fa6f47e7aee228226421c52e61cce4e1ab4cc099,tests/ml/test_loss.py,,test_permutation_invariant_loss_tf,#,40

Before Change

    for shift in range(n_sources):
        sources_a = sources[:, :, :, shift:]
        sources_b = sources[:, :, :, :shift]
        shifted_sources = torch.cat(
            [sources_a, sources_b], dim=-1)
        _loss_b = LossPIT(shifted_sources, sources).item()
        assert _loss_a == _loss_b

After Change

    noise_amount = [0.001, .01, .05, 1.0]

    for n in noise_amount:
        permuted = []
        noisy = sources + n * torch.randn_like(sources)
        _loss_a = LossL1(noisy, sources).item()

        for i in range(n_batch):
            p = random.choice(list(permutations(range(n_sources))))
            permuted_batch = noisy[i, ..., list(p)].unsqueeze(0)
            permuted.append(permuted_batch)

        permuted = torch.cat(permuted, dim=0)
        _loss_b = LossPIT(permuted, sources).item()
        assert np.allclose(_loss_a, _loss_b, atol=1e-6)

def test_combination_invariant_loss_tf():

Instances