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TensorFlow Lite Backend

We will use this example project to show how to make AI inference with a TensorFlow Lite model in WasmEdge and Rust.


Besides the regular WasmEdge and Rust requirements, please make sure that you have the WASI-NN plugin with TensorFlow Lite installed.

Quick start

Because the example already includes a compiled WASM file from the Rust code, we could use WasmEdge CLI to execute the example directly. First, git clone the WasmEdge-WASINN-examples repo.

git clone
cd WasmEdge-WASINN-examples/tflite-birds_v1-image/

Run the inference application in WasmEdge.

wasmedge --dir .:. wasmedge-wasinn-example-tflite-bird-image.wasm lite-model_aiy_vision_classifier_birds_V1_3.tflite bird.jpg

If everything goes well, you should have the terminal output:

Read graph weights, size in bytes: 3561598
Loaded graph into wasi-nn with ID: 0
Created wasi-nn execution context with ID: 0
Read input tensor, size in bytes: 150528
Executed graph inference
1.) [166](198)Aix galericulata
2.) [158](2)Coccothraustes coccothraustes
3.) [34](1)Gallus gallus domesticus
4.) [778](1)Sitta europaea
5.) [819](1)Anas platyrhynchos

Build and run

Let's build the wasm file from the rust source code. First, git clone the WasmEdge-WASINN-examples repo.

git clone
cd WasmEdge-WASINN-examples/tflite-birds_v1-image/rust/

Second, use cargo to build the example project.

cargo build --target wasm32-wasi --release

The output WASM file is target/wasm32-wasi/release/wasmedge-wasinn-example-tflite-bird-image.wasm. Next, let's use WasmEdge to load the Tensorflow Lite model and then use it to classify objects in your image.

wasmedge --dir .:. wasmedge-wasinn-example-tflite-bird-image.wasm lite-model_aiy_vision_classifier_birds_V1_3.tflite bird.jpg

You can replace bird.jpg with your image file.

Improve performance

You can make the inference program run faster by AOT compiling the wasm file first.

wasmedge compile wasmedge-wasinn-example-tflite-bird-image.wasm out.wasm
wasmedge --dir .:. out.wasm lite-model_aiy_vision_classifier_birds_V1_3.tflite bird.jpg

Understand the code

The is the complete example Rust source. First, read the image file and Tensorflow Lite (tflite) model file names from the command line.

let args: Vec<String> = env::args().collect();
let model_bin_name: &str = &args[1]; // File name for the TFLite model
let image_name: &str = &args[2]; // File name for the input image

We use a helper function called image_to_tensor() to convert the input image into tensor data (the tensor type is U8). Now we can load the model, feed the tensor array from the image to the model, and get the inference output tensor array.

// load model
let weights = fs::read(model_bin_name)?;
let graph = GraphBuilder::new(
let mut ctx = graph.init_execution_context()?;

// Load a tensor that precisely matches the graph input tensor
let tensor_data = image_to_tensor(image_name.to_string(), 224, 224);
ctx.set_input(0, TensorType::U8, &[1, 224, 224, 3], &tensor_data)?;

// Execute the inference.

// Retrieve the output.
let mut output_buffer = vec![0u8; imagenet_classes::AIY_BIRDS_V1.len()];
_ = ctx.get_output(0, &mut output_buffer)?;

In the above code, GraphEncoding::TensorflowLite means using the PyTorch backend, and ExecutionTarget::CPU means running the computation on the CPU. Finally, we sort the output and then print the top-5 classification results. Finally, we sort the output and then print the top-5 classification results:

let results = sort_results(&output_buffer);
for i in 0..5 {
" {}.) [{}]({:.4}){}",
i + 1,