def test_mask_conv_output_size_and_seq_lens(
mask_conv1d_input: Tuple[MaskConv1d, Tuple[torch.Tensor, torch.Tensor]]
) -> None:
"""Ensures the MaskConv1d output size and seq_len values are correct."""
mask_conv1d, inputs = mask_conv1d_input
batch, channels, seq_len = inputs[0].size()
out_tensor, out_seq_lens = mask_conv1d(inputs)
out_batch, out_channels, out_seq_len = out_tensor.size()
assert out_batch == len(out_seq_lens) == batch
assert out_channels == mask_conv1d.out_channels
# test out_seq_len dimension of tensor output and set padding value ready
# for testing out_seq_lens
if mask_conv1d._padding_mode == PaddingMode.NONE:
padding = (0, 0)
# out_lens function returns correct expected length if the tests pass
exp_len = out_lens(
seq_lens=torch.tensor(seq_len),
kernel_size=mask_conv1d.kernel_size[0],
stride=mask_conv1d.stride[0],
dilation=mask_conv1d.dilation[0],
padding=0,
).item()
assert out_seq_len == exp_len
elif mask_conv1d._padding_mode == PaddingMode.SAME:
padding = pad_same(
length=seq_len,
kernel_size=mask_conv1d.kernel_size[0],
stride=mask_conv1d.stride[0],
dilation=mask_conv1d.dilation[0],
)
# by definition of SAME padding
assert out_seq_len == math.ceil(float(seq_len) / mask_conv1d.stride[0])
else:
raise ValueError(f"unknown PaddingMode {mask_conv1d._padding_mode}")
# test out_seq_lens
exp_out_seq_lens = out_lens(
seq_lens=inputs[1],
kernel_size=mask_conv1d.kernel_size[0],
stride=mask_conv1d.stride[0],
dilation=mask_conv1d.dilation[0],
padding=sum(padding),
)
assert torch.all(out_seq_lens == exp_out_seq_lens)
def test_pad_same_output_has_correct_size_after_conv(
conv1d_valid_input: Tuple[torch.nn.Conv1d, torch.Tensor]) -> None:
"""Ensures output size after applying [pad_same, Conv1d](x) is correct."""
conv1d, tensor = conv1d_valid_input
batch_size, in_channels, seq_len = tensor.size()
pad = pad_same(
length=seq_len,
kernel_size=conv1d.kernel_size[0],
stride=conv1d.stride[0],
dilation=conv1d.dilation[0],
)
tensor = torch.nn.functional.pad(tensor, pad)
out = conv1d(tensor)
assert out.size(2) == math.ceil(float(seq_len) / conv1d.stride[0])
def test_pad_same_raises_value_error_invalid_parameters(
length: int, kernel_size: int, stride: int, dilation: int) -> None:
"""Ensures pad_same raises ValueError when called with invalid params."""
assume(length <= 0 or kernel_size <= 0 or stride <= 0 or dilation <= 0)
with pytest.raises(ValueError):
pad_same(length, kernel_size, stride, dilation)
