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| import sys |
| from pathlib import Path |
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| import pytest |
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| torch = pytest.importorskip("torch") |
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| REPO = Path(__file__).resolve().parent.parent |
| sys.path.insert(0, str(REPO)) |
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| from model import DepthModule |
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| def _tiny(): |
| torch.manual_seed(0) |
| return DepthModule(d_model=16, num_levels=3, codebook_size=32, d_depth=16, |
| num_layers=1, depth_arch="gru") |
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| def test_none_and_all_ones_are_identical(): |
| m = _tiny() |
| h = torch.randn(7, 16) |
| t = torch.randint(0, 32, (7, 3)) |
| a = m.forward_loss(h, t) |
| b = m.forward_loss(h, t, frame_weights=torch.ones(7)) |
| assert torch.allclose(a, b) |
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| def test_nonuniform_weight_changes_loss_and_backprops(): |
| m = _tiny() |
| h = torch.randn(7, 16, requires_grad=True) |
| t = torch.randint(0, 32, (7, 3)) |
| w = torch.ones(7) |
| w[2] = 5.0 |
| a = m.forward_loss(h, t) |
| b = m.forward_loss(h, t, frame_weights=w) |
| assert not torch.allclose(a, b) |
| b.backward() |
| assert h.grad is not None and torch.isfinite(h.grad).all() |
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| def test_weights_are_normalised(): |
| m = _tiny() |
| h = torch.randn(7, 16) |
| t = torch.randint(0, 32, (7, 3)) |
| w = torch.rand(7) + 0.5 |
| a = m.forward_loss(h, t, frame_weights=w) |
| b = m.forward_loss(h, t, frame_weights=w * 3.0) |
| assert torch.allclose(a, b, atol=1e-6) |
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| def test_transition_mask_selects_pause_to_speech_only(): |
| pause = torch.tensor([5, 9]) |
| prev0 = torch.tensor([-1, 5, 9, 5, 7, 9, 2]) |
| tgt0 = torch.tensor([3, 4, 9, 5, 5, 1, 8]) |
| exit_ = torch.isin(prev0, pause) & ~torch.isin(tgt0, pause) |
| assert exit_.tolist() == [False, True, False, False, False, True, False] |
| w = torch.where(exit_, torch.full_like(prev0, 0, dtype=torch.float32) + 4.0, |
| torch.ones_like(prev0, dtype=torch.float32)) |
| assert w.tolist() == [1.0, 4.0, 1.0, 1.0, 1.0, 4.0, 1.0] |
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