train.py

import time
import torch
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer


if __name__ == "__main__":
    opt = TrainOptions().parse()  # get training options
    dataset = create_dataset(
        opt
    )  # create a dataset given opt.dataset_mode and other options
    dataset_size = len(dataset)  # get the number of images in the dataset.

    model = create_model(opt)
    # create a model given opt.model and other options
    print("The number of training images = %d" % dataset_size)

    visualizer = Visualizer(
        opt
    )  # create a visualizer that display/save images and plots
    opt.visualizer = visualizer
    total_iters = 0  # the total number of training iterations

    optimize_time = 0.1

    times = []
    for epoch in range(
        opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1
    ):  # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
        epoch_start_time = time.time()  # timer for entire epoch
        iter_data_time = time.time()  # timer for data loading per iteration
        epoch_iter = 0  # the number of training iterations in current epoch, reset to 0 every epoch
        visualizer.reset()  # reset the visualizer: make sure it saves the results to HTML at least once every epoch

        dataset.set_epoch(epoch)
        for i, data in enumerate(dataset):  # inner loop within one epoch
            iter_start_time = time.time()  # timer for computation per iteration
            if total_iters % opt.print_freq == 0:
                t_data = iter_start_time - iter_data_time

            batch_size = data["A"].size(0)
            total_iters += batch_size
            epoch_iter += batch_size
            if len(opt.gpu_ids) > 0:
                torch.cuda.synchronize()
            optimize_start_time = time.time()
            if epoch == opt.epoch_count and i == 0:
                model.data_dependent_initialize(data)
                model.setup(
                    opt
                )  # regular setup: load and print networks; create schedulers
                model.parallelize()
            model.set_input(data)  # unpack data from dataset and apply preprocessing
            model.optimize_parameters()  # calculate loss functions, get gradients, update network weights
            if len(opt.gpu_ids) > 0:
                torch.cuda.synchronize()
            optimize_time = (
                time.time() - optimize_start_time
            ) / batch_size * 0.005 + 0.995 * optimize_time

            if (
                total_iters % opt.display_freq == 0
            ):  # display images on visdom and save images to a HTML file
                save_result = total_iters % opt.update_html_freq == 0
                model.compute_visuals()
                visualizer.display_current_results(
                    model.get_current_visuals(), epoch, save_result
                )

            if (
                total_iters % opt.print_freq == 0
            ):  # print training losses and save logging information to the disk
                losses = model.get_current_losses()
                visualizer.print_current_losses(
                    epoch, epoch_iter, losses, optimize_time, t_data
                )
                if opt.display_id is None or opt.display_id > 0:
                    visualizer.plot_current_losses(
                        epoch, float(epoch_iter) / dataset_size, losses
                    )

            if (
                total_iters % opt.save_latest_freq == 0
            ):  # cache our latest model every <save_latest_freq> iterations
                print(
                    "saving the latest model (epoch %d, total_iters %d)"
                    % (epoch, total_iters)
                )
                print(
                    opt.name
                )  # it's useful to occasionally show the experiment name on console
                save_suffix = "iter_%d" % total_iters if opt.save_by_iter else "latest"
                model.save_networks(save_suffix)

            iter_data_time = time.time()

        if (
            epoch % opt.save_epoch_freq == 0
        ):  # cache our model every <save_epoch_freq> epochs
            print(
                "saving the model at the end of epoch %d, iters %d"
                % (epoch, total_iters)
            )
            model.save_networks("latest")
            model.save_networks(epoch)

        print(
            "End of epoch %d / %d \t Time Taken: %d sec"
            % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time)
        )
        model.update_learning_rate()  # update learning rates at the end of every epoch.