#*-coding:utf-8-*
"""
将keras的.h5的模型文件，转换成TensorFlow的pb文件
"""
# ==========================================================
from keras.models import load_model
import tensorflow as tf
import os
from keras import backend
from keras.applications.mobilenetv2 import MobileNetV2
from keras.layers import Input
from keras.preprocessing import image
from keras.applications.mobilenetv2 import preprocess_input, decode_predictions
from keras.applications.inception_resnet_v2 import InceptionResNetV2
def h5_to_pb(h5_model, output_dir, model_name, out_prefix="output_", log_tensorboard=True):
""".h5模型文件转换成pb模型文件
Argument:
h5_model: str
.h5模型文件
output_dir: str
pb模型文件保存路径
model_name: str
pb模型文件名称
out_prefix: str
根据训练，需要修改
log_tensorboard: bool
是否生成日志文件
Return:
pb模型文件
"""
if os.path.exists(output_dir) == False:
os.mkdir(output_dir)
out_nodes = []
for i in range(len(h5_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(h5_model.output[i], out_prefix + str(i + 1))
sess = backend.get_session()
from tensorflow.python.framework import graph_util, graph_io
# 写入pb模型文件
init_graph = sess.graph.as_graph_def()
main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes)
graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False)
# 输出日志文件
if log_tensorboard:
from tensorflow.python.tools import import_pb_to_tensorboard
import_pb_to_tensorboard.import_to_tensorboard(os.path.join(output_dir, model_name), output_dir)
if __name__ == '__main__':
# .h模型文件路径参数
input_path = '../models/'
weight_file = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'
#weight_file = 'mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.4_224.h5'
weight_file_path = os.path.join(input_path, weight_file)
output_graph_name = weight_file[:-3] + '.pb'
# pb模型文件输出输出路径
output_dir = input_path
# 加载模型
h5_model = 0
if weight_file.find("mobile")!=-1:
input_tensor = Input(shape=(224, 224, 3)) # or you could put (None, None, 3) for shape.
h5_model = MobileNetV2(input_tensor=input_tensor, alpha=1.4, include_top=True,weights=input_path+weight_file)
elif weight_file.find("inception_resnet")!=-1:
h5_model = InceptionResNetV2("imagenet",include_top=True,)
h5_model.summary()
h5_to_pb(h5_model, output_dir=output_dir, model_name=output_graph_name)
print('Finished')
