def transition_SE_layer(input_tensor,
numFilters,
compressionFactor=1.0,
se_ratio=16):
numOutPutFilters = int(numFilters * compressionFactor)
if K.image_data_format() == 'channels_last':
bn_axis = -1
else:
bn_axis = 1
x = BatchNormalization(axis=bn_axis)(input_tensor)
x = Activation('relu')(x)
x = Conv2D(numOutPutFilters, (1, 1),
strides=(1, 1),
padding='same',
kernel_initializer='he_normal')(x)
# SE Block
x = squeeze_excitation_block(x, ratio=se_ratio)
#x = BatchNormalization(axis=bn_axis)(x)
# downsampling
x = AveragePooling2D((2, 2),
strides=(2, 2),
padding='valid',
data_format='channels_last',
name='')(x)
#x = squeeze_excitation_block(x, ratio=se_ratio)
return x, numOutPutFilters
def projection_block(input_tensor, filters, stage, block, se_enabled=False, se_ratio=16):
numFilters1, numFilters2 = filters
if K.image_data_format() == 'channels_last':
bn_axis = -1
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + '_' + str(block) + '_branch'
bn_name_base = 'bn' + str(stage) + '_' + str(block) + '_branch'
# downsampling directly by convolution with stride 2
x = Conv2D(numFilters1, (3, 3), padding='same', strides=(2, 2), kernel_initializer='he_normal',
name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(numFilters2, (3, 3), padding='same', kernel_initializer='he_normal', name=conv_name_base + '2b')(x)
# squeeze and excitation block
if se_enabled:
x = squeeze_excitation_block(x, ratio=se_ratio)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
# projection shortcut convolution
x_shortcut = Conv2D(numFilters2, (1, 1), strides=(2, 2), kernel_initializer='he_normal', name=conv_name_base + '1')(
input_tensor)
x_shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(x_shortcut)
# addition of shortcut
x = Add()([x, x_shortcut])
x = Activation('relu')(x)
return x
def identity_block(input_tensor, filters, stage, block, se_enabled=False, se_ratio=16):
numFilters1, numFilters2 = filters
if K.image_data_format() == 'channels_last':
bn_axis = -1
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + '_' + str(block) + '_branch'
bn_name_base = 'bn' + str(stage) + '_' + str(block) + '_branch'
x = Conv2D(numFilters1, (3, 3), padding='same', kernel_initializer='he_normal', name=conv_name_base + '2a')(
input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(numFilters2, (3, 3), padding='same', kernel_initializer='he_normal', name=conv_name_base + '2b')(x)
# squeeze and excitation block
if se_enabled:
x = squeeze_excitation_block(x, ratio=se_ratio)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Add()([x, input_tensor])
x = Activation('relu')(x)
return x
def dense_SE_block(input_tensor,
numInputFilters,
numLayers=1,
growthRate_k=12,
bottleneck_enabled=False,
se_ratio=16):
if K.image_data_format() == 'channels_last':
concat_axis = -1
bn_axis = -1
else:
concat_axis = 1
bn_axis = 1
concat_features = input_tensor
for i in range(numLayers):
x = BatchNormalization(axis=bn_axis, name='')(concat_features)
x = Activation('relu')(x)
if bottleneck_enabled == True:
x = Conv2D(
4 * growthRate_k, (1, 1),
strides=(1, 1),
kernel_initializer='he_normal',
padding='same'
)(
x
) # "in our experiments, we let each 1x1 conv produce 4k feature maps
x = BatchNormalization(axis=bn_axis)(x)
x = Activation('relu')(x)
x = Conv2D(growthRate_k, (3, 3),
strides=(1, 1),
kernel_initializer='he_normal',
padding='same')(x)
concat_features = concatenate([x, concat_features], axis=concat_axis)
numInputFilters += growthRate_k
# SE-Block
concat_features = squeeze_excitation_block(concat_features, ratio=se_ratio)
return concat_features, numInputFilters