noahshen98
79 lines
3.2 KiB
Markdown
79 lines
3.2 KiB
Markdown
# GREW Tutorial
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<!--  -->
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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.
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## Preprocess the dataset
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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`.
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Step 1: Unzip train and test:
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```shell
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unzip -P password train.zip (password is the obtained password)
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tar -xzvf train.tgz
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cd train
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ls *.tgz | xargs -n1 tar xzvf
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```
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```shell
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unzip -P password test.zip (password is the obtained password)
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tar -xzvf test.tgz
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cd test & cd gallery
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ls *.tgz | xargs -n1 tar xzvf
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cd .. & cd probe
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ls *.tgz | xargs -n1 tar xzvf
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```
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After unpacking these compressed files, run this command:
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Step2 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run
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```
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python misc/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
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```
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Step3: Transforming images to pickle file, run
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```
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python misc/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
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```
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Then you will see the structure like:
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- Processed
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```
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GREW-pkl
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├── 00001train (subject in training set)
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├── 00
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├── 4XPn5Z28
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├── 4XPn5Z28.pkl
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├──5TXe8svE
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├── 5TXe8svE.pkl
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......
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├── 00001 (subject in testing set)
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├── 01
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├── 79XJefi8
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├── 79XJefi8.pkl
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├── 02
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├── t16VLaQf
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├── t16VLaQf.pkl
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├── probe
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├── etaGVnWf
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├── etaGVnWf.pkl
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├── eT1EXpgZ
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├── eT1EXpgZ.pkl
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...
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...
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```
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## Train the dataset
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Modify the `dataset_root` in `./config/baseline_GREW.yaml`, and then run this command:
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```shell
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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
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```
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<!-- 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. -->
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## Get the submission file
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```shell
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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 test
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```
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The result will be generated in your working directory, you must rename and compress it as the requirements before submitting.
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## Evaluation locally
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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.
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