[Port HIL-SERL] Add HF vision encoder option in SAC (#651)

Added support with custom pretrained vision encoder to the modeling sac implementation. Great job @ChorntonYoel !

lerobot/common/datasets/transforms.py

@@ -150,6 +150,10 @@ def get_image_transforms(
     sharpness_min_max: tuple[float, float] | None = None,
     max_num_transforms: int | None = None,
     random_order: bool = False,
+    interpolation: str | None = None,
+    image_size: tuple[int, int] | None = None,
+    image_mean: list[float] | None = None,
+    image_std: list[float] | None = None,
 ):
     def check_value(name, weight, min_max):
         if min_max is not None:
@@ -170,6 +174,18 @@ def get_image_transforms(
 
     weights = []
     transforms = []
+    if image_size is not None:
+        interpolations = [interpolation.value for interpolation in v2.InterpolationMode]
+        if interpolation is None:
+            # Use BICUBIC as default interpolation
+            interpolation_mode = v2.InterpolationMode.BICUBIC
+        elif interpolation in interpolations:
+            interpolation_mode = v2.InterpolationMode(interpolation)
+        else:
+            raise ValueError("The interpolation passed is not supported")
+        # Weight for resizing is always 1
+        weights.append(1.0)
+        transforms.append(v2.Resize(size=(image_size[0], image_size[1]), interpolation=interpolation_mode))
     if brightness_min_max is not None and brightness_weight > 0.0:
         weights.append(brightness_weight)
         transforms.append(v2.ColorJitter(brightness=brightness_min_max))
@@ -185,6 +201,15 @@ def get_image_transforms(
     if sharpness_min_max is not None and sharpness_weight > 0.0:
         weights.append(sharpness_weight)
         transforms.append(SharpnessJitter(sharpness=sharpness_min_max))
+    if image_mean is not None and image_std is not None:
+        # Weight for normalization is always 1
+        weights.append(1.0)
+        transforms.append(
+            v2.Normalize(
+                mean=image_mean,
+                std=image_std,
+            )
+        )
 
     n_subset = len(transforms)
     if max_num_transforms is not None: