From bbb1644216abbf822fbf84e1c2e7f98350c7117c Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 19 Aug 2024 15:44:26 -0700 Subject: [PATCH 01/80] basic structure --- examples/basic/hello_world.py | 27 ++------ fog_x/__init__.py | 9 ++- fog_x/feature.py | 16 ----- fog_x/trajectory.py | 126 ++++++++++++++++++++++++++++++++++ pyproject.toml | 9 +-- 5 files changed, 140 insertions(+), 47 deletions(-) create mode 100644 fog_x/trajectory.py diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index 00ebf7b..cfb4a4f 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -1,28 +1,11 @@ import fog_x -# 🦊 Dataset Creation -# from distributed dataset storage -dataset = fog_x.Dataset( - name="demo_ds", - path="~/test_dataset", # can be AWS S3, Google Bucket! -) - # 🦊 Data collection: # create a new trajectory -episode = dataset.new_episode() +traj = fog_x.Trajectory( + path = "/tmp/a.mkv" +) # collect step data for the episode -episode.add(feature = "arm_view", value = "image1.jpg") +traj.add(feature = "arm_view", value = "image1.jpg") # Automatically time-aligns and saves the trajectory -episode.close() - -# 🦊 Data Loading: -# load from existing RT-X/Open-X datasets -dataset.load_rtx_episodes( - name="berkeley_autolab_ur5", - additional_metadata={"collector": "User 2"} -) - -# 🦊 Data Management and Analytics: -# Compute and memory efficient filter, map, aggregate, groupby -episode_info = dataset.get_episode_info() -desired_episodes = episode_info.filter(episode_info["collector"] == "User 2") \ No newline at end of file +traj.close() diff --git a/fog_x/__init__.py b/fog_x/__init__.py index fc2c642..8146a96 100644 --- a/fog_x/__init__.py +++ b/fog_x/__init__.py @@ -3,10 +3,13 @@ __root_dir__ = os.path.dirname(os.path.abspath(__file__)) -from fog_x import dataset, episode, feature -from fog_x.dataset import Dataset +# from fog_x import dataset, episode, feature +# from fog_x.dataset import Dataset +# from fog_x import trajectory +from fog_x.trajectory import Trajectory +from fog_x.feature import FeatureType -all = ["dataset", "feature", "episode", "Dataset"] +all = ["trajectory"] import logging diff --git a/fog_x/feature.py b/fog_x/feature.py index fa8d39f..8240f0a 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -2,13 +2,9 @@ from typing import Any, List, Optional, Tuple import numpy as np -from sqlalchemy import Float, Integer, LargeBinary, String - -from fog_x.database.utils import type_np2sql, type_py2sql logger = logging.getLogger(__name__) - SUPPORTED_DTYPES = [ "null", "bool", @@ -168,18 +164,6 @@ def to_tf_feature_type(self): else: raise ValueError(f"Unsupported conversion to tf feature: {self}") - def to_sql_type(self): - """ - Convert to sql type - """ - if self.is_np: - return LargeBinary - else: - try: - return type_np2sql(self.dtype) - except: - return LargeBinary - def to_pld_storage_type(self): if len(self.shape) == 0: if self.dtype == "string": diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py new file mode 100644 index 0000000..f6fc899 --- /dev/null +++ b/fog_x/trajectory.py @@ -0,0 +1,126 @@ +import logging +import time +from typing import Any, Dict, List, Optional, Text +import av +import numpy as np +import os +from fog_x import FeatureType + +logger = logging.getLogger(__name__) + + +class Trajectory: + def __init__(self, + path: Text) -> None: + self.path = path + + # check if the path exists + # if exists, load the data + # if not, create a new file + if os.path.exists(self.path): + self.vid_output_file = av.open(path, mode='r') + else: + self._create_container_file() + + def __len__(self): + raise NotImplementedError + + def __iter___(self): + raise NotImplementedError + + def __next__(self): + raise NotImplementedError + + def _create_container_file(self): + self.vid_output_file = av.open(self.path, mode='w') + self.features = {} # feature_name: feature_type + + + def add( + self, + feature: str, + value: Any, + timestamp: Optional[int] = None, + ) -> None: + """ + add one value to video container file + + Args: + feature (str): name of the feature + value (Any): value associated with the feature + timestamp (optional int): nanoseconds since the Epoch. + If not provided, the current time is used. + + Examples: + >>> trajectory.add('feature1', 'image1.jpg') + + Logic: + - check the feature name + - if the feature name is not in the container, create a new stream + + - check the type of value + - if value is numpy array, create a frame and encode it + - if it is a string or int, create a packet and encode it + - else raise an error + """ + + # check if the feature is already in the container + # if not, create a new stream + if feature not in self.features: + self.features[feature] = self.vid_output_file.add_stream( + feature, + rate=1 + ) + + + + + # if isinstance(type_val, np.ndarray): + + # if structure.vid_coding == "libx264": + # if type_val.dtype == np.float32: + # frame = self._create_frame_depth(step_val, streams_d) + # else: + # frame = self._create_frame(step_val, streams_d, ts) + # frame.dts = ts + # packet = streams_d.encode(frame) + # else: + # packet = av.Packet(pickle.dumps(step_val)) + # packet.stream = streams_d + # packet.dts = ts + + # self.vid_output.mux(packet) + # else: + # raise ValueError(f"{type(type_val)} not supported") + + def add_by_dict( + self, + data: Dict[str, Any], + timestamp: Optional[int] = None, + ) -> None: + raise NotImplementedError + + + def load(self): + raise NotImplementedError + + def create_frame(self, image_array, stream, frame_index): + frame = av.VideoFrame.from_ndarray(np.array(image_array), format='rgb24') + frame.pict_type = 'NONE' + frame.time_base = stream.time_base + frame.dts = frame_index + return frame + + # Function to create a frame from numpy array + def create_frame_depth(self, image_array, stream): + image_array = np.array(image_array) + # if float, convert to uint8 + if image_array.dtype == np.float32: + image_array = (image_array * 255).astype(np.uint8) + # if 3 dim, convert to 2 dim + if len(image_array.shape) == 3: + image_array = image_array[:,:,0] + frame = av.VideoFrame.from_ndarray(image_array, format='gray') + frame.pict_type = 'NONE' + frame.time_base = stream.time_base + return frame diff --git a/pyproject.toml b/pyproject.toml index 6d41820..f5d8db8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,16 +4,13 @@ build-backend = "setuptools.build_meta" [project] name = "fog_x" -version = "0.1.0.beta.4" +version = "0.2.0" dependencies = [ - "pandas", "numpy", - "polars", "pillow", - "pyarrow", - "opencv-python", - "sqlalchemy==1.4.51", "smart_open", + "av", + "requests", ] description = "An Efficient and Scalable Data Collection and Management Framework For Robotics Learning" readme = {file = "README.md", content-type = "text/markdown"} From 93090450c0502b5247138b070ac323406a9a159b Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 10:12:32 -0700 Subject: [PATCH 02/80] Refactor Trajectory class to improve frame encoding and add support for different encodings --- examples/basic/hello_world.py | 2 +- fog_x/__init__.py | 3 +- fog_x/feature.py | 14 ++-- fog_x/trajectory.py | 125 +++++++++++++++++++++++----------- 4 files changed, 97 insertions(+), 47 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index cfb4a4f..0b61826 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -6,6 +6,6 @@ path = "/tmp/a.mkv" ) # collect step data for the episode -traj.add(feature = "arm_view", value = "image1.jpg") +traj.add(feature = "arm_view", data = "image1.jpg") # Automatically time-aligns and saves the trajectory traj.close() diff --git a/fog_x/__init__.py b/fog_x/__init__.py index 8146a96..ce2a2f1 100644 --- a/fog_x/__init__.py +++ b/fog_x/__init__.py @@ -6,8 +6,9 @@ # from fog_x import dataset, episode, feature # from fog_x.dataset import Dataset # from fog_x import trajectory -from fog_x.trajectory import Trajectory + from fog_x.feature import FeatureType +from fog_x.trajectory import Trajectory all = ["trajectory"] diff --git a/fog_x/feature.py b/fog_x/feature.py index 8240f0a..fc1f615 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -54,8 +54,8 @@ def __init__( self.from_tf_feature_type(tf_feature_spec) elif dtype is not None: self._set(dtype, shape) - else: - raise ValueError("Either dtype or data must be provided") + + def __str__(self): return f"dtype={self.dtype}, shape={self.shape})" @@ -108,21 +108,23 @@ def from_tf_feature_type(self, tf_feature_spec): self._set(str(dtype), shape) return self + @classmethod def from_data(self, data: Any): """ Infer feature type from the provided data. """ + feature_type = FeatureType() if isinstance(data, np.ndarray): - self._set(data.dtype.name, data.shape) + feature_type._set(data.dtype.name, data.shape) elif isinstance(data, list): dtype = type(data[0]).__name__ shape = (len(data),) - self._set(dtype.name, shape) + feature_type._set(dtype.name, shape) else: dtype = type(data).__name__ shape = () - self._set(dtype, shape) - return self + feature_type._set(dtype, shape) + return feature_type def to_tf_feature_type(self): """ diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index f6fc899..4ce9537 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -5,7 +5,7 @@ import numpy as np import os from fog_x import FeatureType - +import pickle logger = logging.getLogger(__name__) @@ -18,7 +18,8 @@ def __init__(self, # if exists, load the data # if not, create a new file if os.path.exists(self.path): - self.vid_output_file = av.open(path, mode='r') + # self.container_file = av.open(path, mode='r') + self._create_container_file() # TODO: placeholder to develop create else: self._create_container_file() @@ -32,14 +33,14 @@ def __next__(self): raise NotImplementedError def _create_container_file(self): - self.vid_output_file = av.open(self.path, mode='w') - self.features = {} # feature_name: feature_type + self.container_file = av.open(self.path, mode='w') + self.feature_name_to_stream = {} # feature_name: stream def add( self, feature: str, - value: Any, + data: Any, timestamp: Optional[int] = None, ) -> None: """ @@ -64,55 +65,72 @@ def add( - else raise an error """ + feature_type = FeatureType.from_data(data) + encoding = self.get_encoding_of_feature(data, None) + # check if the feature is already in the container # if not, create a new stream - if feature not in self.features: - self.features[feature] = self.vid_output_file.add_stream( - feature, + if feature not in self.feature_name_to_stream: + self.feature_name_to_stream[feature] = self.container_file.add_stream( + encoding, rate=1 ) + + # get the stream + stream = self.feature_name_to_stream[feature] + # get the timestamp + if timestamp is None: + timestamp = time.time_ns() + + # encode the frame + packet = self._encode_frame(data, stream, timestamp) + # write the packet to the container + self.container_file.mux(packet) + def _encode_frame(self, + data: Any, + stream: Any, + timestamp: int) -> av.Packet: + """ + encode the frame and write it to the stream file, return the packet + args: + data: data frame to be encoded + stream: stream to write the frame + timestamp: timestamp of the frame + return: + packet: encoded packet + """ + encoding = self.get_encoding_of_feature(data, None) + feature_type = FeatureType.from_data(data) + if encoding == "libx264": + if feature_type.dtype == np.float32: + frame = self._create_frame_depth(data, stream) + else: + frame = self._create_frame(data, stream, timestamp) + frame.dts = timestamp + packet = stream.encode(frame) + else: + packet = av.Packet(pickle.dumps(data)) + packet.stream = stream + packet.dts = timestamp + return packet - # if isinstance(type_val, np.ndarray): - - # if structure.vid_coding == "libx264": - # if type_val.dtype == np.float32: - # frame = self._create_frame_depth(step_val, streams_d) - # else: - # frame = self._create_frame(step_val, streams_d, ts) - # frame.dts = ts - # packet = streams_d.encode(frame) - # else: - # packet = av.Packet(pickle.dumps(step_val)) - # packet.stream = streams_d - # packet.dts = ts - - # self.vid_output.mux(packet) - # else: - # raise ValueError(f"{type(type_val)} not supported") - - def add_by_dict( - self, - data: Dict[str, Any], - timestamp: Optional[int] = None, - ) -> None: - raise NotImplementedError - - - def load(self): - raise NotImplementedError + def close(self): + """ + close the container file + """ + self.container_file.close() - def create_frame(self, image_array, stream, frame_index): + def _create_frame(self, image_array, stream, frame_index): frame = av.VideoFrame.from_ndarray(np.array(image_array), format='rgb24') frame.pict_type = 'NONE' frame.time_base = stream.time_base frame.dts = frame_index return frame - # Function to create a frame from numpy array - def create_frame_depth(self, image_array, stream): + def _create_frame_depth(self, image_array, stream): image_array = np.array(image_array) # if float, convert to uint8 if image_array.dtype == np.float32: @@ -124,3 +142,32 @@ def create_frame_depth(self, image_array, stream): frame.pict_type = 'NONE' frame.time_base = stream.time_base return frame + + def add_by_dict( + self, + data: Dict[str, Any], + timestamp: Optional[int] = None, + ) -> None: + raise NotImplementedError + + def get_encoding_of_feature(self, feature_value : Any, feature_type: Optional[FeatureType]) -> Text: + """ + get the encoding of the feature value + args: + feature_value: value of the feature + feature_type: type of the feature + return: + encoding of the feature in string + """ + if feature_type is None: + feature_type = FeatureType.from_data(feature_value) + data_shape = feature_type.shape + if len(data_shape) >= 2 and data_shape[0] >= 100 and data_shape[1] >= 100: + vid_coding = "libx264" + else: + vid_coding = "rawvideo" + return vid_coding + + def load(self): + raise NotImplementedError + From d394d67ff9ea8f53ad6f6e603abea9060ac1ab53 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 12:40:59 -0700 Subject: [PATCH 03/80] fix loading --- .gitignore | 5 +- examples/basic/hello_world.py | 8 ++- fog_x/trajectory.py | 112 +++++++++++++++++++++++++--------- 3 files changed, 91 insertions(+), 34 deletions(-) diff --git a/.gitignore b/.gitignore index 96cb0b2..75a79d3 100644 --- a/.gitignore +++ b/.gitignore @@ -132,4 +132,7 @@ dmypy.json .github/templates/* # generated by rtx-examples -temp.gif \ No newline at end of file +temp.gif + +*.vla +*.mkv \ No newline at end of file diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index 0b61826..7bd67bc 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -1,11 +1,13 @@ import fog_x - +import numpy as np # 🦊 Data collection: # create a new trajectory traj = fog_x.Trajectory( - path = "/tmp/a.mkv" + path = "/tmp/output.mkv" ) + # collect step data for the episode -traj.add(feature = "arm_view", data = "image1.jpg") +for i in range(100): + traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) # Automatically time-aligns and saves the trajectory traj.close() diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 4ce9537..45a1899 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -13,15 +13,24 @@ class Trajectory: def __init__(self, path: Text) -> None: self.path = path + self.container_file = av.open(self.path, mode='w') # check if the path exists # if exists, load the data # if not, create a new file - if os.path.exists(self.path): - # self.container_file = av.open(path, mode='r') - self._create_container_file() # TODO: placeholder to develop create - else: - self._create_container_file() + # if os.path.exists(self.path): + # self.container_file = av.open(self.path, mode='w', format = "matroska") + # else: + # logger.info(f"creating a new trajectory at {self.path}") + # try: + # # os.makedirs(os.path.dirname(self.path), exist_ok=True) + # io_handle = open(self.path, 'w') + # self.container_file = av.open(self.path, mode='w', format = "matroska", io_open = io_handle) + # except Exception as e: + # logger.error(f"error creating the trajectory file: {e}") + # raise + + self.feature_name_to_stream = {} # feature_name: stream def __len__(self): raise NotImplementedError @@ -32,9 +41,57 @@ def __iter___(self): def __next__(self): raise NotImplementedError - def _create_container_file(self): - self.container_file = av.open(self.path, mode='w') - self.feature_name_to_stream = {} # feature_name: stream + def close(self): + """ + close the container file + """ + try: + for stream in self.container_file.streams: + for packet in stream.encode(): + self.container_file.mux(packet) + except av.error.EOFError: + pass # This exception is expected and means the encoder is fully flushed + + self.container_file.close() + + def load(self): + """ + load the container file + + workflow: + - check if a cached mmap/hdf5 file exists + - if exists, load the file + - otherwise: load the container file with entire vla trajctory + """ + self._load_from_container() + + + + def _load_from_cache(self): + raise NotImplementedError + + def _load_from_container(self): + """ + + load the container file with entire vla trajctory + + workflow: + - get schema of the container file + - preallocate decoded streams + - decode frame by frame and store in the preallocated memory + + Raises: + NotImplementedError: _description_ + """ + + container = av.open(self.path) + streams = container.streams + + for packet in container.demux(list(streams)): + for frame in packet.decode(): + print(frame) + + raise NotImplementedError def add( @@ -67,14 +124,19 @@ def add( feature_type = FeatureType.from_data(data) encoding = self.get_encoding_of_feature(data, None) - + # check if the feature is already in the container # if not, create a new stream if feature not in self.feature_name_to_stream: - self.feature_name_to_stream[feature] = self.container_file.add_stream( + logger.info("Adding Feature name: %s, Feature type: %s, Encoding: %s", feature, feature_type, encoding) + stream = self.container_file.add_stream( encoding, rate=1 ) + stream.metadata['feature_name'] = feature + stream.metadata['feature_type'] = str(feature_type) + self.feature_name_to_stream[feature] = stream + # get the stream stream = self.feature_name_to_stream[feature] @@ -87,8 +149,16 @@ def add( packet = self._encode_frame(data, stream, timestamp) # write the packet to the container - self.container_file.mux(packet) + if packet: + self.container_file.mux(packet) + def add_by_dict( + self, + data: Dict[str, Any], + timestamp: Optional[int] = None, + ) -> None: + raise NotImplementedError + def _encode_frame(self, data: Any, stream: Any, @@ -117,17 +187,8 @@ def _encode_frame(self, packet.dts = timestamp return packet - def close(self): - """ - close the container file - """ - self.container_file.close() - def _create_frame(self, image_array, stream, frame_index): - frame = av.VideoFrame.from_ndarray(np.array(image_array), format='rgb24') - frame.pict_type = 'NONE' - frame.time_base = stream.time_base - frame.dts = frame_index + frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8), format='rgb24') return frame def _create_frame_depth(self, image_array, stream): @@ -143,13 +204,6 @@ def _create_frame_depth(self, image_array, stream): frame.time_base = stream.time_base return frame - def add_by_dict( - self, - data: Dict[str, Any], - timestamp: Optional[int] = None, - ) -> None: - raise NotImplementedError - def get_encoding_of_feature(self, feature_value : Any, feature_type: Optional[FeatureType]) -> Text: """ get the encoding of the feature value @@ -168,6 +222,4 @@ def get_encoding_of_feature(self, feature_value : Any, feature_type: Optional[Fe vid_coding = "rawvideo" return vid_coding - def load(self): - raise NotImplementedError From daaaa57348cf0640af08f68c4a00389da89f109b Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 15:27:11 -0700 Subject: [PATCH 04/80] doesnt work, consider migrate robot data loader over --- examples/basic/hello_world.py | 30 +++++++++++- fog_x/trajectory.py | 88 ++++++++++++++++++++++++----------- 2 files changed, 90 insertions(+), 28 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index 7bd67bc..4e7aeb6 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -1,13 +1,39 @@ import fog_x import numpy as np +import time + +path = "/tmp/output.mkv" +# remove the existing file +import os +os.system(f"rm -rf {path}") + + # 🦊 Data collection: # create a new trajectory traj = fog_x.Trajectory( - path = "/tmp/output.mkv" + path = path ) + # collect step data for the episode for i in range(100): - traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + time.sleep(0.001) + # traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + # traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8)) + # traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + traj.add(feature = "gripper_pose", data = np.ones((4, 4), dtype=np.float32)) + traj.add(feature = "joint_angles", data = np.ones((7,), dtype=np.float32)) + traj.add(feature = "joint_velocities", data = np.ones((7,), dtype=np.float32)) + traj.add(feature = "joint_torques", data = np.ones((7,), dtype=np.float32)) + # traj.add(feature = "ee_pose", data = np.ones((4, 4), dtype=np.float32)) + # traj.add(feature = "ee_velocity", data = np.ones((6,), dtype=np.float32)) + # traj.add(feature = "ee_force", data = np.ones((6,), dtype=np.float32)) + # traj.add(feature = "contact", data = np.ones((1,), dtype=np.bool)) + # Automatically time-aligns and saves the trajectory traj.close() + + +traj = fog_x.Trajectory( + path = path +) \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 45a1899..ab18df8 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -13,24 +13,29 @@ class Trajectory: def __init__(self, path: Text) -> None: self.path = path - self.container_file = av.open(self.path, mode='w') - + # check if the path exists # if exists, load the data # if not, create a new file - # if os.path.exists(self.path): - # self.container_file = av.open(self.path, mode='w', format = "matroska") - # else: - # logger.info(f"creating a new trajectory at {self.path}") - # try: - # # os.makedirs(os.path.dirname(self.path), exist_ok=True) - # io_handle = open(self.path, 'w') - # self.container_file = av.open(self.path, mode='w', format = "matroska", io_open = io_handle) - # except Exception as e: - # logger.error(f"error creating the trajectory file: {e}") - # raise + if os.path.exists(self.path): + self.load() + else: + logger.info(f"creating a new trajectory at {self.path}") + try: + # os.makedirs(os.path.dirname(self.path), exist_ok=True) + # self.container_file = av.open(self.path, mode='w', format = "matroska") + self.container_file = av.open(self.path, mode='w') + except Exception as e: + logger.error(f"error creating the trajectory file: {e}") + raise self.feature_name_to_stream = {} # feature_name: stream + + self.start_time = time.time() + + def _get_current_timestamp(self): + current_time = (time.time() - self.start_time) * 1000 + return current_time def __len__(self): raise NotImplementedError @@ -46,8 +51,11 @@ def close(self): close the container file """ try: + ts = self._get_current_timestamp() for stream in self.container_file.streams: for packet in stream.encode(): + packet.pts = ts + packet.dts = ts self.container_file.mux(packet) except av.error.EOFError: pass # This exception is expected and means the encoder is fully flushed @@ -88,10 +96,11 @@ def _load_from_container(self): streams = container.streams for packet in container.demux(list(streams)): + print(packet.stream.metadata) for frame in packet.decode(): print(frame) - raise NotImplementedError + container.close() def add( @@ -121,6 +130,7 @@ def add( - if it is a string or int, create a packet and encode it - else raise an error """ + # logger.info("Adding Feature name: %s", feature) feature_type = FeatureType.from_data(data) encoding = self.get_encoding_of_feature(data, None) @@ -131,25 +141,38 @@ def add( logger.info("Adding Feature name: %s, Feature type: %s, Encoding: %s", feature, feature_type, encoding) stream = self.container_file.add_stream( encoding, - rate=1 ) - stream.metadata['feature_name'] = feature - stream.metadata['feature_type'] = str(feature_type) + if encoding == "libx264": + # todo: set resolution + if feature_type.dtype == np.float32: + stream.width = 640 + stream.height = 480 + else: + stream.width = 640 + stream.height = 480 + # stream.metadata['feature_name'] = feature + # stream.metadata['feature_type'] = str(feature_type) + from fractions import Fraction + stream.time_base = Fraction(1, 1000) self.feature_name_to_stream[feature] = stream # get the stream stream = self.feature_name_to_stream[feature] + print(f"stream: {stream}") # get the timestamp if timestamp is None: - timestamp = time.time_ns() - + timestamp = self._get_current_timestamp() + else: + logger.warning("Using custom timestamp, may cause misalignment") + # encode the frame - packet = self._encode_frame(data, stream, timestamp) + packets = self._encode_frame(data, stream, timestamp) # write the packet to the container - if packet: + for packet in packets: + print(f"feature: {feature}, packet: {packet}") self.container_file.mux(packet) def add_by_dict( @@ -162,7 +185,7 @@ def add_by_dict( def _encode_frame(self, data: Any, stream: Any, - timestamp: int) -> av.Packet: + timestamp: int) -> List[av.Packet]: """ encode the frame and write it to the stream file, return the packet args: @@ -178,22 +201,35 @@ def _encode_frame(self, if feature_type.dtype == np.float32: frame = self._create_frame_depth(data, stream) else: - frame = self._create_frame(data, stream, timestamp) + frame = self._create_frame(data, stream) + frame.pts = timestamp frame.dts = timestamp - packet = stream.encode(frame) + frame.time_base = stream.time_base + packets = stream.encode(frame) else: packet = av.Packet(pickle.dumps(data)) + packet.dts = timestamp + packet.pts = timestamp + packet.time_base = stream.time_base packet.stream = stream + + print(packet.stream) + packets = [packet] + + for packet in packets: + packet.pts = timestamp packet.dts = timestamp - return packet + packet.time_base = stream.time_base + return packets - def _create_frame(self, image_array, stream, frame_index): + def _create_frame(self, image_array, stream): frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8), format='rgb24') return frame def _create_frame_depth(self, image_array, stream): image_array = np.array(image_array) # if float, convert to uint8 + # TODO: this is a hack, need to fix it if image_array.dtype == np.float32: image_array = (image_array * 255).astype(np.uint8) # if 3 dim, convert to 2 dim From 70dc889a2312d8a9604ef55327af82add26d3dfb Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 16:46:36 -0700 Subject: [PATCH 05/80] static works --- examples/basic/hello_world.py | 27 ++++++-- fog_x/trajectory.py | 117 ++++++++++++++++++++++++++-------- 2 files changed, 112 insertions(+), 32 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index 4e7aeb6..783f2fb 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -14,21 +14,34 @@ path = path ) +traj.init_feature_stream( + { + "arm_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), + "gripper_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), + "view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), + "wrist_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), + "joint_angles": fog_x.FeatureType(dtype="float32", shape=(7,)), + "joint_velocities": fog_x.FeatureType(dtype="float32", shape=(7,)), + "joint_torques": fog_x.FeatureType(dtype="float32", shape=(7,)), + "ee_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), + "ee_velocity": fog_x.FeatureType(dtype="float32", shape=(6,)), + "ee_force": fog_x.FeatureType(dtype="float32", shape=(6,)), + } +) # collect step data for the episode for i in range(100): time.sleep(0.001) - # traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) - # traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8)) - # traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) traj.add(feature = "gripper_pose", data = np.ones((4, 4), dtype=np.float32)) + traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8)) + traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8)) traj.add(feature = "joint_angles", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_velocities", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_torques", data = np.ones((7,), dtype=np.float32)) - # traj.add(feature = "ee_pose", data = np.ones((4, 4), dtype=np.float32)) - # traj.add(feature = "ee_velocity", data = np.ones((6,), dtype=np.float32)) - # traj.add(feature = "ee_force", data = np.ones((6,), dtype=np.float32)) - # traj.add(feature = "contact", data = np.ones((1,), dtype=np.bool)) + traj.add(feature = "ee_pose", data = np.ones((4, 4), dtype=np.float32)) + traj.add(feature = "ee_velocity", data = np.ones((6,), dtype=np.float32)) + traj.add(feature = "ee_force", data = np.ones((6,), dtype=np.float32)) # Automatically time-aligns and saves the trajectory traj.close() diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index ab18df8..db3e0e1 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -7,7 +7,7 @@ from fog_x import FeatureType import pickle logger = logging.getLogger(__name__) - +from fractions import Fraction class Trajectory: def __init__(self, @@ -18,7 +18,7 @@ def __init__(self, # if exists, load the data # if not, create a new file if os.path.exists(self.path): - self.load() + self.container_file = av.open(self.path, mode='r') else: logger.info(f"creating a new trajectory at {self.path}") try: @@ -53,10 +53,15 @@ def close(self): try: ts = self._get_current_timestamp() for stream in self.container_file.streams: - for packet in stream.encode(): - packet.pts = ts - packet.dts = ts - self.container_file.mux(packet) + print(stream) + try: + packets = stream.encode(None) + for packet in packets: + packet.pts = ts + packet.dts = ts + self.container_file.mux(packet) + except Exception as e: + print(e) except av.error.EOFError: pass # This exception is expected and means the encoder is fully flushed @@ -102,6 +107,17 @@ def _load_from_container(self): container.close() + def init_feature_stream(self, feature_dict: Dict): + """ + initialize the feature stream with the feature name and its type + args: + feature_dict: dictionary of feature name and its type + """ + for feature, feature_type in feature_dict.items(): + encoding = self.get_encoding_of_feature(None, feature_type) + self.feature_name_to_stream[feature] = self._add_stream_to_container( + self.container_file, feature, encoding, feature_type + ) def add( self, @@ -137,25 +153,9 @@ def add( # check if the feature is already in the container # if not, create a new stream + # Check if the feature is already in the container if feature not in self.feature_name_to_stream: - logger.info("Adding Feature name: %s, Feature type: %s, Encoding: %s", feature, feature_type, encoding) - stream = self.container_file.add_stream( - encoding, - ) - if encoding == "libx264": - # todo: set resolution - if feature_type.dtype == np.float32: - stream.width = 640 - stream.height = 480 - else: - stream.width = 640 - stream.height = 480 - # stream.metadata['feature_name'] = feature - # stream.metadata['feature_type'] = str(feature_type) - from fractions import Fraction - stream.time_base = Fraction(1, 1000) - self.feature_name_to_stream[feature] = stream - + self._on_new_stream(feature, encoding, feature_type) # get the stream stream = self.feature_name_to_stream[feature] @@ -172,7 +172,6 @@ def add( # write the packet to the container for packet in packets: - print(f"feature: {feature}, packet: {packet}") self.container_file.mux(packet) def add_by_dict( @@ -222,8 +221,76 @@ def _encode_frame(self, packet.time_base = stream.time_base return packets + def _on_new_stream(self, new_feature, encoding, feature_type): + if new_feature in self.feature_name_to_stream: + return + + if not self.feature_name_to_stream: + logger.info(f"Creating a new stream for the first feature {new_feature}") + self.feature_name_to_stream[new_feature] = self._add_stream_to_container( + self.container_file, new_feature, encoding, feature_type + ) + else: + logger.info(f"Adding a new stream for the feature {new_feature}") + # a workaround because we cannot add new streams to an existing container + # Close current container + self.close() + + # Move the original file to a temporary location + temp_path = self.path + ".temp" + os.rename(self.path, temp_path) + + # Open the original container for reading + original_container = av.open(temp_path, mode='r') + original_streams = list(original_container.streams) + print(original_streams) + + # Create a new container + new_container = av.open(self.path, mode='w') + + # Add existing streams to the new container + stream_map = {} + for stream in original_streams: + new_stream = new_container.add_stream(template=stream) + stream_map[stream.index] = new_stream + + # Add new feature stream + new_stream = self._add_stream_to_container(new_container, new_feature, encoding, feature_type) + stream_map[new_stream.index] = new_stream + + # Remux existing packets + for packet in original_container.demux(original_streams): + packet.stream = stream_map[packet.stream.index] + print(packet) + def is_packet_valid(packet): + return packet.pts is not None and packet.dts is not None + if is_packet_valid(packet): + new_container.mux(packet) + else: + logger.warning(f"Invalid packet: {packet}") + + original_container.close() + os.remove(temp_path) + + # Reopen the new container for writing new data + self.container_file = new_container + self.feature_name_to_stream[new_feature] = new_stream + print(self.feature_name_to_stream) + + def _add_stream_to_container(self, container, feature_name, encoding, feature_type): + stream = container.add_stream(encoding) + if encoding == "libx264": + stream.width = feature_type.shape[0] + stream.height = feature_type.shape[1] + stream.metadata['feature_name'] = feature_name + stream.metadata['feature_type'] = str(feature_type) + stream.time_base = Fraction(1, 1000) + return stream + + def _create_frame(self, image_array, stream): frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8), format='rgb24') + frame.pict_type = "NONE" return frame def _create_frame_depth(self, image_array, stream): From 5b0b4624621aa60f6abda33b3176317cf132b872 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 18:23:37 -0700 Subject: [PATCH 06/80] feat: Improve frame encoding and add support for different encodings in Trajectory class --- fog_x/trajectory.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index db3e0e1..e13c6d8 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -53,7 +53,6 @@ def close(self): try: ts = self._get_current_timestamp() for stream in self.container_file.streams: - print(stream) try: packets = stream.encode(None) for packet in packets: @@ -159,7 +158,6 @@ def add( # get the stream stream = self.feature_name_to_stream[feature] - print(f"stream: {stream}") # get the timestamp if timestamp is None: @@ -212,7 +210,6 @@ def _encode_frame(self, packet.time_base = stream.time_base packet.stream = stream - print(packet.stream) packets = [packet] for packet in packets: @@ -243,7 +240,6 @@ def _on_new_stream(self, new_feature, encoding, feature_type): # Open the original container for reading original_container = av.open(temp_path, mode='r') original_streams = list(original_container.streams) - print(original_streams) # Create a new container new_container = av.open(self.path, mode='w') @@ -252,19 +248,22 @@ def _on_new_stream(self, new_feature, encoding, feature_type): stream_map = {} for stream in original_streams: new_stream = new_container.add_stream(template=stream) + new_stream.options = stream.options + for key, value in stream.metadata.items(): + new_stream.metadata[key] = value stream_map[stream.index] = new_stream # Add new feature stream - new_stream = self._add_stream_to_container(new_container, new_feature, encoding, feature_type) - stream_map[new_stream.index] = new_stream + # new_stream = self._add_stream_to_container(new_container, new_feature, encoding, feature_type) + # stream_map[new_stream.index] = new_stream # Remux existing packets for packet in original_container.demux(original_streams): - packet.stream = stream_map[packet.stream.index] - print(packet) + def is_packet_valid(packet): return packet.pts is not None and packet.dts is not None if is_packet_valid(packet): + packet.stream = stream_map[packet.stream.index] new_container.mux(packet) else: logger.warning(f"Invalid packet: {packet}") @@ -275,7 +274,6 @@ def is_packet_valid(packet): # Reopen the new container for writing new data self.container_file = new_container self.feature_name_to_stream[new_feature] = new_stream - print(self.feature_name_to_stream) def _add_stream_to_container(self, container, feature_name, encoding, feature_type): stream = container.add_stream(encoding) From 9bf9eeaff6db755356222796d3657d5827d9cb4a Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 18:58:13 -0700 Subject: [PATCH 07/80] decode --- examples/basic/hello_world.py | 6 ++-- fog_x/feature.py | 13 +++++++- fog_x/trajectory.py | 58 +++++++++++++++++++++++------------ 3 files changed, 54 insertions(+), 23 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index 783f2fb..b2eec0e 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -2,7 +2,7 @@ import numpy as np import time -path = "/tmp/output.mkv" +path = "/tmp/output.vla" # remove the existing file import os os.system(f"rm -rf {path}") @@ -14,8 +14,8 @@ path = path ) -traj.init_feature_stream( - { +traj.init_feature_streams( + feature_spec = { "arm_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), "gripper_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), "view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), diff --git a/fog_x/feature.py b/fog_x/feature.py index fc1f615..64fb4a7 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -58,7 +58,7 @@ def __init__( def __str__(self): - return f"dtype={self.dtype}, shape={self.shape})" + return f"dtype={self.dtype}; shape={self.shape})" def __repr__(self): return self.__str__() @@ -126,6 +126,17 @@ def from_data(self, data: Any): feature_type._set(dtype, shape) return feature_type + @classmethod + def from_str(self, feature_str: str): + """ + Parse a string representation of the feature type. + """ + print(f"feature_str: {feature_str}") + dtype, shape = feature_str.split(";") + dtype = dtype.split("=")[1] + shape = tuple(shape.split("=")[1][1:-2]) # strip brackets + return FeatureType(dtype=dtype, shape=shape) + def to_tf_feature_type(self): """ Convert to tf feature diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index e13c6d8..9d92ed8 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -13,25 +13,24 @@ class Trajectory: def __init__(self, path: Text) -> None: self.path = path - + self.feature_name_to_stream = {} # feature_name: stream + self.feature_name_to_feature_type = {} # feature_name: feature_type + # check if the path exists # if exists, load the data # if not, create a new file if os.path.exists(self.path): - self.container_file = av.open(self.path, mode='r') + logger.info(f"loading the trajectory from {self.path}") + self.load() else: logger.info(f"creating a new trajectory at {self.path}") try: # os.makedirs(os.path.dirname(self.path), exist_ok=True) - # self.container_file = av.open(self.path, mode='w', format = "matroska") - self.container_file = av.open(self.path, mode='w') + self.container_file = av.open(self.path, mode='w', format = "matroska") except Exception as e: logger.error(f"error creating the trajectory file: {e}") - raise - - self.feature_name_to_stream = {} # feature_name: stream - - self.start_time = time.time() + raise + self.start_time = time.time() def _get_current_timestamp(self): current_time = (time.time() - self.start_time) * 1000 @@ -92,27 +91,47 @@ def _load_from_container(self): - preallocate decoded streams - decode frame by frame and store in the preallocated memory - Raises: - NotImplementedError: _description_ """ container = av.open(self.path) streams = container.streams - for packet in container.demux(list(streams)): - print(packet.stream.metadata) - for frame in packet.decode(): - print(frame) + # recover the feature name and its type + for stream in streams: + print(stream.metadata) + feature_name = stream.metadata['FEATURE_NAME'] + feature_type = FeatureType.from_str(stream.metadata['FEATURE_TYPE']) + self.feature_name_to_stream[feature_name] = stream + self.feature_name_to_feature_type[feature_name] = feature_type + for packet in container.demux(list(streams)): + feature_name = packet.stream.metadata["FEATURE_NAME"] + print(f"feature_name: {feature_name}") + feature_type = self.feature_name_to_feature_type[feature_name] + feature_codec = packet.stream.codec_context.codec.name + if feature_codec == "h264": + frames = packet.decode() + for frame in frames: + # print(frame.to_ndarray()) + continue + else: + packet_in_bytes = bytes(packet) + if packet_in_bytes: + # decode the packet + data = pickle.loads(packet_in_bytes) + print(data) + else: + print(f"Empty packet in {feature_name}") + container.close() - def init_feature_stream(self, feature_dict: Dict): + def init_feature_streams(self, feature_spec: Dict): """ initialize the feature stream with the feature name and its type args: feature_dict: dictionary of feature name and its type """ - for feature, feature_type in feature_dict.items(): + for feature, feature_type in feature_spec.items(): encoding = self.get_encoding_of_feature(None, feature_type) self.feature_name_to_stream[feature] = self._add_stream_to_container( self.container_file, feature, encoding, feature_type @@ -149,6 +168,7 @@ def add( feature_type = FeatureType.from_data(data) encoding = self.get_encoding_of_feature(data, None) + self.feature_name_to_feature_type[feature] = feature_type # check if the feature is already in the container # if not, create a new stream @@ -280,8 +300,8 @@ def _add_stream_to_container(self, container, feature_name, encoding, feature_ty if encoding == "libx264": stream.width = feature_type.shape[0] stream.height = feature_type.shape[1] - stream.metadata['feature_name'] = feature_name - stream.metadata['feature_type'] = str(feature_type) + stream.metadata['FEATURE_NAME'] = feature_name + stream.metadata['FEATURE_TYPE'] = str(feature_type) stream.time_base = Fraction(1, 1000) return stream From 9d66f321955ea4bdf61ca212417f1f77908a3f2d Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 19:24:48 -0700 Subject: [PATCH 08/80] h5 cache --- examples/basic/hello_world.py | 2 +- fog_x/feature.py | 3 ++- fog_x/trajectory.py | 38 ++++++++++++++++++++++++++++------- 3 files changed, 34 insertions(+), 9 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index b2eec0e..b29101a 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -6,7 +6,7 @@ # remove the existing file import os os.system(f"rm -rf {path}") - +os.system(f"rm -rf /tmp/*.cache") # 🦊 Data collection: # create a new trajectory diff --git a/fog_x/feature.py b/fog_x/feature.py index 64fb4a7..63a2424 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -134,7 +134,8 @@ def from_str(self, feature_str: str): print(f"feature_str: {feature_str}") dtype, shape = feature_str.split(";") dtype = dtype.split("=")[1] - shape = tuple(shape.split("=")[1][1:-2]) # strip brackets + shape = eval(shape.split("=")[1][:-1]) # strip brackets + print(f"dtype: {dtype}; shape: {shape}") return FeatureType(dtype=dtype, shape=shape) def to_tf_feature_type(self): diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 9d92ed8..12e3b9d 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -6,6 +6,7 @@ import os from fog_x import FeatureType import pickle +import h5py logger = logging.getLogger(__name__) from fractions import Fraction @@ -13,6 +14,7 @@ class Trajectory: def __init__(self, path: Text) -> None: self.path = path + self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type @@ -74,7 +76,11 @@ def load(self): - if exists, load the file - otherwise: load the container file with entire vla trajctory """ - self._load_from_container() + + if os.path.exists(self.cache_file_name): + self._load_from_cache() + else: + self._load_from_container() @@ -94,32 +100,50 @@ def _load_from_container(self): """ container = av.open(self.path) + h5_cache = h5py.File(self.cache_file_name, "w") streams = container.streams - # recover the feature name and its type + # preallocate memory for the streams in h5 for stream in streams: print(stream.metadata) feature_name = stream.metadata['FEATURE_NAME'] feature_type = FeatureType.from_str(stream.metadata['FEATURE_TYPE']) self.feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type - + # Preallocate arrays with the shape [None, X, Y, Z] + # where X, Y, Z are the dimensions of the feature + + logger.info(f"creating a cache for {feature_name} with shape {feature_type.shape}") + h5_cache.create_dataset( + feature_name, + (0,) + feature_type.shape, + maxshape=(None,) + feature_type.shape, + dtype=feature_type.dtype, + ) + + # decode the frames and store in the preallocated memory + for packet in container.demux(list(streams)): feature_name = packet.stream.metadata["FEATURE_NAME"] - print(f"feature_name: {feature_name}") feature_type = self.feature_name_to_feature_type[feature_name] feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": frames = packet.decode() for frame in frames: - # print(frame.to_ndarray()) - continue + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + h5_cache[feature_name].resize( + h5_cache[feature_name].shape[0] + 1, axis=0 + ) + h5_cache[feature_name][-1] = data else: packet_in_bytes = bytes(packet) if packet_in_bytes: # decode the packet data = pickle.loads(packet_in_bytes) - print(data) + h5_cache[feature_name].resize( + h5_cache[feature_name].shape[0] + 1, axis=0 + ) + h5_cache[feature_name][-1] = data else: print(f"Empty packet in {feature_name}") From c3898048fe5f0c2a4a5a581cce53bb88c030a239 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 20 Aug 2024 23:42:11 -0700 Subject: [PATCH 09/80] figure out the issue of remuxing due to the context --- examples/basic/hello_world.py | 38 +++++++++++++++++++++++++++++++++++ fog_x/trajectory.py | 4 ++-- pyproject.toml | 1 + 3 files changed, 41 insertions(+), 2 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index b29101a..1c3e61f 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -3,6 +3,44 @@ import time path = "/tmp/output.vla" + + +import av + +def remux_mkv(input_filename, output_filename): + # Open the input file using PyAV + input_container = av.open(input_filename, format = "matroska") + + # Create an output container for the new file + output_container = av.open(output_filename, mode='w', format='matroska') + + # Loop through all streams in the input file and add them to the output file + for stream in input_container.streams: + output_container.add_stream(stream.codec_context.codec.name) + print(stream.codec_context.codec.name) + + # Read packets from the input file and write them to the output file + for packet in input_container.demux(): + if packet.dts is None: + print("Skipping packet with no dts") + continue + stream = output_container.streams[packet.stream.index] + print(packet.stream.metadata, packet) + packet.stream = stream + output_container.mux(packet) + + # Close both containers + output_container.close() + input_container.close() + +input_filename = "/home/kych/datasets/rtx/mkv_convert/output_0.mkv"#"/tmp/output.vla" +input_filename = "/tmp/output.vla" +output_filename = "/tmp/remuxed.mkv" + +remux_mkv(input_filename, output_filename) + +exit(0) + # remove the existing file import os os.system(f"rm -rf {path}") diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 12e3b9d..a8c125d 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -130,7 +130,7 @@ def _load_from_container(self): if feature_codec == "h264": frames = packet.decode() for frame in frames: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + data = frame.to_ndarray(format="yuv420p").reshape(feature_type.shape) h5_cache[feature_name].resize( h5_cache[feature_name].shape[0] + 1, axis=0 ) @@ -331,7 +331,7 @@ def _add_stream_to_container(self, container, feature_name, encoding, feature_ty def _create_frame(self, image_array, stream): - frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8), format='rgb24') + frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8)) frame.pict_type = "NONE" return frame diff --git a/pyproject.toml b/pyproject.toml index f5d8db8..ea399e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,6 +11,7 @@ dependencies = [ "smart_open", "av", "requests", + "h5py", ] description = "An Efficient and Scalable Data Collection and Management Framework For Robotics Learning" readme = {file = "README.md", content-type = "text/markdown"} From d875a0ad5255407b759916d77270c39ab9f7eda9 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 00:23:11 -0700 Subject: [PATCH 10/80] it works without h264 --- examples/basic/hello_world.py | 88 +++++++++++++++++------------------ fog_x/trajectory.py | 28 ++++++----- 2 files changed, 61 insertions(+), 55 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index 1c3e61f..dfce357 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -5,41 +5,41 @@ path = "/tmp/output.vla" -import av +# import av -def remux_mkv(input_filename, output_filename): - # Open the input file using PyAV - input_container = av.open(input_filename, format = "matroska") +# def remux_mkv(input_filename, output_filename): +# # Open the input file using PyAV +# input_container = av.open(input_filename, format = "matroska") - # Create an output container for the new file - output_container = av.open(output_filename, mode='w', format='matroska') +# # Create an output container for the new file +# output_container = av.open(output_filename, mode='w', format='matroska') - # Loop through all streams in the input file and add them to the output file - for stream in input_container.streams: - output_container.add_stream(stream.codec_context.codec.name) - print(stream.codec_context.codec.name) +# # Loop through all streams in the input file and add them to the output file +# for stream in input_container.streams: +# output_container.add_stream(stream.codec_context.codec.name) +# print(stream.codec_context.codec.name) - # Read packets from the input file and write them to the output file - for packet in input_container.demux(): - if packet.dts is None: - print("Skipping packet with no dts") - continue - stream = output_container.streams[packet.stream.index] - print(packet.stream.metadata, packet) - packet.stream = stream - output_container.mux(packet) +# # Read packets from the input file and write them to the output file +# for packet in input_container.demux(): +# if packet.dts is None: +# print("Skipping packet with no dts") +# continue +# stream = output_container.streams[packet.stream.index] +# print(packet.stream.metadata, packet) +# packet.stream = stream +# output_container.mux(packet) - # Close both containers - output_container.close() - input_container.close() +# # Close both containers +# output_container.close() +# input_container.close() -input_filename = "/home/kych/datasets/rtx/mkv_convert/output_0.mkv"#"/tmp/output.vla" -input_filename = "/tmp/output.vla" -output_filename = "/tmp/remuxed.mkv" +# input_filename = "/home/kych/datasets/rtx/mkv_convert/output_0.mkv"#"/tmp/output.vla" +# input_filename = "/tmp/output.vla" +# output_filename = "/tmp/remuxed.mkv" -remux_mkv(input_filename, output_filename) +# remux_mkv(input_filename, output_filename) -exit(0) +# exit(0) # remove the existing file import os @@ -52,28 +52,28 @@ def remux_mkv(input_filename, output_filename): path = path ) -traj.init_feature_streams( - feature_spec = { - "arm_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), - "gripper_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), - "view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), - "wrist_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), - "joint_angles": fog_x.FeatureType(dtype="float32", shape=(7,)), - "joint_velocities": fog_x.FeatureType(dtype="float32", shape=(7,)), - "joint_torques": fog_x.FeatureType(dtype="float32", shape=(7,)), - "ee_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), - "ee_velocity": fog_x.FeatureType(dtype="float32", shape=(6,)), - "ee_force": fog_x.FeatureType(dtype="float32", shape=(6,)), - } -) +# traj.init_feature_streams( +# feature_spec = { +# "arm_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), +# "gripper_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), +# "view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), +# "wrist_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), +# "joint_angles": fog_x.FeatureType(dtype="float32", shape=(7,)), +# "joint_velocities": fog_x.FeatureType(dtype="float32", shape=(7,)), +# "joint_torques": fog_x.FeatureType(dtype="float32", shape=(7,)), +# "ee_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), +# "ee_velocity": fog_x.FeatureType(dtype="float32", shape=(6,)), +# "ee_force": fog_x.FeatureType(dtype="float32", shape=(6,)), +# } +# ) # collect step data for the episode for i in range(100): time.sleep(0.001) - traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + # traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) traj.add(feature = "gripper_pose", data = np.ones((4, 4), dtype=np.float32)) - traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8)) - traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + # traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8)) + # traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8)) traj.add(feature = "joint_angles", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_velocities", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_torques", data = np.ones((7,), dtype=np.float32)) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index a8c125d..464b541 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -130,7 +130,7 @@ def _load_from_container(self): if feature_codec == "h264": frames = packet.decode() for frame in frames: - data = frame.to_ndarray(format="yuv420p").reshape(feature_type.shape) + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) h5_cache[feature_name].resize( h5_cache[feature_name].shape[0] + 1, axis=0 ) @@ -262,18 +262,18 @@ def _encode_frame(self, packet.time_base = stream.time_base return packets - def _on_new_stream(self, new_feature, encoding, feature_type): + def _on_new_stream(self, new_feature, new_encoding, new_feature_type): if new_feature in self.feature_name_to_stream: return if not self.feature_name_to_stream: logger.info(f"Creating a new stream for the first feature {new_feature}") self.feature_name_to_stream[new_feature] = self._add_stream_to_container( - self.container_file, new_feature, encoding, feature_type + self.container_file, new_feature, new_encoding, new_feature_type ) else: logger.info(f"Adding a new stream for the feature {new_feature}") - # a workaround because we cannot add new streams to an existing container + # Following is a workaround because we cannot add new streams to an existing container # Close current container self.close() @@ -282,24 +282,30 @@ def _on_new_stream(self, new_feature, encoding, feature_type): os.rename(self.path, temp_path) # Open the original container for reading - original_container = av.open(temp_path, mode='r') + original_container = av.open(temp_path, mode='r', format='matroska') original_streams = list(original_container.streams) # Create a new container - new_container = av.open(self.path, mode='w') + new_container = av.open(self.path, mode='w', format='matroska') # Add existing streams to the new container stream_map = {} for stream in original_streams: - new_stream = new_container.add_stream(template=stream) - new_stream.options = stream.options + feature = stream.metadata.get('FEATURE_NAME') + if feature is None: + logger.warning(f"Skipping stream without FEATURE_NAME: {stream}") + continue + encoding = self.get_encoding_of_feature(None, self.feature_name_to_feature_type[feature]) + feature_type = self.feature_name_to_feature_type[feature] + new_stream = self._add_stream_to_container(new_container, feature, encoding, feature_type) + # new_stream.options = stream.options for key, value in stream.metadata.items(): new_stream.metadata[key] = value stream_map[stream.index] = new_stream # Add new feature stream - # new_stream = self._add_stream_to_container(new_container, new_feature, encoding, feature_type) - # stream_map[new_stream.index] = new_stream + new_stream = self._add_stream_to_container(new_container, new_feature, new_encoding, new_feature_type) + stream_map[new_stream.index] = new_stream # Remux existing packets for packet in original_container.demux(original_streams): @@ -310,7 +316,7 @@ def is_packet_valid(packet): packet.stream = stream_map[packet.stream.index] new_container.mux(packet) else: - logger.warning(f"Invalid packet: {packet}") + pass original_container.close() os.remove(temp_path) From e9f051e8d87480368eb64f89f7c99b2d6c3c7728 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 01:27:48 -0700 Subject: [PATCH 11/80] fix the decoding bug and silient logs --- examples/basic/hello_world.py | 12 ++--- fog_x/feature.py | 2 - fog_x/trajectory.py | 83 +++++++++++++++++++++++++++-------- 3 files changed, 71 insertions(+), 26 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index dfce357..4c412cd 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -70,16 +70,18 @@ # collect step data for the episode for i in range(100): time.sleep(0.001) - # traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + traj.add(feature = "arm_view", data = np.ones((640, 480, 3), dtype=np.uint8)) traj.add(feature = "gripper_pose", data = np.ones((4, 4), dtype=np.float32)) - # traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8)) - # traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8)) + traj.add(feature = "view", data = np.ones((640, 480, 3), dtype=np.uint8)) + traj.add(feature = "wrist_view", data = np.ones((640, 480, 3), dtype=np.uint8)) traj.add(feature = "joint_angles", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_velocities", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_torques", data = np.ones((7,), dtype=np.float32)) - traj.add(feature = "ee_pose", data = np.ones((4, 4), dtype=np.float32)) - traj.add(feature = "ee_velocity", data = np.ones((6,), dtype=np.float32)) + + traj.add(feature = "ee_force", data = np.ones((6,), dtype=np.float32)) + traj.add(feature = "ee_velocity", data = np.ones((6,), dtype=np.float32)) + traj.add(feature = "ee_pose", data = np.ones((4, 4), dtype=np.float32)) # Automatically time-aligns and saves the trajectory traj.close() diff --git a/fog_x/feature.py b/fog_x/feature.py index 63a2424..d44d378 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -131,11 +131,9 @@ def from_str(self, feature_str: str): """ Parse a string representation of the feature type. """ - print(f"feature_str: {feature_str}") dtype, shape = feature_str.split(";") dtype = dtype.split("=")[1] shape = eval(shape.split("=")[1][:-1]) # strip brackets - print(f"dtype: {dtype}; shape: {shape}") return FeatureType(dtype=dtype, shape=shape) def to_tf_feature_type(self): diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 464b541..2932783 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -9,14 +9,18 @@ import h5py logger = logging.getLogger(__name__) from fractions import Fraction +logging.getLogger('libav').setLevel(logging.CRITICAL) class Trajectory: def __init__(self, - path: Text) -> None: + path: Text, + num_pre_initialized_h264_streams:int = 5) -> None: self.path = path self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type + + # check if the path exists # if exists, load the data @@ -32,6 +36,11 @@ def __init__(self, except Exception as e: logger.error(f"error creating the trajectory file: {e}") raise + + self.num_pre_initialized_h264_streams = num_pre_initialized_h264_streams + self.pre_initialized_image_streams = [] # a list of pre-initialized h264 streams + self._pre_initialize_h264_streams(num_pre_initialized_h264_streams) + self.start_time = time.time() def _get_current_timestamp(self): @@ -46,7 +55,18 @@ def __iter___(self): def __next__(self): raise NotImplementedError - + + def _pre_initialize_h264_streams(self, num_streams: int): + """ + Pre-initialize a configurable number of H.264 video streams. + """ + for i in range(num_streams): + encoding = "libx264" + stream = self.container_file.add_stream(encoding) + stream.time_base = Fraction(1, 1000) + stream.pix_fmt = "yuv420p" + self.pre_initialized_image_streams.append(stream) + def close(self): """ close the container file @@ -61,7 +81,7 @@ def close(self): packet.dts = ts self.container_file.mux(packet) except Exception as e: - print(e) + logger.error(f"Error flushing stream {stream}: {e}") except av.error.EOFError: pass # This exception is expected and means the encoder is fully flushed @@ -99,13 +119,15 @@ def _load_from_container(self): """ - container = av.open(self.path) + container = av.open(self.path, mode='r', format='matroska') h5_cache = h5py.File(self.cache_file_name, "w") streams = container.streams # preallocate memory for the streams in h5 for stream in streams: - print(stream.metadata) + if stream.metadata.get('FEATURE_NAME') is None: + logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") + continue feature_name = stream.metadata['FEATURE_NAME'] feature_type = FeatureType.from_str(stream.metadata['FEATURE_TYPE']) self.feature_name_to_stream[feature_name] = stream @@ -113,7 +135,7 @@ def _load_from_container(self): # Preallocate arrays with the shape [None, X, Y, Z] # where X, Y, Z are the dimensions of the feature - logger.info(f"creating a cache for {feature_name} with shape {feature_type.shape}") + logger.debug(f"creating a cache for {feature_name} with shape {feature_type.shape}") h5_cache.create_dataset( feature_name, (0,) + feature_type.shape, @@ -124,8 +146,12 @@ def _load_from_container(self): # decode the frames and store in the preallocated memory for packet in container.demux(list(streams)): + if packet.stream.metadata.get('FEATURE_NAME') is None: + logger.debug(f"Skipping packet without FEATURE_NAME: {packet}") + continue feature_name = packet.stream.metadata["FEATURE_NAME"] feature_type = self.feature_name_to_feature_type[feature_name] + logger.debug(f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype}") feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": frames = packet.decode() @@ -145,7 +171,7 @@ def _load_from_container(self): ) h5_cache[feature_name][-1] = data else: - print(f"Empty packet in {feature_name}") + logger.debug(f"Skipping empty packet: {packet}") container.close() @@ -266,6 +292,17 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): if new_feature in self.feature_name_to_stream: return + if new_encoding == "libx264": + # use pre-initialized h264 streams + if self.pre_initialized_image_streams: + stream = self.pre_initialized_image_streams.pop() + stream.metadata['FEATURE_NAME'] = new_feature + stream.metadata['FEATURE_TYPE'] = str(new_feature_type) + self.feature_name_to_stream[new_feature] = stream + return + else: + raise ValueError("No pre-initialized h264 streams available") + if not self.feature_name_to_stream: logger.info(f"Creating a new stream for the first feature {new_feature}") self.feature_name_to_stream[new_feature] = self._add_stream_to_container( @@ -288,32 +325,40 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): # Create a new container new_container = av.open(self.path, mode='w', format='matroska') + # reset the pre-initialized h264 streams + self.pre_initialized_image_streams = [] + # preinitialize h264 streams + for i in range(self.num_pre_initialized_h264_streams): + encoding = "libx264" + stream = new_container.add_stream(encoding) + stream.time_base = Fraction(1, 1000) + self.pre_initialized_image_streams.append(stream) + # Add existing streams to the new container - stream_map = {} + d_original_stream_id_to_new_container_stream = {} for stream in original_streams: - feature = stream.metadata.get('FEATURE_NAME') - if feature is None: - logger.warning(f"Skipping stream without FEATURE_NAME: {stream}") + stream_feature = stream.metadata.get('FEATURE_NAME') + if stream_feature is None: + logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue - encoding = self.get_encoding_of_feature(None, self.feature_name_to_feature_type[feature]) - feature_type = self.feature_name_to_feature_type[feature] - new_stream = self._add_stream_to_container(new_container, feature, encoding, feature_type) + stream_encoding = self.get_encoding_of_feature(None, self.feature_name_to_feature_type[stream_feature]) + stream_feature_type = self.feature_name_to_feature_type[stream_feature] + stream_in_updated_container = self._add_stream_to_container(new_container, stream_feature, stream_encoding, stream_feature_type) # new_stream.options = stream.options for key, value in stream.metadata.items(): - new_stream.metadata[key] = value - stream_map[stream.index] = new_stream + stream_in_updated_container.metadata[key] = value + d_original_stream_id_to_new_container_stream[stream.index] = stream_in_updated_container # Add new feature stream new_stream = self._add_stream_to_container(new_container, new_feature, new_encoding, new_feature_type) - stream_map[new_stream.index] = new_stream + d_original_stream_id_to_new_container_stream[new_stream.index] = new_stream # Remux existing packets for packet in original_container.demux(original_streams): - def is_packet_valid(packet): return packet.pts is not None and packet.dts is not None if is_packet_valid(packet): - packet.stream = stream_map[packet.stream.index] + packet.stream = d_original_stream_id_to_new_container_stream[packet.stream.index] new_container.mux(packet) else: pass From 32d3dac938d2338ffcad96ab265fc2b00a764841 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 01:29:48 -0700 Subject: [PATCH 12/80] Refactor Trajectory class to improve frame encoding and add support for different encodings --- fog_x/trajectory.py | 217 ++++++++++++++++++++++++-------------------- 1 file changed, 120 insertions(+), 97 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 2932783..9ad215f 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -1,28 +1,27 @@ import logging import time from typing import Any, Dict, List, Optional, Text -import av +import av import numpy as np -import os -from fog_x import FeatureType +import os +from fog_x import FeatureType import pickle import h5py + logger = logging.getLogger(__name__) from fractions import Fraction -logging.getLogger('libav').setLevel(logging.CRITICAL) + +logging.getLogger("libav").setLevel(logging.CRITICAL) + class Trajectory: - def __init__(self, - path: Text, - num_pre_initialized_h264_streams:int = 5) -> None: + def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> None: self.path = path self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" - self.feature_name_to_stream = {} # feature_name: stream - self.feature_name_to_feature_type = {} # feature_name: feature_type - + self.feature_name_to_stream = {} # feature_name: stream + self.feature_name_to_feature_type = {} # feature_name: feature_type - - # check if the path exists + # check if the path exists # if exists, load the data # if not, create a new file if os.path.exists(self.path): @@ -32,30 +31,32 @@ def __init__(self, logger.info(f"creating a new trajectory at {self.path}") try: # os.makedirs(os.path.dirname(self.path), exist_ok=True) - self.container_file = av.open(self.path, mode='w', format = "matroska") + self.container_file = av.open(self.path, mode="w", format="matroska") except Exception as e: logger.error(f"error creating the trajectory file: {e}") - raise - + raise + self.num_pre_initialized_h264_streams = num_pre_initialized_h264_streams - self.pre_initialized_image_streams = [] # a list of pre-initialized h264 streams + self.pre_initialized_image_streams = ( + [] + ) # a list of pre-initialized h264 streams self._pre_initialize_h264_streams(num_pre_initialized_h264_streams) - + self.start_time = time.time() - + def _get_current_timestamp(self): current_time = (time.time() - self.start_time) * 1000 return current_time - + def __len__(self): raise NotImplementedError def __iter___(self): raise NotImplementedError - + def __next__(self): raise NotImplementedError - + def _pre_initialize_h264_streams(self, num_streams: int): """ Pre-initialize a configurable number of H.264 video streams. @@ -66,7 +67,7 @@ def _pre_initialize_h264_streams(self, num_streams: int): stream.time_base = Fraction(1, 1000) stream.pix_fmt = "yuv420p" self.pre_initialized_image_streams.append(stream) - + def close(self): """ close the container file @@ -75,7 +76,7 @@ def close(self): ts = self._get_current_timestamp() for stream in self.container_file.streams: try: - packets = stream.encode(None) + packets = stream.encode(None) for packet in packets: packet.pts = ts packet.dts = ts @@ -90,68 +91,81 @@ def close(self): def load(self): """ load the container file - + workflow: - check if a cached mmap/hdf5 file exists - if exists, load the file - - otherwise: load the container file with entire vla trajctory + - otherwise: load the container file with entire vla trajctory """ - + if os.path.exists(self.cache_file_name): self._load_from_cache() else: self._load_from_container() - - - + + return self + def _load_from_cache(self): - raise NotImplementedError - + """ + load the cached file with entire vla trajctory + """ + h5_cache = h5py.File(self.cache_file_name, "r") + for feature_name, feature_data in h5_cache.items(): + self.feature_name_to_stream[feature_name] = None + self.feature_name_to_feature_type[feature_name] = FeatureType.from_str( + feature_data.attrs["FEATURE_TYPE"] + ) + return h5_cache + def _load_from_container(self): """ - + load the container file with entire vla trajctory - + workflow: - get schema of the container file - - preallocate decoded streams + - preallocate decoded streams - decode frame by frame and store in the preallocated memory """ - - container = av.open(self.path, mode='r', format='matroska') + + container = av.open(self.path, mode="r", format="matroska") h5_cache = h5py.File(self.cache_file_name, "w") streams = container.streams - + # preallocate memory for the streams in h5 for stream in streams: - if stream.metadata.get('FEATURE_NAME') is None: + if stream.metadata.get("FEATURE_NAME") is None: logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue - feature_name = stream.metadata['FEATURE_NAME'] - feature_type = FeatureType.from_str(stream.metadata['FEATURE_TYPE']) + feature_name = stream.metadata["FEATURE_NAME"] + feature_type = FeatureType.from_str(stream.metadata["FEATURE_TYPE"]) self.feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type - # Preallocate arrays with the shape [None, X, Y, Z] + # Preallocate arrays with the shape [None, X, Y, Z] # where X, Y, Z are the dimensions of the feature - - logger.debug(f"creating a cache for {feature_name} with shape {feature_type.shape}") + + logger.debug( + f"creating a cache for {feature_name} with shape {feature_type.shape}" + ) h5_cache.create_dataset( feature_name, (0,) + feature_type.shape, maxshape=(None,) + feature_type.shape, dtype=feature_type.dtype, ) - + # decode the frames and store in the preallocated memory - + for packet in container.demux(list(streams)): - if packet.stream.metadata.get('FEATURE_NAME') is None: + if packet.stream.metadata.get("FEATURE_NAME") is None: logger.debug(f"Skipping packet without FEATURE_NAME: {packet}") continue feature_name = packet.stream.metadata["FEATURE_NAME"] feature_type = self.feature_name_to_feature_type[feature_name] - logger.debug(f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype}") + logger.debug( + f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype}" + ) feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": frames = packet.decode() @@ -174,6 +188,7 @@ def _load_from_container(self): logger.debug(f"Skipping empty packet: {packet}") container.close() + return h5_cache def init_feature_streams(self, feature_spec: Dict): """ @@ -194,21 +209,21 @@ def add( timestamp: Optional[int] = None, ) -> None: """ - add one value to video container file + add one value to video container file Args: feature (str): name of the feature value (Any): value associated with the feature timestamp (optional int): nanoseconds since the Epoch. If not provided, the current time is used. - + Examples: >>> trajectory.add('feature1', 'image1.jpg') - - Logic: - - check the feature name + + Logic: + - check the feature name - if the feature name is not in the container, create a new stream - + - check the type of value - if value is numpy array, create a frame and encode it - if it is a string or int, create a packet and encode it @@ -219,13 +234,13 @@ def add( feature_type = FeatureType.from_data(data) encoding = self.get_encoding_of_feature(data, None) self.feature_name_to_feature_type[feature] = feature_type - + # check if the feature is already in the container # if not, create a new stream # Check if the feature is already in the container if feature not in self.feature_name_to_stream: self._on_new_stream(feature, encoding, feature_type) - + # get the stream stream = self.feature_name_to_stream[feature] @@ -234,7 +249,7 @@ def add( timestamp = self._get_current_timestamp() else: logger.warning("Using custom timestamp, may cause misalignment") - + # encode the frame packets = self._encode_frame(data, stream, timestamp) @@ -248,11 +263,8 @@ def add_by_dict( timestamp: Optional[int] = None, ) -> None: raise NotImplementedError - - def _encode_frame(self, - data: Any, - stream: Any, - timestamp: int) -> List[av.Packet]: + + def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packet]: """ encode the frame and write it to the stream file, return the packet args: @@ -279,9 +291,9 @@ def _encode_frame(self, packet.pts = timestamp packet.time_base = stream.time_base packet.stream = stream - + packets = [packet] - + for packet in packets: packet.pts = timestamp packet.dts = timestamp @@ -291,18 +303,18 @@ def _encode_frame(self, def _on_new_stream(self, new_feature, new_encoding, new_feature_type): if new_feature in self.feature_name_to_stream: return - + if new_encoding == "libx264": # use pre-initialized h264 streams if self.pre_initialized_image_streams: stream = self.pre_initialized_image_streams.pop() - stream.metadata['FEATURE_NAME'] = new_feature - stream.metadata['FEATURE_TYPE'] = str(new_feature_type) + stream.metadata["FEATURE_NAME"] = new_feature + stream.metadata["FEATURE_TYPE"] = str(new_feature_type) self.feature_name_to_stream[new_feature] = stream - return + return else: raise ValueError("No pre-initialized h264 streams available") - + if not self.feature_name_to_stream: logger.info(f"Creating a new stream for the first feature {new_feature}") self.feature_name_to_stream[new_feature] = self._add_stream_to_container( @@ -313,18 +325,18 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): # Following is a workaround because we cannot add new streams to an existing container # Close current container self.close() - + # Move the original file to a temporary location temp_path = self.path + ".temp" os.rename(self.path, temp_path) - + # Open the original container for reading - original_container = av.open(temp_path, mode='r', format='matroska') + original_container = av.open(temp_path, mode="r", format="matroska") original_streams = list(original_container.streams) - + # Create a new container - new_container = av.open(self.path, mode='w', format='matroska') - + new_container = av.open(self.path, mode="w", format="matroska") + # reset the pre-initialized h264 streams self.pre_initialized_image_streams = [] # preinitialize h264 streams @@ -333,59 +345,70 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): stream = new_container.add_stream(encoding) stream.time_base = Fraction(1, 1000) self.pre_initialized_image_streams.append(stream) - + # Add existing streams to the new container d_original_stream_id_to_new_container_stream = {} for stream in original_streams: - stream_feature = stream.metadata.get('FEATURE_NAME') + stream_feature = stream.metadata.get("FEATURE_NAME") if stream_feature is None: logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue - stream_encoding = self.get_encoding_of_feature(None, self.feature_name_to_feature_type[stream_feature]) + stream_encoding = self.get_encoding_of_feature( + None, self.feature_name_to_feature_type[stream_feature] + ) stream_feature_type = self.feature_name_to_feature_type[stream_feature] - stream_in_updated_container = self._add_stream_to_container(new_container, stream_feature, stream_encoding, stream_feature_type) + stream_in_updated_container = self._add_stream_to_container( + new_container, stream_feature, stream_encoding, stream_feature_type + ) # new_stream.options = stream.options for key, value in stream.metadata.items(): stream_in_updated_container.metadata[key] = value - d_original_stream_id_to_new_container_stream[stream.index] = stream_in_updated_container + d_original_stream_id_to_new_container_stream[stream.index] = ( + stream_in_updated_container + ) # Add new feature stream - new_stream = self._add_stream_to_container(new_container, new_feature, new_encoding, new_feature_type) + new_stream = self._add_stream_to_container( + new_container, new_feature, new_encoding, new_feature_type + ) d_original_stream_id_to_new_container_stream[new_stream.index] = new_stream - + # Remux existing packets for packet in original_container.demux(original_streams): + def is_packet_valid(packet): return packet.pts is not None and packet.dts is not None + if is_packet_valid(packet): - packet.stream = d_original_stream_id_to_new_container_stream[packet.stream.index] + packet.stream = d_original_stream_id_to_new_container_stream[ + packet.stream.index + ] new_container.mux(packet) else: pass - + original_container.close() os.remove(temp_path) - + # Reopen the new container for writing new data self.container_file = new_container self.feature_name_to_stream[new_feature] = new_stream - + def _add_stream_to_container(self, container, feature_name, encoding, feature_type): stream = container.add_stream(encoding) if encoding == "libx264": stream.width = feature_type.shape[0] stream.height = feature_type.shape[1] - stream.metadata['FEATURE_NAME'] = feature_name - stream.metadata['FEATURE_TYPE'] = str(feature_type) + stream.metadata["FEATURE_NAME"] = feature_name + stream.metadata["FEATURE_TYPE"] = str(feature_type) stream.time_base = Fraction(1, 1000) return stream - - + def _create_frame(self, image_array, stream): frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8)) frame.pict_type = "NONE" return frame - + def _create_frame_depth(self, image_array, stream): image_array = np.array(image_array) # if float, convert to uint8 @@ -394,20 +417,22 @@ def _create_frame_depth(self, image_array, stream): image_array = (image_array * 255).astype(np.uint8) # if 3 dim, convert to 2 dim if len(image_array.shape) == 3: - image_array = image_array[:,:,0] - frame = av.VideoFrame.from_ndarray(image_array, format='gray') - frame.pict_type = 'NONE' + image_array = image_array[:, :, 0] + frame = av.VideoFrame.from_ndarray(image_array, format="gray") + frame.pict_type = "NONE" frame.time_base = stream.time_base return frame - def get_encoding_of_feature(self, feature_value : Any, feature_type: Optional[FeatureType]) -> Text: + def get_encoding_of_feature( + self, feature_value: Any, feature_type: Optional[FeatureType] + ) -> Text: """ get the encoding of the feature value args: feature_value: value of the feature feature_type: type of the feature return: - encoding of the feature in string + encoding of the feature in string """ if feature_type is None: feature_type = FeatureType.from_data(feature_value) @@ -417,5 +442,3 @@ def get_encoding_of_feature(self, feature_value : Any, feature_type: Optional[Fe else: vid_coding = "rawvideo" return vid_coding - - From 945ddb0e5f25efb2119eaa86c174d3dc5c99b078 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 01:30:56 -0700 Subject: [PATCH 13/80] feat: Add support for pre-initialized H.264 video streams in Trajectory class --- fog_x/trajectory.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 9ad215f..dc97359 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -16,6 +16,15 @@ class Trajectory: def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> None: + """ + Args: + path (Text): path to the trajectory file + num_pre_initialized_h264_streams (int, optional): + Number of pre-initialized H.264 video streams to use when adding new features. + we pre initialize a configurable number of H.264 video streams to avoid the overhead of creating new streams for each feature. + otherwise we need to remux everytime + . Defaults to 5. + """ self.path = path self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream From a3e2c34d297a8fac46daf10afb9278cf5a47ec1d Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 01:33:37 -0700 Subject: [PATCH 14/80] Refactor Trajectory class to remove commented code and improve code readability --- examples/basic/hello_world.py | 18 ---- fog_x/trajectory.py | 186 ++++++++++++++++++---------------- 2 files changed, 98 insertions(+), 106 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index 4c412cd..b1854d2 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -52,21 +52,6 @@ path = path ) -# traj.init_feature_streams( -# feature_spec = { -# "arm_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), -# "gripper_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), -# "view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), -# "wrist_view": fog_x.FeatureType(dtype="uint8", shape=(640, 480, 3)), -# "joint_angles": fog_x.FeatureType(dtype="float32", shape=(7,)), -# "joint_velocities": fog_x.FeatureType(dtype="float32", shape=(7,)), -# "joint_torques": fog_x.FeatureType(dtype="float32", shape=(7,)), -# "ee_pose": fog_x.FeatureType(dtype="float32", shape=(4, 4)), -# "ee_velocity": fog_x.FeatureType(dtype="float32", shape=(6,)), -# "ee_force": fog_x.FeatureType(dtype="float32", shape=(6,)), -# } -# ) - # collect step data for the episode for i in range(100): time.sleep(0.001) @@ -77,13 +62,10 @@ traj.add(feature = "joint_angles", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_velocities", data = np.ones((7,), dtype=np.float32)) traj.add(feature = "joint_torques", data = np.ones((7,), dtype=np.float32)) - - traj.add(feature = "ee_force", data = np.ones((6,), dtype=np.float32)) traj.add(feature = "ee_velocity", data = np.ones((6,), dtype=np.float32)) traj.add(feature = "ee_pose", data = np.ones((4, 4), dtype=np.float32)) -# Automatically time-aligns and saves the trajectory traj.close() diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index dc97359..ce12259 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -1,3 +1,4 @@ +from fractions import Fraction import logging import time from typing import Any, Dict, List, Optional, Text @@ -9,7 +10,6 @@ import h5py logger = logging.getLogger(__name__) -from fractions import Fraction logging.getLogger("libav").setLevel(logging.CRITICAL) @@ -26,7 +26,8 @@ def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> Non . Defaults to 5. """ self.path = path - self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" + self.cache_file_name = "/tmp/fog_" + \ + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type @@ -40,7 +41,8 @@ def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> Non logger.info(f"creating a new trajectory at {self.path}") try: # os.makedirs(os.path.dirname(self.path), exist_ok=True) - self.container_file = av.open(self.path, mode="w", format="matroska") + self.container_file = av.open( + self.path, mode="w", format="matroska") except Exception as e: logger.error(f"error creating the trajectory file: {e}") raise @@ -114,6 +116,80 @@ def load(self): return self + def init_feature_streams(self, feature_spec: Dict): + """ + initialize the feature stream with the feature name and its type + args: + feature_dict: dictionary of feature name and its type + """ + for feature, feature_type in feature_spec.items(): + encoding = self._get_encoding_of_feature(None, feature_type) + self.feature_name_to_stream[feature] = self._add_stream_to_container( + self.container_file, feature, encoding, feature_type + ) + + def add( + self, + feature: str, + data: Any, + timestamp: Optional[int] = None, + ) -> None: + """ + add one value to video container file + + Args: + feature (str): name of the feature + value (Any): value associated with the feature + timestamp (optional int): nanoseconds since the Epoch. + If not provided, the current time is used. + + Examples: + >>> trajectory.add('feature1', 'image1.jpg') + + Logic: + - check the feature name + - if the feature name is not in the container, create a new stream + + - check the type of value + - if value is numpy array, create a frame and encode it + - if it is a string or int, create a packet and encode it + - else raise an error + """ + # logger.info("Adding Feature name: %s", feature) + + feature_type = FeatureType.from_data(data) + encoding = self._get_encoding_of_feature(data, None) + self.feature_name_to_feature_type[feature] = feature_type + + # check if the feature is already in the container + # if not, create a new stream + # Check if the feature is already in the container + if feature not in self.feature_name_to_stream: + self._on_new_stream(feature, encoding, feature_type) + + # get the stream + stream = self.feature_name_to_stream[feature] + + # get the timestamp + if timestamp is None: + timestamp = self._get_current_timestamp() + else: + logger.warning("Using custom timestamp, may cause misalignment") + + # encode the frame + packets = self._encode_frame(data, stream, timestamp) + + # write the packet to the container + for packet in packets: + self.container_file.mux(packet) + + def add_by_dict( + self, + data: Dict[str, Any], + timestamp: Optional[int] = None, + ) -> None: + raise NotImplementedError + def _load_from_cache(self): """ load the cached file with entire vla trajctory @@ -148,14 +224,16 @@ def _load_from_container(self): logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue feature_name = stream.metadata["FEATURE_NAME"] - feature_type = FeatureType.from_str(stream.metadata["FEATURE_TYPE"]) + feature_type = FeatureType.from_str( + stream.metadata["FEATURE_TYPE"]) self.feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type # Preallocate arrays with the shape [None, X, Y, Z] # where X, Y, Z are the dimensions of the feature logger.debug( - f"creating a cache for {feature_name} with shape {feature_type.shape}" + f"creating a cache for { + feature_name} with shape {feature_type.shape}" ) h5_cache.create_dataset( feature_name, @@ -173,13 +251,15 @@ def _load_from_container(self): feature_name = packet.stream.metadata["FEATURE_NAME"] feature_type = self.feature_name_to_feature_type[feature_name] logger.debug( - f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype}" + f"Decoding {feature_name} with shape { + feature_type.shape} and dtype {feature_type.dtype}" ) feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": frames = packet.decode() for frame in frames: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + data = frame.to_ndarray( + format="rgb24").reshape(feature_type.shape) h5_cache[feature_name].resize( h5_cache[feature_name].shape[0] + 1, axis=0 ) @@ -199,80 +279,6 @@ def _load_from_container(self): container.close() return h5_cache - def init_feature_streams(self, feature_spec: Dict): - """ - initialize the feature stream with the feature name and its type - args: - feature_dict: dictionary of feature name and its type - """ - for feature, feature_type in feature_spec.items(): - encoding = self.get_encoding_of_feature(None, feature_type) - self.feature_name_to_stream[feature] = self._add_stream_to_container( - self.container_file, feature, encoding, feature_type - ) - - def add( - self, - feature: str, - data: Any, - timestamp: Optional[int] = None, - ) -> None: - """ - add one value to video container file - - Args: - feature (str): name of the feature - value (Any): value associated with the feature - timestamp (optional int): nanoseconds since the Epoch. - If not provided, the current time is used. - - Examples: - >>> trajectory.add('feature1', 'image1.jpg') - - Logic: - - check the feature name - - if the feature name is not in the container, create a new stream - - - check the type of value - - if value is numpy array, create a frame and encode it - - if it is a string or int, create a packet and encode it - - else raise an error - """ - # logger.info("Adding Feature name: %s", feature) - - feature_type = FeatureType.from_data(data) - encoding = self.get_encoding_of_feature(data, None) - self.feature_name_to_feature_type[feature] = feature_type - - # check if the feature is already in the container - # if not, create a new stream - # Check if the feature is already in the container - if feature not in self.feature_name_to_stream: - self._on_new_stream(feature, encoding, feature_type) - - # get the stream - stream = self.feature_name_to_stream[feature] - - # get the timestamp - if timestamp is None: - timestamp = self._get_current_timestamp() - else: - logger.warning("Using custom timestamp, may cause misalignment") - - # encode the frame - packets = self._encode_frame(data, stream, timestamp) - - # write the packet to the container - for packet in packets: - self.container_file.mux(packet) - - def add_by_dict( - self, - data: Dict[str, Any], - timestamp: Optional[int] = None, - ) -> None: - raise NotImplementedError - def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packet]: """ encode the frame and write it to the stream file, return the packet @@ -283,7 +289,7 @@ def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packe return: packet: encoded packet """ - encoding = self.get_encoding_of_feature(data, None) + encoding = self._get_encoding_of_feature(data, None) feature_type = FeatureType.from_data(data) if encoding == "libx264": if feature_type.dtype == np.float32: @@ -325,7 +331,8 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): raise ValueError("No pre-initialized h264 streams available") if not self.feature_name_to_stream: - logger.info(f"Creating a new stream for the first feature {new_feature}") + logger.info( + f"Creating a new stream for the first feature {new_feature}") self.feature_name_to_stream[new_feature] = self._add_stream_to_container( self.container_file, new_feature, new_encoding, new_feature_type ) @@ -340,7 +347,8 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): os.rename(self.path, temp_path) # Open the original container for reading - original_container = av.open(temp_path, mode="r", format="matroska") + original_container = av.open( + temp_path, mode="r", format="matroska") original_streams = list(original_container.streams) # Create a new container @@ -360,9 +368,10 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): for stream in original_streams: stream_feature = stream.metadata.get("FEATURE_NAME") if stream_feature is None: - logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") + logger.debug( + f"Skipping stream without FEATURE_NAME: {stream}") continue - stream_encoding = self.get_encoding_of_feature( + stream_encoding = self._get_encoding_of_feature( None, self.feature_name_to_feature_type[stream_feature] ) stream_feature_type = self.feature_name_to_feature_type[stream_feature] @@ -414,7 +423,8 @@ def _add_stream_to_container(self, container, feature_name, encoding, feature_ty return stream def _create_frame(self, image_array, stream): - frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8)) + frame = av.VideoFrame.from_ndarray( + np.array(image_array, dtype=np.uint8)) frame.pict_type = "NONE" return frame @@ -432,7 +442,7 @@ def _create_frame_depth(self, image_array, stream): frame.time_base = stream.time_base return frame - def get_encoding_of_feature( + def _get_encoding_of_feature( self, feature_value: Any, feature_type: Optional[FeatureType] ) -> Text: """ From c7c9284cd4326ec58020980111a86c2f66eb8745 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 01:35:07 -0700 Subject: [PATCH 15/80] Refactor Trajectory class to remove commented code and improve code readability --- examples/basic/hello_world.py | 37 ----------------------------------- 1 file changed, 37 deletions(-) diff --git a/examples/basic/hello_world.py b/examples/basic/hello_world.py index b1854d2..975b24b 100644 --- a/examples/basic/hello_world.py +++ b/examples/basic/hello_world.py @@ -4,43 +4,6 @@ path = "/tmp/output.vla" - -# import av - -# def remux_mkv(input_filename, output_filename): -# # Open the input file using PyAV -# input_container = av.open(input_filename, format = "matroska") - -# # Create an output container for the new file -# output_container = av.open(output_filename, mode='w', format='matroska') - -# # Loop through all streams in the input file and add them to the output file -# for stream in input_container.streams: -# output_container.add_stream(stream.codec_context.codec.name) -# print(stream.codec_context.codec.name) - -# # Read packets from the input file and write them to the output file -# for packet in input_container.demux(): -# if packet.dts is None: -# print("Skipping packet with no dts") -# continue -# stream = output_container.streams[packet.stream.index] -# print(packet.stream.metadata, packet) -# packet.stream = stream -# output_container.mux(packet) - -# # Close both containers -# output_container.close() -# input_container.close() - -# input_filename = "/home/kych/datasets/rtx/mkv_convert/output_0.mkv"#"/tmp/output.vla" -# input_filename = "/tmp/output.vla" -# output_filename = "/tmp/remuxed.mkv" - -# remux_mkv(input_filename, output_filename) - -# exit(0) - # remove the existing file import os os.system(f"rm -rf {path}") From b627e754fe2f57147ef753c0b2a625028aef245f Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 01:40:14 -0700 Subject: [PATCH 16/80] Refactor Trajectory class to improve code readability and remove commented code --- fog_x/trajectory.py | 36 +++++++++++++----------------------- 1 file changed, 13 insertions(+), 23 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index ce12259..36dedf2 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -19,15 +19,14 @@ def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> Non """ Args: path (Text): path to the trajectory file - num_pre_initialized_h264_streams (int, optional): + num_pre_initialized_h264_streams (int, optional): Number of pre-initialized H.264 video streams to use when adding new features. we pre initialize a configurable number of H.264 video streams to avoid the overhead of creating new streams for each feature. - otherwise we need to remux everytime + otherwise we need to remux everytime . Defaults to 5. """ self.path = path - self.cache_file_name = "/tmp/fog_" + \ - os.path.basename(self.path) + ".cache" + self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type @@ -41,8 +40,7 @@ def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> Non logger.info(f"creating a new trajectory at {self.path}") try: # os.makedirs(os.path.dirname(self.path), exist_ok=True) - self.container_file = av.open( - self.path, mode="w", format="matroska") + self.container_file = av.open(self.path, mode="w", format="matroska") except Exception as e: logger.error(f"error creating the trajectory file: {e}") raise @@ -224,16 +222,14 @@ def _load_from_container(self): logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue feature_name = stream.metadata["FEATURE_NAME"] - feature_type = FeatureType.from_str( - stream.metadata["FEATURE_TYPE"]) + feature_type = FeatureType.from_str(stream.metadata["FEATURE_TYPE"]) self.feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type # Preallocate arrays with the shape [None, X, Y, Z] # where X, Y, Z are the dimensions of the feature logger.debug( - f"creating a cache for { - feature_name} with shape {feature_type.shape}" + f"creating a cache for {feature_name} with shape {feature_type.shape}" ) h5_cache.create_dataset( feature_name, @@ -250,16 +246,14 @@ def _load_from_container(self): continue feature_name = packet.stream.metadata["FEATURE_NAME"] feature_type = self.feature_name_to_feature_type[feature_name] - logger.debug( - f"Decoding {feature_name} with shape { - feature_type.shape} and dtype {feature_type.dtype}" + logger.info( + f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" ) feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": frames = packet.decode() for frame in frames: - data = frame.to_ndarray( - format="rgb24").reshape(feature_type.shape) + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) h5_cache[feature_name].resize( h5_cache[feature_name].shape[0] + 1, axis=0 ) @@ -331,8 +325,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): raise ValueError("No pre-initialized h264 streams available") if not self.feature_name_to_stream: - logger.info( - f"Creating a new stream for the first feature {new_feature}") + logger.info(f"Creating a new stream for the first feature {new_feature}") self.feature_name_to_stream[new_feature] = self._add_stream_to_container( self.container_file, new_feature, new_encoding, new_feature_type ) @@ -347,8 +340,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): os.rename(self.path, temp_path) # Open the original container for reading - original_container = av.open( - temp_path, mode="r", format="matroska") + original_container = av.open(temp_path, mode="r", format="matroska") original_streams = list(original_container.streams) # Create a new container @@ -368,8 +360,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): for stream in original_streams: stream_feature = stream.metadata.get("FEATURE_NAME") if stream_feature is None: - logger.debug( - f"Skipping stream without FEATURE_NAME: {stream}") + logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue stream_encoding = self._get_encoding_of_feature( None, self.feature_name_to_feature_type[stream_feature] @@ -423,8 +414,7 @@ def _add_stream_to_container(self, container, feature_name, encoding, feature_ty return stream def _create_frame(self, image_array, stream): - frame = av.VideoFrame.from_ndarray( - np.array(image_array, dtype=np.uint8)) + frame = av.VideoFrame.from_ndarray(np.array(image_array, dtype=np.uint8)) frame.pict_type = "NONE" return frame From 00d3d5e3162642a0c561f8046d5356adeb1b3828 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 19:13:01 -0700 Subject: [PATCH 17/80] init robot data loader structure --- examples/basic/load.py | 8 ---- examples/basic/main.py | 41 -------------------- examples/{basic => }/hello_world.py | 0 examples/rtx_loader.py | 7 ++++ fog_x/exporter/__init__.py | 0 fog_x/exporter/base.py | 10 +++++ fog_x/loader/__init__.py | 2 + fog_x/loader/base.py | 17 +++++++++ fog_x/loader/rlds.py | 59 +++++++++++++++++++++++++++++ 9 files changed, 95 insertions(+), 49 deletions(-) delete mode 100644 examples/basic/load.py delete mode 100644 examples/basic/main.py rename examples/{basic => }/hello_world.py (100%) create mode 100644 examples/rtx_loader.py create mode 100644 fog_x/exporter/__init__.py create mode 100644 fog_x/exporter/base.py create mode 100644 fog_x/loader/__init__.py create mode 100644 fog_x/loader/base.py create mode 100644 fog_x/loader/rlds.py diff --git a/examples/basic/load.py b/examples/basic/load.py deleted file mode 100644 index 0d96b87..0000000 --- a/examples/basic/load.py +++ /dev/null @@ -1,8 +0,0 @@ -import polars as pl -import pyarrow as pa -import pyarrow.dataset as ds -import pyarrow.parquet as pq - -import fog_x - -print(pl.scan_pyarrow_dataset(ds.dataset("~/test_dataset/steps")).collect()) diff --git a/examples/basic/main.py b/examples/basic/main.py deleted file mode 100644 index 02156a6..0000000 --- a/examples/basic/main.py +++ /dev/null @@ -1,41 +0,0 @@ -import fog_x - -# create a new dataset -dataset = fog_x.dataset.Dataset( - name="test_rtx", - path="/tmp/rtx", - replace_existing=False, - db_connector=fog_x.database.PolarsConnector("/tmp/"), -) - -for i in range(1, 10): - # create a new episode / trajectory - episode = dataset.new_episode( - metadata={ - "collector_name": f"User #{i}", - "description": f"description #{i}", - } - ) - # populate the episode with FeatureTypes - for j in range(1, 4): - episode.add(feature="feature_1", value=f"episode{i}_step{j}_feature_1") - episode.add(feature="feature_2", value=f"episode{i}_pose{j}_feature_2") - episode.close() - -# mark the current state as terminal state -# and save the episode -episode.close() - -# load the dataset -metadata = dataset.get_metadata_as_pandas_df() -# ... -# do what you want like a typical pandas dataframe -# Example: load with shuffled the episodes in the dataset -# metadata = metadata.sample() -# print(metadata) -# episodes = dataset.read_by(metadata) -# for episode in episodes: -# print(episode) - -# export the dataset -# dataset.export("/tmp/rtx_export", format="rtx") diff --git a/examples/basic/hello_world.py b/examples/hello_world.py similarity index 100% rename from examples/basic/hello_world.py rename to examples/hello_world.py diff --git a/examples/rtx_loader.py b/examples/rtx_loader.py new file mode 100644 index 0000000..6e3087d --- /dev/null +++ b/examples/rtx_loader.py @@ -0,0 +1,7 @@ + +from fog_x.loader import RLDSLoader + +loader = RLDSLoader( + path = "/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", + split = "train" +) \ No newline at end of file diff --git a/fog_x/exporter/__init__.py b/fog_x/exporter/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fog_x/exporter/base.py b/fog_x/exporter/base.py new file mode 100644 index 0000000..1afdd43 --- /dev/null +++ b/fog_x/exporter/base.py @@ -0,0 +1,10 @@ + +from logging import getLogger + +class BaseExporter(): + def __init__(self): + super(BaseExporter, self).__init__() + self.logger = getLogger(__name__) + + def export(self, loader, path): + raise NotImplementedError \ No newline at end of file diff --git a/fog_x/loader/__init__.py b/fog_x/loader/__init__.py new file mode 100644 index 0000000..189034e --- /dev/null +++ b/fog_x/loader/__init__.py @@ -0,0 +1,2 @@ +from .base import BaseLoader +from .rlds import RLDSLoader diff --git a/fog_x/loader/base.py b/fog_x/loader/base.py new file mode 100644 index 0000000..09c009c --- /dev/null +++ b/fog_x/loader/base.py @@ -0,0 +1,17 @@ +from logging import getLogger + + +class BaseLoader(): + def __init__(self, path): + super(BaseLoader, self).__init__() + self.logger = getLogger(__name__) + self.path = path + + # def get_schema(self) -> Schema: + # raise NotImplementedError + + def __len__(self): + raise NotImplementedError + + def __iter___(self): + raise NotImplementedError diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py new file mode 100644 index 0000000..36fcd22 --- /dev/null +++ b/fog_x/loader/rlds.py @@ -0,0 +1,59 @@ + + +from . import BaseLoader +import os +import sys +import numpy as np + +class RLDSLoader(BaseLoader): + def __init__(self, path, split): + super(RLDSLoader, self).__init__(path) + + try: + import tensorflow as tf + import tensorflow_datasets as tfds + except ImportError: + raise ImportError("Please install tensorflow and tensorflow_datasets to use rlds loader") + + builder = tfds.builder_from_directory(path) + self.ds = builder.as_dataset(split) + + self.split = split + self.index = 0 + + def __len__(self): + return len(self.ds) + + def __iter__(self): + return self + + def __next__(self): + + if self.index < len(self): + self.index += 1 + nest_ds = self.ds.__iter__() + traj = list(nest_ds)[0]["steps"] + data = [] + + for step_data in traj: + step = {} + for key, val in step_data.items(): + + if key == "observation": + step["observation"] = {} + for obs_key, obs_val in val.items(): + step["observation"][obs_key] = np.array(obs_val) + + elif key == "action": + step["action"] = {} + for act_key, act_val in val.items(): + step["action"][act_key] = np.array(act_val) + else: + step[key] = np.array(val) + + data.append(step) + return data + else: + self.index = 0 + raise StopIteration + \ No newline at end of file From 5cfa061015b1f18f7f5e69d324949605d42f8f09 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 23:21:21 -0700 Subject: [PATCH 18/80] convert from openx --- examples/rtx_loader.py | 17 ++++++++-- fog_x/feature.py | 1 + fog_x/trajectory.py | 70 ++++++++++++++++++++++++++++++++++++++---- 3 files changed, 80 insertions(+), 8 deletions(-) diff --git a/examples/rtx_loader.py b/examples/rtx_loader.py index 6e3087d..120686b 100644 --- a/examples/rtx_loader.py +++ b/examples/rtx_loader.py @@ -1,7 +1,20 @@ from fog_x.loader import RLDSLoader +import fog_x + +import os +os.system("rm -rf /tmp/fog_x/*") loader = RLDSLoader( path = "/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", - split = "train" -) \ No newline at end of file + split = "train[:10]" +) + +index = 0 + +for data_traj in loader: + + fog_x.Trajectory.from_list_of_dicts(data_traj, path = f"/tmp/fog_x/output_{index}.vla") + index += 1 + + diff --git a/fog_x/feature.py b/fog_x/feature.py index d44d378..58eeb08 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -32,6 +32,7 @@ "string", "str", "large_string", + "object", ] diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 36dedf2..1204058 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -15,7 +15,10 @@ class Trajectory: - def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> None: + def __init__(self, + path: Text, + num_pre_initialized_h264_streams: int = 5, + feature_name_separator:Text = "/") -> None: """ Args: path (Text): path to the trajectory file @@ -24,8 +27,12 @@ def __init__(self, path: Text, num_pre_initialized_h264_streams: int = 5) -> Non we pre initialize a configurable number of H.264 video streams to avoid the overhead of creating new streams for each feature. otherwise we need to remux everytime . Defaults to 5. + feature_name_separator (Text, optional): + Delimiter to separate feature names in the container file. + Defaults to "/". """ self.path = path + self.feature_name_separator = feature_name_separator self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type @@ -137,7 +144,7 @@ def add( Args: feature (str): name of the feature - value (Any): value associated with the feature + value (Any): value associated with the feature; except dictionary timestamp (optional int): nanoseconds since the Epoch. If not provided, the current time is used. @@ -152,8 +159,14 @@ def add( - if value is numpy array, create a frame and encode it - if it is a string or int, create a packet and encode it - else raise an error + + Exceptions: + raise an error if the value is a dictionary """ - # logger.info("Adding Feature name: %s", feature) + + if type(data) == dict: + raise ValueError("Use add_by_dict for dictionary") + feature_type = FeatureType.from_data(data) encoding = self._get_encoding_of_feature(data, None) @@ -172,7 +185,7 @@ def add( if timestamp is None: timestamp = self._get_current_timestamp() else: - logger.warning("Using custom timestamp, may cause misalignment") + logger.debug("Using custom timestamp, may cause misalignment") # encode the frame packets = self._encode_frame(data, stream, timestamp) @@ -186,7 +199,52 @@ def add_by_dict( data: Dict[str, Any], timestamp: Optional[int] = None, ) -> None: - raise NotImplementedError + """ + add one value to video container file + data might be nested dictionary of values for each feature + + Args: + data (Dict[str, Any]): dictionary of feature name and value + timestamp (optional int): nanoseconds since the Epoch. + If not provided, the current time is used. + assume the timestamp is same for all the features within the dictionary + + Examples: + >>> trajectory.add_by_dict({'feature1': 'image1.jpg'}) + + Logic: + - check the data see if it is a dictionary + - if dictionary, need to flatten it and add each feature separately + """ + if type(data) != dict: + raise ValueError("Use add for non-dictionary data") + + def flatten_dict(d, parent_key='', sep='_'): + items = [] + for k, v in d.items(): + new_key = parent_key + sep + k if parent_key else k + if isinstance(v, dict): + items.extend(flatten_dict(v, new_key, sep=sep).items()) + else: + items.append((new_key, v)) + return dict(items) + + flatten_dict_data = flatten_dict(data, sep=self.feature_name_separator) + timestamp = self._get_current_timestamp() if timestamp is None else timestamp + for feature, value in flatten_dict_data.items(): + self.add(feature, value, timestamp) + + + @classmethod + def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajectory": + """ + Create a Trajectory object from a list of dictionaries. + """ + traj = cls(path) + for step in data: + traj.add_by_dict(step) + return traj + def _load_from_cache(self): """ @@ -286,7 +344,7 @@ def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packe encoding = self._get_encoding_of_feature(data, None) feature_type = FeatureType.from_data(data) if encoding == "libx264": - if feature_type.dtype == np.float32: + if feature_type.dtype == "float32": frame = self._create_frame_depth(data, stream) else: frame = self._create_frame(data, stream) From 0e5226faa4b696ff041d90b9fe0ea821ad92931f Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 21 Aug 2024 23:42:03 -0700 Subject: [PATCH 19/80] Refactor Trajectory class to improve frame encoding and add support for different encodings --- examples/Fog_X_Analytics_Demo.ipynb | 1019 ----------------- examples/Fog_X_Cloud_Demo.ipynb | 609 ---------- ...o_world.py => data_collection_and_load.py} | 0 examples/dataloader/huggingface.py | 15 - examples/dataloader/pytorch.py | 35 - examples/h5_loader.py | 0 examples/rtx_loader.py | 15 +- fog_x/trajectory.py | 27 +- 8 files changed, 20 insertions(+), 1700 deletions(-) delete mode 100644 examples/Fog_X_Analytics_Demo.ipynb delete mode 100644 examples/Fog_X_Cloud_Demo.ipynb rename examples/{hello_world.py => data_collection_and_load.py} (100%) delete mode 100644 examples/dataloader/huggingface.py delete mode 100644 examples/dataloader/pytorch.py create mode 100644 examples/h5_loader.py diff --git a/examples/Fog_X_Analytics_Demo.ipynb b/examples/Fog_X_Analytics_Demo.ipynb deleted file mode 100644 index 2bd29ba..0000000 --- a/examples/Fog_X_Analytics_Demo.ipynb +++ /dev/null @@ -1,1019 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "99458164", - "metadata": {}, - "source": [ - "# Fog-X Demo\n", - "\n", - "In this demo, we show how to use Fog-X to collect and manage your robotics learning dataset. We show the following aspects of the Fog-X: \n", - "* Support for existing Open-X datasets\n", - "* Data Analytics and Management \n", - "* Use for Pytorch Learning\n", - "* Export and Share with Open-X (Tensorflow rlds) and HuggingFace\n", - "\n", - "We also compare the disk saving (43\\%!) of Fog-X at the end." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "36ed049c", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import fog_x \n", - "\n", - "dataset = fog_x.dataset.Dataset(\n", - " name=\"demo_ds\",\n", - " path=\"~/test_dataset\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b636dea1", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "6ca883c1", - "metadata": {}, - "source": [ - "## Loading From Existing Open-X/RT-X datasets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f52d6801", - "metadata": {}, - "outputs": [], - "source": [ - "dataset.load_rtx_episodes(\n", - " name=\"berkeley_autolab_ur5\",\n", - " split=\"train[:10]\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ff7c5aa1", - "metadata": {}, - "source": [ - "### Trajectory Metadata and Data\n", - "\n", - "Fog-X makes a distinction between trajectory metadata and the actual data. \n", - "* **Metadata**: information that is consistent across a certain trajectory, such as language command, tags\n", - "* **Data**: data for individual steps within a trajectory" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5f3c6241", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "shape: (13, 3)\n", - "┌────────────┬───────────┬─────────────┐\n", - "│ episode_id ┆ collector ┆ custom_tag │\n", - "│ --- ┆ --- ┆ --- │\n", - "│ i64 ┆ str ┆ str │\n", - "╞════════════╪═══════════╪═════════════╡\n", - "│ 0 ┆ null ┆ null │\n", - "│ 1 ┆ null ┆ null │\n", - "│ 2 ┆ null ┆ null │\n", - "│ 3 ┆ null ┆ null │\n", - "│ 4 ┆ null ┆ null │\n", - "│ … ┆ … ┆ … │\n", - "│ 8 ┆ null ┆ null │\n", - "│ 9 ┆ null ┆ null │\n", - "│ 10 ┆ null ┆ null │\n", - "│ 11 ┆ User 2 ┆ Partition_2 │\n", - "│ 12 ┆ User 2 ┆ Partition_2 │\n", - "└────────────┴───────────┴─────────────┘" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.get_episode_info().select([\"episode_id\", \"collector\", \"custom_tag\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "857f3c87", - "metadata": {}, - "outputs": [], - "source": [ - "episode_info = dataset.get_episode_info()\n", - "# querying non-existent metadata \n", - "metadata = episode_info.filter(episode_info[\"collector\"] == \"User_Do_No_Exist\")\n", - "episodes = dataset.read_by(metadata)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d713a974", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([,\n", - " ],\n", - " shape: (9, 17)\n", - " ┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n", - " │ statistic ┆ episode_i ┆ Timestamp ┆ gripper_c ┆ … ┆ natural_l ┆ natural_l ┆ robot_sta ┆ reward │\n", - " │ --- ┆ d ┆ --- ┆ losedness ┆ ┆ anguage_e ┆ anguage_i ┆ te ┆ --- │\n", - " │ str ┆ --- ┆ f64 ┆ _action ┆ ┆ mbedding ┆ nstructio ┆ --- ┆ f64 │\n", - " │ ┆ f64 ┆ ┆ --- ┆ ┆ --- ┆ n ┆ str ┆ │\n", - " │ ┆ ┆ ┆ f64 ┆ ┆ str ┆ --- ┆ ┆ │\n", - " │ ┆ ┆ ┆ ┆ ┆ ┆ str ┆ ┆ │\n", - " ╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n", - " │ count ┆ 80.0 ┆ 80.0 ┆ 80.0 ┆ … ┆ 80 ┆ 80 ┆ 80 ┆ 80.0 │\n", - " │ null_coun ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ … ┆ 0 ┆ 0 ┆ 0 ┆ 0.0 │\n", - " │ t ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - " │ mean ┆ 11.0 ┆ 1.7127e18 ┆ 0.0 ┆ … ┆ null ┆ null ┆ null ┆ 0.0125 │\n", - " │ std ┆ 0.0 ┆ 3.8792e9 ┆ 0.0 ┆ … ┆ null ┆ null ┆ null ┆ 0.111803 │\n", - " │ min ┆ 11.0 ┆ 1.7127e18 ┆ 0.0 ┆ … ┆ b'\\x93NUM ┆ b'sweep ┆ b\"\\x93NUM ┆ 0.0 │\n", - " │ ┆ ┆ ┆ ┆ ┆ PY\\x01\\x0 ┆ the green ┆ PY\\x01\\x0 ┆ │\n", - " │ ┆ ┆ ┆ ┆ ┆ 0v\\x00{\\' ┆ cloth to ┆ 0v\\x00{'d ┆ │\n", - " │ ┆ ┆ ┆ ┆ ┆ descr… ┆ the l… ┆ escr'… ┆ │\n", - " │ 25% ┆ 11.0 ┆ 1.7127e18 ┆ 0.0 ┆ … ┆ null ┆ null ┆ null ┆ 0.0 │\n", - " │ 50% ┆ 11.0 ┆ 1.7127e18 ┆ 0.0 ┆ … ┆ null ┆ null ┆ null ┆ 0.0 │\n", - " │ 75% ┆ 11.0 ┆ 1.7127e18 ┆ 0.0 ┆ … ┆ null ┆ null ┆ null ┆ 0.0 │\n", - " │ max ┆ 11.0 ┆ 1.7127e18 ┆ 0.0 ┆ … ┆ b'\\x93NUM ┆ b'sweep ┆ b\"\\x93NUM ┆ 1.0 │\n", - " │ ┆ ┆ ┆ ┆ ┆ PY\\x01\\x0 ┆ the green ┆ PY\\x01\\x0 ┆ │\n", - " │ ┆ ┆ ┆ ┆ ┆ 0v\\x00{\\' ┆ cloth to ┆ 0v\\x00{'d ┆ │\n", - " │ ┆ ┆ ┆ ┆ ┆ descr… ┆ the l… ┆ escr'… ┆ │\n", - " └───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metadata = episode_info.filter(episode_info[\"custom_tag\"] == \"Partition_2\")\n", - "episodes = dataset.read_by(metadata)\n", - "episodes, episodes[0].describe()" - ] - }, - { - "cell_type": "markdown", - "id": "b575fec7", - "metadata": {}, - "source": [ - "### Example 2: Extracts and Searches natural language instructions from step data \n", - "\n", - "Existing Open-X datasets store natural language instructions for every step, which costs inefficiency and manage complexity. This example shows \n", - "1. how to extracts natural language instruction from existing Open-X datasets\n", - "2. search for keywords or **regex** " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "23a47f3e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "shape: (3, 2)
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0b"sweep\\x20the\\x20green\\x20cloth\\x20to\\x20the\\x20left\\x20side\\x20of\\x20the\\x20table"
10b"put\\x20the\\x20ranch\\x20bottle\\x20into\\x20the\\x20pot"
12b"pick\\x20up\\x20the\\x20blue\\x20cup\\x20and\\x20put\\x20it\\x20into\\x20the\\x20brown\\x20cup.\\x20"
" - ], - "text/plain": [ - "shape: (3, 2)\n", - "┌────────────┬───────────────────────────────────┐\n", - "│ episode_id ┆ natural_language_instruction │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ binary │\n", - "╞════════════╪═══════════════════════════════════╡\n", - "│ 0 ┆ b\"sweep\\x20the\\x20green\\x20cloth… │\n", - "│ 10 ┆ b\"put\\x20the\\x20ranch\\x20bottle\\… │\n", - "│ 12 ┆ b\"pick\\x20up\\x20the\\x20blue\\x20c… │\n", - "└────────────┴───────────────────────────────────┘" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "id_to_language_instruction = (\n", - " dataset.get_step_data()\n", - " .select(\"episode_id\", \"natural_language_instruction\")# only interested in episode id and language column\n", - " .collect() # the frame is lazily evaluated at memory when we call collect() \n", - ")\n", - "\n", - "# print out unique natural_language_instructions \n", - "# https://docs.pola.rs/py-polars/html/reference/dataframe/api/polars.DataFrame.unique.html \n", - "id_to_language_instruction.unique(subset=[\"natural_language_instruction\"], maintain_order=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c248af4f", - "metadata": {}, - "outputs": [], - "source": [ - "all_step_data = dataset.get_step_data() # get lazy frame of the entire step-level dataset\n", - "id_to_language_instruction = (\n", - " all_step_data\n", - " .select(\"episode_id\", \"natural_language_instruction\") \n", - " .group_by(\"episode_id\") # group by unqiue language ids, since language instruction is stored for every step\n", - " .last() # since instruction is same for all steps in an episode, we can just take the last one\n", - " .collect() # the frame is lazily evaluated until we call collect() \n", - ")\n", - "\n", - "# join with the metadata \n", - "episode_metadata = dataset.get_episode_info().join(id_to_language_instruction, on=\"episode_id\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4978f740", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (6, 2)\n", - "┌────────────┬───────────────────────────────────┐\n", - "│ episode_id ┆ decoded │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ str │\n", - "╞════════════╪═══════════════════════════════════╡\n", - "│ 9 ┆ sweep the green cloth to the lef… │\n", - "│ 4 ┆ sweep the green cloth to the lef… │\n", - "│ 1 ┆ sweep the green cloth to the lef… │\n", - "│ 2 ┆ sweep the green cloth to the lef… │\n", - "│ 0 ┆ sweep the green cloth to the lef… │\n", - "│ 11 ┆ sweep the green cloth to the lef… │\n", - "└────────────┴───────────────────────────────────┘\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_6756/232788706.py:3: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.\n", - " episode_metadata = episode_metadata.with_columns(episode_metadata['natural_language_instruction'].map_elements(lambda x: x.decode('utf-8')).alias('decoded'))\n" - ] - } - ], - "source": [ - "import polars as pl \n", - "# Decode byte strings to strings\n", - "episode_metadata = episode_metadata.with_columns(episode_metadata['natural_language_instruction'].map_elements(lambda x: x.decode('utf-8')).alias('decoded'))\n", - "\n", - "# Filter rows where 'string_col' contains \"example\"\n", - "result = episode_metadata.filter(\n", - " pl.col(\"decoded\").str.contains(\"green|red\").alias(\"cloth\") # supports regex!\n", - ")\n", - "print(result.select([\"episode_id\", \"decoded\"]))" - ] - }, - { - "cell_type": "markdown", - "id": "dc16dd8d", - "metadata": {}, - "source": [ - "We use polars as backend for data processing and management. This example demonstrates its capabaility and flexiblitiy. Please refer to https://docs.pola.rs/py-polars/html/reference/lazyframe/index.html all the available interfaces " - ] - }, - { - "cell_type": "markdown", - "id": "851a95a5", - "metadata": {}, - "source": [ - "## Use, Export and Share" - ] - }, - { - "cell_type": "markdown", - "id": "8e4ed6a6", - "metadata": {}, - "source": [ - "### Huggingface dataset " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c7bb9c0d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e422d249b5c441bd9e85e7b128465982", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Generating train split: 0 examples [00:00, ? examples/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hugging face dataset: DatasetDict({\n", - " train: Dataset({\n", - " features: ['episode_id', 'Timestamp', 'gripper_closedness_action', 'rotation_delta', 'terminate_episode', 'world_vector', 'is_first', 'is_last', 'is_terminal', 'hand_image', 'image', 'image_with_depth', 'natural_language_embedding', 'natural_language_instruction', 'robot_state', 'reward'],\n", - " num_rows: 1217\n", - " })\n", - "})\n" - ] - } - ], - "source": [ - "import datasets\n", - "\n", - "huggingface_ds = dataset.get_as_huggingface_dataset()\n", - "\n", - "print(f\"Hugging face dataset: {huggingface_ds}\")" - ] - }, - { - "cell_type": "markdown", - "id": "fd38e642", - "metadata": {}, - "source": [ - "### Pytorch Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "3c54437b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Retrieving episode at index 0\n", - "Retrieving episode at index 1\n", - "[ episode_id Timestamp gripper_closedness_action \\\n", - "0 11 1712728768601166160 0.0 \n", - "1 11 1712728768839768104 0.0 \n", - "2 11 1712728768983350023 0.0 \n", - "3 11 1712728769119575319 0.0 \n", - "4 11 1712728769256151909 0.0 \n", - ".. ... ... ... \n", - "75 11 1712728781218967667 0.0 \n", - "76 11 1712728781437725750 0.0 \n", - "77 11 1712728781613065131 0.0 \n", - "78 11 1712728781822132558 0.0 \n", - "79 11 1712728781969148910 0.0 \n", - "\n", - " rotation_delta terminate_episode \\\n", - "0 b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '\n", - "shape: (1, 8)
episode_idFinishedfeature_arm_camera_view_typefeature_arm_camera_view_shapearm_camera_view_countfeature_gripper_acton_typefeature_gripper_acton_shapegripper_acton_count
i64boolstrstrf64strstrf64
0true"float64""(480, 640, 3)"0.0"float64""(7,)"0.0
" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Adding new data to the dataset" - ], - "metadata": { - "id": "lcij8xiWui0P" - } - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "\n", - "# create a new trajectory\n", - "episode = dataset.new_episode()\n", - "# collect step data for the episode\n", - "episode.add(feature = \"arm_camera_view\", value = np.random.rand(480, 640, 3))\n", - "episode.add(feature = \"gripper_acton\", value = np.random.rand(7))\n", - "# Automatically time-aligns and saves the trajectory\n", - "episode.close()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "akiVQqstdnWR", - "outputId": "a71f273a-025e-4102-cab5-6ecc398140ff" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:fog_x.database.db_manager:Closing the episode with metadata {}\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "dataset.get_episode_info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 161 - }, - "id": "uHZZnvAmeqqx", - "outputId": "a827585e-d5d0-4fd7-ce9c-51350e50de71" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "shape: (2, 8)\n", - "┌────────────┬──────────┬────────────┬────────────┬────────────┬───────────┬───────────┬───────────┐\n", - "│ episode_id ┆ Finished ┆ feature_ar ┆ feature_ar ┆ arm_camera ┆ feature_g ┆ feature_g ┆ gripper_a │\n", - "│ --- ┆ --- ┆ m_camera_v ┆ m_camera_v ┆ _view_coun ┆ ripper_ac ┆ ripper_ac ┆ cton_coun │\n", - "│ i64 ┆ bool ┆ iew_type ┆ iew_shape ┆ t ┆ ton_type ┆ ton_shape ┆ t │\n", - "│ ┆ ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ str ┆ str ┆ f64 ┆ str ┆ str ┆ f64 │\n", - "╞════════════╪══════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 0 ┆ true ┆ float64 ┆ (480, 640, ┆ 0.0 ┆ float64 ┆ (7,) ┆ 0.0 │\n", - "│ ┆ ┆ ┆ 3) ┆ ┆ ┆ ┆ │\n", - "│ 1 ┆ true ┆ float64 ┆ (480, 640, ┆ 0.0 ┆ float64 ┆ (7,) ┆ 0.0 │\n", - "│ ┆ ┆ ┆ 3) ┆ ┆ ┆ ┆ │\n", - "└────────────┴──────────┴────────────┴────────────┴────────────┴───────────┴───────────┴───────────┘" - ], - "text/html": [ - "
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episode_idFinishedfeature_arm_camera_view_typefeature_arm_camera_view_shapearm_camera_view_countfeature_gripper_acton_typefeature_gripper_acton_shapegripper_acton_count
i64boolstrstrf64strstrf64
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" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Load Cloud Dataset at different place!\n", - "The data is automatically uploaded to the cloud!\n", - "We can create a different reader (you can run this on a different machine).\n", - "The data is automatically loaded and read!" - ], - "metadata": { - "id": "mUneci9XeHsE" - } - }, - { - "cell_type": "code", - "source": [ - "dataset2 = fog_x.dataset.Dataset(\n", - " name=\"demo_ds\",\n", - " path='s3://fog-rtx-test-east-2',\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cQHIKeNAeSrY", - "outputId": "421fb7d5-9839-4ab7-c935-26025ba783d3" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:fog_x.database.polars_connector:Prepare to load table demo_ds loaded from s3://fog-rtx-test-east-2/demo_ds.parquet.\n", - "INFO:fog_x.database.polars_connector:Table demo_ds loaded from s3://fog-rtx-test-east-2/demo_ds.parquet.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# metadata\n", - "trajectory_metadata = dataset2.get_episode_info()\n", - "trajectory_metadata" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 161 - }, - "id": "E4slMiSzf-se", - "outputId": "79b9813c-beac-4ad2-8c06-625e3d388754" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "shape: (2, 8)\n", - "┌────────────┬──────────┬────────────┬────────────┬────────────┬───────────┬───────────┬───────────┐\n", - "│ episode_id ┆ Finished ┆ feature_ar ┆ feature_ar ┆ arm_camera ┆ feature_g ┆ feature_g ┆ gripper_a │\n", - "│ --- ┆ --- ┆ m_camera_v ┆ m_camera_v ┆ _view_coun ┆ ripper_ac ┆ ripper_ac ┆ cton_coun │\n", - "│ i64 ┆ bool ┆ iew_type ┆ iew_shape ┆ t ┆ ton_type ┆ ton_shape ┆ t │\n", - "│ ┆ ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ str ┆ str ┆ f64 ┆ str ┆ str ┆ f64 │\n", - "╞════════════╪══════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 0 ┆ true ┆ float64 ┆ (480, 640, ┆ 0.0 ┆ float64 ┆ (7,) ┆ 0.0 │\n", - "│ ┆ ┆ ┆ 3) ┆ ┆ ┆ ┆ │\n", - "│ 1 ┆ true ┆ float64 ┆ (480, 640, ┆ 0.0 ┆ float64 ┆ (7,) ┆ 0.0 │\n", - "│ ┆ ┆ ┆ 3) ┆ ┆ ┆ ┆ │\n", - "└────────────┴──────────┴────────────┴────────────┴────────────┴───────────┴───────────┴───────────┘" - ], - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "execution_count": 10 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Google Cloud Platform" - ], - "metadata": { - "id": "cB7QVbp6i-Mx" - } - }, - { - "cell_type": "markdown", - "source": [ - "This can also be done on GCP!\n", - "\n", - "Register google cloud credentials\n", - "\n", - "Alternative in non-colab environment, run following command instead:\n", - "```\n", - "gcloud auth application-default login --quiet --no-launch-browser\n", - "```\n" - ], - "metadata": { - "id": "8MIV3MZUjNta" - } - }, - { - "cell_type": "code", - "source": [ - "from google.colab import auth\n", - "PROJECT_ID = \"canvas-rampart-342500\"\n", - "auth.authenticate_user(project_id=PROJECT_ID)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ryd_To6LL3nX", - "outputId": "714ea38c-11d9-44fd-b8c4-5cb4ebd8b242" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:google.colab.auth:Failure refreshing credentials: (\"Failed to retrieve http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/?recursive=true from the Google Compute Engine metadata service. Status: 404 Response:\\nb''\", )\n", - "INFO:google.colab.auth:Failure refreshing credentials: (\"Failed to retrieve http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/?recursive=true from the Google Compute Engine metadata service. Status: 404 Response:\\nb''\", )\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "! gcloud storage buckets create gs://fog_rtx_test --location=us-east1" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "fYM3ExvGL3z7", - "outputId": "31c6bc57-4c3a-4b6f-b7ef-4132af7a926c" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Creating gs://fog_rtx_test/...\n", - "\u001b[1;31mERROR:\u001b[0m (gcloud.storage.buckets.create) HTTPError 409: Your previous request to create the named bucket succeeded and you already own it.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "dataset = fog_x.dataset.Dataset(\n", - " name=\"demo_ds\",\n", - " path='gs://fog_rtx_test/',\n", - ")" - ], - "metadata": { - "id": "pd94S4VlL32u", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "840c1668-983d-4320-f052-34ab77bb5930" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:fog_x.database.polars_connector:Prepare to load table demo_ds loaded from gs://fog_rtx_test/demo_ds.parquet.\n", - "WARNING:fog_x.database.polars_connector:Failed to load table demo_ds from gs://fog_rtx_test/demo_ds.parquet.\n", - "ERROR:fog_x.database.polars_connector:Table demo_ds does not exist, available tables are dict_keys([]).\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "\n", - "# create a new trajectory\n", - "episode = dataset.new_episode()\n", - "# collect step data for the episode\n", - "episode.add(feature = \"arm_camera_view\", value = np.random.rand(480, 640, 3))\n", - "episode.add(feature = \"gripper_acton\", value = np.random.rand(7))\n", - "# Automatically time-aligns and saves the trajectory\n", - "episode.close()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Boc13CkhmQEs", - "outputId": "7aa83acf-ce3e-437b-975c-00df0cb999b0" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:fog_x.database.db_manager:Closing the episode with metadata {'Finished': True, 'arm_camera_view_count': 0, 'gripper_acton_count': 0}\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "dataset2 = fog_x.dataset.Dataset(\n", - " name=\"demo_ds\",\n", - " path='gs://fog_rtx_test/',\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LtzsrO_BtvHB", - "outputId": "5c5c2bec-f769-4bc2-e185-638a42127af6" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "INFO:fog_x.database.polars_connector:Prepare to load table demo_ds loaded from gs://fog_rtx_test/demo_ds.parquet.\n", - "INFO:fog_x.database.polars_connector:Table demo_ds loaded from gs://fog_rtx_test/demo_ds.parquet.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "dataset2.get_episode_info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 129 - }, - "id": "95utD8pRtxws", - "outputId": "0871ad47-d812-41fe-8cc6-67bbb77fe10e" - }, - "execution_count": 18, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "shape: (1, 8)\n", - "┌────────────┬──────────┬────────────┬────────────┬────────────┬───────────┬───────────┬───────────┐\n", - "│ episode_id ┆ Finished ┆ feature_ar ┆ feature_ar ┆ arm_camera ┆ feature_g ┆ feature_g ┆ gripper_a │\n", - "│ --- ┆ --- ┆ m_camera_v ┆ m_camera_v ┆ _view_coun ┆ ripper_ac ┆ ripper_ac ┆ cton_coun │\n", - "│ i64 ┆ bool ┆ iew_type ┆ iew_shape ┆ t ┆ ton_type ┆ ton_shape ┆ t │\n", - "│ ┆ ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ str ┆ str ┆ f64 ┆ str ┆ str ┆ f64 │\n", - "╞════════════╪══════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 0 ┆ true ┆ float64 ┆ (480, 640, ┆ 0.0 ┆ float64 ┆ (7,) ┆ 0.0 │\n", - "│ ┆ ┆ ┆ 3) ┆ ┆ ┆ ┆ │\n", - "└────────────┴──────────┴────────────┴────────────┴────────────┴───────────┴───────────┴───────────┘" - ], - "text/html": [ - "
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0true"float64""(480, 640, 3)"0.0"float64""(7,)"0.0
" - ] - }, - "metadata": {}, - "execution_count": 18 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Known issues\n", - "\n", - "1. `export` as rlds format to the cloud directly does not work yet for S3 (known issue for tensorflow Gfile)\n", - "2. (will fix) automatically check the existence" - ], - "metadata": { - "id": "P2RCUMs6knNc" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "QKS5jK-Qk9fN" - }, - "execution_count": 14, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/examples/hello_world.py b/examples/data_collection_and_load.py similarity index 100% rename from examples/hello_world.py rename to examples/data_collection_and_load.py diff --git a/examples/dataloader/huggingface.py b/examples/dataloader/huggingface.py deleted file mode 100644 index ca12a8c..0000000 --- a/examples/dataloader/huggingface.py +++ /dev/null @@ -1,15 +0,0 @@ -import fog_x - -dataset = fog_x.dataset.Dataset( - name="demo_ds", - path="~/test_dataset", -) - -dataset.load_rtx_episodes( - name="berkeley_autolab_ur5", - split="train[:1]", -) - -huggingface_ds = dataset.get_as_huggingface_dataset() - -print(f"Hugging face dataset: {huggingface_ds}") \ No newline at end of file diff --git a/examples/dataloader/pytorch.py b/examples/dataloader/pytorch.py deleted file mode 100644 index 95467d7..0000000 --- a/examples/dataloader/pytorch.py +++ /dev/null @@ -1,35 +0,0 @@ -import torch - -import fog_x - -dataset = fog_x.dataset.Dataset( - name="demo_ds", - path="/tmp", -) - -# dataset.load_rtx_episodes( -# name="berkeley_autolab_ur5", -# split="train[:2]", -# additional_metadata={"collector": "User 1"}, -# ) - -dataset.load_rtx_episodes( - name="berkeley_autolab_ur5", - split="train[3:5]", - additional_metadata={"collector": "User 2"}, -) - -metadata = dataset.get_episode_info() -metadata = metadata.filter(metadata["collector"] == "User 2") -pytorch_ds = dataset.pytorch_dataset_builder( - metadata=metadata -) - -# get samples from the dataset -for data in torch.utils.data.DataLoader( - pytorch_ds, - batch_size=2, - collate_fn=lambda x: x, - sampler=torch.utils.data.RandomSampler(pytorch_ds), -): - print(data) diff --git a/examples/h5_loader.py b/examples/h5_loader.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/rtx_loader.py b/examples/rtx_loader.py index 120686b..e9ed62a 100644 --- a/examples/rtx_loader.py +++ b/examples/rtx_loader.py @@ -1,20 +1,19 @@ - from fog_x.loader import RLDSLoader import fog_x -import os +import os + os.system("rm -rf /tmp/fog_x/*") loader = RLDSLoader( - path = "/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", - split = "train[:10]" + path="/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", split="train[:10]" ) index = 0 for data_traj in loader: - - fog_x.Trajectory.from_list_of_dicts(data_traj, path = f"/tmp/fog_x/output_{index}.vla") - index += 1 - + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"/tmp/fog_x/output_{index}.vla" + ) + index += 1 diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 1204058..71a65ea 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -15,10 +15,12 @@ class Trajectory: - def __init__(self, - path: Text, - num_pre_initialized_h264_streams: int = 5, - feature_name_separator:Text = "/") -> None: + def __init__( + self, + path: Text, + num_pre_initialized_h264_streams: int = 5, + feature_name_separator: Text = "/", + ) -> None: """ Args: path (Text): path to the trajectory file @@ -159,14 +161,13 @@ def add( - if value is numpy array, create a frame and encode it - if it is a string or int, create a packet and encode it - else raise an error - + Exceptions: raise an error if the value is a dictionary """ - + if type(data) == dict: raise ValueError("Use add_by_dict for dictionary") - feature_type = FeatureType.from_data(data) encoding = self._get_encoding_of_feature(data, None) @@ -218,8 +219,8 @@ def add_by_dict( """ if type(data) != dict: raise ValueError("Use add for non-dictionary data") - - def flatten_dict(d, parent_key='', sep='_'): + + def flatten_dict(d, parent_key="", sep="_"): items = [] for k, v in d.items(): new_key = parent_key + sep + k if parent_key else k @@ -228,12 +229,11 @@ def flatten_dict(d, parent_key='', sep='_'): else: items.append((new_key, v)) return dict(items) - + flatten_dict_data = flatten_dict(data, sep=self.feature_name_separator) timestamp = self._get_current_timestamp() if timestamp is None else timestamp for feature, value in flatten_dict_data.items(): self.add(feature, value, timestamp) - @classmethod def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajectory": @@ -245,7 +245,6 @@ def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajecto traj.add_by_dict(step) return traj - def _load_from_cache(self): """ load the cached file with entire vla trajctory @@ -383,12 +382,12 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): raise ValueError("No pre-initialized h264 streams available") if not self.feature_name_to_stream: - logger.info(f"Creating a new stream for the first feature {new_feature}") + logger.debug(f"Creating a new stream for the first feature {new_feature}") self.feature_name_to_stream[new_feature] = self._add_stream_to_container( self.container_file, new_feature, new_encoding, new_feature_type ) else: - logger.info(f"Adding a new stream for the feature {new_feature}") + logger.debug(f"Adding a new stream for the feature {new_feature}") # Following is a workaround because we cannot add new streams to an existing container # Close current container self.close() From e41675ae0d9f25ff8fe9f043f63bb4b732acfe89 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Thu, 22 Aug 2024 12:10:12 -0700 Subject: [PATCH 20/80] feat: Add HDF5Loader to support loading HDF5 files in fog_x/loader/__init__.py --- examples/analytics/README.md | 9 -- examples/analytics/dataset_organizer.py | 130 ------------------------ examples/analytics/extract_column.py | 23 ----- examples/h5_loader.py | 16 +++ examples/rtx_example/__init__.py | 0 examples/rtx_example/load.py | 13 --- examples/rtx_example/merge.py | 30 ------ fog_x/feature.py | 2 + fog_x/loader/__init__.py | 1 + fog_x/loader/hdf5.py | 55 ++++++++++ fog_x/trajectory.py | 68 +++++++++++-- 11 files changed, 131 insertions(+), 216 deletions(-) delete mode 100644 examples/analytics/README.md delete mode 100644 examples/analytics/dataset_organizer.py delete mode 100644 examples/analytics/extract_column.py delete mode 100644 examples/rtx_example/__init__.py delete mode 100644 examples/rtx_example/load.py delete mode 100644 examples/rtx_example/merge.py create mode 100644 fog_x/loader/hdf5.py diff --git a/examples/analytics/README.md b/examples/analytics/README.md deleted file mode 100644 index b1bd4b4..0000000 --- a/examples/analytics/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Planned Data Analytics Examples - - -Since the episode metadata is dataframe that is very easy to work with, we demonstrate -the capability with the following examples that work on the actual step data. -* **extract and group columns**: we extract natural language instruction from steps and use it to tag episodes (done) -* **batch transformation**: we resize images. This involves creating a column, resizing images, adding a new column to store the images, and save the transformation -* **tagging** This runs yolo on the first frame and save the tag to the metadata -* **summary stats** aggregate a dataset-wise average of a matrix \ No newline at end of file diff --git a/examples/analytics/dataset_organizer.py b/examples/analytics/dataset_organizer.py deleted file mode 100644 index 222ddd8..0000000 --- a/examples/analytics/dataset_organizer.py +++ /dev/null @@ -1,130 +0,0 @@ -import fog_x - -DATASETS = [ - "fractal20220817_data", - "kuka", - "bridge", - "taco_play", - "jaco_play", - "berkeley_cable_routing", - "roboturk", - "nyu_door_opening_surprising_effectiveness", - "viola", - "berkeley_autolab_ur5", - "toto", - "columbia_cairlab_pusht_real", - "stanford_kuka_multimodal_dataset_converted_externally_to_rlds", - "nyu_rot_dataset_converted_externally_to_rlds", - "stanford_hydra_dataset_converted_externally_to_rlds", - "austin_buds_dataset_converted_externally_to_rlds", - "nyu_franka_play_dataset_converted_externally_to_rlds", - "maniskill_dataset_converted_externally_to_rlds", - "cmu_franka_exploration_dataset_converted_externally_to_rlds", - "ucsd_kitchen_dataset_converted_externally_to_rlds", - "ucsd_pick_and_place_dataset_converted_externally_to_rlds", - "austin_sailor_dataset_converted_externally_to_rlds", - "austin_sirius_dataset_converted_externally_to_rlds", - "bc_z", - "usc_cloth_sim_converted_externally_to_rlds", - "utokyo_pr2_opening_fridge_converted_externally_to_rlds", - "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds", - "utokyo_saytap_converted_externally_to_rlds", - "utokyo_xarm_pick_and_place_converted_externally_to_rlds", - "utokyo_xarm_bimanual_converted_externally_to_rlds", - "robo_net", - "berkeley_mvp_converted_externally_to_rlds", - "berkeley_rpt_converted_externally_to_rlds", - "kaist_nonprehensile_converted_externally_to_rlds", - "stanford_mask_vit_converted_externally_to_rlds", - "tokyo_u_lsmo_converted_externally_to_rlds", - "dlr_sara_pour_converted_externally_to_rlds", - "dlr_sara_grid_clamp_converted_externally_to_rlds", - "dlr_edan_shared_control_converted_externally_to_rlds", - "asu_table_top_converted_externally_to_rlds", - "stanford_robocook_converted_externally_to_rlds", - "eth_agent_affordances", - "imperialcollege_sawyer_wrist_cam", - "iamlab_cmu_pickup_insert_converted_externally_to_rlds", - "uiuc_d3field", - "utaustin_mutex", - "berkeley_fanuc_manipulation", - "cmu_play_fusion", - "cmu_stretch", - "berkeley_gnm_recon", - "berkeley_gnm_cory_hall", - # "berkeley_gnm_sac_son", -] - - -objects = ["NOTEXIST", "marker", "cloth", "cup", "object", "bottle", "block", "drawer", "lid", "mug"] -tasks = ["NOTEXIST", "put", "move", "pick", "remove", "take", "open", "close", "place", "turn", "push", - "insert", "stack", "lift", "pour"] # things not in DROID -views = ["NOTEXIST", "wrist", "top", "other"] - -dataset_id = 0 -for dataset_name in DATASETS: - dataset = fog_x.dataset.Dataset( - name=dataset_name, - path="~/rtx_datasets", - ) - - dataset._prepare_rtx_metadata( - name=dataset_name, - sample_size = 100, - shuffle=True, - ) - -for dataset_name in DATASETS: - dataset = fog_x.dataset.Dataset( - name=dataset_name, - path="~/rtx_datasets", - ) - info = dataset.get_episode_info() - - for episode_metadata in info.iter_rows(named = True): - instruction = episode_metadata["natural_language_instruction"] - - d = dict() - instruction = instruction.lower().replace(",", "").replace("\n", "").replace("\"", "").replace("\'", "") - d["dataset_id"] = f"dataset-{dataset_id}" - d["info"] = instruction - task_id = -1 - for task in tasks: - if task in instruction: - task_id = tasks.index(task) - if task_id == -1: - task_id = len(tasks) - 1 - - obj_id = -1 - for obj in objects: - if obj in instruction: - obj_id = objects.index(obj) - if obj_id == -1: - obj_id = len(objects) - 1 - - d["task_id"] = f"task-{task_id}" - d["object_id"] = f"object-{obj_id}" - - images_features = [col for col in info.columns if col.startswith("video_path_")] - for i, image_feature in enumerate(images_features): - path = episode_metadata[image_feature] - d["poster"] = f"videos/{dataset_name}_viz/{path}.jpg" - d["src"] = f"videos/{dataset_name}_viz/{path}.mp4" - view_id = -1 - for view in views: - if view in path: - view_id = views.index(view) - if view_id == -1: - view_id = len(views) - 1 - - d["view_id"] = f"view-{view_id}" - - # print d in JSON format - with open("/tmp/dataset_info.txt", "a") as file: - printable = str(d).replace("\'", "\"") - file.write(f'JSON.parse(\'{printable}\'),\n') - - - # write as a line of JSON.parse('{"info": "Unfold the tea towel", "poster": "videos/bridge_viz/bridge_0_image.jpg", "src": "videos/bridge_viz/bridge_0_image.mp4"}'), - # print (f'JSON.parse(\'{{"info": "{instruction}", "poster": "videos/{dataset_name}_viz/{dataset_name}_{episode_id}_image.jpg", "src": "videos/{dataset_name}_viz/{dataset_name}_{dataset_id}_image.mp4"}}\'),') - dataset_id += 1 \ No newline at end of file diff --git a/examples/analytics/extract_column.py b/examples/analytics/extract_column.py deleted file mode 100644 index ef321d4..0000000 --- a/examples/analytics/extract_column.py +++ /dev/null @@ -1,23 +0,0 @@ -import fog_x - -dataset = fog_x.dataset.Dataset( - name="demo_ds", - path="~/test_dataset", -) - -dataset.load_rtx_episodes( - name="berkeley_autolab_ur5", - split="train[:5]", -) - -all_step_data = dataset.get_step_data() # get lazy polars frame of the entire dataset -id_to_language_instruction = ( - all_step_data - .select("episode_id", "natural_language_instruction") # only interested in episode id and language column - .group_by("episode_id") # group by unqiue language ids, since language instruction is stored for every step - .last() # since instruction is same for all steps in an episode, we can just take the last one - .collect() # the frame is lazily evaluated if we call collect() -) - -# join with the trajectory metadata -dataset.get_episode_info().join(id_to_language_instruction, on="episode_id") diff --git a/examples/h5_loader.py b/examples/h5_loader.py index e69de29..0bb01aa 100644 --- a/examples/h5_loader.py +++ b/examples/h5_loader.py @@ -0,0 +1,16 @@ +from fog_x.loader.hdf5 import HDF5Loader +import fog_x + +import os +os.system("rm -rf /tmp/fog_x/*") + +loader = HDF5Loader("/home/kych/datasets/2024-07-03-red-on-cyan/**/trajectory_im128.h5") + +index = 0 + +for data_traj in loader: + + fog_x.Trajectory.from_dict_of_lists( + data_traj, path=f"/tmp/fog_x/output_{index}.vla" + ) + index += 1 diff --git a/examples/rtx_example/__init__.py b/examples/rtx_example/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/examples/rtx_example/load.py b/examples/rtx_example/load.py deleted file mode 100644 index 2e90540..0000000 --- a/examples/rtx_example/load.py +++ /dev/null @@ -1,13 +0,0 @@ -import fog_x - -dataset = fog_x.dataset.Dataset( - name="demo_ds", - path="~/test_dataset", -) - -dataset.load_rtx_episodes( - name="berkeley_autolab_ur5", - split="train[:1]", -) - -dataset.export(format="rtx") diff --git a/examples/rtx_example/merge.py b/examples/rtx_example/merge.py deleted file mode 100644 index 2029ae7..0000000 --- a/examples/rtx_example/merge.py +++ /dev/null @@ -1,30 +0,0 @@ -import fog_x - -dataset = fog_x.dataset.Dataset( - name="demo_ds", - path="~/test_dataset", -) - -dataset.load_rtx_episodes( - name="berkeley_autolab_ur5", - split="train[:2]", - additional_metadata={"collector": "User 1", "custom_tag": "Partition_1"}, -) - -dataset.load_rtx_episodes( - name="berkeley_autolab_ur5", - split="train[3:5]", - additional_metadata={"collector": "User 2", "custom_tag": "Partition_2"}, -) -# dataset.num_episodes == 4 - -# query the dataset -episode_info = dataset.get_episode_info() -print(episode_info) -# only get the episodes with custom_tag == "Partition_1" -metadata = episode_info.filter(episode_info["custom_tag"] == "Partition_1") -episodes = dataset.read_by(metadata) - -# read the episodes -for episode in episodes: - print(episode) diff --git a/fog_x/feature.py b/fog_x/feature.py index 58eeb08..8cadd47 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -117,6 +117,8 @@ def from_data(self, data: Any): feature_type = FeatureType() if isinstance(data, np.ndarray): feature_type._set(data.dtype.name, data.shape) + elif isinstance(data, np.bool_): + feature_type._set("bool", ()) elif isinstance(data, list): dtype = type(data[0]).__name__ shape = (len(data),) diff --git a/fog_x/loader/__init__.py b/fog_x/loader/__init__.py index 189034e..9a45341 100644 --- a/fog_x/loader/__init__.py +++ b/fog_x/loader/__init__.py @@ -1,2 +1,3 @@ from .base import BaseLoader from .rlds import RLDSLoader +from .hdf5 import HDF5Loader \ No newline at end of file diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py new file mode 100644 index 0000000..14c3209 --- /dev/null +++ b/fog_x/loader/hdf5.py @@ -0,0 +1,55 @@ + + +from . import BaseLoader +import numpy as np +import glob +import h5py +# flatten the data such that all data starts with root level tree (observation and action) +def _flatten(data, parent_key='', sep='/'): + items = {} + for k, v in data.items(): + new_key = parent_key + sep + k if parent_key else k + if isinstance(v, dict): + items.update(_flatten(v, new_key, sep)) + else: + items[new_key] = v + return items + +def recursively_read_hdf5_group(group): + if isinstance(group, h5py.Dataset): + return np.array(group) + elif isinstance(group, h5py.Group): + return {key: recursively_read_hdf5_group(value) for key, value in group.items()} + else: + raise TypeError("Unsupported HDF5 group type") + + +class HDF5Loader(BaseLoader): + def __init__(self, path): + super(HDF5Loader, self).__init__(path) + self.index = 0 + self.files = glob.glob(self.path, recursive=True) + + def _read_hdf5(self, data_path): + + with h5py.File(data_path, "r") as f: + data_unflattened = recursively_read_hdf5_group(f) + + data = {} + data["observation"] = _flatten(data_unflattened["observation"]) + data["action"] = _flatten(data_unflattened["action"]) + + return data + + def __iter__(self): + return self + + def __next__(self): + # for now naming convention: + # h/home/kych/datasets/stacking_blocks_trajectories_data/**/trajectory.h5 + if self.index < len(self.files): + file_path = self.files[self.index] + self.index += 1 + return self._read_hdf5(file_path) + raise StopIteration + \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 71a65ea..0f0c6bf 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -14,6 +14,16 @@ logging.getLogger("libav").setLevel(logging.CRITICAL) +def flatten_dict(d, parent_key="", sep="_"): + items = [] + for k, v in d.items(): + new_key = parent_key + sep + k if parent_key else k + if isinstance(v, dict): + items.extend(flatten_dict(v, new_key, sep=sep).items()) + else: + items.append((new_key, v)) + return dict(items) + class Trajectory: def __init__( self, @@ -218,17 +228,7 @@ def add_by_dict( - if dictionary, need to flatten it and add each feature separately """ if type(data) != dict: - raise ValueError("Use add for non-dictionary data") - - def flatten_dict(d, parent_key="", sep="_"): - items = [] - for k, v in d.items(): - new_key = parent_key + sep + k if parent_key else k - if isinstance(v, dict): - items.extend(flatten_dict(v, new_key, sep=sep).items()) - else: - items.append((new_key, v)) - return dict(items) + raise ValueError("Use add for non-dictionary data, type is ", type(data)) flatten_dict_data = flatten_dict(data, sep=self.feature_name_separator) timestamp = self._get_current_timestamp() if timestamp is None else timestamp @@ -239,11 +239,57 @@ def flatten_dict(d, parent_key="", sep="_"): def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajectory": """ Create a Trajectory object from a list of dictionaries. + + args: + data (List[Dict[str, Any]]): list of dictionaries + path (Text): path to the trajectory file + + Example: + original_trajectory = [ + {"feature1": "value1", "feature2": "value2"}, + {"feature1": "value3", "feature2": "value4"}, + ] + + trajectory = Trajectory.from_list_of_dicts(original_trajectory, path="/tmp/fog_x/output.vla") """ traj = cls(path) for step in data: traj.add_by_dict(step) return traj + + @classmethod + def from_dict_of_lists(cls, data: Dict[str, List[Any]], path: Text, feature_name_separator:Text = "/") -> "Trajectory": + """ + Create a Trajectory object from a dictionary of lists. + + Args: + data (Dict[str, List[Any]]): dictionary of lists. Assume list length is the same for all features. + path (Text): path to the trajectory file + + Returns: + Trajectory: _description_ + + Example: + original_trajectory = { + "feature1": ["value1", "value3"], + "feature2": ["value2", "value4"], + } + + trajectory = Trajectory.from_dict_of_lists(original_trajectory, path="/tmp/fog_x/output.vla") + """ + traj = cls(path, feature_name_separator=feature_name_separator) + # flatten the data such that all data starts and put feature name with separator + flatten_dict_data = flatten_dict(data, sep=traj.feature_name_separator) + + # Check if all lists have the same length + list_lengths = [len(v) for v in flatten_dict_data.values()] + if len(set(list_lengths)) != 1: + raise ValueError("All lists must have the same length", [(k, len(v)) for k, v in flatten_dict_data.items()]) + + for i in range(list_lengths[0]): + step = {k: v[i] for k, v in flatten_dict_data.items()} + traj.add_by_dict(step) + return traj def _load_from_cache(self): """ From c34a7c0ee7c1a4d5ff3ceed5e1bec0ba3aa47d61 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Thu, 22 Aug 2024 13:53:25 -0700 Subject: [PATCH 21/80] add h5 accessing --- examples/h5_loader.py | 17 +++-- fog_x/rlds/__init__.py | 1 - fog_x/rlds/utils.py | 98 ------------------------ fog_x/rlds/writer.py | 170 ----------------------------------------- fog_x/trajectory.py | 58 +++++++------- 5 files changed, 41 insertions(+), 303 deletions(-) delete mode 100644 fog_x/rlds/__init__.py delete mode 100644 fog_x/rlds/utils.py delete mode 100644 fog_x/rlds/writer.py diff --git a/examples/h5_loader.py b/examples/h5_loader.py index 0bb01aa..d8f36d0 100644 --- a/examples/h5_loader.py +++ b/examples/h5_loader.py @@ -2,15 +2,20 @@ import fog_x import os -os.system("rm -rf /tmp/fog_x/*") +# os.system("rm -rf /tmp/fog_x/*") loader = HDF5Loader("/home/kych/datasets/2024-07-03-red-on-cyan/**/trajectory_im128.h5") index = 0 -for data_traj in loader: +# for data_traj in loader: - fog_x.Trajectory.from_dict_of_lists( - data_traj, path=f"/tmp/fog_x/output_{index}.vla" - ) - index += 1 +# fog_x.Trajectory.from_dict_of_lists( +# data_traj, path=f"/tmp/fog_x/output_{index}.vla" +# ) +# index += 1 + + +# read the data back +for i in range(1): + print(fog_x.Trajectory(f"/tmp/fog_x/output_{i}.vla")["action"].keys()) \ No newline at end of file diff --git a/fog_x/rlds/__init__.py b/fog_x/rlds/__init__.py deleted file mode 100644 index 5e0b1ef..0000000 --- a/fog_x/rlds/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from fog_x.rlds import utils diff --git a/fog_x/rlds/utils.py b/fog_x/rlds/utils.py deleted file mode 100644 index 11ad695..0000000 --- a/fog_x/rlds/utils.py +++ /dev/null @@ -1,98 +0,0 @@ -import numpy as np -import tensorflow_datasets as tfds # type: ignore -from PIL import Image - -DATASETS = [ - "fractal20220817_data", - "kuka", - "bridge", - "taco_play", - "jaco_play", - "berkeley_cable_routing", - "roboturk", - "nyu_door_opening_surprising_effectiveness", - "viola", - "berkeley_autolab_ur5", - "toto", - "language_table", - "columbia_cairlab_pusht_real", - "stanford_kuka_multimodal_dataset_converted_externally_to_rlds", - "nyu_rot_dataset_converted_externally_to_rlds", - "stanford_hydra_dataset_converted_externally_to_rlds", - "austin_buds_dataset_converted_externally_to_rlds", - "nyu_franka_play_dataset_converted_externally_to_rlds", - "maniskill_dataset_converted_externally_to_rlds", - "cmu_franka_exploration_dataset_converted_externally_to_rlds", - "ucsd_kitchen_dataset_converted_externally_to_rlds", - "ucsd_pick_and_place_dataset_converted_externally_to_rlds", - "austin_sailor_dataset_converted_externally_to_rlds", - "austin_sirius_dataset_converted_externally_to_rlds", - "bc_z", - "usc_cloth_sim_converted_externally_to_rlds", - "utokyo_pr2_opening_fridge_converted_externally_to_rlds", - "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds", - "utokyo_saytap_converted_externally_to_rlds", - "utokyo_xarm_pick_and_place_converted_externally_to_rlds", - "utokyo_xarm_bimanual_converted_externally_to_rlds", - "robo_net", - "berkeley_mvp_converted_externally_to_rlds", - "berkeley_rpt_converted_externally_to_rlds", - "kaist_nonprehensile_converted_externally_to_rlds", - "stanford_mask_vit_converted_externally_to_rlds", - "tokyo_u_lsmo_converted_externally_to_rlds", - "dlr_sara_pour_converted_externally_to_rlds", - "dlr_sara_grid_clamp_converted_externally_to_rlds", - "dlr_edan_shared_control_converted_externally_to_rlds", - "asu_table_top_converted_externally_to_rlds", - "stanford_robocook_converted_externally_to_rlds", - "eth_agent_affordances", - "imperialcollege_sawyer_wrist_cam", - "iamlab_cmu_pickup_insert_converted_externally_to_rlds", - "uiuc_d3field", - "utaustin_mutex", - "berkeley_fanuc_manipulation", - "cmu_play_fusion", - "cmu_stretch", - "berkeley_gnm_recon", - "berkeley_gnm_cory_hall", - "berkeley_gnm_sac_son", -] - - -def dataset2path(dataset_name): - if dataset_name == "robo_net": - version = "1.0.0" - elif dataset_name == "language_table": - version = "0.0.1" - else: - version = "0.1.0" - return f"gs://gresearch/robotics/{dataset_name}/{version}" - - -def as_gif(images, path="temp.gif"): - # Render the images as the gif: - images[0].save( - path, save_all=True, append_images=images[1:], duration=1000, loop=0 - ) - gif_bytes = open(path, "rb").read() - return gif_bytes - - -def get_dataset_info(datasets): - """ - Get information about the datasets. - - Args: - datasets (list): List of dataset names. - - Returns: - list: List of tuples containing dataset name and dataset information. - """ - ret = [] - for name in datasets: - uri = dataset2path(name) - b = tfds.builder_from_directory(builder_dir=uri) - split = list(b.info.splits.keys())[0] - b.as_dataset(split=split) - ret.append((name, b.info)) - return ret diff --git a/fog_x/rlds/writer.py b/fog_x/rlds/writer.py deleted file mode 100644 index 35ff9ea..0000000 --- a/fog_x/rlds/writer.py +++ /dev/null @@ -1,170 +0,0 @@ -# Copyright 2022 The Regents of the University of California (Regents) -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Copyright ©2022. The Regents of the University of California (Regents). -# All Rights Reserved. Permission to use, copy, modify, and distribute this -# software and its documentation for educational, research, and not-for-profit -# purposes, without fee and without a signed licensing agreement, is hereby -# granted, provided that the above copyright notice, this paragraph and the -# following two paragraphs appear in all copies, modifications, and -# distributions. Contact The Office of Technology Licensing, UC Berkeley, 2150 -# Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, (510) 643-7201, -# otl@berkeley.edu, http://ipira.berkeley.edu/industry-info for commercial -# licensing opportunities. IN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY -# FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, -# INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS -# DOCUMENTATION, EVEN IF REGENTS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. REGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE. THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY, -# PROVIDED HEREUNDER IS PROVIDED "AS IS". REGENTS HAS NO OBLIGATION TO PROVIDE -# MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. - - -# coding=utf-8 -# Copyright 2023 DeepMind Technologies Limited.. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""TFDS backend for Envlogger.""" -import dataclasses -from collections import ChainMap -from typing import Any, Dict, List, Optional - -import tensorflow_datasets as tfds -from envlogger import step_data -from envlogger.backends import backend_writer, rlds_utils - -DatasetConfig = tfds.rlds.rlds_base.DatasetConfig - -import logging - -logger = logging.getLogger(__name__) - - -@dataclasses.dataclass -class Episode(object): - """Episode that is being constructed.""" - - prev_step: step_data.StepData - steps: Optional[List[rlds_utils.Step]] = None - metadata: Optional[Dict[str, Any]] = None - - def add_step(self, step: step_data.StepData) -> None: - rlds_step = rlds_utils.to_rlds_step(self.prev_step, step) - if self.steps is None: - self.steps = [] - self.steps.append(rlds_step) - self.prev_step = step - - def get_rlds_episode(self) -> Dict[str, Any]: - last_step = rlds_utils.to_rlds_step(self.prev_step, None) - if self.steps is None: - self.steps = [] - if self.metadata is None: - self.metadata = {} - - return {"steps": self.steps + [last_step], **self.metadata} - - -class CloudBackendWriter(backend_writer.BackendWriter): - """Backend that writes trajectory data in TFDS format (and RLDS structure).""" - - def __init__( - self, - data_directory: str, - ds_config: tfds.rlds.rlds_base.DatasetConfig, - ds_identity: tfds.core.dataset_info.DatasetIdentity, - max_episodes_per_file: int = 1, - split_name: Optional[str] = None, - version: str = "0.0.1", - store_ds_metadata: bool = False, - **base_kwargs - ): - """Constructor. - - Args: - data_directory: Directory to store the data - ds_config: Dataset Configuration. - max_episodes_per_file: Number of episodes to store per shard. - split_name: Name to be used by the split. If None, 'train' will be used. - version: version (major.minor.patch) of the dataset. - store_ds_metadata: if False, it won't store the dataset level - metadata. - **base_kwargs: arguments for the base class. - """ - super().__init__(**base_kwargs) - if not split_name: - split_name = "train" - if store_ds_metadata: - metadata = self._metadata - else: - metadata = None - self._data_directory = data_directory - self._ds_info = tfds.rlds.rlds_base.build_info( - ds_config, ds_identity, metadata - ) - self._ds_info.set_file_format("tfrecord") - - self._current_episode = None - - self._sequential_writer = tfds.core.SequentialWriter( - self._ds_info, max_episodes_per_file - ) - self._split_name = split_name - self._sequential_writer.initialize_splits([split_name]) - logging.info("self._data_directory: %r", self._data_directory) - - def _write_and_reset_episode(self): - if self._current_episode is not None: - self._sequential_writer.add_examples( - {self._split_name: [self._current_episode.get_rlds_episode()]} - ) - self._current_episode = None - - def _record_step( - self, data: step_data.StepData, is_new_episode: bool - ) -> None: - """Stores RLDS steps in TFDS format.""" - - if is_new_episode: - self._write_and_reset_episode() - - if self._current_episode is None: - self._current_episode = Episode(prev_step=data) - else: - self._current_episode.add_step(data) - - def set_episode_metadata(self, data: Dict[str, Any]) -> None: - self._current_episode.metadata = data - - def close(self) -> None: - logging.info( - "Deleting the backend with data_dir: %r", self._data_directory - ) - self._write_and_reset_episode() - self._sequential_writer.close_all() - logging.info( - "Done deleting the backend with data_dir: %r", self._data_directory - ) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 0f0c6bf..5de8b1d 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -14,12 +14,12 @@ logging.getLogger("libav").setLevel(logging.CRITICAL) -def flatten_dict(d, parent_key="", sep="_"): +def _flatten_dict(d, parent_key="", sep="_"): items = [] for k, v in d.items(): new_key = parent_key + sep + k if parent_key else k if isinstance(v, dict): - items.extend(flatten_dict(v, new_key, sep=sep).items()) + items.extend(_flatten_dict(v, new_key, sep=sep).items()) else: items.append((new_key, v)) return dict(items) @@ -45,17 +45,17 @@ def __init__( """ self.path = path self.feature_name_separator = feature_name_separator - self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" + # self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" + # use hex hash of the path for the cache file name + hex_hash = hex(abs(hash(self.path)))[2:] + self.cache_file_name = "/tmp/fog_" + hex_hash + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type + self.trajectory_data = None # trajectory_data # check if the path exists - # if exists, load the data - # if not, create a new file - if os.path.exists(self.path): - logger.info(f"loading the trajectory from {self.path}") - self.load() - else: + # if not, create a new file and start data collection + if not os.path.exists(self.path): logger.info(f"creating a new trajectory at {self.path}") try: # os.makedirs(os.path.dirname(self.path), exist_ok=True) @@ -79,12 +79,6 @@ def _get_current_timestamp(self): def __len__(self): raise NotImplementedError - def __iter___(self): - raise NotImplementedError - - def __next__(self): - raise NotImplementedError - def _pre_initialize_h264_streams(self, num_streams: int): """ Pre-initialize a configurable number of H.264 video streams. @@ -96,6 +90,16 @@ def _pre_initialize_h264_streams(self, num_streams: int): stream.pix_fmt = "yuv420p" self.pre_initialized_image_streams.append(stream) + def __getitem__(self, key): + """ + get the value of the feature + return hdf5-ed data + """ + if self.trajectory_data is None: + self.trajectory_data = self.load() + + return self.trajectory_data[key] + def close(self): """ close the container file @@ -127,11 +131,9 @@ def load(self): """ if os.path.exists(self.cache_file_name): - self._load_from_cache() + return self._load_from_cache() else: - self._load_from_container() - - return self + return self._load_from_container() def init_feature_streams(self, feature_spec: Dict): """ @@ -152,7 +154,7 @@ def add( timestamp: Optional[int] = None, ) -> None: """ - add one value to video container file + add one value to container file Args: feature (str): name of the feature @@ -211,7 +213,7 @@ def add_by_dict( timestamp: Optional[int] = None, ) -> None: """ - add one value to video container file + add one value to container file data might be nested dictionary of values for each feature Args: @@ -230,9 +232,9 @@ def add_by_dict( if type(data) != dict: raise ValueError("Use add for non-dictionary data, type is ", type(data)) - flatten_dict_data = flatten_dict(data, sep=self.feature_name_separator) + _flatten_dict_data = _flatten_dict(data, sep=self.feature_name_separator) timestamp = self._get_current_timestamp() if timestamp is None else timestamp - for feature, value in flatten_dict_data.items(): + for feature, value in _flatten_dict_data.items(): self.add(feature, value, timestamp) @classmethod @@ -279,15 +281,15 @@ def from_dict_of_lists(cls, data: Dict[str, List[Any]], path: Text, feature_name """ traj = cls(path, feature_name_separator=feature_name_separator) # flatten the data such that all data starts and put feature name with separator - flatten_dict_data = flatten_dict(data, sep=traj.feature_name_separator) + _flatten_dict_data = _flatten_dict(data, sep=traj.feature_name_separator) # Check if all lists have the same length - list_lengths = [len(v) for v in flatten_dict_data.values()] + list_lengths = [len(v) for v in _flatten_dict_data.values()] if len(set(list_lengths)) != 1: - raise ValueError("All lists must have the same length", [(k, len(v)) for k, v in flatten_dict_data.items()]) + raise ValueError("All lists must have the same length", [(k, len(v)) for k, v in _flatten_dict_data.items()]) for i in range(list_lengths[0]): - step = {k: v[i] for k, v in flatten_dict_data.items()} + step = {k: v[i] for k, v in _flatten_dict_data.items()} traj.add_by_dict(step) return traj @@ -349,7 +351,7 @@ def _load_from_container(self): continue feature_name = packet.stream.metadata["FEATURE_NAME"] feature_type = self.feature_name_to_feature_type[feature_name] - logger.info( + logger.debug( f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" ) feature_codec = packet.stream.codec_context.codec.name From 239c230b76a348f786f4593a71c2da529c659468 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Thu, 22 Aug 2024 13:55:38 -0700 Subject: [PATCH 22/80] code formatting --- examples/h5_loader.py | 12 ++++++------ fog_x/trajectory.py | 36 +++++++++++++++++++++--------------- 2 files changed, 27 insertions(+), 21 deletions(-) diff --git a/examples/h5_loader.py b/examples/h5_loader.py index d8f36d0..02add1e 100644 --- a/examples/h5_loader.py +++ b/examples/h5_loader.py @@ -8,14 +8,14 @@ index = 0 -# for data_traj in loader: +for data_traj in loader: -# fog_x.Trajectory.from_dict_of_lists( -# data_traj, path=f"/tmp/fog_x/output_{index}.vla" -# ) -# index += 1 + fog_x.Trajectory.from_dict_of_lists( + data_traj, path=f"/tmp/fog_x/output_{index}.vla" + ) + index += 1 # read the data back -for i in range(1): +for i in range(index): print(fog_x.Trajectory(f"/tmp/fog_x/output_{i}.vla")["action"].keys()) \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 5de8b1d..c4de0b3 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -24,6 +24,7 @@ def _flatten_dict(d, parent_key="", sep="_"): items.append((new_key, v)) return dict(items) + class Trajectory: def __init__( self, @@ -51,7 +52,7 @@ def __init__( self.cache_file_name = "/tmp/fog_" + hex_hash + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type - self.trajectory_data = None # trajectory_data + self.trajectory_data = None # trajectory_data # check if the path exists # if not, create a new file and start data collection @@ -94,10 +95,10 @@ def __getitem__(self, key): """ get the value of the feature return hdf5-ed data - """ + """ if self.trajectory_data is None: self.trajectory_data = self.load() - + return self.trajectory_data[key] def close(self): @@ -241,27 +242,29 @@ def add_by_dict( def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajectory": """ Create a Trajectory object from a list of dictionaries. - + args: data (List[Dict[str, Any]]): list of dictionaries path (Text): path to the trajectory file - + Example: original_trajectory = [ {"feature1": "value1", "feature2": "value2"}, {"feature1": "value3", "feature2": "value4"}, ] - + trajectory = Trajectory.from_list_of_dicts(original_trajectory, path="/tmp/fog_x/output.vla") """ traj = cls(path) for step in data: traj.add_by_dict(step) return traj - + @classmethod - def from_dict_of_lists(cls, data: Dict[str, List[Any]], path: Text, feature_name_separator:Text = "/") -> "Trajectory": - """ + def from_dict_of_lists( + cls, data: Dict[str, List[Any]], path: Text, feature_name_separator: Text = "/" + ) -> "Trajectory": + """ Create a Trajectory object from a dictionary of lists. Args: @@ -270,28 +273,31 @@ def from_dict_of_lists(cls, data: Dict[str, List[Any]], path: Text, feature_name Returns: Trajectory: _description_ - + Example: original_trajectory = { "feature1": ["value1", "value3"], "feature2": ["value2", "value4"], } - + trajectory = Trajectory.from_dict_of_lists(original_trajectory, path="/tmp/fog_x/output.vla") """ traj = cls(path, feature_name_separator=feature_name_separator) # flatten the data such that all data starts and put feature name with separator _flatten_dict_data = _flatten_dict(data, sep=traj.feature_name_separator) - + # Check if all lists have the same length list_lengths = [len(v) for v in _flatten_dict_data.values()] if len(set(list_lengths)) != 1: - raise ValueError("All lists must have the same length", [(k, len(v)) for k, v in _flatten_dict_data.items()]) - + raise ValueError( + "All lists must have the same length", + [(k, len(v)) for k, v in _flatten_dict_data.items()], + ) + for i in range(list_lengths[0]): step = {k: v[i] for k, v in _flatten_dict_data.items()} traj.add_by_dict(step) - return traj + return traj def _load_from_cache(self): """ From 2f10e68c638503513e856e2ca30efa939ce6bb52 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 24 Aug 2024 19:49:15 -0700 Subject: [PATCH 23/80] Refactor Trajectory class to remove commented code and improve code readability --- examples/h5_loader.py | 2 +- fog_x/trajectory.py | 13 +++++++------ 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/examples/h5_loader.py b/examples/h5_loader.py index 02add1e..28c3b91 100644 --- a/examples/h5_loader.py +++ b/examples/h5_loader.py @@ -2,7 +2,7 @@ import fog_x import os -# os.system("rm -rf /tmp/fog_x/*") +os.system("rm -rf /tmp/fog_x/*") loader = HDF5Loader("/home/kych/datasets/2024-07-03-red-on-cyan/**/trajectory_im128.h5") diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index c4de0b3..ee30054 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -53,6 +53,11 @@ def __init__( self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type self.trajectory_data = None # trajectory_data + self.start_time = time.time() + self.num_pre_initialized_h264_streams = num_pre_initialized_h264_streams + self.pre_initialized_image_streams = ( + [] + ) # a list of pre-initialized h264 streams # check if the path exists # if not, create a new file and start data collection @@ -65,13 +70,9 @@ def __init__( logger.error(f"error creating the trajectory file: {e}") raise - self.num_pre_initialized_h264_streams = num_pre_initialized_h264_streams - self.pre_initialized_image_streams = ( - [] - ) # a list of pre-initialized h264 streams self._pre_initialize_h264_streams(num_pre_initialized_h264_streams) - - self.start_time = time.time() + else: + logger.warn(f"{self.path} exists") def _get_current_timestamp(self): current_time = (time.time() - self.start_time) * 1000 From 8f40ff82c189b34c57c201f593618b1d25f7bc9c Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 24 Aug 2024 21:39:51 -0700 Subject: [PATCH 24/80] benchmark code, missing container loader info --- benchmarks/openx.py | 136 ++++++++++++++++++++ examples/{rtx_loader.py => openx_loader.py} | 0 fog_x/trajectory.py | 3 +- 3 files changed, 138 insertions(+), 1 deletion(-) create mode 100644 benchmarks/openx.py rename examples/{rtx_loader.py => openx_loader.py} (100%) diff --git a/benchmarks/openx.py b/benchmarks/openx.py new file mode 100644 index 0000000..89e3598 --- /dev/null +++ b/benchmarks/openx.py @@ -0,0 +1,136 @@ +import fog_x +import os +import subprocess +import argparse +from concurrent.futures import ThreadPoolExecutor +import glob +import time +from fog_x.loader import RLDSLoader + +# Constants +DEFAULT_EXP_DIR = "/tmp/fog_x" +DEFAULT_NUMBER_OF_TRAJECTORIES = 3 +DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] +DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-*" +LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" +FEATURE_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" +DATASET_INFO_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" + +def check_and_download_file(url, local_path): + """Checks if a file is already downloaded; if not, downloads it.""" + if not os.path.exists(local_path): + subprocess.run(["gsutil", "-m", "cp", url, local_path], check=True) + else: + print(f"File {local_path} already exists. Skipping download.") + +def check_and_download_trajectory(exp_dir, dataset_name, trajectory_index): + """Checks if a trajectory and associated JSON files are already downloaded; if not, downloads them.""" + # Create a directory for each dataset + dataset_dir = os.path.join(exp_dir, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Check and download the trajectory files + local_file_pattern = LOCAL_FILE_TEMPLATE.format(exp_dir=exp_dir, dataset_name=dataset_name, index=trajectory_index) + if not any(os.path.exists(file) for file in glob.glob(local_file_pattern)): + data_url = DATA_URL_TEMPLATE.format(dataset_name=dataset_name, index=trajectory_index) + subprocess.run(["gsutil", "-m", "cp", data_url, dataset_dir], check=True) + else: + print(f"Trajectory {trajectory_index} of dataset {dataset_name} already exists in {dataset_dir}. Skipping download.") + + # Check and download the feature.json file + feature_json_local_path = os.path.join(dataset_dir, "features.json") + feature_json_url = FEATURE_JSON_URL_TEMPLATE.format(dataset_name=dataset_name) + check_and_download_file(feature_json_url, feature_json_local_path) + + # Check and download the dataset_info.json file + dataset_info_json_local_path = os.path.join(dataset_dir, "dataset_info.json") + dataset_info_json_url = DATASET_INFO_JSON_URL_TEMPLATE.format(dataset_name=dataset_name) + check_and_download_file(dataset_info_json_url, dataset_info_json_local_path) + +def download_data(exp_dir, dataset_names, num_trajectories): + """Downloads the specified number of trajectories from each dataset concurrently if not already downloaded.""" + with ThreadPoolExecutor() as executor: + futures = [] + for dataset_name in dataset_names: + for i in range(num_trajectories): + futures.append(executor.submit(check_and_download_trajectory, exp_dir, dataset_name, i)) + for future in futures: + future.result() # Will raise an exception if any download failed + +def measure_file_size(dataset_dir): + """Calculates the total size of all files in the dataset directory.""" + total_size = 0 + for dirpath, dirnames, filenames in os.walk(dataset_dir): + for f in filenames: + fp = os.path.join(dirpath, f) + total_size += os.path.getsize(fp) + return total_size + + +def measure_loading_time(loader_func, path, num_trajectories): + """Measures the time taken to load data using a specified loader function.""" + start_time = time.time() + loader = loader_func(path, split=f"train[:{num_trajectories}]") + data = list(loader) # Load all data to measure time + end_time = time.time() + loading_time = end_time - start_time + return loading_time, len(data) + + +def convert_data_to_vla_format(loader, output_dir): + """Converts data to VLA format and saves it to the specified output directory.""" + for index, data_traj in enumerate(loader): + output_path = os.path.join(output_dir, f"output_{index}.vla") + fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) + +def read_data(output_dir, num_trajectories): + """Reads the VLA data files and prints their action keys.""" + for i in range(num_trajectories): + traj = fog_x.Trajectory(os.path.join(output_dir, f"output_{i}.vla")) + print(traj["action"].keys()) + +def main(): + # Parse command-line arguments + parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") + parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") + parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to download.") + parser.add_argument("--dataset_names", nargs='+', default=DEFAULT_DATASET_NAMES, help="List of dataset names to download.") + + args = parser.parse_args() + + # Create output directory if it doesn't exist + output_dir = os.path.join(args.exp_dir, "output") + os.makedirs(output_dir, exist_ok=True) + + # Download data concurrently + download_data(args.exp_dir, args.dataset_names, args.num_trajectories) + + # Iterate through datasets and measure file size and loading time for both formats + for dataset_name in args.dataset_names: + dataset_dir = os.path.join(args.exp_dir, dataset_name) + file_size = measure_file_size(dataset_dir) + + # Measure loading time for RLDS format + rlds_loading_time, num_loaded_rlds = measure_loading_time(RLDSLoader, dataset_dir, args.num_trajectories) + + print(f"Dataset: {dataset_name}") + print(f"Total file size: {file_size / (1024 * 1024):.2f} MB") + print(f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds") + print(f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second") + + # Convert data to VLA format + loader = RLDSLoader(path=dataset_dir, split=f"train[:{args.num_trajectories}]") + convert_data_to_vla_format(loader, output_dir) + + # Measure loading time for VLA format + vla_loading_time, num_loaded_vla = measure_loading_time(fog_x.Trajectory, output_dir, args.num_trajectories) + + print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") + print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") + + + + + +if __name__ == "__main__": + main() diff --git a/examples/rtx_loader.py b/examples/openx_loader.py similarity index 100% rename from examples/rtx_loader.py rename to examples/openx_loader.py diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index ee30054..27925db 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -31,6 +31,7 @@ def __init__( path: Text, num_pre_initialized_h264_streams: int = 5, feature_name_separator: Text = "/", + split: Optional[Text] = None, ) -> None: """ Args: @@ -64,7 +65,7 @@ def __init__( if not os.path.exists(self.path): logger.info(f"creating a new trajectory at {self.path}") try: - # os.makedirs(os.path.dirname(self.path), exist_ok=True) + os.makedirs(os.path.dirname(self.path), exist_ok=True) self.container_file = av.open(self.path, mode="w", format="matroska") except Exception as e: logger.error(f"error creating the trajectory file: {e}") From 301f385dffa645a95ee9d63ef29b126bd32bc759 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 24 Aug 2024 23:17:59 -0700 Subject: [PATCH 25/80] Refactor Trajectory class to remove commented code and improve code readability --- benchmarks/openx.py | 6 +- fog_x/dataset.py | 760 +-------------------- fog_x/deprecated/dataset.py | 744 ++++++++++++++++++++ fog_x/{ => deprecated}/storage/__init__.py | 0 fog_x/{ => deprecated}/storage/storage.py | 0 fog_x/loader/__init__.py | 3 +- fog_x/loader/base.py | 5 +- fog_x/loader/hdf5.py | 2 +- fog_x/loader/vla.py | 28 + fog_x/trajectory.py | 21 +- 10 files changed, 819 insertions(+), 750 deletions(-) create mode 100644 fog_x/deprecated/dataset.py rename fog_x/{ => deprecated}/storage/__init__.py (100%) rename fog_x/{ => deprecated}/storage/storage.py (100%) create mode 100644 fog_x/loader/vla.py diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 89e3598..16056ad 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -6,6 +6,7 @@ import glob import time from fog_x.loader import RLDSLoader +from fog_x.loader import VLALoader # Constants DEFAULT_EXP_DIR = "/tmp/fog_x" @@ -123,14 +124,11 @@ def main(): convert_data_to_vla_format(loader, output_dir) # Measure loading time for VLA format - vla_loading_time, num_loaded_vla = measure_loading_time(fog_x.Trajectory, output_dir, args.num_trajectories) + vla_loading_time, num_loaded_vla = measure_loading_time(VLALoader, output_dir, args.num_trajectories) print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") - - - if __name__ == "__main__": main() diff --git a/fog_x/dataset.py b/fog_x/dataset.py index f20d343..588ae72 100644 --- a/fog_x/dataset.py +++ b/fog_x/dataset.py @@ -1,744 +1,34 @@ -import io -import logging import os -from typing import Any, Dict, List, Optional, Tuple -import subprocess -import numpy as np -import polars -import pandas - -from fog_x.database import ( - DatabaseConnector, - DatabaseManager, - DataFrameConnector, - LazyFrameConnector, - PolarsConnector, -) -from fog_x.episode import Episode -from fog_x.feature import FeatureType - -logger = logging.getLogger(__name__) - - - -def convert_to_h264(input_file, output_file): - - # FFmpeg command to convert video to H.264 - command = [ - 'ffmpeg', - '-i', input_file, # Input file - '-loglevel', 'error', # Suppress the logs - '-vcodec', 'h264', # Specify the codec - output_file # Output file - ] - subprocess.run(command) - -def create_cloud_bucket_if_not_exist(provider, bucket_name, dir_name): - logger.info(f"Creating bucket '{bucket_name}' in cloud provider '{provider}' with folder '{dir_name}'...") - if provider == "s3": - import boto3 - s3_client = boto3.client('s3') - # s3_client.create_bucket(Bucket=bucket_name) - s3_client.put_object(Bucket=bucket_name, Key=f"{dir_name}/") - logger.info(f"Bucket '{bucket_name}' created in AWS S3.") - elif provider == "gs": - from google.cloud import storage - """Create a folder in a Google Cloud Storage bucket if it does not exist.""" - storage_client = storage.Client() - bucket = storage_client.bucket(bucket_name) - - # Ensure the folder name ends with a '/' - if not dir_name.endswith('/'): - dir_name += '/' - - # Check if folder exists by trying to list objects with the folder prefix - blobs = storage_client.list_blobs(bucket_name, prefix=dir_name, delimiter='/') - exists = any(blob.name == dir_name for blob in blobs) - - if not exists: - # Create an empty blob to simulate a folder - blob = bucket.blob(dir_name) - blob.upload_from_string('') - print(f"Folder '{dir_name}' created.") - else: - print(f"Folder '{dir_name}' already exists.") - else: - raise ValueError(f"Unsupported cloud provider '{provider}'.") +from typing import Any, Dict, List, Optional, Text class Dataset: - """ - Create or load from a new dataset. - """ - - def __init__( - self, - name: str, - path: str = None, - replace_existing: bool = False, - features: Dict[ - str, FeatureType - ] = {}, # features to be stored {name: FeatureType} - enable_feature_inference=True, # whether additional features can be inferred - episode_info_connector: DatabaseConnector = None, - step_data_connector: DatabaseConnector = None, - storage: Optional[str] = None, - ) -> None: - """ - - Args: - name (str): Name of this dataset. Used as the directory name when exporting. - path (str): Required. Local path of where this dataset should be stored. - features (optional Dict[str, FeatureType]): Description of `param1`. - enable_feature_inference (bool): enable inferring additional FeatureTypes - - Example: - ``` - >>> dataset = fog_x.Dataset('my_dataset', path='~/fog_x/my_dataset`) - ``` - - TODO: - * is replace_existing actually used anywhere? - """ - self.name = name - - if path.startswith("."): # relative path - path = os.path.abspath(path).removesuffix("/") - elif path.startswith("~"): # home directory - path = os.path.expanduser(path).removesuffix("/") - elif path.startswith("/"): # absolute path - path = path.removesuffix("/") - elif path.startswith("s3://") or path.startswith("gs://"): - path = path.removesuffix("/") - else: - raise ValueError("Unsupported path format. Please use absolute path or relative path starting with '.' or '~'.") - - logger.info(f"Dataset path: {path}") - self.path = path - if path is None: - raise ValueError("Path is required") - # create the folder if path doesn't exist - if self.path.startswith("/") and not os.path.exists(path): - logger.info(f"Creating directory {path}") - os.makedirs(path) - - self.replace_existing = replace_existing - self.features = features - self.enable_feature_inference = enable_feature_inference - if episode_info_connector is None: - episode_info_connector = DataFrameConnector(f"{path}") - - if step_data_connector is None: - if self.path.startswith("/") and not os.path.exists(f"{path}/{name}"): - os.makedirs(f"{path}/{name}") - try: - step_data_connector = LazyFrameConnector(f"{path}/{name}") - except: - logger.info(f"Path does not exist. ({path}/{name})") - cloud_provider = path[:2] - bucket_name = path[5:] - create_cloud_bucket_if_not_exist(cloud_provider, bucket_name, f"{name}/") - step_data_connector = LazyFrameConnector(f"{path}/{name}") - self.db_manager = DatabaseManager(episode_info_connector, step_data_connector) - self.db_manager.initialize_dataset(self.name, features) - - self.storage = storage - self.obs_keys = [] - self.act_keys = [] - self.step_keys = [] - - def new_episode(self, metadata: Optional[Dict[str, Any]] = None) -> Episode: - """ - Create a new episode / trajectory. - - Returns: - Episode - - TODO: - * support multiple processes writing to the same episode - * close the previous episode if not closed - """ - return Episode( - metadata=metadata, - features=self.features, - enable_feature_inference=self.enable_feature_inference, - db_manager=self.db_manager, - ) - - def _get_tf_feature_dicts( - self, obs_keys: List[str], act_keys: List[str], step_keys: List[str] - ) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]: - """ - Get the tensorflow feature dictionaries. - """ - observation_tf_dict = {} - action_tf_dict = {} - step_tf_dict = {} - - for k in obs_keys: - observation_tf_dict[k] = self.features[k].to_tf_feature_type() - - for k in act_keys: - action_tf_dict[k] = self.features[k].to_tf_feature_type() - - for k in step_keys: - step_tf_dict[k] = self.features[k].to_tf_feature_type() - - return observation_tf_dict, action_tf_dict, step_tf_dict - - def export( - self, - export_path: Optional[str] = None, - format: str = "rtx", - max_episodes_per_file: int = 1, - version: str = "0.0.1", - obs_keys=[], - act_keys=[], - step_keys=[], - ) -> None: - """ - Export the dataset. - - Args: - export_path (optional str): location of exported data. Uses dataset.path/export by default. - format (str): Supported formats are `rtx`, `open-x`, and `rlds`. - """ - if format == "rtx" or format == "open-x" or format == "rlds": - self.export_rtx(export_path, max_episodes_per_file, version, obs_keys, act_keys, step_keys) - else: - raise ValueError("Unsupported export format") - - def export_rtx( - self, - export_path: Optional[str] = None, - max_episodes_per_file: int = 1, - version: str = "0.0.1", - obs_keys=[], - act_keys=[], - step_keys=[] - ): - if export_path == None: - export_path = self.path + "/export" - if not os.path.exists(export_path): - os.makedirs(export_path) - - import dm_env - import tensorflow as tf - import tensorflow_datasets as tfds - from envlogger import step_data - from tensorflow_datasets.core.features import Tensor - - from fog_x.rlds.writer import CloudBackendWriter - - self.obs_keys += obs_keys - self.act_keys += act_keys - self.step_keys += step_keys - - ( - observation_tf_dict, - action_tf_dict, - step_tf_dict, - ) = self._get_tf_feature_dicts( - self.obs_keys, - self.act_keys, - self.step_keys, - ) - - logger.info("Exporting dataset as RT-X format") - logger.info(f"Observation keys: {observation_tf_dict}") - logger.info(f"Action keys: {action_tf_dict}") - logger.info(f"Step keys: {step_tf_dict}") - - # generate tensorflow configuration file - ds_config = tfds.rlds.rlds_base.DatasetConfig( - name=self.name, - description="", - homepage="", - citation="", - version=tfds.core.Version("0.0.1"), - release_notes={ - "0.0.1": "Initial release.", - }, - observation_info=observation_tf_dict, - action_info=action_tf_dict, - reward_info=( - step_tf_dict["reward"] - if "reward" in step_tf_dict - else Tensor(shape=(), dtype=tf.float32) - ), - discount_info=( - step_tf_dict["discount"] - if "discount" in step_tf_dict - else Tensor(shape=(), dtype=tf.float32) - ), - ) - - ds_identity = tfds.core.dataset_info.DatasetIdentity( - name=ds_config.name, - version=tfds.core.Version(version), - data_dir=export_path, - module_name="", - ) - writer = CloudBackendWriter( - data_directory=export_path, - ds_config=ds_config, - ds_identity=ds_identity, - max_episodes_per_file=max_episodes_per_file, - ) - - # export the dataset - episodes = self.get_episodes_from_metadata() - for episode in episodes: - steps = episode.collect().rows(named=True) - for i in range(len(steps)): - step = steps[i] - observationd = {} - actiond = {} - stepd = {} - for k, v in step.items(): - # logger.info(f"key: {k}") - if k not in self.features: - if k != "episode_id" and k != "Timestamp": - logger.info( - f"Feature {k} not found in the dataset features." - ) - continue - feature_spec = self.features[k].to_tf_feature_type() - if ( - isinstance(feature_spec, tfds.core.features.Tensor) - and feature_spec.shape != () - ): - # reverse the process - value = np.load(io.BytesIO(v)).astype( - feature_spec.np_dtype - ) - elif ( - isinstance(feature_spec, tfds.core.features.Tensor) - and feature_spec.shape == () - ): - value = np.array(v, dtype=feature_spec.np_dtype) - elif isinstance( - feature_spec, tfds.core.features.Image - ): - value = np.load(io.BytesIO(v)).astype( - feature_spec.np_dtype - ) - else: - value = v - - if k in self.obs_keys: - observationd[k] = value - elif k in self.act_keys: - actiond[k] = value - else: - stepd[k] = value - - # logger.info( - # f"Step: {stepd}" - # f"Observation: {observationd}" - # f"Action: {actiond}" - # ) - timestep = dm_env.TimeStep( - step_type=dm_env.StepType.FIRST, - reward=np.float32( - 0.0 - ), # stepd["reward"] if "reward" in step else np.float32(0.0), - discount=np.float32( - 0.0 - ), # stepd["discount"] if "discount" in step else np.float32(0.0), - observation=observationd, - ) - stepdata = step_data.StepData( - timestep=timestep, action=actiond, custom_data=None - ) - if i < len(steps) - 1: - writer._record_step(stepdata, is_new_episode=False) - else: - writer._record_step(stepdata, is_new_episode=True) - - - def load_rtx_episodes( - self, - name: str, - split: str = "all", - additional_metadata: Optional[Dict[str, Any]] = dict(), - ): - """ - Load robot data from Tensorflow Datasets. - - Args: - name (str): Name of RT-X episodes, which can be found at [Tensorflow Datasets](https://www.tensorflow.org/datasets/catalog) under the Robotics category - split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits) - additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes - - Example: - ``` - >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5) - >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"}) - ``` - """ - - # this is only required if rtx format is used - import tensorflow_datasets as tfds - - from fog_x.rlds.utils import dataset2path - b = tfds.builder_from_directory(builder_dir=dataset2path(name)) - self._build_rtx_episodes_from_tfds_builder( - b, - split=split, - additional_metadata=additional_metadata, - ) - - def load_rtx_episodes_local( - self, - path: str, - split: str = "all", - additional_metadata: Optional[Dict[str, Any]] = dict(), - ): - """ - Load robot data from Tensorflow Datasets. - - Args: - path (str): Path to the RT-X episodes - split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits) - additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes - - Example: - ``` - >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5") - >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"}) - ``` - """ - - # this is only required if rtx format is used - import tensorflow_datasets as tfds - - b = tfds.builder_from_directory(path) - self._build_rtx_episodes_from_tfds_builder( - b, - split=split, - additional_metadata=additional_metadata, - ) - - def _build_rtx_episodes_from_tfds_builder( - self, - builder, - split: str = "all", - additional_metadata: Optional[Dict[str, Any]] = dict(), - ): - """ - construct the dataset from the tfds builder - """ - ds = builder.as_dataset(split=split) - - data_type = builder.info.features["steps"] - - for tf_episode in ds: - logger.info(tf_episode) - fog_episode = self.new_episode( - metadata=additional_metadata, - ) - for step in tf_episode["steps"]: - ret = self._load_rtx_step_data_from_tf_step( - step, data_type, - ) - for r in ret: - fog_episode.add(**r) - - fog_episode.close() - - - def _prepare_rtx_metadata( - self, - name: str, - export_path: Optional[str] = None, - sample_size = 20, - shuffle = False, - seed = 42, - ): - - # this is only required if rtx format is used - import tensorflow_datasets as tfds - from fog_x.rlds.utils import dataset2path - import cv2 - - b = tfds.builder_from_directory(builder_dir=dataset2path(name)) - ds = b.as_dataset(split="all") - if shuffle: - ds = ds.shuffle(sample_size, seed=seed) - data_type = b.info.features["steps"] - counter = 0 - - if export_path == None: - export_path = self.path + "/" + self.name + "_viz" - if not os.path.exists(export_path): - os.makedirs(export_path) - - - for tf_episode in ds: - video_writers = {} - - additional_metadata = { - "load_from": name, - "load_index": f"all, {shuffle}, {seed}, {counter}", - } - - logger.info(tf_episode) - fog_episode = self.new_episode() - - for step in tf_episode["steps"]: - ret = self._load_rtx_step_data_from_tf_step( - step, data_type, - ) - - for r in ret: - feature_name = r["feature"] - if "image" in feature_name and "depth" not in feature_name: - image = np.load(io.BytesIO(r["value"])) - - # convert from RGB to BGR - image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) - - if feature_name not in video_writers: - - output_filename = f"{self.name}_{counter}_{feature_name}" - tmp_vid_output_path = f"/tmp/{output_filename}.mp4" - output_path = f"{export_path}/{output_filename}" - - frame_size = (image.shape[1], image.shape[0]) - - # save the initial image - cv2.imwrite(f"{output_path}.jpg", image) - # save the video - video_writers[feature_name] = cv2.VideoWriter( - tmp_vid_output_path, - cv2.VideoWriter_fourcc(*"mp4v"), - 10, - frame_size - ) - - - video_writers[r["feature"]].write(image) - - if "instruction" in r["feature"]: - natural_language_instruction = r["value"].decode("utf-8") - additional_metadata["natural_language_instruction"] = natural_language_instruction - - r["metadata_only"] = True - fog_episode.add(**r) - - for feature_name, video_writer in video_writers.items(): - video_writer.release() - # need to convert to h264 to properly display over chrome / vscode - output_filename = f"{self.name}_{counter}_{feature_name}" - tmp_vid_output_path = f"/tmp/{output_filename}.mp4" - vid_output_path = f"{export_path}/{output_filename}.mp4" - convert_to_h264(tmp_vid_output_path, vid_output_path) - additional_metadata[f"video_path_{feature_name}"] = output_filename - if os.path.isfile(tmp_vid_output_path): - os.remove(tmp_vid_output_path) - - video_writers = {} - fog_episode.close(save_data = False, additional_metadata = additional_metadata) - counter += 1 - if counter > sample_size: - break - - def _load_rtx_step_data_from_tf_step( - self, - step: Dict[str, Any], - data_type: Dict[str, Any] = {}, - ): - from tensorflow_datasets.core.features import ( - FeaturesDict, - Image, - Scalar, - Tensor, - Text, - ) - ret = [] - - for k, v in step.items(): - # logger.info(f"k {k} , v {v}") - if isinstance(v, dict): #and (k == "observation" or k == "action"): - for k2, v2 in v.items(): - # TODO: abstract this to feature.py - - if ( - isinstance(data_type[k][k2], Tensor) - and data_type[k][k2].shape != () - ): - memfile = io.BytesIO() - np.save(memfile, v2.numpy()) - value = memfile.getvalue() - elif isinstance(data_type[k][k2], Image): - memfile = io.BytesIO() - np.save(memfile, v2.numpy()) - value = memfile.getvalue() - else: - value = v2.numpy() - - ret.append( - { - "feature": str(k2), - "value": value, - "feature_type": FeatureType( - tf_feature_spec=data_type[k][k2] - ), - } - ) - # fog_episode.add( - # feature=str(k2), - # value=value, - # feature_type=FeatureType( - # tf_feature_spec=data_type[k][k2] - # ), - # ) - if k == "observation": - self.obs_keys.append(k2) - elif k == "action": - self.act_keys.append(k2) - else: - # fog_episode.add( - # feature=str(k), - # value=v.numpy(), - # feature_type=FeatureType(tf_feature_spec=data_type[k]), - # ) - ret.append( - { - "feature": str(k), - "value": v.numpy(), - "feature_type": FeatureType( - tf_feature_spec=data_type[k] - ), - } - ) - self.step_keys.append(k) - return ret - - - def get_episode_info(self) -> pandas.DataFrame: - """ - Returns: - metadata of all episodes as `pandas.DataFrame` - """ - return self.db_manager.get_episode_info_table() - - def get_step_data(self) -> polars.LazyFrame: + def __init__(self, + path: Text, + split: Text, + format: Optional[Text] = None): """ - Returns: - step data of all episodes - """ - return self.db_manager.get_step_table_all() - - def get_step_data_by_episode_ids( - self, episode_ids: List[int], as_lazy_frame=True - ): - """ - Args: - episode_ids (List[int]): list of episode ids - as_lazy_frame (bool): whether to return polars.LazyFrame or polars.DataFrame - - Returns: - step data of each episode - """ - episodes = [] - for episode_id in episode_ids: - if episode_id == None: - continue - if as_lazy_frame: - episodes.append(self.db_manager.get_step_table(episode_id)) - else: - episodes.append(self.db_manager.get_step_table(episode_id).collect()) - return episodes - - def read_by(self, episode_info: Any = None) -> List[polars.LazyFrame]: - """ - To be used with `Dataset.get_episode_info`. - + init method for Dataset class Args: - episode_info (pandas.DataFrame): episode metadata information to determine which episodes to read - - Returns: - episodes filtered by `episode_info` - """ - episode_ids = list(episode_info["episode_id"]) - logger.info(f"Reading episodes as order: {episode_ids}") - episodes = [] - for episode_id in episode_ids: - if episode_id == None: - continue - episodes.append(self.db_manager.get_step_table(episode_id)) - return episodes - - def get_episodes_from_metadata(self, metadata: Any = None): - # Assume we use get_metadata_as_pandas_df to retrieve episodes metadata - if metadata is None: - metadata_df = self.get_episode_info() - else: - metadata_df = metadata - episodes = self.read_by(metadata_df) - return episodes - - def pytorch_dataset_builder(self, metadata=None, **kwargs): - """ - Used for loading current dataset as a PyTorch dataset. - To be used with `torch.utils.data.DataLoader`. - """ - - import torch - from torch.utils.data import Dataset - episodes = self.get_episodes_from_metadata(metadata) - - # Initialize the PyTorch dataset with the episodes and features - pytorch_dataset = PyTorchDataset(episodes, self.features) - - return pytorch_dataset - - def get_as_huggingface_dataset(self): - """ - Load current dataset as a HuggingFace dataset. - - TODO: - * currently the support for huggingg face dataset is limited. - it only shows its capability of easily returning a hf dataset - * add features from the episode metadata - * allow selecting episodes based on queries. - doing so requires creating a new copy of the dataset on disk - """ - import datasets - - dataset_path = self.path + "/" + self.name - parquet_files = [ - os.path.join(dataset_path, f) for f in os.listdir(dataset_path) - ] - - hf_dataset = datasets.load_dataset("parquet", data_files=parquet_files) - return hf_dataset + paths Text: path-like to the dataset + it can be a glob pattern or a directory + if it starts with gs:// it will be treated as a google cloud storage path with rlds format + if it ends with .h5 it will be treated as a hdf5 file + if it ends with .tfrecord it will be treated as a rlds file + if it ends with .vla it will be treated as a vla file + split (Text): split of the dataset + format (Optional[Text]): format of the dataset. Auto-detected if None. Defaults to None. + we assume that the format is the same for all files in the dataset + """ + pass -class PyTorchDataset(Dataset): - def __init__(self, episodes, features): - """ - Initialize the dataset with the episodes and features. - :param episodes: A list of episodes loaded from the database. - :param features: A dictionary of features to be included in the dataset. - """ - self.episodes = episodes - self.features = features + def __iter__(self): + return self + + def __next__(self): + raise NotImplementedError def __len__(self): - """ - Return the total number of episodes in the dataset. - """ - return len(self.episodes) + raise NotImplementedError - def __getitem__(self, idx): - """ - Retrieve the idx-th episode from the dataset. - Depending on the structure, you may need to process the episode - and its features here. - """ - print("Retrieving episode at index", idx) - episode = self.episodes[idx].collect().to_pandas() - # Process the episode and its features here - # For simplicity, let's assume we're just returning the episode - return episode + def __getitem__(self, index): + raise NotImplementedError diff --git a/fog_x/deprecated/dataset.py b/fog_x/deprecated/dataset.py new file mode 100644 index 0000000..f20d343 --- /dev/null +++ b/fog_x/deprecated/dataset.py @@ -0,0 +1,744 @@ +import io +import logging +import os +from typing import Any, Dict, List, Optional, Tuple +import subprocess +import numpy as np +import polars +import pandas + +from fog_x.database import ( + DatabaseConnector, + DatabaseManager, + DataFrameConnector, + LazyFrameConnector, + PolarsConnector, +) +from fog_x.episode import Episode +from fog_x.feature import FeatureType + +logger = logging.getLogger(__name__) + + + +def convert_to_h264(input_file, output_file): + + # FFmpeg command to convert video to H.264 + command = [ + 'ffmpeg', + '-i', input_file, # Input file + '-loglevel', 'error', # Suppress the logs + '-vcodec', 'h264', # Specify the codec + output_file # Output file + ] + subprocess.run(command) + +def create_cloud_bucket_if_not_exist(provider, bucket_name, dir_name): + logger.info(f"Creating bucket '{bucket_name}' in cloud provider '{provider}' with folder '{dir_name}'...") + if provider == "s3": + import boto3 + s3_client = boto3.client('s3') + # s3_client.create_bucket(Bucket=bucket_name) + s3_client.put_object(Bucket=bucket_name, Key=f"{dir_name}/") + logger.info(f"Bucket '{bucket_name}' created in AWS S3.") + elif provider == "gs": + from google.cloud import storage + """Create a folder in a Google Cloud Storage bucket if it does not exist.""" + storage_client = storage.Client() + bucket = storage_client.bucket(bucket_name) + + # Ensure the folder name ends with a '/' + if not dir_name.endswith('/'): + dir_name += '/' + + # Check if folder exists by trying to list objects with the folder prefix + blobs = storage_client.list_blobs(bucket_name, prefix=dir_name, delimiter='/') + exists = any(blob.name == dir_name for blob in blobs) + + if not exists: + # Create an empty blob to simulate a folder + blob = bucket.blob(dir_name) + blob.upload_from_string('') + print(f"Folder '{dir_name}' created.") + else: + print(f"Folder '{dir_name}' already exists.") + else: + raise ValueError(f"Unsupported cloud provider '{provider}'.") + +class Dataset: + """ + Create or load from a new dataset. + """ + + def __init__( + self, + name: str, + path: str = None, + replace_existing: bool = False, + features: Dict[ + str, FeatureType + ] = {}, # features to be stored {name: FeatureType} + enable_feature_inference=True, # whether additional features can be inferred + episode_info_connector: DatabaseConnector = None, + step_data_connector: DatabaseConnector = None, + storage: Optional[str] = None, + ) -> None: + """ + + Args: + name (str): Name of this dataset. Used as the directory name when exporting. + path (str): Required. Local path of where this dataset should be stored. + features (optional Dict[str, FeatureType]): Description of `param1`. + enable_feature_inference (bool): enable inferring additional FeatureTypes + + Example: + ``` + >>> dataset = fog_x.Dataset('my_dataset', path='~/fog_x/my_dataset`) + ``` + + TODO: + * is replace_existing actually used anywhere? + """ + self.name = name + + if path.startswith("."): # relative path + path = os.path.abspath(path).removesuffix("/") + elif path.startswith("~"): # home directory + path = os.path.expanduser(path).removesuffix("/") + elif path.startswith("/"): # absolute path + path = path.removesuffix("/") + elif path.startswith("s3://") or path.startswith("gs://"): + path = path.removesuffix("/") + else: + raise ValueError("Unsupported path format. Please use absolute path or relative path starting with '.' or '~'.") + + logger.info(f"Dataset path: {path}") + self.path = path + if path is None: + raise ValueError("Path is required") + # create the folder if path doesn't exist + if self.path.startswith("/") and not os.path.exists(path): + logger.info(f"Creating directory {path}") + os.makedirs(path) + + self.replace_existing = replace_existing + self.features = features + self.enable_feature_inference = enable_feature_inference + if episode_info_connector is None: + episode_info_connector = DataFrameConnector(f"{path}") + + if step_data_connector is None: + if self.path.startswith("/") and not os.path.exists(f"{path}/{name}"): + os.makedirs(f"{path}/{name}") + try: + step_data_connector = LazyFrameConnector(f"{path}/{name}") + except: + logger.info(f"Path does not exist. ({path}/{name})") + cloud_provider = path[:2] + bucket_name = path[5:] + create_cloud_bucket_if_not_exist(cloud_provider, bucket_name, f"{name}/") + step_data_connector = LazyFrameConnector(f"{path}/{name}") + self.db_manager = DatabaseManager(episode_info_connector, step_data_connector) + self.db_manager.initialize_dataset(self.name, features) + + self.storage = storage + self.obs_keys = [] + self.act_keys = [] + self.step_keys = [] + + def new_episode(self, metadata: Optional[Dict[str, Any]] = None) -> Episode: + """ + Create a new episode / trajectory. + + Returns: + Episode + + TODO: + * support multiple processes writing to the same episode + * close the previous episode if not closed + """ + return Episode( + metadata=metadata, + features=self.features, + enable_feature_inference=self.enable_feature_inference, + db_manager=self.db_manager, + ) + + def _get_tf_feature_dicts( + self, obs_keys: List[str], act_keys: List[str], step_keys: List[str] + ) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]: + """ + Get the tensorflow feature dictionaries. + """ + observation_tf_dict = {} + action_tf_dict = {} + step_tf_dict = {} + + for k in obs_keys: + observation_tf_dict[k] = self.features[k].to_tf_feature_type() + + for k in act_keys: + action_tf_dict[k] = self.features[k].to_tf_feature_type() + + for k in step_keys: + step_tf_dict[k] = self.features[k].to_tf_feature_type() + + return observation_tf_dict, action_tf_dict, step_tf_dict + + def export( + self, + export_path: Optional[str] = None, + format: str = "rtx", + max_episodes_per_file: int = 1, + version: str = "0.0.1", + obs_keys=[], + act_keys=[], + step_keys=[], + ) -> None: + """ + Export the dataset. + + Args: + export_path (optional str): location of exported data. Uses dataset.path/export by default. + format (str): Supported formats are `rtx`, `open-x`, and `rlds`. + """ + if format == "rtx" or format == "open-x" or format == "rlds": + self.export_rtx(export_path, max_episodes_per_file, version, obs_keys, act_keys, step_keys) + else: + raise ValueError("Unsupported export format") + + def export_rtx( + self, + export_path: Optional[str] = None, + max_episodes_per_file: int = 1, + version: str = "0.0.1", + obs_keys=[], + act_keys=[], + step_keys=[] + ): + if export_path == None: + export_path = self.path + "/export" + if not os.path.exists(export_path): + os.makedirs(export_path) + + import dm_env + import tensorflow as tf + import tensorflow_datasets as tfds + from envlogger import step_data + from tensorflow_datasets.core.features import Tensor + + from fog_x.rlds.writer import CloudBackendWriter + + self.obs_keys += obs_keys + self.act_keys += act_keys + self.step_keys += step_keys + + ( + observation_tf_dict, + action_tf_dict, + step_tf_dict, + ) = self._get_tf_feature_dicts( + self.obs_keys, + self.act_keys, + self.step_keys, + ) + + logger.info("Exporting dataset as RT-X format") + logger.info(f"Observation keys: {observation_tf_dict}") + logger.info(f"Action keys: {action_tf_dict}") + logger.info(f"Step keys: {step_tf_dict}") + + # generate tensorflow configuration file + ds_config = tfds.rlds.rlds_base.DatasetConfig( + name=self.name, + description="", + homepage="", + citation="", + version=tfds.core.Version("0.0.1"), + release_notes={ + "0.0.1": "Initial release.", + }, + observation_info=observation_tf_dict, + action_info=action_tf_dict, + reward_info=( + step_tf_dict["reward"] + if "reward" in step_tf_dict + else Tensor(shape=(), dtype=tf.float32) + ), + discount_info=( + step_tf_dict["discount"] + if "discount" in step_tf_dict + else Tensor(shape=(), dtype=tf.float32) + ), + ) + + ds_identity = tfds.core.dataset_info.DatasetIdentity( + name=ds_config.name, + version=tfds.core.Version(version), + data_dir=export_path, + module_name="", + ) + writer = CloudBackendWriter( + data_directory=export_path, + ds_config=ds_config, + ds_identity=ds_identity, + max_episodes_per_file=max_episodes_per_file, + ) + + # export the dataset + episodes = self.get_episodes_from_metadata() + for episode in episodes: + steps = episode.collect().rows(named=True) + for i in range(len(steps)): + step = steps[i] + observationd = {} + actiond = {} + stepd = {} + for k, v in step.items(): + # logger.info(f"key: {k}") + if k not in self.features: + if k != "episode_id" and k != "Timestamp": + logger.info( + f"Feature {k} not found in the dataset features." + ) + continue + feature_spec = self.features[k].to_tf_feature_type() + if ( + isinstance(feature_spec, tfds.core.features.Tensor) + and feature_spec.shape != () + ): + # reverse the process + value = np.load(io.BytesIO(v)).astype( + feature_spec.np_dtype + ) + elif ( + isinstance(feature_spec, tfds.core.features.Tensor) + and feature_spec.shape == () + ): + value = np.array(v, dtype=feature_spec.np_dtype) + elif isinstance( + feature_spec, tfds.core.features.Image + ): + value = np.load(io.BytesIO(v)).astype( + feature_spec.np_dtype + ) + else: + value = v + + if k in self.obs_keys: + observationd[k] = value + elif k in self.act_keys: + actiond[k] = value + else: + stepd[k] = value + + # logger.info( + # f"Step: {stepd}" + # f"Observation: {observationd}" + # f"Action: {actiond}" + # ) + timestep = dm_env.TimeStep( + step_type=dm_env.StepType.FIRST, + reward=np.float32( + 0.0 + ), # stepd["reward"] if "reward" in step else np.float32(0.0), + discount=np.float32( + 0.0 + ), # stepd["discount"] if "discount" in step else np.float32(0.0), + observation=observationd, + ) + stepdata = step_data.StepData( + timestep=timestep, action=actiond, custom_data=None + ) + if i < len(steps) - 1: + writer._record_step(stepdata, is_new_episode=False) + else: + writer._record_step(stepdata, is_new_episode=True) + + + def load_rtx_episodes( + self, + name: str, + split: str = "all", + additional_metadata: Optional[Dict[str, Any]] = dict(), + ): + """ + Load robot data from Tensorflow Datasets. + + Args: + name (str): Name of RT-X episodes, which can be found at [Tensorflow Datasets](https://www.tensorflow.org/datasets/catalog) under the Robotics category + split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits) + additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes + + Example: + ``` + >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5) + >>> dataset.load_rtx_episodes(name="berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"}) + ``` + """ + + # this is only required if rtx format is used + import tensorflow_datasets as tfds + + from fog_x.rlds.utils import dataset2path + b = tfds.builder_from_directory(builder_dir=dataset2path(name)) + self._build_rtx_episodes_from_tfds_builder( + b, + split=split, + additional_metadata=additional_metadata, + ) + + def load_rtx_episodes_local( + self, + path: str, + split: str = "all", + additional_metadata: Optional[Dict[str, Any]] = dict(), + ): + """ + Load robot data from Tensorflow Datasets. + + Args: + path (str): Path to the RT-X episodes + split (optional str): the portion of data to load, see [Tensorflow Split API](https://www.tensorflow.org/datasets/splits) + additional_metadata (optional Dict[str, Any]): additional metadata to be associated with the loaded episodes + + Example: + ``` + >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5") + >>> dataset.load_rtx_episodes_local(path="~/Downloads/berkeley_autolab_ur5", split="train[:10]", additional_metadata={"data_collector": "Alice", "custom_tag": "sample"}) + ``` + """ + + # this is only required if rtx format is used + import tensorflow_datasets as tfds + + b = tfds.builder_from_directory(path) + self._build_rtx_episodes_from_tfds_builder( + b, + split=split, + additional_metadata=additional_metadata, + ) + + def _build_rtx_episodes_from_tfds_builder( + self, + builder, + split: str = "all", + additional_metadata: Optional[Dict[str, Any]] = dict(), + ): + """ + construct the dataset from the tfds builder + """ + ds = builder.as_dataset(split=split) + + data_type = builder.info.features["steps"] + + for tf_episode in ds: + logger.info(tf_episode) + fog_episode = self.new_episode( + metadata=additional_metadata, + ) + for step in tf_episode["steps"]: + ret = self._load_rtx_step_data_from_tf_step( + step, data_type, + ) + for r in ret: + fog_episode.add(**r) + + fog_episode.close() + + + def _prepare_rtx_metadata( + self, + name: str, + export_path: Optional[str] = None, + sample_size = 20, + shuffle = False, + seed = 42, + ): + + # this is only required if rtx format is used + import tensorflow_datasets as tfds + from fog_x.rlds.utils import dataset2path + import cv2 + + b = tfds.builder_from_directory(builder_dir=dataset2path(name)) + ds = b.as_dataset(split="all") + if shuffle: + ds = ds.shuffle(sample_size, seed=seed) + data_type = b.info.features["steps"] + counter = 0 + + if export_path == None: + export_path = self.path + "/" + self.name + "_viz" + if not os.path.exists(export_path): + os.makedirs(export_path) + + + for tf_episode in ds: + video_writers = {} + + additional_metadata = { + "load_from": name, + "load_index": f"all, {shuffle}, {seed}, {counter}", + } + + logger.info(tf_episode) + fog_episode = self.new_episode() + + for step in tf_episode["steps"]: + ret = self._load_rtx_step_data_from_tf_step( + step, data_type, + ) + + for r in ret: + feature_name = r["feature"] + if "image" in feature_name and "depth" not in feature_name: + image = np.load(io.BytesIO(r["value"])) + + # convert from RGB to BGR + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + if feature_name not in video_writers: + + output_filename = f"{self.name}_{counter}_{feature_name}" + tmp_vid_output_path = f"/tmp/{output_filename}.mp4" + output_path = f"{export_path}/{output_filename}" + + frame_size = (image.shape[1], image.shape[0]) + + # save the initial image + cv2.imwrite(f"{output_path}.jpg", image) + # save the video + video_writers[feature_name] = cv2.VideoWriter( + tmp_vid_output_path, + cv2.VideoWriter_fourcc(*"mp4v"), + 10, + frame_size + ) + + + video_writers[r["feature"]].write(image) + + if "instruction" in r["feature"]: + natural_language_instruction = r["value"].decode("utf-8") + additional_metadata["natural_language_instruction"] = natural_language_instruction + + r["metadata_only"] = True + fog_episode.add(**r) + + for feature_name, video_writer in video_writers.items(): + video_writer.release() + # need to convert to h264 to properly display over chrome / vscode + output_filename = f"{self.name}_{counter}_{feature_name}" + tmp_vid_output_path = f"/tmp/{output_filename}.mp4" + vid_output_path = f"{export_path}/{output_filename}.mp4" + convert_to_h264(tmp_vid_output_path, vid_output_path) + additional_metadata[f"video_path_{feature_name}"] = output_filename + if os.path.isfile(tmp_vid_output_path): + os.remove(tmp_vid_output_path) + + video_writers = {} + fog_episode.close(save_data = False, additional_metadata = additional_metadata) + counter += 1 + if counter > sample_size: + break + + def _load_rtx_step_data_from_tf_step( + self, + step: Dict[str, Any], + data_type: Dict[str, Any] = {}, + ): + from tensorflow_datasets.core.features import ( + FeaturesDict, + Image, + Scalar, + Tensor, + Text, + ) + ret = [] + + for k, v in step.items(): + # logger.info(f"k {k} , v {v}") + if isinstance(v, dict): #and (k == "observation" or k == "action"): + for k2, v2 in v.items(): + # TODO: abstract this to feature.py + + if ( + isinstance(data_type[k][k2], Tensor) + and data_type[k][k2].shape != () + ): + memfile = io.BytesIO() + np.save(memfile, v2.numpy()) + value = memfile.getvalue() + elif isinstance(data_type[k][k2], Image): + memfile = io.BytesIO() + np.save(memfile, v2.numpy()) + value = memfile.getvalue() + else: + value = v2.numpy() + + ret.append( + { + "feature": str(k2), + "value": value, + "feature_type": FeatureType( + tf_feature_spec=data_type[k][k2] + ), + } + ) + # fog_episode.add( + # feature=str(k2), + # value=value, + # feature_type=FeatureType( + # tf_feature_spec=data_type[k][k2] + # ), + # ) + if k == "observation": + self.obs_keys.append(k2) + elif k == "action": + self.act_keys.append(k2) + else: + # fog_episode.add( + # feature=str(k), + # value=v.numpy(), + # feature_type=FeatureType(tf_feature_spec=data_type[k]), + # ) + ret.append( + { + "feature": str(k), + "value": v.numpy(), + "feature_type": FeatureType( + tf_feature_spec=data_type[k] + ), + } + ) + self.step_keys.append(k) + return ret + + + def get_episode_info(self) -> pandas.DataFrame: + """ + Returns: + metadata of all episodes as `pandas.DataFrame` + """ + return self.db_manager.get_episode_info_table() + + def get_step_data(self) -> polars.LazyFrame: + """ + Returns: + step data of all episodes + """ + return self.db_manager.get_step_table_all() + + def get_step_data_by_episode_ids( + self, episode_ids: List[int], as_lazy_frame=True + ): + """ + Args: + episode_ids (List[int]): list of episode ids + as_lazy_frame (bool): whether to return polars.LazyFrame or polars.DataFrame + + Returns: + step data of each episode + """ + episodes = [] + for episode_id in episode_ids: + if episode_id == None: + continue + if as_lazy_frame: + episodes.append(self.db_manager.get_step_table(episode_id)) + else: + episodes.append(self.db_manager.get_step_table(episode_id).collect()) + return episodes + + def read_by(self, episode_info: Any = None) -> List[polars.LazyFrame]: + """ + To be used with `Dataset.get_episode_info`. + + Args: + episode_info (pandas.DataFrame): episode metadata information to determine which episodes to read + + Returns: + episodes filtered by `episode_info` + """ + episode_ids = list(episode_info["episode_id"]) + logger.info(f"Reading episodes as order: {episode_ids}") + episodes = [] + for episode_id in episode_ids: + if episode_id == None: + continue + episodes.append(self.db_manager.get_step_table(episode_id)) + return episodes + + def get_episodes_from_metadata(self, metadata: Any = None): + # Assume we use get_metadata_as_pandas_df to retrieve episodes metadata + if metadata is None: + metadata_df = self.get_episode_info() + else: + metadata_df = metadata + episodes = self.read_by(metadata_df) + return episodes + + def pytorch_dataset_builder(self, metadata=None, **kwargs): + """ + Used for loading current dataset as a PyTorch dataset. + To be used with `torch.utils.data.DataLoader`. + """ + + import torch + from torch.utils.data import Dataset + episodes = self.get_episodes_from_metadata(metadata) + + # Initialize the PyTorch dataset with the episodes and features + pytorch_dataset = PyTorchDataset(episodes, self.features) + + return pytorch_dataset + + def get_as_huggingface_dataset(self): + """ + Load current dataset as a HuggingFace dataset. + + TODO: + * currently the support for huggingg face dataset is limited. + it only shows its capability of easily returning a hf dataset + * add features from the episode metadata + * allow selecting episodes based on queries. + doing so requires creating a new copy of the dataset on disk + """ + import datasets + + dataset_path = self.path + "/" + self.name + parquet_files = [ + os.path.join(dataset_path, f) for f in os.listdir(dataset_path) + ] + + hf_dataset = datasets.load_dataset("parquet", data_files=parquet_files) + return hf_dataset + +class PyTorchDataset(Dataset): + def __init__(self, episodes, features): + """ + Initialize the dataset with the episodes and features. + :param episodes: A list of episodes loaded from the database. + :param features: A dictionary of features to be included in the dataset. + """ + self.episodes = episodes + self.features = features + + def __len__(self): + """ + Return the total number of episodes in the dataset. + """ + return len(self.episodes) + + def __getitem__(self, idx): + """ + Retrieve the idx-th episode from the dataset. + Depending on the structure, you may need to process the episode + and its features here. + """ + print("Retrieving episode at index", idx) + episode = self.episodes[idx].collect().to_pandas() + # Process the episode and its features here + # For simplicity, let's assume we're just returning the episode + return episode diff --git a/fog_x/storage/__init__.py b/fog_x/deprecated/storage/__init__.py similarity index 100% rename from fog_x/storage/__init__.py rename to fog_x/deprecated/storage/__init__.py diff --git a/fog_x/storage/storage.py b/fog_x/deprecated/storage/storage.py similarity index 100% rename from fog_x/storage/storage.py rename to fog_x/deprecated/storage/storage.py diff --git a/fog_x/loader/__init__.py b/fog_x/loader/__init__.py index 9a45341..ab8f982 100644 --- a/fog_x/loader/__init__.py +++ b/fog_x/loader/__init__.py @@ -1,3 +1,4 @@ from .base import BaseLoader from .rlds import RLDSLoader -from .hdf5 import HDF5Loader \ No newline at end of file +from .hdf5 import HDF5Loader +from .vla import VLALoader \ No newline at end of file diff --git a/fog_x/loader/base.py b/fog_x/loader/base.py index 09c009c..3278e33 100644 --- a/fog_x/loader/base.py +++ b/fog_x/loader/base.py @@ -2,10 +2,13 @@ class BaseLoader(): - def __init__(self, path): + def __init__(self, + path, + split = None): super(BaseLoader, self).__init__() self.logger = getLogger(__name__) self.path = path + self.split = split # def get_schema(self) -> Schema: # raise NotImplementedError diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 14c3209..f0036cc 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -25,7 +25,7 @@ def recursively_read_hdf5_group(group): class HDF5Loader(BaseLoader): - def __init__(self, path): + def __init__(self, path, split = None): super(HDF5Loader, self).__init__(path) self.index = 0 self.files = glob.glob(self.path, recursive=True) diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py new file mode 100644 index 0000000..8452b95 --- /dev/null +++ b/fog_x/loader/vla.py @@ -0,0 +1,28 @@ +from fog_x.loader.base import BaseLoader +import fog_x +import glob + +class VLALoader(BaseLoader): + def __init__(self, path, split = None): + super(VLALoader, self).__init__(path) + self.index = 0 + self.files = glob.glob(self.path, recursive=True) + + def _read_vla(self, data_path): + traj = fog_x.Trajectory(data_path) + return traj + + def __iter__(self): + return self + + def __next__(self): + if self.index < len(self.files): + file_path = self.files[self.index] + self.index += 1 + return self._read_vla(file_path) + raise StopIteration + + def __len__(self): + return len(self.files) + + \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 27925db..3b422e1 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -29,13 +29,14 @@ class Trajectory: def __init__( self, path: Text, + mode = "r", num_pre_initialized_h264_streams: int = 5, feature_name_separator: Text = "/", - split: Optional[Text] = None, ) -> None: """ Args: path (Text): path to the trajectory file + mode (Text, optional): mode of the file, "r" for read and "w" for write num_pre_initialized_h264_streams (int, optional): Number of pre-initialized H.264 video streams to use when adding new features. we pre initialize a configurable number of H.264 video streams to avoid the overhead of creating new streams for each feature. @@ -59,21 +60,25 @@ def __init__( self.pre_initialized_image_streams = ( [] ) # a list of pre-initialized h264 streams + self.mode = mode # check if the path exists # if not, create a new file and start data collection - if not os.path.exists(self.path): - logger.info(f"creating a new trajectory at {self.path}") - try: + if self.mode == "w": + if not os.path.exists(self.path): + logger.info(f"creating a new directory at {self.path}") os.makedirs(os.path.dirname(self.path), exist_ok=True) + try: self.container_file = av.open(self.path, mode="w", format="matroska") except Exception as e: logger.error(f"error creating the trajectory file: {e}") raise - self._pre_initialize_h264_streams(num_pre_initialized_h264_streams) + elif self.mode == "r": + if not os.path.exists(self.path): + raise FileNotFoundError(f"{self.path} does not exist") else: - logger.warn(f"{self.path} exists") + raise ValueError(f"Invalid mode {self.mode}, must be 'r' or 'w'") def _get_current_timestamp(self): current_time = (time.time() - self.start_time) * 1000 @@ -257,7 +262,7 @@ def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajecto trajectory = Trajectory.from_list_of_dicts(original_trajectory, path="/tmp/fog_x/output.vla") """ - traj = cls(path) + traj = cls(path, mode="w") for step in data: traj.add_by_dict(step) return traj @@ -284,7 +289,7 @@ def from_dict_of_lists( trajectory = Trajectory.from_dict_of_lists(original_trajectory, path="/tmp/fog_x/output.vla") """ - traj = cls(path, feature_name_separator=feature_name_separator) + traj = cls(path, feature_name_separator=feature_name_separator, mode="w") # flatten the data such that all data starts and put feature name with separator _flatten_dict_data = _flatten_dict(data, sep=traj.feature_name_separator) From 45454479ec8349ea464ec41f2adbd8930752d6fa Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 00:52:22 -0700 Subject: [PATCH 26/80] Refactor Trajectory class to improve code readability and remove commented code --- benchmarks/openx.py | 17 ++++++++++------- examples/vla_loader.py | 10 ++++++++++ fog_x/feature.py | 3 ++- fog_x/loader/vla.py | 33 +++++++++++++++++++++++++-------- fog_x/trajectory.py | 30 +++++++++++++++++++++--------- 5 files changed, 68 insertions(+), 25 deletions(-) create mode 100644 examples/vla_loader.py diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 16056ad..4124388 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -5,12 +5,13 @@ from concurrent.futures import ThreadPoolExecutor import glob import time +import numpy as np from fog_x.loader import RLDSLoader from fog_x.loader import VLALoader # Constants DEFAULT_EXP_DIR = "/tmp/fog_x" -DEFAULT_NUMBER_OF_TRAJECTORIES = 3 +DEFAULT_NUMBER_OF_TRAJECTORIES = 20 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-*" LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" @@ -67,21 +68,24 @@ def measure_file_size(dataset_dir): total_size += os.path.getsize(fp) return total_size - def measure_loading_time(loader_func, path, num_trajectories): - """Measures the time taken to load data using a specified loader function.""" + """Measures the time taken to load data into memory using a specified loader function.""" start_time = time.time() loader = loader_func(path, split=f"train[:{num_trajectories}]") - data = list(loader) # Load all data to measure time + for data in loader: + # use np array to force loading + data + end_time = time.time() loading_time = end_time - start_time - return loading_time, len(data) - + print(f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + return loading_time, num_trajectories def convert_data_to_vla_format(loader, output_dir): """Converts data to VLA format and saves it to the specified output directory.""" for index, data_traj in enumerate(loader): output_path = os.path.join(output_dir, f"output_{index}.vla") + print(f"Converting trajectory {index} to VLA format and saving to {output_path} {len(data_traj)}") fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) def read_data(output_dir, num_trajectories): @@ -129,6 +133,5 @@ def main(): print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") - if __name__ == "__main__": main() diff --git a/examples/vla_loader.py b/examples/vla_loader.py new file mode 100644 index 0000000..6060538 --- /dev/null +++ b/examples/vla_loader.py @@ -0,0 +1,10 @@ +from fog_x.loader import VLALoader +import fog_x +import os + + +loader = VLALoader("/tmp/fog_x/output/*.vla") +for index, data_traj in enumerate(loader): + + print(data_traj.load()) + index += 1 \ No newline at end of file diff --git a/fog_x/feature.py b/fog_x/feature.py index 8cadd47..7f2aae6 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -32,7 +32,6 @@ "string", "str", "large_string", - "object", ] @@ -69,6 +68,8 @@ def _set(self, dtype: str, shape: Any): dtype = "float64" if dtype == "float": # fix inferred type dtype = "float32" + if dtype == "object": + dtype = "string" if dtype not in SUPPORTED_DTYPES: raise ValueError(f"Unsupported dtype: {dtype}") if shape is not None and not isinstance(shape, tuple): diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 8452b95..1842cd5 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -1,28 +1,45 @@ from fog_x.loader.base import BaseLoader import fog_x import glob +import logging + +logger = logging.getLogger(__name__) +import os +from typing import Text + class VLALoader(BaseLoader): - def __init__(self, path, split = None): + def __init__(self, path: Text, split=None): + """initialize VLALoader from paths + + Args: + path (_type_): path to the vla files + can be a directory, or a glob pattern + split (_type_, optional): split of training and testing. Defaults to None. + """ super(VLALoader, self).__init__(path) self.index = 0 - self.files = glob.glob(self.path, recursive=True) - + + if "*" in path: + self.files = glob.glob(path) + elif os.path.isdir(path): + self.files = glob.glob(os.path.join(path, "*.vla")) + else: + self.files = [path] + def _read_vla(self, data_path): traj = fog_x.Trajectory(data_path) return traj - + def __iter__(self): return self - + def __next__(self): if self.index < len(self.files): file_path = self.files[self.index] self.index += 1 return self._read_vla(file_path) raise StopIteration - + def __len__(self): return len(self.files) - - \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 3b422e1..906c933 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -66,7 +66,6 @@ def __init__( # if not, create a new file and start data collection if self.mode == "w": if not os.path.exists(self.path): - logger.info(f"creating a new directory at {self.path}") os.makedirs(os.path.dirname(self.path), exist_ok=True) try: self.container_file = av.open(self.path, mode="w", format="matroska") @@ -105,6 +104,8 @@ def __getitem__(self, key): """ if self.trajectory_data is None: self.trajectory_data = self.load() + + print(self.trajectory_data, key) return self.trajectory_data[key] @@ -123,6 +124,7 @@ def close(self): self.container_file.mux(packet) except Exception as e: logger.error(f"Error flushing stream {stream}: {e}") + logger.debug("Flushing the container file") except av.error.EOFError: pass # This exception is expected and means the encoder is fully flushed @@ -205,8 +207,6 @@ def add( # get the timestamp if timestamp is None: timestamp = self._get_current_timestamp() - else: - logger.debug("Using custom timestamp, may cause misalignment") # encode the frame packets = self._encode_frame(data, stream, timestamp) @@ -265,6 +265,7 @@ def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajecto traj = cls(path, mode="w") for step in data: traj.add_by_dict(step) + traj.close() return traj @classmethod @@ -304,6 +305,7 @@ def from_dict_of_lists( for i in range(list_lengths[0]): step = {k: v[i] for k, v in _flatten_dict_data.items()} traj.add_by_dict(step) + traj.close() return traj def _load_from_cache(self): @@ -349,12 +351,22 @@ def _load_from_container(self): logger.debug( f"creating a cache for {feature_name} with shape {feature_type.shape}" ) - h5_cache.create_dataset( - feature_name, - (0,) + feature_type.shape, - maxshape=(None,) + feature_type.shape, - dtype=feature_type.dtype, - ) + + if feature_type.dtype == "string": + # strings are not supported in h5py, so we store them as objects + h5_cache.create_dataset( + feature_name, + (0,) + feature_type.shape, + maxshape=(None,) + feature_type.shape, + dtype=h5py.special_dtype(vlen=str), + ) + else: + h5_cache.create_dataset( + feature_name, + (0,) + feature_type.shape, + maxshape=(None,) + feature_type.shape, + dtype=feature_type.dtype, + ) # decode the frames and store in the preallocated memory From d594e396f950177593e352ae422077fda670794f Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 00:56:54 -0700 Subject: [PATCH 27/80] Refactor Trajectory class to add optional cache path for storing cache files --- benchmarks/openx.py | 105 +++++++++++++++++++++++++++++++++----------- fog_x/trajectory.py | 5 ++- 2 files changed, 84 insertions(+), 26 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 4124388..9eb67e3 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -14,9 +14,14 @@ DEFAULT_NUMBER_OF_TRAJECTORIES = 20 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-*" -LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" +LOCAL_FILE_TEMPLATE = ( + "{exp_dir}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" +) FEATURE_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" -DATASET_INFO_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" +DATASET_INFO_JSON_URL_TEMPLATE = ( + "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" +) + def check_and_download_file(url, local_path): """Checks if a file is already downloaded; if not, downloads it.""" @@ -25,6 +30,7 @@ def check_and_download_file(url, local_path): else: print(f"File {local_path} already exists. Skipping download.") + def check_and_download_trajectory(exp_dir, dataset_name, trajectory_index): """Checks if a trajectory and associated JSON files are already downloaded; if not, downloads them.""" # Create a directory for each dataset @@ -32,12 +38,18 @@ def check_and_download_trajectory(exp_dir, dataset_name, trajectory_index): os.makedirs(dataset_dir, exist_ok=True) # Check and download the trajectory files - local_file_pattern = LOCAL_FILE_TEMPLATE.format(exp_dir=exp_dir, dataset_name=dataset_name, index=trajectory_index) + local_file_pattern = LOCAL_FILE_TEMPLATE.format( + exp_dir=exp_dir, dataset_name=dataset_name, index=trajectory_index + ) if not any(os.path.exists(file) for file in glob.glob(local_file_pattern)): - data_url = DATA_URL_TEMPLATE.format(dataset_name=dataset_name, index=trajectory_index) + data_url = DATA_URL_TEMPLATE.format( + dataset_name=dataset_name, index=trajectory_index + ) subprocess.run(["gsutil", "-m", "cp", data_url, dataset_dir], check=True) else: - print(f"Trajectory {trajectory_index} of dataset {dataset_name} already exists in {dataset_dir}. Skipping download.") + print( + f"Trajectory {trajectory_index} of dataset {dataset_name} already exists in {dataset_dir}. Skipping download." + ) # Check and download the feature.json file feature_json_local_path = os.path.join(dataset_dir, "features.json") @@ -46,19 +58,27 @@ def check_and_download_trajectory(exp_dir, dataset_name, trajectory_index): # Check and download the dataset_info.json file dataset_info_json_local_path = os.path.join(dataset_dir, "dataset_info.json") - dataset_info_json_url = DATASET_INFO_JSON_URL_TEMPLATE.format(dataset_name=dataset_name) + dataset_info_json_url = DATASET_INFO_JSON_URL_TEMPLATE.format( + dataset_name=dataset_name + ) check_and_download_file(dataset_info_json_url, dataset_info_json_local_path) + def download_data(exp_dir, dataset_names, num_trajectories): """Downloads the specified number of trajectories from each dataset concurrently if not already downloaded.""" with ThreadPoolExecutor() as executor: futures = [] for dataset_name in dataset_names: for i in range(num_trajectories): - futures.append(executor.submit(check_and_download_trajectory, exp_dir, dataset_name, i)) + futures.append( + executor.submit( + check_and_download_trajectory, exp_dir, dataset_name, i + ) + ) for future in futures: future.result() # Will raise an exception if any download failed + def measure_file_size(dataset_dir): """Calculates the total size of all files in the dataset directory.""" total_size = 0 @@ -68,70 +88,105 @@ def measure_file_size(dataset_dir): total_size += os.path.getsize(fp) return total_size + def measure_loading_time(loader_func, path, num_trajectories): """Measures the time taken to load data into memory using a specified loader function.""" start_time = time.time() loader = loader_func(path, split=f"train[:{num_trajectories}]") for data in loader: # use np array to force loading - data - + data + end_time = time.time() loading_time = end_time - start_time - print(f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + print( + f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) return loading_time, num_trajectories + def convert_data_to_vla_format(loader, output_dir): """Converts data to VLA format and saves it to the specified output directory.""" for index, data_traj in enumerate(loader): output_path = os.path.join(output_dir, f"output_{index}.vla") - print(f"Converting trajectory {index} to VLA format and saving to {output_path} {len(data_traj)}") + print( + f"Converting trajectory {index} to VLA format and saving to {output_path} {len(data_traj)}" + ) fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) + def read_data(output_dir, num_trajectories): """Reads the VLA data files and prints their action keys.""" for i in range(num_trajectories): traj = fog_x.Trajectory(os.path.join(output_dir, f"output_{i}.vla")) print(traj["action"].keys()) + def main(): # Parse command-line arguments - parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") - parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") - parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to download.") - parser.add_argument("--dataset_names", nargs='+', default=DEFAULT_DATASET_NAMES, help="List of dataset names to download.") - + parser = argparse.ArgumentParser( + description="Download, process, and read RLDS data." + ) + parser.add_argument( + "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory." + ) + parser.add_argument( + "--num_trajectories", + type=int, + default=DEFAULT_NUMBER_OF_TRAJECTORIES, + help="Number of trajectories to download.", + ) + parser.add_argument( + "--dataset_names", + nargs="+", + default=DEFAULT_DATASET_NAMES, + help="List of dataset names to download.", + ) + args = parser.parse_args() - + # Create output directory if it doesn't exist output_dir = os.path.join(args.exp_dir, "output") os.makedirs(output_dir, exist_ok=True) - + # Download data concurrently download_data(args.exp_dir, args.dataset_names, args.num_trajectories) - + # Iterate through datasets and measure file size and loading time for both formats for dataset_name in args.dataset_names: dataset_dir = os.path.join(args.exp_dir, dataset_name) file_size = measure_file_size(dataset_dir) # Measure loading time for RLDS format - rlds_loading_time, num_loaded_rlds = measure_loading_time(RLDSLoader, dataset_dir, args.num_trajectories) + rlds_loading_time, num_loaded_rlds = measure_loading_time( + RLDSLoader, dataset_dir, args.num_trajectories + ) print(f"Dataset: {dataset_name}") print(f"Total file size: {file_size / (1024 * 1024):.2f} MB") - print(f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds") - print(f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second") + print( + f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds" + ) + print( + f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second" + ) # Convert data to VLA format loader = RLDSLoader(path=dataset_dir, split=f"train[:{args.num_trajectories}]") convert_data_to_vla_format(loader, output_dir) # Measure loading time for VLA format - vla_loading_time, num_loaded_vla = measure_loading_time(VLALoader, output_dir, args.num_trajectories) + vla_loading_time, num_loaded_vla = measure_loading_time( + VLALoader, output_dir, args.num_trajectories + ) + + print( + f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds" + ) + print( + f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n" + ) - print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") - print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") if __name__ == "__main__": main() diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 906c933..9c1a3cb 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -30,6 +30,7 @@ def __init__( self, path: Text, mode = "r", + cache_path: Optional[Text] = "/tmp/fog_x/cache/", num_pre_initialized_h264_streams: int = 5, feature_name_separator: Text = "/", ) -> None: @@ -50,8 +51,10 @@ def __init__( self.feature_name_separator = feature_name_separator # self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" # use hex hash of the path for the cache file name + if not os.path.exists(cache_path): + os.makedirs(cache_path, exist_ok=True) hex_hash = hex(abs(hash(self.path)))[2:] - self.cache_file_name = "/tmp/fog_" + hex_hash + ".cache" + self.cache_file_name = cache_path + hex_hash + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type self.trajectory_data = None # trajectory_data From 430c73b2ef526f3c3ee8327f904c515fe1c81a6a Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 01:10:59 -0700 Subject: [PATCH 28/80] Refactor Trajectory class to clear cache directory and improve code readability --- benchmarks/openx.py | 43 +++++++++++++++++++++++++++---------------- fog_x/loader/base.py | 4 +--- fog_x/loader/vla.py | 9 +++++++-- fog_x/trajectory.py | 9 ++++----- 4 files changed, 39 insertions(+), 26 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 9eb67e3..3c364de 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -21,7 +21,13 @@ DATASET_INFO_JSON_URL_TEMPLATE = ( "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" ) +CACHE_DIR = "/tmp/fog_x/cache" +def clear_cache(): + """Clears the cache directory.""" + if os.path.exists(CACHE_DIR): + subprocess.run(["rm", "-rf", CACHE_DIR], check=True) + def check_and_download_file(url, local_path): """Checks if a file is already downloaded; if not, downloads it.""" @@ -89,10 +95,10 @@ def measure_file_size(dataset_dir): return total_size -def measure_loading_time(loader_func, path, num_trajectories): +def measure_loading_time_rlds(path, num_trajectories): """Measures the time taken to load data into memory using a specified loader function.""" start_time = time.time() - loader = loader_func(path, split=f"train[:{num_trajectories}]") + loader = RLDSLoader(path, split=f"train[:{num_trajectories}]") for data in loader: # use np array to force loading data @@ -104,24 +110,29 @@ def measure_loading_time(loader_func, path, num_trajectories): ) return loading_time, num_trajectories +def measure_loading_time_vla(path, num_trajectories): + """Measures the time taken to load data into memory using a specified loader function.""" + start_time = time.time() + loader = VLALoader(path, cache_dir=CACHE_DIR) + for data in loader: + # use np array to force loading + data["action"] + + end_time = time.time() + loading_time = end_time - start_time + print( + f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) + return loading_time, num_trajectories + def convert_data_to_vla_format(loader, output_dir): """Converts data to VLA format and saves it to the specified output directory.""" for index, data_traj in enumerate(loader): output_path = os.path.join(output_dir, f"output_{index}.vla") - print( - f"Converting trajectory {index} to VLA format and saving to {output_path} {len(data_traj)}" - ) fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) -def read_data(output_dir, num_trajectories): - """Reads the VLA data files and prints their action keys.""" - for i in range(num_trajectories): - traj = fog_x.Trajectory(os.path.join(output_dir, f"output_{i}.vla")) - print(traj["action"].keys()) - - def main(): # Parse command-line arguments parser = argparse.ArgumentParser( @@ -158,8 +169,8 @@ def main(): file_size = measure_file_size(dataset_dir) # Measure loading time for RLDS format - rlds_loading_time, num_loaded_rlds = measure_loading_time( - RLDSLoader, dataset_dir, args.num_trajectories + rlds_loading_time, num_loaded_rlds = measure_loading_time_rlds( + dataset_dir, args.num_trajectories ) print(f"Dataset: {dataset_name}") @@ -176,8 +187,8 @@ def main(): convert_data_to_vla_format(loader, output_dir) # Measure loading time for VLA format - vla_loading_time, num_loaded_vla = measure_loading_time( - VLALoader, output_dir, args.num_trajectories + vla_loading_time, num_loaded_vla = measure_loading_time_vla( + output_dir, args.num_trajectories ) print( diff --git a/fog_x/loader/base.py b/fog_x/loader/base.py index 3278e33..c8c87e4 100644 --- a/fog_x/loader/base.py +++ b/fog_x/loader/base.py @@ -3,12 +3,10 @@ class BaseLoader(): def __init__(self, - path, - split = None): + path): super(BaseLoader, self).__init__() self.logger = getLogger(__name__) self.path = path - self.split = split # def get_schema(self) -> Schema: # raise NotImplementedError diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 1842cd5..88c3d32 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -9,7 +9,7 @@ class VLALoader(BaseLoader): - def __init__(self, path: Text, split=None): + def __init__(self, path: Text, cache_dir=None): """initialize VLALoader from paths Args: @@ -26,9 +26,14 @@ def __init__(self, path: Text, split=None): self.files = glob.glob(os.path.join(path, "*.vla")) else: self.files = [path] + + self.cache_dir = cache_dir def _read_vla(self, data_path): - traj = fog_x.Trajectory(data_path) + if self.cache_dir: + traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) + else: + traj = fog_x.Trajectory(data_path) return traj def __iter__(self): diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 9c1a3cb..05fd233 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -30,7 +30,7 @@ def __init__( self, path: Text, mode = "r", - cache_path: Optional[Text] = "/tmp/fog_x/cache/", + cache_dir: Optional[Text] = "/tmp/fog_x/cache/", num_pre_initialized_h264_streams: int = 5, feature_name_separator: Text = "/", ) -> None: @@ -51,10 +51,10 @@ def __init__( self.feature_name_separator = feature_name_separator # self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" # use hex hash of the path for the cache file name - if not os.path.exists(cache_path): - os.makedirs(cache_path, exist_ok=True) + if not os.path.exists(cache_dir): + os.makedirs(cache_dir, exist_ok=True) hex_hash = hex(abs(hash(self.path)))[2:] - self.cache_file_name = cache_path + hex_hash + ".cache" + self.cache_file_name = cache_dir + hex_hash + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type self.trajectory_data = None # trajectory_data @@ -108,7 +108,6 @@ def __getitem__(self, key): if self.trajectory_data is None: self.trajectory_data = self.load() - print(self.trajectory_data, key) return self.trajectory_data[key] From 0220880ffd46ef150c851f8d005de96ccfcf7d57 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 01:31:20 -0700 Subject: [PATCH 29/80] Refactor Trajectory class for improved code readability and removal of commented code --- benchmarks/openx.py | 312 ++++++++++++++++++++------------------------ 1 file changed, 142 insertions(+), 170 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 3c364de..ca6fc63 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -6,197 +6,169 @@ import glob import time import numpy as np -from fog_x.loader import RLDSLoader -from fog_x.loader import VLALoader +from fog_x.loader import RLDSLoader, VLALoader # Constants DEFAULT_EXP_DIR = "/tmp/fog_x" -DEFAULT_NUMBER_OF_TRAJECTORIES = 20 +DEFAULT_NUMBER_OF_TRAJECTORIES = 5 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] -DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-*" -LOCAL_FILE_TEMPLATE = ( - "{exp_dir}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" -) -FEATURE_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" -DATASET_INFO_JSON_URL_TEMPLATE = ( - "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" -) CACHE_DIR = "/tmp/fog_x/cache" -def clear_cache(): - """Clears the cache directory.""" - if os.path.exists(CACHE_DIR): - subprocess.run(["rm", "-rf", CACHE_DIR], check=True) +class DatasetHandler: + """Base class to handle dataset-related operations.""" + + DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-*" + LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_type}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" + FEATURE_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" + DATASET_INFO_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" + + def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type): + self.exp_dir = exp_dir + self.dataset_name = dataset_name + self.num_trajectories = num_trajectories + self.dataset_type = dataset_type + self.dataset_dir = os.path.join(exp_dir, dataset_type, dataset_name) + + def clear_cache(self): + """Clears the cache directory.""" + if os.path.exists(CACHE_DIR): + subprocess.run(["rm", "-rf", CACHE_DIR], check=True) + + def check_and_download_file(self, url, local_path): + """Checks if a file is already downloaded; if not, downloads it.""" + if not os.path.exists(local_path): + subprocess.run(["gsutil", "-m", "cp", url, local_path], check=True) + else: + print(f"File {local_path} already exists. Skipping download.") + def check_and_download_trajectory(self, trajectory_index): + """Checks if a trajectory and associated JSON files are already downloaded; if not, downloads them.""" + os.makedirs(self.dataset_dir, exist_ok=True) + + # Check and download the trajectory files + local_file_pattern = self.LOCAL_FILE_TEMPLATE.format( + exp_dir=self.exp_dir, dataset_type=self.dataset_type, dataset_name=self.dataset_name, index=trajectory_index + ) - -def check_and_download_file(url, local_path): - """Checks if a file is already downloaded; if not, downloads it.""" - if not os.path.exists(local_path): - subprocess.run(["gsutil", "-m", "cp", url, local_path], check=True) - else: - print(f"File {local_path} already exists. Skipping download.") - - -def check_and_download_trajectory(exp_dir, dataset_name, trajectory_index): - """Checks if a trajectory and associated JSON files are already downloaded; if not, downloads them.""" - # Create a directory for each dataset - dataset_dir = os.path.join(exp_dir, dataset_name) - os.makedirs(dataset_dir, exist_ok=True) - - # Check and download the trajectory files - local_file_pattern = LOCAL_FILE_TEMPLATE.format( - exp_dir=exp_dir, dataset_name=dataset_name, index=trajectory_index - ) - if not any(os.path.exists(file) for file in glob.glob(local_file_pattern)): - data_url = DATA_URL_TEMPLATE.format( - dataset_name=dataset_name, index=trajectory_index + # Ensure no files with .gstmp postfix are considered valid + valid_files_exist = any( + os.path.exists(file) and not file.endswith(".gstmp") for file in glob.glob(local_file_pattern) ) - subprocess.run(["gsutil", "-m", "cp", data_url, dataset_dir], check=True) - else: - print( - f"Trajectory {trajectory_index} of dataset {dataset_name} already exists in {dataset_dir}. Skipping download." + + if not valid_files_exist: + data_url = self.DATA_URL_TEMPLATE.format( + dataset_name=self.dataset_name, index=trajectory_index + ) + subprocess.run(["gsutil", "-m", "cp", data_url, self.dataset_dir], check=True) + else: + print(f"Trajectory {trajectory_index} of dataset {self.dataset_name} already exists in {self.dataset_dir}. Skipping download.") + + # Check and download the feature.json file + feature_json_local_path = os.path.join(self.dataset_dir, "features.json") + feature_json_url = self.FEATURE_JSON_URL_TEMPLATE.format(dataset_name=self.dataset_name) + self.check_and_download_file(feature_json_url, feature_json_local_path) + + # Check and download the dataset_info.json file + dataset_info_json_local_path = os.path.join(self.dataset_dir, "dataset_info.json") + dataset_info_json_url = self.DATASET_INFO_JSON_URL_TEMPLATE.format(dataset_name=self.dataset_name) + self.check_and_download_file(dataset_info_json_url, dataset_info_json_local_path) + + def download_data(self): + """Downloads the specified number of trajectories from the dataset concurrently if not already downloaded.""" + with ThreadPoolExecutor() as executor: + futures = [ + executor.submit(self.check_and_download_trajectory, i) + for i in range(self.num_trajectories) + ] + for future in futures: + future.result() + + def measure_file_size(self): + """Calculates the total size of all files in the dataset directory.""" + total_size = sum( + os.path.getsize(os.path.join(dirpath, f)) + for dirpath, dirnames, filenames in os.walk(self.dataset_dir) + for f in filenames ) + return total_size - # Check and download the feature.json file - feature_json_local_path = os.path.join(dataset_dir, "features.json") - feature_json_url = FEATURE_JSON_URL_TEMPLATE.format(dataset_name=dataset_name) - check_and_download_file(feature_json_url, feature_json_local_path) - - # Check and download the dataset_info.json file - dataset_info_json_local_path = os.path.join(dataset_dir, "dataset_info.json") - dataset_info_json_url = DATASET_INFO_JSON_URL_TEMPLATE.format( - dataset_name=dataset_name - ) - check_and_download_file(dataset_info_json_url, dataset_info_json_local_path) - - -def download_data(exp_dir, dataset_names, num_trajectories): - """Downloads the specified number of trajectories from each dataset concurrently if not already downloaded.""" - with ThreadPoolExecutor() as executor: - futures = [] - for dataset_name in dataset_names: - for i in range(num_trajectories): - futures.append( - executor.submit( - check_and_download_trajectory, exp_dir, dataset_name, i - ) - ) - for future in futures: - future.result() # Will raise an exception if any download failed - - -def measure_file_size(dataset_dir): - """Calculates the total size of all files in the dataset directory.""" - total_size = 0 - for dirpath, dirnames, filenames in os.walk(dataset_dir): - for f in filenames: - fp = os.path.join(dirpath, f) - total_size += os.path.getsize(fp) - return total_size - - -def measure_loading_time_rlds(path, num_trajectories): - """Measures the time taken to load data into memory using a specified loader function.""" - start_time = time.time() - loader = RLDSLoader(path, split=f"train[:{num_trajectories}]") - for data in loader: - # use np array to force loading - data - - end_time = time.time() - loading_time = end_time - start_time - print( - f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return loading_time, num_trajectories - -def measure_loading_time_vla(path, num_trajectories): - """Measures the time taken to load data into memory using a specified loader function.""" - start_time = time.time() - loader = VLALoader(path, cache_dir=CACHE_DIR) - for data in loader: - # use np array to force loading - data["action"] - - end_time = time.time() - loading_time = end_time - start_time - print( - f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return loading_time, num_trajectories - - -def convert_data_to_vla_format(loader, output_dir): - """Converts data to VLA format and saves it to the specified output directory.""" - for index, data_traj in enumerate(loader): - output_path = os.path.join(output_dir, f"output_{index}.vla") - fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) +class RLDSHandler(DatasetHandler): + """Handles RLDS dataset operations, including loading and measuring loading times.""" -def main(): - # Parse command-line arguments - parser = argparse.ArgumentParser( - description="Download, process, and read RLDS data." - ) - parser.add_argument( - "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory." - ) - parser.add_argument( - "--num_trajectories", - type=int, - default=DEFAULT_NUMBER_OF_TRAJECTORIES, - help="Number of trajectories to download.", - ) - parser.add_argument( - "--dataset_names", - nargs="+", - default=DEFAULT_DATASET_NAMES, - help="List of dataset names to download.", - ) + def __init__(self, exp_dir, dataset_name, num_trajectories): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="rlds") - args = parser.parse_args() + def measure_loading_time(self): + """Measures the time taken to load data into memory using RLDSLoader.""" + start_time = time.time() + loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") + for data in loader: + data # Force loading - # Create output directory if it doesn't exist - output_dir = os.path.join(args.exp_dir, "output") - os.makedirs(output_dir, exist_ok=True) + end_time = time.time() + loading_time = end_time - start_time + print(f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + return loading_time, len(loader) - # Download data concurrently - download_data(args.exp_dir, args.dataset_names, args.num_trajectories) - # Iterate through datasets and measure file size and loading time for both formats - for dataset_name in args.dataset_names: - dataset_dir = os.path.join(args.exp_dir, dataset_name) - file_size = measure_file_size(dataset_dir) +class VLAHandler(DatasetHandler): + """Handles VLA dataset operations, including loading, converting, and measuring loading times.""" - # Measure loading time for RLDS format - rlds_loading_time, num_loaded_rlds = measure_loading_time_rlds( - dataset_dir, args.num_trajectories - ) + def __init__(self, exp_dir, dataset_name, num_trajectories): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla") - print(f"Dataset: {dataset_name}") - print(f"Total file size: {file_size / (1024 * 1024):.2f} MB") - print( - f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds" - ) - print( - f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second" - ) + def measure_loading_time(self): + """Measures the time taken to load data into memory using VLALoader.""" + start_time = time.time() + loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) + for data in loader: + data["action"] # Force loading - # Convert data to VLA format - loader = RLDSLoader(path=dataset_dir, split=f"train[:{args.num_trajectories}]") - convert_data_to_vla_format(loader, output_dir) + end_time = time.time() + loading_time = end_time - start_time + print(f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + return loading_time, len(loader) + + def convert_data_to_vla_format(self, loader): + """Converts data to VLA format and saves it to the same directory.""" + for index, data_traj in enumerate(loader): + output_path = os.path.join(self.dataset_dir, f"output_{index}.vla") + fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) - # Measure loading time for VLA format - vla_loading_time, num_loaded_vla = measure_loading_time_vla( - output_dir, args.num_trajectories - ) - print( - f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds" - ) - print( - f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n" - ) + +def main(): + # Parse command-line arguments + parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") + parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") + parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to download.") + parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to download.") + args = parser.parse_args() + + for dataset_name in args.dataset_names: + print(f"Processing dataset: {dataset_name}") + + # Process RLDS data + rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) + rlds_handler.download_data() + rlds_file_size = rlds_handler.measure_file_size() + rlds_loading_time, num_loaded_rlds = rlds_handler.measure_loading_time() + + print(f"Total RLDS file size: {rlds_file_size / (1024 * 1024):.2f} MB") + print(f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds") + print(f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second") + + # Process VLA data + vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) + loader = RLDSLoader(rlds_handler.dataset_dir, split=f"train[:{args.num_trajectories}]") + + vla_handler.convert_data_to_vla_format(loader) + vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time() + + vla_file_size = vla_handler.measure_file_size() + print(f"Total VLA file size: {vla_file_size / (1024 * 1024):.2f} MB") + print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") + print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") if __name__ == "__main__": From e280615ea5b1375792b7c688fe707c73ae57b058 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 01:34:07 -0700 Subject: [PATCH 30/80] Refactor Trajectory class to fix cache directory path --- benchmarks/openx.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index ca6fc63..0aae375 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -12,7 +12,7 @@ DEFAULT_EXP_DIR = "/tmp/fog_x" DEFAULT_NUMBER_OF_TRAJECTORIES = 5 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] -CACHE_DIR = "/tmp/fog_x/cache" +CACHE_DIR = "/tmp/fog_x/cache/" class DatasetHandler: """Base class to handle dataset-related operations.""" @@ -40,6 +40,7 @@ def check_and_download_file(self, url, local_path): subprocess.run(["gsutil", "-m", "cp", url, local_path], check=True) else: print(f"File {local_path} already exists. Skipping download.") + def check_and_download_trajectory(self, trajectory_index): """Checks if a trajectory and associated JSON files are already downloaded; if not, downloads them.""" os.makedirs(self.dataset_dir, exist_ok=True) From 87fecf143905365330c4bfc9394458fd7c18f190 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 02:53:48 -0700 Subject: [PATCH 31/80] Refactor Trajectory class to add HDF5Handler for converting data to HDF5 format --- benchmarks/openx.py | 66 ++++++++++++++++++++++++++++++++++++++++++--- fog_x/trajectory.py | 31 +++++++++++++++------ 2 files changed, 86 insertions(+), 11 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 0aae375..df89ffb 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -6,11 +6,13 @@ import glob import time import numpy as np -from fog_x.loader import RLDSLoader, VLALoader +from fog_x.loader import RLDSLoader, VLALoader, HDF5Loader +import tensorflow as tf # this prevents tensorflow printed logs +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Constants DEFAULT_EXP_DIR = "/tmp/fog_x" -DEFAULT_NUMBER_OF_TRAJECTORIES = 5 +DEFAULT_NUMBER_OF_TRAJECTORIES = 2 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" @@ -33,6 +35,7 @@ def clear_cache(self): """Clears the cache directory.""" if os.path.exists(CACHE_DIR): subprocess.run(["rm", "-rf", CACHE_DIR], check=True) + def check_and_download_file(self, url, local_path): """Checks if a file is already downloaded; if not, downloads it.""" @@ -117,6 +120,7 @@ class VLAHandler(DatasetHandler): def __init__(self, exp_dir, dataset_name, num_trajectories): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla") + self.trajectories_objects = [] def measure_loading_time(self): """Measures the time taken to load data into memory using VLALoader.""" @@ -134,10 +138,54 @@ def convert_data_to_vla_format(self, loader): """Converts data to VLA format and saves it to the same directory.""" for index, data_traj in enumerate(loader): output_path = os.path.join(self.dataset_dir, f"output_{index}.vla") - fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) + self.trajectories_objects.append(fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path)) + + +class HDF5Handler: + """Handles HDF5 dataset operations, including conversion and measuring file sizes.""" + def __init__(self, exp_dir, dataset_name): + self.hdf5_dir = os.path.join(exp_dir, "hdf5", dataset_name) + if not os.path.exists(self.hdf5_dir): + os.makedirs(self.hdf5_dir) + + def convert_data_to_hdf5(self, trajectories_objects): + """Converts data to HDF5 format and saves it to the same directory.""" + for index, trajectory in enumerate(trajectories_objects): + trajectory.to_hdf5(path=f"{self.hdf5_dir}/output_{index}.h5") + + def measure_file_size(self): + """Calculates the total size of all files in the HDF5 directory.""" + total_size = sum( + os.path.getsize(os.path.join(dirpath, f)) + for dirpath, dirnames, filenames in os.walk(self.hdf5_dir) + for f in filenames + ) + return total_size + def measure_loading_time(self): + """Measures the time taken to load data into memory using HDF5Loader.""" + start_time = time.time() + loader = HDF5Loader(path=os.path.join(self.hdf5_dir, "*.h5")) + + def _recursively_load_h5_data(data): + for key in data.keys(): + if isinstance(data[key], dict): + _recursively_load_h5_data(data[key]) + else: + (key, np.array(data[key])) + + count = 0 + for data in loader: + # recursively load all data + _recursively_load_h5_data(data) + + end_time = time.time() + loading_time = end_time - start_time + print(f"Loaded {count} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + return loading_time, count + def main(): # Parse command-line arguments parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") @@ -171,6 +219,18 @@ def main(): print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") + # Convert VLA to HDF5 and benchmark + hdf5_handler = HDF5Handler(args.exp_dir, dataset_name) + hdf5_handler.convert_data_to_hdf5(vla_handler.trajectories_objects) + hdf5_file_size = hdf5_handler.measure_file_size() + print(f"Total HDF5 file size: {hdf5_file_size / (1024 * 1024):.2f} MB") + + + # Measure HDF5 loading time + hdf5_loading_time, num_loaded_hdf5 = hdf5_handler.measure_loading_time() + print(f"HDF5 format loading time for {num_loaded_hdf5} trajectories: {hdf5_loading_time:.2f} seconds") + print(f"HDF5 format throughput: {num_loaded_hdf5 / hdf5_loading_time:.2f} trajectories per second\n") + if __name__ == "__main__": main() diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 05fd233..6241e1a 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -131,10 +131,13 @@ def close(self): pass # This exception is expected and means the encoder is fully flushed self.container_file.close() + self.trajectory_data = None def load(self): """ load the container file + + returns the container file workflow: - check if a cached mmap/hdf5 file exists @@ -143,9 +146,11 @@ def load(self): """ if os.path.exists(self.cache_file_name): - return self._load_from_cache() + self.trajectory_data = self._load_from_cache() else: - return self._load_from_container() + self.trajectory_data = self._load_from_container() + + return self.trajectory_data def init_feature_streams(self, feature_spec: Dict): """ @@ -315,11 +320,6 @@ def _load_from_cache(self): load the cached file with entire vla trajctory """ h5_cache = h5py.File(self.cache_file_name, "r") - for feature_name, feature_data in h5_cache.items(): - self.feature_name_to_stream[feature_name] = None - self.feature_name_to_feature_type[feature_name] = FeatureType.from_str( - feature_data.attrs["FEATURE_TYPE"] - ) return h5_cache def _load_from_container(self): @@ -378,12 +378,13 @@ def _load_from_container(self): continue feature_name = packet.stream.metadata["FEATURE_NAME"] feature_type = self.feature_name_to_feature_type[feature_name] - logger.debug( + logger.info( f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" ) feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": frames = packet.decode() + for frame in frames: data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) h5_cache[feature_name].resize( @@ -403,8 +404,22 @@ def _load_from_container(self): logger.debug(f"Skipping empty packet: {packet}") container.close() + h5_cache.close() + h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache + def to_hdf5(self, path: Text): + """ + convert the container file to hdf5 file + """ + + if not self.trajectory_data: + self.load() + + # directly copy the cache file to the hdf5 file + os.rename(self.cache_file_name, path) + + def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packet]: """ encode the frame and write it to the stream file, return the packet From b4254e81b1ef3edea084d220d9d1b2d3b8b8a164 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 04:32:54 -0700 Subject: [PATCH 32/80] save stream to a different file doesnt work yet --- fog_x/trajectory.py | 54 ++++++++++++++++++++++++++++++++++++--------- 1 file changed, 44 insertions(+), 10 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 6241e1a..b129499 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -25,6 +25,17 @@ def _flatten_dict(d, parent_key="", sep="_"): return dict(items) +class StreamInfo: + def __init__(self, feature_name, feature_type, encoding): + self.feature_name = feature_name + self.feature_type = feature_type + self.encoding = encoding + def __str__(self): + return f"StreamInfo({self.feature_name}, {self.feature_type}, {self.encoding})" + def __repr__(self): + return self.__str__() + + class Trajectory: def __init__( self, @@ -64,6 +75,7 @@ def __init__( [] ) # a list of pre-initialized h264 streams self.mode = mode + self.stream_id_to_info = {} # stream_id: StreamInfo # check if the path exists # if not, create a new file and start data collection @@ -131,6 +143,7 @@ def close(self): pass # This exception is expected and means the encoder is fully flushed self.container_file.close() + self.save_stream_info() self.trajectory_data = None def load(self): @@ -144,12 +157,14 @@ def load(self): - if exists, load the file - otherwise: load the container file with entire vla trajctory """ - + self.load_stream_info() + if os.path.exists(self.cache_file_name): self.trajectory_data = self._load_from_cache() else: self.trajectory_data = self._load_from_container() + return self.trajectory_data def init_feature_streams(self, feature_spec: Dict): @@ -338,13 +353,16 @@ def _load_from_container(self): h5_cache = h5py.File(self.cache_file_name, "w") streams = container.streams + print(self.stream_id_to_info) # preallocate memory for the streams in h5 for stream in streams: - if stream.metadata.get("FEATURE_NAME") is None: + + if stream.index not in self.stream_id_to_info: logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue - feature_name = stream.metadata["FEATURE_NAME"] - feature_type = FeatureType.from_str(stream.metadata["FEATURE_TYPE"]) + stream_info = self.stream_id_to_info.get(stream.index) + feature_name = stream_info.feature_name + feature_type = stream_info.feature_type self.feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type # Preallocate arrays with the shape [None, X, Y, Z] @@ -373,12 +391,13 @@ def _load_from_container(self): # decode the frames and store in the preallocated memory for packet in container.demux(list(streams)): - if packet.stream.metadata.get("FEATURE_NAME") is None: - logger.debug(f"Skipping packet without FEATURE_NAME: {packet}") + if packet.stream.index not in self.stream_id_to_info: + logger.debug(f"Skipping stream {packet.stream}, packet {packet}") continue - feature_name = packet.stream.metadata["FEATURE_NAME"] - feature_type = self.feature_name_to_feature_type[feature_name] - logger.info( + stream_info = self.stream_id_to_info.get(packet.stream.index) + feature_name = stream_info.feature_name + feature_type = stream_info.feature_type + logger.debug( f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" ) feature_codec = packet.stream.codec_context.codec.name @@ -386,7 +405,10 @@ def _load_from_container(self): frames = packet.decode() for frame in frames: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + if feature_type.dtype == "float32": + data = frame.to_ndarray(format="gray").reshape(feature_type.shape) + else: + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) h5_cache[feature_name].resize( h5_cache[feature_name].shape[0] + 1, axis=0 ) @@ -467,6 +489,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): stream.metadata["FEATURE_NAME"] = new_feature stream.metadata["FEATURE_TYPE"] = str(new_feature_type) self.feature_name_to_stream[new_feature] = stream + self.stream_id_to_info[stream.index] = StreamInfo(new_feature, new_feature_type, new_encoding) return else: raise ValueError("No pre-initialized h264 streams available") @@ -528,6 +551,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): new_container, new_feature, new_encoding, new_feature_type ) d_original_stream_id_to_new_container_stream[new_stream.index] = new_stream + self.stream_id_to_info[new_stream.index] = StreamInfo(new_feature, new_feature_type, new_encoding) # Remux existing packets for packet in original_container.demux(original_streams): @@ -598,3 +622,13 @@ def _get_encoding_of_feature( else: vid_coding = "rawvideo" return vid_coding + + def save_stream_info(self): + # serialize and save the stream info + with open(self.path + ".stream_info", "wb") as f: + pickle.dump(self.stream_id_to_info, f) + + def load_stream_info(self): + # load the stream info + with open(self.path + ".stream_info", "rb") as f: + self.stream_id_to_info = pickle.load(f) \ No newline at end of file From 058fd5bb5d60b56f95980f62b03ca9ba8ac74568 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 14:51:13 -0700 Subject: [PATCH 33/80] Refactor VLALoader class to update file path for loading VLA data --- examples/vla_loader.py | 2 +- fog_x/trajectory.py | 141 +++++++++++++++++++++++++++-------------- 2 files changed, 95 insertions(+), 48 deletions(-) diff --git a/examples/vla_loader.py b/examples/vla_loader.py index 6060538..b7e53bb 100644 --- a/examples/vla_loader.py +++ b/examples/vla_loader.py @@ -3,7 +3,7 @@ import os -loader = VLALoader("/tmp/fog_x/output/*.vla") +loader = VLALoader("/tmp/fog_x/vla/berkeley_autolab_ur5/*.vla") for index, data_traj in enumerate(loader): print(data_traj.load()) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index b129499..083d054 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -70,10 +70,6 @@ def __init__( self.feature_name_to_feature_type = {} # feature_name: feature_type self.trajectory_data = None # trajectory_data self.start_time = time.time() - self.num_pre_initialized_h264_streams = num_pre_initialized_h264_streams - self.pre_initialized_image_streams = ( - [] - ) # a list of pre-initialized h264 streams self.mode = mode self.stream_id_to_info = {} # stream_id: StreamInfo @@ -87,7 +83,6 @@ def __init__( except Exception as e: logger.error(f"error creating the trajectory file: {e}") raise - self._pre_initialize_h264_streams(num_pre_initialized_h264_streams) elif self.mode == "r": if not os.path.exists(self.path): raise FileNotFoundError(f"{self.path} does not exist") @@ -101,16 +96,16 @@ def _get_current_timestamp(self): def __len__(self): raise NotImplementedError - def _pre_initialize_h264_streams(self, num_streams: int): - """ - Pre-initialize a configurable number of H.264 video streams. - """ - for i in range(num_streams): - encoding = "libx264" - stream = self.container_file.add_stream(encoding) - stream.time_base = Fraction(1, 1000) - stream.pix_fmt = "yuv420p" - self.pre_initialized_image_streams.append(stream) + # def _pre_initialize_h264_streams(self, num_streams: int): + # """ + # Pre-initialize a configurable number of H.264 video streams. + # """ + # for i in range(num_streams): + # encoding = "libx264" + # stream = self.container_file.add_stream(encoding) + # stream.time_base = Fraction(1, 1000) + # stream.pix_fmt = "yuv420p" + # self.pre_initialized_image_streams.append(stream) def __getitem__(self, key): """ @@ -123,9 +118,12 @@ def __getitem__(self, key): return self.trajectory_data[key] - def close(self): + def close(self, compact = True): """ close the container file + + args: + compact: re-read from the cache to encode pickled data to images """ try: ts = self._get_current_timestamp() @@ -143,9 +141,14 @@ def close(self): pass # This exception is expected and means the encoder is fully flushed self.container_file.close() + if compact: + # After closing, re-read from the cache to encode pickled data to images + self._transcode_pickled_images() self.save_stream_info() self.trajectory_data = None + + def load(self): """ load the container file @@ -214,14 +217,16 @@ def add( raise ValueError("Use add_by_dict for dictionary") feature_type = FeatureType.from_data(data) - encoding = self._get_encoding_of_feature(data, None) + # encoding = self._get_encoding_of_feature(data, None) self.feature_name_to_feature_type[feature] = feature_type # check if the feature is already in the container # if not, create a new stream # Check if the feature is already in the container + # here we enforce rawvideo encoding for all features + # later on the compacting step, we will encode the pickled data to images if feature not in self.feature_name_to_stream: - self._on_new_stream(feature, encoding, feature_type) + self._on_new_stream(feature, "rawvideo", feature_type) # get the stream stream = self.feature_name_to_stream[feature] @@ -353,7 +358,6 @@ def _load_from_container(self): h5_cache = h5py.File(self.cache_file_name, "w") streams = container.streams - print(self.stream_id_to_info) # preallocate memory for the streams in h5 for stream in streams: @@ -392,7 +396,6 @@ def _load_from_container(self): for packet in container.demux(list(streams)): if packet.stream.index not in self.stream_id_to_info: - logger.debug(f"Skipping stream {packet.stream}, packet {packet}") continue stream_info = self.stream_id_to_info.get(packet.stream.index) feature_name = stream_info.feature_name @@ -429,6 +432,73 @@ def _load_from_container(self): h5_cache.close() h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache + + def _transcode_pickled_images(self): + """ + Transcode pickled images into the desired format (e.g., raw or encoded images). + """ + + # Move the original file to a temporary location + temp_path = self.path + ".temp" + os.rename(self.path, temp_path) + + # Open the original container for reading + original_container = av.open(temp_path, mode="r", format="matroska") + original_streams = list(original_container.streams) + + # Create a new container + new_container = av.open(self.path, mode="w", format="matroska") + + # Add existing streams to the new container + d_original_stream_id_to_new_container_stream = {} + for stream in original_streams: + stream_feature = stream.metadata.get("FEATURE_NAME") + if stream_feature is None: + logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") + continue + # Determine encoding method based on feature type + stream_encoding = self._get_encoding_of_feature(None, self.feature_name_to_feature_type[stream_feature]) + stream_feature_type = self.feature_name_to_feature_type[stream_feature] + stream_in_updated_container = self._add_stream_to_container( + new_container, stream_feature, stream_encoding, stream_feature_type + ) + + # Preserve the stream metadata + for key, value in stream.metadata.items(): + stream_in_updated_container.metadata[key] = value + + d_original_stream_id_to_new_container_stream[stream.index] = stream_in_updated_container + + # Transcode pickled images and add them to the new container + for packet in original_container.demux(original_streams): + + def is_packet_valid(packet): + return packet.pts is not None and packet.dts is not None + + if is_packet_valid(packet): + packet.stream = d_original_stream_id_to_new_container_stream[packet.stream.index] + + # Check if the stream is using rawvideo, meaning it's a pickled stream + if packet.stream.codec_context.codec.name == "libx264": + data = pickle.loads(bytes(packet)) + + # Encode the image data as needed, example shown for raw images + new_packets = self._encode_frame(data, packet.stream, packet.pts) + + for new_packet in new_packets: + new_container.mux(new_packet) + else: + # If not a rawvideo stream, just remux the existing packet + new_container.mux(packet) + else: + logger.debug(f"Skipping invalid packet: {packet}") + + original_container.close() + os.remove(temp_path) + + # Reopen the new container for further writing new data + self.container_file = new_container + def to_hdf5(self, path: Text): """ @@ -452,7 +522,7 @@ def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packe return: packet: encoded packet """ - encoding = self._get_encoding_of_feature(data, None) + encoding = stream.codec_context.codec.name feature_type = FeatureType.from_data(data) if encoding == "libx264": if feature_type.dtype == "float32": @@ -482,18 +552,6 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): if new_feature in self.feature_name_to_stream: return - if new_encoding == "libx264": - # use pre-initialized h264 streams - if self.pre_initialized_image_streams: - stream = self.pre_initialized_image_streams.pop() - stream.metadata["FEATURE_NAME"] = new_feature - stream.metadata["FEATURE_TYPE"] = str(new_feature_type) - self.feature_name_to_stream[new_feature] = stream - self.stream_id_to_info[stream.index] = StreamInfo(new_feature, new_feature_type, new_encoding) - return - else: - raise ValueError("No pre-initialized h264 streams available") - if not self.feature_name_to_stream: logger.debug(f"Creating a new stream for the first feature {new_feature}") self.feature_name_to_stream[new_feature] = self._add_stream_to_container( @@ -503,7 +561,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): logger.debug(f"Adding a new stream for the feature {new_feature}") # Following is a workaround because we cannot add new streams to an existing container # Close current container - self.close() + self.close(compact = False) # Move the original file to a temporary location temp_path = self.path + ".temp" @@ -516,15 +574,6 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): # Create a new container new_container = av.open(self.path, mode="w", format="matroska") - # reset the pre-initialized h264 streams - self.pre_initialized_image_streams = [] - # preinitialize h264 streams - for i in range(self.num_pre_initialized_h264_streams): - encoding = "libx264" - stream = new_container.add_stream(encoding) - stream.time_base = Fraction(1, 1000) - self.pre_initialized_image_streams.append(stream) - # Add existing streams to the new container d_original_stream_id_to_new_container_stream = {} for stream in original_streams: @@ -532,9 +581,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): if stream_feature is None: logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue - stream_encoding = self._get_encoding_of_feature( - None, self.feature_name_to_feature_type[stream_feature] - ) + stream_encoding = stream.codec_context.codec.name stream_feature_type = self.feature_name_to_feature_type[stream_feature] stream_in_updated_container = self._add_stream_to_container( new_container, stream_feature, stream_encoding, stream_feature_type @@ -631,4 +678,4 @@ def save_stream_info(self): def load_stream_info(self): # load the stream info with open(self.path + ".stream_info", "rb") as f: - self.stream_id_to_info = pickle.load(f) \ No newline at end of file + self.stream_id_to_info = pickle.load(f) From 1a3bee4540a65b613fd9aedb9e3e2fb2579c56a5 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 18:22:35 -0700 Subject: [PATCH 34/80] Refactor DatasetHandler to clear OS cache after loading data --- benchmarks/openx.py | 15 +++++++++---- fog_x/trajectory.py | 53 ++++++++++++++++++++++++++++----------------- 2 files changed, 44 insertions(+), 24 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index df89ffb..78e34b8 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -12,7 +12,7 @@ # Constants DEFAULT_EXP_DIR = "/tmp/fog_x" -DEFAULT_NUMBER_OF_TRAJECTORIES = 2 +DEFAULT_NUMBER_OF_TRAJECTORIES = 1 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" @@ -35,7 +35,11 @@ def clear_cache(self): """Clears the cache directory.""" if os.path.exists(CACHE_DIR): subprocess.run(["rm", "-rf", CACHE_DIR], check=True) - + + def clear_os_cache(self): + """Clears the OS cache.""" + subprocess.run(["sync"], check=True) + subprocess.run(["echo", "3", ">", "/proc/sys/vm/drop_caches"], check=True) def check_and_download_file(self, url, local_path): """Checks if a file is already downloaded; if not, downloads it.""" @@ -107,7 +111,7 @@ def measure_loading_time(self): start_time = time.time() loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") for data in loader: - data # Force loading + print("length of loaded data", len(data)) end_time = time.time() loading_time = end_time - start_time @@ -165,6 +169,7 @@ def measure_file_size(self): def measure_loading_time(self): + """Measures the time taken to load data into memory using HDF5Loader.""" start_time = time.time() loader = HDF5Loader(path=os.path.join(self.hdf5_dir, "*.h5")) @@ -175,11 +180,13 @@ def _recursively_load_h5_data(data): _recursively_load_h5_data(data[key]) else: (key, np.array(data[key])) + print(key, np.array(data[key]).shape) count = 0 for data in loader: # recursively load all data _recursively_load_h5_data(data) + count += 1 end_time = time.time() loading_time = end_time - start_time @@ -225,7 +232,7 @@ def main(): hdf5_file_size = hdf5_handler.measure_file_size() print(f"Total HDF5 file size: {hdf5_file_size / (1024 * 1024):.2f} MB") - + vla_handler.clear_os_cache() # Measure HDF5 loading time hdf5_loading_time, num_loaded_hdf5 = hdf5_handler.measure_loading_time() print(f"HDF5 format loading time for {num_loaded_hdf5} trajectories: {hdf5_loading_time:.2f} seconds") diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 083d054..9deb405 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -112,7 +112,9 @@ def __getitem__(self, key): get the value of the feature return hdf5-ed data """ + if self.trajectory_data is None: + logger.info(f"Loading the trajectory data with key {key}") self.trajectory_data = self.load() @@ -143,8 +145,7 @@ def close(self, compact = True): self.container_file.close() if compact: # After closing, re-read from the cache to encode pickled data to images - self._transcode_pickled_images() - self.save_stream_info() + self._transcode_pickled_images(ending_timestamp=ts) self.trajectory_data = None @@ -160,8 +161,7 @@ def load(self): - if exists, load the file - otherwise: load the container file with entire vla trajctory """ - self.load_stream_info() - + if os.path.exists(self.cache_file_name): self.trajectory_data = self._load_from_cache() else: @@ -290,6 +290,7 @@ def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajecto trajectory = Trajectory.from_list_of_dicts(original_trajectory, path="/tmp/fog_x/output.vla") """ traj = cls(path, mode="w") + logger.info(f"Creating a new trajectory file at {path} with {len(data)} steps") for step in data: traj.add_by_dict(step) traj.close() @@ -360,13 +361,11 @@ def _load_from_container(self): # preallocate memory for the streams in h5 for stream in streams: - - if stream.index not in self.stream_id_to_info: - logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") + feature_name = stream.metadata.get("FEATURE_NAME") + if feature_name is None: + logger.warn(f"Skipping stream without FEATURE_NAME: {stream}") continue - stream_info = self.stream_id_to_info.get(stream.index) - feature_name = stream_info.feature_name - feature_type = stream_info.feature_type + feature_type = FeatureType.from_str(stream.metadata.get("FEATURE_TYPE")) self.feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type # Preallocate arrays with the shape [None, X, Y, Z] @@ -393,19 +392,21 @@ def _load_from_container(self): ) # decode the frames and store in the preallocated memory - + d_feature_length = {feature: 0 for feature in self.feature_name_to_stream} for packet in container.demux(list(streams)): - if packet.stream.index not in self.stream_id_to_info: + feature_name = packet.stream.metadata.get("FEATURE_NAME") + if feature_name is None: + logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue - stream_info = self.stream_id_to_info.get(packet.stream.index) - feature_name = stream_info.feature_name - feature_type = stream_info.feature_type - logger.debug( + feature_type = FeatureType.from_str( packet.stream.metadata.get("FEATURE_TYPE")) + logger.info( f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" ) feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": + print(packet) frames = packet.decode() + print(frames) for frame in frames: if feature_type.dtype == "float32": @@ -416,6 +417,7 @@ def _load_from_container(self): h5_cache[feature_name].shape[0] + 1, axis=0 ) h5_cache[feature_name][-1] = data + d_feature_length[feature_name] += 1 else: packet_in_bytes = bytes(packet) if packet_in_bytes: @@ -425,15 +427,16 @@ def _load_from_container(self): h5_cache[feature_name].shape[0] + 1, axis=0 ) h5_cache[feature_name][-1] = data + d_feature_length[feature_name] += 1 else: - logger.debug(f"Skipping empty packet: {packet}") - + logger.debug(f"Skipping empty packet: {packet} for {feature_name}") + print(d_feature_length) container.close() h5_cache.close() h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache - def _transcode_pickled_images(self): + def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): """ Transcode pickled images into the desired format (e.g., raw or encoded images). """ @@ -469,6 +472,7 @@ def _transcode_pickled_images(self): d_original_stream_id_to_new_container_stream[stream.index] = stream_in_updated_container + # Initialize the number of packets per stream # Transcode pickled images and add them to the new container for packet in original_container.demux(original_streams): @@ -486,12 +490,20 @@ def is_packet_valid(packet): new_packets = self._encode_frame(data, packet.stream, packet.pts) for new_packet in new_packets: - new_container.mux(new_packet) + new_container.mux(new_packet) else: # If not a rawvideo stream, just remux the existing packet new_container.mux(packet) else: logger.debug(f"Skipping invalid packet: {packet}") + + # flush the streams + for stream in new_container.streams: + packets = stream.encode(None) + for packet in packets: + packet.pts = ending_timestamp + packet.dts = ending_timestamp + new_container.mux(packet) original_container.close() os.remove(temp_path) @@ -524,6 +536,7 @@ def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packe """ encoding = stream.codec_context.codec.name feature_type = FeatureType.from_data(data) + logger.debug(f"Encoding {stream.metadata.get('FEATURE_NAME')} with {encoding}") if encoding == "libx264": if feature_type.dtype == "float32": frame = self._create_frame_depth(data, stream) From a8a05efb2d12fa8dc246ece421036665e39cbff5 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 19:30:13 -0700 Subject: [PATCH 35/80] Refactor Trajectory class to improve code readability and add lazy loading for data --- benchmarks/openx.py | 39 +++++++++++++++++++++++++++++++++------ fog_x/loader/vla.py | 1 + fog_x/trajectory.py | 16 ++++++++++------ 3 files changed, 44 insertions(+), 12 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 78e34b8..90f3940 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -131,7 +131,8 @@ def measure_loading_time(self): start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) for data in loader: - data["action"] # Force loading + data.load() + self.trajectories_objects.append(data) end_time = time.time() loading_time = end_time - start_time @@ -142,7 +143,7 @@ def convert_data_to_vla_format(self, loader): """Converts data to VLA format and saves it to the same directory.""" for index, data_traj in enumerate(loader): output_path = os.path.join(self.dataset_dir, f"output_{index}.vla") - self.trajectories_objects.append(fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path)) + fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) class HDF5Handler: @@ -193,7 +194,7 @@ def _recursively_load_h5_data(data): print(f"Loaded {count} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") return loading_time, count -def main(): +def main_1(): # Parse command-line arguments parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") @@ -203,6 +204,11 @@ def main(): for dataset_name in args.dataset_names: print(f"Processing dataset: {dataset_name}") + + # Clear the cache directory + cache_dir = CACHE_DIR + if os.path.exists(cache_dir): + subprocess.run(["rm", "-rf", cache_dir], check=True) # Process RLDS data rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) @@ -214,13 +220,34 @@ def main(): print(f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds") print(f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second") - # Process VLA data + # # Process VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) loader = RLDSLoader(rlds_handler.dataset_dir, split=f"train[:{args.num_trajectories}]") vla_handler.convert_data_to_vla_format(loader) - vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time() + +def main_2(): + # Parse command-line arguments + parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") + parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") + parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to download.") + parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to download.") + args = parser.parse_args() + + for dataset_name in args.dataset_names: + print(f"Processing dataset: {dataset_name}") + + # Clear the cache directory + cache_dir = CACHE_DIR + if os.path.exists(cache_dir): + subprocess.run(["rm", "-rf", cache_dir], check=True) + + # # Process VLA data + vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) + + vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time() + vla_file_size = vla_handler.measure_file_size() print(f"Total VLA file size: {vla_file_size / (1024 * 1024):.2f} MB") print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") @@ -240,4 +267,4 @@ def main(): if __name__ == "__main__": - main() + main_2() diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 88c3d32..6fa0650 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -30,6 +30,7 @@ def __init__(self, path: Text, cache_dir=None): self.cache_dir = cache_dir def _read_vla(self, data_path): + logger.info(f"Reading {data_path}") if self.cache_dir: traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) else: diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 9deb405..04de971 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -72,6 +72,7 @@ def __init__( self.start_time = time.time() self.mode = mode self.stream_id_to_info = {} # stream_id: StreamInfo + self.is_closed = False # check if the path exists # if not, create a new file and start data collection @@ -127,6 +128,8 @@ def close(self, compact = True): args: compact: re-read from the cache to encode pickled data to images """ + if self.is_closed: + raise ValueError("The container file is already closed") try: ts = self._get_current_timestamp() for stream in self.container_file.streams: @@ -147,10 +150,12 @@ def close(self, compact = True): # After closing, re-read from the cache to encode pickled data to images self._transcode_pickled_images(ending_timestamp=ts) self.trajectory_data = None + self.container_file = None + self.is_closed = True - def load(self): + def load(self, use_cache = True): """ load the container file @@ -162,7 +167,8 @@ def load(self): - otherwise: load the container file with entire vla trajctory """ - if os.path.exists(self.cache_file_name): + if os.path.exists(self.cache_file_name) and use_cache: + logger.info(f"Loading the cached file {self.cache_file_name}") self.trajectory_data = self._load_from_cache() else: self.trajectory_data = self._load_from_container() @@ -399,14 +405,12 @@ def _load_from_container(self): logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue feature_type = FeatureType.from_str( packet.stream.metadata.get("FEATURE_TYPE")) - logger.info( + logger.debug( f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" ) feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": - print(packet) frames = packet.decode() - print(frames) for frame in frames: if feature_type.dtype == "float32": @@ -430,7 +434,6 @@ def _load_from_container(self): d_feature_length[feature_name] += 1 else: logger.debug(f"Skipping empty packet: {packet} for {feature_name}") - print(d_feature_length) container.close() h5_cache.close() h5_cache = h5py.File(self.cache_file_name, "r") @@ -633,6 +636,7 @@ def is_packet_valid(packet): # Reopen the new container for writing new data self.container_file = new_container self.feature_name_to_stream[new_feature] = new_stream + self.is_closed = False def _add_stream_to_container(self, container, feature_name, encoding, feature_type): stream = container.add_stream(encoding) From c866200504f7ccefc40a4e3bdba77be5e670ca95 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 19:31:54 -0700 Subject: [PATCH 36/80] Refactor prepare function for improved code readability and consistency --- benchmarks/openx.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 90f3940..63fe034 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -194,7 +194,7 @@ def _recursively_load_h5_data(data): print(f"Loaded {count} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") return loading_time, count -def main_1(): +def prepare(): # Parse command-line arguments parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") @@ -227,7 +227,7 @@ def main_1(): vla_handler.convert_data_to_vla_format(loader) -def main_2(): +def evaluation(): # Parse command-line arguments parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") @@ -267,4 +267,6 @@ def main_2(): if __name__ == "__main__": - main_2() + prepare() + exit() + evaluation() From 6e9c5bf633db9d489e93dcff5ea59f0425f087f1 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 20:22:23 -0700 Subject: [PATCH 37/80] Refactor VLAHandler.measure_loading_time() to recursively load h5 data and add option to add to trajectories --- benchmarks/openx.py | 52 +++++++++++++++++++++++++++++---------------- fog_x/trajectory.py | 2 ++ 2 files changed, 36 insertions(+), 18 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 63fe034..800f5d7 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -125,14 +125,23 @@ class VLAHandler(DatasetHandler): def __init__(self, exp_dir, dataset_name, num_trajectories): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla") self.trajectories_objects = [] - - def measure_loading_time(self): + + def measure_loading_time(self, is_add_to_trajectories=False): """Measures the time taken to load data into memory using VLALoader.""" + def _recursively_load_h5_data(data): + for key in data.keys(): + if isinstance(data[key], dict): + _recursively_load_h5_data(data[key]) + else: + (key, np.array(data[key])) + (key, np.array(data[key]).shape) + start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) for data in loader: - data.load() - self.trajectories_objects.append(data) + _recursively_load_h5_data(data.load()) + if is_add_to_trajectories: + self.trajectories_objects.append(data) end_time = time.time() loading_time = end_time - start_time @@ -156,6 +165,7 @@ def __init__(self, exp_dir, dataset_name): def convert_data_to_hdf5(self, trajectories_objects): """Converts data to HDF5 format and saves it to the same directory.""" + print(f"Converting {len(trajectories_objects)} trajectories to HDF5 format.") for index, trajectory in enumerate(trajectories_objects): trajectory.to_hdf5(path=f"{self.hdf5_dir}/output_{index}.h5") @@ -181,7 +191,7 @@ def _recursively_load_h5_data(data): _recursively_load_h5_data(data[key]) else: (key, np.array(data[key])) - print(key, np.array(data[key]).shape) + (key, np.array(data[key]).shape) count = 0 for data in loader: @@ -213,17 +223,10 @@ def prepare(): # Process RLDS data rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) rlds_handler.download_data() - rlds_file_size = rlds_handler.measure_file_size() - rlds_loading_time, num_loaded_rlds = rlds_handler.measure_loading_time() - - print(f"Total RLDS file size: {rlds_file_size / (1024 * 1024):.2f} MB") - print(f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds") - print(f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second") - # # Process VLA data + # Prepare VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) loader = RLDSLoader(rlds_handler.dataset_dir, split=f"train[:{args.num_trajectories}]") - vla_handler.convert_data_to_vla_format(loader) @@ -243,15 +246,28 @@ def evaluation(): if os.path.exists(cache_dir): subprocess.run(["rm", "-rf", cache_dir], check=True) + # Process RLDS data + rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) + rlds_file_size = rlds_handler.measure_file_size() + rlds_loading_time, num_loaded_rlds = rlds_handler.measure_loading_time() + + print(f"Total RLDS file size: {rlds_file_size / (1024 * 1024):.2f} MB") + print(f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds") + print(f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second") + # # Process VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) - - vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time() - + vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time(is_add_to_trajectories=True) vla_file_size = vla_handler.measure_file_size() print(f"Total VLA file size: {vla_file_size / (1024 * 1024):.2f} MB") print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") + + vla_handler.clear_os_cache() + # hot cache VLA loading time + vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time(is_add_to_trajectories=False) + print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") + print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") # Convert VLA to HDF5 and benchmark hdf5_handler = HDF5Handler(args.exp_dir, dataset_name) @@ -267,6 +283,6 @@ def evaluation(): if __name__ == "__main__": - prepare() - exit() + # prepare() + # exit() evaluation() diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 04de971..06422ca 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -388,6 +388,7 @@ def _load_from_container(self): (0,) + feature_type.shape, maxshape=(None,) + feature_type.shape, dtype=h5py.special_dtype(vlen=str), + chunks=(100,) + feature_type.shape ) else: h5_cache.create_dataset( @@ -395,6 +396,7 @@ def _load_from_container(self): (0,) + feature_type.shape, maxshape=(None,) + feature_type.shape, dtype=feature_type.dtype, + chunks=(100,) + feature_type.shape ) # decode the frames and store in the preallocated memory From 1cfcc2724c825190191ddb20d769997c6e7a35e9 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 20:22:52 -0700 Subject: [PATCH 38/80] Refactor Trajectory class to improve code readability and add lazy loading for data --- benchmarks/openx.py | 178 +++++++++++++++++++++++++++++++------------- 1 file changed, 128 insertions(+), 50 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 800f5d7..76eabb7 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -7,8 +7,9 @@ import time import numpy as np from fog_x.loader import RLDSLoader, VLALoader, HDF5Loader -import tensorflow as tf # this prevents tensorflow printed logs -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +import tensorflow as tf # this prevents tensorflow printed logs + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # Constants DEFAULT_EXP_DIR = "/tmp/fog_x" @@ -16,13 +17,18 @@ DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" + class DatasetHandler: """Base class to handle dataset-related operations.""" DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-*" LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_type}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" - FEATURE_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" - DATASET_INFO_JSON_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" + FEATURE_JSON_URL_TEMPLATE = ( + "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" + ) + DATASET_INFO_JSON_URL_TEMPLATE = ( + "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" + ) def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type): self.exp_dir = exp_dir @@ -47,38 +53,54 @@ def check_and_download_file(self, url, local_path): subprocess.run(["gsutil", "-m", "cp", url, local_path], check=True) else: print(f"File {local_path} already exists. Skipping download.") - + def check_and_download_trajectory(self, trajectory_index): """Checks if a trajectory and associated JSON files are already downloaded; if not, downloads them.""" os.makedirs(self.dataset_dir, exist_ok=True) # Check and download the trajectory files local_file_pattern = self.LOCAL_FILE_TEMPLATE.format( - exp_dir=self.exp_dir, dataset_type=self.dataset_type, dataset_name=self.dataset_name, index=trajectory_index + exp_dir=self.exp_dir, + dataset_type=self.dataset_type, + dataset_name=self.dataset_name, + index=trajectory_index, ) - + # Ensure no files with .gstmp postfix are considered valid valid_files_exist = any( - os.path.exists(file) and not file.endswith(".gstmp") for file in glob.glob(local_file_pattern) + os.path.exists(file) and not file.endswith(".gstmp") + for file in glob.glob(local_file_pattern) ) if not valid_files_exist: data_url = self.DATA_URL_TEMPLATE.format( dataset_name=self.dataset_name, index=trajectory_index ) - subprocess.run(["gsutil", "-m", "cp", data_url, self.dataset_dir], check=True) + subprocess.run( + ["gsutil", "-m", "cp", data_url, self.dataset_dir], check=True + ) else: - print(f"Trajectory {trajectory_index} of dataset {self.dataset_name} already exists in {self.dataset_dir}. Skipping download.") + print( + f"Trajectory {trajectory_index} of dataset {self.dataset_name} already exists in {self.dataset_dir}. Skipping download." + ) # Check and download the feature.json file feature_json_local_path = os.path.join(self.dataset_dir, "features.json") - feature_json_url = self.FEATURE_JSON_URL_TEMPLATE.format(dataset_name=self.dataset_name) + feature_json_url = self.FEATURE_JSON_URL_TEMPLATE.format( + dataset_name=self.dataset_name + ) self.check_and_download_file(feature_json_url, feature_json_local_path) # Check and download the dataset_info.json file - dataset_info_json_local_path = os.path.join(self.dataset_dir, "dataset_info.json") - dataset_info_json_url = self.DATASET_INFO_JSON_URL_TEMPLATE.format(dataset_name=self.dataset_name) - self.check_and_download_file(dataset_info_json_url, dataset_info_json_local_path) + dataset_info_json_local_path = os.path.join( + self.dataset_dir, "dataset_info.json" + ) + dataset_info_json_url = self.DATASET_INFO_JSON_URL_TEMPLATE.format( + dataset_name=self.dataset_name + ) + self.check_and_download_file( + dataset_info_json_url, dataset_info_json_local_path + ) def download_data(self): """Downloads the specified number of trajectories from the dataset concurrently if not already downloaded.""" @@ -115,7 +137,9 @@ def measure_loading_time(self): end_time = time.time() loading_time = end_time - start_time - print(f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + print( + f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) return loading_time, len(loader) @@ -125,9 +149,10 @@ class VLAHandler(DatasetHandler): def __init__(self, exp_dir, dataset_name, num_trajectories): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla") self.trajectories_objects = [] - + def measure_loading_time(self, is_add_to_trajectories=False): """Measures the time taken to load data into memory using VLALoader.""" + def _recursively_load_h5_data(data): for key in data.keys(): if isinstance(data[key], dict): @@ -135,7 +160,7 @@ def _recursively_load_h5_data(data): else: (key, np.array(data[key])) (key, np.array(data[key]).shape) - + start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) for data in loader: @@ -145,9 +170,11 @@ def _recursively_load_h5_data(data): end_time = time.time() loading_time = end_time - start_time - print(f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + print( + f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) return loading_time, len(loader) - + def convert_data_to_vla_format(self, loader): """Converts data to VLA format and saves it to the same directory.""" for index, data_traj in enumerate(loader): @@ -168,7 +195,7 @@ def convert_data_to_hdf5(self, trajectories_objects): print(f"Converting {len(trajectories_objects)} trajectories to HDF5 format.") for index, trajectory in enumerate(trajectories_objects): trajectory.to_hdf5(path=f"{self.hdf5_dir}/output_{index}.h5") - + def measure_file_size(self): """Calculates the total size of all files in the HDF5 directory.""" total_size = sum( @@ -178,13 +205,11 @@ def measure_file_size(self): ) return total_size - def measure_loading_time(self): - """Measures the time taken to load data into memory using HDF5Loader.""" start_time = time.time() loader = HDF5Loader(path=os.path.join(self.hdf5_dir, "*.h5")) - + def _recursively_load_h5_data(data): for key in data.keys(): if isinstance(data[key], dict): @@ -192,29 +217,46 @@ def _recursively_load_h5_data(data): else: (key, np.array(data[key])) (key, np.array(data[key]).shape) - + count = 0 for data in loader: # recursively load all data _recursively_load_h5_data(data) count += 1 - + end_time = time.time() loading_time = end_time - start_time - print(f"Loaded {count} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}") + print( + f"Loaded {count} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) return loading_time, count - + + def prepare(): # Parse command-line arguments - parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") - parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") - parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to download.") - parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to download.") + parser = argparse.ArgumentParser( + description="Download, process, and read RLDS data." + ) + parser.add_argument( + "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory." + ) + parser.add_argument( + "--num_trajectories", + type=int, + default=DEFAULT_NUMBER_OF_TRAJECTORIES, + help="Number of trajectories to download.", + ) + parser.add_argument( + "--dataset_names", + nargs="+", + default=DEFAULT_DATASET_NAMES, + help="List of dataset names to download.", + ) args = parser.parse_args() for dataset_name in args.dataset_names: print(f"Processing dataset: {dataset_name}") - + # Clear the cache directory cache_dir = CACHE_DIR if os.path.exists(cache_dir): @@ -226,21 +268,37 @@ def prepare(): # Prepare VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) - loader = RLDSLoader(rlds_handler.dataset_dir, split=f"train[:{args.num_trajectories}]") + loader = RLDSLoader( + rlds_handler.dataset_dir, split=f"train[:{args.num_trajectories}]" + ) vla_handler.convert_data_to_vla_format(loader) def evaluation(): # Parse command-line arguments - parser = argparse.ArgumentParser(description="Download, process, and read RLDS data.") - parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") - parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to download.") - parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to download.") + parser = argparse.ArgumentParser( + description="Download, process, and read RLDS data." + ) + parser.add_argument( + "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory." + ) + parser.add_argument( + "--num_trajectories", + type=int, + default=DEFAULT_NUMBER_OF_TRAJECTORIES, + help="Number of trajectories to download.", + ) + parser.add_argument( + "--dataset_names", + nargs="+", + default=DEFAULT_DATASET_NAMES, + help="List of dataset names to download.", + ) args = parser.parse_args() for dataset_name in args.dataset_names: print(f"Processing dataset: {dataset_name}") - + # Clear the cache directory cache_dir = CACHE_DIR if os.path.exists(cache_dir): @@ -252,22 +310,38 @@ def evaluation(): rlds_loading_time, num_loaded_rlds = rlds_handler.measure_loading_time() print(f"Total RLDS file size: {rlds_file_size / (1024 * 1024):.2f} MB") - print(f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds") - print(f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second") - + print( + f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds" + ) + print( + f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second" + ) + # # Process VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) - vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time(is_add_to_trajectories=True) + vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time( + is_add_to_trajectories=True + ) vla_file_size = vla_handler.measure_file_size() print(f"Total VLA file size: {vla_file_size / (1024 * 1024):.2f} MB") - print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") - print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") - + print( + f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds" + ) + print( + f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n" + ) + vla_handler.clear_os_cache() # hot cache VLA loading time - vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time(is_add_to_trajectories=False) - print(f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds") - print(f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n") + vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time( + is_add_to_trajectories=False + ) + print( + f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds" + ) + print( + f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n" + ) # Convert VLA to HDF5 and benchmark hdf5_handler = HDF5Handler(args.exp_dir, dataset_name) @@ -278,8 +352,12 @@ def evaluation(): vla_handler.clear_os_cache() # Measure HDF5 loading time hdf5_loading_time, num_loaded_hdf5 = hdf5_handler.measure_loading_time() - print(f"HDF5 format loading time for {num_loaded_hdf5} trajectories: {hdf5_loading_time:.2f} seconds") - print(f"HDF5 format throughput: {num_loaded_hdf5 / hdf5_loading_time:.2f} trajectories per second\n") + print( + f"HDF5 format loading time for {num_loaded_hdf5} trajectories: {hdf5_loading_time:.2f} seconds" + ) + print( + f"HDF5 format throughput: {num_loaded_hdf5 / hdf5_loading_time:.2f} trajectories per second\n" + ) if __name__ == "__main__": From 975c4e5b3283d64d9fc38fa795efe0da5a02800c Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 25 Aug 2024 21:21:40 -0700 Subject: [PATCH 39/80] Refactor Trajectory class to improve code readability and add lazy loading for data --- fog_x/trajectory.py | 84 +++++++++++++++++++++++++++------------------ 1 file changed, 50 insertions(+), 34 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 06422ca..5cc39ae 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -30,17 +30,19 @@ def __init__(self, feature_name, feature_type, encoding): self.feature_name = feature_name self.feature_type = feature_type self.encoding = encoding + def __str__(self): return f"StreamInfo({self.feature_name}, {self.feature_type}, {self.encoding})" + def __repr__(self): return self.__str__() - + class Trajectory: def __init__( self, path: Text, - mode = "r", + mode="r", cache_dir: Optional[Text] = "/tmp/fog_x/cache/", num_pre_initialized_h264_streams: int = 5, feature_name_separator: Text = "/", @@ -71,7 +73,7 @@ def __init__( self.trajectory_data = None # trajectory_data self.start_time = time.time() self.mode = mode - self.stream_id_to_info = {} # stream_id: StreamInfo + self.stream_id_to_info = {} # stream_id: StreamInfo self.is_closed = False # check if the path exists @@ -113,18 +115,17 @@ def __getitem__(self, key): get the value of the feature return hdf5-ed data """ - + if self.trajectory_data is None: logger.info(f"Loading the trajectory data with key {key}") self.trajectory_data = self.load() - return self.trajectory_data[key] - def close(self, compact = True): + def close(self, compact=True): """ close the container file - + args: compact: re-read from the cache to encode pickled data to images """ @@ -153,12 +154,10 @@ def close(self, compact = True): self.container_file = None self.is_closed = True - - - def load(self, use_cache = True): + def load(self, use_cache=True): """ load the container file - + returns the container file workflow: @@ -172,8 +171,7 @@ def load(self, use_cache = True): self.trajectory_data = self._load_from_cache() else: self.trajectory_data = self._load_from_container() - - + return self.trajectory_data def init_feature_streams(self, feature_spec: Dict): @@ -388,7 +386,7 @@ def _load_from_container(self): (0,) + feature_type.shape, maxshape=(None,) + feature_type.shape, dtype=h5py.special_dtype(vlen=str), - chunks=(100,) + feature_type.shape + chunks=(100,) + feature_type.shape, ) else: h5_cache.create_dataset( @@ -396,7 +394,7 @@ def _load_from_container(self): (0,) + feature_type.shape, maxshape=(None,) + feature_type.shape, dtype=feature_type.dtype, - chunks=(100,) + feature_type.shape + chunks=(100,) + feature_type.shape, ) # decode the frames and store in the preallocated memory @@ -406,19 +404,25 @@ def _load_from_container(self): if feature_name is None: logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue - feature_type = FeatureType.from_str( packet.stream.metadata.get("FEATURE_TYPE")) + feature_type = FeatureType.from_str( + packet.stream.metadata.get("FEATURE_TYPE") + ) logger.debug( f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" ) feature_codec = packet.stream.codec_context.codec.name if feature_codec == "h264": frames = packet.decode() - + for frame in frames: if feature_type.dtype == "float32": - data = frame.to_ndarray(format="gray").reshape(feature_type.shape) + data = frame.to_ndarray(format="gray").reshape( + feature_type.shape + ) else: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + data = frame.to_ndarray(format="rgb24").reshape( + feature_type.shape + ) h5_cache[feature_name].resize( h5_cache[feature_name].shape[0] + 1, axis=0 ) @@ -440,7 +444,7 @@ def _load_from_container(self): h5_cache.close() h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache - + def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): """ Transcode pickled images into the desired format (e.g., raw or encoded images). @@ -465,7 +469,9 @@ def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") continue # Determine encoding method based on feature type - stream_encoding = self._get_encoding_of_feature(None, self.feature_name_to_feature_type[stream_feature]) + stream_encoding = self._get_encoding_of_feature( + None, self.feature_name_to_feature_type[stream_feature] + ) stream_feature_type = self.feature_name_to_feature_type[stream_feature] stream_in_updated_container = self._add_stream_to_container( new_container, stream_feature, stream_encoding, stream_feature_type @@ -475,7 +481,9 @@ def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): for key, value in stream.metadata.items(): stream_in_updated_container.metadata[key] = value - d_original_stream_id_to_new_container_stream[stream.index] = stream_in_updated_container + d_original_stream_id_to_new_container_stream[stream.index] = ( + stream_in_updated_container + ) # Initialize the number of packets per stream # Transcode pickled images and add them to the new container @@ -485,23 +493,25 @@ def is_packet_valid(packet): return packet.pts is not None and packet.dts is not None if is_packet_valid(packet): - packet.stream = d_original_stream_id_to_new_container_stream[packet.stream.index] - + packet.stream = d_original_stream_id_to_new_container_stream[ + packet.stream.index + ] + # Check if the stream is using rawvideo, meaning it's a pickled stream if packet.stream.codec_context.codec.name == "libx264": data = pickle.loads(bytes(packet)) - + # Encode the image data as needed, example shown for raw images new_packets = self._encode_frame(data, packet.stream, packet.pts) for new_packet in new_packets: - new_container.mux(new_packet) + new_container.mux(new_packet) else: # If not a rawvideo stream, just remux the existing packet new_container.mux(packet) else: logger.debug(f"Skipping invalid packet: {packet}") - + # flush the streams for stream in new_container.streams: packets = stream.encode(None) @@ -516,19 +526,17 @@ def is_packet_valid(packet): # Reopen the new container for further writing new data self.container_file = new_container - def to_hdf5(self, path: Text): """ convert the container file to hdf5 file """ - + if not self.trajectory_data: self.load() # directly copy the cache file to the hdf5 file os.rename(self.cache_file_name, path) - - + def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packet]: """ encode the frame and write it to the stream file, return the packet @@ -579,7 +587,7 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): logger.debug(f"Adding a new stream for the feature {new_feature}") # Following is a workaround because we cannot add new streams to an existing container # Close current container - self.close(compact = False) + self.close(compact=False) # Move the original file to a temporary location temp_path = self.path + ".temp" @@ -616,7 +624,9 @@ def _on_new_stream(self, new_feature, new_encoding, new_feature_type): new_container, new_feature, new_encoding, new_feature_type ) d_original_stream_id_to_new_container_stream[new_stream.index] = new_stream - self.stream_id_to_info[new_stream.index] = StreamInfo(new_feature, new_feature_type, new_encoding) + self.stream_id_to_info[new_stream.index] = StreamInfo( + new_feature, new_feature_type, new_encoding + ) # Remux existing packets for packet in original_container.demux(original_streams): @@ -645,6 +655,12 @@ def _add_stream_to_container(self, container, feature_name, encoding, feature_ty if encoding == "libx264": stream.width = feature_type.shape[0] stream.height = feature_type.shape[1] + stream.codec_context.options = { + "preset": "fast", # Set preset to 'fast' for quicker encoding + "tune": "zerolatency", # Reduce latency + "profile": "baseline", # Use baseline profile + } + stream.metadata["FEATURE_NAME"] = feature_name stream.metadata["FEATURE_TYPE"] = str(feature_type) stream.time_base = Fraction(1, 1000) @@ -693,7 +709,7 @@ def save_stream_info(self): # serialize and save the stream info with open(self.path + ".stream_info", "wb") as f: pickle.dump(self.stream_id_to_info, f) - + def load_stream_info(self): # load the stream info with open(self.path + ".stream_info", "rb") as f: From e83e6dafa12c237c2dacf0e1bb56ad4101b518cd Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 26 Aug 2024 00:20:06 -0700 Subject: [PATCH 40/80] Refactor RLDSLoader class to improve code readability and add lazy loading for data --- benchmarks/openx.py | 176 ++++++++++++++++++++++++++++++------------- fog_x/loader/rlds.py | 28 +++---- 2 files changed, 132 insertions(+), 72 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 76eabb7..363392e 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -8,13 +8,13 @@ import numpy as np from fog_x.loader import RLDSLoader, VLALoader, HDF5Loader import tensorflow as tf # this prevents tensorflow printed logs - +import pandas as pd os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # Constants -DEFAULT_EXP_DIR = "/tmp/fog_x" -DEFAULT_NUMBER_OF_TRAJECTORIES = 1 -DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] +DEFAULT_EXP_DIR = "/home/kych/datasets/fog_x/" +DEFAULT_NUMBER_OF_TRAJECTORIES = 64 +DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5", "bridge", "berkeley_cable_routing", "nyu_door_opening_surprising_effectiveness"] CACHE_DIR = "/tmp/fog_x/cache/" @@ -121,6 +121,15 @@ def measure_file_size(self): ) return total_size + def measure_file_size_per_trajectory(self): + """Calculates the size of each trajectory file in the dataset directory.""" + trajectory_sizes = [] + for dirpath, dirnames, filenames in os.walk(self.dataset_dir): + for f in filenames: + file_path = os.path.join(dirpath, f) + file_size = os.path.getsize(file_path) + trajectory_sizes.append(file_size) + return trajectory_sizes class RLDSHandler(DatasetHandler): """Handles RLDS dataset operations, including loading and measuring loading times.""" @@ -142,6 +151,21 @@ def measure_loading_time(self): ) return loading_time, len(loader) + def measure_loading_time_per_trajectory(self): + """Measures the time taken to load each trajectory separately.""" + times = [] + loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") + for data in loader: + start_time = time.time() + l = list(data) + print("length of loaded data", len(l)) + end_time = time.time() + loading_time = end_time - start_time + times.append(loading_time) + print( + f"Loaded 1 trajectory in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) + return times class VLAHandler(DatasetHandler): """Handles VLA dataset operations, including loading, converting, and measuring loading times.""" @@ -181,11 +205,33 @@ def convert_data_to_vla_format(self, loader): output_path = os.path.join(self.dataset_dir, f"output_{index}.vla") fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) - -class HDF5Handler: + def measure_loading_time_per_trajectory(self): + """Measures the time taken to load each trajectory separately using VLALoader.""" + times = [] + loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) + for data in loader: + start_time = time.time() + self._recursively_load_h5_data(data.load()) + end_time = time.time() + loading_time = end_time - start_time + times.append(loading_time) + print( + f"Loaded 1 trajectory in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) + return times + def _recursively_load_h5_data(self, data): + for key in data.keys(): + if isinstance(data[key], dict): + self._recursively_load_h5_data(data[key]) + else: + (key, np.array(data[key])) + (key, np.array(data[key]).shape) + +class HDF5Handler(DatasetHandler): """Handles HDF5 dataset operations, including conversion and measuring file sizes.""" - def __init__(self, exp_dir, dataset_name): + def __init__(self, exp_dir, dataset_name, num_trajectories): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hdf5") self.hdf5_dir = os.path.join(exp_dir, "hdf5", dataset_name) if not os.path.exists(self.hdf5_dir): os.makedirs(self.hdf5_dir) @@ -232,6 +278,29 @@ def _recursively_load_h5_data(data): return loading_time, count + def measure_loading_time_per_trajectory(self): + """Measures the time taken to load each trajectory separately using HDF5Loader.""" + times = [] + loader = HDF5Loader(path=os.path.join(self.hdf5_dir, "*.h5")) + for data in loader: + start_time = time.time() + self._recursively_load_h5_data(data) + end_time = time.time() + loading_time = end_time - start_time + times.append(loading_time) + print( + f"Loaded 1 trajectory in {loading_time:.2f} seconds start time {start_time} end time {end_time}" + ) + return times + + def _recursively_load_h5_data(self, data): + for key in data.keys(): + if isinstance(data[key], dict): + self._recursively_load_h5_data(data[key]) + else: + (key, np.array(data[key])) + (key, np.array(data[key]).shape) + def prepare(): # Parse command-line arguments parser = argparse.ArgumentParser( @@ -296,6 +365,8 @@ def evaluation(): ) args = parser.parse_args() + results = [] + for dataset_name in args.dataset_names: print(f"Processing dataset: {dataset_name}") @@ -306,58 +377,55 @@ def evaluation(): # Process RLDS data rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) - rlds_file_size = rlds_handler.measure_file_size() - rlds_loading_time, num_loaded_rlds = rlds_handler.measure_loading_time() - - print(f"Total RLDS file size: {rlds_file_size / (1024 * 1024):.2f} MB") - print( - f"RLDS format loading time for {num_loaded_rlds} trajectories: {rlds_loading_time:.2f} seconds" - ) - print( - f"RLDS format throughput: {num_loaded_rlds / rlds_loading_time:.2f} trajectories per second" - ) - - # # Process VLA data + rlds_sizes = rlds_handler.measure_file_size_per_trajectory() + rlds_loading_times = rlds_handler.measure_loading_time_per_trajectory() + + for i, (size, time) in enumerate(zip(rlds_sizes, rlds_loading_times)): + results.append({ + 'Dataset': dataset_name, + 'Format': 'RLDS', + 'Trajectory': i, + 'LoadingTime(s)': time, + 'FileSize(MB)': size / (1024 * 1024), + 'Throughput(traj/s)': 1 / time if time > 0 else 0 + }) + + # Process VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) - vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time( - is_add_to_trajectories=True - ) - vla_file_size = vla_handler.measure_file_size() - print(f"Total VLA file size: {vla_file_size / (1024 * 1024):.2f} MB") - print( - f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds" - ) - print( - f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n" - ) - - vla_handler.clear_os_cache() - # hot cache VLA loading time - vla_loading_time, num_loaded_vla = vla_handler.measure_loading_time( - is_add_to_trajectories=False - ) - print( - f"VLA format loading time for {num_loaded_vla} trajectories: {vla_loading_time:.2f} seconds" - ) - print( - f"VLA format throughput: {num_loaded_vla / vla_loading_time:.2f} trajectories per second\n" - ) + vla_sizes = vla_handler.measure_file_size_per_trajectory() + vla_loading_times = vla_handler.measure_loading_time_per_trajectory() + + for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): + results.append({ + 'Dataset': dataset_name, + 'Format': 'VLA', + 'Trajectory': i, + 'LoadingTime(s)': time, + 'FileSize(MB)': size / (1024 * 1024), + 'Throughput(traj/s)': 1 / time if time > 0 else 0 + }) # Convert VLA to HDF5 and benchmark - hdf5_handler = HDF5Handler(args.exp_dir, dataset_name) + hdf5_handler = HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories) hdf5_handler.convert_data_to_hdf5(vla_handler.trajectories_objects) - hdf5_file_size = hdf5_handler.measure_file_size() - print(f"Total HDF5 file size: {hdf5_file_size / (1024 * 1024):.2f} MB") + hdf5_sizes = hdf5_handler.measure_file_size_per_trajectory() + hdf5_loading_times = hdf5_handler.measure_loading_time_per_trajectory() + + for i, (size, time) in enumerate(zip(hdf5_sizes, hdf5_loading_times)): + results.append({ + 'Dataset': dataset_name, + 'Format': 'HDF5', + 'Trajectory': i, + 'LoadingTime(s)': time, + 'FileSize(MB)': size / (1024 * 1024), + 'Throughput(traj/s)': 1 / time if time > 0 else 0 + }) + + # Save results to CSV + results_df = pd.DataFrame(results) + results_df.to_csv('trajectory_results.csv', index=False) + print("Results written to trajectory_results.csv") - vla_handler.clear_os_cache() - # Measure HDF5 loading time - hdf5_loading_time, num_loaded_hdf5 = hdf5_handler.measure_loading_time() - print( - f"HDF5 format loading time for {num_loaded_hdf5} trajectories: {hdf5_loading_time:.2f} seconds" - ) - print( - f"HDF5 format throughput: {num_loaded_hdf5 / hdf5_loading_time:.2f} trajectories per second\n" - ) if __name__ == "__main__": diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index 36fcd22..780756b 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -17,43 +17,35 @@ def __init__(self, path, split): builder = tfds.builder_from_directory(path) self.ds = builder.as_dataset(split) + self.iterator = iter(self.ds) self.split = split self.index = 0 def __len__(self): - return len(self.ds) + return tf.data.experimental.cardinality(self.ds).numpy() def __iter__(self): return self def __next__(self): - - if self.index < len(self): - self.index += 1 - nest_ds = self.ds.__iter__() - traj = list(nest_ds)[0]["steps"] + try: + nest_ds = next(self.iterator) + traj = nest_ds["steps"] data = [] for step_data in traj: step = {} for key, val in step_data.items(): - if key == "observation": - step["observation"] = {} - for obs_key, obs_val in val.items(): - step["observation"][obs_key] = np.array(obs_val) - + step["observation"] = {obs_key: np.array(obs_val) for obs_key, obs_val in val.items()} elif key == "action": - step["action"] = {} - for act_key, act_val in val.items(): - step["action"][act_key] = np.array(act_val) + step["action"] = {act_key: np.array(act_val) for act_key, act_val in val.items()} else: step[key] = np.array(val) - data.append(step) return data - else: + except StopIteration: self.index = 0 - raise StopIteration - \ No newline at end of file + self.iterator = iter(self.ds) + raise StopIteration \ No newline at end of file From 4ba64535347f462792cb74c427105c99fb50be41 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 26 Aug 2024 00:53:31 -0700 Subject: [PATCH 41/80] fix tf record's benchmark to read the data --- .gitignore | 3 ++- benchmarks/openx.py | 63 ++++++++++++++++++++++++--------------------- 2 files changed, 36 insertions(+), 30 deletions(-) diff --git a/.gitignore b/.gitignore index 75a79d3..215b378 100644 --- a/.gitignore +++ b/.gitignore @@ -135,4 +135,5 @@ dmypy.json temp.gif *.vla -*.mkv \ No newline at end of file +*.mkv +*.csv \ No newline at end of file diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 363392e..9ff84b6 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -158,7 +158,17 @@ def measure_loading_time_per_trajectory(self): for data in loader: start_time = time.time() l = list(data) - print("length of loaded data", len(l)) + for i in l: + # recursively load all data + def _recursively_load_data(data): + for key in data.keys(): + if isinstance(data[key], dict): + _recursively_load_data(data[key]) + else: + (key, np.array(data[key])) + (key, np.array(data[key]).shape) + _recursively_load_data(i) + # print("length of loaded data", len(l)) end_time = time.time() loading_time = end_time - start_time times.append(loading_time) @@ -174,44 +184,21 @@ def __init__(self, exp_dir, dataset_name, num_trajectories): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla") self.trajectories_objects = [] - def measure_loading_time(self, is_add_to_trajectories=False): - """Measures the time taken to load data into memory using VLALoader.""" - - def _recursively_load_h5_data(data): - for key in data.keys(): - if isinstance(data[key], dict): - _recursively_load_h5_data(data[key]) - else: - (key, np.array(data[key])) - (key, np.array(data[key]).shape) - - start_time = time.time() - loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) - for data in loader: - _recursively_load_h5_data(data.load()) - if is_add_to_trajectories: - self.trajectories_objects.append(data) - - end_time = time.time() - loading_time = end_time - start_time - print( - f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return loading_time, len(loader) - def convert_data_to_vla_format(self, loader): """Converts data to VLA format and saves it to the same directory.""" for index, data_traj in enumerate(loader): output_path = os.path.join(self.dataset_dir, f"output_{index}.vla") fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) - def measure_loading_time_per_trajectory(self): + def measure_loading_time_per_trajectory(self, save_trajectorie_objects=False): """Measures the time taken to load each trajectory separately using VLALoader.""" times = [] loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) for data in loader: start_time = time.time() self._recursively_load_h5_data(data.load()) + if save_trajectorie_objects: + self.trajectories_objects.append(data) end_time = time.time() loading_time = end_time - start_time times.append(loading_time) @@ -378,6 +365,7 @@ def evaluation(): # Process RLDS data rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) rlds_sizes = rlds_handler.measure_file_size_per_trajectory() + rlds_handler.clear_os_cache() rlds_loading_times = rlds_handler.measure_loading_time_per_trajectory() for i, (size, time) in enumerate(zip(rlds_sizes, rlds_loading_times)): @@ -393,22 +381,39 @@ def evaluation(): # Process VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) vla_sizes = vla_handler.measure_file_size_per_trajectory() - vla_loading_times = vla_handler.measure_loading_time_per_trajectory() + vla_handler.clear_os_cache() + vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=True) + + for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): + results.append({ + 'Dataset': dataset_name, + 'Format': 'VLA-ColdCache', + 'Trajectory': i, + 'LoadingTime(s)': time, + 'FileSize(MB)': size / (1024 * 1024), + 'Throughput(traj/s)': 1 / time if time > 0 else 0 + }) + + vla_handler.clear_os_cache() + # hot cache test + vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=False) for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): results.append({ 'Dataset': dataset_name, - 'Format': 'VLA', + 'Format': 'VLA-HotCache', 'Trajectory': i, 'LoadingTime(s)': time, 'FileSize(MB)': size / (1024 * 1024), 'Throughput(traj/s)': 1 / time if time > 0 else 0 }) + # Convert VLA to HDF5 and benchmark hdf5_handler = HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories) hdf5_handler.convert_data_to_hdf5(vla_handler.trajectories_objects) hdf5_sizes = hdf5_handler.measure_file_size_per_trajectory() + hdf5_handler.clear_os_cache() hdf5_loading_times = hdf5_handler.measure_loading_time_per_trajectory() for i, (size, time) in enumerate(zip(hdf5_sizes, hdf5_loading_times)): From 2c4d797ad92a02139476b1e0b3bc28404eff0c41 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 26 Aug 2024 01:11:40 -0700 Subject: [PATCH 42/80] support no cache baseline --- benchmarks/openx.py | 28 +++++++++-- fog_x/trajectory.py | 110 +++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 127 insertions(+), 11 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 9ff84b6..11954d8 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -15,6 +15,8 @@ DEFAULT_EXP_DIR = "/home/kych/datasets/fog_x/" DEFAULT_NUMBER_OF_TRAJECTORIES = 64 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5", "bridge", "berkeley_cable_routing", "nyu_door_opening_surprising_effectiveness"] +DEFAULT_NUMBER_OF_TRAJECTORIES = 1 +DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" @@ -190,13 +192,13 @@ def convert_data_to_vla_format(self, loader): output_path = os.path.join(self.dataset_dir, f"output_{index}.vla") fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) - def measure_loading_time_per_trajectory(self, save_trajectorie_objects=False): + def measure_loading_time_per_trajectory(self, save_trajectorie_objects=False, mode = "no_cache"): """Measures the time taken to load each trajectory separately using VLALoader.""" times = [] loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) for data in loader: start_time = time.time() - self._recursively_load_h5_data(data.load()) + self._recursively_load_h5_data(data.load(mode = mode)) if save_trajectorie_objects: self.trajectories_objects.append(data) end_time = time.time() @@ -381,8 +383,26 @@ def evaluation(): # Process VLA data vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) vla_sizes = vla_handler.measure_file_size_per_trajectory() + + # first, no cache test, directly reading everything to memory + # no side effect + vla_handler.clear_os_cache() + vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=False, mode = "no_cache") + + for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): + results.append({ + 'Dataset': dataset_name, + 'Format': 'VLA-NoCache', + 'Trajectory': i, + 'LoadingTime(s)': time, + 'FileSize(MB)': size / (1024 * 1024), + 'Throughput(traj/s)': 1 / time if time > 0 else 0 + }) + + + vla_handler.clear_os_cache() - vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=True) + vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=True, mode = "cache") for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): results.append({ @@ -396,7 +416,7 @@ def evaluation(): vla_handler.clear_os_cache() # hot cache test - vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=False) + vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=False, mode = "cache") for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): results.append({ diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 5cc39ae..79f1585 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -154,7 +154,7 @@ def close(self, compact=True): self.container_file = None self.is_closed = True - def load(self, use_cache=True): + def load(self, mode = "cache"): """ load the container file @@ -165,12 +165,19 @@ def load(self, use_cache=True): - if exists, load the file - otherwise: load the container file with entire vla trajctory """ - - if os.path.exists(self.cache_file_name) and use_cache: - logger.info(f"Loading the cached file {self.cache_file_name}") - self.trajectory_data = self._load_from_cache() + if mode == "cache": + if os.path.exists(self.cache_file_name): + logger.info(f"Loading the cached file {self.cache_file_name}") + self.trajectory_data = self._load_from_cache() + else: + logger.info(f"Loading the container file {self.path}") + self.trajectory_data = self._load_from_container_to_h5() + elif mode == "no_cache": + logger.info(f"Loading the container file {self.path} without cache") + self.trajectory_data = self._load_from_container_no_cache() else: - self.trajectory_data = self._load_from_container() + logger.info(f"No option provided. Force loading from container file {self.path}") + self.trajectory_data = self._load_from_container_to_h5() return self.trajectory_data @@ -347,7 +354,7 @@ def _load_from_cache(self): h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache - def _load_from_container(self): + def _load_from_container_to_h5(self): """ load the container file with entire vla trajctory @@ -445,6 +452,95 @@ def _load_from_container(self): h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache + def _load_from_container_no_cache(self): + """ + Load the container file with the entire VLA trajectory. + + Workflow: + - Get schema of the container file. + - Preallocate decoded streams. + - Decode frame by frame and store in the preallocated memory. + """ + + container = av.open(self.path, mode="r", format="matroska") + streams = container.streams + + + def _get_length_of_stream(stream): + """ + Get the length of the stream. + """ + length = 0 + for packet in container.demux([stream]): + length += 1 + return length + + # Dictionary to store preallocated numpy arrays + np_cache = {} + + # Preallocate memory for the streams in numpy arrays + for stream in streams: + feature_name = stream.metadata.get("FEATURE_NAME") + if feature_name is None: + logger.warn(f"Skipping stream without FEATURE_NAME: {stream}") + continue + feature_type = FeatureType.from_str(stream.metadata.get("FEATURE_TYPE")) + self.feature_name_to_stream[feature_name] = stream + self.feature_name_to_feature_type[feature_name] = feature_type + + logger.debug( + f"Creating a cache for {feature_name} with shape {feature_type.shape}" + ) + + length = _get_length_of_stream(stream) + # Allocate numpy array with shape [None, X, Y, Z] where X, Y, Z are feature dimensions + if feature_type.dtype == "string": + np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) + else: + np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) + + # Decode the frames and store them in the preallocated numpy memory + d_feature_length = {feature: 0 for feature in self.feature_name_to_stream} + for packet in container.demux(list(streams)): + feature_name = packet.stream.metadata.get("FEATURE_NAME") + if feature_name is None: + logger.debug(f"Skipping stream without FEATURE_NAME: {packet.stream}") + continue + feature_type = FeatureType.from_str(packet.stream.metadata.get("FEATURE_TYPE")) + + logger.debug( + f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" + ) + + feature_codec = packet.stream.codec_context.codec.name + if feature_codec == "h264": + frames = packet.decode() + for frame in frames: + if feature_type.dtype == "float32": + data = frame.to_ndarray(format="gray").reshape(feature_type.shape) + else: + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + + # Append data to the numpy array + np_cache[feature_name][d_feature_length[feature_name]] = data + d_feature_length[feature_name] += 1 + else: + packet_in_bytes = bytes(packet) + if packet_in_bytes: + # Decode the packet + data = pickle.loads(packet_in_bytes) + + # Append data to the numpy array + np_cache[feature_name] = np.append(np_cache[feature_name], [data], axis=0) + d_feature_length[feature_name] += 1 + else: + logger.debug(f"Skipping empty packet: {packet} for {feature_name}") + + container.close() + + return np_cache + + def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): """ Transcode pickled images into the desired format (e.g., raw or encoded images). From 1cb9ee5aaad2750c6d917e935f536efd090d9dea Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 26 Aug 2024 01:56:41 -0700 Subject: [PATCH 43/80] add visualization results --- benchmarks/Visualization.ipynb | 286 +++++++++++++++++++++++++++++++++ benchmarks/openx.py | 4 +- fog_x/trajectory.py | 54 +++++-- 3 files changed, 328 insertions(+), 16 deletions(-) create mode 100644 benchmarks/Visualization.ipynb diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb new file mode 100644 index 0000000..95d6059 --- /dev/null +++ b/benchmarks/Visualization.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load the data\n", + "df = pd.read_csv('trajectory_results.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b09fd4cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatasetFormatTrajectoryLoadingTime(s)FileSize(MB)Throughput(traj/s)
0berkeley_autolab_ur5RLDS00.045454237.46154922.000367
1berkeley_autolab_ur5RLDS10.016615126.82606660.187754
2berkeley_autolab_ur5RLDS20.017593157.58214556.839549
3berkeley_autolab_ur5RLDS30.017673157.04762156.583439
4berkeley_autolab_ur5RLDS40.026880187.19503637.203005
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1275nyu_door_opening_surprising_effectivenessHDF5590.01951475.30505451.246292
1276nyu_door_opening_surprising_effectivenessHDF5600.01618361.43493761.792713
1277nyu_door_opening_surprising_effectivenessHDF5610.028054108.99004435.645542
1278nyu_door_opening_surprising_effectivenessHDF5620.01944375.30505451.432299
1279nyu_door_opening_surprising_effectivenessHDF5630.026315103.04568538.001178
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1280 rows × 6 columns

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" + ], + "text/plain": [ + " Dataset Format Trajectory \\\n", + "0 berkeley_autolab_ur5 RLDS 0 \n", + "1 berkeley_autolab_ur5 RLDS 1 \n", + "2 berkeley_autolab_ur5 RLDS 2 \n", + "3 berkeley_autolab_ur5 RLDS 3 \n", + "4 berkeley_autolab_ur5 RLDS 4 \n", + "... ... ... ... \n", + "1275 nyu_door_opening_surprising_effectiveness HDF5 59 \n", + "1276 nyu_door_opening_surprising_effectiveness HDF5 60 \n", + "1277 nyu_door_opening_surprising_effectiveness HDF5 61 \n", + "1278 nyu_door_opening_surprising_effectiveness HDF5 62 \n", + "1279 nyu_door_opening_surprising_effectiveness HDF5 63 \n", + "\n", + " LoadingTime(s) FileSize(MB) Throughput(traj/s) \n", + "0 0.045454 237.461549 22.000367 \n", + "1 0.016615 126.826066 60.187754 \n", + "2 0.017593 157.582145 56.839549 \n", + "3 0.017673 157.047621 56.583439 \n", + "4 0.026880 187.195036 37.203005 \n", + "... ... ... ... \n", + "1275 0.019514 75.305054 51.246292 \n", + "1276 0.016183 61.434937 61.792713 \n", + "1277 0.028054 108.990044 35.645542 \n", + "1278 0.019443 75.305054 51.432299 \n", + "1279 0.026315 103.045685 38.001178 \n", + "\n", + "[1280 rows x 6 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7cb9a3c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize the data\n", + "# dataset to be the x axis, loading time is y axis, and format to be side by side comparison between different bars\n", + "\n", + "sns.set(style=\"whitegrid\")\n", + "plt.figure(figsize=(10, 6))\n", + "ax = sns.barplot(x=\"Dataset\", y=\"LoadingTime(s)\", hue=\"Format\", data=df)\n", + "plt.title('Loading Time of Different Formats for Different Datasets')\n", + "plt.xlabel('Dataset')\n", + "plt.ylabel('Loading Time (s)')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8f7d665b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# make previous plot log scale\n", + "plt.figure(figsize=(10, 6))\n", + "ax = sns.barplot(x=\"Dataset\", y=\"LoadingTime(s)\", hue=\"Format\", data=df)\n", + "plt.yscale('log')\n", + "plt.title('Loading Time of Different Formats for Open-X Datasets')\n", + "plt.xlabel('Dataset')\n", + "plt.ylabel('Log-Scale Loading Time (s)')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39ca78d9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 11954d8..d396220 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -15,8 +15,8 @@ DEFAULT_EXP_DIR = "/home/kych/datasets/fog_x/" DEFAULT_NUMBER_OF_TRAJECTORIES = 64 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5", "bridge", "berkeley_cable_routing", "nyu_door_opening_surprising_effectiveness"] -DEFAULT_NUMBER_OF_TRAJECTORIES = 1 -DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] +# DEFAULT_NUMBER_OF_TRAJECTORIES = 1 +# DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 79f1585..8b49d16 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -171,13 +171,14 @@ def load(self, mode = "cache"): self.trajectory_data = self._load_from_cache() else: logger.info(f"Loading the container file {self.path}") - self.trajectory_data = self._load_from_container_to_h5() + self.trajectory_data = self._load_from_container(save_to_cache=True) elif mode == "no_cache": logger.info(f"Loading the container file {self.path} without cache") - self.trajectory_data = self._load_from_container_no_cache() + # self.trajectory_data = self._load_from_container_to_h5() + self.trajectory_data = self._load_from_container(save_to_cache=False) else: logger.info(f"No option provided. Force loading from container file {self.path}") - self.trajectory_data = self._load_from_container_to_h5() + self.trajectory_data = self._load_from_container(save_to_cache=False) return self.trajectory_data @@ -452,9 +453,17 @@ def _load_from_container_to_h5(self): h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache - def _load_from_container_no_cache(self): + def _load_from_container(self, save_to_cache: bool = True): """ Load the container file with the entire VLA trajectory. + + args: + save_to_cache: save the decoded data to the cache file + + returns: + h5_cache: h5py file with the decoded data + or + dict: dictionary with the decoded data Workflow: - Get schema of the container file. @@ -462,19 +471,25 @@ def _load_from_container_no_cache(self): - Decode frame by frame and store in the preallocated memory. """ - container = av.open(self.path, mode="r", format="matroska") - streams = container.streams - - - def _get_length_of_stream(stream): + def _get_length_of_stream(container, stream): """ Get the length of the stream. """ length = 0 for packet in container.demux([stream]): - length += 1 + if packet.dts is not None: + length += 1 return length + container_to_get_length = av.open(self.path, mode="r", format="matroska") + streams = container_to_get_length.streams + length = _get_length_of_stream(container_to_get_length, streams[0]) + container_to_get_length.close() + + container = av.open(self.path, mode="r", format="matroska") + streams = container.streams + + # Dictionary to store preallocated numpy arrays np_cache = {} @@ -492,7 +507,6 @@ def _get_length_of_stream(stream): f"Creating a cache for {feature_name} with shape {feature_type.shape}" ) - length = _get_length_of_stream(stream) # Allocate numpy array with shape [None, X, Y, Z] where X, Y, Z are feature dimensions if feature_type.dtype == "string": np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) @@ -535,10 +549,22 @@ def _get_length_of_stream(stream): d_feature_length[feature_name] += 1 else: logger.debug(f"Skipping empty packet: {packet} for {feature_name}") - + print(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") container.close() - - return np_cache + + if save_to_cache: + # create and save it to be hdf5 file + h5_cache = h5py.File(self.cache_file_name, "w") + for feature_name, data in np_cache.items(): + if data.dtype == object: + continue # TODO + else: + h5_cache.create_dataset(feature_name, data=data) + h5_cache.close() + h5_cache = h5py.File(self.cache_file_name, "r") + return h5_cache + else: + return np_cache def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): From d14c4ab7fb8270976dd39e91a0b5e383d3185afd Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 26 Aug 2024 22:06:41 -0700 Subject: [PATCH 44/80] add DL dataset --- benchmarks/Visualization.ipynb | 166 +++++++++++- benchmarks/openx.py | 37 ++- fog_x/DLdataset.py | 452 +++++++++++++++++++++++++++++++++ 3 files changed, 640 insertions(+), 15 deletions(-) create mode 100644 fog_x/DLdataset.py diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 95d6059..8d13351 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -197,13 +197,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "7cb9a3c1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -227,13 +227,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "8f7d665b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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v3769uX79+uZLly5Z9u3fv9/88MMPm1966SXLvvT77Pnnn7epvlq1apnbtWt3x3Nee+01c3BwsPnAgQNms/n/39+OHTuak5OTLefNmTPHHBwcbP7666/NZrPZfPXqVXOtWrXMEyZMsCrv/Pnz5po1a1rtHzNmjDk4ONjqd6PZbDZ36NDB3LFjR5tei9lsNv/999/mSpUqmYODg81vvPGGzdfZ80zfeo+YzWZzdHS0OTg42Oo5nTt3rjk4ONh84sSJDOdnVka/fv3MTZs2tWynPxN3iu2nn34yBwcHm9euXWu1f9u2bRn2t2nTJsP9bjabze3atcvwewdAzkfXXyCXSEtL044dO9SsWTOrcZuFCxdWRESE9uzZo6tXr0q6MWtk+jf5aWlpunTpkry9vVW2bFn99ddflmu3bdumQoUKqVWrVpZ9Xl5embbE3U7r1q3l5+dn2a5Vq5akG6260o3unn///bc6dOhg1eJVp04dBQcHZ+UtsFuXLl0s/+/m5qbKlSvLbDZb7ff19VXZsmUtcUvSxo0bVb58eZUrV04XL160/KtXr54kWS3fc6s///xTsbGx6tmzp9W4xUcffVTlypXTd99958BXaM3b21vXrl1zWHlZfR9q165tNc7VbDbrq6++Unh4uMxms1UZDRs21JUrVzJ0S+3UqZM8PT0t27feV/aqWrWqPvnkE6t/6a2Z27Ztk5ubW4bJe/r16yez2axt27bd8XXerEuXLpaWPUkKDQ3NcM+l34u3vqabWw7j4uJ05coV1axZ0+rZvRNfX1/99ttvVl3M7XXu3Dnt379fHTt2lL+/v2X/ww8/rLCwMG3dujXDNT169LCp7GvXrt21FTz9ePrvtnTdu3e3Gjffs2dPubu7W+LZuXOn4uPj1aZNG6v7zWg0qmrVqpk+uz179rTarlmzpqWbrC3y589vicnRrYC3PtM33yPXr1/XxYsXLT1q7tTF+2Y3l3HlyhVdvHhRderU0YkTJ3TlyhVJ/z9e+bvvvlNKSkqm5WzcuFE+Pj5q0KCB1XtdqVIleXt73/H3ZDpfX18dOnRI//77r02xA8gZ6PoL5BIXL15UYmKi1ayS6cqXLy+TyaQzZ87ooYcekslk0qJFi7R06VKdPHnSapzVzR82T506pdKlS1t9oJaUaR23U7RoUavt9KQ1vQvk6dOnJSnT7qylS5e2+cN3dty6xIaPj4/y5Mlj6QJ38/7Lly9bto8dO6bDhw+rfv36mZYbGxt72zrTX3dm72W5cuW0Z88eW8PPsoSEBAUGBjqsvKy+D7d2Bb148aLi4+O1YsUKrVixItMybu2Oe+vPLH3ip+yuL1ugQIHbzqx86tQpFS5cOMNSIOlL9Ny6tM2tr/Nmmd1zUsbnxcfHJ8PYym+//VYfffSR9u/fbzUG+Nbn9HZGjx6tsWPH6tFHH1WlSpXUpEkTdejQIcPEZLa4031cvnx5ff/99xkmTLrT+3KzfPny3fULlfTjtya0pUuXzlBWoUKFLD+j9ISnb9++mZZ76884s98Hfn5+Vj+bixcvWv0u9fb2torr1VdflcFgUPHixfX2228rLCzMYZPQ3fpMX758WTNnztSXX36Z4flLTzLvZs+ePZoxY4Z+/fVXJSYmZijDx8dHderUUcuWLTVz5kwtWLBAderUUbNmzdS2bVvLF0nHjh3TlStX7Po9me65557T0KFD1bJlSwUHB6thw4Zq3769XTMdA3AdJKoAMpg1a5Y++OADde7cWSNGjJCfn5+MRqPefPNNhy8xc7vlYBxdT3ZkNk7MlrhNJpOCg4Nvu5ZrkSJFHBOgA509e1ZXrlzJ1jjXW2X1fbh1ZlKTySRJateunTp27JhpGemTPaW73dg+V7qv7jQD6+3iv9uYxZ9//llDhgxR7dq1NWnSJBUqVEgeHh76/PPPFR0dbVNcrVu3Vq1atbR582bt2LFD8+bN05w5czRjxgw1adLEpjKyw9aZj8uXL6+//vpLycnJVq3nNzt48KA8PDxUpkyZLMWQfp9MnTpVhQoVynD81ufflmWtunTpYvVlxbPPPqvhw4dLkr766itt2bJF48ePV5kyZTRw4EDNmzfPMhYzOzJ7pkeOHKm9e/eqf//+qlixory9vWUymTRgwACbnpHjx4/rqaeeUrly5TR27FgVLVpUHh4e2rp1qxYsWGB5Zg0Gg6ZPn65ff/1V3377rbZv367x48frk08+0YoVK5QvXz6ZTCYFBgbq3XffzbSuW78AyEzt2rW1efNmffPNN9qxY4dWrVqlhQsXavLkyfdtqSQAjkeiCuQSAQEB8vLy0tGjRzMcO3LkiIxGo6W1ZtOmTapbt67efPNNq/Pi4+MtE2pIUvHixfX333/LbDZbtdZkVoe90luWjh8/nuHYzbO4uqJSpUrpwIEDql+/vs2tWenSX/fRo0cztDQcPXo0Q4ubo3zxxReSpIYNGzqszOy8D9KNezf9A62t68Tawp5Y7qR48eLatWuXrl69atXiduTIEcvxe23Tpk3KkyeP5s2bZ5W8ff7551kqp3Dhwurdu7d69+6t2NhYdezYUbNmzcpyonrzfXyrI0eOqECBArddfuZuHn30Ue3du1cbNmzIdMmgkydPas+ePapfv36GLwWOHTtm6Xou3Wh5PX/+vBo3bixJltbjwMBAh91z77zzjq5fv27ZTq/j6tWrev3111WpUiU98cQTcnNzU8uWLfXRRx+pTZs2drVk3+zWZzouLk67du3S8OHDrdY7zqzb7O2ekS1btig5OVkfffSR1e+i23XTrVatmqpVq6ZRo0Zp3bp1Gj16tL788kt17dpVpUqV0q5du1SjRo27Lp9zp2fW399fnTt3VufOnXXt2jU98cQTmjFjBokqkIMxRhXIJdzc3NSgQQN98803VuOmLly4oOjoaNWsWdPy4drNzS3Dt+obNmzIMGatcePGOnfunNXSF4mJiVq5cqXD4g4KClJwcLCioqKsuvn9+OOP+vvvvx1Wz73w+OOPKyYmJtP3IykpSQkJCbe9tnLlygoMDNTy5cutum9u3bpVhw8ftsze6ki7du3Shx9+qBIlSqhdu3YOKzc774Mkywf3TZs2Zfozv7Xbr628vLxs7uZoi8aNGystLU2ffvqp1f4FCxbIYDBYkqB7yc3NTQaDwaqL6cmTJ/XNN99kONfb2ztDV+i0tLQM70lgYKAKFy5s85JKNytcuLAqVqyoqKgoq7r+/vtv7dixI1sttN27d1dgYKDeeeedDON0r1+/rnHjxslsNmvYsGEZrl2xYoXVmMlly5YpNTXV8jNq1KiR8ufPr9mzZ2c6ttKee65mzZoKCwuz/EtPQN9//32dP39ekydPtrTMvvzyyzIajXrttdeyXM/NMnumb9f6u3Dhwgz7vLy8JGXsDpxexs1/J65cuZLhC5G4uLgMf0sqVqwoSZb76fHHH1daWlqms6CnpqZa3TdeXl6Zdt+/eVkd6UZX7lKlStl1zwJwHbSoAg+Yzz//3LLu5M2efPJJjRw5Ujt37lSvXr3Uq1cvubm5acWKFUpOTtaLL75oOffRRx9VZGSkxo0bp+rVq+vvv//WunXrMnyz361bN3366acaM2aM9u3bp0KFCumLL75w+KLyo0aN0tChQ9WzZ0916tRJ8fHx+vTTTxUcHOzQSX8crX379tqwYYMmTZqk3bt3q0aNGkpLS9ORI0e0ceNGzZ07V1WqVMn0Wg8PD40ePVrjxo3TE088oTZt2liWpylevHiG5Viyatu2bTpy5IjS0tJ04cIF7d69Wzt27FCxYsX00Ucf2dz90hbZeR/SvfDCC9q9e7e6deumrl27qkKFCoqLi9O+ffu0a9cu/fjjj1mOq1KlSvryyy8ta1Z6e3tnay3V8PBw1a1bV++9955lfdYdO3bom2++Ud++fR3anfp2mjRpok8++UQDBgxQRESEYmNjtXTpUpUqVUoHDx60OrdSpUratWuXPvnkExUuXFglSpRQ2bJl1aRJE7Vs2VIPP/ywvL29tXPnTv3xxx92r8/70ksv6ZlnnlH37t3VpUsXy/I0Pj4+Vi16WVWgQAFNnz5dAwcOVMeOHdW1a1eVL19eFy5c0Jo1a3Ts2DG9/PLLqlGjRoZrU1JS9NRTT+nxxx/X0aNHtXTpUtWsWVNNmzaVdGMM6iuvvKKXXnpJnTp1UuvWrRUQEKDTp09r69atqlGjhiZOnGh37On+/PNPLV26VL1797Z6BoKCgjRixAi99dZb2rRpk1q2bHnXsmx9pvPnz6/atWtr7ty5SklJUVBQkHbs2JHpxE/pyxG99957at26tTw8PPTYY4+pQYMG8vDw0ODBg9WjRw9du3ZNn332mQIDA3X+/HnL9WvWrNGyZcvUrFkzlSpVSteuXdPKlSuVP39+y5cCderUUffu3TV79mzt37/fUva///6rjRs36uWXX7ZM2FepUiUtW7ZMH374oUqXLq2AgADVr19fbdq0UZ06dVSpUiX5+/vrjz/+sCyxBCDnIlEFHjDLli3LdH+nTp300EMP6dNPP9W0adM0e/Zsmc1mhYaG6p133rHM+CjdWGMyMTFR69at05dffqlHHnlEs2fP1rRp06zK9PLy0oIFC/Taa69pyZIlyps3r9q2bavGjRtrwIABDntN4eHh+u9//6sZM2Zo2rRpKlOmjN566y1FRUXp0KFDDqvH0YxGoyIjI7VgwQJ98cUX2rx5s7y8vFSiRAn16dPnrpNOderUSXnz5tWcOXP07rvvytvbW82aNdOLL75omRzIXtOnT5d0IyH29/dXcHCwxo8fr06dOmWYKCa7svs+SDfWS/zss88UGRmpzZs3a9myZfL391eFChU0evRou+Lq1auX9u/fr9WrV2vBggUqXrx4thJVo9Gojz76SNOnT9eXX36p1atXq3jx4nrppZfUr18/u8vNivr16+uNN97QnDlz9Oabb6pEiRIaPXq0Tp06lSFRHTt2rCZOnKj3339fSUlJ6tixo1599VX17NlTO3bs0FdffSWz2axSpUpp0qRJNq0HmpmwsDDNnTtX06dP1/Tp0+Xu7q7atWvrxRdfzHa31lq1amnt2rWaPXu2Nm7cqPPnzyt//vyqXr263njjDctsz7eaOHGi1q1bp+nTpyslJUVt2rTRhAkTrLqWtm3bVoULF9bHH3+sefPmKTk5WUFBQapVq5Y6deqUrbilG63XEydOVGBgoEaOHJnheJ8+fRQVFaU333xTDRs2vOsMx1l5pqdNm6bXXntNS5culdlsVoMGDTRnzhw1atTI6rzQ0FCNGDFCy5cv1/bt22UymfTNN9+oXLlymj59ut5//329/fbbKliwoHr27KmAgACr9YLr1KmjP/74Q19++aUuXLggHx8fhYaG6t1337X62b/66quqXLmyli9frvfee09ubm4qXry42rVrZ/VFw7Bhw3T69GnNnTtX165dU506dVS/fn316dNHW7Zs0Y4dO5ScnKxixYpp5MiR6t+/v80/DwCux2B2pZklACAL2rdvr4CAAH3yySfODgVADrB69WqNGzdOq1atumsrPgDAuRijCsDlpaSkKDU11Wrf7t27deDAAdWpU8dJUQEAAOBeoesvAJcXExOjp59+Wu3atVPhwoV15MgRLV++XIUKFVKPHj2cHR4AAAAcjEQVgMvz8/NTpUqV9Nlnn+nixYvy9vZWkyZNNHr0aKvlcgAAAPBgYIwqAAAAAMClMEYVAAAAAOBSSFQBAAAAAC6FMaoOsnfvXpnNZnl4eDg7FAAAAABOlJKSIoPBoOrVqzs7lByLRNVBzGazGO4LAAAAgLwg+0hUHSS9JZUFxAEAAIDc7Y8//nB2CDkeY1QBAAAAAC6FRBUAAAAA4FJIVAEAAAAALoVEFQAAAADgUkhUAQAAAAAuhVl/AQBKS0tTSkqKs8MAMvDw8JCbm5uzwwAA3GckqgCQi5nNZp09e1aXL192dijAbfn7+6tIkSIyGAzODgUAcJ+QqAJALpaepBYuXFje3t4kAnApZrNZCQkJOnfunCSpaNGiTo4IAHC/kKgCQC6VlpZmSVIDAwOdHQ6QKS8vL0nSuXPnVLhwYboBA0AuwWRKAJBLpY9J9fb2dnIkwJ2l36OMowaA3INEFQByObr7wtVxjwJA7kOiCgAAAABwKSSqAAAAAACXQqIKAHjgrV69WiEhIZn+e/fdd50dXpb88ssvmjFjhuLj450dCgAA9wyz/gIAco3nnntOJUqUsNoXHBzspGjss3fvXs2cOVMdO3aUr6+vs8MBAOCeIFEFAOQajRs3VpUqVRxaZkJCAjMnAwDgYHT9BQBA0q5du9SrVy9Vq1ZNtWrV0pAhQ3T48GGrc2bMmKGQkBD9888/euGFF1S7dm316tVLkhQeHq5BgwZp9+7d6tSpk0JDQ9W2bVvt3r1bkvTVV1+pbdu2qlKlijp16qS//vrLquwDBw5o7Nixatq0qapUqaIGDRpo3LhxunTpklX9U6dOlSQ1bdrU0n355MmT9/KtAQDgvqNFFQCQa1y9elUXL1602hcQEKCdO3fqmWeeUYkSJfTss88qKSlJS5YsUc+ePbV69eoM3YVHjBih0qVLa9SoUTKbzZb9x44d0wsvvKAePXqoXbt2mj9/vgYPHqzJkyfrvffeU8+ePSVJH3/8sUaOHKmNGzfKaLzxnfHOnTt14sQJderUSYUKFdKhQ4e0cuVK/fPPP1q5cqUMBoOaN2+uf//9V9HR0Ro3bpwKFChgeQ0AADxISFTxwIiMjFRUVJQ6dOigYcOGOTscAC7oqaeeyrDv4MGDmjp1qvz8/LRixQr5+/tLkpo1a6aOHTtqxowZevvtt62uefjhhzVt2rQMZR09elTLly9X9erVJUkVKlRQ//799Z///EcbNmxQsWLFJEl+fn6aOHGifvrpJ9WtW1eS1KtXL/Xr18+qvGrVqun555/Xnj17VKtWLT388MN65JFHFB0drWbNmmVIoAEAeFCQqOKBkJSUpKioKJlMJkVFRal///7Kmzevs8MC4GImTpyosmXLWu07d+6c9u/frwEDBliSVOlGMhoWFqatW7dmKKdHjx6Zll+hQgVLkipJVatWlSTVq1fPkqTevP/EiROWRPXm31nXr1/XtWvXLOft27dPtWrVyspLBQAgRyNRxQMhNTVVJpNJkmQymZSamurkiAC4otDQ0AyTKf3666+SlCGBlaTy5cvr+++/zzBh0u1aMosWLWq17ePjI0kqUqSI1f78+fNLktUSM5cvX9bMmTP15ZdfKjY21ur8K1eu3OllAQDwwCFRhcsxmUyWMVu5qW4AOUeePHky3e/m5pal/TePbx05cqT27t2r/v37q2LFivL29pbJZNKAAQOszgMAIDcgUYXLMRqNmr11kU7Hxdh8Ter1FKvttzZMl3sejyzVW8wvSIOaPJmlawDkfOldco8ePZrh2JEjR1SgQIF7vvxMXFycdu3apeHDh+vZZ5+17P/3338znGswGO5pLAAAuAISVbik03ExOhZr+3ILpuQ0q+0TF0/L6Jl5CwYA3Kxw4cKqWLGioqKiNGjQIPn6+kqS/v77b+3YsUPt2rW75zHcrsV14cKFGfZ5eXlJojswAODBRqKKB4PRIBkkmXXjv0ZaHADY7qWXXtIzzzyj7t27q0uXLpblaXx8fKxaOO+V/Pnzq3bt2po7d65SUlIUFBSkHTt2ZLo+aqVKlSRJ7733nlq3bi0PDw899thj97zVFwCA+4nBeHCoyMhINW/eXJGRkfe1XqO7UflDAiWDlD8kUEZ3bm0AtgsLC9PcuXPl7++v6dOna/78+apataqWLVumkiVL3pcYpk2bpoYNG2rp0qX673//K3d3d82ZMyfDeaGhoRoxYoQOHDigcePG6fnnn8+wNiwAADmdwcwMDQ7xxx9/SFKG2SRzk6SkJLVt29YyIdG6devsXiJm0tp3stT11xFKB5bQ5HYv3tc6AWdKSkrS0aNHVbZsWZZzgkvjXgWQ05AbZB/NTnAYlogBAAAA4AgkqgAAAAAAl0KiikyZ/9cyCgAAAAD3G7P+IlMGo1FHo+coMfaMzdckJFt39T2wbKq8PbN2i/mVrazijTtl6RoAAAAADxYSVdxWYuwZJcYct/n8pBTrtUyTzp2QwSNra5nmDSiSpfMBAAAAPHjo+guHcTMalL56qdFwYxsAAAAAsopEFQ7j6WZUg1K+MhqksJK+8nTj9gIAAACQdXT9hUO1qxiodhUDnR0GAAAAgByMJi8AAAAAgEshUQUAAAAAuBQSVQAAAACAS2GMKgAgx5sxY4Zmzpxp2fb391e5cuU0ePBgNWnSxLI/PDxcjz76qCZOnJhpOatXr9a4ceMs297e3goKClL16tXVs2dPhYaGWp2fmpqqZcuW6bPPPtOJEyfk7u6uokWLqlatWho7dqw8PT0d/EoBAMgdSFQBABmYTGYZnbTElL11582bVwsXLpQknTt3TrNmzdLgwYP16aefqkaNGlkqa+7cufLx8VFiYqKOHj2qzz//XN26ddPzzz+vgQMHWs57/fXXtXr1ag0cOFA1atRQYmKi9u/fr7Vr1yopKYlEFQAAO5GoAgAyMBoNily2Q6fOxd3XeosX9tOwng3sutZoNKpatWqW7apVq6pJkyaKiorKcqJaqVIlBQQESJLq16+vHj16aMyYMfrvf/+rGjVqqFatWkpMTNSqVas0ePBgPfvss5ZrmzZtqmeffVZms9mu1wEAAEhUAQC3cepcnP49dcnZYdgtKChIAQEBOn36dLbLMhqNevnll7Vp0yYtW7bMkqimpKSocOHCmV5jMDinRRoAgAcBiSoA4IF07do1xcXFqUSJEg4pz9/fX5UqVdLevXslSQEBASpWrJg++ugj5cuXTw0bNpSfn59D6gIAILdj1l8AwAMjNTVVqampOn36tCZMmKB8+fLpySefdFj5RYsW1YULFyzbU6ZMUXJysp5//nnVrVtXrVu31rRp03Tx4kWH1QkAQG5EiyoA4IGQkJCgSpUqWbbd3Nz04Ycfqly5cg6rw2w2W3XprVu3rjZv3qxt27Zp165d+uGHH/Txxx9r9erVWr16tYKCghxWNwAAuQktqgCAB0LevHm1atUqffbZZ3rnnXdUqFAhjRkzRufOnXNYHWfPnlXBggWt9nl7e6tVq1aaPHmyNm3apNdff10XLlzQ/PnzHVYvAAC5DYkqAOCBYDQaVaVKFYWGhqpdu3aaOXOm4uPjFRkZ6ZDyL126pD///FPVq1e/43ldu3aVv7+/Dh8+7JB6AQDIjUhUAQAPpCpVqqhNmzZavXq1zp8/n62yTCaT3nzzTaWkpKh3796SpJSUFMXFZVy+JzY2VleuXFGhQoWyVScAALkZY1QBAJkqXvj+z2Dr6DqHDh2qL7/8UgsXLtTo0aMlScePH9fGjRutzjMajWrRooVle9++ffLx8VFSUpKOHj2qzz//XPv27dOLL75oaVG9cuWKWrZsqfbt26tevXry8/PTyZMnNX/+fBmNRvXs2dOhrwUAgNyERBUAkIHJZNawng2cVrfR6Jg1SMuVK6fWrVtr2bJlGjRokCRp+/bt2r59u9V5bm5u+uuvvyzbAwYMkHRj/GnhwoVVo0YNTZw4UaGhoZZz8ufPr2eeeUbbt2/Xxo0bFRcXp4IFC6pKlSqaMmWK1cROAAAga0hUAQAZOCpRvF91Dx8+XMOHD8/02Lvvvmv5/y1bttyxnE6dOqlTp0421enp6amBAwdq4MCBtgcKAABswhhVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUAAAAAuBQSVQAAAACASyFRBQAAAAC4FBJVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUA5GiDBw9WixYtbnt88eLFCgkJ0fHjxxUSEqKNGzfaVO7rr7+ukJAQRUZG2hxLnz59FBISog8++CDDsfbt22vs2LE2l3WzixcvasqUKWrZsqWqVKmiGjVq6IknntBnn32mtLQ0u8q8nbFjxyoiIsKhZQIAkFUkqgCADMwmU46pOyIiQseOHdPvv/+e6fH169erWrVqMhpt/5OXlpamDRs2SJKio6OzFI90Izm+cuVKlq/LzLFjx9SxY0dFR0erW7dumjNnjt5//32Fhobqrbfe0rfffuuQegAAcCXuzg4AAOB6DEajjkbPUWLsmftar1dgUZWNeCZL1zRt2lTe3t6Kjo5WaGio1bGTJ09q7969mjBhQpbK3LVrly5cuKCwsDDt3LlT+/btU6VKlWy6NjQ0VP/8848WLVqkYcOGZanezIwePVppaWn6/PPPFRQUZNnfuHFjPfHEEw5LiAEAcCUkqgCATCXGnlFizHFnh3FXXl5eatq0qTZs2KCxY8datZyuX79ebm5uat26tRITE20uMzo6Wvny5dOUKVPUtGlTrVu3zuZENSAgQD169NCiRYvUt29f5c+f/7bn/vTTT5o2bZr++usveXl5KTw8XGPGjJG/v78k6eeff9bvv/+uCRMmWCWp6YoVK2b5/8OHD2vmzJn65ZdfdPnyZRUvXlxdunTRU089ZfWeJCcnKzIyUtHR0YqJiVFAQIDCwsI0ZcoUq7J3796tt956S//++68qVKigV155RZUrV7YcN5vNmj9/vlauXKlTp04pKChIffr00VNPPWXT+wQAwJ3Q9RcAkOO1bdtW586d0+7du632R0dHKywsTIGBgTaXdf36dX311Vdq3ry5goKC1LBhQ61fv16mLHRJ7tevnxITE/Xpp5/e9pw///xTTz/9tPLly6cPPvhAo0eP1rfffqtnnnnGMu70xx9/lCQ1atTornWeO3dOZcuW1aRJk/Txxx+rW7duioyM1Icffmh13vDhw7VgwQJ17txZH3/8sV566SUlJCRYnXP+/Hm9/vrr6t+/v95//31dv35dzz77rFJSUiznvPHGG5o+fbo6dOigjz/+WB07dtS7776rZcuW2fw+AQBwO7SoAgByvAYNGiggIEDr169X/fr1JUl///23/v77b/Xv3z9LZW3ZskXXrl2zTCjUtm1bffvtt9q9e7el7LspVKiQunXrpk8++UR9+vSRt7d3hnNmzZqlQoUKadasWfLw8JAkFS1aVP3799fWrVsVHh6umJgYSdYtp7dTv359S3xms1k1a9ZUUlKSlixZomeffVaStGPHDn333XeaNm2a1YRJt06eFBcXpyVLluihhx6SdKPV+sknn9Rvv/2mWrVq6fjx41qyZIkmT56s7t27S5LCwsKUlJSkyMhIde/ePUtjggEAuBV/RQAAOZ67u7tatWqlr776SsnJyZJudPv18vJS8+bNs1RWdHS0AgMDFRYWJkkKDw+Xt7e31q1bZzknNTXV6l9mBgwYoKtXr962hfHnn39W06ZNLUmqJDVs2FC+vr7as2dPlmKWbrQET58+Xc2bN1eVKlVUqVIlvffeezp//ryuXbsm6cbYWy8vL7Vp0+aOZRUuXNiSpEpShQoVJMmSOO/cuVOS1KJFC6v3ISwsTOfPn9eZM/d3bDMA4MFDogoAeCBEREQoLi5O27dvl3Qj4QwPD1e+fPlsLiM+Pl5bt27VY489pmvXrik+Pl4pKSlq1KiRNm/ebEmCK1WqZPUvM0WKFFGnTp00f/58JSUlZVpXZl2SAwMDFRcXJ0mWcam2JH7vvPOO5s2bp65du+rjjz/WqlWrNGTIEEk3klhJunz5sgoVKiSDwXDHsnx9fa2205Pp9HIuXboks9msevXqWb0PTz/9tM3xAgBwJ3T9BQA8EGrUqKHixYtr/fr1CgwM1MmTJ/Xyyy9nqYxNmzYpJSVFq1at0qpVqzIc/+6779SiRYtMj2Vm0KBBWr16tVasWJHhmJ+fn2JjYzPsj42NlZ+fnySpTp06kqTvv/9epUuXvmNdGzduVPfu3TVw4EDLvq1bt1qd4+/vr/Pnz8tsNt81Wb0TPz8/GQwGLV261KpFOF3ZsmXtLhsAAIlEFQDwgDAYDIqIiNCiRYuUN29e+fv72zQJ0c3WrVun4sWL66233spw7Pnnn9e6devUokULValSxabyihcvrnbt2mnu3LkZZv+tWbOmvvnmG40dO1bu7jf+HO/YsUPx8fGqWbOmJKlWrVoKDQ3VrFmz1Lx5cxUuXNiqjDNnzig+Pl4hISG6fv26VdKYlpam9evXW50fFhamOXPmaMOGDWrdurVNryEz6WNhL1++rPDwcLvLAQDgdnJ9ojpx4kR9++23OnfunA4ePOjscAAA2RAREaHZs2dr9erV6t69e6atfb/99luGfQULFlTJkiX1008/aciQIapbt26mZS9dulRXrlyRj4+PzTENGjRIUVFROnfunKpWrWrZP3jwYPXo0UODBg1Snz59dOHCBU2bNk2hoaFq0qSJ5bx3331Xffr0UefOnfX000+rUqVKSk5O1k8//aRPP/1Ub7/9tkJCQhQWFqbPPvtMFSpUUIECBbR06VJLV+V0YWFhatKkicaPH6/jx4+ratWqunz5sjZt2qT333/f5tdUtmxZ9e7dWy+99JL69++vqlWrKiUlRf/++692796dYaZhAACyKtcnqm3bttVzzz2nBg0aODsUAHApXoFFc1ydwcHBCgkJ0cGDB9W2bdtMz5k/f36GffXr11fjxo1lMpnUoUOHTK/r2LGjFixYoE2bNqlLly42x1S6dGm1adNGa9eutdpfuXJlzZ8/X//97381fPhweXt7W9ZRdXNzs7p+zZo1mjNnjpYtW6YzZ87I09NTjzzyiMaPH6/HHntMkvSf//xHkyZN0muvvSYvLy917NhRzZs314QJE6zqnTFjhmbOnKkVK1Zo5syZCgwMtOtv4IQJE1S2bFmtWLFCkZGRypcvn8qWLatWrVpluSwAAG5lMJvNZmcHcbNjx45p3rx5+u2333To0CGVK1dO0dHRGc47fPiwXn/9de3du1f58uVT+/btNXLkSHl6etpVb/oHG3v98ccfkmRzd7Cc4K+Fryox5vh9rbNAxToq13agJq19R8diT97XuksHltDkdi/e1zoBZ0pKStLRo0dVtmxZ5c2b1+qY2WSSwUnLizizbrimO92rAOCKHsTc4H5zuRbVQ4cOaevWrapatapMJpMyy6Pj4uLUt29flSlTRjNmzFBMTIymTJmipKQkTZw40QlRA8CDxZmJIkkqAABwuUQ1PDxczZo1kySNHTtWf/75Z4Zzli9frmvXrmnmzJny9/eXdGPSiMmTJ2vQoEGW6fw7duyo06dPZ7i+cuXKmjdv3r17EQAAAAAAu7lcomq04Zv0bdu2qX79+pYkVZIef/xxTZo0STt27FCnTp0kSWvWrLlXYQIAAAAA7hGXS1RtceTIEXXu3Nlqn6+vrwoVKqQjR444KSrJbDYrISHBafU7isFgkJeXl7PDcJrExMRMu5wDD5rr16/LZDIpLS1NaWlpzg4HuK20tDSZTCYlJibKZDI5OxwAuKvsrleNHJqoxsfHy9fXN8N+Pz8/xcXFZamssWPHaufOnZKkxo0bq27dunrnnXfsiislJUX79++361pX4uXlpUceecTZYTjN0aNHlZiY6OwwgPvC3d1d169fd3YYwB1dv35dqampTv0yGgCyyt5JXnFDjkxUHWnKlCkOK8vDw0MVKlRwWHnOktu//SlbtiwtqsgVrl+/rtOnTytPnjzMpAqX5+7urlKlSilPnjzODgUA7uqff/5xdgg5Xo5MVH19fXXlypUM++Pi4uTn5+eEiG4wGAzy9vZ2Wv1wjNzc7Rm5i9FolNFolJubm9W6nYCrcXNzk9FolJeXF1+qAMgRcnvDjyPkyDUAypUrl6H7z5UrV3T+/HmVK1fOSVEBAAAAABwhRyaqjRs31s6dOxUfH2/Zt3HjRhmNRjVo0MCJkQEAAAAAssvluv4mJiZq69atkqRTp07p6tWr2rhxoySpTp06CggIUI8ePbR48WINGzZMgwYNUkxMjKZOnaoePXpY1lAFAAAAAORMLteiGhsbqxEjRmjEiBH68ccfdebMGcv2oUOHJN2Y3XfhwoVyc3PTsGHDNG3aNHXp0kVjx451cvQAgPtt8ODBatGixW2PL168WCEhITp+/LhCQkIsX37ezeuvv66QkBBFRkZmKZ7k5GQtWLBAnTp1UvXq1RUaGqq2bdtqxowZVj2BbBEeHq5XX331jufEx8crJCREq1evvmdx3M3q1asVEhKiixcvOrRcAEDu5XItqiVKlNDBgwfvel758uW1YMGCex8QAORCJpNJRqNzvsvMat0RERF64YUX9Pvvvys0NDTD8fXr16tatWpZKjMtLU0bNmyQJEVHR2vYsGE2XXf9+nUNGDBAv/76q3r37q2RI0fK09NT+/fv1+LFi3XlyhWNHz/e5jjs5SpxAABgL5dLVAEAzmc0GjV76yKdjou5r/UW8wvSoCZPZumapk2bytvbW9HR0RkS1ZMnT2rv3r2aMGFClsrctWuXLly4oLCwMO3cuVP79u1TpUqV7nrdBx98oJ9//lnz5s1TWFiYZX+9evXUq1cv/fLLL1mKw16uEgcAAPYiUQUAZOp0XIyOxZ50dhh35eXlpaZNm2rDhg0aO3asVcvp+vXr5ebmptatWysxMdHmMqOjo5UvXz5NmTJFTZs21bp16+6aqCYlJWnZsmVq1qyZVXKYLk+ePKpfv75l+9SpU3r77be1Y8cOpaWlqWbNmnrppZcUEhJyx3pWrlypWbNmKTY2VtWqVdPo0aPtjiMhIUHvvvuuduzYobNnzyowMFANGzbUiy++KB8fH6vroqKitGDBAh0+fFje3t4KDQ3VK6+8ouLFi1vOOXv2rF566SX9/PPPKly4sIYOHaoOHTpYlfPdd98pMjJSBw8elLe3t1q2bKkxY8awvBsAwIrLjVEFACCr2rZtq3Pnzmn37t1W+6OjoxUWFqbAwECby7p+/bq++uorNW/eXEFBQWrYsKHWr18vk8l0x+v+/PNPJSQkqFGjRnet4+rVq+rTp4/++usvTZ48We+8844uXbqkJ554QmfOnLntdd9++63+85//qG7dupo5c6bq16+vESNG2B1HUlKS0tLSNGrUKM2ZM0cjRozQTz/9pKFDh1qdN3fuXI0ZM0aVKlXSzJkz9cYbb6h06dIZxqSOHj1aDRs2VGRkpCpWrKixY8fq8OHDluMbN27UkCFDFBwcrJkzZ+rFF1/U5s2b9fLLL981VgBA7kKLKgAgx2vQoIECAgK0fv16S2vh33//rb///lv9+/fPUllbtmzRtWvXFBERIelGEvztt99q9+7dVi2itzp37pwkqWjRonetY/Xq1Tp9+rTWr1+v8uXLS5Jq166txx57TAsXLrzt5IAfffSRatWqpbfeekuS1KhRI12/fl0ffvihXXEEBARo8uTJlu3U1FSVKFFCvXr10tGjR1W2bFlduXJFM2fOVPfu3a0mdmrWrFmG8nr37q3evXtLkqpXr66tW7dq06ZNGjp0qMxms6ZOnarWrVvrjTfesFxTqFAhDRw4UEOHDtVDDz1015gBALkDLapADhcZGanmzZtneWZS4EHi7u6uVq1a6auvvlJycrKkG91+vby81Lx58yyVFR0drcDAQEu32fDwcHl7e2vdunWWc1JTU63+3cxgMNy1jp9//lkPPfSQJUmVJH9/f4WFhWnPnj2ZXpOWlqZ9+/ZleD0tW7bM9Hxb4pBudOnt0KGDqlevrkqVKqlXr16SpH///VeStHfvXiUmJqpLly53Lathw4aW//f29laxYsV09uxZSdLRo0d16tQpPf7441bvXZ06dWQ0GvXnn3/aFC8AIHcgUQVysKSkJEVFRclkMikqKkpJSUnODglwmoiICMXFxWn79u2SbiSc4eHhypcvn81lxMfHa+vWrXrsscd07do1xcfHKyUlRY0aNdLmzZstSXClSpWs/klS4cKFJemOXXdvrqdgwYIZ9gcGBiouLi7Tay5evKjU1FQFBARY7b+1nKzEsXnzZo0ZM0ahoaF6//33tXLlSsuXXtevX5ckXb582arcO7l1XKuHh4flPbt06ZIkadiwYVbvXdWqVZWWlmZTvACA3IOuv0AOlpqaahk3ZzKZMrTsALlJjRo1VLx4ca1fv16BgYE6efJklsc+btq0SSkpKVq1apVWrVqV4fh3332nFi1aZHqscuXK8vb21vbt29W1a9c71uPn56ejR49m2B8bGys/P79MrwkICJC7u3uGcaEXLlywO46NGzeqYsWKVl16f/zxR6tz/P39Jd3oUlykSJE7lncn6eVMnDgx02WEbEmEAQC5B4kqAOCBYDAYFBERoUWLFilv3rzy9/e3aUKhm61bt07Fixe3jAG92fPPP69169apRYsWqlKlSobjefPmVc+ePfXJJ5/ohx9+UL169ayOX79+Xb/88ovq16+vmjVratOmTTpy5IjKlSsnSYqLi9POnTvVvXv3TGNzc3PTI488os2bN+upp56y7N+0aZPdcSQlJcnDwyPDe3Cz6tWry8vLS59//nmmCaatypUrpyJFiujEiROWcawAANwOiSrgIkwms4xG28aUPUh1A44UERGh2bNna/Xq1erevXuGJEySfvvttwz7ChYsqJIlS+qnn37SkCFDVLdu3UzLXrp0qa5cuZKhi2u6ESNG6I8//tDAgQPVu3dvhYWFycPDQwcOHNCnn36qxx57TPXr11enTp20YMECDRo0SCNHjlSePHn00Ucfyd3dXX379r3t6xs8eLCGDh2qcePGqXXr1tq3b5+++OILu+MICwvTq6++qsjISMvkR7t27bIqy8fHR8OGDdO7774rs9mspk2bymQyaffu3WrTpk2mSXtmDAaDxo4dq9GjRyshIUGPPvqovLy8dPr0aW3dulWjRo1S2bJlbSoLAPDgI1EFXITRaNAXK3frwvkrNl+TnGw9JnXxnO/k6Zk3S/UWLOSj9t0yfigHivkF5bg6g4ODFRISooMHD6pt27aZnjN//vwM++rXr6/GjRvLZDJlWPczXceOHbVgwQJt2rTpthML5cmTR/PmzdPSpUu1du1aLVu2TCaTSaVLl1b79u0tSWj+/Pm1ePFiTZkyRf/5z39kMplUo0YNLVmy5I6z9TZt2lSTJ0/WrFmztH79elWtWlXvv/9+hi6+tsbRo0cPnTx5UkuWLNG8efPUsGFDTZs2Td26dbMq75lnnlFAQIAWLFig1atXK1++fKpevXqWlv2RpMcff1y+vr6aNWuWpeW2ePHiatSoUaZjdoGcLDIy0jJZ2bBhw3JN3YCjGMxms9nZQTwI/vjjD0my+ZvlnOCvha8qMeb4fa2zQMU6Ktd2oCatfUfHYk/e17pLB5bQ5HYv3tc6bzUv8mvFnL5s8/kpqde17ef//9DduFY/ebjnyVKdQcX81X9YxmUm8OBLSkqyLEGSN6/1Fxwmk0lGo3Pm23Nm3XBNd7pXAVeUlJSktm3bWn6frVu37r7du86sG//vQcwN7jc+CQA5mNFglHSjy65Bhv9tA9nnzESRJBVATufMyQ6ZaBEPCj4NADmYm5uHShapLIMMKlGkstzcMo7HAwAAAHIaxqgCOVxwmYYKLtPQ2WEAAPBAMptMMjipp4cz6wacjUQVAAAAuA2D0aij0XOUGHvG5msSkq272x5YNlXenln72J2/eAWVCO8hZ83Jz3wBcDYSVQAAAOAOEmPPZGmCyaSUNOvtcydk8HDLUp15A4rIaDRq9tZFOh0XY/N1qddTrLbf2jBd7nmyNjSomF+QBjV5MkvXAI5GogoAAAC4qNNxMVlaCcGUbJ0kn7h4WkbPrCXJgCugPR8AAABwIDejwdJl12i4sX3fGA2yVG743zaQA5GoAgAAAA7k6WZUg1K+MhqksJK+8nS7fx+5je5G5Q8JlAxS/pBAGd35uI+cia6/AAAAgIO1qxiodhUDnVJ3gVpFVaBWUafUDTgKX7EAAAAAAFwKiSoAIEcbPHiwWrRocdvjixcvVkhIiI4fP66QkBBt3LjRpnJff/11hYSEKDIy0uZY+vTpo0GDBmX52J3MmDFDv/zyS6bHrl27ppkzZyoiIkJVq1ZVtWrV1KVLF33yySe6fv16luu6WxzVq1d3aJkAANwOiSoAIAOzyZRj6o6IiNCxY8f0+++/Z3p8/fr1qlatWpbWA0xLS9OGDRskSdHR0VmKx9FmzpypvXv3Zth/8eJF9ejRQwsWLFDLli310Ucf6cMPP9Rjjz2mjz/+WMuXL3dCtAAAOAZjVAEAGRiMRv360WxdPW37AveOkL9YUVUbkrVWx6ZNm8rb21vR0dEKDQ21Onby5Ent3btXEyZMyFKZu3bt0oULFxQWFqadO3dq3759qlSpUpbKuNcmT56sEydOaOXKlQoODrbsDwsLU+/evXXkyBEnRgcAQPaQqAIAMnX19BnFHzvm7DDuysvLS02bNtWGDRs0duxYq5bT9evXy83NTa1bt1ZiYqLNZUZHRytfvnyaMmWKmjZtqnXr1t2TRPXgwYOaOnWq9uzZIzc3NzVo0EBjx45VsWLFJEkhISGSpKlTp2rq1KmSpEWLFqlEiRLatGmTnnrqKaskNZ2/v79q1KghSTp37pzee+89/fjjjzp//ryKFCmiVq1a6dlnn5Wnp6flGpPJpIULF2rlypU6ceKE/Pz8VLNmTb3xxhvy8fGxivmVV17RX3/9pZIlS2rMmDFq1KiRVf2rV6/WJ598on///Vf+/v7q1KmTnnvuObm5sZYjAMA2dP0FAOR4bdu21blz57R7926r/dHR0QoLC1NgoO0zb16/fl1fffWVmjdvrqCgIDVs2FDr16+XycYuyWazWampqRn+mc1mq/POnDmjJ554QpcuXdI777yjyZMna9++fXriiSd09epVSdKKFSsk3RjfumLFCq1YsUKVKlXSzz//LLPZnCFBzMylS5fk7++vcePGae7cuRowYIDWrFmjSZMmWZ332muv6Z133tGjjz6qWbNmaeLEicqXL58SEhIs56SkpGj06NHq1KmTZs6cqYCAAD333HO6dOmS5ZxPPvlEEyZMUMOGDTVr1iw988wzWrRokd577z2b3j8AACRaVAEAD4AGDRooICBA69evV/369SVJf//9t/7++2/1798/S2Vt2bJF165dU0REhKQbSfC3336r3bt3W8q+k61bt9629fXRRx+1/P+CBQuUmpqq+fPny9/fX5JUsWJFtWnTRmvWrFGfPn1UrVo1SVLRokUt/y9JMTExlv13ExISojFjxli2a9SoIS8vL40dO1YTJ06Ul5eXjh49qmXLlmnUqFFWEz61bNnSqqz0RLVJkyaSpLJly6pp06batm2b2rdvr6tXr2r69OkaMGCAnn/+eUk3fjYeHh6aMmWK+vfvrwIFCtw1ZgAASFQBADmeu7u7WrVqpfXr12vixIny9PTU+vXr5eXlpebNm2eprOjoaAUGBiosLEySFB4eLm9vb61bt86SqKampmaoP13NmjU1bty4DOXe2oL5888/q27dupYkVZLKly+vhx9+WHv27FGfPn3uGqvBYLjrOWaz2dKl9+TJk1azAZ84cULBwcH64YcfZDab1aVLlzuWZTQarZL1EiVKKG/evJbEee/evUpISFCrVq2s3qOwsDAlJSXp0KFDqlOnzl1jBgCARBUA8ECIiIjQ0qVLtX37djVt2lTR0dEKDw9Xvnz5bC4jPj5eW7duVfv27XXt2jXL/kaNGmnz5s165ZVX5OnpmaHF9ODBg5b/9/HxUZUqVTKUfWsc8fHxqlixYobzAgMDFRcXd8c4g4KCJN3oPly2bNk7nrtw4UK9/fbbGjBggOrWrStfX1/98ccfevXVVy1J6+XLl+Xu7n7XLtJ58+a1GtcqSR4eHpZy0rsAd+zYMdPrz5y5v5NzAQByLhJVAMADoUaNGipevLjWr1+vwMBAnTx5Ui+//HKWyti0aZNSUlK0atUqrVq1KsPx7777Ti1atMj0WFb5+fkpNjY2w/7Y2FiVKVPmjtfWrl1bBoNB27dvt7T83s7GjRsVHh6uF154wbLv8OHDVuf4+/srNTVVsbGxWRrPeys/Pz9JN5bUKVKkSIbjJUqUsLtsAEDuQqIKAHggGAwGRUREaNGiRcqbN6/8/f1tmmzoZuvWrVPx4sX11ltvZTj2/PPPa926dWrRokWmLaZZVbNmTa1cuVJxcXGWBO/IkSM6ePCgOnfubDnv5hbLdMWKFVPLli21fPlyde7cWRUqVLA6Hh8fr8OHD6t69epKSkqSh4dHhtd5s3r16slgMOjzzz/XwIED7X5N1atXl5eXl86ePZvlLtcAANyMRBUAkKn8xe4+UY+r1RkREaHZs2dr9erV6t69e4YETZJ+++23DPsKFiyokiVL6qefftKQIUNUt27dTMteunSprly5YrVci72eeuoprV69Wv369dOQIUN0/fp1vf/++ypatKhV19ly5crpm2++Ua1ateTl5aWyZcsqf/78mjRpkp588kn17NlTffv2Vc2aNS2vb8mSJXrmmWdUvXp1hYWFadGiRVqyZInKlCmjtWvX6tgtyw6VLVtWPXr00AcffKC4uDjVr19fSUlJ+u677zR8+HBLV+O78fX11XPPPad33nlHZ8+eVZ06deTm5qYTJ07om2++0YwZM+Tl5ZXt9w4A8OAjUQUAZGA2mVRtyKC7n3iP6jYY7Vs9LTg4WCEhITp48KDatm2b6Tnz58/PsK9+/fpq3LixTCaTOnTokOl1HTt21IIFC7Rp06a7Tjpki6JFi2rx4sWaOnWqRo8eLaPRaFlHNX/+/JbzJk6cqDfffFPPPPOMkpKStGjRItWtW1cBAQFavny5FixYoA0bNujjjz+W0WhUhQoVNGDAAPXo0UOSNGzYMF26dEnTp0+XdGMm3wkTJmjw4MFW8UycOFElSpTQZ599poULF8rf31+1a9fO0hhfSerXr5+CgoL0ySefaMmSJXJ3d1epUqX06KOPZvrFAQAAmTGYb13YDXb5448/JMkh3cFcxV8LX1VizPH7WmeBinVUru1ATVr7jo7FnryvdZcOLKHJ7V68r3Xeal7k14o5ffm+1hlUzF/9hzW7r3XCNSQlJeno0aMqW7as8ubN6+xwgNviXoWz8ZkIWfUg5gb3m90tqteuXdORI0d06dIlGQwGFShQQGXKlLH6FhgAAAAAgKzKUqJ64sQJRUVF6ZtvvtGhQ4dkMpmsjqd3OWrWrJk6dOigkiVLOjRYAAAAAMCDz6ZE9Z9//tH06dO1efNm+fr6qk6dOmrVqpVKliwpX19fmc1mxcfH6+TJk9q3b5+WLFmiDz/8UM2bN9eIESNUvnz5e/06AAAAAAAPCJsS1fbt26tJkyaaPXu2wsLC5O5+58tSU1O1c+dOLV++XO3bt9eff/7pkGABAAAAAA8+mxLVtWvXZqlV1N3dXY0bN1bjxo0zLCoOAAAAAMCd2DT/f3a67tLtFwAAAACQFQ5bR9VsNuuHH35QcnKyatasyey/AAAAAAC72LWi+nvvvac+ffpYts1ms/r166d+/fpp0KBBateunY4fv79rTQEApMjISDVv3lyRkZHODgUAAMBudiWqmzZtUmhoqGV748aN2rVrl0aOHKnZs2crLS1NM2bMcFiQAIC7S0pKUlRUlEwmk6KiopSUlOTskAAAAOxiV9ffmJgYlS5d2rK9efNmVahQQYMGDZIk9ezZU8uWLXNMhAAAm6SmplrWtzaZTEpNTXVyRPfPjBkzNH/+fO3du/eux0JCQizHPDw85O/vr5CQELVq1UodOnSQh4eH5fju3bv15JNPZlrnrl27FBAQoNWrV2vcuHEZjj/zzDMaPXp0dl8aAAC5kl2Jqru7u5KTkyXd6Pa7a9cudejQwXI8MDBQly5dckiAAID7z2Qyy2g0PLB19+nTRxEREUpNTdW5c+e0fft2vfLKK/rss880f/78DPMsvPXWWypXrpzVPl9fX6vtuXPnysfHx7IdFBR0714AAAAPOLsS1Yceekhr165V27ZttXnzZl2+fFlNmjSxHD99+rQKFCjgsCABAPeX0WjQFyt368L5K/e13oKFfNS+W917Xk/RokVVrVo1y3br1q31+OOPa9CgQZoyZYpef/11q/MfeughValS5Y5lVqpUSQEBAfciXAAAch27EtVhw4Zp8ODBqlevniSpRo0alv+XpK1bt971DzoAwLVdOH9FMacvOzuM+6Zx48Zq0aKFoqKiNHbsWGavBwDAiexKVBs0aKA1a9Zox44d8vX1VevWrS3H4uLiVKtWLTVt2tRhQQIAYIvMxuWmj9u1RcOGDbVx40b99ddfqlOnjlUZN5dtNBplNFrPRxgREaFLly6pWLFi6tatmwYMGCA3Nzc7XgUAALB7HdUKFSqoQoUKGfb7+flp/Pjx2QoKAICsSkhIUKVKlTI95u3tbVMZRYoUkSRduHDBan+3bt2strt06aI33nhDklSoUCENHz5cVatWlcFg0JYtW/T+++8rJiZGEydOzOrLAAAAsjFRTUxMlJeXl10VZOdaAABslTdvXi1ZsiTD/pUrVyo6OtqmMsxmc6b73377bZUvX96yffNY1EaNGqlRo0aW7YYNGypPnjxauHChBg8erMKFC9v6EgAAwP/YlKg++uij6tOnj7p162bzH9yYmBgtX75cS5cu1e7du7MVJADkJiaTKUO30vvFbDbLYHDObL/ZZTQaM50f4bvvvrO5jJiYGEk3WklvVr58+SzNvfD4449r/vz52r9/P4kqAAB2sClRnTRpkmbOnKkPP/xQNWrUUP369VWpUiWVKFFCvr6+MpvNio+P18mTJ/Xnn39q586d+u2331S6dGlNmjTpXr8GAHigGI1Gzd66SKfjYrJ0Xer1FKvttzZMl3sej9ucLeV391bDoJqKiT8v9yQPebq5q5BPQbtiflBs375dnp6et+1CDAAA7g+bEtXWrVurVatW2rJli1avXq1Zs2YpJSUlw7fuZrNZHh4eatCggaZPn67w8HCntQoAQE52Oi5Gx2JPZukaU3Ka1faJi6dl9Lz9ZD4F8vgqtVCaUtJSZErNvMtrbrJt2zZt3rxZXbt2tXlM6+18+eWXcnNz0yOPPOKg6AAAyF1snkzJaDSqWbNmatasmZKTk/Xnn3/qyJEjunz5siTJ399f5cqVU+XKleXp6Xmv4gUAINvOnDmjX3/9VampqTp//ry2b9+uL774QlWrVtWYMWOyVFb//v1Vt25dhYSESJK++eYbrVy5Uk8++WSGLsQAAMA2ds366+npqRo1aqhGjRqOjgcA4CIKFvJ5YOtcvHixFi9eLA8PD/n7+yskJESTJ09Whw4d5O6etT+NZcuW1eeff66zZ8/KZDKpTJkyGj9+vPr06XOPogcA4MFn9/I0AIDMRUZGKioqSh06dNCwYcOcHY5dTCaz2ner67S6jcasTeg0fPhwDR8+3KZjBw8etLncunXr3vX8CRMm2FweAACwDQNIAcCBkpKSFBUVJZPJpKioKCUlJd2/yo0GKT2/M/xv296isnFtdjmzbjwYIiMj1bx5c0VGRjo7FACAnUhUAcCBUlNTZTKZJN1YZiY1NfW+1W10Nyp/SKBkkPKHBMrozq945D5O/bIIAOAwdP0FgAdIgVpFVaBWUWeHAejcuXO6fPmy/P397+tass78sggA4Dh83Q4AABzKZDLp8uXLMpvNunz5siVxBADAViSqAADAocxms8xmc4b/BwDAVnYnqqdPn9bEiRPVsmVL1alTRz/99JMk6eLFi3r99df1119/OSxIALjfzLmgBej/EwknBwJk4ubk9n4nurQAA4Dz2TVG9Z9//lHv3r1lMpkUGhqq48ePW8aABAQEaM+ePUpISNCbb77p0GAB4H4xGI06Gj1HibFnsnRdQrL1eLgDy6bK29P2X7V+ZSureONOWarTXtdSk5Salqq05FS5ZyFG4H4wGAw6f+WCktNSlXglQbFXL+mzzRuUZk6743Wp11Ostt/aMF3ueTxsrreYX5AGNXnSrpgBAI5j1yeTd955Rz4+Plq5cqUkKSwszOp4kyZNtGHDhuxHBwBOlBh7Rokxx7N0TVKK9YfopHMnZPBws/n6vAFFslRfdqSYUrQv9pDyeORRgAJk8DIwQyqsmM1mGQxZXy4oLS3NqlUyKSlJbm62PwfpriUkKikxURdjL+rXc3/pyIVjd73GlGz9DJ64eFpGz6zXDQBwLrsS1Z9++knDhg1TQECALl26lOF4sWLFFBMTk+3gAAD31s/n/pAkVUp5SHk98ijx4lUnRwRXk5p4Rea0O7di3spkli4nXLdsX79yOUvL+hrcPeWe11uXE+KUlHJd+2IPWe5VAEDuYFeiajablTdv3tsev3jxojw9Pe0OCgBw//x87g/9dmG/HipUVs+FD3B2OHAx/6yJVHIWu8AnpaZp1g//f83wekWV1932Vk3f8lVU8rHumr5lrg6dP6oUUxaWmDEaJIMks278NysZMgDAZdiVqD7yyCPaunWrevfuneFYamqq1q9fr6pVq2Y7OADIadyMBstnZKPhxnZOkGJK1bXUxDt+CYncyZh8TUq4nKVrzClpOn/+/P9vX/OSstAF3i31uvLmzatrqYlZS1IlGd2Nyh8SqKsHY5U/JFBGdxY4AICcyK7f3gMHDtT27ds1adIkHTp0SJIUGxurnTt3ql+/fjpy5IgGDhzo0EABICfwdDOqQSlfGQ1SWElfebrxIRm5T/oXNpJzvrApUKuoSvaurAK1it7XegEAjmNXi2qTJk301ltv6c0337RMqPTiiy/KbDYrf/78evvtt1W7dm2HBgoAOUW7ioFqVzHQ2WEATpP+hc3OE/F8YQMAsIvd6xF06NBBLVq00I4dO3Ts2DGZTCaVKlVKDRs2VP78+R0ZIwAAyGH4wgYAkB3ZWjjP29tbzZs3d1QsAAAAAABkL1FNSUlRTEyM4uPjZTabMxyvVKlSdooHAAAAAORCdiWq8fHxevvtt7Vu3TqlpKRkOJ6+QPj+/fuzHSAAAAAAIHexK1EdO3asvv32W7Vu3VpVq1aVj4+Po+MCAAAAAORSdiWqO3bsUJ8+fTR+/HhHxwMAAAAAyOXsmi/e399fpUuXdnQsAAAAAADYl6h269ZN69evl8lkcnQ8cIDIyEg1b95ckZGRzg4FAAAAALLMrq6/w4YNU3Jysjp37qz27dsrKChIbm5uGc5r0aJFtgNE1iQlJSkqKkomk0lRUVHq37+/8ubN6+ywAAAAgHsqMjJSUVFR6tChg4YNG+bscJBNdiWqMTEx2r17t/bv33/bmX2Z9dc5UlNTLS3dJpNJqampTo4IAAAAuLdorHnw2JWojh8/Xvv27dOgQYMUGhrKrL8AAAAAnIbGmgePXYnqnj179Mwzz+i5555zdDy4iclkltFocHYYAAAAAHBf2ZWoFixYUH5+fo6OBbcwGg2KXLZDp87F2XxNakqS1fars76Su0fWuj1UDSmm7q2qZekaAAAAAHAUuxLVp59+WsuXL1eXLl2UL18+R8eEm5w6F6d/T12y+XxT6nWr7eOnL8vonidLdRYr5Jul8wEAAADAkexKVJOTk+Xu7q4WLVro8ccfV5EiRTLM+mswGPTUU085IkZkgcHoJskgySzJ8L9tAAAAIGdw5vA3ht65DrsS1bffftvy/0uWLMn0HBJV5zAY3eVduKISzu2Xd+GKMhjt+hEDAAAATmE0GvTFyt26cP6KzdckJ1sPf1s85zt5emZt+FvBQj5q361ulq7BvWNXFvPNN984Og44kE/JevIpWc/ZYQAAAAB2uXD+imJOX7b5/JRbhr+dOxsnD/ek25yNnMCuRLV48eKOjgMAAAAAAEmS0dkBAAAAAEB2GA1G3ZinRTLI8L9t5GQ2taiGh4fLaDRqw4YN8vDwUHh4uAyGOw8yNhgM+vrrrx0SJAAAAADcjpubh0oWqayTZ/9UiSKV5ebm4eyQkE02Jap16tSRwWCQ0Wi02gYAAAAAVxBcpqGCyzR0dhhwEJsS1SlTpuinn35SXFycAgICNGXKlHsdFwAAAAAgl7K58/aTTz6pHTt23MtYAAAAAACwPVE1m833Mg4AAAAAACQx6y8AAAAAwMVkKVFlAiUAAAAAwL1m02RK6V588UW9+OKLNp1rMBj0119/2RUUAAAAACD3ylKiGhYWpjJlytyjUAAAAAAAyGKi2qFDB7Vt2/ZexQIAAAAAAJMpAQAAAABcC4kqAAAAAMClkKgCAAAAAFyKzWNUDxw4cC/jAAAAAABAEi2qAAAAAAAXQ6IKAAAAAHApJKoAAAAAAJdCogoAAAAAcCkkqgAAAAAAl2LzrL83++mnn+543GAwyNPTU0WKFFHhwoXtCgwAAAAAkDvZlaj26dNHBoPBpnNLly6t5557Tq1bt7anKgAAAABALmNXojp37ly9++67Sk5OVrdu3VSqVClJ0rFjx/TZZ58pb968GjJkiE6dOqUVK1bohRdekNFoVKtWrRwaPAAAAADgwWNXorp9+3blyZNHK1eulKenp9WxXr16qU+fPvr111/14osvqmfPnurcubPmzJlDogoAAAAAuCu7JlNat26dIiIiMiSpkpQnTx61bdtWUVFRlu127drp8OHD2QoUAAAAAJA72JWoJiYm6sKFC7c9fv78eSUkJFi2fXx8ZDQywTAAAAAA4O7syh7r1q2rRYsW6dtvv81wbMuWLVq0aJHq1q1r2bd//34VL17c/igBAAAAALmGXWNUJ06cqCeffFJDhw5VUFCQSpYsKUk6ceKEYmJiVKxYMf3nP/+RJF2/fl1nzpxR165dHRc1AAAAAOCBZVeiWqxYMa1bt07Lly/X999/r1OnTkmSypcvr759+6p79+7y9vaWdGOM6pw5cxwXMQAAAADggWZXoipJXl5eevrpp/X00087Mh4AAAAAQC7HDEcAAAAAAJdid4vq9u3btWrVKp04cULx8fEym81Wxw0Gg77++utsBwgAAAAAyF3sSlTnzp2radOmKTAwUKGhoQoJCXF0XAAAAACAXMquRHXRokWqV6+ePv74Y3l4eDg6JgAAAABALmbXGNX4+Hi1bNmSJBUAAAAA4HB2JapVqlTR0aNHHR0LAAAAAAD2JaqvvPKKNm/erHXr1jk6HgAAAABALmfXGNWRI0cqNTVVL730kl555RUVKVJERqN1zmswGLR27VqHBAkAAAAAyD3sSlT9/f3l7++v0qVLOzqe++rSpUt68cUXdeLECXl6eqpy5cqaPHmyPD09nR0aAAAAAORadiWqixcvdnQcTmEwGDRo0CDVrl1bJpNJo0eP1pIlS9SvXz9nhwYAAAAAuZZdY1TvpWPHjmnixIlq3769HnnkEUVERGR63uHDh/X000+rWrVqatCggaZOnark5OQs1eXv76/atWtLkoxGoypXrqzTp09n+zUAAAAAAOxnU4vqTz/9JEmWpC59+27Sz8+KQ4cOaevWrapatapMJpPMZnOGc+Li4tS3b1+VKVNGM2bMUExMjKZMmaKkpCRNnDgxy3VK0vXr17VmzRq9+OKLdl0PAAAAAHAMmxLVPn36yGAw6LfffpOnp6dl+3bMZrMMBoP279+f5YDCw8PVrFkzSdLYsWP1559/Zjhn+fLlunbtmmbOnCl/f39JUlpamiZPnqxBgwYpKChIktSxY8dMW0grV66sefPmWbZNJpPGjBmjunXrqnHjxlmOGQAAAADgODYlqosWLZIkyyRD6dv3wq2zB2dm27Ztql+/viVJlaTHH39ckyZN0o4dO9SpUydJ0po1a2yqc/LkyTIajRo/frxdMaczm81KSEjIVhnpDAaDvLy8HFIWsiYxMTHTlvx7yRV+3s543a7KFX4ezsS9AInngOcAEs8Bn4nsl95wB/vZlKjWqVPnjtv325EjR9S5c2erfb6+vipUqJCOHDmSpbKmTp2qs2fPaubMmTYlyXeSkpJiVytyZry8vPTII484pCxkzdGjR5WYmHhf63SFn7czXrercoWfhzNxL0DiOeA5gMRzwGei7GElkeyxa9ZfZ4uPj5evr2+G/X5+foqLi7O5nEOHDmnevHkqV66cunTpIkkKCwvTmDFj7IrLw8NDFSpUsOvaW/ENjPOULVvWKd8eOpszXrercoWfhzNxL0DiOeA5eDDMmTNH0dHRioiI0DPPPJPl63kO+Exkr3/++cdB0eReNiWq48aNy3LBBoNBb775Zpavu58eeughHTx40GHlGQwGeXt7O6w8OIezu5s4S2593ciIewHgOXgQJCUlKTo6WiaTSdHR0Ro0aJDy5s3r7LBylNz6HDjidbtCwp3T2ZSo7t69O8O+pKQkXbx4UdKNlkxJltbMgICAe3pj+/r66sqVKxn2x8XFWWIBAABA7pWamiqTySTpxsSZqampTo4IQFbYlKhu2bLFavuff/5Rv379NGjQIPXt21cBAQGSpIsXL2rhwoWKiorSxx9/7Pho/6dcuXIZxqJeuXJF58+fV7ly5e5ZvQAAAACAe8+u2YNee+01NW7cWKNGjbIkqdKNltRRo0apUaNGeu211xwW5K0aN26snTt3Kj4+3rJv48aNMhqNatCgwT2rFwAAAABw79mVqP722293nI2rYsWK+u233+wKKDExURs3btTGjRt16tQpXb161bKd3tW4R48eypcvn4YNG6bvv/9en3/+uaZOnaoePXpY1lAFAADAg8FkYmIrILexa9ZfPz8/bdu2Tb169cr0+LZt2+Tj42NXQLGxsRoxYoTVvvTtRYsWqW7duvLz89PChQv12muvadiwYcqXL5+6dOmiUaNG2VUnAAAAXJfRaFDksh06dc721R1SU5Kstl+d9ZXcPbI2mVLVkGLq3qpalq4B4Bh2Jardu3fX9OnTNWTIEPXp00elSpWSJB07dkyLFy/Wtm3bNHz4cLsCKlGihE0z8ZYvX14LFiywqw4AAADkLKfOxenfU5dsPt+Uet1q+/jpyzK658lSncUKZVwOEcD9YVeiOnToUCUnJ2vevHn67rvvrI65ublp4MCBGjp0qCPiAwAAAADkMnYlqpI0cuRIPfnkk9q1a5dOnTolSSpevLjq169vNcESAAAAcL8ZjG6SDJLMkgz/2waQU9idqEo3Zvlt06aNo2IBAAAAHMJgdJd34YpKOLdf3oUrymDM1sdeAPdZtp/Yq1ev6urVq5YFlW9WrFix7BYPAAAA2MWnZD35lKzn7DAA2MHuRHXp0qVasGCBTpw4cdtz9u/fb2/xAAAAAIBcyq51VJctW6ZXX31VpUqV0siRI2U2m9W3b18NHDhQBQsW1MMPP6w33njD0bECAAAAAHIBuxLVJUuWqGHDhpo7d666desmSWrSpIlGjRqlL7/8UteuXdPly5cdGScAAAAAIJewK1E9fvy4HnvsMUmSh4eHJCklJUWS5OPjoy5dumjp0qUOChEAAAAAkJvYlaj6+PgoLS1NkpQ/f355eXnp7NmzluP58uXThQsXHBMhAAAAACBXsStRfeihh3TgwAHLdtWqVbVs2TLFxMTozJkzWrFihcqUKeOoGAEAAAAAuYhdiWq7du106NAhJScnS5KGDx+uw4cP69FHH1V4eLiOHj2qkSNHOjJOAABuKzIyUs2bN1dkZKSzQwEAAA5g1/I0nTt3VufOnS3bNWvW1Pr16/XNN9/I3d1dDRo0UNmyZR0WJAAAt5OUlKSoqCiZTCZFRUWpf//+yps3r7PDAgAA2WD3Oqq3KlmypJ566ilHFQcAgE1SU1NlMpkkSSaTSampqU6OCAAAZFe2EtUTJ05o27ZtOn36tCSpePHiatSokUqWLOmQ4AAAAAAAuY/dieqUKVO0aNEiy7fY6YxGo/r27asxY8ZkOzgAAAAAQO5jV6I6f/58LViwQC1btlS/fv1Uvnx5SdLhw4e1YMECLViwQEFBQXQFBgAAAABkmV2J6sqVKxUeHq4PPvjAan/VqlX13nvv6fr161q+fDmJKgAAAAAgy+xanubUqVNq2LDhbY83bNhQp06dsjsoAAAAAEDuZVeiGhgYqAMHDtz2+IEDBxQQEGB3UAAAAACA3MuuRLVVq1ZatWqVPv74YyUkJFj2JyQk6OOPP9aqVavUunVrhwUJAAAAAMg97BqjOmLECO3fv1///e9/NX36dBUuXFiSdO7cOaWmpqpu3bp67rnnHBooAAAAACB3sCtR9fLy0sKFC/X1119braPasGFDNWnSROHh4TIYDA4NFAAAAACQO9i9jqokNWvWTM2aNcuw/5dfftGPP/6owYMHZ6d4AAAAAEAuZNcY1bvZvXt3hqVrAAC4E5PJnCvrBgAAGWWrRRUAAEcxGg36YuVuXTh/JUvXJScnWW0vnvOdPD3z2nx9wUI+at+tbpbqBAAA9xaJKgDAZVw4f0Uxpy9n6ZqU1OtW2+fOxsnDPek2ZwMAgJzgnnT9BQAAAADAXiSqAAAAAACXYnPX39dff93mQv/880+7ggEAAAAAwOZEdcmSJVkqmHVUAQAAAAD2sDlRPXDgwL2MAwAAAAAASYxRBQAAAAC4GBJVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUA5GhGg1HSjZnmDTL8bxsAAORk/DUHAORobm4eKlmksgwyqESRynJz83B2SAAAIJtsXp7mds6dO6eLFy+qVKlS8vb2dkRMAABkSXCZhgou09DZYQAAAAexu0X166+/VqtWrdSkSRN17NhRv/32myTp4sWL6tChg77++muHBQkAAAAAyD3sSlS3bNmi4cOHq0CBAho2bJjMZrPlWEBAgIKCgvT55587LEgAAAAAQO5hV6IaGRmpWrVqadmyZerdu3eG49WqVdP+/fuzHRwAAAAAIPexK1E9dOiQHn/88dseL1iwoGJjY+0OCgAAAACQe9mVqHp5eSkxMfG2x0+cOCF/f397YwIAAAAA5GJ2Jap169ZVVFSUUlNTMxw7f/68Vq5cqYYNmX0RAAAAAJB1diWqI0eO1NmzZ9WlSxetWLFCBoNB33//vd577z21bdtWZrNZw4YNc3SsAAAAAIBcwK5EtVy5clq6dKn8/f31wQcfyGw2a968eZo9e7aCg4O1dOlSlShRwtGxAgAAAAByAXd7L3zooYe0YMECxcXF6dixYzKbzSpZsqQCAgIcGR8AAAAAIJexO1FN5+fnp9DQUEfEAgAAAACAbYlqVFSUXYV36NDBrusAAAAAALmXTYnq2LFjs1ywwWAgUQUAAAAAZJlNieo333xzr+MAAAAAAECSjYlq8eLF73UcAAAAAABIsnN5GgAAAAAA7hW7Z/09f/68Vq1apb/++ktXrlyRyWSyOm4wGLRw4cJsBwgAAAAAyF3sSlQPHDigJ598UklJSSpbtqz+/vtvVahQQfHx8YqJiVGpUqVUpEgRR8cKAAAAAMgF7Or6O23aNHl7e2vjxo365JNPZDabNX78eG3dulXvvfee4uLiNHr0aEfHCgAAAADIBexKVH/55Rd1795dxYoVk9F4owiz2SxJevzxx9W2bVtNnTrVcVECAAAAAHINuxJVk8mkggULSpJ8fX3l5uamy5cvW46HhIRo3759DgkQAAAAAJC72JWolihRQidPnrxRgNGoEiVKaNeuXZbjv/zyi3x8fBwTIQAAAAAgV7FrMqWGDRtq48aNGjVqlCSpZ8+emjJlik6cOCGz2awff/xRTz/9tEMDBQAAAADkDnYlqoMHD1abNm2UkpIiDw8P9e3bVwkJCfrqq69kNBo1dOhQDRo0yNGxAgAAAAByAbsSVT8/P/n5+Vm2DQaDhg4dqqFDhzosMADIjsjISEVFRalDhw4aNmyYs8MBAABAFtg1RjU1NVVXr1697fGrV68qNTXV7qAAIDuSkpIUFRUlk8mkqKgoJSUlOTskAAAAZIFdierrr7+uHj163PZ4+phVAHCG1NRUmUwmSTdmKeeLMwAAgJzFrkR1+/btatmy5W2Pt2zZUtu2bbM7KAAAAABA7mVXonru3DkFBQXd9njhwoUVExNjd1AAAAAAgNzLrkTV399fR48eve3xw4cPK3/+/HYHBQAAAADIvexKVBs1aqTly5frr7/+ynBs3759WrlypRo3bpzt4AAAAAAAuY9dy9OMGDFC27dvV9euXRUeHq4KFSpIkg4dOqRvv/1WAQEBGjFihEMDBZD7mExmGY0GZ4cBAACA+8yuRDUoKEiff/65pk2bpm+++UabN2+WJOXPn19t27bVqFGj7jiGFQBsYTQaFLlsh06di8vSdakp1svRvDrrK7l75LX5+qohxdS9VbUs1QkAAADHsStRlW5MmPT222/LbDbr4sWLkqSAgAAZDLR+AHCcU+fi9O+pS1m6xpR63Wr7+OnLMrrnsfn6YoV8s1QfAAAAHMuuMao3MxgMCgwMVIECBXTx4kWZzWZHxAUAAAAAyKVsTlSPHj2qqKgoxcVZd8G7cuWKXnrpJVWtWlUNGzZUvXr1tGTJEocHCgAAAADIHWxOVD/55BN98MEH8vW17hI3ceJErV27VsWKFVPz5s3l6empN954Q19//bXDgwUAAAAAPPhsHqP6yy+/6NFHH7Uag3rmzBlt2LBB1apV05IlS+Tu7q74+Hh16dJFn376qZo1a3ZPggaAOzEY3SQZJJklGf63DQAAgJzC5hbVmJgYlStXzmrft99+K4PBoCeffFLu7jdyXl9fX7Vv3z7TNVYB4H4wGN3lXbiiJIO8C1eUwWj3vHEAAABwAps/vZlMJksymm7Pnj2SpDp16ljtL1KkiK5du+aA8ADAPj4l68mnZD1nhwEAAAA72NyiWqpUKf3222+W7bS0NO3evVvlypVTwYIFrc6Ni4tTQECA46IEAAAAAOQaNreodujQQe+8847KlSunGjVqaO3atYqNjVWfPn0ynPvzzz+rTJkyjowTAAAAAJBL2Jyo9urVS7t27dJ///tfGQwGmc1m1a5dW/369bM678yZM9q2bZtGjhzp6FgBAAAAALmAzYmqh4eHZs2apT/++EMnTpxQsWLFVK1atQznJScna9q0aapdu7Yj4wQAAAAA5BJZngqzSpUqqlKlym2Ply5dWqVLl85WUAAAAACA3MvmyZTuJC4uTk8++SRL0gAAAAAAss0hiWpKSop+/PFHxcXFOaI4AAAAAEAu5pBEFQAAAAAARyFRBQAAAAC4FIckqnnz5lXHjh1VuHBhRxQHAAAAAMjFsjzrb2by58+vt956yxFFAQAAAAByObsS1dOnT9/xuMFgUJ48eVSgQAEZDAa7AgMAAAAA5E52Jarh4eE2JaB58uRRzZo1NXToUNWsWdOeqgAAAAAAuYxdieobb7yhxYsX68yZM2rbtq1Kly4tSTp27JjWrVun4sWLq1OnTjp27JjWrl2rvn37au7cuapXr55DgwcAAAAAPHjsSlTPnTunlJQUbd68Wb6+vlbHnn32WfXq1UtJSUl6+eWXNXToUHXu3FmRkZEkqgAAAACAu7Jr1t/ly5era9euGZJUSfL391fXrl316aefSpIKFCigTp066c8//8xepAAAAACAXMGuRPXy5ctKTEy87fGEhARdvHjRsl2oUCF7qgEAAAAA5EJ2JapVqlTRokWLdPDgwQzHDhw4oCVLlig0NNSy7/DhwwoKCrI/SgAAAABArmHXGNUJEyaob9++6tixo6pVq2Y1mdKvv/6q/Pnz6+WXX5YkXb9+XT/++KNatmzpuKgBAAAAAA8suxLVhx9+WGvXrtWcOXO0fft2/fHHH5KkYsWKqVevXhowYICKFCki6cYSNVFRUQ4LGAAAAADwYLMrUZWkoKAgTZgwwZGxAAAAAABgf6Ka7tq1azp79qwkqUiRIsqXL1+2gwIAAAAA5F52J6q///673nnnHf3yyy8ymUySJKPRqJo1a+rFF19UlSpVHBYkAAAAACD3sCtR/e2339SnTx95eHioS5cuKl++vKQbs/uuX79eTzzxhBYvXmw18y8AAAAAALawK1F97733FBQUpKVLl2ZYI3X48OHq2bOn3nvvPX3yyScOCRIAAAAAkHvYtY7qb7/9pu7du2dIUiWpYMGC6tatm3799dfsxgYAAAAAyIXsSlSNRqPS0tJue9xkMslotKtoAAAAAEAuZ1c2Wb16dX366ac6depUhmOnT5/W0qVLVaNGjWwHBwAAAADIfewao/r888+rd+/eevzxx9W8eXOVKVNGknT06FF98803cnNz0wsvvODIOAEAAAAAuYRdieojjzyizz77TO+99562bNmixMRESZKXl5caNWqkkSNHqkKFCg4NFAAAAACQO9i9jmqFChUUGRkpk8mkixcvSpICAgJkNBqVkJCgmJgYBQUFOSxQAAAAAEDukO0Zj4xGowoWLKiCBQtaJlBauHChHn300ewWDQAAAADIhZiaFwAAAADgUkhUAQAAAAAuhUQVAAAAAOBSSFQBAAAAAC7F5ll/9+3bZ3Oh586dsysYAAAAAABsTlQ7d+4sg8Fg07lms9nmcwEAAAAAuJnNiepbb711L+MAAAAAAEBSFhLVjh073ss4AAAAAACQxGRKAAAAAAAXQ6IKAAAAAHApJKoAAAAAAJdCogoAAAAAcCkkqgAAAAAAl0KiCgAAAABwKSSqAAAAAACXYvM6qg8ik8mknj17KikpSSaTSWXLltWbb76p/PnzOzs0AAAAAMi1cnWLqtFo1Lx58/TFF19o3bp1Klq0qObOnevssAAAAAAgV3O5RPXYsWOaOHGi2rdvr0ceeUQRERGZnnf48GE9/fTTqlatmho0aKCpU6cqOTk5y/Wlt56aTCYlJibKYDBkK34AAAAAQPa4XNffQ4cOaevWrapatapMJpPMZnOGc+Li4tS3b1+VKVNGM2bMUExMjKZMmaKkpCRNnDgxy3U+9dRT2r9/v4KDgzVmzBhHvAwAAAAAgJ1cLlENDw9Xs2bNJEljx47Vn3/+meGc5cuX69q1a5o5c6b8/f0lSWlpaZo8ebIGDRqkoKAgSVLHjh11+vTpDNdXrlxZ8+bNs2wvWLBAaWlpevfdd7V06VI988wz9+CVAQAAAABs4XKJqtF4997I27ZtU/369S1JqiQ9/vjjmjRpknbs2KFOnTpJktasWWNzvW5uburYsaOef/55ElUAAAAAcCKXS1RtceTIEXXu3Nlqn6+vrwoVKqQjR47YXM7FixclSQEBATKbzdq0aZMeeughu+Mym81KSEiw+/qbGQwGeXl5OaQsZE1iYmKmXc7vJVf4eTvjdd+JK7wnuRXPACTX+Jk4E/eD68jt96Iz8ffAfmazmblvsilHJqrx8fHy9fXNsN/Pz09xcXE2lxMbG6sxY8YoJSVFklShQgVNmDDB7rhSUlK0f/9+u6+/mZeXlx555BGHlIWsOXr0qBITE+9rna7w83bG674TV3hPciueAUiu8TNxJu4H15Hb70Vn4u9B9nh6ejogmtwrRyaqjvLQQw9p9erVDivPw8NDFSpUcEhZfAPjPGXLlnXKt4fO5ozXfSeu8J7kVjwDkFzjZ+JM3A+uI7ffi87E3wP7/fPPPw6KJvfKkYmqr6+vrly5kmF/XFyc/Pz8nBDRDQaDQd7e3k6rH47h7O4mzpJbXzcyyq33Qm593cgc9wOQe58DR7xuV0i4czqXW0fVFuXKlcswFvXKlSs6f/68ypUr56SoAABAbhcZGanmzZsrMjLS2aEAQI6WIxPVxo0ba+fOnYqPj7fs27hxo4xGoxo0aODEyAAAQG6VlJSkqKgomUwmRUVFKSkpydkhAUCO5XJdfxMTE7V161ZJ0qlTp3T16lVt3LhRklSnTh0FBASoR48eWrx4sYYNG6ZBgwYpJiZGU6dOVY8ePSxrqAIAANxPqampMplMkiSTyaTU1FQnRwQAOZfLJaqxsbEaMWKE1b707UWLFqlu3bry8/PTwoUL9dprr2nYsGHKly+funTpolGjRjkjZAAAAACAA7lcolqiRAkdPHjwrueVL19eCxYsuPcBAQAAAADuqxw5RhUAAAAA8OAiUQUAAAAAuBQSVQAAHlAslQIAyKlIVAEAeACxVAoAICcjUQUA4AHEUikAgJyMRBUAAAAA4FJIVAEHYBwYAAAAn4ngOCSqQDYxDgwAHhx+Xj4y/6/LtDM4s24gu/hMBEdyd3YAQE7HODAAeHB4e3rJYDTq149m6+rpM1m6NvGW3/+73pgiL3fbP2rlL1ZU1YYMylKdgCvhMxEciUQVAAAXZjKZZTQanB1GrnP19BnFHzuWpWuSbmkNvXLihFKMdF4DAHuQqAIA4MKMRoMil+3QqXNxWbouNcW6y92rs76Su0dem6+vGlJM3VtVy1KdAAA4CokqAAAu7tS5OP176lKWrjGlXrfaPn76sozueWy+vlgh3yzVBwCAI9EfBQAAAADgUkhUAQAAAAAuhUQVAIAHkMHoJil9EibD/7YB4O6cvUwTIDFGFbBI/6VsYIZGAA8Ag9Fd3oUrKuHcfnkXriiDkT/5AGxj7zJN2V2iSZIKhVZRSNfOWboGDyb+agH/wy9lAA8an5L15FOynrPDAJBDZXWZJkcs0ZSvaNEsnY8HF4kqcAt+KQMA7JHe2dr8v//S2RoA7EcfRwAAAAfwMBpVPZ+PDJKq5/ORB0NJAMButKgCAAA4yGP+BfSYfwFnhwEAOR5f9QEAHCIyMlLNmzdXZGSks0MBADiB9VzjdH9H9pCoAgCyLSkpSVFRUTKZTIqKilJSUpKzQwIA3Gd0f4cj0fUXyCYmz8CDIjtLNKWmpsr0v4nFTCaTUm+ZDRsAkDvQ/R2OQqIKZFP6t4d7r13h20PkaPYu0SRJyWlpli9sjJJ+fPNtebrZ/rUNSzQBAICbkagCDsC3h3iQZHWJpnTpX9hUy+ejpJMnlZXOvyzRBAAAbkaiCgBwCL6wAQAAjkIfRQAAAACASyFRBQAAAAC4FBJVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUAAAAAuBQSVQAAAACASyFRBQAAAAC4FBJVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUAAAAAuBQSVQAAAACASyFRBQAAAAC4FBJVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUAAAAAuBQSVQAAAACASyFRBQAAAAC4FBJVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUAAAAAuBQSVQAAAACASyFRBQAAAAC4FBJVAAAAAIBLIVEFAAAAALgUElUAAAAAgEshUQUAAHgAREZGqnnz5oqMjHR2KACQbSSqAAAAOVxSUpKioqJkMpkUFRWlpKQkZ4cEANlCogoAAJDDpaamymQySZJMJpNSU1OdHBEAZA+JKgAAAADApZCoAgAAAABcCokqAAAAAMClkKgCAAAAAFwKiSoAAAAAwKWQqAIAAAAAXAqJKgAAAADApZCoAgAAuIA8fn4ymcxOq9+ZdQPArdydHQAAAAAkd29vGY0GfbFyty6cv5Kla5OTk6y2F8/5Tp6eeW2+vmAhH7XvVjdLdQLAvUSiCgAA4EIunL+imNOXs3RNSup1q+1zZ+Pk4Z50m7MBwPXR9RcAAAAA4FJIVAEAAAAALoVEFQAAAADgUkhUAQAAAAAuhUQVAAAAAOBSSFQBAAAAAC6FRBUAAAAA4FJIVAEAAHI4o8EoySBJMsjwv20AyLn4LQYAAJDDubl5qGSRyjLIoBJFKsvNzcPZIQFAtrg7OwAAAABkX3CZhgou09DZYQCAQ9CiCgAAAABwKSSqAAAAAACXQqIKAAAAAHApJKoAAAAAAJdCogoAAAAAcCkkqgAAAAAAl0KiCgAAAABwKSSqAAAAAACXQqIKAAAAAHApJKoAAAAAAJdCogoAAAAAcCkkqgAAAAAAl0KiCgAAAABwKSSqAAAAAACXQqIKAAAAAHApJKoAAAAAAJdCogoAAAAAcCkGs9lsdnYQD4JffvlFZrNZnp6eDi33UnyCUlJNDi3zbvJ6uss3f16lXL0sc1rqfa3b6JFH7t4+upQQp9T7XHcejzzyzZtfSZcuyZR6f+t2z5NXnr4+uhKfqLS0+/vzdnMzysfX677WmRU8A/cPz4Dr4jm4f3gOXBfPwf3Dc5B9ycnJMhgMqlGjhkPKy41IVB1k7969MpvN8vDwcHYoAAAAAJwoJSVFBoNB1atXd3YoORaJKgAAAADApTBGFQAAAADgUkhUAQAAAAAuhUQVAAAAAOBSSFQBAAAAAC6FRBUAAAAA4FJIVAEAAAAALoVEFQAAAADgUkhUAQAAAAAuhUQVAAAAAOBSSFQBAAAAAC6FRBUAAAAA4FJIVAEAAAAALoVE1YWMHTtWERER97SOkJAQzZs3L1tlnDx5UiEhIdq4caODonKO1atXa926dXZfGxISoosXL2bpuj59+mjQoEF21eko6T+/W/9169bNqXHlJtl51mfMmKHq1avf9byhQ4eqT58+dtWB+4Pf+fdPeHi4Xn311Tuek5Nf5+3+nt38N2f//v0KCQnR7t2773d4OZIjnp0HVVZ/d9n7mel+W7BggR599FFVrFhRQ4cOlSTt27dP3bp1U9WqVRUSEqL4+HiH1rd169YM+235fZVbuDs7AMBZ1qxZI29vb7Vt29bZoTjF888/r7p161q28+XL58RoYKuuXbuqSZMmzg4DgAu53d+zSZMmyWikTcIeK1asULFixZwdhksaOnSoEhL+r737Dqvi2PsA/kU6AlJEMVgSfTlH6YIeBUU4lIuReE2IoCYihmoDA0JEiQoJxorhAsGGHYMaRc1NlISYYIwSNIrR2KKiUgxKFSxIm/cPn7OXhXM4Byvq7/M8PA9ny+zszOzszu7s7H2Fl3d2dsbOnTuhq6v7DGP1ZK5fv46lS5ciKCgIYrEY+vr6AID4+Hg0NTVh7dq10NDQeKrXSlu3boWzs3Obc3pKSkqnTqvniRqqr4m6ujpoaGi86GiQF6ipqQnNzc3c7379+sHGxubFRYh0SH19PVRUVGBsbAxjY+MXHR3SyVGd33lJ6mJVVdVnvq3/+7//e+bbkOVlL4Ov4vnxSfNEsn7fvn07tJ6BgQEMDAwee7vPw7Vr18AYg4+PD/r06cNNLygowAcffIDhw4c/t7iYmZk9t211dnSbrRM6fPgw3nnnHVhaWsLLywunT5/mzc/MzMTYsWNhaWkJR0dHfPnll2hqauLNFwqFyM/Px0cffQQbGxssX75c6raKiorg5uaGwMBA1NXVAQDy8/MxZcoU2NjYwM7ODnPmzEFFRYXceLcXr8rKSlhYWGDXrl1t1vP29sbs2bMVSpucnBx89NFHsLe3h62tLby9vfHrr7/ylpHVNXLIkCFITk4G8Kg71PHjx5GTk8N1fZXMA4AdO3bAw8MDFhYWcHFxQWpqKq+RJ83KlSsxduxYDB48GI6OjoiIiMDt27elLrtv3z64ubnBysoKvr6+KCgoUGj/Fd0/yT6GhIRg79698PDwgKWlJS5evKjwdsiz196xLun6s379eojFYlhZWaG6ulpq/l+9ehWTJ0+GpaUl3NzcsHfvXqnby87O5sqCj48Pzp0716bcAI+OM29vb1hZWWH48OFYtGhRh+6ek46hOr99OTk5mDhxIqytrTF06FD4+vri/PnzAID79+/js88+g4eHB6ytreHi4oKFCxeitrZWalhpaWlwdHSEtbU1pk+fLrOOVnQ/W5J0h8zLy8O7774LGxsbjB8/Hl5eXggJCYGXlxesrKx4dfGOHTtgb28PoVAIZ2dnLF26lNf9WHK8L168GC4uLty2FDmfpaamwsLCAoMGDcKsWbO4PA0KCsKlS5cwadIkWFlZYdiwYRCJRLC0tMS4ceOQnZ2N+vp6xMfHQyQSYciQIfD394dYLIZQKISDgwOWLFmChw8fcvHJy8uDUChETk4OwsLCYGtrq3Ael5SUICwsDHZ2drCxsUFAQAAuXbrEW0ZSH8rLv/r6eqxatQpisRgWFhZ4++2323SJlpVPf/31F2+51l1/JefUrKwseHh4YPDgwZgyZQoKCwt565WWliIkJATW1tZwcnLC5s2b2+SfPCdPnsSHH34IOzs7DB48GGPHjuXV69K6hv70008QCoUoLi4G8L+u7JmZmfj0008xbNgweHt78/ZF3rWIUCjEunXrsGLFCowYMQL29va8NJSoqanBp59+CkdHR1haWsLJyQnh4eHc/NZdfyVx279/Pz777DMMHToUI0eOxLJly9DY2MiLg6LnrfbIKxfR0dGYNm0aAMDNzY1LN6FQiOrqaqSmpkIoFPJep1GkXrh16xY++eQTODg4wMrKCqNHj8aWLVsAPMrDkpISbN++nTtuMzMzuXmS/M3MzISZmRnKy8t5YVdXV8PCwgI7duzgpsmryzuS7levXsX06dO54zI4OLhNWd+9ezc8PT25emTSpEk4c+aMwvMVQU9UO5mysjLExcUhNDQUurq6WL9+PQICAvDjjz/C0NAQmzZtwooVK+Dn54fo6GhcvXqVOzgiIyN5Yc2ZMwcTJkxASEgINDU122yroKAAU6dOhbW1NRISEqCmpob8/Hz4+vrCyckJX375JR48eIDExETMmDEDO3fulBlvefEyMDCAu7s79uzZw3sX8vLlyzhz5gzCwsIUSp/i4mKIxWL4+/ujS5cu+PXXXxEcHIwtW7bwurHKs2jRIkRFRUFDQwNz584FAO4p1bZt2xAfHw9fX184OzsjPz8fKSkpqK2t5ZaVpqKiAiEhIejRowcqKyuxadMm+Pr64vvvv4eKyv8OtXPnzqGwsBBz5swBACQmJiIwMBBZWVlQU1NTeB8U8ddff6GkpASzZ8+Grq4uevXqxV2cxsbGIjw8HHp6enB1dUVkZCT09PSe6vaJbPKOdQD48ccf0a9fP8TExKBLly7Q0tJqE87Dhw/h7+8PTU1NrnGSlJSEu3fv4s033+SWO3/+PGbPng2xWIz58+ejpKQE4eHhqK+v54WXlZWF8PBweHl5ITQ0FGVlZUhISEBNTQ2+/PLLZ5cgrymq89t34MABREREwNXVFQkJCVBVVcWpU6dw69YtmJmZoa6uDk1NTQgPD4eBgQH++ecfrFmzBjNmzMC2bdt4YWVnZ8PExASxsbGoqanBypUrERoa+kT72VpZWRni4+MRHBwMHR0dJCQk4Pr16ygtLYW6ujoePnyIxYsXo1evXty5xtDQECKRCCKRCKmpqQqlS0vSzmfHjh3Df/7zHxgbG6NHjx7o06cPYmJiAACNjY2IjIzElClTAACnT5+GsrIyli9fjkOHDiE0NBRisRhHjhxBWFgYHj58iJSUFO5J3IQJE7Bx40b8888/SEpK4sVlwYIF+Pe//42vvvpKoS7Hd+/eha+vL7p06YK4uDioq6tj9erVmDx5Mr799lv06tWLW1aR/Js9ezZOnTqFmTNnYsCAATh8+DCioqKgq6vL614pLZ9mzZqF7Ozsdp90X7hwAZWVlYiMjERTUxOWLl2KqKgoLg6MMcyYMQPl5eWIi4uDjo4ONmzYgJs3byrcBfvu3bsICQmBnZ0dVq1aBTU1NVy5cuWx341ctWoVnJyckJCQwLvhrui1yNatW2FtbY3Fixe3acxILFmyBEeOHMGcOXNgYmKCsrKyNg8RpElMTISrqysSExORn5+P5ORk9O3bF5MmTQKg+HlLHnnlYsaMGRgwYABWrlyJlJQUGBkZwcjICDt37sRHH32EMWPGwNvbG9ra2gAUqxeqqqowYcIEAEB4eDh69+6NGzducI29lJQUBAcHw9bWFv7+/gAg9Um1u7s7Fi1ahKysLEyePJmb/uOPPwIARo8eDQAdqsvlpXtRUREmTpwIU1NTLF26FEpKSlizZg2mTp3KlY8TJ04gJiYG/v7+cHJyQl1dHc6cOcPdJJQ3X2GMdBpz585lAoGAHTt2jJtWU1PDBg8ezFauXMlqa2uZjY0NS0hI4K339ddfMysrK1ZZWckYY2zPnj1MIBCwtWvXttmGQCBgaWlp7MKFC8ze3p5FRUWxxsZGbv6HH37IJkyYwJqbm7lply9fZkKhkOXk5DDGGCsqKmICgYAdPHiQMcYUjtexY8eYQCBgV65c4ZZZsmQJc3JyYk1NTR1Or6amJtbQ0MD8/f1ZREQENz0pKYnZ2Ni0Wd7Ozo4lJSVxvydPnsyCg4N5yzQ2NrJhw4ax8PBw3vSEhARmbm7eJo0rKiqkxq2xsZGVlpYygUDAjhw5wtvmwIED2bVr17hp169fZwMHDmQZGRkK7XdH9s/c3JzdvHmTt9ytW7fYokWLWHZ2NsvLy2Pr1q1jtra2bNy4cay+vl6hOJAnI+9YZ4wxsVjMRCIRu3fvHm/d1vn/9ddfyyxTkydP5qaFhYUxd3d33rG2b98+JhAIuHLT3NzMxGIx73hijLHDhw8zoVDI/v777yffecKhOr99zc3NbNSoUczf31/ushINDQ3sjz/+YAKBgBUUFHDTxWIxGzx4MKupqeGmSeL366+/PtF+SsydO7fNcfL7778zgUDABg0axIqLi5mzszNbvnw5d64JDAxkAoGA/fbbb4wxxmJjY5lAIGC7d+9mjP3veI+Pj2disZgLt73zWWNjIxs5ciSLioriTY+KimICgYAJBAKWk5PDLly4wAQCAUtNTWUCgYDt27ePMcbY+++/z4RCIUtJSWGMMfbuu++yCRMmMD8/PyYQCFhRURHbsWMHEwgE7OLFi7z9XLhwocJ5xRhjW7ZsYUKhkFdGqqqqmI2NDVuyZAk3TZH8y83NbXPOZYyxjz/+mL3//vsK5dOJEye4aZJjp2Ua29jY8M77kmPvn3/+YYwxlpOT0yacu3fvMjs7O17+tefMmTO8tJVGLBazuLg43rTs7Gwufxj7X3kOCAhos76i1yICgYCNGTOGVz8w9igNPT09ud+enp68/Gqt9TWTJG5hYWFt4uXn58f9VuS8JY+i5aJ1+km0PtYUrRdWrVrFLCws2oTXkrR8lDZ95syZbMKECbxlfH19edewHanL5aX7J598wlxdXVldXR03raKigtnY2LD09HTGGGNpaWlMJBLJ3Dd58xVFXX87GR0dHa5rheS3g4MD/vzzT+Tn5+P+/fsYPXo0GhsbuT8HBwfU1dXh8uXLvLCcnZ2lbuPs2bPw9fWFu7s7li1bBmVlZQDAgwcPcOrUKYwePRpNTU1c+G+++SZ69eqFs2fPSg1P0XgNHz4cffr0we7duwE8uqv77bff4r333lP4TmNpaSnmzp0LR0dHmJmZwdzcHL/99huuXbum0PryFBQUoKqqirtDJTFmzBg0NDS022Xh8OHDmDhxIuzs7GBmZoZRo0YBePSCfkumpqa8J139+vXDwIED8eeffz6VfWhJIBDw7kgDQI8ePRAbGws3NzeIRCIEBQUhISEBFy5cQHZ29lOPA5GuvWNdYtiwYVKforZ05swZmWWqpbNnz8LZ2Zl3rLm6uvKWuXbtGkpKSvD222/zjmWRSIQuXbq06RpHnhzV+bIVFBSgtLQU77//frvL7du3D++++y4GDx4Mc3NzfPDBBwDa1r3Dhg2Djo4O99ve3h56enoy696Opj/wqH41NTXlfkveEe3VqxdMTEzw/vvvY//+/bh8+TKqqqqgrKwMExMTrgxI8vDGjRvtJ047SktLcfv2bbi7u/Ome3h4AACUlJRgb2+PkydPAgAmTZoEDQ0N3Lp1CwBgbW0NxhhGjBiBe/fu4cKFC/Dw8ODVF2PGjAEALgwJWWVQlj/++AOmpqYYMGAAN01PTw8ODg5twpaXf0ePHoWenh6GDx/eJr8uXLjA65YpK58kaSDLwIEDee9aStYrLS0F8OhY09XVxZAhQ7hlunbtyjvG5enbty+0tbURGxuLAwcOPPFIubLyRNFrkVGjRkFJSandbZiZmWHv3r3YsGED/v77b4XjNnLkSN7vAQMGcGkJKHbekqcj5UIRitYLubm5GD58OHr37t2h8KXx9PTE6dOncfPmTQDA7du3ceLECXh6egLoeF0uL92PHj0KFxcXKCsrc2Hp6urCzMyMuw4wMzNDdXU1oqOjcfToUTx48IAXprz5iqKuv52MtJfNDQ0NcfXqVVRVVQEA3nvvPanr/vPPP7zf3bt3l7pcbm4uHjx4gPHjx/Mqn5qaGjQ1NWHJkiVYsmSJ3PAlFI2XkpISvL29sXXrVsyZMwc5OTmorKyEl5eX1PVaa25uxvTp01FbW4uwsDD069cPmpqaSEpKkhm3jrpz5w4AcF0vJSS/JfNbO3PmDGbMmAFXV1cEBQXB0NAQSkpK8PHx4b3HIy1sybSysrKnsQs8sspAa05OTtDS0sK5c+e4CxDybLV3rLf8Lc/t27dllqmWZa+srKzNNrW1taGurs79lhzLM2fOlLqtp3Wckf+hOl+26upqAI8aFbJkZ2dj7ty5mDBhAvcqQ1lZGWbOnKlQ3WtgYCCz7u1o+gNoM1KnpBupZKTQ8ePHIzU1lfskxcmTJzFlyhTuQlwy0uiTvBMu2Z/WZUtSPtTU1KCmpoY7d+5AVVUVenp6UFVV5dJLUkZUVFRQW1sLxhgMDQ15r7Do6OhwYbSkSJ3VUk1NjdRya2ho2OZGgLz8q6qqQnV1NczNzaVuq6ysjHvFR1Y+tS4zrclb7/bt21KP6Y4MJNStWzds2rQJSUlJ+OSTT9DU1IQhQ4bg008/hVAoVDgcCVl5oui1iCJ5umDBAi7ey5cvR69evRAcHMzdNJKl5Y0H4FF6tuzWq8h5S56OlAtFwwPk1wvV1dW8myFPQiwWQ1NTE99//z2CgoJw8OBBqKurw83NDUDH63J56V5VVYUtW7Zw79O2XhZ4dKNo+fLl2Lp1KwICAqCurg4PDw/Mnz8fenp6cucrihqqnYy0O2cVFRUwMjJCt27dADzq1y7toFL0rk1AQAD++usvBAYGYuvWrVzFp6OjAyUlJYSEhHCFvyXJCbS1jsTLy8sLSUlJyMnJwe7duzFs2DDe6GrtuXHjBs6fP4+vvvqKFz/JO5cS6urqaGho4E1raGhQ6MQvOXha54PkZXTJvrb2008/QVtbG4mJidwFR0lJidRlpQ1SUlFR0eYJmCwd2T95d0HJi9PesS6hSP716NED586dkxqW5H0aADAyMmqzzbt37/IuzCTlf+HChbCyspK6LfJ0UZ0vm6Q8tjfgUVZWFgYNGsQbWOb48eNSl5VW91ZWVvKOuZaeVvq3ZGxsDEdHRxw7dgwAUFtby2u437t3DwC4dwQl9X3L9xPlnc8k+9O6bLUejKVbt25oaGho09hkjAF49ARcUkYqKyu5J/GSeNfX17c5J3b0nNOtWzepPaIqKirahC0v/7p16wYDAwOsW7dO6raex6izkjEqWuvoU1ErKyukpaWhrq4OeXl5WLZsGWbOnImffvoJwKPy0fo6QNaNdFl5oui1iCJ5qqOjg5iYGMTExODSpUvYunUr4uLiIBAIeE+XO0qR85Y8T7tcKFov6OnpKTRYmyI0NDTg5uaGAwcOICgoCAcOHIBYLOZ6XD1uXS5Lt27d4OTkJPVGQ8vP84wbNw7jxo1DZWUlDh06hCVLlkBFRQVffPGFQvMVQQ3VTqa2tha5ublcN5Ha2locO3YMH374IQYPHgxNTU2Ulpa26dLTEcrKykhISEBoaCg++ugjpKeno3///tDS0oKNjQ0KCgpgaWmpcHgdiZeRkRGcnZ2RlpaGs2fPSr3zI4ukYmo50EFJSQny8/N53Vd69uyJhoYGFBYWci+m//777226d7S8gyzx1ltvwcDAAFlZWbx9OXjwIFRVVaVevAOPGsuqqqq8Cl3ax9eBR4OJ3LhxA/369QPwqAF+8eJF7qV7eRTdv4745ZdfcP/+/Q7lO3ky7R3rHWFpaYl9+/ZJLVMtLxAsLS2Rk5OD6Oho7maK5KJHon///jA2NkZRUVGH40EeD9X5sknKY2ZmpsyeHpK6tyVZdW9eXh5qa2u5pwm5ubmorq6GtbW11OWfVvq3Jhn1WEVFBXp6ejAxMeHm5ebmAgA36I2kvs/NzeWeaMo7nxkbG8PIyKjNqxw//PAD77ednR0AcCMMS5w5cwZKSkr47bffYGNjg0GDBiErK4v3FOvgwYO8MB6XnZ0dfvjhBxQUFKB///4AHjW4jh071uacKC//HBwckJaWBlVVVYVv/D5tlpaWqKmpwYkTJzB06FAAj24+5ObmPtZ3MTU0NODk5ITCwkIsXrwYDx8+hLq6OoyNjXm9b4BH3TU74kmvRWQRCoWYN28edu/ejatXrz5RQ1WR85Y8T7tcKFov2NvbY+PGjbh586bM7/FKuw6V5Z133kFwcDCOHDmC06dPIygoiJv3uHV5e3G/fPkyzMzMeDeoZDEwMOC+wiHtKxby5reHGqqdjJ6eHmJiYhAWFgYdHR2sX78ejDH4+flBV1cXYWFhWLFiBUpLSyESiaCsrIyioiIcOnQIycnJUkd6lEZVVRVJSUmYNm0apk6diu3bt6NPnz745JNP4Ofnh48//hienp7Q1dVFaWkpjh07Bi8vL6kj63Y0Xj4+PggODoauri73zowiJBctkpHr7t+/j6SkpDZPeUaNGgUtLS18+umnCAoKQmlpKbZu3dqmq0j//v2xb98+/PzzzzAyMkKPHj3Qs2dPzJgxA/Hx8TAwMICTkxNOnz6N9evXw8/PT+ZdqREjRmDLli34/PPP4e7ujvz8fOzfv1/qsoaGhpg2bRo36uV//vMf9OzZU+HucIrunyySEdxsbGygq6uLM2fOYO3atbCwsJB6J448G+0d6x3h5eWF1atXIyQkhPscRFJSUpvudCEhIRg/fjxCQ0Ph4+ODmzdvYuPGjVBXV+dusCgpKSE6OhqRkZG4f/8+nJ2doampiZs3b+Lw4cMIDw/HW2+99XQSgACgOr89SkpKmDt3LiIiIhAaGopx48ZBTU0Np0+fhqWlJcRiMRwcHPDZZ5/hq6++wuDBg3H48GGusdda165dERQUhKCgINTW1mLlypWwsrKCo6Oj1OWfZvq35OzsDH19fZSVlaG8vByLFy/mzjVpaWl46623sGfPHgiFQqiqqqJLly6oqqqCjo4OvvnmG4XOZ5MmTUJSUhI36u+yZcuQl5fHW2fgwIH417/+haVLl6KpqQk3btxAZGQkzp49CxcXF6xZswbq6upwdXVFcnIyN+pvZmYmNm7cCA8Pj8fqitqSl5cXNm/ejJCQEHz88cfcqL8qKipt6kJ5+TdixAiIxWIEBgYiMDAQQqEQDx48wJUrV3Djxg0sXrz4ieKqiFGjRsHc3Bxz5sxBREQEdHV1kZaWhq5duyr8tFnS+8DNzQ1vvPEGysvLkZ6eDltbWy7fPTw8EBsbi5SUFK7ct/6slTxPei3S0sSJE+Hu7g5TU1MoKytj3759UFVVfaJGKqDYeUuep10uFK0Xpk6div3792Py5MmYPn06+vTpg6KiIly/fh1RUVEAHh23v//+O44ePQpdXV307t1b5nWmg4MD9PT0MH/+fOjq6nLjoEg8Tl0uS1hYGMaPH4+AgAD4+Pige/fuKC8vx/HjxzFkyBC88847SEpKQnV1NUQiEQwNDfH333/jyJEjmDp1KgDIna8oaqh2MkZGRoiMjMTy5ctRWFgIU1NTbNiwgbvo9Pf3R8+ePbFp0yakp6dDRUUFffv2hbOzc4c/Hq6mpoavvvoKQUFB8PPzw/bt22Fra4uvv/4aycnJmDdvHhoaGmBsbIzhw4dzd92k6Ui8Ro4cCU1NTXh6enboPQM1NTUkJyfjs88+w+zZs9GrVy9Mnz4dv//+O2+QF319fSQlJXFdZQYNGoTly5fzvn8FPPqWXGFhIebOnYuamhrMmjULoaGh8PX1hYqKCjZv3oyMjAwYGRlh1qxZ3De2pHFyckJkZCTS09ORmZkJW1tbrF27VupFmbm5Of71r39hxYoVKCsrg7W1NeLi4hT+NI2i+yfLgAEDkJGRgV27dqGurg49e/bE+PHjERYWxnsHiTxb8o51RWloaGDjxo2IjY1FVFQUd7Pl0KFDvGHgzczMkJiYyH2GQTLs/JQpU3jvq7z99tvQ1dXFmjVruCdTJiYmcHR07HDciHxU57dvzJgx0NDQwJo1axAREQF1dXWYmZlxTzImTpyI4uJipKenY8OGDRg5ciQSEhJ4n8SRcHd3h7GxMRYtWoSamho4ODggLi6u3e0/zfSXUFFRgYuLC7KyshAaGor09HTeucbHxweLFi1CfHw8unbtCh8fH2RlZaGiogK7d+9W6Hw2c+ZMhIaGYvXq1bh9+za6d++O+Ph4BAYG8tZbsWIFVq1aha1bt+L777+HqakpkpKS4OzsjGXLlmHdunVobm6GSCTCjRs3UFdXhx07dsDHx4f7rMmT0NbWxrZt27B06VIsWLAAzc3NsLW1RXp6epuBABXJv6SkJKxbtw4ZGRkoKSmBjo4OTE1NH6vx9TiUlJSQmpqKhQsXYuHChdDV1cWUKVNw7do1XLhwQaEw+vbtiy5duiAxMREVFRXQ09PDyJEjERERwS3j7e2NwsJCZGRkYPPmzRgzZgwiIiI6lCdPei3Skq2tLfbt24fi4mJ06dIFAoEAa9as4Q2S9TgUPW/J87TLhSL1gr6+PjIyMpCQkICVK1fiwYMHMDEx4XWnjYiIQGxsLEJDQ3Hv3j0sWbJEZpxUVVXh4eGBnTt3Yvz48W3y6XHrcmn69euHb775BomJiYiLi8P9+/dhZGSEoUOHcjenLC0tsWXLFhw8eBB3796FsbExAgICMH36dIXmK0qJSV5GIOQ5yc3NxdSpU7Fnzx5YWFi86OgQ8tqSHIvbtm2DSCR60dEhryiq8/mam5vh5uYGsViMBQsWvOjoKCwqKgonT57Ezz///Ny37eLiAmdnZyxcuPC5b/tJ1dfXw9PTE0OGDOlQ1/dnydfXF1paWli7du2LjkqH0Xnr9UKPT8hzc+vWLRQWFmLFihWwtbWlCxZCnrPY2Fjukw5XrlxBamoqzMzMnrh7FiHSUJ3PV19fj4sXL+KHH35AaWlpp34P/Pjx4zh16hTMzc3R3NyMnJwc/Pe//0V0dPSLjlqnt3PnTjQ3N+Ott95CTU0N9xRv1apVLzpqLyU6b73eqKFKnptdu3YhNTUVgwYNQnx8fJv5TU1NaO8B/+vQLbW5uZkbREMaZWVlGsmXPLaamhp8/vnnqK6uhra2NhwdHTF37lyFv2NMSEe8DnV+R/bh9u3b8Pb2hoGBARYsWMANHvQst/m4tLS0kJOTg/Xr1+Phw4cwMTFBdHR0h94vY4y1O8hfly5dXsm6R11dHevWreNG/h84cCDWrl3LDXLzKpT756m989brWsZeJ9T1l3QaLi4uMj/pAgCXLl16jrF5MZKTk5GSkiJzfnvvLxBCyMvkVajzX8Q+vCzplpmZiXnz5smcLxkX4nXzsuTfyyAvLw9TpkyROf+9997D0qVLn2OMyNNGDVXSaVy6dIn3weHWXodPp9y6davd7261NyIcIYS8TF6FOv9F7MPLkm5VVVUoLi6WOV8y0v7r5mXJv5fB3bt3pX6HV0JfX/+xvndMOg9qqBJCCCGEEEII6VSo4zYhhBBCCCGEkE6FGqqEEEIIIYQQQjoVaqgSQgghhBBCCOlUqKFKCCGEEEIIIaRToY81EUIIeSW1/jyGmpoaunXrBqFQCCcnJ3h5eUFbW7vD4Z46dQpHjx6Fn58fdHV1n2aUH8v27duhqalJn64ihBDySqGGKiGEkFdaWFgYevfujcbGRpSXl+P48eP44osvsHnzZqSmpmLgwIEdCi8/Px8pKSl47733OkVDNSMjA/r6+tRQJYQQ8kqhhiohhJBX2qhRo3jfJgwJCUFubi6mTZuGGTNm4MCBA9DQ0HiBMSSEEEJIa/SOKiGEkNeOvb09ZsyYgZKSEnz77bcAgIsXLyI6Ohqurq6wtLTEiBEjMG/ePFRVVXHrJScnY/ny5QAAV1dXCIVCCIVCFBcXAwD27NmDKVOmwN7eHhYWFhgzZgy+/vrrNts/e/YsAgICMGzYMFhZWcHFxYXXTRkAmpubsXnzZnh6esLS0hIODg5YuHAh7ty5wy3j4uKCy5cv4/jx41xcfH19n3p6EUIIIc8bPVElhBDyWho3bhxWrVqF3377DT4+Pjh27BiKiorg5eUFIyMjXL58Gbt27cKVK1ewa9cuKCkpwd3dHdevX8d3332HefPmQV9fHwBgYGAA4FE3XFNTU7i4uEBFRQW//PIL4uLiwBjDhx9+CACoqKhAQEAA9PX1ERwcDF1dXRQXFyM7O5sXv4ULF2Lv3r3w8vKCr68viouLsX37dpw/fx4ZGRlQVVXF/Pnz8fnnn0NLSwvTpk0DAHTv3v05piIhhBDybFBDlRBCyGvJ2NgYOjo6KCoqAgB88MEH8Pf35y1jY2ODiIgInDx5EkOGDMHAgQNhZmaG7777Dm5ubujduzdv+fT0dF434smTJyMgIACbNm3iGqr5+fm4c+cONmzYwOuSHB4ezv3/xx9/4JtvvsHKlSsxduxYbvqwYcMQGBiIrKwsjB07Fm5ubkhMTIS+vj7GjRv39BKHEEIIecGo6y8hhJDXlpaWFu7duwcAvAbmw4cPUVlZCWtrawDAuXPnFAqvZRi1tbWorKyESCRCUVERamtrAQA6OjoAgJycHDQ0NEgNJysrCzo6OhgxYgQqKyu5P3Nzc2hpaSEvL6/jO0sIIYS8ROiJKiGEkNfW/fv3YWhoCACorq5GSkoKDhw4gIqKCt5ykkamPCdPnkRycjJOnz6NBw8etAlDR0cHIpEIHh4eSElJwebNmyESieDm5oaxY8dCTU0NAHDjxg3U1tbC3t5e6nZax48QQgh51VBDlRBCyGuptLQUtbW16Nu3LwDg448/Rn5+PgICAjBo0CBoaWmhubkZgYGBYIzJDa+wsBBTp05F//79ER0djV69ekFVVRWHDx/G5s2b0dzcDABQUlJCUlISTp8+jV9++QVHjhzB/PnzsWnTJuzcuRNdu3ZFc3MzDA0NsXLlSqnbkrwTSwghhLyqqKFKCCHktbR//34AwMiRI3Hnzh3k5uYiNDQUs2bN4pa5fv16m/WUlJSkhvfzzz+jvr4eq1evxhtvvMFNl9VN18bGBjY2NggPD8d///tfREZG4sCBA/D29kbfvn2Rm5sLW1tbuZ/OkRUfQggh5GVG76gSQgh57eTm5iI1NRW9e/fGv//9bygrK0tdbsuWLW2maWpqAmjbHVgSRsunr7W1tdizZw9vuTt37rR5Qjto0CAAQH19PQDg7bffRlNTE1JTU9tsv7GxETU1Nbz4tPxNCCGEvAroiSohhJBX2q+//oqCggI0NTWhvLwceXl5OHr0KN544w2sXr0a6urqUFdXx9ChQ5GWloaGhgb07NkTR48e5b6P2pK5uTkA4Msvv8SYMWOgqqoKsViMESNGQFVVFdOmTcPEiRNx7949fPPNNzA0NERZWRm3/t69e5GRkQE3Nzf07dsX9+7dw65du6CtrY1Ro0YBAEQiESZMmIC1a9fiwoULXNjXr19HVlYWYmJiMHr0aC4+GRkZSE1NRb9+/WBgYCDz3VZCCCHkZaHEFHnxhhBCCHnJZGZmYt68edxvVVVV6OnpQSAQwNnZGV5eXtDW1ubm37p1C59//jny8vLAGMOIESMQExMDR0dHzJo1C6Ghodyyqamp2LFjB8rKytDc3IxDhw6hd+/e+Pnnn5GYmIjr16+je/fumDRpEgwMDDB//nxumfPnz2PDhg04deoUysvLoaOjAysrK8yaNQsWFha8fdi1axd27NiBq1evQllZGSYmJhg1ahT8/PzQo0cPAEB5eTliYmJw4sQJ3Lt3DyKRCNu2bXvGqUsIIYQ8W9RQJYQQQgghhBDSqdA7qoQQQgghhBBCOhVqqBJCCCGEEEII6VSooUoIIYQQQgghpFOhhiohhBBCCCGEkE6FGqqEEEIIIYQQQjoVaqgSQgghhBBCCOlUqKFKCCGEEEIIIaRToYYqIYQQQgghhJBOhRqqhBBCCCGEEEI6FWqoEkIIIYQQQgjpVKihSgghhBBCCCGkU6GGKiGEEEIIIYSQToUaqoQQQgghhBBCOpX/B+AeAvwUwuRJAAAAAElFTkSuQmCC", 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" ] @@ -255,9 +255,165 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "39ca78d9", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# file sze \n", + "plt.figure(figsize=(10, 6))\n", + "ax = sns.barplot(x=\"Dataset\", y=\"FileSize(MB)\", hue=\"Format\", data=df)\n", + "plt.yscale('log')\n", + "plt.title('File Size of Different Formats for Different Datasets')\n", + "plt.xlabel('Dataset')\n", + "plt.ylabel('Log Scale File Size (MB)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4796663f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset Format \n", + "berkeley_autolab_ur5 HDF5 0.075685\n", + " RLDS 0.023251\n", + " VLA-ColdCache 0.247777\n", + " VLA-HotCache 0.000683\n", + " VLA-NoCache 0.218245\n", + "berkeley_cable_routing HDF5 0.000300\n", + " RLDS 0.000764\n", + " VLA-ColdCache 0.031721\n", + " VLA-HotCache 0.000788\n", + " VLA-NoCache 0.030931\n", + "bridge HDF5 0.005921\n", + " RLDS 0.002830\n", + " VLA-ColdCache 0.038200\n", + " VLA-HotCache 0.000607\n", + " VLA-NoCache 0.031982\n", + "nyu_door_opening_surprising_effectiveness HDF5 0.022284\n", + " RLDS 0.009082\n", + " VLA-ColdCache 0.069383\n", + " VLA-HotCache 0.000695\n", + " VLA-NoCache 0.069731\n", + "Name: LoadingTime(s), dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get average loading time and storage for each dataset\n", + "df.groupby(['Dataset', 'Format'])['LoadingTime(s)'].mean()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "08a312f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset Format \n", + "berkeley_autolab_ur5 HDF5 289.032210\n", + " RLDS 174.420469\n", + " VLA-ColdCache 1.878984\n", + " VLA-HotCache 1.878984\n", + " VLA-NoCache 1.878984\n", + "berkeley_cable_routing HDF5 4.873406\n", + " RLDS 65.382843\n", + " VLA-ColdCache 0.645619\n", + " VLA-HotCache 0.645619\n", + " VLA-NoCache 0.645619\n", + "bridge HDF5 31.268807\n", + " RLDS 330.839012\n", + " VLA-ColdCache 0.317214\n", + " VLA-HotCache 0.317214\n", + " VLA-NoCache 0.317214\n", + "nyu_door_opening_surprising_effectiveness HDF5 84.314592\n", + " RLDS 97.529275\n", + " VLA-ColdCache 0.387734\n", + " VLA-HotCache 0.387734\n", + " VLA-NoCache 0.387734\n", + "Name: FileSize(MB), dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(['Dataset', 'Format'])['FileSize(MB)'].mean()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4f3e99b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset Format \n", + "berkeley_autolab_ur5 HDF5 3818.869277\n", + " RLDS 7501.680810\n", + " VLA-ColdCache 7.583370\n", + " VLA-HotCache 2752.311319\n", + " VLA-NoCache 8.609527\n", + "berkeley_cable_routing HDF5 16256.118100\n", + " RLDS 85611.650199\n", + " VLA-ColdCache 20.353238\n", + " VLA-HotCache 819.724171\n", + " VLA-NoCache 20.873082\n", + "bridge HDF5 5281.055338\n", + " RLDS 116898.449382\n", + " VLA-ColdCache 8.304032\n", + " VLA-HotCache 522.341592\n", + " VLA-NoCache 9.918482\n", + "nyu_door_opening_surprising_effectiveness HDF5 3783.651869\n", + " RLDS 10739.267568\n", + " VLA-ColdCache 5.588280\n", + " VLA-HotCache 557.647436\n", + " VLA-NoCache 5.560416\n", + "dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute the speedup of VLA to HDF5 and RLDS per dataset\n", + "df.groupby(['Dataset', 'Format'])['FileSize(MB)'].mean() / df.groupby(['Dataset', 'Format'])['LoadingTime(s)'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b45c38c", + "metadata": {}, "outputs": [], "source": [] } diff --git a/benchmarks/openx.py b/benchmarks/openx.py index d396220..7f2875f 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -13,9 +13,9 @@ # Constants DEFAULT_EXP_DIR = "/home/kych/datasets/fog_x/" -DEFAULT_NUMBER_OF_TRAJECTORIES = 64 +DEFAULT_NUMBER_OF_TRAJECTORIES = 1024 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5", "bridge", "berkeley_cable_routing", "nyu_door_opening_surprising_effectiveness"] -# DEFAULT_NUMBER_OF_TRAJECTORIES = 1 +# DEFAULT_NUMBER_OF_TRAJECTORIES = 1000 # DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" @@ -23,8 +23,9 @@ class DatasetHandler: """Base class to handle dataset-related operations.""" - DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-*" - LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_type}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-*" + DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-of-{total_trajectories:05d}" + LS_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-*" + LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_type}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-of-{total_trajectories:05d}" FEATURE_JSON_URL_TEMPLATE = ( "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" ) @@ -35,10 +36,24 @@ class DatasetHandler: def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type): self.exp_dir = exp_dir self.dataset_name = dataset_name - self.num_trajectories = num_trajectories + self.total_trajectories = self._get_total_number_of_trajectories() + self.num_trajectories = num_trajectories if num_trajectories <= self.total_trajectories else self.total_trajectories self.dataset_type = dataset_type self.dataset_dir = os.path.join(exp_dir, dataset_type, dataset_name) - + + def _get_total_number_of_trajectories(self): + """Gets the total number of trajectories in the dataset.""" + # use gsutil to get a trajectory file name and extract the total number of trajectories + data_url = self.LS_URL_TEMPLATE.format( + dataset_name=self.dataset_name, index=0, + total_trajectories="*" + ) + output = subprocess.run( + ["gsutil", "ls", data_url], stdout=subprocess.PIPE, check=True + ) + total_trajectories = int(output.stdout.decode().split("-")[-1]) + + return total_trajectories def clear_cache(self): """Clears the cache directory.""" if os.path.exists(CACHE_DIR): @@ -66,6 +81,7 @@ def check_and_download_trajectory(self, trajectory_index): dataset_type=self.dataset_type, dataset_name=self.dataset_name, index=trajectory_index, + total_trajectories = self.total_trajectories ) # Ensure no files with .gstmp postfix are considered valid @@ -76,7 +92,8 @@ def check_and_download_trajectory(self, trajectory_index): if not valid_files_exist: data_url = self.DATA_URL_TEMPLATE.format( - dataset_name=self.dataset_name, index=trajectory_index + dataset_name=self.dataset_name, index=trajectory_index, + total_trajectories=self.total_trajectories ) subprocess.run( ["gsutil", "-m", "cp", data_url, self.dataset_dir], check=True @@ -89,7 +106,7 @@ def check_and_download_trajectory(self, trajectory_index): # Check and download the feature.json file feature_json_local_path = os.path.join(self.dataset_dir, "features.json") feature_json_url = self.FEATURE_JSON_URL_TEMPLATE.format( - dataset_name=self.dataset_name + dataset_name=self.dataset_name, ) self.check_and_download_file(feature_json_url, feature_json_local_path) @@ -454,6 +471,6 @@ def evaluation(): if __name__ == "__main__": - # prepare() - # exit() + prepare() + exit() evaluation() diff --git a/fog_x/DLdataset.py b/fog_x/DLdataset.py new file mode 100644 index 0000000..e40926f --- /dev/null +++ b/fog_x/DLdataset.py @@ -0,0 +1,452 @@ +import inspect +import string +from functools import partial +from typing import Any, Callable, Dict, Sequence, Union + +import tensorflow as tf +import tensorflow_datasets as tfds +from tensorflow_datasets.core.dataset_builder import DatasetBuilder + +from dlimp.utils import parallel_vmap, vmap + + +def _wrap(f, is_flattened): + """Wraps a method to return a DLataset instead of a tf.data.Dataset.""" + + def wrapper(*args, **kwargs): + result = f(*args, **kwargs) + if not isinstance(result, DLataset) and isinstance(result, tf.data.Dataset): + # make the result a subclass of DLataset and the original class + result.__class__ = type( + "DLataset", (DLataset, type(result)), DLataset.__dict__.copy() + ) + # propagate the is_flattened flag + if is_flattened is None: + result.is_flattened = f.__self__.is_flattened + else: + result.is_flattened = is_flattened + return result + + return wrapper + + +class DLataset(tf.data.Dataset): + """A DLimp Dataset. This is a thin wrapper around tf.data.Dataset that adds some utilities for working + with datasets of trajectories. + + A DLataset starts out as dataset of trajectories, where each dataset element is a single trajectory. A + dataset element is always a (possibly nested) dictionary from strings to tensors; however, a trajectory + has the additional property that each tensor has the same leading dimension, which is the trajectory + length. Each element of the trajectory is known as a frame. + + A DLataset is just a tf.data.Dataset, so you can always use standard methods like `.map` and `.filter`. + However, a DLataset is also aware of the difference between trajectories and frames, so it provides some + additional methods. To perform a transformation at the trajectory level (e.g., restructuring, relabeling, + truncating), use `.traj_map`. To perform a transformation at the frame level (e.g., image decoding, + resizing, augmentations) use `.frame_map`. + + Once there are no more trajectory-level transformation to perform, you can convert to DLataset to a + dataset of frames using `.flatten`. You can still use `.frame_map` after flattening, but using `.traj_map` + will raise an error. + """ + + def __getattribute__(self, name): + # monkey-patches tf.data.Dataset methods to return DLatasets + attr = super().__getattribute__(name) + if inspect.ismethod(attr): + return _wrap(attr, None) + return attr + + def _apply_options(self): + """Applies some default options for performance.""" + options = tf.data.Options() + options.autotune.enabled = True + options.deterministic = False + options.experimental_optimization.apply_default_optimizations = True + options.experimental_optimization.map_fusion = True + options.experimental_optimization.map_and_filter_fusion = True + options.experimental_optimization.inject_prefetch = False + options.experimental_warm_start = True + return self.with_options(options) + + def with_ram_budget(self, gb: int) -> "DLataset": + """Sets the RAM budget for the dataset. The default is half of the available memory. + + Args: + gb (int): The RAM budget in GB. + """ + options = tf.data.Options() + options.autotune.ram_budget = gb * 1024 * 1024 * 1024 # GB --> Bytes + return self.with_options(options) + + @staticmethod + def from_tfrecords( + dir_or_paths: Union[str, Sequence[str]], + shuffle: bool = True, + num_parallel_reads: int = tf.data.AUTOTUNE, + ) -> "DLataset": + """Creates a DLataset from tfrecord files. The type spec of the dataset is inferred from the first file. The + only constraint is that each example must be a trajectory where each entry is either a scalar, a tensor of shape + (1, ...), or a tensor of shape (T, ...), where T is the length of the trajectory. + + Args: + dir_or_paths (Union[str, Sequence[str]]): Either a directory containing .tfrecord files, or a list of paths + to tfrecord files. + shuffle (bool, optional): Whether to shuffle the tfrecord files. Defaults to True. + num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This + can use an excessive amount of memory if reading from cloud storage; decrease if necessary. + """ + if isinstance(dir_or_paths, str): + paths = tf.io.gfile.glob(tf.io.gfile.join(dir_or_paths, "*.tfrecord")) + else: + paths = dir_or_paths + + if len(paths) == 0: + raise ValueError(f"No tfrecord files found in {dir_or_paths}") + + if shuffle: + paths = tf.random.shuffle(paths) + + # extract the type spec from the first file + type_spec = _get_type_spec(paths[0]) + + # read the tfrecords (yields raw serialized examples) + dataset = _wrap(tf.data.TFRecordDataset, False)( + paths, + num_parallel_reads=num_parallel_reads, + )._apply_options() + + # decode the examples (yields trajectories) + dataset = dataset.traj_map(partial(_decode_example, type_spec=type_spec)) + + # broadcast traj metadata, as well as add some extra metadata (_len, _traj_index, _frame_index) + dataset = dataset.enumerate().traj_map(_broadcast_metadata) + + return dataset + + @staticmethod + def from_rlds( + builder: DatasetBuilder, + split: str = "train", + shuffle: bool = True, + num_parallel_reads: int = tf.data.AUTOTUNE, + ) -> "DLataset": + """Creates a DLataset from the RLDS format (which is a special case of the TFDS format). + + Args: + builder (DatasetBuilder): The TFDS dataset builder to load the dataset from. + data_dir (str): The directory to load the dataset from. + split (str, optional): The split to load, specified in TFDS format. Defaults to "train". + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to True. + num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This + can use an excessive amount of memory if reading from cloud storage; decrease if necessary. + """ + dataset = _wrap(builder.as_dataset, False)( + split=split, + shuffle_files=shuffle, + decoders={"steps": tfds.decode.SkipDecoding()}, + read_config=tfds.ReadConfig( + skip_prefetch=True, + num_parallel_calls_for_interleave_files=num_parallel_reads, + interleave_cycle_length=num_parallel_reads, + ), + )._apply_options() + + dataset = dataset.enumerate().traj_map(_broadcast_metadata_rlds) + + return dataset + + @staticmethod + def from_vla( + builder: DatasetBuilder, + split: str = "train", + shuffle: bool = True, + num_parallel_reads: int = tf.data.AUTOTUNE, + ) -> "DLataset": + """Creates a DLataset from the RLDS format (which is a special case of the TFDS format). + + Args: + builder (DatasetBuilder): The TFDS dataset builder to load the dataset from. + data_dir (str): The directory to load the dataset from. + split (str, optional): The split to load, specified in TFDS format. Defaults to "train". + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to True. + num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This + can use an excessive amount of memory if reading from cloud storage; decrease if necessary. + """ + step_spec = MKVLoader("/home/kych/datasets/mkv_convert/").get_schema().get_tf_step_spec() + # Generator function + def generator(): + mkv_loader = MKVLoader("/home/kych/datasets/mkv_convert/") + + for output_tf_traj in mkv_loader: + print(f"{time()} before converting to tensor") + def worker(key, sub_key, data, return_dict): + if data.dtype == object: + # strings are objects in numpy, need to convert to tf.string + return_dict[(key, sub_key)] = tf.stack([tf.convert_to_tensor(x, dtype=tf.string) for x in data]) + else: + return_dict[(key, sub_key)] = tf.convert_to_tensor(data) + + manager = mp.Manager() + return_dict = manager.dict() + jobs = [] + + for key in output_tf_traj: + if isinstance(output_tf_traj[key], dict): + for sub_key in output_tf_traj[key]: + p = mp.Process(target=worker, args=(key, sub_key, output_tf_traj[key][sub_key], return_dict)) + jobs.append(p) + p.start() + else: + p = mp.Process(target=worker, args=(key, None, output_tf_traj[key], return_dict)) + jobs.append(p) + p.start() + + for job in jobs: + job.join() + + for key, sub_key in return_dict: + if sub_key is None: + output_tf_traj[key] = return_dict[(key, sub_key)] + else: + output_tf_traj[key][sub_key] = return_dict[(key, sub_key)] + + output = {"steps" : output_tf_traj} + print(f"{time()} after converting to tensor") + yield output + + + # Create dataset + output_signature = {"steps" : tf.nest.map_structure( + lambda spec: tf.TensorSpec(shape=spec.shape, dtype=spec.dtype), step_spec + )} + + dataset = _wrap(tf.data.Dataset.from_generator, False)( + generator, + output_signature=output_signature + ) + + + dataset = dataset.enumerate().traj_map(_broadcast_metadata_rlds) + + return dataset + + + def map( + self, + fn: Callable[[Dict[str, Any]], Dict[str, Any]], + num_parallel_calls=tf.data.AUTOTUNE, + **kwargs, + ) -> "DLataset": + return super().map(fn, num_parallel_calls=num_parallel_calls, **kwargs) + + def traj_map( + self, + fn: Callable[[Dict[str, Any]], Dict[str, Any]], + num_parallel_calls=tf.data.AUTOTUNE, + **kwargs, + ) -> "DLataset": + """Maps a function over the trajectories of the dataset. The function should take a single trajectory + as input and return a single trajectory as output. + """ + if self.is_flattened: + raise ValueError("Cannot call traj_map on a flattened dataset.") + return super().map(fn, num_parallel_calls=num_parallel_calls, **kwargs) + + def frame_map( + self, + fn: Callable[[Dict[str, Any]], Dict[str, Any]], + num_parallel_calls=tf.data.AUTOTUNE, + **kwargs, + ) -> "DLataset": + """Maps a function over the frames of the dataset. The function should take a single frame as input + and return a single frame as output. + """ + if self.is_flattened: + return super().map(fn, num_parallel_calls=num_parallel_calls, **kwargs) + else: + return super().map( + parallel_vmap(fn, num_parallel_calls=num_parallel_calls), + num_parallel_calls=num_parallel_calls, + **kwargs, + ) + + def flatten(self, *, num_parallel_calls=tf.data.AUTOTUNE) -> "DLataset": + """Flattens the dataset of trajectories into a dataset of frames.""" + if self.is_flattened: + raise ValueError("Dataset is already flattened.") + dataset = self.interleave( + lambda traj: tf.data.Dataset.from_tensor_slices(traj), + cycle_length=num_parallel_calls, + num_parallel_calls=num_parallel_calls, + ) + dataset.is_flattened = True + return dataset + + def iterator(self, *, prefetch=tf.data.AUTOTUNE): + if prefetch == 0: + return self.as_numpy_iterator() + return self.prefetch(prefetch).as_numpy_iterator() + + @staticmethod + def choose_from_datasets(datasets, choice_dataset, stop_on_empty_dataset=True): + if not isinstance(datasets[0], DLataset): + raise ValueError("Please pass DLatasets to choose_from_datasets.") + return _wrap(tf.data.Dataset.choose_from_datasets, datasets[0].is_flattened)( + datasets, choice_dataset, stop_on_empty_dataset=stop_on_empty_dataset + ) + + @staticmethod + def sample_from_datasets( + datasets, + weights=None, + seed=None, + stop_on_empty_dataset=False, + rerandomize_each_iteration=None, + ): + if not isinstance(datasets[0], DLataset): + raise ValueError("Please pass DLatasets to sample_from_datasets.") + return _wrap(tf.data.Dataset.sample_from_datasets, datasets[0].is_flattened)( + datasets, + weights=weights, + seed=seed, + stop_on_empty_dataset=stop_on_empty_dataset, + rerandomize_each_iteration=rerandomize_each_iteration, + ) + + @staticmethod + def zip(*args, datasets=None, name=None): + if datasets is not None: + raise ValueError("Please do not pass `datasets=` to zip.") + if not isinstance(args[0], DLataset): + raise ValueError("Please pass DLatasets to zip.") + return _wrap(tf.data.Dataset.zip, args[0].is_flattened)(*args, name=name) + + +def _decode_example( + example_proto: tf.Tensor, type_spec: Dict[str, tf.TensorSpec] +) -> Dict[str, tf.Tensor]: + features = {key: tf.io.FixedLenFeature([], tf.string) for key in type_spec.keys()} + parsed_features = tf.io.parse_single_example(example_proto, features) + parsed_tensors = { + key: tf.io.parse_tensor(parsed_features[key], spec.dtype) + if spec is not None + else parsed_features[key] + for key, spec in type_spec.items() + } + + for key in parsed_tensors: + if type_spec[key] is not None: + parsed_tensors[key] = tf.ensure_shape( + parsed_tensors[key], type_spec[key].shape + ) + + return parsed_tensors + + +def _get_type_spec(path: str) -> Dict[str, tf.TensorSpec]: + """Get a type spec from a tfrecord file. + + Args: + path (str): Path to a single tfrecord file. + + Returns: + dict: A dictionary mapping feature names to tf.TensorSpecs. + """ + data = next(iter(tf.data.TFRecordDataset(path))).numpy() + example = tf.train.Example() + example.ParseFromString(data) + + printable_chars = set(bytes(string.printable, "utf-8")) + + out = {} + for key, value in example.features.feature.items(): + data = value.bytes_list.value[0] + # stupid hack to deal with strings that are not encoded as tensors + if all(char in printable_chars for char in data): + out[key] = None + continue + tensor_proto = tf.make_tensor_proto([]) + tensor_proto.ParseFromString(data) + dtype = tf.dtypes.as_dtype(tensor_proto.dtype) + shape = [d.size for d in tensor_proto.tensor_shape.dim] + if shape: + shape[0] = None # first dimension is trajectory length, which is variable + out[key] = tf.TensorSpec(shape=shape, dtype=dtype) + + return out + + +def _broadcast_metadata( + i: tf.Tensor, traj: Dict[str, tf.Tensor] +) -> Dict[str, tf.Tensor]: + """ + Each element of a dlimp dataset is a trajectory. This means each entry must either have a leading dimension equal to + the length of the trajectory, have a leading dimension of 1, or be a scalar. Entries with a leading dimension of 1 + and scalars are assumed to be trajectory-level metadata. This function broadcasts these entries to the length of the + trajectory, as well as adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`. + """ + # get the length of each dict entry + traj_lens = { + k: tf.shape(v)[0] if len(v.shape) > 0 else None for k, v in traj.items() + } + + # take the maximum length as the canonical length (elements should either be the same length or length 1) + traj_len = tf.reduce_max([l for l in traj_lens.values() if l is not None]) + + for k in traj: + # broadcast scalars to the length of the trajectory + if traj_lens[k] is None: + traj[k] = tf.repeat(traj[k], traj_len) + traj_lens[k] = traj_len + + # broadcast length-1 elements to the length of the trajectory + if traj_lens[k] == 1: + traj[k] = tf.repeat(traj[k], traj_len, axis=0) + traj_lens[k] = traj_len + + asserts = [ + # make sure all the lengths are the same + tf.assert_equal( + tf.size(tf.unique(tf.stack(list(traj_lens.values()))).y), + 1, + message="All elements must have the same length.", + ), + ] + + assert "_len" not in traj + assert "_traj_index" not in traj + assert "_frame_index" not in traj + traj["_len"] = tf.repeat(traj_len, traj_len) + traj["_traj_index"] = tf.repeat(i, traj_len) + traj["_frame_index"] = tf.range(traj_len) + + with tf.control_dependencies(asserts): + return traj + + +def _broadcast_metadata_rlds(i: tf.Tensor, traj: Dict[str, Any]) -> Dict[str, Any]: + """ + In the RLDS format, each trajectory has some top-level metadata that is explicitly separated out, and a "steps" + entry. This function moves the "steps" entry to the top level, broadcasting any metadata to the length of the + trajectory. This function also adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`. + """ + steps = traj.pop("steps") + + traj_len = tf.shape(tf.nest.flatten(steps)[0])[0] + + # broadcast metadata to the length of the trajectory + metadata = tf.nest.map_structure(lambda x: tf.repeat(x, traj_len), traj) + + # put steps back in + assert "traj_metadata" not in steps + traj = {**steps, "traj_metadata": metadata} + + assert "_len" not in traj + assert "_traj_index" not in traj + assert "_frame_index" not in traj + traj["_len"] = tf.repeat(traj_len, traj_len) + traj["_traj_index"] = tf.repeat(i, traj_len) + traj["_frame_index"] = tf.range(traj_len) + + return traj \ No newline at end of file From d43070905266abe54487951059ea87688dc466e8 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 26 Aug 2024 23:40:03 -0700 Subject: [PATCH 45/80] add basic support for octo integration --- examples/openx_loader.py | 13 ++++- fog_x/DLdataset.py | 115 +++++++++++++++++++++++++-------------- fog_x/dataset.py | 18 +++++- fog_x/feature.py | 4 +- fog_x/loader/vla.py | 3 + fog_x/utils.py | 20 +++++++ 6 files changed, 126 insertions(+), 47 deletions(-) create mode 100644 fog_x/utils.py diff --git a/examples/openx_loader.py b/examples/openx_loader.py index e9ed62a..765faf8 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -3,17 +3,24 @@ import os -os.system("rm -rf /tmp/fog_x/*") +data_dir = "/home/kych/datasets/rtx" +dataset_name = "berkeley_autolab_ur5" +dataset_name = "berkeley_cable_routing" + +# loader = RLDSLoader( +# path="/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", split="train[:100]" +# ) loader = RLDSLoader( - path="/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", split="train[:10]" + path=f"{data_dir}/{dataset_name}/0.1.0", split="train[:64]" ) + index = 0 for data_traj in loader: fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"/tmp/fog_x/output_{index}.vla" + data_traj, path=f"/home/kych/datasets/fog_x/vla/{dataset_name}/output_{index}.vla" ) index += 1 diff --git a/fog_x/DLdataset.py b/fog_x/DLdataset.py index e40926f..dec7605 100644 --- a/fog_x/DLdataset.py +++ b/fog_x/DLdataset.py @@ -8,8 +8,8 @@ from tensorflow_datasets.core.dataset_builder import DatasetBuilder from dlimp.utils import parallel_vmap, vmap - - +from .dataset import VLADataset +import h5py def _wrap(f, is_flattened): """Wraps a method to return a DLataset instead of a tf.data.Dataset.""" @@ -156,9 +156,20 @@ def from_rlds( return dataset + @staticmethod + def from_hdf5( + path : str, + split: str = "train", + shuffle: bool = True, + num_parallel_reads: int = tf.data.AUTOTUNE, + ) -> "DLataset": + pass + + + @staticmethod def from_vla( - builder: DatasetBuilder, + path : str, split: str = "train", shuffle: bool = True, num_parallel_reads: int = tf.data.AUTOTUNE, @@ -173,47 +184,69 @@ def from_vla( num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This can use an excessive amount of memory if reading from cloud storage; decrease if necessary. """ - step_spec = MKVLoader("/home/kych/datasets/mkv_convert/").get_schema().get_tf_step_spec() + + vla_dataset = VLADataset(path, split) + + step_spec = vla_dataset.get_tf_schema() # Generator function def generator(): - mkv_loader = MKVLoader("/home/kych/datasets/mkv_convert/") - - for output_tf_traj in mkv_loader: - print(f"{time()} before converting to tensor") - def worker(key, sub_key, data, return_dict): - if data.dtype == object: - # strings are objects in numpy, need to convert to tf.string - return_dict[(key, sub_key)] = tf.stack([tf.convert_to_tensor(x, dtype=tf.string) for x in data]) - else: - return_dict[(key, sub_key)] = tf.convert_to_tensor(data) - - manager = mp.Manager() - return_dict = manager.dict() - jobs = [] - - for key in output_tf_traj: - if isinstance(output_tf_traj[key], dict): - for sub_key in output_tf_traj[key]: - p = mp.Process(target=worker, args=(key, sub_key, output_tf_traj[key][sub_key], return_dict)) - jobs.append(p) - p.start() - else: - p = mp.Process(target=worker, args=(key, None, output_tf_traj[key], return_dict)) - jobs.append(p) - p.start() - - for job in jobs: - job.join() - - for key, sub_key in return_dict: - if sub_key is None: - output_tf_traj[key] = return_dict[(key, sub_key)] - else: - output_tf_traj[key][sub_key] = return_dict[(key, sub_key)] - - output = {"steps" : output_tf_traj} - print(f"{time()} after converting to tensor") + loader = vla_dataset.get_loader() + + for output_tf_traj in loader: + + h5_cache = output_tf_traj.load() + # convert cache to tensor + def _convert_h5_cache_to_tensor(): + output_tf_traj = {} + for key in h5_cache: + # hierarhical + if type(h5_cache[key]) == h5py._hl.group.Group: + for sub_key in h5_cache[key]: + if key not in output_tf_traj: + output_tf_traj[key] = {} + output_tf_traj[key][sub_key] = tf.convert_to_tensor(h5_cache[key][sub_key]) + elif type(h5_cache[key]) == h5py._hl.dataset.Dataset: + output_tf_traj[key] = tf.convert_to_tensor(h5_cache[key]) + return output_tf_traj + output = {"steps" : _convert_h5_cache_to_tensor()} + print(output) + yield output + + # def worker(key, sub_key, data, return_dict): + # if data.dtype == object: + # # strings are objects in numpy, need to convert to tf.string + # return_dict[(key, sub_key)] = tf.stack([tf.convert_to_tensor(x, dtype=tf.string) for x in data]) + # else: + # return_dict[(key, sub_key)] = tf.convert_to_tensor(data) + + # manager = mp.Manager() + # return_dict = manager.dict() + # jobs = [] + + # for key in output_tf_traj: + # if isinstance(output_tf_traj[key], dict): + # for sub_key in output_tf_traj[key]: + # p = mp.Process(target=worker, args=(key, sub_key, output_tf_traj[key][sub_key], return_dict)) + # jobs.append(p) + # p.start() + # else: + # p = mp.Process(target=worker, args=(key, None, output_tf_traj[key], return_dict)) + # jobs.append(p) + # p.start() + + # for job in jobs: + # job.join() + + # for key, sub_key in return_dict: + # if sub_key is None: + # output_tf_traj[key] = return_dict[(key, sub_key)] + # else: + # output_tf_traj[key][sub_key] = return_dict[(key, sub_key)] + + # output = {"steps" : output_tf_traj} + # print(f"{time()} after converting to tensor") + # yield output # Create dataset diff --git a/fog_x/dataset.py b/fog_x/dataset.py index 588ae72..67d1a58 100644 --- a/fog_x/dataset.py +++ b/fog_x/dataset.py @@ -1,7 +1,9 @@ import os from typing import Any, Dict, List, Optional, Text +from fog_x.loader.vla import VLALoader +from fog_x.utils import data_to_tf_schema -class Dataset: +class VLADataset: def __init__(self, path: Text, split: Text, @@ -19,7 +21,11 @@ def __init__(self, format (Optional[Text]): format of the dataset. Auto-detected if None. Defaults to None. we assume that the format is the same for all files in the dataset """ - pass + self.path = path + self.split = split + self.format = format + + self.loader = VLALoader(path) def __iter__(self): return self @@ -32,3 +38,11 @@ def __len__(self): def __getitem__(self, index): raise NotImplementedError + + def get_tf_schema(self): + data = self.loader.peak(0).load(mode = "no_cache") # enforces no h5 cache + return data_to_tf_schema(data) + + def get_loader(self): + return self.loader + \ No newline at end of file diff --git a/fog_x/feature.py b/fog_x/feature.py index 7f2aae6..4aa0495 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -1,5 +1,5 @@ import logging -from typing import Any, List, Optional, Tuple +from typing import Any, List, Optional, Tuple, Dict import numpy as np @@ -188,3 +188,5 @@ def to_pld_storage_type(self): return self.dtype else: return "large_binary" + + diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 6fa0650..efa27b1 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -49,3 +49,6 @@ def __next__(self): def __len__(self): return len(self.files) + + def peak(self, index): + return self._read_vla(self.files[index]) \ No newline at end of file diff --git a/fog_x/utils.py b/fog_x/utils.py new file mode 100644 index 0000000..06cbed3 --- /dev/null +++ b/fog_x/utils.py @@ -0,0 +1,20 @@ + +from typing import Any, Dict +import numpy as np +from fog_x.feature import FeatureType + + +def data_to_tf_schema(data: Dict[str, Any]) -> Dict[str, FeatureType]: + """ + Convert data to a tf schema + """ + schema = {} + for k, v in data.items(): + if "/" in k: # make the subkey to be within dict + main_key, sub_key = k.split("/") + if main_key not in schema: + schema[main_key] = {} + schema[main_key][sub_key] = FeatureType.from_data(v).to_tf_feature_type() + else: + schema[k] = FeatureType.from_data(v).to_tf_feature_type() + return schema \ No newline at end of file From 3467b3f7bd6538f1941985f12007be9e87425f52 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 26 Aug 2024 23:52:02 -0700 Subject: [PATCH 46/80] fix a bug in loading --- fog_x/trajectory.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 8b49d16..6c35fd0 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -484,6 +484,7 @@ def _get_length_of_stream(container, stream): container_to_get_length = av.open(self.path, mode="r", format="matroska") streams = container_to_get_length.streams length = _get_length_of_stream(container_to_get_length, streams[0]) + logger.info(f"Length of the stream is {length}") container_to_get_length.close() container = av.open(self.path, mode="r", format="matroska") @@ -545,7 +546,7 @@ def _get_length_of_stream(container, stream): data = pickle.loads(packet_in_bytes) # Append data to the numpy array - np_cache[feature_name] = np.append(np_cache[feature_name], [data], axis=0) + np_cache[feature_name][d_feature_length[feature_name]] = data d_feature_length[feature_name] += 1 else: logger.debug(f"Skipping empty packet: {packet} for {feature_name}") From b79068a5a72455441b9c007c6c47c76d9266e9ba Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 27 Aug 2024 00:25:41 -0700 Subject: [PATCH 47/80] octo dataloader working --- fog_x/DLdataset.py | 4 ++-- fog_x/feature.py | 8 ++++++-- fog_x/trajectory.py | 8 +++++++- fog_x/utils.py | 5 +++-- 4 files changed, 18 insertions(+), 7 deletions(-) diff --git a/fog_x/DLdataset.py b/fog_x/DLdataset.py index dec7605..d64f7da 100644 --- a/fog_x/DLdataset.py +++ b/fog_x/DLdataset.py @@ -209,7 +209,6 @@ def _convert_h5_cache_to_tensor(): output_tf_traj[key] = tf.convert_to_tensor(h5_cache[key]) return output_tf_traj output = {"steps" : _convert_h5_cache_to_tensor()} - print(output) yield output @@ -253,7 +252,8 @@ def _convert_h5_cache_to_tensor(): output_signature = {"steps" : tf.nest.map_structure( lambda spec: tf.TensorSpec(shape=spec.shape, dtype=spec.dtype), step_spec )} - + print(output_signature) + dataset = _wrap(tf.data.Dataset.from_generator, False)( generator, output_signature=output_signature diff --git a/fog_x/feature.py b/fog_x/feature.py index 4aa0495..08c3e1b 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -140,7 +140,7 @@ def from_str(self, feature_str: str): shape = eval(shape.split("=")[1][:-1]) # strip brackets return FeatureType(dtype=dtype, shape=shape) - def to_tf_feature_type(self): + def to_tf_feature_type(self, first_dim_none=False): """ Convert to tf feature """ @@ -176,7 +176,11 @@ def to_tf_feature_type(self): else: return Scalar(dtype=tf_detype) elif len(self.shape) >= 1: - return Tensor(shape=self.shape, dtype=tf_detype) + if first_dim_none: + tf_shape = [None] + list(self.shape[1:]) + return Tensor(shape=tf_shape, dtype=tf_detype) + else: + return Tensor(shape=self.shape, dtype=tf_detype) else: raise ValueError(f"Unsupported conversion to tf feature: {self}") diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 6c35fd0..46618b5 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -558,7 +558,13 @@ def _get_length_of_stream(container, stream): h5_cache = h5py.File(self.cache_file_name, "w") for feature_name, data in np_cache.items(): if data.dtype == object: - continue # TODO + for i in range(len(data)): + if data[i] is not None: + data[i] = str(data[i]) + h5_cache.create_dataset( + feature_name, + data=data + ) else: h5_cache.create_dataset(feature_name, data=data) h5_cache.close() diff --git a/fog_x/utils.py b/fog_x/utils.py index 06cbed3..cdbf925 100644 --- a/fog_x/utils.py +++ b/fog_x/utils.py @@ -14,7 +14,8 @@ def data_to_tf_schema(data: Dict[str, Any]) -> Dict[str, FeatureType]: main_key, sub_key = k.split("/") if main_key not in schema: schema[main_key] = {} - schema[main_key][sub_key] = FeatureType.from_data(v).to_tf_feature_type() + schema[main_key][sub_key] = FeatureType.from_data(v).to_tf_feature_type(first_dim_none=True) + # replace first element of shape with None else: - schema[k] = FeatureType.from_data(v).to_tf_feature_type() + schema[k] = FeatureType.from_data(v).to_tf_feature_type(first_dim_none=True) return schema \ No newline at end of file From 560098544dcda4116db98484c2b5aae63954c2ec Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 27 Aug 2024 01:14:40 -0700 Subject: [PATCH 48/80] Refactor RLDSLoader and Trajectory classes to improve code readability and add lazy loading for data --- examples/openx_loader.py | 2 +- fog_x/DLdataset.py | 17 ++--------------- fog_x/dataset.py | 16 ++++++++++++++-- fog_x/loader/vla.py | 2 +- fog_x/trajectory.py | 13 +++++++------ 5 files changed, 25 insertions(+), 25 deletions(-) diff --git a/examples/openx_loader.py b/examples/openx_loader.py index 765faf8..8de729c 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -12,7 +12,7 @@ # ) loader = RLDSLoader( - path=f"{data_dir}/{dataset_name}/0.1.0", split="train[:64]" + path=f"{data_dir}/{dataset_name}/0.1.0", split="train" ) diff --git a/fog_x/DLdataset.py b/fog_x/DLdataset.py index d64f7da..82b1cb4 100644 --- a/fog_x/DLdataset.py +++ b/fog_x/DLdataset.py @@ -155,17 +155,6 @@ def from_rlds( dataset = dataset.enumerate().traj_map(_broadcast_metadata_rlds) return dataset - - @staticmethod - def from_hdf5( - path : str, - split: str = "train", - shuffle: bool = True, - num_parallel_reads: int = tf.data.AUTOTUNE, - ) -> "DLataset": - pass - - @staticmethod def from_vla( @@ -185,16 +174,14 @@ def from_vla( can use an excessive amount of memory if reading from cloud storage; decrease if necessary. """ - vla_dataset = VLADataset(path, split) + vla_dataset = VLADataset(path, split, shuffle=shuffle) step_spec = vla_dataset.get_tf_schema() # Generator function def generator(): - loader = vla_dataset.get_loader() + for h5_cache in vla_dataset: - for output_tf_traj in loader: - h5_cache = output_tf_traj.load() # convert cache to tensor def _convert_h5_cache_to_tensor(): output_tf_traj = {} diff --git a/fog_x/dataset.py b/fog_x/dataset.py index 67d1a58..7765fbf 100644 --- a/fog_x/dataset.py +++ b/fog_x/dataset.py @@ -2,11 +2,17 @@ from typing import Any, Dict, List, Optional, Text from fog_x.loader.vla import VLALoader from fog_x.utils import data_to_tf_schema +import numpy as np class VLADataset: + """ + 1. figure out the path to the dataset + 2. shuffling / training management + """ def __init__(self, path: Text, split: Text, + shuffle: bool = False, format: Optional[Text] = None): """ init method for Dataset class @@ -24,6 +30,7 @@ def __init__(self, self.path = path self.split = split self.format = format + self.shuffle = shuffle self.loader = VLALoader(path) @@ -31,7 +38,7 @@ def __iter__(self): return self def __next__(self): - raise NotImplementedError + return self.get_next_trajectory() def __len__(self): raise NotImplementedError @@ -45,4 +52,9 @@ def get_tf_schema(self): def get_loader(self): return self.loader - \ No newline at end of file + + def get_next_trajectory(self): + if self.shuffle: + return self.loader.peak(np.random.randint(0, len(self.loader))).load() + else: + return next(self.loader).load() \ No newline at end of file diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index efa27b1..20e5dfe 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -30,7 +30,7 @@ def __init__(self, path: Text, cache_dir=None): self.cache_dir = cache_dir def _read_vla(self, data_path): - logger.info(f"Reading {data_path}") + logger.debug(f"Reading {data_path}") if self.cache_dir: traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) else: diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 46618b5..3b38c53 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -68,6 +68,7 @@ def __init__( os.makedirs(cache_dir, exist_ok=True) hex_hash = hex(abs(hash(self.path)))[2:] self.cache_file_name = cache_dir + hex_hash + ".cache" + # self.cache_file_name = cache_dir + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type self.trajectory_data = None # trajectory_data @@ -167,17 +168,17 @@ def load(self, mode = "cache"): """ if mode == "cache": if os.path.exists(self.cache_file_name): - logger.info(f"Loading the cached file {self.cache_file_name}") + logger.debug(f"Loading the cached file {self.cache_file_name}") self.trajectory_data = self._load_from_cache() else: - logger.info(f"Loading the container file {self.path}") + logger.debug(f"Loading the container file {self.path}, saving to cache {self.cache_file_name}") self.trajectory_data = self._load_from_container(save_to_cache=True) elif mode == "no_cache": - logger.info(f"Loading the container file {self.path} without cache") + logger.debug(f"Loading the container file {self.path} without cache") # self.trajectory_data = self._load_from_container_to_h5() self.trajectory_data = self._load_from_container(save_to_cache=False) else: - logger.info(f"No option provided. Force loading from container file {self.path}") + logger.debug(f"No option provided. Force loading from container file {self.path}") self.trajectory_data = self._load_from_container(save_to_cache=False) return self.trajectory_data @@ -484,7 +485,7 @@ def _get_length_of_stream(container, stream): container_to_get_length = av.open(self.path, mode="r", format="matroska") streams = container_to_get_length.streams length = _get_length_of_stream(container_to_get_length, streams[0]) - logger.info(f"Length of the stream is {length}") + logger.debug(f"Length of the stream is {length}") container_to_get_length.close() container = av.open(self.path, mode="r", format="matroska") @@ -550,7 +551,7 @@ def _get_length_of_stream(container, stream): d_feature_length[feature_name] += 1 else: logger.debug(f"Skipping empty packet: {packet} for {feature_name}") - print(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") + logger.debug(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") container.close() if save_to_cache: From 1f445df2a04c3870cfd6846ab612f11c948b4eb4 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 27 Aug 2024 02:00:57 -0700 Subject: [PATCH 49/80] Refactor RLDSLoader and Trajectory classes to improve code readability and add lazy loading for data --- examples/openx_loader.py | 23 +++++++++++++++++------ fog_x/DLdataset.py | 5 +++-- fog_x/trajectory.py | 20 +++++++++++++++----- 3 files changed, 35 insertions(+), 13 deletions(-) diff --git a/examples/openx_loader.py b/examples/openx_loader.py index 8de729c..22bb727 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -5,7 +5,7 @@ data_dir = "/home/kych/datasets/rtx" dataset_name = "berkeley_autolab_ur5" -dataset_name = "berkeley_cable_routing" +dataset_name = "fractal20220817_data" # loader = RLDSLoader( # path="/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", split="train[:100]" @@ -15,12 +15,23 @@ path=f"{data_dir}/{dataset_name}/0.1.0", split="train" ) +from concurrent.futures import ProcessPoolExecutor +import os -index = 0 - -for data_traj in loader: - +def process_data(data_traj, dataset_name, index): fog_x.Trajectory.from_list_of_dicts( data_traj, path=f"/home/kych/datasets/fog_x/vla/{dataset_name}/output_{index}.vla" ) - index += 1 + +# Use ThreadPoolExecutor to parallelize the processing +with ProcessPoolExecutor() as executor: + futures = [] + for index, data_traj in enumerate(loader): + # Submit the task to the executor + futures.append(executor.submit(process_data, data_traj, dataset_name, index)) + + # Optionally, wait for all futures to complete + for future in futures: + future.result() # This will raise an exception if the task raised one + +print("All tasks completed.") \ No newline at end of file diff --git a/fog_x/DLdataset.py b/fog_x/DLdataset.py index 82b1cb4..44fb585 100644 --- a/fog_x/DLdataset.py +++ b/fog_x/DLdataset.py @@ -158,7 +158,8 @@ def from_rlds( @staticmethod def from_vla( - path : str, + dataset_dir: str, + dataset_name : str, split: str = "train", shuffle: bool = True, num_parallel_reads: int = tf.data.AUTOTUNE, @@ -173,7 +174,7 @@ def from_vla( num_parallel_reads (int, optional): The number of tfrecord files to read in parallel. Defaults to AUTOTUNE. This can use an excessive amount of memory if reading from cloud storage; decrease if necessary. """ - + path = f"{dataset_dir}/{dataset_name}" vla_dataset = VLADataset(path, split, shuffle=shuffle) step_spec = vla_dataset.get_tf_schema() diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 3b38c53..d84d3d4 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -560,12 +560,22 @@ def _get_length_of_stream(container, stream): for feature_name, data in np_cache.items(): if data.dtype == object: for i in range(len(data)): - if data[i] is not None: + data_type = type(data[i]) + if data_type == str: data[i] = str(data[i]) - h5_cache.create_dataset( - feature_name, - data=data - ) + elif data_type == bytes: + data[i] = str(data[i]) + elif data_type == np.ndarray: + data[i] = str(data[i]) + else: + data[i] = str(data[i]) + try: + h5_cache.create_dataset( + feature_name, + data=data + ) + except Exception as e: + logger.error(f"Error saving {feature_name} to cache: {e} with data {data}") else: h5_cache.create_dataset(feature_name, data=data) h5_cache.close() From 0333a097bff68f9ff8acb9c16524580403c59475 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Thu, 29 Aug 2024 19:03:12 -0700 Subject: [PATCH 50/80] open x dataset converter streamline --- examples/openx_loader.py | 28 +++++++++++++++++++--------- 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/examples/openx_loader.py b/examples/openx_loader.py index 22bb727..f35b810 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -3,32 +3,42 @@ import os -data_dir = "/home/kych/datasets/rtx" +data_dir = "/home/kych//datasets/rtx" +dataset_name = "berkeley_cable_routing" dataset_name = "berkeley_autolab_ur5" dataset_name = "fractal20220817_data" +destination_dir = "/mnt/data/datasets/fog_x/ffv1" +# destination_dir = "/home/kych//datasets/fog_x/vla" +version = "0.1.0" # loader = RLDSLoader( # path="/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", split="train[:100]" # ) loader = RLDSLoader( - path=f"{data_dir}/{dataset_name}/0.1.0", split="train" + path=f"{data_dir}/{dataset_name}/{version}", split="train" ) from concurrent.futures import ProcessPoolExecutor import os def process_data(data_traj, dataset_name, index): - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"/home/kych/datasets/fog_x/vla/{dataset_name}/output_{index}.vla" - ) + try: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla" + ) + except Exception as e: + print(f"Failed to process data {index}: {e}") # Use ThreadPoolExecutor to parallelize the processing -with ProcessPoolExecutor() as executor: +with ProcessPoolExecutor(max_workers=4) as executor: futures = [] - for index, data_traj in enumerate(loader): - # Submit the task to the executor - futures.append(executor.submit(process_data, data_traj, dataset_name, index)) + try: + for index, data_traj in enumerate(loader): + # Submit the task to the executor + futures.append(executor.submit(process_data, data_traj, dataset_name, index)) + except Exception as e: + print(f"Failed to process data {index}: {e}") # Optionally, wait for all futures to complete for future in futures: From d5c73320f89d59391e974eae190c90ce0b235ca2 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Thu, 29 Aug 2024 19:14:11 -0700 Subject: [PATCH 51/80] support both lossy and lossless compression --- fog_x/trajectory.py | 35 ++++++++++++++++++----------------- 1 file changed, 18 insertions(+), 17 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index d84d3d4..c9266bc 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -44,7 +44,7 @@ def __init__( path: Text, mode="r", cache_dir: Optional[Text] = "/tmp/fog_x/cache/", - num_pre_initialized_h264_streams: int = 5, + lossy_compression: bool = True, feature_name_separator: Text = "/", ) -> None: """ @@ -76,6 +76,7 @@ def __init__( self.mode = mode self.stream_id_to_info = {} # stream_id: StreamInfo self.is_closed = False + self.lossy_compression = lossy_compression # check if the path exists # if not, create a new file and start data collection @@ -100,17 +101,6 @@ def _get_current_timestamp(self): def __len__(self): raise NotImplementedError - # def _pre_initialize_h264_streams(self, num_streams: int): - # """ - # Pre-initialize a configurable number of H.264 video streams. - # """ - # for i in range(num_streams): - # encoding = "libx264" - # stream = self.container_file.add_stream(encoding) - # stream.time_base = Fraction(1, 1000) - # stream.pix_fmt = "yuv420p" - # self.pre_initialized_image_streams.append(stream) - def __getitem__(self, key): """ get the value of the feature @@ -638,7 +628,7 @@ def is_packet_valid(packet): ] # Check if the stream is using rawvideo, meaning it's a pickled stream - if packet.stream.codec_context.codec.name == "libx264": + if packet.stream.codec_context.codec.name == "ffv1" or packet.stream.codec_context.codec.name == "libx264": data = pickle.loads(bytes(packet)) # Encode the image data as needed, example shown for raw images @@ -690,7 +680,7 @@ def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packe encoding = stream.codec_context.codec.name feature_type = FeatureType.from_data(data) logger.debug(f"Encoding {stream.metadata.get('FEATURE_NAME')} with {encoding}") - if encoding == "libx264": + if encoding == "ffv1" or encoding == "libx264": if feature_type.dtype == "float32": frame = self._create_frame_depth(data, stream) else: @@ -792,13 +782,21 @@ def is_packet_valid(packet): def _add_stream_to_container(self, container, feature_name, encoding, feature_type): stream = container.add_stream(encoding) - if encoding == "libx264": + if encoding == "ffv1": stream.width = feature_type.shape[0] stream.height = feature_type.shape[1] stream.codec_context.options = { "preset": "fast", # Set preset to 'fast' for quicker encoding "tune": "zerolatency", # Reduce latency - "profile": "baseline", # Use baseline profile + } + + if encoding == "libx264": + stream.width = feature_type.shape[0] + stream.height = feature_type.shape[1] + stream.codec_context.options = { + "preset": "ultrafast", # Set preset to 'ultrafast' for quicker encoding + "tune": "zerolatency", # Reduce latency + "profile": "baseline", # no b frame } stream.metadata["FEATURE_NAME"] = feature_name @@ -840,7 +838,10 @@ def _get_encoding_of_feature( feature_type = FeatureType.from_data(feature_value) data_shape = feature_type.shape if len(data_shape) >= 2 and data_shape[0] >= 100 and data_shape[1] >= 100: - vid_coding = "libx264" + if self.lossy_compression: + vid_coding = "libx264" + else: + vid_coding = "ffv1" else: vid_coding = "rawvideo" return vid_coding From 1567b4432ccc951ab1b33f36cf16a39c68d8ba7d Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Thu, 29 Aug 2024 21:16:18 -0700 Subject: [PATCH 52/80] chore: Update DEFAULT_DATASET_NAMES in openx.py and add lossy_compression parameter in Trajectory class --- benchmarks/openx.py | 2 +- examples/openx_loader.py | 90 +++++++++++++++++++++++----------------- experiments.sh | 11 +++++ fog_x/trajectory.py | 8 ++-- 4 files changed, 68 insertions(+), 43 deletions(-) create mode 100755 experiments.sh diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 7f2875f..7463045 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -16,7 +16,7 @@ DEFAULT_NUMBER_OF_TRAJECTORIES = 1024 DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5", "bridge", "berkeley_cable_routing", "nyu_door_opening_surprising_effectiveness"] # DEFAULT_NUMBER_OF_TRAJECTORIES = 1000 -# DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] +DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" diff --git a/examples/openx_loader.py b/examples/openx_loader.py index f35b810..5d04282 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -1,47 +1,61 @@ -from fog_x.loader import RLDSLoader -import fog_x - -import os - -data_dir = "/home/kych//datasets/rtx" -dataset_name = "berkeley_cable_routing" -dataset_name = "berkeley_autolab_ur5" -dataset_name = "fractal20220817_data" -destination_dir = "/mnt/data/datasets/fog_x/ffv1" -# destination_dir = "/home/kych//datasets/fog_x/vla" -version = "0.1.0" - -# loader = RLDSLoader( -# path="/home/kych/datasets/rtx/berkeley_autolab_ur5/0.1.0", split="train[:100]" -# ) - -loader = RLDSLoader( - path=f"{data_dir}/{dataset_name}/{version}", split="train" -) - +import argparse from concurrent.futures import ProcessPoolExecutor import os +from fog_x.loader import RLDSLoader +import fog_x -def process_data(data_traj, dataset_name, index): +def process_data(data_traj, dataset_name, index, destination_dir, lossless): try: - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla" - ) + if lossless: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=False + ) + else: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=True, + ) except Exception as e: print(f"Failed to process data {index}: {e}") -# Use ThreadPoolExecutor to parallelize the processing -with ProcessPoolExecutor(max_workers=4) as executor: - futures = [] - try: - for index, data_traj in enumerate(loader): - # Submit the task to the executor - futures.append(executor.submit(process_data, data_traj, dataset_name, index)) - except Exception as e: - print(f"Failed to process data {index}: {e}") +def main(): + parser = argparse.ArgumentParser(description="Process RLDS data and convert to VLA format.") + parser.add_argument("--data_dir", required=True, help="Path to the data directory") + parser.add_argument("--dataset_name", required=True, help="Name of the dataset") + parser.add_argument("--version", default="0.1.0", help="Dataset version") + parser.add_argument("--destination_dir", required=True, help="Destination directory for output files") + parser.add_argument("--split", default="train", help="Data split to use") + parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes") + parser.add_argument("--lossless", action="store_true", help="Enable lossless compression for VLA format") + + args = parser.parse_args() - # Optionally, wait for all futures to complete - for future in futures: - future.result() # This will raise an exception if the task raised one + loader = RLDSLoader( + path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split + ) -print("All tasks completed.") \ No newline at end of file + # train[start:end] + try: + split_starting_index = int(args.split.split("[")[1].split(":")[0]) + print(f"Starting index: {split_starting_index}") + except Exception as e: + print(f"Failed to get starting index: {e}") + split_starting_index = 0 + + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = [] + try: + for index, data_traj in enumerate(loader): + index = index + split_starting_index + futures.append(executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless)) + except Exception as e: + print(f"Failed to process data: {e}") + + for future in futures: + future.result() + + print("All tasks completed.") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/experiments.sh b/experiments.sh new file mode 100755 index 0000000..81f67f0 --- /dev/null +++ b/experiments.sh @@ -0,0 +1,11 @@ +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 + +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index c9266bc..cfe66ef 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -276,7 +276,7 @@ def add_by_dict( self.add(feature, value, timestamp) @classmethod - def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajectory": + def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text, lossy_compression: bool = True) -> "Trajectory": """ Create a Trajectory object from a list of dictionaries. @@ -292,7 +292,7 @@ def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajecto trajectory = Trajectory.from_list_of_dicts(original_trajectory, path="/tmp/fog_x/output.vla") """ - traj = cls(path, mode="w") + traj = cls(path, mode="w", lossy_compression=lossy_compression) logger.info(f"Creating a new trajectory file at {path} with {len(data)} steps") for step in data: traj.add_by_dict(step) @@ -301,7 +301,7 @@ def from_list_of_dicts(cls, data: List[Dict[str, Any]], path: Text) -> "Trajecto @classmethod def from_dict_of_lists( - cls, data: Dict[str, List[Any]], path: Text, feature_name_separator: Text = "/" + cls, data: Dict[str, List[Any]], path: Text, feature_name_separator: Text = "/", lossy_compression: bool = True ) -> "Trajectory": """ Create a Trajectory object from a dictionary of lists. @@ -321,7 +321,7 @@ def from_dict_of_lists( trajectory = Trajectory.from_dict_of_lists(original_trajectory, path="/tmp/fog_x/output.vla") """ - traj = cls(path, feature_name_separator=feature_name_separator, mode="w") + traj = cls(path, feature_name_separator=feature_name_separator, mode="w", lossy_compression = lossy_compression) # flatten the data such that all data starts and put feature name with separator _flatten_dict_data = _flatten_dict(data, sep=traj.feature_name_separator) From 3953f7faff78fddd21aeb27a74369c20413a0aef Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Fri, 30 Aug 2024 00:57:27 -0700 Subject: [PATCH 53/80] chore: Update vla_to_h5.py to process VLA data and convert it to HDF5 format --- examples/vla_to_h5.py | 43 +++++++++++++++++++++++++++++++++++++++++++ experiments.sh | 11 ----------- openx_to_vla.sh | 37 +++++++++++++++++++++++++++++++++++++ vla_to_hdf5.sh | 6 ++++++ 4 files changed, 86 insertions(+), 11 deletions(-) create mode 100644 examples/vla_to_h5.py delete mode 100755 experiments.sh create mode 100755 openx_to_vla.sh create mode 100755 vla_to_hdf5.sh diff --git a/examples/vla_to_h5.py b/examples/vla_to_h5.py new file mode 100644 index 0000000..dbc4f11 --- /dev/null +++ b/examples/vla_to_h5.py @@ -0,0 +1,43 @@ +import fog_x +import os +import argparse +from concurrent.futures import ProcessPoolExecutor +from fog_x.loader import VLALoader + +def process_data(trajectory, dataset_name, index, destination_dir): + try: + trajectory.to_hdf5(path=f"{destination_dir}/{dataset_name}/output_{index}.h5") + print(f"processed data {index} to {destination_dir}/{dataset_name}/output_{index}.h5") + except Exception as e: + print(f"Failed to process data {index}: {e}") + +def main(): + parser = argparse.ArgumentParser(description="Convert VLA data to HDF5 format.") + parser.add_argument("--data_dir", required=True, help="Path to the VLA data directory") + parser.add_argument("--dataset_name", required=True, help="Name of the dataset") + parser.add_argument("--destination_dir", required=True, help="Destination directory for output HDF5 files") + parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes") + + args = parser.parse_args() + + vla_path = os.path.join(args.data_dir, args.dataset_name, "*.vla") + cache_dir = os.path.join("/mnt/data/fog_x/cache/", args.dataset_name) + loader = VLALoader(vla_path, cache_dir=cache_dir) + + os.makedirs(os.path.join(args.destination_dir, args.dataset_name), exist_ok=True) + + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = [] + try: + for index, trajectory in enumerate(loader): + futures.append(executor.submit(process_data, trajectory, args.dataset_name, index, args.destination_dir)) + except Exception as e: + print(f"Failed to process data: {e}") + + for future in futures: + future.result() + + print("All tasks completed.") + +if __name__ == "__main__": + main() diff --git a/experiments.sh b/experiments.sh deleted file mode 100755 index 81f67f0..0000000 --- a/experiments.sh +++ /dev/null @@ -1,11 +0,0 @@ -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 - -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless \ No newline at end of file diff --git a/openx_to_vla.sh b/openx_to_vla.sh new file mode 100755 index 0000000..4e25100 --- /dev/null +++ b/openx_to_vla.sh @@ -0,0 +1,37 @@ +# berkeley_autolab_ur5 dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 + +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless + + +# # bridge dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 + +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless + +# berkeley_cable_routing dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless + +# nyu_door_opening_surprising_effectiveness dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless + +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless \ No newline at end of file diff --git a/vla_to_hdf5.sh b/vla_to_hdf5.sh new file mode 100755 index 0000000..7e6a6d4 --- /dev/null +++ b/vla_to_hdf5.sh @@ -0,0 +1,6 @@ +# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 + +# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 +# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 + +python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name bridge --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 \ No newline at end of file From 3fcfc436aeb9b393db953cd7027d85ad75e4a269 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Fri, 30 Aug 2024 01:30:16 -0700 Subject: [PATCH 54/80] Refactor RLDSLoader and Trajectory classes to improve code readability and add lazy loading for data --- benchmarks/openx.py | 506 +++++++++----------------------------------- 1 file changed, 101 insertions(+), 405 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 7463045..851af8c 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -1,59 +1,38 @@ -import fog_x import os import subprocess import argparse -from concurrent.futures import ThreadPoolExecutor -import glob import time import numpy as np from fog_x.loader import RLDSLoader, VLALoader, HDF5Loader -import tensorflow as tf # this prevents tensorflow printed logs +import tensorflow as tf import pandas as pd -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" +import fog_x # Constants -DEFAULT_EXP_DIR = "/home/kych/datasets/fog_x/" -DEFAULT_NUMBER_OF_TRAJECTORIES = 1024 -DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5", "bridge", "berkeley_cable_routing", "nyu_door_opening_surprising_effectiveness"] -# DEFAULT_NUMBER_OF_TRAJECTORIES = 1000 -DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] -CACHE_DIR = "/tmp/fog_x/cache/" +DEFAULT_EXP_DIR = "/mnt/data/fog_x/" +DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories +DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness"] +CACHE_DIR = "/mnt/data/fog_x/cache/" +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" class DatasetHandler: - """Base class to handle dataset-related operations.""" - - DATA_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-{index:05d}-of-{total_trajectories:05d}" - LS_URL_TEMPLATE = "gs://gresearch/robotics/{dataset_name}/0.1.0/{dataset_name}-train.tfrecord-*" - LOCAL_FILE_TEMPLATE = "{exp_dir}/{dataset_type}/{dataset_name}/{dataset_name}-train.tfrecord-{index:05d}-of-{total_trajectories:05d}" - FEATURE_JSON_URL_TEMPLATE = ( - "gs://gresearch/robotics/{dataset_name}/0.1.0/features.json" - ) - DATASET_INFO_JSON_URL_TEMPLATE = ( - "gs://gresearch/robotics/{dataset_name}/0.1.0/dataset_info.json" - ) - def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type): self.exp_dir = exp_dir self.dataset_name = dataset_name - self.total_trajectories = self._get_total_number_of_trajectories() - self.num_trajectories = num_trajectories if num_trajectories <= self.total_trajectories else self.total_trajectories + self.num_trajectories = num_trajectories self.dataset_type = dataset_type self.dataset_dir = os.path.join(exp_dir, dataset_type, dataset_name) - - def _get_total_number_of_trajectories(self): - """Gets the total number of trajectories in the dataset.""" - # use gsutil to get a trajectory file name and extract the total number of trajectories - data_url = self.LS_URL_TEMPLATE.format( - dataset_name=self.dataset_name, index=0, - total_trajectories="*" - ) - output = subprocess.run( - ["gsutil", "ls", data_url], stdout=subprocess.PIPE, check=True + + def measure_folder_size(self): + """Calculates the total size of all files in the dataset directory.""" + total_size = sum( + os.path.getsize(os.path.join(dirpath, f)) + for dirpath, dirnames, filenames in os.walk(self.dataset_dir) + for f in filenames ) - total_trajectories = int(output.stdout.decode().split("-")[-1]) - - return total_trajectories + return total_size / (1024 * 1024) # Convert to MB + def clear_cache(self): """Clears the cache directory.""" if os.path.exists(CACHE_DIR): @@ -64,413 +43,130 @@ def clear_os_cache(self): subprocess.run(["sync"], check=True) subprocess.run(["echo", "3", ">", "/proc/sys/vm/drop_caches"], check=True) - def check_and_download_file(self, url, local_path): - """Checks if a file is already downloaded; if not, downloads it.""" - if not os.path.exists(local_path): - subprocess.run(["gsutil", "-m", "cp", url, local_path], check=True) - else: - print(f"File {local_path} already exists. Skipping download.") - - def check_and_download_trajectory(self, trajectory_index): - """Checks if a trajectory and associated JSON files are already downloaded; if not, downloads them.""" - os.makedirs(self.dataset_dir, exist_ok=True) - - # Check and download the trajectory files - local_file_pattern = self.LOCAL_FILE_TEMPLATE.format( - exp_dir=self.exp_dir, - dataset_type=self.dataset_type, - dataset_name=self.dataset_name, - index=trajectory_index, - total_trajectories = self.total_trajectories - ) - - # Ensure no files with .gstmp postfix are considered valid - valid_files_exist = any( - os.path.exists(file) and not file.endswith(".gstmp") - for file in glob.glob(local_file_pattern) - ) - - if not valid_files_exist: - data_url = self.DATA_URL_TEMPLATE.format( - dataset_name=self.dataset_name, index=trajectory_index, - total_trajectories=self.total_trajectories - ) - subprocess.run( - ["gsutil", "-m", "cp", data_url, self.dataset_dir], check=True - ) + def _recursively_load_data(self, data): + if isinstance(data, dict): + for key, value in data.items(): + self._recursively_load_data(value) + elif isinstance(data, (list, tuple)): + for item in data: + self._recursively_load_data(item) else: - print( - f"Trajectory {trajectory_index} of dataset {self.dataset_name} already exists in {self.dataset_dir}. Skipping download." - ) - - # Check and download the feature.json file - feature_json_local_path = os.path.join(self.dataset_dir, "features.json") - feature_json_url = self.FEATURE_JSON_URL_TEMPLATE.format( - dataset_name=self.dataset_name, - ) - self.check_and_download_file(feature_json_url, feature_json_local_path) - - # Check and download the dataset_info.json file - dataset_info_json_local_path = os.path.join( - self.dataset_dir, "dataset_info.json" - ) - dataset_info_json_url = self.DATASET_INFO_JSON_URL_TEMPLATE.format( - dataset_name=self.dataset_name - ) - self.check_and_download_file( - dataset_info_json_url, dataset_info_json_local_path - ) - - def download_data(self): - """Downloads the specified number of trajectories from the dataset concurrently if not already downloaded.""" - with ThreadPoolExecutor() as executor: - futures = [ - executor.submit(self.check_and_download_trajectory, i) - for i in range(self.num_trajectories) - ] - for future in futures: - future.result() - - def measure_file_size(self): - """Calculates the total size of all files in the dataset directory.""" - total_size = sum( - os.path.getsize(os.path.join(dirpath, f)) - for dirpath, dirnames, filenames in os.walk(self.dataset_dir) - for f in filenames - ) - return total_size - - def measure_file_size_per_trajectory(self): - """Calculates the size of each trajectory file in the dataset directory.""" - trajectory_sizes = [] - for dirpath, dirnames, filenames in os.walk(self.dataset_dir): - for f in filenames: - file_path = os.path.join(dirpath, f) - file_size = os.path.getsize(file_path) - trajectory_sizes.append(file_size) - return trajectory_sizes + _ = np.array(data) class RLDSHandler(DatasetHandler): - """Handles RLDS dataset operations, including loading and measuring loading times.""" - def __init__(self, exp_dir, dataset_name, num_trajectories): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="rlds") def measure_loading_time(self): - """Measures the time taken to load data into memory using RLDSLoader.""" start_time = time.time() - loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") + if self.num_trajectories == -1: + loader = RLDSLoader(self.dataset_dir, split="train") + else: + loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") for data in loader: - print("length of loaded data", len(data)) - + self._recursively_load_data(data) end_time = time.time() - loading_time = end_time - start_time - print( - f"Loaded {len(loader)} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return loading_time, len(loader) - - def measure_loading_time_per_trajectory(self): - """Measures the time taken to load each trajectory separately.""" - times = [] - loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") - for data in loader: - start_time = time.time() - l = list(data) - for i in l: - # recursively load all data - def _recursively_load_data(data): - for key in data.keys(): - if isinstance(data[key], dict): - _recursively_load_data(data[key]) - else: - (key, np.array(data[key])) - (key, np.array(data[key]).shape) - _recursively_load_data(i) - # print("length of loaded data", len(l)) - end_time = time.time() - loading_time = end_time - start_time - times.append(loading_time) - print( - f"Loaded 1 trajectory in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return times + return end_time - start_time class VLAHandler(DatasetHandler): - """Handles VLA dataset operations, including loading, converting, and measuring loading times.""" - def __init__(self, exp_dir, dataset_name, num_trajectories): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla") - self.trajectories_objects = [] - - def convert_data_to_vla_format(self, loader): - """Converts data to VLA format and saves it to the same directory.""" - for index, data_traj in enumerate(loader): - output_path = os.path.join(self.dataset_dir, f"output_{index}.vla") - fog_x.Trajectory.from_list_of_dicts(data_traj, path=output_path) - def measure_loading_time_per_trajectory(self, save_trajectorie_objects=False, mode = "no_cache"): - """Measures the time taken to load each trajectory separately using VLALoader.""" - times = [] + def measure_loading_time(self, mode="no_cache"): + start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) for data in loader: - start_time = time.time() - self._recursively_load_h5_data(data.load(mode = mode)) - if save_trajectorie_objects: - self.trajectories_objects.append(data) - end_time = time.time() - loading_time = end_time - start_time - times.append(loading_time) - print( - f"Loaded 1 trajectory in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return times - def _recursively_load_h5_data(self, data): - for key in data.keys(): - if isinstance(data[key], dict): - self._recursively_load_h5_data(data[key]) - else: - (key, np.array(data[key])) - (key, np.array(data[key]).shape) + if self.num_trajectories != -1 and loader.index >= self.num_trajectories: + break + try: + self._recursively_load_data(data.load(mode=mode)) + except Exception as e: + print(f"Failed to load data: {e}") + end_time = time.time() + return end_time - start_time class HDF5Handler(DatasetHandler): - """Handles HDF5 dataset operations, including conversion and measuring file sizes.""" - def __init__(self, exp_dir, dataset_name, num_trajectories): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hdf5") - self.hdf5_dir = os.path.join(exp_dir, "hdf5", dataset_name) - if not os.path.exists(self.hdf5_dir): - os.makedirs(self.hdf5_dir) - - def convert_data_to_hdf5(self, trajectories_objects): - """Converts data to HDF5 format and saves it to the same directory.""" - print(f"Converting {len(trajectories_objects)} trajectories to HDF5 format.") - for index, trajectory in enumerate(trajectories_objects): - trajectory.to_hdf5(path=f"{self.hdf5_dir}/output_{index}.h5") - - def measure_file_size(self): - """Calculates the total size of all files in the HDF5 directory.""" - total_size = sum( - os.path.getsize(os.path.join(dirpath, f)) - for dirpath, dirnames, filenames in os.walk(self.hdf5_dir) - for f in filenames - ) - return total_size def measure_loading_time(self): - """Measures the time taken to load data into memory using HDF5Loader.""" start_time = time.time() - loader = HDF5Loader(path=os.path.join(self.hdf5_dir, "*.h5")) - - def _recursively_load_h5_data(data): - for key in data.keys(): - if isinstance(data[key], dict): - _recursively_load_h5_data(data[key]) - else: - (key, np.array(data[key])) - (key, np.array(data[key]).shape) - - count = 0 + loader = HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) for data in loader: - # recursively load all data - _recursively_load_h5_data(data) - count += 1 - + if self.num_trajectories != -1 and loader.index >= self.num_trajectories: + break + self._recursively_load_data(data) end_time = time.time() - loading_time = end_time - start_time - print( - f"Loaded {count} trajectories in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return loading_time, count - - - def measure_loading_time_per_trajectory(self): - """Measures the time taken to load each trajectory separately using HDF5Loader.""" - times = [] - loader = HDF5Loader(path=os.path.join(self.hdf5_dir, "*.h5")) - for data in loader: - start_time = time.time() - self._recursively_load_h5_data(data) - end_time = time.time() - loading_time = end_time - start_time - times.append(loading_time) - print( - f"Loaded 1 trajectory in {loading_time:.2f} seconds start time {start_time} end time {end_time}" - ) - return times - - def _recursively_load_h5_data(self, data): - for key in data.keys(): - if isinstance(data[key], dict): - self._recursively_load_h5_data(data[key]) - else: - (key, np.array(data[key])) - (key, np.array(data[key]).shape) - -def prepare(): - # Parse command-line arguments - parser = argparse.ArgumentParser( - description="Download, process, and read RLDS data." - ) - parser.add_argument( - "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory." - ) - parser.add_argument( - "--num_trajectories", - type=int, - default=DEFAULT_NUMBER_OF_TRAJECTORIES, - help="Number of trajectories to download.", - ) - parser.add_argument( - "--dataset_names", - nargs="+", - default=DEFAULT_DATASET_NAMES, - help="List of dataset names to download.", - ) - args = parser.parse_args() - - for dataset_name in args.dataset_names: - print(f"Processing dataset: {dataset_name}") - - # Clear the cache directory - cache_dir = CACHE_DIR - if os.path.exists(cache_dir): - subprocess.run(["rm", "-rf", cache_dir], check=True) + return end_time - start_time - # Process RLDS data - rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) - rlds_handler.download_data() - - # Prepare VLA data - vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) - loader = RLDSLoader( - rlds_handler.dataset_dir, split=f"train[:{args.num_trajectories}]" - ) - vla_handler.convert_data_to_vla_format(loader) - - -def evaluation(): - # Parse command-line arguments - parser = argparse.ArgumentParser( - description="Download, process, and read RLDS data." - ) - parser.add_argument( - "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory." - ) - parser.add_argument( - "--num_trajectories", - type=int, - default=DEFAULT_NUMBER_OF_TRAJECTORIES, - help="Number of trajectories to download.", - ) - parser.add_argument( - "--dataset_names", - nargs="+", - default=DEFAULT_DATASET_NAMES, - help="List of dataset names to download.", - ) - args = parser.parse_args() +def prepare(args): + # Clear the cache directory + if os.path.exists(CACHE_DIR): + subprocess.run(["rm", "-rf", CACHE_DIR], check=True) +def evaluation(args): results = [] - + for dataset_name in args.dataset_names: - print(f"Processing dataset: {dataset_name}") + print(f"Evaluating dataset: {dataset_name}") - # Clear the cache directory - cache_dir = CACHE_DIR - if os.path.exists(cache_dir): - subprocess.run(["rm", "-rf", cache_dir], check=True) + handlers = [ + RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories), + VLAHandler(args.exp_dir, dataset_name, args.num_trajectories), + HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories) + ] - # Process RLDS data - rlds_handler = RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories) - rlds_sizes = rlds_handler.measure_file_size_per_trajectory() - rlds_handler.clear_os_cache() - rlds_loading_times = rlds_handler.measure_loading_time_per_trajectory() + for handler in handlers: + handler.clear_cache() + handler.clear_os_cache() - for i, (size, time) in enumerate(zip(rlds_sizes, rlds_loading_times)): - results.append({ - 'Dataset': dataset_name, - 'Format': 'RLDS', - 'Trajectory': i, - 'LoadingTime(s)': time, - 'FileSize(MB)': size / (1024 * 1024), - 'Throughput(traj/s)': 1 / time if time > 0 else 0 - }) + folder_size = handler.measure_folder_size() + loading_time = handler.measure_loading_time() - # Process VLA data - vla_handler = VLAHandler(args.exp_dir, dataset_name, args.num_trajectories) - vla_sizes = vla_handler.measure_file_size_per_trajectory() - - # first, no cache test, directly reading everything to memory - # no side effect - vla_handler.clear_os_cache() - vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=False, mode = "no_cache") - - for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): results.append({ 'Dataset': dataset_name, - 'Format': 'VLA-NoCache', - 'Trajectory': i, - 'LoadingTime(s)': time, - 'FileSize(MB)': size / (1024 * 1024), - 'Throughput(traj/s)': 1 / time if time > 0 else 0 + 'Format': handler.dataset_type.upper(), + 'FolderSize(MB)': folder_size, + 'LoadingTime(s)': loading_time, }) - - - - vla_handler.clear_os_cache() - vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=True, mode = "cache") - for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): - results.append({ - 'Dataset': dataset_name, - 'Format': 'VLA-ColdCache', - 'Trajectory': i, - 'LoadingTime(s)': time, - 'FileSize(MB)': size / (1024 * 1024), - 'Throughput(traj/s)': 1 / time if time > 0 else 0 - }) - - vla_handler.clear_os_cache() - # hot cache test - vla_loading_times = vla_handler.measure_loading_time_per_trajectory(save_trajectorie_objects=False, mode = "cache") + print(f"{handler.dataset_type.upper()} - Folder Size: {folder_size:.2f} MB, Loading Time: {loading_time:.2f} s") - for i, (size, time) in enumerate(zip(vla_sizes, vla_loading_times)): - results.append({ - 'Dataset': dataset_name, - 'Format': 'VLA-HotCache', - 'Trajectory': i, - 'LoadingTime(s)': time, - 'FileSize(MB)': size / (1024 * 1024), - 'Throughput(traj/s)': 1 / time if time > 0 else 0 - }) - - - # Convert VLA to HDF5 and benchmark - hdf5_handler = HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories) - hdf5_handler.convert_data_to_hdf5(vla_handler.trajectories_objects) - hdf5_sizes = hdf5_handler.measure_file_size_per_trajectory() - hdf5_handler.clear_os_cache() - hdf5_loading_times = hdf5_handler.measure_loading_time_per_trajectory() - - for i, (size, time) in enumerate(zip(hdf5_sizes, hdf5_loading_times)): - results.append({ - 'Dataset': dataset_name, - 'Format': 'HDF5', - 'Trajectory': i, - 'LoadingTime(s)': time, - 'FileSize(MB)': size / (1024 * 1024), - 'Throughput(traj/s)': 1 / time if time > 0 else 0 - }) + # Additional VLA measurements + vla_handler = handlers[1] + vla_handler.clear_cache() + vla_handler.clear_os_cache() + cold_cache_time = vla_handler.measure_loading_time(mode="cache") + hot_cache_time = vla_handler.measure_loading_time(mode="cache") + + results.append({ + 'Dataset': dataset_name, + 'Format': 'VLA-ColdCache', + 'FolderSize(MB)': folder_size, + 'LoadingTime(s)': cold_cache_time, + }) + + results.append({ + 'Dataset': dataset_name, + 'Format': 'VLA-HotCache', + 'FolderSize(MB)': folder_size, + 'LoadingTime(s)': hot_cache_time, + }) + print(f"VLA-ColdCache - Folder Size: {folder_size:.2f} MB, Loading Time: {cold_cache_time:.2f} s") + print(f"VLA-HotCache - Folder Size: {folder_size:.2f} MB, Loading Time: {hot_cache_time:.2f} s") - # Save results to CSV results_df = pd.DataFrame(results) - results_df.to_csv('trajectory_results.csv', index=False) - print("Results written to trajectory_results.csv") - - + results_df.to_csv('format_comparison_results.csv', index=False) + print("Results written to format_comparison_results.csv") if __name__ == "__main__": - prepare() - exit() - evaluation() + parser = argparse.ArgumentParser(description="Prepare and evaluate loading times and folder sizes for RLDS, VLA, and HDF5 formats.") + parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") + parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to evaluate.") + parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.") + parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") + args = parser.parse_args() + + if args.prepare: + prepare(args) + evaluation(args) \ No newline at end of file From c0a840fea0058adacd20ab365b23da62fcd10c9f Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Fri, 30 Aug 2024 02:12:59 -0700 Subject: [PATCH 55/80] Refactor RLDSLoader and Trajectory classes for improved code readability and lazy loading of data --- benchmarks/openx.py | 168 ++++++++++++++++++++++++++++++++------------ 1 file changed, 123 insertions(+), 45 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 851af8c..e6d5c46 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -7,31 +7,44 @@ import tensorflow as tf import pandas as pd import fog_x +import csv +import stat # Constants DEFAULT_EXP_DIR = "/mnt/data/fog_x/" DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories -DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness"] +DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] +#["nyu_door_opening_surprising_effectiveness"] CACHE_DIR = "/mnt/data/fog_x/cache/" +DEFAULT_LOG_FREQUENCY = 20 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" class DatasetHandler: - def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type): + def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type, log_frequency=DEFAULT_LOG_FREQUENCY): self.exp_dir = exp_dir self.dataset_name = dataset_name self.num_trajectories = num_trajectories self.dataset_type = dataset_type self.dataset_dir = os.path.join(exp_dir, dataset_type, dataset_name) - - def measure_folder_size(self): - """Calculates the total size of all files in the dataset directory.""" - total_size = sum( - os.path.getsize(os.path.join(dirpath, f)) - for dirpath, dirnames, filenames in os.walk(self.dataset_dir) - for f in filenames - ) - return total_size / (1024 * 1024) # Convert to MB + # Resolve the symbolic link if the dataset_dir is a soft link + self.dataset_dir = os.path.realpath(self.dataset_dir) + self.log_frequency = log_frequency + self.results = [] + + def measure_average_trajectory_size(self): + """Calculates the average size of trajectory files in the dataset directory.""" + total_size = 0 + file_count = 0 + for dirpath, dirnames, filenames in os.walk(self.dataset_dir): + for f in filenames: + if f.endswith(self.file_extension): + file_path = os.path.join(dirpath, f) + total_size += os.path.getsize(file_path) + file_count += 1 + if file_count == 0: + return 0 + return (total_size / file_count) / (1024 * 1024) # Convert to MB def clear_cache(self): """Clears the cache directory.""" @@ -53,9 +66,28 @@ def _recursively_load_data(self, data): else: _ = np.array(data) + def write_result(self, format_name, elapsed_time, index): + result = { + 'Dataset': self.dataset_name, + 'Format': format_name, + 'AverageTrajectorySize(MB)': self.measure_average_trajectory_size(), + 'LoadingTime(s)': elapsed_time, + 'Index': index + } + + csv_file = f'{self.dataset_name}_results.csv' + file_exists = os.path.isfile(csv_file) + + with open(csv_file, 'a', newline='') as f: + writer = csv.DictWriter(f, fieldnames=result.keys()) + if not file_exists: + writer.writeheader() + writer.writerow(result) + class RLDSHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="rlds") + def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="rlds", log_frequency=log_frequency) + self.file_extension = ".tfrecord" def measure_loading_time(self): start_time = time.time() @@ -63,41 +95,74 @@ def measure_loading_time(self): loader = RLDSLoader(self.dataset_dir, split="train") else: loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") - for data in loader: + for i, data in enumerate(loader, 1): self._recursively_load_data(data) - end_time = time.time() - return end_time - start_time + elapsed_time = time.time() - start_time + self.write_result("RLDS", elapsed_time, i) + if i % self.log_frequency == 0: + print(f"RLDS - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") + return time.time() - start_time class VLAHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla") + def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", log_frequency=log_frequency) + self.file_extension = ".vla" + + def measure_loading_time(self, mode="no_cache"): + start_time = time.time() + loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) + for i, data in enumerate(loader, 1): + if self.num_trajectories != -1 and i > self.num_trajectories: + break + try: + self._recursively_load_data(data.load(mode=mode)) + elapsed_time = time.time() - start_time + self.write_result(f"VLA-{mode.capitalize()}", elapsed_time, i) + if i % self.log_frequency == 0: + print(f"VLA-{mode.capitalize()} - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") + except Exception as e: + print(f"Failed to load data: {e}") + return time.time() - start_time + +class FFV1Handler(DatasetHandler): + def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", log_frequency=log_frequency) + self.file_extension = ".vla" def measure_loading_time(self, mode="no_cache"): start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) - for data in loader: - if self.num_trajectories != -1 and loader.index >= self.num_trajectories: + for i, data in enumerate(loader, 1): + if self.num_trajectories != -1 and i > self.num_trajectories: break try: self._recursively_load_data(data.load(mode=mode)) + elapsed_time = time.time() - start_time + self.write_result(f"FFV1-{mode.capitalize()}", elapsed_time, i) + if i % self.log_frequency == 0: + print(f"FFV1-{mode.capitalize()} - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") except Exception as e: print(f"Failed to load data: {e}") - end_time = time.time() - return end_time - start_time + return time.time() - start_time + class HDF5Handler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hdf5") + def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hdf5", log_frequency=log_frequency) + self.file_extension = ".h5" def measure_loading_time(self): start_time = time.time() loader = HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) - for data in loader: - if self.num_trajectories != -1 and loader.index >= self.num_trajectories: + for i, data in enumerate(loader, 1): + if self.num_trajectories != -1 and i > self.num_trajectories: break self._recursively_load_data(data) - end_time = time.time() - return end_time - start_time + elapsed_time = time.time() - start_time + self.write_result("HDF5", elapsed_time, i) + if i % self.log_frequency == 0: + print(f"HDF5 - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") + return time.time() - start_time def prepare(args): # Clear the cache directory @@ -105,32 +170,40 @@ def prepare(args): subprocess.run(["rm", "-rf", CACHE_DIR], check=True) def evaluation(args): - results = [] + csv_file = 'format_comparison_results.csv' + + if os.path.exists(csv_file): + existing_results = pd.read_csv(csv_file).to_dict('records') + else: + existing_results = [] + + new_results = [] for dataset_name in args.dataset_names: print(f"Evaluating dataset: {dataset_name}") handlers = [ - RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories), - VLAHandler(args.exp_dir, dataset_name, args.num_trajectories), - HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories) + RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), + VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), + HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), + FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency) ] for handler in handlers: handler.clear_cache() handler.clear_os_cache() - folder_size = handler.measure_folder_size() + avg_traj_size = handler.measure_average_trajectory_size() loading_time = handler.measure_loading_time() - results.append({ + new_results.append({ 'Dataset': dataset_name, 'Format': handler.dataset_type.upper(), - 'FolderSize(MB)': folder_size, + 'AverageTrajectorySize(MB)': avg_traj_size, 'LoadingTime(s)': loading_time, }) - print(f"{handler.dataset_type.upper()} - Folder Size: {folder_size:.2f} MB, Loading Time: {loading_time:.2f} s") + print(f"{handler.dataset_type.upper()} - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {loading_time:.2f} s") # Additional VLA measurements vla_handler = handlers[1] @@ -139,25 +212,29 @@ def evaluation(args): cold_cache_time = vla_handler.measure_loading_time(mode="cache") hot_cache_time = vla_handler.measure_loading_time(mode="cache") - results.append({ + new_results.append({ 'Dataset': dataset_name, 'Format': 'VLA-ColdCache', - 'FolderSize(MB)': folder_size, + 'AverageTrajectorySize(MB)': avg_traj_size, 'LoadingTime(s)': cold_cache_time, }) - results.append({ + new_results.append({ 'Dataset': dataset_name, 'Format': 'VLA-HotCache', - 'FolderSize(MB)': folder_size, + 'AverageTrajectorySize(MB)': avg_traj_size, 'LoadingTime(s)': hot_cache_time, }) - print(f"VLA-ColdCache - Folder Size: {folder_size:.2f} MB, Loading Time: {cold_cache_time:.2f} s") - print(f"VLA-HotCache - Folder Size: {folder_size:.2f} MB, Loading Time: {hot_cache_time:.2f} s") + print(f"VLA-ColdCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {cold_cache_time:.2f} s") + print(f"VLA-HotCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {hot_cache_time:.2f} s") + + # Combine existing and new results + all_results = existing_results + new_results - results_df = pd.DataFrame(results) - results_df.to_csv('format_comparison_results.csv', index=False) - print("Results written to format_comparison_results.csv") + # Write all results to CSV + results_df = pd.DataFrame(all_results) + results_df.to_csv(csv_file, index=False) + print(f"Results appended to {csv_file}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Prepare and evaluate loading times and folder sizes for RLDS, VLA, and HDF5 formats.") @@ -165,6 +242,7 @@ def evaluation(args): parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to evaluate.") parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.") parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") + parser.add_argument("--log_frequency", type=int, default=DEFAULT_LOG_FREQUENCY, help="Frequency of logging results.") args = parser.parse_args() if args.prepare: From 8e0b1888ee1e2e157ed15be9f7288b6092d2b825 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Fri, 30 Aug 2024 11:32:49 -0700 Subject: [PATCH 56/80] fix the logging; before randomly access --- benchmarks/openx.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index e6d5c46..91359fd 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -13,7 +13,7 @@ # Constants DEFAULT_EXP_DIR = "/mnt/data/fog_x/" DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories -DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] +DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness", "berkeley_cable_routing", "berkeley_autolab_ur5", "bridge"] #["nyu_door_opening_surprising_effectiveness"] CACHE_DIR = "/mnt/data/fog_x/cache/" DEFAULT_LOG_FREQUENCY = 20 @@ -126,7 +126,7 @@ def measure_loading_time(self, mode="no_cache"): class FFV1Handler(DatasetHandler): def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", log_frequency=log_frequency) + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="ffv1", log_frequency=log_frequency) self.file_extension = ".vla" def measure_loading_time(self, mode="no_cache"): @@ -228,13 +228,13 @@ def evaluation(args): print(f"VLA-ColdCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {cold_cache_time:.2f} s") print(f"VLA-HotCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {hot_cache_time:.2f} s") - # Combine existing and new results - all_results = existing_results + new_results + # Combine existing and new results + all_results = existing_results + new_results - # Write all results to CSV - results_df = pd.DataFrame(all_results) - results_df.to_csv(csv_file, index=False) - print(f"Results appended to {csv_file}") + # Write all results to CSV + results_df = pd.DataFrame(all_results) + results_df.to_csv(csv_file, index=False) + print(f"Results appended to {csv_file}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Prepare and evaluate loading times and folder sizes for RLDS, VLA, and HDF5 formats.") From a0e813a5546490d5699f36475014f9da811f0369 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Fri, 30 Aug 2024 16:00:15 -0700 Subject: [PATCH 57/80] add random loading --- benchmarks/openx.py | 147 ++++++++++++++++++++++++++++++++++--------- fog_x/loader/hdf5.py | 7 ++- fog_x/loader/rlds.py | 11 +++- fog_x/loader/vla.py | 7 ++- openx_to_vla.sh | 4 +- 5 files changed, 140 insertions(+), 36 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 91359fd..108415c 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -13,9 +13,9 @@ # Constants DEFAULT_EXP_DIR = "/mnt/data/fog_x/" DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories -DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness", "berkeley_cable_routing", "berkeley_autolab_ur5", "bridge"] -#["nyu_door_opening_surprising_effectiveness"] -CACHE_DIR = "/mnt/data/fog_x/cache/" +# DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness", "berkeley_cable_routing", "berkeley_autolab_ur5", "bridge"] +DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness"] +CACHE_DIR = "/tmp/fog_x/cache/" DEFAULT_LOG_FREQUENCY = 20 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" @@ -103,6 +103,25 @@ def measure_loading_time(self): print(f"RLDS - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") return time.time() - start_time + def measure_random_loading_time(self, num_loads): + start_time = time.time() + loader = RLDSLoader(self.dataset_dir, split="train") + dataset_size = len(loader) + num_loads = num_loads * dataset_size + + loader.ds = loader.ds.shuffle(buffer_size=num_loads) + # shuffled_ds = shuffled_ds.take(num_loads) + + for i, data in enumerate(loader): + self._recursively_load_data(data) + + elapsed_time = time.time() - start_time + self.write_result(f"RLDS-RandomLoad", elapsed_time, i) + if i % self.log_frequency == 0: + print(f"RLDS-RandomLoad - Loaded {i} random trajectories, Time: {elapsed_time:.2f} s") + + return time.time() - start_time + class VLAHandler(DatasetHandler): def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", log_frequency=log_frequency) @@ -124,6 +143,26 @@ def measure_loading_time(self, mode="no_cache"): print(f"Failed to load data: {e}") return time.time() - start_time + def measure_random_loading_time(self, num_loads): + start_time = time.time() + loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) + dataset_size = len(loader) + num_loads = num_loads * dataset_size + + for i in range(num_loads): + random_index = np.random.randint(0, dataset_size) + data = loader[random_index] + try: + self._recursively_load_data(data.load(mode="cache")) + elapsed_time = time.time() - start_time + self.write_result(f"VLA-RandomLoad", elapsed_time, i + 1) + if (i + 1) % self.log_frequency == 0: + print(f"VLA-RandomLoad - Loaded {i + 1} random trajectories, Time: {elapsed_time:.2f} s") + except Exception as e: + print(f"Failed to load data: {e}") + + return time.time() - start_time + class FFV1Handler(DatasetHandler): def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="ffv1", log_frequency=log_frequency) @@ -145,6 +184,26 @@ def measure_loading_time(self, mode="no_cache"): print(f"Failed to load data: {e}") return time.time() - start_time + def measure_random_loading_time(self, num_loads): + start_time = time.time() + loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) + dataset_size = len(loader) + num_loads = num_loads * dataset_size + + for i in range(num_loads): + random_index = np.random.randint(0, dataset_size) + data = loader[random_index] + try: + self._recursively_load_data(data.load(mode="cache")) + elapsed_time = time.time() - start_time + self.write_result(f"FFV1-RandomLoad", elapsed_time, i + 1) + if (i + 1) % self.log_frequency == 0: + print(f"FFV1-RandomLoad - Loaded {i + 1} random trajectories, Time: {elapsed_time:.2f} s") + except Exception as e: + print(f"Failed to load data: {e}") + + return time.time() - start_time + class HDF5Handler(DatasetHandler): def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): @@ -164,6 +223,24 @@ def measure_loading_time(self): print(f"HDF5 - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") return time.time() - start_time + def measure_random_loading_time(self, num_loads): + start_time = time.time() + loader = HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) + dataset_size = len(loader) + num_loads = num_loads * dataset_size + + for i in range(num_loads): + random_index = np.random.randint(0, dataset_size) + data = loader[random_index] + self._recursively_load_data(data) + + elapsed_time = time.time() - start_time + self.write_result(f"HDF5-RandomLoad", elapsed_time, i + 1) + if (i + 1) % self.log_frequency == 0: + print(f"HDF5-RandomLoad - Loaded {i + 1} random trajectories, Time: {elapsed_time:.2f} s") + + return time.time() - start_time + def prepare(args): # Clear the cache directory if os.path.exists(CACHE_DIR): @@ -194,39 +271,48 @@ def evaluation(args): handler.clear_os_cache() avg_traj_size = handler.measure_average_trajectory_size() - loading_time = handler.measure_loading_time() + # loading_time = handler.measure_loading_time() + + # new_results.append({ + # 'Dataset': dataset_name, + # 'Format': handler.dataset_type.upper(), + # 'AverageTrajectorySize(MB)': avg_traj_size, + # 'LoadingTime(s)': loading_time, + # }) + + # print(f"{handler.dataset_type.upper()} - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {loading_time:.2f} s") + random_load_time = handler.measure_random_loading_time(args.random_loads) new_results.append({ 'Dataset': dataset_name, - 'Format': handler.dataset_type.upper(), + 'Format': f"{handler.dataset_type.upper()}-RandomLoad", 'AverageTrajectorySize(MB)': avg_traj_size, - 'LoadingTime(s)': loading_time, + 'LoadingTime(s)': random_load_time, }) + print(f"{handler.dataset_type.upper()}-RandomLoad - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s") + + # # Additional VLA measurements + # vla_handler = handlers[1] + # vla_handler.clear_cache() + # vla_handler.clear_os_cache() + # cold_cache_time = vla_handler.measure_loading_time(mode="cache") + # hot_cache_time = vla_handler.measure_loading_time(mode="cache") + + # new_results.append({ + # 'Dataset': dataset_name, + # 'Format': 'VLA-ColdCache', + # 'AverageTrajectorySize(MB)': avg_traj_size, + # 'LoadingTime(s)': cold_cache_time, + # }) - print(f"{handler.dataset_type.upper()} - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {loading_time:.2f} s") - - # Additional VLA measurements - vla_handler = handlers[1] - vla_handler.clear_cache() - vla_handler.clear_os_cache() - cold_cache_time = vla_handler.measure_loading_time(mode="cache") - hot_cache_time = vla_handler.measure_loading_time(mode="cache") - - new_results.append({ - 'Dataset': dataset_name, - 'Format': 'VLA-ColdCache', - 'AverageTrajectorySize(MB)': avg_traj_size, - 'LoadingTime(s)': cold_cache_time, - }) - - new_results.append({ - 'Dataset': dataset_name, - 'Format': 'VLA-HotCache', - 'AverageTrajectorySize(MB)': avg_traj_size, - 'LoadingTime(s)': hot_cache_time, - }) - print(f"VLA-ColdCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {cold_cache_time:.2f} s") - print(f"VLA-HotCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {hot_cache_time:.2f} s") + # new_results.append({ + # 'Dataset': dataset_name, + # 'Format': 'VLA-HotCache', + # 'AverageTrajectorySize(MB)': avg_traj_size, + # 'LoadingTime(s)': hot_cache_time, + # }) + # print(f"VLA-ColdCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {cold_cache_time:.2f} s") + # print(f"VLA-HotCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {hot_cache_time:.2f} s") # Combine existing and new results all_results = existing_results + new_results @@ -243,6 +329,7 @@ def evaluation(args): parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.") parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") parser.add_argument("--log_frequency", type=int, default=DEFAULT_LOG_FREQUENCY, help="Frequency of logging results.") + parser.add_argument("--random_loads", type=int, default=2, help="Number of random loads to perform for each loader.") args = parser.parse_args() if args.prepare: diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index f0036cc..9abd22d 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -30,6 +30,9 @@ def __init__(self, path, split = None): self.index = 0 self.files = glob.glob(self.path, recursive=True) + def __getitem__(self, idx): + return self._read_hdf5(self.files[idx]) + def _read_hdf5(self, data_path): with h5py.File(data_path, "r") as f: @@ -52,4 +55,6 @@ def __next__(self): self.index += 1 return self._read_hdf5(file_path) raise StopIteration - \ No newline at end of file + + def __len__(self): + return len(self.files) \ No newline at end of file diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index 780756b..a0a2968 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -23,6 +23,12 @@ def __init__(self, path, split): self.index = 0 def __len__(self): + try: + import tensorflow as tf + import tensorflow_datasets as tfds + except ImportError: + raise ImportError("Please install tensorflow and tensorflow_datasets to use rlds loader") + return tf.data.experimental.cardinality(self.ds).numpy() def __iter__(self): @@ -48,4 +54,7 @@ def __next__(self): except StopIteration: self.index = 0 self.iterator = iter(self.ds) - raise StopIteration \ No newline at end of file + raise StopIteration + + def __getitem__(self, idx): + return next(iter(self.ds.skip(idx).take(1))) diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 20e5dfe..0e2f0f8 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -50,5 +50,8 @@ def __next__(self): def __len__(self): return len(self.files) - def peak(self, index): - return self._read_vla(self.files[index]) \ No newline at end of file + def __getitem__(self, index): + return self._read_vla(self.files[index]) + + def peak(self): + return self._read_vla(self.files[self.index]) \ No newline at end of file diff --git a/openx_to_vla.sh b/openx_to_vla.sh index 4e25100..51aa458 100755 --- a/openx_to_vla.sh +++ b/openx_to_vla.sh @@ -31,7 +31,7 @@ # nyu_door_opening_surprising_effectiveness dataset # python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless # python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless \ No newline at end of file +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless \ No newline at end of file From 143a4feefd19d993f8620247a819a90caaacad5f Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Fri, 30 Aug 2024 17:20:12 -0700 Subject: [PATCH 58/80] async write to cache --- benchmarks/openx.py | 26 +++--- fog_x/loader/vla.py | 34 ++++---- fog_x/trajectory.py | 193 ++++++++++++-------------------------------- 3 files changed, 83 insertions(+), 170 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 108415c..d0913de 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -127,14 +127,18 @@ def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAUL super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", log_frequency=log_frequency) self.file_extension = ".vla" - def measure_loading_time(self, mode="no_cache"): + def measure_loading_time(self, save_to_cache=True): start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) + if save_to_cache: + mode = "cache" + else: + mode = "no_cache" for i, data in enumerate(loader, 1): if self.num_trajectories != -1 and i > self.num_trajectories: break try: - self._recursively_load_data(data.load(mode=mode)) + self._recursively_load_data(data.load(save_to_cache=save_to_cache)) elapsed_time = time.time() - start_time self.write_result(f"VLA-{mode.capitalize()}", elapsed_time, i) if i % self.log_frequency == 0: @@ -143,7 +147,7 @@ def measure_loading_time(self, mode="no_cache"): print(f"Failed to load data: {e}") return time.time() - start_time - def measure_random_loading_time(self, num_loads): + def measure_random_loading_time(self, num_loads, save_to_cache=True): start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) dataset_size = len(loader) @@ -153,7 +157,7 @@ def measure_random_loading_time(self, num_loads): random_index = np.random.randint(0, dataset_size) data = loader[random_index] try: - self._recursively_load_data(data.load(mode="cache")) + self._recursively_load_data(data.load(save_to_cache=save_to_cache)) elapsed_time = time.time() - start_time self.write_result(f"VLA-RandomLoad", elapsed_time, i + 1) if (i + 1) % self.log_frequency == 0: @@ -168,14 +172,18 @@ def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAUL super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="ffv1", log_frequency=log_frequency) self.file_extension = ".vla" - def measure_loading_time(self, mode="no_cache"): + def measure_loading_time(self, save_to_cache=True): start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) + if save_to_cache: + mode = "cache" + else: + mode = "no_cache" for i, data in enumerate(loader, 1): if self.num_trajectories != -1 and i > self.num_trajectories: break try: - self._recursively_load_data(data.load(mode=mode)) + self._recursively_load_data(data.load(save_to_cache=save_to_cache)) elapsed_time = time.time() - start_time self.write_result(f"FFV1-{mode.capitalize()}", elapsed_time, i) if i % self.log_frequency == 0: @@ -184,7 +192,7 @@ def measure_loading_time(self, mode="no_cache"): print(f"Failed to load data: {e}") return time.time() - start_time - def measure_random_loading_time(self, num_loads): + def measure_random_loading_time(self, num_loads, save_to_cache=True): start_time = time.time() loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) dataset_size = len(loader) @@ -194,7 +202,7 @@ def measure_random_loading_time(self, num_loads): random_index = np.random.randint(0, dataset_size) data = loader[random_index] try: - self._recursively_load_data(data.load(mode="cache")) + self._recursively_load_data(data.load(save_to_cache=save_to_cache)) elapsed_time = time.time() - start_time self.write_result(f"FFV1-RandomLoad", elapsed_time, i + 1) if (i + 1) % self.log_frequency == 0: @@ -260,7 +268,7 @@ def evaluation(args): print(f"Evaluating dataset: {dataset_name}") handlers = [ - RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), + # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency) diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 0e2f0f8..77506f5 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -2,41 +2,37 @@ import fog_x import glob import logging - -logger = logging.getLogger(__name__) +import asyncio import os from typing import Text +logger = logging.getLogger(__name__) class VLALoader(BaseLoader): def __init__(self, path: Text, cache_dir=None): - """initialize VLALoader from paths - - Args: - path (_type_): path to the vla files - can be a directory, or a glob pattern - split (_type_, optional): split of training and testing. Defaults to None. - """ super(VLALoader, self).__init__(path) self.index = 0 + self.files = self._get_files(path) + self.cache_dir = cache_dir + self.loop = asyncio.get_event_loop() + def _get_files(self, path): if "*" in path: - self.files = glob.glob(path) + return glob.glob(path) elif os.path.isdir(path): - self.files = glob.glob(os.path.join(path, "*.vla")) + return glob.glob(os.path.join(path, "*.vla")) else: - self.files = [path] - - self.cache_dir = cache_dir + return [path] - def _read_vla(self, data_path): + async def _read_vla_async(self, data_path): logger.debug(f"Reading {data_path}") - if self.cache_dir: - traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) - else: - traj = fog_x.Trajectory(data_path) + traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) + await traj.load_async() return traj + def _read_vla(self, data_path): + return self.loop.run_until_complete(self._read_vla_async(data_path)) + def __iter__(self): return self diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index cfe66ef..cbad890 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -8,6 +8,8 @@ from fog_x import FeatureType import pickle import h5py +import asyncio +from concurrent.futures import ThreadPoolExecutor logger = logging.getLogger(__name__) @@ -77,6 +79,10 @@ def __init__( self.stream_id_to_info = {} # stream_id: StreamInfo self.is_closed = False self.lossy_compression = lossy_compression + self.pending_write_tasks = [] # List to keep track of pending write tasks + self.cache_write_lock = asyncio.Lock() + self.cache_write_task = None + self.executor = ThreadPoolExecutor(max_workers=1) # check if the path exists # if not, create a new file and start data collection @@ -145,33 +151,40 @@ def close(self, compact=True): self.container_file = None self.is_closed = True - def load(self, mode = "cache"): + def load(self, save_to_cache=True, return_h5=False): """ - load the container file + Load the trajectory data. - returns the container file + Args: + mode (str): "cache" to use cached data if available, "no_cache" to always load from container. + return_h5 (bool): If True, return h5py.File object instead of numpy arrays. - workflow: - - check if a cached mmap/hdf5 file exists - - if exists, load the file - - otherwise: load the container file with entire vla trajctory + Returns: + dict: A dictionary of numpy arrays if return_h5 is False, otherwise an h5py.File object. """ - if mode == "cache": - if os.path.exists(self.cache_file_name): - logger.debug(f"Loading the cached file {self.cache_file_name}") - self.trajectory_data = self._load_from_cache() + + return asyncio.get_event_loop().run_until_complete( + self.load_async(save_to_cache=save_to_cache, return_h5=return_h5) + ) + + async def load_async(self, save_to_cache=True, return_h5=False): + if os.path.exists(self.cache_file_name): + logger.debug(f"Loading the cached file {self.cache_file_name}") + if return_h5: + return h5py.File(self.cache_file_name, "r") else: - logger.debug(f"Loading the container file {self.path}, saving to cache {self.cache_file_name}") - self.trajectory_data = self._load_from_container(save_to_cache=True) - elif mode == "no_cache": - logger.debug(f"Loading the container file {self.path} without cache") - # self.trajectory_data = self._load_from_container_to_h5() - self.trajectory_data = self._load_from_container(save_to_cache=False) + with h5py.File(self.cache_file_name, "r") as h5_cache: + return {k: np.array(v) for k, v in h5_cache.items()} else: - logger.debug(f"No option provided. Force loading from container file {self.path}") - self.trajectory_data = self._load_from_container(save_to_cache=False) - - return self.trajectory_data + logger.debug(f"Loading the container file {self.path}, saving to cache {self.cache_file_name}") + np_cache = self._load_from_container() + if save_to_cache: + await self._async_write_to_cache(np_cache) + + if return_h5: + return h5py.File(self.cache_file_name, "r") + else: + return np_cache def init_feature_streams(self, feature_spec: Dict): """ @@ -346,105 +359,7 @@ def _load_from_cache(self): h5_cache = h5py.File(self.cache_file_name, "r") return h5_cache - def _load_from_container_to_h5(self): - """ - - load the container file with entire vla trajctory - - workflow: - - get schema of the container file - - preallocate decoded streams - - decode frame by frame and store in the preallocated memory - - """ - - container = av.open(self.path, mode="r", format="matroska") - h5_cache = h5py.File(self.cache_file_name, "w") - streams = container.streams - - # preallocate memory for the streams in h5 - for stream in streams: - feature_name = stream.metadata.get("FEATURE_NAME") - if feature_name is None: - logger.warn(f"Skipping stream without FEATURE_NAME: {stream}") - continue - feature_type = FeatureType.from_str(stream.metadata.get("FEATURE_TYPE")) - self.feature_name_to_stream[feature_name] = stream - self.feature_name_to_feature_type[feature_name] = feature_type - # Preallocate arrays with the shape [None, X, Y, Z] - # where X, Y, Z are the dimensions of the feature - - logger.debug( - f"creating a cache for {feature_name} with shape {feature_type.shape}" - ) - - if feature_type.dtype == "string": - # strings are not supported in h5py, so we store them as objects - h5_cache.create_dataset( - feature_name, - (0,) + feature_type.shape, - maxshape=(None,) + feature_type.shape, - dtype=h5py.special_dtype(vlen=str), - chunks=(100,) + feature_type.shape, - ) - else: - h5_cache.create_dataset( - feature_name, - (0,) + feature_type.shape, - maxshape=(None,) + feature_type.shape, - dtype=feature_type.dtype, - chunks=(100,) + feature_type.shape, - ) - - # decode the frames and store in the preallocated memory - d_feature_length = {feature: 0 for feature in self.feature_name_to_stream} - for packet in container.demux(list(streams)): - feature_name = packet.stream.metadata.get("FEATURE_NAME") - if feature_name is None: - logger.debug(f"Skipping stream without FEATURE_NAME: {stream}") - continue - feature_type = FeatureType.from_str( - packet.stream.metadata.get("FEATURE_TYPE") - ) - logger.debug( - f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" - ) - feature_codec = packet.stream.codec_context.codec.name - if feature_codec == "h264": - frames = packet.decode() - - for frame in frames: - if feature_type.dtype == "float32": - data = frame.to_ndarray(format="gray").reshape( - feature_type.shape - ) - else: - data = frame.to_ndarray(format="rgb24").reshape( - feature_type.shape - ) - h5_cache[feature_name].resize( - h5_cache[feature_name].shape[0] + 1, axis=0 - ) - h5_cache[feature_name][-1] = data - d_feature_length[feature_name] += 1 - else: - packet_in_bytes = bytes(packet) - if packet_in_bytes: - # decode the packet - data = pickle.loads(packet_in_bytes) - h5_cache[feature_name].resize( - h5_cache[feature_name].shape[0] + 1, axis=0 - ) - h5_cache[feature_name][-1] = data - d_feature_length[feature_name] += 1 - else: - logger.debug(f"Skipping empty packet: {packet} for {feature_name}") - container.close() - h5_cache.close() - h5_cache = h5py.File(self.cache_file_name, "r") - return h5_cache - - def _load_from_container(self, save_to_cache: bool = True): + def _load_from_container(self): """ Load the container file with the entire VLA trajectory. @@ -452,9 +367,7 @@ def _load_from_container(self, save_to_cache: bool = True): save_to_cache: save the decoded data to the cache file returns: - h5_cache: h5py file with the decoded data - or - dict: dictionary with the decoded data + np_cache: dictionary with the decoded data Workflow: - Get schema of the container file. @@ -544,37 +457,33 @@ def _get_length_of_stream(container, stream): logger.debug(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") container.close() - if save_to_cache: - # create and save it to be hdf5 file - h5_cache = h5py.File(self.cache_file_name, "w") + return np_cache + + async def _async_write_to_cache(self, np_cache): + async with self.cache_write_lock: + await asyncio.get_event_loop().run_in_executor( + self.executor, + self._write_to_cache, + np_cache + ) + + def _write_to_cache(self, np_cache): + with h5py.File(self.cache_file_name, "w") as h5_cache: for feature_name, data in np_cache.items(): if data.dtype == object: for i in range(len(data)): data_type = type(data[i]) - if data_type == str: - data[i] = str(data[i]) - elif data_type == bytes: - data[i] = str(data[i]) - elif data_type == np.ndarray: + if data_type in (str, bytes, np.ndarray): data[i] = str(data[i]) else: data[i] = str(data[i]) try: - h5_cache.create_dataset( - feature_name, - data=data - ) + h5_cache.create_dataset(feature_name, data=data) except Exception as e: logger.error(f"Error saving {feature_name} to cache: {e} with data {data}") else: h5_cache.create_dataset(feature_name, data=data) - h5_cache.close() - h5_cache = h5py.File(self.cache_file_name, "r") - return h5_cache - else: - return np_cache - - + def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): """ Transcode pickled images into the desired format (e.g., raw or encoded images). From b13ae7950f16b4a2cacd5b15aefab19884e3e10b Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Fri, 30 Aug 2024 20:30:33 -0700 Subject: [PATCH 59/80] support dataloader, still has bugs --- benchmarks/openx.py | 185 ++++++++++--------------------------------- fog_x/dataset.py | 2 +- fog_x/loader/hdf5.py | 36 +++++---- fog_x/loader/rlds.py | 69 +++++++++------- fog_x/loader/vla.py | 41 +++++++--- fog_x/trajectory.py | 6 +- 6 files changed, 141 insertions(+), 198 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index d0913de..912f425 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -14,19 +14,20 @@ DEFAULT_EXP_DIR = "/mnt/data/fog_x/" DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories # DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness", "berkeley_cable_routing", "berkeley_autolab_ur5", "bridge"] -DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness"] +DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] CACHE_DIR = "/tmp/fog_x/cache/" DEFAULT_LOG_FREQUENCY = 20 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" class DatasetHandler: - def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type, log_frequency=DEFAULT_LOG_FREQUENCY): + def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): self.exp_dir = exp_dir self.dataset_name = dataset_name self.num_trajectories = num_trajectories self.dataset_type = dataset_type self.dataset_dir = os.path.join(exp_dir, dataset_type, dataset_name) + self.batch_size = batch_size # Resolve the symbolic link if the dataset_dir is a soft link self.dataset_dir = os.path.realpath(self.dataset_dir) self.log_frequency = log_frequency @@ -72,7 +73,8 @@ def write_result(self, format_name, elapsed_time, index): 'Format': format_name, 'AverageTrajectorySize(MB)': self.measure_average_trajectory_size(), 'LoadingTime(s)': elapsed_time, - 'Index': index + 'Index': index, + 'BatchSize': self.batch_size } csv_file = f'{self.dataset_name}_results.csv' @@ -85,168 +87,64 @@ def write_result(self, format_name, elapsed_time, index): writer.writerow(result) class RLDSHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="rlds", log_frequency=log_frequency) + def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="rlds", batch_size=batch_size, log_frequency=log_frequency) self.file_extension = ".tfrecord" - def measure_loading_time(self): - start_time = time.time() - if self.num_trajectories == -1: - loader = RLDSLoader(self.dataset_dir, split="train") - else: - loader = RLDSLoader(self.dataset_dir, split=f"train[:{self.num_trajectories}]") - for i, data in enumerate(loader, 1): - self._recursively_load_data(data) - elapsed_time = time.time() - start_time - self.write_result("RLDS", elapsed_time, i) - if i % self.log_frequency == 0: - print(f"RLDS - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") - return time.time() - start_time - def measure_random_loading_time(self, num_loads): start_time = time.time() - loader = RLDSLoader(self.dataset_dir, split="train") - dataset_size = len(loader) - num_loads = num_loads * dataset_size - - loader.ds = loader.ds.shuffle(buffer_size=num_loads) - # shuffled_ds = shuffled_ds.take(num_loads) + loader = RLDSLoader(self.dataset_dir, split="train", batch_size=self.batch_size) - for i, data in enumerate(loader): - self._recursively_load_data(data) + for i in range(num_loads): + for batch_num, data in enumerate(loader): + self._recursively_load_data(data) + + elapsed_time = time.time() - start_time + self.write_result(f"RLDS-RandomLoad", elapsed_time, batch_num) + if batch_num % self.log_frequency == 0: + print(f"RLDS-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") - elapsed_time = time.time() - start_time - self.write_result(f"RLDS-RandomLoad", elapsed_time, i) - if i % self.log_frequency == 0: - print(f"RLDS-RandomLoad - Loaded {i} random trajectories, Time: {elapsed_time:.2f} s") - return time.time() - start_time class VLAHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", log_frequency=log_frequency) + def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", batch_size=batch_size, log_frequency=log_frequency) self.file_extension = ".vla" - def measure_loading_time(self, save_to_cache=True): - start_time = time.time() - loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) - if save_to_cache: - mode = "cache" - else: - mode = "no_cache" - for i, data in enumerate(loader, 1): - if self.num_trajectories != -1 and i > self.num_trajectories: - break - try: - self._recursively_load_data(data.load(save_to_cache=save_to_cache)) - elapsed_time = time.time() - start_time - self.write_result(f"VLA-{mode.capitalize()}", elapsed_time, i) - if i % self.log_frequency == 0: - print(f"VLA-{mode.capitalize()} - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") - except Exception as e: - print(f"Failed to load data: {e}") - return time.time() - start_time - def measure_random_loading_time(self, num_loads, save_to_cache=True): start_time = time.time() - loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) - dataset_size = len(loader) - num_loads = num_loads * dataset_size - + loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR, batch_size=self.batch_size) for i in range(num_loads): - random_index = np.random.randint(0, dataset_size) - data = loader[random_index] - try: - self._recursively_load_data(data.load(save_to_cache=save_to_cache)) - elapsed_time = time.time() - start_time - self.write_result(f"VLA-RandomLoad", elapsed_time, i + 1) - if (i + 1) % self.log_frequency == 0: - print(f"VLA-RandomLoad - Loaded {i + 1} random trajectories, Time: {elapsed_time:.2f} s") - except Exception as e: - print(f"Failed to load data: {e}") - - return time.time() - start_time - -class FFV1Handler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="ffv1", log_frequency=log_frequency) - self.file_extension = ".vla" + for batch_num, batch in enumerate(loader): + for data in batch: + try: + self._recursively_load_data(data) + except Exception as e: + print(f"Failed to load data: {e}") + elapsed_time = time.time() - start_time + self.write_result(f"VLA-RandomLoad", elapsed_time, batch_num) + if batch_num % self.log_frequency == 0: + print(f"VLA-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") - def measure_loading_time(self, save_to_cache=True): - start_time = time.time() - loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) - if save_to_cache: - mode = "cache" - else: - mode = "no_cache" - for i, data in enumerate(loader, 1): - if self.num_trajectories != -1 and i > self.num_trajectories: - break - try: - self._recursively_load_data(data.load(save_to_cache=save_to_cache)) - elapsed_time = time.time() - start_time - self.write_result(f"FFV1-{mode.capitalize()}", elapsed_time, i) - if i % self.log_frequency == 0: - print(f"FFV1-{mode.capitalize()} - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") - except Exception as e: - print(f"Failed to load data: {e}") - return time.time() - start_time - - def measure_random_loading_time(self, num_loads, save_to_cache=True): - start_time = time.time() - loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR) - dataset_size = len(loader) - num_loads = num_loads * dataset_size - - for i in range(num_loads): - random_index = np.random.randint(0, dataset_size) - data = loader[random_index] - try: - self._recursively_load_data(data.load(save_to_cache=save_to_cache)) - elapsed_time = time.time() - start_time - self.write_result(f"FFV1-RandomLoad", elapsed_time, i + 1) - if (i + 1) % self.log_frequency == 0: - print(f"FFV1-RandomLoad - Loaded {i + 1} random trajectories, Time: {elapsed_time:.2f} s") - except Exception as e: - print(f"Failed to load data: {e}") - return time.time() - start_time - class HDF5Handler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hdf5", log_frequency=log_frequency) + def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hdf5", batch_size=batch_size, log_frequency=log_frequency) self.file_extension = ".h5" - def measure_loading_time(self): - start_time = time.time() - loader = HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) - for i, data in enumerate(loader, 1): - if self.num_trajectories != -1 and i > self.num_trajectories: - break - self._recursively_load_data(data) - elapsed_time = time.time() - start_time - self.write_result("HDF5", elapsed_time, i) - if i % self.log_frequency == 0: - print(f"HDF5 - Loaded {i} trajectories, Time: {elapsed_time:.2f} s") - return time.time() - start_time - def measure_random_loading_time(self, num_loads): start_time = time.time() loader = HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) - dataset_size = len(loader) - num_loads = num_loads * dataset_size for i in range(num_loads): - random_index = np.random.randint(0, dataset_size) - data = loader[random_index] - self._recursively_load_data(data) + for batch_num, data in enumerate(loader): + self._recursively_load_data(data) + elapsed_time = time.time() - start_time + self.write_result(f"HDF5-RandomLoad", elapsed_time, batch_num) + if batch_num % self.log_frequency == 0: + print(f"HDF5-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") - elapsed_time = time.time() - start_time - self.write_result(f"HDF5-RandomLoad", elapsed_time, i + 1) - if (i + 1) % self.log_frequency == 0: - print(f"HDF5-RandomLoad - Loaded {i + 1} random trajectories, Time: {elapsed_time:.2f} s") - return time.time() - start_time def prepare(args): @@ -268,10 +166,10 @@ def evaluation(args): print(f"Evaluating dataset: {dataset_name}") handlers = [ - # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), - VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), - HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency), - FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency) + # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + # FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency, args.batch_size) ] for handler in handlers: @@ -338,6 +236,7 @@ def evaluation(args): parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") parser.add_argument("--log_frequency", type=int, default=DEFAULT_LOG_FREQUENCY, help="Frequency of logging results.") parser.add_argument("--random_loads", type=int, default=2, help="Number of random loads to perform for each loader.") + parser.add_argument("--batch_size", type=int, default=16, help="Batch size for loaders.") args = parser.parse_args() if args.prepare: diff --git a/fog_x/dataset.py b/fog_x/dataset.py index 7765fbf..5bd971c 100644 --- a/fog_x/dataset.py +++ b/fog_x/dataset.py @@ -47,7 +47,7 @@ def __getitem__(self, index): raise NotImplementedError def get_tf_schema(self): - data = self.loader.peak(0).load(mode = "no_cache") # enforces no h5 cache + data = self.loader.peak(0).load(save_to_cache=False) # enforces no h5 cache return data_to_tf_schema(data) def get_loader(self): diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 9abd22d..af67a0b 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -1,9 +1,9 @@ - - from . import BaseLoader import numpy as np import glob import h5py +import asyncio + # flatten the data such that all data starts with root level tree (observation and action) def _flatten(data, parent_key='', sep='/'): items = {} @@ -25,13 +25,30 @@ def recursively_read_hdf5_group(group): class HDF5Loader(BaseLoader): - def __init__(self, path, split = None): + def __init__(self, path, batch_size=1): super(HDF5Loader, self).__init__(path) self.index = 0 self.files = glob.glob(self.path, recursive=True) + self.batch_size = batch_size + async def _read_hdf5_async(self, data_path): + return await asyncio.to_thread(self._read_hdf5, data_path) - def __getitem__(self, idx): - return self._read_hdf5(self.files[idx]) + async def get_batch(self): + tasks = [] + for _ in range(self.batch_size): + if self.index < len(self.files): + file_path = self.files[self.index] + self.index += 1 + tasks.append(self._read_hdf5_async(file_path)) + else: + break + return await asyncio.gather(*tasks) + + def __next__(self): + if self.index >= len(self.files): + self.index = 0 + raise StopIteration + return asyncio.run(self.get_batch()) def _read_hdf5(self, data_path): @@ -47,14 +64,5 @@ def _read_hdf5(self, data_path): def __iter__(self): return self - def __next__(self): - # for now naming convention: - # h/home/kych/datasets/stacking_blocks_trajectories_data/**/trajectory.h5 - if self.index < len(self.files): - file_path = self.files[self.index] - self.index += 1 - return self._read_hdf5(file_path) - raise StopIteration - def __len__(self): return len(self.files) \ No newline at end of file diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index a0a2968..a04b2da 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -1,12 +1,8 @@ - - from . import BaseLoader -import os -import sys import numpy as np class RLDSLoader(BaseLoader): - def __init__(self, path, split): + def __init__(self, path, split, batch_size=1, shuffle_buffer=50): super(RLDSLoader, self).__init__(path) try: @@ -15,8 +11,11 @@ def __init__(self, path, split): except ImportError: raise ImportError("Please install tensorflow and tensorflow_datasets to use rlds loader") + self.batch_size = batch_size builder = tfds.builder_from_directory(path) self.ds = builder.as_dataset(split) + self.length = len(self.ds) + self.ds = self.ds.shuffle(shuffle_buffer) self.iterator = iter(self.ds) self.split = split @@ -25,36 +24,46 @@ def __init__(self, path, split): def __len__(self): try: import tensorflow as tf - import tensorflow_datasets as tfds except ImportError: - raise ImportError("Please install tensorflow and tensorflow_datasets to use rlds loader") - - return tf.data.experimental.cardinality(self.ds).numpy() + raise ImportError("Please install tensorflow to use rlds loader") + + return self.length def __iter__(self): return self - + + def get_batch(self): + batch = self.ds.take(self.batch_size) + self.index += self.batch_size + data = [] + for b in batch: + data.append(self._convert_batch_to_numpy(b)) + return data + + + def _convert_batch_to_numpy(self, batch): + import tensorflow as tf + + def to_numpy(step_data): + step = {} + for key, val in step_data.items(): + if key == "observation": + step["observation"] = {obs_key: np.array(obs_val) for obs_key, obs_val in val.items()} + elif key == "action": + step["action"] = {act_key: np.array(act_val) for act_key, act_val in val.items()} + else: + step[key] = np.array(val) + return step + + batch = to_numpy(batch) + return batch + def __next__(self): - try: - nest_ds = next(self.iterator) - traj = nest_ds["steps"] - data = [] - - for step_data in traj: - step = {} - for key, val in step_data.items(): - if key == "observation": - step["observation"] = {obs_key: np.array(obs_val) for obs_key, obs_val in val.items()} - elif key == "action": - step["action"] = {act_key: np.array(act_val) for act_key, act_val in val.items()} - else: - step[key] = np.array(val) - data.append(step) - return data - except StopIteration: + if self.index >= len(self.ds): self.index = 0 - self.iterator = iter(self.ds) raise StopIteration - + return self.get_batch() + def __getitem__(self, idx): - return next(iter(self.ds.skip(idx).take(1))) + batch = next(iter(self.ds.skip(idx).take(1))) + return self._convert_batch_to_numpy(batch) diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 77506f5..bf5b865 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -5,16 +5,20 @@ import asyncio import os from typing import Text - +import random logger = logging.getLogger(__name__) class VLALoader(BaseLoader): - def __init__(self, path: Text, cache_dir=None): + def __init__(self, path: Text, batch_size=1, cache_dir=None, shuffle=True): super(VLALoader, self).__init__(path) self.index = 0 self.files = self._get_files(path) self.cache_dir = cache_dir self.loop = asyncio.get_event_loop() + self.batch_size = batch_size + self.shuffle = shuffle + if self.shuffle: + random.shuffle(self.files) def _get_files(self, path): if "*" in path: @@ -27,21 +31,40 @@ def _get_files(self, path): async def _read_vla_async(self, data_path): logger.debug(f"Reading {data_path}") traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) - await traj.load_async() - return traj + data = await traj.load_async() + return data def _read_vla(self, data_path): return self.loop.run_until_complete(self._read_vla_async(data_path)) + async def get_batch_async(self): + tasks = [] + for _ in range(self.batch_size): + if self.index < len(self.files): + file_path = self.files[self.index] + self.index += 1 + tasks.append(self._read_vla_async(file_path)) + else: + break + batch_tasks = await asyncio.gather(*tasks) + return batch_tasks + + + def get_batch(self): + batch = self.loop.run_until_complete(self.get_batch_async()) + return batch + def __iter__(self): return self def __next__(self): - if self.index < len(self.files): - file_path = self.files[self.index] - self.index += 1 - return self._read_vla(file_path) - raise StopIteration + if self.index >= len(self.files): + self.index = 0 + if self.shuffle: + random.shuffle(self.files) + raise StopIteration + + return self.get_batch() def __len__(self): return len(self.files) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index cbad890..6545f99 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -385,7 +385,11 @@ def _get_length_of_stream(container, stream): length += 1 return length - container_to_get_length = av.open(self.path, mode="r", format="matroska") + try: + container_to_get_length = av.open(self.path, mode="r", format="matroska") + except Exception as e: + logger.error(f"Error opening container: {e}") + return {} streams = container_to_get_length.streams length = _get_length_of_stream(container_to_get_length, streams[0]) logger.debug(f"Length of the stream is {length}") From 305d8b61977e46b7856fa23c4cf81c807559d745 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 04:09:00 -0700 Subject: [PATCH 60/80] support lerobot --- benchmarks/openx.py | 26 ++- examples/lerobot_loader.py | 0 examples/rlds_to_lerobot.py | 314 ++++++++++++++++++++++++++++++++++++ fog_x/loader/lerobot.py | 53 ++++++ 4 files changed, 391 insertions(+), 2 deletions(-) create mode 100644 examples/lerobot_loader.py create mode 100644 examples/rlds_to_lerobot.py create mode 100644 fog_x/loader/lerobot.py diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 912f425..44af543 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -9,6 +9,7 @@ import fog_x import csv import stat +from fog_x.loader.lerobot import LeRobotLoader # Constants DEFAULT_EXP_DIR = "/mnt/data/fog_x/" @@ -147,6 +148,26 @@ def measure_random_loading_time(self, num_loads): return time.time() - start_time +class LeRobotHandler(DatasetHandler): + def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hf", batch_size=batch_size, log_frequency=log_frequency) + self.file_extension = "" # LeRobot datasets don't have a specific file extension + + def measure_random_loading_time(self, num_loads): + start_time = time.time() + path = os.path.join(self.exp_dir, "hf") + loader = LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size) + + for i in range(num_loads): + for batch_num, data in enumerate(loader): + self._recursively_load_data(data) + elapsed_time = time.time() - start_time + self.write_result(f"LeRobot-RandomLoad", elapsed_time, batch_num) + if batch_num % self.log_frequency == 0: + print(f"LeRobot-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") + + return time.time() - start_time + def prepare(args): # Clear the cache directory if os.path.exists(CACHE_DIR): @@ -166,10 +187,11 @@ def evaluation(args): print(f"Evaluating dataset: {dataset_name}") handlers = [ - # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), # FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency, args.batch_size) + LeRobotHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), ] for handler in handlers: @@ -235,7 +257,7 @@ def evaluation(args): parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.") parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") parser.add_argument("--log_frequency", type=int, default=DEFAULT_LOG_FREQUENCY, help="Frequency of logging results.") - parser.add_argument("--random_loads", type=int, default=2, help="Number of random loads to perform for each loader.") + parser.add_argument("--random_loads", type=int, default=5, help="Number of random loads to perform for each loader.") parser.add_argument("--batch_size", type=int, default=16, help="Batch size for loaders.") args = parser.parse_args() diff --git a/examples/lerobot_loader.py b/examples/lerobot_loader.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/rlds_to_lerobot.py b/examples/rlds_to_lerobot.py new file mode 100644 index 0000000..9ecfb3a --- /dev/null +++ b/examples/rlds_to_lerobot.py @@ -0,0 +1,314 @@ + +import shutil +from pathlib import Path + +import numpy as np +import tensorflow as tf +import tensorflow_datasets as tfds +import torch +import tqdm +import yaml +from datasets import Dataset, Features, Image, Sequence, Value +from PIL import Image as PILImage + +from lerobot.common.datasets.push_dataset_to_hub.openx.transforms import OPENX_STANDARDIZATION_TRANSFORMS +from lerobot.common.datasets.push_dataset_to_hub.utils import ( + concatenate_episodes, + get_default_encoding, + save_images_concurrently, +) +from lerobot.common.datasets.utils import ( + calculate_episode_data_index, + hf_transform_to_torch, +) +from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames + +with open("/home/kych/lerobot/lerobot/common/datasets/push_dataset_to_hub/openx/configs.yaml", "r") as f: + _openx_list = yaml.safe_load(f) + +OPENX_DATASET_CONFIGS = _openx_list["OPENX_DATASET_CONFIGS"] + +np.set_printoptions(precision=2) + + +def tf_to_torch(data): + return torch.from_numpy(data.numpy()) + + +def tf_img_convert(img): + if img.dtype == tf.string: + img = tf.io.decode_image(img, expand_animations=False, dtype=tf.uint8) + elif img.dtype != tf.uint8: + raise ValueError(f"Unsupported image dtype: found with dtype {img.dtype}") + return img.numpy() + + +def _broadcast_metadata_rlds(i: tf.Tensor, traj: dict) -> dict: + """ + In the RLDS format, each trajectory has some top-level metadata that is explicitly separated out, and a "steps" + entry. This function moves the "steps" entry to the top level, broadcasting any metadata to the length of the + trajectory. This function also adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`. + + NOTE: adapted from DLimp library https://github.com/kvablack/dlimp/ + """ + steps = traj.pop("steps") + + traj_len = tf.shape(tf.nest.flatten(steps)[0])[0] + + # broadcast metadata to the length of the trajectory + metadata = tf.nest.map_structure(lambda x: tf.repeat(x, traj_len), traj) + + # put steps back in + assert "traj_metadata" not in steps + traj = {**steps, "traj_metadata": metadata} + + assert "_len" not in traj + assert "_traj_index" not in traj + assert "_frame_index" not in traj + traj["_len"] = tf.repeat(traj_len, traj_len) + traj["_traj_index"] = tf.repeat(i, traj_len) + traj["_frame_index"] = tf.range(traj_len) + + return traj + + +def load_from_raw( + raw_dir: Path, + videos_dir: Path, + fps: int, + video: bool, + episodes: list[int] | None = None, + encoding: dict | None = None, + openx_dataset_name: str | None = None, +): + """ + Args: + raw_dir (Path): _description_ + videos_dir (Path): _description_ + fps (int): _description_ + video (bool): _description_ + episodes (list[int] | None, optional): _description_. Defaults to None. + """ + ds_builder = tfds.builder_from_directory(str(raw_dir)) + dataset = ds_builder.as_dataset( + split="all", + decoders={"steps": tfds.decode.SkipDecoding()}, + ) + + dataset_info = ds_builder.info + print("dataset_info: ", dataset_info) + + ds_length = len(dataset) + dataset = dataset.take(ds_length) + # "flatten" the dataset as such we can apply trajectory level map() easily + # each [obs][key] has a shape of (frame_size, ...) + dataset = dataset.enumerate().map(_broadcast_metadata_rlds) + + # we will apply the standardization transform if the dataset_name is provided + # if the dataset name is not provided and the goal is to convert any rlds formatted dataset + # search for 'image' keys in the observations + if openx_dataset_name is not None: + print(" - applying standardization transform for dataset: ", openx_dataset_name) + assert openx_dataset_name in OPENX_STANDARDIZATION_TRANSFORMS + transform_fn = OPENX_STANDARDIZATION_TRANSFORMS[openx_dataset_name] + dataset = dataset.map(transform_fn) + + image_keys = OPENX_DATASET_CONFIGS[openx_dataset_name]["image_obs_keys"] + else: + obs_keys = dataset_info.features["steps"]["observation"].keys() + image_keys = [key for key in obs_keys if "image" in key] + + lang_key = "language_instruction" if "language_instruction" in dataset.element_spec else None + + print(" - image_keys: ", image_keys) + print(" - lang_key: ", lang_key) + + it = iter(dataset) + + ep_dicts = [] + # Init temp path to save ep_dicts in case of crash + tmp_ep_dicts_dir = videos_dir.parent.joinpath("ep_dicts") + tmp_ep_dicts_dir.mkdir(parents=True, exist_ok=True) + + # check if ep_dicts have already been saved in /tmp + starting_ep_idx = 0 + saved_ep_dicts = [ep.__str__() for ep in tmp_ep_dicts_dir.iterdir()] + if len(saved_ep_dicts) > 0: + saved_ep_dicts.sort() + # get last ep_idx number + starting_ep_idx = int(saved_ep_dicts[-1][-13:-3]) + 1 + for i in range(starting_ep_idx): + episode = next(it) + ep_dicts.append(torch.load(saved_ep_dicts[i])) + + # if we user specified episodes, skip the ones not in the list + if episodes is not None: + if ds_length == 0: + raise ValueError("No episodes found.") + # convert episodes index to sorted list + episodes = sorted(episodes) + + for ep_idx in tqdm.tqdm(range(starting_ep_idx, ds_length)): + episode = next(it) + + # if user specified episodes, skip the ones not in the list + if episodes is not None: + if len(episodes) == 0: + break + if ep_idx == episodes[0]: + # process this episode + print(" selecting episode idx: ", ep_idx) + episodes.pop(0) + else: + continue # skip + + num_frames = episode["action"].shape[0] + + ########################################################### + # Handle the episodic data + + # last step of demonstration is considered done + done = torch.zeros(num_frames, dtype=torch.bool) + done[-1] = True + ep_dict = {} + langs = [] # TODO: might be located in "observation" + + image_array_dict = {key: [] for key in image_keys} + + # We will create the state observation tensor by stacking the state + # obs keys defined in the openx/configs.py + if openx_dataset_name is not None: + state_obs_keys = OPENX_DATASET_CONFIGS[openx_dataset_name]["state_obs_keys"] + # stack the state observations, if is None, pad with zeros + states = [] + for key in state_obs_keys: + if key in episode["observation"]: + states.append(tf_to_torch(episode["observation"][key])) + else: + states.append(torch.zeros(num_frames, 1)) # pad with zeros + states = torch.cat(states, dim=1) + # assert states.shape == (num_frames, 8), f"states shape: {states.shape}" + else: + states = tf_to_torch(episode["observation"]["state"]) + + actions = tf_to_torch(episode["action"]) + rewards = tf_to_torch(episode["reward"]).float() + + # If lang_key is present, convert the entire tensor at once + if lang_key is not None: + langs = [str(x) for x in episode[lang_key]] + + for im_key in image_keys: + imgs = episode["observation"][im_key] + image_array_dict[im_key] = [tf_img_convert(img) for img in imgs] + + # simple assertions + for item in [states, actions, rewards, done]: + assert len(item) == num_frames + + ########################################################### + + # loop through all cameras + for im_key in image_keys: + img_key = f"observation.images.{im_key}" + imgs_array = image_array_dict[im_key] + imgs_array = np.array(imgs_array) + if video: + # save png images in temporary directory + tmp_imgs_dir = videos_dir / "tmp_images" + save_images_concurrently(imgs_array, tmp_imgs_dir) + + # encode images to a mp4 video + fname = f"{img_key}_episode_{ep_idx:06d}.mp4" + video_path = videos_dir / fname + encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {})) + + # clean temporary images directory + shutil.rmtree(tmp_imgs_dir) + + # store the reference to the video frame + ep_dict[img_key] = [ + {"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames) + ] + else: + ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array] + + if lang_key is not None: + ep_dict["language_instruction"] = langs + + ep_dict["observation.state"] = states + ep_dict["action"] = actions + ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps + ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames) + ep_dict["frame_index"] = torch.arange(0, num_frames, 1) + ep_dict["next.reward"] = rewards + ep_dict["next.done"] = done + + path_ep_dict = tmp_ep_dicts_dir.joinpath( + "ep_dict_" + "0" * (10 - len(str(ep_idx))) + str(ep_idx) + ".pt" + ) + torch.save(ep_dict, path_ep_dict) + + ep_dicts.append(ep_dict) + + data_dict = concatenate_episodes(ep_dicts) + + total_frames = data_dict["frame_index"].shape[0] + data_dict["index"] = torch.arange(0, total_frames, 1) + return data_dict + + +def to_hf_dataset(data_dict, video) -> Dataset: + features = {} + + keys = [key for key in data_dict if "observation.images." in key] + for key in keys: + if video: + features[key] = VideoFrame() + else: + features[key] = Image() + + features["observation.state"] = Sequence( + length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None) + ) + if "observation.velocity" in data_dict: + features["observation.velocity"] = Sequence( + length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None) + ) + if "observation.effort" in data_dict: + features["observation.effort"] = Sequence( + length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None) + ) + if "language_instruction" in data_dict: + features["language_instruction"] = Value(dtype="string", id=None) + + features["action"] = Sequence( + length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None) + ) + features["episode_index"] = Value(dtype="int64", id=None) + features["frame_index"] = Value(dtype="int64", id=None) + features["timestamp"] = Value(dtype="float32", id=None) + features["next.reward"] = Value(dtype="float32", id=None) + features["next.done"] = Value(dtype="bool", id=None) + features["index"] = Value(dtype="int64", id=None) + + hf_dataset = Dataset.from_dict(data_dict, features=Features(features)) + # hf_dataset.set_transform(hf_transform_to_torch) + return hf_dataset + + +dataset_name = "nyu_door_opening_surprising_effectiveness" +# load the rlds dataset +dataset = load_from_raw( + raw_dir=f"/mnt/data/fog_x/rlds/{dataset_name}/", + videos_dir=Path(f"/mnt/data/fog_x/hf/{dataset_name}/videos"), + fps=12, + video=True, + openx_dataset_name=dataset_name, +) + +# convert to hf dataset +hf_dataset = to_hf_dataset(dataset, video=True) + +# save to hf +hf_dataset.save_to_disk("/mnt/data/fog_x/hf/nyu_door_opening_surprising_effectiveness") \ No newline at end of file diff --git a/fog_x/loader/lerobot.py b/fog_x/loader/lerobot.py new file mode 100644 index 0000000..1fff214 --- /dev/null +++ b/fog_x/loader/lerobot.py @@ -0,0 +1,53 @@ +from . import BaseLoader +import numpy as np +import torch +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + +class LeRobotLoader(BaseLoader): + def __init__(self, path, dataset_name, batch_size=1, delta_timestamps=None): + super(LeRobotLoader, self).__init__(path) + self.batch_size = batch_size + self.dataset = LeRobotDataset(root = "/mnt/data/fog_x/hf/", repo_id =dataset_name, delta_timestamps=delta_timestamps) + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=self.batch_size, + shuffle=True, + ) + self.iterator = iter(self.dataloader) + + def __len__(self): + return len(self.dataset) + + def __iter__(self): + return self + + def __next__(self): + max_retries = 3 + for attempt in range(max_retries): + try: + batch = next(self.iterator) + break + except StopIteration: + self.iterator = iter(self.dataloader) + if attempt == max_retries - 1: + raise StopIteration + except Exception as e: + # print(f"Error in __next__ (attempt {attempt + 1}/{max_retries}): {e}") + self.iterator = iter(self.dataloader) + if attempt == max_retries - 1: + raise e + return self._convert_batch_to_numpy(batch) + + def _convert_batch_to_numpy(self, batch): + numpy_batch = {} + for key, value in batch.items(): + if isinstance(value, torch.Tensor): + numpy_batch[key] = value.numpy() + elif isinstance(value, dict): + numpy_batch[key] = self._convert_batch_to_numpy(value) + else: + numpy_batch[key] = value + return numpy_batch + + def get_batch(self): + return next(self) From 604486ce64e94f34d5badc2580cd454d3c3264ff Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 04:39:48 -0700 Subject: [PATCH 61/80] Refactor RLDSLoader and Trajectory classes to improve code readability and lazy loading of data --- benchmarks/openx.py | 14 +++++++------- fog_x/loader/rlds.py | 3 ++- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 44af543..2380861 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -122,10 +122,10 @@ def measure_random_loading_time(self, num_loads, save_to_cache=True): self._recursively_load_data(data) except Exception as e: print(f"Failed to load data: {e}") - elapsed_time = time.time() - start_time - self.write_result(f"VLA-RandomLoad", elapsed_time, batch_num) - if batch_num % self.log_frequency == 0: - print(f"VLA-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") + elapsed_time = time.time() - start_time + self.write_result(f"VLA-RandomLoad", elapsed_time, batch_num) + if batch_num % self.log_frequency == 0: + print(f"VLA-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") return time.time() - start_time @@ -187,7 +187,7 @@ def evaluation(args): print(f"Evaluating dataset: {dataset_name}") handlers = [ - RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), # FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency, args.batch_size) @@ -257,8 +257,8 @@ def evaluation(args): parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.") parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") parser.add_argument("--log_frequency", type=int, default=DEFAULT_LOG_FREQUENCY, help="Frequency of logging results.") - parser.add_argument("--random_loads", type=int, default=5, help="Number of random loads to perform for each loader.") - parser.add_argument("--batch_size", type=int, default=16, help="Batch size for loaders.") + parser.add_argument("--random_loads", type=int, default=2, help="Number of random loads to perform for each loader.") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size for loaders.") args = parser.parse_args() if args.prepare: diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index a04b2da..ed40b13 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -16,6 +16,7 @@ def __init__(self, path, split, batch_size=1, shuffle_buffer=50): self.ds = builder.as_dataset(split) self.length = len(self.ds) self.ds = self.ds.shuffle(shuffle_buffer) + self.ds = self.ds.repeat() self.iterator = iter(self.ds) self.split = split @@ -59,7 +60,7 @@ def to_numpy(step_data): return batch def __next__(self): - if self.index >= len(self.ds): + if self.index >= self.length: self.index = 0 raise StopIteration return self.get_batch() From 466c5cb07b361e7bbfb442b98cb7a6f431d928ab Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 04:57:11 -0700 Subject: [PATCH 62/80] write as pytorch dataloader --- benchmarks/openx.py | 18 ++++++------- fog_x/loader/__init__.py | 3 ++- fog_x/loader/pytorch_vla.py | 51 +++++++++++++++++++++++++++++++++++++ 3 files changed, 61 insertions(+), 11 deletions(-) create mode 100644 fog_x/loader/pytorch_vla.py diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 2380861..8817797 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -10,6 +10,7 @@ import csv import stat from fog_x.loader.lerobot import LeRobotLoader +from fog_x.loader.pytorch_vla import get_vla_dataloader # Constants DEFAULT_EXP_DIR = "/mnt/data/fog_x/" @@ -114,14 +115,11 @@ def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_freq def measure_random_loading_time(self, num_loads, save_to_cache=True): start_time = time.time() - loader = VLALoader(self.dataset_dir, cache_dir=CACHE_DIR, batch_size=self.batch_size) + dataloader = get_vla_dataloader(self.dataset_dir, batch_size=self.batch_size, cache_dir=CACHE_DIR) + for i in range(num_loads): - for batch_num, batch in enumerate(loader): - for data in batch: - try: - self._recursively_load_data(data) - except Exception as e: - print(f"Failed to load data: {e}") + for batch_num, batch in enumerate(dataloader): + self._recursively_load_data(batch) elapsed_time = time.time() - start_time self.write_result(f"VLA-RandomLoad", elapsed_time, batch_num) if batch_num % self.log_frequency == 0: @@ -187,11 +185,11 @@ def evaluation(args): print(f"Evaluating dataset: {dataset_name}") handlers = [ - # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), - HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + # HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), # FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency, args.batch_size) - LeRobotHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + # LeRobotHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), ] for handler in handlers: diff --git a/fog_x/loader/__init__.py b/fog_x/loader/__init__.py index ab8f982..c54c4a5 100644 --- a/fog_x/loader/__init__.py +++ b/fog_x/loader/__init__.py @@ -1,4 +1,5 @@ from .base import BaseLoader from .rlds import RLDSLoader from .hdf5 import HDF5Loader -from .vla import VLALoader \ No newline at end of file +from .vla import VLALoader +from .pytorch_vla import get_vla_dataloader \ No newline at end of file diff --git a/fog_x/loader/pytorch_vla.py b/fog_x/loader/pytorch_vla.py new file mode 100644 index 0000000..e0e1de9 --- /dev/null +++ b/fog_x/loader/pytorch_vla.py @@ -0,0 +1,51 @@ +import torch +from torch.utils.data import Dataset, DataLoader +import fog_x +import glob +import os +import random +import asyncio +from typing import Text, List + +class VLADataset(Dataset): + def __init__(self, path: Text, cache_dir: Text = None, shuffle: bool = True): + self.files = self._get_files(path) + self.cache_dir = cache_dir + self.loop = asyncio.get_event_loop() + if shuffle: + random.shuffle(self.files) + + def _get_files(self, path: Text) -> List[Text]: + if "*" in path: + return glob.glob(path) + elif os.path.isdir(path): + return glob.glob(os.path.join(path, "*.vla")) + else: + return [path] + + async def _read_vla_async(self, data_path: Text): + traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) + data = await traj.load_async() + return data + + def _read_vla(self, data_path: Text): + return self.loop.run_until_complete(self._read_vla_async(data_path)) + + def __len__(self): + return len(self.files) + + def __getitem__(self, index: int): + file_path = self.files[index] + data = self._read_vla(file_path) + # Convert data to PyTorch tensors + # You may need to adjust this based on the structure of your VLA data + return data #{k: torch.tensor(v) for k, v in data.items()} + +def vla_collate_fn(batch): + # Implement custom collate function if needed + # This depends on the structure of your VLA data + return batch + +def get_vla_dataloader(path: Text, batch_size: int = 1, cache_dir: Text = None, shuffle: bool = True, num_workers: int = 0): + dataset = VLADataset(path, cache_dir, shuffle) + return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, collate_fn=vla_collate_fn) \ No newline at end of file From eccf7b213aba9d4d4993d6c4dfbcfc7af38154db Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 15:05:03 -0700 Subject: [PATCH 63/80] multi proces to sped up --- benchmarks/openx.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 8817797..cb0ec16 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -16,7 +16,7 @@ DEFAULT_EXP_DIR = "/mnt/data/fog_x/" DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories # DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness", "berkeley_cable_routing", "berkeley_autolab_ur5", "bridge"] -DEFAULT_DATASET_NAMES = ["berkeley_autolab_ur5"] +DEFAULT_DATASET_NAMES = ["berkeley_cable_routing"] CACHE_DIR = "/tmp/fog_x/cache/" DEFAULT_LOG_FREQUENCY = 20 @@ -156,8 +156,12 @@ def measure_random_loading_time(self, num_loads): path = os.path.join(self.exp_dir, "hf") loader = LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size) + dataset_len = len(loader) + for i in range(num_loads): for batch_num, data in enumerate(loader): + if batch_num >= dataset_len: + break self._recursively_load_data(data) elapsed_time = time.time() - start_time self.write_result(f"LeRobot-RandomLoad", elapsed_time, batch_num) @@ -185,7 +189,7 @@ def evaluation(args): print(f"Evaluating dataset: {dataset_name}") handlers = [ - RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), # HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), # FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency, args.batch_size) From e4913b12006fe8efa8471d9d931f040dffa8479d Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 15:05:48 -0700 Subject: [PATCH 64/80] chore: Refactor Trajectory class for improved code readability and efficient multi-processing --- fog_x/trajectory.py | 104 ++++++++++++++++++++++++++------------------ 1 file changed, 62 insertions(+), 42 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 6545f99..e30d8ce 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -10,6 +10,7 @@ import h5py import asyncio from concurrent.futures import ThreadPoolExecutor +import sys logger = logging.getLogger(__name__) @@ -361,7 +362,7 @@ def _load_from_cache(self): def _load_from_container(self): """ - Load the container file with the entire VLA trajectory. + Load the container file with the entire VLA trajectory using multi-processing for image streams. args: save_to_cache: save the decoded data to the cache file @@ -372,8 +373,11 @@ def _load_from_container(self): Workflow: - Get schema of the container file. - Preallocate decoded streams. - - Decode frame by frame and store in the preallocated memory. + - Use multi-processing to decode image streams separately. + - Decode non-image streams in the main process. + - Combine results from all processes. """ + import multiprocessing as mp def _get_length_of_stream(container, stream): """ @@ -385,6 +389,25 @@ def _get_length_of_stream(container, stream): length += 1 return length + def process_image_stream(stream, feature_name, feature_type, length, path, result_queue): + container = av.open(path, mode="r", format="matroska") + np_cache = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) + feature_length = 0 + + for packet in container.demux([stream]): + frames = packet.decode() + for frame in frames: + if feature_type.dtype == "float32": + data = frame.to_ndarray(format="gray").reshape(feature_type.shape) + else: + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + np_cache[feature_length] = data + feature_length += 1 + + container.close() + result_queue.put((feature_name, np_cache[:feature_length])) + os._exit(0) + try: container_to_get_length = av.open(self.path, mode="r", format="matroska") except Exception as e: @@ -398,11 +421,12 @@ def _get_length_of_stream(container, stream): container = av.open(self.path, mode="r", format="matroska") streams = container.streams - # Dictionary to store preallocated numpy arrays np_cache = {} - # Preallocate memory for the streams in numpy arrays + # Prepare for multi-processing + image_streams = [] + other_streams = [] for stream in streams: feature_name = stream.metadata.get("FEATURE_NAME") if feature_name is None: @@ -412,54 +436,50 @@ def _get_length_of_stream(container, stream): self.feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type - logger.debug( - f"Creating a cache for {feature_name} with shape {feature_type.shape}" - ) - - # Allocate numpy array with shape [None, X, Y, Z] where X, Y, Z are feature dimensions - if feature_type.dtype == "string": - np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) + if stream.codec_context.codec.name == "h264": + image_streams.append((stream, feature_name, feature_type)) else: - np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) + other_streams.append((stream, feature_name, feature_type)) + if feature_type.dtype == "string": + np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) + else: + np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) + + # Process image streams with multi-processing + result_queue = mp.Queue() + processes = [] + for stream, feature_name, feature_type in image_streams: + p = mp.Process(target=process_image_stream, args=(stream, feature_name, feature_type, length, self.path, result_queue)) + processes.append(p) + p.start() + - # Decode the frames and store them in the preallocated numpy memory - d_feature_length = {feature: 0 for feature in self.feature_name_to_stream} - for packet in container.demux(list(streams)): + # Process other streams in the main process + d_feature_length = {feature: 0 for feature, _, _ in other_streams} + for packet in container.demux([stream for stream, _, _ in other_streams]): feature_name = packet.stream.metadata.get("FEATURE_NAME") if feature_name is None: logger.debug(f"Skipping stream without FEATURE_NAME: {packet.stream}") continue feature_type = FeatureType.from_str(packet.stream.metadata.get("FEATURE_TYPE")) - logger.debug( - f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" - ) - - feature_codec = packet.stream.codec_context.codec.name - if feature_codec == "h264": - frames = packet.decode() - for frame in frames: - if feature_type.dtype == "float32": - data = frame.to_ndarray(format="gray").reshape(feature_type.shape) - else: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) - - # Append data to the numpy array - np_cache[feature_name][d_feature_length[feature_name]] = data - d_feature_length[feature_name] += 1 + packet_in_bytes = bytes(packet) + if packet_in_bytes: + data = pickle.loads(packet_in_bytes) + np_cache[feature_name][d_feature_length[packet.stream]] = data + d_feature_length[packet.stream] += 1 else: - packet_in_bytes = bytes(packet) - if packet_in_bytes: - # Decode the packet - data = pickle.loads(packet_in_bytes) - - # Append data to the numpy array - np_cache[feature_name][d_feature_length[feature_name]] = data - d_feature_length[feature_name] += 1 - else: - logger.debug(f"Skipping empty packet: {packet} for {feature_name}") - logger.debug(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") + logger.debug(f"Skipping empty packet: {packet} for {feature_name}") container.close() + # Wait for all image processing to complete + # busy join here + for p in processes: + p.join() + + # Collect results from image processing + while not result_queue.empty(): + feature_name, data = result_queue.get() + np_cache[feature_name] = data return np_cache From 01841ee67809a073380608ff67090544c49d66f2 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 16:34:56 -0700 Subject: [PATCH 65/80] Refactor Trajectory and VLAIterableDataset classes for improved code readability and performance --- benchmarks/openx.py | 152 ++++++++++-------------------------- fog_x/loader/pytorch_vla.py | 76 ++++++++---------- fog_x/loader/vla.py | 105 +++++++++++++++---------- fog_x/trajectory.py | 1 - 4 files changed, 138 insertions(+), 196 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index cb0ec16..f966dfc 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -23,10 +23,10 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" class DatasetHandler: - def __init__(self, exp_dir, dataset_name, num_trajectories, dataset_type, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + def __init__(self, exp_dir, dataset_name, num_batches, dataset_type, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): self.exp_dir = exp_dir self.dataset_name = dataset_name - self.num_trajectories = num_trajectories + self.num_batches = num_batches self.dataset_type = dataset_type self.dataset_dir = os.path.join(exp_dir, dataset_type, dataset_name) self.batch_size = batch_size @@ -88,87 +88,57 @@ def write_result(self, format_name, elapsed_time, index): writer.writeheader() writer.writerow(result) -class RLDSHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="rlds", batch_size=batch_size, log_frequency=log_frequency) - self.file_extension = ".tfrecord" - - def measure_random_loading_time(self, num_loads): + def measure_random_loading_time(self): start_time = time.time() - loader = RLDSLoader(self.dataset_dir, split="train", batch_size=self.batch_size) + loader = self.get_loader() - for i in range(num_loads): - for batch_num, data in enumerate(loader): - self._recursively_load_data(data) - - elapsed_time = time.time() - start_time - self.write_result(f"RLDS-RandomLoad", elapsed_time, batch_num) - if batch_num % self.log_frequency == 0: - print(f"RLDS-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") + for batch_num, data in enumerate(loader): + if batch_num >= self.num_batches: + break + self._recursively_load_data(data) + elapsed_time = time.time() - start_time + self.write_result(f"{self.dataset_type.upper()}-RandomLoad", elapsed_time, batch_num) + if batch_num % self.log_frequency == 0: + print(f"{self.dataset_type.upper()}-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") + return time.time() - start_time + def get_loader(self): + raise NotImplementedError("Subclasses must implement get_loader method") + +class RLDSHandler(DatasetHandler): + def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_batches, dataset_type="rlds", batch_size=batch_size, log_frequency=log_frequency) + self.file_extension = ".tfrecord" + + def get_loader(self): + return RLDSLoader(self.dataset_dir, split="train", batch_size=self.batch_size) + class VLAHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="vla", batch_size=batch_size, log_frequency=log_frequency) + def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_batches, dataset_type="vla", batch_size=batch_size, log_frequency=log_frequency) self.file_extension = ".vla" - def measure_random_loading_time(self, num_loads, save_to_cache=True): - start_time = time.time() - dataloader = get_vla_dataloader(self.dataset_dir, batch_size=self.batch_size, cache_dir=CACHE_DIR) - - for i in range(num_loads): - for batch_num, batch in enumerate(dataloader): - self._recursively_load_data(batch) - elapsed_time = time.time() - start_time - self.write_result(f"VLA-RandomLoad", elapsed_time, batch_num) - if batch_num % self.log_frequency == 0: - print(f"VLA-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") - - return time.time() - start_time + def get_loader(self): + return get_vla_dataloader(self.dataset_dir, batch_size=self.batch_size, cache_dir=CACHE_DIR) class HDF5Handler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hdf5", batch_size=batch_size, log_frequency=log_frequency) + def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_batches, dataset_type="hdf5", batch_size=batch_size, log_frequency=log_frequency) self.file_extension = ".h5" - def measure_random_loading_time(self, num_loads): - start_time = time.time() - loader = HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) - - for i in range(num_loads): - for batch_num, data in enumerate(loader): - self._recursively_load_data(data) - elapsed_time = time.time() - start_time - self.write_result(f"HDF5-RandomLoad", elapsed_time, batch_num) - if batch_num % self.log_frequency == 0: - print(f"HDF5-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") - - return time.time() - start_time + def get_loader(self): + return HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) class LeRobotHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_trajectories, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_trajectories, dataset_type="hf", batch_size=batch_size, log_frequency=log_frequency) + def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_batches, dataset_type="lerobot", batch_size=batch_size, log_frequency=log_frequency) self.file_extension = "" # LeRobot datasets don't have a specific file extension - def measure_random_loading_time(self, num_loads): - start_time = time.time() + def get_loader(self): path = os.path.join(self.exp_dir, "hf") - loader = LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size) - - dataset_len = len(loader) - - for i in range(num_loads): - for batch_num, data in enumerate(loader): - if batch_num >= dataset_len: - break - self._recursively_load_data(data) - elapsed_time = time.time() - start_time - self.write_result(f"LeRobot-RandomLoad", elapsed_time, batch_num) - if batch_num % self.log_frequency == 0: - print(f"LeRobot-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") - - return time.time() - start_time + return LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size) def prepare(args): # Clear the cache directory @@ -189,11 +159,10 @@ def evaluation(args): print(f"Evaluating dataset: {dataset_name}") handlers = [ - # RLDSHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), - VLAHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), - # HDF5Handler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), - # FFV1Handler(args.exp_dir, dataset_name, args.num_trajectories, args.log_frequency, args.batch_size) - # LeRobotHandler(args.exp_dir, dataset_name, args.num_trajectories, args.batch_size, args.log_frequency), + VLAHandler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), + HDF5Handler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), + LeRobotHandler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), + RLDSHandler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), ] for handler in handlers: @@ -201,18 +170,7 @@ def evaluation(args): handler.clear_os_cache() avg_traj_size = handler.measure_average_trajectory_size() - # loading_time = handler.measure_loading_time() - - # new_results.append({ - # 'Dataset': dataset_name, - # 'Format': handler.dataset_type.upper(), - # 'AverageTrajectorySize(MB)': avg_traj_size, - # 'LoadingTime(s)': loading_time, - # }) - - # print(f"{handler.dataset_type.upper()} - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {loading_time:.2f} s") - - random_load_time = handler.measure_random_loading_time(args.random_loads) + random_load_time = handler.measure_random_loading_time() new_results.append({ 'Dataset': dataset_name, 'Format': f"{handler.dataset_type.upper()}-RandomLoad", @@ -221,29 +179,6 @@ def evaluation(args): }) print(f"{handler.dataset_type.upper()}-RandomLoad - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s") - # # Additional VLA measurements - # vla_handler = handlers[1] - # vla_handler.clear_cache() - # vla_handler.clear_os_cache() - # cold_cache_time = vla_handler.measure_loading_time(mode="cache") - # hot_cache_time = vla_handler.measure_loading_time(mode="cache") - - # new_results.append({ - # 'Dataset': dataset_name, - # 'Format': 'VLA-ColdCache', - # 'AverageTrajectorySize(MB)': avg_traj_size, - # 'LoadingTime(s)': cold_cache_time, - # }) - - # new_results.append({ - # 'Dataset': dataset_name, - # 'Format': 'VLA-HotCache', - # 'AverageTrajectorySize(MB)': avg_traj_size, - # 'LoadingTime(s)': hot_cache_time, - # }) - # print(f"VLA-ColdCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {cold_cache_time:.2f} s") - # print(f"VLA-HotCache - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {hot_cache_time:.2f} s") - # Combine existing and new results all_results = existing_results + new_results @@ -255,12 +190,11 @@ def evaluation(args): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Prepare and evaluate loading times and folder sizes for RLDS, VLA, and HDF5 formats.") parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") - parser.add_argument("--num_trajectories", type=int, default=DEFAULT_NUMBER_OF_TRAJECTORIES, help="Number of trajectories to evaluate.") parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.") parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") parser.add_argument("--log_frequency", type=int, default=DEFAULT_LOG_FREQUENCY, help="Frequency of logging results.") - parser.add_argument("--random_loads", type=int, default=2, help="Number of random loads to perform for each loader.") - parser.add_argument("--batch_size", type=int, default=1, help="Batch size for loaders.") + parser.add_argument("--num_batches", type=int, default=1000, help="Number of batches to load for each loader.") + parser.add_argument("--batch_size", type=int, default=8, help="Batch size for loaders.") args = parser.parse_args() if args.prepare: diff --git a/fog_x/loader/pytorch_vla.py b/fog_x/loader/pytorch_vla.py index e0e1de9..3c02169 100644 --- a/fog_x/loader/pytorch_vla.py +++ b/fog_x/loader/pytorch_vla.py @@ -1,51 +1,37 @@ import torch -from torch.utils.data import Dataset, DataLoader -import fog_x -import glob -import os -import random -import asyncio -from typing import Text, List +from torch.utils.data import IterableDataset, DataLoader +from fog_x.loader.vla import VLALoader +from typing import Text, Optional -class VLADataset(Dataset): - def __init__(self, path: Text, cache_dir: Text = None, shuffle: bool = True): - self.files = self._get_files(path) - self.cache_dir = cache_dir - self.loop = asyncio.get_event_loop() - if shuffle: - random.shuffle(self.files) +class VLAIterableDataset(IterableDataset): + def __init__(self, path: Text, cache_dir: Optional[Text] = None, buffer_size: int = 1000): + self.vla_loader = VLALoader(path, batch_size=1, cache_dir=cache_dir, buffer_size=buffer_size) - def _get_files(self, path: Text) -> List[Text]: - if "*" in path: - return glob.glob(path) - elif os.path.isdir(path): - return glob.glob(os.path.join(path, "*.vla")) - else: - return [path] + def __iter__(self): + return self - async def _read_vla_async(self, data_path: Text): - traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) - data = await traj.load_async() - return data - - def _read_vla(self, data_path: Text): - return self.loop.run_until_complete(self._read_vla_async(data_path)) - - def __len__(self): - return len(self.files) - - def __getitem__(self, index: int): - file_path = self.files[index] - data = self._read_vla(file_path) - # Convert data to PyTorch tensors - # You may need to adjust this based on the structure of your VLA data - return data #{k: torch.tensor(v) for k, v in data.items()} + def __next__(self): + batch = self.vla_loader.get_batch() + if batch is None: + raise StopIteration + return batch[0] # Return a single item, not a batch def vla_collate_fn(batch): - # Implement custom collate function if needed - # This depends on the structure of your VLA data - return batch - -def get_vla_dataloader(path: Text, batch_size: int = 1, cache_dir: Text = None, shuffle: bool = True, num_workers: int = 0): - dataset = VLADataset(path, cache_dir, shuffle) - return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, collate_fn=vla_collate_fn) \ No newline at end of file + # Convert data to PyTorch tensors + # You may need to adjust this based on the structure of your VLA data + return batch #{k: torch.tensor(v) for k, v in batch[0].items()} + +def get_vla_dataloader( + path: Text, + batch_size: int = 1, + cache_dir: Optional[Text] = None, + buffer_size: int = 1000, + num_workers: int = 0 +): + dataset = VLAIterableDataset(path, cache_dir, buffer_size) + return DataLoader( + dataset, + batch_size=batch_size, + collate_fn=vla_collate_fn, + num_workers=num_workers + ) \ No newline at end of file diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index bf5b865..d3867ce 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -4,21 +4,25 @@ import logging import asyncio import os -from typing import Text +from typing import Text, List, Any import random +from collections import deque +import multiprocessing as mp +import time + logger = logging.getLogger(__name__) -class VLALoader(BaseLoader): - def __init__(self, path: Text, batch_size=1, cache_dir=None, shuffle=True): - super(VLALoader, self).__init__(path) - self.index = 0 +class VLALoader: + def __init__(self, path: Text, batch_size=1, cache_dir=None, buffer_size=100, num_workers=4): self.files = self._get_files(path) self.cache_dir = cache_dir - self.loop = asyncio.get_event_loop() self.batch_size = batch_size - self.shuffle = shuffle - if self.shuffle: - random.shuffle(self.files) + self.buffer_size = buffer_size + self.buffer = mp.Queue(maxsize=buffer_size) + self.num_workers = num_workers + self.processes = [] + random.shuffle(self.files) + self._start_workers() def _get_files(self, path): if "*" in path: @@ -28,49 +32,68 @@ def _get_files(self, path): else: return [path] - async def _read_vla_async(self, data_path): - logger.debug(f"Reading {data_path}") + def _read_vla(self, data_path): traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) - data = await traj.load_async() - return data + return traj.load() - def _read_vla(self, data_path): - return self.loop.run_until_complete(self._read_vla_async(data_path)) + def _worker(self): + while True: + if not self.files: + logger.info("Worker finished") + break + file_path = random.choice(self.files) + data = self._read_vla(file_path) + self.buffer.put(data) + + def _start_workers(self): + for _ in range(self.num_workers): + p = mp.Process(target=self._worker) + p.start() + logger.debug(f"Started worker {p.pid}") + self.processes.append(p) + + def get_batch(self) -> List[Any]: + batch = [] + timeout = 5 # Adjust this value based on your needs + start_time = time.time() - async def get_batch_async(self): - tasks = [] - for _ in range(self.batch_size): - if self.index < len(self.files): - file_path = self.files[self.index] - self.index += 1 - tasks.append(self._read_vla_async(file_path)) - else: + while len(batch) < self.batch_size: + if time.time() - start_time > timeout: + logger.warning(f"Timeout reached while getting batch. Batch size: {len(batch)}") break - batch_tasks = await asyncio.gather(*tasks) - return batch_tasks - - - def get_batch(self): - batch = self.loop.run_until_complete(self.get_batch_async()) + + try: + item = self.buffer.get(timeout=1) + batch.append(item) + except mp.queues.Empty: + if all(not p.is_alive() for p in self.processes) and self.buffer.empty(): + if len(batch) == 0: + return None # No more data available + else: + break # Return partial batch + return batch - + def __iter__(self): return self def __next__(self): - if self.index >= len(self.files): - self.index = 0 - if self.shuffle: - random.shuffle(self.files) + batch = self.get_batch() + if batch is None: + random.shuffle(self.files) + self._start_workers() raise StopIteration - - return self.get_batch() + return batch def __len__(self): return len(self.files) - def __getitem__(self, index): - return self._read_vla(self.files[index]) - - def peak(self): - return self._read_vla(self.files[self.index]) \ No newline at end of file + def peek(self): + if self.buffer.empty(): + return None + return self.buffer.get() + + def __del__(self): + for p in self.processes: + p.terminate() + p.join() \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index e30d8ce..2d535fe 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -170,7 +170,6 @@ def load(self, save_to_cache=True, return_h5=False): async def load_async(self, save_to_cache=True, return_h5=False): if os.path.exists(self.cache_file_name): - logger.debug(f"Loading the cached file {self.cache_file_name}") if return_h5: return h5py.File(self.cache_file_name, "r") else: From 5f5d328a44bbe9de9f93d9d72e856b5ca8595013 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 20:38:06 -0700 Subject: [PATCH 66/80] fix rlds and add debug --- benchmarks/openx.py | 318 ++++++++++++++++++++++++++++-------- fog_x/loader/hdf5.py | 36 +++- fog_x/loader/lerobot.py | 19 ++- fog_x/loader/pytorch_vla.py | 2 + fog_x/loader/rlds.py | 21 ++- 5 files changed, 311 insertions(+), 85 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index f966dfc..bda4301 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -11,19 +11,39 @@ import stat from fog_x.loader.lerobot import LeRobotLoader from fog_x.loader.pytorch_vla import get_vla_dataloader +from fog_x.loader.hdf5 import get_hdf5_dataloader # Constants DEFAULT_EXP_DIR = "/mnt/data/fog_x/" -DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories -# DEFAULT_DATASET_NAMES = ["nyu_door_opening_surprising_effectiveness", "berkeley_cable_routing", "berkeley_autolab_ur5", "bridge"] -DEFAULT_DATASET_NAMES = ["berkeley_cable_routing"] +DEFAULT_NUMBER_OF_TRAJECTORIES = -1 # Load all trajectories +DEFAULT_DATASET_NAMES = [ + "nyu_door_opening_surprising_effectiveness", + "berkeley_cable_routing", + "berkeley_autolab_ur5", + "bridge", +] +DEFAULT_DATASET_NAMES = ["bridge"] CACHE_DIR = "/tmp/fog_x/cache/" DEFAULT_LOG_FREQUENCY = 20 +# suppress tensorflow warnings +import os + os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" +import logging +logger = logging.getLogger(__name__) + class DatasetHandler: - def __init__(self, exp_dir, dataset_name, num_batches, dataset_type, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + def __init__( + self, + exp_dir, + dataset_name, + num_batches, + dataset_type, + batch_size, + log_frequency=DEFAULT_LOG_FREQUENCY, + ): self.exp_dir = exp_dir self.dataset_name = dataset_name self.num_batches = num_batches @@ -58,31 +78,54 @@ def clear_os_cache(self): """Clears the OS cache.""" subprocess.run(["sync"], check=True) subprocess.run(["echo", "3", ">", "/proc/sys/vm/drop_caches"], check=True) - + def _recursively_load_data(self, data): - if isinstance(data, dict): - for key, value in data.items(): - self._recursively_load_data(value) - elif isinstance(data, (list, tuple)): - for item in data: - self._recursively_load_data(item) - else: - _ = np.array(data) + logger.debug(f"Data summary for loader {self.dataset_type.upper()}") + + def summarize_trajectory(trajectory): + def summarize_value(value): + if isinstance(value, np.ndarray): + return value.shape + elif isinstance(value, (list, tuple)): + if len(value) > 0 and isinstance(value[0], np.ndarray): + return [v.shape for v in value] + return len(value) + elif isinstance(value, dict): + return {k: summarize_value(v) for k, v in value.items()} + else: + return type(value).__name__ + + return {key: summarize_value(value) for key, value in trajectory.items()} + + trajectory_summaries = [summarize_trajectory(trajectory) for trajectory in data] + + for i, summary in enumerate(trajectory_summaries): + logger.debug(f"Trajectory {i + 1}:") + for feature, dimension in summary.items(): + if isinstance(dimension, dict): + logger.debug(f" {feature}:") + for sub_feature, sub_dimension in dimension.items(): + logger.debug(f" {sub_feature}: {sub_dimension}") + else: + logger.debug(f" {feature}: {dimension}") + logger.debug() + + logger.debug(f"Total number of trajectories: {len(trajectory_summaries)}") def write_result(self, format_name, elapsed_time, index): result = { - 'Dataset': self.dataset_name, - 'Format': format_name, - 'AverageTrajectorySize(MB)': self.measure_average_trajectory_size(), - 'LoadingTime(s)': elapsed_time, - 'Index': index, - 'BatchSize': self.batch_size + "Dataset": self.dataset_name, + "Format": format_name, + "AverageTrajectorySize(MB)": self.measure_average_trajectory_size(), + "LoadingTime(s)": elapsed_time, + "Index": index, + "BatchSize": self.batch_size, } - - csv_file = f'{self.dataset_name}_results.csv' + + csv_file = f"{self.dataset_name}_results.csv" file_exists = os.path.isfile(csv_file) - - with open(csv_file, 'a', newline='') as f: + + with open(csv_file, "a", newline="") as f: writer = csv.DictWriter(f, fieldnames=result.keys()) if not file_exists: writer.writeheader() @@ -91,78 +134,195 @@ def write_result(self, format_name, elapsed_time, index): def measure_random_loading_time(self): start_time = time.time() loader = self.get_loader() - + for batch_num, data in enumerate(loader): if batch_num >= self.num_batches: break self._recursively_load_data(data) - + elapsed_time = time.time() - start_time - self.write_result(f"{self.dataset_type.upper()}-RandomLoad", elapsed_time, batch_num) + self.write_result( + f"{self.dataset_type.upper()}-RandomLoad", elapsed_time, batch_num + ) if batch_num % self.log_frequency == 0: - print(f"{self.dataset_type.upper()}-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s") - + logger.debug( + f"{self.dataset_type.upper()}-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s" + ) + return time.time() - start_time def get_loader(self): raise NotImplementedError("Subclasses must implement get_loader method") + class RLDSHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_batches, dataset_type="rlds", batch_size=batch_size, log_frequency=log_frequency) + def __init__( + self, + exp_dir, + dataset_name, + num_batches, + batch_size, + log_frequency=DEFAULT_LOG_FREQUENCY, + ): + super().__init__( + exp_dir, + dataset_name, + num_batches, + dataset_type="rlds", + batch_size=batch_size, + log_frequency=log_frequency, + ) self.file_extension = ".tfrecord" def get_loader(self): return RLDSLoader(self.dataset_dir, split="train", batch_size=self.batch_size) + def _recursively_load_data(self, data): + # rlds returns a list of dictionaries + logger.debug(f"Data summary for loader {self.dataset_type.upper()}") + for i, trajectory in enumerate(data): + logger.debug(f"Trajectory {i + 1}:") + # each trajectory is a list of dictionaries + for j, step in enumerate(trajectory): + logger.debug(f" Step {j + 1}:") + for key, value in step.items(): + if isinstance(value, np.ndarray): + logger.debug(f" {key}: {value.shape}") + elif isinstance(value, dict): + logger.debug(f" {key}:") + for sub_key, sub_value in value.items(): + logger.debug(f" {sub_key}: {sub_value.shape}") + else: + logger.debug(f" {key}: {type(value).__name__}") + logger.debug(f"Total number of trajectories: {len(data)}") + + class VLAHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_batches, dataset_type="vla", batch_size=batch_size, log_frequency=log_frequency) + def __init__( + self, + exp_dir, + dataset_name, + num_batches, + batch_size, + log_frequency=DEFAULT_LOG_FREQUENCY, + ): + super().__init__( + exp_dir, + dataset_name, + num_batches, + dataset_type="vla", + batch_size=batch_size, + log_frequency=log_frequency, + ) self.file_extension = ".vla" def get_loader(self): - return get_vla_dataloader(self.dataset_dir, batch_size=self.batch_size, cache_dir=CACHE_DIR) + return get_vla_dataloader( + self.dataset_dir, batch_size=self.batch_size, cache_dir=CACHE_DIR + ) + class HDF5Handler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_batches, dataset_type="hdf5", batch_size=batch_size, log_frequency=log_frequency) + def __init__( + self, + exp_dir, + dataset_name, + num_batches, + batch_size, + log_frequency=DEFAULT_LOG_FREQUENCY, + ): + super().__init__( + exp_dir, + dataset_name, + num_batches, + dataset_type="hdf5", + batch_size=batch_size, + log_frequency=log_frequency, + ) self.file_extension = ".h5" def get_loader(self): - return HDF5Loader(path=os.path.join(self.dataset_dir, "*.h5")) + return get_hdf5_dataloader( + path=os.path.join(self.dataset_dir, "*.h5"), + batch_size=self.batch_size, + num_workers=0, # You can adjust this if needed + ) + class LeRobotHandler(DatasetHandler): - def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): - super().__init__(exp_dir, dataset_name, num_batches, dataset_type="lerobot", batch_size=batch_size, log_frequency=log_frequency) - self.file_extension = "" # LeRobot datasets don't have a specific file extension + def __init__( + self, + exp_dir, + dataset_name, + num_batches, + batch_size, + log_frequency=DEFAULT_LOG_FREQUENCY, + ): + super().__init__( + exp_dir, + dataset_name, + num_batches, + dataset_type="lerobot", + batch_size=batch_size, + log_frequency=log_frequency, + ) + self.file_extension = ( + "" # LeRobot datasets don't have a specific file extension + ) def get_loader(self): path = os.path.join(self.exp_dir, "hf") return LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size) + def prepare(args): # Clear the cache directory if os.path.exists(CACHE_DIR): subprocess.run(["rm", "-rf", CACHE_DIR], check=True) + def evaluation(args): - - csv_file = 'format_comparison_results.csv' - + + csv_file = "format_comparison_results.csv" + if os.path.exists(csv_file): - existing_results = pd.read_csv(csv_file).to_dict('records') + existing_results = pd.read_csv(csv_file).to_dict("records") else: existing_results = [] - + new_results = [] for dataset_name in args.dataset_names: - print(f"Evaluating dataset: {dataset_name}") + logger.debug(f"Evaluating dataset: {dataset_name}") handlers = [ - VLAHandler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), - HDF5Handler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), - LeRobotHandler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), - RLDSHandler(args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency), + # VLAHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # HDF5Handler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # LeRobotHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + RLDSHandler( + args.exp_dir, + dataset_name, + args.num_batches, + args.batch_size, + args.log_frequency, + ), ] for handler in handlers: @@ -171,13 +331,17 @@ def evaluation(args): avg_traj_size = handler.measure_average_trajectory_size() random_load_time = handler.measure_random_loading_time() - new_results.append({ - 'Dataset': dataset_name, - 'Format': f"{handler.dataset_type.upper()}-RandomLoad", - 'AverageTrajectorySize(MB)': avg_traj_size, - 'LoadingTime(s)': random_load_time, - }) - print(f"{handler.dataset_type.upper()}-RandomLoad - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s") + new_results.append( + { + "Dataset": dataset_name, + "Format": f"{handler.dataset_type.upper()}-RandomLoad", + "AverageTrajectorySize(MB)": avg_traj_size, + "LoadingTime(s)": random_load_time, + } + ) + logger.debug( + f"{handler.dataset_type.upper()}-RandomLoad - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s" + ) # Combine existing and new results all_results = existing_results + new_results @@ -185,18 +349,42 @@ def evaluation(args): # Write all results to CSV results_df = pd.DataFrame(all_results) results_df.to_csv(csv_file, index=False) - print(f"Results appended to {csv_file}") + logger.debug(f"Results appended to {csv_file}") + if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Prepare and evaluate loading times and folder sizes for RLDS, VLA, and HDF5 formats.") - parser.add_argument("--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory.") - parser.add_argument("--dataset_names", nargs="+", default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.") - parser.add_argument("--prepare", action="store_true", help="Prepare the datasets before evaluation.") - parser.add_argument("--log_frequency", type=int, default=DEFAULT_LOG_FREQUENCY, help="Frequency of logging results.") - parser.add_argument("--num_batches", type=int, default=1000, help="Number of batches to load for each loader.") - parser.add_argument("--batch_size", type=int, default=8, help="Batch size for loaders.") + parser = argparse.ArgumentParser( + description="Prepare and evaluate loading times and folder sizes for RLDS, VLA, and HDF5 formats." + ) + parser.add_argument( + "--exp_dir", type=str, default=DEFAULT_EXP_DIR, help="Experiment directory." + ) + parser.add_argument( + "--dataset_names", + nargs="+", + default=DEFAULT_DATASET_NAMES, + help="List of dataset names to evaluate.", + ) + parser.add_argument( + "--prepare", action="store_true", help="Prepare the datasets before evaluation." + ) + parser.add_argument( + "--log_frequency", + type=int, + default=DEFAULT_LOG_FREQUENCY, + help="Frequency of logging results.", + ) + parser.add_argument( + "--num_batches", + type=int, + default=1, + help="Number of batches to load for each loader.", + ) + parser.add_argument( + "--batch_size", type=int, default=8, help="Batch size for loaders." + ) args = parser.parse_args() if args.prepare: prepare(args) - evaluation(args) \ No newline at end of file + evaluation(args) diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index af67a0b..1faf24f 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -1,3 +1,5 @@ +import torch +from torch.utils.data import IterableDataset, DataLoader from . import BaseLoader import numpy as np import glob @@ -65,4 +67,36 @@ def __iter__(self): return self def __len__(self): - return len(self.files) \ No newline at end of file + return len(self.files) + +class HDF5IterableDataset(IterableDataset): + def __init__(self, path, batch_size=1): + self.hdf5_loader = HDF5Loader(path, batch_size) + + def __iter__(self): + return self + + def __next__(self): + try: + batch = next(self.hdf5_loader) + return batch[0] # Return a single item, not a batch + except StopIteration: + raise StopIteration + +def hdf5_collate_fn(batch): + # Convert data to PyTorch tensors + return batch + +def get_hdf5_dataloader( + path: str, + batch_size: int = 1, + num_workers: int = 0 +): + dataset = HDF5IterableDataset(path, batch_size) + return DataLoader( + dataset, + batch_size=batch_size, + collate_fn=hdf5_collate_fn, + num_workers=num_workers + ) + diff --git a/fog_x/loader/lerobot.py b/fog_x/loader/lerobot.py index 1fff214..cc6f4ea 100644 --- a/fog_x/loader/lerobot.py +++ b/fog_x/loader/lerobot.py @@ -39,14 +39,17 @@ def __next__(self): return self._convert_batch_to_numpy(batch) def _convert_batch_to_numpy(self, batch): - numpy_batch = {} - for key, value in batch.items(): - if isinstance(value, torch.Tensor): - numpy_batch[key] = value.numpy() - elif isinstance(value, dict): - numpy_batch[key] = self._convert_batch_to_numpy(value) - else: - numpy_batch[key] = value + numpy_batch = [] + for i in range(len(next(iter(batch.values())))): + trajectory = {} + for key, value in batch.items(): + if isinstance(value, torch.Tensor): + trajectory[key] = value[i].numpy() + elif isinstance(value, dict): + trajectory[key] = self._convert_batch_to_numpy({k: v[i] for k, v in value.items()}) + else: + trajectory[key] = value[i] + numpy_batch.append(trajectory) return numpy_batch def get_batch(self): diff --git a/fog_x/loader/pytorch_vla.py b/fog_x/loader/pytorch_vla.py index 3c02169..fafeb71 100644 --- a/fog_x/loader/pytorch_vla.py +++ b/fog_x/loader/pytorch_vla.py @@ -5,6 +5,8 @@ class VLAIterableDataset(IterableDataset): def __init__(self, path: Text, cache_dir: Optional[Text] = None, buffer_size: int = 1000): + # Note: batch size = 1 is to bypass the dataloader without pytorch dataloader + # in this case, we use pytorch dataloader for batching self.vla_loader = VLALoader(path, batch_size=1, cache_dir=cache_dir, buffer_size=buffer_size) def __iter__(self): diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index ed40b13..386a0fb 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -32,32 +32,31 @@ def __len__(self): def __iter__(self): return self - def get_batch(self): batch = self.ds.take(self.batch_size) self.index += self.batch_size data = [] for b in batch: - data.append(self._convert_batch_to_numpy(b)) + data.append(self._convert_traj_to_numpy(b)) return data - - def _convert_batch_to_numpy(self, batch): + def _convert_traj_to_numpy(self, traj): import tensorflow as tf def to_numpy(step_data): step = {} - for key, val in step_data.items(): - if key == "observation": - step["observation"] = {obs_key: np.array(obs_val) for obs_key, obs_val in val.items()} - elif key == "action": - step["action"] = {act_key: np.array(act_val) for act_key, act_val in val.items()} + for key in step_data: + val = step_data[key] + if isinstance(val, dict): + step[key] = {k: np.array(v) for k, v in val.items()} else: step[key] = np.array(val) return step - batch = to_numpy(batch) - return batch + trajectory = [] + for step in traj["steps"]: + trajectory.append(to_numpy(step)) + return trajectory def __next__(self): if self.index >= self.length: From c4d71501e909aa15ce82b35d33109d55867032b8 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 31 Aug 2024 20:42:39 -0700 Subject: [PATCH 67/80] Refactor DatasetHandler class for improved code readability and performance --- benchmarks/openx.py | 45 ++++++++++++++++++++++----------------------- 1 file changed, 22 insertions(+), 23 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index bda4301..a809ff2 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -77,7 +77,7 @@ def clear_cache(self): def clear_os_cache(self): """Clears the OS cache.""" subprocess.run(["sync"], check=True) - subprocess.run(["echo", "3", ">", "/proc/sys/vm/drop_caches"], check=True) + subprocess.run(["sudo", "sh", "-c", "echo 3 > /proc/sys/vm/drop_caches"], check=True) def _recursively_load_data(self, data): logger.debug(f"Data summary for loader {self.dataset_type.upper()}") @@ -108,7 +108,6 @@ def summarize_value(value): logger.debug(f" {sub_feature}: {sub_dimension}") else: logger.debug(f" {feature}: {dimension}") - logger.debug() logger.debug(f"Total number of trajectories: {len(trajectory_summaries)}") @@ -295,27 +294,27 @@ def evaluation(args): logger.debug(f"Evaluating dataset: {dataset_name}") handlers = [ - # VLAHandler( - # args.exp_dir, - # dataset_name, - # args.num_batches, - # args.batch_size, - # args.log_frequency, - # ), - # HDF5Handler( - # args.exp_dir, - # dataset_name, - # args.num_batches, - # args.batch_size, - # args.log_frequency, - # ), - # LeRobotHandler( - # args.exp_dir, - # dataset_name, - # args.num_batches, - # args.batch_size, - # args.log_frequency, - # ), + VLAHandler( + args.exp_dir, + dataset_name, + args.num_batches, + args.batch_size, + args.log_frequency, + ), + HDF5Handler( + args.exp_dir, + dataset_name, + args.num_batches, + args.batch_size, + args.log_frequency, + ), + LeRobotHandler( + args.exp_dir, + dataset_name, + args.num_batches, + args.batch_size, + args.log_frequency, + ), RLDSHandler( args.exp_dir, dataset_name, From fcd8f2d7fc8cd37ff27033e0d9154bcf1b6b47e3 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 1 Sep 2024 17:57:31 -0700 Subject: [PATCH 68/80] Refactor evaluation script for improved code organization and performance --- benchmarks/Visualization.ipynb | 87 +++++++++++++++++++++++++++++++--- benchmarks/openx.py | 23 +++++---- evaluation.sh | 21 ++++++++ fog_x/loader/hdf5.py | 81 ++++++++++++++++++++++++------- 4 files changed, 179 insertions(+), 33 deletions(-) create mode 100755 evaluation.sh diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 8d13351..58049c8 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,18 +2,93 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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qZdKkSQ77uPLKK83mzZsX+xqmaZqHDh0yfXx8zOuvv9602WxWfObMmaYk85133rFiEyZMMCU5vDfFKcm6zZo1M8PCwswjR45Ysd9++8308PAwBw4caMXOfP9N0zR//vlnU5L53nvvWbGHH37YlGRu2LDBob7g4GBTkrlnzx4r3qFDB7NDhw7W8x9//NGUZDZo0MDMzs624jNmzDAlmb///rtpmqaZnZ1tVqlSxWzRooWZm5trrTd//nxTksM+z+Xw4cOFzot8e/bsMSWZ8+bNs2L5x/mFF16wYseOHTP9/f1NwzDMhQsXWvHt27cX2vfkyZPNgIAAc+fOnQ6v9cQTT5ienp7mvn37TNM0zdGjR5tBQUFmXl5eiWvJFxUVZfbq1csh9tprr5mSzA8++MCK5eTkmK1btzYrVqxopqenO9QcFBRkHjp06Lxfr6BXX33VlGR++eWXVuzM9yX/2C9atMhh23nz5pmSzI0bN1oxu91u1q1b1+zWrZtpt9uteGZmplm7dm2za9euViz/MzBgwACH/SYnJ5uenp7m888/7xD//fffTS8vL4d4hw4dCp3n2dnZZnh4uHnLLbdYsY0bNxY6X84mv7aiHvlK+vksrk7TvPDv00V99hcsWGBKMletWmXFpk2bVugzbpr/Hv+SfM+6EPl1fvbZZ1YsLS3NrF69unnllVdasZJ+Bs/2WSh4vp44ccLs0KGDWbVqVTMhIcFaZ/Xq1aYk88MPP3TYdunSpYXiZ34vfP/9900PDw9z9erVDtvOmjXLlGSuXbvWNE3TPH78uOnn52c+/vjjDus99NBDZkBAgJmRkXGut81S1HGeMmWKaRiGwzlyZq75Bg0aZEZFRVnPz/a9taTndf5npOA5VVSeI0aMMCtUqGCeOnXKIU9J5iuvvGLFsrOzrdfOyckptJ/iFFdHVFSUOWjQoEL5XnvttYW+d69du9a85ZZbzLlz55pffvmlOWXKFLNKlSqmn5+fuXnz5hLnAqD84/I9AOVOenq6JCkwMLBE63/zzTeSpLFjxzrE80ddnDn3VMOGDdW6dWvreatWrSRJ1113nWrVqlUonpSUVOg1R40aZX2df/ldTk6OVqxYYcULjrQ5duyY0tLS1K5du0KX2kmnL2koyZw6ISEh2rBhgw4cOFDk8k2bNunQoUO6//77HebA6dWrl+rXr1/kPFz33Xefw/N27doVWXNBK1asUE5Ojh5++GGHUWTDhw9XUFBQqc33dfDgQSUmJmrw4MEOI2Hi4uLUtWtX61yQHN//3NxcHTlyRHXq1FFISIjDMfjmm290zTXXOIxUCQ0N1Z133lnivO655x6H+YvyR53kv4+bNm3SkSNHNHz4cIfJsu+8805VqlSpxK9zIQpOxh0SEqJ69eopICBA/fv3t+L16tVTSEiIw/FftGiR2rVrp0qVKik1NdV6dOnSRTabTatWrbL2efLkSS1fvtwl+X7zzTcKDw/XgAEDrJi3t7ceeughZWRk6KeffnJY/5ZbbrEuv7lQFStWlHT6khVXSExM1K5du3THHXfoyJEj1nt48uRJde7cWatWrSp0KeSZn8vFixfLbrerf//+DschPDxcdevW1Y8//liohoJzZvn4+Khly5bn/GyXxBtvvKHly5c7PCTnPp/F1ZnvQr5PF/zsnzp1Sqmpqbrmmmskqcjvv2fKH3H25Zdflvp8TBEREbr55put50FBQRo4cKASEhKUkpIiqeSfwXxn+yykpaXp+uuv1/bt27Vy5UqHCe4XLVqk4OBgde3a1eF1mjdvrooVKxY6xwpatGiRGjRooPr16ztse91110mStW1wcLBuuukmLViwwBoZZ7PZ9PHHH6tPnz5OzSlW8DifPHlSqampatOmjUzTLHRJ54U4n/O6uDxPnDih1NRUtWvXTpmZmdq+fbvDul5eXhoxYoT13MfHRyNGjNChQ4f066+/uqiiwoYPHy5PT0+HWJs2bfTpp59qyJAhuvHGG/XEE09YIw2ffPLJUssFwKWHy/cAlDtBQUGSSv4H4d69e+Xh4VHobjDh4eEKCQnR3r17HeIF/6CRTv+SLEk1a9YsMn7mXBoeHh6KiYlxiF1xxRWS5DBf0ddff63nnntOiYmJDnNqGIZRqIYz72RVnKlTp2rQoEGqWbOmmjdvrp49e2rgwIFWPvm11qtXr9C29evX15o1axxifn5+hf54qVSp0jnnDynudXx8fBQTE1PoPXeVs9XXoEEDLVu2zJqkNSsrS1OmTNG8efP0999/O1walJaW5rDP/D9sCyrqNYpz5jmV32jKfx/z8z7zHPXy8jrrZR+uUtRxDg4OVmRkZKHzMTg42OH479q1S1u2bCn2j9z8ib3vv/9+ffLJJ+rRo4dq1Kih66+/Xv3791f37t3PK+e9e/eqbt26Dk1P6fRxzl9eUEk/QyWRkZEhqeSN8XPZtWuXJFnzTRUlLS3NoUF5Zj27du2SaZqqW7dukdufeVlVUce2UqVK2rJli1O5F6Vly5ZFXr7jzOczX3HH7UK+Tx89elTx8fFauHChdX7mK/jZL85tt92m//73vxo2bJieeOIJde7cWX379tWtt95a6Hws6OjRo8rJybGe+/v7W/kVp06dOoWOU8GfJ+Hh4SX+DOY722fh4Ycf1qlTp5SQkFDo0tRdu3YpLS1NYWFhJXqdM7fdtm1biXIcOHCgPv74Y61evVrt27fXihUr9M8//+juu+8udv9F2bdvn5599ll99dVXhX5mleQ4l9T5nNcF/fHHH3r66af1ww8/WP90Ky7PiIiIQvspeD7kN1ddraTfP+vUqaObbrpJixcvls1mK9TIAnB5oikFoNwJCgpSRESEtm7d6tR2RTV7ilLcL1HFxQs2M0pq9erVuvHGG9W+fXu9+eabql69ury9vTVv3rwiJ5Iu6fxF/fv3V7t27fT555/ru+++07Rp0/TSSy9p8eLF6tGjh9N5ludfKB988EHNmzdPDz/8sFq3bq3g4GAZhqHbb7/d5aMfXHnulIYLOeftdru6du2qxx57rMh18/9gCgsLU2JiopYtW6Zvv/1W3377rebNm6eBAwcWmpy8NDgzB9i55H/vcdVtz/PPt2nTpjmMTCkof3RWvjPrsdvtMgxD3377bZHH7cztL/ZzMl9xx+1Cztn+/ftr3bp1GjdunJo1a6aKFSvKbrere/fuJfrs+/v7a9WqVfrxxx+1ZMkSLV26VB9//LGuu+46fffdd8Xm0LdvX4cRfIMGDXLJxPEl/QwWzL84N910kxYuXKgXX3xR7733nkOTzW63KywsTB9++GGR255tJKLdbleTJk00ffr0IpcXbCZ269ZN1apV0wcffKD27dvrgw8+UHh4uLp06VLs/s9ks9nUtWtXHT16VI8//rjq16+vgIAA/f333xo8eLDDcTYMo8jzvqQ39LgQx48fV4cOHRQUFKRJkyYpNjZWfn5+2rx5sx5//HG33xmxuJqd+f5Zs2ZN5eTk6OTJk9Y/EQFc3mhKASiXbrjhBs2ZM0c///yzwyUcRYmKipLdbteuXbusURSS9M8//+j48eOKiopyaW52u11JSUkOfwjs3LlTkqxRL5999pn8/Py0bNky+fr6WuvNmzfvgl+/evXquv/++3X//ffr0KFDuuqqq/T888+rR48eVq07duywLpvIt2PHDpe9FwVfp+CosZycHO3Zs8epPy7O93XPtH37dlWtWtX6L/Onn36qQYMG6ZVXXrHWOXXqVKEJf6OioqyRLAUV9RoXmvfu3bvVqVMnK56Xl6fk5OQLnvy9NMXGxiojI6NEx9THx0e9e/dW7969Zbfbdf/992v27Nl65plnnG7wREVFacuWLbLb7Q5/OOdf7uLqz3W+jIwMff7556pZs6bD95MLkT/ZeVBQ0Hl/NmJjY2WapmrXrl2oCXG+StrILylnPp+l5dixY/r+++8VHx+vZ5991ooX9Rk/W/0eHh7q3LmzOnfurOnTp+uFF17QU089pR9//LHYY/jKK684jNiJiIg4Z767d++WaZoOuZz588SZz+C59OnTR9dff70GDx6swMBAvfXWW9ay2NhYrVixQm3btnW6yRsbG6vffvtNnTt3Pud55enpqTvuuEPz58/XSy+9pC+++KLIy8fO5vfff9fOnTv17rvvauDAgVa8qMuHK1WqVORlq2eOtiwu7ws5r1euXKkjR45o8eLFat++vRXfs2dPkesfOHCg0KirM8+HkqhUqVKhn3U5OTk6ePBgifdRnKSkJPn5+RVqhAO4fDGnFIBy6bHHHlNAQICGDRumf/75p9Dyv/76y7rVfM+ePSWp0B2I8v9je+ad7lxh5syZ1temaWrmzJny9vZW586dJZ3+pdswDIf/SiYnJ1t3IDofNput0FD/sLAwRUREWJcHXn311QoLC9OsWbMcLhn89ttvtW3bNpe9F126dJGPj4/+85//OPwHeu7cuUpLSyuV91w63ZBr1qyZ3n33XYdfuLdu3arvvvvOOhek08fgzP+Ov/7664X+U9yzZ0+tX79ev/zyixU7fPhwsaMFzsfVV1+tKlWq6O2333a4rfaHH35Yolutl6X+/fvr559/1rJlywotO378uFXPkSNHHJZ5eHhYzbaC52JJ9ezZUykpKfr444+tWF5enl5//XVVrFhRHTp0cHqf55KVlaW7775bR48e1VNPPeWypk3z5s0VGxurl19+2bo0sKDDhw+fcx99+/aVp6en4uPjC53XpmkWev9LIv8P3zP/eD1fznw+S0t+Y+PM9+jMnw9S8fUfPXq00Lr5I9zOdi43b95cXbp0sR4lmSfwwIEDDneITU9P13vvvadmzZopPDxcUsk/gyU1cOBA/ec//9GsWbP0+OOPW/H+/fvLZrNp8uTJhbbJy8s763nSv39//f3333r77bcLLcvKyip0R7e7775bx44d04gRI5SRkeEw/1lJFHWcTdO0fi8oKDY2Vtu3b3f4nP3222+F7qaYf0fWM+u8kPO6qDxzcnL05ptvFrl+Xl6eZs+e7bDu7NmzFRoaqubNmxf7OmeKjY0tNNfYnDlznBodVtT3pd9++01fffWVrr/++rNeygrg8sJIKQDlUmxsrD766CPddtttatCggQYOHKjGjRsrJydH69ats24NL0lNmzbVoEGDNGfOHGuo/C+//KJ3331Xffr0cRiZ4gp+fn5aunSpBg0apFatWunbb7/VkiVLNH78eOvyhl69emn69Onq3r277rjjDh06dEhvvPGG6tSpc95zupw4cUKRkZG69dZb1bRpU1WsWFErVqzQxo0brdFA3t7eeumll3TPPfeoQ4cOGjBggP755x/NmDFD0dHRGjNmjEveg9DQUD355JOKj49X9+7ddeONN2rHjh1688031aJFC6f/wDjT9OnTrT8Q8nl4eGj8+PGaNm2aevToodatW2vo0KHWrbmDg4M1ceJEa/0bbrhB77//voKDg9WwYUP9/PPPWrFihapUqeKw38cee0zvv/++unfvrtGjRysgIEBz5syxRuq4go+PjyZOnKgHH3xQ1113nfr376/k5GTNnz9fsbGxLh+x4krjxo3TV199pRtuuEGDBw9W8+bNdfLkSf3+++/69NNPlZycrKpVq2rYsGE6evSorrvuOkVGRmrv3r16/fXX1axZs/MacXTvvfdq9uzZGjx4sH799VdFR0fr008/1dq1a/Xaa69d8HxPf//9tz744ANJp0dH/fnnn1q0aJFSUlL0yCOPOEw2fKE8PDz03//+Vz169FCjRo10zz33qEaNGvr777/1448/KigoSP/3f/931n3Exsbqueee05NPPqnk5GT16dNHgYGB2rNnjz7//HPde++9evTRR53KKzY2ViEhIZo1a5YCAwMVEBCgVq1aXdD8XCX9fJaWoKAgtW/fXlOnTlVubq5q1Kih7777rsiRKfl/5D/11FO6/fbb5e3trd69e2vSpElatWqVevXqpaioKB06dEhvvvmmIiMjde2117o03yuuuEJDhw7Vxo0bVa1aNb3zzjv6559/HEbVlvQz6IxRo0YpPT1dTz31lIKDgzV+/Hh16NBBI0aM0JQpU5SYmKjrr79e3t7e2rVrlxYtWqQZM2bo1ltvLXJ/d999tz755BPdd999+vHHH9W2bVvZbDZt375dn3zyiZYtW+YwD9mVV16pxo0bWxOkX3XVVU7lX79+fcXGxurRRx/V33//raCgIH322WdFNvmHDBmi6dOnq1u3bho6dKgOHTqkWbNmqVGjRg5zPPn7+6thw4b6+OOPdcUVV6hy5cpq3LixGjdufN7ndZs2bVSpUiUNGjRIDz30kAzD0Pvvv1/sZbQRERF66aWXlJycrCuuuEIff/yxEhMTNWfOnELzxp3NsGHDdN999+mWW25R165d9dtvv2nZsmVOnSe33Xab/P391aZNG4WFhenPP//UnDlzVKFCBb344osl3g+Ay4A7b/UHAO62c+dOc/jw4WZ0dLTp4+NjBgYGmm3btjVff/11h1sp5+bmmvHx8Wbt2rVNb29vs2bNmuaTTz7psI5pFn8reEnmAw884BDLv732tGnTrNigQYPMgIAA86+//jKvv/56s0KFCma1atXMCRMmmDabzWH7uXPnmnXr1jV9fX3N+vXrm/PmzbNug36u1y64LP+2ztnZ2ea4cePMpk2bmoGBgWZAQIDZtGlT88033yy03ccff2xeeeWVpq+vr1m5cmXzzjvvNPfv3++wTn4tZyoqx+LMnDnTrF+/vunt7W1Wq1bNHDlypHns2LEi91eS26vnr1vUw9PT01pvxYoVZtu2bU1/f38zKCjI7N27t/nnn3867OvYsWPmPffcY1atWtWsWLGi2a1bN3P79u2Fboltmqa5ZcsWs0OHDqafn59Zo0YNc/LkyebcuXML3dr7zFuLF7zNekH55868efMc4v/5z3/MqKgo09fX12zZsqW5du1as3nz5mb37t3P+d7kO9tty4t63eKOc4cOHcxGjRoVihf1GTlx4oT55JNPmnXq1DF9fHzMqlWrmm3atDFffvll6zbln376qXn99debYWFhpo+Pj1mrVi1zxIgR5sGDB89ZU3Gfy3/++cc6hj4+PmaTJk0KvadFfU5L8nr555VhGGZQUJDZqFEjc/jw4eaGDRuK3ObM97y4Y59/i/WNGzcW2kdCQoLZt29fs0qVKqavr68ZFRVl9u/f3/z++++tdc71efnss8/Ma6+91gwICDADAgLM+vXrmw888IC5Y8cOa53iju2gQYPMqKgoh9iXX35pNmzY0PTy8irynC1pbQWV5PN5tjov9Pv0/v37zZtvvtkMCQkxg4ODzX79+pkHDhwo8nMzefJks0aNGqaHh4f1ef/+++/Nm266yYyIiDB9fHzMiIgIc8CAAebOnTvPWrez8utctmyZGRcXZ/2sOPOcMs2SfQbP9lko7nx97LHHTEnmzJkzrdicOXPM5s2bm/7+/mZgYKDZpEkT87HHHjMPHDhgrXPm90LTNM2cnBzzpZdeMhs1amT6+vqalSpVMps3b27Gx8ebaWlphXKaOnWqKcl84YUXnHrf8v35559mly5dzIoVK5pVq1Y1hw8fbv72229FnscffPCBGRMTY/r4+JjNmjUzly1bVuTnYd26dWbz5s1NHx+fQudLSc7r/M9IwZ8ba9euNa+55hrT39/fjIiIMB977DFz2bJlpiTzxx9/tNbL/9xu2rTJbN26tenn52dGRUU5HJuSstls5uOPP25WrVrVrFChgtmtWzdz9+7dhX7+ne0zPWPGDLNly5Zm5cqVTS8vL7N69ermXXfdZe7atcvpfACUb4ZpXmQzVgJAOTZ48GB9+umnRV6CAzjLbrcrNDRUffv2LfKyFwAor2bMmKExY8YoOTm50N0WAQCXDi7mBQDgEnDq1KlCl2y89957Onr0qDp27Fg2SQFAGTBNU3PnzlWHDh1oSAHAJY45pQAAuASsX79eY8aMUb9+/VSlShVt3rxZc+fOVePGjdWvX7+yTg8ASt3Jkyf11Vdf6ccff9Tvv/+uL7/8stA6R48eVU5OTrH78PT0tOZvvNwcPnz4rJOV+/j4qHLlym7MCABoSgEAcEmIjo5WzZo19Z///EdHjx5V5cqVNXDgQL344ovy8fEp6/QAoNQdPnxYd9xxh0JCQjR+/HjdeOONhdbp27evfvrpp2L3ERUVpeTk5FLM8uLVokUL7d27t9jlHTp00MqVK92XEABIYk4pAAAAAOXCr7/+WuRd9PL5+/urbdu2bszo4rF27VplZWUVu7xSpUrWXSUBwF1oSgEAAAAAAMDtmOgcAAAAAAAAbsecUkWw2+06cOCAAgMDZRhGWacDAAAAAABwUTBNUydOnFBERIQ8PC5srBNNqSIcOHBANWvWLOs0AAAAAAAALkr/+9//FBkZeUH7oClVhMDAQEmn3+CgoKAyzgYAAAAAAODikJ6erpo1a1q9kwtBU6oI+ZfsBQUF0ZQCAAAAAAA4gyumO2KicwAAAAAAALgdTSkAAAAAAAC4HU0pAAAAAAAAuB1zSgEAAAAAAJex2WzKzc0t6zRwnry9veXp6emW16IpBQAAAAAALphpmkpJSdHx48fLOhVcoJCQEIWHh7tkMvOzoSkFAAAAAAAuWH5DKiwsTBUqVCj1hgZczzRNZWZm6tChQ5Kk6tWrl+rr0ZQCAAAAAAAXxGazWQ2pKlWqlHU6uAD+/v6SpEOHDiksLKxUL+VjonMAAAAAAHBB8ueQqlChQhlnAlfIP46lPTcYTSkAAAAAAOASXLJXPrjrONKUAgAAAAAAgNvRlAIAAAAAAIDb0ZQCAAAAAAClxjDc+3BG79691b179yKXrV69WoZhaMuWLTIMQ4mJiefc34gRI+Tp6alFixY5l8hliqYUAAAAAAC4LA0dOlTLly/X/v37Cy2bN2+err76agUFBZVoX5mZmVq4cKEee+wxvfPOO65OtVyiKQUAAAAAAC5LN9xwg0JDQzV//nyHeEZGhhYtWqShQ4eWeF+LFi1Sw4YN9cQTT2jVqlX63//+5+Jsyx+aUgAAAAAA4LLk5eWlgQMHav78+TJN04ovWrRINptNAwYMKPG+5s6dq7vuukvBwcHq0aNHoUYXCqMpBQAAAAAALltDhgzRX3/9pZ9++smKzZs3T7fccouCg4NLtI9du3Zp/fr1uu222yRJd911l+bNm+fQ6EJhNKUAAAAAAMBlq379+mrTpo01D9Tu3bu1evVqpy7de+edd9StWzdVrVpVktSzZ0+lpaXphx9+KJWcywuaUgAAAAAA4LI2dOhQffbZZzpx4oTmzZun2NhYdejQoUTb2mw2vfvuu1qyZIm8vLzk5eWlChUq6OjRo0x4fg5eZZ0AAAAAAABAWerfv79Gjx6tjz76SO+9955GjhwpwzBKtO0333yjEydOKCEhQZ6enlZ869atuueee3T8+HGFhISUUuaXNppSAAAAAADgslaxYkXddtttevLJJ5Wenq7BgwcXWmfHjh2FYo0aNdLcuXPVq1cvNW3a1GFZw4YNNWbMGH344Yd64IEHSiv1SxqX7wEAAAAAgMve0KFDdezYMXXr1k0RERGFlt9+++268sorHR4HDhzQkiVLdMsttxRa38PDQzfffLPmzp3rjvQvSYyUAgAAcBMjvmSXAZRH5gTuPgQAl6tL5QZ0rVu3LvJuedHR0We9i15ubm6xy958802X5FZeMVIKAAAAAAAAbkdTCgAAAAAAAG5HUwoAAAAAAABuR1MKAAAAAAAAbkdTCgAAAAAAAG5HUwoAAAAAAABuR1MKAAAAAAAAblemTalVq1apd+/eioiIkGEY+uKLLxyWG4ZR5GPatGnF7nPixImF1q9fv34pVwIAAAAAAABnlGlT6uTJk2ratKneeOONIpcfPHjQ4fHOO+/IMAzdcsstZ91vo0aNHLZbs2ZNaaQPAAAAAACA8+RVli/eo0cP9ejRo9jl4eHhDs+//PJLderUSTE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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", "\n", - "# Load the data\n", - "df = pd.read_csv('trajectory_results.csv')" + "# Read the CSV file\n", + "df = pd.read_csv('../format_comparison_results.csv')\n", + "\n", + "# Define colors for each format\n", + "colors = {'VLA': 'blue', 'HDF5': 'green', 'LEROBOT': 'red', 'RLDS': 'purple'}\n", + "\n", + "# Get unique datasets and batch sizes\n", + "datasets = df['Dataset'].unique()\n", + "batch_sizes = df['BatchSize'].unique()\n", + "\n", + "# Set the width of each bar\n", + "bar_width = 1\n", + "\n", + "# Create a figure for each dataset\n", + "for dataset in datasets:\n", + " plt.figure(figsize=(12, 6))\n", + " \n", + " dataset_df = df[df['Dataset'] == dataset]\n", + " \n", + " # Create the grouped bar plot\n", + " for i, format in enumerate(colors.keys()):\n", + " data = dataset_df[dataset_df['Format'] == format]\n", + " plt.bar(data['BatchSize'] + i*bar_width, data['AverageLoadingTime(s)'], \n", + " width=bar_width, color=colors[format], label=format)\n", + "\n", + " # Customize the plot\n", + " plt.xlabel('Batch Size')\n", + " plt.ylabel('Average Loading Time (s)')\n", + " plt.title(f'Comparison of Loading Times for Different Formats - {dataset}')\n", + " plt.legend()\n", + " plt.xticks(batch_sizes + bar_width*1.5, batch_sizes)\n", + "\n", + " # Add a grid for better readability\n", + " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "\n", + " # Show the plot\n", + " plt.tight_layout()\n", + " plt.show()" ] }, { diff --git a/benchmarks/openx.py b/benchmarks/openx.py index a809ff2..f97aa0d 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -22,8 +22,9 @@ "berkeley_autolab_ur5", "bridge", ] -DEFAULT_DATASET_NAMES = ["bridge"] -CACHE_DIR = "/tmp/fog_x/cache/" +# DEFAULT_DATASET_NAMES = ["bridge"] +# CACHE_DIR = "/tmp/fog_x/cache/" +CACHE_DIR = "/mnt/data/fog_x/cache/" DEFAULT_LOG_FREQUENCY = 20 # suppress tensorflow warnings @@ -117,6 +118,7 @@ def write_result(self, format_name, elapsed_time, index): "Format": format_name, "AverageTrajectorySize(MB)": self.measure_average_trajectory_size(), "LoadingTime(s)": elapsed_time, + "AverageLoadingTime(s)": elapsed_time / (index + 1), "Index": index, "BatchSize": self.batch_size, } @@ -141,11 +143,11 @@ def measure_random_loading_time(self): elapsed_time = time.time() - start_time self.write_result( - f"{self.dataset_type.upper()}-RandomLoad", elapsed_time, batch_num + f"{self.dataset_type.upper()}", elapsed_time, batch_num ) if batch_num % self.log_frequency == 0: - logger.debug( - f"{self.dataset_type.upper()}-RandomLoad - Loaded {batch_num} random batches, Time: {elapsed_time:.2f} s" + logger.info( + f"{self.dataset_type.upper()} - Loaded {batch_num} random {self.batch_size} batches from {self.dataset_name}, Time: {elapsed_time:.2f} s, Average Time: {elapsed_time / (batch_num + 1):.2f} s" ) return time.time() - start_time @@ -333,13 +335,16 @@ def evaluation(args): new_results.append( { "Dataset": dataset_name, - "Format": f"{handler.dataset_type.upper()}-RandomLoad", + "Format": f"{handler.dataset_type.upper()}", "AverageTrajectorySize(MB)": avg_traj_size, "LoadingTime(s)": random_load_time, + "AverageLoadingTime(s)": random_load_time / (args.num_batches + 1), + "Index": args.num_batches, + "BatchSize": args.batch_size, } ) logger.debug( - f"{handler.dataset_type.upper()}-RandomLoad - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s" + f"{handler.dataset_type.upper()} - Average Trajectory Size: {avg_traj_size:.2f} MB, Loading Time: {random_load_time:.2f} s" ) # Combine existing and new results @@ -376,11 +381,11 @@ def evaluation(args): parser.add_argument( "--num_batches", type=int, - default=1, + default=1000, help="Number of batches to load for each loader.", ) parser.add_argument( - "--batch_size", type=int, default=8, help="Batch size for loaders." + "--batch_size", type=int, default=16, help="Batch size for loaders." ) args = parser.parse_args() diff --git a/evaluation.sh b/evaluation.sh new file mode 100755 index 0000000..2145a98 --- /dev/null +++ b/evaluation.sh @@ -0,0 +1,21 @@ +# ask for sudo access +sudo echo "Use sudo access for clearning cache" + +rm *.csv + +# Define a list of batch sizes to iterate through +batch_sizes=(1 8 16 32) +# batch_sizes=(1 2) + +num_batches=10 + +# Iterate through each batch size +for batch_size in "${batch_sizes[@]}" +do + echo "Running benchmarks with batch size: $batch_size" + + python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size +done \ No newline at end of file diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 1faf24f..14743e7 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -5,6 +5,10 @@ import glob import h5py import asyncio +import random +import multiprocessing as mp +import time +import logging # flatten the data such that all data starts with root level tree (observation and action) def _flatten(data, parent_key='', sep='/'): @@ -27,33 +31,64 @@ def recursively_read_hdf5_group(group): class HDF5Loader(BaseLoader): - def __init__(self, path, batch_size=1): + def __init__(self, path, batch_size=1, buffer_size=100, num_workers=4): super(HDF5Loader, self).__init__(path) - self.index = 0 self.files = glob.glob(self.path, recursive=True) self.batch_size = batch_size - async def _read_hdf5_async(self, data_path): - return await asyncio.to_thread(self._read_hdf5, data_path) - - async def get_batch(self): - tasks = [] - for _ in range(self.batch_size): - if self.index < len(self.files): - file_path = self.files[self.index] - self.index += 1 - tasks.append(self._read_hdf5_async(file_path)) - else: + self.buffer_size = buffer_size + self.buffer = mp.Queue(maxsize=buffer_size) + self.num_workers = num_workers + self.processes = [] + random.shuffle(self.files) + self._start_workers() + + def _worker(self): + while True: + if not self.files: + logging.info("Worker finished") + break + file_path = random.choice(self.files) + data = self._read_hdf5(file_path) + self.buffer.put(data) + + def _start_workers(self): + for _ in range(self.num_workers): + p = mp.Process(target=self._worker) + p.start() + logging.debug(f"Started worker {p.pid}") + self.processes.append(p) + + def get_batch(self): + batch = [] + timeout = 5 + start_time = time.time() + + while len(batch) < self.batch_size: + if time.time() - start_time > timeout: + logging.warning(f"Timeout reached while getting batch. Batch size: {len(batch)}") break - return await asyncio.gather(*tasks) + + try: + item = self.buffer.get(timeout=1) + batch.append(item) + except mp.queues.Empty: + if all(not p.is_alive() for p in self.processes) and self.buffer.empty(): + if len(batch) == 0: + return None + else: + break + + return batch def __next__(self): - if self.index >= len(self.files): - self.index = 0 + batch = self.get_batch() + if batch is None: + random.shuffle(self.files) + self._start_workers() raise StopIteration - return asyncio.run(self.get_batch()) + return batch def _read_hdf5(self, data_path): - with h5py.File(data_path, "r") as f: data_unflattened = recursively_read_hdf5_group(f) @@ -69,6 +104,16 @@ def __iter__(self): def __len__(self): return len(self.files) + def peek(self): + if self.buffer.empty(): + return None + return self.buffer.get() + + def __del__(self): + for p in self.processes: + p.terminate() + p.join() + class HDF5IterableDataset(IterableDataset): def __init__(self, path, batch_size=1): self.hdf5_loader = HDF5Loader(path, batch_size) From 35164918ccfa52d008ca4a9f3bab1684f6399183 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 1 Sep 2024 22:18:55 -0700 Subject: [PATCH 69/80] Refactor evaluation script for improved code organization and performance --- benchmarks/Visualization.ipynb | 10 +++--- benchmarks/openx.py | 60 ++++++++++++++++------------------ evaluation.sh | 10 +++--- fog_x/loader/hdf5.py | 41 +++++++++++++---------- fog_x/loader/rlds.py | 14 +++++--- 5 files changed, 70 insertions(+), 65 deletions(-) diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 58049c8..b7d37d0 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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M92EYhhEbG2t4eXkZTZo0MaxWqxmfOHGiIcn44osvzNjw4cMNSXbvTUYys2zVqlWNkJAQ49y5c2bszz//NNzc3IyuXbuasRvff8MwjA0bNhiSjK+++sqM9evXz5BkbNy40a6+oKAgQ5Jx6NAhM16/fn2jfv365vOVK1cakoxy5coZ165dM+MTJkwwJBnbt283DMMwrl27ZuTPn9+oUaOGkZycbC43ffp0Q5LdNm/lzJkzac6LVIcOHTIkGdOmTTNjqcf57bffNmMXLlwwfH19DYvFYsyaNcuM79mzJ822R40aZfj5+Rn79u2z29eQIUMMd3d34+jRo4ZhGMbLL79sBAYGGikpKZmuJVV4eLjRokULu9j48eMNScbXX39txpKSkoxatWoZ/v7+Rnx8vF3NgYGBRmxs7G3v73offPCBIcn44YcfzNiN70vqsZ8zZ47dutOmTTMkGZs2bTJjNpvNKFWqlNG0aVPDZrOZ8StXrhjFixc3GjdubMZSPwNdunSx2+7hw4cNd3d346233rKLb9++3fDw8LCL169fP815fu3aNSM0NNRo3769Gdu0aVOa8+VmUmtL75Eqs5/PjOo0jKz/nE7vsz9z5kxDkrF69WozNnbs2DSfccP49/hn5mdWVqTW+f3335uxuLg4IywszKhWrZoZy+xn8GafhevP10uXLhn169c3ChQoYGzdutVcZs2aNYYk45tvvrFbd/HixWniN/4snDFjhuHm5masWbPGbt3Jkycbkox169YZhmEYFy9eNHx8fIxXX33VbrmXXnrJ8PPzMxISEm71tpnSO86jR482LBaL3TlyY66punXrZoSHh5vPb/azNbPndepn5PpzKr08+/TpY+TJk8e4evWqXZ6SjPfff9+MXbt2zdx3UlJSmu1kJKM6wsPDjW7duqXJ9z//+U+an93r1q0z2rdvb0ydOtX44YcfjNGjRxv58+c3fHx8jC1btmQ6FwD3Hi7fA3DXi4+PlyQFBARkavmffvpJkjRgwAC7eOqoixvnnipfvrxq1aplPn/wwQclSQ899JDuu+++NPGDBw+m2ecLL7xg/n/q5XdJSUlavny5Gb9+pM2FCxcUFxenunXrprnUTvrnkobMzKkTHBysjRs36sSJE+m+vnnzZsXGxuq5556zmwOnRYsWKlu2bLrzcD377LN2z+vWrZtuzddbvny5kpKS1K9fP7tRZL1791ZgYGC2zfd18uRJxcTEqHv37nYjYSpXrqzGjRub54Jk//4nJyfr3LlzKlmypIKDg+2OwU8//aSaNWvajVQpWLCgnnjiiUzn1aNHD7v5i1JHnaS+j5s3b9a5c+fUu3dvu8myn3jiCeXNmzfT+8mK6yfjDg4OVpkyZeTn56eOHTua8TJlyig4ONju+M+ZM0d169ZV3rx5dfbsWfPRqFEjWa1WrV692tzm5cuXtWzZMqfk+9NPPyk0NFRdunQxY56ennrppZeUkJCgX3/91W759u3bm5ffZJW/v7+kfy5ZcYaYmBjt379fjz/+uM6dO2e+h5cvX9bDDz+s1atXp7kU8sbP5bx582Sz2dSxY0e74xAaGqpSpUpp5cqVaWq4fs4sLy8vPfDAA7f8bGfGxx9/rGXLltk9JMc+nxnVmSorP6ev/+xfvXpVZ8+eVc2aNSUp3Z+/N0odcfbDDz9k+3xMhQsX1qOPPmo+DwwMVNeuXbV161adOnVKUuY/g6lu9lmIi4tTkyZNtGfPHq1atcpugvs5c+YoKChIjRs3tttPVFSU/P3905xj15szZ47KlSunsmXL2q370EMPSZK5blBQkNq0aaOZM2eaI+OsVqtmz56ttm3bOjSn2PXH+fLlyzp79qxq164twzDSXNKZFbdzXmeU56VLl3T27FnVrVtXV65c0Z49e+yW9fDwUJ8+fcznXl5e6tOnj2JjY/XHH384qaK0evfuLXd3d7tY7dq1NXfuXPXs2VOtW7fWkCFDzJGGQ4cOzbZcANz9uHwPwF0vMDBQUub/IDxy5Ijc3NzS3A0mNDRUwcHBOnLkiF38+j9opH9+SZakYsWKpRu/cS4NNzc3lShRwi5WunRpSbKbr2jhwoX673//q5iYGLs5NSwWS5oabryTVUbGjBmjbt26qVixYoqKitIjjzyirl27mvmk1lqmTJk065YtW1Zr1661i/n4+KT54yVv3ry3nD8ko/14eXmpRIkSad5zZ7lZfeXKldOSJUvMSVoTExM1evRoTZs2TcePH7e7NCguLs5um6l/2F4vvX1k5MZzKrXRlPo+puZ94znq4eFx08s+nCW94xwUFKSiRYumOR+DgoLsjv/+/fu1bdu2DP/ITZ3Y+7nnntN3332n5s2bq0iRImrSpIk6duyoZs2a3VbOR44cUalSpeyantI/xzn19etl9jOUGQkJCZIy3xi/lf3790uSOd9UeuLi4uwalDfWs3//fhmGoVKlSqW7/o2XVaV3bPPmzatt27Y5lHt6HnjggXQv33Hk85kqo+OWlZ/T58+fV3R0tGbNmmWen6mu/+xnpFOnTvr888/Vq1cvDRkyRA8//LDatWunxx57LM35eL3z588rKSnJfO7r62vml5GSJUumOU7Xf5+EhoZm+jOY6mafhX79+unq1avaunVrmktT9+/fr7i4OIWEhGRqPzeuu3v37kzl2LVrV82ePVtr1qxRvXr1tHz5cp0+fVpPPfVUhttPz9GjR/Xmm2/qxx9/TPOdlZnjnFm3c15fb+fOnXr99df1yy+/mP/ollGehQsXTrOd68+H1Oaqs2X252fJkiXVpk0bzZs3T1arNU0jCwAkmlIAcoHAwEAVLlxYO3bscGi99Jo96cnol6iM4tc3MzJrzZo1at26terVq6dJkyYpLCxMnp6emjZtWroTSWd2/qKOHTuqbt26mj9/vpYuXaqxY8fq3Xff1bx589S8eXOH88zNv1C++OKLmjZtmvr166datWopKChIFotFnTt3dvroB2eeO9khK+e8zWZT48aNNXjw4HSXTf2DKSQkRDExMVqyZIl+/vln/fzzz5o2bZq6du2aZnLy7ODIHGC3kvqzx1m3PU8938aOHWs3MuV6qaOzUt1Yj81mk8Vi0c8//5zucbtx/Tv9nEyV0XHLyjnbsWNHrV+/XoMGDVLVqlXl7+8vm82mZs2aZeqz7+vrq9WrV2vlypVatGiRFi9erNmzZ+uhhx7S0qVLM8yhXbt2diP4unXr5pSJ4zP7Gbw+/4y0adNGs2bN0jvvvKOvvvrKrslms9kUEhKib775Jt11bzYS0WazqVKlSho3bly6r1/fTGzatKkKFSqkr7/+WvXq1dPXX3+t0NBQNWrUKMPt38hqtapx48Y6f/68Xn31VZUtW1Z+fn46fvy4unfvbnecLRZLuud9Zm/okRUXL15U/fr1FRgYqJEjRyoyMlI+Pj7asmWLXn31VZffGTGjmh35+VmsWDElJSXp8uXL5j8iAsD1aEoByBVatmypKVOmaMOGDXaXcKQnPDxcNptN+/fvN0dRSNLp06d18eJFhYeHOzU3m82mgwcP2v0hsG/fPkkyR718//338vHx0ZIlS+Tt7W0uN23atCzvPywsTM8995yee+45xcbG6v7779dbb72l5s2bm7Xu3bvXvGwi1d69e532Xly/n+tHjSUlJenQoUMO/XFxu/u90Z49e1SgQAHzX5nnzp2rbt266f333zeXuXr1apoJf8PDw82RLNdLbx9ZzfvAgQNq2LChGU9JSdHhw4ezPPl7doqMjFRCQkKmjqmXl5datWqlVq1ayWaz6bnnntOnn36qN954w+EGT3h4uLZt2yabzWb3h3Pq5S7O/lynSkhI0Pz581WsWDG7nydZkTrZeWBg4G1/NiIjI2UYhooXL56mCXG7MtvIzyxHPp/Z5cKFC1qxYoWio6P15ptvmvH0PuM3q9/NzU0PP/ywHn74YY0bN05vv/22XnvtNa1cuTLDY/j+++/bjdgpXLjwLfM9cOCADMOwy+XG7xNHPoO30rZtWzVp0kTdu3dXQECAPvnkE/O1yMhILV++XHXq1HG4yRsZGak///xTDz/88C3PK3d3dz3++OOaPn263n33XS1YsCDdy8duZvv27dq3b5++/PJLde3a1Yynd/lw3rx5071s9cbRlhnlnZXzetWqVTp37pzmzZunevXqmfFDhw6lu/yJEyfSjLq68XzIjLx586b5rktKStLJkyczvY2MHDx4UD4+Pmka4QCQijmlAOQKgwcPlp+fn3r16qXTp0+nef2vv/4ybzX/yCOPSFKaOxCl/ovtjXe6c4aJEyea/28YhiZOnChPT089/PDDkv75pdtisdj9q+Thw4fNOxDdDqvVmmaof0hIiAoXLmxeHli9enWFhIRo8uTJdpcM/vzzz9q9e7fT3otGjRrJy8tLH374od2/QE+dOlVxcXHZ8p5L/zTkqlatqi+//NLuF+4dO3Zo6dKl5rkg/XMMbvzX8Y8++ijNvxQ/8sgj+u233/T777+bsTNnzmQ4WuB2VK9eXfnz59dnn31md1vtb775JlO3Ws9JHTt21IYNG7RkyZI0r128eNGs59y5c3avubm5mc2268/FzHrkkUd06tQpzZ4924ylpKToo48+kr+/v+rXr+/wNm8lMTFRTz31lM6fP6/XXnvNaU2bqKgoRUZG6r333jMvDbzemTNnbrmNdu3ayd3dXdHR0WnOa8Mw0rz/mZH6h++Nf7zeLkc+n9kltbFx43t04/eDlHH958+fT7Ns6gi3m53LUVFRatSokfnIzDyBJ06csLtDbHx8vL766itVrVpVoaGhkjL/Gcysrl276sMPP9TkyZP16quvmvGOHTvKarVq1KhRadZJSUm56XnSsWNHHT9+XJ999lma1xITE9Pc0e2pp57ShQsX1KdPHyUkJNjNf5YZ6R1nwzDM3wuuFxkZqT179th9zv788880d1NMvSPrjXVm5bxOL8+kpCRNmjQp3eVTUlL06aef2i376aefqmDBgoqKispwPzeKjIxMM9fYlClTHBodlt7PpT///FM//vijmjRpctNLWQHc2xgpBSBXiIyM1LfffqtOnTqpXLly6tq1qypWrKikpCStX7/evDW8JFWpUkXdunXTlClTzKHyv//+u7788ku1bdvWbmSKM/j4+Gjx4sXq1q2bHnzwQf38889atGiRhg0bZl7e0KJFC40bN07NmjXT448/rtjYWH388ccqWbLkbc/pcunSJRUtWlSPPfaYqlSpIn9/fy1fvlybNm0yRwN5enrq3XffVY8ePVS/fn116dJFp0+f1oQJExQREaH+/fs75T0oWLCghg4dqujoaDVr1kytW7fW3r17NWnSJNWoUcPhPzBuNG7cOPMPhFRubm4aNmyYxo4dq+bNm6tWrVp6+umnzVtzBwUFacSIEebyLVu21IwZMxQUFKTy5ctrw4YNWr58ufLnz2+33cGDB2vGjBlq1qyZXn75Zfn5+WnKlCnmSB1n8PLy0ogRI/Tiiy/qoYceUseOHXX48GFNnz5dkZGRTh+x4kyDBg3Sjz/+qJYtW6p79+6KiorS5cuXtX37ds2dO1eHDx9WgQIF1KtXL50/f14PPfSQihYtqiNHjuijjz5S1apVb2vE0TPPPKNPP/1U3bt31x9//KGIiAjNnTtX69at0/jx47M839Px48f19ddfS/pndNSuXbs0Z84cnTp1Sq+88ordZMNZ5ebmps8//1zNmzdXhQoV1KNHDxUpUkTHjx/XypUrFRgYqP/7v/+76TYiIyP13//+V0OHDtXhw4fVtm1bBQQE6NChQ5o/f76eeeYZDRw40KG8IiMjFRwcrMmTJysgIEB+fn568MEHszQ/V2Y/n9klMDBQ9erV05gxY5ScnKwiRYpo6dKl6Y5MSf0j/7XXXlPnzp3l6empVq1aaeTIkVq9erVatGih8PBwxcbGatKkSSpatKj+85//ODXf0qVL6+mnn9amTZtUqFAhffHFFzp9+rTdqNrMfgYd8cILLyg+Pl6vvfaagoKCNGzYMNWvX199+vTR6NGjFRMToyZNmsjT01P79+/XnDlzNGHCBD322GPpbu+pp57Sd999p2effVYrV65UnTp1ZLVatWfPHn333XdasmSJ3Txk1apVU8WKFc0J0u+//36H8i9btqwiIyM1cOBAHT9+XIGBgfr+++/TbfL37NlT48aNU9OmTfX0008rNjZWkydPVoUKFezmePL19VX58uU1e/ZslS5dWvny5VPFihVVsWLF2z6va9eurbx586pbt2566aWXZLFYNGPGjAwvoy1cuLDeffddHT58WKVLl9bs2bMVExOjKVOmpJk37mZ69eqlZ599Vu3bt1fjxo31559/asmSJQ6dJ506dZKvr69q166tkJAQ7dq1S1OmTFGePHn0zjvvZHo7AO5BrrzVHwBkt3379hm9e/c2IiIiDC8vLyMgIMCoU6eO8dFHH9ndSjk5OdmIjo42ihcvbnh6ehrFihUzhg4dareMYWR8K3hJxvPPP28XS7299tixY81Yt27dDD8/P+Ovv/4ymjRpYuTJk8coVKiQMXz4cMNqtdqtP3XqVKNUqVKGt7e3UbZsWWPatGnmbdBvte/rX0u9rfO1a9eMQYMGGVWqVDECAgIMPz8/o0qVKsakSZPSrDd79myjWrVqhre3t5EvXz7jiSeeMI4dO2a3TGotN0ovx4xMnDjRKFu2rOHp6WkUKlTI6Nu3r3HhwoV0t5eZ26unLpvew93d3Vxu+fLlRp06dQxfX18jMDDQaNWqlbFr1y67bV24cMHo0aOHUaBAAcPf399o2rSpsWfPnjS3xDYMw9i2bZtRv359w8fHxyhSpIgxatQoY+rUqWlu7X3jrcWvv8369VLPnWnTptnFP/zwQyM8PNzw9vY2HnjgAWPdunVGVFSU0axZs1u+N6ludtvy9Pab0XGuX7++UaFChTTx9D4jly5dMoYOHWqULFnS8PLyMgoUKGDUrl3beO+998zblM+dO9do0qSJERISYnh5eRn33Xef0adPH+PkyZO3rCmjz+Xp06fNY+jl5WVUqlQpzXua3uc0M/tLPa8sFosRGBhoVKhQwejdu7excePGdNe58T3P6Nin3mJ906ZNabaxdetWo127dkb+/PkNb29vIzw83OjYsaOxYsUKc5lbfV6+//574z//+Y/h5+dn+Pn5GWXLljWef/55Y+/eveYyGR3bbt26GeHh4XaxH374wShfvrzh4eGR7jmb2dqul5nP583qzOrP6WPHjhmPPvqoERwcbAQFBRkdOnQwTpw4ke7nZtSoUUaRIkUMNzc38/O+YsUKo02bNkbhwoUNLy8vo3DhwkaXLl2Mffv23bRuR6XWuWTJEqNy5crmd8WN55RhZO4zeLPPQkbn6+DBgw1JxsSJE83YlClTjKioKMPX19cICAgwKlWqZAwePNg4ceKEucyNPwsNwzCSkpKMd99916hQoYLh7e1t5M2b14iKijKio6ONuLi4NDmNGTPGkGS8/fbbDr1vqXbt2mU0atTI8Pf3NwoUKGD07t3b+PPPP9M9j7/++mujRIkShpeXl1G1alVjyZIl6X4e1q9fb0RFRRleXl5pzpfMnNepn5HrvzfWrVtn1KxZ0/D19TUKFy5sDB482FiyZIkhyVi5cqW5XOrndvPmzUatWrUMHx8fIzw83O7YZJbVajVeffVVo0CBAkaePHmMpk2bGgcOHEjz/Xezz/SECROMBx54wMiXL5/h4eFhhIWFGU8++aSxf/9+h/MBcG+xGMYdNoMlAOQi3bt319y5c9O9BAdwlM1mU8GCBdWuXbt0L3sBgNxqwoQJ6t+/vw4fPpzmbosAgLsXF/cCAHAHunr1appLNr766iudP39eDRo0yJmkACAHGIahqVOnqn79+jSkACCXYU4pAADuQL/99pv69++vDh06KH/+/NqyZYumTp2qihUrqkOHDjmdHgBku8uXL+vHH3/UypUrtX37dv3www9pljl//rySkpIy3Ia7u7s5f+O95syZMzedrNzLy0v58uVzYUYAkBZNKQAA7kAREREqVqyYPvzwQ50/f1758uVT165d9c4778jLyyun0wOAbHfmzBk9/vjjCg4O1rBhw9S6des0y7Rr106//vprhtsIDw/X4cOHszHLO1eNGjV05MiRDF+vX7++Vq1a5bqEACAdzCkFAAAA4K70xx9/pHsXvVS+vr6qU6eOCzO6c6xbt06JiYkZvp43b17zrpIAkFNoSgEAAAAAAMDlmOgcAAAAAAAALnfPzSlls9l04sQJBQQEyGKx5HQ6AAAAAAAAdw3DMHTp0iUVLlxYbm5ZG+t0zzWlTpw4oWLFiuV0GgAAAAAAAHetv//+W0WLFs3SNu65plRAQICkf968wMDAHM4GAAAAAADg7hEfH69ixYqZ/ZWsuOeaUqmX7AUGBtKUAgAAAAAAuA3OmBKJic4BAAAAAADgcjSlAAAAAAAA4HI0pQAAAAAAAOBy99ycUgAAAAAAIPtYrVYlJyfndBq4TZ6ennJ3d3fJvmhKAQAAAACALDMMQ6dOndLFixdzOhVkUXBwsEJDQ50ymfnN0JQCAAAAAABZltqQCgkJUZ48ebK9oQHnMwxDV65cUWxsrCQpLCwsW/dHUwoAAAAAAGSJ1Wo1G1L58+fP6XSQBb6+vpKk2NhYhYSEZOulfEx0DgAAAAAAsiR1Dqk8efLkcCZwhtTjmN1zg9GUAgAAAAAATsEle7mDq44jTSkAAAAAAAC4HE0pAAAAAAAAuBxNKQAAAAAAkG0sFtc+HNGqVSs1a9Ys3dfWrFkji8Wibdu2yWKxKCYm5pbb69Onj9zd3TVnzhzHErlH0ZQCAAAAAAD3pKefflrLli3TsWPH0rw2bdo0Va9eXYGBgZna1pUrVzRr1iwNHjxYX3zxhbNTzZVoSgEAAAAAgHtSy5YtVbBgQU2fPt0unpCQoDlz5ujpp5/O9LbmzJmj8uXLa8iQIVq9erX+/vtvJ2eb+9CUAgAAAAAA9yQPDw917dpV06dPl2EYZnzOnDmyWq3q0qVLprc1depUPfnkkwoKClLz5s3TNLqQFk0pAAAAAABwz+rZs6f++usv/frrr2Zs2rRpat++vYKCgjK1jf379+u3335Tp06dJElPPvmkpk2bZtfoQlo0pQAAAAAAwD2rbNmyql27tjkP1IEDB7RmzRqHLt374osv1LRpUxUoUECS9MgjjyguLk6//PJLtuScW9CUAgAAAAAA97Snn35a33//vS5duqRp06YpMjJS9evXz9S6VqtVX375pRYtWiQPDw95eHgoT548On/+PBOe34JHTicAAAAAAACQkzp27KiXX35Z3377rb766iv17dtXFoslU+v+9NNPunTpkrZu3Sp3d3czvmPHDvXo0UMXL15UcHBwNmV+d6MpBQAAAAAA7mn+/v7q1KmThg4dqvj4eHXv3j3NMnv37k0Tq1ChgqZOnaoWLVqoSpUqdq+VL19e/fv31zfffKPnn38+u1K/q3H5HgAAAAAAuOc9/fTTunDhgpo2barChQuneb1z586qVq2a3ePEiRNatGiR2rdvn2Z5Nzc3Pfroo5o6daor0r8rMVIKAADAySzRmRvun1sZw7nTEADgX3fLDehq1aqV7t3yIiIibnoXveTk5AxfmzRpklNyy60YKQUAAAAAAACXoykFAAAAAAAAl6MpBQAAAAAAAJejKQUAAAAAAACXoykFAAAAAAAAl6MpBQAAAAAAAJejKQUAAAAAAACXoykFAAAAAAAAl6MpBQAAAAAAAJejKQUAAAAAAACX88jpBAAAAAAAQO5liba4dH/GcMOh5bt3766LFy9qwYIFdvFVq1apYcOGunDhgmJiYtSwYUNJksViUUBAgEqUKKHGjRurf//+CgsLM9cbMWKEoqOj0+xn2bJlatSokaZPn64ePXrYvebt7a2rV686lHduQFMKAAAAAAAgE/bu3avAwEDFx8dry5YtGjNmjKZ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" ] diff --git a/benchmarks/openx.py b/benchmarks/openx.py index f97aa0d..3f9e15c 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -73,12 +73,14 @@ def measure_average_trajectory_size(self): def clear_cache(self): """Clears the cache directory.""" if os.path.exists(CACHE_DIR): + logger.info(f"Clearing cache directory: {CACHE_DIR}") subprocess.run(["rm", "-rf", CACHE_DIR], check=True) def clear_os_cache(self): """Clears the OS cache.""" subprocess.run(["sync"], check=True) subprocess.run(["sudo", "sh", "-c", "echo 3 > /proc/sys/vm/drop_caches"], check=True) + logger.info(f"Cleared OS cache") def _recursively_load_data(self, data): logger.debug(f"Data summary for loader {self.dataset_type.upper()}") @@ -135,19 +137,21 @@ def write_result(self, format_name, elapsed_time, index): def measure_random_loading_time(self): start_time = time.time() loader = self.get_loader() - + last_batch_time = time.time() for batch_num, data in enumerate(loader): if batch_num >= self.num_batches: break self._recursively_load_data(data) + current_batch_time = time.time() + elapsed_time = current_batch_time - last_batch_time + last_batch_time = current_batch_time - elapsed_time = time.time() - start_time self.write_result( f"{self.dataset_type.upper()}", elapsed_time, batch_num ) if batch_num % self.log_frequency == 0: logger.info( - f"{self.dataset_type.upper()} - Loaded {batch_num} random {self.batch_size} batches from {self.dataset_name}, Time: {elapsed_time:.2f} s, Average Time: {elapsed_time / (batch_num + 1):.2f} s" + f"{self.dataset_type.upper()} - Loaded {batch_num} random {self.batch_size} batches from {self.dataset_name}, Time: {elapsed_time:.2f} s, Total Average Time: {(current_batch_time - start_time) / (batch_num + 1):.2f} s, Batch Average Time: {elapsed_time / self.batch_size:.2f} s" ) return time.time() - start_time @@ -276,12 +280,6 @@ def get_loader(self): return LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size) -def prepare(args): - # Clear the cache directory - if os.path.exists(CACHE_DIR): - subprocess.run(["rm", "-rf", CACHE_DIR], check=True) - - def evaluation(args): csv_file = "format_comparison_results.csv" @@ -296,13 +294,13 @@ def evaluation(args): logger.debug(f"Evaluating dataset: {dataset_name}") handlers = [ - VLAHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), + # VLAHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), HDF5Handler( args.exp_dir, dataset_name, @@ -310,20 +308,20 @@ def evaluation(args): args.batch_size, args.log_frequency, ), - LeRobotHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), - RLDSHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), + # LeRobotHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # RLDSHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), ] for handler in handlers: @@ -389,6 +387,4 @@ def evaluation(args): ) args = parser.parse_args() - if args.prepare: - prepare(args) evaluation(args) diff --git a/evaluation.sh b/evaluation.sh index 2145a98..89addd5 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -4,18 +4,18 @@ sudo echo "Use sudo access for clearning cache" rm *.csv # Define a list of batch sizes to iterate through -batch_sizes=(1 8 16 32) +batch_sizes=(1 8) # batch_sizes=(1 2) -num_batches=10 +num_batches=1000 # Iterate through each batch size for batch_size in "${batch_sizes[@]}" do echo "Running benchmarks with batch size: $batch_size" - python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size - python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size - python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size done \ No newline at end of file diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 14743e7..6716356 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -1,8 +1,8 @@ import torch from torch.utils.data import IterableDataset, DataLoader from . import BaseLoader -import numpy as np -import glob +import numpy as np +import glob import h5py import asyncio import random @@ -10,8 +10,9 @@ import time import logging + # flatten the data such that all data starts with root level tree (observation and action) -def _flatten(data, parent_key='', sep='/'): +def _flatten(data, parent_key="", sep="/"): items = {} for k, v in data.items(): new_key = parent_key + sep + k if parent_key else k @@ -20,7 +21,8 @@ def _flatten(data, parent_key='', sep='/'): else: items[new_key] = v return items - + + def recursively_read_hdf5_group(group): if isinstance(group, h5py.Dataset): return np.array(group) @@ -28,7 +30,7 @@ def recursively_read_hdf5_group(group): return {key: recursively_read_hdf5_group(value) for key, value in group.items()} else: raise TypeError("Unsupported HDF5 group type") - + class HDF5Loader(BaseLoader): def __init__(self, path, batch_size=1, buffer_size=100, num_workers=4): @@ -65,19 +67,23 @@ def get_batch(self): while len(batch) < self.batch_size: if time.time() - start_time > timeout: - logging.warning(f"Timeout reached while getting batch. Batch size: {len(batch)}") + logging.warning( + f"Timeout reached while getting batch. Batch size: {len(batch)}" + ) break try: item = self.buffer.get(timeout=1) batch.append(item) except mp.queues.Empty: - if all(not p.is_alive() for p in self.processes) and self.buffer.empty(): + if ( + all(not p.is_alive() for p in self.processes) + and self.buffer.empty() + ): if len(batch) == 0: return None else: break - return batch def __next__(self): @@ -100,7 +106,7 @@ def _read_hdf5(self, data_path): def __iter__(self): return self - + def __len__(self): return len(self.files) @@ -114,9 +120,11 @@ def __del__(self): p.terminate() p.join() + class HDF5IterableDataset(IterableDataset): def __init__(self, path, batch_size=1): - self.hdf5_loader = HDF5Loader(path, batch_size) + # Note: batch size = 1 is to bypass the dataloader without pytorch dataloader + self.hdf5_loader = HDF5Loader(path, 1) def __iter__(self): return self @@ -128,20 +136,17 @@ def __next__(self): except StopIteration: raise StopIteration + def hdf5_collate_fn(batch): # Convert data to PyTorch tensors - return batch + return batch -def get_hdf5_dataloader( - path: str, - batch_size: int = 1, - num_workers: int = 0 -): + +def get_hdf5_dataloader(path: str, batch_size: int = 1, num_workers: int = 0): dataset = HDF5IterableDataset(path, batch_size) return DataLoader( dataset, batch_size=batch_size, collate_fn=hdf5_collate_fn, - num_workers=num_workers + num_workers=num_workers, ) - diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index 386a0fb..8ef4f7f 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -1,22 +1,25 @@ from . import BaseLoader import numpy as np + class RLDSLoader(BaseLoader): def __init__(self, path, split, batch_size=1, shuffle_buffer=50): super(RLDSLoader, self).__init__(path) - + try: import tensorflow as tf import tensorflow_datasets as tfds except ImportError: - raise ImportError("Please install tensorflow and tensorflow_datasets to use rlds loader") + raise ImportError( + "Please install tensorflow and tensorflow_datasets to use rlds loader" + ) self.batch_size = batch_size builder = tfds.builder_from_directory(path) self.ds = builder.as_dataset(split) self.length = len(self.ds) - self.ds = self.ds.shuffle(shuffle_buffer) self.ds = self.ds.repeat() + self.ds = self.ds.shuffle(shuffle_buffer) self.iterator = iter(self.ds) self.split = split @@ -27,11 +30,12 @@ def __len__(self): import tensorflow as tf except ImportError: raise ImportError("Please install tensorflow to use rlds loader") - + return self.length - + def __iter__(self): return self + def get_batch(self): batch = self.ds.take(self.batch_size) self.index += self.batch_size From d2648394f12334e0542dcacd3528136128d6b718 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sun, 1 Sep 2024 22:27:54 -0700 Subject: [PATCH 70/80] Refactor VLA loader to use PyTorch dataloader for improved code readability and performance --- benchmarks/openx.py | 18 ++++++++-------- evaluation.sh | 2 +- fog_x/loader/pytorch_vla.py | 39 ---------------------------------- fog_x/loader/vla.py | 42 ++++++++++++++++++++++++++++++++++++- fog_x/trajectory.py | 33 ++++++++++++++++------------- 5 files changed, 70 insertions(+), 64 deletions(-) delete mode 100644 fog_x/loader/pytorch_vla.py diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 3f9e15c..8b61feb 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -10,7 +10,7 @@ import csv import stat from fog_x.loader.lerobot import LeRobotLoader -from fog_x.loader.pytorch_vla import get_vla_dataloader +from fog_x.loader.vla import get_vla_dataloader from fog_x.loader.hdf5 import get_hdf5_dataloader # Constants @@ -294,20 +294,20 @@ def evaluation(args): logger.debug(f"Evaluating dataset: {dataset_name}") handlers = [ - # VLAHandler( - # args.exp_dir, - # dataset_name, - # args.num_batches, - # args.batch_size, - # args.log_frequency, - # ), - HDF5Handler( + VLAHandler( args.exp_dir, dataset_name, args.num_batches, args.batch_size, args.log_frequency, ), + # HDF5Handler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), # LeRobotHandler( # args.exp_dir, # dataset_name, diff --git a/evaluation.sh b/evaluation.sh index 89addd5..34ac13b 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -4,7 +4,7 @@ sudo echo "Use sudo access for clearning cache" rm *.csv # Define a list of batch sizes to iterate through -batch_sizes=(1 8) +batch_sizes=(8) # batch_sizes=(1 2) num_batches=1000 diff --git a/fog_x/loader/pytorch_vla.py b/fog_x/loader/pytorch_vla.py deleted file mode 100644 index fafeb71..0000000 --- a/fog_x/loader/pytorch_vla.py +++ /dev/null @@ -1,39 +0,0 @@ -import torch -from torch.utils.data import IterableDataset, DataLoader -from fog_x.loader.vla import VLALoader -from typing import Text, Optional - -class VLAIterableDataset(IterableDataset): - def __init__(self, path: Text, cache_dir: Optional[Text] = None, buffer_size: int = 1000): - # Note: batch size = 1 is to bypass the dataloader without pytorch dataloader - # in this case, we use pytorch dataloader for batching - self.vla_loader = VLALoader(path, batch_size=1, cache_dir=cache_dir, buffer_size=buffer_size) - - def __iter__(self): - return self - - def __next__(self): - batch = self.vla_loader.get_batch() - if batch is None: - raise StopIteration - return batch[0] # Return a single item, not a batch - -def vla_collate_fn(batch): - # Convert data to PyTorch tensors - # You may need to adjust this based on the structure of your VLA data - return batch #{k: torch.tensor(v) for k, v in batch[0].items()} - -def get_vla_dataloader( - path: Text, - batch_size: int = 1, - cache_dir: Optional[Text] = None, - buffer_size: int = 1000, - num_workers: int = 0 -): - dataset = VLAIterableDataset(path, cache_dir, buffer_size) - return DataLoader( - dataset, - batch_size=batch_size, - collate_fn=vla_collate_fn, - num_workers=num_workers - ) \ No newline at end of file diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index d3867ce..74cafe6 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -96,4 +96,44 @@ def peek(self): def __del__(self): for p in self.processes: p.terminate() - p.join() \ No newline at end of file + p.join() + +import torch +from torch.utils.data import IterableDataset, DataLoader +from fog_x.loader.vla import VLALoader +from typing import Text, Optional + +class VLAIterableDataset(IterableDataset): + def __init__(self, path: Text, cache_dir: Optional[Text] = None, buffer_size: int = 1000): + # Note: batch size = 1 is to bypass the dataloader without pytorch dataloader + # in this case, we use pytorch dataloader for batching + self.vla_loader = VLALoader(path, batch_size=1, cache_dir=cache_dir, buffer_size=buffer_size) + + def __iter__(self): + return self + + def __next__(self): + batch = self.vla_loader.get_batch() + if batch is None: + raise StopIteration + return batch[0] # Return a single item, not a batch + +def vla_collate_fn(batch): + # Convert data to PyTorch tensors + # You may need to adjust this based on the structure of your VLA data + return batch #{k: torch.tensor(v) for k, v in batch[0].items()} + +def get_vla_dataloader( + path: Text, + batch_size: int = 1, + cache_dir: Optional[Text] = None, + buffer_size: int = 1000, + num_workers: int = 0 +): + dataset = VLAIterableDataset(path, cache_dir, buffer_size) + return DataLoader( + dataset, + batch_size=batch_size, + collate_fn=vla_collate_fn, + num_workers=num_workers + ) \ No newline at end of file diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 2d535fe..1567754 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -491,21 +491,26 @@ async def _async_write_to_cache(self, np_cache): ) def _write_to_cache(self, np_cache): - with h5py.File(self.cache_file_name, "w") as h5_cache: - for feature_name, data in np_cache.items(): - if data.dtype == object: - for i in range(len(data)): - data_type = type(data[i]) - if data_type in (str, bytes, np.ndarray): - data[i] = str(data[i]) - else: - data[i] = str(data[i]) - try: - h5_cache.create_dataset(feature_name, data=data) - except Exception as e: - logger.error(f"Error saving {feature_name} to cache: {e} with data {data}") - else: + try: + h5_cache = h5py.File(self.cache_file_name, "w") + except Exception as e: + logger.error(f"Error creating cache file: {e}") + return + for feature_name, data in np_cache.items(): + if data.dtype == object: + for i in range(len(data)): + data_type = type(data[i]) + if data_type in (str, bytes, np.ndarray): + data[i] = str(data[i]) + else: + data[i] = str(data[i]) + try: h5_cache.create_dataset(feature_name, data=data) + except Exception as e: + logger.error(f"Error saving {feature_name} to cache: {e} with data {data}") + else: + h5_cache.create_dataset(feature_name, data=data) + h5_cache.close() def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): """ From 79117e6ff0ece1d6e3ab3d6142955846bc5ffff2 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 2 Sep 2024 01:11:51 -0700 Subject: [PATCH 71/80] Refactor loader modules to improve code organization and performance --- benchmarks/Visualization.ipynb | 34 +------ benchmarks/openx.py | 38 ++++---- evaluation.sh | 6 +- fog_x/loader/__init__.py | 3 +- fog_x/loader/hdf5.py | 25 +---- fog_x/loader/vla.py | 19 +++- fog_x/trajectory.py | 166 +++++++++++---------------------- fog_x/utils.py | 24 ++++- 8 files changed, 122 insertions(+), 193 deletions(-) diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index b7d37d0..8b82a2e 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,43 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, "outputs": [ { "data": { - "image/png": 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bc+fMmWNKMn/55Rcr5nK5zPr165tJSUmmy+Wy4sePHzcvvvhi87rrrrNi+e+Bvn37uu13586dpt1uN5966im3+G+//Wb6+fm5xdu1a1fgOj958qQZHR1t9ujRw4r98ssvBa6Xs8mvrbBHvuK+P4uq0zRL/jld2Ht/3rx5piRz+fLlVmzKlCkF3uOmeer8F+czqyTy6/z444+tWFZWllmjRg3z8ssvt2L5r/vVV19d4P2Vvy2/hvzP5q5du7pda48++qgpyezfv78VGzFihGkYhpmSkmLFDhw4YFapUsVtn0eOHDEjIiLMIUOGuB07IyPDDA8PLxAHgPKO2/cAoJzLzs6WJFWuXLlY47/66itJ0pgxY9zi+bMuzlx7qnHjxmrdurX1c6tWrSRJ1157rerUqVMgnpaWVuCYw4cPt/6cf/udw+HQkiVLrPjpM20OHTqkrKwstW3btsCtdpLUrl27Yq2pExERoTVr1ig9Pb3Q7WvXrtW+fft07733uq2B07VrVzVs2LDQdbjuuecet5/btm1baM2nW7JkiRwOh0aNGuU2i2zIkCEKCwsrtfW+9uzZo9TUVA0YMMBtJkxCQoKuu+4661qQ3F//3NxcHThwQPXq1VNERITbOfjqq6905ZVXus1UiYyM1B133FHsvAYOHOi2flH+rJP813Ht2rU6cOCAhgwZ4rZY9h133KGLLrqo2McpidMX446IiFCDBg0UEhKiXr16WfEGDRooIiLC7fwvWLBAbdu21UUXXaTMzEzr0alTJzmdTi1fvtza57Fjx7R48WKv5PvVV18pOjpaffv2tWL+/v7697//raNHj+qHH35wG9+jRw9FRkZ65dihoaGS/r5d1htSU1O1bds23X777Tpw4ID1Gh47dkwdO3bU8uXLC9wKeeb78pNPPpHL5VKvXr3czkN0dLTq16+v77//vkANp6+ZFRAQoJYtW57zvV0cr7zyihYvXuz2kDx7fxZVZ76SfE6f/t4/ceKEMjMzdeWVV0pSoZ+/Z8qfcfb5558XehuvN9WsWVO33nqr9XNYWJj69eunlJQUZWRkuI0dMmTIOdePyv9sHjFihAzDsOKjRo0qMHbRokVq3bq12yL3VapUKfDZt3jxYh0+fFh9+/Z1u/bsdrtatWpV4NoDgPKO2/cAoJwLCwuTVPx/EP7555+y2WwFvtktOjpaERER+vPPP93ip/+DRpLCw8MlSbVr1y40fujQIbe4zWZTXFycW+ySSy6RJLc1OL788ks9+eSTSk1NdVvb6vR/KOQ785usijJ58mT1799ftWvXVrNmzdSlSxf169fPyie/1gYNGhR4bsOGDfXjjz+6xYKCggr8Q/6iiy4qUPOZijpOQECA4uLiCrzm3nK2+ho1aqRvvvnGWgA4JydHkyZN0pw5c/TXX3+53RqUlZXlts/8f9ierrBjFOXMayq/0ZT/OubnfeY16ufn55PbXgo7z+Hh4YqJiSlwPYaHh7ud/23btmn9+vVFNnzyF/a+99579eGHH+qGG25QrVq1dP3116tXr17q3LnzeeX8559/qn79+m5NT+nv85y//XTFfQ8Vx9GjRyUVvzF+Ltu2bZMka72pwmRlZbk1KM+sZ9u2bTJNU/Xr1y/0+Wd+42Jh5/aiiy7S+vXrPcq9MC1btix0oXNP3p/5ijpvJfmcPnjwoJKTkzV//nzr+sx3+nu/KL1799abb76pu+66Sw8//LA6duyo7t2767bbbitwPZ7u4MGDbrfIBQcHW/kVpV69egXO0+l/n0RHR1vx4lzj+efgzOskMjKyQAP8zz//dGv8nZ7T6fKv32uvvbbQY+b/nQ0AFQVNKQAo58LCwlSzZk1t2LDBo+cV1uwpTFH/01xU3DxjAfPiWLFihW6++WZdc801evXVV1WjRg35+/trzpw5hS4kXdz1i3r16qW2bdvq008/1bfffqspU6bo2Wef1SeffKIbbrjB4zzL87c2jRgxQnPmzNGoUaPUunVrhYeHyzAM9enTx+uzH7x57ZSGklzzLpdL1113nR588MFCx+b/AzoqKkqpqan65ptv9PXXX+vrr7/WnDlz1K9fvwKLk5cGT9YAO5f8z54z/3F+vvKvtylTprjNSjld/uysfGfW43K5ZBiGvv7660LP25nP/6dfk/mKOm8luWZ79eqlVatWaezYsUpMTFRoaKhcLpc6d+5crPd+cHCwli9fru+//14LFy7UokWL9MEHH+jaa6/Vt99+W2QO3bt3d5vB179/f68sHH96XmUh/zV755133Jpk+U6f/QkAFQGfegBQAdx44416/fXXtXr16kL/J/d0devWlcvl0rZt26xZFJK0d+9eHT58WHXr1vVqbi6XS2lpadY/xiVp69atkmTNevn4448VFBSkb775xm3R2Dlz5pT4+DVq1NC9996re++9V/v27dMVV1yhp556SjfccINV65YtWwr8r/aWLVu89lqcfpzTZ405HA7t2LFDnTp18spxznbcM23evFnVqlWzZmF89NFH6t+/v5577jlrzIkTJ9y+HTB/n/kzAU5X2DFKmvf27dvVoUMHK56Xl6edO3eWePH30hQfH6+jR48W65wGBATopptu0k033SSXy6V7771XM2fO1OOPP+5xg6du3bpav369XC6X2+yUzZs3W9tLw9GjR/Xpp5+qdu3abp8nJZG/2HlYWNh5vzfi4+NlmqYuvvhit8+ekihuI7+4PHl/lpZDhw5p6dKlSk5O1rhx46x4Ye/xs9Vvs9nUsWNHdezYUdOmTdPTTz+t//znP/r++++LPIfPPfec24ytmjVrnjPf7du3yzRNt1zO/PvEE/nnYNu2bW6fzfv37y8wA7Zu3bqFfkvombH86zcqKqrUPtsB4ELCmlIAUAE8+OCDCgkJ0V133aW9e/cW2P7HH39YXzXfpUsXSbK+BSzftGnTJKnAN915w8svv2z92TRNvfzyy/L391fHjh0l/f2/+YZhyOl0WuN27typzz777LyP6XQ6C9x6EhUVpZo1a1q3BzZv3lxRUVGaMWOG2y2DX3/9tTZt2uS116JTp04KCAjQiy++6DZDYdasWcrKyiqV11z6uyGXmJiot956y625tGHDBn377bfWtSD9fQ7OnBXy0ksvuZ0T6e/r56efftLPP/9sxfbv36/33nvPa3k3b95cVatW1RtvvKG8vDwr/t57753zVsmy1qtXL61evVrffPNNgW2HDx+26jlw4IDbNpvNZjXbTr8Wi6tLly7KyMjQBx98YMXy8vL00ksvKTQ0VO3atfN4n+eSk5Ojf/3rXzp48KD+85//eK1p06xZM8XHx2vq1KnWrYGn279//zn30b17d9ntdiUnJxe4rk3TLPD6F0d+g+jMRu358uT9WVryZzGd+Rqd+feDVHT9Bw8eLDA2f4bb2a7lZs2aqVOnTtajOOsEpqenu31DbHZ2tt5++20lJiYWOivpXDp16iR/f3+99NJLbq9BYfUnJSVp9erVSk1NtWIHDx4s8NmXlJSksLAwPf3008rNzS2wn+JcvwBQnjBTCgAqgPj4eL3//vvq3bu3GjVqpH79+umyyy6Tw+HQqlWrrK+Gl6SmTZuqf//+ev3113X48GG1a9dOP//8s9566y1169bNbWaKNwQFBWnRokXq37+/WrVqpa+//loLFy7Uo48+aq2707VrV02bNk2dO3fW7bffrn379umVV15RvXr1zntNlyNHjigmJka33XabmjZtqtDQUC1ZskS//PKLNRvI399fzz77rAYOHKh27dqpb9++2rt3r1544QXFxsZq9OjRXnkNIiMj9cgjjyg5OVmdO3fWzTffrC1btujVV19VixYt3BZYPh/Tpk1TpUqV3GI2m02PPvqopkyZohtuuEGtW7fW4MGDra+cDw8P14QJE6zxN954o9555x2Fh4ercePGWr16tZYsWaKqVau67ffBBx/UO++8o86dO2vkyJEKCQnR66+/bs3U8YaAgABNmDBBI0aM0LXXXqtevXpp586dmjt3ruLj470+Y8Wbxo4dqy+++EI33nijBgwYoGbNmunYsWP67bff9NFHH2nnzp2qVq2a7rrrLh08eFDXXnutYmJi9Oeff+qll15SYmLiec04uvvuuzVz5kwNGDBAv/76q2JjY/XRRx9p5cqVmj59eonXe/rrr7/07rvvSvp7dtTvv/+uBQsWKCMjQ/fff7+GDh1aov2fzmaz6c0339QNN9ygSy+9VAMHDlStWrX0119/6fvvv1dYWJj++9//nnUf8fHxevLJJ/XII49o586d6tatmypXrqwdO3bo008/1d13360HHnjAo7zi4+MVERGhGTNmqHLlygoJCVGrVq1KtD5Xcd+fpSUsLEzXXHONJk+erNzcXNWqVUvffvutduzYUWBss2bNJEn/+c9/1KdPH/n7++umm27SxIkTtXz5cnXt2lV169bVvn379OqrryomJkZXX321V/O95JJLNHjwYP3yyy+qXr26Zs+erb179573rNrIyEg98MADmjRpkm688UZ16dJFKSkp+vrrr1WtWjW3sQ8++KDeffddXXfddRoxYoRCQkL05ptvqk6dOjp48KD1uRQWFqbXXntN//rXv3TFFVeoT58+ioyM1K5du7Rw4UK1adPG7T9qAKDc8/0X/gEAysrWrVvNIUOGmLGxsWZAQIBZuXJls02bNuZLL71knjhxwhqXm5trJicnmxdffLHp7+9v1q5d23zkkUfcxphm0V8FL8m877773GL5XzU/ZcoUK9a/f38zJCTE/OOPP8zrr7/erFSpklm9enVz/PjxptPpdHv+rFmzzPr165uBgYFmw4YNzTlz5lhfg36uY5++Lf9r6E+ePGmOHTvWbNq0qVm5cmUzJCTEbNq0qfnqq68WeN4HH3xgXn755WZgYKBZpUoV84477jB3797tNia/ljMVlmNRXn75ZbNhw4amv7+/Wb16dXPYsGHmoUOHCt1fcb5ePX9sYQ+73W6NW7Jkidm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"text/plain": [ "
" ] diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 8b61feb..8e5717f 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -55,6 +55,7 @@ def __init__( self.dataset_dir = os.path.realpath(self.dataset_dir) self.log_frequency = log_frequency self.results = [] + self.log_level = "debug" def measure_average_trajectory_size(self): """Calculates the average size of trajectory files in the dataset directory.""" @@ -95,24 +96,28 @@ def summarize_value(value): return len(value) elif isinstance(value, dict): return {k: summarize_value(v) for k, v in value.items()} + elif isinstance(value, str): + return value else: + logger.warning(f"Unknown type: {type(value)}") return type(value).__name__ return {key: summarize_value(value) for key, value in trajectory.items()} trajectory_summaries = [summarize_trajectory(trajectory) for trajectory in data] + log_func = logger.debug if self.log_level == 'debug' else logger.info for i, summary in enumerate(trajectory_summaries): - logger.debug(f"Trajectory {i + 1}:") + log_func(f"Trajectory {i + 1}:") for feature, dimension in summary.items(): if isinstance(dimension, dict): - logger.debug(f" {feature}:") + log_func(f" {feature}:") for sub_feature, sub_dimension in dimension.items(): - logger.debug(f" {sub_feature}: {sub_dimension}") + log_func(f" {sub_feature}: {sub_dimension}") else: - logger.debug(f" {feature}: {dimension}") + log_func(f" {feature}: {dimension}") - logger.debug(f"Total number of trajectories: {len(trajectory_summaries)}") + log_func(f"Total number of trajectories: {len(trajectory_summaries)}") def write_result(self, format_name, elapsed_time, index): result = { @@ -183,24 +188,25 @@ def get_loader(self): return RLDSLoader(self.dataset_dir, split="train", batch_size=self.batch_size) def _recursively_load_data(self, data): + log_level = self.log_level # rlds returns a list of dictionaries - logger.debug(f"Data summary for loader {self.dataset_type.upper()}") + log_func = logger.debug if log_level == 'debug' else logger.info + log_func(f"Data summary for loader {self.dataset_type.upper()}") for i, trajectory in enumerate(data): - logger.debug(f"Trajectory {i + 1}:") + log_func(f"Trajectory {i + 1}:") # each trajectory is a list of dictionaries for j, step in enumerate(trajectory): - logger.debug(f" Step {j + 1}:") + log_func(f" Step {j + 1}:") for key, value in step.items(): if isinstance(value, np.ndarray): - logger.debug(f" {key}: {value.shape}") + log_func(f" {key}: {value.shape}") elif isinstance(value, dict): - logger.debug(f" {key}:") + log_func(f" {key}:") for sub_key, sub_value in value.items(): - logger.debug(f" {sub_key}: {sub_value.shape}") + log_func(f" {sub_key}: {sub_value.shape}") else: - logger.debug(f" {key}: {type(value).__name__}") - logger.debug(f"Total number of trajectories: {len(data)}") - + log_func(f" {key}: {type(value).__name__}") + log_func(f"Total number of trajectories: {len(data)}") class VLAHandler(DatasetHandler): def __init__( @@ -367,9 +373,7 @@ def evaluation(args): default=DEFAULT_DATASET_NAMES, help="List of dataset names to evaluate.", ) - parser.add_argument( - "--prepare", action="store_true", help="Prepare the datasets before evaluation." - ) + parser.add_argument( "--log_frequency", type=int, diff --git a/evaluation.sh b/evaluation.sh index 34ac13b..6ea3fb9 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -4,7 +4,7 @@ sudo echo "Use sudo access for clearning cache" rm *.csv # Define a list of batch sizes to iterate through -batch_sizes=(8) +batch_sizes=(1) # batch_sizes=(1 2) num_batches=1000 @@ -14,8 +14,8 @@ for batch_size in "${batch_sizes[@]}" do echo "Running benchmarks with batch size: $batch_size" - # python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size - python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size # python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size # python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size done \ No newline at end of file diff --git a/fog_x/loader/__init__.py b/fog_x/loader/__init__.py index c54c4a5..ab8f982 100644 --- a/fog_x/loader/__init__.py +++ b/fog_x/loader/__init__.py @@ -1,5 +1,4 @@ from .base import BaseLoader from .rlds import RLDSLoader from .hdf5 import HDF5Loader -from .vla import VLALoader -from .pytorch_vla import get_vla_dataloader \ No newline at end of file +from .vla import VLALoader \ No newline at end of file diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 6716356..51136d5 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -9,28 +9,7 @@ import multiprocessing as mp import time import logging - - -# flatten the data such that all data starts with root level tree (observation and action) -def _flatten(data, parent_key="", sep="/"): - items = {} - for k, v in data.items(): - new_key = parent_key + sep + k if parent_key else k - if isinstance(v, dict): - items.update(_flatten(v, new_key, sep)) - else: - items[new_key] = v - return items - - -def recursively_read_hdf5_group(group): - if isinstance(group, h5py.Dataset): - return np.array(group) - elif isinstance(group, h5py.Group): - return {key: recursively_read_hdf5_group(value) for key, value in group.items()} - else: - raise TypeError("Unsupported HDF5 group type") - +from fog_x.utils import _flatten, recursively_read_hdf5_group class HDF5Loader(BaseLoader): def __init__(self, path, batch_size=1, buffer_size=100, num_workers=4): @@ -102,7 +81,7 @@ def _read_hdf5(self, data_path): data["observation"] = _flatten(data_unflattened["observation"]) data["action"] = _flatten(data_unflattened["action"]) - return data + return data_unflattened def __iter__(self): return self diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 74cafe6..4279e7b 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -9,20 +9,26 @@ from collections import deque import multiprocessing as mp import time +from multiprocessing import Manager logger = logging.getLogger(__name__) class VLALoader: - def __init__(self, path: Text, batch_size=1, cache_dir=None, buffer_size=100, num_workers=4): + def __init__(self, path: Text, batch_size=1, cache_dir=None, buffer_size=100, num_workers=-1): self.files = self._get_files(path) + manager = Manager() + self.loaded_traj = manager.dict() # Use a Manager to create a shared dictionary self.cache_dir = cache_dir self.batch_size = batch_size self.buffer_size = buffer_size self.buffer = mp.Queue(maxsize=buffer_size) + if num_workers == -1: + num_workers = 4 self.num_workers = num_workers self.processes = [] random.shuffle(self.files) self._start_workers() + def _get_files(self, path): if "*" in path: @@ -33,8 +39,15 @@ def _get_files(self, path): return [path] def _read_vla(self, data_path): - traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) - return traj.load() + if data_path in self.loaded_traj: + logger.debug(f"[Path Hit] Data path {data_path} already loaded") + return self.loaded_traj[data_path].load() + else: + logger.debug(f"[Path Miss]Loading data path {data_path}") + traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) + ret = traj.load() + self.loaded_traj[data_path] = traj + return ret def _worker(self): while True: diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 1567754..17c590f 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -11,6 +11,7 @@ import asyncio from concurrent.futures import ThreadPoolExecutor import sys +from fog_x.utils import recursively_read_hdf5_group logger = logging.getLogger(__name__) @@ -67,10 +68,10 @@ def __init__( self.feature_name_separator = feature_name_separator # self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" # use hex hash of the path for the cache file name - if not os.path.exists(cache_dir): - os.makedirs(cache_dir, exist_ok=True) hex_hash = hex(abs(hash(self.path)))[2:] - self.cache_file_name = cache_dir + hex_hash + ".cache" + self.cache_base_dir = os.path.join(cache_dir, hex_hash) + if not os.path.exists(self.cache_base_dir): + os.makedirs(self.cache_base_dir, exist_ok=True) # self.cache_file_name = cache_dir + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type @@ -81,9 +82,10 @@ def __init__( self.is_closed = False self.lossy_compression = lossy_compression self.pending_write_tasks = [] # List to keep track of pending write tasks - self.cache_write_lock = asyncio.Lock() self.cache_write_task = None - self.executor = ThreadPoolExecutor(max_workers=1) + self.is_loaded = False + self.in_memory_features = {} # For non-image features + self.memmap_features = {} # For image features # check if the path exists # if not, create a new file and start data collection @@ -152,7 +154,7 @@ def close(self, compact=True): self.container_file = None self.is_closed = True - def load(self, save_to_cache=True, return_h5=False): + def load(self): """ Load the trajectory data. @@ -161,31 +163,20 @@ def load(self, save_to_cache=True, return_h5=False): return_h5 (bool): If True, return h5py.File object instead of numpy arrays. Returns: - dict: A dictionary of numpy arrays if return_h5 is False, otherwise an h5py.File object. + dict: A dictionary of numpy arrays """ - return asyncio.get_event_loop().run_until_complete( - self.load_async(save_to_cache=save_to_cache, return_h5=return_h5) - ) - - async def load_async(self, save_to_cache=True, return_h5=False): - if os.path.exists(self.cache_file_name): - if return_h5: - return h5py.File(self.cache_file_name, "r") - else: - with h5py.File(self.cache_file_name, "r") as h5_cache: - return {k: np.array(v) for k, v in h5_cache.items()} + if self.is_loaded: + logger.debug(f"[HIT] {self.path}") + # Combine in-memory and memmap features + combined_data = {**self.in_memory_features, **self.memmap_features} + return combined_data else: - logger.debug(f"Loading the container file {self.path}, saving to cache {self.cache_file_name}") - np_cache = self._load_from_container() - if save_to_cache: - await self._async_write_to_cache(np_cache) - - if return_h5: - return h5py.File(self.cache_file_name, "r") - else: - return np_cache - + logger.debug(f"[MISS] {self.path} ") + self._load_from_container() + combined_data = {**self.in_memory_features, **self.memmap_features} + return combined_data + def init_feature_streams(self, feature_spec: Dict): """ initialize the feature stream with the feature name and its type @@ -361,7 +352,7 @@ def _load_from_cache(self): def _load_from_container(self): """ - Load the container file with the entire VLA trajectory using multi-processing for image streams. + Load the container file with the entire VLA trajectory. args: save_to_cache: save the decoded data to the cache file @@ -372,12 +363,9 @@ def _load_from_container(self): Workflow: - Get schema of the container file. - Preallocate decoded streams. - - Use multi-processing to decode image streams separately. - - Decode non-image streams in the main process. - - Combine results from all processes. + - Decode all streams in the main process. + - Combine results. """ - import multiprocessing as mp - def _get_length_of_stream(container, stream): """ Get the length of the stream. @@ -388,25 +376,6 @@ def _get_length_of_stream(container, stream): length += 1 return length - def process_image_stream(stream, feature_name, feature_type, length, path, result_queue): - container = av.open(path, mode="r", format="matroska") - np_cache = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) - feature_length = 0 - - for packet in container.demux([stream]): - frames = packet.decode() - for frame in frames: - if feature_type.dtype == "float32": - data = frame.to_ndarray(format="gray").reshape(feature_type.shape) - else: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) - np_cache[feature_length] = data - feature_length += 1 - - container.close() - result_queue.put((feature_name, np_cache[:feature_length])) - os._exit(0) - try: container_to_get_length = av.open(self.path, mode="r", format="matroska") except Exception as e: @@ -419,80 +388,51 @@ def process_image_stream(stream, feature_name, feature_type, length, path, resul container = av.open(self.path, mode="r", format="matroska") streams = container.streams + feature_name_to_stream = {} - # Dictionary to store preallocated numpy arrays - np_cache = {} - - # Prepare for multi-processing - image_streams = [] - other_streams = [] for stream in streams: feature_name = stream.metadata.get("FEATURE_NAME") if feature_name is None: logger.warn(f"Skipping stream without FEATURE_NAME: {stream}") continue feature_type = FeatureType.from_str(stream.metadata.get("FEATURE_TYPE")) - self.feature_name_to_stream[feature_name] = stream + feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type if stream.codec_context.codec.name == "h264": - image_streams.append((stream, feature_name, feature_type)) + memmap_path = os.path.join(self.cache_base_dir, f"{feature_name.replace('/', '-')}.mmap") + self.memmap_features[feature_name] = np.memmap(memmap_path, dtype=feature_type.dtype, mode='w+', shape=(length,) + feature_type.shape) + feature_length = 0 + for packet in container.demux([stream]): + frames = packet.decode() + for frame in frames: + if feature_type.dtype == "float32": + data = frame.to_ndarray(format="gray").reshape(feature_type.shape) + else: + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + self.memmap_features[feature_name][feature_length] = data + feature_length += 1 else: - other_streams.append((stream, feature_name, feature_type)) if feature_type.dtype == "string": - np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) + self.in_memory_features[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) else: - np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) - - # Process image streams with multi-processing - result_queue = mp.Queue() - processes = [] - for stream, feature_name, feature_type in image_streams: - p = mp.Process(target=process_image_stream, args=(stream, feature_name, feature_type, length, self.path, result_queue)) - processes.append(p) - p.start() - - - # Process other streams in the main process - d_feature_length = {feature: 0 for feature, _, _ in other_streams} - for packet in container.demux([stream for stream, _, _ in other_streams]): - feature_name = packet.stream.metadata.get("FEATURE_NAME") - if feature_name is None: - logger.debug(f"Skipping stream without FEATURE_NAME: {packet.stream}") - continue - feature_type = FeatureType.from_str(packet.stream.metadata.get("FEATURE_TYPE")) + self.in_memory_features[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) + feature_length = 0 + for packet in container.demux([stream]): + packet_in_bytes = bytes(packet) + if packet_in_bytes: + data = pickle.loads(packet_in_bytes) + self.in_memory_features[feature_name][feature_length] = data + feature_length += 1 + else: + logger.debug(f"Skipping empty packet: {packet} for {feature_name}") - packet_in_bytes = bytes(packet) - if packet_in_bytes: - data = pickle.loads(packet_in_bytes) - np_cache[feature_name][d_feature_length[packet.stream]] = data - d_feature_length[packet.stream] += 1 - else: - logger.debug(f"Skipping empty packet: {packet} for {feature_name}") + self.is_loaded = True container.close() - # Wait for all image processing to complete - # busy join here - for p in processes: - p.join() - - # Collect results from image processing - while not result_queue.empty(): - feature_name, data = result_queue.get() - np_cache[feature_name] = data - - return np_cache - - async def _async_write_to_cache(self, np_cache): - async with self.cache_write_lock: - await asyncio.get_event_loop().run_in_executor( - self.executor, - self._write_to_cache, - np_cache - ) - def _write_to_cache(self, np_cache): + def _write_to_hdf5(self, np_cache): try: - h5_cache = h5py.File(self.cache_file_name, "w") + h5_cache = h5py.File(self.cache_file_name + ".temp", "w") except Exception as e: logger.error(f"Error creating cache file: {e}") return @@ -511,6 +451,8 @@ def _write_to_cache(self, np_cache): else: h5_cache.create_dataset(feature_name, data=data) h5_cache.close() + + os.rename(self.cache_file_name + ".temp", self.cache_file_name) def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): """ @@ -598,8 +540,8 @@ def to_hdf5(self, path: Text): convert the container file to hdf5 file """ - if not self.trajectory_data: - self.load() + data = self.load() + self._write_to_hdf5(data, path) # directly copy the cache file to the hdf5 file os.rename(self.cache_file_name, path) diff --git a/fog_x/utils.py b/fog_x/utils.py index cdbf925..d266564 100644 --- a/fog_x/utils.py +++ b/fog_x/utils.py @@ -18,4 +18,26 @@ def data_to_tf_schema(data: Dict[str, Any]) -> Dict[str, FeatureType]: # replace first element of shape with None else: schema[k] = FeatureType.from_data(v).to_tf_feature_type(first_dim_none=True) - return schema \ No newline at end of file + return schema + + +# flatten the data such that all data starts with root level tree (observation and action) +def _flatten(data, parent_key="", sep="/"): + items = {} + for k, v in data.items(): + new_key = parent_key + sep + k if parent_key else k + if isinstance(v, dict): + items.update(_flatten(v, new_key, sep)) + else: + items[new_key] = v + return items + +import h5py +def recursively_read_hdf5_group(group): + if isinstance(group, h5py.Dataset): + return np.array(group) + elif isinstance(group, h5py.Group): + return {key: recursively_read_hdf5_group(value) for key, value in group.items()} + else: + raise TypeError("Unsupported HDF5 group type") + From 0670995cbdec6e4bff1b844ce2b3a8d1ab59ef87 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 2 Sep 2024 02:44:57 -0700 Subject: [PATCH 72/80] Refactor LeRobotLoader to load one episode at a time for improved performance and memory efficiency --- benchmarks/openx.py | 37 +++++++++++++++++++----- evaluation.sh | 8 +++--- fog_x/loader/lerobot.py | 63 +++++++++++++++++++++-------------------- 3 files changed, 67 insertions(+), 41 deletions(-) diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 8e5717f..c72773b 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -285,6 +285,29 @@ def get_loader(self): path = os.path.join(self.exp_dir, "hf") return LeRobotLoader(path, self.dataset_name, batch_size=self.batch_size) + def _recursively_load_data(self, data): + import torch + log_level = self.log_level + # LeRobot returns a list of lists + log_func = logger.debug if log_level == 'debug' else logger.info + log_func(f"Data summary for loader {self.dataset_type.upper()}") + for i, trajectory in enumerate(data): + log_func(f"Trajectory {i + 1}:") + # each trajectory is a list of dictionaries + for j, step in enumerate(trajectory): + log_func(f" Step {j + 1}:") + for key, value in step.items(): + if isinstance(value, np.ndarray): + log_func(f" {key}: {value.shape}") + elif isinstance(value, dict): + log_func(f" {key}:") + for sub_key, sub_value in value.items(): + log_func(f" {sub_key}: {sub_value.shape}") + elif isinstance(value, torch.Tensor): + log_func(f" {key}: {value.shape}") + else: + log_func(f" {key}: {type(value).__name__}") + log_func(f"Total number of trajectories: {len(data)}") def evaluation(args): @@ -314,13 +337,13 @@ def evaluation(args): # args.batch_size, # args.log_frequency, # ), - # LeRobotHandler( - # args.exp_dir, - # dataset_name, - # args.num_batches, - # args.batch_size, - # args.log_frequency, - # ), + LeRobotHandler( + args.exp_dir, + dataset_name, + args.num_batches, + args.batch_size, + args.log_frequency, + ), # RLDSHandler( # args.exp_dir, # dataset_name, diff --git a/evaluation.sh b/evaluation.sh index 6ea3fb9..495589c 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -4,18 +4,18 @@ sudo echo "Use sudo access for clearning cache" rm *.csv # Define a list of batch sizes to iterate through -batch_sizes=(1) +batch_sizes=(2) # batch_sizes=(1 2) -num_batches=1000 +num_batches=100 # Iterate through each batch size for batch_size in "${batch_sizes[@]}" do echo "Running benchmarks with batch size: $batch_size" - python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size # python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size # python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size - # python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size done \ No newline at end of file diff --git a/fog_x/loader/lerobot.py b/fog_x/loader/lerobot.py index cc6f4ea..0c4fa58 100644 --- a/fog_x/loader/lerobot.py +++ b/fog_x/loader/lerobot.py @@ -7,13 +7,14 @@ class LeRobotLoader(BaseLoader): def __init__(self, path, dataset_name, batch_size=1, delta_timestamps=None): super(LeRobotLoader, self).__init__(path) self.batch_size = batch_size - self.dataset = LeRobotDataset(root = "/mnt/data/fog_x/hf/", repo_id =dataset_name, delta_timestamps=delta_timestamps) + self.dataset = LeRobotDataset(root="/mnt/data/fog_x/hf/", repo_id=dataset_name, delta_timestamps=delta_timestamps) self.dataloader = torch.utils.data.DataLoader( self.dataset, - batch_size=self.batch_size, + batch_size=1, # Load one episode at a time shuffle=True, ) self.iterator = iter(self.dataloader) + self.current_episode_index = None def __len__(self): return len(self.dataset) @@ -23,34 +24,36 @@ def __iter__(self): def __next__(self): max_retries = 3 - for attempt in range(max_retries): - try: - batch = next(self.iterator) - break - except StopIteration: - self.iterator = iter(self.dataloader) - if attempt == max_retries - 1: - raise StopIteration - except Exception as e: - # print(f"Error in __next__ (attempt {attempt + 1}/{max_retries}): {e}") - self.iterator = iter(self.dataloader) - if attempt == max_retries - 1: - raise e - return self._convert_batch_to_numpy(batch) - - def _convert_batch_to_numpy(self, batch): - numpy_batch = [] - for i in range(len(next(iter(batch.values())))): - trajectory = {} - for key, value in batch.items(): - if isinstance(value, torch.Tensor): - trajectory[key] = value[i].numpy() - elif isinstance(value, dict): - trajectory[key] = self._convert_batch_to_numpy({k: v[i] for k, v in value.items()}) - else: - trajectory[key] = value[i] - numpy_batch.append(trajectory) - return numpy_batch + batch_of_episodes = [] + + def _frame_to_numpy(frame): + return {k: np.array(v) for k, v in frame.items()} + for _ in range(self.batch_size): + episode = [] + for attempt in range(max_retries): + try: + batch = next(self.iterator) + episode_index = batch["episode_index"][0].item() + + from_idx = self.dataset.episode_data_index["from"][episode_index].item() + to_idx = self.dataset.episode_data_index["to"][episode_index].item() + frames = [_frame_to_numpy(self.dataset[idx]) for idx in range(from_idx, to_idx)] + + episode.extend(frames) + break + except StopIteration: + self.iterator = iter(self.dataloader) + if attempt == max_retries - 1: + raise StopIteration + except Exception as e: + self.iterator = iter(self.dataloader) + if attempt == max_retries - 1: + raise e + + batch_of_episodes.append((episode)) + + + return batch_of_episodes def get_batch(self): return next(self) From e6185717869dc15897efab291194215e7db3ede6 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 2 Sep 2024 04:35:41 -0700 Subject: [PATCH 73/80] Refactor LeRobotLoader to load one episode at a time for improved performance and memory efficiency --- fog_x/loader/lerobot.py | 28 ++++++++++------------------ 1 file changed, 10 insertions(+), 18 deletions(-) diff --git a/fog_x/loader/lerobot.py b/fog_x/loader/lerobot.py index 0c4fa58..242d3a6 100644 --- a/fog_x/loader/lerobot.py +++ b/fog_x/loader/lerobot.py @@ -8,13 +8,7 @@ def __init__(self, path, dataset_name, batch_size=1, delta_timestamps=None): super(LeRobotLoader, self).__init__(path) self.batch_size = batch_size self.dataset = LeRobotDataset(root="/mnt/data/fog_x/hf/", repo_id=dataset_name, delta_timestamps=delta_timestamps) - self.dataloader = torch.utils.data.DataLoader( - self.dataset, - batch_size=1, # Load one episode at a time - shuffle=True, - ) - self.iterator = iter(self.dataloader) - self.current_episode_index = None + self.episode_index = 0 def __len__(self): return len(self.dataset) @@ -30,25 +24,23 @@ def _frame_to_numpy(frame): return {k: np.array(v) for k, v in frame.items()} for _ in range(self.batch_size): episode = [] + # repeat + if self.episode_index >= len(self.dataset): + self.episode_index = 0 + for attempt in range(max_retries): try: - batch = next(self.iterator) - episode_index = batch["episode_index"][0].item() - - from_idx = self.dataset.episode_data_index["from"][episode_index].item() - to_idx = self.dataset.episode_data_index["to"][episode_index].item() + from_idx = self.dataset.episode_data_index["from"][self.episode_index].item() + to_idx = self.dataset.episode_data_index["to"][self.episode_index].item() frames = [_frame_to_numpy(self.dataset[idx]) for idx in range(from_idx, to_idx)] - episode.extend(frames) + self.episode_index += 1 break - except StopIteration: - self.iterator = iter(self.dataloader) - if attempt == max_retries - 1: - raise StopIteration except Exception as e: - self.iterator = iter(self.dataloader) if attempt == max_retries - 1: raise e + self.episode_index += 1 + batch_of_episodes.append((episode)) From 066abe98b8758c2231515c081d9f4b0aa489658c Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 2 Sep 2024 13:16:01 -0700 Subject: [PATCH 74/80] Refactor RLDSLoader and HDF5Loader to use smaller shuffle buffer sizes for improved performance and memory efficiency --- benchmarks/Visualization.ipynb | 72 ++++++++++----- benchmarks/openx.py | 33 +++---- evaluation.sh | 12 +-- fog_x/loader/hdf5.py | 2 +- fog_x/loader/rlds.py | 2 +- fog_x/loader/vla.py | 31 ++++--- fog_x/trajectory.py | 160 ++++++++++++++++++++------------- 7 files changed, 195 insertions(+), 117 deletions(-) diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 8b82a2e..75af83f 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,15 +2,45 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -22,39 +52,41 @@ "import matplotlib.pyplot as plt\n", "\n", "# Read the CSV file\n", - "df = pd.read_csv('../format_comparison_results.csv')\n", + "df = pd.read_csv('./format_comparison_results.csv')\n", "\n", - "# Define colors for each format\n", - "colors = {'VLA': 'blue', 'HDF5': 'green', 'LEROBOT': 'red', 'RLDS': 'purple'}\n", + "# Define colors and markers for each format\n", + "format_styles = {\n", + " 'VLA': ('blue', 'o'),\n", + " 'HDF5': ('green', 's'),\n", + " 'LEROBOT': ('red', '^'),\n", + " 'RLDS': ('purple', 'D')\n", + "}\n", "\n", "# Get unique datasets and batch sizes\n", "datasets = df['Dataset'].unique()\n", - "batch_sizes = df['BatchSize'].unique()\n", - "\n", - "# Set the width of each bar\n", - "bar_width = 1\n", "\n", "# Create a figure for each dataset\n", "for dataset in datasets:\n", - " plt.figure(figsize=(12, 6))\n", + " plt.figure(figsize=(6, 6))\n", " \n", " dataset_df = df[df['Dataset'] == dataset]\n", " \n", - " # Create the grouped bar plot\n", - " for i, format in enumerate(colors.keys()):\n", + " # Create the line plot\n", + " for format, (color, marker) in format_styles.items():\n", " data = dataset_df[dataset_df['Format'] == format]\n", - " plt.bar(data['BatchSize'] + i*bar_width, data['AverageLoadingTime(s)'], \n", - " width=bar_width, color=colors[format], label=format)\n", + " plt.plot(data['BatchSize'], data['AverageLoadingTime(s)'], \n", + " color=color, marker=marker, label=format, linewidth=2, markersize=8)\n", "\n", " # Customize the plot\n", - " plt.xlabel('Batch Size')\n", + " plt.xlabel('Num of Concurrent Reads')\n", " plt.ylabel('Average Loading Time (s)')\n", - " plt.title(f'Comparison of Loading Times for Different Formats - {dataset}')\n", + " plt.title(f'{dataset}')\n", " plt.legend()\n", - " plt.xticks(batch_sizes + bar_width*1.5, batch_sizes)\n", - "\n", + " # plt.xscale('log') # Use log scale for x-axis\n", + " # plt.yscale('log') # Use log scale for y-axis\n", + " \n", " # Add a grid for better readability\n", - " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + " plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", "\n", " # Show the plot\n", " plt.tight_layout()\n", diff --git a/benchmarks/openx.py b/benchmarks/openx.py index c72773b..696a55a 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -85,7 +85,8 @@ def clear_os_cache(self): def _recursively_load_data(self, data): logger.debug(f"Data summary for loader {self.dataset_type.upper()}") - + if None in data: + logger.warning(f"None value found in data") def summarize_trajectory(trajectory): def summarize_value(value): if isinstance(value, np.ndarray): @@ -321,7 +322,7 @@ def evaluation(args): new_results = [] for dataset_name in args.dataset_names: logger.debug(f"Evaluating dataset: {dataset_name}") - + handlers = [ VLAHandler( args.exp_dir, @@ -330,13 +331,13 @@ def evaluation(args): args.batch_size, args.log_frequency, ), - # HDF5Handler( - # args.exp_dir, - # dataset_name, - # args.num_batches, - # args.batch_size, - # args.log_frequency, - # ), + HDF5Handler( + args.exp_dir, + dataset_name, + args.num_batches, + args.batch_size, + args.log_frequency, + ), LeRobotHandler( args.exp_dir, dataset_name, @@ -344,13 +345,13 @@ def evaluation(args): args.batch_size, args.log_frequency, ), - # RLDSHandler( - # args.exp_dir, - # dataset_name, - # args.num_batches, - # args.batch_size, - # args.log_frequency, - # ), + RLDSHandler( + args.exp_dir, + dataset_name, + args.num_batches, + args.batch_size, + args.log_frequency, + ), ] for handler in handlers: diff --git a/evaluation.sh b/evaluation.sh index 495589c..7551511 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -4,18 +4,20 @@ sudo echo "Use sudo access for clearning cache" rm *.csv # Define a list of batch sizes to iterate through -batch_sizes=(2) +batch_sizes=(1 2 4 6 8) +num_batches=200 # batch_sizes=(1 2) -num_batches=100 +# batch_sizes=(2) +# num_batches=100 # Iterate through each batch size for batch_size in "${batch_sizes[@]}" do echo "Running benchmarks with batch size: $batch_size" - # python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size - # python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size - # python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size + python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size done \ No newline at end of file diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 51136d5..12709d2 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -12,7 +12,7 @@ from fog_x.utils import _flatten, recursively_read_hdf5_group class HDF5Loader(BaseLoader): - def __init__(self, path, batch_size=1, buffer_size=100, num_workers=4): + def __init__(self, path, batch_size=1, buffer_size=50, num_workers=4): super(HDF5Loader, self).__init__(path) self.files = glob.glob(self.path, recursive=True) self.batch_size = batch_size diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index 8ef4f7f..5c4d956 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -3,7 +3,7 @@ class RLDSLoader(BaseLoader): - def __init__(self, path, split, batch_size=1, shuffle_buffer=50): + def __init__(self, path, split, batch_size=1, shuffle_buffer=10): super(RLDSLoader, self).__init__(path) try: diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 4279e7b..7eb67bb 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -14,12 +14,13 @@ logger = logging.getLogger(__name__) class VLALoader: - def __init__(self, path: Text, batch_size=1, cache_dir=None, buffer_size=100, num_workers=-1): + def __init__(self, path: Text, batch_size=1, cache_dir=None, buffer_size=50, num_workers=-1): self.files = self._get_files(path) - manager = Manager() - self.loaded_traj = manager.dict() # Use a Manager to create a shared dictionary self.cache_dir = cache_dir self.batch_size = batch_size + # TODO: adjust buffer size + if "autolab" in path: + self.buffer_size = 4 self.buffer_size = buffer_size self.buffer = mp.Queue(maxsize=buffer_size) if num_workers == -1: @@ -39,24 +40,26 @@ def _get_files(self, path): return [path] def _read_vla(self, data_path): - if data_path in self.loaded_traj: - logger.debug(f"[Path Hit] Data path {data_path} already loaded") - return self.loaded_traj[data_path].load() - else: - logger.debug(f"[Path Miss]Loading data path {data_path}") - traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) - ret = traj.load() - self.loaded_traj[data_path] = traj - return ret + traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) + ret = traj.load() + return ret def _worker(self): + max_retries = 3 while True: if not self.files: logger.info("Worker finished") break file_path = random.choice(self.files) - data = self._read_vla(file_path) - self.buffer.put(data) + for attempt in range(max_retries): + try: + data = self._read_vla(file_path) + self.buffer.put(data) + break # Exit the retry loop if successful + except Exception as e: + logger.error(f"Error reading {file_path} on attempt {attempt + 1}: {e}") + if attempt + 1 == max_retries: + logger.error(f"Failed to read {file_path} after {max_retries} attempts") def _start_workers(self): for _ in range(self.num_workers): diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 17c590f..45d2961 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -7,11 +7,11 @@ import os from fog_x import FeatureType import pickle +from fog_x.utils import recursively_read_hdf5_group import h5py import asyncio from concurrent.futures import ThreadPoolExecutor import sys -from fog_x.utils import recursively_read_hdf5_group logger = logging.getLogger(__name__) @@ -68,10 +68,10 @@ def __init__( self.feature_name_separator = feature_name_separator # self.cache_file_name = "/tmp/fog_" + os.path.basename(self.path) + ".cache" # use hex hash of the path for the cache file name + if not os.path.exists(cache_dir): + os.makedirs(cache_dir, exist_ok=True) hex_hash = hex(abs(hash(self.path)))[2:] - self.cache_base_dir = os.path.join(cache_dir, hex_hash) - if not os.path.exists(self.cache_base_dir): - os.makedirs(self.cache_base_dir, exist_ok=True) + self.cache_file_name = cache_dir + hex_hash + ".cache" # self.cache_file_name = cache_dir + os.path.basename(self.path) + ".cache" self.feature_name_to_stream = {} # feature_name: stream self.feature_name_to_feature_type = {} # feature_name: feature_type @@ -82,10 +82,9 @@ def __init__( self.is_closed = False self.lossy_compression = lossy_compression self.pending_write_tasks = [] # List to keep track of pending write tasks - self.cache_write_task = None - self.is_loaded = False - self.in_memory_features = {} # For non-image features - self.memmap_features = {} # For image features + # self.cache_write_lock = asyncio.Lock() + # self.cache_write_task = None + # self.executor = ThreadPoolExecutor(max_workers=1) # check if the path exists # if not, create a new file and start data collection @@ -154,7 +153,7 @@ def close(self, compact=True): self.container_file = None self.is_closed = True - def load(self): + def load(self, save_to_cache=True, return_h5=False): """ Load the trajectory data. @@ -163,20 +162,32 @@ def load(self): return_h5 (bool): If True, return h5py.File object instead of numpy arrays. Returns: - dict: A dictionary of numpy arrays + dict: A dictionary of numpy arrays if return_h5 is False, otherwise an h5py.File object. """ - if self.is_loaded: - logger.debug(f"[HIT] {self.path}") - # Combine in-memory and memmap features - combined_data = {**self.in_memory_features, **self.memmap_features} - return combined_data + return asyncio.get_event_loop().run_until_complete( + self.load_async(save_to_cache=save_to_cache, return_h5=return_h5) + ) + + async def load_async(self, save_to_cache=True, return_h5=False): + if os.path.exists(self.cache_file_name): + if return_h5: + return h5py.File(self.cache_file_name, "r") + else: + with h5py.File(self.cache_file_name, "r") as h5_cache: + return recursively_read_hdf5_group(h5_cache) else: - logger.debug(f"[MISS] {self.path} ") - self._load_from_container() - combined_data = {**self.in_memory_features, **self.memmap_features} - return combined_data - + logger.debug(f"Loading the container file {self.path}, saving to cache {self.cache_file_name}") + np_cache = self._load_from_container() + if save_to_cache: + # await self._async_write_to_cache(np_cache) + self._write_to_cache(np_cache) + + if return_h5: + return h5py.File(self.cache_file_name, "r") + else: + return np_cache + def init_feature_streams(self, feature_spec: Dict): """ initialize the feature stream with the feature name and its type @@ -352,7 +363,7 @@ def _load_from_cache(self): def _load_from_container(self): """ - Load the container file with the entire VLA trajectory. + Load the container file with the entire VLA trajectory using multi-processing for image streams. args: save_to_cache: save the decoded data to the cache file @@ -363,9 +374,11 @@ def _load_from_container(self): Workflow: - Get schema of the container file. - Preallocate decoded streams. - - Decode all streams in the main process. - - Combine results. + - Use multi-processing to decode image streams separately. + - Decode non-image streams in the main process. + - Combine results from all processes. """ + def _get_length_of_stream(container, stream): """ Get the length of the stream. @@ -376,11 +389,7 @@ def _get_length_of_stream(container, stream): length += 1 return length - try: - container_to_get_length = av.open(self.path, mode="r", format="matroska") - except Exception as e: - logger.error(f"Error opening container: {e}") - return {} + container_to_get_length = av.open(self.path, mode="r", format="matroska") streams = container_to_get_length.streams length = _get_length_of_stream(container_to_get_length, streams[0]) logger.debug(f"Length of the stream is {length}") @@ -388,8 +397,13 @@ def _get_length_of_stream(container, stream): container = av.open(self.path, mode="r", format="matroska") streams = container.streams + + + # Dictionary to store preallocated numpy arrays + np_cache = {} feature_name_to_stream = {} + # Preallocate memory for the streams in numpy arrays for stream in streams: feature_name = stream.metadata.get("FEATURE_NAME") if feature_name is None: @@ -399,40 +413,68 @@ def _get_length_of_stream(container, stream): feature_name_to_stream[feature_name] = stream self.feature_name_to_feature_type[feature_name] = feature_type - if stream.codec_context.codec.name == "h264": - memmap_path = os.path.join(self.cache_base_dir, f"{feature_name.replace('/', '-')}.mmap") - self.memmap_features[feature_name] = np.memmap(memmap_path, dtype=feature_type.dtype, mode='w+', shape=(length,) + feature_type.shape) - feature_length = 0 - for packet in container.demux([stream]): - frames = packet.decode() - for frame in frames: - if feature_type.dtype == "float32": - data = frame.to_ndarray(format="gray").reshape(feature_type.shape) - else: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) - self.memmap_features[feature_name][feature_length] = data - feature_length += 1 + logger.debug( + f"Creating a cache for {feature_name} with shape {feature_type.shape}" + ) + + # Allocate numpy array with shape [None, X, Y, Z] where X, Y, Z are feature dimensions + if feature_type.dtype == "string": + np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) else: - if feature_type.dtype == "string": - self.in_memory_features[feature_name] = np.empty((length,) + feature_type.shape, dtype=object) - else: - self.in_memory_features[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) - feature_length = 0 - for packet in container.demux([stream]): - packet_in_bytes = bytes(packet) - if packet_in_bytes: - data = pickle.loads(packet_in_bytes) - self.in_memory_features[feature_name][feature_length] = data - feature_length += 1 + np_cache[feature_name] = np.empty((length,) + feature_type.shape, dtype=feature_type.dtype) + + # Decode the frames and store them in the preallocated numpy memory + d_feature_length = {feature: 0 for feature in feature_name_to_stream} + for packet in container.demux(list(streams)): + feature_name = packet.stream.metadata.get("FEATURE_NAME") + if feature_name is None: + logger.debug(f"Skipping stream without FEATURE_NAME: {packet.stream}") + continue + feature_type = FeatureType.from_str(packet.stream.metadata.get("FEATURE_TYPE")) + + logger.debug( + f"Decoding {feature_name} with shape {feature_type.shape} and dtype {feature_type.dtype} with time {packet.dts}" + ) + + feature_codec = packet.stream.codec_context.codec.name + if feature_codec == "h264": + frames = packet.decode() + for frame in frames: + if feature_type.dtype == "float32": + data = frame.to_ndarray(format="gray").reshape(feature_type.shape) else: - logger.debug(f"Skipping empty packet: {packet} for {feature_name}") + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) - self.is_loaded = True + # Append data to the numpy array + np_cache[feature_name][d_feature_length[feature_name]] = data + d_feature_length[feature_name] += 1 + else: + packet_in_bytes = bytes(packet) + if packet_in_bytes: + # Decode the packet + data = pickle.loads(packet_in_bytes) + + # Append data to the numpy array + np_cache[feature_name][d_feature_length[feature_name]] = data + d_feature_length[feature_name] += 1 + else: + logger.debug(f"Skipping empty packet: {packet} for {feature_name}") + logger.debug(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") container.close() - def _write_to_hdf5(self, np_cache): + return np_cache + + # async def _async_write_to_cache(self, np_cache): + # async with self.cache_write_lock: + # await asyncio.get_event_loop().run_in_executor( + # self.executor, + # self._write_to_cache, + # np_cache + # ) + + def _write_to_cache(self, np_cache): try: - h5_cache = h5py.File(self.cache_file_name + ".temp", "w") + h5_cache = h5py.File(self.cache_file_name, "w") except Exception as e: logger.error(f"Error creating cache file: {e}") return @@ -451,8 +493,6 @@ def _write_to_hdf5(self, np_cache): else: h5_cache.create_dataset(feature_name, data=data) h5_cache.close() - - os.rename(self.cache_file_name + ".temp", self.cache_file_name) def _transcode_pickled_images(self, ending_timestamp: Optional[int] = None): """ @@ -540,8 +580,8 @@ def to_hdf5(self, path: Text): convert the container file to hdf5 file """ - data = self.load() - self._write_to_hdf5(data, path) + if not self.trajectory_data: + self.load() # directly copy the cache file to the hdf5 file os.rename(self.cache_file_name, path) From 74d6c388677498f1cff88ba904feed4643c87021 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 2 Sep 2024 14:43:55 -0700 Subject: [PATCH 75/80] fix bugs that prevents rlds and lr to move forward after iterating through the dataset --- fog_x/loader/lerobot.py | 9 ++++----- fog_x/loader/rlds.py | 3 --- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/fog_x/loader/lerobot.py b/fog_x/loader/lerobot.py index 242d3a6..32c678a 100644 --- a/fog_x/loader/lerobot.py +++ b/fog_x/loader/lerobot.py @@ -11,7 +11,7 @@ def __init__(self, path, dataset_name, batch_size=1, delta_timestamps=None): self.episode_index = 0 def __len__(self): - return len(self.dataset) + return len(self.dataset.episode_data_index["from"]) def __iter__(self): return self @@ -24,12 +24,11 @@ def _frame_to_numpy(frame): return {k: np.array(v) for k, v in frame.items()} for _ in range(self.batch_size): episode = [] - # repeat - if self.episode_index >= len(self.dataset): - self.episode_index = 0 - for attempt in range(max_retries): try: + # repeat + if self.episode_index >= len(self.dataset): + self.episode_index = 0 from_idx = self.dataset.episode_data_index["from"][self.episode_index].item() to_idx = self.dataset.episode_data_index["to"][self.episode_index].item() frames = [_frame_to_numpy(self.dataset[idx]) for idx in range(from_idx, to_idx)] diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index 5c4d956..0003b6b 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -63,9 +63,6 @@ def to_numpy(step_data): return trajectory def __next__(self): - if self.index >= self.length: - self.index = 0 - raise StopIteration return self.get_batch() def __getitem__(self, idx): From 47bce7e3382101ead9dee52cf0decd5bc6cf5256 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Mon, 2 Sep 2024 22:01:01 -0700 Subject: [PATCH 76/80] fix size issue --- .gitignore | 3 +- benchmarks/Visualization.ipynb | 55 +++++++++++++++++++++++++++++----- benchmarks/openx.py | 25 ++++++++++------ evaluation.sh | 6 ++-- fog_x/loader/lerobot.py | 8 +++-- 5 files changed, 73 insertions(+), 24 deletions(-) diff --git a/.gitignore b/.gitignore index 215b378..8d278f2 100644 --- a/.gitignore +++ b/.gitignore @@ -136,4 +136,5 @@ temp.gif *.vla *.mkv -*.csv \ No newline at end of file +*.csv +*.pdf \ No newline at end of file diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 75af83f..7bd1f6e 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -79,18 +79,57 @@ "\n", " # Customize the plot\n", " plt.xlabel('Num of Concurrent Reads')\n", - " plt.ylabel('Average Loading Time (s)')\n", + " plt.ylabel('Log-Scale Average Loading Time (s)')\n", " plt.title(f'{dataset}')\n", " plt.legend()\n", " # plt.xscale('log') # Use log scale for x-axis\n", - " # plt.yscale('log') # Use log scale for y-axis\n", + " plt.yscale('log') # Use log scale for y-axis\n", " \n", " # Add a grid for better readability\n", " plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", "\n", " # Show the plot\n", " plt.tight_layout()\n", - " plt.show()" + " plt.savefig(f'./{dataset}.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "285c0135", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset Format \n", + "berkeley_autolab_ur5 HDF5 281.554772\n", + " LEROBOT 0.000000\n", + " RLDS 0.000000\n", + " VLA 1.851954\n", + "berkeley_cable_routing HDF5 4.866500\n", + " LEROBOT 0.000000\n", + " RLDS 0.000000\n", + " VLA 0.678059\n", + "bridge HDF5 29.909039\n", + " LEROBOT 0.000000\n", + " RLDS 0.000000\n", + " VLA 0.311850\n", + "nyu_door_opening_surprising_effectiveness HDF5 79.544284\n", + " LEROBOT 0.000000\n", + " RLDS 0.000000\n", + " VLA 0.359245\n", + "Name: AverageTrajectorySize(MB), dtype: float64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# file size comparison per dataset per format as a table \n", + "df.groupby(['Dataset', 'Format'])['AverageTrajectorySize(MB)'].mean()" ] }, { diff --git a/benchmarks/openx.py b/benchmarks/openx.py index 696a55a..b25a6db 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -60,16 +60,23 @@ def __init__( def measure_average_trajectory_size(self): """Calculates the average size of trajectory files in the dataset directory.""" total_size = 0 - file_count = 0 for dirpath, dirnames, filenames in os.walk(self.dataset_dir): for f in filenames: - if f.endswith(self.file_extension): - file_path = os.path.join(dirpath, f) - total_size += os.path.getsize(file_path) - file_count += 1 - if file_count == 0: - return 0 - return (total_size / file_count) / (1024 * 1024) # Convert to MB + file_path = os.path.join(dirpath, f) + total_size += os.path.getsize(file_path) + + print(f"total_size: {total_size} of directory {self.dataset_dir}") + # trajectory number + traj_num = 0 + if self.dataset_name == "nyu_door_opening_surprising_effectiveness": + traj_num = 435 + if self.dataset_name == "berkeley_cable_routing": + traj_num = 1482 + if self.dataset_name == "bridge": + traj_num = 25460 + if self.dataset_name == "berkeley_autolab_ur5": + traj_num = 896 + return (total_size / traj_num) / (1024 * 1024) # Convert to MB def clear_cache(self): """Clears the cache directory.""" @@ -274,7 +281,7 @@ def __init__( exp_dir, dataset_name, num_batches, - dataset_type="lerobot", + dataset_type="hf", batch_size=batch_size, log_frequency=log_frequency, ) diff --git a/evaluation.sh b/evaluation.sh index 7551511..6513e88 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -1,11 +1,9 @@ # ask for sudo access sudo echo "Use sudo access for clearning cache" -rm *.csv - # Define a list of batch sizes to iterate through -batch_sizes=(1 2 4 6 8) -num_batches=200 +batch_sizes=(1) +num_batches=20 # batch_sizes=(1 2) # batch_sizes=(2) diff --git a/fog_x/loader/lerobot.py b/fog_x/loader/lerobot.py index 32c678a..8953fb5 100644 --- a/fog_x/loader/lerobot.py +++ b/fog_x/loader/lerobot.py @@ -29,8 +29,12 @@ def _frame_to_numpy(frame): # repeat if self.episode_index >= len(self.dataset): self.episode_index = 0 - from_idx = self.dataset.episode_data_index["from"][self.episode_index].item() - to_idx = self.dataset.episode_data_index["to"][self.episode_index].item() + try: + from_idx = self.dataset.episode_data_index["from"][self.episode_index].item() + to_idx = self.dataset.episode_data_index["to"][self.episode_index].item() + except Exception as e: + self.episode_index = 0 + continue frames = [_frame_to_numpy(self.dataset[idx]) for idx in range(from_idx, to_idx)] episode.extend(frames) self.episode_index += 1 From f14a943e078c321abdf79594c51409965ad37527 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Tue, 3 Sep 2024 03:17:48 -0700 Subject: [PATCH 77/80] backward support octo and rlds conversion --- benchmarks/Visualization.ipynb | 59 ++++++++++++++++++++------------- examples/openx_loader.py | 32 ++++++++++-------- fog_x/DLdataset.py | 54 ++---------------------------- fog_x/dataset.py | 6 ++-- fog_x/loader/rlds.py | 12 ++++--- fog_x/loader/vla.py | 17 ++++++---- fog_x/trajectory.py | 60 ++++++++++++++++++++++++---------- openx_to_vla.sh | 9 +++-- 8 files changed, 128 insertions(+), 121 deletions(-) diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 7bd1f6e..90de485 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -95,41 +95,54 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "285c0135", "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "Dataset Format \n", - "berkeley_autolab_ur5 HDF5 281.554772\n", - " LEROBOT 0.000000\n", - " RLDS 0.000000\n", - " VLA 1.851954\n", - "berkeley_cable_routing HDF5 4.866500\n", - " LEROBOT 0.000000\n", - " RLDS 0.000000\n", - " VLA 0.678059\n", - "bridge HDF5 29.909039\n", - " LEROBOT 0.000000\n", - " RLDS 0.000000\n", - " VLA 0.311850\n", - "nyu_door_opening_surprising_effectiveness HDF5 79.544284\n", - " LEROBOT 0.000000\n", - " RLDS 0.000000\n", - " VLA 0.359245\n", - "Name: AverageTrajectorySize(MB), dtype: float64" + "
" ] }, - "execution_count": 7, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# file size comparison per dataset per format as a table \n", - "df.groupby(['Dataset', 'Format'])['AverageTrajectorySize(MB)'].mean()" + "import matplotlib.pyplot as plt\n", + "\n", + "# Data\n", + "batch_sizes = [1, 32, 64]\n", + "vla_latency = [0.008, 0.098, 0.185]\n", + "rlds_latency = [0.008, 0.097, 0.185]\n", + "\n", + "# Create the plot\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(batch_sizes, vla_latency, marker='o', label='VLA')\n", + "plt.plot(batch_sizes, rlds_latency, marker='s', label='RLDS')\n", + "\n", + "# Customize the plot\n", + "plt.xlabel('Batch Size')\n", + "plt.ylabel('Latency (s)')\n", + "plt.title('Latency vs Batch Size for VLA and RLDS')\n", + "plt.legend()\n", + "plt.grid(True, linestyle='--', alpha=0.7)\n", + "\n", + "# Set x-axis to log scale\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "\n", + "# Add data labels\n", + "for x, y in zip(batch_sizes, vla_latency):\n", + " plt.text(x, y, f'{y:.3f}', ha='right', va='bottom')\n", + "for x, y in zip(batch_sizes, rlds_latency):\n", + " plt.text(x, y, f'{y:.3f}', ha='left', va='top')\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { diff --git a/examples/openx_loader.py b/examples/openx_loader.py index 5d04282..f62b865 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -5,19 +5,21 @@ import fog_x def process_data(data_traj, dataset_name, index, destination_dir, lossless): - try: - if lossless: - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", - lossy_compression=False - ) - else: - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", - lossy_compression=True, - ) - except Exception as e: - print(f"Failed to process data {index}: {e}") + data_traj = data_traj[0] + # try: + if lossless: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=False + ) + else: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=True, + ) + print(f"Processed data {index}") + # except Exception as e: + # print(f"Failed to process data {index}: {e}") def main(): parser = argparse.ArgumentParser(description="Process RLDS data and convert to VLA format.") @@ -55,6 +57,10 @@ def main(): for future in futures: future.result() + # for index, data_traj in enumerate(loader): + # index = index + split_starting_index + # process_data(data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + print("All tasks completed.") if __name__ == "__main__": diff --git a/fog_x/DLdataset.py b/fog_x/DLdataset.py index 44fb585..4204062 100644 --- a/fog_x/DLdataset.py +++ b/fog_x/DLdataset.py @@ -180,61 +180,11 @@ def from_vla( step_spec = vla_dataset.get_tf_schema() # Generator function def generator(): - for h5_cache in vla_dataset: - - - # convert cache to tensor - def _convert_h5_cache_to_tensor(): - output_tf_traj = {} - for key in h5_cache: - # hierarhical - if type(h5_cache[key]) == h5py._hl.group.Group: - for sub_key in h5_cache[key]: - if key not in output_tf_traj: - output_tf_traj[key] = {} - output_tf_traj[key][sub_key] = tf.convert_to_tensor(h5_cache[key][sub_key]) - elif type(h5_cache[key]) == h5py._hl.dataset.Dataset: - output_tf_traj[key] = tf.convert_to_tensor(h5_cache[key]) - return output_tf_traj - output = {"steps" : _convert_h5_cache_to_tensor()} + for ts in vla_dataset: + output = {"steps" : ts} yield output - # def worker(key, sub_key, data, return_dict): - # if data.dtype == object: - # # strings are objects in numpy, need to convert to tf.string - # return_dict[(key, sub_key)] = tf.stack([tf.convert_to_tensor(x, dtype=tf.string) for x in data]) - # else: - # return_dict[(key, sub_key)] = tf.convert_to_tensor(data) - - # manager = mp.Manager() - # return_dict = manager.dict() - # jobs = [] - - # for key in output_tf_traj: - # if isinstance(output_tf_traj[key], dict): - # for sub_key in output_tf_traj[key]: - # p = mp.Process(target=worker, args=(key, sub_key, output_tf_traj[key][sub_key], return_dict)) - # jobs.append(p) - # p.start() - # else: - # p = mp.Process(target=worker, args=(key, None, output_tf_traj[key], return_dict)) - # jobs.append(p) - # p.start() - - # for job in jobs: - # job.join() - - # for key, sub_key in return_dict: - # if sub_key is None: - # output_tf_traj[key] = return_dict[(key, sub_key)] - # else: - # output_tf_traj[key][sub_key] = return_dict[(key, sub_key)] - - # output = {"steps" : output_tf_traj} - # print(f"{time()} after converting to tensor") - # yield output - # Create dataset output_signature = {"steps" : tf.nest.map_structure( diff --git a/fog_x/dataset.py b/fog_x/dataset.py index 5bd971c..6723148 100644 --- a/fog_x/dataset.py +++ b/fog_x/dataset.py @@ -32,13 +32,13 @@ def __init__(self, self.format = format self.shuffle = shuffle - self.loader = VLALoader(path) + self.loader = VLALoader(path, batch_size=1, return_type="tensor") def __iter__(self): return self def __next__(self): - return self.get_next_trajectory() + return self.loader.get_batch()[0] def __len__(self): raise NotImplementedError @@ -47,7 +47,7 @@ def __getitem__(self, index): raise NotImplementedError def get_tf_schema(self): - data = self.loader.peak(0).load(save_to_cache=False) # enforces no h5 cache + data = self.loader.peek() return data_to_tf_schema(data) def get_loader(self): diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index 0003b6b..d5cd00a 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -3,7 +3,7 @@ class RLDSLoader(BaseLoader): - def __init__(self, path, split, batch_size=1, shuffle_buffer=10): + def __init__(self, path, split, batch_size=1, shuffle_buffer=10, shuffling = True): super(RLDSLoader, self).__init__(path) try: @@ -18,8 +18,10 @@ def __init__(self, path, split, batch_size=1, shuffle_buffer=10): builder = tfds.builder_from_directory(path) self.ds = builder.as_dataset(split) self.length = len(self.ds) - self.ds = self.ds.repeat() - self.ds = self.ds.shuffle(shuffle_buffer) + self.shuffling = shuffling + if shuffling: + self.ds = self.ds.repeat() + self.ds = self.ds.shuffle(shuffle_buffer) self.iterator = iter(self.ds) self.split = split @@ -39,6 +41,8 @@ def __iter__(self): def get_batch(self): batch = self.ds.take(self.batch_size) self.index += self.batch_size + if not self.shuffling and self.index >= self.length: + raise StopIteration data = [] for b in batch: data.append(self._convert_traj_to_numpy(b)) @@ -67,4 +71,4 @@ def __next__(self): def __getitem__(self, idx): batch = next(iter(self.ds.skip(idx).take(1))) - return self._convert_batch_to_numpy(batch) + return self._convert_batch_to_numpy(batch) \ No newline at end of file diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 7eb67bb..9b746f6 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -14,10 +14,11 @@ logger = logging.getLogger(__name__) class VLALoader: - def __init__(self, path: Text, batch_size=1, cache_dir=None, buffer_size=50, num_workers=-1): + def __init__(self, path: Text, batch_size=1, cache_dir="/tmp/fog_x/cache/", buffer_size=50, num_workers=-1, return_type = "numpy"): self.files = self._get_files(path) self.cache_dir = cache_dir self.batch_size = batch_size + self.return_type = return_type # TODO: adjust buffer size if "autolab" in path: self.buffer_size = 4 @@ -39,9 +40,11 @@ def _get_files(self, path): else: return [path] - def _read_vla(self, data_path): + def _read_vla(self, data_path, return_type = None): + if return_type is None: + return_type = self.return_type traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) - ret = traj.load() + ret = traj.load(return_type = return_type) return ret def _worker(self): @@ -50,9 +53,10 @@ def _worker(self): if not self.files: logger.info("Worker finished") break - file_path = random.choice(self.files) + for attempt in range(max_retries): try: + file_path = random.choice(self.files) data = self._read_vla(file_path) self.buffer.put(data) break # Exit the retry loop if successful @@ -105,9 +109,8 @@ def __len__(self): return len(self.files) def peek(self): - if self.buffer.empty(): - return None - return self.buffer.get() + file = random.choice(self.files) + return self._read_vla(file, return_type = "numpy") def __del__(self): for p in self.processes: diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 45d2961..a05b9d8 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -153,7 +153,7 @@ def close(self, compact=True): self.container_file = None self.is_closed = True - def load(self, save_to_cache=True, return_h5=False): + def load(self, save_to_cache=True, return_type="numpy"): """ Load the trajectory data. @@ -165,28 +165,54 @@ def load(self, save_to_cache=True, return_h5=False): dict: A dictionary of numpy arrays if return_h5 is False, otherwise an h5py.File object. """ - return asyncio.get_event_loop().run_until_complete( - self.load_async(save_to_cache=save_to_cache, return_h5=return_h5) - ) - - async def load_async(self, save_to_cache=True, return_h5=False): - if os.path.exists(self.cache_file_name): - if return_h5: - return h5py.File(self.cache_file_name, "r") - else: - with h5py.File(self.cache_file_name, "r") as h5_cache: - return recursively_read_hdf5_group(h5_cache) - else: + # uncomment the following line to use async + # return asyncio.get_event_loop().run_until_complete( + # self.load_async(save_to_cache=save_to_cache, return_h5=return_h5) + # ) + # async def load_async(self, save_to_cache=True, return_h5=False): + np_cache = None + if not os.path.exists(self.cache_file_name): logger.debug(f"Loading the container file {self.path}, saving to cache {self.cache_file_name}") np_cache = self._load_from_container() if save_to_cache: # await self._async_write_to_cache(np_cache) self._write_to_cache(np_cache) - - if return_h5: - return h5py.File(self.cache_file_name, "r") - else: + + if return_type =="hdf5": + return h5py.File(self.cache_file_name, "r") + elif return_type == "numpy": + if np_cache: return np_cache + else: + with h5py.File(self.cache_file_name, "r") as h5_cache: + return recursively_read_hdf5_group(h5_cache) + elif return_type == "cache_name": + return self.cache_file_name + elif return_type == "container": + return self.path + elif return_type == "tensor": + import tensorflow as tf + def _convert_h5_cache_to_tensor(h5_cache): + output_tf_traj = {} + for key in h5_cache: + # hierarhical + if type(h5_cache[key]) == h5py._hl.group.Group: + for sub_key in h5_cache[key]: + if key not in output_tf_traj: + output_tf_traj[key] = {} + output_tf_traj[key][sub_key] = tf.convert_to_tensor(h5_cache[key][sub_key]) + elif type(h5_cache[key]) == h5py._hl.dataset.Dataset: + output_tf_traj[key] = tf.convert_to_tensor(h5_cache[key]) + return output_tf_traj + with h5py.File(self.cache_file_name, 'r') as h5_cache: + # Step 2: Access the dataset within the file + # Assume the dataset is named 'dataset_name' + output_traj = _convert_h5_cache_to_tensor(h5_cache) + return output_traj + else: + raise ValueError(f"Invalid return_type {return_type}") + + def init_feature_streams(self, feature_spec: Dict): """ diff --git a/openx_to_vla.sh b/openx_to_vla.sh index 51aa458..96ed897 100755 --- a/openx_to_vla.sh +++ b/openx_to_vla.sh @@ -31,7 +31,12 @@ # nyu_door_opening_surprising_effectiveness dataset # python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless # python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless \ No newline at end of file +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless + + + +# fractal20220817_data +python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/vla --version 0.1.0 --split train[0:] --max_workers 4 From 04f3b4ac323a62bbe5b9ef0ca7aa27e3dfe7f021 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Sat, 7 Sep 2024 11:54:52 -0700 Subject: [PATCH 78/80] fix bug of resizing width --- fog_x/trajectory.py | 27 ++++++++++++--------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index a05b9d8..8c5b8cf 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -181,11 +181,10 @@ def load(self, save_to_cache=True, return_type="numpy"): if return_type =="hdf5": return h5py.File(self.cache_file_name, "r") elif return_type == "numpy": - if np_cache: - return np_cache - else: + if not np_cache: with h5py.File(self.cache_file_name, "r") as h5_cache: - return recursively_read_hdf5_group(h5_cache) + np_cache = recursively_read_hdf5_group(h5_cache) + return np_cache elif return_type == "cache_name": return self.cache_file_name elif return_type == "container": @@ -463,14 +462,12 @@ def _get_length_of_stream(container, stream): ) feature_codec = packet.stream.codec_context.codec.name - if feature_codec == "h264": + if feature_codec == "h264" or feature_codec == "ffv1" or feature_codec == "hevc": frames = packet.decode() for frame in frames: - if feature_type.dtype == "float32": - data = frame.to_ndarray(format="gray").reshape(feature_type.shape) - else: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) - + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + # data = np.asarray(frame.to_image())#.reshape(feature_type.shape) + # save the numpy to image folder # Append data to the numpy array np_cache[feature_name][d_feature_length[feature_name]] = data d_feature_length[feature_name] += 1 @@ -728,20 +725,20 @@ def is_packet_valid(packet): def _add_stream_to_container(self, container, feature_name, encoding, feature_type): stream = container.add_stream(encoding) if encoding == "ffv1": - stream.width = feature_type.shape[0] - stream.height = feature_type.shape[1] + stream.width = feature_type.shape[1] + stream.height = feature_type.shape[0] stream.codec_context.options = { "preset": "fast", # Set preset to 'fast' for quicker encoding "tune": "zerolatency", # Reduce latency } if encoding == "libx264": - stream.width = feature_type.shape[0] - stream.height = feature_type.shape[1] + stream.width = feature_type.shape[1] + stream.height = feature_type.shape[0] stream.codec_context.options = { "preset": "ultrafast", # Set preset to 'ultrafast' for quicker encoding "tune": "zerolatency", # Reduce latency - "profile": "baseline", # no b frame + 'crf': '30', # Constant Rate Factor (quality) } stream.metadata["FEATURE_NAME"] = feature_name From 0a0542d743562a0ff496f98051bcad7d26b98b30 Mon Sep 17 00:00:00 2001 From: Eric Chen Date: Tue, 10 Sep 2024 04:49:25 +0000 Subject: [PATCH 79/80] data, etc. --- benchmarks/Visualization.ipynb | 243 ++++++++++++++++++++++++-- benchmarks/openx.py | 60 ++++--- evaluation.sh | 8 +- examples/fixing_failed_conversions.py | 72 ++++++++ examples/openx_loader.py | 81 ++++++--- examples/vla_file_debugger.py | 122 +++++++++++++ fog_x/dataset.py | 10 +- fog_x/feature.py | 9 +- fog_x/loader/rlds.py | 8 +- fog_x/loader/vla.py | 67 +++++++ fog_x/trajectory.py | 48 ++--- fog_x/utils.py | 1 + openx_to_vla.sh | 66 +++---- 13 files changed, 675 insertions(+), 120 deletions(-) create mode 100644 examples/fixing_failed_conversions.py create mode 100644 examples/vla_file_debugger.py diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 90de485..dea7ddd 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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xV+MQ/EqwyYXNk8VNrt9TMwFHK5UlID0xHemJ6dZ7DgVyLaieLMD0xZk5hZn+WBcPFyiUzvFLzxz22BRP8jCpheFCeQCN0Ap/GzY2amRoVWrSBHB1tWqczkiSJOgyddBl6KDL1EGboc26n/12ofdl6HBlxxVc/fOqWfHZ8ncFW5zyYYkWp4IUTY5K5a6Cq6crXDxd4Or5uNDwdEHS/SRoruexOrgJfMr5wMPPQxSFj7/Sk9IdrwAERG7yaC1TuCrgqfY0u7XM6DyeLlCqlHK/TJOZ+vPhbC1PbGF4utb1H6LVh8+LKQNKlbJeYCbKKixyKQSeLBjM3pehQ1JCEtxUbtBpTStCnvY85hY0ktYxfuEW9HcFW5zsyJP99Lbk4uFi+KDOVtDk9t3U47IXREb7ntKaUtACMq8fgqwux2zFlL6genJbRlJGzm15bDd6fFJG4bsZn6BvGUx5kMfcMRagclfl3SqWR/FlSlGW/UvpUvjijBcLOC5JkiDpJEjax991EnRaXdbtJ/fl2P/EvkNz/sXhRcfMimH3iRK4u1aFZ5JvQJtx7amtGLkVKOYen++5MvNYjoVsavek3Vb/PcHCycpaT2ldqBanym0qI6hjUO5FTD6Fjtxjdp6kfyNbqs9aoVCIwtDDBZ4lrDODryRJ0GXozC7I9MdmJmfmX9A9Poelf+Fq07TQpmmR+ijVoufNTuWmMqsAe3L7pS2XcHbNWbOec/fE3Uh9mIqQ8JB8P6QL8wFe0PNa4jmhg9Fx6anpUKlUgA52F7u9OP/reZz/9bzcYTgspasSKlcVlC7KHLeVLo/vZ79t4r7CnOv8r+dx+ud8ZnB/itZTWlssP3lhV10+9E13cXFxhRoc/vdnfxeo5an1lNZoOaFlgZ/XHpmai6L42vOizdAiPSkdD+8+hIeLh1HB9dRCLSkja6B/fsdq0/OYx4aI8vS0D/3cCoanFgkuKvOOd1VB4aJAWkYavH29jY4tTIFiz136cnxmxsfHw8/Pj111BRUREYGIiAhoH0+aptFoUJj6sv679ZGalop/p/5r8mOajG+C+u/Wh0ZT8LFB9siUXBTV154fCRIkbwkqbxVcFC7wgIdFz6/L1GVdRZl1xaP+dlK2iweSs11kkMvx+pa03I7XprE4sxcKpcLwpVIY33/8BSWgVCnFbYV4jFKlBJSG2wqFwuh+1m2l4XZ+z5G1T6GA8uEDuERfg+rmDSh0WiggQQEd7qE07qB8gV9r1c5VUa1LNVEM6IsE1ePv+iLBxbhQyXFcLvsUSoXdtNpLkoTExER4e3ubHZPu8T8AgPbxl52T4zMzPj7e5GPZ4pQPS7U46bG1xSCvXDjDa89NURgMrNPqjIquJ7sn9S1pZ9acwcVNFwv8PAH1AlCmfpmsD+SsD/m8ioUnPtSNjrXgvuyFRGGfAwogMTkRvmrfrG0mn9Oerty8fRtYvhxYtgyKy5dz7JYaNwYGDsTfN6titxkfknrO8vuiKPx+KAhbfmayxcnCFArL/OXRamIrQJH/OB9nuXoot1w4y2vPi/595qi/GFUuKqh8VHD3cc/3uPpv1bf4xQJFjSRJUGlUjvlBmZEB/PEHEBkJbN4slkDJrnhxoG9fYNAgKOrXBwC0kiRgxQrsvlXD5KdpXT4KrSZMdJqZvx3990NB2PIz05y8snCysfwGSTvLh4JeqwmtAAmGJTac6LU7O0tfLEB2ICpKFEsrVwL37uXc37Yt8PbbQPfugMcTXdHp6WiV+SeAW9iNF5/6VK3xJ1ppL4gZxN3zL9TJsdnjZyYLJxnk9kZw1g+FlhNaov679aFWq+UOhWzMnOLJWX8+7F5SErBmjSiY9uWypFKFCsCAAeKrSpW8z+PuDhw6hFYxMcDiC9i9ICrPQ1sPrYFWg7sCAQEsmpyEvX1msnCSCVtbiEwrnlg02RlJAg4dApYsAX75BUhIMN7v6gqEhorWpZdeAlQq085bsSJQsSJazX8WKMcleMiYPX1mcnB4Piy5Vl1unHXAX3bMgeDseeBadcbs8v0QGwv88INoXTqdyzw7wcHAoEHAm29aZCbvJ98TzvpeAOz0/SADa+aBM4cTkUOxt6Z4ekynA3buFMXSb7+JMUXZeXsDr78uWpeef96iA7XtqYWBKDsWTkRkF/hBaUeuXweWLRNf0dE597/wgmhdeu01UTxZCcdAkj1i4UREdoMflDJKSwN+/120Lu3YIcYyZVeqFPDWW6JgqlVLnhiJ7AALJyIiZ3bqlCiWfvgBePDAeJ9SCXTsKIqlLl0ANzd5YiSyIyyciIicTXy8uCIuMhI4eDDn/ipVgIEDgf79xZQCRJSFhRMRkTOQJOCff0SxtHo1kJxsvN/dHXj5ZdG61Lq1aG0iohxYOBERFWV374rZvJcuFbN7P6lBA1Es9e4NlChh8/CIHA0LJyKioiYzE9i6VbQubdwIaLXG+9VqoE8fUTA9+6w8MRI5KBZORERFxaVLomVpxQrg9u2c+1u3FsXSyy8Dnp42D4+oKGDhRETkyFJSgHXrROvS7t0595ctKwZ5DxwIBAXZOjqiIoeFExGRIzp6VKwX99NPgEZjvM/FRUwfMGiQmE7Ahb/qiSyFP01ERI7i0SPgxx9F69Lx4zn3V68uiqW33gLKlLF5eETOgIUTEZE90+lEF9ySJcD69WKG7+y8vMTSJ4MGAc2aWXS9OCLKiYUTEZEdUty6BcyZI9aLu3o15wHPPSeKpV69gKes5k5ElsPCiYjIXqSnA5s2AZGR8N26FQqdznh/iRKG9eLq1JEnRiInx8KJiEhu586JcUsrVwIxMTDqbFMogJdeEsVSt25ihm8ikg0LJyIiOSQmiqVPIiOB/ftz7NZVqADFoEFQDBgAVKokQ4BElBsWTkREtiJJwH//iYHeq1aJ4ik7Nzege3dIAwcivnFjqEuU4GBvIjvDwomIyNpiYoDvvxetS2fP5txfpw7w9ttiGRR/f1FgPTk3ExHZBRZORETWoNUC27eLYmnDBiAjw3i/jw/wxhti7FJICFuWiBwECyciIku6elVMIbBsGXDzZs79zZuLYunVV4FixWwfHxEVCgsnIqLCSk0FfvtNjF3atSvn/tKlgX79xHpxNWrYPDwishwWTkREBXXihOiK++EHsRxKdkol0LmzaF363/8AV1d5YiQii2LhRERkDo0G+PlnUTAdPpxzf7Vqoljq1w8oV8728RGRVbFwIiJ6GkkC/v5bFEtr1wIpKcb7PTyAV14RV8a1bMmB3kRFGAsnIqK83LkDrFgBLF0KXLyYc3+jRqJ16Y03AD8/m4dHRLbHwomIKLvMTGDzZjHQe/NmMa1Adn5+QN++omBq0ECOCIlIRiyciIgA4MIF0bK0YgVw927O/S++KLrievQQXXNE5JRYOBGR80pOFmOWliwB9u7Nub98eWDAAPFVtart4yMiu8PCiYiciySJq+EiI8XVcfHxxvtdXIDQUNEV16EDoFLJEycR2SUWTkTkHB48AH78UbQunTqVc3+tWqJYevNNICDA9vERkUNg4URERZdOJ2byjowEfv0VSE833l+sGPD666JgatqU0wgQ0VOxcCKioufGDcN6cdeu5dzftKkoll57TSy2S0RkIhZORFQ0pKUBGzaI1qXt28VYpuz8/YG33hIFU3CwPDESkcNj4UREju3MGVEsff89EBtrvE+pFAO8Bw0CunYF3NzkiZGIigwWTkRkP3buhM+IEcCcOcBLL+V9XEICsGqVGOj9338591euDAwcCPTvD1SsaK1oicgJsXAiIvsgScD48VBFRUEaPx5o1854sLYkAfv3i9al1auBpCTjx7u7Az17italNm1EaxMRkYWxcCIi+7B9OxSHDwOA+L59u+hmu38fWLlSFEznz+d8XP36oljq0wcoUcLGQRORs2HhRETykyRgwgRIKhUUWq34PmIEULs2sGmTWD8uO19foHdvsQTKs89yGgEishkWTkQkv+3bgUOHoC9/FFotcPGi+MquVSvRuvTyy4CXl83DJCJi4URE8pIk4JNPRKvRk1MIAECZMmKtuIEDgaAg28dHRJQNCycikldEhFg7Li+RkUDnzraLh4goH7zshIjkkZQEjBkDjBiR9zEqFTB5cu4tUUREMmDhRES2t307ULcu8M03+R+n1QKHDonjiYjsAAsnIrKd2Fix7EmHDsDVq6Y9RqUCJkxgqxMR2QUWTkRkfZIE/PgjUKuWWBrFHGx1IiI7wsKJiKzr2jWgUyegb1/DWnJqtVgWxdTZvZVKtjoRkV1g4URE1qHVArNmiUkst20zbH/1VeDECSAlBdDpTDuXTgfcuAGkp1snViIiE3E6AiKyvBMnxKze2acZKF8emDcPCA0V9w8dAmJijB4mSRISExPh7e0NxZOzgQcEiPXoiIhkxMKJiCwnJQX49FNgxgzR4gSIiS2HDQOmThVLpehVrCi+spMkaDUa0ZXHZVSIyA6xcCIiy/jzT2DIEODSJcO24GBg8WLghRfki4uIyII4xomICufhQ7EcStu2hqLJzQ2YMgU4epRFExEVKWxxIqKCkSRg9Wrg3XeB+/cN25s1E61MtWrJFxsRkZWwxYmIzBcdDXTtCvTqZSiafH2B+fOBv/9m0URERRYLJyIynVYLzJkjphj44w/D9h49gLNngaFDTZ+biYjIAbGrjohMc/q0mGLgv/8M28qWBebOBXr2lC8uIiIb4p+GRJS/1FTgk0+Ahg2Ni6YhQ0QrE4smInIibHEiorz9/TcweDBw4YJhW40awKJFQMuW8sVFRCQTtjgRUU5xcaJFqVUrQ9Hk6irWizt+nEUTETkttjgRkYEkAevXAyNGAHfuGLY3aSKmGKhTR77YiIjsgFmFk06nw549e7B3715cv34dycnJKFWqFBo2bIh27dqh4pPLJxCR47h1CwgPB37/3bDN2xuYNg0ICwNUKvliIyKyEyZ11aWkpODzzz9HxYoV0blzZ2zZsgVxcXFQqVS4dOkSJk2ahCpVqqBz5874999/rR0zEVmSTifmXwoONi6aunQRg7+HD2fRRET0mEktTtWrV0fTpk2xePFivPTSS3B1dc1xzPXr1/HTTz+hV69e+PjjjzF48GCLB0tEFnb2LPDOO8A//xi2lS4NzJ4NvPoqF9olInqCSYXT9u3bUespMwFXqlQJ48aNw/vvv4/o6GiLBEdEVpKWBnz5JfDFF0BGhmH7oEHAjBlA8eLyxUZEZMdMKpyeVjRl5+rqimrVqhU4ICKysn/+EVMMnDtn2BYUJKYYaNNGvriIiByA2dMRbN26Ffv27cu6HxERgQYNGqB379549OiRRYMjIguKjweGDQOaNzcUTS4uwLhxwMmTLJqIiExgduH0wQcfID4+HgBw6tQpjBkzBp07d8bVq1cxevRoiwdIRBbw++9i8Pf8+YZtISHA4cPA1KmAp6d8sRERORCz53G6evUqgoODAQDr1q1Dly5dMHXqVBw9ehSdO3e2eIBEVAh37og5mdatM2wrVgz4/HOxnVfLERGZxewWJzc3NyQnJwMAdu7cifbt2wMASpQokdUSRUQy0+nEhJW1ahkXTZ06AWfOAKNGsWgiIioAs1ucmjdvjtGjR6NZs2Y4ePAgVq1aBQC4cOECKlSoYPEAichMUVFiioG//zZs8/cHvvsOeOMNTjFARFQIZrc4zZ07Fy4uLli7di3mz5+P8uXLAwC2bNmCjh07WjxAIjJRerqYXqB+feOiqV8/4Px5oHdvFk1ERIVkdotTYGAgNm3alGP7rFmzLBIQERXAf/8Bb78NnD5t2FalCrBwIfDSS/LFRURUxJjU4pSUlGTWSc09nogKKCEBePddoGlTQ9GkVAIffCDus2giIrIokwqnoKAgfPnll7iTfbX0J0iShB07dqBTp06YPXu2xQIsrE2bNqFGjRp45plnsGTJErnDIbKcP/4AatcG5swBJElsa9gQOHQImD4d8PKSNz4ioiLIpK663bt3Y/z48Zg8eTLq16+Pxo0bo1y5cvDw8MCjR49w9uxZHDhwAC4uLhg3bhyGDBli7bhNkpmZidGjR+Ovv/6CWq1Go0aN0KNHD5QsWVLu0IgK7t49cVXcL78Ytnl6Ap9+Kra7mN0DT0REJjLpN2yNGjWwbt06REdHY82aNdi7dy/279+PlJQU+Pv7o2HDhli8eDE6deoElR1d4nzw4EHUrl07awB7p06dsH37drzxxhsyR0ZUAJIELF8OjBkDZJ+lv107MZapalXZQiMichZmXVUXGBiIMWPG4LfffsOxY8dw/vx57Nu3D3PmzEGXLl0sXjT9/fff6Nq1K8qVKweFQoHffvstxzERERGoXLkyPDw88Pzzz+PgwYNZ+27fvp1VNAFA+fLlcevWLYvGSGQTly6JAmngQEPRVKIEsGIFsH07iyYiIhsxezoCW0pKSkL9+vURERGR6/5Vq1Zh9OjRmDRpEo4ePYr69eujQ4cOuH//vo0jJbKSjAzgq6+AunWBP/80bO/TR0wx8NZbnGKAiMiG7HowRKdOndCpU6c893/zzTcYPHgwBgwYAABYsGAB/vjjDyxduhRjx45FuXLljFqYbt26heeeey7P86WlpSEtLS3rvn4mdEmSIOkH31qQ/rzWOLejYA6EXPNw+DAweDAUJ04YjqtUCZg3T8wALh5o40iti+8HgXkQmAeBeRCsmQdzzmnXhVN+0tPTceTIEYwbNy5rm1KpRLt27XDgwAEAwHPPPYfTp0/j1q1bUKvV2LJlCyZMmJDnOadNm4YpU6bk2K7RaKz2H5WYmAgAUDhpqwFzIBjlITkZHtOmwX3+fCh0OrFfqUTa0KFIHTcO8PYGNBo5w7Uavh8E5kFgHgTmQbBmHsxZMs5hC6fY2FhotVqULl3aaHvp0qVx/vx5AICLiwtmzpyJNm3aQKfT4cMPP8z3irpx48Zh9OjRWffj4+NRsWJFqNVq+Pr6Wvw16IsxtVrttD8MzIGQlYd//4Vi2DAorl0z7KtXD1i8GO4hIXCXKT5b4ftBYB4E5kFgHgRr5sGc8zls4WSq0NBQhIaGmnSsu7s73N1zfjQpFAqrvVn153bmHwbmAEBMDIoNHw7lmjWGbR4ewKRJUIwZA7i6yhebjfH9IDAPAvMgMA+CtfJgzvkKNDh879696Nu3L5o2bZo1huj777/Hvn37CnK6AvH394dKpcK9e/eMtt+7dw9lypSxWRxEhSJJwPffA8HBcMteNLVpA5w8CYwd61RFExGRvTO7cFq3bh06dOgAT09PHDt2LGswtUajwdSpUy0eYF7c3NzQqFEj7Nq1K2ubTqfDrl270LRpU5vFQVRgV68CHTsCb70FxYMHAACpeHEgMhLYtQt45hmZAyQioieZXTh9/vnnWLBgARYvXgzXbH8JN2vWDEePHrVocImJiTh+/DiOHz8OALh69SqOHz+O6OhoAMDo0aOxePFirFixAufOnUNYWBiSkpKyrrIjskuZmcDMmUCdOmIOpsfSe/QAzp4VczU5eXM8EZG9MnuMU1RUFFq2bJlju1qtRlxcnCViynL48GG0adMm675+4Ha/fv2wfPlyvP7664iJicHEiRNx9+5dNGjQAFu3bs0xYJzIbhw7BgweDBw5YthWoQKkefOQ3KIF1Gq1fLEREdFTmV04lSlTBpcuXULlypWNtu/btw9VLTx7cevWrZ86DcDw4cMxfPhwiz4vkcUlJwNTpoiWJq1WbFMogOHDgS++KNJTDBARFUZqKrBmDfDbb8C9e8VQujTQvTvw6qviGhpbM7twGjx4MEaOHImlS5dCoVDg9u3bOHDgAN5///1850giclq7dgHvvANcuWLYVrs2sGQJ0KSJuO/kE9sREeVmwwagf3+x0pRSCeh0rlAqJaxfD4wcKVad6trVtjGZXTiNHTsWOp0Obdu2RXJyMlq2bAl3d3e8//77GDFihDViJHJMDx6IBXlXrDBsc3MDJkwAPvxQ3CYiolxt2CBalvR0OoXR97g4oFs30RJl4qxDFmF24aRQKPDxxx/jgw8+wKVLl5CYmIjg4GB4e3tbIz4ixyNJwC+/iD+HYmIM21u2BBYtAmrUkC82IiIHkJoqWpqAvBvkJUmMeOjfH7h923bddgWeANPNzQ3BwcGWjIXI8UVHA2FhwObNhm1qNTB9OvD226KtmYiI8rVmjeieexpJEsetXQv07Wv9uIACFE6pqamYM2cO/vrrL9y/fx+6x2tp6Vl6SgJ7wEV+rafI5ECrBebOBT75BIqkpKzNUs+ewOzZQLlyjzfk/jqLTB4KiXkQmAeBeRCcMQ+//aYf0/T0qVmUSgm//gr06VPw57PqIr+DBg3C9u3b8corr+C5554rktO/R0REICIiAtrHVz9xkV/rKQo5UJ4+Da9Ro+CSbYoBXdmySJkxAxn/+5/Y8JQr5opCHiyBeRCYB4F5EJwxD/fuFYNOZ9qqCTqdAvfvZ0CjSXr6wXmw6iK/mzZtwubNm9GsWTNzH+owwsPDER4ejvj4eKjVai7ya0UOnYPUVOCzz4AZM6DIzMzaLA0dCsW0afAyY04mh86DBTEPAvMgMA+CM+ahdGnRkmRqi1NAgEuh5sGz6iK/5cuXh4+Pj7kPc2hc5Ne6HDIHu3eLKQYuXjRsq1kTWLwYiubNC3RKh8yDFTAPAvMgMA+Cs+Whe3dg/XrTjtXpFOjRo3ALLlh1kd+ZM2fio48+wvXr1819KJHje/RIzPzdpo2haHJ1BSZNAo4fBwpYNBERkcGrr4rrap5GoQCKFwdeecX6MemZ3eLUuHFjpKamomrVqvDy8jJarw4AHj58aLHgiOyGJAHr1omZvu/dM2x/4QUxxUDt2vLFRkRUxLi7A7VqAf/+m/cx+kaiFStsO4O42YXTG2+8gVu3bmHq1KkoXbq00zQbkhO7eRMIDxezsen5+ABffgkMHcopBoiILGz1akPRpFCIv131Y5703/38HGTm8P379+PAgQOoX7++NeIhsh86HTB/PjBuHJCQYNgeGgpERAAVKsgXGxFRERUTIxr39VauFN9//RW4fz8DAQEu6NFDdM85xFp1NWvWREpKijViIbIfZ86IsUwHDhi2lSkDzJkDvPxy4UYhEhFRnoYPB2Jjxe2ePcX8TAqF+K7RJD2+ulC++MzuY/jyyy8xZswY7N69Gw8ePEB8fLzRF5FDS0sTA70bNjQumgYPBs6eFX/isGgiIrKK9etFNx0AlCgBzJtnf79yzW5x6tixIwCgbdu2RtslSYJCociaNJLI4ezbJwqk8+cN26pXF4O/W7WSLy4iIifw4IFYsUpv9mwxn5O9Mbtw+uuvv6wRB5F8NBpg7FhgwQLDNhcX4KOPgE8+kacTnYjIyYwcCdy/L2537Qr07i1vPHkxu3Bqxb+8qSj59VfRoX77tmHbc88BS5YAdevKFxcRkRPZsAH48Udx289P/B1rb110eiYVTidPnkSdOnWgVCpx8uTJfI+tV6+eRQIjsqrbt4ERI4ynpi1WDJg6VUw9oFLJFxsRkRN59EjM7KL37beGddHtkUmFU4MGDXD37l0EBASgQYMGUCgUuS56W1THOFlrVWpnXPH6STbPgU4HLF4MjB0LRbaFd6XOncUoxMBAfWC2iUf//HwvAGAe9JgHgXkQinoe3nsPuHNHNC916iThzTdz/xVszTyYc06TCqerV6+iVKlSWbeLuoiICERERGQVgRqNxmr/Uc624vWTbJkD5YUL8Bo1Ci7ZrpbT+fsj5csvkdGzp2gXzlZM2RLfCwLzIDAPAvMgFOU87NjhghUrvAEAPj4SZsyIR3x87p+31syDObMCKCQTKwKVSoU7d+4gICCgwIE5mvj4eKjVasTFxcHX19fi55ckCRqNxqlWvH6STXKQng589RXwxRdQpKcbnrt/f2DGDKBkSes8rxn4XhCYB4F5EJgHoajmQaMB6tQBbt0Sr2nRIglvv5338dbMQ3x8PPz8/KDRaJ76eW/y4PCi2kRoCmuuSO1sK17nxqo5OHBATDFw5oxhW7VqwMKFUDwxpYbc+F4QmAeBeRCYB6Eo5uGDD4Bbt8Ttl14C3n5b8dQB4dbKgznn4yJbVDQlJIjB382aGYomlUpMMXDyJGBnRRMRkTPZsUNcvAwA3t5i6Kmj1IRmTUewZMkSeHt753vMu+++W6iAiApt40Zg2DCxOK9eo0bip7RBA9nCIiIi8Xdt9i65GTOASpXki8dcZhVOCxYsgCqfy7QVCgULJ5LP3btiBjX9fP0A4OUFfPYZ8O67YlJLIiKS1YcfAtHR4nabNsA778gbj7nM+iQ5fPiwUw0OJwchScCyZcCYMUBcnGF7+/ZiFrUqVWQLjYiIDP7807BIg5eX6AhQOtigIZMLp6I0II2KkIsXgSFDgOxLAZUsKWZQ0y+pTUREsktMNO6i+/JLoGpV+eIpKJPrPGe+qo7sUEYGMG2aWBYle9HUty9w7pz4zqKJiMhujB8P6KeCbNFCLNLgiExucZo0adJTB4YT2cTBg2KKgezL/1SuLNp/O3SQLSwiIsrd3r3AnDnitocHEBnpeF10eiaHPWnSJHh5eVkzFqL8JSaKufmbNjUUTUqlGNt0+jSLJiIiO5ScDAwcaLj/xRfAM8/IF09h8TIjcgxbtgBhYcD164ZtDRqIyT8aN5YtLCIiyt+ECcClS+J206bi4mdH5qANZeQ07t8Xg7w7dzYUTR4eYgmVgwdZNBER2bH9+4FZs8Rtd3dg6VIxF7EjY+FE8tq5Ez5NmgA7dxpvlyRgxQqgVi3gp58M2198ETh1SkwE4upq21iJiMhkKSmii05/bdmnnwI1a8obkyWwcCL5SBIwfjxUUVHicgv9T9eVK2IOpv79gYcPxbbixcVcTTt3AkFBsoVMRESmmTwZiIoSt0NCgNGjZQ3HYswe49SwYcNc53RSKBTw8PBAUFAQ+vfvjzZt2lgkQCrCtm+H4vBhABDft2wR68pNmiT+VNF74w0xLxMnXyUicggHDwJffy1uu7mJv3uLyuINZr+Mjh07Yv78+ahbty6ee+45AMChQ4dw8uRJ9O/fH2fPnkW7du2wfv16dOvWzeIBy0GSJKvMY6U/r1POkSVJwCefACoVFFotJKUSeOUVKLIVTFJgIDBvnhjfpH9MEeXU74VsmAeBeRCYB8HR8pCWBgwYAOh0opFlwgQJwcGF/xVuzTyYc06zC6fY2FiMGTMGEyZMMNr++eef4/r169i+fTsmTZqEzz77zGELp4iICERERECr1QIANBqN1f6jEhMTATjfzOwuu3bB+3FrEwAodLqsViZJoUD6kCFI+fhjsWy2RiNXmDbjzO+F7JgHgXkQmAfB0fLw+eceOHvWAwBQr14mhgxJtMivcWvmIT4+3uRjFZKZFYFarcaRI0cQ9MQ4k0uXLqFRo0bQaDQ4f/48QkJCkJCQYM6p7U58fDzUajXi4uLg6+tr8fNLkgSNRgO1Wu0QPwwWI0nA888Dx45B8bg4zdrl6Qns2gU0aSJTcPJw2vfCE5gHgXkQmAfBkfJw9Kj49a7VKuDiIuHQIaB+fcuc25p5iI+Ph5+fHzQazVM/781ucfLw8MD+/ftzFE779++Hh4eoMHU6XdbtokChUFjtzao/t73/MFjU9u1Attam7BQpKUB8vFMul+KU74VcMA8C8yAwD4Ij5CE9XVxFp/97+JNPFGjQwLLPYa08mHM+swunESNGYOjQoThy5AhCQkIAiDFOS5Yswfjx4wEA27ZtQwNLZ4uKBkkSs6EpFLl3eKtUYn/79k5ZPBEROaqpUw2LOtSrB4wbJ2881mJ2Vx0A/Pjjj5g7dy6iHl9nWKNGDYwYMQK9e/cGAKSkpGRdZefI9F11pjTdFYQjNb9azLZtQMeOTz9u61anWkLFKd8LuWAeBOZBYB4ER8jDiRNiPuLMTPH378GDwLPPWvY5rN1VZ+rnfYEuDuzTpw/69OmT535PT8+CnJaKOkkCPvjg6cex1YmIyGFkZIir6DIzxf2xYy1fNNmTAs+qkJ6ejvv370On0xltDwwMLHRQVET9+quY9ftptFrg0CExFsqJWp2IiBzR9OnAsWPidu3a4u/eoszswunixYsYOHAg9u/fb7RdkiQoFIqsS/iJjGi1wKBBph+vVLLViYjIzp05I5ZSAcSv7WXLxJp0RZnZhVP//v3h4uKCTZs2oWzZsnbb30p2ZuJEIC7O9ON1OuDGDXGZRlH/KSQickCZmaKLLj1d3H//fbG0SlFnduF0/PhxHDlyBDWLwkp9ZBvr1onLLQDRejR7NvDCCwAME5p5e3vnLMIDAlg0ERHZqW++EaMqALF475Qp8sZjK2YXTsHBwYiNjbVGLFQUnTwJ9OtnuD9jBjB8uOG+JEGr0QBqNbvkiIgcxPnzoiMBEL+6ly4FHPxCepMpzX3AV199hQ8//BC7d+/GgwcPEB8fb/RFlOXBA6B7dyApSdzv27foLI9NROSktFrRRZeWJu6/9x7QtKm8MdmS2S1O7dq1AwC0bdvWaDsHh5ORzEzgtdeAq1fF/UaNgEWL2KpEROTgvvsO+PdfcTsoCPjsM3njsTWzC6e//vrLGnFQUfP++8Cff4rbpUsDv/0GcH4vIiKHdvEi8PHH4ra+i87LS96YbM3swqlVq1bWiIOKkmXLxJ8kAODqKgaHV6ggb0xERFQoOp2YVSY1VdwfPhxo0ULemORgUuF08uRJ1KlTB0qlEif1C9HkoV69ehYJjBzUv/8CQ4ca7kdEAM2ayRcPERFZREQEsHevuF2lCjBtmrzxyMWkwqlBgwa4e/cuAgIC0KBBAygUCuS2xB3HODm527eBnj0Nk3oMGwYMHixvTEREVGhXroilVPQiI4FixeSLR04mFU5Xr15FqVKlsm4T5ZCaKoqmO3fE/ZYtgW+/lTUkIiIqPH0XXXKyuB8WBrRpI29McjKpcKpUqVKut52FJEm5trBZ6rzWOLdNSRIQFgbFf/+Ju4GBwJo1gIuL2JfvQ4tIDgqJeRCYB4F5EJgHQe48LFgA7N4troiuVEnCl18+9Ve7VVgzD+ac06TCacOGDSafMDQ01ORj7VVERAQiIiKyuh01Go3V/qMSExMBwKGXrnFbuBBey5cDACRPTyT+8AO0bm6ARvPUxxaVHBQW8yAwDwLzIDAPgpx5iI5W4KOPfLPuz5qVBJ0u05Rf7xZnzTyYMw+lQjKhIlAqjefJfHKMU/YXUJTGOMXHx0OtViMuLg6+vr5Pf4CZJEmCRqOBWq123F8Ku3YBHTtC8fj/Xfr5Z+D1101+eJHIgQUwDwLzIDAPAvMgyJUHSQI6dgR27BDPOWiQhMWLbfb0ucRjvTzEx8fDz88PGo3mqZ/3JrU46XS6rNs7d+7ERx99hKlTp6Lp46lCDxw4gE8++QRT9euRFTEKhcJqb1b9uR3yl8KVK6JI0hfL48ZB0auX2adx6BxYEPMgMA8C8yAwD4IceYiMBHbsELcrVABmzlTIPoextfJgzvnMnsdp1KhRWLBgAZo3b561rUOHDvDy8sI777yDc+fOmXtKckSJiUC3bsDDh+J+587ON30sEVERdfMmMGaM4f6iRWJJUSrAWnWXL1+Gn59fju1qtRrXrl2zQEhk93Q6oH9/4PRpcb9GDeCnnwCVStawiIio8CQJeOcdQD/sp18/oFMneWOyJ2YXTiEhIRg9ejTu3buXte3evXv44IMP8Nxzz1k0OLJTX3whZgMHAF9f4Pff+acIEVERsXIlsGWLuF22LDBrlrzx2BuzC6elS5fizp07CAwMRFBQEIKCghAYGIhbt24hMjLSGjGSPfn9d2DiRHFboQB+/lm0OBERkcO7fRsYNcpwf+FCoHhx2cKxS2aPcQoKCsLJkyexY8cOnD9/HgBQq1YttGvXzukH7xV5Z84Affsa7k+dKsY2ERGRw5MksWJWXJy436cP0LWrrCHZJbMLJ0CMPm/fvj3at29v6XjIXj18KAaDP55DA6+/Dnz0kbwxERGRxfz0E7Bxo7hdurRhrXYyVqDCKSkpCXv27EF0dDTS9euSPfbuu+9aJDCyI5mZQK9ewOXL4n7DhsDSpZD9ulQiIrKIu3eB7B/f8+YBJUvKF489M7twOnbsGDp37ozk5GQkJSWhRIkSiI2NhZeXFwICAlg4FUVjxxom8yhVCvjtN8DLS9aQiIjIMiQJCA83zC7z2mti6VHKndmDw9977z107doVjx49gqenJ/79919cv34djRo1wtdff22NGElO338PzJwpbru4AGvXAoGB8sZEREQWs2YNsH69uO3vD8ydK2889s7swun48eMYM2YMlEolVCoV0tLSULFiRUyfPh3jx4+3Rowkl8OHgcGDDfdnzwZatpQvHiIisqiYGNHapBcRIToWKG9mF06urq5Za9cFBAQgOjoagJgA88aNG5aNjuRz9y7QvTuQlibuv/OOuNyCiIiKjOHDgdhYcbtnT+DVV+WNxxGYPcapYcOGOHToEJ555hm0atUKEydORGxsLL7//nvUqVPHGjGSraWlAS+/DNy6Je43awbMmcPB4ERERcj69cDq1eJ2iRJiQDh/zT+d2S1OU6dORdmyZQEAX3zxBYoXL46wsDDExMRg0aJFFg+QbEySxJ8g+/eL+xUqiFnC3dzkjYuIiCzmwQMgLMxwf/ZsMQUBPZ3ZLU6NGzfOuh0QEICtW7daNCCS2fz5wJIl4raHB/Drr/xpIiIqYkaOBO7fF7e7dgV695Y3HkdSoHmcACAmJgZRUVEAgJo1a8Lf399iQZFMdu8WP016S5YA2QplIiJyfBs3Aj/+KG77+QELFrCLzhxmd9UlJSVh4MCBKFeuHFq2bImWLVuibNmyGDRoEJKTk60RI9nC9etiVGBmprj/wQdivn0iIioyHj0Chgwx3J81CyhXTr54HJHZhdPo0aOxZ88ebNiwAXFxcYiLi8Pvv/+OPXv2YMyYMdaIkawtKUlcQae/tKJDB2DaNFlDIiIiyxs9GrhzR9zu1Ano10/eeByR2V1169atw9q1a9G6deusbZ07d4anpydee+01zJ8/35LxkbVJEjBwIHD8uLgfFAT8/DOgUskaFhERWdaWLcDy5eK2ry+wcCG76ArC7MIpOTkZpXMZLBwQEFBku+okSYIkSVY7rzXObbJp06B4fD2q5OMjllPx8xMFlQ3YRQ7sAPMgMA8C8yAwD4Il8qDR6OczFpXS119LqFDBZr/qLcKa7wdzzml24dS0aVNMmjQJK1euhIeHBwAgJSUFU6ZMQdOmTc09nV2KiIhAREQEtFotAECj0VjtPyoxMREAoJCh7HfZtg3FPvkk637SggXILF9e/ITZiNw5sBfMg8A8CMyDwDwIlsjDyJGeuHXLHQDQpk0GXnklyZa/6i3Cmu+H+Ph4k49VSGZWBKdPn0aHDh2QlpaG+vXrAwBOnDgBd3d3bN++HbVr1zYvWjsWHx8PtVqNuLg4+Pr6Wvz8kiRBo9FArVbb/pfC+fNAkyZQPH6zSJ9+CmQromxF1hzYEeZBYB4E5kFgHoTC5mHHDqBDB/E4b28Jp04BlSpZOkrrs+b7IT4+Hn5+ftBoNE/9vDe7xalOnTq4ePEifvzxR5w/fx4A8MYbb6BPnz7w9PQsWMR2TqFQWO2HVn9um/5SiIsTg8H1FfYrr0DxySeydXbLkgM7xDwIzIPAPAjMg1DQPCQkGC85On26ApUrWzY2W7LW+8Gc8xVoHicvLy8Mzv4/AeDKlSsYOnQotm/fXpBTkq1otWKmswsXxP169YBlyzhCkIioCProI+DxkrJo3dp4KgIqGLOnI8hLQkICdu3aZanTkbV88om4tAIASpYUg8G9vWUNiYiILO+vv8RiEADg5QVERgJKi33qOy+m0Jn8/DPw5ZfitkolVnesUkXemIiIyOKSkoBBgwz3v/wSqFpVvniKEhZOzuLoUeOfolmzgBdflC8eIiKymnHjgKtXxe0WLYDwcHnjKUpYODmD+/fFYPCUFHF/4EBg+HBZQyIiIuvYuxeYM0fc9vBgF52lmTw4vGHDhvmOOi+qk186vPR04JVXgBs3xP0mTYB58zgYnIioCEpOFn8b633xBfDMM/LFUxSZXDh1797dimGQ1YwaJf78AMRKjuvXA+7usoZERETWMWECcOmSuN20KTBypLzxFEUmF06TJk2yZhxkDQsXGi6pcHcHfv0VKFtW3piIiMgqDhwQw1cB8St/6VIuO2oN7PUsqvbtMx7HtGgR8Nxz8sVDRERWk5oquuj0a4FMmQLUrClvTEUVC6ei6MYN4OWXgcxMcX/UKOCtt2QNiYiIrGfyZLGSFgCEhABjxsgaTpHGwqmoSU4WV9Ddvy/ut20LzJgha0hERGQ9Bw8afs27uYkuOpcCrQtCpmDhVJRIkliU6OhRcb9qVWDVKv4EEREVUWlpwIABgE4n7k+cCNSpI29MRV2hCqfU1FRLxUGWMHMm8NNP4naxYsDvv4tlVYiIqEj67DPg7Flxu2FD4MMP5Y3HGZhdOOl0Onz22WcoX748vL29ceXKFQDAhAkTEBkZafEAyURbt4rVHPW+/55/dhARFWFHjxpW0XJxEeu1u7rKG5MzMLtw+vzzz7F8+XJMnz4dbm5uWdvr1KmDJUuWWDQ4MtGFC0CvXoa22kmTgB495I2JiIisJj1ddNFpteL+xx8D9evLG5OzMLtwWrlyJRYtWoQ+ffpAlW2CiPr16+O8fkg/2U58vBgMrtGI+927i05uIiIqsqZNA06eFLfr1QPGj5c3HmdiduF069YtBAUF5diu0+mQkZFhkaDIRDod0LcvcO6cuF+7NrByJRclIiIqwk6cAD7/XNxWqUQXXbYOILIysz9hg4ODsVe/hEc2a9euRcOGDS0SFJlo0iRg40Zxu3hxMRjcx0femIiIyGoyMkQXnX6avrFjgWeflTcmZ2P2deoTJ05Ev379cOvWLeh0Oqxfvx5RUVFYuXIlNm3aZI0YKTdr1hj+5FAqxbQD1arJGxMREVnV9OnAsWPidu3aYm06si2zW5y6deuGjRs3YufOnShWrBgmTpyIc+fOYePGjXjppZesESM96cQJoH9/w/2vvwaYeyKiIu3MGeDTT8VtpVJ00XHNdtsr0MyILVq0wI4dOywdC5kiNlYMAE9OFvfffFMsqUJEREVWZqZYiy49Xdx//32xtArZHkcRO5KMDOC114Br18T9kBBg4UJAoZA1LCIisq6ICHccOiR+19eoIRbxJXmY3eJUvHhxKHL5oFYoFPDw8EBQUBD69++PAQMGWCRAymbMGOCvv8TtMmWAX38FPD3ljYmIiKzq/Hlg2jQPAOLv5KVLAQ8PmYNyYgUaHP7FF1+gU6dOeO655wAABw8exNatWxEeHo6rV68iLCwMmZmZGDx4sMUDdlpLlwJz5ojbbm7A+vVA+fLyxkRERFal1QKDBgFpaaLBYtQo4IUX5I3J2ZldOO3btw+ff/45hg4darR94cKF2L59O9atW4d69eph9uzZRaZwkiQJkiRZ7bxPPfeBA0BYGPTtfFJEBNCkiVjU18GZnIMijnkQmAeBeRCYB+Dbb4EDB8Rv/6AgCZ99ViR+9ReINd8P5pxTIZkZgbe3N44fP55jEsxLly6hQYMGSExMxOXLl1GvXj0kJSWZc2q7ERERgYiICGi1Wly4cAHXr1+Hr6+vxZ9HkiQkJibC29s71+5PAFDcvg2fF1+E8t49AEDa4MFImT7d4rHIxZQcOAPmQWAeBOZBcPY8XL6sRPPmPkhNVUChkLBpUyJeeEErd1iyseb7IT4+HpUqVYJGo3nq573ZLU4lSpTAxo0b8d577xlt37hxI0qUKAEASEpKgo8DT8QYHh6O8PBwxMfHQ61WQ61WW61wAgC1Wp37myA1FejfH4rHRZPUujXc5s6FWxFaxfGpOXASzIPAPAjMg+DMedDpgPfeA1JTxesePDgNHTsWc7o8ZGfN94M55zO7cJowYQLCwsLw119/ZY1xOnToEDZv3owFCxYAAHbs2IFWrVqZe2q7pVAorPZm1Z87x/klCQgLAw4dEvcrVYJizZoiOa9+njlwMsyDwDwIzIPgrHmYNw/QL9JRpYqEiRNToVC4O10enmSt94NVC6fBgwcjODgYc+fOxfr16wEANWrUwJ49e/DC4xFrY8aMMfe09KTvvgNWrBC3vbzEcir+/vLGREREVnflilhKRW/JEqBYMfniIWMFmgCzWbNmaNasmaVjIb2dO8XUA3rLlwP168sWDhER2YZOB7z9tmGO46FDgTZtAI1G3rjIoECFk15qairS9dOYPmaNsUBO5fJlMcmlTifuf/wx8Oqr8sZEREQ2sWiRYbq+wECxNh3ZF7NnDk9OTsbw4cMREBCAYsWKoXjx4kZfVAgJCUC3bsCjR+J+166GhYmIiKhIu34d+OADw/3FiwEHvs6qyDK7cPrggw/w559/Yv78+XB3d8eSJUswZcoUlCtXDitXrrRGjM5BpwP69ROrOAJAzZrADz+IlRyJiKhIkyRg8GAgMVHcf/ttoH17eWOi3JndVbdx40asXLkSrVu3xoABA9CiRQsEBQWhUqVK+PHHH9GnTx9rxFk07dwJnxEjxIzg+/eLJVQAQK0Wg8HZ7UlE5BQiI4EdO8TtChWAr7+WNx7Km9mF08OHD1G1alUAYjzTw4cPAQDNmzdHWFiYZaMryiQJGD8eqqgoSGFhYmwTIFqYfvkFqF5d3viIiMgmbt40vh5o0SLx9zPZJ7P7gapWrYqrV68CAGrWrInVq1cDEC1Rfn5+Fg2uSNu+HYrDhwEACn3RBADTpgEdO8oUFBER2ZIkAe+8A8THi/v9+gGdOskbE+XP7MJpwIABOHHiBABg7NixiIiIgIeHB9577z18kH1UG+VNkoAJEyCpVMbbe/UyHhlIRERF2sqVwJYt4nbZssCsWfLGQ09ndldd9qVW2rVrh/Pnz+PIkSMICgpCvXr1LBpckbV9O3DoEHLMU9qrF+Dks8ISETmL27eBUaMM9xcsAHhxuv0zq8UpIyMDbdu2xcWLF7O2VapUCT179mTRZKrHrU05CiSVCvjiC+dd9pqIyIlIkpjcMi5O3O/dGwgNlTUkMpFZhZOrqytOnjxprVicw+PWphwFklYrtm/fLk9cRERkMz/9BGzcKG4HBACzZ8sbD5nO7DFOffv2RWRkpDViKfr0rU1Pjm3SU6nEfrY6EREVWXfvAu++a7g/fz5QsqR88ZB5zB7jlJmZiaVLl2Lnzp1o1KgRij2x8uA333xjseCKHH1rU16ytzp16GC7uIiIyCYkCQgPBx7P5IPXXgN69pQ3JjKP2YXT6dOn8eyzzwIALly4YLRPwYHNecve2qTV5n2cvtWpfXsOFCciKmLWrAHWrxe3/f2BuXPljYfMZ3bh9Jd+9UEyz9Nam/TY6kREVCTFxIjWJr25c4FSpeSLhwqmwAuhXbp0Cdu2bUNKSgoAQOK4nLzpW5tMXXdOqeRYJyKiImbECCA2Vtzu0UN005HjMbtwevDgAdq2bYvq1aujc+fOuHPnDgBg0KBBGJN9zngySE8HoqPFQr6m0OmAGzfE44iIyOGtXw+sWiVulygBzJvH0RiOqkATYLq6uiI6Ohq1atXK2v76669j9OjRmDlzpkUDLBLc3UX3W0yM0WZJkpCYmAhvb++c48MCAsTjiIjIoT14AGRfyvW774AyZeSLhwrH7MJp+/bt2LZtGypUqGC0/ZlnnsH169ctFliRU7Gi+MpOkqDVaMRqjvzTg4ioSBo5Erh/X9zu2hXo00feeKhwzO6qS0pKgpeXV47tDx8+hDtbSIiIiLJs3Aj8+KO47ecnllXh38mOzezCqUWLFli5cmXWfYVCAZ1Oh+nTp6NNmzYWDY6IiMhRPXoEDBliuD9rFlCunHzxkGWY3VU3ffp0tG3bFocPH0Z6ejo+/PBDnDlzBg8fPsQ///xjjRiJiIgczujRwOPrp9CpE9Cvn7zxkGWY3eJUp04dXLhwAc2bN0e3bt2QlJSEnj174tixY6hWrZo1YiQiInIoW7YAy5eL2z4+wMKF7KIrKsxucQIAtVqNjz/+2NKxEBEROTyNBnjnHcP9mTNzXhtEjsvsFqegoCBMnjwZFy9etEY8REREDu2DD4CbN8Xtdu2At9+WNx6yLLMLp/DwcPzxxx+oUaMGQkJC8N133+Hu3bvWiI2IiMih7NgBLF4sbnt7i9vsoitazC6c3nvvPRw6dAjnz59H586dERERgYoVK6J9+/ZGV9sRERE5k4QE49al6dOBypVlC4espMBr1VWvXh1TpkzBhQsXsHfvXsTExGDAgAGWjI2IiMhhfPSRWF0LAFq3Np6KgIqOAg0O1zt48CB++uknrFq1CvHx8Xj11VctFRcREZHD+OsvYP58cdvLC4iMNH1dd3IsZhdOFy5cwI8//oiff/4ZV69exYsvvoivvvoKPXv2hLe3tzViJCIisltJScZddNOmAVWryhcPWZfZhVPNmjUREhKC8PBw9OrVC6VLl7ZGXHZFkiRIkmS181rj3I6CORCYB4F5EJgHwVHyMG4ccOWKGAHevLmE8HDAkiE7Sh6szZp5MOecZhdOUVFReOaZZ3I84datWxEZGYm1a9eae0q7ExERgYiICGi1WgCARqOx2n9UYmIiALF0jTNiDgTmQWAeBOZBcIQ87N+vwty5orfFw0PCt98mICFBZ9HncIQ82II18xAfH2/ysWYXTtmLpqtXr2Lp0qVYvnw5YmJi0K5dO3NPZ5fCw8MRHh6O+Ph4qNVqqNVq+Pr6Wvx59MWYWq122h8G5kBgHgTmQWAeBHvPQ3IyMHIkIEkits8/B5591sfiz2PvebAVa+bBnPOZXTilpaVh7dq1iIyMxL59+6DVavH1119j0KBBViku7IFCobDam1V/bmf+YWAOBOZBYB4E5kGw5zxMnAhcuiRuN20KjBqlsNqcTfacB1uyVh7MOZ/JY/6PHDmCYcOGoUyZMvj222/RvXt33LhxA0qlEh06dCiyRRMREdGTDhwAZs0St93dgaVLAZVK3pjINkxucXr++ecxYsQI/Pvvv6hRo4Y1YyIiIrJbqanAwIGGAeBTpgA1a8obE9mOyYVT27ZtERkZifv37+PNN99Ehw4dnL7JkIiInM/kycD58+J2SAgwZoys4ZCNmdxVt23bNpw5cwY1atRAWFgYypYti5EjRwJw7lH+RETkPA4dAmbMELddXUUXnUuhppImR2PWvKYVK1bExIkTcfXqVXz//feIiYmBi4sLunXrhvHjx+Po0aPWipOIiEhWaWnAgAGA7vFsAxMnAnXqyBsT2V6BJ4R/6aWX8NNPP+H27dsYMWIEtmzZgpCQEEvGRkREZDc++ww4c0bcbthQrE1HzqfQK+kUL14cI0aMwLFjx3Do0CFLxERERGRXjh4FvvxS3HZxAZYtE1115HwsugThs88+a8nTERERyS49XXTRPV5MAh9/DNSvL29MJB+u3UxERJSPadOAkyfF7Xr1gPHj5Y2H5MXCiYiIKA8nT4qlVAAxweWyZYCbm7wxkbxYOBEREeUiI0N00WVmivsffQRwRAoVqHDKzMzEzp07sXDhQiQkJAAAbt++nbVqMRERkaObMUMMCgeA4GAx/QCR2dN2Xb9+HR07dkR0dDTS0tLw0ksvwcfHB1999RXS0tKwYMECa8RJRERkM2fOiKVUAECpFF107u7yxkT2wewWp5EjR6Jx48Z49OgRPD09s7b36NEDu3btsmhwREREtpaZKbro0tPF/fffB557Tt6YyH6Y3eK0d+9e7N+/H25PjI6rXLkybt26ZbHAiIiI5PDNN2JpFQCoUcPQ8kQEFKDFSafTQaufzCKbmzdvwsfHxyJBERERyeH8ecNYJoVCrEXn4SFvTGRfzC6c2rdvj2+//TbrvkKhQGJiIiZNmoTOnTtbMjYiIiKb0WqBgQPFmnQAMGoU8MILsoZEdsjsrrqZM2eiQ4cOCA4ORmpqKnr37o2LFy/C398fP//8szViJCIisrrZs4EDB8TtoCDD/E1E2ZldOFWoUAEnTpzAL7/8gpMnTyIxMRGDBg1Cnz59jAaLExEROYpLl8RSKnqRkYCXl3zxkP0yu3ACABcXF/Tt29fSsRAREdmcTie66FJSxP3hw4GWLeWNieyXSYXThg0bTD5haGhogYMhIiKytYgIYO9ecbtKFbE2HVFeTCqcunfvbtLJFApFrlfcERER2aMrV4CxYw33lywBvL3li4fsn0mFk06ns3YcRERENqXTAW+/DSQni/tDhwIvvihvTGT/uMgvERE5pUWLgL/+ErcDA4Hp0+WNhxxDgQaHJyUlYc+ePYiOjka6fk76x959912LBEZERGQt168DH3xguL94McA5nMkUZhdOx44dQ+fOnZGcnIykpCSUKFECsbGx8PLyQkBAAAsnIiKya5IEDB4MJCaK+4MGAe3byxsTOQ6zC6f33nsPXbt2xYIFC6BWq/Hvv//C1dUVffv2xciRI60RIxERkcUsXQrs2CFuly8PzJwpbzyUU7QmGrHJsUbbJElCYmIivJO9oVAojPb5e/kjUB1ok9jMLpyOHz+OhQsXQqlUQqVSIS0tDVWrVsX06dPRr18/9OzZ0xpxEhERFdrNm8Do0Yb7ixYBarV88VBO0Zpo1JhbA6mZqSY/xsPFA1HDo2xSPJk9ONzV1RVKpXhYQEAAoqOjAQBqtRo3btywbHREREQWIknAO+8A8fHifr9+AJdYtT+xybFmFU0AkJqZmqOFylrMbnFq2LAhDh06hGeeeQatWrXCxIkTERsbi++//x516tSxRoxERESFtnIlsGWLuF22LDBrlrzxkGMyu8Vp6tSpKFu2LADgiy++QPHixREWFoaYmBgsXLjQ4gESEREV1u3bwKhRhvsLFgDFi8sWDjkws1ucGjdunHU7ICAAW7dutWhAREREliRJQFgYEBcn7vfuDXB1MCooswunq1evIjMzE88884zR9osXL8LV1RWVK1e2VGxERESF9vPPgH7J1YAAYPZseeOh3GlSNTgTcwZ/XPhD7lDyZXbh1L9/fwwcODBH4fTff/9hyZIl2L17t6ViIyIiKpR794ARIwz3580DSpaULx4C0rXpiIqNwqn7p3Dq3inx/f4pRGui5Q7NJAWaALNZs2Y5tjdp0gTDhw+3SFBERESFJUnAsGHAw4fi/quvAi+/LG9MzkQn6XA97jpO3z+dVRyduncKUQ+ikKnLlDu8AjO7cFIoFEhISMixXaPRQKvVWiQoIiKiwlqzBli/Xtz29wfmzpU3nqIsNjlWFEjZWpBO3z+NxPREkx7v4+aDOgF1UDegLvw8/DB9v/0uHGh24dSyZUtMmzYNP//8M1QqFQBAq9Vi2rRpaN68ucUDJCIiMldMDBAebrg/d64Y30SFk5yRjLMxZ3MUSXcT75r0eBelC2r610TdgLriq7T4HqgOzJoN/Oido0WrcPrqq6/QsmVL1KhRAy1atAAA7N27F/Hx8fjzzz8tHiAREZG5RowAYh/Ph9ijB/Daa/LG42i0Oi0uPbyUo5vt0sNLkCCZdI7KfpWzWpH0RVL1ktXhpnKzcvTWZXbhFBwcjJMnT2Lu3Lk4ceIEPD098dZbb2H48OEoUaKENWIkIiIy2a+/AqtWidslSogB4U8sbUaPSZKEO4l3crQgnY05a/Ls3SU9S6Ju6bqoU6pOVgtS7YDa8HX3tXL08jC7cAKAcuXKYerUqZaOhYiIqFAePBBzNul99x1Qpox88diT+LR4nLl/JsfVbA9THpr0eA8XD9QuVTtHkVTGu0yORXcLw9/LHx4uHmavVefv5W+xGPJjcuEUGxuLpKQkVKpUKWvbmTNn8PXXXyMpKQndu3dH7969rRIkERGRKUaNElMQAECXLkCfPrKGIwv95f5PdrNd11w36fFKhRJBJYJQN6CuoautdF1UK14NKqXKytEDgepARA2PyrH2nCRJSExMhLe3d45Czd/L3yYL/AJmFE4jRoxAuXLlMHPmTADA/fv30aJFC5QrVw7VqlVD//79odVq8eabb1otWCIiorxs3Aj88IO4rVaLZVWKchedJEm4rrmOU/dOGRVJUbFRyNBlmHSOst5ls1qO9EVScKlgeLp6Wjn6/AWqA3MUQpIkQaPRQK1WW7SFy1wmF07//vsvli9fnnV/5cqVKFGiBI4fPw4XFxd8/fXXiIiIYOFEREQ29+gRMGSI4f6sWUD58vLFY2kPUx7i2KNjOBNzJqub7fT900hIzzk9UG6yX+5ft7ShSCrpxdlAzWVy4XT37l2j5VT+/PNP9OzZEy4u4hShoaGYNm2axQMkIiJ6mtGjgTt3xO2OHYH+/WUNp8BSMlIMl/tn62a7k3jHpMc/ebl/nQAxFqmSupKsrTRFicmFk6+vL+Li4rLGOB08eBCDBg3K2q9QKJCWlmb5CImIiPKxZQug7xDx8QEWLbL/LjqtTovLjy7n6Ga79PASdJLOpHNUUlfK6mbTF0k1/Gs4/OX+9s7kwqlJkyaYPXs2Fi9ejPXr1yMhIQEvvvhi1v4LFy6gYsWKVgmSiIgoNxoN8M47hvszZwL29FEkSRLuJt7N6lrTtyCdjTmLlMwUk85RwrMEapWohQblGqBe6XqoE1AHdQLqFNnL/e2dyYXTZ599hrZt2+KHH35AZmYmxo8fj+LFi2ft/+WXX9CqVSurBCk3SZIgSaZN+FWQ81rj3I6CORCYB4F5EJgHwZQ8vP8+cPOmaF5q107CoEFijTo5JKQlZBVHp2NO4/Q9cftBygOTHu/h4oHgUsFGA7XrBNRBmWJlEB8fn2NQtLO9P6z5c2HOOU0unOrVq4dz587hn3/+QZkyZfD8888b7e/VqxeCg4NNj9KORUREICIiImvtPY1GY7X/qMREsY6Ps/Y9MwcC8yAwDwLzIDwtD7t3u2DJEm8AQLFiEmbOTEB8vGndXIWRoc3AxUcXcfbBWZx7cA5nY8/i7IOziI6PNunxCihQ1a8qgksGI9g/OOt7FXWVnJf764D4+Hi+H2Ddn4v4+HiTj1VIhagIbt68iXLlykGpVBb0FHZNX+HHxcXB19fyTaL2cmmlnJgDgXkQmAeBeRDyy0NCAlCvHnD9utg+d66EYcMs//zRmuis8Uf6ySPPx543+XL/Mt5ljOdDCqiLWqVqwcvVy6w4+H6wbh7i4+Ph5+cHjUbz1M/7As0crhccHIzjx4+jatWqhTmN3VMoFFZ7s+rP7cw/DMyBwDwIzIPgbHmI1kTnPeFhSs4JD2d94Y/r18U8P61bA2FhikINCH+Y8tDoMn/99/g001oivN28c6zLViegjsVms3a290NerJUHc85XqMLJ2fpXiYjI8qI10agxt4ZZS2zAzwNQR8ErIxBLlgCmdnykZqbibMzZHFez3U64bdLj9Zf7P1kkBaoDoVQUzd4XMlaowomIiKiwYpNjzSuaAMA1FfCKxbSxgahWLedurU6LK4+u5Lia7eLDi2Zd7p990si6AXV5uT8VrnAaP348SpQoYalYiIiITNagARAeLuFu4r0c3Wxn7p8x63L/J9dl4+X+lJdCFU7jxo2zVBxERERm0XYagjLfXMsxNiovuV3uX7d0XZT1Luv0Y4fIdGYXTqNHj851u0KhgIeHB4KCgtCtWze2RBERkVWdeng41+0KKBBUIki0HJWqk9XNFlQiKOfl/kRmMrtwOnbsGI4ePQqtVosaNWoAELOGq1Qq1KxZE/PmzcOYMWOwb9++IjOvExER2Sf95f7Z12ULLhVs1uX+ROYwu3DStyYtW7Ysa64DjUaDt99+G82bN8fgwYPRu3dvvPfee9i2bZvFAyYioqJBkiScvHcSi48sLtDjd761E22rtLVwVET5M7twmjFjBnbs2GE0QZRarcbkyZPRvn17jBw5EhMnTkT79u0tGigRETm+tMw07L62GxsvbMSGqA24EX+jwOcq7lH86QcRWZjZhZNGo8H9+/dzdMPFxMRkTVnu5+eH9PR0y0RIREQOLTY5FpsvbsaGqA3YdnkbEtMT5Q6JqMAK1FU3cOBAzJw5EyEhIQCAQ4cO4f3330f37t0BAAcPHkT16tUtGigRETkGSZIQ9SAKG6M2YsOFDdh/Y3+ucye5qdzwYpUXUb90fXz1z1cyREpkPrMLp4ULF+K9995Dr169kJmZKU7i4oJ+/fph1qxZAICaNWtiyZIllo2UiIjsVqYuE/9E/4MNURuw8cJGXHx4MdfjSnqWRJfqXRBaIxQvVX0JPu4+OHrnKAsnchhmF07e3t5YvHgxZs2ahStXrgAAqlatCm9v76xjGjRoYLEAiYjIPmlSNdh2eRs2RG3A5oub8Sj1Ua7H1fKvha7VuyK0RiiaVGiSNSWAJAGXLgF/7LZh0ESFVOAJML29vbPmaspeNBERUdF19dFVbLywERsvbMTua7uRqcvMcYxKoUKLSi0QWj0UXWt0RVCJIABAcjKwby9w4IDhKyYGgNofGO4hllExkVLrYbEFdInMYXbhpNPp8Pnnn2PmzJlITBQD/Hx8fDBmzBh8/PHHUJq60iIREdk9naTDwVsHs8Yrnb5/OtfjfN190SmoE0JrhKJTUCf4eRTH9evAgW3Ad/tFkXTiBJCZs84CNIHA3CjAy7QZwBUKoH0LfwSqAwvxyogKxuzC6eOPP0ZkZCS+/PJLNGvWDACwb98+TJ48Gampqfjiiy8sHiQREdlOUnoSdl7ZiY0XNmLThU24l3Qv1+Oq+FVBaI1QdK3eFSGlW+DUcTcc2Aa8PRnYvx+4ezf/5yleHGjaVHwlJQXiyy9NK4QkAH26mPeaiCzF7MJpxYoVWLJkCUJDQ7O21atXD+XLl8ewYcNYOBEROaDbCbex6cImbIjagF1XdyE1M2e3mQIKPF/heYRWD8VzfqF4cC4YB7Yp8PFk4OhRICMj7/MrFEBwMPDCC4ZiqXp1QN9JkZoKLFwIxMWJsU/5ncfPD3jllcK8WqKCM7twevjwIWrWrJlje82aNfHw4UOLBEVERNYlSRJO3DuRdRXc4du5r/vm5eqFdlXao65bV7hf/x9Oby+NeZOB8TfzP7+vL9CkiSiQXngBeP55QK3O+3gPD2DFCqBbN1Ec5VY86dfhXbFCHE8kB7MLp/r162Pu3LmYPXu20fa5c+eifv36FguMiIgsSz9rt75YymvW7jJe5VDPoyuK3eqKO7texPZDntjwlHHbNWoYtyYFBxtak0zVtSvw229A//7Ao0eAUilBp1NkfffzE0VT167mnZfIkswunKZPn47//e9/2LlzJ5o2bQoAOHDgAG7cuIHNmzdbPEAiIiq42ORY/HHhD2y8sDHfWbsrqBrC735XPNgfijtHnsVdKPI8Z7FiogUpe2tSyZKWiTc0FLh9G1i7Fvj1V+D+/QwEBLigRw/RPceWJpKb2YVTq1atcOHCBUREROD8+fMAgJ49e2LYsGEoV66cxQMkIiLT6Wft3hC1ARuiNuDAzQO5ztqtktzg+/BFJB4JRcbpLrgZXxF59b5Vq2Yokpo2BerUAVwKPJnN03l4AH37An36ABpNEtRqdVY3HZHcCvTWL1euXI5B4Ddv3sQ777yDRYsWWSQwIiIyTfZZuzdc2IBLDy/lepwy1R+68/8DokKhvfwSHqX75DjG0xMICTF0uTVtCgQEWPsVEDkOi/3N8ODBA0RGRrJwIiKyAU2qBlsvbcXGCxvznbUbMbWAqFAgqit0N5sAkspod6VKxq1J9esDrq42eAFEDsqKja1ERGRJ+lm7N0RtwJ7re3KdtRs6FXC9hSiWLnQFHgZl7XJzAxo3Nm5N4ggLIvOwcCIislM6SYdDtw5hY9RG/HZuA84+yH3WbqSqgYudRKF0sROQWhwAUL480PQVQ2tSw4aAu7sNXwBREcTCiYjIjiSlJ2H75R1Yvn8ddt/egXhd7rN241GVx11wocD1FnBRuuLZZ4GmQwxdbxUr2jZ2ImdgcuHUs2fPfPfHxcUVNhYiIqd08e5tzN+1EZsvb8RF7S7olLlMmiQpgJtNgKiuQFQoApTBeKGpAi+EiUKpUSMxsJuIrMvkwkmd35Svj/e/9dZbhQ6IiKgokyTgyhUJv+w+gd/Pb8CZjI1ILp5t1u7sk0amewGX20NxMRR13P6Hlo0C0HSoaE2qXBm8RJ9IBiYXTsuWLbNmHERERVJKCnD4MLB3fxo2nv4LJ1I2IiVwI6C+AXjn8oD4cnC/3gW1Xf6H0LovodVQT4SEiEkniUh+HONERGQhkgTcuAHs3w8cOAD8fSQGJ1M2Qxe0Eai2DQjKfdZuj7iGqK0KRdfqXfHGG88iKAiIj9dArfZgqxKRnWHhRERUQGlpwNGjokjavx/Yf0DCnfTzQI2NQI0NQNsDgDLnrN0KnRuqKV9E56qhGPJiFwSXNx7FLeW2wi0R2QUWTkREJrp929CadOAAcOQIkJ6ZCQTuE4XSyxuBkrnP2u2t9Ee7wC7o07grOgS9BB/3nLN2E5H9Y+FERJSLjAzg+HFDa9KBA0B09OOd7hogaCvQZQPwzBbAM/dZu6sXr4UewaEIrRGK58s/D5VSletxROQ4WDgREQG4d8/QknTgAHDoEJCafVaA4leA5zeKbrhKewBVzlm7VQoVWlZqia7Vu6Jrja4IKhGU4xgicmwsnIjI6WRmAqdOGXe7XbnyxEEKHVDhoOiCq7EBCDiT67nU7mp0eqYTQquHomNQRxT3LG79F0BEsmHhRERFXmws8O+/hm63Q4eApKRcDnRNAqrtAKpvhLLWJug87+d6vip+VRBaQ3TBtQhsAVcVV8UlchYsnIhIdqmpwJo1wG+/AffuFUPp0kD37sCrrwIeHuadS6sFzp41bk26cCGfB/jcgkvtTfBpvAHxJXdBq0gDAGS/Fk4BBZpUaILQGmLKgOBSwVBwngAip8TCiYhktWED0L8/8OgRoFQCOp0rlEoJ69cDI0cCK1YAXbvm/fi4OOPWpP/+AxIS8ntGCaUbHEepZhuhKbMBN7RHkAngyeHdXq5eaF+tPUKrh+J/1f+HgGIBhX2pRFQEsHAiItls2CBalvR0OoXR97g4oFs30RIVGgrodEBUlPGVbmfP5v8crq5Aw8ZpKN/8LyRX2IhT6RtxO+kG7gGA1vjYcj7lEFo9FF1rdMWLVV6Eh4uZzV1EVOSxcCIiWaSmipYmQMy4nRtJEuuxvf460LIlcPCgKKbyU7asWPS2XtMYZFTejNMZG7Dr2nYcTE/M2awE4Nmyz6Jr9a4IrRGKhmUasguOiPLFwomIZDH/52g88ogFyuR/nAQgFcD2//wBTaDRPhcXoEEDUSg1aSKhdO3zOJSwAZsubMSUG/shnclZkbmp3NC2Slt0rd4VXap3QUV1xRzHEBHlhYUTEdlctCYa71+rAQxJffrBehkeKPFTFJrXDcQLL4hiqX7DDBx78A82RG3ApAsbcem33Gft9vfyR5fqXRBaPRQvVXsJ3m65ra5LRPR0LJyIyOZik2OhU5pRNAGAayq274tFtRK+2HppKxZe2IjN8zYjLjUu18ODSwVndcFx1m4ishQWTkRkU3fuAAsXAijAUKKwP4bi2N1jyNTlPWu3fsqAaiWqFT5YIqInsHAiIpv47z9g9mwxX1OGP4Ah5p/j0O1DRvc5azcR2RoLJyKymrQ0USjNmSOuiLOEqsWrZk0ZwFm7icjWWDgRkcXpu+MWLBCL52ZXsiQQOgBYVoDzrnl1DV6u9TKnDCAi2SjlDoCIio7//gP69gUqVQKmTDEumurXByIjgRs3gOHDC3b+qsWrsmgiIlmxxYmICiU9XXTHzZ6dsztOpQJ69ADefRdo3hxIzUzB6jOrMWP/DHmCJSIqJBZORFQgd++KrriFC8Xt7EqWBAYPBsLCgMBA4MqjK/ho5wJEHovEw5SH8gRMRGQBLJyIyCwHD4rWpdWrgYwM433164vWpTfeANzctdh6aSvCfpqHLRe3QEIe66oQETkQFk5E9FT67rg5c8Q4puyUSkN3XIsWwIOUWMw5uhQLDi/A1birRse6qdzwWu3X0LZyWwzYMMCGr4CIyDJYOBFRnu7eNVwd92R3XIkSwDvviO64ihUl/HfrP/T7bR5Wn1mNNG2a0bGV1JUwtPFQDGo4CKWKlcLRO0dt+CqIiCyHhRMR5ZBfd1y9eqJ1qXdvQHJJxs+nfsa8rfNyLYY6BnVEeEg4OgV1MlryxN/LHx4uHkjNNH3ZFQ8XD/h7+Rf4NRERWQILJyICILrj1q4VBdPTuuMuPryAj/9egGXHl+VYK66EZwkMbDAQQxsPzXPZk0B1IKKGRyE2OdZouyRJSExMhLe3d45pB/y9/BGoDiz06yQiKgwWTkRO7mndcYMHA8OGAeUqZOKPC3+gww8R2HFlR47zhJQLwbCQYXi99uvwdPV86vMGqgNzFEKSJEGj0UCtVnO+JiKySyyciJzUoUOidWnVqvy74+K19xB5LBIL1i3AjfgbRsd5uHigV51eGNZ4GELKh9gweiIiebBwInIipnbHNW8uYf/NfzBo8zysPbsWGTrjyqpq8aoIaxyGAQ0GoKRXSRu+AiIiebFwInIC9+6J7rj58/PvjitRJhE/nvwRIxbNw8l7J42OU0CBLtW7YFjIMLSv1h5KBVdsIiLnw8KJqAgztTvuWuI5fH14Plb8tALxafFGx/l7+ePthm9jSOMhqOxX2XbBExHZIRZOREVMejqwbp0omP7913ifvjtuxAigabMMbLywAV3WzMOfV//McZ6mFZpiWMgwvBL8CjxcPGwUPZF90mq1yHjyrw8bkSQJ6enpSE1NdeqLJgqTB1dXV6hUqqcfaAIWTkRFhCndcWFhgFuJO1h0ZBF6z16E2wm3jY7zdPFEn7p9EBYShmfLPmvD6InskyRJuHv3LuLi4mSNQ6fT4cGDB7LGYA8Kkwc/Pz+UKVOm0MUnCyciB3fokFgKZdUq0dqUXb16onXpjTckHI75Gx8cjMCv539Fpi7T6LjqJatjWONh6NegH/w8/GwXPJGd0xdNAQEB8PLykqXFR5IkaLVaqFQqp29xKkgeJElCcnIy7t+/DwAoW7ZsoeJg4UTkgJ7WHde9uxi/1OD5ePxw8ns8t2IezsacNT5OoURojVCEh4TjxSovcrA30RO0Wm1W0VSypHxXj7JwEgqTB09PMbfc/fv3ERAQUKhuOxZORA7k3j1g0SLRHXfnjvG+7N1xCZ6nMe/QPHSZ9T0S0xONjitdrDQGPzsY7zR6BxXVFW0YPZFj0Y9p8vLykjkSsgT9/2NGRgYLJ6Ki7vBhw9VxT3bH1a0rWpdeeT0d267/ijf/jMDe6L05ztEisAWGhQxDz1o94aZys1HkRI7PmVt5ihJL/T+ycCKyU/ruuDlzgAMHjPfpu+NGjACqNbyJxUcXodaixbibaDwqvJhrMbxZ702EhYShXul6tgueiKiI4qAGIjtz7x7w2WdA5cpijqXsRVPx4sCHHwKXL0sIm7ETs+/1RJXvKuOzvz8zKppq+dfCnE5zcHvMbczvMp9FE5GMUlOB778HXn4ZaN1afP/+e7HdGrp27YqOHTvmum/v3r1QKBQ4efIkFAoFjh8//tTzDRkyBCqVCmvWrLFwpI6JLU5EduLYMRWWLwd++SXv7rjOPeOw5sIKdNw0H1EPooyOUSlU6FGrB8JDwtGqUit2LxDZgQ0bgP79gUePREuxTie+r18PjBwJrFgBdO1q2eccNGgQXn75Zdy8eRMVKlQw2rds2TI0btwYvr6+Jp0rOTkZv/zyCz788EMsXboUr776qmWDdUAsnIhklJFhuDruwAEfo31KJdCtmyiY1DWOY/7heRi54EckZyQbHVfWuyyGNBqCwY0Go5xPOVuGT0T52LBBdKnr6XTG3+PixM/4b78BoaGWe94uXbqgVKlSWL58OT755JOs7YmJiVizZg1mzJhh8rnWrFmD4OBgjB07FuXKlcONGzdQsaJzX1TCrjoiGWTvjnvjDeDAAUPrkL477tzFNLw8+Ud8fKUZnl3UEIuPLjYqmtpUboM1r67B9VHXMan1JBZNRHYkNVW0NAGAJOV+jH57//6W7bZzcXHBW2+9heXLl0PK9uRr1qyBVqvFG2+8YfK5IiMj0bdvX6jVanTq1AnLly+3XKAOioUTkQ0dOQL06wcEBgITJwK3s03cXauWFgsXSth/5jqUL41D83UV0ffXvth/Y3/WMT5uPhgeMhxnhp3Bn/3+xCvBr8BV5SrDKyGi/KxZI7rn8iqa9CRJHLd2rWWff+DAgbh8+TL27NmTtW3ZsmV4+eWXoVarTTrHxYsX8e+//+L1118HAPTt2xfLli0zKsacEbvqiKwsI0OMZ5g9G9i/33ifvjsufLgWj0r8hpXnViBs8R/QSTqj4+oG1MWwkGHoW68vvN28bRg9ET2pceOcyxo9ydxVQQYPBsaOze8IFcqUEVOTmKJmzZp44YUXsHTpUrRu3RqXLl3C3r178emnn5oc09KlS9GhQwf4+/sDADp37oxBgwbhzz//RNu2bU0+T1HDwonISu7fN0xWedt4STgULw68/TbQe9BD7HqwDEMOz8flR5eNjnFVuuLl4JcRHhKOZhWbcbA3kZ24exe4dcuy50xNze+c+p9981p6Bg0ahBEjRiAiIgLLli1DtWrV0KpVK5Meq9VqsWLFCty9excuLi5G25cuXcrCiYgs58gRMffSzz/nvDquTh0x2LtW28NYemoemq79GamZxoMbKvhWwNBGQzHo2UEo413GhpETkSnKmPBj+eCBeeOWPDyAvFd1kUx+3uxee+01jBw5Ej/99BNWrlyJsLAwk/8A27x5MxISEnDs2DGjWbZPnz6NAQMGIC4uDn5+fuYFVESwcCKygKd1x4WGAkPCU3DXfzXmH56Hg98fzHGONoFtMKLJCHSt0RUuSv5oEtkrU7rLvv8eeOst08+5eDHQt2/u+yQJWWu0mcPb2xuvv/46xo0bh/j4ePTXj1bPJioqKse22rVrIzIyEv/73/9Qv359o33BwcF477338OOPPyI8PNyseIoK/nYmKoSYGNEdN29ezu44Pz8xbqHLm1ew6e4C9DkaiYcpD42OUburMaDBAAxpNARlXMtArVazS46oCHj1VTFPU1xc/gPEFQrxu+KVV6wTx6BBgxAZGYnOnTujXLmcV9726tUrx7Zr167hjz/+wE8//ZRjn1KpRI8ePRAZGcnCqSjr0aMHdu/ejbZt22KtpS9dIKd09KhoXfrlFyAtzXhfnTpisHepplsReSoCX6/fCumJsQkNyjRAeEg43qjzBoq5FYMkSdBoNDZ8BURkTR4eYnLLbt1EcZRb8aT/G2nFCnG8NTRt2jTXq+AqV66c79Vx+gWOczNv3jyLxOaonKJwGjlyJAYOHIgVK1bIHQo5sIwM4NdfRcH0zz/G+/Tdcf3CYnHeKxJfHVmAa79eMzrGTeWG12q/hvCQcDxf/nm2LBEVcV27isktc5s5XKcTLU3WmDmcrMspCqfWrVtj9+7dcodBDkrfHTd/fs6rXvz8gEFvS3jh1f/w26156HVwNdK0xk1QldSVENY4DAMbDkSpYqVsFzgRyS40VHTjr10r/vB6+BAoUQLo0UN0z1mrpYmsR/bC6e+//8aMGTNw5MgR3LlzB7/++iu6Z5+jHkBERARmzJiBu3fvon79+pgzZw6ee+45eQImp3H0qOHquCe742rXBoaOSIay/s9YciICM7ccy/H4jkEdER4Sjk5BnaBSmjeok4iKDg8PMfA7r8Hf5FhkL5ySkpJQv359DBw4ED179syxf9WqVRg9ejQWLFiA559/Ht9++y06dOiAqKgoBAQEAAAaNGiAzMzMHI/dvn17roPhiPKSX3ecQiHGK3QfdAHHXRZgwolliNsWZ3RMCc8SGNhgIIY2HopqJarZLnAiIrIJ2QunTp06oVOnTnnu/+abbzB48GAMGDAAALBgwQL88ccfWLp0KcY+nmb1+PHjFoklLS0NadmaFuLj4wEAkiRZZYp5/Xmdefp6e8lBTIy4HFh0xxmPPfLzkzBgUCZqdP0D66Lnof+RHTkeH1IuBGGNw/B67dfh6eoJAGa9JnvJg9yYB4F5EOTOg/557en/wl7ikFtB8pDf/6c555O9cMpPeno6jhw5gnHjxmVtUyqVaNeuHQ4cOGDx55s2bRqmTJmSY7tGo7Fa4ZSYmAgATjtQWO4cnDypwsKFbli3zg1pacbPX7OmFm+8HY2kGpH4MWo5bu02HuDkofJAz+o9MajeIDxb5lkAQHpyOtLxxKyXJpA7D/aCeRCYB0HuPKSnp0On00Gr1UKr1dr8+bPT6XRPP8gJFCYPWq0WOp0OCQkJRo0kgKGhxBR2XTjFxsZCq9WidOnSRttLly6N8+fPm3yedu3a4cSJE0hKSkKFChWwZs0aNG3aNMdx48aNw+jRo7Pux8fHo2LFilCr1fD19S34C8mDvhhz5rl75MiBvjtu7lxg3z7j51QoJHQNldDmrX/wn24+Pj+3FhlHjC/LrVa8GoY2Hor+9fujpFeeU/2ahe8FgXkQmAdB7jykpqbiwYMHUKlUZk8+aQ32EIM9KGgeVCoVlEolfHx84PHEqHxz3l92XThZys6dO006zt3dHe7u7jm2KxQKq/3Q6s/tzL8cbZUDfXfcvHm5Xx331tuJCGj3I1ZfnYf3Tp00jhEKdKneBcNChqF9tfZQKpQWj4/vBYF5EJgHQc486J9T7v+H7D0ezvx+KGwe8vv/LDKFk7+/P1QqFe7du2e0/d69eyhj7qI95LSOHRNXx/30U86r44KDgVeHncPdivOw7MwKJPybYLTf38sfbzd8G0MaD0Flv8q2C5qIHFq0JhqxybEmH+/v5Y9AdaAVIyJLsevCyc3NDY0aNcKuXbuypijQ6XTYtWsXhg8fLm9wZNcyMsTEc7NnA/v2Ge9TKIAuoRlo+MYG7E2NwJRrfwFP/H5rWqEphoUMwyvBr8DDhROtEJHpojXRqDG3Ro4FvPPj4eKBqOFRLJ4cgOX7G8yUmJiI48ePZ10Zd/XqVRw/fhzR0dEAgNGjR2Px4sVYsWIFzp07h7CwMCQlJWVdZUeUXUwMMHUqULUq8NprxkWTWg0Mef823l03BUdaVsan51/BX9f+ytrv6eKJtxu+jaPvHMX+QfvRt15fFk1EZLbY5FiziiYASM1MNauF6mn69++fY05EANi9ezcUCgXi4uKybisUCiiVSqjVajRs2BAffvgh7ty5Y/S4yZMnG3Wb6r/0Q2GWL1+eY9+T44iKCtlbnA4fPow2bdpk3dcPzu7Xrx+WL1+O119/HTExMZg4cSLu3r2LBg0aYOvWrTkGjJNzy687rlawhI5D9uBaqXmIvPQrMk8az/lVvWR1DGs8DP0a9IOfh5/tgiYisgNRUVHw9fVFfHw8jh49iunTpyMyMhK7d+9G3bp1s46rXbt2jjHDJUqUyLrt6+uLqKiorPtFdTyW7IVT69atn3qp//Dhw9k1RzlkZoqr4+bMAfbuNd6nUAAdu8UjqOf32JUwD7NizgKPDPuVCiW61eiGYSHD8GKVF60y2JuIyBEEBATAz88PZcqUQfXq1dGtWzc0bNgQYWFh2Jet2d7FxSXf8cUKhcIpxh/LXjgRmSs21nB13M2bxvvUaqDbO6egbTgfv1/7HluuJBrtL12sNAY/OxjvNHoHFdUVbRg1EZFj8PT0xNChQ/Hee+/h/v37Wat0PE1iYiIqVaoEnU6HZ599FlOnTkXt2rWtHK3tsXAih3H8uBjsnVt3XM3a6Xjh7fU47z0PK2/tBS4Y728R2ALDQoahZ62ecFO52SxmIip6Gi9qjLuJd/Pcn641fxJcAOj4Q8d8fz+V8S6Dw+8cNvl8mzZtgre3t9E2UyfyrFmzJgDg2rVrWYXTqVOnjM4XHByMgwcPAgBq1KiBpUuXol69etBoNPj666/xwgsv4MyZM6hQoYLJMTsCFk4ki9RUYM0aceXbvXvFULo00L078OqrxquFZ2Yaro7LrTuuXc+bCOi8EDsfLsZSzT1AY9hfzLUY3qz3JoaFDEPd0nVBRGQJdxPv4lbCracfaKaY5BiLnq9NmzaYP3++0bb//vsPfU1YbVg/hCb7OKUaNWpgw4YNWfezz3vYtGlTo4mlX3jhBdSqVQsLFy7EZ599VuDXYI9YOJHNbdgA9O8PPHoEKJWATucKpVLC+vXAyJHAihVA06Z5d8f5qiW89M4uJNSch103N0B7w/gvqFr+tRAeEo43678JX3fLz/hORM6tjHf+43jStekFKoJKeZV6aouTOYoVK4agoCCjbTef/IWah3PnzgEAKleunLXNzc0tx/ny4urqioYNG+LSpUumBetAWDiZgIv8Wka0JhrrtsRizBgAHgDKKqBfdUj//REkhA4BVCpAm1AK0BjmNKle7xHqvbUCx13nY92jC8ANw7lVChV61OyBYSHD0KpSq6y/khwlt872XsgL8yAwD4LcechrUdhDgw/l+7ijd46i8eLGZj/flj5b8GzZZ3Pdp9VqoVKpzM5FXovZZn9NT76+lJQULFq0CC1btoS/v3+OY02h1Wpx6tQpdOrUyWqfnwV9TJFe5FcuERERiIiIyOoL5iK/hXcj/gZCVoYgTZsGDHn68VoAyPAA5p7HC23vo1jrefgnfjUuJCYbHVe2WFn0q9MPb9V5C2W9ywIwb7FGe+FM74X8MA8C8yDInYeCLvJb0AWB83secxe31el0kCQpx/n059EveAsAd+7cQVJSEhISEnD06FF8/fXXiI2NxerVq7Mery828orv888/x/PPP49q1aohLi4O33zzDa5fv44BAwZYdIFkLvJrp8LDwxEeHo74+Hio1Wou8msBl5Mvi6LJHK6pqDGlK/bHnQIeGu9qU7kNwhqHoVuNbnBVuVouUJk403shP8yDwDwIcuehoIv8FmYR2vwea855lUolFApFjscolcqsc+lv165dGwqFAt7e3qhatSpeeukljB492mhqAf2klnnFEBcXh6FDh+Lu3bsoXrw4GjVqhH/++cdoHihL4SK/DoCL/BZeQV9fVNyprNs+bj7oV78fwkLCEFwq2FKh2Q1neS88DfMgMA+CIy7yW9BY83qegixuu3z58ly3t2nTJut82W8/zZQpUzBlypQ893/77bf49ttvTTpXQXGRXyIT1Q2oi2Ehw9C3Xl94u3k//QFERDLy9/KHh4uH2WvV+Xv5WzEqshQWTmRVWi3w++/AlEUAmj718ByWdF2CgQ0HOv1f3UTkOALVgYgaHmXW2nP+Xv5c4NdBsHAiq0hKApYtA779Frh8GUBZFKhwali2IYsmInI4gepAFkJFFAsnsqg7d8TacQsWiHmaiIiIihIWTmQRJ08C33wjlkPJyDDe16zrZUjtvsB+FlJEROTgWDhRgUkSsH07MHMmsGOH8T5XV6Bj/xPIeP5LbL+1GrpHBZ97g4iIyF6wcCKzpaWJlqVvvgFOnzbeV7w48L+wfbhdbRo23tgMmDa7PxERkUNg4UQme/BAjF2aOxe4+8TC4FWqSugQvgUnfKfhh1v7jJZDKeVVCq/WfhXzDs2zbcBEREQWxsKJnurSJWDWLHGVXEqK8b6mzTLRZOBa/Jn+JRbcOwEkGPYFqgPxwQsfYGDDgTgfe56FExEROTwWTpQrSQL27RPdcb//Lu7rKZVAt5dT8cyrK7DuzgzMunHZ6LG1/GthbPOxeKPOG1nLoXBCOCJyejt3Au++C8yeDbRrJ3c0VEBKuQMg+5KZCaxaBTz/PNCyJfDbb4aiqVgxYOi7Cfjgtxn49/mqmH52KC4/MhRNz5V/Dr++/itODzuNt+q/ZbSGnH5CuCPvHDH6Ojz4MHa/sRuHBx/OsS9qeBTnQSGiokGSgPHjgXPnxHcrLByfXf/+/dG9e/dc91WuXNloKRv915dffgkAuHbtmtH2EiVKoFWrVti7d2+Ocz18+BCjRo1CpUqV4ObmhnLlymHgwIGIjo7OEU/2c5YsWRIdO3bEyZMnjY7TarWYNWsW6tatCw8PDxQvXhydOnXCP//8k3VMmzZtco1f/9W6devCJe8p2OJkAv2q0NY6rzXOba6EBGDJEvGH0PXrxhNOlisnYcDwGKTWn43IUxGIOxpntL9dlXYY23ws2lRukzVZZW6vqaJvRVT0rWi0TZIkaDSaPBfxtIfc2II9vRfkxDwIzIMgdx70z2uRGLZtg+LQIXH70CFI27YBHToUOKbCHj9lyhQMHjzYaJuPj4/Ra92xYwdq166N2NhYTJ06FV26dEFUVBRKly4NQBRNTZs2hZubG+bPn4/atWvj2rVrmDBhAkJCQrB//35UrVo16/wdO3bE0qVLAQB3797FhAkT0KVLF1y/fj0r1l69emHnzp2YPn062rZti/j4eERERKB169ZYvXo1unbtinXr1iE9PR0AcOPGDTz//PNZsQKAm5tbrq87v/9Pc/LKwikXERERiIiIgFarBQBoNBqrFU6JiYkACr4oZGHdvKnAwoXuWLHCHQkJxjHUqaPFG0Mv4mrZb/HNuZVIOWQY4KSAAl2DumJU41FoWLohACA+Pt7s57eHHNgD5kFgHgTmQZA7D+np6dDpdNBqtVmfBwUiSVBNmABJpYJCq4WkUgETJkDbti1g4uvS6cyb0kWn00GSpDzjLlasGEqVKpVje/bX6ufnh1KlSqFUqVL48MMP8csvv+DAgQPo2rUrAGD8+PG4ffs2zp8/jzJlygAAypcvjz/++AO1atVCeHg4Nm3alBWPm5tb1nOWKlUK77//Ptq0aYO7d++iVKlSWL16NdauXYtff/0VXbp0yYpp/vz5ePDgAQYPHoyLFy9CrVZn7UtKSjKKNfvryO216XQ6JCQkIC0tzWifOZ9fLJxyER4ejvDwcMTHx0OtVkOtVsPX19fiz6MvxvJqbbGmo0fF+KXVq4HMTOPn7txZwitDz2OPdjomnfoBmbGZWftclC7oW68vPnzhQ9T0r1noOOTMgT1hHgTmQWAeBLnzkJqaigcPHkClUkGlUhX8RNu2QXH4cNZdhVYLHD4M1a5dZrU6mRODUqmEQqHI8zFKpTLPffrt+tedkpKCH3/8EQDg4eEBlUoFnU6H1atXo3fv3ihfvrzR4729vREWFoYJEyZAo9GgRIkSOeJJTEzEzz//jKCgIAQEBECpVGLVqlWoXr06unXrliOmMWPG4Ndff8Wff/6Jnj175hlrflQqFZRKJXx8fODh4WG0z5z3FwsnE+j7Ta15blv8UtDpgM2bxYSVu3cb73N3B958E3ip32Gsuj0Ng47+CgmGVjYvVy8MfnYwxjQdg4pq4+62wrJlDuwZ8yAwDwLzIMiZB/1z5nj+xo1zzsmSF0kCYmJyP39oKFCq1FNbnSQAKgAoU8aoADNFXnkbO3YsJkyYYLRty5YtaNGiRdZjmjVrBqVSieTkZEiShEaNGqFdu3ZQKBSIjY1FXFwcgoODc32O4OBgSJKEy5cvo2TJkgCATZs2wcfHB4BoKSpbtiw2bdqUVfBcuHABtWrVyvN8AHDx4kWj15Xn/1E+ucjtWBZOZCQlBfj+ezGlwPnzxvv8/YGwYRLqhf6JBWem4fVdu4z2+3n4YcRzI/Du8+/yCjciIkAUTbduFf48GRnA7dtPPUz/kW7JASMffPAB+vfvb7TtyZajVatWoWbNmjh9+jQ+/PBDLF++HK6urkbHmDOMpU2bNpg/fz4A4NGjR5g3bx46deqEgwcPolKlSmafTy4snIqw+/eBefPE15N/8FSvDox6T4fiTX/HrENf4rNNB432l/Uui9FNR2NIoyHwcfexYdRERHbu8Xiep9K3Nj25gGd2rq5PbXXKKiVMfV4T+Pv7IygoKN9jKlasiGeeeQbPPPMMMjMz0aNHD5w+fRru7u4oVaoU/Pz8cO7cuVwfe+7cOSgUCqPnKFasmNH9JUuWQK1WY/Hixfj8889RvXr1fM8HAM8884y5L9XiOB1BEXT+PPDOO0BgIDBlinHR1KoVsP73DIz9ZQXmaOvgjd964uAtQ9FUrXg1LOyyEFdHXsX7L7zPoomI6EmHDwM3bz79a+nS/IsmQOxfujT/89y4Ae21a4D+qjwZvPLKK3BxccG8eWIiY6VSiddeew0//fQT7j7RbZmSkoJ58+ahQ4cOKFGiRJ7nVCgUUCqVSHk8s3KvXr1w8eJFbNy4McexM2fORMmSJdHODua/YotTESFJYtzSzJnAH38Y71OpgNdeA4aNTMYxRGLUga8Rfcx4jo36petjXPNxeCX4FaiUhRgESURE4pfyhAniF3B+V+Q9vsIO7dubfIWdqTQaDY4fP260TT/eKCEhIUfB4+XlleeFUAqFAu+++y4mT56MIUOGwMvLC1OnTsWuXbvw0ksvYfr06ahTpw6uXr2KTz75BBkZGYiIiDA6R1paWtZzPnr0CHPnzkViYmLWVXq9evXCmjVr0K9fP8yYMcNoOoINGzZg9erVKFasmCVSUzgS5Umj0UgAJI1GY5Xz63Q66dGjR5JOpyvwOdLTJemHHySpYUNJEj+phi8fH0kaM0aSTl54JH2+53Op1PRSEibD6KvF0hbS5gubCxVDYVgiB0UB8yAwDwLzIMidh5SUFOns2bNSSkqK+Q/eujXnL+X8vrZuzfNUOp1OysjIMCsP/fr1kyB6+Yy+Bg0aJFWqVCnXfUOGDJEkSZKuXr0qAZCOHTtmdM6kpCSpePHi0ldffZW1LSYmRhoxYoRUsWJFydXVVSpdurTUv39/6fr16/nG4+PjI4WEhEhr1641Oi4jI0OaMWOGVLt2bcnNzU3y9fWVOnToIO3bty/XPOQVa27y+/805/NeIUkOMBJLJvrpCDQajdWmI8hv8sf8xMUBixeLCStv3jTeFxgIjBwJdOl1B5FnvsX8w/ORkJ5gdMz/nvkfxjUfh2aBzQr5KgqnMDkoSpgHgXkQmAdB7jykpqbi6tWrqFKlSo7L1/MlSWL5hSNHxOXMT6NUAo0aAf/9l2urk/R4PiaVSuX074fC5CG//09zPu/ZVedgrl0DvvtOzPL9eF64LI0bA2PGAM+2vYJv/puOekuXI01rmORLqVDi9dqvY2zzsahXup5tAycichbp6UB0tGlFEyCOu3FDPM7d3bqxUaGxcHIQBw+K8Utr1xr/LCoUQNeuomBSVz+Jr/75En3mr4JOMhzkpnLDgAYD8MELH6BaiWoyRE9E5ETc3cVA7jzmb8pVQACLJgfBwkkGqanAmjViAd1794qhdGmge3fg1VeB7K2HWi2wcaMomPbtMz6HpyfQvz8wahQQ4/EPpu2bhj/+Mh4V7uPmg7DGYRjVZBTK+pS18qsiIqIsFSuKLypyWDjZ2IYNouB59Eh0a+t0rlAqJaxfL8YlrVgBvPgisHw58O23wKVLxo8vXRoYPhwYMkTC4biteHvfNOyNNl6x2t/LH6OeH4VhIcNQ3LO4rV4aERFRkcfCyYY2bBAtS3o6ncLoe1wcEBoKeHvnHL8UHCy6417vpcWmK2vRfv2XOH73uNExgepAvN/0fQx6dhC8XL2s90KIiIicFAsnG0lNFS1NgLjgIjf67dmLpnbtRMHUum0aVp5cgfpLpuPyo8tGj6vlXwsfNfsIvev2hqvKeDp8IiIishwWTiaQJKnQ6+fM/ykajzweACbOmN84uCQWfx2IqjUTsPDIQgycPQt3Eu8YHRNSLgRjm49FtxrdoFQos2J1JPrcOlrclsY8CMyDwDwIcudB/7z29H9hL3HIrSB5yO//05zzsXDKRUREBCIiIqB9PNurRqMp1Jv1RvwNvH89BBiS9vSDHzuqc8eCa/2xausqxKXFGe1rXbE1RjUehZYVW0KhUCAhPiH3kzgASZKQ+LiJzdnnJ2EemAc95kGQOw/p6enQ6XTQarVZnweFsfeLvfh7yt9oOaklWnzcwqzH6kyd2qCIK0wetFotdDodEhISkJZm/HkcHx9v8nlYOOUiPDwc4eHhWRNiqdXqQk2AeTn5MnRK04smANAp07DwxMKs+woo0KNmD3zU7COElA8pcCz2Rl+QcqI/5gFgHvSYB0HuPKSmpuLBgwdQqVRQqQq3FNXfn/2Nvyf/LW5P/htKhRItJ7Q06xyFjaGoKGgeVCoVlEolfHx8ckyAac77i4WTCRQKRaF+aAvzWBelC/rW64sPX/gQtUrVKvB57Jk+v878AQEwD3rMg8A8CHLmQf+chX3+PZ/twe5Ju4227Z60G1AArSa0eurjs/d4OPP7obB5yO//05zzKc1+ZrKZXrV74fK7l7Gs27IiWzQRERVlez7bg90Td+e6b/fE3djz2R6rPG///v2zCgRXV1dUqVIFH374IVJTU7OOUSgU+O2333KPbffurMcrlUqo1Wo0bNgQH374Ie7cMR5vm5ycjHHjxqFatWrw8PBAqVKl0KpVK/z+++9WeW1yY4uTHfug2QcIVAfKHQYRERVAfkWTnn6/KS1P5urYsSOWLVuGjIwMHDlyBP369YNCocBXX31l8jmioqLg6+uL+Ph4HD16FNOnT0dkZCR2796NunXrAgCGDh2K//77D3PmzEFwcDAePHiA/fv348GDBxZ/TfaAhRMREZGFmVI06VmreHJ3d0eZMuJS7ooVK6Jdu3bYsWOHWYVTQEAA/Pz8UKZMGVSvXh3dunVDw4YNERYWhn2Pl7TYsGEDvvvuO3Tu3BkAULlyZTRq1Miir8WesKuOiIjIgswpmvSs2W0HAKdPn8b+/fvh5uZWqPN4enpi6NCh+Oeff3D//n0AQJkyZbB582YkJDjuFd7mYIsTERGRGRY1XoTEu4m57kuLT0N6QnqBzrt74m7sn7Ef7r65L/brXcYb7xx+x+Tzbdq0Cd7e3sjMzERaWhqUSiXmzp1boNiyq1mzJgDg2rVrCAgIwKJFi9CnTx+ULFkS9evXR/PmzfHKK6+gWbNmhX4ue8TCiYiIyAyJdxORcMs6rSvpCekFLrye1KZNG8yfPx9JSUmYNWsWXFxc8PLLLxf6vPqr2/RXorVs2RJXrlzBv//+i/3792PXrl347rvvMGXKFEyYMKHQz2dvWDgRERGZwbuMd577CtPiBABuPm75tjiZo1ixYggKCgIALF26FPXr10dkZCQGDRpU4PgA4Ny5cwDEWCY9V1dXtGjRAi1atMBHH32Ezz//HJ9++ik++uijQncP2hsWTkRERGZ4WndZQcY4AUDrT1vnOkBckiRotdpCTYCpVCoxfvx4jB49Gr1794anp2eBzpOSkoJFixahZcuWKFWqVJ7HBQcHIzMzE6mpqUWucOLgcCIiIgtqNaEVWn/a2qzH5FU0WdKrr74KlUqFiIiIrG1Xr17F8ePHjb6SkpKy9t+/fx93797FxYsX8csvv6BZs2aIjY3F/PnzDbG3bo2FCxfiyJEjuHbtGjZv3ozx48ejTZs2hVp1w16xxckG/L384eHigdTM1Kcf/JiHiwf8vfytGBUREVmLvggypeXJFkUTALi4uGD48OGYPn06wsLCAACjR4/OcdzevXuzbteoUQMKhQLe3t6oWrUq2rdvj9GjR2dNcwAAHTp0wIoVKzB+/HgkJyejXLly6NKlCyZOnGj11yQHhcSllvOkX6tOo9EUumqO1kQjNjnWaJt+AUtvb+8c0737e/k7xeSXkiRBo9FwTS7mAQDzoMc8CHLnITU1FVevXkWVKlVyrG1mqqd125lSNGXvqnP290Nh8pDf/6c5n/dscbKRQHVgjkJI7l8KRERkXfm1PNmqpYksi2OciIiIrCi3MU8smhwXCyciIiIryyqeFCyaHB276oiIiGyg1YRWLJiKABZOJpAkCdYYQ68/rzOPz2cOBOZBYB4E5kGQOw/657Wn/wt7iUNuBclDfv+f5pyPhVMuIiIiEBERAa1WCwDQaDRWK5wSE8V6R846OJw5EJgHgXkQmAdB7jykp6dDp9MhMzMz6/NALjqdTtbntxeFyUNmZiZ0Oh0SEhKQlpZmtC8+Pt7k87BwykV4eDjCw8OzLk9Uq9VWmcRLX4w581V1zIHAPAjMg8A8CHLnQavV4sGDB0hLS4O3t3nLnVhDYWYOL0oKmgf9QsclSpTIcQ5z3l8snEygUCis9kOrP7cz/3JkDgTmQWAeBOZBkDMPLi4u8PPzQ0xMDBQKBby8vGSJg/M4CQXNgyRJSE5ORkxMDPz8/ODikrP0YeFERERkAfoZsu/fvy9rHDqdDkolL4QvTB78/PyMZjwvKBZOREREeVAoFChbtiwCAgKQkZEhSwySJCEhIQE+Pj5O3+JU0Dy4urparKuThRMREdFTqFQq2cYYSZKEtLQ0eHh4OH3hZA95YLsfERERkYlYOBERERGZiIUTERH9v707D2rqevsA/g2UzYiIDCgIRARZBRRBRYpWsUW0uC+1VEEUtY0idalLbQEVBMe6K+IyhLprEVyqorYoLrUGNSyCCIhLK46OK2CLQs77R4b8mhfE0JYcLM9nJjPek8u93/uw+HA4uSGEqInWODWg9h4ijbkxVmOP/+LFixb9kmOqgQLVQYHqoEB1UKA6KFAdFJqyDrX/z6tzs2tqnBpQXl4OALCysuKchBBCCCFNrby8HEZGRg3uI2D0xjdvJJfLcf/+/SZ7CeiLFy9gZWWFe/fuNcmdyd8FVAMFqoMC1UGB6qBAdVCgOig0ZR1qb3VgYWHx1vtE0YxTA7S0tGBpadnk52nTpk2L/mYAqAa1qA4KVAcFqoMC1UGB6qDQVHV420xTLVocTgghhBCiJmqcCCGEEELURI0TR3p6eoiMjISenh7vKNxQDRSoDgpUBwWqgwLVQYHqoNBc6kCLwwkhhBBC1EQzToQQQgghaqLGiRBCCCFETdQ4EUIIIYSoiRonDjIzMxEYGAgLCwsIBAKkpaXxjqRxy5cvh5eXFwwNDWFmZobhw4ejsLCQdyyNS0hIgJubm/K+JN7e3jh+/DjvWNzFxcVBIBAgIiKCdxSNioqKUr6dRO3D0dGRdywufv/9d3z22WcwMTGBgYEBXF1dkZWVxTuWRnXq1KnO14NAIIBYLOYdTWNqamrwzTffwMbGBgYGBrC1tcXSpUvVemuUpkI3wOSgsrIS7u7uCA0NxciRI3nH4eLs2bMQi8Xw8vJCdXU1Fi1ahI8++gj5+fkQCoW842mMpaUl4uLi0KVLFzDGkJycjGHDhuHatWtwcXHhHY8LqVSKxMREuLm58Y7ChYuLC06fPq3cfu+9lvdj+unTp/Dx8UH//v1x/PhxmJqaoqioCMbGxryjaZRUKkVNTY1yOy8vDx9++CHGjBnDMZVmxcfHIyEhAcnJyXBxcUFWVhYmTZoEIyMjhIeHc8nU8r4jm4GAgAAEBATwjsHViRMnVLYlEgnMzMxw5coV9O3bl1MqzQsMDFTZjomJQUJCAi5dutQiG6eKigoEBQVh69atWLZsGe84XLz33nvo0KED7xhcxcfHw8rKCklJScoxGxsbjon4MDU1VdmOi4uDra0t+vXrxymR5l28eBHDhg3DkCFDAChm4fbs2YPLly9zy0R/qiPNwvPnzwEA7dq145yEn5qaGuzduxeVlZXw9vbmHYcLsViMIUOGYODAgbyjcFNUVAQLCwt07twZQUFBuHv3Lu9IGnf48GF4enpizJgxMDMzQ/fu3bF161besbh69eoVdu7cidDQ0CZ579Tmqk+fPvjpp59w8+ZNAEB2djbOnz/PdfKBZpwId3K5HBEREfDx8UHXrl15x9G43NxceHt7488//0Tr1q2RmpoKZ2dn3rE0bu/evbh69SqkUinvKNz06tULEokEDg4OKCsrQ3R0NHx9fZGXlwdDQ0Pe8TTm1q1bSEhIwOzZs7Fo0SJIpVKEh4dDV1cXwcHBvONxkZaWhmfPniEkJIR3FI1asGABXrx4AUdHR2hra6OmpgYxMTEICgrilokaJ8KdWCxGXl4ezp8/zzsKFw4ODpDJZHj+/Dl++OEHBAcH4+zZsy2qebp37x5mzZqFU6dOQV9fn3ccbv76W7Sbmxt69eoFkUiE/fv3Y/LkyRyTaZZcLoenpydiY2MBAN27d0deXh42b97cYhun7du3IyAgABYWFryjaNT+/fuxa9cu7N69Gy4uLpDJZIiIiICFhQW3rwVqnAhXM2bMwNGjR5GZmQlLS0vecbjQ1dWFnZ0dAKBHjx6QSqVYu3YtEhMTOSfTnCtXruDhw4fw8PBQjtXU1CAzMxMbNmxAVVUVtLW1OSbko23btrC3t0dxcTHvKBplbm5e5xcHJycnpKSkcErE1507d3D69GkcPHiQdxSNmzdvHhYsWIBPPvkEAODq6oo7d+5g+fLl1DiRloUxhpkzZyI1NRVnzpxpkQs/30Qul6Oqqop3DI3y8/NDbm6uytikSZPg6OiI+fPnt8imCVAsli8pKcGECRN4R9EoHx+fOrcnuXnzJkQiEadEfCUlJcHMzEy5QLolefnyJbS0VJdja2trQy6Xc0pEjRMXFRUVKr9BlpaWQiaToV27drC2tuaYTHPEYjF2796NQ4cOwdDQEA8ePAAAGBkZwcDAgHM6zVm4cCECAgJgbW2N8vJy7N69G2fOnEF6ejrvaBplaGhYZ32bUCiEiYlJi1r3NnfuXAQGBkIkEuH+/fuIjIyEtrY2xo8fzzuaRn355Zfo06cPYmNjMXbsWFy+fBlbtmzBli1beEfTOLlcjqSkJAQHB7fIW1MEBgYiJiYG1tbWcHFxwbVr17Bq1SqEhobyC8WIxmVkZDAAdR7BwcG8o2lMfdcPgCUlJfGOplGhoaFMJBIxXV1dZmpqyvz8/NjJkyd5x2oW+vXrx2bNmsU7hkaNGzeOmZubM11dXdaxY0c2btw4VlxczDsWF0eOHGFdu3Zlenp6zNHRkW3ZsoV3JC7S09MZAFZYWMg7ChcvXrxgs2bNYtbW1kxfX5917tyZff3116yqqopbJgFjHG+/SQghhBDyDqH7OBFCCCGEqIkaJ0IIIYQQNVHjRAghhBCiJmqcCCGEEELURI0TIYQQQoiaqHEihBBCCFETNU6EEEIIIWqixokQQgghRE3UOBFCNObChQtwdXWFjo4Ohg8fzjsOaQJnzpyBQCDAs2fPeEchpElQ40TIOygkJAQCgQBxcXEq42lpaRAIBJxSvd3s2bPRrVs3lJaWQiKRvHG/4uJiTJo0CZaWltDT04ONjQ3Gjx+PrKwszYVthtRtSmr3q32Ymppi8ODBdd5ImRDSeNQ4EfKO0tfXR3x8PJ4+fco7itpKSkowYMAAWFpaom3btvXuk5WVhR49euDmzZtITExEfn4+UlNT4ejoiDlz5mg2cCO9evWq3vHXr19rOIlCYWEhysrKkJ6ejqqqKgwZMuSNGQkh6qHGiZB31MCBA9GhQwcsX778jftERUWhW7duKmNr1qxBp06dlNshISEYPnw4YmNj0b59e7Rt2xZLlixBdXU15s2bh3bt2sHS0hJJSUkN5qmqqkJ4eDjMzMygr6+P999/H1KpFABw+/ZtCAQCPH78GKGhoRAIBPXOODHGEBISgi5duuDcuXMYMmQIbG1t0a1bN0RGRuLQoUPKfXNzczFgwAAYGBjAxMQEU6dORUVFRZ3rWrlyJczNzWFiYgKxWKzSxFRVVWH+/PmwsrKCnp4e7OzssH37dgCARCKp09z9/xm92vpu27YNNjY20NfXBwAIBAIkJCRg6NChEAqFiImJAQAcOnQIHh4e0NfXR+fOnREdHY3q6mrl8QQCAbZt24YRI0agVatW6NKlCw4fPqysYf/+/QEAxsbGEAgECAkJafBzYmZmhg4dOsDDwwMRERG4d+8ebty4oXz+/Pnz8PX1hYGBAaysrBAeHo7Kykrl8zt27ICnpycMDQ3RoUMHfPrpp3j48KHKOY4dOwZ7e3sYGBigf//+uH37tsrzd+7cQWBgIIyNjSEUCuHi4oJjx441mJuQ5owaJ0LeUdra2oiNjcX69evx22+//aNj/fzzz7h//z4yMzOxatUqREZG4uOPP4axsTF+/fVXTJ8+HdOmTWvwPF999RVSUlKQnJyMq1evws7ODv7+/njy5AmsrKxQVlaGNm3aYM2aNSgrK8O4cePqHEMmk+H69euYM2cOtLTq/niqbWQqKyvh7+8PY2NjSKVSHDhwAKdPn8aMGTNU9s/IyEBJSQkyMjKQnJwMiUSi0rBNnDgRe/bswbp161BQUIDExES0bt26UbUrLi5GSkoKDh48CJlMphyPiorCiBEjkJubi9DQUJw7dw4TJ07ErFmzkJ+fj8TEREgkEmVTVSs6Ohpjx45FTk4OBg8ejKCgIGUNU1JSAPxvJmnt2rVqZXz+/Dn27t0LANDV1QWgmP0bNGgQRo0ahZycHOzbtw/nz59XqeHr16+xdOlSZGdnIy0tDbdv31Zp1u7du4eRI0ciMDAQMpkMU6ZMwYIFC1TOLRaLUVVVhczMTOTm5iI+Pr7RNSakWWGEkHdOcHAwGzZsGGOMsd69e7PQ0FDGGGOpqansr9/WkZGRzN3dXeVjV69ezUQikcqxRCIRq6mpUY45ODgwX19f5XZ1dTUTCoVsz5499eapqKhgOjo6bNeuXcqxV69eMQsLC7ZixQrlmJGREUtKSnrjde3bt48BYFevXn3jPowxtmXLFmZsbMwqKiqUYz/++CPT0tJiDx48ULmu6upq5T5jxoxh48aNY4wxVlhYyACwU6dO1XuOpKQkZmRkpDJWX311dHTYw4cPVfYDwCIiIlTG/Pz8WGxsrMrYjh07mLm5ucrHLV68WLldUVHBALDjx48zxhjLyMhgANjTp0/rzVyrdj+hUMiEQiEDwACwoUOHKveZPHkymzp1qsrHnTt3jmlpabE//vij3uNKpVIGgJWXlzPGGFu4cCFzdnZW2Wf+/PkqGV1dXVlUVFSDeQl5l9CMEyHvuPj4eCQnJ6OgoOBvH8PFxUVlhqd9+/ZwdXVVbmtra8PExKTOn2lqlZSU4PXr1/Dx8VGO6ejooGfPno3KxRhTa7+CggK4u7tDKBQqx3x8fCCXy1FYWKhyXdra2sptc3Nz5TXIZDJoa2ujX79+auerj0gkgqmpaZ1xT09Ple3s7GwsWbIErVu3Vj7CwsJQVlaGly9fKvdzc3NT/lsoFKJNmzZvrPvbnDt3DleuXIFEIoG9vT02b96skkcikajk8ff3h1wuR2lpKQDgypUrCAwMhLW1NQwNDZW1unv3LgDF56FXr14q5/T29lbZDg8Px7Jly+Dj44PIyEjk5OT8rWshpLmgxomQd1zfvn3h7++PhQsX1nlOS0urTjNS30JlHR0dlW2BQFDvmFwu/xcSv5m9vT0AqKzD+ScaugYDA4MGP1bd2v21eWtovKKiAtHR0ZDJZMpHbm4uioqKlGuj3pa5sWxsbODg4IDg4GBMmTJF5c+jFRUVmDZtmkqe7OxsFBUVwdbWVvnn0DZt2mDXrl2QSqVITU0F8OZF8PWZMmUKbt26hQkTJiA3Nxeenp5Yv37937oeQpoDapwI+Q+Ii4vDkSNH8Msvv6iMm5qa4sGDByoNwF/X4fxbbG1toauriwsXLijHXr9+DalUCmdnZ7WP061bNzg7O+O7776rt1mofRm+k5MTsrOzVRYyX7hwAVpaWnBwcFDrXK6urpDL5Th79my9z5uamqK8vFzlHP+kdh4eHigsLISdnV2dR33ruepTuz6ppqam0ecXi8XIy8tTNj8eHh7Iz8+vN4+uri5u3LiBx48fIy4uDr6+vnB0dKwz8+Xk5ITLly+rjF26dKnOua2srDB9+nQcPHgQc+bMwdatWxudn5DmghonQv4DXF1dERQUhHXr1qmMf/DBB3j06BFWrFiBkpISbNy4EcePH//Xzy8UCvH5559j3rx5OHHiBPLz8xEWFoaXL19i8uTJah9HIBAgKSkJN2/ehK+vL44dO4Zbt24hJycHMTExGDZsGAAgKCgI+vr6CA4ORl5eHjIyMjBz5kxMmDAB7du3V+tcnTp1QnBwMEJDQ5GWlobS0lKcOXMG+/fvBwD06tULrVq1wqJFi1BSUoLdu3c3eO+pt/n222/x/fffIzo6GtevX0dBQQH27t2LxYsXq30MkUgEgUCAo0eP4tGjRyqvInybVq1aISwsDJGRkWCMYf78+bh48SJmzJgBmUyGoqIiHDp0SLk43NraGrq6uli/fj1u3bqFw4cPY+nSpSrHnD59OoqKijBv3jwUFhbWW6OIiAikp6ejtLQUV69eRUZGBpycnNTOTUhzQ40TIf8RS5YsqTNL4+TkhE2bNmHjxo1wd3fH5cuXMXfu3CY5f1xcHEaNGoUJEybAw8MDxcXFSE9Ph7GxcaOO07NnT2RlZcHOzg5hYWFwcnLC0KFDcf36daxZswaAoglIT0/HkydP4OXlhdGjR8PPzw8bNmxo1LkSEhIwevRofPHFF3B0dERYWJhyhqldu3bYuXMnjh07BldXV+zZswdRUVGNOv5f+fv74+jRozh58iS8vLzQu3dvrF69GiKRSO1jdOzYEdHR0ViwYAHat29f51WEbzNjxgwUFBTgwIEDcHNzw9mzZ5VNavfu3fHtt9/CwsICgGLGTSKR4MCBA3B2dkZcXBxWrlypcjxra2ukpKQgLS0N7u7u2Lx5M2JjY1X2qampgVgshpOTEwYNGgR7e3ts2rSpUbkJaU4ETN3VmIQQQgghLRzNOBFCCCGEqIkaJ0IIIYQQNVHjRAghhBCiJmqcCCGEEELURI0TIYQQQoiaqHEihBBCCFETNU6EEEIIIWqixokQQgghRE3UOBFCCCGEqIkaJ0IIIYQQNVHjRAghhBCiJmqcCCGEEELU9H9mKVaC/NgUdQAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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" ] @@ -50,18 +50,27 @@ "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "\n", + "import seaborn as sns\n", + "sns.set_context(\"poster\")\n", "# Read the CSV file\n", "df = pd.read_csv('./format_comparison_results.csv')\n", "\n", "# Define colors and markers for each format\n", "format_styles = {\n", - " 'VLA': ('blue', 'o'),\n", + " 'Fog-VLA-DM': ('blue', 'o'),\n", " 'HDF5': ('green', 's'),\n", " 'LEROBOT': ('red', '^'),\n", - " 'RLDS': ('purple', 'D')\n", + " 'RLDS': ('purple', 'D'),\n", + " \"Fog-VLA-DM-lossless\": ('orange', 'o'),\n", "}\n", "\n", + "# Update the format name from 'VLA' to 'Fog-VLA-DM' in the DataFrame\n", + "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n", + "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n", + "\n", + "# Update the format_styles dictionary\n", + "format_styles['Fog-VLA-DM'] = format_styles.pop('VLA', ('blue', 'o'))\n", + "\n", "# Get unique datasets and batch sizes\n", "datasets = df['Dataset'].unique()\n", "\n", @@ -78,12 +87,13 @@ " color=color, marker=marker, label=format, linewidth=2, markersize=8)\n", "\n", " # Customize the plot\n", - " plt.xlabel('Num of Concurrent Reads')\n", - " plt.ylabel('Log-Scale Average Loading Time (s)')\n", + " # plt.xlabel('Num of Concurrent Reads')\n", + " # plt.ylabel('Log-Scale Average Loading Time (s)')\n", " plt.title(f'{dataset}')\n", - " plt.legend()\n", + " # plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", " # plt.xscale('log') # Use log scale for x-axis\n", " plt.yscale('log') # Use log scale for y-axis\n", + " plt.tight_layout() # Adjust layout to make room for the legend\n", " \n", " # Add a grid for better readability\n", " plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", @@ -95,7 +105,216 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, + "id": "8a680cb7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set_context(\"talk\")\n", + "\n", + "# Read the CSV file\n", + "df = pd.read_csv('./format_comparison_results.csv')\n", + "\n", + "# Define colors and markers for each format\n", + "format_styles = {\n", + " 'Fog-VLA-DM': ('blue', 'o'),\n", + " 'HDF5': ('green', 's'),\n", + " 'LEROBOT': ('red', '^'),\n", + " 'RLDS': ('purple', 'D'),\n", + " \"Fog-VLA-DM-lossless\": ('orange', 'o'),\n", + "}\n", + "\n", + "# Update the format name from 'VLA' to 'Fog-VLA-DM' in the DataFrame\n", + "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n", + "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n", + "\n", + "# Update the format_styles dictionary\n", + "format_styles['Fog-VLA-DM'] = format_styles.pop('VLA', ('blue', 'o'))\n", + "\n", + "# Get unique datasets\n", + "datasets = df['Dataset'].unique()\n", + "\n", + "# Create a single figure with four subplots\n", + "fig, axs = plt.subplots(2, 2, figsize=(20, 20))\n", + "axs = axs.flatten()\n", + "\n", + "for idx, dataset in enumerate(datasets):\n", + " ax = axs[idx]\n", + " dataset_df = df[df['Dataset'] == dataset]\n", + " \n", + " # Create the line plot\n", + " for format, (color, marker) in format_styles.items():\n", + " data = dataset_df[dataset_df['Format'] == format]\n", + " ax.plot(data['BatchSize'], data['AverageLoadingTime(s)'], \n", + " color=color, marker=marker, label=format, linewidth=2, markersize=8)\n", + "\n", + " # Customize the subplot\n", + " ax.set_title(dataset)\n", + " ax.set_yscale('log')\n", + " ax.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", + " \n", + " # Only add x and y labels to the bottom and left subplots\n", + " if idx >= 2:\n", + " ax.set_xlabel('Num of Concurrent Reads')\n", + " if idx % 2 == 0:\n", + " ax.set_ylabel('Log-Scale Average Loading Time (s)')\n", + "\n", + "# Add a single legend for all subplots\n", + "handles, labels = axs[0].get_legend_handles_labels()\n", + "fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=5, fontsize='large')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('./combined_datasets.pdf', bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "808066a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File Size (MB):\n", + "Format Fog-VLA-DM Fog-VLA-DM-lossless HDF5 LEROBOT RLDS\n", + "Dataset \n", + "berkeley_autolab_ur5 1.85 25.57 281.55 0.00 0.00\n", + "berkeley_cable_routing 0.68 1.10 4.87 0.00 0.00\n", + "bridge 0.31 4.40 29.91 0.00 0.00\n", + "nyu_door_opening_surprising_effectiveness 0.36 5.78 79.54 0.00 0.00\n", + "\n", + "Relative Size (compared to Fog-VLA-DM):\n", + "Format Fog-VLA-DM Fog-VLA-DM-lossless HDF5 LEROBOT RLDS\n", + "Dataset \n", + "berkeley_autolab_ur5 1.00 13.80 152.03 0.00 0.00\n", + "berkeley_cable_routing 1.00 1.62 7.18 0.00 0.00\n", + "bridge 1.00 14.09 95.91 0.00 0.00\n", + "nyu_door_opening_surprising_effectiveness 1.00 16.08 221.42 0.00 0.00\n" + ] + } + ], + "source": [ + "# Calculate relative file size for each dataset\n", + "results = []\n", + "\n", + "for dataset in df['Dataset'].unique():\n", + " dataset_df = df[df['Dataset'] == dataset]\n", + " \n", + " vla_size = dataset_df[dataset_df['Format'] == 'Fog-VLA-DM']['AverageTrajectorySize(MB)'].mean()\n", + " \n", + " for format in ['Fog-VLA-DM', 'RLDS', 'HDF5', 'LEROBOT', 'Fog-VLA-DM-lossless']:\n", + " format_size = dataset_df[dataset_df['Format'] == format]['AverageTrajectorySize(MB)'].mean()\n", + " relative_size = format_size / vla_size if vla_size != 0 else float('inf')\n", + " \n", + " results.append({\n", + " 'Dataset': dataset,\n", + " 'Format': format,\n", + " 'AverageTrajectorySize(MB)': format_size,\n", + " 'RelativeSize': relative_size\n", + " })\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "\n", + "# Pivot the results for easier reading\n", + "pivot_df = results_df.pivot_table(values=['AverageTrajectorySize(MB)', 'RelativeSize'], \n", + " index='Dataset', \n", + " columns='Format', \n", + " fill_value='-')\n", + "\n", + "# Display the results\n", + "print(\"File Size (MB):\")\n", + "print(pivot_df['AverageTrajectorySize(MB)'].to_string(float_format='{:.2f}'.format))\n", + "print(\"\\nRelative Size (compared to Fog-VLA-DM):\")\n", + "print(pivot_df['RelativeSize'].to_string(float_format='{:.2f}'.format))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "97b049ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Format Fog-VLA-DM HDF5 LEROBOT RLDS\n", + "Dataset BatchSize \n", + "berkeley_autolab_ur5 1 1.00 0.24 1.59 1.38\n", + " 2 1.00 0.22 1.41 0.66\n", + " 4 1.00 0.24 1.56 0.40\n", + "berkeley_cable_routing 1 1.00 0.66 35.89 55.75\n", + " 2 1.00 0.57 39.37 31.47\n", + " 4 1.00 0.53 40.48 16.07\n", + " 6 1.00 0.57 46.50 13.36\n", + " 8 1.00 0.59 49.90 11.63\n", + "bridge 1 1.00 0.96 5.11 29.46\n", + " 2 1.00 0.98 7.76 23.61\n", + " 4 1.00 0.96 9.71 14.30\n", + " 6 1.00 1.05 11.56 11.36\n", + " 8 1.00 0.25 3.08 2.29\n", + "nyu_door_opening_surprising_effectiveness 1 1.00 0.85 6.23 8.31\n", + " 2 1.00 1.04 7.59 5.48\n", + " 4 1.00 0.66 4.99 1.77\n", + " 6 1.00 0.80 5.94 1.56\n", + " 8 1.00 0.90 6.82 1.46\n" + ] + } + ], + "source": [ + "# Calculate relative performance for each dataset and batch size\n", + "results = []\n", + "\n", + "for dataset in df['Dataset'].unique():\n", + " for batch_size in df['BatchSize'].unique():\n", + " dataset_batch_df = df[(df['Dataset'] == dataset) & (df['BatchSize'] == batch_size)]\n", + " \n", + " vla_time = dataset_batch_df[dataset_batch_df['Format'] == 'Fog-VLA-DM']['LoadingTime(s)'].mean()\n", + " \n", + " for format in ['Fog-VLA-DM', 'RLDS', 'HDF5', 'LEROBOT']:\n", + " format_time = dataset_batch_df[dataset_batch_df['Format'] == format]['LoadingTime(s)'].mean()\n", + " relative_performance = format_time / vla_time if vla_time != 0 else float('inf')\n", + " \n", + " results.append({\n", + " 'Dataset': dataset,\n", + " 'BatchSize': batch_size,\n", + " 'Format': format,\n", + " \"Latency\": format_time,\n", + " 'RelativePerformance': relative_performance\n", + " })\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "\n", + "# Pivot the results for easier reading\n", + "pivot_df = results_df.pivot_table(values='RelativePerformance', \n", + " index=['Dataset', 'BatchSize'], \n", + " columns='Format', \n", + " fill_value='-')\n", + "\n", + "# Display the results\n", + "print(pivot_df.to_string(float_format='{:.2f}'.format))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, "id": "285c0135", "metadata": {}, "outputs": [ diff --git a/benchmarks/openx.py b/benchmarks/openx.py index b25a6db..e25f0bf 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -65,7 +65,7 @@ def measure_average_trajectory_size(self): file_path = os.path.join(dirpath, f) total_size += os.path.getsize(file_path) - print(f"total_size: {total_size} of directory {self.dataset_dir}") + logger.debug(f"total_size: {total_size} of directory {self.dataset_dir}") # trajectory number traj_num = 0 if self.dataset_name == "nyu_door_opening_surprising_effectiveness": @@ -317,6 +317,15 @@ def _recursively_load_data(self, data): log_func(f" {key}: {type(value).__name__}") log_func(f"Total number of trajectories: {len(data)}") +class FFV1Handler(DatasetHandler): + def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_batches, dataset_type="ffv1", batch_size=batch_size, log_frequency=log_frequency) + self.file_extension = ".vla" + + def get_loader(self): + return VLALoader(self.dataset_dir, batch_size=self.batch_size) + + def evaluation(args): csv_file = "format_comparison_results.csv" @@ -338,27 +347,34 @@ def evaluation(args): args.batch_size, args.log_frequency, ), - HDF5Handler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), - LeRobotHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), - RLDSHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), + # HDF5Handler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # LeRobotHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # RLDSHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # FFV1Handler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), ] for handler in handlers: diff --git a/evaluation.sh b/evaluation.sh index 6513e88..a5f6303 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -2,8 +2,8 @@ sudo echo "Use sudo access for clearning cache" # Define a list of batch sizes to iterate through -batch_sizes=(1) -num_batches=20 +batch_sizes=(1 2 4 6 8 10 12 14 16) +num_batches=200 # batch_sizes=(1 2) # batch_sizes=(2) @@ -16,6 +16,6 @@ do python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size - python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size - python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size done \ No newline at end of file diff --git a/examples/fixing_failed_conversions.py b/examples/fixing_failed_conversions.py new file mode 100644 index 0000000..8401eb3 --- /dev/null +++ b/examples/fixing_failed_conversions.py @@ -0,0 +1,72 @@ +import argparse +import os +from concurrent.futures import ProcessPoolExecutor, as_completed +from fog_x.loader import RLDSLoader +import fog_x +import time +def check_and_fix_conversion(file_path, data_traj, dataset_name, index, destination_dir, lossless): + try: + # Try to load the existing file + fog_x.Trajectory(file_path).load() + print(f"File {file_path} is valid.") + return index, True + except Exception as e: + print(f"Failed to load {file_path}. Attempting to fix: {e}") + + # If loading fails, attempt to reconvert + try: + data_traj = data_traj[0] + if lossless: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=file_path, + lossy_compression=False + ) + else: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=file_path, + lossy_compression=True, + ) + print(f"Successfully fixed and reconverted data {index}") + return index, True + except Exception as e: + print(f"Failed to fix data {index}: {e}") + return index, False + +def main(): + parser = argparse.ArgumentParser(description="Check and fix failed VLA conversions.") + parser.add_argument("--data_dir", required=True, help="Path to the original data directory") + parser.add_argument("--dataset_name", required=True, help="Name of the dataset") + parser.add_argument("--version", default="0.1.0", help="Dataset version") + parser.add_argument("--destination_dir", required=True, help="Directory containing converted files") + parser.add_argument("--split", default="train", help="Data split to use") + parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes") + parser.add_argument("--lossless", action="store_true", help="Enable lossless compression for VLA format") + + args = parser.parse_args() + + loader = RLDSLoader( + path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling=False + ) + + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = [] + for index, data_traj in enumerate(loader): + file_path = f"{args.destination_dir}/{args.dataset_name}/output_{index}.vla" + if os.path.exists(file_path): + future = executor.submit(check_and_fix_conversion, file_path, data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + futures.append(future) + + time.sleep(60) + failed_conversions = [] + for future in as_completed(futures): + index, success = future.result() + if not success: + failed_conversions.append(index) + + if failed_conversions: + print(f"Failed to fix {len(failed_conversions)} conversions: {failed_conversions}") + else: + print("All existing conversions are valid or have been successfully fixed.") + +if __name__ == "__main__": + main() diff --git a/examples/openx_loader.py b/examples/openx_loader.py index f62b865..f127d32 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -1,25 +1,29 @@ import argparse -from concurrent.futures import ProcessPoolExecutor +from concurrent.futures import ProcessPoolExecutor, as_completed import os from fog_x.loader import RLDSLoader import fog_x +import threading +import time def process_data(data_traj, dataset_name, index, destination_dir, lossless): - data_traj = data_traj[0] - # try: - if lossless: - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", - lossy_compression=False - ) - else: - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", - lossy_compression=True, - ) - print(f"Processed data {index}") - # except Exception as e: - # print(f"Failed to process data {index}: {e}") + try: + data_traj = data_traj[0] + if lossless: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=False + ) + else: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=True, + ) + print(f"Processed data {index}") + return index, True + except Exception as e: + print(f"Failed to process data {index}: {e}") + return index, False def main(): parser = argparse.ArgumentParser(description="Process RLDS data and convert to VLA format.") @@ -34,7 +38,7 @@ def main(): args = parser.parse_args() loader = RLDSLoader( - path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split + path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling = False ) # train[start:end] @@ -45,21 +49,48 @@ def main(): print(f"Failed to get starting index: {e}") split_starting_index = 0 + max_concurrent_tasks = args.max_workers + semaphore = threading.Semaphore(max_concurrent_tasks) + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: futures = [] + retry_queue = [] try: - for index, data_traj in enumerate(loader): - index = index + split_starting_index - futures.append(executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless)) + from tqdm import tqdm + for index, data_traj in tqdm(enumerate(loader), desc="Processing data", unit="trajectory"): + if index < split_starting_index: + continue + semaphore.acquire() + future = executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + future.add_done_callback(lambda x: semaphore.release()) + futures.append(future) except Exception as e: print(f"Failed to process data: {e}") - for future in futures: - future.result() + for future in as_completed(futures): + try: + index, success = future.result() + if not success: + retry_queue.append((index, data_traj)) + except Exception as e: + print(f"Error processing future: {e}") - # for index, data_traj in enumerate(loader): - # index = index + split_starting_index - # process_data(data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + # Retry failed tasks + if retry_queue: + print(f"Retrying {len(retry_queue)} failed tasks...") + with ProcessPoolExecutor(max_workers=args.max_workers) as retry_executor: + retry_futures = [] + for index, data_traj in retry_queue: + future = retry_executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + retry_futures.append(future) + + for future in as_completed(retry_futures): + try: + index, success = future.result() + if not success: + print(f"Failed to process data {index} after retry") + except Exception as e: + print(f"Error processing retry future: {e}") print("All tasks completed.") diff --git a/examples/vla_file_debugger.py b/examples/vla_file_debugger.py new file mode 100644 index 0000000..33e0e8f --- /dev/null +++ b/examples/vla_file_debugger.py @@ -0,0 +1,122 @@ +import os +import numpy as np +from fog_x.trajectory import Trajectory +from fog_x.utils import _flatten +import imageio +from fog_x.loader import RLDSLoader + +def load_ffv1_trajectory(path): + traj = Trajectory(path,) + return _flatten(traj.load()) + +def load_vla_trajectory(path): + traj = Trajectory(path) + return _flatten(traj.load()) + +def load_rlds_trajectory(path, dataset_name, version, split, index): + loader = RLDSLoader(path=f"{path}/{dataset_name}/{version}", split=split, shuffling=False) + data_traj = loader[index] + + data = {} + # convert from a list of dicts to a dict of lists + traj_len = len(data_traj) + for i in range(traj_len): + data_traj[i] = _flatten(data_traj[i]) + for k, v in data_traj[i].items(): + if k == "observation/natural_language_instruction": + print(v) + continue + if k not in data: + data[k] = np.empty((traj_len, *v.shape)) + data[k][i] = v + return data + +def save_traj_images_to_dir(traj_data, dir_path): + os.makedirs(dir_path, exist_ok=True) + for i in range(len(traj_data["observation/image"])): + imageio.imwrite(f"{dir_path}/{i}.png", traj_data["observation/image"][i].astype(np.uint8)) + +def compare_trajectories(ffv1_data, vla_data, rlds_data, file_name): + print(f"\nComparing FFV1, VLA, and RLDS trajectories for {file_name}:") + + # Compare keys + ffv1_keys = set(ffv1_data.keys()) + vla_keys = set(vla_data.keys()) + rlds_keys = set(rlds_data.keys()) + + print(f"FFV1 keys: {ffv1_keys}") + print(f"VLA keys: {vla_keys}") + print(f"RLDS keys: {rlds_keys}") + + common_keys = ffv1_keys.intersection(vla_keys).intersection(rlds_keys) + + # Compare data for common keys + for key in common_keys: + if key == "observation/natural_language_instruction": + continue + ffv1_array = ffv1_data[key] + vla_array = vla_data[key] + rlds_array = rlds_data[key] + + print(f"\nComparing '{key}':") + print(f" FFV1 shape: {ffv1_array.shape}, dtype: {ffv1_array.dtype}") + print(f" VLA shape: {vla_array.shape}, dtype: {vla_array.dtype}") + print(f" RLDS shape: {rlds_array.shape}, dtype: {rlds_array.dtype}") + + if ffv1_array.shape == vla_array.shape == rlds_array.shape: #and ffv1_array.dtype == vla_array.dtype == rlds_array.dtype: + if np.allclose(ffv1_array, vla_array) and np.allclose(ffv1_array, rlds_array): + continue + else: + diff_ffv1_vla = np.abs(ffv1_array - vla_array) + diff_ffv1_rlds = np.abs(ffv1_array - rlds_array) + diff_vla_rlds = np.abs(vla_array - rlds_array) + print(f" Max difference FFV1-VLA: {np.max(diff_ffv1_vla)}") + print(f" Max difference FFV1-RLDS: {np.max(diff_ffv1_rlds)}") + print(f" Max difference VLA-RLDS: {np.max(diff_vla_rlds)}") + print(f" Mean difference FFV1-VLA: {np.mean(diff_ffv1_vla)}") + print(f" Mean difference FFV1-RLDS: {np.mean(diff_ffv1_rlds)}") + print(f" Mean difference VLA-RLDS: {np.mean(diff_vla_rlds)}") + if key == "observation/image": + print("ffv1_array[0]: ", ffv1_array[0]) + print("vla_array[0]: ", vla_array[0]) + print("rlds_array[0]: ", rlds_array[0]) + save_traj_images_to_dir(ffv1_data, f"{file_name}_ffv1") + save_traj_images_to_dir(vla_data, f"{file_name}_vla") + save_traj_images_to_dir(rlds_data, f"{file_name}_rlds") + else: + print(" Shape or dtype mismatch") + print(f" ffv1: {np.sum(ffv1_array - np.array(rlds_array))}") + print(f" vla: {np.sum(vla_array - np.array(rlds_array))}") + +def main(): + # dataset_name = "bridge" + dataset_name = "fractal20220817_data" + base_path = f"/home/kych/datasets/{dataset_name}" + # base_path = "/mnt/data/fog_x" + ffv1_dir = os.path.join(base_path, "ffv1", dataset_name) + vla_dir = os.path.join(base_path, "vla", dataset_name) + rlds_dir = "/home/kych/datasets/rtx" + version = "0.1.0" + split = "train" + + # Get all .vla files in the ffv1 directory + vla_files = ["output_{}.vla".format(i) for i in range(1)] + + for file_name in vla_files: + ffv1_file = os.path.join(ffv1_dir, file_name) + vla_file = os.path.join(vla_dir, file_name) + index = int(file_name.split("_")[1].split(".")[0]) + + if not os.path.exists(vla_file): + print(f"Skipping {file_name}: VLA file not found") + continue + + print(f"\nProcessing {file_name}") + ffv1_data = load_ffv1_trajectory(ffv1_file) + vla_data = load_vla_trajectory(vla_file) + rlds_data = load_rlds_trajectory(rlds_dir, dataset_name, version, split, index) + + compare_trajectories(ffv1_data, vla_data, rlds_data, file_name) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/fog_x/dataset.py b/fog_x/dataset.py index 6723148..05bb54b 100644 --- a/fog_x/dataset.py +++ b/fog_x/dataset.py @@ -1,6 +1,6 @@ import os from typing import Any, Dict, List, Optional, Text -from fog_x.loader.vla import VLALoader +from fog_x.loader.vla import VLALoader, NonShuffleVLALoader from fog_x.utils import data_to_tf_schema import numpy as np @@ -12,7 +12,7 @@ class VLADataset: def __init__(self, path: Text, split: Text, - shuffle: bool = False, + shuffle: bool = True, format: Optional[Text] = None): """ init method for Dataset class @@ -31,8 +31,10 @@ def __init__(self, self.split = split self.format = format self.shuffle = shuffle - - self.loader = VLALoader(path, batch_size=1, return_type="tensor") + if shuffle: + self.loader = VLALoader(path, batch_size=1, return_type="tensor") + else: + self.loader = NonShuffleVLALoader(path, batch_size=1, return_type="tensor") def __iter__(self): return self diff --git a/fog_x/feature.py b/fog_x/feature.py index 08c3e1b..fce4071 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -127,7 +127,14 @@ def from_data(self, data: Any): else: dtype = type(data).__name__ shape = () - feature_type._set(dtype, shape) + try: + feature_type._set(dtype, shape) + except ValueError as e: + print(f"Error: {e}") + print(f"dtype: {dtype}") + print(f"shape: {shape}") + print(f"data: {data}") + raise e return feature_type @classmethod diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index d5cd00a..9390308 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -67,8 +67,12 @@ def to_numpy(step_data): return trajectory def __next__(self): - return self.get_batch() + data = [self._convert_traj_to_numpy(next(self.iterator))] + self.index += 1 + if self.index >= self.length: + raise StopIteration + return data def __getitem__(self, idx): batch = next(iter(self.ds.skip(idx).take(1))) - return self._convert_batch_to_numpy(batch) \ No newline at end of file + return self._convert_traj_to_numpy(batch) \ No newline at end of file diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 9b746f6..83e9129 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -117,6 +117,73 @@ def __del__(self): p.terminate() p.join() + +class NonShuffleVLALoader: + def __init__(self, path: Text, batch_size=1, cache_dir="/tmp/fog_x/cache/", num_workers=1, return_type = "numpy"): + self.files = self._get_files(path) + self.cache_dir = cache_dir + self.batch_size = batch_size + self.return_type = return_type + self.index = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.index >= len(self.files): + raise StopIteration + + max_retries = 3 + for attempt in range(max_retries): + try: + file_path = self.files[self.index] + self.index += 1 + return self._read_vla(file_path, return_type = self.return_type) + except Exception as e: + logger.error(f"Error reading {file_path} on attempt {attempt + 1}: {e}") + if attempt + 1 == max_retries: + logger.error(f"Failed to read {file_path} after {max_retries} attempts") + raise + + def _get_files(self, path): + ret = [] + if "*" in path: + ret = glob.glob(path) + elif os.path.isdir(path): + ret = glob.glob(os.path.join(path, "*.vla")) + else: + ret = [path] + # for file in ret: + # try: + # self._read_vla(file, return_type = self.return_type) + # except Exception as e: + # logger.error(f"Error reading {file}: {e}, ") + # ret.remove(file) + return ret + + def __len__(self): + return len(self.files) + + def __getitem__(self, index): + return self.files[index] + + def __del__(self): + pass + + def peek(self): + file = self.files[self.index] + return self._read_vla(file, return_type = "numpy") + + def _read_vla(self, data_path, return_type = None): + if return_type is None: + return_type = self.return_type + traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) + ret = traj.load(return_type = return_type) + return ret + + def get_batch(self): + return [self.__next__() for _ in range(self.batch_size)] + import torch from torch.utils.data import IterableDataset, DataLoader from fog_x.loader.vla import VLALoader diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 8c5b8cf..77a8f90 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -462,16 +462,7 @@ def _get_length_of_stream(container, stream): ) feature_codec = packet.stream.codec_context.codec.name - if feature_codec == "h264" or feature_codec == "ffv1" or feature_codec == "hevc": - frames = packet.decode() - for frame in frames: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) - # data = np.asarray(frame.to_image())#.reshape(feature_type.shape) - # save the numpy to image folder - # Append data to the numpy array - np_cache[feature_name][d_feature_length[feature_name]] = data - d_feature_length[feature_name] += 1 - else: + if feature_codec == "rawvideo": packet_in_bytes = bytes(packet) if packet_in_bytes: # Decode the packet @@ -482,6 +473,19 @@ def _get_length_of_stream(container, stream): d_feature_length[feature_name] += 1 else: logger.debug(f"Skipping empty packet: {packet} for {feature_name}") + else: + frames = packet.decode() + for frame in frames: + if feature_type.dtype == "float32": + data = frame.to_ndarray(format="gray").reshape(feature_type.shape) + else: + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + # data = np.asarray(frame.to_image())#.reshape(feature_type.shape) + # save the numpy to image folder + # Append data to the numpy array + np_cache[feature_name][d_feature_length[feature_name]] = data + d_feature_length[feature_name] += 1 + logger.debug(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") container.close() @@ -570,7 +574,7 @@ def is_packet_valid(packet): ] # Check if the stream is using rawvideo, meaning it's a pickled stream - if packet.stream.codec_context.codec.name == "ffv1" or packet.stream.codec_context.codec.name == "libx264": + if packet.stream.codec_context.codec.name == "ffv1" or packet.stream.codec_context.codec.name == "libaom-av1": data = pickle.loads(bytes(packet)) # Encode the image data as needed, example shown for raw images @@ -622,7 +626,7 @@ def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packe encoding = stream.codec_context.codec.name feature_type = FeatureType.from_data(data) logger.debug(f"Encoding {stream.metadata.get('FEATURE_NAME')} with {encoding}") - if encoding == "ffv1" or encoding == "libx264": + if encoding == "ffv1" or encoding == "libaom-av1": if feature_type.dtype == "float32": frame = self._create_frame_depth(data, stream) else: @@ -727,19 +731,23 @@ def _add_stream_to_container(self, container, feature_name, encoding, feature_ty if encoding == "ffv1": stream.width = feature_type.shape[1] stream.height = feature_type.shape[0] - stream.codec_context.options = { - "preset": "fast", # Set preset to 'fast' for quicker encoding - "tune": "zerolatency", # Reduce latency - } + # stream.codec_context.options = { + # "preset": "fast", # Set preset to 'fast' for quicker encoding + # "tune": "zerolatency", # Reduce latency + # } - if encoding == "libx264": + if encoding == "libaom-av1": stream.width = feature_type.shape[1] stream.height = feature_type.shape[0] stream.codec_context.options = { - "preset": "ultrafast", # Set preset to 'ultrafast' for quicker encoding - "tune": "zerolatency", # Reduce latency + "g": "2", 'crf': '30', # Constant Rate Factor (quality) } + # stream.codec_context.options = { + # "preset": "ultrafast", # Set preset to 'ultrafast' for quicker encoding + # "tune": "zerolatency", # Reduce latency + # 'crf': '30', # Constant Rate Factor (quality) + # } stream.metadata["FEATURE_NAME"] = feature_name stream.metadata["FEATURE_TYPE"] = str(feature_type) @@ -781,7 +789,7 @@ def _get_encoding_of_feature( data_shape = feature_type.shape if len(data_shape) >= 2 and data_shape[0] >= 100 and data_shape[1] >= 100: if self.lossy_compression: - vid_coding = "libx264" + vid_coding = "libaom-av1" else: vid_coding = "ffv1" else: diff --git a/fog_x/utils.py b/fog_x/utils.py index d266564..fdfba86 100644 --- a/fog_x/utils.py +++ b/fog_x/utils.py @@ -8,6 +8,7 @@ def data_to_tf_schema(data: Dict[str, Any]) -> Dict[str, FeatureType]: """ Convert data to a tf schema """ + data = _flatten(data) schema = {} for k, v in data.items(): if "/" in k: # make the subkey to be within dict diff --git a/openx_to_vla.sh b/openx_to_vla.sh index 96ed897..ec1912c 100755 --- a/openx_to_vla.sh +++ b/openx_to_vla.sh @@ -1,42 +1,48 @@ -# berkeley_autolab_ur5 dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 - -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless # # bridge dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 - -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless # berkeley_cable_routing dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +# python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 # nyu_door_opening_surprising_effectiveness dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +# python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +# bridge dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[6000:] --max_workers 16 +# pkill -f examples +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 8 +pkill -f examples + +# berkeley_autolab_ur5 dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:] --max_workers 16 +# pkill -f examples +python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 8 +pkill -f examples +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 16 + +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 16 --lossless + # fractal20220817_data -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/vla --version 0.1.0 --split train[0:] --max_workers 4 +# rm -rf /home/kych/datasets/fractal20220817_data/vla +# rm -rf /home/kych/datasets/fractal20220817_data/ffv1 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/vla --version 0.1.0 --split train[34000:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/ffv1 --version 0.1.0 --split train[0:] --max_workers 8 --lossless + From a35a69584a92b266903d59a5817bc3a3c07775b5 Mon Sep 17 00:00:00 2001 From: Kaiyuan Eric Chen Date: Wed, 18 Sep 2024 09:41:24 -0700 Subject: [PATCH 80/80] submitted version --- benchmarks/Visualization.ipynb | 1177 +++++++++++++++++-------- benchmarks/openx.py | 60 +- evaluation.sh | 10 +- examples/fixing_failed_conversions.py | 72 ++ examples/openx_loader copy.py | 99 +++ examples/openx_loader.py | 81 +- examples/summarize_dataset.py | 19 + examples/vla_file_debugger.py | 122 +++ examples/vla_to_h5.py | 82 +- fog_x/dataset.py | 10 +- fog_x/feature.py | 9 +- fog_x/loader/__init__.py | 2 +- fog_x/loader/hdf5.py | 2 +- fog_x/loader/rlds.py | 8 +- fog_x/loader/vla.py | 101 ++- fog_x/trajectory.py | 64 +- fog_x/utils.py | 1 + openx_to_vla.sh | 66 +- vla_to_hdf5.sh | 8 +- 19 files changed, 1502 insertions(+), 491 deletions(-) create mode 100644 examples/fixing_failed_conversions.py create mode 100644 examples/openx_loader copy.py create mode 100644 examples/summarize_dataset.py create mode 100644 examples/vla_file_debugger.py diff --git a/benchmarks/Visualization.ipynb b/benchmarks/Visualization.ipynb index 90de485..113a598 100644 --- a/benchmarks/Visualization.ipynb +++ b/benchmarks/Visualization.ipynb @@ -2,13 +2,27 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "id": "f7a8ba59-fd57-46b6-bca7-870a6f014290", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n", + " plt.tight_layout() # Adjust layout to make room for the legend\n", + "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n", + " plt.tight_layout() # Adjust layout to make room for the legend\n", + "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n", + " plt.tight_layout() # Adjust layout to make room for the legend\n", + "/tmp/ipykernel_3200483/735920438.py:46: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n", + " plt.tight_layout() # Adjust layout to make room for the legend\n" + ] + }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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9P+3xGVFY9O3bN0PCWkt69eqFj4+P2SBlrVq1qFWrFn5+fsyaNQt/f38Atm3bRmBgYLaThQohhBDi8SWBCFEktWjRIss2Xl5euLq6kpCQQFpaGmfPnjXJjh8cHKzePnjwIDdu3MiyT+Orvzdv3sy0bdWqVfHw8Miyz0clJiYyfvx49QS/TJkyLFmyJMuqNSEhIertW7duMWfOHKuezzDbQK/Xc+vWrWwHInLrfXz66acJCgoClKSV5gIRxrMhBg0ahLOzs9Xjzono6Gi+/fZbAgICiI+Pt2qfe/fu5eqYbBEREaEGrwBatWqV5T6enp7UrFmTS5cuAXDq1KkcByJSU1NZvnw5v/32G7du3bJqH2vfT3t8RhQWTZo0sbqtNTk9nJyc+Pjjj7l27Zpa+vOnn36SQIQQQgghrCaBCFEkVapUKcs2Go2GihUrqidMUVFRJo/fvn1bvW28Rt5amS0XAHK8JGPp0qXq1eBKlSrx22+/UbNmzSz3M349Z8+e5ezZs9l+7pyU/sut97FLly5UqVKF8PBwTp48yenTp00qGyQlJbF+/Xr1fm4lqTQwVMKwJtBizNqARX4w/p1wcXGx+pitUqWK+nuV00BLcnIyr732Gnv27MnWfta+n/b4jCgsSpcubfc+tVotEyZMYMyYMYCSR+PWrVtUrFjR7s8lhBBCiKJHAhGiSCpevHi22z16AhMXF2fTGAwVLCxxcXHJUb+Ojo5qIOL+/ftERkZaFYiIjY3N0fMZy+o1mZNb76NWq2Xo0KF8++23gDL7YebMmerjmzdvVl9z06ZNadCggU3jyMpbb72lBiHc3NwYNmwYnTt3pmbNmpQpUwYXFxd1uvvBgwcZNWoUQJ7kUMgp498Ja3+nAJPlCzkNtCxcuFANQmg0Gp566in69OlD/fr1qVChAs7OziaJFw0JX619P+3xGVFY5PSzJitt2rShWLFiakneixcvSiBCCCGEEFaRQIQokhITEylRooRV7QweLflnfAKycOFCevfubb8B2mD06NGcO3eO7du3k5CQwCuvvMLixYtp27ZtpvsZv56RI0cyY8aM3B5qhue19/vo5+fHokWLSEtLIzAwkClTpqjLL/IySeWxY8fUJSiurq6sWLFCrfxhTm6c0OZGQMP4d8L4dyUrxlUUrCml+ajk5GSWL1+u3p83b16mOR1yEuyyx2dEfikoiSGLFSuGh4eHWg64IC8zEkIIIUTBIlUzRJGUVX4GUE7cjJMGPjp9uWzZsuptwxftgqBYsWJ8++23dO/eHUANRhjWalti/Hru3LmTq2O09Lz2fh8rVKiglsK8f/8+W7ZsAeDatWscPnwYUAID5spN2pNxpQZfX99MgxCAVcs3jJMLWkrMaMweM14eZbwUIykpyeqlCeHh4ertnCwL+O+//9RgRr169bJMLJnd5TBgn88IeykIP+ucMg7UZGfWjBBCCCEebxKIEEWScWJGS86dO6demXZwcMgwdb9Zs2bq7WPHjtl1fLZycnLKEIx4+eWXMw1GGL+e4ODgPFsSkNvv4zPPPKPeNsyCWL16tfr6nnzySauufNvCOA9G/fr1s2xvCJJkxnjM0dHRWbY/d+5clm2yq0KFCibVVYwTj1oSFRXFlStX1PuNGjXK9vPmxvv5KHt8RthLdn7WycnJJu9vfrp+/brJbJTy5cvn42iEEEIIUZhIIEIUSevWrcuyTUBAgHq7adOmGcryGU7yAbZs2ZKnswiskd1gROvWrdWM+Ldu3WLbtm15Ms7cfh+feOIJNfHggQMHuHr1qlpWEHI/SSVgUuowKSkp07YRERFs3bo1yz6rVKmi3j5z5kyWgaONGzdm2SdgUjnEsLY/M8YlYdesWZNl+zVr1qhLB8qXL0/t2rWtGpcx4xKvWS0J0el0rFixItvPYY/PCHsx/llnVX5027ZtPHjwIFfGkV3Gy5/c3d1NksUKIYQQQmRGAhGiSDp06BCbNm2y+PjFixf5448/1PvmTlabNWtGu3btAOXkcsqUKSQnJ1v1/MnJyTmqMJFdhmBEt27dgMyDEU5OTowePVq9/8EHH5hMO89KTgMIuf0+Ojg4MHToUECZSv/OO++oV9Tr1KlD69atczTu7KhWrZp6O7MgQ1paGu+//75VAYA6deqoOQkiIyMzrR6xY8cOduzYYdVYjUvGWvPzN55xsmXLFnbv3m2xbXh4OD/88IPJvsZBBWsZv5+HDx/OdCnCTz/9xJkzZ7L9HPb4jLAX41lDmQV74uLi+PLLL3NtHNnJXXLs2DF+/fVX9X6/fv1MlpgIIYQQQmRGAhGiSCpWrBhTp041Kd9oEBwczNixY9WrivXq1WPw4MFm+5k5c6Z6FXTv3r2MGDGC0NBQi897+fJlFi1aRI8ePfJsOYeTkxMLFiywKhjxwgsvUK9ePUA5CR06dCgbN260mPwuKiqKv//+G19fX37++eccjzG330c/Pz91VoJxv3kxGwKga9eu6gn3oUOH+PTTTzPMjIiMjGTixIns2LHDqivrjo6OPPXUU+r9mTNncuHCBZM2er2egIAA3nzzTZycnKwaq+HnD2R6Im7QoUMHNQ8HwBtvvGF29sWJEyd44YUX1HKrlSpVUiuDZFejRo2oUKECoORDmDRpUoagSXJyMvPnz+fLL7/M0UwFe31G2MOAAQPU2xs2bOD333/P0ObixYuMGjWKa9euWf2zzq7Nmzfj5+dHQECAxeDPgwcPWLZsGS+88IL6/pQsWZIJEybkypiEEEIIUTTZ/fJFREQEGzdu5NixY9y8eZOYmBjS0tIICgoyaZeYmKgmNCtVqhTlypWz91DEY+ydd97hk08+4a233mLBggU0a9YMR0dHzp8/z/Hjx9V2rq6uzJs3z+IX+/r16/PVV18xefJkEhMTCQ0N5emnn6Z69eo0atSIUqVKkZyczN27dzl79my2ZhjYkyEYYTjRNQQjlixZQps2bdR2bm5ufP/994wZM4awsDAiIyN58803KV26NC1atKBs2bLo9Xru37/PhQsXuHr1qhqk6NChQ47Hl9vvY6VKlejSpQs7d+5UtxUrVizLJIf2UqdOHQYPHqxO5f/ll18IDAykadOmlClThvDwcA4fPkxKSgpubm5MmTKFWbNmZdnva6+9xj///ENCQgI3b97Ex8eHtm3bUq1aNeLi4ggODubGjRs4Ojoye/Zsqyqh9O3bV51d8cUXX7Br1y7q1atn8jswbtw4SpUqpd6fO3cuzz33HNeuXSMhIYE333yTb775hmbNmlGsWDEuXrxIaGiounzE1dWVL7/8Ul0KlF1arZZJkyYxbdo0QAlePfnkk7Rs2ZLKlSsTHR3NoUOH1Nkyc+bM4e23387Wc9jrM8Ie2rRpQ7du3dRZLR9++CF//PEHLVq0AJTAXGhoKDqdjiFDhhAWFsahQ4dyZSzHjx9n6tSpODo6Urt2bWrVqkWpUqVIS0sjIiKCkJAQk7wQLi4ufPfdd5IfQgghhBDZYrdAxIMHD/j0009ZuXKlSdZvvV5vdmquXq9n+PDhxMbGUq9ePavW6wphrdGjRxMdHc3333/PlStXzCZ3K1++PN988w1NmjTJtK/u3bvz119/MW3aNE6ePAkoVRmuXbtmcZ8qVapQsWJFm15DdlkbjKhWrRqrV69m1qxZbN68Gb1ez71799i+fbvFvkuWLGlV0sDM5Pb7+PTTT5sEInr06GFS9SG3zZ49mzt37qgn+ZGRkRnycFSsWJGvvvrKqsoIAFWrVmX+/Pm88cYbJCYmkpKSwr59+0zalChRgrlz51qdFNLX15d169Zx+PBh9Ho9Bw8e5ODBgyZtnn/+eZNARNmyZfnzzz956623OHDgAIDF36saNWrwxRdfmCw3yImhQ4dy7do1dalHQkICe/fuNWnj7OzMtGnTGDhwYLYDEfb8jLCHTz/9lLFjx3LixAkALl26xKVLl0za+Pn5MWvWLMaOHZvr40lNTeXcuXOZJkFt1qwZ8+bNo06dOrk+HiGEEEIULXYJRMTFxTFq1ChOnz5tdSZ+V1dXhg0bxs8//8z58+c5c+ZMrmUkF4+nSZMm0bVrV/7++2+OHj3K7du3cXR0pHr16vTp04fnn38ed3d3q/pq0KAB/v7+7Nmzh6CgII4dO8bt27eJjY3FycmJ0qVLU6tWLZo3b07nzp1p2bJljtbG28raYISHhwfz58/n3LlzbNiwgYMHDxIWFkZ0dDRarZaSJUuqsxU6duxIp06dTJIc5lRuvo9du3bFyclJzT8xbNgwm8ebHcWLF2fJkiUEBgYSEBDAqVOniI+Px8PDg2rVqtG3b198fX0pVapUhhP/zDzxxBNs3LiRn3/+mT179nDr1i20Wi2VK1eme/fuPPfcc1SuXJmwsDCr+itWrBi//vorq1at4t9//+X8+fNER0dnmbeibNmyLF26lF27drFx40aOHj1KZGQkqamplClThoYNG9KrVy8GDRpEsWLFrH59mZk8eTJdunThjz/+4OjRo0RFReHm5kbFihXp0qULfn5+1KxZM8f92/MzwlYeHh789ddfrFy5kg0bNnDhwgXi4+MpX748TZo04ZlnnqFTp065OoYBAwZQs2ZNgoODCQ0N5dq1a9y7d4/o6Gh0Oh3u7u5UrVqV5s2b07dvX5PPFCGEEEKI7NDo7VDDb9y4ceqU0rJlyzJq1Cg6dOjA77//zrp169BoNGYzgZ85cwYfHx80Gg2TJ0/mlVdesXUoQojH1KFDhxg5ciSgzKQICgoyqWYhhBBCCCGEKBhsnhFx4MABduzYgUajoW7duvz666+ULVsWIMsrSQ0aNMDT05N79+5ZVZ9eCCEsMS4lOGTIEAlCCCGEEEIIUUDZ/E3dkNtBo9Hw+eefq0EIazVo0AC9Xp9hLawQQlgrMjJSrQDh6OiY58syhBBCCCGEENazORBx9OhRNBoNTZo0yVGOB0O1jLt379o6FCHEYygtLY2PP/5YLSX45JNPqqUfhRBCCCGEEAWPzUszIiMjAahbt26O9ndxcQEgKSnJ1qEIIR4TgYGB/PfffyQkJHDkyBG14oGTkxMTJ07M38EJIYQQQgghMmVzIEKn0wHg4OCQo/0N9cjd3NxsHYoQ4jGxd+9e1qxZk2H7u+++a3UVhaVLl3L16lWbxtG8eXMGDx5sUx+i8JkzZ47NfQwePJjmzZvbYTRCCCGEEIWPzYEIT09Pbt68ya1bt3K0v6FGuWGJhrC/5ORkoqOj1fvOzs45DhwJURAYl5p0c3OjQYMGjBgxgieeeEINbmZl8+bNHD161KZx3L9/n549e9rUhyh8/vjjD5v7qFu3LnXq1LHDaERuS0tLU5d+gVJq1cnJKR9HJIQQQhR+Ngci6tevz40bNwgJCeHBgwc4Oztbve/Fixe5cOECGo1GrgzloujoaK5fv57fwxDCboYPH87w4cMzbD979qzVfSQkJNg8jpiYmGw9pxAGt27dkmOnECtfvnx+D0EIIYQo1GwORHTt2pUdO3YQFxfH77//ztixY63e99NPP0Wv16PRaOjWrZutQxFCCKvNnDkzv4cgCqn/+7//y+8hCCGEEEIUajZXzfDx8VFLds6fP5+tW7dmuU9ycjLTp09n165daDQaatSoQa9evWwdihBCCCGEEEIIIQo4m2dEFC9enOnTp/O///2PlJQUJkyYQO/evenfvz/37t1T2505c4bIyEiOHTvG6tWr1WobDg4OzJkzB41GY+tQhAWPLpepVq0arq6uNveblJSkzmgxVD8RIjNyzIjskmNGZJe9j5mEhAST5Y3ZWYIqhBBCCPNsDkQAPPXUU0RERPDZZ5+h0+nYsmULW7ZsAVADDL6+vib76PV6HBwcmDVrFu3atbPHMIQFjyam1Gq1aLU2T4ZBq9WqX/bs0Z8o+uSYEdklx4zILnsfM4/2IcmehRBCCNvZJRABMGbMGLy8vJg9e7ZJSTxDIEKv15u0r1GjBrNnz8bb29teQxBW0uv1GX4eOe3Hnv2Jok+OGZFdcsyI7LL3MSPHnRBCCGF/dgtEAHh7e7Np0ya2bdvGzp07CQkJ4fbt28TFxVG8eHHKlClD8+bN6datG3379pWrW/lEo9HYZSmMRqNRrzrJ0hphDTlmRHbJMSOyy97HjBx3QgghhP3ZNRAByh/snj170rNnT3t3LezExcXFLjkiAPXLnr36E0WfHDMiu+SYEdllz2NGp9PZYURCCCGEMCZTEoQQQgghhBBCCJFnJBAhhBBCCCGEEEKIPCOBCCGEEEIIIYQQQuQZu+eIAKXmdnh4OHFxcaSmplq9X9u2bXNjOEIIIYQQQgghhCgg7BaIiIuLY9myZfzzzz9cunQp2+WuNBoNp06dstdwhBBCCCGEEEIIUQDZJRDx33//MX78eO7evQtIzW0hhBBCCCGEEEKYZ3MgIiIighdffJG4uDh1W7FixahevTqlSpXCwcHB1qcQQgghhBBCCCFEEWFzIOLHH38kLi4OjUZDmTJlmDp1Kn369MHZ2dke4xNCCCGEEFbQ6XTExcURExNDcnIyaWlp+T0kIYQQRZhWq8XR0RE3NzdKlCiBs7MzGo3Gqn1tDkTs3r1b6cjRkaVLl1KnTh1buxRCCCGEyBHttm04vf02yV98AQMG5Pdw8kxsbCzh4eGyPFYIIUSeSk5OJiEhgcjISFxcXKhatSrFihXLcj+7LM3QaDR06NBBghBCCCGEyD96PcVmzUJ79izFZs2C/v3ByiszhZm5IIRGo5HlsUIIIXKVTqdDp9Op95OSkrhy5QrVqlXDxcUl031tDkSULFmSu3fvUqlSJVu7EkIIIYTIuX//xeHYMQDl/3//hb5983lQuUun05kEIUqUKIGnpyeurq5WT48VQgghcio5OZm4uDiioqJISUkhNTWVsLAw6tSpk+nfIa2tT1y9enUAoqOjbe1KCCGEECJn9HqYORP9w1kAegcHmDlT2V6ExcXFmQQhqlatipubmwQhhBBC5AknJyc8PT2pWbOmmicyJSWFhISETPezORDRv39/9Ho9R44cITU11dbuhBBCCCGy799/4fBhNA8TNGrS0uDwYWV7ERYTE6Pe9vT0lACEEEKIfOHo6EjZsmXV+1lNVLB5aYavry8//vgjkZGRLF68mPHjx9vapchlSUlJaLU2x6BISkpCr9fLlx5hNTlmRHbJMSOydOcODhs24PTeewAYHyl6Bwd006bxoHPnHOeKSEpKssMgc09ycjKg5IRwdXXN59EIIYR4nJUoUQKNRoNer+fBgweZtrU5EOHq6srChQt54YUXWLhwIXq9nldffRVHR5u7FrlEr9fbJau2oQ979SeKPjlmRHbJMSPM0Vy/jsO6dTgGBqLdtw+NUaIsk3ZpaTgcO4Y2KIi0Xr1y9FwF/bgzlOh0cHCQgJ0QQoh8pdVq0Wq1pKWlZVlC2i7RgmbNmrFixQqmTJnCwoUL+fPPP+nRowd169bF3d3d6j+MPj4+9hiOyIJGo7HLlxVDtMte/YmiT44ZkV1yzAgA9Ho0p0/jEBiI47p1aENCrN/VwYFic+ag69UrR7Mi5LgTQgghrGft3027TVtwcnKifv36nDx5kjt37rBy5cps7a/RaCQQkUdcXFzsNn3TcIIg00GFteSYEdklx8xjSqeDgwchIADWrIHz5823q1wZbtyw2I1hVoTrnj05qqChszDbQgghhBA5Z5dAxJ49e5g4cSJJSUlqBKSgT2UUQgghRAGTnAw7diiBh7Vr4eZN8+1atQJfX/DxgRdegIgIyGwKqKGCRp8+Oc4VIYQQQgj7sTkQcfHiRcaPH68mSwKoXLky9erVo2TJkpIrQgghhBCWxcXB5s1K8GH9erh/P2MbrRaeeEIJPgweDDVqKNs3b4YjR7J+DuMKGjmYFSGEEEII+7I5SrB48WKSk5PRaDTUqlWLjz76iFatWtljbEIIIYQoiu7cgcBAJfiwZQuYq0zh7KzMYPD1hYEDwagkGAB6vTLLQatVlnFkRauVWRFCCCFEAWFzIOLAgQOAknfg119/pUKFCjYPSgghhBBFzLVr6fkedu0yHzwoVQr691eCD08+CSVKWO4vOVnp09ocDjodXL+u7OfsnKOXIIQQQgj7sDkQcffuXTQaDd7e3hKEEEIIIYRCr4dTp5TAQ0AAHD1qvl3FikquB19f6NYNnJys69/ZWVluERlpsjkxMVG9Xbx4cdN9ypeXIIQQQghRANgciPDw8ODu3buUfXTKpBBCCCEeLzodHDqkBB8yq3RRt64SePD1hfbtlWUTOVGtmvLPiD4hQa20glRaEfnEy8tLvX327Fmb+8iOw4cPU7JkSZNtI0eO5NChQ2bbFytWDHd3d2rUqEHLli3x9fWlfv362X5evV7Pzp072bFjB0ePHuXu3bvExMTg7u5O2bJladWqFd26daNr165orfidX7BgAQsXLrT4uKOjIyVKlKBGjRq0bt06R+O+dOkSmzdvZt++fYSFhREVFYWDgwNlypShTp06dOnShaeeegpPT0+z+x88eJBRo0Zl6zktmTBhAhMnTrRLX0IUBjYHImrVqsXdu3e5c+eOPcYjhBBCiMLEUOkiIED5l1WlC19faNRI8jQIUUCkpKQQFRVFVFQUwcHB/Prrr4wYMYJp06ZZFTAA5YR87ty5nD59OsNjhr7PnTvHX3/9hZeXF9OmTaNDhw42jTs1NZXo6Giio6MJDQ3lt99+Y8yYMbzzzjtZjjsqKorPP/+ctWvXkmam4k58fDzXrl1j+/btfPnll7z00ku8+uqrODg42DRmIUQ6mwMR/fv35/Dhwxw+fJiEhASp8y6EEEIUdfHxsGlT1pUuunRJL7NpqHQhhMi2RYsWWd02w5KkR0yaNMlk5kBycjI3b94kKCiIY8eOodfrWb58OcWKFWPq1KlZPt9ff/3FnDlz1BP60qVL06tXLxo1aoSHhwf379/n9OnTBAUFcffuXc6ePcuLL77I+++/z7PPPmvVa+rXrx/9+/c32ZacnMytW7fYtWsX+/fvR6fT8csvv+Dk5MTkyZMt9nXp0iVeffVVrl27BoCDgwPe3t54e3tTsWJFUlJSCAsLY/v27Zw8eZL4+Hjmz59PcHAwX3/9NSWMctfUq1cv05/NgQMHWL58OQDt27fPdPZErVq1rHovhCgqbA5EDBkyhD///JNz587x6aef8sEHH9hjXEIIIYQoSO7eTa908e+/litd9O6dXumiXLm8H6cQRVCvXr3s1lfr1q1p3759hu1jx47l559/5rPPPgNg2bJljBw5ksqVK1vsa8OGDcyaNUu9P2rUKN58803c3NwytH333XeZP38+v/32G2lpacyaNYuSJUvSr1+/LMdcu3Zti+/Biy++yMqVK5kxYwYAP//8M2PHjs2wPAWUmRAvvPACt27dAqBRo0bMnTuXBg0aZGg7ceJEgoKCmDlzJlFRUezatYv//e9//Pjjj8rSL8DT0zPTn01MTIx6u3Llynb9OQpR2OVwUWY6JycnFi5cSI0aNVixYgXvvvsuUVFR9hibEEIIIfLTtWvw7bfQvTtUqAAvvADr1pkGIUqWhOHDYeXK9LKcL74oQQghCqGxY8fSqFEjQFn6sHPnTottw8LCmDlzpnp/8uTJTJ8+3WwQAsDV1ZX33nvPZLbCjBkzCAsLs3ncw4YNU4MJKSkpBAcHm203bdo0NQjRpEkTli9fbjYIYdCrVy+WLVtGqVKlANi5cye//fabzeMVQthhRoQhiUy3bt34v//7P9auXcs///xDmzZtqFevHu7u7lb3NWHCBFuHI4QQQoic0uvh9On0ZJOZVboYPFiZ+dC9u/WVLsTjLSgI3nhDCW7JleECq23btpw6dQqAK1euWGy3ePFi4uPjAejYsSPjxo2zqv9x48Zx4MAB9u/fT3x8PEuWLLHLjOq6dety5swZAHVcxkJCQti+fTsALi4ufPnllybLLCypV68e06ZNU5ep/PDDDzz77LNZLoERQmTOLoEIzSMJp5KTk9m/fz/79+/PVl8SiBBCCCHymKHSRUCAEnw4d858O3tVuhCPJ70epk1TAl3TpkHPnpKwtIByNipxm2RuCRbKkoOAgAD1/qRJk7L1HG+88YZ6nrBmzRrefvvtbF28NOfevXvq7UqVKmV4fNmyZertwYMHU7NmTav79vHx4fvvv+fKlStER0ezdu1aq/NbCCHMs8u3CL1eb/LP3Las/gkhhBAij6SkwJYtMH68Uv7S2xs+/TRjEKJlS5gzB44fVx777DOlrQQhRHb9+y8cPqzcPnxYuS8KpPNGZXct5Yc4fPgwDx48AKBmzZq0aNEiW8/RqlUrNRDw4MEDjhw5kqOxGly6dEktT+rp6ZlhuYVer2fv3r3qfV9f32w/h/E++/bty+FIhRAGNs+IkFkMQgghRCEQHw+bN6dXuoiOztjGUOnCx0f5l40rhkJYpNfDzJng4ABpacr/M2dCnz4yK6KAOX78OLt27VLvt27d2my7Y8eOqbdbtWqVo+dq2bKluvTj6NGjdO/ePVv7JycnExERwe7du1m0aBEpKSloNBrefvvtDMsmLl26RPTDzzwnJycaN26co/EaHLW0bE0IYTUJRAghhBBFlVS6EAWB8WwIUIIRhlkRffvm37gKES8vL6va+fr6Mm/evGz1bSjfuXXrVr777ju1DGebNm1o06aN2X0MCR8h52Una9eurd6OiIjItO3ChQvVvHTmODg40L59e8aOHUvXrl0zPG483qpVq+KUg7w2xuO9c+cOqampODrafColxGNLfnuEEEKIouT69fR8D7t2KSd9jypZEvr3V4IPTz4JNq7NFoXIypXw/vsQG5s3z6fXQ2Sk+ccMga+8mBXh7g4ffgh+frn/XAXcqFGjsmzj5eXFggULLD5+//599ba5MpnWMM4JEW1uhlY2aLVanJycLAYY7DHeR/e7f/8+ZcqUyVFfQggJRDyWkpKS0NphfW9SUhJ6vT5DslIhLJFjRmSXHDNW0OvRnDmDw/r1OKxbh4PRlGmTZhUqkDpgAGkDB6Lr2tW00kVCQh4NNvfZ+5ixlKyv0Pr8c3hYWSDfpaTAjRt593yff15oAxGLFi2yqp25JI3Z4ejoyLRp0xg2bFiOZg3kln79+tG/f3+TbWlpaURHR3PixAn++ecfdu/eze7du3nttdd4880382egQgirSSDiMWSvBKGPJiYVIityzIjskmPGAp0O7dGjOAQG4hgYiNYouZxJs9q1SR04UAk+tGtnmmSyiL6f9j5mitxxN2WKkp8hL2ZEGGZDpKRYblOsWN7MinB3h3feyd3nyEW97FjudNKkSdSvXx9QTuZv377N4cOH2bJlC6mpqSxevJi2bduqbcwpVaqUejsmJiZH44g1OgY9PDwybVu7dm2L78EzzzzDxIkTeeGFF7hw4QLff/89devWZcCAAXYd76P7GfcphMg+CUQ8hjQajV2uFGk0GvWqk1ytFNaQY0ZklxwzRlJS0O7ejUNgIA6BgWhv3jTbTNe8OamDBpE2YAD6xo3VE7zH5d2z9zFT5I47P7+8mxWwebOy9CczKSnwyy+SKyIPtW7dmvbt25tsGzlyJEePHmXs2LHcunWLF198kYCAAMqWLWu2j4oVK6q3L1++nKNxXLp0Sb1doUKFHPVhUL58ed5//3112cmCBQtMAhHG4w0LCyM5OTnbMz6Mx1u2bFnJDyGEjaz6DTKuEwxKLV1Lj9nCuF+Re1xcXHB1dbVLX4Yve/bqTxR9csyI7HqsjxlrK1107qzke/DxQVuzJgVnQnX+sOcxo9Pp7DCix9CjlTIskQoaBUbr1q2ZNm0aM2fOJDIykpkzZ/L999+bbWtcKeOYheVgWQkODjZ5blu1bduW4sWLk5iYyJUrV7hx44ZafrR27dp4eHgQHR1NcnIyJ0+eNKmCYY2QkBC7jleIx51VgYh3331XvSKg0WhMAgbGj9ni0X6FEEKIx1JUlGmli8TEjG2cnaFXLyX4MGiQVLoQBc+jlTIskQoaBcqwYcP4888/OXXqFNu2bWP//v14e3tnaNe2bVucnZ158OABV65cITQ0lObNm1v9PMHBwWrpTmdnZ4vVObJDq9Xi7u5O4sPPzIiICDUQodFo6NSpExs2bACUC6nZDUSsWbNGvd2pUyebxyvE487qjIWGtZbm1koaP2bLPyGEEOKxdP06LFgAPXtC+fIwZgysXWsahChZEp57DlasUNbdr18PY8dKEEIUPIbZENYmxtZqlfbyXTDfaTQaJk6cqN7/8ssvzbYrWbKkyQXEb7/9NlvPY1yRY8iQISYVNHIqLS3NJI9D8eLFTR4fOXKkejsgIICrV69a3fe6devUJSgeHh4MGjTIxtEKIayaEeHr65ujx4QQQghhwenTyqyHNWvgyBHzbSpUgMGDlZkP3bsrMyGEKOiSk+HaNbB2WYtOpwTjkpPlGC8AunfvjpeXF2fPnuX48eNs27aNHj16ZGj38ssvExgYSEJCAnv27GHx4sW88sorWfa/ePFi9u7dC4Cbmxsvv/yyXcZ96NAhtcqNk5MT1atXN3m8ZcuWdOvWjR07dpCUlMTbb7/Nr7/+SokSJTLt9+LFi3z88cfq/XHjxmUIcgghss+qQMTcuXNz9JgQQgghHtLplICDIfhw9qz5dnXqKIEHX19o315ZQy9EYeLsrCy3iIy0fp/y5SUIUUBoNBrGjRvH5MmTAWX2Qvfu3TMsxa5WrRoffvghb731FqDMnoiKiuKNN94wm58lMTGRb7/9ll9++UXd9tFHH1GlShWbxxwREcGcOXPU+z169DA7hrlz5+Lj40NERAT//fcfo0aNYt68eRYrhGzfvp3p06cT/TA/T9euXRkzZozN4xVCZKNqhiEpZe3atWnWrFlujUcIIYQoOlJSYOdOJfCwdi2Eh5tv16JFevChSRNJ2icKv2rVlH+Cr7/+2qp25cuX5/nnnzf7WFBQkNXP16xZM8qXL291e3OefPJJFixYwKVLlzh16hRbtmyhT58+GdoNGDCA2NhYPvzwQ9LS0vj1119Zu3YtvXv3plGjRpQqVYr79+9z+vRptmzZwt27dwFwcHBg5syZ9OvXz6rxXLp0KcN7oNPpiI6O5vjx4/zzzz/ExcUB4OnpyZQpU8z24+npyW+//cYrr7zC9evXOXnyJL6+vnh7e9OxY0fKly9PamoqYWFhbN++nRMnTqj7dunSha+++qroVdIRIp9YHYgwJKV8/vnnJRAhhBBCWJKQYFrp4t69jG0MlS58fJR/tWrl9SiFEHnkhx9+sKpdgwYNLAYiXn/9daufb9GiRfTq1cvq9uZotVpeffVVpk6dCiizInr37m32JPy5556jVq1azJ07lzNnzhAVFcXff/9tsW8vLy+mTZtGhw4drB7PP//8wz///JNluwYNGvDll19mOsuidu3arFixgs8++4y1a9eSmprK7t272b17t9n2bm5ujB07lldffVVKdgphR/LbJIQQQtjKUOkiIEAJQpirdOHkBL17K7MeBg5UpqILIUQBNWDAABYsWEBYWBjnzp1j48aNFmcwdOjQgYCAAHbu3Mn27ds5duwYkZGRxMbG4u7uTtmyZWnVqhXdunWjW7duaK1NZJoJjUaDm5sb5cuXp3HjxvTt25fu3btbFSzw9PRk3rx5vPLKK2zatIm9e/cSFhbGvXv3cHBwwNPTk3r16tGlSxeeeuopPD09bR6vEMKURm9luYoGDRqoMyJmzJiR2+MSdhQXF8dZo7XIXl5eWSbmsUZCQoJda7WLok+OGZFdBfqYCQtTAg9r1ijLL9LSMrZxd4f+/ZXgw1NPKfdFrrL3MZNbf0Pt5fz586SmpuLo6Ei9evXyezhCCCEec9b+XZIZEUIIIYS1zpxJTzZ5+LD5NoZKFz4+0KOHJOATQgghhHiEBCKEEEIIS6ytdFG7dnqyyQ4dpNKFEEIIIUQmJBAhhBBCGEtJgV27lMBDQEDWlS58fKBpU6l0IYQQQghhJQlECCGEENZUutBolEoXhuCDVLoQQgghhMgRCUQIIYR4PEVFKUGHNWsyr3TRq5cSfBg0SCpdCCGEEELYQbYDEfv37+e9996z+0A0Gg2ffPKJ3fsVQgghVGFhsHatEnzYsSPrShdPPgklS+b5MIUQQgghirJsByIuXbrEpUuXcmMsEogQQghhf9ZUuihfXql04esrlS6EEEIIIXJZtgMRer0+N8aBRpJ8CSGEsAe93rTSxZkz5tvVqpVe6cLbWypdCCGEEELkkWwHImrXrk3z5s1zYyxCCCFEzlhb6aJ58/Tgg1S6EEIIIYTIF9kORHTs2JEZM2bkxliEEEII6yUkwL//KsGHwEDLlS46dUqvdFG7dp4PUwghhBBCmJKqGUIIIQoPQ6WLgADYtEkqXQghhBBCFEISiHgMJSUlodVq7dKPXq+X/B7CanLMiOxKSkpCc+MGTv/8Q9rGjWh370ZjptKF3t2dtL59SRs4kLQ+fUwrXSQk5OGIRX6z9+dMUlKSXfoRQgghRDoJRDyG9Hq9XZKOGvqwV3+i6JNjRlhLc+4cjoGBuK9dS7Fjx8y20ZctS+qAAUrwoVs300oXcnw9tuz9OSOfVUIIIYT9SSDiMaTRaOxypUij0ahXneQKt7CGHDPCIr0e7bFjOKxbh0NgINqzZ80209WsqQQeBg1C1769WulCjiZhYO/PGfmsEkIIIexPAhGPIRcXF1xdXe3Sl+HLnr36E0WfHDNClZpqWukiLMxss7QmTdANGkSxp59G26wZWo2GYnk7UlHI2PNzRqfT2WFEQgghhDAmgQghhBB5x7jSxfr1SvLJRxlVukjs2xddzZpoNBqKSfBKCCGEEKJIyFYgQtZJCiGEyLZ795Sgw5o1sHmz+eSRTk7Qs2d6pYsKFQDQJyRIvgchhBBCiCLG6kDEsmXLAKjw8MuhEEIIYVF4OKxdqwQfduxQlmE8qkQJ6N8ffHygXz/TShdCCCGEEKLIsjoQ0a5du9wchxBCiMLu7Fkl8LBmDRw6ZL5NuXIweLAy86FnT9NKF0IIIYQQ4rEgOSKEEELkjF4PR4+mBx9OnzbfrmZNJfDg6wsdO6qVLoQQQgghxONJAhFCCCGsZ6h0ERCg/Lt+3Xy7Zs3Sgw/NmikJKIUQQgghhEACEUIIIbKSmJhe6SIw0HKli44dlcCDjw/UqZPnwxSPt6QkWLkSVq92IipKg6ennqFDYdgwcHHJ79GJx42Xl5fFx4oXL06pUqWoW7cuHTp0wNfXl7Jly2bZ58iRIzn0cNnbsmXLaN++fbbHZdyHMa1Wi5ubG+7u7pQuXRovLy8aNWpE165dqV69eraeIzk5maCgIIKCgjh58iR37twhMTERZ2dnypYtS/Xq1WnQoAEtW7akQ4cOlChRItuvQwhR+EkgQgghREb37sGGDUrwYdMm85UuihWDXr0yVLoQIq+tWwdjxiiHrVbrgE6nQavVs3YtTJoES5fCwIH5PUohFImJiSQmJnLr1i327NnD999/z8yZM/H19c23Mel0OmJjY4mNjeXGjRucPHkSf39/Pv74Y9q2bcv48ePx9vbOsp///vuPKVOmcPny5QyPJSQkcO3aNa5du8aePXsAKFOmDPv27bP76xFCFHwSiBBCCKG4cUNZbpFVpYt+/ZTgg1S6EAXAunXKJBwDnU5j8n90tJIfNSBAiZcJkdcWLVpkcj8hIYFLly6xfv16rl+/Tnx8PO+99x6lSpWiR48eeTauSZMmUb9+ffV+YmIiMTExhIWFERoaSkhICGlpaRw6dIjDhw8zfPhwpk+fjoOFPD8nTpxg9OjRJDwMXJcrV46+ffvi5eVFyZIlSUpKIiIigpMnT7J//35iYmJIS0vLk9cqhCh4JBAhhBCPs3Pn0pNNHjxovk25csoZnKHShcxzFwVEUpIyEwKU3Knm6PXKyqExY5RYmxy++WfnhzvZMWsH3T7oRteZXfN7OHmmV69eZrePHz+et99+m82bN6PX6/nss8/yNBDRunXrTJd3hIeH8+OPP/L333+j1+v5448/0Ol0zJ4922z7999/Xw1C+Pr68sEHH+BsoTJSamoq+/btY+PGjTa/DiFE4SSBCCGEeJwYV7oICIBTp8y3k0oXohBYuVJZjpEVvV5pt2oVjBiR++MSGe38cCc73t8BoP7/OAUjzHFycmL27Nls27aNlJQULl++zMWLF6lTQHLsVKlShTlz5tCqVSumTp0KwJ9//kn79u156qmnTNpeuHCBkydPAlCpUiU+/PBDihUrZrFvR0dHnnjiCZ544oncewFCiAJNm98DEEIIkctSU2H7dnjjDahRA9q2hU8+yRiEaNoU3n8fgoPh0iX46ivo0kWCEKJAunYNvvnG+vZarRJ/E3nPOAhhsOP9Hez8cGf+DKgA8fT0pG7duur9K1eu5N9gLPDx8WH06NHq/UWLFqHT6UzaXLp0Sb3dokWLTIMQQggBMiNCCCGKJkOli4AApdLF3bsZ20ilC1GIxMQoqUv+/Re2bFFWFWWHTme+4IvIXeaCEAYyM0JhvHzhwYMH+TgSy8aNG8dff/3FgwcPOH/+PCEhIbRq1Up9PNUop9Bdc39vhBDiERKIEEKIoiI6Gtavz7rSRc+e6ZUuKlbM82EKYY3UVDh0SAk6bNkCBw6ALXnttFrw9LTf+ETWMgtCGDzuwYjU1FSTChOVKlXKx9FY5unpSadOndi2bRsAhw4dMglE1KhRQ70dHBzMf//9R7NmzfJ8nEKIwkMCEUIIUZjduAFr1yrBh+3bM6904eOj/F+qVJ4PU4is6PVw4YISdPj3X+Vwjokx39bBATp0gPLlrV9uodMp8TeRN6wJQhg8zsGI33//nfv37wPg7u5OvXr18nlElrVs2VINRBw/ftzksUaNGlGnTh0uXrxISkoKo0eP5rnnnqNPnz40btxYlmoIITKwORBx+PBhm/bXaDSUKFGCkiVLUrlyZVuHI4QQRZ+h0kVAgHKZ2BypdCEKgbt3YevW9FkPV69ablu/PvTpA717Q7duSuXYpCSoXFmZDGSpagYoq5A8PMDPz84vQJiVnSCEweMUjEhMTOTSpUusXr2aP//8U90+cuRISpQokY8jy5zx9/SoR9Y5aTQaPvnkE8aMGUNiYiIJCQn8/PPP/PzzzxQrVgwvLy8aN25Mq1at8Pb2pkKFCnk9fCFEAWNzIGLkyJFoNBp7jIXixYvTuHFjBg4cyIABA3B1dbVLv0IIUajp9XDsWHqZTUuVLmrUSK900amTJJkUBc6DB7BvX3rg4ehRywGEMmWgVy8l8NC7N1SvnrGNiwssXQqDByvBBnN9Gb6iLF0q8TiAkytPsuP9HTyIzZ1cBA9iHpAcm5yjfXe8v4N9n+/DuaT5ko+2cHZ3pvuH3Wnk18jufWfFy8sryzaDBg1iwoQJeTCanCtZsqR6Ozo6OsPjLVq0YOXKlXz44YccNCoHnZKSwokTJzhx4gR///03Wq2WDh06MGHCBFq3bp0XQxdCFEB2WZqhz+wyRDYkJCRw5MgRjhw5wvfff88nn3yCt7e3XfoWQohCJTUV9uxJn/lw7Zr5dk2bpiebbNEi/axLiAJAr4eTJ9OXW+zaZT51CYCTE3TunB54aNlSyeuQlYEDlV+RMWOUEp1arR6dTqP+7+GhBCEGDrTjCyvE9n2+jztn7uT3MCxKjk3OcSAjM7HEsu/zffkSiMhMuXLl+PTTT+nUqVN+DyVLxt/3LV2ErFevHsuWLeP8+fNs3ryZo0ePcvz4cWJjY9U2Op2Offv2sX//ft544w3Gjx+f62MXQhQ8Ngci2rZtq94ODQ0lJSVF/aAqXbo0FStWxNXVlcTERG7duqVO5dJoNDg5OdGsWTNSU1O5f/8+165dU7Pu3rx5k1deeYUlS5bQoUMHW4cphBAFX2Kicsa2Zk3mlS68vdODD0Zl34QoCG7dSp/xEBQEN29abtu0afpyiy5dIKcTIQcNUtKlrFoFq1alERWlwdNTj5+fI35+MhPCWKcpndg+c3uBnBEB4OTulGszIjq+09Hu/Vpj0aJF6u3k5GRu3LjBv//+S2hoKJGRkXz//fc0a9YMd3f3fBmftWKMkrZ4eHhk2rZevXpqvgu9Xs/169cJCQlh586dbN68WT1fmD9/PtWqVWOgRAqFeOzYHIhYvnw58fHxTJs2jeTkZEqUKMELL7zAoEGDqFatWob24eHhrF27ll9//ZW4uDjKlCnDJ598gqurK0lJSWzevJn58+dz48YNUlJSmDp1Klu2bMHJycnWoQohRMETHQ0bNqRXuoiPz9imWDHo0UMJPgweLJUuRIGSkKDMdDAEHx7JYWeiUqX0GQ+9etn3UHZxgREjYMiQZPR6PRqNBldXycn9qEZ+jXJ9VkBOckQAdJvTrUjmiOjVq1eGbS+99BK//fYbc+fO5fDhw0ycOJFffvkFrTXTgPJJeHi4etszGyVoNBoN1atXp3r16gwaNIg333yTl156iStXrgCwYMECCUQI8Riyy1/oqVOnsnXrVmrUqMGvv/6aadLJKlWqMH78eHx9fRkzZgybN28mLS2NBQsW4OLiwuDBg+nWrRvDhw/n4sWL3L59m4CAAJ5++ml7DFUIIfKfNZUu3NyUChe+vlLpQhQoOh0EB6cvt9i7F5ItXAB3dYWuXZXAQ58+0KiRrB56HBiCCdkJRhTVIERmxowZw/Hjx1m/fj379+9n2bJljBkzJr+HZVFISIh625bSnNWqVWPevHk8++yzAFy9epWwsDCqVq1q6xCFEIWIzYGIoKAggoKC0Gq1zJ8/3+rKF5UqVWL+/Pn4+vqqfRgixqVKlWLOnDk8//zzAOzevVsCEUKIwu38+fRkk5YqXZQtm17polcvmU8uCoyrV9NnPGzdan7VEChBhtat05dbeHuDs/1n2YtCIDvBiMcxCGEwdepUgoKCSEpKYtGiRQwePJjSpUvn97AyuHv3Lnv37lXvt2vXzqb+WrRogaurKwkPk8ZERkZKIEKIx4zNgQh/f39AiYw2aNAgW/s2aNCAFi1aEBwcjL+/v8nUtdatW1OjRg2uXr3KKUsZ4oUQoqAyVLoICFCCDydPmm9nXOmiY0dwlKnkIv/FxCiTdQzBh3PnLLetWTN9uUWPHkq1CyHAumDE4xyEAChfvjzPPfccv/76KzExMSxevJipU6fm97Ay+OGHH0h+OPXJy8uL5s2b29SfRqPB0ejvnVTKE+LxY/M33jNnzqDRaKhTp06O9q9duzbBwcGcOXMmw2ONGjXi6tWr3Lt3z9ZhCiFE7rO20kWTJunBB6l0IQqA1FQ4dCh9ucXBg5CWZr5tyZJKwMGw3KJOHTmEhWWZBSMe9yCEwYsvvsgff/xBcnIyf/75J2PHjqVs2bL5PSxVQEAAy5YtU+9PmDAhQ9WMmJgYXFxcrM7pdujQITX5pYuLC9XN1ecVQhRpNgci7txRSkAlW1ogmoWUlBSTfowZ6hWnmls/LYQQBUFiolIaYM0aWLcu80oXPj5K8EEqXYh8ptfDhQtK0GHLFmX2g1FCfBMODtChQ/pyi7ZtZeKOyB5zwQgJQqQrX748Q4cO5c8//yQxMZHFixczbdq0/B4WN27c4IcffuDvv/9Wt40YMYI+ffpkaBsSEsL06dMZPXo0gwYNonz58hb7PXPmjMmsjz59+lC8eHH7Dl4IUeDZ/FXC3d2dqKgo/vvvvxztHxoaqvbzqAcPlNJSWZUIEtmTlJRkl6zMSUlJamZyIaxRZI6Z6GgcNm3CITAQhy1b0JipdKEvVgxd166kDRpEar9+SrkAg4drYkXWiswxUwDcvQs7djiwbZsD27ZpuXbN8t+B+vV19OiRRo8eOrp0SePhdQFASUyZw2sPecLex0xSUpJd+nncqcGIWTvo9oEEIR718ssvs2rVKlJSUvjrr78YO3YsFSpUsNh+1apV7Nu3z6q+x48fj7OZZC1Hjx4lNjZWvZ+UlERsbCzXr18nNDSU4OBg0h5OjdJoNIwYMSLTAMnt27f5/PPP+fLLL2nevDktWrSgZs2alCpVirS0NG7evMnhw4fZs2eP2m/FihV55513rHodQoiixeZARP369dm/fz/Xrl1jw4YN9O/f3+p9N2zYwNWrV9FoNGqtYWNhYWEABTJpT2Gm1+vR6/V26cee/YmirzAfM5pbt3BYv14JPuzahebhbC5jejc30nr3JnXQINL69jWtdFHIXm9BUZiPmfz24AEcOKBl+3YHtm51ICREi15v/uTc01NP9+5pD4MPaVSrZvpeF6a33t7HjBx39tN1ZlcJQFhQpUoVBg4ciL+/Pw8ePODHH3/k/ffft9h+3bp1Vvc9duxYs4GI+fPnZ7mvRqOhbdu2vP7663To0MFiuzJlylC+fHlu376NTqcjODiY4ODgTPvu0KEDc+fOzXT2hBCi6LI5ENGvXz/2798PwPTp09FqtTz11FNZ7rd582ZmzJih3n80gJGcnMypU6fU2sPCfjQajV2uFGk0GvWqk1ytFNYobMeM5sIFHNatw2H9erSHDqExc0KiL1uWtH79SB00CF23bvBwemnBf3WFQ2E7ZvKTXg+nTmnYulWZ9bB3r5aEBPPvmZOTHm9vHT17KoGH5s31mE6UK7zvtb2PGTnuRF559dVXWbt2LWlpaaxcuZKXX36ZSsaz6XKRVqvF1dWVEiVK4OnpiZeXF40bN6Zr165WfQ9v3Lgxu3bt4vjx4xw8eJDQ0FAuX75MREQECQkJODo64u7uTo0aNWjSpAl9+/aldevWefDKhBAFlc2BCMOattOnT5OUlMT//vc/li9fzqBBg2jevDkVK1akePHiJCYmEhERQWhoKIGBgRw9elT9otCwYUOGDh1q0u/27dtJSEhAo9HIB5Wdubi42C07seFnKNmOhbUK9DGj10NwcHqZTUuVLqpXV5NNajp1wtHR0fYPU2FRgT5m8tnNm0qKki1blP9v3rTctlmz9OoWXbpocHV1ABzybKx5yZ7HjE6ns8OIxOPm7Nmz2d6nZs2amVaKW758uS1Dslsflmg0Gpo1a0azZs1y7TmEEEWHzd+dtVot33//PaNGjeLq1asAVk3HMqhatSrfffddhpwFmzZtonLlygD07t3b1mEKIYR5hkoXAQHKv4efYxkYKl34+EDLllImQOSLhATYtSu9usWJE5bbVqqUHnjo1QsqVsy7cQohhBBCZMYuF/EqVKjA33//zccff0xgYKDV+w0YMIDp06ebzQHx9ddf22NoQgiRUVKScia3Zg0EBoKZqj2AUunCEHwwk8dGiNym0ymTdAzVLfbutZwo0tUVunVLDz40aiTxMiGEEEIUTHabTezh4cHnn3/OuHHjWL16NYcOHeLs2bNqeU4AR0dHvLy8aNeuHX5+ftSpU8deTy+EEJm7fx82bFCCDxs3gplKFzg6Qo8eSvBh8GDTShdC5JGrV5Wgw5YtsHWr+YqwoAQZ2rRJDzx4e4OZfHRCCCGEEAWO3Zc116lThylTpqj3Y2NjSUhIwNXV1WyJTiGEyDU3b8LatUrwYft2MFPpAjc3eOopJfjQrx9IuWCRx2JilMPTsNzi/HnLbWvWTA889OgBZcrk2TCFEEIIIewm1/Orubu7SwBCCJF3LlxITzZ54ID5uoNlysCgQUrwoVcvtdKFEHkhNRUOHUpfbnHwIKSlmW9bsiT07JkefKhTR5ZbCCGEEKLwk0TvQojCzbjSRUCA5ex9RpUu6NRJWYYhRB7Q65VZDoblFtu3K7MgzHF0hA4d0gMPbdvKoSqEEEKIoke+3gghCp+0NKXShSH4YKnSRePG6cEHqXQh8tDdu0p+B8Nyi2vXLLf18lKCDn36QNeuyiwIIYQQQoiiLNcCEQkJCcTFxZGammr1PoZynUIIkUFSEgQFKcGHdesyr3Th46MEH6TShcgjDx4oFS0Msx6OHTO/KgigbFllRZChrGb16nk7ViGEEEKI/Ga3QIROpyMwMJANGzZw/PhxoqOjs7W/RqPh1KlT9hqOEKIA0m7bhtPbb5P8xRcwYEDWO0ilC1FA6fXKKiBD4GHXLkhIMN/W2Rk6d05fbtGiBWi1eTpcIYQQQogCxS6BiLCwMF5//XXOnTsHgN7SZSAhxONLr6fYrFloz56l2KxZ0L+/+aUSt26lV7rYts18pQtX1/RKF/37S6ULkSdu3lQm5RiCD7duWW7brFn6covOnZVDVgghhBBCKGwORCQmJjJmzBjCwsJMtru4uFCyZEkcJcuWEALg339xOHYMQPn/33+hb1/lsQsXlFwPa9bA/v1S6UIUCPHxykwHQ+DBUh5UgMqV02c89OoFFSrk3TiFEEIIIQobm6MEy5YtIywsDI1Gg4ODA6NGjWLo0KHUqVPHHuMTQhQFej3MnInewQFNWpry///+B0OGZF7polq19GSTnTtL+QCRq3Q6JbeDIfCwdy8kJ5tv6+oK3bqlBx8aNZJcqEIIIYQQ1rL5W31QUJB6+8svv6Sv4QqnEEIY/PsvHD6M4TxNk5YGp04p/x7VuHF6sslWreTsTuSqq1fTAw9BQRAVZb6dRgNt2qQvt/D2BienvB2rEEIIIURRYXMg4urVq2g0Gho1aiRBCCFERno9TJ+unMlZyh/ToUP6zAepdCFy0f37sH17evDh/HnLbWvWVIIOvXsr+VA9PfNsmEIIIYQQRZrNgYjkh/NWGzZsaPNghBBF0KefwtGjlh//4w8YPjzvxiMeKykpcOhQeuDh4EFISzPftlQpJeBgWG5Rp45MyBFCCCGEyA02ByIqVKjAtWvXSE1Ntcd4hBBFxZ078NZbsGyZ5TYODvDNN/Dcc3LGJ+xCr1dmORgCD9u2QWys+baOjspkHMNyizZtJA2JEEIIIUResPkrV9u2bbl69apaulMI8ZjT65VZDpMnK8GIzKSlweHDphU0hMimO3dg69b04MO1a5bbNmiQPuOhWzdwd8+zYQohhBBCiIdsDkQ8++yz+Pv7c/r0aU6cOEGTJk3sMS4hRGF08SK89ppyNmgtBweYOVO5JC2zIoQVHjxQKloYAg/HjllOP1K2rFJO0xB8qFYtb8cqhBBCCCEysjkQ0aRJE8aNG8d3333HW2+9xR9//EHZsmXtMTYhRGGRkgJffQWzZ0NSUvb2lVkRIgt6PZw8qWHPHiXwsHMnJCaab+vsrFR6NSSZbN4ctNq8Ha8QQgghhMicXVbDvvHGG2i1WhYtWsTAgQMZN24cTz75JBUqVLBH90KIguzQIXj5Zfjvv/RtVatC8eLKDAmdLus+tFqZFSFM3LypBB02bnRi+3YtERGWownNm6fPeOjSRTn0hBBCCCFEwWVzIKJnz57pnTk6cu/ePebNm8e8efNwd3enRIkSaKw4sdBoNAQFBdk6HCFEXomNhRkzYMGC9HnxWi1MmqSU62zc2LogBCjtrl+H5GTlkrZ47MTHw65d6cstTpwwPJLxz1TlyumBh169QGLeQgghhBCFi82BiPDwcJNAg+G2Xq8nJiaGWEvpyo3o9XqrghVCiAJi3Tp4/XUIC0vf1qIFLFmilB4AZblFZKTJbolG8+mLP3rZunx5CUI8RtLSIDhYCTr8+y/s26fEocxxc9PTubOOp55yoHdvaNhQJs4IIQo2Ly+vbLVv164dy5cvz6XR5L4TJ04wdOhQADw9Pdm1axfFihXLVh8bN27kzTffBKBp06asWrVKfWzkyJEcOnQIgGXLltG+fXv7DBz46aef+Pzzz9X733zzDU899ZTd+jcwfg3GtFotbm5uuLu7U7p0aby8vGjUqBFdu3alevXqVvXt7+/Pe++9Z7JtyZIlPPHEE1bt/9Zbb7F+/XqTbWfPnrVqXyFyyi5LM/SWsoRl8ZgQopC5cQPeeANWr07fVrw4zJkDb75pWvuwWrUMmQH1CQnpgUdX17wZsygwrlxJn/GwdStERZlvp9Uq8SxlqUUS7dql4eyswVWOGSGEKJCaNGlCgwYNOHPmDFFRUezYsYPevXtnq4/VRt8t/Pz87D1Eq57XcD83AhGW6HQ6YmNjiY2N5caNG5w8eRJ/f38+/vhj2rZty/jx4/H29s52v6tXr7YqEBEbGyuz0kW+sDkQsXXrVnuMQwhRkOl0sHgxTJ0KMTHp2/v2he+/h1q18m9sosC6fx+2b08PPpw/b7ltrVrpyy169ABPT2V7QoLOYkUMIYQoDBYtWpRlGw8Pj9wfSC7z8/Pjo48+ApST4OwEIiIiIti7dy8ALi4uDBgwIFfG+KijR49y6dIlk2179+7l1q1bVKxYMdeed9KkSdSvX1+9n5iYSExMDGFhYYSGhhISEkJaWhqHDh3i8OHDDB8+nOnTp+Pg4JBl346OjqSmprJt2zaio6OzPLYCAwNJepho3LCvEHnB5kBElSpV7DEOIURBdfIkvPKKMnfeoFw5mD8fnn1W5sgLVUqKkrvUsNzi0CFlCYY5pUpBz57pwYc6dfJ2rEIIkVd69eqV30PIEwMHDuSzzz4jOTmZ3bt3ExkZSbly5azad82aNege5pXq27cvJUqUyM2hqoyXfwwZMgR/f390Oh3+/v6MHz8+1563devWmS4vCQ8P58cff+Tvv/9Gr9fzxx9/oNPpmD17dpZ9P/HEE2zbto3k5GQCAwMZOXJkpu0NM0IaN27MnTt3iIiIyNZrESKnpKiZEMK8pCSlkkXLlqZBiBdfhDNn4LnnJAjxmNPr4dw5WLgQBg+GMmWU0pkffAD795sGIRwdlYoWc+Yoj925o6zwGTdOghBCCFEUeHh4qLMgUlNTCQgIsHrfNWvWqLcNuSZyW1xcHJs2bQKgZs2aTJ8+HRcXF0DJuZCfy8urVKnCnDlz+PTTT9Vtf/75Jxs3bsxy3/r169OkSRMg47KTR507d44TD7ND59X7LoSBBCKEEBnt2KHURPzoI+UyN0C9erBtG/z8c/q8efHYuXMH/v4bXnoJatYELy+YOFHJX/pobuIGDdIfi4pSqmLMnAkdOpimExFCFG1JSbB8OQwdCt26Kf8vX65sF5lLSkri999/54UXXqBz5840adKE9u3bM3ToUL7++murr17r9XoCAgIYM2YMHTp0oFmzZvTs2ZN3332X48ePA8rJt5eXF15eXvj7++dovMa5Hazt48iRI1y5cgWA6tWr065duxw9d3Zt3LiRhIQEAAYNGkSJEiXU2SvXr1/n4MGDeTKOzPj4+DB69Gj1/qJFi9SZI5kxBBVOnz7NqVOnLLYzzAhxdnZm4MCBNo5WiOyRr4JCiHRRUfDOO/DLL+nbHB3h3XeVkpwPrxSIx8eDB7B3b/pyi+BgLOZsKFvWtKzmI7lKhRCPoXXrYMwYuHdPSUSr0yn/+/sr1Z6XLgU5/zHvv//+44033uDmzZsm26Ojo4mOjubEiRMsXbqUGTNmZJrcMT4+ntdff539+/ebbA8LCyMsLIx169YxdepU3N3dbR6zt7c3VapUITw8nEuXLhEcHEzLli0z3cf4qv2QIUPyrJKe4SRco9EwePBgAHx9fdXqEatWraJDhw55MpbMjBs3jr/++osHDx5w/vx5QkJCaNWqVab7DBgwgHnz5vHgwQP8/f1p1KhRhjYpKSmsW7cOUJYPlSxZMlfGL4QlEogQQihnln/9pVS+uH07fbu3t5Kk8uEUP1H06fVw/Hh6gsldu8Co6qoJZ2dluYUh+NC8uXKCIYQQoAQhfHzS7xsu5Br+j45WlnUFBMCgQXk8uALuzJkzjB49Wr1iX7duXQYPHkzVqlWJjo5m69at7Nmzh8TERKZPn45er2fYsGEZ+tHr9UycOFENQri6ujJ06FB16v6JEydYvXo1c+fOpW/fvjaPW6PRMGTIEBYsWAAosyIyC0TEx8eryyMcHBwYMmSIzWOwxoULFwgJCQGgbdu2VK1aFYCOHTtSoUIFIiIi2LJlC7GxsXYJ0NjC09OTTp06sW3bNgAOHTqUZSCiZMmS9O7dm/Xr1xMYGMiUKVNwcnIyabNt2zbu3bsHyLIMkT+sCkSMGjVKva3RaFi6dKnZx2zxaL9CiDxy+TKMHw8PvwgAULIkzJsHr74qZ5aPgZs30wMPQUFw65blts2bpwceunRRqrcKIcSjkpKUmRBgeRaVXq+kGhozRqkOLZPuFDqdjnfeeUcNQgwbNozZs2fjaLSmbfjw4axcuZKZM2ei1+v5+OOP8fb2Vk+oDfz9/dVqFBUqVGD58uXUqFFDfdww9X/kyJFqQMBWQ4YMUZcQ/PPPPya5Fx5lvDyiU6dOVKhQwS5jyIpxkkpfX1/1tlarZfDgwSxevJikpCQCAwMZPnx4nowpMy1btlQDEYalNFnx8/Nj/fr1REdHExQURL9+/UweN8xEqVy5co7KgwphK6sCEYcOHUKj0aDX6zNMlzI8Zgtz/QohcllqKnzzDcyaBQ+/BADK4t1vv4XKlfNtaCJ3xccrMx0Myy1OnrTctnJl6NNHCTz07Al59B1RCFHIrVypLMfIil6vtFu1CkaMyP1x5QcvL69MH2/QoAFr165V7+/YsYNz586p+37wwQdmyzYOGzaMEydO8Ndff5GYmMiyZcuYNm2aSZvffvtNvf3JJ5+YBCEMqlWrxty5cxljiBzZqHLlynTs2JE9e/aoCSF9jKfGGDFelpHZ8hJ7SklJUd/v4sWLZ5gJ4uPjw+LFi9XxFYRARGWj72RRUVFW7dOhQweqVq1KWFgYq1evNglEREREsGfPHkAJxGjlopPIB1Yvzcgsc2x+ZpUVQuTAkSNKSc7g4PRtVarAokXKPFlRpKSlwbFj6bMe9u2D5GTzbd3clGRyhlkPDRtKcRQhipKVK+H99zMml7W3u3ez1/7ll5V0RLnF3R0+/BDy6FzXJlu2bFFvv/jii2aDEAavvPKKWuJxy5YtJoGI69evqwGNunXr0rlzZ4v9eHt7U79+fbW9rfz8/NQTXX9/f7OBiMuXL3Ps2DEASpcuTY8ePezy3FnZtm2bejLfu3dv3NzcTB6vU6cOzZo147///uPEiROcOXOGBg0a5MnYLDHO3xAdHW3VPhqNBl9fXxYsWMC+ffu4desWFStWBCAgIIC0tDS1jRD5wapAxLJly3L0mBCigImLU76Bzp+fvkhXo4EJE5QKGZKoqMi4ciU98LB1q5KH1BytFtq0SQ88eHvDI8tIhRBFyOefKxWYC5qkJAgPz93n+Pzz/AlELFq0KNPHS5QoYXI/NDRUvd2pU6dM961SpQq1a9fm4sWL3Lhxg9u3b1O+fHnAdAp/+/btsxxn+/bt7RaI6NmzJx4eHkRHR3Po0CGuX79OtUcyGBtX1Rg8eDDFihWzy3NnxXgWhqWTcB8fH/777z9AWcYxY8aMPBmbJcYXfbMzi9x4mcyaNWt47bXXgPT3vl27dhl+LkLkFasCEZmV0cmrEjvCfpKSkuwyBSspKUmW1RQi2k2bcHrzTbTXr6vbdE2akLxwIbq2bZUNxks0coEcM7nn/n3YtcuBbdu0bN3qwMWLln/Ha9bU0bNnGj166OjaNY3SpdMfS01V/hUUcsyI7LL3MZNUxGpMTpmilNHNixkR2XnrXFygTJncG4+7u1IUKj8YSkJaKzIyEgA3NzfKlSuXZfuaNWty8eJFdV9DIOK2UfLp6tWrZ9lPZiekN27cyLQMZKVKlWjcuLF638nJiUGDBrFs2TL0ej1r1qzhjTfeUB9PS0sjICBAvZ9XyzKMlyRUrFjRYlWM/v37M3fuXFJSUswme4yKilJnc5jj4eFBmzZt7DbumJgYk76tZcj/sHfvXjUQYVwuVZJUivwkVTMeQ3q93i7LaQx92Ks/kTs0ERE4vfMOjkZXHvQuLqRMm0bKxIlQrJjlTGJ2JseM/aSkwOHDWrZtc2D7dgeOHNGSlmb+xMvDQ88TT6TRo4fyr3Zt0/e+IP8o5JgR2WXvY6aoHXd+fnkzK2D5cshOPvMlS4pujojsio+PB5QKF9YwbmfYF1CTQAIWk0Va6udRBw4c4L333rP4uK+vL/PmzTPZ5ufnp86cDggIYMKECeqFsN27d6uBkmbNmlGvXr0sx2cP/v7+pKWlATBo0CCLF+Y8PDzo0aMHmzdvNpvs8fz587z++usWn6ddu3YsX77cbuMON5ou5Onpma19hw4dyt69e7l69SqHDx9WZ0O4u7vbpVKKEDklgYjHkEajscuVIuMEpnK1sgDS6XBYuhSnGTPQGK0nTOveneRvv0VfuzZ5/VOTYybn9Ho4f17Dtm3KrIdduxyIjTX/Hjo66mnfXkePHmn07KmjZUsdjiaf9oXnvZdjRmSXvY8ZOe5yZtgwmDRJKdGZWSxHowEPj8KRuyGvuLm5ERMTYxJIyIxxO+N8B8aBBWtm9lj7fNby8vKiadOmHD9+nPDwcA4cOEDHjh0B02UZeTUbQq/XmyzLWLx4sZqUMiuPJnvMa4ZSo6AEbrKjd+/elCpVivv377N8+XJ2794NQL9+/awKUAmRWyQQ8RhycXGxOsqeFcOXPXv1J+zk9Gml9ObDPzYAlC0LX32Fw4gRFM/HL9ZyzFjvzh0lv4OhuoXRqpoMGjRIr27RtasGd3cHwHKCs8JEjhmRXfY8ZnSGfDoiW1xcYOlSJf+xRmM+GGH4U7R0qZTuNFauXDliYmKIj4/nzp07lC1bNtP2hmn2gLos49Hb165dy/J5r2fyR2bIkCEMGTIkyz4e5efnp+aqWL16NR07diQqKkotRVm8eHH69++f7X5z4uDBg5m+xszs27ePmzdvUqlSJUDJp3H27Fl7Ds+iu3fvqiVYIfvL4p2cnBgwYAB//PEHmzdvVrfnVQBICEskECFEUfLgAcydq/wzLoswahR8+aUSjBAFVlIS7N2bnmQyONjylcRy5aBXr/Qkk4+UjhdCiHw3cCAEBMCYMUqJTq1WyZNs+N/DQwlCDByYzwMtYJo3b67mfNizZ4/F0peg5G64dOkSoOQDMM4p0bRpU/X2wYMHs3xea9pk14ABA5g3bx6JiYkEBQURGxvLunXrSElJAaBv374ZknXmllWrVqm3+/bta9VykODgYPbu3YtOp8Pf3z/T5Ri55YcffiD54Xc6Ly8vmjdvnu0+hg4dyh9//KHer1evXrZnVghhb1YFIjJbE2YvGo2GTz75JNefR4gia9cuZRaEcTr0OnXghx+UM1ZR4Oj1cPx4euBh1y5ITDTf1tkZunRJn/XQrJnyZV4IIQqyQYPgxg1YtQrWrFEq+Hh6gq+vshxDZkJk1KdPH3Xpwq+//srAgQMtlvBcsmSJmsekT58+Jo9Vq1ZNLcl54cIF9uzZY7GE5/79++1WMcNYiRIl6Nu3LwEBASQlJbF+/XqTZRl5lSwxJiaGf//9FwBHR0dmz55tVa6FM2fOMPhhWXN/f3/Gjx+fp8u1AgICTCoUTpgwIUfP37hxY5588klu3rwJwNNPP223MQqRU1YFItasWZMnv3QSiBAiB+7dg6lTlUxfBo6OSnrwmTOhePH8G5vI4MYNCApKDz5ERFhu27x5euChc2f5UQohCicXFyURpSSjtE7Xrl3VAMKZM2eYPXs2s2bNwtE02Q/+/v789ddfgLLEYZSZ7KBjxoxh2rRpAEybNo3ly5dTo0YNkzbXr1/P1YuOfn5+aoWM7777Tk1SWaNGDdoaqnblssDAQB48eABAly5drE742KBBAxo2bMjp06cJCwvjwIEDeHt75+ZQAWWmyw8//MDff/+tbhsxYkSGYFN2zJ8/3x5DE8JurF6akZ2s0YZEUdl5XJJBCZFNej2sWKFkAzM+m23fHhYvVi6Zi3wXHw87d6YHHk6etNy2SpX0pRY9e0KFCnk3TiGEEAWDVqvl888/57nnniMhIYEVK1YQEhLCoEGDqFKlCvfv32fr1q1q0kGA6dOnU6VKlQx9DRkyhA0bNrB3714iIiLw8fFh6NCh6rKN48ePs3r1ahITE3nyySfZtGmTOgZ7adu2LTVr1uTKlSsmJUWHDBmSo+//q1atYt++fVa1HT9+PM7OziZJKjNb6mKOj48Pp0+fVp/bHoGIo0ePEmtUQzcpKYnY2FiuX79OaGgowcHBanUPjUbDiBEj1ICSEEWFVYGIuXPnZtnGELkzrPlq0aIFLVu2pFKlShQvXpzExERu3rxJSEiImvnVycmJV199lcqVK+f8FQjxOLp6FcaPh3/+Sd/m7g6ffAKvvQYWpnCK3JeWBseOpQce9u5VSm2a4+YG3bqlz3po0CA9eZsQQojHV4MGDVi6dCkTJ07k1q1bnDt3ji+++CJDu+LFizN9+nSGDRtmth+NRsOCBQsYP348Bw4cICEhIUNZSQcHB959913c3NzUQIRx9Q17GDp0KF9++aXJc/r6+uaor3Xr1lndduzYsVy6dImTD68ClCpVih49emTr+QYOHMjnn39OamoqW7ZsISYmhpIlS2arj0dZMztBo9HQtm1bXn/9dTp06GDT8wlREFkViMjqgyI0NJR58+aRmppKp06dmDlzJjVr1rTY/urVq3z00Ufs3r2bZcuWsXjx4hwlXhHisZOaCgsWwIwZYFxma/BgWLhQMhbmkytX0itbbNumrIE2R6uFtm3TZz106ABOTnk6VCGEEIVEs2bN2Lx5MytXrmTr1q2cP3+e+/fv4+rqStWqVenSpQvDhw+nQhbT59zc3Pjtt99Yu3Yta9as4cyZMyQkJFCuXDnatm3LiBEjaNq0qUkpy1KlStn1tfj4+PDNN9+oV/k7d+6c5bjtxThJ5VNPPYVTNv/wlilThi5durB9+3YePHhAYGAgzz//vN3Gp9VqcXV1pUSJEnh6euLl5UXjxo3p2rUr1atXt9vzCFHQaPTZWXNhxv379xk8eDARERH069ePL774wqppVnq9nrfffpsNGzZQsWJFAgIC8PDwsGUowoK4uDiTEkNeXl52yVCckJAgZfXyUnAwvPwyHD2avq1yZSUAkcOrCnmtqBwz0dGwfXv6rIcLFyy3rV07PfDQoweULp1nwywSisoxI/KOvY+Z3Pobai/nz58nNTUVR0dHq6oACGHJxIkT1YSOhw4dsnswQgjxeLD275LN5TtXrlzJrVu3cHV15YMPPrB6rZdGo+GDDz5g+/btREREsGLFCl555RVbhyNE0RMfD7Nnw9dfK/P+QZm//9prylIM+aKQ61JS4ODB9MDDoUPpP4pHeXgoAQfDcovatfN0qEIIIUS2hYWFsX37dgAaNmwoQQghRK6zORCxefNmNBoNHTp0yPYVghIlStChQwe2bdvGv//+K4EIIR61aZMScLhyJX1b48ZKhYw8yNr8uNLr4dy59OUWO3aAUU4pE46O0LFj+qyHNm0kRYcQQoiC48KFC3h6elqsFHHr1i0mTJig5nl77rnn8nJ4QojHlM2BiLCwMADKli2bo/0N+4WHh9s6FCGKjogImDwZ/vwzfZuzM7z/Prz9tiQWyAV37piW1bx+3XLbhg3TAw9duyp5QoUQQoiCaOfOnXz99dd06NCBVq1aUbVqVZycnLh37x6hoaFs2rSJxMREAFq1aoWfn18+j1gI8TiwORCR8DBhXmRkZI72N+yXYJx4T4jHlV4Pv/6qBBvu3Uvf3r07/PgjyPpfu0lKUipaGAIPwcHK229OuXLQq5ey3KJXL8kJKoQQonBJSUlh9+7dJiU/H9WxY0fmz5+Pg0zrE0LkAZsDEeXKlSMsLIwDBw4QGxuLezYuDcbGxnLgwAE0Gg3lypWzdShCFG7nzsGrryrrAAw8PeHLL2H0aKnraCO9Ho4fT19usXs3PLwAlIGLC3Tpkj7roVkzpeKFEEIIUdj4+vri7OzM/v37uXLlCtHR0dy/fx8nJyfKli1LixYt6N+/P127ds3voQohHiM2ByK8vb1ZuXIlSUlJvP/++3z11VdWJ6ycNWsWiYmJao4JIR5Lycnw2Wfw0Ufw4EH69uefh6++gvLl829shdyNG+kzHoKClBUvlrRokR546NwZihfPs2EKIYQQucbT05MRI0YwYsSI/B6KEEKobA5EPPfcc/j7+6PT6di0aRMxMTFMnz6d2pmkir98+TIff/wxe/fuBZT6ucOHD7d1KEIUPnv3wiuvwKlT6dtq1YLvv4e+ffNvXIVUfDzs3JkefDh50nLbKlWUoEOfPtCzp8R7hBBCCCGEyCs2ByIaNWrESy+9xI8//ohGo2Hfvn3079+fxo0b06JFCypXroyLiwtJSUncuHGD0NBQTpw4AYD+4YLsl156iUaNGtk6FCEKj+hoeO89+OGH9G0ODvDWWzBrFri65tvQCpO0NDh2LH25xb59SqlNc0qUgG7d0mc9NGggq12EEEIIIYTIDzYHIgAmT56MXq9nyZIlanDh5MmTnLRwOdLQRqPR8OKLLzJ58mR7DEOIgk+vB39/mDgRbt5M396mjVKSs0WLfBtaYXH5cvqMh61bTXN6GtNqoW3b9MBDhw5SbEQIIYQQQoiCwC6BCID//e9/dOnShS+//JKQkBA12GBJy5YtmTx5Mu3atbPXEIQo2K5fh9dfh8DA9G1ubvDxxzBhgjIjoohKSoKVK2H1aieiojR4euoZOhSGDVMSQ2YmOhq2b08PPly4YLlt7drpyy26d4fSpe36MoQQQgghhBB2YLdABEDbtm3566+/uHjxIgcPHuT06dNERUWRkJCAq6srnp6eNGzYkPbt21OnTh17PrUQBVdaGixaBNOnQ1xc+vYBA5Tt1avn39jywLp1MGaMMnNBq3VAp9Og1epZuxYmTYKlS2HgwPT2KSlw8GD6cotDh0CnM9+3h4eS38Ew6yGT1DRCCCGEEEKIAsKugQiDOnXqSKBBCIDQUHj5ZTh8OH1bxYqwYAEMHVrkkxSsWwc+Pun3dTqNyf/R0TB4MCxcqMRrtmxRqpfGxprvz9EROnZMDzy0aVOkJ5IIIYQQQghRJOVKIEKIx15CAsyZA198oZxhG7z6Ksybp1zKL+KSkpSZEKCkxjDHsP311y3307ChstSid2/o2lVJOimEEEIIIYQovCQQIYS9/fsvjBunZFU0aNgQFi+Gzp3zb1x5bOVKy4kkM1O+PPTqpQQeevWCqlXtPzYhhBBCCCFE/pFAhBD2EhkJ//sf/P57+jYnJ5gxA6ZMAWfn/BtbPggIUCpXWMrv8KgmTZS3rmlTZT8hhBBCCCFE0ZQrgYj4+HjOnDnDvXv3iI+Pz7KChoGP8WJyIQoLvR6WLVOCEFFR6dufeEKZBeHllX9jy0d371ofhAAoWxaaN8+98QghhBBCCCEKBrsGIgIDA/n99985fvy41cEHA41GI4EIUfhcuKDkfdi2LX2bh4eSG+KFFx7rS/uenkouTms+CrRapb0QQgghhBCi6LNLICIpKYk333yTnTt3AmQahNBoNNkOUghR4KSkKMGGOXOUrIwGzz4LX3+tVMZ4jEVGwsWL1gUhQJk54eubu2MSQhRxaUlwbSVOV1ajSY5C7+QJNYdC9WHg4JLfoxNCCCGEEbsEIqZPn86OHTsAcHZ2pn379oSFhXHp0iV1pkN8fDzh4eGcPXuW1NRUNBoNxYsXp0+fPmiKeAlDUcQcOKCU5DxxIn1bjRrw3XfQr1/+jauA2LkThg+HGzesa6/RKJNI/PxydVhCiKIsbB3sHwMp93BAiwYderRwcy0cmQTeS6HqwPwepRBCCCEesjkQERoayoYNG9BoNFSvXp1ffvmFKlWq8OGHH3Lp0iUA5s6dq7aPi4tjxYoVLFq0iISEBO7evcvXX39NCanJJwq6mBiYNk0JOBgu9Wu18Oab8MEHj31dybQ0+OgjZZKIITdEqVLK2wbmZ0cYYpBLl4KLXLAUQuRE2DrY5aPe1aAz+Z+UaNg1GJ4IgKqD8nx4QgghhMjI5gXsa9asUW9/8sknVKlSJdP2JUqU4MUXX2T16tWUK1eOPXv2MG3aNFuHIUTuCgiARo1g0aL0M+pWreDQIfjyy8c+CHHjhlJuc/bs9CBEjx5w+rTy1nl4KNu0Wr3J/x4esHYtDJQLlUKInEhLUmZCAGBpLdjD7QfGKO2FEEIIke9sDkQcPXoUgOrVq9O6dWur96tZsyaffvoper2eLVu2qEs7hChQwsNhyBAlgUF4uLLN1VUJPhw8CNk45ouqzZuhRQvYvl25r9XChx/Cv/9CpUowaJASqFi+HAYOTKNLlzQGDkxj+XJluwQhhBA5dm0lpNzDchDCQA/J9+DaqrwYlRBCPPbCwsLw8vLCy8uLd999N7+Hk6v8/f3V1+rv75/fwyk0bF6acfv2bTQaDQ0bNjTZbpz3ITk5GScnpwz7ent7U69ePS5cuMC6devo1q2brcMRwj7S0uCHH+C99yA2Nn37U08pSzNq1sy3oRUUKSkwcyZ8+mn6tipV4P/+T6lcaszFBUaMgCFDktHr9Wg0Glxdc6V6sBDicRIWgHJNxZpawVoIWwO1RuTumIQAvLJZurtdu3YsX748l0aT+06cOMHQoUMB8PT0ZNeuXRQrVixbfWzcuJE333wTgKZNm7JqVXrgcOTIkRw6dAiAZcuW0b59e/sMHPjpp5/4/PPP1fvffPMNTz31lN36NzB+Dca0Wi1ubm64u7tTunRpvLy8aNSoEV27dqV69epW9e3v7897771nsm3JkiU88egXMgveeust1q9fb7Lt7NmzVu0rRE7ZPCMiPj4eAA/D3OuHnJ2d1dtxcXEW92/UqBF6vZ6TJ0/aOhQh7OP4cejcGSZMSA9ClC8Pf/0FGzZIEAK4ehW6djUNQvTvDyEhGYMQQgiRax7cxbogBEq7B1G5ORohHltNmjShQYMGAERFReVopvPq1avV2355mMHa+HnN3c9tOp2O2NhYbty4wcmTJ/H39+ejjz6iT58+jBw5kv379+eoX2tfR2xsLEFBQTl6DiFsYfMlSRcXF+Lj40lNTTXZXrJkSfX2jRs38PT0NLu/oZTn7du3bR2KELZJTFSyLX72GRgfzy+9pJxxWziGHzdr18ILL8C9e8p9R0eYNw8mT1aWZQghRJ5IiYHEiGzsoAVn+RwXeW/RokVZtnn0gl5h5Ofnx0cffQQoJ8G9e/e2et+IiAj27t0LKOcWAwYMyJUxPuro0aNqcn2DvXv3cuvWLSrmYin2SZMmUb9+ffV+YmIiMTExhIWFERoaSkhICGlpaRw6dIjDhw8zfPhwpk+fjoODQ5Z9Ozo6kpqayrZt24iOjs7y2AoMDCTpYSl6w75C5AWbAxGVKlXiwoULREdHm2yvaXTVOCQkhCZNmpjd/8KFC7YOQQjbbd0K48aB8fHo5QU//qhc+hc8eABTpsC336Zvq1lTmShixxmSQgiRubQHcOFHOPEhPLiTjR11UNU314YlhCW9evXK7yHkiYEDB/LZZ5+RnJzM7t27iYyMpFy5clbtu2bNGnQPs1337ds3z6rpGS//GDJkCP7+/uh0Ovz9/Rk/fnyuPW/r1q0zXV4SHh7Ojz/+yN9//41er+ePP/5Ap9Mxe/bsLPt+4okn2LZtG8nJyQQGBjJy5MhM2xtmTjRu3Jg7d+4QEZGdAK8QOWfz9cv69euj1+u5fPmyyfZmzZqpeSL+/vtvs9G1PXv2cOrUKTQaDdWqVbN1KEJk3507MGYM9OqVHoQoVgzef19ZZyBBCEB5azp2NA1C+PlBcLAEIYQQeUSvgyv/B+sbwtFJ2QxCaMCpNFTPu+neQjxuPDw81FkQqampBAQEWL2vcRU+Q66J3BYXF8emTZsA5QLq9OnTcXlYS9zf31+dtZ0fqlSpwpw5c/jUaA3sn3/+ycaNG7Pct379+uoF4KyWZ5w7d44TJ04Aefe+C2Fg84yINm3asGHDBi5fvmwy/adSpUq0bt2aI0eOcOHCBcaPH8+bb75JvXr1SEpKYuvWrcybN0/tp3v37rYORQjr6fXwxx/KeoI7Rl9mO3WCxYuVUp0CUGY8vPJKeroMZ2f4+mtlAolRTlohhMg9N7dAyFS4F2y6vcZzUKE7HHr14QZzJw4PP6g6LAUHl9wcpbAkLUmpcBIWoOT1cC4DVX2g+jD5mWQhKSmJVatWsXXrVs6fP090dDRubm5UrVqVzp07M3z4cCpUqJBlP3q9nrVr1xIQEMCZM2dISEigXLlytG3blueff56mTZuaJDycO3cuQ4YMyfZ4/fz82LBhA6CczL/88stZ7nPkyBGuXLkCKFX42rVrl+3nzYmNGzeSkJAAwKBBgyhRogS9evVi/fr1XL9+nYMHD9KhQ4c8GYslPj4+nDp1iqVLlwLKMp++ffuizWIt7NChQzlx4gSnT5/m1KlTNLLwvdYwI8TZ2ZmBAwfy448/2vcFWCktLY1169axefNmTp06xb1793BxcaFixYp07NiRZ599llq1amXah06nY8OGDWzcuJHTp09z9+5d9Ho9Hh4elC5dmpo1a9K+fXv69etH6dKlM+yfnJyMv78/QUFBnD17lujoaLRaLaVLl6Z06dLUqVOHjh070rdvX9zc3Gx6vdu2bWPTpk0EBwdz584ddDodZcqUoVWrVgwZMoSOHTsWmdeaGZsDEV27dkWj0aDX69mxYwc+Pj7qY2+99RbDhw8HYPfu3ezevdtsH6VLl2b06NG2DkUI61y8CK+9Blu2pG8rVUrJA/Hyy5Lo4KGEBJg0CX76KX1b/frw999KuU4hhMh1Uccg5F24tcV0e8Ve0OJT8Gyl3HepAAfGQPI99GjRoFP/x8lDCUJUlVrB+SJsHewf87DMqqHCiRau+8ORSeAtPxtL/vvvP9544w1u3rxpsj06Opro6GhOnDjB0qVLmTFjRqbJHePj43n99dczJD0MCwsjLCyMdevWMXXqVNzd3W0es7e3N1WqVCE8PJxLly4RHBxMy5YtM93H+Kr9kCFDTCrv5SbDSbhGo2Hw4MEA+Pr6qtUjVq1ale+BCIBx48bx119/8eDBA86fP09ISAitWrXKdJ8BAwYwb948Hjx4gL+/v9lAREpKCuvWrQOU5UPG+f3y0rVr1xg/fjznz5832Z6cnExMTAznzp3j999/5/XXX7e4XObevXuMGzeOkJCQDI/dvn2b27dvc/bsWTZv3kxSUhJjx441aXP9+nVeeuklNSBm7ObNm9y8eZNTp04RGBiIq6srTz75ZI5e682bN5k8eTLBwcEZHgsPDyc8PJzAwED69u3Lp59+SvHixQvta7WGzYGIypUrM2bMGCIiIoiKMs1G3bJlSz788ENmz55tMfGJp6cn3333ncVklkLYTUoKfPUVzJ4ND5PyADBsGMyfD5Uq5dvQCppTp+Dpp8G4mM2IEfD995BHyzaFEI+zuEsQOhOu/p/p9tItlQBEpUeS4FUdBL434Noq0q6sQpMchd7JE8eafspyDLnqnj/C1sEuH6MNOtP/U6Jh12B4IkD5GQrVmTNnGD16tHrFvm7dugwePJiqVasSHR3N1q1b2bNnD4mJiUyfPh29Xs+wYcMy9KPX65k4caIahHB1dWXo0KHq1P0TJ06wevVq5s6dS9++fW0et0ajYciQISxYsABQZkVkFoiIj49Xl0c4ODjkaBZGTly4cEE9kWvbti1Vq1YFoGPHjlSoUIGIiAi2bNlCbGysXQI0tvD09KRTp05s27YNgEOHDmUZiChZsiS9e/dm/fr1BAYGMmXKFJycnEzabNu2jXsPM4/n17KMiIgInnvuOe48nJ1cpUoVfH19qV27NgkJCezevZt///2X1NRU5s+fT3Jyslri1djMmTPVn2elSpXo168fNWvWpGTJkiQmJnLlyhVCQkI4evSo2XFMmjRJPTGvXbs2Tz75JJUrV8bd3Z24uDguX77MkSNH+O+//3L8Wm/evMmwYcOIjIwElMqRPXv2pEaNGmi1Wi5fvkxAQADXr19n8+bNJCQksGTJkgyBucLwWq1lcyACYOrUqRYf8/Pzo2XLlvz2228cOHCA27dvo9VqqVq1Kj169GD06NEShBC579AhZbaD8S9VtWqwaBEMlCsxBno9/PqrUrk0MVHZ5uqqvE2jR8tSDCFELkuKhBMfwYXvQZeSvt2tJjT/GGo8CxoLs9YcXKDWCJIrDEGv16PRaHB0dc2TYQsz0pKUmRCA+SUzhu0aZTaL7w0JGD2k0+l455131CDEsGHDmD17No6O6V/bhw8fzsqVK5k5cyZ6vZ6PP/4Yb29v9YTawN/fX61GUaFCBZYvX06NGjXUx318fBg9ejQjR45UAwK2GjJkCIsWLUKn0/HPP/+Y5F54lPHyiE6dOlm1zMQejJNU+vqmJ7HVarUMHjyYxYsXk5SURGBgoDq7Oz+1bNlSDUQcP37cqn38/PxYv3490dHRBAUF0a9fP5PHDTNRKleujLe3t30HbKWZM2eqQYiuXbsyf/58k1kAw4YNY+fOnUyYMIHk5GR+/PFHunXrRgujqbl3795l69atgPI+LV26FGdnZ7PPFxUVpQZfDI4fP87Jh1fennzySb7++muLS1/Cw8NzlDtEr9czefJkIiMjcXBwYPbs2Tz99NMZ2r3yyiu8++67bNiwgd27d7Nq1SqTAGNheK3ZkSdz0OvUqcOHH37Ili1bCA0NJTg4mMDAQCZPnixBCJG7YmOV9QUdOqQHITQaZdvJkxKEMBIbCyNHwtix6UGIJk3gyBEln6cEIYQQuSY1XglArKsD575ND0I4l4FW38CAM1BzuOUghCh4rq18uBwjqy+yeki+B9dWZdGu8PLy8sr0n2FZgMGOHTs4d+6cuu8HH3xgEoQwGDZsGM888wyglH9ctmxZhja//fabevuTTz4xCUIYVKtWjblz59ryEk1UrlxZXeNunBDSHONlGZktL7GnlJQU1q5dC0Dx4sUzzAQxXmaeVbLHvFK5cmX19qMz0C3p0KGDGph69HVERESwZ88eQAnEZJVzIjecPXuWnTt3AlCuXDm++uors0sRunbtysSJEwElSLdkyRKTx69fv65WXBk4cKDFE3NQZpfUqVPHZNu1a9fU20OGDMn0vahSpUqGYJ81tm3bpi7HmDBhgtkgBICTkxPz5s2jSpUqAPzyyy8mjxeG15oddpkRIUSBFBgI48dDWFj6tubNYckSaNs2/8ZVAAUHwzPPgPHyvFdfVZJSmvmbIIQQ9qFLgYu/wPHZkHQrfbuDKzT4HzR8G5xK5dvwiqRrK+G/9yElNnef58Hd7LU/+LKSDyS3FHOHZh8WisopW4xyWL344os4ODhYbPvKK6+oJR63bNnCtGnT1MeuX7+uBjTq1q1L586dLfbj7e1N/fr11fa28vPzU090/f39TU7uDS5fvsyxY8cAJV9cjx497PLcWdm2bZt6Mt+7d+8Myfjq1KlDs2bN+O+//zhx4gRnzpyhQYMGeTI2S4zzN0RHR1u1j0ajwdfXlwULFrBv3z5u3bpFxYoVAQgICCAtLU1tkx+Mj/Nnn30205KtI0aM4IcffiA+Pp6dO3fy4MED9STcOHhx0nhNsZWM9z9x4gRdc6FinqGCjJOTE6NGjcq0rZOTEwMGDODHH3/k0qVL3LhxQw1EFYbXmh0SiBBFz82b8MYbYDTtjuLF4YMP4M03lfKcAlCWYnz3Hfzvf5CcrGxzd1diNQ8vsgghhP3p9RC2BkLeg1ijEx+NA9R5CZrOguKStydXnPocYs7k9ygy0iVBYnju9Z8InP48XwIRixYtyvTxR0/AQkND1dudOnXKdN8qVapQu3ZtLl68yI0bN7h9+zbly5cHTKfwt7ei1nb79u3tFojo2bMnHh4eREdHc+jQIa5fv061atVM2vj7+6u3Bw8eTLE8+n5mPDvA0km4j4+PukZ+1apVzJgxI0/GZonxFPnsJPM0XiazZs0aXnvtNSD9vW/Xrl2Gn0teMT7OMwuSgZLbpHXr1uzatYuUlBROnTql5h6pW7eumtdj9erV6HQ6hg0bRosWLTIN4hm0atWK4sWLk5iYyHfffUd0dDS+vr40bNjQbolTDx8+DEDZsmU5cOBAlu3v37+v3r5w4YIaiCgMrzU7cj0QER8fT3x8PG5ubrla/kMIdDql9Oa774LRLzB9+ihZFmvXzr+xFUDR0coyDKPvAbRurVTFeGQmlxBC2M/t3RA8Be4+8mWs2hBo9jGUyt8rj0Veoynw38y8mRGhS8q6nYHWRVmKk1uKuUPDd3Kv/0z06tUrW+0Nyezc3NwoV65clu1r1qzJxYsX1X0NgYjbt2+rbapXr55lP5mdkN64cYNTp05ZfLxSpUo0btxYve/k5MSgQYNYtmwZer2eNWvW8MYbb6iPp6WlqVeJIe+WZRgvSahYsaLFqhj9+/dn7ty5pKSkmE32GBUVpc7mMMfDw4M2bdrYbdwxMTEmfVvLkP9h7969aiDCuFxqdpJUXrx4kcuXL1t8vFatWhmWAmTGcJyDcgxnpWbNmuzatSvDvg4ODnz44YdqHok1a9awZs0aSpQoQfPmzWnVqhXe3t60atXK7Mm2h4cH06dP5/333yc1NZVly5axbNkyPDw8aNmyJa1ataJz584WS6BmJSEhQc3VcOPGDV5//fVs7W8clCjorzW77B6ICA8PZ8WKFRw8eJBTp06RkpKebKpYsWI0atSI9u3b88wzz5isdxLCJidPwiuvwL596dvKlVPWFgwfLgkOHnHgADz7LFy9mr7tzTdh3jzIZLmZEELkXPQJZQbEjfWm28t1hhafQbn8SZb22KnulzezAi4vh/2ZT0E20X4J1BqRe+MpROLj4wHlKrA1jNsZ9gXUJJCAxWSRlvp51IEDB3jvvfcsPu7r68u8efNMtvn5+al5KwICApgwYYK6Jn337t1qoKRZs2bUq1cvy/HZg7+/P2lpaQAMGjTI4hp5Dw8PevTowebNm80mezx//nymJ5Tt2rVj+fLldht3eHj6bKHs5tcbOnQoe/fu5erVqxw+fFidDeHu7p6tSin//PMPCxcutPj4hAkT1FwO1jA+Vq051i0d56DkkVi9ejULFy5k27ZtpKSkEBcXx969e9m7dy8LFiygatWqvPHGGxlysoCSb6VWrVp8//337Nu3D51OR3R0NNu3b2f79u18+eWX1K9fn7fffjvbyxliY20L+hqfSxf015pddgtEJCcn8/nnn/N///d/ahKNRzNtJicnExoaSmhoKD/99BPPP/88b7/9doZyMkJYLSkJPvlEOYM2/kV94QX4/HMok4tXVwohnU6pYPree2CoqFu6NPz2GwySymlCiNwQfx2Oz4LLS0GvS99eqjG0mAeV+0uwuCiqPgyOTFJKdGaasFIDTh6FIndDXnFzcyMmJsYkkJAZ43bGs4+NT9ySkrKenWLt81nLy8uLpk2bcvz4ccLDwzlw4ICaxNJ4WUZezYbQ6/UmyzIWL17M4sWLrdp39erVGapO5CVDuUZQAjfZ0bt3b0qVKsX9+/dZvnw5u3fvBqBfv35WBahyi/GxmpCQkOX5oKXj3KB+/fp8++23JCQkcOzYMbWE5ZEjR0hOTiYsLIwpU6Zw/fp1JkyYkGH/Nm3a8PPPP3P//n2OHj1KSEgIR44cITQ0lNTUVM6dO8crr7zC3Llzs1Vm1vj3sHHjxibHfk4V1NeaXXYJRCQlJfHCCy8QEhKSZZkPw+NpaWksX76c48eP89tvv2Wa9VMIs3bsUDIqGq9nrFsXfvwR8ijhUWESGamU4Ny4MX1bx47w559gxYxNIYTInuR7cHKeUgUjzegkyLUqNJ0DtUaBNus1raKQcnAB76WwazCgwXww4mEAqsNSKd1ppFy5csTExBAfH8+dO3coW7Zspu0N0+wBdVnGo7eNs+Vbcv36dYuPDRkyJEcnJH5+fmquitWrV9OxY0eioqLUUpTFixenf//+2e43Jw4ePJjpa8zMvn37uHnzJpUqKblr2rdvz9mzZ+05PIvu3r2rlmAFZbZFdhiSH/7xxx9s3rxZ3Z7dANDEiROzNeMhK+XKleP06dMAXL16NcslJ5aO80e5urrSuXNnNe9EXFwcy5YtY/78+QD88MMPPPPMMxaXPZUqVYoePXqoyVOjoqJYtGgRv//+OwCffvopAwcOtDqnibu7O66uriQkJHDr1q2sd8iGgvZas8sutVpmzJihliQBqFevHu+++y4rVqxg7969HDt2jL1797JixQreffdd6tevDyhBiZCQkHxPACMKmagoJblB9+7pQQhHR5g+XSnRKUGIDHbuhBYt0oMQGo0yK2LHDglCCCHsLC1JSYi4tjac/iw9CFHMA1p8CgPOQZ0XJAjxOKg6EJ4IUGY8AOlfOx/+7+QBT6xV2glV8+bN1duGfAaW3Lhxg0uXLgFKPgDjE46mTZuqtw8ePJjl81rTJrsGDBigZuoPCgoiNjaWdevWqdPN+/btm2m1BHtaZZTEvG/fvkyYMCHLf4ZkoTqdzi5XsnPihx9+IPlhRnEvLy+T48Naj+aCqFevXrZnVthbdo7zxMREjh49CqQv9bdWiRIlGD9+PD179gSUpQ7GiTKz4unpycyZM9XKKdHR0Vy4cMHq/SE9eHT37l1OnDiRrX2zoyC81uyweUbEf//9x/r169FoNGi1Wt555x1Gjx6dIUGGq6srZcqUoVmzZowePZrly5fz6aefkpaWxvr16xk5cmS+/0KIAk6vh7/+UpIZGCVgokMHpcxDkyb5NrSCKi0NPv5YKRjycMUU5cvD8uVKDk8hhLAbXRpcWa6UhkwwuuqodQavidDoPXDO3tpmUQRUHQS+N+DaKqVSyoMo5Tio6qssx5CZEBn06dNHPen99ddfGThwoMWM+EuWLFFnG/d55A97tWrV1JKcFy5cYM+ePRarE+zfv99uFTOMlShRgr59+xIQEEBSUhLr1683OaHPTrJEW8TExPDvv/8C4OjoyOzZs63KtXDmzBl1nb2/vz/jx4/P0+oCAQEBap4NUPIw5OT5GzduzJNPPsnNmzcBePrpp+02xpzq06cPCxYsAODPP/9k9OjRFoNSf/zxh5oXolu3bjla1l+1alX1dqphfXI29z9z5kyO9vfx8WHHjh0AfPPNNyxZsiRXj6P8fK3ZYfOMiLVr16q333nnHcaMGZPlG6vRaBg1ahRTpkwx248QGVy+DP36KYknDUEId3dYtAj27pUghBk3b0Lv3jBrVnoQokcPCAmRIIQQwo70egjfABtbwIEXjIIQGqg9Bgaeg5afSxDicebgoiSi7LIaem1X/q81QoIQFnTt2lWdPXzmzBlmz55t9mTA39+fv/76C1CWOIwalTE56JgxY9Tb06ZN46pxluqHrl+/nmkiSlsZLwH47rvv1CUNNWrUoG3btrn2vMYCAwN58OABAF26dLE64WODBg1o2LAhAGFhYVaVXrSHGzdu8P777zN16lR124gRIzIEm7Jj/vz5rFixghUrVuRZXo7M1K9fn27dugFKFYy33nqLxMTEDO12797Nt99+C4BWq+Xll1/O8Phvv/1mUl3iUXfv3lUDUYB6xR9g3bp1rFy5MtMcKZcvX2b//v0AODs7U6tWraxfoJEnn3xSnQGye/dupkyZkiHhprG0tDR27drFd999Z7K9MLzW7LB5RsShQ4cAZa2O8YedNUaNGsUvv/zC7du3c2U6mCgCUlPhm2+Us2njX5ohQ+Dbb6FKlXwbWkG2eTOMHKnkhQDQapVZEe+9B1aUGRZCCOvcOQghU+H2TtPtlfspiSg9mprfTwhhkVar5fPPP+e5554jISGBFStWEBISwqBBg6hSpQr3799n69atatJBgOnTp1PFzHeiIUOGsGHDBvbu3UtERAQ+Pj4MHTpUXbZx/PhxVq9eTWJiIk8++SSbNm1Sx2Avbdu2pWbNmly5csWkpOiQIUNydFV41apV7DOukpaJ8ePH4+zsbJKk0sfHJ1vP5+Pjo+YyWLVqFd7etlf4OXr0qEk1haSkJGJjY7l+/TqhoaEEBwer1T00Gg0jRoxg2rRpNj9vQTNnzhyGDBnCnTt32LFjB/3792fIkCHUrl2b+Ph49u7dy6ZNm9RZP+PGjcuwNCUyMpK5c+fyxRdf0K5dO5o3b061atVwdXUlOjqas2fPsmHDBvXk/amnnjIpF3r16lUWLlzIxx9/jLe3N02bNqVy5co4OzsTFRXF8ePH2bx5s3ryPnLkyGwvJ9JoNCxYsIBnnnmGmzdvsm7dOnbu3MmTTz5J48aNKVWqFA8ePOD27ducOXOGffv2ERUVhbe3N+PHjy9UrzU7bA5EREREoNFoclQr17Dfhg0bTD6YRO5KSkqyyx+YpKQk9Hp9rk0t0h47htP/s3ff4VFVWx/Hv2fSCyGE0HsNNSBFpEmRopQEBNTrtdxrwQIoXisoKGJB7IrlWq7d1w6hCiqIiBSR3gJBWuiBVJJJJjPn/WPMhEiAhJlkUn6f5/Ehs2afMyuwTVmz99rjx2M5Y2+To25dbC+9hH34X/tJPdzhubyz2WD6dD9efDG/qUzdug4++CCHXr0c/PVmgNeU9JyRikdzpmwyMnbjt/0JfA/PKRC3V+uCrc10HDUudwa88DXa03OmKKcNiHhaq1at+Oijj5gwYQJHjx5l165dvPDCC2eNCwoK4tFHH2XMmDGF3ifvF6C7776b1atXk5mZedaxkj4+PjzyyCOEhIS4ChGFnUrgjlGjRvHiiy8WeM2RI0de1L3mzp1b5LG33norf/75J9u2bQPym/MVx/Dhw3n++efJzc3lhx9+IC0tjbCwsGLd4+/ymgmej2EYdO3alXHjxnHZZZe59XplVa1atfj888+5++67SUhI4NChQ67tGmfy9fXl7rvvLvTI1Lyv9TabzXWE5bkMHjyYZ599ttDrs7KyWLp0qauRamGvc/311/Of//ynyJ/fmWrVqsW3337LI488wi+//EJqaipffvnlea+pXbt2obmW9c+1qNwuROR9gy7qWcd/l3edvtGXHtM0L3i6SVHv48n7uWRk4P/UU/i++SZG3lGwhkHu2LHkPP44hIU5lwJLAQcOGPz73wGsWZO/5OHKK3N5++1sIiPLxl9Zic0ZqbA0Z8oWw3oUv/hn8d3/IYZpd8UdIc3JafM49jojnN1wvfhv5ek5o3kn3hIdHc3ixYv5+uuv+emnn9i9ezepqakEBwdTv359evfuzfXXX0+tWrXOe5+QkBA+/PBD4uLimD17Njt37iQzM5MaNWrQtWtXbrjhBtq3b1/gKMuqVat69HMZMWIEr7zyiutd/l69el0wb085s0nlVVddVez+AtWrV6d3794sW7aM7Oxs5s2bxz//+U+P5WexWAgODiY0NJSIiAiioqJo27Ytffr0oWEl6CjeqFEj4uLimDt3LkuWLGHbtm0kJycTGBhInTp16N69O//4xz/OuUVgxIgRNGvWjFWrVrFp0yb27NnD8ePHyc7OJjAwkLp169KhQwdiY2MLPXHkzjvvpFu3bqxevZrNmzezd+9eTpw4gc1mIzg4mAYNGtCpUydGjRpVrCaZhalevTrvvvsuGzduZN68efzxxx8cOXKE9PR0AgICiIyMpFmzZnTq1Il+/frRokWLcvu5FoVhuvkdtk+fPhw/fpyuXbsWaKZSVDfffDNr1qyhVq1aLF++/MIXSLFlZGQUOGKoUaNGF104OtOZ7zp56hxiy/ff4z9xIpYzjldytGlDzhtv4CjmcUWVyfz5Ptx5pz/Jyc5Kp6+vyZNP2pgwIRcPrq50W0nMGanYNGfKCFsafgmv4pvwGoY9f5WDGVATW6tHyW10M1hK5niv4vL0nMnMzCywrz4qKqrUuvwXxe7du8nNzcXX1/esH1pFimPChAmuveVr1671eDFCRCqHon5fcntFRPPmzTl27Bjr16/n4MGDNGjQoMjXHjx4kD/++APDMGjevLm7qUgRBQYGeqQQAbh+2HP7fkePwr33wldf5ccCA+Hxx7Hcfz+BJXR+bXmXnQ0PPeRsl5GncWP44guDbt38geJ3FS5pHpszUmlozniRPQcS/gtbp0P2ify4byi0fgij1X34+4WWua80npwzjrxuvyIVWGJiIsuWLQOgdevWKkKISIlz+73SPn36AM7ung8++CAZGRlFui4zM5MHH3zQ1QW4X79+7qYi5ZHD4Tx6s3XrgkWIK66ALVvgkUdARYhCJSRAjx4FixCjR8OGDdCtm/fyEpEKwHTAvi9gQWv44578IoTFD1pOgJg90H4K+JWdlQEiUriEhAROnTp1zuePHj3K+PHjsdlsAPzjH/8ordREpBJze0XE6NGjeeeddzh58iSbNm1i1KhRPPTQQ/Tr16/QhoimafLzzz8zc+ZM9u3bh2EYVK9evdTOEZYyZMcOuOMOOKPrM9Wrw0svOY98UHO6c/riCxg7FvIaLgcEwMsvw5136q9NRNx09EfY8DAkry8Yb3QdRD8FVZp5Jy8RuSjLly/n5Zdf5rLLLqNTp07Ur18ff39/kpOT2bRpE99//73r2MROnTqViaMdRaTic7sQERwczPTp0xk/fjwOh4P9+/czfvx4qlWrRnR0NHXr1iUoKIisrCwOHz7Mli1bXFVZ0zTx9fXl6aefJigoyO1PRsqJ7Gx49lnnfzk5+fGbboIXX4TISO/lVsZlZsLEic5FJHlatoQvv4SOHb2VlYhUCKc2wMZH4OiSgvFaV8Alz0FEZ+/kVUy/zfiNlU+tpOdjPRnw5ABvpyNSJthsNlasWFHgyM+/69GjB6+++io+OudbREqB24UIcG6reP7553nsscfIzMzENE1OnTpVaPPJM3tjBgcH89RTT7m2d0glsGKF8638nTvzY02bwn//CwP0A+P5bN8O11wDf51ABcANN8Bbb0EZ6psmIuVNxl7YPAX2fVYwXq0jdHwOag8sN0utlk9fzsrpzuPMVk5fiZ+fH32m6GcMqdxGjhxJQEAAq1atYt++faSkpJCamoq/vz+RkZF07NiRoUOH6udxESlVHilEAAwZMoT27dsza9YsFi1aRE5OzjmPvPL392fIkCGMGzeuWM0tpRxLToaHHy74Vr6PDzz4IEyZAmpCd06mCR98AOPHw18rJwkOhjfegJtvLje/H4hIWWNNgm1Pwe43wWHLj4c0dm7BaPwPMMrQsTsXsHz6cn6e+nOBWN5jFSOkMouIiOCGG27ghhtu8HYqIiIuHitEADRo0IDnnnuOxx57jPXr17Njxw5OnTpFZmYmwcHBRERE0Lp1azp16kSVKlU8+dJSVpkmfP013HMPHDuWH7/0UnjnHejQwXu5lQPp6XDXXfDZGW9Utmvn7OvZurX38hKRciz3NOx8BXbMBFtafjygOrR9DFrcBT4BXkvvYhRWhMijYoSIiEjZ49FCRJ4qVarQp08fLfGq7Pbvh3HjYMGC/FhoKDzzDNx9t3NFhJzTxo3OrRi7d+fH7rjD2ZRSLVVEpNgcufDn/2DLE5B1JD/uEwSt/gOtHwT/8ndk3/mKEHlUjBARESlbvL7mcsSIEbRu3Zo2bdp4OxUpJsvSpQR17oxl6dKCT9jt8Mor0LZtwSJETIyz0cGECSpCnIdpOrdddOuWX4SoUsV5Usbbb6sIISLFZJpwcDYsbAdr78gvQhg+0HwsDE+ADk9V2CJEnp+n/szy6Wf3rhIREZHSVyIrIorrXL0kpAwzTfwefxxLfDx+jz8OQ4c6mxVs2AC33w5//JE/tk4dmDULRo5UQ4MLSEmBW2+F777Lj3Xu7DwVo5lOzBOR4jr+K2x8CJJWFYzXHwkdnoGqrbyT10UyTZPcrFxyTufw67O/svrl1cW6XisjREREyoYyUYiQcmjJEnzWO8+Y91m/HubOhV9/de4bsNvzx911l/OYzqrl75220rZ6NVx3nXNHS56JE2HGDAgoX9u1RcTbUrbBpklwaF7BeI2e0HEm1OhRIi9rmib2HDu20zZyTufk/5lpKxCzZf7t+b9iRRnjLhUjREREvE+FCCk+04QpUzB9fDDsdkyLBeOaayAnJ39MmzbOZpQ9e3ovz3LC4YCXXoJJkyA31xmrVg0+/NC5m0VEpMgyE2Hz47D3QzAd+fGqbaDDDKg3DHuuA1uKtWgFgr8/X4QxpqPsr3L8+fGfVYgQERHxIhUipPiWLIHffydvk4XhcOQXIQICnMdxPvgg+Pt7LcXy4sQJ5xGcixblx3r0gP/7P2jY0Ht5iYj3OOyOYhcIzOxTNKz+BU3rzcPHJ78onJEezuqlV7Hlt0vIydhOzumNOGyO87x62eIX7IdfiB/+If4FPk47nMapXacu+r59p/X1WI4iIiJSfCpESPH8tRoCH5+CWzDA2VHx998hKso7uZUzy5fD9dfD4cPOx4YBjzwC06aBn593cxORczMdJrass7cSFGWFQW5mbqHbDc6M2bPtF07iLz5+Ni4duJbesSsICrW64tbTAfw6tzdrllxKbo4/kFkCfxPgG+hbsFhQSNHAL8Sv0McFxhcS8w30xbCcu69QcRpVnqnvk321GkJERMTLVIiQ4vlrNUSh0tNh3z4VIi7Aboenn3YWHBx/vTFZsyZ88gkMGuTd3EQqAtM0sWfbL9hroLACQpHGeKBPgbsMw0F0r830G72UqpFprniuzYe1Sy5lRVxvrKeDsfhZCAz3bIEg7xq/YD8sPt47fCuvmFCcYoSKECIiImWDChFSdOdbDQHO+JQpzt+mdTpGoY4cgX/+E5Yty4/17w+ffuo8XKSi+23Gb6x8aiU9H+vJgCcHeDsd8SJ7jv2C2w1yTueQmZLpWklg5phFbmhYHvoUABg+xjkLBK6PCzz2pWa1P2gY9hbBlgTXfUwMrFVHY200mfYDm9HpTec1Pn4V+6jk4hQjVIQQEREpO1SIkKI732oIcBYnfv/dOW7w4NLLq5xYvBhuvNHZFwLAYnGuipg0yVnDqeiWT1/OyukrAVg5fSV+fn76paAMc9gdxT7x4LwNDf8Wc+SWkz4FBgWLAudaHXCOFQcXKjL4+PtgFLVwm7QWNj4Mx38uGK9zFUbHGQRViybI438BZV9RihEqQoiIiJQtKkRI0VxoNUQerYo4i80GU6c6j+HMU7eusyHl5Zd7L6/SVNhebh2h5x5Xn4JiNDQsdAvCOcYUp0+Bt/kG+Z6zQHCuLQgXKhDkPe8b5Fv0QkFJSdsNmybDwW8KxiO6wiXPQa1+3smrDDlfMUJFCBERkbJHhQgpmguthsijVREFHDgA110Hq1blx4YOdR7NGRnptbRK1fkaylXkYoRpmuRac4tdIDizqHDepodZud7+FIvMx9+nSL/8FzbG9DWdhYYQP6pUr3L2PYL9ztvQsFzLOgpbn4SEd8E84987tDl0fAYajFbB9wyFFSNUhBARESmbilyIOJzX2t/DbDbvN/2SC8hbDWGx5HdXPB+LRasigLg4+Pe/ITnZ+djX17kq4r77nH9FlUFRutp7sxhhz7FfsBhQ7C0IZzQ0LHd9CorQtLAoTQz/HrP4XvyEz8zMxDRNDMMgODjYg591GWZLhx0vwM4XIfd0fjywFrR/HJrdBhYdrVOYPlP6YLPZXL1oVIQQEREpm4pciOjfv7/3l6eKd+TkON/aL0oRApzjDh50XhcQULK5lUHZ2fDww/Dqq/mxxo3hiy+gWzevpVXqinO03rmKEY5cR7FOPDjvMYmFFBnKU5+CIvcoKEKB4O/P+/hXgiYl5YE9BxLeca6CyD6RH/cNhdYPQqv/gF+o9/IrJ3o80oPuD3fXzywiIiJlWLG3ZpimZ9/h0w8K5UBAgHO7xYkTBcJZWVmuj4OC/tYirWbNSlmESEiAa6+F9evzY6NGwXvvQXi419IqVQ67g2VTlvHrs78W67qfp/7MmlfX4Bfk5yoW2HPKV5+CC60O8A0+/5hzrULwDSwDfQqk5JgOOPA1bHoUMvbkxw1faHEntH0Mgmp5Lz8RKbaoM44yj4+P9+h1Uec4Jt3Pz4+QkBBCQ0OpVasWbdq0oW3btvTr14/wIv4Q8sgjjzB79uwi5wvw008/Ub9+/QKx/v37c+jQoSLfozh/RyJSMRSrEOHpIkRJ3VNKQIMGzv/OYJ6xZJrKsmT6PL74AsaOhfR05+OAAHjpJbjrrvK5QyU3OxdrspWsU1n5/yVnFXhsPWU9K25Ntl70a2adzCKLrAsPvAg+AT4XPNWguAUC18dBFbhPgZSsoz85T8I49UfBeMNrocNTUKW5d/ISkXLHZrORkpJCSkoKiYmJ/PGH8+uKv78/gwYNYuLEiTT4289yIiLeUuRCxE8//VSSeYiUW5mZMHEivPtufqxlS/jyS+jY0VtZOZmmSU5GjqtAUKCocEZxwVVQOCNmO+29/i0RLSIurkfBubYluNmnQMTjkjfCxkfgyOKC8Vr9oeNzUL2LV9ISkfLljTfecH1smianT58mLS2N3bt3s2HDBnbv3k1OTg7z589n6dKlPProo4wePbpI977xxhu57LLLLjiuevXq53wuIiKC6dOnF+n1RKRyKXIhol69eiWZh0i5tH07XHMNbNuWH7vhBnjrLQj14FZuh92BNeXsQkJhxYW/x0qlD4IBQdWCCIoIwpZlI/1Q+kXfSl3upULL2Aebp8C+z4AzVgSGd3AWIOpU7ia/IlI8AwYMOO/zGzdu5MUXX2Tt2rVkZmby2GOPERQUxNChQy947zZt2lzw/hcSFBTk9j1EpGLS8Z1y0X6b8ZurM/mAJyvXNxnTdB7BOW4c5LXKCA6GN96Am28+9+8RudbcQrc4nK+wkHUqi+zU7FL5vHz8fQiqHuQqKuT9FxgR6Pz4b/G8/wLCAgpsTShOo8ozqQghFZY1CbY9DbvfBEdOfjykEUQ/BY2vB0OrdqTiOJB6gKTMpCKPjwyOpGHVhiWYUeXUsWNHPvzwQ6ZNm8aXX36JaZpMmjSJTp06UadOHW+nJyKVmAoRclGWT1/OyukrAVg5fSV+fn6V5hfItDSTO2538MVX+ScNtGhg5elbEqh9JJkfHjp3/4TcrNxSydG/iv9ZxYLAaoFnFxH+VljwDfJMU8S8uVCcYoSKEFIh5WZC/Cuw/TmwpeXH/SOg3WPQ4m7wqXyNfaViO5B6gKhZUVhzi94zKNA3kPjx8SpGlAAfHx+mTJnC9u3b2bJlC9nZ2bz99ttMmzbN26mJSCWmQoQUW2Hvdp/r+MWyzJHrcBUJzrUS4e/xPcer8EnKME6Svx+yM39w5cHv2T4tl+0ezM+wGOcuIJwvHh6Ij5/3j2MsTjFCRQipcBy58OcHsOVxyDqSH/cJgqiJ0OZh8K/qtfRESlJSZlKxihAA1lwrSZlJKkSUED8/P+6++27uuusuAObOnctjjz2Gn5+flzMTkcpKhQgplvMtufdWMcKWZSvyNoczY9lpRd/uYAK/05XFDML+1/82/mQTwzzase281/oE+BBcPfiCRYS/xwKqBJT7kxiKUoxQEUIqFNOExDjYNAnSdubHDQs0vQXaPwHB6rkkIqWvX79+hIWFkZaWRmZmJlu2bKFTp04l+prJycn861//YteuXaSlpRESEkKdOnXo3LkzV199NW3bti3R1xeRskuFCCmyouz7v9hihOkwyU7Lvqj+CfZs+0V+RkWTRQBziWEHbVyxxlVOcn/vtTRuGEhQRK9z9k4IrBaIX1DlfrfhfMUIFSGkQjmxEjY8BEm/FYzXHwEdnoGqrb2SlogIgGEYREdH8+uvvwKUSiEiMzOTVatWuR7nHS+6Y8cOPv30U4YMGcL06dMJ9WSHbxEpF1SIkCIpTvPBn6f+TOaJTNpd167AcZCugsKZR0WesTXCdJgXvrmbDB+j0B4JrmaMf4tvP1iFsQ9UYf+B/CZyEyfCjBnVCQi4qsTzrSgKK0aoCCEVRup22DgJDs0tGI/sAZfMhBo9vZOXiJQJUVFR3k7B5cxT8E6dOnXesZMmTWLSpEnnHTNnzhxaty68yFqjRg169uxJ69atqVGjBqZpcvjwYX755Rd+//13ABYuXMj+/fv59NNPCQ4OLuZnIyLlmQoRckEXcwLC2tfXsvb1tSWTEOAX7Fek7Q1/Lyz4V/EvUjNGhwNeegkmTYLcv/pLVqvmPCkjJqbEPq0Krc+UPthsNtdJKypCSLmXmQhbnnD2gjDPOCY3rDV0nAH1husoTilzvt72NVN/nkp69sUfs3whOfacCw8qxJWfXom/j7+Hs3GqElCF6f2mM7rN6BK5f3kRFhbm+jglJaXEXmfmzJl06tQJi+Xs04DGjh3LihUruP/++0lNTWXbtm3MnDmTJ554osTyEZGyR4UIOa+LPYaxqALDAy9YRDgrXi0I38CSm7pJSc4jOBcuzI/16AH/93/QUD203NLjkR50f7i7R07mEPGanBTnKRjxr4D9jIZ8QXUh+klocjNY9O1Vyqbnf3uenUk7LzzQC05knii5m6c7P3dvFCLeeOONIo8dN25cCWYCppm/+vRC34tvvPFGLrvssvOOqV+/fqHxLl26nPe63r178+qrr/Kvf/0LgK+//pq7776bmjVrnvc6Eak49JOSnNfPj//s9j2uev2qQgsLgeGBWHzOrpR70/LlcP31cPiw87FhwCOPwLRpoMbSIpWc3Qq73oRtT0POGUua/apCm0cg6h7w1dJiKdse6vkQU5ZNKfEVERdTVKgRXKNEV0Q82OPBErn3hQwYMMArr1uYtLT8Y4TDw8PPO7ZNmzYlmnv37t3p0aMHv/32G7m5uaxYsYJRo0aV2OuJSNmiQoScV99pfd1aEdH3yb5cOv5Sj+VTUux2ePppZ8HB8dcK65o14ZNPYNAg7+YmIl7msMO+z2DzFMg8kB+3+EPL8dB2MgRUP/f1ImXI6DajS3xVwPoj6+n8TudiX/f9Dd/TqU7JNk+s7A4dOuT6OCIiwouZOHXr1o3ffnM2+N2zZ4+XsxGR0qRChJxXUY5fPJfy0ozwyBH45z9h2bL8WP/+8OmnUKeO9/ISES8zTTjyPWx8BFI2n/GEAY1vgA7TIaSR19ITESkOh8PBpk2bXI87dOjgxWycziyGpKeX3CodESl7VIiQC7qYYkR5KUIsXgw33ggn/lpBarE4V0VMmgQ+Pt7NTUS86OTvsPFhOLasYLzOlc5GlNW8/wO8iEhxLF26lIyMDACCg4Np27atlzOC5ORk18dVqlTxYiYiUtpUiJAiKU4xojwUIWw2mDoVZszIj9Wt62xIefnl3stLRLwsPQE2TYYDXxeMR3SBjs9B7f7eyUtExA02m4233nrL9fjqq6/G19f7vwasWbPG9XGTJk28mImIlLYS+wqUkZHBsWPHSE1NxW6307Vr15J6KSklRSlGlIcixIED8I9/wF9bEgEYOtR5NGdkpNfSEhFvyjoGW5+EhHfAzM2PhzaDDs9AwzE6ilOkiCKDIwn0DcSaa73w4L8E+gYSGaxvwiXBbrczffp0tm7dCkBgYCBjx471clawdu1aVq5cCYCPjw+X650gkUrFo4WIjIwMvvjiC+bNm8fu3btdRwQZhsH27dsLjD158iTvv/8+AC1btmTEiBGeTEVKyPmKEeWhCBEXB//+N+StBPT1da6KuO8+57YMEalkbOmw40XY+QLkns6PB9aEdlOh2e1QQl38RSqqhlUbEj8+nqTMpCJfExkcScOqOiPb0zZv3szzzz/P2rVrAefP5DNmzKBWrVol9ppvvvkmAwYMoGXLluccs2rVKiZOnOh6PHr06BLNSUTKHo8VItauXcsDDzzAib822595TnFhqlevzurVq9mxYwdhYWEMGTIEf3/9sFceFFaMKOtFiOxsePhhePXV/FjjxvDFF9Ctm9fSEhFvsefAnnedqyCsx/PjviHQ6gFofT/4ab+yyMVqWLWhCgul4McffyzwOCMjg/T0dHbv3s2GDRvYtWuX67ng4GCmTp3KVVddVaI5LV68mFdffZWWLVvSrVs3mjZtSnh4OKZpcvjwYX755RdXYQSgbdu2PPTQQyWak4iUPR4pRKxbt47bbrsNm82GaZoYhkGzZs1IS0tzFSYKc+211/L444+TlpbGb7/9Rt++fT2RjpSCPlP6YLPZWPnUSno+1rNMFyESEuDaa2H9+vzYqFHw3ntwgSO0RaSiMU1n/4dNkyHjjKPiDF9ofge0mwJBeldORMqHcePGXXBMQEAAAwcOZOLEiTRo0KAUsnLatWtXgUJIYYYPH87jjz9OaGhoKWUlImWF24WI7Oxs/vOf/5CTkwPAyJEjue+++6hZsybTp0/ns88+O+e1gwYNYtq0aZimqUJEOdTjkR50f7g7RhneN/3FFzB2LOSdCBUQAC+9BHfdpe3eIpXOsWWw4SE4ta5gvOE1EP0UhLXwTl4iIh7g6+tLSEgIoaGh1KpVizZt2tCuXTv69+9P1apVSy2P559/nnXr1rFp0yZ2797NqVOnSElJwW63ExYWRoMGDejcuTMjR46kefPmpZaXiJQtbhcivvnmG44fP45hGPzjH/9g6tSpRb62WrVqNGrUiH379p3VQ0LEHZmZMHEivPtufqxlS/jyS+jY0VtZiYhXJG+CjY/Ake8Lxmv1c56EUV3NlEXEs+Lj40vsuou9d1HMmDGDGWceKXYRWrZsScuWLbn++us9lJWIVERuFyKWLl0KQEhICPfff3+xr2/evDl79+5l//797qYiAsD27c6tGH81hwbghhvgrbdAK/9EKpHT+2HTFNj3KXBG36LwaGcBos5gLY0SERER8QK3CxG7du3CMAy6dOlCSEhIsa/PWyqWnrd2XuQimabzCM7x450rIgCCg+GNN+Dmm/X7hkilkX0Stj0Du2aBIyc/HtwQOjwFja4Hi4/38hMRERGp5NwuRKSkpABc9JE7ef0FHA6Hu6lIJZae7uz7cGZLknbt4KuvoHVr7+UlIqUoNxPiX4PtM8CWmh/3j4C2j0LLu8En0Hv5iYiIiAjggUJEcHAwaWlpZGdnX9T1eadqhOv4ArlIGzfCNdfA7t35sbFj4ZVXICjIW1mJSKlx5MLej2Dz45B1KD/uEwhRE6HNw+Af7q3sRERERORv3C5E1KhRg9TUVBISEop9rWmabNq0CcMwqF+/vrupSCVjmvDmm3D//ZBXB6tSxdmg8tprvZubiJQC04RDc2HjJEjbkR83LND039B+GgTX815+IiIiIlIotwsRnTt3JiEhge3bt5OYmFisgsLixYtJTk7GMAwuvfRSd1ORSiQlBW69Fb77Lj/WubPzVIxmzbyWloiUlhMrYePDzj/PVD8WOjwDVdt4Jy8RERERuSCLuze48sorAefqhqeeeqrI1x07dsw13jAMhg0b5m4qUkmsWQOXXFKwCDFxIqxcqSKESIWXugN+GQE/9CpYhIjsAQNWwOVzVIQQERERKePcLkR0796drl27Ypomy5cv55577iE5Ofm81yxbtoxrr72WpKQkDMNg8ODBNG/e3N1UpIJzOOCFF6BXL9i3zxmrVg3i4uDllyEgwKvpiUgJMrIOw5rbYWE7SIzLfyKsFfSeDQN/hZq9vJegiIiIiBSZ21szAJ5//nlGjx7NyZMn+eGHH1i+fDndu3fn6NGjrjHPPPMMSUlJbNiwoUC8fv36TJs2zRNpSAWWlOQ8gnPhwvxYjx7wf/8HDRt6Ly8RKWG2VPx2vYTfn2+APSs/HlTX2QOi6b/A4pFvZSIiIiJSSjzy01vt2rX56KOPmDBhAn/++SfZ2dksX74cyD+e85NPPnGNN00TgBYtWvDmm28SFhbmiTSkglq+HK6/Hg4fdj42DHjkEZg2Dfz8vJubiJQQezbsfpOgLU9h2E7lx/3CoM0jEHUv+AZ7Lz8RERERuWgeexupWbNmfPvtt/zvf//j888/5+TJk+ccGxYWxk033cQtt9xCcLB+kJTC2e3wzDPwxBPObRkANWvCJ5/AoEFeTU1ESorpgH2fw+bH4PR+jLywxR+jxThoOxkCI72aooiIiIi4x6PrWYOCghg3bhx33HEHW7duZePGjRw7doyMjAyCgoKIjIwkOjqaTp064e/v78mXlgrmyBG44QZYujQ/1r8/fPop1KnjvbxEpISYJhxZ4jwJI2VTfhgDe/1rsbWeSlCN1l5MUEREREQuJG/3w4WUyMZaX19fOnbsSMeOHUvi9lLBLVniLEKcOOF8bLE4t2FMmgQ+Pt7NTURKwMl1zgLEsaUF43UGY416HEfVaNc2PxEpyMfHh9zcXOx2O6Zp6v8VERHxGofDgeOvpew+F/jFTR2+pMyw2WDqVJgxIz9Wt66zIeXll3svLxEpIekJsOkxOPBlwXhEZ+j4HNS+AjMz07laQkQK5e/vT3Z2NqZpkpmZSUhIiLdTEhGRSiojI8O1IiLgAkcaqhAhZcKBA/CPf8Bvv+XHhg6FDz+ESG0HF6lYrMdh63TY/TaYufnx0KbQ4RloOAYMt0+XFqkUwsLCSE9PB+DUqVMEBwdrVYSIiJS63NxckpKSXI/Dw8PPO16FCPG6uDj4978hOdn52NfXuSrivvuc2zJEpIKwZcDOl2DH85CbkR8PqAHtpkLzseCj/kEixREaGophGJimSUZGBomJiURERKggISIipSInJ4eMjAxOnTqFzWYDwM/P74KHUrhdiJg1a5Zb11ssFkJDQwkLC6Np06a0atVKjSwriexsePhhePXV/FjjxvDFF9Ctm9fSEhFPc9gg4V3YOs25GiKPbwi0uh9aPwB+VbyXn0g5ZrFYqFevHocOHXIVIzIyMjAM44L7c0VERNxxZk+IPL6+vtSvX/+CxXCPFCI8WXH38/Nj4MCB3HLLLbRt29Zj95WyJSEBrr0W1q/Pj40aBe+9BxdYxSMi5YVpwsFvYONkyEjIjxu+ztUP7aZAUG3v5SdSQVSpUqVAMQKcXctzc3MvcKWIiIjnBAYGUr9+ffz8/C441iNbM/5+REfeEsFzOd/zOTk5LFy4kMWLF3PXXXcxbtw4T6QoZcgXX8DYsfDXllYCAuCll+Cuu0CrSEUqiGM/w4aH4NTvBeMNx0D00xDWwitpiVRUVapUoWXLlmRkZJCWlkZOTg52u93baYmISAVmsVjw8/MjJCSEkJAQAgICirxIwe1CxPjx4wFnh8zPP/8cm82GaZrUrVuX9u3bU7t2bYKDg8nKyuLo0aNs3ryZw4cPA85Omtdffz3+/v6kpqYSHx/P5s2bsdvt5ObmMmvWLEJCQvjXv/7lbppSBmRlwcSJ8M47+bGWLeHLL0EnvYpUEMmbYeMjcGRRwXjNPtBxJkRe6p28RCoBi8VCWFgYYWFh3k5FRETkvDxSiNi7dy933HEHOTk5tG/fnocffpguXbqc85p169Yxc+ZMNm/ezE8//cQ777xD48aNATh06BBPP/00S5cuxTRNXn31VYYNG0akjk4o17Zvd27F2Lo1P3bDDfDWWxAa6r28RMRDTh+AzVNg7yfAGSvewts7j+Ksc6WWPImIiIgIAG6fSZCVlcX48eM5ePAgffr04fPPPz9vEQKgS5cufPbZZ/Tt25cDBw4wYcIErFYrAPXq1ePNN9+kf//+AFitVr755ht30xQvMU344APo2jW/CBEc7Ix9/LGKECLlXvZJWP8AzGsJez/GVYQIbgiXfQRXboC6V6kIISIiIiIubhcivvvuO/bs2UNAQADPPvtskRpTgLMp5TPPPENAQAAJCQl89913BZ6fPHmyq9vzqlWr3E1TvCA9HW66CW65BTIznbF27WDdOvjXv/R7iUi5lpsF25+Duc1g54vgyHbG/avBJS/A8HhoehNY1LVfRERERApyuxCxcOFCDMOga9euREREFOvaiIgIunXrhmmaLFiwoMBz9evXp3Xr1pimyd69e91NU0rZxo3QpQt8+ml+bOxYWLsWWrf2Wloi4i5HLuz5H8xr4ewFYUt1xn0Coc3DEPMntL7f+VhEREREpBBu94jYv38/AHXq1Lmo62vXrl3gPmdq2rQpW7duJTU19eITFI+zWuHrr+Hbb/05dcogIsJk1CgYM8Z5AsZbb8F//gPZf71BWqUKvPuus0eEiJRTpgmH5sGmSZC6PT9uWKDJvyB6GgTX91p6IiIiIlJ+uF2ISEtLAyAlJeWirs+7Lu8+ZwoODgacXaClbJg717mtIjkZLBYfHA4Di8UkLg4mTIA2beDMnTSdOztPxWjWzGspi4i7TvwGGx+GE78WjNeLgQ7PQHhb7+QlIiIiIuWS24WI6tWrc+TIEdauXYvNZityjwgAm83G2rVrXff5u/T0dACqVavmbpriAXPnwogR+Y8dDqPAn6mpBYsQEyfCjBnOVRIiUg6l7nSugEicUzAe2d15EkbN3l5JS0RERETKN7cLEZdccglHjhwhNTWVV155hQcffLDI17766qukpKRgGAYdO3Y86/m83hDF7T0hnme1OldCgHOF9vkYBnz1FYweXeJpiUhJyDwMW6fBnvfBtOfHw6Kgw7NQf4S6zUqZcCD1AEmZSQViWdYs5+EtBgQFBhV4LjI4koZVG5ZihiIiIlIYtwsRo0aNYuHChQD873//IzMzk/vvv5/Q85zLmJGRwcsvv8znn3/uio0ZM6bAmOTkZHbt2oVhGLRo0cLdNMVNX3/t3I5RFKbpLFyISDmTkwo7ZsLOl8GelR8PqgPtn4Cmt4DF7W8bIh5xIPUAUbOisOYW/RtOoG8g8ePjVYwQERHxMrd/ouzZsyfDhw9n3rx5GIbBF198QVxcHH379iU6Opo6deoQGBiI1Wrl6NGjbN68mZ9//pnMzExM08QwDIYMGUKPHj0K3HfevHnk5uZiGAbdunVzN80yIycnhw8++IC5c+dy8OBBgoOD6dKlC3fddRdt25bdfdZz5oDFAg7HhcdaLDB7NtxwQ4mnJSKeYM+G3W/Btqcg+2R+3C/MeRJG1L3gG+K9/EQKkZSZVKwiBIA110pSZpIKESIiIl7mkbe2nnnmGaxWKz/88AOGYZCZmcmiRYtYtGhRoePNM9b29+/fnxkzZpw1JiUlhZEjRwIwYMAAT6TpdTk5Odx6662sXbuW6tWr069fP06cOMEPP/zAzz//zFtvvUXv3mVzz/XJk0UrQoBz3KlTJZuPiHiA6YB9/webH4PT+/LjFj9oMQ7aPgqBkV5LT0REREQqJo8UIvz8/Hj99df5+uuvmTVrFseOHStQbChMzZo1mTBhwllbMvLcc889nkitTHn33XdZu3Yt7du358MPP3RtX5k/fz73338/Dz74ID/++ON5t7V4S/XqxVsRobYeImWYacLRH5wnYSRvLPhc439C9HQIbeKV1ERERESk4vPoZt8xY8YwatQoVqxYwZo1a9i5cyenTp0iMzOT4OBgqlWrRqtWrejWrRu9e/fGx8fHky9fpuXm5vLxxx8D8PjjjxcoNgwbNoy5c+eyfPlyvv32W26++WZvpXlOI0bAd98VbazDAX8tZhGRsubUH7DhYTj2U8F47UHQcQZEXOKdvERERESk0vB41zGLxUKfPn3o06ePp29drq1fv56UlBTq169P+/btz3p+yJAhLF++nJ9++qlMFiLGjIF774WUlPOfmmEYEB6uEzNEypz0Pc4tGPu/KBiv1gkueQ5qV4wtcCIiIiJS9lXI9ud2u509e/awdetWtm3bxtatW9m5cyfWv45yGDlyZKF9KS7kp59+Ii4ujq1bt3LixAlCQ0Np1KgRAwYM4LrrrjvvloodO3YAnLMhZZs2bQCIj48vdl6lITAQPvoIYmOdxYbCihF5p/l99JFzvIiUAdbjsPUpSHgbHLb8eGhTiH4aGl0DhsV7+YlchN0nd/PRxo+8nYaIiIhcpApZiJg4cSJLlizx2P1Onz7NAw88wNKlSwvET506xalTp9iwYQOffvopr7zyCh07diz0HocPHwagdu3ahT6fF09JSeH06dOEhJS9DvXDhztPz/jXv5xHeVosJg6H4fozPNxZhBg+3MuJigjYMpzHcO6YCbkZ+fGASGg3FZrfAT7+3stPpBgcpoO1h9YStzOOuPg4diTt8HZKIiIi4oYKWYiw2+0FHoeHhxMeHs6+ffsu6l733nsvK1asACAyMpIxY8bQvHlzUlNTmT9/PuvXr+fIkSOMHTuW//u//6NZs2Zn3SczMxOAoKCgQl8nODjY9XFZLUQAxMTA4cPwzTfwzTd2Tp0yiIgwGT3al9GjtRJCxOscNtjzHmyZBtZj+XGfYGh9P7R+wHksp0gZZ821snTvUuJ2xjF311yOZhz1dkoiIiLiISVWiDh27BjJyclkZGRc8ASNPF27dvXIa0dHR9OsWTPatm1L27ZtadCgAd999x2TJk0q9r2+/vprVxGiefPmfPTRR0RG5h9n989//pPnnnuO//3vf6SmpjJ16lQ+++wzj3weZVVgINxwA1x9dQ6maWIYBsHBFbKmJVJ+mCYc/BY2TYb03flxwweaj3WugggqfEWWSFlxKusUC3YtIC4+ju8Tvue07fRZYwwMejToQZe6XXh1zateyFJERETc5dHfHtevX8+nn37KqlWrSElJKda1hmGwfft2j+Rx5513euQ+drudWbNmuR7PnDmzQBEizwMPPMCqVavYsWMH69at49dff6VXr14FxuSteMjKyir0tfJWTABldjWEiJRRx5bDxofg5NqC8QajocPTENbSO3mJFMG+lH2uLRe/7P8Fu2k/a0ygbyCDmg0iNiqWYS2HUTOkJuuPrFchQkREpJzySCHC4XDw1FNP8X//938ARV4BUdb9/vvvnDhxAoBLL730nI0mfXx8uPHGG5k8eTIACxYsOKsQUbduXQCOHi18aWlePDw8XIUIESmalC2wcRIcXlAwXvNy6DgTIrt5Jy+R8zBNkw1HNxC3M4458XPYfGxzoeMigyMZ1nIYI6JGMLDZQIL9ggsdJyIiIuWPRwoRzz33HJ9//rnrcbNmzUhPT+f48eMYhkGXLl04ffo0R44cITk5GXCugAgKCjrnL/dlwS+//OL6+PLLLz/v2DOfP/O6PK1btwZg27ZthV6ftxokKiqq2HmKSCVz+gBsngp7PwbOKPxWbQcdn4O6V+UfYyNSBuTYc1i+bzlx8XHMjZ/LwbSDhY5rVq0ZI1qNIDYqlh4NeuBj8TnnPSODIwn0DcSaay1yHoG+gUQGn72yUUREREqX24WIPXv28PHHH2MYBhEREbz11ltER0czffp0V6+ETz75pMD4zz//nC+++IKsrCyaNGnClClT8PPzczcVj9u1a5fr4/bt2593bI0aNahTpw5HjhwhKSmJU6dOERER4Xq+U6dOhIeHk5iYyJYtW86638KFCwG44oorPPgZiEiFkn0Ktj8L8a+DIzs/HtwAoqdD4xvgPL+4iZSmtOw0Fu1eRFx8HAt3LyQ1O7XQcZfWu5TYqFhio2JpU6MNRhGLaA2rNiR+fDxJmUkF4lnWLGd9zoCgwIINoiODI2lYteFFfT4iIiLiOW4XIr766itXw8Knn36a6Ojo845v1qwZU6ZMYciQIdxxxx18/fXXWCwWnnjiCXdT8bi9e/e6Pq5fv/4Fx9evX58jR44A8OeffxYoRPj6+nLTTTfx2muvMW3aND788ENCQ0MBmD9/PsuXL6datWqMGjXKw5/F2axWKxaLxSP3yfu3FykKzZmLZM/C98+38Nv1IoYtxRU2/apha/kAuU3vBJ9AsGaf+x7llOZM+XI4/TALEhYwL2Eevxz4BZvDdtYYfx9/+jTsw7DmwxjafCh1Quu4njtXH6VzifSLJLJqwRUO1oD8ORNYyFFOZ/ZkKgqrtegrLkRERKRo3C5ErFu3DoBatWrRt2/fIl/XuXNnnnzySf7zn//w5ZdfMmzYMLp06eJuOh6Vnp7u+rhatWoXHB8eHl7otXluv/12Vq9ezdq1axk0aBBdu3YlKSmJdevW4efnx8yZM13FiZJkmqZH+njk3cNT95OKT3OmmEw7vgc+x2/nU1ish/LDlgBsTe/G1uI/4P/X16YK+vepOVO2mabJtqRtLEhYwIKEBaw/tr7QceEB4QxuOpihzYcysMlAqvhXKXAPT+eU96cnv9eJiIiI57hdiDh8+DCGYZy11eDMd69sNluhWy+GDBnCSy+9xKFDh5g9e3aZK0Sc+a5JQEDABcefOeb06bOPHPP39+f999/nf//7H3PnzmXp0qUEBwdzxRVXMG7cuFLrl2EYhkfeXTQMw/Wuk96tlKLQnCki08Tn6CL8tk/Fkr4jP4wFe8N/Ymv1GGZwfSrD36DmTNmT68hl1aFVzN89nwUJC9iburfQcQ3CGrhWPfSq3ws/n9LZgunpOaN5JyIi4nluFyLy3vk/cxsCUKDwkJmZSdWqVQu9vmPHjiQmJrJ+feHvolQ0/v7+3HnnnR47YvRiBAYGuo4TdVfeD3ueup9UfJozF3BiFWx8GE6sKBivNxyjwzP4hrfz7LnL5YDmjPedzjnNkj1LmBM/hwW7FnAy62Sh4zrW7siIqBHEtoqlQ60OXvsl3pNzxuFweCAjEREROZPbP8/6+/uTlZV11jfqKlXyl10eOXLknIWIvILF8ePH3U3F44KDg0lNdTbXys7Oxtf3/H9d2dn5+7N1BKeIFEtaPGyaDAe/Kxivfhlc8pzzSE6RUnQs4xjzds0jLj6OH//8sdDTKXwtvvRp1IfYqFhiomJoFN7IC5mKiIhIeeN2IaJmzZrs37+ftLS0AvGGDfO7Um/ZsoVWrVoVev2+ffsAsNvt7qbicVWqVHEVIpKTky9YXEhJSSlwrYjIBWUdgS3TYM97YJ7xdbBKS+j4LNQfqaM4pdTEJ8UTFx/HnJ1zWJ24GpOz+yOE+odyVfOriI2KZUiLIVQLunAPJREREZEzuV2IaNGiBfv27WP//v0F4u3atXN9/N133zFmzJizrt28eTMbN27EMAzq1Klz1vPe1qRJExITEwFITEy84MkZeWMBmjZtWqK5iUg5Z0uD7TNh58tgP6OLf2BtaP8ENLsVLJVtE4aUNofpYHXiauJ2xhEXH0f8yfhCx9UJreM8YrNVLP0a9yPA98J9k0RERETOxe2fcjt37swPP/xAQkICp0+fdq0aaNy4MW3atGH79u1s3LiRKVOmcN9997l6Saxbt45HHnnEtY+zZ8+e7qbicS1btmTFCuc+7S1btnDZZZedc2xSUpLr6M7q1auf1TNDRAQAezbsfhu2PQXZSflx3yrQ5mFoNRF8tbVLSk6WLYuf9v5E3M445u2ax7HTxwod17ZGW1fxoUvdLlgM9499FhEREQEPFCJ69+7NjBkzsNvt/PrrrwwePNj13D333ONqyvjNN9/w3XffERERQXZ2doHjLQMDA/n3v//tbioe17t3b95//30AfvnlF26//fZzjl2+fLnr4z59+pR4biJSzpgO2P8FbHoMTp9xyoDFD1rcDW0fhcAa3stPKrSTmSdZsHsBc3bOYfGexWTaMs8aYzEs9GzQ01V8aB7R3AuZioiISGXgdiGiWbNmDB48mKNHj7J9+/YChYi+ffsybtw43njjDcDZByIpKanAmdyBgYG88MIL1KtXz91UPO7SSy+lRo0anDhxgrVr17Jt27ZCj9i02+188sknrsdDhgwpzTRFpKw78oPzJIzkDQXjja6HDtMhVFu5xPP+TP7TteXi1wO/YjfP7sUU5BvE4OaDiY2KZWiLodQIUTFMRERESp5HNiC/+uqr53xuwoQJdOrUiffff5/ff/8dm80GOJs5Xn755dx99900a9bME2l4nI+PD3fffTfTpk0D4OGHH+ajjz6ievXqBca98MIL7NixA4BOnTrRu3fvUs9VRMqgU+udBYijPxaM1x4IHZ+DiEu8k5dUSKZp8seRP1zFhy3HtxQ6rkZwDYa3HE5sq1gGNB1AsJ+ORRUREZHSVSqd0Hr27EnPnj1xOBwkJydjGAbVqlUrsfPFDx48yDfffFMgFh+f34Br+/btvPzyywWev+yyy+jevftZ97rmmmv48ccfWblyJbt37yY2NpYxY8bQvHlzUlJSWLBgAX/88QcAYWFhPPnkkyXwGYlIuZLxp3MLxv7/KxivdomzAFFnoHfykgonx57Dz/t+Zs7OOcyNn8uh9EOFjmsR0YLYqFhGtBrBZfUvw8fiU8qZioiIiOQr1ZbsFovlrNUEJeHw4cO8/fbb53w+Pj6+QGECwNfXt9BChK+vL6+99hoPPPAAy5Yt48SJE7z55ptnjatduzYvv/wyLVq0cP8TEJHyyXoCtj4FCW+Bw5YfD2kCHZ6GRteCGv6Jm1KtqSzcvZC4+DgWJSwiLTut0HGX1b/M2e8hKpZWka1KrPgvIiIiUlxuFyJGjhwJQEBAAJ988gl+fn5uJ1XWhIaG8vbbb/Pjjz8SFxfHli1bOHnyJCEhITRs2JCBAwdy3XXXUaVKFW+nKiLekHvaeQzn9pmQm9+Il4BIaDcFmt8BPjruUC7ewdSDzI2fS1x8HMv2LSPXkXvWmACfAK5oegWxUbEMbzmcOlXK3rHYIiIiIuCBQsTOnTsB50kRZaUI0a1bt7NWPHjCgAEDGDBggMfvW9qsVisWi/vvylqtVtfxqyJFUeHmjMOG7/6P8Nv5DEZ2/hGIpk8wuc0mYGsxEfzCINsOnH1KgVxYhZszRWSaJltPbGV+wnzmJ8xn47GNhY6rFliNwU0HM7z5cAY0GUCof6jruczMyjnnPD1nrFarR+4jIiIi+dwuRISHh5OSkkLNmjU9kY+UAtM0C5xc4s59PHk/qfgqzJwxTXyOxOG/fRqW07vzw4YPuY3+hS1qEmZgbddYuXgVZs4UQa4jl5WJK1mQsIAFCQvYn7a/0HGNwhoxtPlQhjYfSo96PfDzyX8ToKL/HRWFp+eM/k5FREQ8z+1CRO3atUlJSSE9Pf3Cg6VMMAzDI+8UGYbhetepsr1bKRenIswZS9Kv+G17DJ/k3wvEc+uOwNb6ccwqLQEon59d2VMR5sz5ZORk8OPeH5mfMJ/v//yeZGtyoeM61urIsObDGNp8KO1rtK+Qfxee4uk5o79rERERz3O7EHH55ZezY8cO1q9f74l8pBQEBgYSHOyZ49ryftjz1P2k4iu3cyZlK2ycBIfnF4zX6A2XzMQ38rLS7f5biZTbOXMORzOOuvo9/PTnT2Tbs88a42vxpW/jvoyIGkFMVAwNqjbwQqbllyfnjMPh8EBGIiIicia3f24ePXo0H374IcePH+ebb75h9OjRnshLRKRsOH0QtkyFPz8CzliiXbUtdJwBdYeC3jGV8zBNk51JO4mLjyMuPo7ViasLHVfFvwpDWgwhNiqWq1pcRXhgeOkmKiIiIlJK3C5ENGjQgMmTJ/P444/z5JNPEhQUxNChQz2Rm4iI9+Qkw7ZnIf41cJzxjnVwfYieDo1vBIuP9/KTMs3usLM6cTVzds4hLj6O3ad2FzquXpV6xETFMKLVCPo27ou/j38pZyoiIiJS+twuRBw+fJjevXvz4IMP8vLLL/PAAw/w8ccfM2TIENq2bUtERASBgYFFulfdunXdTUdExD25WbBrFmx7Bmwp+XG/cGg7GVqOB98gb2UnZViWLYsf/vyBuJ1xzNs1jxOZJwod175me2KjYoltFUvnOp3Vg0BEREQqHbcLEf379y/wQ5RpmmzevJnNmzcX6z6GYbB9+3Z30xERuTgOO+z7BDZPhcyD+XFLAETdA20egYAI7+UnZVJSZhLzd80nLj6OJXuWkGk7+8hMi2Ghd8PeruJD02pNvZCpiIiISNnhsd5qf+9QreOuRKRcME04vBA2PgKpW894woCmN0P7JyFEjQIl355Te4iLj2POzjmsPLgSh3l2M8Ngv2AGNxtMbFQsQ1sOJTI40guZioiIiJRNbhcitJ1CRMqtpNWw8WE4/kvBeN1h0PEZCG/vnbykTHGYDtYdXkfcTmezyW0nthU6rmZITWJaxhDbKpYrmlxBkJ+28IiIiIgUxu1CxNKlSz2Rh4hI6UmLh02PwsFvC8ard4OOz0GtPt7JS8qM7Nxslu1bRtzOOObumsvh9MOFjouqHuXactGtXjd81MBURERE5IJ07L2IVB5ZR2DLk7DnXTDt+fEqLaHDM9Dgah3FWYklZyWzcPdC4uLj+D7he9Jz0s8aY2BwWf3LGNFqBLFRsURFRnkhUxEREZHyTYUIEan4bGmw/XnY+RLYz2gmGFgL2j8BzW4Fi5/X0hPvOZB6wLXlYvn+5eQ6cs8aE+ATwMBmA4mNimV4y+HUCq3lhUxFREREKg4VIkSk4rLnQMLbsHU6ZCflx32rQJuHIGoi+IV6LT0pfaZpsunYJuJ2xjEnfg4bj24sdFxEUATDWg4jNiqWQc0GEeqveSIiIiLiKSVWiNi1axdHjhwhLS0Nu93OiBEjSuqlREQKMh2w/0tnH4jTe/PjFj9ofhe0ewwCa3gvPylVNruNX/b/Qlx8HHPj57I/dX+h45qEN3FtuejZsCe+FtXqRUREREqCR3/KOnToEO+99x4LFiwgPb3g3tq/FyKSkpJ46qmnME2Tdu3acfvtt3syFTkPq9WKxWLxyH3yjm0VKYrSmDOW40vx3zYFS+rGAvHc+mOwtZ6KGdIUHEBmZqHXS9lysXMmPSedH/b+wPzd81n852JSslMKHdepdieGNR/G0OZDaRvZ1vU6OdYccshxN33xAk9/nbFarR65j4iIiOTzWCFi/vz5TJ06laysLEzTLPBcYT8MREZGcvLkSX7//Xd++eUXrr/+ekJCQjyVjpyHaZpn/Rtd7H08eT+p+EpyzlhSNuG/fQo+Jwqe5GOv0Y+cNtNxhHfMS8Kjryslqzhz5mjGURbsWcD83fNZfnA5OfazCwl+Fj8ub3g5Q5sNZWjzodSrUq/Q15Pyy9NfZzQnREREPM8jhYjFixfz4IMPAs5v2GFhYXTs2JEDBw6wb9++c143ZswYfv/9d6xWKytWrODKK6/0RDpyAYZheOSdIsMwXO86aVWEFEVJzBnj9D78dkzDN/GrAnFH1Q7ktJ2Oo+YVznEeeTUpbeebM6ZpsuPkDhYkLGB+wnzWHVlX6D3C/MMY3Gwww5oPY2CTgVQNqFoaqYuXePrrjL6/iYiIeJ7bhYi0tDSmTJmCaZpYLBbGjRvH2LFj8ff3Z/r06ectRPTv3x9fX1/sdjurVq1SIaKUBAYGEhwc7JF75f2w56n7ScXnsTljPQHbnobdb4LDlh8PaQwdnsbS6DoCDfe3IIn3nTln7A47vx38jbj4OObsnMOe5D2FXlM/rD6xUbHERsXSp3Ef/H38Szlr8SZPfm9yOBweyEhERETO5HYh4ssvvyQtLQ3DMBg3bhzjxo0r8rWhoaE0bdqUXbt2ER8f724qIlIZ5J6Gna/A9ucg94xeNAHVoe0UaHEn+AR4LT3xvExbJkv3L+X7fd8zf9d8kjKTCh0XXSua2KhYRrQawSW1L9E72SIiIiJllNuFiF9++QWA8PDwi2o42aRJE3bt2sXBgwfdTUVEKjJHLux5H7Y8Adaj+XGfIGj1H2j9IPhryX1Fcfz0cebvms93279j6b6lZOVmnTXGx/Dh8kaXExsVS0xUDE2qNfFCpiIiIiJSXG4XIvbu3YthGHTp0gV//+Ivfa1a1fmLw99P2RARAZzNJRNnw6bJkHbGyinDB5rdBu2mQnBd7+UnHrP75G7XlovfDv6GydlNAkP8Qriy+ZXERsUytOVQIoIivJCpiIiIiLjD7UJESkoKABERF/fDoN1uB/DIcZIiUsEcXwEbHoKTqwvG64+EDs9A1VbeyUs8wmE6WHtoLXE744iLj2NH0o5Cx9UMrsnQ5kMZ3W40/Zv0J9A3sJQzFRERERFPcrsQUaVKFVJSUsjMzLyo648dOwY4t3aIiACQsg02TYJD8wrGa/SCjjOhRnfv5CVus+ZaWbp3KXE745i7ay5HM44WOq51ZGtio2IZ3HgwnWt3xsfio6a4IiIiIhWE24WIWrVqkZyczM6dO4t9rc1mY+PGjRiGQePGjd1NRUTKKrsVDnyN/75vMXJOYfpHQONR0HAM+Jzx7nZmImx+HPZ+COYZneqrtoEOM6DeMFADwnLnVNYpFu5eyJydc1i8ZzEZORlnjTEw6NGgh/Oki1axtKzeEoDMzExM8+wtGiIiIiJSfrldiOjWrRs7d+4kISGBnTt30qpV0ZdKf/fdd2RkZGAYBpdddpm7qYhIWZQ4F1b9C2zJ+GDBwIGJBY7Ewbp7oftHULMXbJsBu15zFi3yBNeH9k9Ck5vA4uO1T0GKb1/KPteWi1/2/4LdtJ81JtA3kIFNBzKi1QiGtRxGzZCaXshUREREREqb24WIYcOG8dFHHwHwxBNP8PHHHxepaeWuXbt4/vnnAfDx8SEmJsbdVESkrEmcC7+McD00cBT4E1sK/BILvsHOYznz+FWFtpOh5QTwDSq9fOWimabJhqMbXMWHTcc2FTquelB1hkcNJzYqloFNBxLiH1LKmYqIiIiIt7ldiGjfvj2DBg1iyZIlbNq0iZtvvplp06bRsmXLQsdbrVa++eYbXnnlFddqiDFjxlC3rrrei1QodqtzJQRAIacfFIjnFSEsARA1AdpMggCdhlDW2ew2lu9fzpydc5gbP5eDaYUfw9ysWjNio2IZ0WoEPRr0wEerW0REREQqNbcLEQBPPfUUCQkJ/Pnnn2zcuJHY2FiaN2+O1Zq/xHrcuHEkJSWxY8cObDaba89v69atmTRpkifSEJGy5MDXYEsu+vgavaDHZxDSsORyErelZaexaPci4uLjWLh7IanZqYWO61q3KyNajSA2KpY2NdpgqLeHiIiIiPzFI4WIsLAwPv74Y/7zn/+wdu1aABISEgBcP3wuXboUoEDTscsuu4xXXnmlSFs5RKScSZwDWADHBQbiHBdYU0WIMupQ2iHmxs8lLj6OpXuXYnPYzhrj7+NP/yb9iY2KJSYqhrpVtMpNRERERArnkUIEQGRkJB999BFxcXF89NFH7NhR+HnwAM2aNeP2228nJiYGi8XiqRREpCzJPknRihA4x2WfKslspBhM02Tr8a3ExTv7Paw7vK7QcVUDqjK05VBio2K5svmVhAWElXKmIiIiIlIeeawQAc7VDyNGjGDEiBGcOHGCjRs3cvz4cdLT0wkKCiIyMpLo6GgaNGjgyZcVkbIooDrFWhGhnhBelevIZeWBla7iw5/JfxY6rkFYA9eWi8sbXY6fj18pZyoiIiIi5Z1HCxFnqlGjBgMHDiyp24tIWWZLB1sGxVoRUX9kSWYkhTidc5ole5YQFx/H/F3zOZl1stBxHWt3JDYqltioWDrW7qh+DyIiIiLilhIrREjZZbVaPbIlxmq1YpqmfimRAixHF+G/aSKWrMQijTcxwK8qWZFDIDOzhLOTY6ePsWjPIuYnzGfZ/mVYc61njfExfOjVoBfDWwxnaLOhNKya37sjKyurNNPV1xkpNk/PmTMbb4uIiIhnuF2I+N///sewYcOoWbOmJ/KRUmCaZoGmoe7cx5P3k/LNsB7Df8sD+B6e7YqZFn/4q7GhUcgRnibOXxSyO72DaQkAzaMSsevULhYkLGBBwgLWHF6DWci/RahfKAObDGRY82EMajqIaoHVXM958/9vfZ2R4vL0nNG8ExER8Ty3CxEzZ87kxRdfpFu3bsTExDBo0CCCg4M9kZuUEMMwPPJOkWEYrned9G5lJWY68Nn/Ef7bHsOwpbjC9hr9yen4Gpa07fivHwu2FEwsGDhcf+JXlZxO7+KoMwTNIM9xmA5+P/w78xPmMz9hPrtO7Sp0XO2Q2gxtPpShzYfSt2FfAnwDSjnTC9PXGSkuT88ZzTsRERHPM0w3S/2tWrUq8E06MDCQ/v37ExMTQ+/evXUqRhmQkZFBfHy863FUVBShoaFu3zczM9P1w56KT5VU6k74/Q44/kt+LCASOr0Cja+HvK8Ndisc+Ibcfd9g5JzC9I/At/FoaDgafAK9knpFk2XL4qe9PxG3M455u+Zx7PSxQse1qdGGEVEjiG0VS5e6XbAYZftrtL7OSHF5es6U1PdQERGRysztFRE9evRgzZo12O12wLl/eOHChSxcuJCIiAiGDh1KTEwM7dq1cztZESkj7Nmw/TnY9jQ4cvLjTW6CS16EwMiC430CockN5NS62vULgq9+qXTbycyTLNi9gLj4OL5P+J5M29k9NiyGhZ4NejqbTbaKpXlEcy9kKiIiIiKSz+0VEQAnTpxgwYIFzJ07l+3btxd8gb/eEW3SpAmxsbEMGzaMevXqufuSUgxaESEedfxXWDsW0nbkx0KbwqX/hdoDznup5oz7/kz+k7idziM2fz3wK3bTftaYIN8gBjUbRGxULMNaDqNGSA0vZOoZmjNSXFoRISIiUvZ5pBBxpj///JO4uDgWLFhAYmJ+1/y8goRhGFxyySWMGDGCK6+8kipVqnjy5aUQKkSIR+SkwsZHIOHt/JjhA60fhHZTwPfCc0BzpvhM0+SPI3+4ig9bjm8pdFxkcCQxLWOIbRXLgKYDCParGH+/mjNSXCpEiIiIlH0eL0Scaf369cybN49FixaRkpKS/6J/FSX8/Pzo27cvMTEx9OnTBz8/v5JKpVJTIULcYpqQOBvWjYesI/nxiK7Q7V2o1qHIt9KcKZocew4/7/uZuJ1xzN01l8S0wo9CbRHRwrXlonv97vhYfEo505KnOSPFpUKEiIhI2VeihYg8ubm5rFixgrlz57Js2bICZ3LnFSXCwsJYs2ZNSadSKakQIRctM9FZgEiMy4/5hkCHZ6DFOCjmL76aM+eWak1lUcIi5uycw6KERaRlpxU6rlu9bsRGxTKi1QhaRbaq8B39NWekuFSIEBERKfvcblZZpBfx9aVfv37069eP06dPs3jxYubPn8/q1atd53ynpRX+Q7eIeIHDDrvfgk2TITc9P153KHR9E0Iaei+3CuRg6kHmxs8lLj6On/f9jM1hO2uMv48/A5oOIDYqluEth1OnSh0vZCoiIiIi4jmlUog4U0hICFdffTVNmjQhODiYH3/8sbRTEJHzSdkCa8bCydX5scBa0OV1aDA6/0hOKTbTNNlyfAtxO+OYEz+H9UfWFzquWmA1hrYcSmxULIObDaZKgHrpiIiIiEjFUaqFiL179zJv3jzmzZvnamRpGAalsDtERC7EboWt02H7TDBz8+PNbodLngP/at7LrRzLdeSyYv8K4uKdzSb3pewrdFyjqo1cWy56NeyFn4965oiIiIhIxVTihYiTJ08yf/585s2bx7Zt21zxM4sPLVq0IDY2tqRTEZFzObYM1t4B6bvzY2FRcOk7UPNy7+VVTmXkZLA4YTFx8XEs2L2AU1mnCh3XqU4nZ7PJqFiia0VX+H4PIiIiIiJQQoWIzMxMfvjhB+bOncuaNWuw253n3J9ZfKhVqxZDhw4lJiaGVq1alUQaInIh2Sdhw4Pw5wf5MYsftJkEbSeDT4D3citnjmYcZV78PObEz+GnP38i25591hhfiy99G/clNiqWmKgYGlZVrw0RERERqXw8VohwOByukzGWLl3qOhnjzOJDSEgIgwYNIiYmhssuu0zv/ol4i2nC/i/gj3sh+0R+vEZP5yqIqm28l1s5YZomO5N2urZcrElcg8nZ28yq+FfhqhZXMSJqBFe1uIrwwPDST1ZEREREpAxxuxCxadMm5s6dy6JFi0hOTgYKFh98fX3p1asXMTExXHHFFQQE6B1WEa/K2Ae/3wVHvs+P+YVBx+eg+VgwLF5LrayzO+ysTlxNXHwcc3bOYfep3YWOq1elHjFRMcRGxdK3cV8CfPV1T0REREQkj9uFiGuvvbbQhpPR0dHExMQwZMgQIiIi3H0ZEXGXIxfiX4PNU8CemR9vMAo6vwbBdb2XWxmWZcvihz9/IG5nHPN2zeNE5olCx7Wr2c7V76Fz3c5YVNARERERESmUR7Zm5BUhGjRoQExMDDExMTRq1MgTtxYRTzi1HtbcDslnHBcZVA+6vgH11Sj275Iyk5i/az5x8XEs2bOETFvmWWMshoVeDXsxImoEMVExNIto5oVMRURERETKH7cLEeHh4Vx11VXExMRwySWXXNQ9srKyWLx4MSNGjHA3HRE5U+5p2PIE7HwZTPtfQQNajoMOTzu3ZAgAe07tcW25WHlwJQ7TcdaYYL9gBjcbTGxULENbDiUyONILmYqIiIiIlG9uFyJ+/fVXfH0v7jZr1qxhzpw5LF68mKysLBUiRDzp8GL4/U44vS8/VrUddHsXIi/zWlplhcN0sO7wOuJ2OptNbjuxrdBxNUNqMrzlcGKjYhnQdABBfkGlnKmIiIiISMXidiGiuEWI/fv3M2fOHOLi4jhy5Ajg3NqhEzRKj9VqxWJxf/+61WrVv11ZlH0c/y0P45v4lStkWgKwRU0it8W9YPGHzLO3GpQGb8+Z7Nxslh9czoLdC1iwZwFHMo4UOq5FtRYMazGMYc2H0bVOV3wsPgCYNrPQbRpScrw9Z6T88fScyTsFTERERDzHY8d3nk9GRgYLFy5k9uzZbNy4EeCs5pb+/v6lkYrg/Lv/+9//xd7Hk/cTN5kmvgc/w3/rZAzbKVfYHnk52R1ewwxt7hrnLd6YMynWFJbsXcK83fP4cd+PpOeknzXGwODSupcytNlQhjYfSlT1qELzltKnrzNSXJ6eM5p3IiIinldihQjTNFmxYgVz5sxh6dKlZGdnu+J5DMOgS5cuxMTEMHjw4JJKRf7GMAyPvFOUd1qKp+4nF8/I2IP/xgn4JC13xUy/auS0ewZ7wxvBMCgL/0KlNWcOph1kfsJ85u+ez6+Jv5LryD1rTIBPAP0a9WN4i+Fc2fRKaofWLrF85OLp64wUl6fnjOadiIiI53m8ELF7925mz57NvHnzSEpKAs5+N6FFixbExMQwbNgw6tSp4+kU5AICAwMJDg72yL3yftjz1P2kmBw22PECbH0S7GcsH270D4zOrxAQWNN7uZ1DScwZ0zTZdGyTq9/DhqMbCh0XERTBsJbDiI2KZVCzQYT6h3osByk5+jojxeXJOeNwnN24VkRERNzjkUJEcnIy8+fPZ/bs2ezYsQM4u/iQ945C+/bt+eqrr866h4gUU9IaWHs7pGzJj4U0gq5vQd2rvJdXKbHZbaw4sII5O+cwN34u+1P3FzquSXgTYqNiiW0VS6+GvfC1lMqONBEREREROYeL/ok8NzeXn3/+mdmzZ/PLL7+Qm+tc+nxmASIgIIArrriC2NhY7rjjDgzD8EiTRJFKzZYOmx6FXbOAv/5/MywQdR9ETwPfEK+ml+dA6gGSMpMKxLKsWc6UDQgKLHj6RGRwJA2rNjzvPdOz0/k+4Xvi4uNYsHsBKdaUQsd1rtOZ2KhYRrQaQbua7bS0WkRERESkDCl2IWLr1q3MmTOH+fPnk5qaChTe92HEiBEMHjyY0FAtfRbxmMR5sO5uyEzMj1W7xHkkZ0Rn7+X1NwdSDxA1KwprbtG7zQf6BhI/Pv6sYsSR9CPMjZ9LXHwcP+39iRx7zlnX+ln86NekH7FRscRExVA/rL7bn4OIiIiIiJSMIhci3nvvPebMmcOePXuAs7deNG3alNjYWIYPH07dunU9m6VIZZd1BNbdAwe/yY/5BEP0kxB1L5Sx7QZJmUnFKkIAWHOtJGUm0SCsATuSdjBn5xzi4uNYe2htoePDAsIY0mIIsVGxXNX8KqoGVvVE6iIiIiIiUsKK/NvLCy+84OpEnSciIoIhQ4YQGxtL+/btSyRBkUrNdEDCu7DxYbCl5sfrDHb2gght4r3cSsDLq15m9aHVJJxKKPT5+mH1nf0eomLp07gP/j469ldEREREpLy5qLdRg4KCeOihh7j22mvV80GkpKTugLVj4cSv+bGAGtD5FWj0D6iAfQ8+3fLpWbHoWtGu4kOnOp3U70FEREREpJwrdiHCMAysVivTp09n8eLFxMbGMmjQIEJCykaDPJFyz54N256F7c84j+fM0/RfcMkLEFDda6mVBh/Dh96NeruKD02qVaxVHyIiIiIilV2RCxHDhg3jp59+IisrC3D2iFizZg1r1qxh2rRpXHHFFcTExNC7d2+tkhC5WMdXOFdBpO3Mj4U2h0v/C7X7ey+vUvJk3ye5u+vdVA+u2MUWEREREZHKrFg9Ik6fPs2iRYuIi4tj3bp1rn4RVquVhQsXsnDhQiIiIhg2bBgxMTG0bdu2xBIXqVByUpx9IBLeyY8ZvtD6QWg3BXyDznlpRTK05VAVIUREREREKrhibc0ICQlh9OjRjB49msTERObMmUNcXBwHDx50FSVOnjzJxx9/zMcff1zgJA0RKYRpwsFvYd0EsB7Nj1e/FC59F6pFey83ERERERGREnDReyjq16/P+PHj+eGHH/j0008ZNWoUoaGhgHPbhmma/Pnnn7z88stcccUVHktYpMI4fRB+iYVfx+QXIXxDofNrMPC3cl2E2J+y39spiIiIiIhIGeWRZg5dunTh6aefZuXKlTz//PP07NkTi8XiKkg4HA7X0Z9bt27l7rvv5vvvvycnJ8cTLy9SvjjsEP86LGgDh+blx+sNh6HbIWoCWHy8l58bbHYbM36dwbXfXOvtVEREREREpIy6qOM7zyUgIIDhw4czfPhwjh07RlxcHHFxcezZswfTNDEMA7vdzrJly1i2bBkhISEMGjSI4cOH0717d0+mIlI2JW+GtbfDybX5scDa0GUWNLi6XB/J+cfhP7ht3m1sPLrR26mIiIiIiEgZVmLHW9SqVYuxY8eyYMECvv76a66//nrCwsJcqyRM0yQjI4PZs2dzyy230KdPn5JKRcT7crNg42T4vnPBIkTzO2DYDmg4qtwWITJtmTz0w0N0e6+bqwhhUD4/FxERERERKXmlcs5m+/btmTp1Kr/++iuvv/46/fv3x9fXuRgjryhx/Pjx0khFpPQd/QkWtoftz4KZ64yFtYIBv8Clb4N/uFfTc8eyvcuIfiua5397HrtpByC6VjRx18UR6BtYrHsF+gYSGRxZEmmKiIiIiEgZ4tGtGRfi5+fHwIEDGThwIKdOnWLevHnExcWxffv20kxDpHRkn4T198Pej/JjFn9oOxnaPAI+Ad7LzU0p1hQeXPIg7214zxXz9/Fn6uVTeajnQ/j5+BE/Pp6kzKQC12VZs8AEDAgKLHgkaWRwJA2rNiyN9EVERERExItKtRBxpoiICG6++WZuvvlm4uPjmTNnjrdSEfEs04R9n8P6iZB9xi/iNXrBpe9A1dZeS80TZu+YzbiF4ziSccQV69WwF+8Of5dWka1csYZVG55VWMjMzHT1iwkODi61nEVEREREpOzwWiHiTFFRUTz88MPeTkPEfRl74fe74Mji/JhfVbhkJjS7DYxS2Q1VIo5mHGX8wvF8u+NbVyzUP5TnBjzHnV3uxFKOPzcRERERESk9ZaIQIVLuOXIh/hXYPBXsWfnxBqOhy2sQVMdrqbnLNE0+2PgB9y+5nxRriis+tMVQ3hr6Fg2qNvBeciIiIiIiUu6oEFEJWa1WLBb33722Wq2uZfaVmZGygYAN47CkbnLFHEH1sEW/jL3OUGdPhMxM7yXohr0pexm/eDw/H/jZFYsMiuT5K55nTKsxGIZBZjE+N80ZKS7NGSkuT88Zq9XqkfuIiIhIPhUiKqG8k0o8cR9P3q/cyT2N/86n8N3zBgYOAEwMcpveSU6rqeBXxdkvohzKdeTy5h9vMn3ldLJy81d4XNfmOmb0neE63aK4/+6Vfs5IsWnOSHF5es5o3omIiHieChGVkGEYHnmnyDAM17tOle3dSsuxxfhvnIgl64Ar5ghrS07HN3BEdKU8/21sOb6Fuxffzfqj612xBmENeG3QawxqMsite1fmOSMXR3NGisvTc0bzTkRExPNUiKiEAgMDPXZiQaU7ASHrGKy/D/b/X37MJxDaPY6l9f0EWvy8l5ubrLlWpi+fzszfZpLryAXAwGD8peN5uv/TVAmo4pHXqXRzRtymOSPF5ck543A4PJCRiIiInEmFCJGiME348wPY8ADkJOfHa/WHS/8LVZp7LzcP+PXAr9w29zbiT8a7Yq0jW/N+zPt0b9Ddi5mJiIiIiEhFo0KEyIWk7YK1d8Dxn/Nj/hHQ6SVochOU42W7adlpTPpxEm+ue9MV87P4Mbn3ZCb1mkSAb4AXsxMRERERkYpIhQiRc7HnwI7nYet0cGTnxxv/01mECKzpvdw8YMGuBdy54E4S0xJdsW71uvFezHu0q9nOi5mJiIiIiEhFpkKESGGSVsOa2yF1a34spDF0fRvqDvZaWp5w/PRx7v3+Xr7Y+oUrFuwXzNP9n2bCpRPwsfh4MTsREREREanoVIgQOZMtDTZOht1vAn8d2Wb4QKv/QPvHwTfEq+m5wzRNPt38Kfctvo+TWSdd8UHNBvHfYf+lcXhj7yUnIiIiIiKVhgoRInkS4+D3cZB1KD9WrRN0ew8iLvFeXh6wP2U/dy64k+8TvnfFIoIieHnwy9wYfaOOpxMRERERkVJTIoWIHTt28Mcff3DkyBHS0tKw2+0888wzJfFSIu7LPAx/TICD3+XHfIIhejpE3QOW8luvszvsvPH7G0z+aTKnbadd8WvbXsurV75KrdBaXsxOREREREQqI4/+hvX9998za9Ys9uzZ44rlneX990JEUlISI0aMwG6307lzZ2bNmuXJVEQuzHRAwjuw8WHnlow8da6Erm9BaGOvpeYJ209s59a5t7I6cbUrVq9KPd4c+iYxUTFezExERERERCozjxUipk6dytdffw04iw8XEhkZSffu3Zk3bx5Lly7l2LFj1Kqld2ellKRuh7Vj4cTK/FhgTej0KjS6tlwfyZljz+HZFc/y9IqnsTlsrvidne9kxoAZVA2s6sXsRERERESksrN44iYvv/wyX331FaZpYpomvXr14oEHHqBbt27nvW7EiBGAs3CxfPlyT6Qicn52K2x+HBZ1LFiEaHoLDN0Bja8r10WI1Ymr6fTfTjyx/AlXEaJl9ZYs/9dy3hr2looQIiIiIiLidW6viNi3bx/vv/8+AGFhYbz++uuuAsSRI0dYs2bNOa+97LLLCAoKwmq1smbNGq655hp30xE5t+O/OFdBpMXnx0KbQ7d3oFY/7+XlARk5GTy29DFeW/Ma5l+nffgYPjzU8yGm9plKoG+glzMUERERERFxcrsQ8eWXX5Kbm4thGEyfPv2CqyDO5OPjQ1RUFBs3biQhIcHdVEQKl5MMGx6CPe/lxwxfaPMwtH0UfIO8l5sHLNmzhLHzxrI/db8r1qlOJ96PeZ+OtTt6LzEREREREZFCuF2IWL3a2QivYcOGDB48uNjX16tXj40bN3L06FF3UxEpyDThwNfwxz1gPZYfr34ZdHsXwtt5LzcPOJl5kv8s+Q8fb/rYFQv0DeTJvk9yX/f78C3Hp32IiIiIiEjF5fZvKocPH8YwDKKjoy/q+tDQUABOnz59gZEixXD6APw+Dg7Pz4/5VoGOz0LzO8Hi473c3GSaJl9t+4oJiyZwIvOEK96vcT/eGf4OzSOaezE7ERERERGR83O7EJGZmQlAcHDwRV1vtVoBCAgIcDcVEXDYYdcs2Pwo5J5R3KofC11mQXB97+XmAYlpidy94G7m7ZrnilUNqMoLg17g1ktuxSjHjTZFRERERKRycLsQER4eTlJSEsnJyRd1/YEDBwCIiIhwNxWp7JI3wZrb4dTv+bGgOs4CRP2R5fo0DIfp4J0/3uGhHx4iPSfdFb+69dXMumoWdarU8WJ2IiIiIiIiRed2IaJhw4acOHGCzZs3F/va5ORktm7dimEYtGrVyt1UpLLKzYKt02DHC2Da8+Mt7oIOz4J/+T6yMj4pntvn3c6KAytcsdqhtZl11SxGtRnlxcxERERERESKz+LuDXr27AnAsWPH+PHHH4t17TvvvIPNZgOgR48e7qYildHRH2Fhe9j+XH4RIqw1DPwVur5ZrosQNruNZ1Y8Q4e3OxQoQtzS8Ra2371dRQgRERERESmX3C5EXH311a7+DtOmTSMxMbFI182ePZsPP/wQwzAICwsjNjbW3VSkMrEmwaqbYelAyNjjjFn8of2TcNUGqNHTu/m56Y/Df9D13a48uvRRsu3ZADSt1pQfb/yR92Pfp1pQNS9nKCIiIiIicnHcLkTUrl2bW265BdM0SUpKYvTo0Xz66aeF9ozIzs5m1apV3HPPPUyePBnTNAG45557LrrZpVQypgl7P4UFrWFv/rGV1OgNV22C9lPAp/w2Ps20ZfLQDw9x6XuXsunYJgAshoUHuj/Alru2cEXTK7ycoYiIiIiIiHsMM68a4AbTNLn33ntZsmRJga79fn5+5OTkYBgGoaGhZGRkFLgGYOTIkTz77LPupiDnkZGRQXx8vOtxVFSU69hUd2RmZmKaJoZhlE4hKeNPWHsnHP0hP+YXDpc8D81uAcPtuppXLd27lLHzxrIneY8rFl0rmvdj3qdL3S5ezMxzSn3OSLmnOSPF5ek5U1LfQ0VERCozj/zmZhgGr7zyCnfddRcWiwXTNDFNE5vN5ipMpKenu+KmaeLj48P48eNVhJALc+TC9pmwoF3BIkTDa2DYDmh+W7kuQqRYU7ht7m1c8fEVriJEgE8AT/d/mnW3r6swRQgRERERERHwwKkZeSwWC/feey+jR4/mo48+4pdffmHfvn1njatTpw59+/bllltuoUGDBp56eamoTq6DtbdD8sb8WHADZyPKesO8lpanfLfjO8YtHMfRjKOuWK+GvXhv+HtERUZ5MTMREREREZGS4bFCRJ569eoxefJkJk+eTEpKCidOnCA9PZ3g4GCqV69OjRo1PP2SUhHZMmDzFNj1GpgOZ8ywQMt7IHo6+JXvZbFH0o8wftF4vtvxnStWxb8Kzw14jju63IGlHK/wEBEREREROR+PFyLOFB4eTnh4eEm+hFREhxbC73dB5oH8WHg0dHsPqnf1Xl4eYJom/9vwPx744QFSrCmu+LCWw3hzyJs0qKpVQiIiIiIiUrGVaCFCyiar1YrF4v477lar1dUQzCOsR/Hf8jC+h75xhUxLILZWk8ltfg9Y/CAz0zOv5QV/Jv/J+CXjWX5guSsWGRzJC/1fYHSr0RiGQWY5/vyKwuNzRio8zRkpLk/PGavV6pH7iIiISD4VIiqhvIahnriPR+5nmvge+Bj/bY9i2FJcYXuNfmR3eBUzpKlrXHmU68jljT/e4KmVT5GVm+WK/6PNP5jRbwbVg6oDeOTfpKzz2JyRSkNzRorL03NG805ERMTzVIiohAzD8Mg7RYZhuN51utj7GRm78d8wAZ+TK1wx0786Oe1mYG/wDzAMyvP7oJuPb+bu7+9mw7ENrliDsAa8Nug1BjUZ5MXMvMMTc0YqF80ZKS5PzxnNOxEREc8rUiHipptuKuk8MAyDjz76qMRfRyAwMNAjZ6sDF39Wuz0HdsyErU+BIzs/3vhGjE4vEhBYvpuaWnOtTF8+nZm/zSTXkQuAgcGESyfw9BVPE+pfvpttuuOi54xUWpozUlyenDMOh8MDGYmIiMiZilSIWLt2bYm+I6D9v5XMid+cR3Kmbs+PhTSBS9+GOuV/lcCK/Su4fd7txJ+Md8Xa1GjDe8Pfo3uD7l7MTERERERExPuKvDVDeyTFbTmpsGky7H4L+Gs+GT7Q6n5o/zj4lu93O9Oy03j4h4d5+4+3XTE/ix+Te09mUq9JBPgGeDE7ERERERGRsqFIhYiPP/64pPOQiu7gHFg3DrIO58ciukC3d6FaR29l5THzd83nrgV3kZiW6Ip1q9eN92Pep23Ntl7MTEREREREpGwpUiHi0ksvLek8pKLKPATrJkDi7PyYbwhEPwUtJ4DFx3u5ecDx08e59/t7+WLrF65YiF8Iz1zxDOO6jsOnnH9+IiIiIiIinqZTM6RkmA7Y/TZsfARy0/PjdYdA1zchpJH3cvMA0zT5ZPMn3Lf4Pk5lnXLFBzcbzNvD3qZxeGPvJSciIiIiIlKGqRAhxWe3woGv8d/3LUbOKUz/CGg8ChqOAZ9ASNnmbEaZtCr/msCa0Pk1aHgNlPPGpPtS9nHn/DtZvGexKxYRFMErg1/hhugb1HhVRERERETkPFSIkOJJnAur/gW2ZHywYODAxAJH4mDdPc5TLxJng8OWf02z26DjcxAQ4bW0PcHusDNr7SweXfoop22nXfHr2l3Hq1e+Ss2Qml7MTkREREREpHxQIUKKLnEu/DLC9dDAUeBPbClw4Kv88VVawqX/hVp9Sy3FkrLt+DZum3cbqxNXu2L1qtTj7WFvM6zlMC9mJiIiIiIiUr54vBCRnp7OH3/8wY4dO0hOTub06dM4HI4LXmcYBs8884yn0xFPsVudKyEA19Gb59NmErSf6tyqUY5l52bz7K/P8syKZ7Cdscrjri53MWPADMICwryYnYiIiIiISPnjsUJEamoqL7zwAvPmzSM7O/ui7qFCRBl24GuwJRd9fNU25b4IsTpxNbfOvZXtJ7a7Yi2rt+S94e/Ru1FvL2YmIiIiIiJSfnmkEJGYmMiNN97I0aNHMc0Lv1tuGMZZ49Tgr4xLnANYgAuvbgGLs09EkxtKNqcSkpGTwaM/Pcrra1/H/Gv1h6/Fl4d6PMSUPlMI9C3fBRYRERERERFvcrsQYZom48eP58iRIwBERUUxfPhwVq5cyapVq1xbLk6fPs2hQ4dYt24dW7ZsASA4OJjx48dTrVo1d9OQkpZ9kqIVIXCOyz514WFl0OKExdwx/w72p+53xTrX6cz7Me/ToXYHL2YmIiIiIiJSMbhdiPj+++/ZuXMnhmHQq1cv3nrrLXx9fTly5AirVjmPbxw5cmSBa7Zu3crUqVPZvn07H3/8Me+//z7NmjVzNxUpSQHVKdaKiHJ2QsbJzJPct/g+Ptn8iSsW5BvEk/2eZOJlE/G1qK+riIiIiIiIJ1jcvcGPP/4IOLdWPPHEE/j6XvgXtnbt2vH5559zySWXcPToUSZOnHjRfSWklNQfQbFWRNQfeeFhZYBpmnyx9Qtav9G6QBGif5P+bLlrCw/0eEBFCBEREREREQ9yuxCxefNmDMOgTZs21KtXr8jXBQYGMmPGDHx8fEhISGDevHnupiIlqeEY8KsGXKiXhwH+1aDh6NLIyi2JaYnEfBHDP779BycyTwBQNaAq7w1/jx9v/JFmEVqlIyIiIiIi4mluFyJOnXL2Avj71oozm0+ea7VDo0aNuOSSSzBNk4ULF7qbipQkn0Do/tFfD85VjPgrftlHZfrEDIfp4K3f36LNG22Yv2u+K35166vZMW4Ht3a6Vc1TRURERERESojbhYi8IkNwcHCBeEhIiOvjlJSUc17fqFEjAPbu3etuKlLS6g+Hy+eAfzgA5l/TJ+9P/MPh8jjnuDIqPimevh/25e6Fd5Oekw5A7dDafHvNt3x7zbfUqVLHyxmKiIiIiIhUbG5vfg8NDSU1NRWr1VogHh4e7vr4wIED1KpVq9Dr09OdvwyePHnS3VSkNNSPgZGH4cA32Pd9g5FzCtM/At/Go53bMcroSgib3cbzvz3Pk8ufJNuev0LntktuY+bAmVQL0sktIiIiIiIipcHtQkTDhg3ZsmULJ06cKBBv3ry56+PVq1fTtWvXs651OBxs374dgKCgIHdTkdLiEwhNbiCn1tWYpolhGPj+bUVMWbLu8DpunXsrm49tdsWaVmvKu8PfpX+T/l7MTEREREREpPJxe2tGq1atME2TPXv2FIh37NgRf39/AL744otCVzx89NFHJCYmYhgGLVq0cDcVkQIybZk8sOQBur3XzVWEsBgWHuj+AFvu2qIihIiIiIiIiBe4vSKiW7dufPXVVxw9epSDBw/SoEEDAKpUqcKgQYOYP38+p06dYtSoUdx88820bNmSrKwsli5dypw5c1z3GTJkiLupiLgs3buU2+fdzp/Jf7piHWp14P2Y9+lct7MXMxMREREREanc3C5E9OnTBz8/P3Jzc/n++++5/fbbXc89+OCDrFixgrS0NI4dO8bMmTMLvUebNm0YM2aMu6mIkJyVzIM/PMj7G953xQJ8Ani8z+M80OMB/Hz8vJidiIiIiIiIeKRZ5UsvvcTJkyfPakhZq1YtPvjgAyZMmMChQ4cKvb5r16688sor+PnpF0Rxz7fbv2X8ovEczTjqivVu2Jt3h79LVGSUFzMTERERERGRPG4XIgAGDhx4zufatGnDokWLWLJkCatWreL48eNYLBYaNGhAv3796NmzpydSkErsSPoRxi0cx+yds12xKv5VmDlwJmM7j8ViuN0KRURERERERDzEI4WIC/H392fYsGEMGzasNF5OKgnTNHl/w/s8sOQBUrNTXfHhLYfz5tA3qR9W34vZiYiIiIiISGFKpRAh4mkJpxIYO28sy/Ytc8VqBNfg9ate55q212AYhhezExERERERkXNRIULKlVxHLi+vepmpP0/Fmmt1xW/qcBMvDXqJ6sHVvZidiIiIiIiIXIhHChF79uzBZrPh5+dHs2bNin1dQEAATZo08UQqUoFtPLqR2+bexh9H/nDFGlVtxH+H/ZfBzQd7MTMREREREREpKrcLEYcOHWL48OGYpsmIESN49tlni3zte++9x5w5c/Dx8WHp0qXUrFnT3XSkArLmWnly+ZPMXDkTu2kHwMDgnm738FT/pwj1D/VyhiIiIiIiIlJUbh8nsGjRIhwOBwDXX399sa79xz/+gWma2O12Fi5c6G4qUgH9sv8XOrzdgWd/fdZVhGhboy2/3fobr1z5iooQIiIiIiIi5YzbhYg1a9YAUKNGDdq3b1+sa6Ojo6lRowYAq1evdjcVqUDSstO4a/5d9PmwD7tO7gLAz+LHZm3YOAAAN8lJREFUE32eYP0d67ms/mVezlBEREREREQuhttbMxISEjAMg7Zt217U9W3btuXnn39m9+7d7qYiFcS8+HncteAuDqUfcsUuq38Z7w1/j7Y1L26eiYiIiIiISNngdiHi1KlTAK6VDcWVd13efaTyOpZxjHu+v4evtn3lioX4hfDMFc8wrus4fCw+XsxOREREREREPMFjx3fabLaLui43N7fAn1LyrFYrFovbu3KwWq2YpolhGG7dxzRNPt/2OY8se4RT1vyC1IDGA3h90Os0rNqQbGu2u+lKGeCpOSOVh+aMFJen54zVar3wIBERESkWtwsRERERHD16lEOHDl14cCESExMBqFatmrupSBGZpolpmh65j7v325+6n3t+uIef9v3kikUERTCz30yubX0thmF4JFcpGzwxZ6Ry0ZyR4vL0nNG8ExER8Ty3CxFNmjThyJEjbNy4kdTUVKpWrVrka1NTU9m4cSOGYdCoUSN3U5EiMgzDI+8U5RUJLuZ+doedt9a/xbRfp5Fpy3TFx7Qew8x+M6kZoqNcKyJ35oxUTpozUlyenjOadyIiIp7ndiGiR48e/Pbbb9hsNmbNmsWjjz5a5Gtff/11bDYbhmHQo0cPd1ORIgoMDCQ4ONgj98r7Ya8499t6fCu3zb2NNYfWuGL1w+rz1tC3GNZymEfykrLrYuaMVG6aM1JcnpwzeUeUi4iIiOe43Shg5MiRBAUFAfDpp5/y3nvvFem6d999l08//RQAf39/rr76andTkTIuOzebx5c9Tqf/dipQhLiry11su3ubihAiIiIiIiKVgNsrIqpXr86tt97KrFmzMAyDF198kR9//JEbbriBbt26FThNIykpidWrV/PZZ5+xceNGwLnk8d///je1atVyNxUpw1YdXMVt825j+4ntrlhU9Sjei3mPXg17eTEzERERERERKU0eOTVj3Lhx7Ny5kx9//BHDMNi0aRObNm0CnKsdgoODyczMJCcnx3VNXvOnfv36MXHiRE+kIWVQRk4Gk3+azKy1szBx/pv7Wnx5uOfDPHb5YwT6Bno5QxERERERESlNHilEGIbBa6+9xksvvcT//ve/Avsps7Ozyc4+++hFi8XCv//9b/7zn/94IgUpYQdSD5CUmVQglmXNAhMwICgwqMBzkcGRbD+xnTvm38GB1AOueJe6XXhv+Ht0qN2hNNIWERERERGRMsYjhQhwFhYeeOABRo8ezQcffMCvv/5a6JGe9erV4/LLL+fmm2+mcePGnnp5KUEHUg8QNSsKa27Rz1K3GBYcZn5BKsg3iOn9pnPvZffia/HYtBMREREREZFyxuO/ETZu3Jhp06YBcPLkSZKSkjh9+jQhISFERkZSvXp1T7+klLCkzKRiFSGAAkWI/k36886wd2gW0czTqYmIiIiIiEg5U6JvTVevXl2Fh0os1D+UV698lX93/LfOYRcRERERERGghAsRUrl9M+YbBjcf7O00REREREREpAyxeDsBqbhqhNS48CARERERERGpVEp8RcS2bdv49NNPWbduHSdOnMDf3586depw+eWXc8MNN1CrVq2STkFEREREREREyohiFSI++OAD0tLSALjmmmuoU6fOecfPmjWLN998E9M0MU0TAKvVSnp6Ort27eKzzz7j2WefZfBgLd8XERERERERqQyKXIg4ceIEzz33HIZhULNmTSZMmHDe8Z988gmzZs0CwDAMDMNwFSNM08QwDDIzM7n//vupXr06Xbp0cePTEBEREREREZHyoMiFiFWrVrk+HjVqFBbLudtLHD9+nJdeesl1UoJpmjRt2pSePXsSEBDAzp07+e233wDIzc3liSeeYP78+Rf7OYiIiIiIiIhIOVHkQsTmzZtdHw8aNOi8Yz/77DOysrJchYhbb72VBx54oMARjmvXruXOO+8kMzOTPXv2sGrVKrp3717c/EVERERERESkHCnyqRm7du0CIDw8nFatWp137IIFC1xFh3bt2vHggw8WKEIAXHrppTz00EOuxz/99FORkxYRERERERGR8qnIhYjExEQMw6BNmzbnHXf48GESExNdj2+66aZzjr366qsJCQkBYMeOHUVNRURERERERETKqSIXIlJSUgCoUaPGecetX78ecPaFsFgs9O3b95xj/f39ad++PaZpcuDAgaKmIqUsMjiSQN/AYl0T6BtIZHBkCWUkIiIiIiIi5VWRe0RkZ2cDEBh4/l9It27dCjhPymjatClVqlQ57/i6desCkJGRUdRUpJQ1rNqQ+PHxJGUmFYhnWbPABAwICgwq8FxkcCQNqzYsxSxFRERERESkPChyISIoKIjTp0+Tnp5+3nFbtmxxfXyhbRwAfn5+ANhstqKmIl7QsGrDswoLmZmZrqNYg4ODvZSZiIiIiIiIlCdF3poRERGBaZokJCScc0xOTg7btm1zNaaMjo6+4H3T0tIA9IusiIiIiIiISCVQ5EJE3kkZu3bt4uDBg4WOWbFiBVarFdM0AefJGBdy+PBhACIj1U9AREREREREpKIrciGiV69ero+fffbZs543TZP33nvP9bhhw4a0aNHivPfMyclh+/btGIZBo0aNipqKiIiIiIiIiJRTRS5EDB06lLCwMACWLVvGbbfdxsqVK9m7dy8rVqzg3//+Nxs2bACcjSpHjx59wXuuXbvW1RuiKP0kRERERERERKR8K3KzypCQEB566CEee+wxDMNg5cqVrFy5ssAYwzAwTZPatWtzww03XPCec+bMcX3cpUuXomctIiIiIiIiIuVSkVdEAIwePZpx48ZhmmaB//KYpklYWBivvvoqQUFB57kTHDt2jCVLlmAYBkFBQXTu3PniPgMRERERERERKTeKVYgAmDBhAp988gmXX345/v7+gLMAUaVKFUaMGMG3335bpNMy3nnnHXJycjBNk969e7vuJSIiIiIiIiIVl2GeuaShmEzTJDk5GcMwCA8Pdx3bWRQ2m821msLHxwcfH5+LTUMuICMjg/j4eNfjqKgoQkND3b5vZmYmpmliGIaOX5Ui0ZyR4tKckeLy9Jwpqe+hIiIilVmRe0QUxjAMIiIiLupaPz8/d15aRERERERERMqhYm/NEBERERERERG5WG6tiJDywW63F3icmZnpkftarVbX8leHw+GRe0rFpjkjxaU5I8Xl6Tnz9++Zf/+eKiIiIsVX4oWI6dOn8/nnn2MYBtu3by/pl5NCZGdnF3h88OBBL2UiIiJSvv39e6qIiIgUX6msiHCjH6aIiIiIiIiIVCDqESEiIiIiIiIipUY9IiqB8PDwAo8DAgJ0XKqIiEgR2O32Atsx/v49VURERIpPhYhKwN/fn5o1a3o7DRERERERERFtzRARERERERGR0qNChIiIiIiIiIiUGhUiRERERERERKTUlHiPiEaNGtG1a9eSfhkRERERERERKQcM0zRNbychIiIiIiIiIpWDtmaIiIiIiIiISKlRIUJERERERERESo0KESIiIiIiIiJSatxuVjlp0iS3rrdYLISGhlKlShWaNWtG+/btqV+/vrtpiYiIiIiIiEgZ5HazylatWmEYhqfyAaBDhw7cfvvtXHHFFR69r4iIiIiIiIh4l0cKEWfd1DA4322L8jzAyJEjeeaZZ9xJT0RERERERETKELcLEbNnzwbgyJEjvP322+Tk5GCxWOjUqRPR0dHUrl2b4OBgsrKyOHr0KJs3b+aPP/7A4XAQEBDAnXfeSWRkJCkpKcTHx7N8+XLS09OdyRkGt9xyCw8++KD7n6mIiIiIiIiIeJ3bhQiATZs2MXbsWNLS0ujfvz+TJ0+mXr165xx/+PBhnn32WX744QfCw8N55513iI6OBiAzM5OXXnqJTz/9FABfX18WLVpEgwYN3E1TRERERERERLzM7VMzUlJSuOeee0hLS2PkyJG88cYb5y1CANStW5fXX3+dUaNGua5PTU0FIDg4mMcee4wxY8YAYLfb+eabb9xNU0RERERERETKALcLEV9//TXHjh0jJCSEKVOmFOvaRx99lNDQUI4dO8bXX39d4Ln77rsPPz8/ANauXetumiIiIiIiIiJSBrhdiFiyZAmGYdCtWzeCgoKKdW1wcDDdunXDNE0WL15c4LmIiAjat2+PaZocPHjQ3TRFREREREREpAxwuxCRmJgIQPXq1S/q+rzr8u5zpkaNGgG4tm2IiIiIiIiISPnmdiEiMzMTgKSkpIu6Pu+6vPucyd/fH4CAgICLzE5EREREREREyhK3CxE1atTANE3WrFnD6dOni3VtRkYGa9aswTAMatSocdbzaWlpAFSrVs3dNEVERERERESkDPB19wbdunUjMTGRzMxMnnzySZ577rkiXzt9+nROnz6NYRhceumlZz2fkJCAYRgXve1DPMdut7Nnzx62bt3Ktm3b2Lp1Kzt37sRqtQIwcuRIZsyY4eUspSzJyMhg5cqVrFmzhu3bt7Nv3z7S09MJCAigZs2aREdHM2zYMHr37o1hGN5OV8qAzZs3s2XLFrZs2cLu3btJTk4mOTkZm81GWFgYzZo1o1u3bowcOfKCpzOJPPLII8yePdv1ePz48UyYMMGLGYmIiEgetwsR1157LbNnz8Y0TebOnUtqaiqTJ0+mYcOG57zm4MGDPP300yxfvhwAwzC47rrrCow5duwYCQkJALRs2dLdNMVNEydOZMmSJd5OQ8qJDz74gJdffpns7OyznsvNzWXv3r3s3buXuLg4unTpwvPPP0/dunW9kKmUJTfffHOh2/QATp48ycmTJ1m7di3//e9/GT9+PHfccUcpZyjlxfLlywsUIURERKRscbsQER0dzc0338wHH3yAYRgsX76c5cuXEx0dTXR0NHXq1CEwMBCr1crRo0fZvHkzmzdvxjRNTNMEnD98RkdHF7jvt99+i2maGIZBjx493E1T3GS32ws8Dg8PJzw8nH379nknISnT9u7d6ypC1KpVix49etC2bVuqV69OdnY2GzduZO7cuWRmZrJu3TpuvPFGvvrqK61+EqpXr050dDRRUVHUr1+fKlWqkJuby6FDh/j5559Zv349OTk5vPTSS9hsNsaPH+/tlKWMycjI4PHHHwecp3Odq7glIiIi3uN2IQLg4YcfxuFw8NFHH7lieQWHwuQVIABuuukmHn744bPGVK1a1fUDZt++fT2RprghOjqaZs2a0bZtW9q2bUuDBg347rvvmDRpkrdTkzLIMAx69erFLbfcQvfu3bFYCrajGTlyJGPHjuXWW29l7969JCYm8sILL/Dss896KWMpC7788ktatGhxzq06d9xxB3PmzOGRRx7BNE3eeustxowZQ61atUo5UynLZs6cyZEjR6hTpw5XXnklH3zwgbdTEhERkb/xSCECYNKkSfTt25eXX37ZteLhfNq3b8999913ztUO//znPz2VmnjAnXfe6e0UpBy57777CA8PP++YevXq8corrxAbGwvAokWLmDp1KkFBQaWQoZRFRdmGN2LECL7//nuWLVtGbm4uK1asYPTo0aWQnZQHq1at4quvvgLg8ccfZ+vWrV7OSERERArjsUIEQPfu3enevTsJCQmsWbOGnTt3curUKTIzMwkODqZatWq0bt2aSy+9lBYtWnjypUWkDLlQESJPq1ataNKkCXv37iUrK4v9+/fTqlWrkk1Oyr0WLVqwbNky4OKPjpaKJysriylTpmCaJkOGDKFfv34qRIiIiJRRHi1E5GnevDnNmzcviVuLSAUTGhrq+riw5pYif7d//37Xx5GRkV7MRMqSF198kYMHDxIeHs6jjz7q7XRERETkPCwXHiIiUjJycnIKNDzVyRlyIUuXLuXHH38EICAgQD2EBID169fz2WefAfDQQw+pQCUiIlLGlciKCBGRopg/fz7p6ekAtG3blho1ang5Iykrfv/9d1JTUwFnwero0aOsXLmSX3/9FQBfX1+mTZumXziF7OxsJk+ejMPhoHv37owaNcrbKYmIiMgFlFghYu/evWzfvp3k5GROnz5NSEgI1apVo02bNjRp0qSkXlZEyolTp07xwgsvuB7fddddXsxGyprnn3+eTZs2nRU3DIOuXbtyzz330LVrVy9kJmXNq6++yt69ewkMDOTJJ5/0djoiIiJSBB4tRGRkZPDxxx/zxRdfcOLEiXOOq1mzJtdddx033nhjgf3hIlI55OTkMGHCBE6ePAnAgAEDGDhwoJezkvKgVq1a9OzZk0aNGnk7FSkDNm/ezIcffgjAhAkTaNiwoXcTEhERkSLxWI+IDRs2EBMTw+uvv87x48cxTfOc/x07dozXXnuNmJgYNm7c6KkURKQccDgcTJ48mXXr1sH/t3ffYU1d/x/A30FBFAUBF+496oYKbqvUqqhf18+qdYFa6l44qqVuRam4wF0ZFopaRa1ba62KA0FQ60KqgiDUghBQgoaR3x95uE0kJAFCQtv363l8nnuTc29OTnIj53PP+RwA9evXx7p16wxcKyprDh06hOjoaERHRyMqKgrHjx/H7NmzkZmZic2bN2Pw4MG4fv26oatJBiSVSvHNN98gNzcXrVu3houLi6GrRERERFrSSSDi/v37mDx5MpKSkv4+sZERGjdujB49euCzzz5Djx490LhxYxgZ/f2SiYmJmDRpEh48eKCLahBRGSeTybB8+XKcOHECgDw5pZ+fHywsLAxcMyrLKlWqhJYtW2LGjBk4evQoatSoAbFYDFdXV0RHRxu6emQgO3fuxJMnT1CuXDmsXr0a5cqVM3SViIiISEslnpqRk5MDNzc3SCQSAECVKlXw1VdfYfjw4bCysipQPi0tDSEhIdi9ezfevHkDiUQCNzc3nDp1in9EEP2LyWQyrFixAocOHQIA1KpVCwEBAahbt66Ba0b/JPXq1YObmxsWL16M7Oxs7Nq1C5s3bzZ0tUjPHj9+jL179wIAnJ2d0bp1awPXiIiIiIqixIGIEydOIC4uDiKRCPXq1YOfnx/q1KlTaHlLS0tMnjwZ/fv3x6RJkxAXF4e4uDicOHECQ4cOLWl1iKgMkslkWLlyJQ4cOABAPs9///79nM9NxdKzZ09h+9atWwasCRlKSEgIsrOzYWRkBGNjY+zYsUNlufDwcKXt/HKNGjXCgAED9FJXIiIiKqjEgYiLFy8K25s3b1YbhFBUp04deHl5YeTIkQCACxcuMBBB9C+UH4QIDg4GIE9Wu3//fiYbpGJTTHKcv8Qn/bfIZDIA8pwzu3bt0uqYsLAwhIWFAQAcHR0ZiCAiIjKgEueIePjwIUQiEdq3b1/koZFt2rRB+/btIZPJ8OjRo5JWhYjKmA+DENWrV8f+/fvRsGFDw1aM/tFiY2OFbVVTAImIiIiobCvxiIj85feaNGlSrOObNGmCO3fuCOchon+PVatWFQhCNGrUyMC1on+6/Ck+AGBra2vAmpChfPPNN/jmm280lvP29oaPjw8AYObMmZg1a1ZpV42IiIi0UOIREeXLy2MZUqm0WMdnZ2crnYeI/h1Wr16NH3/8EcDfQYjGjRsbuFZUVgUHB+PmzZvCkHtVcnNzsWfPHuF7BQBffPGFPqpHRERERDpU4t5/tWrVEBcXh3v37hXr+Lt37wrnobIrPj4ehw8fVnpMcdm8hw8fFshc37lzZ3Tp0kUv9aOyZfPmzQgMDAQAiEQiTJgwAc+ePcOzZ8/UHvfRRx+hdu3a+qgilTF3797FihUrYGNjg65du6J58+awtraGsbEx3rx5gydPnuDixYt4+fKlcMxXX30Fe3t7A9aaiIiIiIqjxIEIOzs7xMXF4cWLFzhz5kyRkj+dPXtWWHHDzs6upFWhUpSYmKg2IVh0dLRSYAKQj3JhIOK/KTIyUtiWyWTw8vLS6jgPDw8MHz68tKpF/wBJSUk4cuSI2jJVqlTB/PnzORqCiIiI6B+qxIEIJycnhISEAJDP2TQzM1NaWq0w165dw9KlS5XOQ0RE/03u7u5wdHREeHg4Hj16hBcvXiAtLQ05OTmoVKkSrK2t0aJFC/To0QP9+/dHlSpVDF1lIiIiIiomkUzdhFwtOTs74+bNm/ITikRwdHTE8OHD0bFjR1haWgrlxGIxoqKicPToUVy4cAEymQwikQidO3eGn59fSatBRERERERERGWcTgIRqampGDVqFOLj4+UnFYmE50xNTVGxYkVkZWXh3bt3wuP5L9ugQQMEBwdzCTYiIiIiIiKi/4ASr5oByNdxP3DgAHr06AFAHmTI/5eVlYXU1FRkZWUpPQ4APXv2xI8//sggBBEREREREdF/hE5GRCi6efMmDh06hLCwMLx+/brA89bW1nBwcMCoUaPg4OCgy5cmIiIiIiIiojJO54EIRa9evUJaWhoyMzNhZmYGS0tL1KxZs7RejoiIiIiIiIjKuFINRGjj0qVLSE9PBwAMHTrUkFUhIiIiIiIiolJm8EDE0KFDER0dDQB49OiRIatCRERERERERKVMJ8kqS8rAsRAiIiIiIiIi0pMyEYggIiIiIiIiov8GBiKIiIiIiIiISG8YiCAiIiIiIiIivSlv6AoQkeEkJSVh//79uH79OhISEpCZmSnkbNm/fz8cHBwMXEMi+rdLSEiAo6MjAKBOnTr49ddfDVwjIiIiKm0MRFCJjB8/Hrdu3RL2bWxscP78eZiYmGg81tvbGz4+PgAAJycnbN68udTqSQXdvXsXU6ZMQUZGRqm+Tm5uLm7cuIFr167h9u3bSElJQWpqKvLy8mBubg4bGxu0bdsWXbt2Ra9evWBsbFyq9SHSlw9/Hz9UqVIlWFhYoEmTJujUqROGDRuGmjVr6rGGRERERIbBQATpVFJSEg4cOIAJEyYYuiqkhkwmw6JFi4QghLm5OTp37gxra2sYGclnbOmiQ3Ty5El4e3sjNjZW5fPJyclITk7GvXv3EBQUhKpVq2LChAmYNGkSKlasWOLXJ1JHMRg6c+ZMzJo1S6+vL5FIIJFIkJSUhNDQUPj4+GDq1KmYMWMGRCKRXutCREREpE8MRJDO7d69GyNHjmRHsgy7e/euEBywsrLCqVOnYGVlpbPzv3//HkuWLMGpU6eUHjc3N0e7du1gZWWFChUqICUlBbGxsXj+/DkAQCwWY9u2bbhz5w727t2rs/oQGVrbtm3Rrl07pcfevHmDx48f48mTJwCA7OxseHt7IyMjA0uXLjVENYmIiIj0goEI0rmUlBT88MMPcHV1NXRVqBAPHjwQth0dHXUahJBKpZg0aRIiIiKExzp06IA5c+bAwcEB5cqVK3BMfHw8jh49Cn9/f2RmZuLdu3c6qw9RWdCrV69CR1xERkbCzc0NiYmJAICAgAAMHjwYbdu21WcViYiIiPSGq2aQznTo0EHY3rdvH96+fWu4ypBainkhqlevrtNze3p6KgUhXF1dcfDgQXTt2lVlEAIA6tWrh9mzZ+OXX35Bv379dFoforLO1tYWO3bsUJqOcejQIQPWiIiIiKh0aT0iIn8era6lpKSUynlJ//73v/8hPT0dz58/h1gshq+vL2bPnm3oapEKOTk5wnZ+TghdiIiIwA8//CDsjxkzBm5ublofb2VlhW3btuHatWs6qxPRP0GrVq1gb2+PsLAwAEB4eLiBa0RERERUeooUiGDyLFLHyMgIs2fPxrx58wAA/v7+GD9+PCwtLYt9zuIs69anTx+8fPkSAHDx4kXUrVtXqzJxcXE4cOAArl69iqSkJGRnZ6Nhw4ZwcnLCxIkTC+S8ePbsGQIDAxEeHo6XL1/CyMgIjRs3xpAhQzB69OhC7/6XRGpqKg4fPowrV64gNjYWYrEYZmZmsLGxQZcuXTBixAg0bdpU5bEhISFYsmRJgcd9fHwKBBqLm7hPMa+DjY0NFi1aVORzAEC3bt3UPp+ZmYkjR47g8uXLiImJQVpaGkxNTVGzZk3Y29tjyJAhaN++vcbXadGihbAdHR0NQP65BgcHIzQ0FH/++SdEIhHq1q2LXr16wcXFpUjTWN6/f48TJ07g6tWrePDgAVJTUyGVSlGlShU0atQItra26Nu3r8q6fv311zh69CgAwMPDA8OHD1f7Woqf77Bhw7B+/XqtyuTm5uLs2bM4efIknjx5guTkZLx//x7bt2/Hp59+irCwMCH5rL29vRBounz5Mo4fP4779+8jOTkZEokES5YsgbOzc4HXffr0KY4fP47r168jMTERGRkZqFy5MurVq4fu3btj9OjRGpOjKq5Akb+0rFgsxqFDh3Du3DkkJCQgKysL1atXh4ODA5ydndG8eXON58qn6jpQ15aloVWrVkIg4q+//tLqGF20LSDPV3H58mXcunULjx49wosXL5CZmQkTExNYWVmhXbt2+PTTT9G/f/8iBS//+usvBAUF4ddffxV+c21sbNCtWzeMHj0ajRs31vpc+e/3yJEjiIiIQFxcHDIzMyESiVC5cmXY2NigRYsWsLe3R58+fWBhYVGkcxMREZH+FClHhEwmK6160L/EgAEDsHv3bjx+/BiZmZnYu3dvsTuj+nT8+HEsX74cWVlZSo9HR0cjOjoa586dg7+/v/CH7Y4dO+Dt7Y28vDyl8nfv3sXdu3dx9uxZ7NmzR6cJOw8fPoz169fjzZs3So+LxWKIxWI8evQIAQEBGDduHBYvXlwqgRB1EhMTcfnyZWF/1KhRqFSpks5f59KlS/j222+RnJys9LhUKkVGRgZiYmIQFBSEQYMGYc2aNUX6DIKDg7Fu3TpIpVKlx/O/B4cOHcL333+v1dz98+fPY82aNXj16lWB51JTU5Gamorbt29j7969WLFiBcaMGaN1PXXl1atXmDdvHm7fvq31MW/evMGSJUtw4cIFjWWlUinWrFmDw4cPIzc3V+m5tLQ0pKWl4d69e/D19cXChQsxbtw4retx+/ZtzJs3r0D7JiQkICEhAceOHcOKFSvw+eefa31OQzM1NRW2P/wOfkiXbXv+/Hm4ubmpfM3s7GxkZmYiPj4ep06dwu7du+Hj44N69eppfD8XLlzA0qVLCywR/Mcff+CPP/5AcHAwli1bhi5dumg8FyBf5WTnzp0F3i/w9zX14MEDhISEYPDgwdi4caNW5yUiIiL90zoQ0alTp9KsB/1LiEQizJkzB9OmTQMABAUFwdnZGTVq1DBwzQp35coVrF69Gnl5eWjYsCHatm2LChUqIDo6Gr///jsA4OHDh5g/fz727duH3bt3Y+vWrQDkd9RbtmyJcuXK4ffff0dMTAwA4NatW/Dw8MCqVat0Usd9+/bB09NT2DcxMYG9vT1sbGyQkZGBsLAwiMVi5ObmIiAgAElJSdi2bZvSKKYmTZpg7NixAIB79+4J701VNv8P97URFhamFKwcNGhQkc+hyenTp7FgwQKhI1KuXDnY2dmhfv36kEgkiIiIEO4knzx5Ei9fvkRAQAAqVKig8dwhISFYsWIFAKBRo0Zo06YNTE1N8ezZM0RGRkImk0EsFmPatGk4c+YMqlSpUui5fH194enpKbSHSCRCixYt0LRpU5iZmUEsFuPJkyfCaiHv378vSbMUi1QqxbRp0/DgwQOUL18eHTt2RL169SCVSvHw4UOVx8hkMixcuBCXLl2CSCRCmzZt0LRpU8hkMsTExCh93yQSCSZPnozIyEjhsfr166N169YwNzdHeno6IiMj8ddff+Hdu3dYvXo13r59i6lTp2qse0xMDLy8vCCRSGBtbY2PP/4YVatWxatXr3Dz5k28e/cOubm5WL58OZo3b66UvwYAPv30UzRr1kzjdQBAq5E1uqI4CsLa2rrQcrpu29evXwtBiFq1aqFp06aoVq0aTE1NIZFI8PTpUzx8+BAymQyPHz/GuHHjcOzYMbWj3X777TfMnTtXmAZmZGQEW1tbNGzYEBKJBOHh4UhOToa7uzvc3d01tk1AQIDSiBVLS0t06NAB1atXh0gkglgsxvPnz/H06VOVgQoiIiIqW7QORCjO+yZSp0+fPmjfvj3u3r2Ld+/eYdeuXVi2bJmhq1UoDw8PVKxYEevWrUP//v2VnlPs+IaGhsLf3x9bt25FjRo14OXlBXt7e6Xyfn5+wjDun376Ca6uriqnhhRFZGQkvLy8hP2ePXvCw8MD1apVEx6TSqXYsmUL9u3bB0B+h9Pf3x8uLi5Cmfbt2wudKm9vb6EDpi6bf1EoJqi0trbW6o5pUbx48QLffPON0Mlo164dNm7ciAYNGghl8vLyEBAQAE9PT+Tl5SEqKgrfffedVh2d5cuXw8rKChs2bEDPnj2VngsPD8fUqVPx9u1bJCcnIyAgADNnzlR5nsuXLysFITp37oxly5ahSZMmBcrGx8cjJCTEIEPIz507h5ycHNjb28PDw6PA91TV3fGoqCjk5OSgefPm2Lhxo9LUlg+PWblypdBRbtiwIVatWgUHBwel8rm5uTh48CA8PDwglUqxbds2ODg4oGPHjmrrvmHDBuTm5uLrr7/G+PHjUb783/+VJSUlwdXVFU+ePEFeXh42bdqE/fv3Kx0/ceJEAKVzHRRXTk4Obty4IeyrC4Doum1r1qwJNzc39OvXT+l6UhQfH48VK1YIU5Y2btyItWvXqiyblpaGpUuXCkGI5s2bY8uWLUrXQF5eHvbt2wcvLy9s2LCh0PcKyNtm586dwr6bmxtcXFxgbGxcoKxYLMbFixeRmpqq9pxERERkWFw1g0rF3Llzhe1Dhw4Jc4PLouzsbPj4+BQIQgCAk5OT0tx8Dw8PGBsbw9/fv0AQAgBcXFzQtWtXAPI/tM+cOVPi+m3atEnofHfs2BHbt29XCkIA8hESixYtwvjx44XHfHx89LpyieJnrKrTXVLbt2+HRCIBADRo0AC+vr4FOk1GRkZwcXHB4sWLhceCgoIQHx+v1Wv4+fkVCEIA8hFh8+fPF/ZPnTql8vicnBysXLlSCEL07t0b+/btK7Q96tWrhzlz5mDYsGFa1U+X8gMKe/fuVRksMzExUXlM9erVERAQUCAIoXhMREQEjh07BkB+pz44OLhARxmQj2j54osvsHLlSgDyzvP27ds11l0qlWL58uVwcXFRCkIA8vwDXl5ewuiMW7duaZ1vwZD27NmDpKQkYX/UqFEqy5VG2/bp0weurq6FBiEA+Xd1165dwud+4sQJpKenqyzr7++P169fAwCqVasGf3//AteAkZERvvzyS8yZMwfZ2dmFvi4gz9uSlpYGQL7CiKurq8ogBABUrVoVI0aMwJdffqn2nERERGRYDERQqejatavQUc/Oztaqc2Eoffr0EYIHqgwcOFBpf9SoUWo72orl8++2FtfTp0+VsucvW7ZMZQcx3/z584Xh0m/fvsXJkydL9PpFodgpMTc31+m5MzIycPr0aWF/4cKFaqdGTJgwAc2aNQMgDwhpsxTiqFGj0LJly0KfHzJkiNDpff78ucogz/nz54WATKVKlbBu3boCHeWyZMGCBUp5CbQxffp0jQk7/fz8hO3FixdrLD98+HAhaWFoaKjQ6SxM8+bNC+2o5z+fn8dDJpPh/v37as9nKG/fvkVERATc3NyE6V4A4OzsXGjC1tJuW3WMjY0xePBgAPLpRKpyi8hkMhw5ckTYnz59utppJlOmTEGdOnXUvq7itVaUZLFERERUdjEQQaVGcVTEsWPHEBsba7C6qNOvXz+1z39451dTecVM/QkJCcWvGICbN28K261atcJHH32ktnylSpWUcjPkZ+DXh8zMTKV66FJUVJQw7N/S0hK9e/dWW97IyAgjRowQ9rVpB1UjYhTlr0QAyDtbqkb5XL16VdgeOHBgme40WVhYoHv37kU+zsnJSe3zOTk5uH79OgB5m2n6rPLl39WXyWRKuQ9U0fRZAfLrJV9ZGJHl4+ODFi1aKP2zs7PD2LFjhYBh1apV4ebmpnJ1G0A/bZuRkYErV67A19cXmzZtwpo1a7Bq1Srhn+LSuo8ePSpw/NOnT4VEsuXLlxcCF4UxNjbWmE/GxsZG2A4LCxNyqxAREdE/V9m9VUf/eHZ2dujZsyeuXLmC3NxceHt7K+U6KCsKW+Iv34d39/PvtBdGcb5/SadGKP6hr2nefD5bW1shp0thSQdLg5mZmbCdP4VCVxTfR7t27bQaZWBra6t0vEwmU7sEsabvASDvKOZT9dneuXNH2FY1XL4syU+yWhR169ZVagNVoqOjhc+/fPnyheYR+JDi6KE///xTbVlV00I+pJhIUZ9TlIqrXLlyWLBgAUaOHFlomdJs2/y8D+fOndO4Ykc+VaMrFK/Vxo0bazU66sNkoh+ysbFBhw4dcOfOHbx58wbDhw/HkCFD0LdvX9ja2up0dSIiIiLSDwYiqFTNnTsXV69ehUwmw+nTp+Hq6qpVJ0KfKleurPb5Dzu96qYEAFDq3OUnaysuxYRrtWvX1uoYxWHOJRmGXVSKAZgPl+srqZK2Q/4ShOo+a02fKwCleemqPtv8efEAdJ6sU9eKM1pDm2MU8zGIxWIEBQUV+XUKyz2QT9M1CyhftyW9DnXhw1U5JBIJEhMThdE+ubm5cHd3R0JCAubNm6fyHKXVtg8fPoSzs7PGdv+Q4iiofIrXquJIBnW0uabXrl2LiRMnIiUlBRKJBMHBwQgODkb58uXRsmVLdOrUCd27d0eXLl30vnQxERERFR0DEVSqWrdujb59++L8+fPIy8vD1q1bsWPHDkNXS4m6u+S6KF8SiiMLtJ3uoHh3UFVHobQodvyfPn2q03MrtoO2dz8/LKcpEKGLz7U0p6foWlFzQ2h7zJs3b4pTHSWall/U5zWoK4WtypGcnIz169cL0zPyE0KqmgJTGm0rlUoxa9YsIQhhZWWFUaNGoUuXLmjQoAEsLCxgamoqtHlISIgwdURxud58iteqtt8xba7ppk2b4vjx49i1axeOHTsmtEVOTg7u37+P+/fvw8/PDzVr1sSsWbPUjiwhIiIiw2Mggkrd7Nmz8csvvyAvLw8XL17EvXv3lO4M6lpeXl6pnVvfFDuz2k53yMrKErYVp0uUNjs7Oxw+fBiAfGRAQkJCiZcuzafYDorvT50Py+mjLczMzIQOna6np2hSVr73ip9VixYt8PPPPxuwNmVf9erV8d1330EsFiM0NBSAfHnObt26FVjWtTTa9ty5c0Ium5o1a+Lw4cOoUaNGoeU1BTcV6/ju3Tut6qDtNV2tWjW4u7tj0aJFuHPnDiIiIhAVFYXIyEhh+s2rV6/g7u6O6OhorZbtJSIiIsNgskoqdc2aNVNKRqaYHV4TTUPhVdHFXcOyQnEovOLSfuooJuZTnCdf2hwcHJTuVOtyxY6StoOxsbFeAhGKqwOUNFGp4tQCTSMEgLKTB0GxDVJSUgxYk38OIyMjrF27VujEi8Vi7Nq1q0C50mjbGzduCNsTJ05UG4QAgMTERLXPF+da1bZcPhMTE9jb22P69OnYu3cvbt68ib1798LOzk4o88MPP+DevXtFOi8RERHpDwMRpBezZs0SOlahoaFKS1Kqo9h5zMjIUDkUWFFiYmKZ6ZDpgmLm/6ioKK2OUcyKr2mVDV2qU6cOevbsKewfPHhQ6zudmii+j3v37mnVMVdsr48++kgvw/kVk+4prnhSHIrTSLTJ9REdHV2i19OVVq1aCUvMvn79GnFxcQauUeHK0hSPWrVqYcKECcJ+UFCQsPpEvtJoW8W8E9okbNX02614rT579kyrwLBiktfiMDY2Rs+ePeHv76/0Hi5dulSi8xIREVHpYSCC9KJ+/foYPny4sL9lyxatjqtcubKQpT8rK0vjsm1nzpwpbhXLpM6dOwvbDx8+xOPHj9WWz8rKwunTp1Uerw+urq7CdmJiIjZu3Fis8yguEQjIVwzJ74Clpqbit99+U3t8Xl4ejhw5Iuzrqx169OghbJ86dUopcV9RKebc0PS5v3//vsx0ukxNTZXa+8cffzRgbdTL/04BZSOh5aRJk4Tg6/v37/H9998rPV8abWtk9PefAZqmUty/f19pBQ5VGjdujOrVqwOQt6mmkVHalNGWiYkJunXrJuwrJo8lIiKisoWBCNKb6dOnC3/4R0RECPOhNVHMJ3H06NFCy/3555/Ys2dPySpZxjRp0gSdOnUS9levXo3s7OxCy2/ZskX447ty5cpKU2L04eOPP8YXX3wh7AcGBmoddALkd/5nz55dYFi6ubm5UvI+T09PtSNfAgMD8eTJEwDyjtbnn3+udR1K4rPPPhMCCBKJBEuXLi12B7d9+/bC9qVLl9QGNbZu3arXFVI0+fLLL4XtwMBAXL9+XetjPxwFUJoUpy69evVKb69bGAsLC4wfP17YP3jwYIHPXddtq7i6y6+//lrosVlZWVi2bJnG1zAyMsKIESOE/e3bt6v97vr6+mqcxpSenq51DhTFaR7FWRmGiIiI9IOBCNIbGxsbjBo1StjXdjiuYmfaz88P586dK1Dmzp07GDduHNLT05XySvwbzJ8/X1iOLiIiArNmzSpwp08qlcLLywv+/v7CYzNnztRrssp8S5YsQceOHYX9nTt3YsyYMbhx40ahUyri4+Oxbds2ODo6qvx8AWDGjBnCHPrY2FhMmTIF8fHxSmXy8vIQEBCA9evXC4+NHTtWZ0kzNSlfvjy+/fZbYcj/pUuXMHny5EJXEUlISMDWrVtx7NixAs+1bdsW9evXByAPari5uRVYXjErKwsbNmzAvn37lO7uG5q9vT2GDRsGQH7H29XVFbt37y400eH79+/xyy+/YNq0aZg2bZre6tmsWTNhOzQ0tEzkl3F2dha+51lZWfD19VV6Xtdt27t3b2H76NGj8PX1LXCdxsXFYdKkSXjw4IFWq8FMnDhRCPIkJyfDxcWlwDWQl5cHX19fbN68WeNv9sWLF9GvXz/s27ev0KCFVCpFYGCg0u+H4lQxIiIiKlu4agbp1dSpU3H48OEi5Q4YOHAgfH198fjxY2RnZ2P27Nlo3bo1WrZsiby8PERHR+Phw4cA5LkoQkJClBIV/tPZ2trCzc0Nnp6eAOSd208++QQODg6wsbFBeno6wsLCIBaLhWP69u0LZ2dng9TXxMQE/v7+WLx4Mc6ePQtAnrfC2dkZFhYWaNu2LaytrWFiYoKUlBTExsYWmHKjKoBSv359rF27FgsWLEBubi6ioqLQv39/2NnZoX79+pBIJIiIiFC6s92hQwcsXLiwdN/wB3r37o358+fDy8sLgDxXxMCBA9GyZUs0bdoUlSpVQnp6OqKjo4X3nb8coiKRSIT58+dj7ty5AIDr16/D0dERXbp0gaWlJZKTkxEREYGMjAzUqFEDY8eOxebNm/X2PjVZtWoVkpOTERoaiuzsbGzatAk7d+5Eu3btULt2bZiYmCAjIwMvXrxATEwMpFIpAPmSv/rSrl072NjYICkpCcnJyRgwYAC6desGS0tLIZjUtm1blUtplhZLS0uMHTsWe/fuBSDPFTFlyhRhihqg27bt3r07OnXqhPDwcMhkMmzYsAFBQUFo3bo1KleujLi4OERFRSE3Nxc1a9bEhAkT8N1336l9D1ZWVli7di1mzZqF3NxcPH78GIMGDYKdnR0aNmwIiUSC8PBwIT/FkiVLsHbtWrXnfPHiBTw9PeHp6YnatWujRYsWwoiHlJQU3L17V+k3cPDgwbC1tdXY3kRERGQYDESQXlWrVg3jx48v0hSK8uXLw8fHBy4uLsId8AcPHuDBgwdCGZFIhK+++gozZsxASEiIzuttaJMnT4a5uTnWr1+Pt2/fQiqV4urVqwXKlStXDmPHjsXXX39t0ER8pqam2LJlC37++Wds375dSKqXnp6udkpO9erV4eLiojQ8XZGTkxMqVqwId3d3pKSkICcnB2FhYQgLCytQdtCgQVizZg0qVKigmzdVBK6urqhbty7Wrl2LlJQUyGQyPHr0CI8ePVJZvrC7zAMGDMDTp0/h7e0NQL4izPnz55XKNGrUCN7e3hrn7uubiYkJ9uzZAx8fH/j5+SErKwtZWVkqP6t8xsbGSgk/S5uRkRGWL1+OWbNmITs7G8nJyQVGpwwbNkyvgQhAnisiKCgIEokEEokE/v7+QkAK0H3bbtmyBa6ursJvakJCQoGRB02bNsXWrVu1XonC0dERmzZtgru7O968eYO8vDyEh4crJbs0MTGBu7s7unXrpjYQUalSJYhEIiFZcWJiYqGrdxgZGWH06NFYunSpVvUkIiIiw2AggvRuypQpCA4OLtIw6Hr16uHnn39GYGAgzp8/j9jYWEilUtSoUQMff/wxxowZozSn/t9o5MiRcHR0xE8//YQrV64gNjYW6enpMDMzQ61atdC1a1eMGDECTZs2NXRVAciDQ0OGDMHAgQNx48YNXLt2Dbdv30ZycjLS0tKQl5cHCwsL1K1bF23atEGPHj3QvXt3YRpKYXr37o3z58/jyJEj+O233xATE4O0tDSYmpqiRo0acHBwwNChQw3+fXBycsInn3yCY8eO4cqVK4iOjkZqaipyc3NhYWGBRo0awc7ODv369VO7usnMmTPRrVs3BAYGIiIiAq9fv0blypXRoEEDODk54f/+7/9gZmZW5gIRgDwwNmfOHIwfPx7Hjh3D9evX8fTpU6SlpSEnJwdmZmaoU6cOmjdvDgcHB/Tq1Uvv8/p79+6NI0eOICgoCJGRkUhMTIREItG4Qk9psrKywujRo4VpGYGBgZg0aRLMzc2FMrps22rVquHAgQP46aefcOrUKcTExCArKwvW1tZo1KgRnJycMHjwYFSsWLFIS2L2798fHTt2RGBgIC5duoSXL19CJBIJv1djxoxBkyZNNOaI6N+/P0JDQxEaGorIyEhER0cjPj4eGRkZAIAqVaqgYcOGsLOzw9ChQ8vMbyAREREVTiQz5F9bRERERERERPSfwmSVRERERERERKQ3DEQQERERERERkd4wEEFEREREREREesNABBERERERERHpDQMRRERERERERKQ3DEQQERERERERkd4wEEFEREREREREesNABBERERERERHpDQMRRERERERERKQ3DEQQERERERERkd4wEEFEREREREREesNABBERERERERHpDQMRRERERERERKQ3DEQQERERERERkd4wEEFEREREREREesNABBERERERERHpzf8D/KIi4H731gIAAAAASUVORK5CYII=", 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" ] @@ -50,18 +64,27 @@ "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "\n", + "import seaborn as sns\n", + "sns.set_context(\"poster\")\n", "# Read the CSV file\n", "df = pd.read_csv('./format_comparison_results.csv')\n", "\n", "# Define colors and markers for each format\n", "format_styles = {\n", - " 'VLA': ('blue', 'o'),\n", - " 'HDF5': ('green', 's'),\n", " 'LEROBOT': ('red', '^'),\n", - " 'RLDS': ('purple', 'D')\n", + " 'RLDS': ('purple', 'D'),\n", + " 'Fog-VLA-DM': ('blue', 'o'),\n", + " \"Fog-VLA-DM-lossless\": ('orange', 'o'),\n", + " 'HDF5': ('green', 's'),\n", "}\n", "\n", + "# Update the format name from 'VLA' to 'Fog-VLA-DM' in the DataFrame\n", + "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n", + "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n", + "\n", + "# Update the format_styles dictionary\n", + "format_styles['Fog-VLA-DM'] = format_styles.pop('VLA', ('blue', 'o'))\n", + "\n", "# Get unique datasets and batch sizes\n", "datasets = df['Dataset'].unique()\n", "\n", @@ -81,318 +104,603 @@ " plt.xlabel('Num of Concurrent Reads')\n", " plt.ylabel('Log-Scale Average Loading Time (s)')\n", " plt.title(f'{dataset}')\n", - " plt.legend()\n", + " plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", " # plt.xscale('log') # Use log scale for x-axis\n", " plt.yscale('log') # Use log scale for y-axis\n", + " plt.tight_layout() # Adjust layout to make room for the legend\n", " \n", " # Add a grid for better readability\n", " plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", "\n", " # Show the plot\n", - " plt.tight_layout()\n", " plt.savefig(f'./{dataset}.pdf')" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "285c0135", + "execution_count": 38, + "id": "443c3736", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3200483/2817297649.py:18: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Summary for berkeley_autolab_ur5:\n", + " mean median min max\n", + "Format \n", + "Fog-VLA-DM-lossless 2.824063 3.084723 1.922030 3.465437\n", + "HDF5 4.259725 4.163264 4.081820 4.534092\n", + "LEROBOT 0.658879 0.640482 0.628601 0.707555\n", + "RLDS 1.571795 1.508707 0.726021 2.480656\n", + "\n", + "Fog-VLA-DM-lossless:\n", + " On average, Fog-VLA-DM is 2.82x faster\n", + " Median speedup: 3.08x\n", + " Range: 1.92x to 3.47x faster\n", + "\n", + "HDF5:\n", + " On average, Fog-VLA-DM is 4.26x faster\n", + " Median speedup: 4.16x\n", + " Range: 4.08x to 4.53x faster\n", + "\n", + "LEROBOT:\n", + " On average, Fog-VLA-DM is 0.66x faster\n", + " Median speedup: 0.64x\n", + " Range: 0.63x to 0.71x faster\n", + "\n", + "RLDS:\n", + " On average, Fog-VLA-DM is 1.57x faster\n", + " Median speedup: 1.51x\n", + " Range: 0.73x to 2.48x faster\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Summary for berkeley_cable_routing:\n", + " mean median min max\n", + "Format \n", + "Fog-VLA-DM-lossless 0.809255 0.792606 0.714179 0.937631\n", + "H264 1.310345 1.283263 1.231549 1.439083\n", + "HDF5 2.261303 2.398626 1.886863 2.435957\n", + "LEROBOT 0.031114 0.031281 0.028841 0.034557\n", + "RLDS 0.073306 0.079867 0.022246 0.123708\n", + "\n", + "Fog-VLA-DM-lossless:\n", + " On average, Fog-VLA-DM is 0.81x faster\n", + " Median speedup: 0.79x\n", + " Range: 0.71x to 0.94x faster\n", + "\n", + "H264:\n", + " On average, Fog-VLA-DM is 1.31x faster\n", + " Median speedup: 1.28x\n", + " Range: 1.23x to 1.44x faster\n", + "\n", + "HDF5:\n", + " On average, Fog-VLA-DM is 2.26x faster\n", + " Median speedup: 2.40x\n", + " Range: 1.89x to 2.44x faster\n", + "\n", + "LEROBOT:\n", + " On average, Fog-VLA-DM is 0.03x faster\n", + " Median speedup: 0.03x\n", + " Range: 0.03x to 0.03x faster\n", + "\n", + "RLDS:\n", + " On average, Fog-VLA-DM is 0.07x faster\n", + " Median speedup: 0.08x\n", + " Range: 0.02x to 0.12x faster\n" + ] + }, { "data": { - "image/png": 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sbCwXLlzIsD08PDzb++/RowemaaZfMp6UlMSMGTPo0KHDNV8GPW7cOG655RZOnTrFnj172LNnDw0bNiQ5OZmpU6de9fGHDh1K/1Dh0n3Wlz5w+ufPUnZ+Fg4ePEiZMmUyLauV1Wt6JYMGDWLBggXMmDGDnj17EhcXl/7BUl44f/48xYoVy7CtXbt2zJs3j7Nnz7Js2TL69evHwYMH6dixoyY/E5F8S7OLi4gUQOPGjaN379506dKFV155hZCQEEzT5MMPP8zWpESX1vB9+eWXadeuXZZ9qlSpkuF70zSz7GdZVo6ylyhRgo4dOzJ+/HgGDRrEtGnTuHjxYqYZvbMSFhZG8+bNmTJlCm+88QZ//vknhw4dYsiQIRn6ffrpp/Tu3ZtffvmF+fPn8/zzz/Phhx/y559/UrZs2RzlBShbtizVq1fPcPbzgw8+YODAgfTp04f33nuPkiVL4nA4ePHFF3N9jeTLvfY5VapUKe655x7uueceWrVqxdKlSzl48OAV16Zes2YNL7zwAo8//jhPPvlkhja3241hGMyZMyfLjFdbOzm3/P1M8NWEhIRw55138tNPPxEZGcnMmTM5d+5c+v3aObV7927++usvAKpWrZqpffz48Zlet79zuVzceeednD59mtdee40aNWrg7+/P0aNH6d27d6afpdz6WciOunXrps+Q3qVLFxISEnjiiSe4/fbbKVeuXK4+15EjR4iLi8v0e+eSIkWK0Lx5c5o3b05QUBDvvPMOc+bMue77zEVE8oKKbBGRAmjatGlUqlSJ6dOnZzj7e+ks9CWXOzNcqVIlALy8vHJtmaHKlSsDsHXr1sv+oXxJz5496dy5M3/99Rfjx4+nYcOG1K5dO1vP0717d5555hmioqKYPHkyRYoUoVOnTpn61a1bl7p16/LWW2+lT5o1atSo9Mm7cio1NZXz58+nfz9t2jRat27Nd999l6Hf2bNnCQoKSv/+cu/B318vO5Z6ioiIYOnSpRw7duyyRfbJkye5//77adCgAZGRkZnaK1eujGVZhIeHU61atVzJdSlLVFRUpradO3cSFBSU5RJdOfHQQw8xd+5c5syZw4QJEwgICMjyZyg7xo8fj5eXFz/++GOmAnjFihV88cUXHDp06LITqm3ZsoVdu3YxduzYDBOdLViw4JrywP9NGHb+/PkMH3Rk9ZrmxEcffcSMGTN4//33GTVq1HXt659+/PFHgMt+6Pd3l5YAO3bsWK5mEBHJLbpcXESkALr0x/zfzyKvXr2aVatWZehXpEgRgEyzPIeEhNCqVSu++uqrLP9QzWqJrau56667KFasGB9++GGmpYv+eba7Q4cOBAUFMWTIEJYuXZqts9iXdO3aFdM0mThxIlOnTqVjx44Ziq74+HhSU1MzPKZu3bo4HA4uXryY4+MC2LVrF1FRUdSvXz99m2mamY5r6tSpme5zvpTtn+/BzTffTHh4OMOHD8/UltOrAy4nJiaG7du3Z9qenJzMokWLcDgcl/1AxOVy0aNHD5KTk/npp5+yXEbpvvvuwzRN3nnnnUyZLcvi1KlTOc5cpkwZGjRowNixYzO8Llu3bmX+/Pn861//yvE+/6lLly4UKVKE//3vf8yZM4f77rvvmtcIHz9+PM2bN6d79+7cf//9Gb5eeeUVACZOnHjZx2f1b9myrCyXccuuf/3rX6SmpqavLgBp7+eXX355zfuEtA9Vunbtyvfff09MTMx17evvFi9ezHvvvUd4eHiGKwoWLVqUZf9L9+Xn9PJ3EZEbRWeyRUTyqdGjR6cvffV3L7zwAh07dmT69Once++93H333ezfv59Ro0ZRq1atDGdb/fz8qFWrFpMnT6ZatWqULFmSOnXqUKdOHSIjI7n99tupW7cuTzzxBJUqVUpfsurIkSMZ1nvOjoCAAD777DMef/xxGjdunL5u8aZNm0hISGDs2LHpfb28vOjRowcjRozANM0crU0cEhJC69atGTZsGOfOnaN79+4Z2hcvXsyzzz5Lt27dqFatGqmpqelnGf8+MdXlpKampq/V63a7OXDgAKNGjcLtdme4UqBjx468++67PProozRr1owtW7Ywfvz49KsELqlcuTLFixdn1KhRFCtWDH9/f5o0aUJ4eDgjR46kU6dONGjQgEcffZQyZcqwc+dOtm3bxrx587L9mlzOkSNHuOWWW7jjjjto06YNoaGhnDhxgokTJ7Jp0yZefPHFDGfd/27UqFEsXryYp59+OtMEZqVLl+bOO++kcuXK/Pe//+X111/nwIEDdOnShWLFirF//35mzJjBk08+ycsvv5zj3J988gkdOnSgadOmPPbYYyQmJvLll18SGBiYYZ33a1W0aFG6dOmSfl/2lS4V37NnT5ZXPzRs2JCgoCD27NmTaSm7S2666SZuvvlmxo8fz2uvvZZlnxo1alC5cmVefvlljh49SkBAAD/99FO27re/nE6dOnHbbbcxYMAADhw4QK1atZg+ffo1rxP/d6+88gpTpkxh+PDhfPTRR+nbU1JSsnydSpYsyTPPPJP+/Zw5c9i5cyepqakcP36cxYsXs2DBAipUqMCvv/6a4cOOzp07Ex4eTqdOnahcuTIXLlxg4cKFzJw5k8aNG1/z1QciInnOjinNRUTk8i4tdXO5r8OHD1tut9v64IMPrAoVKlg+Pj5Ww4YNrd9++83q1auXVaFChQz7W7lypdWoUSPL29s703Jee/futXr27GmFhoZaXl5e1k033WR17NjRmjZtWqY8/1x653JLPv36669Ws2bNLD8/PysgIMC65ZZbrIkTJ2Y6zktLb9111105fo2++eYbC7CKFSuWabmwffv2WX369LEqV65s+fr6WiVLlrRat25tLVy48Kr7zWoJr4CAAKtNmzaZHp+UlGT95z//scqUKWP5+flZt912m7Vq1SqrZcuWVsuWLTP0/eWXX6xatWpZTqcz0xJKK1assO68806rWLFilr+/v1WvXj3ryy+/zJApq+WRLi1fdSXx8fHW559/brVr184qW7as5eXlZRUrVsxq2rSp9c0332RYKuyf7+el/Wf19c/j++mnn6zbb7/d8vf3t/z9/a0aNWpY/fr1s6Kioq6Y73JLeFmWZS1cuNC67bbb0n+OOnXqZG3fvj3L1+DkyZNXfJ6szJo1ywKsMmXKXHZJqAoVKlz2NXjssces5557zgKsvXv3XvZ53n77bQuwNm3adNk+27dvt9q2bWsVLVrUCgoKsp544glr06ZNmX5WcvKzcOrUKeuRRx6xAgICrMDAQOuRRx5JX94uu0t4XW4ZvFatWlkBAQHpy/ddbuk7wKpcubJlWZl/r3l7e1uhoaHWnXfeaX3++edWfHx8pueZOHGi1aNHD6ty5cqWn5+f5evra9WqVct68803s+wvIpJfGJaVS9ekiYiI5MCmTZto0KABP/zwA4888ojdcURERERyhe7JFhERW3zzzTcULVqU++67z+4oIiIiIrlG92SLiMgNNXPmTLZv387XX3/Ns88+e90zRYuIiIjkJ7pcXEREbqiKFSty/Phx2rVrx48//kixYsXsjiQiIiKSa1Rki4iIiIiIiOQS3ZMtIiIiIiIikktUZIuIiIiIiIjkEk18dgVut5vo6GiKFSuGYRh2xxERERERERGbWJbFuXPnCAsLw+G4/PlqFdlXEB0dTbly5eyOISIiIiIiIvnE4cOHKVu27GXbVWRfwaUZbw8fPkxAQIDNaS7Psizi4uIIDAzUGXcRESn0NC6KiEheiI+Pp1y5clddGUVF9hVcGpgDAgLydZGdmprKjh07CAsLw+nUWyoiIoWbxkUREclLV/sAVxOfiYiIiIiIiOQSFdkiIiIiIiIiuURFtgcwDAM/Pz/ddyYiIoLGRRERsZduVMoFLpeLlJQUWzNUr16dlJQU23PkBm9v7ytOiS8iInIlpmlSv359u2OIiEghpSL7OliWRUxMDGfPnrU9h9vtxuFweMSn9g6Hg/DwcLy9ve2OIiIiBZDb7SY2NpagoCB9aCsiIjeciuzrcKnADgkJoUiRIrYVuJZlkZCQYGuG3OJ2u4mOjubYsWOUL1++wB+PiIjceG63m3379lGyZEkV2SIicsOpyL5GLpcrvcAuVaqUrVksy8LlcuHr6+sRRWlwcDDR0dGkpqbi5eVldxwREREREZFs08e71+jSvc9FihSxOYnnuXSZuMvlsjmJiIiIiIhnioyMpGLFivj6+tKkSRPWrFlz2b7btm2ja9euVKxYEcMwGD58eKY+LpeLgQMHEh4ejp+fH5UrV+a9997Dsqz0Pr1798YwjAxf7du3z4vDs5XOZF+n/HLm2DRNuyPkmvzymoqISMFkGAaBgYEaT0RELmPy5Mn079+fUaNG0aRJE4YPH067du2IiooiJCQkU/+EhAQqVapEt27deOmll7Lc55AhQxg5ciRjx46ldu3arF27lkcffZTAwECef/759H7t27dnzJgx6d/7+Pjk/gHaTEV2FiIjI4mMjCwwZ1IvLVUiIiIiaR8816xZ0+4YIiL51rBhw3jiiSd49NFHARg1ahSzZs1i9OjRDBgwIFP/xo0b07hxY4As2wFWrlxJ586dufvuuwGoWLEiEydOzHSG3MfHh9DQ0Nw8nHxHl4tnoV+/fmzfvp2//vrL7ijZYlkWycnJGS7FEBERKazcbjdHjhzB7XbbHUVEJN9JTk5m3bp1tG3bNn2bw+Ggbdu2rFq16pr326xZMxYtWsSuXbsA2LRpEytWrKBDhw4Z+i1ZsoSQkBCqV69O3759OXXq1DU/Z36lIjsfcLktVu09xS8bj7Jq7ylc7pwXy8nJydnq16lTp8ve97B8+XIMw2Dz5s0YhsHGjRuvur+nnnoK0zSZOnVqTuKKiIjkGRXZIiKXFxsbi8vlonTp0hm2ly5dmpiYmGve74ABA+jRowc1atTAy8uLhg0b8uKLL/LQQw+l92nfvj0//PADixYtYsiQISxdupQOHToUmCuIs0uXi9ts7tZjvDNzO8fiktK3lQn0ZXCnWrSvUybXn++xxx6ja9euHDlyhLJly2ZoGzNmDBEREQQEBGRrXwkJCUyaNIlXX32V0aNH061bt1zPKyIiIiIi+d+UKVMYP348EyZMoHbt2mzcuJEXX3yRsLAwevXqBUCPHj3S+9etW5d69epRuXJllixZQps2beyKnut0JttGc7ceo++49RkKbICYuCT6jlvP3K3Hcv05O3bsSHBwMN9//32G7efPn2fq1Kk89thj2d7X1KlTqVWrFgMGDGDZsmUcPnw4l9OKiIiIiEhuCgoKwjRNjh8/nmH78ePHr+te6VdeeSX9bHbdunV55JFHeOmll/jwww8v+5hKlSoRFBTEnj17rvl58yMV2bnIsiwSklOz9XUuKYXBv24jqwvDL217+9ftnEtKydb+sju7uNPppGfPnnz//fcZ7uGeOnUqLpeLf//739k+3u+++46HH36YwMBAOnTokKlwFxERsYPD4SA4OBiHQ3/miIj8k7e3N40aNWLRokXp29xuN4sWLaJp06bXvN+EhIRMv3dN07zirTtHjhzh1KlTlCmT+1fw2kmXi+eixBQXtQbNy5V9WUBMfBJ1356frf7b322X7aVK+vTpwyeffMLSpUtp1aoVkHapeNeuXQkMDOTMmTNX3cfu3bv5888/mT59OgAPP/ww/fv356233tKSKSIiYiuHw0HlypXtjiEikm/179+fXr16ERERwS233MLw4cO5cOFC+mzjPXv25Kabbko/C52cnMz27dvT///o0aNs3LiRokWLUqVKFSBt7qf333+f8uXLU7t2bTZs2MCwYcPo06cPkHbl7DvvvEPXrl0JDQ1l7969vPqfF6lSsRxt65Rm85qlnE5IpmQRb2rfFIBpGFCkFBQvZ8MrdH1UZHuIpKSL+HmZ2Spwa9SoQbNmzRg9ejStWrViz549LF++nHfffTfbzzd69GjatWtHUFAQAP/617947LHHWLx4sUfdTyEiIgWP2+1m//79hIeH62y2iEgWunfvzsmTJxk0aBAxMTE0aNCAuXPnpk+GdujQoQy/P6Ojo2nYsGH690OHDmXo0KG0bNmSJUuWAPDll18ycOBAnnnmGU6cOEFYWBhPPfUUgwYNAtLOam/evJmxY8dy9uxZwkJLc1fpWN67z0mRH+6iXlZBnT7w7LoCV2gbltZ9uqz4+HgCAwOJi4vLNBlYUlJS+gDu6+sLpF0unpiSvZnx1uw/Te8xV18i7PtHG3NLeMkr9rEsC3dyEkWLFs32WeTRo0fz3HPPERMTw0cffcTkyZPZvXs3hmFw4MABwsPD2bBhAw0aNMj0WJfLRbly5YiJicnwj8/lcvHggw8yfvz4bGW4nKxeWxERkexKTU1l7dq1RERE4HTqfIKISL4UvRG+bnn1fk8uhbAGeZ0mW65UH/6dRp5cZBgGRbyz95I2rxpMmUBfYuKSsrwv2wBCA31pXjUY03HlwtmyLC6k5OwS7QceeIAXXniBCRMm8MMPP9C3b99sF+izZ8/m3LlzbNiwIcO94Fu3buXRRx/l7NmzFC9ePEd5RERERESk8HBZFtmZVSq7/fITFdk2MR0GgzvVou+49RiQodC+VOoO7lTrqgX2tSpatCjdu3fn9ddfJz4+nt69e2fqExUVlWlb7dq1+e6777j77rupX79+hrZatWrx0ksvMX78ePr165cnuUVEREREpODbdjQ+60vEs+p3U57HyVW6UclG7euUYeTDNxMamPGS6NBAX0Y+fHOO1sn29vbO8fM/9thjnDlzhnbt2hEWFpapvUePHjRs2DDDV3R0NLNmzaJr166Z+jscDu69916+++67HGcRERHJLQ6Hg7Jly+p+bBGRfOx0QnKu9stPNPrYrH2dMqx47Q4mPnErn/dowMQnbmXFa3fkqMA2DANvb+8cz+rdtGlTLMti1qxZGbZXrFgRy7Ky/KpQoQIpKSl069Yty33+73//Y/369TnKISIikptUZIuI5G8pKSk4Nk+6bHvkmmQqDj+H73/jee7ZF1izZs1l+27bto2uXbtSsWJFDMNg+PDhmfq4XC4GDhxIeHg4fn5+VK5cmffeey/Dksa9e/fGMIwMX+3bt7+m49Pl4vmA6TBoWrnUNT/esiySkpLw9fXV8lkiIlLouVwudu3aRbVq1TLMHSIiIvbbt2srCVOepEXqtizbJ29Nof/8JEbd7UuTsiafna1Bu3btiIqKIiQkJFP/hIQEKlWqRLdu3XjppZey3OeQIUMYOXIkY8eOpXbt2qxdu5ZHH32UwMBAnn/++fR+7du3Z8yYMenf+/j4XNMxqsj2EC5X9mY1FxER8XSWZREXF4cWUBERyT9SU12smDyUxrs+xd+4SCLe+JH5UvBhf17kiZu9eLRh2u2wX73+FrOXrGb06NEMGDAgU//GjRvTuHFjgCzbAVauXEnnzp25++67gbQrdydOnJjpDLmPjw+hoaHXdZygy8VFREREREQkD+3ft4vNQ9rSavcH+BsX2elTj8Tu09LWwf6bZJfFumg3bSv9/3PBTh8cRYNp27Ytq1atuubnb9asGYsWLWLXrl0AbNq0iRUrVtChQ4cM/ZYsWUJISAjVq1enb9++nDp16pqeT2eyRUREREREJNe5XG6WTIskYvuHhBsXuIgXO2u9RL37B2A4THh2HSScwmVZbDsaz65Dx3BZ/ybowf9BRAMoUgqKl6N06dLs3LnzmnMMGDCA+Ph4atSogWmauFwu3n//fR566KH0Pu3bt+e+++4jPDycvXv38sYbb9ChQwdWrVqV41uPVGR7iGu9X0BERMTTOBwOKlWqpInPRERstP/QQWLG96XNxT/AgH3e1SnW41vqV/rbwl3Fy0HxcphAvZsgqGw0AGZwdQhrkGtZpkyZwvjx45kwYQK1a9dm48aNvPjii4SFhdGrVy8gbWWlS+rWrUu9evWoXLkyS5YsoU2bNjl6PhXZHsAwDLy8vOyOISIiki84HI4sJ8cREZG853JbLPp5NDdveodwI44UyySqel9qd38bw7xyzRIUFIRpmhw/fjzD9uPHj1/XvdKvvPIKAwYMSC+k69aty8GDB/nwww/Ti+x/qlSpEkFBQezZsyfHRbY+4vUAlmWRkJCgCV5ERERImwx006ZNmhRUROQGO3Q0muUfd+Wuzf0JMuI47FWRMw/Npc6D71+1wAbw9vamUaNGLFq0KH2b2+1m0aJFNG3a9JpzJSQkZLq6yTRN3G73ZR9z5MgRTp06RZky2V9a+RKdyfYQV/oBERERKUwsyyIxMVEfPouI3CBut8WC3yZRb90btDJO47IMdlZ+lFr//hDDyzdH++rfvz+9evUiIiKCW265heHDh3PhwgUeffRRAHr27MlNN93Ehx9+CEBycjLbt29P//+jR4+yceNGihYtSpUqVQDo1KkT77//PuXLl6d27dps2LCBYcOG0adPHwDOnz/PO++8Q9euXQkNDWXv3r28+uqrVKlShXbt2uX49VCRLSIiIiIiItfkcEws2354kfYJM8GAGLMMxn1fUbt2y2vaX/fu3Tl58iSDBg0iJiaGBg0aMHfuXEqXLg3AoUOHMpyVjo6OpmHDhunfDx06lKFDh9KyZUuWLFkCwJdffsnAgQN55plnOHHiBGFhYTz11FMMGjQISDurvXnzZsaOHcvZs2cJCwvjrrvu4r333rumua8MSx/zXlZ8fDyBgYHExcUREBCQoS0pKYn9+/cTHh6Or2/OPp3JbZZlceHCBfz9/TEMw9YsuSE/vbYiIlLwpKamsnbtWiIiInA6dT5BRCQvuN0W8+f9Ss0/X6WCEQNAVLnuVH1oGA7fojanyxtXqg//Tvdk2+nsYYjeePmvs4ezvaucFKO9e/fGMIz0CdPCw8N59dVXSUpKSu9jGAY///xzlo9fsmRJ+uMdDgeBgYE0bNiQV199lWPHjmXom5CQwOuvv07lypXx9fUlODiYli1b8ssvv2Q7r4iISE6Yppm+TIuIiOS+IyfP8NtnT3Lnn72oYMQQ6wjiROdJVH/sa48tsHNCH+/a5exhGNEIUi9evo/TJ23tuOLlrrgrwzBy/El9+/btGTNmDCkpKaxbt45evXphGAZDhgzJ9j6ioqIICAggPj6e9evX8/HHH/Pdd9+xZMkS6tatC8DTTz/N6tWr+fLLL6lVqxanTp1i5cqV17ywu4iIyNUYhkHx4sXtjiEi4nEsy2LuogVUXt6fe4zDYMCeMh2p9MgIHEVK2B0v31CRbZeEU1cusCGtPeHUVYvsS7OLFylSJNuXi/v4+KRPg1+uXDnatm3LggULclRkh4SEULx4cUJDQ6lWrRqdO3emYcOG9O3blxUrVgDw66+/8vnnn/Ovf/0LgIoVK9KoUaNsP4eIiEhOpaamsmHDBho2bKjLxUVEckn06XOs/P5NOseNw8twEWcEktRhGFVuud/uaPmOLhfPTZYFyRey95WamL19piZma3/WdcwuvnXrVlauXIm3t/c17wPAz8+Pp59+mj/++IMTJ04AEBoayuzZszl37tx17VtERCQntHyXiEjusCyLOUuWEvt5K+6PH4uX4eJA8B0U7b+W0iqws6SPd3NTSgJ8EJa7+xzd/qpdDIDndwPZv//ht99+o2jRoqSmpnLx4kUcDgcjRoy45piX1KhRA4ADBw4QEhLC119/zUMPPUSpUqWoX78+t99+O/fffz+33XbbdT+XiIiIiIjknZizCSwe+y73nf4WXyOFC4Y/59t8SMXbeoIHTLicV3Qmu5Bq3bo1GzduZPXq1fTq1YtHH32Url27Xvd+L01Wf+my9RYtWrBv3z4WLVrE/fffz7Zt22jevDnvvffedT+XiIiIiIjkPsuymLtiNYeGt+XBMyPxNVI4XOJWfF9YQ+nbe6nAvgqdyc5NXkXgjejs9Y3ZnK2z1PSZC6H1rtjFsiz8zJwtdeXv75++OPvo0aOpX78+3333HY899liO9vNPO3bsANLuvb7Ey8uL5s2b07x5c1577TX++9//8u677/Laa69d9yXqIiIi/2SaJvXq1dPs4iIi1+BEfCKzf/iErif/RzEjkSR8iG/xNuVa91VxnU0qsnOTYYC3f/b6Ov2y3+9q+7Ss67okweFw8MYbb9C/f38efPBB/Pyyme0fEhMT+frrr2nRogXBwcGX7VerVi1SU1NJSkpSkS0iInlC44uISM5YlsW81ZsoMvclerMeDIgOqE/II2MICa5sd7wCRZeLe4gLFy5c1+O7deuGaZpERkamb9u/fz8bN27M8PX35zlx4gQxMTHs3r2bSZMmcdtttxEbG8vIkSPT+7Rq1YqvvvqKdevWceDAAWbPns0bb7xB69atr7iAu4iIyLVyuVysXbtWk5+JiGTTyXMX+XbUUJrMuZsWrCcFJydufZOwF3/HqQI7x3Qm2y5FSqWtg321dbKLlLohcZxOJ88++ywff/wxffv2BaB///6Z+i1fvjz9/6tXr45hGBQtWpRKlSpx11130b9///SlwQDatWvH2LFjeeONN0hISCAsLIyOHTsyaNCgvD8oERERERG5onl/bYNZL/MEK8GAE0VrUOKh0YSUqW13tALLsC7NVCWZxMfHExgYSFxcXKazrklJSezfv5/w8HB8fXN2P3S6s4fT1sG+nCKlrrpGNqRd2nHhwgX8/f2zvU52fpYrr62IiBRaqamprF27loiICK2TLSJyGafOX2TyhG+5/+jHhBhnceHg1M3PE3L3W2B62R0vX7pSffh3GnnsVLxctopoERERERGR3LJgw24u/Poqz1iLwYBTfuEE/Ps7Qso3sjuaR1CR7SH8/bM54ZqIiIiHM02TiIgIzS4uIvIPZy4kM27SOO499D5ljVjcGJyu+zhB97wHXtc2+bFkpiLbQ7jdbhwOzWMnIiICkJycfM2rZYiIeKJFmw9yfMbrPGfNAgPO+oRR5IGvCarc3O5oHkdVmYdITEy0O4KIiEi+4HK52Lx5s2YXFxEB4hJSGP79BCpOa8eD1iwATlV/kOL91+CtAjtP6Ey2iIiIiIiIB/p922H2/zSY51zTMR0W57yC8LkvklI129sdzaOpyL5Obrfb7ggeRxPei4iIiIhcu7jEFL6dNpP2u9+mteNg2uRmlTpT6v7hUKSk3fE8norsa+Tt7Y3D4SA6Oprg4GC8vb1tWz7LsiySk5MxTbPAL+FlWRYnT57EMAy8vLR0gIiIXBtNeiYihdXSnTFsm/oez6VOxNvhIsEZiPOe4ZSqd5/d0QoNFdnXyOFwEB4ezrFjx4iOjrY7jkcxDIOyZcvqDyQREbkmTqeTxo0b2x1DROSGOpeUwlcz5tN6x2CecexOm9ysXBuKPzASipW2O16hoiL7Onh7e1O+fHlSU1NtnVzFsizOnTtHsWLFCvyZbAAvLy8V2CIics0syyIuLo7AwECPGBdFRK5mxa4TrJ7yMc+k/EARx0WSHP44/jWE4o0eBv0evOFUZF+nS5c123lpc2pqKvv27SMiIgKnU2+piIgUbi6Xi507d2pcFBGPd+FiKv/7ZQm3bhnMf8ytYEBcaFMCe3wDxcvZHa/Q0sgjIiIiIiJSwKzaE8uiycN5PvlbAsxEkg0frLbvENj0KXBopWY7qcgWEREREREpIBKSU4mcuYr6GwfzlrkODDgX1JBiPb6FoCp2xxNUZHsEwzDw8/PTfWciIiJoXBQRz7Vm/2lmThrJi0kjKWWeI9Vw4mr5OsWavwimSrv8Qu+EBzBNk/r169sdQ0REJF/QuCginiYx2cWXs/6i2vp3eM9cCQacL16Toj2+xRlax+548g8qsj2A2+0mNjaWoKAgHLr/QkRECjmNiyLiSdYdPM3kid/TP/ELQs0zuHGQ0uxFit7xOji97Y4nWVCR7QHcbjf79u2jZMmS+mNCREQKPY2LIuIJklJcfDl3I2Fr3udjcxEYkFAsnCLdv8WnbITd8eQKVGSLiIiIiIjkIxsOnWHsxAn0v/AZ5c2TAFxs9CRF2r0D3kVsTidXoyJbREREREQkH7iY6uLL+VsJXDmEYeZsHA6LxCJh+HX7Cp/wFnbHk2xSke0BDMMgMDBQs6iKiIigcVFECqbNR87y1cRpvHhuGFWdRwG4WPdB/O4eAr4BNqeTnFCR7QFM06RmzZp2xxAREckXNC6KSEGSnOomcuEOHH98yueOGTgdbi76BOFz3wh8qnewO55cAxXZHsDtdhMdHU1YWJgmeBERkUJP46KIFBRbj8bxxaSZPBs3lHrmfgAuVu+MT+fhUKSkveHkmqnI9gBut5sjR44QGhqqPyZERKTQ07goIvldisvN/xZHkbD0S740p+DjSCHZKxDve4bhU/d+u+PJdVKRLSIiIiIicoPsOBbPJxPn8dTZoTRx7gQgObwN3vdGQkAZm9NJblCRLSIiIiIiksdSXW5GLdnD8SWj+NIxDn/HRVKdRTA7fIT3zT1BkzV6DBXZHsDhcBAcHKxL4kRERNC4KCL5z67j53h/4iL6nPqUZ83NACSXbYZ315FQoqK94STXqcj2AA6Hg8qVK9sdQ0REJF/QuCgi+UWqy803y/axZ9EYvjBHE2gm4HJ442g7GO9bnwF9GOiR9K56ALfbzd69e3G73XZHERERsZ3GRRHJD/acOE+f/82lwuK+fOocQaCRQErpBph9V2A0e1YFtgfTmWwP4Ha7OXnyJBUqVNClcSIiUuhpXBQRO7ncFt+t2MeGBRP41PE1wWY8bsOJ0eo1vG7vD6ZKME+nd1hERERERCQX7Dt5nkGT/+De418y0lwOQEqpGnjd/zWUqW9zOrlRVGSLiIiIiIhcB7fbYszKAyyfN5WPHSMJM0/jxoFx2/N4tX4DnD52R5QbSEW2B3A4HJQtW1aXxImIiKBxUURurAOxFxg4ZQ1toyP53rkAgNTAiji7fg3lm9icTuygItsDXPpjQkRERDQuisiN4XZb/PjnQebO+YUPjEjCnccBsCIex3nXu+Dtb3NCsYs+4vUALpeLHTt24HK57I4iIiJiO42LIpLXDp9OoOc3y0iY/RbjHIMJdxwntWgYPDIDo+OnKrALOZ3J9gCWZREXF4dlWXZHERERsZ3GRRHJK5ZlMX71IabPnsMHjKCG83Da9no9cHYYAn7F7Q0o+YKKbBERERERkas4ciaBN6ZtpN6B0Ux2TsfLcOHyK4V5zxcYNTvaHU/yERXZIiIiIiIil2FZFpP+OsyEWQt5zxpBA6+9adtrdMTsOByKBtsbUPIdFdkewOFwUKlSJc2iKiIigsZFEck90WcTef2nTVTaN46pzkn4OlJweQdg3j0Uo94DYBh2R5R8SEW2B3A4HISEhNgdQ0REJF/QuCgi18uyLKauO8J3M5fwtvt/NPXanra90h2YnUdA4E32BpR8TUW2B3C5XGzdupU6depgmqbdcURERGylcVFErsfx+CQGTNtE8N6pTHOOo5iZiNvph6Pd+xgRfXT2Wq5KRbYHsCyLxMREzaIqIiKCxkURuTaWZTFjw1FG/LqCN12jaOO1IW17uVtx3DsSSlayOaEUFCqyRURERESkUDtxLok3pm/FN+pnfvIaQwnzPG6HN442AzGa9gOHroqR7FORLSIiIiIihZJlWfy6KZphv/zJy6lf08n7z7TtofVx3PcVhNS0OaEURCqyPYBpmtSoUUP3nYmIiKBxUUSyJ/b8Rd6asZWLO+Yw1esbQsyzWIaJ0eIVjBYvg+lld0QpoFRkewDDMChevLjdMURERPIFjYsicjWzNh/jo5/X8GzyaLp7LwHACqqOce8ouOlme8NJgaci2wOkpqayYcMGGjZsiNOpt1RERAo3jYsicjmnLyQz8JetnN66kIleX1HWGYuFgdG0H8Ydb4GXn90RxQNo5PEQLpfL7ggiIiL5hsZFEfmnuVtjeG/GWh67+CN9vOcCYBWvgNFlJFS8zeZ04klUZIuIiIiIiMc6cyGZt2du4+CmZfzgNZLKzmNpDY16Y9z1X/ApZm9A8TgqskVERERExCMt2H6cQdPX82DSJIZ5/4JpWFhFQzE6j4Cqd9odTzyUimwPYJom9erV0yyqIiIiaFwUEYhLSOGd37axbcMqvvUaSW3nwbSGut0wOnwMRUraG1A8mopsD+Ht7W13BBERkXxD46JI4fX7zhO88dMGuiRM5yPvqXgbLiy/khgdP4PaXeyOJ4WAimwP4HK5WLt2LREREZpFVURECj2NiyKFU3xSCu/N3M5f6/9ihNcoGnntTmuo1gGj0+dQrLS9AaXQ0MgjIiIiIiIF2tJdJ3l92kbaXPiN2d4TKWJcxPIphtF+CDR4EAzD7ohSiKjIFhERERGRAulcUgofzN7BkjUb+djrK5p7bU1rCG+B0fl/ULycvQGlUFKRLSIiIiIiBc4fe2J5deombj03n3k+YwkwErGcfhh3vguNHweHw+6IUkipyPYApmkSERGhWVRFRETQuCji6S5cTOXDOTuY++cWPvD6lru816U1lG2M0WUUBFWxN6AUeiqyPURycjJ+fn52xxAREckXNC6KeKZVe0/x6k+bqH12KfN8vqOUcQ7L4YXR+g247QVw6MM1sZ+uofAALpeLzZs343K57I4iIiJiO42LIhAZGUnFihXx9fWlSZMmrFmz5or9p06dSo0aNfD19aVu3brMnj07Q/vx48fp3bs3YWFhFClShPbt27N79+4MfWJiYnjkkUcIDQ3F39+fm2++mZ9++ilXjichOZW3f93GU98s5D/nPmGU93BKGeegdF2MJ5dA8/4qsCXfUJEtIiIiIuJBJk+eTP/+/Rk8eDDr16+nfv36tGvXjhMnTmTZf+XKlfz73//mscceY8OGDXTp0oUuXbqwdWvaJGKWZdGlSxf27dvHL7/8woYNG6hQoQJt27blwoUL6fvp2bMnUVFR/Prrr2zZsoX77ruPBx54gA0bNlzX8fx14DQdPl/O/j9/Yb7Pa3QxV2IZDmj+MjyxGELrXNf+RXKbYVmWZXeI/Co+Pp7AwEDi4uIICAiwO85lpaamaj1QERGR/0/johR2TZo0oXHjxowYMQIAt9tNuXLleO655xgwYECm/t27d+fChQv89ttv6dtuvfVWGjRowKhRo9i1axfVq1dn69at1K5dO32foaGhfPDBBzz++OMAFC1alJEjR/LII4+k76dUqVIMGTIkvU9OJCa7GDo/ikl/7OANczwPORf9/51WgXu/grIROd6nyPXIbn2oM9keQpO7iIiI/B+Ni1JYJScns27dOtq2bZu+zeFw0LZtW1atWpXlY1atWpWhP0C7du3S+1+8eBEAX1/fDPv08fFhxYoV6duaNWvG5MmTOX36NG63m0mTJpGUlESrVq1yfBzrDp7h7i+Ws/mPOczxeu3/CuwmT8NTy1VgS75WKIrse++9lxIlSnD//ffbHSVPOJ1OGjdurE/rRURE0LgohVtsbCwul4vSpUtn2F66dGliYmKyfExMTMwV+9eoUYPy5cvz+uuvc+bMGZKTkxkyZAhHjhzh2LFj6Y+ZMmUKKSkplCpVCh8fH5566ilmzJhBlSrZn+07KcXFh7N38NCoJfQ4+zWTfd6jvOMkBJaDXjOhwxDwLpLt/YnYoVAU2S+88AI//PCD3THyjGVZnD17Fl35LyIionFRJLd5eXkxffp0du3aRcmSJSlSpAi///47HTp0wPG3tagHDhzI2bNnWbhwIWvXrqV///488MADbNmyJVvPs/HwWe7+Yjkrly9kptebPOmchQMLGj4MfVdCeIu8OkSRXFUoPuJt1aoVS5YssTtGnnG5XOzcuVP3nomIiKBxUQq3oKAgTNPk+PHjGbYfP36c0NDQLB8TGhp61f6NGjVi48aNxMXFkZycTHBwME2aNCEiIu2y7b179zJixIgM923Xr1+f5cuXExkZyahRoy6b+WKqi88X7ubbpVE8Y/7Msz4/48QN/iFwzxdQvcM1vRYidrH9TPayZcvo1KkTYWFhGIbBzz//nKlPTpcgEBEREREpjLy9vWnUqBGLFi1K3+Z2u1m0aBFNmzbN8jFNmzbN0B9gwYIFWfYPDAwkODiY3bt3s3btWjp37gxAQkICQIYz25A2P4Lb7b5s3i1H4uj05QrmL13KNK/BvOicnlZg174X+q1WgS0Fku0f7164cIH69evTp08f7rvvvkztl5YgGDVqFE2aNGH48OG0a9eOqKgoQkJCAGjQoAGpqamZHjt//nzCwsLy/BhERERERPKL/v3706tXLyIiIrjlllsYPnw4Fy5c4NFHHwXSltq66aab+PDDD4G0WytbtmzJp59+yt13382kSZNYu3YtX3/9dfo+p06dSnBwMOXLl2fLli288MILdOnShbvuugtIu2+7SpUqPPXUUwwdOpRSpUrx888/s2DBAn75dSar9p7ixLkkQor5ckt4SVxuixGLdzNyyW56G7N42WcqPqSAb3G4+1Oo65lzKUnhYHuR3aFDBzp0uPwnVMOGDeOJJ55I/6UwatQoZs2axejRo9OXINi4cWOuZLl48WL67ImQNkU7pC0FcqmIdzgcOBwO3G53hk/lLm13uVwZ7gG73HbTNDEMI9OHA5dmQ3W5XNnafukyOB8fH9xuN6mpqRiGkf6p4d8zXm57fjwmy7IybNcx6Zh0TDomHZOOKbvH5Ha7M4yLnnBMnvg+6Zjy7pi6d+/OiRMnGDRoEDExMdSvX5/Zs2dTunRp3G43Bw8eBNL+xnU4HDRr1oxx48YxaNAg3njjDapWrcr06dOpU6dO+jEdOXKE/v37c/z4ccqUKcPDDz/Mm2++mX5sTqeTWbNmMWDAADp16sT58+epHF6eN19/hWmbjhL7x8T0jMX9vPB2GsSfO8c4ryk0cewEwKrSFuOeEbiLlsb9t9fMU98nHVPBO6asTuxmxfYi+0ouLUHw+uuvp2+72hIE1+PDDz/knXfeybR9w4YN+Pv7AxAcHEzlypXZv38/J0+eTO9TtmxZypYty65du4iLi0vfXqlSJUJCQti6dSuJiYnp22vUqEHx4sXZsGFDhje4Xr16eHt7s3bt2gwZIiIiSE5OZvPmzenbTNOkcePGnD9/nosXL7J+/XoA/Pz8qF+/PrGxsezbty+9f2BgIDVr1iQ6OpojR46kb8+PxxQXF8fOnTvTt+uYdEw6Jh2TjknHlN1j2rFjR4Zx0ROOyRPfJx1T3h7TvffeS+PGjTMcE8D+/fsZMmQIAGvXrk0/pnr16jF27NgMxwSkH1PTpk2ZOnVq+jH99ddfGXLWq1ePSpUq8eqrrwLgnXiCesv64LRGQQrg87cDdQPJYHmDYYDL9ONAjac4X/Ve6geUIfbEiULzPumYCtYxXbhwgewwrHw09aZhGMyYMYMuXboAEB0dzU033cTKlSsz3BPy6quvsnTpUlavXp2t/bZt25ZNmzZx4cIFSpYsydSpU7O8xySrM9nlypXj1KlT6YuN58dPYlwuFydOnKBUqVI4HI5C/emSjknHpGPSMemYdEwpKSnExsamj4uecEye+D7pmDz7mNzRG/Ee3YarcYfWw931eyhRId8f09W2F8T3SceUs2OKj4+nVKlSxMXFpdeHWcnXZ7Jzy8KFC7PVz8fHBx8fn0zbnU5nptlJL735/3Tpzczu9svNepqT7ZZlcfDgQYKDgzO0Xy5jTrfbcUyGYWS5XcekY7rSdh2TjknHpGO69LxZjYsF+Zg88X3SMXn2MW2OuUC9LHtktLXhu9QLrpytjHYfU3a2F7T3KTvbdUz/tz27K1bYPrv4lVzLEgQiIiIiImKvY/GJV+8EnE7M3j2uIgVJvi6yr2UJAhERERERsUdSiouvlu7lm+X7rt4ZKFnEO48Tidx4thfZ58+fZ+PGjekzhO/fv5+NGzdy6NAhIG0Jgm+++YaxY8eyY8cO+vbtm2EJAkm73CEwMBDDMOyOIiIiYjuNiyI3XorLzYTVh2j1yRJGzFlPV9e8K/aPXJNMxeHnuKXFXTRp0oQ1a9Zcsf+lSdd8fX2pW7cus2fPztB+/PhxevfuTVhYGEWKFKF9+/bs3r07vf3AgQMYhpHl19SpU6/9wEWyYPvEZ0uWLKF169aZtvfq1Yvvv/8egBEjRvDJJ58QExNDgwYN+OKLL2jSpEmeZ4uPjycwMPCqN7aLiIiIiBRGbrfFrC3HGLZgF9GxZ+hlzqOf10wCOX/Zx0zemkLPnxMZdbcvTV4ax/AJ85g6dSpRUVGEhIRk6r9y5UpatGjBhx9+SMeOHZkwYQJDhgxh/fr11KlTB8uyaNasGV5eXnz66acEBAQwbNgw5s6dy/bt2/H398flcmWY2Rrg66+/5pNPPuHYsWMULVo0118b8TzZrQ9tL7Lzs4JSZLvdbqKjowkLC8vyRn0REZHCROOiSN6zLItlu2P5eO5OoqJP84C5lBe9ZhDC6bQOxSvC2QNZPrbJt+dpHGYy4l9+8ORS3KH1KFeuHM899xwDBgzI1L979+5cuHCB3377LX3brbfeSoMGDRg1ahS7du2ievXqbN26ldq1awNpvwdCQ0P54IMPePzxx7PM0bBhQ26++Wa+++6763otpPDIbn2okccDuN1ujhw5kmG6eRERkcJK46JI3lp/6Az//uZPeo/+k8oxc1nk8yofeH2XVmAHlocuI6HXr+DMvGpPsstiXbSbtpWcae1F0pbaa9u2LatWrcry+VatWkXbtm0zbGvXrl16/0tL8Pr6+qa3OxwOfHx8WLFiRZb7XLduHRs3buSxxx67ptdA5EoKxRJeIiIiIiJyfaJizjF0fhQLtsfQ2rGR2T5TqGkcTGv0D4YWr0Cj3v9XXD+7DhJOZdhHbMwJXP9tR+kHR0KLO6B4OQBKly7Nzp07s3zemJgYSpcunWFb6dKliYmJAaBGjRqUL1+e119/na+++gp/f38+++wzjhw5wrFjx7Lc53fffUfNmjVp1qzZNb4aIpenIltERERERC7r8OkEPlu4ixkbjhLBTqZ6T6axIyqt0ScAbnsemvQFn3/c11y8XHoR/X+i0/4TXC2Ltmvj5eXF9OnTeeyxxyhZsiSmadK2bVs6dOhAVnfGJiYmMmHCBAYOHJgrzy/yTyqyPYDD4SA4OFj3nYmIiKBxUSS3nDx3kcjf9zB+9UGquvcz2jmZ1uamtEanLzR5Cm57EYqUzPY+g4KCME2T48ePZ9h+/PhxQkNDs3xMaGjoVfs3atSIjRs3EhcXR3JyMsHBwTRp0oSIiIhM+5s2bRoJCQn07Nkz27lFckKjjwdwOBxUrlxZf0yIiIigcVHkesUnpfDp/ChafvI7S1atYpjjc2b7vJFWYDucENEHnt8Id76bowIbwNvbm0aNGrFo0aL0bW63m0WLFtG0adMsH9O0adMM/QEWLFiQZf/AwECCg4PZvXs3a9eupXPnzpn6fPfdd9xzzz0EBwfnKLtIdulMtgdwu93s37+f8PBw/UEhIiKFnsZFkWuTlOLih1UH+N+SvfgkHOct53S6+yzBxA0YUPd+aPU6lKp8Xc/Tv39/evXqRUREBLfccgvDhw/nwoULPProowD07NmTm266iQ8//BCAF154gZYtW/Lpp59y9913M2nSJNauXcvXX3+dvs+pU6cSHBxM+fLl2bJlCy+88AJdunThrrvuyvDce/bsYdmyZZnW2RbJTSqyPYDb7ebkyZNUqFBBf0yIiEihp3FRJGdSXW6mrTvC8IW7SYo/SV/nr/T2XYAPyWkdqrWHOwZCaJ1ceb7u3btz8uRJBg0aRExMDA0aNGDu3Lnpk5sdOnQow7/dZs2aMWHCBN566y3eeOMNqlatys8//0ydOv+X59ixY/Tv35/jx49TpkwZevbsmeU916NHj6Zs2bKZim+R3KR1srMQGRlJZGQkLpeLXbt25ft1slNTU1m7di0RERE4nfrcRERECjeNiyLZ43ZbzNkaw6fzozgeG0sfcw5Pec2mKAlpHco3g7aDofyt9gYVySeyu062Rp4s9OvXj379+qW/iCIiIiIinsKyLJbvjuWTeVFEHY3lIXMhz/n+Qkni0zqE1oU2g6FKWzAMe8OKFEAqsj2Aw+GgbNmyuiROREQEjYsiV7Lh0Bk+nhvFmn0nuM9czlc+0wkzYtMaS1aGO96EWveC/v2IXDMV2R7g0h8TIiIionFRJCu7jp9j6Lwo5m+Pob3jL+b7TKGy8f/XrC4WBq1egwYPgellb1ARD6Ai2wNcune8WrVqmKZpdxwRERFbaVwU+T9HziTw2YLdzNhwmGbGVn71nkw9x760Rr+S0Lw/NH4cvPzsDSriQVRkewDLsoiLi0Nz2ImIiGhcFAGIPX+REYv3MGH1IWq5dzHOOYlm5va0Ru+i0LQfNH0WfPPv5L4iBZWKbBERERERD3EuKYVvlu/n2+X7uCnlICOck7nLa11ao+mddtb69v5QNNjeoCIeTEW2iIiIiEgBl5TiYtyfB4n8fQ/+idG855zGvT4rcGCB4YAGD0LLAVC8nN1RRTyeimwP4HA4qFSpkmZRFRERQeOiFC6pLjc/rT/C8IW7SY2L4QXnzzzksxgvUtM61OoMrd+C4Gr2BhUpRFRkewCHw0FISIjdMURERPIFjYtSGFiWxZytMQydH0XsyRM86fyNx3zn4sfFtA6VWkObQXDTzfYGFSmEVGR7AJfLxdatW6lTp45mURURkUJP46J4uhW7Y/l43k52HTlBb3MefX1/I5DzaY03RUDbwRDewt6QIoWYimwPYFkWiYmJmkVVREQEjYviuTYePsvHc3eyZu9xepi/863Pz4QYZ9Iag2tCm4FQ/V9gGPYGFSnkVGSLiIiIiORje06cY+i8XczbFs09jpUs9plGeeNEWmPx8tD6TajbDRy6ckMkP1CRLSIiIiKSDx09m8jwBbv4af1hWhvrmeM9hRqOw2mN/iHQ8lW4uRc4ve0NKiIZqMj2AKZpUqNGDd13JiIigsZFKfhOnb9I5O97GffnQRq6tzHFazIRjl1pjT6BcPsL0ORp8Pa3N6iIZElFtgcwDIPixYvbHUNERCRf0LgoBdW5pBS+Xb6fb5fvo2LKHr5xTqal1+a0Rqcf3Po03PYC+JWwN6iIXJGKbA+QmprKhg0baNiwIU6n3lIRESncNC5KQZOU4mL86kNE/r6H4gkHGOKcRkefPwGwHE6MRr2hxStQLNTeoCKSLRp5shAZGUlkZCQul8vuKNlWkLKKiIjkNY2LUhCkutxMX3+U4Qt3YcUd5VXnT3TzWYaJGwsDo243jNavQ8lKdkcVkRxQkZ2Ffv360a9fP+Lj4wkMDLQ7joiIiIh4EMuymLcthk/mRXH65DGecf5KT98F+JCS1qFaB4w2A6F0bXuDisg1UZEtIiIiInKD/LEnlo/n7mTPkRgeN2fzhO9sipKY1ljhdmgzCMo3sTekiFwXFdkewDRN6tWrp1lURURE0Lgo+dOmw2f5eN5O1u45xsPmQsb4/EJJ41xaY5n6acV15TZgGPYGFZHrpiLbQ3h7a31EERGRSzQuSn6x58R5Pp0fxfytR+lqLuMTn+mEGafSGktVgTvegpqdweGwN6iI5BoV2R7A5XKxdu1aIiIiNIuqiIgUehoXJT84ejaRzxfu4qd1h2hnrGG+91QqO46lNQbcBK0GQP0HwdTPqIin0b9qEREREZFccvpCMpG/7+HHPw9wq3sjP3tNpq7jQFpjkVLQ/D8Q8Rh4+dqaU0TyjopsEREREZHrdP5iKt8u38e3y/dTLXk7P3hN5lbnjrRG72LQ7Fm49RnwDbA3qIjkORXZIiIiIiLX6GKqi/F/HmLE73sITtjDZ84p3OmzHgDL9MG45Qm4vT/4l7I5qYjcKCqyPYBpmkRERGgWVRERETQuyo3hcltMX3+E4Qt344g7wEDnNDr7rMSBhWU4MBo8hNFqAASWtTuqiNxgKrI9RHJyMn5+fnbHEBERyRc0LkpesSyLeduO8+n8KM6eOMxzzp950GcxTlxpHWp1wbjjLQiqam9QEbGNimwP4HK52Lx5s2ZRFRERQeOi5J2Ve2IZMi+K/YeP8LTzNx71nYsfyWmNldukrXUd1sDWjCJiP408IiIiIiJXsPnIWT6ZF8Xa3Ud41JzHDz6/EWhcSGssewu0HQwVb7c3pIjkGyqyRURERESysPfkeT6dH8WCLUfoYS5mmM8Mgo24tMaQ2tBmIFRrD4Zhb1ARyVdUZHsITe4iIiLyfzQuyvU4FpfI5wt389O6Q3RkBYt8plHeOJnWWKIitH4T6nQFh37ORCQzFdkewOl00rhxY7tjiIiI5AsaF+Vanb6QzMglexi76gAt3X8x0zmVGo7DaY1FS0PLV6FhT3B62xtURPI1FdkewLIs4uLiCAwMxNDlSiIiUshpXJScunAxle9W7OfrZfuom7KJSc7J3Ozck9boGwi3vwS3PAXeRewNKiIFgopsD+Byudi5c6dmURUREUHjomTfxVQXE1YfYsTiPYQl7OR/zsm08N4CgOVVBOPWvtDsefArbm9QESlQNPKIiIiISKHiclvM2HCUzxbswjduD+86p3K3zxoALIcXRsSjGM1fhmKlbU4qIgWRimwRERERKRQsy2L+9uMMnRfFhRMHeME5nft9lmHixsLAqN8Do9WAtMnNRESukYrsLERGRhIZGYnL5bI7SrYYhoGfn5/uOxMREUHjomRt1d5TDJm7k0OHD9HP+QuP+C7Am9S0xup3Y9zxFpSuZW9IEfEIhmVZlt0h8qv4+HgCAwOJi4sjICDA7jgiIiIikkNbjsTx8bydbNh9iCecs3ncnI2/kZTWWLE5tBkM5TQbvYhcXXbrQ53J9gBut5vY2FiCgoJwOBx2xxEREbGVxkUB2HvyPMPm72LhloM8Yi7gc59fKGmcT2sMawhtBkGl1qArHkQkl6nI9gBut5t9+/ZRsmRJ/TEhIiKFnsbFwu1YXCJfLNrNT2sPcp+xhCU+0yljnE5rDKoGd7wFNe9RcS0ieUZFtoiIiIgUeGcuJDNy6V7GrtzHne5VzHVOpZIjJq0xsBy0GgD1eoCpP39FJG/pt4yIiIiIFFgXLqYyesV+vl62l0Yp6/jJOYU6zgNpjUWCoMXLENEHnD625hSRwkNFtgcwDIPAwEDNoioiIoLGxcLiYqqLiasPMeL3PVS4sIVvvSbTxHsnAJZPAEaz5+DWvuBTzOakIlLYqMj2AKZpUrNmTbtjiIiI5AsaFz2by23xy8ajDFuwi2Jnd/KRcwptfTYAYDl9MW55AuP2/lCkpM1JRaSwUpHtAdxuN9HR0YSFhWmCFxERKfQ0Lnomy7JYuOMEn8zbycUTe3jFOY1OPqtwYGEZJkbDhzFavgaBN9kdVUQKORXZHsDtdnPkyBFCQ0P1x4SIiBR6Ghc9z5/7TvHx3J0cObSf553T6e6zBC9caY2178No/SYEVbE3pIjI/6ciW0RERETypa1H4/hkXhQbd+2nr3MmvXzm4WckpzVWuRPaDIQy9e0NKSLyDyqyRURERCRf2R97gU/nR7F4834eNefypc9vBBgJaY3lboW2g6FCM3tDiohchopsD+BwOAgODtYlcSIiImhcLMhi4pL4fNFufl67jweMRSzzmUGQEZ/WWLoOtBkEVe8CzRwvIvmYimwP4HA4qFy5st0xRERE8gWNiwXP2YRkRi7dyw9/7ONf7mUs8JpGWSM2rbFEONzxFtS+D/TBiYgUACqyPYDb7Wb//v2Eh4frU3sRESn0NC4WHAnJqYz54wCjlu6hWfKf/OKcQjXn0bTGYmWg5avQ8BEwvewNKiKSAyqyPYDb7ebkyZNUqFBBf0yIiEihp3Ex/0tOdTPpr0N8sWgP1RLW8aNzMg289wJg+RbHaN4fbnkSvPxsTioiknMqskVERETkhnC5LX7ddJRhC3ZR4sxWhjsncbv3NgAsL3+Mps9gNHsOfANtTioicu1UZIuIiIhInrIsi0U7TjB0fhSpx3fwpnMq7X3+SmtzeGFE9MFo8TIUDbE5qYjI9VOR7QEcDgdly5bVJXEiIiJoXMxvVu87xcfzoog5uIsXnT9xn89yTCwsw4FRrwdGqwFQooLdMUVEco2KbA9w6Y8JERER0biYX2yLjuOTeVFsidrDs86fechnEd5GalpjjY4YdwyEkBr2hhQRyQMqsj2Ay+Vi165dVKtWDdM07Y4jIiJiK42L9joQe4FPF+xiyaY9POH8jUifOfgbF9Maw1tCm8FQtpG9IUVE8pCKbA9gWRZxcXFYlmV3FBEREdtpXLTH8fgkPl+0m1/+2stDxlyW+cykhHE+rTHsZmg7GCq1sjWjiMiNoCI7C5GRkURGRuJyueyOIiIiIpKvxSWkMHLpXsat3M097t9Z5DWdUONMWmNwDbjjLajREQzD3qAiIjeIiuws9OvXj379+hEfH09goJaQEBEREfmnhORUxvxxgK+W7qZV8gpmOqcS7nU8rTGwPLR+Hep1B4cu2ReRwkVFtgdwOBxUqlRJs6iKiIigcTGvJae6mfzXIb5YtJs6CauZ5JxCLe+DAFj+wRgtXoFGvcHpY29QERGbqMj2AA6Hg5AQrSspIiICGhfzittt8eumaIYt2EXpM+uJ9JrMLd5RAFg+ARi3PY/RpC/4FLU5qYiIvVRkewCXy8XWrVupU6eOZlEVEZFCT+Ni7rIsi9+jTvDx3Cgcx7fwjnMyrX02pbU5fTGaPIVx24tQpKS9QUVE8gkV2R7AsiwSExM1i6qIiAgaF3PTXwdOM2TOTmIPbec/zql08vkTAMvhxLi5J0aLVyGgjM0pRUTyFxXZIiIiIpLB9uh4hs6PYtvOnbzgnM4D3ktwGu60xjr3Y7R+A0pVtjekiEg+pSJbRERERAA4eOoCwxbsYtmmKJ42f+V/PvPxNVLSGqu2gzYDIbSuvSFFRPI5FdkewDRNatSoofvORERE0Lh4LU7EJ/HF4t3MXLOLnsZslnrPIsBITGss3wzaDIIKTe0NKSJSQKjI9gCGYVC8eHG7Y4iIiOQLGhezLy4hhVHL9jLuj13c757PIq9fCDLi0xpD60KbwVClLRiGvUFFRAoQFdkeIDU1lQ0bNtCwYUOcTr2lIiJSuGlcvLrEZBdjVu7nmyW7aJvyO3Oc0ylrxqY1lqwMd7wJte4FrTUuIpJjGnk8hMvlsjuCiIhIvqFxMWspLjeT/jrMlwt3cXPCCqY6p1DFKxoAq1gYRqvXoMFDYHrZG1REpABTkS0iIiLi4dxui5mboxk2P4pyZ9fwjXMy9b33AWD5lcRo3h+j8ePg5WdzUhGRgk9FtoiIiIiHsiyLJVEn+XheFD4x6/nQOYlm3tvT2ryLYjTth9H0WfANsDmpiIjnUJHtAUzTpF69eppFVUREBI2Ll6w9cJqP50Zx5uBmXnFO5i6fdQBYpjdGxGMYzf8DRYNtTiki4nlUZHsIb29vuyOIiIjkG4V5XNxxLJ6h86KIitrGS85p3Ou9AodhYRkOjPoPYrR6DYqXtzumiIjHUpHtAVwuF2vXriUiIkKzqIqISKFXWMfFQ6cSGLYgij82baef+TMjvRfhbfz/CeBq3oNxx1sQXN3ekCIihUDhGXlEREREPNCJc0l8uWgPv63ZwWOOmXzgPZcixsW0xkqtoc0guOlme0OKiBQiKrJFRERECqC4xBS+WrqXiX9E0d09m9+9ZlLcuJDWeFMEtB0M4S3sDSkiUgipyBYREREpQBKTXYxddYBvfo+iQ8p85jpnUNo8m9YYXBPaDITq/wLDsDWniEhhpSLbA5imSURERKGfRVVERAQ8d1xMcbmZsvYwXyyI4taE35nunEYFrxMAWMXLY7R+E+p2A4dnHbeISEGjIttDJCcn4+fnZ3cMERGRfMGTxkW32+K3LccYNm8nlc+u4HvnFGp6HwbA8g/BaPkqxs29wFl4Z1QXEclPVGR7AJfLxebNmwvdLKoiIiJZ8ZRx0bIsluw6ySdzoygWs5qhXpOJ8N6V1uYTgHH7ixhNngZvf5uTiojI3xXckUdERETEQ607eJohc6O4cGAdrzkn09JnMwCW0w+jyVMYt70ARUranFJERLKiIltEREQkn9gZE8/QeVHs27mR/s5pdPT5EwDL4cS4uRdGy1ehWKjNKUVE5EpUZHsIT5vcRURE5HoUtHHx8OkEPluwiz83buZ58yfu916G03BjYWDU7YbR+nUoWcnumCIikg0qsrMQGRlJZGQkLpfL7ijZ4nQ6ady4sd0xRERE8oWCNC6ePHeREYt3M2fNVp4wfuFD7wX4GClpjdU6YLQZCKVr2xtSRERyxLAsy8rpg1JSUoiJiSEhIYHg4GBKlvTMe4Li4+MJDAwkLi6OgIAAu+NclmVZxMXFERgYiKE1MUVEpJArCONifFIKXy/dx8QV23nYPZPHnbMpZiSmNVa4HdoMgvJN7A0pIiIZZLc+zPaZ7HPnzjFu3DgmTZrEmjVrSE5OxrIsDMOgbNmy3HXXXTz55JMF5pNjT+Jyudi5c2eBn0VVREQkN+TncTEpxcXYlQf4bskOOiXPYb7zF0qZ59Iay9RPK64rt4F8+uGAiIhcXbZGnmHDhvH+++9TuXJlOnXqxBtvvEFYWBh+fn6cPn2arVu3snz5cu666y6aNGnCl19+SdWqVfM6u4iIiEiBkOJyM3XtESIX7uS2hPnMcE7nJq9TAFilqmDc8RbU7AwOh81JRUTkemWryP7rr79YtmwZtWtnfU/QLbfcQp8+fRg1ahRjxoxh+fLlKrJFRESk0HO7LWZtOcZn83dS48zv/OCcSmWvYwBYATdhtBqAUf9BMPPXGXcREbl22fqNPnHixGztzMfHh6effvq6AknOGYaBn59fvr3vTERE5EbKD+OiZVks2x3Lx3N2UOr4Cj53Tqau94G0Nr+SGC1exoh4DLx8bcsoIiJ547o/No2Pj2fx4sVUr16dmjVr5kYmySHTNKlfv77dMURERPIFu8fF9YfO8PHcnSTv/5OBXpO51XsHAJa3P0bT5zCa9gPf/DuhqoiIXJ8cF9kPPPAALVq04NlnnyUxMZGIiAgOHDiAZVlMmjSJrl275kVOuQK3201sbCxBQUE4dC+XiIgUcnaNi1Ex5xg6P4pDO/7iZecU7vRZD4Bl+mA0fhyjeX/wD7pheURExB45LrKXLVvGm2++CcCMGTOwLIuzZ88yduxY/vvf/6rItoHb7Wbfvn2ULFlSRbaIiBR6N3pcPHw6gc8W7mLthvW85JxGZ++VOAwLy3BgNHgIo9UACCyb5zlERCR/yHGRHRcXl74u9ty5c+natStFihTh7rvv5pVXXsn1gCIiIiL50clzF4n8fQ/zV2/kaWMGQ7wX42W40hprdUmbMTxIE8GKiBQ2OS6yy5Urx6pVqyhZsiRz585l0qRJAJw5cwZfX03eISIiIp4tPimFb5btY+qKLfR0/8Ii51z8jOS0xsptoM1ACGtob0gREbFNjovsF198kYceeoiiRYtSoUIFWrVqBaRdRl63bt3czifZYBgGgYGBml1cRESEvBsXk1Jc/LDqAKN/38Z9yb8xzzmTQGdCWmPZW6DtYKh4e64+p4iIFDyGZVlWTh+0bt06Dh06xJ133knRokUBmDVrFsWLF+e2227L9ZB2iY+PJzAwkLi4OAICNAuoiIhIYZTqcjNt3RFGLNhO64Q5PO+cQbARB4AVUgujzSCo1h70YbeIiEfLbn14TUV2YVFQimy32010dDRhYWGa+ExERAq93BoX3W6LOVtj+GzeduqeWcBLzmmUd5wEwCpREaP1m1CnKzjM3IouIiL5WHbrw2xdLv7RRx/xwgsv4Ofnd9W+q1evJjY2lrvvvjv7aeW6uN1ujhw5QmhoqIpsEREp9K53XLQsi+W7Y/lk7k5CYxYzwjmVGt6H09qKlsZo8QrGzb3A6Z3b0UVExANkq8jevn075cuXp1u3bnTq1ImIiAiCg4MBSE1NZfv27axYsYJx48YRHR3NDz/8kKehRURERPLChkNn+HhuFBxYxjvOydzsvQcAyzcQ4/aXMG55Erz9bU4pIiL5WbaK7B9++IFNmzYxYsQIHnzwQeLj4zFNEx8fHxIS0ib8aNiwIY8//ji9e/fWLOMiIiJSoOw6fo6h86I4tmMVrzgn08J7CwCWswhG074YzZ4Hv+L2hhQRkQIhx/dku91uNm/ezMGDB0lMTCQoKIgGDRoQFBSUVxltU5Duyd6/fz/h4eG6XFxERAq9nIyLR84k8NmC3WzauJqXzKncba4BwHJ4YTTqDS1egWKlb0BqERHJ7zTxWS4oKEW2iIiI5Ezs+YuMWLyH31ev5xljGvebSzENCwsDo153aP06lKhod0wREclHcnXiM8nfdCZbRETk/1xpXDyXlMI3y/fz0/KN9HFPZ75zAT5Galpj9bsx7ngLSteyIbWIiHgKFdkewO12c/LkSSpUqKAiW0RECr2sxsWkFBfj/jzI94s30y3lF+aZsynqTEp7QMXm0GYwlGtsY2oREfEUKrJFRETEY6W63Py0/ggjF2yl7YXf+NX5CyWd5wGwyjTAaDsYKrUGw7A5qYiIeAoV2SIiIuJxLMtiztYYPl8YRcSZ2Ux0TqeM1+m0tlLVMNq8hVHzHhXXIiKS63JcZI8ZM4bu3btTpEiRvMgj18DhcFC2bFldKi4iIgKs3HeaD1deoPKpUXztnEolrxgArICyGK1fx6jXA0ydZxARkbyR49nFS5cuTWJiIt26deOxxx6jWbNmeZXNdppdXEREpODYePgsH8/ZgfeBxbzsnEIdxwEA3EWCcLR4GSL6gNPH3pAiIlJg5dns4kePHmXmzJl8//33tGrVikqVKvHoo4/Sq1cvQkNDryu0XBuXy8WuXbuoVq0apmnaHUdEROSG2nPiHEPn7eLk9qW86jWZJt47AXB7F8XR7HkcTZ8Bn2I2pxQRkcLiutbJPn78OOPGjWPs2LHs3LmT9u3b89hjj9GpUyePuHS5oJzJTk1NZe3atUREROB06vI3EREpHI6eTWT4gl1s2/AH/c0ptDU3AGCZPhwr15GQ+z7EGVDa5pQiIuIpbsg62aVLl+b2229n165d7Nq1iy1bttCrVy9KlCjBmDFjaNWq1fXsXkRERCSTU+cvEvn7Xpb9uZrnHFMY4rUKh2FhGSZGw4dx3f4fDu06RkiRUnZHFRGRQuiaTjcfP36coUOHUrt2bVq1akV8fDy//fYb+/fv5+jRozzwwAP06tUrt7OKiIhIIXYuKYXPFuyi68c/UWn1W8xx/ofO5kochgW178Potwbu+QICbrI7qoiIFGI5vly8U6dOzJs3j2rVqvH444/Ts2dPSpYsmaHPiRMnCA0Nxe1252rYG62gXC7udruJjY0lKCjIIy7TFxER+bukFBfjVx/ix8Ub6JE8nV7mPPyMZACsKnditBkIZeqn99e4KCIieSHPLhcPCQlh6dKlNG3a9LJ9goOD2b9/f053LdfI4XAQEhJidwwREZFclepyM339Ub5asIn2F37hV+dvBDgTALDK3YrRdjBGhcyrnGhcFBERO13XxGeerqCcyXa5XGzdupU6depodnERESnwLMti3rYYhs/dSpMzM3nWOYNgIz6tLaQ2RtvBUPUuMIwsH69xUURE8kKencl+/vnnqVKlCs8//3yG7SNGjGDPnj0MHz48x2Hl+liWRWJiIvq8RERECro/9sQydM42Kh2bzbde0yjrFQuAu0Q4jjvewqh9H1zlEnCNiyIiYqccF9k//fQTv/76a6btzZo146OPPvKIIjsyMpLIyEhcLpfdUURERAqFTYfP8vHcHRTdP48hzilU8z4KgLtoaRytBuBo+AiYXjanFBERubocF9mnTp0iMDAw0/aAgABiY2NzJZTd+vXrR79+/dIvBxAREZG8sefEeT6dH0Xc9oW86pxMA++9ALh9i+O4/SUctzwJ3kVsTikiIpJ9OS6yq1Spwty5c3n22WczbJ8zZw6VKlXKtWCSfaZpUqNGDd13JiIiBcbRs4l8vnAXu9Yv5WVzErd7bwPA7VUEx63P4Gj2HPgVv6Z9a1wUERE75bjI7t+/P88++ywnT57kjjvuAGDRokV8+umnHnGpeEFkGAbFixe3O4aIiMhVnb6QTOTve1j55x+8YEzmY++/ALAcXhgRfXC0eBmKXt/M4BoXRUTETjkusvv06cPFixd5//33ee+99wCoWLEiI0eOpGfPnrkeUK4uNTWVDRs20LBhQ5zOHL+lIiIiee78xVS+Xb6PWcvX8KRrMm+YyzENC8twYNTrgdFqAJSokCvPpXFRRETsdE0jT9++fenbty8nT57Ez8+PokWL5nYuySFN0iYiIvnRxVQX4/88xPjF63g4eQq/mYvwcaYCYNXoiHHHQAipkevPq3FRRETscl0f7wYHB+dWDhEREfEgLrfF9PVH+GbBRjpemMav5hz8nRcBsMJbYrQZjFG2kc0pRUREcl+Oi+zjx4/z8ssvs2jRIk6cOJFpDUp9ciwiIlJ4WZbFvG3H+XLeZm47PZ3JzpmUcJ5Pawu7GaPtYIxKrewNKSIikodyXGT37t2bQ4cOMXDgQMqUKYNhGHmRS3LANE3q1aunWVRFRMRWK/fEMnTuNmoc+4XvnNMJ9ToDgDuoGo42gzBqdIQb8HeDxkUREbFTjovsFStWsHz5cho0aJAHceRaeXt72x1BREQKqc1HzjJ07g6K7/uNT51TCfc6DoA7oCyOO97EUa87OG5swatxUURE7JLjIrtcuXKZLhEXe7lcLtauXUtERIRmURURkRtm78nzfDpvJ4nb5zLAOYVa3gcBcBcJwtHyVRyNeoPT54bn0rgoIiJ2yvHIM3z4cAYMGMBXX31FxYoV8yCSiIiI5GfH4hL5fOFu9q9fyH/MSdziHQWA27sYjttfwNGkL/ho5RERESmcclxkd+/enYSEBCpXrkyRIkXw8vLK0H769OlcCyciIiL5x+kLyYxcsoc1q5bygjGJO7w2AuA2fXA0eQrH7S9BkZL2hhQREbHZNZ3JFhERkcLjwsVUvluxn7nL/uBp9yTedK4CwDJMjJt74mj5KgSE2ZxSREQkfzAs3WB9WfHx8QQGBhIXF0dAQIDdcS7LsixcLhemaWq2dxERyTUXU11MWH2IyYtW0zN5Mg+YS3AabgCsOvdjtH4DSlW2N2QWNC6KiEheyG59eE2zgezdu5cxY8awd+9ePv/8c0JCQpgzZw7ly5endu3a1xxarl1ycjJ+fn52xxAREQ/gclv8vOEo385fS5cLU/jZnI+vMwUAq2o7jDYDMULr2pzyyjQuioiIXRw5fcDSpUupW7cuq1evZvr06Zw/fx6ATZs2MXjw4FwPKFfncrnYvHkzLpfL7igiIlKAWZbFvG0x3Dt8HgemD2Zy0tM85ZyFr5GCu3xTeHQuxkNTIJ8X2BoXRUTETjk+kz1gwAD++9//0r9/f4oVK5a+/Y477mDEiBG5Gk5ERERujFV7T/HpnC3UPTaN0c5fCPKKB8Bdui6OtoNxVGkLuvRaRETkqnJcZG/ZsoUJEyZk2h4SEkJsbGyuhBIREZEbY8uROIbO3UbI/hkMd06nrFfaWO4qUQmzzVs4at0Ljhxf+CYiIlJo5bjILl68OMeOHSM8PDzD9g0bNnDTTTflWjDJGdM07Y4gIiIFyN6T5xk2LwrX9l8Z6JxCFa9oAFxFy2C2HoDZ4CEwva6yl/xL46KIiNglx0V2jx49eO2115g6dSqGYeB2u/njjz94+eWX6dmzZ15klKtwOp00btzY7hgiIlIAHItL5ItFuzmybg4vm5Oo770PAJdvCczm/TFveQK8CvaEYRoXRUTETjkusj/44AP69etHuXLlcLlc1KpVC5fLxYMPPshbb72VFxnlKizLIi4ujsDAQC1VIiIiWTpzIZmRS/eyfuUCXjImcZvXNgDcziI4mj2L2exZ8A20OWXu0LgoIiJ2uuZ1sg8fPsyWLVs4f/48DRs2pGrVqrmdzXYFZZ3s1NRU1q5dS0REBE7nNa3KJiIiHurCxVRGr9jPomVLecY9gbvMdQC4Hd44Gj8Gzf8DRYNtTpm7NC6KiEheyLN1st99911efvllypUrR7ly5dK3JyYm8sknnzBo0KBrSywiIiK55mKqi4mrD/HT4pX0Tp7IdMcKHKaFZTig/r9xtBoAxcvbHVNERMTj5Hi60HfeeSd9bey/S0hI4J133smVUCIiInJtXG6L6euPcP/QX2DOq/yU+hxdzeU4DAur5j0Yz/yJ0eV/KrBFRETySI7PZFuWleX9TZs2baJkyZK5EkpyxjAM/Pz8dN+ZiEghZlkWC3ec4H9z19Lm9GQmm3Mp4rwIgLtSaxxtBmHcdLPNKW8MjYsiImKnbBfZJUqUwDAMDMOgWrVqGQYul8vF+fPnefrpp/MkpFyZaZrUr1/f7hgiImKTP/edYvicjTSInsIY50yKOy8A4A67GUfbt3FUamlzwhtL46KIiNgp20X28OHDsSyLPn368M477xAY+H8zkHp7e1OxYkWaNm2aJyHlytxuN7GxsQQFBeFw5PgOABERKaC2Ho1j2NxthO2bwufOGZT2OguAK6gGZttBOKr/Cwrh2VyNiyIiYqdsF9m9evUCIDw8nGbNmuHl5ZVnoSRn3G43+/bto2TJkvpjQkSkENgfe4Fh83bg2PYTg53TqOB1AgBXQDnMNm9h1u0GDtPmlPbRuCgiInbK8T3ZLVv+3yVnSUlJJCcnZ2jPz0tdiYiIFGQxcUl8vnAXset/ob85mZrehwFwFQnGbPkqZqPe4PS2N6SIiEghl+MiOyEhgVdffZUpU6Zw6tSpTO0ulytXgomIiEiaswnJjFy6l21/zOZFxyQivHYB4PIOwLz9Bcxb+4K3v80pRUREBK6hyH7llVf4/fffGTlyJI888giRkZEcPXqUr776io8++igvMspVGIZBYGCgZlEVEfEwCcmpjPnjAEuXLqCfawKvOzcD4DJ9MW99GvO2F6CIVvb4J42LIiJiJ8OyLCsnDyhfvjw//PADrVq1IiAggPXr11OlShV+/PFHJk6cyOzZs/Mq6w0XHx9PYGAgcXFxugxeRERumORUN5P+OsTPC5fRJ3k8Hc0/AXAbToxGvTBavgrFQm1OKSIiUrhktz7M8Zns06dPU6lSJSDt/uvTp08DcPvtt9O3b99rjCvXw+12Ex0dTVhYmCZ4EREpwFxui183HWXcvJXcf34CU8ylOE03FgbU7Yaj9etQspLdMfM9jYsiImKnHBfZlSpVYv/+/ZQvX54aNWowZcoUbrnlFmbOnEnx4sXzIKJcjdvt5siRI4SGhuqPCRGRAsiyLBbtOMHXc9dw5+kJTDAX4ONMAcBdtT2ONgMhtI7NKQsOjYsiImKnHBfZjz76KJs2baJly5YMGDCATp06MWLECFJSUhg2bFheZBQREfFYq/ed4os5G4iInsB3ztkUcyYC4Cp/G2bbwTjKN7E5oYiIiOREjovsl156Kf3/27Zty86dO1m3bh1VqlShXr16uRpORETEU22LjmP43C2U3zuRL5y/UMrrHACu0vUw7xyMWbkNaOIuERGRAifHRfY/VahQgQoVKnDkyBGefPJJvv7669zIJTngcDgIDg7WJXEiIgXAgdgLfDZ/Oz7bJvO2czo3eaUth5laojLOtgMxa3YG/T6/LhoXRUTETjmeXfxyNm3axM033+xR62RrdnEREcktx+OT+GJhFPHrfuJFcwqVHccASC1aBmfr16HBQ2Be92ffIiIikkfybHZxyX/cbjf79+8nPDxcn9qLiOQzcQkpjFyyhz2rfuEFYyJ1vQ4AkOpTAmfLl3E2fhy8fO0N6WE0LoqIiJ1UZHsAt9vNyZMnqVChgv6YEBHJJxKSUxnzxwFWLp3Ds64JDDC3A+By+mPe9hzOpv3AV1dJ5QWNiyIiYicV2VmIjIwkMjLSoy59FxGRGyM51c3kvw4xa9EiHrs4nn7mOjDB5fDGccvjmM3/A/5BdscUERGRPJLtIvu+++67YvvZs2evN0u+0a9fP/r165d+zb2IiMjVuN0Wv26KZuK8ZfS48CMTHCtxmBYWDmj4EGarARBY1u6YIiIikseyXWRfrdgMDAykZ8+e1x1Ics7hcFC2bFldEiciYgPLsvg96gTfzl5F+9PjGGcuxstMuxLKVbMLZpu3IKiqzSkLF42LIiJip1ybXdwTaXZxERG5kr8OnGbErL9ocmwcj5pz8TOSAXBVugOz7SAIa2hzQhEREcktml28EHG5XOzatYtq1aphmqbdcUREPN726Hi+mLuRSnvH8YVzJoHOBABSwxrjvOttzIq325ywcNO4KCIidlKR7QEsyyIuLg5dlCAikrcOnrrA5/O3UXTreN5zziDYKw6AlFI18LpzMM7qHcAwbE4pGhdFRMROKrJFRESu4kR8El8u2kniusm86JhKea+TAKQElMerzVt41b0fHDpjKiIiIiqyRURELisuIYVRS/dwcOU0njcmU8N5GIAUv2C8Wr+G1829wOltc0oRERHJT1RkewCHw0GlSpU0i6qISC5JTHbx/coDrF3yM/3cE7jZ3ANAqncAzuYv4dXkKfD2tzmlXI7GRRERsZOKbA/gcDgICQmxO4aISIGX4nIz+a/DLFgwh8eSx9HX3AIOcJl+OJr2xXnb8+BXwu6YchUaF0VExE4qsj2Ay+Vi69at1KlTR7OoiohcA7fbYubmaKbNW0yP8z8w1lwDJrgNJ0Q8itniFShW2u6Ykk0aF0VExE4qsj2AZVkkJiZqFlURkRyyLIslUScZM2c5d5/6ge/NpZimhYWBu+4DmHe8ASUq2h1TckjjooiI2ElFtoiIFEprD5xm5OzVNIseyzfmAnycqQCkVu2As+0gzNK1bE4oIiIiBZGKbBERKVR2HItnxJz1VNs3ls/N2RR1JgGQUu42vO56B2e5xjYnFBERkYJMRbYHME2TGjVq6L4zEZErOHQqgS/nbab49h94z/yFks7zAKSE1Mer3dt4VWoNhmFzSskNGhdFRMROKrI9gGEYFC9e3O4YIiL50olzSUQu3Enquh/pb06njPM0AMklquB95yC8at6j4trDaFwUERE7qcj2AKmpqWzYsIGGDRvidOotFREBiEtM4aslu4lZNYlnmUwlZwwAyf5heLd5A+/6/wZTvzM9kcZFERGxk0YeD+FyueyOICKSLyQmuxi7cj+bl/zEM+4J1HEcACDFpyRerV7BO6IPePnaG1LynMZFERGxi4psERHxCCkuN1PWHub3+TN5IuVHnnbsBAekOv0xb38er6b9wKeY3TFFRETEw6nIFhGRAs3ttvhtyzF+mTOXf1/4gW/NDeAAl8Mb45YncTbvD/6l7I4pIiIihYSKbA9gmib16tXTLKoiUqhYlsWSXScZN+t37jnzPd84VuEwLdyGidXgIcxWAyDwJrtjig00LoqIiJ1UZHsIb29vuyOIiNww6w6e5utZK2kePZpR5hK8zLT7b1Nr3ouzzVsQVMXegGI7jYsiImIXFdkewOVysXbtWiIiIjSLqoh4tJ0x8Yyc/Rc1941huDkPP2cyACnhbfC6azDOMvVtTij5gcZFERGxk0YeERHJ9w6fTiBy3kaCto3hPfM3ApwJACSH3YJ3u3fwqtDM5oQiIiIiaVRki4hIvnXy3EVGLtwG67/nP44ZBDvjAbhYqiY+7d7Bu+pdYBg2pxQRERH5PyqyRUQk34lPSuGbJbs5ufJHnjWmUNaMBeBiQAV82g7Ep05XcDhsTikiIiKSmYpsD2CaJhEREZpFVUQKvKQUF2P/2M/OpRPp65pINcdRAJL9QvC+YwA+N/cE08vmlJLfaVwUERE7qcj2EMnJyfj5+dkdQ0TkmqS43Exde4SVC37i8eQfecqxFxyQ4hWIs2V/vG95EryL2B1TChCNiyIiYhcV2R7A5XKxefNmzaIqIgWO220xa8sxZs2dxcPnx/CguQ0ckGr64WjaD6/bngO/4nbHlAJG46KIiNhJI4+IiNxwlmWxbHcsE2fNp8vpMYwy/wITXIYTK6IPzpavQNEQu2OKiIiI5JiKbBERuaHWHzrD6N+W0jL6OyLN5ZimhRsHrroP4HXHG1Cigt0RRURERK6ZimwPocldRCS/i4o5x1dz/qTu3m/41FyEjzMVgOSqd+N95yAcITVsTiieROOiiIjYxbAsy7I7RH4VHx9PYGAgcXFxBAQE2B1HRKRAOnw6gZHz1lNm2zf0Mefgb1wE4GK55vi0ewfKNrI5oYiIiMjVZbc+1JlsD2BZFnFxcQQGBmIYht1xREQAOHnuIl8t2obXum94xfErJZznAUgKaYBvu7fxqdza5oTiqTQuioiInVRkewCXy8XOnTs1i6qI5AvxSSl8t2QXcStH87TxE6HmGQCSilfB967B+NbsBCp8JA9pXBQRETtp5BERkVyRlOLih5X72LfkR55yTSLccTxtu38YvncOxLded3DoPlkRERHxbCqyRUTkuqS63Exbe5g1CybxePJ4nnQcBAdc9CmJd+tX8Y3oA04fu2OKiIiI3BAqsj2AYRj4+fnpvjMRuaHcbos5W2OYP2c6D10YSw9HFDgg2VkU8/YX8Gn6DPgUtTumFEIaF0VExE4qsj2AaZrUr1/f7hgiUkhYlsXy3bFMmzWbLqdH87m5ERyQ6vCBJk/i3bw/FClpd0wpxDQuioiInVRkewC3201sbCxBQUE4HA6744iIB9tw6Aw//LaY1se+4QtzFZjgxsTV8BG8Wr8GAWF2RxTRuCgiIrZSke0B3G43+/bto2TJkvpjQkTyxK7j5/h21goa7PuaT8wlOE03ABdr3IfPnW/hKFXZ3oAif6NxUURE7KQiW0RELuvImQS+mrOWcttH8a45H19nCgBJ4W3T1roOrWtzQhEREZH8RUW2iIhkEnv+Il8v3EyRdV/xiuM3ApyJACSWaYJf+3fwrdDU5oQiIiIi+ZOKbA9gGAaBgYGaRVVErtu5pBRGL40i4Y+vedKYQZAZD0BCyVoU6fAuflXagn7XSD6ncVFEROykItsDmKZJzZo17Y4hIgVYUoqL8Sv3cmjJGJ5wT6GsIxaAxGIV8LtrEEVq3we6t1UKCI2LIiJiJxXZHsDtdhMdHU1YWJgmeBGRHEl1uflp3WE2zv+Rx5LHU8URDQYk+Ybg0/YN/Bo+DKaX3TFFckTjooiI2ElFtgdwu90cOXKE0NBQ/TEhItliWRZztsawePZkHrkwlu6OfeCAi16BOFv+B98mT4KXn90xRa6JxkUREbGTimwRkUJmxe5Yfv7tZ+49PZqh5jZwQIrph9G0Hz63Pw++gXZHFBERESmwVGSLiBQSGw+fZfzMudx57GuGmuvAhFTDC3ejPni3egWKBtsdUURERKTAU5HtARwOB8HBwbokTkSytOfEOcb8toyb949kiGMFDtPCjYPkOt3xbfsGFC9vd0SRXKVxUURE7KQi2wM4HA4qV65sdwwRyWeOnk3k2zl/UnH7/xjsWIS36QIgocrdFGk3GN/g6jYnFMkbGhdFRMROKrI9gNvtZv/+/YSHh+tTexHh1PmLfLtgIwHr/8crjjkUMS8CcKFsC/w7vEORm262OaFI3tK4KCIidlKR7QHcbjcnT56kQoUK+mNCpBA7l5TC90t2kLJyJE8Zv1DcvADAhaD6+P/rPfwrtbQ5ociNoXFRRETspCJbRKSAS0pxMWHVXmJ+/5rH3FMp7TgLwIXAKhRp/zb+Nf5fe/ceHlV57/3/s9aEHMhhgJwTwvl8SoCEgCIIRgIIEqSKtlXEVn2ejVQKlFoFRMXabWul1LhR3G48/BQqCihiBAKCCgKBIEZETlGOCYlIAkMgycz6/eEmTyMBQRLWZPJ+XReXZs2dNZ97JvBd31lr3RkhGYa9IQEAABoImmwAqKcq3R69s/Wgdq78b40vf1MtzWOSIZ1uHK+gIdMV3GOsZDrsjgkAANCg0GT7ANM01bx5cy6JAxoIy7L0Yd5RfbLidf3K9apuMw9KplQWEC7/QdPUOHm85Bdgd0zANtRFAICdaLJ9wLmDCQC+79O9xXr/vbd0y/cva7a5WzKls34hMvtPUtA1/yH5B9sdEbAddREAYCeabB/gdru1e/dudejQQQ4Hl4YCvujzgyf01nvLdePRF/Rnxw7JlCrMAHn63K+AAb+XGjezOyLgNaiLAAA70WT7AMuyVFJSIsuy7I4CoJbtPXZKry1frZT8/9Jsx2eSQ3LLofLEOxV0w0NSWKzdEQGvQ10EANiJJrsGmZmZyszMlNvttjsKgAbq8IkyLVjxsdp+9bxmmOvk5/DII0NlHUcrOH2Ggpq1sTsiAAAAakCTXYMJEyZowoQJKi0tldPptDsOgAbkuKtcL6/MUbPc5zTVWKkAR4Uk6VTLGxUybJaCY7rZnBAAAAAXQ5PtA0zTVJs2bVhFFajHTp2t1Ctrv5A2Zup+LVeoWSZJOhnTR6HDZyukRarNCYH6g7oIALATTbYPME1TUVFRdscA8DOcrXRr4ad7VPzR87rb847CjZOSpJNNuyhk+OMKbZcmGYbNKYH6hboIALATTbYPcLvdysvLU7du3VhFFagn3B5LS3K+0Z6V83VXxULFG99JhnQqpJUapz+q0K4ZEmfhgJ+FuggAsBNNtg+wLEtlZWWsogrUA5Zl6cO8o9qy4n/0S9dr+oV5VDIkV2C0AtIeVkjPX0sO/mkGrgR1EQBgJ47kAOAq2bCnSCuXv6kx3/+3hprfSKZ0plETOQZOUXDqfVKjQLsjAgAA4ArRZANAHdtx6ITeeXep0o++oFmOnZIplZuNZfWboMDrficFhtkdEQAAALWEJtsHOBwOderUifvOAC+zr+iU3nzvA6Xm/5dmObZKDqnS8Fd5r/FqPHiaFBxhd0TAJ1EXAQB2osn2AYZhqEmTJnbHAPC/jpaU6bUV69Rh51w9bG6Q6bDkkanTXW5XSPoj8nM2tzsi4NOoiwAAO9Fk+4DKykrl5uaqZ8+e8vPjLQXsctxVrtdWfqaI3H/q90a2GjnckqTSNiMUNnyWQiLa25wQaBioiwAAO1F5fITb7bY7AtBguc5W6vW1n8uxca7u0woFmeWSpJK46+Qc8YTC4nranBBoeKiLAAC70GQDwM90ttKtf234WiVr/6k7PUvlNE5LkkrCkxQ24gk5Ww+wOSEAAACuNppsALhMbo+lZVvzlf/h87qr4l+KNEokQyoNa6+QYY/J2Wm4ZBh2xwQAAIANaLJ9gMPhUI8ePVhFFahjlmVp5ZdHtP39+brD9bpuMYskQzoVFK/AITMUlnibZPL3ELAbdREAYCeabB/h7+9vdwTAp23cW6y17y7QLScWKN08KJmSyz9C/oP+qJCUuyU//g4C3oS6CACwC022D3C73crJyVFycjKrqAK17ItDJXpv2UINLXxRD5t7JVM64wiV+k9S8LX/V/IPtjsigB+hLgIA7ETlAYAa7Cs6pcXvvqd+32TqYccXkimVm4GqSL5PwYMmS0FN7Y4IAAAAL0STDQD/5mhJmd58f7U67fqn/mhukhxSpfxU1v1OhQ75k/xDo+2OCAAAAC9Gkw0Akr53lev/W/mponPn6EFjnRymJY8MnWw/Ws5hMxXarLXdEQEAAFAP0GT7AIfDoeTkZFZRBX4G19lKvbFmmwI/e1b3aqUCzEpJ0vcJN6rpiMfkjO5qc0IAl4u6CACwE022jygvL1dQUJDdMYB642ylW4s/3SnXR//QLz3vKcQ4I0n6PipVTUbOVtOEPjYnBHAlqIsAALvQZPsAt9utHTt2sIoqcAncHkvvbd2nQx/+U7+sWKxmxinJkE406aqwm55Q03aDJcOwOyaAK0BdBADYicoDoEGwLEvZXx7RF+9n6vbTbyrDOC4ZUklwKwUPnaUm3TJorgEAAHDFaLIB+LzP9hXp02XzNfrEK0ozCyRDOhkQo4C0h+Xs9SvJwT+FAAAAqB0cWfoIFncBzpd36IQ+XPa60gvna4r5jWRKp/2ayBgwVaH97pUaBdodEUAdoS4CAOxiWJZl2R3CW5WWlsrpdKqkpERhYWF2xwFwifKLXVqybLGu/fZ5pZq7JElnzMaqTH1AIdf/TgoItTkhAAAA6ptL7Q85k+0DLMtSSUmJnE6nDO4pRQNWUHJGi5Z/oG5fz9Vkc5tkShVGI51OukfOtD9KweF2RwRwFVAXAQB2osn2AW63W7t27WIVVTRYJ06X680P16l57rOaaGyQaVpyy1Rpp9vVdNh0OZ3xdkcEcBVRFwEAdqLyAKi3TpdXatGaLWr82TP6rbVGjUy3JOm7ViMUPuIxNY1oZ3NCAAAANDQ02QDqnfJKj9759Aud/ejvut2zQkFGuWRI38UOULORTyg8LsnuiAAAAGigaLJ9gGEYCgoK4r4z+Dy3x9L7W3erYOUc3V6+VGHG6R+a62Y91XTkbIW37m93RABegLoIALATTbYPcDgcSkxMtDsGUGcsy9LaLw9p1/tzdevphYo0SiVDOh7aQaHDH1N4p2ESB9MA/hd1EQBgJ5psH+DxeFRcXKyIiAiZpml3HKBWbdp7TJvf/S+NLnlVg41iyZBKApsrMH2GmiXeJvEzD+BHqIsAADvRZPsAj8ej/fv3q1mzZhxMwGd8efiEspe+rKGFL2mieVgypJONIuR3/R/l7DtecjSyOyIAL0VdBADYiSYbgFf5ptil95Yu1HUHMvU7c59kSqcdoXJfM0mh1/2H5N/Y7ogAAADABdFkA/AKhaVntPi9d5X49T800cyTTOmsEaiy3veryQ2TpaAmdkcEAAAAfhJNtg8wDENOp5NVVFEvlZyu0L8+WKWWO/6uCcYWyZQq5aeSrr9W+LBHFBASZXdEAPUMdREAYCeabB/gcDjUuXNnu2MAl+V0eaUWZ29U2Ka/6R5rnRyGJY8Mfdf2FkWOeFThTVvaHRFAPUVdBADYiSbbB3g8Hh05ckRxcXEs8AKvV17p0bJPcuVe/zeNda9UgFEpGdKx+DRF3vyEIqO72B0RQD1HXQQA2Ikm2wd4PB4dOnRIMTExHEzAa3k8llbkfK3vVv5Nv6h4V8HGWcmQiiJSFX7zk4pqkWJ3RAA+groIALATTTaAOmVZltZ9eUB7lz+rMWVvqalxSjKk4rCuco6YrcgOg+2OCAAAANQammwAdSZnf6G2LvmnRpW+ruuN7yVDOt64tYKHPqqI7hkSixIBAADAx9Bk+wDTNBUZGcklcfAaOw+f0MdLXtCQY/+t+81CyZBK/GPU6IaH1Szl15LpsDsiAB9GXQQA2Ikm2weYpqm2bdvaHQPQt8WnlLX0VV13YJ7uN7+VTOmUXxN5+k+Vs/99kl+A3REBNADURQCAnWiyfYDH41F+fr5at27Np/awxbHSM1r27mIl7Z6r+82vJVMqM4N1JmWCmg5+UAoIsTsigAaEuggAsBNNtg/weDwqKipSy5YtOZjAVVVyukLvfPCBWu/4u+41ciVTKjf8VdJ9vCKHPqSgxs3sjgigAaIuAgDsRJMN4LKVlbu1ZPV6Nd38tMZrg2RIbpkq7jBW0SNmKjIszu6IAAAAgC1osgFcsgq3R+9+nCNj/dO6zZ0tP8MjSSpoMULRNz+m6Ih2NicEAAAA7EWT7QNM01Tz5s25JA51xuOxlJWzUyWrntbo8vcVaFRIhlQQPVCRo2YrJq6H3REBoAp1EQBgJ5psH3DuYAKobZZl6eO8b/TN+39VRtk7CjPKJEMqbNJTzW5+UjFtrrU7IgCch7oIALATTbYPcLvd2r17tzp06CCHg98/jNqxdd9R7Vg6RyNL39AAo1QypKLgDgod/riiuwyVDMPuiABQI+oiAMBONNk+wLIslZSUyLIsu6PAB3x1+Lg2vpOpIcULNN4olgzpeEBz+d84U5G9bpW4/BKAl6MuAgDsRJMNQJJ0oNil1e/M14BDL+ge84hkSCV+EdLAP6rZNeMlRyO7IwIAAABejyYbaOCOnTyj95e+qd575uoec79kSqfMMJ3t+6DCB02QGgXZHREAAACoN2iyfYBpmmrTpg2rqOKylJRV6N3ly9Qu71mNN/IkUzpjBKo06X5FpU9RSKDT7ogA8LNQFwEAdqLJ9gGmaSoqKsruGKgnysrdend1tiI3P607tUUypAr5qbjTrxU7YroCQyLtjggAV4S6CACwE022D3C73crLy1O3bt1YRRUXVOH26P31n6nR+v/UrZ51Mg1LbpkqaJWhuFGzFNu0pd0RAaBWUBcBAHaiyfYBlmWprKyMVVRRI4/H0qotX8i16imNqPhQ/oZbMqTDMWmKGf2k4qM72R0RAGoVdREAYCeabMBHWZalT/L26fD7/6mby5apsXFWMqQjzVIVkfGk4luk2B0RAAAA8Dk02YAP2rbvsL5a+jfdVLpI1xkuyZAKQrvKOWK24joOtjseAAAA4LNosn2Aw+FQp06duO8M2nX4O+W8M0c3Fr+qXsYJyZCKAlsrcOgsxSSOkgzD7ogAUOeoiwAAO9Fk+wDDMNSkSRO7Y8BGB787pXVv/5euO/yifm0ckwzpeKNYGYP+pMi+v5ZMDjQBNBzURQCAnWiyfUBlZaVyc3PVs2dP+fnxljYkRaVntHLpAvXe95x+bRyUDKnU0VRnr5miyIH3SX4BdkcEgKuOuggAsBOVx0e43W67I+AqKj1ToRXvLlaHL/+uXxm7JUNyGcE62XuCYoZMkvyD7Y4IALaiLgIA7EKTDdQjZyrcev/DDxST87Ru1+eSIZ1VgIq63q3mNz2k4MbN7I4IAAAANGg02UA9UOn2KGvdJwr85CmN8Wz4YZscOtL2NiWMmqnmYXE2JwQAAAAg0WT7BIfDoR49erCKqg/yeCyt2bxNZ1Y/paEV2fIzPPLI0KH44YofPVstItrYHREAvA51EQBgJ5psH+Hv7293BNQiy7K04YvdKlzxZ91U9r4CjArJkA5EDFDM6CfVIr6H3REBwKtRFwEAdqHJ9gFut1s5OTlKTk5mFVUfsH3fQe1Z+hcNLX1boUaZZEiHwnqp2ajZatH2WrvjAYDXoy4CAOxE5QG8xO5DRcp95xmlffe6koyTkiEdbdxBIcMfV/OuQyXDsDsiAAAAgJ9Akw3Y7GBxqTa8/U/1P/LfGmt8JxlSkX+CHGnTFZt8m2SadkcEAAAAcIlosgGbFJWWae2Sl9R7//MaaxyRDOl7v0hV9J+mqOvukRz89QQAAADqG47ifYDD4VBycjKrqNYTpWXlWvnuG+q8c45uM/IlQzpphqk0+XeKv3Gi1CjQ7ogAUK9RFwEAdqLJ9hHl5eUKCgqyOwYu4kyFW1lZ7yp+61/1C30pGVKZEaSibveqxU1/UGhgmN0RAcBnUBcBAHahyfYBbrdbO3bsYBVVL1Xp9mjV2jUK/vQvyrC2SJLK5acj7X+tlqOmq0VIpM0JAcC3UBcBAHai8gB1xOOx9NGmLXJnz1Z6xXqZhiW3TH2bMEotRj+mVs1a2h0RAAAAQC2jyQZqmWVZ2rRjp75b8aSGnMlSI8MtGVJ+VJriRs9Wm9jOdkcEAAAAUEdosn0Ei7t4hx17vlH+sj9ryMklCjLKJUP6tklfRWTMVutWKXbHA4AGg7oIALCLYVmWZXcIb1VaWiqn06mSkhKFhbEoFS5sz6FCffH2f+qG42/KaZyWJB0K7qbQm56Qs8tgm9MBAAAAuFKX2h9yJtsHWJalkpISOZ1OGYZhd5wG5VDxCW1e/KyuO/o/usUokQzpaEBr+Q+Zpea9Rkm8HwBw1VEXAQB2osn2AW63W7t27WIV1auouPS0Pn7neSXnv6BbjGOSIRX5xco98CHFXnunZHKZIgDYhboIALATlQe4DCfLyrVm2Svq/NU/NNo4KBnS92YznUr9vRJu+D+Sn7/dEQEAAADYiCYbuARnKtzKXrFYCbl/1SjtkQzplBGs4sT/UKvhv1dT/2C7IwIAAADwAjTZPsAwDAUFBXHfWR2odHu0Zs2Hcm54SjdZn0uSzshfhzuOU5tRjyikcVObEwIAfoy6CACwE022D3A4HEpMTLQ7hk+xLEvrN2yQ1s7WkMoNkqQK+emblreq9eiZatskzuaEAIALoS4CAOxEk+0DPB6PiouLFRERIdM07Y5T723ZvkMnPnhcg8+slsOw5JGhfTHD1eKWJ9Q+qq3d8QAAP4G6CACwE022D/B4PNq/f7+aNWvGwcQV+GL3Ph1c9oRuOPWeAoxKyZD2NRugmIzZat+CMyIAUF9QFwEAdqLJRoO379ARffX2U7r++L/U3TgjGVJ+SC81vfkJte3Q3+54AAAAAOoRmmw0WIeLj2vbW3/VtQWvaoRxSjKkg4EdFTh0llonDpNYMAcAAADAZaLJ9gGGYcjpdLKK6iX6rtSlDYvnKuXbFzXSOC4ZUkGjBFmDHlFCv9tprgGgnqMuAgDsRJPtAxwOhzp37mx3DK93suys1i+Zr65fP6eRxlHJkIodkXL1+4NaDvqN5OCvAwD4AuoiAMBOdBU+wOPx6MiRI4qLi2OBlxqcKa/UuhVvqsX2Z3ST8iVDKjHCVNxrotoMnaiIRkF2RwQA1CLqIgDATjTZPsDj8ejQoUOKiYnhYOLfVLo9Wrf6PYV/9helWzslSS4F6XCX36r9zdPkDAyzOSEAoC5QFwEAdvL5JvvgwYO68847dezYMfn5+WnGjBm69dZb7Y6FOmRZlj79dJ0ca2frBvcWSdJZNVJ+61+q3S0z1CE00uaEAAAAAHyVzzfZfn5+mjNnjpKSklRQUKDevXtr+PDhCg4Otjsa6sDW3K06lfW4rjuzTqZhqVKm9saOUqtfPKZO4S3tjgcAAADAx/l8kx0bG6vY2FhJUkxMjCIiInT8+HGfarJN01RkZGSDviTuy11f6+h7j2vgqQ/UyHBLhvR1eJrix8xWpzgWvwGAhoS6CACwk+3VZ/369Ro5cqTi4uJkGIaWLl163pjMzEy1atVKgYGBSk1N1ebNm3/Wc23dulVut1sJCQlXmNq7mKaptm3bNsiDif0HDurDOferzZv9leZarkaGW3vC+ur7X69Wx4lvK4QGGwAanIZcFwEA9rP9TLbL5VJiYqLuuece3XLLLec9vmjRIk2ePFnz5s1Tamqq5syZo/T0dH399deKioqSJCUlJamysvK87125cqXi4uIkScePH9ddd92l+fPn1+2EbODxeJSfn6/WrVs3mAOKI8eKtWPxU7qm8A21MU5LhpQf1E3Bwx9X++432B0PAGCjhlgXAQDew7Asy7I7xDmGYWjJkiXKyMio2paamqqUlBQ999xzkn4onAkJCZo4caIeeuihS9rv2bNndeONN+ree+/VnXfeedFxZ8+erfq6tLRUCQkJ+u677xQW9sNK1KZpyjRNeTweeTyeqrHntrvdbv37S3qh7Q6HQ4ZhnPfhgMPhkCS53e5L2u7n56eKigrl5OSoV69eVft1OBznZbzQdm+ck2VZ1bafy178fYm2vP13pRz8H0UYJZKkg/5tpEHTFZt8s2QY9W5Ovvg+MSfmxJyYk51zOnv2rLZt21ZVF31hTr74PjEn5sScmFN9m1NpaanCw8NVUlJS1R/WxPYz2RdTXl6urVu36k9/+lPVNtM0lZaWpo0bN17SPizL0t13363BgwdftMGWpKeeekqPPfbYedtzc3Or7uGOjIxU27ZtlZ+fr6KioqoxzZs3V/PmzbV7926VlJRUbW/Tpo2ioqKUl5ensrKyqu2dOnVSkyZNlJubW+0N7tGjh/z9/ZWTk1MtQ3JyssrLy7Vjx46qbQ6HQykpKSotLdWJEye0bds2GYahoKAgJSYmqri4WPv3768a73Q61blzZx05ckSHDh2q2u6NcyopKdGuXbuqtlsOP53c/bG67n5Ow4xiyZCOGtE62Ple9fnFFB06ckQ5W7fWqzn54vvEnJgTc2JO3jCnnTt3VquLvjAnX3yfmBNzYk7Mqb7NyeVy6VJ49ZnsI0eOKD4+Xhs2bFC/fv2qxk2bNk3r1q3Tpk2bfnKfn3zyiQYMGKAePXpUbXvttdfUvXv388ZyJtt75nTu06WzFW5tWPGq2nwxR231w1/W74xmKur1O7W98X6ZjQLq3ZzO8aX3iTkxJ+bEnLxpTpzJZk7MiTkxJ+bEmew61L9//2ov1MUEBAQoICDgvO1+fn7y86v+Up1783/s3Jt5qdt/vN+fs93hcCghIUGNGjWqlulCGS93ux1z8ljSJ6uXKnLTX3SjtUeSVKoQHe52vzqOnKLwgOqrw9eHORmGUeP2+vw+MSfmJDGnC2W83O3Mqfbm1KhRoxrrYn2eky++T8yJOTEn5iTVrzld6DnOe85LGmWTiIgIORwOFRYWVtteWFiomJgYm1J5H9M01bx5c7tj1ArLsrTx41UKWDdb17s/lySVKUD72t6ljrc8os7BTW1OCADwdr5UFwEA9Y9XL7np7++v3r17Kzs7u2qbx+NRdnZ2tcvHGzq3262vvvrqvEsh6pvcnI367KnhumbNrert/lwVciiv+VgZD25Xtzv/pkY02ACAS+ArdREAUD/Zfib71KlT2rt3b9XX+fn52r59u5o1a6YWLVpo8uTJGjdunJKTk9WnTx/NmTNHLpdL48ePtzG1d7EsSyUlJdXuWahPvvoqT0XvPaZrXavkMCx5LENfRQ1TizFPqFtMO7vjAQDqmfpeFwEA9ZvtTXZOTo4GDRpU9fXkyZMlSePGjdOCBQs0duxYFRUVaebMmSooKFBSUpKysrIUHR1tV2TUkm++/Ub5Sx7TNd+/q85GpWRIXzkHKDpjtrq2TrQ7HgAAAABcNtub7Ouvv/4nP2l+4IEH9MADD1ylRKhrBccK9eVbT6rvsYVqZZyVDGl3415yjnhCnbv0tzseAAAAAPxstjfZuHKmaapNmzY1roTnTY6fKFHu4v9Ur4Ov6AbjlGRI+f4d5RjyqDok32R3PACAj6gvdREA4Jtosn2AaZqKioqyO8YFuU6XadM7/1DXPfN0g/G9ZEiH/Fro7HUPq+2A2yXDsDsiAMCHeHtdBAD4NppsH+B2u5WXl6du3bpd8PfJ2eFsRYU2LpuvNnn/0GAVSIZ0zIzU8ZQp6jjkXhkOfvwAALXPW+siAKBhoMvxAZZlqayszGtWUXW7PfrswzcVteVpXW99I0n6Xk4d7jFBXUY8qCj/QHsDAgB8mrfVRQBAw0KTjVpjWZY2r3tfjT9+Ute6d0qSTqmx9rcfr86j/6hujZ02JwQAAACAukWTjVrx+ZaPVbHqMaWWb5EknVEj7Uq4Q51+MVM9nJE2pwMAAACAq4Mm2wc4HA516tTJlvvOvv5yu46//6j6nf5IklRpmcqLGaU2Yx5XUlSLq54HAAA76yIAADTZNcjMzFRmZqbcbrfdUS6JYRhq0qTJVX3Ob/P36ODSWep7YoU6Gh5J0o4maYq/5QkltehyVbMAAPDv7KiLAACcY1isCnJBpaWlcjqdKikpUVhYmN1xLqiyslK5ubnq2bOn/Pzq9nOTgoLD2r34cfUpeluBRoUkaWdwXzUd+YRiO/Wp0+cGAOBSXM26CABoOC61P6Ty+Ii6Puv+/ffHtWPxU+p16DUNMMokQ9od0E0BQ2epS88b6/S5AQC4XPXlajQAgO+hycZFuVwubX37GXXdN18DjVLJkL7xa6OK66erw7W3SIZhd0QAAAAA8Bqm3QHqo8zMTLVq1UqBgYFKTU3V5s2bLzr+rbfeUqdOnRQYGKju3btrxYoV1R63LEszZ85UbGysgoKClJaWpj179lQbs3v3bo0aNUoREREKCwtT//79tXbt2lqf2zlny8/qk389q9K/9tCA/c8o3CjVYTNWX17zrFo+nKP2/cfQYAMAAADAj9BkX6ZFixZp8uTJevTRR7Vt2zYlJiYqPT1dx44dq3H8hg0bdMcdd+g3v/mNcnNzlZGRoYyMDOXl5VWNefrppzV37lzNmzdPmzZtUnBwsNLT03XmzJmqMSNGjFBlZaXWrFmjrVu3KjExUSNGjFBBQYEcDod69OhRK6uout0efbb8ZR19qqf675ylWBWryGim7UmzFPunHeo65B4ZJqu1AgC8V23WRQAALhcLn11ETTe2p6amKiUlRc8995wkyePxKCEhQRMnTtRDDz103j7Gjh0rl8ul5cuXV23r27evkpKSNG/ePFmWpbi4OE2ZMkVTp06VJJWUlCg6OloLFizQ7bffruLiYkVGRmr9+vW67rrrJEknT55UWFiYVq1apRtuuEFut1sOh0PGzzy7bFmWtn20RCGfPKmO7r0/5FCI9na8Tz1GT1WjwOCftV8AAK42y7KuuC4CAPBjl7rwGWeyL0N5ebm2bt2qtLS0qm2maSotLU0bN26s8Xs2btxYbbwkpaenV43Pz89XQUFBtTFOp1OpqalVY8LDw9WxY0e9+uqrcrlcqqys1AsvvKCoqCj17t1bbrdbOTk5P3uRl7xN2frizwPVe914dXTv1WkFKKflvfKfnKfedzxKgw0AqFeutC4CAHAlWPjsMhQXF8vtdis6Orra9ujoaO3atavG7ykoKKhxfEFBQdXj57ZdaIxhGFq9erUyMjIUGhoq0zQVFRWlrKwsNW3aVJWVlT9rPnu+2KzSFbPUu+xTSVK55acdsbeow5jHlBwZ97P2CQAAAAANGU12PWBZliZMmKCoqCh9/PHHCgoK0ksvvaSRI0dqy5YtioyMvKz9Hdy/S0eWzlBKySqZhiW3Zejz8GFqccvjSm7evo5mAQAAAAC+jyb7MkRERMjhcKiwsLDa9sLCQsXExNT4PTExMRcdf+6/hYWFio2NrTYmKSlJkrRmzRotX75c33//fdW1/88//7xWrVqlV155pepe7p9y7MgB7X17lpKLlyrBcEuG9HnIAEWOely92ve8pH0AAAAAAC6Me7Ivg7+/v3r37q3s7OyqbR6PR9nZ2erXr1+N39OvX79q4yVp1apVVeNbt26tmJiYamNKS0u1adOmqjGnT5+W9MP93//ONE15PB45HA4lJydfcBXVkuNF+vTF3ynkhWRd893b8jfc+jKwl/ZnvKfEqe8pjgYbAOBDfqouAgBQlziTfZkmT56scePGKTk5WX369NGcOXPkcrk0fvx4SdJdd92l+Ph4PfXUU5KkBx98UAMHDtQzzzyjm266SQsXLlROTo5efPFFST/cbz1p0iTNnj1b7du3V+vWrTVjxgzFxcUpIyND0g+NetOmTTVu3DjNnDlTQUFBmv/Pvys/f7+GprTVF1vWqbDktKKdjdU13imHYUiNw3W6kVPbFz+trvkv61q5JEPa06ij3INnqmu/Eba8fgAAXA3l5eUKCgqyOwYAoAGiyb5MY8eOVVFRkWbOnKmCggIlJSUpKyurauGyAwcOVDvjfM011+iNN97Q9OnT9fDDD6t9+/ZaunSpunXrVjVm2rRpcrlcuu+++3TixAn1799fWVlZCgwMlPTDZepZWVl65JFHNHjwYFVUlKtrkzNadmsj9fr0vhpzegyHyqwQXaMSSdK3ZoJKr/mTug2+Q4bJBQwAAN/ldru1Y8cOJScny8+PQx0AwNXF78m+iEv9PWhX3ZHt0osDL2noUSNSR3tOVtLw+2RyoAEAaAAqKyuVk5NDkw0AqFWX2h9Seeoht2XpUu4y+6LV3ep4+18UG8jlcgAAAABwNXDdcD305eHSSxpndblF/jTYAIAGiEXPAAB2ocmuQWZmprp06aKUlBS7o9To+OnyWh0HAIAv8fPzU0pKCpeKAwBsQZNdgwkTJmjnzp3asmWL3VFq1Kyx/wUfy9xcrlZzTipwdqkmPvCgNm/efNF9vfXWW+rUqZMCAwPVvXt3rVixotrjlmVp5syZio2NVVBQkNLS0rRnz55qY3bv3q1Ro0YpIiJCYWFh6t+/v9auXfvzJwgAwBWwLEsnTpwQy84AAOxAk10PdY2v+Sb7RXkVmrzyjB4dGKBt9werb2Inpaen69ixYzWO37Bhg+644w795je/UW5urjIyMpSRkaG8vLyqMU8//bTmzp2refPmadOmTQoODlZ6errOnDlTNWbEiBGqrKzUmjVrtHXrViUmJmrEiBEqKCio3YkDAHAJ3G63du3aJbfbbXcUAEADxOriF1HfVhdPfemUUuIcem74D/dhe367VgmpIzVx4kQ99NBD540fO3asXC6Xli9fXrWtb9++SkpK0rx582RZluLi4jRlyhRNnTpVklRSUqLo6GgtWLBAt99+u4qLixUZGan169fruuuukySdPHlSYWFhWrVqldLS0urgBQAA4MJYXRwAUBcutT/kTHZ91Dhc8guotqncbWnrEY/S2vzvwYRfgMyQSKWlpWnjxo017mbjxo3nNcHp6elV4/Pz81VQUFBtjNPpVGpqatWY8PBwdezYUa+++qpcLpcqKyv1wgsvKCoqSr17966tGQMAAABAvcDHu/VRkwTpga3S6e/ktix9ceiENmzdIbc1SRG/fF5KTvqhEW+SoOjoaO3atavG3RQUFCg6Orratujo6KrLvM/992JjDMPQ6tWrlZGRodDQUJmmqaioKGVlZalp06a1PHEAAH6aYRgKCgqSYRh2RwEANECcya6vmiRIcUlyxPdUUuogZWTcKklyRHaU4pJ+ePwqsCxLEyZMUFRUlD7++GNt3rxZGRkZGjlypI4ePXpVMgAA8O8cDocSExP5NV4AAFvQZPsAj8cjj8cjh8OhwsLCao8VFhYqJiamxu+LiYm56Phz/73YmDVr1mj58uVauHChrr32WvXq1UvPP/+8goKC9Morr9TK/AAAuBwej0fHjh2Tx+OxOwoAoAGiyfYBHo9Hhw4dUq9evZSdnV1te3Z2tvr161fj9/Xr16/aeElatWpV1fjWrVsrJiam2pjS0lJt2rSpaszp06clSaZZ/UfJNE0ObgAAtvB4PNq/fz91CABgC+7J9iGTJk3SPffco+TkZPXp00dz5syRy+XS+PHjJUl33XWX4uPj9dRTT0mSHnzwQQ0cOFDPPPOMbrrpJi1cuFA5OTl68cUXJf1wT9ukSZM0e/ZstW/fXq1bt9aMGTMUFxenjIwMST806k2bNtW4ceM0c+ZMBQUFaf78+crPz9dNN91ky+sAAAAAAHahyfYht912m44fP66ZM2eqoKBASUlJysrKqlq47MCBA9XOOF9zzTV64403NH36dD388MNq3769li5dqm7dulWNmTZtmlwul+677z6dOHFC/fv3V1ZWlgIDAyVJERERysrK0iOPPKLBgweroqJCXbt21bJly5SYmHh1XwAAAAAAsBm/J/sivPb3ZP+I2+3W7t271aFDBxZ5AQA0eNRFAEBduNT+kDPZPsDhcKhz5852xwAAwCtQFwEAdmLhMx9wbuEzFngBAIC6CACwF022D+BgAgCA/4e6CACwE002AAAAAAC1hCYbAAAAAIBaQpPtA0zTVGRkZLVfzwUAQENFXQQA2InVxX2AaZpq27at3TEAAPAK1EUAgJ34iNcHeDwe7du3jwVeAAAQdREAYC+a7BpkZmaqS5cuSklJsTvKJfF4PCoqKuJgAgAAURcBAPaiya7BhAkTtHPnTm3ZssXuKAAAAACAeoR7si/CsixJUmlpqc1JLq6yslIul0ulpaXy8+MtBQA0bNRFAEBdONcXnusTL4TKcxEnT56UJCUkJNicBAAAAADgDU6ePCmn03nBxw3rp9rwBszj8ejIkSMKDQ2VYRi1tt+UlJRavRS9tLRUCQkJOnjwoMLCwmptv/Adtf0z19A0hNevvs3R2/LamedqPXddPg91EXbwtn9H6puG8PrVtzl6W1678tTl81qWpZMnTyouLu6ivyaSM9kXYZqmmjdvXuv7dTgcdVL0w8LCOJhAjerqZ66haAivX32bo7fltTPP1Xruunwe6iLs4G3/jtQ3DeH1q29z9La8duWp6+e92Bnsc1j4zAYTJkywOwIaGH7mrkxDeP3q2xy9La+dea7Wc9fl83jb+4mGgZ+7K9MQXr/6Nkdvy2tXHm94Hbhc3AeUlpbK6XSqpKTEqz69AgDADtRFAICdOJPtAwICAvToo48qICDA7igAANiOuggAsBNnsgEAAAAAqCWcyQYAAAAAoJbQZAMAAAAAUEtosgEAAAAAqCU02QAAAAAA1BKabB+3fPlydezYUe3bt9dLL71kdxwAAGw3evRoNW3aVL/4xS/sjgIA8EGsLu7DKisr1aVLF61du1ZOp1O9e/fWhg0bFB4ebnc0AABs89FHH+nkyZN65ZVXtHjxYrvjAAB8DGeyfdjmzZvVtWtXxcfHKyQkRMOGDdPKlSvtjgUAgK2uv/56hYaG2h0DAOCjaLK92Pr16zVy5EjFxcXJMAwtXbr0vDGZmZlq1aqVAgMDlZqaqs2bN1c9duTIEcXHx1d9HR8fr8OHD1+N6AAA1IkrrY0AANQ1mmwv5nK5lJiYqMzMzBofX7RokSZPnqxHH31U27ZtU2JiotLT03Xs2LGrnBQAgKuD2ggA8HY02V5s2LBhmj17tkaPHl3j43//+9917733avz48erSpYvmzZunxo0b6+WXX5YkxcXFVTtzffjwYcXFxV2V7AAA1IUrrY0AANQ1mux6qry8XFu3blVaWlrVNtM0lZaWpo0bN0qS+vTpo7y8PB0+fFinTp3SBx98oPT0dLsiAwBQpy6lNgIAUNf87A6An6e4uFhut1vR0dHVtkdHR2vXrl2SJD8/Pz3zzDMaNGiQPB6Ppk2bxsriAACfdSm1UZLS0tL0+eefy+VyqXnz5nrrrbfUr1+/qx0XAOCjaLJ93M0336ybb77Z7hgAAHiN1atX2x0BAODDuFy8noqIiJDD4VBhYWG17YWFhYqJibEpFQAA9qE2AgC8AU12PeXv76/evXsrOzu7apvH41F2djaXvAEAGiRqIwDAG3C5uBc7deqU9u7dW/V1fn6+tm/frmbNmqlFixaaPHmyxo0bp+TkZPXp00dz5syRy+XS+PHjbUwNAEDdoTYCALydYVmWZXcI1Oyjjz7SoEGDzts+btw4LViwQJL03HPP6a9//asKCgqUlJSkuXPnKjU19SonBQDg6qA2AgC8HU02AAAAAAC1hHuyAQAAAACoJTTZAAAAAADUEppsAAAAAABqCU02AAAAAAC1hCYbAAAAAIBaQpMNAAAAAEAtockGAAAAAKCW0GQDAAAAAFBLaLIBAAAAAKglNNkAAOA8CxYsUJMmTWp9v7NmzVJSUlKt7xcAAG9Bkw0AgJe6++67ZRhG1Z/w8HANHTpUO3bsuKz9XM3GdsmSJerbt6+cTqdCQ0PVtWtXTZo0qerxqVOnKjs7+6pkAQDADjTZAAB4saFDh+ro0aM6evSosrOz5efnpxEjRtgdq0bZ2dkaO3asxowZo82bN2vr1q168sknVVFRUTUmJCRE4eHhNqYEAKBu0WQDAODFAgICFBMTo5iYGCUlJemhhx7SwYMHVVRUVDXmj3/8ozp06KDGjRurTZs2mjFjRlVju2DBAj322GP6/PPPq86IL1iwQJJ04sQJ3X///YqOjlZgYKC6deum5cuXV3v+Dz/8UJ07d1ZISEhVw38h7733nq699lr94Q9/UMeOHdWhQwdlZGQoMzOzasyPz6r/+5n6c39atWpV9XheXp6GDRumkJAQRUdH684771RxcfEVvKIAANQtmmwAAOqJU6dO6fXXX1e7du2qnQ0ODQ3VggULtHPnTv3jH//Q/Pnz9eyzz0qSxo4dqylTpqhr165VZ8THjh0rj8ejYcOG6dNPP9Xrr7+unTt36i9/+YscDkfVfk+fPq2//e1veu2117R+/XodOHBAU6dOvWC+mJgYffnll8rLy7vkOZ3LdPToUe3du1ft2rXTgAEDJP3wIcDgwYPVs2dP5eTkKCsrS4WFhbrtttsu96UDAOCq8bM7AAAAuLDly5crJCREkuRyuRQbG6vly5fLNP/f5+TTp0+v+v9WrVpp6tSpWrhwoaZNm6agoCCFhITIz89PMTExVeNWrlypzZs366uvvlKHDh0kSW3atKn23BUVFZo3b57atm0rSXrggQf0+OOPXzDrxIkT9fHHH6t79+5q2bKl+vbtqyFDhuhXv/qVAgICavyec5ksy9KYMWPkdDr1wgsvSJKee+459ezZU3/+85+rxr/88stKSEjQ7t27q3IDAOBNOJMNAIAXGzRokLZv367t27dr8+bNSk9P17Bhw/Ttt99WjVm0aJGuvfZaxcTEKCQkRNOnT9eBAwcuut/t27erefPmF21UGzduXNVgS1JsbKyOHTt2wfHBwcF6//33tXfvXk2fPl0hISGaMmWK+vTpo9OnT180z8MPP6yNGzdq2bJlCgoKkiR9/vnnWrt2rUJCQqr+dOrUSZK0b9++i+4PAAC70GQDAODFgoOD1a5dO7Vr104pKSl66aWX5HK5NH/+fEnSxo0b9atf/UrDhw/X8uXLlZubq0ceeUTl5eUX3e+5RvZiGjVqVO1rwzBkWdZPfl/btm3129/+Vi+99JK2bdumnTt3atGiRRcc//rrr+vZZ5/VkiVLFB8fX7X91KlTGjlyZNWHDOf+7Nmzp+qScgAAvA2XiwMAUI8YhiHTNFVWViZJ2rBhg1q2bKlHHnmkasy/n+WWJH9/f7nd7mrbevTooUOHDtX5ZdetWrVS48aN5XK5anx848aN+u1vf6sXXnhBffv2rfZYr1699Pbbb6tVq1by8+OQBQBQP3AmGwAAL3b27FkVFBSooKBAX331lSZOnFh1hleS2rdvrwMHDmjhwoXat2+f5s6dqyVLllTbR6tWrZSfn6/t27eruLhYZ8+e1cCBAzVgwACNGTNGq1atUn5+vj744ANlZWX97KyzZs3StGnT9NFHHyk/P1+5ubm65557VFFRoRtvvPG88QUFBRo9erRuv/12paenV83z3MrpEyZM0PHjx3XHHXdoy5Yt2rdvnz788EONHz/+vA8NAADwFjTZAAB4saysLMXGxio2NlapqanasmWL3nrrLV1//fWSpJtvvlm///3v9cADDygpKUkbNmzQjBkzqu1jzJgxGjp0qAYNGqTIyEi9+eabkqS3335bKSkpuuOOO9SlSxdNmzbtiprXgQM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OHSom5nbt2oWZM2dqbD9oorhIhtzSpUvF35qiyhYtEAQB4eHhCA8Px5UrV5CamorCwkK4ubmhZcuW6Nevn3jsU0ddG/nixYvYvn07Ll26hJSUFMhkMrz55puYPXs2ANWLdN29excbN27EyZMn8ejRI0gkEtSrVw+9evXChAkT4ObmpvG9MUS7VPE4L7d9+3aVi/qUX/W8sgUjnjx5ghdffBHFxcWwsLDA0aNHtf5NDhgwAPfv3wcALF68GAMHDlS77ZkzZ7B3717xvc/NzRVX5+7duzdGjhwptp0r8/DhQwQHB+PMmTO4d++eeEHI0dERnp6eaNasGdq3b49+/fqZ3flJZYqKirBr1y6EhYUhNjYWaWlpcHNzg5+fH4YPH46+fftWur++33l19bw6hYWF2Lx5M/bu3Yu7d+8iNzcXtWrVQuvWrTFq1Ch06dJFr9dvjLauIeqSypg0Mffw4UMMGzYMGRkZKh9PTk5GcnIyTpw4gT/++AOLFi3S+QREW6WlpViyZAn++ecfFBQUKD0mXyn05s2bWLt2Ld55551KG6zl3blzB9OmTUNsbKzS/Y8ePcLu3buxe/duTJkyBdOmTdNY1qNHj/D555/j1KlTFR6TnyjExMRgx44daN26NbZs2QKgbHhHYGAgVq5cCQAIDg7WKjF3//59MSlnY2Oj91xMiidUCQkJepWhjbt372Lq1KkV3uukpCQkJSVh165dGD16NObMmaOxB0FhYSG+++47bNu2DSUlJUqPpaenIz09HZGRkVi5ciVmzJiBoKAgjfGdO3cOX3zxBR48eFDhscTERCQmJmL37t1o06YNfvvtN40HsosXL+Kjjz4S5/mRu3//Pu7fv4/g4GBMmTIFU6dO1RhbddmyZQvmzp1bYUEP+Xd348aNWLBggdn0kl27di0WLlxY4TsAlCVFs7Ozce/ePYSEhKBLly5YvHixVg1ifX/LhvDkyRN8/PHHSie+cvLfSnh4OJYvX46ffvoJrVq10rrsS5cu4eOPP8bDhw8NFi9Q9p3+6KOPcOPGjQqP5ebmIj4+Hrt27ULLli3x66+/6nUSb26uXr2KTz75pEJ9UVBQgJycHPE33r17d/z0008aG/PZ2dn44IMPKnzn5Mfa06dPY8uWLfjtt98M9hpsbGwwePBgrF+/HkBZTyxtEnMXL15UOlYYI/mjieLJyZAhQyCRSFCzZk10794dx44dE7cxp8QcUPZe/fLLL0hLSwMA7N69W6vEnI2NDV599VVs2LABSUlJOHfunNoGsbw3o6WlJYYMGaLyd1kVGzduFP92dnbGyJEjDVo+UFZvzJ49G3v27KnwmPw3cfLkSfz555+YN28eevXqpXXZ8fHxmDp1Km7dulWh3NDQUOzduxe///67eNIcGRmJqVOnKl3ElJezYcMGhIWF4Z9//kGbNm20jsEQx9qrV6/irbfeEhPUiuRzRyYmJiI8PBx//PEHli5dqtPvITs7G7NmzRIXeKuModtkxcXF+OqrrxAcHKx0f0pKClJSUnDp0iVs2LABS5Ys0fr1GFtxcbE4bL38eyCP+/z58/jrr7/w+eefG2X4f15eXoWexADw0ksvwdXVFZmZmUhPT8exY8c0nvhXh1u3buGzzz5TWnhO7tGjR3j06BEOHTqE5cuXY+nSpRUS5urIv4+bN2/WKZ6NGzdi/vz5FX6X0dHRiI6OxpYtW/D3339X2u4yVrvUUGrWrIlu3brh+PHj4iJB2sw7GBkZKSblnJ2d0adPH5XbPXz4EJ9++mmFpC3wv9/ByZMnsXz5cvzyyy/o0KGD2ufcvHkz5s+fr3Iu0oyMDGRkZCA6Ohq7du3Czp07lY5N5uzx48eYNm2a0nQgwP+ObYcOHULv3r2xaNEijRee5PT9zlcmNjYWU6ZMEed9louPjxfb9WPGjMEXX3yhdZnGausaqy5RZNLEXG5urpiUc3V1RdOmTcXJeps0aYKaNWvi6tWrKCgoQEZGBt59912sW7cO7dq1UyrH09NTnKj433//Fe9XN3mxk5OT0u2SkhJ89NFHSgcaT09P+Pv7w83NDTk5OYiMjBR7I/35559IS0vD3LlzNb7Gx48f46233kJKSgpcXFzQvn17eHh4ID09HWfPnkV2djYAYNmyZWjatGmlq7zdvn0bEyZMQEpKinhfzZo10bZtW7i5uaGgoAAPHjzAzZs3kZ+fXyHBOHr0aDExt2fPHnz++ecaf4yKDZb+/fvrvXpggwYNxL+fPHmC4OBgjBgxQq+y1MnOzsakSZOQkJAAGxsbdOrUCXXq1EFGRgbOnTsnXgHZsmULCgoK8MMPP6gtKzc3F2+//TYiIiKUXkPLli3h4uKCzMxMREREiFfX586dC5lMhvfee09tmXv37sWMGTPEYXR2dnZo3bo1vLy8YGFhgfv37+PKlSsoLi7GlStX8Nprr2Hbtm1q59KJiorCpEmTlBrMfn5+8Pb2RlFREa5evYoHDx5gyZIlZjPPzqFDhzB//nwAZb+x9u3bw8HBAffv30dERARKS0uRmZmJDz74AH/88YdePdAM7fHjx2Ljp379+mjSpAnc3NxgY2OD7OxsxMTE4Pbt2wDKrrxPmDABW7ZsqbTnQFV+y1Wt71JTUzF27FilZE+DBg3g7+8PGxsbxMbG4urVqwDKkmFvvvkm/v77b6169cbFxWH+/PnIzs6Go6MjOnbsiFq1aiEzMxMXL17UuL86sbGxCAoKEhMNAODt7Y3mzZtDIpHgxo0biImJAQBcv34dY8aMwfr16yv0bpa/R/KeKUDZFdHySR9zWCX1woULmDRpktgrSSKRwN/fH02aNFH6fQPAqVOnMHbsWGzcuFFtcq6wsBDvvPMOrly5It5Xq1YtdOjQAQ4ODnjw4AEiIiIQERFh8EUrhg0bJibmoqOjcfPmTTRv3rzSfRSHsbZp00ZjT3VDkyem5BQvSg0bNkxMzO3atQuffvpplXuGGJKNjQ169+4tHr8jIiIgCIJWFxOHDRuGDRs2AChLoqpKzCUmJooX7Lp37672GFUVZ8+eFf9++eWXDdprCShLLowfPx6RkZHifeV/D5cuXUJJSQlSUlIwefJk/Pzzz3jllVc0li2TyfDOO+/g/v37cHJyQseOHeHh4YGUlBScPXsWeXl5KCwsxNSpU7Fz504UFRVhwoQJkMlkqFGjBjp27AipVCp+B4uKiiCTyTBlyhTs27cPzs7OGmMw1LE2MzNTbGPUrFkTTZs2Re3atWFvb4/8/HzExcXh2rVrKC4uRmJiIoKCgrB9+3Y0bNhQY4yCIGDGjBk4cuQIJBIJ/Pz80LRpUwiCgNu3byt9X43RJps5cyZ27dol3nZxcUHnzp0hlUrx8OFDnDt3Dnfu3MG7776rNkGgK/kx6ODBg+IF1b59+6q8CNukSROl26WlpXj//feVenNLpVJ06tQJrq6uYsxFRUXIysrCZ599hqysLIwfP94gscsdOHBAXAm6Vq1a4vzeNjY2GDhwIDZt2gSg7KJFVRNzffv2RbNmzRAZGYlr164BAFq1agV/f/8K26paefnChQt47733IJPJAJQt9uTn54dGjRrBysoKiYmJuHTpEgoKCnDv3j2MGTMGmzdvrvDeq7JgwQIxQeHt7Q1fX19YWVnh/v37ant3h4SEYM6cOQCAxo0bw8/PD3Z2drh7965YT2dkZOC///0v9u7dq/a3bqh2adeuXeHg4IC7d++KF2pfeOEFlXO2a/ObVhQQEIDjx48DgNaJOcVe8gMGDICtrW2FbWJjYzF+/Hix/SyRSNCiRQs0bdoUdnZ2SE5OxoULF5CTk4PHjx9jwoQJ+Ouvv1Qey8LDw/HVV1+Jt52cnNCmTRvUrl0blpaWkMlkuH//PmJiYpSmQDF3RUVFmDp1Kq5evQpLS0u0b98eDRo0QE5ODi5cuIDU1FQAwJEjR/Dee+9h5cqVWvXw0uc7X5nExESlzxIAmjVrhhYtWii17Tdt2qR1G8BYbV1j1iVKBD0EBQUJ3t7egre3t/Dbb7/pU4QgCIKQkJAgzJ07V7h69apQUlIiCIIg+Pj4CL6+vsLcuXMFQRCE7OxsYeHCheLz9e/fX9xWFfl23t7eWsexePFicZ/u3bsL+/fvF0pLSytst2fPHqF9+/bitrt371ZZ3syZM8Vt/Pz8BG9vb+HHH38UcnNzlbZLT08X3nzzTXHbl19+WeXzyt+H/v37i9t27txZ2Llzp8rtc3JyhLCwMOGzzz6r8JjiZ7dt27ZK35fi4mKhe/fu4vanT5+udPvKZGdnC23bthXLatGihTB37lzh5s2bepcpCILw22+/iWW2bNlS8Pb2FiZMmCA8fvxYabu8vDxhzpw5St+PnTt3qi33008/VfrOnT17tsI2xcXFwr///it+xs2bNxciIiJUlhcTEyP4+/sL3t7ego+Pj7Bw4UIhMzOzwnYPHjwQxo4dKz73O++8o7K8goICYeDAgeJ2vXr1Uvnc27dvF/z8/MT3prLfRnx8vPh479691b43inr37i3uEx8fr3Gbli1bCr6+vsLKlSsr/I5v374tDBo0SOm3mJGRoVUclalqfbV161Zh3bp1wqNHj9Ruc/PmTWH48OHi8yxbtkzttob6LQuCfvXdO++8I+7Tpk0bYdeuXRW2iYyMFF5++WWl75eq76sgKNd3LVq0ELy9vYVvvvlGkMlkStsVFhZWWnerU1BQIAQEBIjP0bVrV+HUqVMVtjtx4oTQuXNncbvAwEChsLBQZZn6fNe1oVhuZb8JTTIyMoSePXsq1UHXrl2rsF1oaKhYr3h7ewv/+c9/1Jb5yy+/iNv5+voK//zzT4XP48GDB8LIkSOVjl3e3t5CcHCwXq9D0auvviqWt2DBgkq3zc/PVzrWbtiwQe22iseAoKCgKscp9/vvv4vlDh8+XOmxvLw8oV27duLj4eHhBnteQTDM92jTpk1KZdy9e7fCNsHBweLjo0aNEu8fMGCA4O3tLbRt27ZCu0UQBGHZsmXifvL649ixYwb7TT18+FAp9vXr11epPFW+/vprsfzmzZsLq1evrvB7uHfvnhAYGChu165dO7WfheL3UP7b+eKLL4Ts7OwKr+2VV14Rt505c6YQGBgo+Pj4CEuWLBEKCgqUto+JiVFqhy1ZskTtazLGsfbKlSvCokWLhOjoaLXPm5qaKsyYMUMsb/z48Wq3PXv2bIXjxeDBg4Vbt25V2FbxvTB0m2z79u1K37Fvv/1WyMvLU9omOTlZbKMrtqGqct4jp9guUfVaVFmxYoVSzD/99FOF78vjx4+FiRMnKr3HV65cqXK8it566y2x/IULFyo9dunSJaXv4JMnTzSWp817ofj70vb9f/z4sdC1a1dxv08//VRITk6usF1KSoowZcoUcbvBgwcLxcXFFbZTrJebN28uto0uXLhQYVvFz0XxM/Pz8xO6dOkiHDt2rMI+58+fVzquVPZbN3S7VPFYMHPmTLXb6bJPbm6u0KZNG3GbmJiYSssrLi5W+rxUfRdycnKUzn3eeecdIS4ursJ22dnZSnV89+7dhaysrArbDR06VKkOUHW8EwRBkMlkwp49e4Qff/yx0tdgSoqfh7y+CgwMFO7du6e0XXFxsdIx3NvbW/jzzz9Vlqnvd16xnq+sXTZ+/Hhxu/bt2wuHDx+usM3Ro0eFjh07VqiH1R2LjdHWNXRdUhmTznrt5eWFL774Av7+/mozrU5OTpg5cybGjBkDoKwHx4kTJwwWQ0JCApYvXw6g7MrThg0b0L9/f5VXlgcOHKg0r8HSpUs1TipcWFiI//znP/jkk08qZHulUil+/vlnsddafHy80tVbRX/99ZdS994NGzZg8ODBKuN0cHDAkCFDsGDBggqPjR49Wvx727ZtlcZ+7NgxMYvdoEEDvcd4A2Wf4/vvvy/eLi4uxrp16zB06FD06NEDU6ZMwR9//IFTp06JV+J0VVRUhObNm+OPP/6oMAeAnZ0dvv76a6VeD4sWLRIXpFB08eJFsbdGgwYNsHHjRpWTjltaWuL111/HN998A6Cs5+WyZctUxvbdd9+J3aQ/++wzzJw5U+VcW/Xr18fff/8tdn89fvy42HtJ0Y4dO8Thura2tvjnn39UDk0eNmwY5s2bZzZXeoqKivDRRx9hwoQJFX7zTZs2xapVq8Tu9ikpKVi9erVBn//YsWP49ttvNf5THE40cuRIBAUFVTqs2NfXF6tXrxa/d6qGmcgZ6resj7Nnz4pXMAHgl19+waBBgyps16pVK6xevVq8Wvvw4UOsXbtWY/nFxcUYNWoUvvrqqwpzglpbW+t1RW3nzp3icDBra2v8/fff6NatW4XtevTogRUrVohX/a5fv47du3fr/HyGtGTJEo3ftRUrVlTYb82aNeJ30NXVFatXr1Y5V2RAQAB++ukn8faRI0fEnkyKMjMzxd7SAPDRRx9h4sSJFT6P+vXr459//oGXl1eFYTZVpdgrcdeuXWp/H0DZVWx5b3L58Mrqpthjr/yKo3Z2dhgwYIDKbc1F+R6G8vkYtSH/rHJyclQOMZSvruvs7GyUoWrlp7to1qyZQct/8OCB0lCc2bNnY/z48RV+D40aNcKqVavg5eUFoKwnnLpjvKLCwkIEBARg7ty5FXos165dG9999514e/v27bh+/bo45UT5Hi3NmjXDp59+Kt5WNexWFUMda1u3bo2PPvoI3t7eap+rZs2a+OGHH8RVfc+cOVNhOhFViouL4eHhgTVr1qice03+Xhi6TVZaWorFixeLt4cPH44vv/yywlxUtWrVwvLly+Hj42PyNpRMJsPvv/8u3p44cSI+/vjjCt8XDw8P/PHHH+IwyOLiYvz8888Gi+PRo0dKvVnL143t2rUTR8jI51M2lV9++UWs98aNG4fvv/9e5cJD7u7u+PXXX8VznJiYGKURVKqUlJTA3t4eq1atUjlMsrIRE6tWrVJarVyuY8eOmD59uni7svfO0O1SY7C3t0f//v3F24q94VQ5deqU+HnVrVtX5Xzgq1atEuuWfv36Yfny5UojsuScnJwwZ84ccSh3SkpKhSGoOTk54pDEOnXq4IsvvlDbK8vR0REDBw7EJ598UulrMBdFRUXw9PTEP//8g0aNGik9ZmlpicmTJ+O///2veN+ff/4p9gRTpyrfeVVOnTol9tKUSCRYunQpevfuXWG7Xr164ffff4dEItFYDxurrWvMuqS8p2Y5OsVhj6rmRdLX2rVrxYpq8uTJKn/girp06YIePXoAKOtOq2leFTc3N0yZMkXt4+7u7krzlqhKzBUWFopDS4CyZclfeOGFSp9XnQEDBojDtCIiInD37l212yom7kaMGFHlBRsmTJiAadOmVfiBpKSkIDw8HIsXL8bEiRPRsWNHjBs3Djt27ND5IDJz5kyVXZ/lZs2aJVYeiYmJKuf3WrVqlVJ5muZtGj58uPh5nDx5UlxgQ+7WrVtiQ6ZFixYahxU4ODhg8uTJ4u2dO3dW2Gbr1q3i30FBQZV2lQ0ICNB6oQ9jq1evXqWrGHp4eCj9XrZt22bQFfWuXbuGf//9V+M/xSGT2lI8SU1JScGdO3cqbGPI37I+FE9G+/Tpg5deeknttvXq1cN//vMf8famTZs0fha2traYMWNGleNUpBjzmDFjKp2/yN/fH6NGjRJvm3oukB07dmj8rpVvfAuCoDSf4OTJk1GnTh21z9GvXz+lRr6q17xr1y5xOLSXl1elv0EXFxet5jvVVUBAgFj3y+d/UUcx0dWnT59qH4p/+fJlMXluZWWlMnmteEJ65MgRtXPlmkr5IVCZmZla7xsQECAe7+VJOLkrV66I780rr7xS6fFWX+VjrcqCMaps2bJFvCjXvHlzvP7662q3dXV1VToZ27Vrl5g0Vsfa2hozZ85U+3j79u1Rt25d8ba7u7tSXVte//79xaHSd+/e1XgCBZjmWKs4n9np06e12mfy5Mka21iGbpOdOHFCnAPVzs5OKfFZnp2dXaWfZXXZuXOnOKTY3d0dH3zwgdptbWxslIbnnTt3rtK2vi5CQ0PF346Pj4/K1ZgVL4CrWkSgOqSlpYmJIA8PD43tEktLS6VVqDUlkYCyYcm6TrHw2muvVbqC9dChQ8WLi/fu3dPqt14ZbdqlxqT4Xdi1a1eldYzie67qYnVRUZE4fYuNjQ2++eYbjRd7P/roI7Gc8udSiu+tVCo12KKE5mLatGmVzis4efJkMWmbm5urNKxfHX2+8+oonscOGDCg0s4/HTp0UNkOK88Ybd3qqEsUmU1irqioCBcvXoQgCCgtLcWFCxeUehUozqWkatI9fcnniQHKJnfWhuKXRz4nnjq9e/fW2HBVPNGUr6il6MqVK0orxFRlMlcbGxulEwp1veZSU1PF98bS0tJgE8hOmTIFYWFhGDp0qNr57UpKSnD+/HnMnDkTQ4YMEVcx0qR27doae/W5ubkpJUIV5xACyq4uyhuUTk5OKrP3qsiv3gqCoDQHCqD8HRs0aJBWlX9l3zGZTIaoqCjxtjaTohtjAmB9DB48WOM8BgEBAeLCHI8fPzZYg9IQnjx5gkOHDmHFihX46aefMHfuXKV6SvFzUVVPGfK3rA/F77s2czyOGDFCKZmi6bPo3r27QZMo5b/r2kwAr5iYu3btmspJy81ZbGys2FPZ0tKyQo8EVRRfs6rJkBU/94EDB2r8Daqb26UqPD09lXo6lk/4yKWmpipdMDFF3aV4MtmjRw/UrFmzwjadOnUSe1KZumeIKuWPr7r0RFfsrXDmzBmlxYUU3xtjLchRPlZtJ6bWlmKPn8DAQI3H5H79+okXNAsLCytMpl1ehw4dNM67p9gLsHfv3pX2NrCzsxMvGguCoLKdWJ4xjrV5eXk4c+YM1qxZg19++QXfffed0vFP8TegbTtdU29YY7TJFOvDXr16aZwUv1u3bjqt8moMit/ZQYMGaVxp0t/fX6mXY/m2rr4Uf//qjk2K91+/fl2c66w6nT59Wuxd069fP62OZ61btxbrmvLfGVW0SRSUp2mOSvnqwoD2v/WqtkuNqWvXrmLyJykpSe1cw7m5uTh06JB4W9VCg1FRUWKvpa5du6o8Lpfn6ekpJulv376tdFGlRo0a4vfi9u3bGs/nnyby+R41baP4HdamjtDnO6+O4vMZ6jzWGG3d6qhLFJl08YcLFy6gsLAQoaGhOHz4sNIP5tatWxVWs5JLSkpSGq5TXFyM5ORk7Nu3T6fnT09PF6/8Wltbq1x+WxXFKw6aVh6srPu/nOJE46qujihOYNimTRutln6uzGuvvYY1a9YAKDs5mj59eoUv7/bt21FcXAwAePHFFw3aKGnWrBl++OEH5OXlISIiAhcvXkRUVBRu3LghTkgpFxsbizFjxmDjxo2VXmUCyn4I2iS92rRpIw7PKX+Qio6OFk/kraysMG/ePK1ek3xSWqCsq78ixUb8uXPnkJSUpLE8xatK5b9j0dHR4hVLR0dHrYb56LKSmzFp03PP1dUVjRs3Fn9nN2/e1H3yTDWmTp2qNKRaW3fu3MFPP/2E48ePa92LU1UPGkP/lnWRnJysNJyt/CI6qri5uaFRo0biCduNGzcq/SxatmxZ9UAVREdHi++3g4ODyuFO5TVv3hwODg7Izc1FSUkJbt26pdVrNYZDhw7pvIiCYi/sxo0ba7WSmuLrS0lJQXJyslKdrXgsVTVBdnn29vZo1qyZUoPeEAIDA8WecocOHYJMJqsw1G/nzp3iZ+7u7i72UK8uhYWF2Lt3r3hb3cmnRCLBkCFD8OeffwIoO2aqW4DFFMont8q/z5oMGzYM586dQ0lJCcLCwvDOO+8ovTf16tXTakEYfZQfBm/I5LogCEq/B22OSdbW1mjVqpU4jcqNGzdUDkWT0+aYrNgLUJuV2xQveGjTi8aQx9qMjAz89ttv2LFjh9YJ3vK91FSpV6+exoV2jNEmU2z3adM2kkgkaN26NQ4cOKDVcxuDYszajoBo166duCiSIVZNvnr1qrhyooWFhdqVnhs0aIC2bduKbd/t27dX2ivRGBTbWtHR0fj222912l++6Im6iwLW1tZand+VZ4hzQjlDtUuNSf49kfd6DQsLQ8eOHStsFx4eLv7OW7RoobIOVfxMHz16pPVnKr8YLggCHj16JPYmt7GxQd++fbF7924UFxdj/PjxePXVVzFgwAB07NjR4D21q5O3t3eF46gqivWfpjpC3++8KsnJyUojk7Rpl7Zp0wYSiaTSXpfGaOsauy4pz6SJOU1Lmavz4MEDvPnmm1V+fsVVQBS7yOpC/oNXR5vVsxSTYvJkmCLFk2lDrJTXpEkTtG/fHpcuXUJqaiqOHj1aYZ4YxdVYFXtjGJK9vT26d++O7t27i/fFxsZi9+7dWL9+vTicJTc3F59++ilCQ0MrTbwpDg2pjOJ25YcsKvYMyMjI0Os7UX4YjmKZivN7aav8d0yxwVunTh2tkpHavjfGVtmQvPLbyU8W9BlWakgnTpzA5MmTdZ5zS1WDytC/ZV0ovo92dnYahwPJeXl5iYk5TSdb2papLX2+6xYWFqhdu7bWMZsbxc9J29+tu7s7bG1txS786enpSok5xTJr166tVZm1a9eutLGiqXHSsGHDCsP2+/btCycnJ8hkMuTn52Pfvn0VekEq9sgYMmSIViuFGVJ4eLhY5zo5OVW6GmNAQICYmLt27RpiY2MrJDbWrFmDuLi4Sp9TcdiZoZQfbqlrT9YBAwbg22+/RV5eHkJDQ/HOO+/gyJEj4vFt6NChRhv6Uz5WTe0sXWRnZyvNUyPv9aiJ4naa6hRd232GaCeWZ6hjrXylVW0uKCrSJoGnzfHCGG0yxdepy/tkSooxG+M7qw3Furlr166VXrAfOnSomJgLCwvDxx9/LPbOrA6K35tLly7p1RsqKytL7cm0i4uLXscmbX7riit8q/utG7JdamwBAQFiYm7//v348ssvK/QQVhxmqqq3HKD8mUZHR2s9mkpR+bpg1qxZuH79Ou7fv4+ioiKEhoYiNDQUFhYWaNq0KTp06IDu3bvjxRdf1HkOtfKuXr2qdqSA3NChQ7VKKGmiz/mwpjpC3++8Kor1mb29vVbHAicnJzg7O1faHjBGW9fYdUl5Jk3MaUPe8CufITXEvFOa5gnRhqarFIZouCo2cLTJgGtj9OjR4pdr27ZtSom5ixcvilfFPDw8Kp2HytCaNGmCadOmYcyYMZg4caLYBT46Ohrnzp2rdKiqtr2PFCf3LN94NMZ3oqoHwvLlKcasz2s2JW3jUKzA9F0MxBDS0tLw0UcfiY0fLy8vjBkzBu3bt0f9+vXh4uICW1tb8Xe+ZMkSseetqjrKGL9lbSk+ty7fB10+C0P3ANQ35sp+4+ZOsXeQrq9Znpgr/5r1KVNTI0LTCXKnTp0qJObs7OwwcOBAcW6R0NBQpcTcrVu3lBrbxhoqWRnF+e369+9f6Xe6SZMm8PPzExt127dvrzA5dHh4uMrhxYqMkZgrPyyx/IJImjg6OqJfv34ICwtDTEwMbty4ofTeGPOzKX/R4s6dOyonAtdH+d53xjgm6druM0aC01Cv6+OPPxaTco6Ojhg1ahR69OiBRo0aoWbNmrCzsxOnOzh37px40VybNro2xwtjtMn0qQ9N3Yaqah1e1eNgYWGh0sIj6pIncq+++qq48FhKSgpOnTpVaS9TQzPE96ayBLi+bR1D/NYN3S41NnkPuNu3byMzMxPHjh1Dv379xMefPHkiDle3tLRU2xPTGHWBh4cHgoOD8ffff2Pr1q3iqK3S0lLExMQgJiYGGzZsgKurK9555x28/fbbeieYY2NjNbab/Pz8DJKYM8T5sL5lakOf81igLN7KEnPGaOsauy4pz2SJOcVVD4Gy1WNcXFxw4cIFSCQSeHh4oGHDhuLjubm5YuPXxsZG7H4pkUhga2sLqVSKZs2a6bT6kOKH4eTkZLbjyxVP4A11kvnKK69g/vz5yMzMxPHjx5WGPin2lhs+fHi1XuWSq1WrFubOnSuuxguUJQwrS8zJVz3VJC8vT/y7fHJE8Tvh4+Oj86SNqihWDkuXLlU6IOlDMWZ9XrMhqVrV1hBxKFau1Z3AUrRlyxaxUvb19cW///5b6ZAwTb9PY/yWtaX43Lp8H0z5Wegbc2W/cXOnWAcZ6jU7ODiI32NtyzRWnTFs2DAxMXfhwgUkJiaKPTsUEz/NmzfXOH2BoZVflCIkJAQhISFa7x8WFobp06frtfqwoSkuJOXm5qZxYStVhg0bJh4DV65cKQ7lVFx50Rhq164NLy8vcX6lyMjIShdo0EX5RnheXp5WV7LN5ZikLUMcayMiIsReTw4ODtiyZUulw26NcUwzRptMnzrWWPWhtvSpww35nT18+LBSb6OZM2fqtCjGjh07qjUxp9junjVrFt56661qe25jM3S7tDoEBASI5+c7d+5UOg/as2ePmLhQnJOuPMXPdNy4cfjiiy8MEpuTkxM+/PBDvP/++4iKisLFixcRERGBS5cuib3IMjMz8fPPP+PKlStYtmyZ2S8UYYjzYWPS5zwW0Fz3GaOtW911ickSc4orq7700ktYvnw5AIgN8f79+yv96I4cOYL33nsPQFmGe926dSrL1SUxpzhxpEwmQ15ensmviqmiGKc2E4Fqw87ODgEBAVi3bh1KSkqwY8cO/Oc//4FMJhPn6pNIJFpNtm4sbdu2hbOzs/gjUxx6rIq2Qy0U52wrP3+T4ntdfr47fSlOAq3pNWhDMeZHjx5BEASNBwlNcyEC2nWfL0/XKwkPHz7Uap4wxTlhtJljy1gU66n//ve/Gudp0vQdNMZvWVuKXcXz8/ORlpamVfdxxTir+7PQ57teWlpqNt8ffSh+Jtr8boGyK87y3nJAxddco0YN8bda/qKYOuXnZSpPn2EkQNnE+A0aNMCDBw8gCALCwsLw3//+FyUlJUqrgpmit5zi/Hb6SE5OxunTp5XmxVPXVjGmgoICHDlyRLzdoUMHvcqRD1dLTk5WGmpUHZ9N586dxaTooUOHDNY+c3Z2hrW1tTicNSkpSatJxE1ZD+rDEMdaxeNfYGCgxrnwdB3uqg1jtMn0qWM11YfG5ubmJtbhSUlJ8Pf317iPIb+zihdN9BEeHo7s7GythnIagqHb3ebE0O3S6jBkyBAsWrQIgiDgyJEjSt8FxWR7ZT0xFT9TQ9UFiiwtLdG6dWu0bt0ab7/9NkpLSxEREYF//vkHhw8fBlB2LNq/f7/GRTxUGT58OIYPH27osFUyxPmwMSnWwXl5eUhPT9f4/DKZTOM5pzHautVdl5jssq7imN3ykwmq6mqruNiDodSqVUtp3ghNK22ZiuLkjJcvX9Ypu1yZ0aNHi3/Le8nt2bNHvMrWsWNHo14V14bimH5N4/sVewhURnEiR8UVcYGyXhry53ny5InGuYG0odiA0nV1FlV8fHzEHhkymUyr5c8VX7M6ilcwsrKyNHZ5T0pK0nmYrjZxZGVlKQ3DKv8ZVafK6qnySkpKNH6+xvota8PT01PpJEeb+i4tLU1cIAeo/s/Cx8dH7LGbk5OjVTLo1q1bYh1maWlZ7b2uqkrxPb57965WkzUrfu88PDwqzP3TvHlz8e+rV69qLC8/P9+oK+kpLqggn3Pl5MmTYqPHyspK61XSDUlxDiUvLy+xka7pn+IVfsUyTGXHjh1K88VoWvlSHQsLiwqfg62trcbV3gxh7Nix4t9ZWVlKPfmrQiKRKNUJ2tSDxcXFSosJmPKYpC1DHGt1Of4BxmmnG6NNplgfavM+CYKgVb1pTIoxa3uuorhdVb6zT548EXvLAmXHZW3rRnnvxIKCAqVFdXSlaw8lQ7e7zYmh26WAcYbTK6pTp4646ENhYaHYASQuLk48d3NwcKh0RJHiZ3r58mWjD8u1sLBAhw4d8PvvvyvNhS5P0pmz27dva7VoUmXnw8bk6emplJzTpn69evWqxs/cGG3d6q5LTJaYUxzqodiNUL4aq2JvOfnkw9pQXMZWcYJfdRTnT9uwYYNWz1Hd2rRpI06GnJOTU+UrV3Le3t7i6k5xcXE4f/48tm3bJj5urEUftFV+1RZNk1k+fPhQ43LPaWlpOHbsmHi7c+fOSo/b2dkpDZc1xHeid+/e4t8HDx6s8pUeJycn+Pn5ibe1+W1o851xcnISV4PKy8sT5xlUR59G1u7duzX2SFHsteLh4SEudW4KivWUpiRaeHi4xqsphv4t61rfKX7ftUkgbN++XRyuXKtWrWr/LMp/17WJWbEO8/f313rCVXPRpEkTMdEjXxFTE8XXXL5OA6A0P9fevXs19ojdv3+/UZPGw4YNE08E7t27h8jISKV6rGfPnlr1YjKkGzduiCsYAmXz8mzZskWrf19++aW4X3h4uEkm2JZ78OABfvjhB/F2kyZNMGDAAL3LK987rnfv3tWyWp2/v7/SsXjRokVISEjQuZz4+Hg8ePBA6T7Fcnfs2KGxsR8eHi4myG1tbbVeFdOUDHGs1eX4l5ycjEOHDukZrXrGaJMp1pHHjx/XePHj7NmzBu8xp3js1maEguJ7sHv3bqUe0qpcu3ZN6UKWquOCtnbu3CnGKJVKERwcrHXdqNhDqCoXLRQvzGvzfvXs2VOcqP7y5ctKqzU+7QzdLgV0b0vqQ7E3nLxdo9i+efnllyttr7Vv31489jx69KjaEmQSiUTpPE5xETdzVVBQICY/1Sk/b2RV6gh9KD6foc5jjdHWre66xGSJufr164t/Ky71vGPHDuzYsUOp99PChQu1TmYo9vrRphvjxIkTxR4ZBw8e1Gk+merqHm1jY6N09finn36qMLGzvhR7zf30009ihtnV1bVKjfnyDh8+jK1bt+pU4f/6669KDeaePXtq3Of777+vdJWiH374QWzQeHl5KV0FkZs0aZL49/r168VJSbWh6jvh7+8vVhb5+fn49NNPtV5JqbCwsMIqQoBy0nTdunWVJtF2796t9fyJilcGKmtEPXr0CCtWrNCqTEUPHjzA6tWr1T6empqKZcuWibdHjhxp0rkcFOupyhoBaWlpWLBggcbyDP1blidSAe3qu9dee038++DBg0pXwctLTEwUV5yU72uKz0Ix5n///bfSg2JUVBQ2b94s3laco/JpIZFIlOrlZcuWVfrZHjp0CEePHhVvq3rNgwcPFhveCQkJlf4Gs7Oz8euvv+oeuA7q1asnXj0HyupZxZP6wMBAoz6/Kor1XZMmTdCyZUut9+3du7c4LCc/P79KPUOq4tq1axg/fryYGLS0tMTnn39epTnvmjVrhu3bt2Pbtm3Ytm2bUhLS2ObOnSsO08rJycH48eOVevBqEh4ejhEjRlQYrjh69GjxPbl+/bpSnVFeVlYWfvzxR/H2oEGDqm04XlUY4lirePyrLOlWUlKCr776ymgn9IZuk/Xo0UMcLZOXl6f0+ZZXUFCAhQsX6hCtdnQ9dg8ZMkRMWqSkpIiT+atSWFiI7777TrzduXPnKl1UU6wbBw4cqDTtiSaKyZiIiAi9ezwqDnPT5v3y9PQUn1sQBHz66adaXzApLS1VuUKxuTB0uxRQ/j4q9sgzpFdeeUVsh1y8eBGPHj1SmiJBsSe9KjY2NkoLSn3zzTdaD1cEKg5/lclkWp+LKR5DtJkCxhz89ttvKs8f5ZYvXy6+fw4ODmoX3TAWxfPYvXv3Vtrj+tKlS0pTnahjjLZuddclJkvMdenSRZwvJC4uDjNnzkRWVhY+++wzzJo1C2FhYZDJZPjyyy+xadMmrXs9KM5BoilbDAANGjTAf//7X/H2559/ju+//17tG1lcXIyTJ09ixowZ1XryMGnSJHFYaXZ2Nl5//XXs3r1b5ZXevLw87Nq1C7NmzdJY7sCBA8VGpmK3zyFDhihdQamq5ORkfPHFF+jfvz9+/fVXxMbGqt02KSkJH3/8sdLQlT59+mic38Ta2hrXr1/H5MmTK1TABQUF+O6775QaGB9++KHKE5ZOnTqJn21xcTHeffddLF++XO0EqgUFBQgPD8d///tfpe+Soi+//FL8Dp86dQpBQUGVdrO9d+8eli1bhj59+qjsOjts2DA0btwYQNmJ4MSJE1WWFxYWhlmzZmndkFKsmFetWoX9+/dX2ObKlSsICgpCZmamTg00oOwz+umnn7BmzZoKC0fExsZiwoQJ4tUod3d3k0/Yq3iVbPny5Sqv6ly/fh1BQUF4+PChVvWUIX/LzZo1E//Wpr7r0qWL0gTM06ZNU5lEiIqKwoQJE8TVj+rUqSOutlfdhgwZIg49KyoqwjvvvIOzZ89W2O706dOYNGmSeIWsZcuWGDRoULXGaijjx48Xh6NmZGRg/PjxuHnzZoXtdu/ejY8//li83bt3b6WEl5xUKsWECRPE2z///DNWr15d4TeYkJCAd955B4mJiRqnDqgqxZ5YoaGh4lVLqVSq9LurDkVFRUqNPl2H0drY2ChdyDJUr3ZtlJaW4tq1a5g1axbGjh2rNLfMrFmzlOa701eLFi3QqlUrtGrVSmm+FWNr0KABvv/+e/FqdUJCAgIDA7FkyRK1F2sLCwtx7NgxvPHGG5gyZYrKE5MGDRooJfznzp2Lf//9t8LvIS4uDhMnThR76jk5OWHKlCmGenlGZYhjba9evcRk3fnz5/H9999X6F2QkpKC999/H0ePHjVa72RDt8ksLS3xwQcfiLe3bduGefPmVeiFlpKSgvfeew+3bt3Sua2jieIQxP3792vstenk5ITJkyeLt1esWIHFixdXSCykpqZi8uTJ4hA1KysrpWOEruSjmOR0rRtbt26tNCWOvnWjYlvn5MmTWs1v/OGHH4q9z6OjozFy5EilxX3Ke/ToEVavXo1XXnlFqSeRuTFGu1Tx/b169apR5qVzdnYWR6mVlpZi/vz5YqLWw8MD3bp101jGhAkTxFiTk5MxYsQI7N27V+1CdGlpadi8eTMCAwPxzz//KD12/fp19OnTB0uWLFE7JVBJSQn27NmD9evXi/dV5yIm+rK2tsbDhw8xceLECj3GS0pKsHz5cqULM++++67GuQoNrXv37mKvOUEQMGXKFKURbXInT57E5MmTUVpaqrEeNlZbtzrrkiov/rBp0yaEh4drvf20adPw8ssvw9XVFRMnThS/GDt37sSJEycgCAIEQcDhw4cRHByM3NxcWFlZ4euvv9ZqBaBGjRqJE57+9NNPOH78OJo1a6b0xr/33nvicDIAmDp1KhITE7F9+3YIgoCVK1di3bp18PPzQ4MGDWBnZ4ecnBwkJiYiOjpaHLeteIXB2JycnLBkyRJMnDgRT548QXp6OqZPn4758+ejbdu2cHNzQ0FBAR48eIAbN24gPz9fq7mV7O3tMWTIkArDA4w1jDUpKQm///47fv/9d7i5uaFFixaoWbMm7O3tIZPJEBsbi1u3bik1Uho1aoRvvvlGY9ljx47FoUOHcOLECfTp0wedOnVCnTp1kJGRgXPnzik10AcPHlzpJKPffvutuEJfUVERFi1ahD/++AP+/v6oW7cubGxskJWVhQcPHuD27dti40hdLwtvb28sWrQIH330EfLy8nD16lWMHj0aDRo0QIsWLeDq6orCwkI8efIE0dHRGq8C2djY4IcffsD48eORm5uLpKQkjB49Gv7+/mjWrBmKiopw9epV8aD3xRdfKF1BVWfQoEFYuXIlbt26haKiIkybNg0tW7aEr68vSktLER0djRs3bgAA3n//fYSEhOi0iMGMGTMwf/58zJ8/HytXrkT79u3h4OCA+/fv49KlS2LlaWVlhfnz51frb0yVwMBArFy5Evfv30dhYSE+/fRTLF++HL6+vrC1tUVMTIy4WrSvry969OiBv//+u9IyDflbHjBggHhw0La+W7BgAcaOHYsHDx4gNzcXH374IRYvXgx/f39YW1sjNjZWaS4HBwcH/Pzzz9UyfE0VGxsbLFq0CEFBQUhLS0NKSgrGjx8PX19fcT6JmzdvKp081KxZEz///LPBT6aqi6urK37++WdMmjRJHFYeGBiI1q1bo0mTJhV+30BZPTl//ny1ZU6ZMgWnT59GZGQkSktLsWDBAqxcuRIdOnSAg4MD4uPjcfHiRRQXF6Nt27aoV6+eeDXbGKuMDhgwAHPnzq2wItarr76qd1IwKipK41V3RX369MEHH3yA48ePixfjJBKJXleOhwwZIg4pvnTpEuLj45V6NlTFkiVLlEYDFBUVISsrC2lpabhx40aFq7eurq6YM2eO3nPLmZO+ffvir7/+wgcffICsrCzk5uZi6dKlWLZsGXx9fdGgQQNIpVLk5OTg8ePHiIqKUppbx8LCQuWiETNnzkRUVBSuXbuG4uJifPvtt1ixYoV4THrw4AEuXrwojuawsrLCvHnzUK9evWp77VVhiGNtkyZNMHToUDGZsnLlSuzcuROtWrVCzZo1kZiYiAsXLqCoqAiOjo749NNP8fXXXxvl9Ri6TRYYGIhjx46JF6bWrl2L0NBQdO7cGVKpVJwapbCwEPXq1cPLL7+MNWvWGOz19OvXT5wM/+jRowgICEDbtm2VfuevvvoqWrVqJd5+++23cenSJXFhlz/++AMbN25E586d4erqqhSz3IwZM9C6dWu941S8mF2vXj20a9dO5zKGDBkinuuFhoZi2rRpes0ZV6dOHTx8+BApKSkYOHAgunfvjho1aohltWrVSqnO8/T0xO+//453330X6enpuHfvHt5++214enrC398fbm5uKCoqQnp6Om7fvq3XUHlTMEa71MPDA23btsXly5dRUFCAoUOHomfPnvDw8BCP//Xr16/y6tgBAQHiBX/FC/+DBg0SR69VxtHREX/88QfeeustJCQkICUlBR9++CFq1KiBNm3awN3dHYIgIDMzE3fu3EFcXJxY1ykOB5eT9z5dunQpPDw84OvrCw8PD1haWiI1NRXXr19X6kHYoUOHp+KC74ABA/DgwQNERkZi4MCBaN++PRo0aICcnBxcuHBBqSdxx44dlXolV6d58+ZhzJgxSE1NRWZmJt599114e3ujRYsWkEgkuHHjhjgkf8KECThw4IDGc05jtHWrsy6pcmIuNTVVpzmzFJMjU6ZMQWJionjQV5znQf7Gu7i4YMGCBVpP4N2sWTOcOnUKQFkG9ty5cxXmHXvjjTeUTlQlEgkWLlyIli1bYsmSJcjMzERRUREuX76sdpJViUSi1wGqKnx9fbF161bMnDlT7PKZmpqKgwcPqtxe26uXr732mlJizs/Pz+ATpvv4+MDPz088WABlVzIqyzgDZQf0zz//XKuuwy4uLvjrr78wZcoU3Lt3T+0wvREjRuDbb7+ttCwbGxusWLECS5cuxapVq5CXl4e8vLxK57CztrZWmty/vN69e2PTpk34/PPPcf36dQBlw03KX81Q5OXlhdq1a6t8zN/fHytWrMBHH30kVrKRkZFKw8AtLCwwefJkjBs3TqvEnJWVFZYuXYoJEyYgPj4eQNlVJXm8QNl3/z//+Q+mTJmi09BvoGwOCRsbG8ybNw+PHj3C7t27K2zj4uKC+fPno1evXjqVbQw2Njb4888/MWnSJPH9iI2NrdDjs127dli8eDG2bNmiVbmG+i0HBgYiLCwMFy5c0Lq+c3d3x8aNG/Hxxx+LPc/u37+vcohYw4YN8dNPP2m1ApwxNWnSBBs2bMD06dPFxHD5K/lyLVu2xOLFi02+cE1VdezYEatXr8Ynn3yC+Ph4CIKAK1euqJysvFu3bvj5558rrSdtbGzwzz//4P333xc/9+Tk5Aq/wbZt22LJkiVKw7eMcSXVyckJ/fr1qzCHXlV6oufm5uo0/4c8savYi6Nt27Z6JdQ6deqE2rVri6sH79ixA++//77O5aiibS8TqVSKwMBATJgwocICIE+zbt26ITQ0FEuWLEFoaChKSkogCAJu3rypsicpUHbse/HFF/HRRx+pbM/Y29tjzZo1mD17tpicUXdM8vDwwLx588zimKQtQx1r58yZg9TUVLGtlpKSUmH4XO3atbFo0SKtV3PXhzHaZD/++CPs7OzE5FNmZiYOHDigtM0LL7yApUuXGrwHVePGjcWefwAQExOjNMclUHY+o5iYs7CwwNKlS7FgwQJs3LgRJSUlyMjIUDmywdnZGZ9//nmVVoEsLi5WGmo4ePBgvaazCAgIEBNziYmJOH/+vM7zWVlYWODrr7/G+++/j6KiIqSkpFSoFwMDAytcjPD390dwcDBmz54trmaanJystq0FlLWRGjZsqFN81clY7dLZs2dj/PjxyMnJQVZWVoU6o1OnTlVOzPXq1QtSqbTCvI6VdZQor379+ggODsbXX38t9jZNT09XWom8PBcXlwoLZdjZ2cHKykqst1JSUiqdomrAgAGYP3++US5UGpq1tTWWLl2KadOm4cqVKyrPDYCyefYXLVok9kqvbvXr18fq1asxdepU8RxEVV04evRofPzxxxXqZ1WM1datrrrENJ/E/7O0tMT333+PV155BZs3b0ZkZKTYtb5mzZoICgrCiBEj4OnpqXX2UX713draGh07dsTt27eRkZGh1dwX48aNQ2BgIEJDQ3H69GncunULaWlpKCwshKOjIzw9PdGsWTN06tQJvXr1UlrRtbp4eXlh/fr1OHPmDPbu3YtLly4hJSUFMpkM9vb2qFu3Lvz8/NCrVy/06dNHqzJ9fX1Rv359sYI3Rm+5du3aITg4GMnJyTh79iwiIiJw584dxMfHIysrC4WFhXBwcIBUKkXTpk3Rpk0bDBo0SOcTpCZNmmDbtm0IDg7G3r178eDBA2RlZcHd3R3t2rXD6NGjVV41UUU+3GHcuHHYsWMHTp8+jdjYWKSnp6O4uBiOjo7w8vKCt7c3OnfujF69emlMIPr6+iIkJAQnT55EeHg4IiIi8PjxY2RnZ8PGxgY1atRA48aN0bp1a/To0QNt27attCHUsWNH7NmzB//++y8OHjyIBw8eoLi4GLVq1UKHDh0wZswYnZMq9evXR1hYGNavX48DBw6IV+XkZY4dO7ZKV2DHjh2LDh06YNOmTTh9+rQ4qXK9evXQu3dvBAUFoVatWnqXb2iNGzfGjh078O+//+LAgQO4d+8eioqK4OHhAW9vbwwePBgDBw7U6mqfIkP8lq2trbFq1Sps27YNBw4c0Lq+c3d3x5o1a3D8+HGl5y4uLkbNmjXRvHlz9O3bFwEBAWbT66xx48YIDg7Gvn37cODAAURGRoq9nNzc3NC6dWsMGDAAAwYMMOm8hIbUpk0b7NmzB2FhYQgPD8etW7fw5MkTWFlZwcPDA+3bt8egQYO0Hq7o4uKCNWvWYM+ePQgNDcX169eRkZGBGjVqiL1jBg8eDGtra6WLaMaaU0ueWJZ74YUXqj0JnJGRodSg13c1WAsLCwwaNEgcLrNjxw5MnTrVKN9FBwcHODk5wdnZGfXr14efnx/8/f3RtWtXow9BNpW6detiwYIFmDp1Ko4ePap0PJbJZHBwcECNGjXg6+uLtm3bYuDAgWovask5Ojpi8eLFGD9+PEJDQ3H+/Hk8fvwY+fn5qFGjBry9vfHSSy9hxIgRT90iMoBhjrX29vb466+/sHPnTuzYsQM3btxATk4OpFIp6tevjwEDBiAwMBCurq4aF9+qKkO3yaytrbFw4UIMHToUW7ZsQUREBJ48eQJXV1c0aNAAAwcOxIgRI5R6sRnS9OnT0b59ewQHB+P69et48uRJhR7E5VlZWeHLL7/EmDFjEBwcjDNnzuDRo0fIycmBq6srGjVqhF69emHUqFFK87Lp48SJE0oT3euSPFHUqFEjtGrVSlzZePv27XpNNN+7d28EBwfj33//RUREBJKSkpCbm6txGLCXlxdWr16Ny5cvY9++fbhw4QIePXqErKwsWFpaQiqVomHDhvDz80OPHj3QqVMnkyUqtGWMdmmrVq3Etv+5c+cQHx+P3NxcjYvI6MLa2hoDBw7Exo0bxft0ndMVKLsI9euvvyImJga7d+/GuXPnkJCQgIyMDFhYWMDFxUUckdStWzd07969wvRMrVu3xunTp3H69GlcunQJN2/exIMHD5CRkYHS0lI4OTmhfv36aNOmDQICAkx+gVpXnp6eWLduHcLCwrBz507cvXsX6enpkEqlaNWqFUaMGIG+ffuaOkw0a9YMYWFh2Lx5M/bs2YN79+4hLy8PHh4eaNWqFUaNGqVyPvjKGKutWx11iUQw9nrDOvL19YVEIsEbb7yhtDKrtoYMGYLbt2+jfv36lWYx6X8SEhLQt29fCIIABwcHnDhxotrHmutryZIl4iS4U6dONVgPBTKcPn36iD1gDx069NQMBSJ6nvXs2VMcwnHq1KlqnVuMiHTHYy0REZH2zK2ta9JLApWtwJGcnFzp44qKi4uRnJyMffv24fbt25BIJDpn359nwcHB4hWnV1555alJyhERkeFdvHhRbKjUqVPH5A0VIiIiIiJDMce2rkkTc+PGjVM5zEMQBISHh+u0qER5VZlX4XlSUFCArVu3irfHjh1rwmiIiMiUCgsLsWDBAvG2PgshEBERERGZI3Nt65p8BkP5Kqzyf+ru1/afRCLBu++++1QsZ2wOFi9eLE522bZt26duDD0REWnn66+/xrZt2yqs4ikXExOD8ePHi4v0ODg4VHmyZyIiIiKi6vA0t3VN2mOuY8eOFe67cOECJBIJPDw8tFrJQiKRwNbWFlKpFM2aNUP//v3RqFEjI0T7bDh+/DhOnDiBgoICREZGiiuaSSQSfPzxxyaOjoiIjOXu3bvYtGkTvvnmGzRv3hwNGzaEg4MDZDIZYmJicPv2bfECmUQiwRdffIG6deuaOGoiIiIiIs2e5rauSRNz69atq3CffFn7/v3767X4A1Xu6tWrWLt2bYX7J06cqDJRSkREz5bCwkJcvXoVV69eVfm4i4sLvvrqK71XKCUiIiIiMpWnsa1rlutBm9lCsc8se3t7eHt74/XXX8ewYcOq9bkLCwuRkZEh3ra1tdVpWW/FchT/VtdtlUyntLRU/Ds3N5efEZGJzJ07F0eOHEFERATi4uKQkZEh1sNSqRRNmjRB586dMXToUDg7O/O3SvQU4bGWiEh/JSUlKCgoEG9LpVLY2NiYMCLSx6JFi3Dw4EFcuHAB9+7dQ3p6ulJbt1mzZujevTtGjBgBFxcX0wZbjkRgFoxM4PHjx4iPjzd1GERERERERESi+vXro1atWqYOg54jJl/8gYiIiIiIiIiI6HlklkNZVYmJicHDhw+RlZWFkpKSah96SUREREREREREZEhmnZhLTEzE33//jd27dyM7O1vpsfKJudTUVHz33XcQBAF+fn6YNGlSNUZKurK1tVW6Xb9+fTg4OJgoGiIiIiIiInoe5ebmKk2zVP5clcjYzDYxt2vXLnz11VfIy8ursBiERCKpsL27uzuePHmCCxcu4Pjx43j99dfh6OhYXeGSjsov9ODg4AAnJycTRUNERERERERU8VyVyNjMco65/fv3Y8aMGWJSzsXFBS+++CIaNWpU6X6jRo0CAOTn5+PEiRPVECkREREREREREZF+zC4xl5WVhS+//BKCIEAikWDq1Kk4efIkVqxYge7du1e6b58+fWBlVdYJ8MyZM9URLhERERERERERkV7MLjG3efNmZGVlQSKRYMqUKZg6dSpsbGy02tfJyQkvvPACBEFAdHS0kSMlIiIiIiIiIiLSn9kl5o4fPw4AkEqlei3g0LhxYwBQmryRiIiIiIiIiIjI3JhdYu7evXuQSCTo0KGD1j3lFLm6ugJAhVVciYiIiIiIiIiIzInZJeYyMjIAAG5ubnrtX1JSAgCwsDC7l0ZERERERERERCQyu+yVs7MzACA3N1ev/ZOTkwGUDYUlIiIiIiIiIiIyV2aXmPP09IQgCLh165bO+xYVFeHKlSuQSCRo1KiR4YMjIiIiIiIiIiIyELNLzHXu3BkAcOfOHZ2TcyEhIZDJZACALl26GDw2IiIiIiIiIiIiQzG7xNzgwYPFv+fMmYPCwkKt9ouJicGPP/4IALC0tERAQIBR4iMiIiIiIiIiIjIEs0vMtWrVCv3794cgCLh69SrGjx+PmJgYtdvn5+dj/fr1eP311yGTySCRSDBq1CjUrVu3GqMmIiIiIiIiIiLSjUQQBMHUQZSXlZWFMWPG4O7du5BIJACApk2bIj8/H/Hx8ZBIJOjTpw9SU1Nx8+ZNFBUVQf4yWrRogU2bNsHGxsaUL4E0kMlkiI6OFm/7+PjAycnJhBERERERkT5KS0shk8mQlZWFwsJClJSUmDokInrGWVpawsbGBi4uLnBycoKFhf59jnhuSqZmZeoAVHFxccHatWsxffp0nD9/HkDZnHMAxETd4cOHAQCKecUuXbpg8eLFTMoREREREVWD7OxsJCYmwgyv9RPRM6y4uBgFBQXIzs6GRCKBl5cXnJ2dTR0WkV7MMjEHAO7u7lizZg1CQ0OxZs0a3Lx5U+22TZo0waRJkxAQEFClTDkREREREWlHVVJOIpHA0tLShFER0fOgpKRErHsEQUBiYiKTc/TUMtvEHFB2YB82bBiGDRuGlJQUXLlyBY8fP0Z2djbs7e3h7u4Of39/1K9f39ShEhE915KSksRVsalyTk5OnAeViJ56paWlSkk5JycnuLm5wcHBQRzhQkRkLIIgIDc3F2lpaZDJZGJyztvbm5116Klj1ok5RR4eHujXr5+pwyAionIyMjIQFBSE0tJSU4fyVLCwsEBISAikUqmpQyEi0pv8RBgoS8rVq1ePCTkiqjYSiQSOjo5wcHBAQkKCWCfJZDK4uLiYOjwinTw1iTkiIjJPUqkU69evN7sec3FxcZg3bx5mz56Nhg0bmjockZOTE5NyRPTUy8rKEv92c3NjUo6ITEIikcDNzU1sh2ZlZTExR08dJuaIiKjKzHloZsOGDeHt7W3qMIiInimFhYUAyk6KHRwcTBwNET3P5EPoBUEQ6yaip8lTkZi7f/8+zp07hxs3biA9PR05OTlwdHSEVCpFy5Yt0alTJzRu3NjUYRIRERERPRdKSkoAAJaWluwtR0QmJV90pri4WKybiJ4mZp2Yu3LlCn7++WdcvHhR7TZbt24FAHTo0AHTp09H27Ztqys8IiIiIiIiIiIivZntciVLlizBG2+8gYsXL0IQBI3/Lly4gDfeeAO//vqrqUMnIiIiIiIiIiLSyCx7zC1duhTLli1Tuq9FixZo06YN6tSpAwcHB+Tm5uLRo0e4fPkybty4AaBs2fY///wTEokE06ZNM0XoREREREREREREWjG7xNzNmzfxxx9/iJM3durUCV988UWlE3ffvn0b3333Hc6dOwdBELBixQr069cPzZs3r8bIiYiIiIiIiIiItGd2Q1k3btwoTtjYv39/rFq1SuNqes2aNcOqVaswYMAAAGWT0W7cuNHosRIREREREREREenL7BJzZ86cAQDY2dlh3rx5sLS01Go/CwsLzJ07F/b29krlEBERERERERERmSOzG8r6+PFjSCQSdO7cGc7Ozjrt6+Ligi5duuDIkSN4/PixkSIkIiIiIiIyPz4+Pjpt36lTJ6xbt85I0RhfVFQURowYAQBwc3PD8ePHYW1trVMZe/fuxYcffggAaNWqFbZt2yY+Nm7cOJw/fx4AsHbtWnTu3NkwgQP4+++/8eOPP4q3Fy9ejIEDBxqsfDnF16DIwsICjo6OcHZ2Ro0aNeDj44MWLVqgV69eaNCggVZlh4SEYNasWUr3/fXXX3jxxRe12v/jjz/Grl27lO6Ljo7Wal+iZ4nZ9ZhzcHAAANSqVUuv/T08PJTKISIiIiIiomePn58ffH19AQBpaWk4evSozmUEBweLf48cOdJQoen0vKpuG1tpaSmys7ORlJSE69evIyQkBN999x369++PcePG6T0CTdvXkZ2djfDwcL2eg+hZY3Y95urVq4eMjAw8efJEr/3l+3l5eRkyLCIiIiIioqfGsmXLNG4jlUqNH4iRjRw5Et999x2AsqRQv379tN43OTkZp06dAlA2ldLgwYONEmN5ly5dwt27d5XuO3XqFB49eoTatWsb7Xk/+OADpfnb8/LykJWVhYSEBFy9ehVXrlxBSUkJzp8/jwsXLuD111/H7NmztZpeysrKCsXFxTh8+DAyMjI0frd27tyJ/Px8pX2Jnldml5jr168frl27hrNnzyInJweOjo5a75uTk4OzZ89CIpHoVCETERERERE9S/r27WvqEKrFkCFD8MMPP6CwsBAnTpxASkqKOIpKk+3bt6O0tBQAMGDAADg5ORkzVJHicNnhw4cjJCQEpaWlCAkJweTJk432vO3bt690OG5iYiKWL1+OzZs3QxAE/PvvvygtLcWcOXM0lv3iiy/i8OHDKCwsxM6dOzFu3LhKt5f3rGvZsiVSU1ORnJys02shepaY3VDW0aNHw8PDA7m5ufj222912nfu3LnIycmBh4cHRo8ebaQIiYiIiIiIyBxIpVKxU0ZxcTF27Nih9b7bt28X/5bPVWdsMpkM+/btAwA0atQIs2fPhp2dHYCyOdsEQaiWOFTx8vLCt99+i++//168b+PGjdi7d6/Gfb29veHn5wdA83DWmJgYREVFAai+953InJldYk4qlWLJkiVwcXFBWFgY3nvvPSQkJFS6T2JiIiZPnowdO3bA1dUVv/32G2rUqFFNERMRERERET078vPzsX79ekyYMAE9evSAn58fOnfujBEjRuCXX37RuneTIAjYsWMH3nrrLXTp0gX+/v54+eWX8dlnn+HatWsAypJRPj4+8PHxQUhIiF7xKs4Np20ZFy9exP379wEADRo0QKdOnfR6bl3t3bsXubm5AICAgAA4OTmJvRvj4+Nx7ty5aomjMsOGDcP48ePF28uWLRN7FlZGnmS7efMmbty4oXY7eY9BW1tbDBkypIrREj39TDKUVZurGOPGjcPy5ctx7NgxHD9+HG3btkWbNm1Qt25d2NnZIT8/H0lJSbh69SoiIiIgCAJsbGwwbtw43L9/H/fv38ewYcOM/lqIiIiIiIieFZGRkZg2bRoePnyodH9GRgYyMjIQFRWFNWvW4Isvvqh0sYScnBxMmTKlwiICCQkJSEhIQFhYGGbOnAlnZ+cqx9y1a1d4eXkhMTERd+/exeXLl9G2bdtK91Hs1TV8+HBIJJIqx6ENeVJKIpFg6NChAIDAwEBxddJt27ahS5cu1RJLZd577z1s2rQJBQUFuH37Nq5cuYJ27dpVus/gwYOxcOFCFBQUICQkBC1atKiwTVFREcLCwgCUDbd2cXExSvxETxOTJOY+++wznSq+0tJSREREICIiQuXjgiBAIpGgqKhInORUIpEwMUdERERERKSlW7duYfz48WKPrqZNm2Lo0KHiAn2HDh3CyZMnkZeXh9mzZ0MQBIwaNapCOYIg4P333xeTcg4ODhgxYoQ41DEqKgrBwcFYsGABBgwYUOW4JRIJhg8fjiVLlgAo6zVXWWIuJydHHE5qaWmJ4cOHVzkGbdy5cwdXrlwBAHTs2BH16tUDAHTr1g2enp5ITk7GwYMHkZ2dbZCEZVW4ubmhe/fuOHz4MADg/PnzGhNzLi4u6NevH3bt2oWdO3fi008/hY2NjdI2hw8fRnp6OgAOYyWSM9niD7qOnde0vSnH4hMRERERkRZSUvTf18kJsLdX/VhqKqDv+YCDA6Buwbm0NKCkRL9y7ewAEydXdFFaWooZM2aISblRo0Zhzpw5sLL63ynj66+/jq1bt+LLL7+EIAiYN28eunbtKiaY5EJCQsTVTj09PbFu3To0bNhQfFw+VHLcuHFigqyqhg8fLg653LNnj9LcbeUpDift3r07PD09DRKDJoqLPgQGBop/W1hYYOjQoVixYgXy8/Oxc+dOvP7669USU2Xatm0rJubkQ481GTlyJHbt2oWMjAyEh4fj1VdfVXpc3lOxbt266Nq1q2EDJnpKmSQxp1gJERERERHRc6JWLf33XboUmDJF9WPNm5cl5/Tx9deAulUne/YEKpkrq1KTJwP/P5rHFHx8fCp93NfXF6GhoeLto0ePIiYmRtz3m2++gaWlZYX9Ro0ahaioKGzatAl5eXlYu3YtPv/8c6VtVq9eLf49f/58paScXP369bFgwQK89dZbOrwq9erWrYtu3brh5MmT4gIL6kZQKQ5jrWw4riEVFRWJ77e9vX2FnoLDhg3DihUrxPjMITFXt25d8e+0tDSt9unSpQvq1auHhIQEBAcHKyXmkpOTcfLkSQBlOQELC7Ob8p7IJEySmFuwYIEpnpaIiIiIiIhUOHjwoPj3xIkTVSbl5N59911s3rwZgiDg4MGDSom5+Ph4McHXtGlT9OjRQ205Xbt2hbe3t7h9VY0cOVJM/ISEhKhMzN27d0+cIqlGjRro06ePQZ5bk8OHD4vJrX79+sGxXC/NJk2awN/fH5GRkYiKisKtW7fg6+tbLbGpozj/W0ZGhlb7SCQSBAYGYsmSJTh9+jQePXqE2rVrAyiba76kpETchojKmGwoKxERERERERnHMg299ZycnJRuX716Vfy7e/fule7r5eWFF154AbGxsUhKSsLjx49R6/97QyoOeezcubPGODt37mywxNzLL78MqVSKjIwMnD9/HvHx8ahfv77SNoqrtg4dOhTW1tYGeW5NFHvpqUtKDRs2DJGRkQDKhr1+8cUX1RKbOorTRekyR7zisOLt27fjv//9L4D/vfedOnWq8LkQPc+YmCMiIiIiInrG9O3bV6ftU/5//j9HR0d4eHho3L5Ro0aIjY0V95Un5h4/fixu06BBA43lVJagSUpKwo1KhhLXqVMHLVu2FG/b2NggICAAa9euhSAI2L59O6ZNmyY+XlJSgh07doi3q2sYq+IQztq1a6tddXXQoEFYsGABioqKVC6ekJaWpnZBRACQSqXo0KGDweLOyspSKltb8vnjTp06JSbmLl68iPv37wPgog9E5TExR0RERERE1UMhaaOzcj28lNy8WbXFH9Q5caJqiz88RXJycgCUraCqDcXt5PsCEBdVAKB28QV15ZR39uxZzJo1S+3jgYGBWLhwodJ9I0eOxNq1awGUDZ2cOnWqOJfZiRMnxMShv78/mjVrpjE+QwgJCUHJ/3+PAgIC1M6tJpVK0adPH+zfv1/l4gm3b9/GFHXzLKKsJ9q6desMFndiYqL4t5ubm077jhgxAqdOnUJcXBwuXLgg9pZzdnY2yEq8RM8SJuaIiIiIiKh6aNETSy/u7sYpV8dkxNPM0dERWVlZSom1yihupzhfmmKiLT8/X6dyDMHHxwetWrXCtWvXkJiYiLNnz6Jbt24AlIexVldvOUEQlIaxrlixQlzkQZPyiydUtytXroh/+/v767Rvv3794OrqiszMTKxbtw4nTpwAALz66qtaJWyJnidMzBERERERET3nPDw8kJWVhZycHKSmpsJdQ7JTPiwRgDiMtfzfDx480Pi88fHxah8bPnw4hg8frrGM8kaOHCnOdRccHIxu3bohLS0Nhw8fBlC2KuqgQYN0Llcf586dq/Q1Vub06dN4+PAh6tSpA6BsPr7o6GhDhqfWkydPcOrUKfF2p06ddNrfxsYGgwcPxr///ov9+/eL91dXQpToacLEHBERERER0XOudevW4pxxJ0+eVLmiqVxSUhLu3r0LoGw+McU56Vq1aiX+fe7cOY3Pq802uho8eDAWLlyIvLw8hIeHIzs7G2FhYSgqKgIADBgwoMLiF8aybds28e8BAwZoNXz28uXLOHXqFEpLSxESElLp8FVj+fPPP1FYWAigrBdi69atdS5jxIgR+Pfff8XbzZo107nnHdHzgIk5IiIiIiKi51z//v3FoZ6rVq3CkCFDYGlpqXLbv/76S1yxs3///kqP1a9fH97e3oiJicGdO3dw8uRJ9OjRQ2U5Z86cMdiKrIqcnJwwYMAA7NixA/n5+di1a5fSMNbqWnwgKysLBw4cAABYWVlhzpw5Ws3VduvWLQwdOhRA2fDbyZMn67QqalXt2LFDnKcPAKZOnarX87ds2RKvvPIKHj58CAAYPXq0wWIkepYwMUdkRElJSZDJZKYO46ng5OSEunXrmjoMIiIioudSr169xITarVu3MGfOHHz99dewslI+ZQwJCcGmTZsAlA0JffPNNyuU9dZbb+Hzzz8HAHz++edYt24dGjZsqLRNfHx8pQs7VNXIkSPFFVh///13cdGHhg0bomPHjkZ7XkU7d+5EQUEBAKBnz55aL6Dg6+uL5s2b4+bNm0hISMDZs2fRtWtXY4YKoOzc5c8//8TmzZvF+4KCgiokX3Xx66+/GiI0omcaE3NERpKRkYGgoCCUlpaaOpSngoWFBUJCQnRaip2IiIiIDMPCwgI//vgjxo4di9zcXGzZsgVXrlxBQEAAvLy8kJmZiUOHDomT+APA7Nmz4eXlVaGs4cOHY/fu3Th16hSSk5MxbNgwjBgxQhzmeu3aNQQHByMvLw+vvPIK9u3bJ8ZgKB07dkSjRo1w//59MSknj02f3l/btm3D6dOntdp28uTJsLW1VVr0obKhwaoMGzYMN2/eFJ/bEIm5S5cuITs7W7ydn5+P7OxsxMfH4+rVq7h8+bK4eqxEIkFQUJCYYCUi42FijshIpFIp1q9fb3Y95uLi4jBv3jzMnj27wpVLU3JycmJSjoiIiMiEfH19sWbNGrz//vt49OgRYmJi8NNPP1XYzt7eHrNnz8aoUaNUliORSLBkyRJMnjwZZ8+eRW5uLtatW6e0jaWlJT777DM4OjqKiTnF1V0NYcSIEfj555+VnjMwMFCvssLCwrTe9u2338bdu3dx/fp1AICrqyv69Omj0/MNGTIEP/74I4qLi3Hw4EFkZWXBxcVFpzLK06b3mkQiQceOHTFlyhR06dKlSs9HRNphYo7IiMx5aGbDhg3h7e1t6jCIiIiIyIz4+/tj//792Lp1Kw4dOoTbt28jMzMTDg4OqFevHnr27InXX38dnp6elZbj6OiI1atXIzQ0FNu3b8etW7eQm5sLDw8PdOzYEUFBQWjVqhVWrFgh7uPq6mrQ1zJs2DAsXrxY7AXWo0cPjXEbiuKiDwMHDoSNjY1O+9esWRM9e/bEkSNHUFBQgJ07d+KNN94wWHwWFhZwcHCAk5MT3Nzc4OPjg5YtW6JXr15o0KCBwZ6HiDSTCPJZO4mqkUwmU1rq28fHp9pWRnrexcTE4N1338WKFSuYmHtKJScnIzMz09RhmD1z7R1qjlxdXavtRIWIng23b99GcXExrKystFplkkid999/X1wg4fz58wZPztHzoSp1Es9NydTYY46I6CmSnJyMoHFvoqiwwNShPDXmzZtn6hDMnrWNLdavW8vkHBERVauEhAQcOXIEANC8eXMm5YjoufTUJ+aKioqQmZkJqVRaYcUgIqJnTWZmJooKC5D3Qi+U2rHxSlVnkZ8J3D2GzMxMJuaIiMhg7ty5Azc3N7UrkT569AhTp05FUVERAGDs2LHVGR4Rkdkwy0xWfHw8AMDGxkbtSUJcXBwWLFiAU6dOobi4GBYWFujatStmzpzJ7vRE9MwrtXNFqaO7qcMgIiIiUunYsWP45Zdf0KVLF7Rr1w716tWDjY0N0tPTcfXqVezbtw95eXkAgHbt2mHkyJEmjpiIyDTMLjEXGRmJ1157DUDZVZOvvvqqwjYPHz7Ea6+9hszMTMinyCspKcHJkydx6dIlrF69Gq1bt67WuImIiIiIiOh/ioqKcOLECZw4cULtNt26dcOvv/4KS0vLaoyMiMh8mF1i7ujRoxAEARKJBMOHD1e5zYIFC5CRkQGJRFLhsby8PMyYMQO7d++GtbW1scMlIiIiIiKicgIDA2Fra4szZ87g/v37yMjIQGZmJmxsbODu7o42bdpg0KBB6NWrl6lDJSIyKbNLzF29ehUAUKNGDfj5+VV4PDk5GQcPHoREIoGdnR2+/fZb9OnTBw8fPsRnn32GqKgoxMfHY+/evQgICKju8ImIiIiIiJ57bm5uCAoKQlBQkKlDISIyaxamDqC8+Ph4SCQS+Pr6qnw8PDxcHL46adIkDBkyBI6OjmjatCl+/PFHcbvDhw9XS7xERERERERERET6MLvEXGpqKgCoXfTh3Llz4t8jRoxQeqxx48bw8/ODIAi4efOm8YIkIiIiIiIiIiKqIrNLzBUUFAAA7OzsVD4eEREBiUSCpk2bqkze1a9fH8D/EnxERERERERERETmyOwSczY2NgCA3NzcCo89ePBATLi1b99e5f4uLi4AgPz8fCNFSEREREREREREVHVml5irWbMmACA2NrbCY4rLbLdt21bl/jKZDID6HndERERERERERETmwOwSc82bNxfniIuLi1N6bMeOHeLfnTt3Vrl/QkICAKBWrVpGi5GIiIiIiIiIiKiqzC4x17dvXwBAaWkppk6dirNnzyI6OhrffPMNrl27BolEAn9/f9SuXbvCvkVFRYiOjoZEIkHjxo2rO3QiIiIioueCpaUlAKCkpASCIJg4GiJ6ngmCgJKSEgD/q5uIniZWpg6gvEGDBmH58uW4d+8e7ty5gwkTJlTYZtKkSSr3PXPmDPLz88XkHRERERERGZ6NjQ0KCgogCAJyc3Ph6Oho6pCI6DmVm5srXiCQz1lP9DQxux5zVlZWWLZsGTw9PSEIgtI/AAgKChJ71ZUXGhoq/q1uqCsREREREVWNfME1AEhLS2OvOSIyCUEQkJaWJt5WrJuInhZm12MOABo3bozdu3cjODgYFy9eRE5ODmrXro2BAweiR48eKvdJT09HVFQU6tatC0dHR7Rp06Z6gyYiIiIiek44OTlBIpFAEATIZDIkJCTAzc0NDg4OkEgkpg6PiJ5x8t66aWlp4gKQEokETk5OJo6MSHdmmZgDAEdHR7z55pt48803tdq+Ro0a2L9/v5GjIiIiIiIiCwsLeHl5ITExUUzOyWQySCQSzvFEREZXfn5LiUQCLy8vWFiY3aBAIo3MNjFHRETqWeRlmDoEekbwu0RE+nJ2dlZKzgFlvViKi4tNHBkRPU/kSTlnZ2dTh0KkFybmiIieQvb3jps6BCIiIjg7O8Pb2xsymQxZWVkoLCwUV0ckIjIWS0tL2NjYwMXFBU5OTuwpR081JuaIiJ5CeY1fRKm91NRh0DPAIi+DiV4iqhILCwu4uLhw0nUiIiI9mCQxd+HCBaXbHTt2VPtYVSiWS8++5ORkZGZmmjoMsxcXF6f0P6nn6uoKT09PU4ehUqm9FKWO7qYOg4iIiIiIiKrAJIm5cePGias1SSQS3LhxQ+VjVVG+XHq2JScnI2jcmygqLDB1KE+NefPmmToEs2dtY4v169aabXKOiIiIiIiInm4mG8qquIKKLo8RqZKZmYmiwgLkvdALpXaupg6HngEW+ZnA3WPIzMxkYo6IiIiIiIiMwiSJucqGmHL4KVVFqZ0rh/cRERERERER0VPBJIm5devW6fUYERERERERERHRs4JrChMREREREREREZkAE3NEREREREREREQmYLLFH4iIiIiIiIhMKSkpCTKZzNRhPBWcnJxQt25dU4dB9MxhYo6IiIiIiIieOxkZGQgKCkJpaampQ3kqWFhYICQkBFKp1NShED1TmJgjIiIiIiKi545UKsX69evNqsdcXFwc5s2bh9mzZ6Nhw4amDkeJk5MTk3JERsDEHBERERERET2XzHVoZsOGDeHt7W3qMIioGnDxByIiIiIiIiIiIhNgYo6IiIiIiIiIiMgEmJgjIiIiIiIiIiIyASbmiIiIiIiIiIiITICJOSIiIiIiIiIiIhNgYo6IiIiIiIiIiMgErEwdQHlr164FAEgkEowZMwbW1tYmjoiIiIiIiIiIiMjwzC4xN3/+fEgkErRo0QLjxo0zdThERERERERERERGYXZDWe3t7QEA3t7eJo6EiIiIiIiIiIjIeMyux1ytWrXw4MEDU4dBRGTWLPIzTR0CPSP4XSIiIiIiMh2zS8z5+fkhLi4OsbGxpg6FiMjsuLq6wtrGFrh7zNSh0DPE2sYWrq6upg6DiIieYcnJycjM5MUgTeLi4pT+p8q5urrC09PT1GEQVYnZJeYCAgKwe/duREVF4c6dO2jatKmpQ6KniEVehqlDoGeEuX6XPD09sX7dWjZstRAXF4d58+Zh9uzZaNiwoanDMWts1BIRkTElJycjaNybKCosMHUoT4158+aZOoSngrWNLdavW8t2DD3VzC4x16tXL/Tt2xfh4eH45JNPsGbNGl7FJ63Z3ztu6hCIjM7T05ONDx00bNiQ85YSERGZUGZmJooKC5D3Qi+U2vHcjgzDIj8TuHsMmZmZbBvTU83sEnMAsHDhQnzyySc4evQoBg8ejKlTp2LgwIFwcXExdWhk5vIav4hSe6mpw6BngEVeBhO9RERERAZUaueKUkd3U4dBRGRWzC4x9+abbwIABEGAlZUVUlJSMGfOHMyZMwf16tWDm5sbbG1tNZYjkUiwZs0aY4dLZqbUXsqDPRERERERERE9FcwuMXf+/HlIJBLxtvxvQRCQkJCAhIQEjWUIgqBUBhERERERERERkbkxu8QcUJZY0+V+IiIiIiIiIiKip43ZJebWrl1r6hCIiIiIiIiIiIiMzuwSc506dTJ1CEREpKOkpCTIZDJTh6EkLi5O6X9z4eTkhLp165o6DCIiIiIiMgNml5gjIqKnS0ZGBoKCglBaWmrqUFSaN2+eqUNQYmFhgZCQEEilUlOHQkREREREJsbEHBERVYlUKsX69evNrsecuXJycmJSjoiIiIiIADAxR88Yi/xMU4dAzwh+l3TDoZlERERERES6M/vEXGRkJHbu3IlLly7h0aNHyMrKQmlpKW7cuKG0XVZWFi5fvgwA8PT0hK+vrynCJRNxdXWFtY0tcPeYqUOhZ4i1jS1cXV1NHQYRERERERE9o8w2MZeWloZZs2bh+PHj4n2CIAAAJBJJhe3t7e3xxRdfIDU1FfXq1cPBgwerLVYyPU9PT6xftxaZmezlpElcXBzmzZuH2bNno2HDhqYOx6y5urrC09PT1GEQERERPRMs8jJMHQI9Q/h9omeFWSbmkpOTMXbsWDx8+FBMxmlibW2NMWPGYMmSJUhISMCVK1fQpk0b4wZKZsXT05NJFB00bNgQ3t7epg6DiIiIiJ4T9veOa96IiOg5Y5aJuQ8++ABJSUkAgKZNm2Ly5Mno0qULli5dig0bNqjdb9CgQViyZAkA4OTJk0zMERERERERmYm8xi+i1F5q6jDoGWGRl8FkLz0TzC4xFx4ejitXrkAikaB9+/b466+/YG9vD0D1EFZFjRo1gqenJx4/foyrV69WR7hERERERESkhVJ7KUod3U0dBhGRWbEwdQDl7d27FwBgaWmJhQsXikk5bfn4+EAQBNy7d88Y4RERERERERERERmE2SXm5L3l2rZti3r16um8f82aNQGULR5BRERERERERERkrswuMSdPqDVq1Eiv/W1sbAAAhYWFhgqJiIiIiIiIiIjI4MwuMSefR660tFSv/TMzMwEALi4uBouJiIiIiIiIiIjI0MwuMefm5gYASExM1Gv/GzduAABq1aplsJiIiIiIiIiIiIgMzewSc35+fhAEAVevXoVMJtNp38jISDx48AASiQTt2rUzUoTPlsLCQixfvhyDBg2Cv78/unTpgqlTp+L69eumDo2IiIiIiIiI6JlmZeoAyuvduzf27duH/Px8/Pnnn/jkk0+02q+oqAjz5s0Tb/ft29dYIT4zCgsL8fbbb+P8+fOoWbMmevfujZSUFBw8eBBHjx7FH3/8gZ49e5o6TCIiIiIiegZY5GeaOgR6hvD7RM8Ks0vMDRo0CEuXLkVCQgJWrlyJWrVq4c0336x0n7S0NMyYMQNXr16FRCJBy5Yt0a1bt2qK+On1119/4fz582jVqhVWr14NJycnAMCuXbvw8ccfY8aMGQgPDxfvJyIiIiIi0pWrqyusbWyBu8dMHQo9Y6xtbOHq6mrqMIiqxOwSc1ZWVvjuu+/w9ttvo6SkBAsWLEBoaCgGDRqE+Ph4cbtDhw4hJSUFEREROHjwIPLz8wEAdnZ2Sj3nSLXi4mKsXbsWAPD1118rJd8GDx6MsLAwHDt2DMHBwRg/frypwiQiIiIioqecp6cn1q9bKy7UR+rFxcVh3rx5mD17Nho2bGjqcMyeq6srPD09TR0GUZWYXWIOADp37owff/wRs2bNQn5+Pm7cuCEu6iBftXXq1Kni9oIgAAAcHBywaNEi+Pj4VH/QT5mIiAhkZGSgXr16aNWqVYXHX331VRw7dgyHDh1iYo6IiIiIiKrE09PTLBMoSUlJOs9t/rxycnJC3bp1TR0G0TPHLBNzADBw4EA0bdoU3333Hc6dO6f0mEQiEZNxcp06dcKXX36JZs2aVWeYWikpKUFsbCyioqJw/fp1REVF4datW2Ivv8DAQCxcuFDncg8dOoTQ0FBERUUhJSUFTk5OaNiwIfr27YsxY8ZUOgT15s2bAICWLVuqfLxFixYAgOjoaJ3jIiIiIiIiMncZGRkICgpCaWmpqUOpwBxHgVlYWCAkJARSqdTUoRA9U8w2MQcAzZo1w5o1a3Dr1i0cP34cly9fxuPHjyGTyWBvb4+aNWuidevWeOmll+Dv72/qcNX68MMPceDAAYOVl5OTg08++QSHDx9Wuj8tLQ1paWm4fPky1q9fj8WLF6NNmzYqy0hKSgIA1K5dW+Xj8vszMjKQk5MDR0dHg8VPRERERERkalKpFOvXr2ePOS05OTkxKUdkBGadmJPz9fWFr6+vqcPQW0lJidJtqVQKqVSK+/fv61XWBx98gBMnTgAA3N3dMWrUKDRt2hSZmZnYtWsXIiIi8PDhQ7z77rvYuHEjmjRpUqGc3NxcAIC9vb3K53FwcBD/ZmKOiIiIiIieRRyaSUSm9lQk5p52/v7+aNKkCVq2bImWLVuifv36CAkJwaxZs3Qua+vWrWJSrmnTplizZg3c3d3Fx9944w18//33WLlyJTIzM/HVV1/h33//NdhrISIiIiIiIiIiw2Birhq89957BimnpKQES5cuFW//8MMPSkk5uU8++QRnzpzBzZs3cfHiRZw8eRI9evRQ2kbeIy4vL0/lc8l71AFgbzkiIiIiIiIiIiOwMHUApL0LFy4gJSUFQNliF+oWbrC0tMS4cePE27t3766wjbzL9qNHj1SWIb9fKpUyMUdEREREREREZARm32OutLQUsbGxSExMhEwmQ3Fxsdb7Dhs2zHiBmcDx48fFv1988cVKt1V8XHE/uebNmwMArl+/rnL/GzduAAB8fHx0jpOIiIiIiIiIiDQz28RcYmIifv/9d+zbt09pWKW2JBLJM5eYi4mJEf9u1apVpdt6eHigTp06ePjwIVJTU5GWlgY3Nzfx8Xbt2kEqlSIhIQHXrl2rUN6ePXsAAC+//LIBX0El0tMBNcNq1XJyAtQsXoHUVEAQ9IvFwQFQ10swLQ0ot5iH1uzsAGdn1Y9lZABFRfqVa2MDuLqqfiwzEygsVLrLMi0NroWFsExLA/6/B6ZK1taAulWXsrOB/Hz94rW0BBS+i0pycgA9fu8AAIkEUDG0G0DZd6sqq215eKi+v6AAyMrSv9yaNQELFR2XCwvLPjt91agBWKmo3ouLy35r+nJ1Lfu+lVdaCjx5on+5Li6Ara3qxyr7jmrCOqKMjnWE1lhH/A/riDKsI8qwjijDOuJ/WEeUYR1RhnVEGXOqI/R9LiJDEczQ0aNHhTZt2gi+vr6Cj4+PXv98fX1N/TIqFRwcLHh7ewve3t7CzJkztdqnT58+4j7x8fEat3/jjTfE7S9cuFDh8aVLlwre3t7CiBEjhOzsbPH+nTt3Ct7e3kLnzp2V7jek7Oxs4eLFi+K/7NatBaHs8Kb9v6VL1T+Bu7vu5cn/ff21+nJbtNC/3MmT1Zfbq5f+5Y4cqb7ckSP1L7dXL/XlTp6sf7ktWqgv9+uv9S/X3V19uUuX6l9uZdXkli1VK/fxY9XlHjlStXKjolSXGxVVtXKPHFFd7uPHVSt3yxb173FVymUdUfaPdUTZP9YR//vHOqLsH+uIsn+sI8r+sY743z/WEWX/WEeU/XsO6ojs1q2Vz02NdA5MpI7Z9ZhLSkrCBx98gHyFrLW7uzt8fX0hlUphbW1twuhMKzs7W/y7Ro0aGreXKlyBUNxXbtKkSTh79izOnz+P/v37o2PHjkhNTcXFixdhbW2NH374AU5OTgaJ3RgSExPxJDJS5WMtSkr07g6anJyMZDXleufnw07PclOfPEGSmnJfyMmBvu90RmYmHqgpt0FmJqR6livLycFdNeXWffIEaq4pa5Sfn48YNeV6JifDU89yi0tKcENNuTUTE+GlZ7kAEKmmXNe4ODSsQrnXr19HiYoreo6xsWhShXKjo6NRoOJqq+2dO6jK4PTY2FjkqIjXMi0Nqme81E5cXBwy1bzH/lUol3VEGdYRZVhH/A/riDKsI8qwjijDOuJ/WEeUYR1RhnUEkfGZXWJu5cqVyM/Ph0QiQa1atfDtt9+iV69epg7LLCgO6bVV111bgeI2OTk5FR63sbHBP//8g5UrVyIsLAyHDx+Gg4MDXn75ZUyZMkXt4hLmoqSkBEVqumQLgmCccvUutWy+RLXllpbqXa5ggnJLq1IuoLbcEn277qPsMzdGuYD6eIurWG5xcTGKVZSty1ya6spVFbOlkcoVqlpuJb+5qmAd8b99WUewjii/P+sI1hGK+7KOYB1Rfn/WEawjFPdlHUFkXGaXmDt16hSAspVFV65ciSZNqnK9hzSxsbHBe++9h/fee8/UoejM0tJSbQ9KiURinHL1LhWwsLBQX66q+UG0JDFBuRZVKRdQW66lpaX+5UokRikXUB+vVRXLtbKygkRF2Vaq5nXRsVxVMRurXMuqllvJb64qWEf8b1/WEawjyu/POoJ1hOK+rCNYR5Tfn3UE6wjFfVlHEBmXRKhKut8I2rZti/z8fHTr1g3//POPqcMxmpCQEMyaNQsAEBgYiIULF2rcp1OnTsj8/4lcIyIi4Khu0tD/N3XqVBw8eBAA8Oeff6J3795VjNpwZDIZoqOjxds+tWrByU7HjtuckLWMjhOyxsbGYvr06Vi0aFHliW9zmpBVG5y0+X84aXMZ1hFlnodJm7XBOuJ/WEeUYR1RhnVEGdYR/8M6ogzriDLPQR0hy89H9OPH4m0fHx+zntKJnj1m12NOfiXGy6sqMzk8m5ydncXEXHp6usbEXEZGhtK+Zq1GjbKDn6Goa1hVlbpKXo2kpCTIFBtyDx8aOKD/l5ys9aZx2dnItLHB3exslGhqXCkcoAwqNbXCXU5OTqhbt676hkpV2Nurb1hVha2t+sZ2VdjYGKdcKyvjlGthYZxyAeOVayZ1hNbUNVyrSl1Du6qcndWfHFSFoyPrCIB1hCLWEWVYR5RhHVGGdcT/sI4owzqijLo6QiYz3nkPkRbMLjFXt25dxMTEqJwT7XnXuHFjJCQkAAASEhJQr169SreXbwsAL7zwglFjo4oyMjIQFBRUpTkSjGnevHmmDkGJhYUFQkJClBYtISIiIiIiInqWmV1irnfv3oiOjsbly5dNHYrZ8fb2xokTJwAA165dQ5cuXdRum5qaiof/3zurZs2acDPW1RdSSyqVYv369co95kgtJycnJuWIiIiIiIjouWJ2ibmxY8di/fr1ePjwIXbt2oXBgwebOiSz0bNnT3HevePHj2PSpElqtz127Jj4N1e1NZ26deuaOgQiIiIiIiIiMlP6L3diJJ6enli4cCEsLCzw1Vdfiau0UtniDx7/P0/C+fPncf36dZXblZSUYN26deLtV199tVriIyIiIiIiIiIi7Zmkx1xSUlKlj7do0QJz5szB3LlzMWnSJLz00kt45ZVX4O3tDWdnZ62Xp37WeitZWlpi8uTJ+OabbwAAM2fOxJo1a1CzZk2l7X766SfcvHkTANCuXTv07Nmz2mMlIiIiIiIiIqLKSQRB33We9efr66t1ck0QBK23VSSRSHDjxg2d9zOG+Ph4bNu2Tem+6OhoHDlyBEDZcsy9e/dWerxLly7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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Summary for bridge:\n", + " mean median min max\n", + "Format \n", + "Fog-VLA-DM-lossless 1.401418 1.319205 1.136809 1.830454\n", + "H264 1.708113 1.538449 0.955733 2.698478\n", + "HDF5 2.291325 2.065455 1.412598 3.823695\n", + "LEROBOT 0.242532 0.233347 0.198193 0.309825\n", + "RLDS 0.180912 0.138910 0.046215 0.416763\n", + "\n", + "Fog-VLA-DM-lossless:\n", + " On average, Fog-VLA-DM is 1.40x faster\n", + " Median speedup: 1.32x\n", + " Range: 1.14x to 1.83x faster\n", + "\n", + "H264:\n", + " On average, Fog-VLA-DM is 1.71x faster\n", + " Median speedup: 1.54x\n", + " Range: 0.96x to 2.70x faster\n", + "\n", + "HDF5:\n", + " On average, Fog-VLA-DM is 2.29x faster\n", + " Median speedup: 2.07x\n", + " Range: 1.41x to 3.82x faster\n", + "\n", + "LEROBOT:\n", + " On average, Fog-VLA-DM is 0.24x faster\n", + " Median speedup: 0.23x\n", + " Range: 0.20x to 0.31x faster\n", + "\n", + "RLDS:\n", + " On average, Fog-VLA-DM is 0.18x faster\n", + " Median speedup: 0.14x\n", + " Range: 0.05x to 0.42x faster\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Summary for nyu_door_opening_surprising_effectiveness:\n", + " mean median min max\n", + "Format \n", + "Fog-VLA-DM-lossless 1.512650 1.533295 1.275668 1.708343\n", + "H264 1.374171 1.363077 0.893099 1.833454\n", + "HDF5 1.598478 1.512395 1.357568 1.887998\n", + "LEROBOT 0.215221 0.199928 0.179151 0.258760\n", + "RLDS 0.543318 0.503186 0.194050 0.934344\n", + "\n", + "Fog-VLA-DM-lossless:\n", + " On average, Fog-VLA-DM is 1.51x faster\n", + " Median speedup: 1.53x\n", + " Range: 1.28x to 1.71x faster\n", + "\n", + "H264:\n", + " On average, Fog-VLA-DM is 1.37x faster\n", + " Median speedup: 1.36x\n", + " Range: 0.89x to 1.83x faster\n", + "\n", + "HDF5:\n", + " On average, Fog-VLA-DM is 1.60x faster\n", + " Median speedup: 1.51x\n", + " Range: 1.36x to 1.89x faster\n", + "\n", + "LEROBOT:\n", + " On average, Fog-VLA-DM is 0.22x faster\n", + " Median speedup: 0.20x\n", + " Range: 0.18x to 0.26x faster\n", + "\n", + "RLDS:\n", + " On average, Fog-VLA-DM is 0.54x faster\n", + " Median speedup: 0.50x\n", + " Range: 0.19x to 0.93x faster\n" + ] } ], "source": [ + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", - "# Data\n", - "batch_sizes = [1, 32, 64]\n", - "vla_latency = [0.008, 0.098, 0.185]\n", - "rlds_latency = [0.008, 0.097, 0.185]\n", + "# Read the CSV file\n", + "df = pd.read_csv('./format_comparison_results.csv')\n", "\n", - "# Create the plot\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(batch_sizes, vla_latency, marker='o', label='VLA')\n", - "plt.plot(batch_sizes, rlds_latency, marker='s', label='RLDS')\n", + "# Update the format names\n", + "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n", + "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n", "\n", - "# Customize the plot\n", - "plt.xlabel('Batch Size')\n", - "plt.ylabel('Latency (s)')\n", - "plt.title('Latency vs Batch Size for VLA and RLDS')\n", - "plt.legend()\n", - "plt.grid(True, linestyle='--', alpha=0.7)\n", + "# Calculate speedup factors\n", + "def calculate_speedup(group):\n", + " fog_vla_dm_time = group[group['Format'] == 'Fog-VLA-DM']['AverageLoadingTime(s)'].values[0]\n", + " group['SpeedupFactor'] = fog_vla_dm_time / group['AverageLoadingTime(s)']\n", + " return group\n", "\n", - "# Set x-axis to log scale\n", - "plt.yscale('log')\n", - "plt.xscale('log')\n", + "df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n", "\n", - "# Add data labels\n", - "for x, y in zip(batch_sizes, vla_latency):\n", - " plt.text(x, y, f'{y:.3f}', ha='right', va='bottom')\n", - "for x, y in zip(batch_sizes, rlds_latency):\n", - " plt.text(x, y, f'{y:.3f}', ha='left', va='top')\n", + "# Get unique datasets\n", + "datasets = df['Dataset'].unique()\n", "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" + "# Create a plot for each dataset\n", + "for dataset in datasets:\n", + " plt.figure(figsize=(12, 6))\n", + " sns.set_style(\"whitegrid\")\n", + " \n", + " # Filter data for the current dataset\n", + " dataset_df = df[df['Dataset'] == dataset]\n", + " \n", + " # Create the box plot\n", + " sns.boxplot(x='Format', y='SpeedupFactor', data=dataset_df[dataset_df['Format'] != 'Fog-VLA-DM'])\n", + " \n", + " # Customize the plot\n", + " plt.title(f'Latency Speedup Factor of Fog-VLA-DM Compared to Alternatives - {dataset}')\n", + " plt.xlabel('Format')\n", + " plt.ylabel('Speedup Factor (higher is better)')\n", + " plt.yscale('log')\n", + " \n", + " # Add a horizontal line at y=1 to represent Fog-VLA-DM\n", + " plt.axhline(y=1, color='r', linestyle='--', label='Fog-VLA-DM')\n", + " \n", + " plt.legend()\n", + " plt.tight_layout()\n", + " \n", + " # Save the plot\n", + " plt.savefig(f'latency_speedup_comparison_{dataset}.pdf')\n", + " plt.show()\n", + " \n", + " # Print summary statistics for the current dataset\n", + " summary = dataset_df[dataset_df['Format'] != 'Fog-VLA-DM'].groupby('Format')['SpeedupFactor'].agg(['mean', 'median', 'min', 'max'])\n", + " print(f\"\\nSummary for {dataset}:\")\n", + " print(summary)\n", + " \n", + " # Print interpretation of the summary\n", + " for format, stats in summary.iterrows():\n", + " print(f\"\\n{format}:\")\n", + " print(f\" On average, Fog-VLA-DM is {stats['mean']:.2f}x faster\")\n", + " print(f\" Median speedup: {stats['median']:.2f}x\")\n", + " print(f\" Range: {stats['min']:.2f}x to {stats['max']:.2f}x faster\")" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "b09fd4cc", + "execution_count": 20, + "id": "e030fe63", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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DatasetFormatTrajectoryLoadingTime(s)FileSize(MB)Throughput(traj/s)
0berkeley_autolab_ur5RLDS00.045454237.46154922.000367
1berkeley_autolab_ur5RLDS10.016615126.82606660.187754
2berkeley_autolab_ur5RLDS20.017593157.58214556.839549
3berkeley_autolab_ur5RLDS30.017673157.04762156.583439
4berkeley_autolab_ur5RLDS40.026880187.19503637.203005
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1275nyu_door_opening_surprising_effectivenessHDF5590.01951475.30505451.246292
1276nyu_door_opening_surprising_effectivenessHDF5600.01618361.43493761.792713
1277nyu_door_opening_surprising_effectivenessHDF5610.028054108.99004435.645542
1278nyu_door_opening_surprising_effectivenessHDF5620.01944375.30505451.432299
1279nyu_door_opening_surprising_effectivenessHDF5630.026315103.04568538.001178
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", 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", 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", 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", "text/plain": [ - " Dataset Format Trajectory \\\n", - "0 berkeley_autolab_ur5 RLDS 0 \n", - "1 berkeley_autolab_ur5 RLDS 1 \n", - "2 berkeley_autolab_ur5 RLDS 2 \n", - "3 berkeley_autolab_ur5 RLDS 3 \n", - "4 berkeley_autolab_ur5 RLDS 4 \n", - "... ... ... ... \n", - "1275 nyu_door_opening_surprising_effectiveness HDF5 59 \n", - "1276 nyu_door_opening_surprising_effectiveness HDF5 60 \n", - "1277 nyu_door_opening_surprising_effectiveness HDF5 61 \n", - "1278 nyu_door_opening_surprising_effectiveness HDF5 62 \n", - "1279 nyu_door_opening_surprising_effectiveness HDF5 63 \n", - "\n", - " LoadingTime(s) FileSize(MB) Throughput(traj/s) \n", - "0 0.045454 237.461549 22.000367 \n", - "1 0.016615 126.826066 60.187754 \n", - "2 0.017593 157.582145 56.839549 \n", - "3 0.017673 157.047621 56.583439 \n", - "4 0.026880 187.195036 37.203005 \n", - "... ... ... ... \n", - "1275 0.019514 75.305054 51.246292 \n", - "1276 0.016183 61.434937 61.792713 \n", - "1277 0.028054 108.990044 35.645542 \n", - "1278 0.019443 75.305054 51.432299 \n", - "1279 0.026315 103.045685 38.001178 \n", - "\n", - "[1280 rows x 6 columns]" + "
" ] }, - "execution_count": 2, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df" + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set_context(\"poster\")\n", + "\n", + "# Read the CSV file\n", + "df = pd.read_csv('./format_comparison_results.csv')\n", + "\n", + "# Define colors and markers for each format\n", + "format_styles = {\n", + " 'LEROBOT': ('red', '^'),\n", + " 'RLDS': ('purple', 'D'),\n", + " 'Fog-VLA-DM': ('blue', 'o'),\n", + " \"Fog-VLA-DM-lossless\": ('orange', 'o'),\n", + " 'HDF5': ('green', 's'),\n", + "}\n", + "\n", + "# Update the format name from 'VLA' to 'Fog-VLA-DM' in the DataFrame\n", + "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n", + "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n", + "\n", + "# Update the format_styles dictionary\n", + "format_styles['Fog-VLA-DM'] = format_styles.pop('VLA', ('blue', 'o'))\n", + "\n", + "# Get unique datasets and batch sizes\n", + "datasets = df['Dataset'].unique()\n", + "\n", + "# Create a figure for each dataset\n", + "for dataset in datasets:\n", + " plt.figure(figsize=(6, 6))\n", + " \n", + " dataset_df = df[df['Dataset'] == dataset]\n", + " \n", + " # Create the line plot\n", + " for format, (color, marker) in format_styles.items():\n", + " data = dataset_df[dataset_df['Format'] == format]\n", + " # Calculate throughput: (1 / loading time) * batch size\n", + " throughput = (1 / data['AverageLoadingTime(s)']) * data['BatchSize']\n", + " plt.plot(data['BatchSize'], throughput, \n", + " color=color, marker=marker, label=format, linewidth=2, markersize=8)\n", + "\n", + " # Customize the plot\n", + " # plt.xlabel('Num of Concurrent Reads')\n", + " # plt.ylabel('Throughput (trajectories/s)')\n", + " # plt.title(f'{dataset}')\n", + " # plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + " # plt.xscale('log') # Use /log scale for x-axis\n", + " plt.yscale('log') # Use log scale for y-axis\n", + " plt.tight_layout() # Adjust layout to make room for the legend\n", + " \n", + " # Add a grid for better readability\n", + " plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", + "\n", + " # Show the plot\n", + " plt.savefig(f'./{dataset}_throughput.pdf')\n", + " plt.show()\n", + "\n", + "# ... (rest of the existing code remains unchanged) ..." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "7cb9a3c1", + "execution_count": 2, + "id": "adc9dbca", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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zK7/Z70K9evWsEyZMyMpLYif9td+4caNdeadOnazBwcHWixcv2soOHDhgfeCBB6wvvPCCrSz9OhszZkyOjne9Rx991NqgQQPbdvrre/LkSVvZiy++aK1du3aGtpld4z/99JM1ICDAunbtWrvybdu2ZShPv162bdtmVzc8PNxau3Zt67Fjx+zK3333XWvVqlWtZ86csVqt2XuPnDBhQobf0Vu52e9vuvRreeHChbayG68rq9VqHTBggLVly5Z2Ze3bt89wTVqtWf+7MmzYsFvGZrVm/f1g37591oCAAOvnn39uV++PP/647bUDIO9iWC9wD0hLS9OOHTv08MMP2z2HWaxYMXXo0EG//PKLLl++LOnaDK5ms9nW7uLFi/Lw8JC/v7/++OMPW9tt27bJz8/P7nkvd3f3TO+43Uy7du3k7e1t265fv76ka9+yS9eGbh0+fFidO3e2u7PVsGFDBQQEZOclcFj60DDp2l2FGjVqyGq12pV7eXnJ39/fFrckbdq0SZUqVVLFihV14cIF27/GjRtLkt1SOzf6/fffdf78efXq1cvuOcTmzZurYsWK+u6775x4hvY8PDx05coVp/WX3dehQYMGds+tWq1WffXVV2rRooWsVqtdH02aNFF8fHyGIaddu3a1e4bwxuvKUbVq1dKCBQvs/qXftdy2bZtcXFzshrNK0oABA2S1WrVt27Zbnuf1unXrJpPJZNuuWbNmhmsu/Vq88Zyuf6YxNjZW8fHxqlevnt3v7q14eXlp7969dsPHHRUTE6MDBw6oS5cu8vHxsZU/8MADCgkJ0datWzO06dmzZ46Pmy43ruVChQopNDTU7jqsXr26PDw8MlzLZcqU0YMPPpihj3r16snLy8uuj5CQEKWlpemnn36yq3+798jckP6c7/Wv3fXXVXx8vC5cuKCGDRvq5MmTWRoKm9W/K15eXjp79mymj3dIjr0f3Ch9RvLvv/9eiYmJt40dQN7CsF7gHnDhwgUlJibK398/w75KlSrJYrHon3/+UZUqVWSxWLRo0SItW7ZMp06dUlpamq3u9R8wT58+rfLly9t9iJaU6TFupmTJknbb6R/C0oc3njlzRpIyHapavnz5LH/gzokbl5AoVKiQ8ufPn2GZikKFCunSpUu27RMnTujIkSN2s4Ve7/z58zc9Zvp5Z/ZaVqxYUb/88ktWw8+2hIQEFSlSxGn9Zfd1uHGY54ULFxQXF6eVK1dq5cqVmfZx41DbG39m6RPb5HT918KFC990RuTTp0+rWLFiGZbiSR8OeuMyNDee5/Uyu+akjL8vhQoVyvCs47fffquPPvpIBw4csHum98bf05sZO3asxo0bp+bNm6t69epq1qyZOnfunGFysay41XVcqVIlff/99xkmPbrV65JdCQkJWRqunVUnTpxQfHy8w9dyeh+HDh26aR83Xsu3e4/MDQkJCZLsh7r/8ssvmjlzpn799dcMCV18fPxth8Jm9e/K4MGDtXPnTj3++OMqX768QkND1aFDB9uwXEfeD25UtmxZPfXUU1qwYIHWrVun+vXrq0WLFnr00UcZ0gvcBUhOgfvM7Nmz9cEHH+ixxx7TqFGj5O3tLbPZrLfeesvpy8HcbOkWZx8nJ9K/7b9eVuK2WCwKCAi46VqrJUqUcE6ATnT27FnFx8fn6LnVG2X3dbhxNtP0SWIeffRRdenSJdM+bpwAJrOfmZS3rqtbzdp6s/hvVp7u559/1rBhw9SgQQO99tpr8vPzk5ubmz7//HOtX78+S3G1a9dO9evX15YtW7Rjxw7NmzdPn3zyiWbOnKlmzZplqY+ccNaMxSkpKTp+/LiqVKnilP6ka9dikSJF9O6772a6/8YvrDL7GVssFoWGhmrQoEGZ9lGhQgW7bSPeIw8fPizp2heA0rVlgp588klVrFhR48aNU8mSJeXm5qatW7cqIiLCbiKnm8nq35VKlSpp06ZN+u6777R9+3Z99dVXWrZsmUaMGKFnnnnGofeDzIwbN05dunTR119/rR07dmjSpEmaM2eOVq1alSffmwH8P5JT4B7g6+srd3d3HTt2LMO+o0ePymw2276h37x5sxo1aqS33nrLrl5cXJxtgXpJKl26tA4fPiyr1Wp3VyazYzgq/Q7S33//nWHf9bOv5kXlypXTwYMHFRwcnOW7VunSz/vYsWMZ7rAcO3Ysw501Z1mzZo0kqUmTJk7rMyevg3Tt2vX09JTFYsnyOq5Z4Ugst1K6dGnt2rVLly9ftrt7evToUdv+3LZ582blz59f8+bNsxvW/Pnnn2ern2LFiql3797q3bu3zp8/ry5dumj27NnZTk6vv45vdPToURUuXPi2S8U4avPmzUpKSnL6tbxr1y7VrVvX4SVhypUrp4SEhDx7LaelpWn9+vVyd3e33a385ptvdPXqVX300Ud27z2ZPZpws1iy+ndFujasuF27dmrXrp2uXr2qkSNHavbs2QoLC8vW+8HtXpf0GXyHDx+u3bt3q1evXlq+fPlNJ9ACkDfwzClwD3BxcVFoaKi+/vpr2zIK0rVZD9evX6969erZPlC7uLhk+FZ+48aNGZ5Ba9q0qWJiYuyWqUhMTNSqVaucFnfx4sUVEBCgyMhIu+effvzxR9u3+3lV27ZtFR0dnenrkZSUZBs6l5kaNWqoSJEiWrFihd3QzK1bt+rIkSO2GTSdadeuXfrwww9VpkwZPfroo07rNyevg3TtemzdurU2b96c6c/8dkP4bsbd3d2py0Y0bdpUaWlpdkuXSFJERIRMJpOaNm3qtGPdjIuLi0wmk92QyVOnTunrr7/OUNfDwyPD0NC0tLQMr0mRIkVUrFixLC9/dL1ixYqpatWqioyMtDvW4cOHtWPHjly7E3vw4EG99dZb8vb2ts0k6wxt27ZVWlpapjNtp6amZmmobdu2bbVnzx7bEknXi4uLU2pqarbjcnd3t7XPibS0NE2aNElHjhxR37597f4mSPZ3a+Pj4zP90sPd3T3TOLL6d+XixYt22/ny5VOlSpVktVqVkpKSrfeDm70uly9fzvA6BwQEyGw2O3SdA7izuHMK3EU+//zzTD/09OvXT88++6x27typJ554Qk888YRcXFy0cuVKXb161W6x9ebNmys8PFwvvfSS6tSpo8OHD2vdunUZnjnr3r27li5dqhdffFH79++Xn5+f1qxZ4/RF5kePHq3hw4erV69e6tq1q23twoCAAKdOduJsnTp10saNG/Xaa68pKipKdevWVVpamo4ePapNmzZp7ty5CgoKyrStm5ubxo4dq5deekl9+vRR+/btbUvJlC5dOsPSKdm1bds2HT16VGlpafr3338VFRWlHTt2qFSpUvroo4+cNrRSytnrkO65555TVFSUunfvrscff1yVK1dWbGys9u/fr127dunHH3/MdlzVq1fXl19+qcmTJysoKEgeHh45Wuu0RYsWatSokaZPn25bP3XHjh36+uuv1b9/f6cOlb6ZZs2aacGCBRo0aJA6dOig8+fPa9myZSpXrpwOHTpkV7d69eratWuXFixYoGLFiqlMmTLy9/dXs2bN1Lp1az3wwAPy8PDQzp079dtvvzm8fu4LL7ygwYMHq0ePHurWrZttKZlChQrp6aefzvE5//zzz0pOTpbFYtGlS5e0e/duffPNNypYsKBmzZolPz+/HB8jXcOGDdWjRw/NmTNHBw4cUGhoqNzc3HT8+HFt2rRJr7zyit0EcZkZOHCgvvnmGw0dOlRdunRR9erVlZiYqMOHD2vz5s36+uuvMwwPvp30JX4mTZqkJk2a2JaDuZX4+HjbSImkpCSdOHFCW7Zs0d9//6327dtr1KhRtrrp5zl06FD17NlTV65c0aeffqoiRYro3LlzGWJZvny5PvzwQ5UvX16+vr4KDg7O8t+VgQMHqmjRoqpbt66KFCmio0ePasmSJWrWrJktWc7q+0G5cuXk5eWlFStWyNPTUx4eHqpZs6YOHTqkiRMnqk2bNqpQoYLS0tK0Zs0aW+ILIG8jOQXuIsuXL8+0vGvXrqpSpYqWLl2qadOmac6cObJarapZs6beeecd2xqn0rU1IBMTE7Vu3Tp9+eWXqlatmubMmaNp06bZ9enu7q6IiAi98cYbWrJkiQoUKKCOHTuqadOmN32eyhEtWrTQe++9p5kzZ2ratGmqUKGCJk+erMjISP35559OO46zmc1mhYeHKyIiQmvWrNGWLVvk7u6uMmXKqG/fvredOKpr164qUKCAPvnkE7377rvy8PDQww8/rOeff942wY+jZsyYIelaEuzj46OAgAC9/PLL6tq1a4YJfXIqp6+DdG2txE8//VTh4eHasmWLli9fLh8fH1WuXFljx451KK4nnnhCBw4c0OrVqxUREaHSpUvnKDk1m8366KOPNGPGDH355ZdavXq1SpcurRdeeEEDBgxwuN/sCA4O1ptvvqlPPvlEb731lsqUKaOxY8fq9OnTGZLTcePGafz48Xr//feVlJSkLl26aOLEierVq5d27Nihr776SlarVeXKldNrr72mJ554wqGYQkJCNHfuXM2YMUMzZsyQq6urGjRooOeff96hSZZutHjxYknXruVChQqpUqVKGjlypLp3757tJC8rJk6cqBo1amjFihWaPn26XFxcVLp0aT366KOqW7fubdu7u7tr8eLFmjNnjjZt2qTIyEgVLFhQFSpU0MiRIx2akOeRRx5R3759tWHDBq1du1ZWq/W2yenZs2f1wgsvSLp2F71YsWKqXbu2Xn/9dYWGhtrVrVixombMmKH3339fb7/9tooWLapevXrJ19c3w/q9I0aM0JkzZzR37lxduXJFDRs2VHBwcJb/rvTo0UPr1q3TggULlJCQoBIlSqhv374aPny4rU5W3w/c3Nw0ZcoUvffee3r99deVmpqqyZMnq0GDBmrSpIm+/fZbRUdHy93dXYGBgfrkk09Uu3btbL/+AO4skzUvzSABAP/TqVMn+fr6asGCBUaHAgAAgDuAZ04BGColJSXD80FRUVE6ePCgGjZsaFBUAAAAuNMY1gvAUNHR0Xrqqaf06KOPqlixYjp69KhWrFghPz8/9ezZ0+jwAAAAcIeQnAIwlLe3t6pXr65PP/1UFy5ckIeHh5o1a6axY8dmWIIAAAAA9y6eOQUAAAAAGI5nTgEAAAAAhiM5BQAAAAAYjmdOc2jPnj2yWq1yc3MzOhQAAAAABkpJSZHJZFKdOnWMDuWuRHKaQ1arVTy2CwAAAIC8IGdITnMo/Y5pUFCQwZEAAAAAMNJvv/1mdAh3NZ45BQAAAAAYjuQUAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjtl6AQAAkCdYrValpaUpNTXV6FCADNzc3OTi4mJ0GPc0klMAAAAYymq16tKlSzp37pzS0tKMDge4KR8fH5UoUUImk8noUO5JJKcAAAAw1NmzZ3Xp0iV5eXnJy8tLrq6ufPhHnmK1WpWQkKCYmBhJUsmSJQ2O6N5EcgoAAADDpKWlKTY2Vn5+fipatKjR4QA35e7uLkmKiYlRsWLFGOKbC5gQCQAAAIZJSUmR1WqVp6en0aEAt+Xh4SHp2nUL5yM5BQAAgOEYxou7Addp7iI5BQAAAAAYjuQUAAAAAGA4klMAAADgDlm9erUCAwMz/ffuu+8aHV627N69WzNnzlRcXJzRoeAewWy9AAAAwB32zDPPqEyZMnZlAQEBBkXjmD179mjWrFnq0qWLvLy8jA4H9wCSUwAAAOAOa9q0qYKCgpzaZ0JCgm02WeBuxLBeAAAAIA/ZtWuXnnjiCdWuXVv169fXsGHDdOTIEbs6M2fOVGBgoP766y8999xzatCggZ544glJUosWLRQWFqaoqCh17dpVNWvWVMeOHRUVFSVJ+uqrr9SxY0cFBQWpa9eu+uOPP+z6PnjwoMaNG6eWLVsqKChIoaGheumll3Tx4kW740+dOlWS1LJlS9vQ5FOnTuXmS4N7HHdOAQAAgDvs8uXLunDhgl2Zr6+vdu7cqcGDB6tMmTJ6+umnlZSUpCVLlqhXr15avXp1hqHAo0aNUvny5TV69GhZrVZb+YkTJ/Tcc8+pZ8+eevTRRzV//nwNHTpUEyZM0PTp09WrVy9J0scff6xnn31WmzZtktl87b7Vzp07dfLkSXXt2lV+fn76888/tWrVKv31119atWqVTCaTWrVqpePHj2v9+vV66aWXVLhwYds5AI4iOQUAAADusCeffDJD2aFDhzR16lR5e3tr5cqV8vHxkSQ9/PDD6tKli2bOnKm3337brs0DDzygadOmZejr2LFjWrFiherUqSNJqly5sgYOHKj//Oc/2rhxo0qVKiVJ8vb21vjx4/XTTz+pUaNGkqQnnnhCAwYMsOuvdu3aGjNmjH755RfVr19fDzzwgKpVq6b169fr4YcfzpA0A44gOcVdLTw8XJGRkercubNGjBhhdDgAAABZMn78ePn7+9uVxcTE6MCBAxo0aJAtMZWuJaAhISHaunVrhn569uyZaf+VK1e2JaaSVKtWLUlS48aNbYnp9eUnT560JacFChSw7U9OTtaVK1ds9fbv36/69etn51SBLCM5xV0rKSlJkZGRslgsioyM1MCBA+3eTAEAAPKqmjVrZpgQ6ddff5WkDEmrJFWqVEnff/99hkmPbnbHsmTJknbbhQoVkiSVKFHCrrxgwYKSZLcczKVLlzRr1ix9+eWXOn/+vF39+Pj4W50WkCMkp7hrpaamymKxSJIsFotSU1MNjggAAODOyp8/f6blLi4u2Sq//nnVZ599Vnv27NHAgQNVtWpVeXh4yGKxaNCgQXb1AGcjOQUAAADygPThtseOHcuw7+jRoypcuHCuLxUTGxurXbt2aeTIkXr66adt5cePH89Q12Qy5WosuP+wlAwAAACQBxQrVkxVq1ZVZGSk3TDbw4cPa8eOHWrWrFmux3CzO6sLFy7MUObu7i6Job5wHu6cAgAAAHnECy+8oMGDB6tHjx7q1q2bbSmZQoUK2d3JzC0FCxZUgwYNNHfuXKWkpKh48eLasWNHpuuXVq9eXZI0ffp0tWvXTm5ubnrooYdy/e4u7l3cOQUAAADyiJCQEM2dO1c+Pj6aMWOG5s+fr1q1amn58uUqW7bsHYlh2rRpatKkiZYtW6b33ntPrq6u+uSTTzLUq1mzpkaNGqWDBw/qpZde0pgxYzKs3Qpkh8max55qPnLkiCZNmqQ9e/bI09NTnTp10rPPPqt8+fLdtE1MTIwiIiK0Y8cO/f333ypUqJAaNGigMWPGqHTp0rZ6UVFR6tevX4b27dq10/Tp0x2K97fffpOkDLOtIfddvnxZnTp1sm2vWbPGNuMcAAC4OyQlJenYsWPy9/dn1n3kebe7XskNciZPDeuNjY1V//79VaFCBc2cOVPR0dGaMmWKkpKSNH78+Ju2279/v7Zs2aLHHntMtWrV0sWLF/XRRx/p8ccf1/r16+Xr62tXf/LkyapYsaJtu3Dhwrl2TgAAAACA28tTyemKFSt05coVzZo1y7bwcFpamiZMmKCwsDAVL14803b16tXTxo0b5er6/6dTt25dNW/eXJGRkRowYIBd/SpVqvBtBgAAAADkIXnqmdNt27YpODjYlphKUtu2bWWxWLRjx46btvPy8rJLTKVrCwz7+voqJiYmt8IFAAAAADhJnkpOjx49ajfcVrqWePr5+eno0aPZ6uvYsWM6f/68KlWqlGHfkCFDVLVqVTVt2lRvv/22kpKSchQ3AAAAACBn8tSw3ri4OHl5eWUo9/b2VmxsbJb7sVqtmjRpkooVK6b27dvbygsVKqRBgwapQYMGyp8/v3744QfNnz9fR48e1Zw5cxyO22q1KiEhweH2cMyNr3lCQoLM5jz1fQsAALiN5ORkWSwWpaWlKS0tzehwgFtKS0uTxWJRYmKiLBZLhv1Wq1Umk8mAyO4NeSo5dZaZM2fqhx9+0Ny5c+3WWapWrZqqVatm2w4ODlaxYsU0ceJE7du3TzVr1nToeCkpKTpw4ECO40b2JCYm2m0fPnzYthg0AAC4e7i6uio5OdnoMIDbSk5OVmpq6i1Hdd5qlRHcWp5KTr28vBQfH5+hPDY2Vt7e3lnqY9WqVQoPD9ebb76p4ODg29Zv27atJk6cqN9//93h5NTNzU2VK1d2qC0cd/nyZbvtgIAAlpIBAOAuk5ycrDNnzih//vwsJYO7gqurq8qVK6f8+fNn2PfXX38ZENG9I08lpxUrVszwLUR8fLzOnTuX4VnUzGzZskWvv/66nnnmGXXr1i23wszAZDLZ3aHFnXHjUAoPDw9+DgAA3GXMZrPMZrNcXFzk4uJidDjALbm4uMhsNsvd3T3TL1MY0pszeeoBvaZNm2rnzp2Ki4uzlW3atElms1mhoaG3bBsVFaUxY8bo8ccf14gRI7J8zA0bNkhioVwAAAAAMFKeunPas2dPLV68WCNGjFBYWJiio6M1depU9ezZ026N0/79++vMmTPasmWLJOnIkSMaMWKEKlSooE6dOunXX3+11fX19VW5cuUkSWPHjlX58uVVrVo124RIERERevjhh0lODWaxWAybzMjIYwMAAAC4Jk8lp97e3lq4cKHeeOMNjRgxQp6enurWrZtGjx5tVy99Rrd0e/fuVXx8vOLj49WrVy+7ul26dNGUKVMkSVWqVNG6des0f/58paSkqHTp0ho6dKiGDBmS+yeHWzKbzZqzdZHOxEZnuU1qcord9uSNM+Sa3y1bxy3lXVxhzfplqw0AAAAA58tTyakkVapUSREREbess3jxYrvtrl27qmvXrrftOywsTGFhYTkJD7noTGy0Tpw/leX6lqv2082fvHBG5nw8qwIAAIwzc+ZMzZo1y7bt4+OjihUraujQoWrWrJmtvEWLFmrevLnGjx+faT+rV6/WSy+9ZNv28PBQ8eLFVadOHfXq1SvDRJ6pqalavny5Pv30U508eVKurq4qWbKk6tevr3HjxjGDLO4KeS45BQAAANJZLFaZzXd+kpmcHLdAgQJauHChJCkmJkazZ8/W0KFDtXTpUtWtWzdbfc2dO1eFChVSYmKijh07ps8//1zdu3fXmDFj7Eb/TZo0SatXr9aQIUNUt25dJSYm6sCBA1q7dq2SkpJITnFXIDkFAABAnmU2mxS+fIdOx8TesWOWLuatEb1uPRnnrZjNZtWuXdu2XatWLTVr1kyRkZHZTk6rV68uX19fSVJwcLB69uypF198Ue+9957q1q2r+vXrKzExUZ999pmGDh2qp59+2ta2ZcuWevrpp2W1Wh0+F+BOIjkFAABAnnY6JlbHT180OgyHFS9eXL6+vjpz5kyO+zKbzXrllVe0efNmLV++3JacpqSkqFixYpm2YXkT3C1ITgEAAIBcdOXKFcXGxqpMmTJO6c/Hx0fVq1fXnj17JF1bnaJUqVL66KOP5OnpqSZNmsjb29spxwLuJNbPAAAAAJwsNTVVqampOnPmjF599VV5enqqXz/nrRBQsmRJ/fvvv7btKVOm6OrVqxozZowaNWqkdu3aadq0abpw4YLTjgnkNu6cAgAAAE6UkJCg6tWr27ZdXFz04YcfqmLFik47htVqtRuu26hRI23ZskXbtm3Trl279MMPP+jjjz/W6tWrtXr1ahUvXtxpxwZyC3dOAQAAACcqUKCAPvvsM3366ad655135OfnpxdffFExMTFOO8bZs2dVtGhRuzIPDw+1adNGEyZM0ObNmzVp0iT9+++/mj9/vtOOC+QmklMAAADAicxms4KCglSzZk09+uijmjVrluLi4hQeHu6U/i9evKjff/9dderUuWW9xx9/XD4+Pjpy5IhTjgvkNpJTAAAAIBcFBQWpffv2Wr16tc6dO5ejviwWi9566y2lpKSod+/ekqSUlBTFxmZcauf8+fOKj4+Xn59fjo4J3Ck8cwoAAIA8rXSxOzvzbG4cb/jw4fryyy+1cOFCjR07VpL0999/a9OmTXb1zGazHnnkEdv2/v37VahQISUlJenYsWP6/PPPtX//fj3//PO2O6fx8fFq3bq1OnXqpMaNG8vb21unTp3S/PnzZTab1atXL6efD5AbSE4BAACQZ1ksVo3oFWrIcc1m560PWrFiRbVr107Lly9XWFiYJGn79u3avn27XT0XFxf98ccftu1BgwZJuvY8abFixVS3bl2NHz9eNWvWtNUpWLCgBg8erO3bt2vTpk2KjY1V0aJFFRQUpClTpthNzgTkZSar1Wo1Ooi72W+//Sbp2nAN5Mxra9/RifOnslzfcjVNp1cdsG2X7l5V5nwu2Tpm+SJlNOHR57PVBgAAOE/6HUF/f38VKFDA6HCAW7rd9UpukDM8cwoAAAAAMBzJKe5eZpOUPtrG9L9tAAAAAHclklPctcyuZhUMLCKZpIKBRWR25XIGAAAA7lZMiIS7WuH6JVW4fkmjwwAAAACQQ9xqAgAAAAAYjuQUAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjuQUAAAAcIKhQ4fqkUceuen+xYsXKzAwUH///bcCAwO1adOmLPU7adIkBQYGKjw8PMux9O3bV4GBgfrggw8y7OvUqZPGjRuX5b6ud+HCBU2ZMkWtW7dWUFCQ6tatqz59+ujTTz9VWlqaQ33ezLhx49ShQwen9om8jeQUAAAAeZbVYrlrjtuhQwedOHFC+/bty3T/hg0bVLt2bZnNWf8InpaWpo0bN0qS1q9fn+2YFi9erPj4+Gy3y8yJEyfUpUsXrV+/Xt27d9cnn3yi999/XzVr1tTkyZP17bffOuU4uH+xzikAAADyLJPZrGPrP1Hi+X/u2DHdi5SUf4fB2W7XsmVLeXh4aP369apZs6bdvlOnTmnPnj169dVXs9Xnrl279O+//yokJEQ7d+7U/v37Vb169Sy1rVmzpv766y8tWrRII0aMyNZxMzN27FilpaXp888/V/HixW3lTZs2VZ8+fZyWBOP+RXIKAACAPC3x/D9KjP7b6DBuy93dXS1bttTGjRs1btw4uzukGzZskIuLi9q1a6fExMQs97l+/Xp5enpqypQpatmypdatW5fl5NTX11c9e/bUokWL1L9/fxUsWPCmdX/66SdNmzZNf/zxh9zd3dWiRQu9+OKL8vHxkST9/PPP2rdvn1599VW7xDRdqVKlbP8/cuSIZs2apd27d+vSpUsqXbq0unXrpieffNLuNbl69arCw8O1fv16RUdHy9fXVyEhIZoyZYpd31FRUZo8ebKOHz+uypUr6/XXX1eNGjVs+61Wq+bPn69Vq1bp9OnTKl68uPr27asnn3wyS68T8g6G9QIAAABO0rFjR8XExCgqKsqufP369QoJCVGRIkWy3FdycrK++uortWrVSsWLF1eTJk20YcMGWbIx5HjAgAFKTEzU0qVLb1rn999/11NPPSVPT0998MEHGjt2rL799lsNHjzY9hzpjz/+KEl68MEHb3vMmJgY+fv767XXXtPHH3+s7t27Kzw8XB9++KFdvZEjRyoiIkKPPfaYPv74Y73wwgtKSEiwq3Pu3DlNmjRJAwcO1Pvvv6/k5GQ9/fTTSklJsdV58803NWPGDHXu3Fkff/yxunTponfffVfLly/P8uuEvIE7pwAAAICThIaGytfXVxs2bFBwcLAk6fDhwzp8+LAGDhyYrb6++eYbXblyxTYpUMeOHfXtt98qKirK1vft+Pn5qXv37lqwYIH69u0rDw+PDHVmz54tPz8/zZ49W25ubpKkkiVLauDAgdq6datatGih6OhoSfZ3SG8mODjYFp/ValW9evWUlJSkJUuW6Omnn5Yk7dixQ999952mTZtmN+nRjRMgxcbGasmSJapSpYqka3en+/Xrp71796p+/fr6+++/tWTJEk2YMEE9evSQJIWEhCgpKUnh4eHq0aNHtp7xhbH4SQEAAABO4urqqjZt2uirr77S1atXJV0b0uvu7q5WrVplq6/169erSJEiCgkJkSS1aNFCHh4eWrduna1Oamqq3b/MDBo0SJcvX77pncSff/5ZLVu2tCWmktSkSRN5eXnpl19+yVbM0rU7vjNmzFCrVq0UFBSk6tWra/r06Tp37pyuXLki6dqztO7u7mrfvv0t+ypWrJgtMZWkypUrS5ItWd65c6ck6ZFHHrF7HUJCQnTu3Dn988+de1YZOUdyCgAAADhRhw4dFBsbq+3bt0u6lmS2aNFCnp6eWe4jLi5OW7du1UMPPaQrV64oLi5OKSkpevDBB7VlyxZb4lu9enW7f5kpUaKEunbtqvnz5yspKSnTY2U23LhIkSKKjY2VJNtzpllJ9t555x3NmzdPjz/+uD7++GN99tlnGjZsmKRriaskXbp0SX5+fjKZTLfsy8vLy247PYFO7+fixYuyWq1q3Lix3evw1FNPZTle5B0M60WOhYeHKzIyUp07d3bKTHAAAAB3s7p166p06dLasGGDihQpolOnTumVV17JVh+bN29WSkqKPvvsM3322WcZ9n/33Xd65JFHMt2XmbCwMK1evVorV67MsM/b21vnz5/PUH7+/Hl5e3tLkho2bChJ+v7771W+fPlbHmvTpk3q0aOHhgwZYivbunWrXR0fHx+dO3dOVqv1tgnqrXh7e8tkMmnZsmV2d37T+fv7O9w37jySU+RIUlKSIiMjZbFYFBkZqYEDB6pAgQJGhwUAAGAYk8mkDh06aNGiRSpQoIB8fHyyNJHQ9datW6fSpUtr8uTJGfaNGTNG69at0yOPPKKgoKAs9Ve6dGk9+uijmjt3boZZe+vVq6evv/5a48aNk6vrtfRgx44diouLU7169SRJ9evXV82aNTV79my1atVKxYoVs+vjn3/+UVxcnAIDA5WcnGyXKKalpWnDhg129UNCQvTJJ59o48aNateuXZbOITPpz7ZeunRJLVq0cLgf5A0kp8iR1NRU24xxFovlps86AAAA3E86dOigOXPmaPXq1erRo0emd/X27t2boaxo0aIqW7asfvrpJw0bNkyNGjXKtO9ly5YpPj5ehQoVynJMYWFhioyMVExMjGrVqmUrHzp0qHr27KmwsDD17dtX//77r6ZNm6aaNWuqWbNmtnrvvvuu+vbtq8cee0xPPfWUqlevrqtXr+qnn37S0qVL9fbbbyswMFAhISH69NNPVblyZRUuXFjLli2zDUNOFxISombNmunll1/W33//rVq1aunSpUvavHmz3n///Syfk7+/v3r37q0XXnhBAwcOVK1atZSSkqLjx48rKioqwwzByNtITgEAAJCnuRcpedcdLyAgQIGBgTp06JA6duyYaZ358+dnKAsODlbTpk1lsVjUuXPnTNt16dJFERER2rx5s7p165blmMqXL6/27dtr7dq1duU1atTQ/Pnz9d5772nkyJHy8PCwrXPq4uJi1/6LL77QJ598ouXLl+uff/5Rvnz5VK1aNb388st66KGHJEn/+c9/9Nprr+mNN96Qu7u7unTpolatWunVV1+1O+7MmTM1a9YsrVy5UrNmzVKRIkUUGhqa5fNJ9+qrr8rf318rV65UeHi4PD095e/vrzZt2mS7LxjLZLVarUYHcTf77bffJCnLQyruNZcvX1anTp1s22vWrLnlAs+38trad3Ti/ClnhZYl5YuU0YRHn7+jxwQAAP8vKSlJx44dk7+/f6aPBlktFpkMWArEqOMib7vd9Xq/5wY5xW8cAAAA8iyjEkQSU+DO47cOAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjuQUAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjuQUAAAAAGA4klMAAADACYYOHapHHnnkpvsXL16swMBA/f333woMDNSmTZuy1O+kSZMUGBio8PDwbMVz9epVRUREqGvXrqpTp45q1qypjh07aubMmYqLi8tWXy1atNDEiRNvWScuLk6BgYFavXp1rsVxO6tXr1ZgYKAuXLjg1H5xZ7gaHQAAAABwMxaLRWbznb+f4shxO3TooOeee0779u1TzZo1M+zfsGGDateuna1+09LStHHjRknS+vXrNWLEiCy1S05O1qBBg/Trr7+qd+/eevbZZ5UvXz4dOHBAixcvVnx8vF5++eUsx+GovBIH7g4kpwAAAMizzGaz5mxdpDOx0XfsmKW8iyusWb9st2vZsqU8PDy0fv36DMnpqVOntGfPHr366qvZ6nPXrl36999/FRISop07d2r//v2qXr36bdt98MEH+vnnnzVv3jyFhITYyhs3bqwnnnhCu3fvzlYcjsorceDuQHIKG6vFIpMB30wCAADcypnYaJ04f8roMG7L3d1dLVu21MaNGzVu3Di7O6QbNmyQi4uL2rVrp8TExCz3uX79enl6emrKlClq2bKl1q1bd9vkNCkpScuXL9fDDz9slxCmy58/v4KDg23bp0+f1ttvv60dO3YoLS1N9erV0wsvvKDAwMBbHmfVqlWaPXu2zp8/r9q1a2vs2LEOx5GQkKB3331XO3bs0NmzZ1WkSBE1adJEzz//vAoVKmTXLjIyUhERETpy5Ig8PDxUs2ZNvf766ypdurStztmzZ/XCCy/o559/VrFixTR8+HB17tzZrp/vvvtO4eHhOnTokDw8PNS6dWu9+OKL8vDwuOV5I/eQnMLGZDbr2PpPlHj+nyy3Sbiaard9cPlUeeTL3mXl7V9DpZt2zVYbAACAvKhjx45at26doqKi7BLA9evXKyQkREWKFNGpU1lLtJOTk/XVV1+pVatWKl68uJo0aaINGzbohRdeuOXQ4N9//10JCQl68MEHb3uMy5cvq2/fvjKbzZowYYLy58+vjz76SH369NHatWtVsmTJTNt9++23+s9//qOuXbuqXbt22r9/v0aNGuVwHElJSUpLS9Po0aPl6+urf/75R7Nnz9bw4cO1ePFiW725c+fqnXfeUbdu3TR69GilpKTohx9+0IULF+yS07Fjx6p79+566qmntGrVKo0bN05BQUGqVKmSJGnTpk0aPXq0unbtqpEjR+rcuXOaNm2a4uLiNH369NvGi9xBcgo7ief/UWL031mun5SSZr8dc1ImN5dsHbOAb4ls1QcAAMirQkND5evrqw0bNtiS08OHD+vw4cMaOHBgtvr65ptvdOXKFXXo0EHStcT322+/zZD43igmJkaSbppYXm/16tU6c+aMNmzYYEvcGjRooIceekgLFy7UuHHjMm330UcfqX79+po8ebIk6cEHH1RycrI+/PBDh+Lw9fXVhAkTbNupqakqU6aMnnjiCR07dkz+/v6Kj4/XrFmz1KNHD7vJmR5++OEM/fXu3Vu9e/eWJNWpU0dbt27V5s2bNXz4cFmtVk2dOlXt2rXTm2++aWvj5+enIUOGaPjw4apSpcptY4bzMYYTAAAAcBJXV1e1adNGX331la5evSrp2pBed3d3tWrVKlt9rV+/XkWKFLENiW3RooU8PDy0bt06W53U1FS7f9czmUy3PcbPP/+sKlWq2BJTSfLx8VFISIh++eWXTNukpaVp//79Gc6ndevWmdbPShzSteG6nTt3Vp06dVS9enU98cQTkqTjx49Lkvbs2aPExER169bttn01adLE9n8PDw+VKlVKZ8+elSQdO3ZMp0+fVtu2be1eu4YNG8psNuv333/PUrxwPpJTAAAAwIk6dOig2NhYbd++XdK1JLNFixby9PTMch9xcXHaunWrHnroIV25ckVxcXFKSUnRgw8+qC1bttgS3+rVq9v9k6RixYpJkv755/aPasXFxalo0aIZyosUKaLY2NhM21y4cEGpqany9fW1K7+xn+zEsWXLFr344ouqWbOm3n//fa1atcq2dE5ycrIk6dKlS3b93sqNz6m6ubnZXrOLFy9KkkaMGGH32tWqVUtpaWlZihe5g2G9AAAAgBPVrVtXpUuX1oYNG2zPmL7yyivZ6mPz5s1KSUnRZ599ps8++yzD/u+++06PPPJIpvtq1KghDw8Pbd++XY8//vgtj+Pt7a1jx45lKD9//ry8vb0zbePr6ytXV9cMa4n++++/DsexadMmVa1a1W647o8//mhXx8fHR9K14cIlSjj+WFh6P+PHj890yZ+sJL/IHSSnAAAAgBOZTCZ16NBBixYtUoECBeTj45OlSYGut27dOpUuXdr2TOf1xowZo3Xr1umRRx5RUFBQhv0FChRQr169tGDBAv3www9q3Lix3f7k5GTt3r1bwcHBqlevnjZv3qyjR4+qYsWKkqTY2Fjt3LlTPXr0yDQ2FxcXVatWTVu2bNGTTz5pK9+8ebPDcSQlJcnNzS3Da3C9OnXqyN3dXZ9//nmmSWVWVaxYUSVKlNDJkydtz6UibyA5BQAAAJysQ4cOmjNnjlavXq0ePXpkSLwkae/evRnKihYtqrJly+qnn37SsGHD1KhRo0z7XrZsmeLj4zMMX003atQo/fbbbxoyZIh69+6tkJAQubm56eDBg1q6dKkeeughBQcHq2vXroqIiFBYWJieffZZ22y9rq6u6t+//03Pb+jQoRo+fLheeukl22y9a9ascTiOkJAQTZw4UeHh4bYJjHbt2mXXV6FChTRixAi9++67slqtatmypSwWi6KiotS+fftME/XMmEwmjRs3TmPHjlVCQoKaN28ud3d3nTlzRlu3btXo0aPl7++fpb7gXCSnAAAAyNNKeRe/644XEBCgwMBAHTp0SB07dsy0zvz58zOUBQcHq2nTprJYLBnW5UzXpUsXRUREaPPmzTedHCh//vyaN2+eli1bprVr12r58uWyWCwqX768OnXqZEs8CxYsqMWLF2vKlCn6z3/+I4vForp162rJkiW3nGW3ZcuWmjBhgmbPnq0NGzaoVq1aev/99zMM381qHD179tSpU6e0ZMkSzZs3T02aNNG0adPUvXt3u/4GDx4sX19fRUREaPXq1fL09FSdOnVUpEiRm8aambZt28rLy0uzZ8+23aEtXbq0HnzwwUyfwcWdYbJarVajg7ib/fbbb5KU5W9q8ro/Fk7M1lIyiSlpeu2b/68/oUU5uWdzKZnCVRuqYschem3tO3d8ge3yRcpowqPP39FjAgCA/5eUlGRbKqRAgQIZ9lsslluu6ZlbjDou8rbbXa/3Wm5wp/EbBwAAgDzLqASRxBS48/itAwAAAAAYjuQUAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjuQUAAAAAGA4klMAAAAAgOFITpEjLmaTTP/7v9l0bRsAAAAAsovkFDmSz8Ws0HJeMpukkLJeyufCJQUAAAAg+8gkkGOPVi2iKY/469GqRYwOBQAAwDBDhw7VI488ctP9ixcvVmBgoP7++28FBgZq06ZNWep30qRJCgwMVHh4eJZj6du3r8LCwrK971Zmzpyp3bt3Z7rvypUrmjVrljp06KBatWqpdu3a6tatmxYsWKDk5ORsH+t2cdSpU8epfSJvIDkFAABAnmW1WO6a43bo0EEnTpzQvn37Mt2/YcMG1a5dW2Zz1j+Cp6WlaePGjZKk9evXZzsmZ5o1a5b27NmTofzChQvq2bOnIiIi1Lp1a3300Uf68MMP9dBDD+njjz/WihUrDIgWdyNXowMAAAAAbsZkNuvXj+bo8pl/7tgxC5YqqdrDsn9nsWXLlvLw8ND69etVs2ZNu32nTp3Snj179Oqrr2arz127dunff/9VSEiIdu7cqf3796t69erZji03TZgwQSdPntSqVasUEBBgKw8JCVHv3r119OhRA6PD3YTkFAAAAHna5TP/KO7ECaPDuC13d3e1bNlSGzdu1Lhx4+zukG7YsEEuLi5q166dEhMTs9zn+vXr5enpqSlTpqhly5Zat25driSnhw4d0tSpU/XLL7/IxcVFoaGhGjdunEqVKiVJCgwMlCRNnTpVU6dOlSQtWrRIZcqU0ebNm/Xkk0/aJabpfHx8VLduXUlSTEyMpk+frh9//FHnzp1TiRIl1KZNGz399NPKly+frY3FYtHChQu1atUqnTx5Ut7e3qpXr57efPNNFSpUyC7m119/XX/88YfKli2rF198UQ8++KDd8VevXq0FCxbo+PHj8vHxUdeuXfXMM8/IxcXFuS8gnIJhvQAAAICTdOzYUTExMYqKirIrX79+vUJCQlSkSNbn6EhOTtZXX32lVq1aqXjx4mrSpIk2bNggSxaHHFutVqWmpmb4Z7Va7er9888/6tOnjy5evKh33nlHEyZM0P79+9WnTx9dvnxZkrRy5UpJ155XXblypVauXKnq1avr559/ltVqzZAUZubixYvy8fHRSy+9pLlz52rQoEH64osv9Nprr9nVe+ONN/TOO++oefPmmj17tsaPHy9PT08lJCTY6qSkpGjs2LHq2rWrZs2aJV9fXz3zzDO6ePGirc6CBQv06quvqkmTJpo9e7YGDx6sRYsWafr06Vl6/XDncecUAAAAcJLQ0FD5+vpqw4YNCg4OliQdPnxYhw8f1sCBA7PV1zfffKMrV66oQ4cOkq4lvt9++62ioqJsfd/K1q1bb3qXtXnz5rb/R0REKDU1VfPnz5ePj48kqWrVqmrfvr2++OIL9e3bV7Vr15YklSxZ0vZ/SYqOjraV305gYKBefPFF23bdunXl7u6ucePGafz48XJ3d9exY8e0fPlyjR492m7SptatW9v1lZ6cNmvWTJLk7++vli1batu2berUqZMuX76sGTNmaNCgQRozZoykaz8bNzc3TZkyRQMHDlThwoVvGzPurDx35/TIkSN66qmnVLt2bYWGhmrq1Km6evXqLdvExMRo6tSp6tSpk+rUqaOmTZvqueee0+nTpzPUjY6O1siRI1WnTh01bNhQr7zyiu0bIQAAACAnXF1d1aZNG3311Ve2z7AbNmyQu7u7WrVqla2+1q9fryJFiigkJESS1KJFC3l4eGjdunW2OjfeFb1evXr19Nlnn2X4d2PC+vPPP6tRo0a2xFSSKlWqpAceeEC//PJLlmI1mW6/1r3ValVERITatWunmjVrqnr16ho7dqxSU1N18uRJSdIPP/wgq9Wqbt263bIvs9lsl6CXKVNGBQoUsCXLe/bsUUJCgtq0aWP3+oSEhCgpKUl//vlnls4Ld1aeunMaGxur/v37q0KFCpo5c6aio6M1ZcoUJSUlafz48Tdtt3//fm3ZskWPPfaYatWqpYsXL+qjjz7S448/rvXr18vX11fStW9YBg0aJEmaNm2akpKS9Pbbb+u5557TnDlz7sg5AgAA4N7WoUMHLVu2TNu3b1fLli21fv16tWjRQp6enlnuIy4uTlu3blWnTp105coVW/mDDz6oLVu26PXXX1e+fPkyJJqHDh2y/b9QoUIKCgrK0PeNccTFxalq1aoZ6hUpUkSxsbG3jLN48eKSrg0N9vf3v2XdhQsX6u2339agQYPUqFEjeXl56bffftPEiRNty81cunRJrq6utx3+XKBAAbvnVCXJzc3N1k/68N4uXbpk2v6ff+7cBFvIujyVnK5YscK2RlL6NzdpaWmaMGGCwsLCbBf/jerVq6eNGzfK1fX/T6du3bpq3ry5IiMjNWDAAEnS5s2b9eeff+rLL79UxYoVJUleXl4aOHCg9u3bl2FWNQAAACC76tatq9KlS2vDhg0qUqSITp06pVdeeSVbfWzevFkpKSm2u503+u677/TII49kui+7vL29df78+Qzl58+fV4UKFW7ZtkGDBjKZTNq+fbvtDu/NbNq0SS1atNBzzz1nKzty5IhdHR8fH6Wmpur8+fPZej73Rt7e3pKuLX9TokSJDPvLlCnjcN/IPXlqWO+2bdsUHBxsN6Sgbdu2slgs2rFjx03beXl52SWmklSiRAn5+voqJibGrv/AwEBbYipdG3vu4+OjrVu3Ou9EAAAAcN8ymUzq0KGDvvnmG61atUo+Pj5ZmjDoeuvWrVPp0qW1aNGiDP+KFi1qG9obFBRk988R9erV0w8//GB3l/To0aM6dOiQ6tWrZyu7/s5kulKlSql169ZasWKF/vrrrwx9x8XF2dZGTUpKkpubW4bzvF7jxo1lMpn0+eefO3Qu6erUqSN3d3edPXs2w2sUFBTE86Z5VJ66c3r06FE99thjdmVeXl7y8/PL9vpIx44d0/nz51WpUiW7/q9PTKVrbx7+/v6svwQAAJBHFSx1+8l28trxOnTooDlz5mj16tXq0aNHhqRMkvbu3ZuhrGjRoipbtqx++uknDRs2TI0aNcq072XLlik+Pt5uaRVHPfnkk1q9erUGDBigYcOGKTk5We+//75KlixpNyy2YsWK+vrrr1W/fn25u7vL399fBQsW1GuvvaZ+/fqpV69e6t+/vy2h3bt3r5YsWaLBgwerTp06CgkJ0aJFi7RkyRJVqFBBa9eu1Ykblgjy9/dXz5499cEHHyg2NlbBwcFKSkrSd999p5EjR950JOWNvLy89Mwzz+idd97R2bNn1bBhQ7m4uOjkyZP6+uuvNXPmTLm7u+f4tYNz5ankNC4uTl5eXhnKvb29bzve/XpWq1WTJk1SsWLF1L59e7v+M/sFzm7/mR3v+qmt70Ymk+m+/gVNTEzMMK06AADIfcnJybJYLEpLS1NaWlqG/WaTSbWHhWXSMndZLRZZcvDZoFKlSgoMDNShQ4fUrl07u3NLXwpm/vz5Gdo1btxYDz74oCwWizp27Jjpa/Loo48qIiJCGzduzHBjxxa/1Sqr1Zpp+xv3FStWTAsXLtQ777yjsWPHymw2KyQkRC+88ILc3d1t9V599VVNnjxZgwcPVlJSkiIiItSwYUN5e3tr2bJlioiI0JdffqmPP/5YZrNZlStX1sCBA/X4448rLS1NQ4cO1fnz5/XBBx9IujYD78svv6zhw4fbrgFJeuWVV1SqVCl99tlnioiIkI+Pjxo0aKACBQooLS1NFovlpud2fT/9+/eXn5+fFi5cqCVLlsjV1VVly5ZV8+bNZTabM21/O+nHT0xMzHRJH6vVmqXJoZA5kzUPfSKvXr26Ro0apSFDhtiVd+jQQXXq1NEbb7yRpX5mzJihOXPmaO7cuXazeD3yyCNq3LixJk6caFc/LCxMKSkpmb5B3M5vv/1229mE7wbu7u6qVq2a/lg4UYnRf9/RYxeu2lAVOw7Ra2vf0Ynzp+7oscsXKaMJjz6vP/74I1sLYgMAAOdJTxry58+fYZ/JZDLkw356AgdcLzk5WSdPnswwM/L18uXL5/AQ6/tdnrpz6uXlpfj4+AzlsbGxtoeab2fVqlUKDw/Xm2++mWH9Jy8vr0yXjYmNjc3S2kw34+bmpsqVKzvcPi+437/h8ff35w8QAAAGSE5O1pkzZ5Q/f34VKFDA6HCA23J1dVW5cuUy/TIls+dukXV5KjmtWLFihmc/4+Pjde7cuQzPimYmfVrtZ555JtO1kSpWrKjDhw/blVmtVh07dkyhoaEOx20ymeTh4eFwexjvfh7SDACAkcxms8xms1xcXOTi4mJ0OMAtubi4yGw2y93dPdMvU+73Gz45ladm623atKl27typuLg4W9mmTZtkNptvmzxGRUVpzJgxevzxxzVixIib9n/w4EEdP37cVrZr1y5dunRJzZo1c8o5AAAAAACyL08lpz179pSnp6dGjBih77//Xp9//rmmTp2qnj172s3M1b9/f7Vq1cq2feTIEY0YMUIVKlRQp06d9Ouvv9r+/f33/z8/2bp1a1WpUkUjR47Ut99+qy+//FIvv/yymjdvzhqnAAAAAGCgPDWs19vbWwsXLtQbb7yhESNGyNPTU926ddPo0aPt6l0/C5d0bZrq+Ph4xcfHq1evXnZ1u3TpoilTpki69mzo3LlzNWnSJI0ZM0aurq5q1aqVXn755dw/OQAAAADATeWp5FS6Nu12RETELessXrzYbrtr167q2rVrlvovXry4Zs6c6Wh4AAAAAIBckKeG9QIAAAAA7k8kpwAAAAAAw5GcAgAAAAAMl+eeOQUAAADuVjNnztT8+fO1Z8+e2+4LDAy07XNzc5OPj48CAwPVpk0bde7cWW5ubrb9UVFR6tevX6bH3LVrl3x9fbV69Wq99NJLGfYPHjxYY8eOzempAbmO5BQAAAB5lsVildlsumeP27dvX3Xo0EGpqamKiYnR9u3b9frrr+vTTz/V/PnzVbBgQbv6kydPVsWKFe3KvLy87Lbnzp2rQoUK2bavX5IRyMtITgEAAJBnmc0mrVkVpX/Pxd+xYxb1K6RO3RvdkWOVLFlStWvXtm23a9dObdu2VVhYmKZMmaJJkybZ1a9SpYqCgoJu2Wf16tXl6+ubG+ECuYrkFAAAAHnav+fiFX3mktFh3DFNmzbVI488osjISI0bNy7D3VPgXsWESAAAAICTpaamZvhnsViy3L5JkyZKSUnRH3/8YVdusVhu22eHDh1UtWpVtWzZUnPmzFFaWlqOzwe4E7hzCgAAADhRQkKCqlevnuk+Dw+PLPVRokQJSdK///5rV969e3e77W7duunNN9+UJPn5+WnkyJGqVauWTCaTvvnmG73//vuKjo7W+PHjs3sawB1HcgoAAAA4UYECBbRkyZIM5atWrdL69euz1IfVas20/O2331alSpVs29c/W/rggw/qwQcftG03adJE+fPn18KFCzV06FAVK1Ysq6cAGILkFAAAAHAis9mc6aRF3333XZb7iI6OlnTtbuj1KlWqdNsJka7Xtm1bzZ8/XwcOHCA5RZ7HM6cAAABAHrN9+3bly5fvpsODgXsRySkAAACQh2zbtk1btmxRly5dsvyM6s18+eWXcnFxUbVq1ZwUHZB7GNYLAAAAGOSff/7Rr7/+qtTUVJ07d07bt2/XmjVrVKtWLb344ovZ6mvgwIFq1KiRAgMDJUlff/21Vq1apX79+mUYHgzkRSSnAAAAyNOK+hW6Z4+3ePFiLV68WG5ubvLx8VFgYKAmTJigzp07y9U1ex/V/f399fnnn+vs2bOyWCyqUKGCXn75ZfXt2zeXogeci+QUAAAAeZbFYlWn7o0MOa7ZbMp2u5EjR2rkyJFZ2nfo0KEs99uoUaPb1n/11Vez3B+QF/HMKQAAAPIsRxLEu/m4wP2M5BQAAAAAYDiSUwAAAACA4UhOAQAAAACGIzkFAAAAABiO5BQAAACGs1qtRocA3BbXae4iOQUAAIBh3NzcZDKZdOXKFaNDAW4rISFB0rXrFs7HOqcAAAAwjIuLi7y9vXXu3DklJyfLy8tLrq6uMplYygV5h9VqVUJCgmJiYuTj4yMXFxejQ7onkZwCAADAUCVKlJC7u7tiYmIUFxdndDjATfn4+KhEiRJGh3HPIjkFAACAoUwmk3x8fOTt7a20tDSlpqYaHRKQgZubG3dMcxnJKQAAAPIEk8kkV1dXubryERW4HzEhEgAAAADAcCSnAAAAAADDkZwCAAAAAAxHcgoAAAAAMBzJKQAAAADAcCSnAAAAAADDkZwCAAAAAAxHcgoAAAAAMBzJKQAAAADAcCSnAAAAAADDkZwCAAAAAAznmt0Gp06d0tdff63du3fryJEjunjxokwmkwoXLqyKFSuqbt26atGihcqWLZsb8QIAAAAA7kFZTk6//fZbzZ8/X7/88ousVqvKlSunMmXKKCAgQFarVXFxcTp48KC++uorTZkyRfXq1dPAgQP10EMP5Wb8AAAAAIB7QJaS0+7du+vgwYNq2bKl3n//fYWEhKhgwYKZ1r18+bJ27NihzZs369lnn9UDDzyglStXOjVoAAAAAMC9JUvJaaNGjfThhx+qaNGit61bsGBBtW7dWq1bt9a5c+e0aNGiHAcJAAAAALi3ZSk5fe655xzq3M/Pz+G2AAAAAID7B7P1AgAAAAAM51ByumvXLs2dO9eu7LPPPlPz5s0VEhKit956S2lpaU4JEAAAAABw73MoOZ05c6YOHjxo2z506JBee+01+fr6qmHDhlq8eLHmzZvntCABAAAAAPc2h5LTI0eOqEaNGrbtNWvWqGDBglq6dKnef/99Pf7441qzZo3TggQAAAAA3NscSk4TExPtlpLZvn27mjRpInd3d0lSUFCQzpw545wIAQAAAAD3PIeS05IlS+q3336TJJ04cUJ//vmnmjRpYtsfGxurfPnyOSdCAAAAAMA9L0tLydyoY8eOCg8PV3R0tP766y95e3urZcuWtv379+9XhQoVnBUjAAAAAOAe51ByOnToUKWkpGjr1q0qWbKkpkyZIi8vL0nSpUuX9OOPP6pfv35ODRQAAAAAcO9yKDl1dXXV6NGjNXr06Az7fHx8tGPHjhwHBgAAAAC4fzj0zCkAAAAAAM6UpeR0/PjxOnnyZLY7//vvvzV+/PhstwMAAAAA3F+yNKz3n3/+Udu2bdW4cWO1a9dOwcHBKlmyZKZ1T506pV27dmnjxo2KiopSaGioUwMGAAAAANx7spScfvLJJ/rll180f/58jR8/XmlpafLx8VHp0qXl7e0tq9Wq2NhYnTp1SnFxcXJxcVHTpk21cOFC1a9fP7fPAQAAAABwl8vyhEj16tVTvXr1dOHCBX377bf69ddfdfToUZ09e1bStYmQHnnkEdWuXVvNmzdXkSJFci1oAAAAAMC9Jduz9fr6+uqxxx7TY489lhvxAAAAAADuQ8zWCwAAAAAwHMkpAAAAAMBwJKcAAAAAAMORnAIAAAAADEdyCgAAAAAwHMkpAAAAAMBw2V5K5nq//vqroqKidP78eT3xxBOqUKGCEhMTdfToUVWoUEGenp7OihMAAAAAcA9zKDm9evWqxowZo6+//lpWq1Umk0kPPfSQKlSoILPZrAEDBujJJ5/UsGHDnB0vAAAAAOAe5NCw3g8++EDfffedXn/9dW3atElWq9W2L3/+/GrTpo2+/vprpwUJAAAAALi3OZScbtiwQT179lSPHj3k7e2dYX+lSpV08uTJHAcHAAAAALg/OJScnj9/XoGBgTfd7+LioqSkJIeDAgAAAADcXxxKTkuWLKmjR4/edP/u3btVrlw5h4MCAAAAANxfHEpOO3TooBUrVmjPnj22MpPJJElatWqVNm7cqM6dOzslQAAAAADAvc+h2XqHDh2qvXv3qk+fPqpYsaJMJpMmT56s2NhYnT17Vs2aNdOTTz7p5FABAAAAAPcqh5LTfPnyae7cuVq7dq02b94si8Wiq1evKjAwUM8++6w6depku5MKAAAAAMDtOJScSteG8Xbq1EmdOnVyZjw6cuSIJk2apD179sjT01OdOnXSs88+q3z58t2y3dKlS7Vt2zbt3btXFy9e1AcffKA2bdrY1YmKilK/fv0ytG3Xrp2mT5/u1PMAAAAAAGSdw8lpboiNjVX//v1VoUIFzZw5U9HR0ZoyZYqSkpI0fvz4W7Zds2aNJKlZs2aKjIy8Zd3JkyerYsWKtu3ChQvnOHYAAAAAgOMcTk5//vlnff755zp16pRiY2NltVrt9ptMJq1duzZbfa5YsUJXrlzRrFmz5OPjI0lKS0vThAkTFBYWpuLFi9+yrdls1qlTp26bnFapUkVBQUHZig0AAAAAkHscmq13wYIF6tu3rzZu3KjLly/L29tbPj4+dv+8vb2z3e+2bdsUHBxsS0wlqW3btrJYLNqxY8ct25rNDp0KAAAAACAPcOjO6bx581S3bl3Nnj1bhQoVclowR48e1WOPPWZX5uXlJT8/v1uuq5pdQ4YM0aVLl+Tn56f27dtr1KhRKlCggMP9Wa1WJSQkOC0+I5hMJrm7uxsdhmESExMz3P0HAAAAssNqtTIxbA44lJwmJiaqY8eOTk1MJSkuLk5eXl4Zyr29vRUbG5vj/gsVKqRBgwapQYMGyp8/v3744QfNnz9fR48e1Zw5cxzuNyUlRQcOHMhxfEZyd3dXtWrVjA7DMMeOHVNiYqLRYQAAAOAud7uJXHFzDiWnjRo10uHDh50dS66rVq2aXQIWHBysYsWKaeLEidq3b59q1qzpUL9ubm6qXLmys8I0xP3+DY+/vz93TgEAAJAjf/31l9Eh3NUcSk7/85//aMCAAZo3b54ee+wxu2dEc8LLy0vx8fEZymNjYx16hjUr2rZtq4kTJ+r33393ODk1mUzy8PBwcmS4k+7nIc0AAABwjvv9hk9OOZSclixZUj169NDUqVP17rvvKn/+/BkmJDKZTPrll1+y1W/FihUzPFsaHx+vc+fO2S39AgAAAAC4tziUnH7wwQeaPXu2ihcvrho1ajjt2dOmTZtq9uzZds+ebtq0SWazWaGhoU45xo02bNggSSwtAwAAAAAGcig5XbFihZo1a6YPP/zQqUu49OzZU4sXL9aIESMUFham6OhoTZ06VT179rRb47R///46c+aMtmzZYiv77bffdPr0aV24cEGStHfvXkmSr6+vGjZsKEkaO3asypcvr2rVqtkmRIqIiNDDDz9McgoAAAAABnIoOU1JSVHz5s2dvraot7e3Fi5cqDfeeEMjRoyQp6enunXrptGjR9vVs1gsSktLsytbunSpvvjiC9v2/PnzJUkNGzbU4sWLJUlVqlTRunXrNH/+fKWkpKh06dIaOnSohgwZ4tTzAAAAAABkj0PJafPmzfXzzz+rZ8+ezo5HlSpVUkRExC3rpCeb15syZYqmTJlyy3ZhYWEKCwvLSXgAAAAAgFzg0K3Pp59+WkeOHNHrr7+u33//XRcuXNClS5cy/AMAAAAAICscunPapk0bSdKBAwe0cuXKm9Y7cOCAY1EBAAAAAO4rDiWnI0aMYA0fAAAAAIDTOJScjhw50tlxAAAAAADuY86dbhcAAAAAAAdk6c7prFmzZDKZNGzYMJnNZs2aNeu2bUwmk0aMGJHjAAEAAAAA975sJaeDBw9Wvnz5SE4BAAAAAE6VpeT04MGDt9wGAAAAACAnsvzMacuWLfX111/nZiwAAAAAgPtUlpPT06dPKyEhITdjAQAAAADcp5itFwAAAABgOJJTAAAAAIDhsjQhUrpVq1Zp586dWaprMpn01ltvORQUAAAAAOD+kq3kdO/evfr999+zVJfkFAAAAACQVdlKTt9880117Ngxt2IBAAAAANyneOYUAAAAAGA4klMAAAAAgOFITgEAAAAAhstycjp58mTVqVMnN2MBAAAAANynsjwhUpcuXXIzDgAAAADAfYxhvQAAAAAAw5GcAgAAAAAMR3IKAAAAADAcySkAAAAAwHAkpwAAAAAAw2V5tt7rtWjRQiaT6ab7TSaT8ufPrxIlSqhRo0bq0aOHvL29HQ4SAAAAAHBvc+jOacOGDeXh4aHTp0/L09NT1apVU7Vq1eTp6anTp0/Lw8NDlSpV0vnz5/Xee++pY8eOOnnypLNjBwAAAADcIxy6c/rwww/r22+/1ZIlS1S/fn27fT/++KNGjhypMWPGqHnz5oqKilJYWJjee+89TZ8+3SlBAwAAAADuLQ7dOf3ggw/Up0+fDImpdO2uau/evfXee+9Jkm1Y786dO3MWKQAAAADgnuVQcnrixAl5eXnddL+3t7dOnDhh265UqZISExMdORQAAAAA4D7gUHJatmxZRUZGKikpKcO+xMRErV69WmXKlLGVxcTEyNfX1/EoAQAAAAD3NIeeOX366ac1ZswYtW3bVp07d1a5cuUkXbujumbNGkVHR2vatGmSpLS0NK1du1Z169Z1XtQAAAAAgHuKQ8lp27Zt5e7urmnTpumjjz6y21elShWNHz9eDz30kCTJarVqwYIFLCUDAAAAALgph5JTSWrevLmaN2+umJgYnTlzRpJUqlQpFStWzP4Arq4qXbp0zqIEAAAAANzTHE5O0xUrVixDQgoAAAAAQHY4nJympaXp+++/18mTJxUbGyur1Wq332QyacSIETkOEAAAAABw73MoOf3tt9/0zDPP6OzZsxmS0nQkpwAAAACArHIoOZ0wYYKSkpIUHh6u+vXr33LNUwAAAAAAbseh5PTQoUMaPXq0WrRo4ex4AAAAAAD3IbMjjUqUKHHT4bwAAAAAAGSXQ8np4MGDtWrVKl2+fNnZ8QAAAAAA7kMODeu9cuWKPD091apVK7Vv314lSpSQi4uLXR2TyaQnn3zSGTECAAAAAO5xDiWnb7/9tu3/S5YsybQOySkAAAAAIKscSk6//vprZ8cBAAAAALiPOZScli5d2tlxAAAAAADuYw5NiAQAAAAAgDNl6c5pixYtZDabtXHjRrm5ualFixYymUy3bGMymfTf//7XKUECAAAAAO5tWUpOGzZsKJPJJLPZbLcNAAAAAIAzZCk5nTJlyi23AQAAAADICZ45BQAAAAAYLkt3Tn/66SeHOm/QoIFD7QAAAAAA95csJad9+/a1e8bUarVm6ZnTAwcOOB4ZAAAAAOC+kaXkdNGiRXbbV69e1TvvvKOkpCR1795d/v7+kqSjR4/q008/lbu7u55//nnnRwsAAAAAuCdlebbe602ePFlubm5atWqV8ufPbytv0aKFevfurT59+mj79u0KDQ11brQAAAAAgHuSQxMirVu3Tp06dbJLTNO5u7urU6dOWrt2bY6DAwAAAADcHxxKThMTE3Xu3Lmb7j937pwSExMdDgoAAAAAcH9xKDkNDg7WokWL9NVXX2XYt3nzZi1atEghISE5Dg4AAAAAcH/I0jOnN3rttdfUr18/jRo1Sn5+fipfvrwk6e+//1ZMTIzKlSun//znP04NFAAAAABw73IoOS1evLjWrl2rFStWaNu2bTpz5owkqXLlyho4cKC6d++uAgUKODVQAAAAAMC9y6HkVJLy58+v/v37q3///s6MBwAAAABwH3LomVMAAAAAAJzJ4Tun586d02effaY//vhD8fHxslgsdvtNJpMWLlyY4wABAAAAAPc+h5LTgwcPql+/fkpKSpK/v78OHz6sypUrKy4uTtHR0SpXrpxKlCjh7FgBAAAAAPcoh4b1Tps2TR4eHtq0aZMWLFggq9Wql19+WVu3btX06dMVGxursWPHOjtWAAAAAMA9yqHkdPfu3erRo4dKlSols/laF1arVZLUtm1bdezYUVOnTnVelAAAAACAe5pDyanFYlHRokUlSV5eXnJxcdGlS5ds+wMDA7V//36nBAgAAAAAuPc5lJyWKVNGp06dutaB2awyZcpo165dtv27d+9WoUKFnBMhAAAAAOCe59CESE2aNNGmTZs0evRoSVKvXr00ZcoUnTx5UlarVT/++KOeeuoppwYKAAAAALh3OZScDh06VO3bt1dKSorc3NzUv39/JSQk6KuvvpLZbNbw4cMVFhbm7FgBAAAAAPcoh5JTb29veXt727ZNJpOGDx+u4cOHOy0wAAAAAMD9w6Hk9HpXrlzR2bNnJUklSpSQp6dnjoMCAAAAANxfHE5O9+3bp3feeUe7d++WxWKRdG1ypHr16un5559XUFCQ04IEAAAAANzbHEpO9+7dq759+8rNzU3dunVTpUqVJElHjhzRhg0b1KdPHy1evFg1a9bMdt9HjhzRpEmTtGfPHnl6eqpTp0569tlnlS9fvlu2W7p0qbZt26a9e/fq4sWL+uCDD9SmTZsM9aKjozVp0iR9//33cnNzU6tWrfTSSy+pYMGC2Y4VAAAAAOAcDiWn06dPV/HixbVs2TL5+fnZ7Rs5cqR69eql6dOna8GCBdnqNzY2Vv3791eFChU0c+ZMRUdHa8qUKUpKStL48eNv2XbNmjWSpGbNmikyMjLTOikpKRo0aJAkadq0aUpKStLbb7+t5557TnPmzMlWrAAAAAAA53H4zumIESMyJKaSVLRoUXXv3l0ffvhhtvtdsWKFrly5olmzZsnHx0eSlJaWpgkTJigsLEzFixe/ZVuz2axTp07dNDndvHmz/vzzT3355ZeqWLGiJMnLy0sDBw7Uvn37HLrTCwAAAADIObNDjcxmpaWl3XS/xWKR2Zz9rrdt26bg4GBbYipJbdu2lcVi0Y4dO24bU1b6DwwMtCWmkhQaGiofHx9t3bo12/ECAAAAAJzDoeS0Tp06Wrp0qU6fPp1h35kzZ7Rs2TLVrVs32/0ePXrULnGUrt3Z9PPz09GjRx0J9bb9m0wm+fv7O6V/AAAAAIBjHBrWO2bMGPXu3Vtt27ZVq1atVKFCBUnSsWPH9PXXX8vFxUXPPfdctvuNi4uTl5dXhnJvb2/FxsY6EmqG/gsVKuT0/q1WqxISEnISmuFMJpPc3d2NDsMwiYmJslqtRocBAACAu5jVapXJZDI6jLuWQ8lptWrV9Omnn2r69On65ptvlJiYKElyd3fXgw8+qGeffVaVK1d2aqB5WUpKig4cOGB0GDni7u6uatWqGR2GYY4dO2a7jgEAAABH3W6VEdycw+ucVq5cWeHh4bJYLLpw4YIkydfXV2azWQkJCYqOjr7lBEaZ8fLyUnx8fIby2NhYeXt7OxqqXf+XL1/OtP+SJUs63K+bm9tdn4zf79/w+Pv7c+cUAAAAOfLXX38ZHcJdzeHkNJ3ZbFbRokXtyhYuXKgZM2Zk+25ixYoVMzz7GR8fr3PnzmV4VtQRFStW1OHDh+3KrFarjh07ptDQUIf7NZlM8vDwyGl4MND9PKQZAAAAznG/3/DJKYcmRMotTZs21c6dOxUXF2cr27Rpk8xmc46Sx+v7P3jwoI4fP24r27Vrly5duqRmzZrluH8AAAAAgGNyfOfUmXr27KnFixdrxIgRCgsLU3R0tKZOnaqePXvaDRHu37+/zpw5oy1bttjKfvvtN50+fdo2xHjv3r2Srg01btiwoSSpdevWmjNnjkaOHKkxY8YoMTFRU6dOVfPmzVnjFAAAAAAMlKeSU29vby1cuFBvvPGGRowYIU9PT3Xr1k2jR4+2q2exWDKss7p06VJ98cUXtu358+dLkho2bKjFixdLuvZs6Ny5czVp0iSNGTNGrq6uatWqlV5++eVcPjMAAAAAwK3kqeRUkipVqqSIiIhb1klPNq83ZcoUTZky5bb9Fy9eXDNnznQ0PAAAAABALshycrp///4sdxoTE+NQMAAAAACA+1OWk9PHHnssy7NPsfgsAAAAACA7spycTp48OTfjAAAAAADcx7KcnHbp0iU34wAAAAAA3Mfy1DqnAAAAAID7E8kpAAAAAMBwJKcAAAAAAMORnAIAAAAADEdyCgAAAAAwHMkpAAAAAMBwJKcAAAAAAMORnAIAAAAADEdyCgAAAAAwHMkpAAAAAMBwJKcAAAAAAMORnAIAAAAADEdyCgAAAAAwHMkpAAAAAMBwJKcAAAAAAMORnAIAAAAADEdyCgAAAAAwHMkpAAAAAMBwJKcAAAAAAMORnAIAAAAADEdyCgAAAAAwHMkpAAAAAMBwJKcAAAAAAMORnAJ3ofDwcLVq1Urh4eFGhwIAAAA4BckpcJdJSkpSZGSkLBaLIiMjlZSUZHRIAAAAQI6RnAJ3mdTUVFksFkmSxWJRamqqwREBAAAAOUdyCgAAAAAwHMkpAAAAAMBwJKcAAAAAAMORnAIAAAAADEdyChjIYrHel8cGAAAAbuRqdADA/cxsNmnNqij9ey4+y22uXrVfOmbxJ98pX74C2TpuUb9C6tS9UbbaAAAAALmJ5BQw2L/n4hV95lKW66ekJtttx5yNlZsra50CAADg7sawXgAAAMBJwsPD1apVK4WHhxsdCnDXITkFAAAAnCApKUmRkZGyWCyKjIxUUtKdHdlEYoy7HckpAAAA4ASpqamyWCySJIvFotTU1Dt2bKMTY8AZSE6Bu4zZZJZkkiSZZPrfNgAAuJ8ZmRgDzsKnWuAu4+LiprIlasgkk8qUqCEXFzejQwIAAAByjNl6gbtQQIUmCqjQxOgwAAAAAKfhzikAAAAAwHAkpwAAAAAAw5GcAgAAANex/m9iISNY7tNjAxLPnAIAAAB2TGazjq3/RInn/8lWu4Sr9jPkHlw+VR75sv5x29u/hko37ao5WxfpTGx0to6dmpxitz154wy55s/6pImlvIsrrFm/bB0TcDaSUwAAAOAGief/UWL039lqk5SSZr8dc1ImN5csty/gW0KSdCY2WifOn8rWsS1X7Y998sIZmfNl/dhAXsCwXgAAAACA4UhOAQAAAACGIzkFAAAAABiO5BQAAABwAhezSab//d9surZ9x5hNsh3c9L9t4C5DcgoAAAA4QT4Xs0LLeclskkLKeimfy537qG12NatgYBHJJBUMLCKzKx/zcfdhtl4AAADASR6tWkSPVi1iyLEL1y+pwvVLGnJswBn4SgUAAAAAYDiSUwAAAACA4UhOAQAAAACGIzkFAAAAABiO5BQAAAAAYDiSUwAAAACA4UhOAQAAAACGIzkFAAAAABiO5BQAAAAAYDiSUwAAAACA4UhOAQAAAACGIzkFAAAAABiO5BQAAAAAYDiSUwAAAACA4UhOAQAAAACGIzkFAAAAABiO5BQAAAAAYDiSUwAAAACA4UhOAQAAAACGy3PJ6ZEjR/TUU0+pdu3aCg0N1dSpU3X16tXbtrNarfr444/VvHlz1axZUz169NCvv/5qVycqKkqBgYEZ/o0ePTqXzgYAAAAAkBWuRgdwvdjYWPXv318VKlTQzJkzFR0drSlTpigpKUnjx4+/ZdtPPvlEM2bM0NixYxUYGKilS5dqwIABWrNmjcqWLWtXd/LkyapYsaJtu3DhwrlyPgAAAACArMlTyemKFSt05coVzZo1Sz4+PpKktLQ0TZgwQWFhYSpevHim7ZKTkzVnzhwNGDBATz75pCSpXr16atOmjebNm6fXX3/drn6VKlUUFBSUi2cCAAAAAMiOPDWsd9u2bQoODrYlppLUtm1bWSwW7dix46btdu/ercuXL6tt27a2snz58qlVq1batm1bboYMAAAAAHCCPHXn9OjRo3rsscfsyry8vOTn56ejR4/esp0ku6G6klSpUiUtXLhQSUlJKlCggK18yJAhunTpkvz8/NS+fXuNGjXKbn92Wa1WJSQkONw+LzCZTHJ3dzc6DMMkJibKarXe0WPmhdfciPMGACAvywt/n43EZ4OcsVqtMplMRodx18pTyWlcXJy8vLwylHt7eys2NvaW7fLly6f8+fPblXt5eclqtSo2NlYFChRQoUKFNGjQIDVo0ED58+fXDz/8oPnz5+vo0aOaM2eOw3GnpKTowIEDDrfPC9zd3VWtWjWjwzDMsWPHlJiYeEePmRdecyPOGwCAvCwv/H02Ep8Nci5fvnxGh3DXylPJaW6rVq2a3ZtNcHCwihUrpokTJ2rfvn2qWbOmQ/26ubmpcuXKzgrTEPf7Nzz+/v6G3Dk1mhHnDQBAXpYX/j4bic8GOfPXX38ZHcJdLU8lp15eXoqPj89QHhsbK29v71u2u3r1qpKTk+3unsbFxclkMt2ybdu2bTVx4kT9/vvvDienJpNJHh4eDrVF3nC/Dt+5X88bAABkjs8GOXO/f7mRU3lqQqSKFStmeLY0Pj5e586dy/A86Y3tpGvDEK539OhRlSpVKkfPkwIAAAAAcl+eSk6bNm2qnTt3Ki4uzla2adMmmc1mhYaG3rRd3bp1VbBgQW3cuNFWlpKSoq+++kpNmza95TE3bNggSSwtAwAAAAAGylPDenv27KnFixdrxIgRCgsLU3R0tKZOnaqePXvarXHav39/nTlzRlu2bJEk5c+fX2FhYZo5c6Z8fX0VEBCg5cuX69KlSxo4cKCt3dixY1W+fHlVq1bNNiFSRESEHn74YZJTAAAAADBQnkpOvb29tXDhQr3xxhsaMWKEPD091a1bN40ePdqunsViUVpaml3Z4MGDZbVaNX/+fF24cEFVq1bVvHnzVLZsWVudKlWqaN26dZo/f75SUlJUunRpDR06VEOGDLkj5wcAAAAAyFyeSk6la2uTRkRE3LLO4sWLM5SZTCaFhYUpLCzspu1utx8AAAAAYIw89cwpAAAAAOD+RHIKAAAAADAcySkAAAAAwHAkpwBwlwsPD1erVq0UHh5udCgAAAAOIzkFgLtYUlKSIiMjZbFYFBkZqaSkJKNDAgAAcAjJKQDcxVJTU2WxWCRdW2YrNTXV4IgAAAAcQ3IKAAAAADAcySkAAAAAwHAkpwAAAAAAw5GcAgAAAAAMR3IKAAAAADAcySkAAAAAwHAkpwAAAAAAw5GcAkAekb5e6f12bAAAAElyNToAAMA1ZrNZc7Yu0pnY6Cy3SU1OsduevHGGXPO7Zeu4pbyLK6xZv2y1AQAAcDaSUwDIQ87ERuvE+VNZrm+5mma3ffLCGZnzuTg7LAAAgFzHsF4AAAAAgOFITgEAAAAAhiM5BQAnCA8PV6tWrRQeHm50KAAAAHclklMAyKGkpCRFRkbKYrEoMjJSSUlJd+7gZpNk+t//Tf/bBu5DfEEEAHc/klMAyKHU1FTbUiwWi0Wpqal37NhmV7MKBhaRTFLBwCIyu/K2DuMYlSAa+gURAMBp+BQDAHe5wvVLqmzvGipcv6TRoeA+ZmSCaOQXRAAA5yE5BQAAOUaCCADIKZJTALiO9X8frgEAAHBnuRodAADkJSazWcfWf6LE8/9kuU3CVfs7RAeXT5VHvuy9vXr711Dppl2z1Qa411gsFpnNxnxvbuSxAQDXkJwCwA0Sz/+jxOi/s1w/KSXNfjvmpExuLtk6ZgHfEtmqD+QWq8Uik0FJmtls1pyti3QmNjpb7VKTU+y2J2+cIdf8blluX8q7uMKa9cvWMQEAzkdyCgAAbBwZPSDlfARB+uiBM7HROnH+VLaObblq/wXRyQtnZM6XvS+IAADGIzkFgBxyMZtkkmTVtWVGXVhrFHe57I4ekHI+giBHowfS1/u1ivV+AeAuxsMVAJBD+VzMCi3nJbNJCinrpXwuvLUCdxLr/QLAvYE7pwDgBI9WLaJHqxYxOgzAMEaPIChcvyRr/QLAXY6vFgEAQI4xggAAkFPcOQUAAE7BCAIAQE7wtSYAAAAAwHAkpwAAAAAAw5GcAgAAAAAMR3J6jwgPD1erVq0UHh5udCgAAAAAkG0kp/eApKQkRUZGymKxKDIyUklJSUaHBAAAANwR3KS5d5Cc3gNSU1NlsVgkSRaLRampqQZHBAAAAOQ+btLcW0hO8xiLxWp0CAAAAMBdgZs09xbWOc1jzGaTwpfv0OmY2Cy3SU2x/4Zo4uyv5OpWIFvHrRVYSj3a1M5WGwAAAABwFpLTPOh0TKyOn76Y5fpWS6okkySrJJNOno2XyZyYrWOW8vPKVn0AAADAWSwWq8xm0313bNgjOb0HmMyu8ihWVQkxB+RRrKpMZn6sAAAAuHuYzSatWRWlf8/FZ6vd1av2IwgXf/Kd8uXL+gjCon6F1Kl7o2wdE7mHLOYeUahsYxUq29joMAAAAACH/HsuXtFnLmWrTUpqst12zNlYubkyKdLdigmRAAAAANyVzCazrj3eJplk+t827lb89AAAAADclVxc3FS2RA2ZZFKZEjXk4uJmdEjIAYb1AgAAALhrBVRoooAKTYwOA07AnVMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjuQUAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjuQUAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAAAAYjuQUAAAAAGA4klMAAAAAgOFITgEAAAAAhiM5BQAAwD0lPDxcrVq1Unh4uNGhAMgGklMAAADcM5KSkhQZGSmLxaLIyEglJSUZHRKALCI5BQAAwD0jNTVVFotFkmSxWJSammpwRACyiuQUAAAAeY7FYjU6BAB3mKvRAQAAAAA3MptNCl++Q6djYrPVLi01RZJJklUymfTGnK/l4uqW5fa1AkupR5va2TomAOcgOQUA3JXCw8MVGRmpzp07a8SIEUaHAyAXnI6J1fHTF7PdzqNYVSXEHJCHX1WdjL6crbal/LyyfTwAzsGwXgDAXYcJTwDcSqGyjVW83lMqVLax0aEAyAaSUwDAXYcJTwAAuPeQnAIADGXkpCdMuAIAQN7BM6cAAEOZzSatWRWlf8/FZ7nN1av2w3gXf/Kd8uUrkK3jFvUrpE7dG2WrDQAAyD0kpwAAw/17Ll7RZy5luX5a2v/PxmmSSf9Gx8vFhedOAQC4mzGsFwBw13FxcVPZEjVkkkllStSQi0vWl4kAAAB5U55LTo8cOaKnnnpKtWvXVmhoqKZOnaqrV6/etp3VatXHH3+s5s2bq2bNmurRo4d+/fXXDPWio6M1cuRI1alTRw0bNtQrr7yiy5ezN8U4gLwpPDxcrVq1Unh4uNGh4A4IqNBELRoPVUCFJkaHAgAAnCBPJaexsbHq37+/UlJSNHPmTI0ePVqrVq3SlClTbtv2k08+0YwZM/Tkk09qzpw58vPz04ABA3Ty5ElbnZSUFA0aNEjHjx/XtGnT9Prrr+v777/Xc889l5unBSCbHJmkhqVFAAAA7m556pnTFStW6MqVK5o1a5Z8fHwkSWlpaZowYYLCwsJUvHjxTNslJydrzpw5GjBggJ588klJUr169dSmTRvNmzdPr7/+uiRp8+bN+vPPP/Xll1+qYsWKkiQvLy8NHDhQ+/btU82aNXP7FAFkgdlsUvjyHTodE5vlNmmpKbak1mK16rXwzXJxzd5Qz1qBpdSjTe1stQEAAIBz5KnkdNu2bQoODrYlppLUtm1bvfbaa9qxY4e6du2aabvdu3fr8uXLatu2ra0sX758atWqlbZs2WLXf2BgoC0xlaTQ0FD5+Pho69atJKdAHnI6JlbHT1/MVhuPYlWVEHNAHn5VdTI6+8P1S/l5ZbsNAISHhysyMlKdO3fWiBEjjA4HAO5aeWpY79GjR+0SR+nanU0/Pz8dPXr0lu0kZWhbqVIlnTlzxja8L7P+TSaT/P39b9k/gLtDobKNVbzeUypUtrHRoQC4Txj9SAHP2gO4l5isVmueWYG8evXqGjVqlIYMGWJX3qFDB9WpU0dvvPFGpu0++ugjffjhh/rtt9/syjdt2qRRo0Zp27ZtKl68uB555BE1btxYEydOtKsXFhamlJQUzZ8/P9sx7969W1arVW5uzpkp0mQyKe5yktIsFqf0l1X53Fzl6Z5PqQnxslrS7uixza5ucingqfiky0q9w8d2c3FVwfyed/SYN0q4kqy0tDv783ZxMcvDM7/y0K+/HX4P+D24E/Ly74HJZDL0+PwOZJ3VatXZs2dt2yVKlHDo5+fIdWi1WhUdHW3bLl68uOHXjjPxt+Du+T1wlnvhb0FKSopMJpPq1q3rlP7uN3lqWO/dKP2PgDP/GHgVzN5C8s7k6lHIsGMXKlDQsGMbycMzv2HHzssfYvg9uL/we5C38DuQdSaTSSVLlnRKP0YdOy/jb8H95V74W2Aymfi7kgN5Kjn18vJSfHx8hvLY2Fh5e3vfst3Vq1eVnJys/Pn//6KOi4uTyWSytfXy8sp02ZjY2FiH39zr1KnjUDsAAAAAwP/LU8+cVqxYMcOzn/Hx8Tp37lyGZ0VvbCdJx44dsys/evSoSpUqpQIFCty0f6vVqmPHjt2yfwAAAABA7spTyWnTpk21c+dOxcXF2co2bdoks9ms0NDQm7arW7euChYsqI0bN9rKUlJS9NVXX6lp06Z2/R88eFDHjx+3le3atUuXLl1Ss2bNnHsyAAAAAIAsy1MTIsXGxqp9+/by9/dXWFiYoqOjNWXKFHXs2FHjx4+31evfv7/OnDljt0zMxx9/rJkzZ2rs2LEKCAjQ8uXL9f3332vNmjUqW7aspGsJa/pyNGPGjFFiYqKmTp2qwMBAzZkz586eLAAAAADAJk8lp5J05MgRvfHGG9qzZ488PT3VqVMnjR49Wvny5bPV6du3r06fPq1vvvnGVma1WvXxxx9r2bJlunDhgqpWraqXXnopwzOh0dHRmjRpkr7//nu5urqqVatWevnll1Ww4P354DkAAAAA5AV5LjkFAAAAANx/8tQzpwAAAACA+xPJKQAAAADAcCSnAAAAAADDkZwCAAAAAAxHcgoAAAAAMBzJKQAAAADAcCSnAAAAAADDkZwabNy4cerQoUOuHiMwMFDz5s3LUR+nTp1SYGCgNm3a5KSojLF69WqtW7fO4baBgYG6cOFCttr17dtXYWFhDh3TWdJ/fjf+6969u6Fx3W9y8vs+c+ZM1alT57b1hg8frr59+zp0DNwZvO/fOS1atNDEiRNvWeduPs+b/U1L/7tz4MABBQYGKioqyoDo7j7O+L25V2X3fcvRz0x3WkREhJo3b66qVatq+PDhkqT9+/ere/fuqlWrlgIDAxUXF+fU423dujVDeVbeq+4XrkYHANxJX3zxhTw8PNSxY0ejQzHEmDFj1KhRI9u2p6engdEgOx5//HE1a9bM6DAA5CE3+5v22muvyWw2Kzk52aDI7k4rV65UqVKljA4jTxo+fLgSEhKyXL958+ZauXKlvLy8cjGqnDl+/LimTJmiwYMH66GHHlLhwoUlSZMmTVJaWprmzJmjAgUKOPWz0qJFi9S8efMMf89nzZqVp1+rO4nk9B6WlJSkAgUKGB0GDJSWliaLxWLbLl++vGrXrm1cQMi2q1evytXVVSVKlFCJEiWMDgd5HO/7eVf6+7Gbm1uuH6ty5cqSpAMHDuT6sa53t19/9+Lfx5z+TNLblytXLlvtfH195evr6/Bx74Rjx47JarWqe/fuKlu2rK386NGjeuKJJ9S4ceM7Fku1atXu2LHyOob15hFbt25Vhw4dFBQUpK5du+rXX3+127969Wp17NhRQUFBevDBBzV9+nSl/V979x0VxfX2AfxLLwJSRDEaTfR1V+lFQUCqEBRijEQsUUQpYgMFxYYNNbFi+AFij13UxJpEiUYDNkSjGI1dsQAGpINYaPf9w7MTht1lFzUS9fmcw9Epe+fO3DJzZ+7cqa3lLRcKhcjMzMSoUaNgaWmJpUuXStxWdnY2PDw8EBwcjOfPnwMAMjMzMWLECFhaWsLGxgaTJ09GUVGRzHg3Fq/i4mKYmppi9+7dYr/z8/PDxIkT5To2qampGDVqFOzt7WFtbQ0/Pz+cOHGCt460Lo/du3dHQkICgJfdnM6dO4fU1FSuW6toGQDs3LkTXl5eMDU1hbu7O5KSkngNO0mWL1+Ofv36wcrKCk5OToiMjMTjx48lrrt//354eHjA3Nwc/v7+yMrKkmv/5d0/0T6GhoZi37598PLygpmZGW7cuCH3dsjb0Vh5F3XtWbduHdzc3GBubo7S0lKJeeDu3bsYPnw4zMzM4OHhgX379knc3tGjR7n8MGjQIFy9elUs7wAvy5qfnx/Mzc3Rs2dPzJ07t0l3yknTUL3fuNTUVAwZMgQWFhbo0aMH/P39ce3aNQDA06dPMX/+fHh5ecHCwgLu7u6YM2cOKioqJIa1fv16ODk5wcLCAmPHjpVaT8u7nyKiro4ZGRn48ssvYWlpiYEDB+Kvv/6Cv78/HB0d8dlnn4nVx5GRkRAKhTAxMYGzszOEQiEOHz7MhZuQkAATExO4u7vztifrnDZixAg4OjqiW7ducHR05NJz7969sLKyws2bNzF48GAYGxvD2NgYJiYm6N+/P44ePQrg5c2whQsXwtbWFhYWFrCzs4OJiQmEQiFmzpzJexKbkZEBoVCI1NRUhIeHw9raWu70zc3NRXh4OGxsbGBpaYmgoCDcvHmTt46oLpSVdlVVVVixYgXc3NxgamqKvn37inV1biyd6mvYrVd0Tk1JSYGXlxesrKwwYsQIPHz4kPe7vLw8hIaGwsLCAi4uLti0aRO++eYbsfRrzIULFzBs2DDY2NjAysoK/fr149Xpkrp9/vbbbxAKhcjJyQHwTxf1vXv3YtasWbCzs4Ofnx9vX2RdiwiFQqxduxbLli2Do6Mj7O3tecdQpLy8HLNmzYKTkxPMzMzg4uKCiIgIbnnDbr2iuB04cADz589Hjx490KtXLyxZsgQ1NTW8OMh7zmqMrHwxffp0jBkzBgDg4eHBHTehUIjS0lIkJSVBKBTyXpORp07Iz8/H1KlT4eDgAHNzc/Tp0webN28G8DINc3NzsX37dq7M7t27l1smSt+9e/fC2NgYhYWFvLBLS0thamqKnTt3cvNk1eNNOe53797F2LFjuXI5evRosbz+448/wsfHB+bm5rCzs8PQoUNx+fJluZfLg56c/gcUFBQgJiYGYWFh0NHRwbp16xAUFIQjR47AwMAAGzduxLJlyxAQEIDp06fj7t27XIGYMmUKL6zJkydj8ODBCA0NhYaGhti2srKyMHLkSFhYWCA2NhaqqqrIzMyEv78/XFxc8N133+HZs2eIi4vDuHHjsGvXLqnxlhUvfX19eHp6Ys+ePbx3G2/fvo3Lly8jPDxcruOTk5MDNzc3BAYGQlFRESdOnMDo0aOxefNmXhdVWebOnYuoqCioq6tj2rRpAMA9idq6dSsWLlwIf39/uLq6IjMzE4mJiaioqODWlaSoqAihoaFo3bo1iouLsXHjRvj7++OXX36BsvI/xevq1at4+PAhJk+eDACIi4tDcHAwUlJSoKqqKvc+yOOvv/5Cbm4uJk6cCB0dHbRt25a7GJ03bx4iIiKgq6uL3r17Y8qUKdDV1X2j2yeNk1XeAeDIkSPo2LEjoqOjoaioCE1NTbFwXrx4gcDAQGhoaHANkvj4eDx58gSffPIJt961a9cwceJEuLm5YebMmcjNzUVERASqqqp44aWkpCAiIgK+vr4ICwtDQUEBYmNjUV5eju++++7fOyAfKKr3G3fo0CFERkaid+/eiI2NhYqKCi5evIj8/HwYGxvj+fPnqK2tRUREBPT19fH3339j9erVGDduHLZu3coL6+jRo2jXrh3mzZuH8vJyLF++HGFhYa+1n/UVFBRg4cKFGD16NLS1tREbG4sJEyagXbt2qKqqQmFhIWbMmIH/+7//Q9u2bbF161b88ssv+OSTTzB79mykpaVhy5YtOHDgAPr27SvX8QHEz2kpKSnYuHEjAgMDcerUKTx9+hTR0dHc+tXV1ZgyZQqUlZWhpKQEIyMj5Ofno0OHDggLC8PKlStx7tw57Ny5E97e3jh48CAMDQ2hoKCAkpIS/Pzzz3jy5Ani4+N58Zg9eza++OILrFy5EoqKsp95PHnyBP7+/lBUVERMTAzU1NSwatUqDB8+HAcPHkTbtm25deVJu4kTJ+LixYsYP348OnfujLS0NERFRUFHR4fXdVJaOh09erTRp9nXr19HcXExpkyZgtraWixevBhRUVFcHBhjGDduHAoLCxETEwNtbW1s2LABjx49kut4iI5JaGgobGxssGLFCqiqquLOnTuv/K7jihUr4OLigtjYWN5NdnmvRbZs2QILCwt88803Yg0YkUWLFuHkyZOYPHky2rVrh4KCArEHB5LExcWhd+/eiIuLQ2ZmJhISEtChQwcMHToUgPznLFlk5Ytx48ahc+fOWL58ORITE2FoaAhDQ0Ps2rULo0aNgre3N/z8/KClpQVAvjqhpKQEgwcPBgBERESgffv2ePDgAdfAS0xMxOjRo2FtbY3AwEAAkPhE2tPTE3PnzkVKSgqGDx/OzT9y5AgAoE+fPgDQpHpc1nHPzs7GkCFD0KVLFyxevBgKCgpYvXo1Ro4cyeWP8+fPIzo6GoGBgXBxccHz589x+fJl7qagrOVyY6RZTZs2jQkEAnbmzBluXnl5ObOysmLLly9nFRUVzNLSksXGxvJ+t2PHDmZubs6Ki4sZY4zt2bOHCQQCtmbNGrFtCAQCtn79enb9+nVmb2/PoqKiWE1NDbd82LBhbPDgwayuro6bd/v2bSYUCllqaipjjLHs7GwmEAjY4cOHGWNM7nidOXOGCQQCdufOHW6dRYsWMRcXF1ZbW9vk41VbW8uqq6tZYGAgi4yM5ObHx8czS0tLsfVtbGxYfHw8Nz18+HA2evRo3jo1NTXMzs6ORURE8ObHxsYyExMTsWNcVFQkMW41NTUsLy+PCQQCdvLkSd42u3btyu7du8fNu3//PuvatStLTk6Wa7+bsn8mJibs0aNHvPXy8/PZ3Llz2dGjR1lGRgZbu3Yts7a2Zv3792dVVVVyxYG8PlnlnTHG3NzcmK2tLausrOT9tmEe2LFjh9R8NXz4cG5eeHg48/T05JW3/fv3M4FAwOWduro65ubmxitTjDGWlpbGhEIhu3Xr1uvvPOFQvd+4uro65uzszAIDA2WuK1JdXc3++OMPJhAIWFZWFjffzc2NWVlZsfLycm6eKH4nTpx4rf1k7GVaNiwjZ8+eZQKBgPXv358ZGxszJycntnTpUsbYy/NE9+7dmUAgYKdOneJtv1u3blzY8fHxzNjYmLm5ufHiIO2cVlNTw3r16sWioqJ486OiophAIGBTp05lAoGAbdu2jQkEApacnMxtd//+/Wzw4MGsX79+zMzMjCUmJrIvv/ySDR48mDHGWEBAABMIBGzVqlVMIBCwGzdu8PZzzpw5cqcTY4xt3ryZCYVCXv4oKSlhlpaWbNGiRdw8edIuPT1d7JzLGGOTJk1iX331lVzpdP78eW6eqNzUP76Wlpa8876o3P3999+MMcZSU1PFwnny5AmzsbERSz9pLl++zDu2kri5ubGYmBjevKNHjzKBQMCys7MZY//kpaCgILHfy3stIhAImLe3N69uYOzlMfTx8eGmfXx8eOnVUMNrJlHcwsPDxeIVEBDATctzzpJF3nzR8PiJNCxn8tYJK1asYKampmLh1ScpHSXNHz9+PFcGRfz9/XnXsE2px2Ud96lTp7LevXuz58+fc/OKioqYpaUl27ZtG2OMsfXr1zNbW1up+yZrubyoW+9/gLa2NtdtQjTt4OCAP//8E5mZmXj69Cn69OmDmpoa7s/BwQHPnz/H7du3eWG5urpK3MaVK1fg7+8PT09PLFmyBEpKSgCAZ8+e4eLFi+jTpw9qa2u58D/55BO0bdsWV65ckRievPHq2bMnPv74Y/z4448AgJqaGhw8eBADBgyQ+45iXl4epk2bBicnJ64b0qlTp3Dv3j25fi9LVlYWSkpKuDtRIt7e3qiurm60O0JaWhqGDBkCGxsbGBsbw9nZGcDLl+zr69KlC+9pVseOHdG1a1f8+eefb2Qf6hMIBLw7zwDQunVrzJs3Dx4eHrC1tUVISAhiY2Nx/fp1rjsXeTsaK+8idnZ2Ep+W1nf58mWp+aq+K1euwNXVlVfeevfuzVvn3r17yM3NRd++fXnl2dbWFoqKimJd38jro3pfuqysLOTl5eGrr75qdL39+/fjyy+/hJWVFUxMTPD1118DEK9/7ezsoK2tzU3b29tDV1dXav3b1OPfunVrdOnShZsWve9ZVVXFjYp+4MAB1NTUICsrC+Xl5dDX1+elP/DyndSmdn8TycvLw+PHj+Hp6cmb7+Xlxf1fUVGRewrWp08ftG/fHurq6sjPz0ffvn1x69YtvHjxAg4ODrh+/Tr3W1F9IcpnFy5c4G1DWv6T5o8//kCXLl3QuXNnbp6uri4cHBzEwpaVdqdPn4auri569uwpllbXr1/ndbmUlk75+fmNxrdr1668dydFv8vLywPwspzp6Oige/fu3DotWrQQS9/GdOjQAVpaWpg3bx4OHTr02iPcSksTea9FnJ2doaCg0Og2jI2NsW/fPmzYsAG3bt2SO269evXiTXfu3Jk7loB85yxZmpIv5CFvnZCeno6ePXuiffv2TQpfEh8fH1y6dAmPHj0CADx+/Bjnz5+Hj48PgKbX47KO++nTp+Hu7g4lJSUuLB0dHRgbG3PXAMbGxigtLcX06dNx+vRpPHv2jBemrOXyom69/wGSXhg3MDDA3bt3UVJSAgAYMGCAxN/+/fffvOlWrVpJXC89PR3Pnj3DwIEDeRVOeXk5amtrsWjRIixatEhm+CLyxktBQQF+fn7YsmULJk+ejNTUVBQXF8PX11fi7xqqq6vD2LFjUVFRgfDwcHTs2BEaGhqIj4+XGremKisrAwCuS6WIaFq0vKHLly9j3Lhx6N27N0JCQmBgYAAFBQUMGjRIbITEhmGL5hUUFLyJXeCRlgcacnFxgaamJq5evQpvb+83Hg8iWWPlvf60LI8fP5aar+rnv4KCArFtamlpQU1NjZsWlefx48dL3NabKmvkH1TvS1daWgrgZWNCmqNHj2LatGkYPHgw96pCQUEBxo8fL1f9q6+vL7X+berxbzjCpqiLaF1dHVq1aoWBAwciKSkJaWlp3KifDS++RaSdb2QR7UvDfFU/b6irq6OyshIqKirc6xwqKip48eIF2rVrB8YYAEBZWRmMMe64if7V0tKCqqqqWBzlqa/qKy8vl5hnDQwMxBr+stKupKQEpaWlMDExkbitgoIC7vUdaekka0RjWb97/PixxPLclMGAWrZsiY0bNyI+Ph5Tp05FbW0tunfvjlmzZkEoFModjoi0NJH3WkSeNJ09ezYX76VLl6Jt27YYPXo0d5NImvo3G4CXx7N+l115zlmyNCVfyBseILtOKC0t5d0AeR1ubm7Q0NDAL7/8gpCQEBw+fBhqamrw8PAA0PR6XNZxLykpwebNm7n3YxuuC7y8ObR06VJs2bIFQUFBUFNTg5eXF2bOnAldXV2Zy+VFjdP/AEl3yIqKimBoaIiWLVsCeNlPXVJBkvfuTFBQEP766y8EBwdjy5YtXGWnra0NBQUFhIaGchm+PtGw2g01JV6+vr6Ij49HamoqfvzxR9jZ2fFGRWvMgwcPcO3aNaxcuZIXP9E7lCJqamqorq7mzauurpZrMBdRgWmYDqIXykX72tBvv/0GLS0txMXFcRcZubm5EteVNMhIUVGR2FMuaZqyf7LudpLm1Vh5F5EnDVu3bo2rV69KDEv0jgwAGBoaim3zyZMnvAsyURmYM2cOzM3NJW6LvFlU70snyo+NDVqUkpKCbt268QaIOXfunMR1JdW/xcXFvDJX35s6/sDLsmxkZAQnJyfs2bOHGyeh/qiw9S+6RdtWU1MTe7rT2DlNtC8N81XDAVVatmyJ6upqlJWV8c5thYWFUFBQAGMMNTU1UFBQ4MISHb8nT56gqqpK7JzY1HNOy5YtJfZ8KioqEgtbVtq1bNkS+vr6WLt2rcRtvY3RYkVjTjTU1Kef5ubmWL9+PZ4/f46MjAwsWbIE48ePx2+//QYAUFVVFbsOkHYzQ1qayHstIk+aamtrIzo6GtHR0bh58ya2bNmCmJgYCAQC3lPkppLnnCXLm84X8tYJurq6cg22Jg91dXV4eHjg0KFDCAkJwaFDh+Dm5sb1qnrVelyali1bwsXFReLNhfqf0unfvz/69++P4uJiHDt2DIsWLYKysjK+/fZbuZbLg7r1/gdUVFQgPT2dN33mzBlYWFjAysoKGhoayMvLg5mZmdifvJlPSUkJsbGxsLKywqhRo7jR2TQ1NWFpaYmsrCyJ4Us7CTclXoaGhnB1dcX69etx8uRJmV216hNVRvUHK8jNzUVmZiZvvTZt2qC6upo3qtjZs2fFTu6iu8T1ffrpp9DX1xf7APvhw4ehoqIi8WIdeNlAVlFR4VXikj6GDrwcDOTBgwfc9IMHD3Djxg1YWFhIXL8hefevKX7//Xc8ffoUZmZmrxwGabrGyntTmJmZSc1XDddLTU3lDYohutgR6dSpE4yMjJCdnS2xPLdp06ZJcSOyUb0vnSg/ikaxlERU/9Ynrf7NyMjgDciRnp6O0tJSqWXuTR3/+vz8/JCWloZDhw5BWVmZ15A2MDCAoqIiFBUVufONgYEB11AUaeycZmRkBENDQ7HXNH799VfetI2NDQCIne9SUlIgEAigpqaG06dPo1u3btw6ovoiLS2NF8arsrGxwa1bt3ijxJaVleHMmTNiYctKOwcHBxQXF0NFRUViWr3pAQclMTMzQ3l5Oc6fP8/Nq6ys5JXvplBXV4eLiwuGDh2KnJwc7prFyMiI18MGeNkVsyle91pEGqFQiBkzZgCAWBybSp5zlixvOl/IWyfY29vj7NmzXFdcSSRdh0rz+eef49q1azh58iQuXbrEdekFXr0el8be3h63b9+GsbGxWFidOnUSW19fXx9+fn5wdHSU+PUJWcsbQ09O/wN0dXURHR2N8PBwaGtrY926dWCMISAgADo6OggPD8eyZcuQl5cHW1tbKCkpITs7G8eOHUNCQoLE0RklUVFRQXx8PMaMGYORI0di+/bt+PjjjzF16lQEBARg0qRJ8PHxgY6ODvLy8nDmzBn4+vpKHBG3qfEaNGgQRo8eDR0dHd47MLKILlJEI849ffoU8fHxYk9ynJ2doampiVmzZiEkJAR5eXnYsmWLWDeQTp06Yf/+/Th+/DgMDQ3RunVrtGnTBuPGjcPChQuhr68PFxcXXLp0CevWrUNAQIDUCxFHR0ds3rwZCxYsgKenJzIzM3HgwAGJ6xoYGGDMmDHcSJX/+9//0KZNG7m7ucm7f9KIRl6ztLSEjo4OLl++jDVr1sDU1FTiHTfy72msvDeFr68vVq1ahdDQUO7zDfHx8WLd5UJDQzFw4ECEhYVh0KBBePToEb7//nuoqalxN1YUFBQwffp0TJkyBU+fPoWrqys0NDTw6NEjpKWlISIiAp9++umbOQAEANX7jVFQUMC0adMQGRmJsLAw9O/fH6qqqrh06RLMzMzg5uYGBwcHzJ8/HytXroSVlRXS0tKkNgZatGiBkJAQhISEoKKiAsuXL4e5uTmcnJwkrv8mj7+Iq6sr9PT0cOnSJQwYMAD79u3jnW8YY1BRUUFqair09PRw8OBBbpTckydPyjyniT5LtHHjRhgYGKCsrAyPHj0SG2m1a9eu+Oyzz7B48WI8f/4cNTU1OHLkCG7fvo2kpCRkZGRg9erV8PHxwYEDB+Dp6ck1DpOSkuDl5fVK3Uzr8/X1xaZNmxAaGopJkyZxo/UqKyuL1YOy0s7R0RFubm4IDg5GcHAwhEIhnj17hjt37uDBgwf45ptvXiuu8nB2doaJiQkmT56MyMhI6OjoYP369WjRooXcT5VFPQw8PDzw0UcfobCwENu2bYO1tTWX5l5eXpg3bx4SExO5PN/w81OyvO61SH1DhgyBp6cnunTpAiUlJezfvx8qKiqv9dQUkO+cJcubzhfy1gkjR47EgQMHMHz4cIwdOxYff/wxsrOzcf/+fURFRQF4WWbPnj2L06dPQ0dHB+3bt5d6neng4ABdXV3MnDkTOjo63LgmIq9Sj0sTHh6OgQMHIigoCIMGDUKrVq1QWFiIc+fOoXv37vj8888RHx+P0tJS2NrawsDAALdu3cLJkycxcuRIAJC5XF7UOP0PMDQ0xJQpU7B06VI8fPgQXbp0wYYNG7iLzMDAQLRp0wYbN27Etm3boKysjA4dOsDV1bXJH/NWVVXFypUrERISgoCAAGzfvh3W1tbYsWMHEhISMGPGDFRXV8PIyAg9e/ZEx44dpYbVlHj16tULGhoa8PHxadJ7A6qqqkhISMD8+fMxceJEtG3bFmPHjsXZs2d5g7To6ekhPj6e6wbTrVs3LF26lPd9KgAICQnBw4cPMW3aNJSXl2PChAkICwuDv78/lJWVsWnTJiQnJ8PQ0BATJkzgvoEliYuLC6ZMmYJt27Zh7969sLa2xpo1ayRehJmYmOCzzz7DsmXLUFBQAAsLC8TExMh9907e/ZOmc+fOSE5Oxu7du/H8+XO0adMGAwcORHh4OO+TN+TfJ6u8y0tdXR3ff/895s2bh6ioKO4my7Fjx3hPGoyNjREXF8d9NkE0TPyIESN476D07dsXOjo6WL16NfcEql27dnBycmpy3IhsVO83ztvbG+rq6li9ejUiIyOhpqYGY2NjbsCfIUOGICcnB9u2bcOGDRvQq1cvxMbG8j5fI+Lp6QkjIyPMnTsX5eXlcHBwQExMTKPbf5PHH3j5Hqe7uztSUlIwf/58WFhY8M43wcHByMrKwsKFC9GiRQsEBQVBS0sLqampTTqn2dra4sCBAygpKYGenh6WLFmC4OBg3m+WLVuGFStWYN26dXj27BmKiooQHx8Pd3d39OrVixvASk1NDaWlpaisrATw8n070dOx16GlpYWtW7di8eLFmD17Nurq6mBtbY1t27aJDeYnT9rFx8dj7dq1SE5ORm5uLrS1tdGlS5dXanC9CgUFBSQlJWHOnDmYM2cOdHR0MGLECNy7dw/Xr1+XK4wOHTpAUVERcXFxKCoqgq6uLnr16oXIyEhuHT8/Pzx8+BDJycnYtGkTvL29ERkZyX0WRh6vey1Sn7W1Nfbv34+cnBwoKipCIBBg9erVvIGuXoW85yxZ3nS+kKdO0NPTQ3JyMmJjY7F8+XI8e/YM7dq143WVjYyMxLx58xAWFobKykosWrRIapxUVFTg5eWFXbt2YeDAgWLp9Kr1uCQdO3bEDz/8gLi4OMTExODp06cwNDREjx49uBtSZmZm2Lx5Mw4fPownT57AyMgIQUFBGDt2rFzL5aXARG/AE/IvSk9Px8iRI7Fnzx6Ympo2d3QI+aCJyuPWrVtha2vb3NEh7ymq9/9RV1cHDw8PuLm5Yfbs2c0dnSaJiorChQsXcPz48be6XXd3d7i6umLOnDlvdbtvQlVVFXx8fNC9e3eJg9U0B39/f2hqamLNmjXNHZUmo3PWh4UemZB/VX5+Ph4+fIhly5bB2tr6g79AIaQ5zJs3j/sEw507d5CUlARjY+PX7n5FiCRU7/+jqqoKN27cwK+//oq8vDwMGzasuaPUqHPnzuHixYswMTFBXV0dUlNT8dNPP2H69OnNHbX/tF27dqGurg6ffvopysvLuad1K1asaO6ovZPonPVho8Yp+Vft3r0bSUlJ6NatGxYuXCi2vLa2Fo09vP8QupzW1dXxXvxvSElJiUbgJa+lvLwcCxYsQGlpKbS0tODk5IRp06bJ/a1hQpriQ6j35d2Hx48fw8/PD/r6+pg9e7bEgUXe5PZel6amJlJTU7Fu3TruEzPTp0+X+50xxlijA/WJBn5636ipqWHt2rXciP1du3bFmjVruAEH34c8/zY1ds76UPPYh4S69ZJm5e7uLvXzKwBw8+bNtxib5pGQkIDExESpyxt7H4EQQt4170O9/7b34V05Znv37m30vVTROA8fmncl/d4FGRkZGDFihNTlAwYMwOLFi99ijMibRo1T0qxu3rzJ+whwQx/CZ07y8/Mb/S5WYyO5EULIu+Z9qPff9j68K8espKQEOTk5UpeLRsj/0Lwr6fcuePLkicTv5Iro6ek1+TMq5L+FGqeEEEIIIYQQQpoddcomhBBCCCGEENLsqHFKCCGEEEIIIaTZUeOUEEIIIYQQQkizo8YpIYQQQgghhJBmRx9WIoQQ8t5o+CkLVVVVtGzZEkKhEC4uLvD19YWWllaTw7148SJOnz6NgIAA6OjovMkov5Lt27dDQ0ODPjNFCCHkvUKNU0IIIe+d8PBwtG/fHjU1NSgsLMS5c+fw7bffYtOmTUhKSkLXrl2bFF5mZiYSExMxYMCA/0TjNDk5GXp6etQ4JYQQ8l6hxikhhJD3jrOzM+/bgaGhoUhPT8eYMWMwbtw4HDp0COrq6s0YQ0IIIYQ0RO+cEkII+SDY29tj3LhxyM3NxcGDBwEAN27cwPTp09G7d2+YmZnB0dERM2bMQElJCfe7hIQELF26FADQu3dvCIVCCIVC5OTkAAD27NmDESNGwN7eHqampvD29saOHTvEtn/lyhUEBQXBzs4O5ubmcHd353VBBoC6ujps2rQJPj4+MDMzg4ODA+bMmYOysjJuHXd3d9y+fRvnzp3j4uLv7//GjxchhBDyttGTU0IIIR+M/v37Y8WKFTh16hQGDRqEM2fOIDs7G76+vjA0NMTt27exe/du3LlzB7t374aCggI8PT1x//59/Pzzz5gxYwb09PQAAPr6+gBedrHt0qUL3N3doaysjN9//x0xMTFgjGHYsGEAgKKiIgQFBUFPTw+jR4+Gjo4OcnJycPToUV785syZg3379sHX1xf+/v7IycnB9u3bce3aNSQnJ0NFRQUzZ87EggULoKmpiTFjxgAAWrVq9RaPIiGEEPLvoMYpIYSQD4aRkRG0tbWRnZ0NAPj6668RGBjIW8fS0hKRkZG4cOECunfvjq5du8LY2Bg///wzPDw80L59e97627Zt43URHj58OIKCgrBx40aucZqZmYmysjJs2LCB1904IiKC+/8ff/yBH374AcuXL0e/fv24+XZ2dggODkZKSgr69esHDw8PxMXFQU9PD/37939zB4cQQghpZtStlxBCyAdFU1MTlZWVAMBrVL548QLFxcWwsLAAAFy9elWu8OqHUVFRgeLiYtja2iI7OxsVFRUAAG1tbQBAamoqqqurJYaTkpICbW1tODo6ori4mPszMTGBpqYmMjIymr6zhBBCyDuEnpwSQgj5oDx9+hQGBgYAgNLSUiQmJuLQoUMoKirirSdqWMpy4cIFJCQk4NKlS3j27JlYGNra2rC1tYWXlxcSExOxadMm2NrawsPDA/369YOqqioA4MGDB6ioqIC9vb3E7TSMHyGEEPK+ocYpIYSQD0ZeXh4qKirQoUMHAMCkSZOQmZmJoKAgdOvWDZqamqirq0NwcDAYYzLDe/jwIUaOHIlOnTph+vTpaNu2LVRUVJCWloZNmzahrq4OAKCgoID4+HhcunQJv//+O06ePImZM2di48aN2LVrF1q0aIG6ujoYGBhg+fLlErcleseVEEIIeV9R45QQQsgH48CBAwCAXr16oaysDOnp6QgLC8OECRO4de7fvy/2OwUFBYnhHT9+HFVVVVi1ahU++ugjbr60LriWlpawtLREREQEfvrpJ0yZMgWHDh2Cn58fOnTogPT0dFhbW8v8zI20+BBCCCHvMnrnlBBCyAchPT0dSUlJaN++Pb744gsoKSlJXG/z5s1i8zQ0NACId/UVhVH/KWtFRQX27NnDW6+srEzsSWy3bt0AAFVVVQCAvn37ora2FklJSWLbr6mpQXl5OS8+9acJIYSQ9wE9OSWEEPLeOXHiBLKyslBbW4vCwkJkZGTg9OnT+Oijj7Bq1SqoqalBTU0NPXr0wPr161FdXY02bdrg9OnT3PdL6zMxMQEAfPfdd/D29oaKigrc3Nzg6OgIFRUVjBkzBkOGDEFlZSV++OEHGBgYoKCggPv9vn37kJycDA8PD3To0AGVlZXYvXs3tLS04OzsDACwtbXF4MGDsWbNGly/fp0L+/79+0hJSUF0dDT69OnDxSc5ORlJSUno2LEj9PX1pb6rSgghhLwrFJg8L9UQQggh74C9e/dixowZ3LSKigp0dXUhEAjg6uoKX19faGlpccvz8/OxYMECZGRkgDEGR0dHREdHw8nJCRMmTEBYWBi3blJSEnbu3ImCggLU1dXh2LFjaN++PY4fP464uDjcv38frVq1wtChQ6Gvr4+ZM2dy61y7dg0bNmzAxYsXUVhYCG1tbZibm2PChAkwNTXl7cPu3buxc+dO3L17F0pKSmjXrh2cnZ0REBCA1q1bAwAKCwsRHR2N8+fPo7KyEra2tti6deu/fHQJIYSQfxc1TgkhhBBCCCGENDt655QQQgghhBBCSLOjxikhhBBCCCGEkGZHjVNCCCGEEEIIIc2OGqeEEEIIIYQQQpodNU4JIYQQQgghhDQ7apwSQgghhBBCCGl21DglhBBCCCGEENLsqHFKCCGEEEIIIaTZUeOUEEIIIYQQQkizo8YpIYQQQgghhJBmR41TQgghhBBCCCHNjhqnhBBCCCGEEEKaHTVOCSGEEEIIIYQ0u/8HlbpVMQnu+toAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + " Dataset Format \\\n", + "2 nyu_door_opening_surprising_effectiveness LEROBOT \n", + "3 nyu_door_opening_surprising_effectiveness RLDS \n", + "6 nyu_door_opening_surprising_effectiveness LEROBOT \n", + "7 nyu_door_opening_surprising_effectiveness RLDS \n", + "10 nyu_door_opening_surprising_effectiveness LEROBOT \n", + "11 nyu_door_opening_surprising_effectiveness RLDS \n", + "14 nyu_door_opening_surprising_effectiveness LEROBOT \n", + "15 nyu_door_opening_surprising_effectiveness RLDS \n", + "18 nyu_door_opening_surprising_effectiveness LEROBOT \n", + "19 nyu_door_opening_surprising_effectiveness RLDS \n", + "22 berkeley_cable_routing LEROBOT \n", + "23 berkeley_cable_routing RLDS \n", + "26 bridge LEROBOT \n", + "27 bridge RLDS \n", + "30 berkeley_autolab_ur5 LEROBOT \n", + "31 berkeley_autolab_ur5 RLDS \n", + "34 berkeley_cable_routing LEROBOT \n", + "35 berkeley_cable_routing RLDS \n", + "38 bridge LEROBOT \n", + "39 bridge RLDS \n", + "42 berkeley_autolab_ur5 LEROBOT \n", + "43 berkeley_autolab_ur5 RLDS \n", + "46 berkeley_cable_routing LEROBOT \n", + "47 berkeley_cable_routing RLDS \n", + "50 bridge LEROBOT \n", + "51 bridge RLDS \n", + "54 berkeley_autolab_ur5 LEROBOT \n", + "55 berkeley_autolab_ur5 RLDS \n", + "58 berkeley_cable_routing LEROBOT \n", + "59 berkeley_cable_routing RLDS \n", + "62 bridge LEROBOT \n", + "63 bridge RLDS \n", + "66 berkeley_cable_routing LEROBOT \n", + "67 berkeley_cable_routing RLDS \n", + "70 bridge LEROBOT \n", + "71 bridge RLDS \n", + "\n", + " AverageTrajectorySize(MB) \n", + "2 0.88 \n", + "3 16.76 \n", + "6 0.88 \n", + "7 16.76 \n", + "10 0.88 \n", + "11 16.76 \n", + "14 0.88 \n", + "15 16.76 \n", + "18 0.88 \n", + "19 16.76 \n", + "22 0.68 \n", + "23 3.23 \n", + "26 0.31 \n", + "27 15.58 \n", + "30 0.00 \n", + "31 0.00 \n", + "34 0.68 \n", + "35 3.23 \n", + "38 0.31 \n", + "39 15.58 \n", + "42 0.00 \n", + "43 0.00 \n", + "46 0.68 \n", + "47 3.23 \n", + "50 0.31 \n", + "51 15.58 \n", + "54 0.00 \n", + "55 0.00 \n", + "58 0.68 \n", + "59 3.23 \n", + "62 0.31 \n", + "63 15.58 \n", + "66 0.68 \n", + "67 3.23 \n", + "70 0.31 \n", + "71 15.58 \n" + ] } ], "source": [ - "# visualize the data\n", - "# dataset to be the x axis, loading time is y axis, and format to be side by side comparison between different bars\n", - "\n", - "sns.set(style=\"whitegrid\")\n", - "plt.figure(figsize=(10, 6))\n", - "ax = sns.barplot(x=\"Dataset\", y=\"LoadingTime(s)\", hue=\"Format\", data=df)\n", - "plt.title('Loading Time of Different Formats for Different Datasets')\n", - "plt.xlabel('Dataset')\n", - "plt.ylabel('Loading Time (s)')\n", - "plt.show()\n" + "# Update RLDS and LEROBOT average trajectory sizes\n", + "rlds_sizes = {\n", + " 'berkeley_cable_routing': 3.23,\n", + " 'bridge': 15.58,\n", + " 'nyu_door_opening_surprising_effectiveness': 16.76\n", + "}\n", + "\n", + "lerobot_sizes = {\n", + " 'berkeley_cable_routing': 0.68,\n", + " 'bridge': 0.31,\n", + " 'nyu_door_opening_surprising_effectiveness': 0.88\n", + "}\n", + "\n", + "# Update the DataFrame\n", + "for dataset in rlds_sizes.keys():\n", + " df.loc[(df['Dataset'] == dataset) & (df['Format'] == 'RLDS'), 'AverageTrajectorySize(MB)'] = rlds_sizes[dataset]\n", + " df.loc[(df['Dataset'] == dataset) & (df['Format'] == 'LEROBOT'), 'AverageTrajectorySize(MB)'] = lerobot_sizes[dataset]\n", + "\n", + "# Verify the changes\n", + "print(df[df['Format'].isin(['RLDS', 'LEROBOT'])][['Dataset', 'Format', 'AverageTrajectorySize(MB)']])" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "8f7d665b", + "execution_count": 3, + "id": "808066a5", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "File Size (MB):\n", + "Format Fog-VLA-DM Fog-VLA-DM-lossless HDF5 LEROBOT RLDS\n", + "Dataset \n", + "berkeley_autolab_ur5 1.85 25.57 281.55 0.00 0.00\n", + "berkeley_cable_routing 0.18 1.10 4.87 0.68 3.23\n", + "bridge 0.21 4.40 29.91 0.31 15.58\n", + "nyu_door_opening_surprising_effectiveness 0.23 5.78 79.54 0.88 16.76\n", + "\n", + "Relative Size (compared to Fog-VLA-DM):\n", + "Format Fog-VLA-DM Fog-VLA-DM-lossless HDF5 LEROBOT RLDS\n", + "Dataset \n", + "berkeley_autolab_ur5 1.00 13.80 152.03 0.00 0.00\n", + "berkeley_cable_routing 1.00 6.14 27.14 3.79 18.02\n", + "bridge 1.00 21.16 144.02 1.49 75.02\n", + "nyu_door_opening_surprising_effectiveness 1.00 25.41 349.87 3.87 73.72\n" + ] } ], "source": [ - "# make previous plot log scale\n", - "plt.figure(figsize=(10, 6))\n", - "ax = sns.barplot(x=\"Dataset\", y=\"LoadingTime(s)\", hue=\"Format\", data=df)\n", - "plt.yscale('log')\n", - "plt.title('Loading Time of Different Formats for Open-X Datasets')\n", - "plt.xlabel('Dataset')\n", - "plt.ylabel('Log-Scale Loading Time (s)')\n", - "plt.show()\n" + "# Calculate relative file size for each dataset\n", + "results = []\n", + "\n", + "for dataset in df['Dataset'].unique():\n", + " dataset_df = df[df['Dataset'] == dataset]\n", + " \n", + " vla_size = dataset_df[dataset_df['Format'] == 'Fog-VLA-DM']['AverageTrajectorySize(MB)'].mean()\n", + " \n", + " for format in ['Fog-VLA-DM', 'RLDS', 'HDF5', 'LEROBOT', 'Fog-VLA-DM-lossless']:\n", + " format_size = dataset_df[dataset_df['Format'] == format]['AverageTrajectorySize(MB)'].mean()\n", + " relative_size = format_size / vla_size if vla_size != 0 else float('inf')\n", + " \n", + " results.append({\n", + " 'Dataset': dataset,\n", + " 'Format': format,\n", + " 'AverageTrajectorySize(MB)': format_size,\n", + " 'RelativeSize': relative_size\n", + " })\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "\n", + "# Pivot the results for easier reading\n", + "pivot_df = results_df.pivot_table(values=['AverageTrajectorySize(MB)', 'RelativeSize'], \n", + " index='Dataset', \n", + " columns='Format', \n", + " fill_value='-')\n", + "\n", + "# Display the results\n", + "print(\"File Size (MB):\")\n", + "print(pivot_df['AverageTrajectorySize(MB)'].to_string(float_format='{:.2f}'.format))\n", + "print(\"\\nRelative Size (compared to Fog-VLA-DM):\")\n", + "print(pivot_df['RelativeSize'].to_string(float_format='{:.2f}'.format))" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "39ca78d9", + "execution_count": 17, + "id": "ca58a7db", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -400,151 +708,320 @@ } ], "source": [ - "# file sze \n", - "plt.figure(figsize=(10, 6))\n", - "ax = sns.barplot(x=\"Dataset\", y=\"FileSize(MB)\", hue=\"Format\", data=df)\n", - "plt.yscale('log')\n", - "plt.title('File Size of Different Formats for Different Datasets')\n", - "plt.xlabel('Dataset')\n", - "plt.ylabel('Log Scale File Size (MB)')\n", - "plt.show()" + "# Filter the data for batch size 8\n", + "batch_8_df = df[df['BatchSize'] == 8]\n", + "\n", + "# Get unique datasets\n", + "datasets = batch_8_df['Dataset'].unique()\n", + "\n", + "# Create a figure for each dataset\n", + "for dataset in datasets:\n", + " plt.figure(figsize=(6, 6))\n", + " \n", + " dataset_df = batch_8_df[batch_8_df['Dataset'] == dataset]\n", + " \n", + " # Create the scatter plot\n", + " for format, (color, marker) in format_styles.items():\n", + " data = dataset_df[dataset_df['Format'] == format]\n", + " plt.scatter(data['AverageTrajectorySize(MB)'], data['LoadingTime(s)'], \n", + " color=color, marker=marker, label=format, s=100)\n", + " \n", + " # Add labels for each point\n", + " # for _, row in data.iterrows():\n", + " # if format == 'LEROBOT':\n", + " # plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n", + " # xytext=(-40, -40), textcoords='offset points', ha='left', va='bottom')\n", + " # elif format == 'RLDS':\n", + " # # move to the left a little bit\n", + " # plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n", + " # xytext=(-10, 10), textcoords='offset points', ha='left', va='bottom')\n", + " # elif format == 'HDF5':\n", + " # plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n", + " # xytext=(-80, -10), textcoords='offset points', ha='left', va='bottom')\n", + " # elif format == 'Fog-VLA-DM-lossless':\n", + " # # move to very left \n", + " # plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n", + " # xytext=(-80, 10), textcoords='offset points', ha='left', va='bottom')\n", + " # else:\n", + " # plt.annotate(format, (row['AverageTrajectorySize(MB)'], row['LoadingTime(s)']),\n", + " # xytext=(5, 5), textcoords='offset points', ha='left', va='bottom')\n", + "\n", + " # Customize the plot\n", + " # plt.xlabel('Average Trajectory Size (MB)')\n", + " # plt.ylabel('Loading Time (s)')\n", + " # plt.title(f'{dataset} - Trajectory Size vs Loading Time (Batch Size 8)')\n", + " # plt.legend()\n", + " plt.xscale('log')\n", + " plt.yscale('log')\n", + " # for nyu_door_opening_surprising_effectiveness, move the x axis to the left\n", + " if dataset == 'nyu_door_opening_surprising_effectiveness':\n", + " plt.ylim(100, 1300)\n", + " plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", + "\n", + " # Show the plot\n", + " plt.tight_layout()\n", + " plt.savefig(f'./{dataset}_cost_vs_time.pdf')\n", + " plt.show()" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "4796663f", + "execution_count": 5, + "id": "46a2410a", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Dataset Format \n", - "berkeley_autolab_ur5 HDF5 0.075685\n", - " RLDS 0.023251\n", - " VLA-ColdCache 0.247777\n", - " VLA-HotCache 0.000683\n", - " VLA-NoCache 0.218245\n", - "berkeley_cable_routing HDF5 0.000300\n", - " RLDS 0.000764\n", - " VLA-ColdCache 0.031721\n", - " VLA-HotCache 0.000788\n", - " VLA-NoCache 0.030931\n", - "bridge HDF5 0.005921\n", - " RLDS 0.002830\n", - " VLA-ColdCache 0.038200\n", - " VLA-HotCache 0.000607\n", - " VLA-NoCache 0.031982\n", - "nyu_door_opening_surprising_effectiveness HDF5 0.022284\n", - " RLDS 0.009082\n", - " VLA-ColdCache 0.069383\n", - " VLA-HotCache 0.000695\n", - " VLA-NoCache 0.069731\n", - "Name: LoadingTime(s), dtype: float64" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + " Dataset Format Size (GB)\n", + "18 AutoLab UR5 Fog-VLA-DM 3.26\n", + "10 AutoLab UR5 Fog-VLA-DM-lossless 23.45\n", + "6 AutoLab UR5 HDF5 258.33\n", + "14 AutoLab UR5 LEROBOT NaN\n", + "2 AutoLab UR5 RLDS 76.39\n", + "19 Bridge Fog-VLA-DM 5.31\n", + "11 Bridge Fog-VLA-DM-lossless 114.63\n", + "7 Bridge HDF5 779.24\n", + "15 Bridge LEROBOT 16.34\n", + "3 Bridge RLDS 387.49\n", + "16 Cable Routing Fog-VLA-DM 0.26\n", + "8 Cable Routing Fog-VLA-DM-lossless 1.67\n", + "4 Cable Routing HDF5 7.38\n", + "12 Cable Routing LEROBOT 0.36\n", + "0 Cable Routing RLDS 4.67\n", + "17 Door Opening Fog-VLA-DM 0.10\n", + "9 Door Opening Fog-VLA-DM-lossless 2.89\n", + "5 Door Opening HDF5 35.35\n", + "13 Door Opening LEROBOT 0.38\n", + "1 Door Opening RLDS 7.12\n" + ] } ], "source": [ - "# get average loading time and storage for each dataset\n", - "df.groupby(['Dataset', 'Format'])['LoadingTime(s)'].mean()\n" + "import pandas as pd\n", + "\n", + "data = {\n", + " 'Dataset': ['Cable Routing', 'Door Opening', 'AutoLab UR5', 'Bridge'],\n", + " 'RLDS': [4.67, 7.12, 76.39, 387.49],\n", + " 'HDF5': [7.38, 35.35, 258.33, 779.24],\n", + " 'Fog-VLA-DM-lossless': [1.67, 2.89, 23.45, 114.63],\n", + " 'LEROBOT': [0.36, 0.38, None, 16.34],\n", + " 'Fog-VLA-DM': [0.26, 0.10, 3.26, 5.31]\n", + "}\n", + "\n", + "df_melted = pd.DataFrame(data)\n", + "\n", + "# Melt the DataFrame to have format and size as separate columns\n", + "df_melted = df_melted.melt(id_vars=['Dataset'], var_name='Format', value_name='Size (GB)')\n", + "\n", + "# Sort the DataFrame by Dataset and Format\n", + "df_melted = df_melted.sort_values(['Dataset', 'Format'])\n", + "\n", + "print(df_melted)" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "08a312f8", + "execution_count": 32, + "id": "b4ea3eb1", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datasets in df_melted: ['AutoLab UR5' 'Bridge' 'Cable Routing' 'Door Opening']\n", + "Datasets in batch_8_df: ['nyu_door_opening_surprising_effectiveness' 'berkeley_cable_routing'\n", + " 'bridge']\n" + ] + }, { "data": { + "image/png": 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", "text/plain": [ - "Dataset Format \n", - "berkeley_autolab_ur5 HDF5 289.032210\n", - " RLDS 174.420469\n", - " VLA-ColdCache 1.878984\n", - " VLA-HotCache 1.878984\n", - " VLA-NoCache 1.878984\n", - "berkeley_cable_routing HDF5 4.873406\n", - " RLDS 65.382843\n", - " VLA-ColdCache 0.645619\n", - " VLA-HotCache 0.645619\n", - " VLA-NoCache 0.645619\n", - "bridge HDF5 31.268807\n", - " RLDS 330.839012\n", - " VLA-ColdCache 0.317214\n", - " VLA-HotCache 0.317214\n", - " VLA-NoCache 0.317214\n", - "nyu_door_opening_surprising_effectiveness HDF5 84.314592\n", - " RLDS 97.529275\n", - " VLA-ColdCache 0.387734\n", - " VLA-HotCache 0.387734\n", - " VLA-NoCache 0.387734\n", - "Name: FileSize(MB), dtype: float64" + "
" ] }, - "execution_count": 13, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "df.groupby(['Dataset', 'Format'])['FileSize(MB)'].mean()\n" + "# Assuming df_melted and df are already created as in the previous code\n", + "\n", + "# Print unique dataset names in both DataFrames\n", + "print(\"Datasets in df_melted:\", df_melted['Dataset'].unique())\n", + "print(\"Datasets in batch_8_df:\", batch_8_df['Dataset'].unique())\n", + "\n", + "# Filter the data for batch size 8\n", + "batch_8_df = df[df['BatchSize'] == 8]\n", + "\n", + "# Get unique datasets\n", + "datasets = batch_8_df['Dataset'].unique()\n", + "\n", + "# Create a mapping between dataset names if necessary\n", + "dataset_mapping = {\n", + " 'berkeley_cable_routing': 'Cable Routing',\n", + " 'nyu_door_opening_surprising_effectiveness': 'Door Opening',\n", + " 'bridge': 'Bridge'\n", + " # Add more mappings if needed\n", + "}\n", + "\n", + "# use the same color for the same format\n", + "color_mapping = {\n", + " 'RLDS': 'purple',\n", + " 'HDF5': 'green',\n", + " 'Fog-VLA-DM-lossless': 'orange',\n", + " 'LEROBOT': 'red',\n", + " 'Fog-VLA-DM': 'blue'\n", + "}\n", + "\n", + "# Create a figure for each dataset\n", + "for dataset in datasets:\n", + " plt.figure(figsize=(6, 6))\n", + " \n", + " dataset_df = batch_8_df[batch_8_df['Dataset'] == dataset]\n", + " \n", + " # Map the dataset name if necessary\n", + " mapped_dataset = dataset_mapping.get(dataset, dataset)\n", + " \n", + " # Create the scatter plot\n", + " for format, (color, marker) in format_styles.items():\n", + " data = dataset_df[dataset_df['Format'] == format]\n", + " try:\n", + " size = df_melted[(df_melted['Dataset'] == mapped_dataset) & (df_melted['Format'] == format)]['Size (GB)'].values[0]\n", + " plt.scatter(size * 0.02, data['LoadingTime(s)'], \n", + " color=color_mapping[format], marker=marker, label=format, s=100)\n", + " except IndexError:\n", + " print(f\"Warning: No data found for dataset '{mapped_dataset}' and format '{format}'\")\n", + " continue\n", + "\n", + " # Customize the plot\n", + " # plt.xlabel('Dataset Size (GB)')\n", + " # plt.ylabel('Throughput (episodes/s)')\n", + " # plt.title(f'{mapped_dataset} - Dataset Size vs Loading Time (Batch Size 8)')\n", + " # plt.legend()\n", + " \n", + " \n", + " plt.xscale('log')\n", + " plt.yscale('log')\n", + " \n", + " plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", + "\n", + " if mapped_dataset == 'Door Opening':\n", + " plt.ylim(100, 1500)\n", + " # Show the plot\n", + " plt.tight_layout()\n", + " plt.savefig(f'./{mapped_dataset}_size_vs_cost_overall.pdf')\n", + " plt.show()" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "4f3e99b4", + "execution_count": 34, + "id": "9a655a70", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3200483/808706995.py:18: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n" + ] + }, { "data": { + "image/png": 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", "text/plain": [ - "Dataset Format \n", - "berkeley_autolab_ur5 HDF5 3818.869277\n", - " RLDS 7501.680810\n", - " VLA-ColdCache 7.583370\n", - " VLA-HotCache 2752.311319\n", - " VLA-NoCache 8.609527\n", - "berkeley_cable_routing HDF5 16256.118100\n", - " RLDS 85611.650199\n", - " VLA-ColdCache 20.353238\n", - " VLA-HotCache 819.724171\n", - " VLA-NoCache 20.873082\n", - "bridge HDF5 5281.055338\n", - " RLDS 116898.449382\n", - " VLA-ColdCache 8.304032\n", - " VLA-HotCache 522.341592\n", - " VLA-NoCache 9.918482\n", - "nyu_door_opening_surprising_effectiveness HDF5 3783.651869\n", - " RLDS 10739.267568\n", - " VLA-ColdCache 5.588280\n", - " VLA-HotCache 557.647436\n", - " VLA-NoCache 5.560416\n", - "dtype: float64" + "
" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean median min max\n", + "Format \n", + "Fog-VLA-DM-lossless 0.785081 0.696694 0.288564 1.400209\n", + "H264 0.733893 0.734583 0.370579 1.119697\n", + "HDF5 0.477196 0.474345 0.220551 0.736611\n", + "LEROBOT 11.711865 4.944148 1.413318 34.672886\n", + "RLDS 9.262323 4.681807 0.403119 44.951988\n" + ] } ], "source": [ - "# compute the speedup of VLA to HDF5 and RLDS per dataset\n", - "df.groupby(['Dataset', 'Format'])['FileSize(MB)'].mean() / df.groupby(['Dataset', 'Format'])['LoadingTime(s)'].mean()" + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Read the CSV file\n", + "df = pd.read_csv('./format_comparison_results.csv')\n", + "\n", + "# Update the format names\n", + "df['Format'] = df['Format'].replace('VLA', 'Fog-VLA-DM')\n", + "df['Format'] = df['Format'].replace('FFV1', 'Fog-VLA-DM-lossless')\n", + "\n", + "# Calculate speedup factors\n", + "def calculate_speedup(group):\n", + " fog_vla_dm_time = group[group['Format'] == 'Fog-VLA-DM']['AverageLoadingTime(s)'].values[0]\n", + " group['SpeedupFactor'] = group['AverageLoadingTime(s)'] / fog_vla_dm_time\n", + " return group\n", + "\n", + "df = df.groupby(['Dataset', 'BatchSize']).apply(calculate_speedup).reset_index(drop=True)\n", + "\n", + "# Set up the plot\n", + "plt.figure(figsize=(12, 8))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "# Create the box plot\n", + "sns.boxplot(x='Format', y='SpeedupFactor', data=df[df['Format'] != 'Fog-VLA-DM'])\n", + "\n", + "# Customize the plot\n", + "plt.title('Latency Speedup Factor of Fog-VLA-DM Compared to Alternatives')\n", + "plt.xlabel('Format')\n", + "plt.ylabel('Speedup Factor (higher is better)')\n", + "plt.yscale('log')\n", + "\n", + "# Add a horizontal line at y=1 to represent Fog-VLA-DM\n", + "plt.axhline(y=1, color='r', linestyle='--', label='Fog-VLA-DM')\n", + "\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "\n", + "# Save the plot\n", + "plt.savefig('latency_speedup_comparison.pdf')\n", + "plt.show()\n", + "\n", + "# Print summary statistics\n", + "summary = df[df['Format'] != 'Fog-VLA-DM'].groupby('Format')['SpeedupFactor'].agg(['mean', 'median', 'min', 'max'])\n", + "print(summary)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b45c38c", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/benchmarks/openx.py b/benchmarks/openx.py index b25a6db..f8db194 100644 --- a/benchmarks/openx.py +++ b/benchmarks/openx.py @@ -65,7 +65,7 @@ def measure_average_trajectory_size(self): file_path = os.path.join(dirpath, f) total_size += os.path.getsize(file_path) - print(f"total_size: {total_size} of directory {self.dataset_dir}") + logger.debug(f"total_size: {total_size} of directory {self.dataset_dir}") # trajectory number traj_num = 0 if self.dataset_name == "nyu_door_opening_surprising_effectiveness": @@ -317,6 +317,15 @@ def _recursively_load_data(self, data): log_func(f" {key}: {type(value).__name__}") log_func(f"Total number of trajectories: {len(data)}") +class FFV1Handler(DatasetHandler): + def __init__(self, exp_dir, dataset_name, num_batches, batch_size, log_frequency=DEFAULT_LOG_FREQUENCY): + super().__init__(exp_dir, dataset_name, num_batches, dataset_type="ffv1", batch_size=batch_size, log_frequency=log_frequency) + self.file_extension = ".vla" + + def get_loader(self): + return VLALoader(self.dataset_dir, batch_size=self.batch_size) + + def evaluation(args): csv_file = "format_comparison_results.csv" @@ -331,13 +340,13 @@ def evaluation(args): logger.debug(f"Evaluating dataset: {dataset_name}") handlers = [ - VLAHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), + # VLAHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), HDF5Handler( args.exp_dir, dataset_name, @@ -345,20 +354,27 @@ def evaluation(args): args.batch_size, args.log_frequency, ), - LeRobotHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), - RLDSHandler( - args.exp_dir, - dataset_name, - args.num_batches, - args.batch_size, - args.log_frequency, - ), + # LeRobotHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # RLDSHandler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), + # FFV1Handler( + # args.exp_dir, + # dataset_name, + # args.num_batches, + # args.batch_size, + # args.log_frequency, + # ), ] for handler in handlers: diff --git a/evaluation.sh b/evaluation.sh index 6513e88..a9a09fb 100755 --- a/evaluation.sh +++ b/evaluation.sh @@ -2,8 +2,8 @@ sudo echo "Use sudo access for clearning cache" # Define a list of batch sizes to iterate through -batch_sizes=(1) -num_batches=20 +batch_sizes=(1 2 4 6 8) +num_batches=200 # batch_sizes=(1 2) # batch_sizes=(2) @@ -14,8 +14,8 @@ for batch_size in "${batch_sizes[@]}" do echo "Running benchmarks with batch size: $batch_size" - python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names nyu_door_opening_surprising_effectiveness --num_batches $num_batches --batch_size $batch_size python3 benchmarks/openx.py --dataset_names berkeley_cable_routing --num_batches $num_batches --batch_size $batch_size - python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size - python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names bridge --num_batches $num_batches --batch_size $batch_size + # python3 benchmarks/openx.py --dataset_names berkeley_autolab_ur5 --num_batches $num_batches --batch_size $batch_size done \ No newline at end of file diff --git a/examples/fixing_failed_conversions.py b/examples/fixing_failed_conversions.py new file mode 100644 index 0000000..8401eb3 --- /dev/null +++ b/examples/fixing_failed_conversions.py @@ -0,0 +1,72 @@ +import argparse +import os +from concurrent.futures import ProcessPoolExecutor, as_completed +from fog_x.loader import RLDSLoader +import fog_x +import time +def check_and_fix_conversion(file_path, data_traj, dataset_name, index, destination_dir, lossless): + try: + # Try to load the existing file + fog_x.Trajectory(file_path).load() + print(f"File {file_path} is valid.") + return index, True + except Exception as e: + print(f"Failed to load {file_path}. Attempting to fix: {e}") + + # If loading fails, attempt to reconvert + try: + data_traj = data_traj[0] + if lossless: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=file_path, + lossy_compression=False + ) + else: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=file_path, + lossy_compression=True, + ) + print(f"Successfully fixed and reconverted data {index}") + return index, True + except Exception as e: + print(f"Failed to fix data {index}: {e}") + return index, False + +def main(): + parser = argparse.ArgumentParser(description="Check and fix failed VLA conversions.") + parser.add_argument("--data_dir", required=True, help="Path to the original data directory") + parser.add_argument("--dataset_name", required=True, help="Name of the dataset") + parser.add_argument("--version", default="0.1.0", help="Dataset version") + parser.add_argument("--destination_dir", required=True, help="Directory containing converted files") + parser.add_argument("--split", default="train", help="Data split to use") + parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes") + parser.add_argument("--lossless", action="store_true", help="Enable lossless compression for VLA format") + + args = parser.parse_args() + + loader = RLDSLoader( + path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling=False + ) + + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = [] + for index, data_traj in enumerate(loader): + file_path = f"{args.destination_dir}/{args.dataset_name}/output_{index}.vla" + if os.path.exists(file_path): + future = executor.submit(check_and_fix_conversion, file_path, data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + futures.append(future) + + time.sleep(60) + failed_conversions = [] + for future in as_completed(futures): + index, success = future.result() + if not success: + failed_conversions.append(index) + + if failed_conversions: + print(f"Failed to fix {len(failed_conversions)} conversions: {failed_conversions}") + else: + print("All existing conversions are valid or have been successfully fixed.") + +if __name__ == "__main__": + main() diff --git a/examples/openx_loader copy.py b/examples/openx_loader copy.py new file mode 100644 index 0000000..a04d368 --- /dev/null +++ b/examples/openx_loader copy.py @@ -0,0 +1,99 @@ +import argparse +from concurrent.futures import ProcessPoolExecutor, as_completed +import os +from fog_x.loader import RLDSLoader +import fog_x +import threading +import time + +def process_data(data_traj, dataset_name, index, destination_dir, lossless): + try: + data_traj = data_traj[0] + steps = len(data_traj) # Count the number of steps in the trajectory + return index, True, steps + except Exception as e: + print(f"Failed to process data {index}: {e}") + return index, False, 0 + +def main(): + parser = argparse.ArgumentParser(description="Process RLDS data and convert to VLA format.") + parser.add_argument("--data_dir", required=True, help="Path to the data directory") + parser.add_argument("--dataset_name", required=True, help="Name of the dataset") + parser.add_argument("--version", default="0.1.0", help="Dataset version") + parser.add_argument("--split", default="train", help="Data split to use") + parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes") + parser.add_argument("--lossless", action="store_true", help="Enable lossless compression for VLA format") + + args = parser.parse_args() + + loader = RLDSLoader( + path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling = False + ) + + # train[start:end] + try: + split_starting_index = int(args.split.split("[")[1].split(":")[0]) + print(f"Starting index: {split_starting_index}") + except Exception as e: + print(f"Failed to get starting index: {e}") + split_starting_index = 0 + + max_concurrent_tasks = args.max_workers + semaphore = threading.Semaphore(max_concurrent_tasks) + + total_steps = 0 + total_trajectories = 0 + + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + futures = [] + retry_queue = [] + try: + from tqdm import tqdm + for index, data_traj in tqdm(enumerate(loader), desc="Processing data", unit="trajectory"): + if index < split_starting_index: + continue + semaphore.acquire() + future = executor.submit(process_data, data_traj, args.dataset_name, index, "", args.lossless) + future.add_done_callback(lambda x: semaphore.release()) + futures.append(future) + except Exception as e: + print(f"Failed to process data: {e}") + + for future in as_completed(futures): + try: + index, success, steps = future.result() + if success: + total_steps += steps + total_trajectories += 1 + else: + retry_queue.append((index, data_traj)) + except Exception as e: + print(f"Error processing future: {e}") + + # Retry failed tasks + if retry_queue: + print(f"Retrying {len(retry_queue)} failed tasks...") + with ProcessPoolExecutor(max_workers=args.max_workers) as retry_executor: + retry_futures = [] + for index, data_traj in retry_queue: + future = retry_executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + retry_futures.append(future) + + for future in as_completed(retry_futures): + try: + index, success, steps = future.result() + if not success: + print(f"Failed to process data {index} after retry") + except Exception as e: + print(f"Error processing retry future: {e}") + + if total_trajectories > 0: + average_steps = total_steps / total_trajectories + print(f"Average steps per trajectory: {average_steps:.2f}") + else: + print("No trajectories were successfully processed.") + + print("All tasks completed.") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/openx_loader.py b/examples/openx_loader.py index f62b865..f127d32 100644 --- a/examples/openx_loader.py +++ b/examples/openx_loader.py @@ -1,25 +1,29 @@ import argparse -from concurrent.futures import ProcessPoolExecutor +from concurrent.futures import ProcessPoolExecutor, as_completed import os from fog_x.loader import RLDSLoader import fog_x +import threading +import time def process_data(data_traj, dataset_name, index, destination_dir, lossless): - data_traj = data_traj[0] - # try: - if lossless: - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", - lossy_compression=False - ) - else: - fog_x.Trajectory.from_list_of_dicts( - data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", - lossy_compression=True, - ) - print(f"Processed data {index}") - # except Exception as e: - # print(f"Failed to process data {index}: {e}") + try: + data_traj = data_traj[0] + if lossless: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=False + ) + else: + fog_x.Trajectory.from_list_of_dicts( + data_traj, path=f"{destination_dir}/{dataset_name}/output_{index}.vla", + lossy_compression=True, + ) + print(f"Processed data {index}") + return index, True + except Exception as e: + print(f"Failed to process data {index}: {e}") + return index, False def main(): parser = argparse.ArgumentParser(description="Process RLDS data and convert to VLA format.") @@ -34,7 +38,7 @@ def main(): args = parser.parse_args() loader = RLDSLoader( - path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split + path=f"{args.data_dir}/{args.dataset_name}/{args.version}", split=args.split, shuffling = False ) # train[start:end] @@ -45,21 +49,48 @@ def main(): print(f"Failed to get starting index: {e}") split_starting_index = 0 + max_concurrent_tasks = args.max_workers + semaphore = threading.Semaphore(max_concurrent_tasks) + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: futures = [] + retry_queue = [] try: - for index, data_traj in enumerate(loader): - index = index + split_starting_index - futures.append(executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless)) + from tqdm import tqdm + for index, data_traj in tqdm(enumerate(loader), desc="Processing data", unit="trajectory"): + if index < split_starting_index: + continue + semaphore.acquire() + future = executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + future.add_done_callback(lambda x: semaphore.release()) + futures.append(future) except Exception as e: print(f"Failed to process data: {e}") - for future in futures: - future.result() + for future in as_completed(futures): + try: + index, success = future.result() + if not success: + retry_queue.append((index, data_traj)) + except Exception as e: + print(f"Error processing future: {e}") - # for index, data_traj in enumerate(loader): - # index = index + split_starting_index - # process_data(data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + # Retry failed tasks + if retry_queue: + print(f"Retrying {len(retry_queue)} failed tasks...") + with ProcessPoolExecutor(max_workers=args.max_workers) as retry_executor: + retry_futures = [] + for index, data_traj in retry_queue: + future = retry_executor.submit(process_data, data_traj, args.dataset_name, index, args.destination_dir, args.lossless) + retry_futures.append(future) + + for future in as_completed(retry_futures): + try: + index, success = future.result() + if not success: + print(f"Failed to process data {index} after retry") + except Exception as e: + print(f"Error processing retry future: {e}") print("All tasks completed.") diff --git a/examples/summarize_dataset.py b/examples/summarize_dataset.py new file mode 100644 index 0000000..0344d5f --- /dev/null +++ b/examples/summarize_dataset.py @@ -0,0 +1,19 @@ +import fog_x +from fog_x.loader import RLDSLoader + +path = "/home/kych/datasets/rtx" +dataset_name = "fractal20220817_data" +version = "0.1.0" +split = "train" + +loader = RLDSLoader(path=f"{path}/{dataset_name}/{version}", split=split, shuffling=False) + +data = loader[0][0] +for k, v in data.items(): + print(k) + if k == "observation" or k == "action": + for k2, v2 in v.items(): + print(k, k2, v2.shape, v2.dtype) + else: + print(k, v.shape, v.dtype) + diff --git a/examples/vla_file_debugger.py b/examples/vla_file_debugger.py new file mode 100644 index 0000000..33e0e8f --- /dev/null +++ b/examples/vla_file_debugger.py @@ -0,0 +1,122 @@ +import os +import numpy as np +from fog_x.trajectory import Trajectory +from fog_x.utils import _flatten +import imageio +from fog_x.loader import RLDSLoader + +def load_ffv1_trajectory(path): + traj = Trajectory(path,) + return _flatten(traj.load()) + +def load_vla_trajectory(path): + traj = Trajectory(path) + return _flatten(traj.load()) + +def load_rlds_trajectory(path, dataset_name, version, split, index): + loader = RLDSLoader(path=f"{path}/{dataset_name}/{version}", split=split, shuffling=False) + data_traj = loader[index] + + data = {} + # convert from a list of dicts to a dict of lists + traj_len = len(data_traj) + for i in range(traj_len): + data_traj[i] = _flatten(data_traj[i]) + for k, v in data_traj[i].items(): + if k == "observation/natural_language_instruction": + print(v) + continue + if k not in data: + data[k] = np.empty((traj_len, *v.shape)) + data[k][i] = v + return data + +def save_traj_images_to_dir(traj_data, dir_path): + os.makedirs(dir_path, exist_ok=True) + for i in range(len(traj_data["observation/image"])): + imageio.imwrite(f"{dir_path}/{i}.png", traj_data["observation/image"][i].astype(np.uint8)) + +def compare_trajectories(ffv1_data, vla_data, rlds_data, file_name): + print(f"\nComparing FFV1, VLA, and RLDS trajectories for {file_name}:") + + # Compare keys + ffv1_keys = set(ffv1_data.keys()) + vla_keys = set(vla_data.keys()) + rlds_keys = set(rlds_data.keys()) + + print(f"FFV1 keys: {ffv1_keys}") + print(f"VLA keys: {vla_keys}") + print(f"RLDS keys: {rlds_keys}") + + common_keys = ffv1_keys.intersection(vla_keys).intersection(rlds_keys) + + # Compare data for common keys + for key in common_keys: + if key == "observation/natural_language_instruction": + continue + ffv1_array = ffv1_data[key] + vla_array = vla_data[key] + rlds_array = rlds_data[key] + + print(f"\nComparing '{key}':") + print(f" FFV1 shape: {ffv1_array.shape}, dtype: {ffv1_array.dtype}") + print(f" VLA shape: {vla_array.shape}, dtype: {vla_array.dtype}") + print(f" RLDS shape: {rlds_array.shape}, dtype: {rlds_array.dtype}") + + if ffv1_array.shape == vla_array.shape == rlds_array.shape: #and ffv1_array.dtype == vla_array.dtype == rlds_array.dtype: + if np.allclose(ffv1_array, vla_array) and np.allclose(ffv1_array, rlds_array): + continue + else: + diff_ffv1_vla = np.abs(ffv1_array - vla_array) + diff_ffv1_rlds = np.abs(ffv1_array - rlds_array) + diff_vla_rlds = np.abs(vla_array - rlds_array) + print(f" Max difference FFV1-VLA: {np.max(diff_ffv1_vla)}") + print(f" Max difference FFV1-RLDS: {np.max(diff_ffv1_rlds)}") + print(f" Max difference VLA-RLDS: {np.max(diff_vla_rlds)}") + print(f" Mean difference FFV1-VLA: {np.mean(diff_ffv1_vla)}") + print(f" Mean difference FFV1-RLDS: {np.mean(diff_ffv1_rlds)}") + print(f" Mean difference VLA-RLDS: {np.mean(diff_vla_rlds)}") + if key == "observation/image": + print("ffv1_array[0]: ", ffv1_array[0]) + print("vla_array[0]: ", vla_array[0]) + print("rlds_array[0]: ", rlds_array[0]) + save_traj_images_to_dir(ffv1_data, f"{file_name}_ffv1") + save_traj_images_to_dir(vla_data, f"{file_name}_vla") + save_traj_images_to_dir(rlds_data, f"{file_name}_rlds") + else: + print(" Shape or dtype mismatch") + print(f" ffv1: {np.sum(ffv1_array - np.array(rlds_array))}") + print(f" vla: {np.sum(vla_array - np.array(rlds_array))}") + +def main(): + # dataset_name = "bridge" + dataset_name = "fractal20220817_data" + base_path = f"/home/kych/datasets/{dataset_name}" + # base_path = "/mnt/data/fog_x" + ffv1_dir = os.path.join(base_path, "ffv1", dataset_name) + vla_dir = os.path.join(base_path, "vla", dataset_name) + rlds_dir = "/home/kych/datasets/rtx" + version = "0.1.0" + split = "train" + + # Get all .vla files in the ffv1 directory + vla_files = ["output_{}.vla".format(i) for i in range(1)] + + for file_name in vla_files: + ffv1_file = os.path.join(ffv1_dir, file_name) + vla_file = os.path.join(vla_dir, file_name) + index = int(file_name.split("_")[1].split(".")[0]) + + if not os.path.exists(vla_file): + print(f"Skipping {file_name}: VLA file not found") + continue + + print(f"\nProcessing {file_name}") + ffv1_data = load_ffv1_trajectory(ffv1_file) + vla_data = load_vla_trajectory(vla_file) + rlds_data = load_rlds_trajectory(rlds_dir, dataset_name, version, split, index) + + compare_trajectories(ffv1_data, vla_data, rlds_data, file_name) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/vla_to_h5.py b/examples/vla_to_h5.py index dbc4f11..df8dd06 100644 --- a/examples/vla_to_h5.py +++ b/examples/vla_to_h5.py @@ -1,15 +1,39 @@ import fog_x import os import argparse -from concurrent.futures import ProcessPoolExecutor -from fog_x.loader import VLALoader +from concurrent.futures import ProcessPoolExecutor, as_completed, TimeoutError +from tqdm import tqdm +import threading +from fog_x.loader import NonShuffleVLALoader +import h5py +import time def process_data(trajectory, dataset_name, index, destination_dir): - try: - trajectory.to_hdf5(path=f"{destination_dir}/{dataset_name}/output_{index}.h5") - print(f"processed data {index} to {destination_dir}/{dataset_name}/output_{index}.h5") + try: + print(f"Processing data {index}") + if trajectory is None: + print(f"Trajectory is None for index {index}") + return index, False + write_to_h5(trajectory, dataset_name, index, destination_dir) + return index, True except Exception as e: print(f"Failed to process data {index}: {e}") + return index, False + +def write_to_h5(trajectory, dataset_name, index, destination_dir): + print(trajectory.keys()) + try: + with h5py.File(f"{destination_dir}/{dataset_name}/output_{index}.h5", "w") as f: + for k in trajectory.keys(): + v = trajectory[k] + print(k, v.shape) + + f.create_dataset(k, data=v, compression="gzip", compression_opts=9) + except Exception as e: + print(f"Failed to write to h5 {index}: {e}") + + # except Exception as e: + # print(f"Failed to process data {index}: {e}") def main(): parser = argparse.ArgumentParser(description="Convert VLA data to HDF5 format.") @@ -17,25 +41,61 @@ def main(): parser.add_argument("--dataset_name", required=True, help="Name of the dataset") parser.add_argument("--destination_dir", required=True, help="Destination directory for output HDF5 files") parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker processes") + parser.add_argument("--timeout", type=int, default=20, help="Timeout for each task in seconds") args = parser.parse_args() vla_path = os.path.join(args.data_dir, args.dataset_name, "*.vla") cache_dir = os.path.join("/mnt/data/fog_x/cache/", args.dataset_name) - loader = VLALoader(vla_path, cache_dir=cache_dir) + print(vla_path, cache_dir) + loader = NonShuffleVLALoader(vla_path, cache_dir=cache_dir) os.makedirs(os.path.join(args.destination_dir, args.dataset_name), exist_ok=True) + max_concurrent_tasks = args.max_workers + semaphore = threading.Semaphore(max_concurrent_tasks) + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: futures = [] + retry_queue = [] try: - for index, trajectory in enumerate(loader): - futures.append(executor.submit(process_data, trajectory, args.dataset_name, index, args.destination_dir)) + for index, trajectory in tqdm(enumerate(loader), desc="Submitting tasks", unit="trajectory"): + semaphore.acquire() + future = executor.submit(process_data, trajectory, args.dataset_name, index, args.destination_dir) + future.add_done_callback(lambda x: semaphore.release()) + futures.append(future) except Exception as e: - print(f"Failed to process data: {e}") + print(f"Failed to submit tasks: {e}") + + for future in tqdm(as_completed(futures), total=len(futures), desc="Processing tasks"): + try: + index, success = future.result(timeout=args.timeout) + if not success: + retry_queue.append((index, trajectory)) + except TimeoutError: + print(f"Task for index {index} timed out") + retry_queue.append((index, trajectory)) + except Exception as e: + print(f"Error processing future: {e}") - for future in futures: - future.result() + # Retry failed tasks + if retry_queue: + print(f"Retrying {len(retry_queue)} failed tasks...") + with ProcessPoolExecutor(max_workers=args.max_workers) as retry_executor: + retry_futures = [] + for index, trajectory in retry_queue: + future = retry_executor.submit(process_data, trajectory, args.dataset_name, index, args.destination_dir) + retry_futures.append(future) + + for future in tqdm(as_completed(retry_futures), total=len(retry_futures), desc="Processing retry tasks"): + try: + index, success = future.result(timeout=args.timeout) + if not success: + print(f"Failed to process data {index} after retry") + except TimeoutError: + print(f"Retry task for index {index} timed out") + except Exception as e: + print(f"Error processing retry future: {e}") print("All tasks completed.") diff --git a/fog_x/dataset.py b/fog_x/dataset.py index 6723148..65ee6fe 100644 --- a/fog_x/dataset.py +++ b/fog_x/dataset.py @@ -1,6 +1,6 @@ import os from typing import Any, Dict, List, Optional, Text -from fog_x.loader.vla import VLALoader +from fog_x.loader.vla import VLALoader, NonShuffleVLALoader from fog_x.utils import data_to_tf_schema import numpy as np @@ -12,7 +12,7 @@ class VLADataset: def __init__(self, path: Text, split: Text, - shuffle: bool = False, + shuffle: bool = True, format: Optional[Text] = None): """ init method for Dataset class @@ -31,8 +31,10 @@ def __init__(self, self.split = split self.format = format self.shuffle = shuffle - - self.loader = VLALoader(path, batch_size=1, return_type="tensor") + if shuffle: + self.loader = VLALoader(path, batch_size=1, return_type="tensor", split=split) + else: + self.loader = NonShuffleVLALoader(path, batch_size=1, return_type="tensor") def __iter__(self): return self diff --git a/fog_x/feature.py b/fog_x/feature.py index 08c3e1b..fce4071 100644 --- a/fog_x/feature.py +++ b/fog_x/feature.py @@ -127,7 +127,14 @@ def from_data(self, data: Any): else: dtype = type(data).__name__ shape = () - feature_type._set(dtype, shape) + try: + feature_type._set(dtype, shape) + except ValueError as e: + print(f"Error: {e}") + print(f"dtype: {dtype}") + print(f"shape: {shape}") + print(f"data: {data}") + raise e return feature_type @classmethod diff --git a/fog_x/loader/__init__.py b/fog_x/loader/__init__.py index ab8f982..da928ba 100644 --- a/fog_x/loader/__init__.py +++ b/fog_x/loader/__init__.py @@ -1,4 +1,4 @@ from .base import BaseLoader from .rlds import RLDSLoader from .hdf5 import HDF5Loader -from .vla import VLALoader \ No newline at end of file +from .vla import VLALoader, NonShuffleVLALoader \ No newline at end of file diff --git a/fog_x/loader/hdf5.py b/fog_x/loader/hdf5.py index 12709d2..4bfab81 100644 --- a/fog_x/loader/hdf5.py +++ b/fog_x/loader/hdf5.py @@ -76,7 +76,7 @@ def __next__(self): def _read_hdf5(self, data_path): with h5py.File(data_path, "r") as f: data_unflattened = recursively_read_hdf5_group(f) - + print(data_unflattened.keys()) data = {} data["observation"] = _flatten(data_unflattened["observation"]) data["action"] = _flatten(data_unflattened["action"]) diff --git a/fog_x/loader/rlds.py b/fog_x/loader/rlds.py index d5cd00a..9390308 100644 --- a/fog_x/loader/rlds.py +++ b/fog_x/loader/rlds.py @@ -67,8 +67,12 @@ def to_numpy(step_data): return trajectory def __next__(self): - return self.get_batch() + data = [self._convert_traj_to_numpy(next(self.iterator))] + self.index += 1 + if self.index >= self.length: + raise StopIteration + return data def __getitem__(self, idx): batch = next(iter(self.ds.skip(idx).take(1))) - return self._convert_batch_to_numpy(batch) \ No newline at end of file + return self._convert_traj_to_numpy(batch) \ No newline at end of file diff --git a/fog_x/loader/vla.py b/fog_x/loader/vla.py index 9b746f6..2db5ace 100644 --- a/fog_x/loader/vla.py +++ b/fog_x/loader/vla.py @@ -14,32 +14,43 @@ logger = logging.getLogger(__name__) class VLALoader: - def __init__(self, path: Text, batch_size=1, cache_dir="/tmp/fog_x/cache/", buffer_size=50, num_workers=-1, return_type = "numpy"): - self.files = self._get_files(path) + def __init__(self, path: Text, batch_size=1, cache_dir="/tmp/fog_x/cache/", buffer_size=50, num_workers=-1, return_type = "numpy", split="all"): + self.files = self._get_files(path, split) + self.split = split + self.cache_dir = cache_dir self.batch_size = batch_size self.return_type = return_type # TODO: adjust buffer size - if "autolab" in path: - self.buffer_size = 4 + # if "autolab" in path: + # self.buffer_size = 4 self.buffer_size = buffer_size self.buffer = mp.Queue(maxsize=buffer_size) if num_workers == -1: - num_workers = 4 + num_workers = 2 self.num_workers = num_workers self.processes = [] random.shuffle(self.files) self._start_workers() - - def _get_files(self, path): + def _get_files(self, path, split): + ret = [] if "*" in path: - return glob.glob(path) + ret = glob.glob(path) elif os.path.isdir(path): - return glob.glob(os.path.join(path, "*.vla")) + ret = glob.glob(os.path.join(path, "*.vla")) else: - return [path] - + ret = [path] + if split == "train": + ret = ret[:int(len(ret)*0.9)] + elif split == "val": + ret = ret[int(len(ret)*0.9):] + elif split == "all": + pass + else: + raise ValueError(f"Invalid split: {split}") + return ret + def _read_vla(self, data_path, return_type = None): if return_type is None: return_type = self.return_type @@ -117,6 +128,74 @@ def __del__(self): p.terminate() p.join() + +class NonShuffleVLALoader: + def __init__(self, path: Text, batch_size=1, cache_dir="/tmp/fog_x/cache/", num_workers=1, return_type = "numpy"): + self.files = self._get_files(path) + self.cache_dir = cache_dir + self.batch_size = batch_size + self.return_type = return_type + self.index = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.index >= len(self.files): + raise StopIteration + + max_retries = 3 + for attempt in range(max_retries): + try: + print(self.index) + file_path = self.files[self.index] + self.index += 1 + return self._read_vla(file_path, return_type = self.return_type) + except Exception as e: + logger.error(f"Error reading {file_path} on attempt {attempt + 1}: {e}") + if attempt + 1 == max_retries: + logger.error(f"Failed to read {file_path} after {max_retries} attempts") + return None + + def _get_files(self, path): + ret = [] + if "*" in path: + ret = glob.glob(path) + elif os.path.isdir(path): + ret = glob.glob(os.path.join(path, "*.vla")) + else: + ret = [path] + # for file in ret: + # try: + # self._read_vla(file, return_type = self.return_type) + # except Exception as e: + # logger.error(f"Error reading {file}: {e}, ") + # ret.remove(file) + return ret + + def __len__(self): + return len(self.files) + + def __getitem__(self, index): + return self.files[index] + + def __del__(self): + pass + + def peek(self): + file = self.files[self.index] + return self._read_vla(file, return_type = "numpy") + + def _read_vla(self, data_path, return_type = None): + if return_type is None: + return_type = self.return_type + traj = fog_x.Trajectory(data_path, cache_dir=self.cache_dir) + ret = traj.load(return_type = return_type) + return ret + + def get_batch(self): + return [self.__next__() for _ in range(self.batch_size)] + import torch from torch.utils.data import IterableDataset, DataLoader from fog_x.loader.vla import VLALoader diff --git a/fog_x/trajectory.py b/fog_x/trajectory.py index 8c5b8cf..da8f9d7 100644 --- a/fog_x/trajectory.py +++ b/fog_x/trajectory.py @@ -176,14 +176,22 @@ def load(self, save_to_cache=True, return_type="numpy"): np_cache = self._load_from_container() if save_to_cache: # await self._async_write_to_cache(np_cache) - self._write_to_cache(np_cache) + try: + self._write_to_cache(np_cache) + except Exception as e: + logger.error(f"Error writing to cache file {self.cache_file_name}: {e}") + return np_cache if return_type =="hdf5": return h5py.File(self.cache_file_name, "r") elif return_type == "numpy": if not np_cache: - with h5py.File(self.cache_file_name, "r") as h5_cache: - np_cache = recursively_read_hdf5_group(h5_cache) + try: + with h5py.File(self.cache_file_name, "r") as h5_cache: + np_cache = recursively_read_hdf5_group(h5_cache) + except Exception as e: + logger.error(f"Error loading cache file {self.cache_file_name}: {e}, reading from container") + np_cache = self._load_from_container() return np_cache elif return_type == "cache_name": return self.cache_file_name @@ -462,16 +470,7 @@ def _get_length_of_stream(container, stream): ) feature_codec = packet.stream.codec_context.codec.name - if feature_codec == "h264" or feature_codec == "ffv1" or feature_codec == "hevc": - frames = packet.decode() - for frame in frames: - data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) - # data = np.asarray(frame.to_image())#.reshape(feature_type.shape) - # save the numpy to image folder - # Append data to the numpy array - np_cache[feature_name][d_feature_length[feature_name]] = data - d_feature_length[feature_name] += 1 - else: + if feature_codec == "rawvideo": packet_in_bytes = bytes(packet) if packet_in_bytes: # Decode the packet @@ -482,6 +481,19 @@ def _get_length_of_stream(container, stream): d_feature_length[feature_name] += 1 else: logger.debug(f"Skipping empty packet: {packet} for {feature_name}") + else: + frames = packet.decode() + for frame in frames: + if feature_type.dtype == "float32": + data = frame.to_ndarray(format="gray").reshape(feature_type.shape) + else: + data = frame.to_ndarray(format="rgb24").reshape(feature_type.shape) + # data = np.asarray(frame.to_image())#.reshape(feature_type.shape) + # save the numpy to image folder + # Append data to the numpy array + np_cache[feature_name][d_feature_length[feature_name]] = data + d_feature_length[feature_name] += 1 + logger.debug(f"Length of the stream {feature_name} is {d_feature_length[feature_name]}") container.close() @@ -500,7 +512,7 @@ def _write_to_cache(self, np_cache): h5_cache = h5py.File(self.cache_file_name, "w") except Exception as e: logger.error(f"Error creating cache file: {e}") - return + raise for feature_name, data in np_cache.items(): if data.dtype == object: for i in range(len(data)): @@ -570,7 +582,7 @@ def is_packet_valid(packet): ] # Check if the stream is using rawvideo, meaning it's a pickled stream - if packet.stream.codec_context.codec.name == "ffv1" or packet.stream.codec_context.codec.name == "libx264": + if packet.stream.codec_context.codec.name == "ffv1" or packet.stream.codec_context.codec.name == "libaom-av1": data = pickle.loads(bytes(packet)) # Encode the image data as needed, example shown for raw images @@ -622,7 +634,7 @@ def _encode_frame(self, data: Any, stream: Any, timestamp: int) -> List[av.Packe encoding = stream.codec_context.codec.name feature_type = FeatureType.from_data(data) logger.debug(f"Encoding {stream.metadata.get('FEATURE_NAME')} with {encoding}") - if encoding == "ffv1" or encoding == "libx264": + if encoding == "ffv1" or encoding == "libaom-av1": if feature_type.dtype == "float32": frame = self._create_frame_depth(data, stream) else: @@ -727,19 +739,23 @@ def _add_stream_to_container(self, container, feature_name, encoding, feature_ty if encoding == "ffv1": stream.width = feature_type.shape[1] stream.height = feature_type.shape[0] - stream.codec_context.options = { - "preset": "fast", # Set preset to 'fast' for quicker encoding - "tune": "zerolatency", # Reduce latency - } + # stream.codec_context.options = { + # "preset": "fast", # Set preset to 'fast' for quicker encoding + # "tune": "zerolatency", # Reduce latency + # } - if encoding == "libx264": + if encoding == "libaom-av1": stream.width = feature_type.shape[1] stream.height = feature_type.shape[0] stream.codec_context.options = { - "preset": "ultrafast", # Set preset to 'ultrafast' for quicker encoding - "tune": "zerolatency", # Reduce latency + "g": "2", 'crf': '30', # Constant Rate Factor (quality) } + # stream.codec_context.options = { + # "preset": "ultrafast", # Set preset to 'ultrafast' for quicker encoding + # "tune": "zerolatency", # Reduce latency + # 'crf': '30', # Constant Rate Factor (quality) + # } stream.metadata["FEATURE_NAME"] = feature_name stream.metadata["FEATURE_TYPE"] = str(feature_type) @@ -781,7 +797,7 @@ def _get_encoding_of_feature( data_shape = feature_type.shape if len(data_shape) >= 2 and data_shape[0] >= 100 and data_shape[1] >= 100: if self.lossy_compression: - vid_coding = "libx264" + vid_coding = "libaom-av1" else: vid_coding = "ffv1" else: diff --git a/fog_x/utils.py b/fog_x/utils.py index d266564..fdfba86 100644 --- a/fog_x/utils.py +++ b/fog_x/utils.py @@ -8,6 +8,7 @@ def data_to_tf_schema(data: Dict[str, Any]) -> Dict[str, FeatureType]: """ Convert data to a tf schema """ + data = _flatten(data) schema = {} for k, v in data.items(): if "/" in k: # make the subkey to be within dict diff --git a/openx_to_vla.sh b/openx_to_vla.sh index 96ed897..ec1912c 100755 --- a/openx_to_vla.sh +++ b/openx_to_vla.sh @@ -1,42 +1,48 @@ -# berkeley_autolab_ur5 dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 - -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless # # bridge dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:200] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 4 - -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:200] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 4 --lossless -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless # berkeley_cable_routing dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +# python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 # nyu_door_opening_surprising_effectiveness dataset -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +# python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 16 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 4 -# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 4 --lossless +# bridge dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[6000:] --max_workers 16 +# pkill -f examples +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name bridge --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 8 +pkill -f examples + +# berkeley_autolab_ur5 dataset +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:] --max_workers 16 +# pkill -f examples +python examples/fixing_failed_conversions.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[0:] --max_workers 8 +pkill -f examples +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[200:400] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[400:600] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[600:800] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/vla --version 0.1.0 --split train[800:] --max_workers 16 + +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[0:] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[200:400] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[400:600] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[600:800] --max_workers 16 --lossless +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/ffv1 --version 0.1.0 --split train[800:] --max_workers 16 --lossless + # fractal20220817_data -python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/vla --version 0.1.0 --split train[0:] --max_workers 4 +# rm -rf /home/kych/datasets/fractal20220817_data/vla +# rm -rf /home/kych/datasets/fractal20220817_data/ffv1 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/vla --version 0.1.0 --split train[34000:] --max_workers 16 +# python examples/openx_loader.py --data_dir /home/kych/datasets/rtx --dataset_name fractal20220817_data --destination_dir /home/kych/datasets/fractal20220817_data/ffv1 --version 0.1.0 --split train[0:] --max_workers 8 --lossless + diff --git a/vla_to_hdf5.sh b/vla_to_hdf5.sh index 7e6a6d4..a83e86e 100755 --- a/vla_to_hdf5.sh +++ b/vla_to_hdf5.sh @@ -1,6 +1,6 @@ -# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 +# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_autolab_ur5 --destination_dir /mnt/data/fog_x/hdf5 --max_workers 14 -# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 -# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 +# python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name nyu_door_opening_surprising_effectiveness --destination_dir /mnt/data/fog_x/hdf5 --max_workers 14 +python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name berkeley_cable_routing --destination_dir /mnt/data/fog_x/hdf5 --max_workers 1 -python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name bridge --destination_dir /mnt/data/fog_x/hdf5 --max_workers 4 \ No newline at end of file +python examples/vla_to_h5.py --data_dir /mnt/data/fog_x/vla/ --dataset_name bridge --destination_dir /mnt/data/fog_x/hdf5 --max_workers 14 \ No newline at end of file