"""
Creates a EfficientNetV2 Model as defined in:
Mingxing Tan, Quoc V. Le. (2021).
EfficientNetV2: Smaller Models and Faster Training
arXiv preprint arXiv:2104.00298.
import from https://github.com/d-li14/mobilenetv2.pytorch
"""
import torch
import torch.nn as nn
import math
__all__ = ['effnetv2_s', 'effnetv2_m', 'effnetv2_l', 'effnetv2_xl']
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
# SiLU (Swish) activation function
if hasattr(nn, 'SiLU'):
SiLU = nn.SiLU
else:
# For compatibility with old PyTorch versions
class SiLU(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
class SELayer(nn.Module):
def __init__(self, inp, oup, reduction=4):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(oup, _make_divisible(inp // reduction, 8)),
SiLU(),
nn.Linear(_make_divisible(inp // reduction, 8), oup),
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
def conv_3x3_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
SiLU()
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
SiLU()
)
class MBConv(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio, use_se):
super(MBConv, self).__init__()
assert stride in [1, 2]
hidden_dim = round(inp * expand_ratio)
self.identity = stride == 1 and inp == oup
if use_se:
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
nn.BatchNorm2d(hidden_dim),
SiLU(),
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
SiLU(),
SELayer(inp, hidden_dim),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
else:
self.conv = nn.Sequential(
# fused
nn.Conv2d(inp, hidden_dim, 3, stride, 1, bias=False),
nn.BatchNorm2d(hidden_dim),
SiLU(),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
def forward(self, x):
if self.identity:
return x + self.conv(x)
else:
return self.conv(x)
class EffNetV2(nn.Module):
def __init__(self, cfgs, num_classes=1000, width_mult=1.):
super(EffNetV2, self).__init__()
self.cfgs = cfgs
# building first layer
input_channel = _make_divisible(24 * width_mult, 8)
layers = [conv_3x3_bn(9, input_channel, 2)]
# building inverted residual blocks
block = MBConv
for t, c, n, s, use_se in self.cfgs:
output_channel = _make_divisible(c * width_mult, 8)
for i in range(n):
layers.append(block(input_channel, output_channel, s if i == 0 else 1, t, use_se))
input_channel = output_channel
self.features = nn.Sequential(*layers)
# building last several layers
output_channel = _make_divisible(1792 * width_mult, 8) if width_mult > 1.0 else 1792
self.conv = conv_1x1_bn(input_channel, output_channel)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Linear(output_channel, num_classes)
self._initialize_weights()
def forward(self, x):
x = self.features(x)
x = self.conv(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
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):
m.weight.data.normal_(0, 0.001)
m.bias.data.zero_()
def effnetv2_s(**kwargs):
"""
Constructs a EfficientNetV2-S model
"""
cfgs = [
# t, c, n, s, SE
[1, 24, 2, 1, 0],
[4, 48, 4, 2, 0],
[4, 64, 4, 2, 0],
[4, 128, 6, 2, 1],
[6, 160, 9, 1, 1],
[6, 256, 15, 2, 1],
]
return EffNetV2(cfgs, **kwargs)
def effnetv2_m(**kwargs):
"""
Constructs a EfficientNetV2-M model
"""
cfgs = [
# t, c, n, s, SE
[1, 24, 3, 1, 0],
[4, 48, 5, 2, 0],
[4, 80, 5, 2, 0],
[4, 160, 7, 2, 1],
[6, 176, 14, 1, 1],
[6, 304, 18, 2, 1],
[6, 512, 5, 1, 1],
]
return EffNetV2(cfgs, **kwargs)
def effnetv2_l(**kwargs):
"""
Constructs a EfficientNetV2-L model
"""
cfgs = [
# t, c, n, s, SE
[1, 32, 4, 1, 0],
[4, 64, 7, 2, 0],
[4, 96, 7, 2, 0],
[4, 192, 10, 2, 1],
[6, 224, 19, 1, 1],
[6, 384, 25, 2, 1],
[6, 640, 7, 1, 1],
]
return EffNetV2(cfgs, **kwargs)
def effnetv2_xl(**kwargs):
"""
Constructs a EfficientNetV2-XL model
"""
cfgs = [
# t, c, n, s, SE
[1, 32, 4, 1, 0],
[4, 64, 8, 2, 0],
[4, 96, 8, 2, 0],
[4, 192, 16, 2, 1],
[6, 256, 24, 1, 1],
[6, 512, 32, 2, 1],
[6, 640, 8, 1, 1],
]
return EffNetV2(cfgs, **kwargs)
