'''MobileNetV3 in PyTorch. See the paper "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation" for more details. ''' import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import init class hswish(nn.Module): def forward(self, x): out = x * F.relu6(x + 3, inplace=True) / 6 return out class hsigmoid(nn.Module): def forward(self, x): out = F.relu6(x + 3, inplace=True) / 6 return out class SeModule(nn.Module): def __init__(self, in_size, reduction=4): super(SeModule, self).__init__() self.se = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(in_size // reduction), nn.ReLU(inplace=True), nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(in_size), hsigmoid() ) def forward(self, x): return x * self.se(x) class Block(nn.Module): '''expand + depthwise + pointwise''' def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride): super(Block, self).__init__() self.stride = stride self.se = semodule self.conv1 = nn.Conv2d(in_size, expand_size, kernel_size=1, stride=1, padding=0, bias=False) self.bn1 = nn.BatchNorm2d(expand_size) self.nolinear1 = nolinear self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size=kernel_size, stride=stride, padding=kernel_size//2, groups=expand_size, bias=False) self.bn2 = nn.BatchNorm2d(expand_size) self.nolinear2 = nolinear self.conv3 = nn.Conv2d(expand_size, out_size, kernel_size=1, stride=1, padding=0, bias=False) self.bn3 = nn.BatchNorm2d(out_size) self.shortcut = nn.Sequential() if stride == 1 and in_size != out_size: self.shortcut = nn.Sequential( nn.Conv2d(in_size, out_size, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(out_size), ) def forward(self, x): out = self.nolinear1(self.bn1(self.conv1(x))) out = self.nolinear2(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) if self.se != None: out = self.se(out) out = out + self.shortcut(x) if self.stride==1 else out return out class MobileNetV3_Large(nn.Module): def __init__(self, num_classes=1000): super(MobileNetV3_Large, self).__init__() self.features = [] self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False) self.features.append(self.conv1) self.bn1 = nn.BatchNorm2d(16) self.features.append(self.bn1) self.hs1 = hswish() self.features.append(self.hs1) self.bneck = nn.Sequential( Block(3, 16, 16, 16, nn.ReLU(inplace=True), None, 1), Block(3, 16, 64, 24, nn.ReLU(inplace=True), None, 2), Block(3, 24, 72, 24, nn.ReLU(inplace=True), None, 1), Block(5, 24, 72, 40, nn.ReLU(inplace=True), SeModule(40), 2), Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1), Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1), Block(3, 40, 240, 80, hswish(), None, 2), Block(3, 80, 200, 80, hswish(), None, 1), Block(3, 80, 184, 80, hswish(), None, 1), Block(3, 80, 184, 80, hswish(), None, 1), Block(3, 80, 480, 112, hswish(), SeModule(112), 1), Block(3, 112, 672, 112, hswish(), SeModule(112), 1), Block(5, 112, 672, 160, hswish(), SeModule(160), 1), Block(5, 160, 672, 160, hswish(), SeModule(160), 2), Block(5, 160, 960, 160, hswish(), SeModule(160), 1), ) self.features.extend([block for block in self.bneck]) self.conv2 = nn.Conv2d(160, 960, kernel_size=1, stride=1, padding=0, bias=False) self.features.append(self.conv2) self.bn2 = nn.BatchNorm2d(960) self.features.append(self.bn2) self.hs2 = hswish() self.features.append(self.hs2) self.linear3 = nn.Linear(960, 1280) self.bn3 = nn.BatchNorm1d(1280) self.hs3 = hswish() self.linear4 = nn.Linear(1280, num_classes) self.init_params() self.features = nn.Sequential(*self.features) def init_params(self): for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): init.normal_(m.weight, std=0.001) if m.bias is not None: init.constant_(m.bias, 0) def forward(self, x): out = self.hs1(self.bn1(self.conv1(x))) out = self.bneck(out) out = self.hs2(self.bn2(self.conv2(out))) out = F.avg_pool2d(out, 7) out = out.view(out.size(0), -1) out = self.hs3(self.bn3(self.linear3(out))) out = self.linear4(out) return out class MobileNetV3_Small(nn.Module): def __init__(self, num_classes=1000): super(MobileNetV3_Small, self).__init__() self.features = [] self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False) self.features.append(self.conv1) self.bn1 = nn.BatchNorm2d(16) self.features.append(self.bn1) self.hs1 = hswish() self.features.append(self.hs1) self.bneck = nn.Sequential( Block(3, 16, 16, 16, nn.ReLU(inplace=True), SeModule(16), 2), Block(3, 16, 72, 24, nn.ReLU(inplace=True), None, 2), Block(3, 24, 88, 24, nn.ReLU(inplace=True), None, 1), Block(5, 24, 96, 40, hswish(), SeModule(40), 2), Block(5, 40, 240, 40, hswish(), SeModule(40), 1), Block(5, 40, 240, 40, hswish(), SeModule(40), 1), Block(5, 40, 120, 48, hswish(), SeModule(48), 1), Block(5, 48, 144, 48, hswish(), SeModule(48), 1), Block(5, 48, 288, 96, hswish(), SeModule(96), 2), Block(5, 96, 576, 96, hswish(), SeModule(96), 1), Block(5, 96, 576, 96, hswish(), SeModule(96), 1), ) self.features.extend([block for block in self.bneck]) self.conv2 = nn.Conv2d(96, 576, kernel_size=1, stride=1, padding=0, bias=False) self.features.append(self.conv2) self.bn2 = nn.BatchNorm2d(576) self.features.append(self.bn2) self.hs2 = hswish() self.features.append(self.hs2) self.linear3 = nn.Linear(576, 1280) self.bn3 = nn.BatchNorm1d(1280) self.hs3 = hswish() self.linear4 = nn.Linear(1280, num_classes) self.init_params() self.features = nn.Sequential(*self.features) def init_params(self): for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): init.normal_(m.weight, std=0.001) if m.bias is not None: init.constant_(m.bias, 0) def forward(self, x): out = self.hs1(self.bn1(self.conv1(x))) out = self.bneck(out) out = self.hs2(self.bn2(self.conv2(out))) out = F.avg_pool2d(out, 7) out = out.view(out.size(0), -1) out = self.hs3(self.bn3(self.linear3(out))) out = self.linear4(out) return out def test(): net = MobileNetV3_Small() x = torch.randn(2,3,224,224) y = net(x) print(y.size()) # test()