Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.
deftrain(data_loader:torch.utils.data.DataLoader,cfg:Config):# create model model=resnet50(num_classes=cfg.n_celeba_classes+cfg.n_digiface1m_classes,pretrained=True)torch.cuda.set_device(cfg.gpu)model=model.cuda(cfg.gpu)model.train()# define lossfunction(criterion)and optimizer criterion=torch....
last_epoch=last_epoch, ) utils.log_rank_zero(log, "Learning rate scheduler is initialized.") return lr_scheduler def _setup_data( self, cfg_dataset: DictConfig, shuffle: bool, batch_size: int, collate_fn: str, ) -> Tuple[DistributedSampler, DataLoader]: """ All data related setup hap...
num_training_steps=num_training_steps, last_epoch=last_epoch, ) log.info("Learning rate scheduler is initialized.") return lr_scheduler def _setup_data( self, cfg_dataset: DictConfig, shuffle: bool, batch_size: int, collate_fn: str, ) -> Tuple[DistributedSampler, DataLoader]: """ All...
( dataset=ds, sampler=sampler, batch_size=batch_size, collate_fn=partial( utils.padded_collate_dpo, padding_idx=self._tokenizer.pad_id, ignore_idx=CROSS_ENTROPY_IGNORE_IDX, ), ) log.info("Dataset and Sampler are initialized.") return sampler, dataloader def save_checkpoint(self, epoch: ...
cfg.n_celeba_classes)dataset=torch.utils.data.ConcatDataset([celeba_dataset,digiface_dataset])loader=torch.utils.data.DataLoader(dataset=dataset,batch_size=cfg.batch_size,shuffle=True,drop_last=True,num_workers=cfg.n_