Tensorflow高性能数据优化增强工具Pipeline使用详解

目录

安装方法

给大家介绍一个非常好用的TensorFlow数据pipeline工具。

高性能的Tensorflow>

pip install tensorflow-addons==0.11.2
pip install tensorflow==2.2.0
pip install sklearn

功能

High>Core tensorflow support for high performanceClassification data supportBbox data supportKeypoints data supportSegmentation data supportGridMask in core tf2.xMosiac Augmentation in core tf2.xCutOut in core tf2.xFlexible and easy configurationGin-config support

高级用户部分

用例1，为训练创建数据Pipeline

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)                                      

用例2，为验证创建数据Pipeline

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)                                     

初学者部分

Keras>

使用keras model.fit来构建非常简单的pipeline。

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)

配置

image_size ->batch_size - pipeline的Batch size。transformations - 应用数据增强字典中的对应关键字。categorical_encoding - 对类别数据进行编码  - ('labelencoder' , 'onehotencoder').

增强：

GridMask

在输入图像上创建gridmask，并在范围内定义旋转。

参数：

ratio ->

fill - 填充值fill value

rotate - 旋转的角度范围

MixUp

使用给定的alpha值，将两个随机采样的图像和标签进行混合。

参数：

alpha ->

RandomErase

在给定的图像上的随机位置擦除一个随机的矩形区域。

参数：

prob ->

CutMix

在给定图像上对另一个随机采样的图像进行随机的缩放，再以完全覆盖的方式贴到这个给定图像上。

params：

prob ->

Mosaic

把4张输入图像组成一张马赛克图像。

参数：

prob ->

CutMix>



Mosaic



Grid>



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