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| 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 | |