insightface-arcface onnx 推理代码和模型 partial-fc-glint360k-r50

#thanks to link:https://bbs.huaweicloud.com/blogs/180532 #http://github.com/deepinsight/insightface # -*- coding: utf-8 -*- import cv2 import torch import numpy as np import onnxruntime # image to tensor def img2tensor(image): img = cv2.cvtColor(image , cv2.COLOR_BGR2RGB) img = np.transpose(img, (2, 0, 1)) img = torch.from_numpy(img).unsqueeze(0).float().cuda() img.div_(255).sub_(0.5).div_(0.5) return img # tensor to numpy def to_numpy(tensor): return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy() # create an inference ort_session = onnxruntime.InferenceSession("partial_fc_glint360k_r50.onnx") # load image image = cv2.imread('../1.jpg') img = img2tensor(image) # inference ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(img)} f1 = ort_session.run(None, ort_inputs)[0] # array emb = f1.reshape(512,) f1 = emb / np.linalg.norm(emb) # load image image = cv2.imread('../2.jpg') img = img2tensor(image) # inference ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(img)} f2 = ort_session.run(None, ort_inputs)[0] # array emb = f2.reshape(512,) f2 = emb / np.linalg.norm(emb) print((f1*f1).sum()) sim = np.dot(f1, f2) print("置信度:",sim)