Support SUSTech1K

README.md

@@ -3,11 +3,14 @@
 <div align="center"><img src="./assets/nm.gif" width = "100" height = "100" alt="nm" /><img src="./assets/bg.gif" width = "100" height = "100" alt="bg" /><img src="./assets/cl.gif" width = "100" height = "100" alt="cl" /></div>
 
 ------------------------------------------
+📣📣📣 **[*SUSTech1K*](https://lidargait.github.io) relseased, pls checking the [tutorial](datasets/SUSTech1K/README.md).** 📣📣📣
+
 🎉🎉🎉 **[*OpenGait*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf) has been accpected by CVPR2023 as a highlight paper！** 🎉🎉🎉
 
 OpenGait is a flexible and extensible gait recognition project provided by the [Shiqi Yu Group](https://faculty.sustech.edu.cn/yusq/) and supported in part by [WATRIX.AI](http://www.watrix.ai).
 
 ## What's New
+- **[July 2023]** [SUSTech1K](datasets/SUSTech1K/README.md) is released and supported by OpenGait.
 - **[May 2023]** A real gait recognition system [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) provided by [Dongyang Jin](https://github.com/jdyjjj) is avaliable.
 - [Apr 2023] [CASIA-E](datasets/CASIA-E/README.md) is supported by OpenGait.
 - [Feb 2023] [HID 2023 competition](https://hid2023.iapr-tc4.org/) is open, welcome to participate. Additionally, tutorial for the competition has been updated in [datasets/HID/](./datasets/HID).
@@ -50,7 +53,7 @@ Results and models are available in the [model zoo](docs/1.model_zoo.md).
 ## Authors:
 **Open Gait Team (OGT)**
 - [Chao Fan (樊超)](https://chaofan996.github.io), [email protected]
-- [Chuanfu Shen (沈川福)](https://faculty.sustech.edu.cn/?p=95396&tagid=yusq&cat=2&iscss=1&snapid=1&orderby=date), [email protected]
+- [Chuanfu Shen (沈川福)](https://chuanfushen.github.io), [email protected]
 - [Junhao Liang (梁峻豪)](https://faculty.sustech.edu.cn/?p=95401&tagid=yusq&cat=2&iscss=1&snapid=1&orderby=date), [email protected]
 
 ## Acknowledgement

configs/lidargait/lidargait_sustech1k.yaml

@@ -0,0 +1,101 @@
+data_cfg:
+  dataset_name: SUSTech1K
+  dataset_root: your_path_of_SUSTech1K-Released-pkl
+  dataset_partition: ./datasets/SUSTech1K/SUSTech1K.json
+  num_workers: 4
+  data_in_use: [false, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false]
+  remove_no_gallery: false # Remove probe if no gallery for it
+  test_dataset_name: SUSTech1K
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: 40000
+  save_name: LidarGait
+  eval_func: evaluate_indoor_dataset #evaluate_Gait3D
+  sampler:
+    batch_shuffle: false
+    batch_size: 4
+    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
+  transform:
+    - type: BaseSilTransform
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.2
+    type: TripletLoss
+    log_prefix: triplet
+  - loss_term_weight: 1.0
+    scale: 16
+    type: CrossEntropyLoss
+    log_prefix: softmax
+    log_accuracy: true
+
+model_cfg:
+  model: Baseline
+  backbone_cfg:
+    type: ResNet9
+    in_channel: 3
+    block: BasicBlock
+    channels: # Layers configuration for automatically model construction
+      - 64
+      - 128
+      - 256
+      - 512
+    layers: 
+      - 1
+      - 1
+      - 1
+      - 1
+    strides:
+      - 1
+      - 2
+      - 2
+      - 1
+    maxpool: false
+  SeparateFCs:
+    in_channels: 512
+    out_channels: 256
+    parts_num: 16
+  SeparateBNNecks:
+    class_num: 250
+    in_channels: 256
+    parts_num: 16
+  bin_num:
+    - 16
+
+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
+    - 30000
+  scheduler: MultiStepLR
+trainer_cfg:
+  enable_float16: true # half_percesion float for memory reduction and speedup
+  fix_BN: false
+  with_test: true #true
+  log_iter: 100
+  restore_ckpt_strict: true
+  restore_hint: 0
+  save_iter: 5000
+  save_name: LidarGait
+  sync_BN: true
+  total_iter: 40000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 8 # TripletSampler, batch_size[0] indicates Number of Identity
+      - 8 #                 batch_size[1] indicates Samples sequqnce for each Identity
+    frames_num_fixed: 10 # fixed frames number for training
+    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: BaseSilTransform

datasets/SUSTech1K/README.md

@@ -0,0 +1,33 @@
+# Tutorial for [SUSTech1K](https://lidargait.github.io)
+
+## Download the SUSTech1K dataset
+Download the dataset from the [link](https://lidargait.github.io).
+decompress these two file by following command:
+```shell
+unzip -P password SUSTech1K-pkl.zip   | xargs -n1 tar xzvf
+```
+password should be obtained by signing [agreement](https://lidargait.github.io/static/resources/SUSTech1KAgreement.pdf) and sending to email ([email protected])
+
+## Train the dataset
+Modify the `dataset_root` in `configs/lidargait/lidargait_sustech1k.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 configs/lidargait/lidargait_sustech1k.yaml --phase train
+```
+
+
+## Process from RAW dataset
+
+### Preprocess the dataset (Optional)
+Download the raw dataset from the [official link](https://lidargait.github.io). You will get two compressed files, i.e. `DATASET_DOWNLOAD.md5`, `SUSTeck1K-RAW.zip`, and `SUSTeck1K-pkl.zip`.
+We recommend using our provided pickle files for convenience, or process raw dataset into pickle by this command:
+```shell
+python datasets/SUSTech1K/pretreatment_SUSTech1K.py -i SUSTech1K-Released-2023 -o SUSTech1K-pkl -n 8
+```
+
+### Projecting PointCloud into Depth image (Optional)
+You can use our processed depth images, or you can process via the command:
+```shell
+python datasets/SUSTech1K/point2depth.py -i SUSTech1K-Released-2023/ -o SUSTech1K-Released-2023/ -n 8
+```
+We recommend using our provided depth images for convenience.
+