def check_mdconv_im2col_step_forward():
conv_offset = nn.Conv2d(inC,
deformable_groups * 2 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
conv_mask = nn.Conv2d(inC,
deformable_groups * 1 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
input = torch.randn(N, inC, inH, inW).cuda()
offset = conv_offset(input)
mask = conv_mask(input)
groups = 2
dcn1 = ModulatedDeformConv(inC,
outC, (kH, kW),
stride=1,
padding=1,
dilation=1,
groups=groups,
deformable_groups=deformable_groups,
im2col_step=1).cuda()
dcn2 = ModulatedDeformConv(inC,
outC, (kH, kW),
stride=1,
padding=1,
dilation=1,
groups=groups,
deformable_groups=deformable_groups,
im2col_step=2).cuda()
dcn1.weight = dcn2.weight
dcn1.bias = dcn2.bias
output1 = dcn1(input, offset, mask)
output2 = dcn2(input, offset, mask)
d = (output1 - output2).abs().max()
if d < 1e-10:
print('mdconv im2col_step forward passed with {}'.format(d))
else:
print('mdconv im2col_step forward failed with {}'.format(d))
print(output1)
print(output2)
print((output1 - output2).abs())
def check_mdconv_zero_offset():
conv_offset = nn.Conv2d(inC,
deformable_groups * 2 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
conv_mask = nn.Conv2d(inC,
deformable_groups * 1 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
dcn = ModulatedDeformConv(inC,
outC, (kH, kW),
stride=1,
padding=1,
dilation=1,
groups=2,
deformable_groups=deformable_groups,
im2col_step=1).cuda()
pcn = nn.Conv2d(inC,
outC, (kH, kW),
stride=1,
padding=1,
dilation=1,
groups=2).cuda()
pcn.weight = dcn.weight
pcn.bias = dcn.bias
print((pcn.weight.data - dcn.weight.data).abs().max())
conv_offset.weight.data.zero_()
conv_offset.bias.data.zero_()
conv_mask.weight.data.zero_()
conv_mask.bias.data.zero_()
input = torch.randn(N, inC, inH, inW).cuda()
offset = conv_offset(input)
mask = conv_mask(input)
mask = torch.sigmoid(mask)
mask *= 2
output_d = dcn(input, offset, mask)
output_p = pcn(input)
d = (output_d - output_p).abs().max()
if d < 1e-5:
print('mdconv zero offset passed with {}'.format(d))
else:
print('mdconv zero offset failed with {}'.format(d))
# print(output_p)
# print(output_d)
print((output_d - output_p).abs())
def check_mdconv_zero_offset_identify():
conv_offset = nn.Conv2d(inC,
deformable_groups * 2 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
conv_mask = nn.Conv2d(inC,
deformable_groups * 1 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
groups = 2
dcn = ModulatedDeformConv(inC,
outC, (kH, kW),
stride=1,
padding=1,
dilation=1,
groups=groups,
deformable_groups=deformable_groups,
im2col_step=1).cuda()
conv_offset.weight.data.zero_()
conv_offset.bias.data.zero_()
conv_mask.weight.data.zero_()
conv_mask.bias.data.zero_()
conv_identify(dcn.weight, dcn.bias, groups)
input = torch.randn(N, inC, inH, inW).cuda()
offset = conv_offset(input)
mask = conv_mask(input)
mask = torch.sigmoid(mask)
output = dcn(input, offset, mask)
output *= 2
d = (input - output).abs().max()
if d < 1e-10:
print('mdconv zero offset identify passed with {}'.format(d))
else:
print('mdconv zero offset identify failed with {}'.format(d))
# print(input)
# print(output)
print((input - output).abs())
def check_ddconv_zero_offset_identify():
conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True).cuda()
stride = 1
padding = 1
groups = 2
dilation = 1
depth_offset = torch.zeros(N, 2 * kW * kH, inH, inW).int().cuda() * 2
groups = 2
dcn = ModulatedDeformConv(inC, outC, (kH, kW),
stride=1, padding=1, dilation=1,
groups=groups,
deformable_groups=deformable_groups,
im2col_step=1).cuda()
input = torch.randn(N, inC, inH, inW).cuda()
depth = torch.ones(N, 1, inH, inW).cuda()
offset = conv_offset(input)
mask = conv_mask(input)
mask = torch.sigmoid(mask)
output = dcn(input, offset, mask)
output2 = _DepthDeformConv(input, depth, depth_offset, offset, mask, dcn.weight, dcn.bias, stride, padding, dilation, groups, deformable_groups, 2)
# output2 = dcn2(input, depth, offset, mask)
d = (output2 - output).abs().max()
if d < 1e-10:
print('ddconv zero offset identify passed with {}'.format(d))
else:
print('ddconv zero offset identify failed with {}'.format(d))
# print(input)
# print(output)
print((output2 - output).abs())