Merge branch 'develop' into 'lukas_issue002_feat_data-prep-class'

# Conflicts:
#   requirements.txt
#   src/inception_model.py

README.md

 # MachineLearningTools
 
-This is a collection of all relevant functions used for ML stuff in the ESDE group
\ No newline at end of file
+This is a collection of all relevant functions used for ML stuff in the ESDE group
+
+## Inception Model
+
+See a description [here](https://towardsdatascience.com/a-simple-guide-to-the-versions-of-the-inception-network-7fc52b863202)
+or take a look on the papers [Going Deeper with Convolutions (Szegedy et al., 2014)](https://arxiv.org/abs/1409.4842)
+and [Network In Network (Lin et al., 2014)](https://arxiv.org/abs/1312.4400).
\ No newline at end of file

requirements.txt

@@ -6,4 +6,5 @@ pandas==0.25.1
 requests==2.22.0
 pytest==5.2.1
 pytest-lazy-fixture==0.6.1
-pytest-cov
\ No newline at end of file
+pytest-cov
+pydot

src/inception_model.py

 __author__ = 'Felix Kleinert, Lukas Leufen'
-__date__ = '2019-10-15'
+__date__ = '2019-10-22'
 
 import keras
-from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, \
-    Concatenate, Reshape, Activation
-from keras.models import Model
-from keras.regularizers import l2
-from keras.optimizers import SGD
+import keras.layers as layers
 
 
 class InceptionModelBase:
@@ -17,13 +13,8 @@ class InceptionModelBase:
     def __init__(self):
         self.number_of_blocks = 0
         self.part_of_block = 0
-        # conversion between chr and ord:
-        # >>> chr(97)
-        # 'a'
-        # >>> ord('a')
-        # 97
-        # set to 96 as always add +1 for new part of block
-        self.ord_base = 96
+        self.act_number = 0
+        self.ord_base = 96  # set to 96 as always add +1 for new part of block, chr(97)='a'
 
     def block_part_name(self):
         """
@@ -32,71 +23,116 @@ class InceptionModelBase:
         """
         return chr(self.ord_base + self.part_of_block)
 
+    def batch_normalisation(self, input_x, **kwargs):
+        block_name = f"Block_{self.number_of_blocks}{self.block_part_name()}_BN"
+        return layers.BatchNormalization(name=block_name, **kwargs)(input_x)
+
     def create_conv_tower(self,
-                          input_X,
+                          input_x,
                           reduction_filter,
                           tower_filter,
                           tower_kernel,
                           activation='relu',
-                          regularizer=l2(0.01)):
+                          batch_normalisation=False,
+                          **kwargs):
         """
-        This function creates a "convolution tower block" containing a 1x1 convolution to reduce filter size followed by convolution
-        with given filter and kernel size
-        :param input_X: Input to network part
+        This function creates a "convolution tower block" containing a 1x1 convolution to reduce filter size followed by
+        convolution with given filter and kernel size
+        :param input_x: Input to network part
         :param reduction_filter: Number of filters used in 1x1 convolution to reduce overall filter size before conv.
         :param tower_filter: Number of filters for n x m convolution
         :param tower_kernel: kernel size for convolution (n,m)
         :param activation: activation function for convolution
+        :param batch_normalisation:
         :return:
         """
         self.part_of_block += 1
+        self.act_number = 1
+        regularizer = kwargs.get('regularizer', keras.regularizers.l2(0.01))
+        bn_settings = kwargs.get('bn_settings', {})
+        act_settings = kwargs.get('act_settings', {})
+        print(f'Inception Block with activation: {activation}')
+
+        block_name = f'Block_{self.number_of_blocks}{self.block_part_name()}_{tower_kernel[0]}x{tower_kernel[1]}'
 
         if tower_kernel == (1, 1):
-            tower = Conv2D(tower_filter,
-                           tower_kernel,
-                           activation=activation,
-                           padding='same',
-                           kernel_regularizer=regularizer,
-                           name='Block_{}{}_{}x{}'.format(self.number_of_blocks,
-                                                          self.block_part_name(),
-                                                          tower_kernel[0],
-                                                          tower_kernel[1]))(input_X)
+            tower = layers.Conv2D(tower_filter,
+                                  tower_kernel,
+                                  padding='same',
+                                  kernel_regularizer=regularizer,
+                                  name=block_name)(input_x)
+            tower = self.act(tower, activation, **act_settings)
         else:
-            tower = Conv2D(reduction_filter,
-                           (1, 1),
-                           activation=activation,
-                           padding='same',
-                           kernel_regularizer=regularizer,
-                           name='Block_{}{}_1x1'.format(self.number_of_blocks, self.block_part_name()))(input_X)
-
-            tower = Conv2D(tower_filter,
-                           tower_kernel,
-                           activation=activation,
-                           padding='same',
-                           kernel_regularizer=regularizer,
-                           name='Block_{}{}_{}x{}'.format(self.number_of_blocks,
-                                                          self.block_part_name(),
-                                                          tower_kernel[0],
-                                                          tower_kernel[1]))(tower)
+            tower = layers.Conv2D(reduction_filter,
+                                  (1, 1),
+                                  padding='same',
+                                  kernel_regularizer=regularizer,
+                                  name=f'Block_{self.number_of_blocks}{self.block_part_name()}_1x1')(input_x)
+            tower = self.act(tower, activation, **act_settings)
+
+            tower = layers.Conv2D(tower_filter,
+                                  tower_kernel,
+                                  padding='same',
+                                  kernel_regularizer=regularizer,
+                                  name=block_name)(tower)
+            if batch_normalisation:
+                tower = self.batch_normalisation(tower, **bn_settings)
+            tower = self.act(tower, activation, **act_settings)
+
         return tower
 
+    def act(self, input_x, activation, **act_settings):
+        block_name = f"Block_{self.number_of_blocks}{self.block_part_name()}_act_{self.act_number}"
+        try:
+            out = getattr(layers, self._get_act_name(activation))(**act_settings, name=block_name)(input_x)
+        except AttributeError:
+            block_name += f"_{activation.lower()}"
+            out = layers.Activation(activation.lower(), name=block_name)(input_x)
+        self.act_number += 1
+        return out
+
     @staticmethod
-    def create_pool_tower(input_X, pool_kernel, tower_filter):
+    def _get_act_name(act_name):
+        if isinstance(act_name, str):
+            mapping = {'relu': 'ReLU', 'prelu': 'PReLU', 'elu': 'ELU'}
+            return mapping.get(act_name.lower(), act_name)
+        else:
+            return act_name.__name__
+
+    def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs):
         """
         This function creates a "MaxPooling tower block"
-        :param input_X: Input to network part
+        :param input_x: Input to network part
         :param pool_kernel: size of pooling kernel
         :param tower_filter: Number of filters used in 1x1 convolution to reduce filter size
+        :param activation:
+        :param max_pooling:
         :return:
         """
-        tower = MaxPooling2D(pool_kernel, strides=(1, 1), padding='same')(input_X)
-        tower = Conv2D(tower_filter, (1, 1), padding='same', activation='relu')(tower)
+        self.part_of_block += 1
+        self.act_number = 1
+        act_settings = kwargs.get('act_settings', {})
+
+        # pooling block
+        block_name = f"Block_{self.number_of_blocks}{self.block_part_name()}_"
+        if max_pooling:
+            block_type = "MaxPool"
+            pooling = layers.MaxPooling2D
+        else:
+            block_type = "AvgPool"
+            pooling = layers.AveragePooling2D
+        tower = pooling(pool_kernel, strides=(1, 1), padding='same', name=block_name+block_type)(input_x)
+
+        # convolution block
+        tower = Conv2D(tower_filter, (1, 1), padding='same', name=block_name+"1x1")(tower)
+        tower = self.act(tower, activation, **act_settings)
+
         return tower
 
-    def inception_block(self, input_X, tower_conv_parts, tower_pool_parts):
+    def inception_block(self, input_x, tower_conv_parts, tower_pool_parts, **kwargs):
         """
         Crate a inception block
-        :param input_X: Input to block
+        :param input_x: Input to block
         :param tower_conv_parts: dict containing settings for parts of inception block; Example:
                                  tower_conv_parts = {'tower_1': {'reduction_filter': 32,
                                                                  'tower_filter': 64,
@@ -116,41 +152,46 @@ class InceptionModelBase:
         self.part_of_block = 0
         tower_build = {}
         for part, part_settings in tower_conv_parts.items():
-            tower_build[part] = self.create_conv_tower(input_X,
-                                                       part_settings['reduction_filter'],
-                                                       part_settings['tower_filter'],
-                                                       part_settings['tower_kernel']
-                                                       )
-        tower_build['pool'] = self.create_pool_tower(input_X,
-                                                     tower_pool_parts['pool_kernel'],
-                                                     tower_pool_parts['tower_filter']
-                                                     )
+            tower_build[part] = self.create_conv_tower(input_x, **part_settings, **kwargs)
+        if 'max_pooling' in tower_pool_parts.keys():
+            max_pooling = tower_pool_parts.get('max_pooling')
+            if not isinstance(max_pooling, bool):
+                raise AttributeError(f"max_pooling has to be either a bool or empty. Given was: {max_pooling}")
+            pool_name = '{}pool'.format('max' if max_pooling else 'avg')
+            tower_build[pool_name] = self.create_pool_tower(input_x, **tower_pool_parts, **kwargs)
+        else:
+            tower_build['maxpool'] = self.create_pool_tower(input_x, **tower_pool_parts, **kwargs)
+            tower_build['avgpool'] = self.create_pool_tower(input_x, **tower_pool_parts, **kwargs, max_pooling=False)
+
         block = keras.layers.concatenate(list(tower_build.values()), axis=3)
         return block
 
-    @staticmethod
-    def flatten_tail(input_X, tail_block):
-        input_X = Flatten()(input_X)
-        tail = tail_block(input_X)
-        return tail
-
 
 if __name__ == '__main__':
     print(__name__)
     from keras.datasets import cifar10
     from keras.utils import np_utils
     from keras.layers import Input
+    from keras.layers.advanced_activations import LeakyReLU
+    from keras.optimizers import SGD
+    from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+    from keras.models import Model
+
+    # network settings
     conv_settings_dict = {'tower_1': {'reduction_filter': 64,
                                       'tower_filter': 64,
-                                      'tower_kernel': (3, 3)},
+                                      'tower_kernel': (3, 3),
+                                      'activation': LeakyReLU},
                           'tower_2': {'reduction_filter': 64,
                                       'tower_filter': 64,
-                                      'tower_kernel': (5, 5)},
+                                      'tower_kernel': (5, 5),
+                                      'activation': 'relu'}
                           }
     pool_settings_dict = {'pool_kernel': (3, 3),
-                          'tower_filter': 64}
-    myclass = True
+                          'tower_filter': 64,
+                          'activation': 'relu'}
 
+    # load data
     (X_train, y_train), (X_test, y_test) = cifar10.load_data()
     X_train = X_train.astype('float32')
     X_test = X_test.astype('float32')
@@ -160,37 +201,19 @@ if __name__ == '__main__':
     y_test = np_utils.to_categorical(y_test)
     input_img = Input(shape=(32, 32, 3))
 
-    if myclass:
-        googLeNet = InceptionModelBase()
-        output = googLeNet.inception_block(input_img, conv_settings_dict, pool_settings_dict)
-    else:
-        tower_1 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
-        tower_1 = Conv2D(64, (3, 3), padding='same', activation='relu')(tower_1)
-
-        tower_2 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
-        tower_2 = Conv2D(64, (5, 5), padding='same', activation='relu')(tower_2)
-
-        tower_3 = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(input_img)
-        tower_3 = Conv2D(64, (1, 1), padding='same', activation='relu')(tower_3)
-
-        output = keras.layers.concatenate([tower_1, tower_2, tower_3], axis=3)
-
+    # create inception net
+    inception_net = InceptionModelBase()
+    output = inception_net.inception_block(input_img, conv_settings_dict, pool_settings_dict)
     output = Flatten()(output)
-    out = Dense(10, activation='softmax')(output)
-    model = Model(inputs=input_img, outputs=out)
+    output = Dense(10, activation='softmax')(output)
+    model = Model(inputs=input_img, outputs=output)
     print(model.summary())
 
+    # compile
     epochs = 10
     lrate = 0.01
     decay = lrate/epochs
     sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
-
     model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
-
     print(X_train.shape)
-    # model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=32)
-    #
-    # scores = model.evaluate(X_test, y_test, verbose=0)
-    # print("Accuracy: %.2f%%" % (scores[1]*100))
-
-
+    keras.utils.plot_model(model, to_file='model.pdf', show_shapes=True, show_layer_names=True)

test/test_inception_model.py

+import pytest
+from src.inception_model import InceptionModelBase
+import keras
+import tensorflow as tf
+
+
+class TestInceptionModelBase:
+
+    @pytest.fixture
+    def base(self):
+        return InceptionModelBase()
+
+    @pytest.fixture
+    def input_x(self):
+        return keras.Input(shape=(32, 32, 3))
+
+    @staticmethod
+    def step_in(element, depth=1):
+        for _ in range(depth):
+            element = element.input._keras_history[0]
+        return element
+
+    def test_init(self, base):
+        assert base.number_of_blocks == 0
+        assert base.part_of_block == 0
+        assert base.ord_base == 96
+        assert base.act_number == 0
+
+    def test_block_part_name(self, base):
+        assert base.block_part_name() == chr(96)
+        base.part_of_block += 1
+        assert base.block_part_name() == 'a'
+
+    def test_create_conv_tower_3x3(self, base, input_x):
+        opts = {'input_x': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (3, 3)}
+        tower = base.create_conv_tower(**opts)
+        # check last element of tower (activation)
+        assert base.part_of_block == 1
+        assert tower.name == 'Block_0a_act_2/Relu:0'
+        act_layer = tower._keras_history[0]
+        assert isinstance(act_layer, keras.layers.advanced_activations.ReLU)
+        assert act_layer.name == "Block_0a_act_2"
+        # check previous element of tower (conv2D)
+        conv_layer = self.step_in(act_layer)
+        assert isinstance(conv_layer, keras.layers.Conv2D)
+        assert conv_layer.filters == 32
+        assert conv_layer.padding == 'same'
+        assert conv_layer.kernel_size == (3, 3)
+        assert conv_layer.strides == (1, 1)
+        assert conv_layer.name == "Block_0a_3x3"
+        # check previous element of tower (activation)
+        act_layer2 = self.step_in(conv_layer)
+        assert isinstance(act_layer2, keras.layers.advanced_activations.ReLU)
+        assert act_layer2.name == "Block_0a_act_1"
+        # check previous element of tower (conv2D)
+        conv_layer2 = self.step_in(act_layer2)
+        assert isinstance(conv_layer2, keras.layers.Conv2D)
+        assert conv_layer2.filters == 64
+        assert conv_layer2.kernel_size == (1, 1)
+        assert conv_layer2.padding == 'same'
+        assert conv_layer2.name == 'Block_0a_1x1'
+        assert conv_layer2.input._keras_shape == (None, 32, 32, 3)
+
+    def test_create_conv_tower_3x3_activation(self, base, input_x):
+        opts = {'input_x': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (3, 3)}
+        # create tower with standard activation function
+        tower = base.create_conv_tower(activation='tanh', **opts)
+        assert tower.name == 'Block_0a_act_2_tanh/Tanh:0'
+        act_layer = tower._keras_history[0]
+        assert isinstance(act_layer, keras.layers.core.Activation)
+        assert act_layer.name == "Block_0a_act_2_tanh"
+        # create tower with activation function class
+        tower = base.create_conv_tower(activation=keras.layers.LeakyReLU, **opts)
+        assert tower.name == 'Block_0b_act_2/LeakyRelu:0'
+        act_layer = tower._keras_history[0]
+        assert isinstance(act_layer, keras.layers.advanced_activations.LeakyReLU)
+        assert act_layer.name == "Block_0b_act_2"
+
+    def test_create_conv_tower_1x1(self, base, input_x):
+        opts = {'input_x': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (1, 1)}
+        tower = base.create_conv_tower(**opts)
+        # check last element of tower (activation)
+        assert base.part_of_block == 1
+        assert tower.name == 'Block_0a_act_1_1/Relu:0'
+        act_layer = tower._keras_history[0]
+        assert isinstance(act_layer, keras.layers.advanced_activations.ReLU)
+        assert act_layer.name == "Block_0a_act_1"
+        # check previous element of tower (conv2D)
+        conv_layer = self.step_in(act_layer)
+        assert isinstance(conv_layer, keras.layers.Conv2D)
+        assert conv_layer.filters == 32
+        assert conv_layer.padding == 'same'
+        assert conv_layer.kernel_size == (1, 1)
+        assert conv_layer.strides == (1, 1)
+        assert conv_layer.name == "Block_0a_1x1"
+        assert conv_layer.input._keras_shape == (None, 32, 32, 3)
+
+    def test_create_conv_towers(self, base, input_x):
+        opts = {'input_x': input_x, 'reduction_filter': 64, 'tower_filter': 32, 'tower_kernel': (3, 3)}
+        _ = base.create_conv_tower(**opts)
+        tower = base.create_conv_tower(**opts)
+        assert base.part_of_block == 2
+        assert tower.name == 'Block_0b_act_2_1/Relu:0'
+
+    def test_create_pool_tower(self, base, input_x):
+        opts = {'input_x': input_x, 'pool_kernel': (3, 3), 'tower_filter': 32}
+        tower = base.create_pool_tower(**opts)
+        # check last element of tower (activation)
+        assert base.part_of_block == 1
+        assert tower.name == 'Block_0a_act_1_3/Relu:0'
+        act_layer = tower._keras_history[0]
+        assert isinstance(act_layer, keras.layers.advanced_activations.ReLU)
+        assert act_layer.name == "Block_0a_act_1"
+        # check previous element of tower (conv2D)
+        conv_layer = self.step_in(act_layer)
+        assert isinstance(conv_layer, keras.layers.Conv2D)
+        assert conv_layer.filters == 32
+        assert conv_layer.padding == 'same'
+        assert conv_layer.kernel_size == (1, 1)
+        assert conv_layer.strides == (1, 1)
+        assert conv_layer.name == "Block_0a_1x1"
+        # check previous element of tower (maxpool)
+        pool_layer = self.step_in(conv_layer)
+        assert isinstance(pool_layer, keras.layers.pooling.MaxPooling2D)
+        assert pool_layer.name == "Block_0a_MaxPool"
+        assert pool_layer.pool_size == (3, 3)
+        assert pool_layer.padding == 'same'
+        # check avg pool tower
+        opts = {'input_x': input_x, 'pool_kernel': (3, 3), 'tower_filter': 32}
+        tower = base.create_pool_tower(max_pooling=False, **opts)
+        pool_layer = self.step_in(tower._keras_history[0], depth=2)
+        assert isinstance(pool_layer, keras.layers.pooling.AveragePooling2D)
+        assert pool_layer.name == "Block_0b_AvgPool"
+        assert pool_layer.pool_size == (3, 3)
+        assert pool_layer.padding == 'same'
+
+    def test_inception_block(self, base, input_x):
+        conv = {'tower_1': {'reduction_filter': 64, 'tower_kernel': (3, 3), 'tower_filter': 64},
+                'tower_2': {'reduction_filter': 64, 'tower_kernel': (5, 5), 'tower_filter': 64, 'activation': 'tanh'}}
+        pool = {'pool_kernel': (3, 3), 'tower_filter': 64}
+        opts = {'input_x': input_x, 'tower_conv_parts': conv, 'tower_pool_parts': pool}
+        block = base.inception_block(**opts)
+        assert base.number_of_blocks == 1
+        concatenated = block._keras_history[0].input
+        assert len(concatenated) == 4
+        block_1a, block_1b, block_pool1, block_pool2 = concatenated
+        assert block_1a.name == 'Block_1a_act_2/Relu:0'
+        assert block_1b.name == 'Block_1b_act_2_tanh/Tanh:0'
+        assert block_pool1.name == 'Block_1c_act_1/Relu:0'
+        assert block_pool2.name == 'Block_1d_act_1/Relu:0'
+        assert self.step_in(block_1a._keras_history[0]).name == "Block_1a_3x3"
+        assert self.step_in(block_1b._keras_history[0]).name == "Block_1b_5x5"
+        assert isinstance(self.step_in(block_pool1._keras_history[0], depth=2), keras.layers.pooling.MaxPooling2D)
+        assert isinstance(self.step_in(block_pool2._keras_history[0], depth=2), keras.layers.pooling.AveragePooling2D)
+        # next block
+        opts['input_x'] = block
+        opts['tower_pool_parts']['max_pooling'] = True
+        block = base.inception_block(**opts)
+        assert base.number_of_blocks == 2
+        concatenated = block._keras_history[0].input
+        assert len(concatenated) == 3
+        block_2a, block_2b, block_pool = concatenated
+        assert block_2a.name == 'Block_2a_act_2/Relu:0'
+        assert block_2b.name == 'Block_2b_act_2_tanh/Tanh:0'
+        assert block_pool.name == 'Block_2c_act_1/Relu:0'
+        assert self.step_in(block_2a._keras_history[0]).name == "Block_2a_3x3"
+        assert self.step_in(block_2b._keras_history[0]).name == "Block_2b_5x5"
+        assert isinstance(self.step_in(block_pool._keras_history[0], depth=2), keras.layers.pooling.MaxPooling2D)
+
+    def test_batch_normalisation(self, base, input_x):
+        base.part_of_block += 1
+        bn = base.batch_normalisation(input_x)._keras_history[0]
+        assert isinstance(bn, keras.layers.normalization.BatchNormalization)
+        assert bn.name == "Block_0a_BN"