model.py 文件源码

def _get_blocks_for_sentence(self, sent):
        block_a = {}
        block_b = {}
        for ws in self.filter_widths:
            if np.isinf(ws):
                sent_flattened, sent_flattened_size = sent.contiguous().view(sent.size(0), 1, -1), sent.size(1) * sent.size(2)
                block_a[ws] = {
                    'max': F.max_pool1d(sent_flattened, sent_flattened_size).view(sent.size(0), -1),
                    'min': F.max_pool1d(-1 * sent_flattened, sent_flattened_size).view(sent.size(0), -1),
                    'mean': F.avg_pool1d(sent_flattened, sent_flattened_size).view(sent.size(0), -1)
                }
                continue

            holistic_conv_out = self.holistic_conv_layers[ws - 1](sent)
            block_a[ws] = {
                'max': F.max_pool1d(holistic_conv_out, holistic_conv_out.size(2)).contiguous().view(-1, self.n_holistic_filters),
                'min': F.max_pool1d(-1 * holistic_conv_out, holistic_conv_out.size(2)).contiguous().view(-1, self.n_holistic_filters),
                'mean': F.avg_pool1d(holistic_conv_out, holistic_conv_out.size(2)).contiguous().view(-1, self.n_holistic_filters)
            }

            per_dim_conv_out = self.per_dim_conv_layers[ws - 1](sent)
            block_b[ws] = {
                'max': F.max_pool1d(per_dim_conv_out, per_dim_conv_out.size(2)).contiguous().view(-1, self.n_word_dim, self.n_per_dim_filters),
                'min': F.max_pool1d(-1 * per_dim_conv_out, per_dim_conv_out.size(2)).contiguous().view(-1, self.n_word_dim, self.n_per_dim_filters)
            }
        return block_a, block_b