Optionally Avoid recomputing features

CHANGELOG.md

@@ -47,6 +47,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `adaptive_k` for the `RetrievalPrecision` metric ([#910](https://github.com/PyTorchLightning/metrics/pull/910))
 
 
+- Added `reset_real_features` argument image quality assesment metrics ([#722](https://github.com/PyTorchLightning/metrics/pull/722))
+
+
 ### Changed
 
 - Made `num_classes` in `jaccard_index` a required argument ([#853](https://github.com/PyTorchLightning/metrics/pull/853), [#914](https://github.com/PyTorchLightning/metrics/pull/914))

tests/image/test_fid.py

@@ -153,3 +153,28 @@ def test_compare_fid(tmpdir, feature=2048):
  tm_res = metric.compute()
 
  assert torch.allclose(tm_res.cpu(), torch.tensor([torch_fid["frechet_inception_distance"]]), atol=1e-3)
+
+
+@pytest.mark.parametrize("reset_real_features", [True, False])
+def test_reset_real_features_arg(reset_real_features):
+ metric = FrechetInceptionDistance(feature=64, reset_real_features=reset_real_features)
+
+ metric.update(torch.randint(0, 180, (2, 3, 299, 299), dtype=torch.uint8), real=True)
+ metric.update(torch.randint(0, 180, (2, 3, 299, 299), dtype=torch.uint8), real=False)
+
+ assert len(metric.real_features) == 1
+ assert list(metric.real_features[0].shape) == [2, 64]
+
+ assert len(metric.fake_features) == 1
+ assert list(metric.fake_features[0].shape) == [2, 64]
+
+ metric.reset()
+
+ # fake features should always reset
+ assert len(metric.fake_features) == 0
+
+ if reset_real_features:
+ assert len(metric.real_features) == 0
+ else:
+ assert len(metric.real_features) == 1
+ assert list(metric.real_features[0].shape) == [2, 64]

tests/image/test_kid.py

@@ -163,3 +163,28 @@ def test_compare_kid(tmpdir, feature=2048):
 
  assert torch.allclose(tm_mean.cpu(), torch.tensor([torch_fid["kernel_inception_distance_mean"]]), atol=1e-3)
  assert torch.allclose(tm_std.cpu(), torch.tensor([torch_fid["kernel_inception_distance_std"]]), atol=1e-3)
+
+
+@pytest.mark.parametrize("reset_real_features", [True, False])
+def test_reset_real_features_arg(reset_real_features):
+ metric = KernelInceptionDistance(feature=64, reset_real_features=reset_real_features)
+
+ metric.update(torch.randint(0, 180, (2, 3, 299, 299), dtype=torch.uint8), real=True)
+ metric.update(torch.randint(0, 180, (2, 3, 299, 299), dtype=torch.uint8), real=False)
+
+ assert len(metric.real_features) == 1
+ assert list(metric.real_features[0].shape) == [2, 64]
+
+ assert len(metric.fake_features) == 1
+ assert list(metric.fake_features[0].shape) == [2, 64]
+
+ metric.reset()
+
+ # fake features should always reset
+ assert len(metric.fake_features) == 0
+
+ if reset_real_features:
+ assert len(metric.real_features) == 0
+ else:
+ assert len(metric.real_features) == 1
+ assert list(metric.real_features[0].shape) == [2, 64]

torchmetrics/image/fid.py

@@ -161,6 +161,10 @@ class FrechetInceptionDistance(Metric):
  - an ``nn.Module`` for using a custom feature extractor. Expects that its forward method returns
  an ``[N,d]`` matrix where ``N`` is the batch size and ``d`` is the feature size.
 
+ reset_real_features: Whether to also reset the real features. Since in many cases the real dataset does not
+ change, the features can cached them to avoid recomputing them which is costly. Set this to ``False`` if
+ your dataset does not change.
+
  compute_on_step:
  Forward only calls ``update()`` and returns None if this is set to False.
 
@@ -186,6 +190,8 @@ class FrechetInceptionDistance(Metric):
  If ``feature`` is set to an ``int`` not in [64, 192, 768, 2048]
  TypeError:
  If ``feature`` is not an ``str``, ``int`` or ``torch.nn.Module``
+ ValueError:
+ If ``reset_real_features`` is not an ``bool``
 
  Example:
  >>> import torch
@@ -203,11 +209,13 @@ class FrechetInceptionDistance(Metric):
  """
  real_features: List[Tensor]
  fake_features: List[Tensor]
- higher_is_better = False
+ higher_is_better: bool = False
+ is_differentiable: bool = False
 
  def __init__(
  self,
  feature: Union[int, torch.nn.Module] = 2048,
+ reset_real_features: bool = True,
  compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
@@ -237,6 +245,10 @@ def __init__(
  else:
  raise TypeError("Got unknown input to argument `feature`")
 
+ if not isinstance(reset_real_features, bool):
+ raise ValueError("Arugment `reset_real_features` expected to be a bool")
+ self.reset_real_features = reset_real_features
+
  self.add_state("real_features", [], dist_reduce_fx=None)
  self.add_state("fake_features", [], dist_reduce_fx=None)
 
@@ -274,3 +286,12 @@ def compute(self) -> Tensor:
 
  # compute fid
  return _compute_fid(mean1, cov1, mean2, cov2).to(orig_dtype)
+
+ def reset(self) -> None:
+ if not self.reset_real_features:
+ # remove temporarily to avoid resetting
+ value = self._defaults.pop("real_features")
+ super().reset()
+ self._defaults["real_features"] = value
+ else:
+ super().reset()

torchmetrics/image/kid.py

@@ -112,6 +112,9 @@ class KernelInceptionDistance(Metric):
  Scale-length of polynomial kernel. If set to ``None`` will be automatically set to the feature size
  coef:
  Bias term in the polynomial kernel.
+ reset_real_features: Whether to also reset the real features. Since in many cases the real dataset does not
+ change, the features can cached them to avoid recomputing them which is costly. Set this to ``False`` if
+ your dataset does not change.
  compute_on_step:
  Forward only calls ``update()`` and returns None if this is set to False.
 
@@ -145,6 +148,8 @@ class KernelInceptionDistance(Metric):
  If ``gamma`` is niether ``None`` or a float larger than 0
  ValueError:
  If ``coef`` is not an float larger than 0
+ ValueError:
+ If ``reset_real_features`` is not an ``bool``
 
  Example:
  >>> import torch
@@ -163,7 +168,8 @@ class KernelInceptionDistance(Metric):
  """
  real_features: List[Tensor]
  fake_features: List[Tensor]
- higher_is_better = False
+ higher_is_better: bool = False
+ is_differentiable: bool = False
 
  def __init__(
  self,
@@ -173,6 +179,7 @@ def __init__(
  degree: int = 3,
  gamma: Optional[float] = None, # type: ignore
  coef: float = 1.0,
+ reset_real_features: bool = True,
  compute_on_step: Optional[bool] = None,
  **kwargs: Dict[str, Any],
  ) -> None:
@@ -222,6 +229,10 @@ def __init__(
  raise ValueError("Argument `coef` expected to be float larger than 0")
  self.coef = coef
 
+ if not isinstance(reset_real_features, bool):
+ raise ValueError("Arugment `reset_real_features` expected to be a bool")
+ self.reset_real_features = reset_real_features
+
  # states for extracted features
  self.add_state("real_features", [], dist_reduce_fx=None)
  self.add_state("fake_features", [], dist_reduce_fx=None)
@@ -267,3 +278,12 @@ def compute(self) -> Tuple[Tensor, Tensor]:
  kid_scores_.append(o)
  kid_scores = torch.stack(kid_scores_)
  return kid_scores.mean(), kid_scores.std(unbiased=False)
+
+ def reset(self) -> None:
+ if not self.reset_real_features:
+ # remove temporarily to avoid resetting
+ value = self._defaults.pop("real_features")
+ super().reset()
+ self._defaults["real_features"] = value
+ else:
+ super().reset()