我在 python 中制作了一个用于图像分类的 tensorflow 模型。我正在使用 Windows 10。
我有一个 Train.py我在 build_graph() 中定义图形的类并在 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 将 GraphDef 导出到 .tflite 文件, Exporting a GraphDef from file和 Exporting a SavedModel .都在这里描述Converter Python API guide .
来自 tf.Session 的 GraphDef
当我尝试使用 GraphDef from tf.Session 导出时指导，我收到以下错误:
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 导出时指导我export_saved_model.py脚本我收到以下错误:
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卡住已保存模型的脚本:
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()
但是当我尝试转换我的 frozen_my_model.pb与我的 export_to_tflite.py 一起玩 tflite脚本:
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
额外信息
当我将模型保存在 model_files 目录中时，它看起来像这样:
我尝试了很多东西，但没有运气。
感谢任何帮助!
请您参考如下方法:
Windows 上的 TOCO 有问题。在找到解决方案之前，我一直遇到这样的问题。 解决方案是将所有保存的模型或 graphdef 上传到 Google Colab notebook。然后，
- 连接 GPU 或 TPU 运行时。 (更改运行时类型选项)
- 在运行时上传saved_model。(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answer .
- 转换将在云端进行。
所以，TOCO 没有问题。 看这个notebook获取信息。
