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import torch
def split_tensors(n, x):
if torch.is_tensor(x):
assert x.shape[0] % n == 0
x = x.reshape(x.shape[0] // n, n, *x.shape[1:]).unbind(1)
elif type(x) is list or type(x) is tuple:
x = [split_tensors(n, _) for _ in x]
elif x is None:
x = [None] * n
return x
# Input: seq, N*D numpy array, with element 0 .. vocab_size. 0 is END token.
#def decode_sequence(ix_to_word, seq):
# # N, D = seq.size()
# N, D = seq.shape
# out = []
# for i in range(N):
# txt = ''
# for j in range(D):
# ix = seq[i,j]
# if ix > 0 :
# if j >= 1:
# txt = txt + ' '
# txt = txt + ix_to_word[str(ix.item())]
# else:
# break
# if int(os.getenv('REMOVE_BAD_ENDINGS', '0')):
# flag = 0
# words = txt.split(' ')
# for j in range(len(words)):
# if words[-j-1] not in bad_endings:
# flag = -j
# break
# txt = ' '.join(words[0:len(words)+flag])
# out.append(txt.replace('@@ ', ''))
# return out
def decode_sequence(ix_to_word, seq, remove_bad_endings = True):
# N, D = seq.size()
N, D = seq.shape
bad_endings = ['a','an','the','in','for','at','of','with','before','after','on','upon','near','to','is','are','am']
bad_endings += ['the']
out = []
for i in range(N):
txt = ''
for j in range(D):
ix = seq[i,j]
if ix > 0 :
if j >= 1:
txt = txt + ' '
txt = txt + ix_to_word[str(ix.item())]
else:
break
if remove_bad_endings is True:
flag = 0
words = txt.split(' ')
for j in range(len(words)):
if words[-j-1] not in bad_endings:
flag = -j
break
txt = ' '.join(words[0:len(words)+flag])
out.append(txt.replace('@@ ', ''))
return out
def penalty_builder(penalty_config):
if penalty_config == '':
return lambda x,y: y
pen_type, alpha = penalty_config.split('_')
alpha = float(alpha)
if pen_type == 'wu':
return lambda x,y: length_wu(x,y,alpha)
if pen_type == 'avg':
return lambda x,y: length_average(x,y,alpha)
def length_wu(length, logprobs, alpha=0.):
"""
NMT length re-ranking score from
"Google's Neural Machine Translation System" :cite:`wu2016google`.
"""
modifier = (((5 + length) ** alpha) /
((5 + 1) ** alpha))
return (logprobs / modifier)
def length_average(length, logprobs, alpha=0.):
"""
Returns the average probability of tokens in a sequence.
"""
return logprobs / length