TFLiteElixir

TensorFlow Lite Elixir bindings with optional EdgeTPU support.

For pure Erlang bindings, please see cocoa-xu/tflite_beam.

Getting Started

A general workflow looks like this,

# will download and install precompiled version
Mix.install([
  {:tflite_elixir, "~> 0.3.0"}
])

# parrot.jpeg and the tflite file can be found in the test/test_data directory
interpreter = TFLiteElixir.Interpreter.new!("/path/to/mobilenet_v2_1.0_224_inat_bird_quant.tflite")
input =
  StbImage.read_file!("/path/to/parrot.jpeg")
  |> StbImage.resize(224, 224)
  |> StbImage.to_nx()

[output_tensor_0] = TFLiteElixir.Interpreter.predict(interpreter, input)
indices_nx = Nx.flatten(output_tensor_0)

# get top k predictions (numerical id of the class)
# classes can be found in this file,
# https://raw.githubusercontent.com/cocoa-xu/tflite_elixir/main/test/test_data/inat_bird_labels.txt
# each line corresponds to a class
# and the first line = id 0
top_k = 5
sorted_indices = Nx.argsort(indices_nx, direction: :desc)
top_k_indices = Nx.take(sorted_indices, Nx.iota({top_k}))
top_k_preds = Nx.to_flat_list(top_k_indices)

And there is an experimental ImageClassification module that does everything for you. It supports both CPU and TPU, and it will show more information, including scores (confidence) and the class name of the predicted results. It's also more flexible where you can adjust different parameters like top_k and threshold (for confidence) and etc.

iex> alias TFLiteElixir.ImageClassification
iex> {:ok, pid} = ImageClassification.start("/path/to/mobilenet_v2_1.0_224_inat_bird_quant.tflite")
iex> ImageClassification.predict(pid, "/path/to/parrot.jpeg")
%{class_id: 923, score: 0.70703125}
iex> ImageClassification.set_label_from_associated_file(pid, "inat_bird_labels.txt")
:ok
iex> ImageClassification.predict(pid, "/path/to/parrot.jpeg")
%{class_id: 923, label: "Ara macao (Scarlet Macaw)", score: 0.70703125}
iex> ImageClassification.predict(pid, "/path/to/parrot.jpeg", top_k: 3)
[
  %{class_id: 923, label: "Ara macao (Scarlet Macaw)", score: 0.70703125},
  %{
    class_id: 837,
    label: "Platycercus elegans (Crimson Rosella)",
    score: 0.078125
  },
  %{
    class_id: 245,
    label: "Coracias caudatus (Lilac-breasted Roller)",
    score: 0.01953125
  }
]

Nerves Support

Prebuilt firmware (Experimental)

Prebuilt firmwares are available here. Nightly builds can be found here.

Select the most recent run and scroll down to the Artifacts section, download the firmware file for your board and run

fwup /path/to/the/downloaded/firmware.fw

In the nerves build, tflite_elixir is integrated as one of the dependencies of the nerves_livebook project. This means that you can use livebook (as well as other pre-pulled libraries) to explore and evaluate the tflite_elixir project.

The default password of the livebook is nerves (as the time of writing, if it does not work, please check the nerves_livebook project).

Build from Source

If prefer precompiled binaries
```
# for example
export MIX_TARGET=rpi4
```

There is no need to explicitly set CPU architecture

for the precompiled libedgetpu binaries. The arch

is automatically detected by the `TARGET_ARCH`,

`TARGET_OS` and `TARGET_ABI` environment vars.

However, if you are using your own nerves target

you can manually set the correct arch, e.g.,

set `aarch64` for rpi4.

Possible values including

- aarch64

- armv7l

- armv6

- riscv64

- x86_64

export TFLITE_BEAM_CORAL_LIBEDGETPU_LIBRARIES=aarch64


2. If prefer not to use precompiled binaries
```shell
# for example
export MIX_TARGET=rpi4
# then set env var TFLITE_BEAM_PREFER_PRECOMPILED to false
export TFLITE_BEAM_PREFER_PRECOMPILED=false

Demo

Mix Task Demo

List all available Edge TPU
```
mix list_edgetpu
```
Image classification
```
mix help classify_image
```

Note: The first inference on Edge TPU is slow because it includes,

loading the model into Edge TPU memory

mix classify_image \ --model test/test_data/mobilenet_v2_1.0_224_inat_bird_quant.tflite \ --input test/test_data/parrot.jpeg \ --labels test/test_data/inat_bird_labels.txt


Output from the mix task

----INFERENCE TIME---- Note: The first inference on Edge TPU is slow because it includes, loading the model into Edge TPU memory. 6.7ms -------RESULTS-------- Ara macao (Scarlet Macaw): 0.70703


2. Object detection
```shell
mix help detect_image

# Note: The first inference on Edge TPU is slow because it includes,
# loading the model into Edge TPU memory
mix detect_image \
  --model test/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite \
  --input test/test_data/cat.jpeg \
  --labels test/test_data/coco_labels.txt

Output from the mix task

INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
----INFERENCE TIME----
13.2ms
cat
  id   : 16
  score: 0.953
  bbox : [3, -1, 294, 240]

test files used here are downloaded from google-coral/test_data and wikipedia.

Demo code

Model: mobilenet_v2_1.0_224_inat_bird_quant.tflite

Input image:

parrot.jpg
Or use pre-converted input parrot.bin

Labels: inat_bird_labels.txt

alias Evision, as: Cv
alias TFLiteElixir, as: TFLite

# load labels
labels = File.read!("inat_bird_labels.txt") |> String.split("\n")

# load tflite model
filename = "mobilenet_v2_1.0_224_inat_bird_quant.tflite"
model = TFLite.FlatBufferModel.build_from_file(filename)
resolver = TFLite.Ops.Builtin.BuiltinResolver.new!()
builder = TFLite.InterpreterBuilder.new!(model, resolver)
interpreter = TFLite.Interpreter.new!()
:ok = TFLite.InterpreterBuilder.build!(builder, interpreter)
:ok = TFLite.Interpreter.allocate_tensors(interpreter)

# verify loaded model, feel free to skip
# [0] = TFLite.Interpreter.inputs!(interpreter)
# [171] = TFLite.Interpreter.outputs!(interpreter)
# "map/TensorArrayStack/TensorArrayGatherV3" = TFLite.Interpreter.get_input_name!(interpreter, 0)
# "prediction" = TFLite.Interpreter.get_output_name!(interpreter, 0)
# input_tensor = TFLite.Interpreter.tensor(interpreter, 0)
# [1, 224, 224, 3] = TFLite.TFLiteTensor.dims(input_tensor)
# {:u, 8} = TFLite.TFLiteTensor.type(input_tensor)
# output_tensor = TFLite.Interpreter.tensor(interpreter, 171)
# [1, 965] = TFLite.TFLiteTensor.dims(output_tensor)
# {:u, 8} = TFLite.TFLiteTensor.type(output_tensor)

# parrot.bin - if you don't have :evision
binary = File.read!("parrot.bin")
# parrot.jpg - if you have :evision
# load image, resize it, covert to RGB and to binary
binary =
  Cv.imread("parrot.jpg")
  |> Cv.resize({224, 224})
  |> Cv.cvtColor(Cv.cv_COLOR_BGR2RGB)
  |> Cv.Mat.to_binary(mat)

# set input, run forwarding, get output
TFLite.Interpreter.input_tensor(interpreter, 0, binary)
TFLite.Interpreter.invoke(interpreter)
output_data = TFLite.Interpreter.output_tensor!(interpreter, 0)

# if you have :nx
# get predicted label
output_data
|> Nx.from_binary(:u8)
|> Nx.argmax()
|> Nx.to_scalar()
|> then(&Enum.at(labels, &1))

Coral Support

Dependencies

For macOS

# only required if not using precompiled binaries
# for compiling libusb
brew install autoconf automake

For some Linux OSes you need to manually execute the following command to update udev rules, otherwise, libedgetpu will fail to initialize Coral devices.

mix deps.get
bash "3rd_party/cache/${TFLITE_BEAM_CORAL_LIBEDGETPU_RUNTIME}/edgetpu_runtime/install.sh"

Compile-Time Environment Variable

TFLITE_BEAM_PREFER_PRECOMPILED

Use precompiled binaries when TFLITE_BEAM_PREFER_PRECOMPILED is true. Otherwise, this library will compile from source.

Defaults to true.
TFLITE_BEAM_CORAL_SUPPORT

Enable Coral Support.

Defaults to true.
TFLITE_BEAM_CORAL_USB_THROTTLE

Throttling USB Coral Devices. Please see the official warning here, google-coral/libedgetpu.

Defaults to true.

Note that only when TFLITE_BEAM_CORAL_USB_THROTTLE is set to false, :tflite_beam will use the non-throttled libedgetpu libraries.

TFLITE_BEAM_CORAL_LIBEDGETPU_LIBRARIES

Choose which ones of the libedgetpu libraries to copy to the priv directory of the :tflite_beam app.

Default value is native - only native libraries will be downloaded and copied. native corresponds to the host OS and CPU architecture when compiling this library.

When set to a specific value, e.g, darwin_arm64 or darwin_x86_64, then the corresponding one will be downloaded and copied. This option is expected to be used for cross-compiling, like with nerves.

Available values for this option are:

Value	OS/CPU
`aarch64`	Linux arm64
`armv7l`	Linux armv7
`armv6`	Linux armv6
`k8`	Linux x86_64
`x86_64`	Linux x86_64
`riscv64`	Linux riscv64
`darwin_arm64`	macOS Apple Silicon
`darwin_x86_64`	macOS x86_64

Installation

Add :tflite_elixir to your list of dependencies in mix.exs:

def deps do
  [
    {:tflite_elixir, "~> 0.3.0"}
  ]
end

Documentation can be found at https://hexdocs.pm/tflite_elixir.

cocoa-xu / tflite_elixir

readme

TFLiteElixir

Getting Started

Nerves Support

Prebuilt firmware (Experimental)

Build from Source

There is no need to explicitly set CPU architecture

for the precompiled libedgetpu binaries. The arch

is automatically detected by the `TARGET_ARCH`,

`TARGET_OS` and `TARGET_ABI` environment vars.

However, if you are using your own nerves target

you can manually set the correct arch, e.g.,

set `aarch64` for rpi4.

Possible values including

- aarch64

- armv7l

- armv6

- riscv64

- x86_64

Demo

Mix Task Demo

Note: The first inference on Edge TPU is slow because it includes,

loading the model into Edge TPU memory

Demo code

Coral Support

Dependencies

Compile-Time Environment Variable

Installation

cocoa-xu / tflite_elixir

readme

TFLiteElixir

Getting Started

Nerves Support

Prebuilt firmware (Experimental)

Build from Source

There is no need to explicitly set CPU architecture

for the precompiled libedgetpu binaries. The arch

is automatically detected by the TARGET_ARCH,

TARGET_OS and TARGET_ABI environment vars.

However, if you are using your own nerves target

you can manually set the correct arch, e.g.,

set aarch64 for rpi4.

Possible values including

- aarch64

- armv7l

- armv6

- riscv64

- x86_64

Demo

Mix Task Demo

Note: The first inference on Edge TPU is slow because it includes,

loading the model into Edge TPU memory

Demo code

Coral Support

Dependencies

Compile-Time Environment Variable

Installation

is automatically detected by the `TARGET_ARCH`,

`TARGET_OS` and `TARGET_ABI` environment vars.

set `aarch64` for rpi4.