Question

我正在使用自定义的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），而二进制掩码作为地面真相分割。

但是，当我尝试使用此生成器训练标准U-Net时，它会经历10批训练数据，然后再给我这个错误：

无效的参数：TypeError：generator产生了一个元素无法转换为预期的类型。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]

这完全让我感到困惑，因为我已经在生成器中对其进行了编码，以将我训练的多通道PET / CT数据转换为Float32（据称它是期望但没有得到的数据类型）。

我已经尝试使用model.fit和model.fit_generator进行此操作。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])

如果任何人有任何建议和/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）上实现此功能。

Keras自定义生成器：TypeError：生成器期望Float32

0 个答案: