提升模型推理速度：合并BN层到卷积层

た入场券 2022-02-03 00:15 274阅读 0赞

参考文章：[https://blog.csdn.net/kangdi7547/article/details/81348254][https_blog.csdn.net_kangdi7547_article_details_81348254]

1.  为什么要合并BN层

在训练深度网络模型时，BN（Batch Normalization）层能够加速网络收敛，并且能够控制过拟合，一般放在卷积层之后。BN 层将数据归一化后，能够有效解决梯度消失与梯度爆炸问题。虽然 BN 层在训练时起到了积极作用，然而，在网络前向推断时多了一些层的运算，影响了模型的性能，且占用了更多的内存或者显存空间。目前，很多先进的网络模型（ResNet，MobileNet，Xception，ShuffleNet 等）都使用了BN技术，因此，我们有必要将 BN 层的参数合并到卷积层，来提升模型前向推断的速度。

2.  BN层与卷积层合并的数学原理

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTc3MDE2OQ_size_16_color_FFFFFF_t_70][]

3.  实验结果

机器：显卡 GTX 1080Ti，i7 CPU

本实验对比了Resnet50 模型合并BN层前后的性能，分类精度保持不变，速度显著提升。

![20190508141000485.png][]

4.  合并的python脚本

该脚本需要caffe的python接口，代码：

#!/usr/bin/env python
    # -*- coding: UTF-8 -*-
     
    import numpy as np
    import sys
    import os
    import os.path as osp
    import google.protobuf as pb
    import google.protobuf.text_format
    from argparse import ArgumentParser
    import caffe
     
    caffe.set_mode_cpu()
     
    def load_and_fill_biases(src_model, src_weights, dst_model, dst_weights):
        with open(src_model) as f:
            model = caffe.proto.caffe_pb2.NetParameter()
            pb.text_format.Merge(f.read(), model)
     
        for i, layer in enumerate(model.layer):
            if layer.type == 'Convolution': # or layer.type == 'Scale':
                # Add bias layer if needed
                if layer.convolution_param.bias_term == False:
                    layer.convolution_param.bias_term = True
                    layer.convolution_param.bias_filler.type = 'constant'
                    layer.convolution_param.bias_filler.value = 0.0
     
        with open(dst_model, 'w') as f:
            f.write(pb.text_format.MessageToString(model))
     
        caffe.set_mode_cpu()
        net_src = caffe.Net(src_model, src_weights, caffe.TEST)
        net_dst = caffe.Net(dst_model, caffe.TEST)
        for key in net_src.params.keys():
            for i in range(len(net_src.params[key])):
                net_dst.params[key][i].data[:] = net_src.params[key][i].data[:]
     
        if dst_weights is not None:
            # Store params
            pass
     
        return net_dst
     
     
    def merge_conv_and_bn(net, i_conv, i_bn, i_scale):
        # This is based on Kyeheyon's work
        assert(i_conv != None)
        assert(i_bn != None)
     
        def copy_double(data):
            return np.array(data, copy=True, dtype=np.double)
     
        key_conv = net._layer_names[i_conv]
        key_bn = net._layer_names[i_bn]
        key_scale = net._layer_names[i_scale] if i_scale else None
     
        # Copy
        bn_mean = copy_double(net.params[key_bn][0].data)
        bn_variance = copy_double(net.params[key_bn][1].data)
        num_bn_samples = copy_double(net.params[key_bn][2].data)
     
        # and Invalidate the BN layer
        net.params[key_bn][0].data[:] = 0
        net.params[key_bn][1].data[:] = 1
        net.params[key_bn][2].data[:] = 1
     
        if num_bn_samples[0] == 0:
            num_bn_samples[0] = 1
     
        if net.params.has_key(key_scale):
            print 'Combine {:s} + {:s} + {:s}'.format(key_conv, key_bn, key_scale)
            scale_weight = copy_double(net.params[key_scale][0].data)
            scale_bias = copy_double(net.params[key_scale][1].data)
            net.params[key_scale][0].data[:] = 1
            net.params[key_scale][1].data[:] = 0
     
        else:
            print 'Combine {:s} + {:s}'.format(key_conv, key_bn)
            scale_weight = 1
            scale_bias = 0
     
        weight = copy_double(net.params[key_conv][0].data)
        bias = copy_double(net.params[key_conv][1].data)
     
        alpha = scale_weight / np.sqrt(bn_variance / num_bn_samples[0] + 1e-5)
        net.params[key_conv][1].data[:] = bias * alpha + (scale_bias - (bn_mean / num_bn_samples[0]) * alpha)
        for i in range(len(alpha)):
            net.params[key_conv][0].data[i] = weight[i] * alpha[i]
     
     
    def merge_batchnorms_in_net(net):
        # for each BN
        for i, layer in enumerate(net.layers):
            if layer.type != 'BatchNorm':
                continue
     
            l_name = net._layer_names[i]
     
            l_bottom = net.bottom_names[l_name]
            assert(len(l_bottom) == 1)
            l_bottom = l_bottom[0]
            l_top = net.top_names[l_name]
            assert(len(l_top) == 1)
            l_top = l_top[0]
     
            can_be_absorbed = True
     
            # Search all (bottom) layers
            for j in xrange(i - 1, -1, -1):
                tops_of_j = net.top_names[net._layer_names[j]]
                if l_bottom in tops_of_j:
                    if net.layers[j].type not in ['Convolution', 'InnerProduct']:
                        can_be_absorbed = False
                    else:
                        # There must be only one layer
                        conv_ind = j
                        break
     
            if not can_be_absorbed:
                continue
     
            # find the following Scale
            scale_ind = None
            for j in xrange(i + 1, len(net.layers)):
                bottoms_of_j = net.bottom_names[net._layer_names[j]]
                if l_top in bottoms_of_j:
                    if scale_ind:
                        # Followed by two or more layers
                        scale_ind = None
                        break
     
                    if net.layers[j].type in ['Scale']:
                        scale_ind = j
     
                        top_of_j = net.top_names[net._layer_names[j]][0]
                        if top_of_j == bottoms_of_j[0]:
                            # On-the-fly => Can be merged
                            break
     
                    else:
                        # Followed by a layer which is not 'Scale'
                        scale_ind = None
                        break
     
     
            merge_conv_and_bn(net, conv_ind, i, scale_ind)
     
        return net
     
     
    def process_model(net, src_model, dst_model, func_loop, func_finally):
        with open(src_model) as f:
            model = caffe.proto.caffe_pb2.NetParameter()
            pb.text_format.Merge(f.read(), model)
     
        for i, layer in enumerate(model.layer):
            map(lambda x: x(layer, net, model, i), func_loop)
     
        map(lambda x: x(net, model), func_finally)
     
        with open(dst_model, 'w') as f:
            f.write(pb.text_format.MessageToString(model))
     
     
    # Functions to remove (redundant) BN and Scale layers
    to_delete_empty = []
    def pick_empty_layers(layer, net, model, i):
        if layer.type not in ['BatchNorm', 'Scale']:
            return
     
        bottom = layer.bottom[0]
        top = layer.top[0]
     
        if (bottom != top):
            # Not supperted yet
            return
     
        if layer.type == 'BatchNorm':
            zero_mean = np.all(net.params[layer.name][0].data == 0)
            one_var = np.all(net.params[layer.name][1].data == 1)
     
            if zero_mean and one_var:
                print 'Delete layer: {}'.format(layer.name)
                to_delete_empty.append(layer)
     
        if layer.type == 'Scale':
            no_scaling = np.all(net.params[layer.name][0].data == 1)
            zero_bias = np.all(net.params[layer.name][1].data == 0)
     
            if no_scaling and zero_bias:
                print 'Delete layer: {}'.format(layer.name)
                to_delete_empty.append(layer)
     
     
    def remove_empty_layers(net, model):
        map(model.layer.remove, to_delete_empty)
     
     
    # A function to add 'engine: CAFFE' param into 1x1 convolutions
    def set_engine_caffe(layer, net, model, i):
        if layer.type == 'Convolution':
            if layer.convolution_param.kernel_size == 1\
                or (layer.convolution_param.kernel_h == layer.convolution_param.kernel_w == 1):
                layer.convolution_param.engine = dict(layer.convolution_param.Engine.items())['CAFFE']
     
     
    def main():
        # Set default output file names
        if args.output_model is None:
           file_name = osp.splitext(args.model)[0]
           args.output_model = file_name + '_inference.prototxt'
        if args.output_weights is None:
           file_name = osp.splitext(args.weights)[0]
           args.output_weights = file_name + '_inference.caffemodel'
     
        net = load_and_fill_biases(args.model, args.weights, args.model + '.temp.pt', None)
        net = merge_batchnorms_in_net(net)
     
        process_model(net, args.model + '.temp.pt', args.output_model,
                      [pick_empty_layers, set_engine_caffe],
                      [remove_empty_layers])
     
        # Store params
        net.save(args.output_weights)
     
     
    if __name__ == '__main__':
       parser = ArgumentParser(
               description="Generate Batch Normalized model for inference")
       parser.add_argument('--model', default="MobileNetSSD_deploy.prototxt", help="The net definition prototxt")
       parser.add_argument('--weights', default="MobileNetSSD_deploy.caffemodel", help="The weights caffemodel")
       parser.add_argument('--output_model')
       parser.add_argument('--output_weights')
       args = parser.parse_args()
       main()

脚本下载地址：

[https://download.csdn.net/download/kangdi7547/10578152][https_download.csdn.net_download_kangdi7547_10578152]

[https_blog.csdn.net_kangdi7547_article_details_81348254]: https://blog.csdn.net/kangdi7547/article/details/81348254
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTc3MDE2OQ_size_16_color_FFFFFF_t_70]: /images/20220203/b447059f1817433e918644a08cc1e809.png
[20190508141000485.png]: /images/20220203/ed4498c247a946d3b40c43300fb4ed24.png
[https_download.csdn.net_download_kangdi7547_10578152]: https://download.csdn.net/download/kangdi7547/10578152