minor changes to args

config.py

@@ -63,8 +63,12 @@ def parse_training_args(parser):
     parser.add_argument('--model', type=str, default='ssas',
                         help='Indicates which model to use: '
                         '[co-occurrence, vrd, ssas] Check paper for details.')
+    parser.add_argument('--use-subject', type=int, default=1,
+                        help='1/0 indicating whether to use the subjects.')
     parser.add_argument('--use-predicate', type=int, default=1,
                         help='1/0 indicating whether to use the predicates.')
+    parser.add_argument('--use-object', type=int, default=1,
+                        help='1/0 indicating whether to use the objects.')
     parser.add_argument('--hidden-dim', type=int, default=1024,
                         help='Number of dimensions in the hidden unit.')
     parser.add_argument('--embedding-dim', type=int, default=512,

models.py

@@ -50,7 +50,6 @@ def __init__(self, args):
         self.iterations = args.iterations
         self.attention_conv_kernel = args.attention_conv_kernel
         self.refinement_conv_kernel = args.refinement_conv_kernel
-        self.upsampling_channels = args.upsampling_channels
         self.output_dim = args.output_dim
         self.embedding_dim = args.embedding_dim
         self.finetune_cnn = args.finetune_cnn
@@ -142,7 +141,7 @@ def build_ssas(self):
             object_att = new_object_att
             subject_att = new_subject_att
 
-        # Upsample the subject and objects regions.
+        # Collect the subject and objects regions.
         if self.use_internal_loss and self.iterations > 0:
             internal_weights = np.array([self.internal_loss_weight**i for i in range(len(subject_outputs))])
             internal_weights = internal_weights / internal_weights.sum()