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Commit 72bc0b23 authored by Felix Kleinert's avatar Felix Kleinert
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include call of padding layers within an inception block #62

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2 merge requests!59Develop,!53Felix issue062 apply advanced pooling in inception block feat
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src/model_modules/inception_model.py
+ 62
− 47
View file @ 72bc0b23
...@@ -54,6 +54,7 @@ class InceptionModelBase: ...@@ -54,6 +54,7 @@ class InceptionModelBase:
regularizer = kwargs.get('regularizer', keras.regularizers.l2(0.01)) regularizer = kwargs.get('regularizer', keras.regularizers.l2(0.01))
bn_settings = kwargs.get('bn_settings', {}) bn_settings = kwargs.get('bn_settings', {})
act_settings = kwargs.get('act_settings', {}) act_settings = kwargs.get('act_settings', {})
padding = kwargs.get('padding', 'ZeroPad2D')
logging.debug(f'Inception Block with activation: {activation}') 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]}' block_name = f'Block_{self.number_of_blocks}{self.block_part_name()}_{tower_kernel[0]}x{tower_kernel[1]}'
...@@ -74,7 +75,7 @@ class InceptionModelBase: ...@@ -74,7 +75,7 @@ class InceptionModelBase:
name=f'Block_{self.number_of_blocks}{self.block_part_name()}_1x1')(input_x) name=f'Block_{self.number_of_blocks}{self.block_part_name()}_1x1')(input_x)
tower = self.act(tower, activation, **act_settings) tower = self.act(tower, activation, **act_settings)
tower = self.padding_layer('SymPad2D')(padding=padding_size, tower = self.padding_layer(padding)(padding=padding_size,
name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad' name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
)(tower) )(tower)
...@@ -116,19 +117,19 @@ class InceptionModelBase: ...@@ -116,19 +117,19 @@ class InceptionModelBase:
} }
if isinstance(padding, str): if isinstance(padding, str):
try: try:
pad2D = allowed_paddings[padding] pad2d = allowed_paddings[padding]
except KeyError as einfo: except KeyError as einfo:
raise NotImplementedError( raise NotImplementedError(
f"`{einfo}' is not implemented as padding. " f"`{einfo}' is not implemented as padding. "
"Use one of those: i) `RefPad2D', ii) `SymPad2D', iii) `ZeroPad2D'") "Use one of those: i) `RefPad2D', ii) `SymPad2D', iii) `ZeroPad2D'")
else: else:
if padding in allowed_paddings.values(): if padding in allowed_paddings.values():
pad2D = padding pad2d = padding
else: else:
raise ValueError(f"`{padding.__name__}' is not a valid padding padding. " raise TypeError(f"`{padding.__name__}' is not a valid padding layer type. "
"Use one of those: " "Use one of those: "
"i) ReflectionPadding2D, ii) `SymmetricPadding2D', iii) `ZeroPadding2D'") "i) ReflectionPadding2D, ii) SymmetricPadding2D, iii) ZeroPadding2D")
return pad2D return pad2d
def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs): def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs):
""" """
...@@ -143,6 +144,7 @@ class InceptionModelBase: ...@@ -143,6 +144,7 @@ class InceptionModelBase:
self.part_of_block += 1 self.part_of_block += 1
self.act_number = 1 self.act_number = 1
act_settings = kwargs.get('act_settings', {}) act_settings = kwargs.get('act_settings', {})
padding = kwargs.get('padding', 'ZeroPad2D')
padding_size = PadUtils.get_padding_for_same(kernel_size=pool_kernel) padding_size = PadUtils.get_padding_for_same(kernel_size=pool_kernel)
# pooling block # pooling block
...@@ -154,11 +156,11 @@ class InceptionModelBase: ...@@ -154,11 +156,11 @@ class InceptionModelBase:
block_type = "AvgPool" block_type = "AvgPool"
pooling = layers.AveragePooling2D 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) tower = pooling(pool_kernel, strides=(1, 1), padding='valid', name=block_name+block_type)(tower)
# convolution block # 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) tower = self.act(tower, activation, **act_settings)
return tower return tower
...@@ -170,16 +172,22 @@ class InceptionModelBase: ...@@ -170,16 +172,22 @@ class InceptionModelBase:
:param tower_conv_parts: dict containing settings for parts of inception block; Example: :param tower_conv_parts: dict containing settings for parts of inception block; Example:
tower_conv_parts = {'tower_1': {'reduction_filter': 32, tower_conv_parts = {'tower_1': {'reduction_filter': 32,
'tower_filter': 64, 'tower_filter': 64,
'tower_kernel': (3, 1)}, 'tower_kernel': (3, 1),
'activation' : 'relu',
'padding' : 'SymPad2D'}
'tower_2': {'reduction_filter': 32, 'tower_2': {'reduction_filter': 32,
'tower_filter': 64, 'tower_filter': 64,
'tower_kernel': (5, 1)}, 'tower_kernel': (5, 1),
'activation' : LeakyReLU,
'padding' : keras.layers.ZeroPadding2D}
'tower_3': {'reduction_filter': 32, 'tower_3': {'reduction_filter': 32,
'tower_filter': 64, '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: :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: :return:
""" """
self.number_of_blocks += 1 self.number_of_blocks += 1
...@@ -201,41 +209,41 @@ class InceptionModelBase: ...@@ -201,41 +209,41 @@ class InceptionModelBase:
return block return block
if __name__ == '__main__': # if __name__ == '__main__':
from keras.models import Model # from keras.models import Model
from keras.layers import Conv2D, Flatten, Dense, Input # from keras.layers import Conv2D, Flatten, Dense, Input
import numpy as np # import numpy as np
#
#
kernel_1 = (3, 3) # kernel_1 = (3, 3)
kernel_2 = (5, 5) # kernel_2 = (5, 5)
x = np.array(range(2000)).reshape(-1, 10, 10, 1) # x = np.array(range(2000)).reshape(-1, 10, 10, 1)
y = x.mean(axis=(1, 2)) # y = x.mean(axis=(1, 2))
#
x_input = Input(shape=x.shape[1:]) # x_input = Input(shape=x.shape[1:])
pad1 = PadUtils.get_padding_for_same(kernel_size=kernel_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 = InceptionModelBase.padding_layer('RefPad2D')(padding=pad1, name="RefPAD1")(x_input)
# x_out = ReflectionPadding2D(padding=pad1, name="RefPAD")(x_input) # # x_out = ReflectionPadding2D(padding=pad1, name="RefPAD")(x_input)
x_out = Conv2D(5, kernel_size=kernel_1, activation='relu')(x_out) # x_out = Conv2D(5, kernel_size=kernel_1, activation='relu')(x_out)
#
pad2 = PadUtils.get_padding_for_same(kernel_size=kernel_2) # pad2 = PadUtils.get_padding_for_same(kernel_size=kernel_2)
x_out = InceptionModelBase.padding_layer(SymmetricPadding2D)(padding=pad2, name="SymPAD1")(x_out) # x_out = InceptionModelBase.padding_layer(SymmetricPadding2D)(padding=pad2, name="SymPAD1")(x_out)
# x_out = SymmetricPadding2D(padding=pad2, name="SymPAD")(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 = Conv2D(2, kernel_size=kernel_2, activation='relu')(x_out)
x_out = Flatten()(x_out) # x_out = Flatten()(x_out)
x_out = Dense(1, activation='linear')(x_out) # x_out = Dense(1, activation='linear')(x_out)
#
model = Model(inputs=x_input, outputs=x_out) # model = Model(inputs=x_input, outputs=x_out)
model.compile('adam', loss='mse') # model.compile('adam', loss='mse')
model.summary() # model.summary()
# model.fit(x, y, epochs=10) # # model.fit(x, y, epochs=10)
if __name__ == '__main__': if __name__ == '__main__':
print(__name__) print(__name__)
from keras.datasets import cifar10 from keras.datasets import cifar10
from keras.utils import np_utils from keras.utils import np_utils
from keras.layers import Input 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.optimizers import SGD
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from keras.models import Model from keras.models import Model
...@@ -244,11 +252,17 @@ if __name__ == '__main__': ...@@ -244,11 +252,17 @@ if __name__ == '__main__':
conv_settings_dict = {'tower_1': {'reduction_filter': 64, conv_settings_dict = {'tower_1': {'reduction_filter': 64,
'tower_filter': 64, 'tower_filter': 64,
'tower_kernel': (3, 3), 'tower_kernel': (3, 3),
'activation': LeakyReLU}, 'activation': LeakyReLU,},
'tower_2': {'reduction_filter': 64, 'tower_2': {'reduction_filter': 64,
'tower_filter': 64, 'tower_filter': 64,
'tower_kernel': (5, 5), '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), pool_settings_dict = {'pool_kernel': (3, 3),
'tower_filter': 64, 'tower_filter': 64,
...@@ -266,7 +280,7 @@ if __name__ == '__main__': ...@@ -266,7 +280,7 @@ if __name__ == '__main__':
# create inception net # create inception net
inception_net = InceptionModelBase() 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 = Flatten()(output)
output = Dense(10, activation='softmax')(output) output = Dense(10, activation='softmax')(output)
model = Model(inputs=input_img, outputs=output) model = Model(inputs=input_img, outputs=output)
...@@ -281,5 +295,6 @@ if __name__ == '__main__': ...@@ -281,5 +295,6 @@ if __name__ == '__main__':
print(X_train.shape) print(X_train.shape)
keras.utils.plot_model(model, to_file='model.pdf', show_shapes=True, show_layer_names=True) 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)) # model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test))
print('test')
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