[Refactor] Add 'to_backend' in BackendManager (open-mmlab#1522)

* Refactor to backend

* export_postprocess_mask = False as defailt

* update zh_cn docs

* solve comment

* fix comment

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 `BackendManager`, 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.

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

@@ -106,7 +106,7 @@ MMDeploy 中的后端必须支持 ONNX，因此后端能直接加载“.onnx”
        call([onnx2ncnn_path, onnx_path, save_param, save_bin])\
    ```
 
-5. 在 `mmdeploy/apis` 中创建新后端库并声明对应 APIs
+   从 BackendManager 派生类，实现 `to_backend` 类方法。
 
    **例子**
 
@@ -128,32 +128,20 @@ MMDeploy 中的后端必须支持 ONNX，因此后端能直接加载“.onnx”
    **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。
+5. 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。
 
-7. 为新后端引擎代码添加相关注释和单元测试:).
+6. 为新后端引擎代码添加相关注释和单元测试:).
 
 ## 支持后端推理
 

mmdeploy/apis/utils/__init__.py

@@ -1,8 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .calibration import create_calib_input_data
-from .utils import build_task_processor, get_predefined_partition_cfg
+from .utils import (build_task_processor, get_predefined_partition_cfg,
+                    to_backend)
 
 __all__ = [
     'create_calib_input_data', 'build_task_processor',
-    'get_predefined_partition_cfg'
+    'get_predefined_partition_cfg', 'to_backend'
 ]

mmdeploy/apis/utils/utils.py

@@ -1,9 +1,13 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import logging
+from typing import Any, Optional, Sequence
+
 import mmcv
 
 from mmdeploy.codebase import BaseTask, get_codebase_class, import_codebase
 from mmdeploy.utils import (get_backend, get_codebase, get_task_type,
                             parse_device_id)
+from ..core import PIPELINE_MANAGER
 
 
 def check_backend_device(deploy_cfg: mmcv.Config, device: str):
@@ -62,3 +66,36 @@ def get_predefined_partition_cfg(deploy_cfg: mmcv.Config, partition_type: str):
     codebase = get_codebase_class(codebase_type)
     task_processor_class = codebase.get_task_class(task)
     return task_processor_class.get_partition_cfg(partition_type)
+
+
+@PIPELINE_MANAGER.register_pipeline()
+def to_backend(backend_name: str,
+               ir_files: Sequence[str],
+               work_dir: str,
+               deploy_cfg: Optional[Any] = None,
+               log_level: int = logging.INFO,
+               device: str = 'cpu',
+               **kwargs) -> Sequence[str]:
+    """Convert intermediate representation to given backend.
+
+    Args:
+        backend_name (str): The name of the backend.
+        ir_files (Sequence[str]): The intermediate representation files.
+        work_dir (str): The work directory, backend files and logs should
+            be save in this directory.
+        deploy_cfg (Any): The deploy config.
+        log_level (int, optional): The log level. Defaults to logging.INFO.
+        device (str, optional): The device type. Defaults to 'cpu'.
+
+    Returns:
+        Seqeuence[str]: Backend files.
+    """
+    from mmdeploy.backend.base import get_backend_manager
+    backend_mgr = get_backend_manager(backend_name)
+    return backend_mgr.to_backend(
+        ir_files=ir_files,
+        work_dir=work_dir,
+        deploy_cfg=deploy_cfg,
+        log_level=log_level,
+        device=device,
+        **kwargs)

mmdeploy/backend/ascend/backend_manager.py

@@ -1,4 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os.path as osp
 from typing import Any, Optional, Sequence
 
 from ..base import BACKEND_MANAGERS, BaseBackendManager
@@ -29,3 +31,38 @@ def build_wrapper(cls,
         """
         from .wrapper import AscendWrapper
         return AscendWrapper(model=backend_files[0], device=device)
+
+    @classmethod
+    def to_backend(cls,
+                   ir_files: Sequence[str],
+                   work_dir: str,
+                   deploy_cfg: Any,
+                   log_level: int = logging.INFO,
+                   device: str = 'cpu',
+                   **kwargs) -> Sequence[str]:
+        """Convert intermediate representation to given backend.
+
+        Args:
+            ir_files (Sequence[str]): The intermediate representation files.
+            work_dir (str): The work directory, backend files and logs should
+                be save in this directory.
+            deploy_cfg (Any): The deploy config.
+            log_level (int, optional): The log level. Defaults to logging.INFO.
+            device (str, optional): The device type. Defaults to 'cpu'.
+
+        Returns:
+            Seqeuence[str]: Backend files.
+        """
+        from mmdeploy.utils import get_model_inputs
+        from .onnx2ascend import from_onnx
+
+        model_inputs = get_model_inputs(deploy_cfg)
+
+        om_files = []
+        for model_id, onnx_path in enumerate(ir_files):
+            om_path = osp.splitext(onnx_path)[0] + '.om'
+            from_onnx(onnx_path, work_dir, model_inputs[model_id])
+            om_files.append(om_path)
+        backend_files = om_files
+
+        return backend_files

mmdeploy/backend/base/__init__.py

@@ -1,10 +1,12 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .backend_manager import BACKEND_MANAGERS, BaseBackendManager
+from .backend_manager import (BACKEND_MANAGERS, BaseBackendManager,
+                              get_backend_manager)
 from .backend_wrapper_registry import (BACKEND_WRAPPER, get_backend_file_count,
                                        get_backend_wrapper_class)
 from .base_wrapper import BaseWrapper
 
 __all__ = [
-    'BACKEND_MANAGERS', 'BaseBackendManager', 'BaseWrapper', 'BACKEND_WRAPPER',
-    'get_backend_wrapper_class', 'get_backend_file_count'
+    'BACKEND_MANAGERS', 'BaseBackendManager', 'get_backend_manager',
+    'BaseWrapper', 'BACKEND_WRAPPER', 'get_backend_wrapper_class',
+    'get_backend_file_count'
 ]

mmdeploy/backend/base/backend_manager.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import importlib
+import logging
 from abc import ABCMeta
 from typing import Any, Optional, Sequence
 
@@ -28,7 +29,31 @@ def build_wrapper(cls,
                 to None.
         """
         raise NotImplementedError(
-            f'build_wrapper has not been implemented for {cls}')
+            f'build_wrapper has not been implemented for `{cls.__name__}`')
+
+    @classmethod
+    def to_backend(cls,
+                   ir_files: Sequence[str],
+                   work_dir: str,
+                   deploy_cfg: Any,
+                   log_level: int = logging.INFO,
+                   device: str = 'cpu',
+                   **kwargs) -> Sequence[str]:
+        """Convert intermediate representation to given backend.
+
+        Args:
+            ir_files (Sequence[str]): The intermediate representation files.
+            work_dir (str): The work directory, backend files and logs should
+                be save in this directory.
+            deploy_cfg (Any): The deploy config.
+            log_level (int, optional): The log level. Defaults to logging.INFO.
+            device (str, optional): The device type. Defaults to 'cpu'.
+
+        Returns:
+            Seqeuence[str]: Backend files.
+        """
+        raise NotImplementedError(
+            f'to_backend has not been implemented for `{cls.__name__}`')
 
 
 class BackendManagerRegistry:
@@ -89,3 +114,18 @@ def find(self, name: str) -> BaseBackendManager:
 
 
 BACKEND_MANAGERS = BackendManagerRegistry()
+
+
+def get_backend_manager(name: str) -> BaseBackendManager:
+    """Get backend manager.
+
+    Args:
+        name (str): name of the backend.
+
+    Returns:
+        BaseBackendManager: The backend manager of given name
+    """
+    from enum import Enum
+    if isinstance(name, Enum):
+        name = name.value
+    return BACKEND_MANAGERS.find(name)

mmdeploy/backend/coreml/backend_manager.py

@@ -1,4 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os.path as osp
 from typing import Any, Optional, Sequence
 
 from ..base import BACKEND_MANAGERS, BaseBackendManager
@@ -29,3 +31,36 @@ def build_wrapper(cls,
         """
         from .wrapper import CoreMLWrapper
         return CoreMLWrapper(model_file=backend_files[0])
+
+    @classmethod
+    def to_backend(cls,
+                   ir_files: Sequence[str],
+                   work_dir: str,
+                   deploy_cfg: Any,
+                   log_level: int = logging.INFO,
+                   device: str = 'cpu',
+                   **kwargs) -> Sequence[str]:
+        """Convert intermediate representation to given backend.
+
+        Args:
+            ir_files (Sequence[str]): The intermediate representation files.
+            work_dir (str): The work directory, backend files and logs should
+                be save in this directory.
+            deploy_cfg (Any): The deploy config.
+            log_level (int, optional): The log level. Defaults to logging.INFO.
+            device (str, optional): The device type. Defaults to 'cpu'.
+
+        Returns:
+            Seqeuence[str]: Backend files.
+        """
+        from .torchscript2coreml import from_torchscript
+
+        coreml_files = []
+        for model_id, torchscript_path in enumerate(ir_files):
+            torchscript_name = osp.splitext(osp.split(torchscript_path)[1])[0]
+            output_file_prefix = osp.join(work_dir, torchscript_name)
+
+            from_torchscript(model_id, torchscript_path, output_file_prefix,
+                             deploy_cfg, coreml_files)
+
+        return coreml_files

mmdeploy/backend/ncnn/backend_manager.py

@@ -1,8 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-
+import logging
+import os.path as osp
+import sys
 from typing import Any, Optional, Sequence
 
-from mmdeploy.utils import get_backend_config
+from mmdeploy.utils import get_backend_config, get_root_logger
 from ..base import BACKEND_MANAGERS, BaseBackendManager
 
 
@@ -43,3 +45,46 @@ def build_wrapper(cls,
             bin_file=backend_files[1],
             output_names=output_names,
             use_vulkan=use_vulkan)
+
+    @classmethod
+    def to_backend(cls,
+                   ir_files: Sequence[str],
+                   work_dir: str,
+                   log_level: int = logging.INFO,
+                   device: str = 'cpu',
+                   **kwargs) -> Sequence[str]:
+        """Convert intermediate representation to given backend.
+
+        Args:
+            ir_files (Sequence[str]): The intermediate representation files.
+            work_dir (str): The work directory, backend files and logs should
+                be save in this directory.
+            log_level (int, optional): The log level. Defaults to logging.INFO.
+            device (str, optional): The device type. Defaults to 'cpu'.
+
+        Returns:
+            Seqeuence[str]: Backend files.
+        """
+        logger = get_root_logger()
+
+        from . import is_available
+
+        if not is_available():
+            logger.error('ncnn support is not available, please make sure:\n'
+                         '1) `mmdeploy_onnx2ncnn` existed in `PATH`\n'
+                         '2) python import ncnn success')
+            sys.exit(1)
+
+        from mmdeploy.apis.ncnn import get_output_model_file
+        from .onnx2ncnn import from_onnx
+
+        backend_files = []
+        for onnx_path in ir_files:
+            model_param_path, model_bin_path = get_output_model_file(
+                onnx_path, work_dir)
+            onnx_name = osp.splitext(osp.split(onnx_path)[1])[0]
+            from_onnx(onnx_path, osp.join(work_dir, onnx_name))
+
+            backend_files += [model_param_path, model_bin_path]
+
+        return backend_files