GAN integration

.gitignore

@@ -99,3 +99,9 @@ ENV/
 
 # mypy
 .mypy_cache/
+
+# my_files
+/models/*
+/models_old/*
+/saved_old_runs/*
+/saved_runs/*

Writing-editing network/configurations.py

@@ -11,7 +11,7 @@ class CommonConfig(object):
     num_exams = 3
     log_interval = 1000
     predict_right_after = 3
-    patience = 5
+    advantage_clipping = 5
 
 class SmallDatasetWithTopics(CommonConfig):
     relative_data_path = '/data/small-json-topics/train.dat'
@@ -153,6 +153,14 @@ class LargeConfig6(LargeDataset):
     use_topics = True
     experiment_name = "lg-with-topics-lr-0.0001-WE-300"
 
+class RandomConfig(SmallDataset):
+    emsize = 512
+    context_dim = 128
+    lr = 0.0001
+    pretrained = None
+    use_topics = False
+    experiment_name = "random"
+
 configuration = {
                  "st1": SmallTopicsConfig1(),
                  "st2": SmallTopicsConfig2(),
@@ -168,7 +176,8 @@ class LargeConfig6(LargeDataset):
                  "l3": LargeConfig3(),
                  "l4": LargeConfig4(),
                  "l5": LargeConfig5(),
-                 "l6": LargeConfig6()}
+                 "l6": LargeConfig6(),
+                 "random": RandomConfig()}
 
 def get_conf(name):
-    return configuration[name]
+    return configuration[name]

Writing-editing network/eval.py

@@ -0,0 +1,87 @@
+import pickle
+import os
+import collections
+import sys
+
+sys.path.append('pycocoevalcap')
+from pycocoevalcap.bleu.bleu import Bleu
+from pycocoevalcap.rouge.rouge import Rouge
+from pycocoevalcap.meteor.meteor import Meteor
+#from pycocoevalcap.cider.cider import Cider
+
+class Evaluate(object):
+    def __init__(self):
+        self.scorers = [
+            (Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
+            (Meteor(), "METEOR"),
+            (Rouge(), "ROUGE_L")
+        ]#,        (Cider(), "CIDEr")
+
+    def convert(self, data):
+        if isinstance(data, basestring):
+            return data.encode('utf-8')
+        elif isinstance(data, collections.Mapping):
+            return dict(map(convert, data.items()))
+        elif isinstance(data, collections.Iterable):
+            return type(data)(map(convert, data))
+        else:
+            return data
+
+    def score(self, ref, hypo):
+        final_scores = {}
+        for scorer, method in self.scorers:
+            score, scores = scorer.compute_score(ref, hypo)
+            if type(score) == list:
+                for m, s in zip(method, score):
+                    final_scores[m] = s
+            else:
+                final_scores[method] = score
+
+        return final_scores
+
+    def evaluate(self, get_scores=True, live=False, **kwargs):
+        if live:
+            temp_ref = kwargs.pop('ref', {})
+            cand = kwargs.pop('cand', {})
+        else:
+            reference_path = kwargs.pop('ref', '')
+            candidate_path = kwargs.pop('cand', '')
+
+            # load caption data
+            with open(reference_path, 'rb') as f:
+                temp_ref = pickle.load(f)
+            with open(candidate_path, 'rb') as f:
+                cand = pickle.load(f)
+
+        # make dictionary
+        hypo = {}
+        ref = {}
+        i = 0
+        for vid, caption in cand.items():
+            hypo[i] = [caption]
+            ref[i] = temp_ref[vid]
+            i += 1
+
+        # compute scores
+        final_scores = self.score(ref, hypo)
+        #"""
+        # print out scores
+        print ('Bleu_1:\t', final_scores['Bleu_1'])
+        print ('Bleu_2:\t', final_scores['Bleu_2'])
+        print ('Bleu_3:\t', final_scores['Bleu_3'])
+        print ('Bleu_4:\t', final_scores['Bleu_4'])
+        print ('METEOR:\t', final_scores['METEOR'])
+        print ('ROUGE_L:', final_scores['ROUGE_L'])
+        #print ('CIDEr:\t', final_scores['CIDEr'])
+        #"""
+
+        if get_scores:
+            return final_scores
+
+
+if __name__ == '__main__':
+    cand = {'generated_description1': 'how are you', 'generated_description2': 'Hello how are you'}
+    ref = {'generated_description1': ['what are you', 'where are you'],
+           'generated_description2': ['Hello how are you', 'Hello how is your day']}
+    x = Evaluate()
+    x.evaluate(live=True, cand=cand, ref=ref)