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137 lines
4.4 KiB
Python
137 lines
4.4 KiB
Python
import torch
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import torch.nn as nn
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class Conv2dSubsampling(nn.Module):
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"""Convolutional 2D subsampling (to 1/4 length).
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Convert an input of shape [N, T, idim] to an output
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with shape [N, T', odim], where
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T' = ((T-1)//2 - 1)//2, which approximates T' == T//4
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It is based on
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https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa
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"""
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def __init__(self, idim: int, odim: int) -> None:
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"""
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Args:
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idim:
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Input dim. The input shape is [N, T, idim].
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Caution: It requires: T >=7, idim >=7
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odim:
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Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim]
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"""
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assert idim >= 7
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(in_channels=1, out_channels=odim, kernel_size=3, stride=2),
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nn.ReLU(),
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nn.Conv2d(in_channels=odim, out_channels=odim, kernel_size=3, stride=2),
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nn.ReLU(),
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)
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self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Subsample x.
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Args:
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x:
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Its shape is [N, T, idim].
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Returns:
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Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim]
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"""
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# On entry, x is [N, T, idim]
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x = x.unsqueeze(1) # [N, T, idim] -> [N, 1, T, idim] i.e., [N, C, H, W]
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x = self.conv(x)
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# Now x is of shape [N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2]
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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# Now x is of shape [N, ((T-1)//2 - 1))//2, odim]
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return x
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class VggSubsampling(nn.Module):
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"""Trying to follow the setup described in the following paper:
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https://arxiv.org/pdf/1910.09799.pdf
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This paper is not 100% explicit so I am guessing to some extent,
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and trying to compare with other VGG implementations.
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Convert an input of shape [N, T, idim] to an output
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with shape [N, T', odim], where
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T' = ((T-1)//2 - 1)//2, which approximates T' = T//4
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"""
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def __init__(self, idim: int, odim: int) -> None:
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"""Construct a VggSubsampling object.
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This uses 2 VGG blocks with 2 Conv2d layers each,
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subsampling its input by a factor of 4 in the time dimensions.
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Args:
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idim:
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Input dim. The input shape is [N, T, idim].
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Caution: It requires: T >=7, idim >=7
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odim:
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Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim]
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"""
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super().__init__()
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cur_channels = 1
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layers = []
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block_dims = [32, 64]
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# The decision to use padding=1 for the 1st convolution, then padding=0
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# for the 2nd and for the max-pooling, and ceil_mode=True, was driven by
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# a back-compatibility concern so that the number of frames at the
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# output would be equal to:
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# (((T-1)//2)-1)//2.
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# We can consider changing this by using padding=1 on the
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# 2nd convolution, so the num-frames at the output would be T//4.
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for block_dim in block_dims:
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layers.append(
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torch.nn.Conv2d(
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in_channels=cur_channels,
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out_channels=block_dim,
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kernel_size=3,
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padding=1,
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stride=1,
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)
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)
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layers.append(torch.nn.ReLU())
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layers.append(
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torch.nn.Conv2d(
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in_channels=block_dim,
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out_channels=block_dim,
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kernel_size=3,
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padding=0,
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stride=1,
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)
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)
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layers.append(
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torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=True)
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)
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cur_channels = block_dim
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self.layers = nn.Sequential(*layers)
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self.out = nn.Linear(block_dims[-1] * (((idim - 1) // 2 - 1) // 2), odim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Subsample x.
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Args:
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x:
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Its shape is [N, T, idim].
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Returns:
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Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim]
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"""
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x = x.unsqueeze(1)
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x = self.layers(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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return x
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