Create inference.py

inference.py

@@ -0,0 +1,69 @@
+import torch
+from torch.utils.data import DataLoader
+import json
+import os
+
+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 = json.load(f)
+
+# --- تعيين الجهاز ---
+device = params['device']
+dataset_path = params['dataset_path']
+input_size = None  # سيتم تحديده بعد تحميل البيانات
+
+# --- تحميل نموذج مدرب ---
+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 ---
+inference_dataset = VRP_Dataset(
+    size=1,
+    num_nodes=params['num_nodes'],
+    num_depots=params['num_depots'],
+    path=dataset_path,
+    device=device
+)
+
+input_size = inference_dataset.model_input_length()
+
+# --- تحميل النموذج ---
+model = Model(
+    input_size=input_size,
+    embedding_size=params["embedding_size"],
+    decoder_input_size=params["decoder_input_size"]
+)
+model.load_state_dict(torch.load(model_path, map_location=device))
+
+# --- تهيئة الممثل (Actor) والـ NN Actor ---
+actor = Actor(model=model,
+              num_movers=params['num_movers'],
+              num_neighbors_encoder=params['num_neighbors_encoder'],
+              num_neighbors_action=params['num_neighbors_action'],
+              device=device,
+              normalize=False)
+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 ---
+dataloader = DataLoader(inference_dataset, batch_size=1, collate_fn=inference_dataset.collate)
+for batch in dataloader:
+    with torch.no_grad():
+        actor.greedy_search()
+        actor_output = actor(batch)
+        total_time = actor_output['total_time'].item()
+
+        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}%")