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Merged
amcadmus merged 3 commits into from
Jul 7, 2020
Merged

Devel #235

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c0e0a14
Update transform.py
GeiduanLiu Jul 6, 2020
8e28cf8
Update Fitting.py
GeiduanLiu Jul 6, 2020
e141573
Update transform.py
GeiduanLiu Jul 6, 2020
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2 changes: 2 additions & 0 deletions source/train/Fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,12 +194,14 @@ def build (self,
if self.numb_fparam > 0 :
ext_fparam = tf.tile(fparam, [1, natoms[2+type_i]])
ext_fparam = tf.reshape(ext_fparam, [-1, self.numb_fparam])
ext_fparam = tf.cast(ext_fparam,self.fitting_precision)
layer = tf.concat([layer, ext_fparam], axis = 1)
if self.numb_aparam > 0 :
ext_aparam = tf.slice(aparam,
[ 0, start_index * self.numb_aparam],
[-1, natoms[2+type_i] * self.numb_aparam])
ext_aparam = tf.reshape(ext_aparam, [-1, self.numb_aparam])
ext_aparam = tf.cast(ext_aparam,self.fitting_precision)
layer = tf.concat([layer, ext_aparam], axis = 1)
start_index += natoms[2+type_i]

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25 changes: 13 additions & 12 deletions source/train/transform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,22 +49,18 @@ def transform_graph(raw_graph,old_graph):

for node in raw_graph_def.node:
if node.name in raw_graph_node.keys():
"""
if precision_dict[old_graph_node[node.name].dtype][1] == "float16" or precision_dict[raw_graph_node[node.name].dtype][1] == "float16":
raise RuntimeError("float16 conversions not currently supported")
"""

check_dim(raw_graph_node, old_graph_node, node.name)
tensor_shape = [dim.size for dim in raw_graph_node[node.name].tensor_shape.dim]
old_graph_dtype = precision_dict[old_graph_node[node.name].dtype]
raw_graph_dtype = precision_dict[raw_graph_node[node.name].dtype]
print("%s is passed from old graph(%s) to raw graph(%s)" % (node.name, old_graph_dtype[1],raw_graph_dtype[1]))

if raw_graph_dtype[1] == "float16":
if old_graph_dtype[1] == "float64" or old_graph_dtype[1] == "float32":
if re.fullmatch("final_layer_type_\d+/bias", node.name) == None:
if (len(tensor_shape) != 1) or (tensor_shape[0] != 1):
tensor_value = np.frombuffer(old_graph_node[node.name].tensor_content, dtype=old_graph_dtype[0])
tensor_value = tensor_value.astype(np.float16)
tensor_shape = [dim.size for dim in raw_graph_node[node.name].tensor_shape.dim]
node.attr["value"].CopyFrom(tf.AttrValue(tensor=tf.make_tensor_proto(tensor_value, tf.float16, tensor_shape)))

else:
Expand All @@ -77,13 +73,12 @@ def transform_graph(raw_graph,old_graph):
node.attr["value"].CopyFrom(tf.AttrValue(tensor=tf.make_tensor_proto(tensor_value,tf.float16, [1])))

elif old_graph_dtype[1] == "float16":
tensor_shape = [dim.size for dim in raw_graph_node[node.name].tensor_shape.dim]
tensor_value = convertMatrix(np.array(old_graph_node[node.name].half_val), tensor_shape)
node.attr["value"].CopyFrom(tf.AttrValue(tensor=tf.make_tensor_proto(tensor_value, tf.float16, tensor_value.shape)))

elif raw_graph_dtype[1] == "float64" or raw_graph_dtype[1] == "float32":
if old_graph_dtype[1] == "float64" or old_graph_dtype[1] == "float32":
if re.fullmatch("final_layer_type_\d+/bias", node.name) == None:
if (len(tensor_shape) != 1) or (tensor_shape[0] != 1):
tensor_value = np.frombuffer(old_graph_node[node.name].tensor_content,dtype = old_graph_dtype[0])
tensor_value = tensor_value.astype(dtype=raw_graph_dtype[0])
node.attr["value"].tensor.tensor_content = tensor_value.tostring()
Expand All @@ -98,13 +93,11 @@ def transform_graph(raw_graph,old_graph):
node.attr["value"].CopyFrom(tf.AttrValue(tensor=tf.make_tensor_proto(tensor_value,raw_graph_dtype[0], [1])))

elif old_graph_dtype[1] == "float16":
if re.fullmatch("final_layer_type_\d+/bias", node.name) == None:
tensor_shape = [dim.size for dim in raw_graph_node[node.name].tensor_shape.dim]
if (len(tensor_shape) != 1) or (tensor_shape[0] != 1):
tensor_value = convertMatrix(np.array(old_graph_node[node.name].half_val), tensor_shape)
tensor_value = tensor_value.astype(raw_graph_dtype[0])
node.attr["value"].tensor.tensor_content = tensor_value.tostring()
else:
tensor_shape = [dim.size for dim in raw_graph_node[node.name].tensor_shape.dim]
tensor_value = convertMatrix(np.array(old_graph_node[node.name].half_val), tensor_shape)
tensor_value = tensor_value.astype(raw_graph_dtype[0])
node.attr["value"].CopyFrom(tf.AttrValue(tensor=tf.make_tensor_proto(tensor_value,raw_graph_dtype[0], tensor_value.shape)))
Expand All @@ -127,8 +120,16 @@ def load_transform_node(graph):
layer_\d+_type_\d+/matrix|\
layer_\d+_type_\d+/bias|\
layer_\d+_type_\d+/idt|\
final_layer_type_\d+/matrix|\
descrpt_attr/t_avg|\
descrpt_attr/t_std|\
final_layer_type_\d+/bias|\
final_layer_type_\d+/matrix\
fitting_attr/t_fparam_avg|\
fitting_attr/t_fparam_istd|\
fitting_attr/t_aparam_avg|\
fitting_attr/t_aparam_istd|\
model_attr/t_tab_info|\
model_attr/t_tab_data|\
"
for node in graph.node:
if re.fullmatch(transform_node_pattern,node.name) != None:
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