We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[Bug] faster-rcnn_r50-caffe_fpn_ms-2x and 3x don't work 
### Prerequisite

- [X] I have searched [Issues](https://github.com/open-mmlab/mmdetection/issues) and [Discussions](https://github.com/open-mmlab/mmdetection/discussions) but cannot get the expected help.
- [X] I have read the [FAQ documentation](https://mmdetection.readthedocs.io/en/latest/faq.html) but cannot get the expected help.
- [X] The bug has not been fixed in the [latest version (master)](https://github.com/open-mmlab/mmdetection) or [latest version (3.x)](https://github.com/open-mmlab/mmdetection/tree/dev-3.x).

### Task

I'm using the official example scripts/configs for the officially supported tasks/models/datasets.

### Branch

3.x branch https://github.com/open-mmlab/mmdetection/tree/3.x

### Environment

OrderedDict([('sys.platform', 'linux'),
             ('Python', '3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]'),
             ('CUDA available', True),
             ('numpy_random_seed', 2147483648),
             ('GPU 0', 'Tesla T4'),
             ('CUDA_HOME', '/usr/local/cuda'),
             ('NVCC', 'Cuda compilation tools, release 11.2, V11.2.152'),
             ('GCC',
              'x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0'),
             ('PyTorch', '1.9.0+cu111'),
             ('PyTorch compiling details',
              'PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.1\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n  - CuDNN 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n'),
             ('TorchVision', '0.10.0+cu111'),
             ('OpenCV', '4.6.0'),
             ('MMEngine', '0.3.0'),
             ('MMDetection', '2.25.3+5b0d5b4')])

### Reproduces the problem - code sample
（the other models work well）
```python
!mim download mmdet --config faster-rcnn_r50-caffe_fpn_ms-2x_coco --dest ./checkpoints
```
and then we see 
`faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth`  or `faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b`



### Reproduces the problem - command or script

``` python
from mmdet.apis import init_detector, inference_detector
from mmdet.utils import register_all_modules
from mmdet.registry import VISUALIZERS
import mmcv
register_all_modules()
config_file = './checkpoints/faster-rcnn_r50-caffe_fpn_ms-2x_coco.py'
checkpoint_file = './checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth'

model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
image = mmcv.imread( "demo/demo.jpg", channel_order='rgb')
result = inference_detector(model, image)

print(result)
```


### Reproduces the problem - error message

and i see:
```
local loads checkpoint from path: ./checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth
<DetDataSample(

    META INFORMATION
    img_path: None
    pad_shape: (800, 1216)
    scale_factor: (1.8734375, 1.873536299765808)
    img_id: 0
    ori_shape: (427, 640)
    batch_input_shape: (800, 1216)
    img_shape: (800, 1199)

    DATA FIELDS
    gt_instances: <InstanceData(
        
            META INFORMATION
        
            DATA FIELDS
            labels: tensor([], dtype=torch.int64)
            bboxes: tensor([], size=(0, 4))
        ) at 0x7f8e7cabc150>
    _ignored_instances: <InstanceData(
        
            META INFORMATION
        
            DATA FIELDS
            labels: tensor([], dtype=torch.int64)
            bboxes: tensor([], size=(0, 4))
        ) at 0x7f8e7cabce90>
    _pred_instances: <InstanceData(
        
            META INFORMATION
        
            DATA FIELDS
            scores: tensor([])
            labels: tensor([], dtype=torch.int64)
            bboxes: tensor([], size=(0, 4))
        ) at 0x7f8e7f14b190>
    _gt_instances: <InstanceData(
        
            META INFORMATION
        
            DATA FIELDS
            labels: tensor([], dtype=torch.int64)
            bboxes: tensor([], size=(0, 4))
        ) at 0x7f8e7cabc150>
```

All results are empty.

### Additional information

_No response_

--- 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/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py`
Content:
```
1 _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
2 model = dict(
3     backbone=dict(
4         norm_cfg=dict(requires_grad=False),
5         norm_eval=True,
6         style='caffe',
7         init_cfg=dict(
8             type='Pretrained',
9             checkpoint='open-mmlab://detectron2/resnet50_caffe')))
10 # use caffe img_norm
11 img_norm_cfg = dict(
12     mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
13 train_pipeline = [
14     dict(type='LoadImageFromFile'),
15     dict(type='LoadAnnotations', with_bbox=True),
16     dict(
17         type='Resize',
18         img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
19                    (1333, 768), (1333, 800)],
20         multiscale_mode='value',
21         keep_ratio=True),
22     dict(type='RandomFlip', flip_ratio=0.5),
23     dict(type='Normalize', **img_norm_cfg),
24     dict(type='Pad', size_divisor=32),
25     dict(type='DefaultFormatBundle'),
26     dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
27 ]
28 test_pipeline = [
29     dict(type='LoadImageFromFile'),
30     dict(
31         type='MultiScaleFlipAug',
32         img_scale=(1333, 800),
33         flip=False,
34         transforms=[
35             dict(type='Resize', keep_ratio=True),
36             dict(type='RandomFlip'),
37             dict(type='Normalize', **img_norm_cfg),
38             dict(type='Pad', size_divisor=32),
39             dict(type='ImageToTensor', keys=['img']),
40             dict(type='Collect', keys=['img']),
41         ])
42 ]
43 data = dict(
44     train=dict(pipeline=train_pipeline),
45     val=dict(pipeline=test_pipeline),
46     test=dict(pipeline=test_pipeline))
47
```

Path: `configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py`
Content:
```
1 _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
2 model = dict(
3     backbone=dict(
4         norm_cfg=dict(requires_grad=False),
5         norm_eval=True,
6         style='caffe',
7         init_cfg=dict(
8             type='Pretrained',
9             checkpoint='open-mmlab://detectron2/resnet50_caffe')))
10 
11 # use caffe img_norm
12 img_norm_cfg = dict(
13     mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
14 train_pipeline = [
15     dict(type='LoadImageFromFile'),
16     dict(type='LoadAnnotations', with_bbox=True),
17     dict(
18         type='Resize',
19         img_scale=[(1333, 640), (1333, 800)],
20         multiscale_mode='range',
21         keep_ratio=True),
22     dict(type='RandomFlip', flip_ratio=0.5),
23     dict(type='Normalize', **img_norm_cfg),
24     dict(type='Pad', size_divisor=32),
25     dict(type='DefaultFormatBundle'),
26     dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
27 ]
28 test_pipeline = [
29     dict(type='LoadImageFromFile'),
30     dict(
31         type='MultiScaleFlipAug',
32         img_scale=(1333, 800),
33         flip=False,
34         transforms=[
35             dict(type='Resize', keep_ratio=True),
36             dict(type='RandomFlip'),
37             dict(type='Normalize', **img_norm_cfg),
38             dict(type='Pad', size_divisor=32),
39             dict(type='ImageToTensor', keys=['img']),
40             dict(type='Collect', keys=['img']),
41         ])
42 ]
43 
44 data = dict(
45     train=dict(dataset=dict(pipeline=train_pipeline)),
46     val=dict(pipeline=test_pipeline),
47     test=dict(pipeline=test_pipeline))
48
```
--- 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/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py
--- a/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py
+++ b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py
@@ -1,5 +1,11 @@
 _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
 model = dict(
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        pad_size_divisor=32),
     backbone=dict(
         norm_cfg=dict(requires_grad=False),
         norm_eval=True,
diff --git a/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py
--- a/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py
+++ b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py
@@ -1,5 +1,11 @@
 _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
 model = dict(
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        pad_size_divisor=32),
     backbone=dict(
         norm_cfg=dict(requires_grad=False),
         norm_eval=True,

{"golden_diff": "diff --git a/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py\n--- a/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py\n+++ b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py\n@@ -1,5 +1,11 @@\n _base_ = './faster-rcnn_r50_fpn_1x_coco.py'\n model = dict(\n+    data_preprocessor=dict(\n+        type='DetDataPreprocessor',\n+        mean=[103.530, 116.280, 123.675],\n+        std=[1.0, 1.0, 1.0],\n+        bgr_to_rgb=False,\n+        pad_size_divisor=32),\n     backbone=dict(\n         norm_cfg=dict(requires_grad=False),\n         norm_eval=True,\ndiff --git a/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py\n--- a/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py\n+++ b/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py\n@@ -1,5 +1,11 @@\n _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'\n model = dict(\n+    data_preprocessor=dict(\n+        type='DetDataPreprocessor',\n+        mean=[103.530, 116.280, 123.675],\n+        std=[1.0, 1.0, 1.0],\n+        bgr_to_rgb=False,\n+        pad_size_divisor=32),\n     backbone=dict(\n         norm_cfg=dict(requires_grad=False),\n         norm_eval=True,\n", "issue": "[Bug] faster-rcnn_r50-caffe_fpn_ms-2x and 3x don't work \n### Prerequisite\r\n\r\n- [X] I have searched [Issues](https://github.com/open-mmlab/mmdetection/issues) and [Discussions](https://github.com/open-mmlab/mmdetection/discussions) but cannot get the expected help.\r\n- [X] I have read the [FAQ documentation](https://mmdetection.readthedocs.io/en/latest/faq.html) but cannot get the expected help.\r\n- [X] The bug has not been fixed in the [latest version (master)](https://github.com/open-mmlab/mmdetection) or [latest version (3.x)](https://github.com/open-mmlab/mmdetection/tree/dev-3.x).\r\n\r\n### Task\r\n\r\nI'm using the official example scripts/configs for the officially supported tasks/models/datasets.\r\n\r\n### Branch\r\n\r\n3.x branch https://github.com/open-mmlab/mmdetection/tree/3.x\r\n\r\n### Environment\r\n\r\nOrderedDict([('sys.platform', 'linux'),\r\n             ('Python', '3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]'),\r\n             ('CUDA available', True),\r\n             ('numpy_random_seed', 2147483648),\r\n             ('GPU 0', 'Tesla T4'),\r\n             ('CUDA_HOME', '/usr/local/cuda'),\r\n             ('NVCC', 'Cuda compilation tools, release 11.2, V11.2.152'),\r\n             ('GCC',\r\n              'x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0'),\r\n             ('PyTorch', '1.9.0+cu111'),\r\n             ('PyTorch compiling details',\r\n              'PyTorch built with:\\n  - GCC 7.3\\n  - C++ Version: 201402\\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\\n  - NNPACK is enabled\\n  - CPU capability usage: AVX2\\n  - CUDA Runtime 11.1\\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\\n  - CuDNN 8.0.5\\n  - Magma 2.5.2\\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \\n'),\r\n             ('TorchVision', '0.10.0+cu111'),\r\n             ('OpenCV', '4.6.0'),\r\n             ('MMEngine', '0.3.0'),\r\n             ('MMDetection', '2.25.3+5b0d5b4')])\r\n\r\n### Reproduces the problem - code sample\r\n\uff08the other models work well\uff09\r\n```python\r\n!mim download mmdet --config faster-rcnn_r50-caffe_fpn_ms-2x_coco --dest ./checkpoints\r\n```\r\nand then we see \r\n`faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth`  or `faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b`\r\n\r\n\r\n\r\n### Reproduces the problem - command or script\r\n\r\n``` python\r\nfrom mmdet.apis import init_detector, inference_detector\r\nfrom mmdet.utils import register_all_modules\r\nfrom mmdet.registry import VISUALIZERS\r\nimport mmcv\r\nregister_all_modules()\r\nconfig_file = './checkpoints/faster-rcnn_r50-caffe_fpn_ms-2x_coco.py'\r\ncheckpoint_file = './checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth'\r\n\r\nmodel = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'\r\nimage = mmcv.imread( \"demo/demo.jpg\", channel_order='rgb')\r\nresult = inference_detector(model, image)\r\n\r\nprint(result)\r\n```\r\n\r\n\r\n### Reproduces the problem - error message\r\n\r\nand i see:\r\n```\r\nlocal loads checkpoint from path: ./checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth\r\n<DetDataSample(\r\n\r\n    META INFORMATION\r\n    img_path: None\r\n    pad_shape: (800, 1216)\r\n    scale_factor: (1.8734375, 1.873536299765808)\r\n    img_id: 0\r\n    ori_shape: (427, 640)\r\n    batch_input_shape: (800, 1216)\r\n    img_shape: (800, 1199)\r\n\r\n    DATA FIELDS\r\n    gt_instances: <InstanceData(\r\n        \r\n            META INFORMATION\r\n        \r\n            DATA FIELDS\r\n            labels: tensor([], dtype=torch.int64)\r\n            bboxes: tensor([], size=(0, 4))\r\n        ) at 0x7f8e7cabc150>\r\n    _ignored_instances: <InstanceData(\r\n        \r\n            META INFORMATION\r\n        \r\n            DATA FIELDS\r\n            labels: tensor([], dtype=torch.int64)\r\n            bboxes: tensor([], size=(0, 4))\r\n        ) at 0x7f8e7cabce90>\r\n    _pred_instances: <InstanceData(\r\n        \r\n            META INFORMATION\r\n        \r\n            DATA FIELDS\r\n            scores: tensor([])\r\n            labels: tensor([], dtype=torch.int64)\r\n            bboxes: tensor([], size=(0, 4))\r\n        ) at 0x7f8e7f14b190>\r\n    _gt_instances: <InstanceData(\r\n        \r\n            META INFORMATION\r\n        \r\n            DATA FIELDS\r\n            labels: tensor([], dtype=torch.int64)\r\n            bboxes: tensor([], size=(0, 4))\r\n        ) at 0x7f8e7cabc150>\r\n```\r\n\r\nAll results are empty.\r\n\r\n### Additional information\r\n\r\n_No response_\n", "before_files": [{"content": "_base_ = './faster-rcnn_r50_fpn_1x_coco.py'\nmodel = dict(\n    backbone=dict(\n        norm_cfg=dict(requires_grad=False),\n        norm_eval=True,\n        style='caffe',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='open-mmlab://detectron2/resnet50_caffe')))\n# use caffe img_norm\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, 672), (1333, 704), (1333, 736),\n                   (1333, 768), (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", "path": "configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py"}, {"content": "_base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'\nmodel = dict(\n    backbone=dict(\n        norm_cfg=dict(requires_grad=False),\n        norm_eval=True,\n        style='caffe',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='open-mmlab://detectron2/resnet50_caffe')))\n\n# use caffe img_norm\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='range',\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]\n\ndata = dict(\n    train=dict(dataset=dict(pipeline=train_pipeline)),\n    val=dict(pipeline=test_pipeline),\n    test=dict(pipeline=test_pipeline))\n", "path": "configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py"}], "after_files": [{"content": "_base_ = './faster-rcnn_r50_fpn_1x_coco.py'\nmodel = dict(\n    data_preprocessor=dict(\n        type='DetDataPreprocessor',\n        mean=[103.530, 116.280, 123.675],\n        std=[1.0, 1.0, 1.0],\n        bgr_to_rgb=False,\n        pad_size_divisor=32),\n    backbone=dict(\n        norm_cfg=dict(requires_grad=False),\n        norm_eval=True,\n        style='caffe',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='open-mmlab://detectron2/resnet50_caffe')))\n# use caffe img_norm\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, 672), (1333, 704), (1333, 736),\n                   (1333, 768), (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", "path": "configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py"}, {"content": "_base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'\nmodel = dict(\n    data_preprocessor=dict(\n        type='DetDataPreprocessor',\n        mean=[103.530, 116.280, 123.675],\n        std=[1.0, 1.0, 1.0],\n        bgr_to_rgb=False,\n        pad_size_divisor=32),\n    backbone=dict(\n        norm_cfg=dict(requires_grad=False),\n        norm_eval=True,\n        style='caffe',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='open-mmlab://detectron2/resnet50_caffe')))\n\n# use caffe img_norm\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='range',\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]\n\ndata = dict(\n    train=dict(dataset=dict(pipeline=train_pipeline)),\n    val=dict(pipeline=test_pipeline),\n    test=dict(pipeline=test_pipeline))\n", "path": "configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py"}]}