self.transforms = ModuleList() self.convs = ModuleList() for i in range(num_convs): if i == 0: trans = FCLayer(in_channels, conv_channels, bias=True, activation=None) conv = DenseEdgeConv( conv_channels, num_fc_layers=conv_num_fc_layers, growth_rate=conv_growth_rate, knn=conv_knn, aggr=conv_aggr, activation=activation, relative_feat_only=True ) else: trans = FCLayer(in_channels, conv_channels, bias=True, activation=activation) conv = DenseEdgeConv( conv_channels, num_fc_layers=conv_num_fc_layers, growth_rate=conv_growth_rate, knn=conv_knn, aggr=conv_aggr, activation=activation, relative_feat_only=False ) self.transforms.append(trans) self.convs.append(conv) in_channels = conv.out_channels

这段代码是一个神经网络模型的定义，其中包含了多个层。在这个模型中，首先创建了两个空的ModuleList()，命名为transforms和convs，用于存储后续创建的层。然后通过for循环，依次创建num_convs个层。当i为0时，创建的是一个FCLayer和一个DenseEdgeConv，其中FCLayer是全连接层，DenseEdgeConv是基于边缘卷积的网络层。当i不为0时，创建的是一个带激活函数的FCLayer和一个带有完整特征的DenseEdgeConv。每次创建完层之后，将层添加到transforms和convs中，并更新in_channels的值。最终，这个模型的输出是convs中最后一层的输出。

相关问题

for i in range(num_convs): if i == 0: trans = FCLayer(in_channels, conv_channels, bias=True, activation=None) conv = DenseEdgeConv( conv_channels, num_fc_layers=conv_num_fc_layers, growth_rate=conv_growth_rate, knn=conv_knn, aggr=conv_aggr, activation=activation, relative_feat_only=True ) else: trans = FCLayer(in_channels, conv_channels, bias=True, activation=activation) conv = DenseEdgeConv( conv_channels, num_fc_layers=conv_num_fc_layers, growth_rate=conv_growth_rate, knn=conv_knn, aggr=conv_aggr, activation=activation, relative_feat_only=False ) self.transforms.append(trans) self.convs.append(conv) in_channels = conv.out_channels

这段代码是一个类的初始化方法，其中包含了一个多层的DenseEdgeConv神经网络模型。代码中的循环语句用来构建多层DenseEdgeConv模块，每一层都由一个FCLayer（全连接层）和一个DenseEdgeConv模块组成。其中第一层的FCLayer没有激活函数，后续层的FCLayer使用了激活函数。在循环中，将每个转换层和卷积层添加到ModuleList中，并将卷积层的输出大小作为下一层转换层的输入大小。最终，这个类就是一个包含多层DenseEdgeConv模块的神经网络模型。

import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms import skimage.segmentation as seg import numpy as np # 超参数 from PIL import Image num_superpixels = 1000 compactness = 10 sigma = 1 # 定义模型 class SuperpixelSegmentation(nn.Module): def init(self): super(SuperpixelSegmentation, self).init() self.convs = nn.Sequential( nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64, num_superpixels, kernel_size=1, stride=1) ) def forward(self, x): x = self.convs(x) return x # 加载图像 imgA = Image.open('1.png').convert('RGB') imgB = Image.open('2.jpg').convert('RGB') # 超像素分割 imgA_np = np.array(imgA) segments = seg.slic(imgA_np, n_segments=num_superpixels, compactness=compactness, sigma=sigma) segments = torch.from_numpy(segments).unsqueeze(0).unsqueeze(0).float() segments = F.interpolate(segments, size=(imgA.height, imgA.width), mode='nearest').long() # 应用超像素块范围到图像B imgB_np = np.array(imgB) for i in range(num_superpixels): mask = (segments == i) imgB_np[mask.expand(3, -1, -1)] = np.mean(imgB_np[mask.expand(3, -1, -1)], axis=1, keepdims=True) # 显示超像素分割图像 imgA_segments = np.zeros_like(imgA_np) for i in range(num_superpixels): mask = (segments == i) imgA_segments[mask.expand(3, -1, -1)] = np.random.randint(0, 255, size=(3,)) imgA_segments = Image.fromarray(imgA_segments.astype(np.uint8)) imgB_segments = Image.fromarray(imgB_np) # 显示图像 transforms.ToPILImage()(imgA).show() transforms.ToPILImage()(imgB).show() imgA_segments.show() imgB_segments.show()上述代码出现错误：RuntimeError: expand(CPUBoolType{[1, 1, 512, 512]}, size=[3, -1, -1]): the number of sizes provided (3) must be greater or equal to the number of dimensions in the tensor (4)

这个错误出现的原因是在这行代码中：

imgB_np[mask.expand(3, -1, -1)] = np.mean(imgB_np[mask.expand(3, -1, -1)], axis=1, keepdims=True)

`mask.expand(3, -1, -1)`的输出的维度是 `[3, 1, 512, 512]`，但是`imgB_np`的维度是 `[512, 512, 3]`，所以二者无法进行广播操作。你需要将`imgB_np`的维度改为`[3, 512, 512]`，然后再进行操作。修改的代码如下所示：

imgB_np = np.transpose(imgB_np, (2, 0, 1))
for i in range(num_superpixels):
    mask = (segments == i)
    imgB_np[:, mask] = np.mean(imgB_np[:, mask], axis=1, keepdims=True)
imgB_np = np.transpose(imgB_np, (1, 2, 0))

这里我们先对`imgB_np`进行了转置操作，将通道维度放在最前面，reshape成了`[3, 512, 512]`的维度，然后进行超像素块的操作，最后再将维度转置回来，得到了`[512, 512, 3]`的维度。