import torch import torch.nn as nn import torch.nn.functional as F __all__ = ["MobileNetV3", "mobilenetv3"] 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 + 3.0, inplace=self.inplace) / 6.0 class Hsigmoid(nn.Module): def __init__(self, inplace=True): super(Hsigmoid, self).__init__() self.inplace = inplace def forward(self, x): return F.relu6(x + 3.0, inplace=self.inplace) / 6.0 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 * 1.0 / 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, n_class=1000, input_size=224, dropout=0.0, mode="small", width_mult=1.0 ): 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 * width_mult) if width_mult > 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 * width_mult) exp_channel = make_divisible(exp * width_mult) 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 * width_mult) 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 * width_mult) 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) # 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 = x.mean(3).mean(2) x = self.classifier(x) return x 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 mobilenetv3(pretrained=False, **kwargs): model = MobileNetV3(**kwargs) if pretrained: state_dict = torch.load("mobilenetv3_small_67.4.pth.tar") model.load_state_dict(state_dict, strict=True) # raise NotImplementedError return model