.riahub/workflows/workflow.yaml

@@ -2,9 +2,11 @@ name: Modulation Recognition Demo
 
 on:
   push:
-    branches: [main]
+    branches:
+      [main]
   pull_request:
-    branches: [main]
+    branches:
+      [main]
 
 jobs:
   ria-demo:
@@ -44,24 +46,22 @@ jobs:
           fi
           pip install -r requirements.txt
 
+
       - name: 1. Generate Recordings
         run: |
-          mkdir -p data/recordings
+         mkdir -p data/recordings
-          PYTHONPATH=. python scripts/dataset_manager/data_gen.py --output-dir data/recordings
+         PYTHONPATH=. python scripts/dataset_manager/data_gen.py --output-dir data/recordings
-
-      - name: 📦 Compress Recordings
-        run: tar -czf recordings.tar.gz -C data/recordings .
 
       - name: ⬆️ Upload recordings
         uses: actions/upload-artifact@v3
         with:
           name: recordings
-          path: recordings.tar.gz
+          path: data/recordings/**
 
       - name: 2. Build HDF5 Dataset
         run: |
-          mkdir -p data/dataset
+         mkdir -p data/dataset
-          PYTHONPATH=. python scripts/dataset_manager/produce_dataset.py
+         PYTHONPATH=. python scripts/dataset_manager/produce_dataset.py
         shell: bash
 
       - name: ⬆️ Upload Dataset
@@ -72,16 +72,16 @@ jobs:
 
       - name: 3. Train Model
         env:
-          NO_NNPACK: 1
+            NO_NNPACK: 1
-          PYTORCH_NO_NNPACK: 1
+            PYTORCH_NO_NNPACK: 1
         run: |
           mkdir -p checkpoint_files
           PYTHONPATH=. python scripts/model_builder/train.py 2>/dev/null
-
+    
       - name: 4. Plot Model
         env:
-          NO_NNPACK: 1
+            NO_NNPACK: 1
-          PYTORCH_NO_NNPACK: 1
+            PYTORCH_NO_NNPACK: 1
         run: |
           PYTHONPATH=. python scripts/model_builder/plot_data.py 2>/dev/null
 
@@ -98,7 +98,7 @@ jobs:
         run: |
           mkdir -p onnx_files
           MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python scripts/application_packager/convert_to_onnx.py 2>/dev/null
-
+          
       - name: ⬆️ Upload ONNX file
         uses: actions/upload-artifact@v3
         with:
@@ -108,13 +108,13 @@ jobs:
       - name: 6. Profile ONNX model
         run: |
           PYTHONPATH=. python scripts/application_packager/profile_onnx.py
-
+      
       - name: ⬆️ Upload JSON trace
         uses: actions/upload-artifact@v3
         with:
-          name: profile-data
+          name: profile-data          
-          path: "**/onnxruntime_profile_*.json"
+          path: '**/onnxruntime_profile_*.json'
-
+      
       - name: 7. Convert ONNX graph to an ORT file
         run: |
           PYTHONPATH=. python scripts/application_packager/convert_to_ort.py

conf/app.yaml

@@ -24,7 +24,7 @@ dataset:
   snr_step: 3
 
   # Number of iterations (signal recordings) per modulation and SNR combination
-  num_iterations: 10
+  num_iterations: 3
 
   # Modulation scheme settings; keys must match the `modulation_types` list above
   # Each entry includes:
@@ -50,7 +50,7 @@ dataset:
 
   # Training and validation split ratios; must sum to 1
   train_split: 0.8
-  val_split: 0.2
+  val_split : 0.2
 
 training:
   # Number of training examples processed together before the model updates its weights

helpers/app_settings.py

@@ -41,11 +41,7 @@ class AppConfig:
 
 
 class AppSettings:
-    """
+    """Application settings, to be initialized from app.yaml configuration file."""
-    Application settings,
-    to be initialized from
-    app.yaml configuration file.
-    """
 
     def __init__(self, config_file: str):
         # Load the YAML configuration file

scripts/application_packager/convert_to_onnx.py

@@ -2,9 +2,9 @@ import os
 
 import numpy as np
 import torch
+from scripts.model_builder.mobilenetv3 import RFClassifier, mobilenetv3
 
 from helpers.app_settings import get_app_settings
-from scripts.model_builder.mobilenetv3 import RFClassifier, mobilenetv3
 
 
 def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
@@ -21,7 +21,7 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
 
     in_channels = 2
     batch_size = 1
-    slice_length = int(dataset_cfg.recording_length / dataset_cfg.num_slices)
+    slice_length = int(1024 / dataset_cfg.num_slices)
     num_classes = len(dataset_cfg.modulation_types)
 
     model = RFClassifier(
@@ -42,7 +42,7 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
     model.eval()
 
     # Generate random sample data
-    base, _ = os.path.splitext(os.path.basename(ckpt_path))
+    base, ext = os.path.splitext(os.path.basename(ckpt_path))
     if fp16:
         output_path = os.path.join("onnx_files", f"{base}.onnx")
         sample_input = torch.from_numpy(

scripts/dataset_manager/data_gen.py

@@ -29,7 +29,7 @@ def generate_modulated_signals(output_dir: str) -> None:
 
     for modulation in settings.modulation_types:
         for snr in np.arange(settings.snr_start, settings.snr_stop, settings.snr_step):
-            for _ in range(settings.num_iterations):
+            for i in range(3):
                 recording_length = settings.recording_length
                 beta = (
                     settings.beta

scripts/dataset_manager/produce_dataset.py

@@ -49,6 +49,8 @@ def write_hdf5_file(records: List, output_path: str, dataset_name: str = "data")
             int(md["sps"]),
         )
 
+    first_rec, _ = records[0]  # records[0] is a tuple of (data, md)
+
     with h5py.File(output_path, "w") as hf:
         data_arr = np.stack([rec[0] for rec in records])
         dset = hf.create_dataset(dataset_name, data=data_arr, compression="gzip")

scripts/dataset_manager/split_dataset.py

@@ -90,7 +90,7 @@ def split_recording(
     snippet_list = []
 
     for data, md in recording_list:
-        _, N = data.shape
+        C, N = data.shape
         L = N // num_snippets
         for i in range(num_snippets):
             start = i * L

scripts/model_builder/mobilenetv3.py

@@ -1,4 +1,5 @@
 import lightning as L
+import numpy as np
 import timm
 import torch
 from torch import nn

scripts/model_builder/plot_data.py

@@ -2,26 +2,26 @@ import os
 
 import numpy as np
 import torch
+from sklearn.metrics import classification_report
+
+os.environ["NNPACK"] = "0"
 from matplotlib import pyplot as plt
 from mobilenetv3 import RFClassifier, mobilenetv3
 from modulation_dataset import ModulationH5Dataset
-from sklearn.metrics import classification_report
 
 from helpers.app_settings import get_app_settings
 
-os.environ["NNPACK"] = "0"
-
 
 def load_validation_data():
     val_dataset = ModulationH5Dataset(
         "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)
+    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,)
     class_names = list(val_dataset.label_encoder.classes_)
 
-    return x, y, class_names
+    return X, y, class_names
 
 
 def build_model_from_ckpt(
@@ -75,7 +75,7 @@ def evaluate_checkpoint(ckpt_path: str):
         classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
     )
 
-    print_confusion_matrix(
+    plot_confusion_matrix_with_counts(
         y_true=np.array(y_true),
         y_pred=np.array(y_pred),
         classes=class_names,
@@ -84,7 +84,7 @@ def evaluate_checkpoint(ckpt_path: str):
     )
 
 
-def print_confusion_matrix(
+def plot_confusion_matrix_with_counts(
     y_true: np.ndarray,
     y_pred: np.ndarray,
     classes: list[str],
@@ -94,7 +94,6 @@ def print_confusion_matrix(
     """
     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)
@@ -103,8 +102,8 @@ def print_confusion_matrix(
         title: title for the plot
     """
     # 1) build raw CM
-    c = len(classes)
+    C = len(classes)
-    cm = np.zeros((c, c), dtype=int)
+    cm = np.zeros((C, C), dtype=int)
     for t, p in zip(y_true, y_pred):
         cm[t, p] += 1
 
@@ -113,48 +112,33 @@ def print_confusion_matrix(
         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)
-        print_confusion_matrix_helper(cm_norm, classes)
     else:
-        print_confusion_matrix_helper(cm, classes)
+        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)
 
-import numpy as np
+    # 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")
-def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=2):
+    plt.tight_layout()
-    """
+    plt.show()
-    Pretty prints a confusion matrix with x/y labels.
-
-    Parameters:
-    - matrix: square 2D numpy array
-    - labels: list of class labels (default: range(num_classes))
-    - normalize: whether to normalize rows to sum to 1
-    - digits: number of decimal places to show for normalized values
-    """
-    matrix = np.array(matrix)
-    num_classes = matrix.shape[0]
-    labels = classes or list(range(num_classes))
-
-    # Header
-    print(" " * 9 + "Ground Truth →")
-    header = "Pred ↓ | " + "  ".join([f"{str(label):>6}" for label in labels])
-    print(header)
-    print("-" * len(header))
-
-    # Rows
-    for i in range(num_classes):
-        row_vals = matrix[i]
-        if normalize:
-            row_sum = row_vals.sum()
-            row_vals = row_vals / row_sum if row_sum != 0 else row_vals
-            row_str = "  ".join([f"{val:>6.{digits}f}" for val in row_vals])
-        else:
-            row_str = "  ".join([f"{int(val):>6}" for val in row_vals])
-        print(f"{str(labels[i]):>7} | {row_str}")
 
 
 if __name__ == "__main__":
     settings = get_app_settings()
-    evaluate_checkpoint(
+    evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
-        os.path.join("checkpoint_files", "inference_recognition_model.ckpt")
-    )

scripts/model_builder/train.py

@@ -1,22 +1,23 @@
 import os
 import sys
 
+os.environ["NNPACK"] = "0"
 import lightning as L
 import mobilenetv3
 import torch
 import torch.nn.functional as F
 import torchmetrics
-from lightning.pytorch.callbacks import ModelCheckpoint, TQDMProgressBar
+from lightning.pytorch.callbacks import ModelCheckpoint
 from modulation_dataset import ModulationH5Dataset
 
 from helpers.app_settings import get_app_settings
 
-os.environ["NNPACK"] = "0"
 script_dir = os.path.dirname(os.path.abspath(__file__))
 data_dir = os.path.abspath(os.path.join(script_dir, ".."))
 project_root = os.path.abspath(os.path.join(os.getcwd(), ".."))
 if project_root not in sys.path:
     sys.path.insert(0, project_root)
+from lightning.pytorch.callbacks import TQDMProgressBar
 
 
 class CustomProgressBar(TQDMProgressBar):
@@ -58,6 +59,8 @@ def train_model():
         print("X shape:", x.shape)
         print("Y values:", y[:10])
         break
+
+    unique_labels = list(set([row[label].decode("utf-8") for row in ds_train.metadata]))
     num_classes = len(ds_train.label_encoder.classes_)
 
     hparams = {