Datasets:
jblitzar
/
github-python-corpus

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fpyr = self.fpyrs_dict[id]
fvec = []
for i, feat in enumerate(fpyr):
res = 2 ** (2 + int(i/2))
v = bilinear_interp_sampling(feat, h, w, self.res, res)
fvec.append(v.squeeze(0))
fvec = torch.cat(fvec, dim=0)
normal = normal[h, w]
return index, fvec, normal
# <FILESEP>
from header import *
from utils import *
from dataloader import *
def load_prediction_greedy_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = TopicPredictDataset(path, mode=args['mode'], max_len=args['src_len_size'])
train_sampler = torch.utils.data.distributed.DistributedSampler(data)
iter_ = DataLoader(data, sampler=train_sampler, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_seq2seq_trs_dataset(args):
zh_tokenizer = False
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = TransformerDataset(path, mode=args['mode'], lang=args['lang'], max_length=args['src_len_size'], n_vocab=args['n_vocab'], zh_tokenizer=zh_tokenizer)
args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
if zh_tokenizer is True:
args['vocab'] = data.vocab
else:
args['vocab'] = None
if args['mode'] == 'train':
train_sampler = torch.utils.data.distributed.DistributedSampler(data)
iter_ = DataLoader(data, sampler=train_sampler, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
else:
iter_ = DataLoader(data, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
def load_seq2seq_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = Seq2SeqDataset(path, mode=args['mode'], lang=args['lang'], n_vocab=args['n_vocab'])
args['vocab'] = data.vocab
if args['mode'] == 'train':
train_sampler = torch.utils.data.distributed.DistributedSampler(data)
iter_ = DataLoader(data, sampler=train_sampler, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
else:
iter_ = DataLoader(data, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
def load_gpt2rl_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = GPT2RLDataset(path, src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'])
iter_ = GPT2RLDataLoader(data, shuffle=True, batch_size=args['batch_size'])
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_gpt2lm_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = GPT2LMDataset(path)
args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
iter_ = DataLoader(data, shuffle=False, batch_size=args['batch_size'], collate_fn=gpt2_lm_collate_fn)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_pfgpt2_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'train_trs', 'dev']:
data = GPT2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'])
args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
iter_ = DataLoader(data, shuffle=False, batch_size=args['batch_size'], collate_fn=gpt2_train_collate_fn)
else:
data = GPT2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'])
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=gpt2_test_collate_fn)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_gpt2retrieval_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'dev']:
data = GPT2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'], ensemble=True, candidates_k=2)
args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=gpt2retrieval_train_collate_fn)
else:
data = GPT2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'], ensemble=True, candidates_k=2)
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=gpt2retrieval_test_collate_fn)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_