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Running
| import os | |
| from options.test_options import TestOptions | |
| from data import create_dataset | |
| from models import create_model | |
| from util.visualizer import save_images | |
| from util import html | |
| try: | |
| import wandb | |
| except ImportError: | |
| print('Warning: wandb package cannot be found. The option "--use_wandb" will result in error.') | |
| if __name__ == '__main__': | |
| opt = TestOptions().parse() # get test options | |
| # hard-code some parameters for test | |
| opt.num_threads = 0 # test code only supports num_threads = 0 | |
| opt.batch_size = 1 # test code only supports batch_size = 1 | |
| opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed. | |
| opt.no_flip = True # no flip; comment this line if results on flipped images are needed. | |
| opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file. | |
| dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options | |
| model = create_model(opt) # create a model given opt.model and other options | |
| model.setup(opt) # regular setup: load and print networks; create schedulers | |
| # initialize logger | |
| if opt.use_wandb: | |
| wandb_run = wandb.init(project=opt.wandb_project_name, name=opt.name, config=opt) if not wandb.run else wandb.run | |
| wandb_run._label(repo='CycleGAN-and-pix2pix') | |
| # create a website | |
| web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch)) # define the website directory | |
| if opt.load_iter > 0: # load_iter is 0 by default | |
| web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter) | |
| print('creating web directory', web_dir) | |
| webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch)) | |
| # test with eval mode. This only affects layers like batchnorm and dropout. | |
| # For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode. | |
| # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout. | |
| if opt.eval: | |
| model.eval() | |
| for i, data in enumerate(dataset): | |
| if i >= opt.num_test: # only apply our model to opt.num_test images. | |
| break | |
| model.set_input(data) # unpack data from data loader | |
| model.test() # run inference | |
| visuals = model.get_current_visuals() # get image results | |
| img_path = model.get_image_paths() # get image paths | |
| if i % 5 == 0: # save images to an HTML file | |
| print('processing (%04d)-th image... %s' % (i, img_path)) | |
| save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize, use_wandb=opt.use_wandb) | |
| webpage.save() # save the HTML | |