[labels out of bound]

[supriya-godge]

I am getting the below error in the testing module.

InvalidArgumentError (see above for traceback): assertion failed: [labels out of bound] [Condition x < y did not hold element-wise:x (mean_iou/confusion_matrix/control_dependency:0) = ] [0 0 0...] [y (mean_iou/ToInt64_2:0) = ] [3] [[Node: mean_iou/confusion_matrix/assert_less/Assert/AssertGuard/Assert = Assert[T=[DT_STRING, DT_STRING, DT_INT64, DT_STRING, DT_INT64], summarize=3, _device="/job:localhost/replica:0/task:0/cpu:0"](mean_iou/confusion_matrix/assert_less/Assert/AssertGuard/Assert/Switch, mean_iou/confusion_matrix/assert_less/Assert/AssertGuard/Assert/data_0, mean_iou/confusion_matrix/assert_less/Assert/AssertGuard/Assert/data_1, mean_iou/confusion_matrix/assert_less/Assert/AssertGuard/Assert/Switch_1, mean_iou/confusion_matrix/assert_less/Assert/AssertGuard/Assert/data_3, mean_iou/confusion_matrix/assert_less/Assert/AssertGuard/Assert/Switch_2)]]

I only have 2 labels as 255 is not included in the classes.

[zx-code123]

I met the same problem,did you slove it?

[violet17]

My python is 2.7 with tensorflow 1.12.
I added these code before caculating mIoU:

indices = tf.cast(tf.less_equal(annotation_batch_tensor, number_of_classes - 1),tf.uint8)
annotation_batch_tensor = tf.multiply(annotation_batch_tensor,indices)

Because the later version of TF has added the assert:

   labels = control_flow_ops.with_dependencies(
      [check_ops.assert_less(
          labels, num_classes_int64, message='`labels` out of bound')],
      labels)
  predictions = control_flow_ops.with_dependencies(
      [check_ops.assert_less(
          predictions, num_classes_int64,
          message='`predictions` out of bound')],
      predictions)

from the comments of @xxxzhi in https://github.com/tensorflow/models/issues/2239.

And it it so wired in TF 1.12 that I can't use the solution of https://github.com/tensorflow/models/issues/2239. After using tf.gather, the shape of annotation_batch_tensor is wired and is not compatable with the input labels in slim.metrics.streaming_mean_iou .

Then I thought I must ignore all labels greater than or equal to number_of_classes from the comments of @amlarraz https://github.com/DrSleep/tensorflow-deeplab-resnet/issues/107.

[cgxcgxcgxcgx]

I met the same problem recently, did you solve it?