New fixes + reformat
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araison
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b73f087a6a
commit
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from torch_geometric.data import Data
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from torch_geometric.explain import Explanation
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from torch_geometric.explain.algorithm.base import ExplainerAlgorithm
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from torch_geometric.explain.config import (ExplainerConfig, MaskType,
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ModelConfig, ModelMode,
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ModelTaskLevel)
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from torch_geometric.nn.models.captum import (_raise_on_invalid_mask_type,
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to_captum_input, to_captum_model)
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from captum.attr import (LRP, DeepLift, DeepLiftShap, FeatureAblation,
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FeaturePermutation, GradientShap, GuidedBackprop,
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GuidedGradCam, InputXGradient, IntegratedGradients,
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Lime, Occlusion, Saliency)
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def _load_FeatureAblation(model):
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return lambda model: FeatureAblation(model)
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def _load_LRP(model):
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# return lambda model: LRP(model)
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raise ValueError("Captum LRP is not supported yet")
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def _load_DeepLift(model):
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# return lambda model: DeepLift(model)
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raise ValueError("Captum DeepLift is not supported yet")
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def _load_DeepLiftShap(model):
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# return lambda model: DeepLiftShap(model)
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raise ValueError("Captum DeepLiftShap is not supported yet")
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def _load_FeaturePermutation(model):
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# return lambda model: FeaturePermutation(model)
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raise ValueError("Captum FeaturePermutation is not supported yet")
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def _load_GradientShap(model):
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# return lambda model: GradientShap(model)
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raise ValueError("Captum GradientShap is not supported yet")
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def _load_GuidedBackPropagation(model):
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return lambda model: GuidedBackprop(model)
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def _load_GuidedGradCam(model):
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# return lambda model: GuidedGradCam(model)
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raise ValueError("Captum GuidedGradCam is not supported yet")
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def _load_InputXGradient(model):
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return lambda model: InputXGradient(model)
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def _load_Lime(model):
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return lambda model: Lime(model)
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def _load_Saliency(model):
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return lambda model: Saliency(model)
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def _load_Occlusion(model):
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# return lambda model: Occlusion(model)
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raise ValueError("Captum Occlusion is not supported yet")
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def _load_IntegratedGradients(model):
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# return lambda model: IntegratedGradients(model)
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raise ValueError("Captum IntegratedGradients is not supported yet")
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class CaptumWrapper(ExplainerAlgorithm):
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def __init__(self, name):
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super().__init__()
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self.name = name
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def supports(self) -> bool:
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task_level = self.model_config.task_level
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if self.name in [
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"LRP",
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"DeepLift",
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"DeepLiftShap",
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"FeatureAblation",
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"FeaturePermutation",
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"GradientShap",
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"GuidedBackPropagation",
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"GuidedGradCam",
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"InputXGradient",
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"IntegratedGradients",
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"Lime",
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"Occlusion",
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"Saliency",
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]:
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if task_level not in [ModelTaskLevel.node, ModelTaskLevel.graph]:
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logging.error(f"Task level '{task_level.value}' not supported")
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return False
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edge_mask_type = self.explainer_config.edge_mask_type
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if edge_mask_type not in [MaskType.object, None]:
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logging.error(
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f"Edge mask type '{edge_mask_type.value}' not " f"supported"
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)
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return False
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node_mask_type = self.explainer_config.node_mask_type
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if node_mask_type not in [
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MaskType.common_attributes,
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MaskType.object,
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MaskType.attributes,
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None,
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]:
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logging.error(
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f"Node mask type '{node_mask_type.value}' not " f"supported."
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)
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return False
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return True
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def _get_mask_type(self):
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edge_mask_type = self.explainer_config.edge_mask_type
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node_mask_type = self.explainer_config.node_mask_type
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if edge_mask_type is None and node_mask_type is None:
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raise ValueError("You need to provide a masking config")
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if not edge_mask_type is None and node_mask_type is None:
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self.mask_type = "edge"
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if edge_mask_type is None and not node_mask_type is None:
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self.mask_type = "node"
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if not edge_mask_type is None and not node_mask_type is None:
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self.mask_type = "node_and_edge"
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_raise_on_invalid_mask_type(self.mask_type)
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return self.mask_type
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def _load_captum_method(self, model):
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if self.name == "LRP":
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return _load_LRP(model)
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elif self.name == "DeepLift":
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return _load_DeepLift(model)
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elif self.name == "DeepLiftShap":
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return _load_DeepLiftShap(model)
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elif self.name == "FeatureAblation":
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return _load_FeatureAblation(model)
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elif self.name == "FeaturePermutation":
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return _load_FeaturePermutation(model)
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elif self.name == "GradientShap":
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return _load_GradientShap(model)
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elif self.name == "GuidedBackPropagation":
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return _load_GuidedBackPropagation(model)
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elif self.name == "GuidedGradCam":
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return _load_GuidedGradCam(model)
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elif self.name == "InputXGradient":
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return _load_InputXGradient(model)
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elif self.name == "IntegratedGradients":
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return _load_IntegratedGradients(model)
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elif self.name == "Lime":
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return _load_Lime(model)
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elif self.name == "Occlusion":
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return _load_Occlusion(model)
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elif self.name == "Saliency":
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return _load_Saliency(model)
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else:
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raise ValueError(f"{self.name} is not a supported Captum method yet !")
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def _parse_attr(self, attr):
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if self.mask_type == "node":
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node_mask = attr
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edge_mask = None
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if "edge" == mask_type:
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node_mask = None
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edge_mask = attr
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if "node_and_mask" == mask_type:
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node_mask = attr[0]
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edge_mask = attr[1]
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# TODO
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pass
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def forward(
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self,
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model: torch.nn.Module,
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x: Tensor,
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edge_index: Tensor,
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target: int,
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**kwargs,
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):
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mask_type = self._get_mask_type()
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converted_model = to_captum_model(model, mask_type=mask_type, output=target)
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self.captum_method = self._load_captum_method(converted_model)
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inputs, additional_forward_args = to_captum_input(
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x, edge_index, mask_type=mask_type
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)
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if self.name in ["InputXGradient", "Lime", "Saliency", "GuidedBackPropagation"]:
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attr = self.captum_method.attribute(
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inputs=inputs,
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additional_forward_args=additional_forward_args,
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target=target,
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)
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elif self.name == "FeatureAblation":
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attr = self.captum_method.attribute(
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inputs=inputs,
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additional_forward_args=additional_forward_args,
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)
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node_mask, edge_mask = self._parse_attr(attr)
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return Explanation(
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x=x, edge_index=edge_index, edge_mask=edge_mask, node_mask=node_mask
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)
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