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deepmd/dpmodel/fitting/property_fitting.py

@@ -1,14 +1,20 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import copy
 from typing import (
-    Callable,
-    Union,
     TYPE_CHECKING,
     Any,
+    Callable,
     List,
     Optional,
+    Union,
 )
 
+from deepmd.dpmodel.common import (
+    DEFAULT_PRECISION,
+)
+from deepmd.dpmodel.fitting.invar_fitting import (
+    InvarFitting,
+)
 from deepmd.dpmodel.output_def import (
     FittingOutputDef,
     OutputVariableDef,
@@ -17,17 +23,11 @@
     DPPath,
 )
 
-from deepmd.dpmodel.common import (
-    DEFAULT_PRECISION,
-)
-from deepmd.dpmodel.fitting.invar_fitting import (
-    InvarFitting,
-)
-
 if TYPE_CHECKING:
     from deepmd.dpmodel.fitting.general_fitting import (
         GeneralFitting,
     )
+
 from deepmd.utils.version import (
     check_version_compatibility,
 )
@@ -91,11 +91,7 @@ def deserialize(cls, data: dict) -> "GeneralFitting":
 
     def serialize(self) -> dict:
         """Serialize the fitting to dict."""
-        return {
-            **super().serialize(),
-            "type": "property",
-            "task_num": self.task_num
-        }
+        return {**super().serialize(), "type": "property", "task_num": self.task_num}
 
     def output_def(self) -> FittingOutputDef:
         return FittingOutputDef(
@@ -134,4 +130,4 @@ def compute_output_stats(
         pass
 
     # make jit happy with torch 2.0.0
-    exclude_types: List[int]
+    exclude_types: List[int]

deepmd/infer/deep_property.py

@@ -133,4 +133,5 @@ def eval(
     def get_numb_task(self) -> int:
         return self.deep_eval.get_numb_task()
 
+
 __all__ = ["DeepProperty"]

deepmd/pt/infer/deep_eval.py

@@ -35,12 +35,12 @@
 from deepmd.infer.deep_pot import (
     DeepPot,
 )
-from deepmd.infer.deep_wfc import (
-    DeepWFC,
-)
 from deepmd.infer.deep_property import (
     DeepProperty,
 )
+from deepmd.infer.deep_wfc import (
+    DeepWFC,
+)
 from deepmd.pt.model.model import (
     get_model,
 )

deepmd/pt/loss/__init__.py

@@ -11,12 +11,12 @@
 from .loss import (
     TaskLoss,
 )
-from .tensor import (
-    TensorLoss,
-)
 from .property import (
     PropertyLoss,
 )
+from .tensor import (
+    TensorLoss,
+)
 
 __all__ = [
     "DenoiseLoss",

deepmd/pt/loss/property.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+import logging
 from typing import (
     List,
 )
@@ -12,15 +13,13 @@
 from deepmd.pt.utils import (
     env,
 )
-from deepmd.pt.utils.env import (
-    GLOBAL_PT_FLOAT_PRECISION,
-)
 from deepmd.utils.data import (
     DataRequirementItem,
 )
-import logging
+
 log = logging.getLogger(__name__)
 
+
 class PropertyLoss(TaskLoss):
     def __init__(
         self,
@@ -33,11 +32,11 @@ def __init__(
 
         Parameters
         ----------
-        starter_learning_rate : float
+        task_num : float
             The learning rate at the start of the training.
-        loss_func: str
+        loss_func : str
             The loss function, such as "smooth_mae", "mae", "rmse"
-        metric: list
+        metric : list
             The metric such as mae,rmse which will be printed.
         **kwargs
             Other keyword arguments.
@@ -62,35 +61,77 @@ def forward(self, model_pred, label, natoms, learning_rate, mae=False):
         -------
         - loss: Loss to minimize.
         """
-        assert label['property'].shape[-1] == self.task_num
-        assert model_pred['property'].shape[-1] == self.task_num
+        assert label["property"].shape[-1] == self.task_num
+        assert model_pred["property"].shape[-1] == self.task_num
         loss = torch.zeros(1, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)[0]
         more_loss = {}
 
-        label_mean = torch.tensor(self.mean, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)
-        label_std = torch.tensor(self.std, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)
+        label_mean = torch.tensor(
+            self.mean, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE
+        )
+        label_std = torch.tensor(
+            self.std, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE
+        )
 
         # loss
         if self.loss_func == "smooth_mae":
-            loss += F.smooth_l1_loss((label['property'] - label_mean) / label_std, model_pred['property'], reduction="sum", beta=self.beta)
+            loss += F.smooth_l1_loss(
+                (label["property"] - label_mean) / label_std,
+                model_pred["property"],
+                reduction="sum",
+                beta=self.beta,
+            )
         elif self.func == "mae":
-            loss += F.l1_loss((label['property'] - label_mean) / label_std, model_pred['property'], reduction="sum")
+            loss += F.l1_loss(
+                (label["property"] - label_mean) / label_std,
+                model_pred["property"],
+                reduction="sum",
+            )
         elif self.func == "mse":
-            loss += F.mse_loss((label['property'] - label_mean) / label_std, model_pred['property'], reduction="sum")
+            loss += F.mse_loss(
+                (label["property"] - label_mean) / label_std,
+                model_pred["property"],
+                reduction="sum",
+            )
         elif self.func == "rmse":
-            loss += torch.sqrt(F.mse_loss((label['property'] - label_mean) / label_std, model_pred['property'], reduction="mean"))
+            loss += torch.sqrt(
+                F.mse_loss(
+                    (label["property"] - label_mean) / label_std,
+                    model_pred["property"],
+                    reduction="mean",
+                )
+            )
         else:
             raise RuntimeError(f"Unknown loss function : {self.func}")
 
         # more loss
         if "smooth_mae" in self.metric:
-            more_loss["smooth_mae"] = F.smooth_l1_loss(label['property'], (model_pred['property'] * label_std) + label_mean, reduction="mean", beta=self.beta).detach()
+            more_loss["smooth_mae"] = F.smooth_l1_loss(
+                label["property"],
+                (model_pred["property"] * label_std) + label_mean,
+                reduction="mean",
+                beta=self.beta,
+            ).detach()
         if "mae" in self.metric:
-            more_loss['mae'] = F.l1_loss(label['property'], (model_pred['property'] * label_std) + label_mean, reduction="mean").detach()
+            more_loss["mae"] = F.l1_loss(
+                label["property"],
+                (model_pred["property"] * label_std) + label_mean,
+                reduction="mean",
+            ).detach()
         if "mse" in self.metric:
-            more_loss['mse'] = F.mse_loss(label['property'], (model_pred['property'] * label_std) + label_mean, reduction="mean").detach()
+            more_loss["mse"] = F.mse_loss(
+                label["property"],
+                (model_pred["property"] * label_std) + label_mean,
+                reduction="mean",
+            ).detach()
         if "rmse" in self.metric:
-            more_loss['rmse'] = torch.sqrt(F.mse_loss(label['property'], (model_pred['property'] * label_std) + label_mean, reduction="mean")).detach()
+            more_loss["rmse"] = torch.sqrt(
+                F.mse_loss(
+                    label["property"],
+                    (model_pred["property"] * label_std) + label_mean,
+                    reduction="mean",
+                )
+            ).detach()
 
         return loss, more_loss
 

deepmd/pt/model/model/__init__.py

@@ -140,7 +140,7 @@ def get_standard_model(model_params):
     fitting_net["type"] = fitting_net.get("type", "ener")
     fitting_net["ntypes"] = descriptor.get_ntypes()
     fitting_net["mixed_types"] = descriptor.mixed_types()
-    if fitting_net["type"] in ["dipole", "polar","denoise"]:
+    if fitting_net["type"] in ["dipole", "polar", "denoise"]:
         fitting_net["embedding_width"] = descriptor.get_dim_emb()
     fitting_net["dim_descrpt"] = descriptor.get_dim_out()
     grad_force = "direct" not in fitting_net["type"]

deepmd/pt/model/model/denoise_model.py

@@ -37,7 +37,7 @@ def forward(
             fparam=fparam,
             aparam=aparam,
             do_atomic_virial=do_atomic_virial,
-        )    
+        )
         model_predict = model_ret
         return model_predict
 
@@ -78,4 +78,4 @@ def forward_lower(
                 model_predict["dforce"] = model_ret["dforce"]
         else:
             model_predict = model_ret
-        return model_predict
+        return model_predict

deepmd/pt/model/model/dp_model.py

@@ -15,6 +15,9 @@
 from deepmd.pt.model.model.model import (
     BaseModel,
 )
+from deepmd.pt.model.task.denoise import (
+    DenoiseFittingNet,
+)
 from deepmd.pt.model.task.dipole import (
     DipoleFittingNet,
 )
@@ -31,9 +34,6 @@
 from deepmd.pt.model.task.property import (
     PropertyFittingNet,
 )
-from deepmd.pt.model.task.denoise import (
-    DenoiseFittingNet
-)
 
 from .make_model import (
     make_model,
@@ -51,6 +51,9 @@ def __new__(
         atomic_model_: Optional[DPAtomicModel] = None,
         **kwargs,
     ):
+        from deepmd.pt.model.model.denoise_model import (
+            DenoiseModel,
+        )
         from deepmd.pt.model.model.dipole_model import (
             DipoleModel,
         )
@@ -66,9 +69,6 @@ def __new__(
         from deepmd.pt.model.model.property_model import (
             PropertyModel,
         )
-        from deepmd.pt.model.model.denoise_model import (
-            DenoiseModel,
-        )
 
         if atomic_model_ is not None:
             fitting = atomic_model_.fitting_net

deepmd/pt/model/model/property_model.py

@@ -40,7 +40,7 @@ def forward(
         )
         model_predict = {}
         model_predict["atom_property"] = model_ret["property"]
-        model_predict["property"] = model_ret["property_redu"]/atype.shape[-1]
+        model_predict["property"] = model_ret["property_redu"] / atype.shape[-1]
         if "mask" in model_ret:
             model_predict["mask"] = model_ret["mask"]
         return model_predict

deepmd/pt/model/task/__init__.py

@@ -21,12 +21,12 @@
 from .polarizability import (
     PolarFittingNet,
 )
-from .type_predict import (
-    TypePredictNet,
-)
 from .property import (
     PropertyFittingNet,
 )
+from .type_predict import (
+    TypePredictNet,
+)
 
 __all__ = [
     "FittingNetAttenLcc",