TFLite Conversion Error: Element Shape Required to be 1D

Currently trying to convert a TF mask rcnn model to TFLite, so I can use it on a TPU. When I try to run the quantization code, I get the following error:

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 error: 'tf.TensorListReserve' op requires element_shape to be 1D tensor during TF Lite transformation pass 

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I’m not sure how to deal with the error, or how to fix it. Here’s the code:

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 import tensorflow as tf import model as modellib import coco import os import sys # Enable eager execution tf.compat.v1.enable_eager_execution() class InferenceConfig(coco.CocoConfig): GPU_COUNT = 1 IMAGES_PER_GPU = 1 config = InferenceConfig() model = modellib.MaskRCNN(mode="inference", model_dir='logs', config=config) model.load_weights('mask_rcnn_coco.h5', by_name=True) model = model.keras_model tf.saved_model.save(model, "tflite") # Preparing before conversion - making the representative dataset ROOT_DIR = os.path.abspath("../") CARS = os.path.join(ROOT_DIR, 'Mask_RCNN\mrcnn\smallCar') IMAGE_SIZE = 224 datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255) def representative_data_gen(): dataset_list = tf.data.Dataset.list_files(CARS) for i in range(100): image = next(iter(dataset_list)) image = tf.io.read_file(image) image = tf.io.decode_jpeg(image, channels=3) image = tf.image.resize(image, [IMAGE_SIZE, IMAGE_SIZE]) image = tf.cast(image / 255., tf.float32) image = tf.expand_dims(image, 0) yield [image] converter = tf.lite.TFLiteConverter.from_keras_model(model) # This enables quantization converter.optimizations = [tf.lite.Optimize.DEFAULT] # This sets the representative dataset for quantization converter.representative_dataset = representative_data_gen # This ensures that if any ops can't be quantized, the converter throws an error converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] # For full integer quantization, though supported types defaults to int8 only, we explicitly declare it for clarity. converter.target_spec.supported_types = [tf.int8] # These set the input and output tensors to uint8 (added in r2.3) converter.inference_input_type = tf.uint8 converter.inference_output_type = tf.uint8 tflite_model = converter.convert() with open('modelQuantized.tflite', 'wb') as f: f.write(tflite_model) 

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Any help is appreciated!