Last time __getitem__ Time is rough 、 Plain 、 Simply say what is an iteratable object . Here is a better one found science explain ：
PythonObjects that can be iterated in(Iterable)It's not a specific data type , It refers to a that stores elements Container object , And the elements in the container can pass through__iter__( )Method or__getitem__( )Method access .
__iter__ Method is used to make objects available for … in obj Loop traversal ,__getitem__( ) The way to do this is to make the object pass through Instance name [index] To access the elements in the instance . The old ape believes that the purpose of these two methods is Python Implement a general external interface that can access the internal data of the iteratable object .
An iteratable object cannot iterate independently ,Python in , Iteration is through for … in obj To complete . All iteratable objects can be used directly for… in obj Loop access , This statement actually does two things ： The first thing is to call __iter__() Get an iterator , The second thing is the loop call __next__().
Common iteratible objects include ：
a) Collection data type , Such as list、tuple、dict、set、str etc. ;
b) generator (generator), Including generator and belt yield The generator function of (generator function), The next section is devoted to .
How to judge whether an object is an iterative object ？ The specific judgment methods are as follows ：
utilize numpy Of iterable Method
from numpy import iterable
print(iterable( Instance name ))
utilize collections Modular Iterable class
from collections import Iterable
isinstance( Instance name , Iterable)
The above contents are from CSDN Of Old ape Python, It's really the work of a master , Speak briefly 、 The key to clarity is to make people understand , bosses ！
Define any object , Does not define __iter__ Method ：
from numpy import iterable
class MyList:
def __init__(self, len: int):
self.list = [i for i in range(len)]
self.length = len
def __repr__(self) -> str:
return f"MyList({
self.length}):{
self.list}"
x = MyList(10)
for i in x:
print(i)
Running results ：
Show MyList Instances are not iterative
Definition __iter__ After the method
range(n)from numpy import iterable
class MyList:
def __init__(self, len: int):
self.cursor = -1
self.length = len
def __iter__(self):
return self
def __next__(self):
if self.cursor+1 < self.length:
self.cursor += 1
return self.cursor
else:
exit(1)
def __repr__(self) -> str:
return f"MyList({
self.length})"
x = MyList(10)
print(iterable(x))
for i in x:
print(i)
Output is ：
True
0
1
2
3
4
5
6
7
8
9
Use next() Step by step iteration can be seen more clearly ：
from numpy import iterable
class MyList:
def __init__(self, len: int):
self.cursor = -1
self.length = len
def __iter__(self):
return self
def __next__(self):
if self.cursor+1 < self.length:
self.cursor += 1
return self.cursor
else:
exit(1)
def __repr__(self) -> str:
return f"MyList({
self.length})"
x = MyList(10)
print(iter(x))
print(next(x))
print(next(x))
print(next(x))
print(next(x))
for i in x:
print(i)
The output is ：
MyList(10)
0
1
2
3
4
5
6
7
8
9
Why is it PyTorch The trace of common iterations in ？
Probably because deep learning model training often requires a large data set , Sometimes even with TB（1024GB） To calculate , as everyone knows , Most personal computers have no more than 32GB, One time handle 1TB Such large data is loaded into memory for use , It's obviously unrealistic .
In this case , Put the required data set Batch by batch Loading into memory for use is obviously a better and more realistic solution , This coincides with the concept and positioning of iteratable objects , It can be asserted that , Precisely because Python Iterations are naturally supported , therefore Python No other language can shake its position in the field of deep learning .
Dataloader Loading instances of data ：
from cgi import test
from torch.utils.data import DataLoader
from torchvision.transforms import ToTensor
from torchvision.datasets import CIFAR10
from numpy import iterable
# Use CIFAR10 Reference data set
test_set = CIFAR10(root='./torch2Learn/dataset/cifar10_data',
train=False, download=True, transform=ToTensor())
# Load datasets in batches , Each batch 64 individual
test_loader = DataLoader(dataset=test_set, batch_size=64,
shuffle=True, drop_last=False)
print(type(test_set))
print(type(test_loader))
print(iterable(test_set))
print(iterable(test_loader))
print(iter(test_loader))
for step, item in enumerate(test_loader):
imgs_arr, kinds_arr = item
