Converting Tensorflow model to TensorRT engine

I like you understand my issue.
So let me show again the issue from the beginning in this reply.

I have this tensorflow model. https://www.dropbox.com/s/um4ojp7i72tqa9v/Screenshot%20from%202019-08-13%2009-39-56.png?dl=0

The model is working well upto Mconv7_stage6_L2 and Mconv7_stage6_L1.

The layers after Mconv7_stage6_L2 and Mconv7_stage6_L1 are added for some functions and made a new model.

When I converted the new model to TensorRT engine, I have messages as

Warning: No conversion function registered for layer: ResizeArea yet.
Converting upsample_heatmat as custom op: ResizeArea
Warning: No conversion function registered for layer: Select yet.
Converting Select as custom op: Select
Warning: No conversion function registered for layer: Fill yet.
Converting zeros_like as custom op: Fill
Warning: No conversion function registered for layer: Equal yet.
Converting Equal as custom op: Equal
No. nodes: 475
UFF Output written to /home/coie/Data/coie/Softwares/HumanActivity_MSF/Multithreading/CreateEnginefromNetwork/CMUNet/cmu/frozen_model.uff
UFF Text Output written to /home/coie/Data/coie/Softwares/HumanActivity_MSF/Multithreading/CreateEngi

What I understood from these warnings is
Tensorflow operations ResizeArea, Select, Fill, and Equal have no equivalent TensorRT layers.
So I need to make custom plugin for those layers.
I made custom layers following the tutorials and examples here https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#extending.

After creating all custom plugins, I prepared all necessary steps as shown in examples as follow.

RESIZEAREA_PLUGIN_LIBRARY = os.path.join(
        WORKING_DIR,
        'plugins/ResizeArea/build/libResizeAreaplugin.so'
    )

    SELECT_PLUGIN_LIBRARY = os.path.join(
        WORKING_DIR,
        'plugins/Select/build/libSelectplugin.so'
    )

    FILL_PLUGIN_LIBRARY = os.path.join(
        WORKING_DIR,
        'plugins/Fill/build/libFillplugin.so'
    )

    EQUAL_PLUGIN_LIBRARY = os.path.join(
        WORKING_DIR,
        'plugins/Equal/build/libEqualplugin.so'
    )

    ENGINE_PATH = os.path.join(
        WORKING_DIR,
        'engine/cmu_32.engine'
    )

    MODEL_PATH = os.path.join(
        WORKING_DIR,
        'frozen_model.pb'
    )

    class ModelData(object):
        INPUT_NAME = "image"
        EQUAL_NAME = "Equal"
        SELECT_NAME = "Select"
        PMAT_NAME = "upsample_pafmat"
        ZERO_LIKE = "zeros_like"
        HMAT_NAME = "upsample_heatmat"
        OUTPUT_NAME = "Openpose/output"


    #engine_path='mobilenet/mobilenet_32.engine'
    #model_path='mobilenet/mobilenet_openpose.uff'
    width=640
    height=480

    def get_sec(time_str):
        m, s = time_str.split(':')
        return int(m) * 60 + int(s)

    # Generates mappings from unsupported TensorFlow operations to TensorRT plugins
    def prepare_namespace_plugin_map():
        # In this sample, the only operation that is not supported by TensorRT
        # is tf.nn.relu6, so we create a new node which will tell UffParser which
        # plugin to run and with which arguments in place of tf.nn.relu6.


        # The "clipMin" and "clipMax" fields of this TensorFlow node will be parsed by createPlugin,
        # and used to create a CustomClipPlugin with the appropriate parameters.
        trt_resizearea = gs.create_plugin_node(name="trt_resizearea", op="ResizeAreaPlugin", in_width=80.0, in_height=60.0, in_channel=3.0, upscale=4.0)
        trt_fill = gs.create_plugin_node(name="trt_fill", op="FillPlugin", in_width=320.0, in_height=240.0, in_channel=3.0, value=0.0)#fill 0
        trt_equal = gs.create_plugin_node(name="trt_equal", op="EqualPlugin", in_width=320.0, in_height=240.0, in_channel=3.0)
        trt_select = gs.create_plugin_node(name="trt_select", op="SelectPlugin", in_width=320.0, in_height=240.0, value=0.0)
        namespace_plugin_map = {
            ModelData.SELECT_NAME: trt_select,
            ModelData.EQUAL_NAME: trt_equal,
            ModelData.PMAT_NAME: trt_resizearea,
            ModelData.HMAT_NAME: trt_resizearea,
            ModelData.ZERO_LIKE: trt_fill
        }
        return namespace_plugin_map

    def model_to_uff(model_path):
        # Transform graph using graphsurgeon to map unsupported TensorFlow
        # operations to appropriate TensorRT custom layer plugins
        dynamic_graph = gs.DynamicGraph(model_path)
        dynamic_graph.collapse_namespaces(prepare_namespace_plugin_map())
        # Save resulting graph to UFF file
        output_uff_path = model_path_to_uff_path(model_path)
        uff.from_tensorflow(
            dynamic_graph.as_graph_def(),
            [ModelData.OUTPUT_NAME],
            output_filename=output_uff_path,
            text=True
        )
        return output_uff_path

    def model_path_to_uff_path(model_path):
        uff_path = os.path.splitext(model_path)[0] + ".uff"
        return uff_path

When I convert again, I still have warnings and have the following error

CHECK failed: (index) < (current_size_):
Warning: No conversion function registered for layer: SelectPlugin yet.
    Converting trt_select as custom op: SelectPlugin
    Warning: No conversion function registered for layer: FillPlugin yet.
    Converting trt_fill as custom op: FillPlugin
    Warning: No conversion function registered for layer: EqualPlugin yet.
    Converting trt_equal as custom op: EqualPlugin
    No. nodes: 470
    UFF Output written to /home/coie/Data/coie/Softwares/HumanActivity_MSF/Multithreading/CreateEnginefromNetwork/CMUNet/cmu/frozen_model.uff
    UFF Text Output written to /home/coie/Data/coie/Softwares/HumanActivity_MSF/Multithreading/CreateEnginefromNetwork/CMUNet/cmu/frozen_model.pbtxt
    [libprotobuf FATAL /home/erisuser/p4sw/sw/gpgpu/MachineLearning/DIT/externals/protobuf/x86_64/10.0/include/google/protobuf/repeated_field.h:1408] CHECK failed: (index) < (current_size_): 
    Traceback (most recent call last):
      File "HumanPoseTRT.py", line 303, in <module>
        main()
      File "HumanPoseTRT.py", line 256, in main
        serializeandsave_engine(MODEL_PATH)
      File "HumanPoseTRT.py", line 124, in serializeandsave_engine
        parser.parse(uff_path, network)
    RuntimeError: CHECK failed: (index) < (current_size_):

What is wrong with my approach?