本文整理汇总了Python中data.data_loader.CreateDataLoader方法的典型用法代码示例。如果您正苦于以下问题：Python data_loader.CreateDataLoader方法的具体用法？Python data_loader.CreateDataLoader怎么用？Python data_loader.CreateDataLoader使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类data.data_loader的用法示例。
在下文中一共展示了data_loader.CreateDataLoader方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_func
# 需要导入模块: from data import data_loader [as 别名]
# 或者: from data.data_loader import CreateDataLoader [as 别名]
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() 示例2: prepare_data
# 需要导入模块: from data import data_loader [as 别名]
# 或者: from data.data_loader import CreateDataLoader [as 别名]
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