Refactor GNO model and enhance Graph class documentation and error handling. Remove TemporalGraph class
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FilippoOlivo
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Nicola Demo
Nicola Demo
parent
bbdd5d4bf1
commit
bd24b0c1c2
109
pina/graph.py
109
pina/graph.py
@@ -25,25 +25,44 @@ class Graph:
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additional_params=None
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):
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"""
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Constructor for the Graph class.
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:param x: The node features.
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Constructor for the Graph class. This object creates a list of PyTorch Geometric Data objects.
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Based on the input of x and pos there could be the following cases:
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1. 1 pos, 1 x: a single graph will be created
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2. N pos, 1 x: N graphs will be created with the same node features
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3. 1 pos, N x: N graphs will be created with the same nodes but different node features
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4. N pos, N x: N graphs will be created
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:param x: Node features. Can be a single 2D tensor of shape [num_nodes, num_node_features],
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or a 3D tensor of shape [n_graphs, num_nodes, num_node_features]
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or a list of such 2D tensors of shape [num_nodes, num_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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:param pos: Node coordinates. Can be a single 2D tensor of shape [num_nodes, num_coordinates],
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or a 3D tensor of shape [n_graphs, num_nodes, num_coordinates]
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or a list of such 2D tensors of shape [num_nodes, num_coordinates].
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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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:param edge_index: The edge index defining connections between nodes.
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It should be a 2D tensor of shape [2, num_edges]
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or a 3D tensor of shape [n_graphs, 2, num_edges]
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or a list of such 2D tensors of shape [2, num_edges].
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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 custom_build_edge_attr: Custom function to build the edge
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attributes.
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:type custom_build_edge_attr: function
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:param additional_params: Additional parameters.
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:type additional_params: dict
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:param edge_attr: Edge features. If provided, should have the shape [num_edges, num_edge_features]
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or be a list of such tensors for multiple graphs.
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:type edge_attr: torch.Tensor or list[torch.Tensor], optional
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:param build_edge_attr: Whether to compute edge attributes during initialization.
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:type build_edge_attr: bool, default=False
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:param undirected: If True, converts the graph(s) into an undirected graph by adding reciprocal edges.
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:type undirected: bool, default=False
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:param custom_build_edge_attr: A user-defined function to generate edge attributes dynamically.
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The function should take (x, pos, edge_index) as input and return a tensor
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of shape [num_edges, num_edge_features].
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:type custom_build_edge_attr: function or callable, optional
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:param additional_params: Dictionary containing extra attributes to be added to each Data object.
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Keys represent attribute names, and values should be tensors or lists of tensors.
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:type additional_params: dict, optional
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Note: if x, pos, and edge_index are both lists or 3D tensors, then len(x) == len(pos) == len(edge_index).
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"""
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self.data = []
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x, pos, edge_index = self._check_input_consistency(x, pos, edge_index)
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@@ -85,7 +104,8 @@ class Graph:
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# Build the edge attributes
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edge_attr = self._check_and_build_edge_attr(edge_attr, build_edge_attr,
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data_len, edge_index, pos, x)
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data_len, edge_index, pos,
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x)
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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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@@ -128,14 +148,32 @@ class Graph:
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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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elif (not (isinstance(x, list) and all(t.ndim == 2 for t in x)) and
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not (isinstance(x, torch.Tensor) and x.ndim == 2)):
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raise TypeError("x must be either a list of 2D tensors or a 2D "
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"tensor or a 3D tensor")
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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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elif not (isinstance(pos, list) and all(
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t.ndim == 2 for t in pos)) and not (
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isinstance(pos, torch.Tensor) and pos.ndim == 2):
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raise TypeError("pos must be either a list of 2D tensors or a 2D "
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"tensor or a 3D tensor")
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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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if edge_index is not None:
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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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elif not (isinstance(edge_index, list) and all(
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t.ndim == 2 for t in edge_index)) and not (
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isinstance(edge_index,
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torch.Tensor) and edge_index.ndim == 2):
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raise TypeError(
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"edge_index must be either a list of 2D tensors or a 2D "
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"tensor or a 3D tensor")
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return x, pos, edge_index
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@staticmethod
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@@ -180,12 +218,12 @@ class Graph:
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"considered.")
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if isinstance(edge_attr, list):
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if len(edge_attr) != data_len:
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raise ValueError("edge_attr must have the same length as x "
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raise TypeError("edge_attr must have the same length as x "
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"and pos.")
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return [edge_attr] * data_len
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if build_edge_attr:
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return [self._build_edge_attr(x,pos_, edge_index_) for
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return [self._build_edge_attr(x, pos_, edge_index_) for
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pos_, edge_index_ in zip(pos, edge_index)]
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@@ -256,34 +294,3 @@ class KNNGraph(Graph):
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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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class TemporalGraph(Graph):
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def __init__(
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self,
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x,
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pos,
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t,
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edge_index=None,
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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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r=None
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):
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x, pos, edge_index = self._check_input_consistency(x, pos, edge_index)
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print(len(pos))
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if edge_index is None:
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edge_index = [RadiusGraph._radius_graph(p, r) for p in pos]
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additional_params = {'t': t}
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self._check_time_consistency(pos, t)
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super().__init__(x=x, pos=pos, edge_index=edge_index,
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edge_attr=edge_attr,
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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 _check_time_consistency(pos, times):
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if len(pos) != len(times):
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raise ValueError("pos and times must have the same length.")
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@@ -1,7 +1,6 @@
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import pytest
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import torch
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from pina import Graph
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from pina.graph import RadiusGraph, KNNGraph, TemporalGraph
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from pina.graph import RadiusGraph, KNNGraph
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@pytest.mark.parametrize(
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@@ -146,19 +145,6 @@ def test_additional_parameters_2(additional_parameters):
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assert all(hasattr(d, 'y') for d in data)
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assert all(torch.isclose(d_.x, x_).all() for (d_, x_) in zip(data, x))
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def test_temporal_graph():
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x = torch.rand(3, 10, 2)
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pos = torch.rand(3, 10, 2)
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t = torch.rand(3)
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graph = TemporalGraph(x=x, pos=pos, build_edge_attr=True, r=.3, t=t)
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assert len(graph.data) == 3
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data = graph.data
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assert all(torch.isclose(d_.x, x_).all() for (d_, x_) in zip(data, x))
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assert all(hasattr(d, 't') for d in data)
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assert all(d_.t == t_ for (d_, t_) in zip(data, t))
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def test_custom_build_edge_attr_func():
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x = torch.rand(3, 10, 2)
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pos = torch.rand(3, 10, 2)
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