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[] inputData = {input0, input1, ...};
* byte[] data = serializeObject(inputData);
* }
*
* For Executorch model, this field is a serialized EValue array. * * @hide */ @NonNull private byte[] mData; /** * 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 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 outputs = new HashMap<>();
* outputs.put(0, output);
* expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
* }
*/
@NonNull private InferenceOutput mExpectedOutputStructure;
public static class Params {
/** A {@link KeyValueStore} where pre-trained model is stored. */
@NonNull private KeyValueStore mKeyValueStore;
/** The key of the table where the corresponding value stores a pre-trained model. */
@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 model is an executorch model. */
@FlaggedApi(Flags.FLAG_EXECUTORCH_INFERENCE_API_ENABLED)
public static final int MODEL_TYPE_EXECUTORCH = 2;
/**
* The type of the model.
*
* @hide
*/
@IntDef(
prefix = "MODEL_TYPE",
value = {MODEL_TYPE_TENSORFLOW_LITE, MODEL_TYPE_EXECUTORCH})
@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} .
*/
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;
/**
* Creates a new Params.
*
* @param keyValueStore A {@link KeyValueStore} where pre-trained model is stored.
* @param modelKey The key of the table where the corresponding value stores a pre-trained
* model.
* @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} .
* @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
*/
public Params(
@NonNull KeyValueStore keyValueStore,
@NonNull String modelKey,
@Delegate int delegateType,
@ModelType int modelType,
@IntRange(from = 1) int recommendedNumThreads) {
this.mKeyValueStore = Objects.requireNonNull(keyValueStore);
this.mModelKey = Objects.requireNonNull(modelKey);
this.mDelegateType = delegateType;
this.mModelType = modelType;
if (!(mModelType == MODEL_TYPE_TENSORFLOW_LITE)
&& !(mModelType == MODEL_TYPE_EXECUTORCH)) {
throw new java.lang.IllegalArgumentException(
"modelType was "
+ mModelType
+ " but must be one of: "
+ "MODEL_TYPE_TENSORFLOW_LITE("
+ MODEL_TYPE_TENSORFLOW_LITE
+ "), "
+ "MODEL_TYPE_EXECUTORCH("
+ MODEL_TYPE_EXECUTORCH
+ ")");
}
this.mRecommendedNumThreads = recommendedNumThreads;
Preconditions.checkState(
recommendedNumThreads >= 1,
"recommend thread number should be large or equal to 1");
}
/** A {@link KeyValueStore} where pre-trained model is stored. */
public @NonNull KeyValueStore getKeyValueStore() {
return mKeyValueStore;
}
/** The key of the table where the corresponding value stores a pre-trained model. */
public @NonNull String getModelKey() {
return mModelKey;
}
/**
* The delegate to run model inference. If not set, the default value is {@link
* #DELEGATE_CPU}.
*/
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} .
*/
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.
*/
public @IntRange(from = 1) int getRecommendedNumThreads() {
return mRecommendedNumThreads;
}
@Override
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
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")
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.
* @param modelKey key of the table where the corresponding value stores a pre-trained
* model.
*/
public Builder(@NonNull KeyValueStore keyValueStore, @NonNull String modelKey) {
mKeyValueStore = Objects.requireNonNull(keyValueStore);
mModelKey = Objects.requireNonNull(modelKey);
}
/** A {@link KeyValueStore} where pre-trained model is stored. */
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. */
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}.
*/
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} .
*/
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.
*/
public @NonNull Builder setRecommendedNumThreads(@IntRange(from = 1) int value) {
mBuilderFieldsSet |= 0x10;
mRecommendedNumThreads = value;
return this;
}
/** Builds the instance. This builder should not be touched after calling this! */
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;
}
}
}
/* package-private */ InferenceInput(
@NonNull Params params,
@NonNull byte[] data,
int batchSize,
@NonNull InferenceOutput expectedOutputStructure) {
this.mParams = Objects.requireNonNull(params);
this.mData = Objects.requireNonNull(data);
this.mBatchSize = batchSize;
this.mExpectedOutputStructure = Objects.requireNonNull(expectedOutputStructure);
}
/** The configuration that controls runtime interpreter behavior. */
public @NonNull Params getParams() {
return mParams;
}
/**
* 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[] inputData = {input0, input1, ...};
* byte[] data = serializeObject(inputData);
* }
*
* For Executorch model, this field is a serialized EValue array. */ @FlaggedApi(Flags.FLAG_EXECUTORCH_INFERENCE_API_ENABLED) public @NonNull byte[] getData() { return mData; } /** * Note: use {@link InferenceInput#getData()} 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, ...};
* }
*
* For LiteRT, this field is mapped to inputs of runForMultipleInputsOutputs:
* https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9
*/
@SuppressLint("ArrayReturn")
public @NonNull Object[] getInputData() {
return (Object[]) deserializeObject(mData);
}
/**
* 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.
*/
public int getBatchSize() {
return mBatchSize;
}
/**
* The empty InferenceOutput representing the expected output structure. 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 outputs = new HashMap<>();
* outputs.put(0, output);
* expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
* }
*/
public @NonNull InferenceOutput getExpectedOutputStructure() {
return mExpectedOutputStructure;
}
@Override
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(mData, that.mData)
&& mBatchSize == that.mBatchSize
&& java.util.Objects.equals(
mExpectedOutputStructure, that.mExpectedOutputStructure);
}
@Override
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(mData);
_hash = 31 * _hash + mBatchSize;
_hash = 31 * _hash + java.util.Objects.hashCode(mExpectedOutputStructure);
return _hash;
}
/** A builder for {@link InferenceInput} */
@SuppressWarnings("WeakerAccess")
public static final class Builder {
private @NonNull Params mParams;
private @NonNull byte[] mData;
private int mBatchSize;
private @NonNull InferenceOutput mExpectedOutputStructure =
new InferenceOutput.Builder().build();
private long mBuilderFieldsSet = 0L;
/**
* Note: use {@link 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, ...};
* }
*
* For LiteRT, the inference code will verify whether the 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();
*
* }
*
* @param params configuration that controls runtime interpreter behavior.
* @param inputData an array of input data.
* @param expectedOutputStructure an empty InferenceOutput representing the expected output
* structure.
*/
public Builder(
@NonNull Params params,
@SuppressLint("ArrayReturn") @NonNull Object[] inputData,
@NonNull InferenceOutput expectedOutputStructure) {
mParams = Objects.requireNonNull(params);
mData = serializeObject(Objects.requireNonNull(inputData));
mExpectedOutputStructure = Objects.requireNonNull(expectedOutputStructure);
}
/**
* 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);
* }
*
* For Executorch, inputData field is mapped to a serialized EValue array. * * @param params configuration that controls runtime interpreter behavior. * @param inputData byte array that holds serialized input data. */ @FlaggedApi(Flags.FLAG_EXECUTORCH_INFERENCE_API_ENABLED) public Builder(@NonNull Params params, @NonNull byte[] inputData) { mParams = Objects.requireNonNull(params); mData = Objects.requireNonNull(inputData); } /** The configuration that controls runtime interpreter behavior. */ public @NonNull Builder setParams(@NonNull Params value) { mBuilderFieldsSet |= 0x1; mParams = value; return this; } /** * 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);
* }
*
* For Executorch model, this field is a serialized EValue array. */ @FlaggedApi(Flags.FLAG_EXECUTORCH_INFERENCE_API_ENABLED) public @NonNull Builder setInputData(@NonNull byte[] value) { mBuilderFieldsSet |= 0x2; mData = value; return this; } /** * Note: use {@link 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, ...};
* }
*
* For LiteRT, this field is mapped to inputs of runForMultipleInputsOutputs:
* https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi#parameters_9
*/
public @NonNull Builder setInputData(@NonNull Object... value) {
mBuilderFieldsSet |= 0x2;
mData = serializeObject(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.
*/
public @NonNull Builder setBatchSize(int value) {
mBuilderFieldsSet |= 0x4;
mBatchSize = value;
return this;
}
/**
* 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 outputs = new HashMap<>();
* outputs.put(0, output);
* expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
* }
*/
public @NonNull Builder setExpectedOutputStructure(@NonNull InferenceOutput value) {
mBuilderFieldsSet |= 0x8;
mExpectedOutputStructure = value;
return this;
}
/** @hide */
private void validateInputData() {
Preconditions.checkArgument(
mData.length > 0, "Input data should not be empty for InferenceInput.");
}
/** @hide */
private void validateOutputStructure() {
// ExecuTorch model doesn't require set output structure.
if (mParams.getModelType() != Params.MODEL_TYPE_TENSORFLOW_LITE) {
return;
}
Preconditions.checkArgument(
!mExpectedOutputStructure.getDataOutputs().isEmpty()
|| mExpectedOutputStructure.getData().length > 0,
"ExpectedOutputStructure field is required for TensorflowLite model.");
}
/** Builds the instance. This builder should not be touched after calling this! */
public @NonNull InferenceInput build() {
mBuilderFieldsSet |= 0x10; // Mark builder used
if ((mBuilderFieldsSet & 0x4) == 0) {
mBatchSize = 1;
}
validateInputData();
validateOutputStructure();
InferenceInput o =
new InferenceInput(mParams, mData, mBatchSize, mExpectedOutputStructure);
return o;
}
}
}