Merge pull request 'liyu-dev' (#3) from liyu-dev into main

Reviewed-on: https://git.riahub.ai/qoherent/modrec-workflow/pulls/3

.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:
@@ -46,22 +44,24 @@ 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
+          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
         with:
           name: recordings
-          path: data/recordings/**
+          path: recordings.tar.gz
 
       - name: 2. Build HDF5 Dataset
         run: |
-         mkdir -p data/dataset
-         PYTHONPATH=. python scripts/dataset_manager/produce_dataset.py
+          mkdir -p data/dataset
+          PYTHONPATH=. python scripts/dataset_manager/produce_dataset.py
         shell: bash
 
       - name: ⬆️ Upload Dataset
@@ -72,16 +72,16 @@ jobs:
 
       - name: 3. Train Model
         env:
-            NO_NNPACK: 1
-            PYTORCH_NO_NNPACK: 1
+          NO_NNPACK: 1
+          PYTORCH_NO_NNPACK: 1
         run: |
           mkdir -p checkpoint_files
           PYTHONPATH=. python scripts/model_builder/train.py 2>/dev/null
-    
+
       - name: 4. Plot Model
         env:
-            NO_NNPACK: 1
-            PYTORCH_NO_NNPACK: 1
+          NO_NNPACK: 1
+          PYTORCH_NO_NNPACK: 1
         run: |
           PYTHONPATH=. python scripts/model_builder/plot_data.py 2>/dev/null
 
@@ -98,7 +98,7 @@ jobs:
         run: |
           mkdir -p onnx_files
           MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python scripts/application_packager/convert_to_onnx.py 2>/dev/null
-          
+
       - name: ⬆️ Upload ONNX file
         uses: actions/upload-artifact@v3
         with:
@@ -108,13 +108,13 @@ jobs:
       - name: 6. Profile ONNX model
         run: |
           PYTHONPATH=. python scripts/application_packager/profile_onnx.py
-      
+
       - name: ⬆️ Upload JSON trace
         uses: actions/upload-artifact@v3
         with:
-          name: profile-data          
-          path: '**/onnxruntime_profile_*.json'
-      
+          name: profile-data
+          path: "**/onnxruntime_profile_*.json"
+
       - name: 7. Convert ONNX graph to an ORT file
         run: |
           PYTHONPATH=. python scripts/application_packager/convert_to_ort.py

conf/app.yaml

@@ -24,7 +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:
@@ -50,14 +50,14 @@ 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
   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

helpers/app_settings.py

@@ -41,7 +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/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.num_iterations):
                 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")

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

@@ -2,139 +2,125 @@ 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(
-       "data/dataset/val.h5", label_name="modulation", data_key="validation_data"
-   )
+    val_dataset = ModulationH5Dataset(
+        "data/dataset/val.h5", label_name="modulation", data_key="validation_data"
+    )
 
+    x = np.stack([x.numpy() for x, _ in val_dataset])  # shape: (N, C, L)
+    y = np.array([y.item() for _, y in val_dataset])  # shape: (N,)
+    class_names = list(val_dataset.label_encoder.classes_)
 
-   x = np.stack([x.numpy() for x, _ in val_dataset])  # shape: (N, C, L)
-   y = np.array([y.item() for _, y in val_dataset])  # shape: (N,)
-   class_names = list(val_dataset.label_encoder.classes_)
-
-
-   return x, y, class_names
+    return x, y, class_names
 
 
 def build_model_from_ckpt(
-   ckpt_path: str, in_channels: int, num_classes: int
+    ckpt_path: str, in_channels: int, num_classes: int
 ) -> torch.nn.Module:
-   """
-   Build and return a PyTorch model loaded from a checkpoint.
-   """
-   model = RFClassifier(
-       model=mobilenetv3(
-           model_size="mobilenetv3_small_050",
-           num_classes=num_classes,
-           in_chans=in_channels,
-       )
-   )
-   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-   checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
-   model.load_state_dict(checkpoint["state_dict"])
-   model.eval()
-   return model
-
-
+    """
+    Build and return a PyTorch model loaded from a checkpoint.
+    """
+    model = RFClassifier(
+        model=mobilenetv3(
+            model_size="mobilenetv3_small_050",
+            num_classes=num_classes,
+            in_chans=in_channels,
+        )
+    )
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
+    model.load_state_dict(checkpoint["state_dict"])
+    model.eval()
+    return model
 
 
 def evaluate_checkpoint(ckpt_path: str):
-   """
-   Loads the model from checkpoint and evaluates it on a validation set.
-   Prints classification metrics and plots a confusion matrix.
-   """
+    """
+    Loads the model from checkpoint and evaluates it on a validation set.
+    Prints classification metrics and plots a confusion matrix.
+    """
 
+    # Load validation data
+    X_val, y_true, class_names = load_validation_data()
+    num_classes = len(class_names)
+    in_channels = X_val.shape[1]
 
-   # Load validation data
-   X_val, y_true, class_names = load_validation_data()
-   num_classes = len(class_names)
-   in_channels = X_val.shape[1]
+    # Load model
+    model = build_model_from_ckpt(
+        ckpt_path, in_channels=in_channels, num_classes=num_classes
+    )
 
+    # Inference
+    y_pred = []
+    with torch.no_grad():
+        for x in X_val:
+            x_tensor = torch.tensor(x[np.newaxis, ...], dtype=torch.float32)
+            logits = model(x_tensor)
+            pred = torch.argmax(logits, dim=1).item()
+            y_pred.append(pred)
 
-   # Load model
-   model = build_model_from_ckpt(
-       ckpt_path, in_channels=in_channels, num_classes=num_classes
-   )
-
-
-   # Inference
-   y_pred = []
-   with torch.no_grad():
-       for x in X_val:
-           x_tensor = torch.tensor(x[np.newaxis, ...], dtype=torch.float32)
-           logits = model(x_tensor)
-           pred = torch.argmax(logits, dim=1).item()
-           y_pred.append(pred)
-
-
-   # Print classification report
-   print("\nClassification Report:")
-   print(
-       classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
-   )
-  
-  
-  
-   print_confusion_matrix(
-       y_true=np.array(y_true),
-       y_pred=np.array(y_pred),
-       classes=class_names,
-       normalize=True,
-       title="Normalized Confusion Matrix",
-   )
-
+    # Print classification report
+    print("\nClassification Report:")
+    print(
+        classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
+    )
 
+    print_confusion_matrix(
+        y_true=np.array(y_true),
+        y_pred=np.array(y_pred),
+        classes=class_names,
+        normalize=True,
+        title="Normalized Confusion Matrix",
+    )
 
 
 def print_confusion_matrix(
-   y_true: np.ndarray,
-   y_pred: np.ndarray,
-   classes: list[str],
-   normalize: bool = True,
-   title: str = "Confusion Matrix (counts and normalized)",
+    y_true: np.ndarray,
+    y_pred: np.ndarray,
+    classes: list[str],
+    normalize: bool = True,
+    title: str = "Confusion Matrix (counts and normalized)",
 ) -> None:
-   """
-   Plot a confusion matrix showing both raw counts and (optionally) normalized values.
+    """
+    Plot a confusion matrix showing both raw counts and (optionally) normalized values.
 
 
-   Args:
-       y_true: true labels (integers 0..C-1)
-       y_pred: predicted labels (same shape as y_true)
-       classes: list of class‐name strings in index order
-       normalize: if True, each row is normalized to sum=1
-       title: title for the plot
-   """
-   # 1) build raw CM
-   c = len(classes)
-   cm = np.zeros((c, c), dtype=int)
-   for t, p in zip(y_true, y_pred):
-       cm[t, p] += 1
-      
-  
-   # 2) normalize if requested
-   if normalize:
-       with np.errstate(divide="ignore", invalid="ignore"):
-           cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
-           cm_norm = np.nan_to_num(cm_norm)
-       print_confusion_matrix_helper(cm_norm, classes)
-   else:
-       print_confusion_matrix_helper(cm, classes)
-
+    Args:
+        y_true: true labels (integers 0..C-1)
+        y_pred: predicted labels (same shape as y_true)
+        classes: list of class‐name strings in index order
+        normalize: if True, each row is normalized to sum=1
+        title: title for the plot
+    """
+    # 1) build raw CM
+    c = len(classes)
+    cm = np.zeros((c, c), dtype=int)
+    for t, p in zip(y_true, y_pred):
+        cm[t, p] += 1
 
+    # 2) normalize if requested
+    if normalize:
+        with np.errstate(divide="ignore", invalid="ignore"):
+            cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
+            cm_norm = np.nan_to_num(cm_norm)
+        print_confusion_matrix_helper(cm_norm, classes)
+    else:
+        print_confusion_matrix_helper(cm, classes)
 
 
 import numpy as np
 
+
 def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=2):
     """
     Pretty prints a confusion matrix with x/y labels.
@@ -148,13 +134,13 @@ def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=
     matrix = np.array(matrix)
     num_classes = matrix.shape[0]
     labels = classes or list(range(num_classes))
-    
+
     # Header
     print(" " * 9 + "Ground Truth →")
     header = "Pred ↓ | " + "  ".join([f"{str(label):>6}" for label in labels])
     print(header)
     print("-" * len(header))
-    
+
     # Rows
     for i in range(num_classes):
         row_vals = matrix[i]
@@ -166,9 +152,9 @@ def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=
             row_str = "  ".join([f"{int(val):>6}" for val in row_vals])
         print(f"{str(labels[i]):>7} | {row_str}")
 
-      
+
 if __name__ == "__main__":
-   settings = get_app_settings()
-   evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
-
-
+    settings = get_app_settings()
+    evaluate_checkpoint(
+        os.path.join("checkpoint_files", "inference_recognition_model.ckpt")
+    )

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 = {