"""Abstract Model class for PyTorch neural networks.""" from abc import ABC, abstractmethod from glob import glob from pathlib import Path import re import shutil from typing import Callable, Dict, Optional, Tuple, Type from loguru import logger import torch from torch import nn from torchsummary import summary WEIGHT_DIRNAME = Path(__file__).parents[1].resolve() / "weights" class Model(ABC): """Abstract Model class with composition of different parts defining a PyTorch neural network.""" def __init__( self, network_fn: Type[nn.Module], network_args: Optional[Dict] = None, data_loader: Optional[Callable] = None, data_loader_args: Optional[Dict] = None, metrics: Optional[Dict] = None, criterion: Optional[Callable] = None, criterion_args: Optional[Dict] = None, optimizer: Optional[Callable] = None, optimizer_args: Optional[Dict] = None, lr_scheduler: Optional[Callable] = None, lr_scheduler_args: Optional[Dict] = None, device: Optional[str] = None, ) -> None: """Base class, to be inherited by model for specific type of data. Args: network_fn (Type[nn.Module]): The PyTorch network. network_args (Optional[Dict]): Arguments for the network. Defaults to None. data_loader (Optional[Callable]): A function that fetches train and val DataLoader. data_loader_args (Optional[Dict]): Arguments for the DataLoader. metrics (Optional[Dict]): Metrics to evaluate the performance with. Defaults to None. criterion (Optional[Callable]): The criterion to evaulate the preformance of the network. Defaults to None. criterion_args (Optional[Dict]): Dict of arguments for criterion. Defaults to None. optimizer (Optional[Callable]): The optimizer for updating the weights. Defaults to None. optimizer_args (Optional[Dict]): Dict of arguments for optimizer. Defaults to None. lr_scheduler (Optional[Callable]): A PyTorch learning rate scheduler. Defaults to None. lr_scheduler_args (Optional[Dict]): Dict of arguments for learning rate scheduler. Defaults to None. device (Optional[str]): Name of the device to train on. Defaults to None. """ # Fetch data loaders. if data_loader_args is not None: self._data_loaders = data_loader(**data_loader_args) dataset_name = self._data_loaders.__name__ self._mapping = self._data_loaders.mapping else: self._mapping = None dataset_name = "*" self._data_loaders = None self._name = f"{self.__class__.__name__}_{dataset_name}_{network_fn.__name__}" if metrics is not None: self._metrics = metrics # Set the device. if device is None: self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu") else: self._device = device # Load network. self._network = None self._network_args = network_args # If no network arguemnts are given, load pretrained weights if they exist. if self._network_args is None: self.load_weights(network_fn) else: self._network = network_fn(**self._network_args) # To device. self._network.to(self._device) # Set criterion. self._criterion = None if criterion is not None: self._criterion = criterion(**criterion_args) # Set optimizer. self._optimizer = None if optimizer is not None: self._optimizer = optimizer(self._network.parameters(), **optimizer_args) # Set learning rate scheduler. self._lr_scheduler = None if lr_scheduler is not None: # OneCycleLR needs the number of steps in an epoch as an input argument. if "OneCycleLR" in str(lr_scheduler): lr_scheduler_args["steps_per_epoch"] = len(self._data_loaders("train")) self._lr_scheduler = lr_scheduler(self._optimizer, **lr_scheduler_args) # Extract the input shape for the torchsummary. if isinstance(self._network_args["input_size"], int): self._input_shape = (1,) + tuple([self._network_args["input_size"]]) else: self._input_shape = (1,) + tuple(self._network_args["input_size"]) # Experiment directory. self.model_dir = None # Flag for stopping training. self.stop_training = False @property def __name__(self) -> str: """Returns the name of the model.""" return self._name @property def input_shape(self) -> Tuple[int, ...]: """The input shape.""" return self._input_shape @property def mapping(self) -> Dict: """Returns the class mapping.""" return self._mapping def eval(self) -> None: """Sets the network to evaluation mode.""" self._network.eval() def train(self) -> None: """Sets the network to train mode.""" self._network.train() @property def device(self) -> str: """Device where the weights are stored, i.e. cpu or cuda.""" return self._device @property def metrics(self) -> Optional[Dict]: """Metrics.""" return self._metrics @property def criterion(self) -> Optional[Callable]: """Criterion.""" return self._criterion @property def optimizer(self) -> Optional[Callable]: """Optimizer.""" return self._optimizer @property def lr_scheduler(self) -> Optional[Callable]: """Learning rate scheduler.""" return self._lr_scheduler @property def data_loaders(self) -> Optional[Dict]: """Dataloaders.""" return self._data_loaders @property def network(self) -> nn.Module: """Neural network.""" return self._network @property def weights_filename(self) -> str: """Filepath to the network weights.""" WEIGHT_DIRNAME.mkdir(parents=True, exist_ok=True) return str(WEIGHT_DIRNAME / f"{self._name}_weights.pt") def summary(self) -> None: """Prints a summary of the network architecture.""" device = re.sub("[^A-Za-z]+", "", self.device) summary(self._network, self._input_shape, device=device) def _get_state_dict(self) -> Dict: """Get the state dict of the model.""" state = {"model_state": self._network.state_dict()} if self._optimizer is not None: state["optimizer_state"] = self._optimizer.state_dict() if self._lr_scheduler is not None: state["scheduler_state"] = self._lr_scheduler.state_dict() return state def load_checkpoint(self, path: Path) -> int: """Load a previously saved checkpoint. Args: path (Path): Path to the experiment with the checkpoint. Returns: epoch (int): The last epoch when the checkpoint was created. """ logger.debug("Loading checkpoint...") if not path.exists(): logger.debug("File does not exist {str(path)}") checkpoint = torch.load(str(path)) self._network.load_state_dict(checkpoint["model_state"]) if self._optimizer is not None: self._optimizer.load_state_dict(checkpoint["optimizer_state"]) if self._lr_scheduler is not None: self._lr_scheduler.load_state_dict(checkpoint["scheduler_state"]) epoch = checkpoint["epoch"] return epoch def save_checkpoint(self, is_best: bool, epoch: int, val_metric: str) -> None: """Saves a checkpoint of the model. Args: is_best (bool): If it is the currently best model. epoch (int): The epoch of the checkpoint. val_metric (str): Validation metric. Raises: ValueError: If the self.model_dir is not set. """ state = self._get_state_dict() state["is_best"] = is_best state["epoch"] = epoch state["network_args"] = self._network_args if self.model_dir is None: raise ValueError("Experiment directory is not set.") self.model_dir.mkdir(parents=True, exist_ok=True) logger.debug("Saving checkpoint...") filepath = str(self.model_dir / "last.pt") torch.save(state, filepath) if is_best: logger.debug( f"Found a new best {val_metric}. Saving best checkpoint and weights." ) shutil.copyfile(filepath, str(self.model_dir / "best.pt")) def load_weights(self, network_fn: Type[nn.Module]) -> None: """Load the network weights.""" logger.debug("Loading network with pretrained weights.") filename = glob(self.weights_filename)[0] if not filename: raise FileNotFoundError( f"Could not find any pretrained weights at {self.weights_filename}" ) # Loading state directory. state_dict = torch.load(filename, map_location=torch.device(self._device)) self._network_args = state_dict["network_args"] weights = state_dict["model_state"] # Initializes the network with trained weights. self._network = network_fn(**self._network_args) self._network.load_state_dict(weights) def save_weights(self, path: Path) -> None: """Save the network weights.""" logger.debug("Saving the best network weights.") shutil.copyfile(str(path / "best.pt"), self.weights_filename)