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# coding: utf-8
from __future__ import division
"""
Creates a MobileNet Model as defined in:
Andrew G. Howard Menglong Zhu Bo Chen, et.al. (2017).
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
Copyright (c) Yang Lu, 2017
Modified By cleardusk
"""
import math
import torch.nn as nn
__all__ = ['MobileNet', 'mobilenet']
# __all__ = ['mobilenet_2', 'mobilenet_1', 'mobilenet_075', 'mobilenet_05', 'mobilenet_025']
class DepthWiseBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, prelu=False):
super(DepthWiseBlock, self).__init__()
inplanes, planes = int(inplanes), int(planes)
self.conv_dw = nn.Conv2d(inplanes, inplanes, kernel_size=3, padding=1, stride=stride, groups=inplanes,
bias=False)
self.bn_dw = nn.BatchNorm2d(inplanes)
self.conv_sep = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False)
self.bn_sep = nn.BatchNorm2d(planes)
if prelu:
self.relu = nn.PReLU()
else:
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
out = self.conv_dw(x)
out = self.bn_dw(out)
out = self.relu(out)
out = self.conv_sep(out)
out = self.bn_sep(out)
out = self.relu(out)
return out
class MobileNet(nn.Module):
def __init__(self, widen_factor=1.0, num_classes=1000, prelu=False, input_channel=3):
""" Constructor
Args:
widen_factor: config of widen_factor
num_classes: number of classes
"""
super(MobileNet, self).__init__()
block = DepthWiseBlock
self.conv1 = nn.Conv2d(input_channel, int(32 * widen_factor), kernel_size=3, stride=2, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(int(32 * widen_factor))
if prelu:
self.relu = nn.PReLU()
else:
self.relu = nn.ReLU(inplace=True)
self.dw2_1 = block(32 * widen_factor, 64 * widen_factor, prelu=prelu)
self.dw2_2 = block(64 * widen_factor, 128 * widen_factor, stride=2, prelu=prelu)
self.dw3_1 = block(128 * widen_factor, 128 * widen_factor, prelu=prelu)
self.dw3_2 = block(128 * widen_factor, 256 * widen_factor, stride=2, prelu=prelu)
self.dw4_1 = block(256 * widen_factor, 256 * widen_factor, prelu=prelu)
self.dw4_2 = block(256 * widen_factor, 512 * widen_factor, stride=2, prelu=prelu)
self.dw5_1 = block(512 * widen_factor, 512 * widen_factor, prelu=prelu)
self.dw5_2 = block(512 * widen_factor, 512 * widen_factor, prelu=prelu)
self.dw5_3 = block(512 * widen_factor, 512 * widen_factor, prelu=prelu)
self.dw5_4 = block(512 * widen_factor, 512 * widen_factor, prelu=prelu)
self.dw5_5 = block(512 * widen_factor, 512 * widen_factor, prelu=prelu)
self.dw5_6 = block(512 * widen_factor, 1024 * widen_factor, stride=2, prelu=prelu)
self.dw6 = block(1024 * widen_factor, 1024 * widen_factor, prelu=prelu)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(int(1024 * widen_factor), num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.dw2_1(x)
x = self.dw2_2(x)
x = self.dw3_1(x)
x = self.dw3_2(x)
x = self.dw4_1(x)
x = self.dw4_2(x)
x = self.dw5_1(x)
x = self.dw5_2(x)
x = self.dw5_3(x)
x = self.dw5_4(x)
x = self.dw5_5(x)
x = self.dw5_6(x)
x = self.dw6(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def mobilenet(**kwargs):
"""
Construct MobileNet.
widen_factor=1.0 for mobilenet_1
widen_factor=0.75 for mobilenet_075
widen_factor=0.5 for mobilenet_05
widen_factor=0.25 for mobilenet_025
"""
# widen_factor = 1.0, num_classes = 1000
# model = MobileNet(widen_factor=widen_factor, num_classes=num_classes)
# return model
model = MobileNet(
widen_factor=kwargs.get('widen_factor', 1.0),
num_classes=kwargs.get('num_classes', 62)
)
return model
def mobilenet_2(num_classes=62, input_channel=3):
model = MobileNet(widen_factor=2.0, num_classes=num_classes, input_channel=input_channel)
return model
def mobilenet_1(num_classes=62, input_channel=3):
model = MobileNet(widen_factor=1.0, num_classes=num_classes, input_channel=input_channel)
return model
def mobilenet_075(num_classes=62, input_channel=3):
model = MobileNet(widen_factor=0.75, num_classes=num_classes, input_channel=input_channel)
return model
def mobilenet_05(num_classes=62, input_channel=3):
model = MobileNet(widen_factor=0.5, num_classes=num_classes, input_channel=input_channel)
return model
def mobilenet_025(num_classes=62, input_channel=3):
model = MobileNet(widen_factor=0.25, num_classes=num_classes, input_channel=input_channel)
return model
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