Update all registries and fix some ut problems

docs/zh_cn/benchmarks.md

@@ -4,16 +4,16 @@
 
 ## 配置
 
-- 硬件：8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
-- 软件：Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.3, numba 0.48.0.
-- 模型：由于不同代码库所实现的模型种类有所不同，在基准测试中我们选择了 SECOND、PointPillars、Part-A2 和 VoteNet 几种模型，分别与其他代码库中的相应模型实现进行了对比。
-- 度量方法：我们使用整个训练过程中的平均吞吐量作为度量方法，并跳过每个 epoch 的前 50 次迭代以消除训练预热的影响。
+* 硬件：8 NVIDIA Tesla V100 (32G) GPUs, Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
+* 软件：Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.3, numba 0.48.0.
+* 模型：由于不同代码库所实现的模型种类有所不同，在基准测试中我们选择了 SECOND、PointPillars、Part-A2 和 VoteNet 几种模型，分别与其他代码库中的相应模型实现进行了对比。
+* 度量方法：我们使用整个训练过程中的平均吞吐量作为度量方法，并跳过每个 epoch 的前 50 次迭代以消除训练预热的影响。
 
 ## 主要结果
 
 对于模型的训练速度（样本/秒），我们将 MMDetection3D 与其他实现了相同模型的代码库进行了对比。结果如下所示，表格内的数字越大，代表模型的训练速度越快。代码库中不支持的模型使用 `×` 进行标识。
 
-|         模型          | MMDetection3D | OpenPCDet | votenet | Det3D |
+|        模型         | MMDetection3D | OpenPCDet | votenet | Det3D |
 | :-----------------: | :-----------: | :-------: | :-----: | :---: |
 |       VoteNet       |      358      |     ×     |   77    |   ×   |
 |  PointPillars-car   |      141      |     ×     |    ×    |  140  |
@@ -25,104 +25,103 @@
 
 ### 为了计算速度所做的修改
 
-- __MMDetection3D__：我们尝试使用与其他代码库中尽可能相同的配置，具体配置细节见 [基准测试配置](https://github.com/open-mmlab/MMDetection3D/blob/master/configs/benchmark)。
+* __MMDetection3D__：我们尝试使用与其他代码库中尽可能相同的配置，具体配置细节见 [基准测试配置](https://github.com/open-mmlab/MMDetection3D/blob/master/configs/benchmark)。
 
-- __Det3D__：为了与 Det3D 进行比较，我们使用了 commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7) 所对应的代码版本。
+* __Det3D__：为了与 Det3D 进行比较，我们使用了 commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7) 所对应的代码版本。
 
-- __OpenPCDet__：为了与 OpenPCDet 进行比较，我们使用了 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 所对应的代码版本。
+* __OpenPCDet__：为了与 OpenPCDet 进行比较，我们使用了 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 所对应的代码版本。
 
-  为了计算训练速度，我们在 `./tools/train_utils/train_utils.py` 文件中添加了用于记录运行时间的代码。我们对每个 epoch 的训练速度进行计算，并报告所有 epoch 的平均速度。
-
-  <details>
+    为了计算训练速度，我们在 `./tools/train_utils/train_utils.py` 文件中添加了用于记录运行时间的代码。我们对每个 epoch 的训练速度进行计算，并报告所有 epoch 的平均速度。
+    <details>
     <summary>
     （为了使用相同方法进行测试所做的具体修改 - 点击展开）
     </summary>
 
-  ```diff
-  diff --git a/tools/train_utils/train_utils.py b/tools/train_utils/train_utils.py
-  index 91f21dd..021359d 100644
-  --- a/tools/train_utils/train_utils.py
-  +++ b/tools/train_utils/train_utils.py
-  @@ -2,6 +2,7 @@ import torch
-   import os
-   import glob
-   import tqdm
-  +import datetime
-   from torch.nn.utils import clip_grad_norm_
-
-
-  @@ -13,7 +14,10 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
-       if rank == 0:
-           pbar = tqdm.tqdm(total=total_it_each_epoch, leave=leave_pbar, desc='train', dynamic_ncols=True)
-
-  +    start_time = None
-       for cur_it in range(total_it_each_epoch):
-  +        if cur_it > 49 and start_time is None:
-  +            start_time = datetime.datetime.now()
-           try:
-               batch = next(dataloader_iter)
-           except StopIteration:
-  @@ -55,9 +59,11 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
-                   tb_log.add_scalar('learning_rate', cur_lr, accumulated_iter)
-                   for key, val in tb_dict.items():
-                       tb_log.add_scalar('train_' + key, val, accumulated_iter)
-  +    endtime = datetime.datetime.now()
-  +    speed = (endtime - start_time).seconds / (total_it_each_epoch - 50)
-       if rank == 0:
-           pbar.close()
-  -    return accumulated_iter
-  +    return accumulated_iter, speed
-
-
-   def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_cfg,
-  @@ -65,6 +71,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
-                   lr_warmup_scheduler=None, ckpt_save_interval=1, max_ckpt_save_num=50,
-                   merge_all_iters_to_one_epoch=False):
-       accumulated_iter = start_iter
-  +    speeds = []
-       with tqdm.trange(start_epoch, total_epochs, desc='epochs', dynamic_ncols=True, leave=(rank == 0)) as tbar:
-           total_it_each_epoch = len(train_loader)
-           if merge_all_iters_to_one_epoch:
-  @@ -82,7 +89,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
-                   cur_scheduler = lr_warmup_scheduler
-               else:
-                   cur_scheduler = lr_scheduler
-  -            accumulated_iter = train_one_epoch(
-  +            accumulated_iter, speed = train_one_epoch(
-                   model, optimizer, train_loader, model_func,
-                   lr_scheduler=cur_scheduler,
-                   accumulated_iter=accumulated_iter, optim_cfg=optim_cfg,
-  @@ -91,7 +98,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
-                   total_it_each_epoch=total_it_each_epoch,
-                   dataloader_iter=dataloader_iter
-               )
-  -
-  +            speeds.append(speed)
-               # save trained model
-               trained_epoch = cur_epoch + 1
-               if trained_epoch % ckpt_save_interval == 0 and rank == 0:
-  @@ -107,6 +114,8 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
-                   save_checkpoint(
-                       checkpoint_state(model, optimizer, trained_epoch, accumulated_iter), filename=ckpt_name,
-                   )
-  +            print(speed)
-  +    print(f'*******{sum(speeds) / len(speeds)}******')
-
-
-   def model_state_to_cpu(model_state):
-  ```
-
-  </details>
+    ```diff
+    diff --git a/tools/train_utils/train_utils.py b/tools/train_utils/train_utils.py
+    index 91f21dd..021359d 100644
+    --- a/tools/train_utils/train_utils.py
+    +++ b/tools/train_utils/train_utils.py
+    @@ -2,6 +2,7 @@ import torch
+     import os
+     import glob
+     import tqdm
+    +import datetime
+     from torch.nn.utils import clip_grad_norm_
+
+
+    @@ -13,7 +14,10 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
+         if rank == 0:
+             pbar = tqdm.tqdm(total=total_it_each_epoch, leave=leave_pbar, desc='train', dynamic_ncols=True)
+
+    +    start_time = None
+         for cur_it in range(total_it_each_epoch):
+    +        if cur_it > 49 and start_time is None:
+    +            start_time = datetime.datetime.now()
+             try:
+                 batch = next(dataloader_iter)
+             except StopIteration:
+    @@ -55,9 +59,11 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
+                     tb_log.add_scalar('learning_rate', cur_lr, accumulated_iter)
+                     for key, val in tb_dict.items():
+                         tb_log.add_scalar('train_' + key, val, accumulated_iter)
+    +    endtime = datetime.datetime.now()
+    +    speed = (endtime - start_time).seconds / (total_it_each_epoch - 50)
+         if rank == 0:
+             pbar.close()
+    -    return accumulated_iter
+    +    return accumulated_iter, speed
+
+
+     def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_cfg,
+    @@ -65,6 +71,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
+                     lr_warmup_scheduler=None, ckpt_save_interval=1, max_ckpt_save_num=50,
+                     merge_all_iters_to_one_epoch=False):
+         accumulated_iter = start_iter
+    +    speeds = []
+         with tqdm.trange(start_epoch, total_epochs, desc='epochs', dynamic_ncols=True, leave=(rank == 0)) as tbar:
+             total_it_each_epoch = len(train_loader)
+             if merge_all_iters_to_one_epoch:
+    @@ -82,7 +89,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
+                     cur_scheduler = lr_warmup_scheduler
+                 else:
+                     cur_scheduler = lr_scheduler
+    -            accumulated_iter = train_one_epoch(
+    +            accumulated_iter, speed = train_one_epoch(
+                     model, optimizer, train_loader, model_func,
+                     lr_scheduler=cur_scheduler,
+                     accumulated_iter=accumulated_iter, optim_cfg=optim_cfg,
+    @@ -91,7 +98,7 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
+                     total_it_each_epoch=total_it_each_epoch,
+                     dataloader_iter=dataloader_iter
+                 )
+    -
+    +            speeds.append(speed)
+                 # save trained model
+                 trained_epoch = cur_epoch + 1
+                 if trained_epoch % ckpt_save_interval == 0 and rank == 0:
+    @@ -107,6 +114,8 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
+                     save_checkpoint(
+                         checkpoint_state(model, optimizer, trained_epoch, accumulated_iter), filename=ckpt_name,
+                     )
+    +            print(speed)
+    +    print(f'*******{sum(speeds) / len(speeds)}******')
+
+
+     def model_state_to_cpu(model_state):
+    ```
+
+    </details>
 
 ### VoteNet
 
-- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
+* __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
 
   ```bash
   ./tools/dist_train.sh configs/votenet/votenet_16x8_sunrgbd-3d-10class.py 8 --no-validate
   ```
 
-- __votenet__：在 commit [2f6d6d3](https://github.com/facebookresearch/votenet/tree/2f6d6d36ff98d96901182e935afe48ccee82d566) 版本下，执行如下命令：
+* __votenet__：在 commit [2f6d6d3](https://github.com/facebookresearch/votenet/tree/2f6d6d36ff98d96901182e935afe48ccee82d566) 版本下，执行如下命令：
 
   ```bash
   python train.py --dataset sunrgbd --batch_size 16
@@ -197,13 +196,13 @@
 
 ### PointPillars-car
 
-- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
+* __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
 
   ```bash
   ./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py 8 --no-validate
   ```
 
-- __Det3D__：在 commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7) 版本下，使用 `kitti_point_pillars_mghead_syncbn.py` 并执行如下命令：
+* __Det3D__：在 commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7) 版本下，使用 `kitti_point_pillars_mghead_syncbn.py` 并执行如下命令：
 
   ```bash
   ./tools/scripts/train.sh --launcher=slurm --gpus=8
@@ -239,13 +238,13 @@
 
 ### PointPillars-3class
 
-- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
+* __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
 
   ```bash
   ./tools/dist_train.sh configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
   ```
 
-- __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令：
+* __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令：
 
   ```bash
   cd tools
@@ -256,13 +255,13 @@
 
 基准测试中的 SECOND 指在 [second.Pytorch](https://github.com/traveller59/second.pytorch) 首次被实现的 [SECONDv1.5](https://github.com/traveller59/second.pytorch/blob/master/second/configs/all.fhd.config)。Det3D 实现的 SECOND 中，使用了自己实现的 Multi-Group Head，因此无法将它的速度与其他代码库进行对比。
 
-- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
+* __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
 
   ```bash
   ./tools/dist_train.sh configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py 8 --no-validate
   ```
 
-- __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令：
+* __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令：
 
   ```bash
   cd tools
@@ -271,13 +270,13 @@
 
 ### Part-A2
 
-- __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
+* __MMDetection3D__：在 v0.1.0 版本下, 执行如下命令：
 
   ```bash
   ./tools/dist_train.sh configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py 8 --no-validate
   ```
 
-- __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令以进行模型训练：
+* __OpenPCDet__：在 commit [b32fbddb](https://github.com/open-mmlab/OpenPCDet/tree/b32fbddbe06183507bad433ed99b407cbc2175c2) 版本下，执行如下命令以进行模型训练：
 
   ```bash
   cd tools

mmdet3d/apis/__init__.py

@@ -3,12 +3,13 @@ from .inference import (convert_SyncBN, inference_detector,
                         inference_mono_3d_detector,
                         inference_multi_modality_detector, inference_segmentor,
                         init_model, show_result_meshlab)
-from .test import single_gpu_test
-from .train import init_random_seed, train_model
 
 __all__ = [
-    'inference_detector', 'init_model', 'single_gpu_test',
-    'inference_mono_3d_detector', 'show_result_meshlab', 'convert_SyncBN',
-    'train_model', 'inference_multi_modality_detector', 'inference_segmentor',
-    'init_random_seed'
+    'inference_detector',
+    'init_model',
+    'inference_mono_3d_detector',
+    'show_result_meshlab',
+    'convert_SyncBN',
+    'inference_multi_modality_detector',
+    'inference_segmentor',
 ]

mmdet3d/apis/test.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from os import path as osp
-
-import mmcv
-import torch
-from mmcv.image import tensor2imgs
-
-from mmdet3d.models import (Base3DDetector, Base3DSegmentor,
-                            SingleStageMono3DDetector)
-
-
-def single_gpu_test(model,
-                    data_loader,
-                    show=False,
-                    out_dir=None,
-                    show_score_thr=0.3):
-    """Test model with single gpu.
-
-    This method tests model with single gpu and gives the 'show' option.
-    By setting ``show=True``, it saves the visualization results under
-    ``out_dir``.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (nn.Dataloader): Pytorch data loader.
-        show (bool, optional): Whether to save viualization results.
-            Default: True.
-        out_dir (str, optional): The path to save visualization results.
-            Default: None.
-
-    Returns:
-        list[dict]: The prediction results.
-    """
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    prog_bar = mmcv.ProgressBar(len(dataset))
-    for i, data in enumerate(data_loader):
-        with torch.no_grad():
-            result = model(return_loss=False, rescale=True, **data)
-
-        if show:
-            # Visualize the results of MMDetection3D model
-            # 'show_results' is MMdetection3D visualization API
-            models_3d = (Base3DDetector, Base3DSegmentor,
-                         SingleStageMono3DDetector)
-            if isinstance(model.module, models_3d):
-                model.module.show_results(
-                    data,
-                    result,
-                    out_dir=out_dir,
-                    show=show,
-                    score_thr=show_score_thr)
-            # Visualize the results of MMDetection model
-            # 'show_result' is MMdetection visualization API
-            else:
-                batch_size = len(result)
-                if batch_size == 1 and isinstance(data['img'][0],
-                                                  torch.Tensor):
-                    img_tensor = data['img'][0]
-                else:
-                    img_tensor = data['img'][0].data[0]
-                img_metas = data['img_metas'][0].data[0]
-                imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
-                assert len(imgs) == len(img_metas)
-
-                for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
-                    h, w, _ = img_meta['img_shape']
-                    img_show = img[:h, :w, :]
-
-                    ori_h, ori_w = img_meta['ori_shape'][:-1]
-                    img_show = mmcv.imresize(img_show, (ori_w, ori_h))
-
-                    if out_dir:
-                        out_file = osp.join(out_dir, img_meta['ori_filename'])
-                    else:
-                        out_file = None
-
-                    model.module.show_result(
-                        img_show,
-                        result[i],
-                        show=show,
-                        out_file=out_file,
-                        score_thr=show_score_thr)
-        results.extend(result)
-
-        batch_size = len(result)
-        for _ in range(batch_size):
-            prog_bar.update()
-    return results

mmdet3d/apis/train.py

-# Copyright (c) OpenMMLab. All rights reserved.
-import random
-import warnings
-
-import numpy as np
-import torch
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
-                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
-                         build_runner, get_dist_info)
-from mmcv.utils import build_from_cfg
-from torch import distributed as dist
-
-from mmdet3d.datasets import build_dataset
-from mmdet3d.utils import find_latest_checkpoint
-from mmdet.core import DistEvalHook as MMDET_DistEvalHook
-from mmdet.core import EvalHook as MMDET_EvalHook
-from mmdet.datasets import build_dataloader as build_mmdet_dataloader
-from mmdet.datasets import replace_ImageToTensor
-from mmdet.utils import get_root_logger as get_mmdet_root_logger
-from mmseg.core import DistEvalHook as MMSEG_DistEvalHook
-from mmseg.core import EvalHook as MMSEG_EvalHook
-from mmseg.datasets import build_dataloader as build_mmseg_dataloader
-from mmseg.utils import get_root_logger as get_mmseg_root_logger
-
-
-def init_random_seed(seed=None, device='cuda'):
-    """Initialize random seed.
-
-    If the seed is not set, the seed will be automatically randomized,
-    and then broadcast to all processes to prevent some potential bugs.
-    Args:
-        seed (int, optional): The seed. Default to None.
-        device (str, optional): The device where the seed will be put on.
-            Default to 'cuda'.
-    Returns:
-        int: Seed to be used.
-    """
-    if seed is not None:
-        return seed
-
-    # Make sure all ranks share the same random seed to prevent
-    # some potential bugs. Please refer to
-    # https://github.com/open-mmlab/mmdetection/issues/6339
-    rank, world_size = get_dist_info()
-    seed = np.random.randint(2**31)
-    if world_size == 1:
-        return seed
-
-    if rank == 0:
-        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
-    else:
-        random_num = torch.tensor(0, dtype=torch.int32, device=device)
-    dist.broadcast(random_num, src=0)
-    return random_num.item()
-
-
-def set_random_seed(seed, deterministic=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
-
-
-def train_segmentor(model,
-                    dataset,
-                    cfg,
-                    distributed=False,
-                    validate=False,
-                    timestamp=None,
-                    meta=None):
-    """Launch segmentor training."""
-    logger = get_mmseg_root_logger(cfg.log_level)
-
-    # prepare data loaders
-    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    data_loaders = [
-        build_mmseg_dataloader(
-            ds,
-            cfg.data.samples_per_gpu,
-            cfg.data.workers_per_gpu,
-            # cfg.gpus will be ignored if distributed
-            len(cfg.gpu_ids),
-            dist=distributed,
-            seed=cfg.seed,
-            drop_last=True) for ds in dataset
-    ]
-
-    # put model on gpus
-    if distributed:
-        find_unused_parameters = cfg.get('find_unused_parameters', False)
-        # Sets the `find_unused_parameters` parameter in
-        # torch.nn.parallel.DistributedDataParallel
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False,
-            find_unused_parameters=find_unused_parameters)
-    else:
-        model = MMDataParallel(
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
-    # build runner
-    optimizer = build_optimizer(model, cfg.optimizer)
-
-    if cfg.get('runner') is None:
-        cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
-        warnings.warn(
-            'config is now expected to have a `runner` section, '
-            'please set `runner` in your config.', UserWarning)
-
-    runner = build_runner(
-        cfg.runner,
-        default_args=dict(
-            model=model,
-            batch_processor=None,
-            optimizer=optimizer,
-            work_dir=cfg.work_dir,
-            logger=logger,
-            meta=meta))
-
-    # register hooks
-    runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
-                                   cfg.checkpoint_config, cfg.log_config,
-                                   cfg.get('momentum_config', None))
-
-    # an ugly walkaround to make the .log and .log.json filenames the same
-    runner.timestamp = timestamp
-
-    # register eval hooks
-    if validate:
-        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-        val_dataloader = build_mmseg_dataloader(
-            val_dataset,
-            samples_per_gpu=1,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False)
-        eval_cfg = cfg.get('evaluation', {})
-        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
-        eval_hook = MMSEG_DistEvalHook if distributed else MMSEG_EvalHook
-        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
-        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
-        runner.register_hook(
-            eval_hook(val_dataloader, **eval_cfg), priority='LOW')
-
-    # user-defined hooks
-    if cfg.get('custom_hooks', None):
-        custom_hooks = cfg.custom_hooks
-        assert isinstance(custom_hooks, list), \
-            f'custom_hooks expect list type, but got {type(custom_hooks)}'
-        for hook_cfg in cfg.custom_hooks:
-            assert isinstance(hook_cfg, dict), \
-                'Each item in custom_hooks expects dict type, but got ' \
-                f'{type(hook_cfg)}'
-            hook_cfg = hook_cfg.copy()
-            priority = hook_cfg.pop('priority', 'NORMAL')
-            hook = build_from_cfg(hook_cfg, HOOKS)
-            runner.register_hook(hook, priority=priority)
-
-    if cfg.resume_from:
-        runner.resume(cfg.resume_from)
-    elif cfg.load_from:
-        runner.load_checkpoint(cfg.load_from)
-    runner.run(data_loaders, cfg.workflow)
-
-
-def train_detector(model,
-                   dataset,
-                   cfg,
-                   distributed=False,
-                   validate=False,
-                   timestamp=None,
-                   meta=None):
-    logger = get_mmdet_root_logger(log_level=cfg.log_level)
-
-    # prepare data loaders
-    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    if 'imgs_per_gpu' in cfg.data:
-        logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
-                       'Please use "samples_per_gpu" instead')
-        if 'samples_per_gpu' in cfg.data:
-            logger.warning(
-                f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
-                f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
-                f'={cfg.data.imgs_per_gpu} is used in this experiments')
-        else:
-            logger.warning(
-                'Automatically set "samples_per_gpu"="imgs_per_gpu"='
-                f'{cfg.data.imgs_per_gpu} in this experiments')
-        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
-
-    runner_type = 'EpochBasedRunner' if 'runner' not in cfg else cfg.runner[
-        'type']
-    data_loaders = [
-        build_mmdet_dataloader(
-            ds,
-            cfg.data.samples_per_gpu,
-            cfg.data.workers_per_gpu,
-            # `num_gpus` will be ignored if distributed
-            num_gpus=len(cfg.gpu_ids),
-            dist=distributed,
-            seed=cfg.seed,
-            runner_type=runner_type,
-            persistent_workers=cfg.data.get('persistent_workers', False))
-        for ds in dataset
-    ]
-
-    # put model on gpus
-    if distributed:
-        find_unused_parameters = cfg.get('find_unused_parameters', False)
-        # Sets the `find_unused_parameters` parameter in
-        # torch.nn.parallel.DistributedDataParallel
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False,
-            find_unused_parameters=find_unused_parameters)
-    else:
-        model = MMDataParallel(
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
-    # build runner
-    optimizer = build_optimizer(model, cfg.optimizer)
-
-    if 'runner' not in cfg:
-        cfg.runner = {
-            'type': 'EpochBasedRunner',
-            'max_epochs': cfg.total_epochs
-        }
-        warnings.warn(
-            'config is now expected to have a `runner` section, '
-            'please set `runner` in your config.', UserWarning)
-    else:
-        if 'total_epochs' in cfg:
-            assert cfg.total_epochs == cfg.runner.max_epochs
-
-    runner = build_runner(
-        cfg.runner,
-        default_args=dict(
-            model=model,
-            optimizer=optimizer,
-            work_dir=cfg.work_dir,
-            logger=logger,
-            meta=meta))
-
-    # an ugly workaround to make .log and .log.json filenames the same
-    runner.timestamp = timestamp
-
-    # fp16 setting
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        optimizer_config = Fp16OptimizerHook(
-            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
-    elif distributed and 'type' not in cfg.optimizer_config:
-        optimizer_config = OptimizerHook(**cfg.optimizer_config)
-    else:
-        optimizer_config = cfg.optimizer_config
-
-    # register hooks
-    runner.register_training_hooks(
-        cfg.lr_config,
-        optimizer_config,
-        cfg.checkpoint_config,
-        cfg.log_config,
-        cfg.get('momentum_config', None),
-        custom_hooks_config=cfg.get('custom_hooks', None))
-
-    if distributed:
-        if isinstance(runner, EpochBasedRunner):
-            runner.register_hook(DistSamplerSeedHook())
-
-    # register eval hooks
-    if validate:
-        # Support batch_size > 1 in validation
-        val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
-        if val_samples_per_gpu > 1:
-            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
-            cfg.data.val.pipeline = replace_ImageToTensor(
-                cfg.data.val.pipeline)
-        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-        val_dataloader = build_mmdet_dataloader(
-            val_dataset,
-            samples_per_gpu=val_samples_per_gpu,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False)
-        eval_cfg = cfg.get('evaluation', {})
-        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
-        eval_hook = MMDET_DistEvalHook if distributed else MMDET_EvalHook
-        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
-        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
-        runner.register_hook(
-            eval_hook(val_dataloader, **eval_cfg), priority='LOW')
-
-    resume_from = None
-    if cfg.resume_from is None and cfg.get('auto_resume'):
-        resume_from = find_latest_checkpoint(cfg.work_dir)
-
-    if resume_from is not None:
-        cfg.resume_from = resume_from
-
-    if cfg.resume_from:
-        runner.resume(cfg.resume_from)
-    elif cfg.load_from:
-        runner.load_checkpoint(cfg.load_from)
-    runner.run(data_loaders, cfg.workflow)
-
-
-def train_model(model,
-                dataset,
-                cfg,
-                distributed=False,
-                validate=False,
-                timestamp=None,
-                meta=None):
-    """A function wrapper for launching model training according to cfg.
-
-    Because we need different eval_hook in runner. Should be deprecated in the
-    future.
-    """
-    if cfg.model.type in ['EncoderDecoder3D']:
-        train_segmentor(
-            model,
-            dataset,
-            cfg,
-            distributed=distributed,
-            validate=validate,
-            timestamp=timestamp,
-            meta=meta)
-    else:
-        train_detector(
-            model,
-            dataset,
-            cfg,
-            distributed=distributed,
-            validate=validate,
-            timestamp=timestamp,
-            meta=meta)

mmdet3d/core/anchor/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.core.anchor import build_prior_generator
 from .anchor_3d_generator import (AlignedAnchor3DRangeGenerator,
                                   AlignedAnchor3DRangeGeneratorPerCls,
                                   Anchor3DRangeGenerator)
+from .builder import (ANCHOR_GENERATORS, PRIOR_GENERATORS,
+                      build_anchor_generator, build_prior_generator)
 
 __all__ = [
     'AlignedAnchor3DRangeGenerator', 'Anchor3DRangeGenerator',
-    'build_prior_generator', 'AlignedAnchor3DRangeGeneratorPerCls'
+    'build_prior_generator', 'AlignedAnchor3DRangeGeneratorPerCls',
+    'build_anchor_generator', 'ANCHOR_GENERATORS', 'PRIOR_GENERATORS'
 ]

mmdet3d/core/anchor/anchor_3d_generator.py

@@ -2,10 +2,10 @@
 import mmcv
 import torch
 
-from mmdet.core.anchor import ANCHOR_GENERATORS
+from mmdet3d.registry import TASK_UTILS
 
 
-@ANCHOR_GENERATORS.register_module()
+@TASK_UTILS.register_module()
 class Anchor3DRangeGenerator(object):
     """3D Anchor Generator by range.
 
@@ -221,7 +221,7 @@ class Anchor3DRangeGenerator(object):
         return ret
 
 
-@ANCHOR_GENERATORS.register_module()
+@TASK_UTILS.register_module()
 class AlignedAnchor3DRangeGenerator(Anchor3DRangeGenerator):
     """Aligned 3D Anchor Generator by range.
 
@@ -340,7 +340,7 @@ class AlignedAnchor3DRangeGenerator(Anchor3DRangeGenerator):
         return ret
 
 
-@ANCHOR_GENERATORS.register_module()
+@TASK_UTILS.register_module()
 class AlignedAnchor3DRangeGeneratorPerCls(AlignedAnchor3DRangeGenerator):
     """3D Anchor Generator by range for per class.
 

mmdet3d/core/anchor/builder.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+from mmdet3d.registry import TASK_UTILS
+
+PRIOR_GENERATORS = TASK_UTILS
+
+ANCHOR_GENERATORS = TASK_UTILS
+
+
+def build_prior_generator(cfg, default_args=None):
+    warnings.warn(
+        '``build_prior_generator`` would be deprecated soon, please use '
+        '``mmdet3d.registry.TASK_UTILS.build()`` ')
+    return TASK_UTILS.build(cfg, default_args=default_args)
+
+
+def build_anchor_generator(cfg, default_args=None):
+    warnings.warn(
+        '``build_anchor_generator`` would be deprecated soon, please use '
+        '``mmdet3d.registry.TASK_UTILS.build()`` ')
+    return TASK_UTILS.build(cfg, default_args=default_args)

mmdet3d/core/bbox/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from .assigners import AssignResult, BaseAssigner, MaxIoUAssigner
-from .coders import DeltaXYZWLHRBBoxCoder
 # from .bbox_target import bbox_target
+from .builder import build_assigner, build_bbox_coder, build_sampler
+from .coders import DeltaXYZWLHRBBoxCoder
 from .iou_calculators import (AxisAlignedBboxOverlaps3D, BboxOverlaps3D,
                               BboxOverlapsNearest3D,
                               axis_aligned_bbox_overlaps_3d, bbox_overlaps_3d,
@@ -26,5 +27,6 @@ __all__ = [
     'LiDARInstance3DBoxes', 'CameraInstance3DBoxes', 'bbox3d2roi',
     'bbox3d2result', 'DepthInstance3DBoxes', 'BaseInstance3DBoxes',
     'bbox3d_mapping_back', 'xywhr2xyxyr', 'limit_period', 'points_cam2img',
-    'points_img2cam', 'get_box_type', 'Coord3DMode', 'mono_cam_box2vis'
+    'points_img2cam', 'get_box_type', 'Coord3DMode', 'mono_cam_box2vis',
+    'build_assigner', 'build_bbox_coder', 'build_sampler'
 ]

mmdet3d/core/bbox/builder.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+from mmdet3d.registry import TASK_UTILS
+
+BBOX_ASSIGNERS = TASK_UTILS
+BBOX_SAMPLERS = TASK_UTILS
+BBOX_CODERS = TASK_UTILS
+
+
+def build_assigner(cfg, **default_args):
+    """Builder of box assigner."""
+    warnings.warn('``build_assigner`` would be deprecated soon, please use '
+                  '``mmdet3d.registry.TASK_UTILS.build()`` ')
+    return TASK_UTILS.build(cfg, default_args=default_args)
+
+
+def build_sampler(cfg, **default_args):
+    """Builder of box sampler."""
+    warnings.warn('``build_sampler`` would be deprecated soon, please use '
+                  '``mmdet3d.registry.TASK_UTILS.build()`` ')
+    return TASK_UTILS.build(cfg, default_args=default_args)
+
+
+def build_bbox_coder(cfg, **default_args):
+    """Builder of box coder."""
+    warnings.warn('``build_bbox_coder`` would be deprecated soon, please use '
+                  '``mmdet3d.registry.TASK_UTILS.build()`` ')
+    return TASK_UTILS.build(cfg, default_args=default_args)

mmdet3d/core/bbox/coders/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.core.bbox import build_bbox_coder
 from .anchor_free_bbox_coder import AnchorFreeBBoxCoder
 from .centerpoint_bbox_coders import CenterPointBBoxCoder
 from .delta_xyzwhlr_bbox_coder import DeltaXYZWLHRBBoxCoder
@@ -12,7 +11,7 @@ from .point_xyzwhlr_bbox_coder import PointXYZWHLRBBoxCoder
 from .smoke_bbox_coder import SMOKECoder
 
 __all__ = [
-    'build_bbox_coder', 'DeltaXYZWLHRBBoxCoder', 'PartialBinBasedBBoxCoder',
+    'DeltaXYZWLHRBBoxCoder', 'PartialBinBasedBBoxCoder',
     'CenterPointBBoxCoder', 'AnchorFreeBBoxCoder', 'GroupFree3DBBoxCoder',
     'PointXYZWHLRBBoxCoder', 'FCOS3DBBoxCoder', 'PGDBBoxCoder', 'SMOKECoder',
     'MonoFlexCoder'

mmdet3d/core/bbox/coders/anchor_free_bbox_coder.py

@@ -2,11 +2,11 @@
 import numpy as np
 import torch
 
-from mmdet.core.bbox.builder import BBOX_CODERS
+from mmdet3d.registry import TASK_UTILS
 from .partial_bin_based_bbox_coder import PartialBinBasedBBoxCoder
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class AnchorFreeBBoxCoder(PartialBinBasedBBoxCoder):
     """Anchor free bbox coder for 3D boxes.
 

mmdet3d/core/bbox/coders/centerpoint_bbox_coders.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import BaseBBoxCoder
-from mmdet.core.bbox.builder import BBOX_CODERS
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class CenterPointBBoxCoder(BaseBBoxCoder):
     """Bbox coder for CenterPoint.
 

mmdet3d/core/bbox/coders/delta_xyzwhlr_bbox_coder.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import BaseBBoxCoder
-from mmdet.core.bbox.builder import BBOX_CODERS
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class DeltaXYZWLHRBBoxCoder(BaseBBoxCoder):
     """Bbox Coder for 3D boxes.
 

mmdet3d/core/bbox/coders/fcos3d_bbox_coder.py

@@ -2,12 +2,12 @@
 import numpy as np
 import torch
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import BaseBBoxCoder
-from mmdet.core.bbox.builder import BBOX_CODERS
 from ..structures import limit_period
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class FCOS3DBBoxCoder(BaseBBoxCoder):
     """Bounding box coder for FCOS3D.
 

mmdet3d/core/bbox/coders/groupfree3d_bbox_coder.py

@@ -2,11 +2,11 @@
 import numpy as np
 import torch
 
-from mmdet.core.bbox.builder import BBOX_CODERS
+from mmdet3d.registry import TASK_UTILS
 from .partial_bin_based_bbox_coder import PartialBinBasedBBoxCoder
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class GroupFree3DBBoxCoder(PartialBinBasedBBoxCoder):
     """Modified partial bin based bbox coder for GroupFree3D.
 

mmdet3d/core/bbox/coders/monoflex_bbox_coder.py

@@ -3,11 +3,11 @@ import numpy as np
 import torch
 from torch.nn import functional as F
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import BaseBBoxCoder
-from mmdet.core.bbox.builder import BBOX_CODERS
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class MonoFlexCoder(BaseBBoxCoder):
     """Bbox Coder for MonoFlex.
 

mmdet3d/core/bbox/coders/partial_bin_based_bbox_coder.py

@@ -2,11 +2,11 @@
 import numpy as np
 import torch
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import BaseBBoxCoder
-from mmdet.core.bbox.builder import BBOX_CODERS
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class PartialBinBasedBBoxCoder(BaseBBoxCoder):
     """Partial bin based bbox coder.
 

mmdet3d/core/bbox/coders/pgd_bbox_coder.py

@@ -3,11 +3,11 @@ import numpy as np
 import torch
 from torch.nn import functional as F
 
-from mmdet.core.bbox.builder import BBOX_CODERS
+from mmdet3d.registry import TASK_UTILS
 from .fcos3d_bbox_coder import FCOS3DBBoxCoder
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class PGDBBoxCoder(FCOS3DBBoxCoder):
     """Bounding box coder for PGD."""
 

mmdet3d/core/bbox/coders/point_xyzwhlr_bbox_coder.py

@@ -2,11 +2,11 @@
 import numpy as np
 import torch
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import BaseBBoxCoder
-from mmdet.core.bbox.builder import BBOX_CODERS
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class PointXYZWHLRBBoxCoder(BaseBBoxCoder):
     """Point based bbox coder for 3D boxes.
 

mmdet3d/core/bbox/coders/smoke_bbox_coder.py

@@ -2,11 +2,11 @@
 import numpy as np
 import torch
 
+from mmdet3d.registry import TASK_UTILS
 from mmdet.core.bbox import BaseBBoxCoder
-from mmdet.core.bbox.builder import BBOX_CODERS
 
 
-@BBOX_CODERS.register_module()
+@TASK_UTILS.register_module()
 class SMOKECoder(BaseBBoxCoder):
     """Bbox Coder for SMOKE.