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Update documentation about the deprecation of train_cfg/test_cfg (#4489)

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* Update documentation about the modification of train_cfg/test_cfg

* Fix docs str

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Haian Huang(深度眸) 4 years ago committed by GitHub
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      docs/tutorials/config.md

142
docs/tutorials/config.md
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@ -64,6 +64,32 @@ We follow the below style to name config files. Contributors are advised to foll
For `20e`, initial learning rate decays by a factor of 10 at the 16th and 19th epochs. For `20e`, initial learning rate decays by a factor of 10 at the 16th and 19th epochs.
- `{dataset}`: dataset like `coco`, `cityscapes`, `voc_0712`, `wider_face`. - `{dataset}`: dataset like `coco`, `cityscapes`, `voc_0712`, `wider_face`.
## Deprecated train_cfg/test_cfg
The `train_cfg` and `test_cfg` are deprecated in config file, please specify them in the model config. The original config structure is as below.
```python
# deprecated
model = dict(
type=...,
...
)
train_cfg=dict(...)
test_cfg=dict(...)
```
The migration example is as below.
```python
# recommended
model = dict(
type=...,
...
train_cfg=dict(...),
test_cfg=dict(...),
)
```
## An Example of Mask R-CNN ## An Example of Mask R-CNN
To help the users have a basic idea of a complete config and the modules in a modern detection system, To help the users have a basic idea of a complete config and the modules in a modern detection system,
@ -157,64 +183,64 @@ model = dict(
type='CrossEntropyLoss', # Type of loss used for segmentation type='CrossEntropyLoss', # Type of loss used for segmentation
use_mask=True, # Whether to only train the mask in the correct class. use_mask=True, # Whether to only train the mask in the correct class.
loss_weight=1.0)))) # Loss weight of mask branch. loss_weight=1.0)))) # Loss weight of mask branch.
train_cfg = dict( # Config of training hyperparameters for rpn and rcnn train_cfg = dict( # Config of training hyperparameters for rpn and rcnn
rpn=dict( # Training config of rpn rpn=dict( # Training config of rpn
assigner=dict( # Config of assigner assigner=dict( # Config of assigner
type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
pos_iou_thr=0.7, # IoU >= threshold 0.7 will be taken as positive samples pos_iou_thr=0.7, # IoU >= threshold 0.7 will be taken as positive samples
neg_iou_thr=0.3, # IoU < threshold 0.3 will be taken as negative samples neg_iou_thr=0.3, # IoU < threshold 0.3 will be taken as negative samples
min_pos_iou=0.3, # The minimal IoU threshold to take boxes as positive samples min_pos_iou=0.3, # The minimal IoU threshold to take boxes as positive samples
match_low_quality=True, # Whether to match the boxes under low quality (see API doc for more details). match_low_quality=True, # Whether to match the boxes under low quality (see API doc for more details).
ignore_iof_thr=-1), # IoF threshold for ignoring bboxes ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
sampler=dict( # Config of positive/negative sampler sampler=dict( # Config of positive/negative sampler
type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
num=256, # Number of samples num=256, # Number of samples
pos_fraction=0.5, # The ratio of positive samples in the total samples. pos_fraction=0.5, # The ratio of positive samples in the total samples.
neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples.
add_gt_as_proposals=False), # Whether add GT as proposals after sampling. add_gt_as_proposals=False), # Whether add GT as proposals after sampling.
allowed_border=-1, # The border allowed after padding for valid anchors. allowed_border=-1, # The border allowed after padding for valid anchors.
pos_weight=-1, # The weight of positive samples during training. pos_weight=-1, # The weight of positive samples during training.
debug=False), # Whether to set the debug mode debug=False), # Whether to set the debug mode
rpn_proposal=dict( # The config to generate proposals during training rpn_proposal=dict( # The config to generate proposals during training
nms_across_levels=False, # Whether to do NMS for boxes across levels nms_across_levels=False, # Whether to do NMS for boxes across levels
nms_pre=2000, # The number of boxes before NMS nms_pre=2000, # The number of boxes before NMS
nms_post=1000, # The number of boxes to be kept by NMS nms_post=1000, # The number of boxes to be kept by NMS
max_num=1000, # The number of boxes to be used after NMS max_num=1000, # The number of boxes to be used after NMS
nms_thr=0.7, # The threshold to be used during NMS nms_thr=0.7, # The threshold to be used during NMS
min_bbox_size=0), # The allowed minimal box size min_bbox_size=0), # The allowed minimal box size
rcnn=dict( # The config for the roi heads. rcnn=dict( # The config for the roi heads.
assigner=dict( # Config of assigner for second stage, this is different for that in rpn assigner=dict( # Config of assigner for second stage, this is different for that in rpn
type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
pos_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples pos_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples
neg_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples neg_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples
min_pos_iou=0.5, # The minimal IoU threshold to take boxes as positive samples min_pos_iou=0.5, # The minimal IoU threshold to take boxes as positive samples
match_low_quality=False, # Whether to match the boxes under low quality (see API doc for more details). match_low_quality=False, # Whether to match the boxes under low quality (see API doc for more details).
ignore_iof_thr=-1), # IoF threshold for ignoring bboxes ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
sampler=dict( sampler=dict(
type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
num=512, # Number of samples num=512, # Number of samples
pos_fraction=0.25, # The ratio of positive samples in the total samples. pos_fraction=0.25, # The ratio of positive samples in the total samples.
neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples.
add_gt_as_proposals=True add_gt_as_proposals=True
), # Whether add GT as proposals after sampling. ), # Whether add GT as proposals after sampling.
mask_size=28, # Size of mask mask_size=28, # Size of mask
pos_weight=-1, # The weight of positive samples during training. pos_weight=-1, # The weight of positive samples during training.
debug=False)) # Whether to set the debug mode debug=False)) # Whether to set the debug mode
test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn
rpn=dict( # The config to generate proposals during testing rpn=dict( # The config to generate proposals during testing
nms_across_levels=False, # Whether to do NMS for boxes across levels nms_across_levels=False, # Whether to do NMS for boxes across levels
nms_pre=1000, # The number of boxes before NMS nms_pre=1000, # The number of boxes before NMS
nms_post=1000, # The number of boxes to be kept by NMS nms_post=1000, # The number of boxes to be kept by NMS
max_num=1000, # The number of boxes to be used after NMS max_num=1000, # The number of boxes to be used after NMS
nms_thr=0.7, # The threshold to be used during NMS nms_thr=0.7, # The threshold to be used during NMS
min_bbox_size=0), # The allowed minimal box size min_bbox_size=0), # The allowed minimal box size
rcnn=dict( # The config for the roi heads. rcnn=dict( # The config for the roi heads.
score_thr=0.05, # Threshold to filter out boxes score_thr=0.05, # Threshold to filter out boxes
nms=dict( # Config of nms in the second stage nms=dict( # Config of nms in the second stage
type='nms', # Type of nms type='nms', # Type of nms
iou_thr=0.5), # NMS threshold iou_thr=0.5), # NMS threshold
max_per_img=100, # Max number of detections of each image max_per_img=100, # Max number of detections of each image
mask_thr_binary=0.5)) # Threshold of mask prediction mask_thr_binary=0.5)) # Threshold of mask prediction
dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset
data_root = 'data/coco/' # Root path of data data_root = 'data/coco/' # Root path of data
img_norm_cfg = dict( # Image normalization config to normalize the input images img_norm_cfg = dict( # Image normalization config to normalize the input images

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