Update to provide resources to PyArmNN examples manually
Change-Id: I9ee751512abd5d4ec9faca499b5cea7c19028d22
Signed-off-by: Pavel Macenauer <pavel.macenauer@nxp.com>
diff --git a/python/pyarmnn/examples/tflite_mobilenetv1_quantized.py b/python/pyarmnn/examples/tflite_mobilenetv1_quantized.py
index aa18a52..cb2c91c 100755
--- a/python/pyarmnn/examples/tflite_mobilenetv1_quantized.py
+++ b/python/pyarmnn/examples/tflite_mobilenetv1_quantized.py
@@ -2,71 +2,54 @@
# 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
+args = eu.parse_command_line()
-def unzip_file(filename):
- """Unzips a file to its current location.
+# 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'
- Args:
- filename (str): Name of the archive.
+archive_url = \
+ 'https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip'
- 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)
+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)
-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')
+# 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)
+# 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
+# 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]
+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 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 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)
+# Load images and resize to expected size
+images = eu.load_images(image_filenames, 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)
+eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info)