from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['mlfn']
model_urls = {
# training epoch = 5, top1 = 51.6
'imagenet':
'https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk',
}
class MLFNBlock(nn.Module):
def __init__(
self, in_channels, out_channels, stride, fsm_channels, groups=32
):
super(MLFNBlock, self).__init__()
self.groups = groups
mid_channels = out_channels // 2
# Factor Modules
self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
self.fm_bn1 = nn.BatchNorm2d(mid_channels)
self.fm_conv2 = nn.Conv2d(
mid_channels,
mid_channels,
3,
stride=stride,
padding=1,
bias=False,
groups=self.groups
)
self.fm_bn2 = nn.BatchNorm2d(mid_channels)
self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
self.fm_bn3 = nn.BatchNorm2d(out_channels)
# Factor Selection Module
self.fsm = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, fsm_channels[0], 1),
nn.BatchNorm2d(fsm_channels[0]),
nn.ReLU(inplace=True),
nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
nn.BatchNorm2d(fsm_channels[1]),
nn.ReLU(inplace=True),
nn.Conv2d(fsm_channels[1], self.groups, 1),
nn.BatchNorm2d(self.groups),
nn.Sigmoid(),
)
self.downsample = None
if in_channels != out_channels or stride > 1:
self.downsample = nn.Sequential(
nn.Conv2d(
in_channels, out_channels, 1, stride=stride, bias=False
),
nn.BatchNorm2d(out_channels),
)
def forward(self, x):
residual = x
s = self.fsm(x)
# reduce dimension
x = self.fm_conv1(x)
x = self.fm_bn1(x)
x = F.relu(x, inplace=True)
# group convolution
x = self.fm_conv2(x)
x = self.fm_bn2(x)
x = F.relu(x, inplace=True)
# factor selection
b, c = x.size(0), x.size(1)
n = c // self.groups
ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
ss = ss.view(b, n, self.groups, 1, 1)
ss = ss.permute(0, 2, 1, 3, 4).contiguous()
ss = ss.view(b, c, 1, 1)
x = ss * x
# recover dimension
x = self.fm_conv3(x)
x = self.fm_bn3(x)
x = F.relu(x, inplace=True)
if self.downsample is not None:
residual = self.downsample(residual)
return F.relu(residual + x, inplace=True), s
class MLFN(nn.Module):
"""Multi-Level Factorisation Net.
Reference:
Chang et al. Multi-Level Factorisation Net for
Person Re-Identification. CVPR 2018.
Public keys:
- ``mlfn``: MLFN (Multi-Level Factorisation Net).
"""
def __init__(
self,
num_classes,
loss='softmax',
groups=32,
channels=[64, 256, 512, 1024, 2048],
embed_dim=1024,
**kwargs
):
super(MLFN, self).__init__()
self.loss = loss
self.groups = groups
# first convolutional layer
self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(channels[0])
self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
# main body
self.feature = nn.ModuleList(
[
# layer 1-3
MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
# layer 4-7
MLFNBlock(
channels[1], channels[2], 2, [256, 128], self.groups
),
MLFNBlock(
channels[2], channels[2], 1, [256, 128], self.groups
),
MLFNBlock(
channels[2], channels[2], 1, [256, 128], self.groups
),
MLFNBlock(
channels[2], channels[2], 1, [256, 128], self.groups
),
# layer 8-13
MLFNBlock(
channels[2], channels[3], 2, [512, 128], self.groups
),
MLFNBlock(
channels[3], channels[3], 1, [512, 128], self.groups
),
MLFNBlock(
channels[3], channels[3], 1, [512, 128], self.groups
),
MLFNBlock(
channels[3], channels[3], 1, [512, 128], self.groups
),
MLFNBlock(
channels[3], channels[3], 1, [512, 128], self.groups
),
MLFNBlock(
channels[3], channels[3], 1, [512, 128], self.groups
),
# layer 14-16
MLFNBlock(
channels[3], channels[4], 2, [512, 128], self.groups
),
MLFNBlock(
channels[4], channels[4], 1, [512, 128], self.groups
),
MLFNBlock(
channels[4], channels[4], 1, [512, 128], self.groups
),
]
)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
# projection functions
self.fc_x = nn.Sequential(
nn.Conv2d(channels[4], embed_dim, 1, bias=False),
nn.BatchNorm2d(embed_dim),
nn.ReLU(inplace=True),
)
self.fc_s = nn.Sequential(
nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
nn.BatchNorm2d(embed_dim),
nn.ReLU(inplace=True),
)
self.classifier = nn.Linear(embed_dim, num_classes)
self.init_params()
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.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x, inplace=True)
x = self.maxpool(x)
s_hat = []
for block in self.feature:
x, s = block(x)
s_hat.append(s)
s_hat = torch.cat(s_hat, 1)
x = self.global_avgpool(x)
x = self.fc_x(x)
s_hat = self.fc_s(s_hat)
v = (x+s_hat) * 0.5
v = v.view(v.size(0), -1)
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 mlfn(num_classes, loss='softmax', pretrained=True, **kwargs):
model = MLFN(num_classes, loss, **kwargs)
if pretrained:
# init_pretrained_weights(model, model_urls['imagenet'])
import warnings
warnings.warn(
'The imagenet pretrained weights need to be manually downloaded from {}'
.format(model_urls['imagenet'])
)
return model