中断训练
This commit is contained in:
parent
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commit
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602
.gitignore
vendored
602
.gitignore
vendored
@ -1,301 +1,301 @@
|
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### Python template
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||||
# Byte-compiled / optimized / DLL files
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||||
__pycache__/
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*.py[cod]
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||||
*$py.class
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||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
### IntelliJ IDEA ###
|
||||
.idea/
|
||||
*.iws
|
||||
*.iml
|
||||
*.ipr
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||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
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||||
eggs/
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||||
.eggs/
|
||||
lib/
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||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
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||||
pip-delete-this-directory.txt
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||||
|
||||
# spec
|
||||
manage.spec
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||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
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||||
.tox/
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||||
.coverage
|
||||
.coverage.*
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||||
.cache
|
||||
nosetests.xml
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||||
coverage.xml
|
||||
*.cover
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||||
.hypothesis/
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||||
|
||||
# Translations
|
||||
*.mo
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||||
*.pot
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||||
|
||||
# Django stuff:
|
||||
staticfiles/
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
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||||
|
||||
# pyenv
|
||||
.python-version
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||||
|
||||
|
||||
|
||||
# Environments
|
||||
.venv
|
||||
venv/
|
||||
ENV/
|
||||
.vscode
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
|
||||
|
||||
### Node template
|
||||
# Logs
|
||||
logs
|
||||
*.log
|
||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
|
||||
# Runtime data
|
||||
pids
|
||||
*.pid
|
||||
*.seed
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||||
*.pid.lock
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||||
|
||||
# Directory for instrumented libs generated by jscoverage/JSCover
|
||||
lib-cov
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||||
|
||||
# Coverage directory used by tools like istanbul
|
||||
coverage
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||||
|
||||
# nyc test coverage
|
||||
.nyc_output
|
||||
|
||||
# Bower dependency directory (https://bower.io/)
|
||||
bower_components
|
||||
|
||||
# node-waf configuration
|
||||
.lock-wscript
|
||||
|
||||
# Compiled binary addons (http://nodejs.org/api/addons.html)
|
||||
build/Release
|
||||
|
||||
# Dependency directories
|
||||
node_modules/
|
||||
jspm_packages/
|
||||
|
||||
# Typescript v1 declaration files
|
||||
typings/
|
||||
|
||||
# Optional npm cache directory
|
||||
.npm
|
||||
|
||||
# Optional eslint cache
|
||||
.eslintcache
|
||||
|
||||
# Optional REPL history
|
||||
.node_repl_history
|
||||
|
||||
# Output of 'npm pack'
|
||||
*.tgz
|
||||
|
||||
# Yarn Integrity file
|
||||
.yarn-integrity
|
||||
|
||||
|
||||
### Linux template
|
||||
*~
|
||||
|
||||
# temporary files which can be created if a process still has a handle open of a deleted file
|
||||
.fuse_hidden*
|
||||
|
||||
# KDE directory preferences
|
||||
.directory
|
||||
|
||||
# Linux trash folder which might appear on any partition or disk
|
||||
.Trash-*
|
||||
|
||||
# .nfs files are created when an open file is removed but is still being accessed
|
||||
.nfs*
|
||||
|
||||
|
||||
### VisualStudioCode template
|
||||
.vscode/*
|
||||
!.vscode/settings.json
|
||||
!.vscode/tasks.json
|
||||
!.vscode/launch.json
|
||||
!.vscode/extensions.json
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### Windows template
|
||||
# Windows thumbnail cache files
|
||||
Thumbs.db
|
||||
ehthumbs.db
|
||||
ehthumbs_vista.db
|
||||
|
||||
# Dump file
|
||||
*.stackdump
|
||||
|
||||
# Folder config file
|
||||
Desktop.ini
|
||||
|
||||
# Recycle Bin used on file shares
|
||||
$RECYCLE.BIN/
|
||||
|
||||
# Windows Installer files
|
||||
*.cab
|
||||
*.msi
|
||||
*.msm
|
||||
*.msp
|
||||
|
||||
# Windows shortcuts
|
||||
*.lnk
|
||||
|
||||
|
||||
### macOS template
|
||||
# General
|
||||
*.DS_Store
|
||||
.AppleDouble
|
||||
.LSOverride
|
||||
|
||||
# Icon must end with two \r
|
||||
Icon
|
||||
|
||||
# Thumbnails
|
||||
._*
|
||||
|
||||
# Files that might appear in the root of a volume
|
||||
.DocumentRevisions-V100
|
||||
.fseventsd
|
||||
.Spotlight-V100
|
||||
.TemporaryItems
|
||||
.Trashes
|
||||
.VolumeIcon.icns
|
||||
.com.apple.timemachine.donotpresent
|
||||
|
||||
# Directories potentially created on remote AFP share
|
||||
.AppleDB
|
||||
.AppleDesktop
|
||||
Network Trash Folder
|
||||
Temporary Items
|
||||
.apdisk
|
||||
|
||||
|
||||
### SublimeText template
|
||||
# Cache files for Sublime Text
|
||||
*.tmlanguage.cache
|
||||
*.tmPreferences.cache
|
||||
*.stTheme.cache
|
||||
|
||||
# Workspace files are user-specific
|
||||
*.sublime-workspace
|
||||
|
||||
# Project files should be checked into the repository, unless a significant
|
||||
# proportion of contributors will probably not be using Sublime Text
|
||||
# *.sublime-project
|
||||
|
||||
# SFTP configuration file
|
||||
sftp-config.json
|
||||
|
||||
# Package control specific files
|
||||
Package Control.last-run
|
||||
Package Control.ca-list
|
||||
Package Control.ca-bundle
|
||||
Package Control.system-ca-bundle
|
||||
Package Control.cache/
|
||||
Package Control.ca-certs/
|
||||
Package Control.merged-ca-bundle
|
||||
Package Control.user-ca-bundle
|
||||
oscrypto-ca-bundle.crt
|
||||
bh_unicode_properties.cache
|
||||
|
||||
# Sublime-github package stores a github token in this file
|
||||
# https://packagecontrol.io/packages/sublime-github
|
||||
GitHub.sublime-settings
|
||||
|
||||
|
||||
### Vim template
|
||||
# Swap
|
||||
[._]*.s[a-v][a-z]
|
||||
[._]*.sw[a-p]
|
||||
[._]s[a-v][a-z]
|
||||
[._]sw[a-p]
|
||||
|
||||
# Session
|
||||
Session.vim
|
||||
|
||||
# Temporary
|
||||
.netrwhist
|
||||
|
||||
# Auto-generated tag files
|
||||
tags
|
||||
|
||||
|
||||
### VirtualEnv template
|
||||
# Virtualenv
|
||||
[Bb]in
|
||||
[Ii]nclude
|
||||
[Ll]ib
|
||||
[Ll]ib64
|
||||
[Ss]cripts
|
||||
pyvenv.cfg
|
||||
pip-selfcheck.json
|
||||
.env
|
||||
|
||||
|
||||
### Project template
|
||||
|
||||
izan/media/
|
||||
|
||||
.pytest_cache/
|
||||
|
||||
!app/yolov5/yolov5s.pt
|
||||
*.pt
|
||||
*.pdparams
|
||||
*.onnx
|
||||
|
||||
|
||||
### Python template
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
### IntelliJ IDEA ###
|
||||
.idea/
|
||||
*.iws
|
||||
*.iml
|
||||
*.ipr
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# spec
|
||||
manage.spec
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
staticfiles/
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
|
||||
|
||||
# Environments
|
||||
.venv
|
||||
venv/
|
||||
ENV/
|
||||
.vscode
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
|
||||
|
||||
### Node template
|
||||
# Logs
|
||||
logs
|
||||
*.log
|
||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
|
||||
# Runtime data
|
||||
pids
|
||||
*.pid
|
||||
*.seed
|
||||
*.pid.lock
|
||||
|
||||
# Directory for instrumented libs generated by jscoverage/JSCover
|
||||
lib-cov
|
||||
|
||||
# Coverage directory used by tools like istanbul
|
||||
coverage
|
||||
|
||||
# nyc test coverage
|
||||
.nyc_output
|
||||
|
||||
# Bower dependency directory (https://bower.io/)
|
||||
bower_components
|
||||
|
||||
# node-waf configuration
|
||||
.lock-wscript
|
||||
|
||||
# Compiled binary addons (http://nodejs.org/api/addons.html)
|
||||
build/Release
|
||||
|
||||
# Dependency directories
|
||||
node_modules/
|
||||
jspm_packages/
|
||||
|
||||
# Typescript v1 declaration files
|
||||
typings/
|
||||
|
||||
# Optional npm cache directory
|
||||
.npm
|
||||
|
||||
# Optional eslint cache
|
||||
.eslintcache
|
||||
|
||||
# Optional REPL history
|
||||
.node_repl_history
|
||||
|
||||
# Output of 'npm pack'
|
||||
*.tgz
|
||||
|
||||
# Yarn Integrity file
|
||||
.yarn-integrity
|
||||
|
||||
|
||||
### Linux template
|
||||
*~
|
||||
|
||||
# temporary files which can be created if a process still has a handle open of a deleted file
|
||||
.fuse_hidden*
|
||||
|
||||
# KDE directory preferences
|
||||
.directory
|
||||
|
||||
# Linux trash folder which might appear on any partition or disk
|
||||
.Trash-*
|
||||
|
||||
# .nfs files are created when an open file is removed but is still being accessed
|
||||
.nfs*
|
||||
|
||||
|
||||
### VisualStudioCode template
|
||||
.vscode/*
|
||||
!.vscode/settings.json
|
||||
!.vscode/tasks.json
|
||||
!.vscode/launch.json
|
||||
!.vscode/extensions.json
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### Windows template
|
||||
# Windows thumbnail cache files
|
||||
Thumbs.db
|
||||
ehthumbs.db
|
||||
ehthumbs_vista.db
|
||||
|
||||
# Dump file
|
||||
*.stackdump
|
||||
|
||||
# Folder config file
|
||||
Desktop.ini
|
||||
|
||||
# Recycle Bin used on file shares
|
||||
$RECYCLE.BIN/
|
||||
|
||||
# Windows Installer files
|
||||
*.cab
|
||||
*.msi
|
||||
*.msm
|
||||
*.msp
|
||||
|
||||
# Windows shortcuts
|
||||
*.lnk
|
||||
|
||||
|
||||
### macOS template
|
||||
# General
|
||||
*.DS_Store
|
||||
.AppleDouble
|
||||
.LSOverride
|
||||
|
||||
# Icon must end with two \r
|
||||
Icon
|
||||
|
||||
# Thumbnails
|
||||
._*
|
||||
|
||||
# Files that might appear in the root of a volume
|
||||
.DocumentRevisions-V100
|
||||
.fseventsd
|
||||
.Spotlight-V100
|
||||
.TemporaryItems
|
||||
.Trashes
|
||||
.VolumeIcon.icns
|
||||
.com.apple.timemachine.donotpresent
|
||||
|
||||
# Directories potentially created on remote AFP share
|
||||
.AppleDB
|
||||
.AppleDesktop
|
||||
Network Trash Folder
|
||||
Temporary Items
|
||||
.apdisk
|
||||
|
||||
|
||||
### SublimeText template
|
||||
# Cache files for Sublime Text
|
||||
*.tmlanguage.cache
|
||||
*.tmPreferences.cache
|
||||
*.stTheme.cache
|
||||
|
||||
# Workspace files are user-specific
|
||||
*.sublime-workspace
|
||||
|
||||
# Project files should be checked into the repository, unless a significant
|
||||
# proportion of contributors will probably not be using Sublime Text
|
||||
# *.sublime-project
|
||||
|
||||
# SFTP configuration file
|
||||
sftp-config.json
|
||||
|
||||
# Package control specific files
|
||||
Package Control.last-run
|
||||
Package Control.ca-list
|
||||
Package Control.ca-bundle
|
||||
Package Control.system-ca-bundle
|
||||
Package Control.cache/
|
||||
Package Control.ca-certs/
|
||||
Package Control.merged-ca-bundle
|
||||
Package Control.user-ca-bundle
|
||||
oscrypto-ca-bundle.crt
|
||||
bh_unicode_properties.cache
|
||||
|
||||
# Sublime-github package stores a github token in this file
|
||||
# https://packagecontrol.io/packages/sublime-github
|
||||
GitHub.sublime-settings
|
||||
|
||||
|
||||
### Vim template
|
||||
# Swap
|
||||
[._]*.s[a-v][a-z]
|
||||
[._]*.sw[a-p]
|
||||
[._]s[a-v][a-z]
|
||||
[._]sw[a-p]
|
||||
|
||||
# Session
|
||||
Session.vim
|
||||
|
||||
# Temporary
|
||||
.netrwhist
|
||||
|
||||
# Auto-generated tag files
|
||||
tags
|
||||
|
||||
|
||||
### VirtualEnv template
|
||||
# Virtualenv
|
||||
[Bb]in
|
||||
[Ii]nclude
|
||||
[Ll]ib
|
||||
[Ll]ib64
|
||||
[Ss]cripts
|
||||
pyvenv.cfg
|
||||
pip-selfcheck.json
|
||||
.env
|
||||
|
||||
|
||||
### Project template
|
||||
|
||||
izan/media/
|
||||
|
||||
.pytest_cache/
|
||||
|
||||
!app/yolov5/yolov5s.pt
|
||||
*.pt
|
||||
*.pdparams
|
||||
*.onnx
|
||||
|
||||
|
||||
|
||||
@ -1,29 +1,29 @@
|
||||
import os
|
||||
|
||||
# 根目录
|
||||
ROOT_PATH = os.path.split(os.path.abspath(__name__))[0]
|
||||
# 开启debug
|
||||
DEBUG = True
|
||||
# 密钥
|
||||
SECRET_KEY = 'WugjsfiYBEVsiQfiSwEbIOEAGnOIFYqoOYHEIK'
|
||||
|
||||
# 数据库配置
|
||||
SQLALCHEMY_DATABASE_URI = 'postgresql+psycopg2://deepLearner:dp2021@124.71.203.3:5432/demo'
|
||||
#SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo'
|
||||
SQLALCHEMY_TRACK_MODIFICATIONS = False
|
||||
# 查询时会显示原始SQL语句
|
||||
SQLALCHEMY_ECHO = True
|
||||
|
||||
# SQLALCHEMY_DATABASE_URI = 'sqlite:///{}'.format(os.path.join(ROOT_PATH, 'demo.db'))
|
||||
# SQLALCHEMY_TRACK_MODIFICATIONS = False
|
||||
|
||||
# 数据库配置
|
||||
db = {
|
||||
'host': '127.0.0.1',
|
||||
'user': 'root',
|
||||
'password': 'sdust2020',
|
||||
'port': 6379,
|
||||
'database': 'school',
|
||||
'charset': 'utf8',
|
||||
'db': 0
|
||||
}
|
||||
import os
|
||||
|
||||
# 根目录
|
||||
ROOT_PATH = os.path.split(os.path.abspath(__name__))[0]
|
||||
# 开启debug
|
||||
DEBUG = True
|
||||
# 密钥
|
||||
SECRET_KEY = 'WugjsfiYBEVsiQfiSwEbIOEAGnOIFYqoOYHEIK'
|
||||
|
||||
# 数据库配置
|
||||
SQLALCHEMY_DATABASE_URI = 'postgresql+psycopg2://deepLearner:dp2021@124.71.203.3:5432/demo'
|
||||
#SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo'
|
||||
SQLALCHEMY_TRACK_MODIFICATIONS = False
|
||||
# 查询时会显示原始SQL语句
|
||||
SQLALCHEMY_ECHO = True
|
||||
|
||||
# SQLALCHEMY_DATABASE_URI = 'sqlite:///{}'.format(os.path.join(ROOT_PATH, 'demo.db'))
|
||||
# SQLALCHEMY_TRACK_MODIFICATIONS = False
|
||||
|
||||
# 数据库配置
|
||||
db = {
|
||||
'host': '127.0.0.1',
|
||||
'user': 'root',
|
||||
'password': 'sdust2020',
|
||||
'port': 6379,
|
||||
'database': 'school',
|
||||
'charset': 'utf8',
|
||||
'db': 0
|
||||
}
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
from .default import * # NOQA F401
|
||||
|
||||
# 数据库配置
|
||||
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo'
|
||||
# SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo?allowPublicKeyRetrieval=true&useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai'
|
||||
SQLALCHEMY_TRACK_MODIFICATIONS = False
|
||||
# 查询时会显示原始SQL语句
|
||||
SQLALCHEMY_ECHO = True
|
||||
from .default import * # NOQA F401
|
||||
|
||||
# 数据库配置
|
||||
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo'
|
||||
# SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo?allowPublicKeyRetrieval=true&useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai'
|
||||
SQLALCHEMY_TRACK_MODIFICATIONS = False
|
||||
# 查询时会显示原始SQL语句
|
||||
SQLALCHEMY_ECHO = True
|
||||
|
||||
@ -1,36 +1,44 @@
|
||||
"""
|
||||
@Time ： 2022/11/15 10:13
|
||||
@Auth ： 东
|
||||
@File ：global_var.py
|
||||
@IDE ：PyCharm
|
||||
@Motto：ABC(Always Be Coding)
|
||||
@Desc：
|
||||
|
||||
"""
|
||||
import json
|
||||
from app.utils.redis_config import redis_client
|
||||
|
||||
|
||||
def _init(): # 初始化
|
||||
dict = {}
|
||||
redis_client.__setattr__("_global_dict", json.dumps(dict))
|
||||
|
||||
|
||||
def set_value(key, value):
|
||||
# 定义一个全局变量
|
||||
dict = redis_client.get_redis().get("_global_dict")
|
||||
if dict is None:
|
||||
dict = {}
|
||||
dict[key] = value
|
||||
# redis_client.get_redis().set("_global_dict", json.dumps(dict))
|
||||
redis_client.__setattr__("_global_dict", json.dumps(dict))
|
||||
|
||||
|
||||
|
||||
def get_value(key):
|
||||
# 获得一个全局变量，不存在则提示读取对应变量失败
|
||||
try:
|
||||
return redis_client.get_redis().get("_global_dict")[key]
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print('读取' + key + '失败\r\n')
|
||||
"""
|
||||
@Time ： 2022/11/15 10:13
|
||||
@Auth ： 东
|
||||
@File ：global_var.py
|
||||
@IDE ：PyCharm
|
||||
@Motto：ABC(Always Be Coding)
|
||||
@Desc：
|
||||
|
||||
"""
|
||||
import multiprocessing
|
||||
|
||||
|
||||
|
||||
def _init(): # 初始化
|
||||
# 中断标志
|
||||
global _global_dict
|
||||
_global_dict = {}
|
||||
|
||||
# # ws列表存储
|
||||
# global _active_connections
|
||||
# _active_connections = multiprocessing.Manager().list()
|
||||
|
||||
# # ws字典存储
|
||||
# global _active_connections_dist
|
||||
# _active_connections_dist = multiprocessing.Manager().dict()
|
||||
|
||||
|
||||
# def get_active_connections():
|
||||
# return _active_connections
|
||||
|
||||
# def get_active_connections_dist():
|
||||
# return _active_connections_dist
|
||||
|
||||
def set_value(key, value):
|
||||
# 定义一个全局变量
|
||||
_global_dict[key] = value
|
||||
|
||||
|
||||
def get_value(key):
|
||||
# 获得一个全局变量，不存在则提示读取对应变量失败
|
||||
try:
|
||||
return _global_dict[key]
|
||||
except:
|
||||
print('读取' + key + '失败\r\n')
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
from .default import * # NOQA F401
|
||||
|
||||
DEBUG = False
|
||||
from .default import * # NOQA F401
|
||||
|
||||
DEBUG = False
|
||||
|
||||
@ -1,14 +1,14 @@
|
||||
import os
|
||||
|
||||
from .default import ROOT_PATH
|
||||
from .default import * # NOQA F401
|
||||
|
||||
|
||||
TEST_BASE_DIR = os.path.join(ROOT_PATH, '.test')
|
||||
SQLALCHEMY_DATABASE_URI = 'sqlite:///{}'.format(
|
||||
os.path.join(TEST_BASE_DIR, 'demo.db'))
|
||||
# SQLALCHEMY_ECHO = True
|
||||
TESTING = True
|
||||
|
||||
if not os.path.exists(TEST_BASE_DIR):
|
||||
os.makedirs(TEST_BASE_DIR)
|
||||
import os
|
||||
|
||||
from .default import ROOT_PATH
|
||||
from .default import * # NOQA F401
|
||||
|
||||
|
||||
TEST_BASE_DIR = os.path.join(ROOT_PATH, '.test')
|
||||
SQLALCHEMY_DATABASE_URI = 'sqlite:///{}'.format(
|
||||
os.path.join(TEST_BASE_DIR, 'demo.db'))
|
||||
# SQLALCHEMY_ECHO = True
|
||||
TESTING = True
|
||||
|
||||
if not os.path.exists(TEST_BASE_DIR):
|
||||
os.makedirs(TEST_BASE_DIR)
|
||||
|
||||
@ -1,486 +1,486 @@
|
||||
"""
|
||||
@Time ： 2022/9/20 16:17
|
||||
@Auth ： 东
|
||||
@File ：AlgorithmController.py
|
||||
@IDE ：PyCharm
|
||||
@Motto：ABC(Always Be Coding)
|
||||
@Desc：算法接口
|
||||
|
||||
"""
|
||||
import json
|
||||
from functools import wraps
|
||||
from threading import Thread
|
||||
from multiprocessing import Process
|
||||
from time import sleep
|
||||
|
||||
from flask import Blueprint, request
|
||||
|
||||
from app.schemas.TrainResult import Report, ProcessValueList
|
||||
from app.utils.RedisMQTool import Task
|
||||
from app.utils.StandardizedOutput import output_wrapped
|
||||
from app.utils.redis_config import redis_client
|
||||
from app.utils.websocket_tool import manager
|
||||
from app.configs.global_var import set_value
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from pynvml import *
|
||||
# FILE = Path(__file__).resolve()
|
||||
# ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
# if str(ROOT) not in sys.path:
|
||||
# sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# sys.path.append("/mnt/sdc/algorithm/AICheck-MaskRCNN/app/maskrcnn_ppx")
|
||||
# import ppx as pdx
|
||||
|
||||
bp = Blueprint('AlgorithmController', __name__)
|
||||
|
||||
ifKillDict = {}
|
||||
|
||||
def start_train_algorithm():
|
||||
"""
|
||||
调用训练算法
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_train_algorithm', methods=['get'])
|
||||
def wrapped_function():
|
||||
param = request.args.get('param')
|
||||
id = request.args.get('id')
|
||||
dict = manager.active_connections_dist
|
||||
# t = Thread(target=func, args=(param, id))
|
||||
t = Process(target=func, args=(param, id, dict[id]), name=id)
|
||||
set_value(key=id, value=False)
|
||||
t.start()
|
||||
return output_wrapped(0, 'success', '成功')
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def start_test_algorithm():
|
||||
"""
|
||||
调用验证算法
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_test_algorithm', methods=['get'])
|
||||
def wrapped_function_test():
|
||||
param = request.args.get('param')
|
||||
id = request.args.get('id')
|
||||
t = Thread(target=func, args=(param, id))
|
||||
t.start()
|
||||
return output_wrapped(0, 'success', '成功')
|
||||
|
||||
return wrapped_function_test
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def start_detect_algorithm():
|
||||
"""
|
||||
调用检测算法
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_detect_algorithm', methods=['get'])
|
||||
def wrapped_function_detect():
|
||||
param = request.args.get('param')
|
||||
id = request.args.get('id')
|
||||
t = Thread(target=func, args=(param, id))
|
||||
t.start()
|
||||
return output_wrapped(0, 'success', '成功')
|
||||
|
||||
return wrapped_function_detect
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def start_download_pt():
|
||||
"""
|
||||
下载模型
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_download_pt', methods=['get'])
|
||||
def wrapped_function_start_download_pt():
|
||||
param = request.args.get('param')
|
||||
data = func(param)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_start_download_pt
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def algorithm_process_value():
|
||||
"""
|
||||
获取中间值, redis订阅发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
print(data)
|
||||
Task(redis_conn=redis_client.get_redis(), channel="ceshi").publish_task(
|
||||
data={'code': 0, 'msg': 'success', 'data': data})
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def algorithm_process_value_websocket():
|
||||
"""
|
||||
获取中间值, websocket发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
id = data["id"]
|
||||
data_res = {'code': 0, "type": 'connected', 'msg': 'success', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
return data
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
def algorithm_kill_value_websocket():
|
||||
"""
|
||||
获取kill值, websocket发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
id = data["id"]
|
||||
data_res = {'code': 1, "type": 'kill', 'msg': 'success', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
return data
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def algorithm_error_value_websocket():
|
||||
"""
|
||||
获取error值, websocket发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
id = data["id"]
|
||||
data_res = {'code': 2, "type": 'error', 'msg': 'fail', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
return data
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
def obtain_train_param():
|
||||
"""
|
||||
获取训练参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_train_param', methods=['get'])
|
||||
def wrapped_function_train_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_train_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
def obtain_test_param():
|
||||
"""
|
||||
获取验证参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_test_param', methods=['get'])
|
||||
def wrapped_function_test_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_test_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def obtain_detect_param():
|
||||
"""
|
||||
获取测试参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_detect_param', methods=['get'])
|
||||
def wrapped_function_inf_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_inf_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def obtain_download_pt_param():
|
||||
"""
|
||||
获取下载模型参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_download_pt_param', methods=['get'])
|
||||
def wrapped_function_obtain_download_pt_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_obtain_download_pt_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
@bp.route('/change_ifKillDIct', methods=['get'])
|
||||
def change_ifKillDIct():
|
||||
"""
|
||||
修改全局变量
|
||||
"""
|
||||
id = request.args.get('id')
|
||||
type = request.args.get('type')
|
||||
set_value(id, type)
|
||||
return output_wrapped(0, 'success')
|
||||
|
||||
|
||||
# @start_train_algorithm()
|
||||
# def start(param: str):
|
||||
# """
|
||||
# 例子
|
||||
# """
|
||||
# print(param)
|
||||
# process_value_list = ProcessValueList(name='1', value=[])
|
||||
# report = Report(rate_of_progess=0, process_value=[process_value_list], id='1')
|
||||
#
|
||||
# @algorithm_process_value_websocket()
|
||||
# def process(v: int):
|
||||
# print(v)
|
||||
# report.rate_of_progess = ((v + 1) / 10) * 100
|
||||
# report.precision[0].value.append(v)
|
||||
# return report.dict()
|
||||
#
|
||||
# for i in range(10):
|
||||
# process(i)
|
||||
# return report.dict()
|
||||
from setparams import TrainParams
|
||||
import os
|
||||
from app.schemas.TrainResult import DetectProcessValueDice, DetectReport
|
||||
from app import file_tool
|
||||
|
||||
|
||||
def error_return(id: str, data):
|
||||
"""
|
||||
算法出错，返回
|
||||
"""
|
||||
data_res = {'code': 2, "type": 'error', 'msg': 'fail', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
|
||||
# 启动训练
|
||||
@start_train_algorithm()
|
||||
def train_R0DY(params_str, id, getsomething):
|
||||
print('**********************************')
|
||||
print(getsomething)
|
||||
print('**********************************')
|
||||
manager.active_connections_dist[id] = getsomething
|
||||
print('**********************************')
|
||||
print(manager.active_connections_dist)
|
||||
print('**********************************')
|
||||
print(params_str)
|
||||
print('**********************************')
|
||||
from app.yolov5.train_server import train_start
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
print(params.get('device').value)
|
||||
data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('CLASS_NAMES').value)
|
||||
weights = params.get('resumeModPath').value # 初始化模型绝对路径
|
||||
img_size = params.get('img_size').value
|
||||
savemodel = os.path.splitext(params.get('saveModDir').value)[0] + '_' + str(img_size) + '.pt' # 模型命名加上图像参数
|
||||
epoches = params.get('epochnum').value
|
||||
batch_size = params.get('batch_size').value
|
||||
device = params.get('device').value
|
||||
#try:
|
||||
train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id, getsomething)
|
||||
print("train down!")
|
||||
# except Exception as e:
|
||||
# print(repr(e))
|
||||
# error_return(id=id,data=repr(e))
|
||||
|
||||
|
||||
# 启动验证程序
|
||||
|
||||
@start_test_algorithm()
|
||||
def validate_RODY(params_str, id):
|
||||
from app.yolov5.validate_server import validate_start
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
weights = params.get('modPath').value # 验证模型绝对路径
|
||||
(filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
|
||||
img_size = int(filename.split('ROD')[1].split('_')[2]) # 获取图像参数
|
||||
# v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
|
||||
output = params.get('outputPath').value
|
||||
batch_size = params.get('batch_size').default
|
||||
device = params.get('device').value
|
||||
|
||||
validate_start(weights, img_size, batch_size, device, output, id)
|
||||
|
||||
|
||||
@start_detect_algorithm()
|
||||
def detect_RODY(params_str, id):
|
||||
from app.yolov5.detect_server import detect_start
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
weights = params.get('modPath').value # 检测模型绝对路径
|
||||
input = params.get('inputPath').value
|
||||
outpath = params.get('outputPath').value
|
||||
# (filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
|
||||
# img_size = int(filename.split('ROD')[1].split('_')[2]) #获取图像参数
|
||||
# v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
|
||||
# batch_size = params.get('batch_size').default
|
||||
device = params.get('device').value
|
||||
|
||||
detect_start(input, weights, outpath, device, id)
|
||||
|
||||
|
||||
@start_download_pt()
|
||||
def Export_model_RODY(params_str):
|
||||
from app.yolov5.export import Start_Model_Export
|
||||
import zipfile
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
exp_inputPath = params.get('exp_inputPath').value # 模型路径
|
||||
print('输入模型：', exp_inputPath)
|
||||
exp_device = params.get('device').value
|
||||
imgsz = params.get('imgsz').value
|
||||
modellist = Start_Model_Export(exp_inputPath, exp_device, imgsz)
|
||||
exp_outputPath = exp_inputPath.replace('pt', 'zip') # 压缩文件
|
||||
print('模型路径：',exp_outputPath)
|
||||
zipf = zipfile.ZipFile(exp_outputPath, 'w')
|
||||
for file in modellist:
|
||||
zipf.write(file, arcname=Path(file).name) # 将torchscript和onnx模型压缩
|
||||
|
||||
return exp_outputPath
|
||||
|
||||
@obtain_train_param()
|
||||
def returnTrainParams():
|
||||
nvmlInit()
|
||||
gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
|
||||
_kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
|
||||
params_list = [
|
||||
{"index": 0, "name": "epochnum", "value": 10, "description": '训练轮次', "default": 100, "type": "I", 'show': True},
|
||||
{"index": 1, "name": "batch_size", "value": 4, "description": '批次图像数量', "default": 1, "type": "I",
|
||||
'show': True},
|
||||
{"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
|
||||
'show': True},
|
||||
{"index": 3, "name": "device", "value": f'{_kernel[0]}', "description": '训练核心', "default": f'{_kernel[0]}', "type": "E",
|
||||
"items": _kernel, 'show': False}, # _kernel
|
||||
{"index": 4, "name": "saveModDir", "value": "E:/alg_demo-master/alg_demo/app/yolov5/best.pt",
|
||||
"description": '保存模型路径',
|
||||
"default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
|
||||
{"index": 5, "name": "resumeModPath", "value": '/yolov5s.pt',
|
||||
"description": '继续训练路径', "default": '', "type": "S",
|
||||
'show': False},
|
||||
{"index": 6, "name": "resumeMod", "value": '', "description": '继续训练模型', "default": '', "type": "E", "items": '',
|
||||
'show': True},
|
||||
{"index": 7, "name": "CLASS_NAMES", "value": ['hole', '456'], "description": '类别名称', "default": '', "type": "L",
|
||||
"items": '',
|
||||
'show': False},
|
||||
{"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori",
|
||||
"description": '数据集路径',
|
||||
"default": "./app/maskrcnn/datasets/test", "type": "S", 'show': False} # ORI_PATH
|
||||
]
|
||||
# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
@obtain_test_param()
|
||||
def returnValidateParams():
|
||||
# nvmlInit()
|
||||
# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
|
||||
# _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
|
||||
params_list = [
|
||||
{"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
|
||||
"description": '验证模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
|
||||
{"index": 1, "name": "batch_size", "value": 1, "description": '批次图像数量', "default": 1, "type": "I",
|
||||
'show': False},
|
||||
{"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
|
||||
'show': False},
|
||||
{"index": 3, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
|
||||
"description": '输出结果路径',
|
||||
"default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
|
||||
{"index": 4, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
|
||||
"items": '', 'show': False} # _kernel
|
||||
]
|
||||
# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
@obtain_detect_param()
|
||||
def returnDetectParams():
|
||||
# nvmlInit()
|
||||
# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
|
||||
# _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
|
||||
params_list = [
|
||||
{"index": 0, "name": "inputPath", "value": 'E:/aicheck/data_set/11442136178662604800/input/',
|
||||
"description": '输入图像路径', "default": './app/maskrcnn/datasets/M006B_waibi/JPEGImages', "type": "S",
|
||||
'show': False},
|
||||
{"index": 1, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
|
||||
"description": '输出结果路径',
|
||||
"default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
|
||||
{"index": 2, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
|
||||
"description": '模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
|
||||
{"index": 3, "name": "device", "value": "0", "description": '推理核', "default": "cpu", "type": "S",
|
||||
'show': False},
|
||||
]
|
||||
# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
@obtain_download_pt_param()
|
||||
def returnDownloadParams():
|
||||
params_list = [
|
||||
{"index": 0, "name": "exp_inputPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt',
|
||||
"description": '转化模型输入路径',
|
||||
"default": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt/',
|
||||
"type": "S", 'show': False},
|
||||
{"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
|
||||
'show': False},
|
||||
{"index": 2, "name": "imgsz", "value": 640, "description": '图像大小', "default": 640, "type": "I",
|
||||
'show': True}
|
||||
]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
par = returnTrainParams()
|
||||
print(par)
|
||||
id='1'
|
||||
train_R0DY(par,id)
|
||||
"""
|
||||
@Time ： 2022/9/20 16:17
|
||||
@Auth ： 东
|
||||
@File ：AlgorithmController.py
|
||||
@IDE ：PyCharm
|
||||
@Motto：ABC(Always Be Coding)
|
||||
@Desc：算法接口
|
||||
|
||||
"""
|
||||
import json
|
||||
from functools import wraps
|
||||
from threading import Thread
|
||||
from multiprocessing import Process
|
||||
from time import sleep
|
||||
|
||||
from flask import Blueprint, request
|
||||
|
||||
from app.schemas.TrainResult import Report, ProcessValueList
|
||||
from app.utils.RedisMQTool import Task
|
||||
from app.utils.StandardizedOutput import output_wrapped
|
||||
from app.utils.redis_config import redis_client
|
||||
from app.utils.websocket_tool import manager
|
||||
from app.configs.global_var import set_value
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from pynvml import *
|
||||
# FILE = Path(__file__).resolve()
|
||||
# ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
# if str(ROOT) not in sys.path:
|
||||
# sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# sys.path.append("/mnt/sdc/algorithm/AICheck-MaskRCNN/app/maskrcnn_ppx")
|
||||
# import ppx as pdx
|
||||
|
||||
bp = Blueprint('AlgorithmController', __name__)
|
||||
|
||||
ifKillDict = {}
|
||||
|
||||
def start_train_algorithm():
|
||||
"""
|
||||
调用训练算法
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_train_algorithm', methods=['get'])
|
||||
def wrapped_function():
|
||||
param = request.args.get('param')
|
||||
id = request.args.get('id')
|
||||
dict = manager.active_connections_dist
|
||||
# t = Thread(target=func, args=(param, id))
|
||||
t = Process(target=func, args=(param, id, dict[id]), name=id)
|
||||
set_value(key=id, value=False)
|
||||
t.start()
|
||||
return output_wrapped(0, 'success', '成功')
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def start_test_algorithm():
|
||||
"""
|
||||
调用验证算法
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_test_algorithm', methods=['get'])
|
||||
def wrapped_function_test():
|
||||
param = request.args.get('param')
|
||||
id = request.args.get('id')
|
||||
t = Thread(target=func, args=(param, id))
|
||||
t.start()
|
||||
return output_wrapped(0, 'success', '成功')
|
||||
|
||||
return wrapped_function_test
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def start_detect_algorithm():
|
||||
"""
|
||||
调用检测算法
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_detect_algorithm', methods=['get'])
|
||||
def wrapped_function_detect():
|
||||
param = request.args.get('param')
|
||||
id = request.args.get('id')
|
||||
t = Thread(target=func, args=(param, id))
|
||||
t.start()
|
||||
return output_wrapped(0, 'success', '成功')
|
||||
|
||||
return wrapped_function_detect
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def start_download_pt():
|
||||
"""
|
||||
下载模型
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/start_download_pt', methods=['get'])
|
||||
def wrapped_function_start_download_pt():
|
||||
param = request.args.get('param')
|
||||
data = func(param)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_start_download_pt
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def algorithm_process_value():
|
||||
"""
|
||||
获取中间值, redis订阅发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
print(data)
|
||||
Task(redis_conn=redis_client.get_redis(), channel="ceshi").publish_task(
|
||||
data={'code': 0, 'msg': 'success', 'data': data})
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def algorithm_process_value_websocket():
|
||||
"""
|
||||
获取中间值, websocket发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
id = data["id"]
|
||||
data_res = {'code': 0, "type": 'connected', 'msg': 'success', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
return data
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
def algorithm_kill_value_websocket():
|
||||
"""
|
||||
获取kill值, websocket发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
id = data["id"]
|
||||
data_res = {'code': 1, "type": 'kill', 'msg': 'success', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
return data
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def algorithm_error_value_websocket():
|
||||
"""
|
||||
获取error值, websocket发布
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
def wrapped_function(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
id = data["id"]
|
||||
data_res = {'code': 2, "type": 'error', 'msg': 'fail', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
return data
|
||||
|
||||
return wrapped_function
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
def obtain_train_param():
|
||||
"""
|
||||
获取训练参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_train_param', methods=['get'])
|
||||
def wrapped_function_train_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_train_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
def obtain_test_param():
|
||||
"""
|
||||
获取验证参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_test_param', methods=['get'])
|
||||
def wrapped_function_test_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_test_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def obtain_detect_param():
|
||||
"""
|
||||
获取测试参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_detect_param', methods=['get'])
|
||||
def wrapped_function_inf_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_inf_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
|
||||
def obtain_download_pt_param():
|
||||
"""
|
||||
获取下载模型参数
|
||||
"""
|
||||
|
||||
def wrapTheFunction(func):
|
||||
@wraps(func)
|
||||
@bp.route('/obtain_download_pt_param', methods=['get'])
|
||||
def wrapped_function_obtain_download_pt_param(*args, **kwargs):
|
||||
data = func(*args, **kwargs)
|
||||
return output_wrapped(0, 'success', data)
|
||||
|
||||
return wrapped_function_obtain_download_pt_param
|
||||
|
||||
return wrapTheFunction
|
||||
|
||||
@bp.route('/change_ifKillDIct', methods=['get'])
|
||||
def change_ifKillDIct():
|
||||
"""
|
||||
修改全局变量
|
||||
"""
|
||||
id = request.args.get('id')
|
||||
type = request.args.get('type')
|
||||
set_value(id, type)
|
||||
return output_wrapped(0, 'success')
|
||||
|
||||
|
||||
# @start_train_algorithm()
|
||||
# def start(param: str):
|
||||
# """
|
||||
# 例子
|
||||
# """
|
||||
# print(param)
|
||||
# process_value_list = ProcessValueList(name='1', value=[])
|
||||
# report = Report(rate_of_progess=0, process_value=[process_value_list], id='1')
|
||||
#
|
||||
# @algorithm_process_value_websocket()
|
||||
# def process(v: int):
|
||||
# print(v)
|
||||
# report.rate_of_progess = ((v + 1) / 10) * 100
|
||||
# report.precision[0].value.append(v)
|
||||
# return report.dict()
|
||||
#
|
||||
# for i in range(10):
|
||||
# process(i)
|
||||
# return report.dict()
|
||||
from setparams import TrainParams
|
||||
import os
|
||||
from app.schemas.TrainResult import DetectProcessValueDice, DetectReport
|
||||
from app import file_tool
|
||||
|
||||
|
||||
def error_return(id: str, data):
|
||||
"""
|
||||
算法出错，返回
|
||||
"""
|
||||
data_res = {'code': 2, "type": 'error', 'msg': 'fail', 'data': data}
|
||||
manager.send_message_proj_json(message=data_res, id=id)
|
||||
|
||||
# 启动训练
|
||||
@start_train_algorithm()
|
||||
def train_R0DY(params_str, id, getsomething):
|
||||
print('**********************************')
|
||||
print(getsomething)
|
||||
print('**********************************')
|
||||
manager.active_connections_dist[id] = getsomething
|
||||
print('**********************************')
|
||||
print(manager.active_connections_dist)
|
||||
print('**********************************')
|
||||
print(params_str)
|
||||
print('**********************************')
|
||||
from app.yolov5.train_server import train_start
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
print(params.get('device').value)
|
||||
data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('CLASS_NAMES').value)
|
||||
weights = params.get('resumeModPath').value # 初始化模型绝对路径
|
||||
img_size = params.get('img_size').value
|
||||
savemodel = os.path.splitext(params.get('saveModDir').value)[0] + '_' + str(img_size) + '.pt' # 模型命名加上图像参数
|
||||
epoches = params.get('epochnum').value
|
||||
batch_size = params.get('batch_size').value
|
||||
device = params.get('device').value
|
||||
#try:
|
||||
train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id, getsomething)
|
||||
print("train down!")
|
||||
# except Exception as e:
|
||||
# print(repr(e))
|
||||
# error_return(id=id,data=repr(e))
|
||||
|
||||
|
||||
# 启动验证程序
|
||||
|
||||
@start_test_algorithm()
|
||||
def validate_RODY(params_str, id):
|
||||
from app.yolov5.validate_server import validate_start
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
weights = params.get('modPath').value # 验证模型绝对路径
|
||||
(filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
|
||||
img_size = int(filename.split('ROD')[1].split('_')[2]) # 获取图像参数
|
||||
# v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
|
||||
output = params.get('outputPath').value
|
||||
batch_size = params.get('batch_size').default
|
||||
device = params.get('device').value
|
||||
|
||||
validate_start(weights, img_size, batch_size, device, output, id)
|
||||
|
||||
|
||||
@start_detect_algorithm()
|
||||
def detect_RODY(params_str, id):
|
||||
from app.yolov5.detect_server import detect_start
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
weights = params.get('modPath').value # 检测模型绝对路径
|
||||
input = params.get('inputPath').value
|
||||
outpath = params.get('outputPath').value
|
||||
# (filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
|
||||
# img_size = int(filename.split('ROD')[1].split('_')[2]) #获取图像参数
|
||||
# v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
|
||||
# batch_size = params.get('batch_size').default
|
||||
device = params.get('device').value
|
||||
|
||||
detect_start(input, weights, outpath, device, id)
|
||||
|
||||
|
||||
@start_download_pt()
|
||||
def Export_model_RODY(params_str):
|
||||
from app.yolov5.export import Start_Model_Export
|
||||
import zipfile
|
||||
params = TrainParams()
|
||||
params.read_from_str(params_str)
|
||||
exp_inputPath = params.get('exp_inputPath').value # 模型路径
|
||||
print('输入模型：', exp_inputPath)
|
||||
exp_device = params.get('device').value
|
||||
imgsz = params.get('imgsz').value
|
||||
modellist = Start_Model_Export(exp_inputPath, exp_device, imgsz)
|
||||
exp_outputPath = exp_inputPath.replace('pt', 'zip') # 压缩文件
|
||||
print('模型路径：',exp_outputPath)
|
||||
zipf = zipfile.ZipFile(exp_outputPath, 'w')
|
||||
for file in modellist:
|
||||
zipf.write(file, arcname=Path(file).name) # 将torchscript和onnx模型压缩
|
||||
|
||||
return exp_outputPath
|
||||
|
||||
@obtain_train_param()
|
||||
def returnTrainParams():
|
||||
nvmlInit()
|
||||
gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
|
||||
_kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
|
||||
params_list = [
|
||||
{"index": 0, "name": "epochnum", "value": 10, "description": '训练轮次', "default": 100, "type": "I", 'show': True},
|
||||
{"index": 1, "name": "batch_size", "value": 4, "description": '批次图像数量', "default": 1, "type": "I",
|
||||
'show': True},
|
||||
{"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
|
||||
'show': True},
|
||||
{"index": 3, "name": "device", "value": f'{_kernel[0]}', "description": '训练核心', "default": f'{_kernel[0]}', "type": "E",
|
||||
"items": _kernel, 'show': False}, # _kernel
|
||||
{"index": 4, "name": "saveModDir", "value": "E:/alg_demo-master/alg_demo/app/yolov5/best.pt",
|
||||
"description": '保存模型路径',
|
||||
"default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
|
||||
{"index": 5, "name": "resumeModPath", "value": '/yolov5s.pt',
|
||||
"description": '继续训练路径', "default": '', "type": "S",
|
||||
'show': False},
|
||||
{"index": 6, "name": "resumeMod", "value": '', "description": '继续训练模型', "default": '', "type": "E", "items": '',
|
||||
'show': True},
|
||||
{"index": 7, "name": "CLASS_NAMES", "value": ['hole', '456'], "description": '类别名称', "default": '', "type": "L",
|
||||
"items": '',
|
||||
'show': False},
|
||||
{"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori",
|
||||
"description": '数据集路径',
|
||||
"default": "./app/maskrcnn/datasets/test", "type": "S", 'show': False} # ORI_PATH
|
||||
]
|
||||
# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
@obtain_test_param()
|
||||
def returnValidateParams():
|
||||
# nvmlInit()
|
||||
# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
|
||||
# _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
|
||||
params_list = [
|
||||
{"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
|
||||
"description": '验证模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
|
||||
{"index": 1, "name": "batch_size", "value": 1, "description": '批次图像数量', "default": 1, "type": "I",
|
||||
'show': False},
|
||||
{"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
|
||||
'show': False},
|
||||
{"index": 3, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
|
||||
"description": '输出结果路径',
|
||||
"default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
|
||||
{"index": 4, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
|
||||
"items": '', 'show': False} # _kernel
|
||||
]
|
||||
# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
@obtain_detect_param()
|
||||
def returnDetectParams():
|
||||
# nvmlInit()
|
||||
# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
|
||||
# _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
|
||||
params_list = [
|
||||
{"index": 0, "name": "inputPath", "value": 'E:/aicheck/data_set/11442136178662604800/input/',
|
||||
"description": '输入图像路径', "default": './app/maskrcnn/datasets/M006B_waibi/JPEGImages', "type": "S",
|
||||
'show': False},
|
||||
{"index": 1, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
|
||||
"description": '输出结果路径',
|
||||
"default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
|
||||
{"index": 2, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
|
||||
"description": '模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
|
||||
{"index": 3, "name": "device", "value": "0", "description": '推理核', "default": "cpu", "type": "S",
|
||||
'show': False},
|
||||
]
|
||||
# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
@obtain_download_pt_param()
|
||||
def returnDownloadParams():
|
||||
params_list = [
|
||||
{"index": 0, "name": "exp_inputPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt',
|
||||
"description": '转化模型输入路径',
|
||||
"default": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt/',
|
||||
"type": "S", 'show': False},
|
||||
{"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
|
||||
'show': False},
|
||||
{"index": 2, "name": "imgsz", "value": 640, "description": '图像大小', "default": 640, "type": "I",
|
||||
'show': True}
|
||||
]
|
||||
params_str = json.dumps(params_list)
|
||||
return params_str
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
par = returnTrainParams()
|
||||
print(par)
|
||||
id='1'
|
||||
train_R0DY(par,id)
|
||||
|
||||
@ -1,33 +1,33 @@
|
||||
import logging
|
||||
|
||||
from flask import Blueprint, app
|
||||
|
||||
from app.exts import redisClient
|
||||
from app.utils.StandardizedOutput import output_wrapped
|
||||
|
||||
bp = Blueprint('WebStatus', __name__)
|
||||
|
||||
|
||||
@bp.route('/ping', methods=['GET'])
|
||||
def ping():
|
||||
""" For health check.
|
||||
"""
|
||||
res = output_wrapped(0, 'pong', '')
|
||||
return res
|
||||
|
||||
|
||||
@bp.route('/redis/set', methods=['post'])
|
||||
def redis_set():
|
||||
redisClient.set('foo', 'bar', ex=60*60*6)
|
||||
res = output_wrapped(0, 'set foo', '')
|
||||
return res
|
||||
|
||||
|
||||
@bp.route('/redis/get', methods=['get'])
|
||||
def redis_get():
|
||||
""" For health check.
|
||||
"""
|
||||
the_food = redisClient.get('foo')
|
||||
if not the_food:
|
||||
return output_wrapped(5006, 'foo', "")
|
||||
return output_wrapped(0, 'foo', the_food.decode("utf-8"))
|
||||
import logging
|
||||
|
||||
from flask import Blueprint, app
|
||||
|
||||
from app.exts import redisClient
|
||||
from app.utils.StandardizedOutput import output_wrapped
|
||||
|
||||
bp = Blueprint('WebStatus', __name__)
|
||||
|
||||
|
||||
@bp.route('/ping', methods=['GET'])
|
||||
def ping():
|
||||
""" For health check.
|
||||
"""
|
||||
res = output_wrapped(0, 'pong', '')
|
||||
return res
|
||||
|
||||
|
||||
@bp.route('/redis/set', methods=['post'])
|
||||
def redis_set():
|
||||
redisClient.set('foo', 'bar', ex=60*60*6)
|
||||
res = output_wrapped(0, 'set foo', '')
|
||||
return res
|
||||
|
||||
|
||||
@bp.route('/redis/get', methods=['get'])
|
||||
def redis_get():
|
||||
""" For health check.
|
||||
"""
|
||||
the_food = redisClient.get('foo')
|
||||
if not the_food:
|
||||
return output_wrapped(5006, 'foo', "")
|
||||
return output_wrapped(0, 'foo', the_food.decode("utf-8"))
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from app.core.common_utils import import_subs
|
||||
|
||||
|
||||
__all__ = import_subs(locals(), modules_only=True)
|
||||
from app.core.common_utils import import_subs
|
||||
|
||||
|
||||
__all__ = import_subs(locals(), modules_only=True)
|
||||
|
||||
Binary file not shown.
@ -1,9 +1,9 @@
|
||||
path: null
|
||||
train: /mnt/sdc/aicheck/IntelligentizeAI/data_set/193120735164768256/trained/images/train/
|
||||
val: /mnt/sdc/aicheck/IntelligentizeAI/data_set/193120735164768256/trained/images/val/
|
||||
train: /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/190857268466688000/trained/images/train/
|
||||
val: /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/190857268466688000/trained/images/val/
|
||||
test: null
|
||||
names:
|
||||
0: hole
|
||||
1: '456'
|
||||
2: dog
|
||||
3: cat
|
||||
2: zui
|
||||
3: mianbang
|
||||
|
||||
503
nohup.out
503
nohup.out
@ -1,51 +1,478 @@
|
||||
nohup: ignoring input
|
||||
2022-11-24 08:58:34,262 INFO sqlalchemy.engine.Engine select pg_catalog.version()
|
||||
2022-11-24 08:58:34,262 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-24 08:58:34,267 INFO sqlalchemy.engine.Engine select current_schema()
|
||||
2022-11-24 08:58:34,267 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-24 08:58:34,272 INFO sqlalchemy.engine.Engine show standard_conforming_strings
|
||||
2022-11-24 08:58:34,272 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-24 08:58:34,277 INFO sqlalchemy.engine.Engine BEGIN (implicit)
|
||||
2022-11-24 08:58:34,277 INFO sqlalchemy.engine.Engine COMMIT
|
||||
[34m[1mexport: [0mdata=app/yolov5/data/coco128.yaml, weights=/mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.pt, imgsz=[640, 640], batch_size=1, device=0, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=11, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
|
||||
2022-11-29 08:44:22,208 INFO sqlalchemy.engine.Engine select pg_catalog.version()
|
||||
2022-11-29 08:44:22,209 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-29 08:44:22,214 INFO sqlalchemy.engine.Engine select current_schema()
|
||||
2022-11-29 08:44:22,214 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-29 08:44:22,219 INFO sqlalchemy.engine.Engine show standard_conforming_strings
|
||||
2022-11-29 08:44:22,219 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-29 08:44:22,225 INFO sqlalchemy.engine.Engine BEGIN (implicit)
|
||||
2022-11-29 08:44:22,225 INFO sqlalchemy.engine.Engine COMMIT
|
||||
[34m[1mdetect_server: [0mid=195095688265211904_17_detect, weights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//核酸检测_190857268466688000_R-ODY_17_640.pt, source=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera, output=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/results, data=app/yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=0, view_img=False, save_txt=True, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=app/yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
|
||||
YOLOv5 🚀 2022-11-7 Python-3.8.13 torch-1.8.0+cu111 CUDA:0 (Tesla T4, 15110MiB)
|
||||
|
||||
2022-11-29 08:44:36.902619: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
|
||||
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
|
||||
2022-11-29 08:44:37.033367: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
|
||||
2022-11-29 08:44:37.070603: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
|
||||
2022-11-29 08:44:37.607302: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/cv2/../../lib64::/usr/local/cuda/lib64
|
||||
2022-11-29 08:44:37.607416: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/cv2/../../lib64::/usr/local/cuda/lib64
|
||||
2022-11-29 08:44:37.607434: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
|
||||
Fusing layers...
|
||||
192.168.0.20 - - [2022-11-29 08:44:42] "GET /api/obtain_detect_param HTTP/1.1" 200 1110 0.000672
|
||||
192.168.0.20 - - [2022-11-29 08:44:42] "GET /api/start_detect_algorithm?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22inputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F195095688265211904%2Fcamera%22%2C+%22description%22%3A+%22%5Cu8f93%5Cu5165%5Cu56fe%5Cu50cf%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2FM006B_waibi%2FJPEGImages%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22outputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F195095688265211904%2Fresults%22%2C+%22description%22%3A+%22%5Cu8f93%5Cu51fa%5Cu7ed3%5Cu679c%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2FM006B_waibi%2Fres%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22modPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%2F%5Cu6838%5Cu9178%5Cu68c0%5Cu6d4b_190857268466688000_R-ODY_17_640.pt%22%2C+%22description%22%3A+%22%5Cu6a21%5Cu578b%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fsaved_model%2Ftest.pt%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+3%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%220%22%2C+%22description%22%3A+%22%5Cu63a8%5Cu7406%5Cu6838%22%2C+%22default%22%3A+%22cpu%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%5D&id=195095688265211904_17_detect HTTP/1.1" 200 161 0.002945
|
||||
[34m[1mdetect_server: [0mid=195095688265211904_17_detect, weights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//核酸检测_190857268466688000_R-ODY_17_640.pt, source=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera, output=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/results, data=app/yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=0, view_img=False, save_txt=True, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=app/yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
|
||||
YOLOv5 🚀 2022-11-7 Python-3.8.13 torch-1.8.0+cu111 CUDA:0 (Tesla T4, 15110MiB)
|
||||
|
||||
Fusing layers...
|
||||
Model summary: 213 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
|
||||
------进入websocket
|
||||
Model summary: 213 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
|
||||
image 1/1 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera/微信截图_20221129084118.png: 640x512 1 zui, 1 mianbang, 23.9ms
|
||||
Speed: 0.4ms pre-process, 23.9ms inference, 1.5ms NMS per image at shape (1, 3, 640, 640)
|
||||
image 1/1 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera/微信截图_20221129084118.png: 640x512 1 zui, 1 mianbang, 21.2ms
|
||||
Speed: 0.4ms pre-process, 21.2ms inference, 1.1ms NMS per image at shape (1, 3, 640, 640)
|
||||
192.168.0.20 - - [2022-11-29 08:46:07] "GET /api/obtain_detect_param HTTP/1.1" 200 1110 0.000634
|
||||
192.168.0.20 - - [2022-11-29 08:46:07] "GET /api/start_detect_algorithm?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22inputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F195095688265211904%2Fcamera%22%2C+%22description%22%3A+%22%5Cu8f93%5Cu5165%5Cu56fe%5Cu50cf%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2FM006B_waibi%2FJPEGImages%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22outputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F195095688265211904%2Fresults%22%2C+%22description%22%3A+%22%5Cu8f93%5Cu51fa%5Cu7ed3%5Cu679c%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2FM006B_waibi%2Fres%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22modPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%2F%5Cu6838%5Cu9178%5Cu68c0%5Cu6d4b_190857268466688000_R-ODY_17_640.pt%22%2C+%22description%22%3A+%22%5Cu6a21%5Cu578b%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fsaved_model%2Ftest.pt%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+3%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%220%22%2C+%22description%22%3A+%22%5Cu63a8%5Cu7406%5Cu6838%22%2C+%22default%22%3A+%22cpu%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%5D&id=195095688265211904_17_detect HTTP/1.1" 200 161 0.002091
|
||||
[34m[1mdetect_server: [0mid=195095688265211904_17_detect, weights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//核酸检测_190857268466688000_R-ODY_17_640.pt, source=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera, output=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/results, data=app/yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=0, view_img=False, save_txt=True, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=app/yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
|
||||
YOLOv5 🚀 2022-11-7 Python-3.8.13 torch-1.8.0+cu111 CUDA:0 (Tesla T4, 15110MiB)
|
||||
|
||||
Fusing layers...
|
||||
Model summary: 213 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
|
||||
image 1/1 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera/微信截图_20221129084118.png: 640x512 1 zui, 1 mianbang, 12.3ms
|
||||
Speed: 0.4ms pre-process, 12.3ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640)
|
||||
------进入websocket
|
||||
192.168.0.20 - - [2022-11-29 08:46:19] "GET /api/obtain_detect_param HTTP/1.1" 200 1110 0.000607
|
||||
192.168.0.20 - - [2022-11-29 08:46:19] "GET /api/start_detect_algorithm?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22inputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F195095688265211904%2Fcamera%22%2C+%22description%22%3A+%22%5Cu8f93%5Cu5165%5Cu56fe%5Cu50cf%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2FM006B_waibi%2FJPEGImages%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22outputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F195095688265211904%2Fresults%22%2C+%22description%22%3A+%22%5Cu8f93%5Cu51fa%5Cu7ed3%5Cu679c%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2FM006B_waibi%2Fres%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22modPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%2F%5Cu6838%5Cu9178%5Cu68c0%5Cu6d4b_190857268466688000_R-ODY_17_640.pt%22%2C+%22description%22%3A+%22%5Cu6a21%5Cu578b%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fsaved_model%2Ftest.pt%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+3%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%220%22%2C+%22description%22%3A+%22%5Cu63a8%5Cu7406%5Cu6838%22%2C+%22default%22%3A+%22cpu%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%5D&id=195095688265211904_17_detect HTTP/1.1" 200 161 0.002050
|
||||
[34m[1mdetect_server: [0mid=195095688265211904_17_detect, weights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//核酸检测_190857268466688000_R-ODY_17_640.pt, source=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera, output=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/results, data=app/yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=0, view_img=False, save_txt=True, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=app/yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
|
||||
YOLOv5 🚀 2022-11-7 Python-3.8.13 torch-1.8.0+cu111 CUDA:0 (Tesla T4, 15110MiB)
|
||||
|
||||
Fusing layers...
|
||||
Model summary: 213 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
|
||||
image 1/1 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/195095688265211904/camera/微信截图_20221129084118.png: 640x512 1 zui, 1 mianbang, 12.3ms
|
||||
Speed: 0.4ms pre-process, 12.3ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640)
|
||||
------进入websocket
|
||||
192.168.0.20 - - [2022-11-29 09:46:07] "GET /api/obtain_train_param HTTP/1.1" 200 1936 0.003772
|
||||
------进入websocket
|
||||
------进入websocket
|
||||
192.168.0.20 - - [2022-11-29 09:52:50] "GET /api/obtain_train_param HTTP/1.1" 200 1936 0.000666
|
||||
------进入websocket
|
||||
------进入websocket
|
||||
------进入websocket
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
-----------回调消息成功------------
|
||||
192.168.0.20 - - [2022-11-29 10:06:37] "GET /api/obtain_train_param HTTP/1.1" 200 1936 0.000656
|
||||
------进入websocket
|
||||
存储ws连接对象
|
||||
[31m[1mrequirements:[0m /mnt/sdc/algorithm/R-ODY/app/yolov5/requirements.txt not found, check failed.
|
||||
True
|
||||
[31m[1mrequirements:[0m /mnt/sdc/algorithm/R-ODY/app/yolov5/requirements.txt not found, check failed.
|
||||
True
|
||||
存储ws连接对象
|
||||
图片总数量： 1
|
||||
处理成功数量： 1
|
||||
处理失败数量： 0
|
||||
图片总数量： 1
|
||||
处理成功数量： 1
|
||||
处理失败数量： 0
|
||||
[31m[1mrequirements:[0m /mnt/sdc/algorithm/R-ODY/app/yolov5/requirements.txt not found, check failed.
|
||||
True
|
||||
图片总数量： 1
|
||||
处理成功数量： 1
|
||||
处理失败数量： 0
|
||||
存储ws连接对象
|
||||
[31m[1mrequirements:[0m /mnt/sdc/algorithm/R-ODY/app/yolov5/requirements.txt not found, check failed.
|
||||
True
|
||||
图片总数量： 1
|
||||
处理成功数量： 1
|
||||
处理失败数量： 0
|
||||
存储ws连接对象
|
||||
存储ws连接对象
|
||||
存储ws连接对象
|
||||
存储ws连接对象
|
||||
存储ws连接对象
|
||||
存储ws连接对象
|
||||
None
|
||||
None
|
||||
None
|
||||
None
|
||||
None
|
||||
None
|
||||
None
|
||||
None
|
||||
None
|
||||
存储ws连接对象
|
||||
192.168.0.20 - - [2022-11-29 10:06:49] "GET /api/start_train_algorithm?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22epochnum%22%2C+%22value%22%3A+10%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu8f6e%5Cu6b21%22%2C+%22default%22%3A+100%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22batch_size%22%2C+%22value%22%3A+4%2C+%22description%22%3A+%22%5Cu6279%5Cu6b21%5Cu56fe%5Cu50cf%5Cu6570%5Cu91cf%22%2C+%22default%22%3A+1%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22img_size%22%2C+%22value%22%3A+640%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu56fe%5Cu50cf%5Cu5927%5Cu5c0f%22%2C+%22default%22%3A+640%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+3%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%22cuda%3A0%22%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu6838%5Cu5fc3%22%2C+%22default%22%3A+%22cuda%3A0%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%5B%22cuda%3A0%22%2C+%22cuda%3A1%22%5D%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+4%2C+%22name%22%3A+%22saveModDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F1128test_194741569180540928_R-ODY_19.pt%22%2C+%22description%22%3A+%22%5Cu4fdd%5Cu5b58%5Cu6a21%5Cu578b%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fsaved_model%2Ftest.pt%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+5%2C+%22name%22%3A+%22resumeModPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%2Fyolov5s.pt%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+6%2C+%22name%22%3A+%22resumeMod%22%2C+%22value%22%3A+%22%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu6a21%5Cu578b%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+7%2C+%22name%22%3A+%22CLASS_NAMES%22%2C+%22value%22%3A+%5B%22hole%22%2C+%22456%22%2C+%22aeroplane%22%2C+%22tvmonitor%22%2C+%22train%22%2C+%22boat%22%2C+%22dog%22%2C+%22chair%22%2C+%22bird%22%2C+%22bicycle%22%2C+%22person%22%2C+%22bottle%22%2C+%22sheep%22%2C+%22cat%22%5D%2C+%22description%22%3A+%22%5Cu7c7b%5Cu522b%5Cu540d%5Cu79f0%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22L%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+8%2C+%22name%22%3A+%22DatasetDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F194741569180540928%2Fori%22%2C+%22description%22%3A+%22%5Cu6570%5Cu636e%5Cu96c6%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2Ftest%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%5D&id=194741569180540928_19_train HTTP/1.1" 200 161 0.057693
|
||||
删除图片数据
|
||||
删除json数据
|
||||
[34m[1mtrain_server: [0mweights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//yolov5s.pt, savemodel=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/1128test_194741569180540928_R-ODY_19_640.pt, cfg=, data=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/coco128.yaml, hyp=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=cuda:0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=/mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
|
||||
[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases
|
||||
[34m[1mClearML: [0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
|
||||
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir /mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train', view at http://localhost:6006/
|
||||
Overriding model.yaml nc=80 with nc=14
|
||||
|
||||
from n params module arguments
|
||||
0 -1 1 3520 app.yolov5.models.common.Conv [3, 32, 6, 2, 2]
|
||||
1 -1 1 18560 app.yolov5.models.common.Conv [32, 64, 3, 2]
|
||||
2 -1 1 18816 app.yolov5.models.common.C3 [64, 64, 1]
|
||||
3 -1 1 73984 app.yolov5.models.common.Conv [64, 128, 3, 2]
|
||||
4 -1 2 115712 app.yolov5.models.common.C3 [128, 128, 2]
|
||||
5 -1 1 295424 app.yolov5.models.common.Conv [128, 256, 3, 2]
|
||||
6 -1 3 625152 app.yolov5.models.common.C3 [256, 256, 3]
|
||||
7 -1 1 1180672 app.yolov5.models.common.Conv [256, 512, 3, 2]
|
||||
8 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1]
|
||||
9 -1 1 656896 app.yolov5.models.common.SPPF [512, 512, 5]
|
||||
10 -1 1 131584 app.yolov5.models.common.Conv [512, 256, 1, 1]
|
||||
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
12 [-1, 6] 1 0 app.yolov5.models.common.Concat [1]
|
||||
13 -1 1 361984 app.yolov5.models.common.C3 [512, 256, 1, False]
|
||||
14 -1 1 33024 app.yolov5.models.common.Conv [256, 128, 1, 1]
|
||||
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
16 [-1, 4] 1 0 app.yolov5.models.common.Concat [1]
|
||||
17 -1 1 90880 app.yolov5.models.common.C3 [256, 128, 1, False]
|
||||
18 -1 1 147712 app.yolov5.models.common.Conv [128, 128, 3, 2]
|
||||
19 [-1, 14] 1 0 app.yolov5.models.common.Concat [1]
|
||||
20 -1 1 296448 app.yolov5.models.common.C3 [256, 256, 1, False]
|
||||
21 -1 1 590336 app.yolov5.models.common.Conv [256, 256, 3, 2]
|
||||
22 [-1, 10] 1 0 app.yolov5.models.common.Concat [1]
|
||||
23 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1, False]
|
||||
24 [17, 20, 23] 1 51243 app.yolov5.models.yolo.Detect [14, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
|
||||
Model summary: 270 layers, 7057387 parameters, 7057387 gradients, 16.1 GFLOPs
|
||||
|
||||
**********************************
|
||||
[<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06880>]
|
||||
**********************************
|
||||
**********************************
|
||||
{'195095688265211904_17_detect': [<geventwebsocket.websocket.WebSocket object at 0x7fd0e269a340>, <geventwebsocket.websocket.WebSocket object at 0x7fcf55fe9820>, <geventwebsocket.websocket.WebSocket object at 0x7fcfb9ef6760>, <geventwebsocket.websocket.WebSocket object at 0x7fcf55fe9e20>], '194741569180540928_14_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06760>], '194741569180540928_15_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06a60>], '194741569180540928_16_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06520>], '194741569180540928_17_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf5499d100>], '194741569180540928_18_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf5499d460>], '194741569180540928_19_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06880>]}
|
||||
**********************************
|
||||
[{"index": 0, "name": "epochnum", "value": 10, "description": "\u8bad\u7ec3\u8f6e\u6b21", "default": 100, "type": "I", "show": true}, {"index": 1, "name": "batch_size", "value": 4, "description": "\u6279\u6b21\u56fe\u50cf\u6570\u91cf", "default": 1, "type": "I", "show": true}, {"index": 2, "name": "img_size", "value": 640, "description": "\u8bad\u7ec3\u56fe\u50cf\u5927\u5c0f", "default": 640, "type": "I", "show": true}, {"index": 3, "name": "device", "value": "cuda:0", "description": "\u8bad\u7ec3\u6838\u5fc3", "default": "cuda:0", "type": "E", "items": ["cuda:0", "cuda:1"], "show": false}, {"index": 4, "name": "saveModDir", "value": "/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/1128test_194741569180540928_R-ODY_19.pt", "description": "\u4fdd\u5b58\u6a21\u578b\u8def\u5f84", "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", "show": false}, {"index": 5, "name": "resumeModPath", "value": "/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//yolov5s.pt", "description": "\u7ee7\u7eed\u8bad\u7ec3\u8def\u5f84", "default": "", "type": "S", "show": false}, {"index": 6, "name": "resumeMod", "value": "", "description": "\u7ee7\u7eed\u8bad\u7ec3\u6a21\u578b", "default": "", "type": "E", "items": "", "show": true}, {"index": 7, "name": "CLASS_NAMES", "value": ["hole", "456", "aeroplane", "tvmonitor", "train", "boat", "dog", "chair", "bird", "bicycle", "person", "bottle", "sheep", "cat"], "description": "\u7c7b\u522b\u540d\u79f0", "default": "", "type": "L", "items": "", "show": false}, {"index": 8, "name": "DatasetDir", "value": "/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/194741569180540928/ori", "description": "\u6570\u636e\u96c6\u8def\u5f84", "default": "./app/maskrcnn/datasets/test", "type": "S", "show": false}]
|
||||
**********************************
|
||||
cuda:0
|
||||
图像： ['2007_000032.jpg', '2007_000241.jpg', '2007_000068.jpg', '4.jpg', '3.jpg', '2007_000033.jpg', '10.jpg', '2007_000042.jpg', '7.jpg', '2007_000170.jpg', '2007_001583.jpg', '8.jpg', '2007_000187.jpg', '1.jpg', '2007_001457.jpg', '2007_000061.jpg', '2007_000027.jpg', '2007_000063.jpg', '2007_000129.jpg', '5.jpg', '2007_000123.jpg', '2007_000121.jpg', '9.jpg', '2007_000175.jpg', '2007_000039.jpg', '2007_001430.jpg', '6.jpg', '2007_001585.jpg', '2.jpg']
|
||||
图像路径 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/194741569180540928/ori/images/2007_000032.jpg
|
||||
1111
|
||||
标签 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/194741569180540928/ori/labels/2007_000032.json
|
||||
2222
|
||||
ROOT############### /mnt/sdc/algorithm/R-ODY/app/yolov5
|
||||
|
||||
opt.device: cuda:0
|
||||
|
||||
|
||||
device: cuda:0
|
||||
|
||||
get in train()
|
||||
Process 194741569180540928_19_train:
|
||||
Traceback (most recent call last):
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/flask_sockets.py", line 40, in __call__
|
||||
handler, values = adapter.match()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/werkzeug/routing.py", line 1945, in match
|
||||
raise NotFound()
|
||||
werkzeug.exceptions.NotFound: 404 Not Found: The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.
|
||||
|
||||
During handling of the above exception, another exception occurred:
|
||||
|
||||
Traceback (most recent call last):
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
|
||||
self.run()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/multiprocessing/process.py", line 108, in run
|
||||
self._target(*self._args, **self._kwargs)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/controller/AlgorithmController.py", line 327, in train_R0DY
|
||||
train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id, getsomething)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 733, in train_start
|
||||
main(opt,data_list,id,getsomething)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 630, in main
|
||||
train(opt.hyp, opt, device, data_list,id,getsomething,callbacks)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 168, in train
|
||||
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 673, in to
|
||||
return self._apply(convert)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/models/yolo.py", line 136, in _apply
|
||||
self = super()._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 409, in _apply
|
||||
param_applied = fn(param)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 671, in convert
|
||||
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/cuda/__init__.py", line 160, in _lazy_init
|
||||
raise RuntimeError(
|
||||
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
|
||||
192.168.0.20 - - [2022-11-29 10:21:16] "GET /api/obtain_train_param HTTP/1.1" 200 1936 0.000878
|
||||
------进入websocket
|
||||
存储ws连接对象
|
||||
192.168.0.20 - - [2022-11-29 10:21:27] "GET /api/start_train_algorithm?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22epochnum%22%2C+%22value%22%3A+10%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu8f6e%5Cu6b21%22%2C+%22default%22%3A+100%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22batch_size%22%2C+%22value%22%3A+4%2C+%22description%22%3A+%22%5Cu6279%5Cu6b21%5Cu56fe%5Cu50cf%5Cu6570%5Cu91cf%22%2C+%22default%22%3A+1%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22img_size%22%2C+%22value%22%3A+640%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu56fe%5Cu50cf%5Cu5927%5Cu5c0f%22%2C+%22default%22%3A+640%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+3%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%22cuda%3A0%22%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu6838%5Cu5fc3%22%2C+%22default%22%3A+%22cuda%3A0%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%5B%22cuda%3A0%22%2C+%22cuda%3A1%22%5D%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+4%2C+%22name%22%3A+%22saveModDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F1128test_194741569180540928_R-ODY_20.pt%22%2C+%22description%22%3A+%22%5Cu4fdd%5Cu5b58%5Cu6a21%5Cu578b%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fsaved_model%2Ftest.pt%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+5%2C+%22name%22%3A+%22resumeModPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%2Fyolov5s.pt%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+6%2C+%22name%22%3A+%22resumeMod%22%2C+%22value%22%3A+%22%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu6a21%5Cu578b%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+7%2C+%22name%22%3A+%22CLASS_NAMES%22%2C+%22value%22%3A+%5B%22hole%22%2C+%22456%22%2C+%22aeroplane%22%2C+%22tvmonitor%22%2C+%22train%22%2C+%22boat%22%2C+%22dog%22%2C+%22chair%22%2C+%22bird%22%2C+%22bicycle%22%2C+%22person%22%2C+%22bottle%22%2C+%22sheep%22%2C+%22cat%22%5D%2C+%22description%22%3A+%22%5Cu7c7b%5Cu522b%5Cu540d%5Cu79f0%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22L%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+8%2C+%22name%22%3A+%22DatasetDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F194741569180540928%2Fori%22%2C+%22description%22%3A+%22%5Cu6570%5Cu636e%5Cu96c6%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2Ftest%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%5D&id=194741569180540928_20_train HTTP/1.1" 200 161 0.050371
|
||||
删除图片数据
|
||||
删除json数据
|
||||
[34m[1mtrain_server: [0mweights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//yolov5s.pt, savemodel=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/1128test_194741569180540928_R-ODY_20_640.pt, cfg=, data=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/coco128.yaml, hyp=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=cuda:0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=/mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
|
||||
[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases
|
||||
[34m[1mClearML: [0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
|
||||
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir /mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train', view at http://localhost:6006/
|
||||
Overriding model.yaml nc=80 with nc=14
|
||||
|
||||
from n params module arguments
|
||||
0 -1 1 3520 app.yolov5.models.common.Conv [3, 32, 6, 2, 2]
|
||||
1 -1 1 18560 app.yolov5.models.common.Conv [32, 64, 3, 2]
|
||||
2 -1 1 18816 app.yolov5.models.common.C3 [64, 64, 1]
|
||||
3 -1 1 73984 app.yolov5.models.common.Conv [64, 128, 3, 2]
|
||||
4 -1 2 115712 app.yolov5.models.common.C3 [128, 128, 2]
|
||||
5 -1 1 295424 app.yolov5.models.common.Conv [128, 256, 3, 2]
|
||||
6 -1 3 625152 app.yolov5.models.common.C3 [256, 256, 3]
|
||||
7 -1 1 1180672 app.yolov5.models.common.Conv [256, 512, 3, 2]
|
||||
8 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1]
|
||||
9 -1 1 656896 app.yolov5.models.common.SPPF [512, 512, 5]
|
||||
10 -1 1 131584 app.yolov5.models.common.Conv [512, 256, 1, 1]
|
||||
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
12 [-1, 6] 1 0 app.yolov5.models.common.Concat [1]
|
||||
13 -1 1 361984 app.yolov5.models.common.C3 [512, 256, 1, False]
|
||||
14 -1 1 33024 app.yolov5.models.common.Conv [256, 128, 1, 1]
|
||||
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
16 [-1, 4] 1 0 app.yolov5.models.common.Concat [1]
|
||||
17 -1 1 90880 app.yolov5.models.common.C3 [256, 128, 1, False]
|
||||
18 -1 1 147712 app.yolov5.models.common.Conv [128, 128, 3, 2]
|
||||
19 [-1, 14] 1 0 app.yolov5.models.common.Concat [1]
|
||||
20 -1 1 296448 app.yolov5.models.common.C3 [256, 256, 1, False]
|
||||
21 -1 1 590336 app.yolov5.models.common.Conv [256, 256, 3, 2]
|
||||
22 [-1, 10] 1 0 app.yolov5.models.common.Concat [1]
|
||||
23 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1, False]
|
||||
24 [17, 20, 23] 1 51243 app.yolov5.models.yolo.Detect [14, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
|
||||
Model summary: 270 layers, 7057387 parameters, 7057387 gradients, 16.1 GFLOPs
|
||||
|
||||
**********************************
|
||||
[<geventwebsocket.websocket.WebSocket object at 0x7fcf5499d7c0>]
|
||||
**********************************
|
||||
**********************************
|
||||
{'195095688265211904_17_detect': [<geventwebsocket.websocket.WebSocket object at 0x7fd0e269a340>, <geventwebsocket.websocket.WebSocket object at 0x7fcf55fe9820>, <geventwebsocket.websocket.WebSocket object at 0x7fcfb9ef6760>, <geventwebsocket.websocket.WebSocket object at 0x7fcf55fe9e20>], '194741569180540928_14_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06760>], '194741569180540928_15_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06a60>], '194741569180540928_16_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06520>], '194741569180540928_17_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf5499d100>], '194741569180540928_18_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf5499d460>], '194741569180540928_19_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06880>], '194741569180540928_20_train': [<geventwebsocket.websocket.WebSocket object at 0x7fcf5499d7c0>]}
|
||||
**********************************
|
||||
[{"index": 0, "name": "epochnum", "value": 10, "description": "\u8bad\u7ec3\u8f6e\u6b21", "default": 100, "type": "I", "show": true}, {"index": 1, "name": "batch_size", "value": 4, "description": "\u6279\u6b21\u56fe\u50cf\u6570\u91cf", "default": 1, "type": "I", "show": true}, {"index": 2, "name": "img_size", "value": 640, "description": "\u8bad\u7ec3\u56fe\u50cf\u5927\u5c0f", "default": 640, "type": "I", "show": true}, {"index": 3, "name": "device", "value": "cuda:0", "description": "\u8bad\u7ec3\u6838\u5fc3", "default": "cuda:0", "type": "E", "items": ["cuda:0", "cuda:1"], "show": false}, {"index": 4, "name": "saveModDir", "value": "/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/1128test_194741569180540928_R-ODY_20.pt", "description": "\u4fdd\u5b58\u6a21\u578b\u8def\u5f84", "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", "show": false}, {"index": 5, "name": "resumeModPath", "value": "/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//yolov5s.pt", "description": "\u7ee7\u7eed\u8bad\u7ec3\u8def\u5f84", "default": "", "type": "S", "show": false}, {"index": 6, "name": "resumeMod", "value": "", "description": "\u7ee7\u7eed\u8bad\u7ec3\u6a21\u578b", "default": "", "type": "E", "items": "", "show": true}, {"index": 7, "name": "CLASS_NAMES", "value": ["hole", "456", "aeroplane", "tvmonitor", "train", "boat", "dog", "chair", "bird", "bicycle", "person", "bottle", "sheep", "cat"], "description": "\u7c7b\u522b\u540d\u79f0", "default": "", "type": "L", "items": "", "show": false}, {"index": 8, "name": "DatasetDir", "value": "/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/194741569180540928/ori", "description": "\u6570\u636e\u96c6\u8def\u5f84", "default": "./app/maskrcnn/datasets/test", "type": "S", "show": false}]
|
||||
**********************************
|
||||
cuda:0
|
||||
图像： ['2007_000032.jpg', '2007_000241.jpg', '2007_000068.jpg', '4.jpg', '3.jpg', '2007_000033.jpg', '10.jpg', '2007_000042.jpg', '7.jpg', '2007_000170.jpg', '2007_001583.jpg', '8.jpg', '2007_000187.jpg', '1.jpg', '2007_001457.jpg', '2007_000061.jpg', '2007_000027.jpg', '2007_000063.jpg', '2007_000129.jpg', '5.jpg', '2007_000123.jpg', '2007_000121.jpg', '9.jpg', '2007_000175.jpg', '2007_000039.jpg', '2007_001430.jpg', '6.jpg', '2007_001585.jpg', '2.jpg']
|
||||
图像路径 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/194741569180540928/ori/images/2007_000032.jpg
|
||||
1111
|
||||
标签 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/194741569180540928/ori/labels/2007_000032.json
|
||||
2222
|
||||
ROOT############### /mnt/sdc/algorithm/R-ODY/app/yolov5
|
||||
|
||||
opt.device: cuda:0
|
||||
|
||||
|
||||
device: cuda:0
|
||||
|
||||
get in train()
|
||||
Process 194741569180540928_20_train:
|
||||
Traceback (most recent call last):
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/flask_sockets.py", line 40, in __call__
|
||||
handler, values = adapter.match()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/werkzeug/routing.py", line 1945, in match
|
||||
raise NotFound()
|
||||
werkzeug.exceptions.NotFound: 404 Not Found: The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.
|
||||
|
||||
During handling of the above exception, another exception occurred:
|
||||
|
||||
Traceback (most recent call last):
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
|
||||
self.run()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/multiprocessing/process.py", line 108, in run
|
||||
self._target(*self._args, **self._kwargs)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/controller/AlgorithmController.py", line 327, in train_R0DY
|
||||
train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id, getsomething)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 733, in train_start
|
||||
main(opt,data_list,id,getsomething)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 630, in main
|
||||
train(opt.hyp, opt, device, data_list,id,getsomething,callbacks)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 168, in train
|
||||
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 673, in to
|
||||
return self._apply(convert)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/models/yolo.py", line 136, in _apply
|
||||
self = super()._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 409, in _apply
|
||||
param_applied = fn(param)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 671, in convert
|
||||
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/cuda/__init__.py", line 160, in _lazy_init
|
||||
raise RuntimeError(
|
||||
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
|
||||
192.168.0.20 - - [2022-11-29 10:21:55] "GET /api/obtain_train_param HTTP/1.1" 200 1936 0.000899
|
||||
------进入websocket
|
||||
存储ws连接对象
|
||||
192.168.0.20 - - [2022-11-29 10:22:24] "GET /api/start_train_algorithm?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22epochnum%22%2C+%22value%22%3A+10%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu8f6e%5Cu6b21%22%2C+%22default%22%3A+100%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22batch_size%22%2C+%22value%22%3A+4%2C+%22description%22%3A+%22%5Cu6279%5Cu6b21%5Cu56fe%5Cu50cf%5Cu6570%5Cu91cf%22%2C+%22default%22%3A+1%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22img_size%22%2C+%22value%22%3A+640%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu56fe%5Cu50cf%5Cu5927%5Cu5c0f%22%2C+%22default%22%3A+640%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+3%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%22cuda%3A0%22%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu6838%5Cu5fc3%22%2C+%22default%22%3A+%22cuda%3A0%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%5B%22cuda%3A0%22%2C+%22cuda%3A1%22%5D%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+4%2C+%22name%22%3A+%22saveModDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%5Cu6838%5Cu9178%5Cu68c0%5Cu6d4b_190857268466688000_R-ODY_18.pt%22%2C+%22description%22%3A+%22%5Cu4fdd%5Cu5b58%5Cu6a21%5Cu578b%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fsaved_model%2Ftest.pt%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+5%2C+%22name%22%3A+%22resumeModPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%2Fyolov5s.pt%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+6%2C+%22name%22%3A+%22resumeMod%22%2C+%22value%22%3A+%22%2F1128test_194741569180540928_R-ODY_13_640.pt%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu6a21%5Cu578b%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+7%2C+%22name%22%3A+%22CLASS_NAMES%22%2C+%22value%22%3A+%5B%22hole%22%2C+%22456%22%2C+%22zui%22%2C+%22mianbang%22%5D%2C+%22description%22%3A+%22%5Cu7c7b%5Cu522b%5Cu540d%5Cu79f0%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22L%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+8%2C+%22name%22%3A+%22DatasetDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F190857268466688000%2Fori%22%2C+%22description%22%3A+%22%5Cu6570%5Cu636e%5Cu96c6%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2Ftest%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%5D&id=190857268466688000_18_train HTTP/1.1" 200 161 0.050019
|
||||
删除图片数据
|
||||
删除json数据
|
||||
192.168.0.20 - - [2022-11-29 10:22:26] "GET /api/obtain_download_pt_param HTTP/1.1" 200 792 0.000835
|
||||
[34m[1mtrain_server: [0mweights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//yolov5s.pt, savemodel=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_18_640.pt, cfg=, data=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/coco128.yaml, hyp=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=cuda:0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=/mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
|
||||
[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases
|
||||
[34m[1mClearML: [0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
|
||||
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir /mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train', view at http://localhost:6006/
|
||||
Overriding model.yaml nc=80 with nc=4
|
||||
|
||||
from n params module arguments
|
||||
0 -1 1 3520 app.yolov5.models.common.Conv [3, 32, 6, 2, 2]
|
||||
1 -1 1 18560 app.yolov5.models.common.Conv [32, 64, 3, 2]
|
||||
2 -1 1 18816 app.yolov5.models.common.C3 [64, 64, 1]
|
||||
3 -1 1 73984 app.yolov5.models.common.Conv [64, 128, 3, 2]
|
||||
4 -1 2 115712 app.yolov5.models.common.C3 [128, 128, 2]
|
||||
5 -1 1 295424 app.yolov5.models.common.Conv [128, 256, 3, 2]
|
||||
6 -1 3 625152 app.yolov5.models.common.C3 [256, 256, 3]
|
||||
7 -1 1 1180672 app.yolov5.models.common.Conv [256, 512, 3, 2]
|
||||
8 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1]
|
||||
9 -1 1 656896 app.yolov5.models.common.SPPF [512, 512, 5]
|
||||
10 -1 1 131584 app.yolov5.models.common.Conv [512, 256, 1, 1]
|
||||
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
12 [-1, 6] 1 0 app.yolov5.models.common.Concat [1]
|
||||
13 -1 1 361984 app.yolov5.models.common.C3 [512, 256, 1, False]
|
||||
14 -1 1 33024 app.yolov5.models.common.Conv [256, 128, 1, 1]
|
||||
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
16 [-1, 4] 1 0 app.yolov5.models.common.Concat [1]
|
||||
17 -1 1 90880 app.yolov5.models.common.C3 [256, 128, 1, False]
|
||||
18 -1 1 147712 app.yolov5.models.common.Conv [128, 128, 3, 2]
|
||||
19 [-1, 14] 1 0 app.yolov5.models.common.Concat [1]
|
||||
20 -1 1 296448 app.yolov5.models.common.C3 [256, 256, 1, False]
|
||||
21 -1 1 590336 app.yolov5.models.common.Conv [256, 256, 3, 2]
|
||||
22 [-1, 10] 1 0 app.yolov5.models.common.Concat [1]
|
||||
23 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1, False]
|
||||
24 [17, 20, 23] 1 24273 app.yolov5.models.yolo.Detect [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
|
||||
Model summary: 270 layers, 7030417 parameters, 7030417 gradients, 16.0 GFLOPs
|
||||
|
||||
**********************************
|
||||
[<geventwebsocket.websocket.WebSocket object at 0x7fcf54d06e80>]
|
||||
**********************************
|
||||
**********************************
|
||||
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|
||||
**********************************
|
||||
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|
||||
**********************************
|
||||
cuda:0
|
||||
图像： ['IMG_20221117_132941~1.jpg', 'IMG_20221117_133002~1.jpg', 'IMG_20221117_152005~1.jpg', 'IMG_20221117_133005~1.jpg', 'IMG_20221117_132939~1.jpg', 'IMG_20221117_151945~1.jpg', 'IMG_20221117_133009~1.jpg', 'IMG_20221117_152023~1.jpg', 'IMG_20221117_133035~1.jpg', 'IMG_20221117_132947~1.jpg', 'IMG_20221117_151925~1.jpg', 'IMG_20221117_152026~1.jpg', 'IMG_20221117_133037~1.jpg', 'IMG_20221117_152018~1.jpg', 'IMG_20221117_152002~1.jpg', 'IMG_20221117_152004~1.jpg', 'IMG_20221117_152019~1.jpg', 'IMG_20221117_133006~1.jpg', 'IMG_20221117_152020~1.jpg', 'IMG_20221117_151959~1.jpg', 'IMG_20221117_152024~1.jpg', 'IMG_20221117_151921~1.jpg', 'IMG_20221117_151923~1.jpg', 'IMG_20221117_133038~1.jpg', 'IMG_20221117_151943~1.jpg', 'IMG_20221117_151924~1.jpg', 'IMG_20221117_152022~1.jpg', 'IMG_20221117_133032~1.jpg', 'IMG_20221117_151957~1.jpg', 'IMG_20221117_151939~1.jpg', 'IMG_20221117_133040~1.jpg', 'IMG_20221117_151946~1.jpg', 'IMG_20221117_151944~1.jpg', 'IMG_20221117_133007~1.jpg', 'IMG_20221117_132946~1.jpg', 'IMG_20221117_133004~1.jpg', 'IMG_20221117_152001~1.jpg', 'IMG_20221117_151941~1.jpg', 'IMG_20221117_151919~1.jpg', 'IMG_20221117_132944~1.jpg']
|
||||
图像路径 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/190857268466688000/ori/images/IMG_20221117_132941~1.jpg
|
||||
1111
|
||||
标签 /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/190857268466688000/ori/labels/IMG_20221117_132941~1.json
|
||||
2222
|
||||
ROOT############### /mnt/sdc/algorithm/R-ODY/app/yolov5
|
||||
|
||||
opt.device: cuda:0
|
||||
|
||||
|
||||
device: cuda:0
|
||||
|
||||
get in train()
|
||||
Process 190857268466688000_18_train:
|
||||
Traceback (most recent call last):
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/flask_sockets.py", line 40, in __call__
|
||||
handler, values = adapter.match()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/werkzeug/routing.py", line 1945, in match
|
||||
raise NotFound()
|
||||
werkzeug.exceptions.NotFound: 404 Not Found: The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.
|
||||
|
||||
During handling of the above exception, another exception occurred:
|
||||
|
||||
Traceback (most recent call last):
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
|
||||
self.run()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/multiprocessing/process.py", line 108, in run
|
||||
self._target(*self._args, **self._kwargs)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/controller/AlgorithmController.py", line 327, in train_R0DY
|
||||
train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id, getsomething)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 733, in train_start
|
||||
main(opt,data_list,id,getsomething)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 630, in main
|
||||
train(opt.hyp, opt, device, data_list,id,getsomething,callbacks)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/train_server.py", line 168, in train
|
||||
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 673, in to
|
||||
return self._apply(convert)
|
||||
File "/mnt/sdc/algorithm/R-ODY/app/yolov5/models/yolo.py", line 136, in _apply
|
||||
self = super()._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
|
||||
module._apply(fn)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 409, in _apply
|
||||
param_applied = fn(param)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/nn/modules/module.py", line 671, in convert
|
||||
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/torch/cuda/__init__.py", line 160, in _lazy_init
|
||||
raise RuntimeError(
|
||||
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
|
||||
[34m[1mexport: [0mdata=app/yolov5/data/coco128.yaml, weights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.pt, imgsz=[640, 640], batch_size=1, device=0, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=11, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
|
||||
YOLOv5 🚀 2022-11-7 Python-3.8.13 torch-1.8.0+cu111 CUDA:0 (Tesla T4, 15110MiB)
|
||||
|
||||
Fusing layers...
|
||||
Model summary: 213 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
|
||||
|
||||
[34m[1mPyTorch:[0m starting from /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.pt with output shape (1, 25200, 9) (13.8 MB)
|
||||
[34m[1mPyTorch:[0m starting from /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.pt with output shape (1, 25200, 9) (13.8 MB)
|
||||
|
||||
[34m[1mTorchScript:[0m starting export with torch 1.8.0+cu111...
|
||||
[34m[1mTorchScript:[0m export success ✅ 0.9s, saved as /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.torchscript (27.3 MB)
|
||||
[34m[1mTorchScript:[0m export success ✅ 1.0s, saved as /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.torchscript (27.3 MB)
|
||||
|
||||
[34m[1mONNX:[0m starting export with onnx 1.12.0...
|
||||
[34m[1mONNX:[0m export success ✅ 1.8s, saved as /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx (27.2 MB)
|
||||
[34m[1mONNX:[0m export success ✅ 1.6s, saved as /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.onnx (27.2 MB)
|
||||
|
||||
Export complete (6.5s)
|
||||
Results saved to [1m/mnt/sdc/aicheck/IntelligentizeAI/data_set/weights[0m
|
||||
Detect: python detect.py --weights /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx
|
||||
Validate: python val.py --weights /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx
|
||||
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '/mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx')
|
||||
Export complete (2.8s)
|
||||
Results saved to [1m/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights[0m
|
||||
Detect: python detect.py --weights /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.onnx
|
||||
Validate: python val.py --weights /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.onnx
|
||||
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.onnx')
|
||||
Visualize: https://netron.app
|
||||
192.168.0.20 - - [2022-11-24 08:59:15] "GET /api/start_download_pt?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22exp_inputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2Faicheck%2FIntelligentizeAI%2Fdata_set%2Fweights%2Fces2_193120735164768256_R-ODY_2_640.pt%22%2C+%22description%22%3A+%22%5Cu8f6c%5Cu5316%5Cu6a21%5Cu578b%5Cu8f93%5Cu5165%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22E%3A%2Falg_demo-master%2Falg_demo%2Fapp%2Fyolov5%2F%5Cu5706%5Cu5b54_123_RODY_1_640.pt%2F%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%22gpu%22%2C+%22description%22%3A+%22CPU%5Cu6216GPU%22%2C+%22default%22%3A+%22gpu%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22imgsz%22%2C+%22value%22%3A+640%2C+%22description%22%3A+%22%5Cu56fe%5Cu50cf%5Cu5927%5Cu5c0f%22%2C+%22default%22%3A+640%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%5D&id=875 HTTP/1.1" 200 240 7.984953
|
||||
[34m[1mexport: [0mdata=app/yolov5/data/coco128.yaml, weights=/mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.pt, imgsz=[640, 640], batch_size=1, device=0, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=11, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
|
||||
YOLOv5 🚀 2022-11-7 Python-3.8.13 torch-1.8.0+cu111 CUDA:0 (Tesla T4, 15110MiB)
|
||||
192.168.0.20 - - [2022-11-29 10:22:31] "GET /api/start_download_pt?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22exp_inputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%5Cu6838%5Cu9178%5Cu68c0%5Cu6d4b_190857268466688000_R-ODY_17_640.pt%22%2C+%22description%22%3A+%22%5Cu8f6c%5Cu5316%5Cu6a21%5Cu578b%5Cu8f93%5Cu5165%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22E%3A%2Falg_demo-master%2Falg_demo%2Fapp%2Fyolov5%2F%5Cu5706%5Cu5b54_123_RODY_1_640.pt%2F%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%22gpu%22%2C+%22description%22%3A+%22CPU%5Cu6216GPU%22%2C+%22default%22%3A+%22gpu%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22imgsz%22%2C+%22value%22%3A+640%2C+%22description%22%3A+%22%5Cu56fe%5Cu50cf%5Cu5927%5Cu5c0f%22%2C+%22default%22%3A+640%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%5D&id=737 HTTP/1.1" 200 270 3.030957
|
||||
------进入websocket
|
||||
输入模型： /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.pt
|
||||
['torchscript', 'onnx']
|
||||
['torchscript', 'onnx']
|
||||
('torchscript', 'onnx', 'openvino', 'engine', 'coreml', 'saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs')
|
||||
True
|
||||
模型路径： /mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/核酸检测_190857268466688000_R-ODY_17_640.zip
|
||||
存储ws连接对象
|
||||
192.168.0.20 - - [2022-11-29 10:44:19] "GET /api/start_train_algorithm?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22epochnum%22%2C+%22value%22%3A+10%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu8f6e%5Cu6b21%22%2C+%22default%22%3A+100%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22batch_size%22%2C+%22value%22%3A+4%2C+%22description%22%3A+%22%5Cu6279%5Cu6b21%5Cu56fe%5Cu50cf%5Cu6570%5Cu91cf%22%2C+%22default%22%3A+1%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22img_size%22%2C+%22value%22%3A+640%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu56fe%5Cu50cf%5Cu5927%5Cu5c0f%22%2C+%22default%22%3A+640%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+3%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%22cuda%3A0%22%2C+%22description%22%3A+%22%5Cu8bad%5Cu7ec3%5Cu6838%5Cu5fc3%22%2C+%22default%22%3A+%22cuda%3A0%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%5B%22cuda%3A0%22%2C+%22cuda%3A1%22%5D%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+4%2C+%22name%22%3A+%22saveModDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F1128test_194741569180540928_R-ODY_21.pt%22%2C+%22description%22%3A+%22%5Cu4fdd%5Cu5b58%5Cu6a21%5Cu578b%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fsaved_model%2Ftest.pt%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+5%2C+%22name%22%3A+%22resumeModPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2Fweights%2F%2Fyolov5s.pt%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+6%2C+%22name%22%3A+%22resumeMod%22%2C+%22value%22%3A+%22%22%2C+%22description%22%3A+%22%5Cu7ee7%5Cu7eed%5Cu8bad%5Cu7ec3%5Cu6a21%5Cu578b%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22E%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+true%7D%2C+%7B%22index%22%3A+7%2C+%22name%22%3A+%22CLASS_NAMES%22%2C+%22value%22%3A+%5B%22hole%22%2C+%22456%22%2C+%22aeroplane%22%2C+%22tvmonitor%22%2C+%22train%22%2C+%22boat%22%2C+%22dog%22%2C+%22chair%22%2C+%22bird%22%2C+%22bicycle%22%2C+%22person%22%2C+%22bottle%22%2C+%22sheep%22%2C+%22cat%22%5D%2C+%22description%22%3A+%22%5Cu7c7b%5Cu522b%5Cu540d%5Cu79f0%22%2C+%22default%22%3A+%22%22%2C+%22type%22%3A+%22L%22%2C+%22items%22%3A+%22%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+8%2C+%22name%22%3A+%22DatasetDir%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2FIntelligentizeAI%2FIntelligentizeAI%2Fdata_set%2F194741569180540928%2Fori%22%2C+%22description%22%3A+%22%5Cu6570%5Cu636e%5Cu96c6%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22.%2Fapp%2Fmaskrcnn%2Fdatasets%2Ftest%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%5D&id=194741569180540928_21_train HTTP/1.1" 200 161 0.051209
|
||||
删除图片数据
|
||||
删除json数据
|
||||
[34m[1mtrain_server: [0mweights=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights//yolov5s.pt, savemodel=/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/1128test_194741569180540928_R-ODY_21_640.pt, cfg=, data=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/coco128.yaml, hyp=/mnt/sdc/algorithm/R-ODY/app/yolov5/data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=cuda:0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=/mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
|
||||
[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases
|
||||
[34m[1mClearML: [0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
|
||||
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir /mnt/sdc/algorithm/R-ODY/app/yolov5/runs/train', view at http://localhost:6006/
|
||||
Overriding model.yaml nc=80 with nc=14
|
||||
|
||||
Fusing layers...
|
||||
Model summary: 213 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
|
||||
|
||||
[34m[1mPyTorch:[0m starting from /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.pt with output shape (1, 25200, 9) (13.8 MB)
|
||||
|
||||
[34m[1mTorchScript:[0m starting export with torch 1.8.0+cu111...
|
||||
[34m[1mTorchScript:[0m export success ✅ 0.8s, saved as /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.torchscript (27.3 MB)
|
||||
|
||||
[34m[1mONNX:[0m starting export with onnx 1.12.0...
|
||||
[34m[1mONNX:[0m export success ✅ 1.6s, saved as /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx (27.2 MB)
|
||||
|
||||
Export complete (2.6s)
|
||||
Results saved to [1m/mnt/sdc/aicheck/IntelligentizeAI/data_set/weights[0m
|
||||
Detect: python detect.py --weights /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx
|
||||
Validate: python val.py --weights /mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx
|
||||
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '/mnt/sdc/aicheck/IntelligentizeAI/data_set/weights/ces2_193120735164768256_R-ODY_2_640.onnx')
|
||||
Visualize: https://netron.app
|
||||
192.168.0.20 - - [2022-11-24 08:59:17] "GET /api/start_download_pt?param=%5B%7B%22index%22%3A+0%2C+%22name%22%3A+%22exp_inputPath%22%2C+%22value%22%3A+%22%2Fmnt%2Fsdc%2Faicheck%2FIntelligentizeAI%2Fdata_set%2Fweights%2Fces2_193120735164768256_R-ODY_2_640.pt%22%2C+%22description%22%3A+%22%5Cu8f6c%5Cu5316%5Cu6a21%5Cu578b%5Cu8f93%5Cu5165%5Cu8def%5Cu5f84%22%2C+%22default%22%3A+%22E%3A%2Falg_demo-master%2Falg_demo%2Fapp%2Fyolov5%2F%5Cu5706%5Cu5b54_123_RODY_1_640.pt%2F%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+1%2C+%22name%22%3A+%22device%22%2C+%22value%22%3A+%22gpu%22%2C+%22description%22%3A+%22CPU%5Cu6216GPU%22%2C+%22default%22%3A+%22gpu%22%2C+%22type%22%3A+%22S%22%2C+%22show%22%3A+false%7D%2C+%7B%22index%22%3A+2%2C+%22name%22%3A+%22imgsz%22%2C+%22value%22%3A+640%2C+%22description%22%3A+%22%5Cu56fe%5Cu50cf%5Cu5927%5Cu5c0f%22%2C+%22default%22%3A+640%2C+%22type%22%3A+%22I%22%2C+%22show%22%3A+true%7D%5D&id=875 HTTP/1.1" 200 240 2.709875
|
||||
from n params module arguments
|
||||
0 -1 1 3520 app.yolov5.models.common.Conv [3, 32, 6, 2, 2]
|
||||
1 -1 1 18560 app.yolov5.models.common.Conv [32, 64, 3, 2]
|
||||
2 -1 1 18816 app.yolov5.models.common.C3 [64, 64, 1]
|
||||
3 -1 1 73984 app.yolov5.models.common.Conv [64, 128, 3, 2]
|
||||
4 -1 2 115712 app.yolov5.models.common.C3 [128, 128, 2]
|
||||
5 -1 1 295424 app.yolov5.models.common.Conv [128, 256, 3, 2]
|
||||
6 -1 3 625152 app.yolov5.models.common.C3 [256, 256, 3]
|
||||
7 -1 1 1180672 app.yolov5.models.common.Conv [256, 512, 3, 2]
|
||||
8 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1]
|
||||
9 -1 1 656896 app.yolov5.models.common.SPPF [512, 512, 5]
|
||||
10 -1 1 131584 app.yolov5.models.common.Conv [512, 256, 1, 1]
|
||||
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
12 [-1, 6] 1 0 app.yolov5.models.common.Concat [1]
|
||||
13 -1 1 361984 app.yolov5.models.common.C3 [512, 256, 1, False]
|
||||
14 -1 1 33024 app.yolov5.models.common.Conv [256, 128, 1, 1]
|
||||
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
||||
16 [-1, 4] 1 0 app.yolov5.models.common.Concat [1]
|
||||
17 -1 1 90880 app.yolov5.models.common.C3 [256, 128, 1, False]
|
||||
18 -1 1 147712 app.yolov5.models.common.Conv [128, 128, 3, 2]
|
||||
19 [-1, 14] 1 0 app.yolov5.models.common.Concat [1]
|
||||
20 -1 1 296448 app.yolov5.models.common.C3 [256, 256, 1, False]
|
||||
21 -1 1 590336 app.yolov5.models.common.Conv [256, 256, 3, 2]
|
||||
22 [-1, 10] 1 0 app.yolov5.models.common.Concat [1]
|
||||
23 -1 1 1182720 app.yolov5.models.common.C3 [512, 512, 1, False]
|
||||
24 [17, 20, 23] 1 51243 app.yolov5.models.yolo.Detect [14, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
|
||||
|
||||
27
train_log.txt
Normal file
27
train_log.txt
Normal file
@ -0,0 +1,27 @@
|
||||
nohup: ignoring input
|
||||
2022-11-28 17:42:23,653 INFO sqlalchemy.engine.Engine select pg_catalog.version()
|
||||
2022-11-28 17:42:23,653 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-28 17:42:23,659 INFO sqlalchemy.engine.Engine select current_schema()
|
||||
2022-11-28 17:42:23,659 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-28 17:42:23,663 INFO sqlalchemy.engine.Engine show standard_conforming_strings
|
||||
2022-11-28 17:42:23,664 INFO sqlalchemy.engine.Engine [raw sql] {}
|
||||
2022-11-28 17:42:23,669 INFO sqlalchemy.engine.Engine BEGIN (implicit)
|
||||
2022-11-28 17:42:23,669 INFO sqlalchemy.engine.Engine COMMIT
|
||||
Traceback (most recent call last):
|
||||
File "./app/run.py", line 134, in <module>
|
||||
server.serve_forever()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/gevent/baseserver.py", line 398, in serve_forever
|
||||
self.start()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/gevent/baseserver.py", line 336, in start
|
||||
self.init_socket()
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/gevent/pywsgi.py", line 1545, in init_socket
|
||||
StreamServer.init_socket(self)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/gevent/server.py", line 180, in init_socket
|
||||
self.socket = self.get_listener(self.address, self.backlog, self.family)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/gevent/server.py", line 192, in get_listener
|
||||
return _tcp_listener(address, backlog=backlog, reuse_addr=cls.reuse_addr, family=family)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/gevent/server.py", line 288, in _tcp_listener
|
||||
sock.bind(address)
|
||||
File "/home/wd/anaconda3/envs/aicheck_RODY/lib/python3.8/site-packages/gevent/_socketcommon.py", line 563, in bind
|
||||
return self._sock.bind(address)
|
||||
OSError: [Errno 98] Address already in use: ('192.168.0.20', 6914)
|
||||
Loading…
Reference in New Issue
Block a user