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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
View file @ 31ef559a
# 教程 1: 配置文件 # 教程 1: 学习配置文件
我们在配置文件中支持了继承和模块化来方便进行各种实验。
如果需要检查配置文件，可以通过运行 `python tools/misc/print_config.py /PATH/TO/CONFIG` 来查看完整的配置。
你也可以传入 `--options xxx.yyy=zzz` 参数来查看更新后的配置。
## 配置文件结构
在 `config/_base_` 文件夹下有 4 个基本组件类型，分别是：数据集 (dataset)，模型 (model)，训练策略 (schedule) 和运行时的默认设置 (default runtime)。
通过从上述每个文件夹中选取一个组件进行组合，许多方法如 SECOND、PointPillars、PartA2 和 VoteNet 都能够很容易地构建出来。
由 `_base_` 下的组件组成的配置，被我们称为 _原始配置 (primitive)_。
对于同一文件夹下的所有配置，推荐**只有一个**对应的 _原始配置_ 文件，所有其他的配置文件都应该继承自这个 _原始配置_ 文件，这样就能保证配置文件的最大继承深度为 3。
为了便于理解，我们建议贡献者继承现有方法。
例如，如果在 PointPillars 的基础上做了一些修改，用户首先可以通过指定 `_base_ = ../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py` 来继承基础的 PointPillars 结构，然后修改配置文件中的必要参数以完成继承。
如果你在构建一个与任何现有方法不共享结构的全新方法，可以在 `configs` 文件夹下创建一个新的例如 `xxx_rcnn` 文件夹。
更多细节请参考 [MMCV](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html) 文档。
## 配置文件名称风格
我们遵循以下样式来命名配置文件，并建议贡献者遵循相同的风格。
```
{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}
```
`{xxx}` 是被要求填写的字段而 `[yyy]` 是可选的。
- `{model}`：模型种类，例如 `hv_pointpillars` (Hard Voxelization PointPillars)、`VoteNet` 等。
- `[model setting]`：某些模型的特殊设定。
- `{backbone}`： 主干网络种类例如 `regnet-400mf`、`regnet-1.6gf` 等。
- `{neck}`：模型颈部的种类包括 `fpn`、`secfpn` 等。
- `[norm_setting]`：如无特殊声明，默认使用 `bn` (Batch Normalization)，其他类型可以有 `gn` (Group Normalization)、`sbn` (Synchronized Batch Normalization) 等。
`gn-head`/`gn-neck` 表示 GN 仅应用于网络的头部或颈部，而 `gn-all` 表示 GN 用于整个模型，例如主干网络、颈部和头部。
- `[misc]`：模型中各式各样的设置/插件，例如 `strong-aug` 意味着在训练过程中使用更强的数据增广策略。
- `[batch_per_gpu x gpu]`：每个 GPU 的样本数和 GPU 数量，默认使用 `4x8`。
- `{schedule}`：训练方案，选项是 `1x`、`2x`、`20e` 等。
`1x` 和 `2x` 分别代表训练 12 和 24 轮。
`20e` 在级联模型中使用，表示训练 20 轮。
对于 `1x`/`2x`，初始学习率在第 8/16 和第 11/22 轮衰减 10 倍；对于 `20e`，初始学习率在第 16 和第 19 轮衰减 10 倍。
- `{dataset}`：数据集，例如 `nus-3d`、`kitti-3d`、`lyft-3d`、`scannet-3d`、`sunrgbd-3d` 等。
当某一数据集存在多种设定时，我们也标记下所使用的类别数量，例如 `kitti-3d-3class` 和 `kitti-3d-car` 分别意味着在 KITTI 的所有三类上和单独车这一类上进行训练。
## 弃用的 train_cfg/test_cfg
遵循 MMDetection 的做法，我们在配置文件中弃用 `train_cfg` 和 `test_cfg`，请在模型配置中指定它们。
原始的配置结构如下：
```python
# 已经弃用的形式
model = dict(
type=...,
...
)
train_cfg=dict(...)
test_cfg=dict(...)
```
迁移后的配置结构如下：
```python
# 推荐的形式
model = dict(
type=...,
...
train_cfg=dict(...),
test_cfg=dict(...),
)
```
## VoteNet 配置文件示例
```python
model = dict(
type='VoteNet', # 检测器的类型，更多细节请参考 mmdet3d.models.detectors
backbone=dict(
type='PointNet2SASSG', # 主干网络的类型，更多细节请参考 mmdet3d.models.backbones
in_channels=4, # 点云输入通道数
num_points=(2048, 1024, 512, 256), # 每个 SA 模块采样的中心点的数量
radius=(0.2, 0.4, 0.8, 1.2), # 每个 SA 层的半径
num_samples=(64, 32, 16, 16), # 每个 SA 层聚集的点的数量
sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
(128, 128, 256)), # SA 模块中每个多层感知器的输出通道数
fp_channels=((256, 256), (256, 256)), # FP 模块中每个多层感知器的输出通道数
norm_cfg=dict(type='BN2d'), # 归一化层的配置
sa_cfg=dict( # 点集抽象 (SA) 模块的配置
type='PointSAModule', # SA 模块的类型
pool_mod='max', # SA 模块的池化方法 (最大池化或平均池化)
use_xyz=True, # 在特征聚合中是否使用 xyz 坐标
normalize_xyz=True)), # 在特征聚合中是否使用标准化的 xyz 坐标
bbox_head=dict(
type='VoteHead', # 检测框头的类型，更多细节请参考 mmdet3d.models.dense_heads
num_classes=18, # 分类的类别数量
bbox_coder=dict(
type='PartialBinBasedBBoxCoder', # 框编码层的类型，更多细节请参考 mmdet3d.core.bbox.coders
num_sizes=18, # 尺寸聚类的数量
num_dir_bins=1, # 编码方向角的间隔数
with_rot=False, # 框是否带有旋转角度
mean_sizes=[[0.76966727, 0.8116021, 0.92573744],
[1.876858, 1.8425595, 1.1931566],
[0.61328, 0.6148609, 0.7182701],
[1.3955007, 1.5121545, 0.83443564],
[0.97949594, 1.0675149, 0.6329687],
[0.531663, 0.5955577, 1.7500148],
[0.9624706, 0.72462326, 1.1481868],
[0.83221924, 1.0490936, 1.6875663],
[0.21132214, 0.4206159, 0.5372846],
[1.4440073, 1.8970833, 0.26985747],
[1.0294262, 1.4040797, 0.87554324],
[1.3766412, 0.65521795, 1.6813129],
[0.6650819, 0.71111923, 1.298853],
[0.41999173, 0.37906948, 1.7513971],
[0.59359556, 0.5912492, 0.73919016],
[0.50867593, 0.50656086, 0.30136237],
[1.1511526, 1.0546296, 0.49706793],
[0.47535285, 0.49249494, 0.5802117]]), # 每一类的平均尺寸，其顺序与类名顺序相同
vote_moudule_cfg=dict( # 投票 (vote) 模块的配置，更多细节请参考 mmdet3d.models.model_utils
in_channels=256, # 投票模块的输入通道数
vote_per_seed=1, # 对于每个种子点生成的投票数
gt_per_seed=3, # 每个种子点的真实标签个数
conv_channels=(256, 256), # 卷积通道数
conv_cfg=dict(type='Conv1d'), # 卷积配置
norm_cfg=dict(type='BN1d'), # 归一化层配置
norm_feats=True, # 是否标准化特征
vote_loss=dict( # 投票分支的损失函数配置
type='ChamferDistance', # 投票分支的损失函数类型
mode='l1', # 投票分支的损失函数模式
reduction='none', # 设置对损失函数输出的聚合方法
loss_dst_weight=10.0)), # 投票分支的目标损失权重
vote_aggregation_cfg=dict( # 投票聚合分支的配置
type='PointSAModule', # 投票聚合模块的类型
num_point=256, # 投票聚合分支中 SA 模块的点的数量
radius=0.3, # 投票聚合分支中 SA 模块的半径
num_sample=16, # 投票聚合分支中 SA 模块的采样点的数量
mlp_channels=[256, 128, 128, 128], # 投票聚合分支中 SA 模块的多层感知器的通道数
use_xyz=True, # 是否使用 xyz 坐标
normalize_xyz=True), # 是否使用标准化后的 xyz 坐标
feat_channels=(128, 128), # 特征卷积的通道数
conv_cfg=dict(type='Conv1d'), # 卷积的配置
norm_cfg=dict(type='BN1d'), # 归一化层的配置
objectness_loss=dict( # 物体性 (objectness) 损失函数的配置
type='CrossEntropyLoss', # 损失函数类型
class_weight=[0.2, 0.8], # 损失函数对每一类的权重
reduction='sum', # 设置损失函数输出的聚合方法
loss_weight=5.0), # 损失函数权重
center_loss=dict( # 中心 (center) 损失函数的配置
type='ChamferDistance', # 损失函数类型
mode='l2', # 损失函数模式
reduction='sum', # 设置损失函数输出的聚合方法
loss_src_weight=10.0, # 源损失权重
loss_dst_weight=10.0), # 目标损失权重
dir_class_loss=dict( # 方向分类损失函数的配置
type='CrossEntropyLoss', # 损失函数类型
reduction='sum', # 设置损失函数输出的聚合方法
loss_weight=1.0), # 损失函数权重
dir_res_loss=dict( # 方向残差 (residual) 损失函数的配置
type='SmoothL1Loss', # 损失函数类型
reduction='sum', # 设置损失函数输出的聚合方法
loss_weight=10.0), # 损失函数权重
size_class_loss=dict( # 尺寸分类损失函数的配置
type='CrossEntropyLoss', # 损失函数类型
reduction='sum', # 设置损失函数输出的聚合方法
loss_weight=1.0), # 损失函数权重
size_res_loss=dict( # 尺寸残差损失函数的配置
type='SmoothL1Loss', # 损失函数类型
reduction='sum', # 设置损失函数输出的聚合方法
loss_weight=3.3333333333333335), # 损失函数权重
semantic_loss=dict( # 语义损失函数的配置
type='CrossEntropyLoss', # 损失函数类型
reduction='sum', # 设置损失函数输出的聚合方法
loss_weight=1.0)), # 损失函数权重
train_cfg = dict( # VoteNet 训练的超参数配置
pos_distance_thr=0.3, # 距离 >= 0.3 阈值的样本将被视为正样本
neg_distance_thr=0.6, # 距离 < 0.6 阈值的样本将被视为负样本
sample_mod='vote'), # 采样方法的模式
test_cfg = dict( # VoteNet 测试的超参数配置
sample_mod='seed', # 采样方法的模式
nms_thr=0.25, # NMS 中使用的阈值
score_thr=0.8, # 剔除框的阈值
per_class_proposal=False)) # 是否使用逐类提议框 (proposal)
dataset_type = 'ScanNetDataset' # 数据集类型
data_root = './data/scannet/' # 数据路径
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin') # 类的名称
train_pipeline = [ # 训练流水线，更多细节请参考 mmdet3d.datasets.pipelines
dict(
type='LoadPointsFromFile', # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
shift_height=True, # 是否使用变换高度
load_dim=6, # 读取的点的维度
use_dim=[0, 1, 2]), # 使用所读取点的哪些维度
dict(
type='LoadAnnotations3D', # 第二个流程，用于读取标注，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
with_bbox_3d=True, # 是否读取 3D 框
with_label_3d=True, # 是否读取 3D 框对应的类别标签
with_mask_3d=True, # 是否读取 3D 实例分割掩码
with_seg_3d=True), # 是否读取 3D 语义分割掩码
dict(
type='PointSegClassMapping', # 选取有效的类别，更多细节请参考 mmdet3d.datasets.pipelines.point_seg_class_mapping
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
36, 39), # 所有有效类别的编号
max_cat_id=40), # 输入语义分割掩码中可能存在的最大类别编号
dict(type='IndoorPointSample', # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
num_points=40000), # 采样的点的数量
dict(type='IndoorFlipData', # 数据增广流程，随机翻转点和 3D 框
flip_ratio_yz=0.5, # 沿着 yz 平面被翻转的概率
flip_ratio_xz=0.5), # 沿着 xz 平面被翻转的概率
dict(
type='IndoorGlobalRotScale', # 数据增广流程，旋转并放缩点和 3D 框，更多细节请参考 mmdet3d.datasets.pipelines.indoor_augment
shift_height=True, # 读取的点是否有高度这一属性
rot_range=[-0.027777777777777776, 0.027777777777777776], # 旋转角范围
scale_range=None), # 缩放尺寸范围
dict(
type='DefaultFormatBundle3D', # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.pipelines.formating
class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')),
dict(
type='Collect3D', # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.pipelines.formating
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
])
]
test_pipeline = [ # 测试流水线，更多细节请参考 mmdet3d.datasets.pipelines
dict(
type='LoadPointsFromFile', # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
shift_height=True, # 是否使用变换高度
load_dim=6, # 读取的点的维度
use_dim=[0, 1, 2]), # 使用所读取点的哪些维度
dict(type='IndoorPointSample', # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
num_points=40000), # 采样的点的数量
dict(
type='DefaultFormatBundle3D', # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.pipelines.formating
class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')),
dict(type='Collect3D', # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.pipelines.formating
keys=['points'])
]
eval_pipeline = [ # 模型验证或可视化所使用的流水线，更多细节请参考 mmdet3d.datasets.pipelines
dict(
type='LoadPointsFromFile', # 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
shift_height=True, # 是否使用变换高度
load_dim=6, # 读取的点的维度
use_dim=[0, 1, 2]), # 使用所读取点的哪些维度
dict(
type='DefaultFormatBundle3D', # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.pipelines.formating
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', # 最后一个流程，决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.pipelines.formating
keys=['points'])
]
data = dict(
samples_per_gpu=8, # 单张 GPU 上的样本数
workers_per_gpu=4, # 每张 GPU 上用于读取数据的进程数
train=dict( # 训练数据集配置
type='RepeatDataset', # 数据集嵌套，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/dataset_wrappers.py
times=5, # 重复次数
dataset=dict(
type='ScanNetDataset', # 数据集类型
data_root='./data/scannet/', # 数据路径
ann_file='./data/scannet/scannet_infos_train.pkl', # 数据标注文件的路径
pipeline=[ # 流水线，这里传入的就是上面创建的训练流水线变量
dict(
type='LoadPointsFromFile',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(type='IndoorPointSample', num_points=40000),
dict(
type='IndoorFlipData',
flip_ratio_yz=0.5,
flip_ratio_xz=0.5),
dict(
type='IndoorGlobalRotScale',
shift_height=True,
rot_range=[-0.027777777777777776, 0.027777777777777776],
scale_range=None),
dict(
type='DefaultFormatBundle3D',
class_names=('cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture',
'counter', 'desk', 'curtain', 'refrigerator',
'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin')),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d',
'pts_semantic_mask', 'pts_instance_mask'
])
],
filter_empty_gt=False, # 是否过滤掉空的标签框
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin'))), # 类别名称
val=dict( # 验证数据集配置
type='ScanNetDataset', # 数据集类型
data_root='./data/scannet/', # 数据路径
ann_file='./data/scannet/scannet_infos_val.pkl', # 数据标注文件的路径
pipeline=[ # 流水线，这里传入的就是上面创建的测试流水线变量
dict(
type='LoadPointsFromFile',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='IndoorPointSample', num_points=40000),
dict(
type='DefaultFormatBundle3D',
class_names=('cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture',
'counter', 'desk', 'curtain', 'refrigerator',
'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin')),
dict(type='Collect3D', keys=['points'])
],
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin'), # 类别名称
test_mode=True), # 是否开启测试模式
test=dict( # 测试数据集配置
type='ScanNetDataset', # 数据集类型
data_root='./data/scannet/', # 数据路径
ann_file='./data/scannet/scannet_infos_val.pkl', # 数据标注文件的路径
pipeline=[ # 流水线，这里传入的就是上面创建的测试流水线变量
dict(
type='LoadPointsFromFile',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='IndoorPointSample', num_points=40000),
dict(
type='DefaultFormatBundle3D',
class_names=('cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture',
'counter', 'desk', 'curtain', 'refrigerator',
'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin')),
dict(type='Collect3D', keys=['points'])
],
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin'), # 类别名称
test_mode=True)) # 是否开启测试模式
evaluation = dict(pipeline=[ # 流水线，这里传入的就是上面创建的验证流水线变量
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
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', keys=['points'])
])
lr = 0.008 # 优化器的学习率
optimizer = dict( # 构建优化器所使用的配置，我们支持所有 PyTorch 中支持的优化器，并且拥有相同的参数名称
type='Adam', # 优化器类型，更多细节请参考 https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/optimizer/default_constructor.py#L12
lr=0.008) # 优化器的学习率，用户可以在 PyTorch 文档中查看这些参数的详细使用方法
optimizer_config = dict( # 构建优化器钩子的配置，更多实现细节可参考 https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/optimizer.py#L22
grad_clip=dict( # 梯度裁剪的配置
max_norm=10, # 梯度的最大模长
norm_type=2)) # 所使用的 p-范数的类型，可以设置成 'inf' 则指代无穷范数
lr_config = dict( # 学习率策略配置，用于注册学习率更新的钩子
policy='step', # 学习率调整的策略，支持 CosineAnnealing、Cyclic 等，更多支持的种类请参考 https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/lr_updater.py#L9
warmup=None, # Warmup 策略，同时也支持 `exp` 和 `constant`
step=[24, 32]) # 学习率衰减的步数
checkpoint_config = dict( # 设置保存模型权重钩子的配置，具体实现请参考 https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py
interval=1) # 保存模型权重的间隔是 1 轮
log_config = dict( # 用于注册输出记录信息钩子的配置
interval=50, # 输出记录信息的间隔
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')]) # 用于记录训练过程的信息记录机制
runner = dict(type='EpochBasedRunner', max_epochs=36) # 程序运行器，将会运行 `workflow` `max_epochs` 次
dist_params = dict(backend='nccl') # 设置分布式训练的配置，通讯端口值也可被设置
log_level = 'INFO' # 输出记录信息的等级
find_unused_parameters = True # 是否查找模型中未使用的参数
work_dir = None # 当前实验存储模型权重和输出信息的路径
load_from = None # 从指定路径读取一个预训练的模型权重，这将不会继续 (resume) 训练
resume_from = None # 从一个指定路径读入模型权重并继续训练，这意味着训练轮数、优化器状态等都将被读取
workflow = [('train', 1)] # 要运行的工作流。[('train', 1)] 意味着只有一个名为 'train' 的工作流，它只会被执行一次。这一工作流依据 `max_epochs` 的值将会训练模型 36 轮。
gpu_ids = range(0, 1) # 所使用的 GPU 编号
```
## 常问问题 (FAQ)
### 忽略基础配置文件里的部分内容
有时，您也许会需要通过设置 `_delete_=True` 来忽略基础配置文件里的一些域内容。
请参照 [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) 来获得一些简单的指导。
例如在 MMDetection3D 中，为了改变如下所示 PointPillars FPN 模块的某些配置：
```python
model = dict(
type='MVXFasterRCNN',
pts_voxel_layer=dict(...),
pts_voxel_encoder=dict(...),
pts_middle_encoder=dict(...),
pts_backbone=dict(...),
pts_neck=dict(
type='FPN',
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
act_cfg=dict(type='ReLU'),
in_channels=[64, 128, 256],
out_channels=256,
start_level=0,
num_outs=3),
pts_bbox_head=dict(...))
```
`FPN` 和 `SECONDFPN` 使用不同的关键词来构建。
```python
_base_ = '../_base_/models/hv_pointpillars_fpn_nus.py'
model = dict(
pts_neck=dict(
_delete_=True,
type='SECONDFPN',
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
in_channels=[64, 128, 256],
upsample_strides=[1, 2, 4],
out_channels=[128, 128, 128]),
pts_bbox_head=dict(...))
```
`_delete_=True` 的标识将会使用新的键值覆盖掉 `pts_neck` 中的所有旧键值。
### 使用配置文件里的中间变量
配置文件里会使用一些中间变量，例如数据集中的 `train_pipeline`/`test_pipeline`。
值得注意的是，当修改子配置文件中的中间变量后，用户还需再次将其传入相应字段。
例如，我们想在训练和测试中，对 PointPillars 使用多尺度策略 (multi scale strategy)，那么 `train_pipeline`/`test_pipeline` 就是我们想要修改的中间变量。
```python
_base_ = './nus-3d.py'
train_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=[0.95, 1.0, 1.05],
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
```
这里，我们首先定义了新的 `train_pipeline`/`test_pipeline`，然后将其传入 `data`。
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