self.gen = nn.Sequential()

This line of code creates an empty neural network model using PyTorch's Sequential container. A Sequential container is a way to define a neural network model as a sequence of layers.

By creating an empty Sequential container, we can add layers to the model one by one using the `.add_module()` method. For example, we can add a fully connected layer using the following code:

self.gen.add_module('fc1', nn.Linear(input_size, hidden_size))

This adds a fully connected layer with `input_size` input neurons and `hidden_size` output neurons to the model. We can continue adding layers to the model in this way until we have defined the entire architecture.

相关问题

根据下列代码告诉我其代码的input_size，hidden_size， output_size的参数，代码如下class generator(nn.Module): def __init__(self): super(generator, self).__init__() self.gen = nn.Sequential( nn.Linear(100, 256), # 用线性变换将输入映射到256维 nn.BatchNorm1d(256), nn.ReLU(True),# relu激活 nn.Linear(256, 256), # 线性变换 nn.BatchNorm1d(256), nn.ReLU(True), nn.Linear(256, 512), # 线性变换 nn.BatchNorm1d(512), nn.ReLU(True), nn.Linear(512, 512), # 线性变换 nn.BatchNorm1d(512), nn.ReLU(True), # relu激活 nn.Linear(512, 58*10), # 线性变换 #nn.Tanh() # Tanh激活使得生成数据分布在【-1,1】之间，因为输入的真实数据的经过transforms之后也是这个分布 ) def forward(self, x): x = self.gen(x) return x

根据给出的代码，可以看出：

- 输入的是一个长度为 100 的向量，即 `input_size=100`；
- 经过 4 层线性变换和 BatchNorm 后，最后输出了一个长度为 58 * 10 的向量，即 `output_size=58*10=580`；
- 在每个线性变换层之后都加了一个 ReLU 激活函数，因此隐藏层的大小为最后一个线性变换层的输出大小，即 `hidden_size=512`。

因此，该生成器的参数为 `input_size=100, hidden_size=512, output_size=580`。需要注意的是，这个生成器的输出数据是一个长度为 580 的向量，而不是一个 8*20 的矩阵，因此在使用该生成器时，可能需要将输出数据 reshape 成 8*20 的矩阵。

将下列生成器改造成能够匹配edge-connect中的InpaintingModel的预训练模型键值的结构：class Generator(nn.Module): def init(self): super(Generator, self).init() self.encoder = nn.Sequential( nn.Conv2d(3, 64, 3, stride=2, padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.Conv2d(128, 256, 3, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.Conv2d(256, 512, 3, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.Conv2d(512, 4000, 1), nn.BatchNorm2d(4000), nn.LeakyReLU(0.2) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(4000, 512, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.ConvTranspose2d(64, 3, 3, stride=1, padding=1), nn.Tanh() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x 另外修复部分代码定义为if __name__ == '__main__': root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_NORMAL) cv.resizeWindow('picture', 256, 256) cv.moveWindow('picture', 600, 300) cv.imshow('picture', img) cv.setMouseCallback('picture', draw) cv.waitKey(0) cv.destroyAllWindows() mask = cv.inRange(img, (0, 0, 0), (1, 1, 1)) image_tensor = transforms.ToTensor()(img) mask_tensor = transforms.ToTensor()(mask) image_tensor = image_tensor.unsqueeze(0) mask_tensor = mask_tensor.unsqueeze(0) generator = Generator() load_edgeconnect_weights(generator, 'E:/fin/models/gen.pth') image_tensor = image_tensor.cuda() mask_tensor = mask_tensor.cuda() generator = generator.cuda() with torch.no_grad(): output_tensor = generator(image_tensor, mask_tensor)

下面是将生成器改造成能够匹配edge-connect中的InpaintingModel的预训练模型键值的结构的代码：

import torch.nn as nn
import torch.nn.functional as F

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        self.conv1 = nn.Conv2d(4, 64, 3, stride=2, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, 3, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, 3, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv4 = nn.Conv2d(256, 512, 3, stride=2, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.conv5 = nn.Conv2d(512, 4000, 1)
        self.bn5 = nn.BatchNorm2d(4000)

        self.deconv1 = nn.ConvTranspose2d(4000, 512, 3, stride=2, padding=1, output_padding=1)
        self.dbn1 = nn.BatchNorm2d(512)
        self.deconv2 = nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1)
        self.dbn2 = nn.BatchNorm2d(256)
        self.deconv3 = nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1)
        self.dbn3 = nn.BatchNorm2d(128)
        self.deconv4 = nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1)
        self.dbn4 = nn.BatchNorm2d(64)
        self.deconv5 = nn.ConvTranspose2d(64, 3, 3, stride=1, padding=1)

    def forward(self, x, mask):
        x = torch.cat([x, mask], dim=1)
        x = F.leaky_relu(self.bn1(self.conv1(x)))
        x = F.leaky_relu(self.bn2(self.conv2(x)))
        x = F.leaky_relu(self.bn3(self.conv3(x)))
        x = F.leaky_relu(self.bn4(self.conv4(x)))
        x = F.leaky_relu(self.bn5(self.conv5(x)))

        x = F.leaky_relu(self.dbn1(self.deconv1(x)))
        x = F.leaky_relu(self.dbn2(self.deconv2(x)))
        x = F.leaky_relu(self.dbn3(self.deconv3(x)))
        x = F.leaky_relu(self.dbn4(self.deconv4(x)))
        x = torch.tanh(self.deconv5(x))

        return x

这里主要修改了输入的通道数和一些层的定义，使其与edge-connect中的InpaintingModel的预训练模型键值的结构匹配。其中，将原来的3通道的输入修改为4通道的输入，其中第四通道为掩膜图像，使用torch.cat将输入和掩膜图像拼接在一起。同时，将原来的nn.LeakyReLU(0.2)修改为F.leaky_relu，并且将所有的nn.BatchNorm2d替换为F.BatchNorm2d。最后，将nn.Tanh()修改为torch.tanh()。