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* Fix an error in TDNN-LSTM training. * WIP: Refactoring * Refactor transformer.py * Remove unused code. * Minor fixes.
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@@ -4,3 +4,4 @@ path.sh | |
| exp | ||
| exp*/ | ||
| *.pt | ||
| download/ | ||
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| Original file line number | Diff line number | Diff line change |
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| import torch | ||
| import torch.nn as nn | ||
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| class Conv2dSubsampling(nn.Module): | ||
| """Convolutional 2D subsampling (to 1/4 length). | ||
| Convert an input of shape [N, T, idim] to an output | ||
| with shape [N, T', odim], where | ||
| T' = ((T-1)//2 - 1)//2, which approximates T' == T//4 | ||
| It is based on | ||
| https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa | ||
| """ | ||
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| def __init__(self, idim: int, odim: int) -> None: | ||
| """ | ||
| Args: | ||
| idim: | ||
| Input dim. The input shape is [N, T, idim]. | ||
| Caution: It requires: T >=7, idim >=7 | ||
| odim: | ||
| Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim] | ||
| """ | ||
| assert idim >= 7 | ||
| super().__init__() | ||
| self.conv = nn.Sequential( | ||
| nn.Conv2d( | ||
| in_channels=1, out_channels=odim, kernel_size=3, stride=2 | ||
| ), | ||
| nn.ReLU(), | ||
| nn.Conv2d( | ||
| in_channels=odim, out_channels=odim, kernel_size=3, stride=2 | ||
| ), | ||
| nn.ReLU(), | ||
| ) | ||
| self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim) | ||
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| def forward(self, x: torch.Tensor) -> torch.Tensor: | ||
| """Subsample x. | ||
| Args: | ||
| x: | ||
| Its shape is [N, T, idim]. | ||
| Returns: | ||
| Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim] | ||
| """ | ||
| # On entry, x is [N, T, idim] | ||
| x = x.unsqueeze(1) # [N, T, idim] -> [N, 1, T, idim] i.e., [N, C, H, W] | ||
| x = self.conv(x) | ||
| # Now x is of shape [N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2] | ||
| b, c, t, f = x.size() | ||
| x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) | ||
| # Now x is of shape [N, ((T-1)//2 - 1))//2, odim] | ||
| return x | ||
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| class VggSubsampling(nn.Module): | ||
| """Trying to follow the setup described in the following paper: | ||
| https://arxiv.org/pdf/1910.09799.pdf | ||
| This paper is not 100% explicit so I am guessing to some extent, | ||
| and trying to compare with other VGG implementations. | ||
| Convert an input of shape [N, T, idim] to an output | ||
| with shape [N, T', odim], where | ||
| T' = ((T-1)//2 - 1)//2, which approximates T' = T//4 | ||
| """ | ||
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| def __init__(self, idim: int, odim: int) -> None: | ||
| """Construct a VggSubsampling object. | ||
| This uses 2 VGG blocks with 2 Conv2d layers each, | ||
| subsampling its input by a factor of 4 in the time dimensions. | ||
| Args: | ||
| idim: | ||
| Input dim. The input shape is [N, T, idim]. | ||
| Caution: It requires: T >=7, idim >=7 | ||
| odim: | ||
| Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim] | ||
| """ | ||
| super().__init__() | ||
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| cur_channels = 1 | ||
| layers = [] | ||
| block_dims = [32, 64] | ||
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| # The decision to use padding=1 for the 1st convolution, then padding=0 | ||
| # for the 2nd and for the max-pooling, and ceil_mode=True, was driven by | ||
| # a back-compatibility concern so that the number of frames at the | ||
| # output would be equal to: | ||
| # (((T-1)//2)-1)//2. | ||
| # We can consider changing this by using padding=1 on the | ||
| # 2nd convolution, so the num-frames at the output would be T//4. | ||
| for block_dim in block_dims: | ||
| layers.append( | ||
| torch.nn.Conv2d( | ||
| in_channels=cur_channels, | ||
| out_channels=block_dim, | ||
| kernel_size=3, | ||
| padding=1, | ||
| stride=1, | ||
| ) | ||
| ) | ||
| layers.append(torch.nn.ReLU()) | ||
| layers.append( | ||
| torch.nn.Conv2d( | ||
| in_channels=block_dim, | ||
| out_channels=block_dim, | ||
| kernel_size=3, | ||
| padding=0, | ||
| stride=1, | ||
| ) | ||
| ) | ||
| layers.append( | ||
| torch.nn.MaxPool2d( | ||
| kernel_size=2, stride=2, padding=0, ceil_mode=True | ||
| ) | ||
| ) | ||
| 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: | ||
| """Subsample x. | ||
| Args: | ||
| x: | ||
| Its shape is [N, T, idim]. | ||
| Returns: | ||
| Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim] | ||
| """ | ||
| x = x.unsqueeze(1) | ||
| x = self.layers(x) | ||
| b, c, t, f = x.size() | ||
| x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) | ||
| return x |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,33 @@ | ||
| #!/usr/bin/env python3 | ||
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| from subsampling import Conv2dSubsampling | ||
| from subsampling import VggSubsampling | ||
| import torch | ||
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| def test_conv2d_subsampling(): | ||
| N = 3 | ||
| odim = 2 | ||
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| for T in range(7, 19): | ||
| for idim in range(7, 20): | ||
| model = Conv2dSubsampling(idim=idim, odim=odim) | ||
| x = torch.empty(N, T, idim) | ||
| y = model(x) | ||
| assert y.shape[0] == N | ||
| assert y.shape[1] == ((T - 1) // 2 - 1) // 2 | ||
| assert y.shape[2] == odim | ||
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| def test_vgg_subsampling(): | ||
| N = 3 | ||
| odim = 2 | ||
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| for T in range(7, 19): | ||
| for idim in range(7, 20): | ||
| model = VggSubsampling(idim=idim, odim=odim) | ||
| x = torch.empty(N, T, idim) | ||
| y = model(x) | ||
| assert y.shape[0] == N | ||
| assert y.shape[1] == ((T - 1) // 2 - 1) // 2 | ||
| assert y.shape[2] == odim |
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