tensorflow/models · error · ValueError
Unequal shapes
Error message
Unequal shapes {}, {} What it means
Static-shape consistency guard in assert_shape_equal: when both shape lists are fully static the mismatch can be decided at graph-construction time, and this raise reports the first such pair that differs — i.e. two tensors the caller claims are aligned but whose static shapes prove otherwise.
Solutions
- Make the two shapes equal before combining the tensors.
- Broadcast or reshape one operand so both shapes match.
Defensive patterns
Strategy: validation
When it happens
Trigger: Thrown at official/vision/utils/object_detection/shape_utils.py:43 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24).
Data as JSON: /api/errors/71bcda804828f4d4.
Report an issue: GitHub.
Appendix: source
Thrown at official/vision/utils/object_detection/shape_utils.py:43
If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
mismatch.
Args:
shape_a: a list containing shape of the first tensor.
shape_b: a list containing shape of the second tensor.
Returns:
Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
when the shapes are dynamic.
Raises:
ValueError: When shapes are both static and unequal.
"""
if (all(isinstance(dim, int) for dim in shape_a) and
all(isinstance(dim, int) for dim in shape_b)):
if shape_a != shape_b:
raise ValueError('Unequal shapes {}, {}'.format(shape_a, shape_b))
else:
return tf.no_op()
else:
return tf.assert_equal(shape_a, shape_b)
def combined_static_and_dynamic_shape(tensor):
"""Returns a list containing static and dynamic values for the dimensions.
Returns a list of static and dynamic values for shape dimensions. This is
useful to preserve static shapes when available in reshape operation.
Args:
tensor: A tensor of any type.
Returns:
A list of size tensor.shape.ndims containing integers or a scalar tensor.
"""View on GitHub (pinned to e006f5f0d5)