Comments (3)
Hi, I think you're referring to this section: https://github.com/adobe/antialiased-cnns/blob/master/antialiased_cnns/resnet.py#L147-L148, which makes it look like there's blurpool(stride2)->conv(stride1)
.
Your understanding is correct. It should be conv first. There is actually a conv before it, which was changed from stride 2 to stride 1: https://github.com/adobe/antialiased-cnns/blob/master/antialiased_cnns/resnet.py#L142. So overall, it is conv(stride1)
first.
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Thank you for your reply! I understand it is conv(stride1)
first in the Bottleneck. What I am confused is the skip connections:
antialiased-cnns/antialiased_cnns/resnet.py
Lines 252 to 253 in b27a34a
conv(stride1)
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Got it, in this case, because there's no non-linearity, the 1x1 conv and NxN blur are interchangeable. It's cheaper to do the blur --> stride --> conv rather than conv --> blur --> stride
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Related Issues (20)
- Is the blur_kernel trainable or parametric? HOT 1
- RuntimeError when training resnext50_32x4d HOT 2
- 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
- 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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