path to the labels file during classifier saving gets joined with scr…

…ipt dir so it works in the installed version. (#456)

added error Log for training and saving model

model.py

@@ -340,7 +340,7 @@ def saveLinearClassifier(classifier, model_path: str, labels: list[str], mode="r
     open(model_path, "wb").write(tflite_model)
 
     if mode == "append":
-        labels = [*utils.readLines(cfg.LABELS_FILE), *labels]
+        labels = [*utils.readLines(os.path.join(SCRIPT_DIR, cfg.LABELS_FILE)), *labels]
 
     # Save labels
     with open(model_path.replace(".tflite", "_Labels.txt"), "w", encoding="utf-8") as f:
@@ -399,7 +399,7 @@ def basic(self, inputs):
     tf.saved_model.save(smodel, model_path, signatures=signatures)
 
     if mode == "append":
-        labels = [*utils.readLines(cfg.LABELS_FILE), *labels]
+        labels = [*utils.readLines(os.path.join(SCRIPT_DIR, cfg.LABELS_FILE)), *labels]
 
     # Save label file
     labelIds = [label[:4].replace(" ", "") + str(i) for i, label in enumerate(labels, 1)]

train.py

@@ -321,34 +321,46 @@ def run_trial(self, trial, *args, **kwargs):
 
     # Train model
     print("Training model...", flush=True)
-    classifier, history = model.trainLinearClassifier(
-        classifier,
-        x_train,
-        y_train,
-        epochs=cfg.TRAIN_EPOCHS,
-        batch_size=cfg.TRAIN_BATCH_SIZE,
-        learning_rate=cfg.TRAIN_LEARNING_RATE,
-        val_split=cfg.TRAIN_VAL_SPLIT,
-        upsampling_ratio=cfg.UPSAMPLING_RATIO,
-        upsampling_mode=cfg.UPSAMPLING_MODE,
-        train_with_mixup=cfg.TRAIN_WITH_MIXUP,
-        train_with_label_smoothing=cfg.TRAIN_WITH_LABEL_SMOOTHING,
-        on_epoch_end=on_epoch_end,
-    )
+    try:
+        classifier, history = model.trainLinearClassifier(
+            classifier,
+            x_train,
+            y_train,
+            epochs=cfg.TRAIN_EPOCHS,
+            batch_size=cfg.TRAIN_BATCH_SIZE,
+            learning_rate=cfg.TRAIN_LEARNING_RATE,
+            val_split=cfg.TRAIN_VAL_SPLIT,
+            upsampling_ratio=cfg.UPSAMPLING_RATIO,
+            upsampling_mode=cfg.UPSAMPLING_MODE,
+            train_with_mixup=cfg.TRAIN_WITH_MIXUP,
+            train_with_label_smoothing=cfg.TRAIN_WITH_LABEL_SMOOTHING,
+            on_epoch_end=on_epoch_end,
+        )
+    except Exception as e:
+        utils.writeErrorLog(e)
+        raise Exception("Error training model")
+
+    print("...Done.", flush=True)
 
     # Best validation AUPRC (at minimum validation loss)
     best_val_auprc = history.history["val_AUPRC"][np.argmin(history.history["val_loss"])]
     best_val_auroc = history.history["val_AUROC"][np.argmin(history.history["val_loss"])]
 
-    if cfg.TRAINED_MODEL_OUTPUT_FORMAT == "both":
-        model.save_raven_model(classifier, cfg.CUSTOM_CLASSIFIER, labels)
-        model.saveLinearClassifier(classifier, cfg.CUSTOM_CLASSIFIER, labels, mode=cfg.TRAINED_MODEL_SAVE_MODE)
-    elif cfg.TRAINED_MODEL_OUTPUT_FORMAT == "tflite":
-        model.saveLinearClassifier(classifier, cfg.CUSTOM_CLASSIFIER, labels, mode=cfg.TRAINED_MODEL_SAVE_MODE)
-    elif cfg.TRAINED_MODEL_OUTPUT_FORMAT == "raven":
-        model.save_raven_model(classifier, cfg.CUSTOM_CLASSIFIER, labels)
-    else:
-        raise ValueError(f"Unknown model output format: {cfg.TRAINED_MODEL_OUTPUT_FORMAT}")
+    print("Saving model...", flush=True)
+
+    try:
+        if cfg.TRAINED_MODEL_OUTPUT_FORMAT == "both":
+            model.save_raven_model(classifier, cfg.CUSTOM_CLASSIFIER, labels)
+            model.saveLinearClassifier(classifier, cfg.CUSTOM_CLASSIFIER, labels, mode=cfg.TRAINED_MODEL_SAVE_MODE)
+        elif cfg.TRAINED_MODEL_OUTPUT_FORMAT == "tflite":
+            model.saveLinearClassifier(classifier, cfg.CUSTOM_CLASSIFIER, labels, mode=cfg.TRAINED_MODEL_SAVE_MODE)
+        elif cfg.TRAINED_MODEL_OUTPUT_FORMAT == "raven":
+            model.save_raven_model(classifier, cfg.CUSTOM_CLASSIFIER, labels)
+        else:
+            raise ValueError(f"Unknown model output format: {cfg.TRAINED_MODEL_OUTPUT_FORMAT}")
+    except Exception as e:
+        utils.writeErrorLog(e)
+        raise Exception("Error saving model")
 
     print(f"...Done. Best AUPRC: {best_val_auprc}, Best AUROC: {best_val_auroc}", flush=True)