【猫狗数据集】利用tensorboard可视化训练和测试过程

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

epoch、batchsize、step之间的关系:https://www.cnblogs.com/xiximayou/p/12405485.html

 

我们已经能够使用学习率衰减策略了,同时也可以训练、验证、测试了。那么,我们可能想要了解训练过程中的损失和准确率的可视化结果。我们可以使用tensorboard来进行可视化。可参考:

利用tensorboard可视化:https://www.cnblogs.com/xiximayou/p/12470678.html

利用tensorboardcolab可视化:https://www.cnblogs.com/xiximayou/p/12470715.html

在此之前,我们还要优化一下我们的训练测试代码。一般情况下,我们只需要关注每一个epoch的结果就行了,可以将输入每一个step的那段代码注释掉,但是,这也存在一个问题。每次只打印出epoch的结果,有可能一个epoch要执行的时间很长,注释掉step之后没有反馈给到我们。那应该怎么办?使用python库tqdm。它会以进度条的形式告诉我们一个epoch还有多久完成,以及完成所需的时间。

接下来,我们结合代码来一起看看改变之后的结果:

main.py

import sys
sys.path.append("/content/drive/My Drive/colab notebooks")
from utils import rdata
from model import resnet
import torch.nn as nn
import torch
import numpy as np
import torchvision
import train
import torch.optim as optim

np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

batch_size=128
train_loader,val_loader,test_loader=rdata.load_dataset(batch_size)

model =torchvision.models.resnet18(pretrained=False)
model.fc = nn.Linear(model.fc.in_features,2,bias=False)
model.cuda()


#定义训练的epochs
num_epochs=100
#定义学习率
learning_rate=0.1
#定义损失函数
criterion=nn.CrossEntropyLoss()
#定义优化方法,简单起见,就是用带动量的随机梯度下降
optimizer = torch.optim.SGD(params=model.parameters(), lr=0.1, momentum=0.9,
                          weight_decay=1*1e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [40,80], 0.1)
print("训练集有:",len(train_loader.dataset))
#print("验证集有:",len(val_loader.dataset))
print("测试集有:",len(test_loader.dataset))
def main():
  trainer=train.Trainer(criterion,optimizer,model)
  trainer.loop(num_epochs,train_loader,val_loader,test_loader,scheduler)
  
main()

这里面没有什么变化。主要是train.py

import torch
from tqdm import tqdm
from tensorflow import summary
import datetime


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
    #for i ,(images, labels) in enumerate(dataloader):
    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)
      loss = self.criterion(outputs, labels)
      # 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))
    with train_summary_writer.as_default():
      summary.scalar('loss', epoch_loss.item(), epoch)
      summary.scalar('accuracy', epoch_acc.item(), epoch)
    if epoch==num_epochs:
      state = { 
        'model': self.model.state_dict(), 
        'optimizer':self.optimizer.state_dict(), 
        'epoch': epoch,
        'train_loss':epoch_loss,
        'train_acc':epoch_acc,
      }
      save_path="/content/drive/My Drive/colab notebooks/output/"   
      torch.save(state,save_path+"/resnet18_final"+".t7")
  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):
    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)
        loss = self.criterion(outputs, labels)
        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))
    with val_summary_writer.as_default():
      summary.scalar('loss', epoch_loss.item(), epoch)
      summary.scalar('accuracy', epoch_acc.item(), epoch)
  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):
    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)
        loss = self.criterion(outputs, labels)
        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))
    with test_summary_writer.as_default():
      summary.scalar('loss', epoch_loss.item(), epoch)
      summary.scalar('accuracy', epoch_acc.item(), epoch)
    if epoch_acc > self.acc1:
      state = {  
      "model": self.model.state_dict(),
      "optimizer": self.optimizer.state_dict(),
      "epoch": epoch,
      "epoch_loss": epoch_loss,
      "epoch_acc": epoch_acc,
      "acc1": self.acc1,
      }
      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,epoch_acc))   
      torch.save(state,save_path+"/resnet18_best"+".t7")
      self.acc1=max(self.acc1,epoch_acc)

首先关注summary.create_file_writer,这个函数的参数是需要存储可视化文件的地址,我们这里有train、val、test。然后是

with test_summary_writer.as_default():
      summary.scalar('loss', epoch_loss.item(), epoch)
      summary.scalar('accuracy', epoch_acc.item(), epoch)

这之类的。我们把想要可视化的损失和准确率随epoch的变化情况传入到summary.scalar中。summary.scalar接受三个参数,第一个是图的名称,第二个是纵坐标,第三个是横坐标。

之后在test.ipynb中,我们一步步进行操作:

首先进入到train目录下:

cd /content/drive/My Drive/colab notebooks/train

然后输入魔法命令:

%load_ext tensorboard.notebook

接着就可以启动tensorboard了:

%tensorboard --logdir "/content/drive/My Drive/colab notebooks/output/tsboardx/"

启动之后会在该代码块下显示tensorboard的界面。还没有开始训练,所以暂时是看不到变化的。

接下来我们就可以开始训练了:

!python main.py

这里的结果就只截部分了。我们设定了训练100个epoch,batchsize设定为128。这里需要说明的是使用大的batchsize的同时要将学习率也设置大些,我们设置初始的学习率为0.1。并在第40个和第80个epoch进行学习率衰减,每次变为原来的0.1呗。也要切记并不是batchsize越大越好,虽然大的batchsize可以加速网络的训练,但是会造成内存不足和模型的泛化能力不好。

 

可以发现我们显示的界面还是比较美观的。

最后截图的是测试准确率最高的那个epoch的结果:

在查看tensorboard之前,我们看下存储内容的位置。

就是根据标红的文件中的内容进行可视化的。 

最后去看一下tensorboard:

红线代表测试,蓝线代表训练。 

至此,网络的训练、测试以及可视化就完成了,接下来是看看整体的目录结构:

 

 

下一节,通过在命令行指定所需的参数,比如batchsize等。 

posted @ 2020-03-12 20:44  西西嘛呦  阅读(1096)  评论(0编辑  收藏