tddfa/models/mobilenet_v3.py

# coding: utf-8


import torch.nn as nn
import torch.nn.functional as F

__all__ = ['MobileNetV3', 'mobilenet_v3']


def conv_bn(inp, oup, stride, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU):
    return nn.Sequential(
        conv_layer(inp, oup, 3, stride, 1, bias=False),
        norm_layer(oup),
        nlin_layer(inplace=True)
    )


def conv_1x1_bn(inp, oup, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU):
    return nn.Sequential(
        conv_layer(inp, oup, 1, 1, 0, bias=False),
        norm_layer(oup),
        nlin_layer(inplace=True)
    )


class Hswish(nn.Module):
    def __init__(self, inplace=True):
        super(Hswish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return x * F.relu6(x + 3., inplace=self.inplace) / 6.


class Hsigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(Hsigmoid, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return F.relu6(x + 3., inplace=self.inplace) / 6.


class SEModule(nn.Module):
    def __init__(self, channel, reduction=4):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            Hsigmoid()
            # nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y.expand_as(x)


class Identity(nn.Module):
    def __init__(self, channel):
        super(Identity, self).__init__()

    def forward(self, x):
        return x


def make_divisible(x, divisible_by=8):
    import numpy as np
    return int(np.ceil(x * 1. / divisible_by) * divisible_by)


class MobileBottleneck(nn.Module):
    def __init__(self, inp, oup, kernel, stride, exp, se=False, nl='RE'):
        super(MobileBottleneck, self).__init__()
        assert stride in [1, 2]
        assert kernel in [3, 5]
        padding = (kernel - 1) // 2
        self.use_res_connect = stride == 1 and inp == oup

        conv_layer = nn.Conv2d
        norm_layer = nn.BatchNorm2d
        if nl == 'RE':
            nlin_layer = nn.ReLU  # or ReLU6
        elif nl == 'HS':
            nlin_layer = Hswish
        else:
            raise NotImplementedError
        if se:
            SELayer = SEModule
        else:
            SELayer = Identity

        self.conv = nn.Sequential(
            # pw
            conv_layer(inp, exp, 1, 1, 0, bias=False),
            norm_layer(exp),
            nlin_layer(inplace=True),
            # dw
            conv_layer(exp, exp, kernel, stride, padding, groups=exp, bias=False),
            norm_layer(exp),
            SELayer(exp),
            nlin_layer(inplace=True),
            # pw-linear
            conv_layer(exp, oup, 1, 1, 0, bias=False),
            norm_layer(oup),
        )

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV3(nn.Module):
    def __init__(self, widen_factor=1.0, num_classes=141, num_landmarks=136, input_size=120, mode='small'):
        super(MobileNetV3, self).__init__()
        input_channel = 16
        last_channel = 1280
        if mode == 'large':
            # refer to Table 1 in paper
            mobile_setting = [
                # k, exp, c,  se,     nl,  s,
                [3, 16, 16, False, 'RE', 1],
                [3, 64, 24, False, 'RE', 2],
                [3, 72, 24, False, 'RE', 1],
                [5, 72, 40, True, 'RE', 2],
                [5, 120, 40, True, 'RE', 1],
                [5, 120, 40, True, 'RE', 1],
                [3, 240, 80, False, 'HS', 2],
                [3, 200, 80, False, 'HS', 1],
                [3, 184, 80, False, 'HS', 1],
                [3, 184, 80, False, 'HS', 1],
                [3, 480, 112, True, 'HS', 1],
                [3, 672, 112, True, 'HS', 1],
                [5, 672, 160, True, 'HS', 2],
                [5, 960, 160, True, 'HS', 1],
                [5, 960, 160, True, 'HS', 1],
            ]
        elif mode == 'small':
            # refer to Table 2 in paper
            mobile_setting = [
                # k, exp, c,  se,     nl,  s,
                [3, 16, 16, True, 'RE', 2],
                [3, 72, 24, False, 'RE', 2],
                [3, 88, 24, False, 'RE', 1],
                [5, 96, 40, True, 'HS', 2],
                [5, 240, 40, True, 'HS', 1],
                [5, 240, 40, True, 'HS', 1],
                [5, 120, 48, True, 'HS', 1],
                [5, 144, 48, True, 'HS', 1],
                [5, 288, 96, True, 'HS', 2],
                [5, 576, 96, True, 'HS', 1],
                [5, 576, 96, True, 'HS', 1],
            ]
        else:
            raise NotImplementedError

        # building first layer
        assert input_size % 32 == 0
        last_channel = make_divisible(last_channel * widen_factor) if widen_factor > 1.0 else last_channel
        self.features = [conv_bn(3, input_channel, 2, nlin_layer=Hswish)]
        # self.classifier = []

        # building mobile blocks
        for k, exp, c, se, nl, s in mobile_setting:
            output_channel = make_divisible(c * widen_factor)
            exp_channel = make_divisible(exp * widen_factor)
            self.features.append(MobileBottleneck(input_channel, output_channel, k, s, exp_channel, se, nl))
            input_channel = output_channel

        # building last several layers
        if mode == 'large':
            last_conv = make_divisible(960 * widen_factor)
            self.features.append(conv_1x1_bn(input_channel, last_conv, nlin_layer=Hswish))
            self.features.append(nn.AdaptiveAvgPool2d(1))
            self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0))
            self.features.append(Hswish(inplace=True))
        elif mode == 'small':
            last_conv = make_divisible(576 * widen_factor)
            self.features.append(conv_1x1_bn(input_channel, last_conv, nlin_layer=Hswish))
            # self.features.append(SEModule(last_conv))  # refer to paper Table2, but I think this is a mistake
            self.features.append(nn.AdaptiveAvgPool2d(1))
            self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0))
            self.features.append(Hswish(inplace=True))
        else:
            raise NotImplementedError

        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # self.fc_param = nn.Linear(int(last_channel), num_classes)
        self.fc = nn.Linear(int(last_channel), num_classes)
        # self.fc_lm = nn.Linear(int(last_channel), num_landmarks)

        # building classifier
        # self.classifier = nn.Sequential(
        #     nn.Dropout(p=dropout),    # refer to paper section 6
        #     nn.Linear(last_channel, n_class),
        # )

        self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x_share = x.mean(3).mean(2)

        # x = self.classifier(x)
        # print(x_share.shape)
        # xp = self.fc_param(x_share)  # param
        # xl = self.fc_lm(x_share)  # lm

        xp = self.fc(x_share)  # param

        return xp  # , xl

    def _initialize_weights(self):
        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)


def mobilenet_v3(**kwargs):
    model = MobileNetV3(
        widen_factor=kwargs.get('widen_factor', 1.0),
        num_classes=kwargs.get('num_classes', 62),
        num_landmarks=kwargs.get('num_landmarks', 136),
        input_size=kwargs.get('size', 128),
        mode=kwargs.get('mode', 'small')
    )

    return model