# Copyright (c) 2020 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. import paddle import paddle.nn as nn from paddle.utils.download import get_weights_path_from_url from ..ops import ConvNormActivation __all__ = [] model_urls = { 'mobilenetv1_1.0': ('https://paddle-hapi.bj.bcebos.com/models/mobilenetv1_1.0.pdparams', '3033ab1975b1670bef51545feb65fc45') } class DepthwiseSeparable(nn.Layer): def __init__(self, in_channels, out_channels1, out_channels2, num_groups, stride, scale): super(DepthwiseSeparable, self).__init__() self._depthwise_conv = ConvNormActivation(in_channels, int(out_channels1 * scale), kernel_size=3, stride=stride, padding=1, groups=int(num_groups * scale)) self._pointwise_conv = ConvNormActivation(int(out_channels1 * scale), int(out_channels2 * scale), kernel_size=1, stride=1, padding=0) def forward(self, x): x = self._depthwise_conv(x) x = self._pointwise_conv(x) return x class MobileNetV1(nn.Layer): """MobileNetV1 model from `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" `_. 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 MobileNetV1 model. Examples: .. code-block:: python import paddle from paddle.vision.models import MobileNetV1 model = MobileNetV1() x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] """ def __init__(self, scale=1.0, num_classes=1000, with_pool=True): super(MobileNetV1, self).__init__() self.scale = scale self.dwsl = [] self.num_classes = num_classes self.with_pool = with_pool self.conv1 = ConvNormActivation(in_channels=3, out_channels=int(32 * scale), kernel_size=3, stride=2, padding=1) dws21 = self.add_sublayer(sublayer=DepthwiseSeparable(in_channels=int( 32 * scale), out_channels1=32, out_channels2=64, num_groups=32, stride=1, scale=scale), name="conv2_1") self.dwsl.append(dws21) dws22 = self.add_sublayer(sublayer=DepthwiseSeparable(in_channels=int( 64 * scale), out_channels1=64, out_channels2=128, num_groups=64, stride=2, scale=scale), name="conv2_2") self.dwsl.append(dws22) dws31 = self.add_sublayer(sublayer=DepthwiseSeparable(in_channels=int( 128 * scale), out_channels1=128, out_channels2=128, num_groups=128, stride=1, scale=scale), name="conv3_1") self.dwsl.append(dws31) dws32 = self.add_sublayer(sublayer=DepthwiseSeparable(in_channels=int( 128 * scale), out_channels1=128, out_channels2=256, num_groups=128, stride=2, scale=scale), name="conv3_2") self.dwsl.append(dws32) dws41 = self.add_sublayer(sublayer=DepthwiseSeparable(in_channels=int( 256 * scale), out_channels1=256, out_channels2=256, num_groups=256, stride=1, scale=scale), name="conv4_1") self.dwsl.append(dws41) dws42 = self.add_sublayer(sublayer=DepthwiseSeparable(in_channels=int( 256 * scale), out_channels1=256, out_channels2=512, num_groups=256, stride=2, scale=scale), name="conv4_2") self.dwsl.append(dws42) for i in range(5): tmp = self.add_sublayer(sublayer=DepthwiseSeparable( in_channels=int(512 * scale), out_channels1=512, out_channels2=512, num_groups=512, stride=1, scale=scale), name="conv5_" + str(i + 1)) self.dwsl.append(tmp) dws56 = self.add_sublayer(sublayer=DepthwiseSeparable( in_channels=int(512 * scale), out_channels1=512, out_channels2=1024, num_groups=512, stride=2, scale=scale), name="conv5_6") self.dwsl.append(dws56) dws6 = self.add_sublayer(sublayer=DepthwiseSeparable(in_channels=int( 1024 * scale), out_channels1=1024, out_channels2=1024, num_groups=1024, stride=1, scale=scale), name="conv6") self.dwsl.append(dws6) if with_pool: self.pool2d_avg = nn.AdaptiveAvgPool2D(1) if num_classes > 0: self.fc = nn.Linear(int(1024 * scale), num_classes) def forward(self, x): x = self.conv1(x) for dws in self.dwsl: x = dws(x) if self.with_pool: x = self.pool2d_avg(x) if self.num_classes > 0: x = paddle.flatten(x, 1) x = self.fc(x) return x def _mobilenet(arch, pretrained=False, **kwargs): model = MobileNetV1(**kwargs) if pretrained: assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format( arch) 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_v1(pretrained=False, scale=1.0, **kwargs): """MobileNetV1 from `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" `_. 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:`MobileNetV1 `. Returns: :ref:`api_paddle_nn_Layer`. An instance of MobileNetV1 model. Examples: .. code-block:: python import paddle from paddle.vision.models import mobilenet_v1 # build model model = mobilenet_v1() # build model and load imagenet pretrained weight # model = mobilenet_v1(pretrained=True) # build mobilenet v1 with scale=0.5 model_scale = mobilenet_v1(scale=0.5) x = paddle.rand([1, 3, 224, 224]) out = model(x) print(out.shape) # [1, 1000] """ model = _mobilenet('mobilenetv1_' + str(scale), pretrained, scale=scale, **kwargs) return model