def extract_layer_norm(self,
input_name,
layer_norm_name,
scope_id,
layer_names=None):
if (layer_names is None):
self.scopes[scope_id] = "LayerNorm"
self.scopes[scope_id + 1] = "gamma"
gamma_name = self.generate_name(self.scopes, scope_id + 2)
self.scopes[scope_id + 1] = "beta"
beta_name = self.generate_name(self.scopes, scope_id + 2)
else:
self.scopes[scope_id] = layer_names[0]
self.scopes[scope_id + 1] = layer_names[1]
gamma_name = self.generate_name(self.scopes, scope_id + 2)
self.scopes[scope_id + 1] = layer_names[2]
beta_name = self.generate_name(self.scopes, scope_id + 2)
gamma = self.get_tensor(gamma_name)
beta = self.get_tensor(beta_name)
layer = caffe_net.LayerParameter(name=layer_norm_name,
type='LayerNorm',
bottom=[input_name],
top=[layer_norm_name])
layer.add_data(gamma, beta)
self.caffe_model.add_layer(layer)
self.data_dict[layer_norm_name] = Operators.layer_norm(
self.data_dict[input_name], gamma, beta, layer_norm_name)
return layer_norm_name
def extract_layer_norm(self, input_name, output_name, scope_id,
layer_names=["LayerNorm", "gamma", "beta"]):
gamma, beta = self.get_weights(scope_id, layer_names)
layer = caffe_net.LayerParameter(name=output_name, type='LayerNorm',
bottom=[input_name], top=[output_name])
layer.add_data(gamma, beta)
self.caffe_model.add_layer(layer)
self.data_dict[output_name] = Operators.layer_norm(self.data_dict[input_name],
gamma, beta,
output_name)
return output_name