[Misc] Black auto fix. (dmlc#4641)

* [Misc] Black auto fix. * sort Co-authored-by: Steve <[email protected]>
XiaoYixin7132 · Sep 26, 2022 · a9f2acf · a9f2acf
1 parent 08c50eb
commit a9f2acf
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diff --git a/benchmarks/benchmarks/model_speed/bench_sage.py b/benchmarks/benchmarks/model_speed/bench_sage.py
@@ -1,21 +1,26 @@
 import time
-import dgl
-from dgl.nn.pytorch import SAGEConv
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+import dgl
+from dgl.nn.pytorch import SAGEConv
+
 from .. import utils
 
+
 class GraphSAGE(nn.Module):
-    def __init__(self,
-                 in_feats,
-                 n_hidden,
-                 n_classes,
-                 n_layers,
-                 activation,
-                 dropout,
-                 aggregator_type):
+    def __init__(
+        self,
+        in_feats,
+        n_hidden,
+        n_classes,
+        n_layers,
+        activation,
+        dropout,
+        aggregator_type,
+    ):
         super(GraphSAGE, self).__init__()
         self.layers = nn.ModuleList()
         self.dropout = nn.Dropout(dropout)
@@ -27,7 +32,9 @@ def __init__(self,
         for i in range(n_layers - 1):
             self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type))
         # output layer
-        self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type)) # activation None
+        self.layers.append(
+            SAGEConv(n_hidden, n_classes, aggregator_type)
+        )  # activation None
 
     def forward(self, graph, inputs):
         h = self.dropout(inputs)
@@ -38,20 +45,21 @@ def forward(self, graph, inputs):
                 h = self.dropout(h)
         return h
 
-@utils.benchmark('time')
-@utils.parametrize('data', ['cora', 'pubmed'])
+
+@utils.benchmark("time")
+@utils.parametrize("data", ["cora", "pubmed"])
 def track_time(data):
     data = utils.process_data(data)
     device = utils.get_bench_device()
     num_epochs = 200
 
     g = data[0].to(device)
 
-    features = g.ndata['feat']
-    labels = g.ndata['label']
-    train_mask = g.ndata['train_mask']
-    val_mask = g.ndata['val_mask']
-    test_mask = g.ndata['test_mask']
+    features = g.ndata["feat"]
+    labels = g.ndata["label"]
+    train_mask = g.ndata["train_mask"]
+    val_mask = g.ndata["val_mask"]
+    test_mask = g.ndata["test_mask"]
 
     in_feats = features.shape[1]
     n_classes = data.num_classes
@@ -60,16 +68,14 @@ def track_time(data):
     g = dgl.add_self_loop(g)
 
     # create model
-    model = GraphSAGE(in_feats, 16, n_classes, 1, F.relu, 0.5, 'gcn')
+    model = GraphSAGE(in_feats, 16, n_classes, 1, F.relu, 0.5, "gcn")
     loss_fcn = torch.nn.CrossEntropyLoss()
 
     model = model.to(device)
     model.train()
 
     # optimizer
-    optimizer = torch.optim.Adam(model.parameters(),
-                                 lr=1e-2,
-                                 weight_decay=5e-4)
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
 
     # dry run
     for i in range(10):