def hard_spatial_augmentations(image_size: Tuple[int, int], rot_angle=45):
return A.Compose([
A.OneOf([
A.NoOp(),
A.RandomGridShuffle(grid=(4, 4)),
A.RandomGridShuffle(grid=(3, 3)),
A.RandomGridShuffle(grid=(2, 2)),
]),
A.MaskDropout(max_objects=10),
A.OneOf([
A.ShiftScaleRotate(scale_limit=0.1,
rotate_limit=rot_angle,
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0),
A.NoOp(),
]),
A.OneOf([
A.ElasticTransform(border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0),
A.GridDistortion(border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0),
A.NoOp(),
]),
# D4
A.Compose([A.Transpose(), A.RandomRotate90()]),
])
def subcase3_cls_train_augs(name, **kwargs):
return [
A.Compose([
Colorout(fill=255, same=True),
A.RandomGridShuffle(grid=(4, 4)),
],
p=1.0),
A.Compose([
Colorout(fill=255, same=False),
A.RandomGridShuffle(grid=(4, 4)),
],
p=1.0),
A.Compose([
A.RandomBrightness(0.1),
A.RandomGridShuffle(grid=(4, 4)),
],
p=1.0),
A.Compose([
A.RandomGamma((100, 150)),
A.RandomGridShuffle(grid=(4, 4)),
],
p=1.0),
A.Compose([
A.HueSaturationValue(),
A.RandomGridShuffle(grid=(4, 4)),
],
p=1.0),
]
def __init__(self, outputs=6):
super().__init__()
self.net = models.resnet34(True)
self.linear = Sequential(ReLU(), Dropout(), Linear(1000, outputs))
df = pd.read_csv(
"/home/dipet/kaggle/prostate/input/prostate-cancer-grade-assessment/train.csv"
)
self.train_df, self.valid_df = train_test_split(df, test_size=0.2)
self.data_dir = "/datasets/panda/train_128_100"
self.train_transforms = A.Compose([
A.InvertImg(p=1),
A.RandomGridShuffle(grid=(10, 10)),
A.RandomScale(0.1),
A.PadIfNeeded(1280, 1280),
A.RandomSizedCrop([1000, 1280], 1280, 1280),
A.Flip(),
A.Rotate(90),
A.RandomBrightnessContrast(0.02, 0.02),
A.HueSaturationValue(0, 10, 10),
A.Normalize(mean, std, 1),
])
self.valid_transforms = A.Compose([
A.InvertImg(p=1),
A.Normalize(mean, std, 1),
])
def __init__(self, outputs=6):
super().__init__()
self.net = models.resnet34(True)
self.linear = Linear(1000, outputs)
df = pd.read_csv("/home/dipet/kaggle/prostate/input/prostate-cancer-grade-assessment/train.csv")
self.train_df, self.valid_df = train_test_split(df, test_size=0.2)
self.data_dir = "/datasets/panda/train_64_100"
self.train_transforms = A.Compose(
[
A.Compose(
[
A.OneOf([A.GaussNoise(), A.MultiplicativeNoise(elementwise=True)]),
A.RandomBrightnessContrast(0.02, 0.02),
A.HueSaturationValue(0, 10, 10),
A.Flip(),
A.RandomGridShuffle(grid=(10, 10)),
A.GridDistortion(),
A.Rotate()
],
p=0.5,
),
A.ToFloat(),
]
)
self.valid_transforms = A.Compose([A.ToFloat()])
def on_epoch_start(self, trainer, pl_module):
if pl_module.hparams.progressive:
ind = int(trainer.current_epoch / 5) if trainer.current_epoch < 5*3 else 3
prog = [512, 1024, 1536, 2048]
#batch = [32, 16, 8, 4]
batch = [64, 32, 16, 8]
# For Progressive Resizing
train_transform = A.Compose([
A.RandomResizedCrop(height=prog[ind], width=prog[ind], scale=(0.8, 1.0), ratio=(1, 1), interpolation=1, always_apply=False, p=1.0),
A.Flip(always_apply=False, p=0.5),
A.RandomGridShuffle(grid=(4, 4), always_apply=False, p=1.0),
A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10, always_apply=False, p=0.5),
A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, brightness_by_max=True, always_apply=False, p=0.5),
A.GaussNoise(var_limit=(10.0, 50.0), mean=0, always_apply=False, p=0.5),
#A.Rotate(limit=90, interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, p=0.5),
A.ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.0, rotate_limit=45, interpolation=1, border_mode=4, value=255, mask_value=None, always_apply=False, p=0.5),
A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0, always_apply=False, p=1.0),
A.Cutout(num_holes=8, max_h_size=8, max_w_size=8, fill_value=0, always_apply=False, p=0.5),
])
valid_transform = A.Compose([A.Resize(height=prog[ind], width=prog[ind], interpolation=1, always_apply=False, p=1.0),
A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0, always_apply=False, p=1.0)
])
pl_module.train_dataset = PANDADataset(pl_module.train_df, pl_module.hparams.data_dir, pl_module.hparams.image_format, transform=train_transform, tile=pl_module.hparams.tile, layer=pl_module.hparams.image_layer)
pl_module.val_dataset = PANDADataset(pl_module.val_df, pl_module.hparams.data_dir, pl_module.hparams.image_format, transform=valid_transform, tile=pl_module.hparams.tile, layer=pl_module.hparams.image_layer)
trainer.train_dataloader = DataLoader(pl_module.train_dataset, batch_size=batch[ind],
shuffle=True, num_workers=4, drop_last=True)
trainer.val_dataloaders = [DataLoader(pl_module.val_dataset, batch_size=batch[ind],
shuffle=True, num_workers=4, drop_last=True)]
trainer.num_training_batches = len(trainer.train_dataloader)#float('inf')
trainer.num_val_batches = len(trainer.val_dataloaders[0])#float('inf')
trainer.val_check_batch = trainer.num_training_batches
def get_hard_augmentations(image_size):
return A.Compose([
A.OneOf([
A.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.1,
rotate_limit=45,
border_mode=cv2.BORDER_CONSTANT, value=0),
A.ElasticTransform(alpha_affine=0,
alpha=35,
sigma=5,
border_mode=cv2.BORDER_CONSTANT,
value=0),
A.OpticalDistortion(distort_limit=0.11, shift_limit=0.15,
border_mode=cv2.BORDER_CONSTANT,
value=0),
A.GridDistortion(border_mode=cv2.BORDER_CONSTANT,
value=0),
A.NoOp()
]),
A.OneOf([
A.RandomSizedCrop(min_max_height=(int(image_size[0] * 0.75), image_size[0]),
height=image_size[0],
width=image_size[1], p=0.3),
A.NoOp()
]),
A.ISONoise(p=0.5),
# Brightness/contrast augmentations
A.OneOf([
A.RandomBrightnessContrast(brightness_limit=0.5,
contrast_limit=0.4),
IndependentRandomBrightnessContrast(brightness_limit=0.25,
contrast_limit=0.24),
A.RandomGamma(gamma_limit=(50, 150)),
A.NoOp()
]),
A.OneOf([
A.RGBShift(r_shift_limit=40, b_shift_limit=30, g_shift_limit=30),
A.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=10),
A.ToGray(p=0.2),
A.NoOp()
]),
A.ChannelDropout(),
A.RandomGridShuffle(p=0.3),
# D4
A.Compose([
A.RandomRotate90(),
A.Transpose()
])
])
def medium_spatial_augmentations(image_size: Tuple[int, int],
no_mask_dropout=False):
return A.Compose([
A.OneOf([
A.NoOp(p=0.8),
A.RandomGridShuffle(grid=(4, 4), p=0.2),
A.RandomGridShuffle(grid=(3, 3), p=0.2),
A.RandomGridShuffle(grid=(2, 2), p=0.2),
],
p=1),
A.ShiftScaleRotate(scale_limit=0.1,
rotate_limit=10,
border_mode=cv2.BORDER_CONSTANT),
# D4 Augmentations
A.Compose([A.Transpose(), A.RandomRotate90()]),
# Spatial-preserving augmentations:
A.RandomBrightnessContrast(),
A.NoOp() if no_mask_dropout else A.MaskDropout(max_objects=10),
])
def subcase5_cls_train_augs(name, **kwargs):
return [
A.Compose([
A.RandomBrightness(),
A.RandomGridShuffle(grid=(4, 4)),
],
p=1.0),
A.Compose([
A.RandomGamma(),
A.RandomGridShuffle(grid=(4, 4)),
], p=1.0),
A.Compose([
A.HueSaturationValue(),
A.RandomGridShuffle(grid=(4, 4)),
],
p=1.0),
A.Compose([
A.RandomGridShuffle(grid=(4, 4)),
], p=1.0),
]
def __init__(self, size):
if isinstance(size,int):
size = (size,size)
self.A_Compose = A.Compose([A.RandomGridShuffle(always_apply=False, p=0.05, grid=(2, 2)),
A.transforms.GridDistortion(num_steps=5, distort_limit=0.4, p=0.5),
A.HorizontalFlip(p=0.5),
A.RandomResizedCrop(always_apply=True, p=1.0, height=size[0], width=size[0],
scale=(0.75, 1),
ratio=(0.98, 1.02), interpolation=3),#ONLY 3 SEEMS TO WORK
A.transforms.ColorJitter(brightness=0.05, contrast=0.07, saturation=0.04, hue=0.07, always_apply=False, p=0.6),#HSV random
A.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
ToTensorV2()
])
def __init__(self, cfg, data, transform=None, train=True):
self.cfg = cfg
self.train = train
self.data = data
self.transform = transform
if self.cfg.gridshuffle:
self.gridshuffle = A.OneOf(
[
A.RandomGridShuffle((2, 2), p=1.0),
# A.RandomGridShuffle((4, 2), p=1.0),
],
p=0.5)
else:
self.gridshuffle = None
def __init__(self, mode=None, resize=224):
# assert type(resize) == list, f'resize type is not list '
if mode == 'train_tfms_mask':
self.transform = A.Compose([
A.OneOf([
A.Perspective(p=1.0),
A.Rotate(limit=20, p=1.0, border_mode=1),
],
p=0.5),
A.OneOf([
A.RandomBrightness(p=1.0),
A.HueSaturationValue(p=1.0),
A.RandomContrast(p=1.0),
],
p=0.5),
A.Compose([
A.Resize(resize, resize),
A.Normalize(),
])
])
elif mode == 'train_age_gender':
self.transform = A.Compose([
A.Rotate(limit=20, p=0.5, border_mode=1),
A.OneOf(
[
A.RandomGridShuffle(grid=(2, 2),
p=1.0), # not using for gender
# A.RandomGridShuffle(grid=(4, 2), p=1.0),
A.Perspective(p=1.0)
],
p=0.5),
A.GaussNoise(p=0.5),
A.Compose([
A.Resize(resize, resize),
A.Normalize(),
])
])
# elif mode =
elif mode == 'valid_tfms':
self.transform = A.Compose([
A.Resize(resize, resize),
A.Normalize(),
])
def get_train_transforms_v2():
return A.Compose(
[
A.Resize(config.SIZE, config.SIZE),
A.OneOf(
[
A.GaussNoise(var_limit=1.15),
A.MultiplicativeNoise(multiplier=1.1),
],
p=0.2,
),
A.RandomBrightnessContrast(
contrast_limit=0.12, brightness_limit=0.12, p=0.2
),
A.OpticalDistortion(distort_limit=0.07, shift_limit=0.07, p=0.25),
A.GaussianBlur(p=0.15),
A.RandomGridShuffle(grid=(4, 4), p=0.2),
ToTensorV2(),
]
)
def hard_augmentations():
return A.Compose([
A.RandomRotate90(),
A.Transpose(),
A.RandomGridShuffle(),
A.ShiftScaleRotate(scale_limit=0.1,
rotate_limit=45,
border_mode=cv2.BORDER_CONSTANT,
mask_value=0,
value=0),
A.ElasticTransform(border_mode=cv2.BORDER_CONSTANT,
alpha_affine=5,
mask_value=0,
value=0),
# Add occasion blur
A.OneOf([
A.GaussianBlur(),
A.GaussNoise(),
A.IAAAdditiveGaussianNoise(),
A.NoOp()
]),
# D4 Augmentations
A.OneOf([A.CoarseDropout(),
A.MaskDropout(max_objects=10),
A.NoOp()]),
# Spatial-preserving augmentations:
A.OneOf([
A.RandomBrightnessContrast(brightness_by_max=True),
A.CLAHE(),
A.HueSaturationValue(),
A.RGBShift(),
A.RandomGamma(),
A.NoOp(),
]),
# Weather effects
A.OneOf([
A.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1),
A.NoOp()
]),
A.Normalize(),
])
def hard_augmentations(mask_dropout=True) -> List[A.DualTransform]:
return [
# D4 Augmentations
A.RandomRotate90(p=1),
A.Transpose(p=0.5),
# Spatial augmentations
A.OneOf(
[
A.ShiftScaleRotate(scale_limit=0.2, rotate_limit=45, border_mode=cv2.BORDER_REFLECT101),
A.ElasticTransform(border_mode=cv2.BORDER_REFLECT101, alpha_affine=5),
]
),
# Color augmentations
A.OneOf(
[
A.RandomBrightnessContrast(brightness_by_max=True),
A.CLAHE(),
A.FancyPCA(),
A.HueSaturationValue(),
A.RGBShift(),
A.RandomGamma(),
]
),
# Dropout & Shuffle
A.OneOf(
[
A.RandomGridShuffle(),
A.CoarseDropout(),
A.MaskDropout(max_objects=2, mask_fill_value=0) if mask_dropout else A.NoOp(),
]
),
# Add occasion blur
A.OneOf([A.GaussianBlur(), A.GaussNoise(), A.IAAAdditiveGaussianNoise()]),
# Weather effects
A.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1),
]
#scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.33, patience=2, verbose=True)
scheduler = OneCycleLRWithWarmup(optimizer,
num_steps=EPOCHS,
lr_range=(0.003, 0.0001),
warmup_steps=1)
LOGDIR = os.path.join(BASE_LOGDIR, f"fold{i}")
train_files = image_files[train_inds]
valid_files = image_files[test_inds]
train_ds = tu.ITSDataset(train_files,
train_transforms=[
albu.VerticalFlip(),
albu.HorizontalFlip(),
albu.ShiftScaleRotate(),
albu.RandomGamma(),
albu.RandomGridShuffle(),
albu.HueSaturationValue()
])
val_ds = tu.ITSDataset(valid_files)
train_loader = DataLoader(train_ds,
batch_size=BATCH_SIZE,
num_workers=6,
shuffle=True)
val_loader = DataLoader(val_ds,
batch_size=BATCH_SIZE,
num_workers=6,
shuffle=False)
loaders = OrderedDict()
loaders["train"] = train_loader
def get_transform_imagenet(use_albu_aug):
if use_albu_aug:
train_transform = al.Compose([
# al.Flip(p=0.5),
al.Resize(256, 256, interpolation=2),
al.RandomResizedCrop(224,
224,
scale=(0.08, 1.0),
ratio=(3. / 4., 4. / 3.),
interpolation=2),
al.HorizontalFlip(),
al.OneOf(
[
al.OneOf(
[
al.ShiftScaleRotate(
border_mode=cv2.BORDER_CONSTANT,
rotate_limit=30), # , p=0.05),
al.OpticalDistortion(
border_mode=cv2.BORDER_CONSTANT,
distort_limit=5.0,
shift_limit=0.1),
# , p=0.05),
al.GridDistortion(border_mode=cv2.BORDER_CONSTANT
), # , p=0.05),
al.ElasticTransform(
border_mode=cv2.BORDER_CONSTANT,
alpha_affine=15), # , p=0.05),
],
p=0.1),
al.OneOf(
[
al.RandomGamma(), # p=0.05),
al.HueSaturationValue(), # p=0.05),
al.RGBShift(), # p=0.05),
al.CLAHE(), # p=0.05),
al.ChannelShuffle(), # p=0.05),
al.InvertImg(), # p=0.05),
],
p=0.1),
al.OneOf(
[
al.RandomSnow(), # p=0.05),
al.RandomRain(), # p=0.05),
al.RandomFog(), # p=0.05),
al.RandomSunFlare(num_flare_circles_lower=1,
num_flare_circles_upper=2,
src_radius=110),
# p=0.05, ),
al.RandomShadow(), # p=0.05),
],
p=0.1),
al.RandomBrightnessContrast(p=0.1),
al.OneOf(
[
al.GaussNoise(), # p=0.05),
al.ISONoise(), # p=0.05),
al.MultiplicativeNoise(), # p=0.05),
],
p=0.1),
al.OneOf(
[
al.ToGray(), # p=0.05),
al.ToSepia(), # p=0.05),
al.Solarize(), # p=0.05),
al.Equalize(), # p=0.05),
al.Posterize(), # p=0.05),
al.FancyPCA(), # p=0.05),
],
p=0.1),
al.OneOf(
[
# al.MotionBlur(blur_limit=1),
al.Blur(blur_limit=[3, 5]),
al.MedianBlur(blur_limit=[3, 5]),
al.GaussianBlur(blur_limit=[3, 5]),
],
p=0.1),
al.OneOf(
[
al.CoarseDropout(), # p=0.05),
al.Cutout(), # p=0.05),
al.GridDropout(), # p=0.05),
al.ChannelDropout(), # p=0.05),
al.RandomGridShuffle(), # p=0.05),
],
p=0.1),
al.OneOf(
[
al.Downscale(), # p=0.1),
al.ImageCompression(quality_lower=60), # , p=0.1),
],
p=0.1),
],
p=0.5),
al.Normalize(),
ToTensorV2()
])
else:
train_transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
])
if use_albu_aug:
train_transform = MultiDataTransformAlbu(train_transform)
else:
train_transform = MultiDataTransform(train_transform)
return train_transform, test_transform
def get_aug(aug_type="val", size=256):
"""aug_type (str): one of `val`, `test`, `light`, `medium`, `hard`
size (int): final size of the crop"""
NORM_TO_TENSOR = albu.Compose([albu.Normalize(), ToTensor()])
CROP_AUG = albu.RandomResizedCrop(size, size, scale=(0.05, 0.4))
VAL_AUG = albu.Compose([
albu.CenterCrop(size, size),
NORM_TO_TENSOR,
])
TEST_AUG = albu.Compose([
albu.Resize(size, size),
NORM_TO_TENSOR,
])
LIGHT_AUG = albu.Compose([
CROP_AUG,
albu.Flip(),
albu.RandomRotate90(),
NORM_TO_TENSOR,
])
MEDIUM_AUG = albu.Compose([
CROP_AUG,
albu.Flip(),
albu.ShiftScaleRotate(), # border_mode=cv2.BORDER_CONSTANT
# Add occasion blur/sharpening
albu.OneOf([albu.GaussianBlur(),
albu.IAASharpen(),
albu.NoOp()]),
# Spatial-preserving augmentations:
# albu.OneOf([albu.CoarseDropout(), albu.MaskDropout(max_objects=5), albu.NoOp()]),
albu.GaussNoise(),
albu.OneOf([
albu.RandomBrightnessContrast(),
albu.CLAHE(),
albu.HueSaturationValue(),
albu.RGBShift(),
albu.RandomGamma(),
]),
# Weather effects
albu.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1),
NORM_TO_TENSOR,
])
HARD_AUG = albu.Compose([
CROP_AUG,
albu.RandomRotate90(),
albu.Transpose(),
albu.RandomGridShuffle(p=0.2),
albu.ShiftScaleRotate(scale_limit=0.1, rotate_limit=45, p=0.2),
albu.ElasticTransform(alpha_affine=5, p=0.2),
# Add occasion blur
albu.OneOf([
albu.GaussianBlur(),
albu.GaussNoise(),
albu.IAAAdditiveGaussianNoise(),
albu.NoOp()
]),
# D4 Augmentations
albu.OneOf([albu.CoarseDropout(), albu.NoOp()]),
# Spatial-preserving augmentations:
albu.OneOf([
albu.RandomBrightnessContrast(brightness_by_max=True),
albu.CLAHE(),
albu.HueSaturationValue(),
albu.RGBShift(),
albu.RandomGamma(),
albu.NoOp(),
]),
# Weather effects
albu.OneOf([
albu.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1),
albu.NoOp()
]),
NORM_TO_TENSOR,
])
types = {
"val": VAL_AUG,
"test": TEST_AUG,
"light": LIGHT_AUG,
"medium": MEDIUM_AUG,
"hard": HARD_AUG,
}
return types[aug_type]
def set_transforms(self) -> dict:
d4_tansforms = [
A.SmallestMaxSize(self.img_size[0], interpolation=0, p=1.),
# D4 Group augmentations
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.RandomRotate90(p=0.5),
A.Transpose(p=0.5),
]
tensor_norm = [
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
geometric = [
A.SmallestMaxSize(self.img_size[0], interpolation=0, p=1.),
A.ShiftScaleRotate(shift_limit=self.shift_limit,
self.scale_limit,
rotate_limit=self.rotate_limit,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0,
p=0.5),
# crop and resize
A.RandomSizedCrop(
(self.crop_size[0],
min(self.crop_size[1], self.img_size[0], self.img_size[1])),
self.img_size[0],
self.img_size[1],
w2h_ratio=1.0,
interpolation=cv2.INTER_LINEAR,
always_apply=False,
p=0.2),
]
resize_crop = [
A.SmallestMaxSize(max(self.img_size[0], self.img_size[1]),
interpolation=0,
p=1.),
A.RandomCrop(self.img_size[0], self.img_size[1], p=1.),
]
train_light = [
A.SmallestMaxSize(self.img_size[0], interpolation=0, p=1.),
A.RandomCrop(self.img_size[0], self.img_size[1], p=1.),
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(p=0.5),
]
train_medium = train_light.append([
A.ShiftScaleRotate(shift_limit=self.shift_limit,
self.scale_limit,
rotate_limit=self.rotate_limit,
p=0.5),
A.OneOf([
A.RandomBrightnessContrast(p=0.7),
A.Equalize(p=0.3),
A.HueSaturationValue(p=0.5),
A.RGBShift(p=0.5),
A.RandomGamma(p=0.4),
A.ChannelShuffle(p=0.05),
],
p=0.9),
A.OneOf([
A.GaussNoise(p=0.5),
A.ISONoise(p=0.5),
A.MultiplicativeNoise(0.5),
],
p=0.2),
])
valid_ade = [
A.SmallestMaxSize(self.img_size, p=1.),
A.Lambda(name="Pad32", image=pad_x32, mask=pad_x32),
]
# from bloodaxe
# https://github.com/BloodAxe/Catalyst-Inria-Segmentation-Example/blob/master/inria/augmentations.py
crop = [ #(image_size: Tuple[int, int], min_scale=0.75, max_scale=1.25, input_size=5000):
A.OneOrOther(
A.RandomSizedCrop((self.crop_size[0],
min(self.crop_size[1], self.img_size[0],
self.img_size[1])), self.img_size[0],
self.img_size[1]),
A.CropNonEmptyMaskIfExists(self.img_size[0], self.img_size[1]),
)
]
safe_augmentations = [A.HorizontalFlip(), A.RandomBrightnessContrast()]
light_augmentations = [
A.HorizontalFlip(),
A.RandomBrightnessContrast(),
A.OneOf([
A.ShiftScaleRotate(scale_limit=self.scale_limit,
rotate_limit=self.rotate_limit,
border_mode=cv2.BORDER_CONSTANT),
A.IAAAffine(),
A.IAAPerspective(),
A.NoOp()
]),
A.HueSaturationValue(),
]
medium_augmentations = [
A.HorizontalFlip(),
A.ShiftScaleRotate(scale_limit=self.scale_limit,
rotate_limit=self.rotate_limit,
border_mode=cv2.BORDER_CONSTANT),
# Add occasion blur/sharpening
A.OneOf([A.GaussianBlur(),
A.IAASharpen(),
A.NoOp()]),
# Spatial-preserving augmentations:
A.OneOf(
[A.CoarseDropout(),
A.MaskDropout(max_objects=5),
A.NoOp()]),
A.GaussNoise(),
A.OneOf([
A.RandomBrightnessContrast(),
A.CLAHE(),
A.HueSaturationValue(),
A.RGBShift(),
A.RandomGamma()
]),
# Weather effects
A.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1),
]
hard_augmentations = [
A.RandomRotate90(),
A.Transpose(),
A.RandomGridShuffle(),
A.ShiftScaleRotate(scale_limit=self.scale_limit,
rotate_limit=self.rotate_limit,
border_mode=cv2.BORDER_CONSTANT,
mask_value=0,
value=0),
A.ElasticTransform(border_mode=cv2.BORDER_CONSTANT,
alpha_affine=5,
mask_value=0,
value=0),
# Add occasion blur
A.OneOf([
A.GaussianBlur(),
A.GaussNoise(),
A.IAAAdditiveGaussianNoise(),
A.NoOp()
]),
# D4 Augmentations
A.OneOf(
[A.CoarseDropout(),
A.MaskDropout(max_objects=10),
A.NoOp()]),
# Spatial-preserving augmentations:
A.OneOf([
A.RandomBrightnessContrast(brightness_by_max=True),
A.HueSaturationValue(),
A.RGBShift(),
A.RandomGamma(),
A.NoOp(),
]),
# Weather effects
A.OneOf([
A.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1),
A.NoOp()
]),
]
TRANSFORMS = {
"d4": d4_tansforms,
"resize_crop": resize_crop,
"geometric": geometric,
"light": train_light,
"medium": train_medium,
"ade_valid": valid_ade,
"flip_bright": safe_augmentations,
"crop": crop,
"inria_light": light_augmentations,
"inria_medium": medium_augmentations,
"inria_hard": hard_augmentations,
"inria_valid": safe_augmentations,
}
return TRANSFORMS
def get_transforms(self):
augs = []
if self.augs_name:
augs = self.set_transforms[self.augs_name]
augs.append(
A.Resize(height=self.img_size[0],
width=self.img_size[1],
p=1.0))
if self.use_d4:
augs.append(self.set_transforms["d4"])
if self.vflip:
augs.append(A.VerticalFlip(p=0.5))
if self.hflip:
augs.append(A.HorizontalFlip(p=0.5))
if self.normalise:
augs.append(A.Normalize())
return A.Compose(augs)
A.GaussNoise(),
A.OneOf([
A.RandomBrightnessContrast(),
A.CLAHE(),
A.HueSaturationValue(),
A.RGBShift(),
A.RandomGamma()
]),
# Weather effects
A.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1),
])
hard_augmentations = A.Compose([
A.RandomRotate90(),
A.Transpose(),
A.RandomGridShuffle(),
A.ShiftScaleRotate(scale_limit=0.3,
rotate_limit=7,
border_mode=cv2.BORDER_CONSTANT,
mask_value=0,
value=0),
A.ElasticTransform(border_mode=cv2.BORDER_CONSTANT,
alpha_affine=5,
mask_value=0,
value=0),
# Add occasion blur
A.OneOf([
A.GaussianBlur(),
A.GaussNoise(),
A.IAAAdditiveGaussianNoise(),
A.NoOp()
def get_train_transforms_mmdetection(input_size,
use_crop=False,
use_no_color_aug=False,
use_center_crop=False,
center_crop_ratio=0.9,
use_gray=False):
if isinstance(input_size, int):
input_size = (input_size[0], input_size[1])
return al.Compose([
al.RandomResizedCrop(height=input_size[0],
width=input_size[1],
scale=(0.4, 1.0),
interpolation=0,
p=0.5),
al.Resize(input_size[0], input_size[1], p=1.0),
al.HorizontalFlip(p=0.5),
al.OneOf([
al.ShiftScaleRotate(border_mode=0,
shift_limit=(-0.2, 0.2),
scale_limit=(-0.2, 0.2),
rotate_limit=(-20, 20)),
al.OpticalDistortion(border_mode=0,
distort_limit=[-0.5, 0.5],
shift_limit=[-0.5, 0.5]),
al.GridDistortion(
num_steps=5, distort_limit=[-0., 0.3], border_mode=0),
al.ElasticTransform(border_mode=0),
al.IAAPerspective(),
al.RandomGridShuffle()
],
p=0.1),
al.Rotate(limit=(-25, 25), border_mode=0, p=0.1),
al.OneOf([
al.RandomBrightnessContrast(brightness_limit=(-0.2, 0.2),
contrast_limit=(-0.2, 0.2)),
al.HueSaturationValue(hue_shift_limit=(-20, 20),
sat_shift_limit=(-30, 30),
val_shift_limit=(-20, 20)),
al.RandomGamma(gamma_limit=(30, 150)),
al.RGBShift(),
al.CLAHE(clip_limit=(1, 15)),
al.ChannelShuffle(),
al.InvertImg(),
],
p=0.1),
al.RandomSnow(p=0.05),
al.RandomRain(p=0.05),
al.RandomFog(p=0.05),
al.RandomSunFlare(num_flare_circles_lower=1,
num_flare_circles_upper=2,
src_radius=110,
p=0.05),
al.RandomShadow(p=0.05),
al.GaussNoise(var_limit=(10, 20), p=0.05),
al.ISONoise(color_shift=(0, 15), p=0.05),
al.MultiplicativeNoise(p=0.05),
al.OneOf([
al.ToGray(p=1. if use_gray else 0.05),
al.ToSepia(p=0.05),
al.Solarize(p=0.05),
al.Equalize(p=0.05),
al.Posterize(p=0.05),
al.FancyPCA(p=0.05),
],
p=0.05),
al.OneOf([
al.MotionBlur(blur_limit=(3, 7)),
al.Blur(blur_limit=(3, 7)),
al.MedianBlur(blur_limit=3),
al.GaussianBlur(blur_limit=3),
],
p=0.05),
al.CoarseDropout(p=0.05),
al.Cutout(num_holes=30,
max_h_size=37,
max_w_size=37,
fill_value=0,
p=0.05),
al.GridDropout(p=0.05),
al.ChannelDropout(p=0.05),
al.Downscale(scale_min=0.5, scale_max=0.9, p=0.1),
al.ImageCompression(quality_lower=60, p=0.2),
al.Normalize(),
ToTensorV2()
])
def get_hard_augmentations_v2(image_size):
return A.Compose([
A.OneOf([
A.ShiftScaleRotate(shift_limit=0.05,
scale_limit=0.1,
rotate_limit=45,
border_mode=cv2.BORDER_CONSTANT,
value=0),
A.ElasticTransform(alpha_affine=0,
alpha=35,
sigma=5,
border_mode=cv2.BORDER_CONSTANT,
value=0),
A.OpticalDistortion(distort_limit=0.11,
shift_limit=0.15,
border_mode=cv2.BORDER_CONSTANT,
value=0),
A.GridDistortion(border_mode=cv2.BORDER_CONSTANT, value=0),
A.NoOp()
]),
A.OneOf([
ZeroTopAndBottom(p=0.3),
A.RandomSizedCrop(min_max_height=(int(image_size[0] * 0.75),
image_size[0]),
height=image_size[0],
width=image_size[1],
p=0.3),
A.NoOp()
]),
A.ISONoise(p=0.5),
A.JpegCompression(p=0.3, quality_lower=75),
# Brightness/contrast augmentations
A.OneOf([
A.RandomBrightnessContrast(brightness_limit=0.5,
contrast_limit=0.4),
IndependentRandomBrightnessContrast(brightness_limit=0.25,
contrast_limit=0.24),
A.RandomGamma(gamma_limit=(50, 150)),
A.NoOp()
]),
A.OneOf([
FancyPCA(alpha_std=6),
A.RGBShift(r_shift_limit=40, b_shift_limit=30, g_shift_limit=30),
A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=10),
A.ToGray(p=0.2),
A.NoOp()
]),
# Intentionally destroy image quality and assign 0 class in this case
# A.Compose([
# BrightnessContrastDestroy(p=0.1),
# A.OneOf([
# MakeTooBlurry(),
# MakeTooBlurryMedian(),
# A.NoOp()
# ], p=0.1),
# ]),
# Add preprocessing method as an augmentation
ChannelIndependentCLAHE(p=0.5),
A.OneOf([
A.ChannelDropout(p=0.2),
A.CoarseDropout(p=0.1,
max_holes=2,
max_width=256,
max_height=256,
min_height=16,
min_width=16),
A.NoOp()
]),
A.RandomGridShuffle(p=0.3),
DiagnosisNoise(p=0.2),
# D4
A.Compose([A.RandomRotate90(), A.Transpose()])
])
def get_train_transforms_atopy(input_size,
use_crop=False,
use_no_color_aug=False):
if use_crop:
resize = [
al.Resize(int(input_size * 1.2), int(input_size * 1.2)),
al.RandomSizedCrop(min_max_height=(int(input_size * 0.6),
int(input_size * 1.2)),
height=input_size,
width=input_size)
]
else:
resize = [al.Resize(input_size, input_size)]
return al.Compose(resize + [
al.Flip(p=0.5),
al.OneOf([
al.RandomRotate90(),
al.Rotate(limit=180),
], p=0.5),
al.OneOf([
al.ShiftScaleRotate(),
al.OpticalDistortion(),
al.GridDistortion(),
al.ElasticTransform(),
],
p=0.3),
al.RandomGridShuffle(p=0.05),
al.OneOf([
al.RandomGamma(),
al.HueSaturationValue(),
al.RGBShift(),
al.CLAHE(),
al.ChannelShuffle(),
al.InvertImg(),
],
p=0.1),
al.RandomSnow(p=0.05),
al.RandomRain(p=0.05),
al.RandomFog(p=0.05),
al.RandomSunFlare(p=0.05),
al.RandomShadow(p=0.05),
al.RandomBrightnessContrast(p=0.05),
al.GaussNoise(p=0.2),
al.ISONoise(p=0.2),
al.MultiplicativeNoise(p=0.2),
al.ToGray(p=0.05),
al.ToSepia(p=0.05),
al.Solarize(p=0.05),
al.Equalize(p=0.05),
al.Posterize(p=0.05),
al.FancyPCA(p=0.05),
al.OneOf([
al.MotionBlur(blur_limit=3),
al.Blur(blur_limit=3),
al.MedianBlur(blur_limit=3),
al.GaussianBlur(blur_limit=3),
],
p=0.05),
al.CoarseDropout(p=0.05),
al.Cutout(p=0.05),
al.GridDropout(p=0.05),
al.ChannelDropout(p=0.05),
al.Downscale(p=0.1),
al.ImageCompression(quality_lower=60, p=0.2),
al.Normalize(),
ToTensorV2()
])
def __init__(self, folds, img_height, img_width, mean, std):
df = pd.read_csv('../input/dataset/train_folds.csv')
df = df[['Image', 'label', 'kfold']]
df = df[df.kfold.isin(folds)].reset_index(drop=True)
self.image_ids = df.Image.values
self.labels = df.label.values
if len(folds) == 1:
self.aug = albumentations.Compose([
albumentations.Resize(img_height, img_width),
albumentations.Normalize(mean, std, always_apply=True)
])
else:
self.aug = albumentations.Compose([
albumentations.Resize(img_height, img_width),
albumentations.OneOf([
albumentations.ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=45),
albumentations.Rotate(limit=5),
albumentations.RandomGamma(),
albumentations.RandomShadow(),
albumentations.RandomGridShuffle(),
albumentations.ElasticTransform(),
albumentations.RGBShift(),
]),
albumentations.OneOf([
albumentations.OneOf([
albumentations.Blur(),
albumentations.MedianBlur(),
albumentations.MotionBlur(),
albumentations.GaussianBlur(),
]),
albumentations.OneOf([
albumentations.GaussNoise(),
albumentations.IAAAdditiveGaussianNoise(),
albumentations.ISONoise()
]),
]),
albumentations.OneOf([
albumentations.RandomBrightness(),
albumentations.RandomContrast(),
albumentations.RandomBrightnessContrast(),
]),
albumentations.OneOf([
albumentations.OneOf([
albumentations.Cutout(),
albumentations.CoarseDropout(),
albumentations.GridDistortion(),
albumentations.GridDropout(),
albumentations.OpticalDistortion()
]),
albumentations.OneOf([
albumentations.HorizontalFlip(),
albumentations.VerticalFlip(),
albumentations.RandomRotate90(),
albumentations.Transpose()
]),
]),
# albumentations.OneOf([
# albumentations.RandomSnow(),
# albumentations.RandomRain(),
# albumentations.RandomFog(),
# ]),
albumentations.Normalize(mean, std, always_apply=True)
])
image = glue_to_one_picture_from_coord(
filepath, regions, window=window, K=K, layer=layer
)
else:
image = glue_to_one_picture_from_coord_lowlayer(
filepath, regions, window=window, K=K, layer=layer
)
image = cv2.resize(image, (outsize * outgrid, outsize * outgrid))
return image
# %% [code]
"""
DataLoader
"""
augops = A.Compose([
A.RandomGridShuffle(grid=(4, 4)),
A.Normalize()
], p=1.0)
def augment_fn(img):
ret = augops(force_apply=False, image=img)
return ret['image']
def GetImage(tdir, idn, image_size, scale_factor, jitter=0):
# 想定: scale_factor = 2.0
gen_images = 16
num_images = 16
num_column = int(np.sqrt(num_images))
imgpath = os.path.join(tdir, idn + '.tiff')
img = LoadImage(imgpath, gen_images, scale_factor, layer=1, auto_ws=True, window=image_size // 4, stride=image_size // 4, jitter=jitter)
img = 255 - img
def prepare_data(self):
df = pd.read_csv(os.path.join(self.hparams.data_dir, 'train.csv'))
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2020)
for fold, (train_index, val_index) in enumerate(
skf.split(df.values, df['isup_grade'])):
df.loc[val_index, 'fold'] = int(fold)
df['fold'] = df['fold'].astype(int)
#print(df)
self.train_df = df[df['fold'] != self.hparams.fold]
self.val_df = df[df['fold'] == self.hparams.fold]
#train_df, val_df = train_test_split(train_df, stratify=train_df['isup_grade'])
train_transform = A.Compose([
A.RandomResizedCrop(height=self.hparams.image_size,
width=self.hparams.image_size,
scale=(0.8, 1.0),
ratio=(1, 1),
interpolation=1,
always_apply=False,
p=1.0),
A.Flip(always_apply=False, p=0.5),
A.RandomGridShuffle(grid=(4, 4), always_apply=False, p=1.0),
A.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=10,
val_shift_limit=10,
always_apply=False,
p=0.5),
A.RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
brightness_by_max=True,
always_apply=False,
p=0.5),
A.GaussNoise(var_limit=(10.0, 50.0),
mean=0,
always_apply=False,
p=0.5),
#A.Rotate(limit=90, interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, p=0.5),
A.ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=45,
interpolation=1,
border_mode=4,
value=255,
mask_value=None,
always_apply=False,
p=0.5),
A.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
max_pixel_value=255.0,
always_apply=False,
p=1.0),
A.Cutout(num_holes=8,
max_h_size=8,
max_w_size=8,
fill_value=0,
always_apply=False,
p=0.5),
])
valid_transform = A.Compose([
A.Resize(height=self.hparams.image_size,
width=self.hparams.image_size,
interpolation=1,
always_apply=False,
p=1.0),
A.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
max_pixel_value=255.0,
always_apply=False,
p=1.0)
])
self.train_dataset = PANDADataset(self.train_df,
self.hparams.data_dir,
self.hparams.image_format,
transform=train_transform,
tile=self.hparams.tile,
layer=self.hparams.image_layer)
self.val_dataset = PANDADataset(self.val_df,
self.hparams.data_dir,
self.hparams.image_format,
transform=valid_transform,
tile=self.hparams.tile,
layer=self.hparams.image_layer)
def get_augmentation(version):
if version == "v1":
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v2":
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.OneOf([
A.RandomCrop(height=256, width=256, p=1),
A.RandomCrop(height=384, width=384, p=1),
A.RandomCrop(height=512, width=512, p=1),
A.RandomCrop(height=640, width=640, p=1),
],
p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v3":
return {
"train":
A.Compose([
A.RandomCrop(height=1024, width=1024, p=1),
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v4":
# for 3x-scaled dataset | 256x256
return {
"train":
A.Compose([
A.RandomCrop(height=512, width=512, p=1),
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v5":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.RandomCrop(height=512, width=512, p=1),
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v6":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v7":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.RandomCrop(height=768, width=768, p=1),
A.Resize(height=256, width=256, p=1.0),
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Resize(height=256, width=256, p=1.0),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v8":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Rotate(p=0.5),
A.RandomCrop(height=512, width=512, p=1),
A.Flip(p=0.5),
# color
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
# transform
A.ElasticTransform(p=1, alpha=120, sigma=6, alpha_affine=0.25),
A.GridDistortion(p=0.25),
A.OpticalDistortion(distort_limit=2, shift_limit=0.5, p=0.25),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v9":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.25),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.25),
A.RandomContrast(limit=0.2, always_apply=False, p=0.25),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.25),
# transform
A.OneOf([
A.ElasticTransform(
alpha=120, sigma=6, alpha_affine=0.25, p=0.25),
A.GridDistortion(p=0.25),
A.OpticalDistortion(
distort_limit=2, shift_limit=0.5, p=0.25)
],
p=0.75),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v10":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
# size
A.OneOf([
A.CropNonEmptyMaskIfExists(height=128, width=128, p=1.0),
A.CropNonEmptyMaskIfExists(height=256, width=256, p=1.0),
A.CropNonEmptyMaskIfExists(height=384, width=384, p=1.0)
],
p=0.25),
A.PadIfNeeded(min_height=512,
min_width=512,
border_mode=4,
p=1),
# array shuffle
A.OneOf([A.MaskDropout(p=1),
A.RandomGridShuffle(p=1)], p=0.5),
# quality
A.Downscale(scale_min=0.25,
scale_max=0.75,
interpolation=0,
always_apply=False,
p=0.5),
A.GaussNoise(p=0.5),
A.OneOf([
A.GlassBlur(p=1),
A.GaussianBlur(p=1),
], p=0.5),
# colors
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.5),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.5),
A.RandomContrast(limit=0.2, always_apply=False, p=0.5),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.5),
A.CLAHE(p=0.5),
A.Equalize(p=0.5),
A.ChannelShuffle(p=0.5),
# transform
A.OneOf([
A.ElasticTransform(
alpha=120, sigma=6, alpha_affine=0.25, p=1),
A.GridDistortion(p=1),
A.OpticalDistortion(distort_limit=2, shift_limit=0.5, p=1)
],
p=0.5),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v11":
# custom normalization (see other/img_normalization.py)
return {
"train":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
elif version == "v12":
# for 3x-scaled dataset | 512x512
return {
"train":
A.Compose([
A.Flip(p=0.5),
A.Rotate(p=0.5),
# size / quality
A.OneOf([
A.CropNonEmptyMaskIfExists(height=128, width=128, p=1.0),
A.CropNonEmptyMaskIfExists(height=256, width=256, p=1.0),
A.CropNonEmptyMaskIfExists(height=384, width=384, p=1.0),
A.Downscale(scale_min=0.25,
scale_max=0.75,
interpolation=0,
always_apply=False,
p=1.0),
],
p=0.25),
A.PadIfNeeded(min_height=512,
min_width=512,
border_mode=4,
p=1),
# array shuffle
A.OneOf([A.MaskDropout(p=1),
A.RandomGridShuffle(p=1)], p=0.15),
# noise
A.OneOf([
A.GaussNoise(p=1),
A.GlassBlur(p=1),
A.GaussianBlur(p=1),
],
p=0.15),
# colors
A.HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
always_apply=False,
p=0.15),
A.RandomBrightness(limit=0.4, always_apply=False, p=0.15),
A.RandomContrast(limit=0.2, always_apply=False, p=0.15),
A.RandomShadow(shadow_roi=(0, 0, 1, 1),
num_shadows_lower=1,
num_shadows_upper=50,
shadow_dimension=5,
always_apply=False,
p=0.15),
A.OneOf([
A.CLAHE(p=1),
A.Equalize(p=1),
A.ChannelShuffle(p=1),
],
p=0.15),
# transform
A.OneOf([
A.ElasticTransform(
alpha=120, sigma=6, alpha_affine=0.25, p=1),
A.GridDistortion(p=1),
A.OpticalDistortion(distort_limit=2, shift_limit=0.5, p=1)
],
p=0.15),
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
"valid":
A.Compose([
A.Normalize(mean=(0.623, 0.520, 0.650),
std=(0.278, 0.305, 0.274),
max_pixel_value=255.0,
p=1.0)
]),
}
else:
raise Exception(f"Augmentation version '{version}' is UNKNOWN!")