grew supported

README.md

@@ -8,7 +8,7 @@ OpenGait is a flexible and extensible gait recognition project provided by the [
 
 
 ## What's New
-- [Mar 2022] Dataset GREW is supported.
+- [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported.
 - [Mar 2022] [HID](http://hid2022.iapr-tc4.org/) support is ready in [misc/HID](./misc/HID).
 
 ## Highlighted features
@@ -19,7 +19,7 @@ OpenGait is a flexible and extensible gait recognition project provided by the [
 
 ## Model Zoo
 
-###  CASIA-B
+###  [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp)
 |                                                                                          Model                                                                                          |     NM     |     BG     |     CL     | Configuration                                                                                | Input Size | Inference Time |   Model Size   |
 | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------: | :--------: | :--------: | :------------------------------------------------------------------------------------------- | :--------: | :------------: | :------------: |
 |                                                                                        Baseline                                                                                         |    96.3    |    92.2    |    77.6    | [baseline.yaml](config/baseline.yaml)                                                        |   64x44    |      12s       |     3.78M      |
@@ -28,7 +28,7 @@ OpenGait is a flexible and extensible gait recognition project provided by the [
 |                                                        [GLN*(ECCV2020)](http://home.ustc.edu.cn/~saihui/papers/eccv2020_gln.pdf)                                                        | 96.4(95.6) | 93.1(92.0) | 81.0(77.2) | [gln_phase1.yaml](config/gln/gln_phase1.yaml), [gln_phase2.yaml](config/gln/gln_phase2.yaml) |   128x88   |    47s/46s     | 8.54M / 14.70M |
 | [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) | 97.4(97.4) | 94.5(94.5) | 83.8(83.6) | [gaitgl.yaml](config/gaitgl.yaml)                                                            |   64x44    |      38s       |     3.10M      |
 
-### OUMVLP
+### [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html)
 |                                                                                          Model                                                                                          |   Rank@1   |                Configuration                 | Input Size | Inference Time | Model Size |
 | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------: | :------------------------------------------: | :--------: | :------------- | :--------: |
 |                                                                                        Baseline                                                                                         |    86.7    | [baseline.yaml](config/baseline_OUMVLP.yaml) |   64x44    | 1m13s          |   44.11M   |
@@ -36,7 +36,7 @@ OpenGait is a flexible and extensible gait recognition project provided by the [
 |                                                   [GaitPart(CVPR2020)](http://home.ustc.edu.cn/~saihui/papers/cvpr2020_gaitpart.pdf)                                                    | 88.6(88.7) | [gaitpart.yaml](config/gaitpart_OUMVLP.yaml) |   64x44    | 8m04s          |   3.78M    |
 | [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) | 89.9(89.7) |   [gaitgl.yaml](config/gaitgl_OUMVLP.yaml)   |   64x44    | 5m23s          |   95.62M   |
 
-### GREW
+### [GREW](https://www.grew-benchmark.org)
 |                                                                                          Model                                                                                          |   Rank@1   |                Configuration                 | Input Size | Inference Time | Model Size |
 | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------: | :------------------------------------------: | :--------: | :------------- | :--------: |
 |                                                                                        Baseline                                                                                         |    43.3    | [baseline.yaml](config/baseline_GREW.yaml) |   64x44    | 2m23s          |   84.12M   |
@@ -131,7 +131,7 @@ You can run commands in [test.sh](test.sh) for testing different models.
 ## Acknowledgement
 - GLN: [Saihui Hou (侯赛辉)](http://home.ustc.edu.cn/~saihui/index_english.html)
 - GaitGL: [Beibei Lin (林贝贝)](https://scholar.google.com/citations?user=KyvHam4AAAAJ&hl=en&oi=ao)
-
+- GREW: [GREW TEAM](https://www.grew-benchmark.org)
 <!-- ## Citation
 ```
 ``` -->

docs/0.prepare_dataset.md

@@ -133,7 +133,7 @@ Step2: [Unzip](https://github.com/GREW-Benchmark/GREW-Benchmark) the dataset, yo
         ...
     ...
             
-Step3 : To rearrange directory of OUMVLP dataset, turning to id-type-view structure, Run 
+Step3 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run 
 ```
 python misc/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
 ```  

misc/GREW/README.md

@@ -0,0 +1,78 @@
+# GREW Tutorial
+<!-- ![](http://hid2022.iapr-tc4.org/wp-content/uploads/sites/7/2022/03/%E5%9B%BE%E7%89%871-2.png) -->
+This is for [GREW-Benchmark](https://github.com/GREW-Benchmark/GREW-Benchmark). We report our result of 48% using the baseline model. In order for participants to better start the first step, we provide a tutorial on how to use OpenGait for GREW.
+
+## Preprocess the dataset
+Download the raw dataset from the [official link](https://www.grew-benchmark.org/download.html). You will get three compressed files, i.e. `train.zip`, `test.zip` and `distractor.zip`.
+
+Step 1: Unzip train and test:
+```shell
+unzip -P password train.zip (password is the obtained password)
+tar -xzvf train.tgz
+cd train
+ls *.tgz | xargs -n1 tar xzvf
+```
+
+```shell
+unzip -P password test.zip (password is the obtained password)
+tar -xzvf test.tgz
+cd test & cd gallery
+ls *.tgz | xargs -n1 tar xzvf
+cd .. & cd probe
+ls *.tgz | xargs -n1 tar xzvf
+```
+
+After unpacking these compressed files, run this command:
+
+Step2 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run 
+```
+python misc/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
+```  
+
+Step3: Transforming images to pickle file, run 
+```
+python misc/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
+```
+Then you will see the structure like:
+
+- Processed
+    ```
+    GREW-pkl
+    ├── 00001train (subject in training set)
+        ├── 00
+            ├── 4XPn5Z28
+                ├── 4XPn5Z28.pkl
+            ├──5TXe8svE
+                ├── 5TXe8svE.pkl
+                ......
+    ├── 00001 (subject in testing set)
+        ├── 01
+            ├── 79XJefi8
+                ├── 79XJefi8.pkl
+        ├── 02
+            ├── t16VLaQf
+                ├── t16VLaQf.pkl
+    ├── probe
+        ├── etaGVnWf
+            ├── etaGVnWf.pkl
+        ├── eT1EXpgZ
+            ├── eT1EXpgZ.pkl
+        ...
+    ...
+    ```
+
+## Train the dataset
+Modify the `dataset_root` in `./config/baseline_GREW.yaml`, and then run this command:
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 lib/main.py --cfgs ./config/baseline_GREW.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 lib/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase test
+```
+The result will be generated in your working directory, you must rename and compress it as the requirements before submitting.
+
+## Evaluation locally
+While the original grew treat both seq_01 and seq_02 as gallery, but there is no ground truth for probe. Therefore, it is nessesary to upload the submission file on grew competitation. We seperate test set to: seq_01 as gallery, seq_02 as probe. Then you can modify `eval_func` in the `./config/baseline_GREW.yaml` to `identification_real_scene`, you can obtain result localy like setting of OUMVLP.