#尝试一

i32=torch.IntTensor([1,2,3])

i64=torch.LongTensor([1,2,3])

#两种转换都报错

#new_i64=torch.IntTensor(i64)

#new_i32=torch.LongTensor(i32)

#didn't match because some of the arguments have invalid types: (!torch.LongTensor!)

#尝试二

new_i32=i32.long()

print(torch.equal(new_i32,i64))  #True

#torch.Tensor对应八种数据转换,各种数据可以相互转换

i32.float()

i32.double()

i32.half()

i32.byte()

i32.char()

i32.short()

i32.int()

i32.long()

#torch.stack(sequence, dim=0, out=None) 连接Tensors

i32=torch.Tensor([1,2,3])

print(torch.stack([i32,i32,i32]))  #默认dim=0,以列为基准

#  1  2  3

#  1  2  3

#  1  2  3

# [torch.FloatTensor of size 3x3]

print(torch.stack([i32,i32,i32],dim=1))

#  1  1  1

#  2  2  2

#  3  3  3

# [torch.FloatTensor of size 3x3]

print(torch.stack((i32,i32,i32),dim=1))

#  1  1  1

#  2  2  2

#  3  3  3

# [torch.FloatTensor of size 3x3]

torch.from_numpy(np.array([1,2,3]))   #torch.IntTensor of size 3

torch.from_numpy(np.array([1.0,2,3])) #torch.DoubleTensor of size 3

torch.nonzero(torch.Tensor([1,2,3,0,4]))==torch.Tensor([0,1,2,4])  #nonzero  非0元素所在位置

# TypeError: eq received an invalid combination of arguments - got (torch.FloatTensor), but expected one of:

#  * (int value)

#       didn't match because some of the arguments have invalid types: (!torch.FloatTensor!)

#  * (torch.LongTensor other)

#       didn't match because some of the arguments have invalid types: (!torch.FloatTensor!)

#torch.unsqueeze(input,dim,out=None)

m=torch.Tensor([1,2,3,4])

print(m)                     #torch.FloatTensor of size 4

m_zero=torch.unsqueeze(m,0)

print(m_zero)                #torch.FloatTensor of size 1x4

m_one=torch.unsqueeze(m,1)

print(m_one)                 #torch.FloatTensor of size 4x1

m_zero_to_m=torch.squeeze(m_zero)

print(m_zero_to_m)           #torch.FloatTensor of size 4

print(m==m_zero_to_m)        #torch.ByteTensor of size 4

# 1

# 1

# 1

# 1

print(m.equal(m_zero_to_m))  True