tf.summary可视化计算流graph 可视化.ckpt和.pb中的计算流graph

'''
对graph的计算流的可视化
'''
import tensorflow as tf
a=tf.constant(1,name="input_a")
b=tf.constant(2,name='input_b')
c=tf.multiply(a,b, name="mul_c")
sess=tf.Session()
summary_writer=tf.summary.FileWriter('./my_graph',sess.graph)
tf.summary.scalar('c', c)
sum_ops = tf.summary.merge_all()#自动管理
metall,c_ = sess.run([sum_ops,c])
summary_writer.add_summary(metall) # 写入文件
print(c_)

 

'''
通过.ckpt文件，可视化.meta中的图
'''
from tensorflow.python.platform import gfile
import tensorflow as tf

model_path = r".\checkpoint\model_yolov3-tiny_ckpt\model.ckpt"
saver = tf.train.import_meta_graph(model_path+'.meta',clear_devices=True)
graph = tf.get_default_graph()
with tf.Session( graph=graph) as sess:
    sess.run(tf.global_variables_initializer()) 
    saver.restore(sess,model_path)
    summaryWriter = tf.summary.FileWriter('./log_graph_model_yolov3-tiny_ckpt/', graph)

 

'''
可视化.pb文件中的图模型
'''
from tensorflow.python.platform import gfile
import tensorflow as tf
model = r'./data/darknet_weights/frozen_darknet_yolov3spp_model.pb'
graph = tf.get_default_graph()
graph_def = graph.as_graph_def()
graph_def.ParseFromString(gfile.FastGFile(model, 'rb').read())
tf.import_graph_def(graph_def, name='graph')
summaryWriter = tf.summary.FileWriter('./log_graph_yolov3spp_pb/', graph)