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import os
from options.test_options import TestOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import save_images
from util import html
if __name__ == '__main__':
opt = TestOptions().parse()
# hard-code some parameters for test
opt.num_threads = 1 # test code only supports num_threads = 1
opt.batch_size = 1 # test code only supports batch_size = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
opt.display_id = -1 # no visdom display
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
model = create_model(opt)
model.setup(opt)
# create a website
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.epoch))
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.
# pix2pix: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
# 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:
break
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
if i % 5 == 0:
print('processing (%04d)-th image... %s' % (i, img_path))
save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
# save the website
webpage.save()
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