| # 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 |