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()

```

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),

{"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"}]}