old_contents: import os.path as osp
  import sys
  
  import mmcv
  import torch
  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
  
  feat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda:0').double()
  mask = torch.randn(
      2, 100, 6, 6, requires_grad=True, device='cuda:0').sigmoid().double()
  
  print('Gradcheck for carafe...')
  test = gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
  print(test)
  
  print('Gradcheck for carafe naive...')
  test = gradcheck(CARAFENAIVE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
  print(test)
  
  feat = torch.randn(
      2, 1024, 100, 100, requires_grad=True, device='cuda:0').float()
  mask = torch.randn(
      2, 25, 200, 200, requires_grad=True, device='cuda:0').sigmoid().float()
  loop_num = 500
  
  time_forward = 0
  time_backward = 0
  bar = mmcv.ProgressBar(loop_num)
  timer = mmcv.Timer()
  for i in range(loop_num):
      x = carafe(feat.clone(), mask.clone(), 5, 1, 2)
      torch.cuda.synchronize()
      time_forward += timer.since_last_check()
      x.sum().backward(retain_graph=True)
      torch.cuda.synchronize()
      time_backward += timer.since_last_check()
      bar.update()
  print('\nCARAFE time forward: {} ms/iter | time backward: {} ms/iter'.format(
      (time_forward + 1e-3) * 1e3 / loop_num,
      (time_backward + 1e-3) * 1e3 / loop_num))
  
  time_naive_forward = 0
  time_naive_backward = 0
  bar = mmcv.ProgressBar(loop_num)
  timer = mmcv.Timer()
  for i in range(loop_num):
      x = carafe_naive(feat.clone(), mask.clone(), 5, 1, 2)
      torch.cuda.synchronize()
      time_naive_forward += timer.since_last_check()
      x.sum().backward(retain_graph=True)
      torch.cuda.synchronize()
      time_naive_backward += timer.since_last_check()
      bar.update()
  print('\nCARAFE naive time forward: {} ms/iter | time backward: {} ms/iter'.
        format((time_naive_forward + 1e-3) * 1e3 / loop_num,
               (time_naive_backward + 1e-3) * 1e3 / loop_num))
  
new_contents: import os.path as osp
  import sys
  
  import mmcv
  import torch
  from torch.autograd import gradcheck
  
  sys.path.append(osp.abspath(osp.join(__file__, '../../')))
  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(
      2, 100, 6, 6, requires_grad=True, device='cuda:0').sigmoid().double()
  
  print('Gradcheck for carafe...')
  test = gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
  print(test)
  
  print('Gradcheck for carafe naive...')
  test = gradcheck(CARAFENaive(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
  print(test)
  
  feat = torch.randn(
      2, 1024, 100, 100, requires_grad=True, device='cuda:0').float()
  mask = torch.randn(
      2, 25, 200, 200, requires_grad=True, device='cuda:0').sigmoid().float()
  loop_num = 500
  
  time_forward = 0
  time_backward = 0
  bar = mmcv.ProgressBar(loop_num)
  timer = mmcv.Timer()
  for i in range(loop_num):
      x = carafe(feat.clone(), mask.clone(), 5, 1, 2)
      torch.cuda.synchronize()
      time_forward += timer.since_last_check()
      x.sum().backward(retain_graph=True)
      torch.cuda.synchronize()
      time_backward += timer.since_last_check()
      bar.update()
  print('\nCARAFE time forward: {} ms/iter | time backward: {} ms/iter'.format(
      (time_forward + 1e-3) * 1e3 / loop_num,
      (time_backward + 1e-3) * 1e3 / loop_num))
  
  time_naive_forward = 0
  time_naive_backward = 0
  bar = mmcv.ProgressBar(loop_num)
  timer = mmcv.Timer()
  for i in range(loop_num):
      x = carafe_naive(feat.clone(), mask.clone(), 5, 1, 2)
      torch.cuda.synchronize()
      time_naive_forward += timer.since_last_check()
      x.sum().backward(retain_graph=True)
      torch.cuda.synchronize()
      time_naive_backward += timer.since_last_check()
      bar.update()
  print('\nCARAFE naive time forward: {} ms/iter | time backward: {} ms/iter'.
        format((time_naive_forward + 1e-3) * 1e3 / loop_num,
               (time_naive_backward + 1e-3) * 1e3 / loop_num))
  
subject: fix a typo (#2296)