python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py

#!/usr/bin/env python3
# Copyright © 2020 NXP and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT

import example_utils as eu
import os

if __name__ == "__main__":
    args = eu.parse_command_line()

    # names of the files in the archive
    labels_filename = 'labels_mobilenet_quant_v1_224.txt'
    model_filename = 'mobilenet_v1_1.0_224_quant.tflite'
    archive_filename = 'mobilenet_v1_1.0_224_quant_and_labels.zip'

    archive_url = \
        'https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip'

    model_filename, labels_filename = eu.get_model_and_labels(args.model_dir, model_filename, labels_filename,
                                                              archive_filename, archive_url)

    image_filenames = eu.get_images(args.data_dir)

    # all 3 resources must exist to proceed further
    assert os.path.exists(labels_filename)
    assert os.path.exists(model_filename)
    assert image_filenames
    for im in image_filenames:
        assert(os.path.exists(im))

    # Create a network from the model file
    net_id, graph_id, parser, runtime = eu.create_tflite_network(model_filename)

    # Load input information from the model
    # tflite has all the need information in the model unlike other formats
    input_names = parser.GetSubgraphInputTensorNames(graph_id)
    assert len(input_names) == 1  # there should be 1 input tensor in mobilenet

    input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
    input_width = input_binding_info[1].GetShape()[1]
    input_height = input_binding_info[1].GetShape()[2]

    # Load output information from the model and create output tensors
    output_names = parser.GetSubgraphOutputTensorNames(graph_id)
    assert len(output_names) == 1  # and only one output tensor
    output_binding_info = parser.GetNetworkOutputBindingInfo(graph_id, output_names[0])

    # Load labels file
    labels = eu.load_labels(labels_filename)

    # Load images and resize to expected size
    images = eu.load_images(image_filenames, input_width, input_height)

    eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info)