42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
from __future__ import annotations
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from ...commands import CommandSpec, append_flag, bash, conda_python, required
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from ...config import settings
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SEGMODEL_DIR = settings.source_root / "Seg_All_In_One_SegModel"
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def build_segmodel_task(job_type: str, params: dict, conda_env: str) -> CommandSpec | None:
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env = {"SEG_CONDA_ENV": conda_env}
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if job_type == "segmodel.train":
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args = conda_python(conda_env, SEGMODEL_DIR / "train.py")
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append_flag(args, "-a", required(params, "architecture"))
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return CommandSpec(args, SEGMODEL_DIR, "train one segmentation_models_pytorch architecture")
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if job_type == "segmodel.batch_train":
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return CommandSpec(bash(SEGMODEL_DIR / "train.sh"), SEGMODEL_DIR, "run legacy SegModel batch training", env=env)
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if job_type == "segmodel.predict":
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args = conda_python(conda_env, SEGMODEL_DIR / "1_predict.py")
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append_flag(args, "-a", required(params, "architecture"))
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choice = str(params.get("run_choice", 1))
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return CommandSpec(args, SEGMODEL_DIR, "predict with one SegModel run", stdin_text=f"{choice}\n")
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if job_type == "segmodel.batch_predict":
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return CommandSpec(bash(SEGMODEL_DIR / "predict.sh"), SEGMODEL_DIR, "run legacy SegModel batch prediction", env=env)
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if job_type == "segmodel.flops":
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script = SEGMODEL_DIR / params.get("script", "2_predict_params_and_FLOPs_V2.py")
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return CommandSpec(conda_python(conda_env, script), SEGMODEL_DIR, "calculate SegModel params/FLOPs/FPS")
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if job_type == "segmodel.raw_mask_check":
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return CommandSpec(conda_python(conda_env, SEGMODEL_DIR / "1_predict_raw_masks_check.py"), SEGMODEL_DIR, "check SegModel raw mask completeness")
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if job_type == "segmodel.metrics":
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return CommandSpec(conda_python(conda_env, SEGMODEL_DIR / "3_predict_matrics_from_log.py"), SEGMODEL_DIR, "parse SegModel training/prediction metrics")
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return None
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