Keras 自定义生成器：TypeError：生成器需要 Float32

Question

我使用的是自定义的 Keras 数据生成器，代码如下：

class DataGen (keras.utils.Sequence):

  def __init__(self, combined_ids, mask_ids, combined_path, mask_path, batch_size = 1, image_size = 256, PET_only = False, CT_only = False):
    self.combined_ids = combined_ids
    self.mask_ids = mask_ids
    self.combined_path = combined_path
    self.mask_path = mask_path
    self.image_size = image_size
    self.batch_size = batch_size
    self.PET_only = PET_only
    self.CT_only = CT_only
    self.on_epoch_end()

  def __loadcombined__ (self, id_name_combined):

    #Path of the image
    combined_path = os.path.join(self.combined_path, id_name_combined)

    #Reading the combined image
    combined_image = np.load(combined_path)

    #Only PET images
    if self.PET_only == True: 
      combined_image = combined_image[:,:,0]
      combined_image = whitening(combined_image)
      combined_image = crop_image(combined_image, 256, 256)
      #combined_image = cv2.resize(combined_image, dsize=(self.image_size,self.image_size), interpolation = cv2.INTER_CUBIC)

    #Only CT images
    elif self.CT_only == True:
      combined_image = combined_image[:,:,1]
      combined_image = whitening(combined_image)
      combined_image = crop_image(combined_image, 256, 256)
      #combined_image = cv2.resize(combined_image, dsize=(self.image_size,self.image_size), interpolation = cv2.INTER_CUBIC)

    #Full combined images
    else: 
      combined_image = combined_image.astype("float32")
      combined_image[:,:,0] = whitening(combined_image[:,:,0])
      combined_image[:,:,1] = whitening(combined_image[:,:,1])
      combined_image = crop_image_3d(combined_image, 256, 256)
      #combined_image = cv2.resize(combined_image, dsize=(self.image_size,self.image_size, 2), interpolation = cv2.INTER_CUBIC)

    return combined_image


  def __loadmask__(self, id_name_mask):

    #Path of the image
    mask_path = os.path.join(self.mask_path, id_name_mask)

    #Reading the mask image
    mask_image = np.load(mask_path) #No normalisation needed

    #Cropping the mask image to remove some useless information
    mask_image = crop_image(mask_image, 256, 256)
    #mask_image = cv2.resize(mask_image, dsize=(self.image_size,self.image_size), interpolation=cv2.INTER_NEAREST)

    #Converting the masks to boolean data type
    mask_image = mask_image.astype(bool)

    return mask_image


  def __getitem__(self, index):

    #Generate one batch of data. 
    if(index+1)*self.batch_size > len(self.combined_ids):
      self.batch_size = len(self.combined_ids) - index*self.batch_size

    files_batch_combined = self.combined_ids[index*self.batch_size : (index+1)*self.batch_size]
    files_batch_mask = self.mask_ids[index*self.batch_size : (index+1)*self.batch_size]

    combined = []
    mask = []

    for id_name_mask in files_batch_mask:
      _mask = self.__loadmask__(id_name_mask)
      mask.append(_mask)

    for id_name_combined in files_batch_combined:
      _combined = self.__loadcombined__(id_name_combined)
      combined.append(_combined)

    combined = np.array(combined)

    if self.PET_only == True:
      combined = np.expand_dims(combined, -1)

    if self.CT_only == True:
      combined = np.expand_dims(combined, -1)

    mask = np.array(mask)
    mask = np.expand_dims(mask, -1)

    return combined, mask

  def on_epoch_end(self):
    pass

  def __len__(self):

    #Denotes the number of batches per epoch
    return int(np.ceil(len(self.combined_ids)/float(self.batch_size)))

这将生成一个 4-D 张量，其中 2 通道图像作为训练数据（PET/CT 图像），二进制掩码作为地面实况分割。

然而，当我尝试用这个生成器训练一个标准的 U-Net 时，它会在给我这个错误之前通过大约 10 批训练数据 go ：

无效参数：TypeError： generator产生了一个无法转换为预期类型的元素。 预期的类型是 float32，但产生的元素是 [[array([[-0.480255, -0.48197123, -0.48379683], [-0.4811588, -0.48264366, -0.48423332]

这让我很困惑，因为在我的生成器中，我已经对其进行了编码，以将我的训练多通道 PET/CT 数据转换为 Float32，这是据称它期望但没有得到的数据类型。

我已经用 model.fit 和 model.fit_generator 尝试了这个。 下面的代码

train_gen = DataGen(train_ids_combined, train_ids_mask, train_path_combined, train_path_mask, batch_size = batch_size, image_size = image_size, PET_only=True)
valid_gen = DataGen(valid_ids_combined, valid_ids_mask, train_path_combined, train_path_mask, batch_size = batch_size, image_size = image_size, PET_only=True)
test_gen = DataGen(test_ids_combined, test_ids_mask, train_path_combined, train_path_mask, batch_size = 1, image_size = image_size, PET_only=True)

train_steps = len(train_ids_combined)//batch_size
valid_steps = len(valid_ids_combined)//batch_size
test_steps = len(test_ids_combined)

history = model.fit(train_gen, validation_data=valid_gen, steps_per_epoch=train_steps, validation_steps=valid_steps, 
                    epochs=epochs, callbacks=[model_checkpoint, early_stopping])

如果有人有任何建议和/或以前遇到过此问题，我将非常感谢您的帮助。 我正在 Google Colab（标准版）上实现这一点。