Refactor the structure of documentation (#1113)

* Merge English and Chinese doc into the same folder

* Add logo image back

* Add logo image back

docs/datasets/waymo_det.md → docs/en/datasets/waymo_det.md

@@ -129,7 +129,7 @@ Then you can evaluate your models on Waymo. An example to evaluate PointPillars
        'submission_prefix=results/waymo-car/kitti_results'
    ```
 
-`pklfile_prefix` should be given in the `--eval-options` if the bin file is needed to be generated. 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 definitions of difficulty and different methods of computing Average Precision.
+`pklfile_prefix` should be given in the `--eval-options` if the bin file is needed to be generated. 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 definitions of difficulty and different methods of computing Average Precision.
 
 **Notice**:
 

docs/getting_started.md → docs/en/getting_started.md

@@ -64,7 +64,7 @@ PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.
 conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
 ```
 
-If you build PyTorch from source instead of installing the prebuilt pacakge,
+If you build PyTorch from source instead of installing the prebuilt package,
 you can use more CUDA versions such as 9.0.
 
 **c. Install [MMCV](https://mmcv.readthedocs.io/en/latest/).**

docs/tutorials/config.md → docs/en/tutorials/config.md

@@ -214,13 +214,13 @@ train_pipeline = [  # Training pipeline, refer to mmdet3d.datasets.pipelines for
         rot_range=[-0.027777777777777776, 0.027777777777777776],  # Range of rotation
         scale_range=None),  # Range of scale
     dict(
-        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formating for more details
+        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formatting for more details
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
     dict(
-        type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formating for more details
+        type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formatting for more details
         keys=[
             'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
             'pts_instance_mask'
@@ -235,12 +235,12 @@ test_pipeline = [  # Testing pipeline, refer to mmdet3d.datasets.pipelines for m
     dict(type='PointSample',  # Sample points, refer to mmdet3d.datasets.pipelines.transforms_3d for more details
         num_points=40000),  # Number of points to be sampled
     dict(
-        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formating for more details
+        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formatting for more details
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
-    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formating for more details
+    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formatting for more details
         keys=['points'])
 ]
 eval_pipeline = [  # Pipeline used for evaluation or visualization, refer to mmdet3d.datasets.pipelines for more details
@@ -250,13 +250,13 @@ eval_pipeline = [  # Pipeline used for evaluation or visualization, refer to mmd
         load_dim=6,  # The dimension of the loaded points
         use_dim=[0, 1, 2]),  # Which dimensions of the points to be used
     dict(
-        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formating for more details
+        type='DefaultFormatBundle3D',  # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formatting for more details
         class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                      'sink', 'bathtub', 'garbagebin')),
         with_label=False),
-    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formating for more details
+    dict(type='Collect3D',  # Pipeline that decides which keys in the data should be passed to the detector, refer to mmdet3d.datasets.pipelines.formatting for more details
         keys=['points'])
 ]
 data = dict(
@@ -384,7 +384,7 @@ evaluation = dict(pipeline=[  # Pipeline is passed by eval_pipeline created befo
 lr = 0.008  # Learning rate of optimizers
 optimizer = dict(  # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
     type='Adam',  # Type of optimizers, refer to https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/optimizer/default_constructor.py#L12 for more details
-    lr=0.008)  # Learning rate of optimizers, see detail usages of the parameters in the documentaion of PyTorch
+    lr=0.008)  # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch
 optimizer_config = dict(  # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/optimizer.py#L22 for implementation details.
     grad_clip=dict(  # Config used to grad_clip
     max_norm=10,  # max norm of the gradients

docs/tutorials/customize_runtime.md → docs/en/tutorials/customize_runtime.md

@@ -123,7 +123,7 @@ Tricks not implemented by the optimizer should be implemented through optimizer
         _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
     ```
 
-    If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to overide the unnecessary settings in the base config. See the [config documentation](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html) for more details.
+    If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to override the unnecessary settings in the base config. See the [config documentation](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html) for more details.
 
 - __Use momentum schedule to accelerate model convergence__:
 
@@ -286,7 +286,7 @@ custom_hooks = [
 
 ### Modify default runtime hooks
 
-There are some common hooks that are not registerd through `custom_hooks`, they are
+There are some common hooks that are not registered through `custom_hooks`, they are
 
 - log_config
 - checkpoint_config

docs/useful_tools.md → docs/en/useful_tools.md

@@ -203,7 +203,7 @@ The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth
 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 ${VERIONS} \
+python -u tools/data_converter/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
                                                     --out-dir ${OUT_DIR} --nproc ${NUM_WORKERS} --extra-tag ${TAG}
 ```