New metric: Perceptual Path Length

CHANGELOG.md

@@ -26,6 +26,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added support for evaluating `"segm"` and `"bbox"` detection in `MeanAveragePrecision` at the same time ([#1928](https://github.com/Lightning-AI/torchmetrics/pull/1928))
 
 
+- Added `PerceptualPathLength` to image package ([#1939](https://github.com/Lightning-AI/torchmetrics/pull/1939))
+
+
 - Added support for multioutput evaluation in `MeanSquaredError` ([#1937](https://github.com/Lightning-AI/torchmetrics/pull/1937))
 
 

docs/source/image/learned_perceptual_image_patch_similarity.rst

@@ -15,3 +15,9 @@ ________________
 .. autoclass:: torchmetrics.image.lpip.LearnedPerceptualImagePatchSimilarity
     :noindex:
     :exclude-members: update, compute
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.image.learned_perceptual_image_patch_similarity
+    :noindex:

docs/source/image/perceptual_path_length.rst

@@ -0,0 +1,23 @@
+.. customcarditem::
+   :header: Perceptual Path Length
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
+   :tags: Image
+
+.. include:: ../links.rst
+
+############################
+Perceptual Path Length (PPL)
+############################
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.image.perceptual_path_length.PerceptualPathLength
+    :noindex:
+    :exclude-members: update, compute
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.image.perceptual_path_length.perceptual_path_length
+    :noindex:

docs/source/links.rst

@@ -144,6 +144,7 @@
 .. _Seamless Scene Segmentation paper: https://arxiv.org/abs/1905.01220
 .. _Fleiss kappa: https://en.wikipedia.org/wiki/Fleiss%27_kappa
 .. _VIF: https://ieeexplore.ieee.org/abstract/document/1576816
+.. _PPL : https://arxiv.org/pdf/1812.04948
 .. _CIOU: https://arxiv.org/abs/2005.03572
 .. _DIOU: https://arxiv.org/abs/1911.08287v1
 .. _GIOU: https://arxiv.org/abs/1902.09630

requirements/image.txt

@@ -3,5 +3,5 @@
 
 scipy >1.0.0, <1.11.0
 torchvision >=0.8, <=0.15.2
-torch-fidelity <=0.3.0
+torch-fidelity <=0.4.0  # bumping to alow install version from master, now used in testing
 lpips <=0.1.4

requirements/image_test.txt

@@ -6,3 +6,4 @@ kornia >=0.6.7, <0.7.1
 pytorch-msssim ==1.0.0
 sewar >=0.4.4, <=0.4.6
 numpy <1.25.0
+torch-fidelity @ git+https://github.com/toshas/torch-fidelity@master

src/torchmetrics/functional/image/__init__.py

@@ -14,6 +14,8 @@
 from torchmetrics.functional.image.d_lambda import spectral_distortion_index
 from torchmetrics.functional.image.ergas import error_relative_global_dimensionless_synthesis
 from torchmetrics.functional.image.gradients import image_gradients
+from torchmetrics.functional.image.lpips import learned_perceptual_image_patch_similarity
+from torchmetrics.functional.image.perceptual_path_length import perceptual_path_length
 from torchmetrics.functional.image.psnr import peak_signal_noise_ratio
 from torchmetrics.functional.image.psnrb import peak_signal_noise_ratio_with_blocked_effect
 from torchmetrics.functional.image.rase import relative_average_spectral_error
@@ -41,4 +43,6 @@
     "total_variation",
     "universal_image_quality_index",
     "visual_information_fidelity",
+    "learned_perceptual_image_patch_similarity",
+    "perceptual_path_length",
 ]

src/torchmetrics/functional/image/lpips.py

@@ -186,7 +186,7 @@ def spatial_average(in_tens: Tensor, keepdim: bool = True) -> Tensor:
     return in_tens.mean([2, 3], keepdim=keepdim)
 
 
-def upsam(in_tens: Tensor, out_hw: Tuple[int, int] = (64, 64)) -> Tensor:
+def upsam(in_tens: Tensor, out_hw: Tuple[int, ...] = (64, 64)) -> Tensor:
     """Upsample input with bilinear interpolation."""
     return nn.Upsample(size=out_hw, mode="bilinear", align_corners=False)(in_tens)
 
@@ -197,6 +197,13 @@ def normalize_tensor(in_feat: Tensor, eps: float = 1e-10) -> Tensor:
     return in_feat / (norm_factor + eps)
 
 
+def resize_tensor(x: Tensor, size: int = 64) -> Tensor:
+    """https://github.com/toshas/torch-fidelity/blob/master/torch_fidelity/sample_similarity_lpips.py#L127C22-L132."""
+    if x.shape[-1] > size and x.shape[-2] > size:
+        return torch.nn.functional.interpolate(x, (size, size), mode="area")
+    return torch.nn.functional.interpolate(x, (size, size), mode="bilinear", align_corners=False)
+
+
 class ScalingLayer(nn.Module):
     """Scaling layer."""
 
@@ -238,6 +245,7 @@ def __init__(
         use_dropout: bool = True,
         model_path: Optional[str] = None,
         eval_mode: bool = True,
+        resize: Optional[int] = None,
     ) -> None:
         """Initializes a perceptual loss torch.nn.Module.
 
@@ -250,6 +258,7 @@ def __init__(
             use_dropout: If dropout layers should be added
             model_path: Model path to load pretained models from
             eval_mode: If network should be in evaluation mode
+            resize: If input should be resized to this size
 
         """
         super().__init__()
@@ -258,6 +267,7 @@ def __init__(
         self.pnet_tune = pnet_tune
         self.pnet_rand = pnet_rand
         self.spatial = spatial
+        self.resize = resize
         self.scaling_layer = ScalingLayer()
 
         if self.pnet_type in ["vgg", "vgg16"]:
@@ -298,31 +308,34 @@ def __init__(
 
     def forward(
         self, in0: Tensor, in1: Tensor, retperlayer: bool = False, normalize: bool = False
-    ) -> Union[int, Tuple[int, List[Tensor]]]:
+    ) -> Union[Tensor, Tuple[Tensor, List[Tensor]]]:
         if normalize:  # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1]
             in0 = 2 * in0 - 1
             in1 = 2 * in1 - 1
 
-        # v0.0 - original release had a bug, where input was not scaled
+        # normalize input
         in0_input, in1_input = self.scaling_layer(in0), self.scaling_layer(in1)
+
+        # resize input if needed
+        if self.resize is not None:
+            in0_input = resize_tensor(in0_input, size=self.resize)
+            in1_input = resize_tensor(in1_input, size=self.resize)
+
         outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input)
         feats0, feats1, diffs = {}, {}, {}
 
         for kk in range(self.L):
             feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
             diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
 
-        if self.spatial:
-            res = [
-                upsam(self.lins[kk](diffs[kk]), out_hw=in0.shape[2:]) for kk in range(self.L)  # type: ignore[arg-type]
-            ]
-        else:
-            res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]
-
-        val = 0
-        for layer in range(self.L):
-            val += res[layer]  # type: ignore[assignment]
+        res = []
+        for kk in range(self.L):
+            if self.spatial:
+                res.append(upsam(self.lins[kk](diffs[kk]), out_hw=tuple(in0.shape[2:])))
+            else:
+                res.append(spatial_average(self.lins[kk](diffs[kk]), keepdim=True))
 
+        val: Tensor = sum(res)  # type: ignore[assignment]
         if retperlayer:
             return (val, res)
         return val