init__(self,*args,**kwargs):super().__init__(*args,**kwargs)fromargparseimportArgumentParserparser=ArgumentParser()parser.add_argument('--classes',type=int,default=10)parser.add_argument('--checkpoint',type=str,
from torch_geometric.nn import SchNet parser = argparse.ArgumentParser() parser.add_argument('--cutoff', type=float, default=10.0, help='Cutoff distance for interatomic interactions') args = parser.parse_args() path = osp.join(osp.dirname(osp.realpath(file)), '..', 'data', 'QM9') data...
model=LightningModel( pretrained_weights=[ os.path.join(args.pretrained_path,"text_encoder"), os.path.join(args.pretrained_path,"unet/diffusion_pytorch_model.safetensors"), os.path.join(args.pretrained_path,"vae/diffusion_pytorch_model.safetensors"), ...
"""For code used in distributed training."""fromtypingimportTupleimporttorchimporttorch.distributedasdistimportosfromtorchimportTensorimporttorch.multiprocessingasmpdefset_sharing_strategy(new_strategy=None):"""https://pytorch.org/docs/stable/multiprocessing.htmlhttps://discuss.pytorch.org/t/how-does-one...