Densenet实现灰色图像到灰色图像的代码。映射前后的每个图像都用一个100*4096的二维数组表示(一个二维数组保存在一个TXT中，已经保存成二维数组形式了)，映射前后的txt分别保存在两个文件夹中，每个文件夹有240个txt，加入数据归一化，数据集划分，训练过程，最后能实现输入一个二维数组可以输出映射后的二维数组的效果

首先，需要将数据读入并进行归一化处理，可以使用以下代码：

import os
import numpy as np

def read_data(folder_path):
    data = []
    for filename in os.listdir(folder_path):
        file_path = os.path.join(folder_path, filename)
        arr = np.loadtxt(file_path)
        arr = arr / 255.0 # 归一化
        data.append(arr)
    return np.array(data)

input_folder = "input_folder"
output_folder = "output_folder"

input_data = read_data(input_folder)
output_data = read_data(output_folder)

接下来，可以使用PyTorch实现Densenet模型。以下是一个简单的Densenet实现：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import densenet121

class Densenet(nn.Module):
    def __init__(self):
        super(Densenet, self).__init__()
        self.densenet = densenet121(pretrained=True)
        self.linear = nn.Linear(1000, 4096)

    def forward(self, x):
        x = self.densenet(x)
        x = self.linear(x)
        return x

接下来，可以定义数据集和数据加载器，使用PyTorch的内置函数进行训练。以下是一个简单的训练过程：

from torch.utils.data import Dataset, DataLoader

class MyDataset(Dataset):
    def __init__(self, input_data, output_data):
        self.input_data = input_data
        self.output_data = output_data
        
    def __len__(self):
        return len(self.input_data)
    
    def __getitem__(self, idx):
        input_arr = self.input_data[idx]
        output_arr = self.output_data[idx]
        return input_arr, output_arr

train_ratio = 0.8
train_size = int(len(input_data) * train_ratio)
train_input = input_data[:train_size]
train_output = output_data[:train_size]
test_input = input_data[train_size:]
test_output = output_data[train_size:]

train_dataset = MyDataset(train_input, train_output)
test_dataset = MyDataset(test_input, test_output)

batch_size = 10
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Densenet().to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

num_epochs = 10
for epoch in range(num_epochs):
    train_loss = 0.0
    for batch_idx, (input_arr, output_arr) in enumerate(train_loader):
        input_arr = input_arr.to(device)
        output_arr = output_arr.to(device)
        optimizer.zero_grad()
        output = model(input_arr.unsqueeze(1).float())
        loss = criterion(output, output_arr.unsqueeze(1).float())
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    train_loss /= len(train_loader)
    
    test_loss = 0.0
    with torch.no_grad():
        for batch_idx, (input_arr, output_arr) in enumerate(test_loader):
            input_arr = input_arr.to(device)
            output_arr = output_arr.to(device)
            output = model(input_arr.unsqueeze(1).float())
            loss = criterion(output, output_arr.unsqueeze(1).float())
            test_loss += loss.item()
        test_loss /= len(test_loader)

    print("Epoch {} Train Loss {:.6f} Test Loss {:.6f}".format(epoch+1, train_loss, test_loss))

最后，可以实现一个函数，输入一个二维数组，输出映射后的二维数组：

def map_array(arr):
    arr = arr / 255.0 # 归一化
    arr = torch.tensor(arr).unsqueeze(0).unsqueeze(0).float().to(device)
    with torch.no_grad():
        output = model(arr)
    return output.squeeze(0).squeeze(0).cpu().numpy() * 255.0

这样，就可以使用以上代码实现灰度图像到灰度图像的Densenet映射了。