模型转换[yolov3模型在keras与darknet之间转换]
首先借助qqwweee/keras-yolo3中的convert.py和,并重新编写了代码，实现将darknet格式的yolov3的yolov3.cfg和yolov3.weights转换成keras（tensorflow）的h5格式
1 将darknet格式的yolov3.cfg和yolov3.weights转换成kears(tf)的h5格式
# -*- coding: utf-8 -*-
import os
import io
import argparse
import configparser
import numpy as np
from keras import backend as K
from keras.layers import (Conv2D, Input, ZeroPadding2D, Add,
UpSampling2D, MaxPooling2D, Concatenate)
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.regularizers import l2
from keras.utils.vis_utils import plot_model as plot
def parser():
parser = argparse.ArgumentParser(description="Darknet\'s yolov3.cfg and yolov3.weights \
converted into Keras\'s yolov3.h5!")
parser.add_argument('-cfg_path', help='yolov3.cfg')
parser.add_argument('-weights_path', help='yolov3.weights')
parser.add_argument('-output_path', help='yolov3.h5')
parser.add_argument('-weights_only', action='store_true',help='only save weights in yolov3.h5')
return parser.parse_args()
class WeightLoader(object):
def __init__(self,weight_path):
self.fhandle = open(weight_path,'rb')
self.read_bytes = 0
def parser_buffer(self,shape,dtype='int32',buffer_size=None):
self.read_bytes += buffer_size
return np.ndarray(shape=shape,
dtype=dtype,
buffer=self.fhandle.read(buffer_size) )
def head(self):
major, minor, revision = self.parser_buffer(
shape=(3,),
dtype='int32',
buffer_size=12)
if major*10+minor >= 2 and major < 1000 and minor < 1000:
seen = self.parser_buffer(
shape=(1,),
dtype='int64',
buffer_size=8)
else:
seen = self.parser_buffer(
shape=(1,),
dtype='int32',
buffer_size=4)
return major, minor, revision, seen
def close(self):
self.fhandle.close()
class DarkNetParser(object):
def __init__(self, cfg_path, weights_path):
self.block_gen = self._get_block(cfg_path)
self.weight_loader = WeightLoader(weights_path)
major, minor, revision, seen = self.weight_loader.head()
print('weights header: ',major, minor, revision, seen)
self.input_layer = Input(shape=(None, None, 3))
self.out_index = []
self.prev_layer = self.input_layer
self.all_layers = []
self.count = [0,0]
def _get_block(self,cfg_path):
block = {}
with open(cfg_path,'r', encoding='utf-8') as fr:
for line in fr:
line = line.strip()
if '[' in line and ']' in line:
if block:
yield block
block = {}
block['type'] = line.strip(' []')
elif not line or '#' in line:
continue
else:
key,val = line.strip().replace(' ','').split('=')
key,val = key.strip(), val.strip()
block[key] = val
yield block
def conv(self, block):
'''在读取darknet的yolov3.weights文件时，顺序是
1 - bias；
2 - 如果有bn，则接着读取三个scale，mean，var
3 - 读取权重
'''
# Darknet serializes convolutional weights as:
# [bias/beta, [gamma, mean, variance], conv_weights]
self.count[0] += 1
# read conv block
filters = int(block['filters'])
size = int(block['size'])
stride = int(block['stride'])
pad = int(block['pad'])
activation = block['activation']
padding = 'same' if pad == 1 and stride == 1 else 'valid'
batch_normalize = 'batch_normalize' in block
prev_layer_shape = K.int_shape(self.prev_layer)
weights_shape = (size, size, prev_layer_shape[-1], filters)
darknet_w_shape = (filters, weights_shape[2], size, size)
weights_size = np.product(weights_shape)
print('+',self.count[0],'conv2d',
'bn' if batch_normalize else ' ',
activation,
weights_shape)
# 读取滤波器个偏置
conv_bias = self.weight_loader.parser_buffer(
shape=(filters,),
dtype='float32',
buffer_size=filters*4)
# 如果有bn，则接着读取滤波器个scale，mean，var
if batch_normalize:
bn_weight_list = self.bn(filters, conv_bias)
# 读取权重
conv_weights = self.weight_loader.parser_buffer(
shape=darknet_w_shape,
dtype='float32',
buffer_size=weights_size*4)
# DarkNet conv_weights are serialized Caffe-style:
# (out_dim, in_dim, height, width)
# We would like to set these to Tensorflow order:
# (height, width, in_dim, out_dim)
conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
conv_weights = [conv_weights] if batch_normalize else \
[conv_weights, conv_bias]
act_fn = None
if activation == 'leaky':
pass
elif activation != 'linear':
raise
if stride > 1:
self.prev_layer = ZeroPadding2D(((1,0),(1,0)))(self.prev_layer)
conv_layer = (Conv2D(
filters, (size, size),
strides=(stride, stride),
kernel_regularizer=l2(self.weight_decay),
use_bias=not batch_normalize,
weights=conv_weights,
activation=act_fn,
padding=padding))(self.prev_layer)
if batch_normalize:
conv_layer = BatchNormalization(weights=bn_weight_list)(conv_layer)
self.prev_layer = conv_layer
if activation == 'linear':
self.all_layers.append(self.prev_layer)
elif activation == 'leaky':
act_layer = LeakyReLU(alpha=0.1)(self.prev_layer)
self.prev_layer = act_layer
self.all_layers.append(act_layer)
def bn(self,filters,conv_bias):
'''bn有4个参数，分别是bias，scale，mean，var，
其中bias已经读取完毕，这里读取剩下三个，scale,mean,var '''
bn_weights = self.weight_loader.parser_buffer(
shape=(3,filters),
dtype='float32',
buffer_size=(filters*3)*4)
# scale, bias, mean,var
bn_weight_list = [bn_weights[0],
conv_bias,
bn_weights[1],
bn_weights[2] ]
return bn_weight_list
def maxpool(self,block):
size = int(block['size'])
stride = int(block['stride'])
maxpool_layer = MaxPooling2D(pool_size=(size,size),
strides=(stride,stride),
padding='same')(self.prev_layer)
self.all_layers.append(maxpool_layer)
self.prev_layer = maxpool_layer
def shortcut(self,block):
index = int(block['from'])
activation = block['activation']
assert activation == 'linear', 'Only linear activation supported.'
shortcut_layer = Add()([self.all_layers[index],self.prev_layer])
self.all_layers.append(shortcut_layer)
self.prev_layer = shortcut_layer
def route(self,block):
layers_ids = block['layers']
ids = [int(i) for i in layers_ids.split(',')]
layers = [self.all_layers[i] for i in ids]
if len(layers) > 1:
print('Concatenating route layers:', layers)
concatenate_layer = Concatenate()(layers)
self.all_layers.append(concatenate_layer)
self.prev_layer = concatenate_layer
else:
skip_layer = layers[0]
self.all_layers.append(skip_layer)
self.prev_layer = skip_layer
def upsample(self,block):
stride = int(block['stride'])
assert stride == 2, 'Only stride=2 supported.'
upsample_layer = UpSampling2D(stride)(self.prev_layer)
self.all_layers.append(upsample_layer)
self.prev_layer = self.all_layers[-1]
def yolo(self,block):
self.out_index.append(len(self.all_layers)-1)
self.all_layers.append(None)
self.prev_layer = self.all_layers[-1]
def net(self, block):
self.weight_decay = block['decay']
def create_and_save(self,weights_only,output_path):
if len(self.out_index) == 0:
self.out_index.append( len(self.all_layers)-1 )
output_layers = [self.all_layers[i] for i in self.out_index]
model = Model(inputs=self.input_layer,
outputs=output_layers)
print(model.summary())
if weights_only:
model.save_weights(output_path)
print('Saved Keras weights to {}'.format(output_path))
else:
model.save(output_path)
print('Saved Keras model to {}'.format(output_path))
def close(self):
self.weight_loader.close()
def main():
args = parser()
print('loading weights...')
cfg_parser = DarkNetParser(args.cfg_path,args.weights_path)
print('creating keras model...')
layers_fun = {'convolutional':cfg_parser.conv,
'net':cfg_parser.net,
'yolo':cfg_parser.yolo,
'route':cfg_parser.route,
'upsample':cfg_parser.upsample,
'maxpool':cfg_parser.maxpool,
'shortcut':cfg_parser.shortcut
}
print('Parsing Darknet config.')
for ind,block in enumerate(cfg_parser.block_gen):
type = block['type']
layers_fun[type](block)
cfg_parser.create_and_save(args.weights_only, args.output_path)
cfg_parser.close()
if __name__ == '__main__':
main()
运行结果
python yolov3_darknet_to_keras.py -cfg_path text.cfg -weights_path yolov3.weights -output_path yolov3c_d2k.h5
2 将kears(tf)的h5格式转换成darknet格式的yolov3.weights
其中上面的与下面的名称转换
bias -> beta
scale -> gamma
mean -> moving_mean
var -> moving_variance
基于此写的脚本为：
# -*- coding: utf-8 -*-
''' yolov3_keras_to_darknet.py'''
import argparse
import numpy
import numpy as np
import keras
from keras.models import load_model
from keras import backend as K
def parser():
parser = argparse.ArgumentParser(description="Darknet\'s yolov3.cfg and yolov3.weights \
converted into Keras\'s yolov3.h5!")
parser.add_argument('-cfg_path', help='yolov3.cfg')
parser.add_argument('-h5_path', help='yolov3.h5')
parser.add_argument('-output_path', help='yolov3.weights')
return parser.parse_args()
class WeightSaver(object):
def __init__(self,h5_path,output_path):
self.model = load_model(h5_path)
# 如果要读取keras调用save_weights的h5文件，可以先读取一次save的h5，
# 然后取消下面的注释，读取save_weights的h5
# self.model.load_weights('text.h5')
self.layers = {weight.name:weight for weight in self.model.weights}
self.sess = K.get_session()
self.fhandle = open(output_path,'wb')
self._write_head()
def _write_head(self):
numpy_data = numpy.ndarray(shape=(3,),
dtype='int32',
buffer=np.array([0,2,0],dtype='int32') )
self.save(numpy_data)
numpy_data = numpy.ndarray(shape=(1,),
dtype='int64',
buffer=np.array([320000],dtype='int64'))
self.save(numpy_data)
def get_bn_layername(self,num):
layer_name = 'batch_normalization_{num}'.format(num=num)
bias = self.layers['{0}/beta:0'.format(layer_name)]
scale = self.layers['{0}/gamma:0'.format(layer_name)]
mean = self.layers['{0}/moving_mean:0'.format(layer_name)]
var = self.layers['{0}/moving_variance:0'.format(layer_name)]
bias_np = self.get_numpy(bias)
scale_np = self.get_numpy(scale)
mean_np = self.get_numpy(mean)
var_np = self.get_numpy(var)
return bias_np,scale_np,mean_np,var_np
def get_convbias_layername(self,num):
layer_name = 'conv2d_{num}'.format(num=num)
bias = self.layers['{0}/bias:0'.format(layer_name)]
bias_np = self.get_numpy(bias)
return bias_np
def get_conv_layername(self,num):
layer_name = 'conv2d_{num}'.format(num=num)
conv = self.layers['{0}/kernel:0'.format(layer_name)]
conv_np = self.get_numpy(conv)
return conv_np
def get_numpy(self,layer_name):
numpy_data = self.sess.run(layer_name)
return numpy_data
def save(self,numpy_data):
bytes_data = numpy_data.tobytes()
self.fhandle.write(bytes_data)
self.fhandle.flush()
def close(self):
self.fhandle.close()
class KerasParser(object):
def __init__(self, cfg_path, h5_path, output_path):
self.block_gen = self._get_block(cfg_path)
self.weights_saver = WeightSaver(h5_path, output_path)
self.count_conv = 0
self.count_bn = 0
def _get_block(self,cfg_path):
block = {}
with open(cfg_path,'r', encoding='utf-8') as fr:
for line in fr:
line = line.strip()
if '[' in line and ']' in line:
if block:
yield block
block = {}
block['type'] = line.strip(' []')
elif not line or '#' in line:
continue
else:
key,val = line.strip().replace(' ','').split('=')
key,val = key.strip(), val.strip()
block[key] = val
yield block
def close(self):
self.weights_saver.close()
def conv(self, block):
self.count_conv += 1
batch_normalize = 'batch_normalize' in block
print('handing.. ',self.count_conv)
# 如果bn存在，则先处理bn，顺序为bias，scale，mean，var
if batch_normalize:
bias,scale,mean,var = self.bn()
self.weights_saver.save(bias)
scale = scale.reshape(1,-1)
mean = mean.reshape(1,-1)
var = var.reshape(1,-1)
remain = np.concatenate([scale,mean,var],axis=0)
self.weights_saver.save(remain)
# 否则，先处理biase
else:
conv_bias = self.weights_saver.get_convbias_layername(self.count_conv)
self.weights_saver.save(conv_bias)
# 接着处理weights
conv_weights = self.weights_saver.get_conv_layername(self.count_conv)
# 需要将(height, width, in_dim, out_dim)转换成(out_dim, in_dim, height, width)
conv_weights = np.transpose(conv_weights,[3,2,0,1])
self.weights_saver.save(conv_weights)
def bn(self):
self.count_bn += 1
bias,scale,mean,var = self.weights_saver.get_bn_layername(self.count_bn)
return bias,scale,mean,var
def main():
args = parser()
keras_loader = KerasParser(args.cfg_path, args.h5_path, args.output_path)
for block in keras_loader.block_gen:
if 'convolutional' in block['type']:
keras_loader.conv(block)
keras_loader.close()
if __name__ == "__main__":
main()
通过读取keras保存的h5文件，并读取其权重，其如下所示,
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