conf/app.yaml

@@ -5,9 +5,6 @@ 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
@@ -27,7 +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:

helpers/app_settings.py

@@ -9,7 +9,6 @@ import yaml
 @dataclass
 class DataSetConfig:
     num_slices: int
-    mult_factor: int
     train_split: float
     seed: int
     modulation_types: list
@@ -42,7 +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/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.mult_factor):
+            for _ in range(settings.num_iterations):
                 recording_length = settings.recording_length
                 beta = (
                     settings.beta

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

@@ -2,15 +2,15 @@ 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(

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 = {