@ -1,486 +1,486 @@
"""
@Time ：
@Auth ：
@File ：
@IDE ：
@Motto：
@Desc：
"""
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 ：
@Auth ：
@File ：
@IDE ：
@Motto：
@Desc：
"""
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 )
