PINTO0309/yolact_edge_onnx_tensorrt_myriad

Provides a conversion flow for YOLACT_Edge to models compatible with ONNX, TensorRT, OpenVINO and Myriad (OAK). My own implementation of post-processing allows for e2e inference. Support for Multi-Class NonMaximumSuppression, CombinedNonMaxSuppression.

yolact_edge_onnx_tensorrt_myriad

Provides a conversion flow for YOLACT_Edge to models compatible with ONNX, TensorRT, OpenVINO and Myriad (OAK). My own implementation of post-processing allows for e2e inference. Support for Multi-Class NonMaximumSuppression, CombinedNonMaxSuppression.

• Replace ReduceMax and ArgMax.

• Multi-Class NonMaximumSuppression, CombinedNonMaxSuppression for ONNX

• Demo Code https://github.com/PINTO0309/PINTO_model_zoo/tree/main/085_Yolact_Edge/32_Multi-class_NonMaximumSuppression_compliant_version

• Multi-Class NonMaximumSuppression, CombinedNonMaxSuppression ONNX sample

  Kazam_screencast_00019_.mp4

  

Official Repo

https://github.com/haotian-liu/yolact_edge

Tools

1. https://github.com/PINTO0309/tflite2tensorflow
2. https://github.com/PINTO0309/simple-onnx-processing-tools

Convert

See sequence below.

https://github.com/PINTO0309/yolact_edge_onnx_tensorrt_myriad/blob/main/convert_script.txt

Conversion to TensorFlow, TensorFlow Lite

https://github.com/PINTO0309/onnx2tf

Benchmark

ONNX + TensorRT

$ sit4onnx --input_onnx_file_path yolact_edge_mobilenetv2_550x550.onnx
INFO: file: yolact_edge_mobilenetv2_550x550.onnx
INFO: providers: ['TensorrtExecutionProvider', 'CPUExecutionProvider']
INFO: input_name.1: input shape: [1, 3, 550, 550] dtype: float32
INFO: test_loop_count: 10
INFO: total elapsed time:  44.979095458984375 ms
INFO: avg elapsed time per pred:  4.4979095458984375 ms
INFO: output_name.1: x1y1x2y2_score_class shape: [1, 0, 6] dtype: float32
INFO: output_name.2: final_masks shape: [0, 138, 138] dtype: float32

How to change NMS parameters

https://github.com/PINTO0309/simple-onnx-processing-tools

### fnms_max_output_boxes_per_class
sam4onnx \
--op_name fnms_nonmaxsuppression11 \
--input_onnx_file_path yolact_edge_mobilenetv2_550x550.onnx \
--output_onnx_file_path yolact_edge_mobilenetv2_550x550.onnx \
--input_constants fnms_max_output_boxes_per_class int64 [10]

### iou_threshold
sam4onnx \
--op_name fnms_nonmaxsuppression11 \
--input_onnx_file_path yolact_edge_mobilenetv2_550x550.onnx \
--output_onnx_file_path yolact_edge_mobilenetv2_550x550.onnx \
--input_constants fnms_iou_threshold float32 [0.6]

### score_threshold
sam4onnx \
--op_name fnms_nonmaxsuppression11 \
--input_onnx_file_path yolact_edge_mobilenetv2_550x550.onnx \
--output_onnx_file_path yolact_edge_mobilenetv2_550x550.onnx \
--input_constants fnms_score_threshold float32 [0.7]

Model Structure

• INPUTS:

  • input: float32 [1, 3, 550, 550]

• OUTPUTS:

  • x1y1x2y2_score_class: float32 [1, N, 6]
    • N = The number of objects detected, filtered by NMS, and therefore less than 1600. max_output_boxes_per_class=20 x 80classes
    • 6 = x1, y1, x2, y2, score, classid
  • final_masks: float32 [N, 138, 138]
    • N = The number of objects detected, filtered by NMS, and therefore less than 1600. max_output_boxes_per_class=20 x 80classes

Acknowledgments

https://github.com/yujin6056/yolactedge-onnx-conversion