disvae/training.py

# import imageio
import logging
import os
from collections import defaultdict
from timeit import default_timer

import torch
from torch.nn import functional as F
from tqdm import trange

from disvae.utils.modelIO import save_model

TRAIN_LOSSES_LOGFILE = "train_losses.log"


class Trainer:
    """
    Class to handle training of model.

    Parameters
    ----------
    model: disvae.vae.VAE

    optimizer: torch.optim.Optimizer

    loss_f: disvae.models.BaseLoss
        Loss function.

    device: torch.device, optional
        Device on which to run the code.

    logger: logging.Logger, optional
        Logger.

    save_dir : str, optional
        Directory for saving logs.

    gif_visualizer : viz.Visualizer, optional
        Gif Visualizer that should return samples at every epochs.

    is_progress_bar: bool, optional
        Whether to use a progress bar for training.
    """

    def __init__(
        self,
        model,
        optimizer,
        loss_f,
        device=torch.device("cpu"),
        logger=logging.getLogger(__name__),
        save_dir="results",
        gif_visualizer=None,
        is_progress_bar=True,
    ):
        self.device = device
        self.model = model.to(self.device)
        self.loss_f = loss_f
        self.optimizer = optimizer
        self.save_dir = save_dir
        self.is_progress_bar = is_progress_bar
        self.logger = logger
        self.losses_logger = LossesLogger(
            os.path.join(self.save_dir, TRAIN_LOSSES_LOGFILE)
        )
        self.gif_visualizer = gif_visualizer
        self.logger.info("Training Device: {}".format(self.device))

    def __call__(self, data_loader, epochs=10, checkpoint_every=10):
        """
        Trains the model.

        Parameters
        ----------
        data_loader: torch.utils.data.DataLoader

        epochs: int, optional
            Number of epochs to train the model for.

        checkpoint_every: int, optional
            Save a checkpoint of the trained model every n epoch.
        """
        start = default_timer()
        self.model.train()
        for epoch in range(epochs):
            storer = defaultdict(list)
            mean_epoch_loss = self._train_epoch(data_loader, storer, epoch)
            self.logger.info(
                "Epoch: {} Average loss per image: {:.2f}".format(
                    epoch + 1, mean_epoch_loss
                )
            )
            self.losses_logger.log(epoch, storer)

            if self.gif_visualizer is not None:
                self.gif_visualizer()

            if epoch % checkpoint_every == 0:
                save_model(
                    self.model, self.save_dir, filename="model-{}.pt".format(epoch)
                )

        if self.gif_visualizer is not None:
            self.gif_visualizer.save_reset()

        self.model.eval()

        delta_time = (default_timer() - start) / 60
        self.logger.info("Finished training after {:.1f} min.".format(delta_time))

    def _train_epoch(self, data_loader, storer, epoch):
        """
        Trains the model for one epoch.

        Parameters
        ----------
        data_loader: torch.utils.data.DataLoader

        storer: dict
            Dictionary in which to store important variables for vizualisation.

        epoch: int
            Epoch number

        Return
        ------
        mean_epoch_loss: float
            Mean loss per image
        """
        epoch_loss = 0.0
        kwargs = dict(
            desc="Epoch {}".format(epoch + 1),
            leave=False,
            disable=not self.is_progress_bar,
        )
        with trange(len(data_loader), **kwargs) as t:
            for _, (data, _) in enumerate(data_loader):
                iter_loss = self._train_iteration(data, storer)
                epoch_loss += iter_loss

                t.set_postfix(loss=iter_loss)
                t.update()

        mean_epoch_loss = epoch_loss / len(data_loader)
        return mean_epoch_loss

    def _train_iteration(self, data, storer):
        """
        Trains the model for one iteration on a batch of data.

        Parameters
        ----------
        data: torch.Tensor
            A batch of data. Shape : (batch_size, channel, height, width).

        storer: dict
            Dictionary in which to store important variables for vizualisation.
        """
        batch_size, channel, height, width = data.size()
        data = data.to(self.device)

        try:
            recon_batch, latent_dist, latent_sample = self.model(data)
            loss = self.loss_f(
                data,
                recon_batch,
                latent_dist,
                self.model.training,
                storer,
                latent_sample=latent_sample,
            )
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()

        except ValueError:
            # for losses that use multiple optimizers (e.g. Factor)
            loss = self.loss_f.call_optimize(data, self.model, self.optimizer, storer)

        return loss.item()


class LossesLogger(object):
    """Class definition for objects to write data to log files in a
    form which is then easy to be plotted.
    """

    def __init__(self, file_path_name):
        """Create a logger to store information for plotting."""
        if os.path.isfile(file_path_name):
            os.remove(file_path_name)

        self.logger = logging.getLogger("losses_logger")
        self.logger.setLevel(1)  # always store
        file_handler = logging.FileHandler(file_path_name)
        file_handler.setLevel(1)
        self.logger.addHandler(file_handler)

        header = ",".join(["Epoch", "Loss", "Value"])
        self.logger.debug(header)

    def log(self, epoch, losses_storer):
        """Write to the log file"""
        for k, v in losses_storer.items():
            log_string = ",".join(str(item) for item in [epoch, k, mean(v)])
            self.logger.debug(log_string)


# HELPERS
def mean(l):
    """Compute the mean of a list"""
    return sum(l) / len(l)