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How to use from_args method in dbt-osmosis

Best Python code snippet using dbt-osmosis_python
mobilenet_edgetpu_v2_model_blocks.py
Source:mobilenet_edgetpu_v2_model_blocks.py
...84      fused_project = False85      conv_type = 'group'86    else:87      raise NotImplementedError(f'Unsupported IBN type {block_op_type.type}.')88    return cls.from_args(89        input_filters=input_filters,90        output_filters=output_filters,91        kernel_size=block_search_config.kernel_size,92        num_repeat=num_repeat,93        expand_ratio=block_search_config.expand_ratio,94        strides=(block_search_config.stride, block_search_config.stride),95        se_ratio=se_ratio,96        id_skip=id_skip,97        fused_expand=fused_expand,98        fused_project=fused_project,99        conv_type=conv_type,100        group_size=block_search_config.group_size)101@dataclasses.dataclass102class BlockGroupConfig(base_config.Config):103  """Config for group of blocks that share the same filter size."""104  blocks: List[BlockSearchConfig] = dataclasses.field(default_factory=list)105  filters: int = 64106def _default_mobilenet_edgetpu_v2_topology():107  return [108      # Block Group 0109      BlockGroupConfig(110          blocks=[111              # BlockSearchConfig: op_type, kernel_size, expand_ratio, stride112              BlockSearchConfig.from_args(113                  BlockType.from_args('ibn_fused'), 3, 1, 1),114          ],115          filters=24),116      # Block Group 1117      BlockGroupConfig(118          blocks=[119              BlockSearchConfig.from_args(120                  BlockType.from_args('ibn_fused'), 3, 8, 2),121              BlockSearchConfig.from_args(122                  BlockType.from_args('ibn_fused_grouped'), 3, 4, 1),123          ],124          filters=48),125      # Block Group 2126      BlockGroupConfig(127          blocks=[128              BlockSearchConfig.from_args(129                  BlockType.from_args('ibn_fused'), 3, 8, 2),130              BlockSearchConfig.from_args(131                  BlockType.from_args('ibn_fused_grouped'), 3, 4, 1),132              BlockSearchConfig.from_args(133                  BlockType.from_args('ibn_fused'), 3, 4, 1),134              BlockSearchConfig.from_args(135                  BlockType.from_args('ibn_fused_grouped'), 3, 4, 1),136          ],137          filters=64),138      # Block Group 3139      BlockGroupConfig(140          blocks=[141              BlockSearchConfig.from_args(142                  BlockType.from_args('ibn_fused'), 3, 8, 2),143              BlockSearchConfig.from_args(144                  BlockType.from_args('ibn_dw'), 3, 4, 1),145              BlockSearchConfig.from_args(146                  BlockType.from_args('ibn_dw'), 3, 4, 1),147              BlockSearchConfig.from_args(148                  BlockType.from_args('ibn_dw'), 3, 4, 1),149          ],150          filters=128),151      # Block Group 4152      BlockGroupConfig(153          blocks=[154              BlockSearchConfig.from_args(155                  BlockType.from_args('ibn_dw'), 3, 8, 1),156              BlockSearchConfig.from_args(157                  BlockType.from_args('ibn_dw'), 3, 4, 1),158              BlockSearchConfig.from_args(159                  BlockType.from_args('ibn_dw'), 3, 4, 1),160              BlockSearchConfig.from_args(161                  BlockType.from_args('ibn_dw'), 3, 4, 1),162          ],163          filters=160),164      # Block Group 5165      BlockGroupConfig(166          blocks=[167              BlockSearchConfig.from_args(168                  BlockType.from_args('ibn_dw'), 3, 8, 2),169              BlockSearchConfig.from_args(170                  BlockType.from_args('ibn_dw'), 3, 4, 1),171              BlockSearchConfig.from_args(172                  BlockType.from_args('ibn_dw'), 3, 4, 1),173              BlockSearchConfig.from_args(174                  BlockType.from_args('ibn_dw'), 3, 4, 1),175          ],176          filters=192),177      # Block Group 6178      BlockGroupConfig(179          blocks=[180              BlockSearchConfig.from_args(181                  BlockType.from_args('ibn_dw'), 3, 8, 1),182          ],183          filters=256),184  ]185@dataclasses.dataclass186class TopologyConfig(base_config.Config):187  """Config for model topology as a collection of BlockGroupConfigs."""188  block_groups: List[BlockGroupConfig] = dataclasses.field(189      default_factory=_default_mobilenet_edgetpu_v2_topology)190@dataclasses.dataclass191class ModelConfig(base_config.Config):192  """Default Config for MobilenetEdgeTPUV2."""193  width_coefficient: float = 1.0194  depth_coefficient: float = 1.0195  resolution: Union[int, Tuple[int, int]] = 224196  dropout_rate: float = 0.1197  stem_base_filters: int = 64198  stem_kernel_size: int = 5199  top_base_filters: int = 1280200  blocks: Tuple[BlockConfig, ...] = (201      # (input_filters, output_filters, kernel_size, num_repeat,202      #  expand_ratio, strides, se_ratio, id_skip, fused_conv, conv_type)203      # pylint: disable=bad-whitespace204      BlockConfig.from_args(205          stem_base_filters, 24, 3, 1, 1, (1, 1), conv_type='full'),206      BlockConfig.from_args(207          24, 48, 3, 1, 8, (2, 2), fused_expand=True, conv_type='full'),208      BlockConfig.from_args(209          48, 48, 3, 1, 4, (1, 1), fused_expand=True, conv_type='group'),210      BlockConfig.from_args(211          48, 64, 3, 1, 8, (2, 2), fused_expand=True, conv_type='full'),212      BlockConfig.from_args(213          64, 64, 3, 1, 4, (1, 1), fused_expand=True, conv_type='group'),214      BlockConfig.from_args(215          64, 64, 3, 1, 4, (1, 1), fused_expand=True, conv_type='full'),216      BlockConfig.from_args(217          64, 64, 3, 1, 4, (1, 1), fused_expand=True, conv_type='group'),218      BlockConfig.from_args(219          64, 128, 3, 1, 8, (2, 2), fused_expand=True, conv_type='full'),220      BlockConfig.from_args(128, 128, 3, 3, 4, (1, 1)),221      BlockConfig.from_args(128, 160, 3, 1, 8, (1, 1)),222      BlockConfig.from_args(160, 160, 3, 3, 4, (1, 1)),223      BlockConfig.from_args(160, 192, 5, 1, 8, (2, 2)),224      BlockConfig.from_args(192, 192, 5, 3, 4, (1, 1)),225      BlockConfig.from_args(192, 256, 5, 1, 8, (1, 1)),226      # pylint: enable=bad-whitespace227  )228  activation: str = 'relu'229  batch_norm: str = 'default'230  bn_momentum: float = 0.99231  bn_epsilon: float = 1e-3232  # While the original implementation used a weight decay of 1e-5,233  # tf.nn.l2_loss divides it by 2, so we halve this to compensate in Keras234  weight_decay: float = 5e-6235  drop_connect_rate: float = 0.1236  depth_divisor: int = 8237  min_depth: Optional[int] = None238  # No Squeeze/Excite for MobilenetEdgeTPUV2239  use_se: bool = False240  input_channels: int = 3241  num_classes: int = 1001242  model_name: str = 'mobilenet_edgetpu_v2'243  rescale_input: bool = False244  data_format: str = 'channels_last'245  dtype: str = 'float32'246  # The number of filters in each group. HW arch dependent.247  group_base_size: int = 64248  backbone_only: bool = False249  features_as_dict: bool = False250def mobilenet_edgetpu_v2_base(251    width_coefficient: float = 1.0,252    depth_coefficient: float = 1.0,253    stem_base_filters: int = 64,254    stem_kernel_size: int = 5,255    top_base_filters: int = 1280,256    group_base_size: int = 64,257    dropout_rate: float = 0.2,258    drop_connect_rate: float = 0.1,259    filter_size_overrides: Optional[Dict[int, int]] = None,260    block_op_overrides: Optional[Dict[int, Dict[int, Dict[str, Any]]]] = None,261    block_group_overrides: Optional[Dict[int, Dict[str, Any]]] = None):262  """Creates MobilenetEdgeTPUV2 ModelConfig based on tuning parameters."""263  config = ModelConfig()264  param_overrides = {265      'width_coefficient': width_coefficient,266      'depth_coefficient': depth_coefficient,267      'stem_base_filters': stem_base_filters,268      'stem_kernel_size': stem_kernel_size,269      'top_base_filters': top_base_filters,270      'group_base_size': group_base_size,271      'dropout_rate': dropout_rate,272      'drop_connect_rate': drop_connect_rate273  }274  config = config.replace(**param_overrides)275  topology_config = TopologyConfig()276  if filter_size_overrides:277    for group_id in filter_size_overrides:278      topology_config.block_groups[group_id].filters = filter_size_overrides[279          group_id]280  if block_op_overrides:281    for group_id in block_op_overrides:282      for block_id in block_op_overrides[group_id]:283        replaced_block = topology_config.block_groups[group_id].blocks[284            block_id].replace(**block_op_overrides[group_id][block_id])285        topology_config.block_groups[group_id].blocks[block_id] = replaced_block286  if block_group_overrides:287    for group_id in block_group_overrides:288      replaced_group = topology_config.block_groups[group_id].replace(289          **block_group_overrides[group_id])290      topology_config.block_groups[group_id] = replaced_group291  blocks = ()292  input_filters = stem_base_filters293  for group in topology_config.block_groups:294    for block_search in group.blocks:295      if block_search.op_type != BlockType.skip:296        block = BlockConfig.from_search_config(297            input_filters=input_filters,298            output_filters=group.filters,299            block_search_config=block_search)300        blocks += (block,)301        # Set input filters for the next block302        input_filters = group.filters303  config = config.replace(blocks=blocks)304  return config305def autoseg_edgetpu_backbone_base(306    width_coefficient: float = 1.0,307    depth_coefficient: float = 1.0,308    stem_base_filters: int = 64,309    stem_kernel_size: int = 5,310    top_base_filters: int = 1280,311    group_base_size: int = 64,312    dropout_rate: float = 0.2,313    drop_connect_rate: float = 0.1,314    blocks_overrides: Optional[Tuple[BlockConfig, ...]] = None):315  """Creates a edgetpu ModelConfig based on search on segmentation."""316  config = ModelConfig()317  config.depth_divisor = 4318  param_overrides = {319      'width_coefficient': width_coefficient,320      'depth_coefficient': depth_coefficient,321      'stem_base_filters': stem_base_filters,322      'stem_kernel_size': stem_kernel_size,323      'top_base_filters': top_base_filters,324      'group_base_size': group_base_size,325      'dropout_rate': dropout_rate,326      'drop_connect_rate': drop_connect_rate,327  }328  if blocks_overrides:329    param_overrides['blocks'] = blocks_overrides330  config = config.replace(**param_overrides)331  return config332def autoseg_edgetpu_backbone_s() -> ModelConfig:333  """AutoML searched model with 2.5ms target simulated latency."""334  stem_base_filters = 32335  stem_kernel_size = 3336  top_base_filters = 1280337  blocks = (338      # (input_filters, output_filters, kernel_size, num_repeat,339      #  expand_ratio, strides, se_ratio, id_skip, fused_conv, conv_type)340      # pylint: disable=bad-whitespace341      BlockConfig.from_args(342          stem_base_filters,343          12,344          3,345          1,346          1, (1, 1),347          fused_expand=True,348          conv_type='full'),349      BlockConfig.from_args(350          12, 36, 3, 1, 6, (2, 2), fused_expand=True, conv_type='full'),351      BlockConfig.from_args(36, 18, 5, 1, 3, (1, 1)),352      BlockConfig.from_args(353          18, 60, 5, 1, 6, (2, 2), fused_expand=True, conv_type='full'),354      BlockConfig.from_args(60, 60, 3, 1, 3, (1, 1)),355      BlockConfig.from_args(60, 120, 5, 1, 6, (2, 2)),356      BlockConfig.from_args(120, 120, 3, 1, 3, (1, 1)),357      BlockConfig.from_args(120, 120, 5, 1, 6, (1, 1)),358      BlockConfig.from_args(120, 112, 3, 1, 6, (1, 1)),359      BlockConfig.from_args(112, 112, 5, 2, 6, (1, 1)),360      BlockConfig.from_args(112, 112, 5, 1, 1, (2, 2), id_skip=False),361      BlockConfig.from_args(362          112, 192, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),363      BlockConfig.from_args(192, 192, 5, 1, 1, (1, 1), id_skip=False),364      BlockConfig.from_args(365          192, 96, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),366      BlockConfig.from_args(96, 96, 5, 1, 3, (1, 1)),367      BlockConfig.from_args(96, 96, 5, 1, 1, (1, 1), id_skip=False),368      BlockConfig.from_args(369          96, 192, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),370      BlockConfig.from_args(192, 192, 5, 1, 1, (1, 1), id_skip=False),371      BlockConfig.from_args(372          192, 160, 1, 1, 3, (1, 1), fused_expand=True, id_skip=False),373      # pylint: enable=bad-whitespace374  )375  return autoseg_edgetpu_backbone_base(376      stem_base_filters=stem_base_filters,377      stem_kernel_size=stem_kernel_size,378      top_base_filters=top_base_filters,379      blocks_overrides=blocks,380      dropout_rate=0.2,381      drop_connect_rate=0.2)382def autoseg_edgetpu_backbone_xs() -> ModelConfig:383  """AutoML searched model with 2ms target simulated latency."""384  stem_base_filters = 32385  stem_kernel_size = 3386  top_base_filters = 1280387  blocks = (388      # (input_filters, output_filters, kernel_size, num_repeat,389      #  expand_ratio, strides, se_ratio, id_skip, fused_conv, conv_type)390      # pylint: disable=bad-whitespace391      BlockConfig.from_args(392          stem_base_filters,393          12,394          3,395          1,396          1, (1, 1),397          fused_expand=True,398          conv_type='full'),399      BlockConfig.from_args(400          12, 24, 3, 1, 6, (2, 2), fused_expand=True, conv_type='full'),401      BlockConfig.from_args(24, 24, 3, 1, 3, (1, 1)),402      BlockConfig.from_args(403          24, 60, 3, 1, 3, (2, 2), fused_expand=True, conv_type='full'),404      BlockConfig.from_args(60, 40, 3, 1, 6, (1, 1)),405      BlockConfig.from_args(40, 40, 5, 1, 3, (2, 2)),406      BlockConfig.from_args(40, 40, 3, 1, 6, (1, 1)),407      BlockConfig.from_args(408          40, 120, 3, 1, 6, (1, 1), fused_expand=True, conv_type='full'),409      BlockConfig.from_args(120, 168, 3, 1, 6, (1, 1)),410      BlockConfig.from_args(168, 84, 5, 1, 6, (1, 1)),411      BlockConfig.from_args(84, 84, 5, 1, 3, (1, 1)),412      BlockConfig.from_args(84, 84, 5, 1, 1, (2, 2), id_skip=False),413      BlockConfig.from_args(414          84, 288, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),415      BlockConfig.from_args(288, 288, 5, 1, 1, (1, 1), id_skip=False),416      BlockConfig.from_args(417          288, 96, 1, 1, 3, (1, 1), fused_expand=True, id_skip=False),418      BlockConfig.from_args(96, 96, 5, 1, 1, (1, 1), id_skip=False),419      BlockConfig.from_args(420          96, 96, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),421      BlockConfig.from_args(96, 96, 5, 1, 1, (1, 1), id_skip=False),422      BlockConfig.from_args(423          96, 96, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),424      BlockConfig.from_args(96, 480, 5, 1, 3, (1, 1)),425      # pylint: enable=bad-whitespace426  )427  return autoseg_edgetpu_backbone_base(428      stem_base_filters=stem_base_filters,429      stem_kernel_size=stem_kernel_size,430      top_base_filters=top_base_filters,431      blocks_overrides=blocks,432      dropout_rate=0.2,433      drop_connect_rate=0.2)434def autoseg_edgetpu_backbone_m() -> ModelConfig:435  """AutoML searched model with 3ms target simulated latency."""436  stem_base_filters = 32437  stem_kernel_size = 3438  top_base_filters = 1280439  blocks = (440      # (input_filters, output_filters, kernel_size, num_repeat,441      #  expand_ratio, strides, se_ratio, id_skip, fused_conv, conv_type)442      # pylint: disable=bad-whitespace443      BlockConfig.from_args(stem_base_filters, 16, 5, 1, 1, (1, 1)),444      BlockConfig.from_args(445          16, 36, 3, 1, 6, (2, 2), fused_expand=True, conv_type='full'),446      BlockConfig.from_args(36, 36, 3, 1, 3, (1, 1)),447      BlockConfig.from_args(448          36, 60, 3, 1, 6, (2, 2), fused_expand=True, conv_type='full'),449      BlockConfig.from_args(60, 60, 3, 1, 6, (1, 1)),450      BlockConfig.from_args(451          60, 120, 5, 1, 6, (2, 2), fused_expand=True, conv_type='full'),452      BlockConfig.from_args(120, 120, 5, 1, 6, (1, 1)),453      BlockConfig.from_args(454          120, 80, 3, 1, 6, (1, 1), fused_expand=True, conv_type='full'),455      BlockConfig.from_args(80, 168, 3, 1, 6, (1, 1)),456      BlockConfig.from_args(168, 168, 5, 1, 6, (1, 1)),457      BlockConfig.from_args(168, 168, 5, 1, 1, (1, 1), id_skip=False),458      BlockConfig.from_args(459          168, 168, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),460      BlockConfig.from_args(168, 168, 3, 1, 1, (2, 2), id_skip=False),461      BlockConfig.from_args(462          168, 192, 1, 1, 3, (1, 1), fused_expand=True, id_skip=False),463      BlockConfig.from_args(192, 192, 5, 1, 1, (1, 1), id_skip=False),464      BlockConfig.from_args(465          192, 288, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),466      BlockConfig.from_args(288, 288, 5, 1, 1, (1, 1), id_skip=False),467      BlockConfig.from_args(468          288, 96, 1, 1, 6, (1, 1), fused_expand=True, id_skip=False),469      BlockConfig.from_args(96, 96, 5, 1, 1, (1, 1), id_skip=False),470      BlockConfig.from_args(471          96, 192, 1, 1, 3, (1, 1), fused_expand=True, id_skip=False),472      BlockConfig.from_args(192, 192, 5, 1, 1, (1, 1), id_skip=False),473      BlockConfig.from_args(474          192, 320, 1, 1, 3, (1, 1), fused_expand=True, id_skip=False),475      # pylint: enable=bad-whitespace476  )477  return autoseg_edgetpu_backbone_base(478      stem_base_filters=stem_base_filters,479      stem_kernel_size=stem_kernel_size,480      top_base_filters=top_base_filters,481      blocks_overrides=blocks,482      dropout_rate=0.3,483      drop_connect_rate=0.3)484def mobilenet_edgetpu_v2_tiny() -> ModelConfig:485  """MobilenetEdgeTPUV2 tiny model config."""486  stem_base_filters = 32487  stem_kernel_size = 5488  top_base_filters = 1280489  filter_sizes = [16, 32, 48, 80, 112, 160, 192]490  filter_size_overrides = {491      k: v for (k, v) in zip(range(len(filter_sizes)), filter_sizes)492  }493  block_op_overrides = {494      2: {495          0: {'op_type': BlockType.from_args('ibn_fused_grouped')},496          2: {'op_type': BlockType.from_args('ibn_fused_grouped')},497      },498      3: {499          0: {'op_type': BlockType.from_args('ibn_fused_grouped')},500      }501  }502  return mobilenet_edgetpu_v2_base(503      stem_base_filters=stem_base_filters,504      stem_kernel_size=stem_kernel_size,505      top_base_filters=top_base_filters,506      filter_size_overrides=filter_size_overrides,507      block_op_overrides=block_op_overrides,508      dropout_rate=0.05,509      drop_connect_rate=0.05)510def mobilenet_edgetpu_v2_xs() -> ModelConfig:511  """MobilenetEdgeTPUV2 extra small model config."""512  stem_base_filters = 32513  stem_kernel_size = 5...
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