main.py --dataset market --testset msmt --data-dir ./reid/datasets/ --exp-dir ./Exp

fpaths:./reid/datasets/market/bounding_box_train/1500_c6s3_086567_01.jpg fpaths:./reid/datasets/market/bounding_box_test/1501_c6s4_001902_01.jpg fpaths:./reid/datasets/market/query/1501_c6s4_001877_00.jpg Market dataset loaded subset | # ids | # images

train | 751 | 12935 query | 750 | 3367 gallery | 751 | 15912

• Finished epoch 1 at lr=[0.0005, 0.005, 0.005]. Loss: 14.812. Acc: 54.97%. Training time: 174 seconds.

• Finished epoch 2 at lr=[0.0005, 0.005, 0.005]. Loss: 13.333. Acc: 61.35%. Training time: 344 seconds.

• Finished epoch 3 at lr=[0.0005, 0.005, 0.005]. Loss: 11.447. Acc: 68.55%. Training time: 514 seconds.

• Finished epoch 4 at lr=[0.0005, 0.005, 0.005]. Loss: 10.338. Acc: 72.09%. Training time: 684 seconds.

• Finished epoch 5 at lr=[0.0005, 0.005, 0.005]. Loss: 9.319. Acc: 75.31%. Training time: 855 seconds.

Decay the learning rate by a factor of 0.1. Final epochs: 7.

• Finished epoch 6 at lr=[5e-05, 0.0005, 0.0005]. Loss: 8.566. Acc: 77.75%. Training time: 1025 seconds.

• Finished epoch 7 at lr=[5e-05, 0.0005, 0.0005]. Loss: 7.732. Acc: 80.22%. Training time: 1195 seconds.

The learning converges at epoch 7.

Evaluate the learned model: test_names: ['msmt'] MSMT dataset loaded subset | # ids | # images

train | 1041 | 32621 query | 3060 | 11659 gallery | 3060 | 82161 /home/luotao/anaconda3/envs/QAConv/lib/python3.6/site-packages/torchvision/transforms/transforms.py:288: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. "Argument interpolation should be of type InterpolationMode instead of int. " Time: 2690.337 seconds. / 1284. similarity 1 / 1284.
已杀死

run: python main.py --dataset market --testset msmt --data-dir ./reid/datasets/ --exp-dir ./Exp
--test_fea_batch --test_gal_batch --test_prob_batch all set to 128. Time: xx seconds, /xx similarity xx/xx. 已杀死.
Those three parameters set to 64， the errer ： Time: 2690.337 seconds. / 1284. similarity 1 / 1284. 已杀死.

Hi, Thanks for sharing your code. However, I ran your code twice and get quite different results. Maybe due to random seed? So did you set a fixed random seed when you train the model?

你好，我使用2块2080TI训练30W数据，64 batch_size 并且使用了fp16来加速训练，但是一个epoch训练了半个多小时才到511 iter，这正常吗？ Epoch: [1][511/4714] 455Time 2.620 (2.646)ec 0.0Data 0.001 (0.002) Loss 456.984 (520.544) Prec 0.00% (0.00%)

In the QAConv codes, I tried to modify the loss function to ClassMemoryLoss as the criterion but the acc is nearly zero. Is the ClassMemoryLoss available to use? Are ClassMemoryLoss and Focal Loss in the paper the same? The code is shown below.

criterion = ClassMemoryLoss(matcher, num_classes, num_features, hei, wid).cuda()

Hi Mr.Liao, I appreciate much your novel idea and your code, and i notice that you choose ResNet as the backbone. ResNet152 has shown great results in the paper and in my own experiments , but it seems that it takes quite some time to train, even if we choose layer 3 of the model. Have you tried some lightweight backbone such as MobileNet? Is there any specific reason for choosing ResNet as feature extractor? Thanks in advance.

廖老师您好，想了解一下为什么graph sampling对于domain generalized re-id能够有很好的提升效果？以往的domain generalized re-id方法往往是采用domain invariant learning, style normalization等方式来解决这一任务，但graph sampling好像跟以往的方法思路不同，是通过加强hard mining的方式来改善domain generalization；对这一点有些不太理解，期待您的回复，谢谢！

thank u for ur work! I got 2 questions about Graph Sampling:

1. intuitively, it should work on normal ReID task.
2. The whole process is like: before training one epoch, the proposed sampler randomly select one img for each class, then computes a distmat for each img. The distmat represents distances between classes. So we can mine hardest samples in entire dataset, not a batch. But I didn't get where does "Graph" have connection to the process above. Looking forward to your help

廖老师您好，我想问一下关于s的取值问题。 您论文提到为了效率选择了s=1， 我是这么理解的，在不使用classmemory 而是使用pair wise match的情况下， 做一次QAconv的时间复杂度为O( B^2 * (HW)^2 * s )。 按照时间复杂度来的化， s取值的大一点或者小小一点感觉没有多影响。 但是，当s=1的时候，可以直接使用矩阵乘法，然后又因为矩阵乘法做了大量的优化，所以实际的时间大大缩短了。所以最终s=1. 不知我的理解是否有问题，望老师您赐教！

廖老师您好，读了您最近的The Graph Sampling work 论文，有两个问题不太清楚，想请教您一下，望您指点：

1. 在每一个epoch建图的时候，随机采样每个类的一张图片会不会造成比较大的偏差？
2. K=2处理梯度太小的问题时，会不会遇到完全采样不到的情况（以前在远大于学术数据集的业务数据集上遇到过着种问题，hardcase采样不到）

I use Market as the training dataset and Duke as the test dataset, when I use --do_tlift, it shows that the size of tensor are not match.

In the evaluators.py document, line 212, the original dist size is 222817661 in Market dataset, and the size of dist_rerank is 2228253 because the num_gal is not the same. The value of num_gal is the length of gallery images in the definition of line 189. However, it is redefined in line 204 as the size of gallery feature.

Recently, I have read your code for QAConv. Now, I have a question to consult you. In the train() method in trainer.py, the following codes class BaseTrainer(object): for i, inputs in enumerate(data_loader): self.model.eval() self.criterion.train() Why you don't set the model in train mode by using self.mode.train(), instead of using model.eval(). And, in the whole code of your project, I also found that there is no other place to use model. train().

Hello,

First of all, thanks so much for your good work!

Here is a question: inside the test_matching.py, you import from reid.loss.qaconv_loss import QAConvLoss, however, it seems that qaconv_loss is no longer here, so I change to other loss functions. Will it influence the performance?

Thanks!