请，我正在一个图像分割项目上，我使用了fastai库（特别是unet_learner）。我已经训练了模型，很好的是这里的代码（在训练阶段）：

#codes = np.loadtxt('codes.txt',dtype=str)
codes = np.array(['bg','edge'],dtype='<U4')# bg= background
get_y_fn = lambda x: path_lbl/f'{x.stem}{x.suffix}'

# fastai codes
data = (SegmentationItemList.from_folder(path_img)
    .split_by_rand_pct()
    .label_from_func(get_y_fn,classes=codes)
    #.add_test_folder()
    #.transform(get_transforms(),tfm_y=True,size=384)
    .databunch(bs=2,path=dataset) # bs = mimi-patch size
    .normalize(imagenet_stats))

 learn = unet_learner(data,models.resnet34,wd=1e-2)

 learn.lr_find() # find learning rate
 learn.recorder.plot() # plot learning rate graph

lr = 1e-02 # pick a lr
learn.fit_one_cycle(3,slice(lr),pct_start=0.3) # train model ---- epochs=3

learn.unfreeze() # unfreeze all layers  

# find and plot lr again
 learn.lr_find()
 learn.recorder.plot()

 learn.fit_one_cycle(10,slice(lr/400,lr/4),pct_start=0.3)

 learn.save('model-stage-1') # save model
 learn.load('model-stage-1');

 learn.export()

我的问题是，当我尝试使用经过训练的模型进行预测时，输出始终为黑色图像。下面是预测阶段的代码：

 img = open_image('/content/generated_samples_masks/545.png')
 prediction = learn.predict(img)
 prediction[0].show(figsize=(8,8))

请问，关于如何解决此问题的任何想法？谢谢