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, ...};
* }
*
* For TFLite, this field is mapped to inputs of runForMultipleInputsOutputs:
* https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9
*/
@NonNull private Object[] mInputData;
/**
* 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.
*/
private int mBatchSize = 1;
/**
* The empty InferenceOutput representing the expected output structure. For TFLite, 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 outputs = new HashMap<>();
* outputs.put(0, output);
* expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
* }
*/
@NonNull private InferenceOutput mExpectedOutputStructure;
@DataClass(genBuilder = true, genHiddenConstructor = true, genEqualsHashCode = true)
public static class Params {
/**
* A {@link KeyValueStore} where pre-trained model is stored. Only supports TFLite model
* now.
*/
@NonNull private KeyValueStore mKeyValueStore;
/**
* The key of the table where the corresponding value stores a pre-trained model. Only
* supports TFLite model now.
*/
@NonNull private String mModelKey;
/** The model inference will run on CPU. */
public static final int DELEGATE_CPU = 1;
/**
* The delegate to run model inference.
*
* @hide
*/
@IntDef(
prefix = "DELEGATE_",
value = {DELEGATE_CPU})
@Retention(RetentionPolicy.SOURCE)
public @interface Delegate {}
/**
* The delegate to run model inference. If not set, the default value is {@link
* #DELEGATE_CPU}.
*/
private @Delegate int mDelegateType = DELEGATE_CPU;
/** The model is a tensorflow lite model. */
public static final int MODEL_TYPE_TENSORFLOW_LITE = 1;
/**
* The type of the model.
*
* @hide
*/
@IntDef(
prefix = "MODEL_TYPE",
value = {MODEL_TYPE_TENSORFLOW_LITE})
@Retention(RetentionPolicy.SOURCE)
public @interface ModelType {}
/**
* The type of the pre-trained model. If not set, the default value is {@link
* #MODEL_TYPE_TENSORFLOW_LITE} . Only supports {@link #MODEL_TYPE_TENSORFLOW_LITE} for now.
*/
private @ModelType int mModelType = MODEL_TYPE_TENSORFLOW_LITE;
/**
* The number of threads used for intraop parallelism on CPU, must be positive number.
* Adopters can set this field based on model architecture. The actual thread number depends
* on system resources and other constraints.
*/
private @IntRange(from = 1) int mRecommendedNumThreads = 1;
// Code below generated by codegen v1.0.23.
//
// DO NOT MODIFY!
// CHECKSTYLE:OFF Generated code
//
// To regenerate run:
// $ codegen
// $ANDROID_BUILD_TOP/packages/modules/OnDevicePersonalization/framework/java/android/adservices/ondevicepersonalization/InferenceInput.java
//
// To exclude the generated code from IntelliJ auto-formatting enable (one-time):
// Settings > Editor > Code Style > Formatter Control
// @formatter:off
/**
* Creates a new Params.
*
* @param keyValueStore A {@link KeyValueStore} where pre-trained model is stored. Only
* supports TFLite model now.
* @param modelKey The key of the table where the corresponding value stores a pre-trained
* model. Only supports TFLite model now.
* @param delegateType The delegate to run model inference. If not set, the default value is
* {@link #DELEGATE_CPU}.
* @param modelType The type of the pre-trained model. If not set, the default value is
* {@link #MODEL_TYPE_TENSORFLOW_LITE} . Only supports {@link
* #MODEL_TYPE_TENSORFLOW_LITE} for now.
* @param recommendedNumThreads The number of threads used for intraop parallelism on CPU,
* must be positive number. Adopters can set this field based on model architecture. The
* actual thread number depends on system resources and other constraints.
* @hide
*/
@DataClass.Generated.Member
public Params(
@NonNull KeyValueStore keyValueStore,
@NonNull String modelKey,
@Delegate int delegateType,
@ModelType int modelType,
@IntRange(from = 1) int recommendedNumThreads) {
this.mKeyValueStore = keyValueStore;
AnnotationValidations.validate(NonNull.class, null, mKeyValueStore);
this.mModelKey = modelKey;
AnnotationValidations.validate(NonNull.class, null, mModelKey);
this.mDelegateType = delegateType;
AnnotationValidations.validate(Delegate.class, null, mDelegateType);
this.mModelType = modelType;
AnnotationValidations.validate(ModelType.class, null, mModelType);
this.mRecommendedNumThreads = recommendedNumThreads;
AnnotationValidations.validate(IntRange.class, null, mRecommendedNumThreads, "from", 1);
// onConstructed(); // You can define this method to get a callback
}
/**
* A {@link KeyValueStore} where pre-trained model is stored. Only supports TFLite model
* now.
*/
@DataClass.Generated.Member
public @NonNull KeyValueStore getKeyValueStore() {
return mKeyValueStore;
}
/**
* The key of the table where the corresponding value stores a pre-trained model. Only
* supports TFLite model now.
*/
@DataClass.Generated.Member
public @NonNull String getModelKey() {
return mModelKey;
}
/**
* The delegate to run model inference. If not set, the default value is {@link
* #DELEGATE_CPU}.
*/
@DataClass.Generated.Member
public @Delegate int getDelegateType() {
return mDelegateType;
}
/**
* The type of the pre-trained model. If not set, the default value is {@link
* #MODEL_TYPE_TENSORFLOW_LITE} . Only supports {@link #MODEL_TYPE_TENSORFLOW_LITE} for now.
*/
@DataClass.Generated.Member
public @ModelType int getModelType() {
return mModelType;
}
/**
* The number of threads used for intraop parallelism on CPU, must be positive number.
* Adopters can set this field based on model architecture. The actual thread number depends
* on system resources and other constraints.
*/
@DataClass.Generated.Member
public @IntRange(from = 1) int getRecommendedNumThreads() {
return mRecommendedNumThreads;
}
@Override
@DataClass.Generated.Member
public boolean equals(@android.annotation.Nullable Object o) {
// You can override field equality logic by defining either of the methods like:
// boolean fieldNameEquals(Params other) { ... }
// boolean fieldNameEquals(FieldType otherValue) { ... }
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
@SuppressWarnings("unchecked")
Params that = (Params) o;
//noinspection PointlessBooleanExpression
return true
&& java.util.Objects.equals(mKeyValueStore, that.mKeyValueStore)
&& java.util.Objects.equals(mModelKey, that.mModelKey)
&& mDelegateType == that.mDelegateType
&& mModelType == that.mModelType
&& mRecommendedNumThreads == that.mRecommendedNumThreads;
}
@Override
@DataClass.Generated.Member
public int hashCode() {
// You can override field hashCode logic by defining methods like:
// int fieldNameHashCode() { ... }
int _hash = 1;
_hash = 31 * _hash + java.util.Objects.hashCode(mKeyValueStore);
_hash = 31 * _hash + java.util.Objects.hashCode(mModelKey);
_hash = 31 * _hash + mDelegateType;
_hash = 31 * _hash + mModelType;
_hash = 31 * _hash + mRecommendedNumThreads;
return _hash;
}
/** A builder for {@link Params} */
@SuppressWarnings("WeakerAccess")
@DataClass.Generated.Member
public static final class Builder {
private @NonNull KeyValueStore mKeyValueStore;
private @NonNull String mModelKey;
private @Delegate int mDelegateType;
private @ModelType int mModelType;
private @IntRange(from = 1) int mRecommendedNumThreads;
private long mBuilderFieldsSet = 0L;
/**
* Creates a new Builder.
*
* @param keyValueStore A {@link KeyValueStore} where pre-trained model is stored. Only
* supports TFLite model now.
* @param modelKey The key of the table where the corresponding value stores a
* pre-trained model. Only supports TFLite model now.
*/
public Builder(@NonNull KeyValueStore keyValueStore, @NonNull String modelKey) {
mKeyValueStore = keyValueStore;
AnnotationValidations.validate(NonNull.class, null, mKeyValueStore);
mModelKey = modelKey;
AnnotationValidations.validate(NonNull.class, null, mModelKey);
}
/**
* A {@link KeyValueStore} where pre-trained model is stored. Only supports TFLite model
* now.
*/
@DataClass.Generated.Member
public @NonNull Builder setKeyValueStore(@NonNull KeyValueStore value) {
mBuilderFieldsSet |= 0x1;
mKeyValueStore = value;
return this;
}
/**
* The key of the table where the corresponding value stores a pre-trained model. Only
* supports TFLite model now.
*/
@DataClass.Generated.Member
public @NonNull Builder setModelKey(@NonNull String value) {
mBuilderFieldsSet |= 0x2;
mModelKey = value;
return this;
}
/**
* The delegate to run model inference. If not set, the default value is {@link
* #DELEGATE_CPU}.
*/
@DataClass.Generated.Member
public @NonNull Builder setDelegateType(@Delegate int value) {
mBuilderFieldsSet |= 0x4;
mDelegateType = value;
return this;
}
/**
* The type of the pre-trained model. If not set, the default value is {@link
* #MODEL_TYPE_TENSORFLOW_LITE} . Only supports {@link #MODEL_TYPE_TENSORFLOW_LITE} for
* now.
*/
@DataClass.Generated.Member
public @NonNull Builder setModelType(@ModelType int value) {
mBuilderFieldsSet |= 0x8;
mModelType = value;
return this;
}
/**
* The number of threads used for intraop parallelism on CPU, must be positive number.
* Adopters can set this field based on model architecture. The actual thread number
* depends on system resources and other constraints.
*/
@DataClass.Generated.Member
public @NonNull Builder setRecommendedNumThreads(@IntRange(from = 1) int value) {
mBuilderFieldsSet |= 0x10;
mRecommendedNumThreads = value;
return this;
}
/** Builds the instance. */
public @NonNull Params build() {
mBuilderFieldsSet |= 0x20; // Mark builder used
if ((mBuilderFieldsSet & 0x4) == 0) {
mDelegateType = DELEGATE_CPU;
}
if ((mBuilderFieldsSet & 0x8) == 0) {
mModelType = MODEL_TYPE_TENSORFLOW_LITE;
}
if ((mBuilderFieldsSet & 0x10) == 0) {
mRecommendedNumThreads = 1;
}
Params o =
new Params(
mKeyValueStore,
mModelKey,
mDelegateType,
mModelType,
mRecommendedNumThreads);
return o;
}
}
@DataClass.Generated(
time = 1709250081597L,
codegenVersion = "1.0.23",
sourceFile =
"packages/modules/OnDevicePersonalization/framework/java/android/adservices/ondevicepersonalization/InferenceInput.java",
inputSignatures =
"private @android.annotation.NonNull android.adservices.ondevicepersonalization.KeyValueStore mKeyValueStore\nprivate @android.annotation.NonNull java.lang.String mModelKey\npublic static final int DELEGATE_CPU\nprivate @android.adservices.ondevicepersonalization.Params.Delegate int mDelegateType\npublic static final int MODEL_TYPE_TENSORFLOW_LITE\nprivate @android.adservices.ondevicepersonalization.Params.ModelType int mModelType\nprivate @android.annotation.IntRange int mRecommendedNumThreads\nclass Params extends java.lang.Object implements []\n@com.android.ondevicepersonalization.internal.util.DataClass(genBuilder=true, genHiddenConstructor=true, genEqualsHashCode=true)")
@Deprecated
private void __metadata() {}
// @formatter:on
// End of generated code
}
// Code below generated by codegen v1.0.23.
//
// DO NOT MODIFY!
// CHECKSTYLE:OFF Generated code
//
// To regenerate run:
// $ codegen
// $ANDROID_BUILD_TOP/packages/modules/OnDevicePersonalization/framework/java/android/adservices/ondevicepersonalization/InferenceInput.java
//
// To exclude the generated code from IntelliJ auto-formatting enable (one-time):
// Settings > Editor > Code Style > Formatter Control
// @formatter:off
@DataClass.Generated.Member
/* package-private */ InferenceInput(
@NonNull Params params,
@NonNull Object[] inputData,
int batchSize,
@NonNull InferenceOutput expectedOutputStructure) {
this.mParams = params;
AnnotationValidations.validate(NonNull.class, null, mParams);
this.mInputData = inputData;
AnnotationValidations.validate(NonNull.class, null, mInputData);
this.mBatchSize = batchSize;
this.mExpectedOutputStructure = expectedOutputStructure;
AnnotationValidations.validate(NonNull.class, null, mExpectedOutputStructure);
// onConstructed(); // You can define this method to get a callback
}
/** The configuration that controls runtime interpreter behavior. */
@DataClass.Generated.Member
public @NonNull Params getParams() {
return mParams;
}
/**
* 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, ...};
* }
*
* For TFLite, this field is mapped to inputs of runForMultipleInputsOutputs:
* https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9
*/
@SuppressLint("ArrayReturn")
@DataClass.Generated.Member
public @NonNull Object[] getInputData() {
return mInputData;
}
/**
* 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.
*/
@DataClass.Generated.Member
public int getBatchSize() {
return mBatchSize;
}
/**
* The empty InferenceOutput representing the expected output structure. For TFLite, 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 outputs = new HashMap<>();
* outputs.put(0, output);
* expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
* }
*/
@DataClass.Generated.Member
public @NonNull InferenceOutput getExpectedOutputStructure() {
return mExpectedOutputStructure;
}
@Override
@DataClass.Generated.Member
public boolean equals(@android.annotation.Nullable Object o) {
// You can override field equality logic by defining either of the methods like:
// boolean fieldNameEquals(InferenceInput other) { ... }
// boolean fieldNameEquals(FieldType otherValue) { ... }
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
@SuppressWarnings("unchecked")
InferenceInput that = (InferenceInput) o;
//noinspection PointlessBooleanExpression
return true
&& java.util.Objects.equals(mParams, that.mParams)
&& java.util.Arrays.equals(mInputData, that.mInputData)
&& mBatchSize == that.mBatchSize
&& java.util.Objects.equals(
mExpectedOutputStructure, that.mExpectedOutputStructure);
}
@Override
@DataClass.Generated.Member
public int hashCode() {
// You can override field hashCode logic by defining methods like:
// int fieldNameHashCode() { ... }
int _hash = 1;
_hash = 31 * _hash + java.util.Objects.hashCode(mParams);
_hash = 31 * _hash + java.util.Arrays.hashCode(mInputData);
_hash = 31 * _hash + mBatchSize;
_hash = 31 * _hash + java.util.Objects.hashCode(mExpectedOutputStructure);
return _hash;
}
/** A builder for {@link InferenceInput} */
@SuppressWarnings("WeakerAccess")
@DataClass.Generated.Member
public static final class Builder {
private @NonNull Params mParams;
private @NonNull Object[] mInputData;
private int mBatchSize;
private @NonNull InferenceOutput mExpectedOutputStructure;
private long mBuilderFieldsSet = 0L;
/**
* Creates a new Builder.
*
* @param params The configuration that controls runtime interpreter behavior.
* @param inputData 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, ...};
*
* }
* For TFLite, this field is mapped to inputs of runForMultipleInputsOutputs:
* https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9
* @param expectedOutputStructure The empty InferenceOutput representing the expected output
* structure. For TFLite, 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 outputs = new HashMap<>();
* outputs.put(0, output);
* expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
*
* }
*/
public Builder(
@NonNull Params params,
@SuppressLint("ArrayReturn") @NonNull Object[] inputData,
@NonNull InferenceOutput expectedOutputStructure) {
mParams = params;
AnnotationValidations.validate(NonNull.class, null, mParams);
mInputData = inputData;
AnnotationValidations.validate(NonNull.class, null, mInputData);
mExpectedOutputStructure = expectedOutputStructure;
AnnotationValidations.validate(NonNull.class, null, mExpectedOutputStructure);
}
/** The configuration that controls runtime interpreter behavior. */
@DataClass.Generated.Member
public @NonNull Builder setParams(@NonNull Params value) {
mBuilderFieldsSet |= 0x1;
mParams = value;
return this;
}
/**
* 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, ...};
* }
*
* For TFLite, this field is mapped to inputs of runForMultipleInputsOutputs:
* https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9
*/
@DataClass.Generated.Member
public @NonNull Builder setInputData(@NonNull Object... value) {
mBuilderFieldsSet |= 0x2;
mInputData = value;
return this;
}
/**
* 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.
*/
@DataClass.Generated.Member
public @NonNull Builder setBatchSize(int value) {
mBuilderFieldsSet |= 0x4;
mBatchSize = value;
return this;
}
/**
* The empty InferenceOutput representing the expected output structure. For TFLite, 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 outputs = new HashMap<>();
* outputs.put(0, output);
* expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
* }
*/
@DataClass.Generated.Member
public @NonNull Builder setExpectedOutputStructure(@NonNull InferenceOutput value) {
mBuilderFieldsSet |= 0x8;
mExpectedOutputStructure = value;
return this;
}
/** Builds the instance. */
public @NonNull InferenceInput build() {
mBuilderFieldsSet |= 0x10; // Mark builder used
if ((mBuilderFieldsSet & 0x4) == 0) {
mBatchSize = 1;
}
InferenceInput o =
new InferenceInput(mParams, mInputData, mBatchSize, mExpectedOutputStructure);
return o;
}
}
@DataClass.Generated(
time = 1709250081618L,
codegenVersion = "1.0.23",
sourceFile =
"packages/modules/OnDevicePersonalization/framework/java/android/adservices/ondevicepersonalization/InferenceInput.java",
inputSignatures =
"private @android.annotation.NonNull android.adservices.ondevicepersonalization.Params mParams\nprivate @android.annotation.NonNull java.lang.Object[] mInputData\nprivate int mBatchSize\nprivate @android.annotation.NonNull android.adservices.ondevicepersonalization.InferenceOutput mExpectedOutputStructure\nclass InferenceInput extends java.lang.Object implements []\n@com.android.ondevicepersonalization.internal.util.DataClass(genBuilder=true, genEqualsHashCode=true)")
@Deprecated
private void __metadata() {}
// @formatter:on
// End of generated code
}