(function(vita) { var systemUserAdd = vita.Backbone.BizView.extend({ events : { "click #isSysUser" : "_isChecked", "click .close" : "_closePage" }, _initialize : function() { var widget = this, element = $(widget.el), global = widget.global, data = element.data("data"); //时间控件类型初始化 var datetime = widget.require("datetime"); var timeTxts = element.find("input[name='timeTxt']"); if (timeTxts.length) { timeTxts.each(function(i, timeTxt) { datetime.register($(timeTxt), { preset : widget.global.preset }); }); } var initEffDate = widget.getNowDate(new Date()); var initExpDate = "2999-12-31"; element.find("#effDate").val(initEffDate); element.find("#expDate").val(initExpDate); //获取登陆员工ID widget.model.set('loginStaffId',data.loginStaffId); }, global : { preset : 'date', chooseArea : "../sm/chooseArea", staffSelectList : "../sm/staffSelectList", selectOrg : "../sm/selectOrg" }, getNowDate : function (date) { var y = date.getFullYear(); var m = date.getMonth() + 1; m = m < 10 ? '0' + m : m; var d = date.getDate(); d = d < 10 ? ('0' + d) : d; return y + '-' + m + '-' + d; }, _isChecked : function(e) { var widget = this, element = $(widget.el); var button = $(e.target).closest("input"); var id = button.attr("id") || ""; var idVal = "#"+id; var ischecked = $(idVal).prop('checked'); if(id=="isSysUser"){ if(ischecked){ widget.model.set({"isSysUser" : '1'}); }else{ widget.model.set({"isSysUser" : "0"}); } } }, _closePage : function() { var widget = this, element = $(widget.el); element.closest("[data-widgetfullname=vita-dialog]").dialog("close"); } }); vita.widget.register("systemUserAdd", systemUserAdd, true); })(window.vita);解释一下这些代码

BackBone教程–第8部分：了解Backbone.js事件

Backbone.Events.onReady:Backbone.Events 的 onReady() 方法

Backbone.Events.onReady 将onReady和triggerReady方法添加到Backbone.Events 。行为：准备好之前- 注册回调。准备就绪时，运行所有挂起的回调，从triggerReady传入任何数据。准备就绪后- 立即运行回调，传入...

ssh_full.zip_AOP java_aop_backbone jsp_backbone struts_ssh

SSH 基干SPRING AOP开发实例,里面有详细的源码,欢迎下载

import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models import os class FCNTransformerNet(nn.Module): def init(self, num_classes): super(FCNTransformerNet, self).init() self.fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True).backbone self.fcn_backbone.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.transformer_layers = nn.TransformerEncoderLayer(d_model=2048, nhead=8) self.transformer_encoder = nn.TransformerEncoder(self.transformer_layers, num_layers=6) self.classification_head = nn.Sequential( nn.Linear(2048, 512), nn.ReLU(), nn.Linear(512, num_classes) ) def forward(self, x): fcn_output = self.fcn_backbone(x)['out'] fcn_output = fcn_output.view(fcn_output.size(0), fcn_output.size(1), -1) fcn_output = fcn_output.permute(2, 0, 1) transformer_output = self.transformer_encoder(fcn_output) transformer_output = transformer_output.permute(1, 2, 0) transformer_output = transformer_output.contiguous().view(transformer_output.size(0), -1, 1, 1) output = self.classification_head(transformer_output) return output FCNTransformerNet net = FCNTransformerNet(num_classes=2) input_batch = torch.randn(4, 3, 512, 512) output_batch = net(input_batch) print(output_batch.size()) # Should print: torch.Size([4, 2, 512, 512]) 运行这段代码，并改错

self.fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True).backbone self.fcn_backbone.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.transformer_layers = ...

this.networdIdCollection.fetch({ success: function () { _parent.networdIdCollection.each(function (model) { _parent.addNetworkIp(model, _parent.networdIdCollection); }, _parent); }, error: function () { console.error("The networdIdCollection request failed"); } }); // users数据获取 this.collection.fetch({ success: function () { // 获取转化后的interface_name字段 var name1 = _parent.transChannel(interface_name[0]); var name2 = _parent.transChannel(interface_name[1]); // 根据interface_name字段获取过滤数据并进行合并 var filterData1 = _parent.collection.byUserCollection(name1).models; var filterData2 = _parent.collection.byUserCollection(name2).models; _parent.collection = new Backbone.Collection(filterData1.concat(filterData2)); // 执行affect函数(生成selection子项) _parent.affect(); // 遍历并执行add函数 _parent.collection.each(function (model) { _parent.add(model, _parent.collection); }, _parent); // 触发选择框的变化事件 _parent.$('#idgroup_by_channel').change(); }, error: function () { console.error("The usersCollection request failed"); } }) }怎么解决这个的异步问题，第二个请求是基于第一个请求的基础上

_parent.collection = new Backbone.Collection(filterData1.concat(filterData2)); _parent.affect(); _parent.collection.each(function (model) { _parent.add(model, _parent.collection); }, _parent); _...

def init(self, backbone=None, head=None,predict=False): super().init() self.backbone = backbone self.backbone.init_weights() self.head = head self.head.init_weights() self.predict = predict

构造函数中的参数包括 backbone、head 和 predict。其中，backbone 和 head 分别代表模型的主干网络和头部网络，用于提取图像特征和进行分类输出。predict 参数用于控制模型是否进行预测。在构造函数中，首先调用...

# setup setup: selflabel # Threshold confidence_threshold: 0.99 # EMA use_ema: True ema_alpha: 0.999 # Loss criterion: confidence-cross-entropy criterion_kwargs: apply_class_balancing: False # Model backbone: resnet50 num_heads: 1 # Dataset train_db_name: imagenet_50 val_db_name: imagenet_50 num_classes: 50 # Transformations augmentation_strategy: ours augmentation_kwargs: crop_size: 224 normalize: mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] num_strong_augs: 4 cutout_kwargs: n_holes: 1 length: 75 random: True transformation_kwargs: crop_size: 224 normalize: mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] # Hyperparameters optimizer: sgd optimizer_kwargs: lr: 0.03 weight_decay: 0.0 nesterov: False momentum: 0.9 epochs: 25 batch_size: 512 num_workers: 16 # Scheduler scheduler: constant ，这段话是什么意思

- backbone: resnet50，num_heads: 1：表示使用ResNet-50作为模型骨架，并设置模型头数为1。 - train_db_name: imagenet_50，val_db_name: imagenet_50，num_classes: 50：表示使用ImageNet-50数据集进行训练和验证...

from collections import OrderedDict import torch import torch.nn.functional as F import torchvision from torch import nn import models.vgg_ as models class BackboneBase_VGG(nn.Module): def init(self, backbone: nn.Module, num_channels: int, name: str, return_interm_layers: bool): super().init() features = list(backbone.features.children()) if return_interm_layers: if name == 'vgg16_bn': self.body1 = nn.Sequential(features[:13]) self.body2 = nn.Sequential(features[13:23]) self.body3 = nn.Sequential(features[23:33]) self.body4 = nn.Sequential(features[33:43]) else: self.body1 = nn.Sequential(features[:9]) self.body2 = nn.Sequential(features[9:16]) self.body3 = nn.Sequential(features[16:23]) self.body4 = nn.Sequential(features[23:30]) else: if name == 'vgg16_bn': self.body = nn.Sequential(features[:44]) # 16x down-sample elif name == 'vgg16': self.body = nn.Sequential(features[:30]) # 16x down-sample self.num_channels = num_channels self.return_interm_layers = return_interm_layers def forward(self, tensor_list): out = [] if self.return_interm_layers: xs = tensor_list for _, layer in enumerate([self.body1, self.body2, self.body3, self.body4]): xs = layer(xs) out.append(xs) else: xs = self.body(tensor_list) out.append(xs) return out class Backbone_VGG(BackboneBase_VGG): """ResNet backbone with frozen BatchNorm.""" def init(self, name: str, return_interm_layers: bool): if name == 'vgg16_bn': backbone = models.vgg16_bn(pretrained=True) elif name == 'vgg16': backbone = models.vgg16(pretrained=True) num_channels = 256 super().init(backbone, num_channels, name, return_interm_layers) def build_backbone(args): backbone = Backbone_VGG(args.backbone, True) return backbone if name == 'main': Backbone_VGG('vgg16', True)

其中包括两个类：BackboneBase_VGG和Backbone_VGG以及一个函数build_backbone。 BackboneBase_VGG类是VGG的基础类，用于构建VGG神经网络模型。它的初始化函数接受四个参数：backbone，num_channels，...

class ContrastiveModel(nn.Module): def init(self, backbone, head='mlp', features_dim=128): super(ContrastiveModel, self).init() self.backbone = backbone['backbone'] self.backbone_dim = backbone['dim'] self.head = head if head == 'linear': self.contrastive_head = nn.Linear(self.backbone_dim, features_dim) elif head == 'mlp': self.contrastive_head = nn.Sequential( nn.Linear(self.backbone_dim, self.backbone_dim), nn.ReLU(), nn.Linear(self.backbone_dim, features_dim)) else: raise ValueError('Invalid head {}'.format(head)) def forward(self, x): features = self.contrastive_head(self.backbone(x)) features = F.normalize(features, dim = 1) return features

它接受一个backbone作为输入，backbone是一个由特征提取器组成的模型。backbone的输出维度是self.backbone_dim。head参数指定了模型的头部结构，可以选择'linear'或'mlp'。如果选择'linear'，则使用一个线性层将...

class End2EndModel(nn.Module): def init(self, backbone, head='mlp', features_dim=128, nheads=1, nclusters=10): super(End2EndModel, self).init() self.backbone = backbone['backbone'] self.backbone_dim = backbone['dim'] self.head = head self.nheads = nheads assert(isinstance(self.nheads, int)) assert(self.nheads > 0) self.cluster_head = nn.ModuleList([nn.Linear(self.backbone_dim, nclusters) for _ in range(self.nheads)]) if head == 'linear': self.contrastive_head = nn.Linear(self.backbone_dim, features_dim) elif head == 'mlp': self.contrastive_head = nn.Sequential( nn.Linear(self.backbone_dim, self.backbone_dim), nn.ReLU(), nn.Linear(self.backbone_dim, features_dim)) else: raise ValueError('Invalid head {}'.format(head)) def forward(self, x): features = self.backbone(x) contrastive_features = self.contrastive_head(features) contrastive_features = F.normalize(contrastive_features, dim = -1) cluster_outs = [cluster_head(features) for cluster_head in self.cluster_head] return contrastive_features, cluster_outs

它接受一个backbone作为输入，backbone是一个由特征提取器组成的模型。backbone的输出维度是self.backbone_dim。head参数指定了模型的头部结构，可以选择'linear'或'mlp'。如果选择'linear'，则使用一个线性层将...

解释这段代码# ema use_ema: False # Threshold confidence_threshold: 0.98 # Criterion criterion: confidence-cross-entropy criterion_kwargs: apply_class_balancing: True # Model backbone: resnet18 num_heads: 1 # Dataset train_db_name: cifar-10 val_db_name: cifar-10 num_classes: 10 # Transformations augmentation_strategy: ours augmentation_kwargs: crop_size: 32 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010] num_strong_augs: 4 cutout_kwargs: n_holes: 1 length: 16 random: True transformation_kwargs: #resize: 40 crop_size: 32 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010] # Hyperparameters epochs: 200 batch_size: 1000 num_workers: 8 optimizer: adam optimizer_kwargs: lr: 0.00005 weight_decay: 0.0001 # Scheduler scheduler: constant

- backbone: resnet18：模型的主干网络，这里使用的是ResNet-18。 - num_heads: 1：注意力头的数量。 - train_db_name: cifar-10：训练数据集的名称，这里使用CIFAR-10数据集。 - val_db_name: cifar-10：...

class ContrastiveModel(nn.Module): def init(self, backbone, head='mlp', features_dim=128): super(ContrastiveModel, self).init() self.backbone = backbone['backbone'] self.backbone_dim = backbone['dim'] self.head = head if head == 'linear': self.contrastive_head = nn.Linear(self.backbone_dim, features_dim) elif head == 'mlp': self.contrastive_head = nn.Sequential( nn.Linear(self.backbone_dim, self.backbone_dim), nn.ReLU(), nn.Linear(self.backbone_dim, features_dim)) else: raise ValueError('Invalid head {}'.format(head)) def forward(self, x): features = self.contrastive_head(self.backbone(x)) features = F.normalize(features, dim = 1) return features class ClusteringModel(nn.Module): def init(self, backbone, nclusters, nheads=1): super(ClusteringModel, self).init() self.backbone = backbone['backbone'] self.backbone_dim = backbone['dim'] self.nheads = nheads assert(isinstance(self.nheads, int)) assert(self.nheads > 0) self.cluster_head = nn.ModuleList([nn.Linear(self.backbone_dim, nclusters) for _ in range(self.nheads)]) def forward(self, x, forward_pass='default'): if forward_pass == 'default': features = self.backbone(x) out = [cluster_head(features) for cluster_head in self.cluster_head] elif forward_pass == 'backbone': out = self.backbone(x) elif forward_pass == 'head': out = [cluster_head(x) for cluster_head in self.cluster_head] elif forward_pass == 'return_all': features = self.backbone(x) out = {'features': features, 'output': [cluster_head(features) for cluster_head in self.cluster_head]} else: raise ValueError('Invalid forward pass {}'.format(forward_pass)) return out，这是什么模型啊

它接收一个 backbone 模型作为输入，该 backbone 模型提取输入图像的特征，然后通过一个 MLP 或 Linear 层将特征映射到一个固定维度的向量。最后，它对向量进行 L2 归一化，以获得最终的特征表示。该模型的作用是...

class _MySegmentationModel(nn.Module): def init(self, backbone1, backbone2, fuselayers1, fuselayers2, classifier): super(_MySegmentationModel, self).init() self.backbone1 = backbone1 self.backbone2 = backbone2 self.fuselayers1 = fuselayers1 self.fuselayers2 = fuselayers2 self.classifier = classifier

这是一个 PyTorch 中的神经网络模型定义，包含了两个 backbone 网络、两个 fuse 层和一个分类器。其中，backbone1 和 backbone2 分别代表两个主干网络，fuselayers1 和 fuselayers2 是用于融合两个主干网络特征的层...

# Setup setup: moco # MoCo is used here # Model backbone: resnet50 model_kwargs: head: mlp features_dim: 128 # Dataset train_db_name: imagenet_50 val_db_name: imagenet_50 num_classes: 50 temperature: 0.07 # Batch size and workers batch_size: 256 num_workers: 8 # Transformations transformation_kwargs: crop_size: 224 normalize: mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225]

这是一个yaml格式的配置文件，用于训练一个基于resnet50模型的MoCo模型，用于图像分类任务。该配置文件中包含了模型架构、数据集、数据增强、训练超参数等信息。具体解释如下： - model_kwargs：模型参数，包括网络...

self.encoder = smp.Unet( encoder_name=cfg.backbone, encoder_weights=weight, in_channels=cfg.in_chans, classes=cfg.target_size, activation=None, )

这段代码使用了 segmentation_models_pytorch 库中的 Unet 模型作为编码器(encoder)，并指定了模型的 backbone、输入通道数、输出通道数、激活函数等参数。其中，encoder_weights 参数用于指定预训练的权重文件路径...

import cv2 import torch import torch.nn as nn import torchvision.models as models class FCNTransformer(nn.Module): def init(self, num_classes): super(FCNTransformer, self).init() # Load pre-trained FCN backbone fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True) self.fcn = fcn_backbone.backbone # Create the transformer encoder self.transformer_encoder = nn.TransformerEncoderLayer(d_model=2048, nhead=8) # Output linear layer self.linear = nn.Linear(2048, num_classes) def forward(self, x): # Pass input through FCN backbone fcn_output = self.fcn(x)['out'] print(fcn_output.shape) x = fcn_output # Reshape output tensor for transformer # From (batch_size, channels, height, width) to (width * height, batch_size, channels) fcn_output = fcn_output.permute(0, 2, 3, 1).contiguous().view(-1, fcn_output.size(0), fcn_output.size(1)) print(fcn_output.shape) # Pass the reshaped tensor through transformer encoder transformed_output = self.transformer_encoder(fcn_output) print(transformed_output.shape) # Reshape output tensor back to (batch_size, channels, height, width) transformed_output = transformed_output.view(1, -1) print(transformed_output.shape) output = self.linear(transformed_output) return output if name == 'main': a = torch.randn(1, 3, 512, 512) model = FCNTransformer(num_classes=2) print(model(a).shape) # print(model) 改进这段代码

2. 模型结构：根据任务需求，可以调整FCN的backbone网络或者使用其他预训练的模型。你可以尝试不同的骨干网络结构或者添加更多的层来提高性能。 3. 超参数调整：可以尝试不同的超参数值，如学习率、批量大小、迭代...

import torch import torch.nn as nn import torchvision.models as models class FCNTransformer(nn.Module): def init(self, num_classes): super(FCNTransformer, self).init() # Load pre-trained V16 model as FCN backbone vgg16 = models.vgg16(pretrained=True) features = list(vgg16.features.children()) self.backbone = nn.Sequential(*features) # FCN layers self.fcn_layers = nn.Sequential( nn.Conv2d(512, 4096, 7), nn.ReLU(inplace=True), nn.Dropout(), nn.Conv2d(4096, 4096, 1), nn.ReLU(inplace=True), nn.Dropout(), nn.Conv2d(4096, num_classes, 1) ) # Transformer layers self.transformer = nn.Transformer( d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1 ) def forward(self,x): # Backbone feature extraction features = self.backbone(x) # FCN layers fcn_out = self.fcn_layers(features) # Reshaping output for transformer input b, c, h, w = fcn_out.size() fcn_out = fcn_out.squeeze().view(c, b, -1).permute(2, 0, 1) # Reshaping for transformer input # Transformer encoding transformer_out = self.transformer.encode(fcn_out) # Reshaping output for segmentation prediction transformer_out = transformer_out.permute(1, 2, 0).view(b, c, h, w) return transformer_out if name == 'main': a = torch.randn(1, 3, 512, 512) model = FCNTransformer(num_classes=2) print(model(a).shape) 改进这段代码

首先，由于我们只需要使用 VGG16 的特征提取部分作为 FCN 的 backbone，而不需要其完整的网络结构，因此可以使用 vgg16.features 而不是 vgg16 来加载预训练模型。这样可以减少模型的参数量。其次，为了更好地...

if isinstance(c2, list): is_backbone = True m_ = m m_.backbone = True else:

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Backbone.Events.onReady:Backbone.Events 的 onReady() 方法

ssh_full.zip_AOP java_aop_backbone jsp_backbone struts_ssh

def __init__(self, backbone=None, head=None,predict=False): super().__init__() self.backbone = backbone self.backbone.init_weights() self.head = head self.head.init_weights() self.predict = predict

self.encoder = smp.Unet( encoder_name=cfg.backbone, encoder_weights=weight, in_channels=cfg.in_chans, classes=cfg.target_size, activation=None, )

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def init(self, backbone=None, head=None,predict=False): super().init() self.backbone = backbone self.backbone.init_weights() self.head = head self.head.init_weights() self.predict = predict