Closed MrMaaoui closed 4 years ago
What is the channel dimension of your segmentation classifier (aka num_classes[0]
in config) ? And what is the largest label in your segmentation masks? When computing the segmentation cross-entropy loss, the largest label must be lower than the channel dimension of the classifier, otherwise you will see that error (more info here in pytorch docs).
Thank you for your response, converting segmented ground truth into class ids solved it
Good evening,
I'm trying to use DenseTorch to train a multi task module on a custom created dataset (rgb, masks and depth with the masks and depth being grayscale). whenever I'm trying to run the training I get the following error that I couldn't solve:
Traceback (most recent call last): File "train.py", line 82, in <module> dt.engine.train(model1, optims, [crit_segm, crit_depth], trainloader, loss_coeffs) File "/media/pfe_historiar/data/dataset/DenseTorch-master/densetorch/engine/trainval.py", line 80, in train target.squeeze(dim=1), File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 547, in __call__ result = self.forward(*input, **kwargs) File "/media/pfe_historiar/data/dataset/DenseTorch-master/densetorch/engine/losses.py", line 27, in forward c = 0.2 * torch.max(err) RuntimeError: cuda runtime error (59) : device-side assert triggered at /pytorch/aten/src/THC/THCReduceAll.cuh:327
Any recommendations on how to solve this issue?
The full terminal log is in this file log.txt.