qualia_codegen_plugin_snn.graph package

Subpackages

• qualia_codegen_plugin_snn.graph.layers package
  • Submodules
    • qualia_codegen_plugin_snn.graph.layers.TIfLayer module
      • TIfLayer
    • qualia_codegen_plugin_snn.graph.layers.TLifLayer module
      • TLifLayer
    • qualia_codegen_plugin_snn.graph.layers.TObjectDetectionPostProcessLayer module
      • TObjectDetectionPostProcessLayer
  • Module contents
    • TIfLayer
      • TIfLayer.v_threshold
      • TIfLayer.v_reset
      • TIfLayer.soft_reset
      • TIfLayer.weights
    • TLifLayer
      • TLifLayer.reciprocal_tau
      • TLifLayer.decay_input
      • TLifLayer.weights
    • TObjectDetectionPostProcessLayer
      • TObjectDetectionPostProcessLayer.num_classes
      • TObjectDetectionPostProcessLayer.num_fms
      • TObjectDetectionPostProcessLayer.image_shape
      • TObjectDetectionPostProcessLayer.score_threshold
      • TObjectDetectionPostProcessLayer.nms_threshold
      • TObjectDetectionPostProcessLayer.topk_candidates
      • TObjectDetectionPostProcessLayer.detections_per_image
      • TObjectDetectionPostProcessLayer.box_coder_weights
      • TObjectDetectionPostProcessLayer.anchors
      • TObjectDetectionPostProcessLayer.weights

Submodules

• qualia_codegen_plugin_snn.graph.TorchModelGraph module
  • SequentialForward
    • SequentialForward.forward()
  • TorchModelGraph
    • TorchModelGraph.MODULE_MAPPING
    • TorchModelGraph.convert()

Module contents

class qualia_codegen_plugin_snn.graph.TorchModelGraph(model: Module)

Bases: TorchModelGraph

MODULE_MAPPING: ClassVar[dict[type[Module], Callable[[Module, TBaseLayer], tuple[type[TBaseLayer], list[Any]]]]] = {<class 'spikingjelly.activation_based.neuron.IFNode'>: <function TorchModelGraph.<lambda>>, <class 'spikingjelly.activation_based.neuron.LIFNode'>: <function TorchModelGraph.<lambda>>, <class 'spikingjelly.activation_based.neuron.ParametricLIFNode'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.activation.ReLU'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.activation.ReLU6'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.batchnorm.BatchNorm1d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.batchnorm.BatchNorm2d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.conv.Conv1d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.conv.Conv2d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.dropout.Dropout'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.flatten.Flatten'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.linear.Identity'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.linear.Linear'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.pooling.AdaptiveAvgPool1d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.pooling.AvgPool1d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.pooling.AvgPool2d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.pooling.MaxPool1d'>: <function TorchModelGraph.<lambda>>, <class 'torch.nn.modules.pooling.MaxPool2d'>: <function TorchModelGraph.<lambda>>}

convert(custom_layers: dict[type[Module], Callable[[Module, TBaseLayer], tuple[type[TBaseLayer], list[Any]]]] | None = None) → ModelGraph | None