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text_helper.py
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text_helper.py
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import torch
from torch.autograd import Variable
from torch.nn.functional import log_softmax
from helper import Helper
import random
import logging
from models.word_model import RNNModel
from utils.text_load import *
logger = logging.getLogger("logger")
POISONED_PARTICIPANT_POS = 0
class TextHelper(Helper):
corpus = None
@staticmethod
def batchify(data, bsz):
# Work out how cleanly we can divide the dataset into bsz parts.
nbatch = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, nbatch * bsz)
# Evenly divide the data across the bsz batches.
data = data.view(bsz, -1).t().contiguous()
return data.cuda()
def poison_dataset(self, data_source, dictionary, poisoning_prob=1.0):
poisoned_tensors = list()
for sentence in self.params['poison_sentences']:
sentence_ids = [dictionary.word2idx[x] for x in sentence.lower().split() if
len(x) > 1 and dictionary.word2idx.get(x, False)]
sen_tensor = torch.LongTensor(sentence_ids)
len_t = len(sentence_ids)
poisoned_tensors.append((sen_tensor, len_t))
## just to be on a safe side and not overflow
no_occurences = (data_source.shape[0] // (self.params['bptt']))
logger.info("CCCCCCCCCCCC: ")
logger.info(len(self.params['poison_sentences']))
logger.info(no_occurences)
for i in range(1, no_occurences + 1):
if random.random() <= poisoning_prob:
# if i>=len(self.params['poison_sentences']):
pos = i % len(self.params['poison_sentences'])
sen_tensor, len_t = poisoned_tensors[pos]
position = min(i * (self.params['bptt']), data_source.shape[0] - 1)
data_source[position + 1 - len_t: position + 1, :] = \
sen_tensor.unsqueeze(1).expand(len_t, data_source.shape[1])
logger.info(f'Dataset size: {data_source.shape} ')
return data_source
def get_sentence(self, tensor):
result = list()
for entry in tensor:
result.append(self.corpus.dictionary.idx2word[entry])
# logger.info(' '.join(result))
return ' '.join(result)
@staticmethod
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(TextHelper.repackage_hidden(v) for v in h)
def get_batch(self, source, i, evaluation=False):
seq_len = min(self.params['bptt'], len(source) - 1 - i)
data = source[i:i + seq_len]
target = source[i + 1:i + 1 + seq_len].view(-1)
return data, target
@staticmethod
def get_batch_poison(source, i, bptt, evaluation=False):
seq_len = min(bptt, len(source) - 1 - i)
data = Variable(source[i:i + seq_len], volatile=evaluation)
target = Variable(source[i + 1:i + 1 + seq_len].view(-1))
return data, target
def load_data(self):
### DATA PART
logger.info('Loading data')
#### check the consistency of # of batches and size of dataset for poisoning
if self.params['size_of_secret_dataset'] % (self.params['bptt']) != 0:
raise ValueError(f"Please choose size of secret dataset "
f"divisible by {self.params['bptt'] }")
dictionary = torch.load(self.params['word_dictionary_path'])
corpus_file_name = f"{self.params['data_folder']}/" \
f"corpus_{self.params['number_of_total_participants']}.pt.tar"
if self.params['recreate_dataset']:
self.corpus = Corpus(self.params, dictionary=dictionary,
is_poison=self.params['is_poison'])
torch.save(self.corpus, corpus_file_name)
else:
self.corpus = torch.load(corpus_file_name)
logger.info('Loading data. Completed.')
if self.params['is_poison']:
self.params['adversary_list'] = [POISONED_PARTICIPANT_POS] + \
random.sample(
range(self.params['number_of_total_participants']),
self.params['number_of_adversaries'] - 1)
logger.info(f"Poisoned following participants: {len(self.params['adversary_list'])}")
else:
self.params['adversary_list'] = list()
### PARSE DATA
eval_batch_size = self.params['test_batch_size']
self.train_data = [self.batchify(data_chunk, self.params['batch_size']) for data_chunk in
self.corpus.train]
self.test_data = self.batchify(self.corpus.test, eval_batch_size)
if self.params['is_poison']:
data_size = self.test_data.size(0) // self.params['bptt']
test_data_sliced = self.test_data.clone()[:data_size * self.params['bptt']]
self.test_data_poison = self.poison_dataset(test_data_sliced, dictionary)
self.poisoned_data = self.batchify(
self.corpus.load_poison_data(number_of_words=self.params['size_of_secret_dataset'] *
self.params['batch_size']),
self.params['batch_size'])
self.poisoned_data_for_train = self.poison_dataset(self.poisoned_data, dictionary,
poisoning_prob=self.params[
'poisoning'])
self.n_tokens = len(self.corpus.dictionary)
def create_model(self):
local_model = RNNModel(name='Local_Model', created_time=self.params['current_time'],
rnn_type='LSTM', ntoken=self.n_tokens,
ninp=self.params['emsize'], nhid=self.params['nhid'],
nlayers=self.params['nlayers'],
dropout=self.params['dropout'], tie_weights=self.params['tied'])
local_model.cuda()
target_model = RNNModel(name='Target', created_time=self.params['current_time'],
rnn_type='LSTM', ntoken=self.n_tokens,
ninp=self.params['emsize'], nhid=self.params['nhid'],
nlayers=self.params['nlayers'],
dropout=self.params['dropout'], tie_weights=self.params['tied'])
target_model.cuda()
if self.params['resumed_model']:
loaded_params = torch.load(f"saved_models/{self.params['resumed_model']}")
target_model.load_state_dict(loaded_params['state_dict'])
self.start_epoch = loaded_params['epoch']
self.params['lr'] = loaded_params.get('lr', self.params['lr'])
logger.info(f"Loaded parameters from saved model: LR is"
f" {self.params['lr']} and current epoch is {self.start_epoch}")
else:
self.start_epoch = 1
self.local_model = local_model
self.target_model = target_model