Faster RCNN 和Retinanet在將影象資料輸送到網路之前，要對影象資料進行預處理。大致上與部落格提到的相同。

事實上還可以採取第三步，將圖片的寬和高擴充套件為32的整倍數，正如在Retinanet使用的。下面是一個簡單的Pytorch資料預處理模組：

class Resizer():
    def __call__(self, sample, targetSize=608, maxSize=1024, pad_N=32):
        image, anns = sample['img'], sample['ann']
        rows, cols = image.shape[:2]
        
        smaller_size, larger_size = min(rows, cols), max(rows, cols)
        scale = targetSize / smaller_size
        if larger_size * scale > maxSize:
            scale = maxSize / larger_size
        image = skimage.transform.resize(image, (int(round(rows*scale)), 
                                                 int(round(cols*scale))), 
                                         mode='constant')
        rows, cols, cns = image.shape[:3]
        
        pad_w, pad_h = (pad_N - cols % pad_N), (pad_N - rows % pad_N)
        new_image = np.zeros((rows + pad_h, cols + pad_w, cns)).astype(np.float32)
        new_image[:rows, :cols, :] = image.astype(np.float32)
        
        anns[:, :4] *= scale
        return {'img': torch.from_numpy(new_image), 
                'ann':torch.from_numpy(anns),
                'scale':scale}