# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...fluid.dygraph import layers from ...tensor import math, manipulation __all__ = [] class FloatFunctionalLayer(layers.Layer): def __init__(self): super(FloatFunctionalLayer, self).__init__() class add(FloatFunctionalLayer): def __init__(self): super(add, self).__init__() def forward(self, x, y, name=None): return math.add(x, y, name) class subtract(FloatFunctionalLayer): def __init__(self): super(subtract, self).__init__() def forward(self, x, y, name=None): return math.subtract(x, y, name) class multiply(FloatFunctionalLayer): def __init__(self): super(multiply, self).__init__() def forward(self, x, y, name=None): return math.multiply(x, y, name) class divide(FloatFunctionalLayer): def __init__(self): super(divide, self).__init__() def forward(self, x, y, name=None): return math.divide(x, y, name) class reshape(FloatFunctionalLayer): def __init__(self): super(reshape, self).__init__() def forward(self, x, shape, name=None): return manipulation.reshape(x, shape, name) class transpose(FloatFunctionalLayer): def __init__(self): super(transpose, self).__init__() def forward(self, x, perm, name=None): return manipulation.transpose(x, perm, name) class concat(FloatFunctionalLayer): def __init__(self): super(concat, self).__init__() def forward(self, x, axis=0, name=None): return manipulation.concat(x, axis, name) class flatten(FloatFunctionalLayer): def __init__(self): super(flatten, self).__init__() def forward(self, x, start_axis=0, stop_axis=-1, name=None): return manipulation.flatten(x, start_axis, stop_axis, name)