Neilstid
commented
6 months ago This cannot work since you are loading a trained model that have been trained to generate RGB images (3 channels). In my opinion there is three solution: -You modify the training_loop.py so that after loading the Generator only outputs one channel (either R, G or B) -> Not the easiest but I think it may work
- You train from scratch your network (if your data has 1 channel, the generated images will be 1 channel too)
- You modify the input images to be 3 channels (same channel repeated 3 times after loading the image in dataset.py). You can then select one of the 3 channel as your grayscale image
I hope it will help you :)
Describe the bug RuntimeError: output with shape [64, 1, 1, 1] doesn't match the broadcast shape [64, 3, 1, 1]
To Reproduce Steps to reproduce the behavior:
Output directory: ~/training-runs-rr/00000-stylegan3-r-256L-gpus1-batch64-gamma2 Number of GPUs: 1 Batch size: 64 images Training duration: 25000 kimg Dataset path: ../Gan/data/data/256L Dataset size: 4813 images Dataset resolution: 256 Dataset labels: False Dataset x-flips: True
Creating output directory... Launching processes... Loading training set...
Num images: 9626 Image shape: [1, 256, 256] Label shape: [0]
Constructing networks... Resuming from "https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-256x256.pkl" Traceback (most recent call last): File "/home/bairu/workspace/DatasetGEN/styleGan3/train.py", line 288, in
main() # pylint: disable=no-value-for-parameter
File "/home/bairu/miniconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1157, in call
return self.main(args, kwargs)
File "/home/bairu/miniconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
File "/home/bairu/miniconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, ctx.params)
File "/home/bairu/miniconda3/envs/stylegan3/lib/python3.9/site-packages/click/core.py", line 783, in invoke
return __callback(args, kwargs)
File "/home/bairu/workspace/DatasetGEN/styleGan3/train.py", line 283, in main
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
File "/home/bairu/workspace/DatasetGEN/styleGan3/train.py", line 98, in launch_training
subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
File "/home/bairu/workspace/DatasetGEN/styleGan3/train.py", line 49, in subprocess_fn
training_loop.training_loop(rank=rank, c)
File "/home/bairu/workspace/DatasetGEN/styleGan3/training/training_loop.py", line 164, in training_loop
misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
File "/home/bairu/workspace/DatasetGEN/styleGan3/torch_utils/misc.py", line 162, in copy_params_andbuffers
tensor.copy(src_tensors[name].detach()).requiresgrad(tensor.requires_grad)
RuntimeError: output with shape [64, 1, 1, 1] doesn't match the broadcast shape [64, 3, 1, 1]
Expected behavior The target data I want to generate is a single channel grayscale image. When I use grayscale images for training, it will improve this error.
Desktop (please complete the following information):
Additional context If pre-trained models are not used, it is feasible. This seems to be because the input of the pre trained model is three channels? What should I do if I want to use a pre-trained model for training single channel images?