思科无线AP1240AG快速配置步骤详解

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"WLN00066-思科无线AP1240AG的快速配置手册提供了针对这款胖AP的初始设置指南,包括登陆信息、默认设置、射频和IP地址配置以及安全注意事项。" 这篇快速配置手册是为思科Aironet 1240AG系列无线接入点设计的,它主要关注的是如何快速有效地配置这个胖AP(FAT AP)。"胖AP"是指不依赖于无线控制器独立工作的接入点,它可以自行管理无线网络的连接和配置。 手册中指出,802.11a频率并不适用于1242G型号的AP,用户应关注802.11b和802.11g的相关内容。默认的登陆信息为用户名"Cisco"(区分大小写),密码同样为"Cisco"。AP的IP地址通常是通过DHCP动态获取,但如果无法获取,用户需要通过控制台接口手动配置IP地址、子网掩码和网关。 射频和IP地址的配置是初始化过程中的关键步骤。新购买的AP射频模块默认关闭,需要在配置时开启。在开始配置之前,确保有一台连接到同一网络的PC,并准备以下信息:AP的设备名称、802.11g和802.11a的SSID、SNMP管理信息(如果需要)、AP的MAC地址(如果使用Cisco IP地址设置软件),以及如果不能使用DHCP,需要手工地设定AP的IP信息。 关于安全,设备已经过FCC认证,RF射频对人体无害。不过,安装和运行时仍需注意:避免在设备运行时让天线靠近人体,特别是头部;设备应根据IEEE 802.3af标准和IEC 60950标准进行安装,同时设备内置电源保护措施,但电源输入不应超过其额定值。 安全警告部分强调了在连接电源前阅读安装手册的重要性,以及设备仅适用于符合特定电气标准的环境。此外,手册还包含了多语言的安全警告,以确保用户在安装和使用过程中遵循正确的操作流程,保障人身安全。 这份快速配置手册为用户提供了全面的指导,帮助他们成功地设置和启动思科1240AG无线AP,确保其在网络中的有效运作。

pytorch部分代码如下:train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) samples, targets = mixup_fn(data, target) output = model(samples) optimizer.zero_grad() if use_amp: with torch.cuda.amp.autocast(): loss = torch.nan_to_num(criterion_train(output, targets)) scaler.scale(loss).backward() torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks or global_forward_hooks or global_forward_pre_hooks): return forward_call(*input, **kwargs) class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) target = torch.clamp(target, 0, index.size(1) - 1) index.scatter_(1, target.unsqueeze(1).type(torch.int64), 1) index = index[:, :x.size(1)] index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight) 报错: File "/home/adminis/hpy/ConvNextV2_Demo/train+ca.py", line 46, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/adminis/hpy/ConvNextV2_Demo/models/utils.py", line 622, in forward index.scatter_(1, target.unsqueeze(1).type(torch.int64), 1) # target.data.view(-1, 1). RuntimeError: Index tensor must have the same number of dimensions as self tensor 帮我看看如何修改源代码

2023-06-10 上传

pytorch代码如下:class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((-1, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) classes=7, cls_num_list = np.zeros(classes) for , label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) criterion_val = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) # 3、将数据输入mixup_fn生成mixup数据 samples, targets = mixup_fn(data, target) targets = torch.tensor(targets).to(torch.long) # 4、将上一步生成的数据输入model,输出预测结果,再计算loss output = model(samples) # 5、梯度清零(将loss关于weight的导数变成0) optimizer.zero_grad() # 6、若使用混合精度 if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm(model.parameters(), CLIP_GRAD) # 梯度裁剪,防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() 报错:File "/home/adminis/hpy/ConvNextV2_Demo/models/losses.py", line 53, in forward return F.cross_entropy(logit, target, weight=self.weight) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/functional.py", line 2824, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15

2023-05-29 上传

INSERT INTO `QHDATA_THEME.DB_DTRK_CZRKDT` (`RID`, `LDBM`, `TJS`, `XZQHDM`, `XB0`, `XB1`, `MNL0`, `MNL1`, `MNL2`, `MNL3`, `MNL4`, `MNL5`, `MNL6`, `MNL7`, `MNL8`, `MNL9`, `MNL10`, `MNL11`, `MNL12`, `MNL13`, `MNL14`, `MNL15`, `MNL16`, `MNL17`, `MNL18`, `MNL19`, `MNL20`, `MNL21`, `WNL0`, `WNL1`, `WNL2`, `WNL3`, `WNL4`, `WNL5`, `WNL6`, `WNL7`, `WNL8`, `WNL9`, `WNL10`, `WNL11`, `WNL12`, `WNL13`, `WNL14`, `WNL15`, `WNL16`, `WNL17`, `WNL18`, `WNL19`, `WNL20`, `WNL21`, `MYE`, `WYE`, `MET`, `WET`, `MWCN`, `WWCN`, `MLN`, `WLN`, `LNWHQ`, `LNXQJY`, `LNXX`, `LNCZ`, `LNGZ`, `LNDXZK`, `LNDXBK`, `LNSSYJS`, `LNBSYJS`, `MLN2`, `WLN2`, `LNWHQ2`, `LNXQJY2`, `LNXX2`, `LNCZ2`, `LNGZ2`, `LNDXZK2`, `LNDXBK2`, `LNSSYJS2`, `LNBSYJS2`, `MLN3`, `WLN3`, `LNWHQ3`, `LNXQJY3`, `LNXX3`, `LNCZ3`, `LNGZ3`, `LNDXZK3`, `LNDXBK3`, `LNSSYJS3`, `LNBSYJS3`, `WHQ`, `XQJY`, `XX`, `CZ`, `GZ`, `DXZK`, `DXBK`, `SSYJS`, `BSYJS`, `SSH`, `FSH`, `lng`, `lat`, `is_qianhai`, `DISTRICT_NAME`, `DISTRICT_CODE`, `STREET_NAME`, `STREET_CODE`, `COMMUNITY_NAME`, `COMMUNITY_CODE`, `occur_period`, `occur_period_year`, `occur_period_month`, `org_id`, `org_name`, `area_code`, `data_time`, `TJNY`) VALUES ('933f35f92e5d4b19a7f9334452fe5a99', '4403060000000000000', 54, '440306000000', 23, 31, 0, 0, 0, 0, 0, 7, 10, 3, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 11, 14, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 5, 8, 36, 2, 0, 16, 38, '113.861892600000004', '22.580441539999999', '1', '宝安区', '440306000000', '西乡街道', '440306000000', '盐田社区', '440306000000', 202212, 2022, 12, NULL, NULL, NULL, '2023-06-01 00:00:00', '2022-12-01 00:00:00');

2023-06-03 上传

pytorch中ConvNeXt v2模型加入CBAM模块后报错:Traceback (most recent call last): File "/home/adminis/hpy/ConvNextV2_Demo/train+.py", line 234, in <module> model_ft = convnextv2_base(pretrained=True) File "/home/adminis/hpy/ConvNextV2_Demo/models/convnext_v2.py", line 201, in convnextv2_base model = ConvNeXtV2(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs) File "/home/adminis/hpy/ConvNextV2_Demo/models/convnext_v2.py", line 114, in init self.apply(self.init_weights) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/modules/module.py", line 616, in apply module.apply(fn) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/modules/module.py", line 616, in apply module.apply(fn) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/modules/module.py", line 616, in apply module.apply(fn) [Previous line repeated 4 more times] File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/modules/module.py", line 617, in apply fn(self) File "/home/adminis/hpy/ConvNextV2_Demo/models/convnext_v2.py", line 121, in init_weights nn.init.constant(m.bias, 0) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/init.py", line 186, in constant return no_grad_fill(tensor, val) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/init.py", line 59, in no_grad_fill return tensor.fill_(val) AttributeError: 'NoneType' object has no attribute 'fill_' 部分代码如下:for i in range(4): stage = nn.Sequential( *[Block(dim=dims[i], drop_path=dp_rates[cur + j]) for j in range(depths[i])], CBAM(gate_channels=dims[i]) ) self.stages.append(stage) cur += depths def _init_weights(self, m): if isinstance(m, (nn.Conv2d, nn.Linear)): trunc_normal_(m.weight, std=.02) nn.init.constant_(m.bias, 0)

2023-05-25 上传