Simulate YOLOv5 Segmentation

tip

This document aims to demonstrate how to use rknn-toolkit2 on an x86 PC to simulate inference of the YOLOv5 object segmentation model without the need for a development board. For the required environment setup, please refer to RKNN Installation.

Prepare the Model

In this example, we will use a pre-trained ONNX format model from the rknn_model_zoo as an example, convert the model, and perform simulated inference on the PC.

If you are using conda, please activate the rknn conda environment first.
```
conda activate rknn
```
Download the yolov5s-seg.onnx model
```
cd rknn_model_zoo/examples/yolov5_seg/model
# Download the pre-trained yolov5s-seg.onnx model
bash download_model.sh
```
If you encounter network issues, you can visit this page to download the corresponding model into the appropriate folder.
Use rknn-toolkit2 to convert it into yolov5s-seg.rknn
```
cd rknn_model_zoo/examples/yolov5_seg/python
python3 convert.py ../model/yolov5s-seg.onnx <TARGET_PLATFORM>
```
Parameter explanations:

<onnx_model>: Specify the path to the ONNX model

<TARGET_PLATFORM>: Specify the name of the NPU platform. Supported platforms can be found here

<dtype>(optional): Specify i8 for int8 quantization or fp for fp16 quantization. The default is i8.

<output_rknn_path>(optional): Specify the save path for the RKNN model. By default, it is saved in the same directory as the ONNX model with the filename yolov5s-seg.rknn.

Run the YOLOv5 Segmentation Simulated Inference Python Demo

Install the required dependencies via pip3

pip3 install torchvision==0.11.2 pycocotools

Run the simulated inference program

Modify rknn_model_zoo/py_utils/rknn_executor.py to the following code, and make sure to back up the original code

from rknn.api import RKNN

class RKNN_model_container():
    def __init__(self, model_path, target=None, device_id=None) -> None:
        rknn = RKNN()
        print('--> Init runtime environment')
        if target==None:
            DATASET_PATH = '../../../datasets/COCO/coco_subset_20.txt'
            onnx_model = model_path[:-4] + 'onnx'
            print('--> Config model')
            rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
            print('done')
            # Load model
            print('--> Loading model')
            ret = rknn.load_onnx(model=onnx_model)
            if ret != 0:
                print('Load model failed!')
                exit(ret)
            print('done')
            # Build model
            print('--> Building model')
            ret = rknn.build(do_quantization=True, dataset=DATASET_PATH)
            if ret != 0:
                print('Build model failed!')
                exit(ret)
            print('done')
            ret = rknn.init_runtime()
        else:
            ret = rknn.init_runtime(target=target, device_id=device_id)
        if ret != 0:
            print('Init runtime environment failed')
            exit(ret)
        print('done')

        self.rknn = rknn

    def run(self, inputs):
        if isinstance(inputs, list) or isinstance(inputs, tuple):
            pass
        else:
            inputs = [inputs]

        result = self.rknn.inference(inputs=inputs)
        return result

Modify rknn_model_zoo/examples/yolov5_seg/python/yolov5_seg.py line 260 to set the default value of target to None

# parser.add_argument('--target', type=str, default=‘rk3566’, help='target RKNPU platform')
parser.add_argument('--target', type=str, default=None, help='target RKNPU platform')

Run the simulated inference program

python3 yolov5_seg.py --model_path ../model/yolov5s-seg.rknn --img_show

Simulated inference results (the simulator only simulates NPU computation results; actual effects and accuracy are based on board inference)

yolov5_seg_result

Simulate YOLOv5 Segmentation

Prepare the Model​

Run the YOLOv5 Segmentation Simulated Inference Python Demo​

Prepare the Model

Run the YOLOv5 Segmentation Simulated Inference Python Demo