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onnx_inference.py
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onnx_inference.py
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# encoding: utf-8
"""
@author: xingyu liao
@contact: [email protected]
"""
import argparse
import glob
import os
import cv2
import numpy as np
import onnxruntime
import tqdm
def get_parser():
parser = argparse.ArgumentParser(description="onnx model inference")
parser.add_argument(
"--model-path",
default="onnx_model/baseline.onnx",
help="onnx model path"
)
parser.add_argument(
"--input",
nargs="+",
help="A list of space separated input images; "
"or a single glob pattern such as 'directory/*.jpg'",
)
parser.add_argument(
"--output",
default='onnx_output',
help='path to save converted caffe model'
)
parser.add_argument(
"--height",
type=int,
default=256,
help="height of image"
)
parser.add_argument(
"--width",
type=int,
default=128,
help="width of image"
)
return parser
def preprocess(image_path, image_height, image_width):
original_image = cv2.imread(image_path)
# the model expects RGB inputs
original_image = original_image[:, :, ::-1]
# Apply pre-processing to image.
img = cv2.resize(original_image, (image_width, image_height), interpolation=cv2.INTER_CUBIC)
img = img.astype("float32").transpose(2, 0, 1)[np.newaxis] # (1, 3, h, w)
return img
def normalize(nparray, order=2, axis=-1):
"""Normalize a N-D numpy array along the specified axis."""
norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
return nparray / (norm + np.finfo(np.float32).eps)
if __name__ == "__main__":
args = get_parser().parse_args()
ort_sess = onnxruntime.InferenceSession(args.model_path)
input_name = ort_sess.get_inputs()[0].name
if not os.path.exists(args.output): os.makedirs(args.output)
if args.input:
if os.path.isdir(args.input[0]):
args.input = glob.glob(os.path.expanduser(args.input[0]))
assert args.input, "The input path(s) was not found"
for path in tqdm.tqdm(args.input):
image = preprocess(path, args.height, args.width)
feat = ort_sess.run(None, {input_name: image})[0]
feat = normalize(feat, axis=1)
np.save(os.path.join(args.output, path.replace('.jpg', '.npy').split('/')[-1]), feat)