cuDNN error about bicyclegan HOT 11 CLOSED

This is quite hard to tell. Did you manage to run the code on our dataset (e.g., facades)?

from bicyclegan.

Yes its working on your dataset(edges2shoes).I will create some more dataset and check it.

from bicyclegan.

Cool. Maybe you have a broken image or 1 channel image in your dataset. But it can be other reasons.

from bicyclegan.

I have the same problem when training on my own dataset with both input and output channel = 1, and HW 256x256. I use the following bash:
python ./train.py --gpu_ids 0 --display_id 1 --dataroot /mnt/ssd/IVS_Data_All_20181213 --dataset_mode alphaL --alphaL_data_json_file_full_path /mnt/ssd/IV S_Data_All_20181213/all_combine_20181229.json --num_threads 0 --name alphaL_bicycle_gan_10_01_2019_15 --model bicycle_gan --netG unet_256 --direction AtoB --c heckpoints_dir ../checkpoints/alphaL/ --preprocess crop --load_size 480 --crop_size 384 --nz 8 --input_nc 1 --output_nc 1 --niter 100 --niter_decay 100 --save _epoch_freq 10 --use_dropout

Additionally, I notice that just as @riktimmondal, error raise at bicycle_gan_model.py, line 129, in backward_D() pred_real = netD(real)

from bicyclegan.

I will have a look next week. Sorry that I am busy with a paper deadline now.

from bicyclegan.

after some dig, I found the problem may caused by un-full mini-batch. After I set dataset length to even, (default batch-size=2), It seems the training goes well.

from bicyclegan.

Yes, the code divides a batch into two. I can add an assertion.

from bicyclegan.

I added a function to check if the mini-batch is good for training.

from bicyclegan.

Thanks. I have another question.
Dut to fake_B=net_G(A,z) and tanh is the last non-linear-layer, fake_B should be in range(-1, 1). However, the real_B has mean 0.5 and std 0.5. This different value range between fake_B and real_B makes net_D easy to tell their different. Do I understand correctly? Thanks again.

from bicyclegan.

It normalizes an image to [-1, 1]. See this post for more details.

from bicyclegan.

So, transforms.Normalize() normalize a image of range [0, 1] to [-1, 1].
Thanks.

from bicyclegan.