Delete arcface_onnx.py.txt

arcface_onnx.py.txt

@@ -1,91 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Organization  : insightface.ai
-# @Author        : Jia Guo
-# @Time          : 2021-05-04
-# @Function      : 
-
-import numpy as np
-import cv2
-import onnx
-import onnxruntime
-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, providers=['CoreMLExecutionProvider','CUDAExecutionProvider'])
-        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, kps):
-        aimg = face_align.norm_crop(img, landmark=kps, image_size=self.input_size[0])
-        embedding = self.get_feat(aimg).flatten()
-        return 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
-
-