F.conv2d中的stride: Union[_int, _size]=1是什么意思

`stride`是指卷积核在进行卷积操作时在输入张量上沿着高度和宽度方向的步长。对于`F.conv2d`函数，它的`stride`参数是一个可选参数，它决定了卷积核每次移动的步长。这个参数可以是一个整数或者一个长度为2的整数元组，其中整数元组的第一个和第二个元素分别表示在高度和宽度方向上的步长。如果`stride`参数是一个整数，那么这个整数会同时作用于高度和宽度方向上。如果没有提供`stride`参数，则默认值为1。

这里的`Union[_int, _size]`表示`stride`参数可以是一个整数或者一个长度为2的整数元组，这两种类型都可以被接受。

相关问题

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

可以将self.conv1、self.conv_offset1和self.conv_mask1加入到NLayerDiscriminator中的nn.Sequential序列中，如下所示：

import torchvision
from torchvision.ops.deform_conv import DeformConv2d

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))
        
        nf_mult = 1
        sequence = [
            nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
            nn.LeakyReLU(0.2, True)
        ]
        
        for n in range(1, n_layers):
            nf_mult_prev = nf_mult
            nf_mult = min(2**n, 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(2**n_layers, 8)
        sequence += [
            DeformConv2d(
                ndf * nf_mult_prev, ndf * nf_mult, kernel_size=3, stride=1, padding=1,
                dilation=1, groups=1, deformable_groups=1, bias=False
            ),
            norm_layer(ndf * nf_mult),
            nn.LeakyReLU(0.2, True),
            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
        ]
        
        if use_sigmoid:
            sequence += [nn.Sigmoid()]
        
        self.model = nn.Sequential(*sequence)
        
        self.conv1 = nn.Conv2d(4 * ndf, 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)
        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)
        
    def forward(self, input):
        x = input
        for module in self.model:
            if isinstance(module, DeformConv2d):
                offset_mask = torch.cat([self.conv_offset1(x), self.conv_mask1(x)], dim=1)
                x = module(x, offset_mask)
            else:
                x = module(x)
        return x

这里使用了torchvision.ops.deform_conv2d中的DeformConv2d作为最后一层卷积层，因此需要将self.conv1、self.conv_offset1和self.conv_mask1的通道数修改为ndf。在forward中，对于DeformConv2d层，需要将self.conv_offset1和self.conv_mask1的输出拼接起来作为offset_mask输入。

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

这段代码是定义了一个名为ACmix的类，继承自PyTorch中的nn.Module。它包含了一些卷积、全连接、非线性激活函数等层，用于实现图像处理任务中的注意力机制和特征融合。

具体来说，逐行解释代码如下：

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__init__()。其中in_planes表示输入特征图的通道数，out_planes表示输出特征图的通道数，kernel_att表示注意力机制的卷积核大小，head表示注意力机制的头数，kernel_conv表示特征融合的卷积核大小，stride表示卷积的步长，dilation表示卷积的膨胀率。

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

将输入的参数保存到类的成员变量中，其中rate1和rate2是需要学习的参数，用于调整注意力机制中的权重。

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)

定义三个卷积层，其中conv1和conv2用于计算注意力机制，conv3用于特征融合。

self.conv_p = nn.Conv2d(2, self.head_dim, kernel_size=1)

定义一个卷积层，用于将注意力机制中的特征图转换为头数的通道数。

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)

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.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)

定义特征融合的卷积层和深度可分离卷积层，其中fc层用于将展开后的特征图进行特征融合，dep_conv层用于将融合后的特征图进行输出。

self.reset_parameters()

初始化模型参数。