Refactor the structure of documentation (#1113)

* Merge English and Chinese doc into the same folder

* Add logo image back

* Add logo image back

.gitignore

@@ -65,7 +65,8 @@ instance/
 .scrapy
 
 # Sphinx documentation
-docs/_build/
+docs/en/_build/
+docs/zh_cn/_build/
 
 # PyBuilder
 target/

docs/1_exist_data_model.md → docs/en/1_exist_data_model.md

@@ -75,7 +75,7 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
 
    The generated results be under `./pointpillars_nuscenes_results` directory.
 
-6. Test SECOND on KITTI with 8 GPUs, and generate the pkl files and submission datas to be submit to the official evaluation server.
+6. Test SECOND on KITTI with 8 GPUs, and generate the pkl files and submission data to be submit to the official evaluation server.
 
    ```shell
    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py \
@@ -107,7 +107,7 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
        'submission_prefix=results/waymo-car/kitti_results'
    ```
 
-   **Notice**: For evaluation on waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`.(Sometimes when using bazel to build `compute_detection_metrics_main`, an error `'round' is not a member of 'std'` may appear. We just need to remove the `std::` before `round` in that file.) `pklfile_prefix` should be given in the `--eval-options` for the bin file generation. For metrics, `waymo` is the recommended official evaluation prototype. Currently, evaluating with choice `kitti` is adapted from KITTI and the results for each difficulty are not exactly the same as the definition of KITTI. Instead, most of objects are marked with difficulty 0 currently, which will be fixed in the future. The reasons of its instability include the large computation for evalution, the lack of occlusion and truncation in the converted data, different definition of difficulty and different methods of computing average precision.
+   **Notice**: For evaluation on waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`.(Sometimes when using bazel to build `compute_detection_metrics_main`, an error `'round' is not a member of 'std'` may appear. We just need to remove the `std::` before `round` in that file.) `pklfile_prefix` should be given in the `--eval-options` for the bin file generation. For metrics, `waymo` is the recommended official evaluation prototype. Currently, evaluating with choice `kitti` is adapted from KITTI and the results for each difficulty are not exactly the same as the definition of KITTI. Instead, most of objects are marked with difficulty 0 currently, which will be fixed in the future. The reasons of its instability include the large computation for evaluation, the lack of occlusion and truncation in the converted data, different definition of difficulty and different methods of computing average precision.
 
 9. Test PointPillars on waymo with 8 GPUs, generate the bin files and make a submission to the leaderboard.
 
@@ -156,7 +156,7 @@ Optional arguments are:
 - `--no-validate` (**not suggested**): By default, the codebase will perform evaluation at every k (default value is 1, which can be modified like [this](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py#L75)) epochs during the training. To disable this behavior, use `--no-validate`.
 - `--work-dir ${WORK_DIR}`: Override the working directory specified in the config file.
 - `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
-- `--options 'Key=value'`: Overide some settings in the used config.
+- `--options 'Key=value'`: Override some settings in the used config.
 
 Difference between `resume-from` and `load-from`:
 - `resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally.

docs/changelog.md → docs/en/changelog.md

@@ -77,7 +77,7 @@ A total of 8 developers contributed to this release.
 
 #### Compatibility
 
-- Unify the camera keys for consistent transformation between coodinate systems on different datasets. The modification change the key names to `lidar2img`, `depth2img`, `cam2img`, etc. for easier understanding. Customized codes using legacy keys may be influenced.
+- Unify the camera keys for consistent transformation between coordinate systems on different datasets. The modification change the key names to `lidar2img`, `depth2img`, `cam2img`, etc. for easier understanding. Customized codes using legacy keys may be influenced.
 - The next release will begin to move files of CUDA ops to [MMCV](https://github.com/open-mmlab/mmcv). It will influence the way to import related functions. We will not break the compatibility but will raise a warning first and please prepare to migrate it.
 
 #### Highlights
@@ -531,7 +531,7 @@ In order to fix the problem that the priority of EvalHook is too low, all hook p
 #### Bug Fixes
 
 - Rename CosineAnealing to CosineAnnealing (#57)
-- Fix device inconsistant bug in 3D IoU computation (#69)
+- Fix device inconsistent bug in 3D IoU computation (#69)
 - Fix a minor bug in json2csv of lyft dataset (#78)
 - Add missed test data for pointnet modules (#85)
 - Fix `use_valid_flag` bug in `CustomDataset` (#106)

docs/datasets/lyft_det.md → docs/en/datasets/lyft_det.md

@@ -192,6 +192,6 @@ An example to test PointPillars on Lyft with 8 GPUs and generate a submission to
 ./tools/dist_test.sh configs/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d.py work_dirs/pp-lyft/latest.pth 8 --out work_dirs/pp-lyft/results_challenge.pkl --format-only --eval-options 'jsonfile_prefix=work_dirs/pp-lyft/results_challenge' 'csv_savepath=results/pp-lyft/results_challenge.csv'
 ```
 
-After generating the `work_dirs/pp-lyft/results_challenge.csv`, you can submit it to the Kaggle evaluation server. Please refer to the [offical website](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles) for more information.
+After generating the `work_dirs/pp-lyft/results_challenge.csv`, you can submit it to the Kaggle evaluation server. Please refer to the [official website](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles) for more information.
 
 We can also visualize the prediction results with our developed visualization tools. Please refer to the [visualization doc](https://mmdetection3d.readthedocs.io/en/latest/useful_tools.html#visualization) for more details.

docs/datasets/nuscenes_det.md → docs/en/datasets/nuscenes_det.md

@@ -249,7 +249,7 @@ An example to test PointPillars on nuScenes with 8 GPUs and generate a submissio
 
 Note that the testing info should be changed to that for testing set instead of validation set [here](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/nus-3d.py#L132).
 
-After generating the `work_dirs/pp-nus/results_eval.json`, you can compress it and submit it to nuScenes benchmark. Please refer to the [nuScenes offical website](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) for more information.
+After generating the `work_dirs/pp-nus/results_eval.json`, you can compress it and submit it to nuScenes benchmark. Please refer to the [nuScenes official website](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) for more information.
 
 We can also visualize the prediction results with our developed visualization tools. Please refer to the [visualization doc](https://mmdetection3d.readthedocs.io/en/latest/useful_tools.html#visualization) for more details.