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,
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:
TENSOR
describes that a type holds a generic tensor using the standard TypeProto message.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.AUDIO
describes that a type holds an audio clip.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 ValueInfoProtodata_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.