[pytorch] 1.7 pytorch and numpy, tensor and array conversion

1、pytorch与numpy 函数对比

2、tensor与ndarray的转换

• torch.Tensor(array)
• torch.tensor(array)
• torch.as_tensor(array)
• torch.from_array(array)

import torch
import numpy as np

array = np.array([1,2,3,4])
print(array.dtype)      #int64

# 用4种方式 将numpy array转换为torch tensor
tensor = torch.tensor(array)
Tensor = torch.Tensor(array)
as_tensor = torch.as_tensor(array)
from_array = torch.from_numpy(array)

# ====== 对比几种不同方法之间的异同 =========

print(tensor.dtype)     #torch.int64
print(Tensor.dtype)     #torch.float32
print(as_tensor.dtype)  #torch.int64
print(from_array.dtype) #torch.int64

# ========== Determine whether to share memory ===========

#修改数组的值
array[0] = 10

print(tensor)       #tensor([1, 2, 3, 4])
print(Tensor)       #tensor([1., 2., 3., 4.])
print(as_tensor)    #tensor([10, 2, 3, 4])
print(from_array)   #tensor([10, 2, 3, 4])

结论：
1）torch.Tensor() 和 torch.tensor() 在转换的时候,Is to open up a new memory space to storetensor的.

• torch.Tensor 在生成tensor的时候,默认生成 float数据类型
• torch.tensor It is based on the situation to infer what kind of data type for longevity.

2）torch.as_tensor() 和 torch.from_array() 转换的tensor,和 numpy array 是共享内存的;So the data must be the same.