{"record":{"id":"6460304f7e4b371c","repo":"tensorflow/models","slug":"filter-size-and-residual-block-repetition-lists-mu","errorCode":null,"errorMessage":"filter size and residual block repetition lists must have the same length","messagePattern":"filter size and residual block repetition lists must have the same length","errorType":"exception","errorClass":null,"httpStatus":null,"severity":"error","filePath":"official/projects/centernet/modeling/layers/cn_nn_blocks.py","lineNumber":170,"sourceCode":"      blocks_per_stage: List[int], list of residual block repetitions per\n        down/upsample. `blocks_per_stage[0]` defines the number of blocks at the\n        current stage and `blocks_per_stage[1:]` is used at further stages.\n        For example, [2, 2, 2, 2, 2, 4].\n      strides: `int`, stride parameter to the Residual block.\n      use_sync_bn: A `bool`, if True, use synchronized batch normalization.\n      norm_momentum: `float`, momentum for the batch normalization layers.\n      norm_epsilon: `float`, epsilon for the batch normalization layers.\n      kernel_initializer: A `str` for kernel initializer of conv layers.\n      kernel_regularizer: A `tf_keras.regularizers.Regularizer` object for\n        Conv2D. Default to None.\n      bias_regularizer: A `tf_keras.regularizers.Regularizer` object for Conv2D.\n        Default to None.\n      **kwargs: Additional keyword arguments to be passed.\n    \"\"\"\n    super(HourglassBlock, self).__init__(**kwargs)\n\n    if len(channel_dims_per_stage) != len(blocks_per_stage):\n      raise ValueError('filter size and residual block repetition '\n                       'lists must have the same length')\n\n    self._num_stages = len(channel_dims_per_stage) - 1\n    self._channel_dims_per_stage = channel_dims_per_stage\n    self._blocks_per_stage = blocks_per_stage\n    self._strides = strides\n    self._use_sync_bn = use_sync_bn\n    self._norm_momentum = norm_momentum\n    self._norm_epsilon = norm_epsilon\n    self._kernel_initializer = kernel_initializer\n    self._kernel_regularizer = kernel_regularizer\n    self._bias_regularizer = bias_regularizer\n\n    self._filters = channel_dims_per_stage[0]\n    if self._num_stages > 0:\n      self._filters_downsampled = channel_dims_per_stage[1]\n\n    self._reps = blocks_per_stage[0]","sourceCodeStart":152,"sourceCodeEnd":188,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/projects/centernet/modeling/layers/cn_nn_blocks.py#L152-L188","documentation":"Error \"filter size and residual block repetition lists must have the same length\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/projects/centernet/modeling/layers/cn_nn_blocks.py:170 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":[],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"e006f5f0d534913e49c1f1dae87364039fa607e2","analyzedAt":"2026-08-24T14:09:15.576Z","schemaVersion":2},"datasetVersion":"2026-08-24T17:17:21.512Z"}