.riahub/workflows/workflow.yaml

@@ -2,11 +2,9 @@ name: Modulation Recognition Demo
 
 on:
   push:
-    branches:
-      [main]
+    branches: [main]
   pull_request:
-    branches:
-      [main]
+    branches: [main]
 
 jobs:
   ria-demo:
@@ -46,17 +44,19 @@ jobs:
           fi
           pip install -r requirements.txt
 
-
       - name: 1. Generate Recordings
         run: |
           mkdir -p 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: data/recordings/**
+          path: recordings.tar.gz
 
       - name: 2. Build HDF5 Dataset
         run: |
@@ -113,7 +113,7 @@ jobs:
         uses: actions/upload-artifact@v3
         with:
           name: profile-data
-          path: '**/onnxruntime_profile_*.json'
+          path: "**/onnxruntime_profile_*.json"
 
       - name: 7. Convert ONNX graph to an ORT file
         run: |

conf/app.yaml

@@ -24,7 +24,7 @@ dataset:
   snr_step: 3
 
   # Number of iterations (signal recordings) per modulation and SNR combination
-  num_iterations: 3
+  num_iterations: 100
 
   # Modulation scheme settings; keys must match the `modulation_types` list above
   # Each entry includes:
@@ -57,7 +57,7 @@ training:
   batch_size: 256
 
   # Number of complete passes through the training dataset during training
-  epochs: 5
+  epochs: 30
 
   # Learning rate: step size for weight updates after each batch
   # Recommended range for fine-tuning: 1e-6 to 1e-4

helpers/app_settings.py

@@ -41,7 +41,11 @@ 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:

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 i in range(3):
+            for _ in range(settings.num_iterations):
                 recording_length = settings.recording_length
                 beta = (
                     settings.beta

scripts/dataset_manager/produce_dataset.py

@@ -49,8 +49,6 @@ 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/model_builder/mobilenetv3.py

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

scripts/model_builder/plot_data.py

@@ -2,27 +2,25 @@ 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)
     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
 
 
@@ -46,27 +44,22 @@ def build_model_from_ckpt(
     return model
 
 
-
-
 def evaluate_checkpoint(ckpt_path: str):
     """
     Loads the model from checkpoint and evaluates it on a validation set.
     Prints classification metrics and plots a confusion matrix.
     """
 
-
     # Load validation data
     X_val, y_true, class_names = load_validation_data()
     num_classes = len(class_names)
     in_channels = X_val.shape[1]
 
-
     # Load model
     model = build_model_from_ckpt(
         ckpt_path, in_channels=in_channels, num_classes=num_classes
     )
 
-
     # Inference
     y_pred = []
     with torch.no_grad():
@@ -76,15 +69,12 @@ def evaluate_checkpoint(ckpt_path: str):
             pred = torch.argmax(logits, dim=1).item()
             y_pred.append(pred)
 
-
     # Print classification report
     print("\nClassification Report:")
     print(
         classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
     )
 
-  
-  
     print_confusion_matrix(
         y_true=np.array(y_true),
         y_pred=np.array(y_pred),
@@ -94,8 +84,6 @@ def evaluate_checkpoint(ckpt_path: str):
     )
 
 
-
-
 def print_confusion_matrix(
     y_true: np.ndarray,
     y_pred: np.ndarray,
@@ -120,7 +108,6 @@ def print_confusion_matrix(
     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"):
@@ -131,10 +118,9 @@ def print_confusion_matrix(
         print_confusion_matrix_helper(cm, classes)
 
 
-
-
 import numpy as np
 
+
 def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=2):
     """
     Pretty prints a confusion matrix with x/y labels.
@@ -169,6 +155,6 @@ def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=
 
 if __name__ == "__main__":
     settings = get_app_settings()
-   evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
-
-
+    evaluate_checkpoint(
+        os.path.join("checkpoint_files", "inference_recognition_model.ckpt")
+    )

scripts/model_builder/train.py

@@ -1,23 +1,22 @@
 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
+from lightning.pytorch.callbacks import ModelCheckpoint, TQDMProgressBar
 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):
@@ -59,8 +58,6 @@ 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 = {