tensorflow/models · error · ValueError

Input must be of size [height, width, C>0]

Error message

Input must be of size [height, width, C>0]

What it means

Error "Input must be of size [height, width, C>0]" thrown in tensorflow/models.

Source

Thrown at official/legacy/image_classification/resnet/imagenet_preprocessing.py:452

    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.
    num_channels: number of color channels in the image that will be distorted.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')

  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  # We have a 1-D tensor of means; convert to 3-D.
  # Note(b/130245863): we explicitly call `broadcast` instead of simply
  # expanding dimensions for better performance.
  means = tf.broadcast_to(means, tf.shape(image))

  return image - means


def _smallest_size_at_least(height, width, resize_min):
  """Computes new shape with the smallest side equal to `smallest_side`.

  Computes new shape with the smallest side equal to `smallest_side` while
  preserving the original aspect ratio.

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/legacy/image_classification/resnet/imagenet_preprocessing.py:452 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/b0cecb0bd08bf0f5. Report an issue: GitHub.