def test_imagenet(self):
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=256,
shuffle=False)
random.seed(0)
tc_data = []
for i, data in enumerate(train_loader):
tc_data.append(data)
print("get", data[0].shape)
if i == check_num_batch: break
from jittor.dataset.dataset import ImageFolder
import jittor.transform as transform
dataset = ImageFolder(traindir).set_attrs(batch_size=256,
shuffle=False)
dataset.set_attrs(transform=transform.Compose([
transform.RandomCropAndResize(224),
transform.RandomHorizontalFlip(),
transform.ImageNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
random.seed(0)
for i, (images, labels) in enumerate(dataset):
print("compare", i)
assert np.allclose(images.numpy(), tc_data[i][0].numpy())
assert np.allclose(labels.numpy(), tc_data[i][1].numpy())
if i == check_num_batch: break
labels = [path.split('/')[-1].split('.')[0] for path in train_list]
## Split
train_list, valid_list = train_test_split(train_list,
test_size=0.2,
stratify=labels,
random_state=42)
print(f"Train Data: {len(train_list)}")
print(f"Validation Data: {len(valid_list)}")
print(f"Test Data: {len(test_list)}")
## Image Augumentation
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomCropAndResize(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
## Load Datasets
class CatsDogsDataset(Dataset):
def __init__(self,
file_list,
transform=None,
batch_size=1,
shuffle=False,
num_workers=0):
super(CatsDogsDataset, self).__init__(batch_size=batch_size,
shuffle=shuffle,
parser.add_argument('--cuda', action='store_true', help='to use cuda')
args = parser.parse_args()
if not os.path.isdir(args.logdir):
os.mkdir(args.logdir)
logging.basicConfig(filename=os.path.join(args.logdir, 'log.txt'),
level=logging.INFO)
logging.info(sys.argv)
jt.flags.use_cuda = 1 if args.cuda else 0
train_transform = transform.Compose([
transform.RandomHorizontalFlip(),
transform.RandomCropAndResize(32, scale=(0.5, 1)),
transform.ImageNormalize(mean=[0.4914, 0.4822, 0.4465],
std=[0.2023, 0.1994, 0.2010])
])
test_transform = transform.Compose([
transform.ImageNormalize(mean=[0.4914, 0.4822, 0.4465],
std=[0.2023, 0.1994, 0.2010])
])
trainloader = CIFAR10(train=True,
shuffle=True,
batch_size=64,
transform=train_transform)
testloader = CIFAR10(train=False,
shuffle=False,