mirror of https://github.com/JDAI-CV/fast-reid.git
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
# coding: utf-8
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"""
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Sun, Y. , Zheng, L. , Yang, Y. , Tian, Q. , & Wang, S. . (2017). Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). Springer, Cham.
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实现和线上一模一样的PCB
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"""
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import torch
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from fastreid.modeling.losses import cross_entropy_loss, log_accuracy
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from fastreid.modeling.meta_arch import Baseline
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from fastreid.modeling.meta_arch import META_ARCH_REGISTRY
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@META_ARCH_REGISTRY.register()
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class PcbOnline(Baseline):
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def forward(self, batched_inputs):
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# TODO: 减均值除方差
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q = batched_inputs['q_img']
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x = batched_inputs['p_img']
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qf = self.cnn(q)
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xf = self.cnn(x)
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# L2 norm TODO: 查看具体数值，确定他到底是什么范数
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qf = self._norm(qf)
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xf = self._norm(xf)
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qf_full, qf_part_0, qf_part_1, qf_part_2 = torch.split(qf, [2048, 512, 512, 512], dim=-1)
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xf_full, xf_part_0, xf_part_1, xf_part_2 = torch.split(xf, [2048, 512, 512, 512], dim=-1)
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m_full = self.dense_matching_full(
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torch.cat([qf_full, xf_full, (qf_full - xf_full).abs(), qf_full * xf_full], dim=-1))
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m_part_0 = self.dense_matching_part_0(
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torch.cat([qf_part_0, xf_part_0, (qf_part_0 - xf_part_0).abs(), qf_part_0 * xf_part_0], dim=-1))
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m_part_1 = self.dense_matching_part_1(
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torch.cat([qf_part_1, xf_part_1, (qf_part_1 - xf_part_1).abs(), qf_part_1 * xf_part_1], dim=-1))
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m_part_2 = self.dense_matching_part_2(
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torch.cat([qf_part_2, xf_part_2, (qf_part_2 - xf_part_2).abs(), qf_part_2 * xf_part_2], dim=-1))
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m_all = self.dense_matching_all(torch.cat([m_full, m_part_0, m_part_1, m_part_2], dim=-1))
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return m_all.squeeze()
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def losses(self, outputs, gt_labels):
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"""
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Compute loss from modeling's outputs, the loss function input arguments
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must be the same as the outputs of the model forwarding.
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"""
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# model predictions
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pred_class_logits = outputs['pred_class_logits'].detach()
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cls_outputs = outputs['cls_outputs']
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# Log prediction accuracy
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log_accuracy(pred_class_logits, gt_labels)
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loss_dict = {}
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loss_names = self.loss_kwargs['loss_names']
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if 'CrossEntropyLoss' in loss_names:
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ce_kwargs = self.loss_kwargs.get('ce')
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loss_dict['loss_cls'] = cross_entropy_loss(
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cls_outputs,
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gt_labels,
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ce_kwargs.get('eps'),
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ce_kwargs.get('alpha')
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) * ce_kwargs.get('scale')
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return loss_dict
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