def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
if isTrain:
augmentors = [
GoogleNetResize(),
imgaug.Flip(horiz=True),
imgaug.ToFloat32(),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), rgb=False, clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_LINEAR),
imgaug.CenterCrop((224, 224)),
imgaug.ToFloat32(),
]
return augmentors
def test_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_test = MultiLabelDataset(folder=self.hparams.data_path,
is_train='test',
fname='test.csv',
types=self.hparams.types,
pathology=self.hparams.pathology,
resize=int(self.hparams.shape))
ds_test.reset_state()
ag_test = [
imgaug.Albumentations(
AB.SmallestMaxSize(self.hparams.shape, p=1.0)),
iimgaug.ColorSpace(mode=cv2.COLOR_GRAY2RGB),
imgaug.Albumentations(AB.CLAHE(p=1)),
imgaug.ToFloat32(),
]
ds_test = AugmentImageComponent(ds_test, ag_test, 0)
ds_test = BatchData(ds_test, self.hparams.batch, remainder=True)
# ds_test = MultiProcessRunner(ds_test, num_proc=4, num_prefetch=16)
ds_test = PrintData(ds_test)
ds_test = MapData(ds_test,
lambda dp: [torch.tensor(np.transpose(dp[0], (0, 3, 1, 2))),
torch.tensor(dp[1]).float()])
return ds_test
def test_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_test = CustomDataSet(folder=self.hparams.data,
train_or_valid='test',
size=np.inf,
hparams=self.hparams)
ds_test.reset_state()
ag_test = [
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_NEAREST),
imgaug.ToFloat32(),
]
# ds_test = AugmentImageComponent(ds_test, [imgaug.Albumentations(AB.CLAHE(tile_grid_size=(32, 32), always_apply=True, p=1)),], 0)
ds_test = AugmentImageComponents(ds_test, ag_test, [0, 1])
ds_test = BatchData(ds_test, self.hparams.batch, remainder=True)
ds_test = MultiProcessRunner(ds_test, num_proc=4, num_prefetch=16)
ds_test = PrintData(ds_test)
ds_test = MapData(
ds_test, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
])
return ds_test
def test_dataloader(self):
ds_test = MultiLabelDataset(folder=self.hparams.data,
is_train='valid',
fname='covid_test_v5.csv',
types=self.hparams.types,
pathology=self.hparams.pathology,
resize=int(self.hparams.shape),
fold_idx=None,
n_folds=1)
ds_test.reset_state()
ag_test = [
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_AREA),
imgaug.ToFloat32(),
]
ds_test = AugmentImageComponent(ds_test, ag_test, 0)
ds_test = BatchData(ds_test, self.hparams.batch, remainder=True)
ds_test = MultiProcessRunner(ds_test, num_proc=4, num_prefetch=16)
ds_test = PrintData(ds_test)
ds_test = MapData(
ds_test, lambda dp: [
torch.tensor(np.transpose(dp[0], (0, 3, 1, 2))),
torch.tensor(dp[1]).float()
])
return ds_test
def val_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_valid = MultiLabelDataset(
folder=self.hparams.data,
is_train='valid',
fname='covid_test_v5.csv',
types=self.hparams.types,
pathology=self.hparams.pathology,
resize=int(self.hparams.shape),
)
ds_valid.reset_state()
ag_valid = [
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_AREA),
imgaug.ToFloat32(),
]
ds_valid = AugmentImageComponent(ds_valid, ag_valid, 0)
ds_valid = BatchData(ds_valid, self.hparams.batch, remainder=True)
ds_valid = MultiProcessRunner(ds_valid, num_proc=4, num_prefetch=16)
ds_valid = PrintData(ds_valid)
ds_valid = MapData(
ds_valid, lambda dp: [
torch.tensor(np.transpose(dp[0], (0, 3, 1, 2))),
torch.tensor(dp[1]).float()
])
return ds_valid
def train_dataloader(self):
ds_train = MultiLabelDataset(folder=self.hparams.data,
is_train='train',
fname='covid_train_v5.csv',
types=self.hparams.types,
pathology=self.hparams.pathology,
resize=int(self.hparams.shape),
balancing=None)
ds_train.reset_state()
ag_train = [
# imgaug.Albumentations(
# AB.SmallestMaxSize(self.hparams.shape, p=1.0)),
imgaug.ColorSpace(mode=cv2.COLOR_GRAY2RGB),
# imgaug.Affine(shear=10),
imgaug.RandomChooseAug([
imgaug.Albumentations(AB.Blur(blur_limit=4, p=0.25)),
imgaug.Albumentations(AB.MotionBlur(blur_limit=4, p=0.25)),
imgaug.Albumentations(AB.MedianBlur(blur_limit=4, p=0.25)),
]),
imgaug.Albumentations(AB.CLAHE(tile_grid_size=(32, 32), p=0.5)),
imgaug.RandomOrderAug([
imgaug.Affine(shear=10,
border=cv2.BORDER_CONSTANT,
interp=cv2.INTER_AREA),
imgaug.Affine(translate_frac=(0.01, 0.02),
border=cv2.BORDER_CONSTANT,
interp=cv2.INTER_AREA),
imgaug.Affine(scale=(0.5, 1.0),
border=cv2.BORDER_CONSTANT,
interp=cv2.INTER_AREA),
]),
imgaug.RotationAndCropValid(max_deg=10, interp=cv2.INTER_AREA),
imgaug.GoogleNetRandomCropAndResize(
crop_area_fraction=(0.8, 1.0),
aspect_ratio_range=(0.8, 1.2),
interp=cv2.INTER_AREA,
target_shape=self.hparams.shape),
imgaug.ColorSpace(mode=cv2.COLOR_RGB2GRAY),
imgaug.ToFloat32(),
]
ds_train = AugmentImageComponent(ds_train, ag_train, 0)
# Label smoothing
ag_label = [
imgaug.BrightnessScale((0.8, 1.2), clip=False),
]
# ds_train = AugmentImageComponent(ds_train, ag_label, 1)
ds_train = BatchData(ds_train, self.hparams.batch, remainder=True)
if self.hparams.debug:
ds_train = FixedSizeData(ds_train, 2)
ds_train = MultiProcessRunner(ds_train, num_proc=4, num_prefetch=16)
ds_train = PrintData(ds_train)
ds_train = MapData(
ds_train, lambda dp: [
torch.tensor(np.transpose(dp[0], (0, 3, 1, 2))),
torch.tensor(dp[1]).float()
])
return ds_train
def get_moco_v1_augmentor():
augmentors = [
TorchvisionCropAndResize(crop_area_fraction=(0.2, 1.)),
RandomGrayScale(0.2),
imgaug.ToFloat32(),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4)),
imgaug.Contrast((0.6, 1.4), rgb=False),
imgaug.Saturation(0.4, rgb=False),
# 72 = 180*0.4
imgaug.Hue(range=(-72, 72), rgb=False)
]),
imgaug.ToUint8(),
imgaug.Flip(horiz=True),
]
return augmentors
def get_moco_v1_augmentor():
augmentors = [
imgaug.GoogleNetRandomCropAndResize(crop_area_fraction=(0.2, 1.)),
imgaug.RandomApplyAug(imgaug.Grayscale(rgb=False, keepshape=True), 0.2),
imgaug.ToFloat32(),
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4)),
imgaug.Contrast((0.6, 1.4), rgb=False),
imgaug.Saturation(0.4, rgb=False),
# 72 = 180*0.4
imgaug.Hue(range=(-72, 72), rgb=False)
]),
imgaug.ToUint8(),
imgaug.Flip(horiz=True),
]
return augmentors
def val_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_valid = CustomDataSet(folder=self.hparams.data,
train_or_valid='valid',
size=np.inf,
hparams=self.hparams)
ds_valid.reset_state()
ag_valid = [
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_NEAREST),
imgaug.ToFloat32(),
]
ds_valid = AugmentImageComponent(ds_valid, [
imgaug.Albumentations(AB.CLAHE(p=1)),
], 0)
if self.hparams.types == 6:
ds_valid = AugmentImageComponents(ds_valid, ag_valid,
[0, 1, 2, 3, 4, 5, 6])
elif self.hparams.types == 1:
ds_valid = AugmentImageComponents(ds_valid, ag_valid, [0, 1])
ds_valid = BatchData(ds_valid, self.hparams.batch, remainder=True)
ds_valid = MultiProcessRunner(ds_valid, num_proc=4, num_prefetch=16)
ds_valid = PrintData(ds_valid)
if self.hparams.types == 6:
ds_valid = MapData(
ds_valid, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
torch.tensor(dp[2][:, np.newaxis, :, :]).float(),
torch.tensor(dp[3][:, np.newaxis, :, :]).float(),
torch.tensor(dp[4][:, np.newaxis, :, :]).float(),
torch.tensor(dp[5][:, np.newaxis, :, :]).float(),
torch.tensor(dp[6][:, np.newaxis, :, :]).float(),
])
elif self.hparams.types == 1:
ds_valid = MapData(
ds_valid, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
])
return ds_valid
def get_moco_v2_augmentor():
augmentors = [
TorchvisionCropAndResize(crop_area_fraction=(0.2, 1.)),
imgaug.ToFloat32(),
imgaug.RandomApplyAug(
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4)),
imgaug.Contrast((0.6, 1.4), rgb=False),
imgaug.Saturation(0.4, rgb=False),
# 18 = 180*0.1
imgaug.Hue(range=(-18, 18), rgb=False)
]),
0.8),
RandomGrayScale(0.2),
imgaug.RandomApplyAug(RandomGaussionBlur([0.1, 2.0], 0.1), 0.5),
imgaug.ToUint8(),
imgaug.Flip(horiz=True),
]
return augmentors
def get_moco_v2_augmentor():
augmentors = [
imgaug.GoogleNetRandomCropAndResize(crop_area_fraction=(0.2, 1.)),
imgaug.ToFloat32(),
imgaug.RandomApplyAug(
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4)),
imgaug.Contrast((0.6, 1.4), rgb=False),
imgaug.Saturation(0.4, rgb=False),
# 18 = 180*0.1
imgaug.Hue(range=(-18, 18), rgb=False)
]), 0.8),
imgaug.RandomApplyAug(imgaug.Grayscale(rgb=False, keepshape=True), 0.2),
imgaug.RandomApplyAug(
# 11 = 0.1*224//2
imgaug.GaussianBlur(size_range=(11, 12), sigma_range=[0.1, 2.0]), 0.5),
imgaug.ToUint8(),
imgaug.Flip(horiz=True),
]
return augmentors
def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
interpolation = cv2.INTER_CUBIC
# linear seems to have more stable performance.
# but we keep cubic for compatibility with old models
if isTrain:
augmentors = [
imgaug.GoogleNetRandomCropAndResize(interp=interpolation),
imgaug.ToFloat32(), # avoid frequent casting in each color augmentation
# It's OK to remove the following augs if your CPU is not fast enough.
# Removing brightness/contrast/saturation does not have a significant effect on accuracy.
# Removing lighting leads to a tiny drop in accuracy.
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4)),
imgaug.Contrast((0.6, 1.4), rgb=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(0.1,
eigval=np.asarray(
[0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array(
[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1]
)]),
imgaug.ToUint8(),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, interp=interpolation),
imgaug.CenterCrop((224, 224)),
]
return augmentors
def train_dataloader(self):
"""Summary
Returns:
TYPE: Description
"""
ds_train = CustomDataSet(folder=self.hparams.data,
train_or_valid='train',
size=np.inf,
hparams=self.hparams)
ds_train.reset_state()
ag_train = [
imgaug.Affine(shear=10, interp=cv2.INTER_NEAREST),
imgaug.Affine(translate_frac=(0.01, 0.02),
interp=cv2.INTER_NEAREST),
imgaug.Affine(scale=(0.25, 1.0), interp=cv2.INTER_NEAREST),
imgaug.RotationAndCropValid(max_deg=10, interp=cv2.INTER_NEAREST),
imgaug.GoogleNetRandomCropAndResize(
crop_area_fraction=(0.8, 1.0),
aspect_ratio_range=(0.8, 1.2),
interp=cv2.INTER_NEAREST,
target_shape=self.hparams.shape),
imgaug.Resize(self.hparams.shape, interp=cv2.INTER_NEAREST),
imgaug.Flip(horiz=True, vert=False, prob=0.5),
imgaug.Flip(horiz=False, vert=True, prob=0.5),
imgaug.Transpose(prob=0.5),
imgaug.Albumentations(AB.RandomRotate90(p=1)),
imgaug.ToFloat32(),
]
ds_train = AugmentImageComponent(
ds_train,
[
# imgaug.Float32(),
# imgaug.RandomChooseAug([
# imgaug.Albumentations(AB.IAAAdditiveGaussianNoise(p=0.25)),
# imgaug.Albumentations(AB.GaussNoise(p=0.25)),
# ]),
# imgaug.ToUint8(),
imgaug.RandomChooseAug([
imgaug.Albumentations(AB.Blur(blur_limit=4, p=0.25)),
imgaug.Albumentations(AB.MotionBlur(blur_limit=4, p=0.25)),
imgaug.Albumentations(AB.MedianBlur(blur_limit=4, p=0.25)),
]),
imgaug.RandomChooseAug([
# imgaug.Albumentations(AB.IAASharpen(p=0.5)),
# imgaug.Albumentations(AB.IAAEmboss(p=0.5)),
imgaug.Albumentations(AB.RandomBrightnessContrast(p=0.5)),
]),
imgaug.ToUint8(),
imgaug.Albumentations(AB.CLAHE(tile_grid_size=(32, 32),
p=0.5)),
],
0)
ds_train = AugmentImageComponents(ds_train, ag_train, [0, 1])
ds_train = BatchData(ds_train, self.hparams.batch, remainder=True)
if self.hparams.debug:
ds_train = FixedSizeData(ds_train, 2)
ds_train = MultiProcessRunner(ds_train, num_proc=4, num_prefetch=16)
ds_train = PrintData(ds_train)
ds_train = MapData(
ds_train, lambda dp: [
torch.tensor(dp[0][:, np.newaxis, :, :]).float(),
torch.tensor(dp[1][:, np.newaxis, :, :]).float(),
])
return ds_train
