71 lines
3.8 KiB
Python
71 lines
3.8 KiB
Python
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"""General-purpose test script for image-to-image translation.
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Once you have trained your model with train.py, you can use this script to test the model.
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It will load a saved model from --checkpoints_dir and save the results to --results_dir.
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It first creates model and dataset given the option. It will hard-code some parameters.
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It then runs inference for --num_test images and save results to an HTML file.
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Example (You need to train models first or download pre-trained models from our website):
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Test a CycleGAN model (both sides):
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python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
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Test a CycleGAN model (one side only):
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python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
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The option '--model test' is used for generating CycleGAN results only for one side.
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This option will automatically set '--dataset_mode single', which only loads the images from one set.
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On the contrary, using '--model cycle_gan' requires loading and generating results in both directions,
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which is sometimes unnecessary. The results will be saved at ./results/.
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Use '--results_dir <directory_path_to_save_result>' to specify the results directory.
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Test a pix2pix model:
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python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
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See options/base_options.py and options/test_options.py for more test 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 os
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from options.test_options import TestOptions
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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 save_images
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from util import html
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import util.util as util
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if __name__ == '__main__':
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opt = TestOptions().parse() # get test options
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# hard-code some parameters for test
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opt.num_threads = 0 # test code only supports num_threads = 1
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opt.batch_size = 1 # test code only supports batch_size = 1
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opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
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opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
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opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
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dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
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# train_dataset = create_dataset(util.copyconf(opt, phase="train"))
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model = create_model(opt) # create a model given opt.model and other options
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# create a webpage for viewing the results
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web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch)) # define the website directory
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print('creating web directory', web_dir)
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webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
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for i, data in enumerate(dataset):
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if i == 0:
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model.data_dependent_initialize(data)
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model.setup(opt) # regular setup: load and print networks; create schedulers
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model.parallelize()
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if opt.eval:
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model.eval()
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if i >= opt.num_test: # only apply our model to opt.num_test images.
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break
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model.set_input(data) # unpack data from data loader
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model.test() # run inference
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visuals = model.get_current_visuals() # get image results
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img_path = model.get_image_paths() # get image paths
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if i % 5 == 0: # save images to an HTML file
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print('processing (%04d)-th image... %s' % (i, img_path))
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save_images(webpage, visuals, img_path, width=opt.display_winsize)
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webpage.save() # save the HTML
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