debn.txt

#-*- coding:utf-8 -*-
#!/usr/bin/env python  
# export PYTHONPATH=/home/wanyouwen/ewenwan/software/caffe_yolo/caffe/python
# sudo python2 examples/ristretto/resNet/caffe_no_batchnorm.py /data4/quantization/models/ResNet/ResNet-152-train-val.prototxt |
# /data4/quantization/models/ResNet/ResNet-152-model.caffemodel --output_model ResNet-152-train-val-remove-bn.prototxt --output_weight ResNet-152-model-remove-bn.caffemodel
#import _init_paths  
import numpy as np  
import sys  
import os  
# caffe python 接口
sys.path.insert(0,"/home/wanyouwen/ewenwan/software/caffe_yolo/caffe/python")
import os.path as osp  
import google.protobuf as pb  
from argparse import ArgumentParser  
import sys  
import caffe  
  
# 载入原始模型 
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):  
    # 卷积层 后面会有 BN层
        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_gpu()  
    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] + np.finfo(np.double).eps)  
    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)  
        #length_is_1 = (net.params['conv1_1/bn'][2].data == 1) or (net.params[layer.name][2].data == 0)  
        length_is_1 =  (net.params[layer.name][2].data == 1)  
  
        if zero_mean and one_var and length_is_1:  
            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(args):  
    # 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', help="The net definition prototxt")# 原模型文件  
    parser.add_argument('weights', help="The weights caffemodel")   # 原权重文件 
    parser.add_argument('--output_model')  # 去除BN层的模型文件
    parser.add_argument('--output_weights')# 去除BN层后的权重文件   
    args = parser.parse_args()  
    main(args)