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228add1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | """
Shared augmentation and preprocessing transforms.
Provides train and eval transform factories based on config.
"""
from torchvision import transforms
# ImageNet normalization constants
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
def get_train_transforms(
img_size: int = 224,
random_crop: bool = True,
horizontal_flip: bool = True,
rotation_degrees: float = 15.0,
color_jitter: bool = True,
color_jitter_strength: float = 0.2,
) -> transforms.Compose:
"""
Build training transforms with data augmentation.
Avoids aggressive distortions that could alter breed-specific features.
"""
transform_list = []
if random_crop:
transform_list.append(transforms.RandomResizedCrop(
img_size,
scale=(0.8, 1.0),
ratio=(0.9, 1.1),
))
else:
transform_list.append(transforms.Resize((img_size, img_size)))
if horizontal_flip:
transform_list.append(transforms.RandomHorizontalFlip(p=0.5))
if rotation_degrees > 0:
transform_list.append(transforms.RandomRotation(degrees=rotation_degrees))
if color_jitter:
transform_list.append(transforms.ColorJitter(
brightness=color_jitter_strength,
contrast=color_jitter_strength,
saturation=color_jitter_strength,
hue=color_jitter_strength * 0.5,
))
transform_list.extend([
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
])
return transforms.Compose(transform_list)
def get_eval_transforms(img_size: int = 224) -> transforms.Compose:
"""
Build evaluation/inference transforms (no augmentation).
"""
return transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
])
def get_denormalize_transform() -> transforms.Compose:
"""
Inverse of ImageNet normalization for visualization.
"""
return transforms.Compose([
transforms.Normalize(
mean=[0.0, 0.0, 0.0],
std=[1.0 / s for s in IMAGENET_STD],
),
transforms.Normalize(
mean=[-m for m in IMAGENET_MEAN],
std=[1.0, 1.0, 1.0],
),
])
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