pix2pix/train.py

"""General-purpose training script for image-to-image translation.



This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and

different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).

You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').



It first creates model, dataset, and visualizer given the option.

It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models.

The script supports continue/resume training. Use '--continue_train' to resume your previous training.



Example:

    Train a CycleGAN model:

        python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan

    Train a pix2pix model:

        python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA



See options/base_options.py and options/train_options.py for more training options.

See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md

See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md

"""
import time
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
    # opt.serial_batches = True
    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.
    print('The number of training images = %d' % dataset_size)

    model = create_model(opt)      # create a model given opt.model and other options
    model.setup(opt)               # regular setup: load and print networks; create schedulers
    visualizer = Visualizer(opt)   # create a visualizer that display/save images and plots

    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
        model.update_learning_rate()    # update learning rates in the beginning of every epoch.
        for i, data in enumerate(dataset):  # inner loop within one epoch
            iter_start_time = time.time()  # timer for computation per iteration

            epoch_iter += opt.batch_size
            model.set_input_train(data)         # unpack data from dataset and apply preprocessing
            model.optimize_parameters()   # calculate loss functions, get gradients, update network weights

            if epoch_iter == dataset_size:
                model.compute_visuals()
                visualizer.display_current_results(model.get_current_visuals(), epoch, True)

            if epoch_iter % 500 == 0 or epoch_iter == dataset_size:    # print training losses and save logging information to the disk
                losses = model.get_current_losses()
                t_data = iter_start_time - iter_data_time
                t_comp = (time.time() - iter_start_time) / opt.batch_size
                visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)


        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' % epoch)
            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))