Builder
class Builder
kotlin.Any | |
↳ | android.adservices.ondevicepersonalization.InferenceInput.Builder |
A builder for InferenceInput
Summary
Public constructors | |
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Builder(params: InferenceInput.Params, inputData: ByteArray) Creates a new Builder with provided runtime parameters and input data. |
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Builder(params: InferenceInput.Params, inputData: Array<Any!>, expectedOutputStructure: InferenceOutput) Note: use |
Public methods | |
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InferenceInput |
build() Builds the instance. |
InferenceInput.Builder |
setBatchSize(value: Int) The number of input examples. |
InferenceInput.Builder |
The empty InferenceOutput representing the expected output structure. |
InferenceInput.Builder |
setInputData(value: ByteArray) A byte array that holds input data. |
InferenceInput.Builder |
setInputData(vararg value: Any!) Note: use |
InferenceInput.Builder |
setParams(value: InferenceInput.Params) The configuration that controls runtime interpreter behavior. |
Public constructors
Builder
Builder(
params: InferenceInput.Params,
inputData: ByteArray)
Creates a new Builder with provided runtime parameters and input data.
For LiteRT, inputData field is mapped to inputs of runForMultipleInputsOutputs: https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9 For example, if a model takes multiple inputs:
<code>String[] input0 = {"foo", "bar"}; // string tensor shape is [2]. int[] input1 = new int[]{3, 2, 1}; // int tensor shape is [3]. Object[] data = {input0, input1, ...}; byte[] inputData = serializeObject(data); </code>
For Executorch, inputData field is mapped to a serialized EValue array.
Parameters | |
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params |
InferenceInput.Params: configuration that controls runtime interpreter behavior. This value cannot be null . |
inputData |
ByteArray: byte array that holds serialized input data. This value cannot be null . |
Builder
Builder(
params: InferenceInput.Params,
inputData: Array<Any!>,
expectedOutputStructure: InferenceOutput)
Note: use InferenceInput.Builder.Builder(Params, byte[])
instead.
Creates a new Builder for LiteRT model inference input. For LiteRT, inputData field is mapped to inputs of runForMultipleInputsOutputs: https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9 The inputs should be in the same order as inputs * of the model. *
For example, if a model takes multiple inputs: *
<code>String[] input0 = {"foo", "bar"}; // string tensor shape is [2]. int[] input1 = new int[]{3, 2, 1}; // int tensor shape is [3]. Object[] inputData = {input0, input1, ...}; </code>
If a model produce string tensors:
<code>String[] output = new String[3][2]; // Output tensor shape is [3, 2]. HashMap<Integer, Object> outputs = new HashMap<>(); outputs.put(0, output); expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build(); </code>
Parameters | |
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params |
InferenceInput.Params: configuration that controls runtime interpreter behavior. This value cannot be null . |
inputData |
Array<Any!>: an array of input data. This value cannot be null . |
expectedOutputStructure |
InferenceOutput: an empty InferenceOutput representing the expected output structure. This value cannot be null . |
Public methods
build
fun build(): InferenceInput
Builds the instance. This builder should not be touched after calling this!
Return | |
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InferenceInput |
This value cannot be null . |
setBatchSize
fun setBatchSize(value: Int): InferenceInput.Builder
The number of input examples. Adopter can set this field to run batching inference. The batch size is 1 by default. The batch size should match the input data size.
Return | |
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InferenceInput.Builder |
This value cannot be null . |
setExpectedOutputStructure
fun setExpectedOutputStructure(value: InferenceOutput): InferenceInput.Builder
The empty InferenceOutput representing the expected output structure. It's only required by LiteRT model. For LiteRT, the inference code will verify whether this expected output structure matches model output signature.
If a model produce string tensors:
<code>String[] output = new String[3][2]; // Output tensor shape is [3, 2]. HashMap<Integer, Object> outputs = new HashMap<>(); outputs.put(0, output); expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build(); </code>
Parameters | |
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value |
InferenceOutput: This value cannot be null . |
Return | |
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InferenceInput.Builder |
This value cannot be null . |
setInputData
fun setInputData(value: ByteArray): InferenceInput.Builder
A byte array that holds input data. The inputs should be in the same order as inputs of the model.
For LiteRT, this field is mapped to inputs of runForMultipleInputsOutputs: https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9
<code>String[] input0 = {"foo", "bar"}; // string tensor shape is [2]. int[] input1 = new int[]{3, 2, 1}; // int tensor shape is [3]. Object[] data = {input0, input1, ...}; byte[] inputData = serializeObject(data); </code>
For Executorch model, this field is a serialized EValue array.
Parameters | |
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value |
ByteArray: This value cannot be null . |
Return | |
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InferenceInput.Builder |
This value cannot be null . |
setInputData
fun setInputData(vararg value: Any!): InferenceInput.Builder
Note: use InferenceInput.Builder.setInputData(byte[])
instead.
An array of input data. The inputs should be in the same order as inputs of the model.
For example, if a model takes multiple inputs:
<code>String[] input0 = {"foo", "bar"}; // string tensor shape is [2]. int[] input1 = new int[]{3, 2, 1}; // int tensor shape is [3]. Object[] inputData = {input0, input1, ...}; </code>
Parameters | |
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value |
Any!: This value cannot be null . |
Return | |
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InferenceInput.Builder |
This value cannot be null . |
setParams
fun setParams(value: InferenceInput.Params): InferenceInput.Builder
The configuration that controls runtime interpreter behavior.
Parameters | |
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value |
InferenceInput.Params: This value cannot be null . |
Return | |
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InferenceInput.Builder |
This value cannot be null . |