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support sharing fitting network parameters (#2211)
Add `layer_name` parameter to share network parameters among different fitting network layers. Signed-off-by: Jinzhe Zeng <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| import numpy as np | ||
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| from deepmd.env import tf | ||
| from common import gen_data, del_data, j_loader | ||
| from common import DataSystem | ||
| from deepmd.descriptor import DescrptSeA | ||
| from deepmd.fit import EnerFitting, DipoleFittingSeA | ||
| from deepmd.model import MultiModel | ||
| from deepmd.common import j_must_have | ||
|
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| GLOBAL_ENER_FLOAT_PRECISION = tf.float64 | ||
| GLOBAL_TF_FLOAT_PRECISION = tf.float64 | ||
| GLOBAL_NP_FLOAT_PRECISION = np.float64 | ||
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| class TestModel(tf.test.TestCase): | ||
| def setUp(self): | ||
| gen_data() | ||
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| def tearDown(self): | ||
| del_data() | ||
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| def test_model(self): | ||
| """Two fittings which share the same parameters should give the same result""" | ||
| jfile = 'water_layer_name.json' | ||
| jdata = j_loader(jfile) | ||
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| 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 | ||
| rcut = j_must_have(jdata['model']['descriptor'], 'rcut') | ||
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| data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) | ||
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| test_data = data.get_test() | ||
| numb_test = 1 | ||
|
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| jdata['model']['descriptor'].pop('type', None) | ||
| jdata['model']['descriptor']['multi_task'] = True | ||
| descrpt = DescrptSeA(**jdata['model']['descriptor'], uniform_seed=True) | ||
| fitting_dict = {} | ||
| fitting_type_dict = {} | ||
| for fitting_key in jdata['model']['fitting_net_dict']: | ||
| item_fitting_param = jdata['model']['fitting_net_dict'][fitting_key] | ||
| item_fitting_type = item_fitting_param.get('type', 'ener') | ||
| fitting_type_dict[fitting_key] = item_fitting_type | ||
| item_fitting_param.pop('type', None) | ||
| item_fitting_param.pop('fit_diag', None) | ||
| item_fitting_param['descrpt'] = descrpt | ||
| if item_fitting_type == 'ener': | ||
| fitting_dict[fitting_key] = EnerFitting(**item_fitting_param, uniform_seed=True) | ||
| elif item_fitting_type == 'dipole': | ||
| fitting_dict[fitting_key] = DipoleFittingSeA(**item_fitting_param, uniform_seed=True) | ||
| else: | ||
| raise RuntimeError('Test should not be here!') | ||
| model = MultiModel(descrpt, fitting_dict, fitting_type_dict) | ||
|
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| 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']] | ||
| } | ||
|
|
||
| for fitting_key in jdata['model']['fitting_net_dict']: | ||
| model._compute_input_stat(input_data, fitting_key=fitting_key) | ||
| model.descrpt.merge_input_stats(model.descrpt.stat_dict) | ||
| model.descrpt.bias_atom_e = data.compute_energy_shift() | ||
|
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| 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 | ||
|
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| model_pred \ | ||
| = model.build(t_coord, | ||
| t_type, | ||
| t_natoms, | ||
| t_box, | ||
| t_mesh, | ||
| t_fparam, | ||
| suffix="_layer_name", | ||
| reuse=False) | ||
|
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| e_energy1 = model_pred['water_ener']['energy'] | ||
| e_force1 = model_pred['water_ener']['force'] | ||
| e_virial1 = model_pred['water_ener']['virial'] | ||
| e_energy2 = model_pred['water_ener2']['energy'] | ||
| e_force2 = model_pred['water_ener2']['force'] | ||
| e_virial2 = model_pred['water_ener2']['virial'] | ||
| 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} | ||
|
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| with self.test_session() as sess: | ||
| sess.run(tf.global_variables_initializer()) | ||
| [e1, f1, v1, e2, f2, v2] = sess.run( | ||
| [e_energy1, e_force1, e_virial1, e_energy2, e_force2, e_virial2], | ||
| feed_dict=feed_dict_test) | ||
| np.testing.assert_allclose(e1, e2, rtol=1e-5, atol=1e-5) | ||
| np.testing.assert_allclose(f1, f2, rtol=1e-5, atol=1e-5) | ||
| np.testing.assert_allclose(v1, v2, rtol=1e-5, atol=1e-5) |
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