def test_integration_y0(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/ode/integration_y0.yml'))
main_setting.trainer.gpu_id = 1
if main_setting.trainer.output_directory.exists():
shutil.rmtree(main_setting.trainer.output_directory)
tr = trainer.Trainer(main_setting)
loss = tr.train()
self.assertLess(loss, 1e-2)
ir = inferer.Inferer(
main_setting,
converter_parameters_pkl=main_setting.data.preprocessed_root /
'preprocessors.pkl')
results = ir.infer(
model=main_setting.trainer.output_directory,
data_directories=main_setting.data.preprocessed_root / 'test')
self.assertLess(results[0]['loss'], 1e-1)
if PLOT:
cmap = plt.get_cmap('tab10')
for i, result in enumerate(results):
plt.plot(result['dict_x']['t'][:, 0, 0],
result['dict_x']['y0'][:, 0, 0],
':',
color=cmap(i),
label=f"y0 answer of data {i}")
plt.plot(result['dict_x']['t'][:, 0, 0],
result['dict_y']['y0'][:, 0, 0],
color=cmap(i),
label=f"y0 inferred of data {i}")
plt.legend()
plt.show()
def test_iso_gcn_rotation_thermal_stress_dict_input_dict_output(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/rotation_thermal_stress'
'/iso_gcn_dict_input_dict_output.yml'))
tr = trainer.Trainer(main_setting)
if tr.setting.trainer.output_directory.exists():
shutil.rmtree(tr.setting.trainer.output_directory)
tr.train()
# Confirm inference result has isometric equivariance
original_path = Path(
'tests/data/rotation_thermal_stress/preprocessed/cube/original')
transformed_paths = self.collect_transformed_paths(
'tests/data/rotation_thermal_stress/preprocessed/cube'
'/*_transformed_*')
ir = inferer.Inferer(
main_setting,
save=False,
converter_parameters_pkl=Path(
'tests/data/rotation_thermal_stress/preprocessed'
'/preprocessors.pkl'))
model_directory = str(main_setting.trainer.output_directory)
original_results = ir.infer(model=model_directory,
data_directories=[original_path])
transformed_results = ir.infer(model=model_directory,
data_directories=transformed_paths)
self.validate_results(original_results,
transformed_results,
rank0='cnt_temperature',
rank2='global_lte_mat')
def test_iso_gcn_rotation_thermal_stress_frame_rank2(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/rotation_thermal_stress/iso_gcn_frame_rank2.yml'))
tr = trainer.Trainer(main_setting)
if tr.setting.trainer.output_directory.exists():
shutil.rmtree(tr.setting.trainer.output_directory)
tr.train()
# Confirm inference result has isometric invariance and equivariance
original_path = Path(
'tests/data/rotation_thermal_stress/preprocessed/cube/original')
transformed_paths = self.collect_transformed_paths(
'tests/data/rotation_thermal_stress/preprocessed/cube'
'/*_transformed_*')
ir = inferer.Inferer(
main_setting,
save=False,
converter_parameters_pkl=Path(
'tests/data/rotation_thermal_stress/preprocessed'
'/preprocessors.pkl'))
model_directory = str(main_setting.trainer.output_directory)
inference_outpout_directory = \
main_setting.trainer.output_directory / 'inferred'
if inference_outpout_directory.exists():
shutil.rmtree(inference_outpout_directory)
original_results = ir.infer(model=model_directory,
data_directories=[original_path])
transformed_results = ir.infer(model=model_directory,
data_directories=transformed_paths)
self.validate_results(original_results,
transformed_results,
rank2='nodal_strain_mat',
threshold_percent=.002)
def test_iso_gcn_rank0_rank0(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/rotation/iso_gcn_rank0_rank0.yml'))
tr = trainer.Trainer(main_setting)
if tr.setting.trainer.output_directory.exists():
shutil.rmtree(tr.setting.trainer.output_directory)
loss = tr.train()
np.testing.assert_array_less(loss, 1.)
self.assertEqual(len(tr.model.dict_block['ISO_GCN1'].subchains), 1)
self.assertEqual(len(tr.model.dict_block['ISO_GCN2'].subchains), 1)
# Confirm results does not change under rigid body transformation
original_path = Path(
'tests/data/rotation/preprocessed/cube/clscale1.0/original')
transformed_paths = self.collect_transformed_paths(
'tests/data/rotation/preprocessed/cube/clscale1.0/rotated')
ir = inferer.Inferer(
main_setting,
save=False,
converter_parameters_pkl=Path(
'tests/data/rotation/preprocessed/preprocessors.pkl'))
model_directory = str(main_setting.trainer.output_directory)
original_results = ir.infer(model=model_directory,
data_directories=[original_path])
transformed_results = ir.infer(model=model_directory,
data_directories=transformed_paths)
self.validate_results(original_results,
transformed_results,
rank0='t_100')
def test_iso_gcn_rotation_thermal_stress_dict_input_list_output(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/rotation_thermal_stress/iso_gcn_dict_input.yml'))
tr = trainer.Trainer(main_setting)
if tr.setting.trainer.output_directory.exists():
shutil.rmtree(tr.setting.trainer.output_directory)
loss = tr.train()
np.testing.assert_array_less(loss, 5.)
# Confirm inference result has isometric equivariance
original_path = Path(
'tests/data/rotation_thermal_stress/preprocessed/cube/original')
transformed_paths = self.collect_transformed_paths(
'tests/data/rotation_thermal_stress/preprocessed/cube'
'/*_transformed_*')
ir = inferer.Inferer(
main_setting,
save=False,
converter_parameters_pkl=Path(
'tests/data/rotation_thermal_stress/preprocessed'
'/preprocessors.pkl'))
model_directory = str(main_setting.trainer.output_directory)
original_results = ir.infer(model=model_directory,
data_directories=[original_path])
transformed_results = ir.infer(model=model_directory,
data_directories=transformed_paths)
self.validate_results(original_results,
transformed_results,
rank2='nodal_strain_mat',
threshold_percent=2.,
decimal=1)
def test_iso_gcn_rotation_thermal_stress_rank2_rank0(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/rotation_thermal_stress/iso_gcn_rank2_rank0.yml'))
tr = trainer.Trainer(main_setting)
if tr.setting.trainer.output_directory.exists():
shutil.rmtree(tr.setting.trainer.output_directory)
loss = tr.train()
np.testing.assert_array_less(loss, .5)
# Confirm inference result has isometric invariance
original_path = Path(
'tests/data/rotation_thermal_stress/preprocessed/cube/original')
transformed_paths = self.collect_transformed_paths(
'tests/data/rotation_thermal_stress/preprocessed/cube'
'/*_transformed_*')
ir = inferer.Inferer(
main_setting,
save=False,
converter_parameters_pkl=Path(
'tests/data/rotation_thermal_stress/preprocessed/'
'preprocessors.pkl'))
model_directory = str(main_setting.trainer.output_directory)
inference_outpout_directory = \
main_setting.trainer.output_directory / 'inferred'
if inference_outpout_directory.exists():
shutil.rmtree(inference_outpout_directory)
original_results = ir.infer(model=model_directory,
data_directories=[original_path])
transformed_results = ir.infer(model=model_directory,
data_directories=transformed_paths)
self.validate_results(original_results,
transformed_results,
rank0='initial_temperature',
decimal=5)
def test_infer_with_raw_data_wo_answer_w_model_directory(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/deform/pretrained/settings.yml'))
def conversion_function(fem_data, raw_directory=None):
adj = fem_data.calculate_adjacency_matrix_element()
nadj = prepost.normalize_adjacency_matrix(adj)
return {'adj': adj, 'nadj': nadj}
ir = inferer.Inferer(
main_setting, conversion_function=conversion_function)
ir.setting.inferer.converter_parameters_pkl = Path(
'tests/data/deform/preprocessed/preprocessors.pkl')
ir.setting.inferer.save = False
ir.setting.inferer.perform_preprocess = True
if ir.setting.trainer.output_directory.exists():
shutil.rmtree(ir.setting.trainer.output_directory)
res_from_raw = ir.infer(
model=Path('tests/data/deform/pretrained'),
data_directories=Path(
'tests/data/deform/external/tet2_4_modulusx0.9500'))
ir.setting.inferer.perform_preprocess = False
res_from_preprocessed = ir.infer(
model=Path('tests/data/deform/pretrained'),
data_directories=Path(
'tests/data/deform/preprocessed/test/tet2_4_modulusx0.9500'))
np.testing.assert_almost_equal(
res_from_raw[0]['dict_y']['elemental_stress'],
res_from_preprocessed[0]['dict_y']['elemental_stress'],
decimal=3)
def test_infer_to_write_simulation_file(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/deform/incomplete_pretrained/settings.yml'))
output_directory = Path('tests/data/deform/write_simulation')
ir = inferer.Inferer(main_setting)
if output_directory.exists():
shutil.rmtree(output_directory)
ir.setting.inferer.converter_parameters_pkl = Path(
'tests/data/deform/preprocessed/preprocessors.pkl')
ir.setting.inferer.output_directory = output_directory
ir.setting.inferer.write_simulation_base = Path(
'tests/data/deform/raw')
ir.setting.inferer.write_simulation = True
ir.setting.inferer.write_simulation_type = 'ucd'
res_from_preprocessed = ir.infer(
model=Path('tests/data/deform/pretrained'),
data_directories=Path(
'tests/data/deform/preprocessed/test/'
'tet2_4_modulusx0.9500'))
fem_data = femio.FEMData.read_files(
'ucd', [output_directory / 'mesh.inp'])
np.testing.assert_almost_equal(
fem_data.elemental_data.get_attribute_data(
'inferred_elemental_stress'),
res_from_preprocessed[0]['dict_y']['elemental_stress'],
decimal=7)
np.testing.assert_almost_equal(
fem_data.elemental_data.get_attribute_data(
'difference_elemental_stress'),
res_from_preprocessed[0]['dict_y']['elemental_stress']
- res_from_preprocessed[0]['dict_x']['elemental_stress'])
def test_infer_multiple_data(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/deform/pretrained/settings.yml'))
def conversion_function(fem_data, raw_directory=None):
adj = fem_data.calculate_adjacency_matrix_element()
nadj = prepost.normalize_adjacency_matrix(adj)
return {'adj': adj, 'nadj': nadj}
ir = inferer.Inferer(
main_setting, conversion_function=conversion_function,
converter_parameters_pkl=Path(
'tests/data/deform/preprocessed/preprocessors.pkl'))
ir.setting.inferer.save = True
ir.setting.inferer.perform_preprocess = False
ir.setting.inferer.write_simulation = True
ir.setting.inferer.write_simulation_base = Path(
'tests/data/deform/raw')
ir.setting.inferer.write_simulation_type = 'ucd'
output_directory_base = Path('tests/data/deform/inferred/multiple')
if output_directory_base.exists():
shutil.rmtree(output_directory_base)
res_from_raw = ir.infer(
model=Path('tests/data/deform/pretrained'),
data_directories=Path('tests/data/deform/preprocessed/test'),
output_directory_base=output_directory_base)
raw_directory_base = Path('tests/data/deform/raw/test')
for i_data, data_basename in enumerate([
'tet2_3_modulusx1.0500', 'tet2_4_modulusx0.9500']):
raw_directory = glob.glob(
str(raw_directory_base / f"**/{data_basename}"),
recursive=True)[0]
raw_fem_data = femio.FEMData.read_directory(
'fistr', raw_directory)
inferred_directory = glob.glob(
str(output_directory_base / f"**/{data_basename}"),
recursive=True)[0]
inferred_fem_data = femio.FEMData.read_directory(
'ucd', inferred_directory)
np.testing.assert_almost_equal(
inferred_fem_data.elemental_data.get_attribute_data(
'inferred_elemental_stress'),
res_from_raw[i_data]['dict_y']['elemental_stress'])
np.testing.assert_almost_equal(
res_from_raw[i_data]['dict_x']['elemental_stress'],
raw_fem_data.elemental_data.get_attribute_data(
'ElementalSTRESS'), decimal=2)
np.testing.assert_almost_equal(
inferred_fem_data.elemental_data.get_attribute_data(
'inferred_elemental_stress'),
raw_fem_data.elemental_data.get_attribute_data(
'ElementalSTRESS'), decimal=-1)
return
def test_infer_with_preprocessed_data(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/linear/pretrained/settings.yml'))
main_setting.inferer.converter_parameters_pkl = Path(
'tests/data/linear/preprocessed/preprocessors.pkl')
main_setting.inferer.output_directory_root = Path(
'tests/data/linear/inferred')
ir = inferer.Inferer(main_setting)
if ir.setting.trainer.output_directory.exists():
shutil.rmtree(ir.setting.trainer.output_directory)
res = ir.infer(
model=Path('tests/data/linear/pretrained'),
data_directories=Path('tests/data/linear/preprocessed/validation'))
np.testing.assert_almost_equal(
res[0]['dict_y']['y'],
np.load('tests/data/linear/interim/validation/0/y.npy'), decimal=3)
np.testing.assert_array_less(res[0]['loss'], 1e-7)
def test_infer_res_gcn(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/deform/pretrained_res_gcn/settings.yml'))
ir = inferer.Inferer(main_setting)
if ir.setting.trainer.output_directory.exists():
shutil.rmtree(ir.setting.trainer.output_directory)
ir.setting.inferer.converter_parameters_pkl = Path(
'tests/data/deform/preprocessed/preprocessors.pkl')
preprocessed_data_directory = Path(
'tests/data/deform/preprocessed/train/tet2_3_modulusx1.0000')
res = ir.infer(
model=Path('tests/data/deform/pretrained_res_gcn'),
data_directories=preprocessed_data_directory)
np.testing.assert_almost_equal(
res[0]['dict_y']['elemental_stress'] * 1e-5,
np.load(
'tests/data/deform/interim/train'
'/tet2_3_modulusx1.0000/elemental_stress.npy') * 1e-5,
decimal=3)
np.testing.assert_array_less(res[0]['loss'], 1e-3)
def test_iso_gcn_inverse_invariant(self):
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/rotation_thermal_stress'
'/iso_gcn_rank0_rank0_inverse.yml'))
tr = trainer.Trainer(main_setting)
if tr.setting.trainer.output_directory.exists():
shutil.rmtree(tr.setting.trainer.output_directory)
loss = tr.train()
np.testing.assert_array_less(loss, 5.)
# Confirm inference result has isometric invariance
original_path = Path(
'tests/data/rotation_thermal_stress/preprocessed/cube/original')
transformed_paths = self.collect_transformed_paths(
'tests/data/rotation_thermal_stress/preprocessed/cube'
'/*_transformed_*')
ir = inferer.Inferer(
main_setting,
save=False,
converter_parameters_pkl=Path(
'tests/data/rotation_thermal_stress/preprocessed'
'/preprocessors.pkl'))
model_directory = str(main_setting.trainer.output_directory)
inference_outpout_directory = \
main_setting.trainer.output_directory / 'inferred'
if inference_outpout_directory.exists():
shutil.rmtree(inference_outpout_directory)
original_results = ir.infer(model=model_directory,
data_directories=[original_path])
transformed_results = ir.infer(model=model_directory,
data_directories=transformed_paths)
# NOTE: LTE is not invariant, but just to validate IsoGCN invariance,
# we use it.
self.validate_results(original_results,
transformed_results,
rank0='global_lte_array',
validate_x=False)
def test_infer_encrypted_data(self):
encrypt_key = Random.get_random_bytes(16)
deploy_wo_encrypt_directory = Path(
'tests/data/linear/deploy_wo_encrypt')
deploy_w_encrypt_directory = Path('tests/data/linear/deploy_w_encrypt')
main_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/linear/pretrained/settings.yml'))
main_setting.inferer.converter_parameters_pkl = Path(
'tests/data/linear/preprocessed/preprocessors.pkl')
ir = inferer.Inferer(main_setting)
if deploy_wo_encrypt_directory.exists():
shutil.rmtree(deploy_wo_encrypt_directory)
ir.deploy(
output_directory=deploy_wo_encrypt_directory,
model=Path('tests/data/linear/pretrained'))
if deploy_w_encrypt_directory.exists():
shutil.rmtree(deploy_w_encrypt_directory)
ir.deploy(
output_directory=deploy_w_encrypt_directory,
model=Path('tests/data/linear/pretrained'),
encrypt_key=encrypt_key)
ir_wo_encrypt = inferer.Inferer.from_model_directory(
deploy_wo_encrypt_directory)
res_wo_encrypt = ir_wo_encrypt.infer(
data_directories=Path('tests/data/linear/preprocessed/validation'))
ir_w_encrypt = inferer.Inferer.from_model_directory(
deploy_w_encrypt_directory, decrypt_key=encrypt_key)
res_w_encrypt = ir_w_encrypt.infer(
data_directories=Path('tests/data/linear/preprocessed/validation'))
np.testing.assert_almost_equal(
res_wo_encrypt[0]['dict_y']['y'], res_w_encrypt[0]['dict_y']['y'])
def test_grad_grad_vs_laplacian(self):
preprocess_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/precalculated/convert.yml'))
shutil.rmtree(preprocess_setting.data.interim, ignore_errors=True)
shutil.rmtree(preprocess_setting.data.preprocessed, ignore_errors=True)
raw_converter = pre.RawConverter(
preprocess_setting,
conversion_function=convert.HeatConversionFuncionCreator(
create_elemental=False),
filter_function=convert.filter_function_heat,
force_renew=True, recursive=True, to_first_order=True,
write_ucd=False, read_npy=False, read_res=True)
raw_converter.convert()
preprocessor = pre.Preprocessor(
preprocess_setting, force_renew=True)
preprocessor.preprocess_interim_data()
# Confirm results does not change under rigid body transformation
grad_grad_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/precalculated/grad_grad.yml'))
grad_grad_tr = trainer.Trainer(grad_grad_setting)
if grad_grad_tr.setting.trainer.output_directory.exists():
shutil.rmtree(grad_grad_tr.setting.trainer.output_directory)
grad_grad_tr.train()
grad_grad_ir = inferer.Inferer(grad_grad_setting)
inference_outpout_directory = \
grad_grad_setting.trainer.output_directory / 'inferred'
if inference_outpout_directory.exists():
shutil.rmtree(inference_outpout_directory)
results = grad_grad_ir.infer(
model=Path('tests/data/precalculated/models/grad_grad'),
preprocessed_data_directory=[
Path(
'tests/data/precalculated/preprocessed/cube/clscale1.0/'
'original'),
Path(
'tests/data/precalculated/preprocessed/cube/clscale1.0/'
'rotated')],
converter_parameters_pkl=Path(
'tests/data/precalculated/preprocessed/preprocessors.pkl'),
output_directory=inference_outpout_directory,
overwrite=True)
np.testing.assert_almost_equal(
results[0]['dict_y']['nodal_t_100'],
results[1]['dict_y']['nodal_t_100'])
laplacian_setting = setting.MainSetting.read_settings_yaml(
Path('tests/data/precalculated/laplacian.yml'))
laplacian_tr = trainer.Trainer(laplacian_setting)
if laplacian_tr.setting.trainer.output_directory.exists():
shutil.rmtree(laplacian_tr.setting.trainer.output_directory)
laplacian_tr.train()
laplacian_ir = inferer.Inferer(laplacian_setting)
inference_outpout_directory = \
laplacian_setting.trainer.output_directory / 'inferred'
if inference_outpout_directory.exists():
shutil.rmtree(inference_outpout_directory)
results = laplacian_ir.infer(
model=Path('tests/data/precalculated/models/laplacian'),
preprocessed_data_directory=[
Path(
'tests/data/precalculated/preprocessed/cube/clscale1.0/'
'original'),
Path(
'tests/data/precalculated/preprocessed/cube/clscale1.0/'
'rotated')],
converter_parameters_pkl=Path(
'tests/data/precalculated/preprocessed/preprocessors.pkl'),
output_directory=inference_outpout_directory,
overwrite=True)
np.testing.assert_almost_equal(
results[0]['dict_y']['nodal_t_100'],
results[1]['dict_y']['nodal_t_100'])
