Comments (2)
This is similar to #11. I use logsoftmax in the first iteration, because the original FullCRF implemented uses log on the data (followed by softmax). Logsoftmax does the same but numerical more stable.
In all further iteration only softmax is applied in FullCRF, since the data is already in "log" space.
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Isn't it softmax followed by log?
Docs: https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.log_softmax
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Related Issues (20)
- Why log on input data? HOT 1
- The condition is always False HOT 1
- On the difference compared to the original paper
- Why input is image rather than network output prediction?
- How to use the ConvCRF for depth estimation
- The size of tensor a (512) must match the size of tensor b (20) at non-singleton dimension 1
- The meaning of these parameters? HOT 1
- typeerror HOT 3
- Can I use ConvCRF in grayscale images?
- about the fullcrf training? HOT 1
- Any plan for the tensorflow implementation?
- Where do you implement the compatibility transform? HOT 1
- Another Question about speed test and GPU-memory test HOT 1
- Question about comparative experiment
- About training in deep network.
- Confusion regarding ordering of log and softmax in inference code
- RuntimeError: non-empty 3D or 4D (batch mode) tensor expected for input
- Sir ,tks for the cpu version,however,i think there have some small problems in your code
- KeyError: 'final_softmax'
- About End-to-end training HOT 2
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