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 random_crop(data, label, crop_size):
height, width = crop_size
data, rect = tfs.RandomCrop((height, width))(data)
label = tfs.FixedCrop(*rect)(label)
return data, label
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
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(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
# 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__)