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Merge pull request #109 from aclex/mobilenetv3
Support for MobileNetV3 backbones
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,224 @@ | ||
| '''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 | ||
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| class hswish(nn.Module): | ||
| def forward(self, x): | ||
| out = x * F.relu6(x + 3, inplace=True) / 6 | ||
| return out | ||
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| class hsigmoid(nn.Module): | ||
| def forward(self, x): | ||
| out = F.relu6(x + 3, inplace=True) / 6 | ||
| return out | ||
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| 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() | ||
| ) | ||
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| def forward(self, x): | ||
| return x * self.se(x) | ||
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| 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 | ||
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| 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) | ||
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| 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), | ||
| ) | ||
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| 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 | ||
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| class MobileNetV3_Large(nn.Module): | ||
| def __init__(self, num_classes=1000): | ||
| super(MobileNetV3_Large, self).__init__() | ||
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| self.features = [] | ||
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| 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) | ||
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| 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), | ||
| ) | ||
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| self.features.extend([block for block in self.bneck]) | ||
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| 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) | ||
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| self.linear3 = nn.Linear(960, 1280) | ||
| self.bn3 = nn.BatchNorm1d(1280) | ||
| self.hs3 = hswish() | ||
| self.linear4 = nn.Linear(1280, num_classes) | ||
| self.init_params() | ||
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| self.features = nn.Sequential(*self.features) | ||
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| 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) | ||
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| 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 | ||
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| class MobileNetV3_Small(nn.Module): | ||
| def __init__(self, num_classes=1000): | ||
| super(MobileNetV3_Small, self).__init__() | ||
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| self.features = [] | ||
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| 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) | ||
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| 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), | ||
| ) | ||
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| self.features.extend([block for block in self.bneck]) | ||
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| 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() | ||
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| self.features = nn.Sequential(*self.features) | ||
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| 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) | ||
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| 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 | ||
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| def test(): | ||
| net = MobileNetV3_Small() | ||
| x = torch.randn(2,3,224,224) | ||
| y = net(x) | ||
| print(y.size()) | ||
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| # test() |
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Hi are there weights available for the mobilenetv3 SSD?