split val into val_subset and val_rest

answer_classifier_cached_features/eval.py

@@ -38,16 +38,23 @@ def create_initializer(graph, sess, model):
     return initializer()
 
 def create_batch_generator(mode):
-    if mode=='val':
+    if mode=='val_subset':
         vqa_resnet_feat_dir = constants.vqa_val_resnet_feat_dir
         vqa_anno = constants.vqa_val_anno
-        num_questions = constants.num_val_questions
+        num_questions = constants.num_val_subset_questions
+        offset = 0
+    elif mode=='val_rest':
+        vqa_resnet_feat_dir = constants.vqa_val_resnet_feat_dir
+        vqa_anno = constants.vqa_val_anno
+        num_questions = constants.num_val_rest_questions
+        offset = constants.num_val_subset_questions
     elif mode=='train':
         vqa_resnet_feat_dir = constants.vqa_train_resnet_feat_dir
         vqa_anno = constants.vqa_train_anno
         num_questions = constants.num_train_questions
+        offset = 0
     else:
-        print "mode needs to be one of {'train','test','val'}, found " + mode
+        print "mode needs to be one of {'train','val_subset','val_rest'}, found " + mode
     
     data_mgr = vqa_data.data(
         vqa_resnet_feat_dir,
@@ -63,7 +70,7 @@ def create_batch_generator(mode):
         constants.answer_batch_size, 
         num_questions, 
         1, 
-        0)
+        offset)
     
     batch_generator = tftools.data.async_batch_generator(
         data_mgr, 
@@ -266,7 +273,10 @@ if __name__=='__main__':
         training=False)
 
     print 'Starting a session...'
-    sess = tf.Session(graph=graph.tf_graph)
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.gpu_options.per_process_gpu_memory_fraction = 0.5
+    sess = tf.Session(config=config, graph=graph.tf_graph)
 
     print 'Creating initializer...'
     initializer = create_initializer(

constants_vision_gpu_2.py

@@ -153,7 +153,9 @@ vqa_answer_vocab_json = os.path.join(
 
 # VQA dataset params
 num_train_questions = 248349
-num_val_questions = 10000 #121512
+num_val_subset_questions = 10000
+num_val_questions = 121512
+num_val_rest_questions = num_val_questions - num_val_subset_questions
 num_test_questions = 0
 
 # Answer classifier training params