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