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eef8873 | 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 85 86 87 88 89 90 | import logging
from torchvision import transforms
from src.config import RESNET_IMAGE_SIZE, FUSION_IMAGE_SIZE
logger = logging.getLogger(__name__)
def get_resnet_train_transforms():
logger.info("Creating ResNet training transforms...")
return transforms.Compose([
transforms.Resize((RESNET_IMAGE_SIZE, RESNET_IMAGE_SIZE)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ColorJitter(
brightness=0.2,
contrast=0.2,
saturation=0.2
),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
def get_resnet_val_transforms():
logger.info("Creating ResNet validation transforms...")
return transforms.Compose([
transforms.Resize((RESNET_IMAGE_SIZE, RESNET_IMAGE_SIZE)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
def get_fusion_train_transforms():
logger.info("Creating Fusion training transforms...")
return transforms.Compose([
transforms.Resize((FUSION_IMAGE_SIZE, FUSION_IMAGE_SIZE)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.ColorJitter(
brightness=0.15,
contrast=0.15,
saturation=0.15
),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
def get_fusion_val_transforms():
logger.info("Creating Fusion validation transforms...")
return transforms.Compose([
transforms.Resize((FUSION_IMAGE_SIZE, FUSION_IMAGE_SIZE)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s"
)
resnet_train = get_resnet_train_transforms()
resnet_val = get_resnet_val_transforms()
fusion_train = get_fusion_train_transforms()
fusion_val = get_fusion_val_transforms()
print("\nTransforms created successfully:")
print("ResNet Train:", resnet_train)
print("ResNet Val:", resnet_val)
print("Fusion Train:", fusion_train)
print("Fusion Val:", fusion_val) |