rl4lms.envs.text_generation.caption_metrics package

Subpackages

• rl4lms.envs.text_generation.caption_metrics.spice package
  • Submodules
  • rl4lms.envs.text_generation.caption_metrics.spice.spice module
    • Spice
      • Spice.__init__()
      • Spice.float_convert()
      • Spice.tokenize()
      • Spice.compute_score()
      • Spice.method()
  • Module contents

Submodules

rl4lms.envs.text_generation.caption_metrics.cider module

rl4lms.envs.text_generation.caption_metrics.cider.precook(s, n=4, out=False)

Takes a string as input and returns an object that can be given to either cook_refs or cook_test. This is optional: cook_refs and cook_test can take string arguments as well. :param s: string : sentence to be converted into ngrams :param n: int : number of ngrams for which representation is calculated :return: term frequency vector for occuring ngrams

rl4lms.envs.text_generation.caption_metrics.cider.cook_refs(refs, n=4)

Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them. :param refs: list of string : reference sentences for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (list of dict)

rl4lms.envs.text_generation.caption_metrics.cider.cook_test(test, n=4)

Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it. :param test: list of string : hypothesis sentence for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (dict)

class rl4lms.envs.text_generation.caption_metrics.cider.CiderScorer(test=None, refs=None, n=4, sigma=6.0)

Bases: object

CIDEr scorer.

copy()

copy the refs.

__init__(test=None, refs=None, n=4, sigma=6.0)

singular instance

cook_append(test, refs)

called by constructor and __iadd__ to avoid creating new instances.

size()

compute_doc_freq()

Compute term frequency for reference data. This will be used to compute idf (inverse document frequency later) The term frequency is stored in the object :return: None

compute_cider()

compute_score(option=None, verbose=0)

class rl4lms.envs.text_generation.caption_metrics.cider.Cider(test=None, refs=None, n=4, sigma=6.0)

Bases: object

Main Class to compute the CIDEr metric

__init__(test=None, refs=None, n=4, sigma=6.0)

tokenize(dict)

compute_score(gts, res)

method()

Module contents