TensorFlow:读取卡住的模型，添加操作，然后保存到新的卡住模型

Question

如果标题不能完全反射(reflect)我的问题(我认为确实可以，但是不确定)，我将在下面对我进行预告。

我正在将Yolo对象检测模型转换为TensorFlow卡住模型.pb，然后将该模型用于手机预测。

我已经成功获得了一个有效的.pb模型(即来自Yolo图的卡住图)。但是由于网络的输出(其中有两个)不是边界框，所以我必须编写一个转换函数(这不是我的问题，我已经有一个可以执行此任务的函数):

def get_boxes_from_output(outputs_of_the_graph, anchors,
        num_classes, input_image_shape,
        score_threshold=score, iou_threshold=iou)
    """
    Apply some operations on the outputs_of_the_graph to obtain bounding boxes information
    """
    return boxes, scores, classes

因此，管道很简单:我必须加载 pb模型，然后将图像数据扔给它以获得两个输出，然后从这两个输出中应用上述函数(包含张量运算)来获取边界框信息。代码如下:

model_path = 'model_data/yolo.pb'
class_names = _get_class('model_data/classes.txt')
anchors = _get_anchors('model_data/yolo_anchors.txt')

score = 0.25
iou = 0.5

# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
    graph_def = tf.GraphDef()
    with tf.gfile.GFile(model_path, 'rb') as fid:
        graph_def.ParseFromString(fid.read())
        tf.import_graph_def(graph_def, name='')

        # Get the input and output nodes (there are two outputs)
        l_input = detection_graph.get_tensor_by_name('input_1:0')
        l_output = [detection_graph.get_tensor_by_name('conv2d_10/BiasAdd:0'),
                    detection_graph.get_tensor_by_name('conv2d_13/BiasAdd:0')]

        #initialize_all_variables
        tf.global_variables_initializer()

        # Generate output tensor targets for filtered bounding boxes.
        input_image_shape = tf.placeholder(dtype=tf.float32,shape=(2, ))
        training = tf.placeholder(tf.bool, name='training')

        boxes, scores, classes = get_boxes_from_output(l_output, anchors,
        len(class_names), input_image_shape,
        score_threshold=score, iou_threshold=iou)


    image = Image.open('./data/image1.jpg')
    image = preprocess_image(image)
    image_data = np.array(image, dtype='float32')
    image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

    sess = tf.Session(graph=detection_graph)

    # Run the session to get the output bounding boxes
    out_boxes, out_scores, out_classes = sess.run(
    [boxes, scores, classes],
    feed_dict={
        l_input: image_data,
        input_image_shape: [image.size[1], image.size[0]],
        training: False
    })
    # Now how do I save a new model that outputs directly [boxes, scores, classes]

现在，我的问题是如何从 session 中保存新的.pb模型，以便可以在其他地方再次加载它并直接输出boxes, scores, classes？

我希望这个问题足够清楚。

预先非常感谢您的帮助!

Answer 1

添加节点并保存到卡住模型
添加新操作后，需要使用tf.train.write_graph编写新图形:

boxes, scores, classes = get_boxes_from_output()
tf.train.write_graph(sess.graph_def,save_dir,'new_cnn_weights.pb')

然后，您需要使用 freeze_graph实用程序卡住上面的图。确保将 output_node_names设置为 boxes, scores, classes，如下所示:

# Freeze graph
from tensorflow.python.tools import freeze_graph
import os

input_graph_path = os.path.join(save_dir, 'new_cnn_weights.pb')
input_saver_def_path = ''
input_binary = False
output_node_names = 'boxes, scores, classes'
restore_op_name = ''
filename_tensor_name = ''
output_graph_path = os.path.join(save_dir, 'new_frozen_cnn_weights.pb')
clear_devices = False
checkpoint_path = os.path.join(save_dir, 'test_model')
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_graph_path, clear_devices, '')

检查优化图

#Load the new optimized graph and check whether the output is consistent,
tf.reset_default_graph()
with tf.gfile.GFile(save_dir+'new_frozen_cnn_weights.pb', 'rb') as f:
   graph_def_optimized = tf.GraphDef()
   graph_def_optimized.ParseFromString(f.read())

G = tf.Graph()

with tf.Session(graph=G) as sess:
   boxes,scores,classes = tf.import_graph_def(graph_def_optimized, return_elements=['boxes:0', 'scores:0', 'classes:0'])
   print('Operations in Optimized Graph:')
   print([op.name for op in G.get_operations()]) 
   x = G.get_tensor_by_name('import/import/input:0')
   print(sess.run([boxes, scores, classes], feed_dict={x: np.expand_dims(img, 0)}))