SAM: expose sizing helpers

src/refiners/foundationals/segment_anything/model.py

@@ -1,17 +1,21 @@
 from dataclasses import dataclass
 from typing import Sequence
 
-import numpy as np
 import torch
 from jaxtyping import Float
 from PIL import Image
 from torch import Tensor, device as Device, dtype as DType
 
 import refiners.fluxion.layers as fl
-from refiners.fluxion.utils import interpolate, no_grad, normalize, pad
+from refiners.fluxion.utils import no_grad
 from refiners.foundationals.segment_anything.image_encoder import SAMViT, SAMViTH
 from refiners.foundationals.segment_anything.mask_decoder import MaskDecoder
 from refiners.foundationals.segment_anything.prompt_encoder import MaskEncoder, PointEncoder
+from refiners.foundationals.segment_anything.utils import (
+    normalize_coordinates,
+    postprocess_masks,
+    preprocess_image,
+)
 
 
 @dataclass
@@ -85,9 +89,8 @@ class SegmentAnything(fl.Chain):
             The computed image embedding.
         """
         original_size = (image.height, image.width)
-        target_size = self.compute_target_size(original_size)
         return ImageEmbedding(
-            features=self.image_encoder(self.preprocess_image(image=image, target_size=target_size)),
+            features=self.image_encoder(self.preprocess_image(image)),
             original_image_size=original_size,
         )
 
@@ -118,12 +121,10 @@ class SegmentAnything(fl.Chain):
         """
         if isinstance(input, ImageEmbedding):
             original_size = input.original_image_size
-            target_size = self.compute_target_size(original_size)
             image_embedding = input.features
         else:
             original_size = (input.height, input.width)
-            target_size = self.compute_target_size(original_size)
-            image_embedding = self.image_encoder(self.preprocess_image(image=input, target_size=target_size))
+            image_embedding = self.image_encoder(self.preprocess_image(input))
 
         coordinates, type_mask = self.point_encoder.points_to_tensor(
             foreground_points=foreground_points,
@@ -139,9 +140,7 @@ class SegmentAnything(fl.Chain):
                 image_embedding_size=self.image_encoder.image_embedding_size
             )
 
-        point_embedding = self.point_encoder(
-            self.normalize(coordinates, target_size=target_size, original_size=original_size)
-        )
+        point_embedding = self.point_encoder(self.normalize(coordinates, original_size=original_size))
         dense_positional_embedding = self.point_encoder.get_dense_positional_embedding(
             image_embedding_size=self.image_encoder.image_embedding_size
         )
@@ -153,9 +152,7 @@ class SegmentAnything(fl.Chain):
 
         low_res_masks, iou_predictions = self.mask_decoder()
 
-        high_res_masks = self.postprocess_masks(
-            masks=low_res_masks, target_size=target_size, original_size=original_size
-        )
+        high_res_masks = self.postprocess_masks(low_res_masks, original_size)
 
         if binarize:
             high_res_masks = high_res_masks > self.mask_threshold
@@ -163,82 +160,43 @@ class SegmentAnything(fl.Chain):
         return high_res_masks, iou_predictions, low_res_masks
 
     @property
-    def image_size(self) -> int:
-        """The image size."""
+    def image_encoder_resolution(self) -> int:
+        """The resolution of the image encoder."""
         w, h = self.image_encoder.image_size
         assert w == h
         return w
 
-    def compute_target_size(self, size: tuple[int, int]) -> tuple[int, int]:
-        """Compute the target size as expected by the image encoder.
-
-        Args:
-            size: The size of the input image.
-
-        Returns:
-            The target height.
-            The target width.
+    def preprocess_image(self, image: Image.Image) -> Tensor:
         """
-        oldh, oldw = size
-        scale = self.image_size * 1.0 / max(oldh, oldw)
-        newh, neww = oldh * scale, oldw * scale
-        neww = int(neww + 0.5)
-        newh = int(newh + 0.5)
-        return (newh, neww)
-
-    def preprocess_image(self, image: Image.Image, target_size: tuple[int, int]) -> Tensor:
-        """Preprocess an image without distorting its aspect ratio.
-
+        See [`preprocess_image`][refiners.foundationals.segment_anything.utils.preprocess_image]
         Args:
             image: The image to preprocess.
-            target_size: The target size.
-
         Returns:
-            The preprocessed image.
+            The preprocessed tensor.
         """
-        h, w = target_size
-        padh = self.image_size - h
-        padw = self.image_size - w
-        resized = image.resize((w, h), resample=Image.Resampling.BILINEAR)  # type: ignore
-        image_tensor = torch.tensor(
-            np.array(resized).astype(np.float32).transpose(2, 0, 1),
-            device=self.device,
-            dtype=self.dtype,
-        ).unsqueeze(0)
-        return pad(
-            normalize(image_tensor, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), (0, padw, 0, padh)
-        )
-
-    def normalize(self, coordinates: Tensor, target_size: tuple[int, int], original_size: tuple[int, int]) -> Tensor:
-        """Normalize the coordinates.
+        return preprocess_image(image, self.image_encoder_resolution, self.device, self.dtype)
 
+    def normalize(self, coordinates: Tensor, original_size: tuple[int, int]) -> Tensor:
+        """
+        See [`normalize_coordinates`][refiners.foundationals.segment_anything.utils.normalize_coordinates]
         Args:
-            coordinates: The coordinates to normalize.
-            target_size: The target size.
-            original_size: The original size.
-
+            coordinates: a tensor of coordinates.
+            original_size: (h, w) the original size of the image.
         Returns:
-            The normalized coordinates.
+            The [0,1] normalized coordinates tensor.
         """
-        coordinates[:, :, 0] = ((coordinates[:, :, 0] * (target_size[1] / original_size[1])) + 0.5) / self.image_size
-        coordinates[:, :, 1] = ((coordinates[:, :, 1] * (target_size[0] / original_size[0])) + 0.5) / self.image_size
-        return coordinates
-
-    def postprocess_masks(self, masks: Tensor, target_size: tuple[int, int], original_size: tuple[int, int]) -> Tensor:
-        """Postprocess the masks.
+        return normalize_coordinates(coordinates, original_size, self.image_encoder_resolution)
 
+    def postprocess_masks(self, low_res_masks: Tensor, original_size: tuple[int, int]) -> Tensor:
+        """
+        See [`postprocess_masks`][refiners.foundationals.segment_anything.utils.postprocess_masks]
         Args:
-            masks: The masks to postprocess.
-            target_size: The target size.
-            original_size: The original size.
-
+            low_res_masks: a mask tensor of size (N, 1, 256, 256)
+            original_size: (h, w) the original size of the image.
         Returns:
-            The postprocessed masks.
+            The mask of shape (N, 1, H, W)
         """
-        masks = interpolate(masks, size=torch.Size((self.image_size, self.image_size)), mode="bilinear")
-        masks = masks[..., : target_size[0], : target_size[1]]  # remove padding added at `preprocess_image` time
-        masks = interpolate(masks, size=torch.Size(original_size), mode="bilinear")
-        return masks
+        return postprocess_masks(low_res_masks, original_size, self.image_encoder_resolution)
 
 
 class SegmentAnythingH(SegmentAnything):

src/refiners/foundationals/segment_anything/utils.py

@@ -0,0 +1,132 @@
+import numpy as np
+from PIL import Image
+from torch import Size, Tensor, device as Device, dtype as DType, tensor
+
+from refiners.fluxion.utils import interpolate, normalize, pad
+
+
+def compute_scaled_size(size: tuple[int, int], image_encoder_resolution: int) -> tuple[int, int]:
+    """Compute the scaled size as expected by the image encoder.
+    This computed size keep the ratio of the input image, and scale it to fit inside the square (image_encoder_resolution, image_encoder_resolution) of image encoder.
+
+    Args:
+        size: The size (h, w) of the input image.
+        image_encoder_resolution: Image encoder resolution.
+
+    Returns:
+        The target height.
+        The target width.
+    """
+    oldh, oldw = size
+    scale = image_encoder_resolution * 1.0 / max(oldh, oldw)
+    newh, neww = oldh * scale, oldw * scale
+    neww = int(neww + 0.5)
+    newh = int(newh + 0.5)
+    return (newh, neww)
+
+
+def image_to_scaled_tensor(
+    image: Image.Image, scaled_size: tuple[int, int], device: Device | None = None, dtype: DType | None = None
+) -> Tensor:
+    """Resize the image to `scaled_size` and convert it to a tensor.
+
+    Args:
+        image: The image.
+        scaled_size: The target size (h, w).
+        device: Tensor device.
+        dtype: Tensor dtype.
+    Returns:
+        a Tensor of shape (1, c, h, w)
+    """
+    h, w = scaled_size
+    resized = image.resize((w, h), resample=Image.Resampling.BILINEAR)  # type: ignore
+    return tensor(
+        np.array(resized).astype(np.float32).transpose(2, 0, 1),
+        device=device,
+        dtype=dtype,
+    ).unsqueeze(0)
+
+
+def preprocess_image(
+    image: Image.Image, image_encoder_resolution: int, device: Device | None = None, dtype: DType | None = None
+) -> Tensor:
+    """Preprocess an image without distorting its aspect ratio.
+
+    Args:
+        image: The image to preprocess before calling the image encoder.
+        image_encoder_resolution: Image encoder resolution.
+        device: Tensor device (None by default).
+        dtype: Tensor dtype (None by default).
+
+    Returns:
+        The preprocessed image.
+    """
+    scaled_size = compute_scaled_size((image.height, image.width), image_encoder_resolution)
+
+    image_tensor = image_to_scaled_tensor(image, scaled_size, device, dtype)
+
+    return pad_image_tensor(
+        normalize(image_tensor, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]),
+        scaled_size,
+        image_encoder_resolution,
+    )
+
+
+def pad_image_tensor(image_tensor: Tensor, scaled_size: tuple[int, int], image_encoder_resolution: int) -> Tensor:
+    """Pad an image with zeros to make it square.
+
+    Args:
+        image_tensor: The image tensor to pad.
+        scaled_size: The scaled size (h, w).
+        image_encoder_resolution: Image encoder resolution.
+
+    Returns:
+        The padded image.
+    """
+    assert len(image_tensor.shape) == 4
+    assert image_tensor.shape[2] <= image_encoder_resolution
+    assert image_tensor.shape[3] <= image_encoder_resolution
+
+    h, w = scaled_size
+    padh = image_encoder_resolution - h
+    padw = image_encoder_resolution - w
+    return pad(image_tensor, (0, padw, 0, padh))
+
+
+def postprocess_masks(low_res_masks: Tensor, original_size: tuple[int, int], image_encoder_resolution: int) -> Tensor:
+    """Postprocess the masks to fit the original image size and remove zero-padding (if any).
+
+    Args:
+        low_res_masks: The masks to postprocess.
+        original_size: The original size (h, w).
+        image_encoder_resolution: Image encoder resolution.
+
+    Returns:
+        The postprocessed masks.
+    """
+    scaled_size = compute_scaled_size(original_size, image_encoder_resolution)
+    masks = interpolate(low_res_masks, size=Size((image_encoder_resolution, image_encoder_resolution)), mode="bilinear")
+    masks = masks[..., : scaled_size[0], : scaled_size[1]]  # remove padding added at `preprocess_image` time
+    masks = interpolate(masks, size=Size(original_size), mode="bilinear")
+    return masks
+
+
+def normalize_coordinates(coordinates: Tensor, original_size: tuple[int, int], image_encoder_resolution: int) -> Tensor:
+    """Normalize the coordinates in the [0,1] range
+
+    Args:
+        coordinates: The coordinates to normalize.
+        original_size: The original image size.
+        image_encoder_resolution: Image encoder resolution.
+
+    Returns:
+        The normalized coordinates.
+    """
+    scaled_size = compute_scaled_size(original_size, image_encoder_resolution)
+    coordinates[:, :, 0] = (
+        (coordinates[:, :, 0] * (scaled_size[1] / original_size[1])) + 0.5
+    ) / image_encoder_resolution
+    coordinates[:, :, 1] = (
+        (coordinates[:, :, 1] * (scaled_size[0] / original_size[0])) + 0.5
+    ) / image_encoder_resolution
+    return coordinates

tests/foundationals/segment_anything/test_utils.py

@@ -0,0 +1,47 @@
+import pytest
+import torch
+from PIL import Image
+
+from refiners.foundationals.segment_anything.utils import (
+    compute_scaled_size,
+    image_to_scaled_tensor,
+    pad_image_tensor,
+    preprocess_image,
+)
+
+
+@pytest.fixture
+def image_encoder_resolution() -> int:
+    return 1024
+
+
+def test_compute_scaled_size(image_encoder_resolution: int) -> None:
+    w, h = (1536, 768)
+    scaled_size = compute_scaled_size((h, w), image_encoder_resolution)
+
+    assert scaled_size == (512, 1024)
+
+
+def test_image_to_scaled_tensor() -> None:
+    image = Image.new("RGB", (1536, 768))
+    tensor = image_to_scaled_tensor(image, (512, 1024))
+
+    assert tensor.shape == (1, 3, 512, 1024)
+
+
+def test_preprocess_image(image_encoder_resolution: int) -> None:
+    image = Image.new("RGB", (1536, 768))
+    preprocessed = preprocess_image(image, image_encoder_resolution)
+
+    assert preprocessed.shape == (1, 3, 1024, 1024)
+
+
+def test_pad_image_tensor(image_encoder_resolution: int) -> None:
+    w, h = (1536, 768)
+    image = Image.new("RGB", (w, h), color="white")
+    scaled_size = compute_scaled_size((h, w), image_encoder_resolution)
+    scaled_image_tensor = image_to_scaled_tensor(image, scaled_size)
+    padded_image_tensor = pad_image_tensor(scaled_image_tensor, scaled_size, image_encoder_resolution)
+
+    assert padded_image_tensor.shape == (1, 3, 1024, 1024)
+    assert torch.all(padded_image_tensor[:, :, 512:, :] == 0)