1. train.py
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import time
#定义训练设备
# device = torch.device('cuda:0')
device = torch.device("cuda"if torch.cuda.is_available() else "cpu")
#准备数据集
train_data = torchvision.datasets.CIFAR10('../dataset',train=True,transform=torchvision.transforms.ToTensor())
test_data = torchvision.datasets.CIFAR10('../dataset',train=False,transform=torchvision.transforms.ToTensor())
#length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print('训练数据集的长度为:{}'.format(train_data_size), '测试数据集的长度为:{}'.format(test_data_size))
#打包数据
train_dataloader = DataLoader(train_data,batch_size=64)
test_dataloader = DataLoader(test_data,batch_size=64)
#创建网络模型 这块也可以单独放一个文件引入
class CIF(nn.Module):
def __init__(self):
super(CIF, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(3,32,5,1,padding=2),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(32,32,5,1,2),
nn.MaxPool2d(2),
nn.Conv2d(32,64,5,1,2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024,64),
nn.Linear(64,10)
)
def forward(self,x):
x = self.model(x)
return x
cif = CIF()
cif.to(device)
# cif = cif.cuda() #网络模型转移到cuda上
#良好的写法:
# if torch.cuda.is_available():
# cif = cif.cuda()
#损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)
# loss_fn = loss_fn.cuda() #损失函数转移到cuda
#优化器
#learing_rate = 0.01 #方便修改
learing_rate = 1e-2 #1x(10)^(-2)
optimzer = torch.optim.SGD(cif.parameters(),lr=learing_rate)
#设置训练网络的一些参数
#记录训练次数
total_train_step = 0
#记录测试的次数
total_test_step = 0
#训练的轮数
epoch = 10
#添加tendorboard
writer = SummaryWriter('../logs')
#计算开始时间
start_time = time.time()
for i in range(epoch):
print('-----------第{}轮训练开始------------'.format(i+1))
#训练步骤开始
cif.train() #和cif.eval()只对部分网络有用(dropout等)
for data in train_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
# imgs = imgs.cuda() #图像数据加载到cuda
# targets = targets.cuda() #标签加载到cuda
output = cif(imgs)
loss = loss_fn(output,targets)
#优化器调优
optimzer.zero_grad()
loss.backward()
optimzer.step()
total_train_step = total_train_step+1
if total_train_step % 100 == 0: #每100次打印一次
end_time = time.time()
print('训练时长:{}'.format(end_time-start_time)) #计算总时长
print('训练次数:{}, loss: {}'.format(total_train_step,loss.item()))
writer.add_scalar('train_loss',loss.item(),total_train_step)
#测试步骤开始
cif.eval()
total_test_loss = 0 #损失指标
total_accuracy = 0 #准确率指标
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
# imgs = imgs.cuda() # 图像数据加载到cuda
# targets = targets.cuda() # 标签加载到cuda
outputs = cif(imgs)
loss = loss_fn(outputs,targets)
accuracy = (outputs.argmax(1) == targets).sum() #1为横着方向
total_test_loss = total_test_loss+loss.item()
total_accuracy = total_accuracy+accuracy
print('整体测试集上的loss:{}'.format(total_test_loss))
print('整体测试集上的正确率:{}'.format(total_accuracy/test_data_size))
writer.add_scalar('test_loss',total_test_loss,total_test_step)
writer.add_scalar('test_accuracy',total_accuracy/test_data_size,total_test_step)
total_test_step = total_test_step+1
#保存模型
torch.save(cif,'cif_{}.pt'.format(i))
print('模型已保存')
writer.close()
2. test.py
import torch
import torchvision
from PIL import Image
from torch import nn
img_path = 'dataset/train/dog/dog.jpg'
img = Image.open(img_path)
image = img.convert('RGB')
transforms = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),torchvision.transforms.ToTensor()])
image = transforms(image)
# print(image.shape)
class CIF(nn.Module):
def __init__(self):
super(CIF, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(3,32,5,1,padding=2),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(32,32,5,1,2),
nn.MaxPool2d(2),
nn.Conv2d(32,64,5,1,2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024,64),
nn.Linear(64,10)
)
def forward(self,x):
x = self.model(x)
return x
model = torch.load('gpu_train/cif_5.pt', map_location=torch.device('cpu')) #GPU训练的模型 cpu时需要映射过来
print(model)
image = torch.reshape(image,(1,3,32,32))
model.eval()
with torch.no_grad():
output = model(image)
print(output)
print(output.argmax(1))