Add Graph class and tests for Graph and Collector + Dataloader refactoring
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FilippoOlivo
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Nicola Demo
Nicola Demo
parent
4fdb5641d4
commit
e63c3d9061
334
pina/graph.py
334
pina/graph.py
@@ -1,118 +1,240 @@
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""" Module for Loss class """
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from logging import warning
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import logging
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from torch_geometric.nn import MessagePassing, InstanceNorm, radius_graph
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from torch_geometric.data import Data
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import torch
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from . import LabelTensor
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from torch_geometric.data import Data
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from torch_geometric.utils import to_undirected
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class Graph:
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"""
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PINA Graph managing the PyG Data class.
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Class for the graph construction.
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"""
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def __init__(self, data):
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self.data = data
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@staticmethod
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def _build_triangulation(**kwargs):
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logging.debug("Creating graph with triangulation mode.")
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# check for mandatory arguments
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if "nodes_coordinates" not in kwargs:
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raise ValueError("Nodes coordinates must be provided in the kwargs.")
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if "nodes_data" not in kwargs:
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raise ValueError("Nodes data must be provided in the kwargs.")
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if "triangles" not in kwargs:
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raise ValueError("Triangles must be provided in the kwargs.")
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nodes_coordinates = kwargs["nodes_coordinates"]
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nodes_data = kwargs["nodes_data"]
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triangles = kwargs["triangles"]
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def less_first(a, b):
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return [a, b] if a < b else [b, a]
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list_of_edges = []
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for triangle in triangles:
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for e1, e2 in [[0, 1], [1, 2], [2, 0]]:
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list_of_edges.append(less_first(triangle[e1],triangle[e2]))
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array_of_edges = torch.unique(torch.Tensor(list_of_edges), dim=0) # remove duplicates
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array_of_edges = array_of_edges.t().contiguous()
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print(array_of_edges)
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# list_of_lengths = []
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# for p1,p2 in array_of_edges:
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# x1, y1 = tri.points[p1]
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# x2, y2 = tri.points[p2]
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# list_of_lengths.append((x1-x2)**2 + (y1-y2)**2)
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# array_of_lengths = np.sqrt(np.array(list_of_lengths))
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# return array_of_edges, array_of_lengths
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return Data(
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x=nodes_data,
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pos=nodes_coordinates.T,
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edge_index=array_of_edges,
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)
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@staticmethod
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def _build_radius(**kwargs):
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logging.debug("Creating graph with radius mode.")
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# check for mandatory arguments
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if "nodes_coordinates" not in kwargs:
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raise ValueError("Nodes coordinates must be provided in the kwargs.")
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if "nodes_data" not in kwargs:
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raise ValueError("Nodes data must be provided in the kwargs.")
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if "radius" not in kwargs:
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raise ValueError("Radius must be provided in the kwargs.")
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nodes_coordinates = kwargs["nodes_coordinates"]
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nodes_data = kwargs["nodes_data"]
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radius = kwargs["radius"]
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edges_data = kwargs.get("edge_data", None)
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loop = kwargs.get("loop", False)
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batch = kwargs.get("batch", None)
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logging.debug(f"radius: {radius}, loop: {loop}, "
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f"batch: {batch}")
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edge_index = radius_graph(
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x=nodes_coordinates.tensor,
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r=radius,
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loop=loop,
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batch=batch,
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)
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logging.debug(f"edge_index computed")
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return Data(
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x=nodes_data.tensor,
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pos=nodes_coordinates.tensor,
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edge_index=edge_index,
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edge_attr=edges_data,
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)
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@staticmethod
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def build(mode, **kwargs):
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def __init__(self,
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x,
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pos,
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edge_index,
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edge_attr=None,
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build_edge_attr=False,
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undirected=False,
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additional_params=None):
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"""
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Constructor for the `Graph` class.
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Constructor for the Graph class.
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:param x: The node features.
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:type x: torch.Tensor or list[torch.Tensor]
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:param pos: The node positions.
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:type pos: torch.Tensor or list[torch.Tensor]
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:param edge_index: The edge index.
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:type edge_index: torch.Tensor or list[torch.Tensor]
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:param edge_attr: The edge attributes.
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:type edge_attr: torch.Tensor or list[torch.Tensor]
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:param build_edge_attr: Whether to build the edge attributes.
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:type build_edge_attr: bool
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:param undirected: Whether to build an undirected graph.
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:type undirected: bool
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:param additional_params: Additional parameters.
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:type additional_params: dict
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"""
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if mode == "radius":
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graph = Graph._build_radius(**kwargs)
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elif mode == "triangulation":
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graph = Graph._build_triangulation(**kwargs)
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self.data = []
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x, pos, edge_index = Graph._check_input_consistency(x, pos, edge_index)
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# Check input dimension consistency and store the number of graphs
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data_len = self._check_len_consistency(x, pos)
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# Initialize additional_parameters (if present)
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if additional_params is not None:
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if not isinstance(additional_params, dict):
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raise TypeError("additional_params must be a dictionary.")
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for param, val in additional_params.items():
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# Check if the values are tensors or lists of tensors
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if isinstance(val, torch.Tensor):
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# If the tensor is 3D, we split it into a list of 2D tensors
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# In this case there must be a additional parameter for each
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# node
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if val.ndim == 3:
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additional_params[param] = [val[i] for i in
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range(val.shape[0])]
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# If the tensor is 2D, we replicate it for each node
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elif val.ndim == 2:
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additional_params[param] = [val] * data_len
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# If the tensor is 1D, each graph has a scalar values as
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# additional parameter
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if val.ndim == 1:
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if len(val) == data_len:
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additional_params[param] = [val[i] for i in
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range(len(val))]
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else:
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additional_params[param] = [val for _ in
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range(data_len)]
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elif not isinstance(val, list):
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raise TypeError("additional_params values must be tensors "
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"or lists of tensors.")
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else:
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raise ValueError(f"Mode {mode} not recognized")
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return Graph(graph)
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additional_params = {}
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# Make the graphs undirected
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if undirected:
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if isinstance(edge_index, list):
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edge_index = [to_undirected(e) for e in edge_index]
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else:
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edge_index = to_undirected(edge_index)
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if build_edge_attr:
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if edge_attr is not None:
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warning("Edge attributes are provided, build_edge_attr is set "
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"to True. The provided edge attributes will be ignored.")
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edge_attr = self._build_edge_attr(pos, edge_index)
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# Prepare internal lists to create a graph list (same positions but
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# different node features)
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if isinstance(x, list) and isinstance(pos,
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(torch.Tensor, LabelTensor)):
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# Replicate the positions, edge_index and edge_attr
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pos, edge_index = [pos] * data_len, [edge_index] * data_len
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if edge_attr is not None:
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edge_attr = [edge_attr] * data_len
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# Prepare internal lists to create a list containing a single graph
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elif isinstance(x, (torch.Tensor, LabelTensor)) and isinstance(pos, (
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torch.Tensor, LabelTensor)):
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# Encapsulate the input tensors into lists
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x, pos, edge_index = [x], [pos], [edge_index]
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if isinstance(edge_attr, torch.Tensor):
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edge_attr = [edge_attr]
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# Prepare internal lists to create a list of graphs (same node features
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# but different positions)
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elif (isinstance(x, (torch.Tensor, LabelTensor))
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and isinstance(pos, list)):
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# Replicate the node features
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x = [x] * data_len
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elif not isinstance(x, list) and not isinstance(pos, list):
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raise TypeError("x and pos must be lists or tensors.")
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# Perform the graph construction
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self._build_graph_list(x, pos, edge_index, edge_attr, additional_params)
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def _build_graph_list(self, x, pos, edge_index, edge_attr,
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additional_params):
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for i, (x_, pos_, edge_index_) in enumerate(zip(x, pos, edge_index)):
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if isinstance(x_, LabelTensor):
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x_ = x_.tensor
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add_params_local = {k: v[i] for k, v in additional_params.items()}
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if edge_attr is not None:
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self.data.append(Data(x=x_, pos=pos_, edge_index=edge_index_,
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edge_attr=edge_attr[i],
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**add_params_local))
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else:
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self.data.append(Data(x=x_, pos=pos_, edge_index=edge_index_,
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**add_params_local))
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@staticmethod
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def _build_edge_attr(pos, edge_index):
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if isinstance(pos, torch.Tensor):
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pos = [pos]
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edge_index = [edge_index]
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distance = [pos_[edge_index_[0]] - pos_[edge_index_[1]] ** 2 for
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pos_, edge_index_ in zip(pos, edge_index)]
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return distance
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@staticmethod
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def _check_len_consistency(x, pos):
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if isinstance(x, list) and isinstance(pos, list):
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if len(x) != len(pos):
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raise ValueError("x and pos must have the same length.")
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return max(len(x), len(pos))
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elif isinstance(x, list) and not isinstance(pos, list):
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return len(x)
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elif not isinstance(x, list) and isinstance(pos, list):
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return len(pos)
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else:
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return 1
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@staticmethod
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def _check_input_consistency(x, pos, edge_index=None):
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# If x is a 3D tensor, we split it into a list of 2D tensors
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if isinstance(x, torch.Tensor) and x.ndim == 3:
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x = [x[i] for i in range(x.shape[0])]
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# If pos is a 3D tensor, we split it into a list of 2D tensors
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if isinstance(pos, torch.Tensor) and pos.ndim == 3:
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pos = [pos[i] for i in range(pos.shape[0])]
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# If edge_index is a 3D tensor, we split it into a list of 2D tensors
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if isinstance(edge_index, torch.Tensor) and edge_index.ndim == 3:
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edge_index = [edge_index[i] for i in range(edge_index.shape[0])]
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return x, pos, edge_index
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def __repr__(self):
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return f"Graph(data={self.data})"
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class RadiusGraph(Graph):
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def __init__(self,
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x,
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pos,
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r,
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build_edge_attr=False,
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undirected=False,
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additional_params=None, ):
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x, pos, edge_index = Graph._check_input_consistency(x, pos)
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if isinstance(pos, (torch.Tensor, LabelTensor)):
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edge_index = RadiusGraph._radius_graph(pos, r)
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else:
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edge_index = [RadiusGraph._radius_graph(p, r) for p in pos]
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super().__init__(x=x, pos=pos, edge_index=edge_index,
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build_edge_attr=build_edge_attr,
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undirected=undirected,
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additional_params=additional_params)
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@staticmethod
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def _radius_graph(points, r):
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"""
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Implementation of the radius graph construction.
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:param points: The input points.
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:type points: torch.Tensor
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:param r: The radius.
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:type r: float
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:return: The edge index.
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:rtype: torch.Tensor
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"""
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dist = torch.cdist(points, points, p=2)
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edge_index = torch.nonzero(dist <= r, as_tuple=False).t()
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return edge_index
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class KNNGraph(Graph):
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def __init__(self,
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x,
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pos,
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k,
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build_edge_attr=False,
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undirected=False,
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additional_params=None,
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):
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x, pos, edge_index = Graph._check_input_consistency(x, pos)
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if isinstance(pos, (torch.Tensor, LabelTensor)):
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edge_index = KNNGraph._knn_graph(pos, k)
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else:
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edge_index = [KNNGraph._knn_graph(p, k) for p in pos]
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super().__init__(x=x, pos=pos, edge_index=edge_index,
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build_edge_attr=build_edge_attr,
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undirected=undirected,
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additional_params=additional_params)
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@staticmethod
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def _knn_graph(points, k):
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"""
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Implementation of the k-nearest neighbors graph construction.
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:param points: The input points.
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:type points: torch.Tensor
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:param k: The number of nearest neighbors.
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:type k: int
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:return: The edge index.
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:rtype: torch.Tensor
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"""
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dist = torch.cdist(points, points, p=2)
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knn_indices = torch.topk(dist, k=k + 1, largest=False).indices[:, 1:]
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row = torch.arange(points.size(0)).repeat_interleave(k)
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col = knn_indices.flatten()
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edge_index = torch.stack([row, col], dim=0)
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return edge_index
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