prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Different mean values of image normalization for HRNet
  I find the different configs of HRNet experiments use different mean values for image normalization.
  
  For example,
  [fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py) uses the `[102.9801, 115.9465, 122.7717]` as the mean value,
  while [fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) uses the `[103.530, 116.280, 123.675]` as the mean value.
  
  Which one is correct?
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py`
  Content:
  ```
  1 _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
  2 img_norm_cfg = dict(
  3     mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
  4 train_pipeline = [
  5     dict(type='LoadImageFromFile'),
  6     dict(type='LoadAnnotations', with_bbox=True),
  7     dict(
  8         type='Resize',
  9         img_scale=[(1333, 640), (1333, 800)],
  10         multiscale_mode='value',
  11         keep_ratio=True),
  12     dict(type='RandomFlip', flip_ratio=0.5),
  13     dict(type='Normalize', **img_norm_cfg),
  14     dict(type='Pad', size_divisor=32),
  15     dict(type='DefaultFormatBundle'),
  16     dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  17 ]
  18 test_pipeline = [
  19     dict(type='LoadImageFromFile'),
  20     dict(
  21         type='MultiScaleFlipAug',
  22         img_scale=(1333, 800),
  23         flip=False,
  24         transforms=[
  25             dict(type='Resize', keep_ratio=True),
  26             dict(type='RandomFlip'),
  27             dict(type='Normalize', **img_norm_cfg),
  28             dict(type='Pad', size_divisor=32),
  29             dict(type='ImageToTensor', keys=['img']),
  30             dict(type='Collect', keys=['img']),
  31         ])
  32 ]
  33 data = dict(
  34     train=dict(pipeline=train_pipeline),
  35     val=dict(pipeline=test_pipeline),
  36     test=dict(pipeline=test_pipeline))
  37 # learning policy
  38 lr_config = dict(step=[16, 22])
  39 total_epochs = 24
  40
  ```
  
  Path: `configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py`
  Content:
  ```
  1 _base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
  2 model = dict(
  3     pretrained='open-mmlab://msra/hrnetv2_w32',
  4     backbone=dict(
  5         _delete_=True,
  6         type='HRNet',
  7         extra=dict(
  8             stage1=dict(
  9                 num_modules=1,
  10                 num_branches=1,
  11                 block='BOTTLENECK',
  12                 num_blocks=(4, ),
  13                 num_channels=(64, )),
  14             stage2=dict(
  15                 num_modules=1,
  16                 num_branches=2,
  17                 block='BASIC',
  18                 num_blocks=(4, 4),
  19                 num_channels=(32, 64)),
  20             stage3=dict(
  21                 num_modules=4,
  22                 num_branches=3,
  23                 block='BASIC',
  24                 num_blocks=(4, 4, 4),
  25                 num_channels=(32, 64, 128)),
  26             stage4=dict(
  27                 num_modules=3,
  28                 num_branches=4,
  29                 block='BASIC',
  30                 num_blocks=(4, 4, 4, 4),
  31                 num_channels=(32, 64, 128, 256)))),
  32     neck=dict(
  33         _delete_=True,
  34         type='HRFPN',
  35         in_channels=[32, 64, 128, 256],
  36         out_channels=256,
  37         stride=2,
  38         num_outs=5))
  39
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
  --- a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
  +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
  @@ -36,3 +36,34 @@
           out_channels=256,
           stride=2,
           num_outs=5))
  +img_norm_cfg = dict(
  +    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
  +train_pipeline = [
  +    dict(type='LoadImageFromFile'),
  +    dict(type='LoadAnnotations', with_bbox=True),
  +    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
  +    dict(type='RandomFlip', flip_ratio=0.5),
  +    dict(type='Normalize', **img_norm_cfg),
  +    dict(type='Pad', size_divisor=32),
  +    dict(type='DefaultFormatBundle'),
  +    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  +]
  +test_pipeline = [
  +    dict(type='LoadImageFromFile'),
  +    dict(
  +        type='MultiScaleFlipAug',
  +        img_scale=(1333, 800),
  +        flip=False,
  +        transforms=[
  +            dict(type='Resize', keep_ratio=True),
  +            dict(type='RandomFlip'),
  +            dict(type='Normalize', **img_norm_cfg),
  +            dict(type='Pad', size_divisor=32),
  +            dict(type='ImageToTensor', keys=['img']),
  +            dict(type='Collect', keys=['img']),
  +        ])
  +]
  +data = dict(
  +    train=dict(pipeline=train_pipeline),
  +    val=dict(pipeline=test_pipeline),
  +    test=dict(pipeline=test_pipeline))
  diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
  --- a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
  +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
  @@ -1,6 +1,6 @@
   _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
   img_norm_cfg = dict(
  -    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
  +    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
   train_pipeline = [
       dict(type='LoadImageFromFile'),
       dict(type='LoadAnnotations', with_bbox=True),
  
verification_info: {"golden_diff": "diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py\n--- a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py\n+++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py\n@@ -36,3 +36,34 @@\n         out_channels=256,\n         stride=2,\n         num_outs=5))\n+img_norm_cfg = dict(\n+    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)\n+train_pipeline = [\n+    dict(type='LoadImageFromFile'),\n+    dict(type='LoadAnnotations', with_bbox=True),\n+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n+    dict(type='RandomFlip', flip_ratio=0.5),\n+    dict(type='Normalize', **img_norm_cfg),\n+    dict(type='Pad', size_divisor=32),\n+    dict(type='DefaultFormatBundle'),\n+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),\n+]\n+test_pipeline = [\n+    dict(type='LoadImageFromFile'),\n+    dict(\n+        type='MultiScaleFlipAug',\n+        img_scale=(1333, 800),\n+        flip=False,\n+        transforms=[\n+            dict(type='Resize', keep_ratio=True),\n+            dict(type='RandomFlip'),\n+            dict(type='Normalize', **img_norm_cfg),\n+            dict(type='Pad', size_divisor=32),\n+            dict(type='ImageToTensor', keys=['img']),\n+            dict(type='Collect', keys=['img']),\n+        ])\n+]\n+data = dict(\n+    train=dict(pipeline=train_pipeline),\n+    val=dict(pipeline=test_pipeline),\n+    test=dict(pipeline=test_pipeline))\ndiff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py\n--- a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py\n+++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py\n@@ -1,6 +1,6 @@\n _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'\n img_norm_cfg = dict(\n-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\n+    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)\n train_pipeline = [\n     dict(type='LoadImageFromFile'),\n     dict(type='LoadAnnotations', with_bbox=True),\n", "issue": "Different mean values of image normalization for HRNet\nI find the different configs of HRNet experiments use different mean values for image normalization.\r\n\r\nFor example,\r\n[fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py) uses the `[102.9801, 115.9465, 122.7717]` as the mean value,\r\nwhile [fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) uses the `[103.530, 116.280, 123.675]` as the mean value.\r\n\r\nWhich one is correct?\n", "before_files": [{"content": "_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'\nimg_norm_cfg = dict(\n    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True),\n    dict(\n        type='Resize',\n        img_scale=[(1333, 640), (1333, 800)],\n        multiscale_mode='value',\n        keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(type='Normalize', **img_norm_cfg),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(type='Normalize', **img_norm_cfg),\n            dict(type='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img']),\n        ])\n]\ndata = dict(\n    train=dict(pipeline=train_pipeline),\n    val=dict(pipeline=test_pipeline),\n    test=dict(pipeline=test_pipeline))\n# learning policy\nlr_config = dict(step=[16, 22])\ntotal_epochs = 24\n", "path": "configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py"}, {"content": "_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'\nmodel = dict(\n    pretrained='open-mmlab://msra/hrnetv2_w32',\n    backbone=dict(\n        _delete_=True,\n        type='HRNet',\n        extra=dict(\n            stage1=dict(\n                num_modules=1,\n                num_branches=1,\n                block='BOTTLENECK',\n                num_blocks=(4, ),\n                num_channels=(64, )),\n            stage2=dict(\n                num_modules=1,\n                num_branches=2,\n                block='BASIC',\n                num_blocks=(4, 4),\n                num_channels=(32, 64)),\n            stage3=dict(\n                num_modules=4,\n                num_branches=3,\n                block='BASIC',\n                num_blocks=(4, 4, 4),\n                num_channels=(32, 64, 128)),\n            stage4=dict(\n                num_modules=3,\n                num_branches=4,\n                block='BASIC',\n                num_blocks=(4, 4, 4, 4),\n                num_channels=(32, 64, 128, 256)))),\n    neck=dict(\n        _delete_=True,\n        type='HRFPN',\n        in_channels=[32, 64, 128, 256],\n        out_channels=256,\n        stride=2,\n        num_outs=5))\n", "path": "configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py"}], "after_files": [{"content": "_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'\nimg_norm_cfg = dict(\n    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True),\n    dict(\n        type='Resize',\n        img_scale=[(1333, 640), (1333, 800)],\n        multiscale_mode='value',\n        keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(type='Normalize', **img_norm_cfg),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(type='Normalize', **img_norm_cfg),\n            dict(type='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img']),\n        ])\n]\ndata = dict(\n    train=dict(pipeline=train_pipeline),\n    val=dict(pipeline=test_pipeline),\n    test=dict(pipeline=test_pipeline))\n# learning policy\nlr_config = dict(step=[16, 22])\ntotal_epochs = 24\n", "path": "configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py"}, {"content": "_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'\nmodel = dict(\n    pretrained='open-mmlab://msra/hrnetv2_w32',\n    backbone=dict(\n        _delete_=True,\n        type='HRNet',\n        extra=dict(\n            stage1=dict(\n                num_modules=1,\n                num_branches=1,\n                block='BOTTLENECK',\n                num_blocks=(4, ),\n                num_channels=(64, )),\n            stage2=dict(\n                num_modules=1,\n                num_branches=2,\n                block='BASIC',\n                num_blocks=(4, 4),\n                num_channels=(32, 64)),\n            stage3=dict(\n                num_modules=4,\n                num_branches=3,\n                block='BASIC',\n                num_blocks=(4, 4, 4),\n                num_channels=(32, 64, 128)),\n            stage4=dict(\n                num_modules=3,\n                num_branches=4,\n                block='BASIC',\n                num_blocks=(4, 4, 4, 4),\n                num_channels=(32, 64, 128, 256)))),\n    neck=dict(\n        _delete_=True,\n        type='HRFPN',\n        in_channels=[32, 64, 128, 256],\n        out_channels=256,\n        stride=2,\n        num_outs=5))\nimg_norm_cfg = dict(\n    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True),\n    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(type='Normalize', **img_norm_cfg),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(type='Normalize', **img_norm_cfg),\n            dict(type='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img']),\n        ])\n]\ndata = dict(\n    train=dict(pipeline=train_pipeline),\n    val=dict(pipeline=test_pipeline),\n    test=dict(pipeline=test_pipeline))\n", "path": "configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py"}]}