use_metric_cuhk03 = False
cuhk03_classic_split = False
########################################################
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
sys.stdout = Logger(osp.join(PATH, 'log_train.txt'))
print("Dataset is being initialized")
dataset = dataset_manager.init_img_dataset(
root='data',
name=dataset_name,
split_id=split_id,
cuhk03_labeled=cuhk03_labeled,
cuhk03_classic_split=cuhk03_classic_split,
)
tfms_train = tfms.Compose([
tfms.Random2DTranslation(height, width),
tfms.RandomHorizontalFlip(),
tfms.ToTensor(),
tfms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
tfms_test = tfms.Compose([
tfms.Resize(size=(height, width)),
tfms.ToTensor(),
tfms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
print_freq = 10
eval_step = 20
start_eval = 0
PATH = 'log'
use_metric_cuhk03 = False
########################################################
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.cuda("cpu")
sys.stdout = Logger(osp.join(PATH, 'log_train.txt'))
print("Dataset is being initialized")
dataset = dataset_manager.init_img_dataset(root='data',
name=dataset_name,
split_id=0)
tfms_train = tfms.Compose([
tfms.Random2DTranslation(256, 128),
tfms.RandomHorizontalFlip(),
tfms.ToTensor(),
tfms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
tfms_test = tfms.Compose([
tfms.Resize((256, 128)),
tfms.ToTensor(),
tfms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])