search

wasidennis / AdaptSegNet

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
845 stars 205 forks source link

Error when running on custom dataset of 256*256 size images #77

Open ambekarsameer96 opened 4 years ago

ambekarsameer96 commented 4 years ago

I am using my own datasets as source and target images but I am encountering an error. Help needed ASAP. Input images size : 256 Target Image size : 256 Nunber of classes :1 (source and target both)

I got this error -

/opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [16,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [17,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [18,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [19,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [20,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [21,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [22,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [23,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [24,0,0] Assertion t >= 0 && t < n_classes failed. /opt/conda/conda-bld/pytorch_1579022021485/work/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype , Dtype , Dtype , long , Dtype , int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [25,0,0] Assertion t >= 0 && t < n_classes failed. Traceback (most recent call last): File "train_com.py", line 419, in main() File "train_com.py", line 306, in main loss_seg2 = loss_calc(pred2, labels, args.gpu) File "train_com.py", line 152, in loss_calc label = Variable(label.long()).cuda(gpu) RuntimeError: CUDA error: device-side assert triggered

Aayushktyagi commented 4 years ago

Yes faced the same issue while training on a custom dataset with a single class.

wasidennis commented 4 years ago

What does your label look like, e.g., only [0, 1] categories? If so, please make sure that you have the correct size in the classifier (final output layer), so that you can output only 2 channels for computing the cross-entropy loss with respect to your ground truth.