commit 98abafa7cc4298033c94dc27b6cedd462516078e Author: admin <572701190@qq.com> Date: Tue Jun 30 11:49:36 2026 +0800 Initial Seg Data Server Net platform diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..25bc061 --- /dev/null +++ b/.env.example @@ -0,0 +1,9 @@ +SEG_SOURCE_ROOT=../Seg +SEG_DATA_SERVER_ROOT=. +SEG_BACKEND_DB=var/seg_data_server.sqlite3 +SEG_BACKEND_LOG_DIR=var/job_logs +SEG_TASK_CONDA_ENV=seg_smp +SEG_BACKEND_CONDA_ENV=seg_server +SEG_WEIGHT_MODE=copy +SEG_ENABLE_SHELL_TASKS=1 +VITE_API_BASE=http://localhost:8000 diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..7d5b6f4 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,5 @@ +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.engine filter=lfs diff=lfs merge=lfs -text + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9f52548 --- /dev/null +++ b/.gitignore @@ -0,0 +1,27 @@ +# Runtime state +.env +var/ +.pytest_cache/ +backend/.pytest_cache/ +backend/__pycache__/ +frontend/node_modules/ +frontend/dist/ +frontend/*.tsbuildinfo + +# Python / JS caches +__pycache__/ +*.py[cod] +.mypy_cache/ +.ruff_cache/ +.vite/ + +# Large runtime assets. Track through Git LFS or release/package storage. +weights/files/ +*.pt +*.pth +*.onnx +*.engine + +# Keep the manifest and placeholder. +!weights/.gitkeep +!weights/manifest.json diff --git a/README.md b/README.md new file mode 100644 index 0000000..7d2d5ef --- /dev/null +++ b/README.md @@ -0,0 +1,79 @@ +# Seg Data Server Net + +Modular web control plane for the existing Seg image segmentation workspace. + +The platform keeps the current training and analysis scripts as the compute +core, then adds: + +- a FastAPI backend for catalog discovery, job orchestration, logs, results, + GPU status, and weight management; +- a React/Vite frontend for launching jobs and inspecting progress; +- a unified `weights/` area with a generated manifest for `.pt`, `.pth`, + `.onnx`, and `.engine` assets. + +## Layout + +```text +Seg_Data_Server_Net/ + backend/ FastAPI API, job runner, module wrappers + frontend/ React + Vite operator UI + scripts/ helper scripts for running services and syncing weights + weights/ copied model weights and manifest.json +``` + +## Quick Start + +```bash +cd Seg_Data_Server_Net +cp .env.example .env + +# Backend. The existing machine already has a seg_server env with FastAPI. +conda run -n seg_server uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8000 + +# Frontend. +cd frontend +npm install +npm run dev -- --host 0.0.0.0 +``` + +Open the Vite URL shown in the terminal. The frontend expects the backend at +`http://localhost:8000` by default. + +## Weight Sync + +The current workspace contains tens of GB of pretrained and trained weights. +They are copied into `weights/files/` and indexed in +`weights/manifest.json`. + +```bash +cd Seg_Data_Server_Net +python scripts/sync_weights.py --mode copy --hash +``` + +For repository storage, use Git LFS or a Gitea release/package store: + +```bash +git lfs install +git lfs track "*.pt" "*.pth" "*.onnx" "*.engine" +``` + +If Git LFS is not available on the host or server, keep the copied weights on +the deployment volume and commit only `weights/manifest.json`. + +## Job Types + +The backend exposes all current Seg capabilities as job types. Examples: + +- `dataset.rename`, `dataset.resize`, `dataset.pair`, `dataset.rebuild_labels`, + `dataset.stack`, `dataset.stitch`, `dataset.video_frames` +- `segmodel.train`, `segmodel.batch_train`, `segmodel.predict`, + `segmodel.batch_predict`, `segmodel.flops`, `segmodel.raw_mask_check` +- `yolo.train`, `yolo.batch_train`, `yolo.predict`, `yolo.batch_predict`, + `yolo.heatmap`, `yolo.compare`, `yolo.raw_mask_check`, `yolo.video_visible` +- `mmseg.generate_data`, `mmseg.generate_alg`, `mmseg.train`, + `mmseg.metrics`, `mmseg.flops_fps`, `mmseg.draw`, `mmseg.extract_loss_miou` +- `analysis.all`, `system.backup`, `mock.echo` + +Use `GET /api/catalog` to inspect supported models, algorithms, datasets, and +task types discovered from the existing `Seg/` workspace. + diff --git a/backend/app/__init__.py b/backend/app/__init__.py new file mode 100644 index 0000000..62a2513 --- /dev/null +++ b/backend/app/__init__.py @@ -0,0 +1,2 @@ +"""Seg Data Server backend.""" + diff --git a/backend/app/catalog.py b/backend/app/catalog.py new file mode 100644 index 0000000..a6ee4e6 --- /dev/null +++ b/backend/app/catalog.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +from .config import settings +from .paths import rel + + +SEGMODEL_ARCHS = [ + "Unet", + "UnetPlusPlus", + "FPN", + "PSPNet", + "DeepLabV3", + "DeepLabV3Plus", + "Linknet", + "MAnet", + "PAN", + "UPerNet", + "Segformer", + "DPT", +] + + +YOLO_MODELS = [ + "YOLOv8n-seg", + "YOLOv8s-seg", + "YOLOv8m-seg", + "YOLOv8l-seg", + "YOLOv8x-seg", + "YOLOv9c-seg", + "YOLOv9e-seg", + "YOLO11n-seg", + "YOLO11s-seg", + "YOLO11m-seg", + "YOLO11l-seg", + "YOLO11x-seg", + "YOLO12-seg", +] + + +TASK_TYPES = [ + "mock.echo", + "system.backup", + "dataset.rename", + "dataset.to_png", + "dataset.resize", + "dataset.pair", + "dataset.rebuild_labels", + "dataset.stack", + "dataset.stitch", + "dataset.video_frames", + "segmodel.train", + "segmodel.batch_train", + "segmodel.predict", + "segmodel.batch_predict", + "segmodel.flops", + "segmodel.raw_mask_check", + "segmodel.metrics", + "yolo.train", + "yolo.batch_train", + "yolo.predict", + "yolo.batch_predict", + "yolo.heatmap", + "yolo.compare", + "yolo.raw_mask_check", + "yolo.video_visible", + "yolo.video_unvisible", + "mmseg.init_weights", + "mmseg.generate_data", + "mmseg.generate_alg", + "mmseg.train", + "mmseg.metrics", + "mmseg.flops_fps", + "mmseg.draw", + "mmseg.extract_loss_miou", + "analysis.all", +] + + +def _read_json(path: Path) -> Any | None: + try: + return json.loads(path.read_text(encoding="utf-8")) + except Exception: + return None + + +def discover_datasets() -> list[dict[str, Any]]: + root = settings.source_root + candidates: list[dict[str, Any]] = [] + for base in ["DataSet_Public", "BestMode_Predict_Results_DataSet_Public", "Hardisk"]: + parent = root / base + if not parent.exists(): + continue + for item in sorted(parent.iterdir()): + if item.is_dir(): + candidates.append({"name": item.name, "path": rel(item, root), "source": base}) + mmseg_params = root / "Seg_All_In_One_MMSeg" / "My_All_In_One" / "1_Data_Parameter" + for item in sorted(mmseg_params.glob("*.json")): + data = _read_json(item) + if item.name == "All_Data_Record.json" or not data: + continue + candidates.append({"name": item.stem, "path": rel(item, root), "source": "mmseg_parameter"}) + return candidates + + +def discover_mmseg_algorithms() -> list[str]: + alg_dir = settings.source_root / "Seg_All_In_One_MMSeg" / "My_All_In_One" / "2_Alg_Program" + if not alg_dir.exists(): + return [] + return sorted(path.stem for path in alg_dir.glob("*.py")) + + +def discover_weights_summary() -> dict[str, Any]: + manifest = settings.weights_root / "manifest.json" + if not manifest.exists(): + return {"manifest": None, "count": 0, "total_bytes": 0} + data = _read_json(manifest) or {} + return { + "manifest": rel(manifest, settings.project_root), + "count": len(data.get("files", [])), + "total_bytes": data.get("total_bytes", 0), + "updated_at": data.get("updated_at"), + } + + +def get_catalog() -> dict[str, Any]: + return { + "source_root": str(settings.source_root), + "project_root": str(settings.project_root), + "task_types": TASK_TYPES, + "segmodel_architectures": SEGMODEL_ARCHS, + "yolo_models": YOLO_MODELS, + "mmseg_algorithms": discover_mmseg_algorithms(), + "datasets": discover_datasets(), + "weights": discover_weights_summary(), + } + diff --git a/backend/app/commands.py b/backend/app/commands.py new file mode 100644 index 0000000..06d932e --- /dev/null +++ b/backend/app/commands.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path + + +@dataclass +class CommandSpec: + command: list[str] + cwd: Path + description: str = "" + env: dict[str, str] = field(default_factory=dict) + stdin_text: str | None = None + + +TaskFactory = callable + + +def conda_python(env_name: str, script: Path, *args: object) -> list[str]: + return ["conda", "run", "-n", env_name, "python", str(script), *[str(a) for a in args]] + + +def python(script: Path, *args: object) -> list[str]: + return ["python", str(script), *[str(a) for a in args]] + + +def bash(script: Path, *args: object) -> list[str]: + return ["bash", str(script), *[str(a) for a in args]] + + +def option(params: dict, name: str, default=None): + value = params.get(name, default) + return value + + +def required(params: dict, name: str): + if name not in params or params[name] in (None, ""): + raise ValueError(f"missing required parameter: {name}") + return params[name] + + +def append_flag(args: list[str], flag: str, value): + if value not in (None, ""): + args.extend([flag, str(value)]) + diff --git a/backend/app/config.py b/backend/app/config.py new file mode 100644 index 0000000..c12ece5 --- /dev/null +++ b/backend/app/config.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path + + +def _resolve_path(value: str | None, default: Path) -> Path: + base = Path(value).expanduser() if value else default + if not base.is_absolute(): + base = default.parent / base + return base.resolve() + + +@dataclass(frozen=True) +class Settings: + project_root: Path + source_root: Path + db_path: Path + log_dir: Path + weights_root: Path + task_conda_env: str + backend_conda_env: str + weight_mode: str + enable_shell_tasks: bool + + +def get_settings() -> Settings: + project_root = Path(os.getenv("SEG_DATA_SERVER_ROOT", Path(__file__).resolve().parents[2])).expanduser() + if not project_root.is_absolute(): + project_root = (Path(__file__).resolve().parents[2] / project_root).resolve() + else: + project_root = project_root.resolve() + + sibling_source = project_root.parent / "Seg" + default_source = sibling_source if sibling_source.exists() else project_root.parent + source_root = _resolve_path(os.getenv("SEG_SOURCE_ROOT"), default_source) + db_path = _resolve_path(os.getenv("SEG_BACKEND_DB"), project_root / "var" / "seg_data_server.sqlite3") + log_dir = _resolve_path(os.getenv("SEG_BACKEND_LOG_DIR"), project_root / "var" / "job_logs") + weights_root = (project_root / "weights").resolve() + + return Settings( + project_root=project_root, + source_root=source_root, + db_path=db_path, + log_dir=log_dir, + weights_root=weights_root, + task_conda_env=os.getenv("SEG_TASK_CONDA_ENV", "seg_smp"), + backend_conda_env=os.getenv("SEG_BACKEND_CONDA_ENV", "seg_server"), + weight_mode=os.getenv("SEG_WEIGHT_MODE", "copy"), + enable_shell_tasks=os.getenv("SEG_ENABLE_SHELL_TASKS", "1") == "1", + ) + + +settings = get_settings() diff --git a/backend/app/db.py b/backend/app/db.py new file mode 100644 index 0000000..074fa1f --- /dev/null +++ b/backend/app/db.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import json +import sqlite3 +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from .config import settings + + +def utc_now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def connect() -> sqlite3.Connection: + settings.db_path.parent.mkdir(parents=True, exist_ok=True) + conn = sqlite3.connect(settings.db_path) + conn.row_factory = sqlite3.Row + return conn + + +def init_db() -> None: + with connect() as conn: + conn.execute( + """ + create table if not exists jobs ( + id text primary key, + type text not null, + status text not null, + params_json text not null, + command_json text not null, + cwd text not null, + description text not null default '', + pid integer, + exit_code integer, + created_at text not null, + started_at text, + finished_at text, + log_path text not null, + error text + ) + """ + ) + conn.execute( + """ + create table if not exists profiles ( + id integer primary key autoincrement, + name text not null, + kind text not null, + data_json text not null, + updated_at text not null, + unique(name, kind) + ) + """ + ) + + +def _job_from_row(row: sqlite3.Row) -> dict[str, Any]: + data = dict(row) + data["params"] = json.loads(data.pop("params_json")) + data["command"] = json.loads(data.pop("command_json")) + return data + + +def insert_job(job: dict[str, Any]) -> None: + with connect() as conn: + conn.execute( + """ + insert into jobs ( + id, type, status, params_json, command_json, cwd, description, + pid, exit_code, created_at, started_at, finished_at, log_path, error + ) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + job["id"], + job["type"], + job["status"], + json.dumps(job["params"], ensure_ascii=False), + json.dumps(job["command"], ensure_ascii=False), + job["cwd"], + job.get("description", ""), + job.get("pid"), + job.get("exit_code"), + job["created_at"], + job.get("started_at"), + job.get("finished_at"), + job["log_path"], + job.get("error"), + ), + ) + + +def update_job(job_id: str, **fields: Any) -> None: + if not fields: + return + allowed = { + "status", + "pid", + "exit_code", + "started_at", + "finished_at", + "error", + } + updates = {key: value for key, value in fields.items() if key in allowed} + if not updates: + return + assignments = ", ".join(f"{key}=?" for key in updates) + with connect() as conn: + conn.execute(f"update jobs set {assignments} where id=?", [*updates.values(), job_id]) + + +def get_job(job_id: str) -> dict[str, Any] | None: + with connect() as conn: + row = conn.execute("select * from jobs where id=?", (job_id,)).fetchone() + return _job_from_row(row) if row else None + + +def list_jobs(limit: int = 100) -> list[dict[str, Any]]: + with connect() as conn: + rows = conn.execute( + "select * from jobs order by created_at desc limit ?", (limit,) + ).fetchall() + return [_job_from_row(row) for row in rows] + + +def upsert_profile(name: str, kind: str, data: dict[str, Any]) -> dict[str, Any]: + updated_at = utc_now() + with connect() as conn: + conn.execute( + """ + insert into profiles (name, kind, data_json, updated_at) + values (?, ?, ?, ?) + on conflict(name, kind) do update set + data_json=excluded.data_json, + updated_at=excluded.updated_at + """, + (name, kind, json.dumps(data, ensure_ascii=False), updated_at), + ) + return {"name": name, "kind": kind, "data": data, "updated_at": updated_at} + + +def list_profiles(kind: str | None = None) -> list[dict[str, Any]]: + sql = "select name, kind, data_json, updated_at from profiles" + params: tuple = () + if kind: + sql += " where kind=?" + params = (kind,) + sql += " order by kind, name" + with connect() as conn: + rows = conn.execute(sql, params).fetchall() + return [ + { + "name": row["name"], + "kind": row["kind"], + "data": json.loads(row["data_json"]), + "updated_at": row["updated_at"], + } + for row in rows + ] + + +def log_tail(log_path: str | Path, max_bytes: int = 8192) -> str: + path = Path(log_path) + if not path.exists(): + return "" + size = path.stat().st_size + with path.open("rb") as handle: + if size > max_bytes: + handle.seek(size - max_bytes) + return handle.read().decode("utf-8", errors="replace") + diff --git a/backend/app/jobs.py b/backend/app/jobs.py new file mode 100644 index 0000000..68b6e9d --- /dev/null +++ b/backend/app/jobs.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +import os +import signal +import subprocess +import threading +import uuid +from pathlib import Path + +from . import db +from .commands import CommandSpec +from .config import settings +from .modules import build_module_task +from .schemas import JobCreate + +_running: dict[str, subprocess.Popen] = {} +_lock = threading.Lock() + + +def _build_task(request: JobCreate) -> CommandSpec: + conda_env = request.conda_env or settings.task_conda_env + spec = build_module_task(request.type, request.params, conda_env) + if spec is None: + raise ValueError(f"unsupported job type: {request.type}") + env = dict(spec.env) + if request.gpus: + env["CUDA_VISIBLE_DEVICES"] = ",".join(str(gpu) for gpu in request.gpus) + return CommandSpec( + command=spec.command, + cwd=spec.cwd, + description=spec.description, + env=env, + stdin_text=spec.stdin_text, + ) + + +def create_job(request: JobCreate) -> dict: + spec = _build_task(request) + job_id = uuid.uuid4().hex + settings.log_dir.mkdir(parents=True, exist_ok=True) + log_path = settings.log_dir / f"{job_id}.log" + job = { + "id": job_id, + "type": request.type, + "status": "queued", + "params": request.params, + "command": spec.command, + "cwd": str(spec.cwd), + "description": spec.description, + "pid": None, + "exit_code": None, + "created_at": db.utc_now(), + "started_at": None, + "finished_at": None, + "log_path": str(log_path), + "error": None, + } + db.insert_job(job) + thread = threading.Thread(target=_run_job, args=(job_id, spec, log_path), daemon=True) + thread.start() + return db.get_job(job_id) + + +def _run_job(job_id: str, spec: CommandSpec, log_path: Path) -> None: + env = os.environ.copy() + env.update(spec.env) + db.update_job(job_id, status="running", started_at=db.utc_now()) + try: + with log_path.open("ab") as log_file: + log_file.write(("COMMAND: " + " ".join(spec.command) + "\n").encode("utf-8")) + log_file.flush() + process = subprocess.Popen( + spec.command, + cwd=str(spec.cwd), + env=env, + stdin=subprocess.PIPE if spec.stdin_text is not None else None, + stdout=log_file, + stderr=subprocess.STDOUT, + start_new_session=True, + ) + with _lock: + _running[job_id] = process + db.update_job(job_id, pid=process.pid) + if spec.stdin_text is not None and process.stdin is not None: + process.stdin.write(spec.stdin_text.encode("utf-8")) + process.stdin.close() + exit_code = process.wait() + with _lock: + _running.pop(job_id, None) + current = db.get_job(job_id) + if current and current["status"] == "cancelled": + db.update_job(job_id, exit_code=exit_code, finished_at=db.utc_now()) + elif exit_code == 0: + db.update_job(job_id, status="success", exit_code=exit_code, finished_at=db.utc_now()) + else: + db.update_job(job_id, status="failed", exit_code=exit_code, finished_at=db.utc_now()) + except Exception as exc: + with _lock: + _running.pop(job_id, None) + db.update_job(job_id, status="failed", error=str(exc), finished_at=db.utc_now()) + + +def cancel_job(job_id: str) -> dict | None: + job = db.get_job(job_id) + if not job: + return None + db.update_job(job_id, status="cancelled", finished_at=db.utc_now()) + with _lock: + process = _running.get(job_id) + if process and process.poll() is None: + try: + os.killpg(process.pid, signal.SIGTERM) + except ProcessLookupError: + pass + return db.get_job(job_id) + diff --git a/backend/app/main.py b/backend/app/main.py new file mode 100644 index 0000000..4f103ee --- /dev/null +++ b/backend/app/main.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import asyncio +import json +from pathlib import Path + +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import FileResponse, StreamingResponse + +from . import db +from .catalog import get_catalog +from .config import settings +from .jobs import cancel_job, create_job +from .modules.system.service import disk_usage, get_conda_envs, get_gpus, scan_results +from .modules.weights.service import load_manifest, sync_weights, verify_weights +from .paths import ensure_inside +from .schemas import JobCreate, ProfileCreate, WeightSyncRequest + +app = FastAPI(title="Seg Data Server Net", version="0.1.0") + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.on_event("startup") +def startup() -> None: + db.init_db() + settings.log_dir.mkdir(parents=True, exist_ok=True) + settings.weights_root.mkdir(parents=True, exist_ok=True) + + +@app.get("/api/health") +def health() -> dict: + return { + "ok": True, + "source_root": str(settings.source_root), + "project_root": str(settings.project_root), + "disk": disk_usage(), + } + + +@app.get("/api/system/gpus") +def api_gpus() -> dict: + return get_gpus() + + +@app.get("/api/system/envs") +def api_envs() -> dict: + return get_conda_envs() + + +@app.get("/api/catalog") +def api_catalog() -> dict: + return get_catalog() + + +@app.get("/api/profiles") +def api_profiles(kind: str | None = None) -> list[dict]: + return db.list_profiles(kind) + + +@app.post("/api/profiles") +def api_save_profile(profile: ProfileCreate) -> dict: + return db.upsert_profile(profile.name, profile.kind, profile.data) + + +@app.post("/api/jobs") +def api_create_job(request: JobCreate) -> dict: + try: + return create_job(request) + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +@app.get("/api/jobs") +def api_jobs(limit: int = 100) -> list[dict]: + return db.list_jobs(limit) + + +@app.get("/api/jobs/{job_id}") +def api_job(job_id: str) -> dict: + job = db.get_job(job_id) + if not job: + raise HTTPException(status_code=404, detail="job not found") + job["log_tail"] = db.log_tail(job["log_path"]) + return job + + +@app.post("/api/jobs/{job_id}/cancel") +def api_cancel_job(job_id: str) -> dict: + job = cancel_job(job_id) + if not job: + raise HTTPException(status_code=404, detail="job not found") + return job + + +@app.get("/api/jobs/{job_id}/events") +async def api_job_events(job_id: str): + async def stream(): + last_size = 0 + while True: + job = db.get_job(job_id) + if not job: + yield "event: error\ndata: job not found\n\n" + return + path = Path(job["log_path"]) + chunk = "" + if path.exists(): + size = path.stat().st_size + if size > last_size: + with path.open("rb") as handle: + handle.seek(last_size) + chunk = handle.read(size - last_size).decode("utf-8", errors="replace") + last_size = size + payload = {"job": job, "chunk": chunk} + yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n" + if job["status"] in {"success", "failed", "cancelled"}: + return + await asyncio.sleep(1) + + return StreamingResponse(stream(), media_type="text/event-stream") + + +@app.get("/api/results") +def api_results() -> list[dict]: + return scan_results() + + +@app.get("/api/artifacts/{artifact_path:path}") +def api_artifact(artifact_path: str): + candidate = Path(artifact_path) + if not candidate.is_absolute(): + candidate = settings.source_root / candidate + try: + resolved = candidate.resolve() + allowed = False + for root in (settings.source_root, settings.project_root): + try: + ensure_inside(resolved, root) + allowed = True + break + except Exception: + continue + if not allowed: + raise ValueError("artifact path is outside allowed roots") + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + if not resolved.exists() or not resolved.is_file(): + raise HTTPException(status_code=404, detail="artifact not found") + return FileResponse(resolved) + + +@app.get("/api/weights") +def api_weights() -> dict: + return load_manifest() + + +@app.post("/api/weights/sync") +def api_weight_sync(request: WeightSyncRequest) -> dict: + return sync_weights(request.mode, request.hash_files, request.skip_existing) + + +@app.post("/api/weights/verify") +def api_weight_verify() -> dict: + return verify_weights() + diff --git a/backend/app/modules/__init__.py b/backend/app/modules/__init__.py new file mode 100644 index 0000000..cdd7629 --- /dev/null +++ b/backend/app/modules/__init__.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from .analysis.tasks import build_analysis_task +from .dataset.tasks import build_dataset_task +from .mmseg.tasks import build_mmseg_task +from .segmodel.tasks import build_segmodel_task +from .system.tasks import build_system_task +from .yolo.tasks import build_yolo_task + + +def build_module_task(job_type: str, params: dict, conda_env: str): + for builder in ( + build_dataset_task, + build_segmodel_task, + build_yolo_task, + build_mmseg_task, + build_analysis_task, + build_system_task, + ): + spec = builder(job_type, params, conda_env) + if spec is not None: + return spec + return None + diff --git a/backend/app/modules/analysis/__init__.py b/backend/app/modules/analysis/__init__.py new file mode 100644 index 0000000..86174a9 --- /dev/null +++ b/backend/app/modules/analysis/__init__.py @@ -0,0 +1,2 @@ +"""Analysis task wrappers.""" + diff --git a/backend/app/modules/analysis/tasks.py b/backend/app/modules/analysis/tasks.py new file mode 100644 index 0000000..a9617b8 --- /dev/null +++ b/backend/app/modules/analysis/tasks.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from ...commands import CommandSpec, append_flag, conda_python +from ...config import settings + + +ANALYSIS_DIR = settings.source_root / "Seg_All_In_One_Analysis" + + +def build_analysis_task(job_type: str, params: dict, conda_env: str) -> CommandSpec | None: + if job_type != "analysis.all": + return None + args = conda_python(conda_env, ANALYSIS_DIR / "1_Analysis_All.py") + append_flag(args, "--input_dir", params.get("input_dir", "../BestMode_Predict_Results_DataSet_Public")) + append_flag(args, "--output_dir", params.get("output_dir", "./")) + stdin = f"{params.get('dataset_choice', 1)}\n" + return CommandSpec(args, ANALYSIS_DIR, "merge SegModel/MMSeg metrics and generate plots", stdin_text=stdin) + diff --git a/backend/app/modules/dataset/__init__.py b/backend/app/modules/dataset/__init__.py new file mode 100644 index 0000000..5d6b5ec --- /dev/null +++ b/backend/app/modules/dataset/__init__.py @@ -0,0 +1,2 @@ +"""Dataset task wrappers.""" + diff --git a/backend/app/modules/dataset/tasks.py b/backend/app/modules/dataset/tasks.py new file mode 100644 index 0000000..53b6811 --- /dev/null +++ b/backend/app/modules/dataset/tasks.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +from pathlib import Path + +from ...commands import CommandSpec, append_flag, bash, conda_python, required +from ...config import settings + + +DATASET_TOOL_DIR = settings.source_root / "DataSet_Own" / "1. 图片预处理（内含使用手册）" +STACK_TOOL_DIR = settings.source_root / "Tool-图片堆叠" +VIDEO_DIR = settings.source_root / "Seg_Predict_Own_Video_V2" +YOLO_DATASET_DIR = settings.source_root / "Seg_All_In_One_YoloModel" / "Yolo数据集构建" + + +def _dataset_script(name: str) -> Path: + return DATASET_TOOL_DIR / name + + +def build_dataset_task(job_type: str, params: dict, conda_env: str) -> CommandSpec | None: + if job_type == "dataset.rename": + args = bash(_dataset_script("1_rename_pics.sh")) + append_flag(args, "-i", required(params, "image_dir")) + append_flag(args, "-l", required(params, "label_dir")) + return CommandSpec(args, DATASET_TOOL_DIR, "rename and normalize image/label names") + + if job_type == "dataset.to_png": + script = _dataset_script("2_1_Trans_to_png.py") + args = conda_python(conda_env, script) + append_flag(args, "-i", params.get("input_dir")) + append_flag(args, "-o", params.get("output_dir")) + return CommandSpec(args, DATASET_TOOL_DIR, "convert images to png") + + if job_type == "dataset.resize": + args = bash(_dataset_script("2_reformate_pics.sh")) + append_flag(args, "-i", params.get("image_dir")) + append_flag(args, "-l", params.get("label_dir")) + append_flag(args, "-w", params.get("width", 1920)) + append_flag(args, "-h", params.get("height", 1080)) + return CommandSpec(args, DATASET_TOOL_DIR, "resize and reformat image/label folders") + + if job_type == "dataset.pair": + args = bash(_dataset_script("3_pair_ori_label.sh")) + append_flag(args, "-i", required(params, "image_dir")) + append_flag(args, "-l", required(params, "label_dir")) + append_flag(args, "-p", params.get("prefix", "")) + append_flag(args, "-s", params.get("suffix", "")) + return CommandSpec(args, DATASET_TOOL_DIR, "check image and label pairing") + + if job_type == "dataset.rebuild_labels": + args = bash(_dataset_script("4_rebuild_labels.sh")) + append_flag(args, "-l", required(params, "label_dir")) + return CommandSpec(args, DATASET_TOOL_DIR, "rebuild color labels into GT masks") + + if job_type == "dataset.stack": + args = bash(_dataset_script("5_TOOL_stack_pics.sh")) + append_flag(args, "-i", required(params, "image_dir")) + append_flag(args, "-l", required(params, "label_dir")) + append_flag(args, "-r", required(params, "result_dir")) + append_flag(args, "-a", params.get("alpha", 0.3)) + append_flag(args, "-p", params.get("prefix", "")) + append_flag(args, "-s", params.get("suffix", "")) + return CommandSpec(args, DATASET_TOOL_DIR, "overlay image and label for inspection") + + if job_type == "dataset.stitch": + args = bash(_dataset_script("6_TOOL_stitch_pics.sh")) + append_flag(args, "-i", required(params, "image_dir")) + append_flag(args, "-l", required(params, "label_dir")) + append_flag(args, "-r", required(params, "result_dir")) + return CommandSpec(args, DATASET_TOOL_DIR, "stitch image and label panels") + + if job_type == "dataset.video_frames": + script = VIDEO_DIR / "1_Save_Frame_V2.py" + args = conda_python(conda_env, script) + append_flag(args, "--video", required(params, "video")) + append_flag(args, "--interval", params.get("interval", 0.5)) + append_flag(args, "--resize", params.get("resize")) + append_flag(args, "--output_dir", params.get("output_dir")) + return CommandSpec(args, VIDEO_DIR, "extract video frames into DataSet_Public layout") + + if job_type == "dataset.yolo_check_pairs": + script = YOLO_DATASET_DIR / "0_1_check_picture_pair.py" + args = conda_python(conda_env, script) + append_flag(args, "-i", required(params, "image_dir")) + append_flag(args, "-l", required(params, "label_dir")) + return CommandSpec(args, YOLO_DATASET_DIR, "check YOLO image/label pairs") + + return None + diff --git a/backend/app/modules/mmseg/__init__.py b/backend/app/modules/mmseg/__init__.py new file mode 100644 index 0000000..e4f38f2 --- /dev/null +++ b/backend/app/modules/mmseg/__init__.py @@ -0,0 +1,2 @@ +"""MMSeg task wrappers.""" + diff --git a/backend/app/modules/mmseg/tasks.py b/backend/app/modules/mmseg/tasks.py new file mode 100644 index 0000000..abbb2c8 --- /dev/null +++ b/backend/app/modules/mmseg/tasks.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +from ...commands import CommandSpec, append_flag, conda_python, required +from ...config import settings + + +MMSEG_DIR = settings.source_root / "Seg_All_In_One_MMSeg" +MY_DIR = MMSEG_DIR / "My_All_In_One" + + +def _stdin_for_generate_alg(params: dict) -> str: + lines = [ + str(params.get("dataset_choice", 1)), + str(params.get("gpu_count", 1)), + ] + gpu_ids = params.get("gpu_ids", [0]) + if isinstance(gpu_ids, str): + gpu_ids = [part.strip() for part in gpu_ids.split(",") if part.strip()] + for index in range(int(params.get("gpu_count", len(gpu_ids) or 1))): + lines.append(str(gpu_ids[index] if index < len(gpu_ids) else 0)) + + mode = str(params.get("schedule_mode", 2)) + lines.append(mode) + if mode == "1": + lines.extend( + [ + str(params.get("train_k", 40)), + str(params.get("check_count", 10)), + str(params.get("logger_interval", 50)), + ] + ) + else: + lines.extend( + [ + str(params.get("max_epochs", 300)), + str(params.get("val_interval", 1)), + str(params.get("checkpoint_interval", 10)), + str(params.get("logger_interval", "")), + ] + ) + lines.append(str(params.get("algorithm_choice", 1))) + return "\n".join(lines) + "\n" + + +def build_mmseg_task(job_type: str, params: dict, conda_env: str) -> CommandSpec | None: + if job_type == "mmseg.init_weights": + return CommandSpec(conda_python(conda_env, MY_DIR / "0_Initial_Save_All_Model_locally.py"), MMSEG_DIR, "download/save MMSeg pretrained weights locally") + + if job_type == "mmseg.generate_data": + return CommandSpec(conda_python(conda_env, MY_DIR / "1_Initial_Data_All_data_from_1_Data_Parameter-V2.py"), MMSEG_DIR, "generate MMSeg dataset configs from JSON parameters") + + if job_type == "mmseg.generate_alg": + script = MY_DIR / "2_Initial_Alg_All_data_from_2_Alg_Program-V2.py" + return CommandSpec( + conda_python(conda_env, script), + MMSEG_DIR, + "generate MMSeg algorithm config and training command", + stdin_text=_stdin_for_generate_alg(params), + ) + + if job_type == "mmseg.train": + config_path = required(params, "config") + args = conda_python(conda_env, MMSEG_DIR / "tools" / "train.py", config_path) + append_flag(args, "--work-dir", params.get("work_dir")) + return CommandSpec(args, MMSEG_DIR, "train MMSeg model") + + if job_type == "mmseg.metrics": + args = conda_python(conda_env, MY_DIR / "4_2_predict_matrics_from_log_V2.py") + append_flag(args, "--input_dir", params.get("input_dir", "../Hardisk")) + append_flag(args, "--output_dir", params.get("output_dir", "../BestMode_Predict_Results_DataSet_Public")) + stdin = f"{params.get('dataset_choice', 1)}\n{params.get('algorithm_choice', 0)}\n" + return CommandSpec(args, MMSEG_DIR, "extract best MMSeg metrics from logs", stdin_text=stdin) + + if job_type == "mmseg.flops_fps": + args = conda_python(conda_env, MY_DIR / "4_1_predict_params_FLOPs_FPS_V2.py") + append_flag(args, "--input_dir", params.get("input_dir", "../Hardisk")) + append_flag(args, "--output_dir", params.get("output_dir", "../BestMode_Predict_Results_DataSet_Public")) + append_flag(args, "--repeat-times", params.get("repeat_times", 3)) + stdin = f"{params.get('dataset_choice', 1)}\n{params.get('algorithm_choice', 0)}\n" + if "shape_h" in params and "shape_w" in params: + stdin += f"{params['shape_h']}\n{params['shape_w']}\n" + return CommandSpec(args, MMSEG_DIR, "calculate MMSeg FLOPs/Params/FPS", stdin_text=stdin) + + if job_type == "mmseg.draw": + return CommandSpec(conda_python(conda_env, MY_DIR / "4_3_predict_draw_pictures_and_tabels.py"), MMSEG_DIR, "generate MMSeg prediction pictures and tables") + + if job_type == "mmseg.extract_loss_miou": + return CommandSpec(conda_python(conda_env, MY_DIR / "4_4_extract_loss_and_best_miou.py"), MMSEG_DIR, "extract MMSeg loss and best mIoU curves") + + if job_type == "mmseg.delete_epoch": + return CommandSpec(conda_python(conda_env, MY_DIR / "3_Find_And_Delete_Special_Epoch.py"), MMSEG_DIR, "find and delete selected epoch checkpoints") + + return None + diff --git a/backend/app/modules/segmodel/__init__.py b/backend/app/modules/segmodel/__init__.py new file mode 100644 index 0000000..1ed492a --- /dev/null +++ b/backend/app/modules/segmodel/__init__.py @@ -0,0 +1,2 @@ +"""SegModel task wrappers.""" + diff --git a/backend/app/modules/segmodel/tasks.py b/backend/app/modules/segmodel/tasks.py new file mode 100644 index 0000000..b006d74 --- /dev/null +++ b/backend/app/modules/segmodel/tasks.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +from ...commands import CommandSpec, append_flag, bash, conda_python, required +from ...config import settings + + +SEGMODEL_DIR = settings.source_root / "Seg_All_In_One_SegModel" + + +def build_segmodel_task(job_type: str, params: dict, conda_env: str) -> CommandSpec | None: + env = {"SEG_CONDA_ENV": conda_env} + + if job_type == "segmodel.train": + args = conda_python(conda_env, SEGMODEL_DIR / "train.py") + append_flag(args, "-a", required(params, "architecture")) + return CommandSpec(args, SEGMODEL_DIR, "train one segmentation_models_pytorch architecture") + + if job_type == "segmodel.batch_train": + return CommandSpec(bash(SEGMODEL_DIR / "train.sh"), SEGMODEL_DIR, "run legacy SegModel batch training", env=env) + + if job_type == "segmodel.predict": + args = conda_python(conda_env, SEGMODEL_DIR / "1_predict.py") + append_flag(args, "-a", required(params, "architecture")) + choice = str(params.get("run_choice", 1)) + return CommandSpec(args, SEGMODEL_DIR, "predict with one SegModel run", stdin_text=f"{choice}\n") + + if job_type == "segmodel.batch_predict": + return CommandSpec(bash(SEGMODEL_DIR / "predict.sh"), SEGMODEL_DIR, "run legacy SegModel batch prediction", env=env) + + if job_type == "segmodel.flops": + script = SEGMODEL_DIR / params.get("script", "2_predict_params_and_FLOPs_V2.py") + return CommandSpec(conda_python(conda_env, script), SEGMODEL_DIR, "calculate SegModel params/FLOPs/FPS") + + if job_type == "segmodel.raw_mask_check": + return CommandSpec(conda_python(conda_env, SEGMODEL_DIR / "1_predict_raw_masks_check.py"), SEGMODEL_DIR, "check SegModel raw mask completeness") + + if job_type == "segmodel.metrics": + return CommandSpec(conda_python(conda_env, SEGMODEL_DIR / "3_predict_matrics_from_log.py"), SEGMODEL_DIR, "parse SegModel training/prediction metrics") + + return None + diff --git a/backend/app/modules/system/__init__.py b/backend/app/modules/system/__init__.py new file mode 100644 index 0000000..e120f6e --- /dev/null +++ b/backend/app/modules/system/__init__.py @@ -0,0 +1,2 @@ +"""System task wrappers.""" + diff --git a/backend/app/modules/system/service.py b/backend/app/modules/system/service.py new file mode 100644 index 0000000..7c981b2 --- /dev/null +++ b/backend/app/modules/system/service.py @@ -0,0 +1,106 @@ +from __future__ import annotations + +import shutil +import subprocess +from pathlib import Path + +from ...config import settings + + +def parse_nvidia_smi_csv(output: str) -> list[dict]: + gpus: list[dict] = [] + for line in output.splitlines(): + if not line.strip(): + continue + parts = [part.strip() for part in line.split(",")] + if len(parts) < 7: + continue + index, name, total, used, free, util, temp = parts[:7] + try: + gpus.append( + { + "index": int(index), + "name": name, + "memory_total_mb": int(total), + "memory_used_mb": int(used), + "memory_free_mb": int(free), + "utilization_gpu_percent": int(util), + "temperature_c": int(temp), + } + ) + except ValueError: + continue + return gpus + + +def get_gpus() -> dict: + cmd = [ + "nvidia-smi", + "--query-gpu=index,name,memory.total,memory.used,memory.free,utilization.gpu,temperature.gpu", + "--format=csv,noheader,nounits", + ] + try: + result = subprocess.run(cmd, capture_output=True, text=True, check=True) + return {"available": True, "gpus": parse_nvidia_smi_csv(result.stdout)} + except Exception as exc: + return {"available": False, "gpus": [], "error": str(exc)} + + +def get_conda_envs() -> dict: + try: + result = subprocess.run(["conda", "env", "list"], capture_output=True, text=True, check=True) + except Exception as exc: + return {"available": False, "envs": [], "error": str(exc)} + envs = [] + for line in result.stdout.splitlines(): + raw = line.strip() + if not raw or raw.startswith("#"): + continue + marker = "*" in raw.split() + parts = raw.replace("*", " ").split() + if len(parts) >= 2: + envs.append({"name": parts[0], "path": parts[-1], "active": marker}) + return {"available": True, "envs": envs, "task_default": settings.task_conda_env} + + +def disk_usage() -> dict: + usage = shutil.disk_usage(settings.source_root) + return { + "path": str(settings.source_root), + "total": usage.total, + "used": usage.used, + "free": usage.free, + } + + +def scan_results() -> list[dict]: + roots = [ + settings.source_root / "DataSet_Public_outputs", + settings.source_root / "BestMode_Predict_Results_DataSet_Public", + settings.source_root / "Hardisk", + settings.source_root / "Seg_All_In_One_Analysis", + ] + exts = {".csv", ".png", ".jpg", ".jpeg", ".svg", ".log", ".pth", ".pt"} + results: list[dict] = [] + for root in roots: + if not root.exists(): + continue + for path in root.rglob("*"): + if path.is_file() and path.suffix.lower() in exts: + try: + stat = path.stat() + results.append( + { + "name": path.name, + "path": str(path.resolve()), + "relative_path": str(path.resolve().relative_to(settings.source_root)), + "size": stat.st_size, + "modified": stat.st_mtime, + "kind": path.suffix.lower().lstrip("."), + } + ) + except OSError: + continue + results.sort(key=lambda item: item["modified"], reverse=True) + return results[:1000] + diff --git a/backend/app/modules/system/tasks.py b/backend/app/modules/system/tasks.py new file mode 100644 index 0000000..b4e60d5 --- /dev/null +++ b/backend/app/modules/system/tasks.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +from ...commands import CommandSpec, bash +from ...config import settings + + +def build_system_task(job_type: str, params: dict, conda_env: str) -> CommandSpec | None: + if job_type == "system.backup": + return CommandSpec(bash(settings.source_root / "Back_Up.sh"), settings.source_root, "run legacy backup script") + if job_type == "mock.echo": + message = params.get("message", "Seg Data Server mock job") + return CommandSpec(["python", "-c", f"print({message!r})"], settings.project_root, "test job runner") + return None + diff --git a/backend/app/modules/weights/__init__.py b/backend/app/modules/weights/__init__.py new file mode 100644 index 0000000..b9bc8a0 --- /dev/null +++ b/backend/app/modules/weights/__init__.py @@ -0,0 +1,2 @@ +"""Weight sync and verification.""" + diff --git a/backend/app/modules/weights/service.py b/backend/app/modules/weights/service.py new file mode 100644 index 0000000..02c324a --- /dev/null +++ b/backend/app/modules/weights/service.py @@ -0,0 +1,153 @@ +from __future__ import annotations + +import hashlib +import json +import os +import shutil +import subprocess +from datetime import datetime, timezone +from pathlib import Path +from typing import Iterable + +from ...config import settings + +WEIGHT_EXTS = {".pt", ".pth", ".onnx", ".engine"} + + +def sha256_file(path: Path, chunk_size: int = 1024 * 1024 * 8) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + while True: + chunk = handle.read(chunk_size) + if not chunk: + break + digest.update(chunk) + return digest.hexdigest() + + +def iter_source_weights() -> Iterable[Path]: + project_root = settings.project_root.resolve() + for path in settings.source_root.rglob("*"): + if not path.is_file() or path.suffix.lower() not in WEIGHT_EXTS: + continue + try: + path.resolve().relative_to(project_root) + continue + except ValueError: + yield path + + +def classify_weight(path: Path) -> dict[str, str]: + try: + rel = str(path.resolve().relative_to(settings.source_root)) + except ValueError: + rel = str(path) + lower = rel.lower() + if "yolo" in lower: + family = "yolo" + elif "mmseg" in lower or "my_local_model" in lower: + family = "mmseg" + elif "segmodel" in lower: + family = "segmodel" + else: + family = "misc" + if "best.pt" in lower or "best.pth" in lower: + role = "trained_best" + elif "last.pt" in lower or "last.pth" in lower: + role = "trained_last" + elif "pretrain" in lower or "my_local_model" in lower: + role = "pretrained" + else: + role = "weight" + return {"family": family, "role": role} + + +def copy_weight(src: Path, dst: Path, mode: str) -> None: + dst.parent.mkdir(parents=True, exist_ok=True) + if mode == "hardlink": + if dst.exists(): + dst.unlink() + os.link(src, dst) + elif mode == "reflink": + subprocess.run(["cp", "--reflink=auto", "--preserve=timestamps", str(src), str(dst)], check=True) + else: + shutil.copy2(src, dst) + + +def sync_weights(mode: str = "copy", hash_files: bool = True, skip_existing: bool = True) -> dict: + files_dir = settings.weights_root / "files" + settings.weights_root.mkdir(parents=True, exist_ok=True) + entries = [] + total_bytes = 0 + for src in sorted(iter_source_weights()): + rel = src.resolve().relative_to(settings.source_root) + dst = files_dir / rel + stat = src.stat() + total_bytes += stat.st_size + copied = False + if not (skip_existing and dst.exists() and dst.stat().st_size == stat.st_size): + copy_weight(src, dst, mode) + copied = True + meta = classify_weight(src) + entry = { + "source_path": str(rel), + "stored_path": str(dst.resolve().relative_to(settings.project_root)), + "size": stat.st_size, + "family": meta["family"], + "role": meta["role"], + "copied": copied, + } + if hash_files: + entry["sha256"] = sha256_file(dst) + entries.append(entry) + + manifest = { + "generated_at": datetime.now(timezone.utc).isoformat(), + "updated_at": datetime.now(timezone.utc).isoformat(), + "source_root": str(settings.source_root), + "mode": mode, + "count": len(entries), + "total_bytes": total_bytes, + "files": entries, + } + manifest_path = settings.weights_root / "manifest.json" + manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + return manifest + + +def load_manifest() -> dict: + manifest_path = settings.weights_root / "manifest.json" + if not manifest_path.exists(): + return { + "generated_at": None, + "source_root": str(settings.source_root), + "count": 0, + "total_bytes": 0, + "files": [], + } + return json.loads(manifest_path.read_text(encoding="utf-8")) + + +def verify_weights() -> dict: + manifest = load_manifest() + checked = [] + ok_count = 0 + for entry in manifest.get("files", []): + path = settings.project_root / entry["stored_path"] + exists = path.exists() + size_ok = exists and path.stat().st_size == entry.get("size") + hash_ok = None + if exists and "sha256" in entry: + hash_ok = sha256_file(path) == entry["sha256"] + ok = bool(exists and size_ok and (hash_ok is not False)) + ok_count += int(ok) + checked.append( + { + "stored_path": entry["stored_path"], + "exists": exists, + "size_ok": size_ok, + "hash_ok": hash_ok, + "ok": ok, + } + ) + return {"count": len(checked), "ok_count": ok_count, "items": checked} diff --git a/backend/app/modules/yolo/__init__.py b/backend/app/modules/yolo/__init__.py new file mode 100644 index 0000000..ae4513d --- /dev/null +++ b/backend/app/modules/yolo/__init__.py @@ -0,0 +1,2 @@ +"""YOLO task wrappers.""" + diff --git a/backend/app/modules/yolo/tasks.py b/backend/app/modules/yolo/tasks.py new file mode 100644 index 0000000..ff1ad71 --- /dev/null +++ b/backend/app/modules/yolo/tasks.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +from ...commands import CommandSpec, append_flag, bash, conda_python, required +from ...config import settings + + +YOLO_DIR = settings.source_root / "Seg_All_In_One_YoloModel" +VIDEO_YOLO_DIR = settings.source_root / "Seg_Predict_YoloModel" + + +def build_yolo_task(job_type: str, params: dict, conda_env: str) -> CommandSpec | None: + env = {"SEG_CONDA_ENV": conda_env} + + if job_type == "yolo.train": + args = conda_python(conda_env, YOLO_DIR / "yolo_train.py") + append_flag(args, "--model", required(params, "model")) + return CommandSpec(args, YOLO_DIR, "train one Ultralytics YOLO segmentation model") + + if job_type == "yolo.batch_train": + return CommandSpec(bash(YOLO_DIR / "yolo_train.sh"), YOLO_DIR, "run legacy YOLO batch training", env=env) + + if job_type == "yolo.predict": + args = conda_python(conda_env, YOLO_DIR / "yolo_predict_V2.py") + append_flag(args, "--model", required(params, "model")) + append_flag(args, "--source", params.get("source")) + append_flag(args, "--pt_name", params.get("pt_name", "best.pt")) + append_flag(args, "--conf", params.get("conf", 0.2)) + choice = str(params.get("run_choice", 1)) + return CommandSpec(args, YOLO_DIR, "predict with one YOLO model", stdin_text=f"{choice}\n") + + if job_type == "yolo.batch_predict": + args = bash(YOLO_DIR / "yolo_predict.sh") + append_flag(args, "--pt_name", params.get("pt_name", "best.pt")) + append_flag(args, "--conf", params.get("conf", 0.2)) + append_flag(args, "--heatmap_method", params.get("heatmap_method")) + return CommandSpec(args, YOLO_DIR, "run legacy YOLO batch prediction", env=env) + + if job_type == "yolo.heatmap": + args = conda_python(conda_env, YOLO_DIR / "yolo_predict_visualize_nn.py") + append_flag(args, "--model", required(params, "model")) + append_flag(args, "--target_layers", params.get("target_layers", "default")) + append_flag(args, "--cam_method", params.get("cam_method", "All")) + append_flag(args, "--pt_name", params.get("pt_name", "best.pt")) + choice = str(params.get("run_choice", 1)) + return CommandSpec(args, YOLO_DIR, "generate YOLO heatmaps", stdin_text=f"{choice}\n") + + if job_type == "yolo.compare": + args = conda_python(conda_env, YOLO_DIR / "yolo_predict_V2_compare_all.py") + append_flag(args, "--pt_name", params.get("pt_name", "all")) + return CommandSpec(args, YOLO_DIR, "compare all YOLO prediction outputs") + + if job_type == "yolo.raw_mask_check": + args = conda_python(conda_env, YOLO_DIR / "yolo_predict_raw_masks_check.py") + append_flag(args, "--pt_name", params.get("pt_name", "best.pt")) + return CommandSpec(args, YOLO_DIR, "check YOLO raw mask completeness") + + if job_type == "yolo.copy_best": + args = bash(YOLO_DIR / "Tool_Yolo_Copy_Best_Model.sh") + append_flag(args, "--pt_name", params.get("pt_name", "best.pt")) + return CommandSpec(args, YOLO_DIR, "copy YOLO best weights into prediction area") + + if job_type == "yolo.video_visible": + return CommandSpec(conda_python(conda_env, VIDEO_YOLO_DIR / "yolo_Seg_Video-V1-Visible.py"), VIDEO_YOLO_DIR, "render visible YOLO video prediction") + + if job_type == "yolo.video_unvisible": + return CommandSpec(conda_python(conda_env, VIDEO_YOLO_DIR / "yolo_Seg_Video-V2-UnVisible.py"), VIDEO_YOLO_DIR, "render invisible/headless YOLO video prediction") + + return None + diff --git a/backend/app/paths.py b/backend/app/paths.py new file mode 100644 index 0000000..ddae6e2 --- /dev/null +++ b/backend/app/paths.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from pathlib import Path + + +class PathSecurityError(ValueError): + pass + + +def ensure_inside(path: Path, root: Path) -> Path: + resolved = path.expanduser().resolve() + root_resolved = root.expanduser().resolve() + try: + resolved.relative_to(root_resolved) + except ValueError as exc: + raise PathSecurityError(f"path escapes root: {resolved}") from exc + return resolved + + +def resolve_user_path(value: str | None, default: Path, root: Path) -> Path: + if value: + candidate = Path(value).expanduser() + if not candidate.is_absolute(): + candidate = root / candidate + else: + candidate = default + return ensure_inside(candidate, root) + + +def rel(path: Path, root: Path) -> str: + try: + return str(path.resolve().relative_to(root.resolve())) + except ValueError: + return str(path.resolve()) + diff --git a/backend/app/schemas.py b/backend/app/schemas.py new file mode 100644 index 0000000..b259a6e --- /dev/null +++ b/backend/app/schemas.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +from typing import Any, Literal + +from pydantic import BaseModel, Field + + +JobStatus = Literal["queued", "running", "success", "failed", "cancelled"] + + +class JobCreate(BaseModel): + type: str = Field(..., examples=["segmodel.train", "yolo.predict"]) + params: dict[str, Any] = Field(default_factory=dict) + gpus: list[int] | None = None + conda_env: str | None = None + + +class JobRecord(BaseModel): + id: str + type: str + status: JobStatus + params: dict[str, Any] + command: list[str] + cwd: str + description: str = "" + pid: int | None = None + exit_code: int | None = None + created_at: str + started_at: str | None = None + finished_at: str | None = None + log_path: str + error: str | None = None + + +class ProfileCreate(BaseModel): + name: str + kind: str + data: dict[str, Any] = Field(default_factory=dict) + + +class WeightSyncRequest(BaseModel): + mode: Literal["copy", "reflink", "hardlink"] = "copy" + hash_files: bool = True + skip_existing: bool = True + diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..2de758c --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,6 @@ +fastapi>=0.110 +uvicorn[standard]>=0.27 +pydantic>=2 +python-multipart>=0.0.9 +pytest>=8 + diff --git a/backend/tests/test_catalog.py b/backend/tests/test_catalog.py new file mode 100644 index 0000000..2b3967c --- /dev/null +++ b/backend/tests/test_catalog.py @@ -0,0 +1,15 @@ +from app.catalog import SEGMODEL_ARCHS, TASK_TYPES, YOLO_MODELS + + +def test_catalog_contains_required_capabilities(): + assert "Unet" in SEGMODEL_ARCHS + assert "YOLOv9e-seg" in YOLO_MODELS + for task in [ + "dataset.video_frames", + "segmodel.train", + "yolo.predict", + "mmseg.flops_fps", + "analysis.all", + ]: + assert task in TASK_TYPES + diff --git a/backend/tests/test_system_service.py b/backend/tests/test_system_service.py new file mode 100644 index 0000000..7c3441f --- /dev/null +++ b/backend/tests/test_system_service.py @@ -0,0 +1,18 @@ +from app.modules.system.service import parse_nvidia_smi_csv + + +def test_parse_nvidia_smi_csv(): + output = "0, NVIDIA GeForce RTX 4090, 24564, 15, 24069, 0, 37\n" + gpus = parse_nvidia_smi_csv(output) + assert gpus == [ + { + "index": 0, + "name": "NVIDIA GeForce RTX 4090", + "memory_total_mb": 24564, + "memory_used_mb": 15, + "memory_free_mb": 24069, + "utilization_gpu_percent": 0, + "temperature_c": 37, + } + ] + diff --git a/backend/tests/test_weights_service.py b/backend/tests/test_weights_service.py new file mode 100644 index 0000000..c571bba --- /dev/null +++ b/backend/tests/test_weights_service.py @@ -0,0 +1,14 @@ +from pathlib import Path + +from app.modules.weights.service import classify_weight + + +def test_classify_weight(): + item = classify_weight(Path("Seg_All_In_One_YoloModel/yolov8n-seg.pt")) + assert item["family"] == "yolo" + assert item["role"] == "weight" + + best = classify_weight(Path("Seg_Predict_YoloModel/YOLOv9e-seg/weights/best.pt")) + assert best["family"] == "yolo" + assert best["role"] == "trained_best" + diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..3e60698 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + Seg Data Server Net + + +
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-0,0 +1,23 @@ +{ + "name": "seg-data-server-net-frontend", + "private": true, + "version": "0.1.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "preview": "vite preview" + }, + "dependencies": { + "@vitejs/plugin-react": "^4.3.4", + "lucide-react": "^0.468.0", + "react": "^19.0.0", + "react-dom": "^19.0.0", + "typescript": "^5.7.2", + "vite": "^6.0.7" + }, + "devDependencies": { + "@types/react": "^19.2.17", + "@types/react-dom": "^19.2.3" + } +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx new file mode 100644 index 0000000..7218dc2 --- /dev/null +++ b/frontend/src/main.tsx @@ -0,0 +1,400 @@ +import React, { useEffect, useMemo, useState } from "react"; +import { createRoot } from "react-dom/client"; +import { + Activity, + BarChart3, + Cpu, + Database, + FileSearch, + Gauge, + HardDrive, + Layers3, + Play, + RefreshCcw, + ShieldCheck, + Square, + Terminal, + UploadCloud, + Zap +} from "lucide-react"; +import "./styles.css"; + +const API_BASE = import.meta.env.VITE_API_BASE ?? "http://localhost:8000"; + +type Job = { + id: string; + type: string; + status: string; + description: string; + created_at: string; + started_at?: string; + finished_at?: string; + log_tail?: string; + params: Record; +}; + +type Catalog = { + task_types: string[]; + segmodel_architectures: string[]; + yolo_models: string[]; + mmseg_algorithms: string[]; + datasets: Array<{ name: string; path: string; source: string }>; + weights: { count: number; total_bytes: number; updated_at?: string }; +}; + +type GpuPayload = { + available: boolean; + gpus: Array<{ + index: number; + name: string; + memory_total_mb: number; + memory_used_mb: number; + memory_free_mb: number; + utilization_gpu_percent: number; + temperature_c: number; + }>; +}; + +async function api(path: string, init?: RequestInit): Promise { + const res = await fetch(`${API_BASE}${path}`, { + headers: { "Content-Type": "application/json" }, + ...init + }); + if (!res.ok) throw new Error(await res.text()); + return res.json(); +} + +const defaultParams: Record> = { + "mock.echo": { message: "hello from Seg Data Server" }, + "dataset.video_frames": { video: "../Seg_Predict_Own_Video_V2/LC_Video_1.mp4", interval: 0.5, resize: "1920x1080" }, + "segmodel.train": { architecture: "Unet" }, + "segmodel.predict": { architecture: "Unet", run_choice: 1 }, + "yolo.train": { model: "YOLOv8n-seg" }, + "yolo.predict": { model: "YOLOv8n-seg", pt_name: "best.pt", conf: 0.2, run_choice: 1 }, + "yolo.heatmap": { model: "YOLOv8n-seg", cam_method: "All", pt_name: "best.pt", run_choice: 1 }, + "mmseg.generate_alg": { dataset_choice: 1, gpu_count: 1, gpu_ids: [0], schedule_mode: 2, max_epochs: 300, algorithm_choice: 1 }, + "mmseg.train": { config: "configs/example.py", work_dir: "../DataSet_Public_outputs/example" }, + "mmseg.metrics": { input_dir: "../Hardisk", output_dir: "../BestMode_Predict_Results_DataSet_Public", dataset_choice: 1, algorithm_choice: 0 }, + "mmseg.flops_fps": { input_dir: "../Hardisk", output_dir: "../BestMode_Predict_Results_DataSet_Public", repeat_times: 3, dataset_choice: 1, algorithm_choice: 0 }, + "analysis.all": { input_dir: "../BestMode_Predict_Results_DataSet_Public", output_dir: "./", dataset_choice: 1 } +}; + +function formatBytes(value?: number) { + if (!value) return "0 B"; + const units = ["B", "KB", "MB", "GB", "TB"]; + let next = value; + let unit = 0; + while (next >= 1024 && unit < units.length - 1) { + next /= 1024; + unit += 1; + } + return `${next.toFixed(unit > 1 ? 2 : 0)} ${units[unit]}`; +} + +function useData() { + const [catalog, setCatalog] = useState(null); + const [gpus, setGpus] = useState(null); + const [jobs, setJobs] = useState([]); + const [results, setResults] = useState>>([]); + const [error, setError] = useState(""); + + async function refresh() { + try { + const [catalogNext, gpusNext, jobsNext, resultsNext] = await Promise.all([ + api("/api/catalog"), + api("/api/system/gpus"), + api("/api/jobs"), + api>>("/api/results") + ]); + setCatalog(catalogNext); + setGpus(gpusNext); + setJobs(jobsNext); + setResults(resultsNext.slice(0, 80)); + setError(""); + } catch (err) { + setError(String(err)); + } + } + + useEffect(() => { + refresh(); + const timer = window.setInterval(refresh, 5000); + return () => window.clearInterval(timer); + }, []); + + return { catalog, gpus, jobs, results, error, refresh }; +} + +function StatusPill({ status }: { status: string }) { + return {status}; +} + +function App() { + const { catalog, gpus, jobs, results, error, refresh } = useData(); + const [taskType, setTaskType] = useState("mock.echo"); + const [params, setParams] = useState(JSON.stringify(defaultParams["mock.echo"], null, 2)); + const [selectedJob, setSelectedJob] = useState(null); + const [log, setLog] = useState(""); + const [busy, setBusy] = useState(false); + + const runningCount = jobs.filter((job) => job.status === "running").length; + const successCount = jobs.filter((job) => job.status === "success").length; + const failedCount = jobs.filter((job) => job.status === "failed").length; + + const taskGroups = useMemo>(() => { + const items = catalog?.task_types ?? []; + return { + dataset: items.filter((task) => task.startsWith("dataset.")), + segmodel: items.filter((task) => task.startsWith("segmodel.")), + yolo: items.filter((task) => task.startsWith("yolo.")), + mmseg: items.filter((task) => task.startsWith("mmseg.")), + analysis: items.filter((task) => task.startsWith("analysis.") || task.startsWith("system.") || task.startsWith("mock.")) + }; + }, [catalog]); + + function pickTask(next: string) { + setTaskType(next); + setParams(JSON.stringify(defaultParams[next] ?? {}, null, 2)); + } + + async function createJob() { + setBusy(true); + try { + await api("/api/jobs", { + method: "POST", + body: JSON.stringify({ type: taskType, params: JSON.parse(params) }) + }); + await refresh(); + } finally { + setBusy(false); + } + } + + async function syncWeights() { + setBusy(true); + try { + await api("/api/weights/sync", { + method: "POST", + body: JSON.stringify({ mode: "copy", hash_files: true, skip_existing: true }) + }); + await refresh(); + } finally { + setBusy(false); + } + } + + async function inspectJob(job: Job) { + const detail = await api(`/api/jobs/${job.id}`); + setSelectedJob(detail); + setLog(detail.log_tail ?? ""); + const source = new EventSource(`${API_BASE}/api/jobs/${job.id}/events`); + source.onmessage = (event) => { + const payload = JSON.parse(event.data); + if (payload.chunk) setLog((prev) => `${prev}${payload.chunk}`); + setSelectedJob(payload.job); + if (["success", "failed", "cancelled"].includes(payload.job.status)) source.close(); + }; + } + + async function cancelSelectedJob() { + if (!selectedJob) return; + await api(`/api/jobs/${selectedJob.id}/cancel`, { method: "POST" }); + await refresh(); + } + + return ( +
+ + +
+
+
+

Segmentation Operations

+

训练、预测、分析与权重资产控制台

+
+ +
+ + {error &&
{error}
} + +
+
+ + 运行中 + {runningCount} +
+
+ + 成功 + {successCount} +
+
+ + 失败 + {failedCount} +
+
+ + 数据集 + {catalog?.datasets.length ?? 0} +
+
+ +
+
+
+
+

Job Builder

+

创建任务

+
+ +
+
+ {Object.entries(taskGroups).map(([group, values]) => ( +
+ {group} + {values.map((task) => ( + + ))} +
+ ))} +
+