declouding_unet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 615,
     "status": "ok",
     "timestamp": 1668243496021,
     "user": {
      "displayName": "Parth Tulsyan",
      "userId": "14552357153811248538"
     },
     "user_tz": -330
    },
    "id": "EcvpPRx-IVgm"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader,Dataset\n",
    "from torchvision import transforms\n",
    "from torchvision.datasets import FashionMNIST,StanfordCars\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import torch.nn.functional as F\n",
    "import math\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "?wandb.init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import ImageFile\n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1668243250815,
     "user": {
      "displayName": "Parth Tulsyan",
      "userId": "14552357153811248538"
     },
     "user_tz": -330
    },
    "id": "3INLn-8KC9Vq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "# Defining the device\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Block(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(in_ch, out_ch, 3)\n",
    "        self.relu  = nn.ReLU()\n",
    "        self.conv2 = nn.Conv2d(out_ch, out_ch, 3)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.conv2(self.relu(self.conv1(x)))\n",
    "\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self, chs=(3,64,128,256,512,1024)):\n",
    "        super().__init__()\n",
    "        self.enc_blocks = nn.ModuleList([Block(chs[i], chs[i+1]) for i in range(len(chs)-1)])\n",
    "        self.pool       = nn.MaxPool2d(2)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        ftrs = []\n",
    "        for block in self.enc_blocks:\n",
    "            x = block(x)\n",
    "            ftrs.append(x)\n",
    "            x = self.pool(x)\n",
    "        return ftrs\n",
    "\n",
    "\n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self, chs=(1024, 512, 256, 128, 64)):\n",
    "        super().__init__()\n",
    "        self.chs         = chs\n",
    "        self.upconvs    = nn.ModuleList([nn.ConvTranspose2d(chs[i], chs[i+1], 2, 2) for i in range(len(chs)-1)])\n",
    "        self.dec_blocks = nn.ModuleList([Block(chs[i], chs[i+1]) for i in range(len(chs)-1)]) \n",
    "        \n",
    "    def forward(self, x, encoder_features):\n",
    "        for i in range(len(self.chs)-1):\n",
    "            x        = self.upconvs[i](x)\n",
    "            enc_ftrs = self.crop(encoder_features[i], x)\n",
    "            x        = torch.cat([x, enc_ftrs], dim=1)\n",
    "            x        = self.dec_blocks[i](x)\n",
    "        return x\n",
    "    \n",
    "    def crop(self, enc_ftrs, x):\n",
    "        _, _, H, W = x.shape\n",
    "        enc_ftrs   = torchvision.transforms.CenterCrop([H, W])(enc_ftrs)\n",
    "        return enc_ftrs\n",
    "\n",
    "\n",
    "class UNet(nn.Module):\n",
    "    def __init__(self, enc_chs=(3,64,128,256,512,1024), dec_chs=(1024, 512, 256, 128, 64), num_class=1, retain_dim=False, out_sz=(480,720)):\n",
    "        super().__init__()\n",
    "        self.out_size    =out_sz\n",
    "        self.encoder     = Encoder(enc_chs)\n",
    "        self.decoder     = Decoder(dec_chs)\n",
    "        self.head        = nn.Conv2d(dec_chs[-1], num_class, 1)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.retain_dim  = retain_dim\n",
    "\n",
    "    def forward(self, x):\n",
    "        enc_ftrs = self.encoder(x)\n",
    "        out      = self.decoder(enc_ftrs[::-1][0], enc_ftrs[::-1][1:])\n",
    "        out      = self.head(out)\n",
    "        out      = self.sigmoid(out)\n",
    "        if self.retain_dim:\n",
    "            out = F.interpolate(out, self.out_size)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[0.4784, 0.4784, 0.4781,  ..., 0.4781, 0.4784, 0.4781],\n",
       "          [0.4784, 0.4784, 0.4781,  ..., 0.4781, 0.4784, 0.4781],\n",
       "          [0.4785, 0.4785, 0.4784,  ..., 0.4784, 0.4784, 0.4784],\n",
       "          ...,\n",
       "          [0.4784, 0.4784, 0.4781,  ..., 0.4781, 0.4784, 0.4781],\n",
       "          [0.4784, 0.4784, 0.4781,  ..., 0.4781, 0.4784, 0.4781],\n",
       "          [0.4784, 0.4784, 0.4784,  ..., 0.4784, 0.4785, 0.4784]],\n",
       "\n",
       "         [[0.4817, 0.4817, 0.4814,  ..., 0.4814, 0.4817, 0.4814],\n",
       "          [0.4817, 0.4817, 0.4814,  ..., 0.4814, 0.4817, 0.4814],\n",
       "          [0.4813, 0.4813, 0.4811,  ..., 0.4811, 0.4813, 0.4811],\n",
       "          ...,\n",
       "          [0.4817, 0.4817, 0.4814,  ..., 0.4814, 0.4817, 0.4814],\n",
       "          [0.4817, 0.4817, 0.4814,  ..., 0.4814, 0.4817, 0.4814],\n",
       "          [0.4813, 0.4813, 0.4811,  ..., 0.4811, 0.4813, 0.4811]],\n",
       "\n",
       "         [[0.4836, 0.4836, 0.4834,  ..., 0.4834, 0.4836, 0.4834],\n",
       "          [0.4836, 0.4836, 0.4834,  ..., 0.4834, 0.4836, 0.4834],\n",
       "          [0.4842, 0.4842, 0.4839,  ..., 0.4839, 0.4842, 0.4838],\n",
       "          ...,\n",
       "          [0.4836, 0.4836, 0.4834,  ..., 0.4834, 0.4836, 0.4834],\n",
       "          [0.4836, 0.4836, 0.4834,  ..., 0.4834, 0.4836, 0.4834],\n",
       "          [0.4842, 0.4842, 0.4838,  ..., 0.4838, 0.4842, 0.4838]],\n",
       "\n",
       "         [[0.4991, 0.4991, 0.4993,  ..., 0.4993, 0.4991, 0.4993],\n",
       "          [0.4991, 0.4991, 0.4993,  ..., 0.4993, 0.4991, 0.4993],\n",
       "          [0.4994, 0.4994, 0.4994,  ..., 0.4994, 0.4994, 0.4993],\n",
       "          ...,\n",
       "          [0.4991, 0.4991, 0.4993,  ..., 0.4993, 0.4991, 0.4993],\n",
       "          [0.4991, 0.4991, 0.4993,  ..., 0.4993, 0.4991, 0.4993],\n",
       "          [0.4994, 0.4994, 0.4993,  ..., 0.4993, 0.4994, 0.4994]]]],\n",
       "       device='cuda:0', grad_fn=<UpsampleNearest2DBackward1>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dims=1024\n",
    "model = UNet(num_class=4,retain_dim=True,out_sz=(480,720)).to(device)\n",
    "test=torch.ones(1,3,480,720).to(device)\n",
    "model(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1668243325562,
     "user": {
      "displayName": "Parth Tulsyan",
      "userId": "14552357153811248538"
     },
     "user_tz": -330
    },
    "id": "c7oyrJkTC1Pf"
   },
   "outputs": [],
   "source": [
    "\n",
    "# Setting the optimiser\n",
    "\n",
    "learning_rate = 1e-3*5\n",
    "\n",
    "optimizer = torch.optim.Adam(\n",
    "    model.parameters(),\n",
    "    lr=learning_rate,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1668243370754,
     "user": {
      "displayName": "Parth Tulsyan",
      "userId": "14552357153811248538"
     },
     "user_tz": -330
    },
    "id": "TMFda0HMDPtF"
   },
   "outputs": [],
   "source": [
    "# Reconstruction + KL divergence losses summed over all elements and batch\n",
    "\n",
    "def loss_function(ỹ, y):\n",
    "    BCE = nn.functional.binary_cross_entropy(\n",
    "        ỹ, y,reduction='sum'\n",
    "    )\n",
    "#     KLD = (-0.5 * torch.mean(-logvar1.exp() + logvar1 + 1.0 - mu1.pow(2)))\n",
    "    return BCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 571,
     "status": "ok",
     "timestamp": 1668243521961,
     "user": {
      "displayName": "Parth Tulsyan",
      "userId": "14552357153811248538"
     },
     "user_tz": -330
    },
    "id": "ejob3DR9DqOp"
   },
   "outputs": [],
   "source": [
    "class MyDataset(Dataset):\n",
    "    def __init__(self, train_path,transform_x=None,transform_y=None):\n",
    "        self.df = pd.read_csv(train_path, sep=',', usecols=['input', 'output'])\n",
    "        self.transform_x=transform_x\n",
    "        self.transform_y=transform_y\n",
    "    def __getitem__(self, index):\n",
    "#         print(self.df.iloc[index, 1])\n",
    "#         print(self.df.iloc[index, 0])\n",
    "        x = Image.open(self.df.iloc[index, 1])\n",
    "        y = Image.open(self.df.iloc[index, 0])\n",
    "        if self.transform_x is not None:\n",
    "            x=self.transform_x(x)\n",
    "            y=self.transform_y(y)\n",
    "        else:\n",
    "            x, y = torch.from_numpy(x), torch.from_numpy(y)\n",
    "        return x, y\n",
    "    def __len__(self):\n",
    "#         return len(self.df)\n",
    "        return 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyDataset_np(Dataset):\n",
    "    def __init__(self, train_path,transform_x=None,transform_y=None):\n",
    "        self.df = pd.read_csv(train_path, sep=',', usecols=['input', 'output'])\n",
    "        self.transform_x=transform_x\n",
    "        self.transform_y=transform_y\n",
    "    def __getitem__(self, index):\n",
    "#         print(self.df.iloc[index, 1])\n",
    "#         print(self.df.iloc[index, 0])\n",
    "        x = np.array(Image.open(self.df.iloc[index, 1]))\n",
    "        y = np.array(Image.open(self.df.iloc[index, 0]))\n",
    "        if self.transform_x is not None:\n",
    "            x=self.transform_x(x)\n",
    "            y=self.transform_y(y)\n",
    "        else:\n",
    "            x, y = torch.from_numpy(x), torch.from_numpy(y)\n",
    "        return x, y\n",
    "    def __len__(self):\n",
    "#         return len(self.df)\n",
    "        return 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 1000\n",
    "batch_size = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "wandb.config = {\n",
    "  \"learning_rate\": learning_rate,\n",
    "  \"epochs\": epochs,\n",
    "  \"batch_size\": batch_size,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpthpth\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
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       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.01666969786666641, max=1.0)…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: ERROR Failed to sample metric: Not Supported\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.13.6 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
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      ]
     },
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    {
     "data": {
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       "Tracking run with wandb version 0.13.5"
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     "data": {
      "text/html": [
       "Run data is saved locally in <code>/home/aryna/Documents/Sem_5/CV/AerialPoseEstimator/wandb/run-20221211_031231-1j9ssi5k</code>"
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       "Syncing run <strong><a href=\"https://wandb.ai/pthpth/AerialPoseEstimator/runs/1j9ssi5k\" target=\"_blank\">major-snowflake-76</a></strong> to <a href=\"https://wandb.ai/pthpth/AerialPoseEstimator\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src=\"https://wandb.ai/pthpth/AerialPoseEstimator/runs/1j9ssi5k?jupyter=true\" style=\"border:none;width:100%;height:420px;display:none;\"></iframe>"
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       "<wandb.sdk.wandb_run.Run at 0x7fddbc64cb20>"
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "wandb.init(project=\"AerialPoseEstimator\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "error",
     "timestamp": 1668243568524,
     "user": {
      "displayName": "Parth Tulsyan",
      "userId": "14552357153811248538"
     },
     "user_tz": -330
    },
    "id": "cxLMV2PJECih",
    "outputId": "c5629b81-d939-4794-d695-cfb00de301ca"
   },
   "outputs": [],
   "source": [
    "train_loader=MyDataset_np(\"./dataset_train.csv\")\n",
    "test_loader=MyDataset_np(\"./dataset_test.csv\")\n",
    "train_loader=DataLoader(train_loader, batch_size=batch_size,shuffle=True)\n",
    "test_loader=DataLoader(test_loader, batch_size=batch_size,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.watch(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_mean_x(loader):\n",
    "    cnt=0\n",
    "    fst_moment=torch.empty(3)\n",
    "    snd_moment=torch.empty(3)\n",
    "    for images,_ in loader:\n",
    "        # c h w b\n",
    "#         print(images.shape)\n",
    "        images=images/255\n",
    "        b,h,w,c = images.shape\n",
    "        nb_pixels=b * h * w\n",
    "        sum_ =  torch.sum(images,dim=[0,1,2])\n",
    "        sum_of_square = torch.sum(images**2,dim=[0,1,2])\n",
    "        \n",
    "        fst_moment = (cnt * fst_moment + sum_) / (cnt + nb_pixels)\n",
    "        snd_moment = (cnt * snd_moment + sum_of_square) / ( cnt + nb_pixels)\n",
    "        \n",
    "        cnt+=nb_pixels\n",
    "    mean,std=fst_moment,torch.sqrt(snd_moment - fst_moment ** 2)\n",
    "    return mean,std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_mean_y(loader):\n",
    "    cnt=0\n",
    "    fst_moment=torch.empty(4)\n",
    "    snd_moment=torch.empty(4)\n",
    "    for _,images in loader:\n",
    "        images=images/255\n",
    "        b,h,w,c = images.shape\n",
    "        nb_pixels=b * h * w\n",
    "        sum_ =  torch.sum(images,dim=[0,1,2])\n",
    "        sum_of_square = torch.sum(images**2,dim=[0,1,2])\n",
    "        \n",
    "        fst_moment = (cnt * fst_moment + sum_) / (cnt + nb_pixels)\n",
    "        snd_moment = (cnt * snd_moment + sum_of_square) / ( cnt + nb_pixels)\n",
    "        \n",
    "        cnt+=nb_pixels\n",
    "    mean,std=fst_moment,torch.sqrt(snd_moment - fst_moment ** 2)\n",
    "    return mean,std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_x,std_x=batch_mean_x(train_loader)\n",
    "mean_y,std_y=batch_mean_y(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader=MyDataset(\"./dataset_train.csv\")\n",
    "test_loader=MyDataset(\"./dataset_test.csv\")\n",
    "train_loader=DataLoader(train_loader, batch_size=batch_size,shuffle=True)\n",
    "test_loader=DataLoader(test_loader, batch_size=batch_size,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_img_normal_x = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean = mean_x,std= std_x)\n",
    "])\n",
    "transform_img_normal_y = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean = 0,std = 1)\n",
    "])\n",
    "train_loader=MyDataset(\"./dataset_train.csv\",\n",
    "                       transform_x=transform_img_normal_x,\n",
    "                       transform_y=transform_img_normal_y)\n",
    "test_loader=MyDataset(\"./dataset_test.csv\",\n",
    "                      transform_x=transform_img_normal_x,\n",
    "                      transform_y=transform_img_normal_y)\n",
    "train_loader=DataLoader(train_loader, batch_size=batch_size,shuffle=True)\n",
    "test_loader=DataLoader(test_loader, batch_size=batch_size,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "error",
     "timestamp": 1668243426197,
     "user": {
      "displayName": "Parth Tulsyan",
      "userId": "14552357153811248538"
     },
     "user_tz": -330
    },
    "id": "mDdzXB6iDayp",
    "outputId": "2123abc6-78cc-461d-9a42-113ffc96dcab"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [27], line 38\u001b[0m\n\u001b[1;32m     36\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m     37\u001b[0m test_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x,y \u001b[38;5;129;01min\u001b[39;00m tqdm(test_loader):\n\u001b[1;32m     39\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     40\u001b[0m     y \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mto(device)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/tqdm/std.py:1195\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1192\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1194\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1195\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1196\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1197\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1198\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py:628\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    625\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    626\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 628\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    629\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    631\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    632\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py:671\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    669\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    670\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 671\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    672\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m    673\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:58\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     56\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     60\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:58\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     56\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     60\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
      "Cell \u001b[0;32mIn [10], line 12\u001b[0m, in \u001b[0;36mMyDataset.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     10\u001b[0m y \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mopen(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf\u001b[38;5;241m.\u001b[39miloc[index, \u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 12\u001b[0m     x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform_x\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_y(y)\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torchvision/transforms/transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[1;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[0;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torchvision/transforms/transforms.py:135\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[0;34m(self, pic)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pic):\n\u001b[1;32m    128\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;124;03m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;124;03m        Tensor: Converted image.\u001b[39;00m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 135\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpic\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torchvision/transforms/functional.py:171\u001b[0m, in \u001b[0;36mto_tensor\u001b[0;34m(pic)\u001b[0m\n\u001b[1;32m    169\u001b[0m img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mpermute((\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[1;32m    170\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(img, torch\u001b[38;5;241m.\u001b[39mByteTensor):\n\u001b[0;32m--> 171\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_float_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m255\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Training and testing the VAE\n",
    "codes = dict(μ=list(), logσ2=list(), x=list())\n",
    "for epoch in tqdm(range(0, epochs + 1)):\n",
    "    # Training\n",
    "    if epoch > 0:  # test untrained net first\n",
    "        model.train()\n",
    "        train_loss = 0\n",
    "        for x,y in tqdm(train_loader):\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "            x=x.view(-1,3,480,720)             \n",
    "            y=y.view(-1,4,480,720)\n",
    "            x=torch.div(x,255)\n",
    "            y=torch.div(y,255)\n",
    "            y_bar=model(x)\n",
    "            loss = loss_function(y_bar, y)\n",
    "            train_loss += loss\n",
    "            # ===================backward====================\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        # ===================log========================\n",
    "\n",
    "    # Testing\n",
    "        print(train_loss)\n",
    "        wandb.log({\"train_loss\":train_loss /len(train_loader.dataset)})\n",
    "        means, logvars, labels = list(), list(), list()\n",
    "        if epoch%10==0:\n",
    "            torch.save({'epoch': epoch,\n",
    "                        'model_state_dict': model.state_dict(),\n",
    "                        'optimizer_state_dict': optimizer.state_dict(),\n",
    "                        'loss': train_loss,}, \n",
    "                       \"./Weights/resnet_unet.pt\")\n",
    "    torch.cuda.empty_cache()\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        test_loss = 0\n",
    "        for x,y in tqdm(test_loader):\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "            x=x.view(-1,3,480,720)\n",
    "            y=y.view(-1,4,480,720)\n",
    "            x=torch.div(x,255)\n",
    "            y=torch.div(y,255)\n",
    "            # ===================forward=====================\n",
    "            ỹ = model(x)\n",
    "            loss = loss_function(ỹ, y)\n",
    "            test_loss+=loss.item()\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print(test_loss)\n",
    "    wandb.log({\"test_loss\":test_loss /len(test_loader.dataset)})\n",
    "    print(epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XAsyU8rHEAOC"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AbAfCjL7DoeU"
   },
   "outputs": [],
   "source": [
    "# test_loader=MyDataset(\"./dataset_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp =np.array(Image.open(\"./Datasets/Input/Echendens-LHS_09620.png_6.png\"), dtype = float)/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tem = torch.from_numpy(temp).view(-1,3,480,720)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tem=tem.to(device,dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ans=(model(tem))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ans=(ans[0]*255).detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ans.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# img=Image.frtomarray(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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