【C语言】swap()函数为什么不能直接交换int整型数据

文章讲述了在C语言中,由于对函数参数传递方式的理解不足,导致在使用`voidswap(int*a,int*b)`时遇到困难。作者介绍了传值、指针和引用三种参数传递方式,并提供了正确的指针传递改进示例。

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常见错误:

void swap(int a,int b);

int main(){
    int a=2,b=3;
    swap(a,b);
    printf("%d",a,b);
    return 0;
}

void swap(int a,int b){
     int t=a;
     a=b;
     b=t;
}

错误原因:对 函数参数的知识点不够熟悉。

在函数中参数传递分为三种情况:

  1. 参数可以使用传值方式传递:默认情况下,C函数参数是通过传值方式传递的,即实参的值被复制到形参中,在函数内部对形参的修改不会影响实参的值。

  2. 参数可以使用指针传递:通过将指针作为参数传递,可以在函数内部修改实参的值。例如 void func(int *a);

  3. 参数可以使用引用传递:通过使用引用作为参数,可以在函数内部直接修改实参的值。例如 void func(int &a);

改进方式:使用指针对参数进行传递

void *swap(int* a, int* b);

int main() {
    int a = 2, b = 3;
    swap(&a, &b);
    printf("%d %d", a, b);
    return 0;
}

void *swap(int *a, int *b) {
    int t = *a;
    *a = *b;
    *b = t;
}

在这一过程中,实际上我在写

    int t = *a;//把*a所指地址存储的2赋给变量t
    *a = *b;   //把*a从指向存储2的地址,换成*b指向的地址
    *b = t;    //将变量t的值赋给*b所指的地址进行存储

这一段时遇到了困难,原因就在于我没办法第一时间判断a和*a的值是多少。只能通过printf来进行判断,实际上:

a=地址,*a=所指地址处的值,而我想要把*a所指的值赋给一个变量t。也就是

int t=*a;
class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 kw = 4 padw = int(np.ceil((kw-1)/2)) nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) self.sequence2 = [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) self.sequence2 += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] self.sequence2 += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: self.sequence2 += [nn.Sigmoid()] def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(*sequence2) nn.LeakyReLU(0.2, True) return self.model(input),上述代码中:出现错误:torchvision.ops.deform_conv2d(input=input, offset=offset1,RuntimeError: Expected weight_c.size(1) * n_weight_grps == input_c.size(1) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)
05-30
如何将self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 与torchvision.ops.deform_conv2d,加入到:class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)中,请给出修改后的代码
05-30
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