我正在做一个项目，我需要就SpaceNet数据集训练Mask RCNN。
因此，当我尝试训练模型时，会出现许多警告和错误。
错误消息是：
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-11-a73fb1f7a961> in <module>
8 learning_rate=config.LEARNING_RATE,
9 epochs=10,
---> 10 layers='heads')
11
12 # Training - Stage 2
~\Desktop\SpaceNet_MaskRCNN\mrcnn\model.py in train(self, train_dataset, val_dataset, learning_rate, epochs, layers, augmentation, custom_callbacks, no_augmentation_sources)
2372 max_queue_size=100,
2373 workers=workers,
-> 2374 use_multiprocessing=True,
2375 )
2376 self.epoch = max(self.epoch, epochs)
~\Anaconda3\envs\MaskRCNN\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\Anaconda3\envs\MaskRCNN\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1656 use_multiprocessing=use_multiprocessing,
1657 shuffle=shuffle,
-> 1658 initial_epoch=initial_epoch)
1659
1660 @interfaces.legacy_generator_methods_support
~\Anaconda3\envs\MaskRCNN\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
179 batch_index = 0
180 while steps_done < steps_per_epoch:
--> 181 generator_output = next(output_generator)
182
183 if not hasattr(generator_output, '__len__'):
~\Desktop\SpaceNet_MaskRCNN\mrcnn\model.py in data_generator(dataset, config, shuffle, augment, augmentation, random_rois, batch_size, detection_targets, no_augmentation_sources)
1707 load_image_gt(dataset, config, image_id, augment=augment,
1708 augmentation=augmentation,
-> 1709 use_mini_mask=config.USE_MINI_MASK)
1710
1711 # Skip images that have no instances. This can happen in cases
~\Desktop\SpaceNet_MaskRCNN\mrcnn\model.py in load_image_gt(dataset, config, image_id, augment, augmentation, use_mini_mask)
1263 _idx = np.sum(mask, axis=(0, 1)) > 0
1264 mask = mask[:, :, _idx]
-> 1265 class_ids = class_ids[_idx]
1266 # Bounding boxes. Note that some boxes might be all zeros
1267 # if the corresponding mask got cropped out.
IndexError: boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 650
警告是：
ERROR：root：处理图像{'id'：219，'source'：'yapi'，'path'：None，'width'：650，'height'：650}时出错 追溯（最近一次通话）： 在data_generator中的文件“ C：\ Users \ MUSTAFAAKTAS \ Desktop \ SpaceNet_MaskRCNN \ mrcnn \ model.py”，行1710 use_mini_mask = config.USE_MINI_MASK） 在load_image_gt的第1266行中，文件“ C：\ Users \ MUSTAFAAKTAS \ Desktop \ SpaceNet_MaskRCNN \ mrcnn \ model.py” class_ids = class_ids [_idx] IndexError：布尔索引与维度0上的索引数组不匹配；维度为1，但相应的布尔维度为650
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ERROR：root：处理图像{'id'：448，'source'：'yapi'，'path'：None，'width'：650，'height'：650}时出错 追溯（最近一次通话）： 在data_generator中的文件“ C：\ Users \ MUSTAFAAKTAS \ Desktop \ SpaceNet_MaskRCNN \ mrcnn \ model.py”，行1710 use_mini_mask = config.USE_MINI_MASK） 在load_image_gt的第1266行中，文件“ C：\ Users \ MUSTAFAAKTAS \ Desktop \ SpaceNet_MaskRCNN \ mrcnn \ model.py” class_ids = class_ids [_idx] IndexError：布尔索引与维度0上的索引数组不匹配；维度为1，但相应的布尔维度为650
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还有image_id的警告：219-348-444-448-3986-3023
答案 0 :(得分：1)
IndexError：布尔索引与维度0上的索引数组不匹配；维度为1，但相应的布尔维度为650
此错误表明您正在尝试传递1个班级ID来放置650个班级。
def load_mask(self, image_id):
"""Load instance masks for the given image.
Different datasets use different ways to store masks. Override this
method to load instance masks and return them in the form of am
array of binary masks of shape [height, width, instances].
Returns:
masks: A bool array of shape [height, width, instance count] with
a binary mask per instance.
class_ids: a 1D array of class IDs of the instance masks.
"""
# Override this function to load a mask from your dataset.
# Otherwise, it returns an empty mask.
logging.warning("You are using the default load_mask(), maybe you need to define your own one.")
mask = np.empty([0, 0, 0])
class_ids = np.empty([0], np.int32)
return mask, class_ids
这是load_mask函数，它将对象类的数组作为class_id。您必须以自己的方式实现它，以便为每个蒙版都有一个对应的对象类。例如，这就是我的操作方式：
首先，我从使用labelimg创建的xml文件中提取边界框及其各自的标签：
# extract bounding boxes from an annotation file
def extract_boxes(self, filename):
# load and parse the file
tree = ElementTree.parse(filename)
# get the root of the document
root = tree.getroot()
# extract each object
boxes = list()
for object in root.findall('.//object'):
box_class_list = list()
#find bbox coordinates
for box in object.findall('.//bndbox'):
xmin = int(box.find('xmin').text)
ymin = int(box.find('ymin').text)
xmax = int(box.find('xmax').text)
ymax = int(box.find('ymax').text)
coors = [xmin, ymin, xmax, ymax]
box_class_list.append(coors)
#get the name of the object class corresponding to the bbox
for name in object.findall('.//name'):
box_class_list.append(name.text)
#append the box coors and respective name to a list
boxes.append(box_class_list)
# extract image dimensions
width = int(root.find('.//size/width').text)
height = int(root.find('.//size/height').text)
return boxes, width, height
然后按如下方式使用load_mask中提取的数据：
def load_mask(self, image_id):
# get details of image
info = self.image_info[image_id]
# define box file location, here the annotation dir in project dir
path = info['annotation']
# load XML
boxes, w, h = self.extract_boxes(path)
# create one array for all masks, each on a different channel
masks = zeros([h, w, len(boxes)], dtype='uint8')
# create masks
class_ids = list()
for i in range(len(boxes)):
box = boxes[i][0]
row_s, row_e = box[1], box[3]
col_s, col_e = box[0], box[2]
masks[row_s:row_e, col_s:col_e, i] = 1
class_ids.append(self.class_names.index(boxes[i][1]))
return masks, asarray(class_ids, dtype='int32')
很抱歉，您没有编写完整的功能示例，但我在atm的时间很少。希望这可以帮助。而且，此示例没有提取任何实际的蒙版，仅用于bboxes和类。