Merge pull request #235 from GeiduanLiu/devel

Devel

Author: amcadmus

source/train/Fitting.py

@@ -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]
 

source/train/transform.py

@@ -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:
@@ -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()
@@ -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)))
@@ -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: