87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
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# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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from torch import nn
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from .backbones.resnet import ResNet
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def weights_init_kaiming(m):
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classname = m.__class__.__name__
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if classname.find('Linear') != -1:
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nn.init.kaiming_normal_(m.weight, a=0, mode='fan_out')
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nn.init.constant_(m.bias, 0.0)
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elif classname.find('Conv') != -1:
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nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0.0)
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elif classname.find('BatchNorm') != -1:
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if m.affine:
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nn.init.constant_(m.weight, 1.0)
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nn.init.constant_(m.bias, 0.0)
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def weights_init_classifier(m):
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classname = m.__class__.__name__
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if classname.find('Linear') != -1:
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nn.init.normal_(m.weight, std=0.001)
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if m.bias:
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nn.init.constant_(m.bias, 0.0)
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class Baseline(nn.Module):
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in_planes = 2048
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def __init__(self, num_classes, last_stride, model_path, neck, neck_feat):
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super(Baseline, self).__init__()
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self.base = ResNet(last_stride)
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self.base.load_param(model_path)
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self.gap = nn.AdaptiveAvgPool2d(1)
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# self.gap = nn.AdaptiveMaxPool2d(1)
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self.num_classes = num_classes
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self.neck = neck
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self.neck_feat = neck_feat
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if self.neck == 'no':
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self.classifier = nn.Linear(self.in_planes, self.num_classes)
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# self.classifier = nn.Linear(self.in_planes, self.num_classes, bias=False) # new add by luo
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# self.classifier.apply(weights_init_classifier) # new add by luo
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elif self.neck == 'bnneck':
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self.bottleneck = nn.BatchNorm1d(self.in_planes)
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self.bottleneck.bias.requires_grad_(False) # no shift
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self.classifier = nn.Linear(self.in_planes, self.num_classes, bias=False)
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self.bottleneck.apply(weights_init_kaiming)
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self.classifier.apply(weights_init_classifier)
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def forward(self, x):
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global_feat = self.gap(self.base(x)) # (b, 2048, 1, 1)
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global_feat = global_feat.view(global_feat.shape[0], -1) # flatten to (bs, 2048)
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if self.neck == 'no':
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feat = global_feat
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elif self.neck == 'bnneck':
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feat = self.bottleneck(global_feat) # normalize for angular softmax
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if self.training:
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cls_score = self.classifier(feat)
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return cls_score, global_feat # global feature for triplet loss
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else:
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if self.neck_feat == 'after':
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# print("Test with feature after BN")
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return feat
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else:
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# print("Test with feature before BN")
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return global_feat
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def load_param(self, trained_path):
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param_dict = torch.load(trained_path)
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for i in param_dict:
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if 'classifier' in i:
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continue
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self.state_dict()[i].copy_(param_dict[i])
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