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Useful Tools

Apart from deploy.py, there are other useful tools under the tools/ directory.

torch2onnx

This tool can be used to convert PyTorch model from OpenMMLab to ONNX.

Usage

python tools/torch2onnx.py \
    ${DEPLOY_CFG} \
    ${MODEL_CFG} \
    ${CHECKPOINT} \
    ${INPUT_IMG} \
    --work-dir ${WORK_DIR} \
    --device cpu \
    --log-level INFO

Description of all arguments

  • deploy_cfg : The path of the deploy config file in MMDeploy codebase.
  • model_cfg : The path of model config file in OpenMMLab codebase.
  • checkpoint : The path of the model checkpoint file.
  • img : The path of the image file used to convert the model.
  • --work-dir : Directory to save output ONNX models Default is ./work-dir.
  • --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.

extract

ONNX model with Mark nodes in it can be partitioned into multiple subgraphs. This tool can be used to extract the subgraph from the ONNX model.

Usage

python tools/extract.py \
    ${INPUT_MODEL} \
    ${OUTPUT_MODEL} \
    --start ${PARITION_START} \
    --end ${PARITION_END} \
    --log-level INFO

Description of all arguments

  • input_model : The path of input ONNX model. The output ONNX model will be extracted from this model.
  • output_model : The path of output ONNX model.
  • --start : The start point of extracted model with format <function_name>:<input/output>. The function_name comes from the decorator @mark.
  • --end : The end point of extracted model with format <function_name>:<input/output>. The function_name comes from the decorator @mark.
  • --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.

Note

To support the model partition, you need to add Mark nodes in the ONNX model. The Mark node comes from the @mark decorator. For example, if we have marked the multiclass_nms as below, we can set end=multiclass_nms:input to extract the subgraph before NMS.

@mark('multiclass_nms', inputs=['boxes', 'scores'], outputs=['dets', 'labels'])
def multiclass_nms(*args, **kwargs):
    """Wrapper function for `_multiclass_nms`."""

onnx2pplnn

This tool helps to convert an ONNX model to an PPLNN model.

Usage

python tools/onnx2pplnn.py \
    ${ONNX_PATH} \
    ${OUTPUT_PATH} \
    --device cuda:0 \
    --opt-shapes [224,224] \
    --log-level INFO

Description of all arguments

  • onnx_path: The path of the ONNX model to convert.
  • output_path: The converted PPLNN algorithm path in json format.
  • device: The device of the model during conversion.
  • opt-shapes: Optimal shapes for PPLNN optimization. The shape of each tensor should be wrap with "[]" or "()" and the shapes of tensors should be separated by ",".
  • --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.

onnx2tensorrt

This tool can be used to convert ONNX to TensorRT engine.

Usage

python tools/onnx2tensorrt.py \
    ${DEPLOY_CFG} \
    ${ONNX_PATH} \
    ${OUTPUT} \
    --device-id 0 \
    --log-level INFO \
    --calib-file /path/to/file

Description of all arguments

  • deploy_cfg : The path of the deploy config file in MMDeploy codebase.
  • onnx_path : The ONNX model path to convert.
  • output : The path of output TensorRT engine.
  • --device-id : The device index, default to 0.
  • --calib-file : The calibration data used to calibrate engine to int8.
  • --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.

onnx2ncnn

This tool helps to convert an ONNX model to an ncnn model.

Usage

python tools/onnx2ncnn.py \
    ${ONNX_PATH} \
    ${NCNN_PARAM} \
    ${NCNN_BIN} \
    --log-level INFO

Description of all arguments

  • onnx_path : The path of the ONNX model to convert from.
  • output_param : The converted ncnn param path.
  • output_bin : The converted ncnn bin path.
  • --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.

profiler

This tool helps to test latency of models with PyTorch, TensorRT and other backends. Note that the pre- and post-processing is excluded when computing inference latency.

Usage

python tools/profiler.py \
    ${DEPLOY_CFG} \
    ${MODEL_CFG} \
    ${IMAGE_DIR} \
    --model ${MODEL} \
    --device ${DEVICE} \
    --shape ${SHAPE} \
    --num-iter ${NUM_ITER} \
    --warmup ${WARMUP} \
    --cfg-options ${CFG_OPTIONS} \
    --batch-size ${BATCH_SIZE} \
    --img-ext ${IMG_EXT}

Description of all arguments

  • deploy_cfg : The path of the deploy config file in MMDeploy codebase.
  • model_cfg : The path of model config file in OpenMMLab codebase.
  • image_dir : The directory to image files that used to test the model.
  • --model : The path of the model to be tested.
  • --shape : Input shape of the model by HxW, e.g., 800x1344. If not specified, it would use input_shape from deploy config.
  • --num-iter : Number of iteration to run inference. Default is 100.
  • --warmup : Number of iteration to warm-up the machine. Default is 10.
  • --device : The device type. If not specified, it will be set to cuda:0.
  • --cfg-options : Optional key-value pairs to be overrode for model config.
  • --batch-size: the batch size for test inference. Default is 1. Note that not all models support batch_size>1.
  • --img-ext: the file extensions for input images from image_dir. Defaults to ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif'].

Example:

python tools/profiler.py \
    configs/mmpretrain/classification_tensorrt_dynamic-224x224-224x224.py \
    ../mmpretrain/configs/resnet/resnet18_8xb32_in1k.py \
    ../mmpretrain/demo/ \
    --model work-dirs/mmpretrain/resnet/trt/end2end.engine \
    --device cuda \
    --shape 224x224 \
    --num-iter 100 \
    --warmup 10 \
    --batch-size 1

And the output look like this:

----- Settings:
+------------+---------+
| batch size |    1    |
|   shape    | 224x224 |
| iterations |   100   |
|   warmup   |    10   |
+------------+---------+
----- Results:
+--------+------------+---------+
| Stats  | Latency/ms |   FPS   |
+--------+------------+---------+
|  Mean  |   1.535    | 651.656 |
| Median |   1.665    | 600.569 |
|  Min   |   1.308    | 764.341 |
|  Max   |   1.689    | 591.983 |
+--------+------------+---------+

generate_md_table

This tool can be used to generate supported-backends markdown table.

Usage

python tools/generate_md_table.py \
    ${YML_FILE} \
    ${OUTPUT} \
    --backends ${BACKENDS}

Description of all arguments

  • yml_file: input yml config path
  • output: output markdown file path
  • --backends: output backends list. If not specified, it will be set 'onnxruntime' 'tensorrt' 'torchscript' 'pplnn' 'openvino' 'ncnn'.

Example:

Generate backends markdown table from mmocr.yml

python tools/generate_md_table.py tests/regression/mmocr.yml tests/regression/mmocr.md --backends  onnxruntime tensorrt torchscript pplnn openvino ncnn

And the output look like this:

model task onnxruntime tensorrt torchscript pplnn openvino ncnn
DBNet TextDetection Y Y Y Y Y Y
DBNetpp TextDetection Y Y N N Y Y
PANet TextDetection Y Y Y Y Y Y
PSENet TextDetection Y Y Y Y Y Y
TextSnake TextDetection Y Y Y N N N
MaskRCNN TextDetection Y Y Y N N N
CRNN TextRecognition Y Y Y Y N Y
SAR TextRecognition Y N Y N N N
SATRN TextRecognition Y Y Y N N N
ABINet TextRecognition Y Y Y N N N