diff options
Diffstat (limited to 'python/pyarmnn/examples/image_classification')
4 files changed, 92 insertions, 92 deletions
diff --git a/python/pyarmnn/examples/image_classification/example_utils.py b/python/pyarmnn/examples/image_classification/example_utils.py index 090ce2f5b3..f0ba91e981 100644 --- a/python/pyarmnn/examples/image_classification/example_utils.py +++ b/python/pyarmnn/examples/image_classification/example_utils.py @@ -38,7 +38,8 @@ def run_inference(runtime, net_id, images, labels, input_binding_info, output_bi runtime.EnqueueWorkload(net_id, input_tensors, output_tensors) # Process output - out_tensor = ann.workload_tensors_to_ndarray(output_tensors)[0] + # output tensor has a shape (1, 1001) + out_tensor = ann.workload_tensors_to_ndarray(output_tensors)[0][0] results = np.argsort(out_tensor)[::-1] print_top_n(5, results, labels, out_tensor) @@ -121,7 +122,7 @@ def __create_network(model_file: str, backends: list, parser=None): return net_id, parser, runtime -def create_tflite_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef']): +def create_tflite_network(model_file: str, backends: list = ('CpuAcc', 'CpuRef')): """Creates a network from a tflite model file. Args: @@ -140,7 +141,7 @@ def create_tflite_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef'] return net_id, graph_id, parser, runtime -def create_onnx_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef']): +def create_onnx_network(model_file: str, backends: list = ('CpuAcc', 'CpuRef')): """Creates a network from an onnx model file. Args: @@ -181,7 +182,7 @@ def preprocess_default(img: Image, width: int, height: int, data_type, scale: fl def load_images(image_files: list, input_width: int, input_height: int, data_type=np.uint8, - scale: float = 1., mean: list = [0., 0., 0.], stddev: list = [1., 1., 1.], + scale: float = 1., mean: list = (0., 0., 0.), stddev: list = (1., 1., 1.), preprocess_fn=preprocess_default): """Loads images, resizes and performs any additional preprocessing to run inference. @@ -218,7 +219,6 @@ def load_labels(label_file: str): with open(label_file, 'r') as f: labels = [l.rstrip() for l in f] return labels - return None def print_top_n(N: int, results: list, labels: list, prob: list): @@ -299,10 +299,10 @@ def get_model_and_labels(model_dir: str, model: str, labels: str, archive: str = download_url = [download_url] for dl in download_url: archive = download_file(dl) - if dl.lower().endswith(".zip"): - unzip_file(archive) + if dl.lower().endswith(".zip"): + unzip_file(archive) except RuntimeError: - print("Unable to download file ({}).".format(archive_url)) + print("Unable to download file ({}).".format(download_url)) if not os.path.exists(labels) or not os.path.exists(model): raise RuntimeError("Unable to provide model and labels.") @@ -310,7 +310,7 @@ def get_model_and_labels(model_dir: str, model: str, labels: str, archive: str = return model, labels -def list_images(folder: str = None, formats: list = ['.jpg', '.jpeg']): +def list_images(folder: str = None, formats: list = ('.jpg', '.jpeg')): """Lists files of a certain format in a folder. Args: @@ -338,7 +338,7 @@ def get_images(image_dir: str, image_url: str = DEFAULT_IMAGE_URL): """Gets image. Args: - image (str): Image filename + image_dir (str): Image filename image_url (str): Image url Returns: diff --git a/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py b/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py index 9e95f76dcc..be28b585ba 100644 --- a/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py +++ b/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py @@ -44,48 +44,49 @@ def preprocess_onnx(img: Image, width: int, height: int, data_type, scale: float return img -args = eu.parse_command_line() - -model_filename = 'mobilenetv2-1.0.onnx' -labels_filename = 'synset.txt' -archive_filename = 'mobilenetv2-1.0.zip' -labels_url = 'https://s3.amazonaws.com/onnx-model-zoo/' + labels_filename -model_url = 'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/' + model_filename - -# Download resources -image_filenames = eu.get_images(args.data_dir) - -model_filename, labels_filename = eu.get_model_and_labels(args.model_dir, model_filename, labels_filename, - archive_filename, - [model_url, labels_url]) - -# 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 a model file -net_id, parser, runtime = eu.create_onnx_network(model_filename) - -# Load input information from the model and create input tensors -input_binding_info = parser.GetNetworkInputBindingInfo("data") - -# Load output information from the model and create output tensors -output_binding_info = parser.GetNetworkOutputBindingInfo("mobilenetv20_output_flatten0_reshape0") -output_tensors = ann.make_output_tensors([output_binding_info]) - -# Load labels -labels = eu.load_labels(labels_filename) - -# Load images and resize to expected size -images = eu.load_images(image_filenames, - 224, 224, - np.float32, - 255.0, - [0.485, 0.456, 0.406], - [0.229, 0.224, 0.225], - preprocess_onnx) - -eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info) +if __name__ == "__main__": + args = eu.parse_command_line() + + model_filename = 'mobilenetv2-1.0.onnx' + labels_filename = 'synset.txt' + archive_filename = 'mobilenetv2-1.0.zip' + labels_url = 'https://s3.amazonaws.com/onnx-model-zoo/' + labels_filename + model_url = 'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/' + model_filename + + # Download resources + image_filenames = eu.get_images(args.data_dir) + + model_filename, labels_filename = eu.get_model_and_labels(args.model_dir, model_filename, labels_filename, + archive_filename, + [model_url, labels_url]) + + # 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 a model file + net_id, parser, runtime = eu.create_onnx_network(model_filename) + + # Load input information from the model and create input tensors + input_binding_info = parser.GetNetworkInputBindingInfo("data") + + # Load output information from the model and create output tensors + output_binding_info = parser.GetNetworkOutputBindingInfo("mobilenetv20_output_flatten0_reshape0") + output_tensors = ann.make_output_tensors([output_binding_info]) + + # Load labels + labels = eu.load_labels(labels_filename) + + # Load images and resize to expected size + images = eu.load_images(image_filenames, + 224, 224, + np.float32, + 255.0, + [0.485, 0.456, 0.406], + [0.229, 0.224, 0.225], + preprocess_onnx) + + eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info) diff --git a/python/pyarmnn/examples/image_classification/requirements.txt b/python/pyarmnn/examples/image_classification/requirements.txt index f97e85636e..289a2b521a 100644 --- a/python/pyarmnn/examples/image_classification/requirements.txt +++ b/python/pyarmnn/examples/image_classification/requirements.txt @@ -1,4 +1,4 @@ requests>=2.23.0 urllib3>=1.25.8 Pillow>=6.1.0 -numpy>=1.18.1 +numpy>=1.19.2 diff --git a/python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py b/python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py index 229a9b6778..6b35f63a00 100644 --- a/python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py +++ b/python/pyarmnn/examples/image_classification/tflite_mobilenetv1_quantized.py @@ -2,54 +2,53 @@ # 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() +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' + # 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' + 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) + 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) + 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)) + # 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]) + # 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 -images = eu.load_images(image_filenames, input_width, input_height) + # 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) + eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info) |