{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6603a8fc-d9da-4037-b845-d9c38bae4ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Dataset, random_split\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8a8cedd-536d-4a48-a1af-7c40489ef0f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7fc99a573830>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "torch.random.manual_seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c28cc123-71be-47ff-b78f-3a4d5592df39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算图像数据中的最大像素值\n",
    "max_pixel_value = 107.49169921875"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "342d21ee-7f31-4c37-a73b-f47cac181763",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GrayScaleDataset(Dataset):\n",
    "    def __init__(self, data_dir):\n",
    "        self.data_dir = data_dir\n",
    "        self.file_list = [x for x in os.listdir(data_dir) if x.endswith('npy')]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.file_list)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file_path = os.path.join(self.data_dir, self.file_list[idx])\n",
    "        data = np.load(file_path)[:,:,0] / max_pixel_value\n",
    "        return torch.tensor(data, dtype=torch.float32).unsqueeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dbfe80ce-4394-449c-a9a4-22ed15b2b8f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checkpoint before Generator is OK\n"
     ]
    }
   ],
   "source": [
    "class NO2Dataset(Dataset):\n",
    "    \n",
    "    def __init__(self, image_dir, mask_dir):\n",
    "        \n",
    "        self.image_dir = image_dir\n",
    "        self.mask_dir = mask_dir\n",
    "        self.image_filenames = [f for f in os.listdir(image_dir) if f.endswith('.npy')]  # 仅加载 .npy 文件\n",
    "        self.mask_filenames = [f for f in os.listdir(mask_dir) if f.endswith('.jpg')]  # 仅加载 .jpg 文件\n",
    "        \n",
    "    def __len__(self):\n",
    "        \n",
    "        return len(self.image_filenames)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        \n",
    "        image_path = os.path.join(self.image_dir, self.image_filenames[idx])\n",
    "        mask_idx = np.random.choice(self.mask_filenames)\n",
    "        mask_path = os.path.join(self.mask_dir, mask_idx)\n",
    "\n",
    "        # 加载图像数据 (.npy 文件)\n",
    "        image = np.load(image_path).astype(np.float32)[:,:,:1] / max_pixel_value  # 形状为 (96, 96, 1)\n",
    "\n",
    "        # 加载掩码数据 (.jpg 文件)\n",
    "        mask = np.array(Image.open(mask_path).convert('L')).astype(np.float32)\n",
    "\n",
    "        # 将掩码数据中非0值设为1，0值保持不变\n",
    "        mask = np.where(mask != 0, 1.0, 0.0)\n",
    "\n",
    "        # 保持掩码数据形状为 (96, 96, 1)\n",
    "        mask = mask[:, :, np.newaxis]  # 将形状调整为 (96, 96, 1)\n",
    "\n",
    "        # 应用掩码\n",
    "        masked_image = image.copy()\n",
    "        masked_image[:, :, 0] = image[:, :, 0] * mask.squeeze()  # 遮盖NO2数据\n",
    "\n",
    "        # cGAN的输入和目标\n",
    "        X = masked_image[:, :, :1]  # 形状为 (96, 96, 8)\n",
    "        y = image[:, :, 0:1]  # 目标输出为NO2数据，形状为 (96, 96, 1)\n",
    "\n",
    "        # 转换形状为 (channels, height, width)\n",
    "        X = np.transpose(X, (2, 0, 1))  # 转换为 (1, 96, 96)\n",
    "        y = np.transpose(y, (2, 0, 1))  # 转换为 (1, 96, 96)\n",
    "        mask = np.transpose(mask, (2, 0, 1))  # 转换为 (1, 96, 96)\n",
    "\n",
    "        return torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.float32), torch.tensor(mask, dtype=torch.float32)\n",
    "\n",
    "# 实例化数据集和数据加载器\n",
    "image_dir = './out_mat/96/train/'\n",
    "mask_dir = './out_mat/96/mask/20/'\n",
    "\n",
    "print(f\"checkpoint before Generator is OK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3a25f29-b16e-4485-9f06-5378b910be6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PatchMasking:\n",
    "    def __init__(self, patch_size, mask_ratio):\n",
    "        self.patch_size = patch_size\n",
    "        self.mask_ratio = mask_ratio\n",
    "\n",
    "    def __call__(self, x):\n",
    "        batch_size, C, H, W = x.shape\n",
    "        num_patches = (H // self.patch_size) * (W // self.patch_size)\n",
    "        num_masked = int(num_patches * self.mask_ratio)\n",
    "        \n",
    "        # 为每个样本生成独立的mask\n",
    "        masks = []\n",
    "        for _ in range(batch_size):\n",
    "            mask = torch.zeros(num_patches, dtype=torch.bool, device=x.device)\n",
    "            mask[:num_masked] = 1\n",
    "            mask = mask[torch.randperm(num_patches)]\n",
    "            mask = mask.view(H // self.patch_size, W // self.patch_size)\n",
    "            mask = mask.repeat_interleave(self.patch_size, dim=0).repeat_interleave(self.patch_size, dim=1)\n",
    "            masks.append(mask)\n",
    "        \n",
    "        # 将所有mask堆叠成一个批量张量\n",
    "        masks = torch.stack(masks, dim=0)\n",
    "        masks = torch.unsqueeze(masks, dim=1)\n",
    "        \n",
    "        # 应用mask到输入x上\n",
    "        masked_x = x * (1- masks.float())\n",
    "        return masked_x, masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41da7319-9795-441d-bde8-8cf390365099",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = './out_mat/96/train/'\n",
    "train_dataset = GrayScaleDataset(train_dir)\n",
    "val_dir = './out_mat/96/valid/'\n",
    "val_dataset = GrayScaleDataset(val_dir)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
    "\n",
    "test_set = NO2Dataset('./out_mat/96/test/', mask_dir)\n",
    "test_loader = DataLoader(test_set, batch_size=64, shuffle=False, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70797703-1619-4be7-b965-5506b3d1e775",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可视化特定特征的函数\n",
    "def visualize_feature(input_feature,masked_feature, output_feature, title):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.imshow(input_feature[0].cpu().numpy(), cmap='RdYlGn_r')\n",
    "    plt.title(title + \" Input\")\n",
    "    plt.subplot(1, 3, 2)\n",
    "    plt.imshow(masked_feature[0].cpu().numpy(), cmap='RdYlGn_r')\n",
    "    plt.title(title + \" Masked\")\n",
    "    plt.subplot(1, 3, 3)\n",
    "    plt.imshow(output_feature[0].detach().cpu().numpy(), cmap='RdYlGn_r')\n",
    "    plt.title(title + \" Recovery\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "645114e8-65a4-4867-b3fe-23395288e855",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Conv(nn.Sequential):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):\n",
    "        super(Conv, self).__init__(\n",
    "            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,\n",
    "                      dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2af52d0e-b785-4a84-838c-6fcfe2568722",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvBNReLU(nn.Sequential):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d,\n",
    "                 bias=False):\n",
    "        super(ConvBNReLU, self).__init__(\n",
    "            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,\n",
    "                      dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),\n",
    "            norm_layer(out_channels),\n",
    "            nn.ReLU()\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31ecf247-e98b-4977-a145-782914a042bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SeparableBNReLU(nn.Sequential):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, norm_layer=nn.BatchNorm2d):\n",
    "        super(SeparableBNReLU, self).__init__(\n",
    "            nn.Conv2d(in_channels, in_channels, kernel_size=kernel_size, stride=stride, dilation=dilation,\n",
    "                      padding=((stride - 1) + dilation * (kernel_size - 1)) // 2, groups=in_channels, bias=False),\n",
    "            # 分离卷积，仅调整空间信息\n",
    "            norm_layer(in_channels),  # 对输入通道进行归一化\n",
    "            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),  # 这里进行升维操作\n",
    "            nn.ReLU6()\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7827bee2-74f7-4e47-b8c6-e41d5670e8b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, stride=1, downsample=None):\n",
    "        super(ResidualBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "\n",
    "        # 如果输入和输出通道不一致，进行降采样操作\n",
    "        self.downsample = downsample\n",
    "        if in_channels != out_channels or stride != 1:\n",
    "            self.downsample = nn.Sequential(\n",
    "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_channels)\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        identity = x\n",
    "        if self.downsample is not None:\n",
    "            identity = self.downsample(x)\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        out += identity\n",
    "        out = self.relu(out)\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7853bf62-02f5-4917-b950-6fdfe467df4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mlp(nn.Module):\n",
    "    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):\n",
    "        super().__init__()\n",
    "        out_features = out_features or in_features\n",
    "        hidden_features = hidden_features or in_features\n",
    "        self.fc1 = nn.Conv2d(in_features, hidden_features, 1, 1, 0, bias=True)\n",
    "\n",
    "        self.act = act_layer()\n",
    "        self.fc2 = nn.Conv2d(hidden_features, out_features, 1, 1, 0, bias=True)\n",
    "        self.drop = nn.Dropout(drop, inplace=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.act(x)\n",
    "        x = self.drop(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.drop(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e2375881-a11b-47a7-8f56-2eadb25010b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttentionBlock(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, dropout=0.1):\n",
    "        super(MultiHeadAttentionBlock, self).__init__()\n",
    "        self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)\n",
    "        self.norm = nn.LayerNorm(embed_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # (B, C, H, W) -> (HW, B, C) for MultiheadAttention compatibility\n",
    "        B, C, H, W = x.shape\n",
    "        x = x.view(B, C, H * W).permute(2, 0, 1)  # (B, C, H, W) -> (HW, B, C)\n",
    "\n",
    "        # Apply multihead attention\n",
    "        attn_output, _ = self.attention(x, x, x)\n",
    "\n",
    "        # Apply normalization and dropout\n",
    "        attn_output = self.norm(attn_output)\n",
    "        attn_output = self.dropout(attn_output)\n",
    "\n",
    "        # Reshape back to (B, C, H, W)\n",
    "        attn_output = attn_output.permute(1, 2, 0).view(B, C, H, W)\n",
    "\n",
    "        return attn_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82a15d3d-2f8d-42ec-9146-87c8a4abe384",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SpatialAttentionBlock(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SpatialAttentionBlock, self).__init__()\n",
    "        self.conv = nn.Conv2d(2, 1, kernel_size=7, padding=3, bias=False)\n",
    "\n",
    "    def forward(self, x): #(B, 64, H, W)\n",
    "        avg_out = torch.mean(x, dim=1, keepdim=True) #(B, 1, H, W)\n",
    "        max_out, _ = torch.max(x, dim=1, keepdim=True)#(B, 1, H, W)\n",
    "        out = torch.cat([avg_out, max_out], dim=1)#(B, 2, H, W)\n",
    "        out = torch.sigmoid(self.conv(out))#(B, 1, H, W)\n",
    "        return x * out #(B, C, H, W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "497bb9f1-1ac5-4d7f-a930-0ea222b9d1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderAttentionBlock(nn.Module):\n",
    "    def __init__(self, in_channels):\n",
    "        super(DecoderAttentionBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels, in_channels // 2, kernel_size=1)\n",
    "        self.conv2 = nn.Conv2d(in_channels // 2, in_channels, kernel_size=1)\n",
    "        self.spatial_attention = SpatialAttentionBlock()\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 通道注意力\n",
    "        b, c, h, w = x.size()\n",
    "        avg_pool = F.adaptive_avg_pool2d(x, 1)\n",
    "        max_pool = F.adaptive_max_pool2d(x, 1)\n",
    "\n",
    "        avg_out = self.conv1(avg_pool)\n",
    "        max_out = self.conv1(max_pool)\n",
    "\n",
    "        out = avg_out + max_out\n",
    "        out = torch.sigmoid(self.conv2(out))\n",
    "\n",
    "        # 添加空间注意力\n",
    "        out = x * out\n",
    "        out = self.spatial_attention(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "15b9d453-d8d9-43b8-aca2-904735fb3a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SEBlock(nn.Module):\n",
    "    def __init__(self, in_channels, reduced_dim):\n",
    "        super(SEBlock, self).__init__()\n",
    "        self.se = nn.Sequential(\n",
    "            nn.AdaptiveAvgPool2d(1),  # 全局平均池化\n",
    "            nn.Conv2d(in_channels, reduced_dim, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(reduced_dim, in_channels, kernel_size=1),\n",
    "            nn.Sigmoid()  # 使用Sigmoid是因为我们要对通道进行权重归一化\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return x * self.se(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9d176a8-bbf6-4043-ab82-1648a99d772a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def masked_mse_loss(preds, target, mask):\n",
    "    loss = (preds - target) ** 2\n",
    "    loss = loss.mean(dim=-1)  # 对每个像素点求平均\n",
    "    loss = (loss * mask).sum() / mask.sum()  # 只计算被mask的像素点的损失\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6379adb7-8a87-4dd8-a695-4013a7b37830",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 定义Masked Autoencoder模型\n",
    "class MaskedAutoencoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MaskedAutoencoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(\n",
    "            Conv(1, 32, kernel_size=3, stride=2),\n",
    "            \n",
    "            nn.ReLU(),\n",
    "            \n",
    "            SEBlock(32,32),\n",
    "            \n",
    "            ConvBNReLU(32, 64, kernel_size=3, stride=2),\n",
    "            \n",
    "            ResidualBlock(64,64),\n",
    "            \n",
    "            SeparableBNReLU(64, 128, kernel_size=3, stride=2),\n",
    "            \n",
    "            MultiHeadAttentionBlock(embed_dim=128, num_heads=4),\n",
    "            \n",
    "            SEBlock(128, 128),\n",
    "            \n",
    "            Mlp(in_features=128, hidden_features=256, out_features=128, act_layer=nn.ReLU6, drop=0.1)\n",
    "        )\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.ConvTranspose2d(128, 32, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
    "            nn.ReLU(),\n",
    "            \n",
    "            nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
    "            nn.ReLU(),            \n",
    "            nn.ConvTranspose2d(16, 1, kernel_size=3, stride=2, padding=1, output_padding=1),  # 修改为 output_padding=1\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        encoded = self.encoder(x)\n",
    "        decoded = self.decoder(encoded)\n",
    "        return decoded\n",
    "\n",
    "# 实例化模型、损失函数和优化器\n",
    "model = MaskedAutoencoder()\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "084f6b1e-ed3a-490b-9020-5479863e803b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, train_loader, val_loader, epochs, criterion, optimizer, device):\n",
    "    model.to(device)\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        train_loss = 0\n",
    "        for data in train_loader:\n",
    "            data = data.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            masked_data, mask = PatchMasking(patch_size=8, mask_ratio=0.2)(data)\n",
    "            output = model(masked_data)\n",
    "            loss = masked_mse_loss(output, data, mask)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += loss.item()\n",
    "        train_loss /= len(train_loader)\n",
    "\n",
    "        model.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for data in val_loader:\n",
    "                data = data.to(device)\n",
    "                masked_data, mask = PatchMasking(patch_size=8, mask_ratio=0.2)(data)\n",
    "                output = model(masked_data)\n",
    "                loss = masked_mse_loss(output, data, mask)\n",
    "                val_loss += loss.item()\n",
    "        val_loss /= len(val_loader)\n",
    "\n",
    "        print(f'Epoch {epoch+1}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "296ba6bd-2239-4948-b278-7edcb29bfd14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# 数据准备\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "16673a37-02e9-4883-8288-aa0e240d6824",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Train Loss: 0.0185, Val Loss: 0.0199\n",
      "Epoch 2, Train Loss: 0.0178, Val Loss: 0.0187\n",
      "Epoch 3, Train Loss: 0.0174, Val Loss: 0.0217\n",
      "Epoch 4, Train Loss: 0.0172, Val Loss: 0.0227\n",
      "Epoch 5, Train Loss: 0.0167, Val Loss: 0.0180\n",
      "Epoch 6, Train Loss: 0.0166, Val Loss: 0.0225\n",
      "Epoch 7, Train Loss: 0.0163, Val Loss: 0.0183\n",
      "Epoch 8, Train Loss: 0.0162, Val Loss: 0.0220\n",
      "Epoch 9, Train Loss: 0.0161, Val Loss: 0.0181\n",
      "Epoch 10, Train Loss: 0.0159, Val Loss: 0.0196\n",
      "Epoch 11, Train Loss: 0.0159, Val Loss: 0.0210\n",
      "Epoch 12, Train Loss: 0.0155, Val Loss: 0.0198\n",
      "Epoch 13, Train Loss: 0.0154, Val Loss: 0.0212\n",
      "Epoch 14, Train Loss: 0.0153, Val Loss: 0.0207\n",
      "Epoch 15, Train Loss: 0.0153, Val Loss: 0.0216\n",
      "Epoch 16, Train Loss: 0.0152, Val Loss: 0.0222\n",
      "Epoch 17, Train Loss: 0.0152, Val Loss: 0.0225\n",
      "Epoch 18, Train Loss: 0.0150, Val Loss: 0.0183\n",
      "Epoch 19, Train Loss: 0.0151, Val Loss: 0.0242\n",
      "Epoch 20, Train Loss: 0.0148, Val Loss: 0.0203\n",
      "Epoch 21, Train Loss: 0.0148, Val Loss: 0.0211\n",
      "Epoch 22, Train Loss: 0.0148, Val Loss: 0.0200\n",
      "Epoch 23, Train Loss: 0.0146, Val Loss: 0.0191\n",
      "Epoch 24, Train Loss: 0.0145, Val Loss: 0.0215\n",
      "Epoch 25, Train Loss: 0.0145, Val Loss: 0.0196\n",
      "Epoch 26, Train Loss: 0.0146, Val Loss: 0.0215\n",
      "Epoch 27, Train Loss: 0.0144, Val Loss: 0.0195\n",
      "Epoch 28, Train Loss: 0.0144, Val Loss: 0.0196\n",
      "Epoch 29, Train Loss: 0.0143, Val Loss: 0.0182\n",
      "Epoch 30, Train Loss: 0.0143, Val Loss: 0.0213\n",
      "Epoch 31, Train Loss: 0.0142, Val Loss: 0.0178\n",
      "Epoch 32, Train Loss: 0.0139, Val Loss: 0.0215\n",
      "Epoch 33, Train Loss: 0.0135, Val Loss: 0.0171\n",
      "Epoch 34, Train Loss: 0.0131, Val Loss: 0.0187\n",
      "Epoch 35, Train Loss: 0.0128, Val Loss: 0.0171\n",
      "Epoch 36, Train Loss: 0.0128, Val Loss: 0.0159\n",
      "Epoch 37, Train Loss: 0.0127, Val Loss: 0.0170\n",
      "Epoch 38, Train Loss: 0.0125, Val Loss: 0.0182\n",
      "Epoch 39, Train Loss: 0.0124, Val Loss: 0.0155\n",
      "Epoch 40, Train Loss: 0.0123, Val Loss: 0.0169\n",
      "Epoch 41, Train Loss: 0.0122, Val Loss: 0.0160\n",
      "Epoch 42, Train Loss: 0.0123, Val Loss: 0.0164\n",
      "Epoch 43, Train Loss: 0.0120, Val Loss: 0.0154\n",
      "Epoch 44, Train Loss: 0.0121, Val Loss: 0.0159\n",
      "Epoch 45, Train Loss: 0.0119, Val Loss: 0.0152\n",
      "Epoch 46, Train Loss: 0.0118, Val Loss: 0.0151\n",
      "Epoch 47, Train Loss: 0.0119, Val Loss: 0.0135\n",
      "Epoch 48, Train Loss: 0.0121, Val Loss: 0.0135\n",
      "Epoch 49, Train Loss: 0.0118, Val Loss: 0.0162\n",
      "Epoch 50, Train Loss: 0.0117, Val Loss: 0.0195\n",
      "Epoch 51, Train Loss: 0.0116, Val Loss: 0.0160\n",
      "Epoch 52, Train Loss: 0.0116, Val Loss: 0.0167\n",
      "Epoch 53, Train Loss: 0.0116, Val Loss: 0.0149\n",
      "Epoch 54, Train Loss: 0.0114, Val Loss: 0.0143\n",
      "Epoch 55, Train Loss: 0.0115, Val Loss: 0.0136\n",
      "Epoch 56, Train Loss: 0.0115, Val Loss: 0.0144\n",
      "Epoch 57, Train Loss: 0.0115, Val Loss: 0.0158\n",
      "Epoch 58, Train Loss: 0.0113, Val Loss: 0.0147\n",
      "Epoch 59, Train Loss: 0.0112, Val Loss: 0.0142\n",
      "Epoch 60, Train Loss: 0.0113, Val Loss: 0.0159\n",
      "Epoch 61, Train Loss: 0.0112, Val Loss: 0.0153\n",
      "Epoch 62, Train Loss: 0.0113, Val Loss: 0.0140\n",
      "Epoch 63, Train Loss: 0.0112, Val Loss: 0.0156\n",
      "Epoch 64, Train Loss: 0.0111, Val Loss: 0.0149\n",
      "Epoch 65, Train Loss: 0.0112, Val Loss: 0.0154\n",
      "Epoch 66, Train Loss: 0.0112, Val Loss: 0.0158\n",
      "Epoch 67, Train Loss: 0.0111, Val Loss: 0.0136\n",
      "Epoch 68, Train Loss: 0.0110, Val Loss: 0.0139\n",
      "Epoch 69, Train Loss: 0.0110, Val Loss: 0.0142\n",
      "Epoch 70, Train Loss: 0.0112, Val Loss: 0.0152\n",
      "Epoch 71, Train Loss: 0.0109, Val Loss: 0.0151\n",
      "Epoch 72, Train Loss: 0.0110, Val Loss: 0.0162\n",
      "Epoch 73, Train Loss: 0.0110, Val Loss: 0.0162\n",
      "Epoch 74, Train Loss: 0.0109, Val Loss: 0.0176\n",
      "Epoch 75, Train Loss: 0.0109, Val Loss: 0.0143\n",
      "Epoch 76, Train Loss: 0.0109, Val Loss: 0.0147\n",
      "Epoch 77, Train Loss: 0.0108, Val Loss: 0.0141\n",
      "Epoch 78, Train Loss: 0.0109, Val Loss: 0.0145\n",
      "Epoch 79, Train Loss: 0.0108, Val Loss: 0.0140\n",
      "Epoch 80, Train Loss: 0.0109, Val Loss: 0.0135\n",
      "Epoch 81, Train Loss: 0.0108, Val Loss: 0.0145\n",
      "Epoch 82, Train Loss: 0.0108, Val Loss: 0.0126\n",
      "Epoch 83, Train Loss: 0.0108, Val Loss: 0.0145\n",
      "Epoch 84, Train Loss: 0.0107, Val Loss: 0.0135\n",
      "Epoch 85, Train Loss: 0.0108, Val Loss: 0.0140\n",
      "Epoch 86, Train Loss: 0.0107, Val Loss: 0.0143\n",
      "Epoch 87, Train Loss: 0.0107, Val Loss: 0.0146\n",
      "Epoch 88, Train Loss: 0.0107, Val Loss: 0.0136\n",
      "Epoch 111, Train Loss: 0.0094, Val Loss: 0.0120\n",
      "Epoch 112, Train Loss: 0.0094, Val Loss: 0.0114\n",
      "Epoch 113, Train Loss: 0.0095, Val Loss: 0.0128\n",
      "Epoch 114, Train Loss: 0.0093, Val Loss: 0.0125\n",
      "Epoch 115, Train Loss: 0.0093, Val Loss: 0.0124\n",
      "Epoch 116, Train Loss: 0.0093, Val Loss: 0.0114\n",
      "Epoch 117, Train Loss: 0.0093, Val Loss: 0.0127\n",
      "Epoch 118, Train Loss: 0.0093, Val Loss: 0.0122\n",
      "Epoch 119, Train Loss: 0.0093, Val Loss: 0.0116\n",
      "Epoch 120, Train Loss: 0.0092, Val Loss: 0.0114\n",
      "Epoch 121, Train Loss: 0.0092, Val Loss: 0.0130\n",
      "Epoch 122, Train Loss: 0.0092, Val Loss: 0.0114\n",
      "Epoch 123, Train Loss: 0.0093, Val Loss: 0.0113\n",
      "Epoch 124, Train Loss: 0.0092, Val Loss: 0.0120\n",
      "Epoch 125, Train Loss: 0.0091, Val Loss: 0.0110\n",
      "Epoch 126, Train Loss: 0.0091, Val Loss: 0.0128\n",
      "Epoch 127, Train Loss: 0.0091, Val Loss: 0.0129\n",
      "Epoch 128, Train Loss: 0.0092, Val Loss: 0.0126\n",
      "Epoch 129, Train Loss: 0.0092, Val Loss: 0.0113\n",
      "Epoch 130, Train Loss: 0.0091, Val Loss: 0.0109\n"
     ]
    }
   ],
   "source": [
    "train_model(model, train_loader, val_loader, epochs=130, criterion=criterion, optimizer=optimizer, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a8467686-0655-4056-8e01-56299eb89d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_percentage_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "efc96935-bbe0-4ca9-b11a-931cdcfc3bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_ioa(y_true, y_pred):\n",
    "    # 计算平均值\n",
    "    mean_observed = np.mean(y_true)\n",
    "    mean_predicted = np.mean(y_pred)\n",
    "\n",
    "    # 计算IoA\n",
    "    numerator = np.sum((y_true - y_pred) ** 2)\n",
    "    denominator = np.sum((np.abs(y_true - mean_observed) + np.abs(y_pred - mean_predicted)) ** 2)\n",
    "    IoA = 1 - (numerator / denominator)\n",
    "\n",
    "    return IoA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "73a0002b-35d6-4e20-a620-5c8f5cd49296",
   "metadata": {},
   "outputs": [],
   "source": [
    "eva_list_frame = list()\n",
    "device = 'cpu'\n",
    "model = model.to(device)\n",
    "best_mape = 1\n",
    "best_img = None\n",
    "best_mask = None\n",
    "best_recov = None\n",
    "with torch.no_grad():\n",
    "    for batch_idx, (X, y, mask) in enumerate(test_loader):\n",
    "        X, y, mask = X.to(device), y.to(device), mask.to(device)\n",
    "        mask_rev = (torch.squeeze(mask, dim=1)==0) * 1 # mask取反获得修复区域\n",
    "        reconstructed = model(X)\n",
    "        rev_data = y * max_pixel_value\n",
    "        rev_recon = reconstructed * max_pixel_value\n",
    "        # todo: 这里需要只评估修补出来的模块\n",
    "        for i, sample in enumerate(rev_data):\n",
    "            used_mask = mask_rev[i]\n",
    "            data_label = sample[0] * used_mask\n",
    "            recon_no2 = rev_recon[i][0] * used_mask\n",
    "            data_label = data_label[used_mask==1]\n",
    "            recon_no2 = recon_no2[used_mask==1]\n",
    "            mae = mean_absolute_error(data_label, recon_no2)\n",
    "            rmse = np.sqrt(mean_squared_error(data_label, recon_no2))\n",
    "            mape = mean_absolute_percentage_error(data_label, recon_no2)\n",
    "            r2 = r2_score(data_label, recon_no2)\n",
    "            ioa = cal_ioa(data_label.detach().numpy(), recon_no2.detach().numpy())\n",
    "            r = np.corrcoef(data_label, recon_no2)[0, 1]\n",
    "            eva_list_frame.append([mae, rmse, mape, r2, ioa, r])\n",
    "            if mape < best_mape:\n",
    "                best_recov = rev_recon[i][0].numpy()\n",
    "                best_mask = used_mask.numpy()\n",
    "                best_img = sample[0].numpy()\n",
    "                best_mape = mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "589e6d80-228d-4e8a-968a-e7477c5e0e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mae</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape</th>\n",
       "      <th>r2</th>\n",
       "      <th>ioa</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4739.000000</td>\n",
       "      <td>4739.000000</td>\n",
       "      <td>4739.000000</td>\n",
       "      <td>4739.000000</td>\n",
       "      <td>4739.000000</td>\n",
       "      <td>4739.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.297680</td>\n",
       "      <td>6.225729</td>\n",
       "      <td>0.489679</td>\n",
       "      <td>-1.978159</td>\n",
       "      <td>-0.362509</td>\n",
       "      <td>0.352984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.930302</td>\n",
       "      <td>4.176386</td>\n",
       "      <td>0.191670</td>\n",
       "      <td>2.447883</td>\n",
       "      <td>1.074637</td>\n",
       "      <td>0.201559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.996953</td>\n",
       "      <td>1.279405</td>\n",
       "      <td>0.202344</td>\n",
       "      <td>-28.276637</td>\n",
       "      <td>-9.562830</td>\n",
       "      <td>-0.500861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.103293</td>\n",
       "      <td>2.741658</td>\n",
       "      <td>0.353414</td>\n",
       "      <td>-2.891019</td>\n",
       "      <td>-0.796581</td>\n",
       "      <td>0.225314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.190869</td>\n",
       "      <td>4.148710</td>\n",
       "      <td>0.457116</td>\n",
       "      <td>-1.093823</td>\n",
       "      <td>0.044020</td>\n",
       "      <td>0.365110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.378542</td>\n",
       "      <td>9.440538</td>\n",
       "      <td>0.586501</td>\n",
       "      <td>-0.406974</td>\n",
       "      <td>0.355992</td>\n",
       "      <td>0.498017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.329165</td>\n",
       "      <td>23.047779</td>\n",
       "      <td>2.242282</td>\n",
       "      <td>0.592645</td>\n",
       "      <td>0.829324</td>\n",
       "      <td>0.839954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mae         rmse         mape           r2          ioa  \\\n",
       "count  4739.000000  4739.000000  4739.000000  4739.000000  4739.000000   \n",
       "mean      5.297680     6.225729     0.489679    -1.978159    -0.362509   \n",
       "std       3.930302     4.176386     0.191670     2.447883     1.074637   \n",
       "min       0.996953     1.279405     0.202344   -28.276637    -9.562830   \n",
       "25%       2.103293     2.741658     0.353414    -2.891019    -0.796581   \n",
       "50%       3.190869     4.148710     0.457116    -1.093823     0.044020   \n",
       "75%       8.378542     9.440538     0.586501    -0.406974     0.355992   \n",
       "max      21.329165    23.047779     2.242282     0.592645     0.829324   \n",
       "\n",
       "                 r  \n",
       "count  4739.000000  \n",
       "mean      0.352984  \n",
       "std       0.201559  \n",
       "min      -0.500861  \n",
       "25%       0.225314  \n",
       "50%       0.365110  \n",
       "75%       0.498017  \n",
       "max       0.839954  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(eva_list_frame, columns=['mae', 'rmse', 'mape', 'r2', 'ioa', 'r']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5f8b2dc4-5ac4-4b52-9dea-de8d29cba6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "eva_list = list()\n",
    "device = 'cpu'\n",
    "model = model.to(device)\n",
    "with torch.no_grad():\n",
    "    for batch_idx, (X, y, mask) in enumerate(test_loader):\n",
    "        X, y, mask = X.to(device), y.to(device), mask.to(device)\n",
    "        mask_rev = (torch.squeeze(mask, dim=1)==0) * 1 # mask取反获得修复区域\n",
    "        reconstructed = model(X)\n",
    "        rev_data = y * max_pixel_value\n",
    "        rev_recon = reconstructed * max_pixel_value\n",
    "        # todo: 这里需要只评估修补出来的模块\n",
    "        data_label = torch.squeeze(rev_data, dim=1) * mask_rev\n",
    "        data_label = data_label[mask_rev==1]\n",
    "        recon_no2 = torch.squeeze(rev_recon, dim=1) * mask_rev\n",
    "        recon_no2 = recon_no2[mask_rev==1]\n",
    "        mae = mean_absolute_error(data_label, recon_no2)\n",
    "        rmse = np.sqrt(mean_squared_error(data_label, recon_no2))\n",
    "        mape = mean_absolute_percentage_error(data_label, recon_no2)\n",
    "        r2 = r2_score(data_label, recon_no2)\n",
    "        ioa = cal_ioa(data_label.detach().numpy(), recon_no2.detach().numpy())\n",
    "        eva_list.append([mae, rmse, mape, r2, ioa])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "755abc3e-f4d2-4056-b01b-3fb085f95f19",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, './models/MAE/final_patch_20.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76449691-74b2-43ef-b092-f71cd8116448",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可视化特定特征的函数\n",
    "def visualize_rst(input_feature,masked_feature, recov_region, output_feature, title):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.subplot(1, 4, 1)\n",
    "    plt.imshow(input_feature, cmap='RdYlGn_r')\n",
    "    plt.gca().axis('off')  # 获取当前坐标轴并关闭\n",
    "    plt.subplot(1, 4, 2)\n",
    "    plt.imshow(masked_feature, cmap='gray')\n",
    "    plt.gca().axis('off')  # 获取当前坐标轴并关闭\n",
    "    plt.subplot(1, 4, 3)\n",
    "    plt.imshow(recov_region, cmap='RdYlGn_r')\n",
    "    plt.gca().axis('off')  # 获取当前坐标轴并关闭\n",
    "    plt.subplot(1, 4, 4)\n",
    "    plt.imshow(output_feature, cmap='RdYlGn_r')\n",
    "    plt.gca().axis('off')  # 获取当前坐标轴并关闭\n",
    "    # plt.savefig('./figures/result/20_samples.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "82467932-3b38-4d2d-83d9-8d76c4f98a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_mask_cp = np.where(best_mask == 0, np.nan, best_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6bb568d1-07bd-49c4-9056-9ad2f2dd36a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_rst(best_img, best_mask, best_recov*best_mask_cp, best_recov, '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb78ecea-809d-40c6-940f-c72cd956ff84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}