Resolve pyi024 warnings (#2146)

Co-authored-by: Jirka Borovec <[email protected]>
Co-authored-by: Nicki Skafte Detlefsen <[email protected]>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
(cherry picked from commit fdaab96)

pyproject.toml

@@ -121,7 +121,6 @@ ignore = [
     "S301",   # todo: `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue  # todo
     "S310",   # todo: Audit URL open for permitted schemes. Allowing use of `file:` or custom schemes is often unexpected.  # todo
     "B905",   # todo: `zip()` without an explicit `strict=` parameter
-    "PYI024",  # todo: Use `typing.NamedTuple` instead of `collections.namedtuple`
 ]
 # Exclude a variety of commonly ignored directories.
 exclude = [

src/torchmetrics/functional/image/lpips.py

@@ -24,7 +24,6 @@
 # License under BSD 2-clause
 import inspect
 import os
-from collections import namedtuple
 from typing import List, NamedTuple, Optional, Tuple, Union
 
 import torch
@@ -86,13 +85,21 @@ def __init__(self, requires_grad: bool = False, pretrained: bool = True) -> None
 
     def forward(self, x: Tensor) -> NamedTuple:
         """Process input."""
-        squeeze_output = namedtuple("squeeze_output", ["relu1", "relu2", "relu3", "relu4", "relu5", "relu6", "relu7"])
+
+        class _SqueezeOutput(NamedTuple):
+            relu1: Tensor
+            relu2: Tensor
+            relu3: Tensor
+            relu4: Tensor
+            relu5: Tensor
+            relu6: Tensor
+            relu7: Tensor
 
         relus = []
         for slice_ in self.slices:
             x = slice_(x)
             relus.append(x)
-        return squeeze_output(*relus)
+        return _SqueezeOutput(*relus)
 
 
 class Alexnet(torch.nn.Module):
@@ -134,8 +141,15 @@ def forward(self, x: Tensor) -> NamedTuple:
         h_relu4 = h
         h = self.slice5(h)
         h_relu5 = h
-        alexnet_outputs = namedtuple("alexnet_outputs", ["relu1", "relu2", "relu3", "relu4", "relu5"])
-        return alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
+
+        class _AlexnetOutputs(NamedTuple):
+            relu1: Tensor
+            relu2: Tensor
+            relu3: Tensor
+            relu4: Tensor
+            relu5: Tensor
+
+        return _AlexnetOutputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
 
 
 class Vgg16(torch.nn.Module):
@@ -177,8 +191,15 @@ def forward(self, x: Tensor) -> NamedTuple:
         h_relu4_3 = h
         h = self.slice5(h)
         h_relu5_3 = h
-        vgg_outputs = namedtuple("vgg_outputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"])
-        return vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+
+        class _VGGOutputs(NamedTuple):
+            relu1_2: Tensor
+            relu2_2: Tensor
+            relu3_3: Tensor
+            relu4_3: Tensor
+            relu5_3: Tensor
+
+        return _VGGOutputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
 
 
 def _spatial_average(in_tens: Tensor, keep_dim: bool = True) -> Tensor:

tests/unittests/__init__.py

@@ -1,7 +1,9 @@
 import os.path
+from typing import NamedTuple
 
 import numpy
 import torch
+from torch import Tensor
 
 from unittests.conftest import (
     BATCH_SIZE,
@@ -26,9 +28,23 @@
     torch.backends.cuda.matmul.allow_tf32 = False
     torch.backends.cudnn.allow_tf32 = False
 
+
+class _Input(NamedTuple):
+    preds: Tensor
+    target: Tensor
+
+
+class _GroupInput(NamedTuple):
+    preds: Tensor
+    target: Tensor
+    groups: Tensor
+
+
 __all__ = [
     "BATCH_SIZE",
     "EXTRA_DIM",
+    "_Input",
+    "_GroupInput",
     "NUM_BATCHES",
     "NUM_CLASSES",
     "NUM_PROCESSES",

tests/unittests/audio/test_c_si_snr.py

@@ -11,24 +11,22 @@
 # 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 collections import namedtuple
 
 import pytest
 import torch
 from scipy.io import wavfile
 from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
 from torchmetrics.functional.audio import complex_scale_invariant_signal_noise_ratio
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_BATCHES, _Input
 from unittests.audio import _SAMPLE_AUDIO_SPEECH, _SAMPLE_AUDIO_SPEECH_BAB_DB
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
 
-inputs = Input(
+inputs = _Input(
     preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 129, 20, 2),
     target=torch.rand(NUM_BATCHES, BATCH_SIZE, 129, 20, 2),
 )

tests/unittests/audio/test_pesq.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 
 import pytest
@@ -22,21 +21,21 @@
 from torchmetrics.audio import PerceptualEvaluationSpeechQuality
 from torchmetrics.functional.audio import perceptual_evaluation_speech_quality
 
+from unittests import _Input
 from unittests.audio import _SAMPLE_AUDIO_SPEECH, _SAMPLE_AUDIO_SPEECH_BAB_DB
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
 
 # for 8k sample rate, need at least 8k/4=2000 samples
-inputs_8k = Input(
+inputs_8k = _Input(
     preds=torch.rand(2, 3, 2100),
     target=torch.rand(2, 3, 2100),
 )
 # for 16k sample rate, need at least 16k/4=4000 samples
-inputs_16k = Input(
+inputs_16k = _Input(
     preds=torch.rand(2, 3, 4100),
     target=torch.rand(2, 3, 4100),
 )

tests/unittests/audio/test_pit.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 from typing import Callable, Tuple
 
@@ -31,23 +30,22 @@
     _find_best_perm_by_linear_sum_assignment,
 )
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_BATCHES, _Input
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
 TIME = 10
 
-Input = namedtuple("Input", ["preds", "target"])
 
 # three speaker examples to test _find_best_perm_by_linear_sum_assignment
-inputs1 = Input(
+inputs1 = _Input(
     preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 3, TIME),
     target=torch.rand(NUM_BATCHES, BATCH_SIZE, 3, TIME),
 )
 # two speaker examples to test _find_best_perm_by_exhuastive_method
-inputs2 = Input(
+inputs2 = _Input(
     preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, TIME),
     target=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, TIME),
 )

tests/unittests/audio/test_sa_sdr.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 
 import pytest
@@ -24,17 +23,16 @@
     source_aggregated_signal_distortion_ratio,
 )
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_BATCHES, _Input
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
 NUM_SAMPLES = 100  # the number of samples
 
-Input = namedtuple("Input", ["preds", "target"])
 
-inputs = Input(
+inputs = _Input(
     preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, NUM_SAMPLES),
     target=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, NUM_SAMPLES),
 )

tests/unittests/audio/test_sdr.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 from typing import Callable
 
@@ -25,19 +24,20 @@
 from torchmetrics.functional import signal_distortion_ratio
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_11
 
+from unittests import _Input
 from unittests.audio import _SAMPLE_AUDIO_SPEECH, _SAMPLE_AUDIO_SPEECH_BAB_DB, _SAMPLE_NUMPY_ISSUE_895
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
 
-inputs_1spk = Input(
+inputs_1spk = _Input(
     preds=torch.rand(2, 1, 1, 500),
     target=torch.rand(2, 1, 1, 500),
 )
-inputs_2spk = Input(
+
+inputs_2spk = _Input(
     preds=torch.rand(2, 1, 2, 500),
     target=torch.rand(2, 1, 2, 500),
 )

tests/unittests/audio/test_si_sdr.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 
 import pytest
@@ -21,17 +20,16 @@
 from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
 from torchmetrics.functional.audio import scale_invariant_signal_distortion_ratio
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_BATCHES, _Input
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
 NUM_SAMPLES = 100
 
-Input = namedtuple("Input", ["preds", "target"])
 
-inputs = Input(
+inputs = _Input(
     preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, NUM_SAMPLES),
     target=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, NUM_SAMPLES),
 )

tests/unittests/audio/test_si_snr.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 
 import pytest
@@ -21,17 +20,16 @@
 from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
 from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_BATCHES, _Input
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
 NUM_SAMPLES = 100
 
-Input = namedtuple("Input", ["preds", "target"])
 
-inputs = Input(
+inputs = _Input(
     preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, NUM_SAMPLES),
     target=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, NUM_SAMPLES),
 )

tests/unittests/audio/test_snr.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 from typing import Callable
 
@@ -22,14 +21,14 @@
 from torchmetrics.audio import SignalNoiseRatio
 from torchmetrics.functional.audio import signal_noise_ratio
 
+from unittests import _Input
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
 
-inputs = Input(
+inputs = _Input(
     preds=torch.rand(2, 1, 1, 25),
     target=torch.rand(2, 1, 1, 25),
 )

tests/unittests/audio/test_stoi.py

@@ -11,7 +11,6 @@
 # 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 collections import namedtuple
 from functools import partial
 
 import pytest
@@ -22,19 +21,19 @@
 from torchmetrics.audio import ShortTimeObjectiveIntelligibility
 from torchmetrics.functional.audio import short_time_objective_intelligibility
 
+from unittests import _Input
 from unittests.audio import _SAMPLE_AUDIO_SPEECH, _SAMPLE_AUDIO_SPEECH_BAB_DB
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
 
-inputs_8k = Input(
+inputs_8k = _Input(
     preds=torch.rand(2, 3, 8000),
     target=torch.rand(2, 3, 8000),
 )
-inputs_16k = Input(
+inputs_16k = _Input(
     preds=torch.rand(2, 3, 16000),
     target=torch.rand(2, 3, 16000),
 )