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Merged
wanghan-iapcm merged 6 commits into from
Nov 29, 2022
Merged

clean deprecated codes #2140

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1df94a9
clean deprecated codes
njzjz Nov 27, 2022
079a830
fix tests
njzjz Nov 27, 2022
b98d111
move DataSystem to tests
njzjz Nov 27, 2022
4056677
fix tests
njzjz Nov 27, 2022
cef564a
fix tests
njzjz Nov 27, 2022
6da16b1
Merge branch 'devel' into clean-deprecated
njzjz Nov 29, 2022
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135 changes: 0 additions & 135 deletions deepmd/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,141 +212,6 @@ def make_default_mesh(
return default_mesh


# TODO not an ideal approach, every class uses this to parse arguments on its own, json
# TODO should be parsed once and the parsed result passed to all objects that need it
class ClassArg:
"""Class that take care of input json/yaml parsing.

The rules for parsing are defined by the `add` method, than `parse` is called to
process the supplied dict

Attributes
----------
arg_dict: Dict[str, Any]
dictionary containing parsing rules
alias_map: Dict[str, Any]
dictionary with keyword aliases
"""

def __init__(self) -> None:
self.arg_dict = {}
self.alias_map = {}

def add(
self,
key: str,
types_: Union[type, List[type]],
alias: Optional[Union[str, List[str]]] = None,
default: Optional[Any] = None,
must: bool = False,
) -> "ClassArg":
"""Add key to be parsed.

Parameters
----------
key : str
key name
types_ : Union[type, List[type]]
list of allowed key types
alias : Optional[Union[str, List[str]]], optional
alias for the key, by default None
default : Any, optional
default value for the key, by default None
must : bool, optional
if the key is mandatory, by default False

Returns
-------
ClassArg
instance with added key
"""
if not isinstance(types_, list):
types = [types_]
else:
types = types_
if alias is not None:
if not isinstance(alias, list):
alias_ = [alias]
else:
alias_ = alias
else:
alias_ = []

self.arg_dict[key] = {
"types": types,
"alias": alias_,
"value": default,
"must": must,
}
for ii in alias_:
self.alias_map[ii] = key

return self

def _add_single(self, key: str, data: Any):
vtype = type(data)
if data is None:
return data
if not (vtype in self.arg_dict[key]["types"]):
for tp in self.arg_dict[key]["types"]:
try:
vv = tp(data)
except TypeError:
pass
else:
break
else:
raise TypeError(
f"cannot convert provided key {key} to type(s) "
f'{self.arg_dict[key]["types"]} '
)
else:
vv = data
self.arg_dict[key]["value"] = vv

def _check_must(self):
for kk in self.arg_dict:
if self.arg_dict[kk]["must"] and self.arg_dict[kk]["value"] is None:
raise RuntimeError(f"key {kk} must be provided")

def parse(self, jdata: Dict[str, Any]) -> Dict[str, Any]:
"""Parse input dictionary, use the rules defined by add method.

Parameters
----------
jdata : Dict[str, Any]
loaded json/yaml data

Returns
-------
Dict[str, Any]
parsed dictionary
"""
for kk in jdata.keys():
if kk in self.arg_dict:
key = kk
self._add_single(key, jdata[kk])
else:
if kk in self.alias_map:
key = self.alias_map[kk]
self._add_single(key, jdata[kk])
self._check_must()
return self.get_dict()

def get_dict(self) -> Dict[str, Any]:
"""Get dictionary built from rules defined by add method.

Returns
-------
Dict[str, Any]
settings dictionary with default values
"""
ret = {}
for kk in self.arg_dict.keys():
ret[kk] = self.arg_dict[kk]["value"]
return ret


# TODO maybe rename this to j_deprecated and only warn about deprecated keys,
# TODO if the deprecated_key argument is left empty function puppose is only custom
# TODO error since dict[key] already raises KeyError when the key is missing
Expand Down
5 changes: 0 additions & 5 deletions deepmd/descriptor/hybrid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@
from typing import Tuple, List

from deepmd.env import tf
from deepmd.common import ClassArg
from deepmd.env import op_module
from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
from deepmd.env import GLOBAL_NP_FLOAT_PRECISION
Expand Down Expand Up @@ -51,10 +50,6 @@ def __init__ (self,
formatted_descript_list.append(Descriptor(**ii))
else:
raise NotImplementedError
# args = ClassArg()\
# .add('list', list, must = True)
# class_data = args.parse(jdata)
# dict_list = class_data['list']
self.descrpt_list = formatted_descript_list
self.numb_descrpt = len(self.descrpt_list)
for ii in range(1, self.numb_descrpt):
Expand Down
6 changes: 0 additions & 6 deletions deepmd/descriptor/loc_frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,12 +46,6 @@ def __init__(self,
"""
Constructor
"""
# args = ClassArg()\
# .add('sel_a', list, must = True) \
# .add('sel_r', list, must = True) \
# .add('rcut', float, default = 6.0) \
# .add('axis_rule',list, must = True)
# class_data = args.parse(jdata)
self.sel_a = sel_a
self.sel_r = sel_r
self.axis_rule = axis_rule
Expand Down
8 changes: 1 addition & 7 deletions deepmd/descriptor/se_a_ebd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
from typing import Optional, Tuple, List

from deepmd.env import tf
from deepmd.common import ClassArg, get_activation_func, get_precision, add_data_requirement
from deepmd.common import get_activation_func, get_precision, add_data_requirement
from deepmd.utils.network import one_layer
from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
from deepmd.env import GLOBAL_NP_FLOAT_PRECISION
Expand Down Expand Up @@ -75,12 +75,6 @@ def __init__ (self,
"""
Constructor
"""
# args = ClassArg()\
# .add('type_nchanl', int, default = 4) \
# .add('type_nlayer', int, default = 2) \
# .add('type_one_side', bool, default = True) \
# .add('numb_aparam', int, default = 0)
# class_data = args.parse(jdata)
DescrptSeA.__init__(self,
rcut,
rcut_smth,
Expand Down
19 changes: 0 additions & 19 deletions deepmd/descriptor/se_a_ef.py
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Original file line number Diff line number Diff line change
Expand Up @@ -309,25 +309,6 @@ def __init__ (self,
precision,
uniform_seed
)
# DescrptSeA.__init__(self, **jdata)
# args = ClassArg()\
# .add('sel', list, must = True) \
# .add('rcut', float, default = 6.0) \
# .add('rcut_smth',float, default = 5.5) \
# .add('neuron', list, default = [10, 20, 40]) \
# .add('axis_neuron', int, default = 4, alias = 'n_axis_neuron') \
# .add('resnet_dt',bool, default = False) \
# .add('trainable',bool, default = True) \
# .add('seed', int)
# class_data = args.parse(jdata)
# self.sel_a = class_data['sel']
# self.rcut_r = class_data['rcut']
# self.rcut_r_smth = class_data['rcut_smth']
# self.filter_neuron = class_data['neuron']
# self.n_axis_neuron = class_data['axis_neuron']
# self.filter_resnet_dt = class_data['resnet_dt']
# self.seed = class_data['seed']
# self.trainable = class_data['trainable']
self.sel_a = sel
self.rcut_r = rcut
self.rcut_r_smth = rcut_smth
Expand Down
14 changes: 0 additions & 14 deletions deepmd/descriptor/se_r.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,20 +70,6 @@ def __init__ (self,
"""
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)
if rcut < rcut_smth:
raise RuntimeError("rcut_smth (%f) should be no more than rcut (%f)!" % (rcut_smth, rcut))
self.sel_r = sel
Expand Down
2 changes: 0 additions & 2 deletions deepmd/fit/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,4 @@
from .ener import EnerFitting
from .wfc import WFCFitting
from .dipole import DipoleFittingSeA
from .polar import PolarFittingSeA
from .polar import GlobalPolarFittingSeA
from .polar import PolarFittingLocFrame
8 changes: 0 additions & 8 deletions deepmd/fit/dipole.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,14 +51,6 @@ def __init__ (self,
"""
self.ntypes = descrpt.get_ntypes()
self.dim_descrpt = descrpt.get_dim_out()
# args = ClassArg()\
# .add('neuron', list, default = [120,120,120], alias = 'n_neuron')\
# .add('resnet_dt', bool, default = True)\
# .add('sel_type', [list,int], default = [ii for ii in range(self.ntypes)], alias = 'dipole_type')\
# .add('seed', int)\
# .add("activation_function", str, default = "tanh")\
# .add('precision', str, default = "default")
# class_data = args.parse(jdata)
self.n_neuron = neuron
self.resnet_dt = resnet_dt
self.sel_type = sel_type
Expand Down
98 changes: 0 additions & 98 deletions deepmd/fit/polar.py
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Expand Up @@ -14,93 +14,6 @@
from deepmd.env import GLOBAL_TF_FLOAT_PRECISION


class PolarFittingLocFrame () :
"""
Fitting polarizability with local frame descriptor.

.. deprecated:: 2.0.0
This class is not supported any more.
"""
def __init__ (self, jdata, descrpt) :
if not isinstance(descrpt, DescrptLocFrame) :
raise RuntimeError('PolarFittingLocFrame only supports DescrptLocFrame')
self.ntypes = descrpt.get_ntypes()
self.dim_descrpt = descrpt.get_dim_out()
args = ClassArg()\
.add('neuron', list, default = [120,120,120], alias = 'n_neuron')\
.add('resnet_dt', bool, default = True)\
.add('sel_type', [list,int], default = [ii for ii in range(self.ntypes)], alias = 'pol_type')\
.add('seed', int)\
.add("activation_function", str, default = "tanh")\
.add('precision', str, default = "default")
class_data = args.parse(jdata)
self.n_neuron = class_data['neuron']
self.resnet_dt = class_data['resnet_dt']
self.sel_type = class_data['sel_type']
self.seed = class_data['seed']
self.fitting_activation_fn = get_activation_func(class_data["activation_function"])
self.fitting_precision = get_precision(class_data['precision'])
self.useBN = False

def get_sel_type(self):
return self.sel_type

def get_out_size(self):
return 9

def build (self,
input_d,
rot_mat,
natoms,
reuse = None,
suffix = '') :
start_index = 0
inputs = tf.cast(tf.reshape(input_d, [-1, natoms[0], self.dim_descrpt]), self.fitting_precision)
rot_mat = tf.reshape(rot_mat, [-1, 9 * natoms[0]])

count = 0
outs_list = []
for type_i in range(self.ntypes):
# cut-out inputs
inputs_i = tf.slice (inputs,
[ 0, start_index, 0],
[-1, natoms[2+type_i], -1] )
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
rot_mat_i = tf.slice (rot_mat,
[ 0, start_index* 9],
[-1, natoms[2+type_i]* 9] )
rot_mat_i = tf.reshape(rot_mat_i, [-1, 3, 3])
start_index += natoms[2+type_i]
if not type_i in self.sel_type :
continue
layer = inputs_i
for ii in range(0,len(self.n_neuron)) :
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii-1] :
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision)
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision)
# (nframes x natoms) x 9
final_layer = one_layer(layer, 9, activation_fn = None, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision, final_layer = True)
# (nframes x natoms) x 3 x 3
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0] * natoms[2+type_i], 3, 3])
# (nframes x natoms) x 3 x 3
final_layer = final_layer + tf.transpose(final_layer, perm = [0,2,1])
# (nframes x natoms) x 3 x 3(coord)
final_layer = tf.matmul(final_layer, rot_mat_i)
# (nframes x natoms) x 3(coord) x 3(coord)
final_layer = tf.matmul(rot_mat_i, final_layer, transpose_a = True)
# nframes x natoms x 3 x 3
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[2+type_i], 3, 3])

# concat the results
outs_list.append(final_layer)
count += 1
outs = tf.concat(outs_list, axis = 1)

tf.summary.histogram('fitting_net_output', outs)
return tf.cast(tf.reshape(outs, [-1]), self.fitting_precision)


class PolarFittingSeA (Fitting) :
"""
Fit the atomic polarizability with descriptor se_a
Expand Down Expand Up @@ -150,17 +63,6 @@ def __init__ (self,
"""
self.ntypes = descrpt.get_ntypes()
self.dim_descrpt = descrpt.get_dim_out()
# args = ClassArg()\
# .add('neuron', list, default = [120,120,120], alias = 'n_neuron')\
# .add('resnet_dt', bool, default = True)\
# .add('fit_diag', bool, default = True)\
# .add('diag_shift', [list,float], default = [0.0 for ii in range(self.ntypes)])\
# .add('scale', [list,float], default = [1.0 for ii in range(self.ntypes)])\
# .add('sel_type', [list,int], default = [ii for ii in range(self.ntypes)], alias = 'pol_type')\
# .add('seed', int)\
# .add("activation_function", str , default = "tanh")\
# .add('precision', str, default = "default")
# class_data = args.parse(jdata)
self.n_neuron = neuron
self.resnet_dt = resnet_dt
self.sel_type = sel_type
Expand Down
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