63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
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from typing import Tuple, Union
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import torch
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import torch.nn as nn
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class CausalConv3d(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size: int = 3,
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stride: Union[int, Tuple[int]] = 1,
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dilation: int = 1,
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groups: int = 1,
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**kwargs,
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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kernel_size = (kernel_size, kernel_size, kernel_size)
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self.time_kernel_size = kernel_size[0]
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dilation = (dilation, 1, 1)
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height_pad = kernel_size[1] // 2
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width_pad = kernel_size[2] // 2
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padding = (0, height_pad, width_pad)
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self.conv = nn.Conv3d(
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in_channels,
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out_channels,
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kernel_size,
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stride=stride,
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dilation=dilation,
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padding=padding,
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padding_mode="zeros",
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groups=groups,
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)
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def forward(self, x, causal: bool = True):
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if causal:
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first_frame_pad = x[:, :, :1, :, :].repeat(
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(1, 1, self.time_kernel_size - 1, 1, 1)
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)
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x = torch.concatenate((first_frame_pad, x), dim=2)
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else:
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first_frame_pad = x[:, :, :1, :, :].repeat(
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(1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
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)
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last_frame_pad = x[:, :, -1:, :, :].repeat(
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(1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
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)
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x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2)
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x = self.conv(x)
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return x
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@property
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def weight(self):
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return self.conv.weight
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