【论文阅读】HIP network:Historical information passing network for extrapolation reasoning on temporal kno

【论文阅读】HIP network: Historical information passing network for extrapolation reasoning on temporal knowledge ---- 前言 关于时间知识图谱的论文：HIP Network: Historical Information Passing Network for Extrapolation Reasoning on OverView 本文提出了Historical Information Passing (HIP) network。 HIP Network 模型架构如下图所示： Structural Information Passing Module 首先论文根据最近的历史 KG \mathcal{G}^{(t-1)} ，更新实体和关系的信息 参考资料 [1] HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal

独家 | 利用深度学习来预测Spotify上的Hip-Hop 流行程度

作者：Nicholas Indorf翻译：Gabriel Ng校对：zrx 本文约10000字，建议阅读13分钟项目中收集并使用了 Spotify 数据库中最近发布的hip-hop曲目的音频预览样本和相关的流行度分数 摘要 在这个项目里面，我想构建一个工具来帮助我的表弟，一位名叫“KC Makes Music”的Hip-Hop艺术家。这个工具将会评估他尚未发布的歌曲是否有在Spotify上流行的潜力。 项目中只收集并使用了 Spotify 数据库中最近发布的hip-hop曲目的音频预览样本和相关的流行度分数。 KC Makes Music 是我表弟的艺名，他是 Spotify 上的Hip-Hop艺术家。我认为如果我利用我的数据科学技能能尝试帮助他在平台上取得听众数，这将是一次有趣的学习体验。 为了更全面地捕捉当代Hip-hop音乐的全貌，我需要重新评估我的收集方法以收集更多歌曲。 除了扩展现在所有的东西之外，我还想看看有什么有用的东西。

又双叒叕有公司想打破Nvidia垄断？这回让CUDA代码直接编译运行于AMD GPU

GPUS开发者，赞119AMD开发了开源的HIP，这是一种C++运行时API和内核语言，使开发人员能够从单个源代码为AMD和Nvidia GPU创建可移植的应用程序。） 虽然HIP不是CUDA，但它基于AMD的ROCm，相当于Nvidia的CUDA。此外，AMD还提供了HIPIFY翻译工具，该工具将CUDA源代码转换为AMD HIP，使其能够在AMD GPU上运行。 一旦翻译或用HIP API编写，代码就可以针对AMD或Nvidia硬件。 虽然AMD HIP和HIPIFY是有效且开放的解决方案，但开发人员通常更喜欢直接进行源代码编译。从实际角度来看，使用单个CUDA或HIP代码库比同时管理两者更可取。 尽管HIP同时面向AMD和Nvidia硬件，但大量Nvidia GPU代码已经并将继续使用CUDA编写。

资源 | AMD 开源高性能机器智能库MIOpen，可加速卷积神经网络

GEMM 提供了池化、Softmax、激活函数、批量归一化梯度算法和 LR 归一化等 MIOpen 采用 4 维张量描述数据，即 Tensors 4D NCHW 格式 支持启用了 OpenCL 和 HIP install.html 基础软件栈，需要包括： OpenCL：OpenCL 库和头文件（header files） HIP：HIP、HCC 库和头文件，还需要 clang-ocl MIOpen 依赖于 对于 HIP，运行： 设置 C++编译器为 hcc。 cmake -DMIOPEN_BACKEND=HIP -DCMAKE_PREFIX_PATH="<hip-installed-path>;<hcc-installed-path>;<clang-ocl-installed-path rocm/hcc;/opt/rocm/hip" ..

利用COCO数据集对人体上半身进行检测

image_id).zfill(12) + '.jpg' left_shoulder = 5*3 right_shoulder = 6*3 left_hip = 11*3 right_hip = 12*3 if num_keypoints < 4: continue +2] flag4 = keypoints[right_hip+2] if flag1 == 0 or flag2 == 0 or flag3 == 0 or flag4 x2 = keypoints[right_shoulder] y2 = keypoints[right_shoulder+1] x3 = keypoints[left_hip ] y3 = keypoints[left_hip+1] x4 = keypoints[right_hip] y4 = keypoints[right_hip

十三、自定义一个运动分析器，实现计时计数02

angleKey: 'left_shoulder', secondKey: 'left_elbow', thirdKey: 'left_hip angleKey: 'right_shoulder', secondKey: 'right_elbow', thirdKey: 'right_hip ', angleKey: 'left_knee', secondKey: 'left_ankle', thirdKey: 'left_hip angleKey: 'right_knee', secondKey: 'right_ankle', thirdKey: 'rgight_hip angleKey: 'left_shoulder', secondKey: 'left_elbow', thirdKey: 'left_hip

Unity & CityEngine 根据地图中建筑矢量数据批量建模

") attr Building_Form = _getInitialBuildingForm @Order(5) @Range("flat","shed","pyramid","gable","hip ","half-hip","gablet","gambrel","mansard","gambrel-flat","mansard-flat","vault","dome","saltbox","butterfly HipRoof --> roofHip(45) RoofMassScale PyramidRoof --> roofPyramid(45) RoofMassScale # gable & hip comp(f){ bottom: NIL | horizontal: set(Roof_Ht,Roof_Ht*0.5) HipRoof } } # ... and invokes a hip roof true) comp(f){ bottom: NIL | horizontal: set(Roof_Ht,Roof_Ht*0.5) GableRoof } } # gable/hip

llama.cpp在各个操作系统本地编译流程

HIP This provides GPU acceleration on HIP-supported AMD GPUs. Make sure to have ROCm installed. Using CMake for Linux (assuming a gfx1030-compatible AMD GPU): HIPCXX="$(hipconfig -l)/clang" HIP_PATH Then, add the following to the start of the command: HIP_DEVICE_LIB_PATH=<directory-you-just-found>, so something like: HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ HIP_DEVICE_LIB_PATH=<directory-you-just-found The environment variable HIP_VISIBLE_DEVICES can be used to specify which GPU(s) will be used.

目标检测 - 基于 SSD: Single Shot MultiBox Detector 的人体上下半身检测

NECK": [871.1877,180.4244], "HEAD": [835.8123,58.5756], "R_ANKLE": [980,322], "R_KNEE": [896,318], "R_HIP ": [865,248], "L_HIP": [943,226], "L_KNEE": [948,290], "L_ANKLE": [881,349], "R_WRIST": [772,294], "R_ELBOW NECK": [795.2738,314.8937], "HEAD": [597.7262,122.1063], "R_ANKLE": [918,456], "R_KNEE": [659,518], "R_HIP ": [713,413], "L_HIP": [979,288], "L_KNEE": [1222,453], "L_ANKLE": [974,399], "R_WRIST": [441,490], " ', 'L_KNEE', 'L_ANKLE', 'R_ANKLE', 'R_KNEE', 'R_HIP'] 以关节点图像中的位置, 设定外扩 50 个 像素,以使得 gtbox 尽可能准确.

Angular2 之 结构型指令几个概念

我们可以通过把短语"Hip! Hip! Hooray!"中间的"hip"包在一个<template>标签中来验证下这个效果。

Hip!

Hip!

Hooray!

论文实践学习 - Multi-Context Attention for Human Pose Estimation

import scipy.misc as scm import matplotlib.pyplot as plt JointsIndex = {'r_ankle': 0, 'r_knee': 1, 'r_hip ': 2, 'l_hip': 3, 'l_knee': 4, 'l_ankle': 5, 'pelvis': 6, 'thorax': r_wrist'], ['l_shoulder', 'l_elbow'], ['l_elbow', 'l_wrist'], \ ['pelvis', 'r_hip '], ['pelvis', 'l_hip'], ['r_hip', 'r_knee'], ['r_knee', 'r_ankle'], \ [' l_hip', 'l_knee'], ['l_knee', 'l_ankle'], ['thorax', 'pelvis']] StickType = ['r-', 'r-'

牛！Python 也能实现图像姿态识别溺水行为了！

']['x']), int(self.dic['left_hip']['y'])), (0, 255, 0), 2) # neck --> right_hip cv2.line ']['x']), int(self.dic['right_hip']['y'])), (0, 255, 0), 2) # left_hip --> left_knee cv2.line(img, (int(self.dic['left_hip']['x']), int(self.dic['left_hip']['y'])), (int (self.dic['left_knee']['x']), int(self.dic['left_knee']['y'])), (0, 255, 0), 2) # right_hip - -> right_knee cv2.line(img, (int(self.dic['right_hip']['x']), int(self.dic['right_hip']['y'])

【一步步开发AI运动小程序】二十一、如果将AI运动项目配置持久化到后端？

左手臂与腰齐垂直',"calc": 'match-angle',"angleKey": 'left_shoulder',"secondKey": 'left_elbow',"thirdKey": 'left_hip 右手臂与腰齐垂直',"calc": 'match-angle',"angleKey": 'right_shoulder',"secondKey": 'right_elbow',"thirdKey": 'right_hip ": '左腿绷直',"calc": 'match-angle',"angleKey": 'left_knee',"secondKey": 'left_ankle',"thirdKey": 'left_hip 右腿绷直',"calc": 'match-angle',"angleKey": 'right_knee',"secondKey": 'right_ankle',"thirdKey": 'rgight_hip

八、利用body-calc进行姿态识别

度检测',calc: '$or',rules: [{name: '左脚90度弯曲',calc: 'match-angle',angleKey: 'left_knee',secondKey: 'left_hip angle: 90,offset: 25}, {name: '右脚90度弯曲',calc: 'match-angle',angleKey: 'right_knee',secondKey: 'right_hip name: '左腋90度',calc: 'match-angle',angleKey: 'left_shoulder',secondKey: 'left_elbow',thirdKey: 'left_hip name: '右腋90度',calc: 'match-angle',angleKey: 'right_shoulder',secondKey: 'right_elbow',thirdKey: 'right_hip

Caffe2 - (二十五) Detectron 之 utils 函数(3)

'), keypoints.index('left_hip')], ] return kp_lines def convert_from_cls_format(cls_boxes, kp_mask = np.copy(img) # Draw mid shoulder / mid hip first for better visualization. 'left_hip')]) / 2.0 sc_mid_hip = np.minimum( kps[2, dataset_keypoints.index('right_hip')] ('left_hip')]) / 2.0 sc_mid_hip = np.minimum( kps[2, dataset_keypoints.index ('right_hip')], kps[2, dataset_keypoints.index('left_hip')]) if (sc_mid_shoulder

Caffe2 - (二十五) Detectron 之 utils 函数(3)

'), keypoints.index('left_hip')], ] return kp_lines def convert_from_cls_format(cls_boxes, kp_mask = np.copy(img) # Draw mid shoulder / mid hip first for better visualization. 'left_hip')]) / 2.0 sc_mid_hip = np.minimum( kps[2, dataset_keypoints.index('right_hip')] ('left_hip')]) / 2.0 sc_mid_hip = np.minimum( kps[2, dataset_keypoints.index ('right_hip')], kps[2, dataset_keypoints.index('left_hip')]) if (sc_mid_shoulder

AMD发布新版ROCm 成为GPU计算方面通用的开源平台

便携异构计算接口（HIP） - HIP让开发人员能够使用HIPIFY将CUDA应用程序移植到ROCm，HIPIFY会自动转换CUDA应用程序成为HIP内核语言和运行时API，使用NVIDIA的CUDA编译器或

activiti 根据 流程实例ID 获取发起人

fhadmin.cn */ protected String getInitiator(String PROC_INST_ID_) { HistoricProcessInstance hip orderByHistoricActivityInstanceId().asc().list(); //获取流程中已经执行的节点，按照执行先后顺序排序 BpmnModel bpmnModel = repositoryService.getBpmnModel(hip.getProcessDefinitionId

十、姿态动作相似度比较

0.70849609375,name:"right_wrist"}, {y:304.5039375289,x:251.342317172392,score:0.87646484375,name:"left_hip "}, {y:303.68360752741575,x:189.6796075527766,score:0.8740234375,name:"right_hip"}, {y:431.38422581120494 0.50927734375,name:"right_wrist"}, {y:321.5624618648858,x:218.59376906208004,score:0.58154296875,name:"left_hip "}, {y:323.43750001184594,x:224.06249998855716,score:0.5615234375,name:"right_hip"}, {y:453.43750001097675

【一步步开发AI运动APP】八、自定义姿态动作识别检测——之姿态相似度比较

"right_wrist" }, { y: 265.3125188252036, x: 224.68751882000163, score: 0.5830078125, name: "left_hip " }, { y: 266.2499997516102, x: 167.81249975301373, score: 0.634765625, name: "right_hip" }, { 0.50927734375,name:"right_wrist"},{y:321.5624618648858,x:218.59376906208004,score:0.58154296875,name:"left_hip "},{y:323.43750001184594,x:224.06249998855716,score:0.5615234375,name:"right_hip"},{y:453.43750001097675