diff --git a/src/model_modules/inception_model.py b/src/model_modules/inception_model.py
index c3fe27476272b18b4e4630e641f43ab03b555f9b..baf1d0250a319008c573109da765613ca87b2c77 100644
--- a/src/model_modules/inception_model.py
+++ b/src/model_modules/inception_model.py
@@ -54,6 +54,7 @@ class InceptionModelBase:
regularizer = kwargs.get('regularizer', keras.regularizers.l2(0.01))
bn_settings = kwargs.get('bn_settings', {})
act_settings = kwargs.get('act_settings', {})
+ padding = kwargs.get('padding', 'ZeroPad2D')
logging.debug(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]}'
@@ -74,9 +75,9 @@ class InceptionModelBase:
name=f'Block_{self.number_of_blocks}{self.block_part_name()}_1x1')(input_x)
tower = self.act(tower, activation, **act_settings)
- tower = self.padding_layer('SymPad2D')(padding=padding_size,
- name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
- )(tower)
+ tower = self.padding_layer(padding)(padding=padding_size,
+ name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
+ )(tower)
tower = layers.Conv2D(tower_filter,
tower_kernel,
@@ -116,19 +117,19 @@ class InceptionModelBase:
}
if isinstance(padding, str):
try:
- pad2D = allowed_paddings[padding]
+ pad2d = allowed_paddings[padding]
except KeyError as einfo:
raise NotImplementedError(
f"`{einfo}' is not implemented as padding. "
"Use one of those: i) `RefPad2D', ii) `SymPad2D', iii) `ZeroPad2D'")
else:
if padding in allowed_paddings.values():
- pad2D = padding
+ pad2d = padding
else:
- raise ValueError(f"`{padding.__name__}' is not a valid padding padding. "
- "Use one of those: "
- "i) ReflectionPadding2D, ii) `SymmetricPadding2D', iii) `ZeroPadding2D'")
- return pad2D
+ raise TypeError(f"`{padding.__name__}' is not a valid padding layer type. "
+ "Use one of those: "
+ "i) ReflectionPadding2D, ii) SymmetricPadding2D, iii) ZeroPadding2D")
+ return pad2d
def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs):
"""
@@ -143,6 +144,7 @@ class InceptionModelBase:
self.part_of_block += 1
self.act_number = 1
act_settings = kwargs.get('act_settings', {})
+ padding = kwargs.get('padding', 'ZeroPad2D')
padding_size = PadUtils.get_padding_for_same(kernel_size=pool_kernel)
# pooling block
@@ -154,11 +156,11 @@ class InceptionModelBase:
block_type = "AvgPool"
pooling = layers.AveragePooling2D
- tower = self.padding_layer('SymPad2D')(padding=padding_size, name=block_name+'Pad')(input_x)
+ tower = self.padding_layer(padding)(padding=padding_size, name=block_name+'Pad')(input_x)
tower = pooling(pool_kernel, strides=(1, 1), padding='valid', name=block_name+block_type)(tower)
# convolution block
- tower = layers.Conv2D(tower_filter, (1, 1), padding='same', name=block_name+"1x1")(tower)
+ tower = layers.Conv2D(tower_filter, (1, 1), padding='valid', name=block_name+"1x1")(tower)
tower = self.act(tower, activation, **act_settings)
return tower
@@ -170,16 +172,22 @@ class InceptionModelBase:
:param tower_conv_parts: dict containing settings for parts of inception block; Example:
tower_conv_parts = {'tower_1': {'reduction_filter': 32,
'tower_filter': 64,
- 'tower_kernel': (3, 1)},
+ 'tower_kernel': (3, 1),
+ 'activation' : 'relu',
+ 'padding' : 'SymPad2D'}
'tower_2': {'reduction_filter': 32,
'tower_filter': 64,
- 'tower_kernel': (5, 1)},
+ 'tower_kernel': (5, 1),
+ 'activation' : LeakyReLU,
+ 'padding' : keras.layers.ZeroPadding2D}
'tower_3': {'reduction_filter': 32,
'tower_filter': 64,
- 'tower_kernel': (1, 1)},
+ 'tower_kernel': (1, 1),
+ 'activation' : ELU,
+ 'padding' : src.model_modules.advanced_paddings.ReflectionPadding2D}
}
:param tower_pool_parts: dict containing settings for pool part of inception block; Example:
- tower_pool_parts = {'pool_kernel': (3, 1), 'tower_filter': 64}
+ tower_pool_parts = {'pool_kernel': (3, 1), 'tower_filter': 64, 'padding': 'RefPad2D'}
:return:
"""
self.number_of_blocks += 1
@@ -201,41 +209,41 @@ class InceptionModelBase:
return block
-if __name__ == '__main__':
- from keras.models import Model
- from keras.layers import Conv2D, Flatten, Dense, Input
- import numpy as np
-
-
- kernel_1 = (3, 3)
- kernel_2 = (5, 5)
- x = np.array(range(2000)).reshape(-1, 10, 10, 1)
- y = x.mean(axis=(1, 2))
-
- x_input = Input(shape=x.shape[1:])
- pad1 = PadUtils.get_padding_for_same(kernel_size=kernel_1)
- x_out = InceptionModelBase.padding_layer('RefPad2D')(padding=pad1, name="RefPAD1")(x_input)
- # x_out = ReflectionPadding2D(padding=pad1, name="RefPAD")(x_input)
- x_out = Conv2D(5, kernel_size=kernel_1, activation='relu')(x_out)
-
- pad2 = PadUtils.get_padding_for_same(kernel_size=kernel_2)
- x_out = InceptionModelBase.padding_layer(SymmetricPadding2D)(padding=pad2, name="SymPAD1")(x_out)
- # x_out = SymmetricPadding2D(padding=pad2, name="SymPAD")(x_out)
- x_out = Conv2D(2, kernel_size=kernel_2, activation='relu')(x_out)
- x_out = Flatten()(x_out)
- x_out = Dense(1, activation='linear')(x_out)
-
- model = Model(inputs=x_input, outputs=x_out)
- model.compile('adam', loss='mse')
- model.summary()
- # model.fit(x, y, epochs=10)
+# if __name__ == '__main__':
+# from keras.models import Model
+# from keras.layers import Conv2D, Flatten, Dense, Input
+# import numpy as np
+#
+#
+# kernel_1 = (3, 3)
+# kernel_2 = (5, 5)
+# x = np.array(range(2000)).reshape(-1, 10, 10, 1)
+# y = x.mean(axis=(1, 2))
+#
+# x_input = Input(shape=x.shape[1:])
+# pad1 = PadUtils.get_padding_for_same(kernel_size=kernel_1)
+# x_out = InceptionModelBase.padding_layer('RefPad2D')(padding=pad1, name="RefPAD1")(x_input)
+# # x_out = ReflectionPadding2D(padding=pad1, name="RefPAD")(x_input)
+# x_out = Conv2D(5, kernel_size=kernel_1, activation='relu')(x_out)
+#
+# pad2 = PadUtils.get_padding_for_same(kernel_size=kernel_2)
+# x_out = InceptionModelBase.padding_layer(SymmetricPadding2D)(padding=pad2, name="SymPAD1")(x_out)
+# # x_out = SymmetricPadding2D(padding=pad2, name="SymPAD")(x_out)
+# x_out = Conv2D(2, kernel_size=kernel_2, activation='relu')(x_out)
+# x_out = Flatten()(x_out)
+# x_out = Dense(1, activation='linear')(x_out)
+#
+# model = Model(inputs=x_input, outputs=x_out)
+# model.compile('adam', loss='mse')
+# model.summary()
+# # model.fit(x, y, epochs=10)
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.layers.advanced_activations import LeakyReLU, ELU
from keras.optimizers import SGD
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from keras.models import Model
@@ -244,11 +252,17 @@ if __name__ == '__main__':
conv_settings_dict = {'tower_1': {'reduction_filter': 64,
'tower_filter': 64,
'tower_kernel': (3, 3),
- 'activation': LeakyReLU},
+ 'activation': LeakyReLU,},
'tower_2': {'reduction_filter': 64,
'tower_filter': 64,
'tower_kernel': (5, 5),
- 'activation': 'relu'}
+ 'activation': 'relu',
+ 'padding': 'SymPad2D'},
+ 'tower_3': {'reduction_filter': 64,
+ 'tower_filter': 64,
+ 'tower_kernel': (1, 1),
+ 'activation': ELU,
+ 'padding': ReflectionPadding2D}
}
pool_settings_dict = {'pool_kernel': (3, 3),
'tower_filter': 64,
@@ -266,7 +280,7 @@ if __name__ == '__main__':
# create inception net
inception_net = InceptionModelBase()
- output = inception_net.inception_block(input_img, conv_settings_dict, pool_settings_dict)
+ output = inception_net.inception_block(input_img, conv_settings_dict, pool_settings_dict, batch_normalisation=True)
output = Flatten()(output)
output = Dense(10, activation='softmax')(output)
model = Model(inputs=input_img, outputs=output)
@@ -281,5 +295,6 @@ if __name__ == '__main__':
print(X_train.shape)
keras.utils.plot_model(model, to_file='model.pdf', show_shapes=True, show_layer_names=True)
# model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test))
+ print('test')