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sacmehta / ESPNet

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
https://sacmehta.github.io/ESPNet/
MIT License
541 stars 112 forks source link

dimension mismatch #2

Closed sriharsha0806 closed 6 years ago

sriharsha0806 commented 6 years ago

Hi I ran your code. I am getting follwoing error: """ Total network parameters: 349449 Data statistics [72.39231 82.908936 73.1584 ] [45.31922 46.152893 44.914833] [ 1.5422179 6.085514 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 10.492059 5.2713513] Learning rate: 0.0005 Traceback (most recent call last): File "main.py", line 413, in trainValidateSegmentation(parser.parse_args()) File "main.py", line 339, in trainValidateSegmentation train(args, trainLoader_scale1, model, criteria, optimizer, epoch) File "main.py", line 103, in train loss = criterion(output, target_var) File "/users/sriharsha.annamaneni/miniconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 325, in call result = self.forward(*input, *kwargs) File "/users/sriharsha.annamaneni/ESPNet/train/Criteria.py", line 23, in forward return self.loss(F.log_softmax(outputs, 1), targets) File "/users/sriharsha.annamaneni/miniconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 325, in call result = self.forward(input, **kwargs) File "/users/sriharsha.annamaneni/miniconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/loss.py", line 147, in forward self.ignore_index, self.reduce) File "/users/sriharsha.annamaneni/miniconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py", line 1051, in nll_loss return torch._C._nn.nll_loss2d(input, target, weight, size_average, ignore_index, reduce) RuntimeError: input and target batch or spatial sizes don't match: target [6 x 768 x 1536], input [6 x 20 x 96 x 192] at /opt/conda/conda-bld/pytorch_1512387374934/work/torch/lib/THCUNN/generic/SpatialClassNLLCriterion.cu:24

""" There is a dimension mismatch between image from model and target image

sacmehta commented 6 years ago

ESPNet-C expects the output dimensions to be 1/8th of input image size. Please pass --scaleIn 8 as a parameter to train ESPNet-C.

sriharsha0806 commented 6 years ago

ok