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[Pyspeech] avoid .item call in feature_processing #4015

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[Pyspeech] avoid .item call in feature_processing #4015

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9 changes: 7 additions & 2 deletions src/torchaudio/functional/functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -817,7 +817,7 @@ def _get_mask_param(mask_param: int, p: float, axis_length: int) -> int:
def mask_along_axis_iid(
specgrams: Tensor,
mask_param: int,
mask_value: float,
mask_value: Union[float, Tensor],
axis: int,
p: float = 1.0,
) -> Tensor:
Expand Down Expand Up @@ -874,7 +874,12 @@ def mask_along_axis_iid(

# Per batch example masking
specgrams = specgrams.transpose(axis, -1)
specgrams = specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value)
# this aims to avoid CPU-GPU sync from upstream
specgrams = (
torch.where((mask >= mask_start) & (mask < mask_end), mask_value.repeat(*specgrams.shape), specgrams)
if isinstance(mask_value, Tensor)
else specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value)
)
specgrams = specgrams.transpose(axis, -1)

return specgrams
Expand Down
2 changes: 1 addition & 1 deletion src/torchaudio/transforms/_transforms.py
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Expand Up @@ -1185,7 +1185,7 @@ def __init__(self, mask_param: int, axis: int, iid_masks: bool, p: float = 1.0)
self.iid_masks = iid_masks
self.p = p

def forward(self, specgram: Tensor, mask_value: float = 0.0) -> Tensor:
def forward(self, specgram: Tensor, mask_value: Union[float, torch.Tensor] = 0.0) -> Tensor:
r"""
Args:
specgram (Tensor): Tensor of dimension `(..., freq, time)`.
Expand Down
14 changes: 14 additions & 0 deletions test/torchaudio_unittest/functional/functional_impl.py
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Expand Up @@ -456,6 +456,20 @@ def test_mask_along_axis_iid(self, mask_param, mask_value, axis, p):
assert mask_specgrams.size() == specgrams.size()
assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel()

@parameterized.expand(list(itertools.product([100], [0.0, 30.0], [2, 3], [0.2, 1.0])))
def test_mask_along_axis_iid_mask_value(self, mask_param, mask_value, axis, p):
specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)
mask_value_tensor = torch.tensor(mask_value, dtype=self.dtype, device=self.device)
torch.manual_seed(0)
# as this operation is random we need to fix the seed for results to match
mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value_tensor, axis, p=p)
torch.manual_seed(0)
mask_specgrams_float = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis, p=p)
assert torch.allclose(
mask_specgrams, mask_specgrams_float
), f"""Masking with float and tensor should be the same diff = {
torch.abs(mask_specgrams - mask_specgrams_float).max()}"""

@parameterized.expand(list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0.0, 30.0], [1, 2])))
def test_mask_along_axis_preserve(self, shape, mask_param, mask_value, axis):
"""mask_along_axis should not alter original input Tensor
Expand Down
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