对numpy和torch数据操作进行对比，避免遗忘。
ndarray和tensor
import torch
import numpy as npnp_data = np.arange(6).reshape((2, 3))
torch_data = torch.arange(6) # 张量
tensor2array = torch_data.numpy()print('\nnumpy array:\n', np_data,'\ntorch tensor\n', torch_data,'\ntensor to array\n', tensor2array
)
"""
numpy array:[[0 1 2][3 4 5]]
torch tensortensor([0, 1, 2, 3, 4, 5])
tensor to array[0 1 2 3 4 5]
"""numpy和tensor的维度
import torch
import numpy as npnp_data = np.array([[i for i in range(r * 4, (r + 1) * 4)] for r in range(5)], dtype=np.float32)
np_data = np_data.reshape((2, 5, 2)) # 5x4 reshape一下变 2x5x2
print('numpy shape', np_data.shape) # 数组的维度
print('numpy ndim', np_data.ndim) # 数组的轴，也就是rank，也就是shape的的大小
print('numpy size', np_data.size) # 数组元素的个数，也就是shape乘起来
print('numpy dtype', np_data.dtype) # 数组元素数据类型
print('numpy itemsize', np_data.itemsize) # 数组有元素数据类型的大小，float32， 32/8=4np_data = np_data[:, :, np.newaxis, :] # 增加维度
print('numpy add dim, np.newaxis', np_data.shape)np_data = np_data.transpose((2, 0, 1, 3)) # 转置 transpose
print('numpy transpose, np.newaxis', np_data.shape)np_data = np_data.squeeze(0) # 移除0维度，只能移除维数为1的维度
print('numpy remove dim, np.squeeze', np_data.shape)#######################
print()
#######################torch_data = torch.Tensor([[i for i in range(r * 4, (r + 1) * 4)] for r in range(5)]
)
torch_data = torch_data.view(2, 5, 2) # 方法变成view， 5x4 view一下变 2x5x2
print('torch shape', torch_data.shape)
print('torch ndim', torch_data.ndim)
print('torch size', torch_data.size()) # 要调用函数
print('torch dtype', torch_data.dtype) # 没有itemsizetorch_data = torch_data.unsqueeze(2) # 增加维度
print('torch add dim, torch.unsqueeze', torch_data.shape)torch_data = torch_data.permute((2, 0, 1, 3)) # 转置 permute
print('torch permute', torch_data.shape)torch_data = torch_data.squeeze(0) # 移除0维度，只能移除维数是1的维度
print('torch remove dim, torch.squeeze', torch_data.shape)"""
numpy shape (2, 5, 2)
numpy ndim 3
numpy size 20
numpy dtype float32
numpy itemsize 4
numpy add dim, np.newaxis (2, 5, 1, 2)
numpy transpose, np.newaxis (1, 2, 5, 2)
numpy remove dim, np.squeeze (2, 5, 2)torch shape torch.Size([2, 5, 2])
torch ndim 3
torch size torch.Size([2, 5, 2])
torch dtype torch.float32
torch add dim, torch.unsqueeze torch.Size([2, 5, 1, 2])
torch permute torch.Size([1, 2, 5, 2])
torch remove dim, torch.squeeze torch.Size([2, 5, 2])
"""计算对比
import torch
import numpy as npnp_data = np.array([[1, 2], [1, 2]])
torch_data = torch.tensor(np_data)np_t = np.array([0, np.pi / 4., np.pi / 2.])
torch_t = torch.tensor(np_t)# 平均值
np_x = np.arange(5)
torch_x = torch.FloatTensor([i for i in range(5)])
# torch.mean 只能计算float的平均值，不能计算int的平均值，所有必须用FloatTensorprint('\nnumpy.abs:\n', np.abs(np_data),'\ntorch.abs:\n', torch.abs(torch_data),'\nnumpy.sin:\n', np.sin(np_t),'\ntorch.sin:\n', torch.sin(torch_t),'\nnumpy.mean\n', np.mean(np_x),'\ntorch.mean\n', torch.mean(torch_x)
)
"""
numpy.abs:[[1 2][1 2]]
torch.abs:tensor([[1, 2],[1, 2]])
numpy.sin:[0. 0.70710678 1. ]
torch.sin:tensor([0.0000, 0.7071, 1.0000], dtype=torch.float64)
numpy.mean2.0
torch.meantensor(2.)
"""矩阵的乘法对比
import torch
import numpy as np# numpy矩阵相乘
na = np.array([[1, 2], [3, 4]])
nb = np.array([[1, 1], [0, 1]])
nc = np.matmul(na, nb)
nd = na * nb
print(nc, nd, sep='\n')# torch矩阵相乘
ta = torch.FloatTensor([[1, 2], [3, 4]])
tb = torch.FloatTensor([[1, 1], [0, 1]])
tc = torch.mm(ta, tb)
td = ta * tb
print(tc, td, sep='\n')"""
[[1 3][3 7]]
[[1 2][0 4]]
tensor([[1., 3.],[3., 7.]])
tensor([[1., 2.],[0., 4.]])
"""tensor求导数
import torch
from torch.autograd import Variable# 变量
tensor = torch.FloatTensor([[1, 2], [3, 4]])
variable = Variable(tensor, requires_grad=True)
print(tensor)
print(variable)
t_mean = torch.mean(tensor * tensor)
v_mean = torch.mean(variable * variable)
print(t_mean)
print(v_mean)# 反向传播求导数
v_mean.backward()
print(variable.grad)print(variable) # Variable形式
print(variable.data) # tensor形式
print(variable.data.numpy()) # numpy形式，随后输出结果一般用numpy形式
tensor的池化操作
import torch
import torch.nn as nn
from torch.autograd import Variable# 最大池化与反池化
pool = nn.MaxPool2d(kernel_size=2,stride=2,return_indices=True,ceil_mode=True
)
unpool = nn.MaxUnpool2d(kernel_size=2, stride=2)
in_data = Variable(torch.Tensor([[[[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12],[13, 14, 15, 16]]]])
)
print('\nin_data\n', in_data)
print(in_data.shape)out_data, indices = pool(in_data)
print('\nout_data\n', out_data)
print('\nindices\n', indices)un_data = unpool(out_data, indices)
print('\nun_data\n', un_data)"""
in_datatensor([[[[ 1., 2., 3., 4.],[ 5., 6., 7., 8.],[ 9., 10., 11., 12.],[13., 14., 15., 16.]]]])
torch.Size([1, 1, 4, 4])out_datatensor([[[[ 6., 8.],[14., 16.]]]])indicestensor([[[[ 5, 7],[13, 15]]]])un_datatensor([[[[ 0., 0., 0., 0.],[ 0., 6., 0., 8.],[ 0., 0., 0., 0.],[ 0., 14., 0., 16.]]]])
"""
