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add aircraft in utilities

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Mhrooz
2024-08-31 15:52:00 +02:00
parent 4df5615380
commit 24f15ad0fe
1 changed files with 16 additions and 2 deletions
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18
src/datasets/utilities.py
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@@ -13,7 +13,8 @@ Dataset2Class = {'cifar10': 10,
'ImageNet16' : 1000, 'ImageNet16' : 1000,
'ImageNet16-120': 120, 'ImageNet16-120': 120,
'ImageNet16-150': 150, 'ImageNet16-150': 150,
'ImageNet16-200': 200} 'ImageNet16-200': 200,
'aircraft': 100}
class RandChannel(object): class RandChannel(object):
# randomly pick channels from input # randomly pick channels from input
@@ -46,6 +47,10 @@ def get_datasets(name, root, input_size, cutout=-1):
elif name.startswith('ImageNet16'): elif name.startswith('ImageNet16'):
mean = [0.481098, 0.45749, 0.407882] mean = [0.481098, 0.45749, 0.407882]
std = [0.247922, 0.240235, 0.255255] std = [0.247922, 0.240235, 0.255255]
elif name == 'aircraft':
mean = [0.4785, 0.5100, 0.5338]
std = [0.1845, 0.1830, 0.2060]
else: else:
raise TypeError("Unknow dataset : {:}".format(name)) raise TypeError("Unknow dataset : {:}".format(name))
@@ -55,6 +60,12 @@ def get_datasets(name, root, input_size, cutout=-1):
if cutout > 0 : lists += [CUTOUT(cutout)] if cutout > 0 : lists += [CUTOUT(cutout)]
train_transform = transforms.Compose(lists) train_transform = transforms.Compose(lists)
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)]) test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
elif name == 'aircraft':
lists = [transforms.RandomCrop(input_size[1], padding=0), transforms.ToTensor(), transforms.Normalize(mean, std)]
if cutout > 0 : lists += [CUTOUT(cutout)]
train_transform = transforms.Compose(lists)
test_transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
elif name.startswith('ImageNet16'): elif name.startswith('ImageNet16'):
lists = [transforms.RandomCrop(input_size[1], padding=0), transforms.ToTensor(), transforms.Normalize(mean, std), RandChannel(input_size[0])] lists = [transforms.RandomCrop(input_size[1], padding=0), transforms.ToTensor(), transforms.Normalize(mean, std), RandChannel(input_size[0])]
if cutout > 0 : lists += [CUTOUT(cutout)] if cutout > 0 : lists += [CUTOUT(cutout)]
@@ -86,9 +97,12 @@ def get_datasets(name, root, input_size, cutout=-1):
train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True) train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
test_data = dset.CIFAR100(root, train=False, transform=test_transform , download=True) test_data = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
assert len(train_data) == 50000 and len(test_data) == 10000 assert len(train_data) == 50000 and len(test_data) == 10000
elif name == 'aircraft':
train_data = dset.ImageFolder(osp.join(root, 'train_sorted_images'), train_transform)
test_data = dset.ImageFolder(osp.join(root, 'test_sorted_images'), test_transform)
elif name.startswith('imagenet-1k'): elif name.startswith('imagenet-1k'):
train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform) train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
test_data = dset.ImageFolder(osp.join(root, 'val'), test_transform) test_data = dset.ImageFolder(osp.join(root, 'test'), test_transform)
elif name == 'ImageNet16': elif name == 'ImageNet16':
root = osp.join(root, 'ImageNet16') root = osp.join(root, 'ImageNet16')
train_data = ImageNet16(root, True , train_transform) train_data = ImageNet16(root, True , train_transform)