415 lines
15 KiB
Python
415 lines
15 KiB
Python
"""
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@Time ： 2022/9/20 16:17
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@Auth ： 东
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@File ：AlgorithmController.py
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@IDE ：PyCharm
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@Motto：ABC(Always Be Coding)
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@Desc：算法接口
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"""
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import json
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from functools import wraps
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from threading import Thread
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from flask import Blueprint, request
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from app.schemas.TrainResult import Report, ProcessValueList
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from app.utils.RedisMQTool import Task
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from app.utils.StandardizedOutput import output_wrapped
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from app.utils.redis_config import redis_client
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from app.utils.websocket_tool import manager
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import sys
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from pathlib import Path
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# from pynvml import *
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# FILE = Path(__file__).resolve()
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# ROOT = FILE.parents[0] # YOLOv5 root directory
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# if str(ROOT) not in sys.path:
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# sys.path.append(str(ROOT)) # add ROOT to PATH
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# sys.path.append("/mnt/sdc/algorithm/AICheck-MaskRCNN/app/maskrcnn_ppx")
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# import ppx as pdx
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bp = Blueprint('AlgorithmController', __name__)
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def start_train_algorithm():
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"""
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调用训练算法
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/start_train_algorithm', methods=['get'])
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def wrapped_function():
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param = request.args.get('param')
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id = request.args.get('id')
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t = Thread(target=func, args=(param, id))
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t.start()
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return output_wrapped(0, 'success', '成功')
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return wrapped_function
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return wrapTheFunction
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def start_test_algorithm():
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"""
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调用验证算法
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/start_test_algorithm', methods=['get'])
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def wrapped_function_test():
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param = request.args.get('param')
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id = request.args.get('id')
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t = Thread(target=func, args=(param, id))
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t.start()
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return output_wrapped(0, 'success', '成功')
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return wrapped_function_test
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return wrapTheFunction
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def start_detect_algorithm():
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"""
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调用检测算法
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/start_detect_algorithm', methods=['get'])
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def wrapped_function_detect():
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param = request.args.get('param')
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id = request.args.get('id')
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t = Thread(target=func, args=(param, id))
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t.start()
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return output_wrapped(0, 'success', '成功')
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return wrapped_function_detect
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return wrapTheFunction
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def start_download_pt():
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"""
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下载模型
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/start_download_pt', methods=['get'])
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def wrapped_function_start_download_pt():
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param = request.args.get('param')
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func(param)
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return output_wrapped(0, 'success', '成功')
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return wrapped_function_start_download_pt
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return wrapTheFunction
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def algorithm_process_value():
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"""
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获取中间值, redis订阅发布
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"""
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def wrapTheFunction(func):
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@wraps(func)
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def wrapped_function(*args, **kwargs):
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data = func(*args, **kwargs)
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print(data)
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Task(redis_conn=redis_client.get_redis(), channel="ceshi").publish_task(
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data={'code': 0, 'msg': 'success', 'data': data})
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return output_wrapped(0, 'success', data)
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return wrapped_function
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return wrapTheFunction
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def algorithm_process_value_websocket():
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"""
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获取中间值, websocket发布
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"""
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def wrapTheFunction(func):
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@wraps(func)
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def wrapped_function(*args, **kwargs):
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data = func(*args, **kwargs)
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id = data["id"]
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data_res = {'code': 0, "type": 'connected', 'msg': 'success', 'data': data}
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manager.send_message_proj_json(message=data_res, id=id)
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return data
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return wrapped_function
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return wrapTheFunction
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def obtain_train_param():
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"""
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获取训练参数
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/obtain_train_param', methods=['get'])
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def wrapped_function_train_param(*args, **kwargs):
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data = func(*args, **kwargs)
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return output_wrapped(0, 'success', data)
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return wrapped_function_train_param
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return wrapTheFunction
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def obtain_test_param():
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"""
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获取验证参数
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/obtain_test_param', methods=['get'])
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def wrapped_function_test_param(*args, **kwargs):
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data = func(*args, **kwargs)
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return output_wrapped(0, 'success', data)
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return wrapped_function_test_param
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return wrapTheFunction
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def obtain_detect_param():
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"""
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获取测试参数
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/obtain_detect_param', methods=['get'])
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def wrapped_function_inf_param(*args, **kwargs):
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data = func(*args, **kwargs)
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return output_wrapped(0, 'success', data)
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return wrapped_function_inf_param
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return wrapTheFunction
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def obtain_download_pt_param():
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"""
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获取下载模型参数
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"""
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def wrapTheFunction(func):
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@wraps(func)
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@bp.route('/obtain_download_pt_param', methods=['get'])
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def wrapped_function_obtain_download_pt_param(*args, **kwargs):
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data = func(*args, **kwargs)
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return output_wrapped(0, 'success', data)
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return wrapped_function_obtain_download_pt_param
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return wrapTheFunction
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# @start_train_algorithm()
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# def start(param: str):
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# """
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# 例子
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# """
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# print(param)
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# process_value_list = ProcessValueList(name='1', value=[])
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# report = Report(rate_of_progess=0, process_value=[process_value_list], id='1')
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#
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# @algorithm_process_value_websocket()
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# def process(v: int):
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# print(v)
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# report.rate_of_progess = ((v + 1) / 10) * 100
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# report.precision[0].value.append(v)
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# return report.dict()
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#
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# for i in range(10):
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# process(i)
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# return report.dict()
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from setparams import TrainParams
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import os
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from app.schemas.TrainResult import DetectProcessValueDice, DetectReport
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from app import file_tool
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# 启动训练
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@start_train_algorithm()
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def train_R0DY(params_str, id):
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from app.yolov5.train_server import train_start
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params = TrainParams()
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params.read_from_str(params_str)
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print(params.get('device').default)
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data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('classname').value)
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print(data_list)
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weights = params.get('resumeModPath').value # 初始化模型绝对路径
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img_size = params.get('img_size').value
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savemodel = os.path.splitext(params.get('saveModDir').value)[0] + '_' + str(img_size) + '.pt' # 模型命名加上图像参数
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epoches = params.get('epochnum').value
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batch_size = params.get('batch_size').value
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device = params.get('device').value
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train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id)
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# 启动验证程序
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# @start_test_algorithm()
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# def validate_RODY(params_str, id):
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# from app.yolov5.validate_server import validate_start
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# params = TrainParams()
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# params.read_from_str(params_str)
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# weights = params.get('modPath').value # 验证模型绝对路径
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# (filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
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# img_size = int(filename.split('ROD')[1].split('_')[2]) # 获取图像参数
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# # v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
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# output = params.get('outputPath').value
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# batch_size = params.get('batch_size').default
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# device = params.get('device').value
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#
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# validate_start(weights, img_size, batch_size, device, output, id)
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#
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#
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# @start_detect_algorithm()
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# def detect_RODY(params_str, id):
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# from app.yolov5.detect_server import detect_start
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# params = TrainParams()
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# params.read_from_str(params_str)
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# weights = params.get('modPath').value # 检测模型绝对路径
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# input = params.get('inputPath').value
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# outpath = params.get('outputPath').value
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# # (filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
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# # img_size = int(filename.split('ROD')[1].split('_')[2]) #获取图像参数
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# # v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
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# # batch_size = params.get('batch_size').default
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# device = params.get('device').value
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#
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# detect_start(input, weights, outpath, device, id)
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#
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#
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# @start_download_pt()
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# def Export_model_RODY(params_str):
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# from app.yolov5.export import Start_Model_Export
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# import zipfile
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# params = TrainParams()
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# params.read_from_str(params_str)
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# exp_inputPath = params.get('exp_inputPath').value # 模型路径
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# exp_device = params.get('device').value
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# modellist = Start_Model_Export(exp_inputPath, exp_device)
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# exp_outputPath = exp_inputPath.replace('pt', 'zip') # 压缩文件
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# zipf = zipfile.ZipFile(exp_outputPath, 'w')
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# for file in modellist:
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# zipf.write(file, arcname=Path(file).name) # 将torchscript和onnx模型压缩
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#
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# return exp_outputPath
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# zipf.write(modellist[1], arcname=modellist[1])
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# zip_inputpath = os.path.join(exp_outputPath, "inference_model")
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# zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
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@obtain_train_param()
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def returnTrainParams():
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# nvmlInit()
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# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
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params_list = [
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{"index": 0, "name": "epochnum", "value": 10, "description": '训练轮次', "default": 100, "type": "I", 'show': True},
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{"index": 1, "name": "batch_size", "value": 4, "description": '批次图像数量', "default": 1, "type": "I",
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'show': True},
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{"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
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'show': True},
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{"index": 3, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
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"items": '', 'show': True}, # _kernel
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{"index": 4, "name": "saveModDir", "value": "E:/alg_demo-master/alg_demo/app/yolov5/best.pt",
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"description": '保存模型路径',
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"default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
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{"index": 5, "name": "resumeModPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/yolov5s.pt',
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"description": '继续训练路径', "default": '', "type": "S",
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'show': False},
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{"index": 6, "name": "resumeMod", "value": '', "description": '继续训练模型', "default": '', "type": "E", "items": '',
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'show': True},
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{"index": 7, "name": "classname", "value": ['logo', '3C'], "description": '类别名称', "default": '', "type": "L",
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"items": '',
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'show': False},
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{"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori/",
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"description": '数据集路径',
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"default": "./app/maskrcnn/datasets/test", "type": "S", 'show': False} # ORI_PATH
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]
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# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
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params_str = json.dumps(params_list)
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return params_str
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# @obtain_test_param()
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# def returnValidateParams():
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# # nvmlInit()
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# # gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# # _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
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# params_list = [
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# {"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
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# "description": '验证模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
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# {"index": 1, "name": "batch_size", "value": 1, "description": '批次图像数量', "default": 1, "type": "I",
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# 'show': False},
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# {"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
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# 'show': False},
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# {"index": 3, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
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# "description": '输出结果路径',
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# "default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
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# {"index": 4, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
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# "items": '', 'show': False} # _kernel
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# ]
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# # {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
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# params_str = json.dumps(params_list)
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# return params_str
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#
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#
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# @obtain_detect_param()
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# def returnDetectParams():
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# # nvmlInit()
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# # gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# # _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
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# params_list = [
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# {"index": 0, "name": "inputPath", "value": 'E:/aicheck/data_set/11442136178662604800/input/',
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# "description": '输入图像路径', "default": './app/maskrcnn/datasets/M006B_waibi/JPEGImages', "type": "S",
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# 'show': False},
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# {"index": 1, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
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# "description": '输出结果路径',
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# "default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
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# {"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
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# "description": '模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
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# {"index": 3, "name": "device", "value": "0", "description": '推理核', "default": "cpu", "type": "S",
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# 'show': False},
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# ]
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# # {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
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# params_str = json.dumps(params_list)
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# return params_str
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#
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#
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# @obtain_download_pt_param()
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# def returnDownloadParams():
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# params_list = [
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# {"index": 0, "name": "exp_inputPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt',
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# "description": '转化模型输入路径',
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# "default": '/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/new磁环检测test_183504733393264640_R-DDM_11.pt/',
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# "type": "S", 'show': False},
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# {"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
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# 'show': False}
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# ]
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# params_str = json.dumps(params_list)
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# return params_str
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if __name__ == '__main__':
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par = returnTrainParams()
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print(par)
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id='1'
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train_R0DY(par,id)
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