Comments (2)
QtacierP
commented on May 18, 2024
If you do not set this function, you will get some troubles when you use multiple GPUs. The MLP flag will be initialized and fixed when you use the nn.DataParallel() to replace the naive model (nn.Module). Therefore, we need to initialize the MLP before we parallelize the model
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ibro45
commented on May 18, 2024
@QtacierP does that imply that there's no other purpose to it except that it's patching the issue with nn.DataParallel() and that if nn.DataParallel() is not used, it'd be safe to remove it?
from contrastive-unpaired-translation.
Related Issues (20)
- Error during running train.py: ConnectionRefusedError: [Errno 111] Connection refused HOT 2
- test.py alters file names of translated images HOT 1
- About the "no_antialias"
- Weird edge pattern in generated images during training
- Model doesnt work well on unpaired data
- The geneated images from train and test are inconsistent
- Applying to a general image restoration task
- One-to-Many translation HOT 2
- 3D CUT for medical images
- I cannot find the dataset HOT 2
- Unbiased / Supervised Patch Sampling
- CUT is not working with grayscale images. HOT 2
- Diagonal masked replacement is filled with -10.0 instead of exp(-10.0)
- About epoch_number
- Training image size changes
- May I ask in networks in ResnetGenerator.networks ,this parameter: no_antialias_up, What is the purpose of up? The code is as follows:
- Model collapse from the get go on grumpycat dataset HOT 6
- I wonder whether it can train on NPU?
- There is no PatchGAN in this repo, despite claims of paper
- Why my generator seems to cheat my discriminator by generating a same picture? HOT 4
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