Description
Hey All
I've succesfully exported several RAVE models before through collabs. I've been training a model and would like to test it now, but when I go to export it, it reports an error I haven't seen before. I'm guessing this may be user error, but any assistance on the matter would be appreciated.
I execute the code following code:
`
model_dir = "/content/drive/MyDrive/RAVE_PianoTube/checkpoints/runs/RAVE_PianoTube_e5ef914ed9/version_3" #@param {type:"string"}
/content/miniconda/bin/rave export --run $model_dir --streaming --fidelity 0.999 --channels 2
`
And my console reports the following:
I0306 12:26:26.680935 137042398381888 export.py:495] building rave /content/miniconda/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning:torch.nn.utils.weight_normis deprecated in favor oftorch.nn.utils.parametrizations.weight_norm. WeightNorm.apply(module, name, dim) I0306 12:26:27.370947 137042398381888 export.py:505] model found : /content/drive/MyDrive/RAVE_PianoTube/checkpoints/runs/RAVE_PianoTube_e5ef914ed9/version_3/checkpoints/epoch-epoch=3499.ckpt Traceback (most recent call last): File "/content/miniconda/bin/rave", line 8, in <module> sys.exit(main()) File "/content/miniconda/lib/python3.9/site-packages/scripts/main_cli.py", line 38, in main app.run(export.main) File "/content/miniconda/lib/python3.9/site-packages/absl/app.py", line 308, in run _run_main(main, args) File "/content/miniconda/lib/python3.9/site-packages/absl/app.py", line 254, in _run_main sys.exit(main(argv)) File "/content/miniconda/lib/python3.9/site-packages/scripts/export.py", line 513, in main pretrained.load_state_dict( File "/content/miniconda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 2581, in load_state_dict raise RuntimeError( RuntimeError: Error(s) in loading state_dict for RAVE: size mismatch for encoder.encoder.net.0.weight_v: copying a param with shape torch.Size([96, 32, 7]) from checkpoint, the shape in current model is torch.Size([96, 16, 7]). size mismatch for decoder.net.21.weight_g: copying a param with shape torch.Size([64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 1, 1]). size mismatch for decoder.net.21.weight_v: copying a param with shape torch.Size([64, 96, 7]) from checkpoint, the shape in current model is torch.Size([32, 96, 7]). size mismatch for discriminator.discriminators.0.layers.0.net.0.weight_v: copying a param with shape torch.Size([96, 2, 5, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 5, 1]). size mismatch for discriminator.discriminators.0.layers.1.net.0.weight_v: copying a param with shape torch.Size([96, 2, 5, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 5, 1]). size mismatch for discriminator.discriminators.0.layers.2.net.0.weight_v: copying a param with shape torch.Size([96, 2, 5, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 5, 1]). size mismatch for discriminator.discriminators.0.layers.3.net.0.weight_v: copying a param with shape torch.Size([96, 2, 5, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 5, 1]). size mismatch for discriminator.discriminators.0.layers.4.net.0.weight_v: copying a param with shape torch.Size([96, 2, 5, 1]) from checkpoint, the shape in current model is torch.Size([96, 1, 5, 1]). size mismatch for discriminator.discriminators.1.layers.0.net.0.weight_v: copying a param with shape torch.Size([96, 2, 15]) from checkpoint, the shape in current model is torch.Size([96, 1, 15]). size mismatch for discriminator.discriminators.1.layers.1.net.0.weight_v: copying a param with shape torch.Size([96, 2, 15]) from checkpoint, the shape in current model is torch.Size([96, 1, 15]). size mismatch for discriminator.discriminators.1.layers.2.net.0.weight_v: copying a param with shape torch.Size([96, 2, 15]) from checkpoint, the shape in current model is torch.Size([96, 1, 15]).