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import math
import torch
from torch import nn, Tensor
from torch.nn.parameter import Parameter
from torch.nn import init
from typing import Optional
import torch.nn as nn
from torchvision.ops.deform_conv import deform_conv2d
def deform_conv1d(
input: Tensor, # shape [B,in_channels,T_in]
offset: Tensor, # shape [B,kernel_size_t,T_out]
weight: Tensor, # shape [out_channels,in_channels,kernel_size_t]
bias: Optional[Tensor] = None, # shape [out_channels]
stride: int = 1,
padding: int = 0,
dilation: int = 1,
mask: Optional[Tensor] = None, # not implemented yet
) -> Tensor:
assert mask == None, "mask is not implemented yet"
input2d = input.unsqueeze(-1) # [B,in_channels,T_in,1]
offset2d = torch.zeros_like(offset).repeat(1, 2, 1).unsqueeze(-1) # [B,2*kernel_size_t*1,T_out,1]
offset2d[:, 0::2, :, 0] += offset # add offset to H dimension
weight2d = weight.unsqueeze(-1) # [out_channels,in_channels,kernel_size_t,1]
out_2d = deform_conv2d(
input=input2d,
offset=offset2d,
weight=weight2d,
bias=bias,
stride=(stride, 1),
padding=(padding, 0),
dilation=(dilation, 1),
mask=mask,
)
out = out_2d.squeeze(-1) # [B,out_channels,T_out]
return out
class DeformConv1d(nn.Module):
"""We use torchvision.ops.deform_conv2d to implement deform_conv1d."""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
groups: int = 1,
bias: bool = True,
):
super().__init__()
if in_channels % groups != 0:
raise ValueError("in_channels must be divisible by groups")
if out_channels % groups != 0:
raise ValueError("out_channels must be divisible by groups")
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.weight = Parameter(torch.empty(out_channels, in_channels // groups, self.kernel_size))
if bias:
self.bias = Parameter(torch.empty(out_channels))
else:
self.register_parameter("bias", None)
self.reset_parameters()
def reset_parameters(self) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def forward(self, input: Tensor, offset: Tensor, mask: Optional[Tensor] = None) -> Tensor:
"""
Args:
input (Tensor[batch_size, in_channels, in_length]): input tensor
offset (Tensor[batch_size, offset_groups * kernel_length, out_length]):
offsets to be applied for each position in the convolution kernel.
mask (Tensor[batch_size, offset_groups * kernel_length, out_length]):
masks to be applied for each position in the convolution kernel.
"""
return deform_conv1d(
input,
offset,
self.weight,
self.bias,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
mask=mask,
)
def __repr__(self) -> str:
s = (
f"{self.__class__.__name__}("
f"{self.in_channels}"
f", {self.out_channels}"
f", kernel_size={self.kernel_size}"
f", stride={self.stride}"
)
s += f", padding={self.padding}" if self.padding != 0 else ""
s += f", dilation={self.dilation}" if self.dilation != 1 else ""
s += f", groups={self.groups}" if self.groups != 1 else ""
s += ", bias=False" if self.bias is None else ""
s += ")"
return s
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