.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 +44,14 @@ 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
+        uses: actions/upload-artifact@v4
         with:
           name: recordings
-          path: data/recordings/**
+          path: recordings.tar.gz
 
       - name: 2. Build HDF5 Dataset
         run: |
@@ -113,7 +108,7 @@ 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

@@ -5,6 +5,9 @@ dataset:
   # Number of samples per recording
   recording_length: 1024
 
+  # Set this to scale the number of generated recordings
+  mult_factor: 5
+
   # List of signal modulation schemes to include in the dataset
   modulation_types:
     - bpsk
@@ -50,7 +53,7 @@ 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

@@ -9,6 +9,7 @@ import yaml
 @dataclass
 class DataSetConfig:
     num_slices: int
+    mult_factor: int
     train_split: float
     seed: int
     modulation_types: list

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.mult_factor):
                 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")