PyTorch中的squeeze()和unsqueeze()解析与应用案例

>>> x = torch.zeros(2, 1, 2, 1, 2)
>>> x.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x)
>>> y.size()
torch.Size([2, 2, 2])
>>> y = torch.squeeze(x, 0)
>>> y.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x, 1)
>>> y.size()
torch.Size([2, 2, 1, 2])

>>> x = torch.tensor([1, 2, 3, 4])
>>> torch.unsqueeze(x, 0)
tensor([[ 1,  2,  3,  4]])
>>> torch.unsqueeze(x, 1)
tensor([[ 1],
        [ 2],
        [ 3],
        [ 4]])

from torchvision.transforms import  ToTensor
import torch as t
from torch import nnimport cv2
import numpy as np
import cv2
to_tensor = ToTensor()
# 加载图像
lena = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imshow('lena', lena)
# input = to_tensor(lena) 将ndarray转换为tensor，自动将[0,255]归一化至[0,1]。
input = to_tensor(lena).unsqueeze(0)
# 初始化卷积参数
kernel = t.ones(1, 1, 3, 3)/-9
kernel[:, :, 1, 1] = 1
conv = nn.Conv2d(1, 1, 3, 1, padding=1, bias=False)
conv.weight.data = kernel.view(1, 1, 3, 3)
# 输出
out = conv(input)
out = out.squeeze(0)
print(out.shape)
out = out.unsqueeze(3)
print(out.shape)
out = out.squeeze(0)
print(out.shape)
out = out.detach().numpy()# 缩放到0~最大值
cv2.normalize(out, out, 1.0, 0, cv2.NORM_INF)
cv2.imshow("lena-result", out)
cv2.waitKey()