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Running
on
Zero
| # -*- coding: utf-8 -*- | |
| # @Organization : insightface.ai | |
| # @Author : Jia Guo | |
| # @Time : 2021-05-04 | |
| # @Function : | |
| from __future__ import division | |
| import numpy as np | |
| import cv2 | |
| import onnx | |
| import onnxruntime | |
| from ..utils import face_align | |
| __all__ = [ | |
| 'ArcFaceONNX', | |
| ] | |
| class ArcFaceONNX: | |
| def __init__(self, model_file=None, session=None): | |
| assert model_file is not None | |
| self.model_file = model_file | |
| self.session = session | |
| self.taskname = 'recognition' | |
| find_sub = False | |
| find_mul = False | |
| model = onnx.load(self.model_file) | |
| graph = model.graph | |
| for nid, node in enumerate(graph.node[:8]): | |
| #print(nid, node.name) | |
| if node.name.startswith('Sub') or node.name.startswith('_minus'): | |
| find_sub = True | |
| if node.name.startswith('Mul') or node.name.startswith('_mul'): | |
| find_mul = True | |
| if find_sub and find_mul: | |
| #mxnet arcface model | |
| input_mean = 0.0 | |
| input_std = 1.0 | |
| else: | |
| input_mean = 127.5 | |
| input_std = 127.5 | |
| self.input_mean = input_mean | |
| self.input_std = input_std | |
| #print('input mean and std:', self.input_mean, self.input_std) | |
| if self.session is None: | |
| self.session = onnxruntime.InferenceSession(self.model_file, None) | |
| input_cfg = self.session.get_inputs()[0] | |
| input_shape = input_cfg.shape | |
| input_name = input_cfg.name | |
| self.input_size = tuple(input_shape[2:4][::-1]) | |
| self.input_shape = input_shape | |
| outputs = self.session.get_outputs() | |
| output_names = [] | |
| for out in outputs: | |
| output_names.append(out.name) | |
| self.input_name = input_name | |
| self.output_names = output_names | |
| assert len(self.output_names)==1 | |
| self.output_shape = outputs[0].shape | |
| def prepare(self, ctx_id, **kwargs): | |
| if ctx_id<0: | |
| self.session.set_providers(['CPUExecutionProvider']) | |
| def get(self, img, face): | |
| aimg = face_align.norm_crop(img, landmark=face.kps, image_size=self.input_size[0]) | |
| face.embedding = self.get_feat(aimg).flatten() | |
| return face.embedding | |
| def compute_sim(self, feat1, feat2): | |
| from numpy.linalg import norm | |
| feat1 = feat1.ravel() | |
| feat2 = feat2.ravel() | |
| sim = np.dot(feat1, feat2) / (norm(feat1) * norm(feat2)) | |
| return sim | |
| def get_feat(self, imgs): | |
| if not isinstance(imgs, list): | |
| imgs = [imgs] | |
| input_size = self.input_size | |
| blob = cv2.dnn.blobFromImages(imgs, 1.0 / self.input_std, input_size, | |
| (self.input_mean, self.input_mean, self.input_mean), swapRB=True) | |
| net_out = self.session.run(self.output_names, {self.input_name: blob})[0] | |
| return net_out | |
| def forward(self, batch_data): | |
| blob = (batch_data - self.input_mean) / self.input_std | |
| net_out = self.session.run(self.output_names, {self.input_name: blob})[0] | |
| return net_out | |