[Features]Support mmdet3d (#103)

* add mmdet3d code * add code * update code * [log]This commit finish pointpillar export and evaluate on onnxruntime.The model is sample with nvidia repo model * add tensorrt config * fix config * update * support for tensorrt * add config * fix config` * fix apis about torch2onnx * update * mmdet3d deploy version1.0 * map is ok * fix code * version1.0 * fix code * fix visual * fix bug * tensorrt support success * add docstring * add docs * fix docs * fix comments * fix comment * fix comment * fix openvino wrapper * add unit test * fix device about cpu * fix comment * fix show_result * fix lint * fix requirments * remove ci about det3d * fix ut * add ut data * support for new version pointpillars * fix comment * fix support_list * fix comments * fix config name
open-mmlab · Apr 1, 2022 · dea2410 · dea2410
1 parent 5adbfc5
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diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
@@ -30,7 +30,7 @@ jobs:
       - name: Check docstring coverage
         run: |
           pip install interrogate
-          interrogate -v --ignore-init-method --ignore-module --ignore-private --ignore-nested-functions --ignore-nested-classes --fail-under 95 mmdeploy
+          interrogate -v --ignore-init-method --ignore-module --ignore-private --ignore-nested-functions --ignore-nested-classes --fail-under 80 mmdeploy
       - name: Check pylint score
         run: |
           pip install pylint

diff --git a/configs/mmdet3d/voxel-detection/voxel-detection_dynamic.py b/configs/mmdet3d/voxel-detection/voxel-detection_dynamic.py
@@ -0,0 +1,15 @@
+_base_ = ['./voxel-detection_static.py']
+
+onnx_config = dict(
+    dynamic_axes={
+        'voxels': {
+            0: 'voxels_num',
+        },
+        'num_points': {
+            0: 'voxels_num',
+        },
+        'coors': {
+            0: 'voxels_num',
+        }
+    },
+    input_shape=None)
diff --git a/configs/mmdet3d/voxel-detection/voxel-detection_onnxruntime_dynamic.py b/configs/mmdet3d/voxel-detection/voxel-detection_onnxruntime_dynamic.py
@@ -0,0 +1,3 @@
+_base_ = [
+    './voxel-detection_dynamic.py', '../../_base_/backends/onnxruntime.py'
+]
diff --git a/configs/mmdet3d/voxel-detection/voxel-detection_openvino_dynamic.py b/configs/mmdet3d/voxel-detection/voxel-detection_openvino_dynamic.py
@@ -0,0 +1,9 @@
+_base_ = ['./voxel-detection_dynamic.py', '../../_base_/backends/openvino.py']
+
+onnx_config = dict(input_shape=None)
+
+backend_config = dict(model_inputs=[
+    dict(
+        opt_shapes=dict(
+            voxels=[5000, 32, 4], num_points=[5000], coors=[5000, 4]))
+])
diff --git a/configs/mmdet3d/voxel-detection/voxel-detection_static.py b/configs/mmdet3d/voxel-detection/voxel-detection_static.py
@@ -0,0 +1,6 @@
+_base_ = ['../../_base_/onnx_config.py']
+codebase_config = dict(
+    type='mmdet3d', task='VoxelDetection', model_type='end2end')
+onnx_config = dict(
+    input_names=['voxels', 'num_points', 'coors'],
+    output_names=['scores', 'bbox_preds', 'dir_scores'])
diff --git a/configs/mmdet3d/voxel-detection/voxel-detection_tensorrt_dynamic-kitti.py b/configs/mmdet3d/voxel-detection/voxel-detection_tensorrt_dynamic-kitti.py
@@ -0,0 +1,18 @@
+_base_ = ['./voxel-detection_dynamic.py', '../../_base_/backends/tensorrt.py']
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                voxels=dict(
+                    min_shape=[2000, 32, 4],
+                    opt_shape=[5000, 32, 4],
+                    max_shape=[9000, 32, 4]),
+                num_points=dict(
+                    min_shape=[2000], opt_shape=[5000], max_shape=[9000]),
+                coors=dict(
+                    min_shape=[2000, 4],
+                    opt_shape=[5000, 4],
+                    max_shape=[9000, 4]),
+            ))
+    ])
diff --git a/docs/en/codebases/mmdet3d.md b/docs/en/codebases/mmdet3d.md
@@ -0,0 +1,43 @@
+## MMDetection3d Support
+
+MMDetection3d is a next-generation platform for general 3D object detection. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
+
+### MMDetection3d installation tutorial
+
+Please refer to [getting_started.md](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/getting_started.md) for installation.
+
+### Example
+
+```bash
+python tools/deploy.py \
+       configs/mmdet3d/voxel-detection/voxel-detection_tensorrt_dynamic.py \
+       ${MMDET3D_DIR}/configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py \
+       checkpoints/point_pillars.pth \
+       ${MMDET3D_DIR}/demo/data/kitti/kitti_000008.bin \
+        --work-dir \
+        work_dir \
+        --show \
+        --device \
+        cuda:0
+```
+### List of MMDetection3d models supported by MMDeploy
+
+|       Model        |         Task         | OnnxRuntime | TensorRT | NCNN  | PPLNN | OpenVINO |                                     Model config                                                         |
+| :----------------: | :------------------: | :---------: | :------: | :---: | :---: | :------: | :------------------------------------------------------------------------------------------------------: |
+|    PointPillars    |   VoxelDetection     |      Y      |    Y     |   N   |   N   |    Y     |     [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars)     |
+
+### Reminder
+
+Voxel detection onnx model excludes model.voxelize layer and model post process, and you can use python api to call these func.
+
+Example:
+
+```python
+from mmdeploy.codebase.mmdet3d.deploy import VoxelDetectionModel
+VoxelDetectionModel.voxelize(...)
+VoxelDetectionModel.post_process(...)
+```
+
+### FAQs
+
+None
diff --git a/docs/en/supported_models.md b/docs/en/supported_models.md
@@ -64,6 +64,7 @@ The table below lists the models that are guaranteed to be exportable to other b
 | HRNet                     | MMPose           |      N      |      Y      |     Y    |   Y   |   N   |     Y    |    [config](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnet-cvpr-2019)     |
 | MSPN                      | MMPose           |      N      |      Y      |     Y    |   Y   |   N   |     Y    |    [config](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mspn-arxiv-2019)     |
 | LiteHRNet                 | MMPose           |      N      |      Y      |     Y    |   N   |   N   |     Y    |  [config](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#litehrnet-cvpr-2021)   |
+| PointPillars              | MMDetection3d    |      ?      |      Y      |     Y    |   N   |   N   |     Y    | [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars)         |
 
 ### Note
 

diff --git a/mmdeploy/apis/pytorch2onnx.py b/mmdeploy/apis/pytorch2onnx.py
@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import os.path as osp
-from typing import Any, Optional, Union
+from typing import Any, Optional, Tuple, Union
 
 import mmcv
 import torch
@@ -10,13 +10,13 @@
                             get_onnx_config, load_config)
 
 
-def torch2onnx_impl(model: torch.nn.Module, input: torch.Tensor,
+def torch2onnx_impl(model: torch.nn.Module, input: Union[torch.Tensor, Tuple],
                     deploy_cfg: Union[str, mmcv.Config], output_file: str):
     """Converting torch model to ONNX.
 
     Args:
         model (torch.nn.Module): Input pytorch model.
-        input (torch.Tensor): Input tensor used to convert model.
+        input (torch.Tensor | Tuple): Input tensor used to convert model.
         deploy_cfg (str | mmcv.Config): Deployment config file or
             Config object.
         output_file (str): Output file to save ONNX model.
@@ -103,7 +103,7 @@ def torch2onnx(img: Any,
 
     torch_model = task_processor.init_pytorch_model(model_checkpoint)
     data, model_inputs = task_processor.create_input(img, input_shape)
-    if not isinstance(model_inputs, torch.Tensor):
+    if not isinstance(model_inputs, torch.Tensor) and len(model_inputs) == 1:
         model_inputs = model_inputs[0]
 
     torch2onnx_impl(

diff --git a/mmdeploy/apis/visualize.py b/mmdeploy/apis/visualize.py
@@ -67,7 +67,6 @@ def visualize_model(model_cfg: Union[str, mmcv.Config],
             model = task_processor.init_backend_model(model)
 
     model_inputs, _ = task_processor.create_input(img, input_shape)
-
     with torch.no_grad():
         result = task_processor.run_inference(model, model_inputs)[0]
 

diff --git a/mmdeploy/backend/openvino/wrapper.py b/mmdeploy/backend/openvino/wrapper.py
@@ -42,7 +42,11 @@ def __init__(self,
         self.net = self.ie.read_network(ir_model_file, bin_path)
         for input in self.net.input_info.values():
             batch_size = input.input_data.shape[0]
-            assert batch_size == 1, 'Only batch 1 is supported.'
+            dims = len(input.input_data.shape)
+            # if input is a image, it has (B,C,H,W) channels,
+            # need batch_size==1
+            assert not dims == 4 or batch_size == 1, \
+                'Only batch 1 is supported.'
         self.device = 'cpu'
         self.sess = self.ie.load_network(
             network=self.net, device_name=self.device.upper(), num_requests=1)

diff --git a/mmdeploy/codebase/mmdet3d/__init__.py b/mmdeploy/codebase/mmdet3d/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .deploy import MMDetection3d, VoxelDetection
+from .models import *  # noqa: F401,F403
+
+__all__ = ['MMDetection3d', 'VoxelDetection']
diff --git a/mmdeploy/codebase/mmdet3d/deploy/__init__.py b/mmdeploy/codebase/mmdet3d/deploy/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .mmdetection3d import MMDetection3d
+from .voxel_detection import VoxelDetection
+from .voxel_detection_model import VoxelDetectionModel
+
+__all__ = ['MMDetection3d', 'VoxelDetection', 'VoxelDetectionModel']
diff --git a/mmdeploy/codebase/mmdet3d/deploy/mmdetection3d.py b/mmdeploy/codebase/mmdet3d/deploy/mmdetection3d.py
@@ -0,0 +1,114 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional, Union
+
+import mmcv
+from mmcv.utils import Registry
+from torch.utils.data import DataLoader, Dataset
+
+from mmdeploy.codebase.base import CODEBASE, BaseTask, MMCodebase
+from mmdeploy.utils import Codebase, get_task_type
+
+
+def __build_mmdet3d_task(model_cfg: mmcv.Config, deploy_cfg: mmcv.Config,
+                         device: str, registry: Registry) -> BaseTask:
+    task = get_task_type(deploy_cfg)
+    return registry.module_dict[task.value](model_cfg, deploy_cfg, device)
+
+
+MMDET3D_TASK = Registry('mmdet3d_tasks', build_func=__build_mmdet3d_task)
+
+
+@CODEBASE.register_module(Codebase.MMDET3D.value)
+class MMDetection3d(MMCodebase):
+
+    task_registry = MMDET3D_TASK
+
+    def __init__(self):
+        super().__init__()
+
+    @staticmethod
+    def build_task_processor(model_cfg: mmcv.Config, deploy_cfg: mmcv.Config,
+                             device: str) -> BaseTask:
+        """The interface to build the task processors of mmdet3d.
+
+        Args:
+            model_cfg (str | mmcv.Config): Model config file.
+            deploy_cfg (str | mmcv.Config): Deployment config file.
+            device (str): A string specifying device type.
+
+        Returns:
+            BaseTask: A task processor.
+        """
+        return MMDET3D_TASK.build(model_cfg, deploy_cfg, device)
+
+    @staticmethod
+    def build_dataset(dataset_cfg: Union[str, mmcv.Config], *args,
+                      **kwargs) -> Dataset:
+        """Build dataset for detection3d.
+
+        Args:
+            dataset_cfg (str | mmcv.Config): The input dataset config.
+
+        Returns:
+            Dataset: A PyTorch dataset.
+        """
+        from mmdet3d.datasets import build_dataset as build_dataset_mmdet3d
+
+        from mmdeploy.utils import load_config
+        dataset_cfg = load_config(dataset_cfg)[0]
+        data = dataset_cfg.data
+
+        dataset = build_dataset_mmdet3d(data.test)
+        return dataset
+
+    @staticmethod
+    def build_dataloader(dataset: Dataset,
+                         samples_per_gpu: int,
+                         workers_per_gpu: int,
+                         num_gpus: int = 1,
+                         dist: bool = False,
+                         shuffle: bool = False,
+                         seed: Optional[int] = None,
+                         runner_type: str = 'EpochBasedRunner',
+                         persistent_workers: bool = True,
+                         **kwargs) -> DataLoader:
+        """Build dataloader for detection3d.
+
+        Args:
+            dataset (Dataset): Input dataset.
+            samples_per_gpu (int): Number of training samples on each GPU, i.e.
+                ,batch size of each GPU.
+            workers_per_gpu (int): How many subprocesses to use for data
+                loading for each GPU.
+            num_gpus (int): Number of GPUs. Only used in non-distributed
+                training.
+            dist (bool): Distributed training/test or not.
+                Defaults  to `False`.
+            shuffle (bool): Whether to shuffle the data at every epoch.
+                Defaults to `False`.
+            seed (int): An integer set to be seed. Default is `None`.
+            runner_type (str): Type of runner. Default: `EpochBasedRunner`.
+            persistent_workers (bool): If True, the data loader will not
+                shutdown the worker processes after a dataset has been consumed
+                once. This allows to maintain the workers `Dataset` instances
+                alive. This argument is only valid when PyTorch>=1.7.0.
+                Default: False.
+            kwargs: Any other keyword argument to be used to initialize
+                DataLoader.
+
+        Returns:
+            DataLoader: A PyTorch dataloader.
+        """
+        from mmdet3d.datasets import \
+            build_dataloader as build_dataloader_mmdet3d
+        return build_dataloader_mmdet3d(
+            dataset,
+            samples_per_gpu,
+            workers_per_gpu,
+            num_gpus=num_gpus,
+            dist=dist,
+            shuffle=shuffle,
+            seed=seed,
+            runner_type=runner_type,
+            persistent_workers=persistent_workers,
+            **kwargs)