# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. # # 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 __future__ import annotations import math from typing import ( TYPE_CHECKING, TypedDict, ) from typing_extensions import NotRequired, Unpack import paddle import paddle.nn.functional as F from paddle import nn from paddle.base.param_attr import ParamAttr from paddle.nn import Conv2D, Dropout, Linear, MaxPool2D, ReLU from paddle.nn.initializer import Uniform from paddle.utils.download import get_weights_path_from_url model_urls = { "alexnet": ( "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams", "7f0f9f737132e02732d75a1459d98a43", ) } __all__ = [] if TYPE_CHECKING: from paddle import Tensor from paddle._typing import Size2 class _AlexNetOptions(TypedDict): num_classes: NotRequired[int] class ConvPoolLayer(nn.Layer): def __init__( self, input_channels: int, output_channels: int, filter_size: Size2, stride: Size2, padding: Size2, stdv: float, groups: int = 1, act: str | None = None, ) -> None: super().__init__() self.relu = ReLU() if act == "relu" else None self._conv = Conv2D( in_channels=input_channels, out_channels=output_channels, kernel_size=filter_size, stride=stride, padding=padding, groups=groups, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), ) self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0) def forward(self, inputs: Tensor) -> Tensor: x = self._conv(inputs) if self.relu is not None: x = self.relu(x) x = self._pool(x) return x class AlexNet(nn.Layer): """AlexNet model from `"ImageNet Classification with Deep Convolutional Neural Networks" `_. Args: num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. Returns: :ref:`api_paddle_nn_Layer`. An instance of AlexNet model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import AlexNet >>> alexnet = AlexNet() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = alexnet(x) >>> print(out.shape) [1, 1000] """ num_classes: int def __init__(self, num_classes: int = 1000) -> None: super().__init__() self.num_classes = num_classes stdv = 1.0 / math.sqrt(3 * 11 * 11) self._conv1 = ConvPoolLayer(3, 64, 11, 4, 2, stdv, act="relu") stdv = 1.0 / math.sqrt(64 * 5 * 5) self._conv2 = ConvPoolLayer(64, 192, 5, 1, 2, stdv, act="relu") stdv = 1.0 / math.sqrt(192 * 3 * 3) self._conv3 = Conv2D( 192, 384, 3, stride=1, padding=1, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), ) stdv = 1.0 / math.sqrt(384 * 3 * 3) self._conv4 = Conv2D( 384, 256, 3, stride=1, padding=1, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), ) stdv = 1.0 / math.sqrt(256 * 3 * 3) self._conv5 = ConvPoolLayer(256, 256, 3, 1, 1, stdv, act="relu") if self.num_classes > 0: stdv = 1.0 / math.sqrt(256 * 6 * 6) self._drop1 = Dropout(p=0.5, mode="downscale_in_infer") self._fc6 = Linear( in_features=256 * 6 * 6, out_features=4096, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), ) self._drop2 = Dropout(p=0.5, mode="downscale_in_infer") self._fc7 = Linear( in_features=4096, out_features=4096, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), ) self._fc8 = Linear( in_features=4096, out_features=num_classes, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), ) def forward(self, inputs: Tensor) -> Tensor: x = self._conv1(inputs) x = self._conv2(x) x = self._conv3(x) x = F.relu(x) x = self._conv4(x) x = F.relu(x) x = self._conv5(x) if self.num_classes > 0: x = paddle.flatten(x, start_axis=1, stop_axis=-1) x = self._drop1(x) x = self._fc6(x) x = F.relu(x) x = self._drop2(x) x = self._fc7(x) x = F.relu(x) x = self._fc8(x) return x def _alexnet( arch: str, pretrained: bool, **kwargs: Unpack[_AlexNetOptions] ) -> AlexNet: model = AlexNet(**kwargs) if pretrained: assert arch in model_urls, ( f"{arch} model do not have a pretrained model now, you should set pretrained=False" ) weight_path = get_weights_path_from_url( model_urls[arch][0], model_urls[arch][1] ) param = paddle.load(weight_path) model.load_dict(param) return model def alexnet( pretrained: bool = False, **kwargs: Unpack[_AlexNetOptions] ) -> AlexNet: """AlexNet model from `"ImageNet Classification with Deep Convolutional Neural Networks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`AlexNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of AlexNet model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import alexnet >>> # Build model >>> model = alexnet() >>> # Build model and load imagenet pretrained weight >>> # model = alexnet(pretrained=True) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] """ return _alexnet('alexnet', pretrained, **kwargs)