def forward(self, x):#正向传播 identity = x if self.downsample is not None:#判断虚实线 identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += identity out = self.relu(out) return out class Bottleneck(nn.Module):#深层残差结构 expansion = 4

脉冲神经网络中这段代码的意思是def forward(self, x): identity = x out = self.conv1(x) out = self.MaxPool1(out) out = self.bn1(out) out = self.sn1(out) out = self.conv2(out) out = self.MaxPool2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.sn2(out) return out

接下来，如果 downsample 不为 None，则将输入张量 x 通过下采样操作得到 identity 张量。然后将 identity 与 out 相加，并再次通过脉冲激活函数层得到 out 张量。最后，将 out 作为函数的输出返回。这个函数实现了...

def GetNo(self,e): #查找第一个为e的元素的序号 j=0 p=self.head.next while p is not None and p.data!=e: j+=1 #查找元素e p=p.next if p is None: return -1 #未找到时返回-1 else: return j #找到后返回其序号 def Insert(self, i, e): #在线性表中序号i位置插入元素e def Delete(self,i): #在线性表中删除序号i位置的元素 def display(self): #输出线性表 p=self.head.next while p is not None: print(p.data,end=' ') p=p.next; print()

while (j < i and p is not None): j += 1 p = p.next return p def Add(self, e): #在线性表的末尾添加一个元素e p = self.head while p.next is not None: p = p.next node = LinkNode(e) p.next = ...

class LinkNode: #单链表结点类 def init(self,data=None): #构造函数 self.data=data #data属性 self.next=None #next属性 class LinkList: #单链表类 def init(self): #构造函数 self.head=LinkNode() #头结点head self.head.next=None def CreateListF(self, a): #头插法：由数组a整体建立单链表 def CreateListR(self, a): #尾插法：由数组a整体建立单链表 def geti(self, i): #返回序号为i的结点 p=self.head j=-1 while (j<i and p is not None): j+=1 p=p.next return p def Add(self, e): #在线性表的末尾添加一个元素e def getsize(self): #返回长度 p=self.head cnt=0 while p.next is not None: #找到尾结点为止 cnt+=1 p=p.next return cnt

while (j < i and p is not None): j += 1 p = p.next return p def Add(self, e): #在线性表的末尾添加一个元素e p = self.head while p.next is not None: p = p.next node = LinkNode(e) p.next = ...

class LinkQueue: #链队类 def init(self): #构造方法 self.front=None #队头指针 self.rear=None #队尾指针 def empty(self): #判断队是否为空 return self.front==None def push(self,e): #元素e进队 def pop(self): #出队操作 def gethead(self): #取队顶元素操作

def empty(self): # 判断队是否为空 return self.front == None def push(self, e): # 元素e进队 new_node = Node(e) # 创建新节点 if self.empty(): # 如果队列为空，则新节点既是队头也是队尾 self.front =...

检查代码并修改：class Node: def init(self, data): self.data = data self.prev = None self.next = None Class DoublyLinkedlist: # def init(self) self.head = None def remove(self,item): current = self.head previous = None found = False while not found: if current.getData() == item found = True else: previous = current current = current.getNext() if previous == None: self.head = current.getNext() current.prev = None else: previous.setNext(current.getNext()) current.prev = previous

if self.head is None: self.head = new_node else: current = self.head while current.next is not None: current = current.next current.next = new_node new_node.prev = current def prepend(self, ...

class LinkNode: #链队结点类 def init(self,data=None): #构造方法 self.data=data #data域 self.next=None #next域 class LinkQueue: #链队类 def init(self): #构造方法 self.front=None #队头指针 self.rear=None #队尾指针 def empty(self): #判断队是否为空 return self.front==None def push(self,e): #元素e进队 new_node = Node(e) # 创建新节点 if self.empty(): # 如果队列为空，则新节点既是队头也是队尾 self.front = new_node self.rear = new_node else: self.rear.next = new_node # 将新节点链到队尾 self.rear = new_node # 更新队尾指针 def pop(self): #出队操作 if self.empty(): # 如果队列为空，返回None return None node = self.front # 取出队头指针指向的节点 self.front = node.next # 更新队头指针 if self.front == None: # 如果删除的是最后一个节点，更新队尾指针 self.rear = None return node.data # 返回出队节点的数据 def gethead(self): #取队顶元素操作 if self.empty(): # 如果队列为空，返回None return None return self.front.data # 返回队头指针指向的节点的数据

def empty(self): #判断队是否为空 return self.front==None def push(self,e): #元素e进队 new_node = LinkNode(e) # 创建新节点 if self.empty(): # 如果队列为空，则新节点既是队头也是队尾 self....

为以下每句代码做注释：class Bottleneck(nn.Module): expansion = 4 def init(self, in_channel, out_channel, stride=1, downsample=None): super(Bottleneck, self).init() self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=1, stride=1, bias=False) # squeeze channels self.bn1 = nn.BatchNorm2d(out_channel) self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=stride, bias=False, padding=1) self.bn2 = nn.BatchNorm2d(out_channel) self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel * self.expansion, kernel_size=1, stride=1, bias=False) # unsqueeze channels self.bn3 = nn.BatchNorm2d(out_channel * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += identity out = self.relu(out) return out

- if self.downsample is not None: identity = self.downsample(x)：如果下采样函数不为空，则使用下采样函数调整输入数据。 - out = self.conv1(x)：第一个卷积层的前向传播。 - out = self.bn1(out)：第一个 ...

class LinkNode: #循环单链表结点类 def init(self,data=None): #构造函数 self.data=data #data属性 self.next=None #next属性 class CLinkList: #循环单链表类 def init(self): #构造函数 self.head=LinkNode() #头结点head self.head.next=self.head #构成循环的 def CreateListF(self, a): #头插法：由数组a整体建立循环单链表 for i in range(0,len(a)): #循环建立数据结点s s=LinkNode(a[i]) #新建存放a[i]元素的结点s s.next=self.head.next #将s结点插入到开始结点之前,头结点之后 self.head.next=s def CreateListR(self, a): #尾插法：由数组a整体建立循环单链表 t=self.head #t始终指向尾结点,开始时指向头结点 for i in range(0,len(a)): #循环建立数据结点s s=LinkNode(a[i]); #新建存放a[i]元素的结点s t.next=s #将s结点插入t结点之后 t=s t.next=self.head #将尾结点的next改为指向头结点 def geti(self, i): #返回序号为i的结 p=self.head #首先p指向头结点 j=-1 while (j<i): j+=1 p=p.next if p==self.head: break return p def Add(self, e): #在线性表的末尾添加一个元素e def getsize(self): #返回长度 p=self.head cnt=0 while p.next!=self.head: #找到尾结点为止 cnt+=1 p=p.next return cnt

def __init__(self,data=None): # 构造函数 self.data = data # data属性 self.next = None # next属性 class CLinkList: # 循环单链表类 def __init__(self): # 构造函数 self.head = LinkNode() # 头...

修改以下代码：MaxSize=5 #全局变量，假设容量为5 class LinkNode: #链队结点类 def init(self,data=None): #构造方法 self.data=[None] *MaxSize #data属性 self.next=None #next属性 class LinkQueue: #链队类 def init(self): #构造方法 self.front=None #队头指针 self.rear=None #队尾指针 def empty(self): # 判断队是否为空 return self.front == self.rear def push(self, e): # 元素e进队 s = LinkNode(e) # 新建结点s if self.empty(): # 原链队为空 self.front = self.rear = s else: # 原链队不空 self.rear.next = s # 将s结点链接到rear结点后面 self.rear = s def pop(self): # 出队操作 assert not self.empty() # 检测空链队 if self.front == self.rear: # 原链队只有一个结点 e = self.front.data # 取首结点值 self.front = self.rear = None # 置为空队 else: # 原链队有多个结点 e = self.front.data # 取首结点值 self.front = self.front.next # front指向下一个结点 return e def gethead(self): # 取队头元素 assert not self.empty() # 检测空链队 e = self.front.data # 取首结点值 return e def size(self): # 返回队中元素个数 return ((self.rear - self.front + MaxSize) % MaxSize) #主程序 if name == 'main': t=LinkQueue() t.push(4) t.push(6) t.push(1) t.push(9) print(" 队列元素个数:%d" % (t.size())) print() print("队头元素: %d" % (t.gethead())) print(" 出队元素:%d" % (t.pop())) print(" 队列元素个数:%d" % (t.size())) print()

def empty(self): # 判断队是否为空 return self.front == None and self.rear == None def push(self, e): # 元素e进队 s = LinkNode(e) # 新建结点s if self.empty(): # 原链队为空 self.front = self...

class SpiralIterator: def init(self, source, x=810, y=500, length=None): self.source = source self.row = np.shape(self.source)[0]#第一个元素是行数 self.col = np.shape(self.source)[1]#第二个元素是列数 if length: self.length = min(length, np.size(self.source)) else: self.length = np.size(self.source) if x: self.x = x else: self.x = self.row // 2 if y: self.y = y else: self.y = self.col // 2 self.i = self.x self.j = self.y self.iteSize = 0 geo_transform = dsm_data.GetGeoTransform() self.x_origin = geo_transform[0] self.y_origin = geo_transform[3] self.pixel_width = geo_transform[1] self.pixel_height = geo_transform[5] def hasNext(self): return self.iteSize < self.length # 不能取更多值了 def get(self): if self.hasNext(): # 还能再取一个值 # 先记录当前坐标的值 —— 准备返回 i = self.i j = self.j val = self.source[i][j] # 计算下一个值的坐标 relI = self.i - self.x # 相对坐标 relJ = self.j - self.y # 相对坐标 if relJ > 0 and abs(relI) < relJ: self.i -= 1 # 上 elif relI < 0 and relJ > relI: self.j -= 1 # 左 elif relJ < 0 and abs(relJ) > relI: self.i += 1 # 下 elif relI >= 0 and relI >= relJ: self.j += 1 # 右 #判断索引是否在矩阵内 x = self.x_origin + (j + 0.5) * self.pixel_width y = self.y_origin + (i + 0.5) * self.pixel_height z = val self.iteSize += 1 return x, y, z这段代码怎么改能利用共线方程将地面点坐标反算其原始航片对应的像素行列号

def __init__(self, source, K, R, t, x=810, y=500, length=None): self.source = source self.row = np.shape(self.source)[0] # 第一个元素是行数 self.col = np.shape(self.source)[1] # 第二个元素是列数 ...

解释每一句 def forward(self, x): if self.conv_down is not None: x = self.conv_down(x) y1 = self.conv1(x) y2 = self.blocks(self.conv2(x)) y = paddle.concat([y1, y2], axis=1) if self.attn is not None: y = self.attn(y) y = self.conv3(y) return y

if self.conv_down is not None: x = self.conv_down(x) 如果self.conv_down不为空，则将输入x执行一次卷积操作self.conv_down。 y1 = self.conv1(x) 执行第一步卷积操作，将输入x执行一次卷积操作...

解释这段代码class BasicBlock(nn.Module): expansion = 1 def init(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(BasicBlock, self).init() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out

如果在初始化方法中传入了downsample参数（非空），则会执行降采样操作，将输入张量x进行降采样以适应维度不匹配的情况。最后，返回处理后的输出特征图。这段代码实现了一个BasicBlock残差块，用于构建深度残差...

def GetNo(self,e): #查找第一个为e的元素的序号 j=0 p=self.head.next #首先p指向首结点 while p!=self.head and p.data!=e: j+=1 #查找元素e p=p.next if p==self.head: return -1 #未找到时返回-1 else: return j #找到后返回其序号 def Insert(self, i, e): #在线性表中序号i位置插入元素e assert i>=0 #检测参数i正确性的断言 s=LinkNode(e) #建立新结点s if (i==0): #插入作为首结点 s.next=self.head.next self.head.next=s else: p=self.geti(i-1) #找到序号为i-1的结点p assert p!=self.head #p不为头结点的检测 s.next=p.next #在p结点后面插入s结点 p.next=s def Delete(self,i): #在线性表中删除序号i位置的元素 def display(self): #输出线性表 p=self.head.next #首先p指向首结点 while p!=self.head: print(p.data,end=' ') p=p.next print()

def __init__(self, data, next=None): #构造函数 self.data = data #结点数据 self.next = next #后继指针 class LinkList: def __init__(self): #构造函数 self.head = LinkNode(None) #头结点 self.head....

def undo(self): # 悔棋 if not self.steps: return False x, y = self.steps.pop() self.board[x][y] = 0 self.player = 1 - self.player return True，解释这段代码

- if not self.steps: 判断 self.steps 是否为空，如果是则返回 False。 - x, y = self.steps.pop() 从 self.steps 中弹出最后一次下棋的坐标，并将其赋值给变量 x 和 y。 - self.board[x][y] = 0 将...

def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x)

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class LinkQueue: #链队类 def __init__(self): #构造方法 self.front=None #队头指针 self.rear=None #队尾指针 def empty(self): #判断队是否为空 return self.front==None def push(self,e): #元素e进队 def pop(self): #出队操作 def gethead(self): #取队顶元素操作

解释每一句 def forward(self, x): if self.conv_down is not None: x = self.conv_down(x) y1 = self.conv1(x) y2 = self.blocks(self.conv2(x)) y = paddle.concat([y1, y2], axis=1) if self.attn is not None: y = self.attn(y) y = self.conv3(y) return y

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class LinkQueue: #链队类 def init(self): #构造方法 self.front=None #队头指针 self.rear=None #队尾指针 def empty(self): #判断队是否为空 return self.front==None def push(self,e): #元素e进队 def pop(self): #出队操作 def gethead(self): #取队顶元素操作