//附件上传绑定 $("input[class*='att_upload']").each((index, jq) => { var _this = $(jq), opt = _this.attr('opt'), name = _this.attr('id'), canEdit = _this.attr('edit'), limit = _this.attr('limit'), acceptType = _this.attr('acceptType'), validate = eval('(' + _this.attr('validate') + ')'), div = $("<div></div>"); _this.addClass("hidden").attr("type", "text"); if (validate && validate.hasOwnProperty('notEmpty')) { validate.notEmpty.message = "请上传文件！" _this.attr('validate', JSON.stringify(validate)) } canEdit = canEdit ? canEdit == 'true' : true opt = $.extend({}, { name: name, pick: name, fileNumLimit: Number(limit), acceptType: acceptType, canEdit: canEdit, canAdd: canEdit, canDel: canEdit }, opt) _this.after(div) div.gsUpload(opt) }) 这串代码需要打开浏览器控制台有效，关闭后调用无效

att_faces_31att_www.31attcom_www.31attcom_faces_matlab_

标题中的"att_faces_31att_www.31attcom_www.31attcom_faces_matlab_"表明这是一个与人脸识别技术相关的项目，可能是一个数据集或者代码库，由31att.com提供，并且使用了MATLAB编程语言进行处理。描述中提到的“人脸...

for img_a, att_a in progressbar(train_dataloader): attgan.train() img_a = img_a.cuda() if args.gpu else img_a att_a = att_a.cuda() if args.gpu else att_a idx = torch.randperm(len(att_a)) att_b = att_a[idx].contiguous() att_a = att_a.type(torch.float) att_b = att_b.type(torch.float) att_a_ = (att_a * 2 - 1) * args.thres_int

然后，通过索引将 att_a 中的属性值重新排序得到 att_b。之后，使用 .type(torch.float) 将属性数据的类型转换为浮点型。最后，将属性数据经过一系列数学计算进行归一化处理。att_a 的值乘以2，并减去1...

jsp附件上传按钮，引用的//附件上传绑定 $("input[class*='att_upload']").each((index, jq) => { var _this = $(jq), opt = _this.attr('opt'), name = _this.attr('id'), canEdit = _this.attr('edit'), limit = _this.attr('limit'), acceptType = _this.attr('acceptType'), validate = eval('(' + _this.attr('validate') + ')'), div = $(""); _this.addClass("hidden").attr("type", "text"); if (validate && validate.hasOwnProperty('notEmpty')) { validate.notEmpty.message = "请上传文件！" _this.attr('validate', JSON.stringify(validate)) } canEdit = canEdit ? canEdit == 'true' : true opt = $.extend({}, { name: name, pick: name, fileNumLimit: Number(limit), acceptType: acceptType, canEdit: canEdit, canAdd: canEdit, canDel: canEdit }, opt) _this.after(div) div.gsUpload(opt) }) ；需要浏览器F12按钮才生效

在页面加载时，它会查找所有class属性中包含"att_upload"的input元素，并为其绑定上传功能。具体来说，代码会为每个input元素创建一个div元素，然后使用GS Upload插件初始化上传功能。其中包含了一些配置参数，...

def sample(): batch_feature, batch_label, batch_att = data.next_batch(opt.batch_size) input_res.copy_(batch_feature) input_att.copy_(batch_att) input_label.copy_(util.map_label(batch_label, data.seenclasses)) def generate_syn_feature(netG, classes, attribute, num): nclass = classes.size(0) syn_feature = torch.FloatTensor(nclassnum, opt.resSize) syn_label = torch.LongTensor(nclassnum) syn_att = torch.FloatTensor(num, opt.attSize) syn_noise = torch.FloatTensor(num, opt.nz) if opt.cuda: syn_att = syn_att.cuda() syn_noise = syn_noise.cuda()

然后，将获取到的数据分别复制到预定义的张量变量input_res、input_att和input_label中。在这段代码中，input_res、input_att和input_label分别表示图像特征、属性特征和标签。 generate_syn_feature...

class ACmix(nn.Module): def init(self, in_planes, out_planes, kernel_att=7, head=4, kernel_conv=3, stride=1, dilation=1): super(ACmix, self).init() self.in_planes = in_planes self.out_planes = out_planes self.head = head self.kernel_att = kernel_att self.kernel_conv = kernel_conv self.stride = stride self.dilation = dilation self.rate1 = torch.nn.Parameter(torch.Tensor(1)) self.rate2 = torch.nn.Parameter(torch.Tensor(1)) self.head_dim = self.out_planes // self.head self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1) self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1) self.conv3 = nn.Conv2d(in_planes, out_planes, kernel_size=1) self.conv_p = nn.Conv2d(2, self.head_dim, kernel_size=1) self.padding_att = (self.dilation * (self.kernel_att - 1) + 1) // 2 self.pad_att = torch.nn.ReflectionPad2d(self.padding_att) self.unfold = nn.Unfold(kernel_size=self.kernel_att, padding=0, stride=self.stride) self.softmax = torch.nn.Softmax(dim=1) self.fc = nn.Conv2d(3 * self.head, self.kernel_conv * self.kernel_conv, kernel_size=1, bias=False) self.dep_conv = nn.Conv2d(self.kernel_conv * self.kernel_conv * self.head_dim, out_planes, kernel_size=self.kernel_conv, bias=True, groups=self.head_dim, padding=1, stride=stride) self.reset_parameters()逐行解释代码

class ACmix(nn.Module): def __init__(self, in_planes, out_planes, kernel_att=7, head=4, kernel_conv=3, stride=1, dilation=1): super(ACmix, self).__init__() 定义ACmix类，其中包含了初始化函数__...

for p in netD.parameters(): # reset requires_grad p.requires_grad = False # avoid computation netG.zero_grad() input_attv = Variable(input_att) noise.normal_(0, 1) noisev = Variable(noise) fake = netG(noisev, input_attv) criticG_fake = netD(fake, input_attv) criticG_fake = criticG_fake.mean() G_cost = -criticG_fake # classification loss c_errG = cls_criterion(pretrain_cls.model(fake), Variable(input_label)) errG = G_cost + opt.cls_weight*c_errG errG.backward() optimizerG.step() mean_lossG /= data.ntrain / opt.batch_size mean_lossD /= data.ntrain / opt.batch_size print('[%d/%d] Loss_D: %.4f Loss_G: %.4f, Wasserstein_dist: %.4f, c_errG:%.4f' % (epoch, opt.nepoch, D_cost.data[0], G_cost.data[0], Wasserstein_D.data[0], c_errG.data[0]))

接下来，创建变量input_attv和noisev，分别将输入的属性特征（input_att）和噪声（noise）转换为可求导的变量。使用生成器netG生成合成图像fake，并将其输入到判别器netD中得到判别器对合成图像的评分criticG_fake...

attgan = AttGAN(args) progressbar = Progressbar() writer = SummaryWriter(join('output', args.experiment_name, 'summary')) fixed_img_a, fixed_att_a = next(iter(valid_dataloader)) fixed_img_a = fixed_img_a.cuda() if args.gpu else fixed_img_a fixed_att_a = fixed_att_a.cuda() if args.gpu else fixed_att_a fixed_att_a = fixed_att_a.type(torch.float) sample_att_b_list = [fixed_att_a]

最后，创建了一个名为 sample_att_b_list 的列表，并将固定的属性数据 fixed_att_a 添加到列表中。这个列表将在后续的代码中用于生成样本。总结起来，这段代码创建了 AttGAN 类的实例，并初始化了 ...

UPDATE att_ad_base SET att_ad_base.ad_name = sc_ad_xt.ad_name FROM sc_ad_xt WHERE att_ad_base.ad_code = sc_ad_xt.ad_code;

这是一条 SQL 语句，它的作用是将表 att_ad_base 中的 ad_name 列更新为表 sc_ad_xt 中对应 ad_code 的 ad_name 值。具体来说，它使用了 SQL 的 UPDATE 和 FROM 语句，先指定要更新的表是 att_ad_base，然后使用 ...

class AttU_Net(nn.Module): def init(self, img_ch=3, output_ch=1): super(AttU_Net, self).init() self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2) self.Conv1 = conv_block(ch_in=img_ch, ch_out=64) self.Conv2 = conv_block(ch_in=64, ch_out=128) self.Conv3 = conv_block(ch_in=128, ch_out=256) self.Conv4 = conv_block(ch_in=256, ch_out=512) self.Conv5 = conv_block(ch_in=512, ch_out=1024) self.Up5 = up_conv(ch_in=1024, ch_out=512) self.Att5 = Attention_block(F_g=512, F_l=512, F_int=256) self.Up_conv5 = conv_block(ch_in=1024, ch_out=512) self.Up4 = up_conv(ch_in=512, ch_out=256) self.Att4 = Attention_block(F_g=256, F_l=256, F_int=128) self.Up_conv4 = conv_block(ch_in=512, ch_out=256) self.Up3 = up_conv(ch_in=256, ch_out=128) self.Att3 = Attention_block(F_g=128, F_l=128, F_int=64) self.Up_conv3 = conv_block(ch_in=256, ch_out=128) self.Up2 = up_conv(ch_in=128, ch_out=64) self.Att2 = Attention_block(F_g=64, F_l=64, F_int=32) self.Up_conv2 = conv_block(ch_in=128, ch_out=64) self.Conv_1x1 = nn.Conv2d(64, output_ch, kernel_size=1, stride=1, padding=0) self.sigmoid = nn.Sigmoid()

在每个上采样操作后，使用Attention_block模块（self.Att5到self.Att2）对特征图进行注意力计算。最后，通过一个1x1的卷积层（self.Conv_1x1）将通道数变为output_ch，并使用Sigmoid函数进行输出。这个AttU_Net...

class SelfAttention(nn.Module): def init(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).init() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias def __getPosCode(self,fm_sz,out_c): x = [] for i in range(fm_sz): x.append([np.sin,np.cos][i % 2](1 / (10000 ** (i // 2 / fm_sz)))) x = torch.from_numpy(np.array([x])).float() return torch.cat([(x + x.t()).unsqueeze(0) for i in range(out_c)]) def forward(self,x): q,k,v = self.w_q(x),self.w_k(x),self.w_v(x) pos_code = torch.cat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])]).to(x.device) if self.pos_bias: att_map = torch.matmul(q,k.permute(0,1,3,2)) + pos_code else: att_map = torch.matmul(q,k.permute(0,1,3,2)) + torch.matmul(q,pos_code.permute(0,1,3,2)) am_shape = att_map.shape att_map = self.softmax(att_map.view(am_shape[0],am_shape[1],am_shape[2] * am_shape[3])).view(am_shape) return att_map * v 改写为tensorflow形式

抱歉，我并不是一个可以执行代码的程序，我只能回答一些关于代码的问题并提供一些思路。对于这段PyTorch代码，如果要转换为TensorFlow的形式，需要进行以下的修改： 1. 将nn.Module改为tf.keras.layers.Layer，...

用tensorflow的layers.Layer模块改写class SelfAttention(nn.Module): def init(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).init() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias def __getPosCode(self,fm_sz,out_c): x = [] for i in range(fm_sz): x.append([np.sin,np.cos][i % 2](1 / (10000 ** (i // 2 / fm_sz)))) x = torch.from_numpy(np.array([x])).float() return torch.cat([(x + x.t()).unsqueeze(0) for i in range(out_c)]) def forward(self,x): q,k,v = self.w_q(x),self.w_k(x),self.w_v(x) pos_code = torch.cat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])]).to(x.device) if self.pos_bias: att_map = torch.matmul(q,k.permute(0,1,3,2)) + pos_code else: att_map = torch.matmul(q,k.permute(0,1,3,2)) + torch.matmul(q,pos_code.permute(0,1,3,2)) am_shape = att_map.shape att_map = self.softmax(att_map.view(am_shape[0],am_shape[1],am_shape[2] * am_shape[3])).view(am_shape) return att_map * v

class SelfAttention(tf.keras.layers.Layer): def __init__(self, in_c, out_c, fm_sz, pos_bias=False): super(SelfAttention, self).__init__() self.w_q = tf.keras.layers.Conv2D(filters=out_c, kernel_...

if (it+1) % args.sample_interval == 0: attgan.eval() with torch.no_grad(): samples = [fixed_img_a] for i, att_b in enumerate(sample_att_b_list): att_b_ = (att_b * 2 - 1) * args.thres_int if i > 0: att_b_[..., i - 1] = att_b_[..., i - 1] * args.test_int / args.thres_int samples.append(attgan.G(fixed_img_a, att_b_)) samples = torch.cat(samples, dim=3) writer.add_image('sample', vutils.make_grid(samples, nrow=1, normalize=True, value_range=(-1., 1.)), it+1) vutils.save_image(samples, os.path.join( 'output', args.experiment_name, 'sample_training', 'Epoch_({:d})_({:d}of{:d}).jpg'.format(epoch, it%it_per_epoch+1, it_per_epoch) ), nrow=1, normalize=True,value_range=(-1., 1.)) it += 1 我这里面有没有模型保存的命令

在您提供的代码片段中，并没有看到明确的模型保存命令。代码片段展示了在每个 args.sample_interval 步骤时进行模型评估和生成样本的过程，但没有包含模型保存的逻辑。如果您希望在训练过程中保存模型，您可以在...

在Java中用Dom4j把这一段xml内容转成Map对象：<?xml version="1.0" encoding="UTF-8"?> <java version="1.6.0_26" class="java.beans.XMLDecoder"> <object class="java.util.HashMap"> <void method="put"> <string>att_20160719180419281NEk9</string> <string>cx</string> </void> <void method="put"> <string>att_20160511094248589ZKYq</string> <string>cnmm</string> </void> <void method="put"> <string>att_20161018153614188aTus</string> <string>zsx;fj</string> </void> <void method="put"> <string>att_20160422110906494XuEf</string> <null/> </void> <void method="put"> <string>att_20160511130210612UULE</string> <string>fjyxapp;jjgjapp;wx;jd;dxjg;tb;wsjy</string> </void> <void method="put"> <string>att_20160719180542840LnFm</string> <string></string> </void> <void method="put"> <string>att_20160511130025203ssle</string> <string>zdgsszta</string> </void> <void method="put"> <string>att_20160810131359346N0ra</string> <string>szjys</string> </void> <void method="put"> <string>att_20160511130031927equg</string> <string></string> </void> <void method="put"> <string>att_20160422110456036qp7Y</string> <null/> </void> <void method="put"> <string>att_20160422110543585f7iI</string> <string>cpjbxx</string> </void> </object> </java>

</void> <void method=\"put\"> <string>att_20160810131359346N0ra</string> <string>szjys</string> </void> <void method=\"put\"> <string>att_20160511130031927equg</string> <string></string> </void> ...

def forward(self,x): q,k,v = self.w_q(x),self.w_k(x),self.w_v(x) pos_code = torch.cat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])]).to(x.device) if self.pos_bias: att_map = torch.matmul(q,k.permute(0,1,3,2)) + pos_code else: att_map = torch.matmul(q,k.permute(0,1,3,2)) + torch.matmul(q,pos_code.permute(0,1,3,2)) am_shape = att_map.shape att_map = self.softmax(att_map.view(am_shape[0],am_shape[1],am_shape[2] * am_shape[3])).view(am_shape) return att_map * v

如果使用了位置偏置，则将pos_code加到att_map上，否则将att_map分别与q和pos_code相乘再相加。接着使用softmax函数对att_map进行归一化处理，得到注意力权重。最后，将注意力权重与值向量v相乘，得到...

解释下面这段代码：class GNNLayer(nn.Module): def init(self, in_channel, out_channel, inter_dim=0, heads=1, node_num=100): super(GNNLayer, self).init() self.gnn = GraphLayer(in_channel, out_channel, inter_dim=inter_dim, heads=heads, concat=False) self.bn = nn.BatchNorm1d(out_channel) self.relu = nn.ReLU() self.leaky_relu = nn.LeakyReLU() def forward(self, x, edge_index, embedding=None, node_num=0): out, (new_edge_index, att_weight) = self.gnn(x, edge_index, embedding, return_attention_weights=True) self.att_weight_1 = att_weight self.edge_index_1 = new_edge_index out = self.bn(out) return self.relu(out)

在 forward 方法中，输入参数为 x、edge_index、embedding、node_num，其中 x 表示节点特征矩阵，edge_index 表示边的索引矩阵，embedding 表示节点嵌入矩阵，node_num 表示节点数。在 forward 方法中，先调用 gnn ...

for i in range(args.n_attrs): tmp = fixed_att_a.clone() tmp[:, i] = 1 - tmp[:, i] tmp = check_attribute_conflict(tmp, args.attrs[i], args.attrs) sample_att_b_list.append(tmp) it = 0 it_per_epoch = len(train_dataset) // args.batch_size

在每次循环中，创建了 fixed_att_a 的一个副本 tmp，然后将 tmp 中的第 i 列属性值取反（1 减去原来的值）。这样做是为了在生成样本时改变固定的属性，以观察模型生成不同属性的样本。接下来，调用 check_...

# gradient penalty gradient_penalty = calc_gradient_penalty(netD, input_res, fake.data, input_att) gradient_penalty.backward() Wasserstein_D = criticD_real - criticD_fake D_cost = criticD_fake - criticD_real + gradient_penalty optimizerD.step()

- input_att：输入图像的属性特征。然后，通过调用backward方法进行反向传播，计算梯度并更新模型参数。接下来，计算 Wasserstein 距离。Wasserstein_D 是真实图像的判别器评分与合成图像的判别器评分之差。...

通过 <select id="modifyType" name="modifyType" class="form-control" onclick="modifyTypeChange()"> <option value="2" <c:if test="${hiddenDanger.modifyType == 2}">selected="selected"</c:if>>限期整改</option> <option value="1" <c:if test="${hiddenDanger.modifyType == 1}">selected="selected"</c:if>>自查自改</option> <%-- <option value="3" <c:if test="${hiddenDanger.modifyType == 3}">selected="selected"</c:if>>停工整改--%> <%-- </option>--%> </select>；控制div 的显示和隐藏，当控制显示时，涉及的 <input id="rectificationAttachments" class="form-control att_upload" name="rectificationAttachments" type="hidden" value="${hiddenDanger.hiddendangerRectification.attachment}"/>通过class="form-control att_upload 调用的上传按钮点不动

这个问题可能是因为上传按钮被隐藏或者被遮盖住了，你需要检查一下是否有 CSS 样式或者其他 JavaScript 代码导致上传按钮不能被点击。你可以使用浏览器的开发者工具来检查上传按钮的样式和事件监听器，以便找到导致...

class SelfAttention(nn.Module): def init(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).init() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias 改写为twensorflow形式

class SelfAttention(keras.layers.Layer): def __init__(self, in_c, out_c, fm_sz, pos_bias=False): super(SelfAttention, self).__init__() self.w_q = keras.layers.Conv2D(filters=out_c, kernel_size=1, ...

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