参考：
TensorFlow 自定义模型导出：将 .ckpt 格式转化为 .pb 格式
TensorFlow 模型保存与恢复
snpe
tensorflow 模型前向传播 保存ckpt tensorbard查看 ckpt转pb pb 转snpe dlc 实例
log文件
输入节点 图像高度 图像宽度 图像通道数
input0 6,6,3
输出节点
--out_node add
snpe-tensorflow-to-dlc --graph ./simple_snpe_log/model200.pb -i input0 6,6,3 --out_node add
#coding:utf-8
#http://blog.csdn.net/zhuiqiuk/article/details/53376283
#http://blog.csdn.net/gan_player/article/details/77586489
from __future__ import absolute_import, unicode_literals
import tensorflow as tf
import shutil
import os.path
from tensorflow.python.framework import graph_util
import mxnet as mx
import numpy as np
import random
import cv2
from time import sleep
from easydict import EasyDict as edict
import logging
import math
import tensorflow as tf
import numpy as np def FullyConnected(input, fc_weight, fc_bias, name):
fc = tf.matmul(input, fc_weight) + fc_bias
return fc def inference(body, name_class,outchannel):
wkernel = 3
inchannel = body.get_shape()[3].value
conv_weight = np.arange(wkernel * wkernel * inchannel * outchannel,dtype=np.float32).reshape((outchannel,inchannel,wkernel,wkernel))
conv_weight = conv_weight / (outchannel*inchannel*wkernel*wkernel)
print("conv_weight ", conv_weight)
conv_weight = conv_weight.transpose(2,3,1,0)
conv_weight = tf.Variable(conv_weight, dtype=np.float32, name = "conv_weight")
body = tf.nn.conv2d(body, conv_weight, strides=[1, 1, 1, 1], padding='SAME', name = "conv0")
conv = body
conv_shape = body.get_shape()
dim = conv_shape[1].value * conv_shape[2].value * conv_shape[3].value
body = tf.reshape(body, [1, dim], name = "fc0")
fc_weight = np.ones((dim, name_class))
fc_bias = np.zeros((1, name_class))
fc_weight = tf.Variable(fc_weight, dtype=np.float32, name="fc_weight")
fc_bias = tf.Variable(fc_bias, dtype=np.float32, name="fc_bias")
# tf.constant(100,dtype=np.float32, shape=(body.get_shape()[1] * body.get_shape()[2] * body.get_shape()[3], name_class])
# fc_bias = tf.constant(10, dtype=np.float32, shape=(1, name_class])
body = FullyConnected(body, fc_weight, fc_bias, "fc0")
return conv, body export_dir = "simple_snpe_log"
def saveckpt():
height = 6
width = 6
inchannel = 3
outchannel = 3
graph = tf.get_default_graph()
with tf.Graph().as_default():
input_image = tf.placeholder("float", [1, height, width, inchannel], name = "input0")
conv, logdit = inference(input_image,10,outchannel)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
img = np.arange(height * width * inchannel, dtype=np.float32).reshape((1,inchannel,height,width)) \
/ (1 * inchannel * height * width) * 255.0 - 127.5
print("img",img)
img = img.transpose(0,2,3,1)
import time
since = time.time()
fc = sess.run(logdit,{input_image:img})
conv = sess.run(conv, {input_image: img})
time_elapsed = time.time() - since
print("tf inference time ", str(time_elapsed))
print("conv", conv.transpose(0, 2, 3, 1))
print("fc", fc)
#np.savetxt("tfconv.txt",fc)
#print( "fc", fc.transpose(0,3,2,1))
#np.savetxt("tfrelu.txt",fc.transpose(0,3,2,1)[0][0]) # #save ckpt
export_dir = "simple_snpe_log"
saver = tf.train.Saver()
step = 200
# if os.path.exists(export_dir):
# os.system("rm -rf " + export_dir)
if not os.path.isdir(export_dir): # Create the log directory if it doesn't exist
os.makedirs(export_dir) checkpoint_file = os.path.join(export_dir, 'model.ckpt')
saver.save(sess, checkpoint_file, global_step=step) def LoadModelToTensorBoard():
graph = tf.get_default_graph()
checkpoint_file = os.path.join(export_dir, 'model.ckpt-200.meta')
saver = tf.train.import_meta_graph(checkpoint_file)
print(saver)
summary_write = tf.summary.FileWriter(export_dir , graph)
print(summary_write) def ckptToPb():
checkpoint_file = os.path.join(export_dir, 'model.ckpt-200.meta')
ckpt = tf.train.get_checkpoint_state(export_dir)
print("model ", ckpt.model_checkpoint_path)
saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path +'.meta')
graph = tf.get_default_graph()
with tf.Session() as sess:
saver.restore(sess,ckpt.model_checkpoint_path)
height = 6
width = 6
input_image = tf.get_default_graph().get_tensor_by_name("input0:0")
fc0_output = tf.get_default_graph().get_tensor_by_name("add:0")
sess.run(tf.global_variables_initializer())
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph.as_graph_def(), ['add'])
model_name = os.path.join(export_dir, 'model200.pb')
with tf.gfile.GFile(model_name, "wb") as f:
f.write(output_graph_def.SerializeToString()) def PbTest():
with tf.Graph().as_default():
output_graph_def = tf.GraphDef()
output_graph_path = os.path.join(export_dir,'model200.pb')
with open(output_graph_path, "rb") as f:
output_graph_def.ParseFromString(f.read())
tf.import_graph_def(output_graph_def, name="") with tf.Session() as sess:
tf.initialize_all_variables().run()
height = 6
width = 6
inchannel = 3
outchannel = 3
input_image = tf.get_default_graph().get_tensor_by_name("input0:0")
fc0_output = tf.get_default_graph().get_tensor_by_name("add:0")
conv = tf.get_default_graph().get_tensor_by_name("conv0:0") img = np.arange(height * width * inchannel, dtype=np.float32).reshape((1,inchannel,height,width)) \
/ (1 * inchannel * height * width) * 255.0 - 127.5
print("img",img)
img = img.transpose(0,2,3,1)
import time
since = time.time()
fc0_output = sess.run(fc0_output,{input_image:img})
conv = sess.run(conv, {input_image: img})
time_elapsed = time.time() - since
print("tf inference time ", str(time_elapsed))
print("conv", conv.transpose(0, 2, 3, 1))
print("fc0_output", fc0_output) if __name__ == '__main__': saveckpt() #1
LoadModelToTensorBoard()#2
ckptToPb()#3
PbTest()#
