https://github.com/sebastian-sz/resnet-rs-keras
ResNet-RS models rewritten in Tensorflow / Keras functional API.
https://github.com/sebastian-sz/resnet-rs-keras
deep-learning deep-neural-networks image-classification keras onnx tensorflow tensorflow-lite tensorflow-models tensorflow2
Last synced: 4 months ago
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ResNet-RS models rewritten in Tensorflow / Keras functional API.
- Host: GitHub
- URL: https://github.com/sebastian-sz/resnet-rs-keras
- Owner: sebastian-sz
- License: apache-2.0
- Archived: true
- Created: 2021-10-16T05:56:59.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-26T08:57:20.000Z (almost 2 years ago)
- Last Synced: 2024-09-27T21:04:41.209Z (8 months ago)
- Topics: deep-learning, deep-neural-networks, image-classification, keras, onnx, tensorflow, tensorflow-lite, tensorflow-models, tensorflow2
- Language: Python
- Homepage:
- Size: 251 KB
- Stars: 26
- Watchers: 2
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Achive Note
The models in this repo can be used from Keras directly.
The only difference is that Keras implementation already includes preprocessing.
```python
from tensorflow.keras.applications.resnet_rs import ResNetRS50 import ResNetRS50
```
I am archiving this repository as the maintenance overhead, for a duplicated functionality is not worth it.
You can still use this repository if you like it, but if something breaks due to Tensorflow/Keras update I probably won't
be able to dedicate my time to fix it.
# Table of contents
1. [Introduction](https://github.com/sebastian-sz/resnet-rs-keras#introduction)
2. [Quickstart](https://github.com/sebastian-sz/resnet-rs-keras#quickstart)
3. [Installation](https://github.com/sebastian-sz/resnet-rs-keras#installation)
4. [How to use](https://github.com/sebastian-sz/resnet-rs-keras#how-to-use)
5. [Original Weights](https://github.com/sebastian-sz/resnet-rs-keras#original-weights)
# Introduction
This is a package with ResNet-RS models adapted to Tensorflow/Keras.
ResNet-RS models are updated versions of ResNet models - [Arxiv Link](https://arxiv.org/abs/2103.07579)
The model's weights are converted from [original repository](https://github.com/tensorflow/tpu/blob/acb331c8878ce5a4124d4d7687df5fe0fadcd43b/models/official/resnet/resnet_rs/).
# Quickstart
The design was meant to mimic the usage of `keras.applications`:
```python
!pip install git+https://github.com/sebastian-sz/resnet-rs-keras@main
# Import package:
from resnet_rs import ResNetRS50
import tensorflow as tf
# Use model directly:
model = ResNetRS50(weights='imagenet', input_shape=(224, 224, 3))
model.summary()
# Or to extract features / fine tune:
backbone = ResNetRS50(
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](https://colab.research.google.com/drive/1FQAGBj4RwkjZcPckYkO0YAcrCeKYN26Z?usp=sharing).
# Installation
There are multiple ways to install.
The only requirements are Tensorflow 2.4+ and Python 3.6+.
### Option A: (recommended) pip install from GitHub
`pip install git+https://github.com/sebastian-sz/resnet-rs-keras@main`
### Option B: Build from source
```bash
git clone https://github.com/sebastian-sz/resnet-rs-keras.git
cd resnet-rs-keras
pip install .
```
### Option C: (alternatively) no install:
If you do not want to install you could just drop the `resnet_rs/` directory 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 resnet_rs_keras .`
Run: `docker run -it --rm resnet_rs_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 resnet_rs import *`
# How to use
There are 7 model variants you can use, with the following depths:
`ResNetRS[50, 101, 152, 200, 270, 350, 420]`
### Imagenet weights
The imagenet weights are automatically downloaded if you pass `weights="imagenet"`
option while creating the models.
Note: for a single depth, sometimes multiple weight variants have been released,
depending on the input shape the network has been trained with. By **default**
the highest input shape weights are downloaded as they yield the best accuracy.
### Possible weight variants:
As of writing this repository the following weight variants are available:
```
50: [160],
101: [160, 192],
152: [192, 224, 256],
200: [256],
270: [256],
350: [256, 320],
420: [320],
```
To pick a specific weight variant (instead of default / highest) pass, `imagenet-i` as a weight argument.
For example, to create ResNetRS152, with weights trained with 224 input shape, run:
`model = ResNetRS152(weights="imagenet-i224")`
### Input shapes
**Weights are input shape agnostic**. You can create a model with different input
shape than what it was trained with:
```python
# Trained with 320x320
# Now has 224x224
model = ResNetRS350(weights="imagenet-i320", input_shape=(224, 224, 3))
```
By default, it is suggested to use shape 224x224.
### Preprocessing
The models expect image, normalized with Imagenet's mean and stddev:
```python
import tensorflow as tf
mean_rgb = [0.485, 0.456, 0.406]
stddev_rgb = [0.229, 0.224, 0.225]
def preprocess(image): # input image is in range 0-255.
image = image / 255.
image -= tf.constant(mean_rgb, shape=[1, 1, 3], dtype=image.dtype)
image /= tf.constant(stddev_rgb, shape=[1, 1, 3], dtype=image.dtype)
return image
```
##### (Alternatively) Preprocessing Layer:
Or you can use [Preprocessing Layer](https://keras.io/guides/preprocessing_layers/):
```python
from resnet_rs import get_preprocessing_layer
layer = get_preprocessing_layer()
inputs = layer(image)
```
### Fine-tuning
For fine-tuning example, check out the [Colab Notebook](https://colab.research.google.com/drive/1FQAGBj4RwkjZcPckYkO0YAcrCeKYN26Z?usp=sharing).
### Tensorflow Lite
The models are TFLite compatible. You can convert them like any other Keras model:
```python
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open("resnet_rs.tflite", "wb") as file:
file.write(tflite_model)
```
### ONNX
The models are ONNX compatible. For ONNX Conversion you can use [tf2onnx](
https://github.com/onnx/tensorflow-onnx) package:
```python
!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 resnet_rs.onnx
```
# Original Weights
The original weights are present in the [original repository](https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs)
in the form of `ckpt` files. I converted the weights to Keras `.h5` format so it's
easier to use with Tensorflow/Keras API.
### (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.sh`
2. `bash scripts/convert_all.sh`
# Closing words
If you found this repo useful, please consider giving it a star!
# Bibliography
1. [Original Repository](https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs)
2. [Keras Reference Implementation](https://github.com/tensorflow/models/blob/master/official/vision/beta/modeling/backbones/resnet.py)