conv = [] conv.extend(PythonNet.conv3x3(2, channels))
时间: 2024-05-28 07:11:54 浏览: 21
这段代码是在使用 PythonNet 库中的 conv3x3 函数,它的功能是生成一个 3x3 的卷积核,然后将它应用于输入的通道数为 2 的张量,生成一个输出通道数为 channels 的张量。其中 extend() 函数是将生成的张量追加到 conv 列表的末尾,最终返回一个包含多个卷积层的列表 conv。
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 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: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.),请问如何解决,给出修改后的代码
这个错误通常是由于在使用分组卷积时,输入通道数和卷积核数量不匹配引起的。根据错误提示,可以得知是在一次卷积中出现了此问题。根据代码,可以看出是在使用`torchvision.ops.deform_conv2d`函数时出现了此问题。
解决方法是检查输入通道数和卷积核数量是否匹配,如果不匹配,则需要对它们进行调整。
下面是修改后的代码:
```
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
self.sequence2 = nn.ModuleList()
for n in range(1, n_layers):
nf_mult_prev = nf_mult
nf_mult = min(2**n, 8)
self.sequence2.extend([
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)
self.sequence2.extend([
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),
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
])
if use_sigmoid:
self.sequence2.append(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
for i, layer in enumerate(sequence2):
sequence2[i] = layer(sequence2[i-1])
return sequence2[-1]
```
修改的主要内容如下:
1. 将`self.sequence2`改为`nn.ModuleList()`类型,这样可以方便地添加和修改网络结构;
2. 在循环中使用`extend`方法添加卷积层,避免在循环中使用`+=`操作符引起的错误;
3. 在`forward`函数中,使用循环遍历`sequence2`中的每一层,并将其输入和输出连接起来。
希望这样的修改能够帮助到你。
class InvertedResidual(nn.Cell): def init(self, inp, oup, stride, expand_ratio): super(InvertedResidual, self).init() assert stride in [1, 2] hidden_dim = int(round(inp * expand_ratio)) self.use_res_connect = stride == 1 and inp == oup layers = [] if expand_ratio != 1: layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) layers.extend([ dw ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), pw-linear nn.Conv2d(hidden_dim, oup, kernel_size=1, stride=1, has_bias=False), nn.BatchNorm2d(oup), ]) self.conv = nn.SequentialCell(layers) self.add = ops.Add() self.cast = ops.Cast() def construct(self, x): identity = x x = self.conv(x) if self.use_res_connect: return self.add(identity, x) return x
这段代码是一个定义了反向残差模块(InResidual)的类。这个模块深度学习的卷积神经网络常用于增加网络的深度和线性表示能力。在该类中构造函数(__init__)接受输入通道数(inp)、输出通道数(oup)、步长(stride)和扩展比例(expand_ratio)作为参数。
在构造函数中,首先进行了一些参数的检查和计算。然后根据是否需要进行残差连接来选择不同网络层。如果步长为1且输入通道数等于输出通道数,则使用残差连接(residual connection)。否则,采用分组卷积(depthwise convolution)和逐点卷积(pointwise convolution)来构建网络层。
在前向传播函数(construct)中,首先保存输入张量作为身份张量(identity),然后通过网络层进行前向传播得到输出张量(x)。最后,根据是否需要进行残差连接,返回相应的结果。
这段代码是使用PyTorch框架编写的,用于定义反向残差模块的类。它可以用于构建深度学习模型中的残差连接部分。