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更多工具介绍

除 deploy.py 以外， tools 目录下有很多实用工具

torch2onnx

把 OpenMMLab 模型转 onnx 格式。

用法

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

参数说明

  • 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

有 Mark 节点的 onnx 模型会被分成多个子图，这个工具用来提取 onnx 模型中的子图。

用法

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

参数说明

  • 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.

注意事项

要支持模型分块，必须在 onnx 模型中添加 mark 节点，用@mark 修饰。 下面这个例子里 mark 了 multiclass_nms，在 NMS 前设置 end=multiclass_nms:input 提取子图。

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

onnx2pplnn

这个工具可以把 onnx 模型转成 pplnn 格式。

用法

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

参数说明

  • 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

这个工具把 onnx 转成 trt .engine 格式。

用法

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

参数说明

  • 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

onnx 转 ncnn

用法

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

参数说明

  • 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

这个工具用来测试 torch 和 trt 等后端的速度，注意测试不包含前后处理。

用法

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}

参数说明

  • 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'].

使用举例

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

输出：

----- 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

生成mmdeploy支持的后端表。

用法

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

参数说明

  • yml_file: 输入 yml 配置路径
  • output: 输出markdown文件路径
  • --backends: 要输出的后端，默认为 onnxruntime tensorrt torchscript pplnn openvino ncnn

使用举例

从 mmocr.yml 生成mmdeploy支持的后端表

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

输出：

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