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Image Caption evaluation

Description

A python3 caption eval lib, for MS COCO image caption challenge and custom task caption evaluation.

Requirements:

Python3

  • Linux with java-1.8

For coco caption eval:

fork from ruotianluo/coco-caption

  • You will first need to download the Stanford CoreNLP 3.6.0 code and models for use by SPICE. To do this, run: bash get_stanford_models.sh
  • Note: SPICE will try to create a cache of parsed sentences in ./pycocoevalcap/spice/cache/. This dramatically speeds up repeated evaluations. The cache directory can be moved by setting 'CACHE_DIR' in ./pycocoevalcap/spice. In the same file, caching can be turned off by removing the '-cache' argument to 'spice_cmd'.
  • You will also need to download the Google News negative 300 word2vec model for use by WMD. To do this, run: bash get_google_word2vec_model.sh

By running get_stanford_models.sh & get_google_word2vec_model.sh,stanford-corenlp-3.6.0.jar stanford-corenlp-3.6.0-models.jar&GoogleNews-vectors-negative300.bin will appear in pycocoevalcap/spice/lib & pycocoevalcap/wmd/data.

For custom caption eval:

kracwarlock/demo_cocoeval.py

Custom caption eval

Script to evaluate Bleu, METEOR, CIDEr and ROUGE_L for any dataset using the coco evaluation api. Just requires the pycocoevalcap folder.

Each image_id can only have one hypothesis caption, but can have multiple reference captions(ground truth)

Repeated reference captions will not affect the BLEU & METEOR score, but can lower CIDer score.

    from custom_caption_eval.pyimport calculate_metrics
    rng = range(2)
    # label caption
    datasetGTS = {
        'annotations': [{u'image_id': 0, u'caption': u'the man is playing a guitar'},
                        {u'image_id': 0, u'caption': u'a man is playing a guitar'},
                        {u'image_id': 1, u'caption': u'a woman is slicing cucumbers'},
                        {u'image_id': 1, u'caption': u'the woman is slicing cucumbers'},
                        {u'image_id': 1, u'caption': u'a woman is cutting cucumbers'}]
        }
    # your generated caption
    datasetRES = {
        'annotations': [{u'image_id': 0, u'caption': u'man is playing guitar'},
                        {u'image_id': 1, u'caption': u'a woman is cutting vegetables'}]
        }
    # calculate score
    calculate_metrics(rng, datasetGTS, datasetRES)

and in custom_caption_eval.py select what are the criteria of your task.

        # =================================================
        # Set up scorers
        # =================================================
        print('setting up scorers...')
        scorers = [
            (Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
            (Meteor(), "METEOR"),
            # (Rouge(), "ROUGE_L"),
            (Cider(), "CIDEr"),
            (Spice(), "SPICE"),
            # (WMD(),   "WMD"),
        ]

Fixed bugs in ruotianluo/coco-caption

First use command which java to get absolute java path, such as /usr/java/jdk1.8.0_191/bin/java. for error like:

No such file or directory: 'java': 'java'

replace 'jave' with absolute java path.

case 1

pycocoevalcap/tokenizer/ptbtokenizer.py:

Replace the path of java, and add shell=True in subprocess.Popen()

replace

....

cmd = ['java', '-cp', STANFORD_CORENLP_3_4_1_JAR, \
                 'edu.stanford.nlp.process.PTBTokenizer', \
                 '-preserveLines', '-lowerCase']
...
cmd.append(os.path.basename(tmp_file.name))
p_tokenizer = subprocess.Popen(cmd, cwd=path_to_jar_dirname, \
                stdout=subprocess.PIPE)
....

with

....
cmd = ['/***absolute java path***/java -cp stanford-corenlp-3.4.1.jar edu.stanford.nlp.process.PTBTokenizer -preserveLines -lowerCase ']
cmd[0] += os.path.join(path_to_jar_dirname, os.path.basename(tmp_file.name))
# add shell=True
p_tokenizer = subprocess.Popen(cmd, cwd=path_to_jar_dirname, \
stdout=subprocess.PIPE, shell=True)
....

ERROR like:

File "/..your project path.../pycocoevalcap/meteor/meteor.py", line 71, in __del__
    self.lock.acquire()
AttributeError: 'Meteor' object has no attribute 'lock'

case 2

pycocoevalcap/meteor/meteor.py:

Replace the path of java with absolute path.

replace

   ....
   self.meteor_cmd = ['java', '-jar', '-Xmx2G', METEOR_JAR, \
            '-', '-', '-stdio', '-l', 'en', '-norm']
   ....

with

   ....
   self.meteor_cmd = ['/***absolute java path***/java', '-jar', '-Xmx2G', METEOR_JAR, \
           '-', '-', '-stdio', '-l', 'en', '-norm']
   ....

case 3

In pycocoevalcap/spice/spice.py:

replace

   ....
    spice_cmd = ['java', '-jar', '-Xmx8G', SPICE_JAR, in_file.name,
      '-cache', self.cache_dir,
      '-out', out_file.name,
      '-subset',
      '-silent'
    ]
   ....

with

   ....
    spice_cmd = ['/***absolute java path***/java', '-jar', '-Xmx8G', SPICE_JAR, in_file.name,
      '-cache', self.cache_dir,
      '-out', out_file.name,
      '-subset',
      '-silent'
    ]
   ....