Kid metric added

docs/user_manual/customize_metric.rst

@@ -187,13 +187,13 @@ For example, if you are implementing a metric that compares two models, you shou
 
 If you are implementing an alignment metric comparing model's output with the input, you should use the ``x_gt`` or ``gt_x`` call type. Examples from |pruna| include ``clip_score``.
 
-If you are implementing a metric that compares the model's output with the ground truth, you should use the ``y_gt`` or ``gt_y`` call type. Examples from |pruna| include ``fid``, ``cmmd``, ``accuracy``, ``recall``, ``precision``.
+If you are implementing a metric that compares the model's output with the ground truth, you should use the ``y_gt`` or ``gt_y`` call type. Examples from |pruna| include ``fid``, ``kid``, ``cmmd``, ``accuracy``, ``recall``, ``precision``.
 
 If you are wrapping an Image Quality Assessment (IQA) metric, that has an internal dataset, you should use the ``y`` call type. Examples from |pruna| include ``arniqa``.
 
-You may want to switch the mode of the metric despite your default ``call_type``. For instance you may want to use ``fid`` in pairwise mode to get a single comparison score for two models.
+You may want to switch the mode of the metric despite your default ``call_type``. For instance you may want to use ``fid`` or ``kid`` in pairwise mode to get a single comparison score for two models.
 
-In this case, you can pass ``pairwise`` to the ``call_type`` parameter of the ``StatefulMetric`` constructor.
+In this case, you can pass ``pairwise`` to the ``call_type`` parameter of the ``StatefulMetric`` constructor`
 
 .. container:: hidden_code
 

docs/user_manual/evaluate.rst

@@ -10,7 +10,7 @@ Evaluation helps you understand how compression affects your models across diffe
 This knowledge is essential for making informed decisions about which compression techniques work best for your specific needs using two types of metrics:
 
 - **Efficiency Metrics:** Measure speed (total time, latency, throughput), memory (disk, inference, training), and energy usage (consumption, CO2 emissions).
-- **Quality Metrics:** Assess fidelity (FID, CMMD), alignment (Clip Score), diversity (PSNR, SSIM), accuracy (accuracy, precision, perplexity), and more. Custom metrics are supported.
+- **Quality Metrics:** Assess fidelity (FID, KID, CMMD), alignment (Clip Score), diversity (PSNR, SSIM), accuracy (accuracy, precision, perplexity), and more. Custom metrics are supported.
 
 .. image:: /_static/assets/images/evaluation_agent.png
     :alt: Evaluation Agent
@@ -275,11 +275,11 @@ This is what's happening under the hood when you pass ``call_type="single"`` or
 
    * - ``y_gt``
      - Model's output first, then ground truth
-     - ``fid``, ``cmmd``, ``accuracy``, ``recall``, ``precision``
+     - ``fid``, ``kid``, ``cmmd``, ``accuracy``, ``recall``, ``precision``
 
    * - ``gt_y``
      - Ground truth first, then model's output
-     - ``fid``, ``cmmd``, ``accuracy``, ``recall``, ``precision``
+     - ``fid``, ``kid``, ``cmmd``, ``accuracy``, ``recall``, ``precision``
 
    * - ``x_gt``
      - Input data first, then ground truth

src/pruna/evaluation/metrics/metric_torch.py

@@ -25,6 +25,7 @@
 from torchmetrics.classification import Accuracy, Precision, Recall
 from torchmetrics.image import (
     FrechetInceptionDistance,
+    KernelInceptionDistance,
     LearnedPerceptualImagePatchSimilarity,
     MultiScaleStructuralSimilarityIndexMeasure,
     PeakSignalNoiseRatio,
@@ -85,6 +86,46 @@ def fid_update(metric: FrechetInceptionDistance, reals: Any, fakes: Any) -> None
     metric.update(fakes, real=False)
 
 
+def kid_update(metric: KernelInceptionDistance, reals: Any, fakes: Any) -> None:
+    """
+    Update handler for KID metric.
+
+    Parameters
+    ----------
+    metric : KernelInceptionDistance instance
+        The KID metric instance.
+    reals : Any
+        The ground truth images tensor.
+    fakes : Any
+        The generated images tensor.
+    """
+    metric.update(reals, real=True)
+    metric.update(fakes, real=False)
+
+
+def kid_compute(metric: KernelInceptionDistance) -> Any:
+    """
+    Compute handler for KID metric.
+
+    KID normally returns (mean, std) but we only need the mean value.
+    The defensive check handles edge cases (e.g., insufficient data, version differences).
+
+    Parameters
+    ----------
+    metric : KernelInceptionDistance
+        The KID metric instance.
+
+    Returns
+    -------
+    Any
+        The computed metric value (mean from tuple, or result as-is if not a tuple).
+    """
+    result = metric.compute()  # type: ignore
+    if isinstance(result, tuple) and len(result) == 2:
+        return result[0]  # Extract mean from tuple (KID returns normally (mean, std))
+    return result
+
+
 def lpips_update(metric: LearnedPerceptualImagePatchSimilarity, preds: Any, target: Any) -> None:
     """
     Update handler for LPIPS metric.
@@ -152,7 +193,8 @@ class TorchMetrics(Enum):
     """
     Enum for available torchmetrics.
 
-    The enum contains triplets of the metric class, the update function and the call type.
+    The enum contains tuples of (metric class, update function, call type) with an optional
+    compute function as a 4th element for metrics that need special compute handling.
 
     Parameters
     ----------
@@ -171,6 +213,7 @@ class TorchMetrics(Enum):
     """
 
     fid = (partial(FrechetInceptionDistance), fid_update, "gt_y")
+    kid = (partial(KernelInceptionDistance), kid_update, "gt_y", kid_compute)
     accuracy = (partial(Accuracy), None, "y_gt")
     perplexity = (partial(Perplexity), None, "y_gt")
     clip_score = (partial(CLIPScore), None, "y_x")
@@ -187,6 +230,7 @@ def __init__(self, *args, **kwargs) -> None:
         self.tm = self.value[0]
         self.update_fn = self.value[1] or default_update
         self.call_type = self.value[2]
+        self.compute_fn = self.value[3] if len(self.value) > 3 else None
 
     def __call__(self, **kwargs) -> Metric:
         """
@@ -260,6 +304,8 @@ def __init__(self, metric_name: str, call_type: str = "", **kwargs) -> None:
 
             # Get the specific update function for the metric, or use the default if not found.
             self.update_fn = TorchMetrics[metric_name].update_fn
+            # Get the compute function if available (e.g., for KID), otherwise None
+            self.compute_fn = TorchMetrics[metric_name].compute_fn
         except KeyError:
             raise ValueError(f"Metric {metric_name} is not supported.")
 
@@ -339,7 +385,8 @@ def compute(self) -> Any:
         Any
             The computed metric value.
         """
-        result = self.metric.compute()
+        # Use metric-specific compute function if available (e.g., KID), otherwise use default
+        result = self.compute_fn(self.metric) if self.compute_fn is not None else self.metric.compute()
 
         # Normally we have a single score for each metric for the entire dataset.
         # For IQA metrics we have a single score per image, so we need to convert the tensor to a list.

tests/evaluation/test_torch_metrics.py

@@ -58,6 +58,41 @@ def test_fid(datamodule_fixture: PrunaDataModule, device: str) -> None:
     assert metric.compute().result == pytest.approx(0.0, abs=1e-2)
 
 
+@pytest.mark.parametrize(
+    "datamodule_fixture, device",
+    [
+        pytest.param("LAION256", "cpu", marks=pytest.mark.cpu),
+        pytest.param("LAION256", "cuda", marks=pytest.mark.cuda),
+    ],
+    indirect=["datamodule_fixture"],
+)
+def test_kid(datamodule_fixture: PrunaDataModule, device: str) -> None:
+    """Test the kid."""
+    dataloader = datamodule_fixture.val_dataloader()
+    dataloader_iter = iter(dataloader)
+
+    # Get multiple batches to ensure enough samples
+    batches = []
+    for _ in range(4):  # Get 4 batches
+        _, batch = next(dataloader_iter)
+        batches.append(batch)
+    gt = torch.cat(batches, dim=0)
+
+    total_samples = gt.shape[0]
+    # subset_size must be strictly smaller than number of samples
+    # Use a subset_size that's safely smaller (at least 1 less)
+    subset_size = min(50, max(2, total_samples - 1))
+
+    metric = TorchMetricWrapper("kid", device=device, subset_size=subset_size)
+    metric.update(gt, gt, gt)
+    result = metric.compute()
+
+    # KID should be close to 0 when comparing identical images
+    # Use absolute value and a slightly larger tolerance due to numerical precision
+    assert not torch.isnan(torch.tensor(result.result)), f"KID returned NaN"
+    assert abs(result.result) < 0.25, f"KID should be close to 0 for identical images, got {result.result}"
+
+
 @pytest.mark.parametrize(
     "datamodule_fixture, device",
     [
@@ -77,6 +112,7 @@ def test_clip_score(datamodule_fixture: PrunaDataModule, device: str) -> None:
     score = metric.compute()
     assert score.result > 0.0 and score.result < 100.0
 
+
 @pytest.mark.cpu
 @pytest.mark.parametrize("datamodule_fixture", ["LAION256"], indirect=True)
 def test_clipiqa(datamodule_fixture: PrunaDataModule) -> None:
@@ -113,6 +149,7 @@ def test_torch_metrics(datamodule_fixture: PrunaDataModule, device: str, metric:
     metric.update(gt, gt, gt)
     assert metric.compute().result == 1.0
 
+
 @pytest.mark.cpu
 @pytest.mark.parametrize("datamodule_fixture", ["LAION256"], indirect=True)
 def test_arniqa(datamodule_fixture: PrunaDataModule) -> None:
@@ -123,6 +160,7 @@ def test_arniqa(datamodule_fixture: PrunaDataModule) -> None:
     x, gt = next(dataloader_iter)
     metric.update(x, gt, gt)
 
+
 @pytest.mark.cpu
 @pytest.mark.parametrize("metric", TorchMetrics.__members__.keys())
 @pytest.mark.parametrize("call_type", ["single", "pairwise"])
@@ -143,6 +181,7 @@ def test_check_call_type(metric: str, call_type: str):
     else:
         assert not metric.call_type.startswith("pairwise")
 
+
 @pytest.mark.cpu
 @pytest.mark.parametrize(
     'metric_name,metric_type',
@@ -155,6 +194,7 @@ def test_ssim_generalization_metric_type(metric_name, metric_type):
     wrapper = TorchMetricWrapper(metric_name=metric_name)
     assert isinstance(wrapper.metric, metric_type)
 
+
 @pytest.mark.cpu
 @pytest.mark.parametrize(
     'metric_name,invalid_param_args',