eval.py 文件源码

def eval_model(dataset, dataset_loader, standard_encoding, model_encoding, model, trustedEncoder = False, image_group = {}):
    model.eval()
    print "evaluating model..."
    if trustedEncoder == False:
      print "not using trusted encoder. This may take signficantly longer as predictions are converted to other encoding."
    mx = len(dataset_loader) 
    batches = []
    top1 = imSituTensorEvaluation(1, 3, image_group)
    top5 = imSituTensorEvaluation(5, 3, image_group)
    for i, (indexes, input, target) in enumerate(dataset_loader):
      if True or i % 10 == 0: print "batch {} out of {}\r".format(i+1,mx),
      input_var = torch.autograd.Variable(input.cuda(), volatile = True)
      #target_var = torch.autograd.Variable(target.cuda(), volatile = True)
      (scores,predictions)  = model.forward_max(input_var)
      (s_sorted, idx) = torch.sort(scores, 1, True)
      if not trustedEncoder:
        predictions = standard_encoding.to_tensor(model_encoding.to_situation(predictions), False, False)
        predictions = predictions.view(target.size()[0], standard_encoding.n_verbs(), -1)  
      else:
        predictions = predictions.data
      #(s_sorted, idx) = torch.sort(scores, 1, True)
      top1.add_point(target, predictions, idx.data, dataset.index_image(indexes))
      top5.add_point(target, predictions, idx.data, dataset.index_image(indexes))
    return (top1, top5) 

#assumes the predictions are grouped by image, and sorted