我正在使用带有Keras后端的U-Net处理一个多标签分割问题。对于每个输入图像，我有两个掩码，属于两个不同的对象。图像和掩码的大小为224 x 224，分别为RGB和灰度。文件夹结构如下：

data
 |_train
     |_image 
     |_label1 (binary masks of object 1)
     |_label2 (binary masks of object 2)

我使用的是Qubvel细分模型https://github.com/qubvel/segmentation_models和vgg-16 Backbone.js 。下面显示的是我的培训管道：

img_width, img_height = 224,224
input_shape = (img_width, img_height, 3)
model_input = Input(shape=input_shape)
n_classes=2 # masks of object 1 and object 2 
activation='sigmoid' #since I want multi-label output and not multi-class
batch_size = 16
n_epochs = 128

BACKBONE = 'vgg16'
model1 = sm.Unet(BACKBONE, 
                 encoder_weights='imagenet', 
                 classes=n_classes, 
                 activation=activation)
opt = keras.optimizers.Adam(lr=0.001) 
loss_func='binary_crossentropy'
model1.compile(optimizer=opt, 
              loss=loss_func, 
              metrics=['binary_accuracy'])

callbacks = [ModelCheckpoint(monitor='val_loss', 
                             filepath='model1.hdf5', 
                             save_best_only=True, 
                             save_weights_only=True, 
                             mode='min', 
                             verbose = 1)]
history1 = model1.fit(X_tr, Y_tr, 
                    batch_size=batch_size, 
                    epochs=n_epochs, 
                    callbacks=callbacks,
                    validation_data=(X_val, Y_val))

模型各层形状如下：

[(None, None, None, 3)]
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 512)
(None, None, None, 1024)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 256)
(None, None, None, 768)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 128)
(None, None, None, 384)
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 64)
(None, None, None, 192)
(None, None, None, 32)
(None, None, None, 32)
(None, None, None, 32)
(None, None, None, 32)
(None, None, None, 32)
(None, None, None, 32)
(None, None, None, 32)
(None, None, None, 16)
(None, None, None, 16)
(None, None, None, 16)
(None, None, None, 16)
(None, None, None, 16)
(None, None, None, 16)
(None, None, None, 2)
(None, None, None, 2)

下面显示的是我的数据准备管道，每个图像有两个掩码。我尝试为每个输入图像堆叠掩码1和掩码2：

ids = next(os.walk("data/train/image"))[2] 
print("No. of images = ", len(ids))
X = np.zeros((len(ids), im_height, im_width, 3), dtype=np.float32) #RGB input
Y = np.zeros((len(ids), im_height, im_width, 1), dtype=np.float32) #grayscale input for the masks
for n, id_ in tqdm(enumerate(ids), total=len(ids)):
    img = load_img("data/train/image/"+id_, color_mode = "rgb")
    x_img = img_to_array(img)
    x_img = resize(x_img, (224,224,3), 
                   mode = 'constant', preserve_range = True)
    # Load mask
    mask1 = img_to_array(load_img("data/train/label1/"+id_, color_mode = "grayscale"))
    mask2 = img_to_array(load_img("data/train/label2/"+id_, color_mode = "grayscale"))
    mask1 = resize(mask1, (224,224,1), 
                  mode = 'constant', preserve_range = True)
    mask2 = resize(mask2, (224,224,1), 
                  mode = 'constant', preserve_range = True)
    mask = np.stack([mask1,mask2], axis=-1)
    # Save images
    X[n] = x_img/255.0
    Y[n] = mask/255.0

X_tr, X_val, Y_tr, Y_val = train_test_split(X, Y, test_size=0.3, random_state=42)

出现以下错误：

Traceback (most recent call last):

  File "/home/codes/untitled1.py", line 482, in <module>
    Y[n] = mask/255.0

ValueError: could not broadcast input array from shape (224,224,1,2) into shape (224,224,1)

我应该使用什么正确的语法，并修改代码来堆叠掩码和训练多标签模型？感谢并期待代码中的更正。