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：mask = cv.inRange(img, (0, 0, 0), (1, 1, 1)) # 转换为张量 image_tensor = transforms.ToTensor()(img) mask_tensor = transforms.ToTensor()(mask) # 扩展维度 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)

以下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
import torch.nn as nn
import cv2 as cv
from torchvision import transforms

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, mask):
x = x * (1 - mask)
x = self.encoder(x)
x = self.decoder(x)
x = x * (1 - mask) + x * mask
return x

def load_edgeconnect_weights(model, weight_path):
state_dict = torch.load(weight_path)
new_state_dict = {}
for key, value in state_dict.items():
if 'netG.' in key:
new_key = key.replace('netG.', '')
new_state_dict[new_key] = value
model.load_state_dict(new_state_dict)

# 读取图像和遮罩
img = cv.imread('example.jpg')[:, :, ::-1] / 255.0
mask = cv.inRange(img, (0, 0, 0), (1, 1, 1))

# 转换为张量
image_tensor = transforms.ToTensor()(img)
mask_tensor = transforms.ToTensor()(mask)

# 扩展维度
image_tensor = image_tensor.unsqueeze(0)
mask_tensor = mask_tensor.unsqueeze(0)

# 加载预训练模型权重
generator = Generator()
load_edgeconnect_weights(generator, 'gen.pth')

# 将张量和模型移动到GPU上
image_tensor = image_tensor.cuda()
mask_tensor = mask_tensor.cuda()
generator = generator.cuda()

# 使用预训练模型生成修复后的图像
with torch.no_grad():
output_tensor = generator(image_tensor, mask_tensor)

# 将张量转换为图像
output = output_tensor.detach().cpu().squeeze().numpy().transpose(1, 2, 0)
output = cv.cvtColor(output, cv.COLOR_RGB2BGR)
cv.imwrite('output.jpg', output * 255)