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apis

mmdeploy.apis.build_task_processor(model_cfg: mmengine.config.config.Config, deploy_cfg: mmengine.config.config.Config, device: str) → mmdeploy.codebase.base.task.BaseTask[source]

Build a task processor to manage the deployment pipeline.

Parameters

  • model_cfg (str | mmengine.Config) – Model config file.

  • deploy_cfg (str | mmengine.Config) – Deployment config file.

  • device (str) – A string specifying device type.

Returns

A task processor.

Return type

BaseTask

mmdeploy.apis.create_calib_input_data(calib_file: str, deploy_cfg: Union[str, mmengine.config.config.Config], model_cfg: Union[str, mmengine.config.config.Config], model_checkpoint: Optional[str] = None, dataset_cfg: Optional[Union[str, mmengine.config.config.Config]] = None, dataset_type: str = 'val', device: str = 'cpu') → None[source]

Create dataset for post-training quantization.

Parameters

  • calib_file (str) – The output calibration data file.

  • deploy_cfg (str | Config) – Deployment config file or Config object.

  • model_cfg (str | Config) – Model config file or Config object.

  • model_checkpoint (str) – A checkpoint path of PyTorch model, defaults to None.

  • dataset_cfg (Optional[Union[str, Config]], optional) – Model config to provide calibration dataset. If none, use model_cfg as the dataset config. Defaults to None.

  • dataset_type (str, optional) – The dataset type. Defaults to ‘val’.

  • device (str, optional) – Device to create dataset. Defaults to ‘cpu’.

mmdeploy.apis.extract_model(model: Union[str, onnx.onnx_ml_pb2.ModelProto], start_marker: Union[str, Iterable[str]], end_marker: Union[str, Iterable[str]], start_name_map: Optional[Dict[str, str]] = None, end_name_map: Optional[Dict[str, str]] = None, dynamic_axes: Optional[Dict[str, Dict[int, str]]] = None, save_file: Optional[str] = None) → onnx.onnx_ml_pb2.ModelProto[source]

Extract partition-model from an ONNX model.

The partition-model is defined by the names of the input and output tensors exactly.

Examples

>>> from mmdeploy.apis import extract_model
>>> model = 'work_dir/fastrcnn.onnx'
>>> start_marker = 'detector:input'
>>> end_marker = ['extract_feat:output', 'multiclass_nms[0]:input']
>>> dynamic_axes = {
    'input': {
        0: 'batch',
        2: 'height',
        3: 'width'
    },
    'scores': {
        0: 'batch',
        1: 'num_boxes',
    },
    'boxes': {
        0: 'batch',
        1: 'num_boxes',
    }
}
>>> save_file = 'partition_model.onnx'
>>> extract_model(model, start_marker, end_marker,                 dynamic_axes=dynamic_axes,                 save_file=save_file)
Parameters

  • model (str | onnx.ModelProto) – Input ONNX model to be extracted.

  • start_marker (str | Sequence[str]) – Start marker(s) to extract.

  • end_marker (str | Sequence[str]) – End marker(s) to extract.

  • start_name_map (Dict[str, str]) – A mapping of start names, defaults to None.

  • end_name_map (Dict[str, str]) – A mapping of end names, defaults to None.

  • dynamic_axes (Dict[str, Dict[int, str]]) – A dictionary to specify dynamic axes of input/output, defaults to None.

  • save_file (str) – A file to save the extracted model, defaults to None.

Returns

The extracted model.

Return type

onnx.ModelProto

mmdeploy.apis.get_predefined_partition_cfg(deploy_cfg: mmengine.config.config.Config, partition_type: str)[source]

Get the predefined partition config.

Notes

Currently only support mmdet codebase.

Parameters

  • deploy_cfg (mmengine.Config) – use deploy config to get the codebase and task type.

  • partition_type (str) – A string specifying partition type.

Returns

A dictionary of partition config.

Return type

dict

mmdeploy.apis.inference_model(model_cfg: Union[str, mmengine.config.config.Config], deploy_cfg: Union[str, mmengine.config.config.Config], backend_files: Sequence[str], img: Union[str, numpy.ndarray], device: str) → Any[source]

Run inference with PyTorch or backend model and show results.

Examples

>>> from mmdeploy.apis import inference_model
>>> model_cfg = ('mmdetection/configs/fcos/'
                 'fcos_r50_caffe_fpn_gn-head_1x_coco.py')
>>> deploy_cfg = ('configs/mmdet/detection/'
                  'detection_onnxruntime_dynamic.py')
>>> backend_files = ['work_dir/fcos.onnx']
>>> img = 'demo.jpg'
>>> device = 'cpu'
>>> model_output = inference_model(model_cfg, deploy_cfg,
                    backend_files, img, device)
Parameters

  • model_cfg (str | mmengine.Config) – Model config file or Config object.

  • deploy_cfg (str | mmengine.Config) – Deployment config file or Config object.

  • backend_files (Sequence[str]) – Input backend model file(s).

  • img (str | np.ndarray) – Input image file or numpy array for inference.

  • device (str) – A string specifying device type.

Returns

The inference results

Return type

Any

mmdeploy.apis.torch2onnx(img: Any, work_dir: str, save_file: str, deploy_cfg: Union[str, mmengine.config.config.Config], model_cfg: Union[str, mmengine.config.config.Config], model_checkpoint: Optional[str] = None, device: str = 'cuda:0')[source]

Convert PyTorch model to ONNX model.

Examples

>>> from mmdeploy.apis import torch2onnx
>>> img = 'demo.jpg'
>>> work_dir = 'work_dir'
>>> save_file = 'fcos.onnx'
>>> deploy_cfg = ('configs/mmdet/detection/'
                  'detection_onnxruntime_dynamic.py')
>>> model_cfg = ('mmdetection/configs/fcos/'
                 'fcos_r50_caffe_fpn_gn-head_1x_coco.py')
>>> model_checkpoint = ('checkpoints/'
                        'fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth')
>>> device = 'cpu'
>>> torch2onnx(img, work_dir, save_file, deploy_cfg,             model_cfg, model_checkpoint, device)
Parameters

  • img (str | np.ndarray | torch.Tensor) – Input image used to assist converting model.

  • work_dir (str) – A working directory to save files.

  • save_file (str) – Filename to save onnx model.

  • deploy_cfg (str | mmengine.Config) – Deployment config file or Config object.

  • model_cfg (str | mmengine.Config) – Model config file or Config object.

  • model_checkpoint (str) – A checkpoint path of PyTorch model, defaults to None.

  • device (str) – A string specifying device type, defaults to ‘cuda:0’.

mmdeploy.apis.torch2torchscript(img: Any, work_dir: str, save_file: str, deploy_cfg: Union[str, mmengine.config.config.Config], model_cfg: Union[str, mmengine.config.config.Config], model_checkpoint: Optional[str] = None, device: str = 'cuda:0')[source]

Convert PyTorch model to torchscript model.

Parameters

  • img (str | np.ndarray | torch.Tensor) – Input image used to assist converting model.

  • work_dir (str) – A working directory to save files.

  • save_file (str) – Filename to save torchscript model.

  • deploy_cfg (str | mmengine.Config) – Deployment config file or Config object.

  • model_cfg (str | mmengine.Config) – Model config file or Config object.

  • model_checkpoint (str) – A checkpoint path of PyTorch model, defaults to None.

  • device (str) – A string specifying device type, defaults to ‘cuda:0’.

mmdeploy.apis.visualize_model(model_cfg: Union[str, mmengine.config.config.Config], deploy_cfg: Union[str, mmengine.config.config.Config], model: Union[str, Sequence[str]], img: Union[str, numpy.ndarray, Sequence[str]], device: str, backend: Optional[mmdeploy.utils.constants.Backend] = None, output_file: Optional[str] = None, show_result: bool = False, **kwargs)[source]

Run inference with PyTorch or backend model and show results.

Examples

>>> from mmdeploy.apis import visualize_model
>>> model_cfg = ('mmdetection/configs/fcos/'
                 'fcos_r50_caffe_fpn_gn-head_1x_coco.py')
>>> deploy_cfg = ('configs/mmdet/detection/'
                  'detection_onnxruntime_dynamic.py')
>>> model = 'work_dir/fcos.onnx'
>>> img = 'demo.jpg'
>>> device = 'cpu'
>>> visualize_model(model_cfg, deploy_cfg, model,             img, device, show_result=True)
Parameters

  • model_cfg (str | mmengine.Config) – Model config file or Config object.

  • deploy_cfg (str | mmengine.Config) – Deployment config file or Config object.

  • model (str | Sequence[str]) – Input model or file(s).

  • img (str | np.ndarray | Sequence[str]) – Input image file or numpy array for inference.

  • device (str) – A string specifying device type.

  • backend (Backend) – Specifying backend type, defaults to None.

  • output_file (str) – Output file to save visualized image, defaults to None. Only valid if show_result is set to False.

  • show_result (bool) – Whether to show plotted image in windows, defaults to False.

apis/tensorrt

mmdeploy.apis.tensorrt.from_onnx(onnx_model: Union[str, onnx.onnx_ml_pb2.ModelProto], output_file_prefix: str, input_shapes: Dict[str, Sequence[int]], max_workspace_size: int = 0, fp16_mode: bool = False, int8_mode: bool = False, int8_param: Optional[dict] = None, device_id: int = 0, log_level: tensorrt.Logger.Severity = tensorrt.Logger.ERROR, **kwargs) → tensorrt.ICudaEngine[source]

Create a tensorrt engine from ONNX.

Parameters

  • onnx_model (str or onnx.ModelProto) – Input onnx model to convert from.

  • output_file_prefix (str) – The path to save the output ncnn file.

  • input_shapes (Dict[str, Sequence[int]]) – The min/opt/max shape of each input.

  • max_workspace_size (int) – To set max workspace size of TensorRT engine. some tactics and layers need large workspace. Defaults to 0.

  • fp16_mode (bool) – Specifying whether to enable fp16 mode. Defaults to False.

  • int8_mode (bool) – Specifying whether to enable int8 mode. Defaults to False.

  • int8_param (dict) – A dict of parameter int8 mode. Defaults to None.

  • device_id (int) – Choice the device to create engine. Defaults to 0.

  • log_level (trt.Logger.Severity) – The log level of TensorRT. Defaults to trt.Logger.ERROR.

Returns

The TensorRT engine created from onnx_model.

Return type

tensorrt.ICudaEngine

Example

>>> from mmdeploy.apis.tensorrt import from_onnx
>>> engine = from_onnx(
>>>             "onnx_model.onnx",
>>>             {'input': {"min_shape" : [1, 3, 160, 160],
>>>                        "opt_shape" : [1, 3, 320, 320],
>>>                        "max_shape" : [1, 3, 640, 640]}},
>>>             log_level=trt.Logger.WARNING,
>>>             fp16_mode=True,
>>>             max_workspace_size=1 << 30,
>>>             device_id=0)
>>>             })
mmdeploy.apis.tensorrt.is_available(with_custom_ops: bool = False) → bool

Check whether backend is installed.

Parameters

with_custom_ops (bool) – check custom ops exists.

Returns

True if backend package is installed.

Return type

bool

mmdeploy.apis.tensorrt.load(path: str, allocator: Optional[Any] = None) → tensorrt.ICudaEngine[source]

Deserialize TensorRT engine from disk.

Parameters

  • path (str) – The disk path to read the engine.

  • allocator (Any) – gpu allocator

Returns

The TensorRT engine loaded from disk.

Return type

tensorrt.ICudaEngine

mmdeploy.apis.tensorrt.onnx2tensorrt(work_dir: str, save_file: str, model_id: int, deploy_cfg: Union[str, mmengine.config.config.Config], onnx_model: Union[str, onnx.onnx_ml_pb2.ModelProto], device: str = 'cuda:0', partition_type: str = 'end2end', **kwargs)[source]

Convert ONNX to TensorRT.

Examples

>>> from mmdeploy.backend.tensorrt.onnx2tensorrt import onnx2tensorrt
>>> work_dir = 'work_dir'
>>> save_file = 'end2end.engine'
>>> model_id = 0
>>> deploy_cfg = ('configs/mmdet/detection/'
                  'detection_tensorrt_dynamic-320x320-1344x1344.py')
>>> onnx_model = 'work_dir/end2end.onnx'
>>> onnx2tensorrt(work_dir, save_file, model_id, deploy_cfg,
        onnx_model, 'cuda:0')
Parameters

  • work_dir (str) – A working directory.

  • save_file (str) – The base name of the file to save TensorRT engine. E.g. end2end.engine.

  • model_id (int) – Index of input model.

  • deploy_cfg (str | mmengine.Config) – Deployment config.

  • onnx_model (str | onnx.ModelProto) – input onnx model.

  • device (str) – A string specifying cuda device, defaults to ‘cuda:0’.

  • partition_type (str) – Specifying partition type of a model, defaults to ‘end2end’.

mmdeploy.apis.tensorrt.save(engine: Any, path: str) → None[source]

Serialize TensorRT engine to disk.

Parameters

  • engine (Any) – TensorRT engine to be serialized.

  • path (str) – The absolute disk path to write the engine.

apis/onnxruntime

mmdeploy.apis.onnxruntime.is_available(with_custom_ops: bool = False) → bool

Check whether backend is installed.

Parameters

with_custom_ops (bool) – check custom ops exists.

Returns

True if backend package is installed.

Return type

bool

apis/ncnn

mmdeploy.apis.ncnn.from_onnx(onnx_model: Union[onnx.onnx_ml_pb2.ModelProto, str], output_file_prefix: str)[source]

Convert ONNX to ncnn.

The inputs of ncnn include a model file and a weight file. We need to use an executable program to convert the .onnx file to a .param file and a .bin file. The output files will save to work_dir.

Example

>>> from mmdeploy.apis.ncnn import from_onnx
>>> onnx_path = 'work_dir/end2end.onnx'
>>> output_file_prefix = 'work_dir/end2end'
>>> from_onnx(onnx_path, output_file_prefix)
Parameters

  • onnx_path (ModelProto|str) – The path of the onnx model.

  • output_file_prefix (str) – The path to save the output ncnn file.

mmdeploy.apis.ncnn.is_available(with_custom_ops: bool = False) → bool

Check whether backend is installed.

Parameters

with_custom_ops (bool) – check custom ops exists.

Returns

True if backend package is installed.

Return type

bool

apis/pplnn

mmdeploy.apis.pplnn.is_available(with_custom_ops: bool = False) → bool

Check whether backend is installed.

Parameters

with_custom_ops (bool) – check custom ops exists.

Returns

True if backend package is installed.

Return type

bool