def train_dataloader(self): trn_aug = get_aug(atype=self.aug_type, size=self.cfg['img_size']) train = pd.read_csv(self.csv_path) trn_ds = CassavaDs(df=train[train.fold != self.fold].reset_index().drop('index', axis=1).drop('fold', axis=1), aug=trn_aug, path=self.trn_path, return_index=True) trn_dl = torch.utils.data.DataLoader(trn_ds, shuffle=True, batch_size=self.batch_size, num_workers=self.num_workers) return trn_dl
def train_dataloader(self): trn_aug = get_aug(atype=self.aug_type, size=self.cfg['img_size']) train = pd.read_csv(self.csv_path) trn_ds = CassavaDs( df=train[train.fold != self.fold].reset_index().drop('index', axis=1).drop( 'fold', axis=1), aug=trn_aug, path=self.trn_path) if self.do_cutmix: trn_ds = CutMix(trn_ds, num_class=5, beta=1.0, prob=0.5, num_mix=2) trn_dl = torch.utils.data.DataLoader(trn_ds, shuffle=True, batch_size=self.batch_size, num_workers=self.num_workers) return trn_dl