使用 tf.sparse.to_dense 函数时出错

Question

我正在尝试解析我的 tfrecord 数据集以将其用于对象检测。当我试图将我的稀疏张量更改为密集张量时，出现以下我无法理解的错误：ValueError: Shapes must be equal rank, but are 1 and 0    From merging shape 3 with other shapes. for '{{node stack}} = Pack[N=5, T=DT_FLOAT, axis=1](SparseToDense, SparseToDense_1, SparseToDense_2, SparseToDense_3, Cast)' with input shapes: [?], [?], [?], [?], [].我的 feature_description 是：feature_description = {    'image/filename': tf.io.FixedLenFeature([], tf.string),    'image/encoded': tf.io.FixedLenFeature([], tf.string),    'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),    'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),    'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),    'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),    'image/object/class/label': tf.io.VarLenFeature(tf.int64),}我的解析代码：def _parse_image_function(example_proto):  # Parse the input tf.Example proto using the dictionary above.  return tf.io.parse_single_example(example_proto, feature_description)def _parse_tfrecord(x):      x_train = tf.image.decode_jpeg(x['image/encoded'], channels=3)    x_train = tf.image.resize(x_train, (416, 416))        labels = tf.cast(1, tf.float32)#    print(type(x['image/object/bbox/xmin']))    tf.print(x['image/object/bbox/xmin'])    y_train = tf.stack([tf.sparse.to_dense(x['image/object/bbox/xmin']),                        tf.sparse.to_dense(x['image/object/bbox/ymin']),                        tf.sparse.to_dense(x['image/object/bbox/xmax']),                        tf.sparse.to_dense(x['image/object/bbox/ymax']),                        labels], axis=1)    paddings = [[0, 100 - tf.shape(y_train)[0]], [0, 0]]    y_train = tf.pad(y_train, paddings)    return x_train, y_traindef load_tfrecord_dataset(train_record_file, size=416):    dataset=tf.data.TFRecordDataset(train_record_file)    parsed_dataset = dataset.map(_parse_image_function)    final = parsed_dataset.map(_parse_tfrecord)    return final我的错误是什么？

Answer 1

问题是labels具有形状()，即零维（它是标量），而您尝试堆叠的所有稀疏张量都是一维的。您应该制作一个label与框数据张量具有相同形状的张量：

# Assuming all box data tensors have the same shape

box_data_shape = tf.shape(x['image/object/bbox/xmin'])

# Make label data

labels = tf.ones(box_data_shape, dtype=tf.float32)

除此之外，由于您正在解析单个示例，因此您的所有稀疏张量都应该是一维且连续的，因此您可以将转换保存为密集并只采用它们.values：

y_train = tf.stack([x['image/object/bbox/xmin'].values,

                    x['image/object/bbox/ymin'].values,

                    x['image/object/bbox/xmax'].values,

                    x['image/object/bbox/ymax'].values,

                    labels], axis=1)