compatibility TM v0.4 (#8206)

* tm compatible

* format

* chlog

* ..

* destroy distributed workaround

Co-authored-by: Adrian Wälchli <[email protected]>

CHANGELOG.md

@@ -24,6 +24,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed setting `worker_init_fn` to seed dataloaders correctly when using DDP ([#7942](https://github.com/PyTorchLightning/pytorch-lightning/pull/7942))
 - Fixed `BaseFinetuning` callback to properly handle parent modules w/ parameters ([#7931](https://github.com/PyTorchLightning/pytorch-lightning/pull/7931))
 
+## [1.3.6] - 2021-06-DD
+
+- Fix compatibility TorchMetrics v0.4 ([#8206](https://github.com/PyTorchLightning/pytorch-lightning/pull/8206))
+
 
 ## [1.3.5] - 2021-06-08
 

pytorch_lightning/metrics/classification/f_beta.py

@@ -16,12 +16,18 @@
 from torchmetrics import F1 as _F1
 from torchmetrics import FBeta as _FBeta
 
-from pytorch_lightning.metrics.utils import deprecated_metrics
+from pytorch_lightning.metrics.utils import (
+    _TORCHMETRICS_GREATER_EQUAL_0_4,
+    _TORCHMETRICS_LOWER_THAN_0_4,
+    deprecated_metrics,
+    void,
+)
 
 
 class FBeta(_FBeta):
 
-    @deprecated_metrics(target=_FBeta)
+    @deprecated_metrics(target=_FBeta, skip_if=_TORCHMETRICS_GREATER_EQUAL_0_4)
+    @deprecated_metrics(target=_FBeta, args_mapping={"multilabel": None}, skip_if=_TORCHMETRICS_LOWER_THAN_0_4)
     def __init__(
         self,
         num_classes: int,
@@ -44,7 +50,8 @@ def __init__(
 
 class F1(_F1):
 
-    @deprecated_metrics(target=_F1)
+    @deprecated_metrics(target=_F1, skip_if=_TORCHMETRICS_GREATER_EQUAL_0_4)
+    @deprecated_metrics(target=_F1, args_mapping={"multilabel": None}, skip_if=_TORCHMETRICS_LOWER_THAN_0_4)
     def __init__(
         self,
         num_classes: int,
@@ -61,4 +68,4 @@ def __init__(
         .. deprecated::
             Use :class:`~torchmetrics.F1`. Will be removed in v1.5.0.
         """
-        _ = num_classes, threshold, average, multilabel, compute_on_step, dist_sync_on_step, process_group
+        void(num_classes, threshold, average, multilabel, compute_on_step, dist_sync_on_step, process_group)

pytorch_lightning/metrics/classification/precision_recall.py

@@ -16,12 +16,25 @@
 from torchmetrics import Precision as _Precision
 from torchmetrics import Recall as _Recall
 
-from pytorch_lightning.metrics.utils import deprecated_metrics
+from pytorch_lightning.metrics.utils import (
+    _TORCHMETRICS_GREATER_EQUAL_0_4,
+    _TORCHMETRICS_LOWER_THAN_0_4,
+    deprecated_metrics,
+    void,
+)
 
 
 class Precision(_Precision):
 
-    @deprecated_metrics(target=_Precision)
+    @deprecated_metrics(target=_Precision, skip_if=_TORCHMETRICS_GREATER_EQUAL_0_4)
+    @deprecated_metrics(
+        target=_Precision,
+        args_mapping={
+            "multilabel": None,
+            "is_multiclass": None
+        },
+        skip_if=_TORCHMETRICS_LOWER_THAN_0_4
+    )
     def __init__(
         self,
         num_classes: Optional[int] = None,
@@ -49,7 +62,13 @@ def __init__(
 
 class Recall(_Recall):
 
-    @deprecated_metrics(target=_Recall)
+    @deprecated_metrics(target=_Recall, skip_if=_TORCHMETRICS_GREATER_EQUAL_0_4)
+    @deprecated_metrics(
+        target=_Recall, args_mapping={
+            "multilabel": None,
+            "is_multiclass": None
+        }, skip_if=_TORCHMETRICS_LOWER_THAN_0_4
+    )
     def __init__(
         self,
         num_classes: Optional[int] = None,
@@ -71,3 +90,7 @@ def __init__(
         .. deprecated::
             Use :class:`~torchmetrics.Recall`. Will be removed in v1.5.0.
         """
+        void(
+            num_classes, threshold, average, multilabel, mdmc_average, ignore_index, top_k, is_multiclass,
+            compute_on_step, dist_sync_on_step, process_group, dist_sync_fn
+        )

pytorch_lightning/metrics/classification/stat_scores.py

@@ -15,12 +15,25 @@
 
 from torchmetrics import StatScores as _StatScores
 
-from pytorch_lightning.metrics.utils import deprecated_metrics
+from pytorch_lightning.metrics.utils import (
+    _TORCHMETRICS_GREATER_EQUAL_0_4,
+    _TORCHMETRICS_LOWER_THAN_0_4,
+    deprecated_metrics,
+    void,
+)
 
 
 class StatScores(_StatScores):
 
-    @deprecated_metrics(target=_StatScores)
+    @deprecated_metrics(target=_StatScores, skip_if=_TORCHMETRICS_GREATER_EQUAL_0_4)
+    @deprecated_metrics(
+        target=_StatScores,
+        args_mapping={
+            "multilabel": None,
+            "is_multiclass": "multiclass"
+        },
+        skip_if=_TORCHMETRICS_LOWER_THAN_0_4
+    )
     def __init__(
         self,
         threshold: float = 0.5,
@@ -41,5 +54,7 @@ def __init__(
         .. deprecated::
             Use :class:`~torchmetrics.StatScores`. Will be removed in v1.5.0.
         """
-        _ = threshold, top_k, reduce, num_classes, ignore_index, mdmc_reduce, is_multiclass, compute_on_step, \
+        void(
+            threshold, top_k, reduce, num_classes, ignore_index, mdmc_reduce, is_multiclass, compute_on_step,
             dist_sync_on_step, process_group, dist_sync_fn
+        )

pytorch_lightning/metrics/compositional.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 from typing import Callable, Union
 
-import torch
+from torch import Tensor
 from torchmetrics import Metric
 from torchmetrics.metric import CompositionalMetric as _CompositionalMetric
 
@@ -26,8 +26,8 @@ class CompositionalMetric(_CompositionalMetric):
     def __init__(
         self,
         operator: Callable,
-        metric_a: Union[Metric, int, float, torch.Tensor],
-        metric_b: Union[Metric, int, float, torch.Tensor, None],
+        metric_a: Union[Metric, int, float, Tensor],
+        metric_b: Union[Metric, int, float, Tensor, None],
     ):
         """
         .. deprecated::

pytorch_lightning/metrics/functional/accuracy.py

@@ -13,20 +13,20 @@
 # limitations under the License.
 from typing import Optional
 
-import torch
+from torch import Tensor
 from torchmetrics.functional import accuracy as _accuracy
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_accuracy)
 def accuracy(
-    preds: torch.Tensor,
-    target: torch.Tensor,
+    preds: Tensor,
+    target: Tensor,
     threshold: float = 0.5,
     top_k: Optional[int] = None,
     subset_accuracy: bool = False,
-) -> torch.Tensor:
+) -> Tensor:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.accuracy`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/auc.py

@@ -11,14 +11,15 @@
 # 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.
-import torch
+
+from torch import Tensor
 from torchmetrics.functional import auc as _auc
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_auc)
-def auc(x: torch.Tensor, y: torch.Tensor, reorder: bool = False) -> torch.Tensor:
+def auc(x: Tensor, y: Tensor, reorder: bool = False) -> Tensor:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.auc`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/auroc.py

@@ -13,22 +13,22 @@
 # limitations under the License.
 from typing import Optional, Sequence
 
-import torch
+from torch import Tensor
 from torchmetrics.functional import auroc as _auroc
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_auroc)
 def auroc(
-    preds: torch.Tensor,
-    target: torch.Tensor,
+    preds: Tensor,
+    target: Tensor,
     num_classes: Optional[int] = None,
     pos_label: Optional[int] = None,
     average: Optional[str] = 'macro',
     max_fpr: Optional[float] = None,
     sample_weights: Optional[Sequence] = None,
-) -> torch.Tensor:
+) -> Tensor:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.auroc`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/average_precision.py

@@ -13,20 +13,20 @@
 # limitations under the License.
 from typing import List, Optional, Sequence, Union
 
-import torch
+from torch import Tensor
 from torchmetrics.functional import average_precision as _average_precision
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_average_precision)
 def average_precision(
-    preds: torch.Tensor,
-    target: torch.Tensor,
+    preds: Tensor,
+    target: Tensor,
     num_classes: Optional[int] = None,
     pos_label: Optional[int] = None,
     sample_weights: Optional[Sequence] = None,
-) -> Union[List[torch.Tensor], torch.Tensor]:
+) -> Union[List[Tensor], Tensor]:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.average_precision`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/confusion_matrix.py

@@ -13,20 +13,16 @@
 # limitations under the License.
 from typing import Optional
 
-import torch
+from torch import Tensor
 from torchmetrics.functional import confusion_matrix as _confusion_matrix
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_confusion_matrix)
 def confusion_matrix(
-    preds: torch.Tensor,
-    target: torch.Tensor,
-    num_classes: int,
-    normalize: Optional[str] = None,
-    threshold: float = 0.5
-) -> torch.Tensor:
+    preds: Tensor, target: Tensor, num_classes: int, normalize: Optional[str] = None, threshold: float = 0.5
+) -> Tensor:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.confusion_matrix`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/explained_variance.py

@@ -13,18 +13,18 @@
 # limitations under the License.
 from typing import Sequence, Union
 
-import torch
+from torch import Tensor
 from torchmetrics.functional import explained_variance as _explained_variance
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_explained_variance)
 def explained_variance(
-    preds: torch.Tensor,
-    target: torch.Tensor,
+    preds: Tensor,
+    target: Tensor,
     multioutput: str = 'uniform_average',
-) -> Union[torch.Tensor, Sequence[torch.Tensor]]:
+) -> Union[Tensor, Sequence[Tensor]]:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.explained_variance`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/f_beta.py

@@ -13,38 +13,44 @@
 # limitations under the License.
 from typing import Optional
 
-import torch
+from torch import Tensor
 from torchmetrics.functional import f1 as _f1
 from torchmetrics.functional import fbeta as _fbeta
 
-from pytorch_lightning.metrics.utils import deprecated_metrics
+from pytorch_lightning.metrics.utils import (
+    _TORCHMETRICS_GREATER_EQUAL_0_4,
+    _TORCHMETRICS_LOWER_THAN_0_4,
+    deprecated_metrics,
+)
 
 
-@deprecated_metrics(target=_fbeta)
+@deprecated_metrics(target=_fbeta, skip_if=_TORCHMETRICS_GREATER_EQUAL_0_4)
+@deprecated_metrics(target=_fbeta, args_mapping={"multilabel": None}, skip_if=_TORCHMETRICS_LOWER_THAN_0_4)
 def fbeta(
-    preds: torch.Tensor,
-    target: torch.Tensor,
+    preds: Tensor,
+    target: Tensor,
     num_classes: int,
     beta: float = 1.0,
     threshold: float = 0.5,
     average: str = "micro",
     multilabel: Optional[bool] = None
-) -> torch.Tensor:
+) -> Tensor:
     """
     .. deprecated::
-        Use :func:`torchmetrics.functional.accuracy`. Will be removed in v1.5.0.
+        Use :func:`torchmetrics.functional.fbeta`. Will be removed in v1.5.0.
     """
 
 
-@deprecated_metrics(target=_f1)
+@deprecated_metrics(target=_f1, skip_if=_TORCHMETRICS_GREATER_EQUAL_0_4)
+@deprecated_metrics(target=_f1, args_mapping={"multilabel": None}, skip_if=_TORCHMETRICS_LOWER_THAN_0_4)
 def f1(
-    preds: torch.Tensor,
-    target: torch.Tensor,
+    preds: Tensor,
+    target: Tensor,
     num_classes: int,
     threshold: float = 0.5,
     average: str = "micro",
     multilabel: Optional[bool] = None
-) -> torch.Tensor:
+) -> Tensor:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.f1`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/hamming_distance.py

@@ -11,14 +11,14 @@
 # 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.
-import torch
+from torch import Tensor
 from torchmetrics.functional import hamming_distance as _hamming_distance
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_hamming_distance)
-def hamming_distance(preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5) -> torch.Tensor:
+def hamming_distance(preds: Tensor, target: Tensor, threshold: float = 0.5) -> Tensor:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.hamming_distance`. Will be removed in v1.5.0.

pytorch_lightning/metrics/functional/image_gradients.py

@@ -13,14 +13,14 @@
 # limitations under the License.
 from typing import Tuple
 
-import torch
+from torch import Tensor
 from torchmetrics.functional import image_gradients as _image_gradients
 
 from pytorch_lightning.metrics.utils import deprecated_metrics
 
 
 @deprecated_metrics(target=_image_gradients)
-def image_gradients(img: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+def image_gradients(img: Tensor) -> Tuple[Tensor, Tensor]:
     """
     .. deprecated::
         Use :func:`torchmetrics.functional.image_gradients`. Will be removed in v1.5.0.