选择要导入的函数ONNX Pretrained网络- MATLAB & Simulink - MathWorks Australia

选择要导入的函数ONNX Pretrained网络

Deep Learning Toolbox™ Converter for ONNX™ Model Formatprovides three functions to import a pretrained ONNX (Open Neural Network Exchange) network:importONNXNetwork,importONNXLayers, andimportONNXFunction.

This flow chart illustrates which import function best suits different scenarios.

Flow chart for selecting ONNX import function

Note

By default,importONNXNetworkandimportONNXLayerstry to generate a custom layer when the software cannot convert an ONNX operator into an equivalent built-in MATLAB^®layer. For a list of operators for which the software supports conversion, seeONNX Operators Supported for Conversion into Built-In MATLAB Layers.

importONNXNetworkandimportONNXLayerssave the generated custom layers in the package+PackageNamein the current folder.

importONNXNetworkandimportONNXLayersdo not automatically generate a custom layer for each ONNX operator that is not supported for conversion into a built-in MATLAB layer.

Decisions

This table describes each decision in the workflow for selecting an ONNX import function.

Decision	Description
Are all the ONNX operators supported for conversion into equivalent built-in MATLAB layers or can the software automatically generate custom layers?	If the imported network contains an ONNX operator not supported for conversion into a built-in MATLAB layer (seeONNX Operators Supported for Conversion into Built-In MATLAB Layers) and`importONNXNetwork`does not generate a custom layer, then`importONNXNetwork`返回一个n error. If the imported network contains an ONNX operator not supported for conversion into a built-in MATLAB layer and`importONNXLayers`does not generate a custom layer, then`importONNXLayers`inserts a placeholder layer in place of the unsupported layer. `importONNXFunction`supports most ONNX operators. For more information, seeONNX Operators That importONNXFunction Supports.
Will you deploy the imported network?	If you use`importONNXNetwork`or`importONNXLayers`, you can generate code for the imported network. To create a`DAGNetwork`object for code generation, seeLoad Pretrained Networks for Code Generation(MATLAB Coder).
Will you load the imported network with Deep Network Designer?	If you use`importONNXNetwork`or`importONNXLayers`, you can load the imported network with theDeep Network Designer应用程序。
If you retrain the imported network, will you use a custom training loop?	If you use`importONNXFunction`, you can retrain the imported network only with a custom training loop. For an example, seeTrain Imported ONNX Function Using Custom Training Loop. Use`importONNXNetwork`with`TargetNetwork`specified as`"dlnetwork"`to import the network as a`dlnetwork`object. A`dlnetwork`enables support for custom training loops using automatic differentiation. Use`importONNXLayers`with`TargetNetwork`specified as`"dlnetwork"`to import the network as a`LayerGraph`object compatible with a`dlnetwork`object. Then convert the layer graph to a`dlnetwork`by using`dlnetwork`. For more information about training options, seeTrain Deep Learning Model in MATLAB.

Actions

This table describes each action in the workflow for selecting an ONNX import function.

Action	Description
Use`importONNXNetwork`	`importONNXNetwork`返回一个`DAGNetwork`or`dlnetwork`object that is ready to use for prediction (for more information, see the`TargetNetwork`name-value argument). Predict class labels by using the`classify`function on the`DAGNetwork`object or the`predict`function on the`dlnetwork`object.
Use`importONNXLayers`	`importONNXLayers`返回一个`LayerGraph`object compatible with a`DAGNetwork`or`dlnetwork`object (for more information, see the`TargetNetwork`name-value argument).`importONNXLayers`插入占位符p层lace of unsupported layers. Find and replace the placeholder layers. Then, you can assemble the layer graph by using`assembleNetwork`, which returns a`DAGNetwork`object, or convert the layer graph to a`dlnetwork`object by using`dlnetwork`.
Use`importONNXFunction`	`importONNXFunction`返回一个n`ONNXParameters`object, which contains the network parameters, and a model function (seeImported ONNX Model Function), which contains the network architecture. The`ONNXParameters`object and the model function are ready to use for prediction. For an example, seePredict Using Imported ONNX Function.
Find and replace the placeholder layers	To find the names and indices of the placeholder layers in the imported network, use the`findPlaceholderLayers`function. You then can replace a placeholder layer with a new layer that you define. To replace a layer, use`replaceLayer`.