将 tensorflow 冻结图转换为 pbtxt 文件失败

Question

在给定 tensorflow 冻结推理图的输入的情况下，我想提取 pbtxt 文件。为了做到这一点，我使用以下脚本：

import tensorflow as tf

#from google.protobuf import text_format
from tensorflow.python.platform import gfile

def converter(filename): 
  with gfile.FastGFile(filename,'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')
    tf.train.write_graph(graph_def, 'pbtxt/', 'protobuf.pbtxt', as_text=True)
    print(graph_def)
  return


#converter('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb')  # here you can write the name of the file to be converted
# and then a new file will be made in pbtxt directory.

converter('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb')

例如，我使用的是 ssd mobilenet 架构。使用上面的代码，我得到了 pbtxt 的输出，但我不能使用它。参考下图

右图：移动网络架构的原始 pbtxt 文件图片

左：使用上述脚本获得的 pbtxt 文件的图像。

当我使用右边的官方 pbtxt 时，我得到了正确的结果。但是，当我使用使用上述脚本生成的 LEFT pbtxt 时，我没有得到任何预测

我在 open cv DNN 模块上使用这些预测

tensorflowNet = cv2.dnn.readNetFromTensorflow('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb', 'pbtxt/protobuf.pbtxt')

如何将 mobilenet 冻结推理图转换为正确的 pbtxt 格式以便进行推理？

参考资料： https://gist.github.com/Arafatk/c063bddb9b8d17a037695d748db4f592

Answer 1

这对我有用

• git 克隆https://github.com/opencv/opencv.git
• 导航到 opencv/samples/dnn/
• 复制frozen_inference_graph.pb，和你的pb文件对应的*.config文件
• 将复制的文件粘贴到opencv/samples/dnn目录中
• 在 den 目录下新建一个文件夹，命名为“exported_pbtxt”

然后运行这个脚本：

python3 tf_text_graph_ssd.py --input frozen_inference_graph.pb --output exported_pbtxt/output.pbtxt --config pipeline.config

这就是你所需要的，现在复制冻结的推理图和新生成的 pbtxt 文件。并且，使用以下脚本通过 OpenCV 运行您的模型：

import cv2

# Load a model imported from Tensorflow
tensorflowNet = cv2.dnn.readNetFromTensorflow('card_graph/frozen_inference_graph.pb', 'exported_pbtxt/output.pbtxt')

# Input image
img = cv2.imread('image.jpg')
rows, cols, channels = img.shape

# Use the given image as input, which needs to be blob(s).
tensorflowNet.setInput(cv2.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))

# Runs a forward pass to compute the net output
networkOutput = tensorflowNet.forward()

# Loop on the outputs
for detection in networkOutput[0,0]:

    score = float(detection[2])
    if score > 0.9:

        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows

        #draw a red rectangle around detected objects
        cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (0, 0, 255), thickness=2)

# Show the image with a rectagle surrounding the detected objects 
cv2.imshow('Image', img)
cv2.waitKey()
cv2.destroyAllWindows()

Answer 2

请遵循本指南：https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API。不修改就创建 .pbtxt 是没有意义的。指南中的脚本创建了一个额外的文本图，用于导入 OpenCV。

Answer 3

可能会帮助某人。从 master 中提取的 OpenCV 4.3.0 的 mars-small128.pb 遇到了同样的问题

import argparse
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants

def save(graph_pb, export_dir):
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)

with tf.gfile.GFile(graph_pb, "rb") as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())

sigs = {}

with tf.Session(graph=tf.Graph()) as sess:
    # INFO: name="" is important to ensure we don't get spurious prefixing
    tf.import_graph_def(graph_def, name='')
    g = tf.get_default_graph()

    # INFO: if name is added the input/output should be prefixed like:
    #       name=net => net/images:0 & net/features:0
    inp = tf.get_default_graph().get_tensor_by_name("images:0")
    out = tf.get_default_graph().get_tensor_by_name("features:0")

    sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
        tf.saved_model.signature_def_utils.predict_signature_def(
            {"in": inp}, {"out": out})

    builder.add_meta_graph_and_variables(sess,
                                         [tag_constants.SERVING],
                                         signature_def_map=sigs)

builder.save(as_text=True)


if __name__ == '__main__':
    # export_dir = './saved'
    # graph_pb = '../models/deep_sort/mars-small128.pb'

    parser = argparse.ArgumentParser()
    parser.add_argument('--input', help="path to frozen pb file")
    parser.add_argument('--output', help="Folder to save")
    args = parser.parse_args()
    if args.input is not None and args.output:
        save(args.input, args.output)
    else:
        print(f"Usage adapt_opencv.py.py --input 'path_to_bp' --output './saved'")

Answer 4

将 pb 转换为 TF 2.xxx 的 pbtxt：

import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.platform import gfile

def graphdef_to_pbtxt(filename): 
    with open(filename,'rb') as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())
    with open('protobuf.txt', 'w') as fp:
        fp.write(str(graph_def))
    
graphdef_to_pbtxt('saved_model.pb')