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* Apply make_divisible for onnx models in Autoshape At line 697 we have this `make_divisible` function for pytorch models. * Context: we want to run inference on varied input sizes instead of fixed image size. * When I test an image of size [720, 720] for a pytorch model (e.g., yolov5n.pt), we can see that it will be reshaped to [736, 736] by the function. This is as expected. * When I test the same image for the onnx model (e.g., yolov5n.onnx, exported with `--dynamic`), I got an error and it's due to the indivisible problem ``` onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running Concat node. Name:'Concat_143' Status Message: concat.cc:156 PrepareForCompute Non concat axis dimensions must match: Axis 3 has mismatched dimensions of 45 and 46 ``` The simple solution is to enable the `make_divisible` function for onnx model too. Signed-off-by: janus-zheng <106574221+janus-zheng@users.noreply.github.com> * revise indent Signed-off-by: janus-zheng <106574221+janus-zheng@users.noreply.github.com> * Apply make_divisible to all formats All formats from DetectMultiBackend should have default stride=32 Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Signed-off-by: janus-zheng <106574221+janus-zheng@users.noreply.github.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> |
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| .. | ||
| hub | ||
| segment | ||
| __init__.py | ||
| common.py | ||
| experimental.py | ||
| tf.py | ||
| yolo.py | ||
| yolov5l.yaml | ||
| yolov5m.yaml | ||
| yolov5n.yaml | ||
| yolov5s.yaml | ||
| yolov5x.yaml | ||