elmo.py 文件源码

def _load_cnn_weights(self):
        cnn_options = self._options['char_cnn']
        filters = cnn_options['filters']
        char_embed_dim = cnn_options['embedding']['dim']

        convolutions = []
        for i, (width, num) in enumerate(filters):
            conv = torch.nn.Conv1d(
                    in_channels=char_embed_dim,
                    out_channels=num,
                    kernel_size=width,
                    bias=True
            )
            # load the weights
            with h5py.File(cached_path(self._weight_file), 'r') as fin:
                weight = fin['CNN']['W_cnn_{}'.format(i)][...]
                bias = fin['CNN']['b_cnn_{}'.format(i)][...]

            w_reshaped = numpy.transpose(weight.squeeze(axis=0), axes=(2, 1, 0))
            if w_reshaped.shape != tuple(conv.weight.data.shape):
                raise ValueError("Invalid weight file")
            conv.weight.data.copy_(torch.FloatTensor(w_reshaped))
            conv.bias.data.copy_(torch.FloatTensor(bias))

            conv.weight.requires_grad = False
            conv.bias.requires_grad = False

            convolutions.append(conv)
            self.add_module('char_conv_{}'.format(i), conv)

        self._convolutions = convolutions