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mmdeploy/docs/tutorials/how_to_convert_model.md

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How to convert model

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

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, PPLNN-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.

How to find the corresponding deployment config of a PyTorch model

  1. Find model's codebase folder in configs/ . Example, convert a yolov3 model you need to find configs/mmdet folder.
  2. Find model's task folder in configs/codebase_folder/ . Just like yolov3 model, you need to find configs/mmdet/single-stage folder.
  3. Find deployment config file in configs/codebase_folder/task_folder/ . Just like deploy yolov3 model you can use configs/mmdet/single-stage/single-stage_onnxruntime_dynamic.py.

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 OnnxRuntime TensorRT NCNN PPLNN OpenVINO model config file(example)
RetinaNet MMDetection Y Y Y Y Y $MMDET_DIR/configs/retinanet/retinanet_r50_fpn_1x_coco.py
Faster R-CNN MMDetection Y Y Y Y Y $MMDET_DIR/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
YOLOv3 MMDetection Y Y Y Y Y $MMDET_DIR/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
YOLOX MMDetection Y Y ? ? Y $MMDET_DIR/configs/yolox/yolox_tiny_8x8_300e_coco.py
FCOS MMDetection Y Y Y N Y $MMDET_DIR/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.
FSAF MMDetection Y Y Y Y Y $MMDET_DIR/configs/fsaf/fsaf_r50_fpn_1x_coco.py
Mask R-CNN MMDetection Y Y N Y Y $MMDET_DIR/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
SSD MMDetection Y Y Y Y Y $MMDET_DIR/configs/ssd/ssd300_coco.py
FoveaBox MMDetection Y ? ? ? Y $MMDET_DIR/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py
ATSS MMDetection Y Y ? ? Y $MMDET_DIR/configs/atss/atss_r50_fpn_1x_coco.py
Cascade R-CNN MMDetection Y ? ? Y Y $MMDET_DIR/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
Cascade Mask R-CNN MMDetection Y ? ? Y Y $MMDET_DIR/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
VFNet MMDetection N ? ? ? Y $MMDET_DIR/configs/vfnet/vfnet_r50_fpn_1x_coco.py
ResNet MMClassification Y Y Y Y N $MMCLS_DIR/configs/resnet/resnet18_b32x8_imagenet.py
ResNeXt MMClassification Y Y Y Y N $MMCLS_DIR/configs/resnext/resnext50_32x4d_b32x8_imagenet.py
SE-ResNet MMClassification Y Y Y Y N $MMCLS_DIR/configs/seresnet/seresnet50_b32x8_imagenet.py
MobileNetV2 MMClassification Y Y Y Y N $MMCLS_DIR/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py
ShuffleNetV1 MMClassification Y Y Y Y N $MMCLS_DIR/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
ShuffleNetV2 MMClassification Y Y Y Y N $MMCLS_DIR/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py
FCN MMSegmentation Y Y Y Y Y $MMSEG_DIR/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
PSPNet MMSegmentation Y Y N Y Y $MMSEG_DIR/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
DeepLabV3 MMSegmentation Y Y Y Y Y $MMSEG_DIR/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
DeepLabV3+ MMSegmentation Y Y Y Y Y $MMSEG_DIR/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
Fast-SCNN MMSegmentation Y Y N Y Y ${MMSEG_DIR}/configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
SRCNN MMEditing Y Y N Y N $MMSEG_DIR/configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py
ESRGAN MMEditing Y Y N Y N $MMSEG_DIR/configs/restorers/esrgan/esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py
DBNet MMOCR Y Y Y Y Y $MMOCR_DIR/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py
CRNN MMOCR Y Y Y Y N $MMOCR_DIR/configs/textrecog/crnn/crnn_academic_dataset.py

Reminders

  • None

FAQs

  • None