backbone mobilenet_light export to onnx about centernet-condinst HOT 3 CLOSED

 UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
:undefined
  "See the documentation of nn.Upsample for details.".format(mode))
torch.onnx.export(model, input, "./bb.onnx", export_params=True)
None
***/lib/python3.6/site-packages/torch/onnx/symbolic_helper.py:198: UserWarning: You are trying to export the model with onnx:Upsample for ONNX opset version 9. This operator might cause results to not match the expected results by PyTorch.
:undefined
ONNX's Upsample/Resize operator did not match Pytorch's Interpolation until opset 11. Attributes to determine how to transform the input were added in onnx:Resize in opset 11 to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).
We recommend using opset 11 and above for models using this operator. 
  "" + str(_export_onnx_opset_version) + ". "

class resize_bilinear(nn.Module):
    def __init__(self, in_shape):
        super(resize_bilinear, self).__init__()
        self.in_shape = in_shape
        self.rhw = 2
        self.out_shape = self.in_shape*self.rhw
        self.cal_parameters()
    
    def cal_parameters(self):
        y = torch.arange(self.out_shape, dtype=torch.float32)
        ty = (y + 1) / self.rhw + 0.5 * (1 - 1.0 / self.rhw) - 1
        zero = torch.zeros([1])
        ty = torch.max(ty, zero)

        ty_floor = ty.floor()
        ty_ceil = ty.ceil()
        dy = ty - ty_floor
        dydy = dy.view(-1,1) * dy.view(-1)
        
        iy0 = ty_floor.long()
        iy1 = torch.clamp(ty_ceil,0,self.in_shape-1).long()

        self.iy0 = nn.Parameter(iy0, requires_grad=False)
        self.iy1 = nn.Parameter(iy1, requires_grad=False)
        self.dy = nn.Parameter(dy, requires_grad=False)
        self.dydy = nn.Parameter(dydy, requires_grad=False)

    def forward(self, x):
        if x is None:
            return x
        im_iy0 = x.index_select(2, self.iy0) 
        im_iy1 = x.index_select(2, self.iy1)
        d = im_iy0.index_select(3, self.iy0) * (1 - 2*self.dy + self.dydy) + \
            im_iy1.index_select(3, self.iy0) * (self.dy - self.dydy) + \
            im_iy0.index_select(3, self.iy1) * (self.dy - self.dydy) + \
            im_iy1.index_select(3, self.iy1) * self.dydy
        
        return d