asus
dee58e4ae5
- config.py: resolve_data_path (legacy abs + rel) + rel_data_path - all file-opening reads wrapped: preview, autolabel, training, model download, live count, projects.get; training_start_point hack replaced - new writes store paths relative to data/ - legacy stale rows (/home/asus/reTraining/...) resolve without migration - requirements: REQ-187 added; REQ-188 (per-class max box) + REQ-186 copy-line amendment drafted for the next task
87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
"""Training and model-version routes (REQ-060…065)."""
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import os
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from typing import Optional, Union
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from fastapi import APIRouter, HTTPException
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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from backend import config, hardware, training
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from backend.api.common import project_or_404
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router = APIRouter(tags=["models"])
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class TrainRequest(BaseModel):
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epochs: int = 50
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batch: Optional[int] = None
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imgsz: Optional[int] = None
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device: Optional[Union[int, str]] = None
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batch_ids: Optional[list] = None
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class_ids: Optional[list] = None
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dataset_ids: Optional[list] = None
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base_dataset_ids: Optional[list] = None
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@router.get("/api/hardware")
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def read_hardware() -> dict:
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return hardware.defaults()
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@router.post("/api/projects/{project_id}/train")
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def start_training(project_id: int, request: TrainRequest) -> dict:
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project_or_404(project_id)
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try:
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return training.start(
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project_id, request.epochs,
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{"batch": request.batch, "imgsz": request.imgsz, "device": request.device},
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batch_ids=request.batch_ids,
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class_ids=request.class_ids,
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dataset_ids=request.dataset_ids,
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base_dataset_ids=request.base_dataset_ids,
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)
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except training.TrainingError as exc:
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raise HTTPException(400, str(exc))
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@router.get("/api/projects/{project_id}/models")
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def list_models(project_id: int) -> dict:
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project = project_or_404(project_id)
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return {"models": training.listing(project_id),
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"base_model_kind": project["base_model_kind"]}
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@router.get("/api/models/{model_id}/weights")
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def download_weights(model_id: int):
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version = training.get_version(model_id)
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weights = config.resolve_data_path(version["weights_path"]) if version else None
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if version is None or not os.path.isfile(weights):
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raise HTTPException(404, "No weights for that version")
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name = version.get('name') or f"v{version['version']}"
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return FileResponse(weights, media_type="application/octet-stream",
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filename=f"{name}-best.pt")
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@router.post("/api/models/{model_id}/promote")
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def promote_model(model_id: int) -> dict:
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try:
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return training.promote(model_id)
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except training.TrainingError as exc:
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raise HTTPException(400, str(exc))
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class RenameRequest(BaseModel):
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name: str
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@router.patch("/api/models/{model_id}/rename")
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def rename_model(model_id: int, request: RenameRequest) -> dict:
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try:
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version = training.rename(model_id, request.name)
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if version is None:
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raise HTTPException(404, "No such model version")
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return version
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except training.TrainingError as exc:
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raise HTTPException(400, str(exc))
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