TensorPipe

High Performance Tensorflow Data Pipeline with State of Art Augmentations and low level optimizations.

Features

• High Performance tf.data pipline
• Core tensorflow support for high performance
• Classification data support
• Bbox data support
• Keypoints data support
• Segmentation data support
• GridMask in core tf2.x
• Mosiac Augmentation in core tf2.x
• CutOut in core tf2.x
• Flexible and easy configuration
• Gin-config support

Advance Users Section:

Example Usage 1

Create a Data Pipeline for Training.

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.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 Validation.

from pipe import Funnel                                                         
from bunch import Bunch                                                         
"""                                                                             
Create a Funnel for the Pipeline!                                               
"""                                                                             


# Config for Funnel
config = {                                                                      
    "batch_size": 1,                                                            
    "image_size": [512,512],                                                    
    "transformations": {                                                                                                       
    },                                                                          
    "categorical_encoding":"labelencoder"                                       
}                                                                               
config = Bunch(config)                                                          
pipeline = Funnel(data_path="testdata", config=config, datatype="categorical", training=False)  
pipeline = pipeline.dataset(type="val")                                       

# use pipeline to validate your data on model.
loss = []
for data in pipeline:
    image_batch , actual_label_batch = data[0], data[1]
    # pred_label_batch = model.predict(image_batch)
    # loss.append(calc_loss(actual_label_batch,pred_label_batch))
    print(image_batch,label_batch)                                     

Beginners Section.

Keras Compatiblity.

Very simple example to use pipeline with keras model.fit as iterable.

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")
pipeline = pipeline.dataset(type="train")

# 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 gridmask on input image with rotation defined on range.

• params:
  • ratio - grid to space ratio
  • fill - fill value
  • rotate - rotation range in degrees

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 – [email protected]

Distributed under the Apache 2.0 license. See LICENSE for more information.