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(input_nc, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, input_nc, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(input_nc, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, input_nc, 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.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) self.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) ] self.sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: self.sequence += [nn.Sigmoid()] def forward(self, 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)) ] sequence = sequence1 + self.sequence self.model = nn.Sequential(*sequence) return self.model(input)，上述代码出现问题：TypeError: torch.cuda.FloatTensor is not a Module subclass，如何修改

这个错误一般出现在将张量作为模型输入时。需要将张量包装成一个 `nn.Parameter` 对象，或者将其转换为一个 `nn.Module` 对象。你可以尝试将最后一行代码修改为以下内容：

return nn.Sequential(*sequence)(input)

这里直接将 `nn.Sequential` 对象应用于输入张量 `input`，然后返回模型的输出。这样就可以避免将张量作为模型输入时出现的 `TypeError` 错误。

相关问题

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)，逐行解释上述代码

这段代码实现了一个深度卷积神经网络的判别器，用于对输入的图像进行分类，判断其是否为真实图像。下面逐行解释代码：

class NLayerDiscriminator(nn.Module):

定义了一个名为 `NLayerDiscriminator` 的类，它继承自 `nn.Module`。

def __init__(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True):

定义了类的构造函数，其中 `input_nc` 表示输入图像的通道数，`ndf` 表示初始特征数量，`n_layers` 表示网络的深度，`norm_layer` 表示使用的归一化方法，`use_sigmoid` 表示是否使用 Sigmoid 函数，`use_parallel` 表示是否使用多 GPU 并行计算。

super(NLayerDiscriminator, self).__init__()

调用父类的构造函数。

self.use_parallel = use_parallel

保存 `use_parallel` 参数。

if type(norm_layer) == functools.partial:
    use_bias = norm_layer.func == nn.InstanceNorm2d
else:
    use_bias = norm_layer == nn.InstanceNorm2d

判断是否使用 InstanceNorm2d 归一化方法。

kw = 4
padw = int(np.ceil((kw-1)/2))

定义卷积核大小和填充大小。

sequence = [
    nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
    nn.LeakyReLU(0.2, True)
]

定义网络的第一层。

nf_mult = 1
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)
    ]

定义网络的中间层，其中使用了多个卷积层和归一化层，激活函数为 LeakyReLU。

nf_mult_prev = nf_mult
nf_mult = min(2**n_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)
]

定义网络的最后一层。

sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]

定义网络的输出层，输出一个标量，表示输入图像是否为真实图像。

if use_sigmoid:
    sequence += [nn.Sigmoid()]

如果 `use_sigmoid` 为真，则在输出层后添加一个 Sigmoid 函数，将输出值映射到 [0,1] 区间。

self.model = nn.Sequential(*sequence)

将所有层组合成一个序列模型。

def forward(self, input):
    return self.model(input)

定义前向传播函数，将输入图像向前传播至输出层，返回输出结果。

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。