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from enum import Enum
from typing import Dict, Optional, Tuple, Union
import torch
from torch import Tensor
from torch_geometric.explain.algorithm.utils import (
clear_masks,
set_hetero_masks,
set_masks,
)
from torch_geometric.explain.config import (
ModelConfig,
ModelMode,
ModelReturnType,
)
from torch_geometric.typing import EdgeType, Metadata, NodeType
class MaskLevelType(Enum):
"""Enum class for the mask level type."""
node = 'node'
edge = 'edge'
node_and_edge = 'node_and_edge'
@property
def with_edge(self) -> bool:
return self in [MaskLevelType.edge, MaskLevelType.node_and_edge]
class CaptumModel(torch.nn.Module):
def __init__(
self,
model: torch.nn.Module,
mask_type: Union[str, MaskLevelType],
output_idx: Optional[Union[int, Tensor]] = None,
model_config: Optional[ModelConfig] = None,
):
super().__init__()
self.mask_type = MaskLevelType(mask_type)
self.model = model
self.output_idx = output_idx
self.model_config = model_config
def forward(self, mask, *args):
"""""" # noqa: D419
# The mask tensor, which comes from Captum's attribution methods,
# contains the number of samples in dimension 0. Since we are
# working with only one sample, we squeeze the tensors below.
assert mask.shape[0] == 1, "Dimension 0 of input should be 1"
if self.mask_type == MaskLevelType.edge:
assert len(args) >= 2, "Expects at least x and edge_index as args."
if self.mask_type == MaskLevelType.node:
assert len(args) >= 1, "Expects at least edge_index as args."
if self.mask_type == MaskLevelType.node_and_edge:
assert args[0].shape[0] == 1, "Dimension 0 of input should be 1"
assert len(args[1:]) >= 1, "Expects at least edge_index as args."
# Set edge mask:
if self.mask_type == MaskLevelType.edge:
set_masks(self.model, mask.squeeze(0), args[1],
apply_sigmoid=False)
elif self.mask_type == MaskLevelType.node_and_edge:
set_masks(self.model, args[0].squeeze(0), args[1],
apply_sigmoid=False)
args = args[1:]
if self.mask_type == MaskLevelType.edge:
x = self.model(*args)
else:
x = self.model(mask.squeeze(0), *args)
return self.postprocess(x)
def postprocess(self, x: Tensor) -> Tensor:
if self.mask_type.with_edge:
clear_masks(self.model)
if self.output_idx is not None: # Filter by output index:
x = x[self.output_idx]
if (isinstance(self.output_idx, int)
or self.output_idx.dim() == 0):
x = x.unsqueeze(0)
# Convert binary classification to multi-class classification:
if (self.model_config is not None
and self.model_config.mode == ModelMode.binary_classification):
assert self.model_config.return_type == ModelReturnType.probs
x = x.view(-1, 1)
x = torch.cat([1 - x, x], dim=-1)
return x
# TODO(jinu) Is there any point of inheriting from `CaptumModel`
class CaptumHeteroModel(CaptumModel):
def __init__(
self,
model: torch.nn.Module,
mask_type: Union[str, MaskLevelType],
output_idx: Optional[Union[int, Tensor]],
metadata: Metadata,
model_config: Optional[ModelConfig] = None,
):
super().__init__(model, mask_type, output_idx, model_config)
self.node_types = metadata[0]
self.edge_types = metadata[1]
self.num_node_types = len(self.node_types)
self.num_edge_types = len(self.edge_types)
def _captum_data_to_hetero_data(
self, *args
) -> Tuple[Dict[NodeType, Tensor], Dict[EdgeType, Tensor], Optional[Dict[
EdgeType, Tensor]]]:
"""Converts tuple of tensors to `x_dict`, `edge_index_dict` and
`edge_mask_dict`.
"""
if self.mask_type == MaskLevelType.node:
node_tensors = args[:self.num_node_types]
node_tensors = [mask.squeeze(0) for mask in node_tensors]
x_dict = dict(zip(self.node_types, node_tensors))
edge_index_dict = args[self.num_node_types]
elif self.mask_type == MaskLevelType.edge:
edge_mask_tensors = args[:self.num_edge_types]
x_dict = args[self.num_edge_types]
edge_index_dict = args[self.num_edge_types + 1]
else:
node_tensors = args[:self.num_node_types]
node_tensors = [mask.squeeze(0) for mask in node_tensors]
x_dict = dict(zip(self.node_types, node_tensors))
edge_mask_tensors = args[self.num_node_types:self.num_node_types +
self.num_edge_types]
edge_index_dict = args[self.num_node_types + self.num_edge_types]
if self.mask_type.with_edge:
edge_mask_tensors = [mask.squeeze(0) for mask in edge_mask_tensors]
edge_mask_dict = dict(zip(self.edge_types, edge_mask_tensors))
else:
edge_mask_dict = None
return x_dict, edge_index_dict, edge_mask_dict
def forward(self, *args):
# Validate args:
if self.mask_type == MaskLevelType.node:
assert len(args) >= self.num_node_types + 1
len_remaining_args = len(args) - (self.num_node_types + 1)
elif self.mask_type == MaskLevelType.edge:
assert len(args) >= self.num_edge_types + 2
len_remaining_args = len(args) - (self.num_edge_types + 2)
else:
assert len(args) >= self.num_node_types + self.num_edge_types + 1
len_remaining_args = len(args) - (self.num_node_types +
self.num_edge_types + 1)
# Get main args:
(x_dict, edge_index_dict,
edge_mask_dict) = self._captum_data_to_hetero_data(*args)
if self.mask_type.with_edge:
set_hetero_masks(self.model, edge_mask_dict, edge_index_dict)
if len_remaining_args > 0:
# If there are args other than `x_dict` and `edge_index_dict`
x = self.model(x_dict, edge_index_dict,
*args[-len_remaining_args:])
else:
x = self.model(x_dict, edge_index_dict)
return self.postprocess(x)
def _to_edge_mask(edge_index: Tensor) -> Tensor:
num_edges = edge_index.shape[1]
return torch.ones(num_edges, requires_grad=True, device=edge_index.device)
def to_captum_input(
x: Union[Tensor, Dict[NodeType, Tensor]],
edge_index: Union[Tensor, Dict[EdgeType, Tensor]],
mask_type: Union[str, MaskLevelType],
*args,
) -> Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]]:
r"""Given :obj:`x`, :obj:`edge_index` and :obj:`mask_type`, converts it
to a format to use in `Captum <https://captum.ai/>`_ attribution
methods. Returns :obj:`inputs` and :obj:`additional_forward_args`
required for :captum:`Captum's` :obj:`attribute` functions.
See :meth:`~torch_geometric.nn.models.to_captum_model` for example usage.
Args:
x (torch.Tensor or Dict[NodeType, torch.Tensor]): The node features.
For heterogeneous graphs this is a dictionary holding node featues
for each node type.
edge_index(torch.Tensor or Dict[EdgeType, torch.Tensor]): The edge
indices. For heterogeneous graphs this is a dictionary holding the
:obj:`edge index` for each edge type.
mask_type (str): Denotes the type of mask to be created with
a Captum explainer. Valid inputs are :obj:`"edge"`, :obj:`"node"`,
and :obj:`"node_and_edge"`.
*args: Additional forward arguments of the model being explained
which will be added to :obj:`additional_forward_args`.
"""
mask_type = MaskLevelType(mask_type)
additional_forward_args = []
if isinstance(x, Tensor) and isinstance(edge_index, Tensor):
if mask_type == MaskLevelType.node:
inputs = [x.unsqueeze(0)]
elif mask_type == MaskLevelType.edge:
inputs = [_to_edge_mask(edge_index).unsqueeze(0)]
additional_forward_args.append(x)
else:
inputs = [x.unsqueeze(0), _to_edge_mask(edge_index).unsqueeze(0)]
additional_forward_args.append(edge_index)
elif isinstance(x, Dict) and isinstance(edge_index, Dict):
node_types = x.keys()
edge_types = edge_index.keys()
inputs = []
if mask_type == MaskLevelType.node:
for key in node_types:
inputs.append(x[key].unsqueeze(0))
elif mask_type == MaskLevelType.edge:
for key in edge_types:
inputs.append(_to_edge_mask(edge_index[key]).unsqueeze(0))
additional_forward_args.append(x)
else:
for key in node_types:
inputs.append(x[key].unsqueeze(0))
for key in edge_types:
inputs.append(_to_edge_mask(edge_index[key]).unsqueeze(0))
additional_forward_args.append(edge_index)
else:
raise ValueError(
"'x' and 'edge_index' need to be either"
f"'Dict' or 'Tensor' got({type(x)}, {type(edge_index)})")
additional_forward_args.extend(args)
return tuple(inputs), tuple(additional_forward_args)
def captum_output_to_dicts(
captum_attrs: Tuple[Tensor, ...],
mask_type: Union[str, MaskLevelType],
metadata: Metadata,
) -> Tuple[Optional[Dict[NodeType, Tensor]], Optional[Dict[EdgeType, Tensor]]]:
r"""Convert the output of `Captum <https://captum.ai/>`_ attribution
methods which is a tuple of attributions to two dictionaries with node and
edge attribution tensors. This function is used while explaining
:class:`~torch_geometric.data.HeteroData` objects.
See :meth:`~torch_geometric.nn.models.to_captum_model` for example usage.
Args:
captum_attrs (tuple[torch.Tensor]): The output of attribution methods.
mask_type (str): Denotes the type of mask to be created with
a Captum explainer. Valid inputs are :obj:`"edge"`, :obj:`"node"`,
and :obj:`"node_and_edge"`:
1. :obj:`"edge"`: :obj:`captum_attrs` contains only edge
attributions. The returned tuple has no node attributions, and
an edge attribution dictionary edge types as keys and edge mask
tensors of shape :obj:`[num_edges]` as values.
2. :obj:`"node"`: :obj:`captum_attrs` contains only node
attributions. The returned tuple has a node attribution
dictionary with node types as keys and node mask tensors of
shape :obj:`[num_nodes, num_features]` as values, and no edge
attributions.
3. :obj:`"node_and_edge"`: :obj:`captum_attrs` contains node and
edge attributions.
metadata (Metadata): The metadata of the heterogeneous graph.
"""
mask_type = MaskLevelType(mask_type)
node_types = metadata[0]
edge_types = metadata[1]
x_attr_dict, edge_attr_dict = None, None
captum_attrs = [captum_attr.squeeze(0) for captum_attr in captum_attrs]
if mask_type == MaskLevelType.node:
assert len(node_types) == len(captum_attrs)
x_attr_dict = dict(zip(node_types, captum_attrs))
elif mask_type == MaskLevelType.edge:
assert len(edge_types) == len(captum_attrs)
edge_attr_dict = dict(zip(edge_types, captum_attrs))
elif mask_type == MaskLevelType.node_and_edge:
assert len(edge_types) + len(node_types) == len(captum_attrs)
x_attr_dict = dict(zip(node_types, captum_attrs[:len(node_types)]))
edge_attr_dict = dict(zip(edge_types, captum_attrs[len(node_types):]))
return x_attr_dict, edge_attr_dict
def convert_captum_output(
captum_attrs: Tuple[Tensor, ...],
mask_type: Union[str, MaskLevelType],
metadata: Optional[Metadata] = None,
):
r"""Convert the output of `Captum.ai <https://captum.ai/>`_ attribution
methods which is a tuple of attributions to either
:obj:`(node_mask, edge_mask)` or :obj:`(node_mask_dict, edge_mask_dict)`.
"""
mask_type = MaskLevelType(mask_type)
if metadata is not None:
return captum_output_to_dicts(captum_attrs, mask_type, metadata)
node_mask = edge_mask = None
if mask_type == MaskLevelType.edge:
edge_mask = captum_attrs[0].squeeze(0)
elif mask_type == MaskLevelType.node:
node_mask = captum_attrs[0].squeeze(0)
else:
node_mask = captum_attrs[0].squeeze(0)
edge_mask = captum_attrs[1].squeeze(0)
return node_mask, edge_mask
|
