Zzier
609aa0e9aa
* Update ParsingGait * Clear up the confusion Clear up the confusion about gait3d and gait3d-parsing. * Update 0.get_started.md * Add BaseParsingCuttingTransform * Update gcn.py * Create gaitbase_gait3d_parsing_btz32x2_fixed.yaml * Add gait3d_parsing config file * Update 1.model_zoo.md Update Gait3D-Parsing checkpoints * Update 1.model_zoo.md add configuration * Update 1.model_zoo.md center text --------- Co-authored-by: Junhao Liang <43094337+darkliang@users.noreply.github.com>
71 lines
2.6 KiB
Markdown
71 lines
2.6 KiB
Markdown
# Get Started
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## Installation
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1. clone this repo.
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```
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git clone https://github.com/ShiqiYu/OpenGait.git
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```
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2. Install dependenices:
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- pytorch >= 1.10
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- torchvision
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- pyyaml
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- tensorboard
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- opencv-python
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- tqdm
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- py7zr
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- kornia
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- einops
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Install dependenices by [Anaconda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html):
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```
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conda install tqdm pyyaml tensorboard opencv kornia einops -c conda-forge
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conda install pytorch==1.10 torchvision -c pytorch
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```
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Or, Install dependenices by pip:
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```
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pip install tqdm pyyaml tensorboard opencv-python kornia einops
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pip install torch==1.10 torchvision==0.11
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```
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## Prepare dataset
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See [prepare dataset](2.prepare_dataset.md).
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## Get trained model
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- Option 1:
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```
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python misc/download_pretrained_model.py
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```
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- Option 2: Go to the [release page](https://github.com/ShiqiYu/OpenGait/releases/), then download the model file and uncompress it to [output](output).
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## Train
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Train a model by
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```
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CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase train
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```
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- `python -m torch.distributed.launch` [DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) launch instruction.
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- `--nproc_per_node` The number of gpus to use, and it must equal the length of `CUDA_VISIBLE_DEVICES`.
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- `--cfgs` The path to config file.
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- `--phase` Specified as `train`.
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<!-- - `--iter` You can specify a number of iterations or use `restore_hint` in the config file and resume training from there. -->
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- `--log_to_file` If specified, the terminal log will be written on disk simultaneously.
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You can run commands in [train.sh](train.sh) for training different models.
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## Test
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Evaluate the trained model by
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```
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CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase test
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```
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- `--phase` Specified as `test`.
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- `--iter` Specify a iteration checkpoint.
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**Tip**: Other arguments are the same as train phase.
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You can run commands in [test.sh](test.sh) for testing different models.
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## Customize
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1. Read the [detailed config](docs/1.detailed_config.md) to figure out the usage of needed setting items;
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2. See [how to create your model](docs/2.how_to_create_your_model.md);
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3. There are some advanced usages, refer to [advanced usages](docs/3.advanced_usages.md), please.
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## Warning
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- In `DDP` mode, zombie processes may be generated when the program terminates abnormally. You can use this command [sh misc/clean_process.sh](./misc/clean_process.sh) to clear them.
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