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Diffstat (limited to 'python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py')
-rw-r--r-- | python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py | 55 |
1 files changed, 55 insertions, 0 deletions
diff --git a/python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py b/python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py new file mode 100644 index 0000000000..229a9b6778 --- /dev/null +++ b/python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +# Copyright © 2020 NXP and Contributors. All rights reserved. +# SPDX-License-Identifier: MIT + +import numpy as np +import pyarmnn as ann +import example_utils as eu +import os + +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) |