From 8201ebcb2c2138dcab650295c5f2432dc7d4c0a7 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Sun, 4 Aug 2024 21:01:14 -0400 Subject: [PATCH] fix: manage testing models in a standard way (#4028) Fix #2103. Migrate three models (se_e2_a, se_e2_r, and fparam_aparam) for the Python unit tests. Fix several bugs. Old files are kept until the C++ tests are also migrated. Note that several models (for example, the dipole model due to #3672) cannot be serialized yet. ## Summary by CodeRabbit ## Summary by CodeRabbit - **New Features** - Introduced a structured framework for managing and testing models with YAML files. - Added comprehensive configurations for energy calculations and molecular simulations in YAML format. - Implemented new test cases for the `DeepPot` and `DeepPotNeighborList` classes. - **Bug Fixes** - Improved robustness in tensor reshaping, resolving potential dimension mismatches. - **Tests** - Enhanced unit tests with a case-based approach for better adaptability and maintainability. - Consolidated tests by relocating obsolete classes to streamline the test suite. - **Chores** - Updated deserialization functions for better type safety and input handling. --------- Signed-off-by: Jinzhe Zeng --- deepmd/pt/infer/deep_eval.py | 22 +- deepmd/pt/utils/serialization.py | 4 +- deepmd/tf/descriptor/se_a.py | 3 +- deepmd/tf/descriptor/se_r.py | 3 +- deepmd/tf/utils/serialization.py | 4 +- source/tests/consistent/model/test_frozen.py | 27 +- source/tests/infer/.gitignore | 1 + source/tests/infer/__init__.py | 1 + source/tests/infer/case.py | 195 + source/tests/infer/deeppot-r-testcase.yaml | 339 ++ source/tests/infer/deeppot-r.yaml | 1631 ++++++++ source/tests/infer/deeppot-testcase.yaml | 534 +++ source/tests/infer/deeppot.dp | Bin 43424 -> 0 bytes source/tests/infer/deeppot.yaml | 3711 +++++++++++++++++ source/tests/infer/deeppot_descpt.txt | 6 - .../tests/infer/fparam_aparam-testcase.yaml | 122 + source/tests/infer/fparam_aparam.yaml | 2033 +++++++++ source/tests/infer/test_models.py | 352 ++ source/tests/pt/model/test_deeppot.py | 21 +- source/tests/tf/test_deeppot_a.py | 951 +---- source/tests/tf/test_deeppot_r.py | 624 +-- source/tests/tf/test_dp_test.py | 103 +- 22 files changed, 8964 insertions(+), 1723 deletions(-) create mode 100644 source/tests/infer/.gitignore create mode 100644 source/tests/infer/__init__.py create mode 100644 source/tests/infer/case.py create mode 100644 source/tests/infer/deeppot-r-testcase.yaml create mode 100644 source/tests/infer/deeppot-r.yaml create mode 100644 source/tests/infer/deeppot-testcase.yaml delete mode 100644 source/tests/infer/deeppot.dp create mode 100644 source/tests/infer/deeppot.yaml delete mode 100644 source/tests/infer/deeppot_descpt.txt create mode 100644 source/tests/infer/fparam_aparam-testcase.yaml create mode 100644 source/tests/infer/fparam_aparam.yaml create mode 100644 source/tests/infer/test_models.py diff --git a/deepmd/pt/infer/deep_eval.py b/deepmd/pt/infer/deep_eval.py index 55c18d5d95..9309af657d 100644 --- a/deepmd/pt/infer/deep_eval.py +++ b/deepmd/pt/infer/deep_eval.py @@ -380,26 +380,28 @@ def _eval_model( natoms = len(atom_types[0]) coord_input = torch.tensor( - coords.reshape([-1, natoms, 3]), + coords.reshape([nframes, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE, ) type_input = torch.tensor(atom_types, dtype=torch.long, device=DEVICE) if cells is not None: box_input = torch.tensor( - cells.reshape([-1, 3, 3]), + cells.reshape([nframes, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE, ) else: box_input = None if fparam is not None: - fparam_input = to_torch_tensor(fparam.reshape(-1, self.get_dim_fparam())) + fparam_input = to_torch_tensor( + fparam.reshape(nframes, self.get_dim_fparam()) + ) else: fparam_input = None if aparam is not None: aparam_input = to_torch_tensor( - aparam.reshape(-1, natoms, self.get_dim_aparam()) + aparam.reshape(nframes, natoms, self.get_dim_aparam()) ) else: aparam_input = None @@ -451,31 +453,33 @@ def _eval_model_spin( natoms = len(atom_types[0]) coord_input = torch.tensor( - coords.reshape([-1, natoms, 3]), + coords.reshape([nframes, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE, ) type_input = torch.tensor(atom_types, dtype=torch.long, device=DEVICE) spin_input = torch.tensor( - spins.reshape([-1, natoms, 3]), + spins.reshape([nframes, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE, ) if cells is not None: box_input = torch.tensor( - cells.reshape([-1, 3, 3]), + cells.reshape([nframes, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE, ) else: box_input = None if fparam is not None: - fparam_input = to_torch_tensor(fparam.reshape(-1, self.get_dim_fparam())) + fparam_input = to_torch_tensor( + fparam.reshape(nframes, self.get_dim_fparam()) + ) else: fparam_input = None if aparam is not None: aparam_input = to_torch_tensor( - aparam.reshape(-1, natoms, self.get_dim_aparam()) + aparam.reshape(nframes, natoms, self.get_dim_aparam()) ) else: aparam_input = None diff --git a/deepmd/pt/utils/serialization.py b/deepmd/pt/utils/serialization.py index 21a2a3fbda..98906ca455 100644 --- a/deepmd/pt/utils/serialization.py +++ b/deepmd/pt/utils/serialization.py @@ -72,7 +72,7 @@ def deserialize_to_file(model_file: str, data: dict) -> None: model = BaseModel.deserialize(data["model"]) # JIT will happy in this way... model.model_def_script = json.dumps(data["model_def_script"]) - model = torch.jit.script(model) if "min_nbor_dist" in data.get("@variables", {}): - model.min_nbor_dist = data["@variables"]["min_nbor_dist"] + model.min_nbor_dist = float(data["@variables"]["min_nbor_dist"]) + model = torch.jit.script(model) torch.jit.save(model, model_file) diff --git a/deepmd/tf/descriptor/se_a.py b/deepmd/tf/descriptor/se_a.py index fe47deba86..721e8e71d1 100644 --- a/deepmd/tf/descriptor/se_a.py +++ b/deepmd/tf/descriptor/se_a.py @@ -720,10 +720,11 @@ def prod_force_virial( """ [net_deriv] = tf.gradients(atom_ener, self.descrpt_reshape) tf.summary.histogram("net_derivative", net_deriv) + nf = tf.shape(self.nlist)[0] net_deriv_reshape = tf.reshape( net_deriv, [ - np.asarray(-1, dtype=np.int64), + nf, natoms[0] * np.asarray(self.ndescrpt, dtype=np.int64), ], ) diff --git a/deepmd/tf/descriptor/se_r.py b/deepmd/tf/descriptor/se_r.py index ea58b276c9..cd99651314 100644 --- a/deepmd/tf/descriptor/se_r.py +++ b/deepmd/tf/descriptor/se_r.py @@ -512,10 +512,11 @@ def prod_force_virial( """ [net_deriv] = tf.gradients(atom_ener, self.descrpt_reshape) tf.summary.histogram("net_derivative", net_deriv) + nf = tf.shape(self.nlist)[0] net_deriv_reshape = tf.reshape( net_deriv, [ - np.asarray(-1, dtype=np.int64), + nf, natoms[0] * np.asarray(self.ndescrpt, dtype=np.int64), ], ) diff --git a/deepmd/tf/utils/serialization.py b/deepmd/tf/utils/serialization.py index 7cf596f5bd..1d2f1b597f 100644 --- a/deepmd/tf/utils/serialization.py +++ b/deepmd/tf/utils/serialization.py @@ -91,13 +91,13 @@ def deserialize_to_file(model_file: str, data: dict) -> None: if model.get_numb_fparam() > 0: inputs["fparam"] = tf.placeholder( GLOBAL_TF_FLOAT_PRECISION, - [None, model.get_numb_fparam()], + [None], name="t_fparam", ) if model.get_numb_aparam() > 0: inputs["aparam"] = tf.placeholder( GLOBAL_TF_FLOAT_PRECISION, - [None, model.get_numb_aparam()], + [None], name="t_aparam", ) model.build( diff --git a/source/tests/consistent/model/test_frozen.py b/source/tests/consistent/model/test_frozen.py index a60a6abb3f..e362aed511 100644 --- a/source/tests/consistent/model/test_frozen.py +++ b/source/tests/consistent/model/test_frozen.py @@ -31,36 +31,29 @@ from deepmd.tf.model.model import Model as FrozenModelTF else: FrozenModelTF = None -from pathlib import ( - Path, -) -from deepmd.entrypoints.convert_backend import ( - convert_backend, -) from deepmd.utils.argcheck import ( model_args, ) -original_model = str(Path(__file__).parent.parent.parent / "infer" / "deeppot.dp") +from ...infer.case import ( + get_cases, +) + pt_model = "deeppot_for_consistent_frozen.pth" tf_model = "deeppot_for_consistent_frozen.pb" -dp_model = original_model +dp_model = "deeppot_for_consistent_frozen.dp" def setUpModule(): - convert_backend( - INPUT=dp_model, - OUTPUT=tf_model, - ) - convert_backend( - INPUT=dp_model, - OUTPUT=pt_model, - ) + case = get_cases()["se_e2_a"] + case.get_model(".dp", dp_model) + case.get_model(".pb", tf_model) + case.get_model(".pth", pt_model) def tearDownModule(): - for model_file in (pt_model, tf_model): + for model_file in (dp_model, pt_model, tf_model): try: os.remove(model_file) except FileNotFoundError: diff --git a/source/tests/infer/.gitignore b/source/tests/infer/.gitignore new file mode 100644 index 0000000000..e6021b6fd9 --- /dev/null +++ b/source/tests/infer/.gitignore @@ -0,0 +1 @@ +deepmd_test_models*/ diff --git a/source/tests/infer/__init__.py b/source/tests/infer/__init__.py new file mode 100644 index 0000000000..6ceb116d85 --- /dev/null +++ b/source/tests/infer/__init__.py @@ -0,0 +1 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later diff --git a/source/tests/infer/case.py b/source/tests/infer/case.py new file mode 100644 index 0000000000..662d84e7e0 --- /dev/null +++ b/source/tests/infer/case.py @@ -0,0 +1,195 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Manage testing models in a standard way. + +For each model, a YAML file ending with `-testcase.yaml` must be given. It should contains the following keys: + +- `key`: The key of the model. +- `filename`: The path to the model file. +- `ntypes`: The number of atomic types. +- `rcut`: The cutoff radius. +- `type_map`: The mapping between atomic types and atomic names. +- `dim_fparam`: The number of frame parameters. +- `dim_aparam`: The number of atomic parameters. +- `results`: A list of results. Each result should contain the following keys: + - `atype`: The atomic types. + - `coord`: The atomic coordinates. + - `box`: The simulation box. + - `atomic_energy` or `energy` (optional): The atomic energies or the total energy. + - `force` (optional): The atomic forces. + - `atomic_virial` or `virial` (optional): The atomic virials or the total virial. +""" + +import tempfile +from functools import ( + lru_cache, +) +from pathlib import ( + Path, +) +from typing import ( + Dict, + Optional, +) + +import numpy as np +import yaml + +from deepmd.entrypoints.convert_backend import ( + convert_backend, +) + +this_directory = Path(__file__).parent.resolve() +# create a temporary directory under this directory +# to store the temporary model files +# it will be deleted when the program exits +tempdir = tempfile.TemporaryDirectory(dir=this_directory, prefix="deepmd_test_models_") + + +class Result: + """Test results. + + Parameters + ---------- + data : dict + Dictionary containing the results. + + Attributes + ---------- + atype : np.ndarray + The atomic types. + nloc : int + The number of atoms. + coord : np.ndarray + The atomic coordinates. + box : np.ndarray + The simulation box. + atomic_energy : np.ndarray + The atomic energies. + energy : np.ndarray + The total energy. + force : np.ndarray + The atomic forces. + atomic_virial : np.ndarray + The atomic virials. + virial : np.ndarray + The total virial. + """ + + def __init__(self, data: dict) -> None: + self.atype = np.array(data["atype"], dtype=np.int64) + self.nloc = self.atype.size + self.coord = np.array(data["coord"], dtype=np.float64).reshape(self.nloc, 3) + if data["box"] is not None: + self.box = np.array(data["box"], dtype=np.float64).reshape(3, 3) + else: + self.box = None + if "fparam" in data: + self.fparam = np.array(data["fparam"], dtype=np.float64).ravel() + else: + self.fparam = None + if "aparam" in data: + self.aparam = np.array(data["aparam"], dtype=np.float64).reshape( + self.nloc, -1 + ) + else: + self.aparam = None + if "atomic_energy" in data: + self.atomic_energy = np.array( + data["atomic_energy"], dtype=np.float64 + ).reshape(self.nloc, 1) + self.energy = np.sum(self.atomic_energy, axis=0) + elif "energy" in data: + self.atomic_energy = None + self.energy = np.array(data["energy"], dtype=np.float64).reshape(1) + else: + self.atomic_energy = None + self.energy = None + if "force" in data: + self.force = np.array(data["force"], dtype=np.float64).reshape(self.nloc, 3) + else: + self.force = None + if "atomic_virial" in data: + self.atomic_virial = np.array( + data["atomic_virial"], dtype=np.float64 + ).reshape(self.nloc, 9) + self.virial = np.sum(self.atomic_virial, axis=0) + elif "virial" in data: + self.atomic_virial = None + self.virial = np.array(data["virial"], dtype=np.float64).reshape(9) + else: + self.atomic_virial = None + self.virial = None + if "descriptor" in data: + self.descriptor = np.array(data["descriptor"], dtype=np.float64).reshape( + self.nloc, -1 + ) + else: + self.descriptor = None + + +class Case: + """Test case. + + Parameters + ---------- + filename : str + The path to the test case file. + """ + + def __init__(self, filename: str): + with open(filename) as file: + config = yaml.safe_load(file) + self.key = config["key"] + self.filename = str(Path(filename).parent / config["filename"]) + self.results = [Result(data) for data in config["results"]] + self.ntypes = config["ntypes"] + self.rcut = config["rcut"] + self.type_map = config["type_map"] + self.dim_fparam = config["dim_fparam"] + self.dim_aparam = config["dim_aparam"] + + @lru_cache + def get_model(self, suffix: str, out_file: Optional[str] = None) -> str: + """Get the model file with the specified suffix. + + Parameters + ---------- + suffix : str + The suffix of the model file. + out_file : str, optional + The path to the output model file. If not given, a temporary file will be created. + + Returns + ------- + str + The path to the model file. + """ + # generate a temporary model file + if out_file is None: + out_file = tempfile.NamedTemporaryFile( + suffix=suffix, dir=tempdir.name, delete=False, prefix=self.key + "_" + ).name + convert_backend(INPUT=self.filename, OUTPUT=out_file) + return out_file + + +@lru_cache +def get_cases() -> Dict[str, Case]: + """Get all test cases. + + Returns + ------- + Dict[str, Case] + A dictionary containing all test cases. + + Examples + -------- + To get a specific case: + + >>> get_cases()["se_e2_a"] + """ + cases = {} + for ff in this_directory.glob("*-testcase.yaml"): + case = Case(ff) + cases[case.key] = case + return cases diff --git a/source/tests/infer/deeppot-r-testcase.yaml b/source/tests/infer/deeppot-r-testcase.yaml new file mode 100644 index 0000000000..84ebc041c7 --- /dev/null +++ b/source/tests/infer/deeppot-r-testcase.yaml @@ -0,0 +1,339 @@ +# se_e2_r + energy +key: se_e2_r +filename: deeppot-r.yaml +ntypes: 2 +rcut: 6.0 +type_map: ["O", "H"] +dim_fparam: 0 +dim_aparam: 0 +results: + - coord: + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + atype: [0, 1, 1, 0, 1, 1] + box: [13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0] + atomic_energy: + [ + -9.320909762801588272e01, + -1.868020345400987878e02, + -1.868011172371355997e02, + -9.320868430396934912e01, + -1.868010398844378415e02, + -1.868016706555875999e02, + ] + force: + [ + 6.385312846474267391e-04, + -6.460452911141417731e-03, + -5.652405655332678417e-04, + -7.516468794343579736e-03, + 1.128804614240160216e-03, + 5.531937784564192051e-03, + 1.914138124904981664e-03, + 5.601819906021693503e-03, + -5.131359585752605541e-03, + -4.847104424804288617e-03, + 1.992071550328819614e-03, + -4.028159855157302516e-03, + 1.236340684486603517e-03, + -5.373955841338794344e-03, + 8.312829460571366513e-03, + 8.574563125108854156e-03, + 3.111712681889538742e-03, + -4.120007238692381148e-03, + ] + atomic_virial: + [ + 5.844056241889131371e-03, + 4.663973497239899614e-04, + -2.268382127762904633e-03, + 4.663973497239897988e-04, + 2.349338784202595950e-03, + -6.908546513234039253e-04, + -2.268382127762904633e-03, + -6.908546513234039253e-04, + 2.040499248150800561e-03, + 4.238130266437327605e-03, + -1.539867187443782223e-04, + -2.393101333240631613e-03, + -1.539867187443782223e-04, + 4.410341945447907377e-04, + 9.544239698119633068e-06, + -2.393101333240631613e-03, + 9.544239698119578858e-06, + 1.877785959095269654e-03, + 5.798992562057291543e-03, + 6.943392552230453693e-04, + -1.180376879311998773e-03, + 6.943392552230453693e-04, + 1.686725132156275536e-03, + -1.461632060145726542e-03, + -1.180376879311998556e-03, + -1.461632060145726325e-03, + 1.749543733794208444e-03, + 7.173915604192910439e-03, + 3.903218041111061569e-04, + -5.747400467123527524e-04, + 3.903218041111061569e-04, + 1.208289706621179949e-03, + -1.826828914132010932e-03, + -5.747400467123527524e-04, + -1.826828914132011148e-03, + 2.856960586657185906e-03, + 4.067553030177322240e-03, + -3.267469855253819430e-05, + -6.980667859103454904e-05, + -3.267469855253830272e-05, + 1.387653029234650918e-03, + -2.096820720698671855e-03, + -6.980667859103444062e-05, + -2.096820720698671855e-03, + 3.218305506720191278e-03, + 4.753992590355240674e-03, + 1.224911338353675992e-03, + -1.683421934571502484e-03, + 1.224911338353676209e-03, + 7.332113564901583539e-04, + -1.025577052190138451e-03, + -1.683421934571502484e-03, + -1.025577052190138234e-03, + 1.456681925652047018e-03, + ] + - coord: + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + atype: [0, 1, 1, 0, 1, 1] + box: null + atomic_energy: + [ + -9.321213823508108476e01, + -1.868044102481340758e02, + -1.868067983858651075e02, + -9.320899631301440991e01, + -1.868014559732615112e02, + -1.868017660713088617e02, + ] + force: + [ + 4.578151103701261042e-03, + -1.917874111009987628e-03, + -3.464546781179331930e-03, + -4.578151103701261042e-03, + 1.917874111009987628e-03, + 3.464546781179331930e-03, + -2.624402581721222913e-03, + 3.566275128489623933e-04, + -2.859315986763691776e-04, + -5.767787273464367384e-03, + 1.907053583551196647e-03, + -3.889064429673861831e-03, + 1.786820066350549132e-04, + -5.327197473636275694e-03, + 8.236236182834734409e-03, + 8.213507848550535492e-03, + 3.063516377236116545e-03, + -4.061240154484504865e-03, + ] + atomic_virial: + [ + 1.984979026299632174e-03, + -8.315452677741701822e-04, + -1.502146290172694243e-03, + -8.315452677741700738e-04, + 3.483500446080982317e-04, + 6.292774999372096039e-04, + -1.502146290172694243e-03, + 6.292774999372097123e-04, + 1.136759354725281907e-03, + 1.402852790439301908e-03, + -5.876815743732210226e-04, + -1.061618327900012114e-03, + -5.876815743732211311e-04, + 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use_aparam_as_mask: false + var_name: energy + pair_exclude_types: [] + preset_out_bias: null + rcond: null + type: standard + type_map: *id001 +model_def_script: + data_bias_nsample: 10 + data_stat_nbatch: 1 + data_stat_protect: 0.01 + descriptor: + activation_function: tanh + axis_neuron: 8 + exclude_types: [] + neuron: *id002 + precision: default + rcut: 6.0 + rcut_smth: 1.0 + resnet_dt: false + seed: 1 + sel: *id003 + set_davg_zero: false + trainable: true + type: se_r + type_one_side: false + fitting_net: + activation_function: tanh + atom_ener: *id004 + neuron: *id005 + numb_aparam: 0 + numb_fparam: 0 + precision: default + rcond: 0.001 + resnet_dt: true + seed: 1 + trainable: true + type: ener + use_aparam_as_mask: false + type_map: *id001 +software: deepmd-kit +tf_version: 2.15.0 +time: "2024-07-27 10:10:04.707439" +version: 3.0.0b3.dev27+gfda408f1c.d20240726 diff --git a/source/tests/infer/deeppot-testcase.yaml b/source/tests/infer/deeppot-testcase.yaml new file mode 100644 index 0000000000..8e031a1638 --- /dev/null +++ b/source/tests/infer/deeppot-testcase.yaml @@ -0,0 +1,534 @@ +# se_e2_a + energy; types are O and H +key: se_e2_a +filename: deeppot.yaml +ntypes: 2 +rcut: 6.0 +type_map: ["O", "H"] +dim_fparam: 0 +dim_aparam: 0 +results: + - coord: + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + atype: [0, 1, 1, 0, 1, 1] + box: [13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0] + atomic_energy: + [ + -9.275780747115504710e01, + -1.863501786584258468e02, + -1.863392472863538103e02, + -9.279281325486221021e01, + -1.863671545232153903e02, + -1.863619822847602165e02, + ] + force: + [ + -3.034045420701179663e-01, + 8.405844663871177014e-01, + 7.696947487118485642e-02, + 7.662001266663505117e-01, + -1.880601391333554251e-01, + -6.183333871091722944e-01, + -5.036172391059643427e-01, + -6.529525836149027151e-01, + 5.432962643022043459e-01, + 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type_map: *id001 +model_def_script: + data_bias_nsample: 10 + data_stat_nbatch: 10 + data_stat_protect: 0.01 + descriptor: + activation_function: tanh + axis_neuron: 4 + exclude_types: [] + neuron: *id002 + precision: default + rcut: 6.0 + rcut_smth: 0.5 + resnet_dt: false + seed: 1 + sel: *id003 + set_davg_zero: false + trainable: true + type: se_e2_a + type_one_side: false + fitting_net: + activation_function: tanh + atom_ener: *id004 + neuron: *id005 + numb_aparam: 0 + numb_fparam: 0 + precision: default + rcond: 0.001 + resnet_dt: true + seed: 1 + trainable: true + type: ener + type_map: *id001 +software: deepmd-kit +tf_version: 2.15.0 +time: "2024-07-26 20:44:52.386315" +version: 3.0.0b3.dev27+gfda408f1c.d20240726 diff --git a/source/tests/infer/deeppot_descpt.txt b/source/tests/infer/deeppot_descpt.txt deleted file mode 100644 index d757dc7d08..0000000000 --- a/source/tests/infer/deeppot_descpt.txt +++ /dev/null @@ -1,6 +0,0 @@ -1.321519961572583446e+00 1.371086996432463234e+00 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@@ -0,0 +1,122 @@ +# se_e2_a + energy with fparam and aparam +key: fparam_aparam +filename: fparam_aparam.yaml +ntypes: 1 +rcut: 6.0 +type_map: ["O"] +dim_fparam: 1 +dim_aparam: 1 +results: + - coord: + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + atype: [0, 0, 0, 0, 0, 0] + box: [13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0] + fparam: 0.25852028 + aparam: + [0.25852028, 0.25852028, 0.25852028, 0.25852028, 0.25852028, 0.25852028] + atomic_energy: + [ + -1.038271183039953804e-01, + -7.285433575272914908e-02, + -9.467600174099155552e-02, + -1.467050086239614082e-01, + -7.660561620618722145e-02, + -7.277295998502930630e-02, + ] + force: + [ + 6.622266817497907132e-02, + 5.278739055693523058e-02, + 2.265727495541422845e-02, + -2.606047850915838363e-02, + -4.538811686410718776e-02, + 1.058247569147072187e-02, + 1.679392490937766935e-01, + -2.257828022687320690e-03, + -4.490145670355452645e-02, + -1.148364103573685929e-01, + -1.169790466695089237e-02, + 6.140402504113953025e-02, + -8.078778132132799494e-02, + -5.838878056243369807e-02, + 6.773639989682191109e-02, + -1.247724708090079161e-02, + 6.494523955924384750e-02, + -1.174787188812918687e-01, + ] + atomic_virial: + [ + -1.589185553287162656e-01, + 2.586163333170100279e-03, + -1.575127933809472624e-04, + -1.855360380105876630e-02, + 1.949822090859933826e-02, + -1.006552056166355388e-02, + 3.177029853276916449e-02, + 1.714349636720383010e-03, + -1.290389175187874483e-03, + -8.553510339477603253e-02, + -5.654637257232508415e-03, + -1.286954833787038420e-02, + 2.464156457499515687e-02, + -2.398202886026797043e-02, + -1.957110465239037672e-02, + 2.233492928605742764e-02, + 6.107843207824020099e-03, + 1.707078295947736047e-03, + -1.653994088976195043e-01, + 3.894358678172111371e-02, + -2.169595969759342477e-02, + 6.819704294738503786e-03, + -5.018242039618424008e-03, + 2.640664428663210429e-03, + -1.985298275686078057e-03, + -3.638421609610945767e-02, + 2.342932331075030239e-02, + -8.501331914753691710e-02, + -2.181253413538992297e-03, + 4.311300069651782287e-03, + -1.910329328333908129e-03, + -1.808810159508548836e-03, + -1.540075281450827612e-03, + -1.173703213175551763e-02, + -2.596306629910121507e-03, + 6.705025662372287101e-03, + -9.038455005073858795e-02, + 3.011717773578577451e-02, + -5.083054073419784880e-02, + -2.951210292616929069e-03, + 2.342445652898489383e-02, + -4.091207474993674431e-02, + -1.648470649301832236e-02, + -2.872261885460645689e-02, + 4.763924972552112391e-02, + -8.300036532764677732e-02, + 1.020429228955421243e-03, + -1.026734151199098881e-03, + 5.678534096113684732e-02, + 1.273635718045938205e-02, + -1.530143225195957322e-02, + -1.061671865629566225e-01, + -2.486859433265622629e-02, + 2.875323131744185121e-02, + ] diff --git a/source/tests/infer/fparam_aparam.yaml b/source/tests/infer/fparam_aparam.yaml new file mode 100644 index 0000000000..e0654e142f --- /dev/null +++ b/source/tests/infer/fparam_aparam.yaml @@ -0,0 +1,2033 @@ +"@variables": + min_nbor_dist: + "@class": np.ndarray + "@is_variable": true + "@version": 1 + dtype: float64 + value: 2.353743796396622 +backend: TensorFlow +model: + "@class": Model + "@variables": + out_bias: + "@class": np.ndarray + "@is_variable": true + "@version": 1 + dtype: float64 + value: + - - - 0.0 + out_std: + "@class": np.ndarray + "@is_variable": true + "@version": 1 + dtype: float64 + value: + - - - 1.0 + "@version": 2 + atom_exclude_types: [] + descriptor: + "@class": Descriptor + "@variables": + davg: + "@class": np.ndarray + "@is_variable": true + "@version": 1 + dtype: float64 + value: + - - - 0.06225070363287908 + - 0.0 + - 0.0 + - 0.0 + - - 0.06225070363287908 + - 0.0 + - 0.0 + - 0.0 + - - 0.06225070363287908 + - 0.0 + - 0.0 + - 0.0 + - - 0.06225070363287908 + - 0.0 + - 0.0 + - 0.0 + - - 0.06225070363287908 + - 0.0 + - 0.0 + - 0.0 + - - 0.06225070363287908 + - 0.0 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"@version": 1 + dtype: float64 + value: + - 0.27935157276531497 + idt: null + w: + "@class": np.ndarray + "@is_variable": true + "@version": 1 + dtype: float64 + value: + - - 0.7197415086181096 + - - -0.20543635513622743 + - - 0.324034267585776 + - - 0.8831348697832359 + - - 0.1948665043063704 + "@version": 1 + activation_function: none + bias: true + precision: float64 + resnet: false + use_timestep: false + neuron: + - 5 + - 5 + - 5 + out_dim: 1 + precision: float64 + resnet_dt: true + ntypes: 1 + neuron: &id005 + - 5 + - 5 + - 5 + ntypes: 1 + numb_aparam: 1 + numb_fparam: 1 + precision: float64 + rcond: 0.001 + resnet_dt: true + spin: null + tot_ener_zero: false + trainable: + - true + - true + - true + - true + type: ener + type_map: *id001 + use_aparam_as_mask: false + var_name: energy + pair_exclude_types: [] + preset_out_bias: null + rcond: null + type: standard + type_map: *id001 +model_def_script: + data_bias_nsample: 10 + data_stat_nbatch: 1 + data_stat_protect: 0.01 + descriptor: + activation_function: tanh + axis_neuron: 8 + exclude_types: [] + neuron: *id002 + precision: default + rcut: 6.0 + rcut_smth: 1.8 + resnet_dt: false + seed: 1 + sel: *id003 + set_davg_zero: false + trainable: true + type: se_e2_a + type_one_side: false + fitting_net: + activation_function: tanh + atom_ener: *id004 + neuron: *id005 + numb_aparam: 1 + numb_fparam: 1 + precision: default + rcond: 0.001 + resnet_dt: true + seed: 1 + trainable: true + type: ener + use_aparam_as_mask: false + type_map: *id001 +software: deepmd-kit +tf_version: 2.15.0 +time: "2024-07-27 04:31:02.669792" +version: 3.0.0b3.dev27+gfda408f1c.d20240726 diff --git a/source/tests/infer/test_models.py b/source/tests/infer/test_models.py new file mode 100644 index 0000000000..f193c09616 --- /dev/null +++ b/source/tests/infer/test_models.py @@ -0,0 +1,352 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import unittest + +import ase +import dpdata +import numpy as np + +from deepmd.infer.deep_eval import ( + DeepEval, +) +from deepmd.infer.deep_pot import ( + DeepPot, +) + +from ..consistent.common import ( + parameterized, +) +from .case import ( + get_cases, +) + +default_places = 7 + + +@parameterized( + ( + "se_e2_a", + "se_e2_r", + "fparam_aparam", + ), # key + (".pb", ".pth"), # model extension +) +class TestDeepPot(unittest.TestCase): + # moved from tests/tf/test_deeppot_a.py + + @classmethod + def setUpClass(cls): + key, extension = cls.param + cls.case = get_cases()[key] + cls.model_name = cls.case.get_model(extension) + cls.dp = DeepEval(cls.model_name) + + @classmethod + def tearDownClass(cls): + cls.dp = None + + def setUp(self): + key, extension = self.param + if key == "se_e2_r" and extension == ".pth": + self.skipTest( + reason="se_e2_r type_one_side is not supported for PyTorch models" + ) + + def test_attrs(self): + assert isinstance(self.dp, DeepPot) + self.assertEqual(self.dp.get_ntypes(), self.case.ntypes) + self.assertAlmostEqual( + self.dp.get_rcut(), self.case.rcut, places=default_places + ) + self.assertEqual(self.dp.get_type_map(), self.case.type_map) + self.assertEqual(self.dp.get_dim_fparam(), self.case.dim_fparam) + self.assertEqual(self.dp.get_dim_aparam(), self.case.dim_aparam) + + def test_1frame(self): + for ii, result in enumerate(self.case.results): + ee, ff, vv = self.dp.eval( + result.coord, + result.box, + result.atype, + atomic=False, + fparam=result.fparam, + aparam=result.aparam, + ) + # check shape of the returns + nframes = 1 + natoms = len(result.atype) + self.assertEqual(ee.shape, (nframes, 1)) + self.assertEqual(ff.shape, (nframes, natoms, 3)) + self.assertEqual(vv.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal( + ff.ravel(), + result.force.ravel(), + default_places, + err_msg=f"Result {ii} force", + ) + expected_se = np.sum(result.atomic_energy.reshape([nframes, -1]), axis=1) + np.testing.assert_almost_equal( + ee.ravel(), + expected_se.ravel(), + default_places, + err_msg=f"Result {ii} energy", + ) + expected_sv = np.sum(result.atomic_virial.reshape([nframes, -1, 9]), axis=1) + np.testing.assert_almost_equal( + vv.ravel(), + expected_sv.ravel(), + default_places, + err_msg=f"Result {ii} virial", + ) + + def test_1frame_atm(self): + for ii, result in enumerate(self.case.results): + ee, ff, vv, ae, av = self.dp.eval( + result.coord, + result.box, + result.atype, + atomic=True, + fparam=result.fparam, + aparam=result.aparam, + ) + # check shape of the returns + nframes = 1 + natoms = len(result.atype) + self.assertEqual(ee.shape, (nframes, 1)) + self.assertEqual(ff.shape, (nframes, natoms, 3)) + self.assertEqual(vv.shape, (nframes, 9)) + self.assertEqual(ae.shape, (nframes, natoms, 1)) + self.assertEqual(av.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal( + ff.ravel(), + result.force.ravel(), + default_places, + err_msg=f"Result {ii} force", + ) + np.testing.assert_almost_equal( + ae.ravel(), + result.atomic_energy.ravel(), + default_places, + err_msg=f"Result {ii} atomic energy", + ) + np.testing.assert_almost_equal( + av.ravel(), + result.atomic_virial.ravel(), + default_places, + err_msg=f"Result {ii} atomic virial", + ) + expected_se = np.sum(result.energy.reshape([nframes, -1]), axis=1) + np.testing.assert_almost_equal( + ee.ravel(), + expected_se.ravel(), + default_places, + err_msg=f"Result {ii} energy", + ) + expected_sv = np.sum(result.virial.reshape([nframes, -1, 9]), axis=1) + np.testing.assert_almost_equal( + vv.ravel(), + expected_sv.ravel(), + default_places, + err_msg=f"Result {ii} virial", + ) + + def test_descriptor(self): + _, extension = self.param + if extension == ".pth": + self.skipTest("eval_descriptor not supported for PyTorch models") + for ii, result in enumerate(self.case.results): + if result.descriptor is None: + continue + descpt = self.dp.eval_descriptor(result.coord, result.box, result.atype) + expected_descpt = result.descriptor + np.testing.assert_almost_equal(descpt.ravel(), expected_descpt.ravel()) + + def test_2frame_atm(self): + for ii, result in enumerate(self.case.results): + coords2 = np.concatenate((result.coord, result.coord)) + if result.box is not None: + box2 = np.concatenate((result.box, result.box)) + else: + box2 = None + ee, ff, vv, ae, av = self.dp.eval( + coords2, + box2, + result.atype, + atomic=True, + fparam=result.fparam, + aparam=result.aparam, + ) + # check shape of the returns + nframes = 2 + natoms = len(result.atype) + self.assertEqual(ee.shape, (nframes, 1)) + self.assertEqual(ff.shape, (nframes, natoms, 3)) + self.assertEqual(vv.shape, (nframes, 9)) + self.assertEqual(ae.shape, (nframes, natoms, 1)) + self.assertEqual(av.shape, (nframes, natoms, 9)) + # check values + expected_f = np.concatenate((result.force, result.force), axis=0) + expected_e = np.concatenate( + (result.atomic_energy, result.atomic_energy), axis=0 + ) + expected_v = np.concatenate( + (result.atomic_virial, result.atomic_virial), axis=0 + ) + np.testing.assert_almost_equal( + ff.ravel(), expected_f.ravel(), default_places + ) + np.testing.assert_almost_equal( + ae.ravel(), expected_e.ravel(), default_places + ) + np.testing.assert_almost_equal( + av.ravel(), expected_v.ravel(), default_places + ) + expected_se = np.sum(expected_e.reshape([nframes, -1]), axis=1) + np.testing.assert_almost_equal( + ee.ravel(), expected_se.ravel(), default_places + ) + expected_sv = np.sum(expected_v.reshape([nframes, -1, 9]), axis=1) + np.testing.assert_almost_equal( + vv.ravel(), expected_sv.ravel(), default_places + ) + + def test_zero_input(self): + _, extension = self.param + if extension == ".pb": + from deepmd.tf.env import ( + tf, + ) + + if tf.test.is_gpu_available(): + # TODO: needs to fix + self.skipTest("Segfault in GPUs") + nframes = 1 + for box in [np.eye(3, dtype=np.float64).reshape(1, 3, 3), None]: + ee, ff, vv = self.dp.eval( + np.zeros([nframes, 0, 3], dtype=np.float64), + box, + np.zeros([0], dtype=int), + atomic=False, + fparam=np.zeros([self.case.dim_fparam], dtype=np.float64) + if self.case.dim_fparam + else None, + aparam=np.zeros([0, self.case.dim_aparam], dtype=np.float64) + if self.case.dim_aparam + else None, + ) + # check shape of the returns + natoms = 0 + self.assertEqual(ee.shape, (nframes, 1)) + self.assertEqual(ff.shape, (nframes, natoms, 3)) + self.assertEqual(vv.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff.ravel(), 0, default_places) + np.testing.assert_almost_equal(ee.ravel(), 0, default_places) + np.testing.assert_almost_equal(vv.ravel(), 0, default_places) + + def test_ase(self): + from ase import ( + Atoms, + ) + + from deepmd.calculator import ( + DP, + ) + + if self.case.dim_fparam or self.case.dim_aparam: + self.skipTest("fparam and aparam not supported") + + for ii, result in enumerate(self.case.results): + water = Atoms( + np.array(self.case.type_map)[result.atype].tolist(), + positions=result.coord.reshape((-1, 3)), + cell=result.box.reshape((3, 3)) if result.box is not None else None, + calculator=DP(self.model_name), + pbc=result.box is not None, + ) + ee = water.get_potential_energy() + ff = water.get_forces() + nframes = 1 + np.testing.assert_almost_equal( + ff.ravel(), + result.force.ravel(), + default_places, + err_msg=f"Result {ii} force", + ) + expected_se = np.sum(result.atomic_energy.reshape([nframes, -1]), axis=1) + np.testing.assert_almost_equal( + ee.ravel(), + expected_se.ravel(), + default_places, + err_msg=f"Result {ii} energy", + ) + + def test_dpdata_driver(self): + if self.case.dim_fparam or self.case.dim_aparam: + self.skipTest("fparam and aparam not supported") + + for ii, result in enumerate(self.case.results): + nframes = 1 + # infer atom_numbs from atype + atom_numbs = np.bincount(result.atype).tolist() + system = dpdata.System( + data={ + "coords": result.coord.reshape((nframes, result.nloc, 3)), + "cells": np.zeros((nframes, 3, 3)) + if result.box is None + else result.box.reshape((nframes, 3, 3)), + "atom_types": np.array(result.atype), + "orig": np.zeros((3,)), + "atom_names": self.case.type_map, + "atom_numbs": atom_numbs, + "nopbc": result.box is None, + } + ) + system_predicted = system.predict(self.dp, driver="dp") + np.testing.assert_almost_equal( + system_predicted["forces"].ravel(), + result.force.ravel(), + default_places, + err_msg=f"Result {ii} force", + ) + expected_se = np.sum(result.energy.reshape([nframes, -1]), axis=1) + np.testing.assert_almost_equal( + system_predicted["energies"].ravel(), + expected_se.ravel(), + default_places, + err_msg=f"Result {ii} energy", + ) + expected_sv = np.sum(result.virial.reshape([nframes, -1, 9]), axis=1) + np.testing.assert_almost_equal( + system_predicted["virials"].ravel(), + expected_sv.ravel(), + default_places, + err_msg=f"Result {ii} virial", + ) + + +@parameterized( + ("se_e2_a",), # key + (".pb",), # model extension +) +class TestDeepPotNeighborList(TestDeepPot): + @classmethod + def setUpClass(cls): + key, extension = cls.param + cls.case = get_cases()[key] + model_name = cls.case.get_model(extension) + cls.dp = DeepEval( + model_name, + neighbor_list=ase.neighborlist.NewPrimitiveNeighborList( + cutoffs=cls.case.rcut, bothways=True + ), + ) + + @unittest.skip("multiple frames not supported") + def test_2frame_atm(self): + pass + + @unittest.skip("Zero atoms not supported") + def test_zero_input(self): + pass diff --git a/source/tests/pt/model/test_deeppot.py b/source/tests/pt/model/test_deeppot.py index 68b1ff65d5..7268181c26 100644 --- a/source/tests/pt/model/test_deeppot.py +++ b/source/tests/pt/model/test_deeppot.py @@ -24,10 +24,6 @@ DeepPot, ) -from ...tf.test_deeppot_a import ( - FparamAparamCommonTest, -) - class TestDeepPot(unittest.TestCase): def setUp(self): @@ -136,19 +132,4 @@ def test_dp_test_cpu(self): self.test_dp_test() -class TestFparamAparamPT(FparamAparamCommonTest, unittest.TestCase): - @classmethod - def setUpClass(cls): - cls.dp = DeepPot( - str(Path(__file__).parent.parent.parent / "infer/fparam_aparam.pth") - ) - - def setUp(self): - super().setUp() - # For unclear reason, the precision is only 1e-7 - # not sure if it is expected... - self.places = 1e-7 - - @classmethod - def tearDownClass(cls): - pass +# TestFparamAparamPT: moved to infer/test_models.py diff --git a/source/tests/tf/test_deeppot_a.py b/source/tests/tf/test_deeppot_a.py index f40b57c213..25477cafea 100644 --- a/source/tests/tf/test_deeppot_a.py +++ b/source/tests/tf/test_deeppot_a.py @@ -3,15 +3,12 @@ import shutil import unittest -import ase.neighborlist -import dpdata import numpy as np from packaging.version import parse as parse_version from deepmd.tf.env import ( GLOBAL_NP_FLOAT_PRECISION, MODEL_VERSION, - tf, ) from deepmd.tf.infer import ( DeepPot, @@ -98,677 +95,7 @@ def test(self): self.assertTrue("0.1000000" in str(context.exception)) -class TestDeepPotAPBC(unittest.TestCase): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot.pbtxt")), "deeppot.pb" - ) - cls.dp = DeepPot("deeppot.pb") - - def setUp(self): - self.coords = np.array( - [ - 12.83, - 2.56, - 2.18, - 12.09, - 2.87, - 2.74, - 00.25, - 3.32, - 1.68, - 3.36, - 3.00, - 1.81, - 3.51, - 2.51, - 2.60, - 4.27, - 3.22, - 1.56, - ] - ) - self.atype = [0, 1, 1, 0, 1, 1] - self.box = np.array([13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) - self.expected_e = np.array( - [ - -9.275780747115504710e01, - -1.863501786584258468e02, - -1.863392472863538103e02, - -9.279281325486221021e01, - -1.863671545232153903e02, - -1.863619822847602165e02, - ] - ) - self.expected_f = np.array( - [ - -3.034045420701179663e-01, - 8.405844663871177014e-01, - 7.696947487118485642e-02, - 7.662001266663505117e-01, - -1.880601391333554251e-01, - -6.183333871091722944e-01, - -5.036172391059643427e-01, - -6.529525836149027151e-01, - 5.432962643022043459e-01, - 6.382357912332115024e-01, - -1.748518296794561167e-01, - 3.457363524891907125e-01, - 1.286482986991941552e-03, - 3.757251165286925043e-01, - -5.972588700887541124e-01, - -5.987006197104716154e-01, - -2.004450304880958100e-01, - 2.495901655353461868e-01, - ] - ) - self.expected_v = np.array( - [ - -2.912234126853306959e-01, - -3.800610846612756388e-02, - 2.776624987489437202e-01, - -5.053761003913598976e-02, - -3.152373041953385746e-01, - 1.060894290092162379e-01, - 2.826389131596073745e-01, - 1.039129970665329250e-01, - -2.584378792325942586e-01, - -3.121722367954994914e-01, - 8.483275876786681990e-02, - 2.524662342344257682e-01, - 4.142176771106586414e-02, - -3.820285230785245428e-02, - -2.727311173065460545e-02, - 2.668859789777112135e-01, - -6.448243569420382404e-02, - -2.121731470426218846e-01, - -8.624335220278558922e-02, - -1.809695356746038597e-01, - 1.529875294531883312e-01, - -1.283658185172031341e-01, - -1.992682279795223999e-01, - 1.409924999632362341e-01, - 1.398322735274434292e-01, - 1.804318474574856390e-01, - -1.470309318999652726e-01, - -2.593983661598450730e-01, - -4.236536279233147489e-02, - 3.386387920184946720e-02, - -4.174017537818433543e-02, - -1.003500282164128260e-01, - 1.525690815194478966e-01, - 3.398976109910181037e-02, - 1.522253908435125536e-01, - -2.349125581341701963e-01, - 9.515545977581392825e-04, - -1.643218849228543846e-02, - 1.993234765412972564e-02, - 6.027265332209678569e-04, - -9.563256398907417355e-02, - 1.510815124001868293e-01, - -7.738094816888557714e-03, - 1.502832772532304295e-01, - -2.380965783745832010e-01, - -2.309456719810296654e-01, - -6.666961081213038098e-02, - 7.955566551234216632e-02, - -8.099093777937517447e-02, - -3.386641099800401927e-02, - 4.447884755740908608e-02, - 1.008593228579038742e-01, - 4.556718179228393811e-02, - -6.078081273849572641e-02, - ] - ) - - @classmethod - def tearDownClass(cls): - os.remove("deeppot.pb") - cls.dp = None - - def test_attrs(self): - self.assertEqual(self.dp.get_ntypes(), 2) - self.assertAlmostEqual(self.dp.get_rcut(), 6.0, places=default_places) - self.assertEqual(self.dp.get_type_map(), ["O", "H"]) - self.assertEqual(self.dp.get_dim_fparam(), 0) - self.assertEqual(self.dp.get_dim_aparam(), 0) - - def test_1frame(self): - ee, ff, vv = self.dp.eval(self.coords, self.box, self.atype, atomic=False) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_1frame_atm(self): - ee, ff, vv, ae, av = self.dp.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - np.testing.assert_almost_equal( - ae.ravel(), self.expected_e.ravel(), default_places - ) - np.testing.assert_almost_equal( - av.ravel(), self.expected_v.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_descriptor(self): - descpt = self.dp.eval_descriptor(self.coords, self.box, self.atype) - expected_descpt = np.loadtxt(str(infer_path / "deeppot_descpt.txt")) - np.testing.assert_almost_equal(descpt.ravel(), expected_descpt.ravel()) - - def test_2frame_atm(self): - coords2 = np.concatenate((self.coords, self.coords)) - box2 = np.concatenate((self.box, self.box)) - ee, ff, vv, ae, av = self.dp.eval(coords2, box2, self.atype, atomic=True) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - expected_f = np.concatenate((self.expected_f, self.expected_f), axis=0) - expected_e = np.concatenate((self.expected_e, self.expected_e), axis=0) - expected_v = np.concatenate((self.expected_v, self.expected_v), axis=0) - np.testing.assert_almost_equal(ff.ravel(), expected_f.ravel(), default_places) - np.testing.assert_almost_equal(ae.ravel(), expected_e.ravel(), default_places) - np.testing.assert_almost_equal(av.ravel(), expected_v.ravel(), default_places) - expected_se = np.sum(expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - # TODO: needs to fix - @unittest.skipIf(tf.test.is_gpu_available(), reason="Segfault in GPUs") - def test_zero_input(self): - nframes = 1 - ee, ff, vv = self.dp.eval( - np.zeros([nframes, 0, 3]), self.box, np.zeros([0]), atomic=False - ) - # check shape of the returns - natoms = 0 - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal(ff.ravel(), 0, default_places) - np.testing.assert_almost_equal(ee.ravel(), 0, default_places) - np.testing.assert_almost_equal(vv.ravel(), 0, default_places) - - -class TestDeepPotANoPBC(unittest.TestCase): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot.pbtxt")), "deeppot.pb" - ) - cls.dp = DeepPot("deeppot.pb") - - def setUp(self): - self.coords = np.array( - [ - 12.83, - 2.56, - 2.18, - 12.09, - 2.87, - 2.74, - 00.25, - 3.32, - 1.68, - 3.36, - 3.00, - 1.81, - 3.51, - 2.51, - 2.60, - 4.27, - 3.22, - 1.56, - ] - ) - self.atype = [0, 1, 1, 0, 1, 1] - self.box = None - self.expected_e = np.array( - [ - -9.255934839310273787e01, - -1.863253376736990106e02, - -1.857237299341402945e02, - -9.279308539717486326e01, - -1.863708105823244239e02, - -1.863635196514972563e02, - ] - ) - self.expected_f = np.array( - [ - -2.161037360255332107e00, - 9.052994347015581589e-01, - 1.635379623977007979e00, - 2.161037360255332107e00, - -9.052994347015581589e-01, - -1.635379623977007979e00, - -1.167128117249453811e-02, - 1.371975700096064992e-03, - -1.575265180249604477e-03, - 6.226508593971802341e-01, - -1.816734122009256991e-01, - 3.561766019664774907e-01, - -1.406075393906316626e-02, - 3.789140061530929526e-01, - -6.018777878642909140e-01, - -5.969188242856223736e-01, - -1.986125696522633155e-01, - 2.472764510780630642e-01, - ] - ) - self.expected_v = np.array( - [ - -7.042445481792056761e-01, - 2.950213647777754078e-01, - 5.329418202437231633e-01, - 2.950213647777752968e-01, - -1.235900311906896754e-01, - -2.232594111831812944e-01, - 5.329418202437232743e-01, - -2.232594111831813499e-01, - -4.033073234276823849e-01, - -8.949230984097404917e-01, - 3.749002169013777030e-01, - 6.772391014992630298e-01, - 3.749002169013777586e-01, - -1.570527935667933583e-01, - -2.837082722496912512e-01, - 6.772391014992631408e-01, - -2.837082722496912512e-01, - -5.125052659994422388e-01, - 4.858210330291591605e-02, - -6.902596153269104431e-03, - 6.682612642430500391e-03, - -5.612247004554610057e-03, - 9.767795567660207592e-04, - -9.773758942738038254e-04, - 5.638322117219018645e-03, - -9.483806049779926932e-04, - 8.493873281881353637e-04, - -2.941738570564985666e-01, - -4.482529909499673171e-02, - 4.091569840186781021e-02, - -4.509020615859140463e-02, - -1.013919988807244071e-01, - 1.551440772665269030e-01, - 4.181857726606644232e-02, - 1.547200233064863484e-01, - -2.398213304685777592e-01, - -3.218625798524068354e-02, - -1.012438450438508421e-02, - 1.271639330380921855e-02, - 3.072814938490859779e-03, - -9.556241797915024372e-02, - 1.512251983492413077e-01, - -8.277872384009607454e-03, - 1.505412040827929787e-01, - -2.386150620881526407e-01, - -2.312295470054945568e-01, - -6.631490213524345034e-02, - 7.932427266386249398e-02, - -8.053754366323923053e-02, - -3.294595881137418747e-02, - 4.342495071150231922e-02, - 1.004599500126941436e-01, - 4.450400364869536163e-02, - -5.951077548033092968e-02, - ] - ) - - @classmethod - def tearDownClass(cls): - os.remove("deeppot.pb") - cls.dp = None - - def test_1frame(self): - ee, ff, vv = self.dp.eval(self.coords, self.box, self.atype, atomic=False) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_1frame_atm(self): - ee, ff, vv, ae, av = self.dp.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - np.testing.assert_almost_equal( - ae.ravel(), self.expected_e.ravel(), default_places - ) - np.testing.assert_almost_equal( - av.ravel(), self.expected_v.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_2frame_atm(self): - coords2 = np.concatenate((self.coords, self.coords)) - ee, ff, vv, ae, av = self.dp.eval(coords2, self.box, self.atype, atomic=True) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - expected_f = np.concatenate((self.expected_f, self.expected_f), axis=0) - expected_e = np.concatenate((self.expected_e, self.expected_e), axis=0) - expected_v = np.concatenate((self.expected_v, self.expected_v), axis=0) - np.testing.assert_almost_equal(ff.ravel(), expected_f.ravel(), default_places) - np.testing.assert_almost_equal(ae.ravel(), expected_e.ravel(), default_places) - np.testing.assert_almost_equal(av.ravel(), expected_v.ravel(), default_places) - expected_se = np.sum(expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_dpdata_driver(self): - nframes = 1 - system = dpdata.System( - data={ - "coords": self.coords.reshape((nframes, -1, 3)), - "cells": np.zeros((nframes, 3, 3)), - "atom_types": np.array(self.atype), - "orig": np.zeros((3,)), - "atom_names": ["O", "H"], - "atom_numbs": [2, 4], - "nopbc": True, - } - ) - system_predicted = system.predict(self.dp, driver="dp") - np.testing.assert_almost_equal( - system_predicted["forces"].ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal( - system_predicted["energies"].ravel(), expected_se.ravel(), default_places - ) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal( - system_predicted["virials"].ravel(), expected_sv.ravel(), default_places - ) - - -class TestDeepPotALargeBoxNoPBC(unittest.TestCase): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot.pbtxt")), "deeppot.pb" - ) - cls.dp = DeepPot("deeppot.pb") - - def setUp(self): - self.coords = np.array( - [ - 12.83, - 2.56, - 2.18, - 12.09, - 2.87, - 2.74, - 00.25, - 3.32, - 1.68, - 3.36, - 3.00, - 1.81, - 3.51, - 2.51, - 2.60, - 4.27, - 3.22, - 1.56, - ] - ) - self.atype = [0, 1, 1, 0, 1, 1] - self.box = np.array([19.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) - self.expected_e = np.array( - [ - -9.255934839310273787e01, - -1.863253376736990106e02, - -1.857237299341402945e02, - -9.279308539717486326e01, - -1.863708105823244239e02, - -1.863635196514972563e02, - ] - ) - self.expected_f = np.array( - [ - -2.161037360255332107e00, - 9.052994347015581589e-01, - 1.635379623977007979e00, - 2.161037360255332107e00, - -9.052994347015581589e-01, - -1.635379623977007979e00, - -1.167128117249453811e-02, - 1.371975700096064992e-03, - -1.575265180249604477e-03, - 6.226508593971802341e-01, - -1.816734122009256991e-01, - 3.561766019664774907e-01, - -1.406075393906316626e-02, - 3.789140061530929526e-01, - -6.018777878642909140e-01, - -5.969188242856223736e-01, - -1.986125696522633155e-01, - 2.472764510780630642e-01, - ] - ) - self.expected_v = np.array( - [ - -7.042445481792056761e-01, - 2.950213647777754078e-01, - 5.329418202437231633e-01, - 2.950213647777752968e-01, - -1.235900311906896754e-01, - -2.232594111831812944e-01, - 5.329418202437232743e-01, - -2.232594111831813499e-01, - -4.033073234276823849e-01, - -8.949230984097404917e-01, - 3.749002169013777030e-01, - 6.772391014992630298e-01, - 3.749002169013777586e-01, - -1.570527935667933583e-01, - -2.837082722496912512e-01, - 6.772391014992631408e-01, - -2.837082722496912512e-01, - -5.125052659994422388e-01, - 4.858210330291591605e-02, - -6.902596153269104431e-03, - 6.682612642430500391e-03, - -5.612247004554610057e-03, - 9.767795567660207592e-04, - -9.773758942738038254e-04, - 5.638322117219018645e-03, - -9.483806049779926932e-04, - 8.493873281881353637e-04, - -2.941738570564985666e-01, - -4.482529909499673171e-02, - 4.091569840186781021e-02, - -4.509020615859140463e-02, - -1.013919988807244071e-01, - 1.551440772665269030e-01, - 4.181857726606644232e-02, - 1.547200233064863484e-01, - -2.398213304685777592e-01, - -3.218625798524068354e-02, - -1.012438450438508421e-02, - 1.271639330380921855e-02, - 3.072814938490859779e-03, - -9.556241797915024372e-02, - 1.512251983492413077e-01, - -8.277872384009607454e-03, - 1.505412040827929787e-01, - -2.386150620881526407e-01, - -2.312295470054945568e-01, - -6.631490213524345034e-02, - 7.932427266386249398e-02, - -8.053754366323923053e-02, - -3.294595881137418747e-02, - 4.342495071150231922e-02, - 1.004599500126941436e-01, - 4.450400364869536163e-02, - -5.951077548033092968e-02, - ] - ) - - @classmethod - def tearDownClass(cls): - os.remove("deeppot.pb") - cls.dp = None - - def test_1frame(self): - ee, ff, vv = self.dp.eval(self.coords, self.box, self.atype, atomic=False) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_1frame_atm(self): - ee, ff, vv, ae, av = self.dp.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - np.testing.assert_almost_equal( - ae.ravel(), self.expected_e.ravel(), default_places - ) - np.testing.assert_almost_equal( - av.ravel(), self.expected_v.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_ase(self): - from ase import ( - Atoms, - ) - - from deepmd.tf.calculator import ( - DP, - ) - - water = Atoms( - "OHHOHH", - positions=self.coords.reshape((-1, 3)), - cell=self.box.reshape((3, 3)), - calculator=DP("deeppot.pb"), - ) - ee = water.get_potential_energy() - ff = water.get_forces() - nframes = 1 - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - - def test_dpdata_driver(self): - nframes = 1 - system = dpdata.System( - data={ - "coords": self.coords.reshape((nframes, -1, 3)), - "cells": self.box.reshape((nframes, 3, 3)), - "atom_types": np.array(self.atype), - "orig": np.zeros((3,)), - "atom_names": ["O", "H"], - "atom_numbs": [2, 4], - } - ) - system_predicted = system.predict("deeppot.pb", driver="dp") - np.testing.assert_almost_equal( - system_predicted["forces"].ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal( - system_predicted["energies"].ravel(), expected_se.ravel(), default_places - ) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal( - system_predicted["virials"].ravel(), expected_sv.ravel(), default_places - ) +# TestDeepPotAPBC, TestDeepPotANoPBC, TestDeepPotALargeBoxNoPBC: moved to infer/test_models.py class TestModelConvert(unittest.TestCase): @@ -894,277 +221,5 @@ def test_eval_typeebd(self): np.testing.assert_almost_equal(eval_typeebd, expected_typeebd, default_places) -class FparamAparamCommonTest: - """Test fparam and aparam.""" - - def setUp(self): - self.coords = np.array( - [ - 12.83, - 2.56, - 2.18, - 12.09, - 2.87, - 2.74, - 00.25, - 3.32, - 1.68, - 3.36, - 3.00, - 1.81, - 3.51, - 2.51, - 2.60, - 4.27, - 3.22, - 1.56, - ] - ) - self.atype = [0, 0, 0, 0, 0, 0] - self.box = np.array([13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) - self.fparam = 0.25852028 - self.aparam = np.repeat(self.fparam, len(self.atype)) - self.expected_e = np.array( - [ - -1.038271183039953804e-01, - -7.285433575272914908e-02, - -9.467600174099155552e-02, - -1.467050086239614082e-01, - -7.660561620618722145e-02, - -7.277295998502930630e-02, - ] - ) - self.expected_f = np.array( - [ - 6.622266817497907132e-02, - 5.278739055693523058e-02, - 2.265727495541422845e-02, - -2.606047850915838363e-02, - -4.538811686410718776e-02, - 1.058247569147072187e-02, - 1.679392490937766935e-01, - -2.257828022687320690e-03, - -4.490145670355452645e-02, - -1.148364103573685929e-01, - -1.169790466695089237e-02, - 6.140402504113953025e-02, - -8.078778132132799494e-02, - -5.838878056243369807e-02, - 6.773639989682191109e-02, - -1.247724708090079161e-02, - 6.494523955924384750e-02, - -1.174787188812918687e-01, - ] - ) - self.expected_v = np.array( - [ - -1.589185553287162656e-01, - 2.586163333170100279e-03, - -1.575127933809472624e-04, - -1.855360380105876630e-02, - 1.949822090859933826e-02, - -1.006552056166355388e-02, - 3.177029853276916449e-02, - 1.714349636720383010e-03, - -1.290389175187874483e-03, - -8.553510339477603253e-02, - -5.654637257232508415e-03, - -1.286954833787038420e-02, - 2.464156457499515687e-02, - -2.398202886026797043e-02, - -1.957110465239037672e-02, - 2.233492928605742764e-02, - 6.107843207824020099e-03, - 1.707078295947736047e-03, - -1.653994088976195043e-01, - 3.894358678172111371e-02, - -2.169595969759342477e-02, - 6.819704294738503786e-03, - -5.018242039618424008e-03, - 2.640664428663210429e-03, - -1.985298275686078057e-03, - -3.638421609610945767e-02, - 2.342932331075030239e-02, - -8.501331914753691710e-02, - -2.181253413538992297e-03, - 4.311300069651782287e-03, - -1.910329328333908129e-03, - -1.808810159508548836e-03, - -1.540075281450827612e-03, - -1.173703213175551763e-02, - -2.596306629910121507e-03, - 6.705025662372287101e-03, - -9.038455005073858795e-02, - 3.011717773578577451e-02, - -5.083054073419784880e-02, - -2.951210292616929069e-03, - 2.342445652898489383e-02, - -4.091207474993674431e-02, - -1.648470649301832236e-02, - -2.872261885460645689e-02, - 4.763924972552112391e-02, - -8.300036532764677732e-02, - 1.020429228955421243e-03, - -1.026734151199098881e-03, - 5.678534096113684732e-02, - 1.273635718045938205e-02, - -1.530143225195957322e-02, - -1.061671865629566225e-01, - -2.486859433265622629e-02, - 2.875323131744185121e-02, - ] - ) - self.places = default_places - - def test_attrs(self): - self.assertEqual(self.dp.get_ntypes(), 1) - self.assertAlmostEqual(self.dp.get_rcut(), 6.0, places=self.places) - self.assertEqual(self.dp.get_dim_fparam(), 1) - self.assertEqual(self.dp.get_dim_aparam(), 1) - - def test_1frame(self): - ee, ff, vv = self.dp.eval( - self.coords, - self.box, - self.atype, - fparam=self.fparam, - aparam=self.aparam, - atomic=False, - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal(ff.ravel(), self.expected_f.ravel(), self.places) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), self.places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), self.places) - - def test_1frame_atm(self): - ee, ff, vv, ae, av = self.dp.eval( - self.coords, - self.box, - self.atype, - fparam=self.fparam, - aparam=self.aparam, - atomic=True, - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal(ff.ravel(), self.expected_f.ravel(), self.places) - np.testing.assert_almost_equal(ae.ravel(), self.expected_e.ravel(), self.places) - np.testing.assert_almost_equal(av.ravel(), self.expected_v.ravel(), self.places) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), self.places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), self.places) - - def test_2frame_atm_single_param(self): - coords2 = np.concatenate((self.coords, self.coords)) - box2 = np.concatenate((self.box, self.box)) - ee, ff, vv, ae, av = self.dp.eval( - coords2, - box2, - self.atype, - fparam=self.fparam, - aparam=self.aparam, - atomic=True, - ) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - expected_f = np.concatenate((self.expected_f, self.expected_f), axis=0) - expected_e = np.concatenate((self.expected_e, self.expected_e), axis=0) - expected_v = np.concatenate((self.expected_v, self.expected_v), axis=0) - np.testing.assert_almost_equal(ff.ravel(), expected_f.ravel(), self.places) - np.testing.assert_almost_equal(ae.ravel(), expected_e.ravel(), self.places) - np.testing.assert_almost_equal(av.ravel(), expected_v.ravel(), self.places) - expected_se = np.sum(expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), self.places) - expected_sv = np.sum(expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), self.places) - - def test_2frame_atm_all_param(self): - coords2 = np.concatenate((self.coords, self.coords)) - box2 = np.concatenate((self.box, self.box)) - ee, ff, vv, ae, av = self.dp.eval( - coords2, - box2, - self.atype, - fparam=np.repeat(self.fparam, 2), - aparam=np.repeat(self.aparam, 2), - atomic=True, - ) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - expected_f = np.concatenate((self.expected_f, self.expected_f), axis=0) - expected_e = np.concatenate((self.expected_e, self.expected_e), axis=0) - expected_v = np.concatenate((self.expected_v, self.expected_v), axis=0) - np.testing.assert_almost_equal(ff.ravel(), expected_f.ravel(), self.places) - np.testing.assert_almost_equal(ae.ravel(), expected_e.ravel(), self.places) - np.testing.assert_almost_equal(av.ravel(), expected_v.ravel(), self.places) - expected_se = np.sum(expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), self.places) - expected_sv = np.sum(expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), self.places) - - -class TestFparamAparam(FparamAparamCommonTest, unittest.TestCase): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("fparam_aparam.pbtxt")), - "fparam_aparam.pb", - ) - cls.dp = DeepPot("fparam_aparam.pb") - - @classmethod - def tearDownClass(cls): - os.remove("fparam_aparam.pb") - cls.dp = None - - -class TestDeepPotAPBCNeighborList(TestDeepPotAPBC): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot.pbtxt")), "deeppot.pb" - ) - cls.dp = DeepPot( - "deeppot.pb", - neighbor_list=ase.neighborlist.NewPrimitiveNeighborList( - cutoffs=6, bothways=True - ), - ) - - @unittest.skip("multiple frames not supported") - def test_2frame_atm(self): - pass - - @unittest.skip("Zero atoms not supported") - def test_zero_input(self): - pass +# TestFparamAparam: moved to infer/test_models.py +# TestDeepPotAPBCNeighborList: moved to infer/test_models.py diff --git a/source/tests/tf/test_deeppot_r.py b/source/tests/tf/test_deeppot_r.py index 482a8c42ee..b3fa6ae5ed 100644 --- a/source/tests/tf/test_deeppot_r.py +++ b/source/tests/tf/test_deeppot_r.py @@ -1,624 +1,2 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -import os -import unittest - -import numpy as np - -from deepmd.tf.env import ( - GLOBAL_NP_FLOAT_PRECISION, - tf, -) -from deepmd.tf.infer import ( - DeepPot, -) -from deepmd.tf.utils.convert import ( - convert_pbtxt_to_pb, -) - -from .common import ( - infer_path, -) - -if GLOBAL_NP_FLOAT_PRECISION == np.float32: - default_places = 4 -else: - default_places = 10 - - -class TestDeepPotRPBC(unittest.TestCase): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot-r.pbtxt")), "deeppot.pb" - ) - cls.dp = DeepPot("deeppot.pb") - - def setUp(self): - self.coords = np.array( - [ - 12.83, - 2.56, - 2.18, - 12.09, - 2.87, - 2.74, - 00.25, - 3.32, - 1.68, - 3.36, - 3.00, - 1.81, - 3.51, - 2.51, - 2.60, - 4.27, - 3.22, - 1.56, - ] - ) - self.atype = [0, 1, 1, 0, 1, 1] - self.box = np.array([13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) - self.expected_e = np.array( - [ - -9.320909762801588272e01, - -1.868020345400987878e02, - -1.868011172371355997e02, - -9.320868430396934912e01, - -1.868010398844378415e02, - -1.868016706555875999e02, - ] - ) - self.expected_f = np.array( - [ - 6.385312846474267391e-04, - -6.460452911141417731e-03, - -5.652405655332678417e-04, - -7.516468794343579736e-03, - 1.128804614240160216e-03, - 5.531937784564192051e-03, - 1.914138124904981664e-03, - 5.601819906021693503e-03, - -5.131359585752605541e-03, - -4.847104424804288617e-03, - 1.992071550328819614e-03, - -4.028159855157302516e-03, - 1.236340684486603517e-03, - -5.373955841338794344e-03, - 8.312829460571366513e-03, - 8.574563125108854156e-03, - 3.111712681889538742e-03, - -4.120007238692381148e-03, - ] - ) - self.expected_v = np.array( - [ - 5.844056241889131371e-03, - 4.663973497239899614e-04, - -2.268382127762904633e-03, - 4.663973497239897988e-04, - 2.349338784202595950e-03, - -6.908546513234039253e-04, - -2.268382127762904633e-03, - -6.908546513234039253e-04, - 2.040499248150800561e-03, - 4.238130266437327605e-03, - -1.539867187443782223e-04, - -2.393101333240631613e-03, - -1.539867187443782223e-04, - 4.410341945447907377e-04, - 9.544239698119633068e-06, - -2.393101333240631613e-03, - 9.544239698119578858e-06, - 1.877785959095269654e-03, - 5.798992562057291543e-03, - 6.943392552230453693e-04, - -1.180376879311998773e-03, - 6.943392552230453693e-04, - 1.686725132156275536e-03, - -1.461632060145726542e-03, - -1.180376879311998556e-03, - -1.461632060145726325e-03, - 1.749543733794208444e-03, - 7.173915604192910439e-03, - 3.903218041111061569e-04, - -5.747400467123527524e-04, - 3.903218041111061569e-04, - 1.208289706621179949e-03, - -1.826828914132010932e-03, - -5.747400467123527524e-04, - -1.826828914132011148e-03, - 2.856960586657185906e-03, - 4.067553030177322240e-03, - -3.267469855253819430e-05, - -6.980667859103454904e-05, - -3.267469855253830272e-05, - 1.387653029234650918e-03, - -2.096820720698671855e-03, - -6.980667859103444062e-05, - -2.096820720698671855e-03, - 3.218305506720191278e-03, - 4.753992590355240674e-03, - 1.224911338353675992e-03, - -1.683421934571502484e-03, - 1.224911338353676209e-03, - 7.332113564901583539e-04, - -1.025577052190138451e-03, - -1.683421934571502484e-03, - -1.025577052190138234e-03, - 1.456681925652047018e-03, - ] - ) - - @classmethod - def tearDownClass(cls): - os.remove("deeppot.pb") - cls.dp = None - - def test_attrs(self): - self.assertEqual(self.dp.get_ntypes(), 2) - self.assertAlmostEqual(self.dp.get_rcut(), 6.0, places=default_places) - self.assertEqual(self.dp.get_type_map(), ["O", "H"]) - self.assertEqual(self.dp.get_dim_fparam(), 0) - self.assertEqual(self.dp.get_dim_aparam(), 0) - - # def test_1frame(self): - # ee, ff, vv, ae, av = self.dp.eval(self.coords, self.box, self.atype, atomic = True) - # np.savetxt('ee.out', ae.reshape([1, -1]), delimiter=',') - # np.savetxt('ff.out', ff.reshape([1, -1]), delimiter=',') - # np.savetxt('vv.out', av.reshape([1, -1]), delimiter=',') - - def test_1frame(self): - ee, ff, vv = self.dp.eval(self.coords, self.box, self.atype, atomic=False) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_1frame_atm(self): - ee, ff, vv, ae, av = self.dp.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - np.testing.assert_almost_equal( - ae.ravel(), self.expected_e.ravel(), default_places - ) - np.testing.assert_almost_equal( - av.ravel(), self.expected_v.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_2frame_atm(self): - coords2 = np.concatenate((self.coords, self.coords)) - box2 = np.concatenate((self.box, self.box)) - ee, ff, vv, ae, av = self.dp.eval(coords2, box2, self.atype, atomic=True) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - expected_f = np.concatenate((self.expected_f, self.expected_f), axis=0) - expected_e = np.concatenate((self.expected_e, self.expected_e), axis=0) - expected_v = np.concatenate((self.expected_v, self.expected_v), axis=0) - np.testing.assert_almost_equal(ff.ravel(), expected_f.ravel(), default_places) - np.testing.assert_almost_equal(ae.ravel(), expected_e.ravel(), default_places) - np.testing.assert_almost_equal(av.ravel(), expected_v.ravel(), default_places) - expected_se = np.sum(expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - -class TestDeepPotRNoPBC(unittest.TestCase): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot-r.pbtxt")), "deeppot.pb" - ) - cls.dp = DeepPot("deeppot.pb") - - def setUp(self): - self.coords = np.array( - [ - 12.83, - 2.56, - 2.18, - 12.09, - 2.87, - 2.74, - 00.25, - 3.32, - 1.68, - 3.36, - 3.00, - 1.81, - 3.51, - 2.51, - 2.60, - 4.27, - 3.22, - 1.56, - ] - ) - self.atype = [0, 1, 1, 0, 1, 1] - self.box = None - self.expected_e = np.array( - [ - -9.321213823508108476e01, - -1.868044102481340758e02, - -1.868067983858651075e02, - -9.320899631301440991e01, - -1.868014559732615112e02, - -1.868017660713088617e02, - ] - ) - self.expected_f = np.array( - [ - 4.578151103701261042e-03, - -1.917874111009987628e-03, - -3.464546781179331930e-03, - -4.578151103701261042e-03, - 1.917874111009987628e-03, - 3.464546781179331930e-03, - -2.624402581721222913e-03, - 3.566275128489623933e-04, - -2.859315986763691776e-04, - -5.767787273464367384e-03, - 1.907053583551196647e-03, - -3.889064429673861831e-03, - 1.786820066350549132e-04, - -5.327197473636275694e-03, - 8.236236182834734409e-03, - 8.213507848550535492e-03, - 3.063516377236116545e-03, - -4.061240154484504865e-03, - ] - ) - self.expected_v = np.array( - [ - 1.984979026299632174e-03, - -8.315452677741701822e-04, - -1.502146290172694243e-03, - -8.315452677741700738e-04, - 3.483500446080982317e-04, - 6.292774999372096039e-04, - -1.502146290172694243e-03, - 6.292774999372097123e-04, - 1.136759354725281907e-03, - 1.402852790439301908e-03, - -5.876815743732210226e-04, - -1.061618327900012114e-03, - -5.876815743732211311e-04, - 2.461909298049979960e-04, - 4.447320022283834766e-04, - -1.061618327900012331e-03, - 4.447320022283834766e-04, - 8.033868427351443728e-04, - 4.143606961846296385e-03, - -5.511382161123719835e-04, - 4.465413399437045397e-04, - -5.511382161123719835e-04, - 1.082271054025323839e-04, - -1.097918001262628728e-04, - 4.465413399437046481e-04, - -1.097918001262628728e-04, - 1.220966982358671871e-04, - 5.263952004497593831e-03, - 2.395243710938091842e-04, - -2.830378939414603329e-04, - 2.395243710938094010e-04, - 1.189969706598244898e-03, - -1.805627331015851201e-03, - -2.830378939414602245e-04, - -1.805627331015851635e-03, - 2.801996513751836820e-03, - 2.208413501170402270e-03, - 5.331756287635716889e-05, - -1.664423506603235218e-04, - 5.331756287635695205e-05, - 1.379626072862918072e-03, - -2.094132943741625064e-03, - -1.664423506603234133e-04, - -2.094132943741625064e-03, - 3.199787996743366607e-03, - 4.047014004814953811e-03, - 1.137904999421357000e-03, - -1.568106936614101698e-03, - 1.137904999421357217e-03, - 7.205982843216952307e-04, - -1.011174600268313238e-03, - -1.568106936614101698e-03, - -1.011174600268313238e-03, - 1.435226522157425754e-03, - ] - ) - - @classmethod - def tearDownClass(cls): - os.remove("deeppot.pb") - cls.dp = None - - def test_1frame(self): - ee, ff, vv = self.dp.eval(self.coords, self.box, self.atype, atomic=False) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_1frame_atm(self): - ee, ff, vv, ae, av = self.dp.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - np.testing.assert_almost_equal( - ae.ravel(), self.expected_e.ravel(), default_places - ) - np.testing.assert_almost_equal( - av.ravel(), self.expected_v.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_2frame_atm(self): - coords2 = np.concatenate((self.coords, self.coords)) - ee, ff, vv, ae, av = self.dp.eval(coords2, self.box, self.atype, atomic=True) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - expected_f = np.concatenate((self.expected_f, self.expected_f), axis=0) - expected_e = np.concatenate((self.expected_e, self.expected_e), axis=0) - expected_v = np.concatenate((self.expected_v, self.expected_v), axis=0) - np.testing.assert_almost_equal(ff.ravel(), expected_f.ravel(), default_places) - np.testing.assert_almost_equal(ae.ravel(), expected_e.ravel(), default_places) - np.testing.assert_almost_equal(av.ravel(), expected_v.ravel(), default_places) - expected_se = np.sum(expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - # TODO: needs to fix - @unittest.skipIf(tf.test.is_gpu_available(), reason="Segfault in GPUs") - def test_zero_input(self): - nframes = 1 - ee, ff, vv = self.dp.eval( - np.zeros([nframes, 0, 3]), self.box, np.zeros([0]), atomic=False - ) - # check shape of the returns - natoms = 0 - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal(ff.ravel(), 0, default_places) - np.testing.assert_almost_equal(ee.ravel(), 0, default_places) - np.testing.assert_almost_equal(vv.ravel(), 0, default_places) - - -class TestDeepPotRLargeBoxNoPBC(unittest.TestCase): - @classmethod - def setUpClass(cls): - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot-r.pbtxt")), "deeppot.pb" - ) - cls.dp = DeepPot("deeppot.pb") - - def setUp(self): - self.coords = np.array( - [ - 12.83, - 2.56, - 2.18, - 12.09, - 2.87, - 2.74, - 00.25, - 3.32, - 1.68, - 3.36, - 3.00, - 1.81, - 3.51, - 2.51, - 2.60, - 4.27, - 3.22, - 1.56, - ] - ) - self.atype = [0, 1, 1, 0, 1, 1] - self.box = np.array([19.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) - self.expected_e = np.array( - [ - -9.321213823508108476e01, - -1.868044102481340758e02, - -1.868067983858651075e02, - -9.320899631301440991e01, - -1.868014559732615112e02, - -1.868017660713088617e02, - ] - ) - self.expected_f = np.array( - [ - 4.578151103701261042e-03, - -1.917874111009987628e-03, - -3.464546781179331930e-03, - -4.578151103701261042e-03, - 1.917874111009987628e-03, - 3.464546781179331930e-03, - -2.624402581721222913e-03, - 3.566275128489623933e-04, - -2.859315986763691776e-04, - -5.767787273464367384e-03, - 1.907053583551196647e-03, - -3.889064429673861831e-03, - 1.786820066350549132e-04, - -5.327197473636275694e-03, - 8.236236182834734409e-03, - 8.213507848550535492e-03, - 3.063516377236116545e-03, - -4.061240154484504865e-03, - ] - ) - self.expected_v = np.array( - [ - 1.984979026299632174e-03, - -8.315452677741701822e-04, - -1.502146290172694243e-03, - -8.315452677741700738e-04, - 3.483500446080982317e-04, - 6.292774999372096039e-04, - -1.502146290172694243e-03, - 6.292774999372097123e-04, - 1.136759354725281907e-03, - 1.402852790439301908e-03, - -5.876815743732210226e-04, - -1.061618327900012114e-03, - -5.876815743732211311e-04, - 2.461909298049979960e-04, - 4.447320022283834766e-04, - -1.061618327900012331e-03, - 4.447320022283834766e-04, - 8.033868427351443728e-04, - 4.143606961846296385e-03, - -5.511382161123719835e-04, - 4.465413399437045397e-04, - -5.511382161123719835e-04, - 1.082271054025323839e-04, - -1.097918001262628728e-04, - 4.465413399437046481e-04, - -1.097918001262628728e-04, - 1.220966982358671871e-04, - 5.263952004497593831e-03, - 2.395243710938091842e-04, - -2.830378939414603329e-04, - 2.395243710938094010e-04, - 1.189969706598244898e-03, - -1.805627331015851201e-03, - -2.830378939414602245e-04, - -1.805627331015851635e-03, - 2.801996513751836820e-03, - 2.208413501170402270e-03, - 5.331756287635716889e-05, - -1.664423506603235218e-04, - 5.331756287635695205e-05, - 1.379626072862918072e-03, - -2.094132943741625064e-03, - -1.664423506603234133e-04, - -2.094132943741625064e-03, - 3.199787996743366607e-03, - 4.047014004814953811e-03, - 1.137904999421357000e-03, - -1.568106936614101698e-03, - 1.137904999421357217e-03, - 7.205982843216952307e-04, - -1.011174600268313238e-03, - -1.568106936614101698e-03, - -1.011174600268313238e-03, - 1.435226522157425754e-03, - ] - ) - - @classmethod - def tearDownClass(cls): - os.remove("deeppot.pb") - cls.dp = None - - def test_1frame(self): - ee, ff, vv = self.dp.eval(self.coords, self.box, self.atype, atomic=False) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) - - def test_1frame_atm(self): - ee, ff, vv, ae, av = self.dp.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee.shape, (nframes, 1)) - self.assertEqual(ff.shape, (nframes, natoms, 3)) - self.assertEqual(vv.shape, (nframes, 9)) - self.assertEqual(ae.shape, (nframes, natoms, 1)) - self.assertEqual(av.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal( - ff.ravel(), self.expected_f.ravel(), default_places - ) - np.testing.assert_almost_equal( - ae.ravel(), self.expected_e.ravel(), default_places - ) - np.testing.assert_almost_equal( - av.ravel(), self.expected_v.ravel(), default_places - ) - expected_se = np.sum(self.expected_e.reshape([nframes, -1]), axis=1) - np.testing.assert_almost_equal(ee.ravel(), expected_se.ravel(), default_places) - expected_sv = np.sum(self.expected_v.reshape([nframes, -1, 9]), axis=1) - np.testing.assert_almost_equal(vv.ravel(), expected_sv.ravel(), default_places) +# moved to source/tests/infer/test_models.py diff --git a/source/tests/tf/test_dp_test.py b/source/tests/tf/test_dp_test.py index 9a3dde3da0..2d9af234dd 100644 --- a/source/tests/tf/test_dp_test.py +++ b/source/tests/tf/test_dp_test.py @@ -14,6 +14,9 @@ convert_pbtxt_to_pb, ) +from ..infer.case import ( + get_cases, +) from .common import ( infer_path, ) @@ -70,103 +73,15 @@ def tearDownClass(cls): class TestDPTestEner(unittest.TestCase, TestDPTest): @classmethod def setUpClass(cls): - cls.model_name = "deeppot.pb" - convert_pbtxt_to_pb( - str(infer_path / os.path.join("deeppot.pbtxt")), cls.model_name - ) + cls.case = get_cases()["se_e2_a"] + cls.model_name = cls.case.get_model(".pb") def setUp(self): + self.result = self.case.results[0] TestDPTest.setUp(self) - self.expected_e = np.array( - [ - -9.275780747115504710e01, - -1.863501786584258468e02, - -1.863392472863538103e02, - -9.279281325486221021e01, - -1.863671545232153903e02, - -1.863619822847602165e02, - ] - ) - self.expected_f = np.array( - [ - -3.034045420701179663e-01, - 8.405844663871177014e-01, - 7.696947487118485642e-02, - 7.662001266663505117e-01, - -1.880601391333554251e-01, - -6.183333871091722944e-01, - -5.036172391059643427e-01, - -6.529525836149027151e-01, - 5.432962643022043459e-01, - 6.382357912332115024e-01, - -1.748518296794561167e-01, - 3.457363524891907125e-01, - 1.286482986991941552e-03, - 3.757251165286925043e-01, - -5.972588700887541124e-01, - -5.987006197104716154e-01, - -2.004450304880958100e-01, - 2.495901655353461868e-01, - ] - ) - self.expected_v = np.array( - [ - -2.912234126853306959e-01, - -3.800610846612756388e-02, - 2.776624987489437202e-01, - -5.053761003913598976e-02, - -3.152373041953385746e-01, - 1.060894290092162379e-01, - 2.826389131596073745e-01, - 1.039129970665329250e-01, - -2.584378792325942586e-01, - -3.121722367954994914e-01, - 8.483275876786681990e-02, - 2.524662342344257682e-01, - 4.142176771106586414e-02, - -3.820285230785245428e-02, - -2.727311173065460545e-02, - 2.668859789777112135e-01, - -6.448243569420382404e-02, - -2.121731470426218846e-01, - -8.624335220278558922e-02, - -1.809695356746038597e-01, - 1.529875294531883312e-01, - -1.283658185172031341e-01, - -1.992682279795223999e-01, - 1.409924999632362341e-01, - 1.398322735274434292e-01, - 1.804318474574856390e-01, - -1.470309318999652726e-01, - -2.593983661598450730e-01, - -4.236536279233147489e-02, - 3.386387920184946720e-02, - -4.174017537818433543e-02, - -1.003500282164128260e-01, - 1.525690815194478966e-01, - 3.398976109910181037e-02, - 1.522253908435125536e-01, - -2.349125581341701963e-01, - 9.515545977581392825e-04, - -1.643218849228543846e-02, - 1.993234765412972564e-02, - 6.027265332209678569e-04, - -9.563256398907417355e-02, - 1.510815124001868293e-01, - -7.738094816888557714e-03, - 1.502832772532304295e-01, - -2.380965783745832010e-01, - -2.309456719810296654e-01, - -6.666961081213038098e-02, - 7.955566551234216632e-02, - -8.099093777937517447e-02, - -3.386641099800401927e-02, - 4.447884755740908608e-02, - 1.008593228579038742e-01, - 4.556718179228393811e-02, - -6.078081273849572641e-02, - ] - ) + self.expected_e = self.result.atomic_energy + self.expected_f = self.result.force + self.expected_v = self.result.atomic_virial def test_1frame(self): detail_file = "test_dp_test_ener_detail"