remove redundancy files
This commit is contained in:
DiaosWang
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"import 必要的包\n",
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"\n",
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"deepface构建keras模型，导出为onnx模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(112, 112) 512 GhostFaceNet\n"
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]
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}
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],
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"source": [
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"# import 必要的包 \n",
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"from deepface import DeepFace \n",
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"import cv2 \n",
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"import time \n",
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"import os \n",
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"import sys\n",
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"import numpy as np \n",
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"import re \n",
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"import pandas as pd \n",
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"\n",
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"from deepface.modules.streaming import (\n",
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" build_demography_models, build_facial_recognition_model,\n",
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" search_identity, highlight_facial_areas, \n",
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" grab_facial_areas, extract_facial_areas, \n",
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" perform_facial_recognition, perform_demography_analysis,\n",
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")\n",
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"\n",
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"from deepface.modules import recognition, verification, detection, representation\n",
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"from deepface.models.FacialRecognition import FacialRecognition\n",
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"from deepface.modules import preprocessing\n",
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"from deepface.commons.logger import Logger\n",
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"import pickle \n",
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"\n",
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"logger = Logger(module=\"commons.realtime\")\n",
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"model: FacialRecognition = DeepFace.build_model(model_name=\"GhostFaceNet\")\n",
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"\n",
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"import tf2onnx\n",
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"import onnxruntime as rt\n",
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"import tensorflow as tf\n",
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"\n",
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"spec = (tf.TensorSpec((None, 112, 112, 3), tf.float32, name=\"input\"),)\n",
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"# spec = (tf.TensorSpec((None, 3, 112, 112, ), tf.float32, name=\"input\"),)\n",
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"output_path = model.model_name + \".onnx\"\n",
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"print(model.input_shape, model.output_shape, model.model_name )\n",
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"\n",
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"model_proto, _ = tf2onnx.convert.from_keras(model.model, input_signature=spec, opset=13, output_path=output_path)\n",
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"output_names = [n.name for n in model_proto.graph.output]\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"使用上述得到的onnx model进行inference得到embeddings：\n",
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"- inference img_path1: C:\\Users\\JIALE\\Desktop\\bns项目资料\\proj_deepface\\face\\biden_id.jpg\n",
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"- inference img_path2: C:\\Users\\JIALE\\Desktop\\bns项目资料\\proj_deepface\\face\\jiangyongqi.jpg\n",
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"\n",
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"计算embeddings之间的normalization L2距离。 \n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(1, 112, 112, 3) (1, 112, 112, 3)\n",
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"distance of source and target embeddings for model inference: 1.2703674196541226\n",
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"distance of embeddings for onnx inference: 1.270367798994846\n"
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]
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}
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],
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"source": [
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"from tensorflow.keras.preprocessing import image\n",
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"\n",
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"img_path1 = r'.\\face\\biden_id.jpg'\n",
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"img_path2 = r\".\\face\\jiangyongqi.jpg\"\n",
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"\n",
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"img1 = cv2.resize(cv2.imread(img_path1),(112, 112)) # bgr格式\n",
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"img2 = cv2.resize(cv2.imread(img_path2),(112, 112)) \n",
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"\n",
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"face1 = image.img_to_array(img1)\n",
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"face1 = np.expand_dims(face1, axis=0)\n",
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"face1 /= 255 # normalize input in [0, 1]\n",
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"\n",
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"face2 = image.img_to_array(img2)\n",
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"face2 = np.expand_dims(face2, axis=0)\n",
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"face2 /= 255 # normalize input in [0, 1]\n",
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"print( face1.shape, face2.shape )\n",
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"\n",
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"source_embedding = model.find_embeddings(\n",
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" preprocessing.normalize_input(img=face1,normalization='base')\n",
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")\n",
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"target_embedding = model.find_embeddings(\n",
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" preprocessing.normalize_input(img=face2,normalization='base')\n",
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")\n",
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"distance_metric = \"euclidean_l2\"\n",
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"distance = verification.find_distance(source_embedding,target_embedding,distance_metric)\n",
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"print(\"distance of source and target embeddings for model inference: \", distance)\n",
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"\n",
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"providers = ['CPUExecutionProvider']\n",
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"m = rt.InferenceSession(output_path, providers=providers)\n",
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"onnx_pred1= m.run(output_names, {\"input\": face1})[0][0].tolist()\n",
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"onnx_pred2 = m.run(output_names, {\"input\": face2})[0][0].tolist()\n",
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"dist2 = verification.find_distance(onnx_pred1,onnx_pred2,distance_metric)\n",
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"print(\"distance of embeddings for onnx inference: \", dist2 )\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.16"
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -1,26 +0,0 @@
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from deepface import DeepFace
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"""
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Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models:
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Model Declared LFW Score
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VGG-Face 98.9%
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Facenet 99.2%
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Facenet512 99.6%
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OpenFace 92.9%
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DeepID 97.4%
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Dlib 99.3%
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SFace 99.5%
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ArcFace 99.5%
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GhostFaceNet 99.7%
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Human-beings 97.5%
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The default configuration uses VGG-Face model.
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"""
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DeepFace.stream(
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db_path='./face',
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model_name = 'Facenet512',
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enable_face_analysis = True,
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time_threshold=2,
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frame_threshold=20, # Given frame_threshold = 20, 如果其在连续的20帧中都检测到人脸，则对第20帧的face进行操作（如：age 分析，人脸识别等等）
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)
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@ -1,276 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Conversion to ONNX from keras model using tf2onnx python api "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"'wget' <20><><EFBFBD><EFBFBD><EFBFBD>ڲ<EFBFBD><DAB2><EFBFBD><EFBFBD>ⲿ<EFBFBD><E2B2BF><EFBFBD>Ҳ<EEA3AC><D2B2><EFBFBD>ǿ<EFBFBD><C7BF><EFBFBD><EFBFBD>еij<D0B5><C4B3><EFBFBD>\n",
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"<22><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ļ<EFBFBD><C4BC><EFBFBD>\n"
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]
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}
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],
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"source": [
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"# get image\n",
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"# !wget -q https://raw.githubusercontent.com/onnx/tensorflow-onnx/main/tests/ade20k.jpg"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install tensorflow tf2onnx onnxruntime"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import tensorflow as tf\n",
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"from tensorflow.keras.applications.resnet50 import ResNet50\n",
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"from tensorflow.keras.preprocessing import image\n",
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"from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
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"import numpy as np\n",
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"import onnxruntime\n",
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"\n",
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"img_path = 'ade20k.jpg'\n",
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"\n",
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"img = image.load_img(img_path, target_size=(224, 224))\n",
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"\n",
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"x = image.img_to_array(img)\n",
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"x = np.expand_dims(x, axis=0)\n",
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"x = preprocess_input(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Run the keras model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1/1 [==============================] - 1s 1s/step\n",
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"Keras Predicted: [('n02974003', 'car_wheel', 0.2852505), ('n04285008', 'sports_car', 0.26048782), ('n03930630', 'pickup', 0.15360694)]\n",
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"WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 53). These functions will not be directly callable after loading.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: ./tmp\\resnet50\\assets\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: ./tmp\\resnet50\\assets\n"
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]
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}
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],
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"source": [
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"model = ResNet50(weights='imagenet')\n",
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"\n",
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"preds = model.predict(x)\n",
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"print('Keras Predicted:', decode_predictions(preds, top=3)[0])\n",
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"model.save(os.path.join(\"./tmp\", model.name))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Convert to ONNX using the Python API"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tf2onnx\n",
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"import onnxruntime as rt\n",
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"\n",
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"spec = (tf.TensorSpec((None, 224, 224, 3), tf.float32, name=\"input\"),)\n",
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"output_path = model.name + \".onnx\"\n",
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"\n",
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"model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13, output_path=output_path)\n",
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"output_names = [n.name for n in model_proto.graph.output]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Run the ONNX model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ONNX Predicted: [('n02974003', 'car_wheel', 0.28524926), ('n04285008', 'sports_car', 0.2604879), ('n03930630', 'pickup', 0.15360788)]\n"
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]
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}
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],
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"source": [
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"providers = ['CPUExecutionProvider']\n",
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"m = rt.InferenceSession(output_path, providers=providers)\n",
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"onnx_pred = m.run(output_names, {\"input\": x})\n",
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"\n",
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"print('ONNX Predicted:', decode_predictions(onnx_pred[0], top=3)[0])\n",
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"\n",
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"# make sure ONNX and keras have the same results\n",
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"np.testing.assert_allclose(preds, onnx_pred[0], rtol=1e-5)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Convert to ONNX using the command line"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\runpy.py:125: RuntimeWarning: 'tf2onnx.convert' found in sys.modules after import of package 'tf2onnx', but prior to execution of 'tf2onnx.convert'; this may result in unpredictable behaviour\n",
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" warn(RuntimeWarning(msg))\n",
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"2024-04-17 13:23:28,986 - WARNING - '--tag' not specified for saved_model. Using --tag serve\n",
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"Traceback (most recent call last):\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\runpy.py\", line 193, in _run_module_as_main\n",
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" \"__main__\", mod_spec)\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\runpy.py\", line 85, in _run_code\n",
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" exec(code, run_globals)\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tf2onnx\\convert.py\", line 714, in <module>\n",
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" main()\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tf2onnx\\convert.py\", line 245, in main\n",
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" use_graph_names=args.use_graph_names)\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tf2onnx\\tf_loader.py\", line 637, in from_saved_model\n",
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" tag, signatures, concrete_function, large_model, use_graph_names)\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tf2onnx\\tf_loader.py\", line 570, in _from_saved_model_v2\n",
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" imported = tf.saved_model.load(model_path, tags=tag) # pylint: disable=no-value-for-parameter\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tensorflow\\python\\saved_model\\load.py\", line 828, in load\n",
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" result = load_partial(export_dir, None, tags, options)[\"root\"]\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tensorflow\\python\\saved_model\\load.py\", line 933, in load_partial\n",
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" loader_impl.parse_saved_model_with_debug_info(export_dir))\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tensorflow\\python\\saved_model\\loader_impl.py\", line 57, in parse_saved_model_with_debug_info\n",
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" saved_model = parse_saved_model(export_dir)\n",
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" File \"c:\\Users\\JIALE\\.conda\\envs\\deepface_v2\\lib\\site-packages\\tensorflow\\python\\saved_model\\loader_impl.py\", line 116, in parse_saved_model\n",
|
||||
" f\"SavedModel file does not exist at: {export_dir}{os.path.sep}\"\n",
|
||||
"OSError: SavedModel file does not exist at: /tmp\\resnet50\\{saved_model.pbtxt|saved_model.pb}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!python -m tf2onnx.convert --opset 13 \\\n",
|
||||
" --saved-model {os.path.join(\"/tmp\", model.name)} \\\n",
|
||||
" --output {os.path.join(\"/tmp\", model.name + \".onnx\")}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.16"
|
||||
},
|
||||
"varInspector": {
|
||||
"cols": {
|
||||
"lenName": 16,
|
||||
"lenType": 16,
|
||||
"lenVar": 40
|
||||
},
|
||||
"kernels_config": {
|
||||
"python": {
|
||||
"delete_cmd_postfix": "",
|
||||
"delete_cmd_prefix": "del ",
|
||||
"library": "var_list.py",
|
||||
"varRefreshCmd": "print(var_dic_list())"
|
||||
},
|
||||
"r": {
|
||||
"delete_cmd_postfix": ") ",
|
||||
"delete_cmd_prefix": "rm(",
|
||||
"library": "var_list.r",
|
||||
"varRefreshCmd": "cat(var_dic_list()) "
|
||||
}
|
||||
},
|
||||
"types_to_exclude": [
|
||||
"module",
|
||||
"function",
|
||||
"builtin_function_or_method",
|
||||
"instance",
|
||||
"_Feature"
|
||||
],
|
||||
"window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue