Test se_a_ebd to increase test coverage

source/tests/test_model_se_a_ebd.py

@@ -0,0 +1,167 @@
+import numpy as np
+from common import (
+    DataSystem,
+    gen_data,
+    j_loader,
+)
+
+from deepmd.common import (
+    j_must_have,
+)
+from deepmd.descriptor.se_a_ebd import (
+    DescrptSeAEbd,
+)
+from deepmd.env import (
+    tf,
+)
+from deepmd.fit import (
+    EnerFitting,
+)
+from deepmd.model import (
+    EnerModel,
+)
+
+GLOBAL_ENER_FLOAT_PRECISION = tf.float64
+GLOBAL_TF_FLOAT_PRECISION = tf.float64
+GLOBAL_NP_FLOAT_PRECISION = np.float64
+
+
+class TestModel(tf.test.TestCase):
+    def setUp(self):
+        gen_data()
+
+    def test_model(self):
+        jfile = "water_se_a_ebd.json"
+        jdata = j_loader(jfile)
+
+        systems = j_must_have(jdata, "systems")
+        set_pfx = j_must_have(jdata, "set_prefix")
+        batch_size = j_must_have(jdata, "batch_size")
+        test_size = j_must_have(jdata, "numb_test")
+        batch_size = 1
+        test_size = 1
+        stop_batch = j_must_have(jdata, "stop_batch")
+        rcut = j_must_have(jdata["model"]["descriptor"], "rcut")
+
+        data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None)
+
+        test_data = data.get_test()
+        numb_test = 1
+
+        jdata["model"]["descriptor"].pop("type", None)
+        descrpt = DescrptSeAEbd(
+            **jdata["model"]["descriptor"],
+        )
+        jdata["model"]["fitting_net"]["descrpt"] = descrpt
+        fitting = EnerFitting(
+            **jdata["model"]["fitting_net"],
+        )
+        # fitting = EnerFitting(jdata['model']['fitting_net'], descrpt)
+        model = EnerModel(descrpt, fitting)
+
+        # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']])
+        input_data = {
+            "coord": [test_data["coord"]],
+            "box": [test_data["box"]],
+            "type": [test_data["type"]],
+            "natoms_vec": [test_data["natoms_vec"]],
+            "default_mesh": [test_data["default_mesh"]],
+        }
+        model._compute_input_stat(input_data)
+        model.descrpt.bias_atom_e = data.compute_energy_shift()
+
+        t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c")
+        t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy")
+        t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force")
+        t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial")
+        t_atom_ener = tf.placeholder(
+            GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener"
+        )
+        t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord")
+        t_type = tf.placeholder(tf.int32, [None], name="i_type")
+        t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2], name="i_natoms")
+        t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box")
+        t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh")
+        is_training = tf.placeholder(tf.bool)
+        t_fparam = None
+
+        model_pred = model.build(
+            t_coord,
+            t_type,
+            t_natoms,
+            t_box,
+            t_mesh,
+            t_fparam,
+            suffix="se_a_ebd",
+            reuse=False,
+        )
+        energy = model_pred["energy"]
+        force = model_pred["force"]
+        virial = model_pred["virial"]
+        atom_ener = model_pred["atom_ener"]
+
+        feed_dict_test = {
+            t_prop_c: test_data["prop_c"],
+            t_energy: test_data["energy"][:numb_test],
+            t_force: np.reshape(test_data["force"][:numb_test, :], [-1]),
+            t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]),
+            t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]),
+            t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]),
+            t_box: test_data["box"][:numb_test, :],
+            t_type: np.reshape(test_data["type"][:numb_test, :], [-1]),
+            t_natoms: test_data["natoms_vec"],
+            t_mesh: test_data["default_mesh"],
+            is_training: False,
+        }
+
+        sess = self.test_session().__enter__()
+        sess.run(tf.global_variables_initializer())
+        [e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test)
+
+        e = e.reshape([-1])
+        f = f.reshape([-1])
+        v = v.reshape([-1])
+
+        refe = [-4.0809183546731935]
+        reff = [
+            -0.0009433080242679126,
+            0.0024540766182440917,
+            7.134654644656728e-05,
+            0.0012476799379696184,
+            0.002212567421017593,
+            -0.0007091290731634111,
+            0.009875291762629728,
+            -0.007876013249122177,
+            -8.78061284553672e-05,
+            -0.013879889764531257,
+            0.005100427326599536,
+            0.00027143866516841334,
+            0.003799286895370519,
+            -0.007567683893582063,
+            0.00024200485149578332,
+            -9.906080717069433e-05,
+            0.005676625776843024,
+            0.00021214513850801415,
+        ]
+        refv = [
+            0.034972620377374586,
+            -0.01539771296182217,
+            -0.0008639588474713173,
+            -0.015397712961822166,
+            0.011757303581302702,
+            0.0005455501828665077,
+            -0.0008639588474713181,
+            0.0005455501828665083,
+            -6.180425284925767e-07,
+        ]
+        refe = np.reshape(refe, [-1])
+        reff = np.reshape(reff, [-1])
+        refv = np.reshape(refv, [-1])
+
+        places = 6
+        for ii in range(e.size):
+            self.assertAlmostEqual(e[ii], refe[ii], places=places)
+        for ii in range(f.size):
+            self.assertAlmostEqual(f[ii], reff[ii], places=places)
+        for ii in range(v.size):
+            self.assertAlmostEqual(v[ii], refv[ii], places=places)

source/tests/water_se_a_ebd.json

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+{
+  "_comment": " model parameters",
+  "model": {
+    "descriptor": {
+      "type": "se_a_ebd",
+      "sel": [
+        46,
+        92
+      ],
+      "rcut_smth": 5.80,
+      "rcut": 6.00,
+      "neuron": [
+        25,
+        50,
+        100
+      ],
+      "resnet_dt": false,
+      "seed": 1
+    },
+    "fitting_net": {
+      "neuron": [
+        240,
+        240,
+        240
+      ],
+      "resnet_dt": true,
+      "seed": 1
+    }
+  },
+
+  "_comment": " traing controls",
+  "systems": [
+    "system"
+  ],
+  "set_prefix": "set",
+  "stop_batch": 100000,
+  "batch_size": 1,
+  "start_lr": 0.005,
+  "decay_steps": 5000,
+  "decay_rate": 0.95,
+
+  "start_pref_e": 0.02,
+  "limit_pref_e": 1,
+  "start_pref_f": 1000,
+  "limit_pref_f": 1,
+  "start_pref_v": 0,
+  "limit_pref_v": 0,
+
+  "seed": 1,
+
+  "_comment": " display and restart",
+  "_comment": " frequencies counted in batch",
+  "disp_file": "lcurve.out",
+  "disp_freq": 100,
+  "numb_test": 1,
+  "save_freq": 1000,
+  "save_ckpt": "model.ckpt",
+  "load_ckpt": "model.ckpt",
+  "disp_training": true,
+  "time_training": true,
+  "profiling": false,
+  "profiling_file": "timeline.json",
+
+  "_comment": "that's all"
+}