from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['mobilenetv2_x1_0', 'mobilenetv2_x1_4']
model_urls = {
# 1.0: top-1 71.3
'mobilenetv2_x1_0':
'https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NUOWvvGPHhaNr-fASF6c',
# 1.4: top-1 73.9
'mobilenetv2_x1_4':
'https://mega.nz/#!RGhgEIwS!xN2s2ZdyqI6vQ3EwgmRXLEW3khr9tpXg96G9SUJugGk',
}
class ConvBlock(nn.Module):
"""Basic convolutional block.
convolution (bias discarded) + batch normalization + relu6.
Args:
in_c (int): number of input channels.
out_c (int): number of output channels.
k (int or tuple): kernel size.
s (int or tuple): stride.
p (int or tuple): padding.
g (int): number of blocked connections from input channels
to output channels (default: 1).
"""
def __init__(self, in_c, out_c, k, s=1, p=0, g=1):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(
in_c, out_c, k, stride=s, padding=p, bias=False, groups=g
)
self.bn = nn.BatchNorm2d(out_c)
def forward(self, x):
return F.relu6(self.bn(self.conv(x)))
class Bottleneck(nn.Module):
def __init__(self, in_channels, out_channels, expansion_factor, stride=1):
super(Bottleneck, self).__init__()
mid_channels = in_channels * expansion_factor
self.use_residual = stride == 1 and in_channels == out_channels
self.conv1 = ConvBlock(in_channels, mid_channels, 1)
self.dwconv2 = ConvBlock(
mid_channels, mid_channels, 3, stride, 1, g=mid_channels
)
self.conv3 = nn.Sequential(
nn.Conv2d(mid_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
)
def forward(self, x):
m = self.conv1(x)
m = self.dwconv2(m)
m = self.conv3(m)
if self.use_residual:
return x + m
else:
return m
[docs]class MobileNetV2(nn.Module):
"""MobileNetV2.
Reference:
Sandler et al. MobileNetV2: Inverted Residuals and
Linear Bottlenecks. CVPR 2018.
Public keys:
- ``mobilenetv2_x1_0``: MobileNetV2 x1.0.
- ``mobilenetv2_x1_4``: MobileNetV2 x1.4.
"""
def __init__(
self,
num_classes,
width_mult=1,
loss='softmax',
fc_dims=None,
dropout_p=None,
**kwargs
):
super(MobileNetV2, self).__init__()
self.loss = loss
self.in_channels = int(32 * width_mult)
self.feature_dim = int(1280 * width_mult) if width_mult > 1 else 1280
# construct layers
self.conv1 = ConvBlock(3, self.in_channels, 3, s=2, p=1)
self.conv2 = self._make_layer(
Bottleneck, 1, int(16 * width_mult), 1, 1
)
self.conv3 = self._make_layer(
Bottleneck, 6, int(24 * width_mult), 2, 2
)
self.conv4 = self._make_layer(
Bottleneck, 6, int(32 * width_mult), 3, 2
)
self.conv5 = self._make_layer(
Bottleneck, 6, int(64 * width_mult), 4, 2
)
self.conv6 = self._make_layer(
Bottleneck, 6, int(96 * width_mult), 3, 1
)
self.conv7 = self._make_layer(
Bottleneck, 6, int(160 * width_mult), 3, 2
)
self.conv8 = self._make_layer(
Bottleneck, 6, int(320 * width_mult), 1, 1
)
self.conv9 = ConvBlock(self.in_channels, self.feature_dim, 1)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = self._construct_fc_layer(
fc_dims, self.feature_dim, dropout_p
)
self.classifier = nn.Linear(self.feature_dim, num_classes)
self._init_params()
def _make_layer(self, block, t, c, n, s):
# t: expansion factor
# c: output channels
# n: number of blocks
# s: stride for first layer
layers = []
layers.append(block(self.in_channels, c, t, s))
self.in_channels = c
for i in range(1, n):
layers.append(block(self.in_channels, c, t))
return nn.Sequential(*layers)
def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
"""Constructs fully connected layer.
Args:
fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
input_dim (int): input dimension
dropout_p (float): dropout probability, if None, dropout is unused
"""
if fc_dims is None:
self.feature_dim = input_dim
return None
assert isinstance(
fc_dims, (list, tuple)
), 'fc_dims must be either list or tuple, but got {}'.format(
type(fc_dims)
)
layers = []
for dim in fc_dims:
layers.append(nn.Linear(input_dim, dim))
layers.append(nn.BatchNorm1d(dim))
layers.append(nn.ReLU(inplace=True))
if dropout_p is not None:
layers.append(nn.Dropout(p=dropout_p))
input_dim = dim
self.feature_dim = fc_dims[-1]
return nn.Sequential(*layers)
def _init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu'
)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def featuremaps(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = self.conv7(x)
x = self.conv8(x)
x = self.conv9(x)
return x
def forward(self, x):
f = self.featuremaps(x)
v = self.global_avgpool(f)
v = v.view(v.size(0), -1)
if self.fc is not None:
v = self.fc(v)
if not self.training:
return v
y = self.classifier(v)
if self.loss == 'softmax':
return y
elif self.loss == 'triplet':
return y, v
else:
raise KeyError("Unsupported loss: {}".format(self.loss))
def init_pretrained_weights(model, model_url):
"""Initializes model with pretrained weights.
Layers that don't match with pretrained layers in name or size are kept unchanged.
"""
pretrain_dict = model_zoo.load_url(model_url)
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
def mobilenetv2_x1_0(num_classes, loss, pretrained=True, **kwargs):
model = MobileNetV2(
num_classes,
loss=loss,
width_mult=1,
fc_dims=None,
dropout_p=None,
**kwargs
)
if pretrained:
# init_pretrained_weights(model, model_urls['mobilenetv2_x1_0'])
import warnings
warnings.warn(
'The imagenet pretrained weights need to be manually downloaded from {}'
.format(model_urls['mobilenetv2_x1_0'])
)
return model
def mobilenetv2_x1_4(num_classes, loss, pretrained=True, **kwargs):
model = MobileNetV2(
num_classes,
loss=loss,
width_mult=1.4,
fc_dims=None,
dropout_p=None,
**kwargs
)
if pretrained:
# init_pretrained_weights(model, model_urls['mobilenetv2_x1_4'])
import warnings
warnings.warn(
'The imagenet pretrained weights need to be manually downloaded from {}'
.format(model_urls['mobilenetv2_x1_4'])
)
return model