Ensure correct encoding for non-contiguous WF #666
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| // planar (fltp). | ||
| TORCH_CHECK(wf.dim() == 2, "waveform must have 2 dimensions, got ", wf.dim()); | ||
| return wf; | ||
| return wf.contiguous(); |
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Confirming that this is load-bearing: newly added test fails if we just return wf without calling contiguous().
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| torch.testing.assert_close(self.decode(encoded_tensor), samples) | ||
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| def test_contiguity(self): |
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Can you provide some narration in the test about what exactly we're testing? I'm afraid I'm not familiar enough with tensor manipulation to fully understand everything. For example, I'm surprised assert_close(contiguous_samples, non_contiguous_samples) passes when they have different strides.
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Done.
The strides have to do with the representation of the tensor in memory. But it's not what uniquely determines the values of a tensor. E.g. a 2D tensor
A B
C D
can be represented in memory as
A B C D
or as
A C B D
They'd have different strides, but equal values.
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TSIA!