Remove deprecated code from image domain (#796)

* Remove deprecated functions, and warnings * Update links for docstring * chlog Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Jirka <[email protected]>
Lightning-AI · Feb 5, 2022 · 8e9eb8e · 8e9eb8e
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -28,6 +28,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Removed deprecated functions, and warnings in Text ([#773](https://github.com/PyTorchLightning/metrics/pull/773))
   * `functional.wer`
   * `WER`
+- Removed deprecated functions and warnings in Image ([#796](https://github.com/PyTorchLightning/metrics/pull/796))
+  * `functional.ssim`
+  * `functional.psnr`
+  * `SSIM`
+  * `PSNR`
 
 
 - Removed deprecated functions, and warnings in detection and pairwise ([#804](https://github.com/PyTorchLightning/metrics/pull/804))

diff --git a/torchmetrics/__init__.py b/torchmetrics/__init__.py
@@ -52,8 +52,6 @@
 )
 from torchmetrics.collections import MetricCollection  # noqa: E402
 from torchmetrics.image import (  # noqa: E402
-    PSNR,
-    SSIM,
     MultiScaleStructuralSimilarityIndexMeasure,
     PeakSignalNoiseRatio,
     StructuralSimilarityIndexMeasure,
@@ -148,7 +146,6 @@
     "PermutationInvariantTraining",
     "Precision",
     "PrecisionRecallCurve",
-    "PSNR",
     "PeakSignalNoiseRatio",
     "R2Score",
     "Recall",
@@ -170,7 +167,6 @@
     "SpearmanCorrCoef",
     "Specificity",
     "SQuAD",
-    "SSIM",
     "StructuralSimilarityIndexMeasure",
     "StatScores",
     "SumMetric",

diff --git a/torchmetrics/functional/__init__.py b/torchmetrics/functional/__init__.py
@@ -35,10 +35,9 @@
 from torchmetrics.functional.classification.specificity import specificity
 from torchmetrics.functional.classification.stat_scores import stat_scores
 from torchmetrics.functional.image.gradients import image_gradients
-from torchmetrics.functional.image.psnr import peak_signal_noise_ratio, psnr
+from torchmetrics.functional.image.psnr import peak_signal_noise_ratio
 from torchmetrics.functional.image.ssim import (
     multiscale_structural_similarity_index_measure,
-    ssim,
     structural_similarity_index_measure,
 )
 from torchmetrics.functional.pairwise.cosine import pairwise_cosine_similarity
@@ -123,7 +122,6 @@
     "precision_recall",
     "precision_recall_curve",
     "peak_signal_noise_ratio",
-    "psnr",
     "r2_score",
     "recall",
     "retrieval_average_precision",
@@ -144,7 +142,6 @@
     "spearman_corrcoef",
     "specificity",
     "squad",
-    "ssim",
     "structural_similarity_index_measure",
     "stat_scores",
     "symmetric_mean_absolute_percentage_error",

diff --git a/torchmetrics/functional/image/__init__.py b/torchmetrics/functional/image/__init__.py
@@ -12,9 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from torchmetrics.functional.image.gradients import image_gradients  # noqa: F401
-from torchmetrics.functional.image.psnr import peak_signal_noise_ratio, psnr  # noqa: F401
+from torchmetrics.functional.image.psnr import peak_signal_noise_ratio  # noqa: F401
 from torchmetrics.functional.image.ssim import (  # noqa: F401
     multiscale_structural_similarity_index_measure,
-    ssim,
     structural_similarity_index_measure,
 )
diff --git a/torchmetrics/functional/image/ms_ssim.py b/torchmetrics/functional/image/ms_ssim.py
diff --git a/torchmetrics/functional/image/psnr.py b/torchmetrics/functional/image/psnr.py
@@ -14,10 +14,9 @@
 from typing import Optional, Tuple, Union
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor, tensor
 
-from torchmetrics.utilities import _future_warning, rank_zero_warn, reduce
+from torchmetrics.utilities import rank_zero_warn, reduce
 
 
 def _psnr_compute(
@@ -149,25 +148,3 @@ def peak_signal_noise_ratio(
         data_range = tensor(float(data_range))
     sum_squared_error, n_obs = _psnr_update(preds, target, dim=dim)
     return _psnr_compute(sum_squared_error, n_obs, data_range, base=base, reduction=reduction)
-
-
-@deprecated(target=peak_signal_noise_ratio, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-def psnr(
-    preds: Tensor,
-    target: Tensor,
-    data_range: Optional[float] = None,
-    base: float = 10.0,
-    reduction: str = "elementwise_mean",
-    dim: Optional[Union[int, Tuple[int, ...]]] = None,
-) -> Tensor:
-    """Computes the peak signal-to-noise ratio.
-
-    .. deprecated:: v0.7
-        Use :func:torchmetrics.functional.psnr. Will be removed in v0.8.
-
-    Example:
-
-        >>> psnr(torch.tensor([[0.0, 1.0], [2.0, 3.0]]), torch.tensor([[3.0, 2.0], [1.0, 0.0]]))
-        tensor(2.5527)
-    """
-    return void(preds, target, data_range, base, reduction, dim)
diff --git a/torchmetrics/functional/image/ssim.py b/torchmetrics/functional/image/ssim.py
@@ -14,12 +14,10 @@
 from typing import List, Optional, Sequence, Tuple, Union
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 from torch.nn import functional as F
 from typing_extensions import Literal
 
-from torchmetrics.utilities import _future_warning
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.distributed import reduce
 
@@ -235,31 +233,6 @@ def structural_similarity_index_measure(
     return _ssim_compute(preds, target, kernel_size, sigma, reduction, data_range, k1, k2)
 
 
-@deprecated(target=structural_similarity_index_measure, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-def ssim(
-    preds: Tensor,
-    target: Tensor,
-    kernel_size: Sequence[int] = (11, 11),
-    sigma: Sequence[float] = (1.5, 1.5),
-    reduction: str = "elementwise_mean",
-    data_range: Optional[float] = None,
-    k1: float = 0.01,
-    k2: float = 0.03,
-) -> Tensor:
-    """Computes Structural Similarity Index Measure.
-
-    .. deprecated:: v0.7
-        Use :func:`torchmetrics.functional.scale_invariant_signal_noise_ratio`. Will be removed in v0.8.
-
-    Example:
-        >>> preds = torch.rand([16, 1, 16, 16])
-        >>> target = preds * 0.75
-        >>> ssim(preds, target)
-        tensor(0.9219)
-    """
-    return void(preds, target, kernel_size, sigma, reduction, data_range, k1, k2)
-
-
 def _get_normalized_sim_and_cs(
     preds: Tensor,
     target: Tensor,

diff --git a/torchmetrics/image/__init__.py b/torchmetrics/image/__init__.py
@@ -11,18 +11,17 @@
 # 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 torchmetrics.image.inception import IS, InceptionScore  # noqa: F401
-from torchmetrics.image.kid import KID, KernelInceptionDistance  # noqa: F401
-from torchmetrics.image.psnr import PSNR, PeakSignalNoiseRatio  # noqa: F401
+from torchmetrics.image.psnr import PeakSignalNoiseRatio  # noqa: F401
 from torchmetrics.image.ssim import (  # noqa: F401
-    SSIM,
     MultiScaleStructuralSimilarityIndexMeasure,
     StructuralSimilarityIndexMeasure,
 )
 from torchmetrics.utilities.imports import _LPIPS_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
 
 if _TORCH_FIDELITY_AVAILABLE:
-    from torchmetrics.image.fid import FID, FrechetInceptionDistance  # noqa: F401
+    from torchmetrics.image.fid import FrechetInceptionDistance  # noqa: F401
+    from torchmetrics.image.inception import InceptionScore  # noqa: F401
+    from torchmetrics.image.kid import KernelInceptionDistance  # noqa: F401
 
 if _LPIPS_AVAILABLE:
-    from torchmetrics.image.lpip import LPIPS, LearnedPerceptualImagePatchSimilarity  # noqa: F401
+    from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity  # noqa: F401
diff --git a/torchmetrics/image/fid.py b/torchmetrics/image/fid.py
@@ -15,12 +15,11 @@
 
 import numpy as np
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 from torch.autograd import Function
 
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning, rank_zero_info, rank_zero_warn
+from torchmetrics.utilities import rank_zero_info, rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
 from torchmetrics.utilities.imports import _SCIPY_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
 
@@ -285,36 +284,3 @@ def compute(self) -> Tensor:
 
         # compute fid
         return _compute_fid(mean1, cov1, mean2, cov2).to(orig_dtype)
-
-
-class FID(FrechetInceptionDistance):
-    r"""
-    Calculates Fréchet inception distance (FID_) which is used to access the quality of generated images.
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.image.FrechetInceptionDistance`. Will be removed in v0.8.
-
-    Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(123)
-        >>> fid = FID(feature=64)
-        >>> # generate two slightly overlapping image intensity distributions
-        >>> imgs_dist1 = torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
-        >>> imgs_dist2 = torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
-        >>> fid.update(imgs_dist1, real=True)
-        >>> fid.update(imgs_dist2, real=False)
-        >>> fid.compute()
-        tensor(12.7202)
-
-    """
-
-    @deprecated(target=FrechetInceptionDistance, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        feature: Union[int, torch.nn.Module] = 2048,
-        compute_on_step: bool = False,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable[[Tensor], List[Tensor]] = None,
-    ) -> None:
-        void(feature, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
diff --git a/torchmetrics/image/inception.py b/torchmetrics/image/inception.py
@@ -14,12 +14,11 @@
 from typing import Any, Callable, List, Optional, Tuple, Union
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.image.fid import NoTrainInceptionV3
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning, rank_zero_warn
+from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
 from torchmetrics.utilities.imports import _TORCH_FIDELITY_AVAILABLE
 
@@ -180,35 +179,3 @@ def compute(self) -> Tuple[Tensor, Tensor]:
 
         # return mean and std
         return kl.mean(), kl.std()
-
-
-class IS(InceptionScore):
-    r"""
-    Calculates the Inception Score (IS) which is used to access how realistic generated images are.
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.image.InceptionScore`. Will be removed in v0.8.
-
-    Example:
-        >>> import torch
-        >>> _ = torch.manual_seed(123)
-        >>> inception = IS()
-        >>> # generate some images
-        >>> imgs = torch.randint(0, 255, (100, 3, 299, 299), dtype=torch.uint8)
-        >>> inception.update(imgs)
-        >>> inception.compute()
-        (tensor(1.0544), tensor(0.0117))
-
-    """
-
-    @deprecated(target=InceptionScore, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        feature: Union[str, int, torch.nn.Module] = "logits_unbiased",
-        splits: int = 10,
-        compute_on_step: bool = False,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable[[Tensor], List[Tensor]] = None,
-    ) -> None:
-        void(feature, splits, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)