We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
little config error in 1.x

mmpose/tree/1.x/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand)/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py

mobilenetv2 out_channels is 1280, however "in_channles" of the head is 2048 in this config file.  


--- 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/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py`
Content:
```
1 _base_ = ['../../../_base_/default_runtime.py']
2 
3 # runtime
4 train_cfg = dict(max_epochs=210, val_interval=10)
5 
6 # optimizer
7 optim_wrapper = dict(optimizer=dict(
8     type='Adam',
9     lr=5e-4,
10 ))
11 
12 # learning policy
13 param_scheduler = [
14     dict(
15         type='LinearLR', begin=0, end=500, start_factor=0.001,
16         by_epoch=False),  # warm-up
17     dict(
18         type='MultiStepLR',
19         begin=0,
20         end=210,
21         milestones=[170, 200],
22         gamma=0.1,
23         by_epoch=True)
24 ]
25 
26 # automatically scaling LR based on the actual training batch size
27 auto_scale_lr = dict(base_batch_size=256)
28 
29 # hooks
30 default_hooks = dict(checkpoint=dict(save_best='AUC', rule='greater'))
31 # codec settings
32 codec = dict(
33     type='MSRAHeatmap', input_size=(256, 256), heatmap_size=(64, 64), sigma=2)
34 
35 # model settings
36 model = dict(
37     type='TopdownPoseEstimator',
38     data_preprocessor=dict(
39         type='PoseDataPreprocessor',
40         mean=[123.675, 116.28, 103.53],
41         std=[58.395, 57.12, 57.375],
42         bgr_to_rgb=True),
43     backbone=dict(
44         type='MobileNetV2',
45         widen_factor=1.,
46         out_indices=(7, ),
47         init_cfg=dict(type='Pretrained', checkpoint='mmcls://mobilenet_v2')),
48     head=dict(
49         type='HeatmapHead',
50         in_channels=2048,
51         out_channels=21,
52         loss=dict(type='KeypointMSELoss', use_target_weight=True),
53         decoder=codec),
54     test_cfg=dict(
55         flip_test=True,
56         flip_mode='heatmap',
57         shift_heatmap=True,
58     ))
59 
60 # base dataset settings
61 dataset_type = 'CocoWholeBodyHandDataset'
62 data_mode = 'topdown'
63 data_root = 'data/coco/'
64 
65 # pipelines
66 train_pipeline = [
67     dict(type='LoadImage', file_client_args={{_base_.file_client_args}}),
68     dict(type='GetBBoxCenterScale'),
69     dict(
70         type='RandomBBoxTransform', rotate_factor=180,
71         scale_factor=(0.7, 1.3)),
72     dict(type='RandomFlip', direction='horizontal'),
73     dict(type='TopdownAffine', input_size=codec['input_size']),
74     dict(type='GenerateTarget', encoder=codec),
75     dict(type='PackPoseInputs')
76 ]
77 val_pipeline = [
78     dict(type='LoadImage', file_client_args={{_base_.file_client_args}}),
79     dict(type='GetBBoxCenterScale'),
80     dict(type='TopdownAffine', input_size=codec['input_size']),
81     dict(type='PackPoseInputs')
82 ]
83 
84 # data loaders
85 train_dataloader = dict(
86     batch_size=32,
87     num_workers=2,
88     persistent_workers=True,
89     sampler=dict(type='DefaultSampler', shuffle=True),
90     dataset=dict(
91         type=dataset_type,
92         data_root=data_root,
93         data_mode=data_mode,
94         ann_file='annotations/coco_wholebody_train_v1.0.json',
95         data_prefix=dict(img='train2017/'),
96         pipeline=train_pipeline,
97     ))
98 val_dataloader = dict(
99     batch_size=32,
100     num_workers=2,
101     persistent_workers=True,
102     drop_last=False,
103     sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
104     dataset=dict(
105         type=dataset_type,
106         data_root=data_root,
107         data_mode=data_mode,
108         ann_file='annotations/coco_wholebody_val_v1.0.json',
109         data_prefix=dict(img='val2017/'),
110         test_mode=True,
111         pipeline=val_pipeline,
112     ))
113 test_dataloader = val_dataloader
114 
115 val_evaluator = [
116     dict(type='PCKAccuracy', thr=0.2),
117     dict(type='AUC'),
118     dict(type='EPE')
119 ]
120 test_evaluator = val_evaluator
121
```
--- 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/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py b/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py
--- a/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py
+++ b/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py
@@ -47,7 +47,7 @@
         init_cfg=dict(type='Pretrained', checkpoint='mmcls://mobilenet_v2')),
     head=dict(
         type='HeatmapHead',
-        in_channels=2048,
+        in_channels=1280,
         out_channels=21,
         loss=dict(type='KeypointMSELoss', use_target_weight=True),
         decoder=codec),

{"golden_diff": "diff --git a/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py b/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py\n--- a/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py\n+++ b/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py\n@@ -47,7 +47,7 @@\n         init_cfg=dict(type='Pretrained', checkpoint='mmcls://mobilenet_v2')),\n     head=dict(\n         type='HeatmapHead',\n-        in_channels=2048,\n+        in_channels=1280,\n         out_channels=21,\n         loss=dict(type='KeypointMSELoss', use_target_weight=True),\n         decoder=codec),\n", "issue": "little config error in 1.x\n\r\nmmpose/tree/1.x/configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand)/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py\r\n\r\nmobilenetv2 out_channels is 1280, however \"in_channles\" of the head is 2048 in this config file.  \r\n\n", "before_files": [{"content": "_base_ = ['../../../_base_/default_runtime.py']\n\n# runtime\ntrain_cfg = dict(max_epochs=210, val_interval=10)\n\n# optimizer\noptim_wrapper = dict(optimizer=dict(\n    type='Adam',\n    lr=5e-4,\n))\n\n# learning policy\nparam_scheduler = [\n    dict(\n        type='LinearLR', begin=0, end=500, start_factor=0.001,\n        by_epoch=False),  # warm-up\n    dict(\n        type='MultiStepLR',\n        begin=0,\n        end=210,\n        milestones=[170, 200],\n        gamma=0.1,\n        by_epoch=True)\n]\n\n# automatically scaling LR based on the actual training batch size\nauto_scale_lr = dict(base_batch_size=256)\n\n# hooks\ndefault_hooks = dict(checkpoint=dict(save_best='AUC', rule='greater'))\n# codec settings\ncodec = dict(\n    type='MSRAHeatmap', input_size=(256, 256), heatmap_size=(64, 64), sigma=2)\n\n# model settings\nmodel = dict(\n    type='TopdownPoseEstimator',\n    data_preprocessor=dict(\n        type='PoseDataPreprocessor',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        bgr_to_rgb=True),\n    backbone=dict(\n        type='MobileNetV2',\n        widen_factor=1.,\n        out_indices=(7, ),\n        init_cfg=dict(type='Pretrained', checkpoint='mmcls://mobilenet_v2')),\n    head=dict(\n        type='HeatmapHead',\n        in_channels=2048,\n        out_channels=21,\n        loss=dict(type='KeypointMSELoss', use_target_weight=True),\n        decoder=codec),\n    test_cfg=dict(\n        flip_test=True,\n        flip_mode='heatmap',\n        shift_heatmap=True,\n    ))\n\n# base dataset settings\ndataset_type = 'CocoWholeBodyHandDataset'\ndata_mode = 'topdown'\ndata_root = 'data/coco/'\n\n# pipelines\ntrain_pipeline = [\n    dict(type='LoadImage', file_client_args={{_base_.file_client_args}}),\n    dict(type='GetBBoxCenterScale'),\n    dict(\n        type='RandomBBoxTransform', rotate_factor=180,\n        scale_factor=(0.7, 1.3)),\n    dict(type='RandomFlip', direction='horizontal'),\n    dict(type='TopdownAffine', input_size=codec['input_size']),\n    dict(type='GenerateTarget', encoder=codec),\n    dict(type='PackPoseInputs')\n]\nval_pipeline = [\n    dict(type='LoadImage', file_client_args={{_base_.file_client_args}}),\n    dict(type='GetBBoxCenterScale'),\n    dict(type='TopdownAffine', input_size=codec['input_size']),\n    dict(type='PackPoseInputs')\n]\n\n# data loaders\ntrain_dataloader = dict(\n    batch_size=32,\n    num_workers=2,\n    persistent_workers=True,\n    sampler=dict(type='DefaultSampler', shuffle=True),\n    dataset=dict(\n        type=dataset_type,\n        data_root=data_root,\n        data_mode=data_mode,\n        ann_file='annotations/coco_wholebody_train_v1.0.json',\n        data_prefix=dict(img='train2017/'),\n        pipeline=train_pipeline,\n    ))\nval_dataloader = dict(\n    batch_size=32,\n    num_workers=2,\n    persistent_workers=True,\n    drop_last=False,\n    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n    dataset=dict(\n        type=dataset_type,\n        data_root=data_root,\n        data_mode=data_mode,\n        ann_file='annotations/coco_wholebody_val_v1.0.json',\n        data_prefix=dict(img='val2017/'),\n        test_mode=True,\n        pipeline=val_pipeline,\n    ))\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n    dict(type='PCKAccuracy', thr=0.2),\n    dict(type='AUC'),\n    dict(type='EPE')\n]\ntest_evaluator = val_evaluator\n", "path": "configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py"}], "after_files": [{"content": "_base_ = ['../../../_base_/default_runtime.py']\n\n# runtime\ntrain_cfg = dict(max_epochs=210, val_interval=10)\n\n# optimizer\noptim_wrapper = dict(optimizer=dict(\n    type='Adam',\n    lr=5e-4,\n))\n\n# learning policy\nparam_scheduler = [\n    dict(\n        type='LinearLR', begin=0, end=500, start_factor=0.001,\n        by_epoch=False),  # warm-up\n    dict(\n        type='MultiStepLR',\n        begin=0,\n        end=210,\n        milestones=[170, 200],\n        gamma=0.1,\n        by_epoch=True)\n]\n\n# automatically scaling LR based on the actual training batch size\nauto_scale_lr = dict(base_batch_size=256)\n\n# hooks\ndefault_hooks = dict(checkpoint=dict(save_best='AUC', rule='greater'))\n# codec settings\ncodec = dict(\n    type='MSRAHeatmap', input_size=(256, 256), heatmap_size=(64, 64), sigma=2)\n\n# model settings\nmodel = dict(\n    type='TopdownPoseEstimator',\n    data_preprocessor=dict(\n        type='PoseDataPreprocessor',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        bgr_to_rgb=True),\n    backbone=dict(\n        type='MobileNetV2',\n        widen_factor=1.,\n        out_indices=(7, ),\n        init_cfg=dict(type='Pretrained', checkpoint='mmcls://mobilenet_v2')),\n    head=dict(\n        type='HeatmapHead',\n        in_channels=1280,\n        out_channels=21,\n        loss=dict(type='KeypointMSELoss', use_target_weight=True),\n        decoder=codec),\n    test_cfg=dict(\n        flip_test=True,\n        flip_mode='heatmap',\n        shift_heatmap=True,\n    ))\n\n# base dataset settings\ndataset_type = 'CocoWholeBodyHandDataset'\ndata_mode = 'topdown'\ndata_root = 'data/coco/'\n\n# pipelines\ntrain_pipeline = [\n    dict(type='LoadImage', file_client_args={{_base_.file_client_args}}),\n    dict(type='GetBBoxCenterScale'),\n    dict(\n        type='RandomBBoxTransform', rotate_factor=180,\n        scale_factor=(0.7, 1.3)),\n    dict(type='RandomFlip', direction='horizontal'),\n    dict(type='TopdownAffine', input_size=codec['input_size']),\n    dict(type='GenerateTarget', encoder=codec),\n    dict(type='PackPoseInputs')\n]\nval_pipeline = [\n    dict(type='LoadImage', file_client_args={{_base_.file_client_args}}),\n    dict(type='GetBBoxCenterScale'),\n    dict(type='TopdownAffine', input_size=codec['input_size']),\n    dict(type='PackPoseInputs')\n]\n\n# data loaders\ntrain_dataloader = dict(\n    batch_size=32,\n    num_workers=2,\n    persistent_workers=True,\n    sampler=dict(type='DefaultSampler', shuffle=True),\n    dataset=dict(\n        type=dataset_type,\n        data_root=data_root,\n        data_mode=data_mode,\n        ann_file='annotations/coco_wholebody_train_v1.0.json',\n        data_prefix=dict(img='train2017/'),\n        pipeline=train_pipeline,\n    ))\nval_dataloader = dict(\n    batch_size=32,\n    num_workers=2,\n    persistent_workers=True,\n    drop_last=False,\n    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n    dataset=dict(\n        type=dataset_type,\n        data_root=data_root,\n        data_mode=data_mode,\n        ann_file='annotations/coco_wholebody_val_v1.0.json',\n        data_prefix=dict(img='val2017/'),\n        test_mode=True,\n        pipeline=val_pipeline,\n    ))\ntest_dataloader = val_dataloader\n\nval_evaluator = [\n    dict(type='PCKAccuracy', thr=0.2),\n    dict(type='AUC'),\n    dict(type='EPE')\n]\ntest_evaluator = val_evaluator\n", "path": "configs/hand_2d_keypoint/topdown_heatmap/coco_wholebody_hand/td-hm_mobilenetv2_8xb32-210e_coco-wholebody-hand-256x256.py"}]}