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| """VGG2L module definition for custom encoder.""" | |
| from typing import Tuple, Union | |
| import torch | |
| class VGG2L(torch.nn.Module): | |
| """VGG2L module for custom encoder. | |
| Args: | |
| idim: Input dimension. | |
| odim: Output dimension. | |
| pos_enc: Positional encoding class. | |
| """ | |
| def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module = None): | |
| """Construct a VGG2L object.""" | |
| super().__init__() | |
| self.vgg2l = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, 64, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(64, 64, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.MaxPool2d((3, 2)), | |
| torch.nn.Conv2d(64, 128, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(128, 128, 3, stride=1, padding=1), | |
| torch.nn.ReLU(), | |
| torch.nn.MaxPool2d((2, 2)), | |
| ) | |
| if pos_enc is not None: | |
| self.output = torch.nn.Sequential( | |
| torch.nn.Linear(128 * ((idim // 2) // 2), odim), pos_enc | |
| ) | |
| else: | |
| self.output = torch.nn.Linear(128 * ((idim // 2) // 2), odim) | |
| def forward(self, feats: torch.Tensor, feats_mask: torch.Tensor) -> Union[ | |
| Tuple[torch.Tensor, torch.Tensor], | |
| Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor], | |
| ]: | |
| """Forward VGG2L bottleneck. | |
| Args: | |
| feats: Feature sequences. (B, F, D_feats) | |
| feats_mask: Mask of feature sequences. (B, 1, F) | |
| Returns: | |
| vgg_output: VGG output sequences. | |
| (B, sub(F), D_out) or ((B, sub(F), D_out), (B, sub(F), D_att)) | |
| vgg_mask: Mask of VGG output sequences. (B, 1, sub(F)) | |
| """ | |
| feats = feats.unsqueeze(1) | |
| vgg_output = self.vgg2l(feats) | |
| b, c, t, f = vgg_output.size() | |
| vgg_output = self.output( | |
| vgg_output.transpose(1, 2).contiguous().view(b, t, c * f) | |
| ) | |
| if feats_mask is not None: | |
| vgg_mask = self.create_new_mask(feats_mask) | |
| else: | |
| vgg_mask = feats_mask | |
| return vgg_output, vgg_mask | |
| def create_new_mask(self, feats_mask: torch.Tensor) -> torch.Tensor: | |
| """Create a subsampled mask of feature sequences. | |
| Args: | |
| feats_mask: Mask of feature sequences. (B, 1, F) | |
| Returns: | |
| vgg_mask: Mask of VGG2L output sequences. (B, 1, sub(F)) | |
| """ | |
| vgg1_t_len = feats_mask.size(2) - (feats_mask.size(2) % 3) | |
| vgg_mask = feats_mask[:, :, :vgg1_t_len][:, :, ::3] | |
| vgg2_t_len = vgg_mask.size(2) - (vgg_mask.size(2) % 2) | |
| vgg_mask = vgg_mask[:, :, :vgg2_t_len][:, :, ::2] | |
| return vgg_mask | |