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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) | |