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docs: add uv workflow and ScoNet eval reproduction notes

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crosstyan
2026-02-26 14:19:00 +08:00
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AGENTS.md
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@@ -44,6 +44,7 @@ OpenGait/
## CONVENTIONS
- Launch pattern is DDP-first (`python -m torch.distributed.launch ... opengait/main.py --cfgs ... --phase ...`).
- DDP Constraints: `world_size` must equal number of visible GPUs; test `evaluator_cfg.sampler.batch_size` must equal `world_size`.
- Model/loss/backbone discoverability is filesystem-driven via package-level dynamic imports.
- Experiment config semantics: custom YAML overlays `configs/default.yaml` (local key precedence).
- Outputs are keyed by config identity: `output/${dataset_name}/${model}/${save_name}`.
@@ -61,6 +62,18 @@ OpenGait/
## COMMANDS
```bash
# install (uv)
uv sync --extra torch
# train (uv)
CUDA_VISIBLE_DEVICES=0,1 uv run python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase train
# test (uv)
CUDA_VISIBLE_DEVICES=0,1 uv run python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase test
# ScoNet 1-GPU eval
CUDA_VISIBLE_DEVICES=0 uv run python -m torch.distributed.launch --nproc_per_node=1 opengait/main.py --cfgs ./configs/sconet/sconet_scoliosis1k_local_eval_1gpu.yaml --phase test
# train
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase train