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"""General-purpose training script for image-to-image translation. |
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This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and |
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different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization). |
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You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model'). |
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It first creates model, dataset, and visualizer given the option. |
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It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models. |
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The script supports continue/resume training. Use '--continue_train' to resume your previous training. |
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Example: |
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Train a CycleGAN model: |
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python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan |
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Train a pix2pix model: |
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python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA |
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See options/base_options.py and options/train_options.py for more training options. |
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See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md |
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See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md |
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""" |
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import time |
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from options.train_options import TrainOptions |
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from data import create_dataset |
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from models import create_model |
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from util.visualizer import Visualizer |
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if __name__ == '__main__': |
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opt = TrainOptions().parse() |
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dataset = create_dataset(opt) |
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dataset_size = len(dataset) |
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print('The number of training images = %d' % dataset_size) |
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model = create_model(opt) |
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model.setup(opt) |
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visualizer = Visualizer(opt) |
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total_iters = 0 |
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for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): |
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epoch_start_time = time.time() |
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iter_data_time = time.time() |
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epoch_iter = 0 |
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visualizer.reset() |
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model.update_learning_rate() |
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for i, data in enumerate(dataset): |
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iter_start_time = time.time() |
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if total_iters % opt.print_freq == 0: |
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t_data = iter_start_time - iter_data_time |
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total_iters += opt.batch_size |
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epoch_iter += opt.batch_size |
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model.set_input(data) |
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model.optimize_parameters() |
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if total_iters % opt.display_freq == 0: |
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save_result = total_iters % opt.update_html_freq == 0 |
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model.compute_visuals() |
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visualizer.display_current_results(model.get_current_visuals(), epoch, save_result) |
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if total_iters % opt.print_freq == 0: |
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losses = model.get_current_losses() |
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t_comp = (time.time() - iter_start_time) / opt.batch_size |
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visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data) |
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if opt.display_id > 0: |
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visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses) |
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if total_iters % opt.save_latest_freq == 0: |
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print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters)) |
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save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest' |
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model.save_networks(save_suffix) |
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iter_data_time = time.time() |
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if epoch % opt.save_epoch_freq == 0: |
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print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters)) |
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model.save_networks('latest') |
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model.save_networks(epoch) |
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print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time)) |
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