tensorflow框架.ckpt .pb模型节点tensor_name打印及ckpt模型转.pb模型

ゝ一纸荒年。 2022-05-15 12:20 176阅读 0赞

转换模型首先要知道的是从哪个节点输出，如果没有源代码是很难清楚节点信息。

### 获取ckpt模型的节点名称 ###

import os
    from tensorflow.python import pywrap_tensorflow
    
    checkpoint_path = os.path.join('./ade20k', "model.ckpt-27150")
    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)
        # print(reader.get_tensor(key)) #相应的值

### 获取pb模型的节点名称 ###

import tensorflow as tf
    import os
    
    model_dir = './'
    model_name = 'model.pb'
    
    def create_graph():
        with tf.gfile.FastGFile(os.path.join(
                model_dir, model_name), 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, name='')
            
    create_graph()
    tensor_name_list = [tensor.name for tensor in tf.get_default_graph().as_graph_def().node]
    for tensor_name in tensor_name_list:
        print(tensor_name,'\n')

### ckpt转换为pb模型 ###

from tensorflow.python.tools import inspect_checkpoint as chkp
    import tensorflow as tf
    
    saver = tf.train.import_meta_graph("./ade20k/model.ckpt-27150.meta", clear_devices=True)
    
    #【敲黑板！】这里就是填写输出节点名称惹
    output_nodes = ["xxx"] 
    
    with tf.Session(graph=tf.get_default_graph()) as sess:
        input_graph_def = sess.graph.as_graph_def()
        saver.restore(sess, "./ade20k/model.ckpt-27150")
        output_graph_def = tf.graph_util.convert_variables_to_constants(sess,
                                                                        input_graph_def,
                                                                        output_nodes)
        with open("frozen_model.pb", "wb") as f:
            f.write(output_graph_def.SerializeToString())

### \[new version\] ckpt to pb ###

import tensorflow as tf
    from tensorflow.python.framework import graph_util
    
    checkpoint = "model.ckpt-xxx"
    graph_file = "xxx.pb"
    
    
    def return_ops(candidate):
        ops = []
        if isinstance(candidate, (list, tuple)):
            for x in candidate:
                ops += return_ops(x)
        else:
            ops.append(candidate.op)
    
        return ops
    
    
    def dump_graph():
        with tf.Graph().as_default():
            inputs = setup_input(dtype=tf.float32,
                                 shape=[None, 224, 224, 3],
                                 name='graph_input')
    
            outputs = model_inference(inputs, 1000)
    
            model_info = gen_info(inputs, outputs)
            print(model_info)
    
            saver = tf.train.Saver(tf.global_variables())
            dest_node = return_ops(outputs)
    
            with tf.Session() as sess:
                saver.restore(sess, checkpoint)
                cur_graphdef = sess.graph.as_graph_def()
                output_graphdef = graph_util.convert_variables_to_constants(
                    sess, cur_graphdef, [n.name for n in dest_node])
    
                with tf.gfile.GFile(graph_file, 'wb') as gf:
                    gf.write(output_graphdef.SerializeToString())
    
                with open(graph_file + '.info', 'w') as info_f:
                    info_f.write(model_info)
    
    
    def setup_input(dtype, shape, name=None):
        p_node = tf.Placeholder(dtype=dtype, shape=shape, name=name)
        return p_node
    
    
    def gen_info(inp, o):
        info_text = '[input tensor]: {0}\n[input shape]: {1}\n'.format(
            inp.name, inp.get_shape())
        print("outp", o)
        info_text += '[output tensor]: {0}\n[output shape]: {1}\n'.format(
            o.name, o.get_shape())
    
        return info_text
    
    
    def model_inference(images, num_classes):
        with tf.variable_scope('xxx'):
            logits = tf.xxx
        return logits
    
    
    if __name__ == "__main__":
        dump_graph()
        print('dump finish!')