Question

我在python中创建了一个tensorflow模型用于图像分类。我正在使用Windows 10。

我有一个Train.py类，其中在build_graph()中定义图并在train()中训练模型。这是main.py脚本：

#import fire
import numpy as np
import data_import as di
import os
import tensorflow as tf


class Train:
    __x_ = []
    __y_ = []
    __logits = []
    __loss = []
    __train_step = []
    __merged_summary_op = []
    __saver = []
    __session = []
    __writer = []
    __is_training = []
    __loss_val = []
    __train_summary = []
    __val_summary = []

    def __init__(self):
        pass

    def build_graph(self):
        self.__x_ = tf.placeholder("float", shape=[None, 60, 60, 3], name='X')
        self.__y_ = tf.placeholder("int32", shape=[None, 3], name='Y')
        self.__is_training = tf.placeholder(tf.bool)


        with tf.name_scope("model") as scope:
            conv1 = tf.layers.conv2d(inputs=self.__x_, filters=64,
                                 kernel_size=[5, 5],
                                 padding="same", activation=tf.nn.relu)
            pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

            conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], padding="same",
                                 activation=tf.nn.relu)

            pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

            conv3 = tf.layers.conv2d(inputs=pool2, filters=32, kernel_size=[5, 5], padding="same",
                                 activation=tf.nn.relu)

            pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)

            pool3_flat = tf.reshape(pool3, [-1, 7 * 7 * 32])

            # FC layers
            FC1 = tf.layers.dense(inputs=pool3_flat, units=128, activation=tf.nn.relu)
            FC2 = tf.layers.dense(inputs=FC1, units=64, activation=tf.nn.relu)
            self.__logits = tf.layers.dense(inputs=FC2, units=3)


        # TensorFlow summary data to display in TensorBoard later
        with tf.name_scope("loss_func") as scope:
            self.__loss = tf.reduce_mean(
                tf.nn.softmax_cross_entropy_with_logits(logits=self.__logits, labels=self.__y_))
            self.__loss_val = tf.reduce_mean(
                tf.nn.softmax_cross_entropy_with_logits(logits=self.__logits, labels=self.__y_))

            # Add loss to tensorboard
            self.__train_summary = tf.summary.scalar("loss_train", self.__loss)
            self.__val_summary = tf.summary.scalar("loss_val", self.__loss_val)


        # summary data to be displayed on TensorBoard during training:
        with tf.name_scope("optimizer") as scope:
            global_step = tf.Variable(0, trainable=False)
            starter_learning_rate = 1e-3
            # decay every 10000 steps with a base of 0.96 function
            learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, 1000, 0.9,
                                                       staircase=True)
            self.__train_step = tf.train.AdamOptimizer(learning_rate).minimize(self.__loss, global_step=global_step)
            tf.summary.scalar("learning_rate", learning_rate)
            tf.summary.scalar("global_step", global_step)


        # Merge op for tensorboard
        self.__merged_summary_op = tf.summary.merge_all()
        # Build graph
        init = tf.global_variables_initializer()
        # Saver for checkpoints
        self.__saver = tf.train.Saver(max_to_keep=None)

        # Configure summary to output at given directory
        self.__session = tf.Session()
        self.__writer = tf.summary.FileWriter("./logs/flight_path", self.__session.graph)
        self.__session.run(init)



def train(self, save_dir='./model_files', batch_size=20):
    #Load dataset and labels
    x = np.asarray(di.load_images())
    y = np.asarray(di.load_labels())

    #Shuffle dataset
    np.random.seed(0)
    shuffled_indeces = np.arange(len(y))
    np.random.shuffle(shuffled_indeces)
    shuffled_x = x[shuffled_indeces].tolist()
    shuffled_y = y[shuffled_indeces].tolist()
    shuffled_y = tf.keras.utils.to_categorical(shuffled_y, 3)

    dataset = (shuffled_x, shuffled_y)
    dataset = tf.data.Dataset.from_tensor_slices(dataset)
    #dataset = dataset.shuffle(buffer_size=300)

    # Using Tensorflow data Api to handle batches
    dataset_train = dataset.take(200)
    dataset_train = dataset_train.repeat()
    dataset_train = dataset_train.batch(batch_size)

    dataset_test = dataset.skip(200)
    dataset_test = dataset_test.repeat()
    dataset_test = dataset_test.batch(batch_size)

    # Create an iterator
    iter_train = dataset_train.make_one_shot_iterator()
    iter_train_op = iter_train.get_next()
    iter_test = dataset_test.make_one_shot_iterator()
    iter_test_op = iter_test.get_next()

    # Build model graph
    self.build_graph()


    # Train Loop
    for i in range(10):
        batch_train = self.__session.run([iter_train_op])
        batch_x_train, batch_y_train = batch_train[0]
        # Print loss from time to time
        if i % 100 == 0:
            batch_test = self.__session.run([iter_test_op])
            batch_x_test, batch_y_test = batch_test[0]
            loss_train, summary_1 = self.__session.run([self.__loss,
                                                    self.__merged_summary_op],
                                                   feed_dict={self.__x_:
                                                                  batch_x_train,
                                                              self.__y_:
                                                                  batch_y_train,
                                                              self.__is_training: True})
            loss_val, summary_2 = self.__session.run([self.__loss_val,
                                              self.__val_summary],
                                             feed_dict={self.__x_: batch_x_test,
                                                        self.__y_: batch_y_test,
                                                        self.__is_training: False})
            print("Loss Train: {0} Loss Val: {1}".format(loss_train,
                                                 loss_val))
            # Write to tensorboard summary
            self.__writer.add_summary(summary_1, i)
            self.__writer.add_summary(summary_2, i)

        # Execute train op
        self.__train_step.run(session=self.__session, feed_dict={
            self.__x_: batch_x_train, self.__y_: batch_y_train,
            self.__is_training: True})
        print(i)


    # Once the training loop is over, we store the final model into a checkpoint file with op
    # __saver.save:

    # converter = tf.contrib.lite.TFLiteConverter.from_session(self.__session, [self.__x_], [self.__y_])
    # tflite_model = converter.convert()
    # open("MobileNet/ConvertedModelFile.tflite", "wb").write(tflite_model)

    # Save model
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
        checkpoint_path = os.path.join(save_dir, "model.ckpt")
        filename = self.__saver.save(self.__session, checkpoint_path)
        tf.train.write_graph(self.__session.graph_def, save_dir, "save_graph.pbtxt")
        print("Model saved in file: %s" % filename)


if __name__ == '__main__':
    cnn = Train()
    cnn.train()

我尝试通过导出GraphDef from tf.Session，Exporting a GraphDef from file和Exporting a SavedModel将GraphDef导出到.tflite文件。所有人都在这里Converter Python API guide中进行描述。

tf.Session中的GraphDef

当我尝试使用GraphDef from tf.Session指南导出时，出现以下错误：

Traceback (most recent call last):
  File "C:/Users/nermi/PycharmProjects/DronePathTracking/main.py", line 226, in <module>
    cnn.train()
  File "C:/Users/nermi/PycharmProjects/DronePathTracking/main.py", line 212, in train
    tflite_model = converter.convert()
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\lite.py", line 453, in convert
    **converter_kwargs)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\convert.py", line 342, in toco_convert_impl
    input_data.SerializeToString())
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\convert.py", line 135, in toco_convert_protos
    (stdout, stderr))
RuntimeError: TOCO failed see console for info.
b'Traceback (most recent call last):\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow_wrap_toco.py", line 18, in swig_import_helper\r\n    fp, pathname, description = imp.find_module(\'_tensorflow_wrap_toco\', [dirname(__file__)])\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\imp.py", line 297, in find_module\r\n    raise ImportError(_ERR_MSG.format(name), name=name)\r\nImportError: No module named \'_tensorflow_wrap_toco\'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\runpy.py", line 193, in _run_module_as_main\r\n    "__main__", mod_spec)\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\runpy.py", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File "C:\\Users\\nermi\\Python\\Python36\\Scripts\\toco_from_protos.exe\\__main__.py", line 5, in <module>\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\toco_from_protos.py", line 22, in <module>\r\n    from tensorflow.contrib.lite.toco.python import tensorflow_wrap_toco\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow_wrap_toco.py", line 28, in <module>\r\n    _tensorflow_wrap_toco = swig_import_helper()\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow_wrap_toco.py", line 20, in swig_import_helper\r\n    import _tensorflow_wrap_toco\r\nModuleNotFoundError: No module named \'_tensorflow_wrap_toco\'\r\n'
None

导出保存的模型

当我尝试使用Exporting a SavedModel脚本中的export_saved_model.py指南导出时，出现以下错误：

Traceback (most recent call last):
  File "C:/Users/nermi/PycharmProjects/DronePathTracking/export_saved_model.py", line 5, in <module>
    converter = tf.contrib.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\lite.py", line 340, in from_saved_model
    output_arrays, tag_set, signature_key)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\convert_saved_model.py", line 239, in freeze_saved_model
    meta_graph = get_meta_graph_def(saved_model_dir, tag_set)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\convert_saved_model.py", line 61, in get_meta_graph_def
    return loader.load(sess, tag_set, saved_model_dir)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 196, in load
    loader = SavedModelLoader(export_dir)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 212, in __init__
    self._saved_model = _parse_saved_model(export_dir)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 82, in _parse_saved_model
    constants.SAVED_MODEL_FILENAME_PB))
OSError: SavedModel file does not exist at: model_files/{saved_model.pbtxt|saved_model.pb}

export_saved_model.py：

import tensorflow as tf

saved_model_dir = "model_files"

converter = tf.contrib.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()
open("MobileNet/converted_model.tflite", "wb").write(tflite_model)

从文件导出GraphDef

最后，我有以下freeze_model.py脚本冻结保存的模型：

from tensorflow.python.tools import freeze_graph

# Freeze the graph
save_path="C:/Users/nermi/PycharmProjects/DronePathTracking/model_files/" #directory to model files
MODEL_NAME = 'my_model' #name of the model optional
input_graph_path = save_path+'save_graph.pbtxt'#complete path to the input graph
checkpoint_path = save_path+'model.ckpt' #complete path to the model's checkpoint file
input_saver_def_path = ""
input_binary = False
output_node_names = "X, Y" #output node's name. Should match to that mentioned in your code
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_frozen_graph_name = save_path+'frozen_'+MODEL_NAME+'.pb' # the name of .pb file you would like to give
clear_devices = True


def freeze():
    freeze_graph.freeze_graph(input_graph_path, input_saver_def_path,
                              input_binary, checkpoint_path, output_node_names,
                              restore_op_name, filename_tensor_name,
                              output_frozen_graph_name, clear_devices, "")


freeze()

但是当我尝试使用frozen_my_model.pb脚本将export_to_tflite.py转换为tflite时：

import tensorflow as tf

grap_def_file = "model_files/frozen_my_model.pb" # the .pb file

input_arrays = ["X"] #Input node
output_arrays = ["Y"] #Output node

converter = tf.contrib.lite.TFLiteConverter.from_frozen_graph(
    grap_def_file, input_arrays, output_arrays
)

tflite_model = converter.convert()

open("MobileNet/my_model.tflite", "wb").write(tflite_model)

我收到以下错误：

Traceback (most recent call last):
  File "C:/Users/nermi/PycharmProjects/DronePathTracking/export_to_tflite.py", line 12, in <module>
    tflite_model = converter.convert()
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\lite.py", line 453, in convert
    **converter_kwargs)
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\convert.py", line 342, in toco_convert_impl
    input_data.SerializeToString())
  File "C:\Users\nermi\Python\Python36\lib\site-packages\tensorflow\contrib\lite\python\convert.py", line 135, in toco_convert_protos
    (stdout, stderr))
RuntimeError: TOCO failed see console for info.
b'Traceback (most recent call last):\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow_wrap_toco.py", line 18, in swig_import_helper\r\n    fp, pathname, description = imp.find_module(\'_tensorflow_wrap_toco\', [dirname(__file__)])\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\imp.py", line 297, in find_module\r\n    raise ImportError(_ERR_MSG.format(name), name=name)\r\nImportError: No module named \'_tensorflow_wrap_toco\'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\runpy.py", line 193, in _run_module_as_main\r\n    "__main__", mod_spec)\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\runpy.py", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File "C:\\Users\\nermi\\Python\\Python36\\Scripts\\toco_from_protos.exe\\__main__.py", line 5, in <module>\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\toco_from_protos.py", line 22, in <module>\r\n    from tensorflow.contrib.lite.toco.python import tensorflow_wrap_toco\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow_wrap_toco.py", line 28, in <module>\r\n    _tensorflow_wrap_toco = swig_import_helper()\r\n  File "c:\\users\\nermi\\python\\python36\\lib\\site-packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow_wrap_toco.py", line 20, in swig_import_helper\r\n    import _tensorflow_wrap_toco\r\nModuleNotFoundError: No module named \'_tensorflow_wrap_toco\'\r\n'
None

其他信息

当我将模型保存在model_files目录中时，它看起来像这样：

我尝试了很多事情，但是没有运气。

感谢您的帮助！

Answer 1

Windows上的TOCO有问题。我遇到了这样的问题，直到找到解决方案。 解决方案是将所有保存的模型或graphdef上传到Google Colab笔记本。然后，

连接GPU或TPU运行时。（更改运行时类型选项）
在运行时上载save_model。（位于左上角的“文件”部分）
在您提到的一个单元格中写入相同的脚本。
确保在运行时中创建必要的目录。参见此answer。
转换将在云中进行。

因此，TOCO没问题。有关信息，请参见此notebook。

将Tensorflow模型转换为tensorflow-lite（.tflite）格式时出现问题

1 个答案: