温故而知新，可以为师矣！
一、参考资料
python中list、numpy、torch.tensor之间的相互转换
二、常用操作
list 转 numpy
ndarray = np.array(list)
import numpy as np
a_list = [[j for j in range(5)] for i in range(3)]
a_ndarray = np.array(a_list)
print(f'a_list = {a_list}, type of a_list: {type(a_list)}')
print(f'a_ndarray = {a_ndarray}, type of a_ndarray: {type(a_ndarray)}')
输出结果
a_list = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], type of a_list: <class 'list'>
a_ndarray = [[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]], type of a_ndarray: <class 'numpy.ndarray'>
list 转 torch.Tensor
如何将装有tensor的多维list转化为torch.Tensor类型
普通 list 转 torch.Tensor
tensor=torch.Tensor(list)
# 普通list转tensor
a_list = [[j for j in range(5)] for i in range(3)]
A_tensor = torch.Tensor(a_list)
print(f'a_list = {a_list}, type of a_list: {type(a_list)}')
print(f'A_tensor = {A_tensor}, type of A_tensor: {type(A_tensor)}')
注意：将list中元素类型为int，转换为tensor后，类型转换为float，如果希望转换为int，则需要加上类型。
A_tensor = torch.Tensor(a_list) # 默认为float
A_tensor = torch.IntTensor(a_list) # 转为int
输出结果
a_list = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], type of a_list: <class 'list'>
A_tensor = tensor([[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.]]), type of A_tensor: <class 'torch.Tensor'>
list包含多维tensor
# list包含tensor，使用torch.Tensor会报错
a = torch.rand((2, 3))
a_list = [a for _ in range(3)]
A_tensor = torch.Tensor(a_list)
输出结果
raceback (most recent call last):
File "/PATH/TO/demo.py", line 13, in <module>
A = torch.Tensor(a_list)
ValueError: only one element tensors can be converted to Python scalars
解决办法
如果该方法无法解决该问题，请参考下文的FAQ。
# 在cpu上
A_tensor= torch.tensor([item.detach().numpy() for item in a_list])
# 在gpu上
A_tensor= torch.tensor([item.cpu().detach().numpy() for item in a_list]).cuda()
注意：因为 gpu上的 tensor 不能直接转为 numpy，需要先在 cpu 上完成操作，再回到 gpu 上。
numpy 转 list
list = ndarray.tolist()
import numpy as np
a_list = [[j for j in range(5)] for i in range(3)]
a_ndarray = np.array(a_list) # ndarray 转为 ndarray
A_list = a_ndarray.tolist() # ndarray 转为 list
print(f'a_list = {a_list}, type of a_list: {type(a_list)}')
print(f'a_ndarray = {a_ndarray}, type of a_ndarray: {type(a_ndarray)}')
print(f'A_list = {A_list}, type of A_list: {type(A_list)}')
输出结果
a_list = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], type of a_list: <class 'list'>
a_ndarray = [[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]], type of a_ndarray: <class 'numpy.ndarray'>
A_list = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], type of A_list: <class 'list'>
numpy 转 torch.Tensor
tensor = torch.from_numpy(ndarray)
import torch
import numpy as np
a_list = [[j for j in range(5)] for i in range(3)]
a_ndarray = np.array(a_list)
a_tensor = torch.from_numpy(a_ndarray)
print(f'a_list = {a_list}, type of a_list: {type(a_list)}')
print(f'a_ndarray = {a_ndarray}, type of a_ndarray: {type(a_ndarray)}')
print(f'a_tensor = {a_tensor}, type of a_tensor: {type(a_tensor)}')
输出结果
a_list = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], type of a_list: <class 'list'>
a_ndarray = [[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]], type of a_ndarray: <class 'numpy.ndarray'>
a_tensor = tensor([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]), type of a_tensor: <class 'torch.Tensor'>
torch.Tensor 转 numpy
# CPU
ndarray = tensor.numpy()
# GPU
ndarray = tensor.cpu().numpy()
注意：gpu上的tensor不能直接转为numpy，须要先在 cpu 上完成操做，再回到 gpu 上。
import torch
a_list = [[j for j in range(5)] for i in range(3)]
# list转tensor
A_tensor = torch.Tensor(a_list)
# CPU
A_ndarray = A_tensor.numpy()
print(f'a_list = {a_list}, type of a_list: {type(a_list)}')
print(f'A_tensor = {A_tensor}, type of A_tensor: {type(A_tensor)}')
print(f'A_ndarray = {A_ndarray}, type of A_ndarray: {type(A_ndarray)}')
输出结果
a_list = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], type of a_list: <class 'list'>
A_tensor = tensor([[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.]]), type of A_tensor: <class 'torch.Tensor'>
A_ndarray = [[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]], type of A_ndarray: <class 'numpy.ndarray'>
torch.Tensor 转 list
tensor先转numpy，后转list。
list = tensor.numpy().tolist()
import torch
a_list = [[j for j in range(5)] for i in range(3)]
# list转tensor
A_tensor = torch.Tensor(a_list)
# tensor先转numpy，再转list
A_list = A_tensor.numpy().tolist()
print(f'a_list = {a_list}, type of a_list: {type(a_list)}')
print(f'A_tensor = {A_tensor}, type of A_tensor: {type(A_tensor)}')
print(f'A_list = {A_list}, type of A_list: {type(A_list)}')
输出结果
a_list = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], type of a_list: <class 'list'>
A_tensor = tensor([[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.]]), type of A_tensor: <class 'torch.Tensor'>
A_list = [[0.0, 1.0, 2.0, 3.0, 4.0], [0.0, 1.0, 2.0, 3.0, 4.0], [0.0, 1.0, 2.0, 3.0, 4.0]], type of A_list: <class 'list'>
三、FAQ
Q：ValueError: only one element tensors can be converted to Python scalars
Pytorch: list, numpy. Tensor 格式转化 （附 only one element tensors can be converted to Python scalars 解决）
ValueError:only one element tensors can be converted to Python scalars解决办法
错误原因：list包含多维tensor，导致类型转换错误。有以下两种解决方法。
方法一：torch.stack
通过torch.stack将包含tensor的多维list转换成tensor。
torch.stack要求两个输入的shape完全相同。
b_tensor = torch.rand((2, 3))
b_list = [b_tensor for _ in range(3)]
B_tensor = torch.stack(b_list)
print(f'b_tensor = {b_tensor}, type of b: {type(b_tensor)}')
print(f'b_list = {b_list}, type of b_list: {type(b_list)}')
print(f'B_tensor = {B_tensor}, type of B_tensor: {type(B_tensor)}, shape of B_tensor: {B_tensor.shape}')
b_tensor = tensor([[0.7443, 0.3041, 0.9545],
[0.3092, 0.2747, 0.6717]]), type of b: <class 'torch.Tensor'>
b_list = [tensor([[0.7443, 0.3041, 0.9545],
[0.3092, 0.2747, 0.6717]]), tensor([[0.7443, 0.3041, 0.9545],
[0.3092, 0.2747, 0.6717]]), tensor([[0.7443, 0.3041, 0.9545],
[0.3092, 0.2747, 0.6717]])], type of b_list: <class 'list'>
B_tensor = tensor([[[0.7443, 0.3041, 0.9545],
[0.3092, 0.2747, 0.6717]],
[[0.7443, 0.3041, 0.9545],
[0.3092, 0.2747, 0.6717]],
[[0.7443, 0.3041, 0.9545],
[0.3092, 0.2747, 0.6717]]]), type of B_tensor: <class 'torch.Tensor'>, shape of B_tensor: torch.Size([3, 2, 3])
方法二：torch.cat
通过torch.cat将包含tensor的多维list转换成tensor。
b_tensor = torch.rand((2, 3))
b_list = [b_tensor for _ in range(3)]
B_tensor = torch.cat(b_list, 0)
print(f'b_tensor = {b_tensor}, type of b: {type(b_tensor)}')
print(f'b_list = {b_list}, type of b_list: {type(b_list)}')
print(f'B_tensor = {B_tensor}, type of B_tensor: {type(B_tensor)}, shape of B_tensor: {B_tensor.shape}')
b_tensor = tensor([[0.4237, 0.4743, 0.5213],
[0.0815, 0.6654, 0.8780]]), type of b: <class 'torch.Tensor'>
b_list = [tensor([[0.4237, 0.4743, 0.5213],
[0.0815, 0.6654, 0.8780]]), tensor([[0.4237, 0.4743, 0.5213],
[0.0815, 0.6654, 0.8780]]), tensor([[0.4237, 0.4743, 0.5213],
[0.0815, 0.6654, 0.8780]])], type of b_list: <class 'list'>
B_tensor = tensor([[0.4237, 0.4743, 0.5213],
[0.0815, 0.6654, 0.8780],
[0.4237, 0.4743, 0.5213],
[0.0815, 0.6654, 0.8780],
[0.4237, 0.4743, 0.5213],
[0.0815, 0.6654, 0.8780]]), type of B_tensor: <class 'torch.Tensor'>, shape of B_tensor: torch.Size([6, 3])