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import albumentations as A
from albumentations.pytorch import ToTensorV2
from torchvision import transforms as transforms
# Define the training tranforms
def get_train_aug():
return A.Compose([
A.MotionBlur(blur_limit=3, p=0.5),
A.Blur(blur_limit=3, p=0.5),
A.RandomBrightnessContrast(
brightness_limit=0.2, p=0.5
),
A.ColorJitter(p=0.5),
# A.Rotate(limit=10, p=0.2),
A.RandomGamma(p=0.2),
A.RandomFog(p=0.2),
# A.RandomSunFlare(p=0.1),
# `RandomScale` for multi-res training,
# `scale_factor` should not be too high, else may result in
# negative convolutional dimensions.
# A.RandomScale(scale_limit=0.15, p=0.1),
# A.Normalize(
# (0.485, 0.456, 0.406),
# (0.229, 0.224, 0.225)
# ),
ToTensorV2(p=1.0),
], bbox_params={
'format': 'pascal_voc',
'label_fields': ['labels']
})
def get_train_transform():
return A.Compose([
# A.Normalize(
# (0.485, 0.456, 0.406),
# (0.229, 0.224, 0.225)
# ),
ToTensorV2(p=1.0),
], bbox_params={
'format': 'pascal_voc',
'label_fields': ['labels']
})
# Define the validation transforms
def get_valid_transform():
return A.Compose([
# A.Normalize(
# (0.485, 0.456, 0.406),
# (0.229, 0.224, 0.225)
# ),
ToTensorV2(p=1.0),
], bbox_params={
'format': 'pascal_voc',
'label_fields': ['labels']
})
def infer_transforms(image):
# Define the torchvision image transforms.
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
])
return transform(image) |