InferenceInput

equals

fun equals(other: Any?): Boolean

Indicates whether some other object is "equal to" this one.

The equals method implements an equivalence relation on non-null object references:

• It is reflexive: for any non-null reference value x, x.equals(x) should return true.
• It is symmetric: for any non-null reference values x and y, x.equals(y) should return true if and only if y.equals(x) returns true.
• It is transitive: for any non-null reference values x, y, and z, if x.equals(y) returns true and y.equals(z) returns true, then x.equals(z) should return true.
• It is consistent: for any non-null reference values x and y, multiple invocations of x.equals(y) consistently return true or consistently return false, provided no information used in equals comparisons on the objects is modified.
• For any non-null reference value x, x.equals(null) should return false.

An equivalence relation partitions the elements it operates on into equivalence classes; all the members of an equivalence class are equal to each other. Members of an equivalence class are substitutable for each other, at least for some purposes.

getBatchSize

fun getBatchSize(): Int

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.

getExpectedOutputStructure

fun getExpectedOutputStructure(): InferenceOutput

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&lt;Integer, Object&gt; outputs = new HashMap&lt;&gt;();
  outputs.put(0, output);
  expectedOutputStructure = new InferenceOutput.Builder().setDataOutputs(outputs).build();
  </code>

getInputData

fun getInputData(): Array<Any!>

Note: use 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, ...};
  </code>

getParams

fun getParams(): InferenceInput.Params

The configuration that controls runtime interpreter behavior.

hashCode

fun hashCode(): Int