New upstream version 1.2.1
This commit is contained in:
szm-min
parent
277191b0b0
commit
2335ec9d1e
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@ -0,0 +1,26 @@
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*.o
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.vscode
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build/
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bin/
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obj-x86_64-linux-gnu/
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debian/tmp/
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debian/files/
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debian/libkysdk-disk/
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debian/libkysdk-disk-dev/
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debian/libkysdk-filesystem/
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debian/libkysdk-filesystem-dev/
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debian/libkysdk-hardware/
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debian/libkysdk-hardware-dev/
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debian/libkysdk-package/
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debian/libkysdk-package-dev/
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debian/libkysdk-proc/
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debian/libkysdk-proc-dev/
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debian/libkysdk-sysinfo/
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debian/libkysdk-sysinfo-dev/
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debian/libkysdk-system/
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debian/libkysdk-system-dev/
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debian/libkysdk-systime/
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debian/libkysdk-systime-dev/
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debian/libkysdk*.substvars
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debian/libkysdk*.debhelper
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debian/.debhelper
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libkysdk-system (1.2.1) v101; urgency=medium
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*Bug号：
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*需求号：
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*其他修改：取消指定版本号依赖
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-- hanpinlong <hanpinlong@kylinos.cn> Fri, 15 Jul 2022 16:22:40 +0800
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libkysdk-system (1.2.1.0build2) v101; urgency=medium
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*Bug号：126838,126836,126834,126922
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*需求号：无
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*其他修改：增加 kdk_system_get_projectSubName 接口，用于获取操作系统项目子编号名
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-- hanpinlong <hanpinlong@kylinos.cn> Fri, 15 Jul 2022 16:22:40 +0800
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libkysdk-system (1.2.1.0build1) v101; urgency=medium
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*Bug号：126838,126836,126834,126922
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*需求号：15924 提供平板特性接口
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*其他修改：增加 kdk_system_get_projectSubName 接口，用于获取操作系统项目子编号名
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-- hanpinlong <hanpinlong@kylinos.cn> Fri, 15 Jul 2022 14:14:10 +0800
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libkysdk-system (1.2.1.0) v101; urgency=medium
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*Bug号：126838,126836,126834,126922
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*需求号：
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*其他修改：增加 kdk_system_get_projectSubName 接口，用于获取操作系统项目子编号名
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-- hanpinlong <hanpinlong@kylinos.cn> Thu, 14 Jul 2022 17:03:53 +0800
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libkysdk-system (1.2.0.5) v101; urgency=medium
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*Bug号：124264
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*需求号：无
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*其他修改：
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修改自测问题 shlibs文件问题
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修改自测问题 libkysdk-systemcommon公用数据包用于指定运行链接库
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-- chenzhikai <chenzhikai@kylinos.cn> Wed, 29 Jun 2022 09:51:29 +0800
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libkysdk-system (1.2.0.4) v101; urgency=medium
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*Bug号：无
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*需求号：13708 为看图增加sw64支持
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*其他修改：无
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-- wangweiran <wangweiran@kylinos.cn> Wed, 22 Jun 2022 10:34:13 +0800
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libkysdk-system (1.2.0.3) v101; urgency=medium
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*Bug号：122537
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*需求号：无
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*其他修改：无
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-- wangweiran <wangweiran@kylinos.cn> Mon, 13 Jun 2022 14:38:58 +0800
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libkysdk-system (1.2.0.2) v101; urgency=medium
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*Bug号：120511,120498,120580
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*需求号：无
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*其他修改：无
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-- shaozhimin <shaozhimin@kylinos.cn> Mon, 13 Jun 2022 10:48:54 +0800
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libkysdk-system (1.2.0.1) v101; urgency=medium
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*Bug号：120474
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*需求号：13708
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*其他修改：
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-- wangweiran <wangweiran@kylinos.cn> Wed, 18 May 2022 10:30:14 +0800
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libkysdk-system (1.2.0kylin1) v101; urgency=medium
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*Bug号：
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*需求号：13735
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*其他修改：
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-- wangweiran <wangweiran@kylinos.cn> Wed, 18 May 2022 10:30:14 +0800
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libkysdk-system (1.1.1kylin1) ultron; urgency=medium
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*Bug号：119833
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*需求号：14071
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*其他修改：
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-- liuyunhe <liuyunhe@kylinos.cn> Tue, 17 May 2022 16:01:54 +0800
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libkysdk-system (1.0.0kylin5) v101; urgency=medium
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*Bug号：90497
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*需求号：无
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*其他修改：解决nvme硬盘信息获取失败问题
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-- chenzhikai <chenzhikai@kylinos.cn> Thu, 02 Dec 2021 17:01:49 +0800
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libkysdk-system (1.0.0kylin4) v101; urgency=medium
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*Bug号：92439
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*需求号：无
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*其他修改：解决d-feet调试服务报错问题
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-- chenzhikai <chenzhikai@kylinos.cn> Fri, 19 Nov 2021 14:17:56 +0800
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libkysdk-system (1.0.0kylin3) v101; urgency=medium
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*Bug号：90619
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*需求号：无
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*其他修改：无
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-- chenzhikai <chenzhikai@kylinos.cn> Fri, 12 Nov 2021 17:12:42 +0800
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libkysdk-system (1.0.0kylin2) v101; urgency=medium
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*Bug号：无
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*需求号：8996
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*其他修改：KYSDK 1.0.0 release
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-- chenzhikai <chenzhikai@kylinos.cn> Fri, 05 Nov 2021 15:35:58 +0800
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@ -0,0 +1,271 @@
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Source: libkysdk-system
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Section: utils
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Priority: optional
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Maintainer: kylin <chenzhikai@kylinos.cn>
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Build-Depends: debhelper-compat (= 12),
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cmake,
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libc6-dev,
|
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libsystemd-dev,
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||||
libdbus-1-dev,
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libdbus-glib-1-dev,
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||||
libglib2.0-dev,
|
||||
libkysdk-log-dev,
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||||
libkysdk-config-dev,
|
||||
libkysdk-utils-dev,
|
||||
libkysdk-timer-dev,
|
||||
libqt5core5a,
|
||||
qtbase5-dev,
|
||||
libudev-dev,
|
||||
libopencv-dev,
|
||||
libarchive-dev,
|
||||
libtesseract-dev
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||||
Standards-Version: 4.4.1
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Homepage: http://gitlab2.kylin.com/kysdk/kysdk-system
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|
||||
Package: libkysdk-system
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||||
Architecture: any
|
||||
Section: utils
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||||
Depends: ${shlibs:Depends},
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||||
${misc:Depends},
|
||||
libkysdk-disk,
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||||
libkysdk-sysinfo,
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||||
libkysdk-systime,
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||||
libkysdk-filesystem,
|
||||
libkysdk-proc,
|
||||
libkysdk-hardware,
|
||||
libkysdk-package,
|
||||
libkysdk-powermanagement,
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||||
systemd,
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||||
libglib2.0-0,
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||||
libkysdk-systemcommon,
|
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Multi-Arch: same
|
||||
Description: 麒麟开发者套件 - 系统层套件，提供系统信息、磁盘信息、系统时间等API与服务
|
||||
|
||||
Package: libkysdk-system-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-system,
|
||||
libkysdk-disk-dev,
|
||||
libkysdk-sysinfo-dev,
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||||
libkysdk-systime-dev,
|
||||
libkysdk-sysinfo-dev,
|
||||
libkysdk-filesystem-dev,
|
||||
libkysdk-proc-dev,
|
||||
libkysdk-hardware-dev,
|
||||
libkysdk-package-dev,
|
||||
libkysdk-powermanagement-dev
|
||||
Multi-Arch: same
|
||||
Description: 麒麟开发者套件 - 系统层套件 - 开发库，提供系统信息、磁盘信息、系统时间等API与服务
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|
||||
Package: libkysdk-disk
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
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||||
${misc:Depends},
|
||||
libkysdk-log,
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||||
libkysdk-systemcommon
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||||
Multi-Arch: same
|
||||
Description: 系统磁盘信息获取库
|
||||
|
||||
Package: libkysdk-disk-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-disk,
|
||||
libkysdk-log-dev,
|
||||
libkysdk-utils-dev,
|
||||
libblkid-dev,
|
||||
libudev-dev
|
||||
Multi-Arch: same
|
||||
Description: 系统磁盘信息获取库 - 开发库
|
||||
|
||||
Package: libkysdk-systime
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-log,
|
||||
dbus,
|
||||
systemd,
|
||||
libglib2.0-0,
|
||||
libkysdk-timer,
|
||||
libkysdk-systemcommon
|
||||
Multi-Arch: same
|
||||
Description: 系统时间相关操作库
|
||||
|
||||
Package: libkysdk-systime-dev
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||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-systime,
|
||||
libglib2.0-dev,
|
||||
libkysdk-timer-dev
|
||||
Multi-Arch: same
|
||||
Description: 系统时间相关操作库 - 开发库
|
||||
|
||||
Package: libkysdk-sysinfo
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-log,
|
||||
dbus,
|
||||
libglib2.0-0,
|
||||
systemd,
|
||||
libkysdk-systemcommon
|
||||
Multi-Arch: same
|
||||
Description: 系统信息获取库
|
||||
|
||||
Package: libkysdk-sysinfo-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-sysinfo,
|
||||
libkysdk-log-dev,
|
||||
libkysdk-utils-dev,
|
||||
libsystemd-dev,
|
||||
libdbus-1-dev,
|
||||
libdbus-glib-1-dev,
|
||||
libglib2.0-dev
|
||||
Multi-Arch: same
|
||||
Description: 系统信息获取库 - 开发库
|
||||
|
||||
Package: libkysdk-filesystem
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-log,
|
||||
libqt5core5a,
|
||||
libkysdk-config,
|
||||
systemd,
|
||||
libkysdk-systemcommon
|
||||
Multi-Arch: same
|
||||
Description: 文件系统库
|
||||
|
||||
Package: libkysdk-filesystem-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-filesystem,
|
||||
libkysdk-log-dev,
|
||||
libkysdk-config-dev,
|
||||
libsystemd-dev,
|
||||
qtbase5-dev
|
||||
Multi-Arch: same
|
||||
Description: 文件系统库 - 开发库
|
||||
|
||||
Package: libkysdk-hardware
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-log,
|
||||
libkysdk-config,
|
||||
systemd,
|
||||
libkysdk-systemcommon
|
||||
Multi-Arch: same
|
||||
Description: 硬件信息获取库
|
||||
|
||||
Package: libkysdk-hardware-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-hardware,
|
||||
libkysdk-log-dev,
|
||||
libkysdk-config-dev,
|
||||
libsystemd-dev
|
||||
Multi-Arch: same
|
||||
Description: 硬件信息获取库 - 开发库
|
||||
|
||||
Package: libkysdk-package
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-systemcommon
|
||||
Multi-Arch: same
|
||||
Description: 包管理库
|
||||
|
||||
Package: libkysdk-package-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-package
|
||||
Multi-Arch: same
|
||||
Description: 包管理库 - 开发库
|
||||
|
||||
Package: libkysdk-proc
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-systemcommon
|
||||
Multi-Arch: same
|
||||
Description: 运行时信息获取库
|
||||
|
||||
Package: libkysdk-proc-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-proc
|
||||
Multi-Arch: same
|
||||
Description: 运行时信息获取库 - 开发库
|
||||
|
||||
Package: libkysdk-powermanagement
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-log,
|
||||
libkysdk-systemcommon
|
||||
Multi-Arch: same
|
||||
Description: 电源管理库
|
||||
|
||||
Package: libkysdk-powermanagement-dev
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libkysdk-log-dev,
|
||||
libkysdk-powermanagement
|
||||
Multi-Arch: same
|
||||
Description: 电源管理 - 开发库
|
||||
|
||||
Package: libkysdk-ocr
|
||||
Architecture: amd64 arm64 mips64el loongarch64 sw64
|
||||
Multi-Arch: same
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends},
|
||||
libopencv-core4.2,
|
||||
libopencv-highgui4.2,
|
||||
libopencv-imgproc4.2,
|
||||
libopencv-imgcodecs4.2,
|
||||
liblept5,
|
||||
libarchive13,
|
||||
libtesseract4,
|
||||
libkysdk-systemcommon
|
||||
Description: AI文字识别功能
|
||||
|
||||
Package: libkysdk-ocr-dev
|
||||
Architecture: amd64 arm64 mips64el loongarch64 sw64
|
||||
Multi-Arch: same
|
||||
Depends: ${shlibs:Depends}, ${misc:Depends},libkysdk-ocr,libleptonica-dev,libopencv-dev,libarchive-dev,libtesseract-dev
|
||||
Description: AI文字识别功能-开发库
|
||||
|
||||
Package: libkysdk-systemcommon
|
||||
Architecture: any
|
||||
Section: utils
|
||||
Depends: ${shlibs:Depends},
|
||||
${misc:Depends}
|
||||
Multi-Arch: same
|
||||
Description: kysdk-system层公用数据包
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
Format: https://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
|
||||
Upstream-Name: kysd-system
|
||||
Upstream-Contact: <preferred name and address to reach the upstream project>
|
||||
Source: <url://example.com>
|
||||
|
||||
Files: *
|
||||
Copyright: <years> <put author's name and email here>
|
||||
<years> <likewise for another author>
|
||||
License: <special license>
|
||||
<Put the license of the package here indented by 1 space>
|
||||
<This follows the format of Description: lines in control file>
|
||||
.
|
||||
<Including paragraphs>
|
||||
|
||||
# If you want to use GPL v2 or later for the /debian/* files use
|
||||
# the following clauses, or change it to suit. Delete these two lines
|
||||
Files: debian/*
|
||||
Copyright: 2021 kylin <chenzhikai@kylinos.cn>
|
||||
License: GPL-2+
|
||||
This package is free software; you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation; either version 2 of the License, or
|
||||
(at your option) any later version.
|
||||
.
|
||||
This package is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
.
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>
|
||||
.
|
||||
On Debian systems, the complete text of the GNU General
|
||||
Public License version 2 can be found in "/usr/share/common-licenses/GPL-2".
|
||||
|
||||
# Please also look if there are files or directories which have a
|
||||
# different copyright/license attached and list them here.
|
||||
# Please avoid picking licenses with terms that are more restrictive than the
|
||||
# packaged work, as it may make Debian's contributions unacceptable upstream.
|
||||
#
|
||||
# If you need, there are some extra license texts available in two places:
|
||||
# /usr/share/debhelper/dh_make/licenses/
|
||||
# /usr/share/common-licenses/
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
usr/include/kysdk/kysdk-system/libkydiskinfo.h
|
||||
development-files/kysdk-disk.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
usr/lib/kysdk/kysdk-system/libkydiskinfo.so*
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
usr/include/kysdk/kysdk-system/libkyfilewatcher.hpp
|
||||
usr/include/kysdk/kysdk-system/libkyfilewatcher_global.hpp
|
||||
development-files/kysdk-filesystem.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
usr/lib/kysdk/kysdk-system/libkyfilewatcher.so*
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
usr/include/kysdk/kysdk-system/libkync.h
|
||||
usr/include/kysdk/kysdk-system/libkycpu.h
|
||||
development-files/kysdk-hardware.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
usr/lib/kysdk/kysdk-system/libkyhw.so*
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
usr/include/kysdk/kysdk-system/libkyocr.hpp
|
||||
development-files/kysdk-ocr.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
usr/lib/kysdk/kysdk-system/libkyocr.so*
|
||||
usr/lib/libpaddle_inference.so
|
||||
src/kdkocr/models/* /etc/kdkocr/
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
#!/bin/sh
|
||||
|
||||
#DEBHELPER#
|
||||
|
||||
if [ -f "/usr/lib/libpaddle_inference.so/libpaddle_inference" ]
|
||||
then
|
||||
rm -rf /usr/lib/libpaddle_inference.so/
|
||||
fi
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
usr/include/kysdk/kysdk-system/libkypackages.h
|
||||
development-files/kysdk-package.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
usr/lib/kysdk/kysdk-system/libkypackage.so*
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
src/powermanagement/libkylockscreen.h usr/include/kysdk/kysdk-system/
|
||||
development-files/kysdk-powermanagement.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
usr/lib/kysdk/kysdk-system/libkypowermanagement.so*
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
usr/include/kysdk/kysdk-system/libkyrtinfo.h
|
||||
development-files/kysdk-proc.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
usr/lib/kysdk/kysdk-system/libkyrtinfo.so*
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
usr/include/kysdk/kysdk-system/libkysysinfo.hpp
|
||||
usr/include/kysdk/kysdk-system/libkysysinfo.h
|
||||
development-files/kysdk-sysinfo.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
usr/lib/kysdk/kysdk-system/libkysysinfo.so*
|
||||
|
|
@ -0,0 +1 @@
|
|||
development-files/kysdk-system.pc usr/share/pkgconfig/
|
||||
|
|
@ -0,0 +1 @@
|
|||
development-files/kysdk-system.conf etc/ld.so.conf.d
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
bin/* usr/bin/
|
||||
src/systemtime/com.kylin.kysdk.TimeServer.conf etc/dbus-1/system.d/
|
||||
src/systemtime/kysdk-systime.service lib/systemd/system/
|
||||
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
systemctl daemon-reload
|
||||
systemctl enable kysdk-systime.service
|
||||
systemctl restart kysdk-systime.service
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
#!/usr/bin/make -f
|
||||
# See debhelper(7) (uncomment to enable)
|
||||
# output every command that modifies files on the build system.
|
||||
#export DH_VERBOSE = 1
|
||||
|
||||
|
||||
# see FEATURE AREAS in dpkg-buildflags(1)
|
||||
#export DEB_BUILD_MAINT_OPTIONS = hardening=+all
|
||||
|
||||
# see ENVIRONMENT in dpkg-buildflags(1)
|
||||
# package maintainers to append CFLAGS
|
||||
#export DEB_CFLAGS_MAINT_APPEND = -Wall -pedantic
|
||||
# package maintainers to append LDFLAGS
|
||||
#export DEB_LDFLAGS_MAINT_APPEND = -Wl,--as-needed
|
||||
|
||||
|
||||
%:
|
||||
dh $@
|
||||
|
||||
|
||||
# dh_make generated override targets
|
||||
# This is example for Cmake (See https://bugs.debian.org/641051 )
|
||||
#override_dh_auto_configure:
|
||||
# dh_auto_configure -- # -DCMAKE_LIBRARY_PATH=$(DEB_HOST_MULTIARCH)
|
||||
|
||||
|
|
@ -0,0 +1 @@
|
|||
3.0 (native)
|
||||
|
|
@ -0,0 +1 @@
|
|||
/usr/lib/kysdk/kysdk-system
|
||||
|
|
@ -15,6 +15,8 @@ elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "mips64")
|
|||
add_subdirectory(kdkocr)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "loongarch64")
|
||||
add_subdirectory(kdkocr)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "sw_64")
|
||||
add_subdirectory(kdkocr)
|
||||
else()
|
||||
message(STATUS "host processor architecture is not supported for ocr")
|
||||
endif()
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
aux_source_directory(. SOURCESCODE)
|
||||
add_library(kydiskinfo SHARED ${SOURCESCODE})
|
||||
set_target_properties(kydiskinfo PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
add_executable(test-getdiskinfo test/getdiskinfo.c)
|
||||
add_executable(test-getdisklist test/getdisklist.c)
|
||||
find_library(UDEV_LIB udev)
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ aux_source_directory(. SOURCECODE)
|
|||
find_package(Qt5Core)
|
||||
include_directories(${Qt5Core_INCLUDE_DIRS})
|
||||
add_library(kyfilewatcher SHARED ${SOURCECODE})
|
||||
set_target_properties(kyfilewatcher PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
add_executable(kyfilewatcher-test test/kyfilewatcher-test.cpp)
|
||||
target_link_libraries(kyfilewatcher kylog kyconf systemd pthread ${Qt5Core_LIBRARIES})
|
||||
target_link_libraries(kyfilewatcher-test kyfilewatcher)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
aux_source_directory(. SOURCESCODE)
|
||||
include_directories(.)
|
||||
add_library(kyhw SHARED ${SOURCESCODE})
|
||||
set_target_properties(kyhw PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
add_executable(kync-test test/kync-test.c)
|
||||
add_executable(kycpu-test test/kycpu-test.c)
|
||||
target_link_libraries(kyhw kylog kyconf pthread systemd)
|
||||
|
|
|
|||
|
|
@ -6,6 +6,8 @@
|
|||
#include <string.h>
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include "sys/sysinfo.h"
|
||||
#include "unistd.h"
|
||||
#ifdef __linux__
|
||||
#include <sys/utsname.h>
|
||||
#endif
|
||||
|
|
@ -29,6 +31,223 @@ struct _cpuInfo{
|
|||
|
||||
struct _cpuInfo *cpuinf;
|
||||
|
||||
struct id_part {
|
||||
const int id;
|
||||
const char* name;
|
||||
};
|
||||
|
||||
static const struct id_part arm_part[] = {
|
||||
{ 0x810, "ARM810" },
|
||||
{ 0x920, "ARM920" },
|
||||
{ 0x922, "ARM922" },
|
||||
{ 0x926, "ARM926" },
|
||||
{ 0x940, "ARM940" },
|
||||
{ 0x946, "ARM946" },
|
||||
{ 0x966, "ARM966" },
|
||||
{ 0xa20, "ARM1020" },
|
||||
{ 0xa22, "ARM1022" },
|
||||
{ 0xa26, "ARM1026" },
|
||||
{ 0xb02, "ARM11 MPCore" },
|
||||
{ 0xb36, "ARM1136" },
|
||||
{ 0xb56, "ARM1156" },
|
||||
{ 0xb76, "ARM1176" },
|
||||
{ 0xc05, "Cortex-A5" },
|
||||
{ 0xc07, "Cortex-A7" },
|
||||
{ 0xc08, "Cortex-A8" },
|
||||
{ 0xc09, "Cortex-A9" },
|
||||
{ 0xc0d, "Cortex-A17" }, /* Originally A12 */
|
||||
{ 0xc0f, "Cortex-A15" },
|
||||
{ 0xc0e, "Cortex-A17" },
|
||||
{ 0xc14, "Cortex-R4" },
|
||||
{ 0xc15, "Cortex-R5" },
|
||||
{ 0xc17, "Cortex-R7" },
|
||||
{ 0xc18, "Cortex-R8" },
|
||||
{ 0xc20, "Cortex-M0" },
|
||||
{ 0xc21, "Cortex-M1" },
|
||||
{ 0xc23, "Cortex-M3" },
|
||||
{ 0xc24, "Cortex-M4" },
|
||||
{ 0xc27, "Cortex-M7" },
|
||||
{ 0xc60, "Cortex-M0+" },
|
||||
{ 0xd01, "Cortex-A32" },
|
||||
{ 0xd03, "Cortex-A53" },
|
||||
{ 0xd04, "Cortex-A35" },
|
||||
{ 0xd05, "Cortex-A55" },
|
||||
{ 0xd06, "Cortex-A65" },
|
||||
{ 0xd07, "Cortex-A57" },
|
||||
{ 0xd08, "Cortex-A72" },
|
||||
{ 0xd09, "Cortex-A73" },
|
||||
{ 0xd0a, "Cortex-A75" },
|
||||
{ 0xd0b, "Cortex-A76" },
|
||||
{ 0xd0c, "Neoverse-N1" },
|
||||
{ 0xd0d, "Cortex-A77" },
|
||||
{ 0xd0e, "Cortex-A76AE" },
|
||||
{ 0xd13, "Cortex-R52" },
|
||||
{ 0xd20, "Cortex-M23" },
|
||||
{ 0xd21, "Cortex-M33" },
|
||||
{ 0xd40, "Neoverse-V1" },
|
||||
{ 0xd41, "Cortex-A78" },
|
||||
{ 0xd42, "Cortex-A78AE" },
|
||||
{ 0xd44, "Cortex-X1" },
|
||||
{ 0xd46, "Cortex-510" },
|
||||
{ 0xd47, "Cortex-710" },
|
||||
{ 0xd48, "Cortex-X2" },
|
||||
{ 0xd49, "Neoverse-N2" },
|
||||
{ 0xd4a, "Neoverse-E1" },
|
||||
{ 0xd4b, "Cortex-A78C" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part brcm_part[] = {
|
||||
{ 0x0f, "Brahma B15" },
|
||||
{ 0x100, "Brahma B53" },
|
||||
{ 0x516, "ThunderX2" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part dec_part[] = {
|
||||
{ 0xa10, "SA110" },
|
||||
{ 0xa11, "SA1100" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part cavium_part[] = {
|
||||
{ 0x0a0, "ThunderX" },
|
||||
{ 0x0a1, "ThunderX 88XX" },
|
||||
{ 0x0a2, "ThunderX 81XX" },
|
||||
{ 0x0a3, "ThunderX 83XX" },
|
||||
{ 0x0af, "ThunderX2 99xx" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part apm_part[] = {
|
||||
{ 0x000, "X-Gene" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part qcom_part[] = {
|
||||
{ 0x00f, "Scorpion" },
|
||||
{ 0x02d, "Scorpion" },
|
||||
{ 0x04d, "Krait" },
|
||||
{ 0x06f, "Krait" },
|
||||
{ 0x201, "Kryo" },
|
||||
{ 0x205, "Kryo" },
|
||||
{ 0x211, "Kryo" },
|
||||
{ 0x800, "Falkor V1/Kryo" },
|
||||
{ 0x801, "Kryo V2" },
|
||||
{ 0x803, "Kryo 3XX Silver" },
|
||||
{ 0x804, "Kryo 4XX Gold" },
|
||||
{ 0x805, "Kryo 4XX Silver" },
|
||||
{ 0xc00, "Falkor" },
|
||||
{ 0xc01, "Saphira" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part samsung_part[] = {
|
||||
{ 0x001, "exynos-m1" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part nvidia_part[] = {
|
||||
{ 0x000, "Denver" },
|
||||
{ 0x003, "Denver 2" },
|
||||
{ 0x004, "Carmel" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part marvell_part[] = {
|
||||
{ 0x131, "Feroceon 88FR131" },
|
||||
{ 0x581, "PJ4/PJ4b" },
|
||||
{ 0x584, "PJ4B-MP" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part apple_part[] = {
|
||||
{ 0x022, "Icestorm" },
|
||||
{ 0x023, "Firestorm" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part faraday_part[] = {
|
||||
{ 0x526, "FA526" },
|
||||
{ 0x626, "FA626" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part intel_part[] = {
|
||||
{ 0x200, "i80200" },
|
||||
{ 0x210, "PXA250A" },
|
||||
{ 0x212, "PXA210A" },
|
||||
{ 0x242, "i80321-400" },
|
||||
{ 0x243, "i80321-600" },
|
||||
{ 0x290, "PXA250B/PXA26x" },
|
||||
{ 0x292, "PXA210B" },
|
||||
{ 0x2c2, "i80321-400-B0" },
|
||||
{ 0x2c3, "i80321-600-B0" },
|
||||
{ 0x2d0, "PXA250C/PXA255/PXA26x" },
|
||||
{ 0x2d2, "PXA210C" },
|
||||
{ 0x411, "PXA27x" },
|
||||
{ 0x41c, "IPX425-533" },
|
||||
{ 0x41d, "IPX425-400" },
|
||||
{ 0x41f, "IPX425-266" },
|
||||
{ 0x682, "PXA32x" },
|
||||
{ 0x683, "PXA930/PXA935" },
|
||||
{ 0x688, "PXA30x" },
|
||||
{ 0x689, "PXA31x" },
|
||||
{ 0xb11, "SA1110" },
|
||||
{ 0xc12, "IPX1200" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part fujitsu_part[] = {
|
||||
{ 0x001, "A64FX" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part hisi_part[] = {
|
||||
{ 0xd01, "Kunpeng-920" }, /* aka tsv110 */
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part ft_part[] = {
|
||||
{ 0x660, "FTC660" },
|
||||
{ 0x661, "FTC661" },
|
||||
{ 0x662, "FTC662" },
|
||||
{ 0x663, "FTC663" },
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
static const struct id_part unknown_part[] = {
|
||||
{ -1, "unknown" },
|
||||
};
|
||||
|
||||
struct hw_impl {
|
||||
const int id;
|
||||
const struct id_part *parts;
|
||||
const char *name;
|
||||
};
|
||||
|
||||
static const struct hw_impl hw_implementer[] = {
|
||||
{ 0x41, arm_part, "ARM" },
|
||||
{ 0x42, brcm_part, "Broadcom" },
|
||||
{ 0x43, cavium_part, "Cavium" },
|
||||
{ 0x44, dec_part, "DEC" },
|
||||
{ 0x46, fujitsu_part, "FUJITSU" },
|
||||
{ 0x48, hisi_part, "HiSilicon" },
|
||||
{ 0x49, unknown_part, "Infineon" },
|
||||
{ 0x4d, unknown_part, "Motorola/Freescale" },
|
||||
{ 0x4e, nvidia_part, "NVIDIA" },
|
||||
{ 0x50, apm_part, "APM" },
|
||||
{ 0x51, qcom_part, "Qualcomm" },
|
||||
{ 0x53, samsung_part, "Samsung" },
|
||||
{ 0x56, marvell_part, "Marvell" },
|
||||
{ 0x61, apple_part, "Apple" },
|
||||
{ 0x66, faraday_part, "Faraday" },
|
||||
{ 0x69, intel_part, "Intel" },
|
||||
{ 0x70, ft_part, "Phytium" },
|
||||
{ 0xc0, unknown_part, "Ampere" },
|
||||
{ -1, unknown_part, "unknown" },
|
||||
};
|
||||
|
||||
static void _free_cpuinfo()
|
||||
{
|
||||
if (cpuinf)
|
||||
|
|
@ -83,23 +302,6 @@ static int lookup(char *line, char *pattern, char **value)
|
|||
return 1;
|
||||
}
|
||||
|
||||
static int do_shell(char *comm, char *buf)
|
||||
{
|
||||
FILE *stream;
|
||||
stream = popen(comm, "r");
|
||||
if (stream == NULL) {
|
||||
return 0;
|
||||
}
|
||||
fread(buf, sizeof(char), MIDSIZE, stream);
|
||||
pclose(stream);
|
||||
buf[strlen(buf) - 1] = '\0';//去掉结尾转行符
|
||||
if (strlen(buf) == 0) {
|
||||
return 0;
|
||||
}
|
||||
|
||||
return 1;
|
||||
}
|
||||
|
||||
static void _get_cpu_info()
|
||||
{
|
||||
// 我知道这里有竞态问题，但是不想引入pthread，所以算了
|
||||
|
|
@ -163,6 +365,11 @@ static void _get_cpu_info()
|
|||
}
|
||||
fclose(fp);
|
||||
|
||||
if(strstr(cpuinf->model, "Loongson"))
|
||||
{
|
||||
cpuinf->vendor = strdup("loongson");
|
||||
}
|
||||
|
||||
if (cpuinf->flags)
|
||||
{
|
||||
if (strstr(cpuinf->flags, " svm "))
|
||||
|
|
@ -174,42 +381,51 @@ static void _get_cpu_info()
|
|||
if(cpuinf->vendor == NULL)
|
||||
{
|
||||
char ret[MIDSIZE];
|
||||
do_shell("lscpu | grep \"厂商 ID\"", ret);
|
||||
if(strcmp(ret, ""))
|
||||
int num = 0;
|
||||
int part, j;
|
||||
const struct id_part *parts = NULL;
|
||||
fp = fopen("/sys/devices/system/cpu/cpu0/regs/identification/midr_el1", "rt");
|
||||
if (!fp)
|
||||
{
|
||||
printf("test\n");
|
||||
char *substr = "：";
|
||||
char *date = strstr(ret, substr);
|
||||
strcpy(ret, date + 27);
|
||||
cpuinf->vendor = strdup(ret);
|
||||
klog_err("midr_el1 读取失败：%s\n", strerror(errno));
|
||||
SAFE_FREE(cpuinf);
|
||||
return ;
|
||||
}
|
||||
fgets(buffer, CPUINF_BUFSIZE, fp);
|
||||
char *substr = "x";
|
||||
char *date = strstr(buffer, substr);
|
||||
strcpy(buffer, date + 9);
|
||||
sscanf(buffer,"%2x",&num) ;
|
||||
|
||||
for (j = 0; hw_implementer[j].id != -1; j++) {
|
||||
if (hw_implementer[j].id == num) {
|
||||
parts = hw_implementer[j].parts;
|
||||
cpuinf->vendor = strdup(hw_implementer[j].name);
|
||||
break;
|
||||
}
|
||||
}
|
||||
fclose(fp);
|
||||
}
|
||||
|
||||
if(cpuinf->model == NULL)
|
||||
{
|
||||
char ret[MIDSIZE];
|
||||
do_shell("lscpu | grep \"型号名称\"", ret);
|
||||
if(strcmp(ret, ""))
|
||||
fp = fopen("/proc/cpuinfo", "rt");
|
||||
if (!fp)
|
||||
{
|
||||
char *substr = "：";
|
||||
char *date = strstr(ret, substr);
|
||||
strcpy(ret, date + 26);
|
||||
cpuinf->model = strdup(ret);
|
||||
klog_err("/proc/cpuinfo 读取失败：%s\n", strerror(errno));
|
||||
SAFE_FREE(cpuinf);
|
||||
return ;
|
||||
}
|
||||
while(fgets(buffer, CPUINF_BUFSIZE, fp))
|
||||
{
|
||||
if (lookup(buffer, "Hardware", &cpuinf->model));//huawei 9A0
|
||||
}
|
||||
fclose(fp);
|
||||
}
|
||||
|
||||
if(cpuinf->corenums == 0)
|
||||
{
|
||||
char ret[MIDSIZE];
|
||||
do_shell("lscpu | grep \"每个座的核数\"", ret);
|
||||
if(strcmp(ret, ""))
|
||||
{
|
||||
char *substr = "：";
|
||||
char *date = strstr(ret, substr);
|
||||
strcpy(ret, date + 20);
|
||||
cpuinf->corenums = atoi(ret);
|
||||
}
|
||||
cpuinf->corenums = sysconf(_SC_NPROCESSORS_ONLN);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
#define _GNU_SOURCE # required for NI_NUMERICHOST
|
||||
#include "libkync.h"
|
||||
#include <cstring-extension.h>
|
||||
#include <errno.h>
|
||||
|
|
@ -12,6 +13,8 @@
|
|||
#include <unistd.h>
|
||||
#include <netinet/in.h>
|
||||
#include <arpa/inet.h>
|
||||
#include <netdb.h>
|
||||
#include <ifaddrs.h>
|
||||
|
||||
enum cardspec{
|
||||
NCSPEC_ALL,
|
||||
|
|
@ -201,9 +204,51 @@ char **_get_nc_cfg(const char *nc, enum cardcfg cfg)
|
|||
memcpy(&sin, &stIf.ifr_ifru.ifru_addr, sizeof(sin));
|
||||
snprintf(res[0], NC_IPv4_SIZE, "%s", inet_ntoa(sin.sin_addr));
|
||||
}break;
|
||||
case NCCFG_IPv6:{
|
||||
struct ifaddrs *ifap, *ifa;
|
||||
struct sockaddr_in6 *sa;
|
||||
char addr[INET6_ADDRSTRLEN] = {0};
|
||||
getifaddrs(&ifap);
|
||||
for (ifa = ifap; ifa; ifa = ifa->ifa_next)
|
||||
{
|
||||
if (ifa->ifa_addr->sa_family == AF_INET6 && !strcmp(ifa->ifa_name, nc))
|
||||
{
|
||||
sa = (struct sockaddr_in6 *)ifa->ifa_addr;
|
||||
getnameinfo(ifa->ifa_addr, sizeof(struct sockaddr_in6), addr,
|
||||
sizeof(addr), NULL, 0, NI_NUMERICHOST);
|
||||
}
|
||||
}
|
||||
|
||||
res = malloc(sizeof(char*));
|
||||
if (!res)
|
||||
{
|
||||
klog_err("内存申请失败:%s\n", strerror(errno));
|
||||
close(sfd);
|
||||
freeifaddrs(ifap);
|
||||
return NULL;
|
||||
}
|
||||
res[0] = malloc(sizeof(char) * INET6_ADDRSTRLEN);
|
||||
if (!res[0])
|
||||
{
|
||||
klog_err("内存申请失败:%s\n", strerror(errno));
|
||||
close(sfd);
|
||||
freeifaddrs(ifap);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
int i = 0;
|
||||
while (addr[i] != '%' && addr[i] != '\0')
|
||||
i++;
|
||||
addr[i] = '\0';
|
||||
|
||||
sprintf(res[0], "%s",addr);
|
||||
|
||||
freeifaddrs(ifap);
|
||||
}break;
|
||||
default:
|
||||
break;
|
||||
}
|
||||
close(sfd);
|
||||
return res;
|
||||
}
|
||||
|
||||
|
|
@ -255,6 +300,19 @@ char* kdk_nc_get_private_ipv4(const char *nc)
|
|||
return ipv4;
|
||||
}
|
||||
|
||||
char* kdk_nc_get_private_ipv6(const char *nc)
|
||||
{
|
||||
if (!nc)
|
||||
return NULL;
|
||||
|
||||
char **ipv6list = _get_nc_cfg(nc, NCCFG_IPv6);
|
||||
if (!ipv6list)
|
||||
return NULL;
|
||||
char *ipv6 = ipv6list[0];
|
||||
free(ipv6list);
|
||||
return ipv6;
|
||||
}
|
||||
|
||||
inline void kdk_nc_freeall(char **list)
|
||||
{
|
||||
if (! list)
|
||||
|
|
|
|||
|
|
@ -11,9 +11,11 @@ int main()
|
|||
{
|
||||
char *mac = kdk_nc_get_phymac(cards[index]);
|
||||
char *ipv4 = kdk_nc_get_private_ipv4(cards[index]);
|
||||
printf("Card %zd: %s\tStatus: %s\tMac: %s\tIPv4: %s\n", index + 1, cards[index], kdk_nc_is_up(cards[index]) == 1 ? "Up" : "Down", mac, ipv4);
|
||||
char *ipv6 = kdk_nc_get_private_ipv6(cards[index]);
|
||||
printf("Card %zd: %s\tStatus: %s\tMac: %s\tIPv4: %s\tIPv6: %s\n", index + 1, cards[index], kdk_nc_is_up(cards[index]) == 1 ? "Up" : "Down", mac, ipv4,ipv6);
|
||||
free(mac);
|
||||
free(ipv4);
|
||||
free(ipv6);
|
||||
index ++;
|
||||
}
|
||||
kdk_nc_freeall(cards);
|
||||
|
|
|
|||
|
|
@ -64,7 +64,11 @@ list(APPEND kyocr_libraries ${TESSERACT_PKG_LIBRARIES})
|
|||
list(APPEND kyocr_libraries -llept)
|
||||
message("kyocr_cflags is ${kyocr_libraries}")
|
||||
|
||||
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "loongarch64")
|
||||
if(NOT EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/libs/amd64/paddle_inference/paddle/lib/libpaddle_inference.so")
|
||||
add_definitions(-DLOONGARCH64)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "loongarch64")
|
||||
add_definitions(-DLOONGARCH64)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "sw_64")
|
||||
add_definitions(-DLOONGARCH64)
|
||||
else()
|
||||
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "i386")
|
||||
|
|
@ -162,22 +166,36 @@ include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
|
|||
|
||||
endif()
|
||||
|
||||
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "loongarch64")
|
||||
if(NOT EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/libs/amd64/paddle_inference/paddle/lib/libpaddle_inference.so")
|
||||
message("no paddle lib")
|
||||
add_library(kyocr SHARED libkyocr.cpp)
|
||||
target_link_libraries(kyocr ${kyocr_libraries})
|
||||
target_compile_options(kyocr PUBLIC ${kyocr_cflags})
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "loongarch64")
|
||||
message(loongarch64)
|
||||
add_library(kyocr SHARED libkyocr.cpp)
|
||||
target_link_libraries(kyocr ${kyocr_libraries})
|
||||
target_compile_options(kyocr PUBLIC ${kyocr_cflags})
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "sw_64")
|
||||
message(loongarch64)
|
||||
add_library(kyocr SHARED libkyocr.cpp)
|
||||
target_link_libraries(kyocr ${kyocr_libraries})
|
||||
target_compile_options(kyocr PUBLIC ${kyocr_cflags})
|
||||
else()
|
||||
add_library(kyocr SHARED libkyocr.cpp ocr_main.cpp utility.cpp preprocess_op.cpp postprocess_op.cpp)
|
||||
target_link_libraries(kyocr ${OpenCV_LIBS} ${DEPS})
|
||||
endif()
|
||||
set_target_properties(kyocr PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
#target_link_libraries(kdkOCR -lleptonica)
|
||||
target_link_libraries(kyocr ${OpenCV_LIBS} ${DEPS})
|
||||
target_link_libraries(kyocr ${kyocr_libraries})
|
||||
target_compile_options(kyocr PUBLIC ${kyocr_cflags})
|
||||
|
||||
|
||||
|
||||
install(TARGETS kyocr LIBRARY DESTINATION lib/kysdk/kysdk-system)
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libkyocr.hpp DESTINATION include/kysdk/kysdk-system)
|
||||
|
||||
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86_64")
|
||||
if(NOT EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/libs/amd64/paddle_inference/paddle/lib/libpaddle_inference.so")
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libs/libpaddle_inference DESTINATION lib/libpaddle_inference.so)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86_64")
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libs/amd64/paddle_inference/paddle/lib/libpaddle_inference.so DESTINATION lib/)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "AMD64")
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libs/amd64/paddle_inference/paddle/lib/libpaddle_inference.so DESTINATION lib/)
|
||||
|
|
@ -186,6 +204,10 @@ elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
|
|||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "mips64")
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libs/mips64el/paddle_inference/paddle/lib/libpaddle_inference.so DESTINATION lib/)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "loongarch64")
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libs/loongarch64/paddle_inference/paddle/lib/libpaddle_inference.so DESTINATION lib/)
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libs/libpaddle_inference DESTINATION lib/libpaddle_inference.so)
|
||||
# install(FILES ${PROJECT_SOURCE_DIR}/libs/loongarch64/paddle_inference/paddle/lib/libpaddle_inference.so DESTINATION lib/)
|
||||
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "sw_64")
|
||||
install(FILES ${PROJECT_SOURCE_DIR}/libs/libpaddle_inference DESTINATION lib/libpaddle_inference.so)
|
||||
# install(FILES ${PROJECT_SOURCE_DIR}/libs/sw64/paddle_inference/paddle/lib/libpaddle_inference.so DESTINATION lib/)
|
||||
endif()
|
||||
#target_link_libraries(test libkdkOCR.so)
|
||||
|
|
|
|||
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/cryptopp/lib/libcryptopp.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/cryptopp/lib/libcryptopp.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/gflags/lib/libgflags.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/gflags/lib/libgflags.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/glog/lib/libglog.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/glog/lib/libglog.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/openblas/lib/libopenblas.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/openblas/lib/libopenblas.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/openblas/lib/libopenblasp-r0.3.7.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/openblas/lib/libopenblasp-r0.3.7.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/protobuf/lib/libprotobuf.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/protobuf/lib/libprotobuf.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/utf8proc/lib/libutf8proc.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/utf8proc/lib/libutf8proc.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/xxhash/lib/libxxhash.a
vendored
Executable file
BIN
src/kdkocr/libs/amd64/paddle_inference/third_party/install/xxhash/lib/libxxhash.a
vendored
Executable file
Binary file not shown.
Binary file not shown.
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/cryptopp/lib/libcryptopp.a
vendored
Executable file
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/cryptopp/lib/libcryptopp.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/gflags/lib/libgflags.a
vendored
Executable file
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/gflags/lib/libgflags.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/glog/lib/libglog.a
vendored
Executable file
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/glog/lib/libglog.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/openblas/lib/libopenblas.a
vendored
Executable file
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/openblas/lib/libopenblas.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/openblas/lib/libopenblas_armv8p-r0.3.7.a
vendored
Executable file
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/openblas/lib/libopenblas_armv8p-r0.3.7.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/protobuf/lib/libprotobuf.a
vendored
Executable file
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/protobuf/lib/libprotobuf.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/xxhash/lib/libxxhash.a
vendored
Executable file
BIN
src/kdkocr/libs/arm64/paddle_inference/third_party/install/xxhash/lib/libxxhash.a
vendored
Executable file
Binary file not shown.
Binary file not shown.
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/cryptopp/lib/libcryptopp.a
vendored
Executable file
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/cryptopp/lib/libcryptopp.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/gflags/lib/libgflags.a
vendored
Executable file
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/gflags/lib/libgflags.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/glog/lib/libglog.a
vendored
Executable file
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/glog/lib/libglog.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/openblas/lib/libopenblas.a
vendored
Executable file
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/openblas/lib/libopenblas.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/openblas/lib/libopenblas_loongson3r4p-r0.3.13.a
vendored
Executable file
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/openblas/lib/libopenblas_loongson3r4p-r0.3.13.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/protobuf/lib/libprotobuf.a
vendored
Executable file
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/protobuf/lib/libprotobuf.a
vendored
Executable file
Binary file not shown.
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/xxhash/lib/libxxhash.a
vendored
Executable file
BIN
src/kdkocr/libs/mips64el/paddle_inference/third_party/install/xxhash/lib/libxxhash.a
vendored
Executable file
Binary file not shown.
|
|
@ -0,0 +1,50 @@
|
|||
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <unordered_map>
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
|
||||
class Cipher {
|
||||
public:
|
||||
Cipher() = default;
|
||||
virtual ~Cipher() {}
|
||||
// encrypt string
|
||||
virtual std::string Encrypt(const std::string& plaintext,
|
||||
const std::string& key) = 0;
|
||||
// decrypt string
|
||||
virtual std::string Decrypt(const std::string& ciphertext,
|
||||
const std::string& key) = 0;
|
||||
|
||||
// encrypt strings and read them to file,
|
||||
virtual void EncryptToFile(const std::string& plaintext,
|
||||
const std::string& key,
|
||||
const std::string& filename) = 0;
|
||||
// read from file and decrypt them
|
||||
virtual std::string DecryptFromFile(const std::string& key,
|
||||
const std::string& filename) = 0;
|
||||
};
|
||||
|
||||
class CipherFactory {
|
||||
public:
|
||||
CipherFactory() = default;
|
||||
static std::shared_ptr<Cipher> CreateCipher(const std::string& config_file);
|
||||
};
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#if !defined(_MSC_VER) && __cplusplus < 199711L
|
||||
#error C++11 or later compatible compiler is required to use Paddle.
|
||||
#endif
|
||||
|
||||
#ifdef _WIN32
|
||||
#ifndef NOMINMAX
|
||||
#define NOMINMAX // msvc max/min macro conflict with std::min/max
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#include "ext_dispatch.h" // NOLINT
|
||||
#include "ext_dtype.h" // NOLINT
|
||||
#include "ext_exception.h" // NOLINT
|
||||
#include "ext_op_meta_info.h" // NOLINT
|
||||
#include "ext_place.h" // NOLINT
|
||||
#include "ext_tensor.h" // NOLINT
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ext_dtype.h" // NOLINT
|
||||
#include "ext_exception.h" // NOLINT
|
||||
|
||||
namespace paddle {
|
||||
|
||||
///////// Basic Marco ///////////
|
||||
|
||||
#define PD_PRIVATE_CASE_TYPE_USING_HINT(NAME, enum_type, type, HINT, ...) \
|
||||
case enum_type: { \
|
||||
using HINT = type; \
|
||||
__VA_ARGS__(); \
|
||||
break; \
|
||||
}
|
||||
|
||||
#define PD_PRIVATE_CASE_TYPE(NAME, enum_type, type, ...) \
|
||||
PD_PRIVATE_CASE_TYPE_USING_HINT(NAME, enum_type, type, data_t, __VA_ARGS__)
|
||||
|
||||
///////// Floating Dispatch Marco ///////////
|
||||
|
||||
#define PD_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
|
||||
[&] { \
|
||||
const auto& __dtype__ = TYPE; \
|
||||
switch (__dtype__) { \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::FLOAT32, float, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::FLOAT64, double, \
|
||||
__VA_ARGS__) \
|
||||
default: \
|
||||
PD_THROW("function " #NAME " is not implemented for data type `", \
|
||||
::paddle::ToString(__dtype__), "`"); \
|
||||
} \
|
||||
}()
|
||||
|
||||
///////// Integral Dispatch Marco ///////////
|
||||
|
||||
#define PD_DISPATCH_INTEGRAL_TYPES(TYPE, NAME, ...) \
|
||||
[&] { \
|
||||
const auto& __dtype__ = TYPE; \
|
||||
switch (__dtype__) { \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT32, int, __VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT64, int64_t, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT8, int8_t, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::UINT8, uint8_t, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT16, int16_t, \
|
||||
__VA_ARGS__) \
|
||||
default: \
|
||||
PD_THROW("function " #NAME " is not implemented for data type `" + \
|
||||
::paddle::ToString(__dtype__) + "`"); \
|
||||
} \
|
||||
}()
|
||||
|
||||
///////// Floating and Integral Dispatch Marco ///////////
|
||||
|
||||
#define PD_DISPATCH_FLOATING_AND_INTEGRAL_TYPES(TYPE, NAME, ...) \
|
||||
[&] { \
|
||||
const auto& __dtype__ = TYPE; \
|
||||
switch (__dtype__) { \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::FLOAT32, float, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::FLOAT64, double, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT32, int, __VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT64, int64_t, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT8, int8_t, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::UINT8, uint8_t, \
|
||||
__VA_ARGS__) \
|
||||
PD_PRIVATE_CASE_TYPE(NAME, ::paddle::DataType::INT16, int16_t, \
|
||||
__VA_ARGS__) \
|
||||
default: \
|
||||
PD_THROW("function " #NAME " is not implemented for data type `" + \
|
||||
::paddle::ToString(__dtype__) + "`"); \
|
||||
} \
|
||||
}()
|
||||
|
||||
// TODO(chenweihang): Add more Marcos in the future if needed
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
|
||||
#if defined(_WIN32)
|
||||
#ifndef PD_DLL_DECL
|
||||
#ifdef PADDLE_DLL_EXPORT
|
||||
#define PD_DLL_DECL __declspec(dllexport)
|
||||
#else
|
||||
#define PD_DLL_DECL __declspec(dllimport)
|
||||
#endif // PADDLE_DLL_EXPORT
|
||||
#endif // PD_DLL_DECL
|
||||
#else
|
||||
#define PD_DLL_DECL
|
||||
#endif // _WIN32
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
#pragma once
|
||||
|
||||
#include <cstdint>
|
||||
#include <string>
|
||||
|
||||
#include "ext_exception.h" // NOLINT
|
||||
|
||||
namespace paddle {
|
||||
|
||||
enum class DataType {
|
||||
BOOL,
|
||||
INT8,
|
||||
UINT8,
|
||||
INT16,
|
||||
INT32,
|
||||
INT64,
|
||||
FLOAT32,
|
||||
FLOAT64,
|
||||
// TODO(JiabinYang) support more data types if needed.
|
||||
};
|
||||
|
||||
inline std::string ToString(DataType dtype) {
|
||||
switch (dtype) {
|
||||
case DataType::BOOL:
|
||||
return "bool";
|
||||
case DataType::INT8:
|
||||
return "int8_t";
|
||||
case DataType::UINT8:
|
||||
return "uint8_t";
|
||||
case DataType::INT16:
|
||||
return "int16_t";
|
||||
case DataType::INT32:
|
||||
return "int32_t";
|
||||
case DataType::INT64:
|
||||
return "int64_t";
|
||||
case DataType::FLOAT32:
|
||||
return "float";
|
||||
case DataType::FLOAT64:
|
||||
return "double";
|
||||
default:
|
||||
PD_THROW("Unsupported paddle enum data type.");
|
||||
}
|
||||
}
|
||||
|
||||
#define PD_FOR_EACH_DATA_TYPE(_) \
|
||||
_(bool, DataType::BOOL) \
|
||||
_(int8_t, DataType::INT8) \
|
||||
_(uint8_t, DataType::UINT8) \
|
||||
_(int16_t, DataType::INT16) \
|
||||
_(int, DataType::INT32) \
|
||||
_(int64_t, DataType::INT64) \
|
||||
_(float, DataType::FLOAT32) \
|
||||
_(double, DataType::FLOAT64)
|
||||
|
||||
template <paddle::DataType T>
|
||||
struct DataTypeToCPPType;
|
||||
|
||||
#define PD_SPECIALIZE_DataTypeToCPPType(cpp_type, data_type) \
|
||||
template <> \
|
||||
struct DataTypeToCPPType<data_type> { \
|
||||
using type = cpp_type; \
|
||||
};
|
||||
|
||||
PD_FOR_EACH_DATA_TYPE(PD_SPECIALIZE_DataTypeToCPPType)
|
||||
|
||||
#undef PD_SPECIALIZE_DataTypeToCPPType
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
|
||||
namespace paddle {
|
||||
|
||||
//////////////// Exception handling and Error Message /////////////////
|
||||
#if !defined(_WIN32)
|
||||
#define PD_UNLIKELY(expr) (__builtin_expect(static_cast<bool>(expr), 0))
|
||||
#define PD_LIKELY(expr) (__builtin_expect(static_cast<bool>(expr), 1))
|
||||
#else
|
||||
#define PD_UNLIKELY(expr) (expr)
|
||||
#define PD_LIKELY(expr) (expr)
|
||||
#endif
|
||||
|
||||
struct PD_Exception : public std::exception {
|
||||
public:
|
||||
template <typename... Args>
|
||||
explicit PD_Exception(const std::string& msg, const char* file, int line,
|
||||
const char* default_msg) {
|
||||
std::ostringstream sout;
|
||||
if (msg.empty()) {
|
||||
sout << default_msg << "\n [" << file << ":" << line << "]";
|
||||
} else {
|
||||
sout << msg << "\n [" << file << ":" << line << "]";
|
||||
}
|
||||
err_msg_ = sout.str();
|
||||
}
|
||||
|
||||
const char* what() const noexcept override { return err_msg_.c_str(); }
|
||||
|
||||
private:
|
||||
std::string err_msg_;
|
||||
};
|
||||
|
||||
class ErrorMessage {
|
||||
public:
|
||||
template <typename... Args>
|
||||
explicit ErrorMessage(const Args&... args) {
|
||||
build_string(args...);
|
||||
}
|
||||
|
||||
void build_string() { oss << ""; }
|
||||
|
||||
template <typename T>
|
||||
void build_string(const T& t) {
|
||||
oss << t;
|
||||
}
|
||||
|
||||
template <typename T, typename... Args>
|
||||
void build_string(const T& t, const Args&... args) {
|
||||
build_string(t);
|
||||
build_string(args...);
|
||||
}
|
||||
|
||||
std::string to_string() { return oss.str(); }
|
||||
|
||||
private:
|
||||
std::ostringstream oss;
|
||||
};
|
||||
|
||||
#if defined _WIN32
|
||||
#define HANDLE_THE_ERROR try {
|
||||
#define END_HANDLE_THE_ERROR \
|
||||
} \
|
||||
catch (const std::exception& e) { \
|
||||
std::cerr << e.what() << std::endl; \
|
||||
throw e; \
|
||||
}
|
||||
#else
|
||||
#define HANDLE_THE_ERROR
|
||||
#define END_HANDLE_THE_ERROR
|
||||
#endif
|
||||
|
||||
#define PD_CHECK(COND, ...) \
|
||||
do { \
|
||||
if (PD_UNLIKELY(!(COND))) { \
|
||||
auto __message__ = ::paddle::ErrorMessage(__VA_ARGS__).to_string(); \
|
||||
throw ::paddle::PD_Exception(__message__, __FILE__, __LINE__, \
|
||||
"Expected " #COND \
|
||||
", but it's not satisfied."); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
#define PD_THROW(...) \
|
||||
do { \
|
||||
auto __message__ = ::paddle::ErrorMessage(__VA_ARGS__).to_string(); \
|
||||
throw ::paddle::PD_Exception(__message__, __FILE__, __LINE__, \
|
||||
"An error occured."); \
|
||||
} while (0)
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,381 @@
|
|||
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <unordered_map>
|
||||
#include <vector>
|
||||
|
||||
#include <boost/any.hpp>
|
||||
|
||||
#include "ext_dll_decl.h" // NOLINT
|
||||
#include "ext_exception.h" // NOLINT
|
||||
#include "ext_tensor.h" // NOLINT
|
||||
|
||||
/**
|
||||
* Op Meta Info Related Define.
|
||||
*
|
||||
* Used to maintain operator core information.
|
||||
*
|
||||
*/
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
class PD_DLL_DECL OpMetaInfoHelper;
|
||||
} // namespace framework
|
||||
|
||||
using Tensor = paddle::Tensor;
|
||||
|
||||
///////////////// Util Marco Define ////////////////
|
||||
|
||||
#define PD_DISABLE_COPY_AND_ASSIGN(classname) \
|
||||
private: \
|
||||
classname(const classname&) = delete; \
|
||||
classname(classname&&) = delete; \
|
||||
classname& operator=(const classname&) = delete; \
|
||||
classname& operator=(classname&&) = delete
|
||||
|
||||
#define STATIC_ASSERT_GLOBAL_NAMESPACE(uniq_name, msg) \
|
||||
struct __test_global_namespace_##uniq_name##__ {}; \
|
||||
static_assert(std::is_same<::__test_global_namespace_##uniq_name##__, \
|
||||
__test_global_namespace_##uniq_name##__>::value, \
|
||||
msg)
|
||||
|
||||
///////////////// Util Define and Function ////////////////
|
||||
|
||||
inline std::string Grad(const std::string& var_name) {
|
||||
std::string result;
|
||||
result.reserve(var_name.size() + 5U);
|
||||
result += var_name;
|
||||
result += "@GRAD";
|
||||
return result;
|
||||
}
|
||||
|
||||
////////////////////// Kernel Function (PD_KERNEL) ////////////////////////
|
||||
|
||||
// Record Op kernel core function
|
||||
using KernelFunc = std::vector<Tensor> (*)(std::vector<Tensor> inputs,
|
||||
std::vector<boost::any> attrs);
|
||||
|
||||
#define PD_SPECIALIZE_ComputeCallHelper(attr_type) \
|
||||
template <typename... Tail> \
|
||||
struct ComputeCallHelper<attr_type, Tail...> { \
|
||||
template <int in_idx, int attr_idx, typename... PreviousArgs> \
|
||||
static Return Compute(std::vector<Tensor> inputs, \
|
||||
std::vector<boost::any> attrs, \
|
||||
const PreviousArgs&... pargs) { \
|
||||
try { \
|
||||
attr_type arg = boost::any_cast<attr_type>(attrs[attr_idx]); \
|
||||
return ComputeCallHelper<Tail...>::template Compute<in_idx, \
|
||||
attr_idx + 1>( \
|
||||
inputs, attrs, pargs..., arg); \
|
||||
} catch (boost::bad_any_cast&) { \
|
||||
PD_THROW( \
|
||||
"Attribute cast error in custom operator. Expected " #attr_type \
|
||||
" value."); \
|
||||
} \
|
||||
} \
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
struct TypeTag {};
|
||||
|
||||
template <typename F, F f>
|
||||
struct KernelFuncImpl;
|
||||
|
||||
template <typename Return, typename... Args, Return (*impl_fn)(Args...)>
|
||||
struct KernelFuncImpl<Return (*)(Args...), impl_fn> {
|
||||
static Return Compute(std::vector<Tensor> inputs,
|
||||
std::vector<boost::any> attrs) {
|
||||
return ComputeCallHelper<Args..., TypeTag<int>>::template Compute<0, 0>(
|
||||
inputs, attrs);
|
||||
}
|
||||
|
||||
private:
|
||||
template <typename... RemainingArgs>
|
||||
struct ComputeCallHelper;
|
||||
|
||||
// for Tensor input
|
||||
template <typename... Tail>
|
||||
struct ComputeCallHelper<const Tensor&, Tail...> {
|
||||
template <int in_idx, int attr_idx, typename... PreviousArgs>
|
||||
static Return Compute(std::vector<Tensor> inputs,
|
||||
std::vector<boost::any> attrs,
|
||||
const PreviousArgs&... pargs) {
|
||||
static_assert(attr_idx == 0,
|
||||
"Input tensor should appear before attributes.");
|
||||
const Tensor& arg = inputs[in_idx];
|
||||
return ComputeCallHelper<Tail...>::template Compute<in_idx + 1, attr_idx>(
|
||||
inputs, attrs, pargs..., arg);
|
||||
}
|
||||
};
|
||||
|
||||
PD_SPECIALIZE_ComputeCallHelper(bool);
|
||||
PD_SPECIALIZE_ComputeCallHelper(int);
|
||||
PD_SPECIALIZE_ComputeCallHelper(float);
|
||||
PD_SPECIALIZE_ComputeCallHelper(int64_t);
|
||||
PD_SPECIALIZE_ComputeCallHelper(std::string);
|
||||
PD_SPECIALIZE_ComputeCallHelper(std::vector<int>);
|
||||
PD_SPECIALIZE_ComputeCallHelper(std::vector<float>);
|
||||
PD_SPECIALIZE_ComputeCallHelper(std::vector<int64_t>);
|
||||
PD_SPECIALIZE_ComputeCallHelper(std::vector<std::string>);
|
||||
// TODO(chenweihang): support other attribute type if needed.
|
||||
// Why not support other attribute type here?
|
||||
// - boost::blank, std::vector<bool> and std::vector<double>
|
||||
// are not used in op
|
||||
// - BlockDesc* and std::vector<BlockDesc*> are used in framework
|
||||
// end: base template
|
||||
template <typename T>
|
||||
struct ComputeCallHelper<TypeTag<T>> {
|
||||
template <int in_idx, int attr_idx>
|
||||
static Return Compute(std::vector<Tensor> inputs,
|
||||
std::vector<boost::any> attrs, const Args&... args) {
|
||||
return impl_fn(args...);
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
#define PD_KERNEL(...) \
|
||||
::paddle::KernelFuncImpl<decltype(&__VA_ARGS__), &__VA_ARGS__>::Compute
|
||||
|
||||
/////////////// InferShape Function (PD_INFER_SHAPE) ///////////////
|
||||
|
||||
// Record Op infershape core function
|
||||
using InferShapeFunc = std::vector<std::vector<int64_t>> (*)(
|
||||
std::vector<std::vector<int64_t>> input_shapes);
|
||||
|
||||
template <typename F, F f>
|
||||
struct InferShapeFuncImpl;
|
||||
|
||||
template <typename Return, typename... Args, Return (*impl_fn)(Args...)>
|
||||
struct InferShapeFuncImpl<Return (*)(Args...), impl_fn> {
|
||||
static Return InferShape(std::vector<std::vector<int64_t>> input_shapes) {
|
||||
return InferShapeCallHelper<Args..., TypeTag<int>>::template InferShape<0>(
|
||||
input_shapes);
|
||||
}
|
||||
|
||||
private:
|
||||
template <typename... RemainingArgs>
|
||||
struct InferShapeCallHelper;
|
||||
|
||||
// only one type input: std::vector<int64_t>
|
||||
template <typename... Tail>
|
||||
struct InferShapeCallHelper<std::vector<int64_t>, Tail...> {
|
||||
template <int in_idx, typename... PreviousArgs>
|
||||
static Return InferShape(std::vector<std::vector<int64_t>> input_shapes,
|
||||
const PreviousArgs&... pargs) {
|
||||
std::vector<int64_t> arg = input_shapes[in_idx];
|
||||
return InferShapeCallHelper<Tail...>::template InferShape<in_idx + 1>(
|
||||
input_shapes, pargs..., arg);
|
||||
}
|
||||
};
|
||||
|
||||
// end: base template
|
||||
template <typename T>
|
||||
struct InferShapeCallHelper<TypeTag<T>> {
|
||||
template <int in_idx>
|
||||
static Return InferShape(std::vector<std::vector<int64_t>> input_shapes,
|
||||
const Args&... args) {
|
||||
return impl_fn(args...);
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
#define PD_INFER_SHAPE(...) \
|
||||
::paddle::InferShapeFuncImpl<decltype(&__VA_ARGS__), &__VA_ARGS__>::InferShape
|
||||
|
||||
/////////////// InferDataType Function (PD_INFER_DTYPE) ///////////////
|
||||
|
||||
// Record Op Infer dtype core function
|
||||
using InferDtypeFunc =
|
||||
std::vector<DataType> (*)(std::vector<DataType> input_dtypes);
|
||||
|
||||
template <typename F, F f>
|
||||
struct InferDtypeFuncImpl;
|
||||
|
||||
template <typename Return, typename... Args, Return (*impl_fn)(Args...)>
|
||||
struct InferDtypeFuncImpl<Return (*)(Args...), impl_fn> {
|
||||
static Return InferDtype(std::vector<DataType> input_dtypes) {
|
||||
return InferDtypeCallHelper<Args..., TypeTag<int>>::template InferDtype<0>(
|
||||
input_dtypes);
|
||||
}
|
||||
|
||||
private:
|
||||
template <typename... RemainingArgs>
|
||||
struct InferDtypeCallHelper;
|
||||
|
||||
// Only one type input now: DataType
|
||||
template <typename... Tail>
|
||||
struct InferDtypeCallHelper<DataType, Tail...> {
|
||||
template <int in_idx, typename... PreviousArgs>
|
||||
static Return InferDtype(std::vector<DataType> input_dtypes,
|
||||
const PreviousArgs&... pargs) {
|
||||
DataType arg = input_dtypes[in_idx];
|
||||
return InferDtypeCallHelper<Tail...>::template InferDtype<in_idx + 1>(
|
||||
input_dtypes, pargs..., arg);
|
||||
}
|
||||
};
|
||||
|
||||
// end: base template
|
||||
template <typename T>
|
||||
struct InferDtypeCallHelper<TypeTag<T>> {
|
||||
template <int in_idx>
|
||||
static Return InferDtype(std::vector<DataType> input_dtypes,
|
||||
const Args&... args) {
|
||||
return impl_fn(args...);
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
#define PD_INFER_DTYPE(...) \
|
||||
::paddle::InferDtypeFuncImpl<decltype(&__VA_ARGS__), &__VA_ARGS__>::InferDtype
|
||||
|
||||
////////////////////// Op Meta Info //////////////////////
|
||||
|
||||
class PD_DLL_DECL OpMetaInfo {
|
||||
public:
|
||||
explicit OpMetaInfo(const std::string& op_name) : name_(op_name) {}
|
||||
|
||||
// format: {"<name1>", "<name2>", ...}
|
||||
OpMetaInfo& Inputs(std::vector<std::string>&& inputs);
|
||||
|
||||
// format: {"<name1>", "<name2>", ...}
|
||||
OpMetaInfo& Outputs(std::vector<std::string>&& outputs);
|
||||
|
||||
// format: {"<name1>:<type1>", "<name1>:<type1>", ...}
|
||||
OpMetaInfo& Attrs(std::vector<std::string>&& attrs);
|
||||
|
||||
// format: PD_KERNEL(...)
|
||||
OpMetaInfo& SetKernelFn(KernelFunc&& func);
|
||||
|
||||
// format: PD_INFER_SHAPE(...)
|
||||
OpMetaInfo& SetInferShapeFn(InferShapeFunc&& func);
|
||||
|
||||
// format: PD_INFER_DTYPE(...)
|
||||
OpMetaInfo& SetInferDtypeFn(InferDtypeFunc&& func);
|
||||
|
||||
private:
|
||||
friend class framework::OpMetaInfoHelper;
|
||||
|
||||
// 1. desc info
|
||||
std::string name_;
|
||||
std::vector<std::string> inputs_;
|
||||
std::vector<std::string> outputs_;
|
||||
std::vector<std::string> attrs_;
|
||||
|
||||
// 2. func info
|
||||
KernelFunc kernel_fn_{nullptr};
|
||||
InferShapeFunc infer_shape_fn_{nullptr};
|
||||
InferDtypeFunc infer_dtype_fn_{nullptr};
|
||||
};
|
||||
|
||||
//////////////// Op Meta Info Map /////////////////
|
||||
|
||||
class PD_DLL_DECL OpMetaInfoMap {
|
||||
public:
|
||||
// this function's impl should keep in header file.
|
||||
// if move to cc file, meta info can not be added
|
||||
// into map
|
||||
static OpMetaInfoMap& Instance() {
|
||||
static OpMetaInfoMap g_custom_op_meta_info_map;
|
||||
return g_custom_op_meta_info_map;
|
||||
}
|
||||
|
||||
std::vector<OpMetaInfo>& operator[](const std::string& name);
|
||||
|
||||
const std::unordered_map<std::string, std::vector<OpMetaInfo>>& GetMap()
|
||||
const;
|
||||
|
||||
private:
|
||||
OpMetaInfoMap() = default;
|
||||
std::unordered_map<std::string, std::vector<OpMetaInfo>> map_;
|
||||
|
||||
PD_DISABLE_COPY_AND_ASSIGN(OpMetaInfoMap);
|
||||
};
|
||||
|
||||
//////////////// Op Meta Info Builder /////////////////
|
||||
|
||||
class PD_DLL_DECL OpMetaInfoBuilder {
|
||||
public:
|
||||
explicit OpMetaInfoBuilder(std::string&& name, size_t index);
|
||||
OpMetaInfoBuilder& Inputs(std::vector<std::string>&& inputs);
|
||||
OpMetaInfoBuilder& Outputs(std::vector<std::string>&& outputs);
|
||||
OpMetaInfoBuilder& Attrs(std::vector<std::string>&& attrs);
|
||||
OpMetaInfoBuilder& SetKernelFn(KernelFunc func);
|
||||
OpMetaInfoBuilder& SetInferShapeFn(InferShapeFunc func);
|
||||
OpMetaInfoBuilder& SetInferDtypeFn(InferDtypeFunc func);
|
||||
|
||||
private:
|
||||
// Forward Op name
|
||||
std::string name_;
|
||||
// ref current info ptr
|
||||
OpMetaInfo* info_ptr_;
|
||||
// The current op meta info index in vector
|
||||
// - 0: op, 1: grad_op, 2: grad_grad_op
|
||||
size_t index_;
|
||||
};
|
||||
|
||||
/////////////////////// Op register API /////////////////////////
|
||||
|
||||
// For inference: compile directly with framework
|
||||
// Call after PD_BUILD_OP(...)
|
||||
void RegisterAllCustomOperator();
|
||||
|
||||
// Using this api to load compiled custom operator's dynamic library and
|
||||
// register Custom
|
||||
// Operator into it
|
||||
void LoadCustomOperatorLib(const std::string& dso_name);
|
||||
|
||||
/////////////////////// Op register Macro /////////////////////////
|
||||
|
||||
#define PD_BUILD_OP(op_name) \
|
||||
STATIC_ASSERT_GLOBAL_NAMESPACE( \
|
||||
__reg_op__##op_name, "PD_BUILD_OP must be called in global namespace."); \
|
||||
static ::paddle::OpMetaInfoBuilder __op_meta_info_##op_name##__ = \
|
||||
::paddle::OpMetaInfoBuilder(#op_name, 0)
|
||||
|
||||
#define PD_BUILD_GRAD_OP(op_name) \
|
||||
STATIC_ASSERT_GLOBAL_NAMESPACE( \
|
||||
__reg_grad_op__##op_name, \
|
||||
"PD_BUILD_GRAD_OP must be called in global namespace."); \
|
||||
static ::paddle::OpMetaInfoBuilder __grad_op_meta_info_##op_name##__ = \
|
||||
::paddle::OpMetaInfoBuilder(#op_name, 1)
|
||||
|
||||
#define PD_BUILD_DOUBLE_GRAD_OP(op_name) \
|
||||
STATIC_ASSERT_GLOBAL_NAMESPACE( \
|
||||
__reg_grad_grad_op__##op_name, \
|
||||
"PD_BUILD_DOUBLE_GRAD_OP must be called in global namespace."); \
|
||||
static ::paddle::OpMetaInfoBuilder __grad_grad_op_meta_info_##op_name##__ = \
|
||||
::paddle::OpMetaInfoBuilder(#op_name, 2)
|
||||
|
||||
} // namespace paddle
|
||||
|
||||
///////////////////// C API ///////////////////
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#if defined(_WIN32)
|
||||
// C-API to get global OpMetaInfoMap.
|
||||
__declspec(dllexport) inline paddle::OpMetaInfoMap& PD_GetOpMetaInfoMap() {
|
||||
return paddle::OpMetaInfoMap::Instance();
|
||||
}
|
||||
#endif // _WIN32
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
namespace paddle {
|
||||
|
||||
// TODO(yangjiabin): Add other place support in next PR
|
||||
enum class PlaceType { kUNK = -1, kCPU, kGPU };
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
#ifdef PADDLE_WITH_CUDA
|
||||
#include <cuda_runtime.h>
|
||||
#endif
|
||||
|
||||
#include "ext_dll_decl.h" // NOLINT
|
||||
#include "ext_dtype.h" // NOLINT
|
||||
#include "ext_place.h" // NOLINT
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
class CustomTensorUtils;
|
||||
} // namespace framework
|
||||
|
||||
class StreamWrapper {
|
||||
public:
|
||||
StreamWrapper() : stream_(nullptr), is_stream_set_(false) {}
|
||||
void SetStream(void* stream) {
|
||||
stream_ = stream;
|
||||
is_stream_set_ = true;
|
||||
}
|
||||
|
||||
void* GetStream() const { return stream_; }
|
||||
|
||||
bool IsStreamSet() const { return is_stream_set_; }
|
||||
|
||||
private:
|
||||
// cudaStream_t stream_;
|
||||
void* stream_;
|
||||
bool is_stream_set_;
|
||||
};
|
||||
|
||||
class PD_DLL_DECL Tensor {
|
||||
public:
|
||||
/// \brief Construct a Tensor on target Place for CustomOp.
|
||||
/// Generally it's only used for user to create Tensor.
|
||||
explicit Tensor(const PlaceType& place);
|
||||
/// \brief Reset the shape of the tensor.
|
||||
/// Generally it's only used for the input tensor.
|
||||
/// Reshape must be called before calling
|
||||
/// mutable_data() or copy_to(const PlaceType& place)
|
||||
/// \param shape The shape to set.
|
||||
void reshape(const std::vector<int64_t>& shape);
|
||||
|
||||
/// \brief Get the memory pointer in CPU or GPU with
|
||||
/// specific data type.
|
||||
/// Please Reshape the tensor first before call this.
|
||||
/// It's usually used to get input data pointer.
|
||||
/// \param place The place of the tensor this will
|
||||
/// override the original place of current tensor.
|
||||
template <typename T>
|
||||
T* mutable_data(const PlaceType& place);
|
||||
|
||||
/// \brief Get the memory pointer in CPU or GPU with
|
||||
/// specific data type. Please Reshape the tensor
|
||||
/// first before call this.It's usually used to get
|
||||
/// input data pointer.
|
||||
template <typename T>
|
||||
T* mutable_data();
|
||||
|
||||
/// \brief Get the memory pointer directly.
|
||||
/// It's usually used to get the output data pointer.
|
||||
/// \return The tensor data buffer pointer.
|
||||
template <typename T>
|
||||
T* data() const;
|
||||
|
||||
/// \brief Copy the host memory to tensor data.
|
||||
/// It's usually used to set the input tensor data.
|
||||
/// \param PlaceType of target place, of which
|
||||
/// the tensor will copy to.
|
||||
|
||||
template <typename T>
|
||||
Tensor copy_to(const PlaceType& place) const;
|
||||
|
||||
/// \brief Return the shape of the Tensor.
|
||||
std::vector<int64_t> shape() const;
|
||||
|
||||
/// \brief Return the data type of the tensor.
|
||||
/// It's usually used to get the output tensor data type.
|
||||
/// \return The data type of the tensor.
|
||||
DataType type() const;
|
||||
|
||||
/// \brief Get the size of current tensor.
|
||||
/// Use this method to get the size of tensor
|
||||
/// \return int64_t.
|
||||
int64_t size() const;
|
||||
|
||||
/// \brief Get the place of current tensor.
|
||||
/// Use this method to get the place of tensor
|
||||
/// \return Place.
|
||||
const PlaceType& place() const;
|
||||
|
||||
/// \brief Cast datatype from one to another
|
||||
Tensor cast(const DataType& target_type) const;
|
||||
|
||||
#ifdef PADDLE_WITH_CUDA
|
||||
/// \bref Get current stream of Tensor
|
||||
cudaStream_t stream() const;
|
||||
#endif
|
||||
|
||||
private:
|
||||
friend class framework::CustomTensorUtils;
|
||||
mutable std::shared_ptr<void> tensor_;
|
||||
mutable PlaceType place_;
|
||||
StreamWrapper stream_;
|
||||
};
|
||||
|
||||
} // namespace paddle
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,680 @@
|
|||
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
///
|
||||
/// \file paddle_analysis_config.h
|
||||
///
|
||||
/// \brief Paddle Analysis Config API信息
|
||||
///
|
||||
/// \author paddle-infer@baidu.com
|
||||
/// \date 2020-03-20
|
||||
/// \since 1.7
|
||||
///
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cassert>
|
||||
#include <map>
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <unordered_set>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
#include "paddle_infer_declare.h" // NOLINT
|
||||
|
||||
/*! \file */
|
||||
// Here we include some header files with relative paths, for that in deploy,
|
||||
// the abstract path of this header file will be changed.
|
||||
#include "paddle_api.h" // NOLINT
|
||||
#include "paddle_pass_builder.h" // NOLINT
|
||||
#ifdef PADDLE_WITH_MKLDNN
|
||||
#include "paddle_mkldnn_quantizer_config.h" // NOLINT
|
||||
#endif
|
||||
|
||||
namespace paddle {
|
||||
|
||||
class AnalysisPredictor;
|
||||
struct MkldnnQuantizerConfig;
|
||||
|
||||
///
|
||||
/// \brief configuration manager for AnalysisPredictor.
|
||||
/// \since 1.7.0
|
||||
///
|
||||
/// AnalysisConfig manages configurations of AnalysisPredictor.
|
||||
/// During inference procedure, there are many parameters(model/params path,
|
||||
/// place of inference, etc.)
|
||||
/// to be specified, and various optimizations(subgraph fusion, memory
|
||||
/// optimazation, TensorRT engine, etc.)
|
||||
/// to be done. Users can manage these settings by creating and modifying an
|
||||
/// AnalysisConfig,
|
||||
/// and loading it into AnalysisPredictor.
|
||||
///
|
||||
struct PD_INFER_DECL AnalysisConfig {
|
||||
AnalysisConfig() = default;
|
||||
///
|
||||
/// \brief Construct a new AnalysisConfig from another
|
||||
/// AnalysisConfig.
|
||||
///
|
||||
/// \param[in] other another AnalysisConfig
|
||||
///
|
||||
explicit AnalysisConfig(const AnalysisConfig& other);
|
||||
///
|
||||
/// \brief Construct a new AnalysisConfig from a no-combined model.
|
||||
///
|
||||
/// \param[in] model_dir model directory of the no-combined model.
|
||||
///
|
||||
explicit AnalysisConfig(const std::string& model_dir);
|
||||
///
|
||||
/// \brief Construct a new AnalysisConfig from a combined model.
|
||||
///
|
||||
/// \param[in] prog_file model file path of the combined model.
|
||||
/// \param[in] params_file params file path of the combined model.
|
||||
///
|
||||
explicit AnalysisConfig(const std::string& prog_file,
|
||||
const std::string& params_file);
|
||||
///
|
||||
/// \brief Precision of inference in TensorRT.
|
||||
///
|
||||
enum class Precision {
|
||||
kFloat32 = 0, ///< fp32
|
||||
kInt8, ///< int8
|
||||
kHalf, ///< fp16
|
||||
};
|
||||
|
||||
///
|
||||
/// \brief Set the no-combined model dir path.
|
||||
///
|
||||
/// \param model_dir model dir path.
|
||||
///
|
||||
void SetModel(const std::string& model_dir) { model_dir_ = model_dir; }
|
||||
|
||||
///
|
||||
/// \brief Set the combined model with two specific pathes for program and
|
||||
/// parameters.
|
||||
///
|
||||
/// \param prog_file_path model file path of the combined model.
|
||||
/// \param params_file_path params file path of the combined model.
|
||||
///
|
||||
void SetModel(const std::string& prog_file_path,
|
||||
const std::string& params_file_path);
|
||||
///
|
||||
/// \brief Set the model file path of a combined model.
|
||||
///
|
||||
/// \param x model file path.
|
||||
///
|
||||
void SetProgFile(const std::string& x) { prog_file_ = x; }
|
||||
///
|
||||
/// \brief Set the params file path of a combined model.
|
||||
///
|
||||
/// \param x params file path.
|
||||
///
|
||||
void SetParamsFile(const std::string& x) { params_file_ = x; }
|
||||
|
||||
///
|
||||
/// \brief Set the path of optimization cache directory.
|
||||
///
|
||||
/// \param opt_cache_dir the path of optimization cache directory.
|
||||
///
|
||||
void SetOptimCacheDir(const std::string& opt_cache_dir) {
|
||||
opt_cache_dir_ = opt_cache_dir;
|
||||
}
|
||||
///
|
||||
/// \brief Get the model directory path.
|
||||
///
|
||||
/// \return const std::string& The model directory path.
|
||||
///
|
||||
const std::string& model_dir() const { return model_dir_; }
|
||||
///
|
||||
/// \brief Get the program file path.
|
||||
///
|
||||
/// \return const std::string& The program file path.
|
||||
///
|
||||
const std::string& prog_file() const { return prog_file_; }
|
||||
///
|
||||
/// \brief Get the combined parameters file.
|
||||
///
|
||||
/// \return const std::string& The combined parameters file.
|
||||
///
|
||||
const std::string& params_file() const { return params_file_; }
|
||||
|
||||
// Padding related.
|
||||
|
||||
///
|
||||
/// \brief Turn off FC Padding.
|
||||
///
|
||||
///
|
||||
void DisableFCPadding();
|
||||
///
|
||||
/// \brief A boolean state telling whether fc padding is used.
|
||||
///
|
||||
/// \return bool Whether fc padding is used.
|
||||
///
|
||||
bool use_fc_padding() const { return use_fc_padding_; }
|
||||
|
||||
// GPU related.
|
||||
|
||||
///
|
||||
/// \brief Turn on GPU.
|
||||
///
|
||||
/// \param memory_pool_init_size_mb initial size of the GPU memory pool in MB.
|
||||
/// \param device_id device_id the GPU card to use (default is 0).
|
||||
///
|
||||
void EnableUseGpu(uint64_t memory_pool_init_size_mb, int device_id = 0);
|
||||
///
|
||||
/// \brief Turn off GPU.
|
||||
///
|
||||
///
|
||||
void DisableGpu();
|
||||
|
||||
void EnableXpu(int l3_workspace_size = 0xfffc00);
|
||||
///
|
||||
/// \brief A boolean state telling whether the GPU is turned on.
|
||||
///
|
||||
/// \return bool Whether the GPU is turned on.
|
||||
///
|
||||
bool use_gpu() const { return use_gpu_; }
|
||||
///
|
||||
/// \brief A boolean state telling whether the XPU is turned on.
|
||||
///
|
||||
/// \return bool Whether the XPU is turned on.
|
||||
///
|
||||
bool use_xpu() const { return use_xpu_; }
|
||||
///
|
||||
/// \brief Get the GPU device id.
|
||||
///
|
||||
/// \return int The GPU device id.
|
||||
///
|
||||
int gpu_device_id() const { return gpu_device_id_; }
|
||||
///
|
||||
/// \brief Get the XPU device id.
|
||||
///
|
||||
/// \return int The XPU device id.
|
||||
///
|
||||
int xpu_device_id() const { return xpu_device_id_; }
|
||||
///
|
||||
/// \brief Get the initial size in MB of the GPU memory pool.
|
||||
///
|
||||
/// \return int The initial size in MB of the GPU memory pool.
|
||||
///
|
||||
int memory_pool_init_size_mb() const { return memory_pool_init_size_mb_; }
|
||||
///
|
||||
/// \brief Get the proportion of the initial memory pool size compared to the
|
||||
/// device.
|
||||
///
|
||||
/// \return float The proportion of the initial memory pool size.
|
||||
///
|
||||
float fraction_of_gpu_memory_for_pool() const;
|
||||
|
||||
// CUDNN related.
|
||||
///
|
||||
/// \brief Turn on CUDNN.
|
||||
///
|
||||
///
|
||||
void EnableCUDNN();
|
||||
///
|
||||
/// \brief A boolean state telling whether to use CUDNN.
|
||||
///
|
||||
/// \return bool Whether to use CUDNN.
|
||||
///
|
||||
bool cudnn_enabled() const { return use_cudnn_; }
|
||||
|
||||
///
|
||||
/// \brief Control whether to perform IR graph optimization.
|
||||
/// If turned off, the AnalysisConfig will act just like a NativeConfig.
|
||||
///
|
||||
/// \param x Whether the ir graph optimization is actived.
|
||||
///
|
||||
void SwitchIrOptim(int x = true) { enable_ir_optim_ = x; }
|
||||
///
|
||||
/// \brief A boolean state telling whether the ir graph optimization is
|
||||
/// actived.
|
||||
///
|
||||
/// \return bool Whether to use ir graph optimization.
|
||||
///
|
||||
bool ir_optim() const { return enable_ir_optim_; }
|
||||
|
||||
///
|
||||
/// \brief INTERNAL Determine whether to use the feed and fetch operators.
|
||||
/// Just for internal development, not stable yet.
|
||||
/// When ZeroCopyTensor is used, this should be turned off.
|
||||
///
|
||||
/// \param x Whether to use the feed and fetch operators.
|
||||
///
|
||||
void SwitchUseFeedFetchOps(int x = true) { use_feed_fetch_ops_ = x; }
|
||||
///
|
||||
/// \brief A boolean state telling whether to use the feed and fetch
|
||||
/// operators.
|
||||
///
|
||||
/// \return bool Whether to use the feed and fetch operators.
|
||||
///
|
||||
bool use_feed_fetch_ops_enabled() const { return use_feed_fetch_ops_; }
|
||||
|
||||
///
|
||||
/// \brief Control whether to specify the inputs' names.
|
||||
/// The ZeroCopyTensor type has a name member, assign it with the
|
||||
/// corresponding
|
||||
/// variable name. This is used only when the input ZeroCopyTensors passed to
|
||||
/// the
|
||||
/// AnalysisPredictor.ZeroCopyRun() cannot follow the order in the training
|
||||
/// phase.
|
||||
///
|
||||
/// \param x Whether to specify the inputs' names.
|
||||
///
|
||||
void SwitchSpecifyInputNames(bool x = true) { specify_input_name_ = x; }
|
||||
///
|
||||
/// \brief A boolean state tell whether the input ZeroCopyTensor names
|
||||
/// specified should
|
||||
/// be used to reorder the inputs in AnalysisPredictor.ZeroCopyRun().
|
||||
///
|
||||
/// \return bool Whether to specify the inputs' names.
|
||||
///
|
||||
bool specify_input_name() const { return specify_input_name_; }
|
||||
|
||||
///
|
||||
/// \brief Turn on the TensorRT engine.
|
||||
/// The TensorRT engine will accelerate some subgraphes in the original Fluid
|
||||
/// computation graph. In some models such as resnet50, GoogleNet and so on,
|
||||
/// it gains significant performance acceleration.
|
||||
///
|
||||
/// \param workspace_size The memory size(in byte) used for TensorRT
|
||||
/// workspace.
|
||||
/// \param max_batch_size The maximum batch size of this prediction task,
|
||||
/// better set as small as possible for less performance loss.
|
||||
/// \param min_subgrpah_size The minimum TensorRT subgraph size needed, if a
|
||||
/// subgraph is smaller than this, it will not be transferred to TensorRT
|
||||
/// engine.
|
||||
/// \param precision The precision used in TensorRT.
|
||||
/// \param use_static Serialize optimization information to disk for reusing.
|
||||
/// \param use_calib_mode Use TRT int8 calibration(post training
|
||||
/// quantization).
|
||||
///
|
||||
///
|
||||
void EnableTensorRtEngine(int workspace_size = 1 << 20,
|
||||
int max_batch_size = 1, int min_subgraph_size = 3,
|
||||
Precision precision = Precision::kFloat32,
|
||||
bool use_static = false,
|
||||
bool use_calib_mode = true);
|
||||
///
|
||||
/// \brief A boolean state telling whether the TensorRT engine is used.
|
||||
///
|
||||
/// \return bool Whether the TensorRT engine is used.
|
||||
///
|
||||
bool tensorrt_engine_enabled() const { return use_tensorrt_; }
|
||||
///
|
||||
/// \brief Set min, max, opt shape for TensorRT Dynamic shape mode.
|
||||
/// \param min_input_shape The min input shape of the subgraph input.
|
||||
/// \param max_input_shape The max input shape of the subgraph input.
|
||||
/// \param opt_input_shape The opt input shape of the subgraph input.
|
||||
/// \param disable_trt_plugin_fp16 Setting this parameter to true means that
|
||||
/// TRT plugin will not run fp16.
|
||||
///
|
||||
void SetTRTDynamicShapeInfo(
|
||||
std::map<std::string, std::vector<int>> min_input_shape,
|
||||
std::map<std::string, std::vector<int>> max_input_shape,
|
||||
std::map<std::string, std::vector<int>> optim_input_shape,
|
||||
bool disable_trt_plugin_fp16 = false);
|
||||
|
||||
///
|
||||
/// \brief Prevent ops running in Paddle-TRT
|
||||
/// NOTE: just experimental, not an official stable API, easy to be broken.
|
||||
///
|
||||
void Exp_DisableTensorRtOPs(const std::vector<std::string>& ops);
|
||||
|
||||
///
|
||||
/// \brief Replace some TensorRT plugins to TensorRT OSS(
|
||||
/// https://github.com/NVIDIA/TensorRT), with which some models's inference
|
||||
/// may be more high-performance. Libnvinfer_plugin.so greater than
|
||||
/// V7.2.1 is needed.
|
||||
///
|
||||
void EnableTensorRtOSS();
|
||||
|
||||
///
|
||||
/// \brief A boolean state telling whether to use the TensorRT OSS.
|
||||
///
|
||||
/// \return bool Whether to use the TensorRT OSS.
|
||||
///
|
||||
bool tensorrt_oss_enabled() { return trt_use_oss_; }
|
||||
|
||||
///
|
||||
/// \brief Enable TensorRT DLA
|
||||
/// \param dla_core ID of DLACore, which should be 0, 1,
|
||||
/// ..., IBuilder.getNbDLACores() - 1
|
||||
///
|
||||
void EnableTensorRtDLA(int dla_core = 0);
|
||||
|
||||
///
|
||||
/// \brief A boolean state telling whether to use the TensorRT DLA.
|
||||
///
|
||||
/// \return bool Whether to use the TensorRT DLA.
|
||||
///
|
||||
bool tensorrt_dla_enabled() { return trt_use_dla_; }
|
||||
|
||||
///
|
||||
/// \brief Turn on the usage of Lite sub-graph engine.
|
||||
///
|
||||
/// \param precision_mode Precion used in Lite sub-graph engine.
|
||||
/// \param passes_filter Set the passes used in Lite sub-graph engine.
|
||||
/// \param ops_filter Operators not supported by Lite.
|
||||
///
|
||||
void EnableLiteEngine(
|
||||
AnalysisConfig::Precision precision_mode = Precision::kFloat32,
|
||||
bool zero_copy = false,
|
||||
const std::vector<std::string>& passes_filter = {},
|
||||
const std::vector<std::string>& ops_filter = {});
|
||||
|
||||
///
|
||||
/// \brief A boolean state indicating whether the Lite sub-graph engine is
|
||||
/// used.
|
||||
///
|
||||
/// \return bool whether the Lite sub-graph engine is used.
|
||||
///
|
||||
bool lite_engine_enabled() const { return use_lite_; }
|
||||
|
||||
///
|
||||
/// \brief Control whether to debug IR graph analysis phase.
|
||||
/// This will generate DOT files for visualizing the computation graph after
|
||||
/// each analysis pass applied.
|
||||
///
|
||||
/// \param x whether to debug IR graph analysis phase.
|
||||
///
|
||||
void SwitchIrDebug(int x = true);
|
||||
|
||||
///
|
||||
/// \brief Turn on MKLDNN.
|
||||
///
|
||||
///
|
||||
void EnableMKLDNN();
|
||||
///
|
||||
/// \brief Set the cache capacity of different input shapes for MKLDNN.
|
||||
/// Default value 0 means not caching any shape.
|
||||
/// Please see MKL-DNN Data Caching Design Document:
|
||||
/// https://github.com/PaddlePaddle/FluidDoc/blob/develop/doc/fluid/design/mkldnn/caching/caching.md
|
||||
///
|
||||
/// \param capacity The cache capacity.
|
||||
///
|
||||
void SetMkldnnCacheCapacity(int capacity);
|
||||
///
|
||||
/// \brief A boolean state telling whether to use the MKLDNN.
|
||||
///
|
||||
/// \return bool Whether to use the MKLDNN.
|
||||
///
|
||||
bool mkldnn_enabled() const { return use_mkldnn_; }
|
||||
|
||||
///
|
||||
/// \brief Set the number of cpu math library threads.
|
||||
///
|
||||
/// \param cpu_math_library_num_threads The number of cpu math library
|
||||
/// threads.
|
||||
///
|
||||
void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads);
|
||||
///
|
||||
/// \brief An int state telling how many threads are used in the CPU math
|
||||
/// library.
|
||||
///
|
||||
/// \return int The number of threads used in the CPU math library.
|
||||
///
|
||||
int cpu_math_library_num_threads() const {
|
||||
return cpu_math_library_num_threads_;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Transform the AnalysisConfig to NativeConfig.
|
||||
///
|
||||
/// \return NativeConfig The NativeConfig transformed.
|
||||
///
|
||||
NativeConfig ToNativeConfig() const;
|
||||
///
|
||||
/// \brief Specify the operator type list to use MKLDNN acceleration.
|
||||
///
|
||||
/// \param op_list The operator type list.
|
||||
///
|
||||
void SetMKLDNNOp(std::unordered_set<std::string> op_list) {
|
||||
mkldnn_enabled_op_types_ = op_list;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Turn on MKLDNN quantization.
|
||||
///
|
||||
///
|
||||
void EnableMkldnnQuantizer();
|
||||
|
||||
///
|
||||
/// \brief Turn on MKLDNN bfloat16.
|
||||
///
|
||||
///
|
||||
void EnableMkldnnBfloat16();
|
||||
|
||||
///
|
||||
/// \brief A boolean state telling whether to use the MKLDNN Bfloat16.
|
||||
///
|
||||
/// \return bool Whether to use the MKLDNN Bfloat16.
|
||||
///
|
||||
bool mkldnn_bfloat16_enabled() const { return use_mkldnn_bfloat16_; }
|
||||
|
||||
/// \brief Specify the operator type list to use Bfloat16 acceleration.
|
||||
///
|
||||
/// \param op_list The operator type list.
|
||||
///
|
||||
void SetBfloat16Op(std::unordered_set<std::string> op_list) {
|
||||
bfloat16_enabled_op_types_ = op_list;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief A boolean state telling whether the thread local CUDA stream is
|
||||
/// enabled.
|
||||
///
|
||||
/// \return bool Whether the thread local CUDA stream is enabled.
|
||||
///
|
||||
bool thread_local_stream_enabled() const { return thread_local_stream_; }
|
||||
|
||||
///
|
||||
/// \brief A boolean state telling whether the MKLDNN quantization is enabled.
|
||||
///
|
||||
/// \return bool Whether the MKLDNN quantization is enabled.
|
||||
///
|
||||
bool mkldnn_quantizer_enabled() const { return use_mkldnn_quantizer_; }
|
||||
|
||||
///
|
||||
/// \brief Get MKLDNN quantizer config.
|
||||
///
|
||||
/// \return MkldnnQuantizerConfig* MKLDNN quantizer config.
|
||||
///
|
||||
MkldnnQuantizerConfig* mkldnn_quantizer_config() const;
|
||||
|
||||
///
|
||||
/// \brief Specify the memory buffer of program and parameter.
|
||||
/// Used when model and params are loaded directly from memory.
|
||||
///
|
||||
/// \param prog_buffer The memory buffer of program.
|
||||
/// \param prog_buffer_size The size of the model data.
|
||||
/// \param params_buffer The memory buffer of the combined parameters file.
|
||||
/// \param params_buffer_size The size of the combined parameters data.
|
||||
///
|
||||
void SetModelBuffer(const char* prog_buffer, size_t prog_buffer_size,
|
||||
const char* params_buffer, size_t params_buffer_size);
|
||||
///
|
||||
/// \brief A boolean state telling whether the model is set from the CPU
|
||||
/// memory.
|
||||
///
|
||||
/// \return bool Whether model and params are loaded directly from memory.
|
||||
///
|
||||
bool model_from_memory() const { return model_from_memory_; }
|
||||
|
||||
///
|
||||
/// \brief Turn on memory optimize
|
||||
/// NOTE still in development.
|
||||
///
|
||||
void EnableMemoryOptim();
|
||||
///
|
||||
/// \brief A boolean state telling whether the memory optimization is
|
||||
/// activated.
|
||||
///
|
||||
/// \return bool Whether the memory optimization is activated.
|
||||
///
|
||||
bool enable_memory_optim() const;
|
||||
|
||||
///
|
||||
/// \brief Turn on profiling report.
|
||||
/// If not turned on, no profiling report will be generated.
|
||||
///
|
||||
void EnableProfile();
|
||||
///
|
||||
/// \brief A boolean state telling whether the profiler is activated.
|
||||
///
|
||||
/// \return bool Whether the profiler is activated.
|
||||
///
|
||||
bool profile_enabled() const { return with_profile_; }
|
||||
|
||||
///
|
||||
/// \brief Mute all logs in Paddle inference.
|
||||
///
|
||||
void DisableGlogInfo();
|
||||
///
|
||||
/// \brief A boolean state telling whether logs in Paddle inference are muted.
|
||||
///
|
||||
/// \return bool Whether logs in Paddle inference are muted.
|
||||
///
|
||||
bool glog_info_disabled() const { return !with_glog_info_; }
|
||||
|
||||
///
|
||||
/// \brief Set the AnalysisConfig to be invalid.
|
||||
/// This is to ensure that an AnalysisConfig can only be used in one
|
||||
/// AnalysisPredictor.
|
||||
///
|
||||
void SetInValid() const { is_valid_ = false; }
|
||||
///
|
||||
/// \brief A boolean state telling whether the AnalysisConfig is valid.
|
||||
///
|
||||
/// \return bool Whether the AnalysisConfig is valid.
|
||||
///
|
||||
bool is_valid() const { return is_valid_; }
|
||||
|
||||
friend class ::paddle::AnalysisPredictor;
|
||||
|
||||
///
|
||||
/// \brief Get a pass builder for customize the passes in IR analysis phase.
|
||||
/// NOTE: Just for developer, not an official API, easy to be broken.
|
||||
///
|
||||
///
|
||||
PassStrategy* pass_builder() const;
|
||||
|
||||
///
|
||||
/// \brief Enable the GPU multi-computing stream feature.
|
||||
/// NOTE: The current behavior of this interface is to bind the computation
|
||||
/// stream to the thread, and this behavior may be changed in the future.
|
||||
///
|
||||
void EnableGpuMultiStream();
|
||||
void PartiallyRelease();
|
||||
|
||||
protected:
|
||||
// Update the config.
|
||||
void Update();
|
||||
|
||||
std::string SerializeInfoCache();
|
||||
|
||||
protected:
|
||||
// Model pathes.
|
||||
std::string model_dir_;
|
||||
mutable std::string prog_file_;
|
||||
mutable std::string params_file_;
|
||||
|
||||
// GPU related.
|
||||
bool use_gpu_{false};
|
||||
int gpu_device_id_{0};
|
||||
int xpu_device_id_{0};
|
||||
uint64_t memory_pool_init_size_mb_{100}; // initial size is 100MB.
|
||||
|
||||
bool use_cudnn_{false};
|
||||
|
||||
// Padding related
|
||||
bool use_fc_padding_{true};
|
||||
|
||||
// TensorRT related.
|
||||
bool use_tensorrt_{false};
|
||||
// For workspace_size, refer it from here:
|
||||
// https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
|
||||
int tensorrt_workspace_size_{1 << 30};
|
||||
// While TensorRT allows an engine optimized for a given max batch size
|
||||
// to run at any smaller size, the performance for those smaller
|
||||
// sizes may not be as well-optimized. Therefore, Max batch is best
|
||||
// equivalent to the runtime batch size.
|
||||
int tensorrt_max_batchsize_{1};
|
||||
// We transform the Ops that can be converted into TRT layer in the model,
|
||||
// and aggregate these Ops into subgraphs for TRT execution.
|
||||
// We set this variable to control the minimum number of nodes in the
|
||||
// subgraph, 3 as default value.
|
||||
int tensorrt_min_subgraph_size_{3};
|
||||
Precision tensorrt_precision_mode_{Precision::kFloat32};
|
||||
bool trt_use_static_engine_{false};
|
||||
bool trt_use_calib_mode_{true};
|
||||
bool trt_use_oss_{false};
|
||||
bool trt_use_dla_{false};
|
||||
int trt_dla_core_{0};
|
||||
std::map<std::string, std::vector<int>> min_input_shape_{};
|
||||
std::map<std::string, std::vector<int>> max_input_shape_{};
|
||||
std::map<std::string, std::vector<int>> optim_input_shape_{};
|
||||
std::vector<std::string> trt_disabled_ops_{};
|
||||
bool disable_trt_plugin_fp16_{false};
|
||||
|
||||
// memory reuse related.
|
||||
bool enable_memory_optim_{false};
|
||||
|
||||
bool use_mkldnn_{false};
|
||||
std::unordered_set<std::string> mkldnn_enabled_op_types_;
|
||||
|
||||
bool model_from_memory_{false};
|
||||
|
||||
bool enable_ir_optim_{true};
|
||||
bool use_feed_fetch_ops_{true};
|
||||
bool ir_debug_{false};
|
||||
|
||||
bool specify_input_name_{false};
|
||||
|
||||
int cpu_math_library_num_threads_{1};
|
||||
|
||||
bool with_profile_{false};
|
||||
|
||||
bool with_glog_info_{true};
|
||||
|
||||
// A runtime cache, shouldn't be transferred to others.
|
||||
std::string serialized_info_cache_;
|
||||
|
||||
mutable std::unique_ptr<PassStrategy> pass_builder_;
|
||||
|
||||
bool use_lite_{false};
|
||||
std::vector<std::string> lite_passes_filter_;
|
||||
std::vector<std::string> lite_ops_filter_;
|
||||
Precision lite_precision_mode_;
|
||||
bool lite_zero_copy_;
|
||||
|
||||
bool thread_local_stream_{false};
|
||||
bool use_xpu_{false};
|
||||
int xpu_l3_workspace_size_;
|
||||
|
||||
// mkldnn related.
|
||||
int mkldnn_cache_capacity_{0};
|
||||
bool use_mkldnn_quantizer_{false};
|
||||
std::shared_ptr<MkldnnQuantizerConfig> mkldnn_quantizer_config_;
|
||||
bool use_mkldnn_bfloat16_{false};
|
||||
std::unordered_set<std::string> bfloat16_enabled_op_types_;
|
||||
|
||||
// If the config is already used on a predictor, it becomes invalid.
|
||||
// Any config can only be used with one predictor.
|
||||
// Variables held by config can take up a lot of memory in some cases.
|
||||
// So we release the memory when the predictor is set up.
|
||||
mutable bool is_valid_{true};
|
||||
std::string opt_cache_dir_;
|
||||
};
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,457 @@
|
|||
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
#pragma once
|
||||
|
||||
/*! \file paddle_api.h
|
||||
*/
|
||||
|
||||
/*! \mainpage Paddle Inference APIs
|
||||
* \section intro_sec Introduction
|
||||
* The Paddle inference library aims to offer an high performance inference SDK
|
||||
* for Paddle users.
|
||||
*/
|
||||
|
||||
#include <cassert>
|
||||
#include <map>
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include "crypto/cipher.h"
|
||||
#include "paddle_infer_declare.h" // NOLINT
|
||||
/*! \namespace paddle
|
||||
*/
|
||||
namespace paddle {
|
||||
|
||||
/// \brief Paddle data type.
|
||||
enum PaddleDType {
|
||||
FLOAT32,
|
||||
INT64,
|
||||
INT32,
|
||||
UINT8,
|
||||
// TODO(Superjomn) support more data types if needed.
|
||||
};
|
||||
|
||||
/// \brief Memory manager for PaddleTensor.
|
||||
///
|
||||
/// The PaddleBuf holds a buffer for data input or output. The memory can be
|
||||
/// allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
|
||||
/// should be reused for better performance.
|
||||
///
|
||||
/// For user allocated memory, the following API can be used:
|
||||
/// - PaddleBuf(void* data, size_t length) to set an external memory by
|
||||
/// specifying the memory address and length.
|
||||
/// - Reset(void* data, size_t length) to reset the PaddleBuf with an external
|
||||
/// memory.
|
||||
/// ATTENTION, for user allocated memory, deallocation should be done by users
|
||||
/// externally after the program finished. The PaddleBuf won't do any allocation
|
||||
/// or deallocation.
|
||||
///
|
||||
/// To have the PaddleBuf allocate and manage the memory:
|
||||
/// - PaddleBuf(size_t length) will allocate a memory of size `length`.
|
||||
/// - Resize(size_t length) resize the memory to no less than `length`,
|
||||
/// ATTENTION
|
||||
/// if the allocated memory is larger than `length`, nothing will done.
|
||||
///
|
||||
/// Usage:
|
||||
///
|
||||
/// Let PaddleBuf manage the memory internally.
|
||||
/// \code{cpp}
|
||||
/// const int num_elements = 128;
|
||||
/// PaddleBuf buf(num_elements/// sizeof(float));
|
||||
/// \endcode
|
||||
///
|
||||
/// Or
|
||||
/// \code{cpp}
|
||||
/// PaddleBuf buf;
|
||||
/// buf.Resize(num_elements/// sizeof(float));
|
||||
/// \endcode
|
||||
/// Works the exactly the same.
|
||||
///
|
||||
/// One can also make the `PaddleBuf` use the external memory.
|
||||
/// \code{cpp}
|
||||
/// PaddleBuf buf;
|
||||
/// void* external_memory = new float[num_elements];
|
||||
/// buf.Reset(external_memory, num_elements*sizeof(float));
|
||||
/// ...
|
||||
/// delete[] external_memory; // manage the memory lifetime outside.
|
||||
/// \endcode
|
||||
///
|
||||
class PD_INFER_DECL PaddleBuf {
|
||||
public:
|
||||
///
|
||||
/// \brief PaddleBuf allocate memory internally, and manage it.
|
||||
///
|
||||
/// \param[in] length The length of data.
|
||||
///
|
||||
explicit PaddleBuf(size_t length)
|
||||
: data_(new char[length]), length_(length), memory_owned_(true) {}
|
||||
///
|
||||
/// \brief Set external memory, the PaddleBuf won't manage it.
|
||||
///
|
||||
/// \param[in] data The start address of the external memory.
|
||||
/// \param[in] length The length of data.
|
||||
///
|
||||
PaddleBuf(void* data, size_t length)
|
||||
: data_(data), length_(length), memory_owned_{false} {}
|
||||
///
|
||||
/// \brief Copy only available when memory is managed externally.
|
||||
///
|
||||
/// \param[in] other another `PaddleBuf`
|
||||
///
|
||||
explicit PaddleBuf(const PaddleBuf& other);
|
||||
///
|
||||
/// \brief Resize the memory.
|
||||
///
|
||||
/// \param[in] length The length of data.
|
||||
///
|
||||
void Resize(size_t length);
|
||||
///
|
||||
/// \brief Reset to external memory, with address and length set.
|
||||
///
|
||||
/// \param[in] data The start address of the external memory.
|
||||
/// \param[in] length The length of data.
|
||||
///
|
||||
void Reset(void* data, size_t length);
|
||||
///
|
||||
/// \brief Tell whether the buffer is empty.
|
||||
///
|
||||
bool empty() const { return length_ == 0; }
|
||||
///
|
||||
/// \brief Get the data's memory address.
|
||||
///
|
||||
void* data() const { return data_; }
|
||||
///
|
||||
/// \brief Get the memory length.
|
||||
///
|
||||
size_t length() const { return length_; }
|
||||
|
||||
~PaddleBuf() { Free(); }
|
||||
PaddleBuf& operator=(const PaddleBuf&);
|
||||
PaddleBuf& operator=(PaddleBuf&&);
|
||||
PaddleBuf() = default;
|
||||
PaddleBuf(PaddleBuf&& other);
|
||||
|
||||
private:
|
||||
void Free();
|
||||
void* data_{nullptr}; ///< pointer to the data memory.
|
||||
size_t length_{0}; ///< number of memory bytes.
|
||||
bool memory_owned_{true};
|
||||
};
|
||||
|
||||
///
|
||||
/// \brief Basic input and output data structure for PaddlePredictor.
|
||||
///
|
||||
struct PD_INFER_DECL PaddleTensor {
|
||||
PaddleTensor() = default;
|
||||
std::string name; ///< variable name.
|
||||
std::vector<int> shape;
|
||||
PaddleBuf data; ///< blob of data.
|
||||
PaddleDType dtype;
|
||||
std::vector<std::vector<size_t>> lod; ///< Tensor+LoD equals LoDTensor
|
||||
};
|
||||
|
||||
enum class PaddlePlace { kUNK = -1, kCPU, kGPU, kXPU };
|
||||
|
||||
/// \brief Represents an n-dimensional array of values.
|
||||
/// The ZeroCopyTensor is used to store the input or output of the network.
|
||||
/// Zero copy means that the tensor supports direct copy of host or device data
|
||||
/// to device,
|
||||
/// eliminating additional CPU copy. ZeroCopyTensor is only used in the
|
||||
/// AnalysisPredictor.
|
||||
/// It is obtained through PaddlePredictor::GetinputTensor()
|
||||
/// and PaddlePredictor::GetOutputTensor() interface.
|
||||
class PD_INFER_DECL ZeroCopyTensor {
|
||||
public:
|
||||
/// \brief Reset the shape of the tensor.
|
||||
/// Generally it's only used for the input tensor.
|
||||
/// Reshape must be called before calling mutable_data() or copy_from_cpu()
|
||||
/// \param shape The shape to set.
|
||||
void Reshape(const std::vector<int>& shape);
|
||||
|
||||
/// \brief Get the memory pointer in CPU or GPU with specific data type.
|
||||
/// Please Reshape the tensor first before call this.
|
||||
/// It's usually used to get input data pointer.
|
||||
/// \param place The place of the tensor.
|
||||
template <typename T>
|
||||
T* mutable_data(PaddlePlace place);
|
||||
|
||||
/// \brief Get the memory pointer directly.
|
||||
/// It's usually used to get the output data pointer.
|
||||
/// \param[out] place To get the device type of the tensor.
|
||||
/// \param[out] size To get the data size of the tensor.
|
||||
/// \return The tensor data buffer pointer.
|
||||
template <typename T>
|
||||
T* data(PaddlePlace* place, int* size) const;
|
||||
|
||||
/// \brief Copy the host memory to tensor data.
|
||||
/// It's usually used to set the input tensor data.
|
||||
/// \param data The pointer of the data, from which the tensor will copy.
|
||||
template <typename T>
|
||||
void copy_from_cpu(const T* data);
|
||||
|
||||
/// \brief Copy the tensor data to the host memory.
|
||||
/// It's usually used to get the output tensor data.
|
||||
/// \param[out] data The tensor will copy the data to the address.
|
||||
template <typename T>
|
||||
void copy_to_cpu(T* data);
|
||||
|
||||
/// \brief Return the shape of the Tensor.
|
||||
std::vector<int> shape() const;
|
||||
|
||||
/// \brief Set lod info of the tensor.
|
||||
/// More about LOD can be seen here:
|
||||
/// https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/basic_concept/lod_tensor.html#lodtensor
|
||||
/// \param x the lod info.
|
||||
void SetLoD(const std::vector<std::vector<size_t>>& x);
|
||||
/// \brief Return the lod info of the tensor.
|
||||
std::vector<std::vector<size_t>> lod() const;
|
||||
/// \brief Return the name of the tensor.
|
||||
const std::string& name() const { return name_; }
|
||||
void SetPlace(PaddlePlace place, int device = -1) {
|
||||
place_ = place;
|
||||
device_ = device;
|
||||
}
|
||||
|
||||
/// \brief Return the data type of the tensor.
|
||||
/// It's usually used to get the output tensor data type.
|
||||
/// \return The data type of the tensor.
|
||||
PaddleDType type() const;
|
||||
|
||||
protected:
|
||||
explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
|
||||
void SetName(const std::string& name) { name_ = name; }
|
||||
void* FindTensor() const;
|
||||
|
||||
private:
|
||||
std::string name_;
|
||||
bool input_or_output_;
|
||||
friend class AnalysisPredictor;
|
||||
void* scope_{nullptr};
|
||||
// The corresponding tensor pointer inside Paddle workspace is cached for
|
||||
// performance.
|
||||
mutable void* tensor_{nullptr};
|
||||
PaddlePlace place_;
|
||||
PaddleDType dtype_;
|
||||
int device_;
|
||||
};
|
||||
|
||||
/// \brief A Predictor for executing inference on a model.
|
||||
/// Base class for AnalysisPredictor and NativePaddlePredictor.
|
||||
class PD_INFER_DECL PaddlePredictor {
|
||||
public:
|
||||
struct Config;
|
||||
PaddlePredictor() = default;
|
||||
PaddlePredictor(const PaddlePredictor&) = delete;
|
||||
PaddlePredictor& operator=(const PaddlePredictor&) = delete;
|
||||
|
||||
/// \brief This interface takes input and runs the network.
|
||||
/// There are redundant copies of data between hosts in this operation,
|
||||
/// so it is more recommended to use the zecopyrun interface
|
||||
/// \param[in] inputs An list of PaddleTensor as the input to the network.
|
||||
/// \param[out] output_data Pointer to the tensor list, which holds the output
|
||||
/// paddletensor
|
||||
/// \param[in] batch_size This setting has been discarded and can be ignored.
|
||||
/// \return Whether the run is successful
|
||||
virtual bool Run(const std::vector<PaddleTensor>& inputs,
|
||||
std::vector<PaddleTensor>* output_data,
|
||||
int batch_size = -1) = 0;
|
||||
|
||||
/// \brief Used to get the name of the network input.
|
||||
/// Be inherited by AnalysisPredictor, Only used in ZeroCopy scenarios.
|
||||
/// \return Input tensor names.
|
||||
virtual std::vector<std::string> GetInputNames() { return {}; }
|
||||
|
||||
/// \brief Get the input shape of the model.
|
||||
/// \return A map contains all the input names and shape defined in the model.
|
||||
virtual std::map<std::string, std::vector<int64_t>> GetInputTensorShape() {
|
||||
return {};
|
||||
}
|
||||
|
||||
/// \brief Used to get the name of the network output.
|
||||
/// Be inherited by AnalysisPredictor, Only used in ZeroCopy scenarios.
|
||||
/// \return Output tensor names.
|
||||
virtual std::vector<std::string> GetOutputNames() { return {}; }
|
||||
|
||||
/// \brief Get the input ZeroCopyTensor by name.
|
||||
/// Be inherited by AnalysisPredictor, Only used in ZeroCopy scenarios.
|
||||
/// The name is obtained from the GetInputNames() interface.
|
||||
/// \param name The input tensor name.
|
||||
/// \return Return the corresponding input ZeroCopyTensor.
|
||||
virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
|
||||
const std::string& name) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
/// \brief Get the output ZeroCopyTensor by name.
|
||||
/// Be inherited by AnalysisPredictor, Only used in ZeroCopy scenarios.
|
||||
/// The name is obtained from the GetOutputNames() interface.
|
||||
/// \param name The output tensor name.
|
||||
/// \return Return the corresponding output ZeroCopyTensor.
|
||||
virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
|
||||
const std::string& name) {
|
||||
return nullptr;
|
||||
}
|
||||
/// \brief Run the network with zero-copied inputs and outputs.
|
||||
/// Be inherited by AnalysisPredictor and only used in ZeroCopy scenarios.
|
||||
/// This will save the IO copy for transfering inputs and outputs to predictor
|
||||
/// workspace
|
||||
/// and get some performance improvement.
|
||||
/// To use it, one should call the AnalysisConfig.SwitchUseFeedFetchOp(false)
|
||||
/// and then use the `GetInputTensor` and `GetOutputTensor`
|
||||
/// to directly write or read the input/output tensors.
|
||||
/// \return Whether the run is successful
|
||||
virtual bool ZeroCopyRun() { return false; }
|
||||
|
||||
///
|
||||
/// \brief Clear the intermediate tensors of the predictor
|
||||
///
|
||||
///
|
||||
virtual void ClearIntermediateTensor() {}
|
||||
|
||||
///
|
||||
/// \brief Release all tmp tensor to compress the size of the memory pool.
|
||||
/// The memory pool is considered to be composed of a list of chunks, if
|
||||
/// the chunk is not occupied, it can be released.
|
||||
///
|
||||
/// \return Number of bytes released. It may be smaller than the actual
|
||||
/// released memory, because part of the memory is not managed by the
|
||||
/// MemoryPool.
|
||||
///
|
||||
virtual uint64_t TryShrinkMemory() { return 0; }
|
||||
|
||||
/// \brief Clone an existing predictor
|
||||
/// When using clone, the same network will be created,
|
||||
/// and the parameters between them are shared.
|
||||
/// \return unique_ptr which contains the pointer of predictor
|
||||
virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
|
||||
|
||||
/// \brief Destroy the Predictor.
|
||||
virtual ~PaddlePredictor() = default;
|
||||
|
||||
virtual std::string GetSerializedProgram() const {
|
||||
assert(false); // Force raise error.
|
||||
return "NotImplemented";
|
||||
}
|
||||
|
||||
/// \brief Base class for NativeConfig and AnalysisConfig.
|
||||
struct Config {
|
||||
std::string model_dir; /*!< path to the model directory. */
|
||||
};
|
||||
};
|
||||
|
||||
///
|
||||
/// \brief configuration manager for `NativePredictor`.
|
||||
///
|
||||
/// `AnalysisConfig` manages configurations of `NativePredictor`.
|
||||
/// During inference procedure, there are many parameters(model/params path,
|
||||
/// place of inference, etc.)
|
||||
///
|
||||
struct PD_INFER_DECL NativeConfig : public PaddlePredictor::Config {
|
||||
NativeConfig();
|
||||
/// GPU related fields.
|
||||
bool use_xpu{false};
|
||||
bool use_gpu{false};
|
||||
int device{0};
|
||||
float fraction_of_gpu_memory{
|
||||
-1.f}; ///< Change to a float in (0,1] if needed.
|
||||
|
||||
std::string prog_file;
|
||||
std::string
|
||||
param_file; ///< Specify the exact path of program and parameter files.
|
||||
|
||||
bool specify_input_name{false}; ///< Specify the variable's name of each
|
||||
///< input if input tensors don't follow the
|
||||
///< `feeds` and `fetches` of the phase
|
||||
///< `save_inference_model`.
|
||||
|
||||
/// Set and get the number of cpu math library threads.
|
||||
void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads) {
|
||||
cpu_math_library_num_threads_ = cpu_math_library_num_threads;
|
||||
}
|
||||
int cpu_math_library_num_threads() const {
|
||||
return cpu_math_library_num_threads_;
|
||||
}
|
||||
|
||||
protected:
|
||||
int cpu_math_library_num_threads_{1}; ///< number of cpu math library (such
|
||||
///< as MKL, OpenBlas) threads for each
|
||||
///< instance.
|
||||
};
|
||||
|
||||
///
|
||||
/// \brief A factory to help create different predictors.
|
||||
///
|
||||
/// Usage:
|
||||
///
|
||||
/// \code{.cpp}
|
||||
/// NativeConfig config;
|
||||
/// ... // change the configs.
|
||||
/// auto native_predictor = CreatePaddlePredictor(config);
|
||||
/// \endcode
|
||||
///
|
||||
/// FOR EXTENSION DEVELOPER:
|
||||
/// Different predictors are designated by config type. Similar configs can be
|
||||
/// merged, but there shouldn't be a huge config containing different fields for
|
||||
/// more than one kind of predictors.
|
||||
////
|
||||
template <typename ConfigT>
|
||||
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
|
||||
|
||||
struct AnalysisConfig;
|
||||
struct NativeConfig;
|
||||
struct DemoConfig;
|
||||
|
||||
template <>
|
||||
PD_INFER_DECL std::unique_ptr<PaddlePredictor>
|
||||
CreatePaddlePredictor<AnalysisConfig>(const AnalysisConfig& config);
|
||||
|
||||
template <>
|
||||
PD_INFER_DECL std::unique_ptr<PaddlePredictor>
|
||||
CreatePaddlePredictor<NativeConfig>(const NativeConfig& config);
|
||||
|
||||
template <>
|
||||
PD_INFER_DECL std::unique_ptr<PaddlePredictor>
|
||||
CreatePaddlePredictor<DemoConfig>(const DemoConfig& config);
|
||||
|
||||
/// NOTE The following APIs are too trivial, we will discard it in the following
|
||||
/// versions.
|
||||
///
|
||||
enum class PaddleEngineKind {
|
||||
kNative = 0, ///< Use the native Fluid facility.
|
||||
kAutoMixedTensorRT, ///< Automatically mix Fluid with TensorRT.
|
||||
kAnalysis, ///< More optimization.
|
||||
};
|
||||
|
||||
template <typename ConfigT, PaddleEngineKind engine>
|
||||
PD_INFER_DECL std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(
|
||||
const ConfigT& config);
|
||||
|
||||
template <>
|
||||
PD_INFER_DECL std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
|
||||
NativeConfig, PaddleEngineKind::kNative>(const NativeConfig& config);
|
||||
|
||||
template <>
|
||||
PD_INFER_DECL std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
|
||||
AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig& config);
|
||||
|
||||
PD_INFER_DECL int PaddleDtypeSize(PaddleDType dtype);
|
||||
|
||||
PD_INFER_DECL std::string get_version();
|
||||
|
||||
PD_INFER_DECL std::string UpdateDllFlag(const char* name, const char* value);
|
||||
|
||||
PD_INFER_DECL std::shared_ptr<framework::Cipher> MakeCipher(
|
||||
const std::string& config_file);
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
|
||||
#if defined(_WIN32)
|
||||
#ifndef PD_INFER_DECL
|
||||
#ifdef PADDLE_DLL_INFERENCE
|
||||
#define PD_INFER_DECL __declspec(dllexport)
|
||||
#else
|
||||
#define PD_INFER_DECL __declspec(dllimport)
|
||||
#endif // PADDLE_DLL_INFERENCE
|
||||
#endif // PD_INFER_DECL
|
||||
#else
|
||||
#define PD_INFER_DECL __attribute__((visibility("default")))
|
||||
#endif // _WIN32
|
||||
|
|
@ -0,0 +1,311 @@
|
|||
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
/*
|
||||
* This file contains the definition of a simple Inference API for Paddle.
|
||||
*
|
||||
* ATTENTION: It requires some C++11 features, for lower version C++ or C, we
|
||||
* might release another API.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cassert>
|
||||
#include <map>
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
|
||||
#include "paddle_analysis_config.h" // NOLINT
|
||||
#include "paddle_api.h" // NOLINT
|
||||
|
||||
///
|
||||
/// \file paddle_inference_api.h
|
||||
///
|
||||
/// \brief Paddle Inference API
|
||||
///
|
||||
/// \author paddle-infer@baidu.com
|
||||
/// \date 2020-09-01
|
||||
/// \since 2.0.0-beta
|
||||
///
|
||||
|
||||
namespace paddle_infer {
|
||||
using DataType = paddle::PaddleDType;
|
||||
using PlaceType = paddle::PaddlePlace;
|
||||
using PrecisionType = paddle::AnalysisConfig::Precision;
|
||||
using Config = paddle::AnalysisConfig;
|
||||
|
||||
///
|
||||
/// \class Tensor
|
||||
///
|
||||
/// \brief Represents an n-dimensional array of values.
|
||||
/// The Tensor is used to store the input or output of the network.
|
||||
/// It is obtained through Predictor::GetinputHandle()
|
||||
/// and Predictor::GetOutputHandle() interface.
|
||||
///
|
||||
class PD_INFER_DECL Tensor {
|
||||
public:
|
||||
// Can only be created by predictor->GetInputHandle(cosnt std::string& name)
|
||||
// or predictor->GetOutputHandle(cosnt std::string& name)
|
||||
Tensor() = delete;
|
||||
explicit Tensor(std::unique_ptr<paddle::ZeroCopyTensor>&& tensor)
|
||||
: tensor_(std::move(tensor)) {}
|
||||
|
||||
///
|
||||
/// \brief Reset the shape of the tensor.
|
||||
/// Generally it's only used for the input tensor.
|
||||
/// Reshape must be called before calling mutable_data() or CopyFromCpu()
|
||||
/// \param shape The shape to set.
|
||||
///
|
||||
void Reshape(const std::vector<int>& shape);
|
||||
|
||||
///
|
||||
/// \brief Copy the host memory to tensor data.
|
||||
/// It's usually used to set the input tensor data.
|
||||
/// \param data The pointer of the data, from which the tensor will copy.
|
||||
///
|
||||
template <typename T>
|
||||
void CopyFromCpu(const T* data);
|
||||
|
||||
///
|
||||
/// \brief Get the memory pointer in CPU or GPU with specific data type.
|
||||
/// Please Reshape the tensor first before call this.
|
||||
/// It's usually used to get input data pointer.
|
||||
/// \param place The place of the tensor.
|
||||
/// \return The tensor data buffer pointer.
|
||||
///
|
||||
template <typename T>
|
||||
T* mutable_data(PlaceType place);
|
||||
|
||||
///
|
||||
/// \brief Copy the tensor data to the host memory.
|
||||
/// It's usually used to get the output tensor data.
|
||||
/// \param[out] data The tensor will copy the data to the address.
|
||||
///
|
||||
template <typename T>
|
||||
void CopyToCpu(T* data);
|
||||
|
||||
///
|
||||
/// \brief Get the memory pointer directly.
|
||||
/// It's usually used to get the output data pointer.
|
||||
/// \param[out] place To get the device type of the tensor.
|
||||
/// \param[out] size To get the data size of the tensor.
|
||||
/// \return The tensor data buffer pointer.
|
||||
///
|
||||
template <typename T>
|
||||
T* data(PlaceType* place, int* size) const;
|
||||
|
||||
///
|
||||
/// \brief Set lod info of the tensor.
|
||||
/// More about LOD can be seen here:
|
||||
/// https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/basic_concept/lod_tensor.html#lodtensor
|
||||
/// \param x the lod info.
|
||||
///
|
||||
void SetLoD(const std::vector<std::vector<size_t>>& x);
|
||||
|
||||
/// \brief Return the lod info of the tensor.
|
||||
std::vector<std::vector<size_t>> lod() const;
|
||||
|
||||
/// \brief Return the data type of the tensor.
|
||||
/// It's usually used to get the output tensor data type.
|
||||
/// \return The data type of the tensor.
|
||||
DataType type() const;
|
||||
|
||||
/// \brief Return the shape of the Tensor.
|
||||
std::vector<int> shape() const;
|
||||
|
||||
/// \brief Return the name of the tensor.
|
||||
const std::string& name() const;
|
||||
|
||||
private:
|
||||
std::unique_ptr<paddle::ZeroCopyTensor> tensor_;
|
||||
};
|
||||
|
||||
///
|
||||
/// \class Predictor
|
||||
///
|
||||
/// \brief Predictor is the interface for model prediction.
|
||||
///
|
||||
/// The predictor has the following typical uses:
|
||||
///
|
||||
/// Get predictor
|
||||
/// \code{cpp}
|
||||
/// auto predictor = CreatePredictor(config);
|
||||
/// \endcode
|
||||
///
|
||||
/// Get input or output names
|
||||
/// \code{cpp}
|
||||
/// auto input_names = predictor->GetInputNames();
|
||||
/// auto output_names = predictor->GetOutputNames();
|
||||
/// \endcode
|
||||
///
|
||||
/// Get input or output handle
|
||||
/// \code{cpp}
|
||||
/// auto input_t = predictor->GetInputHandle(input_names[0]);
|
||||
/// auto output_t = predictor->GetOutputHandle(output_names[0]);
|
||||
/// \endcode
|
||||
///
|
||||
/// Run predictor
|
||||
/// \code{cpp}
|
||||
/// predictor->Run();
|
||||
/// \endcode
|
||||
///
|
||||
class PD_INFER_DECL Predictor {
|
||||
public:
|
||||
Predictor() = delete;
|
||||
~Predictor() {}
|
||||
// Use for clone
|
||||
explicit Predictor(std::unique_ptr<paddle::PaddlePredictor>&& pred)
|
||||
: predictor_(std::move(pred)) {}
|
||||
|
||||
///
|
||||
/// \brief Construct a new Predictor object
|
||||
///
|
||||
/// \param[in] Config config
|
||||
///
|
||||
explicit Predictor(const Config& config);
|
||||
|
||||
///
|
||||
/// \brief Get the input names
|
||||
///
|
||||
/// \return input names
|
||||
///
|
||||
std::vector<std::string> GetInputNames();
|
||||
|
||||
///
|
||||
/// \brief Get the Input Tensor object
|
||||
///
|
||||
/// \param[in] name input name
|
||||
/// \return input tensor
|
||||
///
|
||||
std::unique_ptr<Tensor> GetInputHandle(const std::string& name);
|
||||
|
||||
///
|
||||
/// \brief Run the prediction engine
|
||||
///
|
||||
/// \return Whether the function executed successfully
|
||||
///
|
||||
bool Run();
|
||||
|
||||
///
|
||||
/// \brief Get the output names
|
||||
///
|
||||
/// \return output names
|
||||
///
|
||||
std::vector<std::string> GetOutputNames();
|
||||
|
||||
///
|
||||
/// \brief Get the Output Tensor object
|
||||
///
|
||||
/// \param[in] name otuput name
|
||||
/// \return output tensor
|
||||
///
|
||||
std::unique_ptr<Tensor> GetOutputHandle(const std::string& name);
|
||||
|
||||
///
|
||||
/// \brief Clone to get the new predictor. thread safe.
|
||||
///
|
||||
/// \return get a new predictor
|
||||
///
|
||||
std::unique_ptr<Predictor> Clone();
|
||||
|
||||
/// \brief Clear the intermediate tensors of the predictor
|
||||
void ClearIntermediateTensor();
|
||||
|
||||
///
|
||||
/// \brief Release all tmp tensor to compress the size of the memory pool.
|
||||
/// The memory pool is considered to be composed of a list of chunks, if
|
||||
/// the chunk is not occupied, it can be released.
|
||||
///
|
||||
/// \return Number of bytes released. It may be smaller than the actual
|
||||
/// released memory, because part of the memory is not managed by the
|
||||
/// MemoryPool.
|
||||
///
|
||||
uint64_t TryShrinkMemory();
|
||||
|
||||
private:
|
||||
std::unique_ptr<paddle::PaddlePredictor> predictor_;
|
||||
};
|
||||
|
||||
///
|
||||
/// \brief A factory to help create predictors.
|
||||
///
|
||||
/// Usage:
|
||||
///
|
||||
/// \code{.cpp}
|
||||
/// Config config;
|
||||
/// ... // change the configs.
|
||||
/// auto predictor = CreatePredictor(config);
|
||||
/// \endcode
|
||||
///
|
||||
PD_INFER_DECL std::shared_ptr<Predictor> CreatePredictor(
|
||||
const Config& config); // NOLINT
|
||||
|
||||
PD_INFER_DECL int GetNumBytesOfDataType(DataType dtype);
|
||||
|
||||
PD_INFER_DECL std::string GetVersion();
|
||||
PD_INFER_DECL std::string UpdateDllFlag(const char* name, const char* value);
|
||||
|
||||
template <typename T>
|
||||
void Tensor::CopyFromCpu(const T* data) {
|
||||
tensor_->copy_from_cpu<T>(data);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void Tensor::CopyToCpu(T* data) {
|
||||
return tensor_->copy_to_cpu<T>(data);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
T* Tensor::mutable_data(PlaceType place) {
|
||||
return tensor_->mutable_data<T>(place);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
T* Tensor::data(PlaceType* place, int* size) const {
|
||||
return tensor_->data<T>(place, size);
|
||||
}
|
||||
|
||||
} // namespace paddle_infer
|
||||
|
||||
namespace paddle_infer {
|
||||
namespace services {
|
||||
|
||||
///
|
||||
/// \class PredictorPool
|
||||
///
|
||||
/// \brief PredictorPool is a simple encapsulation of Predictor, suitable for
|
||||
/// use in multi-threaded situations. According to the thread id, the
|
||||
/// corresponding Predictor is taken out from PredictorPool to complete the
|
||||
/// prediction.
|
||||
///
|
||||
class PD_INFER_DECL PredictorPool {
|
||||
public:
|
||||
PredictorPool() = delete;
|
||||
PredictorPool(const PredictorPool&) = delete;
|
||||
PredictorPool& operator=(const PredictorPool&) = delete;
|
||||
|
||||
/// \brief Construct the predictor pool with \param size predictor instances.
|
||||
explicit PredictorPool(const Config& config, size_t size = 1);
|
||||
|
||||
/// \brief Get \param id-th predictor.
|
||||
Predictor* Retrive(size_t idx);
|
||||
|
||||
private:
|
||||
std::shared_ptr<Predictor> main_pred_;
|
||||
std::vector<std::unique_ptr<Predictor>> preds_;
|
||||
};
|
||||
} // namespace services
|
||||
} // namespace paddle_infer
|
||||
|
|
@ -0,0 +1,199 @@
|
|||
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
///
|
||||
/// \file paddle_mkldnn_quantizer_config.h
|
||||
///
|
||||
/// \brief Mkldnn quantizer config.
|
||||
///
|
||||
/// \author paddle-infer@baidu.com
|
||||
/// \date 2020-01-01
|
||||
/// \since 1.7.0
|
||||
///
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cassert>
|
||||
#include <map>
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <unordered_set>
|
||||
#include <vector>
|
||||
|
||||
#include "paddle_api.h" // NOLINT
|
||||
#include "paddle_infer_declare.h" // NOLINT
|
||||
|
||||
namespace paddle {
|
||||
|
||||
///
|
||||
/// \brief Algorithms for finding scale of quantized Tensors.
|
||||
///
|
||||
enum class ScaleAlgo {
|
||||
NONE, ///< Do not compute scale
|
||||
MAX, ///< Find scale based on the max absolute value
|
||||
MAX_CH, ///< Find scale based on the max absolute value per output channel
|
||||
MAX_CH_T, ///< Find scale based on the max absolute value per output channel
|
||||
///< of a transposed tensor
|
||||
KL, ///< Find scale based on KL Divergence
|
||||
};
|
||||
|
||||
///
|
||||
/// \class MkldnnQuantizerConfig
|
||||
///
|
||||
/// \brief Config for mkldnn quantize.
|
||||
///
|
||||
/// The MkldnnQuantizerConfig is used to configure Mkldnn's quantization
|
||||
/// parameters, including scale algorithm, warmup data, warmup batch size,
|
||||
/// quantized op list, etc.
|
||||
///
|
||||
/// It is not recommended to use this config directly, please refer to
|
||||
/// AnalysisConfig::mkldnn_quantizer_config()
|
||||
///
|
||||
struct PD_INFER_DECL MkldnnQuantizerConfig {
|
||||
///
|
||||
/// \brief Construct a new Mkldnn Quantizer Config object
|
||||
///
|
||||
MkldnnQuantizerConfig();
|
||||
|
||||
///
|
||||
/// \brief Set the scale algo
|
||||
///
|
||||
/// Specify a quantization algorithm for a connection (input/output) of the
|
||||
/// operator type.
|
||||
/// \param[in] op_type_name the operator's name.
|
||||
/// \param[in] conn_name name of the connection (input/output) of the
|
||||
/// operator.
|
||||
/// \param[in] algo the algorithm for computing scale.
|
||||
///
|
||||
void SetScaleAlgo(std::string op_type_name, std::string conn_name,
|
||||
ScaleAlgo algo) {
|
||||
rules_[op_type_name][conn_name] = algo;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Get the scale algo
|
||||
///
|
||||
/// Get the quantization algorithm for a connection (input/output) of the
|
||||
/// operator type.
|
||||
///
|
||||
/// \param[in] op_type_name the operator's name.
|
||||
/// \param[in] conn_name name of the connection (input/output) of the
|
||||
/// operator.
|
||||
/// \return the scale algo.
|
||||
///
|
||||
ScaleAlgo scale_algo(const std::string& op_type_name,
|
||||
const std::string& conn_name) const;
|
||||
|
||||
///
|
||||
/// \brief Set the warmup data
|
||||
///
|
||||
/// Set the batch of data to be used for warm-up iteration.
|
||||
///
|
||||
/// \param[in] data batch of data.
|
||||
///
|
||||
void SetWarmupData(std::shared_ptr<std::vector<PaddleTensor>> data) {
|
||||
warmup_data_ = data;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Get the warmup data
|
||||
///
|
||||
/// Get the batch of data used for warm-up iteration.
|
||||
///
|
||||
/// \return the warm up data
|
||||
///
|
||||
std::shared_ptr<std::vector<PaddleTensor>> warmup_data() const {
|
||||
return warmup_data_;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Set the warmup batch size
|
||||
///
|
||||
/// Set the batch size for warm-up iteration.
|
||||
///
|
||||
/// \param[in] batch_size warm-up batch size
|
||||
///
|
||||
void SetWarmupBatchSize(int batch_size) { warmup_bs_ = batch_size; }
|
||||
|
||||
///
|
||||
/// \brief Get the warmup batch size
|
||||
///
|
||||
/// Get the batch size for warm-up iteration.
|
||||
///
|
||||
/// \return the warm up batch size
|
||||
int warmup_batch_size() const { return warmup_bs_; }
|
||||
|
||||
///
|
||||
/// \brief Set quantized op list
|
||||
///
|
||||
/// In the quantization process, set the op list that supports quantization
|
||||
///
|
||||
/// \param[in] op_list List of quantized ops
|
||||
///
|
||||
void SetEnabledOpTypes(std::unordered_set<std::string> op_list) {
|
||||
enabled_op_types_ = op_list;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Get quantized op list
|
||||
///
|
||||
/// \return list of quantized ops
|
||||
///
|
||||
const std::unordered_set<std::string>& enabled_op_types() const {
|
||||
return enabled_op_types_;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Set the excluded op ids
|
||||
///
|
||||
/// \param[in] op_ids_list excluded op ids
|
||||
///
|
||||
void SetExcludedOpIds(std::unordered_set<int> op_ids_list) {
|
||||
excluded_op_ids_ = op_ids_list;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Get the excluded op ids
|
||||
///
|
||||
/// \return exclude op ids
|
||||
///
|
||||
const std::unordered_set<int>& excluded_op_ids() const {
|
||||
return excluded_op_ids_;
|
||||
}
|
||||
|
||||
///
|
||||
/// \brief Set default scale algorithm
|
||||
///
|
||||
/// \param[in] algo Method for calculating scale in quantization process
|
||||
///
|
||||
void SetDefaultScaleAlgo(ScaleAlgo algo) { default_scale_algo_ = algo; }
|
||||
|
||||
///
|
||||
/// \brief Get default scale algorithm
|
||||
///
|
||||
/// \return Method for calculating scale in quantization
|
||||
/// process
|
||||
///
|
||||
ScaleAlgo default_scale_algo() const { return default_scale_algo_; }
|
||||
|
||||
protected:
|
||||
std::map<std::string, std::map<std::string, ScaleAlgo>> rules_;
|
||||
std::unordered_set<std::string> enabled_op_types_;
|
||||
std::unordered_set<int> excluded_op_ids_;
|
||||
std::shared_ptr<std::vector<PaddleTensor>> warmup_data_;
|
||||
int warmup_bs_{1};
|
||||
ScaleAlgo default_scale_algo_{ScaleAlgo::MAX};
|
||||
};
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -0,0 +1,248 @@
|
|||
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "paddle_infer_declare.h" // NOLINT
|
||||
|
||||
///
|
||||
/// \file paddle_pass_builder.h
|
||||
///
|
||||
/// \brief Class Paddle Passs Builder and its subclasses(pass strategies).
|
||||
/// \section sec_intro Introduction
|
||||
/// This class aims to build passes for paddle and define passes' strategies.
|
||||
///
|
||||
/// \author paddle-infer@baidu.com
|
||||
/// \date 2020-3-23
|
||||
/// \since 1.7
|
||||
|
||||
/// \namespace paddle
|
||||
namespace paddle {
|
||||
|
||||
/// \class PaddlePassBuilder
|
||||
/// \brief This class build passes based on vector<string> input. It is part of
|
||||
/// inference API. Users can build passes, insert new passes, delete passes
|
||||
/// using this class and its functions.
|
||||
///
|
||||
/// Example Usage:
|
||||
/// Build a new pass.
|
||||
/// \code{cpp}
|
||||
/// const vector<string> passes(1, "conv_relu_mkldnn_fuse_pass");
|
||||
/// PaddlePassBuilder builder(passes);
|
||||
/// \endcode
|
||||
class PD_INFER_DECL PaddlePassBuilder {
|
||||
public:
|
||||
/// \brief Constructor of the class. It stores the input passes.
|
||||
/// \param[in] passes passes' types.
|
||||
explicit PaddlePassBuilder(const std::vector<std::string> &passes)
|
||||
: passes_(passes) {}
|
||||
|
||||
/// \brief Stores the input passes.
|
||||
/// \param[in] passes passes' types.
|
||||
void SetPasses(std::initializer_list<std::string> passes) {
|
||||
passes_ = passes;
|
||||
}
|
||||
|
||||
/// \brief Append a pass to the end of the passes.
|
||||
/// \param[in] pass_type the type of the new pass.
|
||||
void AppendPass(const std::string &pass_type);
|
||||
|
||||
/// \brief Insert a pass to a specific position.
|
||||
/// \param[in] idx the position to insert.
|
||||
/// \param[in] pass_type the type of insert pass.
|
||||
void InsertPass(size_t idx, const std::string &pass_type);
|
||||
|
||||
/// \brief Delete the pass at certain position 'idx'.
|
||||
/// \param[in] idx the position to delete.
|
||||
void DeletePass(size_t idx);
|
||||
|
||||
/// \brief Delete all passes that has a certain type 'pass_type'.
|
||||
/// \param[in] pass_type the certain pass type to be deleted.
|
||||
void DeletePass(const std::string &pass_type);
|
||||
|
||||
/// \brief Delete all the passes.
|
||||
void ClearPasses();
|
||||
|
||||
/// \brief Append an analysis pass.
|
||||
/// \param[in] pass the type of the new analysis pass.
|
||||
void AppendAnalysisPass(const std::string &pass);
|
||||
|
||||
/// \brief Visualize the computation graph after each pass by generating a DOT
|
||||
/// language file, one can draw them with the Graphviz toolkit.
|
||||
void TurnOnDebug();
|
||||
/// \brief Human-readable information of the passes.
|
||||
std::string DebugString();
|
||||
|
||||
/// \brief Get information of passes.
|
||||
/// \return Return list of the passes.
|
||||
const std::vector<std::string> &AllPasses() const { return passes_; }
|
||||
|
||||
/// \brief Get information of analysis passes.
|
||||
/// \return Return list of analysis passes.
|
||||
std::vector<std::string> AnalysisPasses() const {
|
||||
auto passes = analysis_passes_;
|
||||
// To make sure the ir_graph_to_program should be the last pass so any
|
||||
// modication of IR will persist to the program.
|
||||
passes.push_back("ir_graph_to_program_pass");
|
||||
return passes;
|
||||
}
|
||||
|
||||
protected:
|
||||
/// \cond Protected
|
||||
std::vector<std::string> analysis_passes_{
|
||||
{"ir_graph_build_pass", "ir_graph_clean_pass", "ir_analysis_pass",
|
||||
"ir_params_sync_among_devices_pass", "adjust_cudnn_workspace_size_pass",
|
||||
"inference_op_replace_pass"}};
|
||||
std::vector<std::string> passes_;
|
||||
/// \endcond
|
||||
};
|
||||
|
||||
/// \class PassStrategy
|
||||
/// \brief This class defines the pass strategies like whether to use gpu/cuDNN
|
||||
/// kernel/MKLDNN.
|
||||
class PD_INFER_DECL PassStrategy : public PaddlePassBuilder {
|
||||
public:
|
||||
/// \brief Constructor of PassStrategy class. It works the same as
|
||||
/// PaddlePassBuilder class. \param[in] passes passes' types.
|
||||
explicit PassStrategy(const std::vector<std::string> &passes)
|
||||
: PaddlePassBuilder(passes) {}
|
||||
|
||||
/// \brief Enable the use of cuDNN kernel.
|
||||
virtual void EnableCUDNN() {}
|
||||
|
||||
/// \brief Enable the use of MKLDNN.
|
||||
/// The MKLDNN control exists in both CPU and GPU mode, because there can
|
||||
/// still be some CPU kernels running in GPU mode.
|
||||
virtual void EnableMKLDNN() {}
|
||||
|
||||
/// \brief Enable MKLDNN quantize optimization.
|
||||
virtual void EnableMkldnnQuantizer() {}
|
||||
|
||||
/// \brief Enable MKLDNN bfloat16.
|
||||
virtual void EnableMkldnnBfloat16() {}
|
||||
|
||||
/// \brief Check if we are using gpu.
|
||||
/// \return A bool variable implying whether we are in gpu mode.
|
||||
bool use_gpu() const { return use_gpu_; }
|
||||
|
||||
/// \brief Check if we are using xpu.
|
||||
/// \return A bool variable implying whether we are in xpu mode.
|
||||
bool use_xpu() const { return use_xpu_; }
|
||||
|
||||
/// \brief Default destructor.
|
||||
virtual ~PassStrategy() = default;
|
||||
|
||||
protected:
|
||||
/// \cond Protected
|
||||
bool use_xpu_{false};
|
||||
bool use_gpu_{false};
|
||||
bool use_mkldnn_{false};
|
||||
/// \endcond
|
||||
};
|
||||
|
||||
/// \class CpuPassStrategy
|
||||
/// \brief The CPU passes controller, it is used in AnalysisPredictor with CPU
|
||||
/// mode.
|
||||
class PD_INFER_DECL CpuPassStrategy : public PassStrategy {
|
||||
public:
|
||||
/// \brief Default constructor of CpuPassStrategy.
|
||||
CpuPassStrategy();
|
||||
|
||||
/// \brief Construct by copying another CpuPassStrategy object.
|
||||
/// \param[in] other The CpuPassStrategy object we want to copy.
|
||||
explicit CpuPassStrategy(const CpuPassStrategy &other)
|
||||
: PassStrategy(other.AllPasses()) {
|
||||
use_gpu_ = other.use_gpu_;
|
||||
use_mkldnn_ = other.use_mkldnn_;
|
||||
use_mkldnn_quantizer_ = other.use_mkldnn_quantizer_;
|
||||
use_mkldnn_bfloat16_ = other.use_mkldnn_bfloat16_;
|
||||
}
|
||||
/// \brief Default destructor.
|
||||
virtual ~CpuPassStrategy() = default;
|
||||
|
||||
/// \brief Enable the use of cuDNN kernel.
|
||||
void EnableCUDNN() override;
|
||||
|
||||
/// \brief Enable the use of MKLDNN.
|
||||
void EnableMKLDNN() override;
|
||||
|
||||
/// \brief Enable MKLDNN quantize optimization.
|
||||
void EnableMkldnnQuantizer() override;
|
||||
|
||||
/// \brief Enable MKLDNN bfloat16.
|
||||
void EnableMkldnnBfloat16() override;
|
||||
|
||||
protected:
|
||||
/// \cond Protected
|
||||
bool use_mkldnn_quantizer_{false};
|
||||
bool use_mkldnn_bfloat16_{false};
|
||||
/// \endcond
|
||||
};
|
||||
|
||||
/// \class GpuPassStrategy
|
||||
/// \brief The GPU passes controller, it is used in AnalysisPredictor with GPU
|
||||
/// mode.
|
||||
class PD_INFER_DECL GpuPassStrategy : public PassStrategy {
|
||||
public:
|
||||
/// \brief Default constructor of GpuPassStrategy.
|
||||
GpuPassStrategy();
|
||||
|
||||
/// \brief Construct by copying another GpuPassStrategy object.
|
||||
/// \param[in] other The GpuPassStrategy object we want to copy.
|
||||
explicit GpuPassStrategy(const GpuPassStrategy &other)
|
||||
: PassStrategy(other.AllPasses()) {
|
||||
use_gpu_ = true;
|
||||
use_cudnn_ = other.use_cudnn_;
|
||||
}
|
||||
|
||||
/// \brief Enable the use of cuDNN kernel.
|
||||
void EnableCUDNN() override;
|
||||
|
||||
/// \brief Not supported in GPU mode yet.
|
||||
void EnableMKLDNN() override;
|
||||
|
||||
/// \brief Not supported in GPU mode yet.
|
||||
void EnableMkldnnQuantizer() override;
|
||||
|
||||
/// \brief Not supported in GPU mode yet.
|
||||
void EnableMkldnnBfloat16() override;
|
||||
|
||||
/// \brief Default destructor.
|
||||
virtual ~GpuPassStrategy() = default;
|
||||
|
||||
protected:
|
||||
/// \cond Protected
|
||||
bool use_cudnn_{false};
|
||||
/// \endcond
|
||||
};
|
||||
|
||||
/// \class XpuPassStrategy
|
||||
/// \brief The XPU passes controller, it is used in AnalysisPredictor with XPU
|
||||
/// mode.
|
||||
class PD_INFER_DECL XpuPassStrategy final : public PassStrategy {
|
||||
public:
|
||||
XpuPassStrategy() : PassStrategy({}) {}
|
||||
};
|
||||
|
||||
/// \brief List of tensorRT subgraph passes.
|
||||
PD_INFER_DECL extern const std::vector<std::string> kTRTSubgraphPasses;
|
||||
|
||||
/// \brief List of lite subgraph passes.
|
||||
PD_INFER_DECL extern const std::vector<std::string> kLiteSubgraphPasses;
|
||||
|
||||
} // namespace paddle
|
||||
|
|
@ -377,7 +377,7 @@ namespace kdkocr_infer {
|
|||
|
||||
boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
|
||||
auto postprocess_end = std::chrono::steady_clock::now();
|
||||
std::cout << "Detected boxes num: " << boxes.size() << endl;
|
||||
//std::cout << "Detected boxes num: " << boxes.size() << endl;
|
||||
|
||||
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
|
||||
times->push_back(double(preprocess_diff.count() * 1000));
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
aux_source_directory(. SOURCESCODE)
|
||||
include_directories(.)
|
||||
add_library(kypackage SHARED ${SOURCESCODE})
|
||||
set_target_properties(kypackage PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
add_executable(kypackage-test test/kypackage-test.c)
|
||||
target_link_libraries(kypackage-test kypackage)
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ project(kypowermanagement LANGUAGES CXX)
|
|||
set(POWERMANAGEMENT_TOP_DIR ${CMAKE_CURRENT_LIST_DIR})
|
||||
|
||||
add_library(${PROJECT_NAME} SHARED)
|
||||
set_target_properties(kypowermanagement PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
|
||||
target_compile_options(${PROJECT_NAME} PRIVATE -Wall -g)
|
||||
target_compile_features(${PROJECT_NAME} PRIVATE cxx_std_11)
|
||||
|
|
|
|||
|
|
@ -129,7 +129,7 @@ bool LockScreen::unInhibitLockScreen(uint32_t flag)
|
|||
DBusPendingCall *sendMsgPending = NULL;
|
||||
|
||||
sendMsg = dbus_message_new_method_call(dbusServiceName , dbusObjectPath , dbusInterfaceName , dbusUnInhibitLockScreenMethod);
|
||||
if (!dbus_message_append_args(sendMsg , DBUS_TYPE_UINT32 , &flag)) {
|
||||
if (!dbus_message_append_args(sendMsg , DBUS_TYPE_UINT32 , &flag , DBUS_TYPE_INVALID)) {
|
||||
klog_err("kdk : d-bus append args fail !\n");
|
||||
return false;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
aux_source_directory(. SOURCESCODE)
|
||||
include_directories(.)
|
||||
add_library(kyrtinfo SHARED ${SOURCESCODE})
|
||||
set_target_properties(kyrtinfo PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
add_executable(kyrtinfo-test test/kyrtinfo-test.c)
|
||||
target_link_libraries(kyrtinfo-test kyrtinfo)
|
||||
|
||||
|
|
|
|||
|
|
@ -4,8 +4,9 @@ find_library(GLIBC_LIB glib-2.0)
|
|||
find_library(DBUS_LIB dbus-1)
|
||||
find_library(DBUS_GLIB_LIB dbus-glib-1)
|
||||
add_library(kysysinfo SHARED ${SOURCESCODE})
|
||||
set_target_properties(kysysinfo PROPERTIES VERSION 1.2.0 SOVERSION 1)
|
||||
add_executable(kysysinfo-test test/kysysinfo_test.c)
|
||||
target_link_libraries(kysysinfo kylog systemd kyconf ${GLIBC_LIB} ${DBUS_LIB} ${DBUS_GLIB_LIB})
|
||||
target_link_libraries(kysysinfo dl kylog systemd kyconf ${GLIBC_LIB} ${DBUS_LIB} ${DBUS_GLIB_LIB})
|
||||
target_link_libraries(kysysinfo-test kysysinfo)
|
||||
# target_link_libraries(kysysinfo-test kysysinfo kylin-activation kylog systemd kyconf ${GLIBC_LIB} ${DBUS_LIB} ${DBUS_GLIB_LIB})
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,9 @@
|
|||
#include <kysdk/kysdk-base/libkylog.h>
|
||||
#include <pwd.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <unistd.h>
|
||||
#include <dlfcn.h>
|
||||
|
||||
#define KYLIN_ACTIVATION_DBUS_ADDRESS "org.freedesktop.activation"
|
||||
|
||||
|
|
@ -77,7 +79,10 @@ char* kdk_system_get_version(bool verbose)
|
|||
|
||||
sysversion = get_val_from_file(fp, "milestone");
|
||||
if (!sysversion)
|
||||
{
|
||||
fclose(fp);
|
||||
return NULL;
|
||||
}
|
||||
fclose(fp);
|
||||
}
|
||||
else
|
||||
|
|
@ -88,7 +93,10 @@ char* kdk_system_get_version(bool verbose)
|
|||
|
||||
sysversion = get_val_from_file(fp, "VERSION");
|
||||
if (!sysversion)
|
||||
{
|
||||
fclose(fp);
|
||||
return NULL;
|
||||
}
|
||||
strstrip(sysversion, '\"');
|
||||
fclose(fp);
|
||||
}
|
||||
|
|
@ -107,33 +115,63 @@ int kdk_system_get_activationStatus(int *status_error_num,int *date_error_num)
|
|||
}
|
||||
|
||||
int res = 0;
|
||||
|
||||
// #define _KYLIN_ACTIVATION_H_
|
||||
#ifdef __linux__
|
||||
#ifdef _KYLIN_ACTIVATION_H_
|
||||
int err;
|
||||
res = kylin_activation_activate_status(&err);
|
||||
if (err != NO_ERROR)
|
||||
int ret = -1;
|
||||
|
||||
typedef int (*kylin_activation_activate_status)(int *);
|
||||
typedef int (*kylin_activation_trial_status)(int *);
|
||||
void *hwnd = dlopen("/usr/lib/libkylin-activation.so", RTLD_LAZY);
|
||||
|
||||
if (!hwnd)
|
||||
{
|
||||
klog_err("激活状态获取失败：%d\n", err);
|
||||
return 0;
|
||||
}
|
||||
if (res)
|
||||
{
|
||||
return 1;
|
||||
klog_err("加载libkylin-activation.so失败\n");
|
||||
return ret;
|
||||
}
|
||||
|
||||
res = kylin_activation_trial_status(&err);
|
||||
if (err != NO_ERROR)
|
||||
do
|
||||
{
|
||||
klog_err("试用状态获取失败：%d\n", err);
|
||||
return 0;
|
||||
}
|
||||
kylin_activation_activate_status pkylin_activation_activate_status = (kylin_activation_activate_status)dlsym(hwnd, "kylin_activation_activate_status");
|
||||
kylin_activation_trial_status pkylin_activation_trial_status = (kylin_activation_trial_status)dlsym(hwnd, "kylin_activation_trial_status");
|
||||
|
||||
if (res == 1)
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
if (!pkylin_activation_activate_status || !pkylin_activation_trial_status)
|
||||
{
|
||||
klog_err("获取接口地址失败\n");
|
||||
break;
|
||||
}
|
||||
res = pkylin_activation_activate_status(&err);
|
||||
if (err != 0)
|
||||
{
|
||||
klog_err("激活状态获取失败：%d\n", err);
|
||||
ret = 0;
|
||||
break;
|
||||
}
|
||||
if (res)
|
||||
{
|
||||
ret = 1;
|
||||
break;
|
||||
}
|
||||
|
||||
return -1;
|
||||
res = pkylin_activation_trial_status(&err);
|
||||
if (err != 0)
|
||||
{
|
||||
klog_err("试用状态获取失败：%d\n", err);
|
||||
ret = 0;
|
||||
break;
|
||||
}
|
||||
|
||||
if (res == 1)
|
||||
{
|
||||
ret = 0;
|
||||
break;
|
||||
}
|
||||
} while (false);
|
||||
|
||||
dlclose(hwnd);
|
||||
return ret;
|
||||
#else // 修改dbus通信
|
||||
DBusConnection *conn;
|
||||
DBusError err;
|
||||
|
|
@ -166,6 +204,12 @@ int kdk_system_get_activationStatus(int *status_error_num,int *date_error_num)
|
|||
"org.freedesktop.activation.interface", // interface to call on
|
||||
"status"); // method name
|
||||
|
||||
if (!status_msg)
|
||||
{ // -1 is default timeout
|
||||
klog_err("status_msg：dbus_message_new_method_call调用失败\n");
|
||||
return -1;
|
||||
}
|
||||
|
||||
if (!dbus_connection_send_with_reply (conn, status_msg, &status_pending, -1)) { // -1 is default timeout
|
||||
klog_err("status_msg：dbus_connection_send_with_reply调用失败\n");
|
||||
return -1;
|
||||
|
|
@ -269,8 +313,8 @@ int kdk_system_get_activationStatus(int *status_error_num,int *date_error_num)
|
|||
return 1;
|
||||
}
|
||||
|
||||
#endif // _KYLIN_ACTIVATION_H_
|
||||
#endif // __linux__
|
||||
// #endif // _KYLIN_ACTIVATION_H_
|
||||
// #endif // __linux__
|
||||
if (status_msg)
|
||||
{
|
||||
dbus_message_unref(status_msg);
|
||||
|
|
@ -296,6 +340,8 @@ int kdk_system_get_activationStatus(int *status_error_num,int *date_error_num)
|
|||
dbus_pending_call_unref(date_pending);
|
||||
}
|
||||
return 0;
|
||||
#endif // _KYLIN_ACTIVATION_H_
|
||||
#endif // __linux__
|
||||
}
|
||||
|
||||
char* kdk_system_get_serialNumber()
|
||||
|
|
@ -313,7 +359,22 @@ char* kdk_system_get_serialNumber()
|
|||
strskipspace(serial);
|
||||
#else
|
||||
int err;
|
||||
serial = kylin_activation_get_serial_number(&err);
|
||||
|
||||
typedef char *(*kylin_activation_get_serial_number)(int *);
|
||||
void *hwnd = dlopen("/usr/lib/libkylin-activation.so", RTLD_LAZY);
|
||||
|
||||
if (!hwnd)
|
||||
{
|
||||
klog_err("加载libkylin-activation.so失败\n");
|
||||
return serial;
|
||||
}
|
||||
kylin_activation_get_serial_number pkylin_activation_get_serial_number = (kylin_activation_get_serial_number)dlsym(hwnd,"kylin_activation_get_serial_number");
|
||||
if(!pkylin_activation_get_serial_number)
|
||||
{
|
||||
klog_err("加载接口kylin_activation_get_serial_number失败\n");
|
||||
return serial;
|
||||
}
|
||||
serial = pkylin_activation_get_serial_number(&err);
|
||||
if (!serial)
|
||||
{
|
||||
klog_err("序列号获取失败：%d\n", err);
|
||||
|
|
@ -322,6 +383,7 @@ char* kdk_system_get_serialNumber()
|
|||
{
|
||||
strskipspace(serial);
|
||||
}
|
||||
dlclose(hwnd);
|
||||
return serial;
|
||||
#endif // _KYLIN_ACTIVATION_H_
|
||||
#endif // __linux__
|
||||
|
|
@ -389,6 +451,59 @@ char* kdk_system_get_projectName()
|
|||
return project_codename;
|
||||
}
|
||||
|
||||
char* kdk_system_get_projectSubName()
|
||||
{
|
||||
char *project_subcodename = NULL;
|
||||
#ifdef __linux__
|
||||
FILE *fp = fopen("/etc/lsb-release", "rt");
|
||||
if (fp)
|
||||
{
|
||||
project_subcodename = get_val_from_file(fp, "SUB_PROJECT_CODENAME");
|
||||
fclose(fp);
|
||||
}
|
||||
|
||||
if (!project_subcodename)
|
||||
{
|
||||
fp = fopen("/etc/os-release", "rt");
|
||||
ASSERT_NOT_NULL(fp, NULL);
|
||||
project_subcodename = get_val_from_file(fp, "SUB_PROJECT_CODENAME");
|
||||
fclose(fp);
|
||||
}
|
||||
if (project_subcodename)
|
||||
strstripspace(project_subcodename);
|
||||
#endif
|
||||
return project_subcodename;
|
||||
}
|
||||
|
||||
unsigned int kdk_system_get_productFeatures()
|
||||
{
|
||||
char *product_features = NULL;
|
||||
unsigned int res = 0;
|
||||
#ifdef __linux__
|
||||
FILE *fp = fopen("/etc/lsb-release", "rt");
|
||||
if (fp)
|
||||
{
|
||||
product_features = get_val_from_file(fp, "PRODUCT_FEATURES");
|
||||
fclose(fp);
|
||||
}
|
||||
|
||||
if (!product_features)
|
||||
{
|
||||
fp = fopen("/etc/os-release", "rt");
|
||||
ASSERT_NOT_NULL(fp, 0);
|
||||
product_features = get_val_from_file(fp, "PRODUCT_FEATURES");
|
||||
fclose(fp);
|
||||
}
|
||||
if (product_features)
|
||||
{
|
||||
strstripspace(product_features);
|
||||
res = atoi(product_features);
|
||||
}
|
||||
#endif
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
char* kdk_system_get_hostVirtType()
|
||||
{
|
||||
char *virtType = (char*)malloc(sizeof(char) * 65);
|
||||
|
|
|
|||
|
|
@ -82,6 +82,24 @@ extern char* kdk_system_get_eUser();
|
|||
*/
|
||||
extern char* kdk_system_get_projectName();
|
||||
|
||||
/**
|
||||
* @brief 获取操作系统项目子编号名
|
||||
*
|
||||
* @return char* 成功返回字符串，失败返回NULL。返回的字符串需要被 free 释放
|
||||
*/
|
||||
extern char* kdk_system_get_projectSubName();
|
||||
|
||||
/**
|
||||
* @brief 获取操作系统产品标识码
|
||||
*
|
||||
* @return unsigned int 返回标志码
|
||||
* 0000 信息异常
|
||||
* 0001 仅PC特性
|
||||
* 0010 仅平板特性
|
||||
* 0011 支持平板与PC特性
|
||||
*/
|
||||
extern unsigned int kdk_system_get_productFeatures();
|
||||
|
||||
/**
|
||||
* @brief 获取操作系统宿主机的虚拟机类型
|
||||
*
|
||||
|
|
|
|||
|
|
@ -13,15 +13,17 @@
|
|||
#include <semaphore.h>
|
||||
#include <sys/timerfd.h>
|
||||
#include <errno.h>
|
||||
// 20020721新增时区变化监听
|
||||
#include <sys/inotify.h>
|
||||
|
||||
pthread_mutex_t lock;
|
||||
u_int8_t g_Flag;
|
||||
u_int8_t g_Flag; // 控制启用常驻定时器还是临时定时器
|
||||
|
||||
u_int8_t g_Quit;
|
||||
u_int8_t g_Quit; //退出信号
|
||||
sem_t g_Wait;
|
||||
|
||||
u_int8_t g_TimeChanged;
|
||||
u_int8_t g_TimeSync;
|
||||
u_int8_t g_TimeChanged; // 发生了时间变更
|
||||
u_int8_t g_TimeSync; // 需要进行对时
|
||||
|
||||
void sig_Handler(int sig)
|
||||
{
|
||||
|
|
@ -39,15 +41,16 @@ static void *printClock(void *ptr)
|
|||
time_t current;
|
||||
time(¤t);
|
||||
now = localtime(¤t);
|
||||
|
||||
// printf("%04d/%02d/%02d %02d:%02d:%02d\n", now->tm_year + 1900, now->tm_mon, now->tm_mday, now->tm_hour, now->tm_min, now->tm_sec);
|
||||
// printf("g_TimeChanged is %d\n", g_TimeChanged);
|
||||
// printf("%d",now->tm_sec);
|
||||
// 如果时间发生改变发送TimeVhangeSignal信号
|
||||
// struct timeval tx;
|
||||
// struct timezone tz;
|
||||
// gettimeofday(&tx,&tz);
|
||||
// zone = tz.tz_minuteswest/60;
|
||||
// printf("时差：%d\n",zone);
|
||||
// 如果时间发生改变发送TimeChangeSignal信号
|
||||
if (g_TimeChanged == 1)
|
||||
{
|
||||
char *buf = calloc(1, 128);
|
||||
sprintf(buf, "%04d/%02d/%02d %02d:%02d:%02d", now->tm_year + 1900, now->tm_mon, now->tm_mday, now->tm_hour, now->tm_min, now->tm_sec);
|
||||
sprintf(buf, "%04d/%02d/%02d %02d:%02d:%02d", now->tm_year + 1900, now->tm_mon+1, now->tm_mday, now->tm_hour, now->tm_min, now->tm_sec);
|
||||
printf("%s\n", buf);
|
||||
|
||||
msg = dbus_message_new_signal("/com/kylin/kysdk/Timer",
|
||||
|
|
@ -63,9 +66,10 @@ static void *printClock(void *ptr)
|
|||
dbus_connection_send(conn, msg, &serial);
|
||||
dbus_connection_flush(conn);
|
||||
dbus_message_unref(msg);
|
||||
free(buf);
|
||||
}
|
||||
// 非整点情况
|
||||
|
||||
// 非整点情况
|
||||
if (now->tm_sec != 0)
|
||||
{
|
||||
pthread_mutex_lock(&lock);
|
||||
|
|
@ -85,7 +89,7 @@ static void *printClock(void *ptr)
|
|||
else
|
||||
{
|
||||
char *buf = calloc(1, 128);
|
||||
sprintf(buf, "%04d/%02d/%02d %02d:%02d:%02d", now->tm_year + 1900, now->tm_mon, now->tm_mday, now->tm_hour, now->tm_min, now->tm_sec);
|
||||
sprintf(buf, "%04d/%02d/%02d %02d:%02d:%02d", now->tm_year + 1900, now->tm_mon+1, now->tm_mday, now->tm_hour, now->tm_min, now->tm_sec);
|
||||
// printf("%s\n", buf);
|
||||
|
||||
msg = dbus_message_new_signal("/com/kylin/kysdk/Timer",
|
||||
|
|
@ -108,6 +112,7 @@ static void *printClock(void *ptr)
|
|||
pthread_mutex_unlock(&lock);
|
||||
sem_post(&g_Wait);
|
||||
}
|
||||
free(buf);
|
||||
}
|
||||
|
||||
return NULL;
|
||||
|
|
@ -117,26 +122,29 @@ static void *printClock(void *ptr)
|
|||
void *startBroadcastSystemTimePerMin(void *tmp)
|
||||
{
|
||||
DBusConnection *conn = tmp;
|
||||
size_t timerID = -1;
|
||||
size_t periodicTimerID = 0;
|
||||
while (!g_Quit)
|
||||
{
|
||||
sem_wait(&g_Wait);
|
||||
|
||||
if (g_TimeChanged || g_TimeSync)
|
||||
{
|
||||
printf("Get Time Changed signal or mis-synced. stop timerID %zd\n", timerID);
|
||||
kdk_timer_stop(timerID);
|
||||
// 若临时定时器已启动，则不做处理
|
||||
// 时钟发生变化，需要进行对时调整；关闭常驻定时器timerID，启动临时定时器
|
||||
printf("Get Time Changed signal or mis-synced. stop timerID %zd\n", periodicTimerID);
|
||||
kdk_timer_stop(periodicTimerID);
|
||||
g_TimeChanged = 0;
|
||||
g_TimeSync = 0;
|
||||
timerID = -1;
|
||||
periodicTimerID = 0;
|
||||
}
|
||||
|
||||
if (!g_Flag)
|
||||
kdk_timer_start(200, printClock, KTIMER_SINGLESHOT, KTIMER_RELATIVE, conn, 0);
|
||||
else
|
||||
{
|
||||
timerID = kdk_timer_start(1000 * 60, printClock, KTIMER_PERIODIC, KTIMER_RELATIVE, conn, 0);
|
||||
printf("start periodic timer with ID %zd\n", timerID);
|
||||
// 当启动常驻定时器时，临时定时器肯定不需要再存在了
|
||||
periodicTimerID = kdk_timer_start(1000 * 60, printClock, KTIMER_PERIODIC, KTIMER_RELATIVE, conn, 0);
|
||||
printf("start periodic timer with ID %zd\n", periodicTimerID);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -146,7 +154,7 @@ void *startBroadcastSystemTimePerMin(void *tmp)
|
|||
int monitorSystemTimeChange()
|
||||
{
|
||||
#define TIME_T_MAX (time_t)((1UL << ((sizeof(time_t) << 3) - 1)) - 1)
|
||||
|
||||
// printf("monitorSystemTimeChange\n");
|
||||
struct itimerspec its = {.it_value.tv_sec = TIME_T_MAX};
|
||||
int fd = timerfd_create(CLOCK_REALTIME, TFD_CLOEXEC);
|
||||
if (fd < 0)
|
||||
|
|
@ -162,6 +170,7 @@ int monitorSystemTimeChange()
|
|||
|
||||
u_int64_t dep;
|
||||
ssize_t ret = read(fd, &dep, sizeof(u_int64_t));
|
||||
close(fd);
|
||||
if (ret == -1 && errno == ECANCELED)
|
||||
return 1;
|
||||
|
||||
|
|
@ -176,9 +185,63 @@ void *actionTimeChanged(void *ptr)
|
|||
if (monitorSystemTimeChange() == 1)
|
||||
{
|
||||
printf("System Time Changed.\n");
|
||||
g_TimeChanged = 1;
|
||||
g_Flag = 0;
|
||||
printClock(conn);
|
||||
if (g_Flag)
|
||||
{
|
||||
g_TimeChanged = 1;
|
||||
g_Flag = 0;
|
||||
printClock(conn);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return NULL;
|
||||
}
|
||||
// 20020721新增时区变化监听
|
||||
int monitorSystemTimeZoneChange(){
|
||||
|
||||
char buf[BUFSIZ];
|
||||
int fd = inotify_init();
|
||||
buf[sizeof(buf) - 1] = 0;
|
||||
|
||||
struct inotify_event *event;
|
||||
|
||||
if (fd < 0)
|
||||
{
|
||||
return -1;
|
||||
}
|
||||
|
||||
int ftimezone = inotify_add_watch(fd,"/etc/timezone",IN_DELETE_SELF);
|
||||
|
||||
if (ftimezone < 0)
|
||||
{
|
||||
close(fd);
|
||||
return -1;
|
||||
}
|
||||
int ret = read(fd, buf, sizeof(buf) - 1);
|
||||
close(fd);
|
||||
event = (struct inotify_event *)&buf[0];
|
||||
if (ret)
|
||||
{
|
||||
fprintf(stdout, "%s --- %s\n", event->name, "IN_DELETE_SELF");
|
||||
return 1;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
// 20020721新增时区变化监听
|
||||
void *actionTimeZoneChanged(void *ptr)
|
||||
{
|
||||
DBusConnection *conn = ptr;
|
||||
while (!g_Quit)
|
||||
{
|
||||
if (monitorSystemTimeZoneChange() == 1)
|
||||
{
|
||||
printf("System Time Changed.\n");
|
||||
if (g_Flag)
|
||||
{
|
||||
g_TimeChanged = 1;
|
||||
g_Flag = 0;
|
||||
printClock(conn);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -444,13 +507,20 @@ int main(void)
|
|||
|
||||
sem_init(&g_Wait, 0, 1);
|
||||
|
||||
pthread_mutex_init(&lock, NULL);
|
||||
pthread_attr_t attr;
|
||||
pthread_t tid;
|
||||
pthread_attr_init(&attr);
|
||||
pthread_attr_setdetachstate(&attr, PTHREAD_CREATE_DETACHED);
|
||||
pthread_create(&tid, &attr, actionTimeChanged, conn);
|
||||
|
||||
pthread_mutex_init(&lock, NULL);
|
||||
// 20020721新增时区变化监听
|
||||
pthread_attr_t timezone_attr;
|
||||
pthread_t timezone_id;
|
||||
pthread_attr_init(&timezone_attr);
|
||||
pthread_attr_setdetachstate(&attr, PTHREAD_CREATE_DETACHED);
|
||||
pthread_create(&timezone_id, &timezone_attr, actionTimeZoneChanged, conn);
|
||||
|
||||
/* connect to the daemon bus */
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue