Pytorch框架之one_hot编码函数解读

import torch

targets = torch.tensor([5, 3, 2, 1])

targets_to_one_hot = torch.nn.functional.one_hot(targets)   # 默认按照targets其中的最大值+1作为one_hot编码的长度
# result: 
# tensor(
# [0, 0, 0, 0, 0, 1],
# [0, 0, 0, 1, 0, 0],
# [0, 0, 1, 0, 0, 0],
# [0, 1, 0, 0, 0, 0]
#)

targets_to_one_hot = torch.nn.functional.one_hot(targets, num_classes=7)  3# 指定one_hot编码长度为7
# result: 
# tensor(
# [0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 1, 0, 0, 0],
# [0, 0, 1, 0, 0, 0, 0],
# [0, 1, 0, 0, 0, 0, 0]
#)

y = torch.empty(3, dtype=torch.long).random_(5)

y = torch.Tensor(2,3).random_(10)

y = torch.randn(3,4).random_(10)

y.type
y.dtype

tensor.long()/int()/float()
long(),int(),float() 实现类型的转化

def one_hot(y):
    '''
    y: (N)的一维tensor，值为每个样本的类别
    out:
        y_onehot: 转换为one_hot 编码格式
    '''
    y = y.view(-1, 1)
    # y_onehot = torch.FloatTensor(3, 5)
    # y_onehot.zero_()

    y_onehot = torch.zeros(3,5)  # 等价于上面
    y_onehot.scatter_(1, y, 1)
    return y_onehot

y = torch.empty(3, dtype=torch.long).random_(5) #标签

res = one_hot(y)  # 转化为One_hot类型

# One_hot类型标签转化为整数型列表的两种方法

h = torch.argmax(res,dim=1)
_,h1 = res.max(dim=1)

a=torch.randn(1,1,3,768)

print(a.shape) #torch.Size([1, 1, 3, 768])
b=a.expand(2,-1,-1,-1)

print(b.shape) #torch.Size([2, 1, 3, 768])

c=a.expand(2,1,3,768)
print(c.shape) #torch.Size([2, 1, 3, 768])

a=torch.randn(2,1,768)
print(a)
print(a.shape) #torch.Size([2, 1, 768])
b=a.repeat(1,2,1)
print(b)
print(b.shape) #torch.Size([2, 2, 768])
c=a.repeat(3,3,3)
print(c)
print(c.shape) #torch.Size([6, 3, 2304])