[Dostring]add example in apis docstring (#214)

* add example in apis docstring

* add backend example in docstring

* rm blank line

mmdeploy/apis/calibration.py

@@ -21,6 +21,20 @@ def create_calib_table(calib_file: str,
                        **kwargs) -> None:
     """Create calibration table.
 
+    Examples:
+        >>> from mmdeploy.apis import create_calib_table
+        >>> from mmdeploy.utils import get_calib_filename, load_config
+        >>> deploy_cfg = 'configs/mmdet/detection/' \
+            'detection_tensorrt-int8_dynamic-320x320-1344x1344.py'
+        >>> deploy_cfg = load_config(deploy_cfg)[0]
+        >>> calib_file = get_calib_filename(deploy_cfg)
+        >>> 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'
+        >>> create_calib_table(calib_file, deploy_cfg, \
+            model_cfg, model_checkpoint, device='cuda:0')
+
     Args:
         calib_file (str): Input calibration file.
         deploy_cfg (str | mmcv.Config): Deployment config.

mmdeploy/apis/extract_model.py

@@ -23,6 +23,30 @@ def extract_model(model: Union[str, onnx.ModelProto],
     The sub-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 = 'detector:input'
+        >>> end = ['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, end, dynamic_axes=dynamic_axes, \
+                save_file=save_file)
+
     Args:
         model (str | onnx.ModelProto): Input ONNX model to be extracted.
         start (str | Sequence[str]): Start marker(s) to extract.

mmdeploy/apis/inference.py

@@ -14,6 +14,18 @@ def inference_model(model_cfg: Union[str, mmcv.Config],
                     device: str) -> Any:
     """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)
+
     Args:
         model_cfg (str | mmcv.Config): Model config file or Config object.
         deploy_cfg (str | mmcv.Config): Deployment config file or Config

mmdeploy/apis/pytorch2onnx.py

@@ -62,6 +62,21 @@ def torch2onnx(img: Any,
                device: str = 'cuda:0'):
     """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)
+
     Args:
         img (str | np.ndarray | torch.Tensor): Input image used to assist
             converting model.

mmdeploy/apis/visualize.py

@@ -19,6 +19,18 @@ def visualize_model(model_cfg: Union[str, mmcv.Config],
                     show_result: bool = False):
     """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)
+
     Args:
         model_cfg (str | mmcv.Config): Model config file or Config object.
         deploy_cfg (str | mmcv.Config): Deployment config file or Config

mmdeploy/backend/ncnn/onnx2ncnn.py

@@ -33,6 +33,13 @@ def onnx2ncnn(onnx_path: str, save_param: str, save_bin: str):
     a 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.backend.ncnn.onnx2ncnn import onnx2ncnn
+        >>> onnx_path = 'work_dir/end2end.onnx'
+        >>> save_param = 'work_dir/end2end.param'
+        >>> save_bin = 'work_dir/end2end.bin'
+        >>> onnx2ncnn(onnx_path, save_param, save_bin)
+
     Args:
         onnx_path (str): The path of the onnx model.
         save_param (str): The path to save the output `.param` file.

mmdeploy/backend/openvino/onnx2openvino.py

@@ -58,6 +58,14 @@ def onnx2openvino(input_info: Dict[str, Union[List[int], torch.Size]],
                   output_names: List[str], onnx_path: str, work_dir: str):
     """Convert ONNX to OpenVINO.
 
+    Examples:
+        >>> from mmdeploy.backend.openvino.onnx2openvino import onnx2openvino
+        >>> input_info = {'input': [1,3,800,1344]}
+        >>> output_names = ['dets', 'labels']
+        >>> onnx_path = 'work_dir/end2end.onnx'
+        >>> work_dir = 'work_dir'
+        >>> onnx2openvino(input_info, output_names, onnx_path, work_dir)
+
     Args:
         input_info (Dict[str, Union[List[int], torch.Size]]):
             The shape of each input.

mmdeploy/backend/tensorrt/onnx2tensorrt.py

@@ -21,6 +21,17 @@ def onnx2tensorrt(work_dir: str,
                   **kwargs):
     """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')
+
     Args:
         work_dir (str): A working directory.
         save_file (str): The base name of the file to save TensorRT engine.