# Copyright (c) Alibaba, Inc. and its affiliates.
r""" This model is taken from the official PyTorch model zoo.
- torchvision.models.mobilenet.py on 31th Aug, 2019
"""
from torch import nn
from easycv.framework.errors import ValueError
from ..modelzoo import mobilenetv2 as model_urls
from ..registry import BACKBONES
__all__ = ['MobileNetV2']
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 ConvBNReLU(nn.Sequential):
def __init__(self,
in_planes,
out_planes,
kernel_size=3,
stride=1,
groups=1):
padding = (kernel_size - 1) // 2
super(ConvBNReLU, self).__init__(
nn.Conv2d(
in_planes,
out_planes,
kernel_size,
stride,
padding,
groups=groups,
bias=False), nn.BatchNorm2d(out_planes),
nn.ReLU6(inplace=True))
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
layers.extend([
# dw
ConvBNReLU(
hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
])
self.conv = nn.Sequential(*layers)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
[docs]@BACKBONES.register_module
class MobileNetV2(nn.Module):
[docs] def __init__(self,
num_classes=0,
width_multi=1.0,
inverted_residual_setting=None,
round_nearest=8):
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_multi (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
"""
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 32
last_channel = 1280
if inverted_residual_setting is None:
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(
inverted_residual_setting[0]) != 4:
raise ValueError('inverted_residual_setting should be non-empty '
'or a 4-element list, got {}'.format(
inverted_residual_setting))
# building first layer
input_channel = _make_divisible(input_channel * width_multi,
round_nearest)
self.last_channel = _make_divisible(
last_channel * max(1.0, width_multi), round_nearest)
features = [ConvBNReLU(3, input_channel, stride=2)]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_multi, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(
block(
input_channel, output_channel, stride, expand_ratio=t))
input_channel = output_channel
# building last several layers
features.append(
ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
# make it nn.Sequential
self.features = nn.Sequential(*features)
# building classifier
if num_classes > 0:
self.classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(self.last_channel, num_classes),
)
self.default_pretrained_model_path = model_urls[self.__class__.__name__
+ '_' +
str(width_multi)]
[docs] def init_weights(self):
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)
nn.init.zeros_(m.bias)
[docs] def forward(self, x):
x = self.features(x)
if hasattr(self, 'classifier'):
x = x.mean([2, 3])
x = self.classifier(x)
return [x]