ci: cache reference metrics & clean audio tests (#2335)

* cache reference metrics
* audio
* classif
* regress
* image
* others
* cleaning

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Borda

.gitignore

@@ -40,9 +40,8 @@ pip-delete-this-directory.txt
 # Unit test / coverage reports
 tests/_data/
 data.zip
+tests/_reference-cache/
 htmlcov/
-.tox/
-.nox/
 .coverage
 .coverage.*
 .cache

tests/unittests/audio/__init__.py

@@ -1,7 +1,30 @@
 import os
+from typing import Callable, Optional
+
+from torch import Tensor
 
 from unittests import _PATH_ALL_TESTS
 
 _SAMPLE_AUDIO_SPEECH = os.path.join(_PATH_ALL_TESTS, "_data", "audio", "audio_speech.wav")
 _SAMPLE_AUDIO_SPEECH_BAB_DB = os.path.join(_PATH_ALL_TESTS, "_data", "audio", "audio_speech_bab_0dB.wav")
 _SAMPLE_NUMPY_ISSUE_895 = os.path.join(_PATH_ALL_TESTS, "_data", "audio", "issue_895.npz")
+
+
+def _average_metric_wrapper(
+    preds: Tensor, target: Tensor, metric_func: Callable, res_index: Optional[int] = None
+) -> Tensor:
+    """Average the metric values.
+
+    Args:
+        preds: predictions, shape[batch, spk, time]
+        target: targets, shape[batch, spk, time]
+        metric_func: a function which return best_metric and best_perm
+        res_index: if not None, return best_metric[res_index]
+
+    Returns:
+        the average of best_metric
+
+    """
+    if res_index is not None:
+        return metric_func(preds, target)[res_index].mean()
+    return metric_func(preds, target).mean()

tests/unittests/audio/test_pesq.py

@@ -22,7 +22,7 @@
 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.audio import _SAMPLE_AUDIO_SPEECH, _SAMPLE_AUDIO_SPEECH_BAB_DB, _average_metric_wrapper
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
@@ -41,7 +41,7 @@
 )
 
 
-def _pesq_original_batch(preds: Tensor, target: Tensor, fs: int, mode: str):
+def _reference_pesq_batch(preds: Tensor, target: Tensor, fs: int, mode: str):
     """Comparison function."""
     # shape: preds [BATCH_SIZE, Time] , target [BATCH_SIZE, Time]
     # or shape: preds [NUM_BATCHES*BATCH_SIZE, Time] , target [NUM_BATCHES*BATCH_SIZE, Time]
@@ -54,23 +54,12 @@ def _pesq_original_batch(preds: Tensor, target: Tensor, fs: int, mode: str):
     return torch.tensor(mss)
 
 
-def _average_metric(preds, target, metric_func):
-    # shape: preds [BATCH_SIZE, 1, Time] , target [BATCH_SIZE, 1, Time]
-    # or shape: preds [NUM_BATCHES*BATCH_SIZE, 1, Time] , target [NUM_BATCHES*BATCH_SIZE, 1, Time]
-    return metric_func(preds, target).mean()
-
-
-pesq_original_batch_8k_nb = partial(_pesq_original_batch, fs=8000, mode="nb")
-pesq_original_batch_16k_nb = partial(_pesq_original_batch, fs=16000, mode="nb")
-pesq_original_batch_16k_wb = partial(_pesq_original_batch, fs=16000, mode="wb")
-
-
 @pytest.mark.parametrize(
     "preds, target, ref_metric, fs, mode",
     [
-        (inputs_8k.preds, inputs_8k.target, pesq_original_batch_8k_nb, 8000, "nb"),
-        (inputs_16k.preds, inputs_16k.target, pesq_original_batch_16k_nb, 16000, "nb"),
-        (inputs_16k.preds, inputs_16k.target, pesq_original_batch_16k_wb, 16000, "wb"),
+        (inputs_8k.preds, inputs_8k.target, partial(_reference_pesq_batch, fs=8000, mode="nb"), 8000, "nb"),
+        (inputs_16k.preds, inputs_16k.target, partial(_reference_pesq_batch, fs=16000, mode="nb"), 16000, "nb"),
+        (inputs_16k.preds, inputs_16k.target, partial(_reference_pesq_batch, fs=16000, mode="wb"), 16000, "wb"),
     ],
 )
 class TestPESQ(MetricTester):
@@ -89,7 +78,7 @@ def test_pesq(self, preds, target, ref_metric, fs, mode, num_processes, ddp):
             preds,
             target,
             PerceptualEvaluationSpeechQuality,
-            reference_metric=partial(_average_metric, metric_func=ref_metric),
+            reference_metric=partial(_average_metric_wrapper, metric_func=ref_metric),
             metric_args={"fs": fs, "mode": mode, "n_processes": num_processes},
         )
 

tests/unittests/audio/test_pit.py

@@ -31,27 +31,28 @@
 )
 
 from unittests import BATCH_SIZE, NUM_BATCHES, _Input
+from unittests.audio import _average_metric_wrapper
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-TIME = 10
+TIME_FRAME = 10
 
 
 # three speaker examples to test _find_best_perm_by_linear_sum_assignment
 inputs1 = _Input(
-    preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 3, TIME),
-    target=torch.rand(NUM_BATCHES, BATCH_SIZE, 3, TIME),
+    preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 3, TIME_FRAME),
+    target=torch.rand(NUM_BATCHES, BATCH_SIZE, 3, TIME_FRAME),
 )
 # two speaker examples to test _find_best_perm_by_exhuastive_method
 inputs2 = _Input(
-    preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, TIME),
-    target=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, TIME),
+    preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, TIME_FRAME),
+    target=torch.rand(NUM_BATCHES, BATCH_SIZE, 2, TIME_FRAME),
 )
 
 
-def naive_implementation_pit_scipy(
+def _reference_scipy_pit(
     preds: Tensor,
     target: Tensor,
     metric_func: Callable,
@@ -66,10 +67,8 @@ def naive_implementation_pit_scipy(
         eval_func: min or max
 
     Returns:
-        best_metric:
-            shape [batch]
-        best_perm:
-            shape [batch, spk]
+        best_metric: shape [batch]
+        best_perm: shape [batch, spk]
 
     """
     batch_size, spk_num = target.shape[0:2]
@@ -88,62 +87,59 @@ def naive_implementation_pit_scipy(
     return torch.from_numpy(np.stack(best_metrics)), torch.from_numpy(np.stack(best_perms))
 
 
-def _average_metric(preds: Tensor, target: Tensor, metric_func: Callable) -> Tensor:
-    """Average the metric values.
+def _reference_scipy_pit_snr(preds: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
+    return _reference_scipy_pit(
+        preds=preds,
+        target=target,
+        metric_func=signal_noise_ratio,
+        eval_func="max",
+    )
 
-    Args:
-        preds: predictions, shape[batch, spk, time]
-        target: targets, shape[batch, spk, time]
-        metric_func: a function which return best_metric and best_perm
-
-    Returns:
-        the average of best_metric
 
-    """
-    return metric_func(preds, target)[0].mean()
-
-
-snr_pit_scipy = partial(naive_implementation_pit_scipy, metric_func=signal_noise_ratio, eval_func="max")
-si_sdr_pit_scipy = partial(
-    naive_implementation_pit_scipy, metric_func=scale_invariant_signal_distortion_ratio, eval_func="max"
-)
+def _reference_scipy_pit_si_sdr(preds: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
+    return _reference_scipy_pit(
+        preds=preds,
+        target=target,
+        metric_func=scale_invariant_signal_distortion_ratio,
+        eval_func="max",
+    )
 
 
 @pytest.mark.parametrize(
     "preds, target, ref_metric, metric_func, mode, eval_func",
     [
-        (inputs1.preds, inputs1.target, snr_pit_scipy, signal_noise_ratio, "speaker-wise", "max"),
+        (inputs1.preds, inputs1.target, _reference_scipy_pit_snr, signal_noise_ratio, "speaker-wise", "max"),
         (
             inputs1.preds,
             inputs1.target,
-            si_sdr_pit_scipy,
+            _reference_scipy_pit_si_sdr,
             scale_invariant_signal_distortion_ratio,
             "speaker-wise",
             "max",
         ),
-        (inputs2.preds, inputs2.target, snr_pit_scipy, signal_noise_ratio, "speaker-wise", "max"),
+        (inputs2.preds, inputs2.target, _reference_scipy_pit_snr, signal_noise_ratio, "speaker-wise", "max"),
         (
             inputs2.preds,
             inputs2.target,
-            si_sdr_pit_scipy,
+            _reference_scipy_pit_si_sdr,
             scale_invariant_signal_distortion_ratio,
             "speaker-wise",
             "max",
         ),
-        (inputs1.preds, inputs1.target, snr_pit_scipy, signal_noise_ratio, "permutation-wise", "max"),
+        (inputs1.preds, inputs1.target, _reference_scipy_pit_snr, signal_noise_ratio, "permutation-wise", "max"),
         (
             inputs1.preds,
             inputs1.target,
-            si_sdr_pit_scipy,
+            _reference_scipy_pit_si_sdr,
             scale_invariant_signal_distortion_ratio,
             "permutation-wise",
             "max",
         ),
-        (inputs2.preds, inputs2.target, snr_pit_scipy, signal_noise_ratio, "permutation-wise", "max"),
+        (inputs2.preds, inputs2.target, _reference_scipy_pit_snr, signal_noise_ratio, "permutation-wise", "max"),
         (
             inputs2.preds,
             inputs2.target,
-            si_sdr_pit_scipy,
+            _reference_scipy_pit_si_sdr,
             scale_invariant_signal_distortion_ratio,
             "permutation-wise",
             "max",
@@ -163,7 +159,7 @@ def test_pit(self, preds, target, ref_metric, metric_func, mode, eval_func, ddp)
             preds,
             target,
             PermutationInvariantTraining,
-            reference_metric=partial(_average_metric, metric_func=ref_metric),
+            reference_metric=partial(_average_metric_wrapper, metric_func=ref_metric, res_index=0),
             metric_args={"metric_func": metric_func, "mode": mode, "eval_func": eval_func},
         )
 

tests/unittests/audio/test_sa_sdr.py

@@ -38,7 +38,9 @@
 )
 
 
-def _ref_metric(preds: Tensor, target: Tensor, scale_invariant: bool, zero_mean: bool):
+def _reference_local_sa_sdr(
+    preds: Tensor, target: Tensor, scale_invariant: bool, zero_mean: bool, reduce_mean: bool = False
+):
     # According to the original paper, the sa-sdr equals to si-sdr with inputs concatenated over the speaker
     # dimension if scale_invariant==True. Accordingly, for scale_invariant==False, the sa-sdr equals to snr.
     # shape: preds [BATCH_SIZE, Spk, Time] , target [BATCH_SIZE, Spk, Time]
@@ -51,14 +53,14 @@ def _ref_metric(preds: Tensor, target: Tensor, scale_invariant: bool, zero_mean:
     preds = preds.reshape(preds.shape[0], preds.shape[1] * preds.shape[2])
     target = target.reshape(target.shape[0], target.shape[1] * target.shape[2])
     if scale_invariant:
-        return scale_invariant_signal_distortion_ratio(preds=preds, target=target, zero_mean=False)
-    return signal_noise_ratio(preds=preds, target=target, zero_mean=zero_mean)
-
-
-def _average_metric(preds: Tensor, target: Tensor, scale_invariant: bool, zero_mean: bool):
-    # shape: preds [BATCH_SIZE, 1, Time] , target [BATCH_SIZE, 1, Time]
-    # or shape: preds [NUM_BATCHES*BATCH_SIZE, 1, Time] , target [NUM_BATCHES*BATCH_SIZE, 1, Time]
-    return _ref_metric(preds, target, scale_invariant, zero_mean).mean()
+        sa_sdr = scale_invariant_signal_distortion_ratio(preds=preds, target=target, zero_mean=False)
+    else:
+        sa_sdr = signal_noise_ratio(preds=preds, target=target, zero_mean=zero_mean)
+    if reduce_mean:
+        # shape: preds [BATCH_SIZE, 1, Time] , target [BATCH_SIZE, 1, Time]
+        # or shape: preds [NUM_BATCHES*BATCH_SIZE, 1, Time] , target [NUM_BATCHES*BATCH_SIZE, 1, Time]
+        return sa_sdr.mean()
+    return sa_sdr
 
 
 @pytest.mark.parametrize(
@@ -83,7 +85,9 @@ def test_si_sdr(self, preds, target, scale_invariant, zero_mean, ddp):
             preds,
             target,
             SourceAggregatedSignalDistortionRatio,
-            reference_metric=partial(_average_metric, scale_invariant=scale_invariant, zero_mean=zero_mean),
+            reference_metric=partial(
+                _reference_local_sa_sdr, scale_invariant=scale_invariant, zero_mean=zero_mean, reduce_mean=True
+            ),
             metric_args={
                 "scale_invariant": scale_invariant,
                 "zero_mean": zero_mean,
@@ -96,7 +100,7 @@ def test_sa_sdr_functional(self, preds, target, scale_invariant, zero_mean):
             preds,
             target,
             source_aggregated_signal_distortion_ratio,
-            reference_metric=partial(_ref_metric, scale_invariant=scale_invariant, zero_mean=zero_mean),
+            reference_metric=partial(_reference_local_sa_sdr, scale_invariant=scale_invariant, zero_mean=zero_mean),
             metric_args={
                 "scale_invariant": scale_invariant,
                 "zero_mean": zero_mean,

tests/unittests/audio/test_sdr.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from functools import partial
-from typing import Callable
 
 import numpy as np
 import pytest
@@ -43,7 +42,9 @@
 )
 
 
-def _sdr_original_batch(preds: Tensor, target: Tensor, compute_permutation: bool = False) -> Tensor:
+def _reference_sdr_batch(
+    preds: Tensor, target: Tensor, compute_permutation: bool = False, reduce_mean: bool = False
+) -> Tensor:
     # shape: preds [BATCH_SIZE, spk, Time] , target [BATCH_SIZE, spk, Time]
     # or shape: preds [NUM_BATCHES*BATCH_SIZE, spk, Time] , target [NUM_BATCHES*BATCH_SIZE, spk, Time]
     target = target.detach().cpu().numpy()
@@ -52,56 +53,49 @@ def _sdr_original_batch(preds: Tensor, target: Tensor, compute_permutation: bool
     for b in range(preds.shape[0]):
         sdr_val_np, _, _, _ = bss_eval_sources(target[b], preds[b], compute_permutation)
         mss.append(sdr_val_np)
-    return torch.tensor(np.array(mss))
+    sdr = torch.tensor(np.array(mss))
+    if reduce_mean:
+        # shape: preds [BATCH_SIZE, 1, Time] , target [BATCH_SIZE, 1, Time]
+        # or shape: preds [NUM_BATCHES*BATCH_SIZE, 1, Time] , target [NUM_BATCHES*BATCH_SIZE, 1, Time]
+        return sdr.mean()
+    return sdr
 
 
-def _average_metric(preds: Tensor, target: Tensor, metric_func: Callable) -> Tensor:
-    # shape: preds [BATCH_SIZE, 1, Time] , target [BATCH_SIZE, 1, Time]
-    # or shape: preds [NUM_BATCHES*BATCH_SIZE, 1, Time] , target [NUM_BATCHES*BATCH_SIZE, 1, Time]
-    return metric_func(preds, target).mean()
-
-
-original_impl_compute_permutation = partial(_sdr_original_batch)
-
-
-@pytest.mark.skipif(  # TODO: figure out why tests leads to cuda errors on latest torch
+@pytest.mark.skipif(  # FIXME: figure out why tests leads to cuda errors on latest torch
     _TORCH_GREATER_EQUAL_1_11 and torch.cuda.is_available(), reason="tests leads to cuda errors on latest torch"
 )
 @pytest.mark.parametrize(
-    "preds, target, ref_metric",
-    [
-        (inputs_1spk.preds, inputs_1spk.target, original_impl_compute_permutation),
-        (inputs_2spk.preds, inputs_2spk.target, original_impl_compute_permutation),
-    ],
+    "preds, target",
+    [(inputs_1spk.preds, inputs_1spk.target), (inputs_2spk.preds, inputs_2spk.target)],
 )
 class TestSDR(MetricTester):
     """Test class for `SignalDistortionRatio` metric."""
 
     atol = 1e-2
 
     @pytest.mark.parametrize("ddp", [pytest.param(True, marks=pytest.mark.DDP), False])
-    def test_sdr(self, preds, target, ref_metric, ddp):
+    def test_sdr(self, preds, target, ddp):
         """Test class implementation of metric."""
         self.run_class_metric_test(
             ddp,
             preds,
             target,
             SignalDistortionRatio,
-            reference_metric=partial(_average_metric, metric_func=ref_metric),
+            reference_metric=partial(_reference_sdr_batch, reduce_mean=True),
             metric_args={},
         )
 
-    def test_sdr_functional(self, preds, target, ref_metric):
+    def test_sdr_functional(self, preds, target):
         """Test functional implementation of metric."""
         self.run_functional_metric_test(
             preds,
             target,
             signal_distortion_ratio,
-            ref_metric,
+            _reference_sdr_batch,
             metric_args={},
         )
 
-    def test_sdr_differentiability(self, preds, target, ref_metric):
+    def test_sdr_differentiability(self, preds, target):
         """Test the differentiability of the metric, according to its `is_differentiable` attribute."""
         self.run_differentiability_test(
             preds=preds,
@@ -110,7 +104,7 @@ def test_sdr_differentiability(self, preds, target, ref_metric):
             metric_args={},
         )
 
-    def test_sdr_half_cpu(self, preds, target, ref_metric):
+    def test_sdr_half_cpu(self, preds, target):
         """Test dtype support of the metric on CPU."""
         self.run_precision_test_cpu(
             preds=preds,
@@ -121,7 +115,7 @@ def test_sdr_half_cpu(self, preds, target, ref_metric):
         )
 
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
-    def test_sdr_half_gpu(self, preds, target, ref_metric):
+    def test_sdr_half_gpu(self, preds, target):
         """Test dtype support of the metric on GPU."""
         self.run_precision_test_gpu(
             preds=preds,