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Thanks to the compact characteristic of PSConv, just replace nn.Conv2d with PSConv2d. Note that there exists another hyperparameter named parts you may set in our PSConv operator.
Hi, I'm reading your paper and there comes some problems, hope you could help me figer it out!
Origin Conv2d was Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
PSConv2D are PSGConv2d( (gwconv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=4, bias=False) (gwconv_shift): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), groups=4, bias=False) (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) )
And I add self.weight = self.conv.weight self.bias = self.conv.bias
To solve the problem
" 'PSGConv2d' object has no attribute 'weight' " during building Resnet,
I don't know I'm right or wrong but it works temporarily.
After that ,durning training part,in PSConv2d.forward
self.gwconv(x).shape:
torch.Size([6, 256, 202, 274])
self.conv(x).shape
torch.Size([6, 256, 202, 274])
but x_shift.shape:
torch.Size([6, 256, 204, 276])
So....The size of tensor a (274) must match the size of tensor b (276) at non-singleton dimension 3
I'm using FCOS original code from tianzhi0549,thanks for the contribution, and add "psconv.py | conv_module.py | conv_ws.py " norm.py" from this respository.
Thank you for your time!
The text was updated successfully, but these errors were encountered:
quantumsquirrel
changed the title
Can't simply replace nn.Conv2d with PSConv2d.
Can't simply replace nn.Conv2d with PSConv2d
Oct 20, 2020
Thanks to the compact characteristic of PSConv, just replace
nn.Conv2d
withPSConv2d
. Note that there exists another hyperparameter namedparts
you may set in our PSConv operator.Originally posted by @d-li14 in #3 (comment)
Hi, I'm reading your paper and there comes some problems, hope you could help me figer it out!
Origin Conv2d was
Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
PSConv2D are
PSGConv2d( (gwconv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=4, bias=False) (gwconv_shift): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), groups=4, bias=False) (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) )
And I add
self.weight = self.conv.weight self.bias = self.conv.bias
To solve the problem
" 'PSGConv2d' object has no attribute 'weight' " during building Resnet,
I don't know I'm right or wrong but it works temporarily.
After that ,durning training part,in PSConv2d.forward
self.gwconv(x).shape:
torch.Size([6, 256, 202, 274])
self.conv(x).shape
torch.Size([6, 256, 202, 274])
but x_shift.shape:
torch.Size([6, 256, 204, 276])
So....
The size of tensor a (274) must match the size of tensor b (276) at non-singleton dimension 3
I'm using FCOS original code from tianzhi0549,thanks for the contribution, and add "psconv.py | conv_module.py | conv_ws.py " norm.py" from this respository.
Thank you for your time!
The text was updated successfully, but these errors were encountered: