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mobilenetv3.py
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mobilenetv3.py
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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., inplace=self.inplace) / 6.
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., inplace=self.inplace) / 6.
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. / 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=0.8, 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
if __name__ == '__main__':
net = mobilenetv3()
print('mobilenetv3:\n', net)
print('Total params: %.2fM' % (sum(p.numel() for p in net.parameters())/1000000.0))
input_size=(1, 3, 224, 224)
# pip install --upgrade git+https://github.com/kuan-wang/pytorch-OpCounter.git
from thop import profile
flops, params = profile(net, input_size=input_size)
# print(flops)
# print(params)
print('Total params: %.2fM' % (params/1000000.0))
print('Total flops: %.2fM' % (flops/1000000.0))
x = torch.randn(input_size)
out = net(x)