Comments (1)
Hi, thanks for your interest in my work. Setting trainable to true and then backprop the gradients through the CRF is all which should be needed to train the CRF.
You mentioned that the prediction is always the same as the unary. I believe that this might be the actual issue. If the CRF is not doing anything, then the gradients are zero and nothing will be learned in this case. Make sure that during early iterations the CRF is augmenting the prediction at least slightly (it does not need to change the argmax, but it has to modify the floating point values significantly).
My guess would be that this is an initialization error. I would recommend playing around with the variances [sdims, schan, compat]. Also have a look at my discussion regarding recommended variances for normalized images. In general, make sure that at this line the message is not negligible small compared to the unary.
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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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