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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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