title: Load a saved NN model in different Python file
body: <p>I am trying to implement the code from a Pytorch <a href="https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html#loading-models" rel="nofollow noreferrer">beginner's tutorial</a>. But I have written the code for loading the saved model in another Python file.
  The <code>FashionClassify</code> file contains the code exactly as its in the tutorial.</p>
  <p>Below is the code:</p>
  <pre><code>from FashionClassify import NeuralNetwork
  from FashionClassify import test_data
  import torch
  
  model = NeuralNetwork()
  model.load_state_dict(torch.load(&quot;model.pth&quot;))
  classes = [
      &quot;T-shirt/top&quot;, &quot;Trouser&quot;,&quot;Pullover&quot;,&quot;Dress&quot;,&quot;Coat&quot;,&quot;Sandal&quot;,&quot;Shirt&quot;,&quot;Sneaker&quot;,&quot;Bag&quot;,&quot;Ankle boot&quot;,
  ]
  
  model.eval()
  x, y = test_data[0][0], test_data[0][1]
  with torch.no_grad():
      pred = model(x)
      predicted, actual = classes[pred[0].argmax(0)],classes[y]
      print(f'Predicted: &quot;{predicted}&quot;, Actual: &quot;{actual}&quot;')
  </code></pre>
  <p><strong>However, when I run this, the entire training process starts again</strong>. Why is that so ?
  OR
  Is it an expected behavior ?</p>
  <p>(I have gone through a couple of webpages and StackOverflow answers but couldn't find my problem)</p>
  <p>FashionClassify file code:</p>
  <pre><code>import torch
  from torch import nn
  from torch.utils.data import DataLoader  # wraps an iterable around dataset
  from torchvision import datasets  # stores samples and their label
  from torchvision.transforms import ToTensor, Lambda, Compose
  import matplotlib as plt
  
  training_data = datasets.FashionMNIST(root='data', train=True, download=True, transform=ToTensor(), )
  test_data = datasets.FashionMNIST(root='data', train=False, download=True, transform=ToTensor(), )
  
  batch_size = 64
  train_dataLoader = DataLoader(training_data, batch_size=batch_size)
  test_dataLoader = DataLoader(test_data, batch_size=batch_size)
  
  for X, y in test_dataLoader:
      print('Shape of X [N,C,H,W]:', X.size())
      print('Shape of y:', y.shape, y.dtype)
      break
  
  device = 'cuda' if torch.cuda.is_available() else 'cpu'
  print('Using {} device'.format(device))
  
  
  # to define a NN, we inherit a class from nn.Module
  class NeuralNetwork(nn.Module):
      def __init__(self):
          # will specify how data will proceed in the forward pass
          super(NeuralNetwork, self).__init__()
          self.flatten = nn.Flatten()
          self.linear_relu_stack = nn.Sequential(
              nn.Linear(28 * 28, 512),
              nn.ReLU(),
              nn.Linear(512, 512),
              nn.ReLU(),
              nn.Linear(512, 10),
              nn.ReLU()
          )
  
      def forward(self, x):
          x = self.flatten(x)
          logits = self.linear_relu_stack(x)
          return logits
  
  
  model = NeuralNetwork().to(device)
  print(model)
  
  loss_fn = nn.CrossEntropyLoss()
  optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
  
  
  def train(dataloader, model, loss_fn, optimizer):
      size = len(dataloader.dataset)
      for batch, (X,y) in enumerate(dataloader):
          X,y = X.to(device), y.to(device)
  
          #compute prediction error
          pred = model(X)
          loss = loss_fn(pred, y)
  
          #backprop
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          if batch%100 ==0:
              loss,current = loss.item(), batch * len(X)
              print(f&quot;loss: {loss:&gt;7f} [{current:&gt;5d}/{size:&gt;5d}]&quot;)
  
  def test(dataloader, model):
      size = len(dataloader.dataset)
      model.eval()
      test_loss, correct = 0,0
      with torch.no_grad():
          for X, y in dataloader:
              X,y = X.to(device), y.to(device)
              pred = model(X)
              test_loss += loss_fn(pred, y).item()
              correct +=  (pred.argmax(1) == y).type(torch.float).sum().item()
      test_loss /= size
      correct /= size
      print(f&quot;Test Error: \n Accuracy: {(100*correct):&gt;0.1f}%, Avg loss: {test_loss:&gt;8f} \n&quot;)
  
  epochs = 5
  for t in range(epochs):
      print(f&quot;Epoch {t+1}\n-------------------------------&quot;)
      train(train_dataLoader, model, loss_fn, optimizer)
      test(test_dataLoader, model)
  print(&quot;Done!&quot;)
  
  torch.save(model.state_dict(), &quot;model.pth&quot;)
  print(&quot;Saved PyTorch Model State to model.pth&quot;)
  </code></pre>