fixed paths

.gitignore

@@ -1,5 +1,3 @@
-.venv/
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.ckpt
@@ -7,3 +5,18 @@ __pycache__/
 *.onnx
 *.json
 *.h5
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# PyCharm
+.idea/
+
+# Visual Studio Code
+.vscode/

.riahub/workflows/workflow.yaml

@@ -68,6 +68,7 @@ jobs:
             NO_NNPACK: 1
             PYTORCH_NO_NNPACK: 1
         run: |
+          mkdir -p checkpoint_files
           PYTHONPATH=. python scripts/training/train.py 
           echo "training model"
 
@@ -79,6 +80,8 @@ jobs:
 
       - name: 4. Convert to ONNX file
         run: |
+          mkdir -p onnx_scripts
+          mkdir -p onnx_files
           MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python onnx_scripts/convert_to_onnx.py
           echo "building inference app"
           

onnx_scripts/profile_onnx.py

@@ -1,7 +1,6 @@
 import onnxruntime as ort
 import numpy as np
 from helpers.app_settings import get_app_settings
-from onnx_files import ONNX_DIR
 import os
 
 def profile_onnx_model(path_to_onnx: str, num_runs: int = 100):
@@ -38,5 +37,5 @@ def profile_onnx_model(path_to_onnx: str, num_runs: int = 100):
 
 if __name__ == "__main__":
     settings = get_app_settings()
-    output_path = os.path.join(ONNX_DIR, f"{settings.inference.onnx_model_filename}.onnx")
+    output_path = os.path.join("onnx_files", "convert_to_onnx.onnx")
     profile_onnx_model(output_path)

scripts/dataset_building/data_gen.py

@@ -15,7 +15,7 @@ mods = {
 def generate_modulated_signals(output_dir):
     for modulation in ["bpsk", "qpsk", "qam16", "qam64"]:
         for snr in np.arange(-6, 13, 3):
-            for i in range(100):
+            for i in range(3):
                 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.

scripts/training/train.py

@@ -29,11 +29,9 @@ def train_model():
     batch_size = training_cfg.batch_size
     epochs = training_cfg.epochs
 
-    checkpoint_dir = training_cfg.checkpoint_dir
-    checkpoint_filename = training_cfg.checkpoint_filename
 
-    train_data = f"{dataset_cfg.output_dir}/train.h5"
+    train_data = "data/dataset/train.h5"
-    val_data = f"{dataset_cfg.output_dir}/val.h5"
+    val_data = "data/dataset/val.h5"
 
     label = "modulation"
 
@@ -122,8 +120,8 @@ def train_model():
     
 
     checkpoint_callback = ModelCheckpoint(
-        dirpath=checkpoint_dir,
+        dirpath="checkpoint_files",
-        filename=checkpoint_filename,
+        filename="inference_recognition_model.ckpt",
         save_top_k=1,
         verbose=True,  # Disable checkpoint verbose output
         monitor="val_acc",