1. 前言
近些年来，随着以卷积神经网络（CNN）为代表的深度学习在图像识别领域的突破，越来越多的图像识别算法不断涌现。在去年，我们初步成功尝试了图像识别在测试领域的应用：将网站样式错乱问题、无线领域机型适配问题转换为“特定场景下的正常图片和异常图片的二分类问题”，并借助Goolge开源的Inception V3网络进行迁移学习，重训练出对应场景下的图片分类模型，问题图片的准确率达到95%以上。
过去一年，我们在图片智能识别做的主要工作包括：
- 模型的落地和参数调优
- 模型的服务化
- 模型服务的优化（包括数据库连接池的引入、gunicorn容器的引入、docker化等）
本篇文章主要是对模型重训练的源码进行学习和分析，加深对模型训练过程的理解，以便后续在对模型训练过程进行调整时有的放矢。
这边对迁移学习做个简单解释：图像识别往往包含数以百万计的参数，从头训练需要大量打好标签的图片，还需要大量的计算力（往往数百小时的GPU时间）。对此，迁移学习是一个捷径，它可以在已经训练好的相似工作模型基础上，继续训练新的模型。
2. retrain.py源码分析
目前我们使用的图像智能服务，对于迁移学习的代码，是参考的开源代码 github: tensorflow/hub/image_retraining/retrain.py。
下面是对源码的学习和解读：
2.1 执行主入口main：
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--image_dir',
type=str,
default='',
help='Path to folders of labeled images.'
)
parser.add_argument(
'--output_graph',
type=str,
default='/tmp/output_graph.pb',
help='Where to save the trained graph.'
)
......省略......
parser.add_argument(
'--logging_verbosity',
type=str,
default='INFO',
choices=['DEBUG', 'INFO', 'WARN', 'ERROR', 'FATAL'],
help='How much logging output should be produced.')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
可以看到，程序main入口主要是对输入参数的声明和解析，实际执行时传入的参数会存入到FLAGS变量中，然后执行tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)开始正式训练。
2.2 main(_)方法
def main(_):
# Needed to make sure the logging output is visible.
# See https://github.com/tensorflow/tensorflow/issues/3047
## 设置log级别
logging_verbosity = logging_level_verbosity(FLAGS.logging_verbosity)
tf.logging.set_verbosity(logging_verbosity)
## 判断image_dir参数是否传入，该参数表示用于训练的图片集路径
if not FLAGS.image_dir:
tf.logging.error('Must set flag --image_dir.')
return -1
# Prepare necessary directories that can be used during training
## 重建summaries_dir，并确保intermediate_output_graphs_dir存在
prepare_file_system()
# Look at the folder structure, and create lists of all the images.
## 根据输入的图片集路径、测试图片占比、验证图片占比来划分输入的图集，将图集划分为训练集、测试集、验证集
image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage,
FLAGS.validation_percentage)
## 根据image_dir下的子目录个数，判断要分类的数量。每个子目录为一个类别，每个类别会各自分为训练集、测试集、验证集。如果类别数为0或1，则返回错误，因为分类问题至少要有2个类。
class_count = len(image_lists.keys())
if class_count == 0:
tf.logging.error('No valid folders of images found at ' + FLAGS.image_dir)
return -1
if class_count == 1:
tf.logging.error('Only one valid folder of images found at ' +
FLAGS.image_dir +
' - multiple classes are needed for classification.')
return -1
# See if the command-line flags mean we're applying any distortions.
## 根据传入的参数判断是否要对图片进行一些调整
do_distort_images = should_distort_images(
FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
FLAGS.random_brightness)
# Set up the pre-trained graph.
## 载入module，默认使用inception v3，可以用参数--tfhub_module调整为使用其他已训练的模型
module_spec = hub.load_module_spec(FLAGS.tfhub_module)
## 创建模型图graph
graph, bottleneck_tensor, resized_image_tensor, wants_quantization = (
create_module_graph(module_spec))
# Add the new layer that we'll be training.
## 调用add_final_retrain_ops方法获得训练步骤、交叉熵、瓶颈输入、真实的输入、最终的tensor
with graph.as_default():
(train_step, cross_entropy, bottleneck_input,
ground_truth_input, final_tensor) = add_final_retrain_ops(
class_count, FLAGS.final_tensor_name, bottleneck_tensor,
wants_quantization, is_training=True)
with tf.Session(graph=graph) as sess:
# Initialize all weights: for the module to their pretrained values,
# and for the newly added retraining layer to random initial values.
## 初始化变量
init = tf.global_variables_initializer()
sess.run(init)
# Set up the image decoding sub-graph.
## 调用图片解码操作的函数获得输入的图片tensor和解码后的图片tensor
jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding(module_spec)
if do_distort_images:
# We will be applying distortions, so set up the operations we'll need.
(distorted_jpeg_data_tensor,
distorted_image_tensor) = add_input_distortions(
FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
FLAGS.random_brightness, module_spec)
else:
# We'll make sure we've calculated the 'bottleneck' image summaries and
# cached them on disk.
## 创建各个image的bottlenecks，并缓存到磁盘disk
cache_bottlenecks(sess, image_lists, FLAGS.image_dir,
FLAGS.bottleneck_dir, jpeg_data_tensor,
decoded_image_tensor, resized_image_tensor,
bottleneck_tensor, FLAGS.tfhub_module)
# Create the operations we need to evaluate the accuracy of our new layer.
## 创建评估的operation
evaluation_step, _ = add_evaluation_step(final_tensor, ground_truth_input)
# Merge all the summaries and write them out to the summaries_dir
## 将summary merge并写到summaries_dir目录下
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
sess.graph)
validation_writer = tf.summary.FileWriter(
FLAGS.summaries_dir + '/validation')
# Create a train saver that is used to restore values into an eval graph
# when exporting models.
train_saver = tf.train.Saver()
# Run the training for as many cycles as requested on the command line.
## 根据传入的迭代次数，开始训练
for i in range(FLAGS.how_many_training_steps):
# Get a batch of input bottleneck values, either calculated fresh every
# time with distortions applied, or from the cache stored on disk.
if do_distort_images:
(train_bottlenecks,
train_ground_truth) = get_random_distorted_bottlenecks(
sess, image_lists, FLAGS.train_batch_size, 'training',
FLAGS.image_dir, distorted_jpeg_data_tensor,
distorted_image_tensor, resized_image_tensor, bottleneck_tensor)
else:
## 获取用于training的图片bottlenecks值，默认train_batch_size=100，即每次迭代会批量取100张图片进行训练
(train_bottlenecks,
train_ground_truth, _) = get_random_cached_bottlenecks(
sess, image_lists, FLAGS.train_batch_size, 'training',
FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
FLAGS.tfhub_module)
# Feed the bottlenecks and ground truth into the graph, and run a training
# step. Capture training summaries for TensorBoard with the `merged` op.
## 执行merge操作，并用feed_dict的内容填充placeholder
train_summary, _ = sess.run(
[merged, train_step],
feed_dict={bottleneck_input: train_bottlenecks,
ground_truth_input: train_ground_truth})
train_writer.add_summary(train_summary, i)
# Every so often, print out how well the graph is training.
## 判断是否最后一步训练
is_last_step = (i + 1 == FLAGS.how_many_training_steps)
## 默认eval_step_interval=10，即每训练10次或训练全部完成，打印一下当前的训练结果
if (i % FLAGS.eval_step_interval) == 0 or is_last_step:
## 打印训练精确度和交叉熵
train_accuracy, cross_entropy_value = sess.run(
[evaluation_step, cross_entropy],
feed_dict={bottleneck_input: train_bottlenecks,
ground_truth_input: train_ground_truth})
tf.logging.info('%s: Step %d: Train accuracy = %.1f%%' %
(datetime.now(), i, train_accuracy * 100))
tf.logging.info('%s: Step %d: Cross entropy = %f' %
(datetime.now(), i, cross_entropy_value))
# TODO: Make this use an eval graph, to avoid quantization
# moving averages being updated by the validation set, though in
# practice this makes a negligable difference.
## 获取验证集的图片的bottleneck值，也是每批次取100
validation_bottlenecks, validation_ground_truth, _ = (
get_random_cached_bottlenecks(
sess, image_lists, FLAGS.validation_batch_size, 'validation',
FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
FLAGS.tfhub_module))
# Run a validation step and capture training summaries for TensorBoard
# with the `merged` op.
validation_summary, validation_accuracy = sess.run(
[merged, evaluation_step],
feed_dict={bottleneck_input: validation_bottlenecks,
ground_truth_input: validation_ground_truth})
validation_writer.add_summary(validation_summary, i)
## 打印验证集的测试精确度和测试的图片数
tf.logging.info('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' %
(datetime.now(), i, validation_accuracy * 100,
len(validation_bottlenecks)))
# Store intermediate results
## 存储瞬时结果
intermediate_frequency = FLAGS.intermediate_store_frequency
if (intermediate_frequency > 0 and (i % intermediate_frequency == 0)
and i > 0):
# If we want to do an intermediate save, save a checkpoint of the train
# graph, to restore into the eval graph.
train_saver.save(sess, CHECKPOINT_NAME)
intermediate_file_name = (FLAGS.intermediate_output_graphs_dir +
'intermediate_' + str(i) + '.pb')
tf.logging.info('Save intermediate result to : ' +
intermediate_file_name)
save_graph_to_file(intermediate_file_name, module_spec,
class_count)
# After training is complete, force one last save of the train checkpoint.
train_saver.save(sess, CHECKPOINT_NAME)
# We've completed all our training, so run a final test evaluation on
# some new images we haven't used before.
## 执行最终的评估
run_final_eval(sess, module_spec, class_count, image_lists,
jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,
bottleneck_tensor)
# Write out the trained graph and labels with the weights stored as
# constants.
tf.logging.info('Save final result to : ' + FLAGS.output_graph)
if wants_quantization:
tf.logging.info('The model is instrumented for quantization with TF-Lite')
save_graph_to_file(FLAGS.output_graph, module_spec, class_count)
with tf.gfile.GFile(FLAGS.output_labels, 'w') as f:
f.write('\n'.join(image_lists.keys()) + '\n')
## 保存训练的graph
if FLAGS.saved_model_dir:
export_model(module_spec, class_count, FLAGS.saved_model_dir)main方法中的一些细节解释已经用中文备注在上述代码（使用“##”开头）中，它的主要步骤是：
- 设置log级别
- 准备workspace
- 从image_dir载入输入图片集，并创建image_lists，该image_lists是一个字段，key为各个类别，value为对应类别的图集（包含训练集、测试集、验证集，划分比例默认为0.8、0.1、0.1）
- 载入在ImageNet上已经训练好的Inception V3网络的特征张量
- 针对每个图片，调用图片解码操作获得图片的原始张量和解码后张量
- 针对每个图片的jpeg_data_tensor和decoded_image_tensor，创建其对应的bottlenects（实际上是1*2048维的张量），并缓存到磁盘
- 获取训练步骤、交叉熵
- 开始迭代训练
- 每迭代10次，打印训练的精度和交叉熵，打印验证集的测试结果。默认情况下训练集和测试集都是取100张图
- 训练完成后，使用测试集进行最后的评估
- 结果的打印和保存
2.3 其它方法
分析完代码的主要执行路径，下面解读下其它方法。因为总的代码非常的长，篇幅有限，下面按照顺序简单介绍下其它方法的内容。
2.3.1 create_image_lists
def create_image_lists(image_dir, testing_percentage, validation_percentage):
...... 省略......
result[label_name] = {
'dir': dir_name,
'training': training_images,
'testing': testing_images,
'validation': validation_images,
}
return result根据image_dir的地址，testing_percentage和testing_percentage的比例划分图集，返回的格式类似如下：
{
'correct': {
'dir': correct_image_dir,
'training': correct_training_images,
'testing': correct_testing_images,
'validation': correct_validation_images
},
'error': {
'dir': error_image_dir,
'training': error_training_images,
'testing': error_testing_images,
'validation': error_validation_images
}
}每个training/testing/validation对应的value为image的file_name list。
2.3.2 get_image_path
获取图片的全路径
2.3.3 get_bottleneck_path
获得不同类别（training、testing、validation）的bottleneck路径
2.3.4 create_module_graph
根据给定的已训练好的模型Hub Module，创建模型的图
2.3.5 run_bottleneck_on_image
def run_bottleneck_on_image(sess, image_data, image_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor):
"""Runs inference on an image to extract the 'bottleneck' summary layer.
Args:
sess: Current active TensorFlow Session.
image_data: String of raw JPEG data.
image_data_tensor: Input data layer in the graph.
decoded_image_tensor: Output of initial image resizing and preprocessing.
resized_input_tensor: The input node of the recognition graph.
bottleneck_tensor: Layer before the final softmax.
Returns:
Numpy array of bottleneck values.
"""
# First decode the JPEG image, resize it, and rescale the pixel values.
resized_input_values = sess.run(decoded_image_tensor,
{image_data_tensor: image_data})
# Then run it through the recognition network.
bottleneck_values = sess.run(bottleneck_tensor,
{resized_input_tensor: resized_input_values})
bottleneck_values = np.squeeze(bottleneck_values)
return bottleneck_values根据给定的输入图片解码后的tensor，计算bottleneck_values，并执行squeeze操作（删除单维度条目，把shape中为1的维度去掉）
2.3.6 ensure_dir_exists
确保目录存在：如果目录不存在，则创建目录
2.3.7 create_bottleneck_file
调run_bottleneck_on_image方法计算bottleneck值，并缓存到磁盘文件
2.3.8 get_or_create_bottleneck
批量获取一组图片的bottleneck值
2.3.9 cache_bottlenecks
批量缓存bottleneck
2.3.10 get_random_cached_bottlenecks
随机获取一批缓存的bottlenecks，以及其对应的真实标ground_truths和文件名filenames
2.3.11 add_final_retrain_ops
def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
quantize_layer, is_training):
batch_size, bottleneck_tensor_size = bottleneck_tensor.get_shape().as_list()
assert batch_size is None, 'We want to work with arbitrary batch size.'
with tf.name_scope('input'):
bottleneck_input = tf.placeholder_with_default(
bottleneck_tensor,
shape=[batch_size, bottleneck_tensor_size],
name='BottleneckInputPlaceholder')
ground_truth_input = tf.placeholder(
tf.int64, [batch_size], name='GroundTruthInput')
# Organizing the following ops so they are easier to see in TensorBoard.
layer_name = 'final_retrain_ops'
with tf.name_scope(layer_name):
with tf.name_scope('weights'):
initial_value = tf.truncated_normal(
[bottleneck_tensor_size, class_count], stddev=0.001)
layer_weights = tf.Variable(initial_value, name='final_weights')
variable_summaries(layer_weights)
with tf.name_scope('biases'):
layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
variable_summaries(layer_biases)
with tf.name_scope('Wx_plus_b'):
logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
tf.summary.histogram('pre_activations', logits)
final_tensor = tf.nn.softmax(logits, name=final_tensor_name)
# The tf.contrib.quantize functions rewrite the graph in place for
# quantization. The imported model graph has already been rewritten, so upon
# calling these rewrites, only the newly added final layer will be
# transformed.
if quantize_layer:
if is_training:
tf.contrib.quantize.create_training_graph()
else:
tf.contrib.quantize.create_eval_graph()
tf.summary.histogram('activations', final_tensor)
# If this is an eval graph, we don't need to add loss ops or an optimizer.
if not is_training:
return None, None, bottleneck_input, ground_truth_input, final_tensor
with tf.name_scope('cross_entropy'):
cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
labels=ground_truth_input, logits=logits)
tf.summary.scalar('cross_entropy', cross_entropy_mean)
with tf.name_scope('train'):
optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate)
train_step = optimizer.minimize(cross_entropy_mean)
return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input,
final_tensor)在结尾处添加一个新的softmax层和全连接层（y=WX+b），用于训练和评估。此处与logistic模型是一样的，采用梯度下降的方式来最小化交叉熵进行迭代训练。
2.3.12 add_evaluation_step
def add_evaluation_step(result_tensor, ground_truth_tensor):
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
## 对每组向量按列找到最大值的index
prediction = tf.argmax(result_tensor, 1)
## 将每组张量比较预测的结果和实际的结果的一致性，一致则为True，否则为False
correct_prediction = tf.equal(prediction, ground_truth_tensor)
with tf.name_scope('accuracy'):
## 将True或False转为float格式，并计算平均值
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', evaluation_step)
return evaluation_step, prediction注解见上述代码，返回最终的accuracy和预测的值list。
2.3.13 run_final_eval
执行最终的评估，使用测试集进行结果评估。如果传入参数print_misclassified_test_images，则会打印评估出错的图片的名字和识别结果。
2.3.14 save_graph_to_file
将graph保存到文件
2.3.15 prepare_file_system
准备workspace
2.3.16 add_jpeg_decoding
将输入图片解析为张量，并进行解码
2.3.17 export_model
输出模型