Support for torch.float weighted networks for FID and KID calculations.

CHANGELOG.md

@@ -21,6 +21,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added support for calculating segmentation quality and recognition quality in `PanopticQuality` metric ([#2381](https://github.com/Lightning-AI/torchmetrics/pull/2381))
 
 
+- Added support for `torch.float` weighted networks for FID and KID calculations ([#2483](https://github.com/Lightning-AI/torchmetrics/pull/2483))
+
+
 ### Changed
 
 - Made `__getattr__` and `__setattr__` of `ClasswiseWrapper` more general ([#2424](https://github.com/Lightning-AI/torchmetrics/pull/2424))

src/torchmetrics/image/fid.py

@@ -198,6 +198,15 @@ class FrechetInceptionDistance(Metric):
     flag ``real`` determines if the images should update the statistics of the real distribution or the
     fake distribution.
 
+    Using custom feature extractor is also possible. One can give a torch.nn.Module as `feature` argument. This
+    custom feature extractor is expected to have output shape of ``(1, num_features)``. This would change the
+    used feature extractor from default (Inception v3) to the given network. In case network doesn't have
+    ``num_features`` attribute, a random tensor will be given to the network to infer feature dimensionality.
+    Size of this tensor can be controlled by ``input_img_size`` argument and type of the tensor can be controlled
+    with ``normalize`` argument (``True`` uses float32 tensors and ``False`` uses int8 tensors). In this case, update
+    method expects to have the tensor given to `imgs` argument to be in the correct shape and type that is compatible
+    to the custom feature extractor.
+
     This metric is known to be unstable in its calculatations, and we recommend for the best results using this metric
     that you calculate using `torch.float64` (default is `torch.float32`) which can be set using the `.set_dtype`
     method of the metric.
@@ -228,13 +237,20 @@ class FrechetInceptionDistance(Metric):
         reset_real_features: Whether to also reset the real features. Since in many cases the real dataset does not
             change, the features can be cached them to avoid recomputing them which is costly. Set this to ``False`` if
             your dataset does not change.
-        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
+        normalize:
+            Argument for controlling the input image dtype normalization:
+
+            - If default feature extractor is used, controls whether input imgs have values in range [0, 1] or not:
+
+              - True: if input imgs have values ranged in [0, 1]. They are cast to int8/byte tensors.
+              - False: if input imgs have values ranged in [0, 255]. No casting is done.
+
+            - If custom feature extractor module is used, controls type of the input img tensors:
 
-    .. note::
-        If a custom feature extractor is provided through the `feature` argument it is expected to either have a
-        attribute called ``num_features`` that indicates the number of features returned by the forward pass or
-        alternatively we will pass through tensor of shape ``(1, 3, 299, 299)`` and dtype ``torch.uint8``` to the
-        forward pass and expect a tensor of shape ``(1, num_features)`` as output.
+              - True: if input imgs are expected to be in the data type of torch.float32.
+              - False: if input imgs are expected to be in the data type of torch.int8.
+        input_img_size: tuple of integers. Indicates input img size to the custom feature extractor network if provided.
+        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
         ValueError:
@@ -284,9 +300,16 @@ def __init__(
         feature: Union[int, Module] = 2048,
         reset_real_features: bool = True,
         normalize: bool = False,
+        input_img_size: Tuple[int, int, int] = (3, 299, 299),
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
+
+        if not isinstance(normalize, bool):
+            raise ValueError("Argument `normalize` expected to be a bool")
+        self.normalize = normalize
+        self.used_custom_model = False
+
         if isinstance(feature, int):
             num_features = feature
             if not _TORCH_FIDELITY_AVAILABLE:
@@ -304,10 +327,14 @@ def __init__(
 
         elif isinstance(feature, Module):
             self.inception = feature
+            self.used_custom_model = True
             if hasattr(self.inception, "num_features"):
                 num_features = self.inception.num_features
             else:
-                dummy_image = torch.randint(0, 255, (1, 3, 299, 299), dtype=torch.uint8)
+                if self.normalize:
+                    dummy_image = torch.rand(1, *input_img_size, dtype=torch.float32)
+                else:
+                    dummy_image = torch.randint(0, 255, (1, *input_img_size), dtype=torch.uint8)
                 num_features = self.inception(dummy_image).shape[-1]
         else:
             raise TypeError("Got unknown input to argument `feature`")
@@ -316,10 +343,6 @@ def __init__(
             raise ValueError("Argument `reset_real_features` expected to be a bool")
         self.reset_real_features = reset_real_features
 
-        if not isinstance(normalize, bool):
-            raise ValueError("Argument `normalize` expected to be a bool")
-        self.normalize = normalize
-
         mx_num_feats = (num_features, num_features)
         self.add_state("real_features_sum", torch.zeros(num_features).double(), dist_reduce_fx="sum")
         self.add_state("real_features_cov_sum", torch.zeros(mx_num_feats).double(), dist_reduce_fx="sum")
@@ -330,8 +353,15 @@ def __init__(
         self.add_state("fake_features_num_samples", torch.tensor(0).long(), dist_reduce_fx="sum")
 
     def update(self, imgs: Tensor, real: bool) -> None:
-        """Update the state with extracted features."""
-        imgs = (imgs * 255).byte() if self.normalize else imgs
+        """Update the state with extracted features.
+
+        Args:
+            imgs: Input img tensors to evaluate. If used custom feature extractor please
+                make sure dtype and size is correct for the model.
+            real: Whether given image is real or fake.
+
+        """
+        imgs = (imgs * 255).byte() if self.normalize and (not self.used_custom_model) else imgs
         features = self.inception(imgs)
         self.orig_dtype = features.dtype
         features = features.double()

src/torchmetrics/image/kid.py

@@ -91,6 +91,12 @@ class KernelInceptionDistance(Metric):
     flag ``real`` determines if the images should update the statistics of the real distribution or the
     fake distribution.
 
+    Using custom feature extractor is also possible. One can give a torch.nn.Module as `feature` argument. This
+    custom feature extractor is expected to have output shape of ``(1, num_features)`` This would change the
+    used feature extractor from default (Inception v3) to the given network. ``normalize`` argument won't have any
+    effect and update method expects to have the tensor given to `imgs` argument to be in the correct shape and
+    type that is compatible to the custom feature extractor.
+
     .. note:: using this metric with the default feature extractor requires that ``torch-fidelity``
         is installed. Either install as ``pip install torchmetrics[image]`` or
         ``pip install torch-fidelity``
@@ -103,7 +109,7 @@ class KernelInceptionDistance(Metric):
     As output of `forward` and `compute` the metric returns the following output
 
     - ``kid_mean`` (:class:`~torch.Tensor`): float scalar tensor with mean value over subsets
-    - ``kid_std`` (:class:`~torch.Tensor`): float scalar tensor with mean value over subsets
+    - ``kid_std`` (:class:`~torch.Tensor`): float scalar tensor with standard deviation value over subsets
 
     Args:
         feature: Either an str, integer or ``nn.Module``:
@@ -187,6 +193,8 @@ def __init__(
             UserWarning,
         )
 
+        self.used_custom_model = False
+
         if isinstance(feature, (str, int)):
             if not _TORCH_FIDELITY_AVAILABLE:
                 raise ModuleNotFoundError(
@@ -202,6 +210,7 @@ def __init__(
             self.inception: Module = NoTrainInceptionV3(name="inception-v3-compat", features_list=[str(feature)])
         elif isinstance(feature, Module):
             self.inception = feature
+            self.used_custom_model = True
         else:
             raise TypeError("Got unknown input to argument `feature`")
 
@@ -238,8 +247,15 @@ def __init__(
         self.add_state("fake_features", [], dist_reduce_fx=None)
 
     def update(self, imgs: Tensor, real: bool) -> None:
-        """Update the state with extracted features."""
-        imgs = (imgs * 255).byte() if self.normalize else imgs
+        """Update the state with extracted features.
+
+        Args:
+            imgs: Input img tensors to evaluate. If used custom feature extractor please
+                make sure dtype and size is correct for the model.
+            real: Whether given image is real or fake.
+
+        """
+        imgs = (imgs * 255).byte() if self.normalize and (not self.used_custom_model) else imgs
         features = self.inception(imgs)
 
         if real:
@@ -252,6 +268,10 @@ def compute(self) -> Tuple[Tensor, Tensor]:
 
         Implementation inspired by `Fid Score`_
 
+        Returns:
+            kid_mean (:class:`~torch.Tensor`): float scalar tensor with mean value over subsets
+            kid_std (:class:`~torch.Tensor`): float scalar tensor with standard deviation value over subsets
+
         """
         real_features = dim_zero_cat(self.real_features)
         fake_features = dim_zero_cat(self.fake_features)

tests/unittests/image/test_fid.py

@@ -80,8 +80,19 @@ def test_fid_raises_errors_and_warnings():
         _ = FrechetInceptionDistance(feature=[1, 2])
 
 
+class _DummyFeatureExtractor(Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.flatten = torch.nn.Flatten()
+        self.extractor = torch.nn.Linear(3 * 299 * 299, 64)
+
+    def __call__(self, img) -> torch.Tensor:
+        img = (img / 125.5).float()  # Convert int img input to float as Linear layer expects float inputs
+        return self.extractor(self.flatten(img))
+
+
 @pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
-@pytest.mark.parametrize("feature", [64, 192, 768, 2048])
+@pytest.mark.parametrize("feature", [64, 192, 768, 2048, _DummyFeatureExtractor()])
 def test_fid_same_input(feature):
     """If real and fake are update on the same data the fid score should be 0."""
     metric = FrechetInceptionDistance(feature=feature)

tests/unittests/image/test_kid.py

@@ -102,8 +102,19 @@ def test_kid_extra_parameters():
         KernelInceptionDistance(coef=-1)
 
 
+class _DummyFeatureExtractor(Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.flatten = torch.nn.Flatten()
+        self.extractor = torch.nn.Linear(3 * 299 * 299, 64)
+
+    def __call__(self, img) -> torch.Tensor:
+        img = (img / 125.5).float()  # Convert int img input to float as Linear layer expects float inputs
+        return self.extractor(self.flatten(img))
+
+
 @pytest.mark.skipif(not _TORCH_FIDELITY_AVAILABLE, reason="metric requires torch-fidelity")
-@pytest.mark.parametrize("feature", [64, 192, 768, 2048])
+@pytest.mark.parametrize("feature", [64, 192, 768, 2048, _DummyFeatureExtractor()])
 def test_kid_same_input(feature):
     """Test that the metric works."""
     metric = KernelInceptionDistance(feature=feature, subsets=5, subset_size=2)