Update main_protein.py

ogb_examples/nodeproppred/unimp/main_protein.py

@@ -23,7 +23,7 @@ evaluator = Evaluator(name='ogbn-proteins')
 def get_config():
     parser = argparse.ArgumentParser()
     
-    ## 基本模型参数
+    ## model_arg
     model_group=parser.add_argument_group('model_base_arg')
     model_group.add_argument('--num_layers', default=7, type=int)
     model_group.add_argument('--hidden_size', default=64, type=int)
@@ -31,7 +31,7 @@ def get_config():
     model_group.add_argument('--dropout', default=0.1, type=float)
     model_group.add_argument('--attn_dropout', default=0, type=float)
     
-    ## label embedding模型参数
+    ## label_embed_arg
     embed_group=parser.add_argument_group('embed_arg')
     embed_group.add_argument('--use_label_e', action='store_true')
     embed_group.add_argument('--label_rate', default=0.5, type=float)
@@ -90,15 +90,16 @@ def eval_test(parser, program, model, test_exe, graph, y_true, split_idx):
 
 def train_loop(parser, start_program, main_program, test_program, 
                model, graph, label, split_idx, exe, run_id, wf=None):
-    #启动上文构建的训练器
+    #build up training program
     exe.run(start_program)
     
-    max_acc=0  # 最佳test_acc
-    max_step=0 # 最佳test_acc 对应step
-    max_val_acc=0 # 最佳val_acc
-    max_cor_acc=0 # 最佳val_acc对应test_acc
-    max_cor_step=0 # 最佳val_acc对应step
-    #训练循环
+    max_acc=0  # best test_acc
+    max_step=0 # step for best test_acc 
+    max_val_acc=0 # best val_acc
+    max_cor_acc=0 # test_acc for best val_acc
+    max_cor_step=0 # step for best val_acc
+    #training loop
+
     graph.node_feat["label"] = label
     graph.node_feat["nid"] = np.arange(0, graph.num_nodes) 
     
@@ -112,7 +113,7 @@ def train_loop(parser, start_program, main_program, test_program,
     
     for epoch_id in tqdm(range(parser.epochs)):
         for subgraph in random_partition(num_clusters=9, graph=graph, shuffle=True): 
-            #运行训练器  
+            #start training  
             if parser.use_label_e:
                 feed_dict = model.gw.to_feed(subgraph)
                 sub_idx = set(subgraph.node_feat["nid"])
@@ -139,7 +140,7 @@ def train_loop(parser, start_program, main_program, test_program,
                           fetch_list=[model.avg_cost])
             loss = loss[0]
 
-        #测试结果
+        #eval result
         if (epoch_id+1) > parser.epochs*0.9:
             result = eval_test(parser, test_program, model, exe, graph, label, split_idx)
             train_acc, valid_acc, test_acc = result
@@ -221,7 +222,7 @@ if __name__ == '__main__':
             model.train_program()
             
         
-            adam_optimizer = optimizer_func(parser.lr)#训练优化函数
+            adam_optimizer = optimizer_func(parser.lr)#optimizer
             adam_optimizer.minimize(model.avg_cost)
     
     exe = F.Executor(place)