def load_labels(data_dir,resize=(224,224)):
data_transforms = {
'train': transforms.Compose([
transforms.RandomSizedCrop(max(resize)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, 'train'), data_transforms[x])
for x in ['train']}
return (dsets['train'].classes)
python类RandomSizedCrop()的实例源码
def load_labels(data_dir,resize=(224,224)):
data_transforms = {
'train': transforms.Compose([
transforms.RandomSizedCrop(max(resize)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, 'train'), data_transforms[x])
for x in ['train']}
return (dsets['train'].classes)
def load_data(resize):
data_transforms = {
'train': transforms.Compose([
transforms.RandomSizedCrop(max(resize)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
#Higher scale-up for inception
transforms.Scale(int(max(resize)/224*256)),
transforms.CenterCrop(max(resize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = 'PlantVillage'
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
for x in ['train', 'val']}
dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=batch_size,
shuffle=True)
for x in ['train', 'val']}
dset_sizes = {x: len(dsets[x]) for x in ['train', 'val']}
dset_classes = dsets['train'].classes
return dset_loaders['train'], dset_loaders['val']
def inception_preproccess(input_size, normalize=__imagenet_stats):
return transforms.Compose([
transforms.RandomSizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**normalize)
])
def inception_color_preproccess(input_size, normalize=__imagenet_stats):
return transforms.Compose([
transforms.RandomSizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
),
Lighting(0.1, __imagenet_pca['eigval'], __imagenet_pca['eigvec']),
transforms.Normalize(**normalize)
])
def inception_preproccess(input_size, normalize=__imagenet_stats):
return transforms.Compose([
transforms.RandomSizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**normalize)
])
def inception_color_preproccess(input_size, normalize=__imagenet_stats):
return transforms.Compose([
transforms.RandomSizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
),
Lighting(0.1, __imagenet_pca['eigval'], __imagenet_pca['eigvec']),
transforms.Normalize(**normalize)
])
transform_rules.py 文件源码
项目：kaggle-nips-adversarial-attacks
作者: EdwardTyantov
项目源码
文件源码
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def imagenet_like():
crop_size = 299#224
train_transformations = transforms.Compose([
transforms.RandomSizedCrop(crop_size),
transforms.RandomHorizontalFlip(),
lambda img: img if random.random() < 0.5 else img.transpose(Image.FLIP_TOP_BOTTOM),
transforms.ToTensor(),
ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
normalize,
])
val_transformations = transforms.Compose([
transforms.CenterCrop(crop_size),
transforms.ToTensor(),
normalize,
])
test_transformation = transforms.Compose([
#TenCropPick(224),
SpatialPick(),
#transforms.CenterCrop(crop_size),
transforms.ToTensor(),
normalize,
])
return {'train': train_transformations, 'val': val_transformations, 'test': test_transformation}
def get_transform(data_name, split_name, opt):
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
t_list = []
if split_name == 'train':
t_list = [transforms.RandomSizedCrop(opt.crop_size),
transforms.RandomHorizontalFlip()]
elif split_name == 'val':
t_list = [transforms.Scale(256), transforms.CenterCrop(224)]
elif split_name == 'test':
t_list = [transforms.Scale(256), transforms.CenterCrop(224)]
t_end = [transforms.ToTensor(), normalizer]
transform = transforms.Compose(t_list + t_end)
return transform
