tensorflow/models · error · ValueError
SpineNet-{} is not a valid architecture.
Error message
SpineNet-{} is not a valid architecture. What it means
Error "SpineNet-{} is not a valid architecture." thrown in tensorflow/models.
Source
Thrown at official/vision/modeling/backbones/spinenet.py:560
"""A dict of {level: TensorShape} pairs for the model output."""
return self._output_specs
@factory.register_backbone_builder('spinenet')
def build_spinenet(
input_specs: tf_keras.layers.InputSpec,
backbone_config: hyperparams.Config,
norm_activation_config: hyperparams.Config,
l2_regularizer: tf_keras.regularizers.Regularizer = None) -> tf_keras.Model:
"""Builds SpineNet backbone from a config."""
backbone_type = backbone_config.type
backbone_cfg = backbone_config.get()
assert backbone_type == 'spinenet', (f'Inconsistent backbone type '
f'{backbone_type}')
model_id = str(backbone_cfg.model_id)
if model_id not in SCALING_MAP:
raise ValueError(
'SpineNet-{} is not a valid architecture.'.format(model_id))
scaling_params = SCALING_MAP[model_id]
return SpineNet(
input_specs=input_specs,
min_level=backbone_cfg.min_level,
max_level=backbone_cfg.max_level,
endpoints_num_filters=scaling_params['endpoints_num_filters'],
resample_alpha=scaling_params['resample_alpha'],
block_repeats=scaling_params['block_repeats'],
filter_size_scale=scaling_params['filter_size_scale'],
init_stochastic_depth_rate=backbone_cfg.stochastic_depth_drop_rate,
kernel_regularizer=l2_regularizer,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon)
View on GitHub (pinned to e006f5f0d5)
Solutions
- Use a valid SpineNet architecture id such as '49', '49S', '96', '143', or '49C'.
- Check the model_id in the SpineNet backbone config for typos.
When it happens
Trigger: Thrown at official/vision/modeling/backbones/spinenet.py:560 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/36896fb664d6c4c9.
Report an issue: GitHub.