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
Exemplo n.º 2
0
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)
Exemplo n.º 5
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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
Exemplo n.º 6
0
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