mmdeploy/docs/tutorials/how_to_convert_model.md

How to convert model

• Tutorial : How to convert model
  • How to convert models from Pytorch to BACKEND
    • Prerequisite
    • Usage
    • Description of all arguments
  • How to evaluate the exported models
  • List of supported models exportable to BACKEND
  • Reminders
  • FAQs

This tutorial briefly introduces how to export an OpenMMlab model to a specific backend using MMDeploy tools. Notes:

• Supported backends are ONNXRuntime, TensorRT, NCNN, PPL, OpenVINO.
• Supported codebases are MMClassification, MMDetection, MMSegmentation, MMOCR, MMEditing.

How to convert models from Pytorch to other backends

Prerequisite

1. Install and build your target backend. You could refer to ONNXRuntime-install, TensorRT-install, NCNN-install, PPL-install, OpenVINO-install for more information.
2. Install and build your target codebase. You could refer to MMClassification-install, MMDetection-install, MMSegmentation-install, MMOCR-install, MMEditing-install.

Usage

python ./tools/deploy.py \
    ${DEPLOY_CFG_PATH} \
    ${MODEL_CFG_PATH} \
    ${MODEL_CHECKPOINT_PATH} \
    ${INPUT_IMG} \
    --test-img ${TEST_IMG} \
    --work-dir ${WORK_DIR} \
    --calib-dataset-cfg ${CALIB_DATA_CFG} \
    --device ${DEVICE} \
    --log-level INFO \
    --show \
    --dump-info

Description of all arguments

• deploy_cfg : The path of deploy config file in MMDeploy codebase.
• model_cfg : The path of model config file in OpenMMLab codebase.
• checkpoint : The path of model checkpoint file.
• img : The path of image file that used to convert model.
• --test-img : The path of image file that used to test model. If not specified, it will be set to None.
• --work-dir : The path of work directory that used to save logs and models.
• --calib-dataset-cfg : Config used for calibration. If not specified, it will be set to None.
• --device : The device used for conversion. If not specified, it will be set to cpu.
• --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.
• --show : Whether to show detection outputs.
• --dump-info : Whether to output information for SDK.

Example

python ./tools/deploy.py \
    configs/mmdet/single-stage/single-stage_tensorrt_dynamic-320x320-1344x1344.py \
    $PATH_TO_MMDET/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
    $PATH_TO_MMDET/checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \
    $PATH_TO_MMDET/demo/demo.jpg \
    --work-dir work_dir \
    --show \
    --device cuda:0

How to evaluate the exported models

You can try to evaluate model, referring to how_to_evaluate_a_model.

List of supported models exportable to other backends

The table below lists the models that are guaranteed to be exportable to other backend.

Model	codebase	model config file(example)	OnnxRuntime	TensorRT	NCNN	PPL	OpenVINO
RetinaNet	MMDetection	$PATH_TO_MMDET/configs/retinanet/retinanet_r50_fpn_1x_coco.py	Y	Y	Y	Y	Y
Faster R-CNN	MMDetection	$PATH_TO_MMDET/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py	Y	Y	Y	Y	Y
YOLOv3	MMDetection	$PATH_TO_MMDET/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py	Y	Y	N	Y	N
FCOS	MMDetection	$PATH_TO_MMDET/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py	Y	Y	Y	N	Y
FSAF	MMDetection	$PATH_TO_MMDET/configs/fsaf/fsaf_r50_fpn_1x_coco.py	Y	Y	Y	Y	Y
Mask R-CNN	MMDetection	$PATH_TO_MMDET/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py	Y	Y	N	Y	Y
SSD	MMDetection	$PATH_TO_MMDET/configs/ssd/ssd300_coco.py	Y	?	?	?	Y
Cascade R-CNN	MMDetection	$PATH_TO_MMDET/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py	Y	?	?	?	Y
Cascade Mask R-CNN	MMDetection	$PATH_TO_MMDET/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py	Y	?	?	?	Y
ResNet	MMClassification	$PATH_TO_MMCLS/configs/resnet/resnet18_b32x8_imagenet.py	Y	Y	Y	Y	N
ResNeXt	MMClassification	$PATH_TO_MMCLS/configs/resnext/resnext50_32x4d_b32x8_imagenet.py	Y	Y	Y	Y	N
SE-ResNet	MMClassification	$PATH_TO_MMCLS/configs/seresnet/seresnet50_b32x8_imagenet.py	Y	Y	Y	Y	N
MobileNetV2	MMClassification	$PATH_TO_MMCLS/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py	Y	Y	Y	Y	N
ShuffleNetV1	MMClassification	$PATH_TO_MMCLS/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py	Y	Y	N	Y	N
ShuffleNetV2	MMClassification	$PATH_TO_MMCLS/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py	Y	Y	N	Y	N
FCN	MMSegmentation	$PATH_TO_MMSEG/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py	Y	Y	Y	Y	N
PSPNet	MMSegmentation	$PATH_TO_MMSEG/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py	Y	Y	N	Y	N
DeepLabV3	MMSegmentation	$PATH_TO_MMSEG/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py	Y	Y	Y	Y	N
DeepLabV3+	MMSegmentation	$PATH_TO_MMSEG/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py	Y	Y	Y	Y	N
SRCNN	MMEditing	$PATH_TO_MMSEG/configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py	Y	Y	N	Y	N
ESRGAN	MMEditing	$PATH_TO_MMSEG/configs/restorers/esrgan/esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py	Y	Y	N	Y	N
DBNet	MMOCR	$PATH_TO_MMOCR/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py	Y	Y	Y	Y	N
CRNN	MMOCR	$PATH_TO_MMOCR/configs/textrecog/tps/crnn_tps_academic_dataset.py	Y	Y	Y	N	N

Reminders

• None

FAQs

• None