Comments (2)
just run into the exact sample problem, any update?
I tried switching the group and width_pre_group parameters. It messed up the network dimension even further. So that is not the solution.
Also, I noticed that the model size of this implementation is about 4x of the one built by torchvision, so it seems some dim setup is off by 4x.
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Related Issues (20)
- Is the blur_kernel trainable or parametric? HOT 1
- can Downsample use tensorflow? HOT 1
- Depthwise convolutions HOT 1
- 3D implementation ? HOT 4
- Larger strides/downsampling factors HOT 2
- About Internal feature distance for shift Equivariance visualization HOT 2
- Increased memory usage vs. torchvision equivalent HOT 3
- ResNet parameter "pool_only=True" HOT 1
- Max-blur-pool used in text recognition model (CRNN) HOT 1
- HTTP Error 403: Forbidden when loading weights HOT 2
- Any plans to explore using sinc filter for downsampling? HOT 1
- Padding size issue for small images HOT 2
- Could you please provide a 3D implementation in pytorch?
- Feature Req: Making the channel argument optional
- Feature Req: Separable Convolution
- Is there any particular reason for puting Blurpool before the skip-connection layers? HOT 3
- Why do deeper CNNs have better shift consistency? HOT 2
- If stride=1, is there a difference between BlurPool and maxpool
- HTTP Error 403: Forbidden when loading weights HOT 9
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