pytorch-deform-conv
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oeway
PyTorch implementation of Deformable Convolution
Hello. Thanks for sharing the code. I have a question about the implementation of ```offset```, in [https://github.com/oeway/pytorch-deform-conv/blob/master/torch_deform_conv/deform_conv.py#L182] the code : ``` offsets = offsets.view(batch_size, -1, 2) ``` the input tensor...
Hi, is there visualization for the method, as seen in the provided gif file? How would one ideally implement this feature?
Hello, I am confused about the shape of offset. The paper mentions: "The grid R defines the receptive field size and dilation. For example,R = {(−1,−1),(−1,0),...,(0,1),(1,1)}. In deformable convolution, the...
The paper has offset with shape [B, 2x9, H, W] But this repo has offset with shape [B, 2xC, H, W] I think this means two totally different things.
I trained your deform-conv on the origin data. It gets 70% on origin test and 89% on scaled data, while the the normal CNN model trained on origin data gets...
I am a little bit confused how the weighting is done of each input element of the deformed kernel in your implementation. In your implementation it looks like you are...
First, appreciate for your work which is easy to use and read. In scaled_mninst.py, the normal CNN model is trained on origin data and then, tested on scaled one. It...
Why the deformable pooling part is not included in the implementation. Is it too complicated to be implemented?
