Loads a deep learning model and prepares its execution on a specific target device.
const avl::DetectAnomalies1ModelDirectory& inModelDirectory,
const atl::Optional<avl::DeviceKind::Type>& inDeviceType,
const int inDeviceIndex,
const bool inComputeReconstructionHint,
||A Detect Anomalies 1 model stored in a specific disk directory.
||A type of a device selected for deploying and executing the model. If not set, device depending on version (CPU/GPU) of installed Deep Learning add-on is selected. If not set, device depending on version (CPU/GPU) of installed Deep Learning add-on is selected.
||An index of a device selected for deploying and executing the model.
||Prepares the model for a reconstruction computation in advance
||Identifier of the deployed model
- In most cases, this filter should be placed in the INITIALIZE section.
- Executing this filter may take several seconds.
- This filter should be connected to AvsFilter_DL_DetectAnomalies1 through the ModelId ports.
- You can edit the model directly through the inModelDirectory. Another option is to use the Deep Learning Editor application and just copy the path to the created model.
- If any subsequent AvsFilter_DL_DetectAnomalies1 filter using deployed model is set to compute a reconstruction, it is advisable to set inComputeReconstructionHint to true.
In other case, inComputeReconstructionHint should be set to false. Following this guidelines should ensure an optimal memory usage and no performance hit on first call to AvsFilter_DL_DetectAnomalies1.
- Passing NIL as inTargetDevice (which is default), is identical to passing DeviceKind::CUDA on GPU version of Deep Learning add-on and DeviceKind::CPU on CPU version on Deep Learning add-on.
- GPU version of Deep Learning add-on supports DeviceKind::CUDA and DeviceKind::CPU as inTargetDevice value.
- CPU version of Deep Learning add-on supports only DeviceKind::CPU as inTargetDevice value.