import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
test_data = torchvision.datasets.CIFAR10(root="./dataset",train=False,transform=dataset_transform,download = True)
# test_loader = DataLoader(dataset=test_data,batch_size=64,shuffle=True,num_workers=0,drop_last=False)
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
# self.conv1 = Conv2d(3, 32, 5, padding=2)
# self.maxpool1 = MaxPool2d(2)
# self.conv2 = Conv2d(32, 32, 5, padding=2)
# self.conv3 = Conv2d(32, 64, 5, padding=2)
# self.flatten = Flatten()
# self.linear1 = Linear(1024,64)
# self.linear2 = Linear(64, 10)
self.model1 = Sequential(
Conv2d(3, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, 5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(1024, 64),
Linear(64, 10)
)
def forward(self,x):
x = self.model1(x)
return x
# x1 = self.conv1(input) # 3@32×32 —— 32@32×32
# x2 = self.maxpool1(x1) #32@32×32 —— 32@16×16
# x3 = self.conv2(x2) #32@16×16 —— 32@16×16
# x4 = self.maxpool1(x3) #32@16×16 —— 32@8×8
# x5 = self.conv3(x4) # 32@8×8 —— 64@8×8
# x6 = self.maxpool1(x5) # 64@8×8 —— 64@4×4
# x7 = self.flatten(x6) # 64@4×4 —— 64×4×4
# x8 = self.linear1(x7) #64×4×4 —— 64
# output = self.linear2(x8) # 64 —— 10
image,targe = test_data[1]
image = torch.reshape(image,(-1,3,32,32))
print(image.shape)
model = Model()
print(model)
output = model(image)
print(output)
网络结构如下所示