Remove deprecated code from image domain

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))

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",

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",

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,
 )

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)

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,

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

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)

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)