Profile a model
Profile a model in the Profiler window
To get performance information when you run a model, you can use the following:
- The Profiler window.
- RenderDoc, a third-party graphics debugger.
The Profiler window displays each Sentis layer as a dropdown item in the Module Details panel. Open a layer to get a detailed timeline of the execution of the layer.
When a layer executes methods that include Download or Upload, Sentis transfers data to or from the CPU or the GPU. This might slow down the model.
If your model runs slower than you expect, refer to:
- Understand models in Sentis for information about how the complexity of a model might affect performance.
- Create an engine to run a model for information about different types of worker.
Get output from any layer
To help you profile a model, you can get the output from any layer in a model. Follow these steps:
- Use
Model.AddOutput("layer-name")
to add the layer to the model outputs, before you create the worker. - Run the model.
- Use
IWorker.PeekOutput("layer-name")
to get the output from the layer.
Only use layer outputs to debug your model. The more layers you add as outputs, the more memory the model uses.
For example, to output from a layer named ConvolutionLayer
:
using UnityEngine;
using Unity.Sentis;
public class GetOutputFromALayer : MonoBehaviour
{
ModelAsset modelAsset;
Model runtimeModel;
IWorker worker;
void Start()
{
// Create an input tensor
TensorFloat inputTensor = new TensorFloat(new TensorShape(4), new[] { 2.0f, 1.0f, 3.0f, 0.0f });
// Create the runtime model
runtimeModel = ModelLoader.Load(modelAsset);
// Add the layer to the model outputs
runtimeModel.AddOutput("ConvolutionLayer");
// Create a worker
worker = WorkerFactory.CreateWorker(BackendType.GPUCompute, runtimeModel);
// Run the model with the input data
worker.Execute(inputTensor);
// Get the output from the model
TensorFloat outputTensor = worker.PeekOutput() as TensorFloat;
// Get the output from the ConvolutionLayer layer
TensorFloat convolutionLayerOutputTensor = worker.PeekOutput("ConvolutionLayer") as TensorFloat;
}
}