added updates to confusion matrix/riahub secrets

.riahub/workflows/workflow.yaml

@@ -34,10 +34,14 @@ jobs:
         with:
           python-version: "3.10"
 
-      - name: Install dependencies
+      - name: Install dependencies (incl. RIA Hub utils)
         run: |
           python -m pip install --upgrade pip
-          pip install -r requirements.txt
+          pip install \
+            --index-url "https://${{ secrets.RIAHUB_USER }}:${{ secrets.RIAHUB_TOKEN }}@git.riahub.ai/api/packages/qoherent/pypi/simple/" \
+            utils \
+            -r requirements.txt
+
         
 
       - name: 1. Generate Recordings
@@ -89,7 +93,7 @@ jobs:
           path: checkpoint_files/*
 
 
-      - name: 4. Convert to ONNX file
+      - name: 5. Convert to ONNX file
         env:
           NO_NNPACK: 1
           PYTORCH_NO_NNPACK: 1
@@ -104,11 +108,7 @@ jobs:
           name: onnx-file
           path: onnx_files/inference_recognition_model.onnx
 
-      - name: List checkpoint directory
-        run: ls -lh onnx_files
-
-
-      - name: 5. Profile ONNX model
+      - name: 6. Profile ONNX model
         run: |
           PYTHONPATH=. python scripts/onnx/profile_onnx.py
       
@@ -118,7 +118,7 @@ jobs:
           name: profile-data          
           path: '**/onnxruntime_profile_*.json'
       
-      - name: 6. Convert to ORT file
+      - name: 7. Convert to ORT file
         run: |
           PYTHONPATH=. python scripts/ort/convert_to_ort.py
 

README.md

@@ -23,6 +23,17 @@ dataset:
   val_split : 0.2
 ```
 
+### Configure GitHub Secrets
+
+Before running the pipeline, add the following repository secrets in GitHub (Settings → Secrets and variables → Actions):
+
+- **RIAHUB_USER**: Your RIA Hub username.  
+- **RIAHUB_TOKEN**: RIA Hub access token with `read:packages` scope (from your RIA Hub account **Settings → Access Tokens**).  
+- **CLONER_TOKEN**: Personal access token for `stark_cloner_bot` with `read_repository` scope (from your on-prem Git server user settings).  
+
+Once secrets are configured, you can run the pipeline:
+
+
 3. Run the Pipeline
 Once you update the changes to app.yaml, you can make any push or pull to your repo to start running the workflow
 

scripts/ort/convert_to_ort.py

@@ -13,9 +13,12 @@ command = [
     "-m",
     "onnxruntime.tools.convert_onnx_models_to_ort",
     input_path,
-    "--output_dir", "ort_files",
-    "--optimization_style", optimization_style,
-    "--target_platform", target_platform,
+    "--output_dir",
+    "ort_files",
+    "--optimization_style",
+    optimization_style,
+    "--target_platform",
+    target_platform,
 ]
 
 # Run the command

scripts/training/plot_data.py

@@ -2,6 +2,7 @@ import os
 import torch
 import numpy as np
 from sklearn.metrics import classification_report
+
 os.environ["NNPACK"] = "0"
 from matplotlib import pyplot as plt
 
@@ -13,13 +14,11 @@ from scripts.training.modulation_dataset import ModulationH5Dataset
 
 def load_validation_data():
     val_dataset = ModulationH5Dataset(
-        "data/dataset/val.h5",
-        label_name="modulation",
-        data_key="validation_data"
+        "data/dataset/val.h5", label_name="modulation", data_key="validation_data"
     )
 
     X = np.stack([x.numpy() for x, _ in val_dataset])  # shape: (N, C, L)
-    y = np.array([y.item() for _, y in val_dataset])   # shape: (N,)
+    y = np.array([y.item() for _, y in val_dataset])  # shape: (N,)
     class_names = list(val_dataset.label_encoder.classes_)
 
     return X, y, class_names
@@ -72,16 +71,71 @@ def evaluate_checkpoint(ckpt_path: str):
 
     # Print classification report
     print("\nClassification Report:")
-    print(classification_report(y_true, y_pred, target_names=class_names, zero_division=0))
+    print(
+        classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
+    )
 
-    # Plot confusion matrix
-    plot_confusion_matrix(
+    plot_confusion_matrix_with_counts(
         y_true=np.array(y_true),
         y_pred=np.array(y_pred),
         classes=class_names,
         normalize=True,
         title="Normalized Confusion Matrix",
     )
+
+
+def plot_confusion_matrix_with_counts(
+    y_true: np.ndarray,
+    y_pred: np.ndarray,
+    classes: list[str],
+    normalize: bool = True,
+    title: str = "Confusion Matrix (counts and normalized)",
+) -> None:
+    """
+    Plot a confusion matrix showing both raw counts and (optionally) normalized values.
+
+    Args:
+        y_true: true labels (integers 0..C-1)
+        y_pred: predicted labels (same shape as y_true)
+        classes: list of class‐name strings in index order
+        normalize: if True, each row is normalized to sum=1
+        title: title for the plot
+    """
+    # 1) build raw CM
+    C = len(classes)
+    cm = np.zeros((C, C), dtype=int)
+    for t, p in zip(y_true, y_pred):
+        cm[t, p] += 1
+
+    # 2) normalize if requested
+    if normalize:
+        with np.errstate(divide="ignore", invalid="ignore"):
+            cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
+            cm_norm = np.nan_to_num(cm_norm)
+    else:
+        cm_norm = cm
+
+    # 3) plot
+    fig, ax = plt.subplots(figsize=(8, 8))
+    im = ax.imshow(cm_norm, interpolation="nearest")
+    ax.set_title(title)
+    ax.set_xlabel("Predicted label")
+    ax.set_ylabel("True label")
+    ax.set_xticks(np.arange(C))
+    ax.set_yticks(np.arange(C))
+    ax.set_xticklabels(classes, rotation=45, ha="right")
+    ax.set_yticklabels(classes)
+
+    # 4) annotate
+    for i in range(C):
+        for j in range(C):
+            count = cm[i, j]
+            val = cm_norm[i, j]
+            txt = f"{count}\n{val:.2f}"
+            ax.text(j, i, txt, ha="center", va="center")
+
+    fig.colorbar(im, ax=ax, label="Normalized value" if normalize else "Count")
+    plt.tight_layout()
     plt.show()