78 lines
4.8 KiB
Python
78 lines
4.8 KiB
Python
"""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() # get training options
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dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
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dataset_size = len(dataset) # get the number of images in the dataset.
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print('The number of training images = %d' % dataset_size)
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model = create_model(opt) # create a model given opt.model and other options
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model.setup(opt) # regular setup: load and print networks; create schedulers
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visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
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total_iters = 0 # the total number of training iterations
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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>
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epoch_start_time = time.time() # timer for entire epoch
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iter_data_time = time.time() # timer for data loading per iteration
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epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
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visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
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model.update_learning_rate() # update learning rates in the beginning of every epoch.
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for i, data in enumerate(dataset): # inner loop within one epoch
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iter_start_time = time.time() # timer for computation per iteration
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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) # unpack data from dataset and apply preprocessing
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model.optimize_parameters() # calculate loss functions, get gradients, update network weights
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if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
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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: # print training losses and save logging information to the disk
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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: # cache our latest model every <save_latest_freq> iterations
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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: # cache our model every <save_epoch_freq> epochs
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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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