73 lines
4.7 KiB
Markdown
73 lines
4.7 KiB
Markdown
# How to convert model
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This tutorial briefly introduces how to export an OpenMMlab model to a specific backend using MMDeploy tools.
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Notes:
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- Supported backends are [ONNXRuntime](../05-supported-backends/onnxruntime.md), [TensorRT](../05-supported-backends/tensorrt.md), [ncnn](../05-supported-backends/ncnn.md), [PPLNN](../05-supported-backends/pplnn.md), [OpenVINO](../05-supported-backends/openvino.md).
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- Supported codebases are [MMClassification](../04-supported-codebases/mmcls.md), [MMDetection](../04-supported-codebases/mmdet.md), [MMSegmentation](../04-supported-codebases/mmseg.md), [MMOCR](../04-supported-codebases/mmocr.md), [MMEditing](../04-supported-codebases/mmedit.md).
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## How to convert models from Pytorch to other backends
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### Prerequisite
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1. Install and build your target backend. You could refer to [ONNXRuntime-install](../05-supported-backends/onnxruntime.md), [TensorRT-install](../05-supported-backends/tensorrt.md), [ncnn-install](../05-supported-backends/ncnn.md), [PPLNN-install](../05-supported-backends/pplnn.md), [OpenVINO-install](../05-supported-backends/openvino.md) for more information.
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2. Install and build your target codebase. You could refer to [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/en/get_started.md#installation), [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/get_started.md), [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/get_started.md#installation), [MMOCR-install](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/get_started/install.md), [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/2_get_started.md#installation).
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### Usage
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```bash
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python ./tools/deploy.py \
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${DEPLOY_CFG_PATH} \
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${MODEL_CFG_PATH} \
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${MODEL_CHECKPOINT_PATH} \
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${INPUT_IMG} \
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--test-img ${TEST_IMG} \
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--work-dir ${WORK_DIR} \
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--calib-dataset-cfg ${CALIB_DATA_CFG} \
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--device ${DEVICE} \
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--log-level INFO \
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--show \
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--dump-info
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```
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### Description of all arguments
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- `deploy_cfg` : The deployment configuration of mmdeploy for the model, including the type of inference framework, whether quantize, whether the input shape is dynamic, etc. There may be a reference relationship between configuration files, `mmdeploy/mmcls/classification_ncnn_static.py` is an example.
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- `model_cfg` : Model configuration for algorithm library, e.g. `mmclassification/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py`, regardless of the path to mmdeploy.
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- `checkpoint` : torch model path. It can start with http/https, see the implementation of `mmcv.FileClient` for details.
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- `img` : The path to the image or point cloud file used for testing during the model conversion.
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- `--test-img` : The path of the image file that is used to test the model. If not specified, it will be set to `None`.
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- `--work-dir` : The path of the work directory that is used to save logs and models.
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- `--calib-dataset-cfg` : Only valid in int8 mode. The config used for calibration. If not specified, it will be set to `None` and use the "val" dataset in the model config for calibration.
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- `--device` : The device used for model conversion. If not specified, it will be set to `cpu`. For trt, use `cuda:0` format.
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- `--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
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- `--show` : Whether to show detection outputs.
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- `--dump-info` : Whether to output information for SDK.
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### How to find the corresponding deployment config of a PyTorch model
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1. Find the model's codebase folder in `configs/`. For converting a yolov3 model, you need to check `configs/mmdet` folder.
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2. Find the model's task folder in `configs/codebase_folder/`. For a yolov3 model, you need to check `configs/mmdet/detection` folder.
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3. Find the deployment config file in `configs/codebase_folder/task_folder/`. For deploying a yolov3 model to the onnx backend, you could use `configs/mmdet/detection/detection_onnxruntime_dynamic.py`.
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### Example
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```bash
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python ./tools/deploy.py \
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configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
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$PATH_TO_MMDET/configs/yolo/yolov3_d53_8xb8-ms-608-273e_coco.py \
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$PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth \
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$PATH_TO_MMDET/demo/demo.jpg \
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--work-dir work_dir \
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--show \
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--device cuda:0
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```
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## How to evaluate the exported models
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You can try to evaluate model, referring to [how_to_evaluate_a_model](profile_model.md).
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## List of supported models exportable to other backends
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Refer to [Support model list](../03-benchmark/supported_models.md)
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