From 80937a86d6fefe6cbd4cfc7f80bda0c0cea82198 Mon Sep 17 00:00:00 2001 From: chenruobing Date: Mon, 11 Oct 2021 21:41:08 +0800 Subject: [PATCH 01/16] add weathers in benchmark collector --- core/data/carla_benchmark_collector.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/core/data/carla_benchmark_collector.py b/core/data/carla_benchmark_collector.py index 9b63ac48..93bb9b67 100644 --- a/core/data/carla_benchmark_collector.py +++ b/core/data/carla_benchmark_collector.py @@ -43,6 +43,8 @@ class CarlaBenchmarkCollector(BaseCollector): benchmark_dir=None, suite='FullTown01-v0', seed=None, + dynamic_seed=True, + weathers=None, ) def __init__( @@ -55,6 +57,8 @@ def __init__( self._benchmark_dir = self._cfg.benchmark_dir suite = self._cfg.suite self._seed = self._cfg.seed + self._dynamic_seed = self._cfg.dynamic_seed + self._weathers = self._cfg.weathers if self._benchmark_dir is None: self._benchmark_dir = get_benchmark_dir() self._collect_suite_list = get_suites_list(suite) @@ -102,6 +106,8 @@ def _generate_suite_reset_params(self): reset_params = kwargs.copy() poses_txt = reset_params.pop('poses_txt') weathers = reset_params.pop('weathers') + if self._weathers is not None: + weathers = self._weathers pose_pairs = read_pose_txt(self._benchmark_dir, poses_txt) for (start, end), weather in product(pose_pairs, weathers): param = reset_params.copy() @@ -127,7 +133,7 @@ def reset(self, suite: Union[List, str] = None) -> None: self._collect_suite_reset_params.clear() self._collect_suite_index_dict.clear() self._collect_suite_list = get_suites_list(suite) - print('[COLLECTOR] Find suites:', self._collect_suite_list) + print('[COLLECTOR] Find suites:', [s for s in self._collect_suite_list]) self._suite_num = len(self._collect_suite_list) self._generate_suite_reset_params() From bfea7e12b429a89b8707c77a60639638b33ef659 Mon Sep 17 00:00:00 2001 From: chenruobing Date: Tue, 12 Oct 2021 22:09:01 +0800 Subject: [PATCH 02/16] This is a combination of 9 commits. rename cict update benchmark collector; add cilrs complete cilrs test complete cilrs train clean code clean debug update training code clean code clean again --- .gitignore | 3 + core/data/__init__.py | 4 +- ...et_saver.py => benchmark_dataset_saver.py} | 0 core/data/carla_benchmark_collector.py | 14 +- core/data/cilrs_dataset.py | 42 +++ core/data/coil_dataset.py | 165 ------------ core/models/__init__.py | 2 +- core/models/cilrs_model.py | 105 ++++++++ core/models/coil_model.py | 245 ------------------ core/models/model_wrappers.py | 4 +- core/policy/__init__.py | 2 +- core/policy/base_carla_policy.py | 1 + core/policy/cilrs_policy.py | 210 +++++++++++++++ core/policy/coil_policy.py | 74 ------ core/policy/lbc_policy.py | 6 +- demo/{cict_demo => cict}/.gitignore | 0 .../{cict_demo => cict}/camera/basic_tools.py | 0 .../camera/coordinate_transformation.py | 0 demo/{cict_demo => cict}/camera/parameters.py | 0 demo/{cict_demo => cict}/cict_datasaver.py | 0 demo/{cict_demo => cict}/cict_eval.py | 0 demo/{cict_demo => cict}/cict_eval_GAN.py | 0 demo/{cict_demo => cict}/cict_eval_traj.py | 0 demo/{cict_demo => cict}/cict_model.py | 0 demo/{cict_demo => cict}/cict_policy.py | 0 demo/{cict_demo => cict}/cict_test.py | 0 demo/{cict_demo => cict}/cict_train_GAN.py | 0 demo/{cict_demo => cict}/cict_train_traj.py | 0 demo/{cict_demo => cict}/collect_data.py | 0 demo/{cict_demo => cict}/collect_pm.py | 0 demo/{cict_demo => cict}/post.py | 0 .../cilrs_collect_data.py} | 73 +++--- demo/cilrs/cilrs_env_wrapper.py | 49 ++++ demo/cilrs/cilrs_test.py | 81 ++++++ demo/cilrs/cilrs_train.py | 152 +++++++++++ demo/coil_demo/coil_eval.py | 107 -------- demo/coil_demo/coil_train.py | 174 ------------- docs/source/api_doc/data.rst | 2 +- 38 files changed, 708 insertions(+), 807 deletions(-) rename core/data/{dataset_saver.py => benchmark_dataset_saver.py} (100%) create mode 100644 core/data/cilrs_dataset.py delete mode 100644 core/data/coil_dataset.py create mode 100644 core/models/cilrs_model.py delete mode 100644 core/models/coil_model.py create mode 100644 core/policy/cilrs_policy.py delete mode 100644 core/policy/coil_policy.py rename demo/{cict_demo => cict}/.gitignore (100%) rename demo/{cict_demo => cict}/camera/basic_tools.py (100%) rename demo/{cict_demo => cict}/camera/coordinate_transformation.py (100%) rename demo/{cict_demo => cict}/camera/parameters.py (100%) rename demo/{cict_demo => cict}/cict_datasaver.py (100%) rename demo/{cict_demo => cict}/cict_eval.py (100%) rename demo/{cict_demo => cict}/cict_eval_GAN.py (100%) rename demo/{cict_demo => cict}/cict_eval_traj.py (100%) rename demo/{cict_demo => cict}/cict_model.py (100%) rename demo/{cict_demo => cict}/cict_policy.py (100%) rename demo/{cict_demo => cict}/cict_test.py (100%) rename demo/{cict_demo => cict}/cict_train_GAN.py (100%) rename demo/{cict_demo => cict}/cict_train_traj.py (100%) rename demo/{cict_demo => cict}/collect_data.py (100%) rename demo/{cict_demo => cict}/collect_pm.py (100%) rename demo/{cict_demo => cict}/post.py (100%) rename demo/{coil_demo/coil_data_collect.py => cilrs/cilrs_collect_data.py} (62%) create mode 100644 demo/cilrs/cilrs_env_wrapper.py create mode 100644 demo/cilrs/cilrs_test.py create mode 100644 demo/cilrs/cilrs_train.py delete mode 100644 demo/coil_demo/coil_eval.py delete mode 100644 demo/coil_demo/coil_train.py diff --git a/.gitignore b/.gitignore index 59c8a74a..f243af4f 100644 --- a/.gitignore +++ b/.gitignore @@ -1405,3 +1405,6 @@ htmlcov project_test.py *.csv *.avi +*episode_metainfo.json +*measurements.lmdb +*index.txt diff --git a/core/data/__init__.py b/core/data/__init__.py index f6a068c2..45bccf0c 100644 --- a/core/data/__init__.py +++ b/core/data/__init__.py @@ -1,2 +1,4 @@ from .carla_benchmark_collector import CarlaBenchmarkCollector -from .dataset_saver import BenchmarkDatasetSaver +from .benchmark_dataset_saver import BenchmarkDatasetSaver +from .cilrs_dataset import CILRSDataset +from .cict_dataset import CictDataset diff --git a/core/data/dataset_saver.py b/core/data/benchmark_dataset_saver.py similarity index 100% rename from core/data/dataset_saver.py rename to core/data/benchmark_dataset_saver.py diff --git a/core/data/carla_benchmark_collector.py b/core/data/carla_benchmark_collector.py index 93bb9b67..0ec930e0 100644 --- a/core/data/carla_benchmark_collector.py +++ b/core/data/carla_benchmark_collector.py @@ -2,11 +2,8 @@ import numpy as np from collections import deque from typing import Any, Dict, List, Optional, Union -import copy -import math -from tqdm import tqdm -from easydict import EasyDict from itertools import product +import random from .base_collector import BaseCollector from core.data.benchmark import ALL_SUITES @@ -45,6 +42,8 @@ class CarlaBenchmarkCollector(BaseCollector): seed=None, dynamic_seed=True, weathers=None, + nocrash=False, + shuffle=False, ) def __init__( @@ -59,6 +58,7 @@ def __init__( self._seed = self._cfg.seed self._dynamic_seed = self._cfg.dynamic_seed self._weathers = self._cfg.weathers + self._shuffle = self._cfg.shuffle if self._benchmark_dir is None: self._benchmark_dir = get_benchmark_dir() self._collect_suite_list = get_suites_list(suite) @@ -109,12 +109,16 @@ def _generate_suite_reset_params(self): if self._weathers is not None: weathers = self._weathers pose_pairs = read_pose_txt(self._benchmark_dir, poses_txt) - for (start, end), weather in product(pose_pairs, weathers): + for weather, (start, end) in product(weathers, pose_pairs): param = reset_params.copy() param['start'] = start param['end'] = end param['weather'] = weather + if self._cfg.nocrash: + param['col_is_failure'] = True self._collect_suite_reset_params[suite].append(param) + if self._shuffle: + random.shuffle(self._collect_suite_reset_params[suite]) def reset(self, suite: Union[List, str] = None) -> None: """ diff --git a/core/data/cilrs_dataset.py b/core/data/cilrs_dataset.py new file mode 100644 index 00000000..295b5f36 --- /dev/null +++ b/core/data/cilrs_dataset.py @@ -0,0 +1,42 @@ +import os +import numpy as np +from typing import Any, Dict +import torch +from torch.utils.data import Dataset + +from core.utils.others.image_helper import read_image + + +class CILRSDataset(Dataset): + + def __init__(self, cfg: Dict) -> None: + self._cfg = cfg + self._root_dir = cfg.root_dir + self._transform = self._cfg.transform + + preload_file = cfg.get('preloads', None) + if preload_file is not None: + print('[DATASET] Loading from NPY') + self._sensor_data_names, self._measurements = np.load(preload_file, allow_pickle=True) + + def __len__(self) -> int: + return len(self._sensor_data_names) + + def __getitem__(self, index: int) -> Any: + img_path = os.path.join(self._root_dir, self._sensor_data_names[index]) + img = read_image(img_path) + if self._transform: + img = img.transpose(2, 0, 1) + img = img / 255. + img = img.astype(np.float32) + img = torch.from_numpy(img).type(torch.FloatTensor) + + measurements = self._measurements[index].copy() + data = dict() + data['rgb'] = img + for k, v in measurements.items(): + v = torch.from_numpy(np.asanyarray([v])).type(torch.FloatTensor) + data[k] = v + data['speed'] = data.pop('speed_module') + data['command'] = data.pop('directions') + return data diff --git a/core/data/coil_dataset.py b/core/data/coil_dataset.py deleted file mode 100644 index ae2f8d5c..00000000 --- a/core/data/coil_dataset.py +++ /dev/null @@ -1,165 +0,0 @@ -import collections -import os - -import cv2 -import numpy as np -import torch -from torch.utils.data import Dataset - -from core.utils.data_utils import splitter - - -def parse_remove_configuration(configuration): - """ - Turns the configuration line of sliptting into a name and a set of params. - """ - - if configuration is None: - return "None", None - print('conf', configuration) - conf_dict = collections.OrderedDict(configuration) - - name = 'remove' - for key in conf_dict.keys(): - if key != 'weights' and key != 'boost': - name += '_' - name += key - - return name, conf_dict - - -class CoILDataset(Dataset): - """ The conditional imitation learning dataset""" - - def __init__( - self, - root_dir, - cfg, - transform=None, - ): - # Setting the root directory for this dataset - self.root_dir = root_dir - self.cfg = cfg - - preload_name = str(cfg.NUMBER_OF_HOURS) + 'hours_' + cfg.TRAIN_DATASET_NAME - # We add to the preload name all the remove labels - if cfg.REMOVE is not None and cfg.REMOVE != "None": - name, self._remove_params = parse_remove_configuration(cfg.REMOVE) - self.preload_name = preload_name + '_' + name - self._check_remove_function = getattr(splitter, name) - else: - self._check_remove_function = lambda _, __: False - self._remove_params = [] - self.preload_name = preload_name - - print("preload Name ", self.preload_name) - - if self.preload_name is not None and os.path.exists(os.path.join('_preloads', self.preload_name + '.npy')): - print(" Loading from NPY ") - self.sensor_data_names, self.measurements = np.load( - os.path.join('_preloads', self.preload_name + '.npy'), allow_pickle=True - ) - print(self.sensor_data_names) - else: - raise ValueError('The .npy file doesn\'t exists, please specify the right file path.') - - print("preload Name ", self.preload_name) - - self.transform = transform - self.batch_read_number = 0 - - def __len__(self): - return len(self.measurements) - - def __getitem__(self, index): - """ - Get item function used by the dataset loader - returns all the measurements with the desired image. - - Args: - index: - - Returns: - - """ - try: - img_path = os.path.join( - self.root_dir, self.sensor_data_names[index].split('/')[-2], - self.sensor_data_names[index].split('/')[-1] - ) - - img = cv2.imread(img_path, cv2.IMREAD_COLOR) - # Apply the image transformation - if self.transform is not None: - boost = 1 - img = self.transform(self.batch_read_number * boost, img) - else: - img = img.transpose(2, 0, 1) - - img = img.astype(np.float) - img = torch.from_numpy(img).type(torch.FloatTensor) - img = img / 255. - - measurements = self.measurements[index].copy() - for k, v in measurements.items(): - v = torch.from_numpy(np.asarray([ - v, - ])) - measurements[k] = v.float() - - measurements['rgb'] = img - - self.batch_read_number += 1 - except AttributeError: - print("Blank IMAGE") - - measurements = self.measurements[0].copy() - for k, v in measurements.items(): - v = torch.from_numpy(np.asarray([ - v, - ])) - measurements[k] = v.float() - measurements['steer'] = 0.0 - measurements['throttle'] = 0.0 - measurements['brake'] = 0.0 - measurements['rgb'] = torch.from_numpy(np.zeros((3, 88, 200))).type(torch.FloatTensor) - - return measurements - - def extract_targets(self, data): - """ - Method used to get to know which positions from the dataset are the targets - for this experiments - Args: - labels: the set of all float data got from the dataset - - Returns: - the float data that is actually targets - - Raises - value error when the configuration set targets that didn't exist in metadata - """ - targets_vec = [] - for target_name in self.cfg.TARGETS: - targets_vec.append(data[target_name]) - - return torch.cat(targets_vec, 1) - - def extract_inputs(self, data): - """ - Method used to get to know which positions from the dataset are the inputs - for this experiments - Args: - labels: the set of all float data got from the dataset - - Returns: - the float data that is actually targets - - Raises - value error when the configuration set targets that didn't exist in metadata - """ - inputs_vec = [] - for input_name in self.cfg.INPUTS: - inputs_vec.append(data[input_name]) - - return torch.cat(inputs_vec, 1) diff --git a/core/models/__init__.py b/core/models/__init__.py index 3072f913..4288c6b6 100644 --- a/core/models/__init__.py +++ b/core/models/__init__.py @@ -4,4 +4,4 @@ from .bev_speed_model import BEVSpeedConvEncoder from .vae_model import VanillaVAE from .model_wrappers import SteerNoiseWrapper -from .coil_model import COILModel +from .cilrs_model import CILRSModel diff --git a/core/models/cilrs_model.py b/core/models/cilrs_model.py new file mode 100644 index 00000000..633addc7 --- /dev/null +++ b/core/models/cilrs_model.py @@ -0,0 +1,105 @@ +import importlib + +import numpy as np +import torch +import torch.nn as nn +from torchvision import models + + +class CILRSModel(nn.Module): + + def __init__( + self, + backbone='resnet18', + normalize=False, + num_branch=6, + speed_dim=1, + embedding_dim=512, + hidden_size=256, + input_speed=True, + predict_speed=True + ): + super().__init__() + self._normalize = normalize + assert backbone in ['resnet18', 'resnet34', 'resnet50'], backbone + backbone_cls = { + 'resnet18': models.resnet18, + 'resnet34': models.resnet34, + 'resnet50': models.resnet50, + }[backbone] + self._backbone = backbone_cls(pretrained=True) + self._backbone.fc = nn.Sequential() + self._num_branch = num_branch + self._input_speed = input_speed + self.predict_speed = predict_speed + + # Project input speed measurement to feature size + if input_speed: + self._speed_in = nn.Sequential( + nn.Linear(speed_dim, hidden_size), + nn.ReLU(True), + nn.Linear(hidden_size, embedding_dim), + ) + + # Project feature to speed prediction + if predict_speed: + self._speed_out = nn.Sequential( + nn.Linear(embedding_dim, hidden_size), + nn.ReLU(True), + nn.Linear(hidden_size, hidden_size), + nn.ReLU(True), + nn.Linear(hidden_size, speed_dim), + ) + + # Control branches + fc_branch_list = [] + for i in range(num_branch): + fc_branch_list.append( + nn.Sequential( + nn.Linear(embedding_dim, hidden_size), + nn.ReLU(True), + nn.Linear(hidden_size, hidden_size), + nn.ReLU(True), + nn.Linear(hidden_size, 3), + nn.Sigmoid(), + ) + ) + + self._branches = nn.ModuleList(fc_branch_list) + + def _normalize_imagenet(self, x): + """ Normalize input images according to ImageNet standards. + Args: + x (tensor): input images + """ + x = x.clone() + x[:, 0] = (x[:, 0] - 0.485) / 0.229 + x[:, 1] = (x[:, 1] - 0.456) / 0.224 + x[:, 2] = (x[:, 2] - 0.406) / 0.225 + return x + + def encode(self, input_images): + embedding = 0 + for x in input_images: + if self._normalize: + x = self._normalize_imagenet(x) + embedding += self._backbone(x) + return embedding + + def forward(self, embedding, speed, command): + batch_size = embedding.size(0) + + if self._input_speed: + embedding += self._speed_in(speed) + + control_pred = 0. + for i, branch in enumerate(self._branches): + # Choose control for branch of only active command + # We check for (command - 1) since navigational command 0 is ignored + control_pred += branch(embedding) * (i == (command - 1)).expand(batch_size, 3) + + if self.predict_speed: + speed_pred = self._speed_out(embedding) + return control_pred, speed_pred + + return control_pred diff --git a/core/models/coil_model.py b/core/models/coil_model.py deleted file mode 100644 index 7db40426..00000000 --- a/core/models/coil_model.py +++ /dev/null @@ -1,245 +0,0 @@ -import importlib - -import numpy as np -import scipy -import scipy.misc -import torch -import torch.nn as nn - -from core.utils.model_utils.common import FC, Conv, Branching, Join -from core.utils.others.general_helper import command_number_to_index - - -class CoILICRA(nn.Module): - - def __init__(self, cfg): - # TODO: Improve the model autonaming function - - super(CoILICRA, self).__init__() - params = cfg.MODEL_CONFIGURATION - self.params = params - - number_first_layer_channels = 0 - - for _, sizes in cfg.SENSORS.items(): - number_first_layer_channels += sizes[0] * cfg.NUMBER_FRAMES_FUSION - - # Get one item from the dict - sensor_input_shape = next(iter(cfg.SENSORS.values())) - sensor_input_shape = [number_first_layer_channels, sensor_input_shape[1], sensor_input_shape[2]] - # For this case we check if the perception layer is of the type "conv" - if 'conv' in params['perception']: - perception_convs = Conv( - params={ - 'channels': [number_first_layer_channels] + params['perception']['conv']['channels'], - 'kernels': params['perception']['conv']['kernels'], - 'strides': params['perception']['conv']['strides'], - 'dropouts': params['perception']['conv']['dropouts'], - 'end_layer': True - } - ) - - perception_fc = FC( - params={ - 'neurons': [perception_convs.get_conv_output(sensor_input_shape)] + - params['perception']['fc']['neurons'], - 'dropouts': params['perception']['fc']['dropouts'], - 'end_layer': False - } - ) - - self.perception = nn.Sequential(*[perception_convs, perception_fc]) - - number_output_neurons = params['perception']['fc']['neurons'][-1] - - elif 'res' in params['perception']: # pre defined residual networks - resnet_module = importlib.import_module('core.utils.model_utils.resnet') - resnet_module = getattr(resnet_module, params['perception']['res']['name']) - self.perception = resnet_module( - pretrained=cfg.PRE_TRAINED, num_classes=params['perception']['res']['num_classes'] - ) - number_output_neurons = params['perception']['res']['num_classes'] - - else: - - raise ValueError("invalid convolution layer type") - - self.measurements = FC( - params={ - 'neurons': [len(cfg.INPUTS)] + params['measurements']['fc']['neurons'], - 'dropouts': params['measurements']['fc']['dropouts'], - 'end_layer': False - } - ) - - self.join = Join( - params={ - 'after_process': FC( - params={ - 'neurons': [params['measurements']['fc']['neurons'][-1] + number_output_neurons] + - params['join']['fc']['neurons'], - 'dropouts': params['join']['fc']['dropouts'], - 'end_layer': False - } - ), - 'mode': 'cat' - } - ) - - self.speed_branch = FC( - params={ - 'neurons': [params['join']['fc']['neurons'][-1]] + params['speed_branch']['fc']['neurons'] + [1], - 'dropouts': params['speed_branch']['fc']['dropouts'] + [0.0], - 'end_layer': True - } - ) - - # Create the fc vector separatedely - branch_fc_vector = [] - for i in range(params['branches']['number_of_branches']): - branch_fc_vector.append( - FC( - params={ - 'neurons': [params['join']['fc']['neurons'][-1]] + params['branches']['fc']['neurons'] + - [len(cfg.TARGETS)], - 'dropouts': params['branches']['fc']['dropouts'] + [0.0], - 'end_layer': True - } - ) - ) - - self.branches = Branching(branch_fc_vector) # Here we set branching automatically - - if 'conv' in params['perception']: - for m in self.modules(): - if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - nn.init.constant_(m.bias, 0.1) - else: - for m in self.modules(): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - nn.init.constant_(m.bias, 0.1) - - def forward(self, x, a): - """ ###### APPLY THE PERCEPTION MODULE """ - x, inter = self.perception(x) - # Not a variable, just to store intermediate layers for future vizualization - # self.intermediate_layers = inter - """ ###### APPLY THE MEASUREMENT MODULE """ - m = self.measurements(a) - """ Join measurements and perception""" - j = self.join(x, m) - - branch_outputs = self.branches(j) - - speed_branch_output = self.speed_branch(x) - - # We concatenate speed with the rest. - return branch_outputs + [speed_branch_output] - - def forward_branch(self, x, a, branch_number): - """ - DO a forward operation and return a single branch. - - Args: - x: the image input - a: speed measurement - branch_number: the branch number to be returned - - Returns: - the forward operation on the selected branch - - """ - # Convert to integer just in case . - # TODO: take four branches, this is hardcoded - output_vec = torch.stack(self.forward(x, a)[0:4]) - - return self.extract_branch(output_vec, branch_number) - - def get_perception_layers(self, x): - return self.perception.get_layers_features(x) - - def extract_branch(self, output_vec, branch_number): - - branch_number = command_number_to_index(branch_number) - - if len(branch_number) > 1: - branch_number = torch.squeeze(branch_number.type(torch.cuda.LongTensor)) - else: - branch_number = branch_number.type(torch.cuda.LongTensor) - - branch_number = torch.stack([branch_number, torch.cuda.LongTensor(range(0, len(branch_number)))]) - - return output_vec[branch_number[0], branch_number[1], :] - - -class COILModel(): - - def __init__(self, cfg): - self._cfg = cfg - self.checkpoint = torch.load(self._cfg.model.ckpt_path) - - # do the merge here - - self._model = CoILModule(self._cfg) - self.first_iter = True - # Load the model and prepare set it for evaluation - self._model.load_state_dict(self.checkpoint['state_dict']) - self._model.cuda() - self._model.eval() - - def run_step(self, observations): - - sensor_data = observations['rgb'].float().cuda() - directions = observations['command'] + 1.0 - # Take the forward speed and normalize it for it to go from 0-1 - norm_speed = observations['speed'] / 25. - norm_speed = torch.cuda.FloatTensor([norm_speed]).unsqueeze(0) - directions_tensor = torch.cuda.LongTensor([directions]) - # Compute the forward pass processing the sensors got from CARLA. - model_outputs = self._model.forward_branch(sensor_data, norm_speed, directions_tensor) - - # actions = self._process_model_outputs(model_outputs) - - return model_outputs - - # There is the posibility to replace some of the predictions with oracle predictions. - - def _process_model_outputs(self, outputs): - """ - A bit of heuristics in the control, to eventually make car faster, for instance. - Returns: - - """ - actions = [] - for output in outputs: - - steer, throttle, brake = output[0], output[1], output[2] - if brake < 0.05: - brake = 0.0 - - if throttle > brake: - brake = 0.0 - - action = {'steer': float(steer), 'throttle': float(throttle), 'brake': float(brake)} - actions.append(action) - - return actions - - -def CoILModule(cfg): - """ Factory function - - Note: It is defined with the first letter as uppercase even though is a function to contrast - the actual use of this function that is making classes - """ - # TODO: this could be extended to some more arbitrary definition - - if cfg.MODEL_TYPE == 'coil-icra': - - return CoILICRA(cfg) - - else: - - raise ValueError(" Not found architecture name") diff --git a/core/models/model_wrappers.py b/core/models/model_wrappers.py index 901cbf8d..71238278 100644 --- a/core/models/model_wrappers.py +++ b/core/models/model_wrappers.py @@ -33,6 +33,7 @@ def __init__( self._noise_state = 'drive' self._state_step = 0 self._last_throttle = 0 + self._last_brake = 0 def forward(self, *args, **kwargs) -> Dict: """ @@ -56,7 +57,7 @@ def forward(self, *args, **kwargs) -> Dict: if self._noise_state == 'noise': control['steer'] = self._noise_steer control['throttle'] = self._last_throttle - control['brake'] = 0 + control['brake'] = self._last_brake else: control['steer'] = real_control['steer'] control['throttle'] = real_control['throttle'] @@ -70,6 +71,7 @@ def forward(self, *args, **kwargs) -> Dict: if self._noise_range is not None: np.clip(self._noise_steer, self._noise_range['min'], self._noise_range['max']) self._last_throttle = real_control['throttle'] + self._last_brake = real_control['brake'] return control diff --git a/core/policy/__init__.py b/core/policy/__init__.py index 5f3abf69..4b1b145b 100644 --- a/core/policy/__init__.py +++ b/core/policy/__init__.py @@ -1,3 +1,3 @@ from .auto_policy import AutoPIDPolicy, AutoMPCPolicy -from .coil_policy import CILPolicy +from .cilrs_policy import CILRSPolicy from .lbc_policy import LBCBirdviewPolicy, LBCImagePolicy diff --git a/core/policy/base_carla_policy.py b/core/policy/base_carla_policy.py index 21ebc78d..91d14d76 100644 --- a/core/policy/base_carla_policy.py +++ b/core/policy/base_carla_policy.py @@ -34,6 +34,7 @@ def __init__(self, cfg: dict, model: Any = None, enable_field: Optional[List[str self._enable_field = self.total_field else: self._enable_field = enable_field + self._model = model for field in self._enable_field: getattr(self, '_init_' + field)() diff --git a/core/policy/cilrs_policy.py b/core/policy/cilrs_policy.py new file mode 100644 index 00000000..99514657 --- /dev/null +++ b/core/policy/cilrs_policy.py @@ -0,0 +1,210 @@ +from typing import Dict, List, Optional, Any +from collections import namedtuple +import numpy as np +import torch +from torch.optim import Adam +import torch.nn.functional as F + +from ding.utils.data import default_collate, default_decollate +from ding.torch_utils import to_device +from core.models import CILRSModel +from .base_carla_policy import BaseCarlaPolicy + + +class CILRSPolicy(BaseCarlaPolicy): + config = dict( + cuda=True, + max_throttle=0.75, + model=dict(), + learn=dict( + epoches=200, + lr=1e-4, + batch_size=128, + loss='l1', + speed_weight=0.05, + control_weights=[0.5, 0.45, 0.05], + ), + ) + validate_function = namedtuple( + 'validate_function', [ + 'forward', + 'reset', + 'get_attribute', + 'set_attribute', + 'state_dict', + 'load_state_dict', + ] + ) + + def __init__( + self, + cfg: Dict, + ) -> None: + super().__init__(cfg, enable_field=[]) + self._enable_field = ['eval', 'learn', 'validate'] + self._cuda = self._cfg.cuda + self._max_throttle = self._cfg.max_throttle + self._model = CILRSModel(**self._cfg.model) + if self._cuda: + self._model.cuda() + + for field in self._enable_field: + getattr(self, '_init_' + field)() + + def _process_sensors(self, sensor): + sensor = sensor[:, :, ::-1] # BGR->RGB + sensor = np.transpose(sensor, (2, 0, 1)) + sensor = sensor / 255.0 + + return sensor + + def _process_model_outputs(self, output): + + control_pred, velocity_pred = output + steer = control_pred[:, 0] * 2 - 1. # convert from [0,1] to [-1,1] + throttle = control_pred[:, 1] * self._cfg.max_throttle + brake = control_pred[:, 2] + action = {'steer': steer, 'throttle': throttle, 'brake': brake} + + return action, velocity_pred + + def _reset_eval(self, data_id: Optional[List[int]] = None) -> None: + self._model.eval() + + @torch.no_grad() + def _forward_eval(self, data: Dict): + data_id = list(data.keys()) + + new_data = dict() + for id in data.keys(): + new_data[id] = dict() + new_data[id]['rgb'] = self._process_sensors(data[id]['rgb'].numpy()) + new_data[id]['command'] = data[id]['command'] + new_data[id]['speed'] = data[id]['speed'] + + data = default_collate(list(new_data.values())) + if self._cuda: + data = to_device(data, 'cuda') + + embedding = self._model.encode([data['rgb']]) + output = self._model(embedding, data['speed'], data['command']) + if self._cuda: + output = to_device(output, 'cpu') + + actions, _ = self._process_model_outputs(output) + actions = default_decollate(actions) + return {i: {'action': d} for i, d in zip(data_id, actions)} + + def _init_learn(self) -> None: + self._optimizer = Adam(self._model.parameters(), self._cfg.learn.lr) + if self._cfg.learn.loss == 'l1': + self._criterion = F.l1_loss + elif self._cfg.policy.learn.loss == 'l2': + self._criterion = F.mse_loss + + def _reset_learn(self, data_id: Optional[List[int]] = None) -> None: + self._model.train() + + def _forward_learn(self, data: Dict) -> Dict[str, Any]: + if self._cuda: + data = to_device(data, 'cuda') + + rgb = data['rgb'] + steer_gt, throttle_gt, brake_gt = data['steer'], data['throttle'], data['brake'] + speed = data['speed'] + command = data['command'] + embedding = self._model.encode([rgb]) + output = self._model(embedding, speed, command) + control_pred, speed_pred = output + steer_pred = control_pred[:, 0] + throttle_pred = control_pred[:, 1] + brake_pred = control_pred[:, 2] + + speed_loss = self._criterion(speed_pred.squeeze(), speed.squeeze()).mean() * self._cfg.learn.speed_weight + steer_loss = self._criterion(steer_pred, steer_gt.squeeze()).mean() * self._cfg.learn.control_weights[0] + throttle_loss = self._criterion(throttle_pred, + throttle_gt.squeeze()).mean() * self._cfg.learn.control_weights[1] + brake_loss = self._criterion(brake_pred, brake_gt.squeeze()).mean() * self._cfg.learn.control_weights[2] + + total_loss = speed_loss + steer_loss + throttle_loss + brake_loss + + self._optimizer.zero_grad() + total_loss.backward() + self._optimizer.step() + + return_info = { + 'cur_lr': self._optimizer.defaults['lr'], + 'total_loss': total_loss.item(), + 'speed_loss': speed_loss.item(), + 'steer_loss': steer_loss.item(), + 'throttle_loss': throttle_loss.item(), + 'brake_loss': brake_loss.item(), + # 'steer_mean': steer_pred.item().mean(), + # 'throttle_mean': throttle_pred.item().mean(), + # 'brake_mean': brake_pred.item().mean(), + } + + return return_info + + def _init_validate(self): + if self._cfg.learn.loss == 'l1': + self._criterion = F.l1_loss + elif self._cfg.policy.learn.loss == 'l2': + self._criterion = F.mse_loss + + @torch.no_grad() + def _forward_validate(self, data: Dict) -> Dict[str, Any]: + if self._cuda: + data = to_device(data, 'cuda') + + rgb = data['rgb'] + steer_gt, throttle_gt, brake_gt = data['steer'], data['throttle'], data['brake'] + speed = data['speed'] + command = data['command'] + embedding = self._model.encode([rgb]) + output = self._model(embedding, speed, command) + control_pred, speed_pred = output + steer_pred = control_pred[:, 0] + throttle_pred = control_pred[:, 1] + brake_pred = control_pred[:, 2] + + speed_loss = self._criterion(speed_pred.squeeze(), speed.squeeze()).mean() * self._cfg.learn.speed_weight + steer_loss = self._criterion(steer_pred, steer_gt.squeeze()).mean() * self._cfg.learn.control_weights[0] + throttle_loss = self._criterion(throttle_pred, + throttle_gt.squeeze()).mean() * self._cfg.learn.control_weights[1] + brake_loss = self._criterion(brake_pred, brake_gt.squeeze()).mean() * self._cfg.learn.control_weights[2] + + total_loss = speed_loss + steer_loss + throttle_loss + brake_loss + + return_info = { + 'total_loss': total_loss.item(), + 'speed_loss': speed_loss.item(), + 'steer_loss': steer_loss.item(), + 'throttle_loss': throttle_loss.item(), + 'brake_loss': brake_loss.item(), + # 'steer_mean': steer_pred.item().mean(), + # 'throttle_mean': throttle_pred.item().mean(), + # 'brake_mean': brake_pred.item().mean(), + } + return return_info + + def _state_dict_learn(self) -> Dict[str, Any]: + return { + 'model': self._model.state_dict(), + 'optimizer': self._optimizer.state_dict(), + } + + def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None: + self._model.load_state_dict(state_dict['model']) + self._optimizer.load_state_dict(state_dict['optimizer']) + + @property + def validate_mode(self) -> 'CILRSPolicy.eval_function': # noqa + return CILRSPolicy.validate_function( + self._forward_validate, + self._reset_eval, + self._get_attribute, + self._set_attribute, + self._state_dict_eval, + self._load_state_dict_eval, + ) diff --git a/core/policy/coil_policy.py b/core/policy/coil_policy.py deleted file mode 100644 index 702dabc3..00000000 --- a/core/policy/coil_policy.py +++ /dev/null @@ -1,74 +0,0 @@ -from typing import Dict - -import numpy as np -import scipy -import scipy.misc -import torch -from ding.utils.data import default_collate, default_decollate -from ding.torch_utils import to_device - -from core.models import COILModel -from .base_carla_policy import BaseCarlaPolicy - - -class CILPolicy(BaseCarlaPolicy): - config = dict(debug=False, cuda=True) - - def __init__( - self, - cfg: Dict, - ) -> None: - super().__init__(cfg) - self._enable_field = set(['collect', 'eval']) - self._cuda = True - for field in self._enable_field: - getattr(self, '_init_' + field)() - - def _process_sensors(self, sensor): - sensor = sensor[:, :, ::-1] # BGR->RGB - sensor = sensor[self._cfg.IMAGE_CUT[0]:self._cfg.IMAGE_CUT[1], :, :] # crop - sensor = scipy.misc.imresize(sensor, (self._cfg.SENSORS['rgb'][1], self._cfg.SENSORS['rgb'][2])) - - sensor = np.swapaxes(sensor, 0, 1) - sensor = np.transpose(sensor, (2, 1, 0)) - sensor = sensor / 255.0 - - return sensor - - def _process_model_outputs(self, outputs): - - actions = [] - for output in outputs: - - steer, throttle, brake = output[0], output[1], output[2] - if brake < 0.05: - brake = 0.0 - - if throttle > brake: - brake = 0.0 - - action = {'steer': float(steer), 'throttle': float(throttle), 'brake': float(brake)} - - actions.append({'action': action}) - - return actions - - def _init_eval(self) -> None: - self._eval_model = COILModel(self._cfg) - - def _forward_eval(self, data: Dict) -> dict: - data_id = list(data.keys()) - - for id in data.keys(): - data[id]['rgb'] = self._process_sensors(data[id]['rgb'].numpy()) - - data = default_collate(list(data.values())) - - with torch.no_grad(): - output = self._eval_model.run_step(data) - if self._cuda: - output = to_device(output, 'cpu') - - output = default_decollate(output) - output = self._process_model_outputs(output) - return {i: d for i, d in zip(data_id, output)} diff --git a/core/policy/lbc_policy.py b/core/policy/lbc_policy.py index 798145bc..2f0bad6e 100644 --- a/core/policy/lbc_policy.py +++ b/core/policy/lbc_policy.py @@ -43,7 +43,8 @@ class LBCBirdviewPolicy(BaseCarlaPolicy): ) def __init__(self, cfg: dict) -> None: - super().__init__(cfg, enable_field=set(['eval', 'learn'])) + super().__init__(cfg, enable_field=[]) + self._enable_field = ['eval', 'learn'] self._controller_dict = dict() if self._cfg.model.cuda: @@ -99,6 +100,9 @@ def __init__(self, cfg: dict) -> None: ) self._model.to(self._device) + for field in self._enable_field: + getattr(self, '_init_' + field)() + def _postprocess(self, steer, throttle, brake): control = {} control.update( diff --git a/demo/cict_demo/.gitignore b/demo/cict/.gitignore similarity index 100% rename from demo/cict_demo/.gitignore rename to demo/cict/.gitignore diff --git a/demo/cict_demo/camera/basic_tools.py b/demo/cict/camera/basic_tools.py similarity index 100% rename from demo/cict_demo/camera/basic_tools.py rename to demo/cict/camera/basic_tools.py diff --git a/demo/cict_demo/camera/coordinate_transformation.py b/demo/cict/camera/coordinate_transformation.py similarity index 100% rename from demo/cict_demo/camera/coordinate_transformation.py rename to demo/cict/camera/coordinate_transformation.py diff --git a/demo/cict_demo/camera/parameters.py b/demo/cict/camera/parameters.py similarity index 100% rename from demo/cict_demo/camera/parameters.py rename to demo/cict/camera/parameters.py diff --git a/demo/cict_demo/cict_datasaver.py b/demo/cict/cict_datasaver.py similarity index 100% rename from demo/cict_demo/cict_datasaver.py rename to demo/cict/cict_datasaver.py diff --git a/demo/cict_demo/cict_eval.py b/demo/cict/cict_eval.py similarity index 100% rename from demo/cict_demo/cict_eval.py rename to demo/cict/cict_eval.py diff --git a/demo/cict_demo/cict_eval_GAN.py b/demo/cict/cict_eval_GAN.py similarity index 100% rename from demo/cict_demo/cict_eval_GAN.py rename to demo/cict/cict_eval_GAN.py diff --git a/demo/cict_demo/cict_eval_traj.py b/demo/cict/cict_eval_traj.py similarity index 100% rename from demo/cict_demo/cict_eval_traj.py rename to demo/cict/cict_eval_traj.py diff --git a/demo/cict_demo/cict_model.py b/demo/cict/cict_model.py similarity index 100% rename from demo/cict_demo/cict_model.py rename to demo/cict/cict_model.py diff --git a/demo/cict_demo/cict_policy.py b/demo/cict/cict_policy.py similarity index 100% rename from demo/cict_demo/cict_policy.py rename to demo/cict/cict_policy.py diff --git a/demo/cict_demo/cict_test.py b/demo/cict/cict_test.py similarity index 100% rename from demo/cict_demo/cict_test.py rename to demo/cict/cict_test.py diff --git a/demo/cict_demo/cict_train_GAN.py b/demo/cict/cict_train_GAN.py similarity index 100% rename from demo/cict_demo/cict_train_GAN.py rename to demo/cict/cict_train_GAN.py diff --git a/demo/cict_demo/cict_train_traj.py b/demo/cict/cict_train_traj.py similarity index 100% rename from demo/cict_demo/cict_train_traj.py rename to demo/cict/cict_train_traj.py diff --git a/demo/cict_demo/collect_data.py b/demo/cict/collect_data.py similarity index 100% rename from demo/cict_demo/collect_data.py rename to demo/cict/collect_data.py diff --git a/demo/cict_demo/collect_pm.py b/demo/cict/collect_pm.py similarity index 100% rename from demo/cict_demo/collect_pm.py rename to demo/cict/collect_pm.py diff --git a/demo/cict_demo/post.py b/demo/cict/post.py similarity index 100% rename from demo/cict_demo/post.py rename to demo/cict/post.py diff --git a/demo/coil_demo/coil_data_collect.py b/demo/cilrs/cilrs_collect_data.py similarity index 62% rename from demo/coil_demo/coil_data_collect.py rename to demo/cilrs/cilrs_collect_data.py index 91c68fc3..a58ac60a 100644 --- a/demo/coil_demo/coil_data_collect.py +++ b/demo/cilrs/cilrs_collect_data.py @@ -9,8 +9,7 @@ from easydict import EasyDict from tqdm import tqdm -from core.data import CarlaBenchmarkCollector -from core.data.dataset_saver import BenchmarkDatasetSaver +from core.data import CarlaBenchmarkCollector, BenchmarkDatasetSaver from core.envs import SimpleCarlaEnv, CarlaEnvWrapper from core.policy import AutoPIDPolicy from core.utils.others.tcp_helper import parse_carla_tcp @@ -20,7 +19,6 @@ env_num=5, simulator=dict( disable_two_wheels=True, - waypoint_num=32, planner=dict( type='behavior', resolution=1, @@ -29,9 +27,9 @@ dict( name='rgb', type='rgb', - size=[800, 600], - position=[2.0, 0.0, 1.4], - rotation=[-15, 0, 0], + size=[400, 300], + position=[1.3, 0.0, 2.3], + fov=100, ), ), @@ -44,7 +42,11 @@ context='spawn', max_retry=1, ), - wrapper=dict(), + wrapper=dict( + speed_factor=25., + scale=1, + crop=256, + ), ), server=[ dict(carla_host='localhost', carla_ports=[9000, 9010, 2]), @@ -53,10 +55,12 @@ target_speed=25, noise=True, collect=dict( - n_episode=5, - dir_path='./datasets_train/cils_datasets_train', + n_episode=100, + dir_path='./datasets_train/cilrs_datasets_train', + preloads_name='cilrs_datasets_train.npy', collector=dict( suite='FullTown01-v1', + nocrash=True, ), ) ), @@ -65,11 +69,15 @@ main_config = EasyDict(config) -def cils_postprocess(sensor_data, *args): +def cilrs_postprocess(sensor_data, scale=1, crop=256): rgb = sensor_data['rgb'].copy() - rgb = rgb[115:500, :, :] im = PIL.Image.fromarray(rgb) - rgb = np.array(im.resize([200, 88], PIL.Image.BICUBIC)) + (width, height) = (int(im.width // scale), int(im.height // scale)) + rgb = im.resize((width, height)) + rgb = np.asarray(rgb) + start_x = height // 2 - crop // 2 + start_y = width // 2 - crop // 2 + rgb = rgb[start_x:start_x + crop, start_y:start_y + crop] sensor_data = {'rgb': rgb} others = {} return sensor_data, others @@ -79,24 +87,24 @@ def wrapped_env(env_cfg, wrapper_cfg, host, port, tm_port=None): return CarlaEnvWrapper(SimpleCarlaEnv(env_cfg, host, port, tm_port), wrapper_cfg) -def post_process(datasets_path): - epi_folder = [x for x in os.listdir(datasets_path) if x.startswith('epi')] +def post_process(config): + epi_folder = [x for x in os.listdir(config.policy.collect.dir_path) if x.startswith('epi')] all_img_list = [] all_mea_list = [] for item in tqdm(epi_folder): - lmdb_file = lmdb.open(os.path.join(datasets_path, item, 'measurements.lmdb')).begin(write=False) - png_file = [ - x for x in os.listdir(os.path.join(datasets_path, item)) if (x.endswith('png') and x.startswith('rgb')) + lmdb_file = lmdb.open(os.path.join(config.policy.collect.dir_path, item, 'measurements.lmdb')).begin(write=False) + png_files = [ + x for x in os.listdir(os.path.join(config.policy.collect.dir_path, item)) if (x.endswith('png') and x.startswith('rgb')) ] - png_file.sort() - for k in tqdm(png_file): - index = k.split('_')[1].split('.')[0] + png_files.sort() + for png_file in png_files: + index = png_file.split('_')[1].split('.')[0] measurements = np.frombuffer(lmdb_file.get(('measurements_%05d' % int(index)).encode()), np.float32) data = {} data['control'] = np.array([measurements[12], measurements[13], measurements[14]]).astype(np.float32) - data['speed'] = measurements[10] / 25. + data['speed'] = measurements[10] / config.env.wrapper.speed_factor data['command'] = float(measurements[11]) new_dict = {} new_dict['brake'] = data['control'][2] @@ -104,11 +112,11 @@ def post_process(datasets_path): new_dict['throttle'] = data['control'][1] new_dict['speed_module'] = data['speed'] new_dict['directions'] = data['command'] + 1.0 - all_img_list.append(os.path.join(item, k)) + all_img_list.append(os.path.join(item, png_file)) all_mea_list.append(new_dict) if not os.path.exists('_preloads'): os.mkdir('_preloads') - np.save('_preloads/50hours_cils_datasets_train.npy', [all_img_list, all_mea_list]) + np.save('_preloads/{}'.format(config.policy.collect.preloads_name), [all_img_list, all_mea_list]) def main(cfg, seed=0): @@ -123,7 +131,6 @@ def main(cfg, seed=0): env_fn=[partial(wrapped_env, cfg.env, cfg.env.wrapper, *tcp_list[i]) for i in range(env_num)], cfg=cfg.env.manager, ) - collector_env.seed(seed) policy = AutoPIDPolicy(cfg.policy) @@ -132,18 +139,22 @@ def main(cfg, seed=0): if not os.path.exists(cfg.policy.collect.dir_path): os.makedirs(cfg.policy.collect.dir_path) - collected_episodes = -1 - saver = BenchmarkDatasetSaver(cfg.policy.collect.dir_path, cfg.env.simulator.obs, cils_postprocess) + collected_episodes = 0 + data_postprocess = lambda x: cilrs_postprocess(x, cfg.env.wrapper.scale, cfg.env.wrapper.crop) + saver = BenchmarkDatasetSaver(cfg.policy.collect.dir_path, cfg.env.simulator.obs, data_postprocess) + print('[MAIN] Start collecting data') while collected_episodes < cfg.policy.collect.n_episode: # Sampling data from environments - print('start collect data') - new_data = collector.collect(n_episode=env_num) - print(new_data[0].keys()) - collected_episodes += env_num + n_episode = min(cfg.policy.collect.n_episode - collected_episodes, env_num * 2) + new_data = collector.collect(n_episode=n_episode) saver.save_episodes_data(new_data, start_episode=collected_episodes) + collected_episodes += n_episode + print('[MAIN] Current collected: ', collected_episodes, '/', cfg.policy.collect.n_episode) collector_env.close() - post_process(cfg.policy.collect.dir_path) + saver.make_index() + print('[MAIN] Making preloads') + post_process(cfg) if __name__ == '__main__': diff --git a/demo/cilrs/cilrs_env_wrapper.py b/demo/cilrs/cilrs_env_wrapper.py new file mode 100644 index 00000000..237e1c17 --- /dev/null +++ b/demo/cilrs/cilrs_env_wrapper.py @@ -0,0 +1,49 @@ +import torch +import numpy as np +from typing import Dict, Any +import PIL + +from core.envs import CarlaEnvWrapper +from core.utils.model_utils import common + + +class CILRSEnvWrapper(CarlaEnvWrapper): + + config = dict( + scale=1, + crop=256, + speed_factor=25., + ) + + def _get_obs(self, obs): + new_obs = { + 'command': obs['command'] + 1, + 'speed': np.float32(obs['speed'] / self._cfg.speed_factor), + } + rgb = obs['rgb'] + im = PIL.Image.fromarray(rgb) + (width, height) = (int(im.width // self._cfg.scale), int(im.height // self._cfg.scale)) + im_resized = im.resize((width, height)) + image = np.asarray(im_resized) + start_x = height // 2 - self._cfg.crop // 2 + start_y = width // 2 - self._cfg.crop // 2 + cropped_image = image[start_x:start_x + self._cfg.crop, start_y:start_y + self._cfg.crop] + new_obs['rgb'] = cropped_image + return new_obs + + def reset(self, *args, **kwargs) -> Any: + obs = super().reset() + obs_out = self._get_obs(obs) + return obs_out + + def step(self, action): + action = { + 'steer': action['steer'], + 'throttle': action['throttle'], + 'brake': action['brake'], + } + timestep = super().step(action) + obs = timestep.obs + obs_out = self._get_obs(obs) + timestep = timestep._replace(obs=obs_out) + return timestep diff --git a/demo/cilrs/cilrs_test.py b/demo/cilrs/cilrs_test.py new file mode 100644 index 00000000..989a53e6 --- /dev/null +++ b/demo/cilrs/cilrs_test.py @@ -0,0 +1,81 @@ +from easydict import EasyDict +import torch + +from core.envs import SimpleCarlaEnv +from core.policy import CILRSPolicy +from core.eval import SingleCarlaEvaluator +from core.utils.others.tcp_helper import parse_carla_tcp +from ding.utils import set_pkg_seed +from demo.cilrs.cilrs_env_wrapper import CILRSEnvWrapper + +lbc_config = dict( + env=dict( + simulator=dict( + town='Town01', + disable_two_wheels=True, + n_vehicles=10, + n_pedestrians=10, + verbose=False, + planner=dict( + type='lbc', + resolution=2.5, + threshold_before=9.0, + threshold_after=1.5, + ), + obs=( + dict( + name='rgb', + type='rgb', + size=[400, 300], + position=[1.3, 0.0, 2.3], + fov=100, + ), + ), + ), + visualize=dict( + type='rgb', + outputs=['show'] + ), + wrapper=dict(), + col_is_failure=True, + stuck_is_failure=True, + ), + server=[dict(carla_host='localhost', carla_ports=[9000, 9010, 2])], + policy=dict( + ckpt_path=None, + eval=dict( + evaluator=dict( + #render=True, + transform_obs=True, + ), + ) + ), +) + +main_config = EasyDict(lbc_config) + + +def wrapped_env(env_cfg, host, port, tm_port=None): + return CILRSEnvWrapper(SimpleCarlaEnv(env_cfg, host, port)) + + +def main(cfg, seed=0): + tcp_list = parse_carla_tcp(cfg.server) + assert len(tcp_list) > 0, "No Carla server found!" + + carla_env = wrapped_env(cfg.env, *tcp_list[0]) + carla_env.seed(seed) + set_pkg_seed(seed) + cilrs_policy = CILRSPolicy(cfg.policy).eval_mode + if cfg.policy.ckpt_path is not None: + state_dict = torch.load(cfg.policy.ckpt_path) + cilrs_policy.load_state_dict(state_dict) + + evaluator = SingleCarlaEvaluator(cfg.policy.eval.evaluator, carla_env, cilrs_policy) + res = evaluator.eval() + + evaluator.close() + + +if __name__ == '__main__': + main(main_config) diff --git a/demo/cilrs/cilrs_train.py b/demo/cilrs/cilrs_train.py new file mode 100644 index 00000000..cc7b0528 --- /dev/null +++ b/demo/cilrs/cilrs_train.py @@ -0,0 +1,152 @@ +from collections import defaultdict +import os +import numpy as np +from ding.utils.data.collate_fn import default_collate, default_decollate +from easydict import EasyDict +from tqdm import tqdm +import torch +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +from core.policy import CILRSPolicy +from core.data import CILRSDataset + +config = dict( + exp_name='cilrs_train', + policy=dict( + cuda=True, + cudnn=True, + resume=False, + ckpt_path=None, + learn=dict( + epoches=200, + batch_size=128, + loss='l1', + lr=1e-4, + speed_weight=0.05, + control_weights=[0.5, 0.45, 0.05], + ), + eval=dict( + eval_freq=10, + ) + ), + data=dict( + train=dict( + root_dir='./datasets_train/cilrs_datasets_train', + preloads='./_preloads/cilrs_datasets_train.npy', + transform=True, + ), + val=dict( + root_dir='./datasets_train/cilrs_datasets_val', + preloads='./_preloads/cilrs_datasets_val.npy', + transform=True, + ), + ) +) +main_config = EasyDict(config) + + +def train(policy, loader, tb_logger=None, start_iter=0): + loss_epoch = defaultdict(list) + iter_num = start_iter + policy.reset() + + for data in tqdm(loader): + log_vars = policy.forward(data) + + for k, v in log_vars.items(): + loss_epoch[k] += [log_vars[k]] + if iter_num % 50 == 0 and tb_logger is not None: + tb_logger.add_scalar("train_iter/" + k, v, iter_num) + iter_num += 1 + loss_epoch = {k: np.mean(v) for k, v in loss_epoch.items()} + return iter_num, loss_epoch + + +def validate(policy, loader, tb_logger=None, epoch=0): + loss_epoch = defaultdict(list) + policy.reset() + for data in tqdm(loader): + log_vars = policy.forward(data) + for k in list(log_vars.keys()): + loss_epoch[k] += [log_vars[k]] + loss_epoch = {k: np.mean(v) for k, v in loss_epoch.items()} + if tb_logger is not None: + for k, v in loss_epoch.items(): + tb_logger.add_scalar("validate_epoch/" + k, v, epoch) + return loss_epoch + + +def save_ckpt(state, name=None, exp_name=''): + os.makedirs('checkpoints/' + exp_name, exist_ok=True) + ckpt_path = 'checkpoints/{}/{}_ckpt.pth'.format(exp_name, name) + torch.save(state, ckpt_path) + + +def load_best_ckpt(policy, root_dir='checkpoints', exp_name='', ckpt_path=None): + ckpt_dir = os.path.join(root_dir, exp_name) + assert os.path.isdir(ckpt_dir), ckpt_dir + files = os.listdir(ckpt_dir) + assert files, 'No ckpt files found' + + if ckpt_path and ckpt_path in files: + pass + elif os.path.exists(os.path.join(ckpt_dir, 'best_ckpt.pth')): + ckpt_path = 'best_ckpt.pth' + else: + ckpt_path = sorted(files)[-1] + print('Load ckpt:', ckpt_path) + state_dict = torch.load(os.path.join(ckpt_dir, ckpt_path)) + policy.load_state_dict(state_dict) + epoch = state_dict['epoch'] + iterations = state_dict['iterations'] + best_loss = state_dict['best_loss'] + return epoch, iterations, best_loss + + +def main(cfg): + if cfg.policy.cudnn: + torch.backends.cudnn.benchmark = True + + train_dataset = CILRSDataset(cfg.data.train) + val_dataset = CILRSDataset(cfg.data.val) + train_loader = DataLoader(train_dataset, cfg.policy.learn.batch_size, shuffle=True, num_workers=8) + val_loader = DataLoader(val_dataset, cfg.policy.learn.batch_size, num_workers=8) + + cilrs_policy = CILRSPolicy(cfg.policy) + tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) + iterations = 0 + best_loss = 1e8 + start_epoch = 0 + + if cfg.policy.resume: + start_epoch, iterations, best_loss = load_best_ckpt( + cilrs_policy.learn_mode, exp_name=cfg.exp_name, ckpt_path=cfg.policy.ckpt_path + ) + + for epoch in range(start_epoch, cfg.policy.learn.epoches): + iter_num, loss = train(cilrs_policy.learn_mode, train_loader, tb_logger, iterations) + iterations = iter_num + tqdm.write( + f"Epoch {epoch:03d}, Iter {iter_num:06d}: Total: {loss['total_loss']:2.5f}" + + f" Speed: {loss['speed_loss']:2.5f} Str: {loss['steer_loss']:2.5f}" + + f" Thr: {loss['throttle_loss']:2.5f} Brk: {loss['brake_loss']:2.5f}" + ) + if epoch % cfg.policy.eval.eval_freq == 0: + loss_dict = validate(cilrs_policy.validate_mode, val_loader, tb_logger, iterations) + total_loss = loss_dict['total_loss'] + tqdm.write(f"Validate Total: {total_loss:2.5f}") + state_dict = cilrs_policy.learn_mode.state_dict() + state_dict['epoch'] = epoch + state_dict['iterations'] = iterations + state_dict['best_loss'] = best_loss + if total_loss < best_loss and epoch > 0: + tqdm.write("Best Validation Loss!") + best_loss = total_loss + state_dict['best_loss'] = best_loss + save_ckpt(state_dict, 'best', cfg.exp_name) + save_ckpt(state_dict, '{:05d}'.format(epoch), cfg.exp_name) + + +if __name__ == '__main__': + main(main_config) diff --git a/demo/coil_demo/coil_eval.py b/demo/coil_demo/coil_eval.py deleted file mode 100644 index 17dfa2e3..00000000 --- a/demo/coil_demo/coil_eval.py +++ /dev/null @@ -1,107 +0,0 @@ -''' -Copyright 2021 OpenDILab. All Rights Reserved: -Description: -''' - -from functools import partial - -from easydict import EasyDict -from ding.envs import SyncSubprocessEnvManager -from ding.utils import set_pkg_seed -from ding.utils.default_helper import deep_merge_dicts - -from core.envs import SimpleCarlaEnv, CarlaEnvWrapper -from core.eval import CarlaBenchmarkEvaluator -from core.policy import CILPolicy -from core.utils.others.tcp_helper import parse_carla_tcp - -autoeval_config = dict( - env=dict( - env_num=5, - simulator=dict( - verbose=False, - obs=( - dict( - name='rgb', - type='rgb', - size=[800, 600], - position=[2.0, 0.0, 1.4], - rotation=[-15, 0, 0], - ), - ), - planner=dict(type='behavior', ), - ), - manager=dict( - shared_memory=False, - auto_reset=False, - context='spawn', - max_retry=1, - ), - ), - server=[ - dict(carla_host='localhost', carla_ports=[9000, 9010, 2]) - ], - policy=dict( - target_speed=40, - eval=dict( - evaluator=dict( - suite='FullTown02-v1', - episodes_per_suite=5, - save_files=True, - ), - ), - ), -) - -policy_config = dict( - model=dict( - ckpt_path='_logs/sample/coil_icra/checkpoints/100.pth' - ), - SENSORS=dict(rgb=[3, 88, 200]), - TARGETS=['steer', 'throttle', 'brake'], - INPUTS=['speed_module'], - SPEED_FACTOR=25.0, - MODEL_TYPE='coil-icra', - MODEL_CONFIGURATION=dict( - perception=dict(res=dict(name='resnet34', num_classes=512)), - measurements=dict(fc=dict(neurons=[128, 128], dropouts=[0.0, 0.0])), - join=dict(fc=dict(neurons=[512], dropouts=[0.0])), - speed_branch=dict(fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])), - branches=dict(number_of_branches=4, fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])) - ), - PRE_TRAINED=False, - LEARNING_RATE_DECAY_LEVEL=0.1, - IMAGE_CUT=[115, 500], - NUMBER_FRAMES_FUSION=1, -) -main_config = EasyDict(autoeval_config) - -main_config.policy.update(policy_config) - - -def wrapped_env(env_cfg, host, port, tm_port=None): - return CarlaEnvWrapper(SimpleCarlaEnv(env_cfg, host, port, tm_port)) - - -def main(cfg, seed=0): - cfg.env.manager = deep_merge_dicts(SyncSubprocessEnvManager.default_config(), cfg.env.manager) - - tcp_list = parse_carla_tcp(cfg.server) - env_num = cfg.env.env_num - assert len(tcp_list) >= env_num, \ - "Carla server not enough! Need {} servers but only found {}.".format(env_num, len(tcp_list)) - - evaluate_env = SyncSubprocessEnvManager( - env_fn=[partial(wrapped_env, cfg.env, *tcp_list[i]) for i in range(env_num)], - cfg=cfg.env.manager, - ) - evaluate_env.seed(seed) - set_pkg_seed(seed) - policy = CILPolicy(cfg.policy) - evaluator = CarlaBenchmarkEvaluator(cfg.policy.eval.evaluator, evaluate_env, policy.eval_mode) - evaluator.eval() - evaluator.close() - - -if __name__ == '__main__': - main(main_config) diff --git a/demo/coil_demo/coil_train.py b/demo/coil_demo/coil_train.py deleted file mode 100644 index 1031e5b6..00000000 --- a/demo/coil_demo/coil_train.py +++ /dev/null @@ -1,174 +0,0 @@ -import os -import time - -import torch -import torch.optim as optim -from easydict import EasyDict - -from core.data.coil_dataset import CoILDataset -from core.models.coil_model import CoILModule -from core.utils.data_utils.splitter import select_balancing_strategy -from core.utils.learner_utils.loss_utils import Loss -from core.utils.learner_utils.optim_utils import adjust_learning_rate_auto -from core.utils.others.checkpoint_helper import is_ready_to_save, get_latest_saved_checkpoint -from core.utils.others.general_helper import create_log_folder, create_exp_path, erase_logs - -train_config = dict( - NUMBER_OF_LOADING_WORKERS=12, - SENSORS=dict(rgb=[3, 88, 200]), - TARGETS=['steer', 'throttle', 'brake'], - INPUTS=['speed_module'], - BATCH_SIZE=120, - COMMON=dict( - folder='sample', exp='coil_icra', dataset_path='./datasets_train' - ), - GPU='0', - SAVE_SCHEDULE=[0, 100, 1000001], - NUMBER_ITERATIONS=200000, - SPEED_FACTOR=25.0, - TRAIN_DATASET_NAME='cils_datasets_train', - AUGMENTATION=None, - NUMBER_OF_HOURS=50, - MODEL_TYPE='coil-icra', - MODEL_CONFIGURATION=dict( - perception=dict(res=dict(name='resnet34', num_classes=512)), - measurements=dict(fc=dict(neurons=[128, 128], dropouts=[0.0, 0.0])), - join=dict(fc=dict(neurons=[512], dropouts=[0.0])), - speed_branch=dict(fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])), - branches=dict(number_of_branches=4, fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])) - ), - PRE_TRAINED=False, - LEARNING_RATE_DECAY_INTERVAL=75000, - LEARNING_RATE_DECAY_LEVEL=0.1, - LEARNING_RATE_THRESHOLD=5000, - LEARNING_RATE=0.0002, - BRANCH_LOSS_WEIGHT=[0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.05], - VARIABLE_WEIGHT=dict(Steer=0.5, Gas=0.45, Brake=0.05), - LOSS_FUNCTION='L1', - IMAGE_CUT=[115, 500], - PRELOAD_MODEL_ALIAS=None, - PRELOAD_MODEL_BATCH=None, - PRELOAD_MODEL_CHECKPOINT=None, - REMOVE=None, - NUMBER_IMAGES_SEQUENCE=1, - NUMBER_FRAMES_FUSION=1, -) - - -def execute(cfg): - gpu = cfg.GPU - exp_batch = cfg.COMMON.folder - exp_alias = cfg.COMMON.exp - - os.environ["CUDA_VISIBLE_DEVICES"] = gpu - - if cfg.PRELOAD_MODEL_ALIAS is not None: - checkpoint = torch.load( - os.path.join( - '_logs', cfg.PRELOAD_MODEL_BATCH, cfg.PRELOAD_MODEL_ALIAS, 'checkpoints', - str(cfg.PRELOAD_MODEL_CHECKPOINT) + '.pth' - ) - ) - - checkpoint_file = get_latest_saved_checkpoint(exp_batch, exp_alias) - if checkpoint_file is not None: - checkpoint = torch.load( - os.path.join( - '_logs', exp_batch, exp_alias, 'checkpoints', str(get_latest_saved_checkpoint(exp_batch, exp_alias)) - ) - ) - iteration = checkpoint['iteration'] - best_loss = checkpoint['best_loss'] - best_loss_iter = checkpoint['best_loss_iter'] - else: - if not os.path.exists(os.path.join('_logs', exp_batch, exp_alias, 'checkpoints')): - os.mkdir(os.path.join(os.path.join('_logs', exp_batch, exp_alias, 'checkpoints'))) - iteration = 0 - best_loss = 10000.0 - best_loss_iter = 0 - - full_dataset = os.path.join(cfg.COMMON.dataset_path, cfg.TRAIN_DATASET_NAME) - - # By instantiating the augmenter we get a callable that augment images and transform them - # into tensors. - # augmenter = Augmenter(cfg.AUGMENTATION) - augmenter = None - - # Instantiate the class used to read a dataset. The coil dataset generator - # can be found - dataset = CoILDataset(full_dataset, cfg, transform=augmenter) # this has g_conf - print("Loaded dataset") - - data_loader = select_balancing_strategy(dataset, cfg, iteration) - model = CoILModule(cfg) - model.cuda() - optimizer = optim.Adam(model.parameters(), lr=cfg.LEARNING_RATE) - - if checkpoint_file is not None or cfg.PRELOAD_MODEL_ALIAS is not None: - model.load_state_dict(checkpoint['state_dict']) - optimizer.load_state_dict(checkpoint['optimizer']) - accumulated_time = checkpoint['total_time'] - loss_window = checkpoint['loss_window'] - else: # We accumulate iteration time and keep the average speed - accumulated_time = 0 - loss_window = [] - - print("Before the loss") - - criterion = Loss(cfg.LOSS_FUNCTION) - - # Loss time series window - for data in data_loader: - - iteration += 1 - if iteration % 1000 == 0: - adjust_learning_rate_auto( - optimizer, loss_window, cfg.LEARNING_RATE, cfg.LEARNING_RATE_THRESHOLD, cfg.LEARNING_RATE_DECAY_LEVEL - ) - - capture_time = time.time() - controls = data['directions'] - model.zero_grad() - branches = model(torch.squeeze(data['rgb'].cuda()), dataset.extract_inputs(data).cuda()) - loss_function_params = { - 'branches': branches, - 'targets': dataset.extract_targets(data).cuda(), - 'controls': controls.cuda(), - 'inputs': dataset.extract_inputs(data).cuda(), - 'branch_weights': cfg.BRANCH_LOSS_WEIGHT, - 'variable_weights': cfg.VARIABLE_WEIGHT - } - loss, _ = criterion(loss_function_params) - loss.backward() - optimizer.step() - - if is_ready_to_save(iteration, cfg): - state = { - 'iteration': iteration, - 'state_dict': model.state_dict(), - 'best_loss': best_loss, - 'total_time': accumulated_time, - 'optimizer': optimizer.state_dict(), - 'best_loss_iter': best_loss_iter, - 'loss_window': loss_window - } - torch.save(state, os.path.join('_logs', exp_batch, exp_alias, 'checkpoints', str(iteration) + '.pth')) - - if loss.data < best_loss: - best_loss = loss.data.tolist() - best_loss_iter = iteration - - accumulated_time += time.time() - capture_time - - loss_window.append(loss.data.tolist()) - print("Iteration: %d Loss: %f" % (iteration, loss.data)) - - -if __name__ == '__main__': - cfg = EasyDict(train_config) - create_log_folder(cfg.COMMON.folder) - erase_logs(cfg.COMMON.folder) - - create_exp_path(cfg.COMMON.folder, cfg.COMMON.exp) - - execute(cfg) diff --git a/docs/source/api_doc/data.rst b/docs/source/api_doc/data.rst index c8990ad0..8d54257d 100644 --- a/docs/source/api_doc/data.rst +++ b/docs/source/api_doc/data.rst @@ -13,5 +13,5 @@ CarlaBenchmarkCollector BenchmarkDatasetSaver ======================= -.. autoclass:: core.data.dataset_saver.BenchmarkDatasetSaver +.. autoclass:: core.data.benchmark_dataset_saver.BenchmarkDatasetSaver :members: From f1677abf9f8589f96a9f702ccd5d69750583327c Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Mon, 18 Oct 2021 12:21:01 +0800 Subject: [PATCH 03/16] suite dev --- core/data/carla_benchmark_collector.py | 7 +- core/eval/base_evaluator.py | 5 +- core/eval/carla_benchmark_evaluator.py | 117 +++++++++++++++++++++---- core/eval/serial_evaluator.py | 17 ++-- demo/implicit/train_rl.py | 6 +- demo/latent_rl/latent_dqn_train.py | 6 +- demo/simple_rl/ddpg_train.py | 6 +- demo/simple_rl/dqn_train.py | 6 +- demo/simple_rl/ppo_train.py | 6 +- demo/simple_rl/sac_train.py | 6 +- demo/simple_rl/td3_train.py | 6 +- 11 files changed, 137 insertions(+), 51 deletions(-) diff --git a/core/data/carla_benchmark_collector.py b/core/data/carla_benchmark_collector.py index 0ec930e0..44f65686 100644 --- a/core/data/carla_benchmark_collector.py +++ b/core/data/carla_benchmark_collector.py @@ -188,8 +188,11 @@ def collect( break if self._seed is not None: - for env_id in running_env_params: - self._env_manager.seed({env_id: self._seed}) + if self._dynamic_seed: + self._env_manager.seed(self._seed) + else: + for env_id in running_env_params: + self._env_manager.seed({env_id: self._seed}) self._env_manager.reset(running_env_params) return_data = [] diff --git a/core/eval/base_evaluator.py b/core/eval/base_evaluator.py index 7c4e98b1..cc2278c7 100644 --- a/core/eval/base_evaluator.py +++ b/core/eval/base_evaluator.py @@ -12,7 +12,7 @@ from easydict import EasyDict from core.utils.others.config_helper import deep_merge_dicts -from ding.utils import EasyTimer +from ding.utils import EasyTimer, build_logger class BaseEvaluator(object): @@ -39,6 +39,9 @@ def __init__( self._tb_logger = None if tb_logger is not None: self._tb_logger = tb_logger + self._logger, _ = build_logger( + path='./{}/log/{}'.format(self._exp_name, self._instance_name), name=self._instance_name, need_tb=False + ) self._timer = EasyTimer() if env is not None: diff --git a/core/eval/carla_benchmark_evaluator.py b/core/eval/carla_benchmark_evaluator.py index 98a7a236..15d9f831 100644 --- a/core/eval/carla_benchmark_evaluator.py +++ b/core/eval/carla_benchmark_evaluator.py @@ -5,7 +5,7 @@ from tqdm import tqdm from itertools import product import torch -from typing import Dict, Any, List, NamedTuple, Optional +from typing import Dict, Any, List, Callable, Optional from .base_evaluator import BaseEvaluator from core.data.benchmark import ALL_SUITES @@ -30,10 +30,11 @@ class CarlaBenchmarkEvaluator(BaseEvaluator): - cfg (Dict): Config dict. - env (BaseEnvManager): Env manager used to evaluate. - policy (Any): Policy to evaluate. Must have ``forward`` method. + - tb_logger (SummaryWriter, optional): Tensorboard writter to store values in tensorboard. Defaults to None. - exp_name (str, optional): Name of the experiments. Used to build logger. Defaults to 'default_experiment'. - instance_name (str, optional): Name of the evaluator. Used to build logger. Defaults to 'benchmark_evaluator'. - :Interfaces: reset, eval, close + :Interfaces: reset, eval, close, should_eval :Properties: - env (BaseEnvManager): Env manager with several environments used to evaluate. @@ -45,6 +46,7 @@ class CarlaBenchmarkEvaluator(BaseEvaluator): result_dir='', transform_obs=False, episodes_per_suite=100, + stop_rate=1, resume=False, suite='FullTown01-v0', weathers=None, @@ -57,10 +59,11 @@ def __init__( cfg: Dict, env: BaseEnvManager, policy: Any, + tb_logger: Optional['SummaryWriter'] = None, # noqa exp_name: Optional[str] = 'default_experiment', instance_name: Optional[str] = 'benchmark_evaluator', ) -> None: - super().__init__(cfg, env, policy, exp_name=exp_name, instance_name=instance_name) + super().__init__(cfg, env, policy, tb_logger=tb_logger, exp_name=exp_name, instance_name=instance_name) self._benchmark_dir = self._cfg.benchmark_dir self._result_dir = self._cfg.result_dir self._transform_obs = self._cfg.transform_obs @@ -71,10 +74,14 @@ def __init__( suite = self._cfg.suite self._eval_suite_list = get_suites_list(suite) + self._stop_rate = self._cfg.stop_rate self._seed = self._cfg.seed self._weathers = self._cfg.weathers self._save_files = self._cfg.save_files + self._close_flag = False + self._last_eval_iter = 0 + self._max_success_rate = 0 @property def env(self) -> BaseEnvManager: @@ -96,36 +103,68 @@ def close(self) -> None: return self._close_flag = True self._env_manager.close() + if self._tb_logger is not None: + self._tb_logger.flush() + self._tb_logger.close() def reset(self) -> None: """ Reset evaluator and policies. """ self._policy.reset([i for i in range(self._env_num)]) + self._last_eval_iter = 0 + self._max_success_rate = 0 + + def should_eval(self, train_iter: int) -> bool: + """ + Judge if the training iteration is at frequency value to run evaluation. + + :Arguments: + - train_iter (int): Current training iteration + + :Returns: + bool: Whether should run iteration + """ + if (train_iter - self._last_eval_iter) < self._cfg.eval_freq and train_iter != 0: + return False + self._last_eval_iter = train_iter + return True - def eval(self, policy_kwargs: Optional[Dict] = None, n_episode: Optional[int] = None) -> float: + def eval( + self, + save_ckpt_fn: Callable = None, + train_iter: int = -1, + envstep: int = -1, + policy_kwargs: Optional[Dict] = None, + n_episode: Optional[int] = None + ) -> float: """ Run evaluation with provided policy arguments. It will evaluate all available episodes of the benchmark suite unless `episode_per_suite` is set in config. :Arguments: + - save_ckpt_fn (Callable, optional): Function to save ckpt. Will be called if at best performance. + Defaults to None. + - train_iter (int, optional): Current training iterations. Defaults to -1. + - envstep (int, optional): Current env steps. Defaults to -1. - policy_kwargs (Dict, optional): Additional arguments in policy forward. Defaults to None. - n_episode: (int, optional): Episodes to eval. By default it is set in config. :Returns: - float: Success rate. + Tuple[bool, float]: Whether reach stop value and success rate. """ - total_time = 0.0 if policy_kwargs is None: policy_kwargs = dict() - if self._result_dir != '': - os.makedirs(self._result_dir, exist_ok=True) - self.reset() if n_episode is None: n_episode = self._episodes_per_suite + assert n_episode >= self._env_num, "Env num must be more than episode num!" + if self._result_dir != '': + os.makedirs(self._result_dir, exist_ok=True) + total_time = 0.0 total_episodes = 0 success_episodes = 0 + self.reset() for suite in tqdm(self._eval_suite_list): args, kwargs = ALL_SUITES[suite] @@ -159,7 +198,7 @@ def eval(self, policy_kwargs: Optional[Dict] = None, n_episode: Optional[int] = param['weather'] = weather if self._resume and len(summary) > 0 and ((summary['start'] == start) & (summary['end'] == end) & (summary['weather'] == weather)).any(): - print('[EVALUATOR]', weather, start, end, 'already exist') + self._logger.info('[EVALUATOR]', weather, start, end, 'already exist') continue if running_envs < self._env_num: running_env_params[running_envs] = param @@ -202,20 +241,64 @@ def eval(self, policy_kwargs: Optional[Dict] = None, n_episode: Optional[int] = if self._env_manager.done: break duration = self._timer.value - total_time += duration + success_num = 0 + episode_num = 0 + episode_reward = [] + envstep_num = 0 for result in results: - total_episodes += 1 + episode_num += 1 if result['success']: - success_episodes += 1 + success_num += 1 + episode_reward.append(result['reward']) + envstep_num += result['timecost'] + if self._save_files: summary = pd.DataFrame(results) summary.to_csv(summary_csv, index=False) + info = { + 'suite': suite, + 'train_iter': train_iter, + 'ckpt_name': 'iteration_{}.pth.tar'.format(train_iter), + 'avg_envstep_per_episode': envstep_num / n_episode, + 'evaluate_time': duration, + 'avg_time_per_episode': duration / n_episode, + 'success_rate': 0 if envstep_num == 0 else success_num / episode_num, + 'reward_mean': np.mean(episode_reward), + 'reward_std': np.std(episode_reward), + } + if train_iter == -1: + info.pop('train_iter') + info.pop('ckpt_name') + elif self._tb_logger is not None: + for k, v in info.items(): + if k in ['train_iter', 'ckpt_name', 'suite']: + continue + if not np.isscalar(v): + continue + self._tb_logger.add_scalar('{}_{}_iter/'.format(self._instance_name, suite) + k, v, train_iter) + self._tb_logger.add_scalar('{}_{}_step/'.format(self._instance_name, suite) + k, v, envstep) + self._logger.info(self._logger.get_tabulate_vars_hor(info)) + + total_episodes += episode_num + success_episodes += success_num + total_time += duration if self._save_files: results = gather_results(self._result_dir) print(results) - success_rate = 0 if total_episodes == 0 else success_episodes / total_episodes - print('[EVALUATOR] Total success: {}/{}.'.format(success_episodes, total_episodes)) - print('[EVALUATOR] Total time: {:.3f} hours.'.format(total_time / 3600.0)) - return success_rate + success_rate = 0 if total_episodes == 0 else success_episodes / total_episodes + if success_rate > self._max_success_rate: + if save_ckpt_fn: + save_ckpt_fn('ckpt_best.pth.tar') + self._max_success_rate = success_rate + stop_flag = success_rate > self._stop_rate and train_iter > 0 + if stop_flag: + self._logger.info( + "[EVALUATOR] " + + "Current success rate: {} is greater than stop rate: {}".format(success_rate, self._stop_rate) + + ", so the training is converged." + ) + self._logger.info('[EVALUATOR] Total success: {}/{}.'.format(success_episodes, total_episodes)) + self._logger.info('[EVALUATOR] Total time: {:.3f} hours.'.format(total_time / 3600.0)) + return stop_flag, success_rate diff --git a/core/eval/serial_evaluator.py b/core/eval/serial_evaluator.py index b97e1ca1..847d703f 100644 --- a/core/eval/serial_evaluator.py +++ b/core/eval/serial_evaluator.py @@ -41,7 +41,7 @@ class SerialEvaluator(BaseEvaluator): # Evaluate every "eval_freq" training iterations. eval_freq=100, n_episode=10, - stop_rate=0.8, + stop_rate=1, ) def __init__( @@ -58,13 +58,8 @@ def __init__( self._default_n_episode = self._cfg.n_episode self._stop_rate = self._cfg.stop_rate - self._logger, _ = build_logger( - path='./{}/log/{}'.format(self._exp_name, self._instance_name), name=self._instance_name, need_tb=False - ) - self._last_eval_iter = 0 self._max_success_rate = 0 - self._timer = EasyTimer() @property def env(self) -> BaseEnvManager: @@ -72,6 +67,7 @@ def env(self) -> BaseEnvManager: @env.setter def env(self, _env_manager: BaseEnvManager) -> None: + assert _env_manager._auto_reset, "auto reset for env manager should be opened!" self._end_flag = False self._env_manager = _env_manager self._env_manager.launch() @@ -117,6 +113,7 @@ def eval( save_ckpt_fn: Callable = None, train_iter: int = -1, envstep: int = -1, + policy_kwargs: Optional[Dict] = None, n_episode: Optional[int] = None ) -> Tuple[bool, float]: """ @@ -128,11 +125,14 @@ def eval( Defaults to None. - train_iter (int, optional): Current training iterations. Defaults to -1. - envstep (int, optional): Current env steps. Defaults to -1. + - policy_kwargs (Dict, optional): Additional arguments in policy forward. Defaults to None. - n_episode: (int, optional): Episodes to eval. By default it is set in config. :Returns: Tuple[bool, float]: Whether reach stop value and success rate. """ + if policy_kwargs is None: + policy_kwargs = dict() if n_episode is None: n_episode = self._default_n_episode assert n_episode is not None, "please indicate eval n_episode" @@ -147,7 +147,7 @@ def eval( obs = self._env_manager.ready_obs if self._transform_obs: obs = to_tensor(obs, dtype=torch.float32) - policy_output = self._policy.forward(obs) + policy_output = self._policy.forward(obs, **policy_kwargs) actions = {env_id: output['action'] for env_id, output in policy_output.items()} timesteps = self._env_manager.step(actions) for env_id, t in timesteps.items(): @@ -180,15 +180,12 @@ def eval( info = { 'train_iter': train_iter, 'ckpt_name': 'iteration_{}.pth.tar'.format(train_iter), - 'envstep_count': envstep_count, 'avg_envstep_per_episode': envstep_count / n_episode, 'evaluate_time': duration, - 'avg_envstep_per_sec': envstep_count / duration, 'avg_time_per_episode': duration / n_episode, 'success_rate': success_rate, 'reward_mean': np.mean(episode_reward), 'reward_std': np.std(episode_reward), - 'reward_max': np.max(episode_reward), } self._logger.info(self._logger.get_tabulate_vars_hor(info)) if self._tb_logger is not None: diff --git a/demo/implicit/train_rl.py b/demo/implicit/train_rl.py index 2e374376..62dddc63 100644 --- a/demo/implicit/train_rl.py +++ b/demo/implicit/train_rl.py @@ -14,7 +14,7 @@ from core.utils.others.tcp_helper import parse_carla_tcp from ding.envs import SyncSubprocessEnvManager, BaseEnvManager from ding.policy import DQNPolicy -from ding.worker import BaseLearner, SampleCollector, AdvancedReplayBuffer +from ding.worker import BaseLearner, SampleSerialCollector, AdvancedReplayBuffer from ding.rl_utils import get_epsilon_greedy_fn from ding.utils import set_pkg_seed from ding.utils.default_helper import deep_merge_dicts @@ -170,7 +170,7 @@ def main(cfg, env_args, seed=0): SyncSubprocessEnvManager, DQNPolicy, BaseLearner, - SampleCollector, + SampleSerialCollector, buffer=AdvancedReplayBuffer, ) @@ -201,7 +201,7 @@ def main(cfg, env_args, seed=0): tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) - collector = SampleCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) + collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) replay_buffer = AdvancedReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) learner.call_hook('before_run') diff --git a/demo/latent_rl/latent_dqn_train.py b/demo/latent_rl/latent_dqn_train.py index 03994124..805c3657 100644 --- a/demo/latent_rl/latent_dqn_train.py +++ b/demo/latent_rl/latent_dqn_train.py @@ -11,7 +11,7 @@ from core.envs import CarlaEnvWrapper from ding.envs import SyncSubprocessEnvManager, BaseEnvManager from ding.policy import DQNPolicy -from ding.worker import BaseLearner, SampleCollector, AdvancedReplayBuffer +from ding.worker import BaseLearner, SampleSerialCollector, AdvancedReplayBuffer from ding.utils import set_pkg_seed from ding.rl_utils import get_epsilon_greedy_fn @@ -134,7 +134,7 @@ def main(cfg, seed=0): SyncSubprocessEnvManager, DQNPolicy, BaseLearner, - SampleCollector, + SampleSerialCollector, buffer=AdvancedReplayBuffer, ) tcp_list = parse_carla_tcp(cfg.server) @@ -161,7 +161,7 @@ def main(cfg, seed=0): tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) - collector = SampleCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) + collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) evaluator = SerialEvaluator(cfg.policy.eval.evaluator, evaluate_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name) replay_buffer = AdvancedReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) diff --git a/demo/simple_rl/ddpg_train.py b/demo/simple_rl/ddpg_train.py index 7855ddc0..748bf9c1 100644 --- a/demo/simple_rl/ddpg_train.py +++ b/demo/simple_rl/ddpg_train.py @@ -12,7 +12,7 @@ from core.utils.others.ding_utils import compile_config from ding.envs import SyncSubprocessEnvManager, BaseEnvManager from ding.policy import DDPGPolicy -from ding.worker import BaseLearner, SampleCollector, NaiveReplayBuffer +from ding.worker import BaseLearner, SampleSerialCollector, NaiveReplayBuffer from ding.utils import set_pkg_seed from demo.simple_rl.model import DDPGRLModel from demo.simple_rl.env_wrapper import ContinuousBenchmarkEnvWrapper @@ -129,7 +129,7 @@ def main(cfg, seed=0): SyncSubprocessEnvManager, DDPGPolicy, BaseLearner, - SampleCollector, + SampleSerialCollector, buffer=NaiveReplayBuffer, ) tcp_list = parse_carla_tcp(cfg.server) @@ -158,7 +158,7 @@ def main(cfg, seed=0): tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) - collector = SampleCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) + collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) evaluator = SerialEvaluator(cfg.policy.eval.evaluator, evaluate_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name) replay_buffer = NaiveReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) diff --git a/demo/simple_rl/dqn_train.py b/demo/simple_rl/dqn_train.py index db7af5b5..53ae2f87 100644 --- a/demo/simple_rl/dqn_train.py +++ b/demo/simple_rl/dqn_train.py @@ -10,7 +10,7 @@ from core.eval import SerialEvaluator from ding.envs import SyncSubprocessEnvManager, BaseEnvManager from ding.policy import DQNPolicy -from ding.worker import BaseLearner, SampleCollector, AdvancedReplayBuffer +from ding.worker import BaseLearner, SampleSerialCollector, AdvancedReplayBuffer from ding.utils import set_pkg_seed from ding.rl_utils import get_epsilon_greedy_fn @@ -146,7 +146,7 @@ def main(cfg, seed=0): SyncSubprocessEnvManager, DQNPolicy, BaseLearner, - SampleCollector, + SampleSerialCollector, buffer=AdvancedReplayBuffer, ) tcp_list = parse_carla_tcp(cfg.server) @@ -175,7 +175,7 @@ def main(cfg, seed=0): tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) - collector = SampleCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) + collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) evaluator = SerialEvaluator(cfg.policy.eval.evaluator, evaluate_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name) replay_buffer = AdvancedReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) diff --git a/demo/simple_rl/ppo_train.py b/demo/simple_rl/ppo_train.py index 2974218e..f9fe46ad 100644 --- a/demo/simple_rl/ppo_train.py +++ b/demo/simple_rl/ppo_train.py @@ -11,7 +11,7 @@ from core.eval import SerialEvaluator from ding.envs import SyncSubprocessEnvManager, BaseEnvManager from ding.policy import PPOPolicy -from ding.worker import BaseLearner, SampleCollector +from ding.worker import BaseLearner, SampleSerialCollector from ding.utils import set_pkg_seed from demo.simple_rl.model import PPORLModel @@ -124,7 +124,7 @@ def main(cfg, seed=0): SyncSubprocessEnvManager, PPOPolicy, BaseLearner, - SampleCollector, + SampleSerialCollector, ) tcp_list = parse_carla_tcp(cfg.server) collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num @@ -152,7 +152,7 @@ def main(cfg, seed=0): tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) - collector = SampleCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) + collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) evaluator = SerialEvaluator(cfg.policy.eval.evaluator, evaluate_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name) learner.call_hook('before_run') diff --git a/demo/simple_rl/sac_train.py b/demo/simple_rl/sac_train.py index 3a091374..05d42ade 100644 --- a/demo/simple_rl/sac_train.py +++ b/demo/simple_rl/sac_train.py @@ -11,7 +11,7 @@ from core.eval import SerialEvaluator from ding.envs import SyncSubprocessEnvManager, BaseEnvManager from ding.policy import SACPolicy -from ding.worker import BaseLearner, SampleCollector, NaiveReplayBuffer +from ding.worker import BaseLearner, SampleSerialCollector, NaiveReplayBuffer from ding.utils import set_pkg_seed from demo.simple_rl.env_wrapper import ContinuousBenchmarkEnvWrapper @@ -135,7 +135,7 @@ def main(cfg, seed=0): SyncSubprocessEnvManager, SACPolicy, BaseLearner, - SampleCollector, + SampleSerialCollector, buffer=NaiveReplayBuffer, ) tcp_list = parse_carla_tcp(cfg.server) @@ -164,7 +164,7 @@ def main(cfg, seed=0): tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) - collector = SampleCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) + collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) evaluator = SerialEvaluator(cfg.policy.eval.evaluator, evaluate_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name) replay_buffer = NaiveReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) diff --git a/demo/simple_rl/td3_train.py b/demo/simple_rl/td3_train.py index af6227ea..c9110ec9 100644 --- a/demo/simple_rl/td3_train.py +++ b/demo/simple_rl/td3_train.py @@ -10,7 +10,7 @@ from core.eval import SerialEvaluator from ding.envs import SyncSubprocessEnvManager, BaseEnvManager from ding.policy import TD3Policy -from ding.worker import BaseLearner, SampleCollector, NaiveReplayBuffer +from ding.worker import BaseLearner, SampleSerialCollector, NaiveReplayBuffer from ding.utils import set_pkg_seed from demo.simple_rl.model import TD3RLModel from demo.simple_rl.env_wrapper import ContinuousBenchmarkEnvWrapper @@ -127,7 +127,7 @@ def main(cfg, seed=0): SyncSubprocessEnvManager, TD3Policy, BaseLearner, - SampleCollector, + SampleSerialCollector, buffer=NaiveReplayBuffer, ) tcp_list = parse_carla_tcp(cfg.server) @@ -156,7 +156,7 @@ def main(cfg, seed=0): tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) - collector = SampleCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) + collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name) evaluator = SerialEvaluator(cfg.policy.eval.evaluator, evaluate_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name) replay_buffer = NaiveReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) From 02b0762b976a47feae0f9c508d702f5cc605ed89 Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Mon, 18 Oct 2021 12:21:14 +0800 Subject: [PATCH 04/16] update engine version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 2aec4479..95f475af 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ 'pygame==1.9.6', 'torch>=1.4,<=1.8', 'torchvision', - 'di-engine==0.1.1', + 'di-engine==0.2', 'scikit-image', 'setuptools==50', 'shapely', From 64ae924729720cb1ba8887823a26581ef60cb0be Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Mon, 18 Oct 2021 14:35:44 +0800 Subject: [PATCH 05/16] fix cilrs bug --- core/models/cilrs_model.py | 5 +++-- core/policy/cilrs_policy.py | 2 ++ demo/cilrs/cilrs_collect_data.py | 2 +- demo/cilrs/cilrs_env_wrapper.py | 2 +- 4 files changed, 7 insertions(+), 4 deletions(-) diff --git a/core/models/cilrs_model.py b/core/models/cilrs_model.py index 633addc7..8259dff5 100644 --- a/core/models/cilrs_model.py +++ b/core/models/cilrs_model.py @@ -11,7 +11,8 @@ class CILRSModel(nn.Module): def __init__( self, backbone='resnet18', - normalize=False, + pretrained=True, + normalize=True, num_branch=6, speed_dim=1, embedding_dim=512, @@ -27,7 +28,7 @@ def __init__( 'resnet34': models.resnet34, 'resnet50': models.resnet50, }[backbone] - self._backbone = backbone_cls(pretrained=True) + self._backbone = backbone_cls(pretrained=pretrained) self._backbone.fc = nn.Sequential() self._num_branch = num_branch self._input_speed = input_speed diff --git a/core/policy/cilrs_policy.py b/core/policy/cilrs_policy.py index 99514657..1b068070 100644 --- a/core/policy/cilrs_policy.py +++ b/core/policy/cilrs_policy.py @@ -64,6 +64,8 @@ def _process_model_outputs(self, output): steer = control_pred[:, 0] * 2 - 1. # convert from [0,1] to [-1,1] throttle = control_pred[:, 1] * self._cfg.max_throttle brake = control_pred[:, 2] + if brake < 0.05: + brake = brake - brake action = {'steer': steer, 'throttle': throttle, 'brake': brake} return action, velocity_pred diff --git a/demo/cilrs/cilrs_collect_data.py b/demo/cilrs/cilrs_collect_data.py index a58ac60a..bf276686 100644 --- a/demo/cilrs/cilrs_collect_data.py +++ b/demo/cilrs/cilrs_collect_data.py @@ -108,7 +108,7 @@ def post_process(config): data['command'] = float(measurements[11]) new_dict = {} new_dict['brake'] = data['control'][2] - new_dict['steer'] = data['control'][0] + new_dict['steer'] = (data['control'][0] + 1) / 2 new_dict['throttle'] = data['control'][1] new_dict['speed_module'] = data['speed'] new_dict['directions'] = data['command'] + 1.0 diff --git a/demo/cilrs/cilrs_env_wrapper.py b/demo/cilrs/cilrs_env_wrapper.py index 237e1c17..bc6bbd7a 100644 --- a/demo/cilrs/cilrs_env_wrapper.py +++ b/demo/cilrs/cilrs_env_wrapper.py @@ -32,7 +32,7 @@ def _get_obs(self, obs): return new_obs def reset(self, *args, **kwargs) -> Any: - obs = super().reset() + obs = super().reset(*args, **kwargs) obs_out = self._get_obs(obs) return obs_out From 78ba3818e69fe5c28455c9326b71883f905f96d2 Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Mon, 18 Oct 2021 15:46:33 +0800 Subject: [PATCH 06/16] fix collect bug --- core/data/carla_benchmark_collector.py | 2 +- core/data/cilrs_dataset.py | 2 -- demo/cilrs/cilrs_collect_data.py | 4 ++-- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/core/data/carla_benchmark_collector.py b/core/data/carla_benchmark_collector.py index 44f65686..cc78586b 100644 --- a/core/data/carla_benchmark_collector.py +++ b/core/data/carla_benchmark_collector.py @@ -196,7 +196,7 @@ def collect( self._env_manager.reset(running_env_params) return_data = [] - collected_episodes = running_envs + collected_episodes = running_envs - 1 collected_samples = 0 with self._timer: diff --git a/core/data/cilrs_dataset.py b/core/data/cilrs_dataset.py index 295b5f36..7b0443f6 100644 --- a/core/data/cilrs_dataset.py +++ b/core/data/cilrs_dataset.py @@ -37,6 +37,4 @@ def __getitem__(self, index: int) -> Any: for k, v in measurements.items(): v = torch.from_numpy(np.asanyarray([v])).type(torch.FloatTensor) data[k] = v - data['speed'] = data.pop('speed_module') - data['command'] = data.pop('directions') return data diff --git a/demo/cilrs/cilrs_collect_data.py b/demo/cilrs/cilrs_collect_data.py index bf276686..3ed0ca77 100644 --- a/demo/cilrs/cilrs_collect_data.py +++ b/demo/cilrs/cilrs_collect_data.py @@ -110,8 +110,8 @@ def post_process(config): new_dict['brake'] = data['control'][2] new_dict['steer'] = (data['control'][0] + 1) / 2 new_dict['throttle'] = data['control'][1] - new_dict['speed_module'] = data['speed'] - new_dict['directions'] = data['command'] + 1.0 + new_dict['speed'] = data['speed'] + new_dict['command'] = data['command'] + 1.0 all_img_list.append(os.path.join(item, png_file)) all_mea_list.append(new_dict) if not os.path.exists('_preloads'): From 5d54265d36bfcd27c947d98ae1768b3f4d1aa823 Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Mon, 18 Oct 2021 18:22:55 +0800 Subject: [PATCH 07/16] fix command bug --- demo/cilrs/cilrs_collect_data.py | 2 +- demo/cilrs/cilrs_test.py | 4 ++++ demo/cilrs/cilrs_train.py | 3 +++ 3 files changed, 8 insertions(+), 1 deletion(-) diff --git a/demo/cilrs/cilrs_collect_data.py b/demo/cilrs/cilrs_collect_data.py index 3ed0ca77..90f80d74 100644 --- a/demo/cilrs/cilrs_collect_data.py +++ b/demo/cilrs/cilrs_collect_data.py @@ -111,7 +111,7 @@ def post_process(config): new_dict['steer'] = (data['control'][0] + 1) / 2 new_dict['throttle'] = data['control'][1] new_dict['speed'] = data['speed'] - new_dict['command'] = data['command'] + 1.0 + new_dict['command'] = data['command'] all_img_list.append(os.path.join(item, png_file)) all_mea_list.append(new_dict) if not os.path.exists('_preloads'): diff --git a/demo/cilrs/cilrs_test.py b/demo/cilrs/cilrs_test.py index 989a53e6..405d1aa9 100644 --- a/demo/cilrs/cilrs_test.py +++ b/demo/cilrs/cilrs_test.py @@ -43,6 +43,10 @@ server=[dict(carla_host='localhost', carla_ports=[9000, 9010, 2])], policy=dict( ckpt_path=None, + model=dict( + num_branch=4, + pretrained=False, + ), eval=dict( evaluator=dict( #render=True, diff --git a/demo/cilrs/cilrs_train.py b/demo/cilrs/cilrs_train.py index cc7b0528..a2bf2834 100644 --- a/demo/cilrs/cilrs_train.py +++ b/demo/cilrs/cilrs_train.py @@ -18,6 +18,9 @@ cudnn=True, resume=False, ckpt_path=None, + model=dict( + num_branch=4, + ), learn=dict( epoches=200, batch_size=128, From 1008ea334dfa76a9c4064f99ab3030cb95c99e90 Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Tue, 19 Oct 2021 12:20:20 +0800 Subject: [PATCH 08/16] fix test bug --- demo/cilrs/cilrs_env_wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/cilrs/cilrs_env_wrapper.py b/demo/cilrs/cilrs_env_wrapper.py index bc6bbd7a..e7913710 100644 --- a/demo/cilrs/cilrs_env_wrapper.py +++ b/demo/cilrs/cilrs_env_wrapper.py @@ -17,7 +17,7 @@ class CILRSEnvWrapper(CarlaEnvWrapper): def _get_obs(self, obs): new_obs = { - 'command': obs['command'] + 1, + 'command': obs['command'], 'speed': np.float32(obs['speed'] / self._cfg.speed_factor), } rgb = obs['rgb'] From c93c915ea951a636e635a2dfab50bace173c15cb Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Tue, 19 Oct 2021 13:48:45 +0800 Subject: [PATCH 09/16] update policy eval; add types --- core/models/cilrs_model.py | 5 ++-- core/policy/cilrs_policy.py | 43 ++++++++++++++++++--------------- demo/cilrs/cilrs_env_wrapper.py | 3 +++ 3 files changed, 29 insertions(+), 22 deletions(-) diff --git a/core/models/cilrs_model.py b/core/models/cilrs_model.py index 8259dff5..c36c4fe9 100644 --- a/core/models/cilrs_model.py +++ b/core/models/cilrs_model.py @@ -69,8 +69,9 @@ def __init__( self._branches = nn.ModuleList(fc_branch_list) def _normalize_imagenet(self, x): - """ Normalize input images according to ImageNet standards. - Args: + """ + Normalize input images according to ImageNet standards. + :Arguments: x (tensor): input images """ x = x.clone() diff --git a/core/policy/cilrs_policy.py b/core/policy/cilrs_policy.py index 1b068070..10f4cd11 100644 --- a/core/policy/cilrs_policy.py +++ b/core/policy/cilrs_policy.py @@ -51,30 +51,33 @@ def __init__( for field in self._enable_field: getattr(self, '_init_' + field)() - def _process_sensors(self, sensor): + def _process_sensors(self, sensor: np.ndarray) -> np.ndarray: sensor = sensor[:, :, ::-1] # BGR->RGB sensor = np.transpose(sensor, (2, 0, 1)) sensor = sensor / 255.0 return sensor - def _process_model_outputs(self, output): - - control_pred, velocity_pred = output - steer = control_pred[:, 0] * 2 - 1. # convert from [0,1] to [-1,1] - throttle = control_pred[:, 1] * self._cfg.max_throttle - brake = control_pred[:, 2] - if brake < 0.05: - brake = brake - brake - action = {'steer': steer, 'throttle': throttle, 'brake': brake} - - return action, velocity_pred + def _process_model_outputs(self, data: Dict, output: List) -> List: + action = [] + for i, d in enumerate(data.values()): + control_pred = output[i][0] + steer = control_pred[0] * 2 - 1. # convert from [0,1] to [-1,1] + throttle = min(control_pred[1], self._max_throttle) + brake = control_pred[2] + if d['tick'] < 20 and d['speed'] < 0.1: + throttle = self._max_throttle + brake = 0 + if brake < 0.05: + brake = 0 + action.append({'steer': steer, 'throttle': throttle, 'brake': brake}) + return action def _reset_eval(self, data_id: Optional[List[int]] = None) -> None: self._model.eval() @torch.no_grad() - def _forward_eval(self, data: Dict): + def _forward_eval(self, data: Dict) -> Dict[str, Any]: data_id = list(data.keys()) new_data = dict() @@ -84,17 +87,17 @@ def _forward_eval(self, data: Dict): new_data[id]['command'] = data[id]['command'] new_data[id]['speed'] = data[id]['speed'] - data = default_collate(list(new_data.values())) + new_data = default_collate(list(new_data.values())) if self._cuda: - data = to_device(data, 'cuda') + new_data = to_device(new_data, 'cuda') - embedding = self._model.encode([data['rgb']]) - output = self._model(embedding, data['speed'], data['command']) + embedding = self._model.encode([new_data['rgb']]) + output = self._model(embedding, new_data['speed'], new_data['command']) if self._cuda: output = to_device(output, 'cpu') - actions, _ = self._process_model_outputs(output) - actions = default_decollate(actions) + actions = default_decollate(output) + actions = self._process_model_outputs(data, output) return {i: {'action': d} for i, d in zip(data_id, actions)} def _init_learn(self) -> None: @@ -148,7 +151,7 @@ def _forward_learn(self, data: Dict) -> Dict[str, Any]: return return_info - def _init_validate(self): + def _init_validate(self) -> None: if self._cfg.learn.loss == 'l1': self._criterion = F.l1_loss elif self._cfg.policy.learn.loss == 'l2': diff --git a/demo/cilrs/cilrs_env_wrapper.py b/demo/cilrs/cilrs_env_wrapper.py index e7913710..1010ffaa 100644 --- a/demo/cilrs/cilrs_env_wrapper.py +++ b/demo/cilrs/cilrs_env_wrapper.py @@ -19,6 +19,7 @@ def _get_obs(self, obs): new_obs = { 'command': obs['command'], 'speed': np.float32(obs['speed'] / self._cfg.speed_factor), + 'tick': obs['tick'], } rgb = obs['rgb'] im = PIL.Image.fromarray(rgb) @@ -34,6 +35,7 @@ def _get_obs(self, obs): def reset(self, *args, **kwargs) -> Any: obs = super().reset(*args, **kwargs) obs_out = self._get_obs(obs) + self._tick = obs_out['tick'] return obs_out def step(self, action): @@ -45,5 +47,6 @@ def step(self, action): timestep = super().step(action) obs = timestep.obs obs_out = self._get_obs(obs) + self._tick = obs_out['tick'] timestep = timestep._replace(obs=obs_out) return timestep From 2bdda70b4655f85e74565090c81e6a2054af478e Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Tue, 19 Oct 2021 13:49:28 +0800 Subject: [PATCH 10/16] del tick in env wrapper --- demo/cilrs/cilrs_env_wrapper.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/demo/cilrs/cilrs_env_wrapper.py b/demo/cilrs/cilrs_env_wrapper.py index 1010ffaa..cd64ed53 100644 --- a/demo/cilrs/cilrs_env_wrapper.py +++ b/demo/cilrs/cilrs_env_wrapper.py @@ -35,7 +35,6 @@ def _get_obs(self, obs): def reset(self, *args, **kwargs) -> Any: obs = super().reset(*args, **kwargs) obs_out = self._get_obs(obs) - self._tick = obs_out['tick'] return obs_out def step(self, action): @@ -47,6 +46,5 @@ def step(self, action): timestep = super().step(action) obs = timestep.obs obs_out = self._get_obs(obs) - self._tick = obs_out['tick'] timestep = timestep._replace(obs=obs_out) return timestep From 7d846cbfe584e0ef3b1af1632d3089f6c1a7b7ed Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Tue, 19 Oct 2021 14:23:54 +0800 Subject: [PATCH 11/16] add cilrs eval; fix bugs --- core/models/cilrs_model.py | 6 +-- core/policy/cilrs_policy.py | 2 +- demo/cilrs/cilrs_eval.py | 92 +++++++++++++++++++++++++++++++++++++ demo/cilrs/cilrs_test.py | 12 ++--- 4 files changed, 99 insertions(+), 13 deletions(-) create mode 100644 demo/cilrs/cilrs_eval.py diff --git a/core/models/cilrs_model.py b/core/models/cilrs_model.py index c36c4fe9..b4dcd1d8 100644 --- a/core/models/cilrs_model.py +++ b/core/models/cilrs_model.py @@ -89,16 +89,14 @@ def encode(self, input_images): return embedding def forward(self, embedding, speed, command): - batch_size = embedding.size(0) - if self._input_speed: - embedding += self._speed_in(speed) + embedding += self._speed_in(speed.unsqueeze(1)) control_pred = 0. for i, branch in enumerate(self._branches): # Choose control for branch of only active command # We check for (command - 1) since navigational command 0 is ignored - control_pred += branch(embedding) * (i == (command - 1)).expand(batch_size, 3) + control_pred += branch(embedding) * (i == (command.unsqueeze(1) - 1)) if self.predict_speed: speed_pred = self._speed_out(embedding) diff --git a/core/policy/cilrs_policy.py b/core/policy/cilrs_policy.py index 10f4cd11..6746504d 100644 --- a/core/policy/cilrs_policy.py +++ b/core/policy/cilrs_policy.py @@ -97,7 +97,7 @@ def _forward_eval(self, data: Dict) -> Dict[str, Any]: output = to_device(output, 'cpu') actions = default_decollate(output) - actions = self._process_model_outputs(data, output) + actions = self._process_model_outputs(data, actions) return {i: {'action': d} for i, d in zip(data_id, actions)} def _init_learn(self) -> None: diff --git a/demo/cilrs/cilrs_eval.py b/demo/cilrs/cilrs_eval.py new file mode 100644 index 00000000..d447333a --- /dev/null +++ b/demo/cilrs/cilrs_eval.py @@ -0,0 +1,92 @@ +from easydict import EasyDict +import torch +from functools import partial + +from core.envs import SimpleCarlaEnv +from core.policy import CILRSPolicy +from core.eval import CarlaBenchmarkEvaluator +from core.utils.others.tcp_helper import parse_carla_tcp +from ding.utils import set_pkg_seed, deep_merge_dicts +from ding.envs import AsyncSubprocessEnvManager +from demo.cilrs.cilrs_env_wrapper import CILRSEnvWrapper + +cilrs_config = dict( + env=dict( + env_num=5, + simulator=dict( + town='Town01', + disable_two_wheels=True, + verbose=False, + planner=dict( + type='behavior', + resolution=1, + ), + obs=( + dict( + name='rgb', + type='rgb', + size=[400, 300], + position=[1.3, 0.0, 2.3], + fov=100, + ), + ), + ), + wrapper=dict(), + col_is_failure=True, + stuck_is_failure=True, + manager=dict( + auto_reset=False, + shared_memory=False, + context='spawn', + max_retry=1, + ), + ), + server=[dict(carla_host='localhost', carla_ports=[9000, 9010, 2])], + policy=dict( + ckpt_path=None, + model=dict( + num_branch=4, + pretrained=False, + ), + eval=dict( + evaluator=dict( + suite=['FullTown01-v1'], + transform_obs=True, + ), + ) + ), +) + +main_config = EasyDict(cilrs_config) + + +def wrapped_env(env_cfg, host, port, tm_port=None): + return CILRSEnvWrapper(SimpleCarlaEnv(env_cfg, host, port)) + + +def main(cfg, seed=0): + cfg.env.manager = deep_merge_dicts(AsyncSubprocessEnvManager.default_config(), cfg.env.manager) + + tcp_list = parse_carla_tcp(cfg.server) + env_num = cfg.env.env_num + assert len(tcp_list) >= env_num, \ + "Carla server not enough! Need {} servers but only found {}.".format(env_num, len(tcp_list)) + + carla_env = AsyncSubprocessEnvManager( + env_fn=[partial(wrapped_env, cfg.env, *tcp_list[i]) for i in range(env_num)], + cfg=cfg.env.manager, + ) + carla_env.seed(seed) + set_pkg_seed(seed) + cilrs_policy = CILRSPolicy(cfg.policy).eval_mode + if cfg.policy.ckpt_path is not None: + state_dict = torch.load(cfg.policy.ckpt_path) + cilrs_policy.load_state_dict(state_dict) + + evaluator = CarlaBenchmarkEvaluator(cfg.policy.eval.evaluator, carla_env, cilrs_policy) + success_rate = evaluator.eval() + evaluator.close() + + +if __name__ == '__main__': + main(main_config) diff --git a/demo/cilrs/cilrs_test.py b/demo/cilrs/cilrs_test.py index 405d1aa9..d170ff59 100644 --- a/demo/cilrs/cilrs_test.py +++ b/demo/cilrs/cilrs_test.py @@ -8,19 +8,15 @@ from ding.utils import set_pkg_seed from demo.cilrs.cilrs_env_wrapper import CILRSEnvWrapper -lbc_config = dict( +cilrs_config = dict( env=dict( simulator=dict( town='Town01', disable_two_wheels=True, - n_vehicles=10, - n_pedestrians=10, verbose=False, planner=dict( - type='lbc', - resolution=2.5, - threshold_before=9.0, - threshold_after=1.5, + type='behavior', + resolution=1, ), obs=( dict( @@ -56,7 +52,7 @@ ), ) -main_config = EasyDict(lbc_config) +main_config = EasyDict(cilrs_config) def wrapped_env(env_cfg, host, port, tm_port=None): From c87b19b60ce7d815e728e1a613a323d2169ff0b6 Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Tue, 19 Oct 2021 14:31:52 +0800 Subject: [PATCH 12/16] fix benchmark evaluator log --- core/eval/carla_benchmark_evaluator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/eval/carla_benchmark_evaluator.py b/core/eval/carla_benchmark_evaluator.py index 15d9f831..7d8c4813 100644 --- a/core/eval/carla_benchmark_evaluator.py +++ b/core/eval/carla_benchmark_evaluator.py @@ -198,7 +198,7 @@ def eval( param['weather'] = weather if self._resume and len(summary) > 0 and ((summary['start'] == start) & (summary['end'] == end) & (summary['weather'] == weather)).any(): - self._logger.info('[EVALUATOR]', weather, start, end, 'already exist') + self._logger.info('[EVALUATOR] weather:{}, route:{}, {} already exist'.format(weather, start, end)) continue if running_envs < self._env_num: running_env_params[running_envs] = param From 9062a576406a01c12ca1d87c7247ed21a2e41206 Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Fri, 29 Oct 2021 15:24:51 +0800 Subject: [PATCH 13/16] fix visualizer bug --- core/envs/simple_carla_env.py | 1 + core/eval/single_carla_evaluator.py | 4 ++-- core/utils/others/visualizer.py | 2 +- core/utils/simulator_utils/__init__.py | 1 - demo/auto_run/auto_run_case.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/core/envs/simple_carla_env.py b/core/envs/simple_carla_env.py index c084911d..b2a97e5f 100644 --- a/core/envs/simple_carla_env.py +++ b/core/envs/simple_carla_env.py @@ -232,6 +232,7 @@ def step(self, action: Dict) -> Tuple[Any, float, bool, Dict]: done = self.is_success() or self.is_failure() if done and self._visualizer is not None: self._visualizer.done() + self._visualizer = None return obs, self._reward, done, info diff --git a/core/eval/single_carla_evaluator.py b/core/eval/single_carla_evaluator.py index c9c6d41c..2c3ec223 100644 --- a/core/eval/single_carla_evaluator.py +++ b/core/eval/single_carla_evaluator.py @@ -74,14 +74,14 @@ def eval(self, reset_param: Dict = None) -> float: with self._timer: while True: + if self._render: + self._env.render() if self._transform_obs: obs = to_tensor(obs, dtype=torch.float32) actions = self._policy.forward({0: obs}) action = actions[0]['action'] timestep = self._env.step(action) obs = timestep.obs - if self._render: - self._env.render() if timestep.info.get('abnormal', False): # If there is an abnormal timestep, reset all the related variables(including this env). self._policy.reset(**reset_param) diff --git a/core/utils/others/visualizer.py b/core/utils/others/visualizer.py index 099236b0..e1623dda 100644 --- a/core/utils/others/visualizer.py +++ b/core/utils/others/visualizer.py @@ -183,7 +183,7 @@ def done(self) -> None: Save file or release file writter, destroy windows. """ if self._gif_maker is not None: - self._gif_maker.save(self._name, self._save_dir, self._name) + self._gif_maker.save(self._name, self._save_dir, self._name + '.gif') self._gif_maker.clear(self._name) if self._video_maker is not None: self._video_maker.clear() diff --git a/core/utils/simulator_utils/__init__.py b/core/utils/simulator_utils/__init__.py index f9980f16..c19bd52d 100644 --- a/core/utils/simulator_utils/__init__.py +++ b/core/utils/simulator_utils/__init__.py @@ -4,4 +4,3 @@ ''' from .map_utils import BeVWrapper -from .sensor_utils import SensorHelper, CollisionSensor diff --git a/demo/auto_run/auto_run_case.py b/demo/auto_run/auto_run_case.py index 061d5156..d98a2808 100644 --- a/demo/auto_run/auto_run_case.py +++ b/demo/auto_run/auto_run_case.py @@ -69,11 +69,11 @@ def main(args, cfg, seed=0): auto_policy.reset([0]) obs = carla_env.reset(config) while True: + carla_env.render() actions = auto_policy.forward({0: obs}) action = actions[0]['action'] timestep = carla_env.step(action) obs = timestep.obs - carla_env.render() if timestep.info.get('abnormal', False): # If there is an abnormal timestep, reset all the related variables(including this env). auto_policy.reset([0]) From 2f41972c71ac32038e52170ab8035f63d0be54ba Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Fri, 29 Oct 2021 21:11:21 +0800 Subject: [PATCH 14/16] Merge remote-tracking branch 'origin/dev-doc' into dev --- core/policy/cilrs_policy.py | 53 +++++ docs/figs/dqn_demo1.gif | Bin 0 -> 156806 bytes docs/figs/dqn_demo2.gif | Bin 0 -> 207933 bytes docs/figs/dqn_demo3.gif | Bin 0 -> 447513 bytes docs/figs/dqn_demo4.gif | Bin 0 -> 290690 bytes .../{image-cils_2.png => image-cilrs_1.png} | Bin docs/figs/image-cilrs_2.png | Bin 0 -> 714119 bytes docs/figs/image-cils_1.png | Bin 15770 -> 0 bytes docs/figs/image-il_1.png | Bin 104747 -> 335358 bytes docs/figs/image-il_2.png | Bin 1345391 -> 121109 bytes docs/figs/image-il_3.png | Bin 0 -> 1345391 bytes docs/source/api_doc/policy.rst | 6 + docs/source/index.rst | 2 +- docs/source/model_zoo/cilrs.rst | 217 +++++++++++++++++ docs/source/model_zoo/coil.rst | 221 ------------------ docs/source/model_zoo/implicit.rst | 8 +- docs/source/model_zoo/index.rst | 5 +- docs/source/model_zoo/latent_rl.rst | 20 ++ docs/source/model_zoo/lbc.rst | 2 + docs/source/model_zoo/simple_rl.rst | 104 ++++----- docs/source/tutorial/carla_tutorial.rst | 18 ++ docs/source/tutorial/il_tutorial.rst | 127 ++++++---- docs/source/tutorial/rl_tutorial.rst | 20 +- 23 files changed, 464 insertions(+), 339 deletions(-) create mode 100644 docs/figs/dqn_demo1.gif create mode 100644 docs/figs/dqn_demo2.gif create mode 100644 docs/figs/dqn_demo3.gif create mode 100644 docs/figs/dqn_demo4.gif rename docs/figs/{image-cils_2.png => image-cilrs_1.png} (100%) create mode 100644 docs/figs/image-cilrs_2.png delete mode 100644 docs/figs/image-cils_1.png create mode 100644 docs/figs/image-il_3.png create mode 100644 docs/source/model_zoo/cilrs.rst delete mode 100644 docs/source/model_zoo/coil.rst create mode 100644 docs/source/model_zoo/latent_rl.rst diff --git a/core/policy/cilrs_policy.py b/core/policy/cilrs_policy.py index 6746504d..9624327f 100644 --- a/core/policy/cilrs_policy.py +++ b/core/policy/cilrs_policy.py @@ -12,6 +12,20 @@ class CILRSPolicy(BaseCarlaPolicy): + """ + CILRS driving policy. It has a CILRS NN model which can handle + observations from several environments by collating data into batch. It contains 3 + modes: `eval`, `learn` and `validate`. The validate mode will calculate control + signal together with loss, but will not back-propregate it. In `eval` mode, the + output control signal will be postprocessed to + + :Arguments: + - cfg (Dict): Config Dict. + + :Interfaces: + reset, forward + """ + config = dict( cuda=True, max_throttle=0.75, @@ -74,10 +88,25 @@ def _process_model_outputs(self, data: Dict, output: List) -> List: return action def _reset_eval(self, data_id: Optional[List[int]] = None) -> None: + """ + Reset policy of `eval` mode. It will change the NN model into 'eval' mode. + + :Arguments: + - data_id (List[int], optional): List of env id to reset. Defaults to None. + """ self._model.eval() @torch.no_grad() def _forward_eval(self, data: Dict) -> Dict[str, Any]: + """ + Running forward to get control signal of `eval` mode. + + :Arguments: + - data (Dict): Input dict, with env id in keys and related observations in values, + + :Returns: + Dict: Control and waypoints dict stored in values for each provided env id. + """ data_id = list(data.keys()) new_data = dict() @@ -108,9 +137,24 @@ def _init_learn(self) -> None: self._criterion = F.mse_loss def _reset_learn(self, data_id: Optional[List[int]] = None) -> None: + """ + Reset policy of `learn` mode. It will change the NN model into 'train' mode. + + :Arguments: + - data_id (List[int], optional): List of env id to reset. Defaults to None. + """ self._model.train() def _forward_learn(self, data: Dict) -> Dict[str, Any]: + """ + Running forward to get loss of `learn` mode and back-propregate the loss. + + :Arguments: + - data (Dict): Input dict, with env id in keys and related observations in values, + + :Returns: + Dict: information about training loss. + """ if self._cuda: data = to_device(data, 'cuda') @@ -159,6 +203,15 @@ def _init_validate(self) -> None: @torch.no_grad() def _forward_validate(self, data: Dict) -> Dict[str, Any]: + """ + Running forward to get loss of `validate` mode. + + :Arguments: + - data (Dict): Input dict, with env id in keys and related observations in values, + + :Returns: + Dict: Dict: information about validating loss + """ if self._cuda: data = to_device(data, 'cuda') diff --git a/docs/figs/dqn_demo1.gif b/docs/figs/dqn_demo1.gif new file mode 100644 index 0000000000000000000000000000000000000000..ea3520d1dee9f8630cc315925723fdb2e225d26e GIT binary patch literal 156806 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z`PaDn9DZkEwFRrL;e8t`{cE5?lzA-jMAQ~suz_pG5c>n3`lCYZkW#o`6739?G%G*^ zyagJ1jbXW4ES!S~*SCq=e5zkiMFHAtof7J5%l*%j*6sbgbrO1Qk7xnE<1M`IfMx=< z!MI6Y^2xPZU}dvk;?e%v4wf#Fe@y7CBfP}jN&fDr@}HbM_t$>xg%06h{;0GHq}i{f z!b-18kog8@ymfx9EUYwYL%AKD1SbB&?x##&pS=4^P@e8zf!l$EC%KfBGiJHJ=8KwD zo|GV0b1s-wA7UWS`2P}X{dXt%3uzp^Gw|+8&42z&YP>RXeDAldNn~-)kzaGhO7CpD zMo)~db5D}%S^{V@>;JVAH^oTraP3z8QQONkyY^*#XN^5Gv^NY$nEtg9erz6*Uedk5 zMLzW>6jP4m{nDiN6sw~=h*8&|$fe`Ge#&?M*9@ppUL%oq-h8iXVO5)Y2mexG{%6*d z?@o4ir;W~-T`+5kxo&>=zYZHwP9lQ#dBMA0q3UoskmanTrLu3YUB6;`x#SP`nEwG_ C0ap+J literal 0 HcmV?d00001 diff --git a/docs/source/api_doc/policy.rst b/docs/source/api_doc/policy.rst index 6dfba62a..aca57139 100644 --- a/docs/source/api_doc/policy.rst +++ b/docs/source/api_doc/policy.rst @@ -23,6 +23,12 @@ AutoMPCPolicy :members: _forward_collect, _forward_eval, _reset_collect, _reset_eval +CILRSPolicy +============= +.. autoclass:: core.policy.CILRSPolicy + :members: + + LBCBirdviewPolicy ==================== .. autoclass:: core.policy.LBCBirdviewPolicy diff --git a/docs/source/index.rst b/docs/source/index.rst index dcfb25ea..21887cee 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -15,7 +15,7 @@ DI-drive Documentation Decision Intelligence Platform for Autonomous Driving simulation. -Last updated on 2021.09.19 +Last updated on 2021.10.28 ----- diff --git a/docs/source/model_zoo/cilrs.rst b/docs/source/model_zoo/cilrs.rst new file mode 100644 index 00000000..425aebef --- /dev/null +++ b/docs/source/model_zoo/cilrs.rst @@ -0,0 +1,217 @@ +Conditional Imitation Learning +=================================== + +.. toctree:: + :maxdepth: 2 + +`CILRS `_ together with some other implementations +following `Conditional Imitation Learning(CIL) `_ is +a commonly used imitation learning method for auto-driving in Carla. It is an end-to-end +driving pipeline which takes front RGB camera and vehicle speed as inputs, then predict +vehicle control signals including steer, throttle and brake under certain navigation +command. + +The CIL model uses CNN to capture visual information and concat it with measurements +information (vehicle speed), then use the provided navigation command to select a +branch to give predictions. + +.. figure:: ../../figs/image-cilrs_1.png + :alt: image-cilrs_1 + :align: center + + CIL model structure + +CILRS uses deeper ResNet model and predicts speed in outputs as well to get better +imitating behavior. + +.. figure:: ../../figs/image-cilrs_2.png + :alt: image-cilrs_2 + :align: center + :width: 600px + + CILRS model structure + +**DI-drive** supports full pipeline of CILRS, including datasets collecting, model +training, and benchmark evaluation. Quick start of CILRS has been +introduced in the tutorial. All code in this page can be found in ``./demo/cilrs`` + +Data collection +--------------- + +The configuration of dataset collection has been set up in +``cilrs_data_collect.py``. It uses ``CarlaBenchmarkCollector`` and ``BenchmarkDatasaver`` +to collect training data. You can custom the configuration as follow. + +.. code:: python + + config = dict( + env=dict( + env_num=5, + simulator=dict( + disable_two_wheels=True, + waypoint_num=32, + planner=dict( + type='behavior', + resolution=1, + ), + obs=( + dict( + name='rgb', + type='rgb', + size=[800, 600], + position=[2.0, 0.0, 1.4], + rotation=[-15, 0, 0], + ), + ), + + ), + col_is_failure=True, + stuck_is_failure=True, + manager=dict( + auto_reset=False, + shared_memory=False, + context='spawn', + max_retry=1, + ), + wrapper=dict(), + ), + server=[ + dict(carla_host='localhost', carla_ports=[9000, 9010, 2]), + ], + policy=dict( + target_speed=25, + noise=True, + collect=dict( + n_episode=5, + dir_path='./datasets_train/cils_datasets_train', + collector=dict( + suite='FullTown01-v1', + ), + ) + ), + ) + + +Model training +-------------- + +The training of CILRS is a standard imitation learning process. It takes +RGB image and measurement as input then mimic the export label. You can custom +the training by changing model architecture, learning rate, steps and so +on in ``cilrs_train.py`` + +.. code:: python + + train_config = dict( + exp_name='cilrs_train', + policy=dict( + cuda=True, + cudnn=True, + resume=False, + ckpt_path=None, + model=dict( + num_branch=4, + ), + learn=dict( + epoches=200, + batch_size=128, + loss='l1', + lr=1e-4, + speed_weight=0.05, + control_weights=[0.5, 0.45, 0.05], + ), + eval=dict( + eval_freq=10, + ) + ), + data=dict( + train=dict( + root_dir='cilrs_datasets_train', + preloads='_preloads/cilrs_train.npy', + transform=True, + ), + val=dict( + root_dir='cilrs_datasets_val', + preloads='./_preloads/cilrs_val.npy', + transform=True, + ), + ) + ) + + +Model evaluation +---------------- + +DI-drive supports model evaluation with Carla benchmark. To custom the +evaluation process, you can modify the configuration in ``cilrs_eval.py``. + +.. code:: python + + cilrs_config = dict( + env=dict( + env_num=5, + simulator=dict( + town='Town01', + disable_two_wheels=True, + verbose=False, + planner=dict( + type='behavior', + resolution=1, + ), + obs=( + dict( + name='rgb', + type='rgb', + size=[400, 300], + position=[1.3, 0.0, 2.3], + fov=100, + ), + ), + ), + wrapper=dict(), + col_is_failure=True, + stuck_is_failure=True, + manager=dict( + auto_reset=False, + shared_memory=False, + context='spawn', + max_retry=1, + ), + ), + server=[dict(carla_host='localhost', carla_ports=[9000, 9010, 2])], + policy=dict( + ckpt_path=None, + model=dict( + num_branch=4, + pretrained=False, + ), + eval=dict( + evaluator=dict( + suite=['FullTown01-v1'], + transform_obs=True, + ), + ) + ), + ) + +We provide pre-train weights that can achieve 90% success rate in single lane maps +(Town01 and Town02). You can use it to run evaluation or fintuning your trained models. + +`best_ckpt.pth `_ + +.. code:: + + @inproceedings{2018End, + title={End-to-End Driving Via Conditional Imitation Learning}, + author={ Codevilla, Felipe and Miiller, Matthias and Lopez, Antonio and Koltun, Vladlen and Dosovitskiy, Alexey }, + booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)}, + year={2018}, + } + + @inproceedings{codevilla2019exploring, + title={Exploring the limitations of behavior cloning for autonomous driving}, + author={Codevilla, Felipe and Santana, Eder and L{\'o}pez, Antonio M and Gaidon, Adrien}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={9329--9338}, + year={2019} + } diff --git a/docs/source/model_zoo/coil.rst b/docs/source/model_zoo/coil.rst deleted file mode 100644 index f71d1889..00000000 --- a/docs/source/model_zoo/coil.rst +++ /dev/null @@ -1,221 +0,0 @@ -.. _header-n32: - -Conditional Imitation Learning -=================================== - -.. toctree:: - :maxdepth: 2 - -`Conditional Imitation Learning(CIL) `_ is a -widely used imitation learning method for autoploit. The CIL takes front -RGB camera image and vehicle speed as inputs, then predict vehicle control -singals including steer, throttle and brake under certain navigation -command. - -.. figure:: ../../figs/image-cils_1.png - :alt: image-cilrs_1 - :align: center - -The CIL model use convolutional neural network to capture visual -information and concat it with measurements information (like -vehicle speed), then use the navigation command to select a suitable -branch to give prediction. More details about CIL can be found in the -paper. - -.. figure:: ../../figs/image-cils_2.png - :alt: image-cilrs_2 - :align: center - -DI-drive supports full pipeline of CIL, including datasets collecting, model -training, and benchmark evaluation. Quick start of CIL has been -introduced in the tutorial. All code in this page can be found in ``./demo/coil_demo`` - -.. _header-n66: - -Data collection ---------------- - -The configuration of dataset collection has been set up in -``coil_data_collect.py.`` You can custom it by -modifying the configuration. - -.. code:: python - - config = dict( - env=dict( - env_num=5, - simulator=dict( - disable_two_wheels=True, - waypoint_num=32, - planner=dict( - type='behavior', - resolution=1, - ), - obs=( - dict( - name='rgb', - type='rgb', - size=[800, 600], - position=[2.0, 0.0, 1.4], - rotation=[-15, 0, 0], - ), - ), - - ), - col_is_failure=True, - stuck_is_failure=True, - manager=dict( - auto_reset=False, - shared_memory=False, - context='spawn', - max_retry=1, - ), - wrapper=dict(), - ), - server=[ - dict(carla_host='localhost', carla_ports=[9000, 9010, 2]), - ], - policy=dict( - target_speed=25, - noise=True, - collect=dict( - n_episode=5, - dir_path='./datasets_train/cils_datasets_train', - collector=dict( - suite='FullTown01-v1', - ), - ) - ), - ) - -.. _header-n75: - -Model training --------------- - -The training of CIL is a standard imitation learning process. It takes -RGB image and measurement as input then mimic the export label. You can custom -the training by changing model architecture, learning rate, steps and so -on in ``coil_train.py`` - -.. code:: python - - train_config = dict( - NUMBER_OF_LOADING_WORKERS=12, - SENSORS=dict(rgb=[3, 88, 200]), - TARGETS=['steer', 'throttle', 'brake'], - INPUTS=['speed_module'], - BATCH_SIZE=120, - COMMON=dict( - folder='sample', exp='coil_icra', dataset_path='./datasets_train' - ), - GPU='0', - SAVE_SCHEDULE=[0, 100, 1000001], - NUMBER_ITERATIONS=200000, - SPEED_FACTOR=25.0, - TRAIN_DATASET_NAME='cils_datasets_train', - AUGMENTATION=None, - NUMBER_OF_HOURS=50, - MODEL_TYPE='coil-icra', - MODEL_CONFIGURATION=dict( - perception=dict(res=dict(name='resnet34', num_classes=512)), - measurements=dict(fc=dict(neurons=[128, 128], dropouts=[0.0, 0.0])), - join=dict(fc=dict(neurons=[512], dropouts=[0.0])), - speed_branch=dict(fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])), - branches=dict(number_of_branches=4, fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])) - ), - PRE_TRAINED=False, - LEARNING_RATE_DECAY_INTERVAL=75000, - LEARNING_RATE_DECAY_LEVEL=0.1, - LEARNING_RATE_THRESHOLD=5000, - LEARNING_RATE=0.0002, - BRANCH_LOSS_WEIGHT=[0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.05], - VARIABLE_WEIGHT=dict(Steer=0.5, Gas=0.45, Brake=0.05), - LOSS_FUNCTION='L1', - IMAGE_CUT=[115, 500], - PRELOAD_MODEL_ALIAS=None, - PRELOAD_MODEL_BATCH=None, - PRELOAD_MODEL_CHECKPOINT=None, - REMOVE=None, - NUMBER_IMAGES_SEQUENCE=1, - NUMBER_FRAMES_FUSION=1, - ) - -.. _header-n79: - -Model evaluation ----------------- - -DI-drive supports model evaluation with Carla benchmark. To custom the -evaluation process, you can modify the configuration in ``coil_eval.py``. - -.. code:: python - - autoeval_config = dict( - env=dict( - env_num=5, - simulator=dict( - verbose=False, - obs=( - dict( - name='rgb', - type='rgb', - size=[800, 600], - position=[2.0, 0.0, 1.4], - rotation=[-15, 0, 0], - ), - ), - planner=dict(type='behavior', ), - ), - manager=dict( - shared_memory=False, - auto_reset=False, - context='spawn', - max_retry=1, - ), - ), - server=[ - dict(carla_host='localhost', carla_ports=[9000, 9010, 2]) - ], - policy=dict( - target_speed=40, - eval=dict( - evaluator=dict( - suite='FullTown02-v1', - episodes_per_suite=5, - save_files=True, - ), - ), - ), - ) - - policy_config = dict( - model=dict( - ckpt_path='./coil_icra/checkpoints/100.pth' - ), - SENSORS=dict(rgb=[3, 88, 200]), - TARGETS=['steer', 'throttle', 'brake'], - INPUTS=['speed_module'], - SPEED_FACTOR=25.0, - MODEL_TYPE='coil-icra', - MODEL_CONFIGURATION=dict( - perception=dict(res=dict(name='resnet34', num_classes=512)), - measurements=dict(fc=dict(neurons=[128, 128], dropouts=[0.0, 0.0])), - join=dict(fc=dict(neurons=[512], dropouts=[0.0])), - speed_branch=dict(fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])), - branches=dict(number_of_branches=4, fc=dict(neurons=[256, 256], dropouts=[0.0, 0.5])) - ), - PRE_TRAINED=False, - LEARNING_RATE_DECAY_LEVEL=0.1, - IMAGE_CUT=[115, 500], - NUMBER_FRAMES_FUSION=1, - ) - -.. code:: - - @inproceedings{2018End, - title={End-to-End Driving Via Conditional Imitation Learning}, - author={ Codevilla, Felipe and Miiller, Matthias and Lopez, Antonio and Koltun, Vladlen and Dosovitskiy, Alexey }, - booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)}, - year={2018}, - } diff --git a/docs/source/model_zoo/implicit.rst b/docs/source/model_zoo/implicit.rst index e7403595..e3cd92e5 100644 --- a/docs/source/model_zoo/implicit.rst +++ b/docs/source/model_zoo/implicit.rst @@ -14,15 +14,19 @@ and train the head of network using RL. It takes 4-frame RGB captured by front camera as in put. The model of the two training stage is shown as follow. -.. image:: ../../figs/implicit2.png +.. figure:: ../../figs/implicit2.png :alt: implicit2 :align: center :width: 600px -.. image:: ../../figs/implicit1.png + SL training pipeline + +.. figure:: ../../figs/implicit1.png :alt: implicit1 :align: center + RL training pipeline + We extend and modified some of the training and environment details to make *Implicit Affordances* able to run in multi-lane maps in Carla. This is so far the **first** driving policy to run `FullTown` navigation in Carla using Reinforcement Learning. It achieves the diff --git a/docs/source/model_zoo/index.rst b/docs/source/model_zoo/index.rst index 7c444ada..df95b6c3 100644 --- a/docs/source/model_zoo/index.rst +++ b/docs/source/model_zoo/index.rst @@ -7,7 +7,7 @@ Imitation Learning Model Zoo .. toctree:: :maxdepth: 1 - coil + cilrs lbc cict @@ -18,7 +18,10 @@ Reinforcement Learning Model Zoo .. toctree:: :maxdepth: 1 + simple_rl implicit + latent_rl + Other Method =============== diff --git a/docs/source/model_zoo/latent_rl.rst b/docs/source/model_zoo/latent_rl.rst new file mode 100644 index 00000000..b708f741 --- /dev/null +++ b/docs/source/model_zoo/latent_rl.rst @@ -0,0 +1,20 @@ +Latent Reinforcement Learning +################################# + +.. toctree:: + :maxdepth: 2 + +Latent Reinforcement Learning is a category of policy settings which generate +latent representations that can conpletely characterize the features related to +the task in the input observation. Techically, it uses a Variational Autoencoder +(VAE) to get latent embeddings, and use it to train RL policies. + +Our implementation refers to `Model-free RL for AD `_ +with **DI-drive** and **DI-engine** and transfers it to more general cases. In the +following documents, it will be explained detailly. + + +Training VAE +================= + +Comming soon \ No newline at end of file diff --git a/docs/source/model_zoo/lbc.rst b/docs/source/model_zoo/lbc.rst index 195ebfc5..64808d7e 100644 --- a/docs/source/model_zoo/lbc.rst +++ b/docs/source/model_zoo/lbc.rst @@ -13,6 +13,8 @@ throttle and brake. :alt: image-lbc_1 :align: center + LBC structure + Datasets collection ------------------- diff --git a/docs/source/model_zoo/simple_rl.rst b/docs/source/model_zoo/simple_rl.rst index 133cd1b1..c4490f4b 100644 --- a/docs/source/model_zoo/simple_rl.rst +++ b/docs/source/model_zoo/simple_rl.rst @@ -4,22 +4,36 @@ BeV Speed End-to-end Reinforcement Learning .. toctree:: :maxdepth: 2 -This is a simple Reinforcement Learning demo to show the basic usage of DI-drive environments -and DI-engine RL policies. +This is a simple Reinforcement Learning demo to show the basic usage of **DI-drive** environments +and **DI-engine** RL policies. + +.. image:: ../../figs/dqn_demo1.gif + :alt: dqn_demo1 + :width: 250px +.. image:: ../../figs/dqn_demo2.gif + :alt: dqn_demo2 + :width: 250px +.. image:: ../../figs/dqn_demo3.gif + :alt: dqn_demo3 + :width: 250px +.. image:: ../../figs/dqn_demo4.gif + :alt: dqn_demo4 + :width: 250px Inputs, Env and NN models ===================================== -We use a Bird-eye View image with size of 32x32x5 and speed scalar as observations. The BeV image -is encoded by a conv net to get a 256 size embedding, and concat with the speed value which is -repeated 256 times. +It takes a Bird-eye View image with size of 32x32x5 and speed scalar as observations. The BeV image +consists of roads, lanes, vehicles, pedestrians and target route. Each occupies a channel with 0-1 +value. The traffic light information is excluded from observations. -The encoder output is then send into different heads depends on the required outputs of RL policies. -Currently we have DQN, DDPG, TD3, SAC and PPO demos. We provide training and evaluation entry for -all of them. +The BeV image is encoded by a conv net to get a 256 size embedding, and concat with the speed value +repeated 256 times. The encoder output is then send into different heads depends on the required +outputs of RL policies. Currently we have DQN, DDPG, TD3, SAC and PPO demos. We provide training and +evaluation entry for all of them. -You can refer to the defination of encoder in ``core/models/bev_speed_model.py`` and RL models in +Please refer to the defination of encoder in ``core/models/bev_speed_model.py`` and RL models in ``demo/simple_rl/model.py`` for their details. If you want to build your own RL experiments, you can define NN models similarly. @@ -68,12 +82,12 @@ This will map the descrete action space to continuous control signal in Carla en traffic signal channels in BeV image. Other wrappers work in the same way. -Training loop -=================== +Training and Evaluation +======================== -The entry files of all the RL methods are written in standard way using DI-engine to run RL experiments. -The sub-process env manager in Di-engine is used to run multi-env in parallel. -Off-policy methods use collector, learner, replay buffer in DI-engine and evaluator in DI-drive. On-policy +The entry files of all the RL methods are written in standard way using **DI-engine** to run RL experiments. +The sub-process env manager in **Di-engine** is used to run multi-env in parallel. +Off-policy methods use collector, learner, replay buffer in **DI-engine** and **evaluator** in DI-drive. On-policy method does not use replay buffer. Off-policy training loop: @@ -97,57 +111,23 @@ Off-policy training loop: learner.train(train_data, collector.envstep) replay_buffer.update(learner.priority_info) - -Env Wrappers -================= - -The policy is trained in single-lane maps in Carla. Town01 for training and Town02 for evaluation. -The traffic lights are ignored and also the traffic light channels in BeV images is deleted. - -The environment instance ``SimpleCarlaEnv`` is well defined with specified inputs and outputs. -The standard usage to customize the env interfaces is to add ``EnvWrapper`` and change the input, output -of Env. For example: +On-policy training loop: .. code:: python - DEFAULT_ACC_LIST = [(0, 1), (0.25, 0), (0.75, 0),] - DEFAULT_STEER_LIST = [-0.8,-0.5,-0.2,0,0.2,0.5,0.8,] - - class DiscreteEnvWrapper(gym.Wrapper): - - self._acc_list = [(0, 1), (0.25, 0), (0.75, 0),] - self._steer_list = [-0.8, -0.5, -0.2, 0, 0.2, 0.5, 0.8] + while True: + if evaluator.should_eval(learner.train_iter): + stop, rate = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) + if stop: + break + new_data = collector.collect(n_sample=3000, train_iter=learner.train_iter) + unpack_birdview(new_data) + learner.train(new_data, collector.envstep) - def reset(self, *args, **kwargs) -> Any: - obs = self.env.reset(*args, **kwargs) - obs_out = { - 'birdview': obs['birdview'][..., [0, 1, 5, 6, 8]], - 'speed': (obs['speed'] / 25).astype(np.float32), - } - return obs_out - def step(self, id): - if isinstance(id, torch.Tensor): - id = id.item() - id = np.squeeze(id) - assert id < len(self._acc_list) * len(self._steer_list), (id, len(self._acc_list) * len(self._steer_list)) - mod_value = len(self._acc_list) - acc = self._acc_list[id % mod_value] - steer = self._steer_list[id // mod_value] - action = { - 'steer': steer, - 'throttle': acc[0], - 'brake': acc[1], - } - timestep = self.env.step(action) - obs = timestep.obs - obs_out = { - 'birdview': obs['birdview'][..., [0, 1, 5, 6, 8]], - 'speed': (obs['speed'] / 25).astype(np.float32), - } - timestep = timestep._replace(obs=obs_out) - return timestep - -This will map the descrete action space to continuous control signal in Carla env and delete -traffic signal channels in BeV image. Other wrappers work in the same way. +The are two kinds of evaluation provided in this demo, `Carla benchmark <../features/carla_benchmark.html>`_ +evaluation and close-loop driving test. The former one is to evaluate the general performance +of a policy in a benchmark suite, the latter one is mostly used to visualize driving results in a route. +They can be implement using ``CarlaBenchmarkEvaluator`` and ``SerialEvaluator`` seperately. +For more details, it is recommended to check the `API doc <../api_doc/index.html>`_ for the modules above. diff --git a/docs/source/tutorial/carla_tutorial.rst b/docs/source/tutorial/carla_tutorial.rst index f181fe46..48bc87ff 100644 --- a/docs/source/tutorial/carla_tutorial.rst +++ b/docs/source/tutorial/carla_tutorial.rst @@ -60,6 +60,24 @@ Then, just pull the Carla image and run with port. # with parameters docker run -p 2000-2002:9000-9002 --runtime=nvidia --gpus carlasim/carla:0.9.9.4 /bin/bash CarlaUE4.sh +We also provide an easily used multi-carla docker image that can start an amount of Carla servers. +You can pull the image from `dockerhub `_ and start with your own settings. +For example, the following command will start a container with 8 Carla server whose ports are set +from 9000 to 9014 + +.. code:: bash + + docker pull opendilab/multi-carla:0.9.9 + docker run -p 9000-9016:9000-9016 --runtime=nvidia opendilab/multi-carla:0.9.9 /bin/bash run_carla.sh -n 8 -p 9000 + +Generally, the option ``-n NUMS`` sets the number of Carla server and the option ``-p PORT`` sets +the start port of servers. The other servers' port are set to the following even number. + +.. note:: + + Please pay attention to the Carla port within and out of the container. The internal ones are set + by the parameters in carla scripts, the external ones are set when creating the container. + Carla server settings =========================== diff --git a/docs/source/tutorial/il_tutorial.rst b/docs/source/tutorial/il_tutorial.rst index da693ef1..52fc018c 100644 --- a/docs/source/tutorial/il_tutorial.rst +++ b/docs/source/tutorial/il_tutorial.rst @@ -3,63 +3,76 @@ Simple Imitation Learning ========================== +.. toctree:: + :maxdepth: 2 + Here we will show you how to use **DI-drive** for imitation learning research of autopilot. **DI-drive** supports -`Conditional Imitation Learning(CIL) `__ -for Imitation Learning research. +`Conditional Imitation Learning(CIL) `_ +and its inheritor for Imitation Learning research. -In this tutorial, we will show how to use **DI-drive** to train a CIL model. -CIL takes front RGB camera and vehicle speed as input, then output the prediction of vehicle control singals including -steer, throttle and brake under certain navigation command. +In this tutorial, we will show how to use **DI-drive** to train a CILRS model. +CILRS takes front RGB camera and vehicle speed as input, then output the prediction of vehicle control singals including +steer, throttle and brake under certain navigation command, together with a speed prediction. -IL of autopilot normally includes three parts: +Imitation Learning normally includes three parts: -- Datasets collection +- Collecting expert driving datasets -- Model training +- Training driving model -- Model evaluation +- Close-loop evaluation under driving curcumstance -For now, we will show you step by step +For now, we will show you step by step. All demo codes can be found in ``demo/cilrs`` .. _header-n14: Datasets collection -------------------- +--------------------- -To collect the datasets, the first step is to start a Carla server +To collect expert data, the first step is to start Carla servers .. code:: shell ./CarlaUE4.sh -fps=10 -benchmark -world-port=[PORT NUM] -To customize your training datasets, please refer to the configuration -``coil_data_collect.py``. For this tutorial, you -should change the ``save_dir`` path and Carla server host\port. +To customize your training datasets, please refer to the configuration and entry demo in +``cilrs_data_collect.py``. It use an auto-driving policy to run in Carla benchmark 'suite'. +For this tutorial, you need to change the suite name, dataset path and Carla server host/port. .. code:: python - saver=dict(save_dir='/cils_datasets_train'), server=[ - dict(carla_host='localhost', carla_ports=[9000, 9008, 2]), - ], + dict(carla_host='localhost', carla_ports=[9000, 9010, 2]), + ], + policy=dict( + collect=dict( + n_episode=100, + dir_path='path_to_your_dataset', + collector=dict( + suite='suite_name', + ), + ) + ) Then you can use the python scrips to collect the data. .. code:: shell - cd demo/coil_demo - python coil_data_collect.py + python cilrs_data_collect.py -This collecting process usually cost ~7 hours and ~12 G storage with -single Carla thread. You can speed up this process by starting multi-carla envs. +This collecting process of 100 episodes may cost ~7 hours and ~12 G storage with +single Carla server. You can speed up this process by starting multi-carla servers. +The dataset format is illustrated in the `benchmark datasets <../features/datasets.html>`_. +The CILRS demo builds preload files to speed up the loading of dataset. If you finished +this step, there should be an ``.npy`` file under ``_preloads``. -If you finished this step, there should be a ``.npy`` file under -``_preloads``. +We recommend to collect both training and validating datasets with different suite to get +better performance. -Once you finish the data collection, the next step is to train an CIL model. +Once you finish the data collection, the next step is to train an CILRS model. .. _header-n26: @@ -72,57 +85,83 @@ with **DI-drive** with default training configuration, just run .. code:: - python coil_train.py + python cilrs_train.py -Note that you should modify the dataset path as you have created +Note that you should modify the dataset path and preload path as you have created above. .. code:: python - COMMON=dict(folder='sample', exp='coil_icra', - dataset_path='/datasets_train') + data=dict( + train=dict( + root_dir='path_to_your_train_dataset', + preloads='path_to_your_train_preloads', + transform=True, + ), + val=dict( + root_dir='path_to_your_val_dataset', + preloads='path_to_your_val_preloads', + transform=True, + ), -You will see loss printed on the screen like +You will see training procedure shown on the screen like this. .. figure:: ../../figs/image-il_1.png :alt: image-il_1 :align: center - :width: 300px + :width: 600px -and the training checkpoints will be saved under ``_logs`` folder. The +The training checkpoints will be saved under ``checkpoints`` folder. The training process will last for 7~8 hours (i7-9900k and GeForce RTX 2080Ti), up to your machine hardware. if you want to customize your training configuration such as the model architecture and -learning rates, please modify the config in ``coil_train.py`` +learning rates, please modify the config in ``cilrs_train.py`` .. _header-n35: Model evaluation ---------------- -To evaluate your trained model with **DI-drive**, just run +We provide benchmark evaluation and close-loop test in environment (mostly for visualization) for +a trained CILRS model with **DI-drive**. To evaluate your trained model, just run -.. code:: python +.. code:: bash - python coil_eval.py + python cilrs_eval.py -Note that you have change the checkpoint path and Carla server host/port in ``coil_eval.py`` +Note that you may need to change the checkpoint path and Carla server host/port in ``cilrs_eval.py``. .. code:: python - ckpt_path='' - server=[dict(carla_host='localhost', carla_ports=[9000, 9010, 2])], + server=[ + dict(carla_host='localhost', carla_ports=[9000, 9010, 2]) + ], + policy=dict( + ckpt_path='path_to_your_ckpt', + ... + ) -You can visual the real-time evaluation process like +When the evaluation is finished, you will get a performance table. .. figure:: ../../figs/image-il_2.png :alt: image-il_2 :align: center - :width: 500px + :width: 800px -When the evaluation is finished, you will get a performance table. +Also, you can visual the real-time close-loop test by running + +.. code:: python + + python cilrs_test.py + +You can see a visualized screen of the running policy. + +.. figure:: ../../figs/image-il_3.png + :alt: image-il_3 + :align: center + :width: 500px -You can customize the evaluation configuration by modifying the config in -``coil_eval.py`` +You can customize the evaluation and test configuration by modifying the config in +``cilrs_eval.py`` and ``cilrs_test.py`` Congratulations! You have finished the imitation learning tutorial. diff --git a/docs/source/tutorial/rl_tutorial.rst b/docs/source/tutorial/rl_tutorial.rst index b8741dca..904536ea 100644 --- a/docs/source/tutorial/rl_tutorial.rst +++ b/docs/source/tutorial/rl_tutorial.rst @@ -4,20 +4,24 @@ Simple Reinforcement Learning .. toctree:: :maxdepth: 2 + +**DI-drive** + **DI-engine** make RL for Autonomous Driving very easy. Here we show how to +use **DI-drive** to run a simple Reinforcement Learning driving policy. +The policy takes a small Bird-eye View image together with +current speed as input, and directly outputs control signals. + Prerequisites ---------------- -Ubuntu 16.04 system +Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz + 32G +Ubuntu 16.04 system + Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz + 32G memory + GPU1060 DI-drive RL training using DI-engine -------------------------------------- -DI-drive + DI-engine make RL for Autonomous Driving very easy. We build a simple RL demo that can run -varies RL algorithm with a simple environment setting. It takes a small Bird-eye View image as NN -input, together with current speed, and directly output control signals. All the code can be found in -``demo/simple_rl``. +We build simple RL demos that can run varies RL algorithm with the aforementioned simple environment setting. +All the code can be found in ``demo/simple_rl``, including training, evaluating and testing. Here we show how to run the DQN demo. It follows the standard deployment of a DI-engine RL entry. Other RL demo is written in same way. @@ -28,7 +32,7 @@ Other RL demo is written in same way. python dqn_train.py The config part defines the env and policy settings. Notes that you need to change the Carla server -host and port, and modify the environment nums according to yours. By default it uses 8 Carla server on +host and port, and modify the environment nums according to yours. By default it uses 8 Carla servers on `localhost` with port from 9000 to 9016. .. code:: python @@ -46,7 +50,7 @@ host and port, and modify the environment nums according to yours. By default it ), ) -For more details about how to tune parameters in DQN, you can see their doc. Usually +For more details about how to tune parameters in DQN, you can see **DI-engine**'s doc. Usually you may concern about the replay buffer size and sample num per collection. When you see the information in terminal that contains the content in @@ -56,7 +60,7 @@ model. .. figure:: ../../figs/rl_tutorial_log.png :alt: rl_tutorial_log :align: center - :width: 800px + :width: 1000px In the process of training, you can use the tensorboard as a monitor, the default log path is in your working directory. From acb9164b00011095ef80c2efe1375cdd18b4f58e Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Fri, 29 Oct 2021 21:47:45 +0800 Subject: [PATCH 15/16] Merge branch 'dev-cilrs' into dev --- core/policy/cilrs_policy.py | 99 ++----------------------------------- demo/cilrs/cilrs_train.py | 26 +++++++--- 2 files changed, 23 insertions(+), 102 deletions(-) diff --git a/core/policy/cilrs_policy.py b/core/policy/cilrs_policy.py index 9624327f..e539cb00 100644 --- a/core/policy/cilrs_policy.py +++ b/core/policy/cilrs_policy.py @@ -2,7 +2,6 @@ from collections import namedtuple import numpy as np import torch -from torch.optim import Adam import torch.nn.functional as F from ding.utils.data import default_collate, default_decollate @@ -39,16 +38,6 @@ class CILRSPolicy(BaseCarlaPolicy): control_weights=[0.5, 0.45, 0.05], ), ) - validate_function = namedtuple( - 'validate_function', [ - 'forward', - 'reset', - 'get_attribute', - 'set_attribute', - 'state_dict', - 'load_state_dict', - ] - ) def __init__( self, @@ -130,7 +119,6 @@ def _forward_eval(self, data: Dict) -> Dict[str, Any]: return {i: {'action': d} for i, d in zip(data_id, actions)} def _init_learn(self) -> None: - self._optimizer = Adam(self._model.parameters(), self._cfg.learn.lr) if self._cfg.learn.loss == 'l1': self._criterion = F.l1_loss elif self._cfg.policy.learn.loss == 'l2': @@ -177,92 +165,15 @@ def _forward_learn(self, data: Dict) -> Dict[str, Any]: total_loss = speed_loss + steer_loss + throttle_loss + brake_loss - self._optimizer.zero_grad() - total_loss.backward() - self._optimizer.step() - return_info = { - 'cur_lr': self._optimizer.defaults['lr'], - 'total_loss': total_loss.item(), - 'speed_loss': speed_loss.item(), - 'steer_loss': steer_loss.item(), - 'throttle_loss': throttle_loss.item(), - 'brake_loss': brake_loss.item(), + 'total_loss': total_loss, + 'speed_loss': speed_loss, + 'steer_loss': steer_loss, + 'throttle_loss': throttle_loss, + 'brake_loss': brake_loss, # 'steer_mean': steer_pred.item().mean(), # 'throttle_mean': throttle_pred.item().mean(), # 'brake_mean': brake_pred.item().mean(), } return return_info - - def _init_validate(self) -> None: - if self._cfg.learn.loss == 'l1': - self._criterion = F.l1_loss - elif self._cfg.policy.learn.loss == 'l2': - self._criterion = F.mse_loss - - @torch.no_grad() - def _forward_validate(self, data: Dict) -> Dict[str, Any]: - """ - Running forward to get loss of `validate` mode. - - :Arguments: - - data (Dict): Input dict, with env id in keys and related observations in values, - - :Returns: - Dict: Dict: information about validating loss - """ - if self._cuda: - data = to_device(data, 'cuda') - - rgb = data['rgb'] - steer_gt, throttle_gt, brake_gt = data['steer'], data['throttle'], data['brake'] - speed = data['speed'] - command = data['command'] - embedding = self._model.encode([rgb]) - output = self._model(embedding, speed, command) - control_pred, speed_pred = output - steer_pred = control_pred[:, 0] - throttle_pred = control_pred[:, 1] - brake_pred = control_pred[:, 2] - - speed_loss = self._criterion(speed_pred.squeeze(), speed.squeeze()).mean() * self._cfg.learn.speed_weight - steer_loss = self._criterion(steer_pred, steer_gt.squeeze()).mean() * self._cfg.learn.control_weights[0] - throttle_loss = self._criterion(throttle_pred, - throttle_gt.squeeze()).mean() * self._cfg.learn.control_weights[1] - brake_loss = self._criterion(brake_pred, brake_gt.squeeze()).mean() * self._cfg.learn.control_weights[2] - - total_loss = speed_loss + steer_loss + throttle_loss + brake_loss - - return_info = { - 'total_loss': total_loss.item(), - 'speed_loss': speed_loss.item(), - 'steer_loss': steer_loss.item(), - 'throttle_loss': throttle_loss.item(), - 'brake_loss': brake_loss.item(), - # 'steer_mean': steer_pred.item().mean(), - # 'throttle_mean': throttle_pred.item().mean(), - # 'brake_mean': brake_pred.item().mean(), - } - return return_info - - def _state_dict_learn(self) -> Dict[str, Any]: - return { - 'model': self._model.state_dict(), - 'optimizer': self._optimizer.state_dict(), - } - - def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None: - self._model.load_state_dict(state_dict['model']) - self._optimizer.load_state_dict(state_dict['optimizer']) - - @property - def validate_mode(self) -> 'CILRSPolicy.eval_function': # noqa - return CILRSPolicy.validate_function( - self._forward_validate, - self._reset_eval, - self._get_attribute, - self._set_attribute, - self._state_dict_eval, - self._load_state_dict_eval, - ) diff --git a/demo/cilrs/cilrs_train.py b/demo/cilrs/cilrs_train.py index a2bf2834..17901de7 100644 --- a/demo/cilrs/cilrs_train.py +++ b/demo/cilrs/cilrs_train.py @@ -7,6 +7,7 @@ import torch from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter +from torch.optim import Adam from core.policy import CILRSPolicy from core.data import CILRSDataset @@ -49,16 +50,20 @@ main_config = EasyDict(config) -def train(policy, loader, tb_logger=None, start_iter=0): +def train(policy, optimizer, loader, tb_logger=None, start_iter=0): loss_epoch = defaultdict(list) iter_num = start_iter policy.reset() for data in tqdm(loader): log_vars = policy.forward(data) - + optimizer.zero_grad() + total_loss = log_vars['total_loss'] + total_loss.backward() + optimizer.step() + log_vars['cur_lr'] = optimizer.defaults['lr'] for k, v in log_vars.items(): - loss_epoch[k] += [log_vars[k]] + loss_epoch[k] += [log_vars[k].item()] if iter_num % 50 == 0 and tb_logger is not None: tb_logger.add_scalar("train_iter/" + k, v, iter_num) iter_num += 1 @@ -70,7 +75,8 @@ def validate(policy, loader, tb_logger=None, epoch=0): loss_epoch = defaultdict(list) policy.reset() for data in tqdm(loader): - log_vars = policy.forward(data) + with torch.no_grad(): + log_vars = policy.forward(data) for k in list(log_vars.keys()): loss_epoch[k] += [log_vars[k]] loss_epoch = {k: np.mean(v) for k, v in loss_epoch.items()} @@ -86,7 +92,7 @@ def save_ckpt(state, name=None, exp_name=''): torch.save(state, ckpt_path) -def load_best_ckpt(policy, root_dir='checkpoints', exp_name='', ckpt_path=None): +def load_best_ckpt(policy, optimizer=None, root_dir='checkpoints', exp_name='', ckpt_path=None): ckpt_dir = os.path.join(root_dir, exp_name) assert os.path.isdir(ckpt_dir), ckpt_dir files = os.listdir(ckpt_dir) @@ -101,6 +107,8 @@ def load_best_ckpt(policy, root_dir='checkpoints', exp_name='', ckpt_path=None): print('Load ckpt:', ckpt_path) state_dict = torch.load(os.path.join(ckpt_dir, ckpt_path)) policy.load_state_dict(state_dict) + if 'optimizer' in state_dict: + optimizer.load_state_dict(state_dict['optimizer']) epoch = state_dict['epoch'] iterations = state_dict['iterations'] best_loss = state_dict['best_loss'] @@ -117,6 +125,7 @@ def main(cfg): val_loader = DataLoader(val_dataset, cfg.policy.learn.batch_size, num_workers=8) cilrs_policy = CILRSPolicy(cfg.policy) + optimizer = Adam(cilrs_policy._model.parameters(), cfg.policy.learn.lr) tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name)) iterations = 0 best_loss = 1e8 @@ -124,11 +133,11 @@ def main(cfg): if cfg.policy.resume: start_epoch, iterations, best_loss = load_best_ckpt( - cilrs_policy.learn_mode, exp_name=cfg.exp_name, ckpt_path=cfg.policy.ckpt_path + cilrs_policy.learn_mode, optimizer, exp_name=cfg.exp_name, ckpt_path=cfg.policy.ckpt_path ) for epoch in range(start_epoch, cfg.policy.learn.epoches): - iter_num, loss = train(cilrs_policy.learn_mode, train_loader, tb_logger, iterations) + iter_num, loss = train(cilrs_policy.learn_mode, optimizer, train_loader, tb_logger, iterations) iterations = iter_num tqdm.write( f"Epoch {epoch:03d}, Iter {iter_num:06d}: Total: {loss['total_loss']:2.5f}" + @@ -136,10 +145,11 @@ def main(cfg): f" Thr: {loss['throttle_loss']:2.5f} Brk: {loss['brake_loss']:2.5f}" ) if epoch % cfg.policy.eval.eval_freq == 0: - loss_dict = validate(cilrs_policy.validate_mode, val_loader, tb_logger, iterations) + loss_dict = validate(cilrs_policy.learn_mode, val_loader, tb_logger, iterations) total_loss = loss_dict['total_loss'] tqdm.write(f"Validate Total: {total_loss:2.5f}") state_dict = cilrs_policy.learn_mode.state_dict() + state_dict['optimizer'] = optimizer.state_dict() state_dict['epoch'] = epoch state_dict['iterations'] = iterations state_dict['best_loss'] = best_loss From cb7107127f054926b3fe95eaea5c92d3a1fcdba9 Mon Sep 17 00:00:00 2001 From: Robin Chen Date: Mon, 1 Nov 2021 20:53:01 +0800 Subject: [PATCH 16/16] format code --- .style.yapf | 11 +++++++++++ core/data/lbc_dataset.py | 14 +++++--------- core/policy/lbc_policy.py | 2 +- 3 files changed, 17 insertions(+), 10 deletions(-) create mode 100644 .style.yapf diff --git a/.style.yapf b/.style.yapf new file mode 100644 index 00000000..edd867c2 --- /dev/null +++ b/.style.yapf @@ -0,0 +1,11 @@ +[style] +# For explanation and more information: https://github.com/google/yapf +BASED_ON_STYLE=pep8 +DEDENT_CLOSING_BRACKETS=True +SPLIT_BEFORE_FIRST_ARGUMENT=True +ALLOW_SPLIT_BEFORE_DICT_VALUE=False +JOIN_MULTIPLE_LINES=False +COLUMN_LIMIT=120 +BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF=True +BLANK_LINES_AROUND_TOP_LEVEL_DEFINITION=2 +SPACES_AROUND_POWER_OPERATOR=True diff --git a/core/data/lbc_dataset.py b/core/data/lbc_dataset.py index 5218b427..db271114 100644 --- a/core/data/lbc_dataset.py +++ b/core/data/lbc_dataset.py @@ -10,7 +10,6 @@ from core.utils.others.image_helper import read_image, draw_msra_gaussian from core.utils.data_utils import augmenter - PIXEL_OFFSET = 10 PIXELS_PER_METER = 5 @@ -160,12 +159,7 @@ def __getitem__(self, index) -> Any: regression_offset[i] = center - center_int indices[i] = center_int[1] * output_size + center_int[0] - return { - 'birdview': birdview, - 'location': np.array(locations), - 'command': command, - 'speed': speed - } + return {'birdview': birdview, 'location': np.array(locations), 'command': command, 'speed': speed} class LBCImageDataset(Dataset): @@ -196,7 +190,7 @@ def __init__( self._batch_read_number = batch_read_number self._batch_aug = batch_aug - print ("augment with ", augment_strategy) + print("augment with ", augment_strategy) if augment_strategy is not None and augment_strategy != 'None': self.augmenter = getattr(augmenter, augment_strategy) else: @@ -240,7 +234,9 @@ def __getitem__(self, index) -> Any: rgb_image = read_image(png_file).reshape(160, 384, 3) if self.augmenter: - rgb_images = [self.augmenter(self._batch_read_number).augment_image(rgb_image) for i in range(self._batch_aug)] + rgb_images = [ + self.augmenter(self._batch_read_number).augment_image(rgb_image) for i in range(self._batch_aug) + ] else: rgb_images = [rgb_image for i in range(self._batch_aug)] diff --git a/core/policy/lbc_policy.py b/core/policy/lbc_policy.py index f6f8a939..c147c80d 100644 --- a/core/policy/lbc_policy.py +++ b/core/policy/lbc_policy.py @@ -36,7 +36,7 @@ class LBCBirdviewPolicy(BaseCarlaPolicy): config = dict( cuda=True, model=dict(), - learn=dict(loss='l1',), + learn=dict(loss='l1', ), steer_points=None, pid=None, gap=5,

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