Failed To Convert Tensorflow Frozen Graph To Pbtxt File

I want to extract pbtxt file given an input of tensorflow frozen inference graph. In order to do this I am using the below script : import tensorflow as tf #from google.protobuf i

Solution 1:

Heres what worked for me

• git clone https://github.com/opencv/opencv.git
• Navigate to opencv/samples/dnn/
• Copy frozen_inference_graph.pb, and *.config file corresponding to your pb file
• Paste the copied files in opencv/samples/dnn directory
• Make a new folder in the den directory and name it "exported_pbtxt"

And run this script:

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

That’s all you need, now copy the frozen inference graph and newely generated pbtxt file. And, use the following script to run your model using OpenCV:

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 outputsfor 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()

Solution 2:

Please follow this guide: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API. There is no sense to create a .pbtxt without modifying it. The script from guide creates an extra text graph which is used for import to OpenCV.

Solution 3:

Might help someone. Met the same problem with mars-small128.pb for OpenCV 4.3.0 pulled from master

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

defsave(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.inputisnotNoneand args.output:
        save(args.input, args.output)
    else:
        print(f"Usage adapt_opencv.py.py --input 'path_to_bp' --output './saved'")

Solution 4:

Convert pb to pbtxt for TF 2.xxx:

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

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