[Docs] Update Config Doc to Add WandB Hook (#8663)

* logger hooks samples updated

* [Docs] MMDetWandB LoggerHook Details Added

* [Docs] lint test passed

Author: Nourollah

Diff for: 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.