tensor与ndarrary的转换
Pytorch中的tensor与ndarray在底层数据类型设计有相似之处，在Pytorch框架中tensor与ndarray可以较为方便地转换
tensor转ndarray
tensor转ndarray分为浅拷贝与深拷贝
浅拷贝
浅拷贝一般使用numpy()方法
import torch
import numpy as npdata1 = torch.tensor([1, 2, 3])
print(data1)
data2 = data1.numpy()
print(data2)
data1[0] = 9
print(data1)
print(data2)
# tensor([1, 2, 3])
# [1 2 3]
# tensor([9, 2, 3])
# [9 2 3]可以看到，在对转换成ndarray类型的data2进行修改后，tensor的值也随之改变，这是因为二者底层共用一块，为浅拷贝
深拷贝
深拷贝我们可以对tensor进行clone()后再进行转换，clone()会拷贝一份完全独立的张量，并会拷贝计算图
import torch
import numpy as npdata1 = torch.tensor([1, 2, 3])
print(data1)
data2 = data1.clone().numpy()
print(data2)
data1[0] = 9
print(data1)
print(data2)
# tensor([1, 2, 3])
# [1 2 3]
# tensor([9, 2, 3])
# [1 2 3]
可以看到这里在对张量进行修改后，并不会影响ndarray，因为这里为深拷贝
ndarray转tensor
ndarray转tensor同样分为深拷贝和浅拷贝
浅拷贝
浅拷贝一般是通过torch.from_numpy()实现的
import torch
import numpy as npdata1 = np.array([1, 2, 3])
data2 = torch.from_numpy(data1)
print(data1)
print(data2)
data1[0] = 9
print(data1)
print(data2)
# [1 2 3]
# tensor([1, 2, 3], dtype=torch.int32)
# [9 2 3]
# tensor([9, 2, 3], dtype=torch.int32)可以看到浅拷贝后，对共享内存的任意一个对象修改都会影响到另一个的值
深拷贝
深拷贝这里我们可以通过对ndarray进行copy()进行深拷贝创立副本
import torch
import numpy as npdata1 = np.array([1, 2, 3])
data2 = torch.from_numpy(data1.copy())
print(data1)
print(data2)
data1[0] = 9
print(data1)
print(data2)
# [1 2 3]
# tensor([1, 2, 3], dtype=torch.int32)
# [9 2 3]
# tensor([1, 2, 3], dtype=torch.int32)
张量提取标量
tensor可以分为矢量张量和标量张量，对于从张量中提取标量值一般可以使用item()方法，要求tensor为单个元素才可以使用
import torch
import numpy as npdata1 = torch.tensor(1)
data2 = torch.tensor([1])
print(data1)
print(data2)
print(data1.item())
print(data2.item())
# tensor(1)
# tensor([1])
# 1
# 1import torch
import numpy as npdata1 = torch.tensor([1, 2, 3])
print(data1)print(data1.item())
tensor([1, 2, 3])
# Traceback (most recent call last):
# File "D:\Pythonproject\teach_day_01\demo02.py", line 7, in <module>
# print(data1.item())
# ^^^^^^^^^^^^
# RuntimeError: a Tensor with 3 elements cannot be converted to Scalar可以看到非标量张量无法进行item()标量值提取