solver.py

"""Brain Mapper U-Net Solver

Description:

    This folder contains the Pytorch implementation of the core U-net solver, used for training the network.

Usage:

    To use this module, import it and instantiate is as you wish:

        from solver import Solver
"""

import os
import numpy as np
import torch
import glob

from datetime import datetime
from utils.losses import MSELoss
from utils.data_utils import create_folder
from utils.data_logging_utils import LogWriter
from utils.early_stopping import EarlyStopping
from torch.optim import lr_scheduler

checkpoint_directory = 'checkpoints'
checkpoint_extension = 'path.tar'


class Solver():
    """Solver class for the BrainMapper U-net.

    This class contains the pytorch implementation of the U-net solver required for the BrainMapper project.

    Args:
        model (class): BrainMapper model class
        experiment_name (str): Name of the experiment
        device (int/str): Device type used for training (int - GPU id, str- CPU)
        number_of_classes (int): Number of classes
        optimizer (class): Pytorch class of desired optimizer
        optimizer_arguments (dict): Dictionary of arguments to be optimized
        loss_function (func): Function describing the desired loss function
        model_name (str): Name of the model
        labels (arr): Vector/Array of labels (if applicable)
        number_epochs (int): Number of training epochs
        loss_log_period (int): Period for writing loss value
        learning_rate_scheduler_step_size (int): Period of learning rate decay
        learning_rate_scheduler_gamma (int): Multiplicative factor of learning rate decay
        use_last_checkpoint (bool): Flag for loading the previous checkpoint
        experiment_directory (str): Experiment output directory name
        logs_directory (str): Directory for outputing training logs

    Returns:
        trained model - working on this!

    """

    def __init__(self,
                 model,
                 device,
                 number_of_classes,
                 experiment_name,
                 optimizer=torch.optim.Adam,
                 optimizer_arguments={},
                 loss_function=MSELoss(),
                 model_name='BrainMapper',
                 labels=None,
                 number_epochs=10,
                 loss_log_period=5,
                 learning_rate_scheduler_step_size=5,
                 learning_rate_scheduler_gamma=0.5,
                 use_last_checkpoint=True,
                 experiment_directory='experiments',
                 logs_directory='logs'
                 ):

        self.model = model
        self.device = device
        self.optimizer = optimizer(model.parameters(), **optimizer_arguments)

        if torch.cuda.is_available():
            self.loss_function = loss_function.cuda(device)
        else:
            self.loss_function = loss_function

        self.model_name = model_name
        self.labels = labels
        self.number_epochs = number_epochs
        self.loss_log_period = loss_log_period

        # We use a learning rate scheduler, that decays the LR of each paramter group by gamma every step_size epoch.
        self.learning_rate_scheduler = lr_scheduler.StepLR(self.optimizer,
                                                           step_size=learning_rate_scheduler_step_size,
                                                           gamma=learning_rate_scheduler_gamma)

        self.use_last_checkpoint = use_last_checkpoint

        experiment_directory_path = os.path.join(
            experiment_directory, experiment_name)
        self.experiment_directory_path = experiment_directory_path

        create_folder(experiment_directory)
        create_folder(experiment_directory_path)
        create_folder(os.path.join(
            experiment_directory_path, checkpoint_directory))

        self.start_epoch = 1
        self.start_iteration = 1
        # self.best_mean_score = 0
        # self.best_mean_score_epoch = 0

        self.LogWriter = LogWriter(number_of_classes=number_of_classes,
                                   logs_directory=logs_directory,
                                   experiment_name=experiment_name,
                                   use_last_checkpoint=use_last_checkpoint,
                                   labels=labels)

        self.EarlyStopping = EarlyStopping()
        self.early_stop = False

        if use_last_checkpoint:
            self.load_checkpoint()

    def train(self, train_loader, validation_loader):
        """Training Function

        This function trains a given model using the provided training data.

        Args:
            train_loader (class): Combined dataset and sampler, providing an iterable over the training dataset (torch.utils.data.DataLoader)
            validation_loader (class):  Combined dataset and sampler, providing an iterable over the validationing dataset (torch.utils.data.DataLoader)

        Returns:
            trained model
        """

        model, optimizer, learning_rate_scheduler = self.model, self.optimizer, self.learning_rate_scheduler
        dataloaders = {'train': train_loader, 'validation': validation_loader}

        if torch.cuda.is_available():
            torch.cuda.empty_cache()  # clear memory
            model.cuda(self.device)  # Moving the model to GPU

        print('****************************************************************')
        print('TRAINING IS STARTING!')
        print('=====================')
        print('Model Name: {}'.format(self.model_name))
        if torch.cuda.is_available():
            print('Device Type: {}'.format(
                torch.cuda.get_device_name(self.device)))
        else:
            print('Device Type: {}'.format(self.device))
        start_time = datetime.now()
        print('Started At: {}'.format(start_time))
        print('----------------------------------------')

        iteration = self.start_iteration

        for epoch in range(self.start_epoch, self.number_epochs+1):
            print("Epoch {}/{}".format(epoch, self.number_epochs))

            for phase in ['train', 'validation']:
                print('-> Phase: {}'.format(phase))

                losses = []

                if phase == 'train':
                    model.train()
                    learning_rate_scheduler.step()
                else:
                    model.eval()

                for batch_index, sampled_batch in enumerate(dataloaders[phase]):
                    X = sampled_batch[0].type(torch.FloatTensor)
                    y = sampled_batch[1].type(torch.FloatTensor)

                    # We add an extra dimension (~ number of channels) for the 3D convolutions.
                    X = torch.unsqueeze(X, dim=1)
                    y = torch.unsqueeze(y, dim=1)

                    if model.test_if_cuda:
                        X = X.cuda(self.device, non_blocking=True)
                        y = y.cuda(self.device, non_blocking=True)

                    y_hat = model(X)   # Forward pass

                    loss = self.loss_function(y_hat, y)  # Loss computation

                    if phase == 'train':
                        optimizer.zero_grad()  # Zero the parameter gradients
                        loss.backward()  # Backward propagation
                        optimizer.step()

                        if batch_index % self.loss_log_period == 0:

                            self.LogWriter.loss_per_iteration(
                                loss.item(), batch_index, iteration)

                        iteration += 1

                    losses.append(loss.item())                 

                    # Clear the memory

                    del X, y, y_hat, loss
                    torch.cuda.empty_cache()

                    if phase == 'validation':                

                        if batch_index != len(dataloaders[phase]) - 1:
                            print("#", end='', flush=True)
                        else:
                            print("100%", flush=True)

                with torch.no_grad():

                    self.LogWriter.loss_per_epoch(losses, phase, epoch)

                    if phase == 'validation':
                        early_stop, save_checkpoint = self.EarlyStopping(np.mean(losses))
                        self.early_stop = early_stop
                        if save_checkpoint == True:
                            self.save_checkpoint(state={'epoch': epoch + 1,
                                                        'start_iteration': iteration + 1,
                                                        'arch': self.model_name,
                                                        'state_dict': model.state_dict(),
                                                        'optimizer': optimizer.state_dict(),
                                                        'scheduler': learning_rate_scheduler.state_dict()
                                                        },
                                                filename=os.path.join(self.experiment_directory_path, checkpoint_directory,
                                                                    'checkpoint_epoch_' + str(epoch) + '.' + checkpoint_extension)
                                                )

            print("Epoch {}/{} DONE!".format(epoch, self.number_epochs))

            # Early Stop Condition

            if self.early_stop == True:
                print("ATTENTION!: Training stopped early to prevent overfitting!")
                break
            else:
                continue

        self.LogWriter.close()

        print('----------------------------------------')
        print('TRAINING IS COMPLETE!')
        print('=====================')
        end_time = datetime.now()
        print('Completed At: {}'.format(end_time))
        print('Training Duration: {}'.format(end_time - start_time))
        print('****************************************************************')

    def save_checkpoint(self, state, filename):
        """General Checkpoint Save

        This function saves a general checkpoint for inference and/or resuming training

        Args:
            state (dict): Dictionary of all the relevant model components
        """

        torch.save(state, filename)

    def load_checkpoint(self, epoch=None):
        """General Checkpoint Loader

        This function loads a previous checkpoint for inference and/or resuming training

        Args:
            epoch (int): Current epoch value
        """

        if epoch is not None:
            checkpoint_file_path = os.path.join(
                self.experiment_directory_path, checkpoint_directory, 'checkpoint_epoch_' + str(epoch) + '.' + checkpoint_extension)
            self._checkpoint_reader(checkpoint_file_path)
        else:
            universal_path = os.path.join(
                self.experiment_directory_path, checkpoint_directory, '*.' + checkpoint_extension)
            files_in_universal_path = glob.glob(universal_path)

            # We will sort through all the files in path to see which one is most recent

            if len(files_in_universal_path) > 0:
                checkpoint_file_path = max(
                    files_in_universal_path, key=os.path.getatime)
                self._checkpoint_reader(checkpoint_file_path)

            else:
                self.LogWriter.log("No Checkpoint found at {}".format(
                    os.path.join(self.experiment_directory_path, checkpoint_directory)))

    def _checkpoint_reader(self, checkpoint_file_path):
        """Checkpoint Reader

        This private function reads a checkpoint file and then loads the relevant variables

        Args:
            checkpoint_file_path (str): path to checkpoint file
        """

        self.LogWriter.log(
            "Loading Checkpoint {}".format(checkpoint_file_path))

        checkpoint = torch.load(checkpoint_file_path)
        self.start_epoch = checkpoint['epoch']
        self.start_iteration = checkpoint['start_iteration']
        # We are not loading the model_name as we might want to pre-train a model and then use it.
        self.model.load_state_dict = checkpoint['state_dict']
        self.optimizer.load_state_dict = checkpoint['optimizer']
        self.learning_rate_scheduler.load_state_dict = checkpoint['scheduler']

        for state in self.optimizer.state.values():
            for key, value in state.items():
                if torch.is_tensor(value):
                    state[key] = value.to(self.device)

        self.LogWriter.log(
            "Checkpoint Loaded {} - epoch {}".format(checkpoint_file_path, checkpoint['epoch']))