def define_cnn_architecture_edge(iot_output,
alpha,
depth_multiplier,
strides=(2, 2)):
if PARTITION_SETING == 1:
edge = _depthwise_conv_block(iot_output,
64,
alpha,
depth_multiplier,
block_id=1)
edge = _depthwise_conv_block(edge,
128,
alpha,
depth_multiplier,
strides=strides,
block_id=2)
edge_output = _depthwise_conv_block(edge,
128,
alpha,
depth_multiplier,
block_id=3)
else: # PARTITION_SETING == 2
edge = _depthwise_conv_block(iot_output,
128,
alpha,
depth_multiplier,
strides=strides,
block_id=2)
edge_output = _depthwise_conv_block(edge,
128,
alpha,
depth_multiplier,
block_id=3)
return edge_output
def define_cnn_architecture_cloud(fog_output,
alpha,
depth_multiplier,
classes,
include_top,
pooling,
dropout=1e-3):
cloud = _depthwise_conv_block(fog_output,
512,
alpha,
depth_multiplier,
block_id=9)
cloud = _depthwise_conv_block(cloud,
512,
alpha,
depth_multiplier,
block_id=10)
cloud = _depthwise_conv_block(cloud,
512,
alpha,
depth_multiplier,
block_id=11)
cloud = _depthwise_conv_block(cloud,
1024,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=12)
cloud = _depthwise_conv_block(cloud,
1024,
alpha,
depth_multiplier,
block_id=13)
if include_top:
if K.image_data_format() == 'channels_first':
shape = (int(1024 * alpha), 1, 1)
else:
shape = (1, 1, int(1024 * alpha))
cloud = layers.GlobalAveragePooling2D()(cloud)
cloud = layers.Reshape(shape, name='reshape_1')(cloud)
cloud = layers.Dropout(dropout, name='dropout')(cloud)
cloud = layers.Conv2D(classes, (1, 1),
padding='same',
name='conv_preds')(cloud)
cloud = layers.Reshape((classes, ), name='reshape_2')(cloud)
cloud_output = layers.Activation('softmax', name='output')(cloud)
else:
if pooling == 'avg':
cloud_output = layers.GlobalAveragePooling2D()(cloud)
elif pooling == 'max':
cloud_output = layers.GlobalMaxPooling2D()(cloud)
return cloud_output
def define_cnn_architecture_fog(edge_output, alpha, depth_multiplier):
if PARTITION_SETING == 1:
fog = _depthwise_conv_block(edge_output,
256,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=4)
fog = _depthwise_conv_block(fog,
256,
alpha,
depth_multiplier,
block_id=5)
fog = _depthwise_conv_block(fog,
512,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=6)
fog = _depthwise_conv_block(fog,
512,
alpha,
depth_multiplier,
block_id=7)
fog_output = _depthwise_conv_block(fog,
512,
alpha,
depth_multiplier,
block_id=8)
else: # PARTITION_SETING == 2
fog = _depthwise_conv_block(edge_output,
256,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=4)
fog = _depthwise_conv_block(fog,
256,
alpha,
depth_multiplier,
block_id=5)
fog = _depthwise_conv_block(fog,
512,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=6)
fog_output = _depthwise_conv_block(fog,
512,
alpha,
depth_multiplier,
block_id=7)
return fog_output
def define_cnn_architecture_IoT(img_input, alpha, strides=(2, 2)):
if PARTITION_SETING == 1:
return _conv_block(img_input, 32, alpha, strides=strides)
else: # PARTITION_SETING == 2
iot = _conv_block(img_input, 32, alpha, strides=strides)
return _depthwise_conv_block(iot,
64,
alpha,
depth_multiplier=1,
block_id=1)
def define_cnn_architecture_fog(edge_output, alpha, depth_multiplier):
fog = _depthwise_conv_block(edge_output,
256,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=4)
fog = _depthwise_conv_block(fog, 256, alpha, depth_multiplier, block_id=5)
fog = _depthwise_conv_block(fog,
512,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=6)
fog = _depthwise_conv_block(fog, 512, alpha, depth_multiplier, block_id=7)
fog_output = _depthwise_conv_block(fog,
512,
alpha,
depth_multiplier,
block_id=8)
return fog_output
def define_cnn_architecture_edge(iot_output,
alpha,
depth_multiplier,
strides=(2, 2)):
edge = _depthwise_conv_block(iot_output,
64,
alpha,
depth_multiplier,
block_id=1)
edge = _depthwise_conv_block(edge,
128,
alpha,
depth_multiplier,
strides=strides,
block_id=2)
edge_output = _depthwise_conv_block(edge,
128,
alpha,
depth_multiplier,
block_id=3)
return edge_output
def define_cnn_architecture_cloud(fog_output,
alpha,
depth_multiplier,
classes,
include_top,
pooling,
dropout=1e-3):
if PARTITION_SETING == 1:
cloud = _depthwise_conv_block(fog_output,
512,
alpha,
depth_multiplier,
block_id=9)
cloud = _depthwise_conv_block(cloud,
512,
alpha,
depth_multiplier,
block_id=10)
cloud = _depthwise_conv_block(cloud,
512,
alpha,
depth_multiplier,
block_id=11)
cloud = _depthwise_conv_block(cloud,
1024,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=12)
cloud = _depthwise_conv_block(cloud,
1024,
alpha,
depth_multiplier,
block_id=13)
else: # PARTITION_SETING == 2
cloud = _depthwise_conv_block(fog_output,
512,
alpha,
depth_multiplier,
block_id=8)
cloud = _depthwise_conv_block(cloud,
512,
alpha,
depth_multiplier,
block_id=9)
cloud = _depthwise_conv_block(cloud,
512,
alpha,
depth_multiplier,
block_id=10)
cloud = _depthwise_conv_block(cloud,
512,
alpha,
depth_multiplier,
block_id=11)
cloud = _depthwise_conv_block(cloud,
1024,
alpha,
depth_multiplier,
strides=(2, 2),
block_id=12)
cloud = _depthwise_conv_block(cloud,
1024,
alpha,
depth_multiplier,
block_id=13)
if include_top:
if K.image_data_format() == "channels_first":
shape = (int(1024 * alpha), 1, 1)
else:
shape = (1, 1, int(1024 * alpha))
cloud = layers.GlobalAveragePooling2D()(cloud)
cloud = layers.Reshape(shape, name="reshape_1")(cloud)
cloud = layers.Dropout(dropout, name="dropout")(cloud)
cloud = layers.Conv2D(classes, (1, 1),
padding="same",
name="conv_preds")(cloud)
cloud = layers.Reshape((classes, ), name="reshape_2")(cloud)
cloud_output = layers.Activation("softmax", name="output")(cloud)
else:
if pooling == "avg":
cloud_output = layers.GlobalAveragePooling2D()(cloud)
elif pooling == "max":
cloud_output = layers.GlobalMaxPooling2D()(cloud)
return cloud_output