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"""
Creates a MobileNetV3 Model as defined in:
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (2019).
Searching for MobileNetV3
arXiv preprint arXiv:1905.02244.
"""
import torch.nn as nn
import math
from utils.learning import freeze_params
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.sigmoid = h_sigmoid(inplace=inplace)
def forward(self, x):
return x * self.sigmoid(x)
class SELayer(nn.Module):
def __init__(self, channel, reduction=4):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, _make_divisible(channel // reduction, 8)),
nn.ReLU(inplace=True),
nn.Linear(_make_divisible(channel // reduction, 8), channel),
h_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
def conv_3x3_bn(inp, oup, stride, norm_layer=nn.BatchNorm2d):
return nn.Sequential(nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
norm_layer(oup), h_swish())
def conv_1x1_bn(inp, oup, norm_layer=nn.BatchNorm2d):
return nn.Sequential(nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
norm_layer(oup), h_swish())
class InvertedResidual(nn.Module):
def __init__(self,
inp,
hidden_dim,
oup,
kernel_size,
stride,
use_se,
use_hs,
dilation=1,
norm_layer=nn.BatchNorm2d):
super(InvertedResidual, self).__init__()
assert stride in [1, 2]
self.identity = stride == 1 and inp == oup
if inp == hidden_dim:
self.conv = nn.Sequential(
# dw
nn.Conv2d(hidden_dim,
hidden_dim,
kernel_size,
stride, (kernel_size - 1) // 2 * dilation,
dilation=dilation,
groups=hidden_dim,
bias=False),
norm_layer(hidden_dim),
h_swish() if use_hs else nn.ReLU(inplace=True),
# Squeeze-and-Excite
SELayer(hidden_dim) if use_se else nn.Identity(),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
norm_layer(oup),
)
else:
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
norm_layer(hidden_dim),
h_swish() if use_hs else nn.ReLU(inplace=True),
# dw
nn.Conv2d(hidden_dim,
hidden_dim,
kernel_size,
stride, (kernel_size - 1) // 2 * dilation,
dilation=dilation,
groups=hidden_dim,
bias=False),
norm_layer(hidden_dim),
# Squeeze-and-Excite
SELayer(hidden_dim) if use_se else nn.Identity(),
h_swish() if use_hs else nn.ReLU(inplace=True),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
norm_layer(oup),
)
def forward(self, x):
if self.identity:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV3Large(nn.Module):
def __init__(self,
output_stride=16,
norm_layer=nn.BatchNorm2d,
width_mult=1.,
freeze_at=0):
super(MobileNetV3Large, self).__init__()
"""
Constructs a MobileNetV3-Large model
"""
cfgs = [
# k, t, c, SE, HS, s
[3, 1, 16, 0, 0, 1],
[3, 4, 24, 0, 0, 2],
[3, 3, 24, 0, 0, 1],
[5, 3, 40, 1, 0, 2],
[5, 3, 40, 1, 0, 1],
[5, 3, 40, 1, 0, 1],
[3, 6, 80, 0, 1, 2],
[3, 2.5, 80, 0, 1, 1],
[3, 2.3, 80, 0, 1, 1],
[3, 2.3, 80, 0, 1, 1],
[3, 6, 112, 1, 1, 1],
[3, 6, 112, 1, 1, 1],
[5, 6, 160, 1, 1, 2],
[5, 6, 160, 1, 1, 1],
[5, 6, 160, 1, 1, 1]
]
self.cfgs = cfgs
# building first layer
input_channel = _make_divisible(16 * width_mult, 8)
layers = [conv_3x3_bn(3, input_channel, 2, norm_layer)]
# building inverted residual blocks
block = InvertedResidual
now_stride = 2
rate = 1
for k, t, c, use_se, use_hs, s in self.cfgs:
if now_stride == output_stride:
dilation = rate
rate *= s
s = 1
else:
dilation = 1
now_stride *= s
output_channel = _make_divisible(c * width_mult, 8)
exp_size = _make_divisible(input_channel * t, 8)
layers.append(
block(input_channel, exp_size, output_channel, k, s, use_se,
use_hs, dilation, norm_layer))
input_channel = output_channel
self.features = nn.Sequential(*layers)
self.conv = conv_1x1_bn(input_channel, exp_size, norm_layer)
# building last several layers
self._initialize_weights()
feature_4x = self.features[0:4]
feautre_8x = self.features[4:7]
feature_16x = self.features[7:13]
feature_32x = self.features[13:]
self.stages = [feature_4x, feautre_8x, feature_16x, feature_32x]
self.freeze(freeze_at)
def forward(self, x):
xs = []
for stage in self.stages:
x = stage(x)
xs.append(x)
xs[-1] = self.conv(xs[-1])
return xs
def _initialize_weights(self):
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))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def freeze(self, freeze_at):
if freeze_at >= 1:
for m in self.stages[0][0]:
freeze_params(m)
for idx, stage in enumerate(self.stages, start=2):
if freeze_at >= idx:
freeze_params(stage)
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