feat: add nframe=3 adsorption softmax training

deepmd/infer/deep_property.py

@@ -141,6 +141,18 @@ def eval(
             nframes, self.get_task_dim()
         )
 
+        # --- softmax-weighted averaging over frames (minimal) ---
+        print(f"Nframes == {nframes}")
+        if nframes != 3:
+            raise RuntimeError(f"Expected nframes == 3, got {nframes}")
+        scores = property.mean(axis=1)  # (3,)
+        # If you want to favor *smaller* values (e.g., energies), use: scores = -scores
+        w = np.exp(scores - scores.max())
+        w /= w.sum()  # (3,)
+        avg = (w[:, None] * property).sum(axis=0, keepdims=True)  # (1, D)
+        property[:] = np.repeat(avg, nframes, axis=0)  # (3, D)
+        # --------------------------------------------------------
+
         if atomic:
             return (
                 property,

deepmd/pt/loss/property.py

@@ -99,6 +99,27 @@ def forward(
         """
         model_pred = model(**input_dict)
         var_name = self.var_name
+
+        # ---- Softmax-weighted averaging over the batch added by YL----
+        # model_pred[var_name]: (nbz, task_dim)
+        # 1) get a scalar score per sample (mean over task_dim)
+        #    (If you want to favor smaller values, use `score_per_sample = -model_pred[var_name].mean(dim=1)`.)
+        score_per_sample = model_pred[var_name].mean(dim=1)  # (nbz,)
+        weights = F.softmax(score_per_sample, dim=0)  # (nbz,)
+        # 2) weighted average vector (1, task_dim)
+        avg_vec = (weights.unsqueeze(1) * model_pred[var_name]).sum(dim=0, keepdim=True)
+        # 3) replace all predictions with the averaged vector (broadcast over batch)
+        model_pred[var_name] = avg_vec.expand_as(model_pred[var_name])
+        # ----------------------------------------------------
+
+        nbz = model_pred[var_name].shape[0]
+        # =======Raise error when nbz!=3=======
+        if nbz != 3:
+            raise RuntimeError(
+                f"[PropertyLoss] Expected batch size nbz == 3 for softmax-avg, got nbz == {nbz}. "
+                "Ensure your DataLoader yields triples (batch_size=3, drop_last=True)."
+            )
+
         nbz = model_pred[var_name].shape[0]
         assert model_pred[var_name].shape == (nbz, self.task_dim)
         assert label[var_name].shape == (nbz, self.task_dim)

deepmd/utils/path.py

@@ -331,7 +331,7 @@ def _load_h5py(cls, path: str, mode: str = "r") -> h5py.File:
         # this method has cache to avoid duplicated
         # loading from different DPH5Path
         # However the file will be never closed?
-        return h5py.File(path, mode)
+        return h5py.File(path, mode, locking=False)
 
     def load_numpy(self) -> np.ndarray:
         """Load NumPy array.