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A Complete Example on Object Detection

This example demonstrates how to deploy a pretrained YOLO v3 model with pre-processing and post-processing on host.

First, download the model from ONNX Model Repository. For demonstration purpose, we use XNNPACK-based ODLA runtime, which supports the ODLA interpret programming mode.

Next, compile the model into the C code:

halo -target cc -exec-mode=interpret -emit-value-id-as-int -reorder-data-layout=channel-last -remove-input-transpose -remove-output-transpose  -o out/yolo.c yolov3-10.onnx  --disable-broadcasting -outputs conv2d_59 -outputs conv2d_67 -outputs conv2d_75 -input-shape=input_1:1x3x416x416 -entry-func-name=yolo_v3

Options explained:

  • -target cc: to generate the C99 code.
  • -exec-mode=interpret: to generate the ODLA interpret mode code.
  • -emit-value-as-int: to generate the ODLA value ids as integers.
  • -reorder-data-layout=channel-last: to enable the data layout conversion since ONNX uses NCHW while the XNNPACK runtime prefers NHWC.
  • -remove-input-transpose: to optimize away the input transpose.
  • -remove-output-transpose: to optimize away the output transpose.
  • -disable-broadcasting: to disable the offline weights broadcasting since the ODLA runtime supports element-wise ops with broadcasting.
  • -outputs: to specify the output nodes by their names.
  • -input-shape: to explicitly specify the input shape.
  • -entry-func-name=yolo_v3: to specify the generate function names as yolo_v3(), yolo_v3_init() and yolo_v3_fini().

A complete Yolo application, including the input preprocessing, inferencing, and the result rendering, can be found here.