add mlfn
KaiyangZhou
parent
733d967345
commit
39df4855c1
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@ -17,6 +17,7 @@ from .squeezenet import *
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from .mudeep import *
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from .hacnn import *
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from .pcb import *
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from .mlfn import *
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__model_factory = {
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@ -48,6 +49,7 @@ __model_factory = {
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'hacnn': HACNN,
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'pcb_p6': pcb_p6,
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'pcb_p4': pcb_p4,
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'mlfn': mlfn,
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}
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@ -0,0 +1,210 @@
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from __future__ import absolute_import
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from __future__ import division
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import torch
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from torch import nn
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from torch.nn import functional as F
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import torchvision
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import torch.utils.model_zoo as model_zoo
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__all__ = ['mlfn']
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model_urls = {
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# training epoch = 5, top1 = 51.6
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'imagenet': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/mlfn-9cb5a267.pth.tar',
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}
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class MLFNBlock(nn.Module):
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def __init__(self, in_channels, out_channels, stride, fsm_channels, groups=32):
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super(MLFNBlock, self).__init__()
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self.groups = groups
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mid_channels = out_channels // 2
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# Factor Modules
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self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
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self.fm_bn1 = nn.BatchNorm2d(mid_channels)
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self.fm_conv2 = nn.Conv2d(mid_channels, mid_channels, 3, stride=stride, padding=1, bias=False, groups=self.groups)
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self.fm_bn2 = nn.BatchNorm2d(mid_channels)
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self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
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self.fm_bn3 = nn.BatchNorm2d(out_channels)
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# Factor Selection Module
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self.fsm = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Conv2d(in_channels, fsm_channels[0], 1),
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nn.BatchNorm2d(fsm_channels[0]),
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nn.ReLU(inplace=True),
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nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
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nn.BatchNorm2d(fsm_channels[1]),
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nn.ReLU(inplace=True),
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nn.Conv2d(fsm_channels[1], self.groups, 1),
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nn.BatchNorm2d(self.groups),
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nn.Sigmoid(),
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)
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self.downsample = None
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if in_channels != out_channels or stride > 1:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 1, stride=stride, bias=False),
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nn.BatchNorm2d(out_channels),
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)
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def forward(self, x):
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residual = x
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s = self.fsm(x)
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# reduce dimension
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x = self.fm_conv1(x)
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x = self.fm_bn1(x)
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x = F.relu(x, inplace=True)
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# group convolution
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x = self.fm_conv2(x)
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x = self.fm_bn2(x)
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x = F.relu(x, inplace=True)
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# factor selection
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b, c = x.size(0), x.size(1)
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n = c // self.groups
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ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
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ss = ss.view(b, n, self.groups, 1, 1)
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ss = ss.permute(0, 2, 1, 3, 4).contiguous()
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ss = ss.view(b, c, 1, 1)
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x = ss * x
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# recover dimension
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x = self.fm_conv3(x)
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x = self.fm_bn3(x)
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x = F.relu(x, inplace=True)
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if self.downsample is not None:
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residual = self.downsample(residual)
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return F.relu(residual + x, inplace=True), s
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class MLFN(nn.Module):
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"""
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Multi-Level Factorisation Net
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Reference:
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Chang et al. Multi-Level Factorisation Net for Person Re-Identification. CVPR 2018.
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"""
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def __init__(self, num_classes, loss={'xent'}, groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs):
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super(MLFN, self).__init__()
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self.loss = loss
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self.groups = groups
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# first convolutional layer
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self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
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self.bn1 = nn.BatchNorm2d(channels[0])
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self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
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# main body
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self.feature = nn.ModuleList([
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# layer 1-3
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MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
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MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
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MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
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# layer 4-7
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MLFNBlock(channels[1], channels[2], 2, [256, 128], self.groups),
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MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
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MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
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MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
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# layer 8-13
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MLFNBlock(channels[2], channels[3], 2, [512, 128], self.groups),
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MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
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MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
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MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
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MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
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MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
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# layer 14-16
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MLFNBlock(channels[3], channels[4], 2, [512, 128], self.groups),
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MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
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MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
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])
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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# projection functions
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self.fc_x = nn.Sequential(
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nn.Conv2d(channels[4], embed_dim, 1, bias=False),
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nn.BatchNorm2d(embed_dim),
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nn.ReLU(inplace=True),
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)
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self.fc_s = nn.Sequential(
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nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
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nn.BatchNorm2d(embed_dim),
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nn.ReLU(inplace=True),
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)
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self.classifier = nn.Linear(embed_dim, num_classes)
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self.init_params()
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def init_params(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = F.relu(x, inplace=True)
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x = self.maxpool(x)
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s_hat = []
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for block in self.feature:
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x, s = block(x)
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s_hat.append(s)
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s_hat = torch.cat(s_hat, 1)
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x = self.global_avgpool(x)
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x = self.fc_x(x)
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s_hat = self.fc_s(s_hat)
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v = (x + s_hat) * 0.5
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v = v.view(v.size(0), -1)
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if not self.training:
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return v
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y = self.classifier(v)
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if self.loss == {'xent'}:
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return y
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elif self.loss == {'xent', 'htri'}:
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return y, v
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else:
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raise KeyError("Unsupported loss: {}".format(self.loss))
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def init_pretrained_weights(model, model_url):
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"""
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Initialize model with pretrained weights.
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Layers that don't match with pretrained layers in name or size are kept unchanged.
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"""
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pretrain_dict = model_zoo.load_url(model_url)
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model_dict = model.state_dict()
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pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
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model_dict.update(pretrain_dict)
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model.load_state_dict(model_dict)
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print("Initialized model with pretrained weights from {}".format(model_url))
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def mlfn(num_classes, loss, pretrained='imagenet', **kwargs):
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model = MLFN(num_classes, loss, **kwargs)
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if pretrained == 'imagenet':
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init_pretrained_weights(model, model_urls['imagenet'])
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return model
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