TensorflowLite-bin
Prebuilt binary for TensorflowLite’s standalone installer. For RaspberryPi.
I provide a FlexDelegate, XNNPACK enabled binary.
Here is the Tensorflow’s official README.
If you want the best performance with RaspberryPi4/3, install Ubuntu 18.04+ aarch64 (64bit) instead of Raspbian armv7l (32bit). The official Tensorflow Lite is performance tuned for aarch64. On aarch64 OS, performance is about 4 times higher than on armv7l OS. How to install Ubuntu 19.10 aarch64 (64bit) on RaspberryPi4 - Qiita - PINTO
The full build package for Tensorflow can be found here (Tensorflow-bin).
TensorFlow Lite will continue to have TensorFlow Lite builtin ops optimized for mobile and embedded devices. However, TensorFlow Lite models can now use a subset of TensorFlow ops when TFLite builtin ops are not sufficient. 1. TensorflowLite-flexdelegate (Tensorflow Select Ops) - Github - PINTO0309 2. Select TensorFlow operators to use in TensorFlow Lite
A repository that shares tuning results of trained models generated by Tensorflow. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization), Quantization-aware training. PINTO_model_zoo - Github - PINTO0309
Reference articles
-
Please refer to the following URL for details of performance. Post-training quantization with TF2.0 Keras - nb.o’s Diary. The performance evaluation article was created by @Nextremer_nb_o / Github. Thank you.
-
Add a custom OP to the TFLite runtime to build the whl installer (for Python),
MaxPoolingWithArgmax2D,MaxUnpooling2D,Convolution2DTransposeBias
Python API packages
| Device | OS | Distribution | Architecture | Python ver | Note |
|---|---|---|---|---|---|
| RaspberryPi3/4 | Raspbian/Debian | Stretch | armhf / armv7l | 3.5 | 32bit |
| RaspberryPi3/4 | Raspbian/Debian | Stretch | aarch64 / armv8 | 3.5 | 64bit |
| RaspberryPi3/4 | Raspbian/Debian | Buster | armhf / armv7l | 3.7 / 2.7 | 32bit |
| RaspberryPi3/4 | Raspbian/Debian | Buster | aarch64 / armv8 | 3.7 / 2.7 | 64bit |
| RaspberryPi3/4 | Ubuntu 20.04 | Focal | armhf / armv7l | 3.8 | 32bit |
| RaspberryPi3/4 | Ubuntu 20.04 | Focal | aarch64 / armv8 | 3.8 | 64bit |
| RaspberryPi3/4 | Ubuntu 21.04/Debian/RaspberryPiOS | Hirsute/Bullseye | armhf / armv7l | 3.9 | 32bit |
| RaspberryPi3/4 | Ubuntu 21.04/Debian/RaspberryPiOS | Hirsute/Bullseye | aarch64 / armv8 | 3.9 | 64bit |
| RaspberryPi3/4 | Ubuntu 22.04 | Jammy | armhf / armv7l | 3.10 | 32bit |
| RaspberryPi3/4 | Ubuntu 22.04 | Jammy | aarch64 / armv8 | 3.10 | 64bit |
| RaspberryPi3/4/5 | RaspberryPiOS/Debian | Bookworm | armhf / armv7l | 3.11 | 32bit |
| RaspberryPi3/4/5 | RaspberryPiOS/Debian | Bookworm | aarch64 / armv8 | 3.11 | 64bit |
Usage
sudo apt-get update && \
sudo apt install -y \
swig \
libjpeg-dev \
zlib1g-dev \
python3-dev \
python-is-python3 \
unzip \
wget \
python3-pip \
curl \
git \
cmake \
make
pip install -U pip
pip install numpy
TFVER=2.15.0.post1
PYVER=39
or
PYVER=310
or
PYVER=311
ARCH=aarch64
or
ARCH=armhf
pip install \
--no-cache-dir \
https://github.com/PINTO0309/TensorflowLite-bin/releases/download/v${TFVER}/tflite_runtime-${TFVER/-/}-cp${PYVER}-none-linux_${ARCH}.whl
Note
Unlike tensorflow this will be installed to a tflite_runtime namespace. You can then use the Tensorflow Lite interpreter as.
from tflite_runtime.interpreter import Interpreter
### Tensorflow v2.2.0
interpreter = Interpreter(model_path="foo.tflite")
### Tensorflow v2.3.0+
interpreter = Interpreter(model_path="foo.tflite", num_threads=4)
Build
BRANCH=r2.16-tflite-build
git clone -b ${BRANCH} --depth 1 https://github.com/PINTO0309/tensorflow.git
cd tensorflow/lite/tools/pip_package
make BASE_IMAGE=ubuntu:22.04 PYTHON=python3 PYTHON_VERSION=3.10 TENSORFLOW_TARGET=aarch64 docker-build
make BASE_IMAGE=debian:bookworm PYTHON=python3 PYTHON_VERSION=3.11 TENSORFLOW_TARGET=aarch64 docker-build
make BASE_IMAGE=ubuntu:22.04 PYTHON=python3 PYTHON_VERSION=3.10 TENSORFLOW_TARGET=armhf docker-build
make BASE_IMAGE=debian:bookworm PYTHON=python3 PYTHON_VERSION=3.11 TENSORFLOW_TARGET=armhf docker-build
make BASE_IMAGE=ubuntu:22.04 PYTHON=python3 PYTHON_VERSION=3.10 TENSORFLOW_TARGET=native docker-build
Operation check 【Classification】
Sample of MultiThread x4 by Tensorflow Lite [MobileNetV1 / 75ms]
Sample of MultiThread x4 by Tensorflow Lite [MobileNetV2 / 68ms]
- Environmental preparation
$ cd ~;mkdir test $ curl https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp -o ~/test/grace_hopper.bmp $ curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz | tar xzv -C ~/test mobilenet_v1_1.0_224/labels.txt $ mv ~/test/mobilenet_v1_1.0_224/labels.txt ~/test/ $ curl http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz | tar xzv -C ~/test $ cd ~/test - label_image.py ```python import argparse import numpy as np import time
from PIL import Image
from tflite_runtime.interpreter import Interpreter
def load_labels(filename):
my_labels = []
input_file = open(filename, ‘r’)
for l in input_file:
my_labels.append(l.strip())
return my_labels
if name == “main”:
floating_model = False
parser = argparse.ArgumentParser()
parser.add_argument(
“-i”,
“–image”,
default=”/tmp/grace_hopper.bmp”,
help=”image to be classified”
)
parser.add_argument(
“-m”,
“–model_file”,
default=”/tmp/mobilenet_v1_1.0_224_quant.tflite”,
help=”.tflite model to be executed”
)
parser.add_argument(
“-l”,
“–label_file”,
default=”/tmp/labels.txt”,
help=”name of file containing labels”
)
parser.add_argument(
“–input_mean”,
default=127.5,
help=”input_mean”
)
parser.add_argument(
“–input_std”,
default=127.5,
help=”input standard deviation”
)
parser.add_argument(
“–num_threads”,
default=1,
help=”number of threads”
)
args = parser.parse_args()
interpreter = Interpreter( model_path=”foo.tflite”, num_threads=args.num_threads ) try: interpreter.allocate_tensors() except: pass input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() # check the type of the input tensor if input_details[0][‘dtype’] == np.float32: floating_model = True # NxHxWxC, H:1, W:2 height = input_details[0][‘shape’][1] width = input_details[0][‘shape’][2] img = Image.open(args.image) img = img.resize((width, height)) # add N dim input_data = np.expand_dims(img, axis=0) if floating_model: input_data = (np.float32(input_data) - args.input_mean) / args.input_std
interpreter.set_tensor(input_details[0][‘index’], input_data)
start_time = time.time() interpreter.invoke() stop_time = time.time()
output_data = interpreter.get_tensor(output_details[0][‘index’]) results = np.squeeze(output_data) top_k = results.argsort()[-5:][::-1] labels = load_labels(args.label_file) for i in top_k: if floating_model: print(‘{0:08.6f}’.format(float(results[i]))+”:”, labels[i]) else: print(‘{0:08.6f}’.format(float(results[i]/255.0))+”:”, labels[i])
print(“time: “, stop_time - start_time)
- **Inference test**
```bash
$ python3 label_image.py \
--num_threads 4 \
--image grace_hopper.bmp \
--model_file mobilenet_v1_1.0_224_quant.tflite \
--label_file labels.txt
Operation check 【ObjectDetection】
Sample of MultiThread x4 by Tensorflow Lite + Raspbian Buster (armhf) + RaspberryPi3 [MobileNetV2-SSD / 160ms]
Sample of MultiThread x4 by Tensorflow Lite + Ubuntu18.04 (aarch64) + RaspberryPi3 [MobileNetV2-SSD / 140ms]
Inference test
$ python3 mobilenetv2ssd.py
MobileNetV2-SSD (UINT8) + Corei7 CPU only + USB Camera + 10 Threads + Async
MobileNetV2-SSDLite (UINT8) + RaspberryPi4 CPU only + USB Camera 640x480 + 4 Threads + Sync + Disp 1080p
List of quantized models
https://www.tensorflow.org/lite/guide/hosted_models
Other MobileNetV1 weight files
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md
Other MobileNetV2 weight files
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/README.md
Reference
tflite only python package https://github.com/PINTO0309/Tensorflow-bin/issues/15 Incorrect predictions of Mobilenet_V2 https://github.com/tensorflow/tensorflow/issues/31229#issuecomment-527296093