resnet源码pytorch_pytorch conv1d

# Pytorch 0.4.0 ResNet34实现cifar10分类.
# @Time: 2018/6/17
# @Author: xfLi
import torchvision as tv
import torch as t
import torchvision.transforms as transforms
from torch import nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
t.set_num_threads(8)
class ResidualBloak(nn.Module):
#残差块
def __init__(self, inchannel, outchannel, stride=1, shortcut=None):
super(ResidualBloak, self).__init__()
self.left = nn.Sequential(
nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),
nn.BatchNorm2d(outchannel),
nn.ReLU(inplace=True),
nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),
nn.BatchNorm2d(outchannel))
self.right = shortcut
def forward(self, x):
out = self.left(x)
residual = x if self.right is None else self.right(x)
out += residual
return F.relu(out)
class ResNet34(nn.Module):
#  实现主module：ResNet34  
#  ResNet34 包含多个layer，每个layer又包含多个residual block  
#  用子module来实现residual block，用_make_layer函数来实现layer 
def __init__(self, num_classes):
super(ResNet34, self).__init__()
#前几层图像转换
self.pre = nn.Sequential(
nn.Conv2d(3, 16, 3, 1, 1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 1))
# 重复的layer，分别有3，4，6，3个residual block
self.layer1 = self._make_layer(16, 16, 3, stride=1)
self.layer2 = self._make_layer(16, 32, 4, stride=1)
self.layer3 = self._make_layer(32, 64, 6, stride=1)
self.layer4 = self._make_layer(64, 64, 3, stride=1)
#分类用的全连接
self.fc = nn.Linear(256, num_classes)
def _make_layer(self, inchannel, outchannel, block_num, stride=1):
#构建layer,包含多个residual block
shortcut = nn.Sequential(
nn.Conv2d(inchannel, outchannel, 1, stride, bias=False),
nn.BatchNorm2d(outchannel))
layer = []
layer.append(ResidualBloak(inchannel, outchannel, stride, shortcut))
for i in range(1, block_num):
layer.append(ResidualBloak(outchannel, outchannel))
return nn.Sequential(*layer)
def forward(self, x):
x = self.pre(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = F.avg_pool2d(x, 7)
x = x.view(x.size(0), -1)
return self.fc(x)
def getData(): # 定义对数据的预处理  
transform = transforms.Compose([
transforms.Resize(40),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32),
transforms.ToTensor()])
#训练集
trainset = tv.datasets.CIFAR10(root='/data/', train=True, transform=transform, download=True)
trainset_loader = DataLoader(trainset, batch_size=4, shuffle=True)
#测试集
testset = tv.datasets.CIFAR10(root='/data/', train=False, transform=transform, download=True)
testset_loader = DataLoader(testset, batch_size=4, shuffle=False)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
return trainset_loader, testset_loader, classes
def train(): #训练
trainset_loader, testset_loader, _ = getData() #获取数据
net = ResNet34(10)
print(net)
criterion = nn.CrossEntropyLoss()
optimizer = t.optim.SGD(net.parameters(), lr=0.001, momentum=0.9) #优化器
for epoch in range(1):
for step, (inputs,labels) in enumerate(trainset_loader):
optimizer.zero_grad() #梯度清零
output = net(inputs)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
if step % 10 ==9:
acc = test(net, testset_loader)
print('Epoch', epoch, '|step ', step, 'loss: %.4f' %loss.item(), 'test accuracy:%.4f' %acc)
print('Finished Training')
return net
def test(net, testdata): #测试集
correct, total = .0, .0
for inputs, label in testdata:
net.eval()
output = net(inputs)
_, predicted = t.max(output, 1) #分类结果
total += label.size(0)
correct += (predicted == label).sum()
return float(correct) / total
if __name__ == '__main__':
net = train()