tf1.0中透過帶weight的pb檔案與get_tensor_by_name函式可以獲取每一層的輸出

import os

import os.path as ops

import argparse

import time

import math

import tensorflow as tf

import glob

import numpy as np

import matplotlib.pyplot as plt

import cv2

os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

gragh_path = "./model.pb"

image_path = "./lvds1901.JPG"

inputtensorname = "input_tensor:0"

tensorname = "loss/inference/encode/resize_images/ResizeBilinear"

filepath="./net_output.txt"

HEIGHT=256

WIDTH=256

VGG_MEAN = [103.939, 116.779, 123.68]

with tf.Graph().as_default():

graph_def = tf.GraphDef()

with tf.gfile.GFile(gragh_path, "rb") as fid:

serialized_graph = fid.read()

graph_def.ParseFromString(serialized_graph)

tf.import_graph_def(graph_def, name="")

image = cv2.imread(image_path)

image = cv2.resize(image, (WIDTH, HEIGHT), interpolation=cv2.INTER_CUBIC)

image_np = np.array(image)

image_np = image_np - VGG_MEAN

image_np_expanded = np.expand_dims(image_np, axis=0)

with tf.Session() as sess:

ops = tf.get_default_graph().get_operations()

tensor_name = tensorname + ":0"

tensor_dict = tf.get_default_graph().get_tensor_by_name(tensor_name)

image_tensor = tf.get_default_graph().get_tensor_by_name(inputtensorname)

output = sess.run(tensor_dict, feed_dict={image_tensor: image_np_expanded})

ftxt = open(filepath,"w")

transform = output.transpose(0, 3, 1, 2)

transform = transform.flatten()

weight_count = 0

for i in transform:

if weight_count % 10 == 0 and weight_count != 0:

ftxt.write("\n")

ftxt.write(str(i) + ",")

weight_count += 1

ftxt.close()