diff --git a/configs/baseline/baseline_Gait3D.yaml b/configs/baseline/baseline_Gait3D.yaml new file mode 100644 index 0000000..858f060 --- /dev/null +++ b/configs/baseline/baseline_Gait3D.yaml @@ -0,0 +1,104 @@ +data_cfg: + dataset_name: Gait3D + dataset_root: your_path + dataset_partition: datasets/Gait3D/Gait3D.json + data_in_use: [true, false] + num_workers: 1 + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: Gait3D + +evaluator_cfg: + enable_float16: true + restore_ckpt_strict: true + restore_hint: 60000 + save_name: Baseline + eval_func: evaluate_Gait3D + sampler: + batch_shuffle: false + batch_size: 16 + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + metric: euc # cos euc + transform: + - type: BaseSilCuttingTransform + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + - loss_term_weight: 0.1 + scale: 16 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + +model_cfg: + model: Baseline + backbone_cfg: + in_channels: 1 + layers_cfg: # Layers configuration for automatically model construction + - BC-64 + - BC-64 + - M + - BC-128 + - BC-128 + - M + - BC-256 + - BC-256 + # - M + # - BC-512 + # - BC-512 + type: Plain + SeparateFCs: + in_channels: 256 + out_channels: 256 + parts_num: 31 + SeparateBNNecks: + class_num: 3000 + in_channels: 256 + parts_num: 31 + bin_num: + - 16 + - 8 + - 4 + - 2 + - 1 + +optimizer_cfg: + lr: 0.1 + momentum: 0.9 + solver: SGD + weight_decay: 0.0005 + +scheduler_cfg: + gamma: 0.1 + milestones: # Learning Rate Reduction at each milestones + - 20000 + - 40000 + - 50000 + scheduler: MultiStepLR + +trainer_cfg: + enable_float16: true # half_percesion float for memory reduction and speedup + fix_BN: false + log_iter: 100 + with_test: 10000 + restore_ckpt_strict: true + restore_hint: 0 + save_iter: 10000 + save_name: Baseline + sync_BN: true + total_iter: 60000 + sampler: + batch_shuffle: true + batch_size: + - 32 # TripletSampler, batch_size[0] indicates Number of Identity + - 4 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 30 # fixed frames number for training + frames_num_max: 50 # max frames number for unfixed training + frames_num_min: 25 # min frames number for unfixed traing + sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + transform: + - type: BaseSilCuttingTransform diff --git a/configs/smplgait/smplgait.yaml b/configs/smplgait/smplgait.yaml new file mode 100644 index 0000000..2ae433c --- /dev/null +++ b/configs/smplgait/smplgait.yaml @@ -0,0 +1,108 @@ +data_cfg: + dataset_name: Gait3D + dataset_root: your_path + dataset_partition: datasets/Gait3D/Gait3D.json + data_in_use: [true, true] + num_workers: 1 + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: Gait3D + +evaluator_cfg: + enable_float16: true + restore_ckpt_strict: true + restore_hint: 60000 + save_name: SMPLGait + eval_func: evaluate_Gait3D + sampler: + batch_shuffle: false + batch_size: 16 + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + metric: euc # cos euc + transform: + - type: BaseSilCuttingTransform + img_w: 64 + - type: NoOperation + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + - loss_term_weight: 0.1 + scale: 16 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + +model_cfg: + model: SMPLGait + backbone_cfg: + in_channels: 1 + layers_cfg: # Layers configuration for automatically model construction + - BC-64 + - BC-64 + - M + - BC-128 + - BC-128 + - M + - BC-256 + - BC-256 + # - M + # - BC-512 + # - BC-512 + type: Plain + SeparateFCs: + in_channels: 256 + out_channels: 256 + parts_num: 31 + SeparateBNNecks: + class_num: 3000 + in_channels: 256 + parts_num: 31 + bin_num: + - 16 + - 8 + - 4 + - 2 + - 1 + +optimizer_cfg: + lr: 0.1 + momentum: 0.9 + solver: SGD + weight_decay: 0.0005 + +scheduler_cfg: + gamma: 0.1 + milestones: # Learning Rate Reduction at each milestones + - 20000 + - 40000 + - 50000 + scheduler: MultiStepLR + +trainer_cfg: + enable_float16: true # half_percesion float for memory reduction and speedup + fix_BN: false + log_iter: 100 + with_test: 10000 + restore_ckpt_strict: true + restore_hint: 0 + save_iter: 10000 + save_name: SMPLGait + sync_BN: true + total_iter: 60000 + sampler: + batch_shuffle: true + batch_size: + - 32 # TripletSampler, batch_size[0] indicates Number of Identity + - 4 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 30 # fixed frames number for training + frames_num_max: 50 # max frames number for unfixed training + frames_num_min: 25 # min frames number for unfixed traing + sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + transform: + - type: BaseSilCuttingTransform + img_w: 64 + - type: NoOperation diff --git a/datasets/CASIA-B/README.md b/datasets/CASIA-B/README.md index b6366ec..c317ae4 100644 --- a/datasets/CASIA-B/README.md +++ b/datasets/CASIA-B/README.md @@ -13,7 +13,7 @@ Download URL: http://www.cbsr.ia.ac.cn/GaitDatasetB-silh.zip ...... ...... ``` -- Run `python misc/pretreatment.py --input_path CASIA-B --output_path CASIA-B-pkl` +- Run `python datasets/pretreatment.py --input_path CASIA-B --output_path CASIA-B-pkl` - Processed ``` CASIA-B-pkl diff --git a/datasets/Gait3D/Gait3D.json b/datasets/Gait3D/Gait3D.json new file mode 100644 index 0000000..500f2e5 --- /dev/null +++ b/datasets/Gait3D/Gait3D.json @@ -0,0 +1,5008 @@ +{ + "PROBE_SET": [ + "0002-camid33_videoid2-seq0", + "0007-camid9_videoid1-seq0", + "0011-camid34_videoid2-seq0", + "0017-camid32_videoid2-seq0", + "0022-camid28_videoid2-seq0", + "0023-camid28_videoid2-seq1", + "0027-camid28_videoid3-seq0", + "0028-camid0_videoid2-seq0", + "0035-camid30_videoid3-seq0", + "0037-camid7_videoid2-seq1", + "0040-camid17_videoid2-seq0", + "0041-camid35_videoid2-seq0", + "0051-camid0_videoid2-seq0", + "0055-camid32_videoid3-seq0", + "0057-camid3_videoid2-seq0", + "0061-camid12_videoid3-seq0", + "0063-camid34_videoid1-seq0", + "0070-camid13_videoid3-seq0", + "0073-camid34_videoid3-seq3", + "0075-camid9_videoid2-seq0", + "0079-camid16_videoid2-seq0", + "0080-camid13_videoid4-seq0", + "0100-camid23_videoid1-seq0", + "0105-camid9_videoid3-seq0", + "0106-camid15_videoid1-seq0", + "0107-camid0_videoid2-seq0", + "0108-camid31_videoid2-seq0", + "0122-camid15_videoid1-seq0", + "0124-camid24_videoid3-seq0", + "0125-camid36_videoid3-seq0", + "0127-camid2_videoid3-seq0", + "0128-camid24_videoid4-seq1", + "0129-camid37_videoid3-seq0", + "0133-camid16_videoid4-seq0", + "0139-camid19_videoid1-seq0", + "0142-camid31_videoid1-seq0", + "0148-camid27_videoid4-seq0", + "0158-camid24_videoid1-seq0", + "0166-camid0_videoid3-seq1", + "0168-camid34_videoid2-seq0", + "0171-camid0_videoid3-seq0", + "0184-camid17_videoid4-seq0", + "0186-camid17_videoid5-seq0", + "0188-camid2_videoid3-seq0", + "0191-camid17_videoid4-seq0", + "0197-camid17_videoid6-seq0", + "0199-camid38_videoid2-seq0", + "0206-camid34_videoid4-seq0", + "0208-camid31_videoid2-seq0", + "0219-camid16_videoid1-seq0", + "0229-camid16_videoid2-seq0", + "0237-camid31_videoid4-seq1", + "0241-camid32_videoid1-seq1", + "0246-camid32_videoid2-seq0", + "0249-camid32_videoid3-seq0", + "0259-camid32_videoid1-seq0", + "0264-camid28_videoid3-seq1", + "0265-camid8_videoid3-seq1", + "0266-camid18_videoid2-seq0", + "0267-camid32_videoid3-seq0", + "0268-camid35_videoid3-seq0", + "0269-camid17_videoid6-seq0", + "0270-camid34_videoid3-seq2", + "0271-camid0_videoid3-seq0", + "0272-camid30_videoid2-seq0", + "0280-camid0_videoid3-seq0", + "0285-camid0_videoid3-seq0", + "0290-camid24_videoid2-seq0", + "0294-camid22_videoid1-seq0", + "0296-camid20_videoid1-seq2", + "0297-camid13_videoid2-seq0", + "0299-camid24_videoid1-seq1", + "0301-camid17_videoid5-seq0", + "0305-camid34_videoid1-seq0", + "0310-camid32_videoid1-seq0", + "0311-camid19_videoid1-seq0", + "0312-camid24_videoid1-seq0", + "0314-camid8_videoid1-seq0", + "0315-camid28_videoid1-seq0", + "0319-camid35_videoid2-seq0", + "0321-camid27_videoid2-seq0", + "0323-camid3_videoid1-seq0", + "0325-camid24_videoid2-seq0", + "0328-camid0_videoid3-seq0", + "0330-camid0_videoid1-seq0", + "0337-camid0_videoid2-seq0", + "0341-camid0_videoid1-seq0", + "0342-camid19_videoid1-seq0", + "0346-camid7_videoid3-seq0", + "0347-camid15_videoid3-seq0", + "0348-camid27_videoid3-seq1", + "0359-camid14_videoid2-seq0", + "0360-camid0_videoid2-seq1", + "0364-camid33_videoid2-seq0", + "0366-camid9_videoid3-seq0", + "0368-camid31_videoid3-seq0", + "0374-camid25_videoid2-seq0", + "0376-camid28_videoid2-seq0", + "0381-camid0_videoid2-seq0", + "0386-camid23_videoid2-seq0", + "0389-camid34_videoid2-seq0", + "0396-camid0_videoid2-seq0", + "0397-camid0_videoid2-seq0", + "0411-camid0_videoid3-seq0", + "0414-camid0_videoid3-seq0", + "0418-camid34_videoid2-seq1", + "0419-camid0_videoid3-seq0", + "0423-camid14_videoid3-seq0", + "0424-camid32_videoid2-seq1", + "0425-camid36_videoid3-seq0", + "0430-camid31_videoid3-seq1", + "0432-camid12_videoid3-seq0", + "0433-camid13_videoid6-seq0", + "0436-camid23_videoid4-seq0", + "0437-camid13_videoid5-seq1", + "0443-camid35_videoid3-seq0", + "0449-camid19_videoid2-seq0", + "0451-camid12_videoid3-seq0", + "0453-camid11_videoid4-seq0", + "0455-camid1_videoid3-seq1", + "0462-camid0_videoid4-seq0", + "0466-camid0_videoid1-seq0", + "0471-camid13_videoid7-seq0", + "0475-camid3_videoid4-seq0", + "0477-camid0_videoid3-seq0", + "0480-camid13_videoid2-seq0", + "0482-camid17_videoid2-seq0", + "0484-camid38_videoid3-seq0", + "0486-camid16_videoid3-seq0", + "0487-camid31_videoid2-seq0", + "0489-camid16_videoid2-seq0", + "0496-camid28_videoid1-seq0", + "0502-camid3_videoid1-seq0", + "0509-camid26_videoid1-seq0", + "0524-camid34_videoid2-seq0", + "0525-camid19_videoid2-seq0", + "0526-camid3_videoid4-seq0", + "0530-camid3_videoid3-seq0", + "0533-camid20_videoid4-seq0", + "0536-camid9_videoid3-seq0", + "0548-camid23_videoid3-seq0", + "0549-camid13_videoid6-seq0", + "0551-camid12_videoid8-seq1", + "0558-camid2_videoid2-seq0", + "0561-camid31_videoid2-seq1", + "0562-camid15_videoid2-seq0", + "0565-camid1_videoid2-seq0", + "0567-camid29_videoid3-seq2", + "0572-camid8_videoid1-seq0", + "0582-camid34_videoid2-seq0", + "0589-camid31_videoid1-seq0", + "0598-camid6_videoid4-seq0", + "0601-camid32_videoid3-seq0", + "0602-camid11_videoid3-seq1", + "0603-camid6_videoid5-seq0", + "0609-camid12_videoid3-seq0", + "0613-camid4_videoid4-seq0", + "0622-camid20_videoid1-seq3", + "0626-camid24_videoid2-seq1", + "0634-camid8_videoid1-seq0", + "0638-camid34_videoid2-seq0", + "0647-camid12_videoid2-seq0", + "0652-camid8_videoid2-seq0", + "0653-camid27_videoid5-seq0", + "0655-camid29_videoid3-seq0", + "0659-camid8_videoid3-seq0", + "0664-camid9_videoid1-seq0", + "0665-camid9_videoid1-seq1", + "0680-camid14_videoid2-seq0", + "0682-camid24_videoid2-seq0", + "0687-camid9_videoid2-seq0", + "0688-camid11_videoid2-seq0", + "0691-camid11_videoid2-seq0", + "0694-camid24_videoid3-seq0", + "0696-camid9_videoid3-seq0", + "0700-camid34_videoid1-seq0", + "0703-camid30_videoid2-seq0", + "0716-camid17_videoid5-seq0", + "0720-camid7_videoid4-seq1", + "0721-camid14_videoid2-seq1", + "0724-camid1_videoid1-seq0", + "0727-camid7_videoid3-seq0", + "0729-camid11_videoid3-seq0", + "0732-camid2_videoid1-seq0", + "0740-camid2_videoid2-seq0", + "0742-camid1_videoid3-seq0", + "0743-camid1_videoid1-seq0", + "0751-camid36_videoid1-seq0", + "0752-camid31_videoid1-seq0", + "0754-camid12_videoid2-seq0", + "0758-camid13_videoid1-seq0", + "0764-camid13_videoid3-seq0", + "0765-camid13_videoid3-seq0", + "0766-camid30_videoid2-seq0", + "0767-camid13_videoid3-seq0", + "0773-camid13_videoid4-seq0", + "0774-camid26_videoid2-seq0", + "0776-camid16_videoid1-seq0", + "0777-camid19_videoid2-seq0", + "0779-camid14_videoid2-seq0", + "0781-camid14_videoid3-seq1", + "0783-camid37_videoid1-seq0", + "0785-camid31_videoid1-seq0", + "0787-camid19_videoid2-seq0", + "0788-camid31_videoid2-seq1", + "0797-camid28_videoid1-seq0", + "0799-camid23_videoid3-seq0", + "0801-camid24_videoid2-seq0", + "0805-camid31_videoid3-seq0", + "0809-camid19_videoid1-seq0", + "0811-camid34_videoid4-seq0", + "0818-camid0_videoid3-seq0", + "0822-camid19_videoid1-seq0", + "0823-camid19_videoid1-seq0", + "0826-camid34_videoid3-seq0", + "0830-camid16_videoid3-seq0", + "0832-camid23_videoid2-seq0", + "0838-camid27_videoid2-seq0", + "0841-camid21_videoid3-seq0", + "0842-camid16_videoid2-seq0", + "0846-camid22_videoid2-seq0", + "0856-camid3_videoid2-seq0", + "0878-camid23_videoid1-seq0", + "0882-camid23_videoid2-seq0", + "0884-camid23_videoid2-seq0", + "0893-camid0_videoid3-seq0", + "0907-camid34_videoid2-seq0", + "0923-camid2_videoid3-seq0", + "0926-camid33_videoid3-seq0", + "0930-camid34_videoid2-seq0", + "0931-camid31_videoid2-seq0", + "0934-camid1_videoid3-seq0", + "0939-camid31_videoid3-seq0", + "0940-camid31_videoid3-seq0", + "0945-camid33_videoid1-seq0", + "0947-camid37_videoid1-seq0", + "0948-camid34_videoid1-seq0", + "0949-camid34_videoid1-seq0", + "0962-camid34_videoid4-seq0", + "0963-camid0_videoid1-seq0", + "0967-camid32_videoid1-seq1", + "0973-camid37_videoid3-seq0", + "0978-camid38_videoid2-seq1", + "0979-camid38_videoid4-seq0", + "0984-camid28_videoid2-seq0", + "0998-camid19_videoid1-seq2", + "1004-camid19_videoid1-seq0", + "1005-camid3_videoid1-seq0", + "1007-camid0_videoid1-seq0", + "1008-camid0_videoid1-seq2", + "1011-camid3_videoid1-seq0", + "1013-camid0_videoid1-seq0", + "1015-camid0_videoid2-seq0", + "1018-camid0_videoid1-seq0", + "1019-camid1_videoid1-seq0", + "1021-camid19_videoid1-seq1", + "1023-camid3_videoid1-seq0", + "1025-camid0_videoid1-seq0", + "1027-camid23_videoid1-seq0", + "1032-camid0_videoid2-seq1", + "1036-camid11_videoid2-seq2", + "1038-camid30_videoid2-seq0", + "1040-camid21_videoid2-seq0", + "1041-camid0_videoid2-seq0", + "1044-camid0_videoid2-seq0", + "1052-camid2_videoid3-seq0", + "1053-camid0_videoid2-seq0", + "1056-camid3_videoid2-seq0", + "1057-camid0_videoid2-seq0", + "1062-camid0_videoid2-seq0", + "1064-camid0_videoid3-seq0", + "1066-camid0_videoid2-seq0", + "1068-camid0_videoid2-seq1", + "1069-camid0_videoid2-seq1", + "1072-camid0_videoid3-seq0", + "1074-camid6_videoid6-seq0", + "1078-camid0_videoid4-seq0", + "1083-camid0_videoid3-seq0", + "1084-camid3_videoid1-seq0", + "1085-camid0_videoid3-seq0", + "1088-camid3_videoid3-seq0", + "1097-camid0_videoid4-seq0", + "1109-camid1_videoid1-seq0", + "1110-camid2_videoid2-seq0", + "1111-camid11_videoid2-seq0", + "1113-camid2_videoid1-seq0", + "1120-camid7_videoid1-seq0", + "1125-camid2_videoid2-seq0", + "1127-camid1_videoid3-seq0", + "1130-camid16_videoid2-seq0", + "1136-camid2_videoid3-seq0", + "1141-camid2_videoid2-seq0", + "1145-camid16_videoid3-seq1", + "1154-camid2_videoid3-seq0", + "1155-camid2_videoid2-seq0", + "1156-camid21_videoid1-seq1", + "1157-camid14_videoid4-seq0", + "1159-camid2_videoid3-seq0", + "1178-camid3_videoid1-seq0", + "1181-camid16_videoid2-seq0", + "1183-camid11_videoid3-seq0", + "1184-camid24_videoid3-seq0", + "1188-camid16_videoid2-seq0", + "1195-camid22_videoid2-seq0", + "1197-camid3_videoid4-seq0", + "1198-camid3_videoid1-seq0", + "1200-camid3_videoid2-seq0", + "1202-camid17_videoid5-seq0", + "1204-camid17_videoid4-seq0", + "1206-camid11_videoid2-seq0", + "1209-camid9_videoid2-seq1", + "1212-camid3_videoid2-seq0", + "1213-camid3_videoid2-seq0", + "1214-camid3_videoid3-seq0", + "1215-camid3_videoid2-seq0", + "1225-camid21_videoid3-seq0", + "1227-camid3_videoid3-seq1", + "1239-camid26_videoid2-seq0", + "1251-camid12_videoid1-seq0", + "1254-camid14_videoid2-seq0", + "1261-camid8_videoid2-seq0", + "1272-camid26_videoid2-seq0", + "1279-camid11_videoid2-seq0", + "1281-camid9_videoid2-seq1", + "1287-camid9_videoid3-seq0", + "1290-camid11_videoid3-seq0", + "1292-camid11_videoid3-seq1", + "1295-camid0_videoid1-seq0", + "1300-camid7_videoid1-seq0", + "1301-camid7_videoid1-seq0", + "1303-camid24_videoid2-seq0", + "1313-camid11_videoid2-seq0", + "1315-camid28_videoid3-seq0", + "1316-camid12_videoid3-seq0", + "1322-camid12_videoid10-seq1", + "1323-camid12_videoid6-seq0", + "1325-camid8_videoid1-seq0", + "1328-camid12_videoid11-seq0", + "1332-camid34_videoid3-seq0", + "1336-camid35_videoid4-seq0", + "1347-camid9_videoid4-seq0", + "1354-camid23_videoid3-seq0", + "1359-camid12_videoid6-seq0", + "1361-camid3_videoid3-seq0", + "1362-camid12_videoid7-seq0", + "1363-camid12_videoid7-seq1", + "1365-camid12_videoid8-seq2", + "1370-camid12_videoid9-seq0", + "1380-camid3_videoid3-seq0", + "1381-camid13_videoid1-seq0", + "1400-camid13_videoid4-seq1", + "1409-camid13_videoid6-seq2", + "1416-camid24_videoid1-seq0", + "1419-camid28_videoid3-seq0", + "1422-camid14_videoid2-seq0", + "1424-camid14_videoid2-seq0", + "1427-camid3_videoid2-seq0", + "1429-camid15_videoid2-seq0", + "1430-camid19_videoid2-seq0", + "1436-camid16_videoid1-seq2", + "1439-camid16_videoid2-seq0", + "1443-camid16_videoid2-seq0", + "1444-camid16_videoid2-seq0", + "1445-camid16_videoid3-seq1", + "1447-camid18_videoid2-seq1", + "1448-camid18_videoid2-seq0", + "1456-camid19_videoid1-seq1", + "1459-camid24_videoid2-seq0", + "1465-camid28_videoid2-seq0", + "1466-camid23_videoid2-seq0", + "1468-camid23_videoid2-seq1", + "1470-camid23_videoid3-seq0", + "1473-camid24_videoid1-seq0", + "1474-camid24_videoid3-seq1", + "1476-camid38_videoid4-seq0", + "1479-camid22_videoid2-seq0", + "1484-camid3_videoid4-seq1", + "1487-camid28_videoid2-seq0", + "1490-camid11_videoid2-seq1", + "1491-camid0_videoid1-seq0", + "1492-camid23_videoid1-seq0", + "1493-camid0_videoid1-seq0", + "1506-camid16_videoid1-seq0", + "1519-camid3_videoid2-seq1", + "1522-camid0_videoid3-seq0", + "1527-camid12_videoid1-seq0", + "1530-camid0_videoid1-seq0", + "1532-camid17_videoid3-seq0", + "1533-camid0_videoid1-seq0", + "1534-camid2_videoid2-seq0", + "1537-camid23_videoid1-seq0", + "1540-camid3_videoid2-seq0", + "1542-camid26_videoid1-seq0", + "1547-camid14_videoid2-seq0", + "1549-camid14_videoid2-seq0", + "1557-camid18_videoid2-seq0", + "1559-camid0_videoid2-seq0", + "1562-camid19_videoid2-seq0", + "1567-camid15_videoid2-seq0", + "1570-camid9_videoid2-seq0", + "1572-camid7_videoid2-seq0", + "1574-camid3_videoid2-seq0", + "1577-camid0_videoid2-seq0", + "1580-camid0_videoid2-seq0", + "1584-camid4_videoid2-seq0", + "1586-camid2_videoid2-seq0", + "1588-camid9_videoid3-seq0", + "1589-camid0_videoid3-seq0", + "1596-camid0_videoid3-seq0", + "1598-camid0_videoid3-seq0", + "1603-camid10_videoid3-seq0", + "1618-camid0_videoid3-seq0", + "1621-camid0_videoid3-seq0", + "1623-camid3_videoid3-seq0", + "1629-camid16_videoid3-seq0", + "1630-camid19_videoid1-seq0", + "1640-camid3_videoid4-seq0", + "1646-camid1_videoid1-seq0", + "1648-camid1_videoid1-seq0", + "1659-camid15_videoid4-seq0", + "1660-camid23_videoid4-seq1", + "1663-camid11_videoid2-seq0", + "1677-camid2_videoid2-seq0", + "1684-camid23_videoid1-seq0", + "1687-camid3_videoid1-seq0", + "1690-camid3_videoid2-seq0", + "1695-camid14_videoid2-seq0", + "1698-camid10_videoid2-seq0", + "1701-camid3_videoid3-seq0", + "1706-camid14_videoid2-seq0", + "1707-camid32_videoid4-seq0", + "1712-camid10_videoid1-seq0", + "1714-camid5_videoid1-seq0", + "1717-camid5_videoid2-seq0", + "1718-camid6_videoid4-seq0", + "1720-camid8_videoid2-seq0", + "1729-camid24_videoid3-seq1", + "1731-camid5_videoid3-seq0", + "1736-camid5_videoid3-seq0", + "1737-camid28_videoid2-seq0", + "1738-camid9_videoid1-seq0", + "1740-camid8_videoid4-seq0", + "1744-camid15_videoid3-seq0", + "1747-camid6_videoid4-seq1", + "1751-camid22_videoid3-seq0", + "1761-camid4_videoid2-seq0", + "1762-camid4_videoid2-seq0", + "1777-camid8_videoid2-seq0", + "1778-camid28_videoid3-seq0", + "1779-camid8_videoid2-seq0", + "1785-camid8_videoid2-seq1", + "1787-camid30_videoid3-seq0", + "1792-camid12_videoid3-seq0", + "1798-camid30_videoid1-seq1", + "1802-camid24_videoid1-seq0", + "1804-camid23_videoid1-seq0", + "1809-camid9_videoid1-seq1", + "1818-camid9_videoid3-seq0", + "1820-camid9_videoid4-seq0", + "1825-camid4_videoid4-seq0", + "1829-camid12_videoid3-seq0", + "1830-camid22_videoid4-seq0", + "1831-camid7_videoid2-seq0", + "1832-camid7_videoid2-seq0", + "1834-camid15_videoid3-seq0", + "1851-camid12_videoid3-seq0", + "1852-camid8_videoid1-seq0", + "1854-camid19_videoid2-seq0", + "1856-camid28_videoid4-seq0", + "1859-camid13_videoid1-seq0", + "1863-camid13_videoid1-seq1", + "1870-camid13_videoid2-seq1", + "1872-camid13_videoid3-seq0", + "1873-camid13_videoid3-seq0", + "1875-camid13_videoid2-seq1", + "1879-camid13_videoid2-seq0", + "1880-camid13_videoid2-seq0", + "1884-camid13_videoid3-seq0", + "1886-camid8_videoid2-seq0", + "1887-camid13_videoid3-seq1", + "1896-camid13_videoid6-seq0", + "1899-camid13_videoid6-seq0", + "1907-camid13_videoid6-seq1", + "1911-camid13_videoid7-seq0", + "1915-camid14_videoid1-seq0", + "1917-camid21_videoid1-seq0", + "1922-camid14_videoid1-seq0", + "1925-camid24_videoid3-seq1", + "1929-camid14_videoid3-seq0", + "1933-camid21_videoid2-seq0", + "1938-camid15_videoid1-seq1", + "1940-camid18_videoid2-seq0", + "1941-camid15_videoid3-seq0", + "1945-camid9_videoid1-seq0", + "1946-camid34_videoid3-seq0", + "1947-camid16_videoid3-seq0", + "1963-camid35_videoid2-seq1", + "1967-camid22_videoid1-seq0", + "1969-camid22_videoid3-seq0", + "1970-camid22_videoid3-seq0", + "1973-camid9_videoid2-seq1", + "1974-camid21_videoid2-seq1", + "1975-camid28_videoid3-seq0", + "1976-camid23_videoid1-seq1", + "1982-camid26_videoid3-seq0", + "1983-camid26_videoid3-seq0", + "1986-camid23_videoid3-seq0", + "0094-camid24_videoid1-seq0", + "0750-camid9_videoid1-seq0", + "1334-camid0_videoid1-seq1", + "3070-camid4_videoid2-seq0", + "3072-camid7_videoid3-seq0", + "3080-camid0_videoid3-seq0", + "3083-camid30_videoid3-seq0", + "3085-camid0_videoid1-seq1", + "3088-camid29_videoid3-seq0", + "3089-camid5_videoid3-seq0", + "3090-camid5_videoid3-seq1", + "3095-camid30_videoid2-seq0", + "3097-camid25_videoid2-seq0", + "3103-camid26_videoid4-seq0", + "3104-camid12_videoid2-seq0", + "3107-camid28_videoid3-seq0", + "3110-camid26_videoid1-seq3", + "3114-camid8_videoid2-seq1", + "3122-camid8_videoid3-seq0", + "3125-camid10_videoid3-seq0", + "3128-camid24_videoid4-seq0", + "3130-camid7_videoid1-seq0", + "3133-camid16_videoid2-seq1", + "3135-camid2_videoid3-seq0", + "3145-camid9_videoid2-seq0", + "3147-camid7_videoid2-seq0", + "3152-camid9_videoid2-seq0", + "3153-camid29_videoid2-seq0", + "3160-camid9_videoid2-seq0", + "3161-camid0_videoid1-seq0", + "3162-camid7_videoid3-seq0", + "3168-camid23_videoid3-seq0", + "3173-camid0_videoid2-seq0", + "3181-camid10_videoid3-seq0", + "3182-camid10_videoid3-seq1", + "3184-camid10_videoid3-seq0", + "3186-camid7_videoid1-seq0", + "3191-camid22_videoid2-seq0", + "3192-camid14_videoid2-seq0", + "3202-camid9_videoid3-seq0", + "3208-camid26_videoid2-seq0", + "3211-camid28_videoid2-seq0", + "3212-camid24_videoid3-seq0", + "3214-camid11_videoid3-seq1", + "3221-camid26_videoid3-seq0", + "3227-camid14_videoid3-seq1", + "3228-camid19_videoid1-seq0", + "3233-camid21_videoid4-seq0", + "3235-camid18_videoid4-seq0", + "3243-camid15_videoid3-seq0", + "3246-camid1_videoid2-seq0", + "3249-camid0_videoid2-seq0", + "3250-camid16_videoid3-seq0", + "3256-camid19_videoid1-seq0", + "3258-camid9_videoid2-seq0", + "3259-camid15_videoid1-seq0", + "3261-camid0_videoid2-seq0", + "3266-camid19_videoid1-seq0", + "3268-camid28_videoid2-seq0", + "3271-camid6_videoid4-seq2", + "3280-camid30_videoid3-seq0", + "3287-camid24_videoid2-seq0", + "3292-camid28_videoid2-seq0", + "3293-camid28_videoid2-seq0", + "3299-camid3_videoid1-seq0", + "3305-camid10_videoid2-seq0", + "3311-camid18_videoid3-seq0", + "3316-camid30_videoid1-seq0", + "3317-camid11_videoid2-seq0", + "3325-camid0_videoid3-seq0", + "3326-camid7_videoid1-seq0", + "3335-camid7_videoid1-seq0", + "3342-camid9_videoid2-seq0", + "3344-camid2_videoid4-seq0", + "3346-camid0_videoid3-seq0", + "3347-camid0_videoid1-seq0", + "3357-camid12_videoid1-seq0", + "3359-camid24_videoid1-seq0", + "3366-camid3_videoid1-seq0", + "3371-camid0_videoid1-seq0", + "3375-camid28_videoid1-seq0", + "3382-camid0_videoid2-seq0", + "3384-camid6_videoid3-seq0", + "3387-camid2_videoid3-seq0", + "3388-camid3_videoid2-seq0", + "3389-camid2_videoid3-seq0", + "3393-camid11_videoid2-seq0", + "3405-camid3_videoid2-seq0", + "3407-camid3_videoid4-seq0", + "3417-camid25_videoid2-seq0", + "3419-camid28_videoid2-seq0", + "3422-camid0_videoid2-seq0", + "3423-camid0_videoid2-seq0", + "3424-camid0_videoid2-seq0", + "3427-camid6_videoid4-seq0", + "3429-camid12_videoid2-seq0", + "3434-camid20_videoid1-seq0", + "3440-camid23_videoid2-seq1", + "3442-camid14_videoid3-seq0", + "3444-camid3_videoid3-seq0", + "3445-camid0_videoid3-seq0", + "3447-camid0_videoid3-seq0", + "3449-camid0_videoid3-seq0", + "3452-camid3_videoid3-seq0", + "3462-camid24_videoid3-seq0", + "3465-camid2_videoid1-seq0", + "3466-camid1_videoid1-seq0", + "3471-camid1_videoid1-seq0", + "3474-camid16_videoid1-seq0", + "3481-camid2_videoid2-seq0", + "3483-camid28_videoid2-seq1", + "3489-camid1_videoid2-seq0", + "3498-camid23_videoid1-seq0", + "3501-camid14_videoid1-seq0", + "3502-camid21_videoid3-seq0", + "3506-camid16_videoid2-seq0", + "3515-camid19_videoid2-seq2", + "3517-camid8_videoid3-seq0", + "3523-camid24_videoid2-seq0", + "3526-camid3_videoid1-seq1", + "3532-camid3_videoid1-seq0", + "3536-camid23_videoid2-seq1", + "3543-camid0_videoid3-seq1", + "3548-camid3_videoid3-seq0", + "3551-camid3_videoid3-seq0", + "3552-camid15_videoid3-seq0", + "3555-camid0_videoid3-seq0", + "3556-camid11_videoid4-seq0", + "3558-camid3_videoid4-seq0", + "3560-camid5_videoid1-seq1", + "3564-camid24_videoid1-seq1", + "3565-camid15_videoid3-seq0", + "3568-camid5_videoid1-seq0", + "3569-camid22_videoid1-seq0", + "3571-camid24_videoid2-seq0", + "3580-camid9_videoid3-seq1", + "3581-camid6_videoid6-seq0", + "3584-camid6_videoid5-seq1", + "3588-camid12_videoid3-seq0", + "3595-camid29_videoid4-seq0", + "3598-camid7_videoid1-seq0", + "3600-camid6_videoid1-seq0", + "3603-camid6_videoid2-seq0", + "3610-camid28_videoid2-seq0", + "3616-camid23_videoid3-seq0", + "3619-camid6_videoid6-seq0", + "3620-camid30_videoid4-seq0", + "3621-camid11_videoid1-seq0", + "3623-camid8_videoid1-seq0", + "3625-camid8_videoid2-seq0", + "3635-camid4_videoid4-seq0", + "3638-camid7_videoid1-seq0", + "3641-camid1_videoid3-seq0", + "3643-camid22_videoid1-seq0", + "3646-camid12_videoid1-seq0", + "3654-camid0_videoid2-seq0", + "3655-camid0_videoid1-seq0", + "3656-camid30_videoid2-seq0", + "3665-camid24_videoid3-seq0", + "3666-camid29_videoid3-seq0", + "3667-camid8_videoid3-seq1", + "3669-camid11_videoid3-seq0", + "3676-camid28_videoid4-seq0", + "3677-camid26_videoid3-seq0", + "3683-camid19_videoid1-seq0", + "3686-camid11_videoid1-seq0", + "3690-camid26_videoid1-seq0", + "3698-camid11_videoid4-seq0", + "3699-camid15_videoid3-seq0", + "3700-camid7_videoid1-seq0", + "3703-camid7_videoid3-seq0", + "3707-camid3_videoid3-seq0", + "3712-camid9_videoid3-seq0", + "3714-camid23_videoid4-seq0", + "3721-camid10_videoid1-seq0", + "3723-camid11_videoid1-seq0", + "3727-camid24_videoid1-seq0", + "3729-camid14_videoid2-seq0", + "3736-camid3_videoid3-seq0", + "3741-camid11_videoid3-seq0", + "3745-camid0_videoid3-seq0", + "3746-camid11_videoid4-seq0", + "3749-camid12_videoid1-seq0", + "3750-camid20_videoid1-seq0", + "3758-camid29_videoid3-seq0", + "3763-camid29_videoid1-seq1", + "3773-camid24_videoid3-seq0", + "3775-camid9_videoid3-seq0", + "3779-camid23_videoid4-seq0", + "3782-camid15_videoid1-seq0", + "3785-camid30_videoid2-seq0", + "3790-camid2_videoid2-seq0", + "3796-camid30_videoid1-seq0", + "3798-camid19_videoid2-seq0", + "3800-camid19_videoid2-seq1", + "3804-camid19_videoid2-seq1", + "3806-camid20_videoid2-seq0", + "3811-camid22_videoid4-seq0", + "3817-camid28_videoid1-seq0", + "3826-camid23_videoid2-seq0", + "3828-camid25_videoid3-seq0", + "3831-camid26_videoid3-seq0", + "3832-camid14_videoid4-seq0", + "3838-camid0_videoid4-seq0", + "3853-camid0_videoid1-seq0", + "3858-camid30_videoid2-seq0", + "3863-camid10_videoid3-seq0", + "3869-camid1_videoid1-seq0", + "3871-camid18_videoid1-seq0", + "3879-camid3_videoid1-seq0", + "3881-camid28_videoid3-seq0", + "3883-camid16_videoid2-seq0", + "3886-camid16_videoid2-seq0", + "3888-camid19_videoid1-seq0", + "3890-camid22_videoid2-seq0", + "3899-camid1_videoid2-seq0", + "3907-camid2_videoid1-seq0", + "3909-camid28_videoid1-seq1", + "3914-camid3_videoid3-seq0", + "3917-camid2_videoid1-seq0", + "3919-camid2_videoid1-seq0", + "3921-camid1_videoid4-seq0", + "3926-camid19_videoid1-seq0", + "3933-camid2_videoid1-seq0", + "3938-camid16_videoid1-seq2", + "3940-camid2_videoid3-seq0", + "3943-camid3_videoid2-seq0", + "3945-camid10_videoid3-seq0", + "3953-camid2_videoid3-seq0", + "3955-camid2_videoid3-seq0", + "3962-camid3_videoid1-seq0", + "3963-camid3_videoid2-seq0", + "3964-camid0_videoid1-seq0", + "3966-camid3_videoid1-seq0", + "3967-camid3_videoid1-seq0", + "3974-camid24_videoid1-seq0", + "3976-camid0_videoid2-seq0", + "3978-camid3_videoid2-seq0", + "3979-camid14_videoid2-seq0", + "3982-camid2_videoid1-seq0", + "3993-camid3_videoid3-seq1", + "1999-camid0_videoid1-seq0", + "2001-camid14_videoid1-seq0", + "2004-camid3_videoid3-seq1", + "2005-camid3_videoid3-seq2", + "2014-camid3_videoid4-seq0", + "2017-camid11_videoid3-seq0", + "2023-camid4_videoid2-seq0", + "2026-camid6_videoid1-seq0", + "2027-camid12_videoid2-seq0", + "2031-camid5_videoid1-seq0", + "2037-camid5_videoid3-seq0", + "2041-camid8_videoid2-seq0", + "2043-camid11_videoid3-seq1", + "2049-camid21_videoid2-seq0", + "2051-camid21_videoid3-seq0", + "2054-camid4_videoid2-seq0", + "2055-camid0_videoid2-seq0", + "2058-camid21_videoid3-seq0", + "2061-camid15_videoid1-seq0", + "2063-camid28_videoid1-seq0", + "2070-camid4_videoid2-seq1", + "2077-camid8_videoid2-seq0", + "2079-camid7_videoid2-seq1", + "2085-camid20_videoid2-seq0", + "2089-camid8_videoid2-seq0", + "2092-camid8_videoid2-seq0", + "2093-camid14_videoid3-seq1", + "2094-camid20_videoid3-seq0", + "2095-camid0_videoid3-seq0", + "2097-camid2_videoid3-seq0", + "2098-camid20_videoid1-seq0", + "2112-camid7_videoid2-seq0", + "2113-camid9_videoid2-seq0", + "2122-camid9_videoid2-seq0", + "2127-camid1_videoid2-seq0", + "2131-camid24_videoid3-seq0", + "2133-camid11_videoid3-seq0", + "2134-camid7_videoid3-seq3", + "2140-camid19_videoid2-seq0", + "2161-camid0_videoid2-seq0", + "2163-camid18_videoid4-seq0", + "2164-camid28_videoid1-seq0", + "2167-camid28_videoid1-seq0", + "2172-camid11_videoid1-seq0", + "2173-camid0_videoid4-seq1", + "2175-camid7_videoid3-seq0", + "2182-camid11_videoid3-seq0", + "2185-camid7_videoid1-seq0", + "2187-camid7_videoid4-seq0", + "2191-camid21_videoid1-seq0", + "2192-camid16_videoid4-seq0", + "2197-camid11_videoid2-seq0", + "2202-camid11_videoid3-seq0", + "2204-camid11_videoid3-seq0", + "2205-camid19_videoid2-seq0", + "2207-camid12_videoid1-seq0", + "2208-camid12_videoid11-seq0", + "2209-camid0_videoid3-seq0", + "2222-camid12_videoid12-seq0", + "2229-camid12_videoid12-seq0", + "2230-camid21_videoid1-seq1", + "2232-camid26_videoid2-seq0", + "2235-camid19_videoid2-seq0", + "2241-camid12_videoid8-seq0", + "2245-camid12_videoid9-seq3", + "2246-camid18_videoid1-seq0", + "2250-camid28_videoid3-seq0", + "2257-camid15_videoid3-seq0", + "2261-camid14_videoid3-seq0", + "2263-camid24_videoid3-seq0", + "2266-camid20_videoid1-seq0", + "2268-camid15_videoid2-seq0", + "2270-camid7_videoid1-seq0", + "2275-camid16_videoid1-seq0", + "2276-camid0_videoid2-seq1", + "2287-camid16_videoid2-seq0", + "2300-camid2_videoid2-seq0", + "2302-camid19_videoid2-seq0", + "2308-camid23_videoid4-seq0", + "2309-camid24_videoid2-seq0", + "2314-camid15_videoid2-seq0", + "2316-camid21_videoid1-seq1", + "2322-camid23_videoid1-seq1", + "2325-camid23_videoid2-seq0", + "2331-camid19_videoid1-seq0", + "2335-camid28_videoid3-seq0", + "2336-camid23_videoid3-seq0", + "2342-camid25_videoid2-seq0", + "2343-camid25_videoid2-seq0", + "2345-camid0_videoid3-seq0", + "2354-camid29_videoid2-seq0", + "2355-camid11_videoid3-seq1", + "2370-camid0_videoid2-seq0", + "2374-camid9_videoid1-seq0", + "2378-camid28_videoid1-seq0", + "2384-camid6_videoid2-seq0", + "2395-camid5_videoid1-seq0", + "2399-camid28_videoid1-seq0", + "2401-camid2_videoid3-seq0", + "2413-camid0_videoid1-seq0", + "2416-camid3_videoid1-seq0", + "2417-camid15_videoid1-seq0", + "2420-camid0_videoid1-seq0", + "2421-camid0_videoid3-seq0", + "2424-camid12_videoid2-seq0", + "2426-camid29_videoid3-seq0", + "2428-camid3_videoid2-seq0", + "2434-camid0_videoid3-seq0", + "2436-camid3_videoid2-seq0", + "2443-camid0_videoid2-seq0", + "2444-camid12_videoid3-seq0", + "2445-camid7_videoid2-seq0", + "2448-camid0_videoid2-seq0", + "2449-camid0_videoid2-seq0", + "2456-camid22_videoid2-seq0", + "2458-camid14_videoid2-seq0", + "2467-camid0_videoid3-seq1", + "2468-camid0_videoid2-seq0", + "2474-camid3_videoid3-seq0", + "2481-camid3_videoid3-seq0", + "2487-camid25_videoid3-seq0", + "2494-camid0_videoid3-seq0", + "2504-camid3_videoid3-seq0", + "2508-camid20_videoid4-seq0", + "2519-camid19_videoid1-seq0", + "2524-camid16_videoid1-seq0", + "2526-camid1_videoid1-seq0", + "2534-camid22_videoid1-seq0", + "2535-camid16_videoid2-seq0", + "2538-camid1_videoid2-seq0", + "2539-camid1_videoid3-seq0", + "2540-camid19_videoid2-seq0", + "2541-camid16_videoid3-seq0", + "2543-camid1_videoid3-seq0", + "2546-camid21_videoid3-seq0", + "2549-camid15_videoid3-seq0", + "2551-camid1_videoid4-seq0", + "2555-camid9_videoid1-seq1", + "2559-camid2_videoid1-seq0", + "2573-camid24_videoid2-seq0", + "2581-camid15_videoid3-seq0", + "2586-camid14_videoid4-seq0", + "2587-camid3_videoid1-seq0", + "2591-camid3_videoid1-seq0", + "2596-camid24_videoid1-seq0", + "2602-camid7_videoid1-seq0", + "2603-camid24_videoid2-seq0", + "2611-camid19_videoid2-seq0", + "2612-camid9_videoid3-seq0", + 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"2769-camid9_videoid1-seq0", + "2770-camid7_videoid1-seq0", + "2773-camid30_videoid1-seq0", + "2782-camid9_videoid1-seq0", + "2784-camid9_videoid1-seq1", + "2785-camid9_videoid2-seq0", + "2791-camid19_videoid1-seq0", + "2795-camid16_videoid1-seq0", + "2796-camid22_videoid2-seq1", + "2797-camid9_videoid2-seq0", + "2803-camid19_videoid2-seq0", + "2818-camid9_videoid3-seq0", + "2820-camid28_videoid4-seq1", + "2824-camid9_videoid4-seq1", + "2827-camid26_videoid1-seq0", + "2831-camid2_videoid2-seq0", + "2834-camid24_videoid1-seq0", + "2835-camid7_videoid2-seq0", + "2839-camid7_videoid2-seq0", + "2846-camid2_videoid2-seq0", + "2852-camid1_videoid2-seq0", + "2855-camid2_videoid1-seq0", + "2868-camid12_videoid2-seq0", + "2876-camid23_videoid1-seq0", + "2884-camid23_videoid3-seq0", + "2892-camid14_videoid4-seq0", + "2893-camid26_videoid3-seq0", + "2894-camid20_videoid4-seq0", + "2896-camid15_videoid1-seq0", + "2900-camid2_videoid2-seq0", + "2901-camid24_videoid2-seq0", + 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The original dataset can be found [here](https://gait3d.github.io/). The original dataset is not publicly available. You need to request access to the dataset in order to download it. This README explains how to extract the original dataset and convert it to a format suitable for OpenGait. +## Data Preparation +https://github.com/Gait3D/Gait3D-Benchmark#data-preparation +## Data Pretreatment +```python +python datasets/pretreatment.py --input_path 'Gait3D/2D_Silhouettes' --output_path 'Gait3D-sils-64-64-pkl' +python datasets/pretreatment_smpl.py --input_path 'Gait3D/3D_SMPLs' --output_path 'Gait3D-smpls-pkl' + +(optional) python datasets/pretreatment.py --input_path 'Gait3D/2D_Silhouettes' --img_size 128 --output_path 'Gait3D-sils-128-128-pkl' + +python datasets/Gait3D/merge_two_modality.py --sils_path 'Gait3D-sils-64-64-pkl' --smpls_path 'Gait3D-smpls-pkl' --output_path 'Gait3D-merged-pkl' --link 'hard' +``` + +## Train +### Baseline model: +`CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./configs/baseline/baseline_Gait3D.yaml --phase train` +### SMPLGait model: +`CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./configs/smplgait/smplgait.yaml --phase train` + +## Citation +If you use this dataset in your research, please cite the following paper: +``` +@inproceedings{zheng2022gait3d, +title={Gait Recognition in the Wild with Dense 3D Representations and A Benchmark}, +author={Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei}, +booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, +year={2022} +} +``` + +## Acknowledgements +This dataset was collected by the [Zheng at. al.](https://gait3d.github.io/). The pre-processing instructions are based on (https://github.com/Gait3D/Gait3D-Benchmark). \ No newline at end of file diff --git a/datasets/Gait3D/merge_two_modality.py b/datasets/Gait3D/merge_two_modality.py new file mode 100644 index 0000000..1c030fa --- /dev/null +++ b/datasets/Gait3D/merge_two_modality.py @@ -0,0 +1,46 @@ +import os +import argparse +from pathlib import Path +import shutil + + +def merge(sils_path, smpls_path, output_path, link): + if link == 'hard': + link_method = os.link + elif link == 'soft': + link_method = os.symlink + else: + link_method = shutil.copyfile + for _id in os.listdir(sils_path): + id_path = os.path.join(sils_path, _id) + for _type in os.listdir(id_path): + type_path = os.path.join(id_path, _type) + for _view in os.listdir(type_path): + view_path = os.path.join(type_path, _view) + for _seq in os.listdir(view_path): + sils_seq_path = os.path.join(view_path, _seq) + smpls_seq_path = os.path.join( + smpls_path, _id, _type, _view, _seq) + output_seq_path = os.path.join(output_path, _id, _type, _view) + os.makedirs(output_seq_path, exist_ok=True) + link_method(sils_seq_path, os.path.join( + output_seq_path, "sils-"+_seq)) + link_method(smpls_seq_path, os.path.join( + output_seq_path, "smpls-"+_seq)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Gait3D dataset mergence.') + parser.add_argument('--sils_path', default='', type=str, + help='Root path of raw silhs dataset.') + parser.add_argument('--smpls_path', default='', type=str, + help='Root path of raw smpls dataset.') + parser.add_argument('-o', '--output_path', default='', + type=str, help='Output path of pickled dataset.') + parser.add_argument('-l', '--link', default='hard', type=str, + choices=['hard', 'soft', 'copy'], help='Link type of output data.') + args = parser.parse_args() + + merge(sils_path=Path(args.sils_path), smpls_path=Path( + args.smpls_path), output_path=Path(args.output_path), link=args.link) + \ No newline at end of file diff --git a/datasets/HID/README.md b/datasets/HID/README.md index 2eb3367..a13b2de 100644 --- a/datasets/HID/README.md +++ b/datasets/HID/README.md @@ -6,19 +6,19 @@ This is the official support for competition of [Human Identification at a Dista Download the raw dataset from the [official link](http://hid2022.iapr-tc4.org/). You will get three compressed files, i.e. `train.tar`, `HID2022_test_gallery.zip` and `HID2022_test_probe.zip`. After unpacking these three files, run this command: ```shell -python misc/HID/pretreatment_HID.py --input_train_path="train" --input_gallery_path="HID2022_test_gallery" --input_probe_path="HID2022_test_probe" --output_path="HID-128-pkl" +python datasets/HID/pretreatment_HID.py --input_train_path="train" --input_gallery_path="HID2022_test_gallery" --input_probe_path="HID2022_test_probe" --output_path="HID-128-pkl" ``` ## Train the dataset -Modify the `dataset_root` in `./misc/HID/baseline_hid.yaml`, and then run this command: +Modify the `dataset_root` in `configs/baseline/baseline_hid.yaml`, and then run this command: ```shell -CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase train +CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/baseline/baseline_hid.yaml --phase train ``` You can also download the [trained model](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) and place it in `output` after unzipping. ## Get the submission file ```shell -CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase test +CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/baseline/baseline_hid.yaml --phase test ``` The result will be generated in your working directory. diff --git a/docs/1.model_zoo.md b/docs/1.model_zoo.md index 38d92fc..7c35788 100644 --- a/docs/1.model_zoo.md +++ b/docs/1.model_zoo.md @@ -39,16 +39,18 @@ | [GaitPart(CVPR2020)](http://home.ustc.edu.cn/~saihui/papers/cvpr2020_gaitpart.pdf) | 47.6 | [gaitpart.yaml](../configs/gaitpart/gaitpart_GREW.yaml) | 64x44 | - | - | | [GaitGL(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf) | 41.5 | [gaitgl.yaml](../configs/gaitgl/gaitgl_GREW.yaml) | 64x44 | - | - | | [GaitGL(BNNeck)(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf) | 51.7 | [gaitgl.yaml](../configs/gaitgl/gaitgl_GREW_BNNeck.yaml) | 64x44 | - | - | -| [GaitBase](https://arxiv.org/abs/2211.06597) | 60.1 | -| 64x44 | - | - | +| [GaitBase](https://arxiv.org/abs/2211.06597) | 60.1 | - | 64x44 | - | - | ## [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark) -| Model | `Rank@1` | Configuration | Input Size | Inference Time | Model Size | -| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------: | :-----------: | :--------: | :------------: | :--------: | -| [GaitSet(AAAI2019)](https://arxiv.org/pdf/1811.06186.pdf) | 36.7 | - | 64x44 | - | - | -| [GaitPart(CVPR2020)](http://home.ustc.edu.cn/~saihui/papers/cvpr2020_gaitpart.pdf) | 28.2 | - | 64x44 | - | - | -| [GaitGL(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf) | 29.7 | - | 64x44 | - | - | -| [GaitBase](https://arxiv.org/abs/2211.06597) | 65.6 | - | 64x44 | - | - | +| Model | `Rank@1` | Configuration | Input Size | Input modality | +| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------: | :-----------: | :--------: | :--------:| +| Baseline | 46.9 | [baseling_Gait3D.yaml](../configs/baseline/baseline_Gait3D.yaml) | 64x44 | Silhouette | +| [GaitSet(AAAI2019)](https://arxiv.org/pdf/1811.06186.pdf) | - | - | 64x44 | Silhouette | +| [GaitPart(CVPR2020)](http://home.ustc.edu.cn/~saihui/papers/cvpr2020_gaitpart.pdf) | - | - | 64x44 | Silhouette | +| [GaitGL(ICCV2021)](https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf) | - | - | 64x44 | Silhouette | +| [SMPLGait(CVPR 2022)](https://gait3d.github.io/) | 48.3(46.3) | [smplgait.yaml](../configs/smplgait/smplgait.yaml) | 64x44 | Silhouette + RGB| +| [GaitBase](https://arxiv.org/abs/2211.06597) | 65.6 | - | 64x44 | Silhouette | ------------------------------------------ diff --git a/opengait/evaluation/evaluator.py b/opengait/evaluation/evaluator.py index 2004eb5..411b00c 100644 --- a/opengait/evaluation/evaluator.py +++ b/opengait/evaluation/evaluator.py @@ -3,7 +3,7 @@ from time import strftime, localtime import numpy as np from utils import get_msg_mgr, mkdir -from .metric import mean_iou, cuda_dist, compute_ACC_mAP +from .metric import mean_iou, cuda_dist, compute_ACC_mAP, evaluate_rank from .re_rank import re_ranking @@ -225,3 +225,43 @@ def evaluate_segmentation(data, dataset): miou = mean_iou(pred, labels) get_msg_mgr().log_info('mIOU: %.3f' % (miou.mean())) return {"scalar/test_accuracy/mIOU": miou} + +def evaluate_Gait3D(data, conf, metric='euc'): + msg_mgr = get_msg_mgr() + + features, labels, cams, time_seqs = data['embeddings'], data['labels'], data['types'], data['views'] + import json + probe_sets = json.load( + open('./datasets/Gait3D/Gait3D.json', 'rb'))['PROBE_SET'] + probe_mask = [] + for id, ty, sq in zip(labels, cams, time_seqs): + if '-'.join([id, ty, sq]) in probe_sets: + probe_mask.append(True) + else: + probe_mask.append(False) + probe_mask = np.array(probe_mask) + + # probe_features = features[:probe_num] + probe_features = features[probe_mask] + # gallery_features = features[probe_num:] + gallery_features = features[~probe_mask] + # probe_lbls = np.asarray(labels[:probe_num]) + # gallery_lbls = np.asarray(labels[probe_num:]) + probe_lbls = np.asarray(labels)[probe_mask] + gallery_lbls = np.asarray(labels)[~probe_mask] + + results = {} + msg_mgr.log_info(f"The test metric you choose is {metric}.") + dist = cuda_dist(probe_features, gallery_features, metric).cpu().numpy() + cmc, all_AP, all_INP = evaluate_rank(dist, probe_lbls, gallery_lbls) + + mAP = np.mean(all_AP) + mINP = np.mean(all_INP) + for r in [1, 5, 10]: + results['scalar/test_accuracy/Rank-{}'.format(r)] = cmc[r - 1] * 100 + results['scalar/test_accuracy/mAP'] = mAP * 100 + results['scalar/test_accuracy/mINP'] = mINP * 100 + + # print_csv_format(dataset_name, results) + msg_mgr.log_info(results) + return results \ No newline at end of file diff --git a/opengait/evaluation/metric.py b/opengait/evaluation/metric.py index f538a2d..627ed62 100644 --- a/opengait/evaluation/metric.py +++ b/opengait/evaluation/metric.py @@ -86,3 +86,73 @@ def compute_ACC_mAP(distmat, q_pids, g_pids, q_views=None, g_views=None, rank=1) mAP = np.mean(all_AP) return ACC, mAP + + +def evaluate_rank(distmat, p_lbls, g_lbls, max_rank=50): + ''' + Copy from https://github.com/Gait3D/Gait3D-Benchmark/blob/72beab994c137b902d826f4b9f9e95b107bebd78/lib/utils/rank.py#L12-L63 + ''' + num_p, num_g = distmat.shape + + if num_g < max_rank: + max_rank = num_g + print('Note: number of gallery samples is quite small, got {}'.format(num_g)) + + indices = np.argsort(distmat, axis=1) + + matches = (g_lbls[indices] == p_lbls[:, np.newaxis]).astype(np.int32) + + # compute cmc curve for each probe + all_cmc = [] + all_AP = [] + all_INP = [] + num_valid_p = 0. # number of valid probe + + for p_idx in range(num_p): + # compute cmc curve + # binary vector, positions with value 1 are correct matches + raw_cmc = matches[p_idx] + if not np.any(raw_cmc): + # this condition is true when probe identity does not appear in gallery + continue + + cmc = raw_cmc.cumsum() + + pos_idx = np.where(raw_cmc == 1) # 返回坐标，此处raw_cmc为一维矩阵，所以返回相当于index + max_pos_idx = np.max(pos_idx) + inp = cmc[max_pos_idx] / (max_pos_idx + 1.0) + all_INP.append(inp) + + cmc[cmc > 1] = 1 + + all_cmc.append(cmc[:max_rank]) + num_valid_p += 1. + + # compute average precision + # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision + num_rel = raw_cmc.sum() + pos_idx = np.where(raw_cmc == 1) # 返回坐标，此处raw_cmc为一维矩阵，所以返回相当于index + max_pos_idx = np.max(pos_idx) + inp = cmc[max_pos_idx] / (max_pos_idx + 1.0) + all_INP.append(inp) + + cmc[cmc > 1] = 1 + + all_cmc.append(cmc[:max_rank]) + num_valid_p += 1. + + # compute average precision + # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision + num_rel = raw_cmc.sum() + tmp_cmc = raw_cmc.cumsum() + tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)] + tmp_cmc = np.asarray(tmp_cmc) * raw_cmc + AP = tmp_cmc.sum() / num_rel + all_AP.append(AP) + + assert num_valid_p > 0, 'Error: all probe identities do not appear in gallery' + + all_cmc = np.asarray(all_cmc).astype(np.float32) + all_cmc = all_cmc.sum(0) / num_valid_p + + return all_cmc, all_AP, all_INP diff --git a/opengait/modeling/models/smplgait.py b/opengait/modeling/models/smplgait.py new file mode 100644 index 0000000..bc808af --- /dev/null +++ b/opengait/modeling/models/smplgait.py @@ -0,0 +1,101 @@ +''' +Modifed from https://github.com/Gait3D/Gait3D-Benchmark/blob/72beab994c137b902d826f4b9f9e95b107bebd78/lib/modeling/models/smplgait.py +''' +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable + +from ..base_model import BaseModel +from ..modules import SetBlockWrapper, HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks + + +class SMPLGait(BaseModel): + def __init__(self, cfgs, is_training): + super().__init__(cfgs, is_training) + + def build_network(self, model_cfg): + # Baseline + self.Backbone = self.get_backbone(model_cfg['backbone_cfg']) + self.Backbone = SetBlockWrapper(self.Backbone) + self.FCs = SeparateFCs(**model_cfg['SeparateFCs']) + self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks']) + self.TP = PackSequenceWrapper(torch.max) + self.HPP = HorizontalPoolingPyramid(bin_num=model_cfg['bin_num']) + + # for SMPL + self.fc1 = nn.Linear(85, 128) + self.fc2 = nn.Linear(128, 256) + self.fc3 = nn.Linear(256, 256) + self.bn1 = nn.BatchNorm1d(128) + self.bn2 = nn.BatchNorm1d(256) + self.bn3 = nn.BatchNorm1d(256) + self.dropout2 = nn.Dropout(p=0.2) + self.dropout3 = nn.Dropout(p=0.2) + + def forward(self, inputs): + ipts, labs, _, _, seqL = inputs + + sils = ipts[0] # [n, s, h, w] + smpls = ipts[1] # [n, s, d] + + # extract SMPL features + n, s, d = smpls.size() + sps = smpls.view(-1, d) + del smpls + + sps = F.relu(self.bn1(self.fc1(sps))) + sps = F.relu(self.bn2(self.dropout2(self.fc2(sps)))) # (B, 256) + sps = F.relu(self.bn3(self.dropout3(self.fc3(sps)))) # (B, 256) + sps = sps.reshape(n, 1, s, 16, 16) + iden = Variable(torch.eye(16)).unsqueeze( + 0).repeat(n, 1, s, 1, 1) # [n, 1, s, 16, 16] + if sps.is_cuda: + iden = iden.cuda() + sps_trans = sps + iden # [n, 1, s, 16, 16] + + if len(sils.size()) == 4: + sils = sils.unsqueeze(1) + + del ipts + outs = self.Backbone(sils) # [n, c, s, h, w] + outs_n, outs_c, outs_s, outs_h, outs_w = outs.size() + + zero_tensor = Variable(torch.zeros( + (outs_n, outs_c, outs_s, outs_h, outs_h-outs_w))) + if outs.is_cuda: + zero_tensor = zero_tensor.cuda() + # [n, s, c, h, h] [n, s, c, 16, 16] + outs = torch.cat([outs, zero_tensor], -1) + outs = outs.reshape(outs_n*outs_c*outs_s, outs_h, + outs_h) # [n*c*s, 16, 16] + + sps = sps_trans.repeat(1, outs_c, 1, 1, 1).reshape( + outs_n * outs_c * outs_s, 16, 16) + + outs_trans = torch.bmm(outs, sps) + outs_trans = outs_trans.reshape(outs_n, outs_c, outs_s, outs_h, outs_h) + + # Temporal Pooling, TP + outs_trans = self.TP(outs_trans, seqL, options={"dim": 2})[ + 0] # [n, c, h, w] + # Horizontal Pooling Matching, HPM + feat = self.HPP(outs_trans) # [n, c, p] + embed_1 = self.FCs(feat) # [n, c, p] + + embed_2, logits = self.BNNecks(embed_1) # [n, c, p] + + n, _, s, h, w = sils.size() + retval = { + 'training_feat': { + 'triplet': {'embeddings': embed_1, 'labels': labs}, + 'softmax': {'logits': logits, 'labels': labs} + }, + 'visual_summary': { + 'image/sils': sils.view(n*s, 1, h, w) + }, + 'inference_feat': { + 'embeddings': embed_1 + } + } + return retval