数据集下载地址：
链接：https://pan.baidu.com/s/1l1AnBgkAAEhh0vI5_loWKw
提取码：2xq4
创建数据集：https://www.cnblogs.com/xiximayou/p/12398285.html
读取数据集：https://www.cnblogs.com/xiximayou/p/12422827.html
进行训练：https://www.cnblogs.com/xiximayou/p/12448300.html
保存模型并继续进行训练：https://www.cnblogs.com/xiximayou/p/12452624.html
加载保存的模型并测试：https://www.cnblogs.com/xiximayou/p/12459499.html
划分验证集并边训练边验证：https://www.cnblogs.com/xiximayou/p/12464738.html
使用学习率衰减策略并边训练边测试：https://www.cnblogs.com/xiximayou/p/12468010.html
利用tensorboard可视化训练和测试过程：https://www.cnblogs.com/xiximayou/p/12482573.html
从命令行接收参数：https://www.cnblogs.com/xiximayou/p/12488662.html
epoch、batchsize、step之间的关系：https://www.cnblogs.com/xiximayou/p/12405485.html
之前使用的仅仅是top1准确率。
在图像分类中，一般使用top1和top5来衡量分类模型的好坏。
下面来看看。
首先在util下新建一个acc.py文件，向里面加入计算top1和top5准确率的代码：
import torch
def accu(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred)) res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
重点就是topk()函数：
torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)
input：输入张量
k：指定返回的前几位的值
dim：排序的维度
largest：返回最大值
sorted：返回值是否排序
out：可选输出张量
需要注意的是我们这里只有两类，因此不存在top5。 因此如果设置参数topk=(1,5)，则会报错：
因此我们只能设置topk=(1,2)，而且top2的值肯定是100%。
最终res中第一位存储的是top1准确率，第二位存储的是top2准确率。
然后修改对应的train.py：
import torch
from tqdm import tqdm
from tensorflow import summary
import datetime
from utils import acc """
current_time = str(datetime.datetime.now().timestamp())
train_log_dir = '/content/drive/My Drive/colab notebooks/output/tsboardx/train/' + current_time
test_log_dir = '/content/drive/My Drive/colab notebooks/output/tsboardx/test/' + current_time
val_log_dir = '/content/drive/My Drive/colab notebooks/output/tsboardx/val/' + current_time
train_summary_writer = summary.create_file_writer(train_log_dir)
val_summary_writer = summary.create_file_writer(val_log_dir)
test_summary_writer = summary.create_file_writer(test_log_dir)
"""
class Trainer:
def __init__(self,criterion,optimizer,model):
self.criterion=criterion
self.optimizer=optimizer
self.model=model
def get_lr(self):
for param_group in self.optimizer.param_groups:
return param_group['lr']
def loop(self,num_epochs,train_loader,val_loader,test_loader,scheduler=None,acc1=0.0):
self.acc1=acc1
for epoch in range(1,num_epochs+1):
lr=self.get_lr()
print("epoch：{}，lr：{:.6f}".format(epoch,lr))
self.train(train_loader,epoch,num_epochs)
self.val(val_loader,epoch,num_epochs)
self.test(test_loader,epoch,num_epochs)
if scheduler is not None:
scheduler.step() def train(self,dataloader,epoch,num_epochs):
self.model.train()
with torch.enable_grad():
self._iteration_train(dataloader,epoch,num_epochs) def val(self,dataloader,epoch,num_epochs):
self.model.eval()
with torch.no_grad():
self._iteration_val(dataloader,epoch,num_epochs)
def test(self,dataloader,epoch,num_epochs):
self.model.eval()
with torch.no_grad():
self._iteration_test(dataloader,epoch,num_epochs) def _iteration_train(self,dataloader,epoch,num_epochs):
#total_step=len(dataloader)
#tot_loss = 0.0
#correct = 0
train_loss=AverageMeter()
train_top1=AverageMeter()
train_top2=AverageMeter()
#for i ,(images, labels) in enumerate(dataloader):
#res=[]
for images, labels in tqdm(dataloader,ncols=80):
images = images.cuda()
labels = labels.cuda()
# Forward pass
outputs = self.model(images)
#_, preds = torch.max(outputs.data,1)
pred1_train,pred2_train=acc.accu(outputs,labels,topk=(1,2))
loss = self.criterion(outputs, labels)
train_loss.update(loss.item(),images.size(0))
train_top1.update(pred1_train[0],images.size(0))
train_top2.update(pred2_train[0],images.size(0))
# Backward and optimizer
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
#tot_loss += loss.data
"""
if (i+1) % 2 == 0:
print('Epoch: [{}/{}], Step: [{}/{}], Loss: {:.4f}'
.format(epoch, num_epochs, i+1, total_step, loss.item()))
"""
#correct += torch.sum(preds == labels.data).to(torch.float32)
### Epoch info ####
#epoch_loss = tot_loss/len(dataloader.dataset)
#epoch_acc = correct/len(dataloader.dataset)
#print('train loss: {:.4f},train acc： {:.4f}'.format(epoch_loss,epoch_acc))
print(">>>[{}] train_loss:{:.4f} top1:{:.4f} top2:{:.4f}".format("train", train_loss.avg, train_top1.avg, train_top2.avg))
"""
with train_summary_writer.as_default():
summary.scalar('loss', train_loss.avg, epoch)
summary.scalar('accuracy', train_top1.avg, epoch)
"""
"""
if epoch==num_epochs:
state = {
'model': self.model.state_dict(),
'optimizer':self.optimizer.state_dict(),
'epoch': epoch,
'train_loss':train_loss.avg,
'train_acc':train_top1.avg,
}
save_path="/content/drive/My Drive/colab notebooks/output/"
torch.save(state,save_path+"/resnet18_final_v2"+".t7")
"""
t_loss = train_loss.avg,
t_top1 = train_top1.avg
t_top2 = train_top2.avg
return t_loss,t_top1,t_top2
def _iteration_val(self,dataloader,epoch,num_epochs):
#total_step=len(dataloader)
#tot_loss = 0.0
#correct = 0
#for i ,(images, labels) in enumerate(dataloader):
val_loss=AverageMeter()
val_top1=AverageMeter()
val_top2=AverageMeter()
for images, labels in tqdm(dataloader,ncols=80):
images = images.cuda()
labels = labels.cuda() # Forward pass
outputs = self.model(images)
#_, preds = torch.max(outputs.data,1)
pred1_val,pred2_val=acc.accu(outputs,labels,topk=(1,2))
loss = self.criterion(outputs, labels)
val_loss.update(loss.item(),images.size(0))
val_top1.update(pred1_val[0],images.size(0))
val_top2.update(pred2_val[0],images.size(0))
#tot_loss += loss.data
#correct += torch.sum(preds == labels.data).to(torch.float32)
"""
if (i+1) % 2 == 0:
print('Epoch: [{}/{}], Step: [{}/{}], Loss: {:.4f}'
.format(1, 1, i+1, total_step, loss.item()))
"""
### Epoch info ####
#epoch_loss = tot_loss/len(dataloader.dataset)
#epoch_acc = correct/len(dataloader.dataset)
#print('val loss: {:.4f}，val acc: {:.4f}'.format(epoch_loss,epoch_acc))
print(">>>[{}] val_loss:{:.4f} top1:{:.4f} top2:{:.4f}".format("val", val_loss.avg, val_top1.avg, val_top2.avg))
"""
with val_summary_writer.as_default():
summary.scalar('loss', val_loss.avg, epoch)
summary.scalar('accuracy', val_top1.avg, epoch)
"""
t_loss = val_loss.avg,
t_top1 = val_top1.avg
t_top2 = val_top2.avg
return t_loss,t_top1,t_top2
def _iteration_test(self,dataloader,epoch,num_epochs):
#total_step=len(dataloader)
#tot_loss = 0.0
#correct = 0
#for i ,(images, labels) in enumerate(dataloader):
test_loss=AverageMeter()
test_top1=AverageMeter()
test_top2=AverageMeter()
for images, labels in tqdm(dataloader,ncols=80):
images = images.cuda()
labels = labels.cuda() # Forward pass
outputs = self.model(images)
#_, preds = torch.max(outputs.data,1)
pred1_test,pred2_test=acc.accu(outputs,labels,topk=(1,2))
loss = self.criterion(outputs, labels)
test_loss.update(loss.item(),images.size(0))
test_top1.update(pred1_test[0],images.size(0))
test_top2.update(pred2_test[0],images.size(0))
#tot_loss += loss.data
#correct += torch.sum(preds == labels.data).to(torch.float32)
"""
if (i+1) % 2 == 0:
print('Epoch: [{}/{}], Step: [{}/{}], Loss: {:.4f}'
.format(1, 1, i+1, total_step, loss.item()))
"""
### Epoch info ####
#epoch_loss = tot_loss/len(dataloader.dataset)
#epoch_acc = correct/len(dataloader.dataset)
#print('test loss: {:.4f}，test acc： {:.4f}'.format(epoch_loss,epoch_acc))
print(">>>[{}] test_loss:{:.4f} top1:{:.4f} top2:{:.4f}".format("test", test_loss.avg, test_top1.avg, test_top2.avg))
t_loss = test_loss.avg,
t_top1 = test_top1.avg
t_top2 = test_top2.avg
"""
with test_summary_writer.as_default():
summary.scalar('loss', test_loss.avg, epoch)
summary.scalar('accuracy', test_top1.avg, epoch)
"""
"""
if epoch_acc > self.acc1:
state = {
"model": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
"epoch": epoch,
"epoch_loss": test_loss.avg,
"epoch_acc": test_top1.avg,
}
save_path="/content/drive/My Drive/colab notebooks/output/"
print("在第{}个epoch取得最好的测试准确率，准确率为：{:.4f}".format(epoch,test_loss.avg))
torch.save(state,save_path+"/resnet18_best_v2"+".t7")
self.acc1=max(self.acc1,test_loss.avg)
"""
return t_loss,t_top1,t_top2 class AverageMeter(object):
def __init__(self):
self.reset() def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0 def update(self, val, n=1):
self.val = val
self.sum += float(val) * n
self.count += n
self.avg = self.sum / self.count
我们新建了一个AverageMeter类来存储结果。
最终结果：
下一节：加载预训练的模型并进行微调。
欢迎分享，转载请注明来源：内存溢出
微信扫一扫
支付宝扫一扫
评论列表（0条）