title: pytorch inception model outputs the wrong label for every input image
body: <p>For the pytorch models I found <a href="http://blog.outcome.io/pytorch-quick-start-classifying-an-image/" rel="nofollow noreferrer">this tutorial</a> explaining how to classify an image. I tried to apply the same prcedure for an inception model. However the model fails for every image I load in</p>
  
  <p>Code:</p>
  
  <pre><code># some people need these three lines to make it work
  #from torchvision.models.inception import model_urls
  #name = 'inception_v3_google'
  #model_urls[name] = model_urls[name].replace('https://', 'http://')
  
  from torch.autograd import Variable
  import torchvision
  import requests
  from torchvision import models, transforms
  from PIL import Image
  import io
  from PIL import Image
  
  LABELS_URL = 'https://s3.amazonaws.com/outcome-blog/imagenet/labels.json'
  
  # cat
  IMG_URL1 = 'http://farm2.static.flickr.com/1029/762542019_4f197a0de5.jpg'
  
  # dog
  IMG_URL2 = 'http://farm3.static.flickr.com/2314/2518519714_98b01968ee.jpg'
  
  # lion
  IMG_URL3 = 'http://farm1.static.flickr.com/62/218998565_62930f10fc.jpg'
  
  
  labels = {int(key):value for (key, value)
            in requests.get(LABELS_URL).json().items()}
  
  
  model = torchvision.models.inception_v3(pretrained=True)
  model.training = False
  model.transform_input = False
  
  
  
  def predict_url_img(url):
      response = requests.get(url)
      img_pil = Image.open(io.BytesIO(response.content))
  
      normalize = transforms.Normalize(
         mean=[0.485, 0.456, 0.406],
         std=[0.229, 0.224, 0.225]
      )
      preprocess = transforms.Compose([
         transforms.Scale(256),
         transforms.CenterCrop(299),
         transforms.ToTensor(),
         normalize
      ])
  
      img_tensor = preprocess(img_pil)
      img_tensor.unsqueeze_(0)
      img_variable = Variable(img_tensor)
      fc_out = model(img_variable)
      print("prediction:", labels[fc_out.data.numpy().argmax()])
  
  predict_url_img(IMG_URL1)
  predict_url_img(IMG_URL2)
  predict_url_img(IMG_URL3)
  </code></pre>
  
  <p>As output I get this:</p>
  
  <blockquote>
    <p>('prediction:', u"plunger, plumber's helper")</p>
    
    <p>('prediction:', u'plastic bag')</p>
    
    <p>('prediction:', u"plunger, plumber's helper")</p>
  </blockquote>