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"""General-purpose test script for image-to-image translation.
Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from --checkpoints_dir and save the results to --results_dir.
It first creates model and dataset given the option. It will hard-code some parameters.
It then runs inference for --num_test images and save results to an HTML file.
Example (You need to train models first or download pre-trained models from our website):
Test a CycleGAN model (both sides):
python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
Test a CycleGAN model (one side only):
python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
The option '--model test' is used for generating CycleGAN results only for one side.
This option will automatically set '--dataset_mode single', which only loads the images from one set.
On the contrary, using '--model cycle_gan' requires loading and generating results in both directions,
which is sometimes unnecessary. The results will be saved at ./results/.
Use '--results_dir <directory_path_to_save_result>' to specify the results directory.
Test a pix2pix model:
python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
See options/base_options.py and options/test_options.py for more test 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 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
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 = 1
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
# create a website
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.epoch)) # define the website directory
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)
webpage.save() # save the HTML
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