grew supported

2022-03-24 15:42:33 +08:00
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+# 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.