Loads a deep learning model and prepares its execution on a specific target device.
void avl::DL_ClassifyObject_Deploy ( const avl::ClassifyObjectModelDirectory& inModelDirectory, const atl::Optional<avl::DeviceType::Type>& inTargetDevice, const bool inCreateHeatmapHint, avl::ClassifyObjectModelId& outModelId )
|inModelDirectory||const ClassifyObjectModelDirectory&||A Classify Object model stored in a specific disk directory|
|inTargetDevice||const Optional<DeviceType::Type>&||NIL||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.|
|inCreateHeatmapHint||const bool||False||Prepares the model for a relevance heatmap creation in advance|
|outModelId||ClassifyObjectModelId&||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 DL_ClassifyObject 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 DL_ClassifyObject filter using deployed model is set to create a relevance heatmap, it is advisable to set inCreateHeatmapHint to true. In other case, inCreateHeatmapHint should be set to false. Following this guidelines should ensure an optimal memory usage and no performance hit on first call to DL_ClassifyObject.
- Passing NIL as inTargetDevice (which is default), is identical to passing DeviceType::CUDA on GPU version of Deep Learning Addon and DeviceType::CPU on CPU version on Deep Learning Addon.
- GPU version of Deep Learning Addon supports DeviceType::CUDA and DeviceType::CPU as inTargetDevice value.
- CPU version of Deep Learning Addon supports only DeviceType::CPU as inTargetDevice value.
- DL_ClassifyObject – Executes a Classify Object model on a single input image.