RODY/app/controller/AlgorithmController.py

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2022-11-04 17:37:08 +08:00
"""
@Time 2022/9/20 16:17
@Auth
@File AlgorithmController.py
@IDE PyCharm
@MottoABC(Always Be Coding)
@Desc算法接口
"""
import json
from functools import wraps
from threading import Thread
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
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__)
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')
t = Thread(target=func, args=(param, id))
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')
func(param)
return output_wrapped(0, 'success', '成功')
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 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
# @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
# 启动训练
@start_train_algorithm()
def train_R0DY(params_str, id):
from app.yolov5.train_server import train_start
params = TrainParams()
params.read_from_str(params_str)
print(params.get('device').default)
data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('classname').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] + '_' + 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
train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id)
# 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
exp_device = params.get('device').value
modellist = Start_Model_Export(exp_inputPath, exp_device)
exp_outputPath = exp_inputPath.replace('pt', 'zip') # 压缩文件
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
# zipf.write(modellist[1], arcname=modellist[1])
# zip_inputpath = os.path.join(exp_outputPath, "inference_model")
# zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
@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": "0", "description": '训练核心', "default": "cuda", "type": "S",
"items": '', 'show': True}, # _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": 'E:/alg_demo-master/alg_demo/app/yolov5/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": "classname", "value": ['logo', '3C'], "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": 0, "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": '/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/new磁环检测test_183504733393264640_R-DDM_11.pt/',
"type": "S", 'show': False},
{"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
'show': False}
]
params_str = json.dumps(params_list)
return params_str
if __name__ == '__main__':
par = returnDownloadParams()
print(par)
Export_model_RODY(par)