"""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()
