prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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'.
  
  </issue>
  <code>
  [start of mmdet/ops/carafe/grad_check.py]
  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 of mmdet/ops/carafe/grad_check.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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(
  
verification_info: {"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"}]}