remove more annotations in code

README.md

@@ -25,7 +25,7 @@
 3. 安装 deepmd-kit
 
     ``` sh
-    git clone https://github.com/HydrogenSulfate/deepmd-kit.git -b add_ddle_backend
+    git clone https://github.com/deepmodeling/deepmd-kit.git -b paddle2
     cd deepmd-kit
     # 以 editable 的方式安装，方便调试
     pip install -e . -i https://pypi.tuna.tsinghua.edu.cn/simple

deepmd/descriptor/se_a.py

@@ -162,7 +162,7 @@ def __init__(
         self.compress_activation_fn = get_activation_func(activation_function)
         self.filter_activation_fn = get_activation_func(activation_function)
         self.filter_precision = get_precision(precision)
-        self.exclude_types = set()  # empty
+        self.exclude_types = set()
         for tt in exclude_types:
             assert len(tt) == 2
             self.exclude_types.add((tt[0], tt[1]))
@@ -194,12 +194,12 @@ def __init__(
         assert self.ntypes == len(self.sel_r)
         self.rcut_a = -1
         # numb of neighbors and numb of descrptors
-        self.nnei_a = np.cumsum(self.sel_a)[-1]  # 138 邻域内原子个数
-        self.nnei_r = np.cumsum(self.sel_r)[-1]  # 0
-        self.nnei = self.nnei_a + self.nnei_r  # 138
-        self.ndescrpt_a = self.nnei_a * 4  # 552 原子个数*4([s, s/x, s/y, s/z])
-        self.ndescrpt_r = self.nnei_r * 1  # 0
-        self.ndescrpt = self.ndescrpt_a + self.ndescrpt_r  # 552
+        self.nnei_a = np.cumsum(self.sel_a)[-1]
+        self.nnei_r = np.cumsum(self.sel_r)[-1]
+        self.nnei = self.nnei_a + self.nnei_r
+        self.ndescrpt_a = self.nnei_a * 4
+        self.ndescrpt_r = self.nnei_r * 1
+        self.ndescrpt = self.ndescrpt_a + self.ndescrpt_r
         self.useBN = False
         self.dstd = None
         self.davg = None
@@ -215,7 +215,6 @@ def __init__(
             [self.ntypes, self.ndescrpt], dtype=GLOBAL_PD_FLOAT_PRECISION
         )
         nets = []
-        # self._pass_filter => self._filter => self._filter_lower
         for type_input in range(self.ntypes):
             layer = []
             for type_i in range(self.ntypes):
@@ -587,7 +586,7 @@ def forward(
             suffix=suffix,
             reuse=reuse,
             trainable=self.trainable,
-        )  # [1, all_atom, M1*M2], output_qmat: [1, all_atom, M1*3]
+        )
 
         return self.dout
 
@@ -701,7 +700,7 @@ def _pass_filter(
                     reuse=reuse,
                     trainable=trainable,
                     activation_fn=self.filter_activation_fn,
-                )  # [natom, M1*M2], qmat: [natom, M1, 3]
+                )
                 layer = paddle.reshape(
                     layer, [inputs.shape[0], natoms[2 + type_i], self.get_dim_out()]
                 )

deepmd/entrypoints/test.py

@@ -260,7 +260,7 @@ def test_ener(
     data.add("energy", 1, atomic=False, must=False, high_prec=True)
     data.add("force", 3, atomic=True, must=False, high_prec=False)
     data.add("virial", 9, atomic=False, must=False, high_prec=False)
-    if dp.has_efield:  # False
+    if dp.has_efield:
         data.add("efield", 3, atomic=True, must=True, high_prec=False)
     if has_atom_ener:
         data.add("atom_ener", 1, atomic=True, must=True, high_prec=False)
@@ -278,7 +278,6 @@ def test_ener(
     numb_test = min(nframes, numb_test)
 
     coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
-
     box = test_data["box"][:numb_test]
     if dp.has_efield:
         efield = test_data["efield"][:numb_test].reshape([numb_test, -1])

deepmd/entrypoints/train.py

@@ -207,7 +207,7 @@ def _do_work(jdata: Dict[str, Any], run_opt: RunOptions, is_compress: bool = Fal
     dp_random.seed(seed)
 
     # setup data modifier
-    modifier = get_modifier(jdata["model"].get("modifier", None))  # None
+    modifier = get_modifier(jdata["model"].get("modifier", None))
 
     # check the multi-task mode
     multi_task_mode = "fitting_net_dict" in jdata["model"]
@@ -275,7 +275,6 @@ def _do_work(jdata: Dict[str, Any], run_opt: RunOptions, is_compress: bool = Fal
         origin_type_map = get_data(
             jdata["training"]["training_data"], rcut, None, modifier
         ).get_type_map()
-    print("model.build")
     model.build(train_data, stop_batch, origin_type_map=origin_type_map)
 
     if not is_compress:
@@ -377,7 +376,7 @@ def get_nbor_stat(jdata, rcut, one_type: bool = False):
     if type_map and len(type_map) == 0:
         type_map = None
     multi_task_mode = "data_dict" in jdata["training"]
-    if not multi_task_mode:  # here
+    if not multi_task_mode:
         train_data = get_data(
             jdata["training"]["training_data"], max_rcut, type_map, None
         )
@@ -496,7 +495,7 @@ def update_sel(jdata):
     if descrpt_data["type"] == "hybrid":
         for ii in range(len(descrpt_data["list"])):
             descrpt_data["list"][ii] = update_one_sel(jdata, descrpt_data["list"][ii])
-    else:  # here
+    else:
         descrpt_data = update_one_sel(jdata, descrpt_data)
     jdata["model"]["descriptor"] = descrpt_data
     return jdata

deepmd/fit/ener.py

@@ -494,7 +494,7 @@ def _build_lower(
         if (not self.uniform_seed) and (self.seed is not None):
             self.seed += self.seed_shift
 
-        return final_layer  # [natoms, 1]
+        return final_layer
 
     def forward(
         self,