mirror of https://github.com/open-mmlab/mmcv.git
96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
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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 = [([[[[1.0, 1.25], [1.5, 1.75]]]], [[[[3.0625, 0.4375],
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[0.4375, 0.0625]]]]),
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([[[[1.0, 1.25], [1.5, 1.75]],
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[[4.0, 3.75], [3.5, 3.25]]]], [[[[3.0625, 0.4375],
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[0.4375, 0.0625]],
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[[3.0625, 0.4375],
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[0.4375, 0.0625]]]]),
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([[[[1.9375, 4.75], [7.5625, 10.375]]]],
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[[[[0.47265625, 0.42968750, 0.42968750, 0.04296875],
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[0.42968750, 0.39062500, 0.39062500, 0.03906250],
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[0.42968750, 0.39062500, 0.39062500, 0.03906250],
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[0.04296875, 0.03906250, 0.03906250, 0.00390625]]]])]
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class TestRoiAlign(object):
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def test_roialign_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 RoIAlign
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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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sampling_ratio = 2
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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 = RoIAlign((pool_h, pool_w), spatial_scale,
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sampling_ratio)
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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_align
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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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sampling_ratio = 2
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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_align(x, rois, (pool_h, pool_w), spatial_scale,
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sampling_ratio, 'avg', True)
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output.backward(torch.ones_like(output))
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assert np.allclose(
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output.data.type(torch.float).cpu().numpy(),
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np_output,
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atol=1e-3)
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assert np.allclose(
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x.grad.data.type(torch.float).cpu().numpy(),
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np_grad,
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atol=1e-3)
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def test_roipool_allclose(self):
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self._test_roipool_allclose(torch.float)
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self._test_roipool_allclose(torch.double)
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self._test_roipool_allclose(torch.half)
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