打印pb模型参数及可视化结构
import tensorflow as tf from tensorflow.python.framework import graph_util tf.reset_default_graph() # 重置计算图 output_graph_path = '/home/huihua/NewDisk/stuff_detector_v1.pb' with tf.Session() as sess: tf.global_variables_initializer().run() output_graph_def = tf.GraphDef() # 获得默认的图 graph = tf.get_default_graph() with open(output_graph_path, "rb") as f: output_graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(output_graph_def, name="") # 得到当前图有几个操作节点 print("%d ops in the final graph." % len(output_graph_def.node)) tensor_name = [tensor.name for tensor in output_graph_def.node] print(tensor_name) print('---------------------------') # 在log_graph文件夹下生产日志文件,可以在tensorboard中可视化模型 summaryWriter = tf.summary.FileWriter('log_pb/', graph) for op in graph.get_operations(): # print出tensor的name和值 print(op.name, op.values())
加载ckpt模型到tensorboard可视化
import tensorflow as tf graph = tf.get_default_graph() graphdef = graph.as_graph_def() _ = tf.train.import_meta_graph("/home/huihua/NewDisk1/research/object_detection/ssd_model/eff_ssd_model/model.ckpt-100.meta") summary_write = tf.summary.FileWriter("./log_ck" , graph)
打印模型参数
from tensorflow.python import pywrap_tensorflow import os import tensorflow as tf checkpoint_path=os.path.join('./model.ckpt-300') # 打印参数 reader=pywrap_tensorflow.NewCheckpointReader(checkpoint_path) var_to_shape_map=reader.get_variable_to_shape_map() for key in var_to_shape_map: print('tensor_name: ',key)
修改模型中的参数名称
import tensorflow as tf def rename_var(ckpt_path, new_ckpt_path): with tf.Session() as sess: for var_name, _ in tf.contrib.framework.list_variables(ckpt_path): print(var_name) var = tf.contrib.framework.load_variable(ckpt_path, var_name) new_var_name = var_name.replace('IV','ssd_efficient_net_feature_extractor' ) var = tf.Variable(var, name=new_var_name) saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) saver.save(sess, new_ckpt_path) ckpt_path = './model.ckpt-200' new_ckpt_path = './model.ckpt-300' rename_var(ckpt_path, new_ckpt_path)
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