Shortcuts

Deployment

We provide deployment tools under tools/deployment directory.

Convert to ONNX (experimental)

We provide a script to convert the model to ONNX format. The converted model could be visualized by tools like Netron. Besides, we also support comparing the output results between Pytorch and ONNX model.

python tools/deployment/pytorch2onnx.py
    ${MODEL_CONFIG_PATH} \
    ${MODEL_CKPT_PATH} \
    ${MODEL_TYPE} \
    ${IMAGE_PATH} \
    --output-file ${OUTPUT_FILE} \
    --device-id ${DEVICE_ID} \
    --opset-version ${OPSET_VERSION} \
    --verify \
    --verbose \
    --show \
    --dynamic-export

Description of arguments:

ARGS Type Description
model_config str The path to a model config file.
model_ckpt str The path to a model checkpoint file.
model_type 'recog', 'det' The model type of the config file.
image_path str The path to input image file.
--output-file str The path to output ONNX model. Defaults to tmp.onnx.
--device-id int Which GPU to use. Defaults to 0.
--opset-version int ONNX opset version. Defaults to 11.
--verify bool Determines whether to verify the correctness of an exported model. Defaults to False.
--verbose bool Determines whether to print the architecture of the exported model. Defaults to False.
--show bool Determines whether to visualize outputs of ONNXRuntime and PyTorch. Defaults to False.
--dynamic-export bool Determines whether to export ONNX model with dynamic input and output shapes. Defaults to False.

Note

This tool is still experimental. For now, some customized operators are not supported, and we only support a subset of detection and recognition algorithms.

List of supported models exportable to ONNX

The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime.

Model Config Dynamic Shape Batch Inference Note
DBNet dbnet_r18_fpnc_1200e_icdar2015.py Y N
PSENet psenet_r50_fpnf_600e_ctw1500.py Y Y
PSENet psenet_r50_fpnf_600e_icdar2015.py Y Y
PANet panet_r18_fpem_ffm_600e_ctw1500.py Y Y
PANet panet_r18_fpem_ffm_600e_icdar2015.py Y Y
CRNN crnn_academic_dataset.py Y Y CRNN only accepts input with height 32

Note

  • All models above are tested with Pytorch==1.8.1 and onnxruntime==1.7.0

  • If you meet any problem with the listed models above, please create an issue and it would be taken care of soon.

  • Because this feature is experimental and may change fast, please always try with the latest mmcv and mmocr.

Convert ONNX to TensorRT (experimental)

We also provide a script to convert ONNX model to TensorRT format. Besides, we support comparing the output results between ONNX and TensorRT model.

python tools/deployment/onnx2tensorrt.py
    ${MODEL_CONFIG_PATH} \
    ${MODEL_TYPE} \
    ${IMAGE_PATH} \
    ${ONNX_FILE} \
    --trt-file ${OUT_TENSORRT} \
    --max-shape INT INT INT INT \
    --min-shape INT INT INT INT \
    --workspace-size INT \
    --fp16 \
    --verify \
    --show \
    --verbose

Description of arguments:

ARGS Type Description
model_config str The path to a model config file.
model_type 'recog', 'det' The model type of the config file.
image_path str The path to input image file.
onnx_file str The path to input ONNX file.
--trt-file str The path of output TensorRT model. Defaults to tmp.trt.
--max-shape int * 4 Maximum shape of model input.
--min-shape int * 4 Minimum shape of model input.
--workspace-size int Max workspace size in GiB. Defaults to 1.
--fp16 bool Determines whether to export TensorRT with fp16 mode. Defaults to False.
--verify bool Determines whether to verify the correctness of an exported model. Defaults to False.
--show bool Determines whether to show the output of ONNX and TensorRT. Defaults to False.
--verbose bool Determines whether to verbose logging messages while creating TensorRT engine. Defaults to False.

Note

This tool is still experimental. For now, some customized operators are not supported, and we only support a subset of detection and recognition algorithms.

List of supported models exportable to TensorRT

The table below lists the models that are guaranteed to be exportable to TensorRT engine and runnable in TensorRT.

Model Config Dynamic Shape Batch Inference Note
DBNet dbnet_r18_fpnc_1200e_icdar2015.py Y N
PSENet psenet_r50_fpnf_600e_ctw1500.py Y Y
PSENet psenet_r50_fpnf_600e_icdar2015.py Y Y
PANet panet_r18_fpem_ffm_600e_ctw1500.py Y Y
PANet panet_r18_fpem_ffm_600e_icdar2015.py Y Y
CRNN crnn_academic_dataset.py Y Y CRNN only accepts input with height 32

Note

  • All models above are tested with Pytorch==1.8.1, onnxruntime==1.7.0 and tensorrt==7.2.1.6

  • If you meet any problem with the listed models above, please create an issue and it would be taken care of soon.

  • Because this feature is experimental and may change fast, please always try with the latest mmcv and mmocr.

Evaluate ONNX and TensorRT Models (experimental)

We provide methods to evaluate TensorRT and ONNX models in tools/deployment/deploy_test.py.

Prerequisite

To evaluate ONNX and TensorRT models, ONNX, ONNXRuntime and TensorRT should be installed first. Install mmcv-full with ONNXRuntime custom ops and TensorRT plugins follow ONNXRuntime in mmcv and TensorRT plugin in mmcv.

Usage

python tools/deploy_test.py \
    ${CONFIG_FILE} \
    ${MODEL_PATH} \
    ${MODEL_TYPE} \
    ${BACKEND} \
    --eval ${METRICS} \
    --device ${DEVICE}

Description of all arguments

ARGS Type Description
model_config str The path to a model config file.
model_file str The path to a TensorRT or an ONNX model file.
model_type 'recog', 'det' Detection or recognition model to deploy.
backend 'TensorRT', 'ONNXRuntime' The backend for testing.
--eval 'acc', 'hmean-iou' The evaluation metrics. 'acc' for recognition models, 'hmean-iou' for detection models.
--device str Device for evaluation. Defaults to cuda:0.

Results and Models

Model Config Dataset Metric PyTorch ONNX Runtime TensorRT FP32 TensorRT FP16
DBNet dbnet_r18_fpnc_1200e_icdar2015.py
icdar2015 Recall
0.731 0.731 0.678 0.679
Precision 0.871 0.871 0.844 0.842
Hmean 0.795 0.795 0.752 0.752
DBNet* dbnet_r18_fpnc_1200e_icdar2015.py
icdar2015 Recall
0.720 0.720 0.720 0.718
Precision 0.868 0.868 0.868 0.868
Hmean 0.787 0.787 0.787 0.786
PSENet psenet_r50_fpnf_600e_icdar2015.py
icdar2015 Recall
0.753 0.753 0.753 0.752
Precision 0.867 0.867 0.867 0.867
Hmean 0.806 0.806 0.806 0.805
PANet panet_r18_fpem_ffm_600e_icdar2015.py
icdar2015 Recall
0.740 0.740 0.687 N/A
Precision 0.860 0.860 0.815 N/A
Hmean 0.796 0.796 0.746 N/A
PANet* panet_r18_fpem_ffm_600e_icdar2015.py
icdar2015 Recall
0.736 0.736 0.736 N/A
Precision 0.857 0.857 0.857 N/A
Hmean 0.792 0.792 0.792 N/A
CRNN crnn_academic_dataset.py
IIIT5K Acc 0.806 0.806 0.806 0.806

Note

  • TensorRT upsampling operation is a little different from PyTorch. For DBNet and PANet, we suggest replacing upsampling operations with the nearest mode to operations with bilinear mode. Here for PANet, here and here for DBNet. As is shown in the above table, networks with tag * mean the upsampling mode is changed.

  • Note that changing upsampling mode reduces less performance compared with using the nearest mode. However, the weights of networks are trained through the nearest mode. To pursue the best performance, using bilinear mode for both training and TensorRT deployment is recommended.

  • All ONNX and TensorRT models are evaluated with dynamic shapes on the datasets, and images are preprocessed according to the original config file.

  • This tool is still experimental, and we only support a subset of detection and recognition algorithms for now.

Read the Docs v: latest
Versions
latest
stable
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.