def auxiliary(x, n_classes, **metaparameters):
""" Construct the auxiliary classier
x : input to the auxiliary classifier
n_classes: number of output classes
"""
x = AveragePooling2D((5, 5), strides=(3, 3))(x)
x = Composable.Conv2D(x, 128, (1, 1), strides=(1, 1), padding='same', use_bias=False, **metaparameters)
x = BatchNormalization()(x)
x = Composable.ReLU(x)
x = Flatten()(x)
x = Composable.Dense(x, 1024, activation=Composable.ReLU, **metaparameters)
x = Dropout(0.7)(x)
output = Composable.Dense(x, n_classes, activation='softmax', **metaparameters)
return output
def squeeze_excite_block(x, **metaparameters):
""" Construct a Squeeze and Excite block
x : input to the block
ratio : amount of filter reduction during squeeze
"""
if 'ratio' in metaparameters:
ratio = metaparameters['ratio']
else:
ratio = SEResNeXt.ratio
# Remember the input
shortcut = x
# Get the number of filters on the input
filters = x.shape[-1]
# Squeeze (dimensionality reduction)
# Do global average pooling across the filters, which will the output a 1D vector
x = GlobalAveragePooling2D()(x)
# Reshape into 1x1 feature maps (1x1xC)
x = Reshape((1, 1, filters))(x)
# Reduce the number of filters (1x1xC/r)
x = Composable.Dense(x,
filters // ratio,
activation='relu',
use_bias=False,
**metaparameters)
# Excitation (dimensionality restoration)
# Restore the number of filters (1x1xC)
x = Composable.Dense(x,
filters,
activation='sigmoid',
use_bias=False,
**metaparameters)
# Scale - multiply the squeeze/excitation output with the input (WxHxC)
x = Multiply()([shortcut, x])
return x
def squeeze(x, **metaparameters):
""" Construct a squeeze block
x : input to the squeeze
"""
del metaparameters['activation']
shortcut = x
n_channels = x.shape[-1]
x = GlobalAveragePooling2D()(x)
x = Composable.Dense(x,
n_channels,
activation=Composable.ReLU,
**metaparameters)
x = Composable.Dense(x,
n_channels,
activation=Composable.HS,
**metaparameters)
x = Reshape((1, 1, n_channels))(x)
x = Multiply()([shortcut, x])
return x
