Thanks for a great codes.
Now I am trying to implement the code with my own dataset, and since I need to use tensorflow as a framework, I am modifying toe code based on the TF2.1.
It may be bit awkward to ask here, but I think it can be a question about module mechanism itself.
At ASPP model class, I saw that the Relu layer is coming after Global Average Pooling.
So when I ran it, it was saying that the dimensions of outputs of layer for '(aspp_idx == self.aspp_num - 1)' case and else.
For the first one, shape of the layer output was something like (None, 128, 128, 64) ,
and output of layer for '(aspp_idx == self.aspp_num - 1)' case was sth. like shape=(None, 256)
Is there any reason for using GAP before relu?
and If so, can I fix this error caused by dim. mismatch?
Hi, the ASPP module was proposed by DeepLab which has a global average pooling layer. Please check the TF implementation you have. It seems that the numbers of channels are not set correctly for the GAP layer.
Hi,
Thanks for a great codes. Now I am trying to implement the code with my own dataset, and since I need to use tensorflow as a framework, I am modifying toe code based on the TF2.1.
It may be bit awkward to ask here, but I think it can be a question about module mechanism itself.
At ASPP model class, I saw that the Relu layer is coming after Global Average Pooling. So when I ran it, it was saying that the dimensions of outputs of layer for '(aspp_idx == self.aspp_num - 1)' case and else. For the first one, shape of the layer output was something like (None, 128, 128, 64) , and output of layer for '(aspp_idx == self.aspp_num - 1)' case was sth. like shape=(None, 256)
Is there any reason for using GAP before relu? and If so, can I fix this error caused by dim. mismatch?
Thanks alot!