I have the data directory set up, except for the xml file. Please assist/advise.

├── AIC21/ │ ├── AIC21_Track2_ReID/ │ ├── image_train/ │ ├── image_test/ │ ├── image_query/ │ ├── train_label.xml │ ├── ... │ ├── training_part_seg/ │ ├── cropped_patch/ │ ├── cropped_aic_test │ ├── image_test/ │ ├── image_query/ │ ├── AIC21_Track2_ReID_Simulation/ │ ├── sys_image_train/ │ ├── sys_image_train_tr/

I found that your train_track.txt file is different from the original data file, and you have generated a new query_label.xml file, which also does not exist in the original data. Can you provide these documents?

The requirements.txt file has a number of packages that are pulled from a local drive, e.g.: albumentations @ file:///home/conda/feedstock_root/build_artifacts/albumentations_1594363620148/work

Since I can't access these specific packages, I don't know the version or if any other modifications have been made to the package locally. As a result, attempting the installation with the most current version of each package, but this results in installation errors. Recommendations are appreciated.

CUDA Out of Memory error but CUDA memory is almost empty

I currently want to train this model, but I have a runtime error.

• python train.py --config_file configs/stage1/resnext101a_384.yml MODEL.DEVICE_ID "('0')"

• RuntimeError: CUDA out of memory. Tried to allocate 108.00 MiB (GPU 0; 23.65 GiB total capacity; 22.40 GiB already allocated; 67.06 MiB free; 22.62 GiB reserved in total by PyTorch).

According to the message, I trained a model on small amount of data and changed the number of [CUDA_VISIBLE_DEVICES], but I still have a runtime error.

Should I have more required space..? Any idea what might cause this ?

hello,

thanks for the brilliant project!

I want to know how could I train the model with multi-gpu on one machine. Looking forward to hearing from you, thank you in advance!

In the training process, there is always such an error:

"File "/home/wit/DmtReid/loss/triplet_loss.py", line 102, in hard_example_mining dist_mat[is_neg].contiguous().view(N, -1), 1, keepdim=True) RuntimeError: cannot perform reduction function min on tensor with no elements because the operation does not have an identity"

When I went to check the information printed on the terminal, I found that the number of the query set and quelly set was 1,as follows

"=> AIC loaded Dataset statistics:

subset | # ids | # images | # cameras

train | 440 | 52717 | 40 query | 1 | 1103 | 18 gallery | 1 | 31238 | 18 ----------------------------------------"

At the same time, I print the relevant information of the error line ，as follows

"is_neg tensor: ([[False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False], [False, False, False, False, False, False, False, False]], device='cuda:0') dist_mat: tensor([[8.9935e-01, 1.3378e+01, 9.9388e+00, 1.2196e+01, 8.9468e+00, 1.1898e+01, 1.3501e+01, 6.1112e+00], [1.3378e+01, 3.5938e-01, 1.2367e+01, 1.0789e+01, 1.2657e+01, 9.7843e+00, 1.0603e+01, 1.2570e+01], [9.9388e+00, 1.2367e+01, 1.0000e-06, 1.1123e+01, 6.7836e+00, 1.2270e+01, 1.2560e+01, 9.4772e+00], [1.2196e+01, 1.0789e+01, 1.1123e+01, 1.0000e-06, 1.0024e+01, 9.9253e+00, 7.1910e+00, 1.1304e+01], [8.9468e+00, 1.2657e+01, 6.7836e+00, 1.0024e+01, 1.0000e-06, 1.1718e+01, 1.1748e+01, 8.9490e+00], [1.1898e+01, 9.7843e+00, 1.2270e+01, 9.9253e+00, 1.1718e+01, 5.8055e-01, 1.0002e+01, 1.0799e+01], [1.3501e+01, 1.0603e+01, 1.2560e+01, 7.1910e+00, 1.1748e+01, 1.0002e+01, 4.8299e-01, 1.2935e+01], [6.1112e+00, 1.2570e+01, 9.4772e+00, 1.1304e+01, 8.9490e+00, 1.0799e+01, 1.2935e+01, 1.0000e-06]], device='cuda:0', grad_fn=) dist_ap: tensor([[13.5008], [13.3779], [12.5595], [12.1962], [12.6573], [12.2696], [13.5008], [12.9348]], device='cuda:0', grad_fn=)"

I don't know where the problem is, can you help me ？

罗博士您好，我们最近复现了贵组的训练代码，确有一些问题和疑惑，希望您能帮忙指点一二，感谢！

1、分类Loss为什么没有采用带margin的cosface等，是和训练样本的id分布有关吗； 2、每个epoch的pk采样几乎遍历了全部图片，相比于遍历id每次随机采k张的采样方法，哪种方式更优呢，还是说对于不同的训练数据分布，可以选择不同的pk采样方式； 3、我们用单张v100训练点数确实很高，但换成多张2080Ti的DP训练，掉点比较明显，这个是因为syncBN的原因吗，但感觉掉点幅度偏大； 4、后处理中uda方法，对于mAP涨点幅度大吗，我们比赛时采用uda好像并不能涨太多点；

谢谢指教~

Hi team, thanks for the hard work! A small question is when I tried to switch the reranking from GPU to CPU, I might not get the same reranking result. I tried your numpy implementation as well as to remove cuda in PyTorch implementation and both of them were not able to reproduce the CUDA result. Do you have any idea why this is happening? thanks in advance!

Dear author, I checked the UDA training part of the code, and see there is a function named compute_P2. https://github.com/michuanhaohao/AICITY2021_Track2_DMT/blob/main/train_stage2_v2.py#L113 However, I did not find the corresponding explanation in the paper... Could you please talk a bit more about this proposal? Thanks.

Hi, I have the same problem below the link. https://github.com/michuanhaohao/AICITY2021_Track2_DMT/issues/10#issue-963169121

I don't know where the problem is, could you help me? Thank you.

Hello there! Firstly, thank you for your paper for the AI City Challenge!

I was going through your paper but had difficulty understanding some things that I hope you can spare the time to clarify. It is mentioned in section 4.2 Validation Data:

Since each team has only 20 submissions, it is necessary to use the validation set to evaluate methods offline. We split the training set of CityFlow-V2 into the training set and the validation set. For convenience, the validation set is denoted as Split-Test. Split-Test includes 18701 images of 88 vehicles.

So according to this, the (52,717 images, 440 vehicles) in the Original Training Set of CityFlowV2-ReID are split into

• New Training Set consisting of 34016 images, 352 vehicles
• New Validation Set (Split-Test) consisting of 18701 images, 88 vehicles

The Original Test Set from CityFlowV2-ReID is untouched, as well as the queries for it.

For the results shown in Table 1 and 2 in the paper, it seems we should train on augmented New Training Set, and evaluate on the New Validation Set (Split-Test), with no validation happening during training.

However, in the repository, I can't seem to find code for splitting Original Training Set into New Training Set and New Validation Set (Split-Test). And also instead of evaluating on New Validation Set (Split-Test), we are doing evaluation on New Validation Set (Split-Test)

Instead, it seems like we are just training on the Original Training Set and then evaluating on Original Test Set? I say this because it seems that in https://github.com/michuanhaohao/AICITY2021_Track2_DMT/blob/50f27363532ae712868ff1ceaf128a3bbec426ac/datasets/aic.py it seems that self.query and self.gallery is just the Original Test Set. And then in https://github.com/michuanhaohao/AICITY2021_Track2_DMT/blob/50f27363532ae712868ff1ceaf128a3bbec426ac/test.py we use the Original Test Set.

Is this understanding correct? If it is, then could you advise how we should generate the New Validation Set (Split-Test)? I am hoping to compare other to yours using it as an evaluation dataset

Hi. Can I start with the inference using python test.py --config_file configs/stage2/101a_384.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT './logs/stage2/resnext101a_384/v1/resnext101_ibn_a_2.pth' OUTPUT_DIR './logs/stage2/resnext101a_384/v1'?

keep getting: File "C:\Users\diva\car_finder\model\make_model.py", line 148, in init self.state_dict()[i].copy_(param_dict[i]) RuntimeError: The size of tensor a (64) must match the size of tensor b (128) at non-singleton dimension 0

Can someone help? Thnks