1.Keras H5转TF pb文件

h5_to_pb.py

from keras.models import load_model
import tensorflow as tf
import os
from keras import backend as K
#路径设置
input_path = 'weights/'
weight_file = '****.h5'
weight_file_path = os.path.join(input_path,weight_file)
output_graph_name = weight_file[:-3]+'.pb'
def h5_to_pb(h5_model,output_dir,model_name,out_prefix = "output_",log_tensorboard = True):
    if osp.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 = K.get_session()
    from tensorflow.python.framework import graph_util,graph_io
    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)
    if log_tensorboard:
        from tensorflow.python.tools import import_pb_to_tensorboard
        import_pb_to_tensorboard.import_to_tensorboard(osp.join(output_dir,model_name),output_dir)
#输出路径
output_dir = osp.join(os.getcwd(),"trans_model")
#加载模型
h5_model = load_model(weight_file_path)
h5_to_pb(h5_model,output_dir = output_dir,model_name = output_graph_name)
print('model saved')

time.py

import time
import argparse
from datetime import datetime

def time_tensorflow_run(session, target, info_string):

  # Args:
  # session:the TensorFlow session to run the computation under.
  # target:需要评测的运算算子。
  # info_string:测试名称。

  num_steps_burn_in = 10 # 先定义预热轮数（头几轮跌代有显存加载、cache命中等问题因此可以跳过，只考量10轮迭代之后的计算时间）
  total_duration = 0.0 # 记录总时间
  total_duration_squared = 0.0 # 总时间平方和  -----用来后面计算方差
  for i in xrange(FLAGS.num_batches + num_steps_burn_in): # 迭代轮数
    start_time = time.time() # 记录时间
    _ = session.run(target) # 每次迭代通过session.run(target)
    duration = time.time() - start_time # 
    if i >= num_steps_burn_in: 
      if not i % 10:
        print ('%s: step %d, duration = %.3f' %
               (datetime.now(), i - num_steps_burn_in, duration))
      total_duration += duration  # 累加便于后面计算每轮耗时的均值和标准差
      total_duration_squared += duration * duration
  mn = total_duration / FLAGS.num_batches # 每轮迭代的平均耗时
  vr = total_duration_squared / FLAGS.num_batches - mn * mn # 
  sd = math.sqrt(vr) # 标准差
  print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
         (datetime.now(), info_string, FLAGS.num_batches, mn, sd))