a27bd35443
Refactoring for 0.2 * Data module, data loader and dataset * Refactor LabelTensor * Refactor solvers Co-authored-by: dario-coscia <dariocos99@gmail.com>
118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
""" Module for Loss class """
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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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class Graph:
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"""
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PINA Graph managing the PyG Data class.
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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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"""
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Constructor for the `Graph` class.
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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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else:
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raise ValueError(f"Mode {mode} not recognized")
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return Graph(graph)
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def __repr__(self):
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return f"Graph(data={self.data})" |