test.py

"""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.util import save_current_images


if __name__ == '__main__':
    opt = TestOptions().parse()  # get test options

    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
    # 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.


    save_result_dir = os.path.join(opt.results_dir, '%s_%s_%s' % (opt.phase, opt.epoch, opt.data_type))
    if os.path.exists(save_result_dir) is False:
        os.makedirs(save_result_dir)

    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

        img_name = model.get_image_names()
        img_path = os.path.join(save_result_dir, img_name)
        results = model.get_current_results()  # get all kinds of image results
        if i % 5 == 0:  # save images to an HTML file
            print('processing (%04d)-th image... %s/%s' % (i, opt.phase, img_name))
        save_current_images(results, img_path)