Function export_tflite

export.py:373–400

(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:'))

@try_export
def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
    # YOLOv5 TensorFlow Lite export
    import tensorflow as tf

    LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
    batch_size, ch, *imgsz = list(im.shape)  # BCHW
    f = str(file).replace('.pt', '-fp16.tflite')

    converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
    converter.target_spec.supported_types = [tf.float16]
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    if int8:
        from models.tf import representative_dataset_gen
        dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
        converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
        converter.target_spec.supported_types = []
        converter.inference_input_type = tf.uint8  # or tf.int8
        converter.inference_output_type = tf.uint8  # or tf.int8
        converter.experimental_new_quantizer = True
        f = str(file).replace('.pt', '-int8.tflite')
    if nms or agnostic_nms:
        converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)

    tflite_model = converter.convert()
    open(f, "wb").write(tflite_model)
    return f, None


@try_export