def load_data_transformers(resize_reso=512, crop_reso=448, swap_num=[7, 7]):
center_resize = 600
Normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
data_transforms = {
'swap': transforms.Compose([
transforms.Randomswap((swap_num[0], swap_num[1])),
]),
'common_aug': transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((crop_reso,crop_reso)),
transforms.RandomHorizontalFlip(),
]),
'train_totensor': transforms.Compose([
transforms.Resize((crop_reso, crop_reso)),
# ImageNetPolicy(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'val_totensor': transforms.Compose([
transforms.Resize((crop_reso, crop_reso)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'test_totensor': transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.CenterCrop((crop_reso, crop_reso)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'None': None,
}
return data_transforms
def _train_image_transform(self):
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomRotation(0.2),
transforms.ColorJitter(0.1, 0, 0, 0),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
return transform
def load_transforms(args, random_chance=1):
# s determins random offset factor. If random_chance==0 (e.g. during testing), then we want no random offset.
if random_chance == 0:
s = 0
else:
s = 1
transforms = tfs.ComposeMRI([
tfs.LoadNifti(),
tfs.RandomScaling(scale_range=[.95, 1.05]), #.95,1.05
tfs.RandomRotation(angle_interval=[-10, 10],
rotation_axis=None), #-10,10
tfs.ApplyAffine(so=2, chance=random_chance),
tfs.Gamma(gamma_range=[.9, 1.1], chance=random_chance), #.9,1.1
tfs.ReturnImageData(),
tfs.Crop(dims=args['img_dim'], offset=[0, -0, 0], rand_offset=5 * s),
tfs.ReduceSlices(2, 2),
tfs.PrcCap(),
tfs.UnitInterval(),
tfs.ToTensor(),
])
return transforms
def load_data_transformers(resize_reso=512, crop_reso=448, swap_num=[7, 7]):
center_resize = 600
Normalize = transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
data_transforms = {
'swap':
transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((crop_reso, crop_reso)),
transforms.RandomHorizontalFlip(),
transforms.Randomswap((swap_num[0], swap_num[1])),
]),
'food_swap':
transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.RandomRotation(degrees=90),
#transforms.RandomCrop((crop_reso, crop_reso)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomResizedCrop(size=crop_reso, scale=(0.75, 1)),
transforms.Randomswap((swap_num[0], swap_num[1])),
]),
'food_unswap':
transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.RandomRotation(degrees=90),
#transforms.RandomCrop((crop_reso, crop_reso)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomResizedCrop(size=crop_reso, scale=(0.75, 1)),
]),
'unswap':
transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((crop_reso, crop_reso)),
transforms.RandomHorizontalFlip(),
]),
'train_totensor':
transforms.Compose([
transforms.Resize((crop_reso, crop_reso)),
#ImageNetPolicy(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'val_totensor':
transforms.Compose([
transforms.Resize((crop_reso, crop_reso)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'test_totensor':
transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.CenterCrop((crop_reso, crop_reso)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'None':
None,
'Centered_swap':
transforms.Compose([
transforms.CenterCrop((center_resize, center_resize)),
transforms.Resize((resize_reso, resize_reso)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((crop_reso, crop_reso)),
transforms.RandomHorizontalFlip(),
transforms.Randomswap((swap_num[0], swap_num[1])),
]),
'Centered_unswap':
transforms.Compose([
transforms.CenterCrop((center_resize, center_resize)),
transforms.Resize((resize_reso, resize_reso)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((crop_reso, crop_reso)),
transforms.RandomHorizontalFlip(),
]),
'Tencrop':
transforms.Compose([
transforms.Resize((resize_reso, resize_reso)),
transforms.TenCrop((crop_reso, crop_reso)),
transforms.Lambda(lambda crops: torch.stack(
[transforms.ToTensor()(crop) for crop in crops])),
])
}
return data_transforms
net = ACCNN(n_classes=n_classes,
pre_trained=True,
dataset=dataset,
fold=fold,
virtualize=True,
using_fl=fl).to(DEVICE)
print("------------%s Model Already be Prepared------------" % net_name)
input_img_size = net.input_size
IMG_MEAN = [0.449]
IMG_STD = [0.226]
transform_train = transforms.Compose([
transforms.Resize(
input_img_size
), # 缩放将图片的最小边缩放为 input_img_size，因此如果输入是非正方形的，那么输出也不是正方形的
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(30),
transforms.ToTensor(),
transforms.Normalize(IMG_MEAN, IMG_STD),
])
transform_test = transforms.Compose([
transforms.Resize(
input_img_size
), # 缩放将图片的最小边缩放为 input_img_size，因此如果输入是非正方形的，那么输出也不是正方形的
transforms.ToTensor(),
transforms.Normalize(IMG_MEAN, IMG_STD),
])
print("------------Preparing Data...----------------")
if dataset == "JAFFE":
test_data = JAFFE(is_train=False,
transform=transform_test,
target_type=target_type,
numimage = 6667
print('Dataset:', cfg['dataset'])
print('train images:', train_pd.shape)
print('test images:', test_pd.shape)
print('num classes:', cfg['numcls'])
print('Set transform')
cfg['swap_num'] = 7
data_transforms = {
'swap':
transforms.Compose([
transforms.Resize((512, 512)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((448, 448)),
transforms.RandomHorizontalFlip(),
transforms.Randomswap((cfg['swap_num'], cfg['swap_num'])),
]),
'unswap':
transforms.Compose([
transforms.Resize((512, 512)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((448, 448)),
transforms.RandomHorizontalFlip(),
]),
'totensor':
transforms.Compose([
transforms.Resize((448, 448)),
transforms.ToTensor(),
# Load image
dataset = SLAM('data/SLAM')
data, target = dataset[1]
# Add grid lines (https://stackoverflow.com/a/20473192/5828163)
dx, dy = 80, 80
data[:, ::dy, :] = [0, 0, 0, 0]
data[::dx, :, :] = [0, 0, 0, 0]
target[:, ::dy] = 1
target[::dx, :] = 1
# Plot transformations
plot_data_target(data, target, 'Original')
transform_list = [
transforms.RandomHorizontalFlip(p=1),
transforms.RandomVerticalFlip(p=1),
transforms.MinMaxScaling(),
transforms.RandomElasticDeformation(alpha=200, sigma=10, alpha_affine=40),
transforms.Pad(92, padding_mode='reflect'),
transforms.RandomRotation(180),
transforms.RandomCrop(572, 388),
transforms.ToTensor(),
]
for transform in transform_list:
data, target = transform(data, target)
plot_data_target(data, target, transform.__class__.__name__)
IMG_STD = [0.226]
# for RGB images
# IMG_MEAN = [0.485, 0.456, 0.406]
# IMG_STD = [0.229, 0.224, 0.225]
crop_img_size = int(net.input_size * 1.2)
input_img_size = net.input_size
transform_using_crop = opt.tr_using_crop
if transform_using_crop:
transform_train = transforms.Compose([
transforms.Resize(crop_img_size),
transforms.TenCrop(input_img_size),
transforms.Lambda(lambda crops: torch.stack([
transforms.Normalize(IMG_MEAN, IMG_STD)
(transforms.ToTensor()(transforms.RandomHorizontalFlip()(
transforms.RandomRotation(30)(crop)))) for crop in crops
])),
])
transform_test = transforms.Compose([
transforms.Resize(crop_img_size),
transforms.TenCrop(input_img_size),
transforms.Lambda(lambda crops: torch.stack([
transforms.Normalize(IMG_MEAN, IMG_STD)
(transforms.ToTensor()(crop)) for crop in crops
])),
])
else:
transform_train = transforms.Compose([
transforms.Resize(
input_img_size
), # 缩放将图片的最小边缩放为 input_img_size，因此如果输入是非正方形的，那么输出也不是正方形的