def define_cnn_ResiliNet_architecture_fog(
skip_iotfog,
edge_output,
r,
transition_dilation_rate,
block_fn,
filters,
dropout,
residual_unit,
multiply_hyperconnection_weight_layer_IoTf,
multiply_hyperconnection_weight_layer_ef,
):
if (multiply_hyperconnection_weight_layer_IoTf == None
or multiply_hyperconnection_weight_layer_ef == None):
fog_input = Lambda(InputMux(ResiliNetPlus),
name="node2_input")([skip_iotfog, edge_output])
else:
fog_input = Lambda(InputMux(ResiliNetPlus), name="node2_input")([
multiply_hyperconnection_weight_layer_IoTf(skip_iotfog),
multiply_hyperconnection_weight_layer_ef(edge_output),
])
fog_output, filters = define_cnn_architecture_fog(
fog_input,
r,
transition_dilation_rate,
block_fn,
filters,
dropout,
residual_unit,
)
return fog_output, filters
def define_cnn_ResiliNet_architecture_cloud(
fog_output,
skip_edgecloud,
alpha,
depth_multiplier,
classes,
include_top,
pooling,
multiply_hyperconnection_weight_layer_fc=None,
multiply_hyperconnection_weight_layer_ec=None,
):
if (
multiply_hyperconnection_weight_layer_fc == None
or multiply_hyperconnection_weight_layer_ec == None
):
cloud_input = Lambda(InputMux(ResiliNetPlus), name="node1_input")(
[skip_edgecloud, fog_output]
)
else:
cloud_input = Lambda(InputMux(ResiliNetPlus), name="node1_input")(
[
multiply_hyperconnection_weight_layer_ec(skip_edgecloud),
multiply_hyperconnection_weight_layer_fc(fog_output),
]
)
cloud_output = define_cnn_architecture_cloud(
cloud_input, alpha, depth_multiplier, classes, include_top, pooling
)
return cloud_output
def define_MLP_ResiliNet_architecture_fog1(
fog2_output,
fog3_output,
fog4_output,
hidden_units,
multiply_hyperconnection_weight_layer=None,
):
if (multiply_hyperconnection_weight_layer == None
or multiply_hyperconnection_weight_layer["f2f1"] == None
or multiply_hyperconnection_weight_layer["f3f1"] == None
or multiply_hyperconnection_weight_layer["f4f1"] == None):
skip_hyperconnections = add([fog3_output, fog4_output])
fog1_input = Lambda(InputMux(ResiliNetPlus), name="node2_input")(
[skip_hyperconnections, fog2_output])
else:
skip_hyperconnections = add([
multiply_hyperconnection_weight_layer["f3f1"](fog3_output),
multiply_hyperconnection_weight_layer["f4f1"](fog4_output),
])
fog1_input = Lambda(InputMux(ResiliNetPlus), name="node2_input")([
skip_hyperconnections,
multiply_hyperconnection_weight_layer["f2f1"](fog2_output),
])
fog1_output = define_MLP_architecture_fog_with_two_layers(
fog1_input, hidden_units, "fog1_output_layer", "fog1_input_layer")
return fog1_output
def define_MLP_ResiliNet_architecture_fog2(
edge1_output,
edge2_output,
edge3_output,
edge4_output,
fog3_output,
fog4_output,
hidden_units,
multiply_hyperconnection_weight_layer=None,
):
if (multiply_hyperconnection_weight_layer == None
or multiply_hyperconnection_weight_layer["e1f2"] == None
or multiply_hyperconnection_weight_layer["e2f2"] == None
or multiply_hyperconnection_weight_layer["e3f2"] == None
or multiply_hyperconnection_weight_layer["e4f2"] == None
or multiply_hyperconnection_weight_layer["f3f2"] == None
or multiply_hyperconnection_weight_layer["f4f2"] == None):
fog2_input_left = Lambda(InputMux(ResiliNetPlus),
name="node3_input_left")(
[edge1_output, fog3_output])
skip_hyperconnections = add([edge2_output, edge3_output, edge4_output])
fog2_input_right = Lambda(InputMux(ResiliNetPlus),
name="node3_input_right")(
[skip_hyperconnections, fog4_output])
else:
fog2_input_left = Lambda(
InputMux(ResiliNetPlus), name="node3_input_left")([
multiply_hyperconnection_weight_layer["e1f2"](edge1_output),
multiply_hyperconnection_weight_layer["f3f2"](fog3_output),
])
skip_hyperconnections = add([
multiply_hyperconnection_weight_layer["e2f2"](edge2_output),
multiply_hyperconnection_weight_layer["e3f2"](edge3_output),
multiply_hyperconnection_weight_layer["e4f2"](edge4_output),
])
fog2_input_right = Lambda(
InputMux(ResiliNetPlus), name="node3_input_right")([
skip_hyperconnections,
multiply_hyperconnection_weight_layer["f4f2"](fog4_output),
])
fog2_input = layers.add([fog2_input_left, fog2_input_right],
name="node3_input")
fog2_output = define_MLP_architecture_fog_with_two_layers(
fog2_input, hidden_units, "fog2_output_layer", "fog2_input_layer")
return fog2_output
def define_cnn_ResiliNet_architecture_cloud(
fog_output,
skip_edgecloud,
r1,
r2,
transition_dilation_rate,
block_fn,
filters,
dropout,
residual_unit,
input_shape,
classes,
activation,
include_top,
top,
final_pooling,
multiply_hyperconnection_weight_layer_fc,
multiply_hyperconnection_weight_layer_ec,
):
if (multiply_hyperconnection_weight_layer_fc == None
or multiply_hyperconnection_weight_layer_ec == None):
cloud_input = Lambda(InputMux(ResiliNetPlus),
name="node1_input")([skip_edgecloud, fog_output])
else:
cloud_input = Lambda(InputMux(ResiliNetPlus), name="node1_input")([
multiply_hyperconnection_weight_layer_ec(skip_edgecloud),
multiply_hyperconnection_weight_layer_fc(fog_output),
])
cloud_output = define_cnn_architecture_cloud(
cloud_input,
r1,
r2,
transition_dilation_rate,
block_fn,
filters,
dropout,
residual_unit,
input_shape,
classes,
activation,
include_top,
top,
final_pooling,
)
return cloud_output
def define_MLP_ResiliNet_architecture_cloud(
fog2_output,
fog1_output,
hidden_units,
num_classes,
multiply_hyperconnection_weight_layer,
):
if (multiply_hyperconnection_weight_layer == None
or multiply_hyperconnection_weight_layer["f1c"] == None
or multiply_hyperconnection_weight_layer["f2c"] == None):
cloud_input = Lambda(InputMux(ResiliNetPlus),
name="node1_input")([fog2_output, fog1_output])
else:
cloud_input = Lambda(InputMux(ResiliNetPlus), name="node1_input")([
multiply_hyperconnection_weight_layer["f2c"](fog2_output),
multiply_hyperconnection_weight_layer["f1c"](fog1_output),
])
cloud_output = define_MLP_architecture_cloud(cloud_input, hidden_units,
num_classes)
return cloud_output
def define_cnn_ResiliNet_architecture_fog(
skip_iotfog,
edge_output,
alpha,
depth_multiplier,
multiply_hyperconnection_weight_layer_IoTf=None,
multiply_hyperconnection_weight_layer_ef=None,
strides=(2, 2),
):
if (
multiply_hyperconnection_weight_layer_IoTf == None
or multiply_hyperconnection_weight_layer_ef == None
):
fog_input = Lambda(InputMux(ResiliNetPlus), name="node2_input")(
[skip_iotfog, edge_output]
)
else:
fog_input = Lambda(InputMux(ResiliNetPlus), name="node2_input")(
[
multiply_hyperconnection_weight_layer_IoTf(skip_iotfog),
multiply_hyperconnection_weight_layer_ef(edge_output),
]
)
fog = define_cnn_architecture_fog(fog_input, alpha, depth_multiplier)
# cnn for imagenet does not need padding
if strides == (2, 2):
fog_output = fog
elif strides == (1, 1):
# pad from (7,7,256) to (8,8,256)
fog_output = layers.ZeroPadding2D(
padding=((0, 1), (0, 1)), name="fogcloud_connection_padding"
)(fog)
else:
raise ValueError("Incorrect stride value")
return fog_output