[Refactor] Refactor codebase (open-mmlab#220)

* [WIP] Refactor v2.0 (open-mmlab#163)

* Refactor backend wrapper

* Refactor mmdet.inference

* Fix

* merge

* refactor utils

* Use deployer and deploy_model to manage pipeline

* Resolve comments

* Add a real inference api function

* rename wrappers

* Set execute to private method

* Rename deployer deploy_model

* Refactor task

* remove type hint

* lint

* Resolve comments

* resolve comments

* lint

* docstring

* [Fix]: Fix bugs in details in refactor branch (open-mmlab#192)

* [WIP] Refactor v2.0 (open-mmlab#163)

* Refactor backend wrapper

* Refactor mmdet.inference

* Fix

* merge

* refactor utils

* Use deployer and deploy_model to manage pipeline

* Resolve comments

* Add a real inference api function

* rename wrappers

* Set execute to private method

* Rename deployer deploy_model

* Refactor task

* remove type hint

* lint

* Resolve comments

* resolve comments

* lint

* docstring

* Fix errors

* lint

* resolve comments

* fix bugs

* conflict

* lint and typo

* Resolve comment

* refactor mmseg (open-mmlab#201)

* support mmseg

* fix docstring

* fix docstring

* [Refactor]: Get the count of backend files (open-mmlab#202)

* Fix backend files

* resolve comments

* lint

* Fix ncnn

* [Refactor]: Refactor folders of mmdet (open-mmlab#200)

* Move folders

* lint

* test object detection model

* lint

* reset changes

* fix openvino

* resolve comments

* __init__.py

* Fix path

* [Refactor]: move mmseg (open-mmlab#206)

* [Refactor]: Refactor mmedit (open-mmlab#205)

* feature mmedit

* edit2.0

* edit

* refactor mmedit

* fix __init__.py

* fix __init__

* fix formai

* fix comment

* fix comment

* Fix wrong func_name of ConvFCBBoxHead (open-mmlab#209)

* [Refactor]: Refactor mmdet unit test (open-mmlab#207)

* Move folders

* lint

* test object detection model

* lint

* WIP

* remove print

* finish unit test

* Fix tests

* resolve comments

* Add mask test

* lint

* resolve comments

* Refine cfg file

* Move files

* add files

* Fix path

* [Unittest]: Refine the unit tests in mmdet open-mmlab#214

* [Refactor] refactor mmocr to mmdeploy/codebase (open-mmlab#213)

* refactor mmocr to mmdeploy/codebase

* fix docstring of show_result

* fix docstring of visualize

* refine docstring

* replace print with logging

* refince codes

* resolve comments

* resolve comments

* [Refactor]: mmseg  tests (open-mmlab#210)

* refactor mmseg tests

* rename test_codebase

* update

* add model.py

* fix

* [Refactor] Refactor mmcls and the package (open-mmlab#217)

* refactor mmcls

* fix yapf

* fix isort

* refactor-mmcls-package

* fix print to logging

* fix docstrings according to others comments

* fix comments

* fix comments

* fix allentdans comment in pr215

* remove mmocr init

* [Refactor] Refactor mmedit tests (open-mmlab#212)

* feature mmedit

* edit2.0

* edit

* refactor mmedit

* fix __init__.py

* fix __init__

* fix formai

* fix comment

* fix comment

* buff

* edit test and code refactor

* refactor dir

* refactor tests/mmedit

* fix docstring

* add test coverage

* fix lint

* fix comment

* fix comment

* Update typehint (open-mmlab#216)

* update type hint

* update docstring

* update

* remove file

* fix ppl

* Refine get_predefined_partition_cfg

* fix tensorrt version > 8

* move parse_cuda_device_id to device.py

* Fix cascade

* onnx2ncnn docstring

Co-authored-by: Yifan Zhou <[email protected]>
Co-authored-by: RunningLeon <[email protected]>
Co-authored-by: VVsssssk <[email protected]>
Co-authored-by: AllentDan <[email protected]>
Co-authored-by: hanrui1sensetime <[email protected]>

Author: 6 people

backend_ops/ncnn/pyncnn_ext/CMakeLists.txt

@@ -11,4 +11,4 @@ pybind11_add_module(ncnn_ext ncnn_ext.cpp)
 target_link_libraries(ncnn_ext PUBLIC ncnn ${SHARED_TARGET})
 set_target_properties(
   ncnn_ext PROPERTIES LIBRARY_OUTPUT_DIRECTORY
-                      ${CMAKE_SOURCE_DIR}/mmdeploy/apis/ncnn)
+                      ${CMAKE_SOURCE_DIR}/mmdeploy/backend/ncnn)

mmdeploy/__init__.py

@@ -7,28 +7,3 @@
     importlib.import_module('mmdeploy.mmcv')
 else:
     logging.debug('mmcv is not installed.')
-
-if importlib.util.find_spec('mmcls'):
-    importlib.import_module('mmdeploy.mmcls')
-else:
-    logging.debug('mmcls is not installed.')
-
-if importlib.util.find_spec('mmdet'):
-    importlib.import_module('mmdeploy.mmdet')
-else:
-    logging.debug('mmdet is not installed.')
-
-if importlib.util.find_spec('mmseg'):
-    importlib.import_module('mmdeploy.mmseg')
-else:
-    logging.debug('mmseg is not installed.')
-
-if importlib.util.find_spec('mmocr'):
-    importlib.import_module('mmdeploy.mmocr')
-else:
-    logging.debug('mmocr is not installed.')
-
-if importlib.util.find_spec('mmedit'):
-    importlib.import_module('mmdeploy.mmedit')
-else:
-    logging.debug('mmedit is not installed.')

mmdeploy/apis/__init__.py

@@ -2,13 +2,11 @@
 from .extract_model import extract_model
 from .inference import inference_model
 from .pytorch2onnx import torch2onnx, torch2onnx_impl
-from .test import post_process_outputs, single_gpu_test
-from .utils import (build_dataloader, build_dataset, get_tensor_from_input,
-                    init_backend_model)
+from .utils import build_task_processor, get_predefined_partition_cfg
+from .visualize import visualize_model
 
 __all__ = [
-    'create_calib_table', 'torch2onnx_impl', 'torch2onnx', 'extract_model',
-    'inference_model', 'init_backend_model', 'single_gpu_test',
-    'post_process_outputs', 'build_dataset', 'get_tensor_from_input',
-    'build_dataloader'
+    'create_calib_table', 'extract_model', 'inference_model', 'torch2onnx',
+    'torch2onnx_impl', 'build_task_processor', 'get_predefined_partition_cfg',
+    'visualize_model'
 ]

mmdeploy/apis/calibration.py

@@ -7,9 +7,7 @@
 
 from mmdeploy.core import (RewriterContext, patch_model,
                            reset_mark_function_count)
-from mmdeploy.utils import cfg_apply_marks, get_codebase, load_config
-from .utils import (build_dataloader, build_dataset, get_tensor_from_input,
-                    init_pytorch_model, run_inference)
+from mmdeploy.utils import cfg_apply_marks, load_config
 
 
 def create_calib_table(calib_file: str,
@@ -46,32 +44,33 @@ def create_calib_table(calib_file: str,
     # load dataset_cfg if necessary
     dataset_cfg = load_config(dataset_cfg)[0]
 
-    codebase = get_codebase(deploy_cfg)
+    from mmdeploy.apis.utils import build_task_processor
+    task_processor = build_task_processor(model_cfg, deploy_cfg, device)
+
     apply_marks = cfg_apply_marks(deploy_cfg)
     backend = 'default'
-    model = init_pytorch_model(
-        codebase, model_cfg, model_checkpoint, device=device)
-    dataset = build_dataset(codebase, dataset_cfg, dataset_type)
+    model = task_processor.init_pytorch_model(model_checkpoint)
+    dataset = task_processor.build_dataset(dataset_cfg, dataset_type)
 
     # patch model
     patched_model = patch_model(model, cfg=deploy_cfg, backend=backend)
 
-    with h5py.File(calib_file, mode='w') as calib_file:
-        calib_data_group = calib_file.create_group('calib_data')
+    with h5py.File(calib_file, mode='w') as file:
+        calib_data_group = file.create_group('calib_data')
 
         if not apply_marks:
             # create end2end group
             input_data_group = calib_data_group.create_group('end2end')
             input_group = input_data_group.create_group('input')
-        dataloader = build_dataloader(
-            codebase, dataset, 1, 1, dist=False, shuffle=False)
+        dataloader = task_processor.build_dataloader(
+            dataset, 1, 1, dist=False, shuffle=False)
         patched_model = MMDataParallel(patched_model, device_ids=[device_id])
         prog_bar = mmcv.ProgressBar(len(dataset))
         for data_id, input_data in enumerate(dataloader):
 
             if not apply_marks:
                 # save end2end data
-                input_tensor = get_tensor_from_input(codebase, input_data)
+                input_tensor = task_processor.get_tensor_from_input(input_data)
                 input_ndarray = input_tensor.detach().cpu().numpy()
                 input_group.create_dataset(
                     str(data_id),
@@ -84,10 +83,10 @@ def create_calib_table(calib_file: str,
                     cfg=deploy_cfg,
                     backend=backend,
                     create_calib=True,
-                    calib_file=calib_file,
+                    calib_file=file,
                     data_id=data_id):
                 reset_mark_function_count()
-                _ = run_inference(codebase, input_data, patched_model)
-            calib_file.flush()
+                _ = task_processor.run_inference(patched_model, input_data)
+            file.flush()
 
             prog_bar.update()

mmdeploy/apis/extract_model.py

@@ -16,7 +16,7 @@ def extract_model(model: Union[str, onnx.ModelProto],
                   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):
+                  save_file: Optional[str] = None) -> onnx.ModelProto:
     """Extract sub-model from an ONNX model.
 
     The sub-model is defined by the names of the input and output tensors

mmdeploy/apis/inference.py

@@ -1,75 +1,40 @@
-from typing import Optional, Sequence, Union
+from typing import Any, Sequence, Union
 
 import mmcv
 import numpy as np
 import torch
 
-from mmdeploy.utils import (Backend, get_backend, get_codebase,
-                            get_input_shape, get_task_type, load_config)
-from .utils import (create_input, init_backend_model, init_pytorch_model,
-                    run_inference, visualize)
+from mmdeploy.utils import get_input_shape, load_config
 
 
 def inference_model(model_cfg: Union[str, mmcv.Config],
                     deploy_cfg: Union[str, mmcv.Config],
-                    model: Union[str, Sequence[str], torch.nn.Module],
-                    img: Union[str, np.ndarray],
-                    device: str,
-                    backend: Optional[Backend] = None,
-                    output_file: Optional[str] = None,
-                    show_result: bool = False):
+                    backend_files: Sequence[str], img: Union[str, np.ndarray],
+                    device: str) -> Any:
     """Run inference with PyTorch or backend model and show results.
 
     Args:
         model_cfg (str | mmcv.Config): Model config file or Config object.
         deploy_cfg (str | mmcv.Config): Deployment config file or Config
             object.
-        model (str | list[str], torch.nn.Module): Input model or file(s).
+        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.
-        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`.
+
+    Returns:
+        Any: The inference results
     """
     deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)
 
-    codebase = get_codebase(deploy_cfg)
-    task = get_task_type(deploy_cfg)
-    input_shape = get_input_shape(deploy_cfg)
-    if backend is None:
-        backend = get_backend(deploy_cfg)
-
-    if isinstance(model, str):
-        model = [model]
+    from mmdeploy.apis.utils import build_task_processor
+    task_processor = build_task_processor(model_cfg, deploy_cfg, device)
 
-    if isinstance(model, (list, tuple)):
-        assert len(model) > 0, 'Model should have at least one element.'
-        assert all([isinstance(m, str) for m in model]), 'All elements in the \
-            list should be str'
+    model = task_processor.init_backend_model(backend_files)
 
-        if backend == Backend.PYTORCH:
-            model = init_pytorch_model(codebase, model_cfg, model[0], device)
-        else:
-            device_id = -1 if device == 'cpu' else 0
-            model = init_backend_model(
-                model,
-                model_cfg=model_cfg,
-                deploy_cfg=deploy_cfg,
-                device_id=device_id)
-
-    model_inputs, _ = create_input(codebase, task, model_cfg, img, input_shape,
-                                   device)
+    input_shape = get_input_shape(deploy_cfg)
+    model_inputs, _ = task_processor.create_input(img, input_shape)
 
     with torch.no_grad():
-        result = run_inference(codebase, model_inputs, model)
+        result = task_processor.run_inference(model, model_inputs)
 
-    visualize(
-        codebase,
-        img,
-        result=result,
-        model=model,
-        output_file=output_file,
-        backend=backend,
-        show_result=show_result)
+    return result

mmdeploy/apis/ncnn/__init__.py

@@ -1,28 +1,8 @@
-import importlib
-import os.path as osp
-
-from .init_plugins import get_onnx2ncnn_path, get_ops_path
-
-__all__ = ['get_ops_path', 'get_onnx2ncnn_path']
-
-
-def is_available():
-    """Check whether ncnn with extension is installed.
-
-    Returns:
-        bool: True if ncnn and its extension are installed.
-    """
-    ncnn_ops_path = get_ops_path()
-    if not osp.exists(ncnn_ops_path):
-        return False
-    has_pyncnn = importlib.util.find_spec('ncnn') is not None
-    has_pyncnn_ext = importlib.util.find_spec(
-        'mmdeploy.apis.ncnn.ncnn_ext') is not None
-
-    return has_pyncnn and has_pyncnn_ext
+from mmdeploy.backend.ncnn import is_available
 
+__all__ = ['is_available']
 
 if is_available():
-    from .ncnn_utils import NCNNWrapper
-
-    __all__ += ['NCNNWrapper']
+    from mmdeploy.backend.ncnn.onnx2ncnn import (onnx2ncnn,
+                                                 get_output_model_file)
+    __all__ += ['onnx2ncnn', 'get_output_model_file']

mmdeploy/apis/onnxruntime/__init__.py

@@ -1,22 +1,3 @@
-import importlib
-import os.path as osp
+from mmdeploy.backend.onnxruntime import is_available
 
-from .init_plugins import get_ops_path
-
-
-def is_available():
-    """Check whether onnxruntime and its custom ops are installed.
-
-    Returns:
-        bool: True if onnxruntime package is installed and its
-        custom ops are compiled.
-    """
-    onnxruntime_op_path = get_ops_path()
-    if not osp.exists(onnxruntime_op_path):
-        return False
-    return importlib.util.find_spec('onnxruntime') is not None
-
-
-if is_available():
-    from .onnxruntime_utils import ORTWrapper
-    __all__ = ['get_ops_path', 'ORTWrapper']
+__all__ = ['is_available']

mmdeploy/apis/openvino/__init__.py

@@ -1,19 +1,11 @@
-import importlib
-
-
-def is_available() -> bool:
-    """Checking if OpenVINO is installed.
-
-    Returns:
-        bool: True if OpenVINO is installed.
-    """
-    return importlib.util.find_spec('openvino') is not None
+from mmdeploy.backend.openvino import is_available
 
+__all__ = ['is_available']
 
 if is_available():
-    from .openvino_utils import OpenVINOWrapper, get_input_shape_from_cfg
-    from .onnx2openvino import (onnx2openvino, get_output_model_file)
-    __all__ = [
-        'OpenVINOWrapper', 'onnx2openvino', 'get_output_model_file',
-        'get_input_shape_from_cfg'
+    from mmdeploy.backend.openvino.onnx2openvino \
+        import onnx2openvino, get_output_model_file
+    from .utils import get_input_shape_from_cfg
+    __all__ += [
+        'onnx2openvino', 'get_output_model_file', 'get_input_shape_from_cfg'
     ]

mmdeploy/apis/openvino/utils.py

@@ -0,0 +1,22 @@
+from typing import List
+
+import mmcv
+
+
+def get_input_shape_from_cfg(config: mmcv.Config) -> List[int]:
+    """Get the input shape from the model config for OpenVINO Model Optimizer.
+
+    Args:
+        config (mmcv.Config): Model config.
+    Returns:
+        List[int]: The input shape in [1, 3, H, W] format from config
+            or [1, 3, 800, 1344].
+    """
+    shape = []
+    test_pipeline = config.get('test_pipeline', None)
+    if test_pipeline is not None:
+        img_scale = test_pipeline[1]['img_scale']
+        shape = [1, 3, img_scale[1], img_scale[0]]
+    else:
+        shape = [1, 3, 800, 1344]
+    return shape