from .register import register from x2paddle.core.util import * def InstanceNormalization_shape(input_shape): return input_shape def InstanceNormalization_layer(inputs, name=None): # TODO(lvmengsi@baidu.com): Check the accuracy when using fluid.layers.layer_norm. epsilon = 1e-5 mean = fluid.layers.reduce_mean(inputs, dim=[2, 3], keep_dim=True) var = fluid.layers.reduce_mean(fluid.layers.square(inputs - mean), dim=[2, 3], keep_dim=True) if name is not None: scale_name = name + "_scale" offset_name = name + "_offset" scale_param = fluid.ParamAttr(name=scale_name, initializer=fluid.initializer.Constant(1.0), trainable=True) offset_param = fluid.ParamAttr(name=offset_name, initializer=fluid.initializer.Constant(0.0), trainable=True) scale = fluid.layers.create_parameter(attr=scale_param, shape=inputs.shape[1:2], dtype="float32") offset = fluid.layers.create_parameter(attr=offset_param, shape=inputs.shape[1:2], dtype="float32") tmp = fluid.layers.elementwise_mul(x=(inputs - mean), y=scale, axis=1) tmp = tmp / fluid.layers.sqrt(var + epsilon) tmp = fluid.layers.elementwise_add(tmp, offset, axis=1) return tmp def InstanceNormalization_weights(name, data=None): weights_name = [name + '_scale'] return weights_name register(kind='InstanceNormalization', shape=InstanceNormalization_shape, layer=InstanceNormalization_layer, weights=InstanceNormalization_weights)