Optimizer initialization issue in DeepLabv3+ about deeplab-pytorch

[kazuto1011]

Thank you for suggesting the revision. As far as I can see the last snippet, I think the issue is related to the improved ASPP module in the v3+ rather than the non-biased conv in ResNet. The yielding of the ResNet part is done in the "1x" scope without causing the NoneType error. The script train.py is made just for parsing and training the params in the v2 model. The reported error is due to the fact that the v3+ ASPP does not have biasses, while the v2 one has them.
Anyway, I think we need more strict modification for adapting it to v3/v3+, e.g., batch norms should also be observed/trained. I'm sorry but this codebase does not assume the v3/v3+ training now.

[SunnerLi]

I think you are right!
However, I hope that the the training part of v3/v3+ can be bare soon
Very thanks for your contribution!

[Ericargus]

class _ConvBatchNormReLU(nn.Sequential):
def init(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
relu=True,
):
super(_ConvBatchNormReLU, self).init()
self.add_module(
"conv",
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=False,
),
)
###
I think the problem is in you _ConvBatchNormReLU function, because you set conv's bias to be False

[kazuto1011]

Do you mean the _ConvBatchNormReLU in v3+ ASPP? I have mentioned above:

The reported error is due to the fact that the v3+ ASPP does not have biasses, while the v2 one has them.

The non-biased conv is from the official implementation. And the init part is just for v2 here.