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>
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
import os
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import numpy as np
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import torch
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_USING_PARROTS = True
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try:
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from parrots.autograd import gradcheck
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except ImportError:
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from torch.autograd import gradcheck
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_USING_PARROTS = False
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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inputs = [([[[[1., 2.], [3., 4.]]]], [[0., 0., 0., 1., 1.]]),
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([[[[1., 2.], [3., 4.]], [[4., 3.], [2.,
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1.]]]], [[0., 0., 0., 1., 1.]]),
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([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.],
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[11., 12., 15., 16.]]]], [[0., 0., 0., 3., 3.]])]
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outputs = [([[[[1., 2.], [3., 4.]]]], [[[[1., 1.], [1., 1.]]]]),
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([[[[1., 2.], [3., 4.]], [[4., 3.], [2., 1.]]]], [[[[1., 1.],
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[1., 1.]],
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[[1., 1.],
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[1., 1.]]]]),
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([[[[4., 8.], [12., 16.]]]], [[[[0., 0., 0., 0.], [0., 1., 0., 1.],
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[0., 0., 0., 0.], [0., 1., 0.,
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1.]]]])]
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class TestRoiPool(object):
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def test_roipool_gradcheck(self):
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if not torch.cuda.is_available():
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return
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from mmcv.ops import RoIPool
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pool_h = 2
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pool_w = 2
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spatial_scale = 1.0
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for case in inputs:
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np_input = np.array(case[0])
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np_rois = np.array(case[1])
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x = torch.tensor(np_input, device='cuda', requires_grad=True)
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rois = torch.tensor(np_rois, device='cuda')
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froipool = RoIPool((pool_h, pool_w), spatial_scale)
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if _USING_PARROTS:
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pass
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# gradcheck(froipool, (x, rois), no_grads=[rois])
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else:
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gradcheck(froipool, (x, rois), eps=1e-2, atol=1e-2)
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def _test_roipool_allclose(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 roi_pool
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pool_h = 2
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pool_w = 2
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spatial_scale = 1.0
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for case, output in zip(inputs, outputs):
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np_input = np.array(case[0])
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np_rois = np.array(case[1])
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np_output = np.array(output[0])
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np_grad = np.array(output[1])
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x = torch.tensor(
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np_input, dtype=dtype, device='cuda', requires_grad=True)
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rois = torch.tensor(np_rois, dtype=dtype, device='cuda')
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output = roi_pool(x, rois, (pool_h, pool_w), spatial_scale)
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output.backward(torch.ones_like(output))
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assert np.allclose(output.data.cpu().numpy(), np_output, 1e-3)
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assert np.allclose(x.grad.data.cpu().numpy(), np_grad, 1e-3)
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def test_roipool_allclose(self):
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self._test_roipool_allclose(torch.double)
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self._test_roipool_allclose(torch.float)
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self._test_roipool_allclose(torch.half)
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