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

* add tutorial.config chinese doc

* minor fix

* refine format

* fix typos
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docs/tutorials/config.md
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...@@ -17,7 +17,7 @@ For example, if some modification is made based on PointPillars, user may first ...@@ -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`, 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 ## Config Name Style
...@@ -116,20 +116,20 @@ model = dict( ...@@ -116,20 +116,20 @@ model = dict(
[0.50867593, 0.50656086, 0.30136237], [0.50867593, 0.50656086, 0.30136237],
[1.1511526, 1.0546296, 0.49706793], [1.1511526, 1.0546296, 0.49706793],
[0.47535285, 0.49249494, 0.5802117]]), # Mean sizes for each class, the order is consistent with class_names. [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 in_channels=256, # Input channels for vote_module
vote_per_seed=1, # Number of votes to generate for each seed vote_per_seed=1, # Number of votes to generate for each seed
gt_per_seed=3, # Number of gts for each seed gt_per_seed=3, # Number of gts for each seed
conv_channels=(256, 256), # Channels for convolution conv_channels=(256, 256), # Channels for convolution
conv_cfg=dict(type='Conv1d'), # Config to convolution conv_cfg=dict(type='Conv1d'), # Config of convolution
norm_cfg=dict(type='BN1d'), # Config to normalization norm_cfg=dict(type='BN1d'), # Config of normalization
norm_feats=True, # Whether to normalize features 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 type='ChamferDistance', # Type of loss for voting branch
mode='l1', # Loss mode of voting branch mode='l1', # Loss mode of voting branch
reduction='none', # Specifies the reduction to apply to the output reduction='none', # Specifies the reduction to apply to the output
loss_dst_weight=10.0)), # Destination loss weight of the voting branch 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 type='PointSAModule', # type of vote aggregation module
num_point=256, # Number of points for the set abstraction layer in vote aggregation branch 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 radius=0.3, # Radius for the set abstraction layer in vote aggregation branch
...@@ -138,44 +138,44 @@ model = dict( ...@@ -138,44 +138,44 @@ model = dict(
use_xyz=True, # Whether to use xyz use_xyz=True, # Whether to use xyz
normalize_xyz=True), # Whether to normalize xyz normalize_xyz=True), # Whether to normalize xyz
feat_channels=(128, 128), # Channels for feature convolution feat_channels=(128, 128), # Channels for feature convolution
conv_cfg=dict(type='Conv1d'), # Config to convolution conv_cfg=dict(type='Conv1d'), # Config of convolution
norm_cfg=dict(type='BN1d'), # Config to normalization norm_cfg=dict(type='BN1d'), # Config of normalization
objectness_loss=dict( # Config to objectness loss objectness_loss=dict( # Config of objectness loss
type='CrossEntropyLoss', # Type of loss type='CrossEntropyLoss', # Type of loss
class_weight=[0.2, 0.8], # Class weight of the objectness loss class_weight=[0.2, 0.8], # Class weight of the objectness loss
reduction='sum', # Specifies the reduction to apply to the output reduction='sum', # Specifies the reduction to apply to the output
loss_weight=5.0), # Loss weight of the objectness loss 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 type='ChamferDistance', # Type of loss
mode='l2', # Loss mode of center loss mode='l2', # Loss mode of center loss
reduction='sum', # Specifies the reduction to apply to the output reduction='sum', # Specifies the reduction to apply to the output
loss_src_weight=10.0, # Source loss weight of the voting branch. loss_src_weight=10.0, # Source loss weight of the voting branch.
loss_dst_weight=10.0), # Destination 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 type='CrossEntropyLoss', # Type of loss
reduction='sum', # Specifies the reduction to apply to the output reduction='sum', # Specifies the reduction to apply to the output
loss_weight=1.0), # Loss weight of the direction classification loss 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 type='SmoothL1Loss', # Type of loss
reduction='sum', # Specifies the reduction to apply to the output reduction='sum', # Specifies the reduction to apply to the output
loss_weight=10.0), # Loss weight of the direction residual loss 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 type='CrossEntropyLoss', # Type of loss
reduction='sum', # Specifies the reduction to apply to the output reduction='sum', # Specifies the reduction to apply to the output
loss_weight=1.0), # Loss weight of the size classification loss 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 type='SmoothL1Loss', # Type of loss
reduction='sum', # Specifies the reduction to apply to the output reduction='sum', # Specifies the reduction to apply to the output
loss_weight=3.3333333333333335), # Loss weight of the size residual loss 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 type='CrossEntropyLoss', # Type of loss
reduction='sum', # Specifies the reduction to apply to the output reduction='sum', # Specifies the reduction to apply to the output
loss_weight=1.0)), # Loss weight of the semantic loss 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 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 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 sample_mod='seed', # Mode of the sampling method
nms_thr=0.25, # The threshold to be used during NMS nms_thr=0.25, # The threshold to be used during NMS
score_thr=0.8, # Threshold to filter out boxes score_thr=0.8, # Threshold to filter out boxes
...@@ -195,7 +195,7 @@ train_pipeline = [ # Training pipeline, refer to mmdet3d.datasets.pipelines for ...@@ -195,7 +195,7 @@ train_pipeline = [ # Training pipeline, refer to mmdet3d.datasets.pipelines for
dict( dict(
type='LoadAnnotations3D', # Second pipeline to load annotations, refer to mmdet3d.datasets.pipelines.indoor_loading for more details 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_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_mask_3d=True, # Whether to load 3D instance masks
with_seg_3d=True), # Whether to load 3D semantic masks with_seg_3d=True), # Whether to load 3D semantic masks
dict( dict(
...@@ -210,7 +210,7 @@ train_pipeline = [ # Training pipeline, refer to mmdet3d.datasets.pipelines for ...@@ -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 flip_ratio_xz=0.5), # Probability of being flipped along xz plane
dict( dict(
type='IndoorGlobalRotScale', # Augmentation pipeline that rotate and scale points and 3d boxes, refer to mmdet3d.datasets.pipelines.indoor_augment for more details 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 rot_range=[-0.027777777777777776, 0.027777777777777776], # Range of rotation
scale_range=None), # Range of scale scale_range=None), # Range of scale
dict( dict(
...@@ -261,7 +261,7 @@ eval_pipeline = [ # Pipeline used for evaluation or visualization, refer to mmd ...@@ -261,7 +261,7 @@ eval_pipeline = [ # Pipeline used for evaluation or visualization, refer to mmd
] ]
data = dict( data = dict(
samples_per_gpu=8, # Batch size of a single GPU 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 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. 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 times=5, # Repeat times
...@@ -310,7 +310,7 @@ data = dict( ...@@ -310,7 +310,7 @@ data = dict(
'pts_semantic_mask', 'pts_instance_mask' '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', classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'curtain', 'refrigerator', 'showercurtrain', 'toilet',
...@@ -383,7 +383,7 @@ evaluation = dict(pipeline=[ # Pipeline is passed by eval_pipeline created befo ...@@ -383,7 +383,7 @@ evaluation = dict(pipeline=[ # Pipeline is passed by eval_pipeline created befo
]) ])
lr = 0.008 # Learning rate of optimizers 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 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 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. 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 grad_clip=dict( # Config used to grad_clip
...@@ -393,19 +393,19 @@ lr_config = dict( # Learning rate scheduler config used to register LrUpdater h ...@@ -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. 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`. warmup=None, # The warmup policy, also support `exp` and `constant`.
step=[24, 32]) # Steps to decay the learning rate 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 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 interval=50, # Interval to print the log
hooks=[dict(type='TextLoggerHook'), hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')]) # The logger used to record the training process. 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. dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set.
log_level = 'INFO' # The level of logging. log_level = 'INFO' # The level of logging.
find_unused_parameters = True # Whether to find unused parameters find_unused_parameters = True # Whether to find unused parameters
work_dir = None # Directory to save the model checkpoints and logs for the current experiments. 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. 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. 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 gpu_ids = range(0, 1) # ids of gpus
``` ```
......
docs_zh-CN/tutorials/config.md
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