def compute_features()

in main.py [0:0]

• 23 lines of code
• 8 McCabe index (conditional complexity)

def compute_features(dataloader, model, N):
    if args.verbose:
        print('Compute features')
    batch_time = AverageMeter()
    end = time.time()
    model.eval()
    # discard the label information in the dataloader
    for i, (input_tensor, _) in enumerate(dataloader):
        input_var = torch.autograd.Variable(input_tensor.cuda(), volatile=True)
        aux = model(input_var).data.cpu().numpy()

        if i == 0:
            features = np.zeros((N, aux.shape[1]), dtype='float32')

        aux = aux.astype('float32')
        if i < len(dataloader) - 1:
            features[i * args.batch: (i + 1) * args.batch] = aux
        else:
            # special treatment for final batch
            features[i * args.batch:] = aux

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if args.verbose and (i % 200) == 0:
            print('{0} / {1}\t'
                  'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})'
                  .format(i, len(dataloader), batch_time=batch_time))
    return features