rename data_convert to dataset_converters (#1700)

configs/fcos3d/README.md

@@ -26,7 +26,7 @@ Currently we first support the benchmark on the large-scale nuScenes dataset, wh
 ### Data Preparation
 
 After supporting FCOS3D and monocular 3D object detection in v0.13.0, the coco-style 2D json info files will include related annotations by default
-(see [here](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py#L333) if you would like to change the parameter).
+(see [here](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuscenes_converter.py#L333) if you would like to change the parameter).
 So you can just follow the data preparation steps given in the documentation, then all the needed infos are ready together.
 
 ### Training and Inference

configs/nuimages/README.md

@@ -6,7 +6,7 @@
 
 We support and provide some baseline results on [nuImages dataset](https://www.nuscenes.org/nuimages).
 We follow the class mapping in nuScenes dataset, which maps the original categories into 10 foreground categories.
-The convert script can be found [here](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuimage_converter.py).
+The convert script can be found [here](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuimage_converter.py).
 The baseline results include instance segmentation models, e.g., Mask R-CNN, Cascade Mask R-CNN, and HTC.
 We will support panoptic segmentation models in the future.
 
@@ -15,7 +15,7 @@ We will support panoptic segmentation models in the future.
 The dataset converted by the script of v0.6.0 only supports instance segmentation. Since v0.7.0, we also support to produce semantic segmentation mask of each image; thus, we can train HTC or semantic segmentation models using the dataset. To convert the nuImages dataset into COCO format, please use the command below:
 
 ```shell
-python -u tools/data_converter/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
+python -u tools/dataset_converters/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
                                                     --out-dir ${OUT_DIR} --nproc ${NUM_WORKERS} --extra-tag ${TAG}
 ```
 

docs/en/2_new_data_model.md

@@ -71,7 +71,7 @@ Specific annotation format is described in the official object development [kit]
 Assume we use the Waymo dataset.
 After downloading the data, we need to implement a function to convert both the input data and annotation format into the KITTI style. Then we can implement WaymoDataset inherited from KittiDataset to load the data and perform training and evaluation.
 
-Specifically, we implement a waymo [converter](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/waymo_converter.py) to convert Waymo data into KITTI format and a waymo dataset [class](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/waymo_dataset.py) to process it. Because we preprocess the raw data and reorganize it like KITTI, the dataset class could be implemented more easily by inheriting from KittiDataset. The last thing needed to be noted is the evaluation protocol you would like to use. Because Waymo has its own evaluation approach, we further incorporate it into our dataset class. Afterwards, users can successfully convert the data format and use `WaymoDataset` to train and evaluate the model.
+Specifically, we implement a waymo [converter](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/waymo_converter.py) to convert Waymo data into KITTI format and a waymo dataset [class](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/waymo_dataset.py) to process it. Because we preprocess the raw data and reorganize it like KITTI, the dataset class could be implemented more easily by inheriting from KittiDataset. The last thing needed to be noted is the evaluation protocol you would like to use. Because Waymo has its own evaluation approach, we further incorporate it into our dataset class. Afterwards, users can successfully convert the data format and use `WaymoDataset` to train and evaluate the model.
 
 For more details about the intermediate results of preprocessing of Waymo dataset, please refer to its [tutorial](https://mmdetection3d.readthedocs.io/en/latest/datasets/waymo_det.html).
 

docs/en/compatibility.md

@@ -107,7 +107,7 @@ The modification only influences nuScenes coco-style json files. Please re-run t
 We adopt a new pre-processing procedure for the ScanNet dataset in order to support ImVoxelNet, which is a multi-view method requiring image data. In previous versions of MMDetection3D, ScanNet dataset was only used for point cloud based 3D detection and segmentation methods. We plan adding ImVoxelNet to our model zoo, thus updating ScanNet correspondingly by adding image-related pre-processing steps. Specifically, we made these changes:
 
 - Add [script](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/extract_posed_images.py) for extracting RGB data.
-- Update [script](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/scannet_data_utils.py) for annotation creating.
+- Update [script](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/scannet_data_utils.py) for annotation creating.
 - Add instructions in the documents on preparing image data.
 
 Please refer to the ScanNet [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/README.md/) for more details.

docs/en/data_preparation.md

@@ -126,7 +126,7 @@ Download Lyft 3D detection data [HERE](https://www.kaggle.com/c/3d-object-detect
 
 ```bash
 python tools/create_data.py lyft --root-path ./data/lyft --out-dir ./data/lyft --extra-tag lyft --version v1.01
-python tools/data_converter/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
+python tools/dataset_converters/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
 ```
 
 Note that we follow the original folder names for clear organization. Please rename the raw folders as shown above. Also note that the second command serves the purpose of fixing a corrupted lidar data file. Please refer to the discussion [here](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/discussion/110000) for more details.

docs/en/datasets/kitti_det.md

@@ -110,7 +110,7 @@ kitti
 
 **Note:** the info\['annos'\] is in the referenced camera coordinate system. More details please refer to [this](http://www.cvlibs.net/publications/Geiger2013IJRR.pdf)
 
-The core function to get kitti_infos_xxx.pkl and kitti_infos_xxx_mono3d.coco.json are [get_kitti_image_info](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_data_utils.py#L140) and [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_converter.py#L378). Please refer to [kitti_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_converter.py) for more details.
+The core function to get kitti_infos_xxx.pkl and kitti_infos_xxx_mono3d.coco.json are [get_kitti_image_info](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/dataset_converters/kitti_data_utils.py#L140) and [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/dataset_converters/kitti_converter.py#L378). Please refer to [kitti_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/dataset_converters/kitti_converter.py) for more details.
 
 ## Train pipeline
 

docs/en/datasets/lyft_det.md

@@ -47,7 +47,7 @@ To prepare info files for Lyft, run the following commands:
 
 ```bash
 python tools/create_data.py lyft --root-path ./data/lyft --out-dir ./data/lyft --extra-tag lyft --version v1.01
-python tools/data_converter/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
+python tools/dataset_converters/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
 ```
 
 Note that the second command serves the purpose of fixing a corrupted lidar data file. Please refer to the discussion [here](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/discussion/110000) for more details.
@@ -108,8 +108,8 @@ Next, we will elaborate on the difference compared to nuScenes in terms of the d
 
 Here we only explain the data recorded in the training info files. The same applies to the testing set.
 
-The core function to get `lyft_infos_xxx.pkl` is [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/lyft_converter.py#L93).
-Please refer to [lyft_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/lyft_converter.py) for more details.
+The core function to get `lyft_infos_xxx.pkl` is [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/lyft_converter.py#L93).
+Please refer to [lyft_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/lyft_converter.py) for more details.
 
 ## Training pipeline
 

docs/en/datasets/nuscenes_det.md

@@ -122,8 +122,8 @@ Next, we will elaborate on the details recorded in these info files.
 
 Here we only explain the data recorded in the training info files. The same applies to validation and testing set.
 
-The core function to get `nuscenes_infos_xxx.pkl` and `nuscenes_infos_xxx_mono3d.coco.json` are [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py#L143) and [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py#L397), respectively.
-Please refer to [nuscenes_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py) for more details.
+The core function to get `nuscenes_infos_xxx.pkl` and `nuscenes_infos_xxx_mono3d.coco.json` are [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuscenes_converter.py#L143) and [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuscenes_converter.py#L397), respectively.
+Please refer to [nuscenes_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuscenes_converter.py) for more details.
 
 ## Training pipeline
 

docs/en/tutorials/coord_sys_tutorial.md

@@ -132,7 +132,7 @@ __|____|____|____|_________\ x right
 
 ### KITTI
 
-The raw annotation of KITTI is under camera coordinate system, see [get_label_anno](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/kitti_data_utils.py). In MMDetection3D, to train LiDAR-based models on KITTI, the data is first converted from camera coordinate system to LiDAR coordinate system, see [get_ann_info](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/kitti_dataset.py). For training vision-based models, the data is kept in the camera coordinate system.
+The raw annotation of KITTI is under camera coordinate system, see [get_label_anno](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/kitti_data_utils.py). In MMDetection3D, to train LiDAR-based models on KITTI, the data is first converted from camera coordinate system to LiDAR coordinate system, see [get_ann_info](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/kitti_dataset.py). For training vision-based models, the data is kept in the camera coordinate system.
 
 In SECOND, the LiDAR coordinate system for a box is defined as follows (a bird's eye view):
 
@@ -169,7 +169,7 @@ The raw data of ScanNet is not point cloud but mesh. The sampled point cloud dat
 
 The raw data of SUN RGB-D is not point cloud but RGB-D image. By back projection, we obtain the corresponding point cloud for each image, which is under our Depth coordinate system. However, the annotation is not under our system and thus needs conversion.
 
-For the conversion from raw annotation to annotation under our Depth coordinate system, please refer to [sunrgbd_data_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/sunrgbd_data_utils.py).
+For the conversion from raw annotation to annotation under our Depth coordinate system, please refer to [sunrgbd_data_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/sunrgbd_data_utils.py).
 
 ### S3DIS
 

docs/en/useful_tools.md

@@ -262,12 +262,12 @@ The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth
 
 # Dataset Conversion
 
-`tools/data_converter/` contains tools for converting datasets to other formats. Most of them convert datasets to pickle based info files, like kitti, nuscenes and lyft. Waymo converter is used to reorganize waymo raw data like KITTI style. Users could refer to them for our approach to converting data format. It is also convenient to modify them to use as scripts like nuImages converter.
+`tools/dataset_converters/` contains tools for converting datasets to other formats. Most of them convert datasets to pickle based info files, like kitti, nuscenes and lyft. Waymo converter is used to reorganize waymo raw data like KITTI style. Users could refer to them for our approach to converting data format. It is also convenient to modify them to use as scripts like nuImages converter.
 
 To convert the nuImages dataset into COCO format, please use the command below:
 
 ```shell
-python -u tools/data_converter/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
+python -u tools/dataset_converters/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
                                                     --out-dir ${OUT_DIR} --nproc ${NUM_WORKERS} --extra-tag ${TAG}
 ```
 

docs/zh_cn/2_new_data_model.md

@@ -68,7 +68,7 @@ KITTI 官方提供的目标检测开发[工具包](https://s3.eu-central-1.amazo
 接下来本文将对 Waymo 数据集原始格式进行转换。
 首先需要将下载的 Waymo 数据集的数据文件和标注文件转换到 KITTI 数据集的格式，接着定义一个从 KittiDataset 类继承而来的 WaymoDataset 类，来帮助数据的加载、模型的训练和评估。
 
-具体来说，首先使用[数据转换器](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/waymo_converter.py)将 Waymo 数据集转换成 KITTI 数据集的格式，并定义 [Waymo 类](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/waymo_dataset.py)对转换的数据进行处理。因为我们将 Waymo 原始数据集进行预处理并重新组织成 KITTI 数据集的格式，因此可以比较容易通过继承 KittiDataset 类来实现 WaymoDataset 类。需要注意的是，由于 Waymo 数据集有相应的官方评估方法，我们需要在定义新数据类的过程中引入官方评估方法，此时用户可以顺利的转换 Waymo 数据的格式，并使用 `WaymoDataset` 数据类进行模型的训练和评估。
+具体来说，首先使用[数据转换器](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/waymo_converter.py)将 Waymo 数据集转换成 KITTI 数据集的格式，并定义 [Waymo 类](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/waymo_dataset.py)对转换的数据进行处理。因为我们将 Waymo 原始数据集进行预处理并重新组织成 KITTI 数据集的格式，因此可以比较容易通过继承 KittiDataset 类来实现 WaymoDataset 类。需要注意的是，由于 Waymo 数据集有相应的官方评估方法，我们需要在定义新数据类的过程中引入官方评估方法，此时用户可以顺利的转换 Waymo 数据的格式，并使用 `WaymoDataset` 数据类进行模型的训练和评估。
 
 更多关于 Waymo 数据集预处理的中间结果的细节，请参照对应的[说明文档](https://mmdetection3d.readthedocs.io/zh_CN/latest/datasets/waymo_det.html)。
 

docs/zh_cn/data_preparation.md

@@ -121,7 +121,7 @@ python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./dat
 
 ```bash
 python tools/create_data.py lyft --root-path ./data/lyft --out-dir ./data/lyft --extra-tag lyft --version v1.01
-python tools/data_converter/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
+python tools/dataset_converters/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
 ```
 
 注意，为了文件结构的清晰性，我们遵从了 Lyft 数据原先的文件夹名称。请按照上面展示出的文件结构对原始文件夹进行重命名。

docs/zh_cn/datasets/kitti_det.md

@@ -110,7 +110,7 @@ kitti
 
 **注意**：其中的 info\['annos'\] 中的数据均位于相机参考坐标系中，更多的细节请参考[此处](http://www.cvlibs.net/publications/Geiger2013IJRR.pdf)。
 
-获取 kitti_infos_xxx.pkl 和 kitti_infos_xxx_mono3d.coco.json 的核心函数分别为 [get_kitti_image_info](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_data_utils.py#L140) 和 [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/data_converter/kitti_converter.py#L378).
+获取 kitti_infos_xxx.pkl 和 kitti_infos_xxx_mono3d.coco.json 的核心函数分别为 [get_kitti_image_info](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/dataset_converters/kitti_data_utils.py#L140) 和 [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/7873c8f62b99314f35079f369d1dab8d63f8a3ce/tools/dataset_converters/kitti_converter.py#L378).
 
 ## 训练流程
 

docs/zh_cn/datasets/lyft_det.md

@@ -46,7 +46,7 @@ Lyft 不提供训练集和验证集的官方划分方案，因此 MMDetection3D
 
 ```bash
 python tools/create_data.py lyft --root-path ./data/lyft --out-dir ./data/lyft --extra-tag lyft --version v1.01
-python tools/data_converter/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
+python tools/dataset_converters/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft
 ```
 
 请注意，上面的第二行命令用于修复损坏的 lidar 数据文件，请参考[此处](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/discussion/110000)获取更多细节。
@@ -107,8 +107,8 @@ mmdetection3d
 
 这里仅介绍存储在训练数据文件的数据记录信息，在测试数据集也采用上述的数据记录方式。
 
-获取 `lyft_infos_xxx.pkl` 的核心函数是 [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/lyft_converter.py#L93)。
-请参考 [lyft_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/lyft_converter.py) 获取更多细节。
+获取 `lyft_infos_xxx.pkl` 的核心函数是 [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/lyft_converter.py#L93)。
+请参考 [lyft_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/lyft_converter.py) 获取更多细节。
 
 ## 训练流程
 

docs/zh_cn/datasets/nuscenes_det.md

@@ -120,7 +120,7 @@ mmdetection3d
     - info\['annotations'\]\[i\]\['id'\]：标注 ID。默认为 `i`。
 
 这里我们只解释训练信息文件中记录的数据。这同样适用于验证和测试集。
-获取 `nuscenes_infos_xxx.pkl` 和 `nuscenes_infos_xxx_mono3d.coco.json` 的核心函数分别为 [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py#L143) 和 [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py#L397)。更多细节请参考 [nuscenes_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py)。
+获取 `nuscenes_infos_xxx.pkl` 和 `nuscenes_infos_xxx_mono3d.coco.json` 的核心函数分别为 [\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuscenes_converter.py#L143) 和 [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuscenes_converter.py#L397)。更多细节请参考 [nuscenes_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/nuscenes_converter.py)。
 
 ## 训练流程
 

docs/zh_cn/tutorials/coord_sys_tutorial.md

@@ -132,7 +132,7 @@ __|____|____|____|_________\ x 右
 
 ### KITTI
 
-KITTI 数据集的原始标注是在相机坐标系下的，详见 [get_label_anno](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/kitti_data_utils.py)。在 MMDetection3D 中，为了在 KITTI 数据集上训练基于激光雷达的模型，首先将数据从相机坐标系转换到激光雷达坐标，详见 [get_ann_info](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/kitti_dataset.py)。对于训练基于视觉的模型，数据保持在相机坐标系不变。
+KITTI 数据集的原始标注是在相机坐标系下的，详见 [get_label_anno](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/kitti_data_utils.py)。在 MMDetection3D 中，为了在 KITTI 数据集上训练基于激光雷达的模型，首先将数据从相机坐标系转换到激光雷达坐标，详见 [get_ann_info](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/kitti_dataset.py)。对于训练基于视觉的模型，数据保持在相机坐标系不变。
 
 在 SECOND 中，框的激光雷达坐标系定义如下（鸟瞰图）：
 
@@ -169,7 +169,7 @@ ScanNet 的原始数据不是点云而是网格，需要在我们的深度坐标
 
 SUN RGB-D 的原始数据不是点云而是 RGB-D 图像。我们通过反投影，可以得到每张图像对应的点云，其在我们的深度坐标系下。但是，数据集的标注并不在我们的系统中，所以需要进行转换。
 
-将原始标注转换为我们的深度坐标系下的标注的转换过程请参考 [sunrgbd_data_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/sunrgbd_data_utils.py)。
+将原始标注转换为我们的深度坐标系下的标注的转换过程请参考 [sunrgbd_data_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/dataset_converters/sunrgbd_data_utils.py)。
 
 ### S3DIS
 

docs/zh_cn/useful_tools.md

@@ -256,12 +256,12 @@ python tools/model_converters/publish_model.py work_dirs/faster_rcnn/latest.pth
 
 # 数据集转换
 
-`tools/data_converter/` 包含转换数据集为其他格式的一些工具。其中大多数转换数据集为基于 pickle 的信息文件，比如 KITTI，nuscense 和 lyft。Waymo 转换器被用来重新组织 waymo 原始数据为 KITTI 风格。用户能够参考它们了解我们转换数据格式的方法。将它们修改为 nuImages 转换器等脚本也很方便。
+`tools/dataset_converters/` 包含转换数据集为其他格式的一些工具。其中大多数转换数据集为基于 pickle 的信息文件，比如 KITTI，nuscense 和 lyft。Waymo 转换器被用来重新组织 waymo 原始数据为 KITTI 风格。用户能够参考它们了解我们转换数据格式的方法。将它们修改为 nuImages 转换器等脚本也很方便。
 
 为了转换 nuImages 数据集为 COCO 格式，请使用下面的指令：
 
 ```shell
-python -u tools/data_converter/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
+python -u tools/dataset_converters/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
                                                     --out-dir ${OUT_DIR} --nproc ${NUM_WORKERS} --extra-tag ${TAG}
 ```
 

tools/create_data.py

@@ -3,11 +3,11 @@ import argparse
 import os
 from os import path as osp
 
-from tools.data_converter import indoor_converter as indoor
-from tools.data_converter import kitti_converter as kitti
-from tools.data_converter import lyft_converter as lyft_converter
-from tools.data_converter import nuscenes_converter as nuscenes_converter
-from tools.data_converter.create_gt_database import (
+from tools.dataset_converters import indoor_converter as indoor
+from tools.dataset_converters import kitti_converter as kitti
+from tools.dataset_converters import lyft_converter as lyft_converter
+from tools.dataset_converters import nuscenes_converter as nuscenes_converter
+from tools.dataset_converters.create_gt_database import (
     GTDatabaseCreater, create_groundtruth_database)
 
 
@@ -165,7 +165,7 @@ def waymo_data_prep(root_path,
             Default: 5. Here we store pose information of these frames
             for later use.
     """
-    from tools.data_converter import waymo_converter as waymo
+    from tools.dataset_converters import waymo_converter as waymo
 
     splits = ['training', 'validation', 'testing']
     for i, split in enumerate(splits):
@@ -307,7 +307,7 @@ if __name__ == '__main__':
 
     for file_name in os.listdir(args.out_dir):
         if '_infos_' in file_name and '.pkl' in file_name:
-            cmd = f'python tools/data_converter/update_infos_to_v2.py ' \
+            cmd = f'python tools/dataset_converters/update_infos_to_v2.py ' \
                   f'--dataset {args.dataset} ' \
                   f'--pkl {osp.join(args.out_dir,file_name)} ' \
                   f'--out-dir {args.out_dir}'

tools/data_converter/indoor_converter.py → tools/dataset_converters/indoor_converter.py

@@ -4,9 +4,10 @@ import os
 import mmcv
 import numpy as np
 
-from tools.data_converter.s3dis_data_utils import S3DISData, S3DISSegData
-from tools.data_converter.scannet_data_utils import ScanNetData, ScanNetSegData
-from tools.data_converter.sunrgbd_data_utils import SUNRGBDData
+from tools.dataset_converters.s3dis_data_utils import S3DISData, S3DISSegData
+from tools.dataset_converters.scannet_data_utils import (ScanNetData,
+                                                         ScanNetSegData)
+from tools.dataset_converters.sunrgbd_data_utils import SUNRGBDData
 
 
 def create_indoor_info_file(data_path,