/
configure_data.py
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/
configure_data.py
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import os
import copy
import data_utils
class DataConfig(object):
def __init__(self, parser, defaults={}):
super(DataConfig,self).__init__()
self.parser = parser
self.defaults = defaults
def apply(self, opt):
print('configuring data')
self.apply_defaults(opt)
return make_loaders(opt)
def set_defaults(self, **kwargs):
for k, v in kwargs.items():
self.defaults[k] = v
def apply_defaults(self, opt):
for k, v in self.defaults.items():
k = k.replace('-', '_')
if not hasattr(opt, k):
setattr(opt, k, v)
def make_loaders(opt):
"""makes training/val/test"""
batch_size = opt.batch_size * opt.world_size
eval_batch_size = opt.eval_batch_size * opt.world_size
seq_length = opt.seq_length
if seq_length < 0:
seq_length = seq_length * opt.world_size
eval_seq_length = opt.eval_seq_length
if opt.eval_seq_length < 0:
eval_seq_length = eval_seq_length * opt.world_size
# data_loader_args = {'num_workers': 0, 'shuffle': opt.shuffle, 'batch_size': batch_size,
data_loader_args = {'num_workers': 4, 'shuffle': opt.shuffle, 'batch_size': batch_size,
# data_loader_args = {'num_workers': 1, 'shuffle': opt.shuffle, 'batch_size': batch_size,
'pin_memory': True, 'transpose': opt.transpose, 'distributed': opt.world_size > 1,
'rank': opt.rank, 'world_size': opt.world_size, 'drop_last': opt.world_size > 1}
if opt.data_set_type == 'L2R':
loader_type = data_utils.ShardLoader
data_loader_args.update({'seq_len': seq_length, 'persist_state': opt.persist_state, 'samples_per_shard': opt.samples_per_shard})
else:
loader_type = data_utils.DataLoader
split = get_split(opt)
data_set_args = {
'path': opt.data, 'seq_length': seq_length, 'lazy': opt.lazy, 'delim': opt.delim,
'text_key': opt.text_key, 'label_key': opt.label_key, 'preprocess': opt.preprocess,
'ds_type': opt.data_set_type, 'split': split, 'loose': opt.loose_json,
'tokenizer_type': opt.tokenizer_type, 'tokenizer_model_path': opt.tokenizer_path,
'vocab_size': opt.vocab_size, 'model_type': opt.tokenizer_model_type,
'non_binary_cols': opt.non_binary_cols, 'process_fn': opt.process_fn}
eval_loader_args = copy.copy(data_loader_args)
eval_set_args = copy.copy(data_set_args)
eval_set_args['split']=[1.]
# if optional eval args were set then replace their equivalent values in the arg dict
if opt.eval_batch_size != 0:
eval_loader_args['batch_size'] = eval_batch_size
if opt.eval_seq_length != 0:
eval_set_args['seq_length'] = eval_seq_length
if opt.data_set_type == 'L2R':
eval_loader_args['seq_len'] = eval_seq_length
if opt.eval_text_key is not None:
eval_set_args['text_key'] = opt.eval_text_key
if opt.eval_label_key is not None:
eval_set_args['label_key'] = opt.eval_label_key
train = None
valid = None
test = None
if opt.data is not None:
train, tokenizer = data_utils.make_dataset(**data_set_args)
if should_split(split):
train, valid, test = train
eval_set_args['tokenizer'] = tokenizer
if opt.valid is not None:
eval_set_args['path'] = opt.valid
valid, _ = data_utils.make_dataset(**eval_set_args)
if test is None and opt.test is not None:
eval_set_args['path'] = opt.test
test, _ = data_utils.make_dataset(**eval_set_args)
if train is not None and opt.batch_size > 0:
train = loader_type(train, **data_loader_args)
if valid is not None:
valid = loader_type(valid, **eval_loader_args)
if test is not None:
test = loader_type(test, **eval_loader_args)
return (train, valid, test), tokenizer
def should_split(split):
return max(split) != 1.
def get_split(opt):
splits = []
if opt.split.find(',') != -1:
splits = [float(s) for s in opt.split.split(',')]
elif opt.split.find('/') != -1:
splits = [float(s) for s in opt.split.split('/')]
else:
splits = [float(opt.split)]
split_total = sum(splits)
if split_total < 1.:
splits.append(1-split_total)
while len(splits) < 3:
splits.append(0.)
splits = splits[:3]
if opt.valid is not None:
splits[1] = 0.
if opt.test is not None:
splits[2] = 0.
final_sum = sum(splits)
return [s/final_sum for s in splits]
def configure_data(parser):
"""add cmdline flags for configuring datasets"""
main_parser = parser
group = parser.add_argument_group('data options')
group.add_argument('--data', nargs='+', default=['./data/imdb/unsup.json'],
help="""Filename for training""")
group.add_argument('--valid', nargs='*', default=None,
help="""Filename for validation""")
group.add_argument('--test', nargs='*', default=None,
help="""Filename for testing""")
group.add_argument('--process-fn', type=str, default='process_str', choices=['process_str', 'process_tweet'],
help='what preprocessing function to use to process text. One of [process_str, process_tweet].')
group.add_argument('--batch-size', type=int, default=128,
help='Data Loader batch size')
group.add_argument('--eval-batch-size', type=int, default=0,
help='Data Loader batch size for evaluation datasets')
group.add_argument('--data-size', type=int, default=256,
help='number of tokens in data')
group.add_argument('--loose-json', action='store_true',
help='Use loose json (one json-formatted string per newline), instead of tight json (data file is one json string)')
group.add_argument('--preprocess', action='store_true',
help='force preprocessing of datasets')
group.add_argument('--delim', default=',',
help='delimiter used to parse csv testfiles')
group.add_argument('--non-binary-cols', nargs='*', default=None,
help='labels for columns to non-binary dataset [only works for csv datasets]')
group.add_argument('--split', default='1.',
help='comma-separated list of proportions for training, validation, and test split')
group.add_argument('--text-key', default='sentence',
help='key to use to extract text from json/csv')
group.add_argument('--label-key', default='label',
help='key to use to extract labels from json/csv')
group.add_argument('--eval-text-key', default=None,
help='key to use to extract text from json/csv evaluation datasets')
group.add_argument('--eval-label-key', default=None,
help='key to use to extract labels from json/csv evaluation datasets')
# tokenizer arguments
group.add_argument('--tokenizer-type', type=str, default='CharacterLevelTokenizer', choices=['CharacterLevelTokenizer', 'SentencePieceTokenizer'],
help='what type of tokenizer to use')
group.add_argument('--tokenizer-model-type', type=str, default='bpe', choices=['bpe', 'char', 'unigram', 'word'],
help='Model type to use for sentencepiece tokenization')
group.add_argument('--vocab-size', type=int, default=256,
help='vocab size to use for non-character-level tokenization')
group.add_argument('--tokenizer-path', type=str, default='tokenizer.model',
help='path used to save/load sentencepiece tokenization models')
# These are options that are relevant to data loading functionality, but are not meant to be exposed to the command line user.
# These options are intneded to be set in code by specific scripts.
defaults = {
'world_size': 1,
'rank': -1,
'persist_state': 0,
'lazy': False,
'shuffle': False,
'transpose': False,
'data_set_type': 'supervised',
'seq_length': 256,
'eval_seq_length': 256,
'samples_per_shard': 100
}
return DataConfig(main_parser, defaults=defaults), group