Load onnx model in opencv dnn

I get the following error while loading onnx model in C++ using the given code. What am I doing wrong? Please help me to solve this problem. (opencv version: 4.5.3 both in C++ and Python)

terminate called after throwing an instance of 'cv::Exception'
what(): OpenCV(4.1.0) /home/Downloads/opencv-4.1.0/modules/dnn/src/dnn.cpp:524: error: (-2:Unspecified error) Can't create layer "StatefulPartitionedCall/inception_resnet_v1/AvgPool/Mean_Squeeze__1416:0" of type "Squeeze" in function 'getLayerInstance'

Aborted (core dumped)

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/dnn/dnn.hpp>

using namespace std;
using namespace cv;
using namespace dnn;

int main()
{
    // load the neural network model
    cv::dnn::Net net = cv::dnn::readNetFromONNX("model.onnx");
}

I got the model.onnx using the following conversion convert: python3 -m tf2onnx.convert --saved-model /savedmodel/model --output model.onnx

Using the following code in python, I can load the model:

import onnx
onnx_model = onnx.load('model.onnx')
onnx.checker.check_model(onnx_model)

But using cv2.dnn in python, I get the following error

#net = cv2.dnn.readNet('model.onnx')
net = cv2.dnn.readNetFromONNX('model.onnx')

[ERROR:0] global /tmp/pip-req-build-tjxnaiom/opencv/modules/dnn/src/onnx/onnx_importer.cpp (2125) handleNode DNN/ONNX: ERROR during processing node with 3 inputs and 1 outputs: [Concat]:(StatefulPartitionedCall/inception_resnet_v1/Mixed_6a/concat:0) Traceback (most recent call last): File "./use_onnx_in_cv2.py", line 36, in net = cv2.dnn.readNetFromONNX('model.onnx') cv2.error: OpenCV(4.5.3) /tmp/pip-req-build-tjxnaiom/opencv/modules/dnn/src/onnx/onnx_importer.cpp:2146: error: (-2:Unspecified error) in function 'handleNode'

Node [Concat]:(StatefulPartitionedCall/inception_resnet_v1/Mixed_6a/concat:0) parse error: OpenCV(4.5.3) /tmp/pip-req-build-tjxnaiom/opencv/modules/dnn/src/layers/concat_layer.cpp:102: error: (-201:Incorrect size of input array) Inconsistent shape for ConcatLayer in function 'getMemoryShapes'

If you're using tf2 and if your weights are in .h5 form you will be able to dispose of onnx troubles . you can generate .pb from your .h5 and then easily use in your c++ program.to aim generating .pb use the following code:

after that by using opencv you will be able to import your model and then enjoy!

import tensorflow as tf
from tensorflow import keras
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
from tensorflow.keras.models import load_model
import numpy as np                    
#path of the directory where you want to save your model
frozen_out_path = '/path.../freez/'             # name of the .pb file
frozen_graph_filename = "freez_graph_6cls_try1" #“frozen_graph”

model = load_model("/cls_vgg16_6_cl.h5")
# model = ""# Your model# Convert Keras model to ConcreteFunction
full_model = tf.function(lambda x: model(x))
full_model = full_model.get_concrete_function(
tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))# Get frozen ConcreteFunction
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
layers = [op.name for op in frozen_func.graph.get_operations()]
print("-" * 60)
print("Frozen model layers: ")
for layer in layers:
   print(layer)
   print("-" * 60)
print("Frozen model inputs: ")
print(frozen_func.inputs)
print("Frozen model outputs: ")
print(frozen_func.outputs)# Save frozen graph to disk
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
                  logdir=frozen_out_path,
                  name=f"{frozen_graph_filename}.pb",
                  as_text=False)# Save its text representation
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
                logdir=frozen_out_path,
                name=f"{frozen_graph_filename}.pbtxt",
                as_text=True)