def AlbumentationTrainTransform(self):
tf = tc.Compose([ta.PadIfNeeded(4, 4, always_apply=True),
ta.RandomCrop(height=32, width=32, always_apply=True),
ta.Cutout(num_holes = 1, max_h_size=8, max_w_size=8, always_apply=True),
ta.HorizontalFlip(),
tp.ToTensor(dict (mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
])
return lambda img: tf(image = np.array(img))["image"]
def model12_train_transforms():
transform = C.Compose([
A.PadIfNeeded(min_height=70, min_width=70, border_mode=cv2.BORDER_CONSTANT,
value=0.5),
A.RandomCrop(height=64, width=64),
A.HorizontalFlip(p=0.5),
A.Cutout(num_holes=1, max_h_size=32, max_w_size=32, p=1),
P.ToTensor(dict (mean=(0.4802, 0.4481, 0.3975), std=(0.2302, 0.2265, 0.2262)))
])
return lambda img: transform(image = np.array(img))["image"]
def model11_davidnet_train_transforms():
transform = C.Compose([
A.PadIfNeeded(min_height=36, min_width=36, border_mode=cv2.BORDER_CONSTANT,
value=0.5),
A.RandomCrop(height=32, width=32, p=1),
A.HorizontalFlip(p=0.5),
A.Cutout(num_holes=1, max_h_size=8, max_w_size=8, p=1),
P.ToTensor(dict (mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
])
return lambda img: transform(image = np.array(img))["image"]
def get_presize_combine_transforms_V4():
transforms_presize = A.Compose([
transforms.PadIfNeeded(600, 800),
geometric.Perspective(
scale=[0, .1],
pad_mode=cv2.BORDER_REFLECT,
interpolation=cv2.INTER_AREA, p = .3),
transforms.Flip(),
geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.95, scale_limit=0.0),
crops.RandomResizedCrop(
TARGET_SIZE, TARGET_SIZE,
scale=(config['rrc_scale_min'], config['rrc_scale_max']),
ratio=(.70, 1.4),
interpolation=cv2.INTER_CUBIC,
p=1.0),
transforms.Transpose()
#rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99),
])
transforms_postsize = A.Compose([
#imgaug.IAAPiecewiseAffine(),
transforms.CoarseDropout(),
transforms.CLAHE(p=.1),
transforms.RandomToneCurve(scale=.1, p=0.2),
transforms.RandomBrightnessContrast(
brightness_limit=.1,
contrast_limit=0.4,
p=.8),
transforms.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=50,
val_shift_limit=0,
p=0.5),
transforms.Equalize(p=0.05),
transforms.FancyPCA(p=0.05),
transforms.RandomGridShuffle(p=0.1),
A.OneOf([
transforms.MotionBlur(blur_limit=(3, 9)),
transforms.GaussianBlur(),
transforms.MedianBlur()
], p=0.1),
transforms.ISONoise(p=.2),
transforms.GaussNoise(var_limit=127., p=.3),
A.OneOf([
transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5),
transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3),
], p=.3),
geometric.ElasticTransform(alpha=4, sigma=4, alpha_affine=4, interpolation=cv2.INTER_AREA, p=0.3),
transforms.CoarseDropout(),
transforms.Normalize(),
ToTensorV2()
])
return transforms_presize, transforms_postsize
def get_valid_transforms():
return A.Compose([
transforms.Transpose(),
transforms.PadIfNeeded(600, 800),
rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.90),
crops.RandomResizedCrop(
TARGET_SIZE_VALID, TARGET_SIZE_VALID,
scale=(.75, 1),
interpolation=cv2.INTER_CUBIC,
p=1.0),
transforms.Flip(),
#transforms.RandomToneCurve(scale=.1),
#transforms.RandomBrightnessContrast(brightness_limit=0.0, contrast_limit=0.3, p=.7),
#transforms.HueSaturationValue(hue_shift_limit=10,
# sat_shift_limit=10,
# val_shift_limit=5, p=0.6),
transforms.Normalize(),
ToTensorV2()
])
def get_train_transforms():
return A.Compose([
transforms.PadIfNeeded(600, 800),
geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99, scale_limit=0.8),
geometric.Perspective(pad_mode=cv2.BORDER_REFLECT,interpolation=cv2.INTER_AREA),
crops.RandomResizedCrop(
TARGET_SIZE, TARGET_SIZE,
scale=(config['rrc_scale_min'], config['rrc_scale_max']),
interpolation=cv2.INTER_CUBIC,
p=1.0),
transforms.Transpose(),
transforms.Flip(),
transforms.CoarseDropout(),
transforms.CLAHE(p=.1),
transforms.RandomToneCurve(scale=.1),
transforms.RandomBrightnessContrast(
brightness_limit=.1,
contrast_limit=0.3,
p=.7),
transforms.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=60,
val_shift_limit=0,
p=0.6),
transforms.RandomGridShuffle(p=0.1),
A.OneOf([
transforms.MotionBlur(blur_limit=(3, 9)),
transforms.GaussianBlur(),
transforms.MedianBlur()
], p=0.2),
transforms.ISONoise(p=.3),
transforms.GaussNoise(var_limit=255., p=.3),
A.OneOf([
transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5),
transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3),
], p=.3),
geometric.ElasticTransform(alpha=4, sigma=100, alpha_affine=100, interpolation=cv2.INTER_AREA, p=0.3),
transforms.CoarseDropout(),
transforms.Normalize(),
ToTensorV2()
])
stats = ((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) # normalize image
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
pmda_train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4, padding_mode='reflect'),
transforms.RandomHorizontalFlip(),
transforms.CenterCrop(32),
transforms.ToTensor(),
transforms.Normalize(*stats)
])
train_transform = A.Compose([
albtransforms.PadIfNeeded(min_height=40,
min_width=40,
border_mode=4,
value=[0, 0, 0],
always_apply=False,
p=1.),
#albtransforms.RandomCrop(32,32,always_apply=False, p=1.0),
albtransforms.RandomCrop(32, 32, always_apply=False, p=1.),
#albtransforms.HorizontalFlip(1.0),
albtransforms.HorizontalFlip(0.5),
albtransforms.Cutout(num_holes=8,
max_h_size=8,
max_w_size=8,
always_apply=False,
p=0.1),
A.Normalize(*stats),
ToTensor()
])