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remove the dependency on inputs from inputs_zero #1417

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merged 7 commits into from
Jan 15, 2022
Merged

remove the dependency on inputs from inputs_zero #1417

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85e7acd
remove the dependency on `inputs` from `inputs_zero`
njzjz Jan 13, 2022
c43ac3a
fix UT
njzjz Jan 13, 2022
f8f7be6
revert changes to the interface of the EnerFitting
njzjz Jan 14, 2022
c3e2453
Revert "fix UT"
njzjz Jan 14, 2022
131154d
fix typo
njzjz Jan 14, 2022
01e91e4
initialize input_dict
njzjz Jan 14, 2022
3b0c501
init input_dict
njzjz Jan 14, 2022
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16 changes: 10 additions & 6 deletions deepmd/fit/ener.py
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Original file line number Diff line number Diff line change
Expand Up @@ -332,7 +332,7 @@ def _build_lower(
def build (self,
inputs : tf.Tensor,
natoms : tf.Tensor,
input_dict : dict = {},
input_dict : dict = None,
reuse : bool = None,
suffix : str = '',
) -> tf.Tensor:
Expand Down Expand Up @@ -362,6 +362,8 @@ def build (self,
ener
The system energy
"""
if input_dict is None:
input_dict = {}
bias_atom_e = self.bias_atom_e
if self.numb_fparam > 0 and ( self.fparam_avg is None or self.fparam_inv_std is None ):
raise RuntimeError('No data stat result. one should do data statisitic, before build')
Expand Down Expand Up @@ -401,7 +403,12 @@ def build (self,
inputs = tf.reshape(inputs, [-1, self.dim_descrpt * natoms[0]])
if len(self.atom_ener):
# only for atom_ener
inputs_zero = tf.zeros_like(inputs, dtype=self.fitting_precision)
nframes = input_dict.get('nframes')
if nframes is not None:
# like inputs, but we don't want to add a dependency on inputs
inputs_zero = tf.zeros((nframes, self.dim_descrpt * natoms[0]), dtype=self.fitting_precision)
else:
inputs_zero = tf.zeros_like(inputs, dtype=self.fitting_precision)


if bias_atom_e is not None :
Expand All @@ -419,10 +426,7 @@ def build (self,
aparam = (aparam - t_aparam_avg) * t_aparam_istd
aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]])

if input_dict is not None:
type_embedding = input_dict.get('type_embedding', None)
else:
type_embedding = None
type_embedding = input_dict.get('type_embedding', None)
if type_embedding is not None:
atype_embed = embed_atom_type(self.ntypes, natoms, type_embedding)
atype_embed = tf.tile(atype_embed,[tf.shape(inputs)[0],1])
Expand Down
7 changes: 5 additions & 2 deletions deepmd/model/ener.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,9 @@ def build (self,
frz_model = None,
suffix = '',
reuse = None):


if input_dict is None:
input_dict = {}
with tf.variable_scope('model_attr' + suffix, reuse = reuse) :
t_tmap = tf.constant(' '.join(self.type_map),
name = 'tmap',
Expand All @@ -144,6 +146,7 @@ def build (self,

coord = tf.reshape (coord_, [-1, natoms[1] * 3])
atype = tf.reshape (atype_, [-1, natoms[1]])
input_dict['nframes'] = tf.shape(coord)[0]

# type embedding if any
if self.typeebd is not None:
Expand Down Expand Up @@ -270,4 +273,4 @@ def build (self,

def _import_graph_def_from_frz_model(self, frz_model, feed_dict, return_elements):
graph, graph_def = load_graph_def(frz_model)
return tf.import_graph_def(graph_def, input_map = feed_dict, return_elements = return_elements, name = "")
return tf.import_graph_def(graph_def, input_map = feed_dict, return_elements = return_elements, name = "")