47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
import pytest
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import torch
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from enhancer import data
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from enhancer.utils.config import Files
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from enhancer.models import WaveUnet
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from enhancer.data.dataset import EnhancerDataset
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@pytest.fixture
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def vctk_dataset():
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root_dir = "tests/data/vctk"
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files = Files(train_clean="clean_testset_wav",train_noisy="noisy_testset_wav",
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test_clean="clean_testset_wav", test_noisy="noisy_testset_wav")
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dataset = EnhancerDataset(name="vctk",root_dir=root_dir,files=files)
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return dataset
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@pytest.mark.parametrize("batch_size,samples",[(1,1000)])
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def test_forward(batch_size,samples):
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model = WaveUnet()
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model.eval()
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data = torch.rand(batch_size,1,samples,requires_grad=False)
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with torch.no_grad():
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_ = model(data)
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data = torch.rand(batch_size,2,samples,requires_grad=False)
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with torch.no_grad():
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with pytest.raises(TypeError):
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_ = model(data)
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@pytest.mark.parametrize("dataset,channels,loss",
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[(pytest.lazy_fixture("vctk_dataset"),1,["mae","mse"])])
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def test_demucs_init(dataset,channels,loss):
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with torch.no_grad():
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model = WaveUnet(num_channels=channels,dataset=dataset,loss=loss)
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