def forward(self, x, feat):
out = self.conv_offset(feat)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
offset_absmean = torch.mean(torch.abs(offset))
if offset_absmean > 50:
# logger = get_root_logger()
print(f'Offset abs mean is {offset_absmean}, larger than 50.')
return modulated_deform_conv(x, offset, mask, self.weight, self.bias,
self.stride, self.padding, self.dilation,
self.groups, self.deformable_groups)
def forward(self, x, feat):
out = self.conv_offset(feat)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
# print('o1 shape: {p1}'.format(p1=o1.shape))
# print('offset shape: {p2}'.format(p2=offset.shape))
# print('mask shape: {p3}'.format(p3=mask.shape))
offset_absmean = torch.mean(torch.abs(offset))
if offset_absmean > 50:
logger = get_root_logger()
logger.warning(
f'Offset abs mean is {offset_absmean}, larger than 50.')
return modulated_deform_conv(x, offset, mask, self.weight, self.bias,
self.stride, self.padding, self.dilation,
self.groups, self.deformable_groups)
def my_modulated_deform_conv(ctx, input_data, offset, mask, weight,
bias=None, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1):
return modulated_deform_conv(ctx, input_data, offset, mask, weight, bias, stride, padding, dilation, groups, deformable_groups)