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https://github.com/kartik4949/tensorpipe
High Performance Tensorflow Data Pipeline with State of Art Augmentations and low level optimizations.
https://github.com/kartik4949/tensorpipe
computer-vision datapipeline tensorflow
Last synced: 3 months ago
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High Performance Tensorflow Data Pipeline with State of Art Augmentations and low level optimizations.
- Host: GitHub
- URL: https://github.com/kartik4949/tensorpipe
- Owner: kartik4949
- License: apache-2.0
- Created: 2020-10-08T14:39:00.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-04-22T20:09:31.000Z (over 2 years ago)
- Last Synced: 2024-10-12T04:09:30.120Z (3 months ago)
- Topics: computer-vision, datapipeline, tensorflow
- Language: Python
- Homepage:
- Size: 183 KB
- Stars: 86
- Watchers: 9
- Forks: 20
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# TensorPipe
[![Library][tensorflow-shield]][tensorflow-url]
[![MIT License][license-shield]][license-url]
[![LinkedIn][linkedin-shield]][linkedin-url]
[](https://codecov.io/gh/kartik4949/TensorPipe)
High Performance Tensorflow Data Pipeline with State of Art Augmentations build as a wrapper aroung tensorflow datasets api, with low level optimizations.
## Requirements
* Python 3.8
* Tensorflow 2.2
* Tensorflow addons
* Sklearn
* typeguard
Install using:
```
pip install tensorflow-addons==0.11.2
pip install tensorflow==2.2.0
pip install sklearn
pip install typeguard
```
## Features
- [x] High Performance tf.data pipline
- [x] Core tensorflow support for high performance
- [x] Classification data support
- [x] Bbox data support
- [ ] Keypoints data support
- [ ] Segmentation data support
- [x] GridMask in core tf2.x
- [x] Mosiac Augmentation in core tf2.x
- [x] CutOut in core tf2.x
- [x] Flexible and easy configuration
- [x] Gin-config support
- [x] Custom numpy function injection.
## Advance Users Section:
## Example Usage 1
### Create a Data Pipeline for Training. (Categorical Data).
```python
from pipe import Funnel
from bunch import Bunch
"""
Create a Funnel for the Pipeline!
"""
# Config for Funnel
config = {
"batch_size": 2,
"image_size": [512,512],
"transformations": {
"flip_left_right": None,
"gridmask": None,
"random_rotate":None,
},
"categorical_encoding":"labelencoder"
}
config = Bunch(config)
pipeline = Funnel(data_path="testdata", config=config, datatype="categorical")
pipeline = pipeline.from_dataset(type="train")
# Pipline ready to use, iter over it to use.
# Custom loop example.
for data in pipeline:
image_batch , label_batch = data[0], data[1]
# you can use _loss = loss(label_batch,model.predict(image_batch))
# calculate gradients on loss and optimize the model.
print(image_batch,label_batch)
```
## Example Usage 2
### Create a Data Pipeline for Bounding Boxes with tf records.
```python
import numpy as np
from tensorpipe.pipe import Funnel
"""
Create a Funnel for the Pipeline!
"""
# Custom numpy code for injection.
def numpy_function(image, label):
"""normalize image"""
image = image / 255.0
return image, label
config = {
"batch_size": 2,
"image_size": [512, 512],
"transformations": {
"flip_left_right": None,
"gridmask": None,
"random_rotate": None,
},
"max_instances_per_image": 100,
"categorical_encoding": "labelencoder",
"numpy_function": numpy_function,
}
funnel = Funnel(data_path="tfrecorddata", config=config, datatype="bbox")
dataset = funnel.from_tfrecords(type="train")
for data in dataset:
print(data[1].shape)
```
# Object Detection Usage
### Now build your custom bounding box funnel with subclassing BboxFunnel.
```python
from tensorpipe.funnels import funnels
class CustomObjectDetectionLoader(funnels.BboxFunnel):
def __init__(self, *args):
super().__init__(*args)
def encoder(self,args):
# encoder is overriden to give custom anchors to the model as per the need.
image_id, image, bbox, classes = args
# make custom anchors and encode the image and bboxes as per the model need.
return image, custom_anchors, classes
def decoder(self, args):
# override decoder if using custom tf records and decode your custom tfrecord in this method
return decoded_data
funnel = CustomObjectDetectionLoader(data_path="tfrecorddata", config=config, datatype="bbox")
dataset = funnel.from_tfrecords(type="train")
```
# Steps to build tfrecords and custom input loader.
- use funnels.create_records script to build tfrecord or build your own tfrecords using your custom script.
- if used the create_records script to build records, we dont need to overide decoder, but if using custom
script overriding the decoder function is mandatory.
- If using anchors in the models, please override encoder with custom anchor script.
## Beginners Section.
## Keras Compatiblity.
### Very simple example to use pipeline with keras model.fit as iterable.
```python
import tensorflow as tf
from pipe import Funnel
"""
Create a Funnel for the Pipeline!
"""
config = {
"batch_size": 2,
"image_size": [100, 100],
"transformations": {
"flip_left_right": None,
"gridmask": None,
"random_rotate": None,
},
"categorical_encoding": "labelencoder",
}
pipeline = Funnel(data_path="testdata", config=config, datatype="categorical")
# from dataset i.e normal dataset.
pipeline = pipeline.from_dataset(type="train")
# e.g from tfrecords i.e tfrecord dataset.
# pipeline = pipeline.from_tfrecords(type="train") # testdata/train/*.tfrecord
# Create Keras model
model = tf.keras.applications.VGG16(
include_top=True, weights=None,input_shape=(100,100,3),
pooling=None, classes=2, classifier_activation='sigmoid'
)
# compile
model.compile(loss='mse', optimizer='adam')
# pass pipeline as iterable
model.fit(pipeline , batch_size=2,steps_per_epoch=5,verbose=1)
```
## Config.
* **image_size** - Output Image Size for the pipeline.
* **batch_size** - Batch size for the pipeline.
* **transformations** - Dictionary of transformations to apply with respective keyword arguments.
* **categorical_encoding** - Encoding for categorical data - ('labelencoder' , 'onehotencoder').
## Augmentations:
### GridMask
Creates a gridmask on input image with rotation defined on range.
* **params**:
* **ratio** - grid to space ratio
* **fill** - fill value
* **rotate** - rotation range in degrees
### MixUp
Mixes two randomly sampled images and their respective labels with given alpha.
* **params**:
* **alpha** - value for blend function.
### RandomErase
Randomly erases rectangular chunk with is sampled randomly on given image.
* **params**:
* **prob** - probablity to randomerase on image.
### CutMix
Overlaps a resized randomly sample image on given image with complete overlay on subset portion of image.
* **params**:
* **prob** - probablity to CutMix on image.
### Mosaic
Creates a mosaic of input 4 images into one single image.
* **params**:
* **prob** - Probablity to mosaic.
## CutMix, CutOut, MixUp
#### source (https://www.researchgate.net/publication/340296142/figure/fig1/AS:874996595429376@1585626853032/Comparison-of-our-proposed-Attentive-CutMix-with-Mixup-5-Cutout-1-and-CutMix-3.png)
## Mosaic
#### source (https://hoya012.github.io/assets/img/yolov4/8.PNG)
## Grid Mask
#### source (https://storage.googleapis.com/groundai-web-prod/media/users/user_302546/project_404544/images/x1.png)
## Release History
* v1.0
* Bbox, Keypoints, Custom Py Functions Support.(WIP)
* v1.0-beta
* Classification Support with gridmask and mosaic augmentations.
## Meta
Kartik Sharma – [@linkedIn](https://www.linkedin.com/in/kartik-sharma-aaa021169/) – [email protected]
Distributed under the Apache 2.0 license. See ``LICENSE`` for more information.
[tensorflow-shield]: https://img.shields.io/badge/Tensorflow-2.x-orange
[tensorflow-url]: https://tensorflow.org
[license-shield]: https://img.shields.io/badge/OpenSource-%E2%9D%A4%EF%B8%8F-blue
[license-url]: LICENSE.txt
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/in/kartik-sharma-aaa021169/