"ATT报告评估及进展：2022年8月评估报告展望"

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The Stimson Center's Arms Trade Treaty-Baseline Assessment Project (ATT-BAP) is dedicated to aiding States in comprehending the obligations of the Arms Trade Treaty (ATT) and fostering effective implementation. One of the key areas of focus for ATT-BAP is enhancing the quality and comprehensiveness of reporting on the ATT. By developing tools and resources to serve as a baseline for evaluating State progress in implementing the treaty, ATT-BAP facilitates the measurement of state compliance with ATT requirements. As the project continues to evolve and expand, it strives to empower States to fulfill their responsibilities under the ATT and contribute to a more transparent and accountable global arms trade regime. Through its comprehensive assessments and targeted initiatives, ATT-BAP plays a crucial role in fostering greater understanding and adherence to the principles of the ATT, ultimately contributing to enhanced peace and security worldwide.

贷款评估报告.pdf

2021-12-21 上传
贷款评估报告.pdf

环境评估报告.pdf

2021-10-25 上传
环境评估报告.pdf

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

2023-07-16 上传
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...

class GAT(nn.Module): def __init__(self, in_dim, out_dim, num_heads): super(GAT, self).__init__() self.num_heads = num_heads self.attentions = nn.ModuleList([nn.Linear(in_dim, out_dim) for _ in range(num_heads)]) # self.linear = nn.Linear(512, 128) self.out_att = nn.Linear(in_dim*num_heads, out_dim) def forward(self, x, adj): x = x.unsqueeze(1) x = x.transpose(2,0) x = torch.cat([att(x) for att in self.attentions], dim=1) alpha = F.softmax(torch.matmul(x, x.transpose(1, 2)) / self.num_heads, dim=-1) alpha = torch.where(alpha>0, alpha, torch.zeros_like(alpha)) # alpha = torch.where(adj.unsqueeze(-1).bool(), alpha, torch.zeros_like(alpha)) alpha = alpha / alpha.sum(dim=-2, keepdim=True) out = torch.matmul(alpha, x).squeeze(1) out=out.reshape(-1,128) # out = self.linear(out) out = F.elu(self.out_att(out)) return out这段代码为什么一直报错mat1 and mat2 shapes cannot be multiplied (512x768 and 512x128)，我应该怎么修改呢？它是怎么在x = torch.cat([att(x) for att in self.attentions], dim=1)这一步将输入（24，1，16）变成（512，768）的形状的，在这一步总是报错和（512，128）的全连接层不匹配，怎么修改呢。这是pytorch代码，本意是构建两层GAT用点注意力机制对形状为（16992，307，12，2）的数据集按第二列度特征相同的节点计算注意力系数和隐变量，怎么完善呢

2023-06-05 上传
x = torch.cat([att(x) for att in self.attentions], dim=1) x = x.view(batch_size, self.num_heads, num_nodes, -1) ``` 如果你要使用全连接层将多头注意力的输出映射到期望的输出维度，需要将out_dim*num_heads...

foo 对象有 att 属性，那么获取 at 周的，以下些微是可以的? (多) A. foo. att B.foo(“att”) C. foo[ “att”] D.foo[“att”) E.foo[“a”+”t”+”t”]

2023-04-26 上传
在这个例子中，foo 对象有一个名为 att 的属性，我们可以通过 foo.att 或 foo[“att”] 来获取它。选项 B 不正确，因为这将尝试调用 foo 对象的 att 方法，而不是获取其属性。选项 E 不正确，因为它将尝试获取名为...

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

2023-06-06 上传
定义一些辅助层，其中padding_att表示注意力机制的填充大小，pad_att表示进行反射填充的层，unfold表示对特征图进行展开的层，softmax表示对展开后的特征图进行softmax操作的层。 ``` self.fc = nn.Conv2d(3 * self...

att = spark.sql("SELECT attributes.{attr} as {attr}, category, stars FROM for_att".format(attr=attribute)).dropna()

2023-06-09 上传
这段代码是使用Spark SQL查询数据，并将结果存储在名为“att”的变量中。但是，在这个代码段中，'spark'变量没有被初始化或定义，导致代码无法正常工作。 要使用Spark SQL，您需要通过以下方式初始化SparkSession...

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(nclass*num, opt.resSize) syn_label = torch.LongTensor(nclass*num) 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()

2023-07-13 上传
它按照批次大小`opt.batch_size`从数据集中获取特征（batch_feature）、标签（batch_label）和属性（batch_att）。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...

class GAT(nn.Module): def __init__(self, in_dim, out_dim, num_heads): super(GAT, self).__init__() self.num_heads = num_heads self.attentions = nn.ModuleList([nn.Linear(in_dim, out_dim) for _ in range(num_heads)]) self.out_att = nn.Linear(in_dim*num_heads, out_dim) def forward(self, x, adj): x = x.unsqueeze(1) x = x.transpose(2,0) x = torch.cat([att(x) for att in self.attentions], dim=1) alpha = F.softmax(torch.matmul(x, x.transpose(1, 2)) / self.num_heads, dim=-1) alpha = torch.where(alpha>0, alpha, torch.zeros_like(alpha)) # alpha = torch.where(adj.unsqueeze(-1).bool(), alpha, torch.zeros_like(alpha)) alpha = alpha / alpha.sum(dim=-2, keepdim=True) out = torch.matmul(alpha, x).squeeze(1) out = F.elu(self.out_att(out)) return out 这段代码中out的形状为（192，512），而self.out_att只能接受（128，512）的输入，这段代码应该怎么调整呢。我尝试在self部分增加一个线性全连接层linear（512，128），但是报错缺少必要的位置参数，我应该怎么办呢。这是pytorch版本

2023-06-04 上传
x = torch.cat([att(x) for att in self.attentions], dim=1) alpha = F.softmax(torch.matmul(x, x.transpose(1, 2)) / self.num_heads, dim=-1) alpha = torch.where(alpha>0, alpha, torch.zeros_like(alpha))...

输入out.shape为torch.Size（192，512）和定义的输入通道self.out_att（128，512）怎么才能使用torch.matmul 可以帮我写好pytorch代码吗上述代码不成功，mat1 and mat2 shapes cannot be multiplied (192x512 and 128x512)。请写出能让我成功运行的代码 out = torch.matmul(alpha, x).squeeze(1) # self.out_att=self.out_att.transpose(1,0) out = F.elu(self.out_att(out))这句话怎么修改

2023-06-04 上传
out = torch.matmul(x, self.out_att.t()) # 注意转置 self.out_att out = F.elu(out) ``` 这里使用了 self.out_att.t() 将 self.out_att 进行转置，使得其形状变为 (512, 128)，从而满足矩阵乘积的相容性要求。...
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