We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
ImportError: cannot import name 'CARAFENAIVE' from 'mmdet.ops.carafe'
The module name 'CARAFENAIVE' in file 'mmdet.ops.carafe.grad_check.py' shoud be 'CARAFENaive'. When I run this command 'python mmdet/ops/carafe/grad_check.py', the following error is reported: ImportError: cannot import name 'CARAFENAIVE' from 'mmdet.ops.carafe'.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `mmdet/ops/carafe/grad_check.py`
Content:
```
1 import os.path as osp
2 import sys
3 
4 import mmcv
5 import torch
6 from torch.autograd import gradcheck
7 
8 sys.path.append(osp.abspath(osp.join(__file__, '../../')))
9 from mmdet.ops.carafe import CARAFENAIVE  # noqa: E402, isort:skip
10 from mmdet.ops.carafe import carafe_naive  # noqa: E402, isort:skip
11 from mmdet.ops.carafe import carafe, CARAFE  # noqa: E402, isort:skip
12 
13 feat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double()
14 mask = torch.randn(
15     2, 100, 6, 6, requires_grad=True, device='cuda:0').sigmoid().double()
16 
17 print('Gradcheck for carafe...')
18 test = gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
19 print(test)
20 
21 print('Gradcheck for carafe naive...')
22 test = gradcheck(CARAFENAIVE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
23 print(test)
24 
25 feat = torch.randn(
26     2, 1024, 100, 100, requires_grad=True, device='cuda:0').float()
27 mask = torch.randn(
28     2, 25, 200, 200, requires_grad=True, device='cuda:0').sigmoid().float()
29 loop_num = 500
30 
31 time_forward = 0
32 time_backward = 0
33 bar = mmcv.ProgressBar(loop_num)
34 timer = mmcv.Timer()
35 for i in range(loop_num):
36     x = carafe(feat.clone(), mask.clone(), 5, 1, 2)
37     torch.cuda.synchronize()
38     time_forward += timer.since_last_check()
39     x.sum().backward(retain_graph=True)
40     torch.cuda.synchronize()
41     time_backward += timer.since_last_check()
42     bar.update()
43 print('\nCARAFE time forward: {} ms/iter | time backward: {} ms/iter'.format(
44     (time_forward + 1e-3) * 1e3 / loop_num,
45     (time_backward + 1e-3) * 1e3 / loop_num))
46 
47 time_naive_forward = 0
48 time_naive_backward = 0
49 bar = mmcv.ProgressBar(loop_num)
50 timer = mmcv.Timer()
51 for i in range(loop_num):
52     x = carafe_naive(feat.clone(), mask.clone(), 5, 1, 2)
53     torch.cuda.synchronize()
54     time_naive_forward += timer.since_last_check()
55     x.sum().backward(retain_graph=True)
56     torch.cuda.synchronize()
57     time_naive_backward += timer.since_last_check()
58     bar.update()
59 print('\nCARAFE naive time forward: {} ms/iter | time backward: {} ms/iter'.
60       format((time_naive_forward + 1e-3) * 1e3 / loop_num,
61              (time_naive_backward + 1e-3) * 1e3 / loop_num))
62
```
--- 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/mmdet/ops/carafe/grad_check.py b/mmdet/ops/carafe/grad_check.py
--- a/mmdet/ops/carafe/grad_check.py
+++ b/mmdet/ops/carafe/grad_check.py
@@ -6,9 +6,8 @@
 from torch.autograd import gradcheck
 
 sys.path.append(osp.abspath(osp.join(__file__, '../../')))
-from mmdet.ops.carafe import CARAFENAIVE  # noqa: E402, isort:skip
-from mmdet.ops.carafe import carafe_naive  # noqa: E402, isort:skip
-from mmdet.ops.carafe import carafe, CARAFE  # noqa: E402, isort:skip
+from mmdet.ops.carafe import CARAFE, CARAFENaive  # noqa: E402, isort:skip
+from mmdet.ops.carafe import carafe, carafe_naive  # noqa: E402, isort:skip
 
 feat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double()
 mask = torch.randn(
@@ -19,7 +18,7 @@
 print(test)
 
 print('Gradcheck for carafe naive...')
-test = gradcheck(CARAFENAIVE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
+test = gradcheck(CARAFENaive(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
 print(test)
 
 feat = torch.randn(

{"golden_diff": "diff --git a/mmdet/ops/carafe/grad_check.py b/mmdet/ops/carafe/grad_check.py\n--- a/mmdet/ops/carafe/grad_check.py\n+++ b/mmdet/ops/carafe/grad_check.py\n@@ -6,9 +6,8 @@\n from torch.autograd import gradcheck\n \n sys.path.append(osp.abspath(osp.join(__file__, '../../')))\n-from mmdet.ops.carafe import CARAFENAIVE  # noqa: E402, isort:skip\n-from mmdet.ops.carafe import carafe_naive  # noqa: E402, isort:skip\n-from mmdet.ops.carafe import carafe, CARAFE  # noqa: E402, isort:skip\n+from mmdet.ops.carafe import CARAFE, CARAFENaive  # noqa: E402, isort:skip\n+from mmdet.ops.carafe import carafe, carafe_naive  # noqa: E402, isort:skip\n \n feat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double()\n mask = torch.randn(\n@@ -19,7 +18,7 @@\n print(test)\n \n print('Gradcheck for carafe naive...')\n-test = gradcheck(CARAFENAIVE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)\n+test = gradcheck(CARAFENaive(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)\n print(test)\n \n feat = torch.randn(\n", "issue": "ImportError: cannot import name 'CARAFENAIVE' from 'mmdet.ops.carafe'\nThe module name 'CARAFENAIVE' in file 'mmdet.ops.carafe.grad_check.py' shoud be 'CARAFENaive'. When I run this command 'python mmdet/ops/carafe/grad_check.py', the following error is reported: ImportError: cannot import name 'CARAFENAIVE' from 'mmdet.ops.carafe'.\n", "before_files": [{"content": "import os.path as osp\nimport sys\n\nimport mmcv\nimport torch\nfrom torch.autograd import gradcheck\n\nsys.path.append(osp.abspath(osp.join(__file__, '../../')))\nfrom mmdet.ops.carafe import CARAFENAIVE  # noqa: E402, isort:skip\nfrom mmdet.ops.carafe import carafe_naive  # noqa: E402, isort:skip\nfrom mmdet.ops.carafe import carafe, CARAFE  # noqa: E402, isort:skip\n\nfeat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double()\nmask = torch.randn(\n    2, 100, 6, 6, requires_grad=True, device='cuda:0').sigmoid().double()\n\nprint('Gradcheck for carafe...')\ntest = gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)\nprint(test)\n\nprint('Gradcheck for carafe naive...')\ntest = gradcheck(CARAFENAIVE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)\nprint(test)\n\nfeat = torch.randn(\n    2, 1024, 100, 100, requires_grad=True, device='cuda:0').float()\nmask = torch.randn(\n    2, 25, 200, 200, requires_grad=True, device='cuda:0').sigmoid().float()\nloop_num = 500\n\ntime_forward = 0\ntime_backward = 0\nbar = mmcv.ProgressBar(loop_num)\ntimer = mmcv.Timer()\nfor i in range(loop_num):\n    x = carafe(feat.clone(), mask.clone(), 5, 1, 2)\n    torch.cuda.synchronize()\n    time_forward += timer.since_last_check()\n    x.sum().backward(retain_graph=True)\n    torch.cuda.synchronize()\n    time_backward += timer.since_last_check()\n    bar.update()\nprint('\\nCARAFE time forward: {} ms/iter | time backward: {} ms/iter'.format(\n    (time_forward + 1e-3) * 1e3 / loop_num,\n    (time_backward + 1e-3) * 1e3 / loop_num))\n\ntime_naive_forward = 0\ntime_naive_backward = 0\nbar = mmcv.ProgressBar(loop_num)\ntimer = mmcv.Timer()\nfor i in range(loop_num):\n    x = carafe_naive(feat.clone(), mask.clone(), 5, 1, 2)\n    torch.cuda.synchronize()\n    time_naive_forward += timer.since_last_check()\n    x.sum().backward(retain_graph=True)\n    torch.cuda.synchronize()\n    time_naive_backward += timer.since_last_check()\n    bar.update()\nprint('\\nCARAFE naive time forward: {} ms/iter | time backward: {} ms/iter'.\n      format((time_naive_forward + 1e-3) * 1e3 / loop_num,\n             (time_naive_backward + 1e-3) * 1e3 / loop_num))\n", "path": "mmdet/ops/carafe/grad_check.py"}], "after_files": [{"content": "import os.path as osp\nimport sys\n\nimport mmcv\nimport torch\nfrom torch.autograd import gradcheck\n\nsys.path.append(osp.abspath(osp.join(__file__, '../../')))\nfrom mmdet.ops.carafe import CARAFE, CARAFENaive  # noqa: E402, isort:skip\nfrom mmdet.ops.carafe import carafe, carafe_naive  # noqa: E402, isort:skip\n\nfeat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double()\nmask = torch.randn(\n    2, 100, 6, 6, requires_grad=True, device='cuda:0').sigmoid().double()\n\nprint('Gradcheck for carafe...')\ntest = gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)\nprint(test)\n\nprint('Gradcheck for carafe naive...')\ntest = gradcheck(CARAFENaive(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)\nprint(test)\n\nfeat = torch.randn(\n    2, 1024, 100, 100, requires_grad=True, device='cuda:0').float()\nmask = torch.randn(\n    2, 25, 200, 200, requires_grad=True, device='cuda:0').sigmoid().float()\nloop_num = 500\n\ntime_forward = 0\ntime_backward = 0\nbar = mmcv.ProgressBar(loop_num)\ntimer = mmcv.Timer()\nfor i in range(loop_num):\n    x = carafe(feat.clone(), mask.clone(), 5, 1, 2)\n    torch.cuda.synchronize()\n    time_forward += timer.since_last_check()\n    x.sum().backward(retain_graph=True)\n    torch.cuda.synchronize()\n    time_backward += timer.since_last_check()\n    bar.update()\nprint('\\nCARAFE time forward: {} ms/iter | time backward: {} ms/iter'.format(\n    (time_forward + 1e-3) * 1e3 / loop_num,\n    (time_backward + 1e-3) * 1e3 / loop_num))\n\ntime_naive_forward = 0\ntime_naive_backward = 0\nbar = mmcv.ProgressBar(loop_num)\ntimer = mmcv.Timer()\nfor i in range(loop_num):\n    x = carafe_naive(feat.clone(), mask.clone(), 5, 1, 2)\n    torch.cuda.synchronize()\n    time_naive_forward += timer.since_last_check()\n    x.sum().backward(retain_graph=True)\n    torch.cuda.synchronize()\n    time_naive_backward += timer.since_last_check()\n    bar.update()\nprint('\\nCARAFE naive time forward: {} ms/iter | time backward: {} ms/iter'.\n      format((time_naive_forward + 1e-3) * 1e3 / loop_num,\n             (time_naive_backward + 1e-3) * 1e3 / loop_num))\n", "path": "mmdet/ops/carafe/grad_check.py"}]}