a=torch.tensor([1,2,3],dtype=torch.float32) print(a)#tensor([1., 2., 3.]) #创建全0tensor z=torch.zeros(3,3) print(z) # tensor([[0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.]]) #创建对角线为1的tensor k=torch.eye(3,3) print(k) # tensor([[1., 0., 0.], # [0., 1., 0.], # [0., 0., 1.]]) #对角矩阵 x2=torch.diag(torch.tensor([1,2,3])) print(x2) # tensor([[1, 0, 0], # [0, 2, 0], # [0, 0, 3]]) #切分 linspace print(torch.linspace(2,10,steps=4))#steps表示切分的个数 # tensor([ 2.0000, 4.6667, 7.3333, 10.0000]) #均匀分布,值在0-1 print(torch.rand(10)) #正态分布，均值为0，方差为1 print(torch.randn(10)) #正态分布随机 x1=torch.normal(mean=torch.zeros(4,4),std=torch.ones(4,4)) #选择随机数 print(torch.randperm(10))#0-9是个数随机打乱 # tensor([3, 1, 2, 7, 6, 9, 0, 5, 4, 8]) #生成一个区间的数 print(torch.arange(10,30,5))#开头、结尾、步长 # tensor([10, 15, 20, 25])2.张量的检查torch.is_tensor(检查元素名称)
x=[2,9,8,5] print(torch.is_tensor(x))#检查是否是一个tensor False y=torch.rand(1,2) print(torch.is_tensor(y))#True3.统计元素个数
print(torch.numel(y))#统计元素个数 24.numpy和tensor之间的转换
#将numpy转化为tensor w=np.array([3,4,5,6]) v=torch.from_numpy(w) print(v) # tensor([3, 4, 5, 6], dtype=torch.int32) #tensor转numpy a=torch.zeros(4) b=a.numpy() print(a,b) # tensor([0., 0., 0., 0.]) [0. 0. 0. 0.]5.切片索引
许多都和numpy几乎一样。
(1)不规则切片
torch.index_select
a=torch.linspace(1,12,steps=12).view(2,6) print(a) b=torch.index_select(a,0,torch.tensor([0,1])) print(b) c=torch.index_select(a,1,torch.tensor([0,2])) print(c)
torch.masked_select根据条件返回元素，注意张量 mask须跟input张量有相同数量的元素数目，但形状或维度不需要相同
pytorch中mask_select()的用法_燕小硕的博客-CSDN博客_mask_select
torch.take主要就是，将输入张量视为视为一维张量。结果tensor的形状与索引相同。
见博客详解
pytorch每日一学44(torch.take())根据索引返回指定索引上的数据集合_Fluid_ray的博客-CSDN博客_pytorch 返回索引
(2)修改张量的某些元素得到新的张量
torch.where
torch.masked_fill
torch.index_fill
a=torch.rand(10).view(2,5) print(a) # tensor([[0.9998, 0.5385, 0.3615, 0.0559, 0.9988], # [0.4596, 0.9342, 0.5472, 0.7440, 0.8707]]) b=torch.where(a>0.5,torch.tensor(1.0),torch.tensor(0.0)) print(b) # tensor([[1., 1., 0., 0., 1.], # [0., 1., 1., 1., 1.]]) c=torch.index_fill(a,dim = 1,index = torch.tensor([0,2,4]),value = 100) print(c) # tensor([[1.0000e+02, 5.3855e-01, 1.0000e+02, 5.5887e-02, 1.0000e+02], # [1.0000e+02, 9.3425e-01, 1.0000e+02, 7.4401e-01, 1.0000e+02]]) d=torch.masked_fill(a,a>0.5,1) print(d) # tensor([[1.0000, 1.0000, 0.3615, 0.0559, 1.0000], # [0.4596, 1.0000, 1.0000, 1.0000, 1.0000]])6.维度变换
tensor.reshape,类似tensor的view方法
tensor.squeeze、tensor.unsqueeze可以添加去掉维度或者添加维度
a=torch.rand([1,2,4,5]) print(a.shape)#torch.Size([1, 2, 4, 5]) print(a) # tensor([[[[0.4281, 0.2015, 0.0614, 0.5061, 0.6283], # [0.7777, 0.0322, 0.4097, 0.2483, 0.2258], # [0.7085, 0.3550, 0.1153, 0.5201, 0.2608], # [0.8606, 0.4883, 0.1230, 0.2008, 0.4587]], # # [[0.1211, 0.3047, 0.4763, 0.5203, 0.1127], # [0.2784, 0.1144, 0.5456, 0.1764, 0.0288], # [0.6109, 0.2346, 0.8698, 0.4185, 0.2284], # [0.4811, 0.8747, 0.8924, 0.1246, 0.7283]]]]) b=a.view(1,8,5) print(b.shape)#torch.Size([1, 8, 5]) print(b) # tensor([[[0.4281, 0.2015, 0.0614, 0.5061, 0.6283], # [0.7777, 0.0322, 0.4097, 0.2483, 0.2258], # [0.7085, 0.3550, 0.1153, 0.5201, 0.2608], # [0.8606, 0.4883, 0.1230, 0.2008, 0.4587], # [0.1211, 0.3047, 0.4763, 0.5203, 0.1127], # [0.2784, 0.1144, 0.5456, 0.1764, 0.0288], # [0.6109, 0.2346, 0.8698, 0.4185, 0.2284], # [0.4811, 0.8747, 0.8924, 0.1246, 0.7283]]]) c=torch.reshape(b,[1,2,4,5]) print(c) # tensor([[[[0.4281, 0.2015, 0.0614, 0.5061, 0.6283], # [0.7777, 0.0322, 0.4097, 0.2483, 0.2258], # [0.7085, 0.3550, 0.1153, 0.5201, 0.2608], # [0.8606, 0.4883, 0.1230, 0.2008, 0.4587]], # # [[0.1211, 0.3047, 0.4763, 0.5203, 0.1127], # [0.2784, 0.1144, 0.5456, 0.1764, 0.0288], # [0.6109, 0.2346, 0.8698, 0.4185, 0.2284], # [0.4811, 0.8747, 0.8924, 0.1246, 0.7283]]]]) d=torch.squeeze(a) print(d.shape)#torch.Size([2, 4, 5]) print(d) # tensor([[[0.4281, 0.2015, 0.0614, 0.5061, 0.6283], # [0.7777, 0.0322, 0.4097, 0.2483, 0.2258], # [0.7085, 0.3550, 0.1153, 0.5201, 0.2608], # [0.8606, 0.4883, 0.1230, 0.2008, 0.4587]], # # [[0.1211, 0.3047, 0.4763, 0.5203, 0.1127], # [0.2784, 0.1144, 0.5456, 0.1764, 0.0288], # [0.6109, 0.2346, 0.8698, 0.4185, 0.2284], # [0.4811, 0.8747, 0.8924, 0.1246, 0.7283]]]) e=torch.unsqueeze(d,axis=1) print(e.shape)#torch.Size([2, 1, 4, 5]) print(e) # tensor([[[[0.4281, 0.2015, 0.0614, 0.5061, 0.6283], # [0.7777, 0.0322, 0.4097, 0.2483, 0.2258], # [0.7085, 0.3550, 0.1153, 0.5201, 0.2608], # [0.8606, 0.4883, 0.1230, 0.2008, 0.4587]]], # # # [[[0.1211, 0.3047, 0.4763, 0.5203, 0.1127], # [0.2784, 0.1144, 0.5456, 0.1764, 0.0288], # [0.6109, 0.2346, 0.8698, 0.4185, 0.2284], # [0.4811, 0.8747, 0.8924, 0.1246, 0.7283]]]])
torch.transpose可以交换张量的维度，常用于图片存储格式的变换上
#batch,height,width,channel data=torch.rand([100,256,256,3]) print(data.shape) # torch.Size([100, 256, 256, 3]) data_t=torch.transpose(torch.transpose(data,1,2),1,3) print(data_t.shape) # torch.Size([100, 3, 256, 256])7.合并分割
torch.cat不增加维度的连接
torch.stack堆叠
torch.split分割张量，是torch.cat的逆运算
a=torch.randn(2,6) b=torch.zeros(2,6) c=torch.cat([a,b],dim=0) print(c) # tensor([[-0.8208, 0.9579, 1.0119, 1.2694, 1.3407, 1.9926], # [ 0.6872, -0.3352, 1.3424, -1.3681, 0.7423, -0.0288], # [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], # [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]) d=torch.cat([a,b],dim=1) print(d) # tensor([[-0.8208, 0.9579, 1.0119, 1.2694, 1.3407, 1.9926, 0.0000, 0.0000, # 0.0000, 0.0000, 0.0000, 0.0000], # [ 0.6872, -0.3352, 1.3424, -1.3681, 0.7423, -0.0288, 0.0000, 0.0000, # 0.0000, 0.0000, 0.0000, 0.0000]]) e=torch.stack([a,b],dim=0) f=torch.stack([a,b],dim=1) print(e) # tensor([[[-0.8208, 0.9579, 1.0119, 1.2694, 1.3407, 1.9926], # [ 0.6872, -0.3352, 1.3424, -1.3681, 0.7423, -0.0288]], # # [[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], # [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]) print(f) # tensor([[[-0.8208, 0.9579, 1.0119, 1.2694, 1.3407, 1.9926], # [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]], # # [[ 0.6872, -0.3352, 1.3424, -1.3681, 0.7423, -0.0288], # [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]) g,h=torch.split(c,split_size_or_sections=2,dim=0) i,j=torch.split(c,split_size_or_sections=[5,1],dim=1) print(g) # tensor([[-0.8208, 0.9579, 1.0119, 1.2694, 1.3407, 1.9926], # [ 0.6872, -0.3352, 1.3424, -1.3681, 0.7423, -0.0288]]) print(h) # tensor([[0., 0., 0., 0., 0., 0.], # [0., 0., 0., 0., 0., 0.]]) print(i) # tensor([[-0.8208, 0.9579, 1.0119, 1.2694, 1.3407], # [ 0.6872, -0.3352, 1.3424, -1.3681, 0.7423], # [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], # [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]) print(j) # tensor([[ 1.9926], # [-0.0288], # [ 0.0000], # [ 0.0000]])
