att_a_ = (att_a * 2 - 1) * args.thres_int if args.b_distribution == 'none': att_b_ = (att_b * 2 - 1) * args.thres_int if args.b_distribution == 'uniform': att_b_ = (att_b * 2 - 1) * \ torch.rand_like(att_b) * \ (2 * args.thres_int) if args.b_distribution == 'truncated_normal': att_b_ = (att_b * 2 - 1) * \ (torch.fmod(torch.randn_like(att_b), 2) + 2) / 4.0 * \ (2 * args.thres_int) if (it+1) % (args.n_d+1) != 0: errD = attgan.trainD(img_a, att_a, att_a_, att_b, att_b_) add_scalar_dict(writer, errD, it+1, 'D') else: errG = attgan.trainG(img_a, att_a, att_a_, att_b, att_b_) add_scalar_dict(writer, errG, it+1, 'G') progressbar.say(epoch=epoch, iter=it+1, d_loss=errD['d_loss'], g_loss=errG['g_loss']) if (it+1) % args.save_interval == 0: # To save storage space, I only checkpoint the weights of G. # If you'd like to keep weights of G, D, optim_G, optim_D, # please use save() instead of saveG(). attgan.saveG(os.path.join( 'output', args.experiment_name, 'checkpoint', 'weights.{:d}.pth'.format(epoch) )) 我这里面有模型保存的命令么

mine_game_java.rar_ATT7022B_JAVA 扫雷_Mine!

【标题】"mine_game_java.rar_ATT7022B_JAVA 扫雷_Mine!" 提供的是一个基于Java编程语言实现的扫雷游戏项目。这个项目不仅是一个完整的可运行的游戏，同时也为开发者提供了进一步学习和扩展Java编程以及游戏开发的...

Att702x.rar_ATT70_ATT702_ATT702X driv_att7

《珠海炬力ATT702x计量芯片驱动程序详解》在嵌入式系统和物联网设备领域，驱动程序是连接硬件与操作系统的关键桥梁。本文将深入探讨珠海炬力（ Actions Semiconductor）开发的ATT702x系列计量芯片的驱动程序，帮助...

if args.b_distribution == 'none': att_b_ = (att_b * 2 - 1) * args.thres_int if args.b_distribution == 'uniform': att_b_ = (att_b * 2 - 1) * \ torch.rand_like(att_b) * \ (2 * args.thres_int) if args.b_distribution == 'truncated_normal': att_b_ = (att_b * 2 - 1) * \ (torch.fmod(torch.randn_like(att_b), 2) + 2) / 4.0 * \ (2 * args.thres_int) if (it+1) % (args.n_d+1) != 0: errD = attgan.trainD(img_a, att_a, att_a_, att_b, att_b_) add_scalar_dict(writer, errD, it+1, 'D') else: errG = attgan.trainG(img_a, att_a, att_a_, att_b, att_b_) add_scalar_dict(writer, errG, it+1, 'G') progressbar.say(epoch=epoch, iter=it+1, d_loss=errD['d_loss'], g_loss=errG['g_loss'])

在该分支中，通过 (att_b * 2 - 1) * args.thres_int 对属性 B 进行归一化处理，将其缩放到 -args.thres_int 到 args.thres_int 的范围内。如果 args.b_distribution 的值为 'uniform'，则执行第二个...

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

att_a 的值乘以2，并减去1，然后再乘以命令行参数 args.thres_int 的值。这个过程将属性数据缩放到 -1 到 1 的范围内。总结起来，这段代码通过迭代训练数据加载器获取每个批次的图像和属性数据。然后，对属性...

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

if (epoch+1) % args.save_interval == 0: # 在每个epoch结束后保存模型 torch.save(attgan.state_dict(), 'path_to_save_model') # 保存模型参数请注意，上述代码只是示例，并且需要根据您的具体情况进行...

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 ...

import requestsfrom html.parser import HTMLParserimport argparsefrom concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completedimport multiprocessingprefix = "save/"readed_path = multiprocessing.Manager().Queue()cur_path = multiprocessing.Manager().Queue()new_path = multiprocessing.Manager().Queue()lock = multiprocessing.Lock()class MyHttpParser(HTMLParser): def init(self): super().init() self.tag = [] self.href = "" self.txt = "" def handle_starttag(self, tag, attrs): self.tag.append(tag) if tag == "a": for att in attrs: if att[0] == 'href': self.href = att[1] def handle_endtag(self, tag): if tag == "a" and len(self.tag) > 2 and self.tag[-2] == "div": print("in div, link txt is %s ." % self.txt) print("in div, link url is %s ." % self.href) if not self.href in readed_path.queue: readed_path.put(self.href) new_path.put(self.href) self.tag.pop(-1) def handle_data(self, data): if len(self.tag) >= 1 and self.tag[-1] == "a": self.txt = datadef LoadHtml(path, file_path): if len(file_path) == 0: file_path = "/" url = f"http://{path}{file_path}" try: response = requests.get(url) print(response.status_code, response.reason, response.raw.version) data = response.content.decode("utf-8") if response.status_code == 301: data = response.headers["Location"] if not data in readed_path.queue: new_path.put(data) data = "" return data except Exception as e: print(e.args)def ParseArgs(): parser = argparse.ArgumentParser() parser.add_argument("-p", "--path", help="domain name") parser.add_argument("-d", "--deep", type=int, help="recursion depth") args = parser.parse_args() return argsdef formatPath(path): path = path.removeprefix("https://") path = path.removeprefix("http://") path = path.removeprefix("//") return pathdef doWork(path): path = formatPath(path) m = path.find("/") if m == -1: m = len(path) data = LoadHtml(path[:m], path[m:]) with open(prefix + path[:m] + ".html", "w+", encoding="utf-8") as f: f.write(data) parse.feed(data)def work(maxdeep): args = ParseArgs() cur_path.put(formatPath(args.path)) readed_path.put(formatPath(args.path)) parse = MyHttpParser() with ProcessPoolExecutor(max_workers=4) as executor: for i in range(args.deep): size = cur_path.qsize() futures = [executor.submit(doWork, cur_path.get()) for _ in range(size)] for future in as_completed(futures): try: future.result() except Exception as e: print(e) cur_path.queue.clear() while not new_path.empty(): cur_path.put(new_path.get()) print(i)if name == 'main': work(5)此代码出现Unresolved reference 'parse'

这是因为您在 doWork 函数中使用了 parse 变量，但是该变量未在 doWork 函数中定义或传递。您需要将 parse 变量作为参数传递给 doWork 函数，或者将其定义为全局变量，以便在 doWork 函数中使用它。...

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_...

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...

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...

import http.client from html.parser import HTMLParser import argparse from concurrent.futures import ThreadPoolExecutor import multiprocessing.pool prefix = "save/" readed_path = multiprocessing.Manager().list() cur_path = multiprocessing.Manager().list() new_path = multiprocessing.Manager().list() lock = multiprocessing.Lock() class MyHttpParser(HTMLParser): def init(self): HTMLParser.init(self) self.tag = [] self.href = "" self.txt = "" def handle_starttag(self, tag, attrs): self.tag.append(tag) # print("start tag in list :" + str(self.tag)) if tag == "a": for att in attrs: if att[0] == 'href': self.href = att[1] def handle_endtag(self, tag): if tag == "a" and len(self.tag) > 2 and self.tag[-2] == "div": print("in div, link txt is %s ." % self.txt) print("in div, link url is %s ." % self.href) lock.acquire() if not self.href in readed_path: readed_path.append(self.href) new_path.append(self.href) # print("end tag in list :" + str(self.tag)) lock.release() self.tag.pop(-1) def handle_data(self, data): if len(self.tag) >= 1 and self.tag[-1] == "a": self.txt = data def LoadHtml(path, file_path): if len(file_path) == 0: file_path = "/" conn = http.client.HTTPConnection(path) try: conn.request("GET", file_path) response = conn.getresponse() print(response.status, response.reason, response.version) data = response.read().decode("utf-8") if response.status == 301: data = response.getheader("Location") lock.acquire() new_path.append(data) lock.release() data = "" #print(data) conn.close() return data except Exception as e: print(e.args) def ParseArgs(): # 初始化解析器 parser = argparse.ArgumentParser() # 定义参数 parser.add_argument("-p", "--path", help="域名") parser.add_argument("-d", "--deep", type=int, help="递归深度") # 解析 args = parser.parse_args() return args def formatPath(path): path = path.removeprefix("https://") path = path.removeprefix("http://") path = path.removeprefix("//") return path def doWork(path): path = formatPath(path) m = path.find("/") if m == -1: m = len(path) data = LoadHtml(path[:m], path[m:]) with open(prefix + path[:m] + ".html", "w+", encoding="utf-8") as f: f.write(data) parse.feed(data) def work(deep,maxdeep): if deep > maxdeep: return args = ParseArgs() cur_path.append(formatPath(args.path)) readed_path.append(formatPath(args.path)) parse = MyHttpParser() e = multiprocessing.Pool(4) for i in range(args.deep): size = len(cur_path) e.map(doWork,cur_path) cur_path[:]=[] for p in new_path: cur_path.append(p) new_path[:]=[] print(i)优化此代码能在windows下运行

if tag == "a" and len(self.tag) > 2 and self.tag[-2] == "div": print("in div, link txt is %s ." % self.txt) print("in div, link url is %s ." % self.href) if not self.href in readed_path.queue: ...

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)

这段代码定义了一个 GNNLayer 类，用于实现图神经网络层。其中，in_channel 和 out_channel 分别表示输入和输出的特征通道数，inter_dim 表示中间维度，heads 表示头数，node_num 表示节点数。在 __init__ 方法中，...

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

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=...

如何实现公式 att_midpoint(i,:) = att_midpoint(i-1,:) + 0.5 * (gyr + gyr_next) * dt 的矩阵计算过程？请提供相应的MATLAB代码片段来解决问题。

dim = size(att_midpoint, 2); % 检查输入是否匹配并防止数组越界 assert(size(gyr, 1) == dim && size(gyr_next, 1) == dim, 'Input dimensions mismatch'); assert(num_samples >= 1, 'No samples to update'); ...

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

1. 将nn.Module改为tf.keras.layers.Layer，并且在init方法中调用super().__init__()。 2. 将nn.Conv2d改为tf.keras.layers.Conv2D。 3. 将nn.Softmax改为tf.keras.layers.Softmax。 4. 将torch....

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

att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + tf.matmul(q, tf.transpose(pos_code, perm=[0, 1, 3, 2])) am_shape = tf.shape(att_map) att_map = self.softmax(tf.reshape(att_map, [am_...

#define _CRT_SECURE_NO_WARNINGS #include<stdio.h> #include<windows.h> #include<string.h> #include<stdlib.h> #include<math.h> #include<malloc.h> typedef struct character_name { char name[100]; struct character_name* next; }char_name; typedef struct character_title { char title[100]; struct character_title* next; }char_title; typedef struct character_identity { char identity[100]; struct character_identity* next; }char_iden; typedef struct character_attribute { int strength; struct character_attribute* next; }char_att; typedef struct character_information { char_name* _name; char_title* _title; char_iden* _iden; char_att* _att; struct character_information* next; }char_inf; char_inf* initialization() { char_inf* node = (char_inf)malloc(sizeof(char_inf)); node->_name = (char_name)malloc(sizeof(char_name)); node->_title = (char_title)malloc(sizeof(char_title)); node->_iden = (char_iden)malloc(sizeof(char_iden)); node->_att = (char_att)malloc(sizeof(char_att)); return node; } char_inf ceshi, * current, * end; char_name* name_current, * name_end; char_title* title_current, * title_end; char_iden* iden_current, * iden_end; char_att* att_current, att_end; int main() { ceshi = initialization(); name_current = (char_name)malloc(sizeof(char_name)); name_current = ceshi->_name; strcpy(name_current->name, "ceshi_1_1"); name_current->next = (char_name)malloc(sizeof(char_name)); name_current = name_current->next; strcpy(name_current->name, "ceshi_1_2"); strcpy(ceshi->_title->title, "ceshi_2"); strcpy(ceshi->_iden->identity, "ceshi_3"); ceshi->_att->strength = 4; name_current = ceshi->_name; while (name_current->next != NULL) { printf("%s\n", name_current->name); name_current = name_current->next; } printf("%s\n%s\n%d\n", ceshi->_title->title, ceshi->_iden->identity, ceshi->_att->strength); return 0; } 中为什么在下面两段代码中报错 name_current = (char_name)malloc(sizeof(char_name)); name_current = ceshi->_name; strcpy(name_current->name, "ceshi_1_1"); name_current->next = (char_name)malloc(sizeof(char_name)); name_current = name_current->next; strcpy(name_current->name, "ceshi_1_2"); name_current = ceshi->_name; while (name_current->next != NULL) { printf("%s\n", name_current->name); name_current = name_current->next; }

strcpy(name_current->name, "ceshi_1_2"); 另外，在最后一段代码中，你需要检查name_current是否为NULL，否则可能会出现访问空指针的错误。修改后的代码如下： c name_current = ceshi->_name; while (name...

基于Qt开发的截图工具- 支持全屏截图，支持自定义截图，支持捕获窗口截图，支持固定大小窗口截图，颜色拾取，图片编辑

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