mirror of
https://github.com/open-mmlab/mmcv.git
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e621e08d54
* DCN cpu version * add modulated dcn cpu version * move deform_conv_shape_check to deform conv utils * add inline to deform_conv_shape_check * add tests * run linter * add newline at file end * run pre-commit against modulated deform conv cpp * update saconv test * run clang-format * remove cuda device inline * refactor dcn cuda/cpu functions * remove DCN util * remove DCN util hpp from all included files * Addressing PR comment by refactoring modulated-DCN * fix lint in cpp files
127 lines
4.8 KiB
Python
127 lines
4.8 KiB
Python
import os
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import numpy
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import pytest
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import torch
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from mmcv.utils import TORCH_VERSION, digit_version
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try:
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# If PyTorch version >= 1.6.0 and fp16 is enabled, torch.cuda.amp.autocast
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# would be imported and used; we should test if our modules support it.
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from torch.cuda.amp import autocast
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except ImportError:
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pass
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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, device='cuda'):
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if not torch.cuda.is_available() and device == 'cuda':
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pytest.skip('test requires GPU')
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from mmcv.ops import ModulatedDeformConv2dPack
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input = torch.tensor(input_t, dtype=dtype, device=device)
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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)
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if device == 'cuda':
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dcn.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_amp_mdconv(self, input_dtype=torch.float):
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"""The function to test amp released on pytorch 1.6.0.
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The type of input data might be torch.float or torch.half,
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so we should test mdconv in both cases. With amp, the data
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type of model will NOT be set manually.
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Args:
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input_dtype: torch.float or torch.half.
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"""
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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(input_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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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, device='cpu')
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self._test_mdconv(torch.float, device='cpu')
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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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# test amp when torch version >= '1.6.0', the type of
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# input data for mdconv might be torch.float or torch.half
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if (TORCH_VERSION != 'parrots'
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and digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
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with autocast(enabled=True):
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self._test_amp_mdconv(torch.float)
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self._test_amp_mdconv(torch.half)
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