| services: | |
| # See https://github.com/Dreamacro/clash/wiki/Running-Clash-as-a-service#docker | |
| clash: | |
| # ghcr.io/dreamacro/clash | |
| # ghcr.io/dreamacro/clash-premium | |
| # dreamacro/clash | |
| # dreamacro/clash-premium | |
| image: dreamacro/clash-premium | |
| container_name: clash-premium | |
| volumes: |
| # Partially Working Error on Exit | |
| from PySide2 import QtWidgets, QtGui,QtCore | |
| import open3d as o3d | |
| import win32gui | |
| import sys | |
| import threading | |
| import time | |
| class Worker(QtCore.QObject): |
| version: "3.9" | |
| services: | |
| rapids: | |
| image: rapidsai/rapidsai:0.18-cuda11.0-runtime-ubuntu16.04-py3.8 | |
| ports: | |
| - "8888:8888" # Jupyter | |
| - "8786:8786" # Dask communication | |
| - "8787:8787" # Dask dashboard |
| # Raspberry Pi的GPU对 H264有一系列针对 MPEG4 H264、MPEG2 和 VC1（需要额外授权）的硬件加速优化，为了得到最好的性能，建议先对树莓派的启动设置文件（/boot/config.txt）进行如下的调整： | |
| gpu_mem=256 | |
| framebuffer_depth=16 | |
| # 直接使用 apt-get 安装的ffmpeg没有专门的优化，对硬解的支持不好，所以需要自己从源码编译安装，首先用 apt-get 安装一些必要的包： | |
| apt-get install autoconf automake build-essential libass-dev libfreetype6-dev libtheora-dev libtool libvorbis-dev pkg-config texinfo zlib1g-dev unzip cmake yasm libx264-dev libmp3lame-dev libopus-dev bzip2-dev |
| diff --git a/aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h b/aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h | |
| index 2aac442d21..f2321dad7a 100644 | |
| --- a/aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h | |
| +++ b/aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h | |
| @@ -26,6 +26,9 @@ namespace { | |
| // Most likely we will do aarch32 support with inline asm. | |
| #if defined(__aarch64__) | |
| +// See https://github.com/pytorch/pytorch/issues/47098 | |
| +#if defined(__clang__) || (__GNUC__ > 8 || (__GNUC__ == 8 && __GNUC_MINOR__ > 3)) |
| ################### | |
| # # | |
| # SU.SG # | |
| # # | |
| ################### | |
| # https://mp.weixin.qq.com/s/H3OOqhFRr0YZGorIAlsCjA | |
| # todo | |
| # replace or remove content-security-policy header |
| import numpy as np | |
| import pandas as pd | |
| import bottleneck as bn | |
| # Read And Count | |
| trainDF = pd.read_csv('train.csv',sep=',',header=0) | |
| lenTrain = len(trainDF) | |
| valDF = pd.read_csv('submission.csv',sep=',',header=0) | |
| lenVal = len(valDF) | |
| trainFold = pd.read_csv('train_5fold_20181219.csv',sep=',',header=0) |
| # Holds repositories directory | |
| sudo chown -R -v git:root /var/opt/gitlab/git-data | |
| sudo chmod -R -v 0700 /var/opt/gitlab/git-data | |
| # Holds git repositories | |
| sudo chown -R -v git:git /var/opt/gitlab/git-data/repositories | |
| sudo chmod -R -v 2770 /var/opt/gitlab/git-data/repositories | |
| # Holds large object directories | |
| sudo chown -R -v git:gitlab-www /var/opt/gitlab/gitlab-rails/shared |
原文：深度学习大讲堂
点击上方“深度学习大讲堂”可订阅哦！
编者按：文字，区别于变幻莫测的图像和视频，有着更强的逻辑性和更概括的表达力。随着互联网和移动互联网技术的高速发展，越来越多的新型应用场景需要利用图像中的文字信息。从海量视频中快速检索感兴趣的文字，可以极大提高人类的认知效率。因此，自然场景中的文字提取技术，即从照片或视频中将文字识别出来，成为了近几年计算机视觉领域的热门研究课题。在本文中，来自华中科技大学的白翔教授，将为大家讲述多方向场景文字检测的奥秘。文末，大讲堂特别提供文中提到所有文章的下载链接。
