EANet for triplet loss

is the python 2.7 and pytorch 1.0.0 are not supportive for tripletloss ??

Dec 07 '20 09:12 ssbilakeri

Hi, python 2.7 and pytorch 1.0.0 are absolutely ok to run this code.

By the way, do you run the program in Linux? I ran it in Ubuntu.

Dec 13 '20 07:12 huanghoujing

Hello sir, I too ran the code on ubuntu python 2.7 and pytorch 1.0.0

The code works fine with ID and PS loss but when triplet is added it give error . Why is it so ?

On Sun, 13 Dec 2020, 1:22 pm Houjing Huang, [email protected] wrote:

Hi, python 2.7 and pytorch 1.0.0 are absolutely ok to run this code.

By the way, do you run the program in Linux? I ran it in Ubuntu.

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Dec 13 '20 08:12 ssbilakeri

I'm getting below error when included triplet loss. Help me to resolve this issue

File "/home/padmashree/anaconda3/envs/myenv/lib/python2.7/runpy.py", line 174, in _run_module_as_main "main", fname, loader, pkg_name) File "/home/padmashree/anaconda3/envs/myenv/lib/python2.7/runpy.py", line 72, in _run_code exec code in run_globals File "/home/padmashree/project_dir/EANet2/package/optim/eanet_trainer.py", line 135, in trainer.train_phases() File "/home/padmashree/project_dir/EANet2/package/optim/eanet_trainer.py", line 126, in train_phases self.train() File "package/optim/reid_trainer.py", line 338, in train self.trainer.train_one_epoch(trial_run_steps=3 if cfg.trial_run else None) File "package/optim/trainer.py", line 36, in train_one_epoch self.train_one_step(batch) File "package/optim/trainer.py", line 24, in train_one_step pred = self.train_forward(batch) File "/home/padmashree/project_dir/EANet2/package/optim/eanet_trainer.py", line 102, in train_forward loss += self.loss_funcs[loss_cfg.name](reid_batch, pred, step=self.trainer.current_step)['loss'] File "package/loss/triplet_loss.py", line 124, in call res3 = self.calculate(torch.stack(pred['feat_list']), batch['label'], hard_type=hard_type) File "package/loss/triplet_loss.py", line 107, in calculate dist_mat = compute_dist(feat, feat, dist_type=cfg.dist_type) File "package/eval/torch_distance.py", line 49, in compute_dist dist = euclidean_dist(array1, array2) File "package/eval/torch_distance.py", line 25, in euclidean_dist xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) RuntimeError: expand(torch.cuda.FloatTensor{[9, 1, 256]}, size=[9, 9]): the number of sizes provided (2) must be greater or equal to the number of dimensions in the tensor (3)

On Sun, 13 Dec 2020, 2:08 pm Shavantrevva Bilakeri, [email protected] wrote:

Hello sir, I too ran the code on ubuntu python 2.7 and pytorch 1.0.0

The code works fine with ID and PS loss but when triplet is added it give error . Why is it so ?

On Sun, 13 Dec 2020, 1:22 pm Houjing Huang, [email protected] wrote:

Hi, python 2.7 and pytorch 1.0.0 are absolutely ok to run this code.

By the way, do you run the program in Linux? I ran it in Ubuntu.

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Dec 13 '20 08:12 ssbilakeri

Hi, thank you for your feedback.

To run triplet loss, we have to

Use PK sampling for batch construction
Increase training epochs

I provide a setting file paper_configs/PAP_S_PS_Triplet_Loss_Market1501.txt, and a script script/exp/train_PAP_S_PS_Triplet_Loss_Market1501.sh, to train with both PS loss and triplet loss on Market1501.

Besides, I also fixed a mistake in part index when feeding feature to triplet loss. You can find the detail in this commit.

Now, you can run the script by

bash script/exp/train_PAP_S_PS_Triplet_Loss_Market1501.sh

The result I obtained is

M -> M      [mAP:  86.0%], [cmc1:  95.0%], [cmc5:  98.0%], [cmc10:  98.8%]
M -> C      [mAP:  11.0%], [cmc1:  12.1%], [cmc5:  24.1%], [cmc10:  31.6%]
M -> D      [mAP:  29.2%], [cmc1:  46.7%], [cmc5:  63.2%], [cmc10:  69.5%]

I hope it helps.

Dec 17 '20 06:12 huanghoujing

Thank you so much for ur informative response.

On Thu, 17 Dec 2020, 12:25 pm Houjing Huang, [email protected] wrote:

Hi, thank you for your feedback.

To run triplet loss, we have to

Use PK sampling for batch construction

Increase training epochs

I provide a setting file paper_configs/PAP_S_PS_Triplet_Loss_Market1501.txt, and a script script/exp/train_PAP_S_PS_Triplet_Loss_Market1501.sh, to train with both PS loss and triplet loss on Market1501.

Besides, I also fixed a mistake in part index when feeding feature to triplet loss. You can find the detail in this commit https://github.com/huanghoujing/EANet/commit/e46d49528428fbf0c54b93e557e009012c9a34b4 .

Now, you can run the script by

bash script/exp/train_PAP_S_PS_Triplet_Loss_Market1501.sh

The result I obtained is

M -> M [mAP: 86.0%], [cmc1: 95.0%], [cmc5: 98.0%], [cmc10: 98.8%] M -> C [mAP: 11.0%], [cmc1: 12.1%], [cmc5: 24.1%], [cmc10: 31.6%] M -> D [mAP: 29.2%], [cmc1: 46.7%], [cmc5: 63.2%], [cmc10: 69.5%]

I hope it helps.

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Dec 17 '20 15:12 ssbilakeri

Hi sir, In your EANet paper did u train the model including triplet loss ?? Kindly reply

On Thu, 17 Dec 2020, 9:15 pm Shavantrevva Bilakeri, [email protected] wrote:

Thank you so much for ur informative response.

On Thu, 17 Dec 2020, 12:25 pm Houjing Huang, [email protected] wrote:

Hi, thank you for your feedback.

To run triplet loss, we have to

Use PK sampling for batch construction

Increase training epochs

I provide a setting file paper_configs/PAP_S_PS_Triplet_Loss_Market1501.txt, and a script script/exp/train_PAP_S_PS_Triplet_Loss_Market1501.sh, to train with both PS loss and triplet loss on Market1501.

Besides, I also fixed a mistake in part index when feeding feature to triplet loss. You can find the detail in this commit https://github.com/huanghoujing/EANet/commit/e46d49528428fbf0c54b93e557e009012c9a34b4 .

Now, you can run the script by

bash script/exp/train_PAP_S_PS_Triplet_Loss_Market1501.sh

The result I obtained is

M -> M [mAP: 86.0%], [cmc1: 95.0%], [cmc5: 98.0%], [cmc10: 98.8%] M -> C [mAP: 11.0%], [cmc1: 12.1%], [cmc5: 24.1%], [cmc10: 31.6%] M -> D [mAP: 29.2%], [cmc1: 46.7%], [cmc5: 63.2%], [cmc10: 69.5%]

I hope it helps.

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Dec 20 '20 16:12 ssbilakeri

Hi ssbilakeri, I did not use triplet loss in the paper.

Dec 21 '20 04:12 huanghoujing

Hello sir, I'm trying to re-produce your paper result with re-ranking unfortunately I'm not able to do it.Since I need your paper result with re-ranking applied to compare with my work .could u please do it for me. It will be a great help Thank you

On Mon, 21 Dec 2020, 9:33 am Houjing Huang, [email protected] wrote:

Hi ssbilakeri, I did not use triplet loss in the paper.

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Dec 26 '20 07:12 ssbilakeri

Hi, ssbilakeri, for which Table of the paper do you need the re-ranking score?

Dec 27 '20 11:12 huanghoujing

I need it for PAP_S_PS (where ID loss and segmentation loss is used). Kindly help in in this regard. I will be looking forward to your response. Thank you.

On Sun, 27 Dec 2020, 5:17 pm Houjing Huang, [email protected] wrote:

Hi, ssbilakeri, for which Table of the paper do you need the re-ranking score?

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Dec 27 '20 13:12 ssbilakeri

Hi sir, when I run your code with re-ranking getting below results. kindly suggest what could be the problem.

I have attached the code with this mail. please help me . Thank you.

Extract Feature: 0%| | 0/106 [00:00<?, ? batches/s]Loaded pickle file /home/padmashree/project_dir/dataset/market1501/im_path_to_kpt.pklLoaded pickle file /home/padmashree/project_dir/dataset/market1501/im_path_to_kpt.pkl

Extract Feature: 100%|##########################################################| 106/106 [00:13<00:00, 7.75 batches/s] Extract Feature: 0%| | 0/498 [00:00<?, ? batches/s]Loaded pickle file /home/padmashree/project_dir/dataset/market1501/im_path_to_kpt.pkl Loaded pickle file /home/padmashree/project_dir/dataset/market1501/im_path_to_kpt.pkl Extract Feature: 100%|##########################################################| 498/498 [01:04<00:00, 7.75 batches/s] => Eval Statistics: dic.keys(): ['g_feat', 'q_feat', 'q_visible', 'q_label', 'q_cam', 'g_visible', 'g_label', 'g_cam'] dic['q_feat'].shape: (3368, 2304) dic['q_label'].shape: (3368,) dic['q_cam'].shape: (3368,) dic['g_feat'].shape: (15913, 2304) dic['g_label'].shape: (15913,) dic['g_cam'].shape: (15913,) M -> M [mAP: 1.5%], [cmc1: 7.3%], [cmc5: 14.5%], [cmc10: 19.3%] Extract Feature: 0%| | 0/44 [00:00<?, ? batches/s]Loaded pickle file /home/padmashree/project_dir/dataset/cuhk03_np_detected_jpg/im_path_to_kpt.pkl Loaded pickle file /home/padmashree/project_dir/dataset/cuhk03_np_detected_jpg/im_path_to_kpt.pkl Extract Feature: 100%|############################################################| 44/44 [00:05<00:00, 7.79 batches/s] Extract Feature: 0%| | 0/167 [00:00<?, ? batches/s]Loaded pickle file /home/padmashree/project_dir/dataset/cuhk03_np_detected_jpg/im_path_to_kpt.pkl Loaded pickle file /home/padmashree/project_dir/dataset/cuhk03_np_detected_jpg/im_path_to_kpt.pkl Extract Feature: 100%|##########################################################| 167/167 [00:20<00:00, 8.10 batches/s] => Eval Statistics: dic.keys(): ['g_feat', 'q_feat', 'q_visible', 'q_label', 'q_cam', 'g_visible', 'g_label', 'g_cam'] dic['q_feat'].shape: (1400, 2304) dic['q_label'].shape: (1400,) dic['q_cam'].shape: (1400,) dic['g_feat'].shape: (5332, 2304) dic['g_label'].shape: (5332,) dic['g_cam'].shape: (5332,) M -> C [mAP: 0.2%], [cmc1: 0.1%], [cmc5: 0.5%], [cmc10: 1.7%] Extract Feature: 0%| | 0/70 [00:00<?, ? batches/s]Loaded pickle file /home/padmashree/project_dir/dataset/duke/im_path_to_kpt.pkl Loaded pickle file /home/padmashree/project_dir/dataset/duke/im_path_to_kpt.pkl Extract Feature: 100%|############################################################| 70/70 [00:08<00:00, 7.90 batches/s] Extract Feature: 0%| | 0/552 [00:00<?, ? batches/s]Loaded pickle file /home/padmashree/project_dir/dataset/duke/im_path_to_kpt.pkl Loaded pickle file /home/padmashree/project_dir/dataset/duke/im_path_to_kpt.pkl Extract Feature: 100%|##########################################################| 552/552 [01:07<00:00, 8.15 batches/s] => Eval Statistics: dic.keys(): ['g_feat', 'q_feat', 'q_visible', 'q_label', 'q_cam', 'g_visible', 'g_label', 'g_cam'] dic['q_feat'].shape: (2228, 2304) dic['q_label'].shape: (2228,) dic['q_cam'].shape: (2228,) dic['g_feat'].shape: (17661, 2304) dic['g_label'].shape: (17661,) dic['g_cam'].shape: (17661,) M -> D [mAP: 0.3%], [cmc1: 1.2%], [cmc5: 2.8%], [cmc10: 4.3%]

On Sun, 27 Dec 2020, 6:38 pm Shavantrevva Bilakeri, [email protected] wrote:

I need it for PAP_S_PS (where ID loss and segmentation loss is used). Kindly help in in this regard. I will be looking forward to your response. Thank you.

On Sun, 27 Dec 2020, 5:17 pm Houjing Huang, [email protected] wrote:

Hi, ssbilakeri, for which Table of the paper do you need the re-ranking score?

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Dec 28 '20 05:12 ssbilakeri

It seems that the trained model weight is not loaded.

Dec 28 '20 14:12 huanghoujing

Could you please check with your code hope you have trained weights

On Mon, 28 Dec 2020, 8:26 pm Houjing Huang, [email protected] wrote:

It seems that the trained model weight is not loaded.

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Dec 28 '20 14:12 ssbilakeri

Hi, ssbilakeri. I have tested the re-ranking results for PAP_S_PS models (the script to run this is script/exp/test_PAP_S_PS_reranking.sh).

The original scores, as well as the re-ranking scores are as follows.

	mAP	Rank-1	Rank-5	Rank-10
M -> M	85.6	94.6	98.2	99.0
ReRank M -> M	93.5	95.7	97.5	98.3

M -> C	12.8	14.2	28.1	35.4
ReRank M -> C	19.4	17.6	28.1	35.9

M -> D	31.7	51.4	67.2	72.5
ReRank M -> D	47.6	57.6	67.9	73.4

C -> M	33.3	59.4	73.7	78.7
ReRank C -> M	47.3	64.0	72.0	76.1

C -> C	66.7	72.5	86.1	91.3
ReRank C -> C	80.8	80.1	86.9	92.2

C -> D	22.0	39.3	54.4	60.3
ReRank C -> D	36.1	47.7	57.5	61.8

D -> M	32.8	61.7	77.2	83.0
ReRank D -> M	48.0	65.6	74.1	78.8

D -> C	9.6	11.4	22.7	28.9
ReRank D -> C	15.4	14.4	22.1	28.7

D -> D	74.6	87.5	93.4	95.3
ReRank D -> D	85.5	89.7	93.6	95.2

I update the code, so that it can test with re-ranking now. Please refer to this commit. You can run it for yourself, by setting cfg.eval.rerank to True in package/config/default.py.

Dec 29 '20 03:12 huanghoujing

Thank you very much for your valuable response. Thanks a lot.

On Tue, Dec 29, 2020 at 9:10 AM Houjing Huang [email protected] wrote:

Hi, ssbilakeri. I have tested the re-ranking results for PAP_S_PS models (the script to run this is script/exp/test_PAP_S_PS_reranking.sh).

The original scores, as well as the re-ranking scores are as follows. mAP Rank-1 Rank-5 Rank-10 M -> M 85.6 94.6 98.2 99.0 ReRank M -> M 93.5 95.7 97.5 98.3 M -> C 12.8 14.2 28.1 35.4 ReRank M -> C 19.4 17.6 28.1 35.9 M -> D 31.7 51.4 67.2 72.5 ReRank M -> D 47.6 57.6 67.9 73.4 C -> M 33.3 59.4 73.7 78.7 ReRank C -> M 47.3 64.0 72.0 76.1 C -> C 66.7 72.5 86.1 91.3 ReRank C -> C 80.8 80.1 86.9 92.2 C -> D 22.0 39.3 54.4 60.3 ReRank C -> D 36.1 47.7 57.5 61.8 D -> M 32.8 61.7 77.2 83.0 ReRank D -> M 48.0 65.6 74.1 78.8 D -> C 9.6 11.4 22.7 28.9 ReRank D -> C 15.4 14.4 22.1 28.7 D -> D 74.6 87.5 93.4 95.3 ReRank D -> D 85.5 89.7 93.6 95.2

I update the code, so that it can test with re-ranking now. Please refer to this commit https://github.com/huanghoujing/EANet/commit/a38f12477e3edd625699f5a1beae92181e2c6b62. You can run it for yourself, by setting cfg.eval.rerank to True in package/config/default.py.

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Dec 29 '20 05:12 ssbilakeri

Hello sir, when I train your model considering only segmentation loss it gives so less accuracy. have you run with only segmentation loss ? then what accuracy did you get?

for me result is like this Epoch 60 M->M: 6.8 ( 1.4), M->C: 0.1 ( 0.2), M->D: 5.7 ( 1.2)

kindly respond.

On Tue, Dec 29, 2020 at 11:08 AM Shavantrevva Bilakeri [email protected] wrote:

Thank you very much for your valuable response. Thanks a lot.

On Tue, Dec 29, 2020 at 9:10 AM Houjing Huang [email protected] wrote:

Hi, ssbilakeri. I have tested the re-ranking results for PAP_S_PS models (the script to run this is script/exp/test_PAP_S_PS_reranking.sh).

The original scores, as well as the re-ranking scores are as follows. mAP Rank-1 Rank-5 Rank-10 M -> M 85.6 94.6 98.2 99.0 ReRank M -> M 93.5 95.7 97.5 98.3 M -> C 12.8 14.2 28.1 35.4 ReRank M -> C 19.4 17.6 28.1 35.9 M -> D 31.7 51.4 67.2 72.5 ReRank M -> D 47.6 57.6 67.9 73.4 C -> M 33.3 59.4 73.7 78.7 ReRank C -> M 47.3 64.0 72.0 76.1 C -> C 66.7 72.5 86.1 91.3 ReRank C -> C 80.8 80.1 86.9 92.2 C -> D 22.0 39.3 54.4 60.3 ReRank C -> D 36.1 47.7 57.5 61.8 D -> M 32.8 61.7 77.2 83.0 ReRank D -> M 48.0 65.6 74.1 78.8 D -> C 9.6 11.4 22.7 28.9 ReRank D -> C 15.4 14.4 22.1 28.7 D -> D 74.6 87.5 93.4 95.3 ReRank D -> D 85.5 89.7 93.6 95.2

I update the code, so that it can test with re-ranking now. Please refer to this commit https://github.com/huanghoujing/EANet/commit/a38f12477e3edd625699f5a1beae92181e2c6b62. You can run it for yourself, by setting cfg.eval.rerank to True in package/config/default.py.

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Dec 30 '20 04:12 ssbilakeri

Hi, ssbilakeri. That's normal. Because segmentation does not learn anything about person re-identification. You have to train with at least one kind of re-identification loss, i.e. id loss or triplet loss.

Dec 30 '20 06:12 huanghoujing

I was not knowing that. Thank you for the information.

On Wed, 30 Dec 2020, 12:00 pm Houjing Huang, [email protected] wrote:

Hi, ssbilakeri. That's normal. Because segmentation does not learn anything about person re-identification. You have to train with at least one kind of re-identification loss, i.e. id loss or triplet loss.

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Dec 30 '20 06:12 ssbilakeri

Hi sir, how did you partition the feature map with keypoint delimitation. Which part of the code does that? Help me to understand Thank you.

On Wed, Dec 30, 2020 at 12:22 PM Shavantrevva Bilakeri [email protected] wrote:

I was not knowing that. Thank you for the information.

On Wed, 30 Dec 2020, 12:00 pm Houjing Huang, [email protected] wrote:

Hi, ssbilakeri. That's normal. Because segmentation does not learn anything about person re-identification. You have to train with at least one kind of re-identification loss, i.e. id loss or triplet loss.

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Jan 06 '21 05:01 ssbilakeri

Hi, package/data/kpt_to_pap_mask.py is the code that partitions body into regions using keypoints.

Jan 12 '21 09:01 huanghoujing

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for triplet loss

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