# Copyright (c) 2022 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 __future__ import annotations from typing import ( TYPE_CHECKING, Callable, TypedDict, ) from typing_extensions import NotRequired, Unpack import paddle from paddle import nn from paddle.utils.download import get_weights_path_from_url from ..ops import ConvNormActivation from ._utils import _make_divisible if TYPE_CHECKING: from paddle import Tensor class _MobileNetV2Options(TypedDict): num_classes: NotRequired[int] with_pool: NotRequired[bool] __all__ = [] model_urls = { 'mobilenetv2_1.0': ( 'https://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparams', '0340af0a901346c8d46f4529882fb63d', ) } class InvertedResidual(nn.Layer): def __init__( self, inp: int, oup: int, stride: int, expand_ratio: float, norm_layer: Callable[..., nn.Layer] = nn.BatchNorm2D, ) -> None: super().__init__() self.stride = stride assert stride in [1, 2] hidden_dim = int(round(inp * expand_ratio)) self.use_res_connect = self.stride == 1 and inp == oup layers = [] if expand_ratio != 1: layers.append( ConvNormActivation( inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6, ) ) layers.extend( [ ConvNormActivation( hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer, activation_layer=nn.ReLU6, ), nn.Conv2D(hidden_dim, oup, 1, 1, 0, bias_attr=False), norm_layer(oup), ] ) self.conv = nn.Sequential(*layers) def forward(self, x: Tensor) -> Tensor: if self.use_res_connect: return x + self.conv(x) else: return self.conv(x) class MobileNetV2(nn.Layer): """MobileNetV2 model from `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" `_. Args: scale (float, optional): Scale of channels in each layer. Default: 1.0. num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV2 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import MobileNetV2 >>> model = MobileNetV2() >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] """ num_classes: int with_pool: bool def __init__( self, scale: float = 1.0, num_classes: int = 1000, with_pool: bool = True, ) -> None: super().__init__() self.num_classes = num_classes self.with_pool = with_pool input_channel = 32 last_channel = 1280 block = InvertedResidual round_nearest = 8 norm_layer = nn.BatchNorm2D inverted_residual_setting = [ [1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2], [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2], [6, 320, 1, 1], ] input_channel = _make_divisible(input_channel * scale, round_nearest) self.last_channel = _make_divisible( last_channel * max(1.0, scale), round_nearest ) features = [ ConvNormActivation( 3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6, ) ] for t, c, n, s in inverted_residual_setting: output_channel = _make_divisible(c * scale, round_nearest) for i in range(n): stride = s if i == 0 else 1 features.append( block( input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer, ) ) input_channel = output_channel features.append( ConvNormActivation( input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6, ) ) self.features = nn.Sequential(*features) if with_pool: self.pool2d_avg = nn.AdaptiveAvgPool2D(1) if self.num_classes > 0: self.classifier = nn.Sequential( nn.Dropout(0.2), nn.Linear(self.last_channel, num_classes) ) def forward(self, x: Tensor) -> Tensor: x = self.features(x) if self.with_pool: x = self.pool2d_avg(x) if self.num_classes > 0: x = paddle.flatten(x, 1) x = self.classifier(x) return x def _mobilenet( arch: str, pretrained: bool = False, **kwargs: Unpack[_MobileNetV2Options] ) -> MobileNetV2: model = MobileNetV2(**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 mobilenet_v2( pretrained: bool = False, scale: float = 1.0, **kwargs: Unpack[_MobileNetV2Options], ) -> MobileNetV2: """MobileNetV2 from `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. scale (float, optional): Scale of channels in each layer. Default: 1.0. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV2 `. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV2 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import mobilenet_v2 >>> # Build model >>> model = mobilenet_v2() >>> # Build model and load imagenet pretrained weight >>> # model = mobilenet_v2(pretrained=True) >>> # Build mobilenet v2 with scale=0.5 >>> model = mobilenet_v2(scale=0.5) >>> x = paddle.rand([1, 3, 224, 224]) >>> out = model(x) >>> print(out.shape) [1, 1000] """ model = _mobilenet( 'mobilenetv2_' + str(scale), pretrained, scale=scale, **kwargs ) return model