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convert_2_nonbnn.py
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#coding=utf-8
import os.path as osp
import sys
import copy
import os
from sys import path
import numpy as np
import google.protobuf as pb
path.append('/Users/kkwang/work/caffe/python/')
import argparse
import caffe
import caffe.proto.caffe_pb2 as cp
caffe.set_mode_cpu()
layer_type = ['Convolution', 'InnerProduct']
bnn_type = ['BatchNorm', 'Scale']
temp_file = './temp.prototxt'
EPS = 1e-6
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='convert prototxt to prototxt without batch normalization')
parser.add_argument('--model', dest='caffe_config_filename',
help='prototxt file',
default="./models/deploy_68_new.prototxt",
type=str)
parser.add_argument('--weights', dest='caffe_weights_filename',
help='weights file',
default="./models/vgg_68_new.caffemodel",
type=str)
parser.add_argument('--merged-model',dest='caffe_without_bn_config_filename',
help='mobile config file',
default="./models/result.prototxt",
type=str)
parser.add_argument('--merged-weights',dest='caffe_without_bn_weight_filename',
help='mobile weights file',
default="./models/result.caffemodel",
type=str)
#if len(sys.argv) == 1:
# parser.print_help()
# sys.exit(1)
#
args = parser.parse_args()
return args
class ConvertBnn:
def __init__(self, model, weights, dest_model_dir, dest_weight_dir):
self.net_model = caffe.Net(model, weights, caffe.TEST)
self.net_param = self.get_netparameter(model)
self.dest_model = None
self.dest_param = self.get_netparameter(model)
self.remove_ele = []
self.bnn_layer_location = []
self.dest_dir = dest_model_dir
self.dest_weight_dir = dest_weight_dir
self.pre_process()
def pre_process(self):
net_param = self.dest_param
layer_params = net_param.layer
length = len(layer_params)
i = 0
while i < length:
print i
if layer_params[i].type in layer_type:
if (i + 2 < length) and layer_params[i + 1].type == bnn_type[0] and \
layer_params[i + 2].type == bnn_type[1]:
params = layer_params[i].param
if len(params) ==0:
params.add()
params[0].lr_mult = 1
params[0].decay_mult = 1
if len(params) < 2:
params.add()
params[1].lr_mult = 2
params[1].decay_mult = 0
if layer_params[i].type == 'Convolution':
layer_params[i].convolution_param.bias_term = True
layer_params[i].convolution_param.bias_filler.type = 'constant'
layer_params[i].convolution_param.bias_filler.value = 0
elif layer_params[i].type == 'InnerProduct':
layer_params[i].inner_product_param.bias_term = True
layer_params[i].inner_product_param.bias_filler.type = 'constant'
layer_params[i].inner_product_param.bias_filler.value = 0
#修改配置params
self.bnn_layer_location.extend([i])
self.remove_ele.extend([layer_params[i + 1], layer_params[i + 2]])
i = i + 3
else:
i=i+1
elif layer_params[i].type == 'Scale' and layer_params[i-1].type == bnn_type[0]:
self.bnn_layer_location.extend([i])
self.remove_ele.extend([layer_params[i -1]])
i += 1
else:
i += 1
#for ele in remove_ele:
# layer_params.remove(ele)
with open(temp_file, 'w') as f:
f.write(str(net_param))
print 'asdf'
self.dest_model = caffe.Net(temp_file, caffe.TEST)
model_layers = self.net_model.layers
for i, layer in enumerate(model_layers):
if layer.type == 'Convolution' or layer.type == 'InnerProduct' or layer.type == 'Scale':
self.dest_model.layers[i].blobs[0] = layer.blobs[0]
if len(layer.blobs) > 1:
self.dest_model.layers[i].blobs[1] = layer.blobs[1]
print 'asdf end'
def get_netparameter(self, model):
with open(model) as f:
net = cp.NetParameter()
pb.text_format.Parse(f.read(), net)
return net
def convert(self):
#layer param 需要修改 BIAS 参数 添加bias param 还有设置为 true
out_params = self.dest_param.layer
model_layers = self.net_model.layers
out_model_layers = self.dest_model.layers
length = len(self.bnn_layer_location)
param_layers = self.dest_param.layer
print len(model_layers)
print len(self.net_param.layer)
for layer in param_layers:
print layer.name
print '*******************************'
for layer in self.net_model.layers:
print layer.type
param_layer_type_list = [layer.type for layer in param_layers]
model_layer_type_list = [layer.type for layer in model_layers]
i=j=0
dict_layer_id_param_to_model = {}
while i<len(param_layer_type_list):
if param_layer_type_list[i]==model_layer_type_list[j]:
dict_layer_id_param_to_model[i]=j
i=i+1
j=j+1
else:
j=j+1
print dict_layer_id_param_to_model
l = 0
while l < length:
i = self.bnn_layer_location[l]
print param_layers[i].name, param_layers[i].type
if param_layers[i].type in layer_type:
#i = self.net_model.params.keys().index(param_layers[l].name);
channels = self.net_model.params[param_layers[i].name][0].num
#count = model_layers[i].blobs[0].count / channels
scale = self.net_model.params[param_layers[i+1].name][2].data[0]
#print scale
mean = self.net_model.params[param_layers[i+1].name][0].data / scale
#print mean
std = np.sqrt(self.net_model.params[param_layers[i+1].name][1].data / scale)
a = self.net_model.params[param_layers[i+2].name][0].data
b = self.net_model.params[param_layers[i+2].name][1].data
for k in xrange(channels):
self.dest_model.params[param_layers[i].name][0].data[k] = self.net_model.params[param_layers[i].name][0].data[k] * a[k] / std[k]
self.dest_model.params[param_layers[i].name][1].data[k] = self.dest_model.params[param_layers[i].name][1].data[k] * a[k] / std[k] - a[k] * mean[k] / std[k] + b[k]
elif param_layers[i].type == 'Scale':
channels = self.net_model.params[param_layers[i-1].name][0].num
scale = self.net_model.params[param_layers[i-1].name][2].data[0]
mean = self.net_model.params[param_layers[i-1].name][0].data / scale
std = np.sqrt(self.net_model.params[param_layers[i-1].name][1].data / scale) + EPS
a = copy.deepcopy(self.net_model.params[param_layers[i].name][0].data)
b = copy.deepcopy(self.net_model.params[param_layers[i].name][1].data)
for k in xrange(channels):
self.dest_model.params[param_layers[i].name][0].data[k] = a[k] / std[k]
self.dest_model.params[param_layers[i].name][1].data[k] = a[k] / std[k] - a[k] * mean[k] / std[k] + b[k]
l += 1
self.dest_model.save(self.dest_weight_dir)
for ele in self.remove_ele:
out_params.remove(ele)
with open(self.dest_dir, 'w') as f:
f.write(str(self.dest_param))
os.remove(temp_file)
print 'MERGED SUCCEED!'
if __name__ == '__main__':
args = parse_args()
cb = ConvertBnn(args.caffe_config_filename,args.caffe_weights_filename,args.caffe_without_bn_config_filename,args.caffe_without_bn_weight_filename)
cb.convert()