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jblitzar
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github-python-corpus

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def load_when2talk_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'dev']:
data = When2talkDataset(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 = When2talkDataset(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=False, batch_size=args['batch_size'], collate_fn=gpt2_test_collate_fn)
return iter_
def load_lccc_ir_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train']:
data = WBDataset('/home/lt/data/LCCD_GPT', path, samples=1)
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=data.collate)
else:
# NOTE: TEST PROCEDURE IS ERROR, WAIT TO REWRITE
data = WBDataset('/home/lt/data/LCCD_GPT', path, samples=9)
iter_ = DataLoader(data, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
def load_bert_na_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = BERTNADataset(path, mode=args['mode'], max_size=16)
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
def load_uni_dataset(args):
if args['mode'] in ['train']:
data = UNIDataset('/data/lantian/data/LCCD_GPT', f'data/{args["dataset"]}/LCCC-base.json', samples=1)
args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
train_sampler = torch.utils.data.distributed.DistributedSampler(data)
iter_ = DataLoader(data, sampler=train_sampler, batch_size=args['batch_size'], collate_fn=data.collate)
else:
data = UNIDataset('/home/lt/data/LCCD_GPT', f'data/{args["dataset"]}/LCCC-base_test.json', samples=9)
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
def load_lccc_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = FTWBDataset('/home/lt/data/LCCD_GPT', args['mode'], path)
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=data.collate)
return iter_
def load_gpt2_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'])
args['total_steps'] = len(data) * args['epoch'] / args['batch_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)
else:
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'] = 100
iter_ = DataLoader(data, 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_gpt2v2rl_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'dev']:
data = GPT2V2RLDataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'], candidate=5)
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)
else:
data = GPT2V2RLDataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'], candidate=5)
iter_ = DataLoader(data, 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_gpt2v2_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'dev']:
data = GPT2V2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'], candidate=5)
args['total_steps'] = len(data) * args['epoch'] / args['batch_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)
else:
data = GPT2V2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'], lang=args['lang'], candidate=5)
args['total_steps'] = 100
iter_ = DataLoader(data, 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_kwgpt2_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'dev']:
data = KWGPT2Dataset(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 = KWGPT2Dataset(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_