Custom Layers in the Model Optimizer

Model Optimizer searches for each layer of the input model in the list of known layers before building the model's internal representation, optimizing the model, and producing the Intermediate Representation.

The list of known layers is different for each of supported frameworks. To see the layers supported by your framework, refer to the corresponding section.

Custom layers are layers that are not included into a list of known layers. If your topology contains any layers that are not in the list of known layers, the Model Optimizer classifies them as custom.

Caffe* Models with Custom Layers

You have two options if your Caffe* model has custom layers:

If your model contains Custom Layers, it is important to understand the internal workflow of Model Optimizer. Consider the following example.


The network has:

The custom and standard layer types are:

NOTE: If any of the layers are not in one of three categories described above, the Model Optimizer fails with an appropriate message and a link to the corresponding question in Model Optimizer FAQ.

The general process is as shown:

Example custom layer network
  1. The example model is fed to the Model Optimizer that loads the model with the special parser, built on top of caffe.proto file. In case of failure, Model Optimizer asks you to prepare the parser that can read the model. For more information, refer to Model Optimizer, FAQ #1.
  2. Model Optimizer extracts the attributes of all layers. In particular, it goes through the list of layers and attempts to find the appropriate extractor. In order of priority, Model Optimizer checks if the layer is:

    • Registered in CustomLayersMapping.xml
    • Registered as a Model Optimizer extension
    • Registered as a standard Model Optimizer layer

    When the Model Optimizer finds a satisfying condition from the list above, it extracts the attributes according to the following rules:

    • For bullet #1 - either takes all parameters or no parameters, according to the content of CustomLayersMapping.xml
    • For bullet #2 - takes only the parameters specified in the extension
    • For bullet #3 - takes only the parameters specified in the standard extractor
  3. Model Optimizer calculates the output shape of all layers. The logic is the same as it is for the priorities. Important: the Model Optimizer always takes the first available option.
  4. Model Optimizer optimizes the original model and produces the Intermediate Representation.

TensorFlow* Models with Custom Layers

You have three options for TensorFlow* models with custom layers:

MXNet* Models with Custom Layers

There are two options to convert your MXNet* model that contains custom layers:

  1. Register the custom layers as extensions to the Model Optimizer. For instructions, see Extending MXNet Model Optimizer with New Primitives. When your custom layers are registered as extensions, the Model Optimizer generates a valid and optimized Intermediate Representation. You can create Model Optimizer extensions for both MXNet layers with op Custom and layers which are not standard MXNet layers.
  2. If you have sub-graphs that should not be expressed with the analogous sub-graph in the Intermediate Representation, but another sub-graph should appear in the model, the Model Optimizer provides such an option. In MXNet the function is actively used for ssd models provides an opportunity to for the necessary subgraph sequences and replace them. To read more, see Sub-graph Replacement in the Model Optimizer.