opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
root_dir = os.path.join(opt.result_root_dir, opt.variable)
web_dir = os.path.join(root_dir, opt.variable_value, opt.phase)
webpage = html.HTML(web_dir,
'Experiment = GAN2C, Phase = test, Epoch = latest')
# test
for i, data in enumerate(dataset):
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
print('process image... %s' % img_path)
visualizer.save_images(webpage, visuals, img_path)
short_path = ntpath.basename(''.join(img_path))
test_depth_errors = model.get_depth_errors()
visualizer.test_depth_errors(i + 1, short_path, test_depth_errors)
webpage.save()
print('creating web directory', web_dir)
webpage = html.HTML(
web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' %
(opt.name, opt.phase, opt.epoch))
#if opt.eval:
# model.eval()
for i, data in enumerate(dataset):
if i > 10:
exit()
#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
teacher.set_input(data)
teacher.test()
model.set_input(
data) # unpack data from dataset and apply preprocessing
model.optimize_parameters()
model.compute_visuals()
visuals = model.get_current_visuals() # get image results
img_path = model.get_image_paths() # get image paths
print(img_path)
#save_images(webpage, visuals, img_path)
#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)
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.
dataset = create_dataset(
opt) # create a dataset given opt.dataset_mode and other options
model = CycleGANModel(
opt) # create a model given opt.model and other options
model.setup(
opt) # regular setup: load and print networks; create schedulers
# create results dir
image_dir = create_results_dir(opt)
# test with eval mode. This only affects layers like batchnorm and dropout.
# 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(opt,
image_dir,
visuals,
img_path,
aspect_ratio=opt.aspect_ratio,
width=opt.display_winsize)