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97 lines
3.8 KiB
Python
97 lines
3.8 KiB
Python
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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from nemo.collections.audio.modules.projections import MixtureConsistencyProjection
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class TestMixtureConsistencyProjection:
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@pytest.mark.unit
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@pytest.mark.parametrize('weighting', [None, 'power'])
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@pytest.mark.parametrize('num_sources', [1, 3])
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def test_mixture_consistency(self, weighting: str, num_sources: int):
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batch_size = 4
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num_subbands = 33
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num_samples = 100
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num_examples = 8
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atol = 1e-5
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rng = torch.Generator()
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rng.manual_seed(42)
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# create projection
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uut = MixtureConsistencyProjection(weighting=weighting)
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for n in range(num_examples):
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# single-channel mixture
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mixture = torch.randn(batch_size, 1, num_subbands, num_samples, generator=rng, dtype=torch.cfloat)
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# source estimates
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estimate = torch.randn(
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batch_size, num_sources, num_subbands, num_samples, generator=rng, dtype=torch.cfloat
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)
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# project
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uut_projected = uut(mixture=mixture, estimate=estimate)
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# estimated mixture
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estimated_mixture = torch.sum(estimate, dim=1, keepdim=True)
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if weighting is None:
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weight = 1 / num_sources
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elif weighting == 'power':
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weight = estimate.abs().pow(2)
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weight = weight / (weight.sum(dim=1, keepdim=True) + uut.eps)
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else:
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raise ValueError(f'Weighting {weighting} not implemented')
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correction = weight * (mixture - estimated_mixture)
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ref_projected = estimate + correction
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# check consistency
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assert torch.allclose(uut_projected, ref_projected, atol=atol)
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@pytest.mark.unit
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def test_unsupported_weighting(self):
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# Initialize with unsupported weighting
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with pytest.raises(NotImplementedError):
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MixtureConsistencyProjection(weighting='not-implemented')
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# Initialize with None and change later
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uut = MixtureConsistencyProjection(weighting=None)
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uut.weighting = 'not-implemented'
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with pytest.raises(NotImplementedError):
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uut(
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mixture=torch.randn(1, 1, 1, 1, dtype=torch.cfloat),
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estimate=torch.randn(1, 1, 1, 1, dtype=torch.cfloat),
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)
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@pytest.mark.unit
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def test_unsupported_inputs(self):
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# Multi-channel mixtures are not supported
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uut = MixtureConsistencyProjection(weighting=None)
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with pytest.raises(ValueError):
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uut(
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mixture=torch.randn(1, 2, 1, 1, dtype=torch.cfloat),
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estimate=torch.randn(1, 2, 1, 1, dtype=torch.cfloat),
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)
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# Consistency projection is applied in the time-frequency domain
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# It is expected that the mixture has a single channel, and shape (B, 1, F, N)
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with pytest.raises(TypeError):
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uut(mixture=torch.randn(1, 2, 1), estimate=torch.randn(1, 2, 1))
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# It is expected that the estimate has shape (B, num_sources, F, N)
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with pytest.raises(TypeError):
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uut(mixture=torch.randn(1, 1, 1, 1, dtype=torch.cfloat), estimate=torch.randn(1, 2, 1))
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