图像的尺寸 红色通道到 FC 网络输入

问：

作为扩散模型的一部分，我构建了这个全连接网络：

import torch
from torch import linalg as LA
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.optim as optim
import torch.nn as nn
import matplotlib.pyplot as plt
from tqdm import tqdm
lr = 0.0001
num_data = 3000
batch_size = 3000
num_epochs =3000
x_dim = 2  # dimension of input
beta1 = 0.5  # Beta1 hyperparameter for Adam optimizers
l2 = nn.MSELoss()
colors = px.colors.qualitative.T10
MODEL_PATH = 'denoiser.pth'
CONDITIONAL_MODEL_PATH = 'denoiser_conditional.pth'
# the denoiser
class NetD(nn.Module):
    def __init__(self):
        super(NetD, self).__init__()
        # intput: [x0, x1, t]
        self.fc = nn.Sequential(
            nn.Linear(x_dim + 1, (x_dim + 1) * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear((x_dim + 1) * 8, (x_dim + 1) * 20),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear((x_dim + 1) * 20, x_dim),
            # output: [e0, e1]
        )

    def forward(self, x_t):
        return self.fc(x_t)

首先，我在如下所示的统一数据上对其进行了训练：

它的形状是[3000,2]

现在我想在大小为 （50,50） 的图像的红色通道上训练模型：

import torchvision.transforms as transforms
import torchvision.models as models
# desired size of the output image
imsize =(50,50)  # use small size if no GPU

loader = transforms.Compose([
    transforms.Resize(imsize),  # scale imported image
    transforms.ToTensor()])  # transform it into a torch tensor
def image_loader(image_name):
    image = Image.open(image_name)
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)
cat=image_loader('/content/orange_cat.jpg')
red_channel = cat[:, 0, :, :]  # Extract the red channel (R) of the image
C = 200  # Threshold value for red channel
density = torch.where(red_channel > 0.78, torch.tensor(1.0), torch.tensor(0.0))
data_1 = red_channel.squeeze().to(device)

据我了解，为了将图像的红色通道传递到 FC 网络->netD，数据的维数也应该为 2 （x，y），但我不太确定我应该如何正确转换张量形状 [50,50] ： 我试着像这样转换它：

# Reshape the tensor to a 2-dimensional tensor
reshaped_red_channel = data_1.view(-1, 2)

它创建了一个大小为 [1250,2] 的张量，我不确定它是否正确？据我了解，原始张量中的元素总数 = 重塑张量中的元素总数，因此它确实创建了原始向量的 2500 个元素