新增消耗分析模式，只需在原有的mode中调整为benchmark即可

run.py

@@ -1,17 +1,14 @@
 import os
-import shutil
-from torchview import draw_graph
-
 
 # 检查数据集完整性
 from lib.Download_data import check_and_download_data
+
 data_complete = check_and_download_data()
 assert data_complete is not None, "数据集下载失败，请重试！"
 
 import torch
 from datetime import datetime
 # import time
-
 from config.args_parser import parse_args
 from lib.initializer import init_model, init_optimizer
 from lib.loss_function import get_loss_function
@@ -21,32 +18,41 @@ from trainer.trainer_selector import select_trainer
 import yaml
 
 
+
+
 def main():
     args = parse_args()
 
     # Set device
-    if torch.cuda.is_available():
+    if torch.cuda.is_available() and args['device'] != 'cpu':
         torch.cuda.set_device(int(args['device'].split(':')[1]))
         args['model']['device'] = args['device']
     else:
         args['device'] = 'cpu'
         args['model']['device'] = args['device']
 
-
     # Initialize model
     model = init_model(args['model'], device=args['device'])
 
-    if args['mode'] == "draw":
-        dummy_input = torch.randn(64,12,307,3)
-        model_graph = draw_graph(model,
-                                 input_data = dummy_input,
-                                 device=args['device'],
-                                 show_shapes=True,
-                                 save_graph=True,
-                                 graph_name=f"{args['model']['type']}_graph",
-                                 directory="./",
-                                 format="png"
-                                 )
+    if args['mode'] == "benchmark":
+        # 支持计算消耗分析，设置 mode为 benchmark
+        import torch.profiler as profiler
+        dummy_input = torch.randn((64, 12, args['model']['num_nodes'], 3), device=args['device'])
+        min_val = dummy_input.min(dim=-1, keepdim=True)[0]
+        max_val = dummy_input.max(dim=-1, keepdim=True)[0]
+
+        dummy_input = (dummy_input - min_val) / (max_val - min_val + 1e-6)
+        with profiler.profile(
+                activities=[
+                    profiler.ProfilerActivity.CPU,
+                    profiler.ProfilerActivity.CUDA
+                ],
+                with_stack=True,
+                profile_memory=True,
+                record_shapes=True
+        ) as prof:
+            out = model(dummy_input)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
         return 0
 
     # Load dataset