fixed yaml file advanced setting not populating issue at model traini…

client/src/components/YamlFileUploader.js

@@ -27,6 +27,7 @@ import {
   getModelArchitectures,
 } from "../api";
 import { findCommonPartOfString } from "../utils";
+import { adaptToPytcSchema } from "../utils/yamlSchemaAdapter";
 
 const YamlFileUploader = (props) => {
   const context = useContext(AppContext);
@@ -132,22 +133,39 @@ const YamlFileUploader = (props) => {
   };
 
   const updateInputSelectorInformation = (yamlData) => {
-    const inputImagePath = getPathValue(context.inputImage);
-    const inputLabelPath = getPathValue(context.inputLabel);
+    const ds = yamlData.DATASET || {};
+
+    // Phase 1: Auto-populate Step 0 path slots from YAML if UI fields are empty.
+    if (!getPathValue(context.inputImage) && ds.INPUT_PATH && ds.IMAGE_NAME) {
+      context.setInputImage(ds.INPUT_PATH + ds.IMAGE_NAME);
+    }
+    if (!getPathValue(context.inputLabel) && ds.INPUT_PATH && ds.LABEL_NAME) {
+      context.setInputLabel(ds.INPUT_PATH + ds.LABEL_NAME);
+    }
+    if (!getPathValue(context.outputPath) && ds.OUTPUT_PATH) {
+      context.setOutputPath(ds.OUTPUT_PATH);
+    }
+
+    // Phase 2: Write current UI values back into the YAML (only if set).
+    const inputImagePath =
+      getPathValue(context.inputImage) ||
+      (ds.INPUT_PATH && ds.IMAGE_NAME ? ds.INPUT_PATH + ds.IMAGE_NAME : "");
+    const inputLabelPath =
+      getPathValue(context.inputLabel) ||
+      (ds.INPUT_PATH && ds.LABEL_NAME ? ds.INPUT_PATH + ds.LABEL_NAME : "");
 
     if (!inputImagePath || !inputLabelPath) {
+      yamlData.DATASET = ds;
       return;
     }
 
-    // INPUT_PATH is the shared parent directory for image/label so names can be relative.
     const inputPath = findCommonPartOfString(inputImagePath, inputLabelPath);
-
-    yamlData.DATASET = yamlData.DATASET || {};
-    yamlData.DATASET.INPUT_PATH = inputPath || yamlData.DATASET.INPUT_PATH;
+    yamlData.DATASET = ds;
+    yamlData.DATASET.INPUT_PATH = inputPath || ds.INPUT_PATH;
     yamlData.DATASET.IMAGE_NAME = inputImagePath.replace(inputPath, "");
     yamlData.DATASET.LABEL_NAME = inputLabelPath.replace(inputPath, "");
 
-    const outputPath = getPathValue(context.outputPath);
+    const outputPath = getPathValue(context.outputPath) || ds.OUTPUT_PATH || "";
     if (outputPath) {
       yamlData.DATASET.OUTPUT_PATH = outputPath;
     }
@@ -183,12 +201,25 @@ const YamlFileUploader = (props) => {
     }
   };
 
-  const applyYamlData = (yamlData, sourceLabel) => {
-    if (!yamlData) {
+  const applyYamlData = (rawYamlData, sourceLabel) => {
+    if (!rawYamlData) {
       message.error("Failed to load YAML configuration.");
       return;
     }
 
+    // Detect schema and adapt to pytorch_connectomics format if needed.
+    const {
+      adapted: yamlData,
+      originalSchema,
+      wasAdapted,
+    } = adaptToPytcSchema(rawYamlData);
+    if (wasAdapted) {
+      message.info(
+        `Detected '${originalSchema}' schema — automatically converted to pytorch_connectomics format.`,
+        5,
+      );
+    }
+
     updateInputSelectorInformation(yamlData);
     const serialized = yaml
       .dump(yamlData, { indent: 2 })

client/src/contexts/GlobalContext.js

@@ -73,12 +73,32 @@ function usePersistedState(key, defaultValue) {
   const [isLoaded, setIsLoaded] = useState(false);
 
   useEffect(() => {
-    // Fetch the stored value asynchronously when the component mounts
+    // Fetch the stored value asynchronously when the component mounts.
+    // Migration guard: if localforage has no value but localStorage does,
+    // migrate the legacy value over and clear the stale localStorage entry.
     localforage
       .getItem(key)
       .then((storedValue) => {
         if (storedValue !== null) {
           setState(storedValue);
+        } else {
+          const legacy = localStorage.getItem(key);
+          if (legacy !== null) {
+            try {
+              // localforage can store raw values, but localStorage only stores
+              // strings, so attempt a JSON parse first.
+              const parsed = JSON.parse(legacy);
+              setState(parsed);
+              localforage.setItem(key, parsed).catch(() => {});
+            } catch {
+              setState(legacy);
+              localforage.setItem(key, legacy).catch(() => {});
+            }
+            localStorage.removeItem(key);
+            console.info(
+              `[GlobalContext] Migrated '${key}' from localStorage → localforage.`,
+            );
+          }
         }
         setIsLoaded(true);
       })

client/src/utils/yamlSchemaAdapter.js

@@ -0,0 +1,236 @@
+/**
+ * yamlSchemaAdapter.js
+ *
+ * Detects the schema family of a parsed YAML object and translates it to the
+ * flat uppercase schema required by pytorch_connectomics / yacs.
+ *
+ * Supported schemas:
+ *  - "pytc"    : pytorch_connectomics native  (SYSTEM, MODEL, DATASET, SOLVER, INFERENCE)
+ *  - "lucchi+" : Lucchi++ / MONAI-style        (system, model, data, optimization, inference)
+ */
+
+// ─── Schema Detection ─────────────────────────────────────────────────────────
+
+/**
+ * Returns the schema family name for the given parsed YAML object,
+ * or "unknown" if it cannot be identified.
+ * @param {object} yamlData
+ * @returns {"pytc"|"lucchi+"|"unknown"}
+ */
+export function detectSchema(yamlData) {
+  if (!yamlData || typeof yamlData !== "object") return "unknown";
+
+  const pytcKeys = ["SYSTEM", "MODEL", "DATASET", "SOLVER", "INFERENCE"];
+  if (pytcKeys.some((k) => k in yamlData)) return "pytc";
+
+  const lucchiKeys = ["data", "optimization", "experiment_name"];
+  if (lucchiKeys.some((k) => k in yamlData)) return "lucchi+";
+
+  return "unknown";
+}
+
+// ─── Path Helpers ─────────────────────────────────────────────────────────────
+
+/**
+ * Splits a file path into [directory, filename], handling both / and \ so the
+ * adapter works correctly on Windows as well as POSIX systems.
+ *
+ * Examples:
+ *   "datasets/lucchi++/train_im.h5"   → ["datasets/lucchi++/", "train_im.h5"]
+ *   "C:\\data\\train.tif"             → ["C:\\data\\",           "train.tif"]
+ *   "train_im.h5"                     → ["",                     "train_im.h5"]
+ */
+function _splitPath(fullPath) {
+  if (!fullPath || typeof fullPath !== "string") return ["", ""];
+  const match = fullPath.match(/.*[/\\]/);
+  const dir = match ? match[0] : "";
+  const fileName = fullPath.slice(dir.length);
+  return [dir, fileName];
+}
+
+/**
+ * Maps Lucchi++ architecture names to pytorch_connectomics MODEL_MAP keys.
+ */
+function _mapArchitecture(arch) {
+  const map = {
+    monai_unet: "unet_3d",
+    monai_basic_unet3d: "unet_3d",
+    rsunet: "unet_3d",
+    mednext: "unet_3d",
+    unet: "unet_3d",
+    unet3d: "unet_3d",
+    unet2d: "unet_2d",
+    fpn: "fpn_3d",
+  };
+  return map[(arch || "").toLowerCase()] ?? "unet_3d";
+}
+
+/**
+ * Maps Lucchi++ scheduler names to pytorch_connectomics scheduler names.
+ */
+function _mapScheduler(name) {
+  const map = {
+    ReduceLROnPlateau: "MultiStepLR",
+    reduceLROnPlateau: "MultiStepLR",
+    CosineAnnealingLR: "CosineAnnealingLR",
+    cosineannealinglr: "CosineAnnealingLR",
+    warmupcosine: "WarmupCosineLR",
+    WarmupCosineLR: "WarmupCosineLR",
+  };
+  return map[name] ?? "MultiStepLR";
+}
+
+/**
+ * Computes a STRIDE array from a window size and overlap fraction.
+ * e.g. window [112,112,112] with overlap 0.25 → stride [84,84,84]
+ */
+function _computeStride(windowSize, overlap) {
+  const ws = windowSize ?? [112, 112, 112];
+  const ov = overlap ?? 0.5;
+  return ws.map((s) => Math.round(s * (1 - ov)));
+}
+
+// ─── Adapter: Lucchi++ / MONAI → pytorch_connectomics ────────────────────────
+
+/**
+ * Translates a Lucchi++ schema YAML object to the pytorch_connectomics schema.
+ * Keys that have no equivalent are preserved in a top-level `_EXTRA` object
+ * so that no data is silently discarded during translation.
+ *
+ * @param {object} src - Parsed Lucchi++ YAML object.
+ * @returns {object} - Translated pytorch_connectomics schema object.
+ */
+function adaptLucchiPlus(src) {
+  const out = {};
+
+  // ── SYSTEM ──────────────────────────────────────────────────────────────
+  const sys = src.system || {};
+  const sysTrain = sys.training || {};
+  const sysInfer = sys.inference || {};
+  out.SYSTEM = {
+    NUM_GPUS: sysTrain.num_gpus ?? sysInfer.num_gpus ?? 1,
+    NUM_CPUS: sysTrain.num_cpus ?? sysInfer.num_cpus ?? 4,
+    DISTRIBUTED: false,
+    PARALLEL: "DP",
+  };
+
+  // ── MODEL ────────────────────────────────────────────────────────────────
+  const mod = src.model || {};
+  out.MODEL = {
+    ARCHITECTURE: _mapArchitecture(mod.architecture),
+    IN_PLANES: mod.in_channels ?? 1,
+    OUT_PLANES: mod.out_channels ?? 1,
+    INPUT_SIZE: mod.input_size ?? [112, 112, 112],
+    OUTPUT_SIZE: mod.output_size ?? mod.input_size ?? [112, 112, 112],
+    FILTERS: mod.filters ?? [32, 64, 128, 256],
+  };
+  if (mod.loss_functions) {
+    out.MODEL.LOSS_OPTION = [mod.loss_functions];
+    out.MODEL.LOSS_WEIGHT = [
+      mod.loss_weights ?? mod.loss_functions.map(() => 1.0),
+    ];
+    out.MODEL.OUTPUT_ACT = [mod.loss_functions.map(() => "none")];
+  }
+
+  // ── DATASET ──────────────────────────────────────────────────────────────
+  const data = src.data || {};
+  const [trainInputPath, trainImageName] = _splitPath(data.train_image);
+  const [, trainLabelName] = _splitPath(data.train_label);
+  out.DATASET = {
+    INPUT_PATH: trainInputPath || "path/to/input",
+    IMAGE_NAME: trainImageName || "",
+    LABEL_NAME: trainLabelName || "",
+    OUTPUT_PATH: "path/to/output",
+    IS_ISOTROPIC: true,
+    PAD_SIZE: data.pad_size ?? [0, 0, 0],
+  };
+
+  // ── SOLVER ───────────────────────────────────────────────────────────────
+  const opt = src.optimization || {};
+  const optOpt = opt.optimizer || {};
+  const sched = opt.scheduler || {};
+  out.SOLVER = {
+    NAME: optOpt.name ?? "Adam",
+    BASE_LR: optOpt.lr ?? 0.001,
+    WEIGHT_DECAY: optOpt.weight_decay ?? 0.0001,
+    MOMENTUM: 0.9,
+    BETAS: optOpt.betas ?? [0.9, 0.999],
+    ITERATION_TOTAL:
+      (opt.max_epochs ?? 1000) * (data.iter_num_per_epoch ?? 1000),
+    SAMPLES_PER_BATCH: sysTrain.batch_size ?? 2,
+    LR_SCHEDULER_NAME: _mapScheduler(sched.name),
+    ITERATION_SAVE: 5000,
+    ITERATION_VAL: 5000,
+  };
+
+  // ── INFERENCE ────────────────────────────────────────────────────────────
+  const inf = src.inference || {};
+  const infData = inf.data || {};
+  const sw = inf.sliding_window || {};
+  const [infInputPath, infImageName] = _splitPath(infData.test_image);
+  out.INFERENCE = {
+    INPUT_PATH: infInputPath || out.DATASET.INPUT_PATH,
+    IMAGE_NAME: infImageName || "",
+    INPUT_SIZE: sw.window_size ?? out.MODEL.INPUT_SIZE,
+    OUTPUT_SIZE: sw.window_size ?? out.MODEL.OUTPUT_SIZE,
+    OUTPUT_PATH: "",
+    OUTPUT_NAME: "result.h5",
+    SAMPLES_PER_BATCH: sysInfer.batch_size ?? 4,
+    AUG_MODE: "mean",
+    AUG_NUM: null,
+    BLENDING: sw.blending ?? "gaussian",
+    STRIDE: _computeStride(sw.window_size, sw.overlap),
+  };
+
+  // ── _EXTRA: collect all unmapped top-level keys ──────────────────────────
+  // This ensures no data is lost during translation. Consumers can inspect
+  // _EXTRA to find schema fields that have no pytc equivalent.
+  const mappedKeys = new Set([
+    "system",
+    "model",
+    "data",
+    "optimization",
+    "inference",
+  ]);
+  const extra = {};
+  for (const [key, value] of Object.entries(src)) {
+    if (!mappedKeys.has(key)) {
+      extra[key] = value;
+    }
+  }
+  if (Object.keys(extra).length > 0) {
+    out._EXTRA = extra;
+  }
+
+  return out;
+}
+
+// ─── Public API ───────────────────────────────────────────────────────────────
+
+/**
+ * Takes a parsed YAML object (any supported schema) and returns a parsed YAML
+ * object conforming to the pytorch_connectomics flat uppercase schema.
+ *
+ * - If the schema is already "pytc", returns the object unchanged.
+ * - If the schema is unknown, returns the object unchanged with a console warn.
+ *
+ * @param {object} yamlData
+ * @returns {{ adapted: object, originalSchema: string, wasAdapted: boolean }}
+ */
+export function adaptToPytcSchema(yamlData) {
+  const schema = detectSchema(yamlData);
+
+  if (schema === "pytc") {
+    return { adapted: yamlData, originalSchema: "pytc", wasAdapted: false };
+  }
+  if (schema === "lucchi+") {
+    const adapted = adaptLucchiPlus(yamlData);
+    return { adapted, originalSchema: "lucchi+", wasAdapted: true };
+  }
+
+  console.warn(
+    "[yamlSchemaAdapter] Unknown YAML schema — returning as-is. " +
+      "Ensure top-level keys match pytorch_connectomics conventions.",
+  );
+  return { adapted: yamlData, originalSchema: "unknown", wasAdapted: false };
+}

client/src/views/ModelInference.js

@@ -10,17 +10,15 @@ function ModelInference({ isInferring, setIsInferring }) {
   const handleStartButton = async () => {
     try {
       setIsInferring(true);
-      const inferenceConfig = localStorage.getItem("inferenceConfig");
+      const inferenceConfig = context.inferenceConfig;
 
       const getPath = (val) => {
         if (!val) return "";
         if (typeof val === "string") return val;
         return val.path || "";
       };
 
-      // const res = startModelInference(
       const res = await startModelInference(
-        context.uploadedYamlFile.name,
         inferenceConfig,
         getPath(context.outputPath),
         getPath(context.checkpointPath),

client/src/views/ModelTraining.js

@@ -83,9 +83,7 @@ function ModelTraining() {
       }
 
       console.log(context.uploadedYamlFile);
-      const trainingConfig =
-        localStorage.getItem("trainingConfig") || context.trainingConfig;
-      console.log(trainingConfig);
+      const trainingConfig = context.trainingConfig;
 
       setIsTraining(true);
       setTrainingStatus(

server_pytc/services/model.py

@@ -71,7 +71,7 @@ def start_training(dict: dict):
     command = [sys.executable, str(script_path)]
 
     print(f"[MODEL.PY] Processing command-line arguments...")
-    for key, value in dict["arguments"].items():
+    for key, value in dict.get("arguments", {}).items():
         if value is not None:
             print(f"[MODEL.PY]   Adding --{key} {value}")
             command.extend([f"--{key}", str(value)])