text: from __future__ import print_function, division
  
  import torch
  import torch.nn as nn
  import torch.optim as optim
  from torch.optim import lr_scheduler
  from torch.autograd import Variable
  import numpy as np
  import torchvision
  from torchvision import datasets, transforms,models
  from matplotlib import pyplot as plt
  import time
  import os
  import logging
  
  
  
  logging.basicConfig(level=logging.INFO,
                  format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
                  datefmt='%a, %d %b %Y %H:%M:%S',
                  filename='single_view_size2',
                  filemode='a')
  
  data_transforms = {
      'train': transforms.Compose([
          #transforms.RandomResizedCrop(224),
          #transforms.RandomHorizontalFlip(),
          transforms.ToTensor(),
          #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      ]),
      'val': transforms.Compose([
          #transforms.Resize(256),
          #transforms.CenterCrop(224),
          transforms.ToTensor(),
          #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      ]),
  }
  
  
  
  
  
  
  
  '''
  
  def imshow(inp, title=None):
      """Imshow for Tensor."""
      inp = inp.numpy().transpose((1, 2, 0))
      mean = np.array([0.485, 0.456, 0.406])
      std = np.array([0.229, 0.224, 0.225])
      inp = std * inp + mean
      inp = np.clip(inp, 0, 1)
      plt.imshow(inp)
      if title is not None:
          plt.title(title)
      plt.pause(0.001)  # pause a bit so that plots are updated
  
  
  # Get a batch of training data
  inputs, classes = next(iter(dataloaders['train']))
  
  # Make a grid from batch
  out = torchvision.utils.make_grid(inputs)
  
  imshow(out, title=[class_names[x] for x in classes])
  
  '''
  
  
  def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
      since = time.time()
      best_acc = 0.0
  
      for epoch in range(num_epochs):
          print('Epoch {}/{}'.format(epoch, num_epochs - 1))
          logging.info('Epoch {}/{}'.format(epoch, num_epochs - 1))
          print('-' * 10)
          logging.info('-' * 10)
  
  
          # Each epoch has a training and validation phase
          for phase in ['train', 'val']:
              if phase == 'train':
                  scheduler.step()
                  model.train(True)  # Set model to training mode
              else:
                  model.train(False)  # Set model to evaluate mode
  
              running_loss = 0.0
              running_corrects = 0
  
              # Iterate over data.
              for data in dataloaders[phase]:
                  # get the inputs
                  inputs, labels = data
  
                  # wrap them in Variable
                  inputs, labels = Variable(inputs), Variable(labels)
  
                  # zero the parameter gradients
                  optimizer.zero_grad()
  
                  # forward
                  outputs = model(inputs)
                  _, preds = torch.max(outputs.data, 1)
  
                  loss = criterion(outputs, labels)
  
                  # backward + optimize only if in training phase
                  if phase == 'train':
                      loss.backward()
                      optimizer.step()
               
  
                  # statistics
                  running_loss += loss.data[0] * inputs.size(0)
                  running_corrects += torch.sum(preds == labels.data)
  
  
              epoch_loss = running_loss / dataset_sizes[phase]
              epoch_acc = running_corrects / dataset_sizes[phase]
  
              print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
              logging.info('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
  
              # deep copy the model
              if phase == 'val' and epoch_acc > best_acc:
                  best_acc = epoch_acc
              
  
         
  
          print()
         
  
      time_elapsed = time.time() - since
      print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
      print('Best val Acc: {:4f}'.format(best_acc))
  
      logging.info('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
      logging.info('Best val Acc: {:4f}'.format(best_acc))
  
     
  
      return model
  
  
  
  if __name__=="__main__":
  
      data_dir = '/home/lss/mydatas/single_view/01'
  
  
  
      image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),data_transforms[x]) for x in ['train', 'val']}
      dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=10,shuffle=True, num_workers=4) for x in ['train', 'val']}
      dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
      class_names = image_datasets['train'].classes
      print(class_names)
  
      model_ft = models.resnet18(pretrained=False)
      #num_ftrs = model_ft.fc.in_features
      model_ft.fc = nn.Linear(512, 10)
      
  
  
      criterion = nn.CrossEntropyLoss()
  
      # Observe that all parameters are being optimized
      optimizer = optim.SGD(model_ft.parameters(), lr=0.0005, momentum=0.9)
  
      # Decay LR by a factor of 0.1 every 7 epochs
      exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
  
      model = train_model( model_ft, criterion, optimizer, exp_lr_scheduler,num_epochs=30)
  
  
  
  
  