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| # copyright (c) 2020 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 absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import paddle | |
| from paddle import nn | |
| import paddle.nn.functional as F | |
| from paddle import ParamAttr | |
| __all__ = ['MobileNetV3'] | |
| def make_divisible(v, divisor=8, min_value=None): | |
| if min_value is None: | |
| min_value = divisor | |
| new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) | |
| if new_v < 0.9 * v: | |
| new_v += divisor | |
| return new_v | |
| class MobileNetV3(nn.Layer): | |
| def __init__(self, | |
| in_channels=3, | |
| model_name='large', | |
| scale=0.5, | |
| disable_se=False, | |
| **kwargs): | |
| """ | |
| the MobilenetV3 backbone network for detection module. | |
| Args: | |
| params(dict): the super parameters for build network | |
| """ | |
| super(MobileNetV3, self).__init__() | |
| self.disable_se = disable_se | |
| if model_name == "large": | |
| cfg = [ | |
| # k, exp, c, se, nl, s, | |
| [3, 16, 16, False, 'relu', 1], | |
| [3, 64, 24, False, 'relu', 2], | |
| [3, 72, 24, False, 'relu', 1], | |
| [5, 72, 40, True, 'relu', 2], | |
| [5, 120, 40, True, 'relu', 1], | |
| [5, 120, 40, True, 'relu', 1], | |
| [3, 240, 80, False, 'hardswish', 2], | |
| [3, 200, 80, False, 'hardswish', 1], | |
| [3, 184, 80, False, 'hardswish', 1], | |
| [3, 184, 80, False, 'hardswish', 1], | |
| [3, 480, 112, True, 'hardswish', 1], | |
| [3, 672, 112, True, 'hardswish', 1], | |
| [5, 672, 160, True, 'hardswish', 2], | |
| [5, 960, 160, True, 'hardswish', 1], | |
| [5, 960, 160, True, 'hardswish', 1], | |
| ] | |
| cls_ch_squeeze = 960 | |
| elif model_name == "small": | |
| cfg = [ | |
| # k, exp, c, se, nl, s, | |
| [3, 16, 16, True, 'relu', 2], | |
| [3, 72, 24, False, 'relu', 2], | |
| [3, 88, 24, False, 'relu', 1], | |
| [5, 96, 40, True, 'hardswish', 2], | |
| [5, 240, 40, True, 'hardswish', 1], | |
| [5, 240, 40, True, 'hardswish', 1], | |
| [5, 120, 48, True, 'hardswish', 1], | |
| [5, 144, 48, True, 'hardswish', 1], | |
| [5, 288, 96, True, 'hardswish', 2], | |
| [5, 576, 96, True, 'hardswish', 1], | |
| [5, 576, 96, True, 'hardswish', 1], | |
| ] | |
| cls_ch_squeeze = 576 | |
| else: | |
| raise NotImplementedError("mode[" + model_name + | |
| "_model] is not implemented!") | |
| supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25] | |
| assert scale in supported_scale, \ | |
| "supported scale are {} but input scale is {}".format(supported_scale, scale) | |
| inplanes = 16 | |
| # conv1 | |
| self.conv = ConvBNLayer( | |
| in_channels=in_channels, | |
| out_channels=make_divisible(inplanes * scale), | |
| kernel_size=3, | |
| stride=2, | |
| padding=1, | |
| groups=1, | |
| if_act=True, | |
| act='hardswish') | |
| self.stages = [] | |
| self.out_channels = [] | |
| block_list = [] | |
| i = 0 | |
| inplanes = make_divisible(inplanes * scale) | |
| for (k, exp, c, se, nl, s) in cfg: | |
| se = se and not self.disable_se | |
| start_idx = 2 if model_name == 'large' else 0 | |
| if s == 2 and i > start_idx: | |
| self.out_channels.append(inplanes) | |
| self.stages.append(nn.Sequential(*block_list)) | |
| block_list = [] | |
| block_list.append( | |
| ResidualUnit( | |
| in_channels=inplanes, | |
| mid_channels=make_divisible(scale * exp), | |
| out_channels=make_divisible(scale * c), | |
| kernel_size=k, | |
| stride=s, | |
| use_se=se, | |
| act=nl)) | |
| inplanes = make_divisible(scale * c) | |
| i += 1 | |
| block_list.append( | |
| ConvBNLayer( | |
| in_channels=inplanes, | |
| out_channels=make_divisible(scale * cls_ch_squeeze), | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| groups=1, | |
| if_act=True, | |
| act='hardswish')) | |
| self.stages.append(nn.Sequential(*block_list)) | |
| self.out_channels.append(make_divisible(scale * cls_ch_squeeze)) | |
| for i, stage in enumerate(self.stages): | |
| self.add_sublayer(sublayer=stage, name="stage{}".format(i)) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| out_list = [] | |
| for stage in self.stages: | |
| x = stage(x) | |
| out_list.append(x) | |
| return out_list | |
| class ConvBNLayer(nn.Layer): | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| stride, | |
| padding, | |
| groups=1, | |
| if_act=True, | |
| act=None): | |
| super(ConvBNLayer, self).__init__() | |
| self.if_act = if_act | |
| self.act = act | |
| self.conv = nn.Conv2D( | |
| in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| groups=groups, | |
| bias_attr=False) | |
| self.bn = nn.BatchNorm(num_channels=out_channels, act=None) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| if self.if_act: | |
| if self.act == "relu": | |
| x = F.relu(x) | |
| elif self.act == "hardswish": | |
| x = F.hardswish(x) | |
| else: | |
| print("The activation function({}) is selected incorrectly.". | |
| format(self.act)) | |
| exit() | |
| return x | |
| class ResidualUnit(nn.Layer): | |
| def __init__(self, | |
| in_channels, | |
| mid_channels, | |
| out_channels, | |
| kernel_size, | |
| stride, | |
| use_se, | |
| act=None): | |
| super(ResidualUnit, self).__init__() | |
| self.if_shortcut = stride == 1 and in_channels == out_channels | |
| self.if_se = use_se | |
| self.expand_conv = ConvBNLayer( | |
| in_channels=in_channels, | |
| out_channels=mid_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| if_act=True, | |
| act=act) | |
| self.bottleneck_conv = ConvBNLayer( | |
| in_channels=mid_channels, | |
| out_channels=mid_channels, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=int((kernel_size - 1) // 2), | |
| groups=mid_channels, | |
| if_act=True, | |
| act=act) | |
| if self.if_se: | |
| self.mid_se = SEModule(mid_channels) | |
| self.linear_conv = ConvBNLayer( | |
| in_channels=mid_channels, | |
| out_channels=out_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| if_act=False, | |
| act=None) | |
| def forward(self, inputs): | |
| x = self.expand_conv(inputs) | |
| x = self.bottleneck_conv(x) | |
| if self.if_se: | |
| x = self.mid_se(x) | |
| x = self.linear_conv(x) | |
| if self.if_shortcut: | |
| x = paddle.add(inputs, x) | |
| return x | |
| class SEModule(nn.Layer): | |
| def __init__(self, in_channels, reduction=4): | |
| super(SEModule, self).__init__() | |
| self.avg_pool = nn.AdaptiveAvgPool2D(1) | |
| self.conv1 = nn.Conv2D( | |
| in_channels=in_channels, | |
| out_channels=in_channels // reduction, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| self.conv2 = nn.Conv2D( | |
| in_channels=in_channels // reduction, | |
| out_channels=in_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| def forward(self, inputs): | |
| outputs = self.avg_pool(inputs) | |
| outputs = self.conv1(outputs) | |
| outputs = F.relu(outputs) | |
| outputs = self.conv2(outputs) | |
| outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5) | |
| return inputs * outputs | |