test.py

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
import torch

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()
    im_mse = torch.nn.MSELoss()
    model = create_model(opt)
    model.setup(opt)

    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))
  
    if opt.eval:
        model.eval()
    # PSNR_im = 0.
    # SSIM_im = 0.
    # im_count = 0.
    import time
    start = time.time()
    for i, data in enumerate(dataset):
        # im_count += 1.
        if i >= opt.num_test:
            break
        model.set_input(data)
        model.test()
        visuals = model.get_current_visuals()
        img_path = model.get_image_paths()
        
        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
    end = time.time()
    print("Testing time is:", (end - start))

    webpage.save()