diff --git a/deepmd/jax/atomic_model/linear_atomic_model.py b/deepmd/jax/atomic_model/linear_atomic_model.py index ae9bae6c4a..e25f8a8658 100644 --- a/deepmd/jax/atomic_model/linear_atomic_model.py +++ b/deepmd/jax/atomic_model/linear_atomic_model.py @@ -24,7 +24,7 @@ def __setattr__(self, name: str, value: Any) -> None: if name == "zbl_weight": # discard since it's only used in tests # to fix flax.errors.TraceContextError: Cannot mutate 'FlaxModule' from different trace level - return + return None return super().__setattr__(name, value) def forward_common_atomic( diff --git a/deepmd/kernels/triton/sezm/so2_rotation.py b/deepmd/kernels/triton/sezm/so2_rotation.py index 87b7792121..b69eaef1aa 100644 --- a/deepmd/kernels/triton/sezm/so2_rotation.py +++ b/deepmd/kernels/triton/sezm/so2_rotation.py @@ -271,7 +271,7 @@ def _to_local_fwd_kernel( coeff_rows = tl.load(idx_ptr + row, mask=row_mask, other=0).to(tl.int64) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) - for k0 in range(0, tl.cdiv(dim_full, BLOCK_K)): + for k0 in range(tl.cdiv(dim_full, BLOCK_K)): kk = k0 * BLOCK_K + tl.arange(0, BLOCK_K) # over D k_mask = kk < dim_full w_tile = tl.load( @@ -333,7 +333,7 @@ def _to_local_bwd_dx_kernel( src_idx = tl.load(src_ptr + edge).to(tl.int64) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) - for k0 in range(0, tl.cdiv(reduced_dim, BLOCK_K)): + for k0 in range(tl.cdiv(reduced_dim, BLOCK_K)): mm = k0 * BLOCK_K + tl.arange(0, BLOCK_K) # over Dm m_mask = mm < reduced_dim coeff = tl.load(idx_ptr + mm, mask=m_mask, other=0).to(tl.int64) @@ -395,7 +395,7 @@ def _to_local_bwd_dw_kernel( src_idx = tl.load(src_ptr + edge).to(tl.int64) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) - for k0 in range(0, tl.cdiv(channels, BLOCK_K)): + for k0 in range(tl.cdiv(channels, BLOCK_K)): cc = k0 * BLOCK_K + tl.arange(0, BLOCK_K) # over C c_mask = cc < channels go_tile = tl.load( @@ -454,7 +454,7 @@ def _back_fwd_kernel( chan_mask = chan < channels acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) - for k0 in range(0, tl.cdiv(dim_full, BLOCK_K)): + for k0 in range(tl.cdiv(dim_full, BLOCK_K)): kk = k0 * BLOCK_K + tl.arange(0, BLOCK_K) # over D (contraction) k_mask = kk < dim_full inv_k = tl.load(inv_ptr + kk, mask=k_mask, other=-1).to(tl.int64) @@ -517,7 +517,7 @@ def _back_bwd_dx_kernel( keep = inv_k >= 0 acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) - for k0 in range(0, tl.cdiv(dim_full, BLOCK_K)): + for k0 in range(tl.cdiv(dim_full, BLOCK_K)): dd = k0 * BLOCK_K + tl.arange(0, BLOCK_K) # over D (contraction) d_mask = dd < dim_full w_tile = tl.load( @@ -578,7 +578,7 @@ def _back_bwd_dw_kernel( keep = inv_k >= 0 acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) - for k0 in range(0, tl.cdiv(channels, BLOCK_K)): + for k0 in range(tl.cdiv(channels, BLOCK_K)): cc = k0 * BLOCK_K + tl.arange(0, BLOCK_K) # over C (contraction) c_mask = cc < channels go_tile = tl.load( diff --git a/deepmd/pd/entrypoints/main.py b/deepmd/pd/entrypoints/main.py index f397bc358b..acd49c589c 100644 --- a/deepmd/pd/entrypoints/main.py +++ b/deepmd/pd/entrypoints/main.py @@ -103,7 +103,7 @@ def prepare_trainer_input_single( seed: int | None = None, ) -> tuple[DpLoaderSet, DpLoaderSet | None, DPPath | None]: training_dataset_params = data_dict_single["training_data"] - validation_dataset_params = data_dict_single.get("validation_data", None) + validation_dataset_params = data_dict_single.get("validation_data") validation_systems = ( validation_dataset_params["systems"] if validation_dataset_params else None ) @@ -115,7 +115,7 @@ def prepare_trainer_input_single( validation_systems = process_systems(validation_systems, val_patterns) # stat files - stat_file_path_single = data_dict_single.get("stat_file", None) + stat_file_path_single = data_dict_single.get("stat_file") if rank != 0: stat_file_path_single = None elif stat_file_path_single is not None: diff --git a/deepmd/pd/train/wrapper.py b/deepmd/pd/train/wrapper.py index f61e9867ab..05817920da 100644 --- a/deepmd/pd/train/wrapper.py +++ b/deepmd/pd/train/wrapper.py @@ -207,7 +207,6 @@ def state_dict(self) -> dict[str, Any]: def set_extra_state(self, extra_state: dict[str, Any]) -> None: self.model_params = extra_state["model_params"] self.train_infos = extra_state["train_infos"] - return None def get_extra_state(self) -> dict: extra_state = { diff --git a/deepmd/pd/utils/dataloader.py b/deepmd/pd/utils/dataloader.py index acaadb67aa..2773e27240 100644 --- a/deepmd/pd/utils/dataloader.py +++ b/deepmd/pd/utils/dataloader.py @@ -286,7 +286,7 @@ def __init__( Thread.__init__(self) self._queue = queue self._source = source # Main DL iterator - self._max_len = max_len # + self._max_len = max_len def run(self) -> None: for item in self._source: diff --git a/deepmd/pd/utils/utils.py b/deepmd/pd/utils/utils.py index 0f7b1e7987..158b76f0df 100644 --- a/deepmd/pd/utils/utils.py +++ b/deepmd/pd/utils/utils.py @@ -263,7 +263,7 @@ def to_numpy_array( # Create a reverse mapping of PD_PRECISION_DICT reverse_precision_dict = {v: k for k, v in PD_PRECISION_DICT.items()} # Use the reverse mapping to find keys with the desired value - prec = reverse_precision_dict.get(xx.dtype, None) + prec = reverse_precision_dict.get(xx.dtype) prec = NP_PRECISION_DICT.get(prec, np.float64) if prec is None: raise ValueError(f"unknown precision {xx.dtype}") @@ -293,7 +293,7 @@ def to_paddle_tensor( # Create a reverse mapping of NP_PRECISION_DICT reverse_precision_dict = {v: k for k, v in NP_PRECISION_DICT.items()} # Use the reverse mapping to find keys with the desired value - prec = reverse_precision_dict.get(xx.dtype.type, None) + prec = reverse_precision_dict.get(xx.dtype.type) prec = PD_PRECISION_DICT.get(prec, None) if prec is None: raise ValueError(f"unknown precision {xx.dtype}") diff --git a/deepmd/pt/entrypoints/main.py b/deepmd/pt/entrypoints/main.py index 560ea5a1ba..9da161580b 100644 --- a/deepmd/pt/entrypoints/main.py +++ b/deepmd/pt/entrypoints/main.py @@ -154,19 +154,19 @@ def prepare_trainer_input_single( ]: # get data modifier modifier = None - modifier_params = model_params_single.get("modifier", None) + modifier_params = model_params_single.get("modifier") if modifier_params is not None: modifier = get_data_modifier(modifier_params).to(DEVICE) training_dataset_params = data_dict_single["training_data"] - validation_dataset_params = data_dict_single.get("validation_data", None) + validation_dataset_params = data_dict_single.get("validation_data") validation_systems = ( validation_dataset_params["systems"] if validation_dataset_params else None ) training_systems = training_dataset_params["systems"] # stat files - stat_file_path_single = data_dict_single.get("stat_file", None) + stat_file_path_single = data_dict_single.get("stat_file") if rank != 0: stat_file_path_single = None elif stat_file_path_single is not None: @@ -185,7 +185,7 @@ def _make_dp_loader_set( dataset_params: dict[str, Any], ) -> DpLoaderSet: """Create a DpLoaderSet from systems with pattern expansion.""" - patterns = dataset_params.get("rglob_patterns", None) + patterns = dataset_params.get("rglob_patterns") systems = process_systems(systems, patterns=patterns) return DpLoaderSet( systems, diff --git a/deepmd/pt/optimizer/hybrid_muon.py b/deepmd/pt/optimizer/hybrid_muon.py index d2d96ccdb8..24caaadc41 100644 --- a/deepmd/pt/optimizer/hybrid_muon.py +++ b/deepmd/pt/optimizer/hybrid_muon.py @@ -281,7 +281,7 @@ def _mmt_kernel( b_ptrs = x + (offs_xn[:, None] * stride_xm + offs_k[None, :] * stride_xk) accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_M), dtype=tl.float32) - for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + for k in range(tl.cdiv(K, BLOCK_SIZE_K)): a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) b = tl.load(b_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) accumulator = tl.dot(a, tl.permute(b, (1, 0)), accumulator) diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py index 76a2e9866a..8dd68c8cdb 100644 --- a/deepmd/pt/train/training.py +++ b/deepmd/pt/train/training.py @@ -329,7 +329,7 @@ def get_dataloader_and_iter_lmdb( rank=self.rank, world_size=self.world_size, shuffle=True, - seed=_training_params.get("seed", None), + seed=_training_params.get("seed"), block_targets=_block_targets, ) else: diff --git a/deepmd/pt/train/wrapper.py b/deepmd/pt/train/wrapper.py index da710f4fdf..b2e4d4f229 100644 --- a/deepmd/pt/train/wrapper.py +++ b/deepmd/pt/train/wrapper.py @@ -256,7 +256,6 @@ def _forward_without_loss( def set_extra_state(self, state: dict) -> None: self.model_params = state["model_params"] self.train_infos = state["train_infos"] - return None def get_extra_state(self) -> dict: state = { diff --git a/deepmd/pt/utils/utils.py b/deepmd/pt/utils/utils.py index a3ba852d7e..9f95c59adc 100644 --- a/deepmd/pt/utils/utils.py +++ b/deepmd/pt/utils/utils.py @@ -247,7 +247,7 @@ def to_numpy_array( # Create a reverse mapping of PT_PRECISION_DICT reverse_precision_dict = {v: k for k, v in PT_PRECISION_DICT.items()} # Use the reverse mapping to find keys with the desired value - prec = reverse_precision_dict.get(xx.dtype, None) + prec = reverse_precision_dict.get(xx.dtype) prec = NP_PRECISION_DICT.get(prec, None) if prec is None: raise ValueError(f"unknown precision {xx.dtype}") @@ -277,7 +277,7 @@ def to_torch_tensor( # Create a reverse mapping of NP_PRECISION_DICT reverse_precision_dict = {v: k for k, v in NP_PRECISION_DICT.items()} # Use the reverse mapping to find keys with the desired value - prec = reverse_precision_dict.get(xx.dtype.type, None) + prec = reverse_precision_dict.get(xx.dtype.type) prec = PT_PRECISION_DICT.get(prec, None) if prec is None: raise ValueError(f"unknown precision {xx.dtype}") diff --git a/deepmd/pt_expt/entrypoints/main.py b/deepmd/pt_expt/entrypoints/main.py index 2e4f747ccb..3367ee4579 100644 --- a/deepmd/pt_expt/entrypoints/main.py +++ b/deepmd/pt_expt/entrypoints/main.py @@ -151,7 +151,7 @@ def _build_data_system( ) systems = process_systems( systems_raw, - patterns=dataset_params.get("rglob_patterns", None), + patterns=dataset_params.get("rglob_patterns"), ) return DeepmdDataSystem( systems=systems, @@ -159,7 +159,7 @@ def _build_data_system( test_size=1, type_map=type_map, trn_all_set=True, - sys_probs=dataset_params.get("sys_probs", None), + sys_probs=dataset_params.get("sys_probs"), auto_prob_style=dataset_params.get("auto_prob", "prob_sys_size"), ) diff --git a/deepmd/pt_expt/utils/network.py b/deepmd/pt_expt/utils/network.py index adef443de9..004ba94401 100644 --- a/deepmd/pt_expt/utils/network.py +++ b/deepmd/pt_expt/utils/network.py @@ -94,10 +94,10 @@ def __setattr__(self, name: str, value: Any) -> None: if val is None: if name in self._parameters: self._parameters[name] = None - return + return None if name in self._buffers: self._buffers[name] = None - return + return None return super().__setattr__(name, None) if getattr(self, "trainable", False): param = ( @@ -107,14 +107,14 @@ def __setattr__(self, name: str, value: Any) -> None: ) if name in self._parameters: self._parameters[name] = param - return + return None return super().__setattr__(name, param) if name in self._buffers: self._buffers[name] = val - return + return None # Register on first assignment so tensors are in state_dict and moved by .to(). self.register_buffer(name, val) - return + return None return super().__setattr__(name, value) def call(self, x: torch.Tensor) -> torch.Tensor: diff --git a/deepmd/tf/descriptor/se_a.py b/deepmd/tf/descriptor/se_a.py index 3e1c9b127e..acecaf57f7 100644 --- a/deepmd/tf/descriptor/se_a.py +++ b/deepmd/tf/descriptor/se_a.py @@ -794,7 +794,7 @@ def _pass_filter( trainable: bool = True, ) -> tuple[tf.Tensor, tf.Tensor]: if input_dict is not None: - type_embedding = input_dict.get("type_embedding", None) + type_embedding = input_dict.get("type_embedding") if type_embedding is not None: self.use_tebd = True else: diff --git a/deepmd/tf/descriptor/se_atten.py b/deepmd/tf/descriptor/se_atten.py index 1bbb0a5595..e9feac8d74 100644 --- a/deepmd/tf/descriptor/se_atten.py +++ b/deepmd/tf/descriptor/se_atten.py @@ -732,10 +732,9 @@ def _pass_filter( trainable: bool = True, ) -> tuple[tf.Tensor, None]: assert ( - input_dict is not None - and input_dict.get("type_embedding", None) is not None + input_dict is not None and input_dict.get("type_embedding") is not None ), "se_atten descriptor must use type_embedding" - type_embedding = input_dict.get("type_embedding", None) + type_embedding = input_dict.get("type_embedding") inputs = tf.reshape(inputs, [-1, natoms[0], self.ndescrpt]) output = [] output_qmat = [] @@ -1961,7 +1960,7 @@ def serialize(self, suffix: str = "") -> dict: raise RuntimeError( "The implementation for smooth_type_embedding is inconsistent with other backends" ) - # todo support serialization when tebd_input_mode=='strip' and type_one_side is True + # TODO support serialization when tebd_input_mode=='strip' and type_one_side is True if self.stripped_type_embedding and self.type_one_side: raise NotImplementedError( "serialization is unsupported when tebd_input_mode=='strip' and type_one_side is True" diff --git a/deepmd/tf/descriptor/se_t.py b/deepmd/tf/descriptor/se_t.py index 16bec59bf0..5ea019b2eb 100644 --- a/deepmd/tf/descriptor/se_t.py +++ b/deepmd/tf/descriptor/se_t.py @@ -810,7 +810,7 @@ def clear_ij(type_i: int, type_j: int) -> None: clear_ij(i, j) clear_ij(j, i) for i in range(ntypes): - for j in range(0, i): + for j in range(i): clear_ij(i, j) if suffix != "": diff --git a/deepmd/tf/fit/dipole.py b/deepmd/tf/fit/dipole.py index 917cfe70e1..990c0d41d5 100644 --- a/deepmd/tf/fit/dipole.py +++ b/deepmd/tf/fit/dipole.py @@ -187,7 +187,7 @@ def _build_lower( rot_mat_i = tf.slice(rot_mat, [0, start_index, 0], [-1, natoms, -1]) rot_mat_i = tf.reshape(rot_mat_i, [-1, self.dim_rot_mat_1, 3]) layer = inputs_i - for ii in range(0, len(self.n_neuron)): + for ii in range(len(self.n_neuron)): if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]: layer += one_layer( layer, @@ -282,8 +282,8 @@ def build( """ if input_dict is None: input_dict = {} - type_embedding = input_dict.get("type_embedding", None) - atype = input_dict.get("atype", None) + type_embedding = input_dict.get("type_embedding") + atype = input_dict.get("atype") nframes = input_dict.get("nframes") start_index = 0 inputs = tf.reshape(input_d, [-1, natoms[0], self.dim_descrpt]) diff --git a/deepmd/tf/fit/dos.py b/deepmd/tf/fit/dos.py index 166ed2e355..3d99bcb64d 100644 --- a/deepmd/tf/fit/dos.py +++ b/deepmd/tf/fit/dos.py @@ -352,7 +352,7 @@ def _build_lower( one_layer = one_layer_nvnmd else: one_layer = one_layer_deepmd - for ii in range(0, len(self.n_neuron)): + for ii in range(len(self.n_neuron)): if self.layer_name is not None and self.layer_name[ii] is not None: layer_suffix = "share_" + self.layer_name[ii] + type_suffix layer_reuse = tf.AUTO_REUSE @@ -452,8 +452,8 @@ def build( if input_dict is None: input_dict = {} bias_dos = self.bias_dos - type_embedding = input_dict.get("type_embedding", None) - atype = input_dict.get("atype", None) + type_embedding = input_dict.get("type_embedding") + atype = input_dict.get("atype") if self.numb_fparam > 0: if self.fparam_avg is None: self.fparam_avg = 0.0 diff --git a/deepmd/tf/fit/ener.py b/deepmd/tf/fit/ener.py index e8accb2087..a787e5249c 100644 --- a/deepmd/tf/fit/ener.py +++ b/deepmd/tf/fit/ener.py @@ -419,7 +419,7 @@ def _build_lower( one_layer = one_layer_nvnmd else: one_layer = one_layer_deepmd - for ii in range(0, len(self.n_neuron)): + for ii in range(len(self.n_neuron)): if self.layer_name is not None and self.layer_name[ii] is not None: layer_suffix = "share_" + self.layer_name[ii] + type_suffix layer_reuse = tf.AUTO_REUSE @@ -519,8 +519,8 @@ def build( if input_dict is None: input_dict = {} bias_atom_e = self.bias_atom_e - type_embedding = input_dict.get("type_embedding", None) - atype = input_dict.get("atype", None) + type_embedding = input_dict.get("type_embedding") + atype = input_dict.get("atype") if self.numb_fparam > 0: if self.fparam_avg is None: self.fparam_avg = 0.0 diff --git a/deepmd/tf/fit/polar.py b/deepmd/tf/fit/polar.py index 5e4e48e96f..3143f89c8b 100644 --- a/deepmd/tf/fit/polar.py +++ b/deepmd/tf/fit/polar.py @@ -336,7 +336,7 @@ def _build_lower( ) rot_mat_i = tf.reshape(rot_mat_i, [-1, self.dim_rot_mat_1, 3]) layer = inputs_i - for ii in range(0, len(self.n_neuron)): + for ii in range(len(self.n_neuron)): if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]: layer += one_layer( layer, @@ -470,8 +470,8 @@ def build( """ if input_dict is None: input_dict = {} - type_embedding = input_dict.get("type_embedding", None) - atype = input_dict.get("atype", None) + type_embedding = input_dict.get("type_embedding") + atype = input_dict.get("atype") nframes = input_dict.get("nframes") start_index = 0 diff --git a/deepmd/tf/loss/tensor.py b/deepmd/tf/loss/tensor.py index d63987e908..624195c69b 100644 --- a/deepmd/tf/loss/tensor.py +++ b/deepmd/tf/loss/tensor.py @@ -26,7 +26,7 @@ class TensorLoss(Loss): """Loss function for tensorial properties.""" def __init__(self, jdata: dict | None, **kwarg: Any) -> None: - model = kwarg.get("model", None) + model = kwarg.get("model") if model is not None: self.type_sel = model.get_sel_type() else: diff --git a/deepmd/tf/nvnmd/descriptor/se_a.py b/deepmd/tf/nvnmd/descriptor/se_a.py index 96b89cffa7..0d5c3dd5cc 100644 --- a/deepmd/tf/nvnmd/descriptor/se_a.py +++ b/deepmd/tf/nvnmd/descriptor/se_a.py @@ -15,8 +15,6 @@ op_module, tf, ) - -# from deepmd.tf.nvnmd.utils.config import ( nvnmd_cfg, ) @@ -44,7 +42,6 @@ def build_davg_dstd() -> tuple[Any, Any]: def check_switch_range(davg: np.ndarray, dstd: np.ndarray) -> None: r"""Check the range of switch, let it in range [-2, 14].""" rmin = nvnmd_cfg.dscp["rcut_smth"] - # namelist = [n.name for n in tf.get_default_graph().as_graph_def().node] if "train_attr/min_nbor_dist" in namelist: min_dist = get_tensor_by_name_from_graph( @@ -307,7 +304,6 @@ def filter_GR2D(xyz_scatter_1: tf.Tensor) -> tuple[tf.Tensor, tf.Tensor]: result = tf.ensure_shape(result, [None, M1, M1]) # D': natom x (outputs_size x outputs_size_2) result = tf.reshape(result, [-1, M1 * M1]) - # index_subset = [] for ii in range(M1): for jj in range(ii, ii + M2): @@ -337,7 +333,6 @@ def filter_GR2D(xyz_scatter_1: tf.Tensor) -> tuple[tf.Tensor, tf.Tensor]: # natom x (outputs_size x outputs_size_2) # result = tf.reshape(result, [-1, outputs_size_2 * outputs_size[-1]]) result = tf.reshape(result, [-1, M1 * M1]) - # index_subset = [] for ii in range(M1): for jj in range(ii, ii + M2): diff --git a/deepmd/tf/nvnmd/descriptor/se_atten.py b/deepmd/tf/nvnmd/descriptor/se_atten.py index f8f3085ae8..aa15a8ca7d 100644 --- a/deepmd/tf/nvnmd/descriptor/se_atten.py +++ b/deepmd/tf/nvnmd/descriptor/se_atten.py @@ -14,8 +14,6 @@ op_module, tf, ) - -# from deepmd.tf.nvnmd.utils.config import ( nvnmd_cfg, ) @@ -43,7 +41,6 @@ def check_switch_range(davg: np.ndarray, dstd: np.ndarray) -> None: ntype = nvnmd_cfg.dscp["ntype"] NIDP = nvnmd_cfg.dscp["NIDP"] ndescrpt = NIDP * 4 - # namelist = [n.name for n in tf.get_default_graph().as_graph_def().node] if "train_attr/min_nbor_dist" in namelist: min_dist = get_tensor_by_name_from_graph( @@ -271,7 +268,6 @@ def filter_GR2D(xyz_scatter_1: tf.Tensor) -> tuple[tf.Tensor, tf.Tensor]: result = tf.ensure_shape(result, [None, M1, M1]) # D': natom x (outputs_size x outputs_size_2) result = tf.reshape(result, [-1, M1 * M1]) - # index_subset = [] for ii in range(M1): for jj in range(ii, ii + M2): @@ -301,7 +297,6 @@ def filter_GR2D(xyz_scatter_1: tf.Tensor) -> tuple[tf.Tensor, tf.Tensor]: # natom x (outputs_size x outputs_size_2) # result = tf.reshape(result, [-1, outputs_size_2 * outputs_size[-1]]) result = tf.reshape(result, [-1, M1 * M1]) - # index_subset = [] for ii in range(M1): for jj in range(ii, ii + M2): diff --git a/deepmd/tf/nvnmd/entrypoints/mapt.py b/deepmd/tf/nvnmd/entrypoints/mapt.py index 5da708b311..efd72f03da 100644 --- a/deepmd/tf/nvnmd/entrypoints/mapt.py +++ b/deepmd/tf/nvnmd/entrypoints/mapt.py @@ -108,7 +108,6 @@ def build_map(self) -> dict: if self.Gs_Gt_mode == 1: self.shift_Gs = 0 self.shift_Gt = 1 - # M = nvnmd_cfg.dscp["M1"] if nvnmd_cfg.version == 0: ndim = nvnmd_cfg.dscp["ntype"] @@ -183,7 +182,6 @@ def build_map(self) -> dict: if nvnmd_cfg.version == 1: self.map.update(dic_map3) self.map.update(dic_map4) - # FioDic().save(self.map_file, self.map) log.info("NVNMD: finish building mapping table") return self.map @@ -231,7 +229,6 @@ def mapping2(self, x: np.ndarray, dic_map: dict, cfgs: dict) -> dict: t_table_info = tf.placeholder(tf.float64, [None], "t_table_info") t_y = op_module.map_flt_nvnmd(t_x, t_table, t_table_grad, t_table_info) sess = get_sess() - # n = len(x) dic_val = {} for key in dic_map.keys(): @@ -293,7 +290,6 @@ def build_map_coef( y_i = ys grad_i = grads grad_grad_i = grad_grads - # coef_i = [] coef_grad_i = [] for jj in range(Nc): @@ -339,7 +335,6 @@ def cal_coef4( y1 = y1[:Nd] dy0 = dy0[:Nd] dy1 = dy1[:Nd] - # a = (dx * dy1 - 2 * y1 + dx * dy0 + 2 * y0) / dx**3 b = (3 * y1 - dx * dy1 - 2 * dx * dy0 - 3 * y0) / dx**2 c = dy0 @@ -386,7 +381,6 @@ def build_u2s(self, r2: tf.Tensor) -> tf.Tensor: if dmin > 1e-6: min_dist = dmin min_dist = 0.5 if (min_dist > 0.5) else (min_dist - 0.1) - # r = tf.sqrt(r2) r_ = tf.clip_by_value(r, rmin, rmax) r__ = tf.clip_by_value(r, min_dist, rmax) @@ -423,7 +417,6 @@ def build_u2s_grad(self) -> dict: ndim = nvnmd_cfg.dscp["ntype"] if nvnmd_cfg.version == 1: ndim = 1 - # dic_ph = {} dic_ph["u"] = tf.placeholder(tf.float64, [None, 1], "t_u") dic_ph["s"], dic_ph["h"] = self.build_u2s(dic_ph["u"]) @@ -473,7 +466,6 @@ def run_u2s(self) -> tuple[dict, dict]: res_dic["h"][tt][0] = 0 res_dic["h_grad"][tt][0] = 0 res_dic["h_grad_grad"][tt][0] = 0 - # res_dic2["s"][tt][0] = -avg[tt, 0] / std[tt, 0] res_dic2["s_grad"][tt][0] = 0 res_dic2["s_grad_grad"][tt][0] = 0 @@ -493,7 +485,6 @@ def build_s2g(self, s: tf.Tensor) -> tf.Tensor: ntype = nvnmd_cfg.dscp["ntype"] if nvnmd_cfg.version == 1: ntype = 1 - # xyz_scatters = [] for tt2 in range(ntype): wbs = [get_filter_weight(nvnmd_cfg.weight, tt2, ll) for ll in range(1, 5)] @@ -504,7 +495,6 @@ def build_s2g(self, s: tf.Tensor) -> tf.Tensor: def build_s2g_grad(self) -> dict: r"""Build gradient of G with respect to s.""" M1 = nvnmd_cfg.dscp["M1"] - # if nvnmd_cfg.version == 0: ntypex = nvnmd_cfg.dscp["ntypex"] ntype = nvnmd_cfg.dscp["ntype"] @@ -513,7 +503,6 @@ def build_s2g_grad(self) -> dict: if nvnmd_cfg.version == 1: ndim = 1 shift = self.shift_Gs - # dic_ph = {} dic_ph["s"] = tf.placeholder(tf.float64, [None, 1], "t_s") dic_ph["g"] = [g + shift for g in self.build_s2g(dic_ph["s"])] @@ -550,7 +539,6 @@ def run_s2g(self) -> tuple[dict, dict]: smin_ = np.floor(smin * prec - 1) / prec if nvnmd_cfg.version == 1: smin_ = 0 - # keys = list(dic_ph.keys()) vals = list(dic_ph.values()) @@ -616,10 +604,8 @@ def run_t2g(self) -> dict: tf.reset_default_graph() dic_ph = self.build_t2g() sess = get_sess() - # keys = list(dic_ph.keys()) vals = list(dic_ph.values()) - # res_lst = run_sess(sess, vals, feed_dict={}) res_dic = dict(zip(keys, res_lst, strict=True)) @@ -659,7 +645,6 @@ def build_embedding_net( def build_davg_dstd(self) -> dict: ntype = nvnmd_cfg.dscp["ntype"] davg, dstd = get_normalize(nvnmd_cfg.weight) - # res_dic = {} res_dic["davg_opp"] = np.array([-davg[tt, 0:4] for tt in range(ntype)]) res_dic["dstd_inv"] = np.array([1.0 / dstd[tt, 0:4] for tt in range(ntype)]) diff --git a/deepmd/tf/nvnmd/entrypoints/train.py b/deepmd/tf/nvnmd/entrypoints/train.py index d83362a705..63583ebffa 100644 --- a/deepmd/tf/nvnmd/entrypoints/train.py +++ b/deepmd/tf/nvnmd/entrypoints/train.py @@ -88,7 +88,6 @@ def normalized_input(fn: str, PATH_CNN: str, CONFIG_CNN: str) -> str: jdata_train["save_ckpt"] = os.path.join( PATH_CNN, os.path.split(jdata_train["save_ckpt"])[1] ) - # jdata["model"] = nvnmd_cfg.get_model_jdata() jdata["nvnmd"] = nvnmd_cfg.get_nvnmd_jdata() return jdata @@ -98,7 +97,6 @@ def normalized_input_qnn( jdata: dict, PATH_QNN: str, CONFIG_CNN: str, WEIGHT_CNN: str, MAP_CNN: str ) -> str: r"""Normalize a input script file for quantize neural network.""" - # jdata_nvnmd = jdata_deepmd_input_v0["nvnmd"] jdata_nvnmd["enable"] = True jdata_nvnmd["version"] = nvnmd_cfg.version diff --git a/deepmd/tf/nvnmd/entrypoints/wrap.py b/deepmd/tf/nvnmd/entrypoints/wrap.py index e946a74a5d..74cd420410 100755 --- a/deepmd/tf/nvnmd/entrypoints/wrap.py +++ b/deepmd/tf/nvnmd/entrypoints/wrap.py @@ -131,7 +131,6 @@ def wrap(self) -> None: w4 = w * 4 # nbit nhs.append(h) nws.append(w) - # w_full = np.ceil(w4 / nbit) * nbit d = e.extend_hex(d, w_full) # DEVELOP_DEBUG @@ -319,7 +318,6 @@ def wrap_dscp(self) -> str: ) sGSs = "".join(GSs[::-1]) bs = sGSs + bs - # NIX = dscp["NIX"] ln2_NIX = -int(np.log2(NIX)) bs = e.dec2bin(ln2_NIX, NBIT_FLTE, signed=True)[0] + bs @@ -389,7 +387,6 @@ def wrap_fitn(self) -> tuple[list[str], list[str]]: bdc.append(bdct) bwr.append(bwrt) bwc.append(bwct) - # bfps, bbps = [], [] for ss in range(NSEL): tt = ss // NSTDM @@ -537,7 +534,6 @@ def wrap_map(self) -> tuple[list[str], list[str], list[str], list[str]]: d1 = d[:, :, 0:2] d2 = d[:, :, 2:4] d = np.concatenate([d1, d2]) - # bs = e.flt2bin(d, NBIT_FLTE, NBIT_FLTF) bs = e.reverse_bin(bs, nmerges[ii]) bs = e.merge_bin(bs, nmerges[ii]) @@ -552,7 +548,6 @@ def wrap_map(self) -> tuple[list[str], list[str], list[str], list[str]]: d1 = np.reshape(d[:, :, 0:2], [-1, nd * 2]) d2 = np.reshape(d[:, :, 2:4], [-1, nd * 2]) d = np.concatenate([d1, d2], axis=1) - # bs = e.flt2bin(d, NBIT_FLTE, NBIT_FLTF) bss.append(bs) bswt, bdsw, bfea, bgra = bss diff --git a/deepmd/tf/nvnmd/utils/config.py b/deepmd/tf/nvnmd/utils/config.py index 99fb640aae..c88077ae94 100644 --- a/deepmd/tf/nvnmd/utils/config.py +++ b/deepmd/tf/nvnmd/utils/config.py @@ -118,7 +118,6 @@ def init_from_config(self, jdata: dict) -> None: self.init_config_by_version( jdata["ctrl"]["VERSION"], jdata["ctrl"]["MAX_NNEI"] ) - # self.config = FioDic().update(jdata, self.config) self.config["dscp"] = self.init_dscp(self.config["dscp"], self.config) self.config["fitn"] = self.init_fitn(self.config["fitn"], self.config) @@ -167,7 +166,6 @@ def init_from_deepmd_input(self, jdata: dict) -> None: self.config["fitn"] = self.init_fitn(self.config["fitn"], self.config) dp_in = {"type_map": fioObj.get(jdata, "type_map", [])} self.config["dpin"] = fioObj.update(dp_in, self.config["dpin"]) - # self.init_net_size() self.init_value() @@ -290,7 +288,6 @@ def get_s_range(self, davg: np.ndarray, dstd: np.ndarray) -> None: rmax = nvnmd_cfg.dscp["rcut"] ntype = self.dscp["ntype"] dmin = self.dscp["dmin"] - # s0 = r2s(dmin, rmin, rmax) smin_ = -davg[:ntype, 0] / dstd[:ntype, 0] smax_ = (s0 - davg[:ntype, 0]) / dstd[:ntype, 0] diff --git a/deepmd/tf/nvnmd/utils/encode.py b/deepmd/tf/nvnmd/utils/encode.py index 53a860080f..039c61ab9e 100644 --- a/deepmd/tf/nvnmd/utils/encode.py +++ b/deepmd/tf/nvnmd/utils/encode.py @@ -68,7 +68,6 @@ def flt2bin_one(self, v: float, nbit_expo: int, nbit_frac: int) -> str: if h[ii] == "p": ed = ii + 1 is_zero = h[st] == "0" - # if is_zero: return "0" * (1 + nbit_expo + nbit_frac) else: diff --git a/deepmd/tf/nvnmd/utils/fio.py b/deepmd/tf/nvnmd/utils/fio.py index 0994a8995a..8b452a19c9 100644 --- a/deepmd/tf/nvnmd/utils/fio.py +++ b/deepmd/tf/nvnmd/utils/fio.py @@ -178,7 +178,6 @@ def save(self, file_name: str, data: list[str]) -> None: buff = [] for si in data: buff.extend(list(bytearray.fromhex(si))[::-1]) - # with open(file_name, "wb") as fp: fp.write(struct.pack(f"{len(buff)}B", *buff)) diff --git a/deepmd/tf/nvnmd/utils/network.py b/deepmd/tf/nvnmd/utils/network.py index c0a6bf5248..0486ac9840 100644 --- a/deepmd/tf/nvnmd/utils/network.py +++ b/deepmd/tf/nvnmd/utils/network.py @@ -213,7 +213,6 @@ def one_layer( uniform_seed, name, ) - # NTAVC = nvnmd_cfg.fitn["NTAVC"] nd = inputs.get_shape().as_list()[1] - NTAVC inputs2 = tf.slice(inputs, [0, nd], [-1, NTAVC]) diff --git a/deepmd/tf/op/_map_flt_nvnmd_grad.py b/deepmd/tf/op/_map_flt_nvnmd_grad.py index 5443e3286b..a8bc6c56cc 100644 --- a/deepmd/tf/op/_map_flt_nvnmd_grad.py +++ b/deepmd/tf/op/_map_flt_nvnmd_grad.py @@ -25,7 +25,6 @@ def _MapFltNvnmdGrad(op: tf.Operation, grad: tf.Tensor) -> list[tf.Tensor | None N = shx[0] D = shx[1] M = shw[1] // 4 - # dydx = op_module.map_flt_nvnmd(x, table_grad, tf.zeros_like(table_grad), table_info) dydx = tf.ensure_shape(dydx, [N, D, M]) # calculate diff --git a/deepmd/tf/op/_tanh4_flt_nvnmd_grad.py b/deepmd/tf/op/_tanh4_flt_nvnmd_grad.py index f3582b194b..4d0a0fcdc0 100644 --- a/deepmd/tf/op/_tanh4_flt_nvnmd_grad.py +++ b/deepmd/tf/op/_tanh4_flt_nvnmd_grad.py @@ -22,12 +22,10 @@ def _Tanh4FltNvnmdGrad(op: tf.Operation, grad: tf.Tensor) -> list[tf.Tensor]: xx = xhi * xlo xxhi = xx + tf.stop_gradient(tf.floor(xx * prechi) / prechi - xx) xxlo = xx + tf.stop_gradient(tf.floor(xx * preclo) / preclo - xx) - # dydx = xxlo * (xhi / 4 - 3 / 4) + 1 # dydx = xxhi * (xlo/4 - 3/4) + 1 dydxhi = dydx + tf.stop_gradient(tf.floor(dydx * prechi) / prechi - dydx) dydxlo = dydx + tf.stop_gradient(tf.floor(dydx * preclo) / preclo - dydx) - # gradhi = grad + tf.stop_gradient(tf.floor(grad * prechi) / prechi - grad) dx = dydxlo * gradhi dx = dx + tf.stop_gradient(tf.floor(dx * prechi) / prechi - dx) diff --git a/deepmd/tf/utils/tabulate.py b/deepmd/tf/utils/tabulate.py index 614d18d9d8..53137cab19 100644 --- a/deepmd/tf/utils/tabulate.py +++ b/deepmd/tf/utils/tabulate.py @@ -194,7 +194,7 @@ def _get_bias(self) -> dict[str, list[np.ndarray]]: bias["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.tf.descriptor.DescrptSeA): if self.type_one_side: - for ii in range(0, self.ntypes): + for ii in range(self.ntypes): if not self._all_excluded(ii): node = self.embedding_net_nodes[ f"filter_type_all{self.suffix}/bias_{layer}_{ii}" @@ -203,7 +203,7 @@ def _get_bias(self) -> dict[str, list[np.ndarray]]: else: bias["layer_" + str(layer)].append(np.array([])) else: - for ii in range(0, self.ntypes * self.ntypes): + for ii in range(self.ntypes * self.ntypes): if ( ii // self.ntypes, ii % self.ntypes, @@ -223,7 +223,7 @@ def _get_bias(self) -> dict[str, list[np.ndarray]]: bias["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.tf.descriptor.DescrptSeR): if self.type_one_side: - for ii in range(0, self.ntypes): + for ii in range(self.ntypes): if not self._all_excluded(ii): node = self.embedding_net_nodes[ f"filter_type_all{self.suffix}/bias_{layer}_{ii}" @@ -232,7 +232,7 @@ def _get_bias(self) -> dict[str, list[np.ndarray]]: else: bias["layer_" + str(layer)].append(np.array([])) else: - for ii in range(0, self.ntypes * self.ntypes): + for ii in range(self.ntypes * self.ntypes): if ( ii // self.ntypes, ii % self.ntypes, @@ -260,7 +260,7 @@ def _get_matrix(self) -> dict[str, list[np.ndarray]]: matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.tf.descriptor.DescrptSeA): if self.type_one_side: - for ii in range(0, self.ntypes): + for ii in range(self.ntypes): if not self._all_excluded(ii): node = self.embedding_net_nodes[ f"filter_type_all{self.suffix}/matrix_{layer}_{ii}" @@ -269,7 +269,7 @@ def _get_matrix(self) -> dict[str, list[np.ndarray]]: else: matrix["layer_" + str(layer)].append(np.array([])) else: - for ii in range(0, self.ntypes * self.ntypes): + for ii in range(self.ntypes * self.ntypes): if ( ii // self.ntypes, ii % self.ntypes, @@ -289,7 +289,7 @@ def _get_matrix(self) -> dict[str, list[np.ndarray]]: matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.tf.descriptor.DescrptSeR): if self.type_one_side: - for ii in range(0, self.ntypes): + for ii in range(self.ntypes): if not self._all_excluded(ii): node = self.embedding_net_nodes[ f"filter_type_all{self.suffix}/matrix_{layer}_{ii}" @@ -298,7 +298,7 @@ def _get_matrix(self) -> dict[str, list[np.ndarray]]: else: matrix["layer_" + str(layer)].append(np.array([])) else: - for ii in range(0, self.ntypes * self.ntypes): + for ii in range(self.ntypes * self.ntypes): if ( ii // self.ntypes, ii % self.ntypes, @@ -508,7 +508,7 @@ def _get_layer_size(self) -> int: @cached_property def _n_all_excluded(self) -> int: """Then number of types excluding all types.""" - return sum(int(self._all_excluded(ii)) for ii in range(0, self.ntypes)) + return sum(int(self._all_excluded(ii)) for ii in range(self.ntypes)) def _convert_numpy_to_tensor(self) -> None: """Convert self.data from np.ndarray to tf.Tensor.""" diff --git a/deepmd/tf2/atomic_model/linear_atomic_model.py b/deepmd/tf2/atomic_model/linear_atomic_model.py index f6030291f1..2c810ee7dd 100644 --- a/deepmd/tf2/atomic_model/linear_atomic_model.py +++ b/deepmd/tf2/atomic_model/linear_atomic_model.py @@ -24,7 +24,7 @@ def __setattr__(self, name: str, value: Any) -> None: if name == "zbl_weight": # discard since it's only used in tests # to fix TensorFlow tracing mutation error: Cannot mutate 'FlaxModule' from different trace level - return + return None return super().__setattr__(name, value) def forward_common_atomic( diff --git a/deepmd/utils/compat.py b/deepmd/utils/compat.py index c6b839a8f6..d7609f9897 100644 --- a/deepmd/utils/compat.py +++ b/deepmd/utils/compat.py @@ -119,7 +119,7 @@ def _smth_descriptor(jdata: dict[str, Any]) -> dict[str, Any]: dict with descriptor parameters """ descriptor = {} - seed = jdata.get("seed", None) + seed = jdata.get("seed") if seed is not None: descriptor["seed"] = seed descriptor["type"] = "se_a" @@ -150,7 +150,7 @@ def _fitting_net(jdata: dict[str, Any]) -> dict[str, Any]: """ fitting_net = {} - seed = jdata.get("seed", None) + seed = jdata.get("seed") if seed is not None: fitting_net["seed"] = seed fitting_net["neuron"] = j_deprecated(jdata, "fitting_neuron", ["n_neuron"]) @@ -228,7 +228,7 @@ def _training(jdata: dict[str, Any]) -> dict[str, Any]: dict with training parameters """ training = {} - seed = jdata.get("seed", None) + seed = jdata.get("seed") if seed is not None: training["seed"] = seed diff --git a/deepmd/utils/data_system.py b/deepmd/utils/data_system.py index 9d13cb4699..9713a43d26 100644 --- a/deepmd/utils/data_system.py +++ b/deepmd/utils/data_system.py @@ -897,11 +897,11 @@ def get_data( The data system """ systems = jdata["systems"] - rglob_patterns = jdata.get("rglob_patterns", None) + rglob_patterns = jdata.get("rglob_patterns") systems = process_systems(systems, patterns=rglob_patterns) batch_size = jdata["batch_size"] - sys_probs = jdata.get("sys_probs", None) + sys_probs = jdata.get("sys_probs") auto_prob = jdata.get("auto_prob", "prob_sys_size") optional_type_map = not multi_task_mode diff --git a/deepmd/utils/tabulate_math.py b/deepmd/utils/tabulate_math.py index 93fe903e12..401840ecd5 100644 --- a/deepmd/utils/tabulate_math.py +++ b/deepmd/utils/tabulate_math.py @@ -536,7 +536,7 @@ def _get_network_variable(self, var_name: str) -> dict: result["layer_" + str(layer)].append(node) elif self.descrpt_type == "A": if self.type_one_side: - for ii in range(0, self.ntypes): + for ii in range(self.ntypes): if not self._all_excluded(ii): node = self.embedding_net_nodes[ii]["layers"][layer - 1][ "@variables" @@ -545,7 +545,7 @@ def _get_network_variable(self, var_name: str) -> dict: else: result["layer_" + str(layer)].append(np.array([])) else: - for ii in range(0, self.ntypes * self.ntypes): + for ii in range(self.ntypes * self.ntypes): if ( ii // self.ntypes, ii % self.ntypes, @@ -570,7 +570,7 @@ def _get_network_variable(self, var_name: str) -> dict: result["layer_" + str(layer)].append(node) elif self.descrpt_type == "R": if self.type_one_side: - for ii in range(0, self.ntypes): + for ii in range(self.ntypes): if not self._all_excluded(ii): node = self.embedding_net_nodes[ii]["layers"][layer - 1][ "@variables" @@ -579,7 +579,7 @@ def _get_network_variable(self, var_name: str) -> dict: else: result["layer_" + str(layer)].append(np.array([])) else: - for ii in range(0, self.ntypes * self.ntypes): + for ii in range(self.ntypes * self.ntypes): if ( ii // self.ntypes, ii % self.ntypes, @@ -607,4 +607,4 @@ def _convert_numpy_to_tensor(self) -> None: @cached_property def _n_all_excluded(self) -> int: """The number of types excluding all types.""" - return sum(int(self._all_excluded(ii)) for ii in range(0, self.ntypes)) + return sum(int(self._all_excluded(ii)) for ii in range(self.ntypes)) diff --git a/doc/conf.py b/doc/conf.py index c58073d5c0..d7ad2c5673 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -128,7 +128,6 @@ # Tell sphinx what the pygments highlight language should be. # highlight_language = 'cpp' -# myst_heading_anchors = 4 nb_execution_mode = "off" diff --git a/pyproject.toml b/pyproject.toml index 4f3a707977..17a99823c0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -411,6 +411,14 @@ select = [ "PYI", # flake8-pyi "ANN", # type annotations "B905", # zip-without-explicit-strict + "PIE808", # unnecessary-range-start + "PT022", # pytest-useless-yield-fixture + "RET502", # implicit-return-value + "SIM910", # dict-get-with-none-default + "TD006", # invalid-todo-capitalization + "PLR1711", # useless-return + "PLR1733", # unnecessary-dict-index-lookup + "PLR2044", # empty-comment ] ignore = [ diff --git a/source/tests/dpa_adapt/test_conditions.py b/source/tests/dpa_adapt/test_conditions.py index f2aefad714..182588a36c 100644 --- a/source/tests/dpa_adapt/test_conditions.py +++ b/source/tests/dpa_adapt/test_conditions.py @@ -77,7 +77,6 @@ def _mock_extract_features(self, systems): def _mock_load_descriptor_model(self): self._checkpoint_type_map = ["Cu", "O"] - return None # ====================================================================== diff --git a/source/tests/dpa_adapt/test_finetuner_strategies.py b/source/tests/dpa_adapt/test_finetuner_strategies.py index 6f280ffa90..d332153060 100644 --- a/source/tests/dpa_adapt/test_finetuner_strategies.py +++ b/source/tests/dpa_adapt/test_finetuner_strategies.py @@ -347,7 +347,6 @@ def test_fit_dispatch_calls_training_path(self, tmp_path): def _mock_load_descriptor_model_cache_test(self): self._checkpoint_type_map = ["H", "O"] - return None class TestFitDescriptorCache: diff --git a/source/tests/dpa_adapt/test_predictor.py b/source/tests/dpa_adapt/test_predictor.py index 5f0a8135ba..685183eb59 100644 --- a/source/tests/dpa_adapt/test_predictor.py +++ b/source/tests/dpa_adapt/test_predictor.py @@ -83,7 +83,6 @@ def _mock_extract_features(self, systems): def _mock_load_descriptor_model(self): self._checkpoint_type_map = ["Cu", "O"] - return None # --------------------------------------------------------------------------- diff --git a/source/tests/pd/model/test_force_grad.py b/source/tests/pd/model/test_force_grad.py index eb6975afec..d1387771f1 100644 --- a/source/tests/pd/model/test_force_grad.py +++ b/source/tests/pd/model/test_force_grad.py @@ -37,7 +37,7 @@ def __init__( def get_disturb(self, index, atom_index, axis_index, delta): for i in range( - 0, len(self.dirs) + 1 + len(self.dirs) + 1 ): # note: if different sets can be merged, prefix sum is unused to calculate if index < self.prefix_sum[i]: break diff --git a/source/tests/pd/model/test_rotation.py b/source/tests/pd/model/test_rotation.py index 48f5ae8983..f47f1d8201 100644 --- a/source/tests/pd/model/test_rotation.py +++ b/source/tests/pd/model/test_rotation.py @@ -35,7 +35,7 @@ def __init__( def get_rotation(self, index, rotation_matrix): for i in range( - 0, len(self.dirs) + 1 + len(self.dirs) + 1 ): # note: if different sets can be merged, prefix sum is unused to calculate if index < self.prefix_sum[i]: break diff --git a/source/tests/pt/model/test_force_grad.py b/source/tests/pt/model/test_force_grad.py index 27bf660241..599f0d1830 100644 --- a/source/tests/pt/model/test_force_grad.py +++ b/source/tests/pt/model/test_force_grad.py @@ -37,7 +37,7 @@ def __init__( def get_disturb(self, index, atom_index, axis_index, delta): for i in range( - 0, len(self.dirs) + 1 + len(self.dirs) + 1 ): # note: if different sets can be merged, prefix sum is unused to calculate if index < self.prefix_sum[i]: break diff --git a/source/tests/pt/model/test_rotation.py b/source/tests/pt/model/test_rotation.py index f2abc6f9fd..e940177cb8 100644 --- a/source/tests/pt/model/test_rotation.py +++ b/source/tests/pt/model/test_rotation.py @@ -35,7 +35,7 @@ def __init__( def get_rotation(self, index, rotation_matrix): for i in range( - 0, len(self.dirs) + 1 + len(self.dirs) + 1 ): # note: if different sets can be merged, prefix sum is unused to calculate if index < self.prefix_sum[i]: break diff --git a/source/tests/pt_expt/conftest.py b/source/tests/pt_expt/conftest.py index 228c6104ae..ff9347b132 100644 --- a/source/tests/pt_expt/conftest.py +++ b/source/tests/pt_expt/conftest.py @@ -43,11 +43,9 @@ def _pop_device_contexts() -> list: def _clear_leaked_device_context_session(): """Pop any stale DeviceContext once at session start.""" _pop_device_contexts() - yield @pytest.fixture(autouse=True) def _clear_leaked_device_context(): """Pop any stale ``DeviceContext`` before each test (safety net).""" _pop_device_contexts() - yield diff --git a/source/tests/tf/test_compat_input.py b/source/tests/tf/test_compat_input.py index 4e60b6bf3e..6fa82a4cc9 100644 --- a/source/tests/tf/test_compat_input.py +++ b/source/tests/tf/test_compat_input.py @@ -35,9 +35,9 @@ def assertDictAlmostEqual(self, d1, d2, msg=None, places=7) -> None: self.assertEqual(d1.keys(), d2.keys()) for kk, vv in d1.items(): if isinstance(vv, dict): - self.assertDictAlmostEqual(d1[kk], d2[kk], msg=msg) + self.assertDictAlmostEqual(vv, d2[kk], msg=msg) else: - self.assertAlmostEqual(d1[kk], d2[kk], places=places, msg=msg) + self.assertAlmostEqual(vv, d2[kk], places=places, msg=msg) def test_json_yaml_equal(self) -> None: inputs = ("water_v1", "water_se_a_v1") diff --git a/source/tests/tf/test_descrpt_sea_ef_rot.py b/source/tests/tf/test_descrpt_sea_ef_rot.py index 356e9dd5bc..ff48f8c52b 100644 --- a/source/tests/tf/test_descrpt_sea_ef_rot.py +++ b/source/tests/tf/test_descrpt_sea_ef_rot.py @@ -205,7 +205,7 @@ def test_rot_axis(self, suffix="") -> None: # print(v_ae0) # print(ae0) - for kk in range(0, self.natoms[0]): + for kk in range(self.natoms[0]): # print(f0) theta = 45.0 / 180.0 * np.pi rr0 = self.rotate_mat(defield[0][kk * 3 : kk * 3 + 3], theta) @@ -256,8 +256,8 @@ def test_rot_axis(self, suffix="") -> None: self.tnatoms: self.natoms, }, ) - for ii in range(0, self.natoms[0]): - for jj in range(0, self.natoms[0]): + for ii in range(self.natoms[0]): + for jj in range(self.natoms[0]): diff = ( dcoord[0][3 * jj : 3 * jj + 3] - dcoord[0][3 * ii : 3 * ii + 3] ) @@ -394,7 +394,7 @@ def test_rot_diff_axis(self, suffix="") -> None: }, ) - for ii in range(0, self.natoms[0]): + for ii in range(self.natoms[0]): self.assertNotAlmostEqual(p_ae0[ii], p_ae1[ii]) self.assertNotAlmostEqual(v_ae0[ii], v_ae1[ii]) diff --git a/source/tests/tf/test_nvnmd_entrypoints.py b/source/tests/tf/test_nvnmd_entrypoints.py index 4a6877761e..531b30748c 100644 --- a/source/tests/tf/test_nvnmd_entrypoints.py +++ b/source/tests/tf/test_nvnmd_entrypoints.py @@ -55,7 +55,6 @@ def test_mapt_cnn_v0(self) -> None: # mapt mapObj = MapTable(config_file, weight_file, map_file) mapt = mapObj.build_map() - # N = 32 x = np.reshape(np.arange(N) / N * (8.0**2), [-1, 1]) pred = mapObj.mapping2(x, {"s": mapt["s"]}, mapt["cfg_u2s"]) @@ -127,7 +126,6 @@ def test_mapt_cnn_v0(self) -> None: -0.37758207, ] np.testing.assert_almost_equal(pred, ref_dout, 8) - # N = 4 x = np.reshape(np.arange(N) / N * 16, [-1, 1]) pred = mapObj.mapping2(x, {"g": mapt["g"]}, mapt["cfg_s2g"]) @@ -455,7 +453,6 @@ def test_model_qnn_v0(self) -> None: dic_ph["natoms_vec"]: natoms_vec_dat, dic_ph["default_mesh"]: mesh_dat, } - # sess = self.cached_session().__enter__() sess.run(tf.global_variables_initializer()) # get tensordic @@ -528,7 +525,6 @@ def test_mapt_cnn_v1(self) -> None: mapObj = MapTable(config_file, weight_file, map_file) mapObj.Gs_Gt_mode = 0 mapt = mapObj.build_map() - # N = 32 x = np.reshape(np.arange(N) / N * (8.0**2), [-1, 1]) pred = mapObj.mapping2(x, {"s": mapt["s"]}, mapt["cfg_u2s"]) @@ -568,7 +564,6 @@ def test_mapt_cnn_v1(self) -> None: 0.00000000e00, ] np.testing.assert_almost_equal(pred, ref_dout, 8) - # N = 4 x = np.reshape(np.arange(N) / N * 16, [-1, 1]) pred = mapObj.mapping2(x, {"g": mapt["g"]}, mapt["cfg_s2g"]) @@ -772,7 +767,6 @@ def test_model_qnn_v1(self) -> None: dic_ph["natoms_vec"]: natoms_vec_dat, dic_ph["default_mesh"]: mesh_dat, } - # sess = self.cached_session().__enter__() sess.run(tf.global_variables_initializer()) # get tensordic