# Copyright (c) 2019 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 collections import OrderedDict from ..framework import Parameter from .layers import Layer __all__ = [ 'Sequential', 'ParameterList', 'LayerList', ] class Sequential(Layer): """Sequential container. Sub layers will be added to this container in the order of argument in the constructor. The argument passed to the constructor can be iterable Layers or iterable name Layer pairs. Parameters: *layers(tuple): Layers or iterable name Layer pairs. Examples: .. code-block:: python import paddle import numpy as np data = np.random.uniform(-1, 1, [30, 10]).astype('float32') data = paddle.to_tensor(data) # create Sequential with iterable Layers model1 = paddle.nn.Sequential( paddle.nn.Linear(10, 1), paddle.nn.Linear(1, 2) ) model1[0] # access the first layer res1 = model1(data) # sequential execution # create Sequential with name Layer pairs model2 = paddle.nn.Sequential( ('l1', paddle.nn.Linear(10, 2)), ('l2', paddle.nn.Linear(2, 3)) ) model2['l1'] # access l1 layer model2.add_sublayer('l3', paddle.nn.Linear(3, 3)) # add sublayer res2 = model2(data) # sequential execution """ def __init__(self, *layers): super(Sequential, self).__init__() if len(layers) > 0 and isinstance(layers[0], tuple): for name, layer in layers: self.add_sublayer(name, layer) else: for idx, layer in enumerate(layers): self.add_sublayer(str(idx), layer) def __getitem__(self, name): return self._sub_layers[str(name)] def __setitem__(self, name, layer): assert isinstance(layer, Layer) setattr(self, str(name), layer) def __delitem__(self, name): name = str(name) assert name in self._sub_layers del self._sub_layers[name] def __len__(self): return len(self._sub_layers) def forward(self, input): for layer in self._sub_layers.values(): input = layer(input) return input class ParameterList(Layer): """ParameterList Container. This container acts like a Python list, but parameters it contains will be properly added. Parameters: parameters (iterable, optional): Iterable Parameters to be added Examples: .. code-block:: python import paddle import numpy as np class MyLayer(paddle.nn.Layer): def __init__(self, num_stacked_param): super(MyLayer, self).__init__() # create ParameterList with iterable Parameters self.params = paddle.nn.ParameterList( [paddle.create_parameter( shape=[2, 2], dtype='float32')] * num_stacked_param) def forward(self, x): for i, p in enumerate(self.params): tmp = self._helper.create_variable_for_type_inference('float32') self._helper.append_op( type="mul", inputs={"X": x, "Y": p}, outputs={"Out": tmp}, attrs={"x_num_col_dims": 1, "y_num_col_dims": 1}) x = tmp return x data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32') x = paddle.to_tensor(data_np) num_stacked_param = 4 model = MyLayer(num_stacked_param) print(len(model.params)) # 4 res = model(x) print(res.shape) # [5, 2] replaced_param = paddle.create_parameter(shape=[2, 3], dtype='float32') model.params[num_stacked_param - 1] = replaced_param # replace last param res = model(x) print(res.shape) # [5, 3] model.params.append(paddle.create_parameter(shape=[3, 4], dtype='float32')) # append param print(len(model.params)) # 5 res = model(x) print(res.shape) # [5, 4] """ def __init__(self, parameters=None): super(ParameterList, self).__init__() if parameters is not None: for idx, param in enumerate(parameters): assert isinstance(param, Parameter) self.add_parameter(str(idx), param) def __getitem__(self, idx): return self._parameters[str(idx)] def __setitem__(self, idx, param): assert isinstance(param, Parameter) setattr(self, str(idx), param) def __len__(self): return len(self._parameters) def __iter__(self): return iter(self._parameters.values()) def append(self, parameter): """Appends a given parameter at the end of the list. Parameters: parameter (Parameter): parameter to append """ idx = len(self._parameters) self.add_parameter(str(idx), parameter) return self class LayerList(Layer): """ LayerList holds sublayers, and sublayers it contains are properly registered. Holded sublayers can be indexed like a regular python list. Parameters: sublayers (iterable of Layer, optional): sublayers to hold Examples: .. code-block:: python import paddle import numpy as np class MyLayer(paddle.nn.Layer): def __init__(self): super(MyLayer, self).__init__() self.linears = paddle.nn.LayerList( [paddle.nn.Linear(10, 10) for i in range(10)]) def forward(self, x): # LayerList can act as an iterable, or be indexed using ints for i, l in enumerate(self.linears): x = self.linears[i // 2](x) + l(x) return x """ def __init__(self, sublayers=None): super(LayerList, self).__init__() if sublayers is not None: for idx, layer in enumerate(sublayers): self.add_sublayer(str(idx), layer) def __getitem__(self, idx): if isinstance(idx, slice): return self.__class__(list(self._sub_layers.values())[idx]) else: return self._sub_layers[str(idx)] def __setitem__(self, idx, sublayer): return setattr(self, str(idx), sublayer) def __delitem__(self, idx): if isinstance(idx, slice): for k in range(len(self._sub_layers))[idx]: delattr(self, str(k)) else: delattr(self, str(idx)) str_indices = [str(i) for i in range(len(self._sub_layers))] self._sub_layers = OrderedDict( list(zip(str_indices, self._sub_layers.values()))) def __len__(self): return len(self._sub_layers) def __iter__(self): return iter(self._sub_layers.values()) def append(self, sublayer): """ Appends a sublayer to the end of the list. Parameters: sublayer (Layer): sublayer to append Examples: .. code-block:: python import paddle linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)]) another = paddle.nn.Linear(10, 10) linears.append(another) print(len(linears)) # 11 """ self.add_sublayer(str(len(self)), sublayer) return self def insert(self, index, sublayer): """ Insert a sublayer before a given index in the list. Parameters: index (int): index to insert. sublayer (Layer): sublayer to insert Examples: .. code-block:: python import paddle linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)]) another = paddle.nn.Linear(10, 10) linears.insert(3, another) print(linears[3] is another) # True """ assert isinstance(index, int) and \ 0 <= index < len(self._sub_layers), \ "index should be an integer in range [0, len(self))" for i in range(len(self._sub_layers), index, -1): self._sub_layers[str(i)] = self._sub_layers[str(i - 1)] self._sub_layers[str(index)] = sublayer def extend(self, sublayers): """ Appends sublayers to the end of the list. Parameters: sublayers (iterable of Layer): iterable of sublayers to append Examples: .. code-block:: python import paddle linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)]) another_list = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(5)]) linears.extend(another_list) print(len(linears)) # 15 print(another_list[0] is linears[10]) # True """ offset = len(self) for i, sublayer in enumerate(sublayers): idx = str(offset + i) self.add_sublayer(idx, sublayer) return self