前言

由于存在多种框架下的YOLO算法，例如模型压缩大部分直接使用Darknet53，而计算mAP等可能需要使用keras等框架，这个时候需要将训练所得的.weights模型类型转换为.h5。本文给出了转换代码。

所需材料

1、待转换模型的算法框架，往往是.cfg文件；
2、待转换模型.weights文件；
3、转换代码 convert.py；
4、python对应的包。

实现转换的代码

import argparse
import configparser
import io
import os
from collections import defaultdictimport 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 plotparser = argparse.ArgumentParser(description='Darknet To Keras Converter.')
parser.add_argument('config_path', help='Path to Darknet cfg file.')
parser.add_argument('weights_path', help='Path to Darknet weights file.')
parser.add_argument('output_path', help='Path to output Keras model file.')
parser.add_argument('-p','--plot_model',help='Plot generated Keras model and save as image.',action='store_true')
parser.add_argument('-w','--weights_only',help='Save as Keras weights file instead of model file.',action='store_true')def unique_config_sections(config_file):"""Convert all config sections to have unique names.Adds unique suffixes to config sections for compability with configparser."""section_counters = defaultdict(int)output_stream = io.StringIO()with open(config_file) as fin:for line in fin:if line.startswith('['):section = line.strip().strip('[]')_section = section + '_' + str(section_counters[section])section_counters[section] += 1line = line.replace(section, _section)output_stream.write(line)output_stream.seek(0)return output_stream# %%
def _main(args):config_path = os.path.expanduser(args.config_path)weights_path = os.path.expanduser(args.weights_path)assert config_path.endswith('.cfg'), '{} is not a .cfg file'.format(config_path)assert weights_path.endswith('.weights'), '{} is not a .weights file'.format(weights_path)output_path = os.path.expanduser(args.output_path)assert output_path.endswith('.h5'), 'output path {} is not a .h5 file'.format(output_path)output_root = os.path.splitext(output_path)[0]# Load weights and config.print('Loading weights.')weights_file = open(weights_path, 'rb')major, minor, revision = np.ndarray(shape=(3, ), dtype='int32', buffer=weights_file.read(12))if (major*10+minor)>=2 and major<1000 and minor<1000:seen = np.ndarray(shape=(1,), dtype='int64', buffer=weights_file.read(8))else:seen = np.ndarray(shape=(1,), dtype='int32', buffer=weights_file.read(4))print('Weights Header: ', major, minor, revision, seen)print('Parsing Darknet config.')unique_config_file = unique_config_sections(config_path)cfg_parser = configparser.ConfigParser()cfg_parser.read_file(unique_config_file)print('Creating Keras model.')input_layer = Input(shape=(None, None, 3))prev_layer = input_layerall_layers = []weight_decay = float(cfg_parser['net_0']['decay']) if 'net_0' in cfg_parser.sections() else 5e-4count = 0out_index = []for section in cfg_parser.sections():print('Parsing section {}'.format(section))if section.startswith('convolutional'):filters = int(cfg_parser[section]['filters'])size = int(cfg_parser[section]['size'])stride = int(cfg_parser[section]['stride'])pad = int(cfg_parser[section]['pad'])activation = cfg_parser[section]['activation']batch_normalize = 'batch_normalize' in cfg_parser[section]padding = 'same' if pad == 1 and stride == 1 else 'valid'# Setting weights.# Darknet serializes convolutional weights as:# [bias/beta, [gamma, mean, variance], conv_weights]prev_layer_shape = K.int_shape(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('conv2d', 'bn'if batch_normalize else '  ', activation, weights_shape)conv_bias = np.ndarray(shape=(filters, ),dtype='float32',buffer=weights_file.read(filters * 4))count += filtersif batch_normalize:bn_weights = np.ndarray(shape=(3, filters),dtype='float32',buffer=weights_file.read(filters * 12))count += 3 * filtersbn_weight_list = [bn_weights[0],  # scale gammaconv_bias,  # shift betabn_weights[1],  # running meanbn_weights[2]  # running var]conv_weights = np.ndarray(shape=darknet_w_shape,dtype='float32',buffer=weights_file.read(weights_size * 4))count += weights_size# 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]# Handle activation.act_fn = Noneif activation == 'leaky':pass  # Add advanced activation later.elif activation != 'linear':raise ValueError('Unknown activation function `{}` in section {}'.format(activation, section))# Create Conv2D layerif stride>1:# Darknet uses left and top padding instead of 'same' modeprev_layer = ZeroPadding2D(((1,0),(1,0)))(prev_layer)conv_layer = (Conv2D(filters, (size, size),strides=(stride, stride),kernel_regularizer=l2(weight_decay),use_bias=not batch_normalize,weights=conv_weights,activation=act_fn,padding=padding))(prev_layer)if batch_normalize:conv_layer = (BatchNormalization(weights=bn_weight_list))(conv_layer)prev_layer = conv_layerif activation == 'linear':all_layers.append(prev_layer)elif activation == 'leaky':act_layer = LeakyReLU(alpha=0.1)(prev_layer)prev_layer = act_layerall_layers.append(act_layer)elif section.startswith('route'):ids = [int(i) for i in cfg_parser[section]['layers'].split(',')]layers = [all_layers[i] for i in ids]if len(layers) > 1:print('Concatenating route layers:', layers)concatenate_layer = Concatenate()(layers)all_layers.append(concatenate_layer)prev_layer = concatenate_layerelse:skip_layer = layers[0]  # only one layer to routeall_layers.append(skip_layer)prev_layer = skip_layerelif section.startswith('maxpool'):size = int(cfg_parser[section]['size'])stride = int(cfg_parser[section]['stride'])all_layers.append(MaxPooling2D(pool_size=(size, size),strides=(stride, stride),padding='same')(prev_layer))prev_layer = all_layers[-1]elif section.startswith('shortcut'):index = int(cfg_parser[section]['from'])activation = cfg_parser[section]['activation']assert activation == 'linear', 'Only linear activation supported.'all_layers.append(Add()([all_layers[index], prev_layer]))prev_layer = all_layers[-1]elif section.startswith('upsample'):stride = int(cfg_parser[section]['stride'])assert stride == 2, 'Only stride=2 supported.'all_layers.append(UpSampling2D(stride)(prev_layer))prev_layer = all_layers[-1]elif section.startswith('yolo'):out_index.append(len(all_layers)-1)all_layers.append(None)prev_layer = all_layers[-1]elif section.startswith('net'):passelse:raise ValueError('Unsupported section header type: {}'.format(section))# Create and save model.if len(out_index)==0: out_index.append(len(all_layers)-1)model = Model(inputs=input_layer, outputs=[all_layers[i] for i in out_index])print(model.summary())if args.weights_only:model.save_weights('{}'.format(output_path))print('Saved Keras weights to {}'.format(output_path))else:model.save('{}'.format(output_path))print('Saved Keras model to {}'.format(output_path))# Check to see if all weights have been read.remaining_weights = len(weights_file.read()) / 4weights_file.close()print('Read {} of {} from Darknet weights.'.format(count, count +remaining_weights))if remaining_weights > 0:print('Warning: {} unused weights'.format(remaining_weights))if args.plot_model:plot(model, to_file='{}.png'.format(output_root), show_shapes=True)print('Saved model plot to {}.png'.format(output_root))if __name__ == '__main__':_main(parser.parse_args())

应用举例

例如，需要将进行模型压缩所得的权重文件.weights转换为keras可以使用的.h5类型的权重文件。假设进行剪枝、压缩后的模型为yolov3.cfg，权重文件为yolov3.weights，需要转换为yolov3.h5则在命令行输入：

python convert.py yolov3.cfg yolov3.weights model_data/yolov3.ht

后面三个参数分别就是，需要转换的模型文件，权重文件，输出的路径。

.weights转.pt格式

类似的，可以通过下面的代码实现weights格式转为pt格式：

python -c "from models import *; convert('cfg/yolov3.cfg', 'weights/last.pt')"

参考信息

https://github.com/huangbinz/yolov3-weights2h5

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Darknet框架的权重文件.weights类型转换为keras框架的权重文件类型.h5

YOLO：Darknet框架的.weights模型转keras框架的.h5模型

前言