[Mod] add target dataset cifar10

@@ -17,30 +17,39 @@ import os
 
 
 
 
 class SEResNeXt(Model):
 class SEResNeXt(Model):
-    def __init__(self, weight, input_shape=None, depth=[3, 8, 36, 3], cardinality=32, width=4, reduction_ratio=4, weight_decay=5e-4, classes=1000, channel_axis=None):
+    def __init__(self, weight, input_shape=None):
         '''
         '''
             ResNext Model
             ResNext Model
 
 
             ## Args
             ## Args
+            + weight: 
             + input_shape: optional shape tuple
             + input_shape: optional shape tuple
-            + depth: number or layers in the each block, defined as a list
-            + cardinality: the size of the set of transformations
-            + width: multiplier to the ResNeXt width (number of filters)
-            + redution_ratio: ratio of reducition in SE Block 
-            + weight_decay: weight decay (l2 norm)
-            + classes: number of classes to classify images into
-            + channel_axis: channel axis in keras.backend.image_data_format()
         '''
         '''
+
+        if weights not in {'cifar10', 'imagenet'}:
+            raise ValueError
+
         self.__weight = weight
         self.__weight = weight
-        self.__depth = depth
-        self.__cardinality = cardinality
-        self.__width = width
-        self.__reduction_ratio = reduction_ratio
-        self.__weight_decay = weight_decay
-        self.__classes = classes
+
+        if weight == 'cifar10':
+            self.__depth = 29
+            self.__cardinality = 8
+            self.__width = 64
+            self.__classes = 10
+        else:
+            self.__depth = [3, 8, 36, 3]
+            self.__cardinality = 32
+            self.__width = 4
+            self.__classes = 1000
+
+        self.__reduction_ratio = 4
+        self.__weight_decay = 5e-4
         self.__channel_axis = 1 if K.image_data_format() == "channels_first" else -1
         self.__channel_axis = 1 if K.image_data_format() == "channels_first" else -1
 
 
-        self.__input_shape = _obtain_input_shape(input_shape, default_size = 224, min_size = 112, data_format=K.image_data_format(), require_flatten=True)
+        if weight == 'cifar10':
+            self.__input_shape = _obtain_input_shape(input_shape, default_size=32, min_size=8, data_format=K.image_data_format(), require_flatten=True)
+        else:
+            self.__input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=112, data_format=K.image_data_format(), require_flatten=True)
         self.__img_input = Input(shape=self.__input_shape)
         self.__img_input = Input(shape=self.__input_shape)
 
 
         # Create model.
         # Create model.
@@ -50,14 +59,16 @@ class SEResNeXt(Model):
         '''
         '''
             Adds an initial conv block, with batch norm and relu for the inception resnext
             Adds an initial conv block, with batch norm and relu for the inception resnext
         '''
         '''
-        channel_axis = -1
-
+        if weight == 'cifar10':
+            x = Conv2D(64, (3, 3), padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(self.__weight_decay))(self.__img_input)
+            x = BatchNormalization(axis=self.__channel_axis)(x)
+            x = Activation('relu')(x)
+            return x
+        else:
             x = Conv2D(64, (7, 7), padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(self.__weight_decay), strides=(2, 2))(self.__img_input)
             x = Conv2D(64, (7, 7), padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(self.__weight_decay), strides=(2, 2))(self.__img_input)
-        x = BatchNormalization(axis=channel_axis)(x)
+            x = BatchNormalization(axis=self.__channel_axis)(x)
             x = Activation('relu')(x)
             x = Activation('relu')(x)
-
             x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
             x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
-
             return x
             return x
 
 
     def __grouped_convolution_block(self, input, grouped_channels, strides):
     def __grouped_convolution_block(self, input, grouped_channels, strides):
@@ -126,8 +137,11 @@ class SEResNeXt(Model):
         ''' 
         ''' 
             Creates a ResNeXt model with specified parameters
             Creates a ResNeXt model with specified parameters
         '''
         '''
-
+        if type(self.__depth) is list or type(self.__depth) is tuple:
             N = list(self.__depth)
             N = list(self.__depth)
+        else:
+            N = [(self.__depth - 2) // 9 for _ in range(3)]
+        print(N)
         
         
         filters = self.__cardinality * self.__width
         filters = self.__cardinality * self.__width
         filters_list = []
         filters_list = []

@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 import os
 import os
 
 
 MODEL_SAVE_FOLDER_PATH = os.path.join(os.getcwd(), 'trained')
 MODEL_SAVE_FOLDER_PATH = os.path.join(os.getcwd(), 'trained')
-MODEL_SAVE_PATH = MODEL_SAVE_FOLDER_PATH + '/seresnext_imagenet.h5'
+MODEL_SAVE_PATH = MODEL_SAVE_FOLDER_PATH + '/seresnext_cifar10.h5'
 TEST_IMAGE_FOLDER_PATH = os.path.join(os.getcwd(), 'test')
 TEST_IMAGE_FOLDER_PATH = os.path.join(os.getcwd(), 'test')
 TEST_IMAGE_PATH = TEST_IMAGE_FOLDER_PATH + '/test01.png'
 TEST_IMAGE_PATH = TEST_IMAGE_FOLDER_PATH + '/test01.png'
 
 

@@ -13,14 +13,16 @@ import os
 MODEL_SAVE_FOLDER_PATH = os.path.join(os.getcwd(), 'trained')
 MODEL_SAVE_FOLDER_PATH = os.path.join(os.getcwd(), 'trained')
 if not os.path.exists(MODEL_SAVE_FOLDER_PATH):
 if not os.path.exists(MODEL_SAVE_FOLDER_PATH):
   os.mkdir(MODEL_SAVE_FOLDER_PATH)
   os.mkdir(MODEL_SAVE_FOLDER_PATH)
-MODEL_SAVE_PATH = MODEL_SAVE_FOLDER_PATH + '/seresnext_imagenet.h5'
+MODEL_SAVE_PATH = MODEL_SAVE_FOLDER_PATH + '/seresnext_cifar.h5'
 
 
-model = SEResNeXt((112, 112, 3))
+model = SEResNeXt('cifar-10', (32, 32, 3))
 
 
 model.compile(optimizer=optimizers.Adam(0.001), loss='categorical_crossentropy', metrics=['accuracy'])
 model.compile(optimizer=optimizers.Adam(0.001), loss='categorical_crossentropy', metrics=['accuracy'])
 
 
-ds_train = tfds.load('imagenet2012_corrupted', split='train', shuffle_files=True)
+# ds_train = tfds.load('imagenet2012_corrupted', split='train', shuffle_files=True)
+ds_train = tfds.load('cifar-10', split='train', shuffle_files=True)
 ds_train = ds_train.shuffle(1000).batch(128).prefetch(10)
 ds_train = ds_train.shuffle(1000).batch(128).prefetch(10)
+
 model.fit(ds_train['image'], ds_train['label'], epochs=10, steps_per_epoch=30)
 model.fit(ds_train['image'], ds_train['label'], epochs=10, steps_per_epoch=30)
 
 
 model.save(MODEL_SAVE_PATH)
 model.save(MODEL_SAVE_PATH)