117 lines
4.4 KiB
Markdown
117 lines
4.4 KiB
Markdown
# Seg Data Server Net
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Modular web control plane for the existing Seg image segmentation workspace.
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The platform keeps the current training and analysis scripts as the compute
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core, then adds:
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- a FastAPI backend for catalog discovery, job orchestration, logs, results,
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GPU status, and weight management;
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- a React/Vite frontend for launching jobs and inspecting progress;
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- a unified `weights/` area with a generated manifest for `.pt`, `.pth`,
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`.onnx`, and `.engine` assets.
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## Layout
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```text
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Seg_Data_Server_Net/
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backend/ FastAPI API, job runner, module wrappers
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frontend/ React + Vite operator UI
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scripts/ helper scripts for running services and syncing weights
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weights/ copied model weights and manifest.json
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```
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## Quick Start
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```bash
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cd Seg_Data_Server_Net
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cp .env.example .env
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# Backend. The deployment env is seg_smp so the API and task wrappers share
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# the same segmentation dependency stack.
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conda run -n seg_smp uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8010
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# Frontend.
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cd frontend
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npm install
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npm run dev -- --host 0.0.0.0
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```
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Open the Vite URL shown in the terminal. The frontend expects the backend at
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`http://localhost:8010` by default.
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The web UI includes a dataset bench for creating upload workspaces, uploading
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images/labels/masks, and jumping into the existing rename, PNG conversion,
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resize, pair-check, label rebuild, transparent overlay, stitch, and video-frame
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jobs. Segmentation previews, YOLO heatmaps, and loss/metric artifacts are
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grouped on the results dashboard.
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The coverage panel calls `GET /api/coverage` and verifies that the user-facing
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scripts from the existing `Seg/` workspace are mapped to web jobs. MMSeg
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vendored internals, docs, build outputs, converters, and config templates are
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classified as supporting artifacts rather than direct web actions.
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The same panel can run `POST /api/acceptance/smoke`, a lightweight live smoke
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that creates an upload dataset, uploads a label, downloads it through the
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artifact API, runs a mock job, checks SSE log streaming, and executes one
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legacy image/label overlay job on tiny generated PNGs.
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## Weight Sync
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The current workspace contains tens of GB of pretrained and trained weights.
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They are copied into `weights/files/<original-relative-path>` and indexed in
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`weights/manifest.json`.
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```bash
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cd Seg_Data_Server_Net
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python scripts/sync_weights.py --mode copy --hash
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```
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Weights must remain local to the deployment machine. Do not push `.pt`, `.pth`,
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`.onnx`, `.engine`, or `weights/files/` into Gitea. The repository stores only
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code, `weights/manifest.json`, and helper scripts. Before pushing, run:
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```bash
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scripts/check_no_weight_git.sh
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```
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If a deployment machine needs weights, run the sync command locally on that
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machine after cloning the code.
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## Job Types
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The backend exposes all current Seg capabilities as job types. Examples:
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- `dataset.rename`, `dataset.resize`, `dataset.pair`, `dataset.rebuild_labels`,
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`dataset.stack`, `dataset.stitch`, `dataset.video_frames`,
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`dataset.yolo_check_pairs`, `dataset.yolo_stack`, `dataset.yolo_txt_sort`,
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`dataset.yolo_convert_png`, `dataset.yolo_resize`
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- `segmodel.train`, `segmodel.batch_train`, `segmodel.predict`,
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`segmodel.batch_predict`, `segmodel.flops`, `segmodel.params_flops`,
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`segmodel.benchmark`, `segmodel.raw_mask_check`
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- `yolo.train`, `yolo.batch_train`, `yolo.predict`, `yolo.batch_predict`,
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`yolo.heatmap`, `yolo.compare`, `yolo.raw_mask_check`, `yolo.video_visible`
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- `mmseg.generate_data`, `mmseg.generate_alg`, `mmseg.train`,
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`mmseg.metrics`, `mmseg.flops_fps`, `mmseg.draw`, `mmseg.extract_loss_miou`
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- `visual.train`, `visual.inference`, `visual.fps`,
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`visual.yolo11_heatmap_v1`, `visual.yolo11_heatmap_v2`, `visual.deal_labels`
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- `analysis.all`, `system.backup`, `mock.echo`
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Use `GET /api/catalog` to inspect supported models, algorithms, datasets, and
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task types discovered from the existing `Seg/` workspace.
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Use `GET /api/coverage` to inspect script-to-task coverage and task
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buildability.
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## Agents
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Run the local evaluation and validation agents before publishing changes:
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```bash
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PYTHONPATH=backend conda run -n seg_smp python scripts/run_agents.py --build
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```
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The validation agent checks catalog coverage, the new `seg_smp` env, GPU
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visibility, no-weight Git safety, backend tests, frontend build, and live
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backend/frontend endpoints when the services are running. With live validation
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enabled it also runs the lightweight acceptance smoke above.
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