import torch
from torch.nn import functional as F
 
"""手動(dòng)定義卷積核(weight)和偏置"""
w = torch.rand(16, 3, 5, 5)  # 16種3通道的5乘5卷積核
b = torch.rand(16)  # 和卷積核種類數(shù)保持一致(不同通道共用一個(gè)bias)
 
"""定義輸入樣本"""
x = torch.randn(1, 3, 28, 28)  # 1張3通道的28乘28的圖像
 
"""2D卷積得到輸出"""
out = F.conv2d(x, w, b, stride=1, padding=1)  # 步長(zhǎng)為1,外加1圈padding,即上下左右各補(bǔ)了1圈的0,
print(out.shape)
 
out = F.conv2d(x, w, b, stride=2, padding=2)  # 步長(zhǎng)為2,外加2圈padding
print(out.shape)
out = F.conv2d(x, w)  # 步長(zhǎng)為1,默認(rèn)不padding, 不夠的舍棄，所以對(duì)于28*28的圖片來(lái)說(shuō)，算完之后變成了24*24
print(out.shape)
 

mu1 = F.conv2d(img1, window , padding=padd, dilation=dilation, groups=channel)

import torch
import torch.nn.functional as F
# 小括號(hào)里面有幾個(gè)[]就代表是幾維數(shù)據(jù)
input = torch.tensor([[1,2,0,3,1],
                      [0,1,2,3,1],
                      [1,2,1,0,0],
                      [5,2,3,1,1],
                      [2,1,0,1,1]])

kernel = torch.tensor([[1,2,1],
                       [0,1,0],
                       [2,1,0]])

input = torch.reshape(input,(1,1,5,5))
kernel = torch.reshape(kernel,(1,1,3,3))

# stride代表的是步長(zhǎng)的意思，即每次卷積核向左或者向下移動(dòng)多少步進(jìn)行相乘
#  因?yàn)閏onv2d的input和weight對(duì)應(yīng)的tensor是[batch,channel,h,w],所以上述才將它們進(jìn)行reshape
output = F.conv2d(input,kernel,stride=1)
print(output)

output = F.conv2d(input,kernel,stride=2)
print(output)

# padding代表的是向上下左右填充的行列數(shù)，里面數(shù)字填寫0
output3 = F.conv2d(input,kernel,stride=1,padding=1)
print(output3)

import torch
import torchvision
from torch.utils.data import DataLoader
from torch import nn
from torch.nn import Conv2d
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10('./torchvision_dataset', train=False, download=False,
                                       transform=torchvision.transforms.ToTensor())

# 準(zhǔn)備好數(shù)據(jù)集就放在dataloader中進(jìn)行加載
dataloader = DataLoader(dataset, batch_size=64)


# 開始定義一個(gè)卷積類
class Zkl(nn.Module):
    def __init__(self):
        super(Zkl, self).__init__()
        self.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0)

    def forward(self,x):
        x = self.conv1(x)
        return x

writer = SummaryWriter("nn_conv2d")
zkl = Zkl()
# print(zkl)
step = 0
for data in dataloader:
    imgs,target = data
    output = zkl(imgs)
    #print(imgs.shape)
    #print(output.shape)
    writer.add_images('nn_conv2d_input',imgs,step)
    #因?yàn)檩敵鍪?個(gè)通道，tensorboard無(wú)法解析，所以需要reshape三個(gè)通道
    output = torch.reshape(output,(-1,3,30,30))
    writer.add_images('nn_conv2d_output',output,step)
    step+=1
writer.close()