mirror of https://github.com/JDAI-CV/fast-reid.git
100 lines
2.9 KiB
Python
100 lines
2.9 KiB
Python
# encoding: utf-8
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"""
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@author: l1aoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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import torch.nn.functional as F
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from torch import nn
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from .build import META_ARCH_REGISTRY
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from ..backbones import build_backbone
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from ..heads import build_reid_heads
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from ..model_utils import weights_init_kaiming
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from fastreid.modeling.layers import Flatten
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@META_ARCH_REGISTRY.register()
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class MidNetwork(nn.Module):
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"""Residual network + mid-level features.
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Reference:
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Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for
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Cross-Domain Instance Matching. arXiv:1711.08106.
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Public keys:
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- ``resnet50mid``: ResNet50 + mid-level feature fusion.
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"""
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def __init__(self, cfg):
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super().__init__()
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self._cfg = cfg
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# backbone
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backbone = build_backbone(cfg)
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self.backbone = nn.Sequential(
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backbone.conv1,
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backbone.bn1,
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backbone.relu,
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backbone.maxpool,
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backbone.layer1,
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backbone.layer2,
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backbone.layer3
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)
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# body
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self.res4 = backbone.layer4
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self.avg_pool = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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Flatten(),
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)
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self.fusion = nn.Sequential(
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nn.Linear(4096, 1024, bias=False),
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nn.BatchNorm1d(1024),
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nn.ReLU(True)
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)
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self.fusion.apply(weights_init_kaiming)
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# head
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self.head = build_reid_heads(cfg, 3072, nn.Identity())
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def forward(self, inputs):
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images = inputs['images']
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targets = inputs['targets']
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if not self.training:
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pred_feat = self.inference(images)
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return pred_feat, targets, inputs['camid']
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feat = self.backbone(images)
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feat_4a = self.res4[0](feat)
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feat_4b = self.res4[1](feat_4a)
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feat_4c = self.res4[2](feat_4b)
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feat_4a = self.avg_pool(feat_4a)
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feat_4b = self.avg_pool(feat_4b)
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feat_4c = self.avg_pool(feat_4c)
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feat_4ab = torch.cat([feat_4a, feat_4b], dim=1)
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feat_4ab = self.fusion(feat_4ab)
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feat = torch.cat([feat_4ab, feat_4c], 1)
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logist, feat = self.head(feat, targets)
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return logist, feat, targets
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def losses(self, outputs):
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return self.head.losses(self._cfg, outputs[0], outputs[1], outputs[2])
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def inference(self, images):
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assert not self.training
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feat = self.backbone(images)
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feat_4a = self.res4[0](feat)
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feat_4b = self.res4[1](feat_4a)
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feat_4c = self.res4[2](feat_4b)
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feat_4a = self.avg_pool(feat_4a)
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feat_4b = self.avg_pool(feat_4b)
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feat_4c = self.avg_pool(feat_4c)
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feat_4ab = torch.cat([feat_4a, feat_4b], dim=1)
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feat_4ab = self.fusion(feat_4ab)
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feat = torch.cat([feat_4ab, feat_4c], 1)
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pred_feat = self.head(feat)
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return F.normalize(pred_feat)
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