├── .gitignore
├── images
    └── graph-network.png
├── __pycache__
    ├── blocks.cpython-36.pyc
    ├── graphs.cpython-36.pyc
    ├── modules.cpython-36.pyc
    └── utils.cpython-36.pyc
├── .idea
    ├── libraries
    │   └── R_User_Library.xml
    ├── modules.xml
    ├── misc.xml
    ├── graph_net_pytorch.iml
    └── workspace.xml
├── utils.py
├── modules.py
├── demo.py
├── README.md
├── graphs.py
└── blocks.py


/.gitignore:
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1 | 


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/images/graph-network.png:
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https://raw.githubusercontent.com/TQCAI/graph_nets_pytorch/HEAD/images/graph-network.png


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/.idea/libraries/R_User_Library.xml:
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1 | <component name="libraryTable">
2 |   <library name="R User Library">
3 |     <CLASSES />
4 |     <SOURCES />
5 |   </library>
6 | </component>


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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="ProjectModuleManager">
4 |     <modules>
5 |       <module fileurl="file://$PROJECT_DIR$/.idea/graph_net_pytorch.iml" filepath="$PROJECT_DIR$/.idea/graph_net_pytorch.iml" />
6 |     </modules>
7 |   </component>
8 | </project>


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/.idea/misc.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="JavaScriptSettings">
4 |     <option name="languageLevel" value="ES6" />
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6 |   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (tf2) (2)" project-jdk-type="Python SDK" />
7 | </project>


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/utils.py:
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 1 | import torch
 2 | import torch.nn as nn
 3 | import blocks
 4 | import torch.nn.functional as F
 5 | import numpy as np
 6 | from random import randint
 7 | from graphs import GraphsTuple
 8 | 
 9 | 
10 | def data_dicts_to_graphs_tuple(graph_dicts:dict):
11 |     for k,v in graph_dicts.items():
12 |         graph_dicts[k]=torch.tensor(v)
13 |     return GraphsTuple(**graph_dicts)


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/modules.py:
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 1 | import torch
 2 | import torch.nn as nn
 3 | import blocks
 4 | import torch.nn.functional as F
 5 | import numpy as np
 6 | from random import randint
 7 | from graphs import GraphsTuple
 8 | 
 9 | class GraphNetwork(nn.Module):
10 |     def __init__(self,graph):
11 |         super(GraphNetwork,self).__init__()
12 |         self._edge_block = blocks.EdgeBlock(graph)
13 |         self._node_block = blocks.NodeBlock(graph)
14 |         self._global_block = blocks.GlobalBlock(graph)
15 |     def forward(self, graph):
16 |         return self._node_block(self._edge_block(graph))


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/.idea/graph_net_pytorch.iml:
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 1 | <?xml version="1.0" encoding="UTF-8"?>
 2 | <module type="PYTHON_MODULE" version="4">
 3 |   <component name="NewModuleRootManager">
 4 |     <content url="file://$MODULE_DIR$" />
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10 |   <component name="TestRunnerService">
11 |     <option name="PROJECT_TEST_RUNNER" value="Unittests" />
12 |   </component>
13 | </module>


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/demo.py:
--------------------------------------------------------------------------------
 1 | import matplotlib.pyplot as plt
 2 | import networkx as nx
 3 | import numpy as np
 4 | import modules
 5 | import utils
 6 | 
 7 | def get_graph_data_dict(num_nodes, num_edges):
 8 |     GLOBAL_SIZE = 4
 9 |     NODE_SIZE = 5
10 |     EDGE_SIZE = 6
11 |     return {
12 |       "globals": np.random.rand(GLOBAL_SIZE).astype(np.float32),
13 |       "nodes": np.random.rand(num_nodes, NODE_SIZE).astype(np.float32),
14 |       "edges": np.random.rand(num_edges, EDGE_SIZE).astype(np.float32),
15 |       "senders": np.random.randint(num_nodes, size=num_edges, dtype=np.int32),
16 |       "receivers": np.random.randint(num_nodes, size=num_edges, dtype=np.int32),
17 |     }
18 | 
19 | 
20 | graph_dicts = get_graph_data_dict(num_nodes=9, num_edges=25)
21 | input_graphs = utils.data_dicts_to_graphs_tuple(graph_dicts)
22 | 
23 | print('input_graphs')
24 | print(input_graphs)
25 | 
26 | graph_network = modules.GraphNetwork(input_graphs)
27 | 
28 | output_graphs = graph_network(input_graphs)
29 | 
30 | print('output_graphs')
31 | print(output_graphs)
32 | 
33 | 


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/README.md:
--------------------------------------------------------------------------------
 1 | # Graph Nets implement by pytorch
 2 | 
 3 | 
 4 | 
 5 | [Graph Nets](https://github.com/deepmind/graph_nets) is DeepMind's library for
 6 | building graph networks in Tensorflow and Sonnet.You can see it in https://github.com/deepmind/graph_nets
 7 | 
 8 | I have implemented `Graph Nets` by `Pytorch` framework. You can see my work in https://github.com/TQCAI/graph_nets_pytorch
 9 | 
10 | #### What are graph networks?
11 | 
12 | A graph network takes a graph as input and returns a graph as output. The input
13 | graph has edge- (*E* ), node- (*V* ), and global-level (**u**) attributes. The
14 | output graph has the same structure, but updated attributes. Graph networks are
15 | part of the broader family of "graph neural networks" (Scarselli et al., 2009).
16 | 
17 | To learn more about graph networks, see our arXiv paper: [Relational inductive
18 | biases, deep learning, and graph networks](https://arxiv.org/abs/1806.01261).
19 | 
20 | ![Graph network](images/graph-network.png)
21 | 
22 | 
23 | 
24 | ## Usage example
25 | 
26 | You can see a forward calculation in `demo.py`


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/graphs.py:
--------------------------------------------------------------------------------
 1 | 
 2 | 
 3 | import collections
 4 | 
 5 | 
 6 | NODES = "nodes"
 7 | EDGES = "edges"
 8 | RECEIVERS = "receivers"
 9 | SENDERS = "senders"
10 | GLOBALS = "globals"
11 | N_NODE = "n_node"
12 | N_EDGE = "n_edge"
13 | 
14 | GRAPH_FEATURE_FIELDS = (NODES, EDGES, GLOBALS)
15 | GRAPH_INDEX_FIELDS = (RECEIVERS, SENDERS)
16 | GRAPH_DATA_FIELDS = (NODES, EDGES, RECEIVERS, SENDERS, GLOBALS)
17 | 
18 | 
19 | class GraphsTuple(
20 |     collections.namedtuple("GraphsTuple",
21 |                            GRAPH_DATA_FIELDS )):
22 | 
23 |     def __init__(self, *args, **kwargs):
24 |         del args, kwargs
25 |         # The fields of a `namedtuple` are filled in the `__new__` method.
26 |         # `__init__` does not accept parameters.
27 |         super(GraphsTuple, self).__init__()
28 | 
29 |     def replace(self, **kwargs):
30 |         output = self._replace(**kwargs)
31 |         return output
32 | 
33 |     def map(self, field_fn, fields=GRAPH_FEATURE_FIELDS):
34 |         """Applies `field_fn` to the fields `fields` of the instance.
35 | 
36 |         `field_fn` is applied exactly once per field in `fields`. The result must
37 |         satisfy the `GraphsTuple` requirement w.r.t. `None` fields, i.e. the
38 |         `SENDERS` cannot be `None` if the `EDGES` or `RECEIVERS` are not `None`,
39 |         etc.
40 | 
41 |         Args:
42 |           field_fn: A callable that take a single argument.
43 |           fields: (iterable of `str`). An iterable of the fields to apply
44 |             `field_fn` to.
45 | 
46 |         Returns:
47 |           A copy of the instance, with the fields in `fields` replaced by the result
48 |           of applying `field_fn` to them.
49 |         """
50 |         return self.replace(**{k: field_fn(getattr(self, k)) for k in fields})


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/blocks.py:
--------------------------------------------------------------------------------
  1 | import torch
  2 | import torch.nn as nn
  3 | import torch.nn.functional as F
  4 | import numpy as np
  5 | from random import randint
  6 | from graphs import GraphsTuple
  7 | 
  8 | 
  9 | # import utils_tf
 10 | 
 11 | def broadcast_receiver_nodes_to_edges(graph: GraphsTuple):
 12 |     return graph.nodes.index_select(index=graph.receivers.long(), dim=0)
 13 | 
 14 | 
 15 | def broadcast_sender_nodes_to_edges(graph: GraphsTuple):
 16 |     return graph.nodes.index_select(index=graph.senders.long(), dim=0)
 17 | 
 18 | 
 19 | def broadcast_globals_to_edges(graph: GraphsTuple):
 20 |     N_edges = graph.edges.shape[0]
 21 |     return graph.globals.repeat(N_edges, 1)
 22 | 
 23 | 
 24 | def broadcast_globals_to_nodes(graph: GraphsTuple):
 25 |     N_nodes = graph.nodes.shape[0]
 26 |     return graph.globals.repeat(N_nodes, 1)
 27 | 
 28 | 
 29 | class Aggregator(nn.Module):
 30 |     def __init__(self, mode):
 31 |         super(Aggregator, self).__init__()
 32 |         self.mode = mode
 33 | 
 34 |     def forward(self, graph):
 35 |         edges = graph.edges
 36 |         nodes = graph.nodes
 37 |         if self.mode == 'receivers':
 38 |             indeces = graph.receivers
 39 |         elif self.mode == 'senders':
 40 |             indeces = graph.senders
 41 |         else:
 42 |             raise AttributeError("invalid parameter `mode`")
 43 |         N_edges, N_features = edges.shape
 44 |         N_nodes=nodes.shape[0]
 45 |         aggrated_list = []
 46 |         for i in range(N_nodes):
 47 |             aggrated = edges[indeces == i]
 48 |             if aggrated.shape[0] == 0:
 49 |                 aggrated = torch.zeros(1, N_features)
 50 |             aggrated_list.append(torch.sum(aggrated, dim=0))
 51 |         return torch.stack(aggrated_list,dim=0)
 52 | 
 53 | 
 54 | class EdgeBlock(nn.Module):
 55 |     def __init__(self,
 56 |                  graph: GraphsTuple,
 57 |                  use_edges=True,
 58 |                  use_receiver_nodes=True,
 59 |                  use_sender_nodes=True,
 60 |                  use_globals=True):
 61 |         super(EdgeBlock, self).__init__()
 62 |         self._use_edges = use_edges
 63 |         self._use_receiver_nodes = use_receiver_nodes
 64 |         self._use_sender_nodes = use_sender_nodes
 65 |         self._use_globals = use_globals
 66 |         N_features = 0
 67 |         pre_features=graph.edges.shape[-1]
 68 |         if self._use_edges:
 69 |             N_features += graph.edges.shape[-1]
 70 |         if self._use_receiver_nodes:
 71 |             N_features += graph.nodes.shape[-1]
 72 |         if self._use_sender_nodes:
 73 |             N_features += graph.nodes.shape[-1]
 74 |         if self._use_globals:
 75 |             N_features += graph.globals.shape[-1]
 76 |         self.linear = nn.Linear(N_features, pre_features)
 77 | 
 78 |     def forward(self, graph: GraphsTuple):
 79 |         edges_to_collect = []
 80 | 
 81 |         if self._use_edges:
 82 |             edges_to_collect.append(graph.edges)  # edge feature  (50,6) 50边，6特征
 83 | 
 84 |         if self._use_receiver_nodes:
 85 |             edges_to_collect.append(broadcast_receiver_nodes_to_edges(graph))  # (50,5)
 86 |             # 顶点有5个特征 receiver=(50,) 表示 每个边的汇点index
 87 |             #            得到的是每个边发射终点的顶点的feature
 88 | 
 89 |         if self._use_sender_nodes:
 90 |             edges_to_collect.append(broadcast_sender_nodes_to_edges(graph))  # (50,5)
 91 |             #            同上，只不过换成了起点
 92 | 
 93 |         if self._use_globals:
 94 |             edges_to_collect.append(broadcast_globals_to_edges(graph))  # (50,)
 95 | 
 96 |         collected_edges = torch.cat(edges_to_collect, dim=1)
 97 |         updated_edges = self.linear(collected_edges)
 98 |         return graph.replace(edges=updated_edges)
 99 | 
100 | 
101 | class NodeBlock(nn.Module):
102 | 
103 |     def __init__(self,
104 |                  graph,
105 |                  use_received_edges=True,
106 |                  use_sent_edges=False,
107 |                  use_nodes=True,
108 |                  use_globals=True):
109 |         super(NodeBlock, self).__init__()
110 |         self._use_received_edges = use_received_edges
111 |         self._use_sent_edges = use_sent_edges
112 |         self._use_nodes = use_nodes
113 |         self._use_globals = use_globals
114 |         N_features = 0
115 |         pre_features=graph.nodes.shape[-1]
116 |         if self._use_nodes:
117 |             N_features += graph.nodes.shape[-1]
118 |         if self._use_received_edges:
119 |             N_features += graph.edges.shape[-1]
120 |         if self._use_sent_edges:
121 |             N_features += graph.edges.shape[-1]
122 |         if self._use_globals:
123 |             N_features += graph.globals.shape[-1]
124 |         self.linear = nn.Linear(N_features, pre_features)
125 |         self._received_edges_aggregator = Aggregator('receivers')
126 |         self._sent_edges_aggregator = Aggregator('senders')
127 | 
128 |     def forward(self, graph):
129 | 
130 |         nodes_to_collect = []
131 |         # nodes: (24,5)
132 |         # edges: (50,10)  # 上一轮更新了
133 |         # global: (4,4)
134 | 
135 |         if self._use_received_edges:
136 |             nodes_to_collect.append(self._received_edges_aggregator(graph))  # (24,10)
137 |             # 在上一轮对边的处理中， 使用的是 _received_nodes_aggregator 将边相连的顶点信息考虑进来
138 |             # 现在是将与顶点相连的边考虑进来
139 | 
140 |         if self._use_sent_edges:
141 |             nodes_to_collect.append(self._sent_edges_aggregator(graph))
142 | 
143 |         if self._use_nodes:
144 |             nodes_to_collect.append(graph.nodes)
145 | 
146 |         if self._use_globals:
147 |             nodes_to_collect.append(broadcast_globals_to_nodes(graph))  # (24,4)
148 | 
149 |         collected_nodes = torch.cat(nodes_to_collect, dim=1)  # 24,19
150 |         updated_nodes = self.linear(collected_nodes)  # 24,11
151 |         return graph.replace(nodes=updated_nodes)
152 | 
153 | 
154 | class GlobalBlock(nn.Module):
155 |     def __init__(self,
156 |                  use_edges=True,
157 |                  use_nodes=True,
158 |                  use_globals=True):
159 | 
160 |         super(GlobalBlock, self).__init__()
161 | 
162 |         self._use_edges = use_edges
163 |         self._use_nodes = use_nodes
164 |         self._use_globals = use_globals
165 | 
166 | 
167 |     def forward(self, graph):
168 |         globals_to_collect = []
169 | 
170 |         if self._use_edges:
171 |             globals_to_collect.append(self._edges_aggregator(graph))
172 | 
173 |         if self._use_nodes:
174 |             globals_to_collect.append(self._nodes_aggregator(graph))
175 | 
176 |         if self._use_globals:
177 |             globals_to_collect.append(graph.globals)
178 | 
179 |         collected_globals = torch.cat(globals_to_collect, dim=1)
180 |         updated_globals = self._global_model(collected_globals)
181 |         return graph.replace(globals=updated_globals)
182 | 


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