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: 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

scripts/application_packager/convert_to_onnx.py

@@ -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/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