def __init__(self, rcut: float, rcut_smth: float, sel: List[str], neuron: List[int] = [24, 48, 96], axis_neuron: int = 8, resnet_dt: bool = False, trainable: bool = True, seed: int = None, type_one_side: bool = True, exclude_types: List[List[int]] = [], set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False) -> None: """ Constructor """ if rcut < rcut_smth: raise RuntimeError( "rcut_smth (%f) should be no more than rcut (%f)!" % (rcut_smth, rcut)) self.sel_a = sel self.rcut_r = rcut self.rcut_r_smth = rcut_smth self.filter_neuron = neuron self.n_axis_neuron = axis_neuron self.filter_resnet_dt = resnet_dt self.seed = seed self.uniform_seed = uniform_seed self.seed_shift = embedding_net_rand_seed_shift(self.filter_neuron) self.trainable = trainable self.compress_activation_fn = get_activation_func(activation_function) self.filter_activation_fn = get_activation_func(activation_function) self.filter_precision = get_precision(precision) self.exclude_types = set() for tt in exclude_types: assert (len(tt) == 2) self.exclude_types.add((tt[0], tt[1])) self.exclude_types.add((tt[1], tt[0])) self.set_davg_zero = set_davg_zero self.type_one_side = type_one_side # descrpt config self.sel_r = [0 for ii in range(len(self.sel_a))] self.ntypes = len(self.sel_a) assert (self.ntypes == len(self.sel_r)) self.rcut_a = -1 # numb of neighbors and numb of descrptors self.nnei_a = np.cumsum(self.sel_a)[-1] self.nnei_r = np.cumsum(self.sel_r)[-1] self.nnei = self.nnei_a + self.nnei_r self.ndescrpt_a = self.nnei_a * 4 self.ndescrpt_r = self.nnei_r * 1 self.ndescrpt = self.ndescrpt_a + self.ndescrpt_r self.useBN = False self.dstd = None self.davg = None self.compress = False self.embedding_net_variables = None self.mixed_prec = None self.place_holders = {} nei_type = np.array([]) for ii in range(self.ntypes): nei_type = np.append(nei_type, ii * np.ones(self.sel_a[ii])) # like a mask self.nei_type = tf.constant(nei_type, dtype=tf.int32) avg_zero = np.zeros([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) std_ones = np.ones([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) sub_graph = tf.Graph() with sub_graph.as_default(): name_pfx = 'd_sea_' for ii in ['coord', 'box']: self.place_holders[ii] = tf.placeholder( GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + 't_' + ii) self.place_holders['type'] = tf.placeholder(tf.int32, [None, None], name=name_pfx + 't_type') self.place_holders['natoms_vec'] = tf.placeholder( tf.int32, [self.ntypes + 2], name=name_pfx + 't_natoms') self.place_holders['default_mesh'] = tf.placeholder( tf.int32, [None], name=name_pfx + 't_mesh') self.stat_descrpt, descrpt_deriv, rij, nlist \ = op_module.prod_env_mat_a(self.place_holders['coord'], self.place_holders['type'], self.place_holders['natoms_vec'], self.place_holders['box'], self.place_holders['default_mesh'], tf.constant(avg_zero), tf.constant(std_ones), rcut_a = self.rcut_a, rcut_r = self.rcut_r, rcut_r_smth = self.rcut_r_smth, sel_a = self.sel_a, sel_r = self.sel_r) self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config) self.original_sel = None
def __init__(self, rcut: float, rcut_smth: float, sel: List[str], neuron: List[int] = [24, 48, 96], resnet_dt: bool = False, trainable: bool = True, seed: int = None, type_one_side: bool = True, exclude_types: List[List[int]] = [], set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False) -> None: """ Constructor """ # args = ClassArg()\ # .add('sel', list, must = True) \ # .add('rcut', float, default = 6.0) \ # .add('rcut_smth',float, default = 0.5) \ # .add('neuron', list, default = [10, 20, 40]) \ # .add('resnet_dt',bool, default = False) \ # .add('trainable',bool, default = True) \ # .add('seed', int) \ # .add('type_one_side', bool, default = False) \ # .add('exclude_types', list, default = []) \ # .add('set_davg_zero', bool, default = False) \ # .add("activation_function", str, default = "tanh") \ # .add("precision", str, default = "default") # class_data = args.parse(jdata) self.sel_r = sel self.rcut = rcut self.rcut_smth = rcut_smth self.filter_neuron = neuron self.filter_resnet_dt = resnet_dt self.seed = seed self.uniform_seed = uniform_seed self.seed_shift = embedding_net_rand_seed_shift(self.filter_neuron) self.trainable = trainable self.filter_activation_fn = get_activation_func(activation_function) self.filter_precision = get_precision(precision) exclude_types = exclude_types self.exclude_types = set() for tt in exclude_types: assert (len(tt) == 2) self.exclude_types.add((tt[0], tt[1])) self.exclude_types.add((tt[1], tt[0])) self.set_davg_zero = set_davg_zero self.type_one_side = type_one_side # descrpt config self.sel_a = [0 for ii in range(len(self.sel_r))] self.ntypes = len(self.sel_r) # numb of neighbors and numb of descrptors self.nnei_a = np.cumsum(self.sel_a)[-1] self.nnei_r = np.cumsum(self.sel_r)[-1] self.nnei = self.nnei_a + self.nnei_r self.ndescrpt_a = self.nnei_a * 4 self.ndescrpt_r = self.nnei_r * 1 self.ndescrpt = self.nnei_r self.useBN = False self.davg = None self.dstd = None self.embedding_net_variables = None self.place_holders = {} avg_zero = np.zeros([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) std_ones = np.ones([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) sub_graph = tf.Graph() with sub_graph.as_default(): name_pfx = 'd_ser_' for ii in ['coord', 'box']: self.place_holders[ii] = tf.placeholder( GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + 't_' + ii) self.place_holders['type'] = tf.placeholder(tf.int32, [None, None], name=name_pfx + 't_type') self.place_holders['natoms_vec'] = tf.placeholder( tf.int32, [self.ntypes + 2], name=name_pfx + 't_natoms') self.place_holders['default_mesh'] = tf.placeholder( tf.int32, [None], name=name_pfx + 't_mesh') self.stat_descrpt, descrpt_deriv, rij, nlist \ = op_module.prod_env_mat_r(self.place_holders['coord'], self.place_holders['type'], self.place_holders['natoms_vec'], self.place_holders['box'], self.place_holders['default_mesh'], tf.constant(avg_zero), tf.constant(std_ones), rcut = self.rcut, rcut_smth = self.rcut_smth, sel = self.sel_r) self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config)
def __init__(self, rcut: float, rcut_smth: float, sel: List[str], neuron: List[int] = [24, 48, 96], resnet_dt: bool = False, trainable: bool = True, seed: int = None, set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False) -> None: """ Constructor """ self.sel_a = sel self.rcut_r = rcut self.rcut_r_smth = rcut_smth self.filter_neuron = neuron self.filter_resnet_dt = resnet_dt self.seed = seed self.uniform_seed = uniform_seed self.seed_shift = embedding_net_rand_seed_shift(self.filter_neuron) self.trainable = trainable self.filter_activation_fn = get_activation_func(activation_function) self.filter_precision = get_precision(precision) # self.exclude_types = set() # for tt in exclude_types: # assert(len(tt) == 2) # self.exclude_types.add((tt[0], tt[1])) # self.exclude_types.add((tt[1], tt[0])) self.set_davg_zero = set_davg_zero # descrpt config self.sel_r = [0 for ii in range(len(self.sel_a))] self.ntypes = len(self.sel_a) assert (self.ntypes == len(self.sel_r)) self.rcut_a = -1 # numb of neighbors and numb of descrptors self.nnei_a = np.cumsum(self.sel_a)[-1] self.nnei_r = np.cumsum(self.sel_r)[-1] self.nnei = self.nnei_a + self.nnei_r self.ndescrpt_a = self.nnei_a * 4 self.ndescrpt_r = self.nnei_r * 1 self.ndescrpt = self.ndescrpt_a + self.ndescrpt_r self.useBN = False self.dstd = None self.davg = None self.place_holders = {} avg_zero = np.zeros([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) std_ones = np.ones([self.ntypes, self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION) sub_graph = tf.Graph() with sub_graph.as_default(): name_pfx = 'd_sea_' for ii in ['coord', 'box']: self.place_holders[ii] = tf.placeholder( GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + 't_' + ii) self.place_holders['type'] = tf.placeholder(tf.int32, [None, None], name=name_pfx + 't_type') self.place_holders['natoms_vec'] = tf.placeholder( tf.int32, [self.ntypes + 2], name=name_pfx + 't_natoms') self.place_holders['default_mesh'] = tf.placeholder( tf.int32, [None], name=name_pfx + 't_mesh') self.stat_descrpt, descrpt_deriv, rij, nlist \ = op_module.prod_env_mat_a(self.place_holders['coord'], self.place_holders['type'], self.place_holders['natoms_vec'], self.place_holders['box'], self.place_holders['default_mesh'], tf.constant(avg_zero), tf.constant(std_ones), rcut_a = self.rcut_a, rcut_r = self.rcut_r, rcut_r_smth = self.rcut_r_smth, sel_a = self.sel_a, sel_r = self.sel_r) self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config)