https://vimsky.com/examples/detail/python-method-onnxruntime.InferenceSession.html
https://github.com/htshinichi/caffe-onnx/blob/master/onnxmodel/test_resnet.py
import onnxruntime
import numpy as np
import PIL.Image as Image
import argparse
def process_image(img_path,input_shape):
img = Image.open(img_path).convert("RGB")
img = img.resize(input_shape)
image = np.array(img, dtype=np.float32)
image = image.transpose((2,0,1))[np.newaxis, ...]
return image
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input_shape', help="caffe's caffemodel file path", nargs='+', default=(224,224))
parser.add_argument('--img_path', help="test image path", type=str, default="./onnxmodel/airplane.jpg")
parser.add_argument('--onnx_path', help="onnx model file path", type=str, default="./onnxmodel/resnet50.onnx")
args = parser.parse_args()
input_shape = [int(x) for x in args.input_shape] #模型输入尺寸
img_path = args.img_path
onnx_path = args.onnx_path
print("image path:",img_path)
print("onnx model path:",onnx_path)
data_input = process_image(img_path,input_shape)
session = onnxruntime.InferenceSession(onnx_path)
inname = [input.name for input in session.get_inputs()]
outname = [output.name for output in session.get_outputs()]
print("inputs name:",inname,"|| outputs name:",outname)
data_output = session.run(outname, {inname[0]: data_input})
output = data_output[0]
print("Label predict: ", output.argmax())
if __name__ == '__main__':
main()
标签：img,onnxruntime,onnx,image,InferenceSession,shape,input,path From： https://www.cnblogs.com/sinferwu/p/17116395.html