def test_model(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')
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 = DescrptSeA(**jdata['model']['descriptor'], uniform_seed = True)
jdata['model']['fitting_net']['descrpt'] = descrpt
fitting = EnerFitting(**jdata['model']['fitting_net'], uniform_seed = True)
typeebd_param = jdata['model']['type_embedding']
typeebd = TypeEmbedNet(
neuron = typeebd_param['neuron'],
resnet_dt = typeebd_param['resnet_dt'],
seed = typeebd_param['seed'],
uniform_seed = True)
model = EnerModel(descrpt, fitting, typeebd)
# 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
inputs_dict = {}
model_pred \
= model.build (t_coord,
t_type,
t_natoms,
t_box,
t_mesh,
inputs_dict,
suffix = "se_a_type",
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)
# print(sess.run(model.type_embedding))
# np.savetxt('tmp.out', sess.run(descrpt.dout, feed_dict = feed_dict_test), fmt='%.10e')
# # print(sess.run(model.atype_embed, feed_dict = feed_dict_test))
# print(sess.run(fitting.inputs, feed_dict = feed_dict_test))
# print(sess.run(fitting.outs, feed_dict = feed_dict_test))
# print(sess.run(fitting.atype_embed, feed_dict = feed_dict_test))
e = e.reshape([-1])
f = f.reshape([-1])
v = v.reshape([-1])
np.savetxt('e.out', e.reshape([1, -1]), delimiter=',')
np.savetxt('f.out', f.reshape([1, -1]), delimiter=',')
np.savetxt('v.out', v.reshape([1, -1]), delimiter=',')
refe = [6.049065170680415804e+01]
reff = [1.021832439441947293e-01,1.122650466359011306e-01,3.927874278714531091e-03,1.407089812207832635e-01,1.312473824343091400e-01,-1.228371057389851181e-02,-1.109672154547165501e-01,6.582735820731049070e-02,1.251568633647655391e-03,7.933758749748777428e-02,-1.831777072317984367e-01,-6.173090134630876760e-03,-2.703597126460742794e-01,4.817856571062521104e-02,1.491963457594796399e-02,5.909711543832503466e-02,-1.743406457563475287e-01,-1.642276779780762769e-03]
refv = [-6.932736357193732823e-01,1.453756052949563837e-01,2.138263139115256783e-02,1.453756052949564392e-01,-3.880901656480436612e-01,-7.782259726407755700e-03,2.138263139115256437e-02,-7.782259726407749628e-03,-1.225285973678705374e-03]
refe = np.reshape(refe, [-1])
reff = np.reshape(reff, [-1])
refv = np.reshape(refv, [-1])
places = 10
np.testing.assert_almost_equal(e, refe, places)
np.testing.assert_almost_equal(f, reff, places)
np.testing.assert_almost_equal(v, refv, places)
def _init_param(self, jdata):
# model config
model_param = j_must_have(jdata, 'model')
descrpt_param = j_must_have(model_param, 'descriptor')
fitting_param = j_must_have(model_param, 'fitting_net')
typeebd_param = model_param.get('type_embedding', None)
self.model_param = model_param
self.descrpt_param = descrpt_param
# descriptor
try:
descrpt_type = descrpt_param['type']
except KeyError:
raise KeyError('the type of descriptor should be set by `type`')
self.descrpt = Descriptor(**descrpt_param)
# fitting net
fitting_type = fitting_param.get('type', 'ener')
self.fitting_type = fitting_type
fitting_param.pop('type', None)
fitting_param['descrpt'] = self.descrpt
if fitting_type == 'ener':
self.fitting = EnerFitting(**fitting_param)
# elif fitting_type == 'wfc':
# self.fitting = WFCFitting(fitting_param, self.descrpt)
elif fitting_type == 'dipole':
if descrpt_type == 'se_e2_a':
self.fitting = DipoleFittingSeA(**fitting_param)
else :
raise RuntimeError('fitting dipole only supports descrptors: se_e2_a')
elif fitting_type == 'polar':
# if descrpt_type == 'loc_frame':
# self.fitting = PolarFittingLocFrame(fitting_param, self.descrpt)
if descrpt_type == 'se_e2_a':
self.fitting = PolarFittingSeA(**fitting_param)
else :
raise RuntimeError('fitting polar only supports descrptors: loc_frame and se_e2_a')
elif fitting_type == 'global_polar':
if descrpt_type == 'se_e2_a':
self.fitting = GlobalPolarFittingSeA(**fitting_param)
else :
raise RuntimeError('fitting global_polar only supports descrptors: loc_frame and se_e2_a')
else :
raise RuntimeError('unknow fitting type ' + fitting_type)
# type embedding
if typeebd_param is not None:
self.typeebd = TypeEmbedNet(
neuron=typeebd_param['neuron'],
resnet_dt=typeebd_param['resnet_dt'],
activation_function=typeebd_param['activation_function'],
precision=typeebd_param['precision'],
trainable=typeebd_param['trainable'],
seed=typeebd_param['seed']
)
else:
self.typeebd = None
# init model
# infer model type by fitting_type
if fitting_type == 'ener':
self.model = EnerModel(
self.descrpt,
self.fitting,
self.typeebd,
model_param.get('type_map'),
model_param.get('data_stat_nbatch', 10),
model_param.get('data_stat_protect', 1e-2),
model_param.get('use_srtab'),
model_param.get('smin_alpha'),
model_param.get('sw_rmin'),
model_param.get('sw_rmax')
)
# elif fitting_type == 'wfc':
# self.model = WFCModel(model_param, self.descrpt, self.fitting)
elif fitting_type == 'dipole':
self.model = DipoleModel(
self.descrpt,
self.fitting,
model_param.get('type_map'),
model_param.get('data_stat_nbatch', 10),
model_param.get('data_stat_protect', 1e-2)
)
elif fitting_type == 'polar':
self.model = PolarModel(
self.descrpt,
self.fitting,
model_param.get('type_map'),
model_param.get('data_stat_nbatch', 10),
model_param.get('data_stat_protect', 1e-2)
)
elif fitting_type == 'global_polar':
self.model = GlobalPolarModel(
self.descrpt,
self.fitting,
model_param.get('type_map'),
model_param.get('data_stat_nbatch', 10),
model_param.get('data_stat_protect', 1e-2)
)
else :
raise RuntimeError('get unknown fitting type when building model')
# learning rate
lr_param = j_must_have(jdata, 'learning_rate')
scale_by_worker = lr_param.get('scale_by_worker', 'linear')
if scale_by_worker == 'linear':
self.scale_lr_coef = float(self.run_opt.world_size)
elif scale_by_worker == 'sqrt':
self.scale_lr_coef = np.sqrt(self.run_opt.world_size).real
else:
self.scale_lr_coef = 1.
lr_type = lr_param.get('type', 'exp')
if lr_type == 'exp':
self.lr = LearningRateExp(lr_param['start_lr'],
lr_param['stop_lr'],
lr_param['decay_steps'])
else :
raise RuntimeError('unknown learning_rate type ' + lr_type)
# loss
# infer loss type by fitting_type
loss_param = jdata.get('loss', None)
loss_type = loss_param.get('type', 'ener')
if fitting_type == 'ener':
loss_param.pop('type', None)
loss_param['starter_learning_rate'] = self.lr.start_lr()
if loss_type == 'ener':
self.loss = EnerStdLoss(**loss_param)
elif loss_type == 'ener_dipole':
self.loss = EnerDipoleLoss(**loss_param)
else:
raise RuntimeError('unknow loss type')
elif fitting_type == 'wfc':
self.loss = TensorLoss(loss_param,
model = self.model,
tensor_name = 'wfc',
tensor_size = self.model.get_out_size(),
label_name = 'wfc')
elif fitting_type == 'dipole':
self.loss = TensorLoss(loss_param,
model = self.model,
tensor_name = 'dipole',
tensor_size = 3,
label_name = 'dipole')
elif fitting_type == 'polar':
self.loss = TensorLoss(loss_param,
model = self.model,
tensor_name = 'polar',
tensor_size = 9,
label_name = 'polarizability')
elif fitting_type == 'global_polar':
self.loss = TensorLoss(loss_param,
model = self.model,
tensor_name = 'global_polar',
tensor_size = 9,
atomic = False,
label_name = 'polarizability')
else :
raise RuntimeError('get unknown fitting type when building loss function')
# training
tr_data = jdata['training']
self.disp_file = tr_data.get('disp_file', 'lcurve.out')
self.disp_freq = tr_data.get('disp_freq', 1000)
self.save_freq = tr_data.get('save_freq', 1000)
self.save_ckpt = tr_data.get('save_ckpt', 'model.ckpt')
self.display_in_training = tr_data.get('disp_training', True)
self.timing_in_training = tr_data.get('time_training', True)
self.profiling = self.run_opt.is_chief and tr_data.get('profiling', False)
self.profiling_file = tr_data.get('profiling_file', 'timeline.json')
self.tensorboard = self.run_opt.is_chief and tr_data.get('tensorboard', False)
self.tensorboard_log_dir = tr_data.get('tensorboard_log_dir', 'log')
self.tensorboard_freq = tr_data.get('tensorboard_freq', 1)
# self.sys_probs = tr_data['sys_probs']
# self.auto_prob_style = tr_data['auto_prob']
self.useBN = False
if fitting_type == 'ener' and self.fitting.get_numb_fparam() > 0 :
self.numb_fparam = self.fitting.get_numb_fparam()
else :
self.numb_fparam = 0
if tr_data.get("validation_data", None) is not None:
self.valid_numb_batch = tr_data["validation_data"].get("numb_btch", 1)
else:
self.valid_numb_batch = 1
# if init the graph with the frozen model
self.frz_model = None
self.model_type = None
def test_model(self):
jfile = 'water_se_a_fparam.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 = DescrptSeA(**jdata['model']['descriptor'], uniform_seed=True)
jdata['model']['fitting_net']['descrpt'] = descrpt
fitting = EnerFitting(**jdata['model']['fitting_net'],
uniform_seed=True)
# descrpt = DescrptSeA(jdata['model']['descriptor'])
# 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']],
'fparam': [test_data['fparam']],
}
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')
t_fparam = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None],
name='i_fparam')
is_training = tf.placeholder(tf.bool)
input_dict = {}
input_dict['fparam'] = t_fparam
model_pred\
= model.build (t_coord,
t_type,
t_natoms,
t_box,
t_mesh,
input_dict,
suffix = "se_a_fparam",
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'],
t_fparam: np.reshape(test_data['fparam'][:numb_test, :], [-1]),
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 = [61.35473702079649]
reff = [
7.789591210641927388e-02, 9.411176646369459609e-02,
3.785806413688173194e-03, 1.430830954178063386e-01,
1.146964190520970150e-01, -1.320340288927138173e-02,
-7.308720494747594776e-02, 6.508269338140809657e-02,
5.398739145542804643e-04, 5.863268336973800898e-02,
-1.603409523950408699e-01, -5.083084610994957619e-03,
-2.551569799443983988e-01, 3.087934885732580501e-02,
1.508590526622844222e-02, 4.863249399791078065e-02,
-1.444292753594846324e-01, -1.125098094204559241e-03
]
refv = [
-6.069498397488943819e-01, 1.101778888191114192e-01,
1.981907430646132409e-02, 1.101778888191114608e-01,
-3.315612988100872793e-01, -5.999739184898976799e-03,
1.981907430646132756e-02, -5.999739184898974197e-03,
-1.198656608172396325e-03
]
refe = np.reshape(refe, [-1])
reff = np.reshape(reff, [-1])
refv = np.reshape(refv, [-1])
places = 10
np.testing.assert_almost_equal(e, refe, places)
np.testing.assert_almost_equal(f, reff, places)
np.testing.assert_almost_equal(v, refv, places)
def test_model(self):
jfile = 'water_se_a_srtab.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 = DescrptSeA(**jdata['model']['descriptor'], uniform_seed = True)
jdata['model']['fitting_net']['descrpt'] = descrpt
fitting = EnerFitting(**jdata['model']['fitting_net'], uniform_seed = True)
# descrpt = DescrptSeA(jdata['model']['descriptor'])
# fitting = EnerFitting(jdata['model']['fitting_net'], descrpt)
model = EnerModel(
descrpt,
fitting,
None,
jdata['model'].get('type_map'),
jdata['model'].get('data_stat_nbatch'),
jdata['model'].get('data_stat_protect'),
jdata['model'].get('use_srtab'),
jdata['model'].get('smin_alpha'),
jdata['model'].get('sw_rmin'),
jdata['model'].get('sw_rmax')
)
# 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_srtab",
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 = [1.141610882066236599e+02]
reff = [-1.493121233165248043e+02,-1.831419491743885715e+02,-8.439542992300344437e+00,-1.811987095947552859e+02,-1.476380826187439084e+02,1.264271856742560018e+01,1.544377958934875323e+02,-7.816520233903435866e+00,1.287925245463442225e+00,-4.000393268449002449e+00,1.910748885843098890e+02,7.134789955349889468e+00,1.826908441979261113e+02,3.677156386479059513e+00,-1.122312112141401741e+01,-2.617413911684622008e+00,1.438445070562470391e+02,-1.402769654524568033e+00]
refv = [3.585047655925112622e+02,-7.569252978336677984e+00,-1.068382043878426124e+01,-7.569252978336677096e+00,3.618439481685132932e+02,5.448668500896081568e+00,-1.068382043878426302e+01,5.448668500896082456e+00,1.050393462151727686e+00]
refe = np.reshape(refe, [-1])
reff = np.reshape(reff, [-1])
refv = np.reshape(refv, [-1])
places = 10
np.testing.assert_almost_equal(e, refe, places)
np.testing.assert_almost_equal(f, reff, places)
np.testing.assert_almost_equal(v, refv, places)
def test_model(self):
jfile = 'water_se_t.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 = DescrptSeT(**jdata['model']['descriptor'], uniform_seed=True)
jdata['model']['fitting_net']['descrpt'] = descrpt
fitting = EnerFitting(**jdata['model']['fitting_net'],
uniform_seed=True)
model = EnerModel(descrpt, fitting)
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_t",
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])
np.savetxt('e.out', e.reshape([1, -1]))
np.savetxt('f.out', f.reshape([1, -1]), delimiter=',')
np.savetxt('v.out', v.reshape([1, -1]), delimiter=',')
refe = [4.8436558582194039e+01]
reff = [
5.2896335066946598e+00, 5.5778402259211131e+00,
2.6839994229557251e-01, 5.3528786387686784e+00,
5.2477755362164968e+00, -4.0486366542657343e-01,
-5.1297084055340498e+00, 3.4607112287117253e-01,
-5.1800783428369482e-02, 1.5557068351407846e-01,
-5.9071343228741506e+00, -2.2012359669589748e-01,
-5.9156735320857488e+00, 8.8397615509389127e-02,
3.6701215949753935e-01, 2.4729910864238122e-01,
-5.3529501776440211e+00, 4.1375943757728552e-02
]
refv = [
-1.3159448660141607e+01, 4.6952048725161544e-01,
3.5482003698976106e-01, 4.6952048725161577e-01,
-1.2178990983673918e+01, -1.6867277410496895e-01,
3.5482003698976106e-01, -1.6867277410496900e-01,
-3.3986741457321945e-02
]
refe = np.reshape(refe, [-1])
reff = np.reshape(reff, [-1])
refv = np.reshape(refv, [-1])
places = 6
np.testing.assert_almost_equal(e, refe, places)
np.testing.assert_almost_equal(f, reff, places)
np.testing.assert_almost_equal(v, refv, places)
def test_model(self):
jfile = 'water_se_r.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 = DescrptSeR(**jdata['model']['descriptor'], uniform_seed=True)
jdata['model']['fitting_net']['descrpt'] = descrpt
fitting = EnerFitting(**jdata['model']['fitting_net'],
uniform_seed=True)
# 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_r",
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 = [6.152085988309423925e+01]
reff = [
-1.714443151616400110e-04, -1.315836609370952051e-04,
-5.584120460897444674e-06, -7.197863450669731334e-05,
-1.384609799994930676e-04, 8.856091902774708468e-06,
1.120578238869146797e-04, -7.428703645877488470e-05,
9.370560731488587317e-07, -1.048347129617610465e-04,
1.977876923815685781e-04, 7.522050342771599598e-06,
2.361772659657814205e-04, -5.774651813388292487e-05,
-1.233143271630744828e-05, 2.257277740226381951e-08,
2.042905031476775584e-04, 6.003548585097267914e-07
]
refv = [
1.035180911513190792e-03, -1.118982949050497126e-04,
-2.383287813436022850e-05, -1.118982949050497126e-04,
4.362023915782403281e-04, 8.119543218224559240e-06,
-2.383287813436022850e-05, 8.119543218224559240e-06,
1.201142938802945237e-06
]
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)