diff --git a/train.py b/train.py index 9ff8b3d3f15..b931d219a9f 100644 --- a/train.py +++ b/train.py @@ -1,78 +1,101 @@ -"""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. +"""General training script for image-to-image translation Example: Train a CycleGAN model: - python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan + python train --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 + python train --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA + +Options: + Available models ('--model') + pix2pix, cyclegan, colorization + + Available datasets ('--dataset_mode') + aligned, unaligned, single, colorization + +Required parameters: + You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model'). -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 +Features: + Loads networks, dataset, etc + 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. + +References: + 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 options.train_options import TrainOptions 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. + # Get training options + opt = TrainOptions().parse() + + dataset = create_dataset(opt) + dataset_size = len(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 - total_iters = 0 # the total number of training iterations + model = create_model(opt) + model.setup(opt) # regular stuff; load and print networks; create schedulers + visualizer = Visualizer(opt) # create a visualiser that displays/saves 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 , + + total_iters = 0 + + # Apparently training is easy enough that we'll do it inline! + for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): 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 + iter_data_time = time.time() # timer for data loading + + epoch_iter = 0 + visualizer.reset() + + for i, data in enumerate(dataset): + iter_start_time = time.time() # timer for computation - 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 total_iters += opt.batch_size epoch_iter += opt.batch_size - model.set_input(data) # unpack data from dataset and apply preprocessing - model.optimize_parameters() # calculate loss functions, get gradients, update network weights - if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file + # this is equivalent to metrics = train(x,y) in the BigGAN code + model.set_input(data) # unpack from the dataset + model.optimize_parameters() + + if total_iters % opt.display_freq == 0: 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 + if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk losses = model.get_current_losses() t_comp = (time.time() - iter_start_time) / opt.batch_size + visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data) if 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 iterations + if total_iters % opt.save_latest_freq == 0: # cache our latest model every iterations print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters)) + 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 epochs + + if epoch % opt.save_epoch_freq == 0: # cache our model every 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. + 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.