fix: precommit

deepmodeling · Apr 8, 2024 · f6ebec1 · f6ebec1
1 parent f9278eb
commit f6ebec1
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Showing 2 changed files with 21 additions and 38 deletions.
diff --git a/deepmd/pt/utils/stat.py b/deepmd/pt/utils/stat.py
@@ -264,43 +264,26 @@ def compute_output_stats(
         keys = new_keys
 
         # split system based on label
-        atomic_sampled_idx = defaultdict(set)
-        global_sampled_idx = defaultdict(set)
+        atomic_sampled_idx = defaultdict(list)
+        global_sampled_idx = defaultdict(list)
 
         for kk in keys:
             for idx, system in enumerate(sampled):
-                if (
-                    (("find_atom_" + kk) in system)
-                    and (system["find_atom_" + kk] > 0.0)
-                    and (idx not in atomic_sampled_idx[kk])
-                ):
-                    atomic_sampled_idx[kk].add(idx)
-                elif (
-                    (("find_" + kk) in system)
-                    and (system["find_" + kk] > 0.0)
-                    and (idx not in global_sampled_idx[kk])
-                ):
-                    global_sampled_idx[kk].add(idx)
+
+
+                if (("find_atom_" + kk) in system) and (system["find_atom_" + kk] > 0.0) and (len(atomic_sampled_idx[kk])==0 or idx > atomic_sampled_idx[kk][-1]):
+                    atomic_sampled_idx[kk].append(idx)
+                elif (("find_" + kk) in system) and (system["find_" + kk] > 0.0) and (len(global_sampled_idx[kk])==0 or idx > global_sampled_idx[kk][-1]):
+                    global_sampled_idx[kk].append(idx)
+
                 else:
                     continue
 
         # use index to gather model predictions for the corresponding systems.
-        model_pred_g = (
-            {
-                kk: [vv[idx] for idx in sorted(list(global_sampled_idx[kk]))]
-                for kk, vv in model_pred.items()
-            }
-            if model_pred
-            else None
-        )
-        model_pred_a = (
-            {
-                kk: [vv[idx] for idx in sorted(list(atomic_sampled_idx[kk]))]
-                for kk, vv in model_pred.items()
-            }
-            if model_pred
-            else None
-        )
+
+        model_pred_g = {kk: [vv[idx] for idx in global_sampled_idx[kk]] for kk, vv in model_pred.items()} if model_pred else None
+        model_pred_a = {kk: [vv[idx] for idx in atomic_sampled_idx[kk]] for kk, vv in model_pred.items()} if model_pred else None
+
         # concat all frames within those systmes
         model_pred_g = (
             {
@@ -396,11 +379,11 @@ def compute_output_stats_global(
         for kk in keys
     }
     # shape: (nframes, ndim)
-    merged_output = {kk: to_numpy_array(torch.cat(outputs[kk])) for kk in keys}
+    merged_output = {kk: to_numpy_array(torch.cat(outputs[kk])) for kk in keys if len(outputs[kk])>0}
     # shape: (nframes, ntypes)
-    merged_natoms = {
-        kk: to_numpy_array(torch.cat(input_natoms[kk])[:, 2:]) for kk in keys
-    }
+
+    merged_natoms = {kk: to_numpy_array(torch.cat(input_natoms[kk])[:, 2:]) for kk in keys if len(input_natoms[kk])>0}
+
     nf = {kk: merged_natoms[kk].shape[0] for kk in keys}
     if preset_bias is not None:
         assigned_atom_ener = {

diff --git a/source/tests/pt/model/test_atomic_bias.py b/source/tests/pt/model/test_atomic_bias.py
@@ -87,7 +87,7 @@ def forward(
                     [4.0, 5.0, 6.0],
                 ]
             )
-            .view([nf, nloc] + self.output_def()["foo"].shape)
+            .view([nf, nloc, *self.output_def()["foo"].shape])
             .to(env.GLOBAL_PT_FLOAT_PRECISION)
             .to(env.DEVICE)
         )
@@ -98,7 +98,7 @@ def forward(
                     [4.0, 5.0, 6.0, 10.0, 11.0, 12.0],
                 ]
             )
-            .view([nf, nloc] + self.output_def()["bar"].shape)
+            .view([nf, nloc, *self.output_def()["bar"].shape])
             .to(env.GLOBAL_PT_FLOAT_PRECISION)
             .to(env.DEVICE)
         )
@@ -198,13 +198,13 @@ def cvt_ret(x):
                 [1.0, 2.0, 3.0],
                 [4.0, 5.0, 6.0],
             ]
-        ).reshape([nf, nloc] + md0.fitting_output_def()["foo"].shape)
+        ).reshape([nf, nloc, *md0.fitting_output_def()["foo"].shape])
         expected_ret0["bar"] = np.array(
             [
                 [1.0, 2.0, 3.0, 7.0, 8.0, 9.0],
                 [4.0, 5.0, 6.0, 10.0, 11.0, 12.0],
             ]
-        ).reshape([nf, nloc] + md0.fitting_output_def()["bar"].shape)
+        ).reshape([nf, nloc, *md0.fitting_output_def()["bar"].shape])
         for kk in ["foo", "bar"]:
             np.testing.assert_almost_equal(ret0[kk], expected_ret0[kk])