# Type Denotation Type Denotation is used to describe semantic information around what the inputs and outputs are. It is stored on the TypeProto message. ## Motivation The motivation of such a mechanism can be illustrated via a simple example. In the neural network SqueezeNet, it takes in an NCHW image input float[1,3,244,244] and produces a output float[1,1000,1,1]: ``` input_in_NCHW -> data_0 -> SqueezeNet() -> output_softmaxout_1 ``` In order to run this model the user needs a lot of information. In this case the user needs to know: * the input is an image * the image is in the format of NCHW * the color channels are in the order of bgr * the pixel data is 8 bit * the pixel data is normalized as values 0-255 This proposal consists of three key components to provide all of this information: * Type Denotation, * [Dimension Denotation](DimensionDenotation.md), * [Model Metadata](MetadataProps.md). ## Type Denotation Definition To begin with, we define a set of semantic types that define what models generally consume as inputs and produce as outputs. Specifically, in our first proposal we define the following set of standard denotations: 0. `TENSOR` describes that a type holds a generic tensor using the standard TypeProto message. 1. `IMAGE` describes that a type holds an image. You can use dimension denotation to learn more about the layout of the image, and also the optional model metadata_props. 2. `AUDIO` describes that a type holds an audio clip. 3. `TEXT` describes that a type holds a block of text. Model authors SHOULD add type denotation to inputs and outputs for the model as appropriate. ## An Example with input IMAGE Let's use the same SqueezeNet example from above and show everything to properly annotate the model: * First set the TypeProto.denotation =`IMAGE` for the ValueInfoProto `data_0` * Because it's an image, the model consumer now knows to go look for image metadata on the model * Then include 3 metadata strings on ModelProto.metadata_props * `Image.BitmapPixelFormat` = `Bgr8` * `Image.ColorSpaceGamma` = `SRGB` * `Image.NominalPixelRange` = `NominalRange_0_255` * For that same ValueInfoProto, make sure to also use Dimension Denotations to denote NCHW * TensorShapeProto.Dimension[0].denotation = `DATA_BATCH` * TensorShapeProto.Dimension[1].denotation = `DATA_CHANNEL` * TensorShapeProto.Dimension[2].denotation = `DATA_FEATURE` * TensorShapeProto.Dimension[3].denotation = `DATA_FEATURE` Now there is enough information in the model to know everything about how to pass a correct image into the model.