模型导出
SSCMA 目前支持以下模型导出方式，您可以参考对应的教程，完成模型的导出，然后将导出的模型投入部署。
TIP
默认情况下，会同时导出 ONNX 和 TFLite 模型，如果您只需要导出其中一个，可以使用 --targets 参数指定导出的模型类型，例如 --targets onnx 或 --targets tflite。
PyTorch 转 ONNX: 将 PyTorch 模型和
.pth权重转换为 ONNX 模型.onnxPyTorch 转 TFLite: 将 PyTorch 模型和
.pth权重转换为 TFLite 模型.tflite
TIP
在开始导出前，您需要先完成模型训练部分内容并获得 .pth 模型权重。
参数说明
您需要将以下部分参数根据实际情况进行替换，各个不同参数的具体说明如下:
sh
python3 tools/export.py --help
# Convert and export PyTorch model to TFLite or ONNX models
# positional arguments:
# config the model config file path
# checkpoint the PyTorch checkpoint file path
# targets the target type of model(s) to export e.g. tflite onnx
# optional arguments:
# -h, --help show this help message and exit
# --targets TARGETS [TARGETS ...]
# the target type of model(s) to export e.g. tflite onnx
# --precisions PRECISIONS [PRECISIONS ...]
# the precisions exported model, e.g. 'int8', 'uint8', 'int16', 'float16' and 'float32'
# --work_dir WORK_DIR, --work-dir WORK_DIR
# the directory to save logs and models
# --output_stem OUTPUT_STEM, --output-stem OUTPUT_STEM
# the stem of output file name (with path)
# --device DEVICE the device used for convert & export
# --input_shape INPUT_SHAPE [INPUT_SHAPE ...], --input-shape INPUT_SHAPE [INPUT_SHAPE ...]
# the shape of input data, e.g. 1 3 224 224
# --input_type {audio,image,sensor}, --input-type {audio,image,sensor}
# the type of input data
# --cfg_options CFG_OPTIONS [CFG_OPTIONS ...], --cfg-options CFG_OPTIONS [CFG_OPTIONS ...]
# override some settings in the used config, the key-value pair in 'xxx=yyy' format will be merged into config file
# --simplify SIMPLIFY the level of graph simplification, 0 means disable, max: 5
# --opset_version OPSET_VERSION, --opset-version OPSET_VERSION
# ONNX: operator set version of exported model
# --dynamic_export, --dynamic-export
# ONNX: export with a dynamic input shape
# --algorithm {l2,kl} TFLite: conversion algorithm
# --backend {qnnpack,fbgemm}
# TFLite: converter backend
# --calibration_epochs CALIBRATION_EPOCHS, --calibration-epochs CALIBRATION_EPOCHS
# TFLite: max epoches for quantization calibration
# --mean MEAN [MEAN ...]
# TFLite: mean for model input (quantization), range: [0, 1], applied to all channels, using the average if multiple values are provided
# --mean_and_std MEAN_AND_STD [MEAN_AND_STD ...], --mean-and-std MEAN_AND_STD [MEAN_AND_STD ...]
# TFLite: mean and std for model input(s), default: [((0.0,), (1.0,))], calculated on normalized input(s), applied to all channel(s), using the average if multiple values are provided