Merge branch 'refactor_property' of github.com:Chengqian-Zhang/deepmd…

…-kit into refactor_property

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -460,9 +460,7 @@ def change_out_bias(
             self._store_out_stat(delta_bias, out_std, add=True)
         elif bias_adjust_mode == "set-by-statistic":
             property_name = (
-                self.fitting_net.property_name
-                if "property" in self.bias_keys
-                else None
+                self.fitting_net.property_name if "property" in self.bias_keys else None
             )
             bias_out, std_out = compute_output_stats(
                 sample_merged,

doc/model/train-fitting-property.md

@@ -43,7 +43,7 @@ The JSON of `property` type should be provided like
 
 ## Loss
 
-DeepProperty supports trainings of the global system (one or more global labels are provided in a frame). For example, when fitting `property`, each frame will provide a `1 x task_dim` vector which gives the fitting properties. 
+DeepProperty supports trainings of the global system (one or more global labels are provided in a frame). For example, when fitting `property`, each frame will provide a `1 x task_dim` vector which gives the fitting properties.
 
 The loss section should be provided like
 
@@ -64,6 +64,7 @@ The loss section should be provided like
 The label should be named `{property_name}.npy/raw`, `property_name` is defined by `fitting_net/property_name` in `input.json`.
 
 To prepare the data, you can use `dpdata` tools, for example:
+
 ```
 import dpdata
 import numpy as np
@@ -133,7 +134,7 @@ dp --pt train input.json
 The detailed loss can be found in `lcurve.out`:
 
 ```
-# step        mae_val     mae_trn   lr      
+# step        mae_val     mae_trn   lr
 # If there is no available reference data, rmse_*_{val,trn} will print nan
       1      2.72e-02    2.40e-02    2.0e-04
     100      1.79e-02    1.34e-02    2.0e-04
@@ -183,10 +184,12 @@ for `*.property.out.*`, it contains matrix with shape of `(2, task_dim)`,
 ```
 
 ## Data Normalization
-When `fitting_net/type` is `ener`, the energy bias layer “$e_{bias}$” adds a constant bias to the atomic energy contribution according to the atomic number.i.e., 
+
+When `fitting_net/type` is `ener`, the energy bias layer “$e_{bias}$” adds a constant bias to the atomic energy contribution according to the atomic number.i.e.,
 $$e_{bias} (Z_i) (MLP(D_i))= MLP(D_i) + e_{bias} (Z_i)$$
 
 But when `fitting_net/type` is `property`. The property bias layer is used to normalize the property output of the model.i.e.,
 $$p_{bias} (MLP(D_i))= MLP(D_i) * std+ mean$$
+
 1. `std`: The standard deviation of the property label
-2. `mean`: The average value of the property label
+2. `mean`: The average value of the property label

examples/property/train/README.md

@@ -1,4 +1,5 @@
 Some explanations of the parameters in `input.json`:
+
 1. `fitting_net/property_name` is the name of the property to be predicted. It should be consistent with the property name in the dataset. In each system, code will read `set.*/{property_name}.npy` file as prediction label if you use NumPy format data.
 2. `fitting_net/task_dim` is the dimension of model output. It should be consistent with the property dimension in the dataset, which means if the shape of data stored in `set.*/{property_name}.npy` is `batch size * 3`, `fitting_net/task_dim` should be set to 3.
-3. `fitting/intensive` indicates whether the fitting property is intensive. If `intensive` is `true`, the model output is the average of the property contribution of each atom. If `intensive` is `false`, the model output is the sum of the property contribution of each atom.
+3. `fitting/intensive` indicates whether the fitting property is intensive. If `intensive` is `true`, the model output is the average of the property contribution of each atom. If `intensive` is `false`, the model output is the sum of the property contribution of each atom.

examples/property/train/input_torch.json

@@ -54,7 +54,9 @@
   },
   "loss": {
     "type": "property",
-    "metric": ["mae"],
+    "metric": [
+      "mae"
+    ],
     "loss_func": "smooth_mae",
     "beta": 1.0,
     "_comment": " that's all"