add pcb
KaiyangZhou
parent
f77b9ada10
commit
bfba6389d6
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@ -16,6 +16,7 @@ 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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__model_factory = {
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@ -45,6 +46,8 @@ __model_factory = {
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# reid-specific models
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'mudeep': MuDeep,
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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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}
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@ -0,0 +1,232 @@
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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__ = ['pcb_p6', 'pcb_p4']
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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}
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class DimReduceLayer(nn.Module):
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def __init__(self, in_channels, out_channels, nonlinear):
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super(DimReduceLayer, self).__init__()
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layers = []
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layers.append(nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False))
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layers.append(nn.BatchNorm2d(out_channels))
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if nonlinear == 'relu':
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layers.append(nn.ReLU(inplace=True))
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elif nonlinear == 'leakyrelu':
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layers.append(nn.LeakyReLU(0.1))
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self.layers = nn.Sequential(*layers)
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def forward(self, x):
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return self.layers(x)
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class PCB(nn.Module):
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"""
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Part-based Convolutional Baseline
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Reference:
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Sun et al. Beyond Part Models: Person Retrieval with Refined
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Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.
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"""
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def __init__(self, num_classes, loss, block, layers,
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parts=6,
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reduced_dim=256,
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nonlinear='relu',
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**kwargs):
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self.inplanes = 64
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super(PCB, self).__init__()
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self.loss = loss
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self.parts = parts
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self.feature_dim = 512 * block.expansion
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# backbone network
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
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# pcb layers
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self.parts_avgpool = nn.AdaptiveAvgPool2d((self.parts, 1))
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self.dropout = nn.Dropout(p=0.5)
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self.conv5 = DimReduceLayer(512 * block.expansion, reduced_dim, nonlinear=nonlinear)
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self.feature_dim = reduced_dim
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self.classifier = nn.ModuleList([nn.Linear(self.feature_dim, num_classes) for _ in range(self.parts)])
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self._init_params()
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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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.BatchNorm1d):
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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 featuremaps(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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return x
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def forward(self, x):
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f = self.featuremaps(x)
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v_g = self.parts_avgpool(f)
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v_h = self.conv5(self.dropout(v_g))
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if not self.training:
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v_g = F.normalize(v_g, p=2, dim=1)
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return v_g.view(v_g.size(0), -1)
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y = []
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for i in range(self.parts):
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v_h_i = v_h[:, :, i, :]
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v_h_i = v_h_i.view(v_h_i.size(0), -1)
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y_i = self.classifier[i](v_h_i)
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y.append(y_i)
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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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v_g = F.normalize(v_g, p=2, dim=1)
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return y, v_g.view(v_g.size(0), -1)
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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 pcb_p6(num_classes, loss, pretrained='imagenet', **kwargs):
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model = PCB(
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num_classes=num_classes,
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loss=loss,
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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last_stride=1,
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parts=6,
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reduced_dim=256,
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nonlinear='relu',
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**kwargs
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)
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if pretrained == 'imagenet':
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init_pretrained_weights(model, model_urls['resnet50'])
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return model
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def pcb_p4(num_classes, loss, pretrained='imagenet', **kwargs):
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model = PCB(
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num_classes=num_classes,
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loss=loss,
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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last_stride=1,
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parts=4,
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reduced_dim=256,
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nonlinear='relu',
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**kwargs
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)
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if pretrained == 'imagenet':
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init_pretrained_weights(model, model_urls['resnet50'])
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return model
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