- independend base support

mini_imagenet_dataloader.py

@@ -30,6 +30,7 @@
 parser.add_argument('--final_name', dest='final_name',  type=str, default='final_model')
 parser.add_argument('--shuffle_images', dest='shuffle_images', action='store_true')
 parser.add_argument('--enable_idx_increase', dest='enable_idx_increase', action='store_true')
+parser.add_argument('--use_independent_base', dest='use_independent_base', action='store_true')
 args = parser.parse_args()
 
 class MiniImageNetDataLoader(object):
@@ -246,7 +247,7 @@ def get_batch(self, phase='train', idx=0):
         return this_inputa, this_labela, this_inputb, this_labelb
 
 cathegories = 5
-dataloader = MiniImageNetDataLoader(shot_num=5, way_num=cathegories, episode_test_sample_num=15)
+dataloader = MiniImageNetDataLoader(shot_num=5 * 2, way_num=cathegories, episode_test_sample_num=15) #twice shot_num is because one might be uses as the base for the samples
 
 dataloader.generate_data_list(phase='train')
 dataloader.generate_data_list(phase='val')
@@ -263,7 +264,7 @@ def get_batch(self, phase='train', idx=0):
 base_train_img, base_train_label, base_test_img, base_test_label = \
         dataloader.get_batch(phase='train', idx=0) 
 
-train_epoch_size = base_train_img.shape[0]
+train_epoch_size = int(base_train_img.shape[0] / 2) # as double is generated for the base and train 
 test_epoch_size = base_test_img.shape[0]
 
 print("epoch training size:", train_epoch_size, base_train_label.shape[0], "epoch testing size", test_epoch_size)
@@ -280,11 +281,11 @@ def generate(self, phase='train'):
 #                dataloader.get_batch(phase='train', idx=idx)
             if phase == 'train':
                 #print(episode_train_img.shape[0])
-                for i in range(base_train_img.shape[0]):
+                for i in range(train_epoch_size):
                     yield base_train_img[i:i+1], base_train_label[i:i+1]
             else:
                 #print(episode_test_img.shape[0])
-                for i in range(base_test_img.shape[0]):
+                for i in range(test_epoch_size):
                     yield base_test_img[i:i+1], base_test_label[i:i+1]
 
     def generate_add_samples(self, phase = 'train'):
@@ -296,8 +297,16 @@ def generate_add_samples(self, phase = 'train'):
             # this depends on what we are trying to train.
             # care must be taken, that with a different dataset the labels have a different meaning. Thus if we use a new dataset, we must 
             # use network_base which fits to the database. Therefore there must be taken images with label from the same dataset.
-            network_base_img = episode_train_img
-            network_base_label = episode_train_label
+            network_base_img = episode_train_img[:train_epoch_size]
+            network_base_img = episode_train_label[:train_epoch_size]
+
+            #only half is used now, as the rest is reserved for independend base    
+            episode_train_img = episode_train_img[train_epoch_size:]
+            episode_train_label = episode_train_label[train_epoch_size:]
+
+            if not args.use_independent_base:
+                network_base_img = episode_train_img
+                network_base_label = episode_train_label
 
             if phase == 'train':
                 if args.enable_idx_increase:
@@ -310,14 +319,14 @@ def generate_add_samples(self, phase = 'train'):
 
                 #print(episode_train_img.shape[0])
                 #assert(episode_train_img.shape[0] == 25)
-                for i in range(episode_train_img.shape[0]):
+                for i in range(train_epoch_size):
                     yield [[episode_train_img[i:i+1]], [network_base_img], [network_base_label]], episode_train_label[i:i+1]
             else:
                 #print(episode_test_img.shape[0])
                 #assert(0)
                 #assert(episode_test_img.shape[0] == 75)
                 #assert(self.idx < 50)
-                for i in range(episode_test_img.shape[0]):
+                for i in range(test_epoch_size):
                     #print('i',i)
                     yield [[episode_test_img[i:i+1]], [network_base_img], [network_base_label]], episode_test_label[i:i+1]
 
@@ -371,7 +380,7 @@ def generate_add_samples(self, phase = 'train'):
 print('the shape', inputs.shape)
 conv1 = TimeDistributed(Conv2D(50, 7, 1 , activation = 'relu'))(inputs)
 conv2 = TimeDistributed(MaxPooling2D(pool_size = (3,3)))(conv1)
-conv3 = TimeDistributed(Conv2D(200, 7, 1 , activation = 'relu'))(conv2)
+conv3 = TimeDistributed(Conv2D(100, 7, 1 , activation = 'relu'))(conv2)
 conv4 = TimeDistributed(MaxPooling2D(pool_size = (3,3)))(conv3)
 #conv3 = TimeDistributed(Conv2D(5, 5, (3,3) , padding='same', activation = 'relu'))(conv2)
 flat = TimeDistributed(Flatten())(conv4)