Update inference.py

inference.py

@@ -2,26 +2,36 @@ import torch
 from torch.utils.data import DataLoader
 import json
 import os
+import csv
+from datetime import datetime
 
 from nets.model import Model
 from Actor.actor import Actor
 from dataloader import VRP_Dataset
 
 # --- تحميل الإعدادات ---
-with open('/data/params_saved.json', 'r') as f:
+params_path = '/data/params_saved.json'
+if not os.path.exists(params_path):
+    raise FileNotFoundError(f"Settings file not found at {params_path}")
+
+with open(params_path, 'r') as f:
     params = json.load(f)
 
 # --- تعيين الجهاز ---
 device = params['device']
 dataset_path = params['dataset_path']
-input_size = None  # سيتم تحديده بعد تحميل البيانات
 
-# --- تحميل نموذج مدرب ---
+# --- مسار النموذج المحفوظ ---
 model_path = "/data/model_state_dict.pt"
 if not os.path.exists(model_path):
     raise FileNotFoundError(f"Model not found at {model_path}")
 
-# --- إعداد بيانات عشوائية للاختبار ---
+# --- تحضير مجلد النتائج ---
+os.makedirs("/data", exist_ok=True)
+txt_results_file = "/data/inference_results.txt"
+csv_results_file = "/data/inference_results.csv"
+
+# --- إعداد بيانات inference ---
 inference_dataset = VRP_Dataset(
     size=1,
     num_nodes=params['num_nodes'],
@@ -29,7 +39,6 @@ inference_dataset = VRP_Dataset(
     path=dataset_path,
     device=device
 )
-
 input_size = inference_dataset.model_input_length()
 
 # --- تحميل النموذج ---
@@ -40,7 +49,7 @@ model = Model(
 )
 model.load_state_dict(torch.load(model_path, map_location=device))
 
-# --- تهيئة الممثل (Actor) والـ NN Actor ---
+# --- تهيئة الممثل والـ NN Actor ---
 actor = Actor(model=model,
               num_movers=params['num_movers'],
               num_neighbors_encoder=params['num_neighbors_encoder'],
@@ -52,8 +61,10 @@ actor.eval_mode()
 nn_actor = Actor(model=None, num_movers=1, num_neighbors_action=1, device=device)
 nn_actor.nearest_neighbors()
 
-# --- تنفيذ الاستدلال على دفعة واحدة ---
+# --- استدلال وتخزين النتائج ---
 dataloader = DataLoader(inference_dataset, batch_size=1, collate_fn=inference_dataset.collate)
+output_lines = []
+
 for batch in dataloader:
     with torch.no_grad():
         actor.greedy_search()
@@ -63,7 +74,31 @@ for batch in dataloader:
         nn_output = nn_actor(batch)
         nn_time = nn_output['total_time'].item()
 
-        print("\n===== INFERENCE RESULT =====")
-        print(f"Actor Model Total Cost: {total_time:.4f}")
-        print(f"Nearest Neighbor Cost : {nn_time:.4f}")
-        print(f"Improvement over NN    : {(nn_time - total_time) / nn_time * 100:.2f}%")
+        improvement = (nn_time - total_time) / nn_time * 100
+
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        result_text = (
+            "\n===== INFERENCE RESULT =====\n"
+            f"Time: {timestamp}\n"
+            f"Actor Model Total Cost: {total_time:.4f}\n"
+            f"Nearest Neighbor Cost : {nn_time:.4f}\n"
+            f"Improvement over NN    : {improvement:.2f}%\n"
+        )
+        print(result_text)
+        output_lines.append(result_text)
+
+        # حفظ النتائج إلى CSV
+        write_header = not os.path.exists(csv_results_file)
+        with open(csv_results_file, 'a', newline='') as csvfile:
+            writer = csv.writer(csvfile)
+            if write_header:
+                writer.writerow(["Timestamp", "Actor Cost", "NN Cost", "Improvement (%)"])
+            writer.writerow([timestamp, f"{total_time:.4f}", f"{nn_time:.4f}", f"{improvement:.2f}"])
+
+# --- حفظ النتائج إلى ملف نصي ---
+with open(txt_results_file, 'a') as f:
+    f.write("\n".join(output_lines))
+    f.write("\n=============================\n")
+
+print(f"\n✅ Results saved to:\n- {txt_results_file}\n- {csv_results_file}")