remove deprecated compute_on_step from torchmetrics/image (#979)

* remove deprecated compute_on_step from torchmetrics/image

* flake8

* update changelog

* fix overwrite in changelog

Co-authored-by: Jirka <[email protected]>
Co-authored-by: Nicki Skafte Detlefsen <[email protected]>

CHANGELOG.md

@@ -41,6 +41,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Removed deprecated `compute_on_step` argument in Regression ([#967](https://github.com/PyTorchLightning/metrics/pull/967))
 
 
+- Removed deprecated `compute_on_step` argument in Image ([#979](https://github.com/PyTorchLightning/metrics/pull/979))
+
+
 ### Fixed
 
 - Fixed "Sort currently does not support bool dtype on CUDA" error in MAP for empty preds ([#983](https://github.com/PyTorchLightning/metrics/pull/983))

torchmetrics/image/fid.py

@@ -148,10 +148,6 @@ class FrechetInceptionDistance(Metric):
         is installed. Either install as ``pip install torchmetrics[image]`` or
         ``pip install torch-fidelity``
 
-    .. note:: the ``forward`` method can be used but ``compute_on_step`` is disabled by default (oppesit of
-        all other metrics) as this metric does not really make sense to calculate on a single batch. This
-        means that by default ``forward`` will just call ``update`` underneat.
-
     Args:
         feature:
             Either an integer or ``nn.Module``:
@@ -164,13 +160,6 @@ 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 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.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     References:
@@ -215,10 +204,9 @@ 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:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
 
         rank_zero_warn(
             "Metric `FrechetInceptionDistance` will save all extracted features in buffer."

torchmetrics/image/inception.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -46,10 +46,6 @@ class InceptionScore(Metric):
         is installed. Either install as ``pip install torchmetrics[image]`` or
         ``pip install torch-fidelity``
 
-    .. note:: the ``forward`` method can be used but ``compute_on_step`` is disabled by default (oppesit of
-        all other metrics) as this metric does not really make sense to calculate on a single batch. This
-        means that by default ``forward`` will just call ``update`` underneat.
-
     Args:
         feature:
             Either an str, integer or ``nn.Module``:
@@ -60,13 +56,6 @@ class InceptionScore(Metric):
               an ``[N,d]`` matrix where ``N`` is the batch size and ``d`` is the feature size.
 
         splits: integer determining how many splits the inception score calculation should be split among
-
-        compute_on_step:
-            Forward only calls ``update()`` and returns None if this is set to False.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     References:
@@ -105,10 +94,9 @@ def __init__(
         self,
         feature: Union[str, int, torch.nn.Module] = "logits_unbiased",
         splits: int = 10,
-        compute_on_step: Optional[bool] = None,
         **kwargs: Dict[str, Any],
     ) -> None:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
 
         rank_zero_warn(
             "Metric `InceptionScore` will save all extracted features in buffer."

torchmetrics/image/kid.py

@@ -89,10 +89,6 @@ class KernelInceptionDistance(Metric):
         is installed. Either install as ``pip install torchmetrics[image]`` or
         ``pip install torch-fidelity``
 
-    .. note:: the ``forward`` method can be used but ``compute_on_step`` is disabled by default (oppesit of
-        all other metrics) as this metric does not really make sense to calculate on a single batch. This
-        means that by default ``forward`` will just call ``update`` underneat.
-
     Args:
         feature: Either an str, integer or ``nn.Module``:
 
@@ -109,12 +105,6 @@ class KernelInceptionDistance(Metric):
         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.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     References:
@@ -173,10 +163,9 @@ def __init__(
         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:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
 
         rank_zero_warn(
             "Metric `Kernel Inception Distance` will save all extracted features in buffer."

torchmetrics/image/lpip.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 import torch
 from torch import Tensor
@@ -59,12 +59,6 @@ class LearnedPerceptualImagePatchSimilarity(Metric):
     Args:
         net_type: str indicating backbone network type to use. Choose between `'alex'`, `'vgg'` or `'squeeze'`
         reduction: str indicating how to reduce over the batch dimension. Choose between `'sum'` or `'mean'`.
-        compute_on_step:
-            Forward only calls ``update()`` and returns None if this is set to False.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -98,10 +92,9 @@ def __init__(
         self,
         net_type: str = "alex",
         reduction: Literal["sum", "mean"] = "mean",
-        compute_on_step: Optional[bool] = None,
         **kwargs: Dict[str, Any],
     ) -> None:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
 
         if not _LPIPS_AVAILABLE:
             raise ModuleNotFoundError(

torchmetrics/image/psnr.py

@@ -44,12 +44,6 @@ class PeakSignalNoiseRatio(Metric):
         dim:
             Dimensions to reduce PSNR scores over, provided as either an integer or a list of integers. Default is
             None meaning scores will be reduced across all dimensions and all batches.
-        compute_on_step:
-            Forward only calls ``update()`` and returns None if this is set to False.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -78,10 +72,9 @@ def __init__(
         base: float = 10.0,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
         dim: Optional[Union[int, Tuple[int, ...]]] = None,
-        compute_on_step: Optional[bool] = None,
         **kwargs: Dict[str, Any],
     ) -> None:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
 
         if dim is None and reduction != "elementwise_mean":
             rank_zero_warn(f"The `reduction={reduction}` will not have any effect when `dim` is None.")

torchmetrics/image/ssim.py

@@ -47,12 +47,6 @@ class StructuralSimilarityIndexMeasure(Metric):
         return_contrast_sensitivity: If true, the constant term is returned as a second argument.
             The luminance term can be obtained with luminance=ssim/contrast
             Mutually exclusive with ``return_full_image``
-        compute_on_step:
-            Forward only calls ``update()`` and returns None if this is set to False.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Return:
@@ -81,12 +75,11 @@ def __init__(
         data_range: Optional[float] = None,
         k1: float = 0.01,
         k2: float = 0.03,
-        compute_on_step: Optional[bool] = None,
         return_full_image: bool = False,
         return_contrast_sensitivity: bool = False,
         **kwargs: Dict[str, Any],
     ) -> None:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
         rank_zero_warn(
             "Metric `SSIM` will save all targets and"
             " predictions in buffer. For large datasets this may lead"
@@ -157,12 +150,6 @@ class MultiScaleStructuralSimilarityIndexMeasure(Metric):
         normalize: When MultiScaleStructuralSimilarityIndexMeasure loss is used for training, it is desirable to use
             normalizes to improve the training stability. This `normalize` argument is out of scope of the original
             implementation [1], and it is adapted from https://github.com/jorge-pessoa/pytorch-msssim instead.
-        compute_on_step:
-            Forward only calls ``update()`` and returns None if this is set to False.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Return:
@@ -206,10 +193,9 @@ def __init__(
         k2: float = 0.03,
         betas: Tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
         normalize: Literal["relu", "simple", None] = None,
-        compute_on_step: Optional[bool] = None,
         **kwargs: Dict[str, Any],
     ) -> None:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
         rank_zero_warn(
             "Metric `MS_SSIM` will save all targets and"
             " predictions in buffer. For large datasets this may lead"

torchmetrics/image/uqi.py

@@ -35,12 +35,6 @@ class UniversalImageQualityIndex(Metric):
             - ``'none'`` or ``None``: no reduction will be applied
 
         data_range: Range of the image. If ``None``, it is determined from the image (max - min)
-        compute_on_step:
-            Forward only calls ``update()`` and returns None if this is set to False.
-
-            .. deprecated:: v0.8
-                Argument has no use anymore and will be removed v0.9.
-
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
 
@@ -67,10 +61,9 @@ def __init__(
         sigma: Sequence[float] = (1.5, 1.5),
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
         data_range: Optional[float] = None,
-        compute_on_step: Optional[bool] = None,
         **kwargs: Dict[str, Any],
     ) -> None:
-        super().__init__(compute_on_step=compute_on_step, **kwargs)
+        super().__init__(**kwargs)
         rank_zero_warn(
             "Metric `UniversalImageQualityIndex` will save all targets and"
             " predictions in buffer. For large datasets this may lead"