title: AttributeError: module 'torch.nn.functional' has no attribute 'resize'
body: <p>Each time I am getting this error while <strong>resizing the images</strong>.
  Here is the code.</p>
  
  <p>why I am getting this error?</p>
  
  <pre><code>import models as m
  import densenet as x
  from torch.autograd import Variable
  import torch.nn as nn
  import torch.optim as optim
  from torch.utils.data import DataLoader
  from torchvision.datasets import ImageFolder
  from torchvision.transforms import transforms
  #import matplotlib.pyplot as plt 
  import torch
  
  
  from sklearn.metrics import confusion_matrix
  import numpy as np
  
  tr1=  transforms.Compose([transforms.Resize((224,224))])
  
  train_dataset = ImageFolder('../../cat_dog_dataset /training_set',transform=tr1)
  train_loader = DataLoader(train_dataset, 
                        batch_size = 1, 
                        shuffle = True, 
                        num_workers = 1)
  
  test_dataset = ImageFolder('../../cat_dog_dataset/test_set',transform=tr1)
  test_loader = DataLoader(test_dataset, 
                        batch_size = 1, 
                        shuffle = False, 
                        num_workers = 1)
  
  # GLOBAL VARIABLES
  noe = 25
  noc = 2
  
  # MODEL INSTANCE 1
  net = m.resnet50()
  net.fc = nn.Linear(2048,noc)
  #net=net.cuda()
  
  criterion = nn.CrossEntropyLoss()
  optimizer = optim.Adam(net.parameters())
  
  def train(epoch):
      net.train()
  
      train_loss_epoch=0.0
      #    running_loss=0
      for batch in train_loader:
  
          inputs, labels = batch
          inputs, labels = Variable(inputs), Variable(labels)
          optimizer.zero_grad()
          #        print(inputs)
          # FORWARD PASS
  
          out = net (inputs)
          # CALCULATE LOSS
          loss = criterion(out, labels)
          # BACK PROPAGATION
          loss.backward()
          # WEIGHT UPDATION
          optimizer.step()
  #        print(loss.data[0])
  
          train_loss_epoch+=loss.item()
  #    total_loss_epoch+=t
      print(epoch,train_loss_epoch)
  
  def test():
      final_updated_cm=np.zeros((2,2),dtype=int)
  #    confusion_matrix = meter.ConfusionMeter(3)
      net.eval()
      test_loss=0.0
      correct=0.0
      for inputs,labels in test_loader:
   #           updated_cm=torch.zeros(23,23)
          inputs, labels = Variable(inputs), Variable(labels)
          out = net (inputs)
          #print(out)
          loss=criterion(out,labels)
          test_loss+=loss.item()
  
  #        pred=out.data.max(1, keepdim=True)[1]
          pred=torch.max(out.data)
          #print(pred)
  #        pred=torch.max(out.data,1)[1]
          correct+=pred.eq(labels.data.view_as(pred)).cpu().sum()
  #        confusion_matrix.add(out.data.squeeze(), labels)
          cm=confusion_matrix(labels.data,pred.data,labels=[0,1])
          final_updated_cm=final_updated_cm+cm
      test_loss /=len(test_loader.dataset)
      print('\nTest Set: Average loss: {:.4f},Accuracy: {}/{}  ({:.0f}%)\n'.format(test_loss,correct,len(test_loader.dataset),
         100.0 * correct / len(test_loader.dataset)))
  
  #    t=(100.0*correct/len(test_loader.dataset)).item()
  #    f=open('2.txt','w')
  #    f.write(str(t))
  #    f.close()    
  #    print(t)
  
      print(final_updated_cm)
  
  for epoch in range(noe):
      train(epoch)
  test()
  </code></pre>
  
  <p>Error I am getting is:</p>
  
  <pre><code>AttributeError: Traceback (most recent call last):
      File "/home/ankur/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 57, in _worker_loop
      samples = collate_fn([dataset[i] for i in batch_indices])
    File "/home/ankur/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 57, in &lt;listcomp&gt;
      samples = collate_fn([dataset[i] for i in batch_indices])
    File "/home/ankur/projects/Codes/Building_classifier_model/torchvision/datasets/folder.py", line 118, in __getitem__
      sample = self.transform(sample)
    File "/home/ankur/projects/Codes/Building_classifier_model/torchvision/transforms/transforms.py", line 49, in __call__
      img = t(img)
    File "/home/ankur/projects/Codes/Building_classifier_model/torchvision/transforms/transforms.py", line 175, in __call__
      return F.resize(img, self.size, self.interpolation)
  AttributeError: module 'torch.nn.functional' has no attribute 'resize'  
  </code></pre>