encoders.py 文件源码

def forward(self, x):
        # Transpose to the shape (sentence_batch size, word dim, sequence length)
        x = x.transpose(0, 1).transpose(1, 2)

        feature_maps = []

        for layer in self.layers:
            x = layer(x)
            feature_maps.append(F.max_pool1d(x, kernel_size=x.size(2)).squeeze())

        features = torch.cat(feature_maps, dim=1)
        return features