The Truth of Sisyphus
  • Introduction
  • Deep Learning
    • Basics
      • Hinge Loss
      • Regularizations
      • Linear Classification
      • Multi-Class and Cross Entropy Loss
      • Batch Norm and other Normalizations
      • Optimization
      • Optimization Functions
      • Convolution im2col
      • Activation Functions
      • Derivatives
        • Derivatives of Softmax
        • A Smooth (differentiable) Max Function
      • Model Ensemble
      • Layers Python Implementation
    • Classification
      • Mobile friendly networks
      • Non-local Neural Networks
      • Squeeze-and-Excitation Networks
      • Further Attention Utilization -- Efficience & Segmentation
      • Group Norm
      • ShuffleNet V2
    • Segmentation
      • Several Instance Segmentation
      • A Peek at Semantic Segmentation
      • Design Choices for Mobile Friendly Deep Learning Models, Semantic Segmentation
      • Efficient Video Object Segmentation via Network Modulation
      • BiSeNet
      • DeepLabV3+
    • Detection
      • CornerNet
      • IoU-Net
      • Why smooth L1 is popular in BBox Regression
      • MTCNN-NCNN
      • DetNet
      • SSD Illustration
    • RNN Related
      • GRU vs LSTM
      • BERT
    • Reinforcement Learning
      • AutoML in Practice Review
      • DRL for optimal execution of profolio transaction
    • Multi-task
      • Multi-task Overview
      • What are the tricks in Multi-Task network design?
    • Neural Network Interpretation
      • Neuron Visualization
    • Deep Learning Frameworks
      • How does Caffe work
      • [Gluon] When to use (Hybrid)Sequential and (Hybrid)Block
      • Gluon Hybrid Intro
      • Gluon HybridBlocks Walk-Through
      • A quick tour of Torch internals
      • NCHW / NHWC in Pytorch
      • Static & Dynamic Computation Graph
    • Converting Between DL Frameworks
      • Things To Be Considered When Doing Model Converting
      • Caffe to TensorFlow
    • Computation Graph Optimization
      • Two ways of TensorRT to optimize Neural Network Computation Graph
      • Customized Caffe Memory Optimization
      • NCNN Memory Optimization
      • Symbolic Programs Advantages: More Efficient, Reuse Intermediate Memory, Operation Folding
    • Deep Learning Debug
      • Problems caused by dead ReLU
      • Loss jumps to 87.3365
      • Common Causes of NANs During Training
    • Deployment
      • Efficient Convolution Operation
      • Quantization
    • What I read recently
      • Know Google the Paper Way
      • ECCV 2018
      • Neural Machine Translation
      • Street View OCR Extraction System
      • Teaching Machines to Draw
      • Pixel to Graph
      • Burst Image Deblurring
      • Material for Masses
      • Learning to Separate Object Sounds by Watching Unlabeled Video
    • Papers / Posts to be read
    • Dummy thoughts
  • Machine Learning
    • Classification
    • Regression
    • Clustering
    • Dimension Reduction
    • Metrics
    • Regularization
    • Bayesian Example
    • Machine Learning System Design
    • Recommendation
    • Essentials of Machine Learning
    • Linear Regression
    • Logistic Regression
      • Logistic Function
    • Gaussian Discriminant Analysis
    • Naive Bayes
    • SVM
    • MLE vs MAP
    • Boosting
    • Frequent Questions
    • Conclusion of Machine Learning
  • Python notes
    • Python _ or __ underscores usage
    • Python Multiprocess and Threading Differences
    • Heapq vs. Q.PriorityQueue
    • Python decorator
    • Understanding Python super()
    • @ property
    • Python __all__
    • Is Python List a Linked List or Array
    • What is the "u" in u'Hello world'
    • Python "self"
    • Python object and class
    • Python Class' Instance method, Class method, and Static Methods Demystified
    • Python WTF
    • Python find first value index in a list: [list].index(val)
    • Sort tuples, and lambda usecase
    • Reverse order of range()
    • Python check list is empty
    • Python get ASCII value from character
    • An A-Z of useful Python tricks
    • Python nested function variable scope
    • Python reverse a list
    • Python priority queue -- heapq
  • C++ Notes
    • Templates
    • std::string (C++) and char* (or c-string "string" for C)
    • C++ printf and cout
    • Class Member Function
    • Inline
    • Scope Resolution Operator ::
    • Constructor
    • Destructor
    • Garbage Collection is Critical
    • C++ Question Lists
  • Operating System
    • Basics
    • Mutex & Semaphore
    • Ticket Selling System
    • OS and Memory
    • Sort implementation in STL
    • Compile, link, loading & run
    • How to understand Multithreading and Multiprocessing from the view of Operating System
  • Linux & Productivity
    • Jupyter Notebook on Remote Server
    • Nividia-smi monitoring
  • Leetcode Notes
    • Array
      • 11. Container With Most Water
      • 35. Search Insert Position
    • Linked List
      • Difference between Linked List and Array
      • Linked List Insert
      • Design of Linked List
      • Two Pointers
        • 141. Linked List Cycle
        • 142. Linked List Cycle II
        • 160. Intersection of two Linked List
        • 19. Remove N-th node from the end of linked list
      • 206. Reverse Linked List
      • 203. Remove Linked List Elements
      • 328. Odd Even Linked List
      • 234. Palindrome Linked List
      • 21. Merge Two Sorted Lists
      • 430. Flatten a Multilevel Doubly Linked List
      • 430. Flatten a Multilevel Doubly Linked List
      • 708. Insert into a Cyclic Sorted List
      • 138. Copy List with Random Pointer
      • 61. Rotate List
    • Binary Tree
      • 144. Binary Tree Preorder Traversal
      • 94. Binary Tree Iterative In-order Traverse
    • Binary Search Tree
      • 98. Validate Binary Search Tree
      • 285. Inorder Successor in BST
      • 173. Binary Search Tree Iterator
      • 700. Search in a Binary Search Tree
      • 450. Delete Node in a BST
      • 701. Insert into a Binary Search Tree
      • Kth Largest Element in a Stream
      • Lowest Common Ancestor of a BST
      • Contain Duplicate III
      • Balanced BST
      • Convert Sorted Array to Binary Search Tree
    • Dynamic Programming
      • 198. House Robber
      • House Robber II
      • Unique Path
      • Unique Path II
      • Best time to buy and sell
      • Partition equal subset sum
      • Target Sum
      • Burst Ballons
    • DFS
      • Clone Graph
      • General Introduction
      • Array & String
      • Sliding Window
  • Quotes
    • Concert Violinist Joke
    • 船 Ship
    • What I cannot create, I do not understand
    • Set your course by the stars
    • To-do list
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  • Instance Methods -- accesses to object state and class state
  • Class Methods -- only have access to class state
  • Static Methods -- no accesses to either state
  1. Python notes

Python Class' Instance method, Class method, and Static Methods Demystified

Let’s begin by writing a (Python 3, no need for inheriting "object", see?) class that contains simple examples for all three method types:

class MyClass:
    def method(self):
        return 'instance method called', self

    @classmethod
    def classmethod(cls):
        return 'class method called', cls

    @staticmethod
    def staticmethod():
        return 'static method called'

NOTE: For Python 2 users: The @staticmethod and @classmethod decorators are available as of Python 2.4 and this example will work as is. Instead of using a plain class MyClass: declaration you might choose to declare a new-style class inheriting from object with the class MyClass(object)

Instance Methods -- accesses to object state and class state

The first method on MyClass, called method, is a regular instance method. That’s the basic, no-frills method type you’ll use most of the time. You can see the method takes one parameter, self, which points to an instance of MyClass when the method is called (but of course instance methods can accept more than just one parameter).

Through the self parameter, instance methods can freely access attributes and other methods on the same object. This gives them a lot of power when it comes to modifying an object’s state.

Not only can they modify object state, instance methods can also access the class itself through the self.__class__ attribute. This means instance methods can also modify class state.

Class Methods -- only have access to class state

Instead of accepting a self parameter, class methods take a clsparameter that points to the class—and not the object instance—when the method is called.

Because the class method only has access to this cls argument, it can’t modify object instance state. That would require access to self. However, class methods can still modify class state that applies across all instances of the class.

Static Methods -- no accesses to either state

This type of method takes neither a self nor a cls parameter (but of course it’s free to accept an arbitrary number of other parameters).

Therefore a static method can neither modify object state nor class state. Static methods are restricted in what data they can access - and they’re primarily a way to namespace your methods.

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Last updated 6 years ago

Let’s compare that to the second method, MyClass.classmethod. I marked this method with a decorator to flag it as a class method.

The third method, MyClass.staticmethod was marked with a decorator to flag it as a static method.

@classmethod
@staticmethod
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