1 files changed, 0 insertions, 59 deletions
diff --git a/src/mlia/nn/tensorflow/utils.py b/src/mlia/nn/tensorflow/utils.py
index b8d45c6..1612447 100644
--- a/src/mlia/nn/tensorflow/utils.py
+++ b/src/mlia/nn/tensorflow/utils.py
@@ -4,31 +4,11 @@
 """Collection of useful functions for optimizations."""
 from __future__ import annotations
 
-import logging
 from pathlib import Path
 from typing import Any
-from typing import Callable
-from typing import cast
-from typing import Iterable
 
-import numpy as np
 import tensorflow as tf
 
-from mlia.utils.logging import redirect_output
-
-
-def representative_dataset(
-    input_shape: Any, sample_count: int = 100, input_dtype: type = np.float32
-) -> Callable:
-    """Sample dataset used for quantization."""
-
-    def dataset() -> Iterable:
-        for _ in range(sample_count):
-            data = np.random.rand(1, *input_shape[1:])
-            yield [data.astype(input_dtype)]
-
-    return dataset
-
 
 def get_tf_tensor_shape(model: str) -> list:
     """Get input shape for the TensorFlow tensor model."""
@@ -49,14 +29,6 @@ def get_tf_tensor_shape(model: str) -> list:
     ]
 
 
-def convert_to_tflite(model: tf.keras.Model | str, quantized: bool = False) -> bytes:
-    """Convert Keras model to TensorFlow Lite."""
-    converter = get_tflite_converter(model, quantized)
-
-    with redirect_output(logging.getLogger("tensorflow")):
-        return cast(bytes, converter.convert())
-
-
 def save_keras_model(
     model: tf.keras.Model, save_path: str | Path, include_optimizer: bool = True
 ) -> None:
@@ -94,37 +66,6 @@ def is_saved_model(model: str | Path) -> bool:
     return model_path.is_dir() and not is_keras_model(model)
 
 
-def get_tflite_converter(
-    model: tf.keras.Model | str | Path, quantized: bool = False
-) -> tf.lite.TFLiteConverter:
-    """Configure TensorFlow Lite converter for the provided model."""
-    if isinstance(model, (str, Path)):
-        # converter's methods accept string as input parameter
-        model = str(model)
-
-    if isinstance(model, tf.keras.Model):
-        converter = tf.lite.TFLiteConverter.from_keras_model(model)
-        input_shape = model.input_shape
-    elif isinstance(model, str) and is_saved_model(model):
-        converter = tf.lite.TFLiteConverter.from_saved_model(model)
-        input_shape = get_tf_tensor_shape(model)
-    elif isinstance(model, str) and is_keras_model(model):
-        keras_model = tf.keras.models.load_model(model)
-        input_shape = keras_model.input_shape
-        converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
-    else:
-        raise ValueError(f"Unable to create TensorFlow Lite converter for {model}")
-
-    if quantized:
-        converter.optimizations = [tf.lite.Optimize.DEFAULT]
-        converter.representative_dataset = representative_dataset(input_shape)
-        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
-        converter.inference_input_type = tf.int8
-        converter.inference_output_type = tf.int8
-
-    return converter
-
-
 def get_tflite_model_type_map(model_filename: str | Path) -> dict[str, type]:
     """Get type map from tflite model."""
     model_type_map: dict[str, Any] = {}