WHENet - ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L

Last update: Sep 21, 2022

Overview

HeadPoseEstimation-WHENet-yolov4-onnx-openvino

ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L

1. Usage

$ git clone https://github.com/PINTO0309/HeadPoseEstimation-WHENet-yolov4-onnx-openvino
$ cd HeadPoseEstimation-WHENet-yolov4-onnx-openvino
$ wget https://github.com/PINTO0309/HeadPoseEstimation-WHENet-yolov4-onnx-openvino/releases/download/v1.0.0/saved_model_224x224.tar.gz
$ tar -zxvf saved_model_224x224.tar.gz && rm saved_model_224x224.tar.gz

$ python3 demo_video.py

usage: demo_video.py \
[-h] \
[--whenet_mode {onnx,openvino}] \
[--device DEVICE] \
[--height_width HEIGHT_WIDTH]

optional arguments:
  -h, --help
      show this help message and exit
  --whenet_mode {onnx,openvino}
      Choose whether to infer WHENet with ONNX or OpenVINO. Default: onnx
  --device DEVICE
      Path of the mp4 file or device number of the USB camera. Default: 0
  --height_width HEIGHT_WIDTH
      {H}x{W} Default: 480x640

2. Reference

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Comments

yaw will output nan or inf after some infers with trt model

Hello ,Sir,

I have tested, the input of network has no problem. With one single image, this trt model infers with different results of yaw angle on different batches, while the roll and pitch keep the same. When this model infers on webcam, the first almost ten batches are fully ok, but after that, the yaw results turn out inf or nan, while the roll and pitch also are ok.

This question might be somewhat too specific, I would appreciate a lot if you can give me some hints about this.

Thank you!

opened by thunder95 2

invalid dimensions

I tried to transform keras model to onnx, and this is my code,

onnx_model = keras2onnx.convert_keras(self.model, self.model.name)
temp_model_file = './mymodel.onnx'
onnx.save_model(onnx_model, temp_model_file)

However, when I tried to run my onnx model, I got a crash and error code is like this,

onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: input_1 for the following indices
 index: 1 Got: 3 Expected: 224
 index: 3 Got: 224 Expected: 3
 Please fix either the inputs or the model.

I also noticed your onnx model runs successfully, coud you please tell me how do you solve this problem?

opened by liyewu 2

Releases(v1.0.4)

v1.0.4(Jun 18, 2022)
Add pre-processing and post-processing to ONNX in WHENet

Pre-processing adds normalization processing

Post-processing adds conversion process to angles (-180° <= 0° <= 180°)

disable_front_side_detection

Source code(tar.gz)
Source code(zip)
shrunk.onnx(1.77 KB)
whenet_1x3x224x224_prepost.onnx(16.51 MB)
whenet_1x3x224x224_prepost_disable_front_side_detection.onnx(16.51 MB)
v1.0.3(Mar 15, 2022)
add script for initializing batch size

add ONNX with variable batch size

ONNX (1 batch, 4 batch, 8 batch, 16 batch, N batch)

TensorFlow saved_model v2

TFLite (Float32/Float16/INT8/Dynamic Range Quant)

CoreML

EdgeTPU

TensorRT

TFJS

YOLOv4 / YOLOv4-tiny-3L Head Detection

Source code(tar.gz)
Source code(zip)
saved_model_224x224.tar.gz(499.45 MB)
v1.0.2(Nov 18, 2021)
ONNX (1batch, 4batch, 8batch, 16batch)

TensorFlow saved_model v2

TFLite (Float32/Float16/INT8/Dynamic Range Quant)

CoreML

EdgeTPU

TensorRT

TFJS

YOLOv4 / YOLOv4-tiny-3L Head Detection

Source code(tar.gz)
Source code(zip)
saved_model_224x224.tar.gz(484.14 MB)
v1.0.1(Nov 15, 2021)
ONNX

TensorFlow saved_model v2

TFLite (Float32/Float16/INT8/Dynamic Range Quant)

CoreML

EdgeTPU

TensorRT

TFJS

YOLOv4 / YOLOv4-tiny-3L Head Detection

Source code(tar.gz)
Source code(zip)
saved_model_224x224.tar.gz(438.23 MB)
v1.0.0(Nov 15, 2021)
ONNX

TensorFlow saved_model v2

TFLite (Float32/Float16/INT8/Dynamic Range Quant)

CoreML

EdgeTPU

TensorRT

TFJS

Source code(tar.gz)
Source code(zip)
saved_model_224x224.tar.gz(438.23 MB)

Owner

Katsuya Hyodo

Hobby programmer. Intel Software Innovator Program member.

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