I try to run the newest code and always report errors. Finally i find that there some little mistake in the code after multiple tests 1st demo.py line97 should change to 'if args.cpu:' 2nd convcrf.py line523 should delete the 'raise ValueError' However，i don't know if i am right or not:) since i am a rookie and even did not fully understand your paper and all the codes

Since you already provide a pure-PyTorch implementation that does not require PyINN, the code does not actually require CUDA. This PR removes the use_gpu assertions from the implementation and adds a --cpu flag both to the demo and benchmark script. (Side note: pydensecrf is actually faster on CPU, so in practice ConvCRF is only useful on GPU, but running on CPU is still useful for testing purposes.)

Hello, when I run the convcrf.py, arise error: Traceback (most recent call last): File "F:/googledownload/ConvCRF-master/convcrf/convcrf.py", line 620, in tcrf = GaussCRF(conf, [10, 10], None).cuda() File "F:/googledownload/ConvCRF-master/convcrf/convcrf.py", line 169, in init final_softmax=conf['final_softmax'], KeyError: 'final_softmax'

have you met such problem? how to solve it?

from convcrf import convcrf

config = convcrf.default_conf
shape = [3,416,416]
nclasses = 15

crf = convcrf.GaussCRF(config, shape, nclasses)
crf.cuda()

img = torch.randn(1,3,416,416).cuda()
un = torch.randn(1,15,416,416).cuda()
un = torch.softmax(un,dim=1)

pred = crf.forward(un, img)

ERROR: Traceback (most recent call last):

File "", line 1, in pred = crf.forward(un, img)

File "...\anaconda3\lib\site-packages\convcrf-1.0-py3.7.egg\convcrf\convcrf.py", line 202, in forward compats, conf['merge'])

File "...\anaconda3\lib\site-packages\convcrf-1.0-py3.7.egg\convcrf\convcrf.py", line 574, in add_pairwise_energies pyinn=self.pyinn)

File "...\anaconda3\lib\site-packages\convcrf-1.0-py3.7.egg\convcrf\convcrf.py", line 311, in init gaussian = self._create_convolutional_filters(feats)

File "...\anaconda3\lib\site-packages\convcrf-1.0-py3.7.egg\convcrf\convcrf.py", line 353, in _create_convolutional_filters count_include_pad=False)

RuntimeError: non-empty 3D or 4D (batch mode) tensor expected for input

Hi Marvin,

Great project! I am trying to utilize it in my own research and went through your convcrf.py code. I have a doubt around this line where if I am not wrong, if default_conf["logsoftmax"] is True you take softmax followed by log using the log_softmax function (docs) but if it's False you take the log followed by the softmax. Can you please clarify on this?

Thanks

Hi, @MarvinTeichmann Thanks for your work, I have two questions about the ConvCRF.

1. I run demo.py and got a perfect result, but how the weights of Gaussian Kernels been updated?
2. I put the ConvCRF as a module into a deep network, and train the whole network by BP for semantic segmentation, No error but the loss is so unstable and the mIoU is too low. How should I choose the training protocol？

Thanks a lot.

Hello

ConvCRF is an amazing work.

I have a question about the experiment on ConvCRF and CRFasRNN. In general, DenseCRF can increase MIoU by 3%, Which means that even without end-to-end jointly fine tuning, the CNN-DenseCRF system can achieve a better MIoU than the unary (CNN). But I notice that in this paper, MIoU of FCN-ResNet101-CRFasRNN is about 69, lower than the raw MIoU of its unary module FCN-ResNet101 (about 71).

What happends to DenseCRF that makes its accuracy declining so hard?