从python中的一些特殊方法讲到pytorch的官方例子mnist（主要针对pytorch的自定义dataset中的几个特殊函数进行说明）
2018-08-24 17:10
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__str__(self)
- 该方法中必须有一个return
- 调用该方法的时机是print对象时
- return的内容就是print打印的内容
__repr__(self)
- 该方法与
__str__(self)
方法一样都是打印时调用的 - 其中也必须含有return
- 下面用pytorch官方例子mnist来进行说明：
#每一个自定义的dataset都必须继承类torch.utils.data.Dataset，还必须重写__len__(self)方法和__getitem__(self,index)方法。
#顺便可能会重写__repr__(self)方法，这个不是必须，下面的例子就是自定义dataset类--MNIST类的部分代码：
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = 'train' if self.train is True else 'test'
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
#当用如下代码实例化MNIST类之后，运用print函数就能调用MNIST类的__repr__(self)函数
d = datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))]))
print(d)#此时就会调用函数__repr__(self)函数
#输出结果如下： Dataset MNIST Number of datapoints: 60000 Split: train Root Location: ../data Transforms (if any): Compose( ToTensor() Normalize(mean=(0.1307,), std=(0.3081,)) ) Target Transforms (if any): None
__getitem__(self,index)
- 既然说到了
__getitem__(self,index)
，也对其进行讲解。该方法也必须有返回return。 - 该方法的调用方式是对象名后面加上中括号，中括号中填上索引值。
- 下面以代码的方式进行展示：
#跟上面代码一样，我们实例化了自定义dataset，然后用索引来取值：
d = datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))]))
print(d[0])#此时就会调用函数__getitem__(self,index)函数
- 打印的结果如下，里面包含了一张28×28大小的图像，就是MNIST数据集里面每张图像的大小；还有后面一个一维的tensor就是这张图像的标签索引值。
- 从打印结果也能够看出，
__getitem__(self,index)
函数的主要功能就是获取数据和标签，并将其返回。
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-0.4242]]]), tensor(5))阅读更多
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