94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# @version : 1.0
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# @Create Time : 2025/04/03 10:32
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# @File : crud.py
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# @IDE : PyCharm
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# @desc : 数据访问层
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from sqlalchemy.ext.asyncio import AsyncSession
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from core.crud import DalBase
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from . import models, schemas
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from utils import os_utils as os
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from utils.csv_utils import read_csv
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class ProjectTrainDal(DalBase):
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def __init__(self, db: AsyncSession):
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super(ProjectTrainDal, self).__init__()
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self.db = db
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self.model = models.ProjectTrain
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self.schema = schemas.ProjectTrainOut
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async def get_result(self, train_id: int):
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"""
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查询训练报告
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"""
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data = await self.get_data(data_id=train_id)
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if data is None:
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return None
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result_csv_path = os.file_path(data.train_url, 'results.csv')
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result_row = read_csv(result_csv_path)
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report_data = {}
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# 轮数
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epoch_data = []
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# 边界框回归损失（Bounding Box Loss），衡量预测框位置（中心坐标、宽高）与真实框的差异，值越低表示定位越准。
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train_box_loss = []
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# 目标置信度损失（Objectness Loss），衡量检测到目标的置信度误差（即是否包含物体），值越低表示模型越能正确判断有无物体。
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train_obj_loss = []
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# 分类损失（Classification Loss），衡量预测类别与真实类别的差异，值越低表示分类越准。
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train_cls_loss = []
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# 验证集的边界框回归损失，反映模型在未见数据上的定位能力。
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val_box_loss = []
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# 验证集的目标置信度损失，反映模型在未见数据上判断物体存在的能力。
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val_obj_loss = []
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# 验证集的分类损失，反映模型在未见数据上的分类准确性。
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val_cls_loss = []
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# 精确率（Precision）：正确检测的正样本占所有预测为正样本的比例，反映“误检率”。值越高说明误检越少。
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m_p = []
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# 召回率（Recall）：正确检测的正样本占所有真实正样本的比例，反映“漏检率”。值越高说明漏检越少。
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m_r = []
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# 主干网络（Backbone）的学习率。
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x_lr0 = []
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# 检测头（Head）的学习率。
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x_lr1 = []
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for row in result_row:
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epoch_data.append(row[0].strip())
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train_box_loss.append(row[1].strip())
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train_obj_loss.append(row[2].strip())
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train_cls_loss.append(row[3].strip())
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val_box_loss.append(row[8].strip())
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val_obj_loss.append(row[9].strip())
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val_cls_loss.append(row[10].strip())
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m_p.append(row[4].strip())
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m_r.append(row[5].strip())
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x_lr0.append(row[11].strip())
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x_lr1.append(row[12].strip())
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report_data['epoch_data'] = epoch_data
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report_data['train_box_loss'] = train_box_loss
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report_data['train_obj_loss'] = train_obj_loss
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report_data['train_cls_loss'] = train_cls_loss
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report_data['val_box_loss'] = val_box_loss
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report_data['val_obj_loss'] = val_obj_loss
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report_data['val_cls_loss'] = val_cls_loss
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report_data['m_p'] = m_p
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report_data['m_r'] = m_r
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report_data['x_lr0'] = x_lr0
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report_data['x_lr1'] = x_lr1
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return report_data
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