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# Copyright (c) OpenMMLab. All rights reserved.
import os
import numpy as np
import torch
_USING_PARROTS = True
try:
from parrots.autograd import gradcheck
except ImportError:
from torch.autograd import gradcheck
_USING_PARROTS = False
cur_dir = os.path.dirname(os.path.abspath(__file__))
inputs = [([[[[1., 2.], [3., 4.]]]], [[0., 0., 0., 1., 1.]]),
([[[[1., 2.], [3., 4.]], [[4., 3.], [2.,
1.]]]], [[0., 0., 0., 1., 1.]]),
([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.],
[11., 12., 15., 16.]]]], [[0., 0., 0., 3., 3.]])]
outputs = [([[[[1, 1.25], [1.5, 1.75]]]], [[[[3.0625, 0.4375],
[0.4375, 0.0625]]]]),
([[[[1., 1.25], [1.5, 1.75]], [[4, 3.75],
[3.5, 3.25]]]], [[[[3.0625, 0.4375],
[0.4375, 0.0625]],
[[3.0625, 0.4375],
[0.4375,
0.0625]]]]),
([[[[1.9375, 4.75],
[7.5625,
10.375]]]], [[[[0.47265625, 0.4296875, 0.4296875, 0.04296875],
[0.4296875, 0.390625, 0.390625, 0.0390625],
[0.4296875, 0.390625, 0.390625, 0.0390625],
[0.04296875, 0.0390625, 0.0390625,
0.00390625]]]])]
class TestDeformRoIPool:
def test_deform_roi_pool_gradcheck(self):
if not torch.cuda.is_available():
return
from mmcv.ops import DeformRoIPoolPack
pool_h = 2
pool_w = 2
spatial_scale = 1.0
sampling_ratio = 2
for case in inputs:
np_input = np.array(case[0])
np_rois = np.array(case[1])
x = torch.tensor(
np_input, device='cuda', dtype=torch.float, requires_grad=True)
rois = torch.tensor(np_rois, device='cuda', dtype=torch.float)
output_c = x.size(1)
droipool = DeformRoIPoolPack((pool_h, pool_w),
output_c,
spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio).cuda()
if _USING_PARROTS:
gradcheck(droipool, (x, rois), no_grads=[rois])
else:
gradcheck(droipool, (x, rois), eps=1e-2, atol=1e-2)
def test_modulated_deform_roi_pool_gradcheck(self):
if not torch.cuda.is_available():
return
from mmcv.ops import ModulatedDeformRoIPoolPack
pool_h = 2
pool_w = 2
spatial_scale = 1.0
sampling_ratio = 2
for case in inputs:
np_input = np.array(case[0])
np_rois = np.array(case[1])
x = torch.tensor(
np_input, device='cuda', dtype=torch.float, requires_grad=True)
rois = torch.tensor(np_rois, device='cuda', dtype=torch.float)
output_c = x.size(1)
droipool = ModulatedDeformRoIPoolPack(
(pool_h, pool_w),
output_c,
spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio).cuda()
if _USING_PARROTS:
gradcheck(droipool, (x, rois), no_grads=[rois])
else:
gradcheck(droipool, (x, rois), eps=1e-2, atol=1e-2)
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