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| """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 | |
| python train.py --dataroot E:\Graduate_Student\datasets\GAN\selfie2anime\selfie2anime --name selfie2anime --model cycle_gan | |
| 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 | |
| import os | |
| os.environ['CUDA_VISIBLE_DEVICES'] = "0" | |
| 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. | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 <save_latest_freq> 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 <save_epoch_freq> 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)) | |