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1.将numpy矩阵转换为Tensor张量
sub_ts = torch.from_numpy(sub_img) #sub_img为numpy类型
2.将Tensor张量转化为numpy矩阵
sub_np1 = sub_ts.numpy() #sub_ts为tensor张量
3.将numpy转换为Variable
sub_va = Variable(torch.from_numpy(sub_img))
4.将Variable张量转化为numpy
sub_np2 = sub_va.data.numpy()
应用实例1
# coding=utf-8
import numpy as np
import torch
"""Pytorch中tensor了解"""
def just_try():
# Tensor可以认为是一个高维数组，和numpy相似，但tensor可以用GPU加速
x = torch.FloatTensor(5, 3) # [torch.FloatTensor of size 5x3],预先分配空间
print('x: {}'.format(x))
print('x.size(): {}'.format(x.size())) # torch.Size([5, 3], torch.Size是tuple对象的子类，支持tuple的所有操作
y_tensor_gpu = x.cuda() # 转化为在GPU上的tensor
print('y_tensor_gpu: {}'.format(y_tensor_gpu))
def multiple_add():
x = torch.FloatTensor(3, 2)
y = torch.FloatTensor(3, 2)
# 第一种加法
result_1 = x + y
# 第二种加法
result_2 = torch.add(x, y)
# 第三种加法
result_3 = torch.FloatTensor(3, 2)
torch.add(x, y, out=result_3)
print('result_1: {}'.format(result_1))
print('result_1.size():{}'.format(result_1.size()))
print('result_2: {}'.format(result_2))
print('result_2.size():{}'.format(result_2.size()))
print('result_3: {}'.format(result_3))
print('result_3.size():{}'.format(result_3.size()))
def inplace_operation():
x = torch.FloatTensor(3, 2)
y = torch.FloatTensor(3, 2)
print('original y: {}'.format(y))
# 普通加法,不改变原始的y值
result_common = y.__add__(x)
print('common add, result_common: {}'.format(result_common))
print('common add, y: {}'.format(y))
# inplace 加法，改变y值
y.__iadd__(x)
print('inplace add, y: {}'.format(y))
def tensor_vs_numpy():
"""
tensor 和 numpy 之间的联系
:return:
"""
y = torch.FloatTensor(3, 2)
print('y: {}'.format(y))
# tensor的slice操作与numpy类似
print('y slice: {}'.format(y[:, 1]))
# 技巧: tensor与numpy之间的转换，互操作比较容易且快速，
# Tensor不支持的操作，可以先转换为numpy数组处理，之后再转回tensor
aa_tensor = torch.ones(3, 2)
print('orignal aa_tensor: {}'.format(aa_tensor))
# tensor ---> numpy
bb_numpy = aa_tensor.numpy() # Note: tensor和numpy之间共享内存，所以他们之间的转换很快，同时也意味着如果其中一个变了，
# 另外一个也会随之改变
print('bb_numpy: {}'.format(bb_numpy))
# numpy ---> tensor
cc_tensor = torch.from_numpy(bb_numpy)
print('cc_tensor: {}'.format(cc_tensor))
bb_numpy += 1
print('after adding one, bb_numpy: {}'.format(bb_numpy))
print('after adding one, aa_tensor: {}'.format(aa_tensor))
print('after adding one, cc_tensor: {}'.format(cc_tensor))
if __name__ == '__main__':
just_try()
print("********************")
multiple_add()
print("********************")
inplace_operation()
print("********************")
tensor_vs_numpy()
运行结果
x: tensor([[ 8.4735e-01, 4.5852e-41, 1.4709e-28],
[ 3.0645e-41, 9.5032e-04, 4.5852e-41],
[ 2.5129e-39, 4.5852e-41, -4.3164e-02],
[ 4.5850e-41, 2.6068e-39, 4.5852e-41],
[ 3.0926e+00, 4.5852e-41, 2.5129e-39]])
x.size(): torch.Size([5, 3])
y_tensor_gpu: tensor([[ 8.4735e-01, 4.5852e-41, 1.4709e-28],
[ 3.0645e-41, 9.5032e-04, 4.5852e-41],
[ 2.5129e-39, 4.5852e-41, -4.3164e-02],
[ 4.5850e-41, 2.6068e-39, 4.5852e-41],
[ 3.0926e+00, 4.5852e-41, 2.5129e-39]], device='cuda:0')
********************
result_1: tensor([[8.4734e-01, 7.6497e-41],
[2.3627e-29, 3.0672e-41],
[2.2296e-29, 3.0645e-41]])
result_1.size():torch.Size([3, 2])
result_2: tensor([[8.4734e-01, 7.6497e-41],
[2.3627e-29, 3.0672e-41],
[2.2296e-29, 3.0645e-41]])
result_2.size():torch.Size([3, 2])
result_3: tensor([[8.4734e-01, 7.6497e-41],
[2.3627e-29, 3.0672e-41],
[2.2296e-29, 3.0645e-41]])
result_3.size():torch.Size([3, 2])
********************
original y: tensor([[1.4718e-28, 3.0645e-41],
[2.3627e-29, 3.0672e-41],
[2.2296e-29, 3.0645e-41]])
common add, result_common: tensor([[1.7051e-28, 6.1290e-41],
[4.7253e-29, 6.1343e-41],
[4.4592e-29, 6.1290e-41]])
common add, y: tensor([[1.4718e-28, 3.0645e-41],
[2.3627e-29, 3.0672e-41],
[2.2296e-29, 3.0645e-41]])
inplace add, y: tensor([[1.7051e-28, 6.1290e-41],
[4.7253e-29, 6.1343e-41],
[4.4592e-29, 6.1290e-41]])
********************
y: tensor([[1.4718e-28, 3.0645e-41],
[4.7253e-29, 6.1343e-41],
[4.4592e-29, 6.1290e-41]])
y slice: tensor([3.0645e-41, 6.1343e-41, 6.1290e-41])
orignal aa_tensor: tensor([[1., 1.],
[1., 1.],
[1., 1.]])
bb_numpy: [[1. 1.]
[1. 1.]
[1. 1.]]
cc_tensor: tensor([[1., 1.],
[1., 1.],
[1., 1.]])
after adding one, bb_numpy: [[2. 2.]
[2. 2.]
[2. 2.]]
after adding one, aa_tensor: tensor([[2., 2.],
[2., 2.],
[2., 2.]])
after adding one, cc_tensor: tensor([[2., 2.],
[2., 2.],
[2., 2.]])
应用实例2
# coding=utf-8
import numpy as np
import torch
from torch.autograd import Variable
"""pytorch中Variable了解"""
"""
Variable是Pytorch中autograd自动微分模块的核心。
它封装了Tensor,支持几乎所有的tensor操作。
主要包含如下3个属性：
1. data: 保存Variable所包含的Tensor
2. grad: 保存data对应的梯度，grad也是一个Variable，而不是一个Tensor，和data的形状一样
3. grad_fn: 指向一个Function对象，这个Function用来反向传播计算输入的梯度
"""
def about_variable():
x = Variable(torch.ones(3, 2), requires_grad=True)
y = x.detach().numpy()
z = torch.from_numpy(y)
print('x: {}'.format(x))
print('***************')
print('y: {}'.format(y))
print('***************')
print('z: {}'.format(z))
print('***************')
print('x.data: {}'.format(x.data))
print('***************')
print('x.grad: {}'.format(x.grad))
# Variable和Tensor具有几乎一致的接口
aa_variable = Variable(torch.ones(3, 2))
print('torch.cos(aa_variable): {}'.format(torch.cos(aa_variable)))
print('torch.cos(aa_variable.data): {}'.format(torch.cos(aa_variable.data)))
if __name__ == '__main__':
about_variable()
运行结果
x: tensor([[1., 1.],
[1., 1.],
[1., 1.]], requires_grad=True)
***************
y: [[1. 1.]
[1. 1.]
[1. 1.]]
***************
z: tensor([[1., 1.],
[1., 1.],
[1., 1.]])
***************
x.data: tensor([[1., 1.],
[1., 1.],
[1., 1.]])
***************
x.grad: None
torch.cos(aa_variable): tensor([[0.5403, 0.5403],
[0.5403, 0.5403],
[0.5403, 0.5403]])
torch.cos(aa_variable.data): tensor([[0.5403, 0.5403],
[0.5403, 0.5403],
[0.5403, 0.5403]])