- prepared for BiasLayer

few_shot_tests.py

@@ -10,7 +10,7 @@
 import ast
 from mini_imagenet_dataloader import MiniImageNetDataLoader
 from tensorflow.keras.models import Model
-from tensorflow.keras.layers import Activation, Dense, Input, Flatten, Conv2D, Lambda, TimeDistributed, MaxPooling2D
+from tensorflow.keras.layers import Activation, Dense, Input, Flatten, Conv2D, Lambda, TimeDistributed, MaxPooling2D, Layer
 from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
 import tensorflow.keras.backend as K
 import tensorflow.keras
@@ -23,6 +23,7 @@
 parser.add_argument('--dataset', dest='dataset',  type=str, default='train')
 parser.add_argument('--lr', dest='lr',  type=float, default=1e-3)
 parser.add_argument('--epochs', dest='epochs',  type=int, default=10)
+parser.add_argument('--hidden_size', dest='hidden_size',  type=int, default=64)
 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')
@@ -59,7 +60,8 @@ def idx_to_big(self, phase, idx):
 
 
 cathegories = 5
-dataloader = OurMiniImageNetDataLoader(shot_num=5 * 2, way_num=cathegories, episode_test_sample_num=args.episode_test_sample_num, shuffle_images = args.shuffle_images) #twice shot_num is because one might be uses as the base for the samples
+shots = 5
+dataloader = OurMiniImageNetDataLoader(shot_num=shots * 2, way_num=cathegories, episode_test_sample_num=args.episode_test_sample_num, shuffle_images = args.shuffle_images) #twice shot_num is because one might be uses as the base for the samples
 
 dataloader.generate_data_list(phase=args.dataset)
 
@@ -164,17 +166,43 @@ def generate_add_samples(self, phase = 'train'):
         # Memory growth must be set before GPUs have been initialized
         print(e)
 
+
+class BiasLayer(Layer):
+
+    def __init__(self, proto_num, mult_bias = 1, **kwargs):
+        self.proto_num = proto_num
+        self.mult_bias = mult_bias
+        super(BiasLayer, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        # Create a trainable weight variable for this layer.
+        self.bias = self.add_weight(name='bias',
+                                      shape=(self.proto_num,input_shape[2], input_shape[3], input_shape[4]),
+                                      initializer='zeros',
+                                      trainable=True)
+        super(BiasLayer, self).build(input_shape)  # Be sure to call this at the end
+
+    def call(self, x):
+        #return tf.expand_dims(self.bias, axis = 0)# let
+        return self.bias * self.mult_bias + (1-self.mult_bias) * x
+
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
+    def get_config(self):
+        return {'proto_num': self.proto_num, 'mult_bias' : self.mult_bias}
+
 inputs = Input(shape=(None,84,84,3))
 print('the shape', inputs.shape)
-conv1 = TimeDistributed(Conv2D(64, 3, padding='same', activation = 'relu'))(inputs)
+conv1 = TimeDistributed(Conv2D(args.hidden_size, 3, padding='same', activation = 'relu'))(inputs)
 pool1 = TimeDistributed(MaxPooling2D(pool_size = 2))(conv1)
-conv2 = TimeDistributed(Conv2D(64, 3, padding='same', activation = 'relu'))(pool1)
+conv2 = TimeDistributed(Conv2D(args.hidden_size, 3, padding='same', activation = 'relu'))(pool1)
 pool2 = TimeDistributed(MaxPooling2D(pool_size = 2))(conv2)
-conv3 = TimeDistributed(Conv2D(64, 3, padding='same', activation = 'relu'))(pool2)
+conv3 = TimeDistributed(Conv2D(args.hidden_size, 3, padding='same', activation = 'relu'))(pool2)
 pool3 = TimeDistributed(MaxPooling2D(pool_size = 2))(conv3)
-conv4 = TimeDistributed(Conv2D(64, 3, padding='same', activation = 'relu'))(pool3)
+conv4 = TimeDistributed(Conv2D(args.hidden_size, 3, padding='same', activation = 'relu'))(pool3)
 pool4 = TimeDistributed(MaxPooling2D(pool_size = 2))(conv4)
-conv5 = TimeDistributed(Conv2D(64, 3, padding='same', activation = 'relu'))(pool4)
+conv5 = TimeDistributed(Conv2D(args.hidden_size, 3, padding='same', activation = 'relu'))(pool4)
 pool5 = TimeDistributed(MaxPooling2D(pool_size = 2))(conv5)
 
 flat = TimeDistributed(Flatten())(pool5)
@@ -192,8 +220,9 @@ def generate_add_samples(self, phase = 'train'):
 input1 = Input(shape=(None,84,84,3))
 input2 = Input(shape=(None,84,84,3)) #, tensor = K.variable(episode_train_img[0:0]))
 
+input2b = BiasLayer(shots * cathegories, mult_bias = 0)(input2)
 encoded_l = model_img(input1)
-encoded_r = model_img(input2)
+encoded_r = model_img(input2b)
 
 # Add a customized layer to compute the absolute difference between the encodings
 L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))
@@ -233,7 +262,7 @@ def call(x):
 
 if args.pretrained_name is not None:
     from tensorflow.keras.models import load_model
-    lambda_model = load_model(args.pretrained_name, custom_objects = { "keras": tensorflow.keras , "args":args})
+    lambda_model = load_model(args.pretrained_name, custom_objects = { "keras": tensorflow.keras , "args":args, "BiasLayer": BiasLayer})
     print("loaded model",lambda_model)
 
 # models in models forget the layer name, therefore one must use the automatically given layer name and iterate throught the models by hand
@@ -281,10 +310,10 @@ def all_layers(model):
 #lambda_model.get_layer("dense_1").trainable = False
 
 # testing with additional batch axis ?!
-i=1
-test_lambda = lambda_model([K.expand_dims(K.variable(base_train_img[0:0+1]),axis=0),K.expand_dims(K.variable(base_train_img), axis=0), K.expand_dims(K.variable(base_train_label), axis=0)])
+#i=1
+#test_lambda = lambda_model([K.expand_dims(K.variable(base_train_img[0:0+1]),axis=0),K.expand_dims(K.variable(base_train_img), axis=0), K.expand_dims(K.variable(base_train_label), axis=0)])
 #        
-print('test lambda', K.eval(test_lambda))
+#print('test lambda', K.eval(test_lambda))