[Docs] Add tutorial/config.md Chinese version (#788)

* add tutorial.config chinese doc

* minor fix

* refine format

* fix typos

docs/tutorials/config.md

@@ -17,7 +17,7 @@ For example, if some modification is made based on PointPillars, user may first
 
 If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder `xxx_rcnn` under `configs`,
 
-Please refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#config) for detailed documentation.
+Please refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html) for detailed documentation.
 
 ## Config Name Style
 
@@ -116,20 +116,20 @@ model = dict(
                         [0.50867593, 0.50656086, 0.30136237],
                         [1.1511526, 1.0546296, 0.49706793],
                         [0.47535285, 0.49249494, 0.5802117]]),  # Mean sizes for each class, the order is consistent with class_names.
-        vote_moudule_cfg=dict(  # Config to vote module branch, refer to mmdet3d.models.model_utils for more details
+        vote_moudule_cfg=dict(  # Config of vote module branch, refer to mmdet3d.models.model_utils for more details
             in_channels=256,  # Input channels for vote_module
             vote_per_seed=1,  # Number of votes to generate for each seed
             gt_per_seed=3,  # Number of gts for each seed
             conv_channels=(256, 256),  # Channels for convolution
-            conv_cfg=dict(type='Conv1d'),  # Config to convolution
-            norm_cfg=dict(type='BN1d'),  # Config to normalization
+            conv_cfg=dict(type='Conv1d'),  # Config of convolution
+            norm_cfg=dict(type='BN1d'),  # Config of normalization
             norm_feats=True,  # Whether to normalize features
-            vote_loss=dict(  # Config to the loss function for voting branch
+            vote_loss=dict(  # Config of the loss function for voting branch
                 type='ChamferDistance',  # Type of loss for voting branch
                 mode='l1',  # Loss mode of voting branch
                 reduction='none',  # Specifies the reduction to apply to the output
                 loss_dst_weight=10.0)),  # Destination loss weight of the voting branch
-        vote_aggregation_cfg=dict(  # Config to vote aggregation branch
+        vote_aggregation_cfg=dict(  # Config of vote aggregation branch
             type='PointSAModule',  # type of vote aggregation module
             num_point=256,  # Number of points for the set abstraction layer in vote aggregation branch
             radius=0.3,  # Radius for the set abstraction layer in vote aggregation branch
@@ -138,44 +138,44 @@ model = dict(
             use_xyz=True,  # Whether to use xyz
             normalize_xyz=True),  # Whether to normalize xyz
         feat_channels=(128, 128),  # Channels for feature convolution
-        conv_cfg=dict(type='Conv1d'),  # Config to convolution
-        norm_cfg=dict(type='BN1d'),  # Config to normalization
-        objectness_loss=dict(  # Config to objectness loss
+        conv_cfg=dict(type='Conv1d'),  # Config of convolution
+        norm_cfg=dict(type='BN1d'),  # Config of normalization
+        objectness_loss=dict(  # Config of objectness loss
             type='CrossEntropyLoss',  # Type of loss
             class_weight=[0.2, 0.8],  # Class weight of the objectness loss
             reduction='sum',  # Specifies the reduction to apply to the output
             loss_weight=5.0),  # Loss weight of the objectness loss
-        center_loss=dict(  # Config to center loss
+        center_loss=dict(  # Config of center loss
             type='ChamferDistance',  # Type of loss
             mode='l2',  # Loss mode of center loss
             reduction='sum',  # Specifies the reduction to apply to the output
             loss_src_weight=10.0,  # Source loss weight of the voting branch.
             loss_dst_weight=10.0),  # Destination loss weight of the voting branch.
-        dir_class_loss=dict(  # Config to direction classification loss
+        dir_class_loss=dict(  # Config of direction classification loss
             type='CrossEntropyLoss',  # Type of loss
             reduction='sum',  # Specifies the reduction to apply to the output
             loss_weight=1.0),  # Loss weight of the direction classification loss
-        dir_res_loss=dict(  # Config to direction residual loss
+        dir_res_loss=dict(  # Config of direction residual loss
             type='SmoothL1Loss',  # Type of loss
             reduction='sum',  # Specifies the reduction to apply to the output
             loss_weight=10.0),  # Loss weight of the direction residual loss
-        size_class_loss=dict(  # Config to size classification loss
+        size_class_loss=dict(  # Config of size classification loss
             type='CrossEntropyLoss',  # Type of loss
             reduction='sum',  # Specifies the reduction to apply to the output
             loss_weight=1.0),  # Loss weight of the size classification loss
-        size_res_loss=dict(  # Config to size residual loss
+        size_res_loss=dict(  # Config of size residual loss
             type='SmoothL1Loss',  # Type of loss
             reduction='sum',  # Specifies the reduction to apply to the output
             loss_weight=3.3333333333333335),  # Loss weight of the size residual loss
-        semantic_loss=dict(  # Config to semantic loss
+        semantic_loss=dict(  # Config of semantic loss
             type='CrossEntropyLoss',  # Type of loss
             reduction='sum',  # Specifies the reduction to apply to the output
             loss_weight=1.0)),  # Loss weight of the semantic loss
-    train_cfg = dict(  # Config of training hyperparameters for votenet
+    train_cfg = dict(  # Config of training hyperparameters for VoteNet
         pos_distance_thr=0.3,  # distance >= threshold 0.3 will be taken as positive samples
-        neg_distance_thr=0.6,  # distance < threshold 0.6 will be taken as positive samples
+        neg_distance_thr=0.6,  # distance < threshold 0.6 will be taken as negative samples
         sample_mod='vote'),  # Mode of the sampling method
-    test_cfg = dict(  # Config of testing hyperparameters for votenet
+    test_cfg = dict(  # Config of testing hyperparameters for VoteNet
         sample_mod='seed',  # Mode of the sampling method
         nms_thr=0.25,  # The threshold to be used during NMS
         score_thr=0.8,  # Threshold to filter out boxes
@@ -195,7 +195,7 @@ train_pipeline = [  # Training pipeline, refer to mmdet3d.datasets.pipelines for
     dict(
         type='LoadAnnotations3D',  # Second pipeline to load annotations, refer to mmdet3d.datasets.pipelines.indoor_loading for more details
         with_bbox_3d=True,  # Whether to load 3D boxes
-        with_label_3d=True,  # Whether to load 3D labels
+        with_label_3d=True,  # Whether to load 3D labels corresponding to each 3D box
         with_mask_3d=True,  # Whether to load 3D instance masks
         with_seg_3d=True),  # Whether to load 3D semantic masks
     dict(
@@ -210,7 +210,7 @@ train_pipeline = [  # Training pipeline, refer to mmdet3d.datasets.pipelines for
         flip_ratio_xz=0.5),  # Probability of being flipped along xz plane
     dict(
         type='IndoorGlobalRotScale',  # Augmentation pipeline that rotate and scale points and 3d boxes, refer to mmdet3d.datasets.pipelines.indoor_augment for more details
-        shift_height=True,  # Whether to use height
+        shift_height=True,  # Whether the loaded points use `shift_height` attribute
         rot_range=[-0.027777777777777776, 0.027777777777777776],  # Range of rotation
         scale_range=None),  # Range of scale
     dict(
@@ -261,7 +261,7 @@ eval_pipeline = [  # Pipeline used for evaluation or visualization, refer to mmd
 ]
 data = dict(
     samples_per_gpu=8,  # Batch size of a single GPU
-    workers_per_gpu=4,  # Worker to pre-fetch data for each single GPU
+    workers_per_gpu=4,  # Number of workers to pre-fetch data for each single GPU
     train=dict(  # Train dataset config
         type='RepeatDataset',  # Wrapper of dataset, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/dataset_wrappers.py for details.
         times=5,  # Repeat times
@@ -310,7 +310,7 @@ data = dict(
                         'pts_semantic_mask', 'pts_instance_mask'
                     ])
             ],
-            filter_empty_gt=False,  # Whether to filter ground empty truth boxes
+            filter_empty_gt=False,  # Whether to filter empty ground truth boxes
             classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                      'window', 'bookshelf', 'picture', 'counter', 'desk',
                      'curtain', 'refrigerator', 'showercurtrain', 'toilet',
@@ -383,7 +383,7 @@ 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,   # 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
+    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
 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
@@ -393,19 +393,19 @@ lr_config = dict(  # Learning rate scheduler config used to register LrUpdater h
     policy='step',  # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/lr_updater.py#L9.
     warmup=None,  # The warmup policy, also support `exp` and `constant`.
     step=[24, 32])  # Steps to decay the learning rate
-checkpoint_config = dict(  # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
+checkpoint_config = dict(  # Config of set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
     interval=1)  # The save interval is 1
-log_config = dict(  # config to register logger hook
+log_config = dict(  # config of register logger hook
     interval=50,  # Interval to print the log
     hooks=[dict(type='TextLoggerHook'),
            dict(type='TensorboardLoggerHook')])  # The logger used to record the training process.
-runner = dict(type='EpochBasedRunner', max_epochs=36) # Runner that runs the workflow in total max_epochs
+runner = dict(type='EpochBasedRunner', max_epochs=36) # Runner that runs the `workflow` in total `max_epochs`
 dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set.
 log_level = 'INFO'  # The level of logging.
 find_unused_parameters = True  # Whether to find unused parameters
 work_dir = None  # Directory to save the model checkpoints and logs for the current experiments.
 load_from = None # load models as a pre-trained model from a given path. This will not resume training.
-resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
+resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved. The training state such as the epoch number and optimizer state will be restored.
 workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 36 epochs according to the max_epochs.
 gpu_ids = range(0, 1)  # ids of gpus
 ```