Comments (5)
Hi, this issue occurs because you cannot call C++ compiling tools of Window cl.exe. You can try to solve it by adding the path to cl.exe in the environment variable PATH. However, unless you are experienced in compiling Pytorch C++ code in Windows, I would advice you to use a Ubuntu machine to setup the project. Or you can opt for PatchGAN discriminator instead of StyleGAN one as some code of StyleGAN discriminator require C++ compiling
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Thanks a lot for the answer i'll try tomorrow and close the issue of it works
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Hi @bastvdn , how is it going ? If some issues occur, don't be shy to ask 😄
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Hi, I tried swapping the Discriminator and added cl.exe to my environment variable but i still have problem because i don't have ninja on windows
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
I guess it's like you said, i should use a ubuntu machine but it's too much of a bother because of how i work (already on remote desktop) i'll try another similar implementation to fix my problem i think it'll be easier, Thanks a lot for the answers tho
from enhancing-transformers.
Hi, I tried swapping the Discriminator and added cl.exe to my environment variable but i still have problem because i don't have ninja on windows subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. I guess it's like you said, i should use a ubuntu machine but it's too much of a bother because of how i work (already on remote desktop) i'll try another similar implementation to fix my problem i think it'll be easier, Thanks a lot for the answers tho
hi @bastvdn, you can easily install Ninja with anaconda or pip install Ninja.
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Related Issues (18)
- Pre-norm and Post-norm HOT 6
- An inplace operation in the forward process HOT 1
- Reconstruction Visualization HOT 2
- stage2 transformer HOT 2
- stage1 pretraining HOT 7
- Model license HOT 4
- stage2 transform HOT 9
- about training loss HOT 1
- The question is done. HOT 1
- ImageNet version.
- Reconstruction results HOT 4
- Learning rate and scheduler for stage 1 training. HOT 1
- Results HOT 5
- Smaller images HOT 10
- OOM for imagenet_gpt_vitvq_base and a 100M params GPT on A100 40G HOT 3
- Pretrained Stage 2 Transformer for ViT-VQGAN HOT 8
- Training time and number of GPUS HOT 6
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