Function: set_aipp_by_input_index

Applicability

Product	Supported (√/x)
Atlas A3 training products / Atlas A3 inference products	√
Atlas A2 training products / Atlas A2 inference products	√
Atlas training products	√
Atlas inference products	√
Atlas 200I/500 A2 inference products	√

Function Usage

Sets the dynamic AIPP attributes for model inference based on an input index.

Dynamic AIPP provides the following operations. They are executed in the exact sequence.

Cropping
Color space conversion
Mean subtraction/Normalization
Padding

Prototype

C Prototype

        
             aclError aclmdlSetAIPPByInputIndex(uint32_t modelId, aclmdlDataset *dataset, size_t index, const aclmdlAIPP *aippParmsSet)

Python Function

        
             ret = acl.mdl.set_aipp_by_input_index(model_id, dataset_in, index, aipp)

Parameter Description

Parameter	Description
model_id	Int, model ID. You can obtain the model ID after the model is successfully loaded by calling the following APIs: acl.mdl.load_from_file acl.mdl.load_from_mem acl.mdl.load_from_file_with_mem acl.mdl.load_from_mem_with_mem
dataset_in	Int, pointer address of the input data for model inference. Data of type aclmdlDataset describes the input data for model inference, while data of type aclDataBuffer describes the input buffer size and address. For details, see aclmdlDataset.
index	Int, index of the dynamic AIPP input. If the input is not associated with a dynamic AIPP input, an error is reported. Call acl.mdl.get_aipp_type to query whether a specified model input has an associated dynamic AIPP input. If yes, the index argument passed to this API call is the same as that of acl.mdl.get_aipp_type. If no, you do not need to set dynamic AIPP attributes.
aipp	Int, pointer address of the dynamic AIPP parameter object. acl.mdl.create_aipp is called in advance to create data of the aclmdlAIPP type.

Return Value Description

Return Value	Description
ret	Int, error code: 0 on success; else, failure.

Restrictions

The image size after dynamic AIPP processing must be the same as that defined by the input shape required by the original model.
In batched inference scenarios, the image size after dynamic AIPP is calculated based on the configuration of each batch. After dynamic AIPP, the output image size must be consistent across batches. Table 1 describes the formulas for calculating the width and height of the output image.

After image cropping, resizing, or padding, the width and height of the image are verified as follows. aippOutputW and aippOutputH indicate the width and height of the output image, respectively. Other parameters are the input parameters of the acl.mdl.set_aipp_srcImage_size, acl.mdl.set_aipp_scf_params, acl.mdl.set_aipp_crop_params and acl.mdl.set_aipp_padding_params APIs. See Table 1.

**Table 1** Formulas for calculating the output image size
Cropping	Resizing	Padding	Output Width and Height
No	No	No	aippOutputW = srcImageSizeW aippOutputH = srcImageSizeH
Yes	No	No	aippOutputW = cropSizeW aippOutputH = cropSizeH
Yes	Yes	No	aippOutputW = scfOutputSizeW aippOutputH = scfOutputSizeH
Yes	No	Yes	aippOutputW = cropSizeW + paddingSizeLeft + paddingSizeRight aippOutputH = cropSizeH + paddingSizeTop + paddingSizeBottom
No	No	Yes	aippOutputW = srcImageSizeW + paddingSizeLeft + paddingSizeRight aippOutputH = srcImageSizeH + paddingSizeTop + paddingSizeBottom
No	Yes	Yes	aippOutputW = scfOutputSizeW + paddingSizeLeft + paddingSizeRight aippOutputH = scfOutputSizeH + paddingSizeTop + paddingSizeBottom
No	Yes	No	aippOutputW = scfOutputSizeW aippOutputH = scfOutputSizeH
Yes	Yes	Yes	aippOutputW = scfOutputSizeW + paddingSizeLeft + paddingSizeRight aippOutputH = scfOutputSizeH + paddingSizeTop + paddingSizeBottom

Reference

For the API call example, see Dynamic AIPP (Multiple Dynamic AIPP Inputs).

Parent topic: Model Execution