Mobile application developers typically interact with typed objects such as bitmaps or primitives such as integers. However, the TensorFlow Lite Interpreter that runs the on-device machine learning model uses tensors in the form of ByteBuffer, which can be difficult to debug and manipulate. The TensorFlow Lite Android Support Library is designed to help process the input and output of TensorFlow Lite models, and make the TensorFlow Lite interpreter easier to use.
We welcome feedback from the community as we develop this support library, especially around:
Copy the .tflite model file to the assets directory for the Android module where the model will be run. Specify that the file should not be compressed, and add the TensorFlow Lite library to the module’s build.gradle file:
android { // Other settings // Specify tflite file should not be compressed for the app apk aaptOptions { noCompress "tflite" } } dependencies { // Other dependencies // Import tflite dependencies implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly' implementation 'org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly' implementation 'org.tensorflow:tensorflow-lite-support:0.0.0-nightly' }
The TensorFlow Lite Support Library has a suite of basic image manipulation methods such as crop and resize. To use it, create an ImagePreprocessor and add the required operations. To convert the image into the tensor format required by the TensorFlow Lite interpreter, create a TensorImage to be used as input:
import org.tensorflow.lite.support.image.ImageProcessor; import org.tensorflow.lite.support.image.TensorImage; import org.tensorflow.lite.support.image.ops.ResizeOp; // Initialization code // Create an ImageProcessor with all ops required. For more ops, please // refer to the ImageProcessor Architecture section in this README. ImageProcessor imageProcessor = new ImageProcessor.Builder() .add(new ResizeOp(224, 224, ResizeOp.ResizeMethod.BILINEAR)) .build(); // Create a TensorImage object, this creates the tensor the TensorFlow Lite // interpreter needs TensorImage tImage = new TensorImage(DataType.UINT8); // Analysis code for every frame // Preprocess the image tImage.load(bitmap); tImage = imageProcessor.process(tImage);
Before running the model, we need to create the container objects that will store the result:
import org.tensorflow.lite.support.tensorbuffer.TensorBuffer; // Create a container for the result and specify that this is a quantized model. // Hence, the 'DataType' is defined as UINT8 (8-bit unsigned integer) TensorBuffer probabilityBuffer = TensorBuffer.createFixedSize(new int[]{1, 1001}, DataType.UINT8);
Loading the model and running inference:
import org.tensorflow.lite.support.model.Model; // Initialise the model try{ MappedByteBuffer tfliteModel = FileUtil.loadMappedFile(activity, "mobilenet_v1_1.0_224_quant.tflite"); Interpreter tflite = new Interpreter(tfliteModel) } catch (IOException e){ Log.e("tfliteSupport", "Error reading model", e); } // Running inference if(null != tflite) tflite.run(tImage.getBuffer(), probabilityBuffer.getBuffer());
Developers can access the output directly through probabilityBuffer.getFloatArray()
. If the model produces a quantized output, remember to convert the result. For the MobileNet quantized model, the developer needs to divide each output value by 255 to obtain the probability ranging from 0 (least likely) to 1 (most likely) for each category.
Developers can also optionally map the results to labels. First, copy the text file containing labels into the module’s assets directory. Next, load the label file using the following code:
import org.tensorflow.lite.support.common.FileUtil; final String ASSOCIATED_AXIS_LABELS = "labels.txt"; List<String> associatedAxisLabels = null; try{ associatedAxisLabels = FileUtil.loadLabels(this, ASSOCIATED_AXIS_LABELS); } catch (IOException e){ Log.e("tfliteSupport", "Error reading label file", e); }
The following snippet demonstrates how to associate the probabilities with category labels:
import org.tensorflow.lite.support.common.TensorProcessor; import org.tensorflow.lite.support.label.TensorLabel; // Post-processor which dequantize the result TensorProcessor probabilityProcessor = new TensorProcessor.Builder().add(new NormalizeOp(0, 255)).build(); if (null != associatedAxisLabels) { // Map of labels and their corresponding probability TensorLabel labels = new TensorLabel(associatedAxisLabels, probabilityProcessor.process(probabilityBuffer)); // Create a map to access the result based on label Map<String, Float> floatMap = labels.getMapWithFloatValue(); }
The current experimental version of the TensorFlow Lite Support Library covers:
Future versions will improve support for text-related applications.
The design of the ImageProcessor
allowed the image manipulation operations to be defined up front and optimised during the build process. The ImageProcessor
currently supports three basic preprocessing operations:
int width = bitmap.getWidth(); int height = bitmap.getHeight(); int size = height > width ? width : height; ImageProcessor imageProcessor = new ImageProcessor.Builder() // Center crop the image to the largest square possible .add(new ResizeWithCropOrPadOp(size , size)) // Resize using Bilinear or Nearest neighbour .add(new ResizeOp(224, 224, ResizeOp.ResizeMethod.BILINEAR)); // Rotation counter-clockwise in 90 degree increments .add(new Rot90Op(rotateDegrees/90)) .build();
The eventual goal of the support library is to support all tf.image
transformations. This means the transformation will be the same as TensorFlow and the implementation will be independent of the operating system.
Developers are also welcome to create custom processors. It is important in these cases to be aligned with the training process - i.e. the same preprocessing should apply to both training and inference to increase reproducibility.
When initiating input or output objects such as TensorImage
or TensorBuffer
the developer will need to specify whether they are to be quantized, by specifying their type to be DataType.UINT8
or DataType.FLOAT32
.
The TensorProcessor
can be used to quantize input tensors or dequantize output tensors. For example, when processing a quantized output TensorBuffer
, the developer can use NormalizeOp
to dequantize the result to a floating point probability between 0 and 1:
import org.tensorflow.lite.support.common.TensorProcessor; // Post-processor which dequantize the result TensorProcessor probabilityProcessor = new TensorProcessor.Builder().add(new NormalizeOp(0, 255)).build(); // Post-processor which dequantize the result TensorBuffer dequantizedBuffer = probabilityProcessor.process(probabilityBuffer)