remove never use code (#1151)
| | |
| | | return features |
| | | |
| | | |
| | | class CAMLayer(nn.Module): |
| | | |
| | | def __init__(self, |
| | | bn_channels, |
| | | out_channels, |
| | | kernel_size, |
| | | stride, |
| | | padding, |
| | | dilation, |
| | | bias, |
| | | reduction=2): |
| | | super(CAMLayer, self).__init__() |
| | | self.linear_local = nn.Conv1d( |
| | | bn_channels, |
| | | out_channels, |
| | | kernel_size, |
| | | stride=stride, |
| | | padding=padding, |
| | | dilation=dilation, |
| | | bias=bias) |
| | | self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1) |
| | | self.relu = nn.ReLU(inplace=True) |
| | | self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1) |
| | | self.sigmoid = nn.Sigmoid() |
| | | |
| | | def forward(self, x): |
| | | y = self.linear_local(x) |
| | | context = x.mean(-1, keepdim=True) + self.seg_pooling(x) |
| | | context = self.relu(self.linear1(context)) |
| | | m = self.sigmoid(self.linear2(context)) |
| | | return y * m |
| | | |
| | | def seg_pooling(self, x, seg_len=100, stype='avg'): |
| | | if stype == 'avg': |
| | | seg = F.avg_pool1d( |
| | | x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) |
| | | elif stype == 'max': |
| | | seg = F.max_pool1d( |
| | | x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) |
| | | else: |
| | | raise ValueError('Wrong segment pooling type.') |
| | | shape = seg.shape |
| | | seg = seg.unsqueeze(-1).expand(*shape, |
| | | seg_len).reshape(*shape[:-1], -1) |
| | | seg = seg[..., :x.shape[-1]] |
| | | return seg |
| | | |
| | | |
| | | def postprocess(segments: list, vad_segments: list, |
| | | labels: np.ndarray, embeddings: np.ndarray) -> list: |
| | | assert len(segments) == len(labels) |