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Diffstat (limited to 'python/pyarmnn/examples/example_utils.py')
| -rw-r--r-- | python/pyarmnn/examples/example_utils.py | 221 |
1 files changed, 221 insertions, 0 deletions
diff --git a/python/pyarmnn/examples/example_utils.py b/python/pyarmnn/examples/example_utils.py new file mode 100644 index 0000000000..f4d1e4eb1f --- /dev/null +++ b/python/pyarmnn/examples/example_utils.py @@ -0,0 +1,221 @@ +# Copyright 2020 NXP +# SPDX-License-Identifier: MIT + +from urllib.parse import urlparse +import os +from PIL import Image +import pyarmnn as ann +import numpy as np +import requests +import argparse +import warnings + + +def parse_command_line(desc: str = ""): + """Adds arguments to the script. + + Args: + desc(str): Script description. + + Returns: + Namespace: Arguments to the script command. + """ + parser = argparse.ArgumentParser(description=desc) + parser.add_argument("-v", "--verbose", help="Increase output verbosity", + action="store_true") + return parser.parse_args() + + +def __create_network(model_file: str, backends: list, parser=None): + """Creates a network based on a file and parser type. + + Args: + model_file (str): Path of the model file. + backends (list): List of backends to use when running inference. + parser_type: Parser instance. (pyarmnn.ITFliteParser/pyarmnn.IOnnxParser...) + + Returns: + int: Network ID. + int: Graph ID. + IParser: TF Lite parser instance. + IRuntime: Runtime object instance. + """ + args = parse_command_line() + options = ann.CreationOptions() + runtime = ann.IRuntime(options) + + if parser is None: + # try to determine what parser to create based on model extension + _, ext = os.path.splitext(model_file) + if ext == ".onnx": + parser = ann.IOnnxParser() + elif ext == ".tflite": + parser = ann.ITfLiteParser() + assert (parser is not None) + + network = parser.CreateNetworkFromBinaryFile(model_file) + + preferred_backends = [] + for b in backends: + preferred_backends.append(ann.BackendId(b)) + + opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), + ann.OptimizerOptions()) + if args.verbose: + for m in messages: + warnings.warn(m) + + net_id, w = runtime.LoadNetwork(opt_network) + if args.verbose and w: + warnings.warn(w) + + return net_id, parser, runtime + + +def create_tflite_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef']): + """Creates a network from an onnx model file. + + Args: + model_file (str): Path of the model file. + backends (list): List of backends to use when running inference. + + Returns: + int: Network ID. + int: Graph ID. + ITFliteParser: TF Lite parser instance. + IRuntime: Runtime object instance. + """ + net_id, parser, runtime = __create_network(model_file, backends, ann.ITfLiteParser()) + graph_id = parser.GetSubgraphCount() - 1 + + return net_id, graph_id, parser, runtime + + +def create_onnx_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef']): + """Creates a network from a tflite model file. + + Args: + model_file (str): Path of the model file. + backends (list): List of backends to use when running inference. + + Returns: + int: Network ID. + IOnnxParser: ONNX parser instance. + IRuntime: Runtime object instance. + """ + return __create_network(model_file, backends, ann.IOnnxParser()) + + +def preprocess_default(img: Image, width: int, height: int, data_type, scale: float, mean: list, + stddev: list): + """Default preprocessing image function. + + Args: + img (PIL.Image): PIL.Image object instance. + width (int): Width to resize to. + height (int): Height to resize to. + data_type: Data Type to cast the image to. + scale (float): Scaling value. + mean (list): RGB mean offset. + stddev (list): RGB standard deviation. + + Returns: + np.array: Resized and preprocessed image. + """ + img = img.resize((width, height), Image.BILINEAR) + img = img.convert('RGB') + img = np.array(img) + img = np.reshape(img, (-1, 3)) # reshape to [RGB][RGB]... + img = ((img / scale) - mean) / stddev + img = img.flatten().astype(data_type) + return img + + +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.], + preprocess_fn=preprocess_default): + """Loads images, resizes and performs any additional preprocessing to run inference. + + Args: + img (list): List of PIL.Image object instances. + input_width (int): Width to resize to. + input_height (int): Height to resize to. + data_type: Data Type to cast the image to. + scale (float): Scaling value. + mean (list): RGB mean offset. + stddev (list): RGB standard deviation. + preprocess_fn: Preprocessing function. + + Returns: + np.array: Resized and preprocessed images. + """ + images = [] + for i in image_files: + img = Image.open(i) + img = preprocess_fn(img, input_width, input_height, data_type, scale, mean, stddev) + images.append(img) + return images + + +def load_labels(label_file: str): + """Loads a labels file containing a label per line. + + Args: + label_file (str): Labels file path. + + Returns: + list: List of labels read from a file. + """ + 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): + """Prints TOP-N results + + Args: + N (int): Result count to print. + results (list): Top prediction indices. + labels (list): A list of labels for every class. + prob (list): A list of probabilities for every class. + + Returns: + None + """ + assert (len(results) >= 1 and len(results) == len(labels) == len(prob)) + for i in range(min(len(results), N)): + print("class={0} ; value={1}".format(labels[results[i]], prob[results[i]])) + + +def download_file(url: str, force: bool = False, filename: str = None, dest: str = "tmp"): + """Downloads a file. + + Args: + url (str): File url. + force (bool): Forces to download the file even if it exists. + filename (str): Renames the file when set. + + Returns: + str: Path to the downloaded file. + """ + if filename is None: # extract filename from url when None + filename = urlparse(url) + filename = os.path.basename(filename.path) + + if str is not None: + if not os.path.exists(dest): + os.makedirs(dest) + filename = os.path.join(dest, filename) + + print("Downloading '{0}' from '{1}' ...".format(filename, url)) + if not os.path.exists(filename) or force is True: + r = requests.get(url) + with open(filename, 'wb') as f: + f.write(r.content) + print("Finished.") + else: + print("File already exists.") + + return filename |