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# 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
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