Python 将Tensorflow模型转换为Tensorflow lite（.tflite）格式时出现问题
我用python制作了一个用于图像分类的tensorflow模型。我正在使用Windows10 我有一个Python 将Tensorflow模型转换为Tensorflow lite（.tflite）格式时出现问题,python,tensorflow,tensorflow-lite,Python,Tensorflow,Tensorflow Lite,我用python制作了一个用于图像分类的tensorflow模型。我正在使用Windows10 我有一个Train.py类，我在build\u graph（）中定义图形，并在Train（）中训练模型。下面是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_ = []
Train.pybuild\u graph（）Train（）main.py#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()
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)
GraphDef、从文件导出GraphDef和导出保存的模型来将GraphDef导出到.tflite文件。所有这些都在这里描述
tf.Session的GraphDef
当我尝试从tf.Session
guide使用GraphDef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
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}
导出保存的\u 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
脚本来冻结保存的模型：
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，当我尝试使用我的export\u to\u tflite.py
脚本将我的freezed\u my\u model.pb
转换为tflite时：
#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()
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)
我得到以下错误：
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
回溯（最近一次呼叫最后一次）：
文件“C:/Users/nermi/PycharmProjects/DronePathTracking/export_to_tflite.py”，第12行，在
tflite_model=converter.convert（）
文件“C:\Users\nermi\Python\Python36\lib\site packages\tensorflow\contrib\lite\Python\lite.py”，第453行，转换格式
**转换器（kwargs）
文件“C:\Users\nermi\Python\Python36\lib\site packages\tensorflow\contrib\lite\Python\convert.py”，第342行，位于toco\u convert\u impl
输入_data.SerializeToString（））
文件“C:\Users\nermi\Python\Python36\lib\site packages\tensorflow\contrib\lite\Python\convert.py”，第135行，在toco\u convert\u protos中
（标准的，标准的）
运行时错误：TOCO失败。有关信息，请参阅控制台。
回溯（最后一次调用）：\r\n文件“c:\\users\\nermi\\python\\python36\\lib\\site packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow\u wrap\u toco.py”，第18行，在swig\u import\u helper\r\n fp中，路径名，描述=imp.find\u模块（\'''''u tensorflow\u wrap\u toco\'，[dirname（'u File\uuuu）]）\r\n文件“c:\\users\\nermi\\python\\python36\\lib\\imp.py”，第297行，在find\u module\r\n raise importorror（\u ERR\u MSG.format（name），name=name）\r\n端口错误：没有名为“\u tensorflow\u wrap\u toco”的模块\r\n\r\n在处理上述异常时，发生另一异常：\r\n\r\n\n回溯（最近的调用最后一次）：\r\n File”c:\\users\\nermi\\python\\python 36\\lib\\runpy.py”，第193行，在运行模块中作为主\r\n“\uu主\uuuuuuuuuu”，mod\u spec）\r\n文件“c:\\users\\nermi\\python\\python 36\\lib\\runpy.py”，第85行，在运行代码\r\n exec（代码，运行全局文件）中c:\\users\\nermi\\python\\python 36\\lib\\site packages\\tensorflow\\contrib\\lite\\toco\\python\\toco\u from\u protos.py”，第22行，在tensorflow.contrib.lite.toco.python导入tensorflow\u wrap\r\n文件“c:\\users\\nermi\\python\\python\\python\\lib\\site packages\\tensorflow\\contrib\\lite\\toco\\python\\t“，第28行，在\r\n\u tensorflow\u wrap\u toco=swig\u import\u helper（）\r\n文件“c:\\users\\nermi\\python\\python36\\lib\\site packages\\tensorflow\\contrib\\lite\\toco\\python\\tensorflow\u wrap\u toco.py”，第20行，在swig\r\u import\r\n导入\u tensorflow\u wrap\u toco\r\r\n模块发现错误：没有名为“tensorflow\u wrap”的模块\r\n”
没有一个
额外信息
当我将模型保存在model_files目录中时，它如下所示：
我尝试过很多事情，但都没有成功
感谢您的帮助 Windows上的TOCO存在问题。在我找到解决办法之前，我一直面临着这样的问题解决方案是将所有保存的模型或graphdef上传到Google Colab笔记本。那么
与GPU或TPU运行时连接。（更改运行时类型选项）
在运行时上载保存的_模型。（左上角的文件部分）
在您提到的一个单元格中编写相同的脚本
确保在运行时创建必要的目录。看这个
转换将在云中进行。
所以，TOCO没有问题。
有关详细信息，请参见此部分。Windows上的TOCO很少能正常工作。另外，一个已经打开了。感谢@Shubnam Panchal的输入，但我不久前试着在谷歌的Colab笔记本上这样做。虽然我同意使用这种方法已经帮助了许多开发人员转换了他们的模型，但它对我来说并不起作用（尽管你仍然得到了一张赞成票；）。我得到了一些我记不起来的新错误，可能是因为我没有像你发送的链接那样使用keras模型（谁知道呢）。但是，通过创建一个单独的python虚拟环境，通过安装tf Nightly
No-issues，我最终成功地将我的模型转换为tflite。我们希望TensorFlow尽早解决这个问题，这样我们就可以使用TensorFlow
包转换模型，而不是通过Colab或tf夜间构建。将keras模型转换为tflite:：OSError:SavedModel文件不存在于：model.hdh5/{saved_model.pbtxt | saved_model.pb@ShubhamPanchal