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src/preprocess.py

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+"""Image preprocessing pipeline for Imager-1-Tiny."""
+from torchvision import transforms
+
+
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
+
+def get_transform(train: bool = False):
+    """
+    Return the torchvision transform pipeline.
+
+    Args:
+        train: If True, returns the training pipeline with augmentation.
+               If False, returns the deterministic inference pipeline.
+    """
+    if train:
+        return transforms.Compose([
+            transforms.Resize(256),
+            transforms.RandomResizedCrop(224, scale=(0.8, 1.0)),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
+        ])
+    return transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
+    ])