[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

deepmd/descriptor/hybrid.py

@@ -243,10 +243,8 @@ def build(
             The output descriptor
         """
         with tf.variable_scope("descrpt_attr" + suffix, reuse=reuse):
-            t_rcut = tf.constant(
-                self.get_rcut(), name="rcut", dtype=GLOBAL_TF_FLOAT_PRECISION
-            )
-            t_ntypes = tf.constant(self.get_ntypes(), name="ntypes", dtype=tf.int32)
+            tf.constant(self.get_rcut(), name="rcut", dtype=GLOBAL_TF_FLOAT_PRECISION)
+            tf.constant(self.get_ntypes(), name="ntypes", dtype=tf.int32)
         all_dout = []
         for idx, ii in enumerate(self.descrpt_list):
             dout = ii.build(

deepmd/descriptor/loc_frame.py

@@ -258,12 +258,12 @@ def build(
                 davg = np.zeros([self.ntypes, self.ndescrpt])
             if dstd is None:
                 dstd = np.ones([self.ntypes, self.ndescrpt])
-            t_rcut = tf.constant(
+            tf.constant(
                 np.max([self.rcut_r, self.rcut_a]),
                 name="rcut",
                 dtype=GLOBAL_TF_FLOAT_PRECISION,
             )
-            t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
+            tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
             self.t_avg = tf.get_variable(
                 "t_avg",
                 davg.shape,

deepmd/descriptor/se_a.py

@@ -556,13 +556,13 @@ def build(
                 davg = np.zeros([self.ntypes, self.ndescrpt])
             if dstd is None:
                 dstd = np.ones([self.ntypes, self.ndescrpt])
-            t_rcut = tf.constant(
+            tf.constant(
                 np.max([self.rcut_r, self.rcut_a]),
                 name="rcut",
                 dtype=GLOBAL_TF_FLOAT_PRECISION,
             )
-            t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
-            t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
+            tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
+            tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
             t_sel = tf.constant(self.sel_a, name="sel", dtype=tf.int32)
             t_original_sel = tf.constant(
                 self.original_sel if self.original_sel is not None else self.sel_a,

deepmd/descriptor/se_a_ebd.py

@@ -229,7 +229,7 @@ def _embedding_net(
         """
         # natom x (nei x 4)
         inputs = tf.reshape(inputs, [-1, self.ndescrpt])
-        shape = inputs.get_shape().as_list()
+        inputs.get_shape().as_list()
         outputs_size = [1, *filter_neuron]
         with tf.variable_scope(name, reuse=reuse):
             xyz_scatter_total = []

deepmd/descriptor/se_a_ef.py

@@ -470,14 +470,14 @@ def build(
                 davg = np.zeros([self.ntypes, self.ndescrpt])
             if dstd is None:
                 dstd = np.ones([self.ntypes, self.ndescrpt])
-            t_rcut = tf.constant(
+            tf.constant(
                 np.max([self.rcut_r, self.rcut_a]),
                 name="rcut",
                 dtype=GLOBAL_TF_FLOAT_PRECISION,
             )
-            t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
-            t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
-            t_sel = tf.constant(self.sel_a, name="sel", dtype=tf.int32)
+            tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
+            tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
+            tf.constant(self.sel_a, name="sel", dtype=tf.int32)
             self.t_avg = tf.get_variable(
                 "t_avg",
                 davg.shape,

deepmd/descriptor/se_a_mask.py

@@ -175,12 +175,8 @@ def __init__(
             nei_type = np.append(nei_type, ii * np.ones(self.sel_a[ii]))  # like a mask
         self.nei_type = tf.constant(nei_type, dtype=tf.int32)
 
-        avg_zero = np.zeros([self.ntypes, self.ndescrpt]).astype(
-            GLOBAL_NP_FLOAT_PRECISION
-        )
-        std_ones = np.ones([self.ntypes, self.ndescrpt]).astype(
-            GLOBAL_NP_FLOAT_PRECISION
-        )
+        np.zeros([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION)
+        np.ones([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION)
         sub_graph = tf.Graph()
         with sub_graph.as_default():
             name_pfx = "d_sea_mask_"
@@ -306,7 +302,7 @@ def build(
         """
         aparam = input_dict["aparam"]
         with tf.variable_scope("fitting_attr" + suffix, reuse=reuse):
-            t_aparam_nall = tf.constant(True, name="aparam_nall", dtype=tf.bool)
+            tf.constant(True, name="aparam_nall", dtype=tf.bool)
         self.mask = tf.cast(aparam, tf.int32)
         self.mask = tf.reshape(self.mask, [-1, natoms[1]])
 
@@ -315,14 +311,14 @@ def build(
                 davg = np.zeros([self.ntypes, self.ndescrpt])
             if dstd is None:
                 dstd = np.ones([self.ntypes, self.ndescrpt])
-            t_rcut = tf.constant(
+            tf.constant(
                 self.rcut,
                 name="rcut",
                 dtype=GLOBAL_TF_FLOAT_PRECISION,
             )
-            t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
-            t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
-            t_sel = tf.constant(self.sel_a, name="sel", dtype=tf.int32)
+            tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
+            tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
+            tf.constant(self.sel_a, name="sel", dtype=tf.int32)
             """
             self.t_avg = tf.get_variable('t_avg',
                                          davg.shape,

deepmd/descriptor/se_atten.py

@@ -556,13 +556,13 @@ def build(
                 davg = np.zeros([self.ntypes, self.ndescrpt])
             if dstd is None:
                 dstd = np.ones([self.ntypes, self.ndescrpt])
-            t_rcut = tf.constant(
+            tf.constant(
                 np.max([self.rcut_r, self.rcut_a]),
                 name="rcut",
                 dtype=GLOBAL_TF_FLOAT_PRECISION,
             )
-            t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
-            t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
+            tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
+            tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
             t_sel = tf.constant(self.sel_a, name="sel", dtype=tf.int32)
             t_original_sel = tf.constant(
                 self.original_sel if self.original_sel is not None else self.sel_a,
@@ -1257,7 +1257,7 @@ def _filter(
         reuse=None,
         trainable=True,
     ):
-        nframes = tf.shape(tf.reshape(inputs, [-1, natoms[0], self.ndescrpt]))[0]
+        tf.shape(tf.reshape(inputs, [-1, natoms[0], self.ndescrpt]))[0]
         # natom x (nei x 4)
         shape = inputs.get_shape().as_list()
         outputs_size = [1, *self.filter_neuron]

deepmd/descriptor/se_r.py

@@ -417,12 +417,10 @@ def build(
                 davg = np.zeros([self.ntypes, self.ndescrpt])
             if dstd is None:
                 dstd = np.ones([self.ntypes, self.ndescrpt])
-            t_rcut = tf.constant(
-                self.rcut, name="rcut", dtype=GLOBAL_TF_FLOAT_PRECISION
-            )
-            t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
-            t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
-            t_sel = tf.constant(self.sel_a, name="sel", dtype=tf.int32)
+            tf.constant(self.rcut, name="rcut", dtype=GLOBAL_TF_FLOAT_PRECISION)
+            tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
+            tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
+            tf.constant(self.sel_a, name="sel", dtype=tf.int32)
             self.t_avg = tf.get_variable(
                 "t_avg",
                 davg.shape,

deepmd/descriptor/se_t.py

@@ -432,14 +432,14 @@ def build(
                 davg = np.zeros([self.ntypes, self.ndescrpt])
             if dstd is None:
                 dstd = np.ones([self.ntypes, self.ndescrpt])
-            t_rcut = tf.constant(
+            tf.constant(
                 np.max([self.rcut_r, self.rcut_a]),
                 name="rcut",
                 dtype=GLOBAL_TF_FLOAT_PRECISION,
             )
-            t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
-            t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
-            t_sel = tf.constant(self.sel_a, name="sel", dtype=tf.int32)
+            tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
+            tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
+            tf.constant(self.sel_a, name="sel", dtype=tf.int32)
             self.t_avg = tf.get_variable(
                 "t_avg",
                 davg.shape,
@@ -545,10 +545,8 @@ def prod_force_virial(
     def _pass_filter(
         self, inputs, atype, natoms, input_dict, reuse=None, suffix="", trainable=True
     ):
-        start_index = 0
         inputs = tf.reshape(inputs, [-1, natoms[0], self.ndescrpt])
         output = []
-        output_qmat = []
         inputs_i = inputs
         inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
         type_i = -1
@@ -632,7 +630,7 @@ def _filter(
         trainable=True,
     ):
         # natom x (nei x 4)
-        shape = inputs.get_shape().as_list()
+        inputs.get_shape().as_list()
         outputs_size = [1, *self.filter_neuron]
         with tf.variable_scope(name, reuse=reuse):
             start_index_i = 0

deepmd/entrypoints/compress.py

@@ -186,7 +186,7 @@ def compress(
 def _check_compress_type(graph: tf.Graph):
     try:
         t_model_type = bytes.decode(get_tensor_by_name_from_graph(graph, "model_type"))
-    except GraphWithoutTensorError as e:
+    except GraphWithoutTensorError:
         # Compatible with the upgraded model, which has no 'model_type' info
         t_model_type = None
 

deepmd/entrypoints/test.py

@@ -101,7 +101,6 @@ def test(
     if len(all_sys) == 0:
         raise RuntimeError("Did not find valid system")
     err_coll = []
-    siz_coll = []
 
     # init random seed
     if rand_seed is not None:

deepmd/fit/dos.py

@@ -426,9 +426,9 @@ def build(
                 self.aparam_inv_std = 1.0
 
         with tf.variable_scope("fitting_attr" + suffix, reuse=reuse):
-            t_dfparam = tf.constant(self.numb_fparam, name="dfparam", dtype=tf.int32)
-            t_daparam = tf.constant(self.numb_aparam, name="daparam", dtype=tf.int32)
-            t_numb_dos = tf.constant(self.numb_dos, name="numb_dos", dtype=tf.int32)
+            tf.constant(self.numb_fparam, name="dfparam", dtype=tf.int32)
+            tf.constant(self.numb_aparam, name="daparam", dtype=tf.int32)
+            tf.constant(self.numb_dos, name="numb_dos", dtype=tf.int32)
 
             self.t_bias_dos = tf.get_variable(
                 "t_bias_dos",
@@ -530,7 +530,6 @@ def build(
             inputs = tf.concat(
                 [tf.reshape(inputs, [-1, self.dim_descrpt]), atype_embed], axis=1
             )
-            original_dim_descrpt = self.dim_descrpt
             self.dim_descrpt = self.dim_descrpt + type_shape[1]
             inputs = tf.reshape(inputs, [-1, natoms[0], self.dim_descrpt])
             final_layer = self._build_lower(

deepmd/fit/ener.py

@@ -515,8 +515,8 @@ def build(
             self.bias_atom_e = self.bias_atom_e[:ntypes_atom]
 
         with tf.variable_scope("fitting_attr" + suffix, reuse=reuse):
-            t_dfparam = tf.constant(self.numb_fparam, name="dfparam", dtype=tf.int32)
-            t_daparam = tf.constant(self.numb_aparam, name="daparam", dtype=tf.int32)
+            tf.constant(self.numb_fparam, name="dfparam", dtype=tf.int32)
+            tf.constant(self.numb_aparam, name="daparam", dtype=tf.int32)
             self.t_bias_atom_e = tf.get_variable(
                 "t_bias_atom_e",
                 self.bias_atom_e.shape,

deepmd/fit/polar.py

@@ -163,7 +163,7 @@ def compute_input_stats(self, all_stat, protection=1e-2):
 
         # YWolfeee: support polar normalization, initialize to a more appropriate point
         if self.shift_diag:
-            mean_polar = np.zeros([len(self.sel_type), 9])
+            np.zeros([len(self.sel_type), 9])
             sys_matrix, polar_bias = [], []
             for ss in range(len(all_stat["type"])):
                 atom_has_polar = [

deepmd/infer/data_modifier.py

@@ -91,34 +91,29 @@ def __init__(
     def build_fv_graph(self) -> tf.Tensor:
         """Build the computational graph for the force and virial inference."""
         with tf.variable_scope("modifier_attr"):
-            t_mdl_name = tf.constant(self.model_name, name="mdl_name", dtype=tf.string)
-            t_modi_type = tf.constant(
-                self.modifier_prefix, name="type", dtype=tf.string
-            )
-            t_mdl_charge_map = tf.constant(
+            tf.constant(self.model_name, name="mdl_name", dtype=tf.string)
+            tf.constant(self.modifier_prefix, name="type", dtype=tf.string)
+            tf.constant(
                 " ".join([str(ii) for ii in self.model_charge_map]),
                 name="mdl_charge_map",
                 dtype=tf.string,
             )
-            t_sys_charge_map = tf.constant(
+            tf.constant(
                 " ".join([str(ii) for ii in self.sys_charge_map]),
                 name="sys_charge_map",
                 dtype=tf.string,
             )
-            t_ewald_h = tf.constant(self.ewald_h, name="ewald_h", dtype=tf.float64)
-            t_ewald_b = tf.constant(
-                self.ewald_beta, name="ewald_beta", dtype=tf.float64
-            )
+            tf.constant(self.ewald_h, name="ewald_h", dtype=tf.float64)
+            tf.constant(self.ewald_beta, name="ewald_beta", dtype=tf.float64)
         with self.graph.as_default():
             return self._build_fv_graph_inner()
 
     def _build_fv_graph_inner(self):
         self.t_ef = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_ef")
         nf = 10
-        nfxnas = 64 * nf
+        64 * nf
         nfxna = 192 * nf
         nf = -1
-        nfxnas = -1
         nfxna = -1
         self.t_box_reshape = tf.reshape(self.t_box, [-1, 9])
         t_nframes = tf.shape(self.t_box_reshape)[0]
@@ -348,7 +343,6 @@ def _eval_fv(self, coords, cells, atom_types, ext_f):
         default_mesh = make_default_mesh(True, False)
 
         # evaluate
-        tensor = []
         feed_dict_test = {}
         feed_dict_test[self.t_natoms] = natoms_vec
         feed_dict_test[self.t_type] = np.tile(atom_types, [nframes, 1]).reshape([-1])
@@ -436,7 +430,7 @@ def modify_data(self, data: dict, data_sys: DeepmdData) -> None:
         box = data["box"][:get_nframes, :]
         atype = data["type"][:get_nframes, :]
         atype = atype[0]
-        nframes = coord.shape[0]
+        coord.shape[0]
 
         tot_e, tot_f, tot_v = self.eval(coord, box, atype)
 

deepmd/loss/dos.py

@@ -100,7 +100,7 @@ def build(self, learning_rate, natoms, model_dict, label_dict, suffix):
                 tf.square(diff_atom_cdf), name="l2_acdf_" + suffix
             )
 
-        atom_norm = 1.0 / global_cvt_2_tf_float(natoms[0])
+        1.0 / global_cvt_2_tf_float(natoms[0])
         atom_norm_ener = 1.0 / global_cvt_2_ener_float(natoms[0])
         pref_dos = global_cvt_2_ener_float(
             find_dos

deepmd/loss/ener.py

@@ -440,7 +440,7 @@ def build(self, learning_rate, natoms, model_dict, label_dict, suffix):
         force_label = label_dict["force"]
         virial_label = label_dict["virial"]
         atom_ener_label = label_dict["atom_ener"]
-        atom_pref = label_dict["atom_pref"]
+        label_dict["atom_pref"]
         find_energy = label_dict["find_energy"]
         find_force = label_dict["find_force"]
         find_virial = label_dict["find_virial"]

deepmd/model/dos.py

@@ -144,10 +144,10 @@ def build(
         if input_dict is None:
             input_dict = {}
         with tf.variable_scope("model_attr" + suffix, reuse=reuse):
-            t_tmap = tf.constant(" ".join(self.type_map), name="tmap", dtype=tf.string)
-            t_mt = tf.constant(self.model_type, name="model_type", dtype=tf.string)
-            t_ver = tf.constant(MODEL_VERSION, name="model_version", dtype=tf.string)
-            t_od = tf.constant(self.numb_dos, name="output_dim", dtype=tf.int32)
+            tf.constant(" ".join(self.type_map), name="tmap", dtype=tf.string)
+            tf.constant(self.model_type, name="model_type", dtype=tf.string)
+            tf.constant(MODEL_VERSION, name="model_version", dtype=tf.string)
+            tf.constant(self.numb_dos, name="output_dim", dtype=tf.int32)
 
         coord = tf.reshape(coord_, [-1, natoms[1] * 3])
         atype = tf.reshape(atype_, [-1, natoms[1]])

deepmd/model/ener.py

@@ -176,9 +176,9 @@ def build(
         if input_dict is None:
             input_dict = {}
         with tf.variable_scope("model_attr" + suffix, reuse=reuse):
-            t_tmap = tf.constant(" ".join(self.type_map), name="tmap", dtype=tf.string)
-            t_mt = tf.constant(self.model_type, name="model_type", dtype=tf.string)
-            t_ver = tf.constant(MODEL_VERSION, name="model_version", dtype=tf.string)
+            tf.constant(" ".join(self.type_map), name="tmap", dtype=tf.string)
+            tf.constant(self.model_type, name="model_type", dtype=tf.string)
+            tf.constant(MODEL_VERSION, name="model_version", dtype=tf.string)
 
             if self.srtab is not None:
                 tab_info, tab_data = self.srtab.get()
@@ -211,7 +211,7 @@ def build(
             input_dict["type_embedding"] = type_embedding
         # spin if any
         if self.spin is not None:
-            type_spin = self.spin.build(
+            self.spin.build(
                 reuse=reuse,
                 suffix=suffix,
             )