Comments (1)
This repo contains a completed code that runs.
qkv_transform is just simply a convolution, but with customized initialization:
https://github.com/csrhddlam/axial-deeplab/blob/master/lib/models/axialnet.py#L176
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Related Issues (20)
- About the class activation map HOT 3
- position-sensitive attention HOT 1
- Seems dist_train.py didn't wrap the model with the synchronize batch norm HOT 2
- Confused about the `transpose` in positional encoding of key HOT 1
- Pretrained weights HOT 3
- Confused about the shape of relative position encoding HOT 4
- how does axial-attention support multi-scale training/testing? HOT 1
- Question about table 9 in paper HOT 3
- What's HERE?? HOT 1
- Training with non-square images HOT 1
- Question about Axial-Res50 HOT 2
- 关于AxialAttention中kernel_size的问题 HOT 1
- Shape of relative position encoding r^q, r^k, r^v HOT 1
- About local constraints HOT 2
- Pretrain_weights HOT 1
- about function parameter “s=0.5” in code
- why batchnormalization after qkv transform?
- Different resolution for inference
- Can it be used in video tasks?
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from axial-deeplab.