[Bug Fix] Fix package reliability bug of networkx (#949)

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test for mxnet TAGCov

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

* reproduce the bug

* Fix concurrency bug reported at #755.
Also make test_shared_mem_store.py more deterministic.

* Update test_shared_mem_store.py

* Update dmlc/core

* networkx >= 2.4 will break our examples

* Update tutorials/requirements

* fix selfloop edges

* upd version

examples/mxnet/gat/train.py

@@ -11,6 +11,7 @@ Pytorch implementation: https://github.com/Diego999/pyGAT
 """
 
 import argparse
+import networkx as nx
 import time
 import mxnet as mx
 from mxnet import gluon
@@ -58,7 +59,7 @@ def main(args):
     # create graph
     g = data.graph
     # add self-loop
-    g.remove_edges_from(g.selfloop_edges())
+    g.remove_edges_from(nx.selfloop_edges(g))
     g = DGLGraph(g)
     g.add_edges(g.nodes(), g.nodes())
     # create model

examples/mxnet/gcn/train.py

 """Training GCN model on citation graphs."""
 import argparse, time
 import numpy as np
+import networkx as nx
 import mxnet as mx
 from mxnet import gluon
 
@@ -54,7 +55,7 @@ def main(args):
     # create GCN model
     g = data.graph
     if args.self_loop:
-        g.remove_edges_from(g.selfloop_edges())
+        g.remove_edges_from(nx.selfloop_edges(g))
         g.add_edges_from(zip(g.nodes(), g.nodes()))
     g = DGLGraph(g)
     # normalization

examples/mxnet/tagcn/train.py

 import argparse, time
 import numpy as np
+import networkx as nx
 import mxnet as mx
 from mxnet import gluon
 
@@ -52,7 +53,7 @@ def main(args):
     g = data.graph
     # add self loop
     if args.self_loop:
-        g.remove_edges_from(g.selfloop_edges())
+        g.remove_edges_from(nx.selfloop_edges(g))
         g.add_edges_from(zip(g.nodes(), g.nodes()))
     g = DGLGraph(g)
 

examples/pytorch/cluster_gcn/cluster_gcn.py

@@ -4,6 +4,7 @@ import time
 import random
 
 import numpy as np
+import networkx as nx
 import sklearn.preprocessing
 import torch
 import torch.nn as nn
@@ -75,7 +76,7 @@ def main(args):
     # create GCN model
     g = data.graph
     if args.self_loop and not args.dataset.startswith('reddit'):
-        g.remove_edges_from(g.selfloop_edges())
+        g.remove_edges_from(nx.selfloop_edges(g))
         g.add_edges_from(zip(g.nodes(), g.nodes()))
         print("adding self-loop edges")
     g = DGLGraph(g, readonly=True)

examples/pytorch/dgi/train.py

 import argparse, time
 import numpy as np
+import networkx as nx
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -49,7 +50,7 @@ def main(args):
     g = data.graph
     # add self loop
     if args.self_loop:
-        g.remove_edges_from(g.selfloop_edges())
+        g.remove_edges_from(nx.selfloop_edges(g))
         g.add_edges_from(zip(g.nodes(), g.nodes()))
     g = DGLGraph(g)
     n_edges = g.number_of_edges()

examples/pytorch/gat/train.py

@@ -12,6 +12,7 @@ Pytorch implementation: https://github.com/Diego999/pyGAT
 
 import argparse
 import numpy as np
+import networkx as nx
 import time
 import torch
 import torch.nn.functional as F
@@ -76,7 +77,7 @@ def main(args):
 
     g = data.graph
     # add self loop
-    g.remove_edges_from(g.selfloop_edges())
+    g.remove_edges_from(nx.selfloop_edges(g))
     g = DGLGraph(g)
     g.add_edges(g.nodes(), g.nodes())
     n_edges = g.number_of_edges()

examples/pytorch/gcn/gcn_mp.py

@@ -7,6 +7,7 @@ References:
 """
 import argparse, time, math
 import numpy as np
+import networkx as nx
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -153,7 +154,7 @@ def main(args):
 
     # graph preprocess and calculate normalization factor
     g = data.graph
-    g.remove_edges_from(g.selfloop_edges())
+    g.remove_edges_from(nx.selfloop_edges(g))
     g = DGLGraph(g)
     # add self loop
     g.add_edges(g.nodes(), g.nodes())

examples/pytorch/gcn/train.py

 import argparse, time
 import numpy as np
+import networkx as nx
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -62,7 +63,7 @@ def main(args):
     g = data.graph
     # add self loop
     if args.self_loop:
-        g.remove_edges_from(g.selfloop_edges())
+        g.remove_edges_from(nx.selfloop_edges(g))
         g.add_edges_from(zip(g.nodes(), g.nodes()))
     g = DGLGraph(g)
     n_edges = g.number_of_edges()

examples/pytorch/graphsage/graphsage.py

@@ -8,6 +8,7 @@ import argparse
 import time
 import abc
 import numpy as np
+import networkx as nx
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -96,7 +97,7 @@ def main(args):
 
     # graph preprocess and calculate normalization factor
     g = data.graph
-    g.remove_edges_from(g.selfloop_edges())
+    g.remove_edges_from(nx.selfloop_edges(g))
     g = DGLGraph(g)
     n_edges = g.number_of_edges()
 

examples/pytorch/model_zoo/citation_network/run.py

@@ -7,6 +7,7 @@ from dgl import DGLGraph
 from dgl.data import register_data_args, load_data
 from models import *
 from conf import *
+import networkx as nx
 
 
 def get_model_and_config(name):
@@ -82,7 +83,7 @@ def main(args):
     g = data.graph
     # add self loop
     if args.self_loop:
-        g.remove_edges_from(g.selfloop_edges())
+        g.remove_edges_from(nx.selfloop_edges(g))
         g.add_edges_from(zip(g.nodes(), g.nodes()))
     g = DGLGraph(g)
     n_edges = g.number_of_edges()

examples/pytorch/tagcn/train.py

 import argparse, time
 import numpy as np
+import networkx as nx
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -60,8 +61,8 @@ def main(args):
     g = data.graph
     # add self loop
     if args.self_loop:
-            g.remove_edges_from(g.selfloop_edges())
-            g.add_edges_from(zip(g.nodes(), g.nodes()))
+        g.remove_edges_from(nx.selfloop_edges(g))
+        g.add_edges_from(zip(g.nodes(), g.nodes()))
     g = DGLGraph(g)
     n_edges = g.number_of_edges()
 

tutorials/models/1_gnn/1_gcn.py

@@ -101,6 +101,7 @@ print(net)
 # We load the cora dataset using DGL's built-in data module.
 
 from dgl.data import citation_graph as citegrh
+import networkx as nx
 def load_cora_data():
     data = citegrh.load_cora()
     features = th.FloatTensor(data.features)
@@ -108,7 +109,7 @@ def load_cora_data():
     mask = th.ByteTensor(data.train_mask)
     g = data.graph
     # add self loop
-    g.remove_edges_from(g.selfloop_edges())
+    g.remove_edges_from(nx.selfloop_edges(g))
     g = DGLGraph(g)
     g.add_edges(g.nodes(), g.nodes())
     return g, features, labels, mask

tutorials/models/1_gnn/9_gat.py

@@ -274,6 +274,7 @@ class GAT(nn.Module):
 
 from dgl import DGLGraph
 from dgl.data import citation_graph as citegrh
+import networkx as nx
 
 def load_cora_data():
     data = citegrh.load_cora()
@@ -282,7 +283,7 @@ def load_cora_data():
     mask = torch.ByteTensor(data.train_mask)
     g = data.graph
     # add self loop
-    g.remove_edges_from(g.selfloop_edges())
+    g.remove_edges_from(nx.selfloop_edges(g))
     g = DGLGraph(g)
     g.add_edges(g.nodes(), g.nodes())
     return g, features, labels, mask

tutorials/requirements.txt

-networkx
+networkx>=2.1
 torch
 numpy
 seaborn