def extract_group_norm(self,
input_name,
groups,
output_name,
scope_id,
data_format="NCHW",
axis=1,
layer_names=["GroupNorm", "gamma", "beta"]):
assert (data_format == "NCHW")
gamma, beta = self.get_weights(scope_id, layer_names)
layer = caffe_net.LayerParameter(name=output_name,
type='GroupNorm',
bottom=[input_name],
top=[output_name])
layer.add_data(gamma, beta)
layer.group_norm_param(groups)
self.caffe_model.add_layer(layer)
if (self.data_dict[input_name] is not None):
input_data, input_shape, inv_transpose_dims = self.preprocess_nchwc8_nchw_input(
input_name, axis)
output_data = Operators.group_norm(input_data, groups, gamma, beta,
output_name)
self.data_dict[output_name] = self.postprocess_nchwc8_nchw_output(
output_data, input_shape, inv_transpose_dims)
else:
self.data_dict[output_name] = None
return output_name
