pytorch vision image prediction

prediction.py

# Resize the input image nad preprocess it.
image = T.Resize(target_size=(224, 224))(image)
image = T.ToTensor()(image)

# Convert to Torch.Tensor and normalize.
image = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(image)

with torch.no_grad():
    image = torch.autograd.Variable(image[None])
      
# Get prediction results
preds = F.softmax(model(image), dim=1)
# Prepare first k items, from preditions result
results = torch.topk(preds.cpu().data, k=4, dim=1)