This paragraph provides an overview of how a deep learning network is represented in various deep learning frameworks.
A deep learning network is usually represented as a directed graph describing the flow of data from the network input data to the inference results. Input data can be represented as a photograph, video, audio information or some preprocessed data that represent object from the target area of interest in a convenient way.
Here is an illustration of a small graph representing a model that consists of a single Convolutional layer and activation function:
Vertices in the graph represent layers or operation instances, like convolution, pooling or element-wise operations with tensors. Layer and operation terms are used interchangeably along the OpenVINO™ documentation and define how input data is processed to produce output data for a node in a graph. An operation node in a graph may consume data at one or multiple input ports. For example, element-wise addition operation has two input ports which accepts tensors that are added together. Some operations don't have any input ports, for example Const operation which knowns the data to be produced without any input. An edge between operations represent data flow or data dependency implied from one operation node to another operation node.
Each operation produces data on one or multiple output ports. For example, convolution produces output tensor with activations at a single output port. Split operation usually has multiple output ports each producing part of an input tensor.
Depending on a deep learning framework, the graph can also contain extra nodes that explicitly represent tensors between operations. In such representations, operation nodes are not connected directly to each other, rather using data nodes as intermediate stops for data flow. If data nodes are not used, the produced data is associated with an output port of a corresponding operation node that produces the data.
A set of various operations used in a network is usually fixed for each deep learning framework. It determines expressiveness and level of representation available in that framework. It may happen that a network that can be represented in one framework is hard or impossible to be represented in another one or should use significantly different graph because operation sets used in those two frameworks do not match.
OpenVINO™ toolkit introduces its own format of graph representation and its own operation set. A graph is represented with two files: an XML file and a binary file. This representation is commonly referred to as the Intermediate Representation or IR.
XML file describes a network topology using
<layer> tag for an operation node and
<edge> tag is for a data-flow connection. Each operation has a fixed number of attributes that define operation flavor used for a node. For example,
Convolution operation has such attributes as
XML file doesn't have big constant values, like convolution weights. Instead, it refers to a part of accompanying binary file that stores such values in a binary format.
Here is an example of a small IR XML file that corresponds to a graph from the previous section:
The IR doesn't use explicit data nodes described in the previous section. In contrast, properties of data such as tensor dimensions and their data types are described as properties of input and output ports of operations.
Operations in the OpenVINO™ Operation Set are selected based on capabilities of supported deep learning frameworks and hardware capabilities of the target inference device. It consists of several groups of operations:
Refer to the complete description of the supported operation sets in the Available Operation Sets document.
The expressiveness of operations in OpenVINO™ is highly dependent on the supported frameworks and target hardware capabilities. As the frameworks and hardware capabilities grow over time, the operation set is constantly evolving to support new models. To maintain backward compatibility and growing demands, both IR format and operation set have versioning.
Version of IR specifies the rules which are used to read the XML and binary files that represent a model. It defines an XML schema and compatible operation set that can be used to describe operations.
Historically, there are two major IR version epochs.
The first supported operation set in the new epoch is
opset1. The number after
opset is going to be increased each time when new operations are added or old operations deleted at the release cadence.
The operations from the new epoch cover more TensorFlow* and ONNX* operators in a form that is closer to the original operation semantics from the frameworks in comparison to the operation set used in former versions of IR (7 and lower).
The name of the opset is specified for each operation in IR. The IR version is specified once per whole IR. Here is an example from the IR snippet:
version="opset1" in the example above mean "use that version of operation `Parameter` that is included into the operation set `opset1`".
When a new operation set is introduced, the significant part of the operations remains unchanged and it is just aliased from the previous operation set within a new one. The goal of operation set versions evolution is adding new operations, and probably changing of small fraction of existing operations (fixing bugs and extending semantics). However such changes affect only new versions of operations from a new operation set, while old operations are used by specifying an appropriate
version. When the old
version is specified, the behavior is kept unchanged from that specified version to provide the backward compatibility with older IRs.
xml file with IR may contain operations from different opsets. An operation that is included into several opsets may be referred to with
version which points to any opset that includes that operation. For example, the same
Convolution can be used with
version="opset2" because both opsets have the same operations
In the Available Operation Sets there are opsets and there are operations. Each opset specification has a list of links to operations descriptions that are included into that specific opset. Two or more opsets may refer to the same operation. That means an operation is kept unchanged from one operation set to another.
Each operation description has a field
Versioned name. For example,
ReLU entry point in `opset1` refers to `ReLU-1` as the versioned name. And
opset2 refers to the same
ReLU-1 and both
ReLU operations are the same operation and it has a single description. So
opset2 share the same operation
To differentiate versions of the same operation type, like
ReLU, the suffix
-N is used in a versioned name of the operation.
N usually refers to the first
opsetN where this version of the operation is introduced. It is not guaranteed that new operations will be named according to that rule, the naming convention might be changed, but not for old operations which are frozen completely.