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This happens when executing nll_loss located in code/torchfcn/utils.py under the mode of sourceonly. The training data is from GTA5.

The error occurs because the snippet inside the cross_entropy2d() first use a mask to exclude elements whose values are less than 0 in target(that is, labels). In other words, mislabeled pixels are not involved when calculating cross entropy.

However, the corresponding prediction values for those removed pixels still exist in log_p, which leads to the array size conflict.

Hi

Has anyone validated the results on the given 3 datasets and then tried to test with other datasets ?

I have tried the implementation with some datasets I have available but there is no improvement in the F networks performance on Target dataset after training for few epochs. The generator and discriminator learn to produce realistic images though. Does changing the adv_weight in F network loss improve the chances of domain adaptation in practice?

Thanks in advance.

Hi @swamiviv , thanks for your code. Your results of source only vgg16-fcn8s with GTA to cityscapes is 29.6, which is much higher than Curriculum domain adaptation with 22.0. What is the difference ?

Hi, I run your code on GTA2Cityscapes but failed to reproduce the evaluations claimed in your paper. During the training, I also noticed that the F's loss is not optimized at all, meaning that the domain shift is not address well? Here is what I got and it is worse than sourceonly. Any ideas? Did I do something wrong?
===>road: 83.28
===>sidewalk: 28.78
===>building: 61.49
===>wall: 6.52
===>fence: 0.19
===>pole: 8.44
===>light: 2.17
===>sign: 0.48
===>vegetation: 58.63
===>terrain: 14.29
===>sky: 55.88
===>person: 18.45
===>rider: 0.11
===>car: 53.92
===>truck: 2.9
===>bus: 1.52
===>train: 0.0
===>motocycle: 0.12
===>bicycle: 0.03
===> mIoU: 20.91

I can not find the synthia_mapped_to_cityscapes(labels) in dataset. Who can help me?

First thank you for sharing the great work. From previous issues you said we needed to map the labels of GTA V to CityScapes, could you pls upload the scripts to do that?

If for some reason the script is unavailable now, I want to ask whether we can directly use the code in the website https://download.visinf.tu-darmstadt.de/data/from_games/ readImage.m.

using default setting, the mIoU is 28%.

Does anybody else get better result?

I think the training of GAN is very tricky.

Hi, could you please tell which exact version of SYNTHIA dataset you used for the experiments.
Is it SYNTHIA-RAND-CVPR16 or SYNTHIA-RAND-CITYSCAPES?
Thank you.

Hi,

when I set the image size to be 1024,512 then there appears the out of memory error. I was using TITAN X gpu. Did you run into the same problem? how did you train with image size 1024,5112?

In the paper: about SYNTHIA dataset, you use 16 common classes with cityscapes,

why these places indicate that class number is 19?

https://github.com/swamiviv/LSD-seg/blob/master/code/train.py#L62

https://github.com/swamiviv/LSD-seg/blob/master/code/torchfcn/datasets/segmentation_datasets.py#L261

Please attach comment on the code related to IoU.
The following code is sort of hard to understand:

BTW, I got bad evaluation results on cityscapes dataset after training on GTA5 dataset.
While the final mean accuracy for validation on GTA5 is 54.8536%, we got the following evaluation results:

These are quite different from those listed in your paper: