Moving to py3

README.md

@@ -12,7 +12,7 @@ This is based on my [ImageCaptioning.pytorch](https://github.com/ruotianluo/Imag
 - Add transformer (merged from [Transformer_captioning](https://github.com/ruotianluo/Transformer_Captioning))
 
 ## Requirements
-Python 2.7 (because there is no [coco-caption](https://github.com/tylin/coco-caption) version for python 3)
+Python 2 or 3 (My [coco-caption](https://github.com/ruotianluo/coco-caption) supports python 3)
 
 PyTorch 1.3 (along with torchvision)
 

eval.py

@@ -36,7 +36,7 @@
 opt = parser.parse_args()
 
 # Load infos
-with open(opt.infos_path) as f:
+with open(opt.infos_path, 'rb') as f:
     infos = utils.pickle_load(f)
 
 # override and collect parameters

models/CaptionModel.py

@@ -184,7 +184,7 @@ def beam_step(logprobsf, unaug_logprobsf, beam_size, t, beam_seq, beam_seq_logpr
 
         # all beams are sorted by their log-probabilities
         done_beams_table = [sorted(done_beams_table[i], key=lambda x: -x['p'])[:bdash] for i in range(group_size)]
-        done_beams = reduce(lambda a,b:a+b, done_beams_table)
+        done_beams = sum(done_beams_table, [])
         return done_beams
 
     def sample_next_word(self, logprobs, sample_method, temperature):

scripts/dump_to_lmdb.py

@@ -1,5 +1,9 @@
 # copy from https://github.com/Lyken17/Efficient-PyTorch/tools
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import os
 import os.path as osp
 import os, sys
@@ -51,7 +55,7 @@ def __getitem__(self, index):
             else:
                 feat = np.load(buf)
         except Exception as e:
-            print self.keys[index], e
+            print(self.keys[index], e)
             return None
 
         return feat
@@ -87,9 +91,9 @@ def raw_npz_reader(path):
     try:
         npz_data = np.load(six.BytesIO(bin_data))['feat']
     except Exception as e:
-        print path
+        print(path)
         npz_data = None
-        print e
+        print(e)
     return bin_data, npz_data
 
 
@@ -99,9 +103,9 @@ def raw_npy_reader(path):
     try:
         npy_data = np.load(six.BytesIO(bin_data))
     except Exception as e:
-        print path
+        print(path)
         npy_data = None
-        print e
+        print(e)
     return bin_data, npy_data
 
 

scripts/prepro_ngrams.py

@@ -41,8 +41,8 @@ def precook(s, n=4, out=False):
   """
   words = s.split()
   counts = defaultdict(int)
-  for k in xrange(1,n+1):
-    for i in xrange(len(words)-k+1):
+  for k in range(1,n+1):
+    for i in range(len(words)-k+1):
       ngram = tuple(words[i:i+k])
       counts[ngram] += 1
   return counts
@@ -74,7 +74,7 @@ def compute_doc_freq(crefs):
   document_frequency = defaultdict(float)
   for refs in crefs:
     # refs, k ref captions of one image
-    for ngram in set([ngram for ref in refs for (ngram,count) in ref.iteritems()]):
+    for ngram in set([ngram for ref in refs for (ngram,count) in ref.items()]):
       document_frequency[ngram] += 1
       # maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
   return document_frequency
@@ -132,8 +132,8 @@ def main(params):
 
   ngram_words, ngram_idxs, ref_len = build_dict(imgs, wtoi, params)
 
-  utils.pickle_dump({'document_frequency': ngram_words, 'ref_len': ref_len}, open(params['output_pkl']+'-words.p','w'))
-  utils.pickle_dump({'document_frequency': ngram_idxs, 'ref_len': ref_len}, open(params['output_pkl']+'-idxs.p','w'))
+  utils.pickle_dump({'document_frequency': ngram_words, 'ref_len': ref_len}, open(params['output_pkl']+'-words.p','wb'))
+  utils.pickle_dump({'document_frequency': ngram_idxs, 'ref_len': ref_len}, open(params['output_pkl']+'-idxs.p','wb'))
 
 if __name__ == "__main__":
 

scripts/prepro_reference_json.py

@@ -49,7 +49,7 @@ def main(params):
     # imgs = tmp
 
     # create output json file
-    out = {u'info': {u'description': u'This is stable 1.0 version of the 2014 MS COCO dataset.', u'url': u'http://mscoco.org', u'version': u'1.0', u'year': 2014, u'contributor': u'Microsoft COCO group', u'date_created': u'2015-01-27 09:11:52.357475'}, u'licenses': [{u'url': u'http://creativecommons.org/licenses/by-nc-sa/2.0/', u'id': 1, u'name': u'Attribution-NonCommercial-ShareAlike License'}, {u'url': u'http://creativecommons.org/licenses/by-nc/2.0/', u'id': 2, u'name': u'Attribution-NonCommercial License'}, {u'url': u'http://creativecommons.org/licenses/by-nc-nd/2.0/', u'id': 3, u'name': u'Attribution-NonCommercial-NoDerivs License'}, {u'url': u'http://creativecommons.org/licenses/by/2.0/', u'id': 4, u'name': u'Attribution License'}, {u'url': u'http://creativecommons.org/licenses/by-sa/2.0/', u'id': 5, u'name': u'Attribution-ShareAlike License'}, {u'url': u'http://creativecommons.org/licenses/by-nd/2.0/', u'id': 6, u'name': u'Attribution-NoDerivs License'}, {u'url': u'http://flickr.com/commons/usage/', u'id': 7, u'name': u'No known copyright restrictions'}, {u'url': u'http://www.usa.gov/copyright.shtml', u'id': 8, u'name': u'United States Government Work'}], u'type': u'captions'}
+    out = {'info': {'description': 'This is stable 1.0 version of the 2014 MS COCO dataset.', 'url': 'http://mscoco.org', 'version': '1.0', 'year': 2014, 'contributor': 'Microsoft COCO group', 'date_created': '2015-01-27 09:11:52.357475'}, 'licenses': [{'url': 'http://creativecommons.org/licenses/by-nc-sa/2.0/', 'id': 1, 'name': 'Attribution-NonCommercial-ShareAlike License'}, {'url': 'http://creativecommons.org/licenses/by-nc/2.0/', 'id': 2, 'name': 'Attribution-NonCommercial License'}, {'url': 'http://creativecommons.org/licenses/by-nc-nd/2.0/', 'id': 3, 'name': 'Attribution-NonCommercial-NoDerivs License'}, {'url': 'http://creativecommons.org/licenses/by/2.0/', 'id': 4, 'name': 'Attribution License'}, {'url': 'http://creativecommons.org/licenses/by-sa/2.0/', 'id': 5, 'name': 'Attribution-ShareAlike License'}, {'url': 'http://creativecommons.org/licenses/by-nd/2.0/', 'id': 6, 'name': 'Attribution-NoDerivs License'}, {'url': 'http://flickr.com/commons/usage/', 'id': 7, 'name': 'No known copyright restrictions'}, {'url': 'http://www.usa.gov/copyright.shtml', 'id': 8, 'name': 'United States Government Work'}], 'type': 'captions'}
     out.update({'images': [], 'annotations': []})
 
     cnt = 0
@@ -58,7 +58,7 @@ def main(params):
         if img['split'] == 'train':
             continue
         out['images'].append(
-            {u'id': img.get('cocoid', img['imgid'])})
+            {'id': img.get('cocoid', img['imgid'])})
         for j, s in enumerate(img['sentences']):
             if len(s) == 0:
                 continue