def __init__(self,
filters,
dropout_rate,
kernel_initializer,
kernel_regularizer,
name='bottleneck_composite_function'):
layers = [
L.BatchNormalization(),
L.Activation(tf.nn.relu),
L.Conv2D(
filters * 4,
1,
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.Dropout(dropout_rate),
L.BatchNormalization(),
L.Activation(tf.nn.relu),
L.Conv2D(
filters,
3,
padding='same',
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.Dropout(dropout_rate),
]
super().__init__(layers, name=name)
def __init__(self,
filters,
strides,
expansion_factor,
dropout_rate,
kernel_initializer,
kernel_regularizer,
name='bottleneck'):
super().__init__(name=name)
self.expand_conv = Sequential([
L.Conv2D(
filters *
expansion_factor, # FIXME: should be `input_shape[3].value * expansion_factor`
1,
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.BatchNormalization(),
L.Activation(tf.nn.relu6),
L.Dropout(dropout_rate)
])
self.depthwise_conv = Sequential([
L.DepthwiseConv2D(3,
strides=strides,
padding='same',
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.BatchNormalization(),
L.Activation(tf.nn.relu6),
L.Dropout(dropout_rate)
])
self.linear_conv = Sequential([
L.Conv2D(filters,
1,
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.BatchNormalization(),
L.Dropout(dropout_rate)
])
def __init__(self,
input_filters,
compression_factor,
dropout_rate,
kernel_initializer,
kernel_regularizer,
name='transition_layer'):
self.input_filters = input_filters
filters = int(input_filters * compression_factor)
layers = [
L.BatchNormalization(),
L.Conv2D(
filters,
1,
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.Dropout(dropout_rate),
L.AveragePooling2D(2, 2, padding='same')
]
super().__init__(layers, name=name)
def __init__(self,
dropout_rate,
kernel_initializer=None,
kernel_regularizer=None,
name='mobilenet_v2'):
if kernel_initializer is None:
kernel_initializer = tf.contrib.layers.variance_scaling_initializer(
factor=2.0, mode='FAN_IN', uniform=False)
if kernel_regularizer is None:
kernel_regularizer = tf.contrib.layers.l2_regularizer(scale=4e-5)
super().__init__(name=name)
self.input_conv = Sequential([
L.Conv2D(32,
3,
strides=2,
padding='same',
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.BatchNormalization(),
L.Activation(tf.nn.relu6),
L.Dropout(dropout_rate)
])
self.bottleneck_1_1 = Bottleneck(16,
expansion_factor=1,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_2_1 = Bottleneck(24,
expansion_factor=6,
strides=2,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_2_2 = Bottleneck(24,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_3_1 = Bottleneck(32,
expansion_factor=6,
strides=2,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_3_2 = Bottleneck(32,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_3_3 = Bottleneck(32,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_4_1 = Bottleneck(64,
expansion_factor=6,
strides=2,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_4_2 = Bottleneck(64,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_4_3 = Bottleneck(64,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_4_4 = Bottleneck(64,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_5_1 = Bottleneck(96,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_5_2 = Bottleneck(96,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_5_3 = Bottleneck(96,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_6_1 = Bottleneck(160,
expansion_factor=6,
strides=2,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_6_2 = Bottleneck(160,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_6_3 = Bottleneck(160,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.bottleneck_7_1 = Bottleneck(320,
expansion_factor=6,
strides=1,
dropout_rate=dropout_rate,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
self.output_conv = Sequential([
L.Conv2D(32,
1,
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer),
L.BatchNormalization(),
L.Activation(tf.nn.relu6),
L.Dropout(dropout_rate)
])