[Refactor] Add backend manager for 1.x (#1515)

* backend manager 1.x

* update pplnn init

* rename file

* add to backend

* add check env and misc

* fix action

* fix ut

* fix comment

Author: grimoire

.github/workflows/backend-ncnn.yml

@@ -79,4 +79,4 @@ jobs:
           python -m pip install torch==1.8.2 torchvision==0.9.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu
           python -m pip install mmcv-lite
           python tools/scripts/build_ubuntu_x64_ncnn.py
-          python -c 'import mmdeploy.apis.ncnn as ncnn_api; assert ncnn_api.is_available() and ncnn_api.is_custom_ops_available()'
+          python -c 'import mmdeploy.apis.ncnn as ncnn_api; assert ncnn_api.is_available(with_custom_ops=True)'

.github/workflows/backend-ort.yml

@@ -36,7 +36,7 @@ jobs:
           python -m pip install torch==1.8.2 torchvision==0.9.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu
           python -m pip install mmcv-lite openmim
           python tools/scripts/build_ubuntu_x64_ort.py
-          python -c 'import mmdeploy.apis.onnxruntime as ort_api; assert ort_api.is_available() and ort_api.is_custom_ops_available()'
+          python -c 'import mmdeploy.apis.onnxruntime as ort_api; assert ort_api.is_available(with_custom_ops=True)'
       - name: test mmcls full pipeline
         run: |
           python -m mim install $(cat requirements/codebases.txt | grep mmcls)

csrc/mmdeploy/backend_ops/torchscript/ops/CMakeLists.txt

@@ -31,3 +31,6 @@ mmdeploy_export(${PROJECT_NAME}_obj)
 mmdeploy_add_module(${PROJECT_NAME} MODULE EXCLUDE "")
 target_link_libraries(${PROJECT_NAME} PUBLIC ${PROJECT_NAME}_obj)
 add_library(mmdeploy::torchscript_ops ALIAS ${PROJECT_NAME})
+
+set(_TORCHJIT_OPS_DIR ${CMAKE_SOURCE_DIR}/mmdeploy/lib)
+install(TARGETS ${PROJECT_NAME} DESTINATION ${_TORCHJIT_OPS_DIR})

docs/en/07-developer-guide/support_new_backend.md

@@ -123,32 +123,20 @@ The backends in MMDeploy must support the ONNX. The backend loads the ".onnx" fi
        __all__ += ['onnx2ncnn', 'get_output_model_file']
    ```
 
-   Then add the codes about conversion to `tools/deploy.py` using these APIs if necessary.
+   Create a backend manager class which derive from `BaseBackendManager`, implement its `to_backend` static method.
 
    **Example:**
 
    ```Python
-   # tools/deploy.py
-   # ...
-       elif backend == Backend.NCNN:
-           from mmdeploy.apis.ncnn import is_available as is_available_ncnn
-
-           if not is_available_ncnn():
-               logging.error('ncnn support is not available.')
-               exit(-1)
-
-           from mmdeploy.apis.ncnn import onnx2ncnn, get_output_model_file
-
-           backend_files = []
-           for onnx_path in onnx_files:
-               create_process(
-                   f'onnx2ncnn with {onnx_path}',
-                   target=onnx2ncnn,
-                   args=(onnx_path, args.work_dir),
-                   kwargs=dict(),
-                   ret_value=ret_value)
-               backend_files += get_output_model_file(onnx_path, args.work_dir)
-   # ...
+    @classmethod
+    def to_backend(cls,
+                ir_files: Sequence[str],
+                deploy_cfg: Any,
+                work_dir: str,
+                log_level: int = logging.INFO,
+                device: str = 'cpu',
+                **kwargs) -> Sequence[str]:
+        return ir_files
    ```
 
 6. Convert the models of OpenMMLab to backends (if necessary) and inference on backend engine. If you find some incompatible operators when testing, you can try to rewrite the original model for the backend following the [rewriter tutorial](support_new_model.md) or add custom operators.
@@ -209,23 +197,26 @@ Although the backend engines are usually implemented in C/C++, it is convenient
            self.sess.run_with_iobinding(io_binding)
    ```
 
-4. Add a default initialization method for the new wrapper in `mmdeploy/codebase/base/backend_model.py`
+4. Create a backend manager class which derive from `BaseBackendManager`, implement its `build_wrapper` static method.
 
    **Example:**
 
    ```Python
-       @staticmethod
-       def _build_wrapper(backend: Backend,
-                          backend_files: Sequence[str],
-                          device: str,
-                          input_names: Optional[Sequence[str]] = None,
-                          output_names: Optional[Sequence[str]] = None):
-           if backend == Backend.ONNXRUNTIME:
-               from mmdeploy.backend.onnxruntime import ORTWrapper
-               return ORTWrapper(
-                   onnx_file=backend_files[0],
-                   device=device,
-                   output_names=output_names)
+        @BACKEND_MANAGERS.register('onnxruntime')
+        class ONNXRuntimeManager(BaseBackendManager):
+            @classmethod
+            def build_wrapper(cls,
+                              backend_files: Sequence[str],
+                              device: str = 'cpu',
+                              input_names: Optional[Sequence[str]] = None,
+                              output_names: Optional[Sequence[str]] = None,
+                              deploy_cfg: Optional[Any] = None,
+                              **kwargs):
+                from .wrapper import ORTWrapper
+                return ORTWrapper(
+                    onnx_file=backend_files[0],
+                    device=device,
+                    output_names=output_names)
    ```
 
 5. Add docstring and unit tests for new code :).

docs/zh_cn/07-developer-guide/support_new_backend.md

@@ -123,32 +123,20 @@ MMDeploy 中的后端必须支持 ONNX，因此后端能直接加载“.onnx”
        __all__ += ['onnx2ncnn', 'get_output_model_file']
    ```
 
-   然后根据需要使用这些 APIs 为 `tools/deploy.py` 添加相关转换代码
+   从 BaseBackendManager 派生类，实现 `to_backend` 类方法。
 
    **例子**
 
    ```Python
-   # tools/deploy.py
-   # ...
-       elif backend == Backend.NCNN:
-           from mmdeploy.apis.ncnn import is_available as is_available_ncnn
-
-           if not is_available_ncnn():
-               logging.error('ncnn support is not available.')
-               exit(-1)
-
-           from mmdeploy.apis.ncnn import onnx2ncnn, get_output_model_file
-
-           backend_files = []
-           for onnx_path in onnx_files:
-               create_process(
-                   f'mmdeploy_onnx2ncnn with {onnx_path}',
-                   target=onnx2ncnn,
-                   args=(onnx_path, args.work_dir),
-                   kwargs=dict(),
-                   ret_value=ret_value)
-               backend_files += get_output_model_file(onnx_path, args.work_dir)
-   # ...
+   @classmethod
+    def to_backend(cls,
+                   ir_files: Sequence[str],
+                   deploy_cfg: Any,
+                   work_dir: str,
+                   log_level: int = logging.INFO,
+                   device: str = 'cpu',
+                   **kwargs) -> Sequence[str]:
+        return ir_files
    ```
 
 6. 将 OpenMMLab 的模型转换后(如有必要)并在后端引擎上进行推理。如果在测试时发现一些不兼容的算子，可以尝试按照[重写器教程](support_new_model.md)为后端重写原始模型或添加自定义算子。
@@ -210,22 +198,26 @@ MMDeploy 中的后端必须支持 ONNX，因此后端能直接加载“.onnx”
            self.sess.run_with_iobinding(io_binding)
    ```
 
-4. 为新封装装器添加默认初始化方法 `mmdeploy/codebase/base/backend_model.py`
+4. 从 `BaseBackendManager` 派生接口类，实现 `build_wrapper` 静态方法
 
    **例子**
 
    ```Python
-       @staticmethod
-       def _build_wrapper(backend: Backend,
-                          backend_files: Sequence[str],
-                          device: str,
-                          output_names: Optional[Sequence[str]] = None):
-           if backend == Backend.ONNXRUNTIME:
-               from mmdeploy.backend.onnxruntime import ORTWrapper
-               return ORTWrapper(
-                   onnx_file=backend_files[0],
-                   device=device,
-                   output_names=output_names)
+        @BACKEND_MANAGERS.register('onnxruntime')
+        class ONNXRuntimeManager(BaseBackendManager):
+            @classmethod
+            def build_wrapper(cls,
+                              backend_files: Sequence[str],
+                              device: str = 'cpu',
+                              input_names: Optional[Sequence[str]] = None,
+                              output_names: Optional[Sequence[str]] = None,
+                              deploy_cfg: Optional[Any] = None,
+                              **kwargs):
+                from .wrapper import ORTWrapper
+                return ORTWrapper(
+                    onnx_file=backend_files[0],
+                    device=device,
+                    output_names=output_names)
    ```
 
 5. 为新后端引擎代码添加相关注释和单元测试 :).

mmdeploy/apis/__init__.py

@@ -1,19 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from .calibration import create_calib_input_data
+from .extract_model import extract_model
+from .inference import inference_model
+from .pytorch2onnx import torch2onnx
+from .pytorch2torchscript import torch2torchscript
+from .utils import build_task_processor, get_predefined_partition_cfg
+from .visualize import visualize_model
 
-# mmcv & mmengine dependency
-try:
-    from .calibration import create_calib_input_data
-    from .extract_model import extract_model
-    from .inference import inference_model
-    from .pytorch2onnx import torch2onnx
-    from .pytorch2torchscript import torch2torchscript
-    from .utils import build_task_processor, get_predefined_partition_cfg
-    from .visualize import visualize_model
-
-    __all__ = [
-        'create_calib_input_data', 'extract_model', 'inference_model',
-        'torch2onnx', 'torch2torchscript', 'build_task_processor',
-        'get_predefined_partition_cfg', 'visualize_model'
-    ]
-except Exception:
-    pass
+__all__ = [
+    'create_calib_input_data', 'extract_model', 'inference_model',
+    'torch2onnx', 'torch2torchscript', 'build_task_processor',
+    'get_predefined_partition_cfg', 'visualize_model'
+]

mmdeploy/apis/calibration.py

@@ -4,11 +4,7 @@
 
 from mmengine import Config
 
-from mmdeploy.core import patch_model
-from mmdeploy.utils import (IR, cfg_apply_marks, get_backend, get_ir_config,
-                            load_config)
 from .core import PIPELINE_MANAGER, no_mp
-from .utils import create_calib_input_data as create_calib_input_data_impl
 
 
 @PIPELINE_MANAGER.register_pipeline()
@@ -34,6 +30,11 @@ def create_calib_input_data(calib_file: str,
         dataset_type (str, optional): The dataset type. Defaults to 'val'.
         device (str, optional): Device to create dataset. Defaults to 'cpu'.
     """
+
+    from mmdeploy.core import patch_model
+    from mmdeploy.utils import (IR, cfg_apply_marks, get_backend,
+                                get_ir_config, load_config)
+    from .utils import create_calib_input_data as create_calib_input_data_impl
     with no_mp():
         if dataset_cfg is None:
             dataset_cfg = model_cfg

mmdeploy/apis/extract_model.py

@@ -5,7 +5,6 @@
 import onnx
 
 from .core import PIPELINE_MANAGER
-from .onnx import extract_partition
 
 
 @PIPELINE_MANAGER.register_pipeline()
@@ -63,5 +62,6 @@ def extract_model(model: Union[str, onnx.ModelProto],
         onnx.ModelProto: The extracted model.
     """
 
+    from .onnx import extract_partition
     return extract_partition(model, start_marker, end_marker, start_name_map,
                              end_name_map, dynamic_axes, save_file)

mmdeploy/apis/inference.py

@@ -3,9 +3,6 @@
 
 import mmengine
 import numpy as np
-import torch
-
-from mmdeploy.utils import get_input_shape, load_config
 
 
 def inference_model(model_cfg: Union[str, mmengine.Config],
@@ -37,6 +34,9 @@ def inference_model(model_cfg: Union[str, mmengine.Config],
     Returns:
         Any: The inference results
     """
+    import torch
+
+    from mmdeploy.utils import get_input_shape, load_config
     deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)
 
     from mmdeploy.apis.utils import build_task_processor

mmdeploy/apis/ncnn/__init__.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmdeploy.backend.ncnn import from_onnx as _from_onnx
-from mmdeploy.backend.ncnn import is_available, is_custom_ops_available
+from mmdeploy.backend.ncnn import is_available
 from ..core import PIPELINE_MANAGER
 
 from_onnx = PIPELINE_MANAGER.register_pipeline()(_from_onnx)
 
-__all__ = ['is_available', 'is_custom_ops_available', 'from_onnx']
+__all__ = ['is_available', 'from_onnx']
 
 if is_available():
     try: