lcerman / efficientnet-lite-keras

Keras reimplementation of EfficientNet Lite.

EfficientNet Lite models adapted to Keras functional API.

Changelog:

• Nov 2021:
  • Added separate get_preprocessing_layer utility function.

Table of contents

1. Introduction
2. Quickstart
3. Installation
4. How to use
5. Original Weights

Introduction

This is a package with EfficientNet-Lite model variants adapted to Keras.

EfficientNet-Lite variants are modified versions of EfficientNet models, better suited for mobile and embedded devices.

The model's weights are converted from original repository.

Quickstart

The design was meant to mimic the usage of keras.applications:

# Install
!pip install git+https://github.com/sebastian-sz/efficientnet-lite-keras@main

# Import package:
from efficientnet_lite import EfficientNetLiteB0
import tensorflow as tf

# Use model directly:
model = EfficientNetLiteB0(weights='imagenet', input_shape=(224, 224, 3))
model.summary()

# Or to extract features / fine tune:
backbone = EfficientNetLiteB0(
   weights='imagenet', 
   input_shape=(224,224, 3),
   include_top=False
)

model = tf.keras.Sequential([
    backbone,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(10)  # 10 = num classes
])
model.compile(...)
model.fit(...)

You can fine tune these models, just like other Keras models.

For end-to-end fine-tuning and conversion examples check out the Colab Notebook.

Installation

There are multiple ways to install.
The only requirements are Tensorflow 2.2+ and Python 3.6+.
(Although, Tensorflow at least 2.4 is strongly recommended)

Option A: (recommended) pip install from GitHub

pip install git+https://github.com/sebastian-sz/efficientnet-lite-keras@main

Option B: Build from source

git clone https://github.com/sebastian-sz/efficientnet-lite-keras.git  
cd efficientnet_lite_keras  
pip install .

Option C: (alternatively) no install:

If you do not want to install you could just drop the efficientnet_lite/efficientnet_lite.py file directly into your project.

Option D: Docker

You can also install this package as an extension to official Tensorflow docker container:

Build: docker build -t efficientnet_lite_keras .
Run: docker run -it --rm efficientnet_lite_keras

For GPU support or different TAG you can (for example) pass
--build-arg IMAGE_TAG=2.5.0-gpu
in build command.

Verify installation

If all goes well you should be able to import:
from efficientnet_lite import *

How to use

There are 5 lite model variants you can use (B0-B4).

Imagenet weights

The imagenet weights are automatically downloaded if you pass weights="imagenet" option while creating the models.

Preprocessing

The models expect image values in range -1:+1. In more detail the preprocessing function (for pretrained models) looks as follows:

def preprocess(image):  # input image is in range 0-255.
    return (image - 127.00) / 128.00

(Alternatively) Preprocessing Layer:

Or you can use Preprocessing Layer:

from efficientnet_lite import get_preprocessing_layer

layer = get_preprocessing_layer()
inputs = layer(image)

Input shapes

The following table shows input shapes for each model variant:

Model variant	Input shape
B0	224,224
B1	240,240
B2	260,260
B3	280,280
B4	300,300

Fine-tuning

For fine-tuning example, check out the Colab Notebook.

Tensorflow Lite

The models are TFLite compatible. You can convert them like any other Keras model:

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open("efficientnet_lite.tflite", "wb") as file:
  file.write(tflite_model)

ONNX

The models are ONNX compatible. For ONNX Conversion you can use tf2onnx package:

!pip install tf2onnx==1.8.4

# Save the model in TF's Saved Model format:
model.save("my_saved_model/")

# Convert:
!python -m tf2onnx.convert \
  --saved-model my_saved_model/ \
  --output efficientnet_lite.onnx

TF's Model Optimization Toolkit

Lite model variants were intended for mobile use and embedded systems, so I tested if they work with Tensorflow Model Optimization Toolkit.

For example, preparing the model for pruning should work:

import tensorflow_model_optimization as tfmot
from efficientnet_lite import EfficientNetLiteB0

model = EfficientNetLiteB0()
model = tfmot.sparsity.keras.prune_low_magnitude(model)

Original Weights

The original weights are present in the original repository for Efficient Net Lite in the form of Tensorflow's .ckpt files. Also, on Tensorflow's GitHub, there is a utility script for converting EfficientNet weights.

The scripts worked for me, after I modified the model's architecture, to match the description of Lite variants.

(Optionally) Convert the weights

The converted weights are on this repository's GitHub. If, for some reason, you wish to download and convert original weights yourself, I prepared the utility scripts:

1. bash scripts/download_all_weights.sh
2. bash scripts/convert_all_weights.sh

Bibliography

[1] Original repository
[2] Existing non-lite Keras EfficientNet models
[3] Weight update util

Closing words

If you found this repo useful, please consider giving it a star!