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Source code for tllib.vision.models.reid.identifier

"""
@author: Baixu Chen
@contact: cbx_99_hasta@outlook.com
"""
from typing import List, Dict, Optional
import torch
import torch.nn as nn
from torch.nn import init


[docs]class ReIdentifier(nn.Module): r"""Person reIdentifier from `Bag of Tricks and A Strong Baseline for Deep Person Re-identification (CVPR 2019) <https://arxiv.org/pdf/1903.07071.pdf>`_. Given 2-d features :math:`f` from backbone network, the authors pass :math:`f` through another `BatchNorm1d` layer and get :math:`bn\_f`, which will then pass through a `Linear` layer to output predictions. During training, we use :math:`f` to compute triplet loss. While during testing, :math:`bn\_f` is used as feature. This may be a little confusing. The figures in the origin paper will help you understand better. """ def __init__(self, backbone: nn.Module, num_classes: int, bottleneck: Optional[nn.Module] = None, bottleneck_dim: Optional[int] = -1, finetune=True, pool_layer=None): super(ReIdentifier, self).__init__() if pool_layer is None: self.pool_layer = nn.Sequential( nn.AdaptiveAvgPool2d(output_size=(1, 1)), nn.Flatten() ) else: self.pool_layer = pool_layer self.backbone = backbone self.num_classes = num_classes if bottleneck is None: feature_bn = nn.BatchNorm1d(backbone.out_features) self.bottleneck = feature_bn self._features_dim = backbone.out_features else: feature_bn = nn.BatchNorm1d(bottleneck_dim) self.bottleneck = nn.Sequential( bottleneck, feature_bn ) self._features_dim = bottleneck_dim self.head = nn.Linear(self.features_dim, num_classes, bias=False) self.finetune = finetune # initialize feature_bn and head feature_bn.bias.requires_grad_(False) init.constant_(feature_bn.weight, 1) init.constant_(feature_bn.bias, 0) init.normal_(self.head.weight, std=0.001) @property def features_dim(self) -> int: """The dimension of features before the final `head` layer""" return self._features_dim def forward(self, x: torch.Tensor): """""" f = self.pool_layer(self.backbone(x)) bn_f = self.bottleneck(f) if not self.training: return bn_f predictions = self.head(bn_f) return predictions, f
[docs] def get_parameters(self, base_lr=1.0, rate=0.1) -> List[Dict]: """A parameter list which decides optimization hyper-parameters, such as the relative learning rate of each layer """ params = [ {"params": self.backbone.parameters(), "lr": rate * base_lr if self.finetune else 1.0 * base_lr}, {"params": self.bottleneck.parameters(), "lr": 1.0 * base_lr}, {"params": self.head.parameters(), "lr": 1.0 * base_lr}, ] return params

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