| #!/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) |