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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numpy as np | |
| import pytest | |
| import torch | |
| from mmcv.ops import RiRoIAlignRotated | |
| if torch.__version__ == 'parrots': | |
| from parrots.autograd import gradcheck | |
| _USING_PARROTS = True | |
| else: | |
| from torch.autograd import gradcheck | |
| _USING_PARROTS = False | |
| np_feature = np.array([[[[1, 2], [3, 4]], [[1, 2], [4, 3]], [[4, 3], [2, 1]], | |
| [[1, 2], [5, 6]], [[3, 4], [7, 8]], [[9, 10], [13, | |
| 14]], | |
| [[11, 12], [15, 16]], [[1, 1], [2, 2]]]]) | |
| np_rois = np.array([[0., 0.5, 0.5, 1., 1., np.pi / 3], | |
| [0., 1., 1., 3., 3., np.pi / 2]]) | |
| expect_output = np.array([[[[1.8425, 1.3516], [2.3151, 1.8241]], | |
| [[2.4779, 1.7416], [3.2173, 2.5632]], | |
| [[2.7149, 2.2638], [2.6540, 2.3673]], | |
| [[2.9461, 2.8638], [2.8028, 2.7205]], | |
| [[4.1943, 2.7214], [5.6119, 4.1391]], | |
| [[7.5276, 6.0547], [8.9453, 7.4724]], | |
| [[12.1943, 10.7214], [13.6119, 12.1391]], | |
| [[9.5489, 8.4237], [10.5763, 9.4511]]], | |
| [[[7.6562, 12.5625], [4.0000, 6.6250]], | |
| [[1.0000, 1.3125], [0.5000, 0.6562]], | |
| [[1.6562, 1.9375], [1.0000, 1.3125]], | |
| [[1.8438, 2.0547], [0.7500, 1.1562]], | |
| [[0.8438, 3.0625], [0.2500, 1.1875]], | |
| [[2.6562, 2.5625], [1.5000, 1.6250]], | |
| [[3.6562, 4.5625], [2.0000, 2.6250]], | |
| [[6.6562, 10.5625], [3.5000, 5.6250]]]]) | |
| expect_grad = np.array([[[[1.4727, 1.5586], [1.5586, 1.6602]], | |
| [[1.4727, 1.5586], [1.5586, 1.6602]], | |
| [[1.4727, 1.5586], [1.5586, 1.6602]], | |
| [[1.4727, 1.5586], [1.5586, 1.6602]], | |
| [[1.4727, 1.5586], [1.5586, 1.6602]], | |
| [[1.4727, 1.5586], [1.5586, 1.6602]], | |
| [[1.4727, 1.5586], [1.5586, 1.6602]], | |
| [[1.4727, 1.5586], [1.5586, 1.6602]]]]) | |
| pool_h = 2 | |
| pool_w = 2 | |
| spatial_scale = 1.0 | |
| num_samples = 2 | |
| sampling_ratio = 2 | |
| num_orientations = 8 | |
| clockwise = False | |
| def test_roialign_rotated_gradcheck(): | |
| x = torch.tensor( | |
| np_feature, dtype=torch.float, device='cuda', requires_grad=True) | |
| rois = torch.tensor(np_rois, dtype=torch.float, device='cuda') | |
| froipool = RiRoIAlignRotated((pool_h, pool_w), spatial_scale, num_samples, | |
| num_orientations, clockwise) | |
| if _USING_PARROTS: | |
| gradcheck( | |
| froipool, (x, rois), no_grads=[rois], delta=1e-3, pt_atol=1e-3) | |
| else: | |
| gradcheck(froipool, (x, rois), eps=1e-3, atol=1e-3) | |
| def test_roialign_rotated_allclose(): | |
| x = torch.tensor( | |
| np_feature, dtype=torch.float, device='cuda', requires_grad=True) | |
| rois = torch.tensor(np_rois, dtype=torch.float, device='cuda') | |
| froipool = RiRoIAlignRotated((pool_h, pool_w), spatial_scale, num_samples, | |
| num_orientations, clockwise) | |
| output = froipool(x, rois) | |
| output.backward(torch.ones_like(output)) | |
| assert np.allclose( | |
| output.data.type(torch.float).cpu().numpy(), expect_output, atol=1e-3) | |
| assert np.allclose( | |
| x.grad.data.type(torch.float).cpu().numpy(), expect_grad, atol=1e-3) | |