def check_gradient():
input = torch.randn(N, inC, inH, inW).cuda()
input.requires_grad = True
offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW).cuda()
# offset.data.zero_()
# offset.data -= 0.5
offset.requires_grad = True
mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW).cuda()
# mask.data.zero_()
mask.requires_grad = True
mask = torch.sigmoid(mask)
weight = torch.randn(outC, inC, kH, kW).cuda()
weight.requires_grad = True
bias = torch.rand(outC).cuda()
bias.requires_grad = True
func = DCNv2Function(stride=1,
padding=1,
dilation=1,
deformable_groups=deformable_groups)
print(
gradcheck(func, (input, offset, mask, weight, bias),
eps=1e-3,
atol=1e-3,
rtol=1e-2))
def forward(self, input):
out = self.conv_offset_mask(input)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
func = DCNv2Function(self.stride, self.padding, self.dilation, self.deformable_groups)
return func(input, offset, mask, self.weight, self.bias)
def forward(self, input, fea):
'''input: input features for deformable conv
fea: other features used for generating offsets and mask'''
out = self.conv_offset_mask(fea)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
offset_mean = torch.mean(torch.abs(offset))
# if offset_mean > 100:
# logger.warning('Offset mean is {}, larger than 100.'.format(offset_mean))
mask = torch.sigmoid(mask)
func = DCNv2Function(self.stride, self.padding, self.dilation,
self.deformable_groups)
return func(input, offset, mask, self.weight, self.bias)
def forward(self, input, offset, mask):
func = DCNv2Function(self.stride, self.padding, self.dilation, self.deformable_groups)
return func(input, offset, mask, self.weight, self.bias)
