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_presize_combine_tune_transforms():
transforms_presize = A.Compose([
transforms.Transpose(),
transforms.Flip(),
#transforms.PadIfNeeded(600, 800),
crops.RandomResizedCrop(
TARGET_SIZE, TARGET_SIZE,
scale=(.75, 1),
interpolation=cv2.INTER_CUBIC,
p=1.0),
rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99),
])
transforms_postsize = A.Compose([
transforms.CoarseDropout(),
# transforms.CLAHE(p=.1),
transforms.RandomToneCurve(scale=.1),
transforms.RandomBrightnessContrast(
brightness_limit=.1,
contrast_limit=0.2,
p=.7),
transforms.HueSaturationValue(
hue_shift_limit=20,
sat_shift_limit=60,
val_shift_limit=0,
p=0.6),
#transforms.Equalize(p=0.1),
#transforms.FancyPCA(p=0.05),
#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()
])
return transforms_presize, transforms_postsize
def CoarseDropout(max_holes=1,
max_height=30,
max_width=30,
fill_value=0,
p=0.5):
return transforms.CoarseDropout(max_holes=max_holes,
max_height=max_height,
max_width=max_width,
fill_value=fill_value,
p=p)
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()
])
def train_dataloader(self):
augmentations = Compose(
[
A.RandomResizedCrop(
height=self.hparams.sz,
width=self.hparams.sz,
scale=(0.7, 1.0),
),
# AdvancedHairAugmentation(),
A.GridDistortion(),
A.RandomBrightnessContrast(),
A.ShiftScaleRotate(),
A.Flip(p=0.5),
A.CoarseDropout(
max_height=int(self.hparams.sz / 10),
max_width=int(self.hparams.sz / 10),
),
# A.HueSaturationValue(),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255,
),
ToTensorV2(),
]
)
train_ds = MelanomaDataset(
df=self.train_df,
images_path=self.train_images_path,
augmentations=augmentations,
train_or_valid=True,
)
return DataLoader(
train_ds,
# sampler=sampler,
batch_size=self.hparams.bs,
shuffle=True,
num_workers=os.cpu_count(),
pin_memory=True,
)
def get_tta_transforms():
return Compose([
A.RandomResizedCrop(
height=hparams.sz,
width=hparams.sz,
scale=(0.7, 1.0),
),
# AdvancedHairAugmentation(),
A.GridDistortion(),
A.RandomBrightnessContrast(),
A.ShiftScaleRotate(),
A.Flip(p=0.5),
A.CoarseDropout(
max_height=int(hparams.sz / 10),
max_width=int(hparams.sz / 10),
),
# A.HueSaturationValue(),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255,
),
ToTensorV2(),
])
def create_transform(self, args, is_train):
"""
Convert numpy array into Tensor if dataset is for validation.
Apply data augmentation method to train dataset while cv or test if args.use_aug is 1.
is_train: boolean
flg that dataset is for validation in cv or test
return: Compose of albumentations
"""
if is_train and args.use_aug == 1:
transform = A.Compose([
trans.Normalize(mean=self.cifar_10_mean,
std=self.cifar_10_std,
max_pixel_value=1.0),
trans.HorizontalFlip(p=0.5),
trans.ShiftScaleRotate(shift_limit=0,
scale_limit=0.25,
rotate_limit=30,
p=1),
trans.CoarseDropout(max_holes=1,
min_holes=1,
min_width=12,
min_height=12,
max_height=12,
max_width=12,
p=0.5),
ToTensorV2()
])
else:
transform = A.Compose([
trans.Normalize(mean=self.cifar_10_mean,
std=self.cifar_10_std,
max_pixel_value=1.0),
ToTensorV2()
])
return transform