Create convert_tflite_2_onnx.py

convert_tflite_2_onnx.py

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+import tensorflow as tf
+
+# Load the TFLite model
+tflite_model_path = 'model.tflite'
+interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
+interpreter.allocate_tensors()
+
+# Export the TFLite model back to a TensorFlow SavedModel
+saved_model_dir = 'saved_model'
+
+# Convert the TFLite model back to a TensorFlow model
+converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
+tf.saved_model.save(interpreter, saved_model_dir)
+
+
+pip install tf2onnx
+pip install onnx_runtime
+python -m tf2onnx.convert --saved-model saved_model --output model.onnx --opset 11
+
+
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+
+# Load ONNX model
+onnx_model_path = 'model.onnx'
+session = ort.InferenceSession(onnx_model_path)
+
+# Load image and preprocess (resize, normalize)
+image_path = 'image.jpg'
+image = Image.open(image_path).resize((320, 320))  # Assuming 320x320 model input size
+image_data = np.array(image).astype('float32')
+image_data = np.expand_dims(image_data, axis=0)  # Add batch dimension
+
+# Run inference
+input_name = session.get_inputs()[0].name
+output = session.run(None, {input_name: image_data})
+
+# Output contains predictions, including bounding boxes and class labels
+print(output)
+
+