Open ssbilakeri opened 3 years ago
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.
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, notifications@github.com 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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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
On Sun, 13 Dec 2020, 2:08 pm Shavantrevva Bilakeri, ssbilakeri@gmail.com 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, notifications@github.com 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.
— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/huanghoujing/EANet/issues/35#issuecomment-743969812, or unsubscribe https://github.com/notifications/unsubscribe-auth/AOOYJBK4QGEBTE73W7PTKB3SURXCJANCNFSM4UQGVKRA .
Hi, thank you for your feedback.
To run triplet loss, we have to
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.
Thank you so much for ur informative response.
On Thu, 17 Dec 2020, 12:25 pm Houjing Huang, notifications@github.com 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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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, ssbilakeri@gmail.com wrote:
Thank you so much for ur informative response.
On Thu, 17 Dec 2020, 12:25 pm Houjing Huang, notifications@github.com 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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Hi ssbilakeri, I did not use triplet loss in the paper.
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, notifications@github.com wrote:
Hi ssbilakeri, I did not use triplet loss in the paper.
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Hi, ssbilakeri, for which Table of the paper do you need the re-ranking score?
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, notifications@github.com wrote:
Hi, ssbilakeri, for which Table of the paper do you need the re-ranking score?
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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, ssbilakeri@gmail.com 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, notifications@github.com wrote:
Hi, ssbilakeri, for which Table of the paper do you need the re-ranking score?
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It seems that the trained model weight is not loaded.
Could you please check with your code hope you have trained weights
On Mon, 28 Dec 2020, 8:26 pm Houjing Huang, notifications@github.com wrote:
It seems that the trained model weight is not loaded.
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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
.
Thank you very much for your valuable response. Thanks a lot.
On Tue, Dec 29, 2020 at 9:10 AM Houjing Huang notifications@github.com 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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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 ssbilakeri@gmail.com wrote:
Thank you very much for your valuable response. Thanks a lot.
On Tue, Dec 29, 2020 at 9:10 AM Houjing Huang notifications@github.com 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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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.
I was not knowing that. Thank you for the information.
On Wed, 30 Dec 2020, 12:00 pm Houjing Huang, notifications@github.com 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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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 ssbilakeri@gmail.com wrote:
I was not knowing that. Thank you for the information.
On Wed, 30 Dec 2020, 12:00 pm Houjing Huang, notifications@github.com 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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Hi, package/data/kpt_to_pap_mask.py
is the code that partitions body into regions using keypoints.
is the python 2.7 and pytorch 1.0.0 are not supportive for tripletloss ??