Added TensorFlow-free npy and h5 weight conversions

convert_weights.py

@@ -0,0 +1,48 @@
+import os
+import numpy as np
+import tensorflow as tf  # Needed to handle TF tensors
+import h5py
+
+def convert_to_numpy(value):
+    """
+    Convert TensorFlow tensors/variables to NumPy arrays (float32).
+    Ensures we remove any TensorFlow-specific data.
+    """
+    if isinstance(value, tf.Tensor) or isinstance(value, tf.Variable):
+        return value.numpy().astype(np.float32)
+    elif isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.number):
+        return value.astype(np.float32)
+    else:
+        return None  # Ignore non-numeric data
+
+def convert_weights(npy_path):
+    """Convert `weights.npy` to a TensorFlow-free HDF5 format."""
+    if not os.path.exists(npy_path):
+        print(f"❌ Error: {npy_path} not found!")
+        return
+
+    h5_path = npy_path.replace(".npy", "_tf_free.h5")
+
+    # Load the weights
+    print(f"🔍 Loading {npy_path}...")
+    weights = np.load(npy_path, allow_pickle=True)
+
+    # Convert all elements to NumPy arrays (remove TensorFlow dtypes)
+    converted_weights = [convert_to_numpy(w) for w in weights if convert_to_numpy(w) is not None]
+
+    # Save to HDF5 format
+    with h5py.File(h5_path, "w") as hf:
+        for i, w in enumerate(converted_weights):
+            hf.create_dataset(f"weight_{i}", data=w)
+
+    print(f"✅ Converted: {npy_path} -> {h5_path}")
+
+if __name__ == "__main__":
+    # Search for all `weights.npy` files and convert them
+    for root, _, files in os.walk("."):
+        for file in files:
+            if file == "weights.npy":
+                convert_weights(os.path.join(root, file))
+
+    print("🚀 All weight files converted successfully!")
+