tensorflow/models · error · ValueError

Unsupported input shape: {input_shape.as_list()}.

Error message

Unsupported input shape: {input_shape.as_list()}.

What it means

Error "Unsupported input shape: {input_shape.as_list()}." thrown in tensorflow/models.

Source

Thrown at official/projects/maxvit/modeling/layers.py:420

    self._dropout = dropout
    self._rel_attn_type = rel_attn_type
    self._scale_ratio = scale_ratio
    self._survival_prob = survival_prob
    self._ln_epsilon = ln_epsilon
    self._ln_dtype = ln_dtype
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer

  def build(self, input_shape: tf.TensorShape) -> None:
    if len(input_shape.as_list()) == 4:
      _, height, width, _ = input_shape.as_list()
    elif len(input_shape.as_list()) == 3:
      _, seq_len, _ = input_shape.as_list()
      height, width = common_ops.get_shape_from_length(
          seq_len, self._input_origin_height, self._input_origin_width
      )
    else:
      raise ValueError(f'Unsupported input shape: {input_shape.as_list()}.')

    self.height, self.width = height, width
    input_size = input_shape.as_list()[-1]

    if input_size != self._hidden_size:
      self._shortcut_proj = TrailDense(
          self._hidden_size,
          kernel_initializer=self._kernel_initializer,
          bias_initializer=self._bias_initializer,
          name='shortcut_proj',
      )
    else:
      self._shortcut_proj = None

    self._attn_layer_norm = tf_keras.layers.LayerNormalization(
        axis=-1,
        epsilon=self._ln_epsilon,
        dtype=self._ln_dtype,

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/maxvit/modeling/layers.py:420 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/3a5d05d26f99a817. Report an issue: GitHub.