[Docs] improve documentation; add reproducibility notice

README.md

@@ -64,10 +64,16 @@ Note on legacy FFSP code: the initial version we made was not yet integrated in
 
 ### Testing
 
-You may run the `test.py` script to evaluate the model, e.g. with:
+You may run the `test.py` script to evaluate the model, e.g. with greedy decoding:
 
 ```bash
-python test.py --problem hcvrp --decode_type greedy --batch_size 128 --sample_size 1
+python test.py --problem hcvrp --decode_type greedy --batch_size 128
+```
+
+(note: we measure time with single instance -- batch size 1, but larger makes the overall evaluation faster), or with sampling:
+
+```bash
+python test.py --problem hcvrp --decode_type sampling --batch_size 1 --sample_size 1280
 ```
 
 

test.py

@@ -40,10 +40,10 @@
     parser.add_argument(
         "--sample_size",
         type=int,
-        default=1,
+        default=None,
         help="Number of samples to use for sampling decoding",
     )
-    parser.add_argument("--batch_size", type=int, default=128)
+    parser.add_argument("--batch_size", type=int, default=1)
     parser.add_argument("--checkpoint", type=str, default=None)
     parser.add_argument("--device", type=str, default="cuda")
 
@@ -68,12 +68,15 @@
         ), "Problem must be specified if checkpoint is not provided"
         checkpoint_path = f"./checkpoints/{problem}/parco.ckpt"
     if decode_type == "greedy":
-        assert sample_size == 1
+        assert (sample_size == 1 or sample_size is None), "Greedy decoding only uses 1 sample"
     if opts.datasets is None:
         assert problem is not None, "Problem must be specified if dataset is not provided"
         data_paths = [f"./data/{problem}/{f}" for f in os.listdir(f"./data/{problem}")]
     else:
         data_paths = [opts.datasets] if isinstance(opts.datasets, str) else opts.datasets
+    if decode_type == "sampling":
+        assert sample_size is not None, "Sample size must be specified for sampling decoding with --sample_size"
+        assert batch_size == 1, "Only batch_size=1 is supported for sampling decoding currently"
     data_paths = sorted(data_paths)  # Sort for consistency
 
     # Load the checkpoint as usual
@@ -85,7 +88,7 @@
     policy = model.policy.to(device).eval()  # Use mixed precision if supported
 
     for dataset in data_paths:
-        tour_lengths = []
+        costs = []
         inference_times = []
         eval_steps = []
 
@@ -108,15 +111,15 @@
                     end_time = time.time()
                     inference_time = end_time - start_time
                     if decode_type == "greedy":
-                        tour_lengths.append(-out["reward"].mean().item())
+                        costs.append(-out["reward"].mean().item())
                     else:
-                        tour_lengths.extend(
+                        costs.extend(
                             -out["reward"].reshape(-1, sample_size).max(dim=-1)[0]
                         )
                     inference_times.append(inference_time)
                     eval_steps.append(out["steps"])
 
-            print(f"Average tour length: {sum(tour_lengths)/len(tour_lengths):.4f}")
+            print(f"Average cost: {sum(costs)/len(costs):.4f}")
             print(
                 f"Per step inference time: {sum(inference_times)/len(inference_times):.4f}s"
             )