mirror of
https://github.com/open-mmlab/mmcv.git
synced 2025-06-03 21:54:52 +08:00
c0f5492ee9
* add ext ops, support parrots * fix lint * fix lint * update op from mmdetection * support non-pytorch env * fix import bug * test not import mmcv.op * rename mmcv.op to mmcv.ops * fix compile warning * 1. fix syncbn warning in pytorch 1.5 2. support only cpu compile 3. add point_sample from mmdet * fix text bug * update docstrings * fix line endings * minor updates * remove non_local from ops * bug fix for nonlocal2d * rename ops_ext to _ext and _ext to _flow_warp_ext * update the doc * try clang-format github action * fix github action * add ops to api.rst * fix cpp format * fix clang format issues * remove .clang-format Co-authored-by: Kai Chen <chenkaidev@gmail.com>
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import os
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import numpy
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import torch
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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input_t = [[[[1., 2., 3.], [1., 2., 3.], [1., 2., 3.]]]]
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output_t = [[[[0.5, 1.5, 2.5, 1.5], [1.0, 3.0, 5.0, 3.0], [1.0, 3.0, 5.0, 3.0],
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[0.5, 1.5, 2.5, 1.5]]]]
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input_grad = [[[[2., 2., 2.], [2., 2., 2.], [2., 2., 2.]]]]
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dcn_w_grad = [[[[9., 9.], [9., 9.]]]]
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dcn_offset_w_grad = [[[[-7.0, -4.0], [0.0, 0.0]]], [[[-9.0, 7.5], [-6.0,
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5.0]]],
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[[[-4.0, -7.0], [0.0, 0.0]]],
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[[[-7.5, -9.0], [-5.0, -6.0]]],
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[[[-7.0, -4.0], [-7.0, -4.0]]],
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[[[-6.0, 5.0], [-9.0, 7.5]]],
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[[[-4.0, -7.0], [-4.0, -7.0]]],
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[[[-5.0, -6.0], [-7.5, -9.0]]], [[[10.5, 6.0], [7.0,
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4.0]]],
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[[[6.0, 10.5], [4.0, 7.0]]], [[[7.0, 4.0], [10.5, 6.0]]],
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[[[4.0, 7.0], [6.0, 10.5]]]]
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dcn_offset_b_grad = [
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-3.0, -1.5, -3.0, -1.5, -3.0, -1.5, -3.0, -1.5, 4.5, 4.5, 4.5, 4.5
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]
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class TestMdconv(object):
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def _test_mdconv(self, dtype=torch.float):
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if not torch.cuda.is_available():
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return
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from mmcv.ops import ModulatedDeformConv2dPack
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input = torch.tensor(input_t).cuda().type(dtype)
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input.requires_grad = True
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dcn = ModulatedDeformConv2dPack(
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1,
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1,
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kernel_size=(2, 2),
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stride=1,
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padding=1,
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deform_groups=1,
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bias=False).cuda()
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dcn.weight.data.fill_(1.)
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dcn.type(dtype)
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output = dcn(input)
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output.sum().backward()
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assert numpy.allclose(output.cpu().detach().numpy(), output_t, 1e-2)
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assert numpy.allclose(input.grad.cpu().detach().numpy(), input_grad,
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1e-2)
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assert numpy.allclose(dcn.weight.grad.cpu().detach().numpy(),
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dcn_w_grad, 1e-2)
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assert numpy.allclose(
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dcn.conv_offset.weight.grad.cpu().detach().numpy(),
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dcn_offset_w_grad, 1e-2)
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assert numpy.allclose(dcn.conv_offset.bias.grad.cpu().detach().numpy(),
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dcn_offset_b_grad, 1e-2)
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def test_mdconv(self):
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self._test_mdconv(torch.double)
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self._test_mdconv(torch.float)
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self._test_mdconv(torch.half)
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