[Doc] update location of test_cfg and train_cfg (open-mmlab#8792)

* Update config.md

* Update config.md

docs/en/tutorials/config.md

@@ -181,71 +181,72 @@ model = dict(
             loss_mask=dict(  # Config of loss function for the mask branch.
                 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
+                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
+
 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