I checked the "torch_resnet_cspn_nyu.py" to understand how you implemented the depth network before CSPN module. I found many dead codes in that file. For example, could you please tell me where you use those self.up_proj_layer(1/2/3/4) in forward function? In addition, when I check the paper the implementation of UpProj is not same as that depicted in Fig. 5 (see, conference paper)? After up-sampling by self._up_pool (object of Unpool), the output is used by the shortcut layer. However, the figure shows that there are conv, BN and Relu before feeding the output of the upsample to the shortcut. Could you please clarify those points for me?
Thanks for providing the code. I appreciate it. However, I think that the code could have been written far better to help people working in academic area and understand your idea better.
Hi,
I checked the "torch_resnet_cspn_nyu.py" to understand how you implemented the depth network before CSPN module. I found many dead codes in that file. For example, could you please tell me where you use those self.up_proj_layer(1/2/3/4) in forward function? In addition, when I check the paper the implementation of UpProj is not same as that depicted in Fig. 5 (see, conference paper)? After up-sampling by self._up_pool (object of Unpool), the output is used by the shortcut layer. However, the figure shows that there are conv, BN and Relu before feeding the output of the upsample to the shortcut. Could you please clarify those points for me?
Thanks for providing the code. I appreciate it. However, I think that the code could have been written far better to help people working in academic area and understand your idea better.
Thank you,