model_proto_generator.py 文件源码

def get_tpp_phocnet(self, word_image_lmdb_path, phoc_lmdb_path, phoc_size, tpp_levels=5,
                        generate_deploy=False):
        '''
        Returns a NetSpec definition of the TPP-PHOCNet. The definition can then be transformed
        into a protobuffer message by casting it into a str.
        '''
        n = NetSpec()
        # Data
        self.set_phocnet_data(n=n, generate_deploy=generate_deploy,
                              word_image_lmdb_path=word_image_lmdb_path,
                              phoc_lmdb_path=phoc_lmdb_path)

        # Conv Part
        self.set_phocnet_conv_body(n=n, relu_in_place=True)

        # FC Part
        n.tpp5 = L.TPP(n.relu4_3, tpp_param=dict(pool=P.TPP.MAX, pyramid_layer=range(1, tpp_levels + 1), engine=self.spp_engine))
        n.fc6, n.relu6, n.drop6 = self.fc_relu(bottom=n.tpp5, layer_size=4096,
                                               dropout_ratio=0.5, relu_in_place=True)
        n.fc7, n.relu7, n.drop7 = self.fc_relu(bottom=n.drop6, layer_size=4096,
                                               dropout_ratio=0.5, relu_in_place=True)
        n.fc8 = L.InnerProduct(n.drop7, num_output=phoc_size,
                               weight_filler=dict(type=self.initialization),
                               bias_filler=dict(type='constant'))
        n.sigmoid = L.Sigmoid(n.fc8, include=dict(phase=self.phase_test))

        # output part
        if not generate_deploy:
            n.silence = L.Silence(n.sigmoid, ntop=0, include=dict(phase=self.phase_test))
            n.loss = L.SigmoidCrossEntropyLoss(n.fc8, n.phocs)

        return n.to_proto()