keras转tensorflow lite【方法一】2步走

叁歲伎倆 2022-03-29 09:52 359阅读 0赞

本文介绍的将keras转tensorflow lite的方法，共分为两步：

keras转tensorflow，再将tensorflow转tensorflow lite

**1、keras-tensorflow：我是在mac上做的**

如果relu6报错，就放开这一行：

> with CustomObjectScope(\{'relu6': mobilenet.relu6, 'DepthwiseConv2D':

# coding: utf-8
    '''
    Input arguments:
    num_output: this value has nothing to do with the number of classes, batch_size, etc., 
    and it is mostly equal to 1. If the network is a **multi-stream network** 
    (forked network with multiple outputs), set the value to the number of outputs.
    quantize: if set to True, use the quantize feature of Tensorflow
    (https://www.tensorflow.org/performance/quantization) [default: False]
    use_theano: Thaeno and Tensorflow implement convolution in different ways.
    When using Keras with Theano backend, the order is set to 'channels_first'.
    This feature is not fully tested, and doesn't work with quantizization [default: False]
    input_fld: directory holding the keras weights file [default: .]
    output_fld: destination directory to save the tensorflow files [default: .]
    input_model_file: name of the input weight file [default: 'model.h5']
    output_model_file: name of the output weight file [default: args.input_model_file + '.pb']
    graph_def: if set to True, will write the graph definition as an ascii file [default: False]
    output_graphdef_file: if graph_def is set to True, the file name of the 
    graph definition [default: model.ascii]
    output_node_prefix: the prefix to use for output nodes. [default: output_node]
    '''
    
    # Parse input arguments
    import argparse
    parser = argparse.ArgumentParser(description='set input arguments')
    parser.add_argument('-input_fld', action="store",
                        dest='input_fld', type=str, default='.')
    parser.add_argument('-output_fld', action="store",
                        dest='output_fld', type=str, default='')
    parser.add_argument('-input_model_file', action="store",
                        dest='input_model_file', type=str, default='model.h5')
    parser.add_argument('-output_model_file', action="store",
                        dest='output_model_file', type=str, default='')
    parser.add_argument('-output_graphdef_file', action="store",
                        dest='output_graphdef_file', type=str, default='model.ascii')
    parser.add_argument('-num_outputs', action="store",
                        dest='num_outputs', type=int, default=1)
    parser.add_argument('-graph_def', action="store",
                        dest='graph_def', type=bool, default=False)
    parser.add_argument('-output_node_prefix', action="store",
                        dest='output_node_prefix', type=str, default='output_node')
    parser.add_argument('-quantize', action="store",
                        dest='quantize', type=bool, default=False)
    parser.add_argument('-theano_backend', action="store",
                        dest='theano_backend', type=bool, default=False)
    parser.add_argument('-f')
    args = parser.parse_args()
    parser.print_help()
    print('input args: ', args)
    
    if args.theano_backend is True and args.quantize is True:
        raise ValueError("Quantize feature does not work with theano backend.")
    
    # initialize
    from keras.models import load_model
    import tensorflow as tf
    from pathlib import Path
    from keras import backend as K
    from keras.applications import mobilenet
    from keras.utils.generic_utils import CustomObjectScope
    
    output_fld =  args.input_fld if args.output_fld == '' else args.output_fld
    if args.output_model_file == '':
        args.output_model_file = str(Path(args.input_model_file).name) + '.pb'
    Path(output_fld).mkdir(parents=True, exist_ok=True)
    weight_file_path = str(Path(args.input_fld) / args.input_model_file)
    
    # Load keras model and rename output
    K.set_learning_phase(0)
    if args.theano_backend:
        K.set_image_data_format('channels_first')
    else:
        K.set_image_data_format('channels_last')
    
    # try:
    # 主要修改在这里，需要加上这行，否则会报错
    # with CustomObjectScope({'relu6': mobilenet.relu6, 'DepthwiseConv2D': mobilenet.DepthwiseConv2D}):
        net_model = load_model(weight_file_path)
    # except ValueError as err:
    #     print('''Input file specified ({}) only holds the weights, and not the model defenition.
    #     Save the model using mode.save(filename.h5) which will contain the network architecture
    #     as well as its weights.
    #     If the model is saved using model.save_weights(filename.h5), the model architecture is
    #     expected to be saved separately in a json format and loaded prior to loading the weights.
    #     Check the keras documentation for more details (https://keras.io/getting-started/faq/)'''
    #           .format(weight_file_path))
    #     raise err
    num_output = args.num_outputs
    pred = [None]*num_output
    pred_node_names = [None]*num_output
    for i in range(num_output):
        pred_node_names[i] = args.output_node_prefix+str(i)
        pred[i] = tf.identity(net_model.outputs[i], name=pred_node_names[i])
    print('output nodes names are: ', pred_node_names)
    
    
    # [optional] write graph definition in ascii
    sess = K.get_session()
    if args.graph_def:
        f = args.output_graphdef_file
        tf.train.write_graph(sess.graph.as_graph_def(), output_fld, f, as_text=True)
        print('saved the graph definition in ascii format at: ', str(Path(output_fld) / f))
    
    
    # convert variables to constants and save
    from tensorflow.python.framework import graph_util
    from tensorflow.python.framework import graph_io
    if args.quantize:
        from tensorflow.tools.graph_transforms import TransformGraph
        transforms = ["quantize_weights", "quantize_nodes"]
        transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [], pred_node_names, transforms)
        constant_graph = graph_util.convert_variables_to_constants(sess, transformed_graph_def, pred_node_names)
    else:
        constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names)
    graph_io.write_graph(constant_graph, output_fld, args.output_model_file, as_text=False)
    print('saved the freezed graph (ready for inference) at: ', str(Path(output_fld) / args.output_model_file))

结果：

![20190108120256900.png][]

**2、tensorflow-tensorflow lite：linux下**

用toco将.pb文件转成.tflite文件

toco --graph_def_file /home/xuqiong/data/ssrnet_3_3_3_64_1_1_morph2_age_all.pb --output_format=TFLITE --output_file=/home/xuqiong/data/ssrnet_3_3_3_64_1_1_morph2_age_all.tflite --inference_type=FLOAT --input_arrays=input_1 --output_arrays=output_node0 --input_shapes=1,64,64,3

> toco --graph\_def\_file /home/xuqiong/data/ssrnet\_3\_3\_3\_64\_1\_1\_morph2\_age\_all.pb --output\_format=TFLITE --output\_file=/home/xuqiong/data/ssrnet\_3\_3\_3\_64\_1\_1\_morph2\_age\_all.tflite --inference\_type=FLOAT --input\_arrays=input\_1 --output\_arrays=output\_node0 --input\_shapes=1,64,64,3

结果：

![20190108120147757.png][]

下载到本地后：  
![20190108120217634.png][]

参考文档：[https://juejin.im/post/5b7bccc6e51d453887102e0a][https_juejin.im_post_5b7bccc6e51d453887102e0a]

[https://blog.csdn.net/mogoweb/article/details/80152774][https_blog.csdn.net_mogoweb_article_details_80152774]

[20190108120256900.png]: /images/20220329/6b8e001b49c1439497db04092bba893e.png
[20190108120147757.png]: /images/20220329/826506ae76a749bf9024436cd1ca1b18.png
[20190108120217634.png]: /images/20220329/38427c9f432c48d9bffec9adfe4aade3.png
[https_juejin.im_post_5b7bccc6e51d453887102e0a]: https://juejin.im/post/5b7bccc6e51d453887102e0a
[https_blog.csdn.net_mogoweb_article_details_80152774]: https://blog.csdn.net/mogoweb/article/details/80152774