From c61954653de692285ea4c345bf601522a84301e9 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 13 Oct 2022 11:57:16 +0200 Subject: [PATCH 01/61] Use Database for DataGeneration in non remote mode. --- src/AsyncSocket/AbstractEnvironment.py | 2 +- src/Database/BaseDataset.py | 252 +++++++++++++++++++++++ src/Database/BaseDatasetConfig.py | 96 +++++++++ src/Database/__init__.py | 0 src/Environment/BaseEnvironment.py | 66 ++++-- src/Environment/BaseEnvironmentConfig.py | 5 + src/Manager/DataManager.py | 155 +++++++------- src/Manager/DatabaseManager.py | 164 +++++++++++++++ src/Manager/DatasetManager.py | 2 +- src/Manager/EnvironmentManager.py | 101 +++------ src/Manager/Manager.py | 22 +- src/Manager/NetworkManager.py | 2 +- src/Manager/__init__.py | 6 +- src/Pipelines/BaseDataGenerator.py | 100 +++++---- src/Pipelines/BasePipeline.py | 121 ++++++----- src/Utils/configs.py | 33 +++ src/Utils/path.py | 79 +++++++ src/Utils/pathUtils.py | 75 ------- 18 files changed, 915 insertions(+), 366 deletions(-) create mode 100644 src/Database/BaseDataset.py create mode 100644 src/Database/BaseDatasetConfig.py create mode 100644 src/Database/__init__.py create mode 100644 src/Manager/DatabaseManager.py create mode 100644 src/Utils/configs.py create mode 100644 src/Utils/path.py delete mode 100644 src/Utils/pathUtils.py diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index c8519105..63fdba35 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -31,7 +31,7 @@ def __init__(self, self.input: ndarray = array([]) self.output: ndarray = array([]) self.loss_data: Any = None - self.compute_essential_data: bool = True + self.compute_training_data: bool = True # Dataset data variables self.sample_in: Optional[ndarray] = None diff --git a/src/Database/BaseDataset.py b/src/Database/BaseDataset.py new file mode 100644 index 00000000..4b155513 --- /dev/null +++ b/src/Database/BaseDataset.py @@ -0,0 +1,252 @@ +from typing import Dict, List, Optional, Tuple +from numpy import array, ndarray, concatenate, save, arange +from numpy.random import shuffle +from collections import namedtuple + + +class BaseDataset: + """ + | BaseDataset is a dataset class to store any data from a BaseEnvironment or from files. + | Given data is split into input data and output data. + | Saving data results in multiple partitions of input and output data. + + :param namedtuple config: Namedtuple which contains BaseDataset parameters + """ + + def __init__(self, config: namedtuple): + + self.name: str = self.__class__.__name__ + + # Data fields containers + self.data_type = ndarray + self.fields: List[str] = ['input', 'output'] + self.data: Dict[str, ndarray] = {'input': array([]), 'output': array([])} + self.shape: Dict[str, Optional[List[int]]] = {'input': None, 'output': None} + + # Indexing + self.shuffle_pattern: Optional[List[int]] = None + self.current_sample: int = 0 + + # Dataset memory + self.max_size: int = config.max_file_size + self.batch_per_field: Dict[str, int] = {field: 0 for field in ['input', 'output']} + self.__empty: bool = True + + @property + def nb_samples(self) -> int: + """ + | Property returning the current number of samples + + :return: The current number of samples in all partitions + """ + + return max([len(self.data[field]) for field in self.fields]) + + def is_empty(self) -> bool: + """ + | Check if the fields of the dataset are empty. A field is considered as non-empty if it is filled with another + sample. + + :return: The Dataset is empty or not + """ + + # The empty flag is set to False once the Dataset is considered as non-empty + if not self.__empty: + return False + # Check each registered data field + for field in self.fields: + # Dataset is considered as non-empty if a field is filled with another sample + if self.batch_per_field[field] > 1: + self.__empty = False + return False + # If all field are considered as non-empty then the Dataset is empty + return True + + def init_data_size(self, field: str, shape: List[int]) -> None: + """ + | Store the original shape of data. Reshape data containers. + + :param str field: Data field name + :param List[int] shape: Shape of the corresponding tensor + """ + + # Store the original data shape + self.shape[field] = shape + # Reshape the data container + self.data[field] = array([]).reshape((0, *shape)) + + def get_data_shape(self, field: str) -> List[int]: + """ + | Returns the data shape of field. + + :param str field: Data field name + :return: Data shape for field + """ + + return self.shape[field] + + def init_additional_field(self, field: str, shape: List[int]) -> None: + """ + | Register a new data field. + + :param str field: Name of the data field + :param List[int] shape: Data shape + """ + + # Register the data field + self.fields.append(field) + # Init the number of adds in the field + self.batch_per_field[field] = 0 + # Init the field shape + self.init_data_size(field, shape) + + def empty(self) -> None: + """ + | Empty the dataset. + """ + + # Reinit each data container + for field in self.fields: + self.data[field] = array([]) if self.shape[field] is None else array([]).reshape((0, *self.shape[field])) + # Reinit indexing variables + self.shuffle_pattern = None + self.current_sample = 0 + self.batch_per_field = {field: 0 for field in self.fields} + self.__empty = True + + def memory_size(self, field: Optional[str] = None) -> int: + """ + | Return the actual memory size of the dataset if field is None. Otherwise, return the actual memory size of the + field. + + :param Optional[str] field: Name of the data field + :return: Size in bytes of the current dataset. + """ + + # Return the total memory size + if field is None: + return sum([self.data[field].nbytes for field in self.fields]) + # Return the memory size for the specified field + return self.data[field].nbytes + + def check_data(self, field: str, data: ndarray) -> None: + """ + | Check if the data is a numpy array. + + :param str field: Values at 'input' or anything else. Define if the associated shape is correspond to input + shape or output one. + :param ndarray data: New data + """ + + if type(data) != self.data_type: + raise TypeError(f"[{self.name}] Wrong data type in field '{field}': numpy array required, got {type(data)}") + + def add(self, field: str, data: ndarray, partition_file: Optional[str] = None) -> None: + """ + | Add data to the dataset. + + :param str field: Name of the data field + :param ndarray data: New data as batch of samples + :param Optional[str] partition_file: Path to the file in which to write the data + """ + + # Check data type + self.check_data(field, data) + + # Check if field is registered + if field not in self.fields: + # Fields can be register only if Dataset is empty + if not self.is_empty(): + raise ValueError(f"[{self.name}] A new field {field} tries to be created as Dataset is non empty. This " + f"will lead to a different number of sample for each field of the dataset.") + # Add new field if not registered + self.init_additional_field(field, data[0].shape) + + # Check data size initialization + if self.shape[field] is None: + self.init_data_size(field, data[0].shape) + + # Add batched samples + self.data[field] = concatenate((self.data[field], data)) + # Save in partition + if partition_file is not None: + self.save(field, partition_file) + + # Update sample indexing in dataset + self.batch_per_field[field] += 1 + self.current_sample = max([len(self.data[f]) for f in self.fields]) + + def save(self, field: str, file: str) -> None: + """ + | Save the corresponding field of the Dataset. + + :param str field: Name of the data field + :param str file: Path to the file in which to write the data + """ + + save(file, self.data[field]) + + def set(self, field: str, data: ndarray) -> None: + """ + | Set a full field of the dataset. + + :param str field: Name of the data field + :param ndarray data: New data as batch of samples + """ + + # Check data type + self.check_data(field, data) + + # Check if field is registered + if field not in self.fields: + # Add new field if not registered + self.init_additional_field(field, data[0].shape) + + # Check data size initialization + if self.shape[field] is None: + self.init_data_size(field, data[0].shape) + + # Set the full field + self.data[field] = data + + # Update sample indexing in dataset + self.__empty = False + self.current_sample = 0 + + def get(self, field: str, idx_begin: int, idx_end: int) -> ndarray: + """ + | Get a batch of data in 'field' container. + + :param str field: Name of the data field + :param int idx_begin: Index of the first sample + :param int idx_end: Index of the last sample + :return: Batch of data from 'field' + """ + + indices = slice(idx_begin, idx_end) if self.shuffle_pattern is None else self.shuffle_pattern[idx_begin:idx_end] + return self.data[field][indices] + + def shuffle(self) -> None: + """ + | Define a random shuffle pattern. + """ + + # Nothing to shuffle if Dataset is empty + if self.is_empty(): + return + # Generate a shuffle pattern + self.shuffle_pattern = arange(self.nb_samples) + shuffle(self.shuffle_pattern) + + def __str__(self) -> str: + """ + :return: String containing information about the BaseDatasetConfig object + """ + + description = "\n" + description += f" {self.name}\n" + description += f" Max size: {self.max_size}\n" + description += f" Data fields: {self.fields}" + for field in self.fields: + description += f" {field} shape: {self.shape[field]}" + return description diff --git a/src/Database/BaseDatasetConfig.py b/src/Database/BaseDatasetConfig.py new file mode 100644 index 00000000..d1bf8bfb --- /dev/null +++ b/src/Database/BaseDatasetConfig.py @@ -0,0 +1,96 @@ +from typing import Type, Optional +from os.path import isdir, sep, join + +from DeepPhysX.Core.Database.BaseDataset import BaseDataset +from DeepPhysX.Core.Utils.configs import make_config, namedtuple + + +class BaseDatasetConfig: + + def __init__(self, + dataset_class: Type[BaseDataset] = BaseDataset, + existing_dir: Optional[str] = None, + mode: Optional[str] = None, + max_file_size: Optional[float] = None, + shuffle: bool = True, + normalize: bool = True, + recompute_normalization: bool = False): + """ + Configuration class to parameterize the Dataset and its Manager. + + :param dataset_class: BaseDataset class from which an instance will be created. + :param existing_dir: Path to an existing Dataset repository. + :param mode: Specify the Dataset mode that should be used between 'training', 'validation' and 'running'. + :param max_file_size: Maximum size (in Gb) of a single dataset file. + :param shuffle: Specify if the Dataset should be shuffled when a batch is taken. + :param normalize: If True, the data will be normalized using standard score. + :param recompute_normalization: If True, compute the normalization coefficients. + """ + + self.name: str = self.__class__.__name__ + + # Check directory variable + if existing_dir is not None: + if type(existing_dir) != str: + raise TypeError(f"[{self.name}] The given 'existing_dir'={existing_dir} must be a str.") + if not isdir(existing_dir): + raise ValueError(f"[{self.name}] The given 'existing_dir'={existing_dir} does not exist.") + if len(existing_dir.split(sep)) > 1 and existing_dir.split(sep)[-1] == 'dataset': + existing_dir = join(*existing_dir.split(sep)[:-1]) + + # Check storage variables + if mode is not None: + if type(mode) != str: + raise TypeError(f"[{self.name}] The given 'mode'={mode} must be a str.") + if mode.lower() not in (available_modes := ['training', 'validation', 'running']): + raise ValueError(f"[{self.name}] The given 'mode'={mode} must be in {available_modes}.") + if max_file_size is not None: + if type(max_file_size) not in [int, float]: + raise TypeError(f"[{self.name}] The given 'max_file_size'={max_file_size} must be a float.") + max_file_size = int(max_file_size * 1e9) if max_file_size > 0 else None + if type(shuffle) != bool: + raise TypeError(f"[{self.name}] The given 'shuffle'={shuffle} must be a bool.") + if type(normalize) != bool: + raise TypeError(f"[{self.name}] The given 'normalize'={normalize} must be a bool.") + if type(recompute_normalization) != bool: + raise TypeError(f"[{self.name}] The given 'recompute_normalization'={recompute_normalization} must be a " + f"bool.") + + # BaseDataset parameterization + self.dataset_class: Type[BaseDataset] = dataset_class + self.dataset_config: namedtuple = make_config(configuration_object=self, + configuration_name='dataset_config', + max_file_size=max_file_size) + + # DatasetManager parameterization + self.existing_dir: Optional[str] = existing_dir + self.mode: Optional[str] = mode + self.max_file_size: int = max_file_size + self.shuffle: bool = shuffle + self.normalize: bool = normalize + self.recompute_normalization: bool = recompute_normalization + + def create_dataset(self) -> BaseDataset: + """ + Create an instance of BaseDataset with given parameters. + + :return: BaseDataset instance. + """ + + dataset = self.dataset_class(config=self.dataset_config) + if not isinstance(dataset, BaseDataset): + raise TypeError(f"[{self.name}] The given 'dataset_class'={self.dataset_class} must be a BaseDataset.") + return dataset + + def __str__(self) -> str: + + description = "\n" + description += f"{self.name}\n" + description += f" Dataset Class: {self.dataset_class.__name__}\n" + description += f" Existing directory: {False if self.existing_dir is None else self.existing_dir}\n" + description += f" Mode: {self.mode}\n" + description += f" Max size: {self.dataset_config.max_size}\n" + description += f" Shuffle: {self.shuffle}\n" + description += f" Normalize: {self.normalize}\n" + description += f" Recompute normalization: {self.recompute_normalization}\n" + return description diff --git a/src/Database/__init__.py b/src/Database/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index ff3bf23f..43cacc69 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Dict, Union, Tuple +from typing import Any, Optional, Dict, Union, Tuple, List from numpy import array, ndarray from SSD.Core.Storage.Database import Database @@ -16,6 +16,7 @@ def __init__(self, number_of_instances: int = 1, as_tcp_ip_client: bool = True, environment_manager: Optional[Any] = None, + data_db: Optional[Union[Database, Tuple[str, str]]] = None, visu_db: Optional[Union[Database, Tuple[str, str]]] = None): """ BaseEnvironment is an environment class to compute simulated data for the network and its optimization process. @@ -37,8 +38,10 @@ def __init__(self, port=port) # Input and output to give to the network - self.input: ndarray = array([]) - self.output: ndarray = array([]) + self.__training_data: Dict[str, ndarray] = {} + self.__additional_data: Dict[str, ndarray] = {} + self.__first_add: List[bool] = [True, True] + # Variables to store samples from Dataset self.sample_in: Optional[ndarray] = None self.sample_out: Optional[ndarray] = None @@ -47,6 +50,17 @@ def __init__(self, # Manager if the Environment is not a TcpIpClient self.environment_manager: Any = environment_manager + # Connect the Environment to the data Database + self.database: Optional[Database] = None + if data_db is not None: + if type(data_db) == list: + pass + else: + self.database = data_db + self.database.create_fields(table_name='Training', + fields=('env_id', int)) + + # Connect the Factory to the visualization Database self.factory: Optional[VedoFactory] = None if visu_db is not None: if type(visu_db) == list: @@ -100,9 +114,17 @@ def send_parameters(self) -> Dict[Any, Any]: return {} + def init_database(self) -> None: + """ + Define the fields of the training dataset. + Required. + """ + + raise NotImplementedError + def init_visualization(self) -> None: """ - Define the visualization objects to send to he Visualizer. + Define the visualization objects to send to the Visualizer. Not mandatory. :return: Dictionary of visualization data @@ -160,19 +182,39 @@ def close(self) -> None: ########################################################################################## def set_training_data(self, - input_array: ndarray, - output_array: ndarray) -> None: + **kwargs) -> None: """ Set the training data to send to the TcpIpServer or the EnvironmentManager. - - :param input_array: Network input - :param output_array: Network expected output """ + # Check kwargs + if self.__first_add[0]: + self.__first_add[0] = False + required_fields = list(set(self.database.get_fields(table_name='Training')) - {'id', 'env_id'}) + for field in kwargs.keys(): + if field not in required_fields: + raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." + f"Required fields are {required_fields}.") + for field in required_fields: + if field not in kwargs.keys(): + raise ValueError(f"[{self.name}] The field '{field}' was not defined in training data." + f"Required fields are {required_fields}.") + # Training data is set if the Environment can compute data - if self.compute_essential_data: - self.input = input_array - self.output = output_array + if self.compute_training_data: + self.__training_data = kwargs + self.__training_data['env_id'] = self.instance_id + + def _reset_training_data(self) -> None: + self.__training_data = {} + + def _send_data(self) -> None: + line_id = self.database.add_data(table_name='Training', + data=self.__training_data) + self.database.add_data(table_name='Sync', + data={'env': line_id}) + self.database.add_data(table_name='Additional', + data=self.__additional_data) def set_loss_data(self, loss_data: Any) -> None: diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index c9da9ea5..b7595f4a 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -5,6 +5,8 @@ from subprocess import run as subprocessRun from sys import modules, executable +from SSD.Core.Storage.Database import Database + from DeepPhysX.Core.AsyncSocket.TcpIpServer import TcpIpServer from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer @@ -172,6 +174,7 @@ def start_client(self, def create_environment(self, environment_manager: Any, + data_db: Optional[Database] = None, visu_db: Optional[Any] = None) -> BaseEnvironment: """ Create an Environment that will not be a TcpIpObject. @@ -184,11 +187,13 @@ def create_environment(self, # Create instance environment = self.environment_class(environment_manager=environment_manager, as_tcp_ip_client=False, + data_db=data_db, visu_db=visu_db) # Create & Init Environment environment.recv_parameters(self.param_dict) environment.create() environment.init() + environment.init_database() environment.init_visualization() return environment diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 2e2708a2..cb69d6e1 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -3,107 +3,96 @@ from numpy import ndarray from json import load as json_load -from DeepPhysX.Core.Manager.DatasetManager import DatasetManager +from DeepPhysX.Core.Manager.DatabaseManager import DatasetManager from DeepPhysX.Core.Manager.EnvironmentManager import EnvironmentManager from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig class DataManager: def __init__(self, - dataset_config: BaseDatasetConfig, - environment_config: BaseEnvironmentConfig, - session: str, + dataset_config: Optional[BaseDatasetConfig] = None, + environment_config: Optional[BaseEnvironmentConfig] = None, manager: Optional[Any] = None, + session: str = 'sessions/default', new_session: bool = True, - training: bool = True, - offline: bool = False, - store_data: bool = True, + is_training: bool = True, + produce_data: bool = True, batch_size: int = 1): """ - DataManager deals with the generation of input / output tensors. His job is to call get_data on either the - DatasetManager or the EnvironmentManager according to the context. + DataManager deals with the generation, storage and loading of training data. + A batch is given with a call to 'get_data' on either the DatasetManager or the EnvironmentManager according to + the context. - :param dataset_config: Specialisation containing the parameters of the dataset manager - :param environment_config: Specialisation containing the parameters of the environment manager + :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param environment_config: Specialisation containing the parameters of the environment manager. + :param manager: Manager that handle the DataManager :param session: Path to the session directory. - :param manager: Manager that handle The DataManager - :param new_session: Define the creation of new directories to store data - :param training: True if this session is a network training - :param offline: True if the session is done offline - :param store_data: Format {\'in\': bool, \'out\': bool} save the tensor when bool is True - :param batch_size: Number of samples in a batch + :param bool new_session: Flag that indicates whether if the session is new + :param is_training: Flag that indicates whether if this session is training a Network. + :param produce_data: Flag that indicates whether if this session is producing data. + :param int batch_size: Number of samples in a batch """ self.name: str = self.__class__.__name__ - # Manager references + # Managers variables self.manager: Optional[Any] = manager self.dataset_manager: Optional[DatasetManager] = None self.environment_manager: Optional[EnvironmentManager] = None - # DataManager parameters - self.is_training: bool = training - self.allow_dataset_fetch: bool = True - self.data: Optional[Dict[str, ndarray]] = None - # Data normalization coefficients (default: mean = 0, standard deviation = 1) self.normalization: Dict[str, List[float]] = {'input': [0., 1.], 'output': [0., 1.]} - # If normalization flag is set to True, try to load existing coefficients - if dataset_config is not None and dataset_config.normalize: - json_file_path = None - # Existing Dataset in the current session - if os.path.exists(os.path.join(session, 'dataset')): - json_file_path = os.path.join(session, 'dataset', 'dataset.json') - # Dataset provided by config - elif dataset_config.dataset_dir is not None: - dataset_dir = dataset_config.dataset_dir - if dataset_dir[-1] != "/": - dataset_dir += "/" - if dataset_dir[-8:] != "dataset/": - dataset_dir += "dataset/" - if os.path.exists(dataset_dir): - json_file_path = os.path.join(dataset_dir, 'dataset.json') - # If Dataset exists then a json file is associated - if json_file_path is not None: - with open(json_file_path) as json_file: - json_dict = json_load(json_file) - # Get the normalization coefficients - for field in self.normalization.keys(): - if field in json_dict['normalization']: - self.normalization[field] = json_dict['normalization'][field] - - # Training - if self.is_training: - # Always create a dataset_manager for training - create_dataset = True - # Create an environment if prediction must be applied else ask DatasetManager - create_environment = False - if environment_config is not None: - create_environment = None if not environment_config.use_dataset_in_environment else True - # Prediction - else: - # Create an environment for prediction if an environment config is provided - create_environment = environment_config is not None - # Create a dataset if data will be stored from environment during prediction - create_dataset = store_data - # Create a dataset also if data should be loaded from any partition - create_dataset = create_dataset or (dataset_config is not None and dataset_config.dataset_dir is not None) - - # Create dataset if required + # # If normalization flag is set to True, try to load existing coefficients + # if dataset_config is not None and dataset_config.normalize: + # json_file_path = None + # # Existing Dataset in the current session + # if os.path.exists(os.path.join(session, 'dataset')): + # json_file_path = os.path.join(session, 'dataset', 'dataset.json') + # # Dataset provided by config + # elif dataset_config.existing_dir is not None: + # dataset_dir = dataset_config.existing_dir + # if dataset_dir[-1] != "/": + # dataset_dir += "/" + # if dataset_dir[-8:] != "dataset/": + # dataset_dir += "dataset/" + # if os.path.exists(dataset_dir): + # json_file_path = os.path.join(dataset_dir, 'dataset.json') + # # If Dataset exists then a json file is associated + # if json_file_path is not None: + # with open(json_file_path) as json_file: + # json_dict = json_load(json_file) + # # Get the normalization coefficients + # for field in self.normalization.keys(): + # if field in json_dict['normalization']: + # self.normalization[field] = json_dict['normalization'][field] + + # Create a DatasetManager if required + create_dataset = is_training or produce_data + database = None if create_dataset: - self.dataset_manager = DatasetManager(data_manager=self, dataset_config=dataset_config, - session=session, new_session=new_session, training=self.is_training, - offline=offline, store_data=store_data) - # Create environment if required - if create_environment is None: # If None then the dataset_manager exists - create_environment = self.dataset_manager.new_dataset() + self.dataset_manager = DatasetManager(dataset_config=dataset_config, + session=session, + data_manager=self, + new_session=new_session, + is_training=is_training, + produce_data=produce_data) + database = self.dataset_manager.database + + # Create an EnvironmentManager if required + create_environment = produce_data or environment_config is not None if create_environment: - self.environment_manager = EnvironmentManager(data_manager=self, environment_config=environment_config, - session=session, batch_size=batch_size, - training=self.is_training) + self.environment_manager = EnvironmentManager(environment_config=environment_config, + data_manager=self, + session=session, + data_db=database, + batch_size=batch_size) + + # DataManager variables + self.produce_data = produce_data + self.data: Optional[Dict[str, ndarray]] = None def get_manager(self) -> Any: """ @@ -117,7 +106,7 @@ def get_manager(self) -> Any: def get_data(self, epoch: int = 0, batch_size: int = 1, - animate: bool = True) -> Dict[str, ndarray]: + animate: bool = True) -> None: """ Fetch data from the EnvironmentManager or the DatasetManager according to the context. @@ -128,14 +117,11 @@ def get_data(self, """ # Training - if self.is_training: - data = None + if self.produce_data: # Get data from environment if used and if the data should be created at this epoch - if data is None and self.environment_manager is not None and \ - (epoch == 0 or self.environment_manager.always_create_data) and self.dataset_manager.new_dataset(): - self.allow_dataset_fetch = False - data = self.environment_manager.get_data(animate=animate, get_inputs=True, get_outputs=True) - self.dataset_manager.add_data(data) + if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data): + self.environment_manager.get_data(animate=animate) + # self.dataset_manager.add_data(data) # Force data from the dataset else: data = self.dataset_manager.get_data(batch_size=batch_size, get_inputs=True, get_outputs=True) @@ -174,9 +160,6 @@ def get_data(self, if self.dataset_manager is not None: self.dataset_manager.add_data(data) - self.data = data - return data - def get_prediction(self, network_input: ndarray) -> ndarray: """ diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py new file mode 100644 index 00000000..5719a398 --- /dev/null +++ b/src/Manager/DatabaseManager.py @@ -0,0 +1,164 @@ +from typing import Any, Dict, Tuple, List, Optional, Union +from os.path import isfile, isdir, abspath, join +from os import listdir, symlink, sep +from json import dump as json_dump +from json import load as json_load +from numpy import load, squeeze, ndarray, concatenate, float64 + +from SSD.Core.Storage.Database import Database + +from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDataset import BaseDataset +from DeepPhysX.Core.Utils.path import create_dir, copy_dir +from DeepPhysX.Core.Utils.jsonUtils import CustomJSONEncoder + + +class DatasetManager: + + def __init__(self, + dataset_config: Optional[BaseDatasetConfig] = None, + session: str = 'sessions/default', + data_manager: Optional[Any] = None, + new_session: bool = True, + is_training: bool = True, + produce_data: bool = True): + """ + DatasetManager handle all operations with input / output files. Allows saving and read tensors from files. + + :param dataset_config: Specialisation containing the parameters of the dataset manager + :param data_manager: DataManager that handles the DatasetManager + :param new_session: Define the creation of new directories to store data + :param is_training: True if the session is done offline + :param produce_data: True if this session is a network training + """ + + self.name: str = self.__class__.__name__ + + # Manager variables + dataset_config = BaseDatasetConfig() if dataset_config is None else dataset_config + self.data_manager: Optional[Any] = data_manager + self.dataset_dir: str = join(session, 'dataset') + self.database: Optional[Database] = None + + # Dataset parameters + self.max_file_size: int = dataset_config.max_file_size + self.shuffle: bool = dataset_config.shuffle + self.produce_data = produce_data + self.normalize: bool = dataset_config.normalize + self.recompute_normalization: bool = dataset_config.recompute_normalization + + # Dataset modes + self.modes: List[str] = ['training', 'validation', 'running'] + self.mode: str = 'training' if produce_data else 'running' + self.mode = self.mode if dataset_config.mode is None else dataset_config.mode + + # Dataset partitions + session_name = session.split(sep)[-1] + self.partition_template: Dict[str, str] = {mode: f'{session_name}_{mode}_' + '{}' for mode in self.modes} + self.partitions: Dict[str, List[str]] = {mode: [] for mode in self.modes} + self.partition_index: Dict[str, int] = {mode: 0 for mode in self.modes} + + # Dataset json file + self.json_default: Dict[str, Dict[str, Any]] = {'data_shape': {}, + 'nb_samples': {mode: [] for mode in self.modes}, + 'partitions': {mode: [] for mode in self.modes}, + 'normalization': {}} + self.json_content: Dict[str, Dict[str, Any]] = self.json_default.copy() + + # Produce training data + if produce_data: + # Produce training data in a new session + if new_session: + # Produce training data in a new session from scratch + # --> Create a new '/dataset' directory + if dataset_config.existing_dir is None: + create_dir(session_dir=session, session_name='dataset') + self.init_directory() + # Produce training data in a new session from an existing Dataset + # --> Copy the 'existing_dir/dataset' directory then load the 'session/dataset' directory + else: + copy_dir(src_dir=dataset_config.existing_dir, dest_dir=session, sub_folders='dataset') + self.load_directory() + # Produce training data in an existing session + # --> Load the 'session/dataset' directory + else: + self.load_directory() + # Load training data + else: + # Load training data in a new session + # --> Link to the 'existing_dir/dataset' directory the load the 'session/dataset' directory + if new_session: + symlink(src=join(dataset_config.existing_dir, 'dataset'), + dst=join(session, 'dataset')) + self.load_directory() + # Load training data in an existing session + # --> Load the 'session/dataset' directory + else: + self.load_directory() + + def init_directory(self): + """ + + """ + + partition_path = self.partition_template[self.mode].format(self.partition_index[self.mode]) + self.database = Database(database_dir=self.dataset_dir, + database_name=partition_path).new() + self.database.create_table(table_name='Sync', + storing_table=False, + fields=[('env', int), ('net', int)]) + self.database.create_table(table_name='Training') + self.database.create_table(table_name='Additional') + self.partition_index[self.mode] += 1 + + def load_directory(self): + + # 1. Check the directory existence to prevent bugs + if not isdir(self.dataset_dir): + raise Warning(f"[{self.name}] Impossible to load Dataset from {self.dataset_dir}.") + + # 2. Get the .json description file + json_found = False + if isfile(join(self.dataset_dir, 'dataset.json')): + json_found = True + with open(join(self.dataset_dir, 'dataset.json')) as json_file: + self.json_content = json_load(json_file) + + # 3. Load partitions for each mode + self.partitions = self.json_content['partitions'] if json_found else self.search_partitions() + + # 4. Update json file if not found + if not json_found or self.json_content == self.json_default: + self.search_partitions_info() + self.update_json(update_partitions_lists=True) + if self.recompute_normalization or ( + self.normalize and self.json_content['normalization'] == self.json_default['normalization']): + self.update_json(update_normalization=True) + + # 5. Load data from partitions + self.load_partitions() + + def search_partitions(self): + """ + + """ + + raw_partitions = {mode: [f for f in listdir(self.dataset_dir) if isfile(join(self.dataset_dir, f)) + and f.endswith('.db') and f.__contains__(mode)] for mode in self.modes} + return {mode: sorted(raw_partitions[mode]) for mode in self.modes} + + def search_partitions_info(self): + pass + + def load_partitions(self): + pass + + def update_json(self, update_shapes: bool = False, update_nb_samples: bool = False, + update_partitions_lists: bool = False, update_normalization: bool = False) -> None: + pass + + def close(self): + + if self.normalize and self.produce_data: + self.update_json(update_normalization=True) + self.database.close() diff --git a/src/Manager/DatasetManager.py b/src/Manager/DatasetManager.py index 048512a9..6860de5f 100644 --- a/src/Manager/DatasetManager.py +++ b/src/Manager/DatasetManager.py @@ -7,7 +7,7 @@ from numpy import load, squeeze, ndarray, concatenate, float64 from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig -from DeepPhysX.Core.Utils.pathUtils import create_dir +from DeepPhysX.Core.Utils.path import create_dir from DeepPhysX.Core.Utils.jsonUtils import CustomJSONEncoder diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index f8c93c5e..af07b971 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -4,6 +4,8 @@ from copy import copy from os.path import join +from SSD.Core.Storage.Database import Database + from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig, TcpIpServer, BaseEnvironment from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer @@ -11,11 +13,11 @@ class EnvironmentManager: def __init__(self, - environment_config: BaseEnvironmentConfig, - session: str, + environment_config: Optional[BaseEnvironmentConfig] = None, data_manager: Any = None, - batch_size: int = 1, - training: bool = True): + session: str = 'sessions/default', + data_db: Optional[Database] = None, + batch_size: int = 1): """ Deals with the online generation of data for both training and running of the neural networks. @@ -23,7 +25,6 @@ def __init__(self, :param session: Path to the session directory. :param data_manager: DataManager that handles the EnvironmentManager. :param batch_size: Number of samples in a batch of data. - :param training: True if this session is a network training. """ self.name: str = self.__class__.__name__ @@ -37,7 +38,6 @@ def __init__(self, self.use_dataset_in_environment: bool = environment_config.use_dataset_in_environment self.simulations_per_step: int = environment_config.simulations_per_step self.max_wrong_samples_per_step: int = environment_config.max_wrong_samples_per_step - self.train: bool = training self.dataset_batch: Optional[Dict[str, Dict[int, Any]]] = None # Create the Visualizer @@ -59,6 +59,7 @@ def __init__(self, visu_db=visu_db.get_path()) else: self.environment = environment_config.create_environment(environment_manager=self, + data_db=data_db, visu_db=visu_db) # Define get_data and dispatch methods @@ -110,87 +111,45 @@ def get_data_from_server(self, return training_data def get_data_from_environment(self, - get_inputs: bool = True, - get_outputs: bool = True, - animate: bool = True) -> Dict[str, Union[ndarray, dict]]: + animate: bool = True) -> None: """ Compute a batch of data directly from Environment. - :param get_inputs: If True, compute and return input. - :param get_outputs: If True, compute and return output. :param animate: If True, triggers an environment step. - :return: Dictionary containing all labeled data sent by the clients in their own dictionary + in and out key - corresponding to the batch. """ - # Init training data container, define production conditions - input_condition = lambda x: len(x) < self.batch_size if get_inputs else lambda _: False - output_condition = lambda x: len(x) < self.batch_size if get_outputs else lambda _: False - training_data = {'input': [], 'output': []} - # 1. Produce batch while batch size is not complete - while input_condition(training_data['input']) and output_condition(training_data['output']): - - # 1.1 Send a sample if a batch from dataset is given - if self.dataset_batch is not None: - # Extract a sample from dataset batch: input - self.environment.sample_in = self.dataset_batch['input'][0] - self.dataset_batch['input'] = self.dataset_batch['input'][1:] - # Extract a sample from dataset batch: output - self.environment.sample_out = self.dataset_batch['output'][0] - self.dataset_batch['output'] = self.dataset_batch['output'][1:] - # Extract a sample from dataset batch: additional fields - additional_fields = {} - if 'additional_fields' in self.dataset_batch: - for field in self.dataset_batch['additional_fields']: - additional_fields[field] = self.dataset_batch['additional_fields'][field][0] - self.dataset_batch['additional_fields'][field] = self.dataset_batch['additional_fields'][field][1:] - self.environment.additional_fields = additional_fields + nb_sample = 0 + while nb_sample < self.batch_size: + + # # 1.1 Send a sample if a batch from dataset is given + # if self.dataset_batch is not None: + # # Extract a sample from dataset batch: input + # self.environment.sample_in = self.dataset_batch['input'][0] + # self.dataset_batch['input'] = self.dataset_batch['input'][1:] + # # Extract a sample from dataset batch: output + # self.environment.sample_out = self.dataset_batch['output'][0] + # self.dataset_batch['output'] = self.dataset_batch['output'][1:] + # # Extract a sample from dataset batch: additional fields + # additional_fields = {} + # if 'additional_fields' in self.dataset_batch: + # for field in self.dataset_batch['additional_fields']: + # additional_fields[field] = self.dataset_batch['additional_fields'][field][0] + # self.dataset_batch['additional_fields'][field] = self.dataset_batch['additional_fields'][field][1:] + # self.environment.additional_fields = additional_fields # 1.2 Run the defined number of step if animate: for current_step in range(self.simulations_per_step): # Sub-steps do not produce data - self.environment.compute_essential_data = current_step == self.simulations_per_step - 1 + self.environment.compute_training_data = current_step == self.simulations_per_step - 1 async_run(self.environment.step()) # 1.3 Add the produced sample to the batch if the sample is validated if self.environment.check_sample(): - # Network's input - if get_inputs: - training_data['input'].append(self.environment.input) - self.environment.input = array([]) - # Network's output - if get_outputs: - training_data['output'].append(self.environment.output) - self.environment.output = array([]) - # Check if there is loss data - if self.environment.loss_data: - if 'loss' not in training_data: - training_data['loss'] = [] - training_data['loss'].append(self.environment.loss_data) - self.environment.loss_data = None - # Check if there is additional dataset fields - if self.environment.additional_fields != {}: - if 'additional_fields' not in training_data: - training_data['additional_fields'] = {} - for field in self.environment.additional_fields: - if field not in training_data['additional_fields']: - training_data['additional_fields'][field] = [] - training_data['additional_fields'][field].append(self.environment.additional_fields[field]) - self.environment.additional_fields = {} - - # 2. Convert data in ndarray - for key in training_data: - # If key does not contain a dict, convert value directly - if key != 'additional_fields': - training_data[key] = array(training_data[key]) - # If key contains a dict, convert item by item - else: - for field in training_data[key]: - training_data[key][field] = array(training_data[key][field]) - - return training_data + nb_sample += 1 + self.environment._send_data() + self.environment._reset_training_data() def dispatch_batch_to_server(self, batch: Dict[str, Union[ndarray, dict]], diff --git a/src/Manager/Manager.py b/src/Manager/Manager.py index fbbfb1a3..2a400e70 100644 --- a/src/Manager/Manager.py +++ b/src/Manager/Manager.py @@ -10,21 +10,21 @@ from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Utils.pathUtils import get_first_caller, create_dir +from DeepPhysX.Core.Utils.path import get_first_caller, create_dir class Manager: def __init__(self, - network_config: BaseNetworkConfig, - dataset_config: BaseDatasetConfig, - environment_config: BaseEnvironmentConfig, + network_config: Optional[BaseNetworkConfig] = None, + dataset_config: Optional[BaseDatasetConfig] = None, + environment_config: Optional[BaseEnvironmentConfig] = None, pipeline: Optional[Any] = None, session_dir: Optional[str] = None, session_name: str = 'DPX_default', new_session: bool = True, - training: bool = True, - store_data: bool = True, + is_training: bool = True, + produce_data: bool = True, batch_size: int = 1): """ Collection of all the specialized managers. Allows for some basic functions call. @@ -34,9 +34,11 @@ def __init__(self, :param dataset_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. :param session_name: Name of the newly created directory if session is not defined. - :param session_dir: Name of the directory in which to write all the necessary data - :param bool new_session: Define the creation of new directories to store data - :param int batch_size: Number of samples in a batch + :param session_dir: Name of the directory in which to write all the necessary data. + :param bool new_session: Flag that indicates whether if the session is new + :param is_training: Flag that indicates whether if this session is training a Network. + :param produce_data: Flag that indicates whether if this session is producing data. + :param int batch_size: Number of samples in a batch. """ self.pipeline: Optional[Any] = pipeline @@ -51,7 +53,7 @@ def __init__(self, self.session: str = osPathJoin(session_dir, session_name) # Trainer: must create a new session to avoid overwriting - if training: + if is_training: # Avoid unwanted overwritten data if new_session: self.session: str = create_dir(self.session, dir_name=session_name) diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index 15006278..52d22f82 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -5,7 +5,7 @@ from numpy import copy, array, ndarray from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Utils.pathUtils import copy_dir, create_dir +from DeepPhysX.Core.Utils.path import copy_dir, create_dir class NetworkManager: diff --git a/src/Manager/__init__.py b/src/Manager/__init__.py index e7481a52..d99a2553 100644 --- a/src/Manager/__init__.py +++ b/src/Manager/__init__.py @@ -5,6 +5,6 @@ exceptions = ['__init__.py', '__pycache__'] modules = [module for module in listdir(package) if module.endswith('.py') and module not in exceptions] __all__ = [] -for module in sorted(modules): - exec(f"from DeepPhysX.Core.Manager.{module[:-3]} import {module[:-3]}") - __all__.append(module[:-3]) +# for module in sorted(modules): +# exec(f"from DeepPhysX.Core.Manager.{module[:-3]} import {module[:-3]}") +# __all__.append(module[:-3]) diff --git a/src/Pipelines/BaseDataGenerator.py b/src/Pipelines/BaseDataGenerator.py index 949aeba4..5ff201e4 100644 --- a/src/Pipelines/BaseDataGenerator.py +++ b/src/Pipelines/BaseDataGenerator.py @@ -1,86 +1,84 @@ -import os.path -from os.path import join as osPathJoin -from os.path import basename +from os.path import join, sep, exists from sys import stdout from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline from DeepPhysX.Core.Manager.DataManager import DataManager -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Utils.progressbar import Progressbar -from DeepPhysX.Core.Utils.pathUtils import create_dir, get_first_caller +from DeepPhysX.Core.Utils.path import get_first_caller, create_dir class BaseDataGenerator(BasePipeline): - """ - | BaseDataGenerator implement a minimalist execute function that simply produce and store data without - training a neural network. - - :param BaseDatasetConfig dataset_config: Specialisation containing the parameters of the dataset manager - :param BaseEnvironmentConfig environment_config: Specialisation containing the parameters of the environment manager - :param str session_name: Name of the newly created directory if session is not defined - :param int nb_batches: Number of batches - :param int batch_size: Size of a batch - :param bool record_input: True if the input must be stored - :param bool record_output: True if the output must be stored - """ def __init__(self, dataset_config: BaseDatasetConfig, environment_config: BaseEnvironmentConfig, - session_name: str = 'default', - nb_batches: int = 0, - batch_size: int = 0, - record_input: bool = True, - record_output: bool = True): + session_dir: str = 'sessions', + session_name: str = 'data_generation', + batch_nb: int = 0, + batch_size: int = 0): + """ + BaseDataGenerator implement the main loop that only produces and stores data (no Network training). + + :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param environment_config: Specialisation containing the parameters of the environment manager. + :param session_dir: Relative path to the directory which contains sessions directories. + :param session_name: Name of the new the session directory. + :param batch_nb: Number of batches to produce. + :param batch_size: Number of samples in a single batch. + """ BasePipeline.__init__(self, dataset_config=dataset_config, environment_config=environment_config, + session_dir=session_dir, session_name=session_name, - pipeline='dataset') + pipeline='data_generation') + + # Define the session repository + root = get_first_caller() + session_dir = join(root, session_dir) - # Init session repository - dataset_dir = dataset_config.dataset_dir - if dataset_dir is not None: - if dataset_dir[-1] == "/": - dataset_dir = dataset_dir[:-1] - if dataset_dir[-8:] == "/dataset": - dataset_dir = dataset_dir[:-8] - if osPathJoin(get_first_caller(), session_name) != osPathJoin(get_first_caller(), dataset_dir): - dataset_dir = None - elif not os.path.exists(osPathJoin(get_first_caller(), dataset_dir)): - dataset_dir = None - if dataset_dir is None: - session_dir = create_dir(osPathJoin(get_first_caller(), session_name), dir_name=session_name) - session_name = (session_name if session_name is not None else basename(session_dir)).split("/")[-1] - else: - session_dir = osPathJoin(get_first_caller(), dataset_dir) - session_name = (session_name if session_name is not None else basename(session_dir)).split("/")[-1] + # Configure 'new_session' flags + # Option 1: existing_dir == None --> new_session = True + # Option 2: existing_dir == session_dir/session_name --> new_session = False + # Option 3: existing_dir != session_dir/session_name --> new_session = True + new_session = True + if dataset_config is not None and dataset_config.existing_dir is not None and \ + join(session_dir, session_name) == join(root, dataset_config.existing_dir): + new_session = False - # Create a DataManager directly - self.data_manager = DataManager(manager=self, - dataset_config=dataset_config, + # Create a new session if required + if new_session: + session_name = create_dir(session_dir=session_dir, + session_name=session_name).split(sep)[-1] + + # Create a DataManager + self.data_manager = DataManager(dataset_config=dataset_config, environment_config=environment_config, - session_name=session_name, - session_dir=session_dir, - new_session=True, - offline=True, - record_data={'input': record_input, 'output': record_output}, + session=join(session_dir, session_name), + new_session=new_session, + is_training=False, + produce_data=True, batch_size=batch_size) - self.nb_batch: int = nb_batches + self.nb_batch: int = batch_nb self.progress_bar = Progressbar(start=0, stop=self.nb_batch, c='orange', title="Data Generation") def execute(self) -> None: """ - | Run the data generation and recording process. + Launch the data generation Pipeline. """ + # Produce each batch of data for i in range(self.nb_batch): + # Produce a batch self.data_manager.get_data() + # Update progress bar stdout.write("\033[K") self.progress_bar.print(counts=i + 1) - # Close manager + + # Close DataManager self.data_manager.close() diff --git a/src/Pipelines/BasePipeline.py b/src/Pipelines/BasePipeline.py index a792bc3d..bf384b9b 100644 --- a/src/Pipelines/BasePipeline.py +++ b/src/Pipelines/BasePipeline.py @@ -1,7 +1,7 @@ -from typing import Dict, Optional, Any, List, Union +from typing import Optional, Any, List, Union from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Manager.Manager import Manager from DeepPhysX.Core.Manager.NetworkManager import NetworkManager @@ -12,60 +12,71 @@ class BasePipeline: - """ - | Base class defining Pipelines common variables. - - :param BaseNetworkConfig network_config: Specialisation containing the parameters of the network manager - :param BaseDatasetConfig dataset_config: Specialisation containing the parameters of the dataset manager - :param BaseEnvironmentConfig environment_config: Specialisation containing the parameters of the environment manager - :param str session_name: Name of the newly created directory if session is not defined - :param Optional[str] session_dir: Name of the directory in which to write all the necessary data - :param Optional[str] pipeline: Values at either 'training' or 'prediction' - """ def __init__(self, network_config: Optional[BaseNetworkConfig] = None, dataset_config: Optional[BaseDatasetConfig] = None, environment_config: Optional[BaseEnvironmentConfig] = None, + session_dir: str = 'sessions', session_name: str = 'default', - session_dir: Optional[str] = None, - pipeline: Optional[str] = None): + pipeline: str = ''): + """ + Pipelines implement the main loop that defines data flow through components (Environment, Dataset, Network...). + + :param network_config: Specialisation containing the parameters of the network manager. + :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param environment_config: Specialisation containing the parameters of the environment manager. + :param session_dir: Name of the directory in which to write all the necessary data. + :param session_name: Name of the newly created directory if session is not defined. + :param pipeline: Name of the Pipeline. + """ self.name: str = self.__class__.__name__ - # Check the arguments + # Check the configurations if network_config is not None and not isinstance(network_config, BaseNetworkConfig): - raise TypeError(f"[{self.name}] The network configuration must be a BaseNetworkConfig") - if environment_config is not None and not isinstance(environment_config, BaseEnvironmentConfig): - raise TypeError(f"[{self.name}] The environment configuration must be a BaseEnvironmentConfig") + raise TypeError(f"[{self.name}] The Network configuration must be a BaseNetworkConfig object.") if dataset_config is not None and not isinstance(dataset_config, BaseDatasetConfig): - raise TypeError(f"[{self.name}] The dataset configuration must be a BaseDatasetConfig") - if type(session_name) != str: - raise TypeError(f"[{self.name}] The network config must be a BaseNetworkConfig object.") - if session_dir is not None and type(session_dir) != str: - raise TypeError(f"[{self.name}] The session directory must be a str.") + raise TypeError(f"[{self.name}] The Dataset configuration must be a BaseDatasetConfig object.") + if environment_config is not None and not isinstance(environment_config, BaseEnvironmentConfig): + raise TypeError(f"[{self.name}] The Environment configuration must be a BaseEnvironmentConfig object.") - self.type: str = pipeline # Either training or prediction - self.debug: bool = False - self.new_session: bool = True - self.record_data: Optional[Dict[str, bool]] = None # Can be of type {'in': bool, 'out': bool} + # Check the session path variables + if type(session_dir) != str: + raise TypeError(f"[{self.name}] The given 'session_dir'={session_dir} must be a str.") + elif len(session_dir) == 0: + session_dir = 'sessions' + if type(session_name) != str: + raise TypeError(f"[{self.name}] The given 'session=name'={session_name} must be a str.") + elif len(session_name) == 0: + session_name = pipeline - # Dataset variables + # Configuration variables self.dataset_config: BaseDatasetConfig = dataset_config - # Network variables self.network_config: BaseNetworkConfig = network_config - # Simulation variables self.environment_config: BaseEnvironmentConfig = environment_config + + # Session variables + self.session_dir = session_dir + self.session_name = session_name + # Main manager self.manager: Optional[Manager] = None - def get_any_manager(self, manager_names: Union[str, List[str]]) -> Optional[Any]: + def execute(self): + """ + Launch the Pipeline. + """ + + raise NotImplemented + + def __get_any_manager(self, + manager_names: Union[str, List[str]]) -> Optional[Any]: """ - | Return the desired Manager associated with the pipeline if it exists. + Return the desired Manager associated with the pipeline if it exists. - :param Union[str, List[str]] manager_names: Name of the desired Manager or order of access to the desired - Manager - :return: Manager associated with the Pipeline + :param manager_names: Name of the desired Manager or order of access to this desired Manager. + :return: The desired Manager associated with the Pipeline. """ # If manager variable is not defined, cannot access other manager @@ -85,47 +96,47 @@ def get_any_manager(self, manager_names: Union[str, List[str]]) -> Optional[Any] return None return accessed_manager - def get_network_manager(self) -> NetworkManager: + def get_network_manager(self) -> Optional[NetworkManager]: """ - | Return the NetworkManager associated with the pipeline. + Return the NetworkManager associated with the pipeline if it exists. - :return: NetworkManager associated with the pipeline + :return: The NetworkManager associated with the pipeline. """ - return self.get_any_manager('network_manager') + return self.__get_any_manager(manager_names='network_manager') - def get_data_manager(self) -> DataManager: + def get_data_manager(self) -> Optional[DataManager]: """ - | Return the DataManager associated with the pipeline. + Return the DataManager associated with the pipeline if it exists. - :return: DataManager associated with the pipeline + :return: The DataManager associated with the pipeline. """ - return self.get_any_manager('data_manager') + return self.__get_any_manager(manager_names='data_manager') - def get_stats_manager(self) -> StatsManager: + def get_stats_manager(self) -> Optional[StatsManager]: """ - | Return the StatsManager associated with the pipeline. + Return the StatsManager associated with the pipeline if it exists. - :return: StatsManager associated with the pipeline + :return: The StatsManager associated with the pipeline. """ - return self.get_any_manager('stats_manager') + return self.__get_any_manager(manager_names='stats_manager') - def get_dataset_manager(self) -> DatasetManager: + def get_dataset_manager(self) -> Optional[DatasetManager]: """ - | Return the DatasetManager associated with the pipeline. + Return the DatasetManager associated with the pipeline if it exists. - :return: DatasetManager associated with the pipeline + :return: The DatasetManager associated with the pipeline. """ - return self.get_any_manager(['data_manager', 'dataset_manager']) + return self.__get_any_manager(manager_names=['data_manager', 'dataset_manager']) - def get_environment_manager(self) -> EnvironmentManager: + def get_environment_manager(self) -> Optional[EnvironmentManager]: """ - | Return the EnvironmentManager associated with the pipeline. + Return the EnvironmentManager associated with the pipeline if it exists. - :return: EnvironmentManager associated with the pipeline + :return: The EnvironmentManager associated with the pipeline. """ - return self.get_any_manager(['data_manager', 'environment_manager']) + return self.__get_any_manager(manager_names=['data_manager', 'environment_manager']) diff --git a/src/Utils/configs.py b/src/Utils/configs.py new file mode 100644 index 00000000..692290d7 --- /dev/null +++ b/src/Utils/configs.py @@ -0,0 +1,33 @@ +from typing import Any +from collections import namedtuple + + +def make_config(configuration_object: Any, + configuration_name: str, + **kwargs) -> namedtuple: + """ + Create a namedtuple which gathers all the parameters for any configuration object. + For a child config class, only new items are required since parent's items will be added by default. + + :param configuration_object: Instance of any Config class. + :param configuration_name: Name of the variable containing the namedtuple. + :param kwargs: Parameters to add to the namedtuple. + :return: Namedtuple which contains newly added parameters. + """ + + # Get items set as keyword arguments + fields = tuple(kwargs.keys()) + args = tuple(kwargs.values()) + + # Check if a dataset_config already exists (child class will have the parent's config by default) + if configuration_name in configuration_object.__dict__: + configuration: namedtuple = configuration_object.__getattr__(configuration_name) + # Get items set in the existing config + for key, value in configuration._asdict().items(): + # Only new items are required for children, check if the parent's items are set again anyway + if key not in fields: + fields += (key,) + args += (value,) + + # Create namedtuple with collected items + return namedtuple(configuration_name, fields)._make(args) diff --git a/src/Utils/path.py b/src/Utils/path.py new file mode 100644 index 00000000..14ede966 --- /dev/null +++ b/src/Utils/path.py @@ -0,0 +1,79 @@ +from typing import Optional +from os.path import join, isdir, abspath, normpath, dirname, basename +from os import listdir, pardir, makedirs +from inspect import getmodule, stack +from shutil import copytree + + +def get_first_caller() -> str: + """ + Return the repertory in which the main script is stored. + """ + + # Get the stack of calls + scripts_list = stack()[-1] + # Get the first one (the one launched by the user) + module = getmodule(scripts_list[0]) + # Return the path of this script + return dirname(abspath(module.__file__)) + + +def create_dir(session_dir: str, session_name: str) -> str: + """ + Create a new directory of the given name in the given directory. + If it already exists, add a unique identifier at the end of the directory name. + + :param session_dir: Path where to create the directory. + :param session_name: Name of the directory to create. + :return: Path to the newly created directory. + """ + + if isdir(join(session_dir, session_name)): + print(f"Directory conflict: you are going to overwrite {join(session_dir, session_name)}.") + # Find all the duplicated folders + session_name += '_' + copies = [folder for folder in listdir(session_dir) if isdir(join(session_dir, folder)) and + folder.find(session_name) == 0] + # Get the indices of copies + indices = [int(folder[len(session_name):]) for folder in copies] + # The new copy is the max index + 1 + max_idx = max(indices) if len(indices) > 0 else 0 + session_name += f'{max_idx + 1}' + + session = join(session_dir, session_name) + print(f"Create a new directory {session} for this session.") + makedirs(session) + return session + + +def copy_dir(src_dir: str, dest_dir: str, dest_name: Optional[str] = None, sub_folders: Optional[str] = None) -> str: + """ + Copy the source directory to the destination directory. + + :param src_dir: Source directory to copy. + :param dest_dir: Parent of the destination directory to copy. + :param dest_name: Destination directory to copy to. + :param sub_folders: If sub folders are specified, the latest is actually copied. + + :return: Path to the newly copied directory. + """ + + if dest_name is not None and isdir(join(dest_dir, dest_name)): + print(f"Directory conflict: you are going to overwrite {join(dest_dir, dest_name)}.") + # Find all the duplicated folders + dest_name += '_' + copies = [folder for folder in listdir(dest_dir) if isdir(join(dest_dir, folder)) and + folder.find(dest_name) == 0] + # Get the indices of the copies + indices = [int(folder[len(dest_name):]) for folder in copies] + # The new copy is the max index + 1 + max_id = max(indices) if len(indices) > 0 else 0 + dest_name += f'{max_id + 1}' + + dest = join(dest_dir, dest_name) if dest_name is not None else dest_dir + print(f"Copying the source directory {src_dir} to {dest} for this session.") + if sub_folders is None: + copytree(src_dir, dest) + else: + copytree(join(src_dir, sub_folders), join(dest, sub_folders)) + return dest diff --git a/src/Utils/pathUtils.py b/src/Utils/pathUtils.py deleted file mode 100644 index 4f25ceb3..00000000 --- a/src/Utils/pathUtils.py +++ /dev/null @@ -1,75 +0,0 @@ -from os.path import join as osPathJoin -from os.path import isdir, abspath, normpath, dirname, basename -from os import listdir, pardir, makedirs -from inspect import getmodule, stack -from shutil import copytree - - -def create_dir(dir_path: str, dir_name: str) -> str: - """ - Create a directory of the given name. If it already exist and specified, add a unique identifier at the end. - - :param str dir_path: Absolute directory to create - :param str dir_name: Name of the directory to check for existence of a similar directories - - :return: Name of the created directory as string - """ - if isdir(dir_path): - print(f"Directory conflict: you are going to overwrite {dir_path}.") - # Get the parent dir of training sessions - parent = abspath(osPathJoin(dir_path, pardir)) - # Find all the duplicated folder - deepest_repertory = dir_name.split('/')[-1] + '_' - copies_list = [folder for folder in listdir(parent) if - isdir(osPathJoin(parent, folder)) and - folder.__contains__(deepest_repertory) and - folder.find(deepest_repertory) == 0 and - len(folder) in [len(deepest_repertory) + i for i in range(1, 4)]] - # Get the indices of copies - indices = [int(folder[len(deepest_repertory):]) for folder in copies_list] - # The new copy is the max int + 1 - max_ind = max(indices) if len(indices) > 0 else 0 - new_name = basename(normpath(dir_path)) + f'_{max_ind + 1}/' - dir_path = osPathJoin(parent, new_name) - print(f"Create a new directory {dir_path} for this session.") - makedirs(dir_path) - return dir_path - - -def copy_dir(src_dir: str, dest_parent_dir: str, dest_dir: str) -> str: - """ - Copy source directory to destination directory at the end of destination parent directory - - :param str src_dir: Source directory to copy - :param str dest_parent_dir: Parent of the destination directory to copy - :param str dest_dir: Destination directory to copy to - - :return: destination directory that source has been copied to - """ - dest_dir = osPathJoin(dest_parent_dir, dest_dir) - if isdir(dest_dir): - print("Directory conflict: you are going to overwrite by copying in {}.".format(dest_dir)) - copies_list = [folder for folder in listdir(dest_parent_dir) if - isdir(osPathJoin(dest_parent_dir, folder)) and - folder.__contains__(dest_dir)] - new_name = dest_dir + '({})/'.format(len(copies_list)) - dest_dir = osPathJoin(dest_parent_dir, new_name) - print("Copying {} into the new directory {} for this session.".format(src_dir, dest_dir)) - else: - new_name = dest_dir + '/' - dest_dir = osPathJoin(dest_parent_dir, new_name) - copytree(src_dir, dest_dir) - return dest_dir - - -def get_first_caller() -> str: - """ - Return the repertory in which the main script is - """ - # Get the stack of called scripts - scripts_list = stack()[-1] - # Get the first one (the one launched by the user) - module = getmodule(scripts_list[0]) - # Return the path of this script - return dirname(abspath(module.__file__)) - From 5c547645ec23961a992adab03ea21f689457c705 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 14 Oct 2022 10:33:23 +0200 Subject: [PATCH 02/61] Use Database for DataGeneration in remote mode. --- src/AsyncSocket/AbstractEnvironment.py | 14 ++ src/AsyncSocket/TcpIpClient.py | 41 +----- src/AsyncSocket/TcpIpServer.py | 142 ++++++--------------- src/Environment/BaseEnvironment.py | 12 +- src/Environment/BaseEnvironmentConfig.py | 18 ++- src/Environment/launcherBaseEnvironment.py | 9 +- src/Manager/DataManager.py | 7 +- src/Manager/EnvironmentManager.py | 30 +---- src/Pipelines/BaseDataGenerator.py | 2 +- 9 files changed, 99 insertions(+), 176 deletions(-) diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 63fdba35..509ddeac 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -100,6 +100,14 @@ def init_visualization(self) -> None: pass + def init_database(self) -> None: + """ + Define the fields of the training dataset. + Required. + """ + + raise NotImplementedError + def send_parameters(self) -> dict: """ Create a dictionary of parameters to send to the manager. Automatically called after create and init. @@ -119,3 +127,9 @@ def apply_prediction(self, prediction: ndarray) -> None: """ pass + + def _send_training_data(self) -> None: + raise NotImplementedError + + def _reset_training_data(self) -> None: + raise NotImplementedError diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 6ac2e00c..35b395c8 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -76,6 +76,8 @@ async def __initialize(self) -> None: # Create the environment self.create() self.init() + if self.instance_id == 0: + self.init_database() self.init_visualization() # Send parameters @@ -147,37 +149,6 @@ async def __close(self) -> None: ########################################################################################## ########################################################################################## - async def __send_training_data(self, - loop: Optional[EventLoop] = None, - receiver: Optional[socket] = None) -> None: - """ - Send the training data to the TcpIpServer. - - :param loop: get_event_loop() return. - :param receiver: TcpIpObject receiver. - """ - - loop = get_event_loop() if loop is None else loop - receiver = self.sock if receiver is None else receiver - - # Send and reset network input - if self.input.tolist(): - await self.send_labeled_data(data_to_send=self.input, label="input", loop=loop, receiver=receiver) - self.input = array([]) - # Send network output - if self.output.tolist(): - await self.send_labeled_data(data_to_send=self.output, label="output", loop=loop, receiver=receiver) - self.output = array([]) - # Send loss data - if self.loss_data: - await self.send_labeled_data(data_to_send=self.loss_data, label='loss', loop=loop, receiver=receiver) - self.loss_data = None - # Send additional dataset fields - for key in self.additional_fields.keys(): - await self.send_labeled_data(data_to_send=self.additional_fields[key], label='dataset_' + key, - loop=loop, receiver=receiver) - self.additional_fields = {} - def send_prediction_data(self, network_input: ndarray, receiver: socket = None) -> ndarray: @@ -312,18 +283,18 @@ async def action_on_step(self, # Execute the required number of steps for step in range(self.simulations_per_step): # Compute data only on final step - self.compute_essential_data = step == self.simulations_per_step - 1 + self.compute_training_data = step == self.simulations_per_step - 1 await self.step() # If produced sample is not usable, run again - # Todo: add the max_rate here if self.sample_in is None and self.sample_out is None: while not self.check_sample(): for step in range(self.simulations_per_step): # Compute data only on final step - self.compute_essential_data = step == self.simulations_per_step - 1 + self.compute_training_data = step == self.simulations_per_step - 1 await self.step() # Sent training data to Server - await self.__send_training_data() + self._send_training_data() + self._reset_training_data() await self.send_command_done() diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index fa5a8761..e8130adb 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -136,74 +136,37 @@ async def __initialize(self, param_dict: Dict[Any, Any]) -> None: ########################################################################################## def get_batch(self, - get_inputs: bool = True, - get_outputs: bool = True, - animate: bool = True) -> Dict[str, Union[ndarray, dict]]: + animate: bool = True) -> None: """ Build a batch from clients samples. - :param get_inputs: If True, compute and return input. - :param get_outputs: If True, compute and return output. :param animate: If True, triggers an environment step. - :return: Batch (list of samples) & additional data in a dictionary. """ # Trigger communication protocol - async_run(self.__request_data_to_clients(get_inputs=get_inputs, get_outputs=get_outputs, animate=animate)) - - # Sort stored data between following fields - data_sorter = {'input': [], 'output': [], 'additional_fields': {}, 'loss': []} - list_fields = [key for key in data_sorter.keys() if type(data_sorter[key]) == list] - # Map produced samples with clients ID - self.sample_to_client_id = [] - - # Process while queue is empty or batch is full - while max([len(data_sorter[key]) for key in list_fields]) < self.batch_size and not self.data_fifo.empty(): - # Get data dict from queue - data = self.data_fifo.get() - # Network in / out / loss - for field in ['input', 'output', 'loss']: - if field in data: - data_sorter[field].append(data[field]) - # Additional fields - field = 'additional_fields' - if field in data: - for key in data[field]: - if key not in data_sorter[field].keys(): - data_sorter[field][key] = [] - data_sorter[field][key].append(data[field][key]) - # ID of client - if 'ID' in data: - self.sample_to_client_id.append(data['ID']) - return data_sorter + async_run(self.__request_data_to_clients(animate=animate)) async def __request_data_to_clients(self, - get_inputs: bool = True, - get_outputs: bool = True, animate: bool = True) -> None: """ Trigger a communication protocol for each client. Wait for all clients before to launch another communication protocol while the batch is not full. - :param get_inputs: If True, compute and return input - :param get_outputs: If True, compute and return output :param animate: If True, triggers an environment step """ - client_launched = 0 + nb_sample = 0 # Launch the communication protocol while the batch needs to be filled - while client_launched < self.batch_size: + while nb_sample < self.batch_size: # Run communicate protocol for each client and wait for the last one to finish - await gather(*[self.__communicate(client=client, client_id=client_id, get_inputs=get_inputs, - get_outputs=get_outputs, animate=animate) - for client_id, client in self.clients]) - client_launched += len(self.clients) + await gather(*[self.__communicate(client=client, + client_id=client_id, + animate=animate) for client_id, client in self.clients]) + nb_sample += len(self.clients) async def __communicate(self, client: Optional[socket] = None, client_id: Optional[int] = None, - get_inputs: bool = True, - get_outputs: bool = True, animate: bool = True) -> None: """ | Communication protocol with a client. It goes through different steps: @@ -213,47 +176,45 @@ async def __communicate(self, :param client: TcpIpObject client to communicate with. :param client_id: Index of the client. - :param get_inputs: If True, compute and return input. - :param get_outputs: If True, compute and return output. :param animate: If True, triggers an environment step. """ loop = get_event_loop() - # 1) If a sample from Dataset is given, sent it to the TcpIpClient - if self.batch_from_dataset is not None: - # Check if there is remaining samples, otherwise client is not used - if len(self.batch_from_dataset['input']) == 0 or len(self.batch_from_dataset['output']) == 0: - return - # Send the sample to the TcpIpClient - await self.send_command_sample(loop=loop, receiver=client) - # Pop the first sample of the numpy batch for network in / out - for field in ['input', 'output']: - # Tell if there is something to read - await self.send_data(data_to_send=field in self.batch_from_dataset, loop=loop, receiver=client) - if field in self.batch_from_dataset: - # Pop sample from array if there are some - sample = self.batch_from_dataset[field][0] - self.batch_from_dataset[field] = self.batch_from_dataset[field][1:] - # Keep the sample in memory - self.data_dict[client_id][field] = sample - # Send network in / out sample - await self.send_data(data_to_send=sample, loop=loop, receiver=client) - # Pop the first sample of the numpy batch for each additional dataset field - field = 'additional_fields' - # Tell TcpClient if there is additional data for this field - await self.send_data(data_to_send=field in self.batch_from_dataset, loop=loop, receiver=client) - if field in self.batch_from_dataset: - sample = {} - # Get each additional data field - for key in self.batch_from_dataset[field]: - # Pop sample from array - sample[key] = self.batch_from_dataset[field][key][0] - self.batch_from_dataset[field][key] = self.batch_from_dataset[field][key][1:] - # Keep the sample in memory - self.data_dict[client_id][field + '_' + key] = sample[key] - # Send additional in / out sample - await self.send_dict(name="additional_fields", dict_to_send=sample, loop=loop, receiver=client) + # # 1) If a sample from Dataset is given, sent it to the TcpIpClient + # if self.batch_from_dataset is not None: + # # Check if there is remaining samples, otherwise client is not used + # if len(self.batch_from_dataset['input']) == 0 or len(self.batch_from_dataset['output']) == 0: + # return + # # Send the sample to the TcpIpClient + # await self.send_command_sample(loop=loop, receiver=client) + # # Pop the first sample of the numpy batch for network in / out + # for field in ['input', 'output']: + # # Tell if there is something to read + # await self.send_data(data_to_send=field in self.batch_from_dataset, loop=loop, receiver=client) + # if field in self.batch_from_dataset: + # # Pop sample from array if there are some + # sample = self.batch_from_dataset[field][0] + # self.batch_from_dataset[field] = self.batch_from_dataset[field][1:] + # # Keep the sample in memory + # self.data_dict[client_id][field] = sample + # # Send network in / out sample + # await self.send_data(data_to_send=sample, loop=loop, receiver=client) + # # Pop the first sample of the numpy batch for each additional dataset field + # field = 'additional_fields' + # # Tell TcpClient if there is additional data for this field + # await self.send_data(data_to_send=field in self.batch_from_dataset, loop=loop, receiver=client) + # if field in self.batch_from_dataset: + # sample = {} + # # Get each additional data field + # for key in self.batch_from_dataset[field]: + # # Pop sample from array + # sample[key] = self.batch_from_dataset[field][key][0] + # self.batch_from_dataset[field][key] = self.batch_from_dataset[field][key][1:] + # # Keep the sample in memory + # self.data_dict[client_id][field + '_' + key] = sample[key] + # # Send additional in / out sample + # await self.send_dict(name="additional_fields", dict_to_send=sample, loop=loop, receiver=client) # 2) Execute n steps, the last one send data computation signal if animate: @@ -262,25 +223,6 @@ async def __communicate(self, await self.listen_while_not_done(loop=loop, sender=client, data_dict=self.data_dict, client_id=client_id) - # 3.1) Add all received in / out data to queue - data = {} - for get_data, net_field in zip([get_inputs, get_outputs], ['input', 'output']): - if get_data: - # Add network field - data[net_field] = self.data_dict[client_id][net_field] - # 3.2) Add loss data if provided - if 'loss' in self.data_dict[client_id]: - data['loss'] = self.data_dict[client_id]['loss'] - # 3.3) Add additional fields (transform key from 'dataset_{FIELD}' to '{FIELD}') - additional_fields = [key for key in self.data_dict[client_id].keys() if key.__contains__('dataset_')] - data['additional_fields'] = {} - for field in additional_fields: - data['additional_fields'][field[len('dataset_'):]] = self.data_dict[client_id][field] - # 3.4) Identify sample - data['ID'] = client_id - # 3.5) Add data to the Queue - self.data_fifo.put(data) - def set_dataset_batch(self, batch: Dict[str, Union[ndarray, Dict]]) -> None: """ diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 43cacc69..1714bbac 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -54,9 +54,11 @@ def __init__(self, self.database: Optional[Database] = None if data_db is not None: if type(data_db) == list: - pass + self.database = Database(database_dir=data_db[0], + database_name=data_db[1]).load() else: self.database = data_db + if self.instance_id == 0: self.database.create_fields(table_name='Training', fields=('env_id', int)) @@ -189,16 +191,18 @@ def set_training_data(self, # Check kwargs if self.__first_add[0]: + if self.instance_id != 0: + self.database.load() self.__first_add[0] = False required_fields = list(set(self.database.get_fields(table_name='Training')) - {'id', 'env_id'}) for field in kwargs.keys(): if field not in required_fields: raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." - f"Required fields are {required_fields}.") + f"Required fields are {required_fields}.{self.instance_id}") for field in required_fields: if field not in kwargs.keys(): raise ValueError(f"[{self.name}] The field '{field}' was not defined in training data." - f"Required fields are {required_fields}.") + f"Required fields are {required_fields}.{self.instance_id}") # Training data is set if the Environment can compute data if self.compute_training_data: @@ -208,7 +212,7 @@ def set_training_data(self, def _reset_training_data(self) -> None: self.__training_data = {} - def _send_data(self) -> None: + def _send_training_data(self) -> None: line_id = self.database.add_data(table_name='Training', data=self.__training_data) self.database.add_data(table_name='Sync', diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index b7595f4a..8b6170e5 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -105,7 +105,8 @@ def __init__(self, def create_server(self, environment_manager: Optional[Any] = None, batch_size: int = 1, - visu_db: Optional[int] = None) -> TcpIpServer: + data_db: Optional[str] = None, + visu_db: Optional[str] = None) -> TcpIpServer: """ Create a TcpIpServer and launch TcpIpClients in subprocesses. @@ -116,16 +117,19 @@ def create_server(self, """ # Create server - server = TcpIpServer(max_client_count=self.max_client_connections, batch_size=batch_size, - nb_client=self.number_of_thread, manager=environment_manager, - ip_address=self.ip_address, port=self.port) + server = TcpIpServer(ip_address=self.ip_address, + port=self.port, + nb_client=self.number_of_thread, + max_client_count=self.max_client_connections, + batch_size=batch_size, + manager=environment_manager) server_thread = Thread(target=self.start_server, args=(server,)) server_thread.start() # Create clients client_threads = [] for i in range(self.number_of_thread): - client_thread = Thread(target=self.start_client, args=(i, visu_db)) + client_thread = Thread(target=self.start_client, args=(i, data_db, visu_db)) client_threads.append(client_thread) for client in client_threads: client.start() @@ -152,7 +156,8 @@ def start_server(self, def start_client(self, idx: int = 1, - visu_db: Optional[int] = None) -> None: + data_db: Optional[str] = None, + visu_db: Optional[str] = None) -> None: """ Run a subprocess to start a TcpIpClient. @@ -170,6 +175,7 @@ def start_client(self, str(self.port), str(idx), str(self.number_of_thread), + str(data_db), str(visu_db)]) def create_environment(self, diff --git a/src/Environment/launcherBaseEnvironment.py b/src/Environment/launcherBaseEnvironment.py index 15c68b28..38fa8823 100644 --- a/src/Environment/launcherBaseEnvironment.py +++ b/src/Environment/launcherBaseEnvironment.py @@ -7,9 +7,9 @@ if __name__ == '__main__': # Check script call - if len(argv) != 8: + if len(argv) != 9: print(f"Usage: python3 {argv[0]} " - f" ") + f" ") exit(1) # Import environment_class @@ -18,9 +18,10 @@ exec(f"from {module_name} import {argv[2]} as Environment") # Create, init and run Tcp-Ip environment - visu_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] + data_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] + visu_db = None if argv[8] == 'None' else [s[1:-1] for s in argv[8][1:-1].split(', ')] client = Environment(ip_address=argv[3], port=int(argv[4]), instance_id=int(argv[5]), - number_of_instances=int(argv[6]), visu_db=visu_db) + number_of_instances=int(argv[6]), data_db=data_db, visu_db=visu_db) client.initialize() client.launch() diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index cb69d6e1..51062cf7 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -1,9 +1,7 @@ from typing import Any, Optional, Dict, List -import os.path from numpy import ndarray -from json import load as json_load -from DeepPhysX.Core.Manager.DatabaseManager import DatasetManager +from DeepPhysX.Core.Manager.DatabaseManager import DatasetManager, Database from DeepPhysX.Core.Manager.EnvironmentManager import EnvironmentManager from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig @@ -103,6 +101,9 @@ def get_manager(self) -> Any: return self.manager + def get_database(self) -> Database: + return self.dataset_manager.database + def get_data(self, epoch: int = 0, batch_size: int = 1, diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index af07b971..031c2177 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -1,5 +1,5 @@ from typing import Any, Dict, Optional, Union -from numpy import array, ndarray +from numpy import ndarray from asyncio import run as async_run from copy import copy from os.path import join @@ -56,7 +56,9 @@ def __init__(self, if environment_config.as_tcp_ip_client: self.server = environment_config.create_server(environment_manager=self, batch_size=batch_size, - visu_db=visu_db.get_path()) + data_db=data_db if data_db is None else data_db.get_path(), + visu_db=visu_db if visu_db is None else visu_db.get_path()) + self.data_manager.get_database().load() else: self.environment = environment_config.create_environment(environment_manager=self, data_db=data_db, @@ -82,33 +84,15 @@ def get_data_manager(self) -> Any: return self.data_manager def get_data_from_server(self, - get_inputs: bool = True, - get_outputs: bool = True, - animate: bool = True) -> Dict[str, Union[ndarray, dict]]: + animate: bool = True) -> None: """ Compute a batch of data from Environments requested through TcpIpServer. - :param get_inputs: If True, compute and return input. - :param get_outputs: If True, compute and return output. :param animate: If True, triggers an environment step. - :return: Dictionary containing all labeled data sent by the clients in their own dictionary + in and out key - corresponding to the batch. """ # Get data from server - batch = self.server.get_batch(get_inputs, get_outputs, animate) - # Filter input and output - training_data = {'input': array(batch['input']) if get_inputs else array([]), - 'output': array(batch['output']) if get_outputs else array([])} - # Convert each additional field - for field in batch['additional_fields']: - batch['additional_fields'][field] = array(batch['additional_fields'][field]) - training_data['additional_fields'] = batch['additional_fields'] - # Convert loss data - if 'loss' in batch and len(batch['loss']) != 0: - training_data['loss'] = array(batch['loss']) - # Return batch - return training_data + self.server.get_batch(animate) def get_data_from_environment(self, animate: bool = True) -> None: @@ -148,7 +132,7 @@ def get_data_from_environment(self, # 1.3 Add the produced sample to the batch if the sample is validated if self.environment.check_sample(): nb_sample += 1 - self.environment._send_data() + self.environment._send_training_data() self.environment._reset_training_data() def dispatch_batch_to_server(self, diff --git a/src/Pipelines/BaseDataGenerator.py b/src/Pipelines/BaseDataGenerator.py index 5ff201e4..3cc621ff 100644 --- a/src/Pipelines/BaseDataGenerator.py +++ b/src/Pipelines/BaseDataGenerator.py @@ -1,4 +1,4 @@ -from os.path import join, sep, exists +from os.path import join, sep from sys import stdout from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline From af577690d57ff448353bf53811c7bf236665af9d Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 14 Oct 2022 11:23:08 +0200 Subject: [PATCH 03/61] Make the DataGeneration mainloop modular. --- src/Pipelines/BaseDataGenerator.py | 75 +++++++++++++++++++++++++----- 1 file changed, 63 insertions(+), 12 deletions(-) diff --git a/src/Pipelines/BaseDataGenerator.py b/src/Pipelines/BaseDataGenerator.py index 3cc621ff..1e281f4b 100644 --- a/src/Pipelines/BaseDataGenerator.py +++ b/src/Pipelines/BaseDataGenerator.py @@ -1,3 +1,4 @@ +from typing import Optional from os.path import join, sep from sys import stdout @@ -12,14 +13,14 @@ class BaseDataGenerator(BasePipeline): def __init__(self, - dataset_config: BaseDatasetConfig, environment_config: BaseEnvironmentConfig, + dataset_config: Optional[BaseDatasetConfig] = None, session_dir: str = 'sessions', session_name: str = 'data_generation', batch_nb: int = 0, batch_size: int = 0): """ - BaseDataGenerator implement the main loop that only produces and stores data (no Network training). + BaseDataGenerator implements the main loop that only produces and stores data (no Network training). :param dataset_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. @@ -54,6 +55,11 @@ def __init__(self, session_name = create_dir(session_dir=session_dir, session_name=session_name).split(sep)[-1] + # Data generation variables + self.batch_nb: int = batch_nb + self.batch_id: int = 0 + self.progress_bar = Progressbar(start=0, stop=self.batch_id, c='orange', title="Data Generation") + # Create a DataManager self.data_manager = DataManager(dataset_config=dataset_config, environment_config=environment_config, @@ -62,23 +68,68 @@ def __init__(self, is_training=False, produce_data=True, batch_size=batch_size) - self.nb_batch: int = batch_nb - self.progress_bar = Progressbar(start=0, stop=self.nb_batch, c='orange', title="Data Generation") def execute(self) -> None: """ Launch the data generation Pipeline. + Each event is already implemented for a basic pipeline but can also be rewritten via inheritance to describe a + more complex pipeline. + """ + + self.data_generation_begin() + while self.batch_condition(): + self.batch_begin() + self.batch_produce() + self.batch_count() + self.batch_end() + self.data_generation_end() + + def data_generation_begin(self) -> None: + """ + Called once at the beginning of the data generation pipeline. + """ + + pass + + def batch_condition(self) -> bool: + """ + Check the batch number condition. + """ + + return self.batch_id < self.batch_nb + + def batch_begin(self) -> None: + """ + Called once at the beginning of a batch production. """ - # Produce each batch of data - for i in range(self.nb_batch): + pass - # Produce a batch - self.data_manager.get_data() + def batch_produce(self) -> None: + """ + Trigger the data production. + """ + + self.data_manager.get_data() + + def batch_count(self) -> None: + """ + Increment the batch counter. + """ + + self.batch_id += 1 - # Update progress bar - stdout.write("\033[K") - self.progress_bar.print(counts=i + 1) + def batch_end(self) -> None: + """ + Called once at the beginning of a batch production. + """ + + stdout.write("\033[K") + self.progress_bar.print(counts=self.batch_id + 1) + + def data_generation_end(self) -> None: + """ + Called once at the beginning of the data generation pipeline. + """ - # Close DataManager self.data_manager.close() From f216f33f3de26690a5ea9d725bb671615805b213 Mon Sep 17 00:00:00 2001 From: robin Date: Tue, 18 Oct 2022 12:09:21 +0200 Subject: [PATCH 04/61] Allow multiple partitions of SQL Database. --- src/AsyncSocket/AbstractEnvironment.py | 3 + src/AsyncSocket/TcpIpClient.py | 32 +- src/AsyncSocket/TcpIpObject.py | 616 ++++++++++++++--------- src/AsyncSocket/TcpIpServer.py | 24 + src/Database/BaseDataset.py | 252 ---------- src/Database/BaseDatasetConfig.py | 28 +- src/Environment/BaseEnvironmentConfig.py | 18 +- src/Manager/DataManager.py | 56 ++- src/Manager/DatabaseManager.py | 244 ++++++++- src/Manager/EnvironmentManager.py | 28 +- src/Manager/Manager.py | 115 ++--- src/Manager/StatsManager.py | 2 +- src/Pipelines/BaseDataGenerator.py | 4 +- src/Utils/jsonUtils.py | 2 +- 14 files changed, 760 insertions(+), 664 deletions(-) delete mode 100644 src/Database/BaseDataset.py diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 509ddeac..466c1784 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -1,6 +1,8 @@ from typing import Optional, Dict, Any from numpy import array, ndarray +from SSD.Core.Storage.Database import Database + class AbstractEnvironment: @@ -34,6 +36,7 @@ def __init__(self, self.compute_training_data: bool = True # Dataset data variables + self.database: Optional[Database] = None self.sample_in: Optional[ndarray] = None self.sample_out: Optional[ndarray] = None self.additional_fields: Dict[str, Any] = {} diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 35b395c8..3e9de830 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -1,12 +1,12 @@ -from typing import Any, Dict, Optional +from typing import Any, Dict from socket import socket from asyncio import get_event_loop from asyncio import AbstractEventLoop as EventLoop from asyncio import run as async_run -from numpy import ndarray, array +from numpy import ndarray from DeepPhysX.Core.AsyncSocket.TcpIpObject import TcpIpObject -from DeepPhysX.Core.AsyncSocket.AbstractEnvironment import AbstractEnvironment +from DeepPhysX.Core.AsyncSocket.AbstractEnvironment import AbstractEnvironment, Database class TcpIpClient(TcpIpObject, AbstractEnvironment): @@ -211,7 +211,7 @@ async def action_on_exit(self, :param data: Dict storing data. :param client_id: ID of the TcpIpClient. - :param loop: asyncio.get_event_loop() return. + :param loop: Asyncio event loop. :param sender: TcpIpObject sender. """ @@ -228,7 +228,7 @@ async def action_on_prediction(self, :param data: Dict storing data. :param client_id: ID of the TcpIpClient. - :param loop: asyncio.get_event_loop() return. + :param loop: Asyncio event loop. :param sender: TcpIpObject sender. """ @@ -247,7 +247,7 @@ async def action_on_sample(self, :param data: Dict storing data. :param client_id: ID of the TcpIpClient. - :param loop: asyncio.get_event_loop() return. + :param loop: Asyncio event loop. :param sender: TcpIpObject sender. """ @@ -276,7 +276,7 @@ async def action_on_step(self, :param data: Dict storing data. :param client_id: ID of the TcpIpClient. - :param loop: asyncio.get_event_loop() return. + :param loop: Asyncio event loop. :param sender: TcpIpObject sender. """ @@ -298,3 +298,21 @@ async def action_on_step(self, self._send_training_data() self._reset_training_data() await self.send_command_done() + + async def action_on_change_db(self, + data: Dict[Any, Any], + client_id: int, sender: socket, + loop: EventLoop) -> None: + """ + Action to run when receiving the 'step' command. + + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. + """ + + new_database = (await self.receive_data(loop=loop, sender=sender), + await self.receive_data(loop=loop, sender=sender)) + self.database = Database(database_dir=new_database[0], + database_name=new_database[1]).load() diff --git a/src/AsyncSocket/TcpIpObject.py b/src/AsyncSocket/TcpIpObject.py index 7d2f247d..aa1994cd 100644 --- a/src/AsyncSocket/TcpIpObject.py +++ b/src/AsyncSocket/TcpIpObject.py @@ -15,16 +15,16 @@ class TcpIpObject: - """ - | TcpIpObject defines communication protocols to send and receive data and commands. - - :param str ip_address: IP address of the TcpIpObject - :param int port: Port number of the TcpIpObject - """ def __init__(self, ip_address: str = 'localhost', port: int = 10000): + """ + TcpIpObject defines communication protocols to send and receive data and commands. + + :param ip_address: IP address of the TcpIpObject. + :param port: Port number of the TcpIpObject. + """ self.name: str = self.__class__.__name__ @@ -39,7 +39,7 @@ def __init__(self, # Available commands self.command_dict: Dict[str, bytes] = {'exit': b'exit', 'step': b'step', 'done': b'done', 'finished': b'fini', 'prediction': b'pred', 'read': b'read', 'sample': b'samp', - 'visualisation': b'visu'} + 'visualisation': b'visu', 'db': b'chdb'} self.action_on_command: Dict[bytes, Any] = { self.command_dict["exit"]: self.action_on_exit, self.command_dict["step"]: self.action_on_step, @@ -49,6 +49,7 @@ def __init__(self, self.command_dict["read"]: self.action_on_read, self.command_dict["sample"]: self.action_on_sample, self.command_dict["visualisation"]: self.action_on_visualisation, + self.command_dict['db']: self.action_on_change_db } # Synchronous variables # self.send_lock = Lock() @@ -60,15 +61,16 @@ def __init__(self, ########################################################################################## ########################################################################################## - async def send_data(self, data_to_send: Convertible, loop: Optional[EventLoop] = None, + async def send_data(self, + data_to_send: Convertible, + loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: """ - | Send data through the given socket. + Send data through the given socket. - :param data_to_send: Data that will be sent on socket - :type data_to_send: Union[type(None), bytes, str, bool, int, float, List, ndarray] - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: socket receiver + :param data_to_send: Data that will be sent on socket. + :param loop: Asyncio event loop. + :param receiver: Socket receiver. """ loop = get_event_loop() if loop is None else loop @@ -79,14 +81,15 @@ async def send_data(self, data_to_send: Convertible, loop: Optional[EventLoop] = if await loop.sock_sendall(sock=receiver, data=data_as_bytes) is not None: ValueError(f"[{self.name}] Could not send all of the data for an unknown reason") - def sync_send_data(self, data_to_send: Convertible, receiver: Optional[socket] = None) -> None: + def sync_send_data(self, + data_to_send: Convertible, + receiver: Optional[socket] = None) -> None: """ - | Send data through the given socket. - | Synchronous version of 'TcpIpObject.send_data'. + Send data through the given socket. + Synchronous version of 'TcpIpObject.send_data'. - :param data_to_send: Data that will be sent on socket* - :type data_to_send: Union[type(None), bytes, str, bool, int, float, List, ndarray] - :param Optional[socket] receiver: socket receiver + :param data_to_send: Data that will be sent on socket. + :param receiver: Socket receiver. """ receiver = self.sock if receiver is None else receiver @@ -95,13 +98,15 @@ def sync_send_data(self, data_to_send: Convertible, receiver: Optional[socket] = # Send the whole message receiver.sendall(data_as_bytes) - async def receive_data(self, loop: EventLoop, sender: socket) -> Convertible: + async def receive_data(self, + loop: EventLoop, + sender: socket) -> Convertible: """ - | Receive data from a socket. + Receive data from a socket. - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: socket sender - :return: Converted data + :param loop: Asyncio event loop. + :param sender: Socket sender. + :return: Converted data. """ # Receive the number of fields to receive @@ -117,10 +122,10 @@ async def receive_data(self, loop: EventLoop, sender: socket) -> Convertible: def sync_receive_data(self) -> Convertible: """ - | Receive data from a socket. - | Synchronous version of 'TcpIpObject.receive_data'. + Receive data from a socket. + Synchronous version of 'TcpIpObject.receive_data'. - :return: Converted data + :return: Converted data. """ self.sock.setblocking(True) @@ -135,14 +140,17 @@ def sync_receive_data(self) -> Convertible: # Return the data in the expected format return self.data_converter.bytes_to_data(bytes_fields) - async def read_data(self, loop: EventLoop, sender: socket, read_size: int) -> bytes: + async def read_data(self, + loop: EventLoop, + sender: socket, + read_size: int) -> bytes: """ - | Read the data on the socket with value of buffer size as relatively small powers of 2. + Read the data on the socket with value of buffer size as relatively small powers of 2. - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: socket sender - :param int read_size: Amount of data to read on the socket - :return: Bytes field with 'read_size' length + :param loop: Asyncio event loop. + :param sender: Socket sender. + :param read_size: Amount of data to read on the socket. + :return: Bytes field with 'read_size' length. """ # Maximum read size @@ -165,13 +173,14 @@ async def read_data(self, loop: EventLoop, sender: socket, read_size: int) -> by return bytes_field - def sync_read_data(self, read_size: int) -> bytes: + def sync_read_data(self, + read_size: int) -> bytes: """ - | Read the data on the socket with value of buffer size as relatively small powers of 2. - | Synchronous version of 'TcpIpObject.read_data'. + Read the data on the socket with value of buffer size as relatively small powers of 2. + Synchronous version of 'TcpIpObject.read_data'. - :param int read_size: Amount of data to read on the socket - :return: Bytes field with 'read_size' length + :param read_size: Amount of data to read on the socket. + :return: Bytes field with 'read_size' length. """ # Maximum read sizes array @@ -199,17 +208,20 @@ def sync_read_data(self, read_size: int) -> bytes: ########################################################################################## ########################################################################################## - async def send_labeled_data(self, data_to_send: Convertible, label: str, loop: Optional[EventLoop] = None, - receiver: Optional[socket] = None, send_read_command: bool = True) -> None: + async def send_labeled_data(self, + data_to_send: Convertible, + label: str, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None, + send_read_command: bool = True) -> None: """ - | Send data with an associated label. + Send data with an associated label. - :param data_to_send: Data that will be sent on socket - :type data_to_send: Union[type(None), bytes, str, bool, int, float, List, ndarray] - :param str label: Associated label - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver - :param bool send_read_command: If True, the command 'read' is sent before sending data + :param data_to_send: Data that will be sent on socket. + :param label: Associated label. + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. + :param send_read_command: If True, the command 'read' is sent before sending data. """ loop = get_event_loop() if loop is None else loop @@ -222,18 +234,19 @@ async def send_labeled_data(self, data_to_send: Convertible, label: str, loop: O # Send data await self.send_data(data_to_send=data_to_send, loop=loop, receiver=receiver) - def sync_send_labeled_data(self, data_to_send: Convertible, label: str, receiver: Optional[socket] = None, + def sync_send_labeled_data(self, + data_to_send: Convertible, + label: str, + receiver: Optional[socket] = None, send_read_command: bool = True) -> None: """ - | Send data with an associated label. - | Synchronous version of 'TcpIpObject.send_labeled_data'. + Send data with an associated label. + Synchronous version of 'TcpIpObject.send_labeled_data'. - :param data_to_send: Data that will be sent on socket - :type data_to_send: Union[type(None), bytes, str, bool, int, float, List, ndarray] - :param str label: Associated label - :param Optional[socket] receiver: TcpIpObject receiver - :param bool send_read_command: If True, the command 'read' is sent before sending data - :return: + :param data_to_send: Data that will be sent on socket. + :param label: Associated label. + :param receiver: TcpIpObject receiver. + :param send_read_command: If True, the command 'read' is sent before sending data. """ receiver = self.sock if receiver is None else receiver @@ -245,13 +258,15 @@ def sync_send_labeled_data(self, data_to_send: Convertible, label: str, receiver # Send data self.sync_send_data(data_to_send=data_to_send, receiver=receiver) - async def receive_labeled_data(self, loop: EventLoop, sender: socket) -> Tuple[str, Convertible]: + async def receive_labeled_data(self, + loop: EventLoop, + sender: socket) -> Tuple[str, Convertible]: """ - | Receive data and an associated label. + Receive data and an associated label. - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender - :return: Label, Data + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. + :return: Label, Data. """ # Listen to sender @@ -267,10 +282,10 @@ async def receive_labeled_data(self, loop: EventLoop, sender: socket) -> Tuple[s def sync_receive_labeled_data(self) -> Tuple[str, Convertible]: """ - | Receive data and an associated label. - | Synchronous version of 'TcpIpObject.receive_labeled_data'. + Receive data and an associated label. + Synchronous version of 'TcpIpObject.receive_labeled_data'. - :return: Label, Data + :return: Label, Data. """ # Listen to sender @@ -284,15 +299,18 @@ def sync_receive_labeled_data(self) -> Tuple[str, Convertible]: data = self.sync_receive_data() return label, data - async def send_dict(self, name: str, dict_to_send: Dict[Any, Any], loop: Optional[EventLoop] = None, + async def send_dict(self, + name: str, + dict_to_send: Dict[Any, Any], + loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: """ - | Send a whole dictionary field by field as labeled data. + Send a whole dictionary field by field as labeled data. - :param str name: Name of the dictionary - :param Dict[Any, Any] dict_to_send: Dictionary to send - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param name: Name of the dictionary. + :param dict_to_send: Dictionary to send. + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ loop = get_event_loop() if loop is None else loop @@ -324,14 +342,17 @@ async def send_dict(self, name: str, dict_to_send: Dict[Any, Any], loop: Optiona await self.send_command_finished(loop=loop, receiver=receiver) await self.send_command_finished(loop=loop, receiver=receiver) - def sync_send_dict(self, name: str, dict_to_send: Dict[Any, Any], receiver: Optional[socket] = None) -> None: + def sync_send_dict(self, + name: str, + dict_to_send: Dict[Any, Any], + receiver: Optional[socket] = None) -> None: """ - | Send a whole dictionary field by field as labeled data. - | Synchronous version of 'TcpIpObject.receive_labeled_data'. + Send a whole dictionary field by field as labeled data. + Synchronous version of 'TcpIpObject.receive_labeled_data'. - :param str name: Name of the dictionary - :param Dict[Any, Any] dict_to_send: Dictionary to send - :param Optional[socket] receiver: TcpIpObject receiver + :param name: Name of the dictionary. + :param dict_to_send: Dictionary to send. + :param receiver: TcpIpObject receiver. """ receiver = self.sock if receiver is None else receiver @@ -362,14 +383,16 @@ def sync_send_dict(self, name: str, dict_to_send: Dict[Any, Any], receiver: Opti self.sync_send_command_finished(receiver=receiver) self.sync_send_command_finished(receiver=receiver) - async def __send_unnamed_dict(self, dict_to_send: Dict[Any, Any], loop: Optional[EventLoop] = None, + async def __send_unnamed_dict(self, + dict_to_send: Dict[Any, Any], + loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: """ - | Send a whole dictionary field by field as labeled data. Dictionary will be unnamed. + Send a whole dictionary field by field as labeled data. Dictionary will be unnamed. - :param dict dict_to_send: Dictionary to send - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param dict_to_send: Dictionary to send + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ loop = get_event_loop() if loop is None else loop @@ -391,13 +414,15 @@ async def __send_unnamed_dict(self, dict_to_send: Dict[Any, Any], loop: Optional # The sending is finished await self.send_command_finished(loop=loop, receiver=receiver) - def __sync_send_unnamed_dict(self, dict_to_send: Dict[Any, Any], receiver: Optional[socket] = None) -> None: + def __sync_send_unnamed_dict(self, + dict_to_send: Dict[Any, Any], + receiver: Optional[socket] = None) -> None: """ - | Send a whole dictionary field by field as labeled data. Dictionary will be unnamed. - | Synchronous version of 'TcpIpObject.receive_labeled_data'. + Send a whole dictionary field by field as labeled data. Dictionary will be unnamed. + Synchronous version of 'TcpIpObject.receive_labeled_data'. - :param Dict[Any, Any] dict_to_send: Dictionary to send - :param Optional[socket] receiver: TcpIpObject receiver + :param dict_to_send: Dictionary to send. + :param receiver: TcpIpObject receiver. """ receiver = self.sock if receiver is None else receiver @@ -420,14 +445,16 @@ def __sync_send_unnamed_dict(self, dict_to_send: Dict[Any, Any], receiver: Optio # The sending is finished self.sync_send_command_finished(receiver=receiver) - async def receive_dict(self, recv_to: Dict[Any, Any], loop: Optional[EventLoop] = None, + async def receive_dict(self, + recv_to: Dict[Any, Any], + loop: Optional[EventLoop] = None, sender: Optional[socket] = None) -> None: """ - | Receive a whole dictionary field by field as labeled data. + Receive a whole dictionary field by field as labeled data. - :param Dict[Any, Any] recv_to: Dictionary to fill with received fields - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] sender: TcpIpObject sender + :param recv_to: Dictionary to fill with received fields. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ loop = get_event_loop() if loop is None else loop @@ -445,13 +472,15 @@ async def receive_dict(self, recv_to: Dict[Any, Any], loop: Optional[EventLoop] else: recv_to[label] = param - def sync_receive_dict(self, recv_to: Dict[Any, Any], sender: Optional[socket] = None) -> None: + def sync_receive_dict(self, + recv_to: Dict[Any, Any], + sender: Optional[socket] = None) -> None: """ - | Receive a whole dictionary field by field as labeled data. - | Synchronous version of 'TcpIpObject.receive_labeled_data'. + Receive a whole dictionary field by field as labeled data. + Synchronous version of 'TcpIpObject.receive_labeled_data'. - :param Dict[Any, Any] recv_to: Dictionary to fill with received fields - :param Optional[socket] sender: TcpIpObject sender + :param recv_to: Dictionary to fill with received fields. + :param sender: TcpIpObject sender. """ sender = self.sock if sender is None else sender @@ -474,14 +503,17 @@ def sync_receive_dict(self, recv_to: Dict[Any, Any], sender: Optional[socket] = ########################################################################################## ########################################################################################## - async def __send_command(self, loop: EventLoop, receiver: socket, command: str = '') -> None: + async def __send_command(self, + loop: EventLoop, + receiver: socket, + command: str = '') -> None: """ - | Send a bytes command among the available commands. - | Do not use this one. Use the dedicated function 'send_command_{cmd}(...)'. + Send a bytes command among the available commands. + Do not use this one. Use the dedicated function 'send_command_{cmd}(...)'. - :param EventLoop loop: asyncio.get_event_loop() return - :param socket receiver: TcpIpObject receiver - :param str command: Name of the command, must be in 'self.command_dict' + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. + :param command: Name of the command, must be in 'self.command_dict'. """ # Check if the command exists @@ -492,14 +524,16 @@ async def __send_command(self, loop: EventLoop, receiver: socket, command: str = # Send command as a byte data await self.send_data(data_to_send=cmd, loop=loop, receiver=receiver) - def __sync_send_command(self, receiver: socket, command: str = '') -> None: + def __sync_send_command(self, + receiver: socket, + command: str = '') -> None: """ - | Send a bytes command among the available commands. - | Do not use this one. Use the dedicated function 'sync_send_command_{cmd}(...)'. - | Synchronous version of 'TcpIpObject.send_command'. + Send a bytes command among the available commands. + Do not use this one. Use the dedicated function 'sync_send_command_{cmd}(...)'. + Synchronous version of 'TcpIpObject.send_command'. - :param str command: name of the command to send - :param socket receiver: TcpIpObject receiver + :param command: Name of the command to send. + :param receiver: TcpIpObject receiver. """ # Check if the command exists @@ -510,203 +544,254 @@ def __sync_send_command(self, receiver: socket, command: str = '') -> None: # Send command as a byte data self.sync_send_data(data_to_send=cmd, receiver=receiver) - async def send_command_compute(self, loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: + async def send_command_compute(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: """ - | Send the 'compute' command. + Send the 'compute' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='compute') - def sync_send_command_compute(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_compute(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'compute' command. - | Synchronous version of 'TcpIpObject.send_command_compute'. + Send the 'compute' command. + Synchronous version of 'TcpIpObject.send_command_compute'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='compute') - async def send_command_done(self, loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: + async def send_command_done(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: """ - | Send the 'done' command. + Send the 'done' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='done') - def sync_send_command_done(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_done(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'done' command. - | Synchronous version of 'TcpIpObject.send_command_done'. + Send the 'done' command. + Synchronous version of 'TcpIpObject.send_command_done'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='done') - async def send_command_exit(self, loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: + async def send_command_exit(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: """ - | Send the 'exit' command. + Send the 'exit' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='exit') - def sync_send_command_exit(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_exit(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'exit' command. - | Synchronous version of 'TcpIpObject.send_command_exit'. + Send the 'exit' command. + Synchronous version of 'TcpIpObject.send_command_exit'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='exit') - async def send_command_finished(self, loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: + async def send_command_finished(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: """ - | Send the 'finished' command. + Send the 'finished' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='finished') - def sync_send_command_finished(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_finished(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'finished' command. - | Synchronous version of 'TcpIpObject.send_command_finished'. + Send the 'finished' command. + Synchronous version of 'TcpIpObject.send_command_finished'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='finished') - async def send_command_prediction(self, loop: Optional[EventLoop] = None, + async def send_command_prediction(self, + loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: """ - | Send the 'prediction' command. + Send the 'prediction' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='prediction') - def sync_send_command_prediction(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_prediction(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'prediction' command. - | Synchronous version of 'TcpIpObject.send_command_prediction'. + Send the 'prediction' command. + Synchronous version of 'TcpIpObject.send_command_prediction'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='prediction') - async def send_command_read(self, loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: + async def send_command_read(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: """ - | Send the 'read' command. + Send the 'read' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='read') - def sync_send_command_read(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_read(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'read' command. - | Synchronous version of 'TcpIpObject.send_command_read'. + Send the 'read' command. + Synchronous version of 'TcpIpObject.send_command_read'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='read') - async def send_command_sample(self, loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: + async def send_command_sample(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: """ - | Send the 'sample' command. + Send the 'sample' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='sample') - def sync_send_command_sample(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_sample(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'sample' command. - | Synchronous version of 'TcpIpObject.send_command_sample'. + Send the 'sample' command. + Synchronous version of 'TcpIpObject.send_command_sample'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='sample') - async def send_command_step(self, loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: + async def send_command_step(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: """ - | Send the 'step' command. + Send the 'step' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='step') - def sync_send_command_step(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_step(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'step' command. - | Synchronous version of 'TcpIpObject.send_command_step'. + Send the 'step' command. + Synchronous version of 'TcpIpObject.send_command_step'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='step') - async def send_command_visualisation(self, loop: Optional[EventLoop] = None, + async def send_command_visualisation(self, + loop: Optional[EventLoop] = None, receiver: Optional[socket] = None) -> None: """ - | Send the 'visualisation' command. + Send the 'visualisation' command. - :param Optional[EventLoop] loop: asyncio.get_event_loop() return - :param Optional[socket] receiver: TcpIpObject receiver + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. """ await self.__send_command(loop=loop, receiver=receiver, command='visualisation') - def sync_send_command_visualisation(self, receiver: Optional[socket] = None) -> None: + def sync_send_command_visualisation(self, + receiver: Optional[socket] = None) -> None: """ - | Send the 'visualisation' command. - | Synchronous version of 'TcpIpObject.send_command_visualisation'. + Send the 'visualisation' command. + Synchronous version of 'TcpIpObject.send_command_visualisation'. - :param Optional[socket] receiver: TcpIpObject receiver + :param receiver: TcpIpObject receiver. """ self.__sync_send_command(receiver=receiver, command='visualisation') + async def send_command_change_db(self, + loop: Optional[EventLoop] = None, + receiver: Optional[socket] = None) -> None: + """ + Send the 'change_database' command. + + :param loop: Asyncio event loop. + :param receiver: TcpIpObject receiver. + """ + + await self.__send_command(loop=loop, receiver=receiver, command='db') + + def sync_send_command_change_db(self, + receiver: Optional[socket] = None) -> None: + """ + Send the 'change_database' command. + Synchronous version of 'TcpIpObject.send_command_change_database'. + + :param receiver: TcpIpObject receiver. + """ + + self.__sync_send_command(receiver=receiver, command='db') + ########################################################################################## ########################################################################################## # Actions to perform on commands # ########################################################################################## ########################################################################################## - async def listen_while_not_done(self, loop: EventLoop, sender: socket, data_dict: Dict[Any, Any], + async def listen_while_not_done(self, + loop: EventLoop, + sender: socket, + data_dict: Dict[Any, Any], client_id: int = None) -> Dict[Any, Any]: """ - | Compute actions until 'done' command is received. + Compute actions until 'done' command is received. - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender - :param Dict[Any, Any] data_dict: Dictionary to collect data - :param int client_id: ID of a Client + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. + :param data_dict: Dictionary to collect data. + :param client_id: ID of a Client. """ # Compute actions until 'done' command is received @@ -717,74 +802,98 @@ async def listen_while_not_done(self, loop: EventLoop, sender: socket, data_dict # Return collected data return data_dict - async def action_on_compute(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_compute(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'compute' command. + Action to run when receiving the 'compute' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass - async def action_on_done(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_done(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'done' command. + Action to run when receiving the 'done' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass - async def action_on_exit(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_exit(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'exit' command. + Action to run when receiving the 'exit' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass - async def action_on_finished(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_finished(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'finished' command. + Action to run when receiving the 'finished' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass - async def action_on_prediction(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_prediction(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'prediction' command. + Action to run when receiving the 'prediction' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass - async def action_on_read(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_read(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'read' command. + Action to run when receiving the 'read' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ # Receive labeled data @@ -797,39 +906,66 @@ async def action_on_read(self, data: Dict[Any, Any], client_id: int, sender: soc else: data[client_id][label] = param - async def action_on_sample(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_sample(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'sample' command. + Action to run when receiving the 'sample' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass - async def action_on_step(self, data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: + async def action_on_step(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: """ - | Action to run when receiving the 'step' command. + Action to run when receiving the 'step' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass - async def action_on_visualisation(self, data: Dict[Any, Any], client_id: int, sender: socket, + async def action_on_visualisation(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, loop: EventLoop) -> None: """ - | Action to run when receiving the 'visualisation' command. + Action to run when receiving the 'visualisation' command. + + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. + """ + + pass + + async def action_on_change_db(self, + data: Dict[Any, Any], + client_id: int, + sender: socket, + loop: EventLoop) -> None: + """ + Action to run when receiving the 'change_database' command. - :param Dict[Any, Any] data: Dict storing data - :param int client_id: ID of the TcpIpClient - :param EventLoop loop: asyncio.get_event_loop() return - :param socket sender: TcpIpObject sender + :param data: Dict storing data. + :param client_id: ID of the TcpIpClient. + :param loop: Asyncio event loop. + :param sender: TcpIpObject sender. """ pass diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index e8130adb..07afb533 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -238,6 +238,30 @@ def set_dataset_batch(self, # Define batch from dataset self.batch_from_dataset = batch.copy() + def change_database(self, + database: str): + """ + Change the Database to connect to for each Client. + + :param database: Path to the new Database. + """ + + async_run(self.__change_database(database)) + + async def __change_database(self, + database: str): + """ + Change the Database to connect to for each Client. + + :param database: Path to the new Database. + """ + + loop = get_event_loop() + for client_id, client in self.clients: + await self.send_command_change_db(loop=loop, receiver=client) + await self.send_data(data_to_send=database[0], loop=loop, receiver=client) + await self.send_data(data_to_send=database[1], loop=loop, receiver=client) + ########################################################################################## ########################################################################################## # Server & Client shutdown # diff --git a/src/Database/BaseDataset.py b/src/Database/BaseDataset.py deleted file mode 100644 index 4b155513..00000000 --- a/src/Database/BaseDataset.py +++ /dev/null @@ -1,252 +0,0 @@ -from typing import Dict, List, Optional, Tuple -from numpy import array, ndarray, concatenate, save, arange -from numpy.random import shuffle -from collections import namedtuple - - -class BaseDataset: - """ - | BaseDataset is a dataset class to store any data from a BaseEnvironment or from files. - | Given data is split into input data and output data. - | Saving data results in multiple partitions of input and output data. - - :param namedtuple config: Namedtuple which contains BaseDataset parameters - """ - - def __init__(self, config: namedtuple): - - self.name: str = self.__class__.__name__ - - # Data fields containers - self.data_type = ndarray - self.fields: List[str] = ['input', 'output'] - self.data: Dict[str, ndarray] = {'input': array([]), 'output': array([])} - self.shape: Dict[str, Optional[List[int]]] = {'input': None, 'output': None} - - # Indexing - self.shuffle_pattern: Optional[List[int]] = None - self.current_sample: int = 0 - - # Dataset memory - self.max_size: int = config.max_file_size - self.batch_per_field: Dict[str, int] = {field: 0 for field in ['input', 'output']} - self.__empty: bool = True - - @property - def nb_samples(self) -> int: - """ - | Property returning the current number of samples - - :return: The current number of samples in all partitions - """ - - return max([len(self.data[field]) for field in self.fields]) - - def is_empty(self) -> bool: - """ - | Check if the fields of the dataset are empty. A field is considered as non-empty if it is filled with another - sample. - - :return: The Dataset is empty or not - """ - - # The empty flag is set to False once the Dataset is considered as non-empty - if not self.__empty: - return False - # Check each registered data field - for field in self.fields: - # Dataset is considered as non-empty if a field is filled with another sample - if self.batch_per_field[field] > 1: - self.__empty = False - return False - # If all field are considered as non-empty then the Dataset is empty - return True - - def init_data_size(self, field: str, shape: List[int]) -> None: - """ - | Store the original shape of data. Reshape data containers. - - :param str field: Data field name - :param List[int] shape: Shape of the corresponding tensor - """ - - # Store the original data shape - self.shape[field] = shape - # Reshape the data container - self.data[field] = array([]).reshape((0, *shape)) - - def get_data_shape(self, field: str) -> List[int]: - """ - | Returns the data shape of field. - - :param str field: Data field name - :return: Data shape for field - """ - - return self.shape[field] - - def init_additional_field(self, field: str, shape: List[int]) -> None: - """ - | Register a new data field. - - :param str field: Name of the data field - :param List[int] shape: Data shape - """ - - # Register the data field - self.fields.append(field) - # Init the number of adds in the field - self.batch_per_field[field] = 0 - # Init the field shape - self.init_data_size(field, shape) - - def empty(self) -> None: - """ - | Empty the dataset. - """ - - # Reinit each data container - for field in self.fields: - self.data[field] = array([]) if self.shape[field] is None else array([]).reshape((0, *self.shape[field])) - # Reinit indexing variables - self.shuffle_pattern = None - self.current_sample = 0 - self.batch_per_field = {field: 0 for field in self.fields} - self.__empty = True - - def memory_size(self, field: Optional[str] = None) -> int: - """ - | Return the actual memory size of the dataset if field is None. Otherwise, return the actual memory size of the - field. - - :param Optional[str] field: Name of the data field - :return: Size in bytes of the current dataset. - """ - - # Return the total memory size - if field is None: - return sum([self.data[field].nbytes for field in self.fields]) - # Return the memory size for the specified field - return self.data[field].nbytes - - def check_data(self, field: str, data: ndarray) -> None: - """ - | Check if the data is a numpy array. - - :param str field: Values at 'input' or anything else. Define if the associated shape is correspond to input - shape or output one. - :param ndarray data: New data - """ - - if type(data) != self.data_type: - raise TypeError(f"[{self.name}] Wrong data type in field '{field}': numpy array required, got {type(data)}") - - def add(self, field: str, data: ndarray, partition_file: Optional[str] = None) -> None: - """ - | Add data to the dataset. - - :param str field: Name of the data field - :param ndarray data: New data as batch of samples - :param Optional[str] partition_file: Path to the file in which to write the data - """ - - # Check data type - self.check_data(field, data) - - # Check if field is registered - if field not in self.fields: - # Fields can be register only if Dataset is empty - if not self.is_empty(): - raise ValueError(f"[{self.name}] A new field {field} tries to be created as Dataset is non empty. This " - f"will lead to a different number of sample for each field of the dataset.") - # Add new field if not registered - self.init_additional_field(field, data[0].shape) - - # Check data size initialization - if self.shape[field] is None: - self.init_data_size(field, data[0].shape) - - # Add batched samples - self.data[field] = concatenate((self.data[field], data)) - # Save in partition - if partition_file is not None: - self.save(field, partition_file) - - # Update sample indexing in dataset - self.batch_per_field[field] += 1 - self.current_sample = max([len(self.data[f]) for f in self.fields]) - - def save(self, field: str, file: str) -> None: - """ - | Save the corresponding field of the Dataset. - - :param str field: Name of the data field - :param str file: Path to the file in which to write the data - """ - - save(file, self.data[field]) - - def set(self, field: str, data: ndarray) -> None: - """ - | Set a full field of the dataset. - - :param str field: Name of the data field - :param ndarray data: New data as batch of samples - """ - - # Check data type - self.check_data(field, data) - - # Check if field is registered - if field not in self.fields: - # Add new field if not registered - self.init_additional_field(field, data[0].shape) - - # Check data size initialization - if self.shape[field] is None: - self.init_data_size(field, data[0].shape) - - # Set the full field - self.data[field] = data - - # Update sample indexing in dataset - self.__empty = False - self.current_sample = 0 - - def get(self, field: str, idx_begin: int, idx_end: int) -> ndarray: - """ - | Get a batch of data in 'field' container. - - :param str field: Name of the data field - :param int idx_begin: Index of the first sample - :param int idx_end: Index of the last sample - :return: Batch of data from 'field' - """ - - indices = slice(idx_begin, idx_end) if self.shuffle_pattern is None else self.shuffle_pattern[idx_begin:idx_end] - return self.data[field][indices] - - def shuffle(self) -> None: - """ - | Define a random shuffle pattern. - """ - - # Nothing to shuffle if Dataset is empty - if self.is_empty(): - return - # Generate a shuffle pattern - self.shuffle_pattern = arange(self.nb_samples) - shuffle(self.shuffle_pattern) - - def __str__(self) -> str: - """ - :return: String containing information about the BaseDatasetConfig object - """ - - description = "\n" - description += f" {self.name}\n" - description += f" Max size: {self.max_size}\n" - description += f" Data fields: {self.fields}" - for field in self.fields: - description += f" {field} shape: {self.shape[field]}" - return description diff --git a/src/Database/BaseDatasetConfig.py b/src/Database/BaseDatasetConfig.py index d1bf8bfb..23e90c9b 100644 --- a/src/Database/BaseDatasetConfig.py +++ b/src/Database/BaseDatasetConfig.py @@ -1,14 +1,10 @@ -from typing import Type, Optional +from typing import Optional from os.path import isdir, sep, join -from DeepPhysX.Core.Database.BaseDataset import BaseDataset -from DeepPhysX.Core.Utils.configs import make_config, namedtuple - class BaseDatasetConfig: def __init__(self, - dataset_class: Type[BaseDataset] = BaseDataset, existing_dir: Optional[str] = None, mode: Optional[str] = None, max_file_size: Optional[float] = None, @@ -18,7 +14,6 @@ def __init__(self, """ Configuration class to parameterize the Dataset and its Manager. - :param dataset_class: BaseDataset class from which an instance will be created. :param existing_dir: Path to an existing Dataset repository. :param mode: Specify the Dataset mode that should be used between 'training', 'validation' and 'running'. :param max_file_size: Maximum size (in Gb) of a single dataset file. @@ -56,12 +51,6 @@ def __init__(self, raise TypeError(f"[{self.name}] The given 'recompute_normalization'={recompute_normalization} must be a " f"bool.") - # BaseDataset parameterization - self.dataset_class: Type[BaseDataset] = dataset_class - self.dataset_config: namedtuple = make_config(configuration_object=self, - configuration_name='dataset_config', - max_file_size=max_file_size) - # DatasetManager parameterization self.existing_dir: Optional[str] = existing_dir self.mode: Optional[str] = mode @@ -70,26 +59,13 @@ def __init__(self, self.normalize: bool = normalize self.recompute_normalization: bool = recompute_normalization - def create_dataset(self) -> BaseDataset: - """ - Create an instance of BaseDataset with given parameters. - - :return: BaseDataset instance. - """ - - dataset = self.dataset_class(config=self.dataset_config) - if not isinstance(dataset, BaseDataset): - raise TypeError(f"[{self.name}] The given 'dataset_class'={self.dataset_class} must be a BaseDataset.") - return dataset - def __str__(self) -> str: description = "\n" description += f"{self.name}\n" - description += f" Dataset Class: {self.dataset_class.__name__}\n" description += f" Existing directory: {False if self.existing_dir is None else self.existing_dir}\n" description += f" Mode: {self.mode}\n" - description += f" Max size: {self.dataset_config.max_size}\n" + description += f" Max size: {self.max_file_size}\n" description += f" Shuffle: {self.shuffle}\n" description += f" Normalize: {self.normalize}\n" description += f" Recompute normalization: {self.recompute_normalization}\n" diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index 8b6170e5..e7979908 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -2,7 +2,7 @@ from os import cpu_count from os.path import join, dirname from threading import Thread -from subprocess import run as subprocessRun +from subprocess import run from sys import modules, executable from SSD.Core.Storage.Database import Database @@ -167,16 +167,9 @@ def start_client(self, script = join(dirname(modules[BaseEnvironment.__module__].__file__), 'launcherBaseEnvironment.py') # Usage: python3 script.py " - subprocessRun([executable, - script, - self.environment_file, - self.environment_class.__name__, - self.ip_address, - str(self.port), - str(idx), - str(self.number_of_thread), - str(data_db), - str(visu_db)]) + run([executable, script, self.environment_file, self.environment_class.__name__, + self.ip_address, str(self.port), str(idx), str(self.number_of_thread), + str(data_db), str(visu_db)]) def create_environment(self, environment_manager: Any, @@ -195,6 +188,9 @@ def create_environment(self, as_tcp_ip_client=False, data_db=data_db, visu_db=visu_db) + if not isinstance(environment, BaseEnvironment): + raise TypeError(f"[{self.name}] The given 'environment_class'={self.environment_class} must be a " + f"BaseEnvironment.") # Create & Init Environment environment.recv_parameters(self.param_dict) environment.create() diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 51062cf7..72afad16 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Dict, List +from typing import Any, Optional, Dict, List, Union from numpy import ndarray from DeepPhysX.Core.Manager.DatabaseManager import DatasetManager, Database @@ -15,7 +15,7 @@ def __init__(self, manager: Optional[Any] = None, session: str = 'sessions/default', new_session: bool = True, - is_training: bool = True, + pipeline: str = '', produce_data: bool = True, batch_size: int = 1): @@ -68,19 +68,19 @@ def __init__(self, # self.normalization[field] = json_dict['normalization'][field] # Create a DatasetManager if required - create_dataset = is_training or produce_data + create_dataset = pipeline in ['data_generation', 'training'] or produce_data database = None if create_dataset: self.dataset_manager = DatasetManager(dataset_config=dataset_config, session=session, data_manager=self, new_session=new_session, - is_training=is_training, + pipeline=pipeline, produce_data=produce_data) database = self.dataset_manager.database # Create an EnvironmentManager if required - create_environment = produce_data or environment_config is not None + create_environment = environment_config is not None if create_environment: self.environment_manager = EnvironmentManager(environment_config=environment_config, data_manager=self, @@ -89,8 +89,10 @@ def __init__(self, batch_size=batch_size) # DataManager variables + self.pipeline = pipeline self.produce_data = produce_data - self.data: Optional[Dict[str, ndarray]] = None + self.batch_size = batch_size + self.data_lines: Union[List[int], int] = [] def get_manager(self) -> Any: """ @@ -104,42 +106,48 @@ def get_manager(self) -> Any: def get_database(self) -> Database: return self.dataset_manager.database + def change_database(self) -> None: + # self.manager.change_database(self.dataset_manager.database) + self.environment_manager.change_database(self.dataset_manager.database) + def get_data(self, epoch: int = 0, - batch_size: int = 1, animate: bool = True) -> None: """ Fetch data from the EnvironmentManager or the DatasetManager according to the context. :param epoch: Current epoch number. - :param batch_size: Size of the desired batch. :param animate: Allow EnvironmentManager to generate a new sample. :return: Dict containing the newly computed data. """ - # Training - if self.produce_data: - # Get data from environment if used and if the data should be created at this epoch + # Data generation case + if self.pipeline == 'data_generation': + self.data_lines = self.environment_manager.get_data(animate=animate) + self.dataset_manager.add_data() + + # Training case + elif self.pipeline == 'training': + + # Get data from Environment(s) if used and if the data should be created at this epoch if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data): - self.environment_manager.get_data(animate=animate) - # self.dataset_manager.add_data(data) - # Force data from the dataset + self.data_lines = self.environment_manager.get_data(animate=animate) + self.dataset_manager.add_data() + + # Get data from Dataset else: - data = self.dataset_manager.get_data(batch_size=batch_size, get_inputs=True, get_outputs=True) + self.data_lines = self.dataset_manager.get_data(batch_size=self.batch_size) + print(self.data_lines) + # Dispatch a batch to clients if self.environment_manager is not None and self.environment_manager.use_dataset_in_environment: - new_data = self.environment_manager.dispatch_batch(batch=data) - if len(new_data['input']) != 0: - data['input'] = new_data['input'] - if len(new_data['output']) != 0: - data['output'] = new_data['output'] - if 'loss' in new_data: - data['loss'] = new_data['loss'] + self.data_lines = self.environment_manager.dispatch_batch(batch=self.data_lines) + + # Environment is no longer used elif self.environment_manager is not None: - # EnvironmentManager is no longer used self.environment_manager.close() self.environment_manager = None - # Prediction + # Prediction pipeline else: if self.dataset_manager is not None and not self.dataset_manager.new_dataset(): # Get data from dataset diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 5719a398..4136cc14 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -1,15 +1,15 @@ -from typing import Any, Dict, Tuple, List, Optional, Union -from os.path import isfile, isdir, abspath, join +from typing import Any, Dict, List, Optional +from os.path import isfile, isdir, join from os import listdir, symlink, sep from json import dump as json_dump from json import load as json_load -from numpy import load, squeeze, ndarray, concatenate, float64 +from numpy import arange, ndarray, array, abs, mean, sqrt +from numpy.random import shuffle from SSD.Core.Storage.Database import Database from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig -from DeepPhysX.Core.Database.BaseDataset import BaseDataset -from DeepPhysX.Core.Utils.path import create_dir, copy_dir +from DeepPhysX.Core.Utils.path import create_dir, copy_dir, get_first_caller from DeepPhysX.Core.Utils.jsonUtils import CustomJSONEncoder @@ -20,7 +20,7 @@ def __init__(self, session: str = 'sessions/default', data_manager: Optional[Any] = None, new_session: bool = True, - is_training: bool = True, + pipeline: str = '', produce_data: bool = True): """ DatasetManager handle all operations with input / output files. Allows saving and read tensors from files. @@ -39,6 +39,7 @@ def __init__(self, self.data_manager: Optional[Any] = data_manager self.dataset_dir: str = join(session, 'dataset') self.database: Optional[Database] = None + self.pipeline: str = pipeline # Dataset parameters self.max_file_size: int = dataset_config.max_file_size @@ -58,26 +59,36 @@ def __init__(self, self.partitions: Dict[str, List[str]] = {mode: [] for mode in self.modes} self.partition_index: Dict[str, int] = {mode: 0 for mode in self.modes} + # Dataset indexing + self.shuffle_pattern: ndarray = arange(0) + self.current_sample: int = 0 + self.first_add = True + # Dataset json file - self.json_default: Dict[str, Dict[str, Any]] = {'data_shape': {}, + self.json_default: Dict[str, Dict[str, Any]] = {'partitions': {mode: [] for mode in self.modes}, 'nb_samples': {mode: [] for mode in self.modes}, - 'partitions': {mode: [] for mode in self.modes}, + 'architecture': {}, + 'data_shape': {}, 'normalization': {}} self.json_content: Dict[str, Dict[str, Any]] = self.json_default.copy() # Produce training data + root = get_first_caller() if produce_data: # Produce training data in a new session if new_session: # Produce training data in a new session from scratch # --> Create a new '/dataset' directory if dataset_config.existing_dir is None: - create_dir(session_dir=session, session_name='dataset') - self.init_directory() + create_dir(session_dir=session, + session_name='dataset') + self.create_partition() # Produce training data in a new session from an existing Dataset # --> Copy the 'existing_dir/dataset' directory then load the 'session/dataset' directory else: - copy_dir(src_dir=dataset_config.existing_dir, dest_dir=session, sub_folders='dataset') + copy_dir(src_dir=join(root, dataset_config.existing_dir), + dest_dir=session, + sub_folders='dataset') self.load_directory() # Produce training data in an existing session # --> Load the 'session/dataset' directory @@ -88,7 +99,7 @@ def __init__(self, # Load training data in a new session # --> Link to the 'existing_dir/dataset' directory the load the 'session/dataset' directory if new_session: - symlink(src=join(dataset_config.existing_dir, 'dataset'), + symlink(src=join(root, dataset_config.existing_dir, 'dataset'), dst=join(session, 'dataset')) self.load_directory() # Load training data in an existing session @@ -96,7 +107,7 @@ def __init__(self, else: self.load_directory() - def init_directory(self): + def create_partition(self): """ """ @@ -109,9 +120,38 @@ def init_directory(self): fields=[('env', int), ('net', int)]) self.database.create_table(table_name='Training') self.database.create_table(table_name='Additional') + self.partitions[self.mode].append(partition_path) self.partition_index[self.mode] += 1 + def additional_partition(self): + + # 1. If a DB exists, get all the Fields to re-create them + fields = {} + types = {'INT': int, 'FLOAT': float, 'STR': str, 'BOOL': bool, 'NUMPY': ndarray} + if self.partition_index[self.mode] > 0: + for table_name in ['Training', 'Additional']: + fields[table_name] = [] + F = self.database.get_fields(table_name=table_name, + only_names=False) + for field in [f for f in F if f not in ['id', '_dt_']]: + fields[table_name].append((field, types[F[field].field_type])) + + # 2. Create a new Database + self.create_partition() + + # 3. Re-create the Fields if this is not the first partition + if self.partition_index[self.mode] > 1: + for table_name in fields.keys(): + self.database.create_fields(table_name=table_name, + fields=fields[table_name]) + + # 4. Tell the other components to communicate on a new DB + self.data_manager.change_database() + def load_directory(self): + """ + + """ # 1. Check the directory existence to prevent bugs if not isdir(self.dataset_dir): @@ -135,9 +175,6 @@ def load_directory(self): self.normalize and self.json_content['normalization'] == self.json_default['normalization']): self.update_json(update_normalization=True) - # 5. Load data from partitions - self.load_partitions() - def search_partitions(self): """ @@ -148,17 +185,182 @@ def search_partitions(self): return {mode: sorted(raw_partitions[mode]) for mode in self.modes} def search_partitions_info(self): - pass + """ - def load_partitions(self): - pass + """ + + # TODO: fill json - def update_json(self, update_shapes: bool = False, update_nb_samples: bool = False, - update_partitions_lists: bool = False, update_normalization: bool = False) -> None: pass + def update_json(self, + update_partitions_lists: bool = False, + update_nb_samples: bool = False, + update_architecture: bool = False, + update_shapes: bool = False, + update_normalization: bool = False) -> None: + """ + + + """ + + # Update partitions lists + if update_partitions_lists: + self.json_content['partitions'] = self.partitions + + # Update number of samples + if update_nb_samples: + if len(self.json_content['nb_samples'][self.mode]) == self.partition_index[self.mode]: + self.json_content['nb_samples'][self.mode][-1] = self.nb_samples + else: + self.json_content['nb_samples'][self.mode].append(self.nb_samples) + + # Update DB architecture + if update_architecture: + architecture = self.database.get_architecture() + for fields in architecture.values(): + for field in fields.copy(): + if field.split(' ')[0] in ['id', '_dt_']: + fields.remove(field) + architecture.pop('Sync') + self.json_content['architecture'] = architecture + + # Update data shapes + if update_shapes: + for table_name, fields in self.json_content['architecture'].items(): + data = self.database.get_line(table_name=table_name) + for field in fields: + if 'NUMPY' in field: + field_name = field.split(' ')[0] + self.json_content['data_shape'][f'{table_name}.{field_name}'] = data[field_name].shape + + # Update normalization coefficients + if update_normalization: + self.json_content['normalization'] = self.compute_normalization() + + # Overwrite json file + with open(join(self.dataset_dir, 'dataset.json'), 'w') as json_file: + json_dump(self.json_content, json_file, indent=3, cls=CustomJSONEncoder) + + @property + def nb_samples(self) -> int: + return self.database.nb_lines(table_name='Training') + + def shuffle(self): + """ + + """ + + self.shuffle_pattern = arange(self.nb_samples) + if self.shuffle: + shuffle(self.shuffle_pattern) + + def add_data(self): + """ + + """ + + # 1. Update the json file + self.update_json(update_nb_samples=True) + if self.first_add: + self.update_json(update_partitions_lists=True, update_shapes=True, update_architecture=True) + self.first_add = False + + # 2. Check the size of the partition + if self.max_file_size is not None: + if self.database.memory_size > self.max_file_size: + self.additional_partition() + self.update_json(update_partitions_lists=True, update_nb_samples=True) + + def get_data(self, + batch_size: int) -> ndarray: + """ + + """ + + # 1. Check if dataset is loaded and if the current sample is not the last + # TODO + + # 2. Update dataset index + idx = self.current_sample + self.current_sample += batch_size + + # 3. Get a batch of data + if self.shuffle: + return self.shuffle_pattern[idx:self.current_sample] + return arange(idx, self.current_sample) + def close(self): + """ + + """ if self.normalize and self.produce_data: self.update_json(update_normalization=True) self.database.close() + + def change_mode(self, mode: int) -> None: + """ + + """ + + pass + + def compute_normalization(self) -> Dict[str, List[float]]: + """ + + """ + + # Get the fields to normalize + fields = [] + for field in self.json_content['data_shape']: + table_name, field_name = field.split('.') + fields += [field_name] if table_name == 'Training' else [] + + # Init result + normalization = {field: [0., 0.] for field in fields} + + # Compute the mean for each field + means = {field: [] for field in fields} + nb_samples = [] + for partition in self.partitions['training']: + data_to_normalize = self.load_partitions_fields(partition=partition, + fields=fields) + nb_samples.append(data_to_normalize['id'][-1]) + for field in fields: + data = array(data_to_normalize[field]) + means[field].append(data.mean()) + for field in fields: + normalization[field][0] = sum([(n / sum(nb_samples)) * m + for n, m in zip(nb_samples, means[field])]) + + # Compute the standard deviation for each field + stds = {field: [] for field in fields} + for partition in self.partitions['training']: + data_to_normalize = self.load_partitions_fields(partition=partition, + fields=fields) + for field in fields: + data = array(data_to_normalize[field]) + stds[field].append(mean(abs(data - normalization[field][0]) ** 2)) + for field in fields: + normalization[field][1] = sqrt(sum([(n / sum(nb_samples)) * std + for n, std in zip(nb_samples, stds[field])])) + + return normalization + + def load_partitions_fields(self, + partition: str, + fields: List[str]): + db = Database(database_dir=self.dataset_dir, + database_name=partition).load() + return db.get_lines(table_name='Training', + fields=fields, + batched=True) + + def __str__(self): + + description = "\n" + description += f"# {self.name}\n" + description += f" Dataset Repository: {self.dataset_dir}\n" + description += f" Partitions size: {self.max_file_size * 1e-9} Go\n" + return description diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 031c2177..a89729cc 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Optional, Union +from typing import Any, Dict, Optional, Union, List from numpy import ndarray from asyncio import run as async_run from copy import copy @@ -13,8 +13,8 @@ class EnvironmentManager: def __init__(self, - environment_config: Optional[BaseEnvironmentConfig] = None, - data_manager: Any = None, + environment_config: BaseEnvironmentConfig, + data_manager: Optional[Any] = None, session: str = 'sessions/default', data_db: Optional[Database] = None, batch_size: int = 1): @@ -65,6 +65,7 @@ def __init__(self, visu_db=visu_db) # Define get_data and dispatch methods + self.change_database = self.change_database_in_server if self.server else self.change_database_in_environment self.get_data = self.get_data_from_server if self.server else self.get_data_from_environment self.dispatch_batch = self.dispatch_batch_to_server if self.server else self.dispatch_batch_to_environment @@ -74,17 +75,14 @@ def __init__(self, self.visualizer.get_database().load() self.visualizer.init_visualizer() - def get_data_manager(self) -> Any: - """ - Get the DataManager of this EnvironmentManager. - - :return: The DataManager of this EnvironmentManager. - """ + def change_database_in_server(self, database: Database): + self.server.change_database(database.get_path()) - return self.data_manager + def change_database_in_environment(self, database): + self.environment.database = database def get_data_from_server(self, - animate: bool = True) -> None: + animate: bool = True) -> List[int]: """ Compute a batch of data from Environments requested through TcpIpServer. @@ -92,10 +90,11 @@ def get_data_from_server(self, """ # Get data from server - self.server.get_batch(animate) + # TODO: return the indices of samples + return self.server.get_batch(animate) def get_data_from_environment(self, - animate: bool = True) -> None: + animate: bool = True) -> List[int]: """ Compute a batch of data directly from Environment. @@ -135,6 +134,9 @@ def get_data_from_environment(self, self.environment._send_training_data() self.environment._reset_training_data() + # TODO: return the indices of samples + return [] + def dispatch_batch_to_server(self, batch: Dict[str, Union[ndarray, dict]], animate: bool = True) -> Dict[str, Union[ndarray, dict]]: diff --git a/src/Manager/Manager.py b/src/Manager/Manager.py index 2a400e70..7747f7a9 100644 --- a/src/Manager/Manager.py +++ b/src/Manager/Manager.py @@ -1,7 +1,5 @@ -from typing import Any, Dict, Tuple, Optional -from os.path import join as osPathJoin -from os.path import isfile, basename, exists -from datetime import datetime +from typing import Dict, Tuple, Optional +from os.path import join, exists from numpy import ndarray from DeepPhysX.Core.Manager.DataManager import DataManager @@ -19,13 +17,13 @@ def __init__(self, network_config: Optional[BaseNetworkConfig] = None, dataset_config: Optional[BaseDatasetConfig] = None, environment_config: Optional[BaseEnvironmentConfig] = None, - pipeline: Optional[Any] = None, - session_dir: Optional[str] = None, - session_name: str = 'DPX_default', + session_dir: str = 'sessions', + session_name: str = 'default', new_session: bool = True, - is_training: bool = True, + pipeline: str = '', produce_data: bool = True, - batch_size: int = 1): + batch_size: int = 1, + debug_session: bool = False): """ Collection of all the specialized managers. Allows for some basic functions call. More specific behaviour have to be directly call from the corresponding manager. @@ -41,60 +39,68 @@ def __init__(self, :param int batch_size: Number of samples in a batch. """ - self.pipeline: Optional[Any] = pipeline + self.name = self.__class__.__name__ - # Constructing the session with the provided arguments - if session_name is None: - raise ValueError("[Manager] The session name cannot be set to None (will raise error).") - if session_dir is None: - # Create manager directory from the session name - self.session: str = osPathJoin(get_first_caller(), session_name) - else: - self.session: str = osPathJoin(session_dir, session_name) + # Define the session repository + root = get_first_caller() + session_dir = join(root, session_dir) - # Trainer: must create a new session to avoid overwriting - if is_training: + if pipeline in ['data_generation', 'training']: # Avoid unwanted overwritten data if new_session: - self.session: str = create_dir(self.session, dir_name=session_name) - # Prediction: work in an existing session + self.session = create_dir(session_dir=session_dir, + session_name=session_name) + else: + self.session = join(session_dir, session_name) else: + self.session = join(session_dir, session_name) if not exists(self.session): - raise ValueError("[Manager] The session directory {} does not exists.".format(self.session)) + raise ValueError(f"[{self.name}] The running session directory {self.session} does not exist.") - # Always create the NetworkMmanager - self.network_manager = NetworkManager(manager=self, - network_config=network_config, - session=self.session, - new_session=new_session, - training=training) - # Always create the DataManager for same reason - self.data_manager = DataManager(manager=self, - dataset_config=dataset_config, + # Create a DataManager + self.data_manager = DataManager(dataset_config=dataset_config, environment_config=environment_config, + manager=self, session=self.session, new_session=new_session, - training=training, - store_data=store_data, + pipeline=pipeline, + produce_data=produce_data, batch_size=batch_size) - # Create the StatsManager for training + database = self.data_manager.get_database() + self.batch_size = batch_size + + # Create a NetworkMmanager + self.network_manager = NetworkManager(network_config=network_config, + manager=self, + session=self.session, + new_session=new_session, + pipeline=pipeline, + data_db=database) + + # Create a StatsManager for training only + self.debug_session = debug_session self.stats_manager = StatsManager(manager=self, - session=self.session) if training else None + session=self.session) if pipeline == 'training' else None - def get_data(self, epoch: int = 0, batch_size: int = 1, animate: bool = True) -> None: + def change_database(self, database): + self.network_manager.change_database(database) + + def get_data(self, + epoch: int = 0, + animate: bool = True) -> None: """ - | Fetch data from the DataManager. + Fetch data from the DataManager. :param int epoch: Epoch ID - :param int batch_size: Size of a batch :param bool animate: If True allows running environment step """ - self.data_manager.get_data(epoch=epoch, batch_size=batch_size, animate=animate) + self.data_manager.get_data(epoch=epoch, + animate=animate) def optimize_network(self) -> Tuple[ndarray, Dict[str, float]]: """ - | Compute a prediction and run a back propagation with the current batch. + Compute a prediction and run a back propagation with the current batch. :return: The network prediction and the associated loss value """ @@ -110,14 +116,14 @@ def optimize_network(self) -> Tuple[ndarray, Dict[str, float]]: def save_network(self) -> None: """ - | Save network weights as a pth file + Save network parameters. """ self.network_manager.save_network() def close(self) -> None: """ - | Call all managers close procedure + Call all managers close procedure. """ if self.network_manager is not None: @@ -127,30 +133,7 @@ def close(self) -> None: if self.data_manager is not None: self.data_manager.close() - def save_info_file(self) -> None: - """ - | Called by the Trainer to save a .txt file which provides a quick description template to the user and lists - the description of all the components. - """ - - filename = osPathJoin(self.session, 'infos.txt') - date_time = datetime.now().strftime("%d/%m/%Y %H:%M:%S") - if not isfile(filename): - f = open(filename, "w+") - # Session description template for user - f.write("## DeepPhysX Training Session ##\n") - f.write(date_time + "\n\n") - f.write("Purpose of the training session:\nNetwork Input:\nNetwork Output:\nComments:\n\n") - # Listing every component descriptions - f.write("## List of Components Parameters ##\n") - f.write(str(self.pipeline)) - f.write(str(self)) - f.close() - def __str__(self) -> str: - """ - :return: A string containing valuable information about the Managers - """ manager_description = "" if self.network_manager is not None: diff --git a/src/Manager/StatsManager.py b/src/Manager/StatsManager.py index c79e7bdc..ee3c9580 100644 --- a/src/Manager/StatsManager.py +++ b/src/Manager/StatsManager.py @@ -39,7 +39,7 @@ def __init__(self, self.writer: SummaryWriter = SummaryWriter(self.log_dir) # Open Tensorboard - if not self.manager.pipeline.debug: + if not self.manager.debug_session: tb = program.TensorBoard() tb.configure(argv=[None, '--logdir', self.log_dir]) url = tb.launch() diff --git a/src/Pipelines/BaseDataGenerator.py b/src/Pipelines/BaseDataGenerator.py index 1e281f4b..5d8f3df0 100644 --- a/src/Pipelines/BaseDataGenerator.py +++ b/src/Pipelines/BaseDataGenerator.py @@ -65,7 +65,7 @@ def __init__(self, environment_config=environment_config, session=join(session_dir, session_name), new_session=new_session, - is_training=False, + pipeline='data_generation', produce_data=True, batch_size=batch_size) @@ -121,7 +121,7 @@ def batch_count(self) -> None: def batch_end(self) -> None: """ - Called once at the beginning of a batch production. + Called once at the end of a batch production. """ stdout.write("\033[K") diff --git a/src/Utils/jsonUtils.py b/src/Utils/jsonUtils.py index abc49337..bce413b1 100644 --- a/src/Utils/jsonUtils.py +++ b/src/Utils/jsonUtils.py @@ -64,7 +64,7 @@ def encode(self, o: Iterable) -> str: elif isinstance(o, dict): self.indentation_level += 1 output = [f"{self.indent_str}{json.dumps(key)}: {self.encode(value)}" for key, value in o.items()] - join_output = ",\n".join(output) + join_output = ",\n".join(output) if self.indentation_level != 1 else ",\n\n".join(output) self.indentation_level -= 1 return f"\n{self.indent_str}{'{'}\n{join_output}\n{self.indent_str}{'}'}" From 2221b3988fd293814c9cac9d43788a9e7bf3cf86 Mon Sep 17 00:00:00 2001 From: robin Date: Tue, 18 Oct 2022 18:38:04 +0200 Subject: [PATCH 05/61] Use Database for offline training. Dispatch batches of data in non remote mode. --- src/AsyncSocket/TcpIpServer.py | 14 +- src/Environment/BaseEnvironment.py | 69 ++++---- src/Manager/DataManager.py | 69 ++------ src/Manager/DatabaseManager.py | 38 ++++- src/Manager/EnvironmentManager.py | 52 +++--- src/Manager/Manager.py | 12 +- src/Manager/NetworkManager.py | 244 ++++++++++++++-------------- src/Network/BaseNetwork.py | 30 ++-- src/Network/BaseNetworkConfig.py | 149 +++++++---------- src/Network/BaseOptimization.py | 44 +++--- src/Network/DataTransformation.py | 50 +++--- src/Pipelines/BaseTrainer.py | 246 +++++++++++++++-------------- 12 files changed, 479 insertions(+), 538 deletions(-) diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index 07afb533..c8cb1cef 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Optional from asyncio import get_event_loop, gather from asyncio import AbstractEventLoop as EventLoop from asyncio import run as async_run @@ -216,7 +216,7 @@ async def __communicate(self, # # Send additional in / out sample # await self.send_dict(name="additional_fields", dict_to_send=sample, loop=loop, receiver=client) - # 2) Execute n steps, the last one send data computation signal + # 2. Execute n steps, the last one send data computation signal if animate: await self.send_command_step(loop=loop, receiver=client) # Receive data @@ -224,19 +224,15 @@ async def __communicate(self, client_id=client_id) def set_dataset_batch(self, - batch: Dict[str, Union[ndarray, Dict]]) -> None: + data_lines: List[int]) -> None: """ Receive a batch of data from the Dataset. Samples will be dispatched between clients. - :param batch: Batch of data. + :param data_lines: Batch of data. """ - # Check batch size - if len(batch['input']) != self.batch_size: - raise ValueError(f"[{self.name}] The size of batch from Dataset is {len(batch['input'])} while the batch " - f"size was set to {self.batch_size}.") # Define batch from dataset - self.batch_from_dataset = batch.copy() + self.batch_from_dataset = data_lines.copy() def change_database(self, database: str): diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 1714bbac..55473e71 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -1,5 +1,5 @@ from typing import Any, Optional, Dict, Union, Tuple, List -from numpy import array, ndarray +from numpy import ndarray from SSD.Core.Storage.Database import Database @@ -43,8 +43,8 @@ def __init__(self, self.__first_add: List[bool] = [True, True] # Variables to store samples from Dataset - self.sample_in: Optional[ndarray] = None - self.sample_out: Optional[ndarray] = None + self.sample_training: Optional[Dict[str, Any]] = None + self.sample_additional: Optional[Dict[str, Any]] = None # Loss data self.loss_data: Any = None # Manager if the Environment is not a TcpIpClient @@ -209,49 +209,46 @@ def set_training_data(self, self.__training_data = kwargs self.__training_data['env_id'] = self.instance_id + def set_additional_data(self, + **kwargs) -> None: + # Additional data is also set if the Environment can compute data + if self.compute_training_data: + self.__additional_data = kwargs + self.__additional_data['env_id'] = self.instance_id + def _reset_training_data(self) -> None: self.__training_data = {} + self.__additional_data = {} + self.sample_training = None + self.sample_additional = None def _send_training_data(self) -> None: line_id = self.database.add_data(table_name='Training', data=self.__training_data) - self.database.add_data(table_name='Sync', - data={'env': line_id}) self.database.add_data(table_name='Additional', data=self.__additional_data) + self.database.add_data(table_name='Sync', + data={'env': line_id}) - def set_loss_data(self, - loss_data: Any) -> None: - """ - Set the loss data to send to the TcpIpServer or the EnvironmentManager. - - :param loss_data: Optional data to compute loss. - """ - - # Training data is set if the Environment can compute data - if self.compute_essential_data: - self.loss_data = loss_data if type(loss_data) in [list, ndarray] else array([loss_data]) - - def set_additional_dataset(self, - label: str, - data: ndarray) -> None: - """ - Set additional data fields to store in the dataset. - - :param label: Name of the data field. - :param data: Data to store. - """ - - # Training data is set if the Environment can compute data - if self.compute_essential_data: - self.additional_fields[label] = data if type(data) in [list, ndarray] else array([data]) - - def reset_additional_datasets(self) -> None: - """ - Reset the additional dataset dictionaries. - """ + def _update_training_data(self, + line_id: int) -> None: + self.database.update(table_name='Training', + data=self.__training_data, + line_id=line_id) + if self.sample_additional is not None: + self.database.update(table_name='Additional', + data=self.__additional_data, + line_id=line_id) + self.database.add_data(table_name='Sync', + data={'env': line_id}) - self.additional_fields = {} + def _get_training_data(self, + line: int) -> None: + self.sample_training = self.database.get_line(table_name='Training', + line_id=line) + self.sample_additional = self.database.get_line(table_name='Additional', + line_id=line) + self.sample_additional = None if len(self.sample_additional) == 1 else self.sample_additional ########################################################################################## ########################################################################################## diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 72afad16..ec4c188e 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Dict, List, Union +from typing import Any, Optional, Dict, List from numpy import ndarray from DeepPhysX.Core.Manager.DatabaseManager import DatasetManager, Database @@ -28,8 +28,8 @@ def __init__(self, :param environment_config: Specialisation containing the parameters of the environment manager. :param manager: Manager that handle the DataManager :param session: Path to the session directory. - :param bool new_session: Flag that indicates whether if the session is new - :param is_training: Flag that indicates whether if this session is training a Network. + :param new_session: Flag that indicates whether if the session is new + :param pipeline: Flag that indicates whether if this session is training a Network. :param produce_data: Flag that indicates whether if this session is producing data. :param int batch_size: Number of samples in a batch """ @@ -41,32 +41,6 @@ def __init__(self, self.dataset_manager: Optional[DatasetManager] = None self.environment_manager: Optional[EnvironmentManager] = None - # Data normalization coefficients (default: mean = 0, standard deviation = 1) - self.normalization: Dict[str, List[float]] = {'input': [0., 1.], 'output': [0., 1.]} - # # If normalization flag is set to True, try to load existing coefficients - # if dataset_config is not None and dataset_config.normalize: - # json_file_path = None - # # Existing Dataset in the current session - # if os.path.exists(os.path.join(session, 'dataset')): - # json_file_path = os.path.join(session, 'dataset', 'dataset.json') - # # Dataset provided by config - # elif dataset_config.existing_dir is not None: - # dataset_dir = dataset_config.existing_dir - # if dataset_dir[-1] != "/": - # dataset_dir += "/" - # if dataset_dir[-8:] != "dataset/": - # dataset_dir += "dataset/" - # if os.path.exists(dataset_dir): - # json_file_path = os.path.join(dataset_dir, 'dataset.json') - # # If Dataset exists then a json file is associated - # if json_file_path is not None: - # with open(json_file_path) as json_file: - # json_dict = json_load(json_file) - # # Get the normalization coefficients - # for field in self.normalization.keys(): - # if field in json_dict['normalization']: - # self.normalization[field] = json_dict['normalization'][field] - # Create a DatasetManager if required create_dataset = pipeline in ['data_generation', 'training'] or produce_data database = None @@ -92,7 +66,7 @@ def __init__(self, self.pipeline = pipeline self.produce_data = produce_data self.batch_size = batch_size - self.data_lines: Union[List[int], int] = [] + self.data_lines: List[int] = [] def get_manager(self) -> Any: """ @@ -107,7 +81,7 @@ def get_database(self) -> Database: return self.dataset_manager.database def change_database(self) -> None: - # self.manager.change_database(self.dataset_manager.database) + self.manager.change_database(self.dataset_manager.database) self.environment_manager.change_database(self.dataset_manager.database) def get_data(self, @@ -123,24 +97,27 @@ def get_data(self, # Data generation case if self.pipeline == 'data_generation': - self.data_lines = self.environment_manager.get_data(animate=animate) + self.environment_manager.get_data(animate=animate) self.dataset_manager.add_data() # Training case elif self.pipeline == 'training': # Get data from Environment(s) if used and if the data should be created at this epoch - if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data): + # TODO + if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data)\ + and self.produce_data: self.data_lines = self.environment_manager.get_data(animate=animate) self.dataset_manager.add_data() # Get data from Dataset else: self.data_lines = self.dataset_manager.get_data(batch_size=self.batch_size) - print(self.data_lines) # Dispatch a batch to clients + # TODO if self.environment_manager is not None and self.environment_manager.use_dataset_in_environment: - self.data_lines = self.environment_manager.dispatch_batch(batch=self.data_lines) + self.environment_manager.dispatch_batch(data_lines=self.data_lines, + animate=animate) # Environment is no longer used elif self.environment_manager is not None: @@ -148,6 +125,7 @@ def get_data(self, self.environment_manager = None # Prediction pipeline + # TODO else: if self.dataset_manager is not None and not self.dataset_manager.new_dataset(): # Get data from dataset @@ -199,24 +177,9 @@ def apply_prediction(self, # Apply prediction self.environment_manager.environment.apply_prediction(prediction) - def normalize_data(self, - data: ndarray, - field: str, - reverse: bool = False) -> ndarray: - """ - Apply or unapply normalization following current standard score. - - :param data: Data to normalize. - :param field: Specify if data is an 'input' or an 'output'. - :param reverse: If False, apply normalization; if False, unapply normalization. - :return: Data with applied or misapplied normalization. - """ - - if not reverse: - # Apply normalization - return (data - self.normalization[field][0]) / self.normalization[field][1] - # Unapply normalization - return (data * self.normalization[field][1]) + self.normalization[field][0] + @property + def normalization(self) -> Dict[str, List[float]]: + return self.dataset_manager.normalization def close(self) -> None: """ diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 4136cc14..949fae10 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -50,7 +50,7 @@ def __init__(self, # Dataset modes self.modes: List[str] = ['training', 'validation', 'running'] - self.mode: str = 'training' if produce_data else 'running' + self.mode: str = 'training' if produce_data or pipeline == 'training' else 'running' self.mode = self.mode if dataset_config.mode is None else dataset_config.mode # Dataset partitions @@ -61,7 +61,7 @@ def __init__(self, # Dataset indexing self.shuffle_pattern: ndarray = arange(0) - self.current_sample: int = 0 + self.current_sample: int = 1 self.first_add = True # Dataset json file @@ -175,6 +175,23 @@ def load_directory(self): self.normalize and self.json_content['normalization'] == self.json_default['normalization']): self.update_json(update_normalization=True) + # 5. Load the Database + partition_path = self.partitions[self.mode][self.partition_index[self.mode]] + self.database = Database(database_dir=self.dataset_dir, + database_name=partition_path).load() + + def load_next_partition(self): + + # 1. Define next partition + self.partition_index[self.mode] = (self.partition_index[self.mode] + 1) % len(self.partitions[self.mode]) + partition_path = self.partitions[self.mode][self.partition_index[self.mode]] + self.database = Database(database_dir=self.dataset_dir, + database_name=partition_path).load() + + # 2. Shuffle + if self.shuffle: + self.shuffle_samples() + def search_partitions(self): """ @@ -246,7 +263,7 @@ def update_json(self, def nb_samples(self) -> int: return self.database.nb_lines(table_name='Training') - def shuffle(self): + def shuffle_samples(self): """ """ @@ -273,13 +290,15 @@ def add_data(self): self.update_json(update_partitions_lists=True, update_nb_samples=True) def get_data(self, - batch_size: int) -> ndarray: + batch_size: int) -> List[int]: """ """ # 1. Check if dataset is loaded and if the current sample is not the last - # TODO + if self.current_sample > self.nb_samples: + self.load_next_partition() + self.current_sample = 1 # 2. Update dataset index idx = self.current_sample @@ -288,7 +307,7 @@ def get_data(self, # 3. Get a batch of data if self.shuffle: return self.shuffle_pattern[idx:self.current_sample] - return arange(idx, self.current_sample) + return list(arange(idx, self.current_sample)) def close(self): """ @@ -348,6 +367,10 @@ def compute_normalization(self) -> Dict[str, List[float]]: return normalization + @property + def normalization(self) -> Dict[str, List[float]]: + return None if self.json_content['normalization'] == {} else self.json_content['normalization'] + def load_partitions_fields(self, partition: str, fields: List[str]): @@ -362,5 +385,6 @@ def __str__(self): description = "\n" description += f"# {self.name}\n" description += f" Dataset Repository: {self.dataset_dir}\n" - description += f" Partitions size: {self.max_file_size * 1e-9} Go\n" + size = f"No limits" if self.max_file_size is None else f"{self.max_file_size * 1e-9} Go" + description += f" Partitions size: {size}\n" return description diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index a89729cc..8fd65259 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -1,7 +1,5 @@ -from typing import Any, Dict, Optional, Union, List -from numpy import ndarray +from typing import Any, Optional, List from asyncio import run as async_run -from copy import copy from os.path import join from SSD.Core.Storage.Database import Database @@ -38,7 +36,7 @@ def __init__(self, self.use_dataset_in_environment: bool = environment_config.use_dataset_in_environment self.simulations_per_step: int = environment_config.simulations_per_step self.max_wrong_samples_per_step: int = environment_config.max_wrong_samples_per_step - self.dataset_batch: Optional[Dict[str, Dict[int, Any]]] = None + self.dataset_batch: Optional[List[int]] = None # Create the Visualizer self.visualizer: Optional[VedoVisualizer] = None @@ -105,21 +103,11 @@ def get_data_from_environment(self, nb_sample = 0 while nb_sample < self.batch_size: - # # 1.1 Send a sample if a batch from dataset is given - # if self.dataset_batch is not None: - # # Extract a sample from dataset batch: input - # self.environment.sample_in = self.dataset_batch['input'][0] - # self.dataset_batch['input'] = self.dataset_batch['input'][1:] - # # Extract a sample from dataset batch: output - # self.environment.sample_out = self.dataset_batch['output'][0] - # self.dataset_batch['output'] = self.dataset_batch['output'][1:] - # # Extract a sample from dataset batch: additional fields - # additional_fields = {} - # if 'additional_fields' in self.dataset_batch: - # for field in self.dataset_batch['additional_fields']: - # additional_fields[field] = self.dataset_batch['additional_fields'][field][0] - # self.dataset_batch['additional_fields'][field] = self.dataset_batch['additional_fields'][field][1:] - # self.environment.additional_fields = additional_fields + # 1.1 Send a sample if a batch from dataset is given + update_line = None + if self.dataset_batch is not None: + update_line = self.dataset_batch.pop(0) + self.environment._get_training_data(update_line) # 1.2 Run the defined number of step if animate: @@ -131,34 +119,34 @@ def get_data_from_environment(self, # 1.3 Add the produced sample to the batch if the sample is validated if self.environment.check_sample(): nb_sample += 1 - self.environment._send_training_data() + if update_line is None: + self.environment._send_training_data() + else: + self.environment._update_training_data(update_line) self.environment._reset_training_data() # TODO: return the indices of samples return [] def dispatch_batch_to_server(self, - batch: Dict[str, Union[ndarray, dict]], - animate: bool = True) -> Dict[str, Union[ndarray, dict]]: + data_lines: List[int], + animate: bool = True) -> None: """ Send samples from dataset to the Environments. Get back the training data. - :param batch: Batch of samples. + :param data_lines: Batch of samples. :param animate: If True, triggers an environment step. :return: Batch of training data. """ # Define the batch to dispatch - self.server.set_dataset_batch(batch) - # Empty the server queue - while not self.server.data_fifo.empty(): - self.server.data_fifo.get() + self.server.set_dataset_batch(data_lines) # Get data - return self.get_data(animate=animate) + self.get_data_from_server(animate=animate) def dispatch_batch_to_environment(self, - batch: Dict[str, Union[ndarray, dict]], - animate: bool = True) -> Dict[str, Union[ndarray, dict]]: + data_lines: List[int], + animate: bool = True) -> None: """ Send samples from dataset to the Environments. Get back the training data. @@ -168,9 +156,9 @@ def dispatch_batch_to_environment(self, """ # Define the batch to dispatch - self.dataset_batch = copy(batch) + self.dataset_batch = data_lines.copy() # Get data - return self.get_data(animate=animate) + self.get_data_from_environment(animate=animate) def update_visualizer(self, instance: int) -> None: diff --git a/src/Manager/Manager.py b/src/Manager/Manager.py index 7747f7a9..4e30cff5 100644 --- a/src/Manager/Manager.py +++ b/src/Manager/Manager.py @@ -105,14 +105,12 @@ def optimize_network(self) -> Tuple[ndarray, Dict[str, float]]: :return: The network prediction and the associated loss value """ - # Normalize input and output data - data = self.data_manager.data - for field in ['input', 'output']: - if field in data: - data[field] = self.data_manager.normalize_data(data[field], field) # Forward pass and optimization step - prediction, loss_dict = self.network_manager.compute_prediction_and_loss(data, optimize=True) - return prediction, loss_dict + data_pred, data_loss = self.network_manager.compute_prediction_and_loss( + data_lines=self.data_manager.data_lines, + normalization=self.data_manager.normalization, + optimize=True) + return data_pred, data_loss def save_network(self) -> None: """ diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index 52d22f82..90c2071d 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -1,8 +1,9 @@ -from typing import Any, Dict, Tuple, Optional -from os import listdir, sep -from os.path import join as osPathJoin -from os.path import isdir, isfile -from numpy import copy, array, ndarray +from typing import Any, Dict, Tuple, Optional, List +from os import listdir +from os.path import join, isdir, isfile +from numpy import copy, ndarray, array + +from SSD.Core.Storage.Database import Database from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Utils.path import copy_dir, create_dir @@ -12,175 +13,158 @@ class NetworkManager: def __init__(self, network_config: BaseNetworkConfig, - session: str, manager: Optional[Any] = None, + session: str = 'sessions/default', new_session: bool = True, - training: bool = True): + pipeline: str = '', + data_db: Optional[Database] = None): """ - Deals with all the interactions with the neural network. Predictions, saves, initialisation, loading, - back-propagation, etc... + Deals with all the interactions with the neural network: predictions, saves, initialisation, loading, + back-propagation, etc. - :param network_config: Specialisation containing the parameters of the network manager - :param manager: Manager that handle the network manager - :param new_session: Define the creation of new directories to store data - :param training: If True prediction will cause tensors gradient creation + :param network_config: Specialisation containing the parameters of the network manager. + :param manager: Manager that handle the network manager. + :param session: Path to the session directory. + :param new_session: Define the creation of new directories to store data. + :param is_training: If True prediction will cause tensors gradient creation. """ self.name: str = self.__class__.__name__ - # Check network_config type - if not isinstance(network_config, BaseNetworkConfig): - raise TypeError(f"[{self.name}] Wrong 'network_config' type: BaseNetworkConfig required, " - f"get {type(network_config)}") - # Check session type and existence - if type(session) != str: - raise TypeError(f"[{self.name}] Wrong 'session' type: str required, get {type(session)}") - if not isdir(session): - raise ValueError(f"[{self.name}] Given 'session' does not exists: {session}") - # Check new_session type - if type(new_session) != bool: - raise TypeError(f"[{self.name}] Wrong 'new_session' type: bool required, get {type(new_session)}") - # Check train type - if type(training) != bool: - raise TypeError(f"[{self.name}] Wrong 'train' type: bool required, get {type(training)}") - - # Storage management + # Managers architecture + self.manager: Any = manager + + # Storage variables + self.data_db: Database = data_db + self.batch: Optional[Any] = None self.session: str = session self.new_session: bool = new_session self.network_dir: Optional[str] = None - self.network_template_name: str = session.split(sep)[-1] + '_network_{}' - - # Network management - self.manager: Any = manager - if training and not network_config.training_stuff: - raise ValueError(f"[{self.name}] Training requires a loss and an optimizer in your NetworkConfig") - self.training: bool = training - self.save_each_epoch: bool = network_config.save_each_epoch self.saved_counter: int = 0 + self.save_each_epoch: bool = network_config.save_each_epoch - # Init network objects: Network, Optimization, DataTransformation - self.network: Any = None - self.optimization: Any = None - self.data_transformation: Any = None - self.network_config: BaseNetworkConfig = network_config - self.set_network() - - def get_manager(self) -> Any: - """ - | Return the Manager of the NetworkManager. - - :return: Manager that handles the NetworkManager - """ - - return self.manager - - def set_network(self) -> None: - """ - | Set the network to the corresponding weight from a given file. - """ - - # Init network - self.network = self.network_config.create_network() + # Init Network + self.network = network_config.create_network() self.network.set_device() - # Init optimization - self.optimization = self.network_config.create_optimization() - self.optimization.manager = self - if self.optimization.loss_class: + if pipeline == 'training' and not network_config.training_stuff: + raise ValueError(f"[{self.name}] Training requires a loss and an optimizer in your NetworkConfig") + self.is_training: bool = pipeline == 'training' + + # Init Optimization + self.optimization = network_config.create_optimization() + if self.optimization.loss_class is not None: self.optimization.set_loss() # Init DataTransformation - self.data_transformation = self.network_config.create_data_transformation() + self.data_transformation = network_config.create_data_transformation() - # Training - if self.training: - # Configure as training + # Training configuration + if self.is_training: self.network.set_train() self.optimization.set_optimizer(self.network) # Setting network directory - if self.new_session and self.network_config.network_dir and isdir(self.network_config.network_dir): - self.network_dir = self.network_config.network_dir - self.network_dir = copy_dir(self.network_dir, self.session, dest_dir='network') - self.load_network() + if new_session and network_config.network_dir is not None and isdir(network_config.network_dir): + self.network_dir = copy_dir(src_dir=network_config.network_dir, + dest_dir=session, + sub_folders='network') + self.load_network(which_network=network_config.which_network) else: - self.network_dir = osPathJoin(self.session, 'network/') - self.network_dir = create_dir(self.network_dir, dir_name='network') + self.network_dir = create_dir(session_dir=session, session_name='network') - # Prediction + # Prediction configuration else: - # Configure as prediction self.network.set_eval() - # Need an existing network - self.network_dir = osPathJoin(self.session, 'network/') - # Load parameters - self.load_network() + self.network_dir = join(session, 'network/') + self.load_network(which_network=network_config.which_network) + + def change_database(self, database): + self.data_db = database - def load_network(self) -> None: + def load_network(self, + which_network: int = -1) -> None: """ - | Load an existing set of parameters to the network. + Load an existing set of parameters to the network. """ # Get eventual epoch saved networks - networks_list = [osPathJoin(self.network_dir, f) for f in listdir(self.network_dir) if - isfile(osPathJoin(self.network_dir, f)) and f.__contains__('_network_.')] + networks_list = [join(self.network_dir, f) for f in listdir(self.network_dir) if + isfile(join(self.network_dir, f)) and f.__contains__('network_')] networks_list = sorted(networks_list) # Add the final saved network - last_saved_network = [osPathJoin(self.network_dir, f) for f in listdir(self.network_dir) if - isfile(osPathJoin(self.network_dir, f)) and f.__contains__('network.')] + last_saved_network = [join(self.network_dir, f) for f in listdir(self.network_dir) if + isfile(join(self.network_dir, f)) and f.__contains__('network.')] networks_list = networks_list + last_saved_network - which_network = self.network_config.which_network if len(networks_list) == 0: - print(f"[{self.name}]: There is no network in {self.network_dir}. Shutting down.") - quit(0) + raise FileNotFoundError(f"[{self.name}]: There is no network in {self.network_dir}.") elif len(networks_list) == 1: which_network = 0 - elif len(networks_list) > 1 and which_network is None: - print(f"[{self.name}] There is more than one network in this directory, loading the most trained by " - f"default. If you want to load another network please use the 'which_network' variable.") - which_network = -1 - elif which_network > len(networks_list) > 1: - print(f"[{self.name}] The selected network doesn't exist (index is too big), loading the most trained " + elif which_network > len(networks_list): + print(f"[{self.name}] The network 'network_{self.saved_counter} doesn't exist, loading the most trained " f"by default.") which_network = -1 print(f"[{self.name}]: Loading network from {networks_list[which_network]}.") self.network.load_parameters(networks_list[which_network]) - def compute_prediction_and_loss(self, batch: Dict[str, ndarray], - optimize: bool) -> Tuple[ndarray, Dict[str, float]]: + def compute_prediction_and_loss(self, + optimize: bool, + data_lines: List[int], + normalization: Optional[Dict[str, List[float]]] = None) -> Tuple[ndarray, Dict[str, float]]: """ - | Make a prediction with the data passed as argument, optimize or not the network + Make a prediction with the data passed as argument, optimize or not the network - :param Dict[str, ndarray] batch: Format {'input': numpy.ndarray, 'output': numpy.ndarray}. - Contains the input value and ground truth to compare against - :param bool optimize: If true run a back propagation + :param batch_indices: Indices of the line of the Database that correspond to the current bach + :param optimize: If true run a back propagation :return: The prediction and the associated loss value """ - # Getting data from the data manager - data_in = self.network.transform_from_numpy(batch['input'], grad=optimize) - data_gt = self.network.transform_from_numpy(batch['output'], grad=optimize) - loss_data = self.network.transform_from_numpy(batch['loss'], grad=False) if 'loss' in batch.keys() else None + # Define in and out batches + batches = {} + normalization = {} if normalization is None else normalization + for side, fields in zip(['net', 'opt'], [self.network.net_fields, self.network.opt_fields]): + + # Get the batch from the Database + batch = self.data_db.get_lines(table_name='Training', + fields=fields, + lines_id=data_lines, + batched=True) + del batch['id'] + + # Apply normalization and convert to tensor + for field in batch.keys(): + batch[field] = array(batch[field]) + if field in normalization: + batch[field] = self.normalize_data(data=batch[field], + normalization=normalization[field]) + batch[field] = self.network.numpy_to_tensor(data=batch[field], + grad=optimize) + batches[side] = batch + data_net, data_opt = batches.values() # Compute prediction - data_in = self.data_transformation.transform_before_prediction(data_in) - data_out = self.network.predict(data_in) + data_net = self.data_transformation.transform_before_prediction(data_net) + data_pred = self.network.predict(data_net) # Compute loss - data_out, data_gt = self.data_transformation.transform_before_loss(data_out, data_gt) - loss_dict = self.optimization.compute_loss(data_out.reshape(data_gt.shape), data_gt, loss_data) + data_pred, data_opt = self.data_transformation.transform_before_loss(data_pred, data_opt) + data_loss = self.optimization.compute_loss(data_pred, data_opt) + # Optimizing network if training if optimize: self.optimization.optimize() + # Transform prediction to be compatible with environment - data_out = self.data_transformation.transform_before_apply(data_out) - prediction = self.network.transform_to_numpy(data_out) - return prediction, loss_dict + data_pred = self.data_transformation.transform_before_apply(data_pred) + for field in data_pred: + data_pred[field] = self.network.tensor_to_numpy(data_pred[field]) + return data_pred, data_loss - def compute_online_prediction(self, network_input: ndarray) -> ndarray: + def compute_online_prediction(self, + network_input: ndarray) -> ndarray: """ - | Make a prediction with the data passed as argument. + Make a prediction with the data passed as argument. - :param ndarray network_input: Input of the network= + :param network_input: Input of the network :return: The prediction """ @@ -195,6 +179,26 @@ def compute_online_prediction(self, network_input: ndarray) -> ndarray: pred = self.network.transform_to_numpy(pred) return pred.reshape(-1) + @classmethod + def normalize_data(cls, + data: ndarray, + normalization: List[float], + reverse: bool = False) -> ndarray: + """ + Apply or unapply normalization following current standard score. + + :param data: Data to normalize. + :param field: Specify if data is an 'input' or an 'output'. + :param reverse: If False, apply normalization; if False, unapply normalization. + :return: Data with applied or misapplied normalization. + """ + + if reverse: + # Unapply normalization + return (data * normalization[1]) + normalization[0] + # Apply normalization + return (data - normalization[0]) / normalization[1] + def save_network(self, last_save: bool = False) -> None: """ | Save the network with the corresponding suffix, so they do not erase the last save. @@ -217,18 +221,14 @@ def save_network(self, last_save: bool = False) -> None: def close(self) -> None: """ - | Closing procedure. + Closing procedure. """ - if self.training: + if self.is_training: self.save_network(last_save=True) del self.network - del self.network_config def __str__(self) -> str: - """ - :return: String containing information about the BaseNetwork object - """ description = "\n" description += f"# {self.__class__.__name__}\n" diff --git a/src/Network/BaseNetwork.py b/src/Network/BaseNetwork.py index e61139fb..4a6d8881 100644 --- a/src/Network/BaseNetwork.py +++ b/src/Network/BaseNetwork.py @@ -4,27 +4,33 @@ class BaseNetwork: - """ - | BaseNetwork is a network class to compute predictions from input data according to actual state. - :param namedtuple config: namedtuple containing BaseNetwork parameters - """ + def __init__(self, + config: namedtuple): + """ + BaseNetwork is a network class to compute predictions from input data according to actual state. - def __init__(self, config: namedtuple): + :param namedtuple config: namedtuple containing BaseNetwork parameters + """ # Config self.device = None self.config = config - def predict(self, input_data: Any) -> Any: + # Data fields + self.net_fields = ['input'] + self.opt_fields = ['ground_truth'] + self.pred_fields = ['prediction'] + + def predict(self, data_net: Dict[str, Any]) -> Dict[str, Any]: """ - | Same as forward + Compute a forward pass of the network. - :param Any input_data: Input tensor - :return: Network prediction + :param data_net: Data used by the Network. + :return: Data produced by the Network. """ - return self.forward(input_data) + return {'prediction': self.forward(data_net['input'])} def forward(self, input_data: Any) -> Any: """ @@ -93,7 +99,7 @@ def nb_parameters(self) -> int: raise NotImplementedError - def transform_from_numpy(self, data: ndarray, grad: bool = True) -> Any: + def numpy_to_tensor(self, data: ndarray, grad: bool = True) -> Any: """ | Transform and cast data from numpy to the desired tensor type. @@ -104,7 +110,7 @@ def transform_from_numpy(self, data: ndarray, grad: bool = True) -> Any: return data.astype(self.config.data_type) - def transform_to_numpy(self, data: Any) -> ndarray: + def tensor_to_numpy(self, data: Any) -> ndarray: """ | Transform and cast data from tensor type to numpy. diff --git a/src/Network/BaseNetworkConfig.py b/src/Network/BaseNetworkConfig.py index 24a943b2..69f93caa 100644 --- a/src/Network/BaseNetworkConfig.py +++ b/src/Network/BaseNetworkConfig.py @@ -1,38 +1,14 @@ from typing import Any, Optional, Type -from collections import namedtuple from os.path import isdir from numpy import typeDict from DeepPhysX.Core.Network.BaseNetwork import BaseNetwork from DeepPhysX.Core.Network.BaseOptimization import BaseOptimization from DeepPhysX.Core.Network.DataTransformation import DataTransformation - -NetworkType = BaseNetwork -OptimizationType = BaseOptimization -DataTransformationType = DataTransformation +from DeepPhysX.Core.Utils.configs import make_config, namedtuple class BaseNetworkConfig: - """ - | BaseNetworkConfig is a configuration class to parameterize and create BaseNetwork, BaseOptimization and - DataTransformation for the NetworkManager. - - :param Type[BaseNetwork] network_class: BaseNetwork class from which an instance will be created - :param Type[BaseOptimization] optimization_class: BaseOptimization class from which an instance will be created - :param Type[DataTransformation] data_transformation_class: DataTransformation class from which an instance will - be created - :param Optional[str] network_dir: Name of an existing network repository - :param str network_name: Name of the network - :param str network_type: Type of the network - :param int which_network: If several networks in network_dir, load the specified one - :param bool save_each_epoch: If True, network state will be saved at each epoch end; if False, network state - will be saved at the end of the training - :param str data_type: Type of the training data - :param Optional[float] lr: Learning rate - :param bool require_training_stuff: If specified, loss and optimizer class can be not necessary for training - :param Optional[Any] loss: Loss class - :param Optional[Any] optimizer: Network's parameters optimizer class - """ def __init__(self, network_class: Type[BaseNetwork] = BaseNetwork, @@ -41,13 +17,34 @@ def __init__(self, network_dir: Optional[str] = None, network_name: str = 'Network', network_type: str = 'BaseNetwork', - which_network: int = 0, + which_network: int = -1, save_each_epoch: bool = False, data_type: str = 'float32', lr: Optional[float] = None, require_training_stuff: bool = True, loss: Optional[Any] = None, optimizer: Optional[Any] = None): + """ + BaseNetworkConfig is a configuration class to parameterize and create BaseNetwork, BaseOptimization and + DataTransformation for the NetworkManager. + + :param network_class: BaseNetwork class from which an instance will be created. + :param optimization_class: BaseOptimization class from which an instance will be created. + :param data_transformation_class: DataTransformation class from which an instance will be created. + :param network_dir: Name of an existing network repository. + :param network_name: Name of the network. + :param network_type: Type of the network. + :param which_network: If several networks in network_dir, load the specified one. + :param save_each_epoch: If True, network state will be saved at each epoch end; if False, network state + will be saved at the end of the training. + :param data_type: Type of the training data. + :param lr: Learning rate. + :param require_training_stuff: If specified, loss and optimizer class can be not necessary for training. + :param loss: Loss class. + :param optimizer: Network's parameters optimizer class. + """ + + self.name = self.__class__.__name__ # Check network_dir type and existence if network_dir is not None: @@ -68,8 +65,6 @@ def __init__(self, if type(which_network) != int: raise TypeError( f"[{self.__class__.__name__}] Wrong 'which_network' type: int required, get {type(which_network)}") - if which_network < 0: - raise ValueError(f"[{self.__class__.__name__}] Given 'which_network' value is negative") # Check save_each_epoch type if type(save_each_epoch) != bool: raise TypeError( @@ -81,110 +76,74 @@ def __init__(self, # BaseNetwork parameterization self.network_class: Type[BaseNetwork] = network_class - self.network_config: namedtuple = self.make_config(config_name='network_config', - network_name=network_name, - network_type=network_type, - data_type=data_type) + self.network_config: namedtuple = make_config(configuration_object=self, + configuration_name='network_config', + network_name=network_name, + network_type=network_type, + data_type=data_type) # BaseOptimization parameterization self.optimization_class: Type[BaseOptimization] = optimization_class - self.optimization_config: namedtuple = self.make_config(config_name='optimization_config', - loss=loss, - lr=lr, - optimizer=optimizer) + self.optimization_config: namedtuple = make_config(configuration_object=self, + configuration_name='optimization_config', + loss=loss, + lr=lr, + optimizer=optimizer) self.training_stuff: bool = (loss is not None) and (optimizer is not None) or (not require_training_stuff) # NetworkManager parameterization self.data_transformation_class: Type[DataTransformation] = data_transformation_class - self.data_transformation_config: namedtuple = self.make_config(config_name='data_transformation_config') + self.data_transformation_config: namedtuple = make_config(configuration_object=self, + configuration_name='data_transformation_config') + + # NetworkManager parameterization self.network_dir: str = network_dir self.which_network: int = which_network self.save_each_epoch: bool = save_each_epoch and self.training_stuff - def make_config(self, config_name: str, **kwargs) -> namedtuple: - """ - | Create a namedtuple which gathers all the parameters for an Object configuration (Network or Optimization). - | For a child config class, only new items are required since parent's items will be added by default. - - :param str config_name: Name of the configuration to fill - :param kwargs: Items to add to the Object configuration - :return: Namedtuple which contains Object parameters + def create_network(self) -> BaseNetwork: """ - - # Get items set as keyword arguments - fields = tuple(kwargs.keys()) - args = tuple(kwargs.values()) - # Check if a config already exists with the same name (child class will have the parent's config by default) - if config_name in self.__dict__: - config = self.__getattribute__(config_name) - for key, value in config._asdict().items(): - # Only new items are required for children, check if the parent's items are set again anyway - if key not in fields: - fields += (key,) - args += (value,) - # Create namedtuple with collected items - return namedtuple(config_name, fields)._make(args) - - def create_network(self) -> NetworkType: - """ - | Create an instance of network_class with given parameters. + Create an instance of network_class with given parameters. :return: BaseNetwork object from network_class and its parameters. """ - try: - network = self.network_class(config=self.network_config) - except: - raise ValueError( - f"[{self.__class__.__name__}] Given 'network_class' cannot be created in {self.__class__.__name__}") + # Create instance + network = self.network_class(config=self.network_config) if not isinstance(network, BaseNetwork): - raise TypeError( - f"[{self.__class__.__name__}] Wrong 'network_class' type: BaseNetwork required, get " - f"{self.network_class}") + raise TypeError(f"[{self.name}] The given 'network_class'={self.network_class} must be a BaseNetwork.") return network - def create_optimization(self) -> OptimizationType: + def create_optimization(self) -> BaseOptimization: """ - | Create an instance of optimization_class with given parameters. + Create an instance of optimization_class with given parameters. :return: BaseOptimization object from optimization_class and its parameters. """ - try: - optimization = self.optimization_class(config=self.optimization_config) - except: - raise ValueError( - f"[{self.__class__.__name__}] Given 'optimization_class' got an unexpected keyword argument 'config'") + # Create instance + optimization = self.optimization_class(config=self.optimization_config) if not isinstance(optimization, BaseOptimization): - raise TypeError(f"[{self.__class__.__name__}] Wrong 'optimization_class' type: BaseOptimization required, " - f"get {self.optimization_class}") + raise TypeError(f"[{self.name}] The given 'optimization_class'={self.optimization_class} must be a " + f"BaseOptimization.") return optimization - def create_data_transformation(self) -> DataTransformationType: + def create_data_transformation(self) -> DataTransformation: """ - | Create an instance of data_transformation_class with given parameters. + Create an instance of data_transformation_class with given parameters. :return: DataTransformation object from data_transformation_class and its parameters. """ - try: - data_transformation = self.data_transformation_class(config=self.data_transformation_config) - except: - raise ValueError( - f"[{self.__class__.__name__}] Given 'data_transformation_class' got an unexpected keyword argument " - f"'config'") + # Create instance + data_transformation = self.data_transformation_class(config=self.data_transformation_config) if not isinstance(data_transformation, DataTransformation): - raise TypeError( - f"[{self.__class__.__name__}] Wrong 'data_transformation_class' type: DataTransformation required, " - f"get {self.data_transformation_class}") + raise TypeError(f"[{self.name}] The given 'data_transformation_class'={self.data_transformation_class} " + f"must be a DataTransformation.") return data_transformation def __str__(self) -> str: - """ - :return: String containing information about the BaseDatasetConfig object - """ - # Todo: fields in Configs are the set in Managers or objects, then remove __str__ method description = "\n" description += f"{self.__class__.__name__}\n" description += f" Network class: {self.network_class.__name__}\n" diff --git a/src/Network/BaseOptimization.py b/src/Network/BaseOptimization.py index 1c75d66c..2419d866 100644 --- a/src/Network/BaseOptimization.py +++ b/src/Network/BaseOptimization.py @@ -5,14 +5,14 @@ class BaseOptimization: - """ - | BaseOptimization is dedicated to network optimization: compute loss between prediction and target, update - network parameters. - - :param namedtuple config: Namedtuple containing BaseOptimization parameters - """ def __init__(self, config: namedtuple): + """ + BaseOptimization is dedicated to network optimization: compute loss between prediction and target, update + network parameters. + + :param config: Namedtuple containing BaseOptimization parameters + """ self.manager: Any = None @@ -28,28 +28,31 @@ def __init__(self, config: namedtuple): def set_loss(self) -> None: """ - | Initialize the loss function. + Initialize the loss function. """ raise NotImplementedError - def compute_loss(self, prediction: Any, ground_truth: Any, data: Dict[str, Any]) -> Dict[str, float]: + def compute_loss(self, + data_pred: Dict[str, Any], + data_opt: Dict[str, Any]) -> Dict[str, Any]: """ - | Compute loss from prediction / ground truth. + Compute loss from prediction / ground truth. - :param Any prediction: Tensor produced by the forward pass of the Network - :param Any ground_truth: Ground truth tensor to be compared with prediction - :param Dict[str, Any] data: Additional data sent as dict to compute loss value - :return: Loss value + :param data_pred: Tensor produced by the forward pass of the Network. + :param data_opt: Ground truth tensor to be compared with prediction. + :return: Loss value. """ + # WARNING: self.optimization.compute_loss(data_pred.reshape(data_gt.shape), data_gt, loss_data) raise NotImplementedError - def transform_loss(self, data: Dict[str, Any]) -> Dict[str, float]: + def transform_loss(self, + data_opt: Dict[str, Any]) -> Dict[str, float]: """ - | Apply a transformation on the loss value using the potential additional data. + Apply a transformation on the loss value using the potential additional data. - :param Dict[str, Any] data: Additional data sent as dict to compute loss value + :param data_opt: Additional data sent as dict to compute loss value :return: Transformed loss value """ @@ -57,24 +60,21 @@ def transform_loss(self, data: Dict[str, Any]) -> Dict[str, float]: def set_optimizer(self, net: BaseNetwork) -> None: """ - | Define an optimization process. + Define an optimization process. - :param BaseNetwork net: Network whose parameters will be optimized. + :param net: Network whose parameters will be optimized. """ raise NotImplementedError def optimize(self) -> None: """ - | Run an optimization step. + Run an optimization step. """ raise NotImplementedError def __str__(self) -> str: - """ - :return: String containing information about the BaseOptimization object - """ description = "\n" description += f" {self.__class__.__name__}\n" diff --git a/src/Network/DataTransformation.py b/src/Network/DataTransformation.py index 8e3d7fbb..cb758fdc 100644 --- a/src/Network/DataTransformation.py +++ b/src/Network/DataTransformation.py @@ -1,15 +1,17 @@ -from typing import Callable, Any, Optional, Tuple +from typing import Callable, Any, Optional, Tuple, Dict from collections import namedtuple +DTYPE = Dict[str, Any] -class DataTransformation: - """ - | DataTransformation is dedicated to data operations before and after network predictions. - :param namedtuple config: Namedtuple containing the parameters of the network manager - """ +class DataTransformation: def __init__(self, config: namedtuple): + """ + DataTransformation is dedicated to data operations before and after network predictions. + + :param namedtuple config: Namedtuple containing the parameters of the network manager. + """ self.name = self.__class__.__name__ @@ -27,36 +29,40 @@ def inner(self, *args): return inner - def transform_before_prediction(self, data_in: Any) -> Any: + def transform_before_prediction(self, + data_net: DTYPE) -> DTYPE: """ - | Apply data operations before network's prediction. + Apply data operations before network's prediction. - :param Any data_in: Input data - :return: Transformed input data + :param data_net: Data used by the Network. + :return: Transformed data_net. """ - return data_in + return data_net - def transform_before_loss(self, data_out: Any, data_gt: Optional[Any] = None) -> Tuple[Any, Optional[Any]]: + def transform_before_loss(self, + data_pred: DTYPE, + data_opt: Optional[DTYPE] = None) -> Tuple[DTYPE, Optional[DTYPE]]: """ - | Apply data operations between network's prediction and loss computation. + Apply data operations between network's prediction and loss computation. - :param Any data_out: Prediction data - :param Optional[Any] data_gt: Ground truth data - :return: Transformed prediction data, transformed ground truth data + :param data_pred: Data produced by the Network. + :param data_opt: Data used by the Optimizer. + :return: Transformed data_pred, data_opt. """ - return data_out, data_gt + return data_pred, data_opt - def transform_before_apply(self, data_out: Any) -> Any: + def transform_before_apply(self, + data_pred: DTYPE) -> DTYPE: """ - | Apply data operations between loss computation and prediction apply in environment. + Apply data operations between loss computation and prediction apply in environment. - :param Any data_out: Prediction data - :return: Transformed prediction data + :param data_pred: Data produced by the Network. + :return: Transformed data_pred. """ - return data_out + return data_pred def __str__(self) -> str: """ diff --git a/src/Pipelines/BaseTrainer.py b/src/Pipelines/BaseTrainer.py index 2c98addd..124b1c77 100644 --- a/src/Pipelines/BaseTrainer.py +++ b/src/Pipelines/BaseTrainer.py @@ -1,96 +1,96 @@ from typing import Optional from sys import stdout +from os.path import join, isfile +from datetime import datetime from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline from DeepPhysX.Core.Manager.Manager import Manager from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Utils.progressbar import Progressbar class BaseTrainer(BasePipeline): - """ - | BaseTrainer is a pipeline defining the training process of an artificial neural network. - | It provides a highly tunable learning process that can be used with any machine learning library. - - :param BaseNetworkConfig network_config: Specialisation containing the parameters of the network manager - :param BaseDatasetConfig dataset_config: Specialisation containing the parameters of the dataset manager - :param Optional[BaseEnvironmentConfig] environment_config: Specialisation containing the parameters of the - environment manager - :param str session_name: Name of the newly created directory if session is not defined - :param Optional[str] session_dir: Name of the directory in which to write all the necessary data - :param bool new_session: Define the creation of new directories to store data - :param int nb_epochs: Number of epochs - :param int nb_batches: Number of batches - :param int batch_size: Size of a batch - :param bool debug: If True, main training features will not be launched - """ def __init__(self, network_config: BaseNetworkConfig, dataset_config: BaseDatasetConfig, environment_config: Optional[BaseEnvironmentConfig] = None, - session_name: str = 'default', - session_dir: Optional[str] = None, - new_session: bool = True, - nb_epochs: int = 0, - nb_batches: int = 0, + session_dir: str = 'sessions', + session_name: str = 'training', + epoch_nb: int = 0, + batch_nb: int = 0, batch_size: int = 0, - debug: bool = False): + new_session: bool = True, + debug_session: bool = False): + """ + BaseTrainer implements the main loop that defines the training process of an artificial neural network. + Training can be launched with several data sources (from a Dataset, from an Environment, from combined sources). + It provides a highly tunable learning process that can be used with any machine learning library. - if environment_config is None and dataset_config.dataset_dir is None: - print("BaseTrainer: You have to give me a dataset source (existing dataset directory or simulation to " - "create data on the fly") - quit(0) + :param network_config: Specialisation containing the parameters of the network manager. + :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param environment_config: Specialisation containing the parameters of the environment manager. + :param session_dir: Relative path to the directory which contains sessions directories. + :param session_name: Name of the new the session directory. + :param epoch_nb: Number of epochs to run. + :param batch_nb: Number of batches to use. + :param batch_size: Number of samples in a single batch. + :param new_session: Define the creation of new directories to store data. + :param debug_session: If True, main training features will not be launched. + """ BasePipeline.__init__(self, network_config=network_config, dataset_config=dataset_config, environment_config=environment_config, - session_name=session_name, session_dir=session_dir, + session_name=session_name, pipeline='training') # Training variables - self.nb_epochs = nb_epochs - self.id_epoch = 0 - self.nb_batches = nb_batches + self.epoch_nb = epoch_nb + self.epoch_id = 0 + self.batch_nb = batch_nb self.batch_size = batch_size - self.id_batch = 0 - self.nb_samples = nb_batches * batch_size * nb_epochs + self.batch_id = 0 + self.nb_samples = batch_nb * batch_size * epoch_nb self.loss_dict = None + self.debug = debug_session - # Tell if data is recording while predicting (output is recorded only if input too) - self.record_data = {'input': True, 'output': True} + # Configure 'produce_data' flag + if environment_config is not None and dataset_config.existing_dir is None: + raise ValueError(f"[{self.name}] No data source provided.") + produce_data = dataset_config.existing_dir is None - self.debug = debug + # Progressbar if not self.debug: self.progress_counter = 0 - self.digits = ['{' + f':0{len(str(self.nb_epochs))}d' + '}', - '{' + f':0{len(str(self.nb_batches))}d' + '}'] - id_epoch, nb_epoch = self.digits[0].format(0), self.digits[0].format(self.nb_epochs) - id_batch, nb_batch = self.digits[1].format(0), self.digits[1].format(self.nb_batches) - self.progress_bar = Progressbar(start=0, stop=self.nb_batches * self.nb_epochs, c='orange', - title=f'Epoch n°{id_epoch}/{nb_epoch} - Batch n°{id_batch}/{nb_batch} ') - - self.manager = Manager(pipeline=self, - network_config=self.network_config, - dataset_config=dataset_config, + self.digits = ['{' + f':0{len(str(self.epoch_nb))}d' + '}', + '{' + f':0{len(str(self.batch_nb))}d' + '}'] + epoch_id, epoch_nb = self.digits[0].format(0), self.digits[0].format(self.epoch_nb) + batch_id, batch_nb = self.digits[1].format(0), self.digits[1].format(self.batch_nb) + self.progress_bar = Progressbar(start=0, stop=self.batch_nb * self.epoch_nb, c='orange', + title=f'Epoch n°{epoch_id}/{epoch_nb} - Batch n°{batch_id}/{batch_nb}') + + self.manager = Manager(network_config=self.network_config, + dataset_config=self.dataset_config, environment_config=self.environment_config, - session_name=session_name, session_dir=session_dir, + session_name=session_name, new_session=new_session, - batch_size=batch_size) - - self.manager.save_info_file() + pipeline='training', + produce_data=produce_data, + batch_size=batch_size, + debug_session=debug_session) + self.save_info_file(self.manager.session) def execute(self) -> None: """ - | Main function of the training process \"execute\" call the functions associated with the learning process. - | Each of the called functions are already implemented so one can start a basic training. - | Each of the called function can also be rewritten via inheritance to provide more specific / complex training - process. + Launch the training Pipeline. + Each event is already implemented for a basic pipeline but can also be rewritten via inheritance to describe a + more complex pipeline. """ self.train_begin() @@ -106,128 +106,132 @@ def execute(self) -> None: self.save_network() self.train_end() - def optimize(self) -> None: - """ - | Pulls data from the manager and run a prediction and optimizer step. - """ - - self.manager.get_data(self.id_epoch, self.batch_size) - _, self.loss_dict = self.manager.optimize_network() - - def save_network(self) -> None: - """ - | Registers the network weights and biases in the corresponding directory (session_name/network or - session/network) - """ - - self.manager.save_network() - def train_begin(self) -> None: """ - | Called once at the very beginning of the training process. - | Allows the user to run some pre-computations. + Called once at the beginning of the training pipeline. """ pass - def train_end(self) -> None: + def epoch_condition(self) -> bool: """ - | Called once at the very end of the training process. - | Allows the user to run some post-computations. + Check the epoch number condition. """ - self.manager.close() + return self.epoch_id < self.epoch_nb def epoch_begin(self) -> None: """ - | Called one at the start of each epoch. - | Allows the user to run some pre-epoch computations. + Called one at the beginning of each epoch. """ - self.id_batch = 0 + self.batch_id = 0 - def epoch_end(self) -> None: + def batch_condition(self) -> bool: """ - | Called one at the end of each epoch. - | Allows the user to run some post-epoch computations. + Check the batch number condition. """ - self.manager.stats_manager.add_train_epoch_loss(self.loss_dict['loss'], self.id_epoch) + return self.batch_id < self.batch_nb - def epoch_condition(self) -> bool: + def batch_begin(self) -> None: """ - | Condition that characterize the end of the training process. - - :return: False if the training needs to stop. + Called one at the beginning of a batch production. """ - return self.id_epoch < self.nb_epochs + if not self.debug: + stdout.write("\033[K") + self.progress_counter += 1 + id_epoch, nb_epoch = self.digits[0].format(self.epoch_id + 1), self.digits[0].format(self.epoch_nb) + id_batch, nb_batch = self.digits[1].format(self.batch_id + 1), self.digits[1].format(self.batch_nb) + self.progress_bar.title = f'Epoch n°{id_epoch}/{nb_epoch} - Batch n°{id_batch}/{nb_batch} ' + self.progress_bar.print(counts=self.progress_counter) - def epoch_count(self) -> None: + def optimize(self) -> None: """ - | Allows user for custom update of epochs count. + Pulls data, run a prediction and an optimizer step. """ - self.id_epoch += 1 + self.manager.get_data(self.epoch_id) + _, self.loss_dict = self.manager.optimize_network() - def batch_begin(self) -> None: + def batch_count(self) -> None: """ - | Called one at the start of each batch. - | Allows the user to run some pre-batch computations. + Increment the batch counter. """ - if not self.debug: - stdout.write("\033[K") - self.progress_counter += 1 - id_epoch, nb_epoch = self.digits[0].format(self.id_epoch + 1), self.digits[0].format(self.nb_epochs) - id_batch, nb_batch = self.digits[1].format(self.id_batch + 1), self.digits[1].format(self.nb_batches) - self.progress_bar.title = f'Epoch n°{id_epoch}/{nb_epoch} - Batch n°{id_batch}/{nb_batch} ' - self.progress_bar.print(counts=self.progress_counter) + self.batch_id += 1 def batch_end(self) -> None: """ - | Called one at the start of each batch. - | Allows the user to run some post-batch computations. + Called one at the end of a batch production. """ self.manager.stats_manager.add_train_batch_loss(self.loss_dict['loss'], - self.id_epoch * self.nb_batches + self.id_batch) + self.epoch_id * self.batch_nb + self.batch_id) for key in self.loss_dict.keys(): if key != 'loss': self.manager.stats_manager.add_custom_scalar(tag=key, value=self.loss_dict[key], - count=self.id_epoch * self.nb_batches + self.id_batch) + count=self.epoch_id * self.batch_nb + self.batch_id) - def batch_condition(self) -> bool: + def epoch_count(self) -> None: """ - | Condition that characterize the end of the epoch. - - :return: False if the epoch needs to stop. + Increment the epoch counter. """ - return self.id_batch < self.nb_batches + self.epoch_id += 1 - def batch_count(self): + def epoch_end(self) -> None: """ - | Allows user for custom update of batches count. - - :return: + Called one at the end of each epoch. """ - self.id_batch += 1 + self.manager.stats_manager.add_train_epoch_loss(self.loss_dict['loss'], self.epoch_id) - def __str__(self) -> str: + def save_network(self) -> None: + """ + Store the network parameters in the corresponding directory. + """ + + self.manager.save_network() + + def train_end(self) -> None: """ - :return: str Contains training information about the training process + Called once at the end of the training pipeline. """ + self.manager.close() + + def save_info_file(self, + directory: str) -> None: + """ + Save a .txt file that provides a template for user notes and the description of all the components. + """ + + filename = join(directory, 'info.txt') + date_time = datetime.now().strftime('%d/%m/%Y %H:%M:%S') + if not isfile(filename): + f = open(filename, "w+") + # Session description template for user + f.write("## DeepPhysX Training Session ##\n") + f.write(date_time + "\n\n") + f.write("Personal notes on the training session:\nNetwork Input:\nNetwork Output:\nComments:\n\n") + # Listing every component descriptions + f.write("## List of Components Parameters ##\n") + f.write(str(self)) + f.write(str(self.manager)) + f.close() + + def __str__(self) -> str: + description = "\n" description += f"# {self.__class__.__name__}\n" description += f" Session directory: {self.manager.session}\n" - description += f" Number of epochs: {self.nb_epochs}\n" - description += f" Number of batches per epoch: {self.nb_batches}\n" + description += f" Number of epochs: {self.epoch_nb}\n" + description += f" Number of batches per epoch: {self.batch_nb}\n" description += f" Number of samples per batch: {self.batch_size}\n" - description += f" Number of samples per epoch: {self.nb_batches * self.batch_size}\n" - description += f" Total: Number of batches : {self.nb_batches * self.nb_epochs}\n" + description += f" Number of samples per epoch: {self.batch_nb * self.batch_size}\n" + description += f" Total: Number of batches : {self.batch_nb * self.epoch_nb}\n" description += f" Number of samples : {self.nb_samples}\n" return description From be654f46067b3689a39a4d0b7ad77c02d38662b8 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 19 Oct 2022 11:39:03 +0200 Subject: [PATCH 06/61] Dispatch batches of data in remote mode. --- src/AsyncSocket/AbstractEnvironment.py | 21 ++++++++---- src/AsyncSocket/TcpIpClient.py | 32 ++++++------------ src/AsyncSocket/TcpIpServer.py | 46 ++++++-------------------- src/Environment/BaseEnvironment.py | 26 +++++++++------ src/Manager/DataManager.py | 5 ++- 5 files changed, 52 insertions(+), 78 deletions(-) diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 466c1784..f2c9687f 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -1,5 +1,5 @@ from typing import Optional, Dict, Any -from numpy import array, ndarray +from numpy import ndarray from SSD.Core.Storage.Database import Database @@ -30,16 +30,15 @@ def __init__(self, self.number_of_instances: int = number_of_instances # Training data variables - self.input: ndarray = array([]) - self.output: ndarray = array([]) - self.loss_data: Any = None + self.__training_data: Dict[str, ndarray] = {} + self.__additional_data: Dict[str, ndarray] = {} self.compute_training_data: bool = True # Dataset data variables self.database: Optional[Database] = None - self.sample_in: Optional[ndarray] = None - self.sample_out: Optional[ndarray] = None - self.additional_fields: Dict[str, Any] = {} + self.update_line: Optional[int] = None + self.sample_training: Optional[Dict[str, Any]] = None + self.sample_additional: Optional[Dict[str, Any]] = None def create(self) -> None: """ @@ -136,3 +135,11 @@ def _send_training_data(self) -> None: def _reset_training_data(self) -> None: raise NotImplementedError + + def _update_training_data(self, + line_id: int) -> None: + raise NotImplementedError + + def _get_training_data(self, + line: int) -> None: + raise NotImplementedError diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 3e9de830..a082d564 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -251,20 +251,8 @@ async def action_on_sample(self, :param sender: TcpIpObject sender. """ - # Receive input sample - if await self.receive_data(loop=loop, sender=sender): - self.sample_in = await self.receive_data(loop=loop, sender=sender) - # Receive output sample - if await self.receive_data(loop=loop, sender=sender): - self.sample_out = await self.receive_data(loop=loop, sender=sender) - - additional_fields = {} - # Receive additional input sample if there are any - if await self.receive_data(loop=loop, sender=sender): - await self.receive_dict(recv_to=additional_fields, loop=loop, sender=sender) - - # Set the samples from Dataset - self.additional_fields = additional_fields.get('additional_fields', {}) + dataset_batch = await self.receive_data(loop=loop, sender=sender) + self._get_training_data(dataset_batch) async def action_on_step(self, data: ndarray, @@ -287,15 +275,17 @@ async def action_on_step(self, await self.step() # If produced sample is not usable, run again - if self.sample_in is None and self.sample_out is None: - while not self.check_sample(): - for step in range(self.simulations_per_step): - # Compute data only on final step - self.compute_training_data = step == self.simulations_per_step - 1 - await self.step() + while not self.check_sample(): + for step in range(self.simulations_per_step): + # Compute data only on final step + self.compute_training_data = step == self.simulations_per_step - 1 + await self.step() # Sent training data to Server - self._send_training_data() + if self.update_line is None: + self._send_training_data() + else: + self._update_training_data(self.update_line) self._reset_training_data() await self.send_command_done() diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index c8cb1cef..ce9ebcd8 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -3,7 +3,6 @@ from asyncio import AbstractEventLoop as EventLoop from asyncio import run as async_run from socket import socket -from numpy import ndarray from queue import SimpleQueue from DeepPhysX.Core.AsyncSocket.TcpIpObject import TcpIpObject @@ -49,7 +48,7 @@ def __init__(self, self.data_fifo: SimpleQueue = SimpleQueue() self.data_dict: Dict[Any, Any] = {} self.sample_to_client_id: List[int] = [] - self.batch_from_dataset: Optional[Dict[str, ndarray]] = None + self.batch_from_dataset: Optional[List[int]] = None self.first_time: bool = True # Reference to EnvironmentManager @@ -181,40 +180,15 @@ async def __communicate(self, loop = get_event_loop() - # # 1) If a sample from Dataset is given, sent it to the TcpIpClient - # if self.batch_from_dataset is not None: - # # Check if there is remaining samples, otherwise client is not used - # if len(self.batch_from_dataset['input']) == 0 or len(self.batch_from_dataset['output']) == 0: - # return - # # Send the sample to the TcpIpClient - # await self.send_command_sample(loop=loop, receiver=client) - # # Pop the first sample of the numpy batch for network in / out - # for field in ['input', 'output']: - # # Tell if there is something to read - # await self.send_data(data_to_send=field in self.batch_from_dataset, loop=loop, receiver=client) - # if field in self.batch_from_dataset: - # # Pop sample from array if there are some - # sample = self.batch_from_dataset[field][0] - # self.batch_from_dataset[field] = self.batch_from_dataset[field][1:] - # # Keep the sample in memory - # self.data_dict[client_id][field] = sample - # # Send network in / out sample - # await self.send_data(data_to_send=sample, loop=loop, receiver=client) - # # Pop the first sample of the numpy batch for each additional dataset field - # field = 'additional_fields' - # # Tell TcpClient if there is additional data for this field - # await self.send_data(data_to_send=field in self.batch_from_dataset, loop=loop, receiver=client) - # if field in self.batch_from_dataset: - # sample = {} - # # Get each additional data field - # for key in self.batch_from_dataset[field]: - # # Pop sample from array - # sample[key] = self.batch_from_dataset[field][key][0] - # self.batch_from_dataset[field][key] = self.batch_from_dataset[field][key][1:] - # # Keep the sample in memory - # self.data_dict[client_id][field + '_' + key] = sample[key] - # # Send additional in / out sample - # await self.send_dict(name="additional_fields", dict_to_send=sample, loop=loop, receiver=client) + # 1. Send a sample to the Client if a batch from the Dataset is given + if self.batch_from_dataset is not None: + # Check if there is remaining samples, otherwise the Client is not used + if len(self.batch_from_dataset) == 0: + return + # Send the sample to the Client + await self.send_command_sample(loop=loop, receiver=client) + line = int(self.batch_from_dataset.pop(0)) + await self.send_data(data_to_send=line, loop=loop, receiver=client) # 2. Execute n steps, the last one send data computation signal if animate: diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 55473e71..498d161d 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -37,16 +37,18 @@ def __init__(self, ip_address=ip_address, port=port) - # Input and output to give to the network + # Training data variables self.__training_data: Dict[str, ndarray] = {} self.__additional_data: Dict[str, ndarray] = {} - self.__first_add: List[bool] = [True, True] + self.compute_training_data: bool = True - # Variables to store samples from Dataset + # Dataset data variables + self.database: Optional[Database] = None + self.update_line: Optional[int] = None self.sample_training: Optional[Dict[str, Any]] = None self.sample_additional: Optional[Dict[str, Any]] = None - # Loss data - self.loss_data: Any = None + self.__first_add: List[bool] = [True, True] + # Manager if the Environment is not a TcpIpClient self.environment_manager: Any = environment_manager @@ -216,12 +218,6 @@ def set_additional_data(self, self.__additional_data = kwargs self.__additional_data['env_id'] = self.instance_id - def _reset_training_data(self) -> None: - self.__training_data = {} - self.__additional_data = {} - self.sample_training = None - self.sample_additional = None - def _send_training_data(self) -> None: line_id = self.database.add_data(table_name='Training', data=self.__training_data) @@ -230,6 +226,13 @@ def _send_training_data(self) -> None: self.database.add_data(table_name='Sync', data={'env': line_id}) + def _reset_training_data(self) -> None: + self.__training_data = {} + self.__additional_data = {} + self.sample_training = None + self.sample_additional = None + self.update_line = None + def _update_training_data(self, line_id: int) -> None: self.database.update(table_name='Training', @@ -244,6 +247,7 @@ def _update_training_data(self, def _get_training_data(self, line: int) -> None: + self.update_line = line self.sample_training = self.database.get_line(table_name='Training', line_id=line) self.sample_additional = self.database.get_line(table_name='Additional', diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index ec4c188e..a7b60bb0 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -114,11 +114,10 @@ def get_data(self, else: self.data_lines = self.dataset_manager.get_data(batch_size=self.batch_size) # Dispatch a batch to clients - # TODO - if self.environment_manager is not None and self.environment_manager.use_dataset_in_environment: + if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data)\ + and self.environment_manager.use_dataset_in_environment: self.environment_manager.dispatch_batch(data_lines=self.data_lines, animate=animate) - # Environment is no longer used elif self.environment_manager is not None: self.environment_manager.close() From 63349fac0e4ed76b119a04f8938c96b86a6f12c7 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 19 Oct 2022 15:52:52 +0200 Subject: [PATCH 07/61] Use Database for online training in local mode. --- src/Environment/BaseEnvironment.py | 3 +- src/Manager/DataManager.py | 2 +- src/Manager/DatabaseManager.py | 73 ++++++++++++++++++++++++++---- src/Manager/EnvironmentManager.py | 9 ++-- src/Pipelines/BaseTrainer.py | 2 +- 5 files changed, 72 insertions(+), 17 deletions(-) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 498d161d..b46c4130 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -218,13 +218,14 @@ def set_additional_data(self, self.__additional_data = kwargs self.__additional_data['env_id'] = self.instance_id - def _send_training_data(self) -> None: + def _send_training_data(self) -> int: line_id = self.database.add_data(table_name='Training', data=self.__training_data) self.database.add_data(table_name='Additional', data=self.__additional_data) self.database.add_data(table_name='Sync', data={'env': line_id}) + return line_id def _reset_training_data(self) -> None: self.__training_data = {} diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index a7b60bb0..1b27a468 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -108,7 +108,7 @@ def get_data(self, if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data)\ and self.produce_data: self.data_lines = self.environment_manager.get_data(animate=animate) - self.dataset_manager.add_data() + self.dataset_manager.add_data(self.data_lines) # Get data from Dataset else: diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 949fae10..977447dd 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -46,6 +46,7 @@ def __init__(self, self.shuffle: bool = dataset_config.shuffle self.produce_data = produce_data self.normalize: bool = dataset_config.normalize + self.total_nb_sample: int = 0 self.recompute_normalization: bool = dataset_config.recompute_normalization # Dataset modes @@ -58,6 +59,7 @@ def __init__(self, self.partition_template: Dict[str, str] = {mode: f'{session_name}_{mode}_' + '{}' for mode in self.modes} self.partitions: Dict[str, List[str]] = {mode: [] for mode in self.modes} self.partition_index: Dict[str, int] = {mode: 0 for mode in self.modes} + self.current_partition: str = '' # Dataset indexing self.shuffle_pattern: ndarray = arange(0) @@ -112,15 +114,15 @@ def create_partition(self): """ - partition_path = self.partition_template[self.mode].format(self.partition_index[self.mode]) + self.current_partition = self.partition_template[self.mode].format(self.partition_index[self.mode]) self.database = Database(database_dir=self.dataset_dir, - database_name=partition_path).new() + database_name=self.current_partition).new() self.database.create_table(table_name='Sync', storing_table=False, fields=[('env', int), ('net', int)]) self.database.create_table(table_name='Training') self.database.create_table(table_name='Additional') - self.partitions[self.mode].append(partition_path) + self.partitions[self.mode].append(self.current_partition) self.partition_index[self.mode] += 1 def additional_partition(self): @@ -176,17 +178,21 @@ def load_directory(self): self.update_json(update_normalization=True) # 5. Load the Database - partition_path = self.partitions[self.mode][self.partition_index[self.mode]] + self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] self.database = Database(database_dir=self.dataset_dir, - database_name=partition_path).load() + database_name=self.current_partition).load() + + # 6. Shuffle Database indices + if self.shuffle: + self.shuffle_samples() def load_next_partition(self): # 1. Define next partition self.partition_index[self.mode] = (self.partition_index[self.mode] + 1) % len(self.partitions[self.mode]) - partition_path = self.partitions[self.mode][self.partition_index[self.mode]] + self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] self.database = Database(database_dir=self.dataset_dir, - database_name=partition_path).load() + database_name=self.current_partition).load() # 2. Shuffle if self.shuffle: @@ -272,7 +278,8 @@ def shuffle_samples(self): if self.shuffle: shuffle(self.shuffle_pattern) - def add_data(self): + def add_data(self, + data_lines: Optional[List[int]] = None): """ """ @@ -282,6 +289,9 @@ def add_data(self): if self.first_add: self.update_json(update_partitions_lists=True, update_shapes=True, update_architecture=True) self.first_add = False + if self.normalize and self.mode == 'training' and self.pipeline == 'training' and data_lines is not None: + self.json_content['normalization'] = self.update_normalization(data_lines=data_lines) + self.update_json() # 2. Check the size of the partition if self.max_file_size is not None: @@ -289,6 +299,9 @@ def add_data(self): self.additional_partition() self.update_json(update_partitions_lists=True, update_nb_samples=True) + # 3. Update sample counter + self.current_sample = self.nb_samples + 1 + def get_data(self, batch_size: int) -> List[int]: """ @@ -306,7 +319,7 @@ def get_data(self, # 3. Get a batch of data if self.shuffle: - return self.shuffle_pattern[idx:self.current_sample] + return list(self.shuffle_pattern[idx:self.current_sample]) return list(arange(idx, self.current_sample)) def close(self): @@ -314,7 +327,7 @@ def close(self): """ - if self.normalize and self.produce_data: + if self.normalize and self.pipeline == 'data_generation': self.update_json(update_normalization=True) self.database.close() @@ -367,6 +380,46 @@ def compute_normalization(self) -> Dict[str, List[float]]: return normalization + def update_normalization(self, + data_lines: List[int]) -> Dict[str, List[float]]: + + previous_normalization = self.normalization + previous_nb_samples = self.total_nb_sample + self.total_nb_sample += len(data_lines) + + # First update + if previous_normalization is None: + return self.compute_normalization() + new_normalization = previous_normalization.copy() + + # Compute the mean for each field + fields = list(previous_normalization.keys()) + data_to_normalize = self.database.get_lines(table_name='Training', + fields=fields, + lines_id=data_lines, + batched=True) + for field in fields: + data = array(data_to_normalize[field]) + m = (previous_nb_samples / self.total_nb_sample) * previous_normalization[field][0] + \ + (len(data_lines) / self.total_nb_sample) * data.mean() + new_normalization[field][0] = m + + # Compute standard deviation for each field + stds = {field: [] for field in fields} + nb_samples = [] + for partition in self.partitions['training']: + data_to_normalize = self.load_partitions_fields(partition=partition, + fields=fields) + nb_samples.append(data_to_normalize['id'][-1]) + for field in fields: + data = array(data_to_normalize[field]) + stds[field].append(mean(abs(data - new_normalization[field][0]) ** 2)) + for field in fields: + new_normalization[field][1] = sqrt(sum([(n / sum(nb_samples)) * std + for n, std in zip(nb_samples, stds[field])])) + + return new_normalization + @property def normalization(self) -> Dict[str, List[float]]: return None if self.json_content['normalization'] == {} else self.json_content['normalization'] diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 8fd65259..d8a3ccf2 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -101,6 +101,7 @@ def get_data_from_environment(self, # 1. Produce batch while batch size is not complete nb_sample = 0 + dataset_lines = [] while nb_sample < self.batch_size: # 1.1 Send a sample if a batch from dataset is given @@ -120,13 +121,14 @@ def get_data_from_environment(self, if self.environment.check_sample(): nb_sample += 1 if update_line is None: - self.environment._send_training_data() + new_line = self.environment._send_training_data() + dataset_lines.append(new_line) else: self.environment._update_training_data(update_line) + dataset_lines.append(update_line) self.environment._reset_training_data() - # TODO: return the indices of samples - return [] + return dataset_lines def dispatch_batch_to_server(self, data_lines: List[int], @@ -194,6 +196,5 @@ def __str__(self) -> str: # description += f" Record wrong samples: {self.record_wrong_samples}\n" description += f" Number of threads: {self.number_of_thread}\n" # description += f" Managed objects: Environment: {self.environment.env_name}\n" - # Todo: manage the print log of each Environment since they can have different parameters # description += str(self.environment) return description diff --git a/src/Pipelines/BaseTrainer.py b/src/Pipelines/BaseTrainer.py index 124b1c77..15cf6041 100644 --- a/src/Pipelines/BaseTrainer.py +++ b/src/Pipelines/BaseTrainer.py @@ -60,7 +60,7 @@ def __init__(self, self.debug = debug_session # Configure 'produce_data' flag - if environment_config is not None and dataset_config.existing_dir is None: + if environment_config is None and dataset_config.existing_dir is None: raise ValueError(f"[{self.name}] No data source provided.") produce_data = dataset_config.existing_dir is None From 84fc59a8753f67f2ee7e139d4ff25d8b864e1fd9 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 19 Oct 2022 18:16:46 +0200 Subject: [PATCH 08/61] Use Database for online training in remote mode. Request prediction with Database in local mode. --- src/AsyncSocket/TcpIpClient.py | 4 +- src/AsyncSocket/TcpIpServer.py | 7 ++- src/Environment/BaseEnvironment.py | 45 +++++++++++++------ src/Environment/BaseEnvironmentConfig.py | 2 +- src/Manager/DataManager.py | 18 +++++--- src/Manager/DatabaseManager.py | 3 +- src/Manager/EnvironmentManager.py | 1 - src/Manager/Manager.py | 1 + src/Manager/NetworkManager.py | 55 +++++++++++++++++++----- 9 files changed, 102 insertions(+), 34 deletions(-) diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index a082d564..a63bd8c9 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -283,11 +283,13 @@ async def action_on_step(self, # Sent training data to Server if self.update_line is None: - self._send_training_data() + line = self._send_training_data() else: self._update_training_data(self.update_line) + line = self.update_line self._reset_training_data() await self.send_command_done() + await self.send_data(data_to_send=line, loop=loop, receiver=sender) async def action_on_change_db(self, data: Dict[Any, Any], diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index ce9ebcd8..1b392fd3 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -50,6 +50,7 @@ def __init__(self, self.sample_to_client_id: List[int] = [] self.batch_from_dataset: Optional[List[int]] = None self.first_time: bool = True + self.data_lines: List[int] = [] # Reference to EnvironmentManager self.environment_manager: Optional[Any] = manager @@ -135,7 +136,7 @@ async def __initialize(self, param_dict: Dict[Any, Any]) -> None: ########################################################################################## def get_batch(self, - animate: bool = True) -> None: + animate: bool = True) -> List[int]: """ Build a batch from clients samples. @@ -144,6 +145,7 @@ def get_batch(self, # Trigger communication protocol async_run(self.__request_data_to_clients(animate=animate)) + return self.data_lines async def __request_data_to_clients(self, animate: bool = True) -> None: @@ -155,6 +157,7 @@ async def __request_data_to_clients(self, """ nb_sample = 0 + self.data_lines = [] # Launch the communication protocol while the batch needs to be filled while nb_sample < self.batch_size: # Run communicate protocol for each client and wait for the last one to finish @@ -196,6 +199,8 @@ async def __communicate(self, # Receive data await self.listen_while_not_done(loop=loop, sender=client, data_dict=self.data_dict, client_id=client_id) + line = await self.receive_data(loop=loop, sender=client) + self.data_lines.append(line) def set_dataset_batch(self, data_lines: List[int]) -> None: diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index b46c4130..dfe42ef6 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Dict, Union, Tuple, List +from typing import Any, Optional, Dict, Union, Tuple, List, Type from numpy import ndarray from SSD.Core.Storage.Database import Database @@ -63,6 +63,8 @@ def __init__(self, if self.instance_id == 0: self.database.create_fields(table_name='Training', fields=('env_id', int)) + self.database.create_fields(table_name='Additional', + fields=('env_id', int)) # Connect the Factory to the visualization Database self.factory: Optional[VedoFactory] = None @@ -185,6 +187,14 @@ def close(self) -> None: ########################################################################################## ########################################################################################## + def define_training_fields(self, + fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + self.database.create_fields(table_name='Training', fields=fields) + + def define_additional_fields(self, + fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + self.database.create_fields(table_name='Additional', fields=fields) + def set_training_data(self, **kwargs) -> None: """ @@ -200,11 +210,11 @@ def set_training_data(self, for field in kwargs.keys(): if field not in required_fields: raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." - f"Required fields are {required_fields}.{self.instance_id}") + f"Required fields are {required_fields}.") for field in required_fields: if field not in kwargs.keys(): raise ValueError(f"[{self.name}] The field '{field}' was not defined in training data." - f"Required fields are {required_fields}.{self.instance_id}") + f"Required fields are {required_fields}.") # Training data is set if the Environment can compute data if self.compute_training_data: @@ -262,7 +272,7 @@ def _get_training_data(self, ########################################################################################## def get_prediction(self, - input_array: ndarray) -> ndarray: + **kwargs) -> ndarray: """ Request a prediction from Network. @@ -270,6 +280,17 @@ def get_prediction(self, :return: Network prediction. """ + # Check kwargs + if self.__first_add[1]: + if self.instance_id != 0: + self.database.load() + self.__first_add[1] = False + required_fields = list(set(self.database.get_fields(table_name='Prediction')) - {'id'}) + for field in kwargs.keys(): + if field not in required_fields: + raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." + f"Required fields are {required_fields}.") + # If Environment is a TcpIpClient, send request to the Server if self.as_tcp_ip_client: return TcpIpClient.request_get_prediction(self, input_array=input_array) @@ -277,15 +298,15 @@ def get_prediction(self, # Otherwise, check the hierarchy of managers if self.environment_manager.data_manager is None: raise ValueError("Cannot request prediction if DataManager does not exist") - elif self.environment_manager.data_manager.manager is None: - raise ValueError("Cannot request prediction if Manager does not exist") - elif not hasattr(self.environment_manager.data_manager.manager, 'network_manager'): - raise AttributeError("Cannot request prediction if NetworkManager does not exist. If using a data " - "generation pipeline, please disable get_prediction requests.") - elif self.environment_manager.data_manager.manager.network_manager is None: - raise ValueError("Cannot request prediction if NetworkManager does not exist") # Get a prediction - return self.environment_manager.data_manager.get_prediction(network_input=input_array[None, ]) + self.database.update(table_name='Prediction', + data=kwargs, + line_id=self.instance_id) + self.environment_manager.data_manager.get_prediction(self.instance_id) + data_pred = self.database.get_line(table_name='Prediction', + line_id=self.instance_id) + del data_pred['id'] + return data_pred def update_visualisation(self) -> None: """ diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index e7979908..11e5991a 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -129,7 +129,7 @@ def create_server(self, # Create clients client_threads = [] for i in range(self.number_of_thread): - client_thread = Thread(target=self.start_client, args=(i, data_db, visu_db)) + client_thread = Thread(target=self.start_client, args=(i + 1, data_db, visu_db)) client_threads.append(client_thread) for client in client_threads: client.start() diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 1b27a468..fd78d19d 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -84,6 +84,12 @@ def change_database(self) -> None: self.manager.change_database(self.dataset_manager.database) self.environment_manager.change_database(self.dataset_manager.database) + @property + def nb_environment(self): + if self.environment_manager is None: + return None + return 1 if self.environment_manager.server is None else self.environment_manager.number_of_thread + def get_data(self, epoch: int = 0, animate: bool = True) -> None: @@ -147,20 +153,18 @@ def get_data(self, self.dataset_manager.add_data(data) def get_prediction(self, - network_input: ndarray) -> ndarray: + instance_id: int) -> None: """ Get a Network prediction from an input array. Normalization is applied on input and prediction. - :param network_input: Input array of the Network. :return: Network prediction. """ - # Apply normalization - network_input = self.normalize_data(network_input, 'input') # Get a prediction - prediction = self.manager.network_manager.compute_online_prediction(network_input=network_input) - # Unapply normalization on prediction - return self.normalize_data(prediction, 'output', reverse=True) + if self.manager is None: + raise ValueError("Cannot request prediction if Manager (and then NetworkManager) does not exist.") + self.manager.network_manager.compute_online_prediction(instance_id=instance_id, + normalization=self.normalization) def apply_prediction(self, prediction: ndarray) -> None: diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 977447dd..58718802 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -122,6 +122,7 @@ def create_partition(self): fields=[('env', int), ('net', int)]) self.database.create_table(table_name='Training') self.database.create_table(table_name='Additional') + self.database.create_table(table_name='Prediction') self.partitions[self.mode].append(self.current_partition) self.partition_index[self.mode] += 1 @@ -131,7 +132,7 @@ def additional_partition(self): fields = {} types = {'INT': int, 'FLOAT': float, 'STR': str, 'BOOL': bool, 'NUMPY': ndarray} if self.partition_index[self.mode] > 0: - for table_name in ['Training', 'Additional']: + for table_name in ['Training', 'Additional', 'Prediction']: fields[table_name] = [] F = self.database.get_fields(table_name=table_name, only_names=False) diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index d8a3ccf2..bef46176 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -88,7 +88,6 @@ def get_data_from_server(self, """ # Get data from server - # TODO: return the indices of samples return self.server.get_batch(animate) def get_data_from_environment(self, diff --git a/src/Manager/Manager.py b/src/Manager/Manager.py index 4e30cff5..c30ec50a 100644 --- a/src/Manager/Manager.py +++ b/src/Manager/Manager.py @@ -76,6 +76,7 @@ def __init__(self, new_session=new_session, pipeline=pipeline, data_db=database) + self.network_manager.link_clients(self.data_manager.nb_environment) # Create a StatsManager for training only self.debug_session = debug_session diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index 90c2071d..289084f9 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -1,7 +1,7 @@ from typing import Any, Dict, Tuple, Optional, List from os import listdir from os.path import join, isdir, isfile -from numpy import copy, ndarray, array +from numpy import ndarray, array from SSD.Core.Storage.Database import Database @@ -80,6 +80,14 @@ def __init__(self, def change_database(self, database): self.data_db = database + def link_clients(self, + nb_clients: Optional[int] = None): + if nb_clients is not None: + fields = [(field_name, ndarray) for field_name in self.network.net_fields + self.network.pred_fields] + self.data_db.create_fields(table_name='Prediction', fields=fields) + for _ in range(nb_clients): + self.data_db.add_data(table_name='Prediction', data={}) + def load_network(self, which_network: int = -1) -> None: """ @@ -157,10 +165,15 @@ def compute_prediction_and_loss(self, data_pred = self.data_transformation.transform_before_apply(data_pred) for field in data_pred: data_pred[field] = self.network.tensor_to_numpy(data_pred[field]) + if field in normalization.keys(): + data_pred[field] = self.normalize_data(data=data_pred[field], + normalization=normalization[field], + reverse=True) return data_pred, data_loss def compute_online_prediction(self, - network_input: ndarray) -> ndarray: + instance_id: int, + normalization: Optional[Dict[str, List[float]]] = None) -> None: """ Make a prediction with the data passed as argument. @@ -168,16 +181,38 @@ def compute_online_prediction(self, :return: The prediction """ - # Getting data from the data manager - data_in = self.network.transform_from_numpy(copy(network_input), grad=False) + # Get Network data + normalization = {} if normalization is None else normalization + sample = self.data_db.get_line(table_name='Prediction', + fields=self.network.net_fields, + line_id=instance_id) + del sample['id'] + + # Apply normalization and convert to tensor + for field in sample.keys(): + sample[field] = array(sample[field]) + if field in normalization.keys(): + sample[field] = self.normalize_data(data=sample[field], + normalization=normalization[field]) + sample[field] = self.network.numpy_to_tensor(data=sample[field]) # Compute prediction - data_in = self.data_transformation.transform_before_prediction(data_in) - pred = self.network.predict(data_in) - pred, _ = self.data_transformation.transform_before_loss(pred) - pred = self.data_transformation.transform_before_apply(pred) - pred = self.network.transform_to_numpy(pred) - return pred.reshape(-1) + data_net = self.data_transformation.transform_before_prediction(sample) + data_pred = self.network.predict(data_net) + data_pred, _ = self.data_transformation.transform_before_loss(data_pred) + data_pred = self.data_transformation.transform_before_apply(data_pred) + + # Return the prediction + for field in data_pred.keys(): + data_pred[field] = self.network.tensor_to_numpy(data=data_pred[field]) + if field in normalization.keys(): + data_pred[field] = self.normalize_data(data=data_pred[field], + normalization=normalization[field], + reverse=True) + data_pred[field].reshape(-1) + self.data_db.update(table_name='Prediction', + data=data_pred, + line_id=instance_id) @classmethod def normalize_data(cls, From 8b49dc11c123808f459b5ada25154f2875739700 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 19 Oct 2022 18:43:09 +0200 Subject: [PATCH 09/61] Request prediction with Database in remote mode. --- src/AsyncSocket/TcpIpClient.py | 17 +++++++++++++---- src/AsyncSocket/TcpIpServer.py | 19 ++----------------- src/Environment/BaseEnvironment.py | 8 ++++---- 3 files changed, 19 insertions(+), 25 deletions(-) diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index a63bd8c9..36f35480 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -76,7 +76,7 @@ async def __initialize(self) -> None: # Create the environment self.create() self.init() - if self.instance_id == 0: + if self.instance_id == 1: self.init_database() self.init_visualization() @@ -175,8 +175,8 @@ def send_prediction_data(self, ########################################################################################## ########################################################################################## - def request_get_prediction(self, - input_array: ndarray) -> ndarray: + def get_prediction(self, + **kwargs) -> Dict[str, ndarray]: """ Request a prediction from Network. @@ -184,7 +184,16 @@ def request_get_prediction(self, :return: Prediction of the Network. """ - return self.send_prediction_data(network_input=input_array) + # Get a prediction + self.database.update(table_name='Prediction', + data=kwargs, + line_id=self.instance_id) + self.sync_send_command_prediction() + _ = self.sync_receive_data() + data_pred = self.database.get_line(table_name='Prediction', + line_id=self.instance_id) + del data_pred['id'] + return data_pred def request_update_visualization(self) -> None: """ diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index 1b392fd3..e9cb04eb 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -300,25 +300,10 @@ async def action_on_prediction(self, :param sender: TcpIpObject sender. """ - # Receive network input - label, network_input = await self.receive_labeled_data(loop=loop, sender=sender) - - # Check that manager hierarchy is well-defined if self.environment_manager.data_manager is None: raise ValueError("Cannot request prediction if DataManager does not exist") - elif self.environment_manager.data_manager.manager is None: - raise ValueError("Cannot request prediction if Manager does not exist") - elif not hasattr(self.environment_manager.data_manager.manager, 'network_manager'): - raise AttributeError("Cannot request prediction if NetworkManager does not exist. If using a data " - "generation pipeline, please disable get_prediction requests.") - elif self.environment_manager.data_manager.manager.network_manager is None: - raise ValueError("Cannot request prediction if NetworkManager does not exist") - - # Get the prediction from NetworkPrediction - prediction = self.environment_manager.data_manager.get_prediction(network_input=network_input[None, ]) - # Send back the prediction to the Client - await self.send_labeled_data(data_to_send=prediction, label="prediction", receiver=sender, - send_read_command=False) + self.environment_manager.data_manager.get_prediction(client_id) + await self.send_data(data_to_send=True, receiver=sender) async def action_on_visualisation(self, data: Dict[Any, Any], diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index dfe42ef6..074b9a67 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -60,7 +60,7 @@ def __init__(self, database_name=data_db[1]).load() else: self.database = data_db - if self.instance_id == 0: + if self.instance_id == 1: self.database.create_fields(table_name='Training', fields=('env_id', int)) self.database.create_fields(table_name='Additional', @@ -163,7 +163,7 @@ def check_sample(self) -> bool: return True def apply_prediction(self, - prediction: ndarray) -> None: + prediction: Dict[str, ndarray]) -> None: """ Apply network prediction in environment. Not mandatory. @@ -272,7 +272,7 @@ def _get_training_data(self, ########################################################################################## def get_prediction(self, - **kwargs) -> ndarray: + **kwargs) -> Dict[str, ndarray]: """ Request a prediction from Network. @@ -293,7 +293,7 @@ def get_prediction(self, # If Environment is a TcpIpClient, send request to the Server if self.as_tcp_ip_client: - return TcpIpClient.request_get_prediction(self, input_array=input_array) + return TcpIpClient.get_prediction(self, **kwargs) # Otherwise, check the hierarchy of managers if self.environment_manager.data_manager is None: From 5f4a68941dbe8fcff79a65bd9ac63e5d089dd7d7 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 20 Oct 2022 14:39:04 +0200 Subject: [PATCH 10/61] Dispatch samples from Database in online training. --- src/Environment/BaseEnvironment.py | 9 +++++---- src/Manager/DataManager.py | 5 ++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 074b9a67..04481b81 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -246,10 +246,11 @@ def _reset_training_data(self) -> None: def _update_training_data(self, line_id: int) -> None: - self.database.update(table_name='Training', - data=self.__training_data, - line_id=line_id) - if self.sample_additional is not None: + if self.__training_data != {}: + self.database.update(table_name='Training', + data=self.__training_data, + line_id=line_id) + if self.__additional_data != {}: self.database.update(table_name='Additional', data=self.__additional_data, line_id=line_id) diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index fd78d19d..248fd441 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -110,7 +110,6 @@ def get_data(self, elif self.pipeline == 'training': # Get data from Environment(s) if used and if the data should be created at this epoch - # TODO if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data)\ and self.produce_data: self.data_lines = self.environment_manager.get_data(animate=animate) @@ -120,8 +119,8 @@ def get_data(self, else: self.data_lines = self.dataset_manager.get_data(batch_size=self.batch_size) # Dispatch a batch to clients - if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data)\ - and self.environment_manager.use_dataset_in_environment: + if self.environment_manager is not None and (epoch == 0 or + self.environment_manager.use_dataset_in_environment): self.environment_manager.dispatch_batch(data_lines=self.data_lines, animate=animate) # Environment is no longer used From df7feb00b2ebde6ec39796f5508db6b5cfdecae8 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 20 Oct 2022 17:08:55 +0200 Subject: [PATCH 11/61] Remove parameters exchange with TCP-IP, replace with a dedicated Database. --- src/AsyncSocket/AbstractEnvironment.py | 98 ++++---------- src/AsyncSocket/TcpIpClient.py | 18 +-- src/AsyncSocket/TcpIpServer.py | 31 +---- ...DatasetConfig.py => BaseDatabaseConfig.py} | 8 +- src/Environment/BaseEnvironment.py | 122 ++++++++--------- src/Environment/BaseEnvironmentConfig.py | 125 ++++++++---------- src/Environment/launcherBaseEnvironment.py | 12 +- src/Manager/DataManager.py | 72 +++++----- src/Manager/DatabaseManager.py | 52 ++++---- src/Manager/EnvironmentManager.py | 20 +-- src/Manager/Manager.py | 10 +- src/Manager/NetworkManager.py | 6 +- ...DataGenerator.py => BaseDataGeneration.py} | 18 +-- src/Pipelines/BasePipeline.py | 24 ++-- .../{BaseRunner.py => BasePrediction.py} | 68 +++++----- .../{BaseTrainer.py => BaseTraining.py} | 18 +-- 16 files changed, 300 insertions(+), 402 deletions(-) rename src/Database/{BaseDatasetConfig.py => BaseDatabaseConfig.py} (95%) rename src/Pipelines/{BaseDataGenerator.py => BaseDataGeneration.py} (86%) rename src/Pipelines/{BaseRunner.py => BasePrediction.py} (65%) rename src/Pipelines/{BaseTrainer.py => BaseTraining.py} (93%) diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index f2c9687f..57495df1 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -40,95 +40,47 @@ def __init__(self, self.sample_training: Optional[Dict[str, Any]] = None self.sample_additional: Optional[Dict[str, Any]] = None - def create(self) -> None: - """ - Create the Environment. Automatically called when Environment is launched. - Must be implemented by user. - """ + ########################################################################################## + ########################################################################################## + # Initializing Environment # + ########################################################################################## + ########################################################################################## + def create(self) -> None: raise NotImplementedError def init(self) -> None: - """ - Initialize the Environment. Automatically called when Environment is launched. - Not mandatory. - """ - pass - def close(self) -> None: - """ - Close the Environment. Automatically called when Environment is shut down. - Not mandatory. - """ + def init_database(self) -> None: + raise NotImplementedError + def init_visualization(self) -> None: pass - async def step(self) -> None: - """ - Compute the number of steps in the Environment specified by simulations_per_step in EnvironmentConfig. - Must be implemented by user. - """ + ########################################################################################## + ########################################################################################## + # Environment behavior # + ########################################################################################## + ########################################################################################## + async def step(self) -> None: raise NotImplementedError def check_sample(self) -> bool: - """ - Check if the current produced sample is usable. - Not mandatory. - - :return: Current sample can be used or not - """ - return True - def recv_parameters(self, - param_dict: Dict[str, Any]) -> None: - """ - Receive simulation parameters set in Config from the Server. Automatically called before create and init. - - :param param_dict: Dictionary of parameters. - """ - + def apply_prediction(self, prediction: Dict[str, ndarray]) -> None: pass - def init_visualization(self) -> None: - """ - Define the visualization objects to send to the Visualizer. Automatically called after create and init. - Not mandatory. - - :return: Initial visualization data dictionary. - """ - + def close(self) -> None: pass - def init_database(self) -> None: - """ - Define the fields of the training dataset. - Required. - """ - - raise NotImplementedError - - def send_parameters(self) -> dict: - """ - Create a dictionary of parameters to send to the manager. Automatically called after create and init. - Not mandatory. - - :return: Dictionary of parameters. - """ - - return {} - - def apply_prediction(self, prediction: ndarray) -> None: - """ - Apply network prediction in environment. - Not mandatory. - - :param prediction: Prediction data. - """ - - pass + ########################################################################################## + ########################################################################################## + # Defining a sample # + ########################################################################################## + ########################################################################################## def _send_training_data(self) -> None: raise NotImplementedError @@ -136,10 +88,8 @@ def _send_training_data(self) -> None: def _reset_training_data(self) -> None: raise NotImplementedError - def _update_training_data(self, - line_id: int) -> None: + def _update_training_data(self, line_id: int) -> None: raise NotImplementedError - def _get_training_data(self, - line: int) -> None: + def _get_training_data(self, line: int) -> None: raise NotImplementedError diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 36f35480..bc7d8fc1 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -53,37 +53,27 @@ def __init__(self, def initialize(self) -> None: """ - Run __initialize method with asyncio. + Receive parameters from the server to create environment. """ async_run(self.__initialize()) async def __initialize(self) -> None: """ - Receive parameters from the server to create environment, send parameters to the server in exchange. + Receive parameters from the server to create environment. """ loop = get_event_loop() + # Receive number of sub-steps self.simulations_per_step = await self.receive_data(loop=loop, sender=self.sock) - # Receive parameters - recv_param_dict = {} - await self.receive_dict(recv_to=recv_param_dict, sender=self.sock, loop=loop) - # Use received parameters - if 'parameters' in recv_param_dict: - self.recv_parameters(recv_param_dict['parameters']) # Create the environment self.create() self.init() - if self.instance_id == 1: - self.init_database() + self.init_database() self.init_visualization() - # Send parameters - param_dict = self.send_parameters() - await self.send_dict(name="parameters", dict_to_send=param_dict, loop=loop, receiver=self.sock) - # Initialization done await self.send_command_done(loop=loop, receiver=self.sock) diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index e9cb04eb..e3d17700 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -63,7 +63,7 @@ def __init__(self, def connect(self) -> None: """ - Run __connect method with asyncio. + Accept connections from clients. """ print(f"[{self.name}] Waiting for clients...") @@ -90,43 +90,26 @@ async def __connect(self) -> None: ########################################################################################## ########################################################################################## - def initialize(self, - param_dict: Dict[Any, Any]) -> Dict[Any, Any]: + def initialize(self) -> None: """ - Run __initialize method with asyncio. Manage parameters exchange. - - :param param_dict: Dictionary of parameters to send to the client's environment. - :return: Dictionary of parameters for each environment to send the manager. + Send parameters to the clients to create their environments. """ print(f"[{self.name}] Initializing clients...") - async_run(self.__initialize(param_dict)) - # Return param dict - param_dict = {} - for client_id in self.data_dict: - if 'parameters' in self.data_dict[client_id]: - param_dict[client_id] = self.data_dict[client_id]['parameters'] - return param_dict + async_run(self.__initialize()) - async def __initialize(self, param_dict: Dict[Any, Any]) -> None: + async def __initialize(self) -> None: """ - Send parameters to the clients to create their environments, receive parameters from clients in exchange. - - :param param_dict: Dictionary of parameters to send to the client's environment. + Send parameters to the clients to create their environments. """ loop = get_event_loop() - # Empty dictionaries for received parameters from clients - self.data_dict = {client_ID: {} for client_ID in range(len(self.clients))} + # Initialisation process for each client for client_id, client in self.clients: # Send number of sub-steps nb_steps = self.environment_manager.simulations_per_step if self.environment_manager else 1 await self.send_data(data_to_send=nb_steps, loop=loop, receiver=client) - # Send parameters to client - await self.send_dict(name="parameters", dict_to_send=param_dict, loop=loop, receiver=client) - # Receive visualization data and parameters - await self.listen_while_not_done(loop=loop, sender=client, data_dict=self.data_dict, client_id=client_id) print(f"[{self.name}] Client n°{client_id} initialisation done") ########################################################################################## diff --git a/src/Database/BaseDatasetConfig.py b/src/Database/BaseDatabaseConfig.py similarity index 95% rename from src/Database/BaseDatasetConfig.py rename to src/Database/BaseDatabaseConfig.py index 23e90c9b..187585b1 100644 --- a/src/Database/BaseDatasetConfig.py +++ b/src/Database/BaseDatabaseConfig.py @@ -2,14 +2,14 @@ from os.path import isdir, sep, join -class BaseDatasetConfig: +class BaseDatabaseConfig: def __init__(self, existing_dir: Optional[str] = None, mode: Optional[str] = None, max_file_size: Optional[float] = None, - shuffle: bool = True, - normalize: bool = True, + shuffle: bool = False, + normalize: bool = False, recompute_normalization: bool = False): """ Configuration class to parameterize the Dataset and its Manager. @@ -51,7 +51,7 @@ def __init__(self, raise TypeError(f"[{self.name}] The given 'recompute_normalization'={recompute_normalization} must be a " f"bool.") - # DatasetManager parameterization + # DatabaseManager parameterization self.existing_dir: Optional[str] = existing_dir self.mode: Optional[str] = mode self.max_file_size: int = max_file_size diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 04481b81..4f311e0e 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -1,5 +1,6 @@ from typing import Any, Optional, Dict, Union, Tuple, List, Type from numpy import ndarray +from os.path import isfile, join from SSD.Core.Storage.Database import Database @@ -16,8 +17,8 @@ def __init__(self, number_of_instances: int = 1, as_tcp_ip_client: bool = True, environment_manager: Optional[Any] = None, - data_db: Optional[Union[Database, Tuple[str, str]]] = None, - visu_db: Optional[Union[Database, Tuple[str, str]]] = None): + training_db: Optional[Union[Database, Tuple[str, str]]] = None, + visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): """ BaseEnvironment is an environment class to compute simulated data for the network and its optimization process. @@ -27,7 +28,7 @@ def __init__(self, :param number_of_instances: Number of simultaneously launched instances. :param as_tcp_ip_client: Environment is owned by a TcpIpClient if True, by an EnvironmentManager if False. :param environment_manager: EnvironmentManager that handles the Environment if 'as_tcp_ip_client' is False. - :param visu_db: The path to the visualization Database or the visualization Database object to connect to. + :param visualization_db: The path to the visualization Database or the visualization Database object to connect to. """ TcpIpClient.__init__(self, @@ -54,12 +55,12 @@ def __init__(self, # Connect the Environment to the data Database self.database: Optional[Database] = None - if data_db is not None: - if type(data_db) == list: - self.database = Database(database_dir=data_db[0], - database_name=data_db[1]).load() + if training_db is not None: + if type(training_db) == list: + self.database = Database(database_dir=training_db[0], + database_name=training_db[1]).load() else: - self.database = data_db + self.database = training_db if self.instance_id == 1: self.database.create_fields(table_name='Training', fields=('env_id', int)) @@ -68,13 +69,13 @@ def __init__(self, # Connect the Factory to the visualization Database self.factory: Optional[VedoFactory] = None - if visu_db is not None: - if type(visu_db) == list: - self.factory = VedoFactory(database_path=visu_db, + if visualization_db is not None: + if type(visualization_db) == list: + self.factory = VedoFactory(database_path=visualization_db, idx_instance=instance_id, remote=True) else: - self.factory = VedoFactory(database=visu_db, + self.factory = VedoFactory(database=visualization_db, idx_instance=instance_id) ########################################################################################## @@ -83,20 +84,9 @@ def __init__(self, ########################################################################################## ########################################################################################## - def recv_parameters(self, - param_dict: Dict[Any, Any]) -> None: - """ - Exploit received parameters before scene creation. - Not mandatory. - - :param param_dict: Dictionary of parameters. - """ - - pass - def create(self) -> None: """ - Create the Environment. + Create the Environment. Automatically called when Environment is launched. Must be implemented by user. """ @@ -104,36 +94,24 @@ def create(self) -> None: def init(self) -> None: """ - Initialize the Environment. + Initialize the Environment. Automatically called when Environment is launched. Not mandatory. """ pass - def send_parameters(self) -> Dict[Any, Any]: - """ - Create a dictionary of parameters to send to the manager. - Not mandatory. - - :return: Dictionary of parameters - """ - - return {} - def init_database(self) -> None: """ - Define the fields of the training dataset. - Required. + Define the fields of the training dataset. Automatically called when Environment is launched. + Must be implemented by user. """ raise NotImplementedError def init_visualization(self) -> None: """ - Define the visualization objects to send to the Visualizer. + Define the visualization objects to send to the Visualizer. Automatically called when Environment is launched. Not mandatory. - - :return: Dictionary of visualization data """ pass @@ -162,8 +140,7 @@ def check_sample(self) -> bool: return True - def apply_prediction(self, - prediction: Dict[str, ndarray]) -> None: + def apply_prediction(self, prediction: Dict[str, ndarray]) -> None: """ Apply network prediction in environment. Not mandatory. @@ -175,7 +152,7 @@ def apply_prediction(self, def close(self) -> None: """ - Close the Environment. + Close the Environment. Automatically called when Environment is shut down. Not mandatory. """ @@ -187,16 +164,48 @@ def close(self) -> None: ########################################################################################## ########################################################################################## - def define_training_fields(self, - fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - self.database.create_fields(table_name='Training', fields=fields) + def save_parameters(self, **kwargs) -> None: + """ + Save a set of parameters in the Database. + """ - def define_additional_fields(self, - fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - self.database.create_fields(table_name='Additional', fields=fields) + database_dir = self.database.get_path()[0] + if isfile(join(database_dir, 'environment_parameters.db')): + database = Database(database_dir=database_dir, + database_name='environment_parameters').load() + else: + database = Database(database_dir=database_dir, + database_name='environment_parameters').new() + fields = [(field, type(value)) for field, value in kwargs.items()] + database.create_table(table_name=f'Environment_{self.instance_id}', fields=fields) + database.add_data(table_name=f'Environment_{self.instance_id}', data=kwargs) + database.close() + + def load_parameters(self) -> Dict[str, Any]: + """ + Load a set of parameters from the Database. + """ - def set_training_data(self, - **kwargs) -> None: + database_dir = self.database.get_path()[0] + if isfile(join(database_dir, 'environment_parameters.db')): + database = Database(database_dir=database_dir, + database_name='environment_parameters').load() + parameters = database.get_line(table_name=f'Environment_{self.instance_id}') + del parameters['id'] + return parameters + return {} + + def define_training_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + self.database.load() + if len(self.database.get_fields(table_name='Training')) == 2: + self.database.create_fields(table_name='Training', fields=fields) + + def define_additional_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + self.database.load() + if len(self.database.get_fields(table_name='Additional')) == 2: + self.database.create_fields(table_name='Additional', fields=fields) + + def set_training_data(self, **kwargs) -> None: """ Set the training data to send to the TcpIpServer or the EnvironmentManager. """ @@ -233,8 +242,6 @@ def _send_training_data(self) -> int: data=self.__training_data) self.database.add_data(table_name='Additional', data=self.__additional_data) - self.database.add_data(table_name='Sync', - data={'env': line_id}) return line_id def _reset_training_data(self) -> None: @@ -254,8 +261,6 @@ def _update_training_data(self, self.database.update(table_name='Additional', data=self.__additional_data, line_id=line_id) - self.database.add_data(table_name='Sync', - data={'env': line_id}) def _get_training_data(self, line: int) -> None: @@ -272,12 +277,10 @@ def _get_training_data(self, ########################################################################################## ########################################################################################## - def get_prediction(self, - **kwargs) -> Dict[str, ndarray]: + def get_prediction(self, **kwargs) -> Dict[str, ndarray]: """ Request a prediction from Network. - :param input_array: Network input. :return: Network prediction. """ @@ -320,9 +323,6 @@ def update_visualisation(self) -> None: self.factory.render() def __str__(self) -> str: - """ - :return: String containing information about the BaseEnvironment object - """ description = "\n" description += f" {self.name}\n" diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index 11e5991a..2e9a0ebe 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Optional, Type +from typing import Any, Optional, Type from os import cpu_count from os.path import join, dirname from threading import Thread @@ -16,45 +16,34 @@ class BaseEnvironmentConfig: def __init__(self, environment_class: Type[BaseEnvironment], - visualizer: Optional[Type[VedoVisualizer]] = None, - simulations_per_step: int = 1, - max_wrong_samples_per_step: int = 10, - always_create_data: bool = False, - record_wrong_samples: bool = False, - screenshot_sample_rate: int = 0, - use_dataset_in_environment: bool = False, - param_dict: Optional[Dict[Any, Any]] = None, as_tcp_ip_client: bool = True, number_of_thread: int = 1, - max_client_connection: int = 1000, - environment_file: Optional[str] = None, ip_address: str = 'localhost', - port: int = 10000): + port: int = 10000, + simulations_per_step: int = 1, + max_wrong_samples_per_step: int = 10, + load_samples: bool = False, + only_first_epoch: bool = True, + visualizer: Optional[Type[VedoVisualizer]] = None, + record_wrong_samples: bool = False): """ BaseEnvironmentConfig is a configuration class to parameterize and create a BaseEnvironment for the EnvironmentManager. :param environment_class: Class from which an instance will be created. - :param visualizer: Class of the Visualizer to use. - :param simulations_per_step: Number of iterations to compute in the Environment at each time step. - :param max_wrong_samples_per_step: Maximum number of wrong samples to produce in a step. - :param always_create_data: If True, data will always be created from environment. If False, data will be - created from the environment during the first epoch and then re-used from the - Dataset. - :param record_wrong_samples: If True, wrong samples are recorded through Visualizer. - :param screenshot_sample_rate: A screenshot of the viewer will be done every x sample. - :param use_dataset_in_environment: If True, the dataset will always be used in the environment. - :param param_dict: Dictionary containing specific environment parameters. :param as_tcp_ip_client: Environment is owned by a TcpIpClient if True, by an EnvironmentManager if False. :param number_of_thread: Number of thread to run. - :param max_client_connection: Maximum number of handled instances. - :param environment_file: Path of the file containing the Environment class. :param ip_address: IP address of the TcpIpObject. :param port: Port number of the TcpIpObject. + :param simulations_per_step: Number of iterations to compute in the Environment at each time step. + :param max_wrong_samples_per_step: Maximum number of wrong samples to produce in a step. + :param load_samples: If True, the dataset will always be used in the environment. + :param only_first_epoch: If True, data will always be created from environment. If False, data will be created + from the environment during the first epoch and then re-used from the Dataset. + :param visualizer: Class of the Visualizer to use. + :param record_wrong_samples: If True, wrong samples are recorded through Visualizer. """ - if param_dict is None: - param_dict = {} self.name: str = self.__class__.__name__ # Check simulations_per_step type and value @@ -69,50 +58,49 @@ def __init__(self, f"{type(max_wrong_samples_per_step)}") if max_wrong_samples_per_step < 1: raise ValueError(f"[{self.name}] Given max_wrong_simulations_per_step value is negative or null") - # Check always_create_data type - if type(always_create_data) != bool: + # Check only_first_epoch type + if type(only_first_epoch) != bool: raise TypeError(f"[{self.name}] Wrong always_create_data type: bool required, get " - f"{type(always_create_data)}") + f"{type(only_first_epoch)}") if type(number_of_thread) != int: raise TypeError(f"[{self.name}] The number_of_thread number must be a positive integer.") if number_of_thread < 0: raise ValueError(f"[{self.name}] The number_of_thread number must be a positive integer.") - # TcpIpClients parameterization + # TcpIpClients variables self.environment_class: Type[BaseEnvironment] = environment_class - self.environment_file: str = environment_file if environment_file is not None else modules[ - self.environment_class.__module__].__file__ - self.param_dict: Optional[Dict[Any, Any]] = param_dict + self.environment_file: str = modules[self.environment_class.__module__].__file__ self.as_tcp_ip_client: bool = as_tcp_ip_client - # EnvironmentManager parameterization - self.received_parameters: Dict[Any, Any] = {} - self.always_create_data: bool = always_create_data - self.record_wrong_samples: bool = record_wrong_samples - self.screenshot_sample_rate: int = screenshot_sample_rate - self.use_dataset_in_environment: bool = use_dataset_in_environment - self.simulations_per_step: int = simulations_per_step - self.max_wrong_samples_per_step: int = max_wrong_samples_per_step - self.visualizer: Optional[Type[VedoVisualizer]] = visualizer - - # TcpIpServer parameterization + # TcpIpServer variables + self.number_of_thread: int = min(max(number_of_thread, 1), cpu_count()) # Assert nb is between 1 and cpu_count self.ip_address: str = ip_address self.port: int = port self.server_is_ready: bool = False - self.number_of_thread: int = min(max(number_of_thread, 1), cpu_count()) # Assert nb is between 1 and cpu_count - self.max_client_connections = max_client_connection + self.max_client_connections: int = 100 + + # EnvironmentManager variables + self.simulations_per_step: int = simulations_per_step + self.max_wrong_samples_per_step: int = max_wrong_samples_per_step + self.load_samples: bool = load_samples + self.only_first_epoch: bool = only_first_epoch + + # Visualizer variables + self.visualizer: Optional[Type[VedoVisualizer]] = visualizer + self.record_wrong_samples: bool = record_wrong_samples def create_server(self, environment_manager: Optional[Any] = None, batch_size: int = 1, - data_db: Optional[str] = None, - visu_db: Optional[str] = None) -> TcpIpServer: + training_db: Optional[str] = None, + visualization_db: Optional[str] = None) -> TcpIpServer: """ Create a TcpIpServer and launch TcpIpClients in subprocesses. :param environment_manager: EnvironmentManager. :param batch_size: Number of sample in a batch. - :param visu_db: The path to the visualization Database to connect to. + :param training_db: Path of the training Database to connect to. + :param visualization_db: Path to the visualization Database to connect to. :return: TcpIpServer object. """ @@ -129,12 +117,12 @@ def create_server(self, # Create clients client_threads = [] for i in range(self.number_of_thread): - client_thread = Thread(target=self.start_client, args=(i + 1, data_db, visu_db)) + client_thread = Thread(target=self.start_client, args=(i + 1, training_db, visualization_db)) client_threads.append(client_thread) for client in client_threads: client.start() - # Return server to manager when ready + # Return server to manager when it is ready while not self.server_is_ready: pass return server @@ -147,52 +135,49 @@ def start_server(self, :param server: TcpIpServer. """ - # Allow clients connections server.connect() - # Send and receive parameters with clients - self.received_parameters = server.initialize(self.param_dict) - # Server is ready - self.server_is_ready: bool = True + server.initialize() + self.server_is_ready = True def start_client(self, idx: int = 1, - data_db: Optional[str] = None, - visu_db: Optional[str] = None) -> None: + training_db: Optional[str] = None, + visualization_db: Optional[str] = None) -> None: """ Run a subprocess to start a TcpIpClient. :param idx: Index of client. - :param visu_db: The path to the visualization Database to connect to. + :param training_db: Path of the training Database to connect to. + :param visualization_db: Path to the visualization Database to connect to. """ script = join(dirname(modules[BaseEnvironment.__module__].__file__), 'launcherBaseEnvironment.py') - # Usage: python3 script.py " run([executable, script, self.environment_file, self.environment_class.__name__, self.ip_address, str(self.port), str(idx), str(self.number_of_thread), - str(data_db), str(visu_db)]) + str(training_db), str(visualization_db)]) def create_environment(self, environment_manager: Any, - data_db: Optional[Database] = None, - visu_db: Optional[Any] = None) -> BaseEnvironment: + training_db: Optional[Database] = None, + visualization_db: Optional[Any] = None) -> BaseEnvironment: """ Create an Environment that will not be a TcpIpObject. :param environment_manager: EnvironmentManager that handles the Environment. - :param visu_db: The visualisation Database to connect to. + :param training_db: Path of the training Database to connect to. + :param visualization_db: Path to the visualization Database to connect to. :return: Environment object. """ # Create instance environment = self.environment_class(environment_manager=environment_manager, as_tcp_ip_client=False, - data_db=data_db, - visu_db=visu_db) + training_db=training_db, + visualization_db=visualization_db) if not isinstance(environment, BaseEnvironment): raise TypeError(f"[{self.name}] The given 'environment_class'={self.environment_class} must be a " f"BaseEnvironment.") # Create & Init Environment - environment.recv_parameters(self.param_dict) environment.create() environment.init() environment.init_database() @@ -200,15 +185,11 @@ def create_environment(self, return environment def __str__(self) -> str: - """ - :return: String containing information about the BaseEnvironmentConfig object - """ - # Todo: fields in Configs are the set in Managers or objects, remove __str__ method description = "\n" description += f"{self.name}\n" description += f" Environment class: {self.environment_class.__name__}\n" description += f" Simulations per step: {self.simulations_per_step}\n" description += f" Max wrong samples per step: {self.max_wrong_samples_per_step}\n" - description += f" Always create data: {self.always_create_data}\n" + description += f" Always create data: {self.only_first_epoch}\n" return description diff --git a/src/Environment/launcherBaseEnvironment.py b/src/Environment/launcherBaseEnvironment.py index 38fa8823..b2ed4a3d 100644 --- a/src/Environment/launcherBaseEnvironment.py +++ b/src/Environment/launcherBaseEnvironment.py @@ -18,10 +18,14 @@ exec(f"from {module_name} import {argv[2]} as Environment") # Create, init and run Tcp-Ip environment - data_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] - visu_db = None if argv[8] == 'None' else [s[1:-1] for s in argv[8][1:-1].split(', ')] - client = Environment(ip_address=argv[3], port=int(argv[4]), instance_id=int(argv[5]), - number_of_instances=int(argv[6]), data_db=data_db, visu_db=visu_db) + training_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] + visualization_db = None if argv[8] == 'None' else [s[1:-1] for s in argv[8][1:-1].split(', ')] + client = Environment(ip_address=argv[3], + port=int(argv[4]), + instance_id=int(argv[5]), + number_of_instances=int(argv[6]), + training_db=training_db, + visualization_db=visualization_db) client.initialize() client.launch() diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 248fd441..3807edff 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -1,16 +1,16 @@ from typing import Any, Optional, Dict, List from numpy import ndarray -from DeepPhysX.Core.Manager.DatabaseManager import DatasetManager, Database +from DeepPhysX.Core.Manager.DatabaseManager import DatabaseManager, Database from DeepPhysX.Core.Manager.EnvironmentManager import EnvironmentManager from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig class DataManager: def __init__(self, - dataset_config: Optional[BaseDatasetConfig] = None, + database_config: Optional[BaseDatabaseConfig] = None, environment_config: Optional[BaseEnvironmentConfig] = None, manager: Optional[Any] = None, session: str = 'sessions/default', @@ -21,10 +21,10 @@ def __init__(self, """ DataManager deals with the generation, storage and loading of training data. - A batch is given with a call to 'get_data' on either the DatasetManager or the EnvironmentManager according to + A batch is given with a call to 'get_data' on either the DatabaseManager or the EnvironmentManager according to the context. - :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param database_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. :param manager: Manager that handle the DataManager :param session: Path to the session directory. @@ -38,28 +38,24 @@ def __init__(self, # Managers variables self.manager: Optional[Any] = manager - self.dataset_manager: Optional[DatasetManager] = None + self.database_manager: Optional[DatabaseManager] = None self.environment_manager: Optional[EnvironmentManager] = None - # Create a DatasetManager if required - create_dataset = pipeline in ['data_generation', 'training'] or produce_data - database = None - if create_dataset: - self.dataset_manager = DatasetManager(dataset_config=dataset_config, - session=session, - data_manager=self, - new_session=new_session, - pipeline=pipeline, - produce_data=produce_data) - database = self.dataset_manager.database + # Create a DatabaseManager + self.database_manager = DatabaseManager(database_config=database_config, + session=session, + data_manager=self, + new_session=new_session, + pipeline=pipeline, + produce_data=produce_data) + training_db = self.database_manager.database # Create an EnvironmentManager if required - create_environment = environment_config is not None - if create_environment: + if environment_config is not None: self.environment_manager = EnvironmentManager(environment_config=environment_config, data_manager=self, session=session, - data_db=database, + training_db=training_db, batch_size=batch_size) # DataManager variables @@ -78,11 +74,11 @@ def get_manager(self) -> Any: return self.manager def get_database(self) -> Database: - return self.dataset_manager.database + return self.database_manager.database def change_database(self) -> None: - self.manager.change_database(self.dataset_manager.database) - self.environment_manager.change_database(self.dataset_manager.database) + self.manager.change_database(self.database_manager.database) + self.environment_manager.change_database(self.database_manager.database) @property def nb_environment(self): @@ -94,7 +90,7 @@ def get_data(self, epoch: int = 0, animate: bool = True) -> None: """ - Fetch data from the EnvironmentManager or the DatasetManager according to the context. + Fetch data from the EnvironmentManager or the DatabaseManager according to the context. :param epoch: Current epoch number. :param animate: Allow EnvironmentManager to generate a new sample. @@ -104,23 +100,23 @@ def get_data(self, # Data generation case if self.pipeline == 'data_generation': self.environment_manager.get_data(animate=animate) - self.dataset_manager.add_data() + self.database_manager.add_data() # Training case elif self.pipeline == 'training': # Get data from Environment(s) if used and if the data should be created at this epoch - if self.environment_manager is not None and (epoch == 0 or self.environment_manager.always_create_data)\ + if self.environment_manager is not None and (epoch == 0 or self.environment_manager.only_first_epoch) \ and self.produce_data: self.data_lines = self.environment_manager.get_data(animate=animate) - self.dataset_manager.add_data(self.data_lines) + self.database_manager.add_data(self.data_lines) # Get data from Dataset else: - self.data_lines = self.dataset_manager.get_data(batch_size=self.batch_size) + self.data_lines = self.database_manager.get_data(batch_size=self.batch_size) # Dispatch a batch to clients if self.environment_manager is not None and (epoch == 0 or - self.environment_manager.use_dataset_in_environment): + self.environment_manager.load_samples): self.environment_manager.dispatch_batch(data_lines=self.data_lines, animate=animate) # Environment is no longer used @@ -131,9 +127,9 @@ def get_data(self, # Prediction pipeline # TODO else: - if self.dataset_manager is not None and not self.dataset_manager.new_dataset(): + if self.database_manager is not None and not self.database_manager.new_dataset(): # Get data from dataset - data = self.dataset_manager.get_data(batch_size=1, get_inputs=True, get_outputs=True) + data = self.database_manager.get_data(batch_size=1, get_inputs=True, get_outputs=True) if self.environment_manager is not None: new_data = self.environment_manager.dispatch_batch(batch=data, animate=animate) else: @@ -148,8 +144,8 @@ def get_data(self, # Get data from environment data = self.environment_manager.get_data(animate=animate, get_inputs=True, get_outputs=True) # Record data - if self.dataset_manager is not None: - self.dataset_manager.add_data(data) + if self.database_manager is not None: + self.database_manager.add_data(data) def get_prediction(self, instance_id: int) -> None: @@ -181,7 +177,7 @@ def apply_prediction(self, @property def normalization(self) -> Dict[str, List[float]]: - return self.dataset_manager.normalization + return self.database_manager.normalization def close(self) -> None: """ @@ -190,8 +186,8 @@ def close(self) -> None: if self.environment_manager is not None: self.environment_manager.close() - if self.dataset_manager is not None: - self.dataset_manager.close() + if self.database_manager is not None: + self.database_manager.close() def __str__(self) -> str: """ @@ -201,6 +197,6 @@ def __str__(self) -> str: data_manager_str = "" if self.environment_manager: data_manager_str += str(self.environment_manager) - if self.dataset_manager: - data_manager_str += str(self.dataset_manager) + if self.database_manager: + data_manager_str += str(self.database_manager) return data_manager_str diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 58718802..687f64eb 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -8,25 +8,25 @@ from SSD.Core.Storage.Database import Database -from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Utils.path import create_dir, copy_dir, get_first_caller from DeepPhysX.Core.Utils.jsonUtils import CustomJSONEncoder -class DatasetManager: +class DatabaseManager: def __init__(self, - dataset_config: Optional[BaseDatasetConfig] = None, + database_config: Optional[BaseDatabaseConfig] = None, session: str = 'sessions/default', data_manager: Optional[Any] = None, new_session: bool = True, pipeline: str = '', produce_data: bool = True): """ - DatasetManager handle all operations with input / output files. Allows saving and read tensors from files. + DatabaseManager handle all operations with input / output files. Allows saving and read tensors from files. - :param dataset_config: Specialisation containing the parameters of the dataset manager - :param data_manager: DataManager that handles the DatasetManager + :param database_config: Specialisation containing the parameters of the dataset manager + :param data_manager: DataManager that handles the DatabaseManager :param new_session: Define the creation of new directories to store data :param is_training: True if the session is done offline :param produce_data: True if this session is a network training @@ -35,24 +35,24 @@ def __init__(self, self.name: str = self.__class__.__name__ # Manager variables - dataset_config = BaseDatasetConfig() if dataset_config is None else dataset_config + database_config = BaseDatabaseConfig() if database_config is None else database_config self.data_manager: Optional[Any] = data_manager self.dataset_dir: str = join(session, 'dataset') self.database: Optional[Database] = None self.pipeline: str = pipeline # Dataset parameters - self.max_file_size: int = dataset_config.max_file_size - self.shuffle: bool = dataset_config.shuffle + self.max_file_size: int = database_config.max_file_size + self.shuffle: bool = database_config.shuffle self.produce_data = produce_data - self.normalize: bool = dataset_config.normalize + self.normalize: bool = database_config.normalize self.total_nb_sample: int = 0 - self.recompute_normalization: bool = dataset_config.recompute_normalization + self.recompute_normalization: bool = database_config.recompute_normalization # Dataset modes self.modes: List[str] = ['training', 'validation', 'running'] self.mode: str = 'training' if produce_data or pipeline == 'training' else 'running' - self.mode = self.mode if dataset_config.mode is None else dataset_config.mode + self.mode = self.mode if database_config.mode is None else database_config.mode # Dataset partitions session_name = session.split(sep)[-1] @@ -81,14 +81,14 @@ def __init__(self, if new_session: # Produce training data in a new session from scratch # --> Create a new '/dataset' directory - if dataset_config.existing_dir is None: + if database_config.existing_dir is None: create_dir(session_dir=session, session_name='dataset') self.create_partition() # Produce training data in a new session from an existing Dataset # --> Copy the 'existing_dir/dataset' directory then load the 'session/dataset' directory else: - copy_dir(src_dir=join(root, dataset_config.existing_dir), + copy_dir(src_dir=join(root, database_config.existing_dir), dest_dir=session, sub_folders='dataset') self.load_directory() @@ -101,7 +101,7 @@ def __init__(self, # Load training data in a new session # --> Link to the 'existing_dir/dataset' directory the load the 'session/dataset' directory if new_session: - symlink(src=join(root, dataset_config.existing_dir, 'dataset'), + symlink(src=join(root, database_config.existing_dir, 'dataset'), dst=join(session, 'dataset')) self.load_directory() # Load training data in an existing session @@ -117,12 +117,8 @@ def create_partition(self): self.current_partition = self.partition_template[self.mode].format(self.partition_index[self.mode]) self.database = Database(database_dir=self.dataset_dir, database_name=self.current_partition).new() - self.database.create_table(table_name='Sync', - storing_table=False, - fields=[('env', int), ('net', int)]) self.database.create_table(table_name='Training') self.database.create_table(table_name='Additional') - self.database.create_table(table_name='Prediction') self.partitions[self.mode].append(self.current_partition) self.partition_index[self.mode] += 1 @@ -132,12 +128,14 @@ def additional_partition(self): fields = {} types = {'INT': int, 'FLOAT': float, 'STR': str, 'BOOL': bool, 'NUMPY': ndarray} if self.partition_index[self.mode] > 0: - for table_name in ['Training', 'Additional', 'Prediction']: + for table_name in self.database.get_tables(): fields[table_name] = [] F = self.database.get_fields(table_name=table_name, only_names=False) for field in [f for f in F if f not in ['id', '_dt_']]: fields[table_name].append((field, types[F[field].field_type])) + if 'Prediction' in fields.keys(): + self.database.remove_table(table_name='Prediction') # 2. Create a new Database self.create_partition() @@ -242,21 +240,23 @@ def update_json(self, # Update DB architecture if update_architecture: architecture = self.database.get_architecture() + if 'Prediction' in architecture.keys(): + del architecture['Prediction'] for fields in architecture.values(): for field in fields.copy(): if field.split(' ')[0] in ['id', '_dt_']: fields.remove(field) - architecture.pop('Sync') self.json_content['architecture'] = architecture # Update data shapes if update_shapes: for table_name, fields in self.json_content['architecture'].items(): - data = self.database.get_line(table_name=table_name) - for field in fields: - if 'NUMPY' in field: - field_name = field.split(' ')[0] - self.json_content['data_shape'][f'{table_name}.{field_name}'] = data[field_name].shape + if self.database.nb_lines(table_name=table_name) > 0: + data = self.database.get_line(table_name=table_name) + for field in fields: + if 'NUMPY' in field: + field_name = field.split(' ')[0] + self.json_content['data_shape'][f'{table_name}.{field_name}'] = data[field_name].shape # Update normalization coefficients if update_normalization: diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index bef46176..f815d68d 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -14,7 +14,7 @@ def __init__(self, environment_config: BaseEnvironmentConfig, data_manager: Optional[Any] = None, session: str = 'sessions/default', - data_db: Optional[Database] = None, + training_db: Optional[Database] = None, batch_size: int = 1): """ Deals with the online generation of data for both training and running of the neural networks. @@ -32,20 +32,20 @@ def __init__(self, # Data producing parameters self.batch_size: int = batch_size - self.always_create_data: bool = environment_config.always_create_data - self.use_dataset_in_environment: bool = environment_config.use_dataset_in_environment + self.only_first_epoch: bool = environment_config.only_first_epoch + self.load_samples: bool = environment_config.load_samples self.simulations_per_step: int = environment_config.simulations_per_step self.max_wrong_samples_per_step: int = environment_config.max_wrong_samples_per_step self.dataset_batch: Optional[List[int]] = None # Create the Visualizer self.visualizer: Optional[VedoVisualizer] = None - visu_db = None + visualization_db = None if environment_config.visualizer is not None: self.visualizer = environment_config.visualizer(database_dir=join(session, 'dataset'), database_name='Visualization', remote=environment_config.as_tcp_ip_client) - visu_db = self.visualizer.get_database() + visualization_db = self.visualizer.get_database() # Create a single Environment or a TcpIpServer self.number_of_thread: int = environment_config.number_of_thread @@ -54,13 +54,13 @@ def __init__(self, if environment_config.as_tcp_ip_client: self.server = environment_config.create_server(environment_manager=self, batch_size=batch_size, - data_db=data_db if data_db is None else data_db.get_path(), - visu_db=visu_db if visu_db is None else visu_db.get_path()) + training_db=training_db if training_db is None else training_db.get_path(), + visualization_db=visualization_db if visualization_db is None else visualization_db.get_path()) self.data_manager.get_database().load() else: self.environment = environment_config.create_environment(environment_manager=self, - data_db=data_db, - visu_db=visu_db) + training_db=training_db, + visualization_db=visualization_db) # Define get_data and dispatch methods self.change_database = self.change_database_in_server if self.server else self.change_database_in_environment @@ -191,7 +191,7 @@ def __str__(self) -> str: description = "\n" description += f"# {self.name}\n" - description += f" Always create data: {self.always_create_data}\n" + description += f" Always create data: {self.only_first_epoch}\n" # description += f" Record wrong samples: {self.record_wrong_samples}\n" description += f" Number of threads: {self.number_of_thread}\n" # description += f" Managed objects: Environment: {self.environment.env_name}\n" diff --git a/src/Manager/Manager.py b/src/Manager/Manager.py index c30ec50a..5ff6fed6 100644 --- a/src/Manager/Manager.py +++ b/src/Manager/Manager.py @@ -6,7 +6,7 @@ from DeepPhysX.Core.Manager.NetworkManager import NetworkManager from DeepPhysX.Core.Manager.StatsManager import StatsManager from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Utils.path import get_first_caller, create_dir @@ -15,12 +15,12 @@ class Manager: def __init__(self, network_config: Optional[BaseNetworkConfig] = None, - dataset_config: Optional[BaseDatasetConfig] = None, + database_config: Optional[BaseDatabaseConfig] = None, environment_config: Optional[BaseEnvironmentConfig] = None, session_dir: str = 'sessions', session_name: str = 'default', - new_session: bool = True, pipeline: str = '', + new_session: bool = True, produce_data: bool = True, batch_size: int = 1, debug_session: bool = False): @@ -29,7 +29,7 @@ def __init__(self, More specific behaviour have to be directly call from the corresponding manager. :param network_config: Specialisation containing the parameters of the network manager. - :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param database_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. :param session_name: Name of the newly created directory if session is not defined. :param session_dir: Name of the directory in which to write all the necessary data. @@ -58,7 +58,7 @@ def __init__(self, raise ValueError(f"[{self.name}] The running session directory {self.session} does not exist.") # Create a DataManager - self.data_manager = DataManager(dataset_config=dataset_config, + self.data_manager = DataManager(database_config=database_config, environment_config=environment_config, manager=self, session=self.session, diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index 289084f9..c9e04322 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -84,7 +84,7 @@ def link_clients(self, nb_clients: Optional[int] = None): if nb_clients is not None: fields = [(field_name, ndarray) for field_name in self.network.net_fields + self.network.pred_fields] - self.data_db.create_fields(table_name='Prediction', fields=fields) + self.data_db.create_table(table_name='Prediction', fields=fields) for _ in range(nb_clients): self.data_db.add_data(table_name='Prediction', data={}) @@ -243,8 +243,8 @@ def save_network(self, last_save: bool = False) -> None: # Final session saving if last_save: - path = self.network_dir + "network" - print(f"[{self.name}] Saving final network at {path}.") + path = join(self.network_dir, 'network') + print(f"[{self.name}] Saving final network at {self.network_dir}.") self.network.save_parameters(path) # Intermediate states saving diff --git a/src/Pipelines/BaseDataGenerator.py b/src/Pipelines/BaseDataGeneration.py similarity index 86% rename from src/Pipelines/BaseDataGenerator.py rename to src/Pipelines/BaseDataGeneration.py index 5d8f3df0..6a0b1dfc 100644 --- a/src/Pipelines/BaseDataGenerator.py +++ b/src/Pipelines/BaseDataGeneration.py @@ -4,25 +4,25 @@ from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline from DeepPhysX.Core.Manager.DataManager import DataManager -from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Utils.progressbar import Progressbar from DeepPhysX.Core.Utils.path import get_first_caller, create_dir -class BaseDataGenerator(BasePipeline): +class BaseDataGeneration(BasePipeline): def __init__(self, environment_config: BaseEnvironmentConfig, - dataset_config: Optional[BaseDatasetConfig] = None, + database_config: Optional[BaseDatabaseConfig] = None, session_dir: str = 'sessions', session_name: str = 'data_generation', batch_nb: int = 0, batch_size: int = 0): """ - BaseDataGenerator implements the main loop that only produces and stores data (no Network training). + BaseDataGeneration implements the main loop that only produces and stores data (no Network training). - :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param database_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. :param session_dir: Relative path to the directory which contains sessions directories. :param session_name: Name of the new the session directory. @@ -31,7 +31,7 @@ def __init__(self, """ BasePipeline.__init__(self, - dataset_config=dataset_config, + database_config=database_config, environment_config=environment_config, session_dir=session_dir, session_name=session_name, @@ -46,8 +46,8 @@ def __init__(self, # Option 2: existing_dir == session_dir/session_name --> new_session = False # Option 3: existing_dir != session_dir/session_name --> new_session = True new_session = True - if dataset_config is not None and dataset_config.existing_dir is not None and \ - join(session_dir, session_name) == join(root, dataset_config.existing_dir): + if database_config is not None and database_config.existing_dir is not None and \ + join(session_dir, session_name) == join(root, database_config.existing_dir): new_session = False # Create a new session if required @@ -61,7 +61,7 @@ def __init__(self, self.progress_bar = Progressbar(start=0, stop=self.batch_id, c='orange', title="Data Generation") # Create a DataManager - self.data_manager = DataManager(dataset_config=dataset_config, + self.data_manager = DataManager(database_config=database_config, environment_config=environment_config, session=join(session_dir, session_name), new_session=new_session, diff --git a/src/Pipelines/BasePipeline.py b/src/Pipelines/BasePipeline.py index bf384b9b..4d935479 100644 --- a/src/Pipelines/BasePipeline.py +++ b/src/Pipelines/BasePipeline.py @@ -1,13 +1,13 @@ from typing import Optional, Any, List, Union from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Manager.Manager import Manager -from DeepPhysX.Core.Manager.NetworkManager import NetworkManager from DeepPhysX.Core.Manager.DataManager import DataManager from DeepPhysX.Core.Manager.StatsManager import StatsManager -from DeepPhysX.Core.Manager.DatasetManager import DatasetManager +from DeepPhysX.Core.Manager.NetworkManager import NetworkManager +from DeepPhysX.Core.Manager.DatabaseManager import DatabaseManager from DeepPhysX.Core.Manager.EnvironmentManager import EnvironmentManager @@ -15,7 +15,7 @@ class BasePipeline: def __init__(self, network_config: Optional[BaseNetworkConfig] = None, - dataset_config: Optional[BaseDatasetConfig] = None, + database_config: Optional[BaseDatabaseConfig] = None, environment_config: Optional[BaseEnvironmentConfig] = None, session_dir: str = 'sessions', session_name: str = 'default', @@ -24,7 +24,7 @@ def __init__(self, Pipelines implement the main loop that defines data flow through components (Environment, Dataset, Network...). :param network_config: Specialisation containing the parameters of the network manager. - :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param database_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. :param session_dir: Name of the directory in which to write all the necessary data. :param session_name: Name of the newly created directory if session is not defined. @@ -36,8 +36,8 @@ def __init__(self, # Check the configurations if network_config is not None and not isinstance(network_config, BaseNetworkConfig): raise TypeError(f"[{self.name}] The Network configuration must be a BaseNetworkConfig object.") - if dataset_config is not None and not isinstance(dataset_config, BaseDatasetConfig): - raise TypeError(f"[{self.name}] The Dataset configuration must be a BaseDatasetConfig object.") + if database_config is not None and not isinstance(database_config, BaseDatabaseConfig): + raise TypeError(f"[{self.name}] The Dataset configuration must be a BaseDatabaseConfig object.") if environment_config is not None and not isinstance(environment_config, BaseEnvironmentConfig): raise TypeError(f"[{self.name}] The Environment configuration must be a BaseEnvironmentConfig object.") @@ -52,7 +52,7 @@ def __init__(self, session_name = pipeline # Configuration variables - self.dataset_config: BaseDatasetConfig = dataset_config + self.database_config: BaseDatabaseConfig = database_config self.network_config: BaseNetworkConfig = network_config self.environment_config: BaseEnvironmentConfig = environment_config @@ -123,14 +123,14 @@ def get_stats_manager(self) -> Optional[StatsManager]: return self.__get_any_manager(manager_names='stats_manager') - def get_dataset_manager(self) -> Optional[DatasetManager]: + def get_dataset_manager(self) -> Optional[DatabaseManager]: """ - Return the DatasetManager associated with the pipeline if it exists. + Return the DatabaseManager associated with the pipeline if it exists. - :return: The DatasetManager associated with the pipeline. + :return: The DatabaseManager associated with the pipeline. """ - return self.__get_any_manager(manager_names=['data_manager', 'dataset_manager']) + return self.__get_any_manager(manager_names=['data_manager', 'database_manager']) def get_environment_manager(self) -> Optional[EnvironmentManager]: """ diff --git a/src/Pipelines/BaseRunner.py b/src/Pipelines/BasePrediction.py similarity index 65% rename from src/Pipelines/BaseRunner.py rename to src/Pipelines/BasePrediction.py index 12771724..f774d949 100644 --- a/src/Pipelines/BaseRunner.py +++ b/src/Pipelines/BasePrediction.py @@ -2,66 +2,60 @@ from numpy import ndarray from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline from DeepPhysX.Core.Manager.Manager import Manager -class BaseRunner(BasePipeline): - """ - | BaseRunner is a pipeline defining the running process of an artificial neural network. - | It provides a highly tunable learning process that can be used with any machine learning library. - - :param BaseNetworkConfig network_config: Specialisation containing the parameters of the network manager - :param BaseEnvironmentConfig environment_config: Specialisation containing the parameters of the environment manager - :param Optional[BaseDatasetConfig] dataset_config: Specialisation containing the parameters of the dataset manager - :param str session_name: Name of the newly created directory if session is not defined - :param Optional[str] session_dir: Name of the directory in which to write all the necessary data - :param int nb_steps: Number of simulation step to play - :param bool record_inputs: Save or not the input in a numpy file - :param bool record_outputs: Save or not the output in a numpy file - """ +class BasePrediction(BasePipeline): def __init__(self, network_config: BaseNetworkConfig, environment_config: Optional[BaseEnvironmentConfig] = None, - dataset_config: Optional[BaseDatasetConfig] = None, - session_name: str = 'default', - session_dir: Optional[str] = None, - nb_steps: int = 0, - record_inputs: bool = False, - record_outputs: bool = False): + database_config: Optional[BaseDatabaseConfig] = None, + session_dir: str = 'session', + session_name: str = 'training', + nb_steps: int = -1, + record: bool = False): + """ + BasePrediction is a pipeline defining the running process of an artificial neural network. + It provides a highly tunable learning process that can be used with any machine learning library. + + :param network_config: Specialisation containing the parameters of the network manager. + :param environment_config: Specialisation containing the parameters of the environment manager. + :param database_config: Specialisation containing the parameters of the dataset manager. + :param session_name: Name of the newly created directory if session is not defined. + :param session_dir: Name of the directory in which to write all the necessary data. + :param nb_steps: Number of simulation step to play. + :param record: Save or not the prediction data. + """ BasePipeline.__init__(self, network_config=network_config, - dataset_config=dataset_config, + database_config=database_config, environment_config=environment_config, - session_name=session_name, session_dir=session_dir, + session_name=session_name, pipeline='prediction') - self.name = self.__class__.__name__ - - if type(nb_steps) != int or nb_steps < 0: - raise TypeError("[BaseRunner] The number of steps must be a positive int") - + # Prediction variables self.nb_samples = nb_steps self.idx_step = 0 - # Tell if data is recording while predicting (output is recorded only if input too) - self.record_data = {'input': False, 'output': False} - if dataset_config is not None: - self.record_data = {'input': record_inputs, 'output': record_outputs and record_inputs} + # Tell if data is recording while predicting + self.record_data = record self.is_environment = environment_config is not None - self.manager = Manager(pipeline=self, - network_config=self.network_config, - dataset_config=dataset_config, + self.manager = Manager(network_config=self.network_config, + database_config=database_config, environment_config=self.environment_config, - session_name=session_name, session_dir=session_dir, - new_session=True) + session_name=session_name, + new_session=False, + pipeline='prediction', + produce_data=environment_config is not None and record, + batch_size=1) def execute(self) -> None: """ diff --git a/src/Pipelines/BaseTrainer.py b/src/Pipelines/BaseTraining.py similarity index 93% rename from src/Pipelines/BaseTrainer.py rename to src/Pipelines/BaseTraining.py index 15cf6041..521bdad4 100644 --- a/src/Pipelines/BaseTrainer.py +++ b/src/Pipelines/BaseTraining.py @@ -6,16 +6,16 @@ from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline from DeepPhysX.Core.Manager.Manager import Manager from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Database.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Utils.progressbar import Progressbar -class BaseTrainer(BasePipeline): +class BaseTraining(BasePipeline): def __init__(self, network_config: BaseNetworkConfig, - dataset_config: BaseDatasetConfig, + database_config: BaseDatabaseConfig, environment_config: Optional[BaseEnvironmentConfig] = None, session_dir: str = 'sessions', session_name: str = 'training', @@ -25,12 +25,12 @@ def __init__(self, new_session: bool = True, debug_session: bool = False): """ - BaseTrainer implements the main loop that defines the training process of an artificial neural network. + BaseTraining implements the main loop that defines the training process of an artificial neural network. Training can be launched with several data sources (from a Dataset, from an Environment, from combined sources). It provides a highly tunable learning process that can be used with any machine learning library. :param network_config: Specialisation containing the parameters of the network manager. - :param dataset_config: Specialisation containing the parameters of the dataset manager. + :param database_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. :param session_dir: Relative path to the directory which contains sessions directories. :param session_name: Name of the new the session directory. @@ -43,7 +43,7 @@ def __init__(self, BasePipeline.__init__(self, network_config=network_config, - dataset_config=dataset_config, + database_config=database_config, environment_config=environment_config, session_dir=session_dir, session_name=session_name, @@ -60,9 +60,9 @@ def __init__(self, self.debug = debug_session # Configure 'produce_data' flag - if environment_config is None and dataset_config.existing_dir is None: + if environment_config is None and database_config.existing_dir is None: raise ValueError(f"[{self.name}] No data source provided.") - produce_data = dataset_config.existing_dir is None + produce_data = database_config.existing_dir is None # Progressbar if not self.debug: @@ -75,7 +75,7 @@ def __init__(self, title=f'Epoch n°{epoch_id}/{epoch_nb} - Batch n°{batch_id}/{batch_nb}') self.manager = Manager(network_config=self.network_config, - dataset_config=self.dataset_config, + database_config=self.database_config, environment_config=self.environment_config, session_dir=session_dir, session_name=session_name, From c91d32337a60f0773fb8781c0285150f3f3f6074 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 20 Oct 2022 17:47:14 +0200 Subject: [PATCH 12/61] Possibility to add data to an existing Dataset. --- src/Database/BaseDatabaseConfig.py | 2 +- src/Manager/DatabaseManager.py | 92 +++++++++++++++++++----------- 2 files changed, 59 insertions(+), 35 deletions(-) diff --git a/src/Database/BaseDatabaseConfig.py b/src/Database/BaseDatabaseConfig.py index 187585b1..804846a1 100644 --- a/src/Database/BaseDatabaseConfig.py +++ b/src/Database/BaseDatabaseConfig.py @@ -37,7 +37,7 @@ def __init__(self, if mode is not None: if type(mode) != str: raise TypeError(f"[{self.name}] The given 'mode'={mode} must be a str.") - if mode.lower() not in (available_modes := ['training', 'validation', 'running']): + if mode.lower() not in (available_modes := ['training', 'validation', 'prediction']): raise ValueError(f"[{self.name}] The given 'mode'={mode} must be in {available_modes}.") if max_file_size is not None: if type(max_file_size) not in [int, float]: diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 687f64eb..37694bc1 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -40,6 +40,7 @@ def __init__(self, self.dataset_dir: str = join(session, 'dataset') self.database: Optional[Database] = None self.pipeline: str = pipeline + root = get_first_caller() # Dataset parameters self.max_file_size: int = database_config.max_file_size @@ -50,8 +51,8 @@ def __init__(self, self.recompute_normalization: bool = database_config.recompute_normalization # Dataset modes - self.modes: List[str] = ['training', 'validation', 'running'] - self.mode: str = 'training' if produce_data or pipeline == 'training' else 'running' + self.modes: List[str] = ['training', 'validation', 'prediction'] + self.mode: str = 'training' if produce_data or pipeline == 'training' else 'prediction' self.mode = self.mode if database_config.mode is None else database_config.mode # Dataset partitions @@ -74,40 +75,58 @@ def __init__(self, 'normalization': {}} self.json_content: Dict[str, Dict[str, Any]] = self.json_default.copy() - # Produce training data - root = get_first_caller() - if produce_data: - # Produce training data in a new session + # DataGeneration case + if self.pipeline == 'data_generation': + # Generate data in a new session if new_session: - # Produce training data in a new session from scratch - # --> Create a new '/dataset' directory + # Generate data from scratch --> create a new directory if database_config.existing_dir is None: - create_dir(session_dir=session, - session_name='dataset') + create_dir(session_dir=session, session_name='dataset') self.create_partition() - # Produce training data in a new session from an existing Dataset - # --> Copy the 'existing_dir/dataset' directory then load the 'session/dataset' directory + # Complete a Database in a new session else: - copy_dir(src_dir=join(root, database_config.existing_dir), - dest_dir=session, + copy_dir(src_dir=join(root, database_config.existing_dir), dest_dir=session, sub_folders='dataset') - self.load_directory() - # Produce training data in an existing session - # --> Load the 'session/dataset' directory + self.load_directory(last_partition=True) + # Complete a Database in the same session else: - self.load_directory() - # Load training data + self.load_directory(last_partition=True) + + else: - # Load training data in a new session - # --> Link to the 'existing_dir/dataset' directory the load the 'session/dataset' directory - if new_session: - symlink(src=join(root, database_config.existing_dir, 'dataset'), - dst=join(session, 'dataset')) - self.load_directory() - # Load training data in an existing session - # --> Load the 'session/dataset' directory + # Produce training data + if produce_data: + # Produce training data in a new session + if new_session: + # Produce training data in a new session from scratch + # --> Create a new '/dataset' directory + if database_config.existing_dir is None: + create_dir(session_dir=session, + session_name='dataset') + self.create_partition() + # Produce training data in a new session from an existing Dataset + # --> Copy the 'existing_dir/dataset' directory then load the 'session/dataset' directory + else: + copy_dir(src_dir=join(root, database_config.existing_dir), + dest_dir=session, + sub_folders='dataset') + self.load_directory() + # Produce training data in an existing session + # --> Load the 'session/dataset' directory + else: + self.load_directory() + # Load training data else: - self.load_directory() + # Load training data in a new session + # --> Link to the 'existing_dir/dataset' directory the load the 'session/dataset' directory + if new_session: + symlink(src=join(root, database_config.existing_dir, 'dataset'), + dst=join(session, 'dataset')) + self.load_directory() + # Load training data in an existing session + # --> Load the 'session/dataset' directory + else: + self.load_directory() def create_partition(self): """ @@ -120,7 +139,6 @@ def create_partition(self): self.database.create_table(table_name='Training') self.database.create_table(table_name='Additional') self.partitions[self.mode].append(self.current_partition) - self.partition_index[self.mode] += 1 def additional_partition(self): @@ -138,6 +156,7 @@ def additional_partition(self): self.database.remove_table(table_name='Prediction') # 2. Create a new Database + self.partition_index[self.mode] += 1 self.create_partition() # 3. Re-create the Fields if this is not the first partition @@ -149,7 +168,8 @@ def additional_partition(self): # 4. Tell the other components to communicate on a new DB self.data_manager.change_database() - def load_directory(self): + def load_directory(self, + last_partition: bool): """ """ @@ -177,9 +197,13 @@ def load_directory(self): self.update_json(update_normalization=True) # 5. Load the Database - self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] - self.database = Database(database_dir=self.dataset_dir, - database_name=self.current_partition).load() + if len(self.partitions[self.mode]) == 0: + self.create_partition() + else: + self.partition_index[self.mode] = len(self.partitions[self.mode]) - 1 if last_partition else 0 + self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] + self.database = Database(database_dir=self.dataset_dir, + database_name=self.current_partition).load() # 6. Shuffle Database indices if self.shuffle: @@ -232,7 +256,7 @@ def update_json(self, # Update number of samples if update_nb_samples: - if len(self.json_content['nb_samples'][self.mode]) == self.partition_index[self.mode]: + if len(self.json_content['nb_samples'][self.mode]) == self.partition_index[self.mode] + 1: self.json_content['nb_samples'][self.mode][-1] = self.nb_samples else: self.json_content['nb_samples'][self.mode].append(self.nb_samples) From 28b69e219b83561d84a7d2c610c120c31ff13d78 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 20 Oct 2022 17:54:23 +0200 Subject: [PATCH 13/61] Fix DatabaseManager behavior. --- src/Manager/DatabaseManager.py | 43 +++++++++++++++------------------- 1 file changed, 19 insertions(+), 24 deletions(-) diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 37694bc1..82581c09 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -83,50 +83,45 @@ def __init__(self, if database_config.existing_dir is None: create_dir(session_dir=session, session_name='dataset') self.create_partition() - # Complete a Database in a new session + # Complete a Database in a new session --> copy and load the existing directory else: copy_dir(src_dir=join(root, database_config.existing_dir), dest_dir=session, sub_folders='dataset') self.load_directory(last_partition=True) - # Complete a Database in the same session + # Complete a Database in the same session --> load the directory else: self.load_directory(last_partition=True) - + # Training and prediction cases else: - # Produce training data + + # Generate data if produce_data: - # Produce training data in a new session + # Generate data in a new session if new_session: - # Produce training data in a new session from scratch - # --> Create a new '/dataset' directory + # Generate data from scratch --> create a new directory if database_config.existing_dir is None: - create_dir(session_dir=session, - session_name='dataset') + create_dir(session_dir=session, session_name='dataset') self.create_partition() - # Produce training data in a new session from an existing Dataset - # --> Copy the 'existing_dir/dataset' directory then load the 'session/dataset' directory + # Complete a Database in a new session --> copy and load the existing directory else: - copy_dir(src_dir=join(root, database_config.existing_dir), - dest_dir=session, + copy_dir(src_dir=join(root, database_config.existing_dir), dest_dir=session, sub_folders='dataset') - self.load_directory() - # Produce training data in an existing session - # --> Load the 'session/dataset' directory + self.load_directory(last_partition=False) + # Complete a Database in the same directory --> load the directory else: - self.load_directory() - # Load training data + self.load_directory(last_partition=False) + + # Load data else: - # Load training data in a new session - # --> Link to the 'existing_dir/dataset' directory the load the 'session/dataset' directory + # Load data in a new session --> link and load the existing directory if new_session: symlink(src=join(root, database_config.existing_dir, 'dataset'), dst=join(session, 'dataset')) - self.load_directory() - # Load training data in an existing session - # --> Load the 'session/dataset' directory + self.load_directory(last_partition=False) + # Load data in the same session --> load the directory else: - self.load_directory() + self.load_directory(last_partition=False) def create_partition(self): """ From a87a65cf59ec68ee376c564e3afeb5ca5554d768 Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 24 Oct 2022 18:02:40 +0200 Subject: [PATCH 14/61] Fix shuffle in online training. --- src/Manager/DataManager.py | 5 ++--- src/Manager/DatabaseManager.py | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 3807edff..1bcbcfe4 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -106,7 +106,7 @@ def get_data(self, elif self.pipeline == 'training': # Get data from Environment(s) if used and if the data should be created at this epoch - if self.environment_manager is not None and (epoch == 0 or self.environment_manager.only_first_epoch) \ + if self.environment_manager is not None and (epoch == 0 or not self.environment_manager.only_first_epoch) \ and self.produce_data: self.data_lines = self.environment_manager.get_data(animate=animate) self.database_manager.add_data(self.data_lines) @@ -115,8 +115,7 @@ def get_data(self, else: self.data_lines = self.database_manager.get_data(batch_size=self.batch_size) # Dispatch a batch to clients - if self.environment_manager is not None and (epoch == 0 or - self.environment_manager.load_samples): + if self.environment_manager is not None and (epoch == 0 or self.environment_manager.load_samples): self.environment_manager.dispatch_batch(data_lines=self.data_lines, animate=animate) # Environment is no longer used diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 82581c09..fb46504c 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -294,7 +294,7 @@ def shuffle_samples(self): """ - self.shuffle_pattern = arange(self.nb_samples) + self.shuffle_pattern = arange(1, self.nb_samples + 1) if self.shuffle: shuffle(self.shuffle_pattern) From 5585aefc5fdcc63c432a1a73ea87208fac1462d4 Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 24 Oct 2022 19:19:26 +0200 Subject: [PATCH 15/61] Use Database for Prediction pipeline. --- src/Environment/BaseEnvironment.py | 12 +++++ src/Manager/DataManager.py | 46 +++++------------ src/Manager/DatabaseManager.py | 38 ++++++++++---- src/Manager/EnvironmentManager.py | 15 ++++-- src/Pipelines/BasePrediction.py | 80 ++++++++++-------------------- 5 files changed, 92 insertions(+), 99 deletions(-) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 4f311e0e..ab94350d 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -312,6 +312,18 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: del data_pred['id'] return data_pred + def _get_prediction(self): + """ + + """ + + training_data = self.__training_data.copy() + required_fields = self.database.get_fields(table_name='Prediction') + for field in self.__training_data.keys(): + if field not in required_fields: + del training_data[field] + self.apply_prediction(self.get_prediction(**training_data)) + def update_visualisation(self) -> None: """ Triggers the Visualizer update. diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 1bcbcfe4..f1706345 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -88,7 +88,8 @@ def nb_environment(self): def get_data(self, epoch: int = 0, - animate: bool = True) -> None: + animate: bool = True, + request_prediction: bool = False) -> None: """ Fetch data from the EnvironmentManager or the DatabaseManager according to the context. @@ -124,27 +125,20 @@ def get_data(self, self.environment_manager = None # Prediction pipeline - # TODO else: - if self.database_manager is not None and not self.database_manager.new_dataset(): - # Get data from dataset - data = self.database_manager.get_data(batch_size=1, get_inputs=True, get_outputs=True) - if self.environment_manager is not None: - new_data = self.environment_manager.dispatch_batch(batch=data, animate=animate) - else: - new_data = data - if len(new_data['input']) != 0: - data['input'] = new_data['input'] - if len(new_data['output']) != 0: - data['output'] = new_data['output'] - if 'loss' in new_data: - data['loss'] = new_data['loss'] + + # Get data from Dataset + if self.produce_data: + self.data_lines = self.database_manager.get_data(batch_size=1) + self.environment_manager.dispatch_batch(data_lines=self.data_lines, + animate=animate, + request_prediction=True) + # Get data from Environment else: - # Get data from environment - data = self.environment_manager.get_data(animate=animate, get_inputs=True, get_outputs=True) - # Record data + self.data_lines = self.environment_manager.get_data(animate=animate, + request_prediction=True) if self.database_manager is not None: - self.database_manager.add_data(data) + self.database_manager.add_data(self.data_lines) def get_prediction(self, instance_id: int) -> None: @@ -160,20 +154,6 @@ def get_prediction(self, self.manager.network_manager.compute_online_prediction(instance_id=instance_id, normalization=self.normalization) - def apply_prediction(self, - prediction: ndarray) -> None: - """ - Apply the Network prediction in the Environment. - - :param prediction: Prediction of the Network to apply. - """ - - if self.environment_manager is not None: - # Unapply normalization on prediction - prediction = self.normalize_data(prediction, 'output', reverse=True) - # Apply prediction - self.environment_manager.environment.apply_prediction(prediction) - @property def normalization(self) -> Dict[str, List[float]]: return self.database_manager.normalization diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index fb46504c..c059fd5c 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -1,6 +1,6 @@ from typing import Any, Dict, List, Optional from os.path import isfile, isdir, join -from os import listdir, symlink, sep +from os import listdir, symlink, sep, remove from json import dump as json_dump from json import load as json_load from numpy import arange, ndarray, array, abs, mean, sqrt @@ -92,8 +92,8 @@ def __init__(self, else: self.load_directory(last_partition=True) - # Training and prediction cases - else: + # Training case + elif self.pipeline == 'training': # Generate data if produce_data: @@ -123,6 +123,16 @@ def __init__(self, else: self.load_directory(last_partition=False) + # Prediction case + else: + + # Generate data + if produce_data: + self.load_directory(last_partition=True) + + else: + self.load_directory(load_partition=False) + def create_partition(self): """ @@ -164,7 +174,8 @@ def additional_partition(self): self.data_manager.change_database() def load_directory(self, - last_partition: bool): + last_partition: bool = True, + load_partition: bool = True): """ """ @@ -192,13 +203,19 @@ def load_directory(self, self.update_json(update_normalization=True) # 5. Load the Database - if len(self.partitions[self.mode]) == 0: - self.create_partition() + if load_partition: + if len(self.partitions[self.mode]) == 0: + self.create_partition() + else: + self.partition_index[self.mode] = len(self.partitions[self.mode]) - 1 if last_partition else 0 + self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] + self.database = Database(database_dir=self.dataset_dir, + database_name=self.current_partition).load() else: - self.partition_index[self.mode] = len(self.partitions[self.mode]) - 1 if last_partition else 0 - self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] self.database = Database(database_dir=self.dataset_dir, - database_name=self.current_partition).load() + database_name='temp').new() + self.database.create_table(table_name='Training') + self.database.create_table(table_name='Additional') # 6. Shuffle Database indices if self.shuffle: @@ -350,6 +367,9 @@ def close(self): if self.normalize and self.pipeline == 'data_generation': self.update_json(update_normalization=True) self.database.close() + if self.pipeline == 'prediction' and not self.produce_data: + path = self.database.get_path() + remove(join(path[0], f'{path[1]}.db')) def change_mode(self, mode: int) -> None: """ diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index f815d68d..a7f4deaf 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -91,7 +91,8 @@ def get_data_from_server(self, return self.server.get_batch(animate) def get_data_from_environment(self, - animate: bool = True) -> List[int]: + animate: bool = True, + request_prediction: bool = False) -> List[int]: """ Compute a batch of data directly from Environment. @@ -125,6 +126,8 @@ def get_data_from_environment(self, else: self.environment._update_training_data(update_line) dataset_lines.append(update_line) + if request_prediction: + self.environment._get_prediction() self.environment._reset_training_data() return dataset_lines @@ -147,7 +150,8 @@ def dispatch_batch_to_server(self, def dispatch_batch_to_environment(self, data_lines: List[int], - animate: bool = True) -> None: + animate: bool = True, + request_prediction: bool = False) -> None: """ Send samples from dataset to the Environments. Get back the training data. @@ -159,7 +163,12 @@ def dispatch_batch_to_environment(self, # Define the batch to dispatch self.dataset_batch = data_lines.copy() # Get data - self.get_data_from_environment(animate=animate) + self.get_data_from_environment(animate=animate, + request_prediction=request_prediction) + + def request_prediction(self): + + self.environment._get_prediction() def update_visualizer(self, instance: int) -> None: diff --git a/src/Pipelines/BasePrediction.py b/src/Pipelines/BasePrediction.py index f774d949..66721cbd 100644 --- a/src/Pipelines/BasePrediction.py +++ b/src/Pipelines/BasePrediction.py @@ -1,5 +1,4 @@ from typing import Optional -from numpy import ndarray from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig @@ -12,7 +11,7 @@ class BasePrediction(BasePipeline): def __init__(self, network_config: BaseNetworkConfig, - environment_config: Optional[BaseEnvironmentConfig] = None, + environment_config: BaseEnvironmentConfig, database_config: Optional[BaseDatabaseConfig] = None, session_dir: str = 'session', session_name: str = 'training', @@ -45,7 +44,6 @@ def __init__(self, # Tell if data is recording while predicting self.record_data = record - self.is_environment = environment_config is not None self.manager = Manager(network_config=self.network_config, database_config=database_config, @@ -54,57 +52,33 @@ def __init__(self, session_name=session_name, new_session=False, pipeline='prediction', - produce_data=environment_config is not None and record, + produce_data=record, batch_size=1) def execute(self) -> None: """ - | Main function of the running process "execute" call the functions associated with the learning process. - | Each of the called functions are already implemented so one can start a basic run session. - | Each of the called function can also be rewritten via inheritance to provide more specific / complex running - process. + Launch the prediction Pipeline. + Each event is already implemented for a basic pipeline but can also be rewritten via inheritance to describe a + more complex pipeline. """ - self.run_begin() - while self.running_condition(): + self.prediction_begin() + while self.prediction_condition(): self.sample_begin() - self.sample_end(self.predict()) + self.predict() + self.sample_end() self.run_end() - def predict(self, animate: bool = True) -> ndarray: + def prediction_begin(self) -> None: """ - | Pull the data from the manager and return the prediction - - :param bool animate: True if getData fetch from the environment - :return: Prediction from the Network - """ - - self.manager.get_data(animate=animate) - data = self.manager.data_manager.data['input'] - data = self.manager.data_manager.normalize_data(data, 'input') - return self.manager.network_manager.compute_online_prediction(data) - - def run_begin(self) -> None: - """ - | Called once at the very beginning of the Run process. - | Allows the user to run some pre-computations. + Called once at the beginning of the prediction pipeline. """ pass - def run_end(self) -> None: - """ - | Called once at the very end of the Run process. - | Allows the user to run some post-computations. + def prediction_condition(self) -> bool: """ - - pass - - def running_condition(self) -> bool: - """ - | Condition that characterize the end of the running process. - - :return: False if the training needs to stop. + Condition that characterize the end of the prediction pipeline. """ running = self.idx_step < self.nb_samples if self.nb_samples > 0 else True @@ -113,39 +87,37 @@ def running_condition(self) -> bool: def sample_begin(self) -> None: """ - | Called one at the start of each step. - | Allows the user to run some pre-step computations. + Called one at the beginning of each sample. """ pass - def sample_end(self, prediction: ndarray) -> None: + def predict(self) -> None: + """ + Pull the data from the manager and return the prediction. """ - | Called one at the end of each step. - | Allows the user to run some post-step computations. - :param ndarray prediction: Prediction of the Network. + self.manager.get_data(animate=True) + + def sample_end(self) -> None: + """ + Called one at the end of each sample. """ - if self.is_environment: - self.manager.data_manager.apply_prediction(prediction) + pass - def close(self) -> None: + def run_end(self) -> None: """ - | End the running process and close all the managers + Called once at the end of the prediction pipeline. """ self.manager.close() def __str__(self) -> str: - """ - :return: str Contains running information about the running process - """ description = "" description += f"Running statistics :\n" description += f"Number of simulation step: {self.nb_samples}\n" - description += f"Record inputs : {self.record_data[0]}\n" - description += f"Record outputs : {self.record_data[1]}\n" + description += f"Record data : {self.record_data}\n" return description From d6df347954cb169d803384a56c4ac26e4e67fe42 Mon Sep 17 00:00:00 2001 From: robin Date: Tue, 25 Oct 2022 18:16:13 +0200 Subject: [PATCH 16/61] New definition of partitions. Create DB handlers for Managers. Adapt DataGeneration pipeline. --- src/AsyncSocket/AbstractEnvironment.py | 38 ++-- src/AsyncSocket/BytesConverter.py | 35 ++-- src/AsyncSocket/TcpIpClient.py | 105 +++++------ src/AsyncSocket/TcpIpObject.py | 3 - src/Database/DatabaseHandler.py | 90 ++++++++++ src/Environment/BaseEnvironment.py | 133 +++++++------- src/Environment/BaseEnvironmentConfig.py | 13 +- src/Environment/launcherBaseEnvironment.py | 8 +- src/Manager/DataManager.py | 13 +- src/Manager/DatabaseManager.py | 192 ++++++++++++--------- src/Manager/DatasetManager.py | 2 +- src/Manager/EnvironmentManager.py | 24 ++- src/Pipelines/BaseDataGeneration.py | 26 ++- src/Pipelines/BasePipeline.py | 3 + 14 files changed, 389 insertions(+), 296 deletions(-) create mode 100644 src/Database/DatabaseHandler.py diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 57495df1..c1d9d0ef 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -1,4 +1,4 @@ -from typing import Optional, Dict, Any +from typing import Optional, Dict, Any, Union, Tuple from numpy import ndarray from SSD.Core.Storage.Database import Database @@ -9,33 +9,35 @@ class AbstractEnvironment: def __init__(self, as_tcp_ip_client: bool = True, instance_id: int = 0, - number_of_instances: int = 1): + instance_nb: int = 1, + visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): """ AbstractEnvironment sets the Environment API for TcpIpClient. - Do not use AbstractEnvironment to implement a personal Environment, use BaseEnvironment instead. + Do not use AbstractEnvironment to implement an Environment, use BaseEnvironment instead. - :param instance_id: ID of the instance. - :param number_of_instances: Number of simultaneously launched instances. - :param as_tcp_ip_client: Environment is a TcpIpObject if True, is owned by an EnvironmentManager if False. + :param as_tcp_ip_client: If True, the Environment is a TcpIpObject, else it is owned by an EnvironmentManager. + :param instance_id: Index of this instance. + :param instance_nb: Number of simultaneously launched instances. """ self.name: str = self.__class__.__name__ + f" n°{instance_id}" # TcpIpClient variables - if instance_id > number_of_instances: + if instance_id > instance_nb: raise ValueError(f"[{self.name}] Instance ID ({instance_id}) is bigger than max instances " - f"({number_of_instances}).") + f"({instance_nb}).") self.as_tcp_ip_client: bool = as_tcp_ip_client self.instance_id: int = instance_id - self.number_of_instances: int = number_of_instances + self.instance_nb: int = instance_nb + + # Manager of the Environment + self.environment_manager: Any = None + self.tcp_ip_client: Any = None # Training data variables - self.__training_data: Dict[str, ndarray] = {} - self.__additional_data: Dict[str, ndarray] = {} self.compute_training_data: bool = True # Dataset data variables - self.database: Optional[Database] = None self.update_line: Optional[int] = None self.sample_training: Optional[Dict[str, Any]] = None self.sample_additional: Optional[Dict[str, Any]] = None @@ -50,13 +52,13 @@ def create(self) -> None: raise NotImplementedError def init(self) -> None: - pass + raise NotImplementedError def init_database(self) -> None: raise NotImplementedError def init_visualization(self) -> None: - pass + raise NotImplementedError ########################################################################################## ########################################################################################## @@ -68,13 +70,13 @@ async def step(self) -> None: raise NotImplementedError def check_sample(self) -> bool: - return True + raise NotImplementedError def apply_prediction(self, prediction: Dict[str, ndarray]) -> None: - pass + raise NotImplementedError def close(self) -> None: - pass + raise NotImplementedError ########################################################################################## ########################################################################################## @@ -91,5 +93,5 @@ def _reset_training_data(self) -> None: def _update_training_data(self, line_id: int) -> None: raise NotImplementedError - def _get_training_data(self, line: int) -> None: + def _get_training_data(self, line_id: int) -> None: raise NotImplementedError diff --git a/src/AsyncSocket/BytesConverter.py b/src/AsyncSocket/BytesConverter.py index 4992472c..37f63361 100644 --- a/src/AsyncSocket/BytesConverter.py +++ b/src/AsyncSocket/BytesConverter.py @@ -6,12 +6,12 @@ class BytesConverter: - """ - | Convert usual types to bytes and vice versa. - | Available types: None, bytes, str, bool, int, float, list, ndarray. - """ def __init__(self): + """ + Convert usual types to bytes and vice versa. + Available types: None, bytes, str, bool, int, float, list, ndarray. + """ # Data to bytes conversions self.__data_to_bytes_conversion: Dict[type, Callable[[Convertible], bytes]] = { @@ -42,17 +42,17 @@ def __init__(self): self.size_from_bytes: Callable[[bytes], int] = lambda b: self.__bytes_to_data_conversion[int.__name__](b) self.int_size: int = calcsize("i") - def data_to_bytes(self, data: Convertible, as_list: bool = False) -> Union[bytes, List[bytes]]: + def data_to_bytes(self, + data: Convertible, + as_list: bool = False) -> Union[bytes, List[bytes]]: """ - | Convert data to bytes. - | Available types: None, bytes, str, bool, signed int, float, list, ndarray. + Convert data to bytes. + Available types: None, bytes, str, bool, signed int, float, list, ndarray. :param data: Data to convert. - :type data: Union[type(None), bytes, str, bool, int, float, List, ndarray] - :param bool as_list: (For tests only, False by default) - If False, the whole bytes message is returned. - If True, the return will be a list of bytes fields. - :return: Concatenated bytes fields (Number of fields, Size of fields, Type, Data, Args) + :param as_list: (For tests only, False by default) If False, the whole bytes message is returned. If True, the + return will be a list of bytes fields. + :return: Concatenated bytes fields (Number of fields, Size of fields, Type, Data, Args). """ # Convert the type of 'data' from str to bytes @@ -91,13 +91,14 @@ def data_to_bytes(self, data: Convertible, as_list: bool = False) -> Union[bytes bytes_message += f return bytes_message - def bytes_to_data(self, bytes_fields: List[bytes]) -> Convertible: + def bytes_to_data(self, + bytes_fields: List[bytes]) -> Convertible: """ - | Recover data from bytes fields. - | Available types: None, bytes, str, bool, signed int, float, list, ndarray. + Recover data from bytes fields. + Available types: None, bytes, str, bool, signed int, float, list, ndarray. - :param List[bytes] bytes_fields: Bytes fields (Type, Data, Args) - :return: Converted data + :param bytes_fields: Bytes fields (Type, Data, Args). + :return: Converted data. """ # Recover the data type diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index bc7d8fc1..3c19e8a2 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -1,4 +1,4 @@ -from typing import Any, Dict +from typing import Any, Dict, Type, Optional, Union, Tuple from socket import socket from asyncio import get_event_loop from asyncio import AbstractEventLoop as EventLoop @@ -9,39 +9,41 @@ from DeepPhysX.Core.AsyncSocket.AbstractEnvironment import AbstractEnvironment, Database -class TcpIpClient(TcpIpObject, AbstractEnvironment): +class TcpIpClient(TcpIpObject): def __init__(self, + environment: Type[AbstractEnvironment], ip_address: str = 'localhost', port: int = 10000, - as_tcp_ip_client: bool = True, instance_id: int = 0, - number_of_instances: int = 1,): + instance_nb: int = 1, + training_db: Optional[Union[Database, Tuple[str, str]]] = None, + visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): """ TcpIpClient is both a TcpIpObject which communicate with a TcpIpServer and an AbstractEnvironment to compute simulated data. :param ip_address: IP address of the TcpIpObject. :param port: Port number of the TcpIpObject. - :param as_tcp_ip_client: Environment is a TcpIpObject if True, is owned by an EnvironmentManager if False. - :param instance_id: ID of the instance. - :param number_of_instances: Number of simultaneously launched instances. + :param instance_id: Index of this instance. + :param instance_nb: Number of simultaneously launched instances. """ - AbstractEnvironment.__init__(self, - as_tcp_ip_client=as_tcp_ip_client, - instance_id=instance_id, - number_of_instances=number_of_instances) + TcpIpObject.__init__(self, + ip_address=ip_address, + port=port) + + self.environment = environment(as_tcp_ip_client=True, + instance_id=instance_id, + instance_nb=instance_nb, + visualization_db=visualization_db) + self.environment.tcp_ip_client = self # Bind to client address - if self.as_tcp_ip_client: - TcpIpObject.__init__(self, - ip_address=ip_address, - port=port) - self.sock.connect((ip_address, port)) - # Send ID - self.sync_send_labeled_data(data_to_send=instance_id, label="instance_ID", receiver=self.sock, - send_read_command=False) + self.sock.connect((ip_address, port)) + # Send ID + self.sync_send_labeled_data(data_to_send=instance_id, label="instance_ID", receiver=self.sock, + send_read_command=False) # Flag to trigger client's shutdown self.close_client: bool = False @@ -69,10 +71,10 @@ async def __initialize(self) -> None: self.simulations_per_step = await self.receive_data(loop=loop, sender=self.sock) # Create the environment - self.create() - self.init() - self.init_database() - self.init_visualization() + self.environment.create() + self.environment.init() + self.environment.init_database() + self.environment.init_visualization() # Initialization done await self.send_command_done(loop=loop, receiver=self.sock) @@ -85,7 +87,7 @@ async def __initialize(self) -> None: def launch(self) -> None: """ - Run __launch method with asyncio. + Trigger the main communication protocol with the server. """ async_run(self.__launch()) @@ -124,7 +126,7 @@ async def __close(self) -> None: # Close environment try: - self.close() + self.environment.close() except NotImplementedError: pass # Confirm exit command to the server @@ -133,32 +135,6 @@ async def __close(self) -> None: # Close socket self.sock.close() - ########################################################################################## - ########################################################################################## - # Data sending to Server # - ########################################################################################## - ########################################################################################## - - def send_prediction_data(self, - network_input: ndarray, - receiver: socket = None) -> ndarray: - """ - Request a prediction from the Environment. - - :param network_input: Data to send under the label 'input'. - :param receiver: TcpIpObject receiver. - :return: Prediction of the Network. - """ - - receiver = self.sock if receiver is None else receiver - # Send prediction command - self.sync_send_command_prediction() - # Send the network input - self.sync_send_labeled_data(data_to_send=network_input, label='input', receiver=receiver) - # Receive the network prediction - _, pred = self.sync_receive_labeled_data() - return pred - ########################################################################################## ########################################################################################## # Available requests to Server # @@ -170,18 +146,17 @@ def get_prediction(self, """ Request a prediction from Network. - :param input_array: Network input. :return: Prediction of the Network. """ # Get a prediction self.database.update(table_name='Prediction', data=kwargs, - line_id=self.instance_id) + line_id=self.environment.instance_id) self.sync_send_command_prediction() _ = self.sync_receive_data() data_pred = self.database.get_line(table_name='Prediction', - line_id=self.instance_id) + line_id=self.environment.instance_id) del data_pred['id'] return data_pred @@ -191,7 +166,7 @@ def request_update_visualization(self) -> None: """ self.sync_send_command_visualisation() - self.sync_send_labeled_data(data_to_send=self.instance_id, + self.sync_send_labeled_data(data_to_send=self.environment.instance_id, label='instance') ########################################################################################## @@ -234,7 +209,7 @@ async def action_on_prediction(self, # Receive prediction prediction = await self.receive_data(loop=loop, sender=sender) # Apply the prediction in Environment - self.apply_prediction(prediction) + self.environment.apply_prediction(prediction) async def action_on_sample(self, data: ndarray, @@ -251,7 +226,7 @@ async def action_on_sample(self, """ dataset_batch = await self.receive_data(loop=loop, sender=sender) - self._get_training_data(dataset_batch) + self.environment._get_training_data(dataset_batch) async def action_on_step(self, data: ndarray, @@ -271,22 +246,22 @@ async def action_on_step(self, for step in range(self.simulations_per_step): # Compute data only on final step self.compute_training_data = step == self.simulations_per_step - 1 - await self.step() + await self.environment.step() # If produced sample is not usable, run again - while not self.check_sample(): + while not self.environment.check_sample(): for step in range(self.simulations_per_step): # Compute data only on final step self.compute_training_data = step == self.simulations_per_step - 1 - await self.step() + await self.environment.step() # Sent training data to Server - if self.update_line is None: - line = self._send_training_data() + if self.environment.update_line is None: + line = self.environment._send_training_data() else: - self._update_training_data(self.update_line) - line = self.update_line - self._reset_training_data() + self.environment._update_training_data(self.environment.update_line) + line = self.environment.update_line + self.environment._reset_training_data() await self.send_command_done() await self.send_data(data_to_send=line, loop=loop, receiver=sender) diff --git a/src/AsyncSocket/TcpIpObject.py b/src/AsyncSocket/TcpIpObject.py index aa1994cd..32bf1b7a 100644 --- a/src/AsyncSocket/TcpIpObject.py +++ b/src/AsyncSocket/TcpIpObject.py @@ -51,9 +51,6 @@ def __init__(self, self.command_dict["visualisation"]: self.action_on_visualisation, self.command_dict['db']: self.action_on_change_db } - # Synchronous variables - # self.send_lock = Lock() - # self.receive_lock = Lock() ########################################################################################## ########################################################################################## diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py new file mode 100644 index 00000000..32c984b5 --- /dev/null +++ b/src/Database/DatabaseHandler.py @@ -0,0 +1,90 @@ +from typing import Union, List, Dict, Any, Callable, Optional, Type, Tuple +from numpy import ndarray + +from SSD.Core.Storage.Database import Database + + +class DatabaseHandler: + + def __init__(self, + remote: bool = False, + on_init_handler: Optional[Callable] = None): + + self.remote = remote + self.__partitions: List[Database] = [] + self.__on_init_handler = self.__default_handler if on_init_handler is None else on_init_handler + + def __default_handler(self): + pass + + ### + # Manage partitions + ### + + def init(self, partitions: List[Database]) -> None: + self.__partitions = partitions.copy() + self.__on_init_handler() + + def init_remote(self, partitions: List[List[str]]) -> None: + self.__partitions = [Database(database_dir=partition[0], + database_name=partition[1]).load() for partition in partitions] + self.__on_init_handler() + + def update_list_partitions(self, + partition: Database) -> None: + self.__partitions.append(partition) + + def get_database_dir(self): + return self.__partitions[0].get_path()[0] + + def load(self): + for db in self.__partitions: + db.load() + + ### + # Database Architecture + ### + + def create_fields(self, + table_name: str, + fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + if len(self.__partitions) == 1: + self.__partitions[0].create_fields(table_name=table_name, + fields=fields) + + def define_fields(self, + table_name: str, + fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + if len(self.__partitions) == 1: + self.__partitions[0].load() + if len(self.__partitions[0].get_fields(table_name=table_name)) == 2: + self.__partitions[0].create_fields(table_name=table_name, + fields=fields) + + def get_fields(self, + table_name: str) -> List[str]: + return self.__partitions[0].get_fields(table_name=table_name) + + ### + # Data Read / Write + ### + + def get_line(self, + table_name: str, + line_id: List[int]) -> Dict[str, ndarray]: + return self.__partitions[line_id[0]].get_line(table_name=table_name, + line_id=line_id[1]) + + def add_data(self, + table_name: str, + data: Dict[str, Any]) -> int: + return self.__partitions[-1].add_data(table_name=table_name, + data=data) + + def update(self, + table_name: str, + data: Dict[str, Any], + line_id: int) -> None: + self.__partitions[-1].update(table_name=table_name, + data=data, + line_id=line_id) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index ab94350d..e7f6c3aa 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -5,67 +5,44 @@ from SSD.Core.Storage.Database import Database from DeepPhysX.Core.Visualization.VedoFactory import VedoFactory -from DeepPhysX.Core.AsyncSocket.TcpIpClient import TcpIpClient +from DeepPhysX.Core.AsyncSocket.AbstractEnvironment import AbstractEnvironment +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler -class BaseEnvironment(TcpIpClient): +class BaseEnvironment(AbstractEnvironment): def __init__(self, - ip_address: str = 'localhost', - port: int = 10000, - instance_id: int = 0, - number_of_instances: int = 1, as_tcp_ip_client: bool = True, - environment_manager: Optional[Any] = None, - training_db: Optional[Union[Database, Tuple[str, str]]] = None, + instance_id: int = 0, + instance_nb: int = 1, visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): """ BaseEnvironment is an environment class to compute simulated data for the network and its optimization process. - :param ip_address: IP address of the TcpIpObject. - :param port: Port number of the TcpIpObject. :param instance_id: ID of the instance. - :param number_of_instances: Number of simultaneously launched instances. - :param as_tcp_ip_client: Environment is owned by a TcpIpClient if True, by an EnvironmentManager if False. - :param environment_manager: EnvironmentManager that handles the Environment if 'as_tcp_ip_client' is False. + :param instance_nb: Number of simultaneously launched instances. + :param as_tcp_ip_client: Environment is a TcpIpObject if True, is owned by an EnvironmentManager if False. :param visualization_db: The path to the visualization Database or the visualization Database object to connect to. """ - TcpIpClient.__init__(self, - instance_id=instance_id, - number_of_instances=number_of_instances, - as_tcp_ip_client=as_tcp_ip_client, - ip_address=ip_address, - port=port) + AbstractEnvironment.__init__(self, + as_tcp_ip_client=as_tcp_ip_client, + instance_id=instance_id, + instance_nb=instance_nb) # Training data variables - self.__training_data: Dict[str, ndarray] = {} - self.__additional_data: Dict[str, ndarray] = {} + self.__data_training: Dict[str, ndarray] = {} + self.__data_additional: Dict[str, ndarray] = {} self.compute_training_data: bool = True # Dataset data variables - self.database: Optional[Database] = None self.update_line: Optional[int] = None self.sample_training: Optional[Dict[str, Any]] = None self.sample_additional: Optional[Dict[str, Any]] = None self.__first_add: List[bool] = [True, True] - # Manager if the Environment is not a TcpIpClient - self.environment_manager: Any = environment_manager - # Connect the Environment to the data Database - self.database: Optional[Database] = None - if training_db is not None: - if type(training_db) == list: - self.database = Database(database_dir=training_db[0], - database_name=training_db[1]).load() - else: - self.database = training_db - if self.instance_id == 1: - self.database.create_fields(table_name='Training', - fields=('env_id', int)) - self.database.create_fields(table_name='Additional', - fields=('env_id', int)) + self.__database_handler = DatabaseHandler(on_init_handler=self.__database_handler_init) # Connect the Factory to the visualization Database self.factory: Optional[VedoFactory] = None @@ -84,6 +61,16 @@ def __init__(self, ########################################################################################## ########################################################################################## + def __database_handler_init(self): + if self.instance_id == 1: + self.__database_handler.create_fields(table_name='Training', + fields=('env_id', int)) + self.__database_handler.create_fields(table_name='Additional', + fields=('env_id', int)) + + def get_database_handler(self) -> DatabaseHandler: + return self.__database_handler + def create(self) -> None: """ Create the Environment. Automatically called when Environment is launched. @@ -169,7 +156,7 @@ def save_parameters(self, **kwargs) -> None: Save a set of parameters in the Database. """ - database_dir = self.database.get_path()[0] + database_dir = self.__database_handler.get_database_dir() if isfile(join(database_dir, 'environment_parameters.db')): database = Database(database_dir=database_dir, database_name='environment_parameters').load() @@ -186,7 +173,7 @@ def load_parameters(self) -> Dict[str, Any]: Load a set of parameters from the Database. """ - database_dir = self.database.get_path()[0] + database_dir = self.__database_handler.get_database_dir() if isfile(join(database_dir, 'environment_parameters.db')): database = Database(database_dir=database_dir, database_name='environment_parameters').load() @@ -196,14 +183,12 @@ def load_parameters(self) -> Dict[str, Any]: return {} def define_training_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - self.database.load() - if len(self.database.get_fields(table_name='Training')) == 2: - self.database.create_fields(table_name='Training', fields=fields) + self.__database_handler.define_fields(table_name='Training', + fields=fields) def define_additional_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - self.database.load() - if len(self.database.get_fields(table_name='Additional')) == 2: - self.database.create_fields(table_name='Additional', fields=fields) + self.__database_handler.define_fields(table_name='Additional', + fields=fields) def set_training_data(self, **kwargs) -> None: """ @@ -213,9 +198,9 @@ def set_training_data(self, **kwargs) -> None: # Check kwargs if self.__first_add[0]: if self.instance_id != 0: - self.database.load() + self.__database_handler.load() self.__first_add[0] = False - required_fields = list(set(self.database.get_fields(table_name='Training')) - {'id', 'env_id'}) + required_fields = list(set(self.__database_handler.get_fields(table_name='Training')) - {'id', 'env_id'}) for field in kwargs.keys(): if field not in required_fields: raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." @@ -227,21 +212,21 @@ def set_training_data(self, **kwargs) -> None: # Training data is set if the Environment can compute data if self.compute_training_data: - self.__training_data = kwargs - self.__training_data['env_id'] = self.instance_id + self.__data_training = kwargs + self.__data_training['env_id'] = self.instance_id def set_additional_data(self, **kwargs) -> None: # Additional data is also set if the Environment can compute data if self.compute_training_data: - self.__additional_data = kwargs - self.__additional_data['env_id'] = self.instance_id + self.__data_additional = kwargs + self.__data_additional['env_id'] = self.instance_id def _send_training_data(self) -> int: - line_id = self.database.add_data(table_name='Training', - data=self.__training_data) - self.database.add_data(table_name='Additional', - data=self.__additional_data) + line_id = self.__database_handler.add_data(table_name='Training', + data=self.__data_training) + self.__database_handler.add_data(table_name='Additional', + data=self.__data_additional) return line_id def _reset_training_data(self) -> None: @@ -254,21 +239,21 @@ def _reset_training_data(self) -> None: def _update_training_data(self, line_id: int) -> None: if self.__training_data != {}: - self.database.update(table_name='Training', - data=self.__training_data, - line_id=line_id) + self.__database_handler.update(table_name='Training', + data=self.__training_data, + line_id=line_id) if self.__additional_data != {}: - self.database.update(table_name='Additional', - data=self.__additional_data, - line_id=line_id) + self.__database_handler.update(table_name='Additional', + data=self.__additional_data, + line_id=line_id) def _get_training_data(self, - line: int) -> None: + line: List[int]) -> None: self.update_line = line - self.sample_training = self.database.get_line(table_name='Training', - line_id=line) - self.sample_additional = self.database.get_line(table_name='Additional', - line_id=line) + self.sample_training = self.__database_handler.get_line(table_name='Training', + line_id=line) + self.sample_additional = self.__database_handler.get_line(table_name='Additional', + line_id=line) self.sample_additional = None if len(self.sample_additional) == 1 else self.sample_additional ########################################################################################## @@ -287,9 +272,9 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: # Check kwargs if self.__first_add[1]: if self.instance_id != 0: - self.database.load() + self.__database_handler.load() self.__first_add[1] = False - required_fields = list(set(self.database.get_fields(table_name='Prediction')) - {'id'}) + required_fields = list(set(self.__database_handler.get_fields(table_name='Prediction')) - {'id'}) for field in kwargs.keys(): if field not in required_fields: raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." @@ -303,12 +288,12 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: if self.environment_manager.data_manager is None: raise ValueError("Cannot request prediction if DataManager does not exist") # Get a prediction - self.database.update(table_name='Prediction', - data=kwargs, - line_id=self.instance_id) + self.__database_handler.update(table_name='Prediction', + data=kwargs, + line_id=self.instance_id) self.environment_manager.data_manager.get_prediction(self.instance_id) - data_pred = self.database.get_line(table_name='Prediction', - line_id=self.instance_id) + data_pred = self.__database_handler.get_line(table_name='Prediction', + line_id=[0, self.instance_id]) del data_pred['id'] return data_pred @@ -318,7 +303,7 @@ def _get_prediction(self): """ training_data = self.__training_data.copy() - required_fields = self.database.get_fields(table_name='Prediction') + required_fields = self.__database_handler.get_fields(table_name='Prediction') for field in self.__training_data.keys(): if field not in required_fields: del training_data[field] diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index 2e9a0ebe..8447a36e 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -157,31 +157,20 @@ def start_client(self, str(training_db), str(visualization_db)]) def create_environment(self, - environment_manager: Any, - training_db: Optional[Database] = None, visualization_db: Optional[Any] = None) -> BaseEnvironment: """ Create an Environment that will not be a TcpIpObject. - :param environment_manager: EnvironmentManager that handles the Environment. - :param training_db: Path of the training Database to connect to. :param visualization_db: Path to the visualization Database to connect to. :return: Environment object. """ # Create instance - environment = self.environment_class(environment_manager=environment_manager, - as_tcp_ip_client=False, - training_db=training_db, + environment = self.environment_class(as_tcp_ip_client=False, visualization_db=visualization_db) if not isinstance(environment, BaseEnvironment): raise TypeError(f"[{self.name}] The given 'environment_class'={self.environment_class} must be a " f"BaseEnvironment.") - # Create & Init Environment - environment.create() - environment.init() - environment.init_database() - environment.init_visualization() return environment def __str__(self) -> str: diff --git a/src/Environment/launcherBaseEnvironment.py b/src/Environment/launcherBaseEnvironment.py index b2ed4a3d..f60112de 100644 --- a/src/Environment/launcherBaseEnvironment.py +++ b/src/Environment/launcherBaseEnvironment.py @@ -3,6 +3,7 @@ from sys import argv, path from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment as Environment +from DeepPhysX.Core.AsyncSocket.TcpIpClient import TcpIpClient if __name__ == '__main__': @@ -20,12 +21,11 @@ # Create, init and run Tcp-Ip environment training_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] visualization_db = None if argv[8] == 'None' else [s[1:-1] for s in argv[8][1:-1].split(', ')] - client = Environment(ip_address=argv[3], + client = TcpIpClient(environment=Environment, + ip_address=argv[3], port=int(argv[4]), instance_id=int(argv[5]), - number_of_instances=int(argv[6]), - training_db=training_db, - visualization_db=visualization_db) + instance_nb=int(argv[6])) client.initialize() client.launch() diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index f1706345..e24ed1ff 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -40,6 +40,7 @@ def __init__(self, self.manager: Optional[Any] = manager self.database_manager: Optional[DatabaseManager] = None self.environment_manager: Optional[EnvironmentManager] = None + self.connected_managers: List[Any] = [] # Create a DatabaseManager self.database_manager = DatabaseManager(database_config=database_config, @@ -48,14 +49,12 @@ def __init__(self, new_session=new_session, pipeline=pipeline, produce_data=produce_data) - training_db = self.database_manager.database # Create an EnvironmentManager if required if environment_config is not None: self.environment_manager = EnvironmentManager(environment_config=environment_config, data_manager=self, session=session, - training_db=training_db, batch_size=batch_size) # DataManager variables @@ -73,12 +72,18 @@ def get_manager(self) -> Any: return self.manager + def connect_handler(self, handler): + self.database_manager.connect_handler(handler) + def get_database(self) -> Database: return self.database_manager.database + def connect_manager(self, manager: Any): + self.connected_managers.append(manager) + def change_database(self) -> None: - self.manager.change_database(self.database_manager.database) - self.environment_manager.change_database(self.database_manager.database) + for manager in self.connected_managers: + manager.change_database(self.database_manager.database) @property def nb_environment(self): diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index c059fd5c..7b97bb4f 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -9,6 +9,7 @@ from SSD.Core.Storage.Database import Database from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler from DeepPhysX.Core.Utils.path import create_dir, copy_dir, get_first_caller from DeepPhysX.Core.Utils.jsonUtils import CustomJSONEncoder @@ -28,19 +29,18 @@ def __init__(self, :param database_config: Specialisation containing the parameters of the dataset manager :param data_manager: DataManager that handles the DatabaseManager :param new_session: Define the creation of new directories to store data - :param is_training: True if the session is done offline :param produce_data: True if this session is a network training """ self.name: str = self.__class__.__name__ # Manager variables - database_config = BaseDatabaseConfig() if database_config is None else database_config - self.data_manager: Optional[Any] = data_manager - self.dataset_dir: str = join(session, 'dataset') - self.database: Optional[Database] = None self.pipeline: str = pipeline + self.data_manager: Optional[Any] = data_manager + self.database_dir: str = join(session, 'dataset') + self.database_handlers: List[DatabaseHandler] = [] root = get_first_caller() + database_config = BaseDatabaseConfig() if database_config is None else database_config # Dataset parameters self.max_file_size: int = database_config.max_file_size @@ -52,15 +52,20 @@ def __init__(self, # Dataset modes self.modes: List[str] = ['training', 'validation', 'prediction'] - self.mode: str = 'training' if produce_data or pipeline == 'training' else 'prediction' - self.mode = self.mode if database_config.mode is None else database_config.mode + if pipeline == 'data_generation': + self.mode: str = 'training' if database_config.mode is None else database_config.mode + elif pipeline == 'training': + self.mode: str = 'training' + else: + self.mode: str = 'prediction' if database_config.mode is None else database_config.mode + self.mode = 'prediction' if produce_data else self.mode # Dataset partitions session_name = session.split(sep)[-1] self.partition_template: Dict[str, str] = {mode: f'{session_name}_{mode}_' + '{}' for mode in self.modes} - self.partitions: Dict[str, List[str]] = {mode: [] for mode in self.modes} self.partition_index: Dict[str, int] = {mode: 0 for mode in self.modes} - self.current_partition: str = '' + self.partition_names: Dict[str, List[str]] = {mode: [] for mode in self.modes} + self.partitions: Dict[str, List[Database]] = {mode: [] for mode in self.modes} # Dataset indexing self.shuffle_pattern: ndarray = arange(0) @@ -133,45 +138,13 @@ def __init__(self, else: self.load_directory(load_partition=False) - def create_partition(self): - """ - - """ - - self.current_partition = self.partition_template[self.mode].format(self.partition_index[self.mode]) - self.database = Database(database_dir=self.dataset_dir, - database_name=self.current_partition).new() - self.database.create_table(table_name='Training') - self.database.create_table(table_name='Additional') - self.partitions[self.mode].append(self.current_partition) - - def additional_partition(self): - - # 1. If a DB exists, get all the Fields to re-create them - fields = {} - types = {'INT': int, 'FLOAT': float, 'STR': str, 'BOOL': bool, 'NUMPY': ndarray} - if self.partition_index[self.mode] > 0: - for table_name in self.database.get_tables(): - fields[table_name] = [] - F = self.database.get_fields(table_name=table_name, - only_names=False) - for field in [f for f in F if f not in ['id', '_dt_']]: - fields[table_name].append((field, types[F[field].field_type])) - if 'Prediction' in fields.keys(): - self.database.remove_table(table_name='Prediction') - - # 2. Create a new Database - self.partition_index[self.mode] += 1 - self.create_partition() - - # 3. Re-create the Fields if this is not the first partition - if self.partition_index[self.mode] > 1: - for table_name in fields.keys(): - self.database.create_fields(table_name=table_name, - fields=fields[table_name]) - - # 4. Tell the other components to communicate on a new DB - self.data_manager.change_database() + def connect_handler(self, + handler: DatabaseHandler) -> None: + if handler.remote: + handler.init_remote(self.get_partition_names()) + else: + handler.init(self.get_partition_objects()) + self.database_handlers.append(handler) def load_directory(self, last_partition: bool = True, @@ -181,14 +154,14 @@ def load_directory(self, """ # 1. Check the directory existence to prevent bugs - if not isdir(self.dataset_dir): - raise Warning(f"[{self.name}] Impossible to load Dataset from {self.dataset_dir}.") + if not isdir(self.database_dir): + raise Warning(f"[{self.name}] Impossible to load Dataset from {self.database_dir}.") # 2. Get the .json description file json_found = False - if isfile(join(self.dataset_dir, 'dataset.json')): + if isfile(join(self.database_dir, 'dataset.json')): json_found = True - with open(join(self.dataset_dir, 'dataset.json')) as json_file: + with open(join(self.database_dir, 'dataset.json')) as json_file: self.json_content = json_load(json_file) # 3. Load partitions for each mode @@ -209,10 +182,10 @@ def load_directory(self, else: self.partition_index[self.mode] = len(self.partitions[self.mode]) - 1 if last_partition else 0 self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] - self.database = Database(database_dir=self.dataset_dir, + self.database = Database(database_dir=self.database_dir, database_name=self.current_partition).load() else: - self.database = Database(database_dir=self.dataset_dir, + self.database = Database(database_dir=self.database_dir, database_name='temp').new() self.database.create_table(table_name='Training') self.database.create_table(table_name='Additional') @@ -221,12 +194,55 @@ def load_directory(self, if self.shuffle: self.shuffle_samples() + def create_partition(self): + """ + + """ + + # 1. Define the partition name + partition_name = self.partition_template[self.mode].format(self.partition_index[self.mode]) + self.partition_names[self.mode].append(partition_name) + + # 2. Create the Database partition + db = Database(database_dir=self.database_dir, + database_name=partition_name).new() + db.create_table(table_name='Training') + db.create_table(table_name='Additional') + self.partitions[self.mode].append(db) + + # 3. If the partition is an additional one, create all fields + if self.partition_index[self.mode] > 0: + # Get fields + fields = {} + types = {'INT': int, 'FLOAT': float, 'STR': str, 'BOOL': bool, 'NUMPY': ndarray} + for table_name in self.partitions[self.mode][0].get_tables(): + fields[table_name] = [] + F = self.partitions[self.mode][0].get_fields(table_name=table_name, + only_names=False) + for field in [f for f in F if f not in ['id', '_dt_']]: + fields[table_name].append((field, types[F[field].field_type])) + # Re-create them + for table_name in fields.keys(): + self.partitions[self.mode][-1].create_fields(table_name=table_name, + fields=fields[table_name]) + + # 4. Update the partitions in handlers + for handler in self.database_handlers: + handler.update_list_partitions(self.partitions[self.mode][-1]) + self.partition_index[self.mode] += 1 + + def get_partition_objects(self) -> List[Database]: + return self.partitions[self.mode] + + def get_partition_names(self) -> List[List[str]]: + return [db.get_path() for db in self.partitions[self.mode]] + def load_next_partition(self): # 1. Define next partition self.partition_index[self.mode] = (self.partition_index[self.mode] + 1) % len(self.partitions[self.mode]) self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] - self.database = Database(database_dir=self.dataset_dir, + self.database = Database(database_dir=self.database_dir, database_name=self.current_partition).load() # 2. Shuffle @@ -238,7 +254,7 @@ def search_partitions(self): """ - raw_partitions = {mode: [f for f in listdir(self.dataset_dir) if isfile(join(self.dataset_dir, f)) + raw_partitions = {mode: [f for f in listdir(self.database_dir) if isfile(join(self.database_dir, f)) and f.endswith('.db') and f.__contains__(mode)] for mode in self.modes} return {mode: sorted(raw_partitions[mode]) for mode in self.modes} @@ -251,6 +267,18 @@ def search_partitions_info(self): pass + def remove_empty_partitions(self): + + for mode in self.modes: + if len(self.partitions[mode]) > 0 and self.partitions[mode][-1].nb_lines(table_name='Training') == 0: + database = self.partitions[mode].pop(-1) + path = database.get_path() + remove(join(path[0], f'{path[1]}.db')) + self.partition_names[mode].pop(-1) + self.partition_index[mode] -= 1 + self.json_content['nb_samples'][mode].pop(-1) + self.update_json(update_partitions_lists=True) + def update_json(self, update_partitions_lists: bool = False, update_nb_samples: bool = False, @@ -262,20 +290,22 @@ def update_json(self, """ + database = self.partitions[self.mode][-1] + # Update partitions lists if update_partitions_lists: - self.json_content['partitions'] = self.partitions + self.json_content['partitions'] = self.partition_names # Update number of samples if update_nb_samples: - if len(self.json_content['nb_samples'][self.mode]) == self.partition_index[self.mode] + 1: + if len(self.json_content['nb_samples'][self.mode]) == self.partition_index[self.mode]: self.json_content['nb_samples'][self.mode][-1] = self.nb_samples else: self.json_content['nb_samples'][self.mode].append(self.nb_samples) # Update DB architecture if update_architecture: - architecture = self.database.get_architecture() + architecture = database.get_architecture() if 'Prediction' in architecture.keys(): del architecture['Prediction'] for fields in architecture.values(): @@ -287,8 +317,8 @@ def update_json(self, # Update data shapes if update_shapes: for table_name, fields in self.json_content['architecture'].items(): - if self.database.nb_lines(table_name=table_name) > 0: - data = self.database.get_line(table_name=table_name) + if database.nb_lines(table_name=table_name) > 0: + data = database.get_line(table_name=table_name) for field in fields: if 'NUMPY' in field: field_name = field.split(' ')[0] @@ -299,12 +329,12 @@ def update_json(self, self.json_content['normalization'] = self.compute_normalization() # Overwrite json file - with open(join(self.dataset_dir, 'dataset.json'), 'w') as json_file: + with open(join(self.database_dir, 'dataset.json'), 'w') as json_file: json_dump(self.json_content, json_file, indent=3, cls=CustomJSONEncoder) @property def nb_samples(self) -> int: - return self.database.nb_lines(table_name='Training') + return self.partitions[self.mode][-1].nb_lines(table_name='Training') def shuffle_samples(self): """ @@ -332,8 +362,8 @@ def add_data(self, # 2. Check the size of the partition if self.max_file_size is not None: - if self.database.memory_size > self.max_file_size: - self.additional_partition() + if self.partitions[self.mode][-1].memory_size > self.max_file_size: + self.create_partition() self.update_json(update_partitions_lists=True, update_nb_samples=True) # 3. Update sample counter @@ -364,9 +394,18 @@ def close(self): """ + # Check non-empty last partition + self.remove_empty_partitions() + if self.normalize and self.pipeline == 'data_generation': self.update_json(update_normalization=True) - self.database.close() + + # Cose databases + for mode in self.modes: + for database in self.partitions[mode]: + database.close() + + # Remove prediction pipeline DB if self.pipeline == 'prediction' and not self.produce_data: path = self.database.get_path() remove(join(path[0], f'{path[1]}.db')) @@ -434,10 +473,10 @@ def update_normalization(self, # Compute the mean for each field fields = list(previous_normalization.keys()) - data_to_normalize = self.database.get_lines(table_name='Training', - fields=fields, - lines_id=data_lines, - batched=True) + data_to_normalize = self.partitions[self.mode][-1].get_lines(table_name='Training', + fields=fields, + lines_id=data_lines, + batched=True) for field in fields: data = array(data_to_normalize[field]) m = (previous_nb_samples / self.total_nb_sample) * previous_normalization[field][0] + \ @@ -465,19 +504,18 @@ def normalization(self) -> Dict[str, List[float]]: return None if self.json_content['normalization'] == {} else self.json_content['normalization'] def load_partitions_fields(self, - partition: str, + partition: Database, fields: List[str]): - db = Database(database_dir=self.dataset_dir, - database_name=partition).load() - return db.get_lines(table_name='Training', - fields=fields, - batched=True) + partition.load() + return partition.get_lines(table_name='Training', + fields=fields, + batched=True) def __str__(self): description = "\n" description += f"# {self.name}\n" - description += f" Dataset Repository: {self.dataset_dir}\n" + description += f" Dataset Repository: {self.database_dir}\n" size = f"No limits" if self.max_file_size is None else f"{self.max_file_size * 1e-9} Go" description += f" Partitions size: {size}\n" return description diff --git a/src/Manager/DatasetManager.py b/src/Manager/DatasetManager.py index 6860de5f..02257d64 100644 --- a/src/Manager/DatasetManager.py +++ b/src/Manager/DatasetManager.py @@ -258,7 +258,7 @@ def get_data(self, get_inputs: bool, get_outputs: bool, batch_size: int = 1, if data['input'].shape[0] != data['output'].shape[0]: raise ValueError(f"[{self.name}] Size of loaded batch mismatch for input and output " f"(in: {data['input'].shape} / out: {data['output'].shape}") - if 'additional_data' in data.keys(): + if '__data_additional' in data.keys(): for field in data['additional_fields'].keys(): if data['additional_fields'][field].shape[0] != data['input'].shape[0]: raise ValueError(f"[{self.name}] Size of loaded batch mismatch for additional field {field} " diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index a7f4deaf..2d170320 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -14,7 +14,6 @@ def __init__(self, environment_config: BaseEnvironmentConfig, data_manager: Optional[Any] = None, session: str = 'sessions/default', - training_db: Optional[Database] = None, batch_size: int = 1): """ Deals with the online generation of data for both training and running of the neural networks. @@ -54,15 +53,22 @@ def __init__(self, if environment_config.as_tcp_ip_client: self.server = environment_config.create_server(environment_manager=self, batch_size=batch_size, - training_db=training_db if training_db is None else training_db.get_path(), - visualization_db=visualization_db if visualization_db is None else visualization_db.get_path()) + visualization_db=None if visualization_db is None else visualization_db.get_path()) self.data_manager.get_database().load() else: - self.environment = environment_config.create_environment(environment_manager=self, - training_db=training_db, - visualization_db=visualization_db) + self.environment = environment_config.create_environment(visualization_db=visualization_db) + self.environment.environment_manager = self + self.data_manager.connect_handler(self.environment.get_database_handler()) + + # Create & Init Environment + self.environment.create() + self.environment.init() + self.environment.init_database() + self.environment.init_visualization() + # Define get_data and dispatch methods + self.get_database_handler = self.get_server_database_handler if self.server else self.get_environment_database_handler self.change_database = self.change_database_in_server if self.server else self.change_database_in_environment self.get_data = self.get_data_from_server if self.server else self.get_data_from_environment self.dispatch_batch = self.dispatch_batch_to_server if self.server else self.dispatch_batch_to_environment @@ -73,6 +79,12 @@ def __init__(self, self.visualizer.get_database().load() self.visualizer.init_visualizer() + def get_server_database_handler(self): + return self.server.get_database_handler() + + def get_environment_database_handler(self): + return self.environment.get_database_handler() + def change_database_in_server(self, database: Database): self.server.change_database(database.get_path()) diff --git a/src/Pipelines/BaseDataGeneration.py b/src/Pipelines/BaseDataGeneration.py index 6a0b1dfc..d18bc85c 100644 --- a/src/Pipelines/BaseDataGeneration.py +++ b/src/Pipelines/BaseDataGeneration.py @@ -1,5 +1,5 @@ from typing import Optional -from os.path import join, sep +from os.path import join, sep, exists from sys import stdout from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline @@ -17,6 +17,7 @@ def __init__(self, database_config: Optional[BaseDatabaseConfig] = None, session_dir: str = 'sessions', session_name: str = 'data_generation', + new_session: bool = True, batch_nb: int = 0, batch_size: int = 0): """ @@ -26,6 +27,7 @@ def __init__(self, :param environment_config: Specialisation containing the parameters of the environment manager. :param session_dir: Relative path to the directory which contains sessions directories. :param session_name: Name of the new the session directory. + :param new_session: If True, the session will be run in a new repository. :param batch_nb: Number of batches to produce. :param batch_size: Number of samples in a single batch. """ @@ -35,31 +37,20 @@ def __init__(self, environment_config=environment_config, session_dir=session_dir, session_name=session_name, + new_session=new_session, pipeline='data_generation') # Define the session repository root = get_first_caller() session_dir = join(root, session_dir) - # Configure 'new_session' flags - # Option 1: existing_dir == None --> new_session = True - # Option 2: existing_dir == session_dir/session_name --> new_session = False - # Option 3: existing_dir != session_dir/session_name --> new_session = True - new_session = True - if database_config is not None and database_config.existing_dir is not None and \ - join(session_dir, session_name) == join(root, database_config.existing_dir): - new_session = False - # Create a new session if required + if not new_session: + new_session = not exists(session_dir) if new_session: session_name = create_dir(session_dir=session_dir, session_name=session_name).split(sep)[-1] - # Data generation variables - self.batch_nb: int = batch_nb - self.batch_id: int = 0 - self.progress_bar = Progressbar(start=0, stop=self.batch_id, c='orange', title="Data Generation") - # Create a DataManager self.data_manager = DataManager(database_config=database_config, environment_config=environment_config, @@ -69,6 +60,11 @@ def __init__(self, produce_data=True, batch_size=batch_size) + # Data generation variables + self.batch_nb: int = batch_nb + self.batch_id: int = 0 + self.progress_bar = Progressbar(start=0, stop=self.batch_id, c='orange', title="Data Generation") + def execute(self) -> None: """ Launch the data generation Pipeline. diff --git a/src/Pipelines/BasePipeline.py b/src/Pipelines/BasePipeline.py index 4d935479..f62637b0 100644 --- a/src/Pipelines/BasePipeline.py +++ b/src/Pipelines/BasePipeline.py @@ -19,6 +19,7 @@ def __init__(self, environment_config: Optional[BaseEnvironmentConfig] = None, session_dir: str = 'sessions', session_name: str = 'default', + new_session: bool = True, pipeline: str = ''): """ Pipelines implement the main loop that defines data flow through components (Environment, Dataset, Network...). @@ -28,6 +29,7 @@ def __init__(self, :param environment_config: Specialisation containing the parameters of the environment manager. :param session_dir: Name of the directory in which to write all the necessary data. :param session_name: Name of the newly created directory if session is not defined. + :param new_session: If True, the session will be run in a new repository. :param pipeline: Name of the Pipeline. """ @@ -59,6 +61,7 @@ def __init__(self, # Session variables self.session_dir = session_dir self.session_name = session_name + self.new_session = new_session # Main manager self.manager: Optional[Manager] = None From b248ff05ad706c222b4f402f637594b851b23c61 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 26 Oct 2022 13:30:53 +0200 Subject: [PATCH 17/61] Fix all DataGeneration use cases. --- src/Manager/DatabaseManager.py | 148 ++++++++++++++++++---------- src/Pipelines/BaseDataGeneration.py | 2 +- 2 files changed, 96 insertions(+), 54 deletions(-) diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 7b97bb4f..14b18bb4 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -1,6 +1,6 @@ from typing import Any, Dict, List, Optional from os.path import isfile, isdir, join -from os import listdir, symlink, sep, remove +from os import listdir, symlink, sep, remove, rename from json import dump as json_dump from json import load as json_load from numpy import arange, ndarray, array, abs, mean, sqrt @@ -92,10 +92,10 @@ def __init__(self, else: copy_dir(src_dir=join(root, database_config.existing_dir), dest_dir=session, sub_folders='dataset') - self.load_directory(last_partition=True) + self.load_directory(rename_partitions=True) # Complete a Database in the same session --> load the directory else: - self.load_directory(last_partition=True) + self.load_directory() # Training case elif self.pipeline == 'training': @@ -112,10 +112,10 @@ def __init__(self, else: copy_dir(src_dir=join(root, database_config.existing_dir), dest_dir=session, sub_folders='dataset') - self.load_directory(last_partition=False) + self.load_directory() # Complete a Database in the same directory --> load the directory else: - self.load_directory(last_partition=False) + self.load_directory() # Load data else: @@ -123,20 +123,20 @@ def __init__(self, if new_session: symlink(src=join(root, database_config.existing_dir, 'dataset'), dst=join(session, 'dataset')) - self.load_directory(last_partition=False) + self.load_directory() # Load data in the same session --> load the directory else: - self.load_directory(last_partition=False) + self.load_directory() # Prediction case else: # Generate data if produce_data: - self.load_directory(last_partition=True) + self.load_directory() else: - self.load_directory(load_partition=False) + self.load_directory(load_partitions=False) def connect_handler(self, handler: DatabaseHandler) -> None: @@ -147,8 +147,8 @@ def connect_handler(self, self.database_handlers.append(handler) def load_directory(self, - last_partition: bool = True, - load_partition: bool = True): + load_partitions: bool = True, + rename_partitions: bool = False): """ """ @@ -164,26 +164,36 @@ def load_directory(self, with open(join(self.database_dir, 'dataset.json')) as json_file: self.json_content = json_load(json_file) - # 3. Load partitions for each mode - self.partitions = self.json_content['partitions'] if json_found else self.search_partitions() - - # 4. Update json file if not found + # 3. Update json file if not found if not json_found or self.json_content == self.json_default: self.search_partitions_info() - self.update_json(update_partitions_lists=True) + self.update_json() if self.recompute_normalization or ( self.normalize and self.json_content['normalization'] == self.json_default['normalization']): self.update_json(update_normalization=True) - # 5. Load the Database - if load_partition: - if len(self.partitions[self.mode]) == 0: + # 4. Load partitions for each mode + self.partition_names = self.json_content['partitions'] + self.partition_index = {mode: len(self.partition_names[mode]) for mode in self.modes} + if rename_partitions: + for mode in self.modes: + current_name = self.partition_template[mode].split(f'_{mode}_')[0] + for i, name in enumerate(self.partition_names[mode]): + if name.split(f'_{mode}_')[0] != current_name: + self.partition_names[mode][i] = current_name + f'_{mode}_{i}' + rename(src=join(self.database_dir, f'{name}.db'), + dst=join(self.database_dir, f'{self.partition_names[mode][i]}.db')) + + # 5. Load the partitions + if load_partitions: + for mode in self.modes: + for name in self.partition_names[mode]: + db = Database(database_dir=self.database_dir, + database_name=name).load() + self.partitions[mode].append(db) + if len(self.partitions[self.mode]) == 0 or self.partitions[self.mode][-1].memory_size > self.max_file_size: self.create_partition() - else: - self.partition_index[self.mode] = len(self.partitions[self.mode]) - 1 if last_partition else 0 - self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] - self.database = Database(database_dir=self.database_dir, - database_name=self.current_partition).load() + else: self.database = Database(database_dir=self.database_dir, database_name='temp').new() @@ -191,7 +201,7 @@ def load_directory(self, self.database.create_table(table_name='Additional') # 6. Shuffle Database indices - if self.shuffle: + if self.shuffle and load_partitions: self.shuffle_samples() def create_partition(self): @@ -230,6 +240,7 @@ def create_partition(self): for handler in self.database_handlers: handler.update_list_partitions(self.partitions[self.mode][-1]) self.partition_index[self.mode] += 1 + self.update_json(update_partitions_lists=True, update_nb_samples=True) def get_partition_objects(self) -> List[Database]: return self.partitions[self.mode] @@ -249,23 +260,70 @@ def load_next_partition(self): if self.shuffle: self.shuffle_samples() - def search_partitions(self): + def search_partitions_info(self): """ """ + # 1. Get all the partitions raw_partitions = {mode: [f for f in listdir(self.database_dir) if isfile(join(self.database_dir, f)) and f.endswith('.db') and f.__contains__(mode)] for mode in self.modes} - return {mode: sorted(raw_partitions[mode]) for mode in self.modes} - - def search_partitions_info(self): - """ + raw_partitions = {mode: [f.split('.')[0] for f in raw_partitions[mode]] for mode in self.modes} + self.json_content['partitions'] = {mode: sorted(raw_partitions[mode]) for mode in self.modes} - """ - - # TODO: fill json - - pass + # 2. Get the number of samples + for mode in self.modes: + for name in self.json_content['partitions'][mode]: + db = Database(database_dir=self.database_dir, + database_name=name).load() + self.json_content['nb_samples'][mode].append(db.nb_lines(table_name='Training')) + db.close() + + # 3. Get the Database architecture + self.json_content['architecture'] = self.get_database_architecture() + self.first_add = False + + # 4. Get the data shapes + self.json_content['data_shape'] = self.get_data_shapes() + + def get_database_architecture(self): + if len(self.json_content['partitions']['training']) != 0: + db = Database(database_dir=self.database_dir, + database_name=self.json_content['partitions']['training'][0]).load() + elif len(self.json_content['partitions']['validation']) != 0: + db = Database(database_dir=self.database_dir, + database_name=self.json_content['partitions']['validation'][0]).load() + else: + return {} + architecture = db.get_architecture() + if 'Prediction' in architecture.keys(): + del architecture['Prediction'] + for fields in architecture.values(): + for field in fields.copy(): + if field.split(' ')[0] in ['id', '_dt_']: + fields.remove(field) + db.close() + return architecture + + def get_data_shapes(self): + if len(self.json_content['partitions']['training']) != 0: + db = Database(database_dir=self.database_dir, + database_name=self.json_content['partitions']['training'][0]).load() + elif len(self.json_content['partitions']['validation']) != 0: + db = Database(database_dir=self.database_dir, + database_name=self.json_content['partitions']['validation'][0]).load() + else: + return {} + shapes = {} + for table_name, fields in self.json_content['architecture'].items(): + if db.nb_lines(table_name=table_name) > 0: + data = db.get_line(table_name=table_name) + for field in fields: + if 'NUMPY' in field: + field_name = field.split(' ')[0] + shapes[f'{table_name}.{field_name}'] = data[field_name].shape + db.close() + return shapes def remove_empty_partitions(self): @@ -290,8 +348,6 @@ def update_json(self, """ - database = self.partitions[self.mode][-1] - # Update partitions lists if update_partitions_lists: self.json_content['partitions'] = self.partition_names @@ -305,24 +361,11 @@ def update_json(self, # Update DB architecture if update_architecture: - architecture = database.get_architecture() - if 'Prediction' in architecture.keys(): - del architecture['Prediction'] - for fields in architecture.values(): - for field in fields.copy(): - if field.split(' ')[0] in ['id', '_dt_']: - fields.remove(field) - self.json_content['architecture'] = architecture + self.json_content['architecture'] = self.get_database_architecture() # Update data shapes if update_shapes: - for table_name, fields in self.json_content['architecture'].items(): - if database.nb_lines(table_name=table_name) > 0: - data = database.get_line(table_name=table_name) - for field in fields: - if 'NUMPY' in field: - field_name = field.split(' ')[0] - self.json_content['data_shape'][f'{table_name}.{field_name}'] = data[field_name].shape + self.json_content['data_shape'] = self.get_data_shapes() # Update normalization coefficients if update_normalization: @@ -364,7 +407,6 @@ def add_data(self, if self.max_file_size is not None: if self.partitions[self.mode][-1].memory_size > self.max_file_size: self.create_partition() - self.update_json(update_partitions_lists=True, update_nb_samples=True) # 3. Update sample counter self.current_sample = self.nb_samples + 1 diff --git a/src/Pipelines/BaseDataGeneration.py b/src/Pipelines/BaseDataGeneration.py index d18bc85c..7a612e44 100644 --- a/src/Pipelines/BaseDataGeneration.py +++ b/src/Pipelines/BaseDataGeneration.py @@ -46,7 +46,7 @@ def __init__(self, # Create a new session if required if not new_session: - new_session = not exists(session_dir) + new_session = not exists(join(session_dir, session_name)) if new_session: session_name = create_dir(session_dir=session_dir, session_name=session_name).split(sep)[-1] From 9fd83d991c47f22778255015dc5e92f7ea774282 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 26 Oct 2022 17:46:38 +0200 Subject: [PATCH 18/61] Adapt offline Training in remote mode for DB handlers. --- src/Database/DatabaseHandler.py | 122 ++++++++++++++++++------- src/Environment/BaseEnvironment.py | 22 ++--- src/Manager/DataManager.py | 46 ++++------ src/Manager/DatabaseManager.py | 134 ++++++++++++++++------------ src/Manager/EnvironmentManager.py | 5 +- src/Manager/NetworkManager.py | 35 +++----- src/Manager/StatsManager.py | 35 +++----- src/Pipelines/BaseDataGeneration.py | 4 +- src/Pipelines/BasePipeline.py | 1 + src/Pipelines/BaseTraining.py | 117 +++++++++++++++--------- 10 files changed, 299 insertions(+), 222 deletions(-) diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py index 32c984b5..bd362fde 100644 --- a/src/Database/DatabaseHandler.py +++ b/src/Database/DatabaseHandler.py @@ -1,5 +1,6 @@ from typing import Union, List, Dict, Any, Callable, Optional, Type, Tuple -from numpy import ndarray +from numpy import array, where +from itertools import chain from SSD.Core.Storage.Database import Database @@ -11,7 +12,8 @@ def __init__(self, on_init_handler: Optional[Callable] = None): self.remote = remote - self.__partitions: List[Database] = [] + self.__storing_partitions: List[Database] = [] + self.__exchange_db: Optional[Database] = None self.__on_init_handler = self.__default_handler if on_init_handler is None else on_init_handler def __default_handler(self): @@ -21,25 +23,36 @@ def __default_handler(self): # Manage partitions ### - def init(self, partitions: List[Database]) -> None: - self.__partitions = partitions.copy() + def init(self, + storing_partitions: List[Database], + exchange_db: Database) -> None: + self.__storing_partitions = storing_partitions.copy() + self.__exchange_db = exchange_db self.__on_init_handler() - def init_remote(self, partitions: List[List[str]]) -> None: - self.__partitions = [Database(database_dir=partition[0], - database_name=partition[1]).load() for partition in partitions] + def init_remote(self, + storing_partitions: List[List[str]], + exchange_db: List[str]) -> None: + self.__storing_partitions = [Database(database_dir=partition[0], + database_name=partition[1]).load() for partition in storing_partitions] + self.__exchange_db = Database(database_dir=exchange_db[0], + database_name=exchange_db[1]).load() self.__on_init_handler() def update_list_partitions(self, partition: Database) -> None: - self.__partitions.append(partition) + self.__storing_partitions.append(partition) def get_database_dir(self): - return self.__partitions[0].get_path()[0] + return self.__storing_partitions[0].get_path()[0] def load(self): - for db in self.__partitions: + for db in self.__storing_partitions: db.load() + self.__exchange_db.load() + + def get_partitions(self): + return self.__storing_partitions ### # Database Architecture @@ -48,43 +61,84 @@ def load(self): def create_fields(self, table_name: str, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - if len(self.__partitions) == 1: - self.__partitions[0].create_fields(table_name=table_name, - fields=fields) - def define_fields(self, - table_name: str, - fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - if len(self.__partitions) == 1: - self.__partitions[0].load() - if len(self.__partitions[0].get_fields(table_name=table_name)) == 2: - self.__partitions[0].create_fields(table_name=table_name, - fields=fields) + if table_name == 'Exchange': + self.__exchange_db.load() + if len(self.__exchange_db.get_fields(table_name=table_name)) == 1: + self.__exchange_db.create_fields(table_name=table_name, + fields=fields) + else: + if len(self.__storing_partitions) == 1: + self.__storing_partitions[0].load() + if len(self.__storing_partitions[0].get_fields(table_name=table_name)) <= 2: + self.__storing_partitions[0].create_fields(table_name=table_name, + fields=fields) def get_fields(self, table_name: str) -> List[str]: - return self.__partitions[0].get_fields(table_name=table_name) + + database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[0] + return database.get_fields(table_name=table_name) ### # Data Read / Write ### - def get_line(self, - table_name: str, - line_id: List[int]) -> Dict[str, ndarray]: - return self.__partitions[line_id[0]].get_line(table_name=table_name, - line_id=line_id[1]) - def add_data(self, table_name: str, data: Dict[str, Any]) -> int: - return self.__partitions[-1].add_data(table_name=table_name, - data=data) + + database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[-1] + return database.add_data(table_name=table_name, data=data) def update(self, table_name: str, data: Dict[str, Any], - line_id: int) -> None: - self.__partitions[-1].update(table_name=table_name, - data=data, - line_id=line_id) + line_id: Union[int, List[int]]) -> None: + + database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[line_id[0]] + line_id = line_id[1] if type(line_id) == list else line_id + database.update(table_name=table_name, data=data, line_id=line_id) + + def get_line(self, + table_name: str, + line_id: Union[int, List[int]], + fields: Optional[Union[str, List[str]]] = None) -> Dict[str, Any]: + + database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[line_id[0]] + line_id = line_id[1] if type(line_id) == list else line_id + return database.get_line(table_name=table_name, + line_id=line_id, + fields=fields) + + def get_lines(self, + table_name: str, + lines_id: List[List[int]], + fields: Optional[Union[str, List[str]]] = None) -> Dict[str, Any]: + + # Transform list of lines to batch of lines per partition + batch_indices = array(lines_id) + partition_batch_indices = [] + for i in range(len(self.__storing_partitions)): + partition_indices = where(batch_indices[:, 0] == i)[0] + if len(partition_indices) > 0: + partition_batch_indices.append([i, batch_indices[partition_indices, 1].tolist()]) + + # Get lines of data + partition_batches = [] + for partition_indices in partition_batch_indices: + data = self.__storing_partitions[partition_indices[0]].get_lines(table_name=table_name, + lines_id=partition_indices[1], + fields=fields, + batched=True) + del data['id'] + partition_batches.append(data) + + # Merge batches + if len(partition_batches) == 1: + return partition_batches[0] + else: + return dict(zip(partition_batches[0].keys(), + [list(chain.from_iterable([partition_batches[i][key] + for i in range(len(partition_batches))])) + for key in partition_batches[0].keys()])) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index e7f6c3aa..2c23b512 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -183,11 +183,11 @@ def load_parameters(self) -> Dict[str, Any]: return {} def define_training_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - self.__database_handler.define_fields(table_name='Training', + self.__database_handler.create_fields(table_name='Training', fields=fields) def define_additional_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: - self.__database_handler.define_fields(table_name='Additional', + self.__database_handler.create_fields(table_name='Additional', fields=fields) def set_training_data(self, **kwargs) -> None: @@ -230,21 +230,21 @@ def _send_training_data(self) -> int: return line_id def _reset_training_data(self) -> None: - self.__training_data = {} - self.__additional_data = {} + self.__data_training = {} + self.__data_additional = {} self.sample_training = None self.sample_additional = None self.update_line = None def _update_training_data(self, - line_id: int) -> None: - if self.__training_data != {}: + line_id: List[int]) -> None: + if self.__data_training != {}: self.__database_handler.update(table_name='Training', - data=self.__training_data, + data=self.__data_training, line_id=line_id) - if self.__additional_data != {}: + if self.__data_additional != {}: self.__database_handler.update(table_name='Additional', - data=self.__additional_data, + data=self.__data_additional, line_id=line_id) def _get_training_data(self, @@ -302,9 +302,9 @@ def _get_prediction(self): """ - training_data = self.__training_data.copy() + training_data = self.__data_training.copy() required_fields = self.__database_handler.get_fields(table_name='Prediction') - for field in self.__training_data.keys(): + for field in self.__data_training.keys(): if field not in required_fields: del training_data[field] self.apply_prediction(self.get_prediction(**training_data)) diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index e24ed1ff..edae03a1 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -1,5 +1,4 @@ from typing import Any, Optional, Dict, List -from numpy import ndarray from DeepPhysX.Core.Manager.DatabaseManager import DatabaseManager, Database from DeepPhysX.Core.Manager.EnvironmentManager import EnvironmentManager @@ -10,12 +9,11 @@ class DataManager: def __init__(self, + pipeline: Any, database_config: Optional[BaseDatabaseConfig] = None, environment_config: Optional[BaseEnvironmentConfig] = None, - manager: Optional[Any] = None, session: str = 'sessions/default', new_session: bool = True, - pipeline: str = '', produce_data: bool = True, batch_size: int = 1): @@ -24,30 +22,29 @@ def __init__(self, A batch is given with a call to 'get_data' on either the DatabaseManager or the EnvironmentManager according to the context. + :param pipeline: Pipeline that handle the DataManager. :param database_config: Specialisation containing the parameters of the dataset manager. :param environment_config: Specialisation containing the parameters of the environment manager. - :param manager: Manager that handle the DataManager :param session: Path to the session directory. - :param new_session: Flag that indicates whether if the session is new - :param pipeline: Flag that indicates whether if this session is training a Network. + :param new_session: Flag that indicates whether if the session is new. :param produce_data: Flag that indicates whether if this session is producing data. - :param int batch_size: Number of samples in a batch + :param int batch_size: Number of samples in a batch. """ self.name: str = self.__class__.__name__ # Managers variables - self.manager: Optional[Any] = manager + self.pipeline: Optional[Any] = pipeline self.database_manager: Optional[DatabaseManager] = None self.environment_manager: Optional[EnvironmentManager] = None self.connected_managers: List[Any] = [] # Create a DatabaseManager self.database_manager = DatabaseManager(database_config=database_config, - session=session, data_manager=self, + pipeline=pipeline.type, + session=session, new_session=new_session, - pipeline=pipeline, produce_data=produce_data) # Create an EnvironmentManager if required @@ -58,19 +55,9 @@ def __init__(self, batch_size=batch_size) # DataManager variables - self.pipeline = pipeline self.produce_data = produce_data self.batch_size = batch_size - self.data_lines: List[int] = [] - - def get_manager(self) -> Any: - """ - Return the Manager of this DataManager. - - :return: The Manager of this DataManager. - """ - - return self.manager + self.data_lines: List[List[int]] = [] def connect_handler(self, handler): self.database_manager.connect_handler(handler) @@ -104,16 +91,16 @@ def get_data(self, """ # Data generation case - if self.pipeline == 'data_generation': + if self.pipeline.type == 'data_generation': self.environment_manager.get_data(animate=animate) self.database_manager.add_data() # Training case - elif self.pipeline == 'training': + elif self.pipeline.type == 'training': # Get data from Environment(s) if used and if the data should be created at this epoch - if self.environment_manager is not None and (epoch == 0 or not self.environment_manager.only_first_epoch) \ - and self.produce_data: + if self.environment_manager is not None and self.produce_data and \ + (epoch == 0 or not self.environment_manager.only_first_epoch): self.data_lines = self.environment_manager.get_data(animate=animate) self.database_manager.add_data(self.data_lines) @@ -121,7 +108,8 @@ def get_data(self, else: self.data_lines = self.database_manager.get_data(batch_size=self.batch_size) # Dispatch a batch to clients - if self.environment_manager is not None and (epoch == 0 or self.environment_manager.load_samples): + if self.environment_manager is not None and self.environment_manager.load_samples and \ + (epoch == 0 or not self.environment_manager.only_first_epoch): self.environment_manager.dispatch_batch(data_lines=self.data_lines, animate=animate) # Environment is no longer used @@ -154,10 +142,10 @@ def get_prediction(self, """ # Get a prediction - if self.manager is None: + if self.pipeline is None: raise ValueError("Cannot request prediction if Manager (and then NetworkManager) does not exist.") - self.manager.network_manager.compute_online_prediction(instance_id=instance_id, - normalization=self.normalization) + self.pipeline.network_manager.compute_online_prediction(instance_id=instance_id, + normalization=self.normalization) @property def normalization(self) -> Dict[str, List[float]]: diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 14b18bb4..b31a4805 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -3,7 +3,7 @@ from os import listdir, symlink, sep, remove, rename from json import dump as json_dump from json import load as json_load -from numpy import arange, ndarray, array, abs, mean, sqrt +from numpy import arange, ndarray, array, abs, mean, sqrt, empty, concatenate from numpy.random import shuffle from SSD.Core.Storage.Database import Database @@ -18,10 +18,10 @@ class DatabaseManager: def __init__(self, database_config: Optional[BaseDatabaseConfig] = None, - session: str = 'sessions/default', data_manager: Optional[Any] = None, - new_session: bool = True, pipeline: str = '', + session: str = 'sessions/default', + new_session: bool = True, produce_data: bool = True): """ DatabaseManager handle all operations with input / output files. Allows saving and read tensors from files. @@ -68,8 +68,8 @@ def __init__(self, self.partitions: Dict[str, List[Database]] = {mode: [] for mode in self.modes} # Dataset indexing - self.shuffle_pattern: ndarray = arange(0) - self.current_sample: int = 1 + self.sample_indices: ndarray = empty((0, 2), dtype=int) + self.sample_id: int = 0 self.first_add = True # Dataset json file @@ -82,6 +82,7 @@ def __init__(self, # DataGeneration case if self.pipeline == 'data_generation': + # Generate data in a new session if new_session: # Generate data from scratch --> create a new directory @@ -138,13 +139,14 @@ def __init__(self, else: self.load_directory(load_partitions=False) - def connect_handler(self, - handler: DatabaseHandler) -> None: - if handler.remote: - handler.init_remote(self.get_partition_names()) - else: - handler.init(self.get_partition_objects()) - self.database_handlers.append(handler) + # Finally create an exchange database + self.exchange = Database(database_dir=self.database_dir, + database_name='Exchange').new() + self.exchange.create_table(table_name='Exchange') + + ######################### + # Partitions Management # + ######################### def load_directory(self, load_partitions: bool = True, @@ -191,7 +193,9 @@ def load_directory(self, db = Database(database_dir=self.database_dir, database_name=name).load() self.partitions[mode].append(db) - if len(self.partitions[self.mode]) == 0 or self.partitions[self.mode][-1].memory_size > self.max_file_size: + if len(self.partitions[self.mode]) == 0: + self.create_partition() + elif self.max_file_size is not None and self.partitions[self.mode][-1].memory_size > self.max_file_size: self.create_partition() else: @@ -200,9 +204,8 @@ def load_directory(self, self.database.create_table(table_name='Training') self.database.create_table(table_name='Additional') - # 6. Shuffle Database indices - if self.shuffle and load_partitions: - self.shuffle_samples() + # 6. Index partitions + self.index_samples() def create_partition(self): """ @@ -248,17 +251,21 @@ def get_partition_objects(self) -> List[Database]: def get_partition_names(self) -> List[List[str]]: return [db.get_path() for db in self.partitions[self.mode]] - def load_next_partition(self): + def remove_empty_partitions(self): - # 1. Define next partition - self.partition_index[self.mode] = (self.partition_index[self.mode] + 1) % len(self.partitions[self.mode]) - self.current_partition = self.partitions[self.mode][self.partition_index[self.mode]] - self.database = Database(database_dir=self.database_dir, - database_name=self.current_partition).load() + for mode in self.modes: + if len(self.partitions[mode]) > 0 and self.partitions[mode][-1].nb_lines(table_name='Training') == 0: + database = self.partitions[mode].pop(-1) + path = database.get_path() + remove(join(path[0], f'{path[1]}.db')) + self.partition_names[mode].pop(-1) + self.partition_index[mode] -= 1 + self.json_content['nb_samples'][mode].pop(-1) + self.update_json(update_partitions_lists=True) - # 2. Shuffle - if self.shuffle: - self.shuffle_samples() + ################## + # JSON info file # + ################## def search_partitions_info(self): """ @@ -325,18 +332,6 @@ def get_data_shapes(self): db.close() return shapes - def remove_empty_partitions(self): - - for mode in self.modes: - if len(self.partitions[mode]) > 0 and self.partitions[mode][-1].nb_lines(table_name='Training') == 0: - database = self.partitions[mode].pop(-1) - path = database.get_path() - remove(join(path[0], f'{path[1]}.db')) - self.partition_names[mode].pop(-1) - self.partition_index[mode] -= 1 - self.json_content['nb_samples'][mode].pop(-1) - self.update_json(update_partitions_lists=True) - def update_json(self, update_partitions_lists: bool = False, update_nb_samples: bool = False, @@ -375,18 +370,34 @@ def update_json(self, with open(join(self.database_dir, 'dataset.json'), 'w') as json_file: json_dump(self.json_content, json_file, indent=3, cls=CustomJSONEncoder) - @property - def nb_samples(self) -> int: - return self.partitions[self.mode][-1].nb_lines(table_name='Training') + ######################### + # Database read / write # + ######################### - def shuffle_samples(self): - """ + def connect_handler(self, + handler: DatabaseHandler) -> None: + if handler.remote: + handler.init_remote(storing_partitions=self.get_partition_names(), + exchange_db=self.exchange.get_path()) + else: + handler.init(storing_partitions=self.get_partition_objects(), + exchange_db=self.exchange) + self.database_handlers.append(handler) - """ + def index_samples(self): - self.shuffle_pattern = arange(1, self.nb_samples + 1) + for i, nb_sample in enumerate(self.json_content['nb_samples'][self.mode]): + partition_indices = empty((nb_sample, 2), dtype=int) + partition_indices[:, 0] = i + partition_indices[:, 1] = arange(1, nb_sample + 1) + self.sample_indices = concatenate((self.sample_indices, partition_indices)) + self.sample_id = 0 if self.shuffle: - shuffle(self.shuffle_pattern) + shuffle(self.sample_indices) + + @property + def nb_samples(self) -> int: + return self.partitions[self.mode][-1].nb_lines(table_name='Training') def add_data(self, data_lines: Optional[List[int]] = None): @@ -408,28 +419,31 @@ def add_data(self, if self.partitions[self.mode][-1].memory_size > self.max_file_size: self.create_partition() - # 3. Update sample counter - self.current_sample = self.nb_samples + 1 - def get_data(self, - batch_size: int) -> List[int]: + batch_size: int) -> List[List[int]]: """ """ # 1. Check if dataset is loaded and if the current sample is not the last - if self.current_sample > self.nb_samples: - self.load_next_partition() - self.current_sample = 1 + if self.sample_id >= len(self.sample_indices): + self.index_samples() # 2. Update dataset index - idx = self.current_sample - self.current_sample += batch_size + idx = self.sample_id + self.sample_id += batch_size # 3. Get a batch of data - if self.shuffle: - return list(self.shuffle_pattern[idx:self.current_sample]) - return list(arange(idx, self.current_sample)) + lines = self.sample_indices[idx:self.sample_id].tolist() + + # 4. Ensure the batch has th good size + if len(lines) < batch_size: + lines += self.get_data(batch_size=batch_size - len(lines)) + return lines + + ############ + # Behavior # + ############ def close(self): """ @@ -459,6 +473,10 @@ def change_mode(self, mode: int) -> None: pass + ################# + # Normalization # + ################# + def compute_normalization(self) -> Dict[str, List[float]]: """ @@ -543,7 +561,7 @@ def update_normalization(self, @property def normalization(self) -> Dict[str, List[float]]: - return None if self.json_content['normalization'] == {} else self.json_content['normalization'] + return None if self.json_content['normalization'] == {} or not self.normalize else self.json_content['normalization'] def load_partitions_fields(self, partition: Database, diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 2d170320..21cd4c7d 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -35,7 +35,7 @@ def __init__(self, self.load_samples: bool = environment_config.load_samples self.simulations_per_step: int = environment_config.simulations_per_step self.max_wrong_samples_per_step: int = environment_config.max_wrong_samples_per_step - self.dataset_batch: Optional[List[int]] = None + self.dataset_batch: Optional[List[List[int]]] = None # Create the Visualizer self.visualizer: Optional[VedoVisualizer] = None @@ -53,7 +53,8 @@ def __init__(self, if environment_config.as_tcp_ip_client: self.server = environment_config.create_server(environment_manager=self, batch_size=batch_size, - visualization_db=None if visualization_db is None else visualization_db.get_path()) + visualization_db=None if visualization_db is None else + visualization_db.get_path()) self.data_manager.get_database().load() else: self.environment = environment_config.create_environment(visualization_db=visualization_db) diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index c9e04322..a75172c7 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -3,8 +3,7 @@ from os.path import join, isdir, isfile from numpy import ndarray, array -from SSD.Core.Storage.Database import Database - +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Utils.path import copy_dir, create_dir @@ -13,29 +12,23 @@ class NetworkManager: def __init__(self, network_config: BaseNetworkConfig, - manager: Optional[Any] = None, - session: str = 'sessions/default', - new_session: bool = True, pipeline: str = '', - data_db: Optional[Database] = None): + session: str = 'sessions/default', + new_session: bool = True): """ Deals with all the interactions with the neural network: predictions, saves, initialisation, loading, back-propagation, etc. :param network_config: Specialisation containing the parameters of the network manager. - :param manager: Manager that handle the network manager. + :param pipeline: Type of the Pipeline. :param session: Path to the session directory. :param new_session: Define the creation of new directories to store data. - :param is_training: If True prediction will cause tensors gradient creation. """ self.name: str = self.__class__.__name__ - # Managers architecture - self.manager: Any = manager - # Storage variables - self.data_db: Database = data_db + self.database_handler: DatabaseHandler = DatabaseHandler() self.batch: Optional[Any] = None self.session: str = session self.new_session: bool = new_session @@ -77,16 +70,16 @@ def __init__(self, self.network_dir = join(session, 'network/') self.load_network(which_network=network_config.which_network) - def change_database(self, database): - self.data_db = database + def get_database_handler(self): + return self.database_handler def link_clients(self, nb_clients: Optional[int] = None): if nb_clients is not None: fields = [(field_name, ndarray) for field_name in self.network.net_fields + self.network.pred_fields] - self.data_db.create_table(table_name='Prediction', fields=fields) + self.database_handler.create_fields(table_name='Exchange', fields=fields) for _ in range(nb_clients): - self.data_db.add_data(table_name='Prediction', data={}) + self.database_handler.add_data(table_name='Exchange', data={}) def load_network(self, which_network: int = -1) -> None: @@ -115,7 +108,7 @@ def load_network(self, def compute_prediction_and_loss(self, optimize: bool, - data_lines: List[int], + data_lines: List[List[int]], normalization: Optional[Dict[str, List[float]]] = None) -> Tuple[ndarray, Dict[str, float]]: """ Make a prediction with the data passed as argument, optimize or not the network @@ -132,11 +125,9 @@ def compute_prediction_and_loss(self, for side, fields in zip(['net', 'opt'], [self.network.net_fields, self.network.opt_fields]): # Get the batch from the Database - batch = self.data_db.get_lines(table_name='Training', - fields=fields, - lines_id=data_lines, - batched=True) - del batch['id'] + batch = self.database_handler.get_lines(table_name='Training', + fields=fields, + lines_id=data_lines) # Apply normalization and convert to tensor for field in batch.keys(): diff --git a/src/Manager/StatsManager.py b/src/Manager/StatsManager.py index ee3c9580..c119c89c 100644 --- a/src/Manager/StatsManager.py +++ b/src/Manager/StatsManager.py @@ -17,33 +17,29 @@ def generate_default_material(): class StatsManager: - """ - | Record all given values using the tensorboard framework. Open a tab in the navigator to inspect these values - during the training. - - :param str log_dir: Path of the created directory - :param Manager manager: Manager that handles the StatsManager - :param bool keep_losses: If True Allow saving loss to .csv file - """ def __init__(self, session: str, - manager: Any = None, keep_losses: bool = False): + """ + Record all given values using the tensorboard framework. Open a tab in the navigator to inspect these values + during the training. + + :param str log_dir: Path of the created directory + :param bool keep_losses: If True Allow saving loss to .csv file + """ self.name: str = self.__class__.__name__ # Init writer - self.manager = manager self.log_dir: str = join(session, 'stats/') self.writer: SummaryWriter = SummaryWriter(self.log_dir) # Open Tensorboard - if not self.manager.debug_session: - tb = program.TensorBoard() - tb.configure(argv=[None, '--logdir', self.log_dir]) - url = tb.launch() - w_open(url) + tb = program.TensorBoard() + tb.configure(argv=[None, '--logdir', self.log_dir]) + url = tb.launch() + w_open(url) # Values self.mean: ndarray = full(4, inf) # Contains in the 1st dimension the mean, and 2nd the variance of the mean @@ -51,15 +47,6 @@ def __init__(self, self.keep_losses: bool = keep_losses self.tag_dict: Dict[str, int] = {} - def get_manager(self) -> Any: - """ - | Return the Manager of the StatsManager. - - :return: Manager that handles the StatsManager - """ - - return self.manager - def add_train_batch_loss(self, value: float, count: int) -> None: """ | Add batch loss to tensorboard framework. Also compute mean and variance. diff --git a/src/Pipelines/BaseDataGeneration.py b/src/Pipelines/BaseDataGeneration.py index 7a612e44..25342895 100644 --- a/src/Pipelines/BaseDataGeneration.py +++ b/src/Pipelines/BaseDataGeneration.py @@ -52,11 +52,11 @@ def __init__(self, session_name=session_name).split(sep)[-1] # Create a DataManager - self.data_manager = DataManager(database_config=database_config, + self.data_manager = DataManager(pipeline=self, + database_config=database_config, environment_config=environment_config, session=join(session_dir, session_name), new_session=new_session, - pipeline='data_generation', produce_data=True, batch_size=batch_size) diff --git a/src/Pipelines/BasePipeline.py b/src/Pipelines/BasePipeline.py index f62637b0..9f61ef1f 100644 --- a/src/Pipelines/BasePipeline.py +++ b/src/Pipelines/BasePipeline.py @@ -62,6 +62,7 @@ def __init__(self, self.session_dir = session_dir self.session_name = session_name self.new_session = new_session + self.type = pipeline # Main manager self.manager: Optional[Manager] = None diff --git a/src/Pipelines/BaseTraining.py b/src/Pipelines/BaseTraining.py index 521bdad4..03f84a85 100644 --- a/src/Pipelines/BaseTraining.py +++ b/src/Pipelines/BaseTraining.py @@ -1,14 +1,17 @@ from typing import Optional from sys import stdout -from os.path import join, isfile +from os.path import join, isfile, exists, sep from datetime import datetime from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline -from DeepPhysX.Core.Manager.Manager import Manager +from DeepPhysX.Core.Manager.DataManager import DataManager +from DeepPhysX.Core.Manager.NetworkManager import NetworkManager +from DeepPhysX.Core.Manager.StatsManager import StatsManager from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Utils.progressbar import Progressbar +from DeepPhysX.Core.Utils.path import get_first_caller, create_dir class BaseTraining(BasePipeline): @@ -19,11 +22,11 @@ def __init__(self, environment_config: Optional[BaseEnvironmentConfig] = None, session_dir: str = 'sessions', session_name: str = 'training', + new_session: bool = True, epoch_nb: int = 0, batch_nb: int = 0, batch_size: int = 0, - new_session: bool = True, - debug_session: bool = False): + debug: bool = False): """ BaseTraining implements the main loop that defines the training process of an artificial neural network. Training can be launched with several data sources (from a Dataset, from an Environment, from combined sources). @@ -34,11 +37,11 @@ def __init__(self, :param environment_config: Specialisation containing the parameters of the environment manager. :param session_dir: Relative path to the directory which contains sessions directories. :param session_name: Name of the new the session directory. + :param new_session: Define the creation of new directories to store data. :param epoch_nb: Number of epochs to run. :param batch_nb: Number of batches to use. :param batch_size: Number of samples in a single batch. - :param new_session: Define the creation of new directories to store data. - :param debug_session: If True, main training features will not be launched. + :param debug: If True, main training features will not be launched. """ BasePipeline.__init__(self, @@ -47,8 +50,47 @@ def __init__(self, environment_config=environment_config, session_dir=session_dir, session_name=session_name, + new_session=new_session, pipeline='training') + # Define the session repository + root = get_first_caller() + session_dir = join(root, session_dir) + + # Create a new session if required + if not new_session: + new_session = not exists(join(session_dir, session_name)) + if new_session: + session_name = create_dir(session_dir=session_dir, + session_name=session_name).split(sep)[-1] + self.session = join(session_dir, session_name) + + # Configure 'produce_data' flag + if environment_config is None and database_config.existing_dir is None: + raise ValueError(f"[{self.name}] No data source provided.") + produce_data = database_config.existing_dir is None + + # Create a DataManager + self.data_manager = DataManager(pipeline=self, + database_config=database_config, + environment_config=environment_config, + session=self.session, + new_session=new_session, + produce_data=produce_data, + batch_size=batch_size) + self.batch_size = batch_size + + # Create a NetworkMmanager + self.network_manager = NetworkManager(network_config=network_config, + pipeline=self.type, + session=self.session, + new_session=new_session) + self.data_manager.connect_handler(self.network_manager.get_database_handler()) + self.network_manager.link_clients(self.data_manager.nb_environment) + + # Create a StatsManager + self.stats_manager = StatsManager(session=self.session) if not debug else None + # Training variables self.epoch_nb = epoch_nb self.epoch_id = 0 @@ -57,12 +99,7 @@ def __init__(self, self.batch_id = 0 self.nb_samples = batch_nb * batch_size * epoch_nb self.loss_dict = None - self.debug = debug_session - - # Configure 'produce_data' flag - if environment_config is None and database_config.existing_dir is None: - raise ValueError(f"[{self.name}] No data source provided.") - produce_data = database_config.existing_dir is None + self.debug = debug # Progressbar if not self.debug: @@ -74,17 +111,7 @@ def __init__(self, self.progress_bar = Progressbar(start=0, stop=self.batch_nb * self.epoch_nb, c='orange', title=f'Epoch n°{epoch_id}/{epoch_nb} - Batch n°{batch_id}/{batch_nb}') - self.manager = Manager(network_config=self.network_config, - database_config=self.database_config, - environment_config=self.environment_config, - session_dir=session_dir, - session_name=session_name, - new_session=new_session, - pipeline='training', - produce_data=produce_data, - batch_size=batch_size, - debug_session=debug_session) - self.save_info_file(self.manager.session) + self.save_info_file() def execute(self) -> None: """ @@ -152,8 +179,11 @@ def optimize(self) -> None: Pulls data, run a prediction and an optimizer step. """ - self.manager.get_data(self.epoch_id) - _, self.loss_dict = self.manager.optimize_network() + self.data_manager.get_data(epoch=self.epoch_id, + animate=True) + _, self.loss_dict = self.network_manager.compute_prediction_and_loss(data_lines=self.data_manager.data_lines, + normalization=self.data_manager.normalization, + optimize=True) def batch_count(self) -> None: """ @@ -167,13 +197,14 @@ def batch_end(self) -> None: Called one at the end of a batch production. """ - self.manager.stats_manager.add_train_batch_loss(self.loss_dict['loss'], - self.epoch_id * self.batch_nb + self.batch_id) - for key in self.loss_dict.keys(): - if key != 'loss': - self.manager.stats_manager.add_custom_scalar(tag=key, - value=self.loss_dict[key], - count=self.epoch_id * self.batch_nb + self.batch_id) + if self.stats_manager is not None: + self.stats_manager.add_train_batch_loss(self.loss_dict['loss'], + self.epoch_id * self.batch_nb + self.batch_id) + for key in self.loss_dict.keys(): + if key != 'loss': + self.stats_manager.add_custom_scalar(tag=key, + value=self.loss_dict[key], + count=self.epoch_id * self.batch_nb + self.batch_id) def epoch_count(self) -> None: """ @@ -187,29 +218,32 @@ def epoch_end(self) -> None: Called one at the end of each epoch. """ - self.manager.stats_manager.add_train_epoch_loss(self.loss_dict['loss'], self.epoch_id) + if self.stats_manager is not None: + self.stats_manager.add_train_epoch_loss(self.loss_dict['loss'], self.epoch_id) def save_network(self) -> None: """ Store the network parameters in the corresponding directory. """ - self.manager.save_network() + self.network_manager.save_network() def train_end(self) -> None: """ Called once at the end of the training pipeline. """ - self.manager.close() + self.data_manager.close() + self.network_manager.close() + if self.stats_manager is not None: + self.stats_manager.close() - def save_info_file(self, - directory: str) -> None: + def save_info_file(self) -> None: """ Save a .txt file that provides a template for user notes and the description of all the components. """ - filename = join(directory, 'info.txt') + filename = join(self.session, 'info.txt') date_time = datetime.now().strftime('%d/%m/%Y %H:%M:%S') if not isfile(filename): f = open(filename, "w+") @@ -220,14 +254,17 @@ def save_info_file(self, # Listing every component descriptions f.write("## List of Components Parameters ##\n") f.write(str(self)) - f.write(str(self.manager)) + f.write(str(self.network_manager)) + f.write(str(self.data_manager)) + if self.stats_manager is not None: + f.write(str(self.stats_manager)) f.close() def __str__(self) -> str: description = "\n" description += f"# {self.__class__.__name__}\n" - description += f" Session directory: {self.manager.session}\n" + description += f" Session directory: {self.session}\n" description += f" Number of epochs: {self.epoch_nb}\n" description += f" Number of batches per epoch: {self.batch_nb}\n" description += f" Number of samples per batch: {self.batch_size}\n" From 19d9d15d6e2cbd460c0fd1359200a7aed55cd826 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 10:54:21 +0200 Subject: [PATCH 19/61] Adapt online Training pipeline in local mode for DB Handlers. --- src/Database/DatabaseHandler.py | 9 ++++++--- src/Environment/BaseEnvironmentConfig.py | 4 ++-- src/Manager/DataManager.py | 19 ++++++++++--------- src/Manager/DatabaseManager.py | 1 + src/Manager/EnvironmentManager.py | 3 ++- 5 files changed, 21 insertions(+), 15 deletions(-) diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py index bd362fde..7eae31f8 100644 --- a/src/Database/DatabaseHandler.py +++ b/src/Database/DatabaseHandler.py @@ -86,10 +86,13 @@ def get_fields(self, def add_data(self, table_name: str, - data: Dict[str, Any]) -> int: + data: Dict[str, Any]) -> Union[int, List[int]]: - database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[-1] - return database.add_data(table_name=table_name, data=data) + if table_name == 'Exchange': + return self.__exchange_db.add_data(table_name=table_name, data=data) + else: + return [len(self.__storing_partitions) - 1, + self.__storing_partitions[-1].add_data(table_name=table_name, data=data)] def update(self, table_name: str, diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index 8447a36e..825583c8 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -5,8 +5,6 @@ from subprocess import run from sys import modules, executable -from SSD.Core.Storage.Database import Database - from DeepPhysX.Core.AsyncSocket.TcpIpServer import TcpIpServer from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer @@ -24,6 +22,7 @@ def __init__(self, max_wrong_samples_per_step: int = 10, load_samples: bool = False, only_first_epoch: bool = True, + always_produce: bool = False, visualizer: Optional[Type[VedoVisualizer]] = None, record_wrong_samples: bool = False): """ @@ -84,6 +83,7 @@ def __init__(self, self.max_wrong_samples_per_step: int = max_wrong_samples_per_step self.load_samples: bool = load_samples self.only_first_epoch: bool = only_first_epoch + self.always_produce: bool = always_produce # Visualizer variables self.visualizer: Optional[Type[VedoVisualizer]] = visualizer diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index edae03a1..0cb80f65 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -100,7 +100,7 @@ def get_data(self, # Get data from Environment(s) if used and if the data should be created at this epoch if self.environment_manager is not None and self.produce_data and \ - (epoch == 0 or not self.environment_manager.only_first_epoch): + (epoch == 0 or self.environment_manager.always_produce): self.data_lines = self.environment_manager.get_data(animate=animate) self.database_manager.add_data(self.data_lines) @@ -108,14 +108,15 @@ def get_data(self, else: self.data_lines = self.database_manager.get_data(batch_size=self.batch_size) # Dispatch a batch to clients - if self.environment_manager is not None and self.environment_manager.load_samples and \ - (epoch == 0 or not self.environment_manager.only_first_epoch): - self.environment_manager.dispatch_batch(data_lines=self.data_lines, - animate=animate) - # Environment is no longer used - elif self.environment_manager is not None: - self.environment_manager.close() - self.environment_manager = None + if self.environment_manager is not None: + if self.environment_manager.load_samples and \ + (epoch == 0 or not self.environment_manager.only_first_epoch): + self.environment_manager.dispatch_batch(data_lines=self.data_lines, + animate=animate) + # Environment is no longer used + else: + self.environment_manager.close() + self.environment_manager = None # Prediction pipeline else: diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index b31a4805..32e80dc8 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -460,6 +460,7 @@ def close(self): for mode in self.modes: for database in self.partitions[mode]: database.close() + self.exchange.close(erase_file=True) # Remove prediction pipeline DB if self.pipeline == 'prediction' and not self.produce_data: diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 21cd4c7d..7241ae1f 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -33,6 +33,7 @@ def __init__(self, self.batch_size: int = batch_size self.only_first_epoch: bool = environment_config.only_first_epoch self.load_samples: bool = environment_config.load_samples + self.always_produce: bool = environment_config.always_produce self.simulations_per_step: int = environment_config.simulations_per_step self.max_wrong_samples_per_step: int = environment_config.max_wrong_samples_per_step self.dataset_batch: Optional[List[List[int]]] = None @@ -105,7 +106,7 @@ def get_data_from_server(self, def get_data_from_environment(self, animate: bool = True, - request_prediction: bool = False) -> List[int]: + request_prediction: bool = False) -> List[List[int]]: """ Compute a batch of data directly from Environment. From 8db542477f306f80fc89c9720baf23e932c6f653 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 12:01:34 +0200 Subject: [PATCH 20/61] Adapt Prediction pipeline in local mode for DB Handlers. --- src/Environment/BaseEnvironment.py | 10 ++--- src/Manager/DataManager.py | 14 ++++--- src/Manager/DatabaseManager.py | 39 +++++------------- src/Manager/EnvironmentManager.py | 22 ++++++---- src/Manager/NetworkManager.py | 13 +++--- src/Pipelines/BasePrediction.py | 66 +++++++++++++++++++----------- src/Pipelines/BaseTraining.py | 10 +---- 7 files changed, 86 insertions(+), 88 deletions(-) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 2c23b512..1bc31289 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -274,7 +274,7 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: if self.instance_id != 0: self.__database_handler.load() self.__first_add[1] = False - required_fields = list(set(self.__database_handler.get_fields(table_name='Prediction')) - {'id'}) + required_fields = list(set(self.__database_handler.get_fields(table_name='Exchange')) - {'id'}) for field in kwargs.keys(): if field not in required_fields: raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." @@ -288,12 +288,12 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: if self.environment_manager.data_manager is None: raise ValueError("Cannot request prediction if DataManager does not exist") # Get a prediction - self.__database_handler.update(table_name='Prediction', + self.__database_handler.update(table_name='Exchange', data=kwargs, line_id=self.instance_id) self.environment_manager.data_manager.get_prediction(self.instance_id) - data_pred = self.__database_handler.get_line(table_name='Prediction', - line_id=[0, self.instance_id]) + data_pred = self.__database_handler.get_line(table_name='Exchange', + line_id=self.instance_id) del data_pred['id'] return data_pred @@ -303,7 +303,7 @@ def _get_prediction(self): """ training_data = self.__data_training.copy() - required_fields = self.__database_handler.get_fields(table_name='Prediction') + required_fields = self.__database_handler.get_fields(table_name='Exchange') for field in self.__data_training.keys(): if field not in required_fields: del training_data[field] diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 0cb80f65..be29997a 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -51,6 +51,7 @@ def __init__(self, if environment_config is not None: self.environment_manager = EnvironmentManager(environment_config=environment_config, data_manager=self, + pipeline=pipeline.type, session=session, batch_size=batch_size) @@ -80,8 +81,7 @@ def nb_environment(self): def get_data(self, epoch: int = 0, - animate: bool = True, - request_prediction: bool = False) -> None: + animate: bool = True) -> None: """ Fetch data from the EnvironmentManager or the DatabaseManager according to the context. @@ -122,16 +122,18 @@ def get_data(self, else: # Get data from Dataset - if self.produce_data: + if self.environment_manager.load_samples: self.data_lines = self.database_manager.get_data(batch_size=1) self.environment_manager.dispatch_batch(data_lines=self.data_lines, animate=animate, - request_prediction=True) + request_prediction=True, + save_data=self.produce_data) # Get data from Environment else: self.data_lines = self.environment_manager.get_data(animate=animate, - request_prediction=True) - if self.database_manager is not None: + request_prediction=True, + save_data=self.produce_data) + if self.produce_data: self.database_manager.add_data(self.data_lines) def get_prediction(self, diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 32e80dc8..d0e3a8d6 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -131,13 +131,7 @@ def __init__(self, # Prediction case else: - - # Generate data - if produce_data: - self.load_directory() - - else: - self.load_directory(load_partitions=False) + self.load_directory() # Finally create an exchange database self.exchange = Database(database_dir=self.database_dir, @@ -149,7 +143,6 @@ def __init__(self, ######################### def load_directory(self, - load_partitions: bool = True, rename_partitions: bool = False): """ @@ -187,22 +180,15 @@ def load_directory(self, dst=join(self.database_dir, f'{self.partition_names[mode][i]}.db')) # 5. Load the partitions - if load_partitions: - for mode in self.modes: - for name in self.partition_names[mode]: - db = Database(database_dir=self.database_dir, - database_name=name).load() - self.partitions[mode].append(db) - if len(self.partitions[self.mode]) == 0: - self.create_partition() - elif self.max_file_size is not None and self.partitions[self.mode][-1].memory_size > self.max_file_size: - self.create_partition() - - else: - self.database = Database(database_dir=self.database_dir, - database_name='temp').new() - self.database.create_table(table_name='Training') - self.database.create_table(table_name='Additional') + for mode in self.modes: + for name in self.partition_names[mode]: + db = Database(database_dir=self.database_dir, + database_name=name).load() + self.partitions[mode].append(db) + if len(self.partitions[self.mode]) == 0: + self.create_partition() + elif self.max_file_size is not None and self.partitions[self.mode][-1].memory_size > self.max_file_size: + self.create_partition() # 6. Index partitions self.index_samples() @@ -462,11 +448,6 @@ def close(self): database.close() self.exchange.close(erase_file=True) - # Remove prediction pipeline DB - if self.pipeline == 'prediction' and not self.produce_data: - path = self.database.get_path() - remove(join(path[0], f'{path[1]}.db')) - def change_mode(self, mode: int) -> None: """ diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 7241ae1f..b4d33661 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -13,6 +13,7 @@ class EnvironmentManager: def __init__(self, environment_config: BaseEnvironmentConfig, data_manager: Optional[Any] = None, + pipeline: str = '', session: str = 'sessions/default', batch_size: int = 1): """ @@ -48,10 +49,11 @@ def __init__(self, visualization_db = self.visualizer.get_database() # Create a single Environment or a TcpIpServer - self.number_of_thread: int = environment_config.number_of_thread + force_local = pipeline == 'prediction' + self.number_of_thread: int = 1 if force_local else environment_config.number_of_thread self.server: Optional[TcpIpServer] = None self.environment: Optional[BaseEnvironment] = None - if environment_config.as_tcp_ip_client: + if environment_config.as_tcp_ip_client and not force_local: self.server = environment_config.create_server(environment_manager=self, batch_size=batch_size, visualization_db=None if visualization_db is None else @@ -106,6 +108,7 @@ def get_data_from_server(self, def get_data_from_environment(self, animate: bool = True, + save_data: bool = True, request_prediction: bool = False) -> List[List[int]]: """ Compute a batch of data directly from Environment. @@ -134,14 +137,15 @@ def get_data_from_environment(self, # 1.3 Add the produced sample to the batch if the sample is validated if self.environment.check_sample(): nb_sample += 1 - if update_line is None: - new_line = self.environment._send_training_data() - dataset_lines.append(new_line) - else: - self.environment._update_training_data(update_line) - dataset_lines.append(update_line) if request_prediction: self.environment._get_prediction() + if save_data: + if update_line is None: + new_line = self.environment._send_training_data() + dataset_lines.append(new_line) + else: + self.environment._update_training_data(update_line) + dataset_lines.append(update_line) self.environment._reset_training_data() return dataset_lines @@ -165,6 +169,7 @@ def dispatch_batch_to_server(self, def dispatch_batch_to_environment(self, data_lines: List[int], animate: bool = True, + save_data: bool = True, request_prediction: bool = False) -> None: """ Send samples from dataset to the Environments. Get back the training data. @@ -178,6 +183,7 @@ def dispatch_batch_to_environment(self, self.dataset_batch = data_lines.copy() # Get data self.get_data_from_environment(animate=animate, + save_data=save_data, request_prediction=request_prediction) def request_prediction(self): diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index a75172c7..093a92a0 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -174,10 +174,9 @@ def compute_online_prediction(self, # Get Network data normalization = {} if normalization is None else normalization - sample = self.data_db.get_line(table_name='Prediction', - fields=self.network.net_fields, - line_id=instance_id) - del sample['id'] + sample = self.database_handler.get_line(table_name='Exchange', + fields=self.network.net_fields, + line_id=instance_id) # Apply normalization and convert to tensor for field in sample.keys(): @@ -201,9 +200,9 @@ def compute_online_prediction(self, normalization=normalization[field], reverse=True) data_pred[field].reshape(-1) - self.data_db.update(table_name='Prediction', - data=data_pred, - line_id=instance_id) + self.database_handler.update(table_name='Exchange', + data=data_pred, + line_id=instance_id) @classmethod def normalize_data(cls, diff --git a/src/Pipelines/BasePrediction.py b/src/Pipelines/BasePrediction.py index 66721cbd..de5c3778 100644 --- a/src/Pipelines/BasePrediction.py +++ b/src/Pipelines/BasePrediction.py @@ -1,10 +1,13 @@ from typing import Optional +from os.path import join, exists +from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline +from DeepPhysX.Core.Manager.DataManager import DataManager +from DeepPhysX.Core.Manager.NetworkManager import NetworkManager from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline -from DeepPhysX.Core.Manager.Manager import Manager +from DeepPhysX.Core.Utils.path import get_first_caller class BasePrediction(BasePipeline): @@ -15,7 +18,7 @@ def __init__(self, database_config: Optional[BaseDatabaseConfig] = None, session_dir: str = 'session', session_name: str = 'training', - nb_steps: int = -1, + step_nb: int = -1, record: bool = False): """ BasePrediction is a pipeline defining the running process of an artificial neural network. @@ -26,7 +29,7 @@ def __init__(self, :param database_config: Specialisation containing the parameters of the dataset manager. :param session_name: Name of the newly created directory if session is not defined. :param session_dir: Name of the directory in which to write all the necessary data. - :param nb_steps: Number of simulation step to play. + :param step_nb: Number of simulation step to play. :param record: Save or not the prediction data. """ @@ -36,24 +39,36 @@ def __init__(self, environment_config=environment_config, session_dir=session_dir, session_name=session_name, + new_session=False, pipeline='prediction') + # Define the session repository + root = get_first_caller() + session_dir = join(root, session_dir) + if not exists(join(session_dir, session_name)): + raise ValueError(f"[{self.name}] The following directory does not exist: {join(session_dir, session_name)}") + self.session = join(session_dir, session_name) + + # Create a DataManager + self.data_manager = DataManager(pipeline=self, + database_config=database_config, + environment_config=environment_config, + session=self.session, + new_session=False, + produce_data=record, + batch_size=1) + + # Create a NetworkManager + self.network_manager = NetworkManager(network_config=network_config, + pipeline=self.type, + session=self.session, + new_session=False) + self.data_manager.connect_handler(self.network_manager.get_database_handler()) + self.network_manager.link_clients(self.data_manager.nb_environment) + # Prediction variables - self.nb_samples = nb_steps - self.idx_step = 0 - - # Tell if data is recording while predicting - self.record_data = record - - self.manager = Manager(network_config=self.network_config, - database_config=database_config, - environment_config=self.environment_config, - session_dir=session_dir, - session_name=session_name, - new_session=False, - pipeline='prediction', - produce_data=record, - batch_size=1) + self.step_nb = step_nb + self.step_id = 0 def execute(self) -> None: """ @@ -81,8 +96,8 @@ def prediction_condition(self) -> bool: Condition that characterize the end of the prediction pipeline. """ - running = self.idx_step < self.nb_samples if self.nb_samples > 0 else True - self.idx_step += 1 + running = self.step_id < self.step_nb if self.step_nb > 0 else True + self.step_id += 1 return running def sample_begin(self) -> None: @@ -97,7 +112,8 @@ def predict(self) -> None: Pull the data from the manager and return the prediction. """ - self.manager.get_data(animate=True) + self.data_manager.get_data(epoch=0, + animate=True) def sample_end(self) -> None: """ @@ -111,13 +127,13 @@ def run_end(self) -> None: Called once at the end of the prediction pipeline. """ - self.manager.close() + self.data_manager.close() + self.network_manager.close() def __str__(self) -> str: description = "" description += f"Running statistics :\n" - description += f"Number of simulation step: {self.nb_samples}\n" - description += f"Record data : {self.record_data}\n" + description += f"Number of simulation step: {self.step_nb}\n" return description diff --git a/src/Pipelines/BaseTraining.py b/src/Pipelines/BaseTraining.py index 03f84a85..9578a6ef 100644 --- a/src/Pipelines/BaseTraining.py +++ b/src/Pipelines/BaseTraining.py @@ -80,7 +80,7 @@ def __init__(self, batch_size=batch_size) self.batch_size = batch_size - # Create a NetworkMmanager + # Create a NetworkManager self.network_manager = NetworkManager(network_config=network_config, pipeline=self.type, session=self.session, @@ -130,7 +130,6 @@ def execute(self) -> None: self.batch_end() self.epoch_count() self.epoch_end() - self.save_network() self.train_end() def train_begin(self) -> None: @@ -220,12 +219,6 @@ def epoch_end(self) -> None: if self.stats_manager is not None: self.stats_manager.add_train_epoch_loss(self.loss_dict['loss'], self.epoch_id) - - def save_network(self) -> None: - """ - Store the network parameters in the corresponding directory. - """ - self.network_manager.save_network() def train_end(self) -> None: @@ -233,6 +226,7 @@ def train_end(self) -> None: Called once at the end of the training pipeline. """ + self.network_manager.save_network(last_save=True) self.data_manager.close() self.network_manager.close() if self.stats_manager is not None: From 4e4554bde969c3ec6239a7d229f7fd75d88478ac Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 13:02:41 +0200 Subject: [PATCH 21/61] Remove unused Manages. --- src/Manager/DatasetManager.py | 749 ---------------------------------- src/Manager/Manager.py | 144 ------- 2 files changed, 893 deletions(-) delete mode 100644 src/Manager/DatasetManager.py delete mode 100644 src/Manager/Manager.py diff --git a/src/Manager/DatasetManager.py b/src/Manager/DatasetManager.py deleted file mode 100644 index 02257d64..00000000 --- a/src/Manager/DatasetManager.py +++ /dev/null @@ -1,749 +0,0 @@ -from typing import Any, Dict, Tuple, List, Optional, Union -from os.path import join as osPathJoin -from os.path import isfile, isdir, abspath -from os import listdir, symlink, sep -from json import dump as json_dump -from json import load as json_load -from numpy import load, squeeze, ndarray, concatenate, float64 - -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig -from DeepPhysX.Core.Utils.path import create_dir -from DeepPhysX.Core.Utils.jsonUtils import CustomJSONEncoder - - -class DatasetManager: - - def __init__(self, - dataset_config: BaseDatasetConfig, - session: str, - data_manager: Optional[Any] = None, - new_session: bool = True, - training: bool = True, - offline: bool = False, - store_data: bool = True): - """ - | DatasetManager handle all operations with input / output files. Allows saving and read tensors from files. - - :param dataset_config: Specialisation containing the parameters of the dataset manager - :param data_manager: DataManager that handles the DatasetManager - :param new_session: Define the creation of new directories to store data - :param training: True if this session is a network training - :param offline: True if the session is done offline - :param store_data: Format {\'in\': bool, \'out\': bool} save the tensor when bool is True - """ - - self.name: str = self.__class__.__name__ - self.data_manager: Optional[Any] = data_manager - - # Checking arguments - if dataset_config is not None and not isinstance(dataset_config, BaseDatasetConfig): - raise TypeError(f"[{self.name}] The dataset config must be a BaseDatasetConfig object.") - if type(session) != str: - raise TypeError(f"[{self.name}] The session name must be a str.") - elif not isdir(session): - raise ValueError(f"[{self.name}] Given 'session' does not exists: {session}") - if type(new_session) != bool: - raise TypeError(f"[{self.name}] The 'new_network' argument must be a boolean.") - if type(training) != bool: - raise TypeError(f"[{self.name}] The 'train' argument must be a boolean.") - if type(store_data) != bool: - raise TypeError(f"[{self.name}] The 'store_data' argument must be a boolean.") - - # Create the Dataset object (default if no config specified) - dataset_config = BaseDatasetConfig() if dataset_config is None else dataset_config - self.dataset = dataset_config.create_dataset() - - # Dataset parameters - self.max_size: int = self.dataset.max_size - self.shuffle_dataset: bool = dataset_config.shuffle_dataset - self.record_data: bool = store_data - self.first_add: bool = True - self.__writing: bool = False - self.normalize: bool = dataset_config.normalize - self.normalization_security = dataset_config.recompute_normalization - self.offline = offline - - # Dataset modes - self.modes: Dict[str, int] = {'Training': 0, 'Validation': 1, 'Running': 2} - self.mode: int = self.modes['Training'] if training else self.modes['Running'] - self.mode = self.mode if dataset_config.use_mode is None else self.modes[dataset_config.use_mode] - self.last_loaded_dataset_mode: int = self.mode - - # Dataset partitions - session_name = session.split(sep)[-1] - self.partitions_templates: Tuple[str, str, str] = (session_name + '_training_{}_{}.npy', - session_name + '_validation_{}_{}.npy', - session_name + '_running_{}_{}.npy') - self.fields: List[str] = ['input', 'output'] - self.list_partitions: Dict[str, Optional[List[List[ndarray]]]] = { - 'input': [[], [], []] if self.record_data else None, - 'output': [[], [], []] if self.record_data else None} - self.idx_partitions: List[int] = [0, 0, 0] - self.current_partition_path: Dict[str, Optional[str]] = {'input': None, 'output': None} - - # Dataset loading with multiple partitions variables - self.mul_part_list_path: Optional[List[Dict[str, str]]] = None - self.mul_part_slices: Optional[List[List[int]]] = None - self.mul_part_idx: Optional[int] = None - - # Dataset Json file - self.json_filename: str = 'dataset.json' - self.json_empty: Dict[str, Dict[str, Union[List[int], Dict[Any, Any]]]] = {'data_shape': {}, - 'nb_samples': {mode: [] for mode in - self.modes}, - 'partitions': {mode: {} for mode in - self.modes}, - 'normalization': {}} - self.json_dict: Dict[str, Dict[str, Union[List[int], Dict[Any, Any]]]] = self.json_empty.copy() - self.json_found: bool = False - - # Dataset repository - self.session: str = session - dataset_dir: str = dataset_config.dataset_dir - self.new_session: bool = new_session - self.__new_dataset: bool = False - - # Training - if training: - # New training session - if new_session: - # New training session with new dataset - if dataset_dir is None: - self.dataset_dir: str = create_dir(dir_path=osPathJoin(self.session, 'dataset/'), - dir_name='dataset') - self.__new_dataset = True - # New training session with existing dataset - else: - if dataset_dir[-1] != "/": - dataset_dir += "/" - if dataset_dir[-8:] != "dataset/": - dataset_dir += "dataset/" - self.dataset_dir = dataset_dir - if abspath(self.dataset_dir) != osPathJoin(self.session, 'dataset'): - symlink(abspath(self.dataset_dir), osPathJoin(self.session, 'dataset')) - self.load_directory() - # Special case: adding data in existing Dataset with DataGeneration - else: - self.load_directory(load_data=False) - self.__new_dataset = True - # Existing training session - else: - self.dataset_dir = osPathJoin(self.session, 'dataset/') - self.load_directory() - # Prediction - else: - # Saving running data - if dataset_dir is None: - self.dataset_dir = osPathJoin(self.session, 'dataset/') - self.__new_dataset = True - # self.create_running_partitions() - self.load_directory(load_data=False) - # Loading partitions - else: - if dataset_dir[-1] != "/": - dataset_dir += "/" - if dataset_dir[-8:] != "dataset/": - dataset_dir += "dataset/" - self.dataset_dir = dataset_dir - self.load_directory() - - def get_data_manager(self) -> Any: - """ - | Return the Manager of the DataManager. - - :return: DataManager that handle The DatasetManager - """ - - return self.data_manager - - def add_data(self, data: Dict[str, Union[ndarray, Dict[str, ndarray]]]) -> None: - """ - | Push the data in the dataset. If max size is reached generate a new partition and write into it. - - :param data: Format {'input':numpy.ndarray, 'output':numpy.ndarray} contain in 'input' input tensors and - in 'output' output tensors - :type data: Dict[str, Union[ndarray, Dict[str, ndarray]]] - """ - - # 1. If first add, create first partitions - if self.first_add: - self.__writing = True - if 'additional_fields' in data: - self.register_new_fields(list(data['additional_fields'].keys())) - self.create_partitions() - - # 2. Add network data - for field in ['input', 'output']: - if self.record_data: - self.dataset.add(field, data[field], self.current_partition_path[field]) - - # 3. Add additional data - # 3.1 If there is additional data, convert field names then add each field - if 'additional_fields' in data.keys(): - # Check all registered are in additional data - for field in self.fields[2:]: - if field not in data['additional_fields']: - raise ValueError(f"[{self.name}] No data received for the additional field {field}.") - # Add each field to the dataset - for field in data['additional_fields']: - self.dataset.add(field, data['additional_fields'][field], self.current_partition_path[field]) - # 3.2 If there is no additional data but registered additional data - elif 'additional_fields' not in data.keys() and len(self.fields) > 2: - raise ValueError(f"[{self.name}] No data received for the additional fields {self.fields[:2]}") - - # 4. Update json file - self.update_json(update_nb_samples=True) - if self.first_add: - self.update_json(update_partitions_lists=True, update_shapes=True) - self.first_add = False - if self.normalize and not self.offline and self.mode == 0: - self.update_json(update_normalization=True) - - # 5. Check the size of the dataset - if self.dataset.memory_size() > self.max_size: - self.save_data() - self.create_partitions() - self.update_json(update_partitions_lists=True, update_nb_samples=True) - self.dataset.empty() - - def get_data(self, get_inputs: bool, get_outputs: bool, batch_size: int = 1, - batched: bool = True) -> Dict[str, ndarray]: - """ - | Fetch tensors from the dataset or reload partitions if dataset is empty or specified. - - :param bool get_inputs: If True fill the data['input'] field - :param bool get_outputs: If True fill the data['output'] field - :param int batch_size: Size of a batch - :param bool batched: Add an empty dimension before [4,100] -> [0,4,100] - - :return: Dict of format {'input':numpy.ndarray, 'output':numpy.ndarray} filled with desired data - """ - - # Do not allow overwriting partitions - self.__writing = False - - # 1. Check if a dataset is loaded and if the current sample is not the last - if self.current_partition_path['input'] is None or self.dataset.current_sample >= self.dataset.nb_samples: - # if not force_partition_reload: - # return None - self.load_partitions() - if self.shuffle_dataset: - self.dataset.shuffle() - self.dataset.current_sample = 0 - - # 2. Update dataset indices with batch size - idx = self.dataset.current_sample - self.dataset.current_sample += batch_size - - # 3. Get a batch of each data field - data = {} - fields = self.fields[2:] - fields += ['input'] if get_inputs else [] - fields += ['output'] if get_outputs else [] - for field in fields: - # Network input and output fields - if field in ['input', 'output']: - data[field] = self.dataset.get(field, idx, idx + batch_size) - if not batched: - data[field] = squeeze(data[field], axis=0) - # Additional data fields - else: - if 'additional_fields' not in data.keys(): - data['additional_fields'] = {} - data['additional_fields'][field] = self.dataset.get(field, idx, idx + batch_size) - if not batched: - data['additional_fields'][field] = squeeze(data['additional_fields'][field], axis=0) - - # 4. Ensure each field received the same batch size - if data['input'].shape[0] != data['output'].shape[0]: - raise ValueError(f"[{self.name}] Size of loaded batch mismatch for input and output " - f"(in: {data['input'].shape} / out: {data['output'].shape}") - if '__data_additional' in data.keys(): - for field in data['additional_fields'].keys(): - if data['additional_fields'][field].shape[0] != data['input'].shape[0]: - raise ValueError(f"[{self.name}] Size of loaded batch mismatch for additional field {field} " - f"(net: {data['input'].shape} / {field}: {data['additional_fields'][field].shape}") - - # 5. Ensure the batch has the good size, otherwise load new data to complete it - if data['input'].shape[0] < batch_size: - # Load next samples from the dataset - self.load_partitions() - if self.shuffle_dataset: - self.dataset.shuffle() - self.dataset.current_sample = 0 - # Get the remaining samples - missing_samples = batch_size - data['input'].shape[0] - missing_data = self.get_data(get_inputs=get_inputs, get_outputs=get_outputs, batch_size=missing_samples) - # Merge fields - for field in ['input', 'output']: - data[field] = concatenate((data[field], missing_data[field])) - if 'additional_fields' in data.keys(): - for field in data['additional_fields'].keys(): - data['additional_fields'][field] = concatenate((data['additional_fields'][field], - missing_data['additional_fields'][field])) - return data - - def register_new_fields(self, new_fields: List[str]) -> None: - """ - | Add new data fields in the dataset. - - :param List[str] new_fields: Name of the new fields split in either 'IN' side or 'OUT' side of the dataset - """ - - for field in new_fields: - self.register_new_field(field) - - def register_new_field(self, new_field: str) -> None: - """ - | Add a new data field in the dataset. - - :param str new_field: Name of the new field. - """ - - if new_field not in self.fields or self.list_partitions[new_field] is None: - self.fields.append(new_field) - self.list_partitions[new_field] = [[], [], []] - - def create_partitions(self) -> None: - """ - | Create a new partition for current mode and for each registered fields. - """ - - print(f"[{self.name}] New partitions added for each field with max size ~{float(self.max_size) / 1e9}Gb.") - for field in self.fields: - if self.record_data: - # Fill partition name template - if field in ['input', 'output']: - name = 'IN' if field == 'input' else 'OUT' - else: - name = 'ADD_' + field - partition_path = self.partitions_templates[self.mode].format(name, self.idx_partitions[self.mode]) - # Make it current partition - self.list_partitions[field][self.mode].append(partition_path) - self.current_partition_path[field] = self.dataset_dir + partition_path - # Index partitions - self.idx_partitions[self.mode] += 1 - - def create_running_partitions(self) -> None: - """ - | Run specific function. Handle partitions creation when not training. - """ - - # 1. Load the directory without loading data - self.load_directory(load_data=False) - - # 2. Find out how many partitions exists for running mode - mode = list(self.modes.keys())[self.mode] - partitions_dict = self.json_dict['partitions'][mode] - nb_running_partitions = max([len(partitions_dict[field]) for field in partitions_dict.keys()]) - - # 3. Create a new partition partitions - self.idx_partitions[self.mode] = nb_running_partitions - self.create_partitions() - - def load_directory(self, load_data: bool = True) -> None: - """ - | Load the desired directory. Try to find partition list and upload it. - | No data loading here. - """ - - # 1. Check the directory exists - if not isdir(self.dataset_dir): - raise Warning(f"[{self.name}] Loading directory: The given path is not an existing directory") - if load_data: - print(f"[{self.name}] Loading directory: Read dataset from {self.dataset_dir}") - - # 2. Look for the json info file - if isfile(osPathJoin(self.dataset_dir, self.json_filename)): - self.json_found = True - with open(osPathJoin(self.dataset_dir, self.json_filename)) as json_file: - self.json_dict = json_load(json_file) - - # 3. Load partitions for each mode - for mode in self.modes: - # 3.1. Get sorted partitions - partitions_dict = self.json_dict['partitions'][mode] if self.json_found else self.search_partitions(mode) - # 3.2. Register additional fields - for field in partitions_dict: - self.register_new_field(field) - # 3.3. Register each partition - for field in partitions_dict: - self.list_partitions[field][self.modes[mode]] = partitions_dict[field] - # 3.4. Check that the number of partitions is the same for each field - number_of_partitions = len(self.list_partitions[self.fields[0]][self.modes[mode]]) - for field in self.fields: - if len(self.list_partitions[field][self.modes[mode]]) != number_of_partitions: - raise ValueError(f"[{self.name}] The number of partitions is different for {field} with " - f"{len(self.list_partitions[field][self.modes[mode]])} partitions found.") - - # 4. Update Json file if not found - if not self.json_found or self.empty_json_fields(): - self.search_partitions_info() - self.update_json(update_partitions_lists=True) - if self.normalization_security or (self.normalize and self.json_dict['normalization'] == self.json_empty['normalization']): - self.update_json(update_normalization=True) - - # 4. Load data from partitions - self.idx_partitions = [len(partitions_list) for partitions_list in self.list_partitions['input']] - if load_data: - self.load_partitions(force_reload=True) - - def search_partitions(self, mode: str) -> Dict[str, List[str]]: - """ - | If loading a directory without JSON info file, search for existing partitions manually. - - :param str mode: Mode of the partitions to find. - :return: Dictionary with found partitions for specified mode - """ - - # 1. Get all the partitions for the mode - partitions_dict = {} - partitions_list = [f for f in listdir(self.dataset_dir) if isfile(osPathJoin(self.dataset_dir, f)) - and f.endswith('.npy') and f.__contains__(mode.lower())] - - # 2. Sort partitions by side (IN, OUT, ADD) and by name - in_partitions = sorted([file for file in partitions_list if file.__contains__('_IN_')]) - out_partitions = sorted([file for file in partitions_list if file.__contains__('_OUT_')]) - add_partitions = sorted([file for file in partitions_list if file.__contains__('_ADD_')]) - - # 3. Sort partitions by data field - for side, partitions, field_name in zip(['IN', 'OUT', 'ADD'], [in_partitions, out_partitions, add_partitions], - ['input', 'output', '']): - for partition in partitions: - # Extract information from the filenames - split_name = partition.split('_') - clues = split_name[split_name.index(side):] - # Partition name for network data: {NAME_OF_SESSION}_SIDE_IDX.npy - if len(clues) == 2: - if field_name not in partitions_dict.keys(): - partitions_dict[field_name] = [] - partitions_dict[field_name].append(partition) - # Partition name for additional data: {NAME_OF_SESSION}_SIDE_{NAME_OF_FIELD}_IDX.npy - else: - field_name = '_'.join(clues[:-1]) - if field_name not in partitions_dict.keys(): - partitions_dict[field_name] = [] - partitions_dict[field_name].append(partition) - - return partitions_dict - - def search_partitions_info(self) -> None: - """ - | If loading a directory without JSON info file. - """ - - # 1. Get the shape of each partition - partition_shapes = [{field: [] for field in self.fields} for _ in self.modes] - for mode in self.modes: - for field in self.fields: - for partition in [self.dataset_dir + path for path in self.list_partitions[field][self.modes[mode]]]: - partition_data = load(partition) - partition_shapes[self.modes[mode]][field].append(partition_data.shape) - del partition_data - - # 2. Get the number of samples per partition for each mode - for mode in self.modes: - number_of_samples = {} - for field in self.fields: - number_of_samples[field] = [shape[0] for shape in partition_shapes[self.modes[mode]][field]] - # Number of samples through partitions must be the same along fields - if number_of_samples[field] != list(number_of_samples.values())[0]: - raise ValueError(f"[{self.name}] The number of sample in each partition is not consistent:\n" - f"{number_of_samples}") - # Store the number of samples per partition for the mode - self.json_dict['nb_samples'][mode] = list(number_of_samples.values())[0] - - # 3. Get the data shape for each field - data_shape = {field: [] for field in self.fields} - for mode in self.modes: - for field in self.fields: - for i, shape in enumerate(partition_shapes[self.modes[mode]][field]): - if len(data_shape[field]) == 0: - data_shape[field] = shape[1:] - # Data shape must be the same along partitions and mode - if shape[1:] != data_shape[field]: - raise ValueError(f"[{self.name}] Two different data sizes found for mode {mode}, field {field}," - f" partition n°{i}: {data_shape[field]} vs {shape[1:]}") - # Store the data shapes - self.json_dict['data_shape'] = data_shape - - def load_partitions(self, force_reload: bool = False) -> None: - """ - | Load data from partitions. - - :param bool force_reload: If True, force partitions reload - """ - - # 1. If there is only one partition for the current mode for input field at least, don't need to reload it - if self.last_loaded_dataset_mode == self.mode and self.idx_partitions[self.mode] == 1 and not force_reload: - if self.shuffle_dataset: - self.dataset.shuffle() - return - - # 2. Check partitions existence for the current mode - if self.idx_partitions[self.mode] == 0: - raise ValueError(f"[{self.name}] No partitions to read for {list(self.modes.keys())[self.mode]} mode.") - - # 3. Load new data in dataset - self.dataset.empty() - # Training mode with mixed dataset: read multiple partitions per field - if self.mode == self.modes['Training'] and self.idx_partitions[self.modes['Running']] > 0: - if self.mul_part_idx is None: - self.load_multiple_partitions([self.modes['Training'], self.modes['Running']]) - self.read_multiple_partitions() - return - # Training mode without mixed dataset or other modes: check the number of partitions per field to read - if self.idx_partitions[self.mode] == 1: - self.read_last_partitions() - else: - if self.mul_part_idx is None: - self.load_multiple_partitions([self.mode]) - self.read_multiple_partitions() - - def read_last_partitions(self) -> None: - """ - | Load the last loaded partitions for each data field. - """ - - for field in self.fields: - self.current_partition_path[field] = self.dataset_dir + self.list_partitions[field][self.mode][-1] - data = load(self.current_partition_path[field]) - self.dataset.set(field, data) - - def load_multiple_partitions(self, modes: List[int]) -> None: - """ - | Specialisation of the load_partitions() function. It can load a list of partitions. - - :param List[int] modes: Recommended to use modes['name_of_desired_mode'] in order to correctly load the dataset - """ - - # 1. Initialize multiple partition loading variables - self.mul_part_list_path = {field: [] for field in self.fields} - self.mul_part_slices = [] - self.mul_part_idx = 0 - nb_sample_per_partition = {field: [] for field in self.fields} - - # 2. For each field, load all partitions - for field in self.fields: - for mode in modes: - # 2.1. Add partitions to the list of partitions to read - self.mul_part_list_path[field] += [self.dataset_dir + partition - for partition in self.list_partitions[field][mode]] - # 2.2. Find the number of samples in each partition - nb_sample_per_partition[field] += self.json_dict['nb_samples'][list(self.modes.keys())[mode]] - - # 3. Invert the partitions list structure - nb_partition = len(nb_sample_per_partition[self.fields[0]]) - inverted_list = [{} for _ in range(nb_partition)] - for i in range(nb_partition): - for field in self.fields: - inverted_list[i][field] = self.mul_part_list_path[field][i] - self.mul_part_list_path = inverted_list - - # 4. Define the slicing pattern of reading for partitions - for idx in nb_sample_per_partition[self.fields[0]]: - idx_slicing = [0] - for _ in range(nb_partition - 1): - idx_slicing.append(idx_slicing[-1] + idx // nb_partition + 1) - idx_slicing.append(idx) - self.mul_part_slices.append(idx_slicing) - - def read_multiple_partitions(self) -> None: - """ - | Read data in a list of partitions. - """ - - for i, partitions in enumerate(self.mul_part_list_path): - for field in partitions.keys(): - dataset = load(partitions[field]) - samples = slice(self.mul_part_slices[i][self.mul_part_idx], - self.mul_part_slices[i][self.mul_part_idx + 1]) - self.dataset.add(field, dataset[samples]) - del dataset - self.mul_part_idx = (self.mul_part_idx + 1) % (len(self.mul_part_slices[0]) - 1) - self.current_partition_path['input'] = self.mul_part_list_path[0][self.fields[0]] - self.dataset.current_sample = 0 - - def update_json(self, update_shapes: bool = False, update_nb_samples: bool = False, - update_partitions_lists: bool = False, update_normalization: bool = False) -> None: - """ - | Update the json info file with the current Dataset repository information. - - :param bool update_shapes: If True, data shapes per field are overwritten - :param bool update_nb_samples: If True, number of samples per partition are overwritten - :param bool update_partitions_lists: If True, list of partitions is overwritten - :param bool update_normalization: If True, compute and save current normalization coefficients - """ - - # Update data shapes - if update_shapes: - for field in self.fields: - self.json_dict['data_shape'][field] = self.dataset.get_data_shape(field) - - # Update number of samples - if update_nb_samples: - idx_mode = list(self.modes.keys())[self.mode] - if len(self.json_dict['nb_samples'][idx_mode]) == self.idx_partitions[self.mode]: - self.json_dict['nb_samples'][idx_mode][-1] = self.dataset.current_sample - else: - self.json_dict['nb_samples'][idx_mode].append(self.dataset.current_sample) - - # Update partitions lists - if update_partitions_lists: - for mode in self.modes: - for field in self.fields: - self.json_dict['partitions'][mode][field] = self.list_partitions[field][self.modes[mode]] - - # Update normalization coefficients - if update_normalization: - for field in ['input', 'output']: - # Normalization is only done on training data (mode = 0) - if len(self.list_partitions[field][0]) > 0: - self.json_dict['normalization'][field] = self.compute_normalization(field) - - # Overwrite json file - with open(self.dataset_dir + self.json_filename, 'w') as json_file: - json_dump(self.json_dict, json_file, indent=3, cls=CustomJSONEncoder) - - def empty_json_fields(self) -> bool: - """ - | Check if the json info file contains empty fields. - - :return: The json file contains empty fields or not - """ - - for key in self.json_empty: - if self.json_dict[key] == self.json_empty[key]: - return True - return False - - def save_data(self) -> None: - """ - | Close all open files - """ - - if self.__new_dataset: - for field in self.current_partition_path.keys(): - self.dataset.save(field, self.current_partition_path[field]) - - def set_mode(self, mode: int) -> None: - """ - | Set the DatasetManager working mode. - - :param int mode: Recommended to use modes['name_of_desired_mode'] in order to correctly set up the - DatasetManager - """ - - # Nothing has to be done if you do not change mode - if mode == self.mode: - return - if self.mode == self.modes['Running']: - print(f"[{self.name}] It's not possible to switch dataset mode while running.") - else: - # Save dataset before changing mode - self.save_data() - self.mode = mode - self.dataset.empty() - # Create or load partition for the new mode - if self.idx_partitions[self.mode] == 0: - print(f"[{self.name}] Change to {self.mode} mode, create a new partition") - self.create_partitions() - else: - print(f"[{self.name}] Change to {self.mode} mode, load last partition") - self.read_last_partitions() - - def compute_normalization(self, field: str) -> List[float]: - """ - | Compute the normalization coefficients (mean & standard deviation) of input and output data fields. - | Compute the normalization on the whole set of partitions for a given data field. - - :param str field: Field for which normalization coefficients should be computed - :return: List containing normalization coefficients (mean, standard deviation) - """ - - partitions_content = [0., 0.] - for i, partition in enumerate(self.list_partitions[field][0]): - loaded = load(self.dataset_dir + partition).astype(float64) - partitions_content[0] += loaded.mean() #Computing the mean - partitions_content[1] += (loaded**2).mean() #Computing the variance - if len(self.list_partitions[field][0]) > 0: - partitions_content[0] /= len(self.list_partitions[field][0]) #Computing the global mean - # Computing the global std - partitions_content[1] = (partitions_content[1] / len(self.list_partitions[field][0]) - partitions_content[0]**2)**(0.5) - if self.data_manager is not None: - self.data_manager.normalization[field] = partitions_content - return partitions_content - - def new_dataset(self) -> bool: - """ - | Check if the Dataset is a new entity or not. - - :return: The Dataset is new or not - """ - - return self.__new_dataset - - def get_next_batch(self, batch_size: int) -> Dict[str, ndarray]: - """ - | Specialization of get_data to get a batch. - - :param int batch_size: Size of the batch - :return: Batch with format {'input': numpy.ndarray, 'output': numpy.ndarray} - """ - - return self.get_data(get_inputs=True, get_outputs=True, batch_size=batch_size, batched=True) - - def get_next_sample(self, batched: bool = True) -> Dict[str, ndarray]: - """ - | Specialization of get_data to get a sample. - - :param batched: If True, return the sample as a batch with batch_size = 1 - :return: Sample with format {'input': numpy.ndarray, 'output': numpy.ndarray} - """ - - return self.get_data(get_inputs=True, get_outputs=True, batched=batched) - - def get_next_input(self, batched: bool = False) -> Dict[str, ndarray]: - """ - | Specialization of get_data to get an input sample. - - :param batched: If True, return the sample as a batch with batch_size = 1 - :return: Sample with format {'input': numpy.ndarray, 'output': numpy.ndarray} where only the input field is - filled - """ - - return self.get_data(get_inputs=True, get_outputs=False, batched=batched) - - def getNextOutput(self, batched: bool = False) -> Dict[str, ndarray]: - """ - | Specialization of get_data to get an output sample. - - :param batched: If True, return the sample as a batch with batch_size = 1 - :return: Sample with format {'input': numpy.ndarray, 'output': numpy.ndarray} where only the output field is - filled - """ - - return self.get_data(get_inputs=False, get_outputs=True, batched=batched) - - def close(self) -> None: - """ - | Launch the close procedure of the dataset manager - """ - - if self.__writing: - self.save_data() - if self.offline and self.normalize: - self.update_json(update_normalization=True) - - def __str__(self) -> str: - """ - :return: A string containing valuable information about the DatasetManager - """ - - description = "\n" - description += f"# {self.name}\n" - description += f" Dataset Repository: {self.dataset_dir}\n" - description += f" Partitions size: {self.max_size * 1e-9} Go\n" - description += f" Managed objects: Dataset: {self.dataset.name}\n" - description += str(self.dataset) - return description diff --git a/src/Manager/Manager.py b/src/Manager/Manager.py deleted file mode 100644 index 5ff6fed6..00000000 --- a/src/Manager/Manager.py +++ /dev/null @@ -1,144 +0,0 @@ -from typing import Dict, Tuple, Optional -from os.path import join, exists -from numpy import ndarray - -from DeepPhysX.Core.Manager.DataManager import DataManager -from DeepPhysX.Core.Manager.NetworkManager import NetworkManager -from DeepPhysX.Core.Manager.StatsManager import StatsManager -from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig -from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Utils.path import get_first_caller, create_dir - - -class Manager: - - def __init__(self, - network_config: Optional[BaseNetworkConfig] = None, - database_config: Optional[BaseDatabaseConfig] = None, - environment_config: Optional[BaseEnvironmentConfig] = None, - session_dir: str = 'sessions', - session_name: str = 'default', - pipeline: str = '', - new_session: bool = True, - produce_data: bool = True, - batch_size: int = 1, - debug_session: bool = False): - """ - Collection of all the specialized managers. Allows for some basic functions call. - More specific behaviour have to be directly call from the corresponding manager. - - :param network_config: Specialisation containing the parameters of the network manager. - :param database_config: Specialisation containing the parameters of the dataset manager. - :param environment_config: Specialisation containing the parameters of the environment manager. - :param session_name: Name of the newly created directory if session is not defined. - :param session_dir: Name of the directory in which to write all the necessary data. - :param bool new_session: Flag that indicates whether if the session is new - :param is_training: Flag that indicates whether if this session is training a Network. - :param produce_data: Flag that indicates whether if this session is producing data. - :param int batch_size: Number of samples in a batch. - """ - - self.name = self.__class__.__name__ - - # Define the session repository - root = get_first_caller() - session_dir = join(root, session_dir) - - if pipeline in ['data_generation', 'training']: - # Avoid unwanted overwritten data - if new_session: - self.session = create_dir(session_dir=session_dir, - session_name=session_name) - else: - self.session = join(session_dir, session_name) - else: - self.session = join(session_dir, session_name) - if not exists(self.session): - raise ValueError(f"[{self.name}] The running session directory {self.session} does not exist.") - - # Create a DataManager - self.data_manager = DataManager(database_config=database_config, - environment_config=environment_config, - manager=self, - session=self.session, - new_session=new_session, - pipeline=pipeline, - produce_data=produce_data, - batch_size=batch_size) - database = self.data_manager.get_database() - self.batch_size = batch_size - - # Create a NetworkMmanager - self.network_manager = NetworkManager(network_config=network_config, - manager=self, - session=self.session, - new_session=new_session, - pipeline=pipeline, - data_db=database) - self.network_manager.link_clients(self.data_manager.nb_environment) - - # Create a StatsManager for training only - self.debug_session = debug_session - self.stats_manager = StatsManager(manager=self, - session=self.session) if pipeline == 'training' else None - - def change_database(self, database): - self.network_manager.change_database(database) - - def get_data(self, - epoch: int = 0, - animate: bool = True) -> None: - """ - Fetch data from the DataManager. - - :param int epoch: Epoch ID - :param bool animate: If True allows running environment step - """ - - self.data_manager.get_data(epoch=epoch, - animate=animate) - - def optimize_network(self) -> Tuple[ndarray, Dict[str, float]]: - """ - Compute a prediction and run a back propagation with the current batch. - - :return: The network prediction and the associated loss value - """ - - # Forward pass and optimization step - data_pred, data_loss = self.network_manager.compute_prediction_and_loss( - data_lines=self.data_manager.data_lines, - normalization=self.data_manager.normalization, - optimize=True) - return data_pred, data_loss - - def save_network(self) -> None: - """ - Save network parameters. - """ - - self.network_manager.save_network() - - def close(self) -> None: - """ - Call all managers close procedure. - """ - - if self.network_manager is not None: - self.network_manager.close() - if self.stats_manager is not None: - self.stats_manager.close() - if self.data_manager is not None: - self.data_manager.close() - - def __str__(self) -> str: - - manager_description = "" - if self.network_manager is not None: - manager_description += str(self.network_manager) - if self.data_manager is not None: - manager_description += str(self.data_manager) - if self.stats_manager is not None: - manager_description += str(self.stats_manager) - return manager_description From deaba56915a40e283a88a937dd934a881ba67419 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 13:05:01 +0200 Subject: [PATCH 22/61] Remove previous Dataset package. --- src/Dataset/BaseDataset.py | 252 ------------------------------- src/Dataset/BaseDatasetConfig.py | 116 -------------- src/Dataset/__init__.py | 10 -- 3 files changed, 378 deletions(-) delete mode 100644 src/Dataset/BaseDataset.py delete mode 100644 src/Dataset/BaseDatasetConfig.py delete mode 100644 src/Dataset/__init__.py diff --git a/src/Dataset/BaseDataset.py b/src/Dataset/BaseDataset.py deleted file mode 100644 index decd434c..00000000 --- a/src/Dataset/BaseDataset.py +++ /dev/null @@ -1,252 +0,0 @@ -from typing import Dict, List, Optional, Tuple -from numpy import array, ndarray, concatenate, save, arange -from numpy.random import shuffle -from collections import namedtuple - - -class BaseDataset: - """ - | BaseDataset is a dataset class to store any data from a BaseEnvironment or from files. - | Given data is split into input data and output data. - | Saving data results in multiple partitions of input and output data. - - :param namedtuple config: Namedtuple which contains BaseDataset parameters - """ - - def __init__(self, config: namedtuple): - - self.name: str = self.__class__.__name__ - - # Data fields containers - self.data_type = ndarray - self.fields: List[str] = ['input', 'output'] - self.data: Dict[str, ndarray] = {'input': array([]), 'output': array([])} - self.shape: Dict[str, Optional[List[int]]] = {'input': None, 'output': None} - - # Indexing - self.shuffle_pattern: Optional[List[int]] = None - self.current_sample: int = 0 - - # Dataset memory - self.max_size: int = config.max_size - self.batch_per_field: Dict[str, int] = {field: 0 for field in ['input', 'output']} - self.__empty: bool = True - - @property - def nb_samples(self) -> int: - """ - | Property returning the current number of samples - - :return: The current number of samples in all partitions - """ - - return max([len(self.data[field]) for field in self.fields]) - - def is_empty(self) -> bool: - """ - | Check if the fields of the dataset are empty. A field is considered as non-empty if it is filled with another - sample. - - :return: The Dataset is empty or not - """ - - # The empty flag is set to False once the Dataset is considered as non-empty - if not self.__empty: - return False - # Check each registered data field - for field in self.fields: - # Dataset is considered as non-empty if a field is filled with another sample - if self.batch_per_field[field] > 1: - self.__empty = False - return False - # If all field are considered as non-empty then the Dataset is empty - return True - - def init_data_size(self, field: str, shape: List[int]) -> None: - """ - | Store the original shape of data. Reshape data containers. - - :param str field: Data field name - :param List[int] shape: Shape of the corresponding tensor - """ - - # Store the original data shape - self.shape[field] = shape - # Reshape the data container - self.data[field] = array([]).reshape((0, *shape)) - - def get_data_shape(self, field: str) -> List[int]: - """ - | Returns the data shape of field. - - :param str field: Data field name - :return: Data shape for field - """ - - return self.shape[field] - - def init_additional_field(self, field: str, shape: List[int]) -> None: - """ - | Register a new data field. - - :param str field: Name of the data field - :param List[int] shape: Data shape - """ - - # Register the data field - self.fields.append(field) - # Init the number of adds in the field - self.batch_per_field[field] = 0 - # Init the field shape - self.init_data_size(field, shape) - - def empty(self) -> None: - """ - | Empty the dataset. - """ - - # Reinit each data container - for field in self.fields: - self.data[field] = array([]) if self.shape[field] is None else array([]).reshape((0, *self.shape[field])) - # Reinit indexing variables - self.shuffle_pattern = None - self.current_sample = 0 - self.batch_per_field = {field: 0 for field in self.fields} - self.__empty = True - - def memory_size(self, field: Optional[str] = None) -> int: - """ - | Return the actual memory size of the dataset if field is None. Otherwise, return the actual memory size of the - field. - - :param Optional[str] field: Name of the data field - :return: Size in bytes of the current dataset. - """ - - # Return the total memory size - if field is None: - return sum([self.data[field].nbytes for field in self.fields]) - # Return the memory size for the specified field - return self.data[field].nbytes - - def check_data(self, field: str, data: ndarray) -> None: - """ - | Check if the data is a numpy array. - - :param str field: Values at 'input' or anything else. Define if the associated shape is correspond to input - shape or output one. - :param ndarray data: New data - """ - - if type(data) != self.data_type: - raise TypeError(f"[{self.name}] Wrong data type in field '{field}': numpy array required, got {type(data)}") - - def add(self, field: str, data: ndarray, partition_file: Optional[str] = None) -> None: - """ - | Add data to the dataset. - - :param str field: Name of the data field - :param ndarray data: New data as batch of samples - :param Optional[str] partition_file: Path to the file in which to write the data - """ - - # Check data type - self.check_data(field, data) - - # Check if field is registered - if field not in self.fields: - # Fields can be register only if Dataset is empty - if not self.is_empty(): - raise ValueError(f"[{self.name}] A new field {field} tries to be created as Dataset is non empty. This " - f"will lead to a different number of sample for each field of the dataset.") - # Add new field if not registered - self.init_additional_field(field, data[0].shape) - - # Check data size initialization - if self.shape[field] is None: - self.init_data_size(field, data[0].shape) - - # Add batched samples - self.data[field] = concatenate((self.data[field], data)) - # Save in partition - if partition_file is not None: - self.save(field, partition_file) - - # Update sample indexing in dataset - self.batch_per_field[field] += 1 - self.current_sample = max([len(self.data[f]) for f in self.fields]) - - def save(self, field: str, file: str) -> None: - """ - | Save the corresponding field of the Dataset. - - :param str field: Name of the data field - :param str file: Path to the file in which to write the data - """ - - save(file, self.data[field]) - - def set(self, field: str, data: ndarray) -> None: - """ - | Set a full field of the dataset. - - :param str field: Name of the data field - :param ndarray data: New data as batch of samples - """ - - # Check data type - self.check_data(field, data) - - # Check if field is registered - if field not in self.fields: - # Add new field if not registered - self.init_additional_field(field, data[0].shape) - - # Check data size initialization - if self.shape[field] is None: - self.init_data_size(field, data[0].shape) - - # Set the full field - self.data[field] = data - - # Update sample indexing in dataset - self.__empty = False - self.current_sample = 0 - - def get(self, field: str, idx_begin: int, idx_end: int) -> ndarray: - """ - | Get a batch of data in 'field' container. - - :param str field: Name of the data field - :param int idx_begin: Index of the first sample - :param int idx_end: Index of the last sample - :return: Batch of data from 'field' - """ - - indices = slice(idx_begin, idx_end) if self.shuffle_pattern is None else self.shuffle_pattern[idx_begin:idx_end] - return self.data[field][indices] - - def shuffle(self) -> None: - """ - | Define a random shuffle pattern. - """ - - # Nothing to shuffle if Dataset is empty - if self.is_empty(): - return - # Generate a shuffle pattern - self.shuffle_pattern = arange(self.nb_samples) - shuffle(self.shuffle_pattern) - - def __str__(self) -> str: - """ - :return: String containing information about the BaseDatasetConfig object - """ - - description = "\n" - description += f" {self.name}\n" - description += f" Max size: {self.max_size}\n" - description += f" Data fields: {self.fields}" - for field in self.fields: - description += f" {field} shape: {self.shape[field]}" - return description diff --git a/src/Dataset/BaseDatasetConfig.py b/src/Dataset/BaseDatasetConfig.py deleted file mode 100644 index acec6302..00000000 --- a/src/Dataset/BaseDatasetConfig.py +++ /dev/null @@ -1,116 +0,0 @@ -from typing import Type, Optional -from os.path import isdir -from collections import namedtuple - -from DeepPhysX.Core.Dataset.BaseDataset import BaseDataset - - -class BaseDatasetConfig: - """ - | BaseDatasetConfig is a configuration class to parameterize and create a BaseDataset for the DatasetManager. - - :param Type[BaseDataset] dataset_class: BaseDataset class from which an instance will be created - :param Optional[str] dataset_dir: Name of an existing dataset repository - :param float partition_size: Maximum size in Gb of a single dataset partition - :param bool shuffle_dataset: Specify if existing dataset should be shuffled - :param Optional[str] use_mode: Specify the Dataset mode that should be used between 'Training', 'Validation' and - 'Running' - :param bool normalize: If True, normalizing dataset using standard score - :param bool recompute_normalization: If True, triggers a normalization coefficients computation - """ - - def __init__(self, - dataset_class: Type[BaseDataset] = BaseDataset, - dataset_dir: Optional[str] = None, - partition_size: float = 1., - shuffle_dataset: bool = True, - use_mode: Optional[str] = None, - normalize: bool = True, - recompute_normalization: bool = False): - - self.name: str = self.__class__.__name__ - - # Check dataset_dir type and existence - if dataset_dir is not None: - if type(dataset_dir) != str: - raise TypeError(f"[{self.name}] Wrong dataset_dir type: str required, get {type(dataset_dir)}") - if not isdir(dataset_dir): - raise ValueError(f"[{self.name}] Given dataset_dir doesn't exists: {dataset_dir}") - # Check partition_size type and value - if type(partition_size) != int and type(partition_size) != float: - raise TypeError(f"[{self.name}] Wrong partition_size type: float required, get {type(partition_size)}") - if partition_size <= 0: - raise ValueError(f"[{self.name}] Given partition_size is negative or null") - # Check shuffle_dataset type - if type(shuffle_dataset) != bool: - raise TypeError(f"[{self.name}] Wrong shuffle_dataset type: bool required, get {type(shuffle_dataset)}") - # Check use_mode type and value - if use_mode is not None: - if type(use_mode) != str: - raise TypeError(f"[{self.name}] Wrong use_mode type: str required, get {type(dataset_dir)}") - if use_mode not in ['Training', 'Validation', 'Running']: - raise ValueError(f"[{self.name}] Wrong use_mode value, must be in " - f"{['Training', 'Validation', 'Running']}") - - # BaseDataset parameterization - self.dataset_class: Type[BaseDataset] = dataset_class - self.dataset_config: namedtuple = self.make_config(max_size=int(partition_size * 1e9)) - - # DatasetManager parameterization - self.dataset_dir: str = dataset_dir - self.shuffle_dataset: bool = shuffle_dataset - self.use_mode: Optional[str] = use_mode - self.normalize: bool = normalize - self.recompute_normalization = recompute_normalization - - def make_config(self, **kwargs) -> namedtuple: - """ - | Create a namedtuple which gathers all the parameters for the Dataset configuration. - | For a child config class, only new items are required since parent's items will be added by default. - - :param kwargs: Items to add to the Dataset configuration. - :return: Namedtuple which contains Dataset parameters - """ - - # Get items set as keyword arguments - fields = tuple(kwargs.keys()) - args = tuple(kwargs.values()) - # Check if a dataset_config already exists (child class will have the parent's config by default) - if 'dataset_config' in self.__dict__: - # Get items set in the existing config - for key, value in self.dataset_config._asdict().items(): - # Only new items are required for children, check if the parent's items are set again anyway - if key not in fields: - fields += (key,) - args += (value,) - # Create namedtuple with collected items - return namedtuple('dataset_config', fields)._make(args) - - def create_dataset(self) -> BaseDataset: - """ - | Create an instance of dataset_class with given parameters. - - :return: Dataset object - """ - - try: - dataset = self.dataset_class(config=self.dataset_config) - except: - raise ValueError(f"[{self.name}] Given dataset_class got an unexpected keyword argument 'config'") - if not isinstance(dataset, BaseDataset): - raise TypeError(f"[{self.name}] Wrong dataset_class type: BaseDataset required, get {self.dataset_class}") - return dataset - - def __str__(self) -> str: - """ - :return: String containing information about the BaseDatasetConfig object - """ - - # Todo: fields in Configs are the set in Managers or objects, the remove __str__ method - description = "\n" - description += f"{self.name}\n" - description += f" Dataset class: {self.dataset_class.__name__}\n" - description += f" Max size: {self.dataset_config.max_size}\n" - description += f" Dataset dir: {self.dataset_dir}\n" - description += f" Shuffle dataset: {self.shuffle_dataset}\n" - return description diff --git a/src/Dataset/__init__.py b/src/Dataset/__init__.py deleted file mode 100644 index 6501d21a..00000000 --- a/src/Dataset/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -from os.path import dirname -from os import listdir - -package = dirname(__file__) -exceptions = ['__init__.py', '__pycache__'] -modules = [module for module in listdir(package) if module.endswith('.py') and module not in exceptions] -__all__ = [] -for module in sorted(modules): - exec(f"from DeepPhysX.Core.Dataset.{module[:-3]} import {module[:-3]}") - __all__.append(module[:-3]) From 23daa56695179203505f6e14be58e5b3b9b51f4f Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 15:34:55 +0200 Subject: [PATCH 23/61] Adapt DataGeneration Pipeline in remote mode for DB Handlers. --- src/AsyncSocket/AbstractEnvironment.py | 5 ++++ src/AsyncSocket/TcpIpClient.py | 16 ++++++---- src/AsyncSocket/TcpIpServer.py | 35 ++++++++++++++++++++++ src/Database/DatabaseHandler.py | 24 +++++++++++---- src/Environment/BaseEnvironmentConfig.py | 9 ++---- src/Environment/launcherBaseEnvironment.py | 10 +++---- src/Manager/DataManager.py | 13 +------- src/Manager/DatabaseManager.py | 11 ++++--- src/Manager/EnvironmentManager.py | 1 - src/Pipelines/BasePipeline.py | 14 ++------- 10 files changed, 85 insertions(+), 53 deletions(-) diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index c1d9d0ef..573ef6cb 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -3,6 +3,8 @@ from SSD.Core.Storage.Database import Database +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler + class AbstractEnvironment: @@ -84,6 +86,9 @@ def close(self) -> None: ########################################################################################## ########################################################################################## + def get_database_handler(self) -> DatabaseHandler: + raise NotImplementedError + def _send_training_data(self) -> None: raise NotImplementedError diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 3c19e8a2..8e0fd539 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -17,7 +17,6 @@ def __init__(self, port: int = 10000, instance_id: int = 0, instance_nb: int = 1, - training_db: Optional[Union[Database, Tuple[str, str]]] = None, visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): """ TcpIpClient is both a TcpIpObject which communicate with a TcpIpServer and an AbstractEnvironment to compute @@ -70,6 +69,15 @@ async def __initialize(self) -> None: # Receive number of sub-steps self.simulations_per_step = await self.receive_data(loop=loop, sender=self.sock) + # Receive partitions + partitions_list = await self.receive_data(loop=loop, sender=self.sock) + partitions_list, exchange = partitions_list.split('%%%') + partitions = [[partitions_list.split('///')[0], partition_name] + for partition_name in partitions_list.split('///')[1:]] + exchange = [exchange.split('///')[0], exchange.split('///')[1]] + self.environment.get_database_handler().init_remote(storing_partitions=partitions, + exchange_db=exchange) + # Create the environment self.environment.create() self.environment.init() @@ -278,7 +286,5 @@ async def action_on_change_db(self, :param sender: TcpIpObject sender. """ - new_database = (await self.receive_data(loop=loop, sender=sender), - await self.receive_data(loop=loop, sender=sender)) - self.database = Database(database_dir=new_database[0], - database_name=new_database[1]).load() + new_database = await self.receive_data(loop=loop, sender=sender) + self.environment.get_database_handler().update_list_partitions_remote(new_database.split('///')) diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index e3d17700..e0a7eab2 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -6,6 +6,7 @@ from queue import SimpleQueue from DeepPhysX.Core.AsyncSocket.TcpIpObject import TcpIpObject +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler class TcpIpServer(TcpIpObject): @@ -55,12 +56,28 @@ def __init__(self, # Reference to EnvironmentManager self.environment_manager: Optional[Any] = manager + # Connect the Server to the Database + self.database_handler = DatabaseHandler(on_partitions_handler=self.__database_handler_partitions, + name='ServerHandler') + self.environment_manager.data_manager.connect_handler(self.database_handler) + ########################################################################################## ########################################################################################## # Connect Clients # ########################################################################################## ########################################################################################## + def __database_handler_partitions(self): + + for _, client in self.clients: + self.sync_send_command_change_db(receiver=client) + new_partition = self.database_handler.get_partitions()[-1] + self.sync_send_data(data_to_send=f'{new_partition.get_path()[0]}///{new_partition.get_path()[1]}', + receiver=client) + + def get_database_handler(self) -> DatabaseHandler: + return self.database_handler + def connect(self) -> None: """ Accept connections from clients. @@ -107,9 +124,27 @@ async def __initialize(self) -> None: # Initialisation process for each client for client_id, client in self.clients: + # Send number of sub-steps nb_steps = self.environment_manager.simulations_per_step if self.environment_manager else 1 await self.send_data(data_to_send=nb_steps, loop=loop, receiver=client) + + # Send partitions + partitions = self.database_handler.get_partitions() + if len(partitions) == 0: + partitions_list = 'None' + else: + partitions_list = partitions[0].get_path()[0] + for partition in partitions: + partitions_list += f'///{partition.get_path()[1]}' + partitions_list += '%%%' + exchange = self.database_handler.get_exchange() + if exchange is None: + partitions += 'None' + else: + partitions_list += f'{exchange.get_path()[0]}///{exchange.get_path()[1]}' + await self.send_data(data_to_send=partitions_list, loop=loop, receiver=client) + print(f"[{self.name}] Client n°{client_id} initialisation done") ########################################################################################## diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py index 7eae31f8..4e031ec9 100644 --- a/src/Database/DatabaseHandler.py +++ b/src/Database/DatabaseHandler.py @@ -8,13 +8,15 @@ class DatabaseHandler: def __init__(self, - remote: bool = False, - on_init_handler: Optional[Callable] = None): + name: str = '', + on_init_handler: Optional[Callable] = None, + on_partitions_handler: Optional[Callable] = None): - self.remote = remote + self.name = name self.__storing_partitions: List[Database] = [] self.__exchange_db: Optional[Database] = None self.__on_init_handler = self.__default_handler if on_init_handler is None else on_init_handler + self.__on_partitions_handler = self.__default_handler if on_partitions_handler is None else on_partitions_handler def __default_handler(self): pass @@ -42,6 +44,13 @@ def init_remote(self, def update_list_partitions(self, partition: Database) -> None: self.__storing_partitions.append(partition) + self.__on_partitions_handler() + + def update_list_partitions_remote(self, + partition: List[str]): + self.__storing_partitions.append(Database(database_dir=partition[0], + database_name=partition[1]).load()) + self.__on_partitions_handler() def get_database_dir(self): return self.__storing_partitions[0].get_path()[0] @@ -54,6 +63,9 @@ def load(self): def get_partitions(self): return self.__storing_partitions + def get_exchange(self): + return self.__exchange_db + ### # Database Architecture ### @@ -70,9 +82,9 @@ def create_fields(self, else: if len(self.__storing_partitions) == 1: self.__storing_partitions[0].load() - if len(self.__storing_partitions[0].get_fields(table_name=table_name)) <= 2: - self.__storing_partitions[0].create_fields(table_name=table_name, - fields=fields) + if len(self.__storing_partitions[0].get_fields(table_name=table_name)) <= 2: + self.__storing_partitions[0].create_fields(table_name=table_name, + fields=fields) def get_fields(self, table_name: str) -> List[str]: diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index 825583c8..208f4f3b 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -92,14 +92,12 @@ def __init__(self, def create_server(self, environment_manager: Optional[Any] = None, batch_size: int = 1, - training_db: Optional[str] = None, visualization_db: Optional[str] = None) -> TcpIpServer: """ Create a TcpIpServer and launch TcpIpClients in subprocesses. :param environment_manager: EnvironmentManager. :param batch_size: Number of sample in a batch. - :param training_db: Path of the training Database to connect to. :param visualization_db: Path to the visualization Database to connect to. :return: TcpIpServer object. """ @@ -117,7 +115,7 @@ def create_server(self, # Create clients client_threads = [] for i in range(self.number_of_thread): - client_thread = Thread(target=self.start_client, args=(i + 1, training_db, visualization_db)) + client_thread = Thread(target=self.start_client, args=(i + 1, visualization_db)) client_threads.append(client_thread) for client in client_threads: client.start() @@ -141,20 +139,17 @@ def start_server(self, def start_client(self, idx: int = 1, - training_db: Optional[str] = None, visualization_db: Optional[str] = None) -> None: """ Run a subprocess to start a TcpIpClient. :param idx: Index of client. - :param training_db: Path of the training Database to connect to. :param visualization_db: Path to the visualization Database to connect to. """ script = join(dirname(modules[BaseEnvironment.__module__].__file__), 'launcherBaseEnvironment.py') run([executable, script, self.environment_file, self.environment_class.__name__, - self.ip_address, str(self.port), str(idx), str(self.number_of_thread), - str(training_db), str(visualization_db)]) + self.ip_address, str(self.port), str(idx), str(self.number_of_thread), str(visualization_db)]) def create_environment(self, visualization_db: Optional[Any] = None) -> BaseEnvironment: diff --git a/src/Environment/launcherBaseEnvironment.py b/src/Environment/launcherBaseEnvironment.py index f60112de..d533f1ec 100644 --- a/src/Environment/launcherBaseEnvironment.py +++ b/src/Environment/launcherBaseEnvironment.py @@ -8,9 +8,9 @@ if __name__ == '__main__': # Check script call - if len(argv) != 9: + if len(argv) != 8: print(f"Usage: python3 {argv[0]} " - f" ") + f" ") exit(1) # Import environment_class @@ -19,13 +19,13 @@ exec(f"from {module_name} import {argv[2]} as Environment") # Create, init and run Tcp-Ip environment - training_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] - visualization_db = None if argv[8] == 'None' else [s[1:-1] for s in argv[8][1:-1].split(', ')] + visualization_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[8][1:-1].split(', ')] client = TcpIpClient(environment=Environment, ip_address=argv[3], port=int(argv[4]), instance_id=int(argv[5]), - instance_nb=int(argv[6])) + instance_nb=int(argv[6]), + visualization_db=visualization_db) client.initialize() client.launch() diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index be29997a..9f6a94a7 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -1,6 +1,6 @@ from typing import Any, Optional, Dict, List -from DeepPhysX.Core.Manager.DatabaseManager import DatabaseManager, Database +from DeepPhysX.Core.Manager.DatabaseManager import DatabaseManager from DeepPhysX.Core.Manager.EnvironmentManager import EnvironmentManager from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig @@ -37,7 +37,6 @@ def __init__(self, self.pipeline: Optional[Any] = pipeline self.database_manager: Optional[DatabaseManager] = None self.environment_manager: Optional[EnvironmentManager] = None - self.connected_managers: List[Any] = [] # Create a DatabaseManager self.database_manager = DatabaseManager(database_config=database_config, @@ -63,16 +62,6 @@ def __init__(self, def connect_handler(self, handler): self.database_manager.connect_handler(handler) - def get_database(self) -> Database: - return self.database_manager.database - - def connect_manager(self, manager: Any): - self.connected_managers.append(manager) - - def change_database(self) -> None: - for manager in self.connected_managers: - manager.change_database(self.database_manager.database) - @property def nb_environment(self): if self.environment_manager is None: diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index d0e3a8d6..e2a00f8b 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -362,12 +362,9 @@ def update_json(self, def connect_handler(self, handler: DatabaseHandler) -> None: - if handler.remote: - handler.init_remote(storing_partitions=self.get_partition_names(), - exchange_db=self.exchange.get_path()) - else: - handler.init(storing_partitions=self.get_partition_objects(), - exchange_db=self.exchange) + + handler.init(storing_partitions=self.get_partition_objects(), + exchange_db=self.exchange) self.database_handlers.append(handler) def index_samples(self): @@ -394,6 +391,8 @@ def add_data(self, # 1. Update the json file self.update_json(update_nb_samples=True) if self.first_add: + for handler in self.database_handlers: + handler.load() self.update_json(update_partitions_lists=True, update_shapes=True, update_architecture=True) self.first_add = False if self.normalize and self.mode == 'training' and self.pipeline == 'training' and data_lines is not None: diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index b4d33661..29da4128 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -58,7 +58,6 @@ def __init__(self, batch_size=batch_size, visualization_db=None if visualization_db is None else visualization_db.get_path()) - self.data_manager.get_database().load() else: self.environment = environment_config.create_environment(visualization_db=visualization_db) self.environment.environment_manager = self diff --git a/src/Pipelines/BasePipeline.py b/src/Pipelines/BasePipeline.py index 9f61ef1f..92d1b414 100644 --- a/src/Pipelines/BasePipeline.py +++ b/src/Pipelines/BasePipeline.py @@ -3,7 +3,6 @@ from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Manager.Manager import Manager from DeepPhysX.Core.Manager.DataManager import DataManager from DeepPhysX.Core.Manager.StatsManager import StatsManager from DeepPhysX.Core.Manager.NetworkManager import NetworkManager @@ -64,9 +63,6 @@ def __init__(self, self.new_session = new_session self.type = pipeline - # Main manager - self.manager: Optional[Manager] = None - def execute(self): """ Launch the Pipeline. @@ -83,16 +79,12 @@ def __get_any_manager(self, :return: The desired Manager associated with the Pipeline. """ - # If manager variable is not defined, cannot access other manager - if self.manager is None: - return None - # Direct access to manager if type(manager_names) == str: - return getattr(self.manager, manager_names) if hasattr(self.manager, manager_names) else None + return getattr(self, manager_names) if hasattr(self, manager_names) else None # Intermediates to access manager - accessed_manager = self.manager + accessed_manager = self for next_manager in manager_names: if hasattr(accessed_manager, next_manager): accessed_manager = getattr(accessed_manager, next_manager) @@ -127,7 +119,7 @@ def get_stats_manager(self) -> Optional[StatsManager]: return self.__get_any_manager(manager_names='stats_manager') - def get_dataset_manager(self) -> Optional[DatabaseManager]: + def get_database_manager(self) -> Optional[DatabaseManager]: """ Return the DatabaseManager associated with the pipeline if it exists. From 3426e07cf6dd9688b2a5efc6b4112100729598fa Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 15:51:58 +0200 Subject: [PATCH 24/61] Adapt Training Pipelines in remote mode for DB Handlers. --- src/AsyncSocket/TcpIpClient.py | 3 +-- src/AsyncSocket/TcpIpServer.py | 4 ++-- src/Environment/BaseEnvironment.py | 1 + src/Manager/EnvironmentManager.py | 1 - 4 files changed, 4 insertions(+), 5 deletions(-) diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 8e0fd539..5c58e2fa 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -85,7 +85,7 @@ async def __initialize(self) -> None: self.environment.init_visualization() # Initialization done - await self.send_command_done(loop=loop, receiver=self.sock) + await self.send_data(data_to_send='done', loop=loop, receiver=self.sock) ########################################################################################## ########################################################################################## @@ -270,7 +270,6 @@ async def action_on_step(self, self.environment._update_training_data(self.environment.update_line) line = self.environment.update_line self.environment._reset_training_data() - await self.send_command_done() await self.send_data(data_to_send=line, loop=loop, receiver=sender) async def action_on_change_db(self, diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index e0a7eab2..c99c4455 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -145,6 +145,8 @@ async def __initialize(self) -> None: partitions_list += f'{exchange.get_path()[0]}///{exchange.get_path()[1]}' await self.send_data(data_to_send=partitions_list, loop=loop, receiver=client) + # Wait Client init + await self.receive_data(loop=loop, sender=client) print(f"[{self.name}] Client n°{client_id} initialisation done") ########################################################################################## @@ -215,8 +217,6 @@ async def __communicate(self, if animate: await self.send_command_step(loop=loop, receiver=client) # Receive data - await self.listen_while_not_done(loop=loop, sender=client, data_dict=self.data_dict, - client_id=client_id) line = await self.receive_data(loop=loop, sender=client) self.data_lines.append(line) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 1bc31289..d076306d 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -62,6 +62,7 @@ def __init__(self, ########################################################################################## def __database_handler_init(self): + self.__database_handler.load() if self.instance_id == 1: self.__database_handler.create_fields(table_name='Training', fields=('env_id', int)) diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 29da4128..776d89d0 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -69,7 +69,6 @@ def __init__(self, self.environment.init_database() self.environment.init_visualization() - # Define get_data and dispatch methods self.get_database_handler = self.get_server_database_handler if self.server else self.get_environment_database_handler self.change_database = self.change_database_in_server if self.server else self.change_database_in_environment From 71efa8bada1a2140ae8afd6107d2a18e926b6a05 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 17:01:49 +0200 Subject: [PATCH 25/61] Fix prediction requests in local and remote modes. --- src/AsyncSocket/TcpIpClient.py | 11 ++++++----- src/AsyncSocket/TcpIpServer.py | 4 +++- src/Environment/BaseEnvironment.py | 16 +++++++++++----- src/Pipelines/BaseTraining.py | 1 - 4 files changed, 20 insertions(+), 12 deletions(-) diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 5c58e2fa..5b192e12 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -158,13 +158,13 @@ def get_prediction(self, """ # Get a prediction - self.database.update(table_name='Prediction', - data=kwargs, - line_id=self.environment.instance_id) + self.environment.get_database_handler().update(table_name='Exchange', + data=kwargs, + line_id=self.environment.instance_id) self.sync_send_command_prediction() _ = self.sync_receive_data() - data_pred = self.database.get_line(table_name='Prediction', - line_id=self.environment.instance_id) + data_pred = self.environment.get_database_handler().get_line(table_name='Exchange', + line_id=self.environment.instance_id) del data_pred['id'] return data_pred @@ -270,6 +270,7 @@ async def action_on_step(self, self.environment._update_training_data(self.environment.update_line) line = self.environment.update_line self.environment._reset_training_data() + await self.send_command_done(loop=loop, receiver=sender) await self.send_data(data_to_send=line, loop=loop, receiver=sender) async def action_on_change_db(self, diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index c99c4455..b9e89e2a 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -210,13 +210,15 @@ async def __communicate(self, return # Send the sample to the Client await self.send_command_sample(loop=loop, receiver=client) - line = int(self.batch_from_dataset.pop(0)) + line = self.batch_from_dataset.pop(0) await self.send_data(data_to_send=line, loop=loop, receiver=client) # 2. Execute n steps, the last one send data computation signal if animate: await self.send_command_step(loop=loop, receiver=client) # Receive data + await self.listen_while_not_done(loop=loop, sender=client, data_dict=self.data_dict, + client_id=client_id) line = await self.receive_data(loop=loop, sender=client) self.data_lines.append(line) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index d076306d..72c10e73 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -198,8 +198,7 @@ def set_training_data(self, **kwargs) -> None: # Check kwargs if self.__first_add[0]: - if self.instance_id != 0: - self.__database_handler.load() + self.__database_handler.load() self.__first_add[0] = False required_fields = list(set(self.__database_handler.get_fields(table_name='Training')) - {'id', 'env_id'}) for field in kwargs.keys(): @@ -272,8 +271,9 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: # Check kwargs if self.__first_add[1]: - if self.instance_id != 0: - self.__database_handler.load() + if len(kwargs) == 0 and len(self.__data_training) == 0: + raise ValueError(f"[{self.name}] The prediction request requires the network fields.") + self.__database_handler.load() self.__first_add[1] = False required_fields = list(set(self.__database_handler.get_fields(table_name='Exchange')) - {'id'}) for field in kwargs.keys(): @@ -281,9 +281,15 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." f"Required fields are {required_fields}.") + # Avoid empty sample + if len(kwargs) == 0: + required_fields = set(self.__database_handler.get_fields(table_name='Exchange')) - {'id'} + necessary_fields = list(required_fields.intersection(self.__data_training.keys())) + kwargs = {field: self.__data_training[field] for field in necessary_fields} + # If Environment is a TcpIpClient, send request to the Server if self.as_tcp_ip_client: - return TcpIpClient.get_prediction(self, **kwargs) + return self.tcp_ip_client.get_prediction(**kwargs) # Otherwise, check the hierarchy of managers if self.environment_manager.data_manager is None: diff --git a/src/Pipelines/BaseTraining.py b/src/Pipelines/BaseTraining.py index 9578a6ef..c4901910 100644 --- a/src/Pipelines/BaseTraining.py +++ b/src/Pipelines/BaseTraining.py @@ -226,7 +226,6 @@ def train_end(self) -> None: Called once at the end of the training pipeline. """ - self.network_manager.save_network(last_save=True) self.data_manager.close() self.network_manager.close() if self.stats_manager is not None: From 860c51f7177e589c1aa54cc566d2c34bbe1f973b Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 27 Oct 2022 17:48:55 +0200 Subject: [PATCH 26/61] Prevent prediction requests in DataGeneration Pipeline. --- src/AsyncSocket/TcpIpClient.py | 3 +++ src/AsyncSocket/TcpIpServer.py | 4 ++++ src/Environment/BaseEnvironment.py | 5 +++++ src/Manager/EnvironmentManager.py | 10 ++++++---- 4 files changed, 18 insertions(+), 4 deletions(-) diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 5b192e12..dbcf48d0 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -66,6 +66,9 @@ async def __initialize(self) -> None: loop = get_event_loop() + # Receive prediction requests authorization + self.allow_prediction_requests = await self.receive_data(loop=loop, sender=self.sock) + # Receive number of sub-steps self.simulations_per_step = await self.receive_data(loop=loop, sender=self.sock) diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index b9e89e2a..cd8de58a 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -125,6 +125,10 @@ async def __initialize(self) -> None: # Initialisation process for each client for client_id, client in self.clients: + # Send prediction request authorization + await self.send_data(data_to_send=self.environment_manager.allow_prediction_requests, + loop=loop, receiver=client) + # Send number of sub-steps nb_steps = self.environment_manager.simulations_per_step if self.environment_manager else 1 await self.send_data(data_to_send=nb_steps, loop=loop, receiver=client) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 72c10e73..81f664f4 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -271,6 +271,11 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: # Check kwargs if self.__first_add[1]: + + if (self.environment_manager is not None and not self.environment_manager.allow_prediction_requests) or \ + (self.tcp_ip_client is not None and not self.tcp_ip_client.allow_prediction_requests): + raise ValueError(f"[{self.name}] Prediction request is not available in data generation Pipeline.") + if len(kwargs) == 0 and len(self.__data_training) == 0: raise ValueError(f"[{self.name}] The prediction request requires the network fields.") self.__database_handler.load() diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 776d89d0..5d7275c6 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -19,15 +19,16 @@ def __init__(self, """ Deals with the online generation of data for both training and running of the neural networks. - :param environment_config: Specialisation containing the parameters of the environment manager. - :param session: Path to the session directory. + :param environment_config: Configuration object with the parameters of the Environment. :param data_manager: DataManager that handles the EnvironmentManager. - :param batch_size: Number of samples in a batch of data. + :param pipeline: Type of the pipeline. + :param session: Path to the session repository. + :param batch_size: Number of samples in a single batch. """ self.name: str = self.__class__.__name__ - # Managers architecture + # Session variables self.data_manager: Any = data_manager # Data producing parameters @@ -37,6 +38,7 @@ def __init__(self, self.always_produce: bool = environment_config.always_produce self.simulations_per_step: int = environment_config.simulations_per_step self.max_wrong_samples_per_step: int = environment_config.max_wrong_samples_per_step + self.allow_prediction_requests: bool = pipeline != 'data_generation' self.dataset_batch: Optional[List[List[int]]] = None # Create the Visualizer From c3330a29b3720fdb5287b68991b9fcea7e0996ba Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 28 Oct 2022 09:55:48 +0200 Subject: [PATCH 27/61] Prevent prediction request in DataGeneration Pipeline. --- src/Environment/BaseEnvironment.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 81f664f4..95fd910e 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -293,21 +293,25 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: kwargs = {field: self.__data_training[field] for field in necessary_fields} # If Environment is a TcpIpClient, send request to the Server - if self.as_tcp_ip_client: + if self.tcp_ip_client is not None: return self.tcp_ip_client.get_prediction(**kwargs) # Otherwise, check the hierarchy of managers - if self.environment_manager.data_manager is None: - raise ValueError("Cannot request prediction if DataManager does not exist") - # Get a prediction - self.__database_handler.update(table_name='Exchange', - data=kwargs, - line_id=self.instance_id) - self.environment_manager.data_manager.get_prediction(self.instance_id) - data_pred = self.__database_handler.get_line(table_name='Exchange', - line_id=self.instance_id) - del data_pred['id'] - return data_pred + elif self.environment_manager is not None: + if self.environment_manager.data_manager is None: + raise ValueError("Cannot request prediction if DataManager does not exist") + # Get a prediction + self.__database_handler.update(table_name='Exchange', + data=kwargs, + line_id=self.instance_id) + self.environment_manager.data_manager.get_prediction(self.instance_id) + data_pred = self.__database_handler.get_line(table_name='Exchange', + line_id=self.instance_id) + del data_pred['id'] + return data_pred + + else: + raise ValueError(f"[{self.name}] This Environment has not Manager.") def _get_prediction(self): """ From 22b9c8acde5d25134d2457d98ee72a2eb0f18204 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 28 Oct 2022 15:51:21 +0200 Subject: [PATCH 28/61] Update comments and docstrings. --- src/AsyncSocket/AbstractEnvironment.py | 4 +- src/AsyncSocket/TcpIpClient.py | 22 +- src/AsyncSocket/TcpIpServer.py | 60 ++--- src/Database/BaseDatabaseConfig.py | 4 +- src/Database/DatabaseHandler.py | 139 +++++++++-- src/Environment/BaseEnvironment.py | 241 +++++++++++------- src/Environment/BaseEnvironmentConfig.py | 3 +- src/Manager/DataManager.py | 62 +++-- src/Manager/DatabaseManager.py | 304 ++++++++++++++--------- src/Manager/EnvironmentManager.py | 143 ++++++----- src/Manager/NetworkManager.py | 146 ++++++----- src/Manager/StatsManager.py | 41 ++- src/Network/BaseNetwork.py | 68 ++--- src/Network/BaseNetworkConfig.py | 2 +- src/Network/BaseOptimization.py | 12 +- src/Network/DataTransformation.py | 19 +- src/Pipelines/BaseDataGeneration.py | 27 +- src/Pipelines/BasePipeline.py | 46 ++-- src/Pipelines/BasePrediction.py | 28 +-- src/Pipelines/BaseTraining.py | 33 ++- 20 files changed, 848 insertions(+), 556 deletions(-) diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 573ef6cb..55423c61 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -46,7 +46,7 @@ def __init__(self, ########################################################################################## ########################################################################################## - # Initializing Environment # + # Environment initialization # ########################################################################################## ########################################################################################## @@ -82,7 +82,7 @@ def close(self) -> None: ########################################################################################## ########################################################################################## - # Defining a sample # + # Defining data samples # ########################################################################################## ########################################################################################## diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index dbcf48d0..66c5e821 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -6,7 +6,7 @@ from numpy import ndarray from DeepPhysX.Core.AsyncSocket.TcpIpObject import TcpIpObject -from DeepPhysX.Core.AsyncSocket.AbstractEnvironment import AbstractEnvironment, Database +from DeepPhysX.Core.AsyncSocket.AbstractEnvironment import AbstractEnvironment class TcpIpClient(TcpIpObject): @@ -17,33 +17,33 @@ def __init__(self, port: int = 10000, instance_id: int = 0, instance_nb: int = 1, - visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): + visualization_db: Optional[Union[Tuple[str, str]]] = None): """ - TcpIpClient is both a TcpIpObject which communicate with a TcpIpServer and an AbstractEnvironment to compute - simulated data. + TcpIpClient is a TcpIpObject which communicate with a TcpIpServer and manages an Environment to compute data. + :param environment: Environment class. :param ip_address: IP address of the TcpIpObject. :param port: Port number of the TcpIpObject. :param instance_id: Index of this instance. :param instance_nb: Number of simultaneously launched instances. + :param visualization_db: Path to the visualization Database. """ TcpIpObject.__init__(self, ip_address=ip_address, port=port) + # Create instance self.environment = environment(as_tcp_ip_client=True, instance_id=instance_id, instance_nb=instance_nb, visualization_db=visualization_db) self.environment.tcp_ip_client = self - # Bind to client address + # Bind to client address and send ID self.sock.connect((ip_address, port)) - # Send ID self.sync_send_labeled_data(data_to_send=instance_id, label="instance_ID", receiver=self.sock, send_read_command=False) - # Flag to trigger client's shutdown self.close_client: bool = False ########################################################################################## @@ -81,7 +81,7 @@ async def __initialize(self) -> None: self.environment.get_database_handler().init_remote(storing_partitions=partitions, exchange_db=exchange) - # Create the environment + # Initialize the environment self.environment.create() self.environment.init() self.environment.init_database() @@ -152,8 +152,7 @@ async def __close(self) -> None: ########################################################################################## ########################################################################################## - def get_prediction(self, - **kwargs) -> Dict[str, ndarray]: + def get_prediction(self, **kwargs) -> Dict[str, ndarray]: """ Request a prediction from Network. @@ -288,6 +287,7 @@ async def action_on_change_db(self, :param loop: Asyncio event loop. :param sender: TcpIpObject sender. """ - + + # Update the partition list in the DatabaseHandler new_database = await self.receive_data(loop=loop, sender=sender) self.environment.get_database_handler().update_list_partitions_remote(new_database.split('///')) diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index cd8de58a..7dc5ff64 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -51,32 +51,45 @@ def __init__(self, self.sample_to_client_id: List[int] = [] self.batch_from_dataset: Optional[List[int]] = None self.first_time: bool = True - self.data_lines: List[int] = [] + self.data_lines: List[List[int]] = [] # Reference to EnvironmentManager self.environment_manager: Optional[Any] = manager # Connect the Server to the Database - self.database_handler = DatabaseHandler(on_partitions_handler=self.__database_handler_partitions, - name='ServerHandler') + self.database_handler = DatabaseHandler(on_partitions_handler=self.__database_handler_partitions) self.environment_manager.data_manager.connect_handler(self.database_handler) ########################################################################################## ########################################################################################## - # Connect Clients # + # DatabaseHandler management # ########################################################################################## ########################################################################################## - def __database_handler_partitions(self): + def get_database_handler(self) -> DatabaseHandler: + """ + Get the DatabaseHandler of the TcpIpServer. + """ + + return self.database_handler + + def __database_handler_partitions(self) -> None: + """ + Partition update event of the DatabaseHandler. + """ + # Send the new partition to every Client for _, client in self.clients: self.sync_send_command_change_db(receiver=client) new_partition = self.database_handler.get_partitions()[-1] self.sync_send_data(data_to_send=f'{new_partition.get_path()[0]}///{new_partition.get_path()[1]}', receiver=client) - def get_database_handler(self) -> DatabaseHandler: - return self.database_handler + ########################################################################################## + ########################################################################################## + # Connect Clients # + ########################################################################################## + ########################################################################################## def connect(self) -> None: """ @@ -160,7 +173,7 @@ async def __initialize(self) -> None: ########################################################################################## def get_batch(self, - animate: bool = True) -> List[int]: + animate: bool = True) -> List[List[int]]: """ Build a batch from clients samples. @@ -195,10 +208,7 @@ async def __communicate(self, client_id: Optional[int] = None, animate: bool = True) -> None: """ - | Communication protocol with a client. It goes through different steps: - | 1) Eventually send samples to Client - | 2) Running steps & Receiving training data - | 3) Add data to the Queue + Communication protocol with a client. :param client: TcpIpObject client to communicate with. :param client_id: Index of the client. @@ -231,36 +241,12 @@ def set_dataset_batch(self, """ Receive a batch of data from the Dataset. Samples will be dispatched between clients. - :param data_lines: Batch of data. + :param data_lines: Batch of indices of samples. """ # Define batch from dataset self.batch_from_dataset = data_lines.copy() - def change_database(self, - database: str): - """ - Change the Database to connect to for each Client. - - :param database: Path to the new Database. - """ - - async_run(self.__change_database(database)) - - async def __change_database(self, - database: str): - """ - Change the Database to connect to for each Client. - - :param database: Path to the new Database. - """ - - loop = get_event_loop() - for client_id, client in self.clients: - await self.send_command_change_db(loop=loop, receiver=client) - await self.send_data(data_to_send=database[0], loop=loop, receiver=client) - await self.send_data(data_to_send=database[1], loop=loop, receiver=client) - ########################################################################################## ########################################################################################## # Server & Client shutdown # diff --git a/src/Database/BaseDatabaseConfig.py b/src/Database/BaseDatabaseConfig.py index 804846a1..0ffe5934 100644 --- a/src/Database/BaseDatabaseConfig.py +++ b/src/Database/BaseDatabaseConfig.py @@ -12,7 +12,7 @@ def __init__(self, normalize: bool = False, recompute_normalization: bool = False): """ - Configuration class to parameterize the Dataset and its Manager. + BaseDatabaseConfig is a configuration class to parameterize the Database and the DatabaseManager. :param existing_dir: Path to an existing Dataset repository. :param mode: Specify the Dataset mode that should be used between 'training', 'validation' and 'running'. @@ -59,7 +59,7 @@ def __init__(self, self.normalize: bool = normalize self.recompute_normalization: bool = recompute_normalization - def __str__(self) -> str: + def __str__(self): description = "\n" description += f"{self.name}\n" diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py index 4e031ec9..44ebd548 100644 --- a/src/Database/DatabaseHandler.py +++ b/src/Database/DatabaseHandler.py @@ -8,26 +8,42 @@ class DatabaseHandler: def __init__(self, - name: str = '', on_init_handler: Optional[Callable] = None, on_partitions_handler: Optional[Callable] = None): + """ + DatabaseHandler allows components to be synchronized with the Database partitions and to read / write data. - self.name = name + :param on_init_handler: Event to trigger when the DatabaseHandler is initialized. + :param on_partitions_handler: Event to trigger when the list of partitions is updated. + """ + + # Databases variables self.__storing_partitions: List[Database] = [] self.__exchange_db: Optional[Database] = None - self.__on_init_handler = self.__default_handler if on_init_handler is None else on_init_handler - self.__on_partitions_handler = self.__default_handler if on_partitions_handler is None else on_partitions_handler - def __default_handler(self): + # Event handlers + self.__on_init_handler = self.default_handler if on_init_handler is None else on_init_handler + self.__on_partitions_handler = self.default_handler if on_partitions_handler is None else on_partitions_handler + + def default_handler(self): pass - ### - # Manage partitions - ### + ########################################################################################## + ########################################################################################## + # Partitions management # + ########################################################################################## + ########################################################################################## def init(self, storing_partitions: List[Database], exchange_db: Database) -> None: + """ + Initialize the list of the partitions. + + :param storing_partitions: List of the storing Database partitions. + :param exchange_db: Exchange Database. + """ + self.__storing_partitions = storing_partitions.copy() self.__exchange_db = exchange_db self.__on_init_handler() @@ -35,6 +51,13 @@ def init(self, def init_remote(self, storing_partitions: List[List[str]], exchange_db: List[str]) -> None: + """ + Initialize the list of partitions in remote DatabaseHandlers. + + :param storing_partitions: List of paths to the storing Database partitions. + :param exchange_db: Path to the exchange Database. + """ + self.__storing_partitions = [Database(database_dir=partition[0], database_name=partition[1]).load() for partition in storing_partitions] self.__exchange_db = Database(database_dir=exchange_db[0], @@ -43,42 +66,81 @@ def init_remote(self, def update_list_partitions(self, partition: Database) -> None: + """ + Add a new storing partition to the list. + + :param partition: New storing partition to add. + """ + self.__storing_partitions.append(partition) self.__on_partitions_handler() def update_list_partitions_remote(self, - partition: List[str]): + partition: List[str]) -> None: + """ + Add a new storing partition to the list in remote DatabaseHandler. + + :param partition: Path to the new storing partition. + """ + self.__storing_partitions.append(Database(database_dir=partition[0], database_name=partition[1]).load()) self.__on_partitions_handler() - def get_database_dir(self): - return self.__storing_partitions[0].get_path()[0] + def load(self) -> None: + """ + Load the Database partitions stored by the component. + """ - def load(self): for db in self.__storing_partitions: db.load() self.__exchange_db.load() - def get_partitions(self): + def get_database_dir(self) -> str: + """ + Get the database repository of the session. + """ + + return self.__storing_partitions[0].get_path()[0] + + def get_partitions(self) -> List[Database]: + """ + Get the storing Database partitions. + """ + return self.__storing_partitions - def get_exchange(self): + def get_exchange(self) -> Database: + """ + Get the exchange Database. + """ + return self.__exchange_db - ### - # Database Architecture - ### + ########################################################################################## + ########################################################################################## + # Databases architecture # + ########################################################################################## + ########################################################################################## def create_fields(self, table_name: str, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + """ + Create new Fields in a Table from one of the Databases. + + :param table_name: Name of the Table. + :param fields: Field or list of Fields names and types. + """ + # Create the Field(s) in the exchange Database if table_name == 'Exchange': self.__exchange_db.load() if len(self.__exchange_db.get_fields(table_name=table_name)) == 1: self.__exchange_db.create_fields(table_name=table_name, fields=fields) + + # Create the Field(s) in the storing Database else: if len(self.__storing_partitions) == 1: self.__storing_partitions[0].load() @@ -88,20 +150,36 @@ def create_fields(self, def get_fields(self, table_name: str) -> List[str]: + """ + Get the list of Fields in a Table. + + :param table_name: Name of the Table. + """ database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[0] return database.get_fields(table_name=table_name) - ### - # Data Read / Write - ### + ########################################################################################## + ########################################################################################## + # Databases editing # + ########################################################################################## + ########################################################################################## def add_data(self, table_name: str, data: Dict[str, Any]) -> Union[int, List[int]]: + """ + Add a new line of data in a Database. + + :param table_name: Name of the Table. + :param data: New line of the Table. + """ + # Add data in the exchange Database if table_name == 'Exchange': return self.__exchange_db.add_data(table_name=table_name, data=data) + + # Add data in the storing Database else: return [len(self.__storing_partitions) - 1, self.__storing_partitions[-1].add_data(table_name=table_name, data=data)] @@ -110,6 +188,13 @@ def update(self, table_name: str, data: Dict[str, Any], line_id: Union[int, List[int]]) -> None: + """ + Update a line in a Database. + + :param table_name: Name of the Table. + :param data: Updated line of the Table. + :param line_id: Index of the line to edit. + """ database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[line_id[0]] line_id = line_id[1] if type(line_id) == list else line_id @@ -119,6 +204,13 @@ def get_line(self, table_name: str, line_id: Union[int, List[int]], fields: Optional[Union[str, List[str]]] = None) -> Dict[str, Any]: + """ + Get a line of data from a Database. + + :param table_name: Name of the Table. + :param line_id: Index of the line to get. + :param fields: Data fields to extract. + """ database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[line_id[0]] line_id = line_id[1] if type(line_id) == list else line_id @@ -130,6 +222,13 @@ def get_lines(self, table_name: str, lines_id: List[List[int]], fields: Optional[Union[str, List[str]]] = None) -> Dict[str, Any]: + """ + Get lines of data from a Database. + + :param table_name: Name of the Table. + :param lines_id: Indices of the lines to get. + :param fields: Data fields to extract. + """ # Transform list of lines to batch of lines per partition batch_indices = array(lines_id) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 95fd910e..2f3c2b70 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -17,12 +17,12 @@ def __init__(self, instance_nb: int = 1, visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): """ - BaseEnvironment is an environment class to compute simulated data for the network and its optimization process. + BaseEnvironment computes simulated data for the Network and its training process. + :param as_tcp_ip_client: Environment is a TcpIpObject if True, is owned by an EnvironmentManager if False. :param instance_id: ID of the instance. :param instance_nb: Number of simultaneously launched instances. - :param as_tcp_ip_client: Environment is a TcpIpObject if True, is owned by an EnvironmentManager if False. - :param visualization_db: The path to the visualization Database or the visualization Database object to connect to. + :param visualization_db: The path to the visualization Database or the visualization Database object to connect. """ AbstractEnvironment.__init__(self, @@ -57,21 +57,10 @@ def __init__(self, ########################################################################################## ########################################################################################## - # Initializing Environment # + # Environment initialization # ########################################################################################## ########################################################################################## - def __database_handler_init(self): - self.__database_handler.load() - if self.instance_id == 1: - self.__database_handler.create_fields(table_name='Training', - fields=('env_id', int)) - self.__database_handler.create_fields(table_name='Additional', - fields=('env_id', int)) - - def get_database_handler(self) -> DatabaseHandler: - return self.__database_handler - def create(self) -> None: """ Create the Environment. Automatically called when Environment is launched. @@ -104,6 +93,41 @@ def init_visualization(self) -> None: pass + def save_parameters(self, **kwargs) -> None: + """ + Save a set of parameters in the Database. + """ + + # Create a dedicated Database + database_dir = self.__database_handler.get_database_dir() + if isfile(join(database_dir, 'environment_parameters.db')): + database = Database(database_dir=database_dir, + database_name='environment_parameters').load() + else: + database = Database(database_dir=database_dir, + database_name='environment_parameters').new() + + # Create fields and add data + fields = [(field, type(value)) for field, value in kwargs.items()] + database.create_table(table_name=f'Environment_{self.instance_id}', fields=fields) + database.add_data(table_name=f'Environment_{self.instance_id}', data=kwargs) + database.close() + + def load_parameters(self) -> Dict[str, Any]: + """ + Load a set of parameters from the Database. + """ + + # Load the dedicated Database and the parameters + database_dir = self.__database_handler.get_database_dir() + if isfile(join(database_dir, 'environment_parameters.db')): + database = Database(database_dir=database_dir, + database_name='environment_parameters').load() + parameters = database.get_line(table_name=f'Environment_{self.instance_id}') + del parameters['id'] + return parameters + return {} + ########################################################################################## ########################################################################################## # Environment behavior # @@ -128,7 +152,8 @@ def check_sample(self) -> bool: return True - def apply_prediction(self, prediction: Dict[str, ndarray]) -> None: + def apply_prediction(self, + prediction: Dict[str, ndarray]) -> None: """ Apply network prediction in environment. Not mandatory. @@ -148,46 +173,29 @@ def close(self) -> None: ########################################################################################## ########################################################################################## - # Defining a sample # + # Defining data samples # ########################################################################################## ########################################################################################## - def save_parameters(self, **kwargs) -> None: - """ - Save a set of parameters in the Database. + def define_training_fields(self, + fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: """ + Specify the training data fields names and types. - database_dir = self.__database_handler.get_database_dir() - if isfile(join(database_dir, 'environment_parameters.db')): - database = Database(database_dir=database_dir, - database_name='environment_parameters').load() - else: - database = Database(database_dir=database_dir, - database_name='environment_parameters').new() - fields = [(field, type(value)) for field, value in kwargs.items()] - database.create_table(table_name=f'Environment_{self.instance_id}', fields=fields) - database.add_data(table_name=f'Environment_{self.instance_id}', data=kwargs) - database.close() - - def load_parameters(self) -> Dict[str, Any]: - """ - Load a set of parameters from the Database. + :param fields: Field or list of fields to tag as training data. """ - database_dir = self.__database_handler.get_database_dir() - if isfile(join(database_dir, 'environment_parameters.db')): - database = Database(database_dir=database_dir, - database_name='environment_parameters').load() - parameters = database.get_line(table_name=f'Environment_{self.instance_id}') - del parameters['id'] - return parameters - return {} - - def define_training_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: self.__database_handler.create_fields(table_name='Training', fields=fields) - def define_additional_fields(self, fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + def define_additional_fields(self, + fields: Union[List[Tuple[str, Type]], Tuple[str, Type]]) -> None: + """ + Specify the additional data fields names and types. + + :param fields: Field or list of Fields to tag as additional data. + """ + self.__database_handler.create_fields(table_name='Additional', fields=fields) @@ -217,45 +225,15 @@ def set_training_data(self, **kwargs) -> None: def set_additional_data(self, **kwargs) -> None: + """ + Set the additional data to send to the TcpIpServer or the EnvironmentManager. + """ + # Additional data is also set if the Environment can compute data if self.compute_training_data: self.__data_additional = kwargs self.__data_additional['env_id'] = self.instance_id - def _send_training_data(self) -> int: - line_id = self.__database_handler.add_data(table_name='Training', - data=self.__data_training) - self.__database_handler.add_data(table_name='Additional', - data=self.__data_additional) - return line_id - - def _reset_training_data(self) -> None: - self.__data_training = {} - self.__data_additional = {} - self.sample_training = None - self.sample_additional = None - self.update_line = None - - def _update_training_data(self, - line_id: List[int]) -> None: - if self.__data_training != {}: - self.__database_handler.update(table_name='Training', - data=self.__data_training, - line_id=line_id) - if self.__data_additional != {}: - self.__database_handler.update(table_name='Additional', - data=self.__data_additional, - line_id=line_id) - - def _get_training_data(self, - line: List[int]) -> None: - self.update_line = line - self.sample_training = self.__database_handler.get_line(table_name='Training', - line_id=line) - self.sample_additional = self.__database_handler.get_line(table_name='Additional', - line_id=line) - self.sample_additional = None if len(self.sample_additional) == 1 else self.sample_additional - ########################################################################################## ########################################################################################## # Available requests # @@ -313,9 +291,20 @@ def get_prediction(self, **kwargs) -> Dict[str, ndarray]: else: raise ValueError(f"[{self.name}] This Environment has not Manager.") - def _get_prediction(self): + def update_visualisation(self) -> None: """ + Triggers the Visualizer update. + """ + + # If Environment is a TcpIpClient, request to the Server + if self.as_tcp_ip_client: + self.tcp_ip_client.request_update_visualization() + self.factory.render() + def _get_prediction(self): + """ + Request a prediction from Network and apply it to the Environment. + Should not be used by users. """ training_data = self.__data_training.copy() @@ -325,17 +314,93 @@ def _get_prediction(self): del training_data[field] self.apply_prediction(self.get_prediction(**training_data)) - def update_visualisation(self) -> None: + ########################################################################################## + ########################################################################################## + # Database communication # + ########################################################################################## + ########################################################################################## + + def get_database_handler(self) -> DatabaseHandler: """ - Triggers the Visualizer update. + Get the DatabaseHandler of the Environment. """ - # If Environment is a TcpIpClient, request to the Server - if self.as_tcp_ip_client: - self.request_update_visualization() - self.factory.render() + return self.__database_handler + + def __database_handler_init(self): + """ + Init event of the DatabaseHandler. + """ + + # Load Database and create basic fields + self.__database_handler.load() + if self.instance_id == 1: + self.__database_handler.create_fields(table_name='Training', + fields=('env_id', int)) + self.__database_handler.create_fields(table_name='Additional', + fields=('env_id', int)) + + def _send_training_data(self) -> List[int]: + """ + Add the training data and the additional data in their respective Databases. + Should not be used by users. + + :return: Index of the samples in the Database. + """ + + line_id = self.__database_handler.add_data(table_name='Training', + data=self.__data_training) + self.__database_handler.add_data(table_name='Additional', + data=self.__data_additional) + return line_id + + def _update_training_data(self, + line_id: List[int]) -> None: + """ + Update the training data and the additional data in their respective Databases. + Should not be used by users. + + :param line_id: Index of the samples to update. + """ + + if self.__data_training != {}: + self.__database_handler.update(table_name='Training', + data=self.__data_training, + line_id=line_id) + if self.__data_additional != {}: + self.__database_handler.update(table_name='Additional', + data=self.__data_additional, + line_id=line_id) + + def _get_training_data(self, + line_id: List[int]) -> None: + """ + Get the training data and the additional data from their respective Databases. + Should not be used by users. + + :param line_id: Index of the sample to get. + """ + + self.update_line = line_id + self.sample_training = self.__database_handler.get_line(table_name='Training', + line_id=line_id) + self.sample_additional = self.__database_handler.get_line(table_name='Additional', + line_id=line_id) + self.sample_additional = None if len(self.sample_additional) == 1 else self.sample_additional + + def _reset_training_data(self) -> None: + """ + Reset the training data and the additional data variables. + Should not be used by users. + """ + + self.__data_training = {} + self.__data_additional = {} + self.sample_training = None + self.sample_additional = None + self.update_line = None - def __str__(self) -> str: + def __str__(self): description = "\n" description += f" {self.name}\n" diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index 208f4f3b..25bcfcfb 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -39,6 +39,7 @@ def __init__(self, :param load_samples: If True, the dataset will always be used in the environment. :param only_first_epoch: If True, data will always be created from environment. If False, data will be created from the environment during the first epoch and then re-used from the Dataset. + :param always_produce: If True, data will always be produced in Environment(s). :param visualizer: Class of the Visualizer to use. :param record_wrong_samples: If True, wrong samples are recorded through Visualizer. """ @@ -168,7 +169,7 @@ def create_environment(self, f"BaseEnvironment.") return environment - def __str__(self) -> str: + def __str__(self): description = "\n" description += f"{self.name}\n" diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 9f6a94a7..cc112799 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -4,6 +4,7 @@ from DeepPhysX.Core.Manager.EnvironmentManager import EnvironmentManager from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler class DataManager: @@ -19,21 +20,19 @@ def __init__(self, """ DataManager deals with the generation, storage and loading of training data. - A batch is given with a call to 'get_data' on either the DatabaseManager or the EnvironmentManager according to - the context. :param pipeline: Pipeline that handle the DataManager. - :param database_config: Specialisation containing the parameters of the dataset manager. - :param environment_config: Specialisation containing the parameters of the environment manager. - :param session: Path to the session directory. - :param new_session: Flag that indicates whether if the session is new. - :param produce_data: Flag that indicates whether if this session is producing data. - :param int batch_size: Number of samples in a batch. + :param database_config: Configuration object with the parameters of the Database. + :param environment_config: Configuration object with the parameters of the Environment. + :param session: Path to the session repository. + :param new_session: If True, the session is done in a new repository. + :param produce_data: If True, this session will store data in the Database. + :param batch_size: Number of samples in a single batch. """ self.name: str = self.__class__.__name__ - # Managers variables + # Session variables self.pipeline: Optional[Any] = pipeline self.database_manager: Optional[DatabaseManager] = None self.environment_manager: Optional[EnvironmentManager] = None @@ -59,15 +58,34 @@ def __init__(self, self.batch_size = batch_size self.data_lines: List[List[int]] = [] - def connect_handler(self, handler): - self.database_manager.connect_handler(handler) - @property - def nb_environment(self): + def nb_environment(self) -> Optional[int]: + """ + Get the number of Environments managed by the EnvironmentManager. + """ + if self.environment_manager is None: return None return 1 if self.environment_manager.server is None else self.environment_manager.number_of_thread + @property + def normalization(self) -> Dict[str, List[float]]: + """ + Get the normalization coefficients computed by the DatabaseManager. + """ + + return self.database_manager.normalization + + def connect_handler(self, + handler: DatabaseHandler) -> None: + """ + Add a new DatabaseHandler to the list of handlers of the DatabaseManager. + + :param handler: New handler to register. + """ + + self.database_manager.connect_handler(handler) + def get_data(self, epoch: int = 0, animate: bool = True) -> None: @@ -75,8 +93,7 @@ def get_data(self, Fetch data from the EnvironmentManager or the DatabaseManager according to the context. :param epoch: Current epoch number. - :param animate: Allow EnvironmentManager to generate a new sample. - :return: Dict containing the newly computed data. + :param animate: Allow EnvironmentManager to trigger a step itself in order to generate a new sample. """ # Data generation case @@ -128,9 +145,7 @@ def get_data(self, def get_prediction(self, instance_id: int) -> None: """ - Get a Network prediction from an input array. Normalization is applied on input and prediction. - - :return: Network prediction. + Get a Network prediction for the specified Environment instance. """ # Get a prediction @@ -139,13 +154,9 @@ def get_prediction(self, self.pipeline.network_manager.compute_online_prediction(instance_id=instance_id, normalization=self.normalization) - @property - def normalization(self) -> Dict[str, List[float]]: - return self.database_manager.normalization - def close(self) -> None: """ - Launch the closing procedure of Managers. + Launch the closing procedure of the DataManager. """ if self.environment_manager is not None: @@ -153,10 +164,7 @@ def close(self) -> None: if self.database_manager is not None: self.database_manager.close() - def __str__(self) -> str: - """ - :return: A string containing valuable information about the DataManager - """ + def __str__(self): data_manager_str = "" if self.environment_manager: diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index e2a00f8b..3c7f9b2c 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -26,15 +26,17 @@ def __init__(self, """ DatabaseManager handle all operations with input / output files. Allows saving and read tensors from files. - :param database_config: Specialisation containing the parameters of the dataset manager - :param data_manager: DataManager that handles the DatabaseManager - :param new_session: Define the creation of new directories to store data - :param produce_data: True if this session is a network training + :param database_config: Configuration object with the parameters of the Database. + :param data_manager: DataManager that handles the DatabaseManager. + :param pipeline: Type of the Pipeline. + :param session: Path to the session repository. + :param new_session: If True, the session is done in a new repository. + :param produce_data: If True, this session will store data in the Database. """ self.name: str = self.__class__.__name__ - # Manager variables + # Session variables self.pipeline: str = pipeline self.data_manager: Optional[Any] = data_manager self.database_dir: str = join(session, 'dataset') @@ -138,14 +140,19 @@ def __init__(self, database_name='Exchange').new() self.exchange.create_table(table_name='Exchange') - ######################### - # Partitions Management # - ######################### + ########################################################################################## + ########################################################################################## + # Partitions Management # + ########################################################################################## + ########################################################################################## def load_directory(self, - rename_partitions: bool = False): + rename_partitions: bool = False) -> None: """ + Get the Database information from the json file (partitions, samples, etc). + Load all the partitions or create one if necessary. + :param rename_partitions: If True, the existing partitions should be renamed to match the session name. """ # 1. Check the directory existence to prevent bugs @@ -165,7 +172,8 @@ def load_directory(self, self.update_json() if self.recompute_normalization or ( self.normalize and self.json_content['normalization'] == self.json_default['normalization']): - self.update_json(update_normalization=True) + self.json_content['normalization'] = self.compute_normalization() + self.update_json() # 4. Load partitions for each mode self.partition_names = self.json_content['partitions'] @@ -193,9 +201,9 @@ def load_directory(self, # 6. Index partitions self.index_samples() - def create_partition(self): + def create_partition(self) -> None: """ - + Create a new partition of the Database. """ # 1. Define the partition name @@ -229,33 +237,61 @@ def create_partition(self): for handler in self.database_handlers: handler.update_list_partitions(self.partitions[self.mode][-1]) self.partition_index[self.mode] += 1 - self.update_json(update_partitions_lists=True, update_nb_samples=True) + self.json_content['partitions'] = self.partition_names + self.get_nb_samples() + self.update_json() def get_partition_objects(self) -> List[Database]: + """ + Get the list of partitions of the Database for the current mode. + """ + return self.partitions[self.mode] def get_partition_names(self) -> List[List[str]]: + """ + Get the list of partition paths of the Database for the current mode. + """ + return [db.get_path() for db in self.partitions[self.mode]] def remove_empty_partitions(self): + """ + Remove every empty partitions of the Database. + """ for mode in self.modes: + # Check if the last partition for the mode is empty if len(self.partitions[mode]) > 0 and self.partitions[mode][-1].nb_lines(table_name='Training') == 0: - database = self.partitions[mode].pop(-1) - path = database.get_path() + # Erase partition file + path = self.partitions[mode].pop(-1).get_path() remove(join(path[0], f'{path[1]}.db')) + # Remove from information self.partition_names[mode].pop(-1) self.partition_index[mode] -= 1 self.json_content['nb_samples'][mode].pop(-1) - self.update_json(update_partitions_lists=True) + self.json_content['partitions'] = self.partition_names + self.update_json() - ################## - # JSON info file # - ################## + def change_mode(self, + mode: str) -> None: + """ + Change the current Database mode. - def search_partitions_info(self): + :param mode: Name of the Database mode. """ + pass + + ########################################################################################## + ########################################################################################## + # JSON Information file # + ########################################################################################## + ########################################################################################## + + def search_partitions_info(self) -> None: + """ + Get the information about the Database manually if the json file is not found. """ # 1. Get all the partitions @@ -279,7 +315,12 @@ def search_partitions_info(self): # 4. Get the data shapes self.json_content['data_shape'] = self.get_data_shapes() - def get_database_architecture(self): + def get_database_architecture(self) -> Dict[str, List[str]]: + """ + Get the Tables and Fields structure of the Database. + """ + + # Get a training or validation partition if len(self.json_content['partitions']['training']) != 0: db = Database(database_dir=self.database_dir, database_name=self.json_content['partitions']['training'][0]).load() @@ -288,17 +329,23 @@ def get_database_architecture(self): database_name=self.json_content['partitions']['validation'][0]).load() else: return {} + + # Get the architecture, keep relevant fields only architecture = db.get_architecture() - if 'Prediction' in architecture.keys(): - del architecture['Prediction'] for fields in architecture.values(): for field in fields.copy(): if field.split(' ')[0] in ['id', '_dt_']: fields.remove(field) db.close() + return architecture - def get_data_shapes(self): + def get_data_shapes(self) -> Dict[str, List[int]]: + """ + Get the shape of data Fields. + """ + + # Get a training or validation partition if len(self.json_content['partitions']['training']) != 0: db = Database(database_dir=self.database_dir, database_name=self.json_content['partitions']['training'][0]).load() @@ -307,6 +354,8 @@ def get_data_shapes(self): database_name=self.json_content['partitions']['validation'][0]).load() else: return {} + + # Get the data shape for each numpy Field shapes = {} for table_name, fields in self.json_content['architecture'].items(): if db.nb_lines(table_name=table_name) > 0: @@ -316,90 +365,90 @@ def get_data_shapes(self): field_name = field.split(' ')[0] shapes[f'{table_name}.{field_name}'] = data[field_name].shape db.close() + return shapes - def update_json(self, - update_partitions_lists: bool = False, - update_nb_samples: bool = False, - update_architecture: bool = False, - update_shapes: bool = False, - update_normalization: bool = False) -> None: + def get_nb_samples(self) -> None: """ - - + Get the number of sample in each partition. """ - # Update partitions lists - if update_partitions_lists: - self.json_content['partitions'] = self.partition_names - - # Update number of samples - if update_nb_samples: - if len(self.json_content['nb_samples'][self.mode]) == self.partition_index[self.mode]: - self.json_content['nb_samples'][self.mode][-1] = self.nb_samples - else: - self.json_content['nb_samples'][self.mode].append(self.nb_samples) - - # Update DB architecture - if update_architecture: - self.json_content['architecture'] = self.get_database_architecture() - - # Update data shapes - if update_shapes: - self.json_content['data_shape'] = self.get_data_shapes() + nb_samples = self.partitions[self.mode][-1].nb_lines(table_name='Training') + if len(self.json_content['nb_samples'][self.mode]) == self.partition_index[self.mode]: + self.json_content['nb_samples'][self.mode][-1] = nb_samples + else: + self.json_content['nb_samples'][self.mode].append(nb_samples) - # Update normalization coefficients - if update_normalization: - self.json_content['normalization'] = self.compute_normalization() + def update_json(self) -> None: + """ + Update the JSON info file with the current Database information. + """ # Overwrite json file with open(join(self.database_dir, 'dataset.json'), 'w') as json_file: json_dump(self.json_content, json_file, indent=3, cls=CustomJSONEncoder) - ######################### - # Database read / write # - ######################### + ########################################################################################## + ########################################################################################## + # Database access and edition # + ########################################################################################## + ########################################################################################## def connect_handler(self, handler: DatabaseHandler) -> None: + """ + Add and init a new DatabaseHandler to the list. + + :param handler: New DatabaseHandler. + """ handler.init(storing_partitions=self.get_partition_objects(), exchange_db=self.exchange) self.database_handlers.append(handler) - def index_samples(self): + def index_samples(self) -> None: + """ + Create a new indexing list of samples. Samples are identified by [partition_id, line_id]. + """ + # Create the indices for each sample such as [partition_id, line_id] for i, nb_sample in enumerate(self.json_content['nb_samples'][self.mode]): partition_indices = empty((nb_sample, 2), dtype=int) partition_indices[:, 0] = i partition_indices[:, 1] = arange(1, nb_sample + 1) self.sample_indices = concatenate((self.sample_indices, partition_indices)) + # Init current sample position self.sample_id = 0 + # Shuffle the indices if required if self.shuffle: shuffle(self.sample_indices) - @property - def nb_samples(self) -> int: - return self.partitions[self.mode][-1].nb_lines(table_name='Training') - def add_data(self, - data_lines: Optional[List[int]] = None): + data_lines: Optional[List[int]] = None) -> None: """ + Manage new lines adding in the Database. + :param data_lines: Indices of the newly added lines. """ # 1. Update the json file - self.update_json(update_nb_samples=True) + self.get_nb_samples() + self.update_json() + # 1.1. Init partitions information on the first sample if self.first_add: for handler in self.database_handlers: handler.load() - self.update_json(update_partitions_lists=True, update_shapes=True, update_architecture=True) + self.json_content['partitions'] = self.partition_names + self.json_content['architecture'] = self.get_database_architecture() + self.json_content['data_shape'] = self.get_data_shapes() + self.update_json() self.first_add = False + # 1.2. Update the normalization coefficients if required if self.normalize and self.mode == 'training' and self.pipeline == 'training' and data_lines is not None: self.json_content['normalization'] = self.update_normalization(data_lines=data_lines) self.update_json() - # 2. Check the size of the partition + # 2. Check the size of the current partition if self.max_file_size is not None: if self.partitions[self.mode][-1].memory_size > self.max_file_size: self.create_partition() @@ -407,93 +456,79 @@ def add_data(self, def get_data(self, batch_size: int) -> List[List[int]]: """ + Select a batch of indices to read in the Database. + :param batch_size: Number of sample in a single batch. """ # 1. Check if dataset is loaded and if the current sample is not the last if self.sample_id >= len(self.sample_indices): self.index_samples() - # 2. Update dataset index + # 2. Update dataset index and get a batch of data idx = self.sample_id self.sample_id += batch_size - - # 3. Get a batch of data lines = self.sample_indices[idx:self.sample_id].tolist() - # 4. Ensure the batch has th good size + # 3. Ensure the batch has the good size if len(lines) < batch_size: lines += self.get_data(batch_size=batch_size - len(lines)) - return lines - ############ - # Behavior # - ############ - - def close(self): - """ - - """ - - # Check non-empty last partition - self.remove_empty_partitions() - - if self.normalize and self.pipeline == 'data_generation': - self.update_json(update_normalization=True) + return lines - # Cose databases - for mode in self.modes: - for database in self.partitions[mode]: - database.close() - self.exchange.close(erase_file=True) + ########################################################################################## + ########################################################################################## + # Data normalization computation # + ########################################################################################## + ########################################################################################## - def change_mode(self, mode: int) -> None: + @property + def normalization(self) -> Optional[Dict[str, List[float]]]: """ - + Get the normalization coefficients. """ - pass - - ################# - # Normalization # - ################# + if self.json_content['normalization'] == {} or not self.normalize: + return None + return self.json_content['normalization'] def compute_normalization(self) -> Dict[str, List[float]]: """ - + Compute the mean and the standard deviation of all the training samples for each data field. """ - # Get the fields to normalize + # 1. Get the fields to normalize fields = [] for field in self.json_content['data_shape']: table_name, field_name = field.split('.') fields += [field_name] if table_name == 'Training' else [] - - # Init result normalization = {field: [0., 0.] for field in fields} - # Compute the mean for each field + # 2. Compute the mean of samples for each field means = {field: [] for field in fields} nb_samples = [] + # 2.1. Compute the mean for each partition for partition in self.partitions['training']: - data_to_normalize = self.load_partitions_fields(partition=partition, - fields=fields) + data_to_normalize = self.load_partitions_fields(partition=partition, fields=fields) nb_samples.append(data_to_normalize['id'][-1]) for field in fields: data = array(data_to_normalize[field]) means[field].append(data.mean()) + # 2.2. Compute the global mean for field in fields: normalization[field][0] = sum([(n / sum(nb_samples)) * m for n, m in zip(nb_samples, means[field])]) - # Compute the standard deviation for each field + # 3. Compute the standard deviation of samples for each field stds = {field: [] for field in fields} + # 3.1. Compute the standard deviation for each partition for partition in self.partitions['training']: data_to_normalize = self.load_partitions_fields(partition=partition, fields=fields) for field in fields: data = array(data_to_normalize[field]) stds[field].append(mean(abs(data - normalization[field][0]) ** 2)) + # 3.2. Compute the global standard deviation for field in fields: normalization[field][1] = sqrt(sum([(n / sum(nb_samples)) * std for n, std in zip(nb_samples, stds[field])])) @@ -502,17 +537,22 @@ def compute_normalization(self) -> Dict[str, List[float]]: def update_normalization(self, data_lines: List[int]) -> Dict[str, List[float]]: + """ + Update the mean and the standard deviation of all the training samples with newly added samples for each data + field. - previous_normalization = self.normalization - previous_nb_samples = self.total_nb_sample - self.total_nb_sample += len(data_lines) + :param data_lines: Indices of the newly added lines. + """ - # First update + # 1. Get the previous normalization coefficients and number of samples + previous_normalization = self.normalization if previous_normalization is None: return self.compute_normalization() new_normalization = previous_normalization.copy() + previous_nb_samples = self.total_nb_sample + self.total_nb_sample += len(data_lines) - # Compute the mean for each field + # 2. Compute the global mean of samples for each field fields = list(previous_normalization.keys()) data_to_normalize = self.partitions[self.mode][-1].get_lines(table_name='Training', fields=fields, @@ -524,9 +564,10 @@ def update_normalization(self, (len(data_lines) / self.total_nb_sample) * data.mean() new_normalization[field][0] = m - # Compute standard deviation for each field + # 3. Compute standard deviation of samples for each field stds = {field: [] for field in fields} nb_samples = [] + # 3.1. Recompute the standard deviation for each partition with the new mean value for partition in self.partitions['training']: data_to_normalize = self.load_partitions_fields(partition=partition, fields=fields) @@ -534,29 +575,58 @@ def update_normalization(self, for field in fields: data = array(data_to_normalize[field]) stds[field].append(mean(abs(data - new_normalization[field][0]) ** 2)) + # 3.2. Compute the global standard deviation for field in fields: new_normalization[field][1] = sqrt(sum([(n / sum(nb_samples)) * std for n, std in zip(nb_samples, stds[field])])) return new_normalization - @property - def normalization(self) -> Dict[str, List[float]]: - return None if self.json_content['normalization'] == {} or not self.normalize else self.json_content['normalization'] + @staticmethod + def load_partitions_fields(partition: Database, + fields: List[str]) -> Dict[str, ndarray]: + """ + Load all the samples from a Field of a Table in the Database. + + :param partition: Database partition to load. + :param fields: Data Fields to get. + """ - def load_partitions_fields(self, - partition: Database, - fields: List[str]): partition.load() return partition.get_lines(table_name='Training', fields=fields, batched=True) + ########################################################################################## + ########################################################################################## + # Manager behavior # + ########################################################################################## + ########################################################################################## + + def close(self): + """ + Launch the closing procedure of the DatabaseManager. + """ + + # Check non-empty last partition + self.remove_empty_partitions() + + # Compute final normalization if required + if self.normalize and self.pipeline == 'data_generation': + self.json_content['normalization'] = self.compute_normalization() + self.update_json() + + # Close Database partitions + for mode in self.modes: + for database in self.partitions[mode]: + database.close() + self.exchange.close(erase_file=True) + def __str__(self): description = "\n" description += f"# {self.name}\n" description += f" Dataset Repository: {self.database_dir}\n" - size = f"No limits" if self.max_file_size is None else f"{self.max_file_size * 1e-9} Go" + size = f"No limits" if self.max_file_size is None else f"{self.max_file_size * 1e-9} Gb" description += f" Partitions size: {size}\n" return description diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 5d7275c6..389596fa 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -2,9 +2,8 @@ from asyncio import run as async_run from os.path import join -from SSD.Core.Storage.Database import Database - from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig, TcpIpServer, BaseEnvironment +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer @@ -17,7 +16,7 @@ def __init__(self, session: str = 'sessions/default', batch_size: int = 1): """ - Deals with the online generation of data for both training and running of the neural networks. + EnvironmentManager handle the communication with Environment(s). :param environment_config: Configuration object with the parameters of the Environment. :param data_manager: DataManager that handles the EnvironmentManager. @@ -31,7 +30,7 @@ def __init__(self, # Session variables self.data_manager: Any = data_manager - # Data producing parameters + # Data production variables self.batch_size: int = batch_size self.only_first_epoch: bool = environment_config.only_first_epoch self.load_samples: bool = environment_config.load_samples @@ -41,7 +40,7 @@ def __init__(self, self.allow_prediction_requests: bool = pipeline != 'data_generation' self.dataset_batch: Optional[List[List[int]]] = None - # Create the Visualizer + # Create a Visualizer to provide the visualization Database self.visualizer: Optional[VedoVisualizer] = None visualization_db = None if environment_config.visualizer is not None: @@ -55,140 +54,161 @@ def __init__(self, self.number_of_thread: int = 1 if force_local else environment_config.number_of_thread self.server: Optional[TcpIpServer] = None self.environment: Optional[BaseEnvironment] = None + # Create Server if environment_config.as_tcp_ip_client and not force_local: self.server = environment_config.create_server(environment_manager=self, batch_size=batch_size, visualization_db=None if visualization_db is None else visualization_db.get_path()) + # Create Environment else: self.environment = environment_config.create_environment(visualization_db=visualization_db) self.environment.environment_manager = self self.data_manager.connect_handler(self.environment.get_database_handler()) - - # Create & Init Environment self.environment.create() self.environment.init() self.environment.init_database() self.environment.init_visualization() - # Define get_data and dispatch methods - self.get_database_handler = self.get_server_database_handler if self.server else self.get_environment_database_handler - self.change_database = self.change_database_in_server if self.server else self.change_database_in_environment - self.get_data = self.get_data_from_server if self.server else self.get_data_from_environment - self.dispatch_batch = self.dispatch_batch_to_server if self.server else self.dispatch_batch_to_environment + # Define whether methods are used for environment or server + self.get_database_handler = self.__get_server_db_handler if self.server else self.__get_environment_db_handler + self.get_data = self.__get_data_from_server if self.server else self.__get_data_from_environment + self.dispatch_batch = self.__dispatch_batch_to_server if self.server else self.__dispatch_batch_to_environment - # Init visualizer + # Init the Visualizer once Environments are initialized if self.visualizer is not None: if len(self.visualizer.get_database().get_tables()) == 1: self.visualizer.get_database().load() self.visualizer.init_visualizer() - def get_server_database_handler(self): + ########################################################################################## + ########################################################################################## + # DatabaseHandler management # + ########################################################################################## + ########################################################################################## + + def __get_server_db_handler(self) -> DatabaseHandler: + """ + Get the DatabaseHandler of the TcpIpServer. + """ + return self.server.get_database_handler() - def get_environment_database_handler(self): - return self.environment.get_database_handler() + def __get_environment_db_handler(self) -> DatabaseHandler: + """ + Get the DatabaseHandler of the Environment. + """ - def change_database_in_server(self, database: Database): - self.server.change_database(database.get_path()) + return self.environment.get_database_handler() - def change_database_in_environment(self, database): - self.environment.database = database + ########################################################################################## + ########################################################################################## + # Data creation management # + ########################################################################################## + ########################################################################################## - def get_data_from_server(self, - animate: bool = True) -> List[int]: + def __get_data_from_server(self, + animate: bool = True) -> List[List[int]]: """ Compute a batch of data from Environments requested through TcpIpServer. - :param animate: If True, triggers an environment step. + :param animate: If True, triggers an environment step. """ - # Get data from server return self.server.get_batch(animate) - def get_data_from_environment(self, - animate: bool = True, - save_data: bool = True, - request_prediction: bool = False) -> List[List[int]]: + def __get_data_from_environment(self, + animate: bool = True, + save_data: bool = True, + request_prediction: bool = False) -> List[List[int]]: """ Compute a batch of data directly from Environment. :param animate: If True, triggers an environment step. + :param save_data: If True, data must be stored in the Database. + :param request_prediction: If True, a prediction request will be triggered. """ - # 1. Produce batch while batch size is not complete + # Produce batch while batch size is not complete nb_sample = 0 dataset_lines = [] while nb_sample < self.batch_size: - # 1.1 Send a sample if a batch from dataset is given + # 1. Send a sample from the Database if one is given update_line = None if self.dataset_batch is not None: update_line = self.dataset_batch.pop(0) self.environment._get_training_data(update_line) - # 1.2 Run the defined number of step + # 2. Run the defined number of steps if animate: for current_step in range(self.simulations_per_step): # Sub-steps do not produce data self.environment.compute_training_data = current_step == self.simulations_per_step - 1 async_run(self.environment.step()) - # 1.3 Add the produced sample to the batch if the sample is validated + # 3. Add the produced sample index to the batch if the sample is validated if self.environment.check_sample(): nb_sample += 1 + # 3.1. The prediction Pipeline triggers a prediction request if request_prediction: self.environment._get_prediction() + # 3.2. Add the data to the Database if save_data: + # Update the line if the sample was given by the database if update_line is None: new_line = self.environment._send_training_data() dataset_lines.append(new_line) + # Create a new line otherwise else: self.environment._update_training_data(update_line) dataset_lines.append(update_line) + # 3.3. Rest the data variables self.environment._reset_training_data() return dataset_lines - def dispatch_batch_to_server(self, - data_lines: List[int], - animate: bool = True) -> None: + def __dispatch_batch_to_server(self, + data_lines: List[int], + animate: bool = True) -> None: """ - Send samples from dataset to the Environments. Get back the training data. + Send samples from the Database to the Environments and get back the produced data. - :param data_lines: Batch of samples. + :param data_lines: Batch of indices of samples. :param animate: If True, triggers an environment step. - :return: Batch of training data. """ # Define the batch to dispatch self.server.set_dataset_batch(data_lines) # Get data - self.get_data_from_server(animate=animate) + self.__get_data_from_server(animate=animate) - def dispatch_batch_to_environment(self, - data_lines: List[int], - animate: bool = True, - save_data: bool = True, - request_prediction: bool = False) -> None: + def __dispatch_batch_to_environment(self, + data_lines: List[int], + animate: bool = True, + save_data: bool = True, + request_prediction: bool = False) -> None: """ - Send samples from dataset to the Environments. Get back the training data. + Send samples from the Database to the Environment and get back the produced data. - :param batch: Batch of samples. + :param data_lines: Batch of indices of samples. :param animate: If True, triggers an environment step. - :return: Batch of training data. + :param save_data: If True, data must be stored in the Database. + :param request_prediction: If True, a prediction request will be triggered. """ # Define the batch to dispatch self.dataset_batch = data_lines.copy() # Get data - self.get_data_from_environment(animate=animate, - save_data=save_data, - request_prediction=request_prediction) - - def request_prediction(self): + self.__get_data_from_environment(animate=animate, + save_data=save_data, + request_prediction=request_prediction) - self.environment._get_prediction() + ########################################################################################## + ########################################################################################## + # Requests management # + ########################################################################################## + ########################################################################################## def update_visualizer(self, instance: int) -> None: @@ -201,28 +221,29 @@ def update_visualizer(self, if self.visualizer is not None: self.visualizer.render_instance(instance) + ########################################################################################## + ########################################################################################## + # Manager behavior # + ########################################################################################## + ########################################################################################## + def close(self) -> None: """ - Close the environment + Launch the closing procedure of the EnvironmentManager. """ # Server case if self.server: self.server.close() - # No server case + + # Environment case if self.environment: self.environment.close() def __str__(self) -> str: - """ - :return: A string containing valuable information about the EnvironmentManager - """ description = "\n" description += f"# {self.name}\n" description += f" Always create data: {self.only_first_epoch}\n" - # description += f" Record wrong samples: {self.record_wrong_samples}\n" description += f" Number of threads: {self.number_of_thread}\n" - # description += f" Managed objects: Environment: {self.environment.env_name}\n" - # description += str(self.environment) return description diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index 093a92a0..aef785b3 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -1,6 +1,6 @@ -from typing import Any, Dict, Tuple, Optional, List +from typing import Any, Dict, Optional, List from os import listdir -from os.path import join, isdir, isfile +from os.path import join, isdir, isfile, sep from numpy import ndarray, array from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler @@ -16,13 +16,13 @@ def __init__(self, session: str = 'sessions/default', new_session: bool = True): """ - Deals with all the interactions with the neural network: predictions, saves, initialisation, loading, - back-propagation, etc. + NetworkManager deals with all the interactions with a neural network: predictions, saves, initialisation, + loading, optimization. - :param network_config: Specialisation containing the parameters of the network manager. + :param network_config: Configuration object with the parameters of the Network. :param pipeline: Type of the Pipeline. - :param session: Path to the session directory. - :param new_session: Define the creation of new directories to store data. + :param session: Path to the session repository. + :param new_session: If True, the session is done in a new repository. """ self.name: str = self.__class__.__name__ @@ -33,6 +33,7 @@ def __init__(self, self.session: str = session self.new_session: bool = new_session self.network_dir: Optional[str] = None + self.network_template_name: str = session.split(sep)[-1] + '_network_{}' self.saved_counter: int = 0 self.save_each_epoch: bool = network_config.save_each_epoch @@ -70,31 +71,58 @@ def __init__(self, self.network_dir = join(session, 'network/') self.load_network(which_network=network_config.which_network) - def get_database_handler(self): + ########################################################################################## + ########################################################################################## + # DatabaseHandler management # + ########################################################################################## + ########################################################################################## + + def get_database_handler(self) -> DatabaseHandler: + """ + Get the DatabaseHandler of the NetworkManager. + """ + return self.database_handler def link_clients(self, - nb_clients: Optional[int] = None): + nb_clients: Optional[int] = None) -> None: + """ + Update the data Exchange Database with a new line for each TcpIpClient. + + :param nb_clients: Number of Clients to connect. + """ + if nb_clients is not None: + # Create the network fields in the Exchange Database fields = [(field_name, ndarray) for field_name in self.network.net_fields + self.network.pred_fields] self.database_handler.create_fields(table_name='Exchange', fields=fields) + # Add an empty line for each Client for _ in range(nb_clients): self.database_handler.add_data(table_name='Exchange', data={}) + ########################################################################################## + ########################################################################################## + # Network parameters management # + ########################################################################################## + ########################################################################################## + def load_network(self, which_network: int = -1) -> None: """ - Load an existing set of parameters to the network. + Load an existing set of parameters of the Network. + + :param which_network: If several sets of parameters were saved, specify which one to load. """ - # Get eventual epoch saved networks + # 1. Get the list of all sets of saved parameters networks_list = [join(self.network_dir, f) for f in listdir(self.network_dir) if isfile(join(self.network_dir, f)) and f.__contains__('network_')] networks_list = sorted(networks_list) - # Add the final saved network last_saved_network = [join(self.network_dir, f) for f in listdir(self.network_dir) if isfile(join(self.network_dir, f)) and f.__contains__('network.')] networks_list = networks_list + last_saved_network + + # 2. Check the Network to access if len(networks_list) == 0: raise FileNotFoundError(f"[{self.name}]: There is no network in {self.network_dir}.") elif len(networks_list) == 1: @@ -103,32 +131,59 @@ def load_network(self, print(f"[{self.name}] The network 'network_{self.saved_counter} doesn't exist, loading the most trained " f"by default.") which_network = -1 + + # 3. Load the set of parameters print(f"[{self.name}]: Loading network from {networks_list[which_network]}.") self.network.load_parameters(networks_list[which_network]) + def save_network(self, + last_save: bool = False) -> None: + """ + Save the current set of parameters of the Network. + + :param last_save: If True, the Network training is done then give a special denomination. + """ + + # Final session saving + if last_save: + path = join(self.network_dir, 'network') + print(f"[{self.name}] Saving final network at {self.network_dir}.") + self.network.save_parameters(path) + + # Intermediate states saving + elif self.save_each_epoch: + path = self.network_dir + self.network_template_name.format(self.saved_counter) + self.saved_counter += 1 + print(f"[{self.name}] Saving intermediate network at {path}.") + self.network.save_parameters(path) + + ########################################################################################## + ########################################################################################## + # Network optimization and prediction # + ########################################################################################## + ########################################################################################## + def compute_prediction_and_loss(self, optimize: bool, data_lines: List[List[int]], - normalization: Optional[Dict[str, List[float]]] = None) -> Tuple[ndarray, Dict[str, float]]: + normalization: Optional[Dict[str, List[float]]] = None) -> Dict[str, float]: """ Make a prediction with the data passed as argument, optimize or not the network - :param batch_indices: Indices of the line of the Database that correspond to the current bach - :param optimize: If true run a back propagation - + :param optimize: If true, run a backward propagation. + :param data_lines: Batch of indices of samples in the Database. + :param normalization: Normalization coefficients. :return: The prediction and the associated loss value """ - # Define in and out batches + # 1. Define Network and Optimization batches batches = {} normalization = {} if normalization is None else normalization for side, fields in zip(['net', 'opt'], [self.network.net_fields, self.network.opt_fields]): - # Get the batch from the Database batch = self.database_handler.get_lines(table_name='Training', fields=fields, lines_id=data_lines) - # Apply normalization and convert to tensor for field in batch.keys(): batch[field] = array(batch[field]) @@ -140,27 +195,19 @@ def compute_prediction_and_loss(self, batches[side] = batch data_net, data_opt = batches.values() - # Compute prediction + # 2. Compute prediction data_net = self.data_transformation.transform_before_prediction(data_net) data_pred = self.network.predict(data_net) - # Compute loss + # 3. Compute loss data_pred, data_opt = self.data_transformation.transform_before_loss(data_pred, data_opt) data_loss = self.optimization.compute_loss(data_pred, data_opt) - # Optimizing network if training + # 4. Optimize network if training if optimize: self.optimization.optimize() - # Transform prediction to be compatible with environment - data_pred = self.data_transformation.transform_before_apply(data_pred) - for field in data_pred: - data_pred[field] = self.network.tensor_to_numpy(data_pred[field]) - if field in normalization.keys(): - data_pred[field] = self.normalize_data(data=data_pred[field], - normalization=normalization[field], - reverse=True) - return data_pred, data_loss + return data_loss def compute_online_prediction(self, instance_id: int, @@ -168,8 +215,8 @@ def compute_online_prediction(self, """ Make a prediction with the data passed as argument. - :param network_input: Input of the network - :return: The prediction + :param instance_id: Index of the Environment instance to provide a prediction. + :param normalization: Normalization coefficients. """ # Get Network data @@ -213,40 +260,27 @@ def normalize_data(cls, Apply or unapply normalization following current standard score. :param data: Data to normalize. - :param field: Specify if data is an 'input' or an 'output'. - :param reverse: If False, apply normalization; if False, unapply normalization. + :param normalization: Normalization coefficients. + :param reverse: If True, apply normalization; if False, unapply normalization. :return: Data with applied or misapplied normalization. """ + # Unapply normalization if reverse: - # Unapply normalization return (data * normalization[1]) + normalization[0] + # Apply normalization return (data - normalization[0]) / normalization[1] - def save_network(self, last_save: bool = False) -> None: - """ - | Save the network with the corresponding suffix, so they do not erase the last save. - - :param bool last_save: Do not add suffix if it's the last save - """ - - # Final session saving - if last_save: - path = join(self.network_dir, 'network') - print(f"[{self.name}] Saving final network at {self.network_dir}.") - self.network.save_parameters(path) - - # Intermediate states saving - elif self.save_each_epoch: - path = self.network_dir + self.network_template_name.format(self.saved_counter) - self.saved_counter += 1 - print(f"[{self.name}] Saving intermediate network at {path}.") - self.network.save_parameters(path) + ########################################################################################## + ########################################################################################## + # Manager behavior # + ########################################################################################## + ########################################################################################## def close(self) -> None: """ - Closing procedure. + Launch the closing procedure of the NetworkManager. """ if self.is_training: diff --git a/src/Manager/StatsManager.py b/src/Manager/StatsManager.py index c119c89c..4ee80d3b 100644 --- a/src/Manager/StatsManager.py +++ b/src/Manager/StatsManager.py @@ -1,4 +1,4 @@ -from typing import Dict, Union, Any, Iterable, Optional +from typing import Dict, Any, Iterable, Optional from tensorboardX import SummaryWriter from tensorboard import program from webbrowser import open as w_open @@ -22,11 +22,11 @@ def __init__(self, session: str, keep_losses: bool = False): """ - Record all given values using the tensorboard framework. Open a tab in the navigator to inspect these values - during the training. + StatsManager records all the given values using the Tensorboard framework. + Open a tab in the navigator to inspect these values during the training. - :param str log_dir: Path of the created directory - :param bool keep_losses: If True Allow saving loss to .csv file + :param session: Path to the session repository. + :param keep_losses: If True, allow saving loss to .csv file. """ self.name: str = self.__class__.__name__ @@ -49,7 +49,7 @@ def __init__(self, def add_train_batch_loss(self, value: float, count: int) -> None: """ - | Add batch loss to tensorboard framework. Also compute mean and variance. + Add batch loss to tensorboard framework. Also compute mean and variance. :param float value: Value to store :param int count: ID of the value @@ -65,7 +65,7 @@ def add_train_batch_loss(self, value: float, count: int) -> None: def add_train_epoch_loss(self, value: float, count: int) -> None: """ - | Add epoch loss to tensorboard framework. Also compute mean and variance. + Add epoch loss to tensorboard framework. Also compute mean and variance. :param float value: Value to store :param int count: ID of the value @@ -79,7 +79,7 @@ def add_train_epoch_loss(self, value: float, count: int) -> None: def add_train_test_batch_loss(self, train_value: float, test_value: float, count: int) -> None: """ - | Add train and test batch loss to tensorboard framework. + Add train and test batch loss to tensorboard framework. :param float train_value: Value of the training batch :param float test_value: Value of the testing batch @@ -93,7 +93,7 @@ def add_train_test_batch_loss(self, train_value: float, test_value: float, count def add_values_multi_plot(self, graph_name: str, tags: Iterable, values: Iterable, counts: Iterable) -> None: """ - | Plot multiples value on the same graph + Plot multiples value on the same graph :param str graph_name: Name of the graph :param Iterable tags: Iterable containing the names of the values @@ -106,7 +106,7 @@ def add_values_multi_plot(self, graph_name: str, tags: Iterable, values: Iterabl def add_test_loss(self, value: float, count: int) -> None: """ - | Add test loss to tensorboard framework. Also compute mean and variance. + Add test loss to tensorboard framework. Also compute mean and variance. :param float value: Value to store :param int count: ID of the value @@ -120,7 +120,7 @@ def add_test_loss(self, value: float, count: int) -> None: def add_test_loss_OOB(self, value: float, count: int) -> None: """ - | Add out of bound test loss to tensorboard framework. Also compute mean and variance. + Add out of bound test loss to tensorboard framework. Also compute mean and variance. :param float value: Value to store :param int count: ID of the value @@ -134,7 +134,7 @@ def add_test_loss_OOB(self, value: float, count: int) -> None: def add_custom_scalar(self, tag: str, value: float, count: int) -> None: """ - | Add a custom scalar to tensorboard framework. + Add a custom scalar to tensorboard framework. :param str tag: Graph name :param float value: Value to store @@ -145,7 +145,7 @@ def add_custom_scalar(self, tag: str, value: float, count: int) -> None: def add_custom_scalar_full(self, tag: str, value: float, count: int) -> None: """ - | Add a custom scalar to tensorboard framework. Also compute mean and variance. + Add a custom scalar to tensorboard framework. Also compute mean and variance. :param str tag: Graph name :param float value: Value to store @@ -164,7 +164,7 @@ def add_custom_scalar_full(self, tag: str, value: float, count: int) -> None: def update_mean_get_var(self, index: int, value: float, count: int) -> Optional[ndarray]: """ - | Update mean and return the variance of the selected value + Update mean and return the variance of the selected value :param float value: Value to add in the computation of the mean :param int index: Target that is updated by the value @@ -214,7 +214,7 @@ def add_3D_mesh(self, tag: str, vertices: ndarray, colors: Optional[ndarray] = N faces: Optional[ndarray] = None, b_n_3: bool = False, config_dict: Optional[Dict[Any, Any]] = None) -> None: """ - | Add 3D Mesh cloud to tensorboard framework. + Add 3D Mesh cloud to tensorboard framework. :param str tag: Data identifier :param ndarray vertices: List of the 3D coordinates of vertices. @@ -242,7 +242,7 @@ def add_3D_mesh(self, tag: str, vertices: ndarray, colors: Optional[ndarray] = N def add_network_weight_grad(self, network: Any, count: int, save_weights: bool = False, save_gradients: bool = True) -> None: """ - | Add network weights and gradiant if specified to tensorboard framework. + Add network weights and gradiant if specified to tensorboard framework. :param BaseNetwork network: Network you want to display :param int count: ID of the sample @@ -259,18 +259,13 @@ def add_network_weight_grad(self, network: Any, count: int, save_weights: bool = def close(self) -> None: """ - | Closing procedure - - :return: + Launch the closing procedure of the StatsManager. """ self.writer.close() del self.train_loss - def __str__(self) -> str: - """ - :return: A string containing valuable information about the StatsManager - """ + def __str__(self): description = "\n" description += f"# {self.name}\n" diff --git a/src/Network/BaseNetwork.py b/src/Network/BaseNetwork.py index 4a6d8881..eef6bb3f 100644 --- a/src/Network/BaseNetwork.py +++ b/src/Network/BaseNetwork.py @@ -8,9 +8,9 @@ class BaseNetwork: def __init__(self, config: namedtuple): """ - BaseNetwork is a network class to compute predictions from input data according to actual state. + BaseNetwork computes predictions from input data according to actual set of weights. - :param namedtuple config: namedtuple containing BaseNetwork parameters + :param config: Set of BaseNetwork parameters. """ # Config @@ -22,9 +22,10 @@ def __init__(self, self.opt_fields = ['ground_truth'] self.pred_fields = ['prediction'] - def predict(self, data_net: Dict[str, Any]) -> Dict[str, Any]: + def predict(self, + data_net: Dict[str, Any]) -> Dict[str, Any]: """ - Compute a forward pass of the network. + Compute a forward pass of the Network. :param data_net: Data used by the Network. :return: Data produced by the Network. @@ -32,26 +33,27 @@ def predict(self, data_net: Dict[str, Any]) -> Dict[str, Any]: return {'prediction': self.forward(data_net['input'])} - def forward(self, input_data: Any) -> Any: + def forward(self, + input_data: Any) -> Any: """ - | Gives input_data as raw input to the neural network. + Compute a forward pass of the Network. - :param Any input_data: Input tensor - :return: Network prediction + :param input_data: Input tensor. + :return: Output tensor. """ raise NotImplementedError def set_train(self) -> None: """ - | Set the Network in train mode (compute gradient). + Set the Network in training mode (compute gradient). """ raise NotImplementedError def set_eval(self) -> None: """ - | Set the Network in eval mode (does not compute gradient). + Set the Network in prediction mode (does not compute gradient). """ raise NotImplementedError @@ -63,67 +65,69 @@ def set_device(self) -> None: raise NotImplementedError - def load_parameters(self, path: str) -> None: + def load_parameters(self, + path: str) -> None: """ - | Load network parameter from path. + Load network parameter from path. - :param str path: Path to Network parameters to load + :param path: Path to Network parameters to load. """ raise NotImplementedError def get_parameters(self) -> Dict[str, Any]: """ - | Return the current state of Network parameters. + Return the current state of Network parameters. - :return: Network parameters + :return: Network parameters. """ raise NotImplementedError - def save_parameters(self, path) -> None: + def save_parameters(self, + path: str) -> None: """ - | Saves the network parameters to the path location. + Saves the network parameters to the path location. - :param str path: Path where to save the parameters. + :param path: Path where to save the parameters. """ raise NotImplementedError def nb_parameters(self) -> int: """ - | Return the number of parameters of the network. + Return the number of parameters of the network. - :return: Number of parameters + :return: Number of parameters. """ raise NotImplementedError - def numpy_to_tensor(self, data: ndarray, grad: bool = True) -> Any: + def numpy_to_tensor(self, + data: ndarray, + grad: bool = True) -> Any: """ - | Transform and cast data from numpy to the desired tensor type. + Transform and cast data from numpy to the desired tensor type. - :param ndarray data: Array data to convert - :param bool grad: If True, gradient will record operations on this tensor - :return: Converted tensor + :param data: Array data to convert. + :param grad: If True, gradient will record operations on this tensor. + :return: Converted tensor. """ return data.astype(self.config.data_type) - def tensor_to_numpy(self, data: Any) -> ndarray: + def tensor_to_numpy(self, + data: Any) -> ndarray: """ - | Transform and cast data from tensor type to numpy. + Transform and cast data from tensor type to numpy. - :param Any data: Any to convert - :return: Converted array + :param data: Tensor to convert. + :return: Converted array. """ return data.astype(self.config.data_type) def __str__(self) -> str: - """ - :return: String containing information about the BaseNetwork object - """ description = "\n" description += f" {self.__class__.__name__}\n" diff --git a/src/Network/BaseNetworkConfig.py b/src/Network/BaseNetworkConfig.py index 69f93caa..6d05b36c 100644 --- a/src/Network/BaseNetworkConfig.py +++ b/src/Network/BaseNetworkConfig.py @@ -142,7 +142,7 @@ def create_data_transformation(self) -> DataTransformation: f"must be a DataTransformation.") return data_transformation - def __str__(self) -> str: + def __str__(self): description = "\n" description += f"{self.__class__.__name__}\n" diff --git a/src/Network/BaseOptimization.py b/src/Network/BaseOptimization.py index 2419d866..eaa49edc 100644 --- a/src/Network/BaseOptimization.py +++ b/src/Network/BaseOptimization.py @@ -8,10 +8,9 @@ class BaseOptimization: def __init__(self, config: namedtuple): """ - BaseOptimization is dedicated to network optimization: compute loss between prediction and target, update - network parameters. + BaseOptimization computes loss between prediction and target and optimizes the Network parameters. - :param config: Namedtuple containing BaseOptimization parameters + :param config: Set of BaseOptimization parameters """ self.manager: Any = None @@ -53,12 +52,13 @@ def transform_loss(self, Apply a transformation on the loss value using the potential additional data. :param data_opt: Additional data sent as dict to compute loss value - :return: Transformed loss value + :return: Transformed loss value. """ raise NotImplementedError - def set_optimizer(self, net: BaseNetwork) -> None: + def set_optimizer(self, + net: BaseNetwork) -> None: """ Define an optimization process. @@ -74,7 +74,7 @@ def optimize(self) -> None: raise NotImplementedError - def __str__(self) -> str: + def __str__(self): description = "\n" description += f" {self.__class__.__name__}\n" diff --git a/src/Network/DataTransformation.py b/src/Network/DataTransformation.py index cb758fdc..e41e8ca4 100644 --- a/src/Network/DataTransformation.py +++ b/src/Network/DataTransformation.py @@ -1,16 +1,14 @@ from typing import Callable, Any, Optional, Tuple, Dict from collections import namedtuple -DTYPE = Dict[str, Any] - class DataTransformation: def __init__(self, config: namedtuple): """ - DataTransformation is dedicated to data operations before and after network predictions. + DataTransformation manages data operations before and after network predictions. - :param namedtuple config: Namedtuple containing the parameters of the network manager. + :param config: Set of DataTransformation parameters. """ self.name = self.__class__.__name__ @@ -30,7 +28,7 @@ def inner(self, *args): return inner def transform_before_prediction(self, - data_net: DTYPE) -> DTYPE: + data_net: Dict[str, Any]) -> Dict[str, Any]: """ Apply data operations before network's prediction. @@ -41,8 +39,8 @@ def transform_before_prediction(self, return data_net def transform_before_loss(self, - data_pred: DTYPE, - data_opt: Optional[DTYPE] = None) -> Tuple[DTYPE, Optional[DTYPE]]: + data_pred: Dict[str, Any], + data_opt: Optional[Dict[str, Any]] = None) -> Tuple[Dict[str, Any], Optional[Dict[str, Any]]]: """ Apply data operations between network's prediction and loss computation. @@ -54,7 +52,7 @@ def transform_before_loss(self, return data_pred, data_opt def transform_before_apply(self, - data_pred: DTYPE) -> DTYPE: + data_pred: Dict[str, Any]) -> Dict[str, Any]: """ Apply data operations between loss computation and prediction apply in environment. @@ -64,10 +62,7 @@ def transform_before_apply(self, return data_pred - def __str__(self) -> str: - """ - :return: String containing information about the DataTransformation object - """ + def __str__(self): description = "\n" description += f" {self.__class__.__name__}\n" diff --git a/src/Pipelines/BaseDataGeneration.py b/src/Pipelines/BaseDataGeneration.py index 25342895..dad878e2 100644 --- a/src/Pipelines/BaseDataGeneration.py +++ b/src/Pipelines/BaseDataGeneration.py @@ -23,11 +23,11 @@ def __init__(self, """ BaseDataGeneration implements the main loop that only produces and stores data (no Network training). - :param database_config: Specialisation containing the parameters of the dataset manager. - :param environment_config: Specialisation containing the parameters of the environment manager. - :param session_dir: Relative path to the directory which contains sessions directories. - :param session_name: Name of the new the session directory. - :param new_session: If True, the session will be run in a new repository. + :param database_config: Configuration object with the parameters of the Database. + :param environment_config: Configuration object with the parameters of the Environment. + :param session_dir: Relative path to the directory which contains sessions repositories. + :param session_name: Name of the new the session repository. + :param new_session: If True, a new repository will be created for this session. :param batch_nb: Number of batches to produce. :param batch_size: Number of samples in a single batch. """ @@ -50,6 +50,7 @@ def __init__(self, if new_session: session_name = create_dir(session_dir=session_dir, session_name=session_name).split(sep)[-1] + self.session = join(session_dir, session_name) # Create a DataManager self.data_manager = DataManager(pipeline=self, @@ -63,13 +64,14 @@ def __init__(self, # Data generation variables self.batch_nb: int = batch_nb self.batch_id: int = 0 + self.batch_size = batch_size self.progress_bar = Progressbar(start=0, stop=self.batch_id, c='orange', title="Data Generation") def execute(self) -> None: """ Launch the data generation Pipeline. - Each event is already implemented for a basic pipeline but can also be rewritten via inheritance to describe a - more complex pipeline. + Each event is already implemented for a basic Pipeline but can also be rewritten via inheritance to describe a + more complex Pipeline. """ self.data_generation_begin() @@ -82,7 +84,7 @@ def execute(self) -> None: def data_generation_begin(self) -> None: """ - Called once at the beginning of the data generation pipeline. + Called once at the beginning of the data generation Pipeline. """ pass @@ -125,7 +127,14 @@ def batch_end(self) -> None: def data_generation_end(self) -> None: """ - Called once at the beginning of the data generation pipeline. + Called once at the beginning of the data generation Pipeline. """ self.data_manager.close() + + def __str__(self): + + description = BasePipeline.__str__(self) + description += f" Number of batches: {self.batch_nb}\n" + description += f" Number of sample per batch: {self.batch_size}\n" + return description diff --git a/src/Pipelines/BasePipeline.py b/src/Pipelines/BasePipeline.py index 92d1b414..31103f01 100644 --- a/src/Pipelines/BasePipeline.py +++ b/src/Pipelines/BasePipeline.py @@ -1,4 +1,5 @@ from typing import Optional, Any, List, Union +from os.path import join from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig @@ -23,12 +24,12 @@ def __init__(self, """ Pipelines implement the main loop that defines data flow through components (Environment, Dataset, Network...). - :param network_config: Specialisation containing the parameters of the network manager. - :param database_config: Specialisation containing the parameters of the dataset manager. - :param environment_config: Specialisation containing the parameters of the environment manager. - :param session_dir: Name of the directory in which to write all the necessary data. - :param session_name: Name of the newly created directory if session is not defined. - :param new_session: If True, the session will be run in a new repository. + :param network_config: Configuration object with the parameters of the Network. + :param database_config: Configuration object with the parameters of the Database. + :param environment_config: Configuration object with the parameters of the Environment. + :param session_dir: Relative path to the directory which contains sessions repositories. + :param session_name: Name of the new the session repository. + :param new_session: If True, a new repository will be created for this session. :param pipeline: Name of the Pipeline. """ @@ -58,8 +59,7 @@ def __init__(self, self.environment_config: BaseEnvironmentConfig = environment_config # Session variables - self.session_dir = session_dir - self.session_name = session_name + self.session = join(session_dir, session_name) self.new_session = new_session self.type = pipeline @@ -73,7 +73,7 @@ def execute(self): def __get_any_manager(self, manager_names: Union[str, List[str]]) -> Optional[Any]: """ - Return the desired Manager associated with the pipeline if it exists. + Return the desired Manager associated with the Pipeline if it exists. :param manager_names: Name of the desired Manager or order of access to this desired Manager. :return: The desired Manager associated with the Pipeline. @@ -94,45 +94,53 @@ def __get_any_manager(self, def get_network_manager(self) -> Optional[NetworkManager]: """ - Return the NetworkManager associated with the pipeline if it exists. + Return the NetworkManager associated with the Pipeline if it exists. - :return: The NetworkManager associated with the pipeline. + :return: The NetworkManager associated with the Pipeline. """ return self.__get_any_manager(manager_names='network_manager') def get_data_manager(self) -> Optional[DataManager]: """ - Return the DataManager associated with the pipeline if it exists. + Return the DataManager associated with the Pipeline if it exists. - :return: The DataManager associated with the pipeline. + :return: The DataManager associated with the Pipeline. """ return self.__get_any_manager(manager_names='data_manager') def get_stats_manager(self) -> Optional[StatsManager]: """ - Return the StatsManager associated with the pipeline if it exists. + Return the StatsManager associated with the Pipeline if it exists. - :return: The StatsManager associated with the pipeline. + :return: The StatsManager associated with the Pipeline. """ return self.__get_any_manager(manager_names='stats_manager') def get_database_manager(self) -> Optional[DatabaseManager]: """ - Return the DatabaseManager associated with the pipeline if it exists. + Return the DatabaseManager associated with the Pipeline if it exists. - :return: The DatabaseManager associated with the pipeline. + :return: The DatabaseManager associated with the Pipeline. """ return self.__get_any_manager(manager_names=['data_manager', 'database_manager']) def get_environment_manager(self) -> Optional[EnvironmentManager]: """ - Return the EnvironmentManager associated with the pipeline if it exists. + Return the EnvironmentManager associated with the Pipeline if it exists. - :return: The EnvironmentManager associated with the pipeline. + :return: The EnvironmentManager associated with the Pipeline. """ return self.__get_any_manager(manager_names=['data_manager', 'environment_manager']) + + def __str__(self): + + description = "\n" + description += f"# {self.name}\n" + description += f" Pipeline type: {self.type}\n" + description += f" Session repository: {self.session}\n" + return description diff --git a/src/Pipelines/BasePrediction.py b/src/Pipelines/BasePrediction.py index de5c3778..290a4fc1 100644 --- a/src/Pipelines/BasePrediction.py +++ b/src/Pipelines/BasePrediction.py @@ -24,13 +24,13 @@ def __init__(self, BasePrediction is a pipeline defining the running process of an artificial neural network. It provides a highly tunable learning process that can be used with any machine learning library. - :param network_config: Specialisation containing the parameters of the network manager. - :param environment_config: Specialisation containing the parameters of the environment manager. - :param database_config: Specialisation containing the parameters of the dataset manager. - :param session_name: Name of the newly created directory if session is not defined. - :param session_dir: Name of the directory in which to write all the necessary data. + :param network_config: Configuration object with the parameters of the Network. + :param environment_config: Configuration object with the parameters of the Environment. + :param database_config: Configuration object with the parameters of the Database. + :param session_dir: Relative path to the directory which contains sessions repositories. + :param session_name: Name of the new the session repository. :param step_nb: Number of simulation step to play. - :param record: Save or not the prediction data. + :param record: If True, prediction data will be saved in a dedicated Database. """ BasePipeline.__init__(self, @@ -74,7 +74,7 @@ def execute(self) -> None: """ Launch the prediction Pipeline. Each event is already implemented for a basic pipeline but can also be rewritten via inheritance to describe a - more complex pipeline. + more complex Pipeline. """ self.prediction_begin() @@ -86,14 +86,14 @@ def execute(self) -> None: def prediction_begin(self) -> None: """ - Called once at the beginning of the prediction pipeline. + Called once at the beginning of the prediction Pipeline. """ pass def prediction_condition(self) -> bool: """ - Condition that characterize the end of the prediction pipeline. + Condition that characterize the end of the prediction Pipeline. """ running = self.step_id < self.step_nb if self.step_nb > 0 else True @@ -124,16 +124,14 @@ def sample_end(self) -> None: def run_end(self) -> None: """ - Called once at the end of the prediction pipeline. + Called once at the end of the prediction Pipeline. """ self.data_manager.close() self.network_manager.close() - def __str__(self) -> str: - - description = "" - description += f"Running statistics :\n" - description += f"Number of simulation step: {self.step_nb}\n" + def __str__(self): + description = BasePipeline.__str__(self) + description += f" Number of step: {self.step_nb}\n" return description diff --git a/src/Pipelines/BaseTraining.py b/src/Pipelines/BaseTraining.py index c4901910..5cc7d480 100644 --- a/src/Pipelines/BaseTraining.py +++ b/src/Pipelines/BaseTraining.py @@ -32,13 +32,13 @@ def __init__(self, Training can be launched with several data sources (from a Dataset, from an Environment, from combined sources). It provides a highly tunable learning process that can be used with any machine learning library. - :param network_config: Specialisation containing the parameters of the network manager. - :param database_config: Specialisation containing the parameters of the dataset manager. - :param environment_config: Specialisation containing the parameters of the environment manager. - :param session_dir: Relative path to the directory which contains sessions directories. - :param session_name: Name of the new the session directory. - :param new_session: Define the creation of new directories to store data. - :param epoch_nb: Number of epochs to run. + :param network_config: Configuration object with the parameters of the Network. + :param database_config: Configuration object with the parameters of the Database. + :param environment_config: Configuration object with the parameters of the Environment. + :param session_dir: Relative path to the directory which contains sessions repositories. + :param session_name: Name of the new the session repository. + :param new_session: If True, a new repository will be created for this session. + :param epoch_nb: Number of epochs to perform. :param batch_nb: Number of batches to use. :param batch_size: Number of samples in a single batch. :param debug: If True, main training features will not be launched. @@ -117,7 +117,7 @@ def execute(self) -> None: """ Launch the training Pipeline. Each event is already implemented for a basic pipeline but can also be rewritten via inheritance to describe a - more complex pipeline. + more complex Pipeline. """ self.train_begin() @@ -134,7 +134,7 @@ def execute(self) -> None: def train_begin(self) -> None: """ - Called once at the beginning of the training pipeline. + Called once at the beginning of the training Pipeline. """ pass @@ -180,9 +180,10 @@ def optimize(self) -> None: self.data_manager.get_data(epoch=self.epoch_id, animate=True) - _, self.loss_dict = self.network_manager.compute_prediction_and_loss(data_lines=self.data_manager.data_lines, - normalization=self.data_manager.normalization, - optimize=True) + self.loss_dict = self.network_manager.compute_prediction_and_loss( + data_lines=self.data_manager.data_lines, + normalization=self.data_manager.normalization, + optimize=True) def batch_count(self) -> None: """ @@ -223,7 +224,7 @@ def epoch_end(self) -> None: def train_end(self) -> None: """ - Called once at the end of the training pipeline. + Called once at the end of the training Pipeline. """ self.data_manager.close() @@ -253,11 +254,9 @@ def save_info_file(self) -> None: f.write(str(self.stats_manager)) f.close() - def __str__(self) -> str: + def __str__(self): - description = "\n" - description += f"# {self.__class__.__name__}\n" - description += f" Session directory: {self.session}\n" + description = BasePipeline.__str__(self) description += f" Number of epochs: {self.epoch_nb}\n" description += f" Number of batches per epoch: {self.batch_nb}\n" description += f" Number of samples per batch: {self.batch_size}\n" From bf9856a4d70e0e3ae17a970e287958dde693fabc Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 2 Nov 2022 11:21:01 +0100 Subject: [PATCH 29/61] Fix number of samples production and Client synchronization with Database. --- src/AsyncSocket/TcpIpClient.py | 4 ++++ src/AsyncSocket/TcpIpServer.py | 9 +++++++-- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 66c5e821..4d9f7cd0 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -90,6 +90,10 @@ async def __initialize(self) -> None: # Initialization done await self.send_data(data_to_send='done', loop=loop, receiver=self.sock) + # Synchronize Database + _ = await self.receive_data(loop=loop, sender=self.sock) + self.environment.get_database_handler().load() + ########################################################################################## ########################################################################################## # Running Client # diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index 7dc5ff64..f54e0074 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -166,6 +166,10 @@ async def __initialize(self) -> None: await self.receive_data(loop=loop, sender=client) print(f"[{self.name}] Client n°{client_id} initialisation done") + # Synchronize Clients + for client_id, client in self.clients: + await self.send_data(data_to_send='sync', loop=loop, receiver=client) + ########################################################################################## ########################################################################################## # Data: produce batch & dispatch batch # @@ -197,11 +201,12 @@ async def __request_data_to_clients(self, self.data_lines = [] # Launch the communication protocol while the batch needs to be filled while nb_sample < self.batch_size: + clients = self.clients[:min(len(self.clients), self.batch_size - nb_sample)] # Run communicate protocol for each client and wait for the last one to finish await gather(*[self.__communicate(client=client, client_id=client_id, - animate=animate) for client_id, client in self.clients]) - nb_sample += len(self.clients) + animate=animate) for client_id, client in clients]) + nb_sample += len(clients) async def __communicate(self, client: Optional[socket] = None, From be901b03fca0c82807c88b2ea1436ccd0868486e Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 2 Nov 2022 11:44:37 +0100 Subject: [PATCH 30/61] Change the moment of 'env_id' Field creation to avoid duplication. --- src/Environment/BaseEnvironment.py | 5 ----- src/Manager/DatabaseManager.py | 5 +++++ 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 2f3c2b70..c519f975 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -334,11 +334,6 @@ def __database_handler_init(self): # Load Database and create basic fields self.__database_handler.load() - if self.instance_id == 1: - self.__database_handler.create_fields(table_name='Training', - fields=('env_id', int)) - self.__database_handler.create_fields(table_name='Additional', - fields=('env_id', int)) def _send_training_data(self) -> List[int]: """ diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index 3c7f9b2c..b922f993 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -232,6 +232,11 @@ def create_partition(self) -> None: for table_name in fields.keys(): self.partitions[self.mode][-1].create_fields(table_name=table_name, fields=fields[table_name]) + else: + self.partitions[self.mode][-1].create_fields(table_name='Training', + fields=('env_id', int)) + self.partitions[self.mode][-1].create_fields(table_name='Additional', + fields=('env_id', int)) # 4. Update the partitions in handlers for handler in self.database_handlers: From 06addf30e0eb4769da3e2fcaaa2413714c854288 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 2 Nov 2022 12:02:00 +0100 Subject: [PATCH 31/61] Update tutorials. --- examples/tutorial/T1_environment.py | 66 +++++++++++-------------- examples/tutorial/T2_network.py | 46 ++++++++++++----- examples/tutorial/T3_configuration.py | 53 +++++++++++--------- examples/tutorial/T4_dataGeneration.py | 29 ++++++++--- examples/tutorial/T5_offlineTraining.py | 46 ++++++++++------- examples/tutorial/T6_onlineTraining.py | 46 +++++++++++++---- examples/tutorial/T7_prediction.py | 32 ++++++------ 7 files changed, 196 insertions(+), 122 deletions(-) diff --git a/examples/tutorial/T1_environment.py b/examples/tutorial/T1_environment.py index 81390f4b..36732817 100644 --- a/examples/tutorial/T1_environment.py +++ b/examples/tutorial/T1_environment.py @@ -4,7 +4,7 @@ """ # Python related imports -from numpy import array +from numpy import array, ndarray # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -14,57 +14,50 @@ class DummyEnvironment(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, + as_tcp_ip_client=True, instance_id=1, - number_of_instances=1, - as_tcp_ip_client=True, - environment_manager=None): + instance_nb=1, + visualization_db=None): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, + as_tcp_ip_client=as_tcp_ip_client, instance_id=instance_id, - number_of_instances=number_of_instances, - as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_nb=instance_nb, + visualization_db=visualization_db) - self.nb_step = 0 - self.increment = 0 + self.step_nb: int = 0 """ INITIALIZING ENVIRONMENT - Methods will be automatically called it this order: - - recv_parameters: Receive a dictionary of parameters that can be set in EnvironmentConfig - create: Create the Environment - init: Initialize the Environment if required - - send_parameters: Same as recv_parameters, Environment can send back a set of parameters if required - - send_visualization: Send initial visualization data (see Example/CORE/Features to add visualization data) + - init_database: Define the training data fields + - init_visualization: Define and send initial visualization data """ - # Optional - def recv_parameters(self, param_dict): - # Set data size - self.increment = param_dict['increment'] if 'increment' in param_dict else 1 - # MANDATORY def create(self): + # Nothing to create in our DummyEnvironment pass # Optional def init(self): + # Nothing to init in our DummyEnvironment pass - # Optional - def send_parameters(self): - # Nothing to send back - return {} + # MANDATORY + def init_database(self): + + # Define the fields of the training Database + self.define_training_fields(fields=[('input', ndarray), ('ground_truth', ndarray)]) # Optional - def send_visualization(self): - # Nothing to visualize (see Example/CORE/Features to add visualization data) - return {} + def init_visualization(self): + + # Nothing to visualize + pass """ ENVIRONMENT BEHAVIOR - Methods will be automatically called at each simulation step in this order: @@ -79,25 +72,26 @@ def send_visualization(self): # MANDATORY async def step(self): + # Setting (and sending) training data - self.set_training_data(input_array=array([self.nb_step]), - output_array=array([self.nb_step])) - self.nb_step += self.increment - # Other data fields can be filled: - # - set_loss_data: Define an additional data to compute loss value (see Optimization.transform_loss) - # - set_additional_dataset: Add a field to the dataset + self.step_nb += 1 + self.set_training_data(input=array([self.step_nb]), + ground_truth=array([self.step_nb])) # Optional - def check_sample(self, check_input=True, check_output=True): + def check_sample(self): + # Nothing to check in our DummyEnvironment return True # Optional def apply_prediction(self, prediction): + # Nothing to apply in our DummyEnvironment - print(f"Prediction at step {self.nb_step - 1} = {prediction}") + print(f"Prediction at step {self.step_nb} = {prediction}") # Optional def close(self): + # Shutdown procedure print("Bye!") diff --git a/examples/tutorial/T2_network.py b/examples/tutorial/T2_network.py index c59ad8bb..9af80612 100644 --- a/examples/tutorial/T2_network.py +++ b/examples/tutorial/T2_network.py @@ -5,24 +5,27 @@ """ # Python related imports -from numpy import save, array +from numpy import save, array, ndarray # DeepPhysX related imports from DeepPhysX.Core.Network.BaseNetwork import BaseNetwork from DeepPhysX.Core.Network.BaseOptimization import BaseOptimization +from DeepPhysX.Core.Network.DataTransformation import DataTransformation # Create a Network as a BaseNetwork child class class DummyNetwork(BaseNetwork): def __init__(self, config): + BaseNetwork.__init__(self, config) # There is no Network architecture to define in our DummyNetwork # MANDATORY - def forward(self, x): + def forward(self, input_data): + # Return the input - return x + return input_data """ The following methods should be already defined in a DeepPhysX AI package. @@ -57,19 +60,12 @@ def save_parameters(self, path): def nb_parameters(self): return 0 - # MANDATORY - def transform_from_numpy(self, x, grad=True): - return x - - # MANDATORY - def transform_to_numpy(self, x): - return x - # Create an Optimization as a BaseOptimization child class class DummyOptimization(BaseOptimization): def __init__(self, config): + BaseOptimization.__init__(self, config) """ @@ -82,11 +78,11 @@ def set_loss(self): pass # MANDATORY - def compute_loss(self, prediction, ground_truth, data): + def compute_loss(self, data_pred, data_opt): return {'loss': 0.} # Optional - def transform_loss(self, data): + def transform_loss(self, data_opt): pass # MANDATORY @@ -96,3 +92,27 @@ def set_optimizer(self, net): # MANDATORY def optimize(self): pass + + +# Create a DataTransformation as a DataTransformation child class +class DummyTransformation(DataTransformation): + + def __init__(self, config): + + DataTransformation.__init__(self, config) + self.data_type = ndarray + + def transform_before_prediction(self, data_net): + + # Do not transform the Network data + return data_net + + def transform_before_loss(self, data_pred, data_opt=None): + + # Do not transform the Prediction data and the Optimizer data + return data_pred, data_opt + + def transform_before_apply(self, data_pred): + + # Do not transform Prediction data + return data_pred diff --git a/examples/tutorial/T3_configuration.py b/examples/tutorial/T3_configuration.py index 964831d0..69d965f2 100644 --- a/examples/tutorial/T3_configuration.py +++ b/examples/tutorial/T3_configuration.py @@ -6,35 +6,42 @@ # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig # Tutorial related imports from T1_environment import DummyEnvironment -from T2_network import DummyNetwork, DummyOptimization - +from T2_network import DummyNetwork, DummyOptimization, DummyTransformation # Create the Environment config -env_config = BaseEnvironmentConfig(environment_class=DummyEnvironment, # The Environment class to create - visualizer=None, # The Visualizer to use - simulations_per_step=1, # The number of bus-steps to run - use_dataset_in_environment=False, # Dataset will not be sent to Environment - param_dict={'increment': 1}, # Parameters to send at init - as_tcp_ip_client=True, # Create a Client / Server architecture - number_of_thread=3, # Number of Clients connected to Server - ip_address='localhost', # IP address to use for communication - port=10001) # Port number to use for communication +env_config = BaseEnvironmentConfig(environment_class=DummyEnvironment, # The Environment class to create + as_tcp_ip_client=True, # Create a Client / Server architecture + number_of_thread=3, # Number of Clients connected to Server + ip_address='localhost', # IP address to use for communication + port=10001, # Port number to use for communication + simulations_per_step=1, # The number of bus-steps to run + load_samples=False, # Load samples from Database to Environment + only_first_epoch=True, # Use the Environment on the first epoch only + always_produce=False) # Environment is always producing data # Create the Network config -net_config = BaseNetworkConfig(network_class=DummyNetwork, # The Network class to create - optimization_class=DummyOptimization, # The Optimization class to create - network_name='DummyNetwork', # Nickname of the Network - network_type='Dummy', # Type of the Network - save_each_epoch=False, # Do not save the network at each epoch - require_training_stuff=False, # loss and optimizer can remain at None - lr=None, # Learning rate - loss=None, # Loss class - optimizer=None) # Optimizer class +net_config = BaseNetworkConfig(network_class=DummyNetwork, # The Network class to create + optimization_class=DummyOptimization, # The Optimization class to create + data_transformation_class=DummyTransformation, # The DataTransformation class to create + network_dir=None, # Path to an existing Network repository + network_name='DummyNetwork', # Nickname of the Network + network_type='Dummy', # Type of the Network + which_network=-1, # The index of Network to load + save_each_epoch=False, # Do not save the network at each epoch + data_type='float32', # Training data type + require_training_stuff=False, # loss and optimizer can remain at None + lr=None, # Learning rate + loss=None, # Loss class + optimizer=None) # Optimizer class # Create the Dataset config -dataset_config = BaseDatasetConfig(partition_size=1, # Max size of the Dataset - shuffle_dataset=False) # Dataset should be shuffled +database_config = BaseDatabaseConfig(existing_dir=None, # Path to an existing Database + mode='training', # Database mode + max_file_size=1, # Max size of the Dataset (Gb) + shuffle=False, # Dataset should be shuffled + normalize=False, # Database should be normalized + recompute_normalization=False) # Normalization should be recomputed at loading diff --git a/examples/tutorial/T4_dataGeneration.py b/examples/tutorial/T4_dataGeneration.py index e5039e11..729ea9d5 100644 --- a/examples/tutorial/T4_dataGeneration.py +++ b/examples/tutorial/T4_dataGeneration.py @@ -4,19 +4,34 @@ """ # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseDataGenerator import BaseDataGenerator +from DeepPhysX.Core.Pipelines.BaseDataGeneration import BaseDataGeneration +from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig # Tutorial related imports -from T3_configuration import env_config, dataset_config +from T1_environment import DummyEnvironment def launch_data_generation(): + + # Create the Environment config + env_config = BaseEnvironmentConfig(environment_class=DummyEnvironment, + as_tcp_ip_client=True, + number_of_thread=3) + + # Create the Database config + database_config = BaseDatabaseConfig(max_file_size=1, + normalize=False) + # Create the Pipeline - pipeline = BaseDataGenerator(session_name='sessions/tutorial_data_generation', - dataset_config=dataset_config, - environment_config=env_config, - nb_batches=100, - batch_size=10) + pipeline = BaseDataGeneration(environment_config=env_config, + database_config=database_config, + session_dir='sessions', + session_name='tutorial_data_generation', + new_session=True, + batch_nb=20, + batch_size=10) + # Launch the Pipeline pipeline.execute() diff --git a/examples/tutorial/T5_offlineTraining.py b/examples/tutorial/T5_offlineTraining.py index d8761412..63fece14 100644 --- a/examples/tutorial/T5_offlineTraining.py +++ b/examples/tutorial/T5_offlineTraining.py @@ -3,31 +3,41 @@ Launch a training session with an existing Dataset. """ -# Python related imports -import os - # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseTrainer import BaseTrainer -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BaseTraining import BaseTraining +from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig # Tutorial related imports -from T3_configuration import env_config, net_config +from T2_network import DummyNetwork, DummyOptimization, DummyTransformation def launch_training(): - # Adapt the Dataset config with the existing dataset directory - dataset_config = BaseDatasetConfig(dataset_dir=os.path.join(os.getcwd(), 'sessions/tutorial_data_generation'), - partition_size=1, - shuffle_dataset=False) + + # Create the Network config + net_config = BaseNetworkConfig(network_class=DummyNetwork, + optimization_class=DummyOptimization, + data_transformation_class=DummyTransformation, + network_name='DummyNetwork', + network_type='Dummy', + save_each_epoch=False, + require_training_stuff=False) + + # Create the Dataset config + database_config = BaseDatabaseConfig(existing_dir='sessions/tutorial_data_generation', + shuffle=False) + # Create the Pipeline - pipeline = BaseTrainer(session_dir='sessions', - session_name='tutorial_offline_training', - environment_config=env_config, - dataset_config=dataset_config, - network_config=net_config, - nb_epochs=2, - nb_batches=100, - batch_size=10) + pipeline = BaseTraining(network_config=net_config, + database_config=database_config, + session_dir='sessions', + session_name='tutorial_offline_training', + new_session=True, + epoch_nb=2, + batch_nb=20, + batch_size=10, + debug=True) + # Launch the Pipeline pipeline.execute() diff --git a/examples/tutorial/T6_onlineTraining.py b/examples/tutorial/T6_onlineTraining.py index 3d77eb4b..3d73587a 100644 --- a/examples/tutorial/T6_onlineTraining.py +++ b/examples/tutorial/T6_onlineTraining.py @@ -4,22 +4,48 @@ """ # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseTrainer import BaseTrainer +from DeepPhysX.Core.Pipelines.BaseTraining import BaseTraining +from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig +from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig # Tutorial related imports -from T3_configuration import env_config, net_config, dataset_config +from T1_environment import DummyEnvironment +from T2_network import DummyNetwork, DummyOptimization, DummyTransformation def launch_training(): + + # Create the Environment config + env_config = BaseEnvironmentConfig(environment_class=DummyEnvironment, + as_tcp_ip_client=True, + number_of_thread=3) + + # Create the Network config + net_config = BaseNetworkConfig(network_class=DummyNetwork, + optimization_class=DummyOptimization, + data_transformation_class=DummyTransformation, + network_name='DummyNetwork', + network_type='Dummy', + save_each_epoch=False, + require_training_stuff=False) + + # Create the Dataset config + database_config = BaseDatabaseConfig(max_file_size=1, + normalize=False) + # Create the Pipeline - pipeline = BaseTrainer(session_dir='sessions', - session_name='tutorial_online_training', - environment_config=env_config, - dataset_config=dataset_config, - network_config=net_config, - nb_epochs=2, - nb_batches=100, - batch_size=10) + pipeline = BaseTraining(network_config=net_config, + database_config=database_config, + environment_config=env_config, + session_dir='sessions', + session_name='tutorial_online_training', + new_session=True, + epoch_nb=2, + batch_nb=20, + batch_size=10, + debug=True) + # Launch the Pipeline pipeline.execute() diff --git a/examples/tutorial/T7_prediction.py b/examples/tutorial/T7_prediction.py index 526eb26a..4752f349 100644 --- a/examples/tutorial/T7_prediction.py +++ b/examples/tutorial/T7_prediction.py @@ -4,28 +4,30 @@ """ # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig +from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig # Tutorial related imports -from T3_configuration import DummyEnvironment, net_config, dataset_config +from T1_environment import DummyEnvironment +from T2_network import DummyNetwork def launch_prediction(): - # Adapt the Environment config to avoid using Client / Server Architecture - env_config = BaseEnvironmentConfig(environment_class=DummyEnvironment, - visualizer=None, - simulations_per_step=1, - use_dataset_in_environment=False, - param_dict={'increment': 1}, - as_tcp_ip_client=False) + + # Create the Environment config + env_config = BaseEnvironmentConfig(environment_class=DummyEnvironment) + + # Create the Network config + net_config = BaseNetworkConfig(network_class=DummyNetwork) + # Create the Pipeline - pipeline = BaseRunner(session_dir='sessions', - session_name='tutorial_online_training', - environment_config=env_config, - dataset_config=dataset_config, - network_config=net_config, - nb_steps=20) + pipeline = BasePrediction(network_config=net_config, + environment_config=env_config, + session_dir='sessions', + session_name='tutorial_online_training', + step_nb=20) + # Launch the Pipeline pipeline.execute() From 69a6278072e724d040138f1fb8577d6a855d8a6e Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 2 Nov 2022 16:11:23 +0100 Subject: [PATCH 32/61] Rename DataTransformation class. --- src/Network/BaseNetwork.py | 4 ++-- src/Network/BaseNetworkConfig.py | 18 +++++++++--------- src/Network/BaseOptimization.py | 2 +- ...Transformation.py => BaseTransformation.py} | 6 +++--- 4 files changed, 15 insertions(+), 15 deletions(-) rename src/Network/{DataTransformation.py => BaseTransformation.py} (93%) diff --git a/src/Network/BaseNetwork.py b/src/Network/BaseNetwork.py index eef6bb3f..b390795c 100644 --- a/src/Network/BaseNetwork.py +++ b/src/Network/BaseNetwork.py @@ -39,7 +39,7 @@ def forward(self, Compute a forward pass of the Network. :param input_data: Input tensor. - :return: Output tensor. + :return: Network prediction. """ raise NotImplementedError @@ -60,7 +60,7 @@ def set_eval(self) -> None: def set_device(self) -> None: """ - | Set computer device on which Network's parameters will be stored and tensors will be computed. + Set computer device on which Network's parameters will be stored and tensors will be computed. """ raise NotImplementedError diff --git a/src/Network/BaseNetworkConfig.py b/src/Network/BaseNetworkConfig.py index 6d05b36c..efde937a 100644 --- a/src/Network/BaseNetworkConfig.py +++ b/src/Network/BaseNetworkConfig.py @@ -4,7 +4,7 @@ from DeepPhysX.Core.Network.BaseNetwork import BaseNetwork from DeepPhysX.Core.Network.BaseOptimization import BaseOptimization -from DeepPhysX.Core.Network.DataTransformation import DataTransformation +from DeepPhysX.Core.Network.BaseTransformation import BaseTransformation from DeepPhysX.Core.Utils.configs import make_config, namedtuple @@ -13,7 +13,7 @@ class BaseNetworkConfig: def __init__(self, network_class: Type[BaseNetwork] = BaseNetwork, optimization_class: Type[BaseOptimization] = BaseOptimization, - data_transformation_class: Type[DataTransformation] = DataTransformation, + data_transformation_class: Type[BaseTransformation] = BaseTransformation, network_dir: Optional[str] = None, network_name: str = 'Network', network_type: str = 'BaseNetwork', @@ -26,11 +26,11 @@ def __init__(self, optimizer: Optional[Any] = None): """ BaseNetworkConfig is a configuration class to parameterize and create BaseNetwork, BaseOptimization and - DataTransformation for the NetworkManager. + BaseTransformation for the NetworkManager. :param network_class: BaseNetwork class from which an instance will be created. :param optimization_class: BaseOptimization class from which an instance will be created. - :param data_transformation_class: DataTransformation class from which an instance will be created. + :param data_transformation_class: BaseTransformation class from which an instance will be created. :param network_dir: Name of an existing network repository. :param network_name: Name of the network. :param network_type: Type of the network. @@ -92,7 +92,7 @@ def __init__(self, self.training_stuff: bool = (loss is not None) and (optimizer is not None) or (not require_training_stuff) # NetworkManager parameterization - self.data_transformation_class: Type[DataTransformation] = data_transformation_class + self.data_transformation_class: Type[BaseTransformation] = data_transformation_class self.data_transformation_config: namedtuple = make_config(configuration_object=self, configuration_name='data_transformation_config') @@ -128,18 +128,18 @@ def create_optimization(self) -> BaseOptimization: f"BaseOptimization.") return optimization - def create_data_transformation(self) -> DataTransformation: + def create_data_transformation(self) -> BaseTransformation: """ Create an instance of data_transformation_class with given parameters. - :return: DataTransformation object from data_transformation_class and its parameters. + :return: BaseTransformation object from data_transformation_class and its parameters. """ # Create instance data_transformation = self.data_transformation_class(config=self.data_transformation_config) - if not isinstance(data_transformation, DataTransformation): + if not isinstance(data_transformation, BaseTransformation): raise TypeError(f"[{self.name}] The given 'data_transformation_class'={self.data_transformation_class} " - f"must be a DataTransformation.") + f"must be a BaseTransformation.") return data_transformation def __str__(self): diff --git a/src/Network/BaseOptimization.py b/src/Network/BaseOptimization.py index eaa49edc..729c54b5 100644 --- a/src/Network/BaseOptimization.py +++ b/src/Network/BaseOptimization.py @@ -10,7 +10,7 @@ def __init__(self, config: namedtuple): """ BaseOptimization computes loss between prediction and target and optimizes the Network parameters. - :param config: Set of BaseOptimization parameters + :param config: Set of BaseOptimization parameters. """ self.manager: Any = None diff --git a/src/Network/DataTransformation.py b/src/Network/BaseTransformation.py similarity index 93% rename from src/Network/DataTransformation.py rename to src/Network/BaseTransformation.py index e41e8ca4..e2f4cb7d 100644 --- a/src/Network/DataTransformation.py +++ b/src/Network/BaseTransformation.py @@ -2,13 +2,13 @@ from collections import namedtuple -class DataTransformation: +class BaseTransformation: def __init__(self, config: namedtuple): """ - DataTransformation manages data operations before and after network predictions. + BaseTransformation manages data operations before and after network predictions. - :param config: Set of DataTransformation parameters. + :param config: Set of BaseTransformation parameters. """ self.name = self.__class__.__name__ From 53392c1b68eae9c6a2d5afdbcbba96b0bc5ac700 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 2 Nov 2022 16:11:57 +0100 Subject: [PATCH 33/61] Restaure additional parameters sending to Environments. --- src/AsyncSocket/AbstractEnvironment.py | 3 ++- src/AsyncSocket/TcpIpClient.py | 26 +++++++++++++++------- src/AsyncSocket/TcpIpServer.py | 15 ++++++++++--- src/Environment/BaseEnvironment.py | 3 ++- src/Environment/BaseEnvironmentConfig.py | 12 ++++++---- src/Environment/launcherBaseEnvironment.py | 2 +- 6 files changed, 43 insertions(+), 18 deletions(-) diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 55423c61..791bfab5 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -12,7 +12,8 @@ def __init__(self, as_tcp_ip_client: bool = True, instance_id: int = 0, instance_nb: int = 1, - visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): + visualization_db: Optional[Union[Database, Tuple[str, str]]] = None, + **kwargs): """ AbstractEnvironment sets the Environment API for TcpIpClient. Do not use AbstractEnvironment to implement an Environment, use BaseEnvironment instead. diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 4d9f7cd0..bd722ae2 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -33,12 +33,11 @@ def __init__(self, ip_address=ip_address, port=port) - # Create instance - self.environment = environment(as_tcp_ip_client=True, - instance_id=instance_id, - instance_nb=instance_nb, - visualization_db=visualization_db) - self.environment.tcp_ip_client = self + # Environment instance + self.environment: AbstractEnvironment + self.environment_class = environment + self.environment_instance = (instance_id, instance_nb) + self.environment_visualization = visualization_db # Bind to client address and send ID self.sock.connect((ip_address, port)) @@ -66,6 +65,17 @@ async def __initialize(self) -> None: loop = get_event_loop() + # Receive additional arguments + env_kwargs = {} + await self.receive_dict(recv_to=env_kwargs, loop=loop, sender=self.sock) + + self.environment = self.environment_class(as_tcp_ip_client=True, + instance_id=self.environment_instance[0], + instance_nb=self.environment_instance[1], + visualization_db=self.environment_visualization, + **env_kwargs['env_kwargs']) + self.environment.tcp_ip_client = self + # Receive prediction requests authorization self.allow_prediction_requests = await self.receive_data(loop=loop, sender=self.sock) @@ -279,8 +289,8 @@ async def action_on_step(self, await self.send_command_done(loop=loop, receiver=sender) await self.send_data(data_to_send=line, loop=loop, receiver=sender) - async def action_on_change_db(self, - data: Dict[Any, Any], + async def action_on_change_db(self, + data: Dict[Any, Any], client_id: int, sender: socket, loop: EventLoop) -> None: """ diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index f54e0074..3eddf1b1 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -120,17 +120,23 @@ async def __connect(self) -> None: ########################################################################################## ########################################################################################## - def initialize(self) -> None: + def initialize(self, + env_kwargs: Dict[str, Any]) -> None: """ Send parameters to the clients to create their environments. + + :param env_kwargs: Additional arguments to pass to the Environment. """ print(f"[{self.name}] Initializing clients...") - async_run(self.__initialize()) + async_run(self.__initialize(env_kwargs)) - async def __initialize(self) -> None: + async def __initialize(self, + env_kwargs: Dict[str, Any]) -> None: """ Send parameters to the clients to create their environments. + + :param env_kwargs: Additional arguments to pass to the Environment. """ loop = get_event_loop() @@ -138,6 +144,9 @@ async def __initialize(self) -> None: # Initialisation process for each client for client_id, client in self.clients: + # Send additional arguments + await self.send_dict(name='env_kwargs', dict_to_send=env_kwargs, loop=loop, receiver=client) + # Send prediction request authorization await self.send_data(data_to_send=self.environment_manager.allow_prediction_requests, loop=loop, receiver=client) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index c519f975..712a3feb 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -15,7 +15,8 @@ def __init__(self, as_tcp_ip_client: bool = True, instance_id: int = 0, instance_nb: int = 1, - visualization_db: Optional[Union[Database, Tuple[str, str]]] = None): + visualization_db: Optional[Union[Database, Tuple[str, str]]] = None, + **kwargs): """ BaseEnvironment computes simulated data for the Network and its training process. diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index 25bcfcfb..ba75ef63 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Type +from typing import Any, Optional, Type, Dict from os import cpu_count from os.path import join, dirname from threading import Thread @@ -24,7 +24,8 @@ def __init__(self, only_first_epoch: bool = True, always_produce: bool = False, visualizer: Optional[Type[VedoVisualizer]] = None, - record_wrong_samples: bool = False): + record_wrong_samples: bool = False, + env_kwargs: Optional[Dict[str, Any]] = None): """ BaseEnvironmentConfig is a configuration class to parameterize and create a BaseEnvironment for the EnvironmentManager. @@ -42,6 +43,7 @@ def __init__(self, :param always_produce: If True, data will always be produced in Environment(s). :param visualizer: Class of the Visualizer to use. :param record_wrong_samples: If True, wrong samples are recorded through Visualizer. + :param env_kwargs: Additional arguments to pass to the Environment. """ self.name: str = self.__class__.__name__ @@ -85,6 +87,7 @@ def __init__(self, self.load_samples: bool = load_samples self.only_first_epoch: bool = only_first_epoch self.always_produce: bool = always_produce + self.env_kwargs: Dict[str, Any] = {} if env_kwargs is None else env_kwargs # Visualizer variables self.visualizer: Optional[Type[VedoVisualizer]] = visualizer @@ -135,7 +138,7 @@ def start_server(self, """ server.connect() - server.initialize() + server.initialize(env_kwargs=self.env_kwargs) self.server_is_ready = True def start_client(self, @@ -163,7 +166,8 @@ def create_environment(self, # Create instance environment = self.environment_class(as_tcp_ip_client=False, - visualization_db=visualization_db) + visualization_db=visualization_db, + **self.env_kwargs) if not isinstance(environment, BaseEnvironment): raise TypeError(f"[{self.name}] The given 'environment_class'={self.environment_class} must be a " f"BaseEnvironment.") diff --git a/src/Environment/launcherBaseEnvironment.py b/src/Environment/launcherBaseEnvironment.py index d533f1ec..6449468b 100644 --- a/src/Environment/launcherBaseEnvironment.py +++ b/src/Environment/launcherBaseEnvironment.py @@ -19,7 +19,7 @@ exec(f"from {module_name} import {argv[2]} as Environment") # Create, init and run Tcp-Ip environment - visualization_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[8][1:-1].split(', ')] + visualization_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] client = TcpIpClient(environment=Environment, ip_address=argv[3], port=int(argv[4]), From 0a3f18dca9cc0ec77be3b99ecd7cd320cd86fcf8 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 2 Nov 2022 18:20:48 +0100 Subject: [PATCH 34/61] Update mean example. --- examples/features/Environment.py | 132 +++++++++++---------- examples/features/dataGeneration_multi.py | 33 ++++-- examples/features/dataGeneration_single.py | 36 +++--- examples/features/gradientDescent.py | 46 +++---- examples/features/offlineTraining.py | 35 +++--- examples/features/onlineTraining.py | 46 +++---- examples/features/prediction.py | 31 ++--- examples/tutorial/T2_network.py | 8 +- src/AsyncSocket/AbstractEnvironment.py | 4 +- src/Environment/BaseEnvironment.py | 4 +- src/Manager/NetworkManager.py | 5 +- src/Network/BaseOptimization.py | 1 - src/Network/BaseTransformation.py | 5 +- src/Utils/configs.py | 2 +- 14 files changed, 215 insertions(+), 173 deletions(-) diff --git a/examples/features/Environment.py b/examples/features/Environment.py index e8034717..818ecd5f 100644 --- a/examples/features/Environment.py +++ b/examples/features/Environment.py @@ -5,7 +5,7 @@ """ # Python related imports -from numpy import mean, pi, array +from numpy import mean, pi, array, ndarray from numpy.random import random, randint from time import sleep @@ -17,69 +17,65 @@ class MeanEnvironment(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None, - visu_db=None): + instance_id=1, + instance_nb=1, + visualization_db=None, + constant=False, + data_size=(30, 2), + delay=False, + allow_request=True): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager, - visu_db=visu_db) + instance_id=instance_id, + instance_nb=instance_nb, + visualization_db=visualization_db) # Define training data values - self.input_value = array([]) - self.output_value = array([]) + self.pcd = array([]) + self.mean = array([]) # Environment parameters - self.constant = False - self.data_size = [30, 2] - self.sleep = False - self.allow_requests = True + self.constant = constant + self.data_size = data_size + self.delay = delay + self.allow_requests = allow_request """ ENVIRONMENT INITIALIZATION Methods will be automatically called in this order to create and initialize Environment. """ - def recv_parameters(self, param_dict): - # If True, the same data is always sent so one can observe how the prediction crawl toward the ground truth. - self.constant = param_dict['constant'] if 'constant' in param_dict else self.constant - # Define the data size - self.data_size = param_dict['data_size'] if 'data_size' in param_dict else self.data_size - # If True, step will sleep a random time to simulate longer processes - self.sleep = param_dict['sleep'] if 'sleep' in param_dict else self.sleep - # If True, requests can be performed - self.allow_requests = param_dict['allow_requests'] if 'allow_requests' in param_dict else self.allow_requests - def create(self): - # The vector is the input of the network and the ground truth is the mean. - self.input_value = pi * random(self.data_size) - self.output_value = mean(self.input_value, axis=0) + + self.pcd = pi * random(self.data_size) + self.mean = mean(self.pcd, axis=0) + + def init_database(self): + + # Define the fields of the training Database + self.define_training_fields(fields=[('input', ndarray), ('ground_truth', ndarray)]) def init_visualization(self): - # Point cloud (object will have id = 0) - self.factory.add_points(positions=self.input_value, - at=self.instance_id, - c='blue', - point_size=8) - # Ground truth value (object will have id = 1) - self.factory.add_points(positions=self.output_value, - at=self.instance_id, - c='green', - point_size=10) - # Prediction value (object will have id = 2) - self.factory.add_points(positions=self.output_value, - at=self.instance_id, - c='orange', - point_size=12) + + if self.factory is not None: + + # Point cloud (object will have id = 0) + self.factory.add_points(positions=self.pcd, + at=self.instance_id, + c='blue', + point_size=8) + # Ground truth value (object will have id = 1) + self.factory.add_points(positions=self.mean, + at=self.instance_id, + c='green', + point_size=10) + # Prediction value (object will have id = 2) + self.factory.add_points(positions=self.mean, + at=self.instance_id, + c='orange', + point_size=12) """ ENVIRONMENT BEHAVIOR @@ -88,34 +84,42 @@ def init_visualization(self): """ async def step(self): - # Compute new data - if not self.constant: - self.input_value = pi * random(self.data_size) - self.output_value = mean(self.input_value, axis=0) + # Simulate longer process - if self.sleep: + if self.delay: sleep(0.01 * randint(0, 10)) + + # Compute new data + if not self.constant: + self.pcd = pi * random(self.data_size) + self.mean = mean(self.pcd, axis=0) + + # Update visualization data + if not self.constant: + # Point cloud + self.factory.update_points(object_id=0, + positions=self.pcd) + # Ground truth value + self.factory.update_points(object_id=1, + positions=self.mean) + # Send the training data - self.set_training_data(input_array=self.input_value, - output_array=self.output_value) + self.set_training_data(input=self.pcd, + ground_truth=self.mean) + if self.allow_requests: # Request a prediction for the given input - prediction = self.get_prediction(input_array=self.input_value) + prediction = self.get_prediction(input=self.pcd) # Apply this prediction in Environment self.apply_prediction(prediction) + else: + self.update_visualisation() def apply_prediction(self, prediction): - # Update visualization with new input and ground truth - if not self.constant: - # Point cloud - self.factory.update_points(object_id=0, - positions=self.input_value) - # Ground truth value - self.factory.update_points(object_id=1, - positions=self.output_value) + # Update visualization with prediction self.factory.update_points(object_id=2, - positions=prediction) + positions=prediction['prediction']) # Send visualization data to update self.update_visualisation() diff --git a/examples/features/dataGeneration_multi.py b/examples/features/dataGeneration_multi.py index 74aa3533..e9e98994 100644 --- a/examples/features/dataGeneration_multi.py +++ b/examples/features/dataGeneration_multi.py @@ -9,34 +9,41 @@ from time import time # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseDataGenerator import BaseDataGenerator +from DeepPhysX.Core.Pipelines.BaseDataGeneration import BaseDataGeneration from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer # Session related imports from Environment import MeanEnvironment def launch_data_generation(use_tcp_ip): + # Define the number of points and the dimension nb_points = 30 dimension = 3 + # Environment configuration environment_config = BaseEnvironmentConfig(environment_class=MeanEnvironment, - param_dict={'constant': False, - 'data_size': [nb_points, dimension], - 'sleep': True, - 'allow_requests': False}, + visualizer=VedoVisualizer, as_tcp_ip_client=use_tcp_ip, - number_of_thread=10) + number_of_thread=5, + env_kwargs={'constant': False, + 'data_size': [nb_points, dimension], + 'delay': True, + 'allow_request': False}) # Dataset configuration - dataset_config = BaseDatasetConfig(normalize=False) + database_config = BaseDatabaseConfig(max_file_size=1, + normalize=False) # Create DataGenerator - data_generator = BaseDataGenerator(session_name='sessions/data_generation_compare', - environment_config=environment_config, - dataset_config=dataset_config, - nb_batches=20, - batch_size=10) + data_generator = BaseDataGeneration(environment_config=environment_config, + database_config=database_config, + session_dir='sessions', + session_name='data_generation_compare', + batch_nb=20, + batch_size=10) + # Launch the training session start_time = time() data_generator.execute() diff --git a/examples/features/dataGeneration_single.py b/examples/features/dataGeneration_single.py index 23c5de46..200d3d9b 100644 --- a/examples/features/dataGeneration_single.py +++ b/examples/features/dataGeneration_single.py @@ -4,33 +4,41 @@ """ # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseDataGenerator import BaseDataGenerator +from DeepPhysX.Core.Pipelines.BaseDataGeneration import BaseDataGeneration from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer # Session related imports from Environment import MeanEnvironment def launch_data_generation(): + # Define the number of points and the dimension nb_points = 30 dimension = 3 + # Environment configuration environment_config = BaseEnvironmentConfig(environment_class=MeanEnvironment, - param_dict={'constant': False, - 'data_size': [nb_points, dimension], - 'sleep': False, - 'allow_requests': False}, - as_tcp_ip_client=False) - # Dataset configuration - dataset_config = BaseDatasetConfig(normalize=False) + visualizer=VedoVisualizer, + as_tcp_ip_client=False, + env_kwargs={'constant': False, + 'data_size': (nb_points, dimension), + 'delay': False, + 'allow_request': False}) + # Database configuration + database_config = BaseDatabaseConfig(max_file_size=1, + normalize=False) + # Create DataGenerator - data_generator = BaseDataGenerator(session_name='sessions/data_generation', - environment_config=environment_config, - dataset_config=dataset_config, - nb_batches=500, - batch_size=10) + data_generator = BaseDataGeneration(environment_config=environment_config, + database_config=database_config, + session_dir='sessions', + session_name='data_generation', + batch_nb=500, + batch_size=10) + # Launch the training session data_generator.execute() diff --git a/examples/features/gradientDescent.py b/examples/features/gradientDescent.py index dcb5de75..87fe600b 100644 --- a/examples/features/gradientDescent.py +++ b/examples/features/gradientDescent.py @@ -9,9 +9,9 @@ from torch.optim import Adam # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseTrainer import BaseTrainer +from DeepPhysX.Core.Pipelines.BaseTraining import BaseTraining from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.FC.FCConfig import FCConfig @@ -20,37 +20,43 @@ def launch_training(): + # Define the number of points and the dimension nb_points = 30 dimension = 3 + # Environment configuration environment_config = BaseEnvironmentConfig(environment_class=MeanEnvironment, visualizer=VedoVisualizer, - param_dict={'constant': True, - 'data_size': [nb_points, dimension], - 'sleep': False, - 'allow_requests': True}, as_tcp_ip_client=True, - number_of_thread=2) + number_of_thread=1, + env_kwargs={'constant': True, + 'data_size': [nb_points, dimension], + 'delay': False, + 'allow_request': True}) + # Fully Connected configuration (the number of neurones on the first and last layer is defined by the total amount # of parameters in the input and the output vectors respectively) - network_config = FCConfig(loss=MSELoss, - lr=1e-3, + network_config = FCConfig(lr=1e-3, + loss=MSELoss, optimizer=Adam, dim_layers=[nb_points * dimension, nb_points * dimension, dimension], dim_output=dimension) - # Dataset configuration - dataset_config = BaseDatasetConfig(normalize=False) + + # Database configuration + database_config = BaseDatabaseConfig(normalize=False) + # Create Trainer - trainer = BaseTrainer(session_dir='sessions', - session_name='gradient_descent', - environment_config=environment_config, - dataset_config=dataset_config, - network_config=network_config, - nb_epochs=1, - nb_batches=200, - batch_size=1, - debug=True) + trainer = BaseTraining(network_config=network_config, + environment_config=environment_config, + database_config=database_config, + session_dir='sessions', + session_name='gradient_descent', + epoch_nb=1, + batch_nb=200, + batch_size=1, + debug=True) + # Launch the training session trainer.execute() diff --git a/examples/features/offlineTraining.py b/examples/features/offlineTraining.py index b03e986e..bc5abb6c 100644 --- a/examples/features/offlineTraining.py +++ b/examples/features/offlineTraining.py @@ -9,42 +9,49 @@ from torch.optim import Adam # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseTrainer import BaseTrainer -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BaseTraining import BaseTraining +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Torch.FC.FCConfig import FCConfig def launch_training(): + # Define the number of points and the dimension nb_points = 30 dimension = 3 + # Fully Connected configuration (the number of neurones on the first and last layer is defined by the total amount # of parameters in the input and the output vectors respectively) - network_config = FCConfig(loss=MSELoss, - lr=1e-3, + network_config = FCConfig(lr=1e-3, + loss=MSELoss, optimizer=Adam, dim_layers=[nb_points * dimension, nb_points * dimension, dimension], dim_output=dimension) + # Dataset configuration with the path to the existing Dataset - dataset_config = BaseDatasetConfig(dataset_dir=os.path.join(os.getcwd(), 'sessions/data_generation'), - shuffle_dataset=True, - normalize=False) + database_config = BaseDatabaseConfig(existing_dir='sessions/data_generation', + shuffle=True, + normalize=False) + # Create DataGenerator - trainer = BaseTrainer(session_dir='sessions', - session_name='offline_training', - dataset_config=dataset_config, - network_config=network_config, - nb_epochs=1, - nb_batches=500, - batch_size=10) + trainer = BaseTraining(network_config=network_config, + database_config=database_config, + session_dir='sessions', + session_name='offline_training', + epoch_nb=1, + batch_nb=500, + batch_size=10) + # Launch the training session trainer.execute() if __name__ == '__main__': + if not os.path.exists(os.path.join(os.getcwd(), 'sessions/data_generation')): print("Existing Dataset required, 'sessions/data_generation' not found. " "Run dataGeneration_single.py script first.") from dataGeneration_single import launch_data_generation launch_data_generation() + launch_training() diff --git a/examples/features/onlineTraining.py b/examples/features/onlineTraining.py index 172b5a78..780849ec 100644 --- a/examples/features/onlineTraining.py +++ b/examples/features/onlineTraining.py @@ -8,48 +8,54 @@ from torch.optim import Adam # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseTrainer import BaseTrainer +from DeepPhysX.Core.Pipelines.BaseTraining import BaseTraining from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.FC.FCConfig import FCConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer # Session imports from Environment import MeanEnvironment def launch_training(): + # Define the number of points and the dimension nb_points = 30 dimension = 3 + # Environment configuration environment_config = BaseEnvironmentConfig(environment_class=MeanEnvironment, visualizer=VedoVisualizer, - param_dict={'constant': False, - 'data_size': [nb_points, dimension], - 'sleep': False, - 'allow_requests': True}, as_tcp_ip_client=True, - number_of_thread=10) + number_of_thread=5, + env_kwargs={'constant': False, + 'data_size': [nb_points, dimension], + 'delay': False, + 'allow_request': True}) + # Fully Connected configuration (the number of neurones on the first and last layer is defined by the total amount # of parameters in the input and the output vectors respectively) - network_config = FCConfig(loss=MSELoss, - lr=1e-3, + network_config = FCConfig(lr=1e-3, + loss=MSELoss, optimizer=Adam, dim_layers=[nb_points * dimension, nb_points * dimension, dimension], dim_output=dimension) + # Dataset configuration with the path to the existing Dataset - dataset_config = BaseDatasetConfig(shuffle_dataset=True, - normalize=False) + database_config = BaseDatabaseConfig(max_file_size=1, + shuffle=True, + normalize=False) # Create DataGenerator - trainer = BaseTrainer(session_dir='sessions', - session_name='online_training', - environment_config=environment_config, - dataset_config=dataset_config, - network_config=network_config, - nb_epochs=5, - nb_batches=100, - batch_size=10) + trainer = BaseTraining(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='online_training', + epoch_nb=5, + batch_nb=100, + batch_size=10) + # Launch the training session trainer.execute() diff --git a/examples/features/prediction.py b/examples/features/prediction.py index 67a8e34e..7fd8adf3 100644 --- a/examples/features/prediction.py +++ b/examples/features/prediction.py @@ -7,41 +7,42 @@ import os # DeepPhysX related imports -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.FC.FCConfig import FCConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig + # Session imports from Environment import MeanEnvironment def launch_prediction(session): + # Define the number of points and the dimension nb_points = 30 dimension = 3 + # Environment configuration environment_config = BaseEnvironmentConfig(environment_class=MeanEnvironment, visualizer=VedoVisualizer, - param_dict={'constant': False, + env_kwargs={'constant': False, 'data_size': [nb_points, dimension], - 'sleep': True, - 'allow_requests': False}, - as_tcp_ip_client=False) + 'delay': True, + 'allow_request': False}) + # Fully Connected configuration (the number of neurones on the first and last layer is defined by the total amount # of parameters in the input and the output vectors respectively) network_config = FCConfig(dim_layers=[nb_points * dimension, nb_points * dimension, dimension], dim_output=dimension) - # Dataset configuration - dataset_config = BaseDatasetConfig(normalize=False) + # Create DataGenerator - trainer = BaseRunner(session_dir='sessions', - session_name=session, - environment_config=environment_config, - network_config=network_config, - dataset_config=dataset_config, - nb_steps=100) + trainer = BasePrediction(environment_config=environment_config, + network_config=network_config, + session_dir='sessions', + session_name=session, + step_nb=100) + # Launch the training session trainer.execute() diff --git a/examples/tutorial/T2_network.py b/examples/tutorial/T2_network.py index 9af80612..df8b5aa8 100644 --- a/examples/tutorial/T2_network.py +++ b/examples/tutorial/T2_network.py @@ -10,7 +10,7 @@ # DeepPhysX related imports from DeepPhysX.Core.Network.BaseNetwork import BaseNetwork from DeepPhysX.Core.Network.BaseOptimization import BaseOptimization -from DeepPhysX.Core.Network.DataTransformation import DataTransformation +from DeepPhysX.Core.Network.BaseTransformation import BaseTransformation # Create a Network as a BaseNetwork child class @@ -94,12 +94,12 @@ def optimize(self): pass -# Create a DataTransformation as a DataTransformation child class -class DummyTransformation(DataTransformation): +# Create a BaseTransformation as a BaseTransformation child class +class DummyTransformation(BaseTransformation): def __init__(self, config): - DataTransformation.__init__(self, config) + BaseTransformation.__init__(self, config) self.data_type = ndarray def transform_before_prediction(self, data_net): diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 791bfab5..1373a901 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -10,7 +10,7 @@ class AbstractEnvironment: def __init__(self, as_tcp_ip_client: bool = True, - instance_id: int = 0, + instance_id: int = 1, instance_nb: int = 1, visualization_db: Optional[Union[Database, Tuple[str, str]]] = None, **kwargs): @@ -30,7 +30,7 @@ def __init__(self, raise ValueError(f"[{self.name}] Instance ID ({instance_id}) is bigger than max instances " f"({instance_nb}).") self.as_tcp_ip_client: bool = as_tcp_ip_client - self.instance_id: int = instance_id + self.instance_id: int = max(1, instance_id) self.instance_nb: int = instance_nb # Manager of the Environment diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 712a3feb..51df8963 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -13,7 +13,7 @@ class BaseEnvironment(AbstractEnvironment): def __init__(self, as_tcp_ip_client: bool = True, - instance_id: int = 0, + instance_id: int = 1, instance_nb: int = 1, visualization_db: Optional[Union[Database, Tuple[str, str]]] = None, **kwargs): @@ -300,6 +300,8 @@ def update_visualisation(self) -> None: # If Environment is a TcpIpClient, request to the Server if self.as_tcp_ip_client: self.tcp_ip_client.request_update_visualization() + else: + self.environment_manager.update_visualizer(self.instance_id) self.factory.render() def _get_prediction(self): diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index aef785b3..1a4d8c5c 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -224,10 +224,11 @@ def compute_online_prediction(self, sample = self.database_handler.get_line(table_name='Exchange', fields=self.network.net_fields, line_id=instance_id) + del sample['id'] # Apply normalization and convert to tensor for field in sample.keys(): - sample[field] = array(sample[field]) + sample[field] = array([sample[field]]) if field in normalization.keys(): sample[field] = self.normalize_data(data=sample[field], normalization=normalization[field]) @@ -241,7 +242,7 @@ def compute_online_prediction(self, # Return the prediction for field in data_pred.keys(): - data_pred[field] = self.network.tensor_to_numpy(data=data_pred[field]) + data_pred[field] = self.network.tensor_to_numpy(data=data_pred[field][0]) if field in normalization.keys(): data_pred[field] = self.normalize_data(data=data_pred[field], normalization=normalization[field], diff --git a/src/Network/BaseOptimization.py b/src/Network/BaseOptimization.py index 729c54b5..03116fac 100644 --- a/src/Network/BaseOptimization.py +++ b/src/Network/BaseOptimization.py @@ -43,7 +43,6 @@ def compute_loss(self, :return: Loss value. """ - # WARNING: self.optimization.compute_loss(data_pred.reshape(data_gt.shape), data_gt, loss_data) raise NotImplementedError def transform_loss(self, diff --git a/src/Network/BaseTransformation.py b/src/Network/BaseTransformation.py index e2f4cb7d..522bfc58 100644 --- a/src/Network/BaseTransformation.py +++ b/src/Network/BaseTransformation.py @@ -21,8 +21,9 @@ def check_type(func: Callable[[Any, Any], Any]): def inner(self, *args): for data in args: - if data is not None and type(data) != self.data_type: - raise TypeError(f"[{self.name}] Wrong data type: {self.data_type} required, get {type(data)}") + for value in data.values(): + if value is not None and type(value) != self.data_type: + raise TypeError(f"[{self.name}] Wrong data type: {self.data_type} required, get {type(value)}") return func(self, *args) return inner diff --git a/src/Utils/configs.py b/src/Utils/configs.py index 692290d7..8ac16c6f 100644 --- a/src/Utils/configs.py +++ b/src/Utils/configs.py @@ -21,7 +21,7 @@ def make_config(configuration_object: Any, # Check if a dataset_config already exists (child class will have the parent's config by default) if configuration_name in configuration_object.__dict__: - configuration: namedtuple = configuration_object.__getattr__(configuration_name) + configuration: namedtuple = configuration_object.__getattribute__(configuration_name) # Get items set in the existing config for key, value in configuration._asdict().items(): # Only new items are required for children, check if the parent's items are set again anyway From f069e4d6568950dd28eb051339a397095ec6bb0c Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 3 Nov 2022 15:32:50 +0100 Subject: [PATCH 35/61] Dataset converter from Numpy to SQL. --- src/Database/DatabaseHandler.py | 16 ++++ src/Utils/converter.py | 146 ++++++++++++++++++++++++++++++++ 2 files changed, 162 insertions(+) create mode 100644 src/Utils/converter.py diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py index 44ebd548..7c284cf5 100644 --- a/src/Database/DatabaseHandler.py +++ b/src/Database/DatabaseHandler.py @@ -184,6 +184,22 @@ def add_data(self, return [len(self.__storing_partitions) - 1, self.__storing_partitions[-1].add_data(table_name=table_name, data=data)] + def add_batch(self, + table_name: str, + batch: Dict[str, List[Any]]) -> None: + """ + Add a batch of data in a Database. + + :param table_name: Name of the Table. + :param batch: New lines of the Table. + """ + + # Only available in the storing Database + if table_name == 'Exchange': + raise ValueError(f"Cannot add a batch in the Exchange Database.") + self.__storing_partitions[-1].add_batch(table_name=table_name, + batch=batch) + def update(self, table_name: str, data: Dict[str, Any], diff --git a/src/Utils/converter.py b/src/Utils/converter.py new file mode 100644 index 00000000..bec3c387 --- /dev/null +++ b/src/Utils/converter.py @@ -0,0 +1,146 @@ +from typing import Dict, List, Optional +from os import listdir +from os.path import join, exists, isfile, dirname, sep +from numpy import ndarray, load +from vedo import ProgressBar + +from DeepPhysX.Core.Utils.path import get_first_caller, create_dir +from DeepPhysX.Core.Manager.DatabaseManager import DatabaseManager +from DeepPhysX.Core.Database.DatabaseHandler import DatabaseHandler +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig + + +class DatasetConverter: + + def __init__(self, + session_path: str): + """ + Convert a Dataset between the previous Numpy partitions and the new SQL Database. + + :param session_path: Relative path to the session to convert. + """ + + root = get_first_caller() + self.dataset_dir = join(root, session_path, 'dataset') + if not exists(self.dataset_dir): + raise ValueError(f"The given path does not exist: {self.dataset_dir}") + + def numpy_to_database(self, + batch_size: int, + max_file_size: Optional[float] = None, + normalize: bool = False): + """ + Convert a Database from the previous Numpy partitions to the new SQL Database. + """ + + # 1. Get the partitions files + partitions = self.load_numpy_partitions() + + # 2. Create a new repository + session_name = f'{self.dataset_dir.split(sep)[-2]}_converted' + session = create_dir(session_dir=dirname(dirname(self.dataset_dir)), + session_name=session_name) + database_manager = DatabaseManager(pipeline='data_generation', + session=session) + database_manager.close() + + # 3. Create the Database for each mode + for mode in partitions.keys(): + print(f"\nConverting {mode} partitions...") + + # 3.1. Create a DatabaseManager with a DatabaseHandler + database_config = BaseDatabaseConfig(mode=mode, + max_file_size=max_file_size, + normalize=normalize) + database_manager = DatabaseManager(database_config=database_config, + pipeline='data_generation', + session=session, + new_session=False) + database_handler = DatabaseHandler() + database_manager.connect_handler(database_handler) + + # 3.2. Create Fields in Tables + training_fields = [] + additional_fields = [] + nb_partition = 0 + for field in partitions[mode].keys(): + if field in ['input', 'ground_truth']: + training_fields.append((field, ndarray)) + + else: + additional_fields.append((field.split('_')[-1], ndarray)) + if nb_partition == 0: + nb_partition = len(partitions[mode][field]) + elif len(partitions[mode][field]) != nb_partition: + raise ValueError(f"The number of partition is not consistent in {mode} mode.") + database_handler.create_fields(table_name='Training', fields=training_fields) + database_handler.create_fields(table_name='Additional', fields=additional_fields) + + # 3.3. Add each partition to the Database + if nb_partition == 0: + print(" No partition.") + for i in range(nb_partition): + data_training = {field: load(join(self.dataset_dir, f'{partitions[mode][field][i]}.npy')) + for field, _ in training_fields} + data_additional = {field: load(join(self.dataset_dir, f'{partitions[mode][field][i]}.npy')) + for field, _ in additional_fields} + nb_sample = 0 + for data in [data_training, data_additional]: + for field in data.keys(): + if nb_sample == 0: + nb_sample = data[field].shape[0] + elif data[field].shape[0] != nb_sample: + raise ValueError(f"The number of sample is not consistent in {mode} mode.") + id_sample = 0 + pb = ProgressBar(0, nb_sample, c='orange', title=f" Loading partition {i + 1}/{nb_partition}") + while id_sample < nb_sample: + last_sample = min(nb_sample, id_sample + batch_size) + if len(data_training.keys()) > 0: + sample_training = {field: data_training[field][id_sample:last_sample] + for field in data_training.keys()} + database_handler.add_batch(table_name='Training', batch=sample_training) + if len(data_additional.keys()) > 0: + sample_additional = {field: data_additional[field][id_sample:last_sample] + for field in data_additional.keys()} + database_handler.add_batch(table_name='Additional', batch=sample_additional) + database_manager.add_data() + id_sample += batch_size + pb.print(counts=id_sample) + + # 3. Close DatabaseManager + database_manager.close() + + print("\nConversion done.") + + def load_numpy_partitions(self) -> Dict[str, Dict[str, List[str]]]: + """ + Get all the partition files in the repository. Do not use the JSON file to prevent bugs. + """ + + # 1. Get the partition files for each mode + modes = ['training', 'validation', 'prediction'] + partitions = {mode: [f.split('.')[0] for f in listdir(self.dataset_dir) if isfile(join(self.dataset_dir, f)) + and f.endswith('.npy') and f.__contains__(mode)] for mode in modes} + + # 2. Sort partitions by field (IN, OUT, ADD) and by name + sorted_partitions = {mode: {field[1:-1]: sorted([f for f in partitions[mode] if f.__contains__(field)]) + for field in ['_IN_', '_OUT_', '_ADD_']} + for mode in modes} + all_partitions = {} + for mode in modes: + all_partitions[mode] = {} + for field, name in zip(['IN', 'OUT', 'ADD'], ['input', 'ground_truth', '']): + for partition in sorted_partitions[mode][field]: + # Extract information from the filename + partition_name = partition.split('_') + partition_name = partition_name[partition_name.index(field):] + # Additional data: ___ + if len(partition_name) == 3: + name = partition_name[-2] + # Add partition + if name not in all_partitions[mode]: + all_partitions[mode][name] = [] + all_partitions[mode][name].append(partition) + + return all_partitions + From e1a2e268e15f41334c66599adf926dd246ee2eb7 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 3 Nov 2022 15:39:05 +0100 Subject: [PATCH 36/61] Fix Environment kwargs in TcpIpClient. --- src/AsyncSocket/TcpIpClient.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index bd722ae2..59a67995 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -68,12 +68,13 @@ async def __initialize(self) -> None: # Receive additional arguments env_kwargs = {} await self.receive_dict(recv_to=env_kwargs, loop=loop, sender=self.sock) + env_kwargs = env_kwargs['env_kwargs'] if 'env_kwargs' in env_kwargs else {} self.environment = self.environment_class(as_tcp_ip_client=True, instance_id=self.environment_instance[0], instance_nb=self.environment_instance[1], visualization_db=self.environment_visualization, - **env_kwargs['env_kwargs']) + **env_kwargs) self.environment.tcp_ip_client = self # Receive prediction requests authorization From a5ca637ebb00c910b6e131e1bbaf6ab0e6ef9d41 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 3 Nov 2022 15:45:40 +0100 Subject: [PATCH 37/61] Use VedoProgressBar. --- src/Pipelines/BaseDataGeneration.py | 8 +++---- src/Pipelines/BaseTraining.py | 30 +++++++++++-------------- src/Utils/progressbar.py | 34 ----------------------------- 3 files changed, 16 insertions(+), 56 deletions(-) delete mode 100644 src/Utils/progressbar.py diff --git a/src/Pipelines/BaseDataGeneration.py b/src/Pipelines/BaseDataGeneration.py index dad878e2..fb2751b1 100644 --- a/src/Pipelines/BaseDataGeneration.py +++ b/src/Pipelines/BaseDataGeneration.py @@ -1,12 +1,11 @@ from typing import Optional from os.path import join, sep, exists -from sys import stdout +from vedo import ProgressBar from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline from DeepPhysX.Core.Manager.DataManager import DataManager from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Utils.progressbar import Progressbar from DeepPhysX.Core.Utils.path import get_first_caller, create_dir @@ -65,7 +64,7 @@ def __init__(self, self.batch_nb: int = batch_nb self.batch_id: int = 0 self.batch_size = batch_size - self.progress_bar = Progressbar(start=0, stop=self.batch_id, c='orange', title="Data Generation") + self.progress_bar = ProgressBar(start=0, stop=self.batch_nb, c='orange', title="Data Generation") def execute(self) -> None: """ @@ -122,8 +121,7 @@ def batch_end(self) -> None: Called once at the end of a batch production. """ - stdout.write("\033[K") - self.progress_bar.print(counts=self.batch_id + 1) + self.progress_bar.print(counts=self.batch_id) def data_generation_end(self) -> None: """ diff --git a/src/Pipelines/BaseTraining.py b/src/Pipelines/BaseTraining.py index 5cc7d480..f326d515 100644 --- a/src/Pipelines/BaseTraining.py +++ b/src/Pipelines/BaseTraining.py @@ -1,7 +1,7 @@ from typing import Optional -from sys import stdout from os.path import join, isfile, exists, sep from datetime import datetime +from vedo import ProgressBar from DeepPhysX.Core.Pipelines.BasePipeline import BasePipeline from DeepPhysX.Core.Manager.DataManager import DataManager @@ -10,7 +10,6 @@ from DeepPhysX.Core.Network.BaseNetworkConfig import BaseNetworkConfig from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Utils.progressbar import Progressbar from DeepPhysX.Core.Utils.path import get_first_caller, create_dir @@ -102,14 +101,13 @@ def __init__(self, self.debug = debug # Progressbar - if not self.debug: - self.progress_counter = 0 - self.digits = ['{' + f':0{len(str(self.epoch_nb))}d' + '}', - '{' + f':0{len(str(self.batch_nb))}d' + '}'] - epoch_id, epoch_nb = self.digits[0].format(0), self.digits[0].format(self.epoch_nb) - batch_id, batch_nb = self.digits[1].format(0), self.digits[1].format(self.batch_nb) - self.progress_bar = Progressbar(start=0, stop=self.batch_nb * self.epoch_nb, c='orange', - title=f'Epoch n°{epoch_id}/{epoch_nb} - Batch n°{batch_id}/{batch_nb}') + self.progress_counter = 0 + self.digits = ['{' + f':0{len(str(self.epoch_nb))}d' + '}', + '{' + f':0{len(str(self.batch_nb))}d' + '}'] + epoch_id, epoch_nb = self.digits[0].format(0), self.digits[0].format(self.epoch_nb) + batch_id, batch_nb = self.digits[1].format(0), self.digits[1].format(self.batch_nb) + self.progress_bar = ProgressBar(start=0, stop=self.batch_nb * self.epoch_nb, c='orange', + title=f'Epoch n°{epoch_id}/{epoch_nb} - Batch n°{batch_id}/{batch_nb}') self.save_info_file() @@ -165,13 +163,11 @@ def batch_begin(self) -> None: Called one at the beginning of a batch production. """ - if not self.debug: - stdout.write("\033[K") - self.progress_counter += 1 - id_epoch, nb_epoch = self.digits[0].format(self.epoch_id + 1), self.digits[0].format(self.epoch_nb) - id_batch, nb_batch = self.digits[1].format(self.batch_id + 1), self.digits[1].format(self.batch_nb) - self.progress_bar.title = f'Epoch n°{id_epoch}/{nb_epoch} - Batch n°{id_batch}/{nb_batch} ' - self.progress_bar.print(counts=self.progress_counter) + self.progress_counter += 1 + id_epoch, nb_epoch = self.digits[0].format(self.epoch_id + 1), self.digits[0].format(self.epoch_nb) + id_batch, nb_batch = self.digits[1].format(self.batch_id + 1), self.digits[1].format(self.batch_nb) + self.progress_bar.title = f'Epoch n°{id_epoch}/{nb_epoch} - Batch n°{id_batch}/{nb_batch} ' + self.progress_bar.print(counts=self.progress_counter) def optimize(self) -> None: """ diff --git a/src/Utils/progressbar.py b/src/Utils/progressbar.py deleted file mode 100644 index 836223f5..00000000 --- a/src/Utils/progressbar.py +++ /dev/null @@ -1,34 +0,0 @@ -from vedo import ProgressBar - - -class Progressbar(ProgressBar): - - def __init__(self, start, stop, step=1, c=None, title=''): - - ProgressBar.__init__(self, start=start, stop=stop, step=step, c=c, title=title) - - self.percent_int = 0 - - def _update(self, counts): - if counts < self.start: - counts = self.start - elif counts > self.stop: - counts = self.stop - self._counts = counts - self.percent = (self._counts - self.start) * 100 - dd = self.stop - self.start - if dd: - self.percent /= self.stop - self.start - else: - self.percent = 0 - self.percent_int = int(round(self.percent)) - af = self.width - 2 - nh = int(round(self.percent_int / 100 * af)) - br_bk = "\x1b[2m" + self.char_back * (af - nh) - br = "%s%s%s" % (self.char * (nh - 1), self.char_arrow, br_bk) - self.bar = self.title + self.char0 + br + self.char1 - if self.percent < 100: - ps = " " + str(self.percent_int) + "%" - else: - ps = "" - self.bar += ps From 3963fa53729b49f6aea6a6d9114c821d10c640eb Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 3 Nov 2022 16:01:13 +0100 Subject: [PATCH 38/61] Fix Visualization in local mode. --- examples/features/dataGeneration_single.py | 2 +- examples/features/gradientDescent.py | 3 +-- examples/features/offlineTraining.py | 2 +- src/Environment/BaseEnvironment.py | 2 -- 4 files changed, 3 insertions(+), 6 deletions(-) diff --git a/examples/features/dataGeneration_single.py b/examples/features/dataGeneration_single.py index 200d3d9b..c9c0295b 100644 --- a/examples/features/dataGeneration_single.py +++ b/examples/features/dataGeneration_single.py @@ -36,7 +36,7 @@ def launch_data_generation(): database_config=database_config, session_dir='sessions', session_name='data_generation', - batch_nb=500, + batch_nb=100, batch_size=10) # Launch the training session diff --git a/examples/features/gradientDescent.py b/examples/features/gradientDescent.py index 87fe600b..23e8d613 100644 --- a/examples/features/gradientDescent.py +++ b/examples/features/gradientDescent.py @@ -28,8 +28,7 @@ def launch_training(): # Environment configuration environment_config = BaseEnvironmentConfig(environment_class=MeanEnvironment, visualizer=VedoVisualizer, - as_tcp_ip_client=True, - number_of_thread=1, + as_tcp_ip_client=False, env_kwargs={'constant': True, 'data_size': [nb_points, dimension], 'delay': False, diff --git a/examples/features/offlineTraining.py b/examples/features/offlineTraining.py index bc5abb6c..0f5b8646 100644 --- a/examples/features/offlineTraining.py +++ b/examples/features/offlineTraining.py @@ -39,7 +39,7 @@ def launch_training(): session_dir='sessions', session_name='offline_training', epoch_nb=1, - batch_nb=500, + batch_nb=100, batch_size=10) # Launch the training session diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 51df8963..714c53bf 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -300,8 +300,6 @@ def update_visualisation(self) -> None: # If Environment is a TcpIpClient, request to the Server if self.as_tcp_ip_client: self.tcp_ip_client.request_update_visualization() - else: - self.environment_manager.update_visualizer(self.instance_id) self.factory.render() def _get_prediction(self): From c7280541dfb27c67c192f5b14d5c77cca49d32f9 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 4 Nov 2022 10:19:39 +0100 Subject: [PATCH 39/61] Manage non-existing Database case. --- src/Database/DatabaseHandler.py | 5 ++++- src/Environment/BaseEnvironment.py | 17 +++++++++-------- src/Pipelines/BasePrediction.py | 4 ++-- 3 files changed, 15 insertions(+), 11 deletions(-) diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py index 7c284cf5..fce07dcb 100644 --- a/src/Database/DatabaseHandler.py +++ b/src/Database/DatabaseHandler.py @@ -94,7 +94,8 @@ def load(self) -> None: for db in self.__storing_partitions: db.load() - self.__exchange_db.load() + if self.__exchange_db is not None: + self.__exchange_db.load() def get_database_dir(self) -> str: """ @@ -156,6 +157,8 @@ def get_fields(self, :param table_name: Name of the Table. """ + if self.__exchange_db is None and len(self.__storing_partitions) == 0: + return [] database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[0] return database.get_fields(table_name=table_name) diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 714c53bf..7a11f031 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -210,14 +210,15 @@ def set_training_data(self, **kwargs) -> None: self.__database_handler.load() self.__first_add[0] = False required_fields = list(set(self.__database_handler.get_fields(table_name='Training')) - {'id', 'env_id'}) - for field in kwargs.keys(): - if field not in required_fields: - raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." - f"Required fields are {required_fields}.") - for field in required_fields: - if field not in kwargs.keys(): - raise ValueError(f"[{self.name}] The field '{field}' was not defined in training data." - f"Required fields are {required_fields}.") + if len(required_fields) > 0: + for field in kwargs.keys(): + if field not in required_fields: + raise ValueError(f"[{self.name}] The field '{field}' is not in the training Database." + f"Required fields are {required_fields}.") + for field in required_fields: + if field not in kwargs.keys(): + raise ValueError(f"[{self.name}] The field '{field}' was not defined in training data." + f"Required fields are {required_fields}.") # Training data is set if the Environment can compute data if self.compute_training_data: diff --git a/src/Pipelines/BasePrediction.py b/src/Pipelines/BasePrediction.py index 290a4fc1..a7bd0062 100644 --- a/src/Pipelines/BasePrediction.py +++ b/src/Pipelines/BasePrediction.py @@ -82,7 +82,7 @@ def execute(self) -> None: self.sample_begin() self.predict() self.sample_end() - self.run_end() + self.prediction_end() def prediction_begin(self) -> None: """ @@ -122,7 +122,7 @@ def sample_end(self) -> None: pass - def run_end(self) -> None: + def prediction_end(self) -> None: """ Called once at the end of the prediction Pipeline. """ From 995385c0cb0530cb5674b32fa9a6830d758a2e06 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 4 Nov 2022 15:01:03 +0100 Subject: [PATCH 40/61] Remove visualization_db from Environment parameters. --- examples/features/Environment.py | 36 ++++++++++------------ examples/tutorial/T1_environment.py | 6 ++-- src/AsyncSocket/AbstractEnvironment.py | 7 +++-- src/AsyncSocket/TcpIpClient.py | 16 +++++----- src/AsyncSocket/TcpIpServer.py | 16 +++++++--- src/Environment/BaseEnvironment.py | 33 +++++++++++--------- src/Environment/BaseEnvironmentConfig.py | 26 ++++++++-------- src/Environment/launcherBaseEnvironment.py | 8 ++--- src/Manager/EnvironmentManager.py | 12 ++++---- 9 files changed, 83 insertions(+), 77 deletions(-) diff --git a/examples/features/Environment.py b/examples/features/Environment.py index 818ecd5f..9093e6b7 100644 --- a/examples/features/Environment.py +++ b/examples/features/Environment.py @@ -20,7 +20,6 @@ def __init__(self, as_tcp_ip_client=True, instance_id=1, instance_nb=1, - visualization_db=None, constant=False, data_size=(30, 2), delay=False, @@ -29,8 +28,7 @@ def __init__(self, BaseEnvironment.__init__(self, as_tcp_ip_client=as_tcp_ip_client, instance_id=instance_id, - instance_nb=instance_nb, - visualization_db=visualization_db) + instance_nb=instance_nb) # Define training data values self.pcd = array([]) @@ -59,23 +57,21 @@ def init_database(self): def init_visualization(self): - if self.factory is not None: - - # Point cloud (object will have id = 0) - self.factory.add_points(positions=self.pcd, - at=self.instance_id, - c='blue', - point_size=8) - # Ground truth value (object will have id = 1) - self.factory.add_points(positions=self.mean, - at=self.instance_id, - c='green', - point_size=10) - # Prediction value (object will have id = 2) - self.factory.add_points(positions=self.mean, - at=self.instance_id, - c='orange', - point_size=12) + # Point cloud (object will have id = 0) + self.factory.add_points(positions=self.pcd, + at=self.instance_id, + c='blue', + point_size=8) + # Ground truth value (object will have id = 1) + self.factory.add_points(positions=self.mean, + at=self.instance_id, + c='green', + point_size=10) + # Prediction value (object will have id = 2) + self.factory.add_points(positions=self.mean, + at=self.instance_id, + c='orange', + point_size=12) """ ENVIRONMENT BEHAVIOR diff --git a/examples/tutorial/T1_environment.py b/examples/tutorial/T1_environment.py index 36732817..4220745f 100644 --- a/examples/tutorial/T1_environment.py +++ b/examples/tutorial/T1_environment.py @@ -16,14 +16,12 @@ class DummyEnvironment(BaseEnvironment): def __init__(self, as_tcp_ip_client=True, instance_id=1, - instance_nb=1, - visualization_db=None): + instance_nb=1): BaseEnvironment.__init__(self, as_tcp_ip_client=as_tcp_ip_client, instance_id=instance_id, - instance_nb=instance_nb, - visualization_db=visualization_db) + instance_nb=instance_nb) self.step_nb: int = 0 diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/AsyncSocket/AbstractEnvironment.py index 1373a901..229f2de7 100644 --- a/src/AsyncSocket/AbstractEnvironment.py +++ b/src/AsyncSocket/AbstractEnvironment.py @@ -11,9 +11,7 @@ class AbstractEnvironment: def __init__(self, as_tcp_ip_client: bool = True, instance_id: int = 1, - instance_nb: int = 1, - visualization_db: Optional[Union[Database, Tuple[str, str]]] = None, - **kwargs): + instance_nb: int = 1): """ AbstractEnvironment sets the Environment API for TcpIpClient. Do not use AbstractEnvironment to implement an Environment, use BaseEnvironment instead. @@ -90,6 +88,9 @@ def close(self) -> None: def get_database_handler(self) -> DatabaseHandler: raise NotImplementedError + def _create_visualization(self, visualization_db: Union[Database, Tuple[str, str]]) -> None: + raise NotImplementedError + def _send_training_data(self) -> None: raise NotImplementedError diff --git a/src/AsyncSocket/TcpIpClient.py b/src/AsyncSocket/TcpIpClient.py index 59a67995..c46a9fa2 100644 --- a/src/AsyncSocket/TcpIpClient.py +++ b/src/AsyncSocket/TcpIpClient.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Type, Optional, Union, Tuple +from typing import Any, Dict, Type from socket import socket from asyncio import get_event_loop from asyncio import AbstractEventLoop as EventLoop @@ -16,8 +16,7 @@ def __init__(self, ip_address: str = 'localhost', port: int = 10000, instance_id: int = 0, - instance_nb: int = 1, - visualization_db: Optional[Union[Tuple[str, str]]] = None): + instance_nb: int = 1): """ TcpIpClient is a TcpIpObject which communicate with a TcpIpServer and manages an Environment to compute data. @@ -26,7 +25,6 @@ def __init__(self, :param port: Port number of the TcpIpObject. :param instance_id: Index of this instance. :param instance_nb: Number of simultaneously launched instances. - :param visualization_db: Path to the visualization Database. """ TcpIpObject.__init__(self, @@ -37,7 +35,6 @@ def __init__(self, self.environment: AbstractEnvironment self.environment_class = environment self.environment_instance = (instance_id, instance_nb) - self.environment_visualization = visualization_db # Bind to client address and send ID self.sock.connect((ip_address, port)) @@ -73,7 +70,6 @@ async def __initialize(self) -> None: self.environment = self.environment_class(as_tcp_ip_client=True, instance_id=self.environment_instance[0], instance_nb=self.environment_instance[1], - visualization_db=self.environment_visualization, **env_kwargs) self.environment.tcp_ip_client = self @@ -92,11 +88,17 @@ async def __initialize(self) -> None: self.environment.get_database_handler().init_remote(storing_partitions=partitions, exchange_db=exchange) + # Receive visualization database + visualization_db = await self.receive_data(loop=loop, sender=self.sock) + visualization_db = None if visualization_db == 'None' else visualization_db.split('///') + # Initialize the environment self.environment.create() self.environment.init() self.environment.init_database() - self.environment.init_visualization() + if visualization_db is not None: + self.environment._create_visualization(visualization_db=visualization_db) + self.environment.init_visualization() # Initialization done await self.send_data(data_to_send='done', loop=loop, receiver=self.sock) diff --git a/src/AsyncSocket/TcpIpServer.py b/src/AsyncSocket/TcpIpServer.py index 3eddf1b1..e8b3f350 100644 --- a/src/AsyncSocket/TcpIpServer.py +++ b/src/AsyncSocket/TcpIpServer.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Tuple from asyncio import get_event_loop, gather from asyncio import AbstractEventLoop as EventLoop from asyncio import run as async_run @@ -121,22 +121,26 @@ async def __connect(self) -> None: ########################################################################################## def initialize(self, - env_kwargs: Dict[str, Any]) -> None: + env_kwargs: Dict[str, Any], + visualization_db: Optional[Tuple[str, str]] = None) -> None: """ Send parameters to the clients to create their environments. :param env_kwargs: Additional arguments to pass to the Environment. + :param visualization_db: Path to the visualization Database to connect to. """ print(f"[{self.name}] Initializing clients...") - async_run(self.__initialize(env_kwargs)) + async_run(self.__initialize(env_kwargs, visualization_db)) async def __initialize(self, - env_kwargs: Dict[str, Any]) -> None: + env_kwargs: Dict[str, Any], + visualization_db: Optional[Tuple[str, str]] = None) -> None: """ Send parameters to the clients to create their environments. :param env_kwargs: Additional arguments to pass to the Environment. + :param visualization_db: Path to the visualization Database to connect to. """ loop = get_event_loop() @@ -171,6 +175,10 @@ async def __initialize(self, partitions_list += f'{exchange.get_path()[0]}///{exchange.get_path()[1]}' await self.send_data(data_to_send=partitions_list, loop=loop, receiver=client) + # Send visualization Database + visualization = 'None' if visualization_db is None else f'{visualization_db[0]}///{visualization_db[1]}' + await self.send_data(data_to_send=visualization, loop=loop, receiver=client) + # Wait Client init await self.receive_data(loop=loop, sender=client) print(f"[{self.name}] Client n°{client_id} initialisation done") diff --git a/src/Environment/BaseEnvironment.py b/src/Environment/BaseEnvironment.py index 7a11f031..41bc90b6 100644 --- a/src/Environment/BaseEnvironment.py +++ b/src/Environment/BaseEnvironment.py @@ -15,7 +15,6 @@ def __init__(self, as_tcp_ip_client: bool = True, instance_id: int = 1, instance_nb: int = 1, - visualization_db: Optional[Union[Database, Tuple[str, str]]] = None, **kwargs): """ BaseEnvironment computes simulated data for the Network and its training process. @@ -23,7 +22,6 @@ def __init__(self, :param as_tcp_ip_client: Environment is a TcpIpObject if True, is owned by an EnvironmentManager if False. :param instance_id: ID of the instance. :param instance_nb: Number of simultaneously launched instances. - :param visualization_db: The path to the visualization Database or the visualization Database object to connect. """ AbstractEnvironment.__init__(self, @@ -47,14 +45,6 @@ def __init__(self, # Connect the Factory to the visualization Database self.factory: Optional[VedoFactory] = None - if visualization_db is not None: - if type(visualization_db) == list: - self.factory = VedoFactory(database_path=visualization_db, - idx_instance=instance_id, - remote=True) - else: - self.factory = VedoFactory(database=visualization_db, - idx_instance=instance_id) ########################################################################################## ########################################################################################## @@ -298,10 +288,11 @@ def update_visualisation(self) -> None: Triggers the Visualizer update. """ - # If Environment is a TcpIpClient, request to the Server - if self.as_tcp_ip_client: - self.tcp_ip_client.request_update_visualization() - self.factory.render() + if self.factory is not None: + # If Environment is a TcpIpClient, request to the Server + if self.as_tcp_ip_client: + self.tcp_ip_client.request_update_visualization() + self.factory.render() def _get_prediction(self): """ @@ -337,6 +328,20 @@ def __database_handler_init(self): # Load Database and create basic fields self.__database_handler.load() + def _create_visualization(self, + visualization_db: Union[Database, Tuple[str, str]]) -> None: + """ + Create a Factory for the Environment. + """ + + if type(visualization_db) == list: + self.factory = VedoFactory(database_path=visualization_db, + idx_instance=self.instance_id, + remote=True) + else: + self.factory = VedoFactory(database=visualization_db, + idx_instance=self.instance_id) + def _send_training_data(self) -> List[int]: """ Add the training data and the additional data in their respective Databases. diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Environment/BaseEnvironmentConfig.py index ba75ef63..94d840a9 100644 --- a/src/Environment/BaseEnvironmentConfig.py +++ b/src/Environment/BaseEnvironmentConfig.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Type, Dict +from typing import Any, Optional, Type, Dict, Tuple from os import cpu_count from os.path import join, dirname from threading import Thread @@ -96,7 +96,7 @@ def __init__(self, def create_server(self, environment_manager: Optional[Any] = None, batch_size: int = 1, - visualization_db: Optional[str] = None) -> TcpIpServer: + visualization_db: Optional[Tuple[str, str]] = None) -> TcpIpServer: """ Create a TcpIpServer and launch TcpIpClients in subprocesses. @@ -113,13 +113,13 @@ def create_server(self, max_client_count=self.max_client_connections, batch_size=batch_size, manager=environment_manager) - server_thread = Thread(target=self.start_server, args=(server,)) + server_thread = Thread(target=self.start_server, args=(server, visualization_db)) server_thread.start() # Create clients client_threads = [] for i in range(self.number_of_thread): - client_thread = Thread(target=self.start_client, args=(i + 1, visualization_db)) + client_thread = Thread(target=self.start_client, args=(i + 1,)) client_threads.append(client_thread) for client in client_threads: client.start() @@ -130,43 +130,41 @@ def create_server(self, return server def start_server(self, - server: TcpIpServer) -> None: + server: TcpIpServer, + visualization_db: Optional[Tuple[str, str]] = None) -> None: """ Start TcpIpServer. :param server: TcpIpServer. + :param visualization_db: Path to the visualization Database to connect to. """ server.connect() - server.initialize(env_kwargs=self.env_kwargs) + server.initialize(visualization_db=visualization_db, + env_kwargs=self.env_kwargs) self.server_is_ready = True def start_client(self, - idx: int = 1, - visualization_db: Optional[str] = None) -> None: + idx: int = 1) -> None: """ Run a subprocess to start a TcpIpClient. :param idx: Index of client. - :param visualization_db: Path to the visualization Database to connect to. """ script = join(dirname(modules[BaseEnvironment.__module__].__file__), 'launcherBaseEnvironment.py') run([executable, script, self.environment_file, self.environment_class.__name__, - self.ip_address, str(self.port), str(idx), str(self.number_of_thread), str(visualization_db)]) + self.ip_address, str(self.port), str(idx), str(self.number_of_thread)]) - def create_environment(self, - visualization_db: Optional[Any] = None) -> BaseEnvironment: + def create_environment(self) -> BaseEnvironment: """ Create an Environment that will not be a TcpIpObject. - :param visualization_db: Path to the visualization Database to connect to. :return: Environment object. """ # Create instance environment = self.environment_class(as_tcp_ip_client=False, - visualization_db=visualization_db, **self.env_kwargs) if not isinstance(environment, BaseEnvironment): raise TypeError(f"[{self.name}] The given 'environment_class'={self.environment_class} must be a " diff --git a/src/Environment/launcherBaseEnvironment.py b/src/Environment/launcherBaseEnvironment.py index 6449468b..2d5b63e6 100644 --- a/src/Environment/launcherBaseEnvironment.py +++ b/src/Environment/launcherBaseEnvironment.py @@ -8,9 +8,9 @@ if __name__ == '__main__': # Check script call - if len(argv) != 8: + if len(argv) != 7: print(f"Usage: python3 {argv[0]} " - f" ") + f"") exit(1) # Import environment_class @@ -19,13 +19,11 @@ exec(f"from {module_name} import {argv[2]} as Environment") # Create, init and run Tcp-Ip environment - visualization_db = None if argv[7] == 'None' else [s[1:-1] for s in argv[7][1:-1].split(', ')] client = TcpIpClient(environment=Environment, ip_address=argv[3], port=int(argv[4]), instance_id=int(argv[5]), - instance_nb=int(argv[6]), - visualization_db=visualization_db) + instance_nb=int(argv[6])) client.initialize() client.launch() diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 389596fa..1fb426e9 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -42,12 +42,10 @@ def __init__(self, # Create a Visualizer to provide the visualization Database self.visualizer: Optional[VedoVisualizer] = None - visualization_db = None if environment_config.visualizer is not None: self.visualizer = environment_config.visualizer(database_dir=join(session, 'dataset'), database_name='Visualization', remote=environment_config.as_tcp_ip_client) - visualization_db = self.visualizer.get_database() # Create a single Environment or a TcpIpServer force_local = pipeline == 'prediction' @@ -58,17 +56,19 @@ def __init__(self, if environment_config.as_tcp_ip_client and not force_local: self.server = environment_config.create_server(environment_manager=self, batch_size=batch_size, - visualization_db=None if visualization_db is None else - visualization_db.get_path()) + visualization_db=None if self.visualizer is None else + self.visualizer.get_path()) # Create Environment else: - self.environment = environment_config.create_environment(visualization_db=visualization_db) + self.environment = environment_config.create_environment() self.environment.environment_manager = self self.data_manager.connect_handler(self.environment.get_database_handler()) self.environment.create() self.environment.init() self.environment.init_database() - self.environment.init_visualization() + if self.visualizer is not None: + self.environment._create_visualization(visualization_db=self.visualizer.get_database()) + self.environment.init_visualization() # Define whether methods are used for environment or server self.get_database_handler = self.__get_server_db_handler if self.server else self.__get_environment_db_handler From 63668408542cb5af211d13e117ae14531ccd1389 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 4 Nov 2022 18:10:52 +0100 Subject: [PATCH 41/61] Fix sample loading in SofaPrediction. Fix unormalization in Prediction. --- src/Database/DatabaseHandler.py | 2 ++ src/Manager/DataManager.py | 10 ++++++++-- src/Manager/DatabaseManager.py | 3 ++- src/Manager/NetworkManager.py | 4 ++-- src/Network/BaseNetwork.py | 1 + 5 files changed, 15 insertions(+), 5 deletions(-) diff --git a/src/Database/DatabaseHandler.py b/src/Database/DatabaseHandler.py index fce07dcb..ca3d16a8 100644 --- a/src/Database/DatabaseHandler.py +++ b/src/Database/DatabaseHandler.py @@ -233,6 +233,8 @@ def get_line(self, database = self.__exchange_db if table_name == 'Exchange' else self.__storing_partitions[line_id[0]] line_id = line_id[1] if type(line_id) == list else line_id + if database.nb_lines(table_name=table_name) == 0: + return {} return database.get_line(table_name=table_name, line_id=line_id, fields=fields) diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index cc112799..103446d9 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -88,7 +88,8 @@ def connect_handler(self, def get_data(self, epoch: int = 0, - animate: bool = True) -> None: + animate: bool = True, + load_samples: bool = True) -> None: """ Fetch data from the EnvironmentManager or the DatabaseManager according to the context. @@ -129,7 +130,8 @@ def get_data(self, # Get data from Dataset if self.environment_manager.load_samples: - self.data_lines = self.database_manager.get_data(batch_size=1) + if load_samples: + self.data_lines = self.database_manager.get_data(batch_size=1) self.environment_manager.dispatch_batch(data_lines=self.data_lines, animate=animate, request_prediction=True, @@ -142,6 +144,10 @@ def get_data(self, if self.produce_data: self.database_manager.add_data(self.data_lines) + def load_sample(self) -> List[int]: + self.data_lines = self.database_manager.get_data(batch_size=1) + return self.data_lines[0] + def get_prediction(self, instance_id: int) -> None: """ diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index b922f993..dbf47624 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -195,7 +195,8 @@ def load_directory(self, self.partitions[mode].append(db) if len(self.partitions[self.mode]) == 0: self.create_partition() - elif self.max_file_size is not None and self.partitions[self.mode][-1].memory_size > self.max_file_size: + elif self.max_file_size is not None and self.partitions[self.mode][-1].memory_size > self.max_file_size \ + and self.produce_data: self.create_partition() # 6. Index partitions diff --git a/src/Manager/NetworkManager.py b/src/Manager/NetworkManager.py index 1a4d8c5c..d00d995a 100644 --- a/src/Manager/NetworkManager.py +++ b/src/Manager/NetworkManager.py @@ -243,9 +243,9 @@ def compute_online_prediction(self, # Return the prediction for field in data_pred.keys(): data_pred[field] = self.network.tensor_to_numpy(data=data_pred[field][0]) - if field in normalization.keys(): + if self.network.pred_norm_fields[field] in normalization.keys(): data_pred[field] = self.normalize_data(data=data_pred[field], - normalization=normalization[field], + normalization=normalization[self.network.pred_norm_fields[field]], reverse=True) data_pred[field].reshape(-1) self.database_handler.update(table_name='Exchange', diff --git a/src/Network/BaseNetwork.py b/src/Network/BaseNetwork.py index b390795c..32830ded 100644 --- a/src/Network/BaseNetwork.py +++ b/src/Network/BaseNetwork.py @@ -21,6 +21,7 @@ def __init__(self, self.net_fields = ['input'] self.opt_fields = ['ground_truth'] self.pred_fields = ['prediction'] + self.pred_norm_fields = {'prediction': 'ground_truth'} def predict(self, data_net: Dict[str, Any]) -> Dict[str, Any]: From 4af2f24a97b0d03f4cdb0913bf8c4f66c0c33bf9 Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 7 Nov 2022 11:45:17 +0100 Subject: [PATCH 42/61] Use Visualizer in local mode in Prediction. --- src/Manager/EnvironmentManager.py | 4 ++-- src/Pipelines/BasePrediction.py | 12 ++++++------ 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index 1fb426e9..d8963b4d 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -41,14 +41,14 @@ def __init__(self, self.dataset_batch: Optional[List[List[int]]] = None # Create a Visualizer to provide the visualization Database + force_local = pipeline == 'prediction' self.visualizer: Optional[VedoVisualizer] = None if environment_config.visualizer is not None: self.visualizer = environment_config.visualizer(database_dir=join(session, 'dataset'), database_name='Visualization', - remote=environment_config.as_tcp_ip_client) + remote=environment_config.as_tcp_ip_client and not force_local) # Create a single Environment or a TcpIpServer - force_local = pipeline == 'prediction' self.number_of_thread: int = 1 if force_local else environment_config.number_of_thread self.server: Optional[TcpIpServer] = None self.environment: Optional[BaseEnvironment] = None diff --git a/src/Pipelines/BasePrediction.py b/src/Pipelines/BasePrediction.py index a7bd0062..34ca2b52 100644 --- a/src/Pipelines/BasePrediction.py +++ b/src/Pipelines/BasePrediction.py @@ -49,6 +49,12 @@ def __init__(self, raise ValueError(f"[{self.name}] The following directory does not exist: {join(session_dir, session_name)}") self.session = join(session_dir, session_name) + # Create a NetworkManager + self.network_manager = NetworkManager(network_config=network_config, + pipeline=self.type, + session=self.session, + new_session=False) + # Create a DataManager self.data_manager = DataManager(pipeline=self, database_config=database_config, @@ -57,12 +63,6 @@ def __init__(self, new_session=False, produce_data=record, batch_size=1) - - # Create a NetworkManager - self.network_manager = NetworkManager(network_config=network_config, - pipeline=self.type, - session=self.session, - new_session=False) self.data_manager.connect_handler(self.network_manager.get_database_handler()) self.network_manager.link_clients(self.data_manager.nb_environment) From 0725ebbc3a9c2929b22170717ff309c8afcb94e8 Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 7 Nov 2022 12:15:13 +0100 Subject: [PATCH 43/61] Manage Exchange and Visualization Databases files. --- src/Manager/DataManager.py | 8 ++++++++ src/Manager/DatabaseManager.py | 2 +- src/Manager/EnvironmentManager.py | 9 ++++++++- src/Visualization/VedoVisualizer.py | 13 ++++++++++++- 4 files changed, 29 insertions(+), 3 deletions(-) diff --git a/src/Manager/DataManager.py b/src/Manager/DataManager.py index 103446d9..c4241984 100644 --- a/src/Manager/DataManager.py +++ b/src/Manager/DataManager.py @@ -51,6 +51,7 @@ def __init__(self, data_manager=self, pipeline=pipeline.type, session=session, + produce_data=produce_data, batch_size=batch_size) # DataManager variables @@ -95,6 +96,7 @@ def get_data(self, :param epoch: Current epoch number. :param animate: Allow EnvironmentManager to trigger a step itself in order to generate a new sample. + :param load_samples: If True, trigger a sample loading from the Database. """ # Data generation case @@ -145,6 +147,12 @@ def get_data(self, self.database_manager.add_data(self.data_lines) def load_sample(self) -> List[int]: + """ + Load a sample from the Database. + + :return: Index of the loaded line. + """ + self.data_lines = self.database_manager.get_data(batch_size=1) return self.data_lines[0] diff --git a/src/Manager/DatabaseManager.py b/src/Manager/DatabaseManager.py index dbf47624..3efc35b1 100644 --- a/src/Manager/DatabaseManager.py +++ b/src/Manager/DatabaseManager.py @@ -137,7 +137,7 @@ def __init__(self, # Finally create an exchange database self.exchange = Database(database_dir=self.database_dir, - database_name='Exchange').new() + database_name='Exchange').new(remove_existing=True) self.exchange.create_table(table_name='Exchange') ########################################################################################## diff --git a/src/Manager/EnvironmentManager.py b/src/Manager/EnvironmentManager.py index d8963b4d..1e85c39f 100644 --- a/src/Manager/EnvironmentManager.py +++ b/src/Manager/EnvironmentManager.py @@ -14,6 +14,7 @@ def __init__(self, data_manager: Optional[Any] = None, pipeline: str = '', session: str = 'sessions/default', + produce_data: bool = True, batch_size: int = 1): """ EnvironmentManager handle the communication with Environment(s). @@ -22,6 +23,7 @@ def __init__(self, :param data_manager: DataManager that handles the EnvironmentManager. :param pipeline: Type of the pipeline. :param session: Path to the session repository. + :param produce_data: If True, this session will store data in the Database. :param batch_size: Number of samples in a single batch. """ @@ -46,7 +48,8 @@ def __init__(self, if environment_config.visualizer is not None: self.visualizer = environment_config.visualizer(database_dir=join(session, 'dataset'), database_name='Visualization', - remote=environment_config.as_tcp_ip_client and not force_local) + remote=environment_config.as_tcp_ip_client and not force_local, + record=produce_data) # Create a single Environment or a TcpIpServer self.number_of_thread: int = 1 if force_local else environment_config.number_of_thread @@ -240,6 +243,10 @@ def close(self) -> None: if self.environment: self.environment.close() + # Visualizer + if self.visualizer: + self.visualizer.close() + def __str__(self) -> str: description = "\n" diff --git a/src/Visualization/VedoVisualizer.py b/src/Visualization/VedoVisualizer.py index d84e367c..d4187f1d 100644 --- a/src/Visualization/VedoVisualizer.py +++ b/src/Visualization/VedoVisualizer.py @@ -12,7 +12,8 @@ def __init__(self, database_name: Optional[str] = None, remove_existing: bool = False, offscreen: bool = False, - remote: bool = False): + remote: bool = False, + record: bool = True): """ Manage the creation, update and rendering of Vedo Actors. @@ -22,6 +23,7 @@ def __init__(self, :param remove_existing: If True, overwrite a Database with the same path. :param offscreen: If True, visual data will be saved but not rendered. :param remote: If True, the Visualizer will treat the Factories as remote. + :param record: If True, the visualization Database is saved in memory. """ # Define Database @@ -47,6 +49,7 @@ def __init__(self, if not remote: self.__database.register_post_save_signal(table_name='Sync', handler=self.__sync_visualizer) + self.record = record def render_instance(self, instance: int): """ @@ -80,3 +83,11 @@ def render_instance(self, instance: int): # 3. Render Plotter if offscreen is False if not self.__offscreen: self.__plotter.render() + + def close(self): + """ + Launch the closing procedure of the Visualizer. + """ + + if not self.record: + self.__database.close(erase_file=True) From 66513613afce0a24caeb057ffc15759a3323c974 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 9 Nov 2022 11:44:57 +0100 Subject: [PATCH 44/61] Update demos. --- .../Armadillo/FC/Environment/Armadillo.py | 122 ++++----- .../FC/Environment/ArmadilloInteractive.py | 25 +- examples/demos/Armadillo/FC/download.py | 8 +- examples/demos/Armadillo/FC/interactive.py | 31 +-- examples/demos/Armadillo/FC/prediction.py | 38 ++- .../Armadillo/UNet/Environment/Armadillo.py | 171 ++++++------ examples/demos/Armadillo/UNet/download.py | 7 +- examples/demos/Armadillo/UNet/prediction.py | 52 ++-- examples/demos/Beam/FC/Environment/Beam.py | 126 ++++----- .../Beam/FC/Environment/BeamInteractive.py | 25 +- examples/demos/Beam/FC/download.py | 8 +- examples/demos/Beam/FC/interactive.py | 31 +-- examples/demos/Beam/FC/prediction.py | 38 ++- examples/demos/Beam/UNet/Environment/Beam.py | 133 ++++------ .../demos/Beam/UNet/Environment/parameters.py | 3 +- examples/demos/Beam/UNet/download.py | 6 +- examples/demos/Beam/UNet/prediction.py | 50 ++-- examples/demos/Liver/FC/Environment/Liver.py | 188 ++++++-------- .../Liver/FC/Environment/LiverInteractive.py | 27 +- examples/demos/Liver/FC/download.py | 8 +- examples/demos/Liver/FC/interactive.py | 31 +-- examples/demos/Liver/FC/prediction.py | 40 ++- .../demos/Liver/UNet/Environment/Liver.py | 245 ++++++++---------- examples/demos/Liver/UNet/download.py | 9 +- examples/demos/Liver/UNet/prediction.py | 53 ++-- 25 files changed, 642 insertions(+), 833 deletions(-) diff --git a/examples/demos/Armadillo/FC/Environment/Armadillo.py b/examples/demos/Armadillo/FC/Environment/Armadillo.py index 0140e923..e5fd74ca 100644 --- a/examples/demos/Armadillo/FC/Environment/Armadillo.py +++ b/examples/demos/Armadillo/FC/Environment/Armadillo.py @@ -10,10 +10,10 @@ import os import sys -import numpy as np +from numpy import ndarray, array, concatenate, arange, zeros, reshape +from numpy.random import randint, uniform from vedo import Mesh from math import pow -from time import sleep # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -28,20 +28,14 @@ class Armadillo(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topology self.mesh = None @@ -53,12 +47,12 @@ def __init__(self, # Force fields self.forces = [] self.areas = [] - self.compute_sample = True # Force pattern step = 0.05 - self.amplitudes = np.concatenate((np.arange(0, 1, step), - np.arange(1, 0, -step))) + self.amplitudes = concatenate((arange(0, 1, step), + arange(1, 0, -step))) + self.amplitudes[0] = 0 self.idx_amplitude = 0 self.force_value = None self.idx_zone = 0 @@ -73,11 +67,10 @@ def __init__(self, Methods will be automatically called in this order to create and initialize Environment. """ - def recv_parameters(self, param_dict): + def init_database(self): - # Get the model definition parameters - self.compute_sample = param_dict['compute_sample'] if 'compute_sample' in param_dict else True - self.amplitudes[0] = 0 if self.compute_sample else 1 + # Define the fields of the Training database + self.define_training_fields(fields=[('input', ndarray), ('ground_truth', ndarray)]) def create(self): @@ -97,34 +90,26 @@ def create(self): if sphere(pts, p_forces.centers[zone]) <= pow(p_forces.radius[zone], 2): self.areas[-1].append(i) # Init force value - self.forces.append(np.zeros(3, )) + self.forces.append(zeros(3, )) - def send_visualization(self): + def init_visualization(self): # Mesh representing detailed Armadillo (object will have id = 0) - self.factory.add_object(object_type="Mesh", - data_dict={"positions": self.mesh.points(), - 'cells': self.mesh.cells(), - 'wireframe': True, - "c": "orange", - "at": self.instance_id}) - + self.factory.add_mesh(positions=self.mesh.points(), + cells=self.mesh.cells(), + wireframe=True, + c='orange', + at=self.instance_id) # Arrows representing the force fields (object will have id = 1) - self.factory.add_object(object_type='Arrows', - data_dict={'positions': [0, 0, 0], - 'vectors': [0, 0, 0], - 'c': 'green', - 'at': self.instance_id}) - + self.factory.add_arrows(positions=array([0., 0., 0.]), + vectors=array([0., 0., 0.]), + c='green', + at=self.instance_id) # Points representing the grid (object will have id = 2) - self.factory.add_object(object_type='Points', - data_dict={'positions': self.sparse_grid.points(), - 'r': 1., - 'c': 'black', - 'at': self.instance_id}) - - # Return the visualization data - return self.factory.objects_dict + self.factory.add_points(positions=self.sparse_grid.points(), + point_size=1, + c='black', + at=self.instance_id) """ ENVIRONMENT BEHAVIOR @@ -134,38 +119,31 @@ def send_visualization(self): async def step(self): - # Compute a force sample - if self.compute_sample: - # Generate a new force - if self.idx_amplitude == 0: - self.idx_zone = np.random.randint(0, len(self.forces)) - zone = p_forces.zones[self.idx_zone] - self.force_value = np.random.uniform(low=-1, high=1, size=(3,)) * p_forces.amplitude[zone] - - # Update current force amplitude - self.forces[self.idx_zone] = self.force_value * self.amplitudes[self.idx_amplitude] + # Generate a new force + if self.idx_amplitude == 0: + self.idx_zone = randint(0, len(self.forces)) + zone = p_forces.zones[self.idx_zone] + self.force_value = uniform(low=-1, high=1, size=(3,)) * p_forces.amplitude[zone] - # Update force amplitude index - self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) + # Update current force amplitude + self.forces[self.idx_zone] = self.force_value * self.amplitudes[self.idx_amplitude] - # Create input array - F = np.zeros(self.input_size) - F[self.areas[self.idx_zone]] = self.forces[self.idx_zone] + # Update force amplitude index + self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) - # Load a force sample from Dataset - else: - sleep(0.5) - F = np.zeros(self.input_size) if self.sample_in is None else self.sample_in + # Create input array + F = zeros(self.input_size) + F[self.areas[self.idx_zone]] = self.forces[self.idx_zone] # Set training data self.F = F - self.set_training_data(input_array=F.copy(), - output_array=np.zeros(self.output_size)) + self.set_training_data(input=F.copy(), + ground_truth=zeros(self.output_size)) def apply_prediction(self, prediction): # Reshape to correspond to sparse grid - U = np.reshape(prediction, self.output_size) + U = reshape(prediction['prediction'], self.output_size) self.update_visual(U) def update_visual(self, U): @@ -176,20 +154,20 @@ def update_visual(self, U): mesh_coarse_position = self.mapping_coarse.apply(updated_position) # Update surface mesh - self.factory.update_object_dict(object_id=0, - new_data_dict={'positions': mesh_position}) + self.factory.update_mesh(object_id=0, + positions=mesh_position) # Update arrows representing force fields - self.factory.update_object_dict(object_id=1, - new_data_dict={'positions': mesh_coarse_position, - 'vectors': 0.25 * self.F / p_model.scale}) + self.factory.update_arrows(object_id=1, + positions=mesh_coarse_position, + vectors=0.25 * self.F / p_model.scale) # Update sparse grid positions - self.factory.update_object_dict(object_id=2, - new_data_dict={'positions': updated_position}) + self.factory.update_points(object_id=2, + positions=updated_position) # Send visualization data to update - self.update_visualisation(visu_dict=self.factory.updated_object_dict) + self.update_visualisation() def close(self): # Shutdown message diff --git a/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py b/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py index 2baad605..cc7893fe 100644 --- a/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py +++ b/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py @@ -28,20 +28,14 @@ class Armadillo(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topologies & mappings self.mesh = None @@ -68,6 +62,11 @@ def __init__(self, self.input_size = (p_model.nb_nodes_mesh, 3) self.output_size = (p_model.nb_nodes_grid, 3) + def init_database(self): + + # Define the fields of the Training database + self.define_training_fields(fields=[('input', np.ndarray), ('ground_truth', np.ndarray)]) + def create(self): # Load the meshes and the sparse grid @@ -121,8 +120,8 @@ async def step(self): # Launch Vedo window self.plotter.show().close() # Smooth close - self.set_training_data(input_array=np.zeros(self.input_size), - output_array=np.zeros(self.output_size)) + self.set_training_data(input=np.zeros(self.input_size), + ground_truth=np.zeros(self.output_size)) def key_press(self, evt): @@ -179,7 +178,7 @@ def mouse_move(self, evt): F[self.areas[self.selected]] = move_3D * amp # Apply output displacement - U = self.get_prediction(F).reshape(self.output_size) + U = self.get_prediction(input=F)['prediction'].reshape(self.output_size) updated_grid = self.sparse_grid.points().copy() + U updated_coarse = self.mapping_coarse.apply(updated_grid) diff --git a/examples/demos/Armadillo/FC/download.py b/examples/demos/Armadillo/FC/download.py index 983b2f07..ad285792 100644 --- a/examples/demos/Armadillo/FC/download.py +++ b/examples/demos/Armadillo/FC/download.py @@ -20,11 +20,11 @@ def __init__(self): 'session': [240], 'network': [193], 'stats': [192], - 'dataset_info': [242], - 'dataset_valid': [244, 239], - 'dataset_train': [241, 243]} + 'dataset_info': [317], + 'dataset_valid': [319], + 'dataset_train': [318]} if __name__ == '__main__': - ArmadilloDownloader().get_session('valid_data') + ArmadilloDownloader().get_session('all') diff --git a/examples/demos/Armadillo/FC/interactive.py b/examples/demos/Armadillo/FC/interactive.py index 15ea305b..440f89af 100644 --- a/examples/demos/Armadillo/FC/interactive.py +++ b/examples/demos/Armadillo/FC/interactive.py @@ -8,15 +8,15 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Torch.FC.FCConfig import FCConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import ArmadilloDownloader -ArmadilloDownloader().get_session('predict') +ArmadilloDownloader().get_session('all') from Environment.ArmadilloInteractive import Armadillo from Environment.parameters import p_model @@ -24,31 +24,28 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Armadillo, - as_tcp_ip_client=False) + environment_config = BaseEnvironmentConfig(environment_class=Armadillo) # FC config nb_hidden_layers = 3 nb_neurons = p_model.nb_nodes_mesh * 3 nb_final_neurons = p_model.nb_nodes_grid * 3 layers_dim = [nb_neurons] + [nb_neurons for _ in range(nb_hidden_layers)] + [nb_final_neurons] - net_config = FCConfig(network_name='armadillo_FC', - dim_output=3, - dim_layers=layers_dim, - biases=True) + network_config = FCConfig(dim_layers=layers_dim, + dim_output=3, + biases=True) # Dataset config - dataset_config = BaseDatasetConfig() + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='armadillo_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=1) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='armadillo_dpx', + step_nb=1) runner.execute() - runner.close() if __name__ == '__main__': diff --git a/examples/demos/Armadillo/FC/prediction.py b/examples/demos/Armadillo/FC/prediction.py index dbc339e2..65d40446 100644 --- a/examples/demos/Armadillo/FC/prediction.py +++ b/examples/demos/Armadillo/FC/prediction.py @@ -8,16 +8,16 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.FC.FCConfig import FCConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import ArmadilloDownloader -ArmadilloDownloader().get_session('valid_data') +ArmadilloDownloader().get_session('all') from Environment.Armadillo import Armadillo from Environment.parameters import p_model @@ -25,35 +25,29 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Armadillo, - visualizer=VedoVisualizer, - as_tcp_ip_client=False, - param_dict={'compute_sample': True}) + environment_config = BaseEnvironmentConfig(environment_class=Armadillo, + visualizer=VedoVisualizer) # FC config nb_hidden_layers = 3 nb_neurons = p_model.nb_nodes_mesh * 3 nb_final_neurons = p_model.nb_nodes_grid * 3 layers_dim = [nb_neurons] + [nb_neurons for _ in range(nb_hidden_layers)] + [nb_final_neurons] - net_config = FCConfig(network_name='armadillo_FC', - dim_output=3, - dim_layers=layers_dim, - biases=True) + network_config = FCConfig(dim_layers=layers_dim, + dim_output=3, + biases=True) # Dataset config - dataset_config = BaseDatasetConfig(dataset_dir='sessions/armadillo_dpx', - normalize=True, - use_mode='Validation') + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='armadillo_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=500) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='armadillo_dpx', + step_nb=500) runner.execute() - runner.close() if __name__ == '__main__': diff --git a/examples/demos/Armadillo/UNet/Environment/Armadillo.py b/examples/demos/Armadillo/UNet/Environment/Armadillo.py index 41691080..7d321de6 100644 --- a/examples/demos/Armadillo/UNet/Environment/Armadillo.py +++ b/examples/demos/Armadillo/UNet/Environment/Armadillo.py @@ -9,11 +9,10 @@ # Python related imports import os import sys - -import numpy as np +from numpy import ndarray, array, concatenate, arange, zeros, unique, reshape +from numpy.random import randint, uniform from vedo import Mesh from math import pow -from time import sleep, time # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -28,20 +27,14 @@ class Armadillo(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topology self.mesh = None @@ -56,12 +49,12 @@ def __init__(self, self.forces = [] self.areas = [] self.g_areas = [] - self.compute_sample = None # Force pattern step = 0.05 - self.amplitudes = np.concatenate((np.arange(step, 1, step), - np.arange(1, step, -step))) + self.amplitudes = concatenate((arange(step, 1, step), + arange(1, step, -step))) + self.amplitudes[0] = 0 self.idx_amplitude = 0 self.force_value = None self.idx_zone = 0 @@ -75,11 +68,10 @@ def __init__(self, Methods will be automatically called in this order to create and initialize Environment. """ - def recv_parameters(self, param_dict): + def init_database(self): - # Get the model definition parameters - self.compute_sample = param_dict['compute_sample'] if 'compute_sample' in param_dict else True - self.amplitudes[0] = 0 if self.compute_sample else 1 + # Define the fields of the Training database + self.define_training_fields(fields=[('input', ndarray), ('ground_truth', ndarray)]) def create(self): @@ -92,19 +84,19 @@ def create(self): x_reg = [p_grid.origin[0] + i * p_grid.size[0] / p_grid.nb_cells[0] for i in range(p_grid.nb_cells[0] + 1)] y_reg = [p_grid.origin[1] + i * p_grid.size[1] / p_grid.nb_cells[1] for i in range(p_grid.nb_cells[1] + 1)] z_reg = [p_grid.origin[2] + i * p_grid.size[2] / p_grid.nb_cells[2] for i in range(p_grid.nb_cells[2] + 1)] - grid_positions = np.array([[[[x, y, z] for x in x_reg] for y in y_reg] for z in z_reg]).reshape(-1, 3) + grid_positions = array([[[[x, y, z] for x in x_reg] for y in y_reg] for z in z_reg]).reshape(-1, 3) cell_corner = lambda x, y, z: len(x_reg) * (len(y_reg) * z + y) + x - grid_cells = np.array([[[[[cell_corner(ix, iy, iz), - cell_corner(ix + 1, iy, iz), - cell_corner(ix + 1, iy + 1, iz), - cell_corner(ix, iy + 1, iz), - cell_corner(ix, iy, iz + 1), - cell_corner(ix + 1, iy, iz + 1), - cell_corner(ix + 1, iy + 1, iz + 1), - cell_corner(ix, iy + 1, iz + 1)] - for ix in range(p_grid.nb_cells[0])] - for iy in range(p_grid.nb_cells[1])] - for iz in range(p_grid.nb_cells[2])]]).reshape(-1, 8) + grid_cells = array([[[[[cell_corner(ix, iy, iz), + cell_corner(ix + 1, iy, iz), + cell_corner(ix + 1, iy + 1, iz), + cell_corner(ix, iy + 1, iz), + cell_corner(ix, iy, iz + 1), + cell_corner(ix + 1, iy, iz + 1), + cell_corner(ix + 1, iy + 1, iz + 1), + cell_corner(ix, iy + 1, iz + 1)] + for ix in range(p_grid.nb_cells[0])] + for iy in range(p_grid.nb_cells[1])] + for iz in range(p_grid.nb_cells[2])]]).reshape(-1, 8) self.grid = Mesh([grid_positions, grid_cells]) # Init mappings between meshes and sparse grid @@ -112,16 +104,16 @@ def create(self): self.mapping_coarse = GridMapping(self.sparse_grid, self.mesh_coarse) # Init correspondences between sparse and regular grid - self.grid_correspondences = np.zeros(self.sparse_grid.N(), dtype=int) - x_sparse = np.unique(self.sparse_grid.points()[:, 0]) - y_sparse = np.unique(self.sparse_grid.points()[:, 1]) - z_sparse = np.unique(self.sparse_grid.points()[:, 2]) + self.grid_correspondences = zeros(self.sparse_grid.N(), dtype=int) + x_sparse = unique(self.sparse_grid.points()[:, 0]) + y_sparse = unique(self.sparse_grid.points()[:, 1]) + z_sparse = unique(self.sparse_grid.points()[:, 2]) if len(x_reg) != len(x_sparse) or len(y_reg) != len(y_sparse) or len(z_reg) != len(z_sparse): raise ValueError('Grids should have the same dimension') d = [x_sparse[1] - x_sparse[0], y_sparse[1] - y_sparse[0], z_sparse[1] - z_sparse[0]] origin = [x_sparse[0], y_sparse[0], z_sparse[0]] for i, node in enumerate(self.sparse_grid.points()): - p = np.array(node) - np.array(origin) + p = array(node) - array(origin) ix = int(round(p[0] / d[0])) iy = int(round(p[1] / d[1])) iz = int(round(p[2] / d[2])) @@ -129,7 +121,8 @@ def create(self): self.grid_correspondences[i] = idx # Define force fields - sphere = lambda x, z: sum([pow(x_i - c_i, 2) for x_i, c_i in zip(x, p_forces.centers[z])]) <= pow(p_forces.radius[z], 2) + sphere = lambda x, z: sum([pow(x_i - c_i, 2) for x_i, c_i in zip(x, p_forces.centers[z])]) <= pow( + p_forces.radius[z], 2) for zone in p_forces.zones: self.areas.append([]) self.g_areas.append([]) @@ -137,36 +130,28 @@ def create(self): if sphere(node, zone): self.areas[-1].append(i) self.g_areas[-1] += self.mapping_coarse.cells[i].tolist() - self.areas[-1] = np.array(self.areas[-1]) - self.g_areas[-1] = np.unique(self.g_areas[-1]) - self.forces.append(np.zeros((len(self.areas[-1]), 3))) + self.areas[-1] = array(self.areas[-1]) + self.g_areas[-1] = unique(self.g_areas[-1]) + self.forces.append(zeros((len(self.areas[-1]), 3))) - def send_visualization(self): + def init_visualization(self): # Mesh representing detailed Armadillo (object will have id = 0) - self.factory.add_object(object_type='Mesh', - data_dict={'positions': self.mesh.points(), - 'cells': self.mesh.cells(), - 'wireframe': True, - 'c': 'orange', - 'at': self.instance_id}) - + self.factory.add_mesh(positions=self.mesh.points(), + cells=self.mesh.cells(), + wireframe=True, + c='orange', + at=self.instance_id) # Arrows representing the force fields (object will have id = 1) - self.factory.add_object(object_type='Arrows', - data_dict={'positions': [0, 0, 0], - 'vectors': [0., 0., 0.], - 'c': 'green', - 'at': self.instance_id}) - + self.factory.add_arrows(positions=array([0., 0., 0.]), + vectors=array([0., 0., 0.]), + c='green', + at=self.instance_id) # Sparse grid (object will have id = 2) - self.factory.add_object(object_type='Points', - data_dict={'positions': self.sparse_grid.points(), - 'r': 2, - 'c': 'grey', - 'at': self.instance_id}) - - # Return the visualization data - return self.factory.objects_dict + self.factory.add_points(positions=self.sparse_grid.points(), + point_size=2, + c='grey', + at=self.instance_id) """ ENVIRONMENT BEHAVIOR @@ -176,38 +161,30 @@ def send_visualization(self): async def step(self): - # Compute a force sample - if self.compute_sample: - # Generate a new force - if self.idx_amplitude == 0: - self.idx_zone = np.random.randint(0, len(self.forces)) - zone = p_forces.zones[self.idx_zone] - self.force_value = np.random.uniform(low=-1, high=1, size=(3,)) * p_forces.amplitude[zone] - - # Update current force amplitude - self.forces[self.idx_zone] = self.force_value * self.amplitudes[self.idx_amplitude] - self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) - - # Create input array - F = np.zeros(self.data_size) - F[self.grid_correspondences[self.g_areas[self.idx_zone]]] = self.forces[self.idx_zone] - self.F = np.zeros((self.mesh_coarse.N(), 3)) - self.F[self.areas[self.idx_zone]] = self.forces[self.idx_zone] - - # Load a force sample from Dataset - else: - sleep(0.5) - F = np.zeros(self.data_size) if self.sample_in is None else self.sample_in - self.F = F[self.grid_correspondences] + # Generate a new force + if self.idx_amplitude == 0: + self.idx_zone = randint(0, len(self.forces)) + zone = p_forces.zones[self.idx_zone] + self.force_value = uniform(low=-1, high=1, size=(3,)) * p_forces.amplitude[zone] + + # Update current force amplitude + self.forces[self.idx_zone] = self.force_value * self.amplitudes[self.idx_amplitude] + self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) + + # Create input array + F = zeros(self.data_size) + F[self.grid_correspondences[self.g_areas[self.idx_zone]]] = self.forces[self.idx_zone] + self.F = zeros((self.mesh_coarse.N(), 3)) + self.F[self.areas[self.idx_zone]] = self.forces[self.idx_zone] # Set training data - self.set_training_data(input_array=F.copy(), - output_array=np.zeros(self.data_size)) + self.set_training_data(input=F.copy(), + ground_truth=zeros(self.data_size)) def apply_prediction(self, prediction): # Reshape to correspond to sparse grid - U = np.reshape(prediction, self.data_size) + U = reshape(prediction['prediction'], self.data_size) U_sparse = U[self.grid_correspondences] self.update_visual(U_sparse) @@ -219,20 +196,20 @@ def update_visual(self, U): mesh_coarse_position = self.mapping_coarse.apply(updated_position) # Update surface mesh - self.factory.update_object_dict(object_id=0, - new_data_dict={'positions': mesh_position}) + self.factory.update_mesh(object_id=0, + positions=mesh_position) # Update arrows representing force fields - self.factory.update_object_dict(object_id=1, - new_data_dict={'positions': mesh_coarse_position if self.compute_sample else updated_position, - 'vectors': 0.25 * self.F / p_model.scale}) + self.factory.update_arrows(object_id=1, + positions=mesh_coarse_position, + vectors=0.25 * self.F / p_model.scale) # Update sparse grid positions - self.factory.update_object_dict(object_id=2, - new_data_dict={'positions': updated_position}) + self.factory.update_points(object_id=2, + positions=updated_position) # Send visualization data to update - self.update_visualisation(visu_dict=self.factory.updated_object_dict) + self.update_visualisation() def close(self): # Shutdown message diff --git a/examples/demos/Armadillo/UNet/download.py b/examples/demos/Armadillo/UNet/download.py index e34eae10..8172d9f6 100644 --- a/examples/demos/Armadillo/UNet/download.py +++ b/examples/demos/Armadillo/UNet/download.py @@ -20,10 +20,9 @@ def __init__(self): 'session': [247], 'network': [230], 'stats': [224], - 'dataset_info': [253], - 'dataset_valid': [245, 257], - 'dataset_train': [252, 255, 246, 256, 258, 251, - 250, 248, 254, 249, 259, 260]} + 'dataset_info': [321], + 'dataset_valid': [320], + 'dataset_train': [323, 322]} if __name__ == '__main__': diff --git a/examples/demos/Armadillo/UNet/prediction.py b/examples/demos/Armadillo/UNet/prediction.py index 2f4019e5..9752f206 100644 --- a/examples/demos/Armadillo/UNet/prediction.py +++ b/examples/demos/Armadillo/UNet/prediction.py @@ -8,16 +8,16 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.UNet.UNetConfig import UNetConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import ArmadilloDownloader -ArmadilloDownloader().get_session('valid_data') +ArmadilloDownloader().get_session('all') from Environment.Armadillo import Armadillo from Environment.parameters import grid_resolution @@ -25,38 +25,32 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Armadillo, - visualizer=VedoVisualizer, - as_tcp_ip_client=False, - param_dict={'compute_sample': True}) + environment_config = BaseEnvironmentConfig(environment_class=Armadillo, + visualizer=VedoVisualizer) # UNet config - net_config = UNetConfig(network_name='armadillo_UNet', - input_size=grid_resolution, - nb_dims=3, - nb_input_channels=3, - nb_first_layer_channels=128, - nb_output_channels=3, - nb_steps=3, - two_sublayers=True, - border_mode='same', - skip_merge=False) + network_config = UNetConfig(input_size=grid_resolution, + nb_dims=3, + nb_input_channels=3, + nb_first_layer_channels=128, + nb_output_channels=3, + nb_steps=3, + two_sublayers=True, + border_mode='same', + skip_merge=False) # Dataset config - dataset_config = BaseDatasetConfig(dataset_dir='sessions/armadillo_dpx', - normalize=True, - use_mode='Validation') + database_config = BaseDatabaseConfig(normalize=True) + # Runner - runner = BaseRunner(session_dir='sessions', - session_name='armadillo_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=0) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='armadillo_dpx', + step_nb=500) runner.execute() - runner.close() if __name__ == '__main__': - launch_runner() diff --git a/examples/demos/Beam/FC/Environment/Beam.py b/examples/demos/Beam/FC/Environment/Beam.py index ad59fd24..e99644be 100644 --- a/examples/demos/Beam/FC/Environment/Beam.py +++ b/examples/demos/Beam/FC/Environment/Beam.py @@ -12,7 +12,6 @@ import numpy as np from vedo import Mesh -from time import sleep # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -26,30 +25,24 @@ class Beam(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topology self.mesh = None self.surface = None # Force - self.compute_sample = True step = 0.1 self.amplitudes = np.concatenate((np.arange(0, 1, step), np.arange(1, 0, -step))) + self.amplitudes[0] = 0 self.idx_amplitude = 0 self.force_value = None self.zone = None @@ -63,11 +56,10 @@ def __init__(self, Methods will be automatically called in this order to create and initialize Environment. """ - def recv_parameters(self, param_dict): + def init_database(self): - # Get the model definition parameters - self.compute_sample = param_dict['compute_sample'] if 'compute_sample' in param_dict else True - self.amplitudes[0] = 0 if self.compute_sample else 1 + # Define the fields of the Training database + self.define_training_fields(fields=[('input', np.ndarray), ('ground_truth', np.ndarray)]) def create(self): @@ -83,24 +75,19 @@ def create(self): np.argwhere(pts[:, 2] == np.max(pts[:, 2])))).reshape(-1) self.surface = np.unique(self.surface) - def send_visualization(self): + def init_visualization(self): # Mesh representing the grid (object will have id = 0) - self.factory.add_object(object_type='Mesh', - data_dict={'positions': self.mesh.points(), - 'cells': self.mesh.cells(), - 'wireframe': True, - 'c': 'orange', - 'at': self.instance_id}) - + self.factory.add_mesh(positions=self.mesh.points(), + cells=self.mesh.cells(), + wireframe=True, + c='orange', + at=self.instance_id) # Arrows representing the force fields (object will have id = 1) - self.factory.add_object(object_type='Arrows', - data_dict={'positions': [0, 0, 0], - 'vectors': [0, 0, 0], - 'c': 'green', - 'at': self.instance_id}) - - return self.factory.objects_dict + self.factory.add_arrows(positions=np.array([0., 0., 0.]), + vectors=np.array([0., 0., 0.]), + c='green', + at=self.instance_id) """ ENVIRONMENT BEHAVIOR @@ -110,65 +97,58 @@ def send_visualization(self): async def step(self): - # Compute a force sample - if self.compute_sample: - # Generate a new force - if self.idx_amplitude == 0: - # Define zone - pts = self.mesh.points().copy() - side = np.random.randint(0, 6) - x_min = np.min(pts[:, 0]) if side == 0 else np.random.randint(np.min(pts[:, 0]), np.max(pts[:, 0]) - 10) - y_min = np.min(pts[:, 1]) if side == 1 else np.random.randint(np.min(pts[:, 1]), np.max(pts[:, 1]) - 10) - z_min = np.min(pts[:, 2]) if side == 2 else np.random.randint(np.min(pts[:, 2]), np.max(pts[:, 2]) - 10) - x_max = np.max(pts[:, 0]) if side == 3 else np.random.randint(x_min + 10, np.max(pts[:, 0]) + 1) - y_max = np.max(pts[:, 1]) if side == 4 else np.random.randint(y_min + 10, np.max(pts[:, 1]) + 1) - z_max = np.max(pts[:, 2]) if side == 5 else np.random.randint(z_min + 10, np.max(pts[:, 2]) + 1) - # Find points on surface and in box - self.zone = np.where((pts[:, 0] >= x_min) & (pts[:, 0] <= x_max) & (pts[:, 1] >= y_min) & - (pts[:, 1] <= y_max) & (pts[:, 2] >= z_min) & (pts[:, 2] <= z_max)) - self.zone = np.array(list(set(self.zone[0].tolist()).intersection(set(list(self.surface))))) - # Force value - F = np.random.uniform(low=-1, high=1, size=(3,)) * np.random.randint(20, 30) - self.force_value = F - - # Update current amplitude - self.force = self.force_value * self.amplitudes[self.idx_amplitude] - self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) - - # Create input array - F = np.zeros(self.data_size) - F[self.zone] = self.force - - # Load a force sample from Dataset - else: - sleep(0.5) - F = np.zeros(self.data_size) if self.sample_in is None else self.sample_in + # Generate a new force + if self.idx_amplitude == 0: + # Define zone + pts = self.mesh.points().copy() + side = np.random.randint(0, 6) + x_min = np.min(pts[:, 0]) if side == 0 else np.random.randint(np.min(pts[:, 0]), np.max(pts[:, 0]) - 10) + y_min = np.min(pts[:, 1]) if side == 1 else np.random.randint(np.min(pts[:, 1]), np.max(pts[:, 1]) - 10) + z_min = np.min(pts[:, 2]) if side == 2 else np.random.randint(np.min(pts[:, 2]), np.max(pts[:, 2]) - 10) + x_max = np.max(pts[:, 0]) if side == 3 else np.random.randint(x_min + 10, np.max(pts[:, 0]) + 1) + y_max = np.max(pts[:, 1]) if side == 4 else np.random.randint(y_min + 10, np.max(pts[:, 1]) + 1) + z_max = np.max(pts[:, 2]) if side == 5 else np.random.randint(z_min + 10, np.max(pts[:, 2]) + 1) + # Find points on surface and in box + self.zone = np.where((pts[:, 0] >= x_min) & (pts[:, 0] <= x_max) & (pts[:, 1] >= y_min) & + (pts[:, 1] <= y_max) & (pts[:, 2] >= z_min) & (pts[:, 2] <= z_max)) + self.zone = np.array(list(set(self.zone[0].tolist()).intersection(set(list(self.surface))))) + # Force value + F = np.random.uniform(low=-1, high=1, size=(3,)) * np.random.randint(20, 30) + self.force_value = F + + # Update current amplitude + force = self.force_value * self.amplitudes[self.idx_amplitude] + self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) + + # Create input array + F = np.zeros(self.data_size) + F[self.zone] = force # Set training data self.F = F - self.set_training_data(input_array=F.copy(), - output_array=np.zeros(self.data_size)) + self.set_training_data(input=F.copy(), + ground_truth=np.zeros(self.data_size)) def apply_prediction(self, prediction): # Reshape to correspond to sparse grid - U = np.reshape(prediction, self.data_size) + U = np.reshape(prediction['prediction'], self.data_size) self.update_visual(U) def update_visual(self, U): # Update surface mesh updated_mesh = self.mesh.clone().points(self.mesh.points().copy() + U) - self.factory.update_object_dict(object_id=0, - new_data_dict={'positions': updated_mesh.points().copy()}) + self.factory.update_mesh(object_id=0, + positions=updated_mesh.points().copy()) # Update arrows representing force fields - self.factory.update_object_dict(object_id=1, - new_data_dict={'positions': updated_mesh.points().copy(), - 'vectors': 0.25 * self.F}) + self.factory.update_arrows(object_id=1, + positions=updated_mesh.points().copy(), + vectors=0.25 * self.F) # Send visualization data to update - self.update_visualisation(visu_dict=self.factory.updated_object_dict) + self.update_visualisation() def close(self): # Shutdown message diff --git a/examples/demos/Beam/FC/Environment/BeamInteractive.py b/examples/demos/Beam/FC/Environment/BeamInteractive.py index c60830d4..6950405c 100644 --- a/examples/demos/Beam/FC/Environment/BeamInteractive.py +++ b/examples/demos/Beam/FC/Environment/BeamInteractive.py @@ -26,20 +26,14 @@ class Beam(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topologies & mappings self.mesh = None @@ -62,6 +56,11 @@ def __init__(self, # Data sizes self.data_size = (p_model.nb_nodes, 3) + def init_database(self): + + # Define the fields of the Training database + self.define_training_fields(fields=[('input', np.ndarray), ('ground_truth', np.ndarray)]) + def create(self): # Load the meshes and the sparse grid @@ -138,8 +137,8 @@ async def step(self): # Launch Vedo window self.plotter.show().close() # Smooth close - self.set_training_data(input_array=np.zeros(self.data_size), - output_array=np.zeros(self.data_size)) + self.set_training_data(input=np.zeros(self.data_size), + ground_truth=np.zeros(self.data_size)) def key_press(self, evt): @@ -195,7 +194,7 @@ def mouse_move(self, evt): F[self.areas[self.selected]] = move_3D * 2 # Apply output displacement - U = self.get_prediction(F).reshape(self.data_size) + U = self.get_prediction(input=F)['prediction'].reshape(self.data_size) updated_grid = self.mesh_init + U # Update view diff --git a/examples/demos/Beam/FC/download.py b/examples/demos/Beam/FC/download.py index 530d0457..4397eb7a 100644 --- a/examples/demos/Beam/FC/download.py +++ b/examples/demos/Beam/FC/download.py @@ -20,11 +20,11 @@ def __init__(self,): 'session': [266], 'network': [219], 'stats': [216], - 'dataset_info': [262], - 'dataset_valid': [263, 264], - 'dataset_train': [261, 265]} + 'dataset_info': [324], + 'dataset_valid': [326], + 'dataset_train': [325]} if __name__ == '__main__': - BeamDownloader().get_session('valid_data') + BeamDownloader().get_session('all') diff --git a/examples/demos/Beam/FC/interactive.py b/examples/demos/Beam/FC/interactive.py index 463dbe40..1b399d86 100644 --- a/examples/demos/Beam/FC/interactive.py +++ b/examples/demos/Beam/FC/interactive.py @@ -8,15 +8,15 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Torch.FC.FCConfig import FCConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import BeamDownloader -BeamDownloader().get_session('predict') +BeamDownloader().get_session('all') from Environment.BeamInteractive import Beam from Environment.parameters import p_model @@ -24,30 +24,27 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Beam, - as_tcp_ip_client=False) + environment_config = BaseEnvironmentConfig(environment_class=Beam) # FC config nb_hidden_layers = 3 nb_neurons = p_model.nb_nodes * 3 layers_dim = [nb_neurons] + [nb_neurons for _ in range(nb_hidden_layers)] + [nb_neurons] - net_config = FCConfig(network_name='beam_FC', - dim_output=3, - dim_layers=layers_dim, - biases=True) + network_config = FCConfig(dim_layers=layers_dim, + dim_output=3, + biases=True) # Dataset config - dataset_config = BaseDatasetConfig() + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='beam_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=1) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='beam_dpx', + step_nb=1) runner.execute() - runner.close() if __name__ == '__main__': diff --git a/examples/demos/Beam/FC/prediction.py b/examples/demos/Beam/FC/prediction.py index 17af8802..c0fc1a70 100644 --- a/examples/demos/Beam/FC/prediction.py +++ b/examples/demos/Beam/FC/prediction.py @@ -8,16 +8,16 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.FC.FCConfig import FCConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import BeamDownloader -BeamDownloader().get_session('valid_data') +BeamDownloader().get_session('all') from Environment.Beam import Beam from Environment.parameters import p_model @@ -25,34 +25,28 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Beam, - visualizer=VedoVisualizer, - as_tcp_ip_client=False, - param_dict={'compute_sample': True}) + environment_config = BaseEnvironmentConfig(environment_class=Beam, + visualizer=VedoVisualizer) # FC config nb_hidden_layers = 3 nb_neurons = p_model.nb_nodes * 3 layers_dim = [nb_neurons] + [nb_neurons for _ in range(nb_hidden_layers)] + [nb_neurons] - net_config = FCConfig(network_name='beam_FC', - dim_output=3, - dim_layers=layers_dim, - biases=True) + network_config = FCConfig(dim_layers=layers_dim, + dim_output=3, + biases=True) # Dataset config - dataset_config = BaseDatasetConfig(normalize=True, - dataset_dir='sessions/beam_dpx', - use_mode='Validation') + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='beam_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=500) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='beam_dpx', + step_nb=500) runner.execute() - runner.close() if __name__ == '__main__': diff --git a/examples/demos/Beam/UNet/Environment/Beam.py b/examples/demos/Beam/UNet/Environment/Beam.py index e255ef39..4e827773 100644 --- a/examples/demos/Beam/UNet/Environment/Beam.py +++ b/examples/demos/Beam/UNet/Environment/Beam.py @@ -12,7 +12,6 @@ import numpy as np from vedo import Mesh -from time import sleep # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -26,31 +25,24 @@ class Beam(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topology self.mesh = None self.surface = None # Force - self.compute_sample = True - step = 0.1 - self.amplitudes = np.concatenate((np.arange(0, 1, step), - np.arange(1, -1, -step), - np.arange(-1, 0, step))) + step = 0.05 + self.amplitudes = np.concatenate((np.arange(step, 1, step), + np.arange(1, step, -step))) + self.amplitudes[0] = 0 self.idx_amplitude = 0 self.force_value = None self.zone = None @@ -64,11 +56,10 @@ def __init__(self, Methods will be automatically called in this order to create and initialize Environment. """ - def recv_parameters(self, param_dict): + def init_database(self): - # Get the model definition parameters - self.compute_sample = param_dict['compute_sample'] if 'compute_sample' in param_dict else True - self.amplitudes[0] = 0 if self.compute_sample else 1 + # Define the fields of the Training database + self.define_training_fields(fields=[('input', np.ndarray), ('ground_truth', np.ndarray)]) def create(self): @@ -84,24 +75,19 @@ def create(self): np.argwhere(pts[:, 2] == np.max(pts[:, 2])))).reshape(-1) self.surface = np.unique(self.surface) - def send_visualization(self): + def init_visualization(self): # Mesh representing the grid (object will have id = 0) - self.factory.add_object(object_type='Mesh', - data_dict={'positions': self.mesh.points(), - 'cells': self.mesh.cells(), - 'wireframe': True, - 'c': 'orange', - 'at': self.instance_id}) - + self.factory.add_mesh(positions=self.mesh.points(), + cells=self.mesh.cells(), + wireframe=True, + c='orange', + at=self.instance_id) # Arrows representing the force fields (object will have id = 1) - self.factory.add_object(object_type='Arrows', - data_dict={'positions': [0, 0, 0], - 'vectors': [0, 0, 0], - 'c': 'green', - 'at': self.instance_id}) - - return self.factory.objects_dict + self.factory.add_arrows(positions=np.array([0., 0., 0.]), + vectors=np.array([0., 0., 0.]), + c='green', + at=self.instance_id) """ ENVIRONMENT BEHAVIOR @@ -111,65 +97,58 @@ def send_visualization(self): async def step(self): - # Compute a force sample - if self.compute_sample: - # Generate a new force - if self.idx_amplitude == 0: - # Define zone - pts = self.mesh.points().copy() - side = np.random.randint(0, 11) - x_min = np.min(pts[:, 0]) if side in (3, 4) else np.random.randint(np.min(pts[:, 0]), np.max(pts[:, 0]) - 10) - y_min = np.min(pts[:, 1]) if side in (7, 8) else np.random.randint(np.min(pts[:, 1]), np.max(pts[:, 1]) - 10) - z_min = np.min(pts[:, 2]) if side == 0 else np.random.randint(np.min(pts[:, 2]), np.max(pts[:, 2]) - 10) - x_max = np.max(pts[:, 0]) if side in (5, 6) else np.random.randint(x_min + 10, np.max(pts[:, 0]) + 1) - y_max = np.max(pts[:, 1]) if side in (9, 10) else np.random.randint(y_min + 10, np.max(pts[:, 1]) + 1) - z_max = np.max(pts[:, 2]) if side in (1, 2, 3) else np.random.randint(z_min + 10, np.max(pts[:, 2]) + 1) - # Find points on surface and in box - self.zone = np.where((pts[:, 0] >= x_min) & (pts[:, 0] <= x_max) & (pts[:, 1] >= y_min) & - (pts[:, 1] <= y_max) & (pts[:, 2] >= z_min) & (pts[:, 2] <= z_max)) - self.zone = np.array(list(set(self.zone[0].tolist()).intersection(set(list(self.surface))))) - # Force value - F = np.random.uniform(low=-1, high=1, size=(3,)) - self.force_value = (F / np.linalg.norm(F)) * np.random.randint(10, 50) - - # Update current amplitude - self.force = self.force_value * self.amplitudes[self.idx_amplitude] - self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) - - # Create input array - F = np.zeros(self.data_size) - F[self.zone] = self.force - - # Load a force sample from Dataset - else: - sleep(0.5) - F = np.zeros(self.data_size) if self.sample_in is None else self.sample_in + # Generate a new force + if self.idx_amplitude == 0: + # Define zone + pts = self.mesh.points().copy() + side = np.random.randint(0, 11) + x_min = np.min(pts[:, 0]) if side in (3, 4) else np.random.randint(np.min(pts[:, 0]), np.max(pts[:, 0]) - 10) + y_min = np.min(pts[:, 1]) if side in (7, 8) else np.random.randint(np.min(pts[:, 1]), np.max(pts[:, 1]) - 10) + z_min = np.min(pts[:, 2]) if side == 0 else np.random.randint(np.min(pts[:, 2]), np.max(pts[:, 2]) - 10) + x_max = np.max(pts[:, 0]) if side in (5, 6) else np.random.randint(x_min + 10, np.max(pts[:, 0]) + 1) + y_max = np.max(pts[:, 1]) if side in (9, 10) else np.random.randint(y_min + 10, np.max(pts[:, 1]) + 1) + z_max = np.max(pts[:, 2]) if side in (1, 2, 3) else np.random.randint(z_min + 10, np.max(pts[:, 2]) + 1) + # Find points on surface and in box + self.zone = np.where((pts[:, 0] >= x_min) & (pts[:, 0] <= x_max) & (pts[:, 1] >= y_min) & + (pts[:, 1] <= y_max) & (pts[:, 2] >= z_min) & (pts[:, 2] <= z_max)) + self.zone = np.array(list(set(self.zone[0].tolist()).intersection(set(list(self.surface))))) + # Force value + F = np.random.uniform(low=-1, high=1, size=(3,)) + self.force_value = (F / np.linalg.norm(F)) * np.random.randint(10, 50) + + # Update current amplitude + force = self.force_value * self.amplitudes[self.idx_amplitude] + self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) + + # Create input array + F = np.zeros(self.data_size) + F[self.zone] = force # Set training data self.F = F - self.set_training_data(input_array=F.copy(), - output_array=np.zeros(self.data_size)) + self.set_training_data(input=F.copy(), + ground_truth=np.zeros(self.data_size)) def apply_prediction(self, prediction): # Reshape to correspond to sparse grid - U = np.reshape(prediction, self.data_size) + U = np.reshape(prediction['prediction'], self.data_size) self.update_visual(U) def update_visual(self, U): # Update surface mesh updated_position = self.mesh.points().copy() + U - self.factory.update_object_dict(object_id=0, - new_data_dict={'positions': updated_position}) + self.factory.update_mesh(object_id=0, + positions=updated_position) # Update arrows representing force fields - self.factory.update_object_dict(object_id=1, - new_data_dict={'positions': updated_position, - 'vectors': 0.25 * self.F}) + self.factory.update_arrows(object_id=1, + positions=updated_position, + vectors=0.25 * self.F) # Send visualization data to update - self.update_visualisation(visu_dict=self.factory.updated_object_dict) + self.update_visualisation() def close(self): # Shutdown message diff --git a/examples/demos/Beam/UNet/Environment/parameters.py b/examples/demos/Beam/UNet/Environment/parameters.py index 60485aa0..aa1a9da3 100644 --- a/examples/demos/Beam/UNet/Environment/parameters.py +++ b/examples/demos/Beam/UNet/Environment/parameters.py @@ -5,14 +5,13 @@ """ import os -from numpy import array from vedo import Mesh from collections import namedtuple # Model parameters grid = os.path.dirname(os.path.abspath(__file__)) + '/models/beam.obj' -grid_resolution = array([5, 5, 25]) +grid_resolution = [5, 5, 25] model = {'grid': grid, 'nb_nodes': Mesh(grid).N()} p_model = namedtuple('p_model', model)(**model) diff --git a/examples/demos/Beam/UNet/download.py b/examples/demos/Beam/UNet/download.py index f78be871..8ddcdab8 100644 --- a/examples/demos/Beam/UNet/download.py +++ b/examples/demos/Beam/UNet/download.py @@ -20,9 +20,9 @@ def __init__(self): 'session': [312], 'network': [315], 'stats': [314], - 'dataset_info': [309], - 'dataset_valid': [313, 316], - 'dataset_train': [310, 311]} + 'dataset_info': [329], + 'dataset_valid': [328], + 'dataset_train': [327]} if __name__ == '__main__': diff --git a/examples/demos/Beam/UNet/prediction.py b/examples/demos/Beam/UNet/prediction.py index 067ff21f..8f194c29 100644 --- a/examples/demos/Beam/UNet/prediction.py +++ b/examples/demos/Beam/UNet/prediction.py @@ -8,16 +8,16 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.UNet.UNetConfig import UNetConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import BeamDownloader -BeamDownloader().get_session('valid_data') +BeamDownloader().get_session('all') from Environment.Beam import Beam from Environment.parameters import grid_resolution @@ -25,37 +25,31 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Beam, - visualizer=VedoVisualizer, - as_tcp_ip_client=False, - param_dict={'compute_sample': True}) + environment_config = BaseEnvironmentConfig(environment_class=Beam, + visualizer=VedoVisualizer) # UNet config - net_config = UNetConfig(network_name='beam_UNet', - input_size=grid_resolution.tolist(), - nb_dims=3, - nb_input_channels=3, - nb_first_layer_channels=128, - nb_output_channels=3, - nb_steps=3, - two_sublayers=True, - border_mode='same', - skip_merge=False) + network_config = UNetConfig(input_size=grid_resolution, + nb_dims=3, + nb_input_channels=3, + nb_first_layer_channels=128, + nb_output_channels=3, + nb_steps=3, + two_sublayers=True, + border_mode='same', + skip_merge=False) # Dataset config - dataset_config = BaseDatasetConfig(normalize=True, - dataset_dir='sessions/beam_dpx', - use_mode='Validation') + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='beam_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=0) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='beam_dpx', + step_nb=500) runner.execute() - runner.close() if __name__ == '__main__': diff --git a/examples/demos/Liver/FC/Environment/Liver.py b/examples/demos/Liver/FC/Environment/Liver.py index c34f741d..d8cb9963 100644 --- a/examples/demos/Liver/FC/Environment/Liver.py +++ b/examples/demos/Liver/FC/Environment/Liver.py @@ -9,10 +9,11 @@ # Python related imports import os import sys -import numpy as np +from numpy import ndarray, array, concatenate, arange, zeros, reshape +from numpy.random import randint, uniform +from numpy.linalg import norm from vedo import Mesh from math import pow -from time import sleep # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -27,20 +28,15 @@ class Liver(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1, + nb_forces=3): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topology self.mesh = None @@ -50,15 +46,15 @@ def __init__(self, self.mapping_coarse = None # Force fields - self.forces = None - self.areas = None - self.compute_sample = True + self.nb_forces = nb_forces + self.forces = [[]] * self.nb_forces + self.areas = [[]] * self.nb_forces # Force pattern step = 0.05 - self.amplitudes = np.concatenate((np.arange(0, 1, step), - np.arange(1, 0, -step))) - self.nb_forces = p_forces.nb_simultaneous_forces + self.amplitudes = concatenate((arange(0, 1, step), + arange(1, 0, -step))) + self.amplitudes[0] = 0 self.idx_amplitude = 0 self.force_value = None self.F = None @@ -72,16 +68,10 @@ def __init__(self, Methods will be automatically called in this order to create and initialize Environment. """ - def recv_parameters(self, param_dict): - - # Get the model definition parameters - self.compute_sample = param_dict['compute_sample'] if 'compute_sample' in param_dict else True - self.amplitudes[0] = 0 if self.compute_sample else 1 + def init_database(self): - # Receive the number of forces - self.nb_forces = min(param_dict['nb_forces'], self.nb_forces) if 'nb_forces' in param_dict else self.nb_forces - self.forces = [None] * self.nb_forces - self.areas = [None] * self.nb_forces + # Define the fields of the Training database + self.define_training_fields(fields=[('input', ndarray), ('ground_truth', ndarray)]) def create(self): @@ -92,32 +82,24 @@ def create(self): self.mapping = GridMapping(self.sparse_grid, self.mesh) self.mapping_coarse = GridMapping(self.sparse_grid, self.mesh_coarse) - def send_visualization(self): + def init_visualization(self): # Mesh representing detailed Armadillo (object will have id = 0) - self.factory.add_object(object_type="Mesh", - data_dict={"positions": self.mesh.points(), - 'cells': self.mesh.cells(), - 'wireframe': True, - "c": "orange", - "at": self.instance_id}) - + self.factory.add_mesh(positions=self.mesh.points(), + cells=self.mesh.cells(), + wireframe=True, + c='orange', + at=self.instance_id) # Arrows representing the force fields (object will have id = 1) - self.factory.add_object(object_type='Arrows', - data_dict={'positions': p_model.fixed_point, - 'vectors': [0, 0, 0], - 'c': 'green', - 'at': self.instance_id}) - + self.factory.add_arrows(positions=p_model.fixed_point, + vectors=array([0., 0., 0.]), + c='green', + at=self.instance_id) # Points representing the grid (object will have id = 2) - self.factory.add_object(object_type='Points', - data_dict={'positions': self.sparse_grid.points(), - 'r': 1., - 'c': 'black', - 'at': self.instance_id}) - - # Return the visualization data - return self.factory.objects_dict + self.factory.add_points(positions=self.sparse_grid.points(), + point_size=1, + c='black', + at=self.instance_id) """ ENVIRONMENT BEHAVIOR @@ -127,63 +109,55 @@ def send_visualization(self): async def step(self): - # Compute a force sample - if self.compute_sample: - - # Generate a new force - if self.idx_amplitude == 0: - - # Define zones - selected_centers = [] - pts = self.mesh_coarse.points().copy() - for i in range(self.nb_forces): - # Pick a random sphere center, check distance with other spheres - current_point = pts[np.random.randint(0, self.mesh_coarse.N())] - distance_check = True - for p in selected_centers: - distance = np.linalg.norm(current_point - p) - if distance < p_forces.inter_distance_thresh: - distance_check = False - break - # Reset force field value and indices - self.areas[i] = [] - self.forces[i] = np.array([0, 0, 0]) - # Fill the force field - if distance_check: - # Add center - selected_centers.append(current_point) - # Find node in the sphere - sphere = lambda x, y: sum([pow(x_i - y_i, 2) for x_i, y_i in zip(x, y)]) - for j, p in enumerate(pts): - if sphere(p, current_point) <= pow(p_forces.inter_distance_thresh / 2, 2): - self.areas[i].append(j) - # If the sphere is non-empty, create a force vector - if len(self.areas[i]) > 0: - f = np.random.uniform(low=-1, high=1, size=(3,)) - self.forces[i] = (f / np.linalg.norm(f)) * p_forces.amplitude - - # Create input array - F = np.zeros(self.input_size) - for i, force in enumerate(self.forces): - F[self.areas[i]] = self.forces[i] * self.amplitudes[self.idx_amplitude] - - # Update current force amplitude - self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) - - # Load a force sample from Dataset - else: - sleep(0.5) - F = np.zeros(self.input_size) if self.sample_in is None else self.sample_in + # Generate a new force + if self.idx_amplitude == 0: + + # Define zones + selected_centers = [] + pts = self.mesh_coarse.points().copy() + for i in range(self.nb_forces): + # Pick a random sphere center, check distance with other spheres + current_point = pts[randint(0, self.mesh_coarse.N())] + distance_check = True + for p in selected_centers: + distance = norm(current_point - p) + if distance < p_forces.inter_distance_thresh: + distance_check = False + break + # Reset force field value and indices + self.areas[i] = [] + self.forces[i] = array([0, 0, 0]) + # Fill the force field + if distance_check: + # Add center + selected_centers.append(current_point) + # Find node in the sphere + sphere = lambda x, y: sum([pow(x_i - y_i, 2) for x_i, y_i in zip(x, y)]) + for j, p in enumerate(pts): + if sphere(p, current_point) <= pow(p_forces.inter_distance_thresh / 2, 2): + self.areas[i].append(j) + # If the sphere is non-empty, create a force vector + if len(self.areas[i]) > 0: + f = uniform(low=-1, high=1, size=(3,)) + self.forces[i] = (f / norm(f)) * p_forces.amplitude + + # Create input array + F = zeros(self.input_size) + for i, force in enumerate(self.forces): + F[self.areas[i]] = self.forces[i] * self.amplitudes[self.idx_amplitude] + + # Update current force amplitude + self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) # Set training data self.F = F - self.set_training_data(input_array=F.copy(), - output_array=np.zeros(self.output_size)) + self.set_training_data(input=F.copy(), + ground_truth=zeros(self.output_size)) def apply_prediction(self, prediction): # Reshape to correspond to sparse grid - U = np.reshape(prediction, self.output_size) + U = reshape(prediction['prediction'], self.output_size) self.update_visual(U) def update_visual(self, U): @@ -194,20 +168,20 @@ def update_visual(self, U): mesh_coarse_position = self.mapping_coarse.apply(updated_position) # Update surface mesh - self.factory.update_object_dict(object_id=0, - new_data_dict={'positions': mesh_position}) + self.factory.update_mesh(object_id=0, + positions=mesh_position) # Update arrows representing force fields - self.factory.update_object_dict(object_id=1, - new_data_dict={'positions': mesh_coarse_position, - 'vectors': self.F}) + self.factory.update_arrows(object_id=1, + positions=mesh_coarse_position, + vectors=self.F) # Update sparse grid positions - self.factory.update_object_dict(object_id=2, - new_data_dict={'positions': updated_position}) + self.factory.update_points(object_id=2, + positions=updated_position) # Send visualization data to update - self.update_visualisation(visu_dict=self.factory.updated_object_dict) + self.update_visualisation() def close(self): # Shutdown message diff --git a/examples/demos/Liver/FC/Environment/LiverInteractive.py b/examples/demos/Liver/FC/Environment/LiverInteractive.py index f7e432c9..4efed83b 100644 --- a/examples/demos/Liver/FC/Environment/LiverInteractive.py +++ b/examples/demos/Liver/FC/Environment/LiverInteractive.py @@ -12,7 +12,7 @@ import vtk import numpy as np -from vedo import Mesh, Sphere, Arrows, Plotter, Text2D, Box, fitSphere +from vedo import Mesh, Sphere, Arrows, Plotter, Text2D, Box # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -28,20 +28,14 @@ class Liver(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topologies & mappings self.mesh = None @@ -68,6 +62,11 @@ def __init__(self, self.input_size = (p_model.nb_nodes_mesh, 3) self.output_size = (p_model.nb_nodes_grid, 3) + def init_database(self): + + # Define the fields of the Training database + self.define_training_fields(fields=[('input', np.ndarray), ('ground_truth', np.ndarray)]) + def create(self): # Load the meshes and the sparse grid @@ -114,8 +113,8 @@ async def step(self): # Launch Vedo window self.plotter.show().close() # Smooth close - self.set_training_data(input_array=np.zeros(self.input_size), - output_array=np.zeros(self.output_size)) + self.set_training_data(input=np.zeros(self.input_size), + ground_truth=np.zeros(self.output_size)) def key_press(self, evt): @@ -171,7 +170,7 @@ def mouse_move(self, evt): F[self.areas[self.selected]] = move_3D * p_forces.amplitude # Apply output displacement - U = self.get_prediction(F).reshape(self.output_size) + U = self.get_prediction(input=F)['prediction'].reshape(self.output_size) updated_grid = self.sparse_grid.points().copy() + U updated_coarse = self.mapping_coarse.apply(updated_grid) diff --git a/examples/demos/Liver/FC/download.py b/examples/demos/Liver/FC/download.py index f17fa317..1e7d616f 100644 --- a/examples/demos/Liver/FC/download.py +++ b/examples/demos/Liver/FC/download.py @@ -20,11 +20,11 @@ def __init__(self): 'session': [281], 'network': [195], 'stats': [198], - 'dataset_info': [276], - 'dataset_valid': [279, 278], - 'dataset_train': [277, 282, 280, 283]} + 'dataset_info': [331], + 'dataset_valid': [330], + 'dataset_train': [332]} if __name__ == '__main__': - LiverDownloader().get_session('valid_data') + LiverDownloader().get_session('all') diff --git a/examples/demos/Liver/FC/interactive.py b/examples/demos/Liver/FC/interactive.py index e16fc4ba..1e2eb974 100644 --- a/examples/demos/Liver/FC/interactive.py +++ b/examples/demos/Liver/FC/interactive.py @@ -8,15 +8,15 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig from DeepPhysX.Torch.FC.FCConfig import FCConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import LiverDownloader -LiverDownloader().get_session('predict') +LiverDownloader().get_session('all') from Environment.LiverInteractive import Liver from Environment.parameters import p_model @@ -24,31 +24,28 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Liver, - as_tcp_ip_client=False) + environment_config = BaseEnvironmentConfig(environment_class=Liver) # FC config nb_hidden_layers = 3 nb_neurons = p_model.nb_nodes_mesh * 3 nb_final_neurons = p_model.nb_nodes_grid * 3 layers_dim = [nb_neurons] + [nb_neurons for _ in range(nb_hidden_layers)] + [nb_final_neurons] - net_config = FCConfig(network_name='liver_FC', - dim_output=3, - dim_layers=layers_dim, - biases=True) + network_config = FCConfig(dim_layers=layers_dim, + dim_output=3, + biases=True) # Dataset config - dataset_config = BaseDatasetConfig() + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='liver_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=1) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='liver_dpx', + step_nb=1) runner.execute() - runner.close() if __name__ == '__main__': diff --git a/examples/demos/Liver/FC/prediction.py b/examples/demos/Liver/FC/prediction.py index 9833fa58..83b3a02e 100644 --- a/examples/demos/Liver/FC/prediction.py +++ b/examples/demos/Liver/FC/prediction.py @@ -8,16 +8,16 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.FC.FCConfig import FCConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import LiverDownloader -LiverDownloader().get_session('valid_data') +LiverDownloader().get_session('all') from Environment.Liver import Liver from Environment.parameters import p_model @@ -25,36 +25,30 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Liver, - visualizer=VedoVisualizer, - as_tcp_ip_client=False, - param_dict={'compute_sample': True, - 'nb_forces': 3}) + environment_config = BaseEnvironmentConfig(environment_class=Liver, + visualizer=VedoVisualizer, + env_kwargs={'nb_forces': 3}) # FC config nb_hidden_layers = 3 nb_neurons = p_model.nb_nodes_mesh * 3 nb_final_neurons = p_model.nb_nodes_grid * 3 layers_dim = [nb_neurons] + [nb_neurons for _ in range(nb_hidden_layers)] + [nb_final_neurons] - net_config = FCConfig(network_name='liver_FC', - dim_output=3, - dim_layers=layers_dim, - biases=True) + network_config = FCConfig(dim_layers=layers_dim, + dim_output=3, + biases=True) # Dataset config - dataset_config = BaseDatasetConfig(dataset_dir='sessions/liver_dpx', - normalize=True, - use_mode='Validation') + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='liver_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=500) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='liver_dpx', + step_nb=500) runner.execute() - runner.close() if __name__ == '__main__': diff --git a/examples/demos/Liver/UNet/Environment/Liver.py b/examples/demos/Liver/UNet/Environment/Liver.py index ee2ef3e1..6e9c10f4 100644 --- a/examples/demos/Liver/UNet/Environment/Liver.py +++ b/examples/demos/Liver/UNet/Environment/Liver.py @@ -9,10 +9,11 @@ # Python related imports import os import sys -import numpy as np +from numpy import ndarray, array, concatenate, arange, zeros, unique, reshape +from numpy.random import randint, uniform +from numpy.linalg import norm from vedo import Mesh from math import pow -from time import sleep # DeepPhysX related imports from DeepPhysX.Core.Environment.BaseEnvironment import BaseEnvironment @@ -27,20 +28,15 @@ class Liver(BaseEnvironment): def __init__(self, - ip_address='localhost', - port=10000, - instance_id=0, - number_of_instances=1, as_tcp_ip_client=True, - environment_manager=None): + instance_id=1, + instance_nb=1, + nb_forces=3): BaseEnvironment.__init__(self, - ip_address=ip_address, - port=port, - instance_id=instance_id, - number_of_instances=number_of_instances, as_tcp_ip_client=as_tcp_ip_client, - environment_manager=environment_manager) + instance_id=instance_id, + instance_nb=instance_nb) # Topology self.mesh = None @@ -52,9 +48,10 @@ def __init__(self, self.grid_correspondences = None # Force fields - self.forces = None - self.areas = None - self.g_areas = None + self.nb_forces = nb_forces + self.forces = [[]] * self.nb_forces + self.areas = [[]] * self.nb_forces + self.g_areas = [[]] * self.nb_forces self.compute_sample = True self.cell_corner = None self.sparse_grid_position = None @@ -64,9 +61,9 @@ def __init__(self, # Force pattern step = 0.05 - self.amplitudes = np.concatenate((np.arange(0, 1, step), - np.arange(1, 0, -step))) - self.nb_forces = p_forces.nb_simultaneous_forces + self.amplitudes = concatenate((arange(step, 1, step), + arange(1, step, -step))) + self.amplitudes[0] = 0 self.idx_amplitude = 0 self.force_value = None self.F = None @@ -80,17 +77,10 @@ def __init__(self, Methods will be automatically called in this order to create and initialize Environment. """ - def recv_parameters(self, param_dict): - - # Get the model definition parameters - self.compute_sample = param_dict['compute_sample'] if 'compute_sample' in param_dict else True - self.amplitudes[0] = 0 if self.compute_sample else 1 + def init_database(self): - # Receive the number of forces - self.nb_forces = min(param_dict['nb_forces'], self.nb_forces) if 'nb_forces' in param_dict else self.nb_forces - self.forces = [None] * self.nb_forces - self.areas = [None] * self.nb_forces - self.g_areas = [None] * self.nb_forces + # Define the fields of the Training database + self.define_training_fields(fields=[('input', ndarray), ('ground_truth', ndarray)]) def create(self): @@ -103,19 +93,19 @@ def create(self): x_reg = [p_grid.origin[0] + i * p_grid.size[0] / p_grid.nb_cells[0] for i in range(p_grid.nb_cells[0] + 1)] y_reg = [p_grid.origin[1] + i * p_grid.size[1] / p_grid.nb_cells[1] for i in range(p_grid.nb_cells[1] + 1)] z_reg = [p_grid.origin[2] + i * p_grid.size[2] / p_grid.nb_cells[2] for i in range(p_grid.nb_cells[2] + 1)] - grid_positions = np.array([[[[x, y, z] for x in x_reg] for y in y_reg] for z in z_reg]).reshape(-1, 3) + grid_positions = array([[[[x, y, z] for x in x_reg] for y in y_reg] for z in z_reg]).reshape(-1, 3) cell_corner = lambda x, y, z: len(x_reg) * (len(y_reg) * z + y) + x - grid_cells = np.array([[[[[cell_corner(ix, iy, iz), - cell_corner(ix + 1, iy, iz), - cell_corner(ix + 1, iy + 1, iz), - cell_corner(ix, iy + 1, iz), - cell_corner(ix, iy, iz + 1), - cell_corner(ix + 1, iy, iz + 1), - cell_corner(ix + 1, iy + 1, iz + 1), - cell_corner(ix, iy + 1, iz + 1)] - for ix in range(p_grid.nb_cells[0])] - for iy in range(p_grid.nb_cells[1])] - for iz in range(p_grid.nb_cells[2])]]).reshape(-1, 8) + grid_cells = array([[[[[cell_corner(ix, iy, iz), + cell_corner(ix + 1, iy, iz), + cell_corner(ix + 1, iy + 1, iz), + cell_corner(ix, iy + 1, iz), + cell_corner(ix, iy, iz + 1), + cell_corner(ix + 1, iy, iz + 1), + cell_corner(ix + 1, iy + 1, iz + 1), + cell_corner(ix, iy + 1, iz + 1)] + for ix in range(p_grid.nb_cells[0])] + for iy in range(p_grid.nb_cells[1])] + for iz in range(p_grid.nb_cells[2])]]).reshape(-1, 8) self.regular_grid = Mesh([grid_positions, grid_cells]) # Init mappings between meshes and sparse grid @@ -123,16 +113,16 @@ def create(self): self.mapping_coarse = GridMapping(self.sparse_grid, self.mesh_coarse) # Init correspondences between sparse and regular grid - self.grid_correspondences = np.zeros(self.sparse_grid.N(), dtype=int) - x_sparse = np.unique(self.sparse_grid.points()[:, 0]) - y_sparse = np.unique(self.sparse_grid.points()[:, 1]) - z_sparse = np.unique(self.sparse_grid.points()[:, 2]) + self.grid_correspondences = zeros(self.sparse_grid.N(), dtype=int) + x_sparse = unique(self.sparse_grid.points()[:, 0]) + y_sparse = unique(self.sparse_grid.points()[:, 1]) + z_sparse = unique(self.sparse_grid.points()[:, 2]) if len(x_reg) != len(x_sparse) or len(y_reg) != len(y_sparse) or len(z_reg) != len(z_sparse): raise ValueError('Grids should have the same dimension') d = [x_sparse[1] - x_sparse[0], y_sparse[1] - y_sparse[0], z_sparse[1] - z_sparse[0]] origin = [x_sparse[0], y_sparse[0], z_sparse[0]] for i, node in enumerate(self.sparse_grid.points()): - p = np.array(node) - np.array(origin) + p = array(node) - array(origin) ix = int(round(p[0] / d[0])) iy = int(round(p[1] / d[1])) iz = int(round(p[2] / d[2])) @@ -140,40 +130,32 @@ def create(self): self.grid_correspondences[i] = idx # Init grid force field stuff - self.x_sparse = np.unique(self.sparse_grid.points()[:, 0]) - self.y_sparse = np.unique(self.sparse_grid.points()[:, 1]) - self.z_sparse = np.unique(self.sparse_grid.points()[:, 2]) + self.x_sparse = unique(self.sparse_grid.points()[:, 0]) + self.y_sparse = unique(self.sparse_grid.points()[:, 1]) + self.z_sparse = unique(self.sparse_grid.points()[:, 2]) self.sparse_grid_position = self.sparse_grid.points().tolist() self.cell_corner = lambda x, y, z: self.sparse_grid_position.index([self.x_sparse[x], self.y_sparse[y], self.z_sparse[z]]) - def send_visualization(self): + def init_visualization(self): # Mesh representing detailed Armadillo (object will have id = 0) - self.factory.add_object(object_type="Mesh", - data_dict={"positions": self.mesh.points(), - 'cells': self.mesh.cells(), - 'wireframe': True, - "c": "orange", - "at": self.instance_id}) - + self.factory.add_mesh(positions=self.mesh.points(), + cells=self.mesh.cells(), + wireframe=True, + c='orange', + at=self.instance_id) # Arrows representing the force fields (object will have id = 1) - self.factory.add_object(object_type='Arrows', - data_dict={'positions': p_model.fixed_point, - 'vectors': [0, 0, 0], - 'c': 'green', - 'at': self.instance_id}) - + self.factory.add_arrows(positions=p_model.fixed_point, + vectors=array([0., 0., 0.]), + c='green', + at=self.instance_id) # Sparse grid (object will have id = 2) - self.factory.add_object(object_type='Points', - data_dict={'positions': self.sparse_grid.points(), - 'r': 2, - 'c': 'grey', - 'at': self.instance_id}) - - # Return the visualization data - return self.factory.objects_dict + self.factory.add_points(positions=self.sparse_grid.points(), + point_size=2, + c='grey', + at=self.instance_id) """ ENVIRONMENT BEHAVIOR @@ -183,70 +165,61 @@ def send_visualization(self): async def step(self): - # Compute a force sample - if self.compute_sample: - - # Generate a new force - if self.idx_amplitude == 0: - - # Define zones - selected_centers = [] - pts = self.mesh_coarse.points().copy() - for i in range(self.nb_forces): - # Pick a random sphere center, check distance with other spheres - current_point = pts[np.random.randint(0, self.mesh_coarse.N())] - distance_check = True - for p in selected_centers: - distance = np.linalg.norm(current_point - p) - if distance < p_forces.inter_distance_thresh: - distance_check = False - break - # Reset force field value and indices - self.areas[i] = [] - self.g_areas[i] = [] - self.forces[i] = np.array([0, 0, 0]) - # Fill the force field - if distance_check: - # Add center - selected_centers.append(current_point) - # Find node in the sphere - sphere = lambda x, c: sum([pow(x_i - c_i, 2) for x_i, c_i in zip(x, c)]) <= \ - pow(p_forces.inter_distance_thresh / 2, 2) - for j, p in enumerate(pts): - if sphere(p, current_point): - self.areas[i].append(j) - self.g_areas[i] += self.mapping_coarse.cells[j].tolist() - # If the sphere is non-empty, create a force vector - if len(self.areas[i]) > 0: - f = np.random.uniform(low=-1, high=1, size=(3,)) - self.forces[i] = (f / np.linalg.norm(f)) * p_forces.amplitude - self.areas[i] = np.array(self.areas[i]) - self.g_areas[i] = np.unique(self.g_areas[i]) - - # Create input array - F = np.zeros(self.data_size) - self.F = np.zeros((self.mesh_coarse.N(), 3)) - for i, force in enumerate(self.forces): - F[self.grid_correspondences[self.g_areas[i]]] = force * self.amplitudes[self.idx_amplitude] - self.F[self.areas[i]] = force * self.amplitudes[self.idx_amplitude] - - # Update current force amplitude - self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) - - # Load a force sample from Dataset - else: - sleep(0.5) - F = np.zeros(self.data_size) if self.sample_in is None else self.sample_in - self.F = F[self.grid_correspondences] + # Generate a new force + if self.idx_amplitude == 0: + + # Define zones + selected_centers = [] + pts = self.mesh_coarse.points().copy() + for i in range(self.nb_forces): + # Pick a random sphere center, check distance with other spheres + current_point = pts[randint(0, self.mesh_coarse.N())] + distance_check = True + for p in selected_centers: + distance = norm(current_point - p) + if distance < p_forces.inter_distance_thresh: + distance_check = False + break + # Reset force field value and indices + self.areas[i] = [] + self.g_areas[i] = [] + self.forces[i] = array([0, 0, 0]) + # Fill the force field + if distance_check: + # Add center + selected_centers.append(current_point) + # Find node in the sphere + sphere = lambda x, c: sum([pow(x_i - c_i, 2) for x_i, c_i in zip(x, c)]) <= \ + pow(p_forces.inter_distance_thresh / 2, 2) + for j, p in enumerate(pts): + if sphere(p, current_point): + self.areas[i].append(j) + self.g_areas[i] += self.mapping_coarse.cells[j].tolist() + # If the sphere is non-empty, create a force vector + if len(self.areas[i]) > 0: + f = uniform(low=-1, high=1, size=(3,)) + self.forces[i] = (f / norm(f)) * p_forces.amplitude + self.areas[i] = array(self.areas[i]) + self.g_areas[i] = unique(self.g_areas[i]) + + # Create input array + F = zeros(self.data_size) + self.F = zeros((self.mesh_coarse.N(), 3)) + for i, force in enumerate(self.forces): + F[self.grid_correspondences[self.g_areas[i]]] = force * self.amplitudes[self.idx_amplitude] + self.F[self.areas[i]] = force * self.amplitudes[self.idx_amplitude] + + # Update current force amplitude + self.idx_amplitude = (self.idx_amplitude + 1) % len(self.amplitudes) # Set training data - self.set_training_data(input_array=F.copy(), - output_array=np.zeros(self.data_size)) + self.set_training_data(input=F.copy(), + ground_truth=zeros(self.data_size)) def apply_prediction(self, prediction): # Reshape to correspond to sparse grid - U = np.reshape(prediction, self.data_size) + U = reshape(prediction['prediction'], self.data_size) U_sparse = U[self.grid_correspondences] self.update_visual(U_sparse) @@ -258,20 +231,20 @@ def update_visual(self, U): mesh_coarse_position = self.mapping_coarse.apply(updated_position) # Update surface mesh - self.factory.update_object_dict(object_id=0, - new_data_dict={'positions': mesh_position}) + self.factory.update_mesh(object_id=0, + positions=mesh_position) # Update arrows representing force fields - self.factory.update_object_dict(object_id=1, - new_data_dict={'positions': mesh_coarse_position if self.compute_sample else updated_position, - 'vectors': self.F}) + self.factory.update_arrows(object_id=1, + positions=mesh_coarse_position, + vectors=self.F) # Update sparse grid positions - self.factory.update_object_dict(object_id=2, - new_data_dict={'positions': updated_position}) + self.factory.update_points(object_id=2, + positions=updated_position) # Send visualization data to update - self.update_visualisation(visu_dict=self.factory.updated_object_dict) + self.update_visualisation() def close(self): # Shutdown message diff --git a/examples/demos/Liver/UNet/download.py b/examples/demos/Liver/UNet/download.py index 490d6aed..5c354326 100644 --- a/examples/demos/Liver/UNet/download.py +++ b/examples/demos/Liver/UNet/download.py @@ -20,12 +20,11 @@ def __init__(self): 'session': [290], 'network': [302], 'stats': [285], - 'dataset_info': [291], - 'dataset_valid': [292, 296], - 'dataset_train': [295, 286, 297, 299, 294, 287, - 289, 300, 288, 301, 298, 293]} + 'dataset_info': [333], + 'dataset_valid': [336], + 'dataset_train': [335, 334]} if __name__ == '__main__': - LiverDownloader().get_session('valid_data') + LiverDownloader().get_session('all') diff --git a/examples/demos/Liver/UNet/prediction.py b/examples/demos/Liver/UNet/prediction.py index 47770dd2..9d937001 100644 --- a/examples/demos/Liver/UNet/prediction.py +++ b/examples/demos/Liver/UNet/prediction.py @@ -8,16 +8,16 @@ import sys # DeepPhysX related imports -from DeepPhysX.Core.Dataset.BaseDatasetConfig import BaseDatasetConfig +from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction from DeepPhysX.Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig -from DeepPhysX.Core.Visualizer.VedoVisualizer import VedoVisualizer -from DeepPhysX.Core.Pipelines.BaseRunner import BaseRunner +from DeepPhysX.Core.Database.BaseDatabaseConfig import BaseDatabaseConfig +from DeepPhysX.Core.Visualization.VedoVisualizer import VedoVisualizer from DeepPhysX.Torch.UNet.UNetConfig import UNetConfig # Session related imports sys.path.append(os.path.dirname(os.path.abspath(__file__))) from download import LiverDownloader -LiverDownloader().get_session('valid_data') +LiverDownloader().get_session('all') from Environment.Liver import Liver from Environment.parameters import grid_resolution @@ -25,39 +25,32 @@ def launch_runner(): # Environment config - env_config = BaseEnvironmentConfig(environment_class=Liver, - visualizer=VedoVisualizer, - as_tcp_ip_client=False, - param_dict={'compute_sample': True, - 'nb_forces': 3}) + environment_config = BaseEnvironmentConfig(environment_class=Liver, + visualizer=VedoVisualizer, + env_kwargs={'nb_forces': 3}) # UNet config - net_config = UNetConfig(network_name='liver_UNet', - save_each_epoch=True, - input_size=grid_resolution, - nb_dims=3, - nb_input_channels=3, - nb_first_layer_channels=128, - nb_output_channels=3, - nb_steps=3, - two_sublayers=True, - border_mode='same', - skip_merge=False) + network_config = UNetConfig(input_size=grid_resolution, + nb_dims=3, + nb_input_channels=3, + nb_first_layer_channels=128, + nb_output_channels=3, + nb_steps=3, + two_sublayers=True, + border_mode='same', + skip_merge=False) # Dataset config - dataset_config = BaseDatasetConfig(dataset_dir='sessions/liver_dpx', - normalize=True, - use_mode='Validation') + database_config = BaseDatabaseConfig(normalize=True) # Runner - runner = BaseRunner(session_dir='sessions', - session_name='liver_dpx', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=500) + runner = BasePrediction(network_config=network_config, + database_config=database_config, + environment_config=environment_config, + session_dir='sessions', + session_name='liver_dpx', + step_nb=500) runner.execute() - runner.close() if __name__ == '__main__': From f47c6ecb440e7e3e5bd36670aa7a166c780becc2 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 10 Nov 2022 17:25:31 +0100 Subject: [PATCH 45/61] Update Vedo version. --- .../demos/Armadillo/FC/Environment/ArmadilloInteractive.py | 3 ++- examples/demos/Beam/FC/Environment/BeamInteractive.py | 5 +++-- examples/demos/Liver/FC/Environment/LiverInteractive.py | 3 ++- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py b/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py index cc7893fe..ba9e071c 100644 --- a/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py +++ b/examples/demos/Armadillo/FC/Environment/ArmadilloInteractive.py @@ -100,6 +100,7 @@ def create(self): # Create plotter self.plotter = Plotter(title='Interactive Armadillo', N=1, interactive=True, offscreen=False, bg2='lightgray') + self.plotter.render() self.plotter.add(*self.spheres) self.plotter.add(self.mesh) self.plotter.add(Plane(pos=plane_origin, normal=[0, 1, 0], s=(10 * p_model.scale, 10 * p_model.scale), @@ -169,7 +170,7 @@ def mouse_move(self, evt): if not self.interactive_window and self.selected is not None: # Compute input force vector - mouse_3D = self.plotter.computeWorldPosition(evt.picked2d) + mouse_3D = self.plotter.compute_world_position(evt.picked2d) move_3D = (mouse_3D - self.spheres_init[self.selected]) / self.mouse_factor if np.linalg.norm(move_3D) > 2: move_3D = 2 * move_3D / np.linalg.norm(move_3D) diff --git a/examples/demos/Beam/FC/Environment/BeamInteractive.py b/examples/demos/Beam/FC/Environment/BeamInteractive.py index 6950405c..3c98d7d9 100644 --- a/examples/demos/Beam/FC/Environment/BeamInteractive.py +++ b/examples/demos/Beam/FC/Environment/BeamInteractive.py @@ -114,10 +114,11 @@ def create(self): fixed = np.where(mesh_x <= np.min(mesh_x) + 0.05 * (np.max(mesh_x) - np.min(mesh_x))) plane_origin = [np.min(mesh_x), np.mean(self.mesh.points()[:, 1][fixed]), - np.mean(self.mesh.points()[:, 2][fixed])] + np.mean(self.mesh.points()[:, 2][fixed])+10] # Create plotter self.plotter = Plotter(title='Interactive Beam', N=1, interactive=True, offscreen=False, bg2='lightgray') + self.plotter.render() self.plotter.add(*self.spheres) self.plotter.add(self.mesh) self.plotter.add(Plane(pos=plane_origin, normal=[1, 0, 0], s=(20, 20), c='darkred', alpha=0.2)) @@ -186,7 +187,7 @@ def mouse_move(self, evt): if not self.interactive_window and self.selected is not None: # Compute input force vector - mouse_3D = self.plotter.computeWorldPosition(evt.picked2d) + mouse_3D = self.plotter.compute_world_position(evt.picked2d) move_3D = (mouse_3D - self.mesh_init[self.spheres_init[self.selected]]) / self.mouse_factor if np.linalg.norm(move_3D) > 10: move_3D = 10 * move_3D / np.linalg.norm(move_3D) diff --git a/examples/demos/Liver/FC/Environment/LiverInteractive.py b/examples/demos/Liver/FC/Environment/LiverInteractive.py index 4efed83b..81fed433 100644 --- a/examples/demos/Liver/FC/Environment/LiverInteractive.py +++ b/examples/demos/Liver/FC/Environment/LiverInteractive.py @@ -94,6 +94,7 @@ def create(self): # Create plotter self.plotter = Plotter(title='Interactive Armadillo', N=1, interactive=True, offscreen=False, bg2='lightgray') + self.plotter.render() self.plotter.add(*self.spheres) self.plotter.add(box) self.plotter.add(self.mesh) @@ -162,7 +163,7 @@ def mouse_move(self, evt): if not self.interactive_window and self.selected is not None: # Compute input force vector - mouse_3D = self.plotter.computeWorldPosition(evt.picked2d) + mouse_3D = self.plotter.compute_world_position(evt.picked2d) move_3D = (mouse_3D - self.mesh_coarse.points()[self.spheres_init[self.selected]]) / self.mouse_factor if np.linalg.norm(move_3D) > 1.5: move_3D = 1.5 * move_3D / np.linalg.norm(move_3D) From 927b1858ef57031c6fd467572bf61de7ecb0e21f Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 14 Nov 2022 17:55:12 +0100 Subject: [PATCH 46/61] Update doc (1/2). --- README.md | 2 + docs/source/_static/image/about_tree.png | Bin 0 -> 85571 bytes .../_static/image/about_working_session.png | Bin 102935 -> 0 bytes docs/source/_static/image/database.png | Bin 0 -> 92351 bytes docs/source/_static/image/dataset.png | Bin 136354 -> 0 bytes .../_static/image/environment_tcp_ip.png | Bin 0 -> 224014 bytes .../_static/image/environment_tcpip.png | Bin 235474 -> 0 bytes docs/source/_static/image/logo.png | Bin 0 -> 552105 bytes .../_static/image/overview_architecture.png | Bin 136782 -> 0 bytes .../_static/image/overview_components.png | Bin 0 -> 123487 bytes docs/source/component/dataset.rst | 4 +- docs/source/component/environment.rst | 2 +- docs/source/conf.py | 25 ++++++------ docs/source/index.rst | 2 + docs/source/presentation/about.rst | 2 +- docs/source/presentation/install.rst | 36 +++++++++--------- docs/source/presentation/overview.rst | 24 ++++-------- 17 files changed, 47 insertions(+), 50 deletions(-) create mode 100644 docs/source/_static/image/about_tree.png delete mode 100644 docs/source/_static/image/about_working_session.png create mode 100644 docs/source/_static/image/database.png delete mode 100644 docs/source/_static/image/dataset.png create mode 100644 docs/source/_static/image/environment_tcp_ip.png delete mode 100644 docs/source/_static/image/environment_tcpip.png create mode 100644 docs/source/_static/image/logo.png delete mode 100644 docs/source/_static/image/overview_architecture.png create mode 100644 docs/source/_static/image/overview_components.png diff --git a/README.md b/README.md index 196f9ccd..3af98e74 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ ## DeepPhysX +![logo](docs/source/_static/image/logo.png) + ### Interfacing AI with simulation The **DeepPhysX** project provides Python packages allowing users to easily interface their **numerical simulations** diff --git a/docs/source/_static/image/about_tree.png b/docs/source/_static/image/about_tree.png new file mode 100644 index 0000000000000000000000000000000000000000..47caba3ed1a41ee45f3ece83f5f8849c77639a76 GIT binary patch literal 85571 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z+X6$)UzeboMe5vKD=-Pi18c>MkIMHX{ySDRtAK~3m;b|v?z8q!glk6MCfuf1sCHjV KrSPt0;J*RJvd(G% literal 0 HcmV?d00001 diff --git a/docs/source/component/dataset.rst b/docs/source/component/dataset.rst index ea122a3e..ff5f6e9c 100644 --- a/docs/source/component/dataset.rst +++ b/docs/source/component/dataset.rst @@ -14,8 +14,8 @@ prediction data) and can be multiple for each data field in order not to exceed of loaded data. Each partition will have a unique name: ``___.npy``. -.. figure:: ../_static/image/dataset.png - :alt: dataset.png +.. figure:: ../_static/image/database.png + :alt: database.png :align: center :width: 80% diff --git a/docs/source/component/environment.rst b/docs/source/component/environment.rst index 3fd00c30..688d9f77 100644 --- a/docs/source/component/environment.rst +++ b/docs/source/component/environment.rst @@ -285,7 +285,7 @@ Client-Server Architecture The :guilabel:`Core/AsyncSocket` module defines a **Client-Server architecture** where a *TcpIpServer* communicates with several *TcpIpClients* using a **TcpIp protocol**. -.. figure:: ../_static/image/environment_tcpip.png +.. figure:: ../_static/image/environment_tcp_ip.png :alt: environment_tcpip.png :width: 80% :align: center diff --git a/docs/source/conf.py b/docs/source/conf.py index 1de1c421..feee0ceb 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,7 +15,7 @@ # DeepPhysX root root = abspath(join(abspath(__file__), pardir, pardir, pardir, 'src')) -all_modules = ['AsyncSocket', 'Dataset', 'Environment', 'Manager', 'Network', 'Pipelines', 'Visualizer'] +all_modules = ['AsyncSocket', 'Database', 'Environment', 'Manager', 'Network', 'Pipelines', 'Visualization'] # Import all modules sys.path.append(root) @@ -73,14 +73,15 @@ html_static_path = ['_static'] html_css_files = ['theme.css'] -extlinks = {'Caribou': ('https://caribou.readthedocs.io/', None), - 'CaribouI': ('https://caribou.readthedocs.io/en/latest/Building.html#', None), - 'Numpy': ('https://numpy.org/', None), - 'PyTorch': ('https://pytorch.org/', None), - 'SOFA': ('https://www.sofa-framework.org/%s', None), - 'SOFAI': ('https://www.sofa-framework.org/community/doc/getting-started/build/linux/', None), - 'SP3': ('https://sofapython3.readthedocs.io/en/latest/', None), - 'SP3I': ('https://sofapython3.readthedocs.io/en/latest/menu/Compilation.html', None), - 'Tensorboard': ('https://www.tensorflow.org/tensorboard/', None), - 'Vedo': ('https://vedo.embl.es/', None), - 'VedoObject': ('https://vedo.embl.es/autodocs/content/vedo/%s', '%s')} +extlinks = {'Caribou': ('https://caribou.readthedocs.io/%s', '%s'), + 'CaribouI': ('https://caribou.readthedocs.io/en/latest/Building.html#/%s', '%s'), + 'Numpy': ('https://numpy.org/%s', '%s'), + 'PyTorch': ('https://pytorch.org/%s', '%s'), + 'SOFA': ('https://www.sofa-framework.org/%s', '%s'), + 'SOFAI': ('https://www.sofa-framework.org/community/doc/getting-started/build/linux/%s', '%s'), + 'SP3': ('https://sofapython3.readthedocs.io/en/latest/%s', '%s'), + 'SP3I': ('https://sofapython3.readthedocs.io/en/latest/menu/Compilation.html/%s', '%s'), + 'Tensorboard': ('https://www.tensorflow.org/tensorboard/%s', '%s'), + 'Vedo': ('https://vedo.embl.es/%s', '%s'), + 'VedoObject': ('https://vedo.embl.es/autodocs/content/vedo/%s', '%s'), + 'SSD': ('https://github.com/RobinEnjalbert/SimulationSimpleDatabase/%s', '%s')} diff --git a/docs/source/index.rst b/docs/source/index.rst index 2329b202..8429c2b8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -10,6 +10,8 @@ with **learning algorithms**. **DeepPhysX** is mainly designed for :SOFA:`SOFA <>` and :PyTorch:`PyTorch <>` frameworks, but other simulation and AI frameworks can also be used. +The project is closely linked to the :SSD:`SSD <>` external Python library. + Let's get started ----------------- diff --git a/docs/source/presentation/about.rst b/docs/source/presentation/about.rst index 1aca8eb1..105d1bb4 100644 --- a/docs/source/presentation/about.rst +++ b/docs/source/presentation/about.rst @@ -70,5 +70,5 @@ typically contains the following **tree structure**: :width: 80% :align: center - * - .. image:: ../_static/image/about_working_session.png + * - .. image:: ../_static/image/about_tree.png :alt: about_working_session.png diff --git a/docs/source/presentation/install.rst b/docs/source/presentation/install.rst index 6f9a757a..58083f50 100644 --- a/docs/source/presentation/install.rst +++ b/docs/source/presentation/install.rst @@ -20,23 +20,25 @@ Prerequisites .. table:: :widths: 20 20 10 30 - +-----------------------------+-------------------------------+--------------+-------------------------------------+ - | **Package** | **Dependency** | **Type** | **Install** | - +=============================+===============================+==============+=====================================+ - | :guilabel:`DeepPhysX_Core` | :Numpy:`Numpy <>` | **Required** | ``pip install numpy`` | - | +-------------------------------+--------------+-------------------------------------+ - | | :Vedo:`Vedo <>` | **Required** | ``pip install vedo`` | - | +-------------------------------+--------------+-------------------------------------+ - | | :Tensorboard:`Tensorboard <>` | **Required** | ``pip install tensorboard`` | - +-----------------------------+-------------------------------+--------------+-------------------------------------+ - | :guilabel:`DeepPhysX_Sofa` | :SOFA:`SOFA Framework <>` | **Required** | :SOFAI:`Follow instructions <>` | - | +-------------------------------+--------------+-------------------------------------+ - | | :SP3:`SofaPython3 <>` | **Required** | :SP3I:`Follow instructions <>` | - | +-------------------------------+--------------+-------------------------------------+ - | | :Caribou:`Caribou <>` | Optional | :CaribouI:`Follow instructions <>` | - +-----------------------------+-------------------------------+--------------+-------------------------------------+ - | :guilabel:`DeepPhysX_Torch` | :PyTorch:`PyTorch <>` | **Required** | ``pip install torch`` | - +-----------------------------+-------------------------------+--------------+-------------------------------------+ + +-----------------------------+-------------------------------+--------------+------------------------------------------+ + | **Package** | **Dependency** | **Type** | **Install** | + +=============================+===============================+==============+==========================================+ + | :guilabel:`DeepPhysX_Core` | :Numpy:`Numpy <>` | **Required** | ``pip install numpy`` | + | +-------------------------------+--------------+------------------------------------------+ + | | :Vedo:`Vedo <>` | **Required** | ``pip install vedo`` | + | +-------------------------------+--------------+------------------------------------------+ + | | :Tensorboard:`Tensorboard <>` | **Required** | ``pip install tensorboard`` | + | +-------------------------------+--------------+------------------------------------------+ + | | :SSD:`SSD <>` | **Required** | ``pip install SimulationSimpleDatabase`` | + +-----------------------------+-------------------------------+--------------+------------------------------------------+ + | :guilabel:`DeepPhysX_Sofa` | :SOFA:`SOFA Framework <>` | **Required** | :SOFAI:`Follow instructions <>` | + | +-------------------------------+--------------+------------------------------------------+ + | | :SP3:`SofaPython3 <>` | **Required** | :SP3I:`Follow instructions <>` | + | +-------------------------------+--------------+------------------------------------------+ + | | :Caribou:`Caribou <>` | Optional | :CaribouI:`Follow instructions <>` | + +-----------------------------+-------------------------------+--------------+------------------------------------------+ + | :guilabel:`DeepPhysX_Torch` | :PyTorch:`PyTorch <>` | **Required** | ``pip install torch`` | + +-----------------------------+-------------------------------+--------------+------------------------------------------+ .. note:: :guilabel:`DeepPhysX.Sofa` has a dependency to :Caribou:`Caribou <>` to run the demo scripts from diff --git a/docs/source/presentation/overview.rst b/docs/source/presentation/overview.rst index 87a920fe..8d1ac8de 100644 --- a/docs/source/presentation/overview.rst +++ b/docs/source/presentation/overview.rst @@ -31,7 +31,7 @@ This package is named :guilabel:`DeepPhysX.Core`. .. admonition:: Dependencies - NumPy, Tensorboard, Vedo + NumPy, Tensorboard, Vedo, SimulationSimpleDatabase Simulation """""""""" @@ -70,7 +70,7 @@ Users might use one of the provided *Pipelines* for their **data generation**, t These *Pipelines* trigger a **loop** which defines the number of samples to produce, the number of epochs to perform during a training session or the number of steps of prediction. -.. figure:: ../_static/image/overview_architecture.png +.. figure:: ../_static/image/overview_components.png :alt: overview_architecture.png :width: 80% :align: center @@ -81,12 +81,12 @@ The *Pipeline* will involve several components (data producers and data consumer communicate with their *Manager* first. A main *Manager* will provide the *Pipeline* an intermediary with all the existing *Managers*: -:``DatasetManager``: It will manage the *Dataset* component to create **storage** partitions, to fill these partitions - with the synthetic training data produced by the *Environment* and to **reload** an existing *Dataset* for training or +:``DatabaseManager``: It will manage the *Database* component to create **storage** partitions, to fill these partitions + with the synthetic training data produced by the *Environment* and to **reload** an existing *Database* for training or prediction sessions. .. note:: - If training and data generation are done simultaneously (by default for the training *Pipeline*), the *Dataset* + If training and data generation are done simultaneously (by default for the training *Pipeline*), the *Database* can be built only during the first epoch and then reloaded for the remaining epochs. :``EnvironmentManager``: It will manage the *Environment* (the numerical simulation) component to **create** it, to @@ -99,10 +99,10 @@ A main *Manager* will provide the *Pipeline* an intermediary with all the existi :ref:`dedicated section `). .. note:: - The two above *Managers* are managed by the ``DataManager`` since both the *Environment* and the *Dataset* + The two above *Managers* are managed by the ``DataManager`` since both the *Environment* and the *Database* components provide training data to the *Network*. This ``DataManager`` is the one who decides if data should be requested from the *Environment* or from the - *Dataset* depending on the current state of the *Pipeline* and on the components configurations. + *Database* depending on the current state of the *Pipeline* and on the components configurations. :``NetworkManager``: It will manage several objects to **train** or **exploit** your *Network*: @@ -126,16 +126,6 @@ A main *Manager* will provide the *Pipeline* an intermediary with all the existi mean and the variance of this loss value per batch and per epoch), but other custom fields can be added and filled as well. -:``VisualizerManager``: It will manage the *Visualizer* which **initialize** and **update** visualization data. - Then, it updates the **render** of the simulated objects defined in the visualization data. - *Factories* are provided to easily **template** visualization data for a wide variety of objects (meshes, point clouds, - etc.). - - .. note:: - It must be specified in an *Environment* which objects to add in the *Visualizer* and when they must be updated. - In the case where several *Environments* are running in parallel, the rendering windows will be split in several - sub-windows to gather all the renderings. - .. warning:: It is not possible to use the default *Network* and *Environment* provided in the :core:`CORE` package, since they are not implemented at all. From 66fecd43e1abbc6b157e0a4afcc74536478fa975 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 24 Nov 2022 15:03:31 +0100 Subject: [PATCH 47/61] Include examples in installation. --- examples/demos/Armadillo/__init__.py | 0 examples/demos/Beam/__init__.py | 0 examples/demos/Liver/__init__.py | 0 examples/demos/__init__.py | 0 setup.py | 81 +++++++++------------------- 5 files changed, 26 insertions(+), 55 deletions(-) create mode 100644 examples/demos/Armadillo/__init__.py create mode 100644 examples/demos/Beam/__init__.py create mode 100644 examples/demos/Liver/__init__.py create mode 100644 examples/demos/__init__.py diff --git a/examples/demos/Armadillo/__init__.py b/examples/demos/Armadillo/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/demos/Beam/__init__.py b/examples/demos/Beam/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/demos/Liver/__init__.py b/examples/demos/Liver/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/demos/__init__.py b/examples/demos/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/setup.py b/setup.py index 7bc7443a..48d3d403 100644 --- a/setup.py +++ b/setup.py @@ -1,69 +1,40 @@ -# SHARED: packages are installed within the shared namespace 'DeepPhysX'. Local install with pip. -# SINGLE: only Core package is installed with name 'DeepPhysX'. Distant install with pip. - from setuptools import setup, find_packages -from os.path import join, pardir, exists -from json import load, dump +from os.path import join PROJECT = 'DeepPhysX' PACKAGE = 'Core' -PACKAGES = {'Torch': False, - 'Sofa': False} -DEPENDENCIES = {'Core': ['numpy', 'SimulationSimpleDatabase', 'tensorboard', 'tensorboardX', 'pyDataverse'], - 'Sofa': [], - 'Torch': ['torch']} - -# (SHARED) Loading existing configuration file -if exists('config.json'): - with open('config.json') as file: - PACKAGES = load(file) - # Check config validity - correct_config = True - for package_name, do_install in PACKAGES.items(): - if do_install and not exists(join(pardir, package_name)): - PACKAGES[package_name] = False - correct_config = False - # Write correction - if not correct_config: - with open('config.json', 'w') as file: - dump(PACKAGES, file) -# (SINGLE / SHARED) Getting the packages to be installed -roots = [PACKAGE] -for package_name, do_install in PACKAGES.items(): - if do_install: - roots.append(package_name) -packages = [] -packages_dir = {} -requires = [] +packages = [f'{PROJECT}.{PACKAGE}'] +packages_dir = {f'{PROJECT}.{PACKAGE}': 'src'} -# (SINGLE) Specifying package list and corresponding directories -if len(roots) == 1: - packages.append(f'{PROJECT}.{PACKAGE}') - packages_dir[f'{PROJECT}.{PACKAGE}'] = 'src' - requires += DEPENDENCIES[PACKAGE] - for subpackage in find_packages(where='src'): - packages.append(f'{PROJECT}.{PACKAGE}.{subpackage}') - packages_dir[f'{PROJECT}.{PACKAGE}.{subpackage}'] = join('src', *subpackage.split('.')) +# Configure packages list and directories +for subpackage in find_packages(where='src'): + packages.append(f'{PROJECT}.{PACKAGE}.{subpackage}') + packages_dir[f'{PROJECT}.{PACKAGE}.{subpackage}'] = join('src', *subpackage.split('.')) -# (SHARED) Specifying package list and corresponding directories -else: - for package_name in roots: - packages.append(f'{PROJECT}.{package_name}') - packages_dir[f'{PROJECT}.{package_name}'] = join(pardir, package_name, 'src') - requires += DEPENDENCIES[package_name] - for sub_package in find_packages(where=join(pardir, package_name, 'src')): - packages.append(f'{PROJECT}.{package_name}.{sub_package}') - packages_dir[f'{PROJECT}.{package_name}.{sub_package}'] = join(pardir, package_name, 'src', - *sub_package.split('.')) +# Add examples as subpackages +packages.append(f'{PROJECT}.examples.{PACKAGE}') +packages_dir[f'{PROJECT}.examples.{PACKAGE}'] = 'examples' +for example_dir in find_packages(where='examples'): + packages.append(f'{PROJECT}.examples.{PACKAGE}.{example_dir}') -# (SINGLE / SHARED) Extract README.md content +# Extract README.md content with open('README.md') as f: long_description = f.read() -# (SINGLE / SHARED) Installation + +def get_SSD(): + # If SSD was installed in dev mode, pip will re-install it + try: + import SSD + except ModuleNotFoundError: + return ['SimulationSimpleDatabase'] + return [] + + +# Installation setup(name='DeepPhysX', - version='22.06', + version='22.12', description='A Python framework interfacing AI with numerical simulation.', long_description=long_description, long_description_content_type='text/markdown', @@ -72,4 +43,4 @@ url='https://github.com/mimesis-inria/DeepPhysX', packages=packages, package_dir=packages_dir, - install_requires=requires) + install_requires=get_SSD() + ['numpy', 'tensorboard', 'tensorboardX', 'pyDataverse']) From 54085be9a1f3e41a6dc469013fb1869c85b499b7 Mon Sep 17 00:00:00 2001 From: robin Date: Thu, 24 Nov 2022 15:35:07 +0100 Subject: [PATCH 48/61] Installation under namespace DeepPhysX. --- src/Database/__init__.py => __init__.py | 0 setup.py | 11 ++++++----- src/{ => Core}/AsyncSocket/AbstractEnvironment.py | 0 src/{ => Core}/AsyncSocket/BytesConverter.py | 0 src/{ => Core}/AsyncSocket/TcpIpClient.py | 0 src/{ => Core}/AsyncSocket/TcpIpObject.py | 0 src/{ => Core}/AsyncSocket/TcpIpServer.py | 0 src/{ => Core}/AsyncSocket/__init__.py | 0 src/{ => Core}/Database/BaseDatabaseConfig.py | 0 src/{ => Core}/Database/DatabaseHandler.py | 0 src/{Utils/Visualizer => Core/Database}/__init__.py | 0 src/{ => Core}/Environment/BaseEnvironment.py | 0 src/{ => Core}/Environment/BaseEnvironmentConfig.py | 0 src/{ => Core}/Environment/__init__.py | 0 src/{ => Core}/Environment/launcherBaseEnvironment.py | 0 src/{ => Core}/Manager/DataManager.py | 0 src/{ => Core}/Manager/DatabaseManager.py | 0 src/{ => Core}/Manager/EnvironmentManager.py | 0 src/{ => Core}/Manager/NetworkManager.py | 0 src/{ => Core}/Manager/StatsManager.py | 0 src/{ => Core}/Manager/__init__.py | 0 src/{ => Core}/Network/BaseNetwork.py | 0 src/{ => Core}/Network/BaseNetworkConfig.py | 0 src/{ => Core}/Network/BaseOptimization.py | 0 src/{ => Core}/Network/BaseTransformation.py | 0 src/{ => Core}/Network/__init__.py | 0 src/{ => Core}/Pipelines/BaseDataGeneration.py | 0 src/{ => Core}/Pipelines/BasePipeline.py | 0 src/{ => Core}/Pipelines/BasePrediction.py | 0 src/{ => Core}/Pipelines/BaseTraining.py | 0 src/{ => Core}/Pipelines/__init__.py | 0 src/{ => Core}/Utils/Visualizer/GridMapping.py | 0 src/{ => Core}/Utils/Visualizer/SampleVisualizer.py | 0 .../Utils/Visualizer}/__init__.py | 0 .../Utils/Visualizer/barycentric_mapping.py | 0 src/{ => Core}/Utils/__init__.py | 0 src/{ => Core}/Utils/configs.py | 0 src/{ => Core}/Utils/converter.py | 0 src/{ => Core}/Utils/data_downloader.py | 0 src/{ => Core}/Utils/jsonUtils.py | 0 src/{ => Core}/Utils/mathUtils.py | 0 src/{ => Core}/Utils/path.py | 0 src/{ => Core}/Utils/tensor_transform_utils.py | 0 src/{ => Core}/Visualization/VedoFactory.py | 0 src/{ => Core}/Visualization/VedoVisualizer.py | 0 src/Core/Visualization/__init__.py | 0 src/Core/__init__.py | 9 +++++++++ src/__init__.py | 10 +--------- 48 files changed, 16 insertions(+), 14 deletions(-) rename src/Database/__init__.py => __init__.py (100%) rename src/{ => Core}/AsyncSocket/AbstractEnvironment.py (100%) rename src/{ => Core}/AsyncSocket/BytesConverter.py (100%) rename src/{ => Core}/AsyncSocket/TcpIpClient.py (100%) rename src/{ => Core}/AsyncSocket/TcpIpObject.py (100%) rename src/{ => Core}/AsyncSocket/TcpIpServer.py (100%) rename src/{ => Core}/AsyncSocket/__init__.py (100%) rename src/{ => Core}/Database/BaseDatabaseConfig.py (100%) rename src/{ => Core}/Database/DatabaseHandler.py (100%) rename src/{Utils/Visualizer => Core/Database}/__init__.py (100%) rename src/{ => Core}/Environment/BaseEnvironment.py (100%) rename src/{ => Core}/Environment/BaseEnvironmentConfig.py (100%) rename src/{ => Core}/Environment/__init__.py (100%) rename src/{ => Core}/Environment/launcherBaseEnvironment.py (100%) rename src/{ => Core}/Manager/DataManager.py (100%) rename src/{ => Core}/Manager/DatabaseManager.py (100%) rename src/{ => Core}/Manager/EnvironmentManager.py (100%) rename src/{ => Core}/Manager/NetworkManager.py (100%) rename src/{ => Core}/Manager/StatsManager.py (100%) rename src/{ => Core}/Manager/__init__.py (100%) rename src/{ => Core}/Network/BaseNetwork.py (100%) rename src/{ => Core}/Network/BaseNetworkConfig.py (100%) rename src/{ => Core}/Network/BaseOptimization.py (100%) rename src/{ => Core}/Network/BaseTransformation.py (100%) rename src/{ => Core}/Network/__init__.py (100%) rename src/{ => Core}/Pipelines/BaseDataGeneration.py (100%) rename src/{ => Core}/Pipelines/BasePipeline.py (100%) rename src/{ => Core}/Pipelines/BasePrediction.py (100%) rename src/{ => Core}/Pipelines/BaseTraining.py (100%) rename src/{ => Core}/Pipelines/__init__.py (100%) rename src/{ => Core}/Utils/Visualizer/GridMapping.py (100%) rename src/{ => Core}/Utils/Visualizer/SampleVisualizer.py (100%) rename src/{Visualization => Core/Utils/Visualizer}/__init__.py (100%) rename src/{ => Core}/Utils/Visualizer/barycentric_mapping.py (100%) rename src/{ => Core}/Utils/__init__.py (100%) rename src/{ => Core}/Utils/configs.py (100%) rename src/{ => Core}/Utils/converter.py (100%) rename src/{ => Core}/Utils/data_downloader.py (100%) rename src/{ => Core}/Utils/jsonUtils.py (100%) rename src/{ => Core}/Utils/mathUtils.py (100%) rename src/{ => Core}/Utils/path.py (100%) rename src/{ => Core}/Utils/tensor_transform_utils.py (100%) rename src/{ => Core}/Visualization/VedoFactory.py (100%) rename src/{ => Core}/Visualization/VedoVisualizer.py (100%) create mode 100644 src/Core/Visualization/__init__.py create mode 100644 src/Core/__init__.py diff --git a/src/Database/__init__.py b/__init__.py similarity index 100% rename from src/Database/__init__.py rename to __init__.py diff --git a/setup.py b/setup.py index 48d3d403..1cc1b7e7 100644 --- a/setup.py +++ b/setup.py @@ -4,13 +4,13 @@ PROJECT = 'DeepPhysX' PACKAGE = 'Core' -packages = [f'{PROJECT}.{PACKAGE}'] -packages_dir = {f'{PROJECT}.{PACKAGE}': 'src'} +packages = [] +packages_dir = {} # Configure packages list and directories for subpackage in find_packages(where='src'): - packages.append(f'{PROJECT}.{PACKAGE}.{subpackage}') - packages_dir[f'{PROJECT}.{PACKAGE}.{subpackage}'] = join('src', *subpackage.split('.')) + packages.append(f'{PROJECT}.{subpackage}') + packages_dir[f'{PROJECT}.{subpackage}'] = join('src', *subpackage.split('.')) # Add examples as subpackages packages.append(f'{PROJECT}.examples.{PACKAGE}') @@ -33,7 +33,7 @@ def get_SSD(): # Installation -setup(name='DeepPhysX', +setup(name=f'{PROJECT}', version='22.12', description='A Python framework interfacing AI with numerical simulation.', long_description=long_description, @@ -43,4 +43,5 @@ def get_SSD(): url='https://github.com/mimesis-inria/DeepPhysX', packages=packages, package_dir=packages_dir, + namespace_packages=[PROJECT], install_requires=get_SSD() + ['numpy', 'tensorboard', 'tensorboardX', 'pyDataverse']) diff --git a/src/AsyncSocket/AbstractEnvironment.py b/src/Core/AsyncSocket/AbstractEnvironment.py similarity index 100% rename from src/AsyncSocket/AbstractEnvironment.py rename to src/Core/AsyncSocket/AbstractEnvironment.py diff --git a/src/AsyncSocket/BytesConverter.py b/src/Core/AsyncSocket/BytesConverter.py similarity index 100% rename from src/AsyncSocket/BytesConverter.py rename to src/Core/AsyncSocket/BytesConverter.py diff --git a/src/AsyncSocket/TcpIpClient.py b/src/Core/AsyncSocket/TcpIpClient.py similarity index 100% rename from src/AsyncSocket/TcpIpClient.py rename to src/Core/AsyncSocket/TcpIpClient.py diff --git a/src/AsyncSocket/TcpIpObject.py b/src/Core/AsyncSocket/TcpIpObject.py similarity index 100% rename from src/AsyncSocket/TcpIpObject.py rename to src/Core/AsyncSocket/TcpIpObject.py diff --git a/src/AsyncSocket/TcpIpServer.py b/src/Core/AsyncSocket/TcpIpServer.py similarity index 100% rename from src/AsyncSocket/TcpIpServer.py rename to src/Core/AsyncSocket/TcpIpServer.py diff --git a/src/AsyncSocket/__init__.py b/src/Core/AsyncSocket/__init__.py similarity index 100% rename from src/AsyncSocket/__init__.py rename to src/Core/AsyncSocket/__init__.py diff --git a/src/Database/BaseDatabaseConfig.py b/src/Core/Database/BaseDatabaseConfig.py similarity index 100% rename from src/Database/BaseDatabaseConfig.py rename to src/Core/Database/BaseDatabaseConfig.py diff --git a/src/Database/DatabaseHandler.py b/src/Core/Database/DatabaseHandler.py similarity index 100% rename from src/Database/DatabaseHandler.py rename to src/Core/Database/DatabaseHandler.py diff --git a/src/Utils/Visualizer/__init__.py b/src/Core/Database/__init__.py similarity index 100% rename from src/Utils/Visualizer/__init__.py rename to src/Core/Database/__init__.py diff --git a/src/Environment/BaseEnvironment.py b/src/Core/Environment/BaseEnvironment.py similarity index 100% rename from src/Environment/BaseEnvironment.py rename to src/Core/Environment/BaseEnvironment.py diff --git a/src/Environment/BaseEnvironmentConfig.py b/src/Core/Environment/BaseEnvironmentConfig.py similarity index 100% rename from src/Environment/BaseEnvironmentConfig.py rename to src/Core/Environment/BaseEnvironmentConfig.py diff --git a/src/Environment/__init__.py b/src/Core/Environment/__init__.py similarity index 100% rename from src/Environment/__init__.py rename to src/Core/Environment/__init__.py diff --git a/src/Environment/launcherBaseEnvironment.py b/src/Core/Environment/launcherBaseEnvironment.py similarity index 100% rename from src/Environment/launcherBaseEnvironment.py rename to src/Core/Environment/launcherBaseEnvironment.py diff --git a/src/Manager/DataManager.py b/src/Core/Manager/DataManager.py similarity index 100% rename from src/Manager/DataManager.py rename to src/Core/Manager/DataManager.py diff --git a/src/Manager/DatabaseManager.py b/src/Core/Manager/DatabaseManager.py similarity index 100% rename from src/Manager/DatabaseManager.py rename to src/Core/Manager/DatabaseManager.py diff --git a/src/Manager/EnvironmentManager.py b/src/Core/Manager/EnvironmentManager.py similarity index 100% rename from src/Manager/EnvironmentManager.py rename to src/Core/Manager/EnvironmentManager.py diff --git a/src/Manager/NetworkManager.py b/src/Core/Manager/NetworkManager.py similarity index 100% rename from src/Manager/NetworkManager.py rename to src/Core/Manager/NetworkManager.py diff --git a/src/Manager/StatsManager.py b/src/Core/Manager/StatsManager.py similarity index 100% rename from src/Manager/StatsManager.py rename to src/Core/Manager/StatsManager.py diff --git a/src/Manager/__init__.py b/src/Core/Manager/__init__.py similarity index 100% rename from src/Manager/__init__.py rename to src/Core/Manager/__init__.py diff --git a/src/Network/BaseNetwork.py b/src/Core/Network/BaseNetwork.py similarity index 100% rename from src/Network/BaseNetwork.py rename to src/Core/Network/BaseNetwork.py diff --git a/src/Network/BaseNetworkConfig.py b/src/Core/Network/BaseNetworkConfig.py similarity index 100% rename from src/Network/BaseNetworkConfig.py rename to src/Core/Network/BaseNetworkConfig.py diff --git a/src/Network/BaseOptimization.py b/src/Core/Network/BaseOptimization.py similarity index 100% rename from src/Network/BaseOptimization.py rename to src/Core/Network/BaseOptimization.py diff --git a/src/Network/BaseTransformation.py b/src/Core/Network/BaseTransformation.py similarity index 100% rename from src/Network/BaseTransformation.py rename to src/Core/Network/BaseTransformation.py diff --git a/src/Network/__init__.py b/src/Core/Network/__init__.py similarity index 100% rename from src/Network/__init__.py rename to src/Core/Network/__init__.py diff --git a/src/Pipelines/BaseDataGeneration.py b/src/Core/Pipelines/BaseDataGeneration.py similarity index 100% rename from src/Pipelines/BaseDataGeneration.py rename to src/Core/Pipelines/BaseDataGeneration.py diff --git a/src/Pipelines/BasePipeline.py b/src/Core/Pipelines/BasePipeline.py similarity index 100% rename from src/Pipelines/BasePipeline.py rename to src/Core/Pipelines/BasePipeline.py diff --git a/src/Pipelines/BasePrediction.py b/src/Core/Pipelines/BasePrediction.py similarity index 100% rename from src/Pipelines/BasePrediction.py rename to src/Core/Pipelines/BasePrediction.py diff --git a/src/Pipelines/BaseTraining.py b/src/Core/Pipelines/BaseTraining.py similarity index 100% rename from src/Pipelines/BaseTraining.py rename to src/Core/Pipelines/BaseTraining.py diff --git a/src/Pipelines/__init__.py b/src/Core/Pipelines/__init__.py similarity index 100% rename from src/Pipelines/__init__.py rename to src/Core/Pipelines/__init__.py diff --git a/src/Utils/Visualizer/GridMapping.py b/src/Core/Utils/Visualizer/GridMapping.py similarity index 100% rename from src/Utils/Visualizer/GridMapping.py rename to src/Core/Utils/Visualizer/GridMapping.py diff --git a/src/Utils/Visualizer/SampleVisualizer.py b/src/Core/Utils/Visualizer/SampleVisualizer.py similarity index 100% rename from src/Utils/Visualizer/SampleVisualizer.py rename to src/Core/Utils/Visualizer/SampleVisualizer.py diff --git a/src/Visualization/__init__.py b/src/Core/Utils/Visualizer/__init__.py similarity index 100% rename from src/Visualization/__init__.py rename to src/Core/Utils/Visualizer/__init__.py diff --git a/src/Utils/Visualizer/barycentric_mapping.py b/src/Core/Utils/Visualizer/barycentric_mapping.py similarity index 100% rename from src/Utils/Visualizer/barycentric_mapping.py rename to src/Core/Utils/Visualizer/barycentric_mapping.py diff --git a/src/Utils/__init__.py b/src/Core/Utils/__init__.py similarity index 100% rename from src/Utils/__init__.py rename to src/Core/Utils/__init__.py diff --git a/src/Utils/configs.py b/src/Core/Utils/configs.py similarity index 100% rename from src/Utils/configs.py rename to src/Core/Utils/configs.py diff --git a/src/Utils/converter.py b/src/Core/Utils/converter.py similarity index 100% rename from src/Utils/converter.py rename to src/Core/Utils/converter.py diff --git a/src/Utils/data_downloader.py b/src/Core/Utils/data_downloader.py similarity index 100% rename from src/Utils/data_downloader.py rename to src/Core/Utils/data_downloader.py diff --git a/src/Utils/jsonUtils.py b/src/Core/Utils/jsonUtils.py similarity index 100% rename from src/Utils/jsonUtils.py rename to src/Core/Utils/jsonUtils.py diff --git a/src/Utils/mathUtils.py b/src/Core/Utils/mathUtils.py similarity index 100% rename from src/Utils/mathUtils.py rename to src/Core/Utils/mathUtils.py diff --git a/src/Utils/path.py b/src/Core/Utils/path.py similarity index 100% rename from src/Utils/path.py rename to src/Core/Utils/path.py diff --git a/src/Utils/tensor_transform_utils.py b/src/Core/Utils/tensor_transform_utils.py similarity index 100% rename from src/Utils/tensor_transform_utils.py rename to src/Core/Utils/tensor_transform_utils.py diff --git a/src/Visualization/VedoFactory.py b/src/Core/Visualization/VedoFactory.py similarity index 100% rename from src/Visualization/VedoFactory.py rename to src/Core/Visualization/VedoFactory.py diff --git a/src/Visualization/VedoVisualizer.py b/src/Core/Visualization/VedoVisualizer.py similarity index 100% rename from src/Visualization/VedoVisualizer.py rename to src/Core/Visualization/VedoVisualizer.py diff --git a/src/Core/Visualization/__init__.py b/src/Core/Visualization/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/Core/__init__.py b/src/Core/__init__.py new file mode 100644 index 00000000..d431a0bb --- /dev/null +++ b/src/Core/__init__.py @@ -0,0 +1,9 @@ +from os.path import dirname +from os import listdir + +package = dirname(__file__) +exceptions = ['__init__.py', '__pycache__'] +modules = [module for module in listdir(package) if module not in exceptions] +__all__ = [] +for module in sorted(modules): + __all__.append(module) diff --git a/src/__init__.py b/src/__init__.py index d431a0bb..6745185f 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -1,9 +1 @@ -from os.path import dirname -from os import listdir - -package = dirname(__file__) -exceptions = ['__init__.py', '__pycache__'] -modules = [module for module in listdir(package) if module not in exceptions] -__all__ = [] -for module in sorted(modules): - __all__.append(module) +__import__('pkg_resources').declare_namespace('DeepPhysX') From 3d0c097095714660343f054a091a1b399c7844e0 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 25 Nov 2022 15:25:13 +0100 Subject: [PATCH 49/61] Update user and dev installation processes. --- config.py | 86 ------------------------------- dev.py | 52 ------------------- setup_dev.py | 137 ++++++++++++++++++++++++++++++++++++++++++++++++++ setup_user.py | 25 +++++++++ 4 files changed, 162 insertions(+), 138 deletions(-) delete mode 100644 config.py delete mode 100644 dev.py create mode 100644 setup_dev.py create mode 100644 setup_user.py diff --git a/config.py b/config.py deleted file mode 100644 index 294405f7..00000000 --- a/config.py +++ /dev/null @@ -1,86 +0,0 @@ -from os import chdir, pardir, system, sep, rename, listdir, getcwd, mkdir -from os.path import exists, abspath, join -from json import dump -from shutil import move - -PROJECT = 'DeepPhysX' -PACKAGES = {'Torch': False, - 'Sofa': False} -AVAILABLE = {'AI': ['Torch'], - 'Simulation': ['Sofa']} -GIT = {'Torch': 'https://github.com/mimesis-inria/DeepPhysX.Torch.git', - 'Sofa': 'https://github.com/mimesis-inria/DeepPhysX.Sofa.git'} -ANSWERS = ['y', 'yes', 'n', 'no'] - - -def check_repositories(): - - # Check current repository - path = abspath(join(__file__, pardir, pardir)) - repository = abspath(join(__file__, pardir)).split(sep)[-1] - chdir(join(path, repository)) - size = 2 - if repository != 'Core': - print(f"WARNING: Wrong repository, moving '{repository}' --> '{join(PROJECT, 'Core')}'") - chdir(pardir) - rename(repository, 'Core') - mkdir(PROJECT) - move(src=join(path, f'Core{sep}'), - dst=join(path, PROJECT)) - size += 1 - path = join(path, PROJECT) - chdir(join(path, 'Core')) - - # Check other repositories - for i in range(size): - for repository in listdir(getcwd()): - if 'DeepPhysX.' in repository: - if repository[10:] in PACKAGES.keys(): - print(f"WARNING: Wrong repository, moving '{repository}' --> '{join(PROJECT, repository[10:])}'") - rename(repository, repository[10:]) - if not exists(join(path, f'{repository[10:]}{sep}')): - move(src=join(getcwd(), f'{repository[10:]}{sep}'), - dst=path) - chdir(pardir) - chdir(join(path, 'Core')) - - -if __name__ == '__main__': - - # Check repositories names - check_repositories() - - # Get user entry for each package - for package_type, package_names in AVAILABLE.items(): - print(f"\nAvailable {package_type} packages : {package_names}") - for package_name in package_names: - while (do_install := input(f" >> Installing package {package_name} (y/n): ").lower()) not in ANSWERS: - pass - PACKAGES[package_name] = do_install in ANSWERS[:2] - - # Ask user confirmation - print("\nApplying following configuration: \n * DeepPhysX.Core: True (default)") - for package_name, do_install in PACKAGES.items(): - print(f" * DeepPhysX.{package_name}: {do_install}") - while (do_validate := input("Confirm (y/n): ")) not in ANSWERS: - pass - if do_validate in ANSWERS[2:]: - print("Aborting") - quit() - - # Save config - with open('config.json', 'w') as file: - dump(PACKAGES, file) - print("Configuration saved in 'config.json'") - - # Clone missing packages - chdir(pardir) - for package_name, do_install in PACKAGES.items(): - if do_install and not exists(package_name): - print(f"\nPackage {package_name} not found, cloning from {GIT[package_name]}") - system(f'git clone {GIT[package_name]} {package_name}') - - # End config - print("\nConfiguration done, install DeepPhysX with the following commands:" - "\n - 'pip install .' for user mode" - "\n - 'python3 dev.py set' for developer mode") diff --git a/dev.py b/dev.py deleted file mode 100644 index 4b59b5f3..00000000 --- a/dev.py +++ /dev/null @@ -1,52 +0,0 @@ -from os import listdir, symlink, unlink, mkdir -from os.path import join, islink, abspath, pardir, isdir -from pathlib import Path -from sys import argv -from site import USER_SITE -from shutil import rmtree - -from config import check_repositories - - -# Check user entry -if len(argv) != 2 or argv[1] not in ['set', 'del']: - print("\nInvalid script option." - "\nRun 'python3 dev.py set' to link DPX to your site package." - "\nRun 'python3 dev.py del' to remove DPX links in your site package.") - quit() - -# Check repositories names -check_repositories() - -# Init DeepPhysX packages and dependencies to install -PROJECT = 'DeepPhysX' -packages = ['Core'] -available = ['Torch', 'Sofa'] -root = abspath(join(Path(__file__).parent.absolute(), pardir)) - -# Option 1: create the symbolic links -if argv[1] == 'set': - - # Create main repository in site-packages - if not isdir(join(USER_SITE, PROJECT)): - mkdir(join(USER_SITE, PROJECT)) - - # Link to every existing packages - for package_name in listdir(root): - if package_name in available: - packages.append(package_name) - - # Create symbolic links in site-packages - for package_name in packages: - if not islink(join(USER_SITE, PROJECT, package_name)): - symlink(src=join(root, package_name, 'src'), dst=join(USER_SITE, PROJECT, package_name)) - print(f"Linked {join(USER_SITE, PROJECT, package_name)} -> {join(root, package_name, 'src')}") - -# Option 2: remove the symbolic links -else: - - if isdir(join(USER_SITE, PROJECT)): - for package_name in listdir(join(USER_SITE, PROJECT)): - unlink(join(USER_SITE, PROJECT, package_name)) - print(f"Unlinked {join(USER_SITE, PROJECT, package_name)} -> {join(root, package_name, 'src')}") - rmtree(join(USER_SITE, PROJECT)) diff --git a/setup_dev.py b/setup_dev.py new file mode 100644 index 00000000..21996c1e --- /dev/null +++ b/setup_dev.py @@ -0,0 +1,137 @@ +from os import sep, chdir, mkdir, listdir, getcwd, rename, symlink, unlink, remove +from os.path import dirname, join, isfile, exists, isdir, islink +from shutil import move, rmtree, which +from site import USER_SITE +from subprocess import run +from sys import argv + +PROJECT = 'DeepPhysX' +AI_PACKAGES = ['Torch'] +SIMU_PACKAGES = ['Sofa'] +PACKAGES = ['Core'] + AI_PACKAGES + SIMU_PACKAGES +GIT = {'Torch': 'https://github.com/mimesis-inria/DeepPhysX.Torch.git', + 'Sofa': 'https://github.com/mimesis-inria/DeepPhysX.Sofa.git'} + + +def check_repositories(): + + dpx_path = dirname(dirname(__file__)) + + # Check the DeepPhysX root + if dpx_path.split(sep)[-1] != PROJECT: + # Create the right root + chdir(dpx_path) + if dirname(__file__).split(sep)[-1] == PROJECT: + rename(PROJECT, f'{PROJECT}.Core') + mkdir(PROJECT) + # Move DeepPhysX packages in the root + for repository in listdir(getcwd()): + if is_dpx_package(repository): + move(src=join(dpx_path, repository), + dst=join(dpx_path, PROJECT)) + dpx_path = join(dpx_path, PROJECT) + + # Check the packages repositories + for repository in listdir(dpx_path): + # Core package + if repository == PROJECT: + rename(repository, 'Core') + # Layers + if 'DeepPhysX.' in repository and repository[10:] in PACKAGES: + rename(src=join(dpx_path, repository), + dst=join(dpx_path, repository[10:])) + + return dpx_path + + +def is_dpx_package(repository): + + for key in ['DeepPhysX', 'DeepPhysX.'] + PACKAGES: + if key in repository and isfile(join(repository, 'README.md')): + with open(join(repository, 'README.md')) as f: + if PROJECT in f.readline(): + return True + return False + + +def define_config(root_dir): + + # Get the user configuration + config = ['Core'] + answers = ['y', 'yes', 'n', 'no'] + for package_list, package_type in zip([SIMU_PACKAGES, AI_PACKAGES], ['SIMULATION', 'AI']): + print(f"\nAvailable {package_type} packages: {[f'{PROJECT}.{pkg}' for pkg in package_list]}") + for pkg in package_list: + while (user := input(f" >> Install package {f'{PROJECT}.{pkg}'} (y/n): ").lower()) not in answers: + pass + if user in answers[:2]: + config.append(pkg) + print(f"\nThe following packages will be installed : {[f'{PROJECT}.{package}' for package in config]}") + while (user := input("Confirm (y/n): ").lower()) not in answers: + pass + if user in answers[2:]: + quit(print("Aborting.")) + + # Clone the missing packages + chdir(root_dir) + if len(config) > 1: + for pkg in config[1:]: + if not exists(pkg): + print(f"\nPackage {f'{PROJECT}.{pkg}'} not found, cloning from {GIT[pkg]}...") + run(['git', 'clone', f'{GIT[pkg]}', f'{pkg}'], cwd=root_dir) + + return config + + +if __name__ == '__main__': + + # Check the project tree + root = check_repositories() + + # Check user entry + if len(argv) == 2 and argv[1] not in ['set', 'del']: + quit(print(f"\nInvalid script option." + f"\nRun 'python3 setup_dev.py set to link {PROJECT} to your site-packages folder." + f"\nRun 'python3 setup_dev.py del to remove {PROJECT} link from your site-packages folder.")) + + # Option 1: create the symbolic links + if len(argv) == 1 or argv[1] == 'set': + + # Get the user configuration + packages = define_config(root) + + # Create the main repository in site-packages + if not isdir(join(USER_SITE, PROJECT)): + mkdir(join(USER_SITE, PROJECT)) + + # Create symbolic links in site-packages + for package in packages: + if not islink(join(USER_SITE, PROJECT, package)): + symlink(src=join(root, package, 'src', package), + dst=join(USER_SITE, PROJECT, package)) + print(f"\nLinked {join(USER_SITE, PROJECT, package)} -> {join(root, package)}") + + # Add examples and the CLI script + # TODO + + # Create the CLI + # TODO + + # Option 2: remove the symbolic links + else: + + # Remove everything from site-packages + if isdir(join(USER_SITE, PROJECT)): + for package in listdir(join(USER_SITE, PROJECT)): + if islink(join(USER_SITE, PROJECT, package)): + unlink(join(USER_SITE, PROJECT, package)) + print(f"Unlinked {join(USER_SITE, PROJECT, package)} -> {join(root, package)}") + elif isdir(join(USER_SITE, PROJECT, package)): + rmtree(join(USER_SITE, PROJECT, package)) + elif isfile(join(USER_SITE, PROJECT, package)): + remove(join(USER_SITE, PROJECT, package)) + rmtree(join(USER_SITE, PROJECT)) + + # Remove the CLI + if isfile(which('DPX')): + remove(which('DPX')) diff --git a/setup_user.py b/setup_user.py new file mode 100644 index 00000000..bc7cd348 --- /dev/null +++ b/setup_user.py @@ -0,0 +1,25 @@ +from os.path import join +from subprocess import run + +from setup_dev import check_repositories, define_config + +PROJECT = 'DeepPhysX' +AI_PACKAGES = ['Torch'] +SIMU_PACKAGES = ['Sofa'] +PACKAGES = ['Core'] + AI_PACKAGES + SIMU_PACKAGES +GIT = {'Torch': 'https://github.com/mimesis-inria/DeepPhysX.Torch.git', + 'Sofa': 'https://github.com/mimesis-inria/DeepPhysX.Sofa.git'} + + +if __name__ == '__main__': + + # Check the project tree + root = check_repositories() + + # Get the user configuration + config = define_config(root) + + # Pip install packages + for package in config: + print(f"\nInstalling {f'{PROJECT}.{package}'} package...") + run(['pip', 'install', '.'], cwd=join(root, package)) From 7c41e7d00c606324d315cd2e0c7f8f99dfa18840 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 25 Nov 2022 16:12:46 +0100 Subject: [PATCH 50/61] Add a CLI to run the demos. --- setup.py | 7 ++- setup_dev.py | 19 +++++- src/cli.py | 161 +++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 182 insertions(+), 5 deletions(-) create mode 100644 src/cli.py diff --git a/setup.py b/setup.py index 1cc1b7e7..7a4c1bc5 100644 --- a/setup.py +++ b/setup.py @@ -4,8 +4,8 @@ PROJECT = 'DeepPhysX' PACKAGE = 'Core' -packages = [] -packages_dir = {} +packages = [f'{PROJECT}'] +packages_dir = {f'{PROJECT}': 'src'} # Configure packages list and directories for subpackage in find_packages(where='src'): @@ -44,4 +44,5 @@ def get_SSD(): packages=packages, package_dir=packages_dir, namespace_packages=[PROJECT], - install_requires=get_SSD() + ['numpy', 'tensorboard', 'tensorboardX', 'pyDataverse']) + install_requires=get_SSD() + ['numpy', 'tensorboard', 'tensorboardX', 'pyDataverse'], + entry_points={'console_scripts': ['DPX=DeepPhysX.cli:execute_cli']}) diff --git a/setup_dev.py b/setup_dev.py index 21996c1e..948f487c 100644 --- a/setup_dev.py +++ b/setup_dev.py @@ -1,10 +1,13 @@ from os import sep, chdir, mkdir, listdir, getcwd, rename, symlink, unlink, remove from os.path import dirname, join, isfile, exists, isdir, islink +from pathlib import Path from shutil import move, rmtree, which from site import USER_SITE from subprocess import run from sys import argv +from pip._internal.operations.install.wheel import PipScriptMaker + PROJECT = 'DeepPhysX' AI_PACKAGES = ['Torch'] SIMU_PACKAGES = ['Sofa'] @@ -112,10 +115,22 @@ def define_config(root_dir): print(f"\nLinked {join(USER_SITE, PROJECT, package)} -> {join(root, package)}") # Add examples and the CLI script - # TODO + if not isdir(join(USER_SITE, PROJECT, 'examples')): + symlink(src=join(Path(__file__).parent.absolute(), 'examples'), + dst=join(USER_SITE, PROJECT, 'examples')) + print(f"\nLinked {join(USER_SITE, PROJECT, 'examples')} -> {join(Path(__file__).parent.absolute(), 'examples')}") + if not isfile(join(USER_SITE, PROJECT, 'cli.py')): + symlink(src=join(Path(__file__).parent.absolute(), 'src', 'cli.py'), + dst=join(USER_SITE, PROJECT, 'cli.py')) # Create the CLI - # TODO + if which('DPX') is None: + # Generate the scripts + maker = PipScriptMaker(None, dirname(which('vedo'))) + generated_scripts = maker.make_multiple(['DPX = DeepPhysX.cli:execute_cli']) + for script in generated_scripts: + if script.split(sep)[-1].split('.')[0] != 'DPX': + remove(script) # Option 2: remove the symbolic links else: diff --git a/src/cli.py b/src/cli.py new file mode 100644 index 00000000..6ef4f533 --- /dev/null +++ b/src/cli.py @@ -0,0 +1,161 @@ +from argparse import ArgumentParser +from json import load +from os import listdir, getcwd, readlink, chdir +from os.path import islink, dirname, join, abspath, isdir +from platform import system +from shutil import copytree, rmtree +from subprocess import run +from sys import executable + + +def is_pip_installed(): + + import DeepPhysX.Core + return not islink(DeepPhysX.Core.__path__[0]) + + +def get_sources(): + + import DeepPhysX + site_packages = dirname(DeepPhysX.__path__[0]) + metadata_repo = [f for f in listdir(site_packages) if 'DeepPhysX' in f and + ('.dist-info' in f or '.egg-info' in f)] + if len(metadata_repo) == 0: + quit(print("The project does not seem to be properly installed. Try to re-install using 'pip'.")) + elif len(metadata_repo) > 1: + quit(print("There might be several version of the project, try to clean your site-packages.")) + metadata_repo = metadata_repo.pop(0) + if 'direct_url.json' not in listdir(join(site_packages, metadata_repo)): + return None + with open(join(site_packages, metadata_repo, 'direct_url.json'), 'r') as file: + direct_url = load(file) + if system() == 'Linux': + return abspath(direct_url['url'].split('//')[1]) + elif system() == 'Windows': + return abspath(direct_url['url'].split('///')[1]) + else: + return abspath(direct_url['url'].split('///')[1]) + + +def copy_examples_dir(): + + user = input(f"WARNING: The project was installed with pip, examples must be run in a new repository to avoid " + f"writing data in your installation of SSD. Allow the creation of this new repository " + f"'{join(getcwd(), 'DPX_examples')}' to run examples (use 'DPX --clean' to cleanly" + f"remove it afterward) (y/n):") + if user.lower() not in ['y', 'yes']: + quit(print("Aborting.")) + + import DeepPhysX.examples + copytree(src=DeepPhysX.examples.__path__[0], + dst=join(getcwd(), 'DPX_examples')) + + +def clean_examples_dir(): + + if not isdir(examples_dir := join(getcwd(), 'DPX_examples')): + quit(print(f"The directory '{examples_dir}' does not exists.")) + user = input(f"Do you want to remove the repository '{examples_dir}' (y/n):") + if user.lower() not in ['y', 'yes']: + quit(print("Aborting.")) + rmtree(examples_dir) + + +def print_available_examples(examples): + + example_names = sorted(list(examples.keys())) + example_per_repo = {} + for example_name in example_names: + if type(examples[example_name]) == str: + root, repo = examples[example_name].split('.')[0], examples[example_name].split('.')[1] + else: + root, repo = examples[example_name][0].split('.')[0], examples[example_name][0].split('.')[1] + repo = 'rendering' if repo == 'rendering-offscreen' else repo + if root not in example_per_repo: + example_per_repo[root] = {} + if repo not in example_per_repo[root]: + example_per_repo[root][repo] = [] + example_per_repo[root][repo].append(example_name) + + description = '\navailable examples:' + for repo, sub_repos in example_per_repo.items(): + for sub_repo, names in sub_repos.items(): + description += f'\n {repo}.{sub_repo}: {names}' + print(description) + + +def execute_cli(): + + description = "Command Line Interface dedicated to DPX examples." + parser = ArgumentParser(prog='SSD', description=description) + parser.add_argument('-c', '--clean', help='clean the example repository.', action='store_true') + parser.add_argument('-g', '--get', help='get the full example repository locally.', action='store_true') + parser.add_argument('-r', '--run', type=str, help='run one of the demo sessions.', metavar='') + args = parser.parse_args() + + # Get a copy of the example repository if pip installed from PyPi.org + if args.get: + # Installed with setup_dev.py + if not is_pip_installed(): + quit(print("The project was installed from sources in dev mode, examples will then be run in " + "'DeepPhysX..examples'.")) + # Installed with pip from sources + if (source_dir := get_sources()) is not None: + quit(print(f"The project was installed with pip from sources, examples will then be run in " + f"'{join(source_dir, 'examples')}'.")) + # Installed with pip from PyPi + copy_examples_dir() + return + + # Clean the examples repository if pip installed from PyPi.org + elif args.clean: + # Installed with setup_dev.py + if not is_pip_installed(): + quit(print("The project was installed from sources in dev mode, you cannot clean " + "'DPX..examples'.")) + # Installed with pip from sources + if (source_dir := get_sources()) is not None: + quit(print(f"The project was installed with pip from sources, you cannot clean " + f"'{join(source_dir, 'examples')}'.")) + # Installed with pip from PyPi + clean_examples_dir() + return + + examples = {'armadillo': 'Core/demos/Armadillo/FC/interactive.py', + 'beam': 'Core/demos/Beam/FC/interactive.py', + 'liver': 'Core/demos/Liver/FC/interactive.py'} + + # Run a demo script + if (example := args.run) is not None: + # Check the example name + if example.lower() not in examples.keys(): + print(f"Unknown demo '{example}'.") + quit(print_available_examples(examples)) + # Get the example directory + if not is_pip_installed(): + import DeepPhysX.Core + source_dir = readlink(DeepPhysX.Core.__path__[0]) + examples_dir = join(dirname(dirname(source_dir)), 'examples') + elif (source_dir := get_sources()) is not None: + examples_dir = join(source_dir, 'examples') + else: + if not isdir(join(getcwd(), 'DPX_examples')): + print(f"The directory '{join(getcwd(), 'DPX_examples')}' does not exists.") + copy_examples_dir() + examples_dir = join(getcwd(), 'DPX_examples') + # Run the example + repo = join(*examples[example].split('/')[:-1]) if is_pip_installed() else join(*examples[example].split('/')[1:-1]) + script = examples[example].split('/')[-1] + chdir(join(examples_dir, repo)) + run([f'{executable}', f'{script}'], cwd=join(examples_dir, repo)) + + return + + # No command + else: + parser.print_help() + print_available_examples(examples) + + +if __name__ == '__main__': + execute_cli() From 8a2ea7008813172dc7d0d8b1b465d377b5d1e256 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 25 Nov 2022 16:42:41 +0100 Subject: [PATCH 51/61] Update installation instructions. --- README.md | 26 +++--------- demo.py | 18 -------- docs/source/conf.py | 2 +- docs/source/presentation/install.rst | 63 +++++++++++++--------------- 4 files changed, 36 insertions(+), 73 deletions(-) delete mode 100644 demo.py diff --git a/README.md b/README.md index 3af98e74..4d564c2e 100644 --- a/README.md +++ b/README.md @@ -40,34 +40,18 @@ $ pip install DeepPhysX.Sofa # Install simulation package $ pip install DeepPhysX.Torch # Install AI package ``` -If cloning sources, create a `DeepPhysX` repository to store every package. -Packages must be cloned in a directory with the corresponding name as shown below: - -``` bash -$ mkdir DeepPhysX -$ cd DeepPhysX -$ git clone https://github.com/mimesis-inria/DeepPhysX.git Core # Clone default package -$ git clone https://github.com/mimesis-inria/DeepPhysX.Sofa.git Sofa # Clone simulation package -$ git clone https://github.com/mimesis-inria/DeepPhysX.Torch.git Torch # Clone AI package -$ ls -Core Sofa Torch -``` - ### Demos **DeepPhysX** includes a set of detailed tutorials, examples and demos. -Following this installation process to directly try the **interactive demos**: +As these scripts are producing data, they cannot be run in the python site-packages, thus they should be run locally. +Use the *command line interface* to get the examples or to run **interactive demos**: ``` bash -$ mkdir DeepPhysX -$ cd DeepPhysX -$ git clone https://github.com/mimesis/deepphysx.git Core # Make shure to clone this repository in 'DeepPhysX/Core' -$ cd Core -$ python3 config.py # Answer 'yes' to install Torch package to launch examples -$ pip install . +$ DPX --get # Get the full example repository locally +$ DPX --run # Run one of the demo scripts ``` -| **Armadillo**
    `python3 demo.py armadillo` | **Beam**
    `python3 demo.py beam` | **Liver**
    `python3 demo.py liver` | +| **Armadillo**
    `DPX -r armadillo` | **Beam**
    `DPX -r beam` | **Liver**
    `DPX -r liver` | |:-----------------------------------------------------:|:-------------------------------------------:|:---------------------------------------------:| | ![armadillo](docs/source/_static/image/armadillo.png) | ![beam](docs/source/_static/image/beam.png) | ![liver](docs/source/_static/image/liver.png) | diff --git a/demo.py b/demo.py deleted file mode 100644 index aec206f2..00000000 --- a/demo.py +++ /dev/null @@ -1,18 +0,0 @@ -import os -import sys -import argparse - - -parser = argparse.ArgumentParser(prog='demo', description='Specify the name of the demo.') -parser.add_argument('Demo', metavar='demo', type=str, help='Name of the demo to run') -args = parser.parse_args() - -demo = args.Demo -demos = ['armadillo', 'beam', 'liver'] - -if demo not in demos: - raise ValueError(f"Unknown demo '{demo}', available are: {demos}") - -repo = os.path.join(os.path.dirname(__file__), 'examples', 'demos', demo[0].upper() + demo[1:].lower(), 'FC') -os.chdir(repo) -os.system(f'{sys.executable} interactive.py') diff --git a/docs/source/conf.py b/docs/source/conf.py index feee0ceb..7f35763d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,7 +14,7 @@ import sys # DeepPhysX root -root = abspath(join(abspath(__file__), pardir, pardir, pardir, 'src')) +root = abspath(join(abspath(__file__), pardir, pardir, pardir, 'src', 'Core')) all_modules = ['AsyncSocket', 'Database', 'Environment', 'Manager', 'Network', 'Pipelines', 'Visualization'] # Import all modules diff --git a/docs/source/presentation/install.rst b/docs/source/presentation/install.rst index 58083f50..516f7183 100644 --- a/docs/source/presentation/install.rst +++ b/docs/source/presentation/install.rst @@ -55,17 +55,17 @@ They can easily be installed with ``pip``: .. code-block:: bash - pip3 install DeepPhysX - pip3 install DeepPhysX.Sofa - pip3 install DeepPhysX.Torch + $ pip3 install DeepPhysX + $ pip3 install DeepPhysX.Sofa + $ pip3 install DeepPhysX.Torch Then, you should be able to run: .. code-block:: bash - pip3 show DeepPhysX - pip3 show DeepPhysX.Sofa - pip3 show DeepPhysX.Torch + $ pip3 show DeepPhysX + $ pip3 show DeepPhysX.Sofa + $ pip3 show DeepPhysX.Torch .. code-block:: python @@ -94,51 +94,48 @@ Start by cloning the **DeepPhysX** source code from its Github repository in a d $ mkdir DeepPhysX $ cd DeepPhysX $ git clone https://github.com/mimesis-inria/DeepPhysX.git Core - $ cd Core -Specify which packages to install by running the configuration script. -This way, all the packages are gathered in a single installation. +Then, you can add compatibility layers to your **DeepPhysX** environment and install packages: -.. code-block:: bash +* **Option 1 (recommended):** run one of the ``setup_.py`` scripts that handle the installation of all packages - $ python3 config.py - > Available AI packages : ['Torch'] - > >> Installing package Torch (y/n): yes - > - > Available Simulation packages : ['Sofa'] - > >> Installing package Sofa (y/n): yes - > - > Applying following configuration: - > * DeepPhysX.Core: True (default) - > * DeepPhysX.Torch: True - > * DeepPhysX.Sofa: True - > Confirm (y/n): yes - > Configuration saved in 'config.json' + * Use ``setup_user.py`` to install and manage packages with ``pip`` as non-editable. + .. code-block:: bash -.. note:: - Configuration script will **automatically clone** missing packages. + $ python3 setup_user.py -Finally, install the defined packages: + * Use ``setup_dev.py`` to link packages in the site-packages. -* by using ``pip`` to install and manage them as non-editable + .. code-block:: bash - .. code-block:: bash + $ python3 setup_dev.py set - $ pip3 install . + .. note:: + Both scripts will asks the packages to install and will **automatically clone** missing packages. -* by running ``dev.py`` to link them as editable in the site-packages +* **Option 2:** clone the corresponding Github repositories in the created ``DeepPhysX`` directory, then install + packages manually. .. code-block:: bash - $ python3 dev.py set + # Clone compatibility layers + $ git clone https://github.com/mimesis-inria/DeepPhysX.Sofa.git Sofa + $ git clone https://github.com/mimesis-inria/DeepPhysX.Torch.git Torch -Then, you should be able to run: + # Install packages manually + $ cd Core ; pip3 install . + $ cd ../Sofa ; pip3 install . + $ cd ../Torch ; pip3 install . + +Finally, you should be able to run: .. code-block:: bash # If installed with pip - $ pip show DeepPhysX + $ pip3 show DeepPhysX + $ pip3 show DeepPhysX.Sofa + $ pip3 show DeepPhysX.Torch .. code-block:: python From 419d93e6ffe45535b3aa784c4186206560a0ed3f Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 25 Nov 2022 17:11:19 +0100 Subject: [PATCH 52/61] Specify dependencies versions. --- setup.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 7a4c1bc5..c481050e 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ def get_SSD(): try: import SSD except ModuleNotFoundError: - return ['SimulationSimpleDatabase'] + return ['SimulationSimpleDatabase >= 22.12'] return [] @@ -44,5 +44,8 @@ def get_SSD(): packages=packages, package_dir=packages_dir, namespace_packages=[PROJECT], - install_requires=get_SSD() + ['numpy', 'tensorboard', 'tensorboardX', 'pyDataverse'], + install_requires=get_SSD() + ['numpy >1.23.5', + 'tensorboard >= 2.10.0', + 'tensorboardX >= 2.5.1', + 'pyDataverse >= 0.3.1'], entry_points={'console_scripts': ['DPX=DeepPhysX.cli:execute_cli']}) From fd1869c81588085be56c861f11007b336d12c499 Mon Sep 17 00:00:00 2001 From: robin Date: Fri, 25 Nov 2022 17:44:50 +0100 Subject: [PATCH 53/61] Remove tensorboardX dependency and update torch version. --- requirements.txt | 1 - setup.py | 3 ++- src/Core/Manager/StatsManager.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 859b901f..8b68c135 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ numpy SimulationSimpleDatabase tensorboard -tensorboardX pyDataverse torch \ No newline at end of file diff --git a/setup.py b/setup.py index c481050e..41dba749 100644 --- a/setup.py +++ b/setup.py @@ -47,5 +47,6 @@ def get_SSD(): install_requires=get_SSD() + ['numpy >1.23.5', 'tensorboard >= 2.10.0', 'tensorboardX >= 2.5.1', - 'pyDataverse >= 0.3.1'], + 'pyDataverse >= 0.3.1' + 'torch >= 1.13.0'], entry_points={'console_scripts': ['DPX=DeepPhysX.cli:execute_cli']}) diff --git a/src/Core/Manager/StatsManager.py b/src/Core/Manager/StatsManager.py index 4ee80d3b..060c986f 100644 --- a/src/Core/Manager/StatsManager.py +++ b/src/Core/Manager/StatsManager.py @@ -1,5 +1,5 @@ from typing import Dict, Any, Iterable, Optional -from tensorboardX import SummaryWriter +from torch.utils.tensorboard import SummaryWriter from tensorboard import program from webbrowser import open as w_open from numpy import full, inf, array, ndarray, append, concatenate From 4173c018ecd89f2159bda1d3e35e98a41e06ea0d Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 28 Nov 2022 14:32:48 +0100 Subject: [PATCH 54/61] Center plane in beam demo. --- examples/demos/Beam/FC/Environment/BeamInteractive.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/examples/demos/Beam/FC/Environment/BeamInteractive.py b/examples/demos/Beam/FC/Environment/BeamInteractive.py index 3c98d7d9..14f9a069 100644 --- a/examples/demos/Beam/FC/Environment/BeamInteractive.py +++ b/examples/demos/Beam/FC/Environment/BeamInteractive.py @@ -65,6 +65,7 @@ def create(self): # Load the meshes and the sparse grid self.mesh = Mesh(p_model.grid, c='o').lineWidth(0.1).lighting('ambient') + self.mesh.shift(0, -7.5, -7.5) self.mesh_init = self.mesh.clone().points() # Get the surface points @@ -109,19 +110,12 @@ def create(self): (pts[:, other[1]] >= o1_min) & (pts[:, other[1]] <= o1_max)) self.areas.append(np.array(list(set(zone[0].tolist()).intersection(set(list(self.surface)))))) - # Define fixed plane - mesh_x = self.mesh.points()[:, 0] - fixed = np.where(mesh_x <= np.min(mesh_x) + 0.05 * (np.max(mesh_x) - np.min(mesh_x))) - plane_origin = [np.min(mesh_x), - np.mean(self.mesh.points()[:, 1][fixed]), - np.mean(self.mesh.points()[:, 2][fixed])+10] - # Create plotter self.plotter = Plotter(title='Interactive Beam', N=1, interactive=True, offscreen=False, bg2='lightgray') self.plotter.render() self.plotter.add(*self.spheres) self.plotter.add(self.mesh) - self.plotter.add(Plane(pos=plane_origin, normal=[1, 0, 0], s=(20, 20), c='darkred', alpha=0.2)) + self.plotter.add(Plane(pos=[0., 0., 0.], normal=[1, 0, 0], s=(20, 20), c='darkred', alpha=0.2)) self.plotter.add(Text2D("Press 'Alt' to interact with the object.\n" "Left click to select a sphere.\n" "Right click to unselect a sphere.", s=0.75)) From e314d2b7669b92b478a0f7ba5c72185d4e6e5850 Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 28 Nov 2022 16:00:07 +0100 Subject: [PATCH 55/61] Smal fix with DataTransormation types. --- src/Core/Network/BaseTransformation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/Core/Network/BaseTransformation.py b/src/Core/Network/BaseTransformation.py index 522bfc58..f2956770 100644 --- a/src/Core/Network/BaseTransformation.py +++ b/src/Core/Network/BaseTransformation.py @@ -20,7 +20,7 @@ def __init__(self, config: namedtuple): def check_type(func: Callable[[Any, Any], Any]): def inner(self, *args): - for data in args: + for data in [a for a in args if a is not None]: for value in data.values(): if value is not None and type(value) != self.data_type: raise TypeError(f"[{self.name}] Wrong data type: {self.data_type} required, get {type(value)}") From 2f489bdaf7f660ad38d31ccd250b87a817c950b9 Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 28 Nov 2022 16:48:32 +0100 Subject: [PATCH 56/61] Fix install dependencies error. --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 41dba749..72acda40 100644 --- a/setup.py +++ b/setup.py @@ -44,9 +44,9 @@ def get_SSD(): packages=packages, package_dir=packages_dir, namespace_packages=[PROJECT], - install_requires=get_SSD() + ['numpy >1.23.5', + install_requires=get_SSD() + ['numpy >= 1.23.5', 'tensorboard >= 2.10.0', 'tensorboardX >= 2.5.1', - 'pyDataverse >= 0.3.1' + 'pyDataverse >= 0.3.1', 'torch >= 1.13.0'], entry_points={'console_scripts': ['DPX=DeepPhysX.cli:execute_cli']}) From 37c3816cc0dad52c4aca91c959a165ce34a3f79a Mon Sep 17 00:00:00 2001 From: robin Date: Mon, 28 Nov 2022 17:04:02 +0100 Subject: [PATCH 57/61] Fix cli running path in local pip installation mode. --- src/cli.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/cli.py b/src/cli.py index 6ef4f533..44c21431 100644 --- a/src/cli.py +++ b/src/cli.py @@ -18,7 +18,7 @@ def get_sources(): import DeepPhysX site_packages = dirname(DeepPhysX.__path__[0]) - metadata_repo = [f for f in listdir(site_packages) if 'DeepPhysX' in f and + metadata_repo = [f for f in listdir(site_packages) if f.split('-')[0] == 'DeepPhysX' and ('.dist-info' in f or '.egg-info' in f)] if len(metadata_repo) == 0: quit(print("The project does not seem to be properly installed. Try to re-install using 'pip'.")) @@ -136,15 +136,17 @@ def execute_cli(): import DeepPhysX.Core source_dir = readlink(DeepPhysX.Core.__path__[0]) examples_dir = join(dirname(dirname(source_dir)), 'examples') + repo = join(*examples[example].split('/')[1:-1]) elif (source_dir := get_sources()) is not None: examples_dir = join(source_dir, 'examples') + repo = join(*examples[example].split('/')[1:-1]) else: if not isdir(join(getcwd(), 'DPX_examples')): print(f"The directory '{join(getcwd(), 'DPX_examples')}' does not exists.") copy_examples_dir() examples_dir = join(getcwd(), 'DPX_examples') + repo = join(*examples[example].split('/')[:-1]) # Run the example - repo = join(*examples[example].split('/')[:-1]) if is_pip_installed() else join(*examples[example].split('/')[1:-1]) script = examples[example].split('/')[-1] chdir(join(examples_dir, repo)) run([f'{executable}', f'{script}'], cwd=join(examples_dir, repo)) From 3800e1c93e09198c89e8c8d081bcab80f7867ec6 Mon Sep 17 00:00:00 2001 From: robin Date: Tue, 29 Nov 2022 17:44:31 +0100 Subject: [PATCH 58/61] Update the documentation. --- docs/source/api/core.rst | 21 +- docs/source/api/core/asyncsocket.rst | 9 - docs/source/api/core/dataset.rst | 16 +- docs/source/api/core/environment.rst | 3 - docs/source/api/core/factories.rst | 59 ------ docs/source/api/core/manager.rst | 24 +-- docs/source/api/core/network.rst | 2 +- docs/source/api/core/pipeline.rst | 20 +- docs/source/api/core/visualizer.rst | 11 +- docs/source/component/dataset.rst | 47 ++--- docs/source/component/environment.rst | 161 ++++----------- docs/source/component/network.rst | 22 ++- docs/source/component/pipelines.rst | 77 ++++---- docs/source/component/visualizer.rst | 272 +------------------------- docs/source/conf.py | 3 +- 15 files changed, 163 insertions(+), 584 deletions(-) delete mode 100644 docs/source/api/core/factories.rst diff --git a/docs/source/api/core.rst b/docs/source/api/core.rst index 51945bd6..c7dec455 100644 --- a/docs/source/api/core.rst +++ b/docs/source/api/core.rst @@ -11,25 +11,22 @@ CORE - :doc:`core/environment` - :doc:`core/manager` - * - | :ref:`asyncsocket.abstractenvironment` - | :ref:`asyncsocket.bytesconverter` + * - | :ref:`asyncsocket.bytesconverter` | :ref:`asyncsocket.tcpipclient` | :ref:`asyncsocket.tcpipobject` | :ref:`asyncsocket.tcpipserver` - - | :ref:`dataset.basedataset` - | :ref:`dataset.basedatasetconfig` + - | :ref:`database.databasehandler` + | :ref:`database.basedatabaseconfig` - | :ref:`environment.baseenvironment` | :ref:`environment.baseenvironmentconfig` - | :ref:`manager.datamanager` - | :ref:`manager.datasetmanager` + | :ref:`manager.databasemanager` | :ref:`manager.environmentmanager` - | :ref:`manager.manager` | :ref:`manager.networkmanager` | :ref:`manager.statsmanager` - | :ref:`manager.visualizermanager` .. list-table:: @@ -46,15 +43,13 @@ CORE | :ref:`network.baseoptimization` | :ref:`network.datatransformation` - - | :ref:`pipelines.basedatagenerator` + - | :ref:`pipelines.basedatageneration` | :ref:`pipelines.basepipeline` - | :ref:`pipelines.baserunner` - | :ref:`pipelines.basetrainer` + | :ref:`pipelines.baseprediction` + | :ref:`pipelines.basetraining` - - | :ref:`visualizer.vedoobjects` + - | :ref:`visualizer.vedofactory` | :ref:`visualizer.vedovisualizer` - | - | :ref:`Visualizer.Factories ` .. toctree:: diff --git a/docs/source/api/core/asyncsocket.rst b/docs/source/api/core/asyncsocket.rst index bf4d54e8..b097d540 100644 --- a/docs/source/api/core/asyncsocket.rst +++ b/docs/source/api/core/asyncsocket.rst @@ -1,14 +1,6 @@ AsyncSocket =========== -.. _asyncsocket.abstractenvironment: - -AbstractEnvironment -___________________ - -.. autoclass:: AbstractEnvironment.AbstractEnvironment - :members: - .. _asyncsocket.bytesconverter: BytesConverter @@ -23,7 +15,6 @@ TcpIpClient ___________ Bases: -:py:class:`AbstractEnvironment.AbstractEnvironment` :py:class:`TcpIpObject.TcpIpObject` .. autoclass:: TcpIpClient.TcpIpClient diff --git a/docs/source/api/core/dataset.rst b/docs/source/api/core/dataset.rst index 69d9eb87..469d2f7e 100644 --- a/docs/source/api/core/dataset.rst +++ b/docs/source/api/core/dataset.rst @@ -1,18 +1,18 @@ Dataset ======= -.. _dataset.basedataset: +.. _database.databasehandler: -BaseDataset ------------ +DatabaseHandler +--------------- -.. autoclass:: BaseDataset.BaseDataset +.. autoclass:: DatabaseHandler.DatabaseHandler :members: -.. _dataset.basedatasetconfig: +.. _database.basedatabaseconfig: -BaseDatasetConfig ------------------ +BaseDatabaseConfig +------------------ -.. autoclass:: BaseDatasetConfig.BaseDatasetConfig +.. autoclass:: BaseDatabaseConfig.BaseDatabaseConfig :members: diff --git a/docs/source/api/core/environment.rst b/docs/source/api/core/environment.rst index f14829bc..53d1fdc4 100644 --- a/docs/source/api/core/environment.rst +++ b/docs/source/api/core/environment.rst @@ -6,9 +6,6 @@ Environment BaseEnvironment --------------- -Bases: -:py:class:`TcpIpClient.TcpIpClient` - .. autoclass:: BaseEnvironment.BaseEnvironment :members: diff --git a/docs/source/api/core/factories.rst b/docs/source/api/core/factories.rst deleted file mode 100644 index 9c7c8681..00000000 --- a/docs/source/api/core/factories.rst +++ /dev/null @@ -1,59 +0,0 @@ -.. _factories: - -Factories -========= - -ArrowsFactory -------------- - -.. autoclass:: VedoObjectFactories.ArrowsFactory.ArrowsFactory - :members: - - -BaseObjectFactory ------------------ - -.. autoclass:: VedoObjectFactories.BaseObjectFactory.BaseObjectFactory - :members: - - -GlyphFactory ------------- - -.. autoclass:: VedoObjectFactories.GlyphFactory.GlyphFactory - :members: - - -MarkerFactory -------------- - -.. autoclass:: VedoObjectFactories.MarkerFactory.MarkerFactory - :members: - - -MeshFactory ------------ - -.. autoclass:: VedoObjectFactories.MeshFactory.MeshFactory - :members: - - -PointsFactory -------------- - -.. autoclass:: VedoObjectFactories.PointsFactory.PointsFactory - :members: - - -VedoObjectFactory ------------------ - -.. autoclass:: VedoObjectFactories.VedoObjectFactory.VedoObjectFactory - :members: - - -WindowFactory -------------- - -.. autoclass:: VedoObjectFactories.WindowFactory.WindowFactory - :members: diff --git a/docs/source/api/core/manager.rst b/docs/source/api/core/manager.rst index 379febbc..ce177ab3 100644 --- a/docs/source/api/core/manager.rst +++ b/docs/source/api/core/manager.rst @@ -9,12 +9,12 @@ DataManager .. autoclass:: DataManager.DataManager :members: -.. _manager.datasetmanager: +.. _manager.databasemanager: -DatasetManager --------------- +DatabaseManager +--------------- -.. autoclass:: DatasetManager.DatasetManager +.. autoclass:: DatabaseManager.DatabaseManager :members: .. _manager.environmentmanager: @@ -25,14 +25,6 @@ EnvironmentManager .. autoclass:: EnvironmentManager.EnvironmentManager :members: -.. _manager.manager: - -Manager -------- - -.. autoclass:: Manager.Manager - :members: - .. _manager.networkmanager: NetworkManager @@ -48,11 +40,3 @@ StatsManager .. autoclass:: StatsManager.StatsManager :members: - -.. _manager.visualizermanager: - -VisualizerManager ------------------ - -.. autoclass:: VisualizerManager.VisualizerManager - :members: diff --git a/docs/source/api/core/network.rst b/docs/source/api/core/network.rst index a25b0b0f..7fb22845 100644 --- a/docs/source/api/core/network.rst +++ b/docs/source/api/core/network.rst @@ -30,5 +30,5 @@ BaseOptimization DataTransformation ------------------ -.. autoclass:: DataTransformation.DataTransformation +.. autoclass:: BaseTransformation.BaseTransformation :members: diff --git a/docs/source/api/core/pipeline.rst b/docs/source/api/core/pipeline.rst index 39f44301..05acebc3 100644 --- a/docs/source/api/core/pipeline.rst +++ b/docs/source/api/core/pipeline.rst @@ -1,15 +1,15 @@ Pipelines ========= -.. _pipelines.basedatagenerator: +.. _pipelines.basedatageneration: -BaseDataGenerator ------------------ +BaseDataGeneration +------------------ Bases: :py:class:`BasePipeline.BasePipeline` -.. autoclass:: BaseDataGenerator.BaseDataGenerator +.. autoclass:: BaseDataGeneration.BaseDataGeneration :members: .. _pipelines.basepipeline: @@ -20,18 +20,18 @@ BasePipeline .. autoclass:: BasePipeline.BasePipeline :members: -.. _pipelines.baserunner: +.. _pipelines.baseprediction: -BaseRunner ----------- +BasePrediction +-------------- Bases: :py:class:`BasePipeline.BasePipeline` -.. autoclass:: BaseRunner.BaseRunner +.. autoclass:: BasePrediction.BasePrediction :members: -.. _pipelines.basetrainer: +.. _pipelines.basetraining: BaseTrainer ----------- @@ -39,5 +39,5 @@ BaseTrainer Bases: :py:class:`BasePipeline.BasePipeline` -.. autoclass:: BaseTrainer.BaseTrainer +.. autoclass:: BaseTraining.BaseTraining :members: diff --git a/docs/source/api/core/visualizer.rst b/docs/source/api/core/visualizer.rst index 14e87172..df95549a 100644 --- a/docs/source/api/core/visualizer.rst +++ b/docs/source/api/core/visualizer.rst @@ -1,17 +1,12 @@ Visualizer ========== -.. toctree:: - :hidden: +.. _visualizer.vedofactory: - factories.rst - -.. _visualizer.vedoobjects: - -VedoObjects +VedoFactory ----------- -.. autoclass:: VedoObjects.VedoObjects +.. autoclass:: VedoFactory.VedoFactory :members: .. _visualizer.vedovisualizer: diff --git a/docs/source/component/dataset.rst b/docs/source/component/dataset.rst index ff5f6e9c..a751ef41 100644 --- a/docs/source/component/dataset.rst +++ b/docs/source/component/dataset.rst @@ -6,9 +6,9 @@ Dedicated code in :guilabel:`Core/Dataset` module. Behavior -------- -**DeepPhysX** comes with its own *Dataset* management system. -The synthetic data produced in *Environments* is stored as Numpy arrays in a dedicated repository named ``dataset`` in -the training session. +**DeepPhysX** comes with its own *Dataset* management system, using the features from the :SSD:`SSD <>` library. +The synthetic data produced in *Environments* is stored on a *Database* that is in a dedicated repository named +``dataset`` in the training session. Data is stored as partitions: these partitions correspond to the different *Dataset* modes (training data, test data, prediction data) and can be multiple for each data field in order not to exceed the maximum size of the current amount of loaded data. @@ -24,16 +24,14 @@ Each partition will have a unique name: ``___ When adding a batch to the *Dataset*, a new partition is created for each data field if the current *Dataset* size exceeds the threshold. The batch is then appended to the *Dataset* for each data field. -Default *Dataset* fields are inputs and outputs, but users can add any data to the *Dataset* from *Environment* using -``additional_in_dataset`` or ``additional_out_dataset`` (see :ref:`dedicated section `). +Default *Dataset* fields are inputs and outputs, but users can define any data field from *Environment* +(see :ref:`dedicated section `). Each field must always be filled at each batch. -A ``dataset.json`` file gathers information about the produced dataset. +A ``dataset.json`` file gathers information about the produced dataset and normalization coefficients if the +normalization is applied. -When loading data from an existing *Dataset*, the repository is loaded first. -If there is a single partition for each field, only those partitions are loaded into the *Dataset*. -Otherwise, a proportional part of each partition will be loaded each time. -Batches of data are accessed in read order (random or not) until the read cursor reaches the end, triggering either the -reloading of a single partition or the loading of the subsequent slices of partitions. +When loading data from an existing *Dataset*, the partitions are loaded and can be accessed randomly or not among the +whole set of partitions. Configuration @@ -49,33 +47,30 @@ Here is a description of attributes related to *Dataset* configuration. :width: 100% :widths: 15 85 - * - ``dataset_class`` - - *Dataset* class from which an instance will be created (*BaseDataset* by default). - - * - ``dataset_dir`` + * - ``existing_dir`` - Path to an existing *Dataset* repository if this repository needs to be loaded or completed. - * - ``partition_size`` + * - ``max_file_size`` - Maximum size (in Gb) of the total *Dataset* object. - * - ``shuffle_dataset`` - - Specify if the loading order is random or not (True by default). - - * - ``use_mode`` + * - ``mode`` - Specify the *Dataset* mode between "Training", "Validation" and "Running". - * - ``normalize_data`` + * - ``normalize`` - If True, normalization parameters are computed from training data and applied to any loaded data. + * - ``shuffle`` + - Specify if the loading order is random or not (True by default). + .. highlight:: python See following example:: # Import DatasetConfig - from DeepPhysX_Core.Dataset.BaseDatasetConfig import BaseDatasetConfig + from DeepPhysX_Core.Database.BaseDatabaseConfig import BaseDatabaseConfig # Create the config - dataset_config = BaseDatasetConfig(partition_size=1, - shuffle_dataset=True, - use_mode='Training', - normalize_data=True) + database_config = BaseDatabaseConfig(max_file_size=1, + shuffle=True, + mode='Training', + normalize=True) diff --git a/docs/source/component/environment.rst b/docs/source/component/environment.rst index 688d9f77..e7912fb1 100644 --- a/docs/source/component/environment.rst +++ b/docs/source/component/environment.rst @@ -26,6 +26,11 @@ implement its *Environment* regardless of this dependence. This method is automatically called when the *Environment* component is created. + * - ``init_database`` + - Define the fields of the training dataset. + + This method is automatically called when the *Environment* component is created. + * - ``step`` - Describe the transitions between simulation states. @@ -47,17 +52,12 @@ implement its *Environment* regardless of this dependence. This method is automatically called when the *Environment* component is created, right after the create method. - * - ``recv_parameters`` - - In the case of using multiple *Environments* connected to one *TcpIpServer*, each of them can be parameterize - differently. - - These parameters can be set in the *EnvironmentConfig* and are then automatically sent to *Environments* right - after their initialization. + * - ``init_visualization`` + - Define the visualization objects to send to the *Visualizer*. - * - ``send_parameters`` - - On the other side, one might want to send back data from *Environments* to the *EnvironmentConfig*. + A *Factory* is available in the *Environment* to easily design visualization objects. - If the method is implemented, parameters will be sent right after the previous receipt. + This method is automatically called when the *Environment* component is created, right after the init. * - ``check_sample`` - Each step will be followed by a sample checking. @@ -83,32 +83,22 @@ implement its *Environment* regardless of this dependence. :width: 100% :widths: 15 85 + * - ``define_training_data`` + - This method must be used in ``init_database`` to specify the training data fields names and types. + + * - ``define_additional_data`` + - This method must be used in ``init_database`` to specify the additional data fields names and types. + * - ``set_training_data`` - This method defines which data will be considered as training data (data sent to the *NetworkManager* to feed the *Network*). - * - ``set_loss_data`` - - This method allows adding specific data to compute the loss function with numeric values from the simulation. - * - ``set_additional_dataset`` - In addition to the training data, some additional data can be sent directly to the dataset to replay some simulation states. This method adds a new field to the *Dataset* and must be then called at each step. - * - ``set_dataset_sample`` - - This method is already implemented and must not be overwritten by the user. - - When loading existing data, the *DataManager* might need to send *Dataset* samples to the *Environment* to - produce training data. - - These samples are set in the variables ``sample_in`` & ``sample_out``. - - If *Dataset* has additional data fields, these additional fields are set in ``additional_inputs`` & - ``additional_outputs``. - - Each sample can then be processed in the step function. - | **Requests** | The *Environment* is also able to perform some requests. These requests are sent either directly to the *EnvironmentManager* or through a *TcpIpServer*. @@ -117,6 +107,12 @@ implement its *Environment* regardless of this dependence. :width: 100% :widths: 15 85 + * - ``save_parameters`` + - Save a set of parameters in the Database. + + * - ``load_parameters`` + - Load a set of parameters from the Database. + * - ``get_prediction`` - Depending on the application, a prediction of the *Network* can be useful before the end of a step. @@ -125,23 +121,11 @@ implement its *Environment* regardless of this dependence. The training data must obviously be set before triggering this request. - * - ``send_visualization`` - - The framework comes with an integrated *Visualizer* tool to render some components of the simulation. - - Parts of the simulation to render must be defined when creating or when initializing the *Environment*. - - An *Environment* has a visualization factory to easily create visualization data from templates with - ``addObject`` method. - - User only has to set the object type and to fill the required fields to init this type of object in the - *Visualizer* (see :ref:`dedicated section `). - * - ``update_visualization`` - If a *Visualizer* was created, it must be manually updated at each step by sending the updated state of the simulation. - The factory allows to easily create updated visualization data from objects ids with ``updateObject_dict`` - method. + The factory allows to easily create updated visualization data from objects ids with ``update_obj`` methods. User only has to fill the object id (same as initialization order) and the required fields (detailed for each object in :ref:`dedicated section `). @@ -151,8 +135,8 @@ Configuration ------------- Using an *Environment* in one of the **DeepPhysX** *Pipeline* always requires an *EnvironmentConfig*. -This component’s role is both to bring together all the options for configuring an *Environment* and to either create -an instance of a single *Environment* or launch a *TcpIpServer* with several *TcpIpClients*. +The role of this component is both to bring together all the options for configuring an *Environment* and to create an +instance of a single *Environment* or launch a *TcpIpServer* with several *TcpIpClients*. In the first case, the single *Environment* will simply be created within the ``create_environment`` method, while in the other case, the ``create_server`` method will simply create and launch a *TcpIpServer* and then start several subprocesses, each using the ``launcherBaseEnvironment.py`` script to create and launch an *Environment* as a *Client*. @@ -169,19 +153,19 @@ subprocesses, each using the ``launcherBaseEnvironment.py`` script to create and The attribute requires the class and not an instance, as it will be automatically created as explained above. - * - ``visualizer`` - - A visualization tool is provided, which renders the specified parts of each *Environment*. - - If no *Visualizer* is provided, the pipeline will run without any render window. - * - ``simulations_per_step`` - The number of iterations to compute in the *Environment* at each time step. An *Environment* will compute one iteration by default. - * - ``use_prediction_in_environment`` - - Each *Network* prediction will be automatically applied in the *Environment* if this flag is set to True - (set to False by default). + * - ``visualizer`` + - A visualization tool is provided, which renders the specified parts of each *Environment*. + + If no *Visualizer* is provided, the pipeline will run without any render window. + + * - ``env_kwargs`` + - *Environments* can receive additional parameters within this dictionary if they need to be parameterized + differently. | **Data parameters** | Here is a description of attributes related to sample generation. @@ -190,7 +174,7 @@ subprocesses, each using the ``launcherBaseEnvironment.py`` script to create and :width: 100% :widths: 15 85 - * - ``always_create_data`` + * - ``always_produce`` - This flag is useful for the training *Pipeline*. If False (by default), the *DataManager* requests batches to the *Environment* during the first epoch and @@ -198,26 +182,13 @@ subprocesses, each using the ``launcherBaseEnvironment.py`` script to create and If set to True, the *DataManager* requests new batches to the *Environment* during the whole training session. - * - ``screenshot_sample_rate`` - - This option is only available if a *Visualizer* is defined. - - In addition to *Dataset* partitions, samples can also be saved as screenshots so representative ones can be - easily found. - - A screenshot of the viewer will be taken every x samples (set to 0 by default). + * - ``load_samples`` + - If True, the dataset will always be used in the environment. - * - ``record_wrong_samples`` - - By default, only the good samples are stored in the *Dataset* (sorted by check_sample, see the section above). + * - ``only_first_epoch`` + - If True, data will always be created from *Environment*. - If this flag is set to True, the wrong samples will also be saved to dedicated partitions. - - * - ``max_wrong_samples_per_step`` - - If an *Environment* produces too many wrong samples, it may be configured incorrectly. - - To avoid an unnecessary extended data generation, a threshold can be set so that the session can be stopped - early. - - This happens when too many wrong samples are produced to fill a single batch. + If False, data will be created from the *Environment* during the first epoch and then re-used from the *Dataset*. | **TcpIP parameters** | Here is a description of attributes related to the *Client* configuration. @@ -238,27 +209,11 @@ subprocesses, each using the ``launcherBaseEnvironment.py`` script to create and The default value is “localhost” to host the *Server* and *Clients* locally. - * - ``port`` - - TCP port’s number through which *TcpIpObjects* will communicate (10000 by default). - - * - ``environment_file`` - - When launching an *Environment* as a *Client*, the *EnvironmentConfig* starts a subprocess involving that - *Environment*. - - To do this, the launcher will need the script path in which the *Environment* is defined. - - This script is in most cases automatically detected, but users may need to enter the path to their python file. - * - ``number_of_thread`` - The number of *Environments* to launch simultaneously if the flag ``as_tcp_ip_client`` is True. - * - ``max_client_connection`` - - The maximum number of *Client* connections allowed by a *Server*. - - * - ``param_dict`` - - *Environments* can receive additional parameters if they need to be parameterized differently. - - These parameters are sent in the form of dictionaries by the *Server* when creating the *Environment*. + * - ``port`` + - TCP port’s number through which *TcpIpObjects* will communicate (10000 by default). .. highlight:: python @@ -309,39 +264,3 @@ The data is sent as a custom bytes message converted with a *BytesConverter*, wh NumPy arrays. On top of these low level data exchange methods are built higher level protocols to send labeled data, labeled dictionaries and commands. - -A list of available commands is defined. *TcpIpServer* and *TcpIpClients* have then their own action implementations -to perform when receiving a command: - -* A *TcpIpClient* defines the following actions to perform on commands: - - .. list-table:: - :width: 90% - :widths: 15 85 - - * - ``exit`` - - Set the closing flag to True to terminate the communication loop. - - * - ``prediction`` - - Receive the prediction sent by the *TcpIpServer* and apply it in the *Environment*. - - * - ``sample`` - - When using data from *Dataset*, the sample is received and defined in the *Environment* on this command - - * - ``step`` - - Trigger a simulation step to produce data. - - Data should be sent to the *TcpIpServer* when the produced sample is identified as usable by sample - checking. - -* A *TcpIpServer* defines the following actions to perform on commands: - - .. list-table:: - :width: 90% - :widths: 15 85 - - * - ``prediction`` - - Receive data to feed the *Network*, then send back the prediction to the same *TcpIpClient*. - - * - ``visualization`` - - Receive initial or updated visualization data, then call the *Visualizer* update. diff --git a/docs/source/component/network.rst b/docs/source/component/network.rst index 114e32f5..11ef7c95 100644 --- a/docs/source/component/network.rst +++ b/docs/source/component/network.rst @@ -23,6 +23,9 @@ Even if a *BaseNetwork* is not usable, any **DeepPhysX** AI package provides the * - ``__init__`` - Define the *Network* architecture. + * - ``predict`` + - Define the data fields used to compute a prediction. + * - ``forward`` - Define the forward pass of the *Network*. @@ -57,16 +60,23 @@ Even if a *BaseNetwork* is not usable, any **DeepPhysX** AI package provides the * - ``nb_parameters`` - Return the number of parameters in the architecture. - * - ``transform_from_numpy`` + * - ``numpy_to_tensor`` - Convert a Numpy array to a tensor with the compatible type. Received data from Core will always be Numpy arrays. - * - ``transform_to_numpy`` + * - ``tensor_to_numpy`` - Convert a tensor to a Numpy array. Data provided to Core must be converted to Numpy arrays. +| **Data fields** +| The training data is given to the *Network* as a dictionary of tensors. + By default, the data used for inference has a single "input" field, the data produced by the *Network* has a single + "prediction" field, the data used for optimization has a single "ground_truth" field. + If another field should be defined, the *Network* class should specify them in the corresponding ``self.net_fields``, + ``self.pred_fields`` & ``self.opt_fields`` variables. + .. _network-optimization: Optimization Implementation @@ -126,14 +136,14 @@ Users are then free to define their own tensor transformations with the followin :widths: 15 85 * - ``transform_before_prediction`` - - Apply a tensor transformation to the input data before *Network* forward pass. + - Apply a tensor transformation to the *Network* data (before a prediction). * - ``transform_before_loss`` - - Apply a tensor transformation to the ground truth data and / or the *Network* output before the loss - computation. + - Apply a tensor transformation to the ground truth data and / or the prediction data (after the prediction, + before the loss computation). * - ``transform_before_apply`` - - Apply a tensor transformation to the *Network* prediction before sending it to the *Environment*. + - Apply a tensor transformation to the prediction data (before sending it to the *Environment*). Configurations diff --git a/docs/source/component/pipelines.rst b/docs/source/component/pipelines.rst index f61e3099..c84f34ea 100644 --- a/docs/source/component/pipelines.rst +++ b/docs/source/component/pipelines.rst @@ -8,9 +8,9 @@ General Policy Several *Pipelines* are available with **DeepPhysX**, allowing the user to: - * **Generate** synthetic data from simulations → ``DataGenerator`` - * **Train** artificial neural networks with synthetic data → ``Trainer`` - * Use the **predictions** of trained networks inside a simulation → ``Runner`` + * **Generate** synthetic data from simulations → ``DataGeneration`` + * **Train** artificial neural networks with synthetic data → ``Training`` + * Use the **predictions** of trained networks inside a simulation → ``Prediction`` A *Pipeline* is always associated with a :ref:`working session `, whether it already exists or whether it is automatically created when the *Pipeline* is launched. @@ -27,8 +27,8 @@ Once these *Configurations* are defined, the *Pipeline* can be created and launc Pipeline - Data generation -------------------------- -The *DataGenerator* will only involve an *Environment* and a *Dataset*, so this *Pipeline* requires the corresponding -*Configurations* +The *DataGeneration* will only involve an *Environment* and a *Dataset*, so this *Pipeline* requires the corresponding +*Configurations*. As the purpose of this *Pipeline* is only to create synthetic data, the working session will always be created at the same time. @@ -48,8 +48,8 @@ Furthermore, users have to define which data to save and how much : See following example:: - # Import BaseDataGenerator and Config objects - from DeepPhysX_Core.Pipelines.BaseDataGenerator import BaseDataGenerator + # Import BaseDataGeneration and Config objects + from DeepPhysX.Core.Pipelines.BaseDataGeneration import BaseDataGeneration ... # Define configs @@ -57,11 +57,12 @@ See following example:: environment_config = ... # Create the pipeline - data_generator = BaseDataGenerator(session_name='sessions/my_data_generation', - dataset_config=dataset_config, - environment_config=environment_config, - nb_batches=500, - batch_size=16) + data_generator = BaseDataGeneration(session_dir='sessions', + session_name='my_data_generation', + dataset_config=dataset_config, + environment_config=environment_config, + batch_nb=500, + batch_size=16) # Launch the pipeline data_generator.execute() @@ -70,12 +71,12 @@ See following example:: Pipeline - Training ------------------- -The *Trainer* can involve an *Environment*, a *Dataset* and a *Network*, so this *Pipeline* might require the +The *Training* can involve an *Environment*, a *Dataset* and a *Network*, so this *Pipeline* might require the corresponding *Configurations*. There are several ways to use this pipeline: **Training a Network from scratch** - To train a *Network* from scratch, the *Trainer* requires the whole set of *Configurations*. + To train a *Network* from scratch, the *Training* requires the whole set of *Configurations*. A new working session will be created, whose name can be set as a parameter. **Training a Network with an existing Dataset** @@ -88,7 +89,7 @@ There are several ways to use this pipeline: **Training a Network from an existing Network state** Training from an existing *Network* state can be done both in an existing session or in a new session. - If you want to work in the same session, you have to configure the *Trainer* to do so, otherwise a new working + If you want to work in the same session, you have to configure the *Training* to do so, otherwise a new working session will be automatically created. In the same session, a new set of trained parameters will be added in the ``network`` repository, either trained with data from an external *Dataset* (whose path must be provided) or with data from the *Environment* (whose @@ -109,8 +110,8 @@ The last parameters to set in the *Trainer* are: See following example:: - # Import BaseTrainer and Config objects - from DeepPhysX_Core.Pipelines.BaseTrainer import BaseTrainer + # Import BaseTraining and Config objects + from DeepPhysX.Core.Pipelines.BaseTraining import BaseTraining ... # Define configs @@ -119,13 +120,14 @@ See following example:: network_config = ... # Create the pipeline - trainer = BaseTrainer(session_name='sessions/my_training', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_epochs=100, - nb_batches=500, - batch_size=16) + trainer = BaseTraining(session_dir='sessions', + session_name='my_training', + dataset_config=dataset_config, + environment_config=env_config, + network_config=net_config, + epoch_nb=100, + batch_nb=500, + batch_size=16) # Launch the pipeline trainer.execute() @@ -134,25 +136,25 @@ See following example:: Pipeline - Prediction --------------------- -The *Runner* always requires a *Network* to compute predictions and an *Environment* to apply them, so this *Pipeline* -will always require the corresponding *Configurations*. +The *Prediction* always requires a *Network* to compute predictions and an *Environment* to apply them, so this +*Pipeline* will always require the corresponding *Configurations*. -This *Pipeline* always works with an existing working session, no new sessions can be created within a *Runner*. +This *Pipeline* always works with an existing working session, no new sessions can be created within a *Prediction*. The path to the session is therefore required, assuming that it contains a trained *Network*. -The *Runner* can either run a specified **number of steps** or run an **infinite loop**. +The *Prediction* can either run a specified **number of steps** or run an **infinite loop**. A *Dataset* configuration can be provided. -In this case, the *Runner* can record input or / and output data. +In this case, the *Prediction* can record prediction data. Each sample computed during the prediction phase will then be added to the *Dataset* in dedicated partitions. -With a *Dataset*, the *Runner* can also load its data to **replay** stored samples. +With a *Dataset*, the *Prediction* can also load its data to **replay** stored samples. .. highlight:: python See following example:: - # Import BaseRunner and Config objects - from DeepPhysX_Core.Pipelines.BaseRunner import BaseTrainer + # Import BasePrediction and Config objects + from DeepPhysX.Core.Pipelines.BasePrediction import BasePrediction ... # Define configs @@ -161,11 +163,12 @@ See following example:: network_config = ... # Create the pipeline - runner = BaseRunner(session_dir='sessions/my_training', - dataset_config=dataset_config, - environment_config=env_config, - network_config=net_config, - nb_steps=-1) + runner = BasePrediction(session_dir='sessions', + session_name='my_training', + dataset_config=dataset_config, + environment_config=env_config, + network_config=net_config, + step_nb=-1) # Launch the pipeline runner.execute() diff --git a/docs/source/component/visualizer.rst b/docs/source/component/visualizer.rst index b30d7234..577ac3c9 100644 --- a/docs/source/component/visualizer.rst +++ b/docs/source/component/visualizer.rst @@ -9,7 +9,7 @@ How to use ---------- **DeepPhysX** provides a visualization tool written with :Vedo:`Vedo <>` (a Python library based on Numpy and VTK) -called *VedoVisualizer*. +called *VedoVisualizer*, using the implementation form the :SSD:`SSD <>` library. This *Visualizer* brings several advantages: * Users can add any component of the simulation in the *Visualizer*; @@ -17,272 +17,20 @@ This *Visualizer* brings several advantages: * Parallel running *Environments* are rendered in the same window with sub-windows; * A *Factory* is created with each *Environment* so that users can access templates to define visualization data. -Objects are created using the ``add_object`` method of the *Factory* in the *Environment*. -This method requires the object type name and a dictionary containing the required fields detailed above (common fields -and object-specific fields). -These objects must be defined in the ``send_visualization`` method of the *Environment*, which must return the objects -dictionary of the *Factory*. +Objects are created using the ``add_`` methods of the *Factory* in the *Environment*. +These methods require the fields detailed above (common fields and object-specific fields). +These objects must be added in the ``init_visualization`` method of the *Environment*, which is automatically called to +create the objects within the *Factory*. -Objects are updated using the ``update_object_dict`` method of the *Factory* in the *Environment*. -This method requires the object index (indices follow the order of creation) and a dictionary containing the updated -data fields. +Objects are updated using the ``update_`` methods of the *Factory* in the *Environment*. +These methods require the object index (indices follow the order of creation) and a the updated data fields. The *Factory* will only use templates to create an updated objects dictionary which must be sent with the request ``update_visualization`` to update the view. -| **General parameters** -| Visual objects share default data fields that could also be filled at init and are all optional: - -.. list-table:: - :width: 95% - :widths: 21 11 11 57 - :header-rows: 1 - - * - Field - - Init - - Update - - Description - - * - ``c`` - - Optional - - Unnecessary - - Opacity of the object between 0 and 1. - - * - ``alpha`` - - Optional - - Unnecessary - - Marker object. - - * - ``at`` - - Optional - - Unnecessary - - Sub-window in which the object will be rendered. - - Set to -1 by default, meaning a new window is created for the object. - - Advise: set to ``self.instance_id`` to gather objects from an *Environment* in the same sub-window. - - * - ``colormap`` - - Optional - - Unnecessary - - Name of color palette that samples a continuous function between two end colors. - - * - ``scalar_field`` - - Optional - - Unnecessary - - List of scalar values to set individual points or cell color. - - * - ``scalar_field_name`` - - Optional - - Unnecessary - - Name of the scalar field. | **Visual Objects Parameters** -| A list of templates are available in the *Factory* to initialize and update a list of objects. - Here is a description of available objects and the required data fields: - -* Create a :VedoObject:`Mesh `: - - .. list-table:: - :width: 95% - :widths: 21 11 11 57 - :header-rows: 1 - - * - Field - - Init - - Update - - Description - - * - ``positions`` - - **Required** - - **Required** - - List of vertices. - Updated position vector must always have the same size. - - * - ``cells`` - - **Required** - - Unnecessary - - List of connections between vertices. - - * - ``computeNormals`` - - Optional - - Unnecessary - - Compute cells and points normals at creation. - -* Create a :VedoObject:`Point Cloud `: - - .. list-table:: - :width: 95% - :widths: 21 11 11 57 - :header-rows: 1 - - * - Field - - Init - - Update - - Description - - * - ``positions`` - - **Required** - - **Required** - - List of vertices. - Updated position vector must always have the same size. - - * - ``r`` - - Optional - - Optional - - Radius of points. - -* Create a :VedoObject:`Marker ` (single point with associated symbol): - - .. list-table:: - :width: 95% - :widths: 21 11 11 57 - :header-rows: 1 - - * - Field - - Init - - Update - - Description - - * - ``positions`` - - **Required** - - **Required** - - Position of the Marker. - - * - ``symbol`` - - **Required** - - Unnecessary - - Associated symbol. - - * - ``s`` - - Optional - - Unnecessary - - Radius of symbol. - - * - ``filled`` - - Optional - - Unnecessary - - Fill the shape or only draw outline. - -* Create a :VedoObject:`Glyph ` (point cloud with oriented markers): - - .. list-table:: - :width: 95% - :widths: 21 11 11 57 - :header-rows: 1 - - * - Field - - Init - - Update - - Description - - * - ``positions`` - - **Required** - - **Required** - - Position of the Markers. - - * - ``glyphObj`` - - **Required** - - Unnecessary - - Marker object. - - * - ``orientationArray`` - - **Required** - - Unnecessary - - List of orientation vectors. - - * - ``scaleByScalar`` - - Optional - - Unnecessary - - Glyph is scaled by the scalar field. - - * - ``scaleByVectorSize`` - - Optional - - Unnecessary - - Glyph is scaled by the size of the orientation vectors. - - * - ``scaleByVectorComponents`` - - Optional - - Unnecessary - - Glyph is scaled by the components of the orientation vectors. - - * - ``colorByScalar`` - - Optional - - Unnecessary - - Glyph is colored based on the colormap and the scalar field. - - * - ``colorByVectorSize`` - - Optional - - Unnecessary - - Glyph is colored based on the size of the orientation vectors. - - * - ``tol`` - - Optional - - Unnecessary - - Minimum distance between two Glyphs. - -* Create :VedoObject:`3D Arrows `: - - .. list-table:: - :width: 95% - :widths: 21 11 11 57 - :header-rows: 1 - - * - Field - - Init - - Update - - Description - - * - ``positions`` - - **Required** - - **Required** - - Start points of the arrows. - - * - ``vectors`` - - **Required** - - **Required** - - Vector that must represent the arrows. - - * - ``res`` - - Optional - - Unnecessary - - Arrows visual resolution. - -* Change window parameters - - .. list-table:: - :width: 95% - :widths: 21 11 11 57 - :header-rows: 1 - - * - Field - - Init - - Update - - Description - - * - ``objects_id`` - - **Required** - - Unnecessary - - Indices of objects to set in this particular window. - - * - ``title`` - - Optional - - Unnecessary - - Title of the window. - - * - ``axes`` - - Optional - - Unnecessary - - Type of axes to show. - - * - ``sharecam`` - - Optional - - Unnecessary - - If True (default), all subwindows will share the same camera parameters. - - * - ``interactive`` - - Optional - - Unnecessary - - If True (default), the window will be interactive. +| All the available objects and their parameters are listed on the + :SSDd:`SSD documentation `. Configuration @@ -300,7 +48,7 @@ See following example:: # Import EnvironmentConfig and Visualizer from DeepPhysX_Core.Environment.BaseEnvironmentConfig import BaseEnvironmentConfig - from DeepPhysX_Core.Visualizer.VedoVisualizer import VedoVisualizer + from DeepPhysX_Core.Visualization.VedoVisualizer import VedoVisualizer # Create the config env_config = BaseEnvironmentConfig(environment_class=MyEnvironment, diff --git a/docs/source/conf.py b/docs/source/conf.py index 7f35763d..ae492141 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -84,4 +84,5 @@ 'Tensorboard': ('https://www.tensorflow.org/tensorboard/%s', '%s'), 'Vedo': ('https://vedo.embl.es/%s', '%s'), 'VedoObject': ('https://vedo.embl.es/autodocs/content/vedo/%s', '%s'), - 'SSD': ('https://github.com/RobinEnjalbert/SimulationSimpleDatabase/%s', '%s')} + 'SSD': ('https://github.com/RobinEnjalbert/SimulationSimpleDatabase/%s', '%s'), + 'SSDd': ('https://simulationsimpledatabase.readthedocs.io/en/latest/%s', '%s')} From 60ae650d4a8e25dc7e97cc1fcde7c1cab8717222 Mon Sep 17 00:00:00 2001 From: robin Date: Tue, 29 Nov 2022 17:48:05 +0100 Subject: [PATCH 59/61] Update the documentation. --- docs/source/api/sofa.rst | 2 +- docs/source/api/torch.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/api/sofa.rst b/docs/source/api/sofa.rst index 71a4752d..3d2c866c 100644 --- a/docs/source/api/sofa.rst +++ b/docs/source/api/sofa.rst @@ -1,4 +1,4 @@ SOFA ==== -Find **DeepPhysX_Sofa** API `here `_ +Find **DeepPhysX.Sofa** API `here `_ diff --git a/docs/source/api/torch.rst b/docs/source/api/torch.rst index 62141b27..ba810e24 100644 --- a/docs/source/api/torch.rst +++ b/docs/source/api/torch.rst @@ -1,4 +1,4 @@ TORCH ===== -Find **DeepPhysX_Torch** API `here `_. +Find **DeepPhysX.Torch** API `here `_. From e971785d32648e98d450a4d955d771deb9ceb88f Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 14 Dec 2022 10:29:09 +0100 Subject: [PATCH 60/61] Fix Dataset converter. --- LICENSE | 0 src/Core/Utils/converter.py | 3 ++- 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..e69de29b diff --git a/src/Core/Utils/converter.py b/src/Core/Utils/converter.py index bec3c387..4302df12 100644 --- a/src/Core/Utils/converter.py +++ b/src/Core/Utils/converter.py @@ -58,6 +58,7 @@ def numpy_to_database(self, new_session=False) database_handler = DatabaseHandler() database_manager.connect_handler(database_handler) + database_manager.first_add = True # 3.2. Create Fields in Tables training_fields = [] @@ -79,6 +80,7 @@ def numpy_to_database(self, # 3.3. Add each partition to the Database if nb_partition == 0: print(" No partition.") + database_manager.normalize = False for i in range(nb_partition): data_training = {field: load(join(self.dataset_dir, f'{partitions[mode][field][i]}.npy')) for field, _ in training_fields} @@ -143,4 +145,3 @@ def load_numpy_partitions(self) -> Dict[str, Dict[str, List[str]]]: all_partitions[mode][name].append(partition) return all_partitions - From acfeda4851fcc229afead13f9437dfa29436aca4 Mon Sep 17 00:00:00 2001 From: robin Date: Wed, 14 Dec 2022 10:29:39 +0100 Subject: [PATCH 61/61] Add license. --- LICENSE | 661 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 8 + 2 files changed, 669 insertions(+) diff --git a/LICENSE b/LICENSE index e69de29b..be3f7b28 100644 --- a/LICENSE +++ b/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md index 4d564c2e..2452a651 100644 --- a/README.md +++ b/README.md @@ -55,3 +55,11 @@ $ DPX --run # Run one of the demo scripts | **Armadillo**
    `DPX -r armadillo` | **Beam**
    `DPX -r beam` | **Liver**
    `DPX -r liver` | |:-----------------------------------------------------:|:-------------------------------------------:|:---------------------------------------------:| | ![armadillo](docs/source/_static/image/armadillo.png) | ![beam](docs/source/_static/image/beam.png) | ![liver](docs/source/_static/image/liver.png) | + + +### References + +Did this project help you for your research ? Please cite us as: + +R. Enjalbert, A. Odot and S. Cotin, *DeepPhysX, a python framework to interface AI with numerical simulation*, +Zenodo, 2022, [**DOI**](https://doi.org/10.5281/zenodo.7389505)

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