85 lines
2.3 KiB
Markdown
85 lines
2.3 KiB
Markdown
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# VSI NBG Runner Python Binding
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This project is a python package that wraps OpenVX API using pybind11. It provides simple python API to load, query and run model NBG.
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## Installation
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```cmake
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cmake -B build -DVIVANTE_SDK_DIR=${VIV_SDK_INSTALL_PATH}
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cmake --build build
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```
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The built python binding lib can be found at
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`build/src/_nbg_runner.cpython-{python_version}-{platform}.so`. Place the lib into `python/nbg_runner/_binding/`.
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## Usage
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### Tensor Info
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| Field | Type | Value Sample |
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|:-----------------:|:-----------------:|:-------------:|
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| rank | int | 4 |
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| shape | Tuple[int, ...] | (1,3,224,224) |
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| dtype | str | "uint8" |
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| qtype | str | "affine" |
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| scale | float | 0.007874 |
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| zero_point | int | 128 |
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| fixed_point_pos | int | 0 |
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- `shape` is in C-style row major order, which is consistent with NumPy.
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### Set Environment Vars
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```shell
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# Set HW target If the driver is compiled with vsimulator.
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VSIMULATOR_CONFIG=VIP9000ULSI_PID0XBA
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# Locate the OVX driver.
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VIVANTE_SDK_DIR=${VIV_SDK_INSTALL_PATH}
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LD_LIBRARY_PATH=${VIVANTE_SDK_DIR}/[lib|lib64|drivers]
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# Set PYTHONPATH to the dir containing nbg_runner module.
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PYTHONPATH=${workspaceFolder}/python
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```
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### Example
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See detailed examples in `examples/*.py`
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```python
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from nbg_runner import OVXExecutor
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# Load a model NBG file.
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executor = OVXExecutor("path/to/model.nbg")
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# Query model I/O tensors count.
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num_inputs = executor.get_num_inputs()
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num_outputs = executor.get_num_outputs()
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# Get I/O tensor info by index.
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input_info = executor.get_input_info(0)
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output_info = executor.get_output_info(0)
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# Or get all I/O tensors infos at once.
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input_infos = executor.get_input_infos()
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output_infos = executor.get_output_infos()
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# Prepare inputs.
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input_tensors: List[NDArray] = ...
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# Set input tensor by index.
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for i, input_tensor in enumerate(input_tensors):
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executor.set_input(i, input_tensor)
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# Or set all input tensors at once.
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executor.set_inputs(input_tensors)
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# Run inference.
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executor.run()
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# Get output tensor by index.
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for i in range(num_outputs):
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output_tensor = executor.get_output(i)
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# Or get all output tensors at once.
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output_tensors = executor.get_outputs()
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```
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