deleted old recordings, updated gpu for training,

.gitignore

@@ -6,3 +6,4 @@ __pycache__/
 *.ckpt
 *.ipynb
 *.onnx
+*.npy

conf/app.yaml

@@ -20,9 +20,7 @@ training:
   use_gpu: true
 
 inference:
-  model_path: checkpoints/inference_recognition_model.ckpt
   num_classes: 4
-  output_path: onnx_files/inference_recognition_model.onnx
 
 app:
   build_dir: dist

convert_to_onnx.py

@@ -80,4 +80,6 @@ if __name__ == "__main__":
         ckpt_path=os.path.join(CHECKPOINTS_DIR, model_checkpoint), fp16=False
     )
     
-    print("Conversion complete stored at: ", os.path.join(ONNX_DIR, model_checkpoint))
+    output_file = "inference_recognition_model.onnx"
+    
+    print("Conversion complete stored at: ", os.path.join(ONNX_DIR, output_file))

data/scripts/data_gen.py

@@ -13,7 +13,7 @@ mods = {
 def generate_modulated_signals():
     for modulation in ["bpsk", "qpsk", "qam16", "qam64"]:
         for snr in np.arange(-6, 13, 3):
-
+            for i in range(100):
                 recording_length = 1024
                 beta = 0.3  # the rolloff factor, can be changed to add variety
                 sps = 4  # samples per symbol, or the relative bandwidth of the digital signal. Can also be changed.

data/training/train.py

@@ -31,7 +31,7 @@ def train_model():
 
     train_flag = True
     batch_size = 128
-    epochs = 1
+    epochs = 50
 
     checkpoint_dir = training_cfg.checkpoint_dir
     checkpoint_filename = training_cfg.checkpoint_filename
@@ -76,8 +76,8 @@ def train_model():
     hparams = {
         "drop_path_rate": 0.2,
         "drop_rate": 0.5,
-        "learning_rate": 3e-4,
-        "wd": 0.2,
+        "learning_rate": 1e-4,
+        "wd": 0.01,
     }
 
     class RFClassifier(L.LightningModule):

helpers/app_settings.py

@@ -33,9 +33,7 @@ class TrainingConfig:
 
 @dataclass
 class InferenceConfig:
-    model_path: str
     num_classes: int
-    output_path: str
 
 
 @dataclass