Update run.py

run.py

@@ -9,8 +9,7 @@ from torch.utils.data import DataLoader
 import json
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
-q = os.path.join(dir_path, '..')
-sys.path.append(q)
+sys.path.append(os.path.join(dir_path, '..'))
 
 from nets.model import Model
 from Actor.actor import Actor
@@ -21,32 +20,16 @@ from google_solver.google_model import evaluate_google_model
 with open('params.json', 'r') as f:
     params = json.load(f)
 
+# Save params into a local file for tracking
+with open('params_saved.json', 'w') as f:
+    json.dump(params, f)
+
 # Settings
 device = params['device']
 run_tests = params['run_tests']
 save_results = params['save_results']
 dataset_path = params['dataset_path']
 
-# Create results directory
-now = datetime.now()
-dt_string = now.strftime("%d-%m-%y %H-%M-%S")
-print('Current time: ' + dt_string + '\n')
-
-if save_results:
-    results_dir = os.path.join(os.getcwd() , "results")
-    os.makedirs(results_dir, exist_ok=True)
-
-    experiment_path = os.path.join(results_dir, dt_string)
-    os.makedirs(experiment_path, exist_ok=True)
-    open(os.path.join(experiment_path, 'train_results.txt'), 'w').close()
-    open(os.path.join(experiment_path, 'test_results.txt'), 'w').close()
-
-    with open(os.path.join(experiment_path, 'params.json'), 'w') as f:
-        json.dump(params, f)
-
-    os.mkdir(os.path.join(experiment_path, 'problem_instances'))
-
-
 # Dataset sizes
 train_dataset_size = params['train_dataset_size']
 validation_dataset_size = params['validation_dataset_size']
@@ -81,26 +64,14 @@ tot_google_scores = google_scores.sum().item()
 input_size = validation_dataset.model_input_length()
 
 # Models
-model = Model(
-    input_size=input_size,
-    embedding_size=embedding_size,
-    decoder_input_size=params["decoder_input_size"]
-)
-actor = Actor(model=model, num_movers=num_movers,
-              num_neighbors_encoder=num_neighbors_encoder,
-              num_neighbors_action=num_neighbors_action,
-              device=device, normalize=False)
+model = Model(input_size=input_size, embedding_size=embedding_size, decoder_input_size=params["decoder_input_size"])
+actor = Actor(model=model, num_movers=num_movers, num_neighbors_encoder=num_neighbors_encoder,
+              num_neighbors_action=num_neighbors_action, device=device, normalize=False)
 actor.train_mode()
 
-baseline_model = Model(
-    input_size=input_size,
-    embedding_size=embedding_size,
-    decoder_input_size=params["decoder_input_size"]
-)
-baseline_actor = Actor(model=baseline_model, num_movers=num_movers,
-                        num_neighbors_encoder=num_neighbors_encoder,
-                        num_neighbors_action=num_neighbors_action,
-                        device=device, normalize=False)
+baseline_model = Model(input_size=input_size, embedding_size=embedding_size, decoder_input_size=params["decoder_input_size"])
+baseline_actor = Actor(model=baseline_model, num_movers=num_movers, num_neighbors_encoder=num_neighbors_encoder,
+                       num_neighbors_action=num_neighbors_action, device=device, normalize=False)
 baseline_actor.greedy_search()
 baseline_actor.load_state_dict(actor.state_dict())
 
@@ -151,7 +122,7 @@ for epoch in range(params['num_epochs']):
         result = f"{epoch}, {i}, {actor_nn_ratio:.4f}, {actor_baseline_ratio:.4f}, {train_batch_record:.4f}"
         print(result, flush=True)
         if save_results:
-            with open(os.path.join(experiment_path, 'train_results.txt'), 'a') as f:
+            with open('train_results.txt', 'a') as f:
                 f.write(result + '\n')
         del batch
 
@@ -196,12 +167,9 @@ for epoch in range(params['num_epochs']):
         ratio = tot_cost / tot_nn_cost
         validation_record = min(validation_record, ratio)
 
-        if save_results:
-            torch.save(actor.state_dict(), os.path.join(experiment_path, 'model_state_dict.pt'))
-            torch.save(optimizer.state_dict(), os.path.join(experiment_path, 'optimizer_state_dict.pt'))
-
         actor_google_ratio = tot_cost / tot_google_scores
         print(f"\nTest results:\nActor/Google: {actor_google_ratio:.4f}, Actor/NN: {ratio:.4f}, Best NN Ratio: {validation_record:.4f}\n")
+
         if save_results:
-            with open(os.path.join(experiment_path, 'test_results.txt'), 'a') as f:
+            with open('test_results.txt', 'a') as f:
                 f.write(f"{epoch}, {actor_google_ratio:.4f}, {ratio:.4f}, {validation_record:.4f}\n")