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utils.py
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import torch
import random
import numpy as np
def tensor2text(vocab, tensor):
tensor = tensor.cpu().numpy()
text = []
index2word = vocab.itos
eos_idx = vocab.stoi['<eos>']
unk_idx = vocab.stoi['<unk>']
stop_idxs = [vocab.stoi['!'], vocab.stoi['.'], vocab.stoi['?']]
for sample in tensor:
sample_filtered = []
prev_token = None
for idx in list(sample):
if prev_token in stop_idxs:
break
if idx == unk_idx or idx == prev_token or idx == eos_idx:
continue
prev_token = idx
sample_filtered.append(index2word[idx])
sample = ' '.join(sample_filtered)
text.append(sample)
return text
def calc_ppl(log_probs, tokens_mask):
return (log_probs.sum() / tokens_mask.sum()).exp()
def idx2onehot(x, num_classes):
y = x.unsqueeze(-1)
x_onehot = torch.zeros_like(y.expand(x.size() + torch.Size((num_classes, ))))
x_onehot.scatter_(-1, y, 1)
return x_onehot.float()
def word_shuffle(x, l, shuffle_len):
if not shuffle_len:
return x
noise = torch.rand(x.size(), dtype=torch.float).to(x.device)
pos_idx = torch.arange(x.size(1)).unsqueeze(0).expand_as(x).to(x.device)
pad_mask = (pos_idx >= l.unsqueeze(1)).float()
scores = pos_idx.float() + ((1 - pad_mask) * noise + pad_mask) * shuffle_len
x2 = x.clone()
x2 = x2.gather(1, scores.argsort(1))
return x2
def word_dropout_raw(x, l, unk_drop_prob, rand_drop_prob, vocab):
if not unk_drop_prob and not rand_drop_prob:
return x
assert unk_drop_prob + rand_drop_prob <= 1
noise = torch.rand(x.size(), dtype=torch.float).to(x.device)
pos_idx = torch.arange(x.size(1)).unsqueeze(0).expand_as(x).to(x.device)
token_mask = pos_idx < l.unsqueeze(1)
x2 = x.clone()
# drop to <unk> token
if unk_drop_prob:
unk_idx = vocab.stoi['<unk>']
unk_drop_mask = (noise < unk_drop_prob) & token_mask
x2.masked_fill_(unk_drop_mask, unk_idx)
# drop to random_mask
if rand_drop_prob:
rand_drop_mask = (noise > 1 - rand_drop_prob) & token_mask
rand_tokens = torch.randint_like(x, len(vocab))
rand_tokens.masked_fill_(1 - rand_drop_mask, 0)
x2.masked_fill_(rand_drop_mask, 0)
x2 = x2 + rand_tokens
return x2
def unk_dropout_(x, l, drop_prob, unk_idx):
noise = torch.rand(x.size(), dtype=torch.float).to(x.device)
pos_idx = torch.arange(x.size(1)).unsqueeze(0).expand_as(x).to(x.device)
token_mask = pos_idx < l.unsqueeze(1)
unk_drop_mask = (noise < drop_prob) & token_mask
x.masked_fill_(unk_drop_mask, unk_idx)
def rand_dropout_(x, l, drop_prob, vocab_size):
noise = torch.rand(x.size(), dtype=torch.float).to(x.device)
pos_idx = torch.arange(x.size(1)).unsqueeze(0).expand_as(x).to(x.device)
token_mask = pos_idx < l.unsqueeze(1)
rand_drop_mask = (noise < drop_prob) & token_mask
rand_tokens = torch.randint_like(x, vocab_size)
rand_tokens.masked_fill_(1 - rand_drop_mask, 0)
x.masked_fill_(rand_drop_mask, 0)
x += rand_tokens
def word_dropout_new(x, l, unk_drop_fac, rand_drop_fac, drop_prob, vocab):
if not unk_drop_fac and not rand_drop_fac:
return x
assert unk_drop_fac + rand_drop_fac <= 1
batch_size = x.size(0)
unk_idx = vocab.stoi['<unk>']
unk_drop_idx = int(batch_size * unk_drop_fac)
rand_drop_idx = int(batch_size * rand_drop_fac)
shuffle_idx = torch.argsort(torch.rand(batch_size))
orignal_idx = torch.argsort(shuffle_idx)
x2 = x.clone()
x2 = x2[shuffle_idx]
if unk_drop_idx:
unk_dropout_(x2[:unk_drop_idx], l[:unk_drop_idx], drop_prob, unk_idx)
if rand_drop_idx:
rand_dropout_(x2[-rand_drop_idx:], l[-rand_drop_idx:], drop_prob, len(vocab))
x2 = x2[orignal_idx]
return x2
def word_dropout(x, l, drop_prob, unk_idx):
if not drop_prob:
return x
noise = torch.rand(x.size(), dtype=torch.float).to(x.device)
pos_idx = torch.arange(x.size(1)).unsqueeze(0).expand_as(x).to(x.device)
token_mask = pos_idx < l.unsqueeze(1)
drop_mask = (noise < drop_prob) & token_mask
x2 = x.clone()
x2.masked_fill_(drop_mask, unk_idx)
return x2
def word_drop(x, l, drop_prob, pad_idx):
if not drop_prob:
return x
noise = torch.rand(x.size(), dtype=torch.float).to(x.device)
pos_idx = torch.arange(x.size(1)).unsqueeze(0).expand_as(x).to(x.device)
token_mask = pos_idx < (l.unsqueeze(1) - 1)
drop_mask = (noise < drop_prob) & token_mask
x2 = x.clone()
pos_idx.masked_fill_(drop_mask, x.size(1) - 1)
pos_idx = torch.sort(pos_idx, 1)[0]
x2 = x2.gather(1, pos_idx)
return x2
def add_noise(words, lengths, shuffle_len, drop_prob, unk_idx):
words = word_shuffle(words, lengths, shuffle_len)
words = word_dropout(words, lengths, drop_prob, unk_idx)
return words
def init_logger():
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if not args.no_cuda and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
def compute_metrics(preds, labels):
assert len(preds) == len(labels)
return acc_score(preds, labels)
def simple_accuracy(preds, labels):
return (preds == labels).mean()
def acc_score(preds, labels):
return {
"acc": simple_accuracy(preds, labels),
}