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#!/usr/bin/env python3
# Copyright 2020 NXP
# SPDX-License-Identifier: MIT
from zipfile import ZipFile
import numpy as np
import pyarmnn as ann
import example_utils as eu
import os
def unzip_file(filename):
"""Unzips a file to its current location.
Args:
filename (str): Name of the archive.
Returns:
str: Directory path of the extracted files.
"""
with ZipFile(filename, 'r') as zip_obj:
zip_obj.extractall(os.path.dirname(filename))
return os.path.dirname(filename)
if __name__ == "__main__":
# Download resources
archive_filename = eu.download_file(
'https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip')
dir_path = unzip_file(archive_filename)
# names of the files in the archive
labels_filename = os.path.join(dir_path, 'labels_mobilenet_quant_v1_224.txt')
model_filename = os.path.join(dir_path, 'mobilenet_v1_1.0_224_quant.tflite')
kitten_filename = eu.download_file('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
# 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])
output_tensors = ann.make_output_tensors([output_binding_info])
# Load labels file
labels = eu.load_labels(labels_filename)
# Load images and resize to expected size
image_names = [kitten_filename]
images = eu.load_images(image_names, input_width, input_height)
for idx, im in enumerate(images):
# Create input tensors
input_tensors = ann.make_input_tensors([input_binding_info], [im])
# Run inference
print("Running inference on '{0}' ...".format(image_names[idx]))
runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
# Process output
out_tensor = ann.workload_tensors_to_ndarray(output_tensors)[0][0]
results = np.argsort(out_tensor)[::-1]
eu.print_top_n(5, results, labels, out_tensor)
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