主函数main：
python
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms
# from Lenet5 import Lenet5
from Resnet import ResNet18
def main():
batchsz = 128 # how to make sure?
# load train data
cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]), download=True)
cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)
cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]), download=True)
cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)
x, label = iter(cifar_train).__next__() # ?
print('x:',x.shape,'label:',label.shape)
device = torch.device('cuda')
# model = Lenet5().to(device)
model = ResNet18().to(device)
criteon = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
print(model)
for epoch in range(1000):
model.train()
for batch_idx, (x, label) in enumerate(cifar_train):
x, label = x.to(device), label.to(device)
logits = model(x)
loss = criteon(logits, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# test
model.eval()
with torch.no_grad(): # test has no user for caculating grad.
total_correct = 0
total_num = 0
for x, label in cifar_test:
x, label = x.to(device), label.to(device)
logits = model(x)
pred = logits.argmax(dim=1)
correct = torch.eq(pred, label).float().sum().item()
total_correct += correct
total_num += x.size(0)
acc = total_correct/total_num
print(epoch, acc)
if __name__ == '__main__':
main()Lenet5类：
python
import torch
from torch import nn
from torch.nn import functional as F
class Lenet5(nn.Module):
def __init__(self):
super(Lenet5, self).__init__()
self.conv_unit = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=0),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=0),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
# flatten
self.fc_unit = nn.Sequential(
nn.Linear(32*5*5, 32),
nn.ReLU(),
# nn.Linear(120, 84),
# nn.ReLU(),
nn.Linear(32, 10)
)
tmp = torch.randn(2, 3, 32, 32)
out = self.conv_unit(tmp)
print('con out:', out.shape)
# con out: torch.Size([2, 32, 5, 5])
def forward(self, x): # indent make no mistake
batchsz = x.size(0)
x = self.conv_unit(x)
x = x.view(batchsz, 32 * 5 * 5)
logits = self.fc_unit(x)
return logits
def main():
net = Lenet5()
tmp = torch.randn(2, 3, 32, 32) # ?
out = net(tmp)
print('lenet out:', out.shape)
if __name__ == '__main__':
main()Resnet类：
python
import torch
from torch import nn
from torch.nn import functional as F
class ResBlk(nn.Module):
def __init__(self, ch_in, ch_out, stride=1):
super(ResBlk, self).__init__()
self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(ch_out)
self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(ch_out)
self.extra = nn.Sequential()
if ch_out != ch_in:
self.extra = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
nn.BatchNorm2d(ch_out)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
# short cut.
out = self.extra(x) + out # aim:x is the same of out.
out = F.relu(out)
return out # the result of the final prediction
class ResNet18(nn.Module):
def __init__(self):
super(ResNet18, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),
nn.BatchNorm2d(64)
)
self.blk1 = ResBlk(64, 128, stride=2)
self.blk2 = ResBlk(128, 256, stride=2)
self.blk3 = ResBlk(256, 512, stride=2)
self.blk4 = ResBlk(512, 512, stride=2)
self.outlayer = nn.Linear(512*1*1, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.blk1(x)
x = self.blk2(x)
x = self.blk3(x)
x = self.blk4(x)
x = F.adaptive_avg_pool2d(x, [1, 1])
x = x.view(x.size(0), -1)
x = self.outlayer(x)
return x
def main():
blk = ResBlk(64, 128, stride=4)
tmp = torch.randn(2, 64, 32, 32)
out = blk(tmp)
print('block:', out.shape)
x = torch.randn(2, 3, 32, 32)
model = ResNet18()
out = model(x)
print('resnet:', out.shape)
if __name__ == '__main__':
main()想的时候都是困难，做的时候才是答案。