Verify YOLO heatmap generation in deep acceptance

README.md

@@ -66,7 +66,8 @@ weight discovery. MMSeg full-model readiness is validated in
 
 For stronger runtime proof, `POST /api/acceptance/deep` runs minimal training
 loops for the three model families: one SegModel optimizer step, one YOLO
-segmentation epoch on a synthetic 64x64 dataset, and one MMSeg optimizer step
+segmentation epoch on a synthetic 64x64 dataset, one YOLO GradCAM heatmap
+generation pass from the trained tiny checkpoint, and one MMSeg optimizer step
 through the full `mmcv._ext` runtime. The latest report is available from
 `GET /api/acceptance/deep/latest` and is surfaced in the coverage panel.
 

backend/app/acceptance.py

@@ -100,6 +100,29 @@ def _yolo_tiny_train_snippet(root: Path, weight: Path) -> str:
     )
 
 
+def _yolo_heatmap_snippet(root: Path) -> str:
+    script_path = settings.source_root / "Seg_All_In_One_YoloModel" / "yolo_predict_visualize_nn.py"
+    return (
+        "from pathlib import Path; "
+        "import importlib.util, shutil, sys, types; "
+        "fake=types.ModuleType('yolo_config'); "
+        "fake.MODEL_CONFIGS={'YOLO11n-seg': {}}; "
+        "fake.TEST_IMAGE_DIR=''; fake.PREDICT_BEST_MODEL_DIR=Path('.'); fake.show_config_summary=lambda: None; "
+        "sys.modules['yolo_config']=fake; "
+        f"script=Path({str(script_path)!r}); "
+        "spec=importlib.util.spec_from_file_location('yolo_heatmap_mod', script); "
+        "mod=importlib.util.module_from_spec(spec); spec.loader.exec_module(mod); "
+        f"root=Path({str(root)!r}); "
+        "base=root/'runs'/'tiny'; "
+        "heatmap_root=base/'HeartMap_Visual'; "
+        "shutil.rmtree(heatmap_root, ignore_errors=True); "
+        "mod.visualize_nn_comprehensive(str(base/'weights'/'best.pt'), str(root/'images'/'val'/'sample.jpg'), base, 'best.pt', 'GradCAM', 'model.model.model[9]', 'YOLO11n-seg'); "
+        "outputs=sorted(heatmap_root.rglob('*.jpg')); "
+        "assert len(outputs) >= 2; "
+        "print('heatmaps', len(outputs), [str(item.relative_to(base)) for item in outputs[:4]])"
+    )
+
+
 def _mmseg_train_step_snippet(config_path: Path) -> str:
     return (
         "import torch; "
@@ -329,23 +352,43 @@ def run_deep_acceptance() -> dict[str, Any]:
     yolo_weight = settings.source_root / "Seg_All_In_One_YoloModel" / "yolo11n-seg.pt"
     mmseg_config = settings.source_root / "Seg_All_In_One_MMSeg" / "configs" / "fcn" / "fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py"
 
+    yolo_root = fixture_root / "yolo_tiny"
     checks = [
         {
             "name": "segmodel_tiny_train_step",
             "passed": False,
             "detail": _run_snippet(SEGMODEL_TRAIN_STEP_SNIPPET, timeout=90),
         },
-        {
-            "name": "yolo_tiny_segment_train_epoch",
-            "passed": False,
-            "detail": _run_snippet(_yolo_tiny_train_snippet(fixture_root / "yolo_tiny", yolo_weight), timeout=180),
-        },
+    ]
+    yolo_train = {
+        "name": "yolo_tiny_segment_train_epoch",
+        "passed": False,
+        "detail": _run_snippet(_yolo_tiny_train_snippet(yolo_root, yolo_weight), timeout=180),
+    }
+    checks.append(yolo_train)
+    if yolo_train["detail"].get("passed"):
+        checks.append(
+            {
+                "name": "yolo_tiny_heatmap_generation",
+                "passed": False,
+                "detail": _run_snippet(_yolo_heatmap_snippet(yolo_root), timeout=90),
+            }
+        )
+    else:
+        checks.append(
+            {
+                "name": "yolo_tiny_heatmap_generation",
+                "passed": False,
+                "detail": {"passed": False, "error": "skipped because yolo_tiny_segment_train_epoch failed"},
+            }
+        )
+    checks.append(
         {
             "name": "mmseg_tiny_train_step",
             "passed": False,
             "detail": _run_conda_snippet(settings.mmseg_conda_env, _mmseg_train_step_snippet(mmseg_config), timeout=120),
-        },
-    ]
+        }
+    )
     for check in checks:
         check["passed"] = bool(check["detail"].get("passed"))
 

backend/app/agents/evaluation_agent.py

@@ -34,6 +34,7 @@ def evaluate_project() -> dict:
 
     frontend_text = frontend.read_text(encoding="utf-8") if frontend.exists() else ""
     backend_text = backend.read_text(encoding="utf-8") if backend.exists() else ""
+    acceptance_text = (settings.project_root / "backend" / "app" / "acceptance.py").read_text(encoding="utf-8")
     readme_text = readme.read_text(encoding="utf-8") if readme.exists() else ""
 
     expectations = {
@@ -44,6 +45,7 @@ def evaluate_project() -> dict:
         "curve_api": "/api/results/curves" in backend_text,
         "deep_acceptance_api": "/api/acceptance/deep" in backend_text,
         "deep_acceptance_ui": "runDeepAcceptance" in frontend_text and "深度训练" in frontend_text,
+        "deep_yolo_heatmap_validation": "yolo_tiny_heatmap_generation" in acceptance_text,
         "coverage_api": "/api/coverage" in backend_text and coverage["task_build_passed"],
         "visual_tools": "visual.yolo11_heatmap_v2" in catalog["task_types"] and "visual.fps" in catalog["task_types"],
         "yolo_dataset_tools": "dataset.yolo_txt_sort" in catalog["task_types"] and "dataset.yolo_resize" in catalog["task_types"],
@@ -64,7 +66,7 @@ def evaluate_project() -> dict:
     if coverage["unmapped_user_scripts"]:
         suggestions.append(f"Map remaining user-facing scripts: {len(coverage['unmapped_user_scripts'])}")
     if not suggestions:
-        suggestions.append("Current platform covers the requested control-plane features; next focus is real dataset/training acceptance tests.")
+        suggestions.append("Current platform covers the requested control-plane features and synthetic deep training/heatmap acceptance; next focus is a user-supplied dataset end-to-end run.")
 
     score = sum(1 for item in checks if item["passed"]) / max(len(checks), 1)
     return {

backend/app/modules/results/service.py

@@ -32,6 +32,9 @@ def result_roots() -> list[Path]:
     upload_root = project / "var" / "uploads" / "datasets"
     if upload_root.exists():
         roots.extend(path for path in upload_root.glob("*/results") if path.is_dir())
+    acceptance_root = project / "var" / "acceptance"
+    if acceptance_root.exists():
+        roots.extend(path for path in acceptance_root.glob("deep_*/yolo_tiny/runs/tiny/HeartMap_Visual") if path.is_dir())
     return roots