[WIP]: Refactor DETR

.github/workflows/stale.yml

@@ -0,0 +1,25 @@
+name: 'Close stale issues and PRs'
+
+on:
+ schedule:
+ # check issue and pull request once every day
+ - cron: '25 11 * * *'
+
+jobs:
+ invalid-stale-close:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/stale@v4
+ with:
+ stale-issue-message: 'This issue is marked as stale because it has been marked as invalid or awaiting response for 7 days without any further response. It will be closed in 5 days if the stale label is not removed or if there is no further response.'
+ stale-pr-message: 'This PR is marked as stale because there has been no activity in the past 45 days. It will be closed in 10 days if the stale label is not removed or if there is no further updates.'
+ close-issue-message: 'This issue is closed because it has been stale for 5 days. Please open a new issue if you have similar issues or you have any new updates now.'
+ close-pr-message: 'This PR is closed because it has been stale for 10 days. Please reopen this PR if you have any updates and want to keep contributing the code.'
+ # only issues/PRS with any of invalid and awaiting response labels are checked
+ any-of-labels: 'invalid, awaiting response'
+ days-before-issue-stale: 7
+ days-before-pr-stale: 45
+ days-before-issue-close: 5
+ days-before-pr-close: 10
+ # automatically remove the stale label when the issues or the pull reqquests are updated or commented
+ remove-stale-when-updated: true

configs/detr/detr_r50_8x2_150e_coco.py

@@ -3,6 +3,7 @@
 ]
 model = dict(
  type='DETR',
+ num_query=100,
  backbone=dict(
  type='ResNet',
  depth=50,
@@ -13,45 +14,34 @@
  norm_eval=True,
  style='pytorch',
  init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+ encoder_cfg=dict(
+ num_layers=6,
+ layers_cfg=dict(
+ self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.1),
+ ffn_cfg=dict(
+ embed_dims=256,
+ feedforward_channels=2048,
+ num_fcs=2,
+ ffn_drop=0.1,
+ act_cfg=dict(type='ReLU', inplace=True)))),
+ decoder_cfg=dict(
+ num_layers=6,
+ layers_cfg=dict(
+ self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.1),
+ cross_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.1),
+ ffn_cfg=dict(
+ embed_dims=256,
+ feedforward_channels=2048,
+ num_fcs=2,
+ ffn_drop=0.1,
+ act_cfg=dict(type='ReLU', inplace=True))),
+ return_intermediate=True),
+ positional_encoding_cfg=dict(num_feats=128, normalize=True),
  bbox_head=dict(
  type='DETRHead',
  num_classes=80,
+ embed_dims=256,
  in_channels=2048,
- transformer=dict(
- type='Transformer',
- encoder=dict(
- type='DetrTransformerEncoder',
- num_layers=6,
- transformerlayers=dict(
- type='BaseTransformerLayer',
- attn_cfgs=[
- dict(
- type='MultiheadAttention',
- embed_dims=256,
- num_heads=8,
- dropout=0.1)
- ],
- feedforward_channels=2048,
- ffn_dropout=0.1,
- operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
- decoder=dict(
- type='DetrTransformerDecoder',
- return_intermediate=True,
- num_layers=6,
- transformerlayers=dict(
- type='DetrTransformerDecoderLayer',
- attn_cfgs=dict(
- type='MultiheadAttention',
- embed_dims=256,
- num_heads=8,
- dropout=0.1),
- feedforward_channels=2048,
- ffn_dropout=0.1,
- operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
- 'ffn', 'norm')),
- )),
- positional_encoding=dict(
- type='SinePositionalEncoding', num_feats=128, normalize=True),
  loss_cls=dict(
  type='CrossEntropyLoss',
  bg_cls_weight=0.1,

configs/htc/README.md

@@ -1,6 +1,6 @@
 # HTC
 
-> [Hybrid Task Cascade for Instance Segmentation](ttps://arxiv.org/abs/1901.07518)
+> [Hybrid Task Cascade for Instance Segmentation](https://arxiv.org/abs/1901.07518)
 
 <!-- [ALGORITHM] -->
 

docs/en/tutorials/config.md

@@ -71,8 +71,8 @@ The `train_cfg` and `test_cfg` are deprecated in config file, please specify the
 ```python
 # deprecated
 model = dict(
- type=...,
- ...
+  type=...,
+  ...
 )
 train_cfg=dict(...)
 test_cfg=dict(...)
@@ -83,10 +83,10 @@ The migration example is as below.
 ```python
 # recommended
 model = dict(
- type=...,
- ...
- train_cfg=dict(...),
- test_cfg=dict(...),
+  type=...,
+  ...
+train_cfg=dict(...),
+  test_cfg=dict(...),
 )
 ```
 
@@ -109,8 +109,8 @@ model = dict(
  type='BN', # Type of norm layer, usually it is BN or GN
  requires_grad=True), # Whether to train the gamma and beta in BN
  norm_eval=True, # Whether to freeze the statistics in BN
- style='pytorch'， # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
-  init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), # The ImageNet pretrained backbone to be loaded
+ style='pytorch', # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
+  init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), # The ImageNet pretrained backbone to be loaded
  neck=dict(
  type='FPN', # The neck of detector is FPN. We also support 'NASFPN', 'PAFPN', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10 for more details.
  in_channels=[256, 512, 1024, 2048], # The input channels, this is consistent with the output channels of backbone
@@ -182,70 +182,70 @@ model = dict(
  type='CrossEntropyLoss', # Type of loss used for segmentation
  use_mask=True, # Whether to only train the mask in the correct class.
  loss_weight=1.0)))) # Loss weight of mask branch.
- train_cfg = dict( # Config of training hyperparameters for rpn and rcnn
-  rpn=dict( # Training config of rpn
-  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.
-  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
-  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).
-  ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
-  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.
-  num=256, # Number of 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.
-  add_gt_as_proposals=False), # Whether add GT as proposals after sampling.
-  allowed_border=-1, # The border allowed after padding for valid anchors.
-  pos_weight=-1, # The weight of positive samples during training.
-  debug=False), # Whether to set the debug mode
-  rpn_proposal=dict( # The config to generate proposals during training
-  nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
-  nms_pre=2000, # The number of boxes before NMS
-  nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
-  max_per_img=1000, # The number of boxes to be kept after NMS.
-  nms=dict( # Config of NMS
-  type='nms', # Type of NMS
-  iou_threshold=0.7 # NMS threshold
-  ),
-  min_bbox_size=0), # The allowed minimal box size
-  rcnn=dict( # The config for the roi heads.
-  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.
-  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 negative 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).
-  ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
-  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.
-  num=512, # Number of 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.
-  add_gt_as_proposals=True
-  ), # Whether add GT as proposals after sampling.
-  mask_size=28, # Size of mask
-  pos_weight=-1, # The weight of positive samples during training.
-  debug=False)) # Whether to set the debug mode
- test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn
-  rpn=dict( # The config to generate proposals during testing
-  nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
-  nms_pre=1000, # The number of boxes before NMS
-  nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
-  max_per_img=1000, # The number of boxes to be kept after NMS.
-  nms=dict( # Config of NMS
-  type='nms', #Type of NMS
-  iou_threshold=0.7 # NMS threshold
-  ),
-  min_bbox_size=0), # The allowed minimal box size
-  rcnn=dict( # The config for the roi heads.
-  score_thr=0.05, # Threshold to filter out boxes
-  nms=dict( # Config of NMS in the second stage
-  type='nms', # Type of NMS
-  iou_thr=0.5), # NMS threshold
-  max_per_img=100, # Max number of detections of each image
-  mask_thr_binary=0.5)) # Threshold of mask prediction
+train_cfg = dict( # Config of training hyperparameters for rpn and rcnn
+ rpn=dict( # Training config of rpn
+ 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.
+ 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
+ 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).
+ ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
+ 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.
+ num=256, # Number of 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.
+ add_gt_as_proposals=False), # Whether add GT as proposals after sampling.
+ allowed_border=-1, # The border allowed after padding for valid anchors.
+ pos_weight=-1, # The weight of positive samples during training.
+ debug=False), # Whether to set the debug mode
+ rpn_proposal=dict( # The config to generate proposals during training
+ nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
+ nms_pre=2000, # The number of boxes before NMS
+ nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
+ max_per_img=1000, # The number of boxes to be kept after NMS.
+ nms=dict( # Config of NMS
+ type='nms', # Type of NMS
+ iou_threshold=0.7 # NMS threshold
+ ),
+ min_bbox_size=0), # The allowed minimal box size
+ rcnn=dict( # The config for the roi heads.
+ 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.
+ 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 negative 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).
+ ignore_iof_thr=-1), # IoF threshold for ignoring bboxes
+ 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.
+ num=512, # Number of 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.
+ add_gt_as_proposals=True
+ ), # Whether add GT as proposals after sampling.
+ mask_size=28, # Size of mask
+ pos_weight=-1, # The weight of positive samples during training.
+ debug=False)) # Whether to set the debug mode
+test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn
+ rpn=dict( # The config to generate proposals during testing
+ nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
+ nms_pre=1000, # The number of boxes before NMS
+ nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
+ max_per_img=1000, # The number of boxes to be kept after NMS.
+ nms=dict( # Config of NMS
+ type='nms', #Type of NMS
+ iou_threshold=0.7 # NMS threshold
+ ),
+ min_bbox_size=0), # The allowed minimal box size
+ rcnn=dict( # The config for the roi heads.
+ score_thr=0.05, # Threshold to filter out boxes
+ nms=dict( # Config of NMS in the second stage
+ type='nms', # Type of NMS
+ iou_thr=0.5), # NMS threshold
+ max_per_img=100, # Max number of detections of each image
+ mask_thr_binary=0.5)) # Threshold of mask prediction
 dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset
 data_root = 'data/coco/' # Root path of data
 img_norm_cfg = dict( # Image normalization config to normalize the input images
@@ -381,7 +381,7 @@ data = dict(
  ])
  ],
  samples_per_gpu=2 # Batch size of a single GPU used in testing
-  ))
+ ))
 evaluation = dict( # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.
  interval=1, # Evaluation interval
  metric=['bbox', 'segm']) # Metrics used during evaluation
@@ -407,9 +407,15 @@ checkpoint_config = dict( # Config to set the checkpoint hook, Refer to https:/
 log_config = dict( # config to register logger hook
  interval=50, # Interval to print the log
  hooks=[
- # dict(type='TensorboardLoggerHook') # The Tensorboard logger is also supported
- dict(type='TextLoggerHook')
+ dict(type='TextLoggerHook', by_epoch=False),
+ dict(type='TensorboardLoggerHook', by_epoch=False),
+ dict(type='MMDetWandbHook', by_epoch=False, # The Wandb logger is also supported, It requires `wandb` to be installed.
+ init_kwargs={'entity': "OpenMMLab", # The entity used to log on Wandb
+ 'project': "MMDet", # Project name in WandB
+ 'config': cfg_dict}), # Check https://docs.wandb.ai/ref/python/init for more init arguments.
+ # MMDetWandbHook is mmdet implementation of WandbLoggerHook. ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation.
  ]) # The logger used to record the training process.
+
 dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set.
 log_level = 'INFO' # The level of logging.
 load_from = None # load models as a pre-trained model from a given path. This will not resume training.

docs/zh_cn/index.rst

@@ -15,6 +15,7 @@ Welcome to MMDetection's documentation!
 
  1_exist_data_model.md
  2_new_data_model.md
+ 3_exist_data_new_model.md
 
 .. toctree::
  :maxdepth: 2