200 lines
7.4 KiB
Python
200 lines
7.4 KiB
Python
'''MobileNetV3 in PyTorch.
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See the paper "Inverted Residuals and Linear Bottlenecks:
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Mobile Networks for Classification, Detection and Segmentation" for more details.
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'''
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn import init
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from config import config as conf
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class hswish(nn.Module):
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def forward(self, x):
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out = x * F.relu6(x + 3, inplace=True) / 6
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return out
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class hsigmoid(nn.Module):
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def forward(self, x):
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out = F.relu6(x + 3, inplace=True) / 6
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return out
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class SeModule(nn.Module):
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def __init__(self, in_size, reduction=4):
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super(SeModule, self).__init__()
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self.se = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(in_size // reduction),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(in_size),
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hsigmoid()
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)
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def forward(self, x):
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return x * self.se(x)
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class Block(nn.Module):
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'''expand + depthwise + pointwise'''
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def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride):
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super(Block, self).__init__()
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self.stride = stride
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self.se = semodule
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self.conv1 = nn.Conv2d(in_size, expand_size, kernel_size=1, stride=1, padding=0, bias=False)
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self.bn1 = nn.BatchNorm2d(expand_size)
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self.nolinear1 = nolinear
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self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size=kernel_size, stride=stride, padding=kernel_size//2, groups=expand_size, bias=False)
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self.bn2 = nn.BatchNorm2d(expand_size)
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self.nolinear2 = nolinear
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self.conv3 = nn.Conv2d(expand_size, out_size, kernel_size=1, stride=1, padding=0, bias=False)
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self.bn3 = nn.BatchNorm2d(out_size)
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self.shortcut = nn.Sequential()
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if stride == 1 and in_size != out_size:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_size, out_size, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(out_size),
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)
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def forward(self, x):
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out = self.nolinear1(self.bn1(self.conv1(x)))
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out = self.nolinear2(self.bn2(self.conv2(out)))
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out = self.bn3(self.conv3(out))
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if self.se != None:
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out = self.se(out)
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out = out + self.shortcut(x) if self.stride==1 else out
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return out
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class MobileNetV3_Large(nn.Module):
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def __init__(self, num_classes=conf.embedding_size):
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super(MobileNetV3_Large, self).__init__()
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self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(16)
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self.hs1 = hswish()
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self.bneck = nn.Sequential(
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Block(3, 16, 16, 16, nn.ReLU(inplace=True), None, 1),
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Block(3, 16, 64, 24, nn.ReLU(inplace=True), None, 2),
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Block(3, 24, 72, 24, nn.ReLU(inplace=True), None, 1),
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Block(5, 24, 72, 40, nn.ReLU(inplace=True), SeModule(40), 2),
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Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1),
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Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1),
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Block(3, 40, 240, 80, hswish(), None, 2),
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Block(3, 80, 200, 80, hswish(), None, 1),
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Block(3, 80, 184, 80, hswish(), None, 1),
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Block(3, 80, 184, 80, hswish(), None, 1),
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Block(3, 80, 480, 112, hswish(), SeModule(112), 1),
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Block(3, 112, 672, 112, hswish(), SeModule(112), 1),
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Block(5, 112, 672, 160, hswish(), SeModule(160), 1),
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Block(5, 160, 672, 160, hswish(), SeModule(160), 2),
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Block(5, 160, 960, 160, hswish(), SeModule(160), 1),
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)
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self.conv2 = nn.Conv2d(160, 960, kernel_size=1, stride=1, padding=0, bias=False)
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self.bn2 = nn.BatchNorm2d(960)
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self.hs2 = hswish()
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self.linear3 = nn.Linear(960, 1280)
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self.bn3 = nn.BatchNorm1d(1280)
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self.hs3 = hswish()
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self.linear4 = nn.Linear(1280, num_classes)
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self.init_params()
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def init_params(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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init.normal_(m.weight, std=0.001)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, x):
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out = self.hs1(self.bn1(self.conv1(x)))
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out = self.bneck(out)
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out = self.hs2(self.bn2(self.conv2(out)))
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out = F.avg_pool2d(out, conf.img_size // 32)
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out = out.view(out.size(0), -1)
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out = self.hs3(self.bn3(self.linear3(out)))
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out = self.linear4(out)
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return out
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class MobileNetV3_Small(nn.Module):
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def __init__(self, num_classes=conf.embedding_size):
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super(MobileNetV3_Small, self).__init__()
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self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(16)
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self.hs1 = hswish()
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self.bneck = nn.Sequential(
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Block(3, 16, 16, 16, nn.ReLU(inplace=True), SeModule(16), 2),
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Block(3, 16, 72, 24, nn.ReLU(inplace=True), None, 2),
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Block(3, 24, 88, 24, nn.ReLU(inplace=True), None, 1),
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Block(5, 24, 96, 40, hswish(), SeModule(40), 2),
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Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
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Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
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Block(5, 40, 120, 48, hswish(), SeModule(48), 1),
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Block(5, 48, 144, 48, hswish(), SeModule(48), 1),
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Block(5, 48, 288, 96, hswish(), SeModule(96), 2),
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Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
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Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
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)
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self.conv2 = nn.Conv2d(96, 576, kernel_size=1, stride=1, padding=0, bias=False)
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self.bn2 = nn.BatchNorm2d(576)
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self.hs2 = hswish()
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self.linear3 = nn.Linear(576, 1280)
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self.bn3 = nn.BatchNorm1d(1280)
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self.hs3 = hswish()
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self.linear4 = nn.Linear(1280, num_classes)
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self.init_params()
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def init_params(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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init.normal_(m.weight, std=0.001)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, x):
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out = self.hs1(self.bn1(self.conv1(x)))
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out = self.bneck(out)
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out = self.hs2(self.bn2(self.conv2(out)))
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out = F.avg_pool2d(out, conf.img_size // 32)
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out = out.view(out.size(0), -1)
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out = self.hs3(self.bn3(self.linear3(out)))
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out = self.linear4(out)
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return out
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def test():
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net = MobileNetV3_Small()
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x = torch.randn(2,3,224,224)
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y = net(x)
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print(y.size())
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# test() |