CSI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://arxiv.org/pdf/1606.03073.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torchvision.models as models\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from skimage import io, transform, color\n",
    "from torchvision import transforms, utils\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "use_gpu = torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageDataset(Dataset):\n",
    "    def __init__(self, root_dir, indices, transform=None):\n",
    "        self.root_dir = root_dir\n",
    "        self.indices = indices  # Which image numbers to take\n",
    "        self.transform = transform\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.indices)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        try:\n",
    "            edge_name = os.path.join(self.root_dir, 'edges/', str(index + 1) + '.png')\n",
    "            icon_name = os.path.join(self.root_dir, 'icons/', str(index + 1) + '.png')\n",
    "            edge = io.imread(edge_name, as_gray=True)\n",
    "            edge = edge.reshape((120, 120, 1))\n",
    "            edge = edge.transpose((2, 0, 1))\n",
    "            icon = io.imread(icon_name)\n",
    "            icon = color.rgba2rgb(icon)\n",
    "            icon = icon.transpose((2, 0, 1))\n",
    "\n",
    "            return edge, icon\n",
    "        except ValueError:\n",
    "            print(index)\n",
    "            raise ValueError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.arange(1, 10001)\n",
    "np.random.shuffle(indices)\n",
    "\n",
    "train_data = ImageDataset('./data/', indices[:7000])\n",
    "validation_data = ImageDataset('./data/', indices[7000:8500])\n",
    "test_data = ImageDataset('./data/', indices[8500:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Res(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Res, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128)\n",
    "        )\n",
    "        \n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        y = self.conv1(x)\n",
    "        y = F.relu(y)\n",
    "        y = self.conv2(y)\n",
    "        return y + x\n",
    "\n",
    "    \n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, kernel_size=9, padding=4),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        \n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        \n",
    "        self.residual = nn.Sequential(\n",
    "            Res(),\n",
    "            Res(),\n",
    "            Res(),\n",
    "            Res(),\n",
    "            Res(),\n",
    "        )\n",
    "        \n",
    "        self.deconv1 = nn.Sequential(\n",
    "            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        \n",
    "        self.deconv2 = nn.Sequential(\n",
    "            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        \n",
    "        self.final = nn.Sequential(\n",
    "            nn.Conv2d(32, 3, kernel_size=9, padding=4),\n",
    "            nn.BatchNorm2d(3),\n",
    "            nn.Tanh(),\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.residual(x)\n",
    "        x = self.deconv1(x)\n",
    "        x = self.deconv2(x)\n",
    "        x = self.final(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 4\n",
    "LR = 0.001\n",
    "EPOCHS = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Second component of loss is a VGG-16 DNN\n",
    "vgg = models.vgg16(pretrained=True).cuda()\n",
    "clipped = list(vgg.children())[0][:9]\n",
    "clipped_model = torch.nn.Sequential(*clipped).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Borrowed from https://dsp.stackexchange.com/questions/38065/peak-signal-to-noise-ratio-psnr-in-python-for-an-image\n",
    "# Credit to user Himanshu Tyagi\n",
    "def psnr(img1, img2):\n",
    "    mse = np.mean( (img1 - img2) ** 2 )\n",
    "    if mse == 0:\n",
    "        return 100\n",
    "    PIXEL_MAX = 1\n",
    "    return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validate(model, val_loader):\n",
    "    model.eval()\n",
    "    val_loss = 0\n",
    "    for edges, icons in tqdm(val_loader):\n",
    "        if use_gpu:\n",
    "            edges = edges.cuda()\n",
    "        icons = icons.float()\n",
    "        res = model(edges.float())\n",
    "        res = res.detach().cpu().numpy()\n",
    "        icons = icons.numpy()\n",
    "        val_loss += psnr(res, icons)      \n",
    "    print(f\"Validation PSNR: {val_loss / len(val_loader)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_loader, val_loader, criterion, optimizer):\n",
    "    for e in range(EPOCHS):\n",
    "        running_loss = 0\n",
    "        model.train()\n",
    "        for edges, icons in tqdm(train_loader):\n",
    "            if use_gpu:\n",
    "                edges = edges.cuda()\n",
    "                icons = icons.cuda()\n",
    "            \n",
    "            res = model(edges.float())\n",
    "#             print(res[0])\n",
    "#             print(icons[0])\n",
    "#             print(res.shape)\n",
    "#             print(icons.shape)\n",
    "            icons = icons.float()\n",
    "    \n",
    "            \n",
    "            loss = criterion(res, icons)\n",
    "            with torch.no_grad():\n",
    "                vgg_clip = clipped_model(icons)\n",
    "            loss += criterion(clipped_model(res), vgg_clip)\n",
    "            \n",
    "#             print(icons[:, :, :, :-1])\n",
    "#             print(icons[:,:,1:,:].shape)\n",
    "            var_loss = 0.00001 * (torch.sum(torch.sqrt(\n",
    "                torch.pow(icons[:, :, :, :-1] - icons[:, :, :, 1:], 2).flatten() + \\\n",
    "                torch.pow(icons[:,:,:-1,:] - icons[:,:,1:,:], 2).flatten()\n",
    "            )))\n",
    "            \n",
    "            loss += var_loss\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            running_loss += loss.item()\n",
    "        else:\n",
    "            print(f\"Epoch {e+1}\")\n",
    "            print(f\"Training loss: {running_loss / len(train_loader)}\")\n",
    "            validate(model, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)\n",
    "validation_loader = DataLoader(validation_data, batch_size=1, shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "text": [
      "\n",
      "Epoch 1\n",
      "Training loss: 1.3758543936184475\n"
     ]
    },
    {
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     "text": [
      "\n",
      "Validation PSNR: 10.703395256320755\n"
     ]
    },
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     "text": [
      "\n",
      "Epoch 2\n",
      "Training loss: 0.9612137578896114\n"
     ]
    },
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     "text": [
      "\n",
      "Validation PSNR: 11.906334205544896\n"
     ]
    },
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     "text": [
      "\n",
      "Epoch 3\n",
      "Training loss: 0.8833379963806697\n"
     ]
    },
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     "text": [
      "\n",
      "Validation PSNR: 10.665639243031832\n"
     ]
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     "text": [
      "\n",
      "Epoch 4\n",
      "Training loss: 0.8372574869053704\n"
     ]
    },
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     "text": [
      "\n",
      "Validation PSNR: 12.649184758157896\n"
     ]
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     "text": [
      "\n",
      "Epoch 5\n",
      "Training loss: 0.7907763450145722\n"
     ]
    },
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     "text": [
      "\n",
      "Validation PSNR: 13.354692121401357\n"
     ]
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     "text": [
      "\n",
      "Epoch 6\n",
      "Training loss: 0.7579229264089039\n"
     ]
    },
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     "text": [
      "\n",
      "Validation PSNR: 13.306074182529832\n"
     ]
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     "text": [
      "\n",
      "Epoch 7\n",
      "Training loss: 0.7251307086263384\n"
     ]
    },
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     "text": [
      "\n",
      "Validation PSNR: 13.800969589462007\n"
     ]
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    {
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     "text": [
      "\n",
      "Epoch 8\n",
      "Training loss: 0.7016713921342578\n"
     ]
    },
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    {
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     "text": [
      "\n",
      "Validation PSNR: 13.82539754870898\n"
     ]
    },
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     "text": [
      "\n",
      "Epoch 9\n",
      "Training loss: 0.6782462356431144\n"
     ]
    },
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    {
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     "text": [
      "\n",
      "Validation PSNR: 13.910496084655563\n"
     ]
    },
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     "text": [
      "\n",
      "Epoch 10\n",
      "Training loss: 0.6511988655669348\n"
     ]
    },
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    {
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     "text": [
      "\n",
      "Validation PSNR: 14.229609383014415\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 11\n",
      "Training loss: 0.6339321947608675\n"
     ]
    },
    {
     "data": {
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     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 14.135191231818554\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 12\n",
      "Training loss: 0.6176373557703836\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 14.438512803746503\n"
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    {
     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 13\n",
      "Training loss: 0.5958799731050218\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 14.591794423063831\n"
     ]
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    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 14\n",
      "Training loss: 0.5790038439546312\n"
     ]
    },
    {
     "data": {
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     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 14.623762009399373\n"
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    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 15\n",
      "Training loss: 0.5666462978294917\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 14.796149295445018\n"
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    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 16\n",
      "Training loss: 0.5538654321432114\n"
     ]
    },
    {
     "data": {
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 14.992192732680513\n"
     ]
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    {
     "data": {
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 17\n",
      "Training loss: 0.5389305361935071\n"
     ]
    },
    {
     "data": {
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    {
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     "text": [
      "\n",
      "Validation PSNR: 15.272812821199546\n"
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    {
     "data": {
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    {
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     "text": [
      "\n",
      "Epoch 18\n",
      "Training loss: 0.5268234277623041\n"
     ]
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    {
     "data": {
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    {
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     "text": [
      "\n",
      "Validation PSNR: 15.263700919066878\n"
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    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 19\n",
      "Training loss: 0.5144130053435053\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "Validation PSNR: 15.503434372734208\n"
     ]
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    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 20\n",
      "Training loss: 0.5023371081096786\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 15.177398283063228\n"
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    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 21\n",
      "Training loss: 0.49330180181775773\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 15.74837598843546\n"
     ]
    },
    {
     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 22\n",
      "Training loss: 0.4889867992571422\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 15.50764493646962\n"
     ]
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    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 23\n",
      "Training loss: 0.4732492472870009\n"
     ]
    },
    {
     "data": {
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       "model_id": "431cfe641749487fb3fea138fac8266b",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 15.903432814510873\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(IntProgress(value=0, max=1750), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 24\n",
      "Training loss: 0.4707472071307046\n"
     ]
    },
    {
     "data": {
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       "model_id": "b274c472d38a4e0e93a42874b8c4b910",
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       "HBox(children=(IntProgress(value=0, max=1500), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 15.049976209583306\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b05601b5f4044e3b93f62879c674dd5d",
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       "version_minor": 0
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       "HBox(children=(IntProgress(value=0, max=1750), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 25\n",
      "Training loss: 0.47028747284412387\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb745b8f337a4c1cb8f7c9f89b813e71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation PSNR: 16.28601680766952\n"
     ]
    }
   ],
   "source": [
    "net = Net()\n",
    "if use_gpu:\n",
    "    net = net.cuda()\n",
    "\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(net.parameters(), lr=LR, betas=(0.9, 0.999), eps=1e-8)\n",
    "\n",
    "train(net, train_loader, validation_loader, criterion, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(net.state_dict(), 'CSI.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (conv1): Sequential(\n",
       "    (0): Conv2d(1, 32, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4))\n",
       "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "  )\n",
       "  (conv2): Sequential(\n",
       "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "  )\n",
       "  (conv3): Sequential(\n",
       "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "  )\n",
       "  (residual): Sequential(\n",
       "    (0): Res(\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Res(\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (2): Res(\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (3): Res(\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (4): Res(\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (deconv1): Sequential(\n",
       "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "  )\n",
       "  (deconv2): Sequential(\n",
       "    (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "  )\n",
       "  (final): Sequential(\n",
       "    (0): Conv2d(32, 3, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4))\n",
       "    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net()\n",
    "device = torch.device('cuda')\n",
    "net.load_state_dict(torch.load('CSI.pt'))\n",
    "net.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, test_loader, visualize=False, samples=1):\n",
    "    model.train()\n",
    "    test_loss = 0\n",
    "    for edges, icons in tqdm(test_loader):\n",
    "        if use_gpu:\n",
    "            edges = edges.cuda()\n",
    "        \n",
    "        res = model(edges.float())\n",
    "        res = res.detach().cpu().numpy()[0,:,:,:]\n",
    "        icons = icons.float().numpy()[0, :, :, :]\n",
    "        test_loss += psnr(res, icons)\n",
    "        \n",
    "        if visualize and samples > 0:\n",
    "            samples -= 1\n",
    "            res = res.transpose((1, 2, 0))\n",
    "            res = res.clip(0, 1)\n",
    "            icons = icons.transpose((1, 2, 0))\n",
    "            edges = edges.detach().cpu().numpy()[0, 0, :, :]\n",
    "            plt.imshow(edges, cmap='gray')\n",
    "            plt.axis('off')\n",
    "            plt.show()\n",
    "            plt.imshow(res)\n",
    "            plt.axis('off')\n",
    "            plt.show()\n",
    "            plt.imshow(icons)\n",
    "            plt.axis('off')\n",
    "            plt.show()\n",
    "            \n",
    "    print(f\"Test PSNR is {test_loss / len(test_loader)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52c04741154b4915bd2d8e3e5fb36f0f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test PSNR is 14.252988022412756\n"
     ]
    }
   ],
   "source": [
    "test(net, test_loader, visualize=True, samples=1)"
   ]
  }
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