add UT for type_one_side

Author: Han Wang

source/tests/test_descrpt_se_a_type.py

@@ -20,7 +20,7 @@ class TestModel(unittest.TestCase):
     def setUp(self) :
         gen_data()
 
-    def test_model(self):
+    def test_descriptor_two_sides(self):
         jfile = 'water_se_a_type.json'
         jdata = j_loader(jfile)
 
@@ -82,7 +82,7 @@ def test_model(self):
 
         type_embedding = typeebd.build(
             ntypes, 
-            suffix = "_se_a_type_des_ebd"
+            suffix = "_se_a_type_des_ebd_2sdies"
         )
 
         dout \
@@ -94,7 +94,7 @@ def test_model(self):
                 t_mesh,
                 {'type_embedding' : type_embedding},
                 reuse = False,
-                suffix = "_se_a_type_des"
+                suffix = "_se_a_type_des_2sides"
             )
 
         feed_dict_test = {t_prop_c:        test_data['prop_c'],
@@ -126,6 +126,115 @@ def test_model(self):
         for ii in range(model_dout.size) :
             self.assertAlmostEqual(model_dout[ii], ref_dout[ii], places = places)
 
+
+    def test_descriptor_one_side(self):
+        jfile = 'water_se_a_type.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')
+        sel = j_must_have (jdata['model']['descriptor'], 'sel')
+        ntypes=len(sel)
+        
+        data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt = None)
+        
+        test_data = data.get_test ()
+        numb_test = 1
+
+        # set parameters
+        jdata['model']['descriptor']['neuron'] = [5, 5, 5]
+        jdata['model']['descriptor']['axis_neuron'] = 2
+        jdata['model']['descriptor']['type_one_side'] = True
+        typeebd_param = {'neuron' : [5, 5, 5], 
+                         'resnet_dt': False,
+                         'seed': 1,
+        }
+
+        # init models
+        typeebd = TypeEmbedNet(
+            neuron = typeebd_param['neuron'],
+            resnet_dt = typeebd_param['resnet_dt'],
+            seed = typeebd_param['seed'], 
+        )
+
+        jdata['model']['descriptor'].pop('type', None)        
+        descrpt = DescrptSeA(**jdata['model']['descriptor'])
+
+        # 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']]
+        }
+        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,   [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
+
+        type_embedding = typeebd.build(
+            ntypes, 
+            suffix = "_se_a_type_des_ebd_1side"
+        )
+
+        dout \
+            = descrpt.build(
+                t_coord,
+                t_type,
+                t_natoms,
+                t_box,
+                t_mesh,
+                {'type_embedding' : type_embedding},
+                reuse = False,
+                suffix = "_se_a_type_des_1side"
+            )
+
+        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 = tf.Session()
+        sess.run(tf.global_variables_initializer())
+        [model_dout] = sess.run([dout], 
+                             feed_dict = feed_dict_test)
+        model_dout = model_dout.reshape([-1])
+
+        ref_dout = [0.0009704469114440277,0.0007136310372560243,0.0007136310372560243,0.000524968274824758,-0.0019790100690810016,-0.0014556100390424947,-0.001318691223889266,-0.0009698525512440269,0.001937780602605409,
+                    0.0014251755182315322,0.0008158935519461114,0.0005943870925895051,0.0005943870925895051,0.0004340263490412088,-0.0016539827195947239,-0.0012066241021841376,-0.0011042186455562336,-0.0008051343572505189,
+                    0.0016229491738044255,0.0011833923257801077,0.0006020440527161554,0.00047526899287409847,0.00047526899287409847,0.00037538142786805136,-0.0012811397377036637,-0.0010116898098710776,-0.0008465095301785942,
+                    -0.0006683577463042215,0.0012459039620461505,0.0009836962283627838,0.00077088529431722,0.0006105807630364827,0.0006105807630364827,0.00048361458700877996,-0.0016444700616024337,-0.001302510079662288,
+                    -0.0010856603485807576,-0.0008598975276238373,0.00159730642327918,0.001265146946434076,0.0008495806081447204,0.000671787466824433,0.000671787466824433,0.0005312928157964384,-0.0018105890543181475,
+                    -0.001431844407277983,-0.0011956722392735362,-0.000945544277375045,0.0017590147511761475,0.0013910348287283414,0.0007393644735054756,0.0005850536182149991,0.0005850536182149991,0.0004631887654949332,
+                    -0.0015760302086346792,-0.0012475134925387294,-0.001041074331192672,-0.0008239586048523492,0.0015319673563669856,0.0012124704278707746]
+    
+        places = 10
+        for ii in range(model_dout.size) :
+            self.assertAlmostEqual(model_dout[ii], ref_dout[ii], places = places)
+
 
 
 

source/tests/test_fitting_ener_type.py

@@ -19,7 +19,7 @@ class TestModel(unittest.TestCase):
     def setUp(self) :
         gen_data()
 
-    def test_model(self):
+    def test_fitting(self):
         jfile = 'water_se_a_type.json'
         jdata = j_loader(jfile)