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import time |
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from options.train_options import TrainOptions |
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from dataloader.data_loader import dataloader |
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from model 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 = dataloader(opt) |
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dataset_size = len(dataset) * opt.batch_size |
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print('training images = %d' % dataset_size) |
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model = create_model(opt) |
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visualizer = Visualizer(opt) |
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total_iters = opt.iter_count |
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epoch = 0 |
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max_iteration = opt.n_iter + opt.n_iter_decay |
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while (total_iters < max_iteration): |
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epoch_start_time = time.time() |
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iter_data_time = time.time() |
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epoch += 1 |
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epoch_iter = 0 |
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visualizer.reset() |
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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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if total_iters == 0: |
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model.setup(opt) |
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model.parallelize() |
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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.log_imgs() |
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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, total_iters, losses, t_comp, t_data) |
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if opt.display_id is None or 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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print(opt.name) |
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model.save_networks('latest') |
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if total_iters % opt.save_iters_freq == 0: |
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print('saving the model at the end of iters %d' % (total_iters)) |
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model.save_networks('latest') |
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model.save_networks(total_iters) |
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print('End of iters %d / %d \t Time Taken: %d sec' % (total_iters, max_iteration, time.time() - epoch_start_time)) |
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model.update_learning_rate() |