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Small Refactoring of the conv.py file #62

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Small Refactoring of the conv.py file #62

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refacto conv.py file
GuichardVictor Jun 24, 2023
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lint code
GuichardVictor Jun 24, 2023
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39 changes: 20 additions & 19 deletions encodec/modules/conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,10 +28,9 @@ def apply_parametrization_norm(module: nn.Module, norm: str = 'none') -> nn.Modu
return weight_norm(module)
elif norm == 'spectral_norm':
return spectral_norm(module)
else:
# We already check was in CONV_NORMALIZATION, so any other choice
# doesn't need reparametrization.
return module
# We already check was in CONV_NORMALIZATION, so any other choice
# doesn't need reparametrization.
return module


def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs) -> nn.Module:
Expand All @@ -47,8 +46,8 @@ def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none',
raise ValueError("GroupNorm doesn't support causal evaluation.")
assert isinstance(module, nn.modules.conv._ConvNd)
return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
else:
return nn.Identity()

return nn.Identity()


def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
Expand Down Expand Up @@ -83,18 +82,21 @@ def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', val
length = x.shape[-1]
padding_left, padding_right = paddings
assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
if mode == 'reflect':
max_pad = max(padding_left, padding_right)
extra_pad = 0
if length <= max_pad:
extra_pad = max_pad - length + 1
x = F.pad(x, (0, extra_pad))
padded = F.pad(x, paddings, mode, value)
end = padded.shape[-1] - extra_pad
return padded[..., :end]
else:

# Constant padding
if mode != 'reflect':
return F.pad(x, paddings, mode, value)

# Reflect padding
max_pad = max(padding_left, padding_right)
extra_pad = 0
if length <= max_pad:
extra_pad = max_pad - length + 1
x = F.pad(x, (0, extra_pad))
padded = F.pad(x, paddings, mode, value)
end = padded.shape[-1] - extra_pad
return padded[..., :end]


def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
"""Remove padding from x, handling properly zero padding. Only for 1d!"""
Expand Down Expand Up @@ -193,7 +195,6 @@ def __init__(self, in_channels: int, out_channels: int,
self.pad_mode = pad_mode

def forward(self, x):
B, C, T = x.shape
kernel_size = self.conv.conv.kernel_size[0]
stride = self.conv.conv.stride[0]
dilation = self.conv.conv.dilation[0]
Expand Down Expand Up @@ -244,10 +245,10 @@ def forward(self, x):
# if trim_right_ratio = 1.0, trim everything from right
padding_right = math.ceil(padding_total * self.trim_right_ratio)
padding_left = padding_total - padding_right
y = unpad1d(y, (padding_left, padding_right))
else:
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
y = unpad1d(y, (padding_left, padding_right))

y = unpad1d(y, (padding_left, padding_right))
return y