darknet(yolov2) to onnx in barracuda

[yyyubn]

All of this was done by colab.

This is the file we used.
https://drive.google.com/drive/folders/1CyP5i498dxbd5XLoM5qNS6nRNd6eZWHc?usp=sharing

Our ultimate goal is to get onnx for use in Barracuda.

Our weights file and cfg file are these.

With this, we did a darknet to keras/keras to onnx

The version we used is as follows.

keras==2.4.3
tensorflow==2.3.0
To match the onnxopset version to 8,
onnx==1.3.0

When we convert darknet to keras,
!conda create -n yad2k python=3.6.0
!pip install protobuf==3.6.1

You set up your virtual environment using the code above, and made other efforts.

As a result of running darknet to keras,
I was able to get an h5 file like this.
!python yad2k.py off-yolov2-tiny.cfg off-yolov2-tiny.weights yolov2.h5

---

2022-08-25 15:05:29.819139: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Loading weights.
Weights Header: [ 0 2 0 26316800]
Parsing Darknet config.
Creating Keras model.
Parsing section net_0
Parsing section convolutional_0
conv2d bn leaky (3, 3, 3, 16)
2022-08-25 15:05:31.036783: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2022-08-25 15:05:31.045699: E tensorflow/stream_executor/cuda/cuda_driver.cc:314] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2022-08-25 15:05:31.045752: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (065e3bdef5e0): /proc/driver/nvidia/version does not exist
2022-08-25 15:05:31.046078: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-08-25 15:05:31.053293: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2199995000 Hz
2022-08-25 15:05:31.053580: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x564118db8840 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2022-08-25 15:05:31.053621: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
Parsing section maxpool_0
Parsing section convolutional_1
conv2d bn leaky (3, 3, 16, 32)
Parsing section maxpool_1
Parsing section convolutional_2
conv2d bn leaky (3, 3, 32, 64)
Parsing section maxpool_2
Parsing section convolutional_3
conv2d bn leaky (3, 3, 64, 128)
Parsing section maxpool_3
Parsing section convolutional_4
conv2d bn leaky (3, 3, 128, 256)
Parsing section maxpool_4
Parsing section convolutional_5
conv2d bn leaky (3, 3, 256, 512)
Parsing section maxpool_5
Parsing section convolutional_6
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_7
conv2d bn leaky (3, 3, 1024, 512)
Parsing section convolutional_8
conv2d linear (1, 1, 512, 425)
Parsing section region_0
Model: "functional_1"

Layer (type) Output Shape Param #

input_1 (InputLayer) [(None, 416, 416, 3)] 0

conv2d (Conv2D) (None, 416, 416, 16) 432

batch_normalization (BatchNo (None, 416, 416, 16) 64

leaky_re_lu (LeakyReLU) (None, 416, 416, 16) 0

max_pooling2d (MaxPooling2D) (None, 208, 208, 16) 0

conv2d_1 (Conv2D) (None, 208, 208, 32) 4608

batch_normalization_1 (Batch (None, 208, 208, 32) 128

leaky_re_lu_1 (LeakyReLU) (None, 208, 208, 32) 0

max_pooling2d_1 (MaxPooling2 (None, 104, 104, 32) 0

conv2d_2 (Conv2D) (None, 104, 104, 64) 18432

batch_normalization_2 (Batch (None, 104, 104, 64) 256

leaky_re_lu_2 (LeakyReLU) (None, 104, 104, 64) 0

max_pooling2d_2 (MaxPooling2 (None, 52, 52, 64) 0

conv2d_3 (Conv2D) (None, 52, 52, 128) 73728

batch_normalization_3 (Batch (None, 52, 52, 128) 512

leaky_re_lu_3 (LeakyReLU) (None, 52, 52, 128) 0

max_pooling2d_3 (MaxPooling2 (None, 26, 26, 128) 0

conv2d_4 (Conv2D) (None, 26, 26, 256) 294912

batch_normalization_4 (Batch (None, 26, 26, 256) 1024

leaky_re_lu_4 (LeakyReLU) (None, 26, 26, 256) 0

max_pooling2d_4 (MaxPooling2 (None, 13, 13, 256) 0

conv2d_5 (Conv2D) (None, 13, 13, 512) 1179648

batch_normalization_5 (Batch (None, 13, 13, 512) 2048

leaky_re_lu_5 (LeakyReLU) (None, 13, 13, 512) 0

max_pooling2d_5 (MaxPooling2 (None, 13, 13, 512) 0

conv2d_6 (Conv2D) (None, 13, 13, 1024) 4718592

batch_normalization_6 (Batch (None, 13, 13, 1024) 4096

leaky_re_lu_6 (LeakyReLU) (None, 13, 13, 1024) 0

conv2d_7 (Conv2D) (None, 13, 13, 512) 4718592

batch_normalization_7 (Batch (None, 13, 13, 512) 2048

leaky_re_lu_7 (LeakyReLU) (None, 13, 13, 512) 0

conv2d_8 (Conv2D) (None, 13, 13, 425) 218025

Total params: 11,237,145
Trainable params: 11,232,057
Non-trainable params: 5,088

None
Saved Keras model to yolov2.h5
Read 11237145 of 11237146.0 from Darknet weights.
Warning: 1.0 unused weights

---

!pip install keras2onnx

As a result of the installation of this method and the execution of Keras to onnx,
I was able to get an onnx file like this.
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import load_model
import onnx
import keras2onnx

onnx_model_name = 'yolov2-tiny_825.onnx'

model = load_model('yolov2-tiny_825.h5')
onnx_model = keras2onnx.convert_keras(model, model.name)
onnx.save_model(onnx_model, onnx_model_name)

---

WARNING:tensorflow:No training configuration found in the save file, so the model was not compiled. Compile it manually.
tf executing eager_mode: True
tf.keras model eager_mode: False
/usr/local/lib/python3.7/site-packages/keras2onnx/ke2onnx/batch_norm.py:45: RuntimeWarning: invalid value encountered in sqrt
gamma = params[0] / np.sqrt(params[3] + op.epsilon)
/usr/local/lib/python3.7/site-packages/keras2onnx/ke2onnx/batch_norm.py:46: RuntimeWarning: invalid value encountered in sqrt
beta = params[1] - params[0] * params[2] / np.sqrt(params[3] + op.epsilon)
The ONNX operator number change on the optimization: 96 -> 33

---

After inserting the onnx file into the barracuda in unity,

Put this txt file in
Conversion of the number of classes /
There is an error that is not recognized properly when setting yolov2-tiny in camera image.

I want to know why h5 and onnx files are not recognized when they are obtained normally.
also, Errors do not appear.