Show dataset YOLO output readiness

2026-06-30 15:29:30 +08:00
parent 826027629b
commit b60fcc5112
6 changed files with 175 additions and 12 deletions
--- a/README.md
+++ b/README.md
@@ -54,6 +54,8 @@ checks YOLO txt labels and mask dimensions, and can generate a `dataset.yaml`
 for the `yolo.train_custom` task. The selected upload dataset also exposes
 direct YOLO custom train, predict, and heatmap actions; custom outputs are
 written under `var/custom_yolo_runs` and are scanned by the results dashboard.
+When a dataset is selected, the dataset panel shows its custom YOLO `best.pt`,
+prediction previews, heatmap previews, and detected training curves.
 Segmentation previews, YOLO heatmaps, and loss/metric artifacts are grouped on
 the results dashboard, and YOLO-style `results.csv` files are parsed into
 lightweight training curves.
--- a/backend/app/agents/evaluation_agent.py
+++ b/backend/app/agents/evaluation_agent.py
@@ -44,6 +44,7 @@ def evaluate_project() -> dict:
        "upload_ui": "uploadDatasetFiles" in frontend_text and "labels" in frontend_text and "masks" in frontend_text,
        "dataset_quality_ui": "DatasetQuality" in frontend_text and "generateSelectedYoloYaml" in frontend_text,
        "uploaded_yolo_workflow_ui": "startSelectedYoloTrain" in frontend_text and "startSelectedYoloPredict" in frontend_text and "startSelectedYoloHeatmap" in frontend_text,
+        "dataset_yolo_outputs_ui": "DatasetYoloOutputs" in frontend_text and "selectedYoloOutputs" in frontend_text and "BEST.PT READY" in frontend_text,
        "loss_result_ui": "loss" in frontend_text.lower() and "heatmap" in frontend_text.lower() and "CurvePanel" in frontend_text,
        "job_progress_ui": "JobProgressBar" in frontend_text and "progressTrack" in frontend_text,
        "runtime_readiness_ui": "runtimeReadiness" in frontend_text and "环境就绪" in frontend_text,
--- a/backend/app/modules/system/service.py
+++ b/backend/app/modules/system/service.py
@@ -14,6 +14,7 @@ import fcntl
 from ...config import settings

 READINESS_CACHE_SECONDS = 300
+READINESS_FAILURE_CACHE_SECONDS = 30
 _readiness_cache: tuple[float, dict[str, Any]] | None = None
 _readiness_thread_lock = threading.Lock()

@@ -215,18 +216,22 @@ def inspect_conda_env(env_name: str, required_imports: list[dict[str, str]], tim
 def get_runtime_readiness(force: bool = False) -> dict[str, Any]:
    global _readiness_cache
    now = time.time()
-    if not force and _readiness_cache and now - _readiness_cache[0] < READINESS_CACHE_SECONDS:
-        cached = dict(_readiness_cache[1])
-        cached["cached"] = True
-        return cached
-
-    with readiness_probe_lock():
-        now = time.time()
-        if not force and _readiness_cache and now - _readiness_cache[0] < READINESS_CACHE_SECONDS:
+    if not force and _readiness_cache:
+        cache_ttl = READINESS_CACHE_SECONDS if _readiness_cache[1].get("passed") else READINESS_FAILURE_CACHE_SECONDS
+        if now - _readiness_cache[0] < cache_ttl:
            cached = dict(_readiness_cache[1])
            cached["cached"] = True
            return cached

+    with readiness_probe_lock():
+        now = time.time()
+        if not force and _readiness_cache:
+            cache_ttl = READINESS_CACHE_SECONDS if _readiness_cache[1].get("passed") else READINESS_FAILURE_CACHE_SECONDS
+            if now - _readiness_cache[0] < cache_ttl:
+                cached = dict(_readiness_cache[1])
+                cached["cached"] = True
+                return cached
+
        conda = get_conda_envs()
        env_paths = {item["name"]: item["path"] for item in conda.get("envs", [])}
        envs: list[dict[str, Any]] = []
@@ -254,6 +259,7 @@ def get_runtime_readiness(force: bool = False) -> dict[str, Any]:
            "passed": bool(conda.get("available")) and all(item["passed"] for item in envs),
            "generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime(now)),
            "cache_seconds": READINESS_CACHE_SECONDS,
+            "failure_cache_seconds": READINESS_FAILURE_CACHE_SECONDS,
            "cached": False,
            "envs": envs,
            "specs": {
--- a/backend/tests/test_system_service.py
+++ b/backend/tests/test_system_service.py
@@ -71,3 +71,15 @@ def test_runtime_readiness_aggregates_probe_results(monkeypatch):
    assert readiness["passed"] is True
    assert readiness["envs"][0]["path"] == "/envs/seg_smp"
    assert readiness["specs"]["env_files"] == ["envs/seg_smp.yml", "envs/seg_mmcv.yml"]
+
+
+def test_runtime_readiness_refreshes_stale_failure_cache(monkeypatch):
+    monkeypatch.setattr(service, "_readiness_cache", (100.0, {"passed": False, "envs": [], "available": True}))
+    monkeypatch.setattr(service.time, "time", lambda: 100.0 + service.READINESS_FAILURE_CACHE_SECONDS + 1)
+    monkeypatch.setattr(service, "runtime_environment_specs", lambda: [])
+    monkeypatch.setattr(service, "get_conda_envs", lambda: {"available": True, "envs": []})
+
+    readiness = get_runtime_readiness(force=False)
+
+    assert readiness["cached"] is False
+    assert readiness["passed"] is True
--- a/frontend/src/main.tsx
+++ b/frontend/src/main.tsx
@@ -115,6 +115,14 @@ type TrainingCurve = {
  series: CurveSeries[];
 };

+type DatasetYoloOutputsPayload = {
+  bestWeight?: ResultItem;
+  artifacts: ResultItem[];
+  curves: TrainingCurve[];
+  predictions: ResultItem[];
+  heatmaps: ResultItem[];
+};
+
 type CoveragePayload = {
  scripts_total: number;
  user_scripts_total: number;
@@ -352,7 +360,7 @@ function useData() {
      setRuntimeReadiness(readinessNext);
      setCapabilities(capabilitiesNext);
      setJobs(jobsNext);
-      setResults(resultsNext.slice(0, 80));
+      setResults(resultsNext.slice(0, 240));
      setCurves(curvesNext.slice(0, 12));
      setDatasets(datasetsNext);
      const validationEntries: Array<[string, DatasetValidation]> = [];
@@ -444,6 +452,26 @@ function App() {
  );
  const selectedValidation = selectedDataset ? datasetValidations[selectedDataset.name] : undefined;
  const selectedCurve = curves.find((curve) => curve.relative_path === selectedCurvePath) ?? curves[0];
+  const selectedYoloOutputs = useMemo<DatasetYoloOutputsPayload>(() => {
+    if (!selectedDataset) {
+      return { artifacts: [], curves: [], predictions: [], heatmaps: [] };
+    }
+    const prefixes = [
+      `var/custom_yolo_runs/${selectedDataset.name}`,
+      `var/custom_yolo_runs/${selectedDataset.name}_predict`,
+      `var/custom_yolo_runs/${selectedDataset.name}_heatmap`
+    ];
+    const matches = (relativePath: string) => prefixes.some((prefix) => relativePath === prefix || relativePath.startsWith(`${prefix}/`));
+    const artifacts = results.filter((item) => matches(item.relative_path));
+    return {
+      bestWeight: artifacts.find((item) => item.relative_path.endsWith("/weights/best.pt")),
+      artifacts,
+      curves: curves.filter((curve) => matches(curve.relative_path)),
+      predictions: artifacts.filter((item) => item.role === "segmentation" && item.previewable),
+      heatmaps: artifacts.filter((item) => item.role === "heatmap" && item.previewable)
+    };
+  }, [curves, results, selectedDataset]);
+  const selectedYoloWeightReady = Boolean(selectedYoloOutputs.bestWeight);

  function pickTask(next: string) {
    setTaskType(next);
@@ -562,7 +590,7 @@ function App() {

  function customYoloWeightPath(dataset: UploadedDataset) {
    const expected = `var/custom_yolo_runs/${dataset.name}/weights/best.pt`;
-    return results.find((item) => item.relative_path === expected || item.relative_path.endsWith(`/custom_yolo_runs/${dataset.name}/weights/best.pt`))?.relative_path ?? expected;
+    return selectedYoloOutputs.bestWeight?.relative_path ?? results.find((item) => item.relative_path === expected || item.relative_path.endsWith(`/custom_yolo_runs/${dataset.name}/weights/best.pt`))?.relative_path ?? expected;
  }

  async function createSelectedYoloYaml() {
@@ -879,10 +907,10 @@ function App() {
                <button className="iconButton" disabled={busy || !selectedValidation?.ready.yolo} onClick={startSelectedYoloTrain} title="启动自定义 YOLO 训练">
                  <Play size={18} />
                </button>
-                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout} onClick={startSelectedYoloPredict} title="使用自定义 best.pt 预测">
+                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout || !selectedYoloWeightReady} onClick={startSelectedYoloPredict} title={selectedYoloWeightReady ? "使用自定义 best.pt 预测" : "best.pt 尚未生成"}>
                  <FileImage size={18} />
                </button>
-                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout} onClick={startSelectedYoloHeatmap} title="使用自定义 best.pt 生成热度图">
+                <button className="iconButton" disabled={busy || !selectedDataset?.absolute_layout || !selectedYoloWeightReady} onClick={startSelectedYoloHeatmap} title={selectedYoloWeightReady ? "使用自定义 best.pt 生成热度图" : "best.pt 尚未生成"}>
                  <Zap size={18} />
                </button>
                <FileImage size={22} />
@@ -929,6 +957,7 @@ function App() {
              ))}
            </div>
            {selectedValidation && <DatasetQuality validation={selectedValidation} />}
+            {selectedDataset && <DatasetYoloOutputs dataset={selectedDataset} outputs={selectedYoloOutputs} />}
          </div>
        </section>

@@ -1258,6 +1287,48 @@ function DatasetQuality({ validation }: { validation: DatasetValidation }) {
  );
 }

+function DatasetYoloOutputs({ dataset, outputs }: { dataset: UploadedDataset; outputs: DatasetYoloOutputsPayload }) {
+  const previewItems = [...outputs.heatmaps.slice(0, 3), ...outputs.predictions.slice(0, 3)].slice(0, 6);
+  return (
+    <div className="datasetOutputBox">
+      <div className="qualityHead">
+        <strong>{dataset.name} · YOLO</strong>
+        <span>{outputs.bestWeight ? "BEST.PT READY" : "BEST.PT MISSING"}</span>
+      </div>
+      <div className="qualityStats">
+        <div><span>Weights</span><strong>{outputs.bestWeight ? 1 : 0}</strong></div>
+        <div><span>Predict</span><strong>{outputs.predictions.length}</strong></div>
+        <div><span>Heatmap</span><strong>{outputs.heatmaps.length}</strong></div>
+        <div><span>Curves</span><strong>{outputs.curves.length}</strong></div>
+      </div>
+      <div className="datasetOutputLinks">
+        {outputs.bestWeight && (
+          <a href={`${API_BASE}/api/artifacts/${outputs.bestWeight.relative_path}`} target="_blank" rel="noreferrer">
+            <span>best.pt</span>
+            <small>{formatBytes(outputs.bestWeight.size)}</small>
+          </a>
+        )}
+        {outputs.curves.slice(0, 2).map((curve) => (
+          <a key={curve.relative_path} href={`${API_BASE}/api/artifacts/${curve.relative_path}`} target="_blank" rel="noreferrer">
+            <span>{curve.name}</span>
+            <small>{curve.row_count} epochs</small>
+          </a>
+        ))}
+      </div>
+      {!!previewItems.length && (
+        <div className="datasetOutputPreview">
+          {previewItems.map((item) => (
+            <a key={item.relative_path} href={`${API_BASE}/api/artifacts/${item.relative_path}`} target="_blank" rel="noreferrer">
+              <img src={`${API_BASE}/api/artifacts/${item.relative_path}`} alt={item.name} />
+              <span>{item.role}</span>
+            </a>
+          ))}
+        </div>
+      )}
+    </div>
+  );
+}
+
 function CurvePanel({
  curves,
  selected,
--- a/frontend/src/styles.css
+++ b/frontend/src/styles.css
@@ -761,6 +761,16 @@ textarea {
  background: #0b0d0b;
 }

+.datasetOutputBox {
+  display: grid;
+  gap: 10px;
+  margin-top: 12px;
+  padding: 12px;
+  border: 1px solid var(--line);
+  border-radius: 7px;
+  background: #0b0d0b;
+}
+
 .qualityHead {
  display: flex;
  justify-content: space-between;
@@ -817,6 +827,65 @@ textarea {
  border-color: rgba(240, 113, 103, 0.55);
 }

+.datasetOutputLinks {
+  display: grid;
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.datasetOutputLinks a {
+  min-width: 0;
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) auto;
+  gap: 8px;
+  padding: 8px;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: #101310;
+  color: var(--ink);
+  text-decoration: none;
+}
+
+.datasetOutputLinks span,
+.datasetOutputLinks small {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.datasetOutputPreview {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.datasetOutputPreview a {
+  min-width: 0;
+  overflow: hidden;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: #101310;
+  color: var(--muted);
+  text-decoration: none;
+}
+
+.datasetOutputPreview img {
+  display: block;
+  width: 100%;
+  aspect-ratio: 1.35 / 1;
+  object-fit: cover;
+  background: #060806;
+}
+
+.datasetOutputPreview span {
+  display: block;
+  padding: 6px;
+  font-size: 11px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
 .jobList, .resultList {
  display: grid;
  gap: 8px;
@@ -1201,6 +1270,8 @@ meter {

  .opGrid,
  .sampleStrip,
+  .datasetOutputLinks,
+  .datasetOutputPreview,
  .taskCheckList,
  .agentCheckList,
  .qualityStats,