improving the pt selection and fix E0 bug

mace/cli/fine_tuning_select.py

@@ -92,6 +92,7 @@ def parse_args() -> argparse.Namespace:
         type=float,
         default=1.0,
     )
+    parser.add_argument("--seed", help="random seed", type=int, default=42)
     return parser.parse_args()
 
 
@@ -197,6 +198,8 @@ def assemble_descriptors(self) -> np.ndarray:
 def select_samples(
     args: argparse.Namespace,
 ) -> None:
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
     if args.model in ["small", "medium", "large"]:
         calc = mace_mp(args.model, device=args.device, default_dtype=args.default_dtype)
     else:
@@ -217,18 +220,34 @@ def select_samples(
             atoms_list_pt = ase.io.read(args.configs_pt, index=":")
             for i, atoms in enumerate(atoms_list_pt):
                 atoms.info["mace_descriptors"] = descriptors[i]
-            atoms_list_pt = [
+            atoms_list_pt_filtered = [
                 x
                 for x in atoms_list_pt
                 if filter_atoms(x, all_species_ft, "combinations")
             ]
         else:
             atoms_list_pt = ase.io.read(args.configs_pt, index=":")
-            atoms_list_pt = [
+            atoms_list_pt_filtered = [
                 x
                 for x in atoms_list_pt
                 if filter_atoms(x, all_species_ft, "combinations")
             ]
+        if len(atoms_list_pt_filtered) <= args.num_samples:
+            logging.info(
+                "Number of configurations after filtering is less than the number of samples, "
+                "selecting random configurations, for the rest."
+            )
+            atoms_list_pt_minus_filtered = [
+                x for x in atoms_list_pt if x not in atoms_list_pt_filtered
+            ]
+            atoms_list_pt_random = np.random.choice(
+                atoms_list_pt_minus_filtered,
+                args.num_samples - len(atoms_list_pt_filtered),
+            ).tolist()
+            atoms_list_pt = atoms_list_pt_filtered + atoms_list_pt_random
+        else:
+            atoms_list_pt = atoms_list_pt_filtered
+
     else:
         atoms_list_pt = ase.io.read(args.configs_pt, index=":")
         if args.descriptors is not None:

mace/cli/run_train.py

@@ -238,7 +238,7 @@ def main() -> None:
                 args_samples = {
                     "configs_pt": dataset_mp,
                     "configs_ft": args.train_file,
-                    "num_samples": 1000,
+                    "num_samples": args.num_samples_pt,
                     "seed": args.seed,
                     "model": args.foundation_model,
                     "head_pt": "pbe_mp",

mace/tools/arg_parser.py

@@ -350,6 +350,12 @@ def build_default_arg_parser() -> argparse.ArgumentParser:
         type=float,
         default=1.0,
     )
+    parser.add_argument(
+        "--num_samples_pt",
+        help="Number of samples in the pretrained head",
+        type=int,
+        default=1000,
+    )
     parser.add_argument(
         "--keep_isolated_atoms",
         help="Keep isolated atoms in the dataset, useful for transfer learning",

mace/tools/scripts_utils.py

@@ -153,7 +153,14 @@ def get_atomic_energies(E0s, train_collection, z_table, heads) -> dict:
                 atomic_energies_dict = json.load(open(E0s, "r"))
             else:
                 try:
-                    atomic_energies_dict = ast.literal_eval(E0s)
+                    atomic_energies_eval = ast.literal_eval(E0s)
+                    if not all(
+                        isinstance(value, dict)
+                        for value in atomic_energies_eval.values()
+                    ):
+                        atomic_energies_dict = {"Default": atomic_energies_eval}
+                    else:
+                        atomic_energies_dict = atomic_energies_eval
                     assert isinstance(atomic_energies_dict, dict)
                 except Exception as e:
                     raise RuntimeError(