Python data.data_loader.CreateDataLoader() Examples

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Example #1
Source File: test_function.py    From non-stationary_texture_syn with MIT License 5 votes vote down vote up 
def test_func(opt_train, webpage, epoch='latest'):
	opt = copy.deepcopy(opt_train)
	print(opt)
	# specify the directory to save the results during training
	opt.results_dir = './results/'
	opt.isTrain = False
	opt.nThreads = 1   # test code only supports nThreads = 1
	opt.batchSize = 1  # test code only supports batchSize = 1
	opt.serial_batches = True  # no shuffle
	opt.no_flip = True  # no flip
	opt.dataroot = opt.dataroot + '/test'
	opt.model = 'test'
	opt.dataset_mode = 'single'
	opt.which_epoch = epoch
	opt.how_many = 50
	opt.phase = 'test'
	# opt.name = name

	data_loader = CreateDataLoader(opt)
	dataset = data_loader.load_data()
	model = create_model(opt)
	visualizer = Visualizer(opt)
	# create website
	# web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
	# web_dir = os.path.join(opt.results_dir, opt.name)
	# webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
	# test
	for i, data in enumerate(dataset):
	    if i >= opt.how_many:
	        break
	    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_epoch(webpage, visuals, img_path, epoch)

	webpage.save()
Example #2
Source File: ocrd_anybaseocr_dewarp.py    From ocrd_anybaseocr with Apache License 2.0 5 votes vote down vote up 
def prepare_data(self, opt, page_img, path):

        sys.path.append(path)
        from data.data_loader import CreateDataLoader
        
        data_loader = CreateDataLoader(opt)
        data_loader.dataset.A_paths = [page_img.filename]
        data_loader.dataset.dataset_size = len(data_loader.dataset.A_paths)
        data_loader.dataloader = torch.utils.data.DataLoader(data_loader.dataset,
                                                             batch_size=opt.batchSize,
                                                             shuffle=not opt.serial_batches,
                                                             num_workers=int(opt.nThreads))
        dataset = data_loader.load_data()
        return dataset   
        # test