Update main_product.py

ogb_examples/nodeproppred/unimp/main_product.py

@@ -22,14 +22,14 @@ evaluator = Evaluator(name='ogbn-products')
 def get_config():
     parser = argparse.ArgumentParser()
     
-    ## 采样参数
+    ## data_sampling_arg
     data_group= parser.add_argument_group('data_arg')
     data_group.add_argument('--batch_size', default=1500, type=int)
     data_group.add_argument('--num_workers', default=12, type=int)
     data_group.add_argument('--sizes', default=[10, 10, 10], type=int, nargs='+' )
     data_group.add_argument('--buf_size', default=1000, type=int)
 
-    ## 基本模型参数
+    ## model_arg
     model_group=parser.add_argument_group('model_base_arg')
     model_group.add_argument('--num_layers', default=3, type=int)
     model_group.add_argument('--hidden_size', default=128, type=int)
@@ -37,7 +37,7 @@ def get_config():
     model_group.add_argument('--dropout', default=0.3, 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=0.625, type=float)
@@ -113,7 +113,7 @@ def eval_test(parser, test_p_list, model, test_exe, dataset, split_idx):
 
 def train_loop(parser, start_program, main_program, test_p_list,
                model, feat_init, place, dataset, 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)
     feat_init(place)
     
@@ -122,10 +122,10 @@ def train_loop(parser, start_program, main_program, test_p_list,
     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
+    #training loop
 
     for epoch_id in range(parser.epochs):
-        #运行训练器  
+        #start training  
          
         if parser.use_label_e:
             train_idx_temp=copy.deepcopy(split_idx['train'])
@@ -158,8 +158,7 @@ def train_loop(parser, start_program, main_program, test_p_list,
 
             print('acc: ', (acc_num/unlabel_idx.shape[0])*100)
 
-        #测试结果
-#         total=0.0
+        #eval result
         if (epoch_id+1)>=50 and (epoch_id+1)%10==0:
             result = eval_test(parser, test_p_list, model, exe, dataset, split_idx)
             train_acc, valid_acc, test_acc = result
@@ -242,17 +241,14 @@ if __name__ == '__main__':
 #             test_prog=train_prog.clone(for_test=True)
             model.train_program()
            
-#             ave_loss = train_program(pred_output)#训练程序
-#             lr, global_step= linear_warmup_decay(0.01, 50, 500)
-#             adam_optimizer = optimizer_func(lr)#训练优化函数
-            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)
     
     test_p_list=[] 
     
     with F.unique_name.guard():  
 
-        ## input层
+        ## build up eval program
         test_p=F.Program()
         with F.program_guard(test_p, ):
             gw_test=pgl.graph_wrapper.GraphWrapper(
@@ -281,7 +277,7 @@ if __name__ == '__main__':
             with F.program_guard(test_p, ):
                 gw_test=pgl.graph_wrapper.GraphWrapper(
                     name="product_"+str(0))
-#                 feature_batch=model.get_batch_feature(label_feature, test=True)  #  把图在CPU存起
+#                 feature_batch=model.get_batch_feature(label_feature, test=True) 
                 feature_batch = F.data( 'hidden_node_feat',
                                     shape=[None, model.num_heads*model.hidden_size],
                                     dtype='float32')