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