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
-
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
| 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 |
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}.whlUnlike 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)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-buildSample 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
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
$ python3 label_image.py \
--num_threads 4 \
--image grace_hopper.bmp \
--model_file mobilenet_v1_1.0_224_quant.tflite \
--label_file labels.txtSample 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]
$ python3 mobilenetv2ssd.py
MobileNetV2-SSDLite (UINT8) + RaspberryPi4 CPU only + USB Camera 640x480 + 4 Threads + Sync + Disp 1080p
https://www.tensorflow.org/lite/guide/hosted_models
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/README.md
tflite only python package PINTO0309/Tensorflow-bin#15 Incorrect predictions of Mobilenet_V2 tensorflow/tensorflow#31229 (comment)
