diffusion-model-with-mnist-data-conditional.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a4e73684",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "The module jupytext is used to treat this .py file as a jupyter notebook file. To keep the output after every session, go to \"File\" -> \"Jupytext\" -> \"Pair Notebook with ipynb document\". This generates a file PY_FILENAME.ipynb."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dce3feb",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e3cee5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision import datasets, transforms\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import math\n",
    "import time\n",
    "import datetime\n",
    "from ignite.engine import Engine, Events\n",
    "from ignite.metrics import InceptionScore, FID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1521f127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU.\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    print(\"Using GPU.\")\n",
    "    device = \"cuda\"\n",
    "else:\n",
    "    print(\"Using CPU.\")\n",
    "    device = \"cpu\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f323dd1b",
   "metadata": {},
   "source": [
    "# Load MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc38ac68",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_path = \"./data/mnist\"\n",
    "\n",
    "train_dataset = datasets.MNIST(root=folder_path, train=True, transform=transforms.ToTensor(), download=True)\n",
    "test_dataset = datasets.MNIST(root=folder_path, train=False, transform=transforms.ToTensor(), download=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4e5616",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c07a893",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_data = train_dataset.data.numpy()\n",
    "train_labels = train_dataset.targets.numpy()\n",
    "\n",
    "assert (train_image_data.shape == np.array([60000, 28, 28])).all()\n",
    "assert (train_labels.shape == np.array([60000,])).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c0f2497",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_images(images):\n",
    "    \"\"\"Plots up to 9 images.\"\"\"\n",
    "    amount_images_to_show = min(9, images.shape[0])\n",
    "    grid_shape = np.array([3, 3])\n",
    "    for image_index in range(amount_images_to_show):\n",
    "        plt.subplot(grid_shape[0], grid_shape[1], image_index + 1)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        image_to_show = images[image_index]\n",
    "        plt.imshow(image_to_show, cmap=\"gray\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "370c2661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(train_image_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3cbeb8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (train_labels[:9] == np.array([5, 0, 4, 1, 9, 2, 1, 3, 1])).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82517a73",
   "metadata": {},
   "source": [
    "# Forward Noise Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d1ceae7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_noise(images, beta):\n",
    "    alpha = 1 - beta\n",
    "    alpha_cum = torch.prod(alpha)\n",
    "    noise = torch.randn(images.shape).to(device)\n",
    "    noisy_images = noise * (1 - alpha_cum) + torch.sqrt(alpha_cum) * images\n",
    "    noisy_images = noisy_images.to(device)\n",
    "    return noisy_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "94a91f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variances = torch.tensor([0.5, 0.5, 0.5, 0.5, 0.5, 0.5]).to(device)\n",
    "noisy_images = add_noise(train_dataset.data.to(device), variances).cpu()\n",
    "plot_images(noisy_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "25be9ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "variances = torch.Tensor(np.linspace(1e-4, 1e-2, 1000)).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2209f2c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = train_dataset.data[0].numpy()\n",
    "transformer = transforms.ToTensor()\n",
    "image = transformer(image).to(device)\n",
    "noisy_images = torch.zeros([9, *image.shape])\n",
    "for i in range(9):\n",
    "    index_stepsize = (variances.shape[0] - 1) / 8\n",
    "    timestep = int(i * index_stepsize) + 1\n",
    "    noisy_image = add_noise(image, variances[:timestep])\n",
    "    noisy_images[i] = noisy_image\n",
    "plot_images(noisy_images.permute(0, 2, 3, 1).numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c91eee1a",
   "metadata": {},
   "source": [
    "# Noise Prediction Model Implementation\n",
    "Uses a UNET architecture to predict the noise of given noisy images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f62cc25",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNISTConditionalEmbeddingLayer(nn.Module):\n",
    "    def __init__(self, cond_emb_dim):\n",
    "        super(MNISTConditionalEmbeddingLayer, self).__init__()\n",
    "        self.cond_emb_dim = cond_emb_dim\n",
    "        self.amount_unique_classes = 10\n",
    "        self.linear_layer = nn.Linear(self.amount_unique_classes, self.cond_emb_dim)\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "    def forward(self, c):\n",
    "        c = nn.functional.one_hot(c, self.amount_unique_classes).float().to(device)\n",
    "        c = self.linear_layer(c)\n",
    "        c = self.relu(c)\n",
    "        return c\n",
    "        \n",
    "        \n",
    "class ResidualConv2dBlock(nn.Module):\n",
    "    def __init__(self, amount_channels_input, amount_channels_output, time_emb_dim, cond_emb_dim):\n",
    "        super(ResidualConv2dBlock, self).__init__()\n",
    "        self.time_emb_mlp = nn.Sequential(nn.Linear(time_emb_dim, amount_channels_input),\n",
    "                                            nn.ReLU())\n",
    "        self.layers = nn.Sequential(nn.Conv2d(amount_channels_input, amount_channels_output, 3, padding=1),\n",
    "                                    nn.GroupNorm(2, amount_channels_output),\n",
    "                                    nn.ReLU(),\n",
    "                                    nn.Conv2d(amount_channels_output, amount_channels_output, 3, padding=1),\n",
    "                                    nn.GroupNorm(2, amount_channels_output),\n",
    "                                    nn.ReLU())\n",
    "        self.residual_layer = nn.Conv2d(amount_channels_input, amount_channels_output, 1)\n",
    "        self.cond_emb_mlp = nn.Sequential(nn.Linear(cond_emb_dim, amount_channels_input),\n",
    "                                            nn.ReLU())\n",
    "\n",
    "    def forward(self, x, t, c):\n",
    "        t = self.time_emb_mlp(t)\n",
    "        t = t[(..., ) + (None, ) * 2]\n",
    "        x = x + t\n",
    "        \n",
    "        if c is not None:\n",
    "            c = self.cond_emb_mlp(c)\n",
    "            c = c[(..., ) + (None, ) * 2]\n",
    "            x = x + c\n",
    "            \n",
    "        conv_output = self.layers(x)\n",
    "        residual_output = self.residual_layer(x)\n",
    "        return conv_output + residual_output\n",
    "\n",
    "class ApplyAttentionBlock(nn.Module):\n",
    "    def __init__(self, amount_channels, amount_channels_attention):\n",
    "        super(ApplyAttentionBlock, self).__init__()\n",
    "        self.amount_channels_attention = amount_channels_attention\n",
    "        self.query_conv2d = nn.Conv2d(amount_channels, self.amount_channels_attention, 1)\n",
    "        self.keys_conv2d = nn.Conv2d(amount_channels, self.amount_channels_attention, 1)\n",
    "        self.values_conv2d = nn.Conv2d(amount_channels, self.amount_channels_attention, 1)\n",
    "        self.attention_conv2d = nn.Conv2d(self.amount_channels_attention, amount_channels, 1)\n",
    "        self.group_norm = nn.GroupNorm(2, amount_channels)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        original_shape = x.shape\n",
    "\n",
    "        query = self.query_conv2d(x).flatten(start_dim=-2, end_dim=-1).permute(0, 2, 1)\n",
    "        keys = self.keys_conv2d(x).flatten(start_dim=-2, end_dim=-1)\n",
    "        values = self.values_conv2d(x).flatten(start_dim=-2, end_dim=-1)\n",
    "\n",
    "        x = torch.matmul(query, keys)\n",
    "        x = self.softmax(x)\n",
    "        x = x.permute(0, 2, 1)\n",
    "        x = torch.matmul(values, x)\n",
    "\n",
    "        x = x.permute(0, 2, 1).reshape(original_shape[0], self.amount_channels_attention, original_shape[2], original_shape[3])\n",
    "        x = self.attention_conv2d(x)\n",
    "        x = self.group_norm(x)\n",
    "        x = self.relu(x)\n",
    "        \n",
    "        x = x + residual\n",
    "        return x\n",
    "\n",
    "class UnetConv2dBlock(nn.Module):\n",
    "    def __init__(self, amount_channels_input, amount_channels_output, time_emb_dim, cond_emb_dim, use_attention=False):\n",
    "        super(UnetConv2dBlock, self).__init__()\n",
    "        self.block1 = ResidualConv2dBlock(amount_channels_input, amount_channels_output, time_emb_dim, cond_emb_dim)\n",
    "        self.block2 = ResidualConv2dBlock(amount_channels_output, amount_channels_output, time_emb_dim, cond_emb_dim)\n",
    "        self.attention_block = None\n",
    "        if use_attention:\n",
    "            amount_channels_attention = amount_channels_output\n",
    "            self.attention_block = ApplyAttentionBlock(amount_channels_output, amount_channels_attention)\n",
    "\n",
    "    def forward(self, x, t, c):\n",
    "        x = self.block1(x, t, c)\n",
    "        if self.attention_block is not None:\n",
    "            x = self.attention_block(x)\n",
    "        x = self.block2(x, t, c)\n",
    "        return x\n",
    "    \n",
    "class EncoderBlock(nn.Module):\n",
    "    def __init__(self, amount_channels_input, amount_channels_output, time_emb_dim, cond_emb_dim, use_attention=False):\n",
    "        super(EncoderBlock, self).__init__()\n",
    "        self.conv_block = UnetConv2dBlock(amount_channels_input, amount_channels_output, time_emb_dim, cond_emb_dim, use_attention)\n",
    "        self.downsampling_layer = nn.MaxPool2d(2)\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "    def forward(self, x, t, c):\n",
    "        x = self.conv_block(x, t, c)\n",
    "        skip_values = x\n",
    "        x = self.downsampling_layer(x)\n",
    "        x = self.relu(x)\n",
    "        return x, skip_values\n",
    "\n",
    "class DecoderBlock(nn.Module):\n",
    "    def __init__(self, amount_channels_input, amount_channels_output, time_emb_dim, cond_emb_dim, use_attention=False):\n",
    "        super(DecoderBlock, self).__init__()\n",
    "        self.conv_block = UnetConv2dBlock(amount_channels_output * 2, amount_channels_output, time_emb_dim, cond_emb_dim, use_attention)\n",
    "        self.upsampling_layer = nn.ConvTranspose2d(amount_channels_input, amount_channels_output, 2, 2)\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "    def forward(self, x, t, skip_values, c):\n",
    "        x = self.upsampling_layer(x)\n",
    "        x = self.relu(x)\n",
    "        x = torch.cat([x, skip_values], axis=1)\n",
    "        x = self.conv_block(x, t, c)\n",
    "        return x\n",
    "\n",
    "class SinusoidalPositionEmbeddingLayer(nn.Module):\n",
    "    def __init__(self, time_emb_dim):\n",
    "        super(SinusoidalPositionEmbeddingLayer, self).__init__()\n",
    "        self.time_emb_dim = time_emb_dim\n",
    "\n",
    "    def forward(self, timesteps):\n",
    "        half_dim = self.time_emb_dim // 2\n",
    "        embeddings = math.log(10000) / (half_dim - 1)\n",
    "        embeddings = torch.exp(torch.arange(half_dim).to(device) * -embeddings).to(device)\n",
    "        embeddings = timesteps[:, None] * embeddings[None, :]\n",
    "        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1).to(device)\n",
    "        return embeddings\n",
    "\n",
    "class NoisePredictionUnet(nn.Module):\n",
    "    def __init__(self, amounts_channels):\n",
    "        super(NoisePredictionUnet, self).__init__()\n",
    "        self.time_emb_dim = 32\n",
    "        self.cond_emb_dim = 32\n",
    "        \n",
    "        encoder_blocks = []\n",
    "        decoder_blocks = []\n",
    "        layers_between = []\n",
    "        final_layers = []\n",
    "        self.divisor = 2 ** (len(amounts_channels) - 2)\n",
    "\n",
    "        for i, current_amount_channels in enumerate(amounts_channels):\n",
    "            next_amount_channels = amounts_channels[i + 1]\n",
    "            if i == 0:\n",
    "                final_layers.append(nn.Conv2d(next_amount_channels, current_amount_channels, 1))\n",
    "\n",
    "                encoding_amount_channels_input = current_amount_channels\n",
    "                encoding_amount_channels_output = next_amount_channels\n",
    "                \n",
    "                assert encoding_amount_channels_input <= encoding_amount_channels_output\n",
    "                \n",
    "                encoder_block = EncoderBlock(encoding_amount_channels_input, encoding_amount_channels_output, self.time_emb_dim, self.cond_emb_dim, True)\n",
    "                encoder_blocks.append(encoder_block)\n",
    "                \n",
    "            elif i == len(amounts_channels) - 2:\n",
    "                layers_between.append(UnetConv2dBlock(current_amount_channels, next_amount_channels, self.time_emb_dim, self.cond_emb_dim))\n",
    "                \n",
    "                decoding_amount_channels_input = next_amount_channels\n",
    "                decoding_amount_channels_output = current_amount_channels\n",
    "                \n",
    "                assert decoding_amount_channels_input >= decoding_amount_channels_output\n",
    "                \n",
    "                decoder_block = DecoderBlock(decoding_amount_channels_input, decoding_amount_channels_output, self.time_emb_dim, self.cond_emb_dim, True)\n",
    "                decoder_blocks.insert(0, decoder_block)\n",
    "                break\n",
    "            else:                \n",
    "                encoding_amount_channels_input = current_amount_channels\n",
    "                encoding_amount_channels_output = next_amount_channels\n",
    "                \n",
    "                decoding_amount_channels_input = next_amount_channels\n",
    "                decoding_amount_channels_output = current_amount_channels\n",
    "\n",
    "                assert encoding_amount_channels_input <= encoding_amount_channels_output\n",
    "                assert decoding_amount_channels_input >= decoding_amount_channels_output\n",
    "                \n",
    "                encoder_block = EncoderBlock(encoding_amount_channels_input, encoding_amount_channels_output, self.time_emb_dim, self.cond_emb_dim, True)\n",
    "                encoder_blocks.append(encoder_block)\n",
    "                \n",
    "                decoder_block = DecoderBlock(decoding_amount_channels_input, decoding_amount_channels_output, self.time_emb_dim, self.cond_emb_dim, True)\n",
    "                decoder_blocks.insert(0, decoder_block)\n",
    "                \n",
    "        layers = encoder_blocks + layers_between + decoder_blocks + final_layers\n",
    "        \n",
    "        self.module_list = nn.ModuleList(layers)\n",
    "        self.time_emb_mlp = nn.Sequential(SinusoidalPositionEmbeddingLayer(self.time_emb_dim),\n",
    "                                            nn.Linear(self.time_emb_dim, self.time_emb_dim),\n",
    "                                            nn.ReLU())\n",
    "        self.cond_emb_mlp = nn.Sequential(MNISTConditionalEmbeddingLayer(self.cond_emb_dim),\n",
    "                                            nn.Linear(self.cond_emb_dim, self.cond_emb_dim),\n",
    "                                            nn.ReLU())\n",
    "\n",
    "    def apply_padding(self, x):\n",
    "        pad_width = self.divisor - x.shape[3] % self.divisor\n",
    "        pad_height = self.divisor - x.shape[2] % self.divisor\n",
    "        \n",
    "        pad_left = int(pad_width / 2)\n",
    "        pad_right = pad_width - pad_left\n",
    "        \n",
    "        pad_up = int(pad_height / 2)\n",
    "        pad_down = pad_height - pad_up\n",
    "        padding = (pad_left, pad_right, pad_up, pad_down)\n",
    "        x = torch.nn.functional.pad(x, padding, mode='constant', value=0)\n",
    "        return x, padding\n",
    "        \n",
    "    def undo_padding(self, x, padding):\n",
    "        (pad_left, pad_right, pad_up, pad_down) = padding\n",
    "        return x[:, :, pad_left:-pad_right, pad_up:-pad_down]\n",
    "    \n",
    "    def forward(self, x, t, c):\n",
    "        skip_values = []\n",
    "        t = self.time_emb_mlp(t)\n",
    "        x, padding = self.apply_padding(x)\n",
    "\n",
    "        if c is not None:\n",
    "            c = self.cond_emb_mlp(c)\n",
    "        for layer in self.module_list:\n",
    "            if isinstance(layer, EncoderBlock):\n",
    "                x, skip_value = layer(x, t, c)\n",
    "                skip_values.append(skip_value)\n",
    "            elif isinstance(layer, DecoderBlock):\n",
    "                skip_value = skip_values.pop()\n",
    "                x = layer(x, t, skip_value, c)\n",
    "            elif isinstance(layer, UnetConv2dBlock):\n",
    "                x = layer(x, t, c)\n",
    "            else:\n",
    "                x = layer(x)\n",
    "                \n",
    "        x = self.undo_padding(x, padding)\n",
    "        assert len(skip_values) == 0\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "161e8788",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_loss(predicted_noise, expected_noise):\n",
    "    mse_loss = nn.MSELoss().to(device)\n",
    "    loss = mse_loss(predicted_noise, expected_noise)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effa52d7",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2f724a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 4\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)\n",
    "amount_batches_train = round(np.ceil(train_dataset.data.shape[0] / batch_size))\n",
    "amount_batches_test = round(np.ceil(test_dataset.data.shape[0] / batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3bcd821e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NoisePredictionUnet([1, 2, 4, 8, 16, 32]).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a2b3f19",
   "metadata": {},
   "source": [
    "## Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d0115de",
   "metadata": {},
   "outputs": [],
   "source": [
    "def denoising_process(images, beta, noise_predictor, labels=None, simple_variance=True):\n",
    "    \"sample image\"\n",
    "    \n",
    "    images_size = images.shape\n",
    "    timesteps = beta.shape[0] - 1\n",
    "    alpha = 1 - beta\n",
    "    alpha_cum = torch.cumprod(alpha, dim=0).to(device)\n",
    "    if simple_variance:\n",
    "        variances = beta\n",
    "    else:\n",
    "        alpha_cum_t_minus_1 = torch.cat([torch.Tensor([0]).to(device), alpha_cum[:-1]], axis=0).to(device)\n",
    "        variances = (1-alpha_cum_t_minus_1)/(1-alpha_cum) \n",
    "        variances = variances * beta\n",
    "    \n",
    "    variances = variances.to(device)\n",
    "    x_t = images.to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for timestep in range(timesteps, 0, -1):\n",
    "            predicted_noise = noise_predictor(x_t, torch.ones(x_t.shape[0]).to(device) * timestep, labels)\n",
    "            z = torch.normal(torch.zeros(images_size), torch.ones(images_size))\n",
    "\n",
    "            if timestep == 1:\n",
    "                z = torch.zeros(images_size)\n",
    "\n",
    "            z = z.to(device)\n",
    "            mean = (x_t - (1 - alpha[timestep]) / torch.sqrt(1 - alpha_cum[timestep]) * predicted_noise) \\\n",
    "                    / torch.sqrt(alpha[timestep])\n",
    "            std = torch.sqrt(variances[timestep])\n",
    "            x_t = (std * z + mean).to(device)\n",
    "\n",
    "    # Normalizes to [0, 1] instead of [-1, 1].\n",
    "    x_t = (x_t + 1) / 2\n",
    "    return x_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "734cbb7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_training_data_single_timestep(images, variances):\n",
    "    # Normalizes to [-1, 1] instead of [0, 1].\n",
    "    images = images * 2 - 1\n",
    "    batch_size = images.shape[0]\n",
    "    \n",
    "    sampled_timestep = np.random.randint(1, variances.shape[0] + 1)\n",
    "    selected_variances = variances[:sampled_timestep]\n",
    "    alpha_cum = torch.prod(1 - selected_variances, dim=0)\n",
    "\n",
    "    expected_noise = torch.randn(images.shape).to(device)\n",
    "    noisy_images = expected_noise * (1 - alpha_cum) + torch.sqrt(alpha_cum) * images\n",
    "        \n",
    "    timesteps = torch.ones(batch_size).int().to(device) * sampled_timestep\n",
    "    return noisy_images, timesteps, expected_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4b7a8706",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_loss():\n",
    "    mean_test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        batch_test_losses = []\n",
    "        for batch_index, test_data_batch in enumerate(test_loader):\n",
    "            images, labels = test_data_batch\n",
    "            images = images.to(device)\n",
    "            noisy_images, timesteps, expected_noise = get_training_data_single_timestep(images, variances)\n",
    "            predicted_noise = model(noisy_images, timesteps, labels)\n",
    "            loss = get_loss(predicted_noise, expected_noise)\n",
    "            batch_test_losses.append(loss.item())\n",
    "        mean_test_loss = np.mean(batch_test_losses)\n",
    "        print(f\"Average Test Loss: {mean_test_loss}\")\n",
    "        print(\"\")\n",
    "    return mean_test_loss\n",
    "\n",
    "def evaluate_model_results():\n",
    "    data_batch, labels = next(iter(test_loader))\n",
    "\n",
    "    if data_batch.shape[0] > 9:\n",
    "        data_batch = data_batch[:9]\n",
    "\n",
    "    noisy_images = add_noise(data_batch.to(device), variances)\n",
    "    labels = labels.to(device)\n",
    "    denoised_images = denoising_process(noisy_images, variances, model, labels).cpu().permute(0, 2, 3, 1).numpy()\n",
    "\n",
    "    for i, image in enumerate(denoised_images):\n",
    "        plt.subplot(1, 9, i + 1)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.imshow(image, cmap=\"gray\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "576954d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_batch, _ = next(iter(test_loader))\n",
    "if data_batch.shape[0] > 9:\n",
    "    data_batch = data_batch[:9]\n",
    "\n",
    "amount_images = data_batch.shape[0]\n",
    "\n",
    "sample_images = data_batch.permute(0, 2, 3, 1).numpy()\n",
    "noisy_images = add_noise(data_batch.to(device), variances).cpu().permute(0, 2, 3, 1).numpy()\n",
    "\n",
    "for i, image in enumerate(sample_images):\n",
    "    plt.subplot(2, amount_images, i + 1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(image, cmap=\"gray\")\n",
    "\n",
    "    plt.subplot(2, amount_images, i + amount_images + 1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    noisy_image = noisy_images[i]\n",
    "    plt.imshow(noisy_image, cmap=\"gray\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "11db5fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                          \n",
      "Epoch 1:\n",
      "Average Training Loss: 0.4835505355971555\n",
      "Average Test Loss: 0.17746522543877363\n",
      "\n",
      "                                                                                          \n",
      "Epoch 2:\n",
      "Average Training Loss: 0.1524205860859404\n",
      "Average Test Loss: 0.13911320362240076\n",
      "\n",
      "                                                                                          \n",
      "Epoch 3:\n",
      "Average Training Loss: 0.12983571442520866\n",
      "Average Test Loss: 0.120800888780877\n",
      "\n",
      "                                                                                          \n",
      "Epoch 4:\n",
      "Average Training Loss: 0.1209849293064947\n",
      "Average Test Loss: 0.11532273123301566\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                          \n",
      "Epoch 5:\n",
      "Average Training Loss: 0.11713211464143047\n",
      "Average Test Loss: 0.11163419397175312\n",
      "\n",
      "                                                                                          \n",
      "Epoch 6:\n",
      "Average Training Loss: 0.10836655665996174\n",
      "Average Test Loss: 0.10606947212703526\n",
      "\n",
      "                                                                                          \n",
      "Epoch 7:\n",
      "Average Training Loss: 0.10492584940257171\n",
      "Average Test Loss: 0.10222818624600769\n",
      "\n",
      "                                                                                          \n",
      "Epoch 8:\n",
      "Average Training Loss: 0.10069341343489165\n",
      "Average Test Loss: 0.10831143072228878\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                          \n",
      "Epoch 9:\n",
      "Average Training Loss: 0.09796633268970376\n",
      "Average Test Loss: 0.09682586890310049\n",
      "\n",
      "                                                                                          \n",
      "Epoch 10:\n",
      "Average Training Loss: 0.09600175862324735\n",
      "Average Test Loss: 0.092973515448533\n",
      "\n",
      "                                                                                          \n",
      "Epoch 11:\n",
      "Average Training Loss: 0.09489511197206253\n",
      "Average Test Loss: 0.0943596962844953\n",
      "\n",
      "                                                                                          \n",
      "Epoch 12:\n",
      "Average Training Loss: 0.09345321195044866\n",
      "Average Test Loss: 0.09366697954442352\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                          \n",
      "Epoch 13:\n",
      "Average Training Loss: 0.09263027748788397\n",
      "Average Test Loss: 0.09073378318753093\n",
      "\n",
      "                                                                                          \n",
      "Epoch 14:\n",
      "Average Training Loss: 0.0923219343268002\n",
      "Average Test Loss: 0.08765198474302888\n",
      "\n",
      "                                                                                          \n",
      "Epoch 15:\n",
      "Average Training Loss: 0.09361497754979257\n",
      "Average Test Loss: 0.09251263303216546\n",
      "\n",
      "                                                                                          \n",
      "Epoch 16:\n",
      "Average Training Loss: 0.08850275612560411\n",
      "Average Test Loss: 0.09000065706260503\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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FYhGyLLP14XK5MDIygkAgwLgIhV9kWcbIyAg2NjawurrK8dFSqYR2uz3Q1KVnnoRHtRAg4dzv9yGKIrxeL2MV1WoVnU4HFosFMzMz2NzcRKPRYAsyFArB4XAgEokwVtNqtTiW3Gg0GMAk01qtSOj5pImPw9ephJEEQYDf78fIyAgSiQS2trbQbrfhcDjgdrvx+7//+zzISqWCRqOBYrGITqeDZrMJQRAwPT0NSZJgtVoxPj4Oj8eDTqeDGzduoFgswmaz8WZ+9913MTo6ivPnzyOdTmN9fZ3Nn1wuh06nA6vVikQigWq1ys88CalR4l6vh2aziXw+z4vs9/vh9Xrhdrvhdrtx6dIlWK1WFItFNJtN7O3tsc9eq9UQCoUQDAZx4cIFAEAgEOBDPT8/z5J7bW0Ne3t7HE7TSwA4SeqknvTXIuyDND/554Sa12o1NJtNVCoVPHz4EG63G2+88Qbi8TiazSY8Hg88Hg9MJhMKhQL+9m//ltNMi8Ui8vm8buWOFmQ7CZ/0nUxoNX/Ej8ViQbfbRblcxsbGBq/94uIiSqUSfvKTn6BQKEAURczMzECSJORyOQa2lpaWOHyUTqexu7uLdrv9TC68Xt67do6fR6emgbvdLur1OtLpNICn6ClJX7fbzb5HIBDguK3J9PTxNpsNNpuN/WiDwYBWq8VgjyzL6HQ6bFJ6vV74fD4GSAwGA8rlMmRZ5g1F9yAg5LgAlp5vos626Xa7sFgssNlssNvtGB0dhc1mQzAY5LH4fD54vV5YrVZYLBY2ISlbi0xQAOwbWywWTE9Po1Kp4MmTJywAnU4nS/jTCBmpiUJ2BBpqD8xBGpDi1+pQEllHRqMRlUqFM7VIa9Pf6DP0jEG+/Wlo3kF8U1ZYJBLB2NgYbDYb7z0CQMPhMGtQitX7/X6Ew2GYzWY+hL1eD+12G7lcDqVSCbVabV/EgkA/wmjIfSD//hfiA+/s7LDEITSSBuPxeOD1ehGPx+H3+9HpdBihM5vNcLlcsNvtKBaLaLfbyGQyaLVaaLVaCIVCcLlcWFtb4xQ2QRDQ7Xaxvr4Og8GASCTClSLRaJT9l2azyQdNnRxxVKRWqxGazSYSiQSbg8FgELFYDH6/H+fPn0cgEEAqlUKv10MsFsPly5exsLCAXC6HVquFQCCAcrmMarXK4M/169cxOjqK2dlZ9om/973vwWazYW1tDZIkwWg0Ynp6GqVSCbu7u5Ak6Rm+jkP0Ocqm6nQ6yOVy+/6n1YK02brdLoxGI4xGI294WZYRCARw4cIFXL58GR6Ph/1Gt9vNYKLT6USlUsHu7i4AwOv17jvM9Bw1HVVoaV0PPRDQYDDAbrdjbGwMb7zxBl555RXOqqJQVqPRYKSassoURcHrr7+OmZkZ2Gw2dDodtNttdoseP36MTqfDzyU3stlsolwu4+LFi/D5fLhz5w4rA7r+sHRqGrjT6ezzNwVBQKPRQDKZhM/nQ7PZxMbGBqdCGo1GuFwuTExMoFQqIZFIYGVlBaIoMnBFB5wOXCgUQjQaRa/XQ6FQQK1WQzgcxtjYGOLxOKrVKnZ2dlCv11Eul1Gv13lijotcamOt6sT0fr/PbgD5Sh6PB4FAgLN47t27B4PBgG63y1ZIv9/H8vIybt68ic8++4zBm2AwyNaIxWJBp9PB9PQ0vv/976NQKKDb7cLtdiOVSqHf7yOVSu0Dt44LXgGAz+djlNhut2NqaornUGuFaH+nEBdZQH6/H8FgEIFAgFFYr9eLvb09rKys4OHDh8jn89jc3ORYcq1WQ6vVOrWkDTV/aoGtvj+tLeEVDocDtVoNm5ubuHXrFiqVCrtflKdApjCZ/uQ60byQL62O9QLgkOi3vvUtJJNJ3Lt3jzEOCinSPv2FHGAyn9QoG/muV69ehcFgwNraGm9Qiu+aTCaUSiU8fvwYN27cQLlcRqvVgsVi2Zd3Oj09Da/Xi9nZWYiiCFEUkc1m4XQ64XA4EA6HYbFYsLa2xge42Wyi3W6fmqmp3rjqumAy0Y1GI+x2O8eoi8UilpeXGSSJRqOwWCwQBAGPHj3CBx98wKZbPB5Hp9OB2WyG1WqF2WxGs9lEPB7HxMQEtra2IIoi508Xi0UUi8UT+/ZEXq8Xfr8ftVoNFosF4XCYk2+0/pmeW0GH2Gg0sotDglsQBAQCAWxsbODmzZu4desWYyA+nw/hcJiTeAaR1oc9Dml9e/pyOBxc/9toNLCzs4Pr16+jXC7vA+nUn7VYLJxZZbfbIUkSzGYzZ29RxIEqzuLxOKanp/H2229jZWVl3/ppc/qPQqd2gNULSymDFy5cwOjoKLa3t+FyuXD16lXcu3cPS0tLvJHff/99lmpGo5FNUQqfENr867/+64jFYhgZGcGnn36KarWKeDwOAEgkEmySUprb/fv3IUkSa86THGCt6UjAHfntJFA6nQ6q1SpMJhPW19fx5MkTLC8vY21tjRNOCCTp9Xqc3GE2mzE6Ogqn04l2u83IeyaT4fDDv/zLv2BtbY0rXygspTfW44Ba8/PzWFhYQCQSQaFQwO3bt/dpAzVyq7Vm1Nf0ej3k83kOJ83Pz8NkMmF1dRW9Xg8LCwt48OABgKfhG0mSkM1m0el0OAIhSRJrbr1nHIe0IJh6PzidTsRiMVy9ehVWqxWCIOCtt95CtVqFKIrY2dlBKpWC2WzmElFJkpBKpfDJJ59gYmICY2NjmJiYwMLCAiYnJzmkNjY2hgsXLqBYLHLW2tzcHCwWC1ZXV1EoFJDP51GpVJDL5VCv148U8jz1VEra4ARGAUC1WmXHvtlsotlsolarsQlKMVa3282ZMCS9ut0uzGbzvgNNIQm73c5gFeXMtttttFot1Ov1U+UJ2O87q4sZQqEQAoEAHA4HRFFEvV7H+vo6dnZ2IEnSPpCCXIdgMMhmpsVigc/ngyRJ2Nzc5ENOLofZbMb29jaSySTn0ZK01ku4OA7RfCuKwhgFYQ7aeOWg5wiCwL4kAZhkGhLYSNlOajek3W5z5xJac/IZyf04DdKLkwuCgFgshomJCcYXarUa7HY7er0eA1CUd09joj27u7sLWZYRiUTYL7ZarfB4PDhz5gxisRhGR0cZgKVy0tnZWTQaDcaKAHBxzs/1AGuBAbVpsrq6itXVVUYjScJQzJAYps+Xy2UGAwj4MpvNaDQa+K//+i/Mzs5iYWEB4XAY0WiUN0a73Ybb7YbRaES1Wt3nF55mfFRrfhE49nu/93s8tk8++QRffvkl7t27x2ExSsqgjB+/34+xsTFMTk7i2rVrMJvNePDgAe7evYt//dd/5fmyWq1cyVSr1dhkVydQUBxTO/9H5a1QKCCRSGBzcxPlchnJZJKRdZpPPRxBLchsNhumpqbw53/+5xgbG0M4HMb777+PRqOBxcVF5HK5Z0JFetqcFACtKcWX6f/HjeOr540EhsViwW/91m9hbm4OZ86cwX/8x39geXmZUfL79+9zgQWZ06VSidHn5eVlZDIZvPbaa6hUKlhdXYUoihAEAe+99x5EUWRAtd1u4969e1hYWMC1a9cgCAJXplmtVnQ6HY4bH5ZODYVWb5p+v8+oHYU/1En52g4L9J0Kn8+dO4discgaBwCDQJStRH5ILBbD1NQUtre3USqVEAqFUCwWAZzO4VXfR30/yv11Op0IBoNwu93sB7lcLr5GG/ujahYy77/44gu0220kEglks1koisIgVrvdRjQaxcTEBHZ2dlCtVhnAUpcfatfiKEThHrfbDZfLBZfLxfm+ZNXQodHzIck6IkEqiiLnAdRqtX0htGw2ixs3bqBare67l3p+er0ep91SuOWkpF4/j8fDYcCxsTEGQP1+P4fwXC4XNjc32b2hRKNKpcJCuN1uo1QqYXR0FGNjY/D7/Sysl5aW0O128c4770BRFDidTj6gkiRhZ2cHivK06COfz2NtbQ3lchnZbPbIGYOndoBpgigO1mg02EdUbwKqUNLzRywWC7xeL86ePYvbt2/va8xdq9XYjBFFkWNwVDj/4MEDFAoFTtvTi/udJJaorWihhHZKDXQ4HJwy53a7WfBIksTmFvmQtVqNE9pv3ryJYrGIUqnEtc4+n483fDwex7Vr1+DxeLC3t4d8Pv8MoKRNtDgSivn//DL68nq9rHFJAGm1pfpAkAlILlK1WsXa2hojsfF4nGP+mUwGt2/f3lc2ql2Xfr8Pu90On8+HYrF4agUosixzXjkBj5OTkzh//jwikQg8Hg8EQYDL5YLP50OlUuEDTIKN3Aq73c4hyitXruDMmTPw+Xxs7j98+BCiKOLVV1/lz1IaZbVaxe7uLjY3N5HL5VCtVrG+vo5Go7GvRvjQ63cakzMosE9F7NQ/iiQ6xQzVC2c0GvHWW2/B4XDg888/h6IoWFxchMvlQqfTwfr6OjqdDlKpFB+ARCKB1dVV/PjHP0alUkG73d6X3aU3puOSojxNhJ+enobb7UY4HMbU1BRGRka4nDASieDDDz/EjRs3cPbsWTSbTSwtLbEPFYvFODxE3Sui0ShGRkYwOzuLvb09bG9v4y/+4i8wMTGB+/fvI5FI4JNPPuFEedp87XabtYReh8SjkCzLXNdKIZVBa6r+TogrWQNUlfPpp58yqEMgXDKZxM7ODn+OhD3lSFNiCsVZSTgQlqI2gY+6jmoTulgsIhgM4u2338bVq1dx5swZeDweVhJTU1Ow2+34/PPPGU+RJAkWiwWXL1+G2+2GzWZjEHFxcZHj/pSoc/78eU4pHRsb48zCVCqFW7duIZ/PI51O8z1EUWQcQA8kPIi+kXpgcvIJgKGkdUpuoAWkQ0amMWXqkNaiTC3g65I10lzUprVcLmNvb4+1mzaschKtq3cP2qjkP1F+tizLyOVyKBaLKJfLiMViMJvNCIfD7P+Qeeb1ehnoIZNpamqK65knJiY4DktthtxuNyOkNpsNDoeDE15O0syONK0oijAajYywaq8ZRGqtTOtVqVTg8/k4Bt/v95FIJLh5gXZO1X/r9/vscxqNRrRaLXaJTsIjPYuUiLpbKP2dKsOazSaDbgRcmc1mzM3NIRQKQVEUXutgMAiXy8WfVRSFk24ov5sy7mw2G+r1Ohd6UFulk8TxT/0AE4pM/mkkEsH3v/99JJNJ/M3f/A1PJG16ChMFAgE8ePAANpuNQ09bW1uc+F4oFBAOh+Hz+TA3N8exuydPnuDu3bvwer0QBAHNZvPACTmqiUnXE1iztbXF456YmMDk5CT+7M/+DKVSCX/1V3/Fea83b95ELBbDd7/7XXzxxRe4f/8++v0+YrEY/vRP/5RdCZLExWIRkUgE9Xod9Xqds7ZGRkbw5ptvIhaLQRRFPHr0CKFQCBcuXEC5XIbJZGLfGTg6iEUlfP1+n8eil0xBIBYJXW3nTPIvBUGAJElwuVxcxFAul7kcT2sZUVSC7tFutxGJRDA/P4+RkRG2tNSllCcJCSqKgnw+j+vXryMUCvGearfb2Nraws2bN/Hw4UPU63Uu0KGquR/+8IcIhUJYX1/Hz372M2xubiIajcLlcuHGjRtYW1vj0BCVzhJoR7ntKysrKJfLnCGoKMq+eSMFcVjf/xsJI1FQmpDT27dvo91uY3Z2luO/2WyWc2bPnTuHmZkZ9Pt9WCwWjI6OwuPxsMljMBiwsLDA5WiBQAAGw9MywUqlAoPB8IzWAPQT84+zCeg+ZDkQf9R8YHNzk80iKsIHnnbcXF1dRaVSYd89l8thdXUVExMTiMfjcDgcbCru7OxwDJU0YyqVwvLyMubm5mAymXD27FkEAgGMjY2xMNFLtDjKegHgWLxe83n13JFGIt9YC2KRH1gqlbCzs8MFDoQJDBIOdH+r1cppt2SNERp/GoCWOrb96NEjSJKEQqEARXlaw0v152rAtVKpoNfrccjIYrFgfHwckiQhmUyi1+txEY3D4YCiKKjX67h//z6azSb3+yKgivx6NV50XF//GzvAVM1Rq9Xw05/+FIFAAGfPnoXT6YTZbMadO3c4ofvKlSt45ZVX+ABTZRExDwCXLl3iA+P3+zk1k9DuQaakdnMfx5wm7avuyUwoOAXkc7kcstksg1U0JtK8VIWUTqextLQEo9GIcDjMJjiZcpQAQM9LJpP4/PPP0el0EIlEOH82FApxzJT4JB6Po6G0HUvUh4p+V7tFdJisVisAMGqtDrUQEk2loAD2gWJE9Bwya71eL6LRKAqFAjqdDocW9T57FNJaUw8ePMCTJ09Qr9dZwNIzKUnGYDBwCeHa2hp6vR5GRkYwPj4Ov9+PDz74gK0lWZa5pr1er2NpaQmpVAqbm5tIJpP7TGrq60apl8e1Kk4dhVZPMKU5xmIxBAIBRCIRLqSmXlWlUomzUajzPfW0SiaT+OEPf4hgMMhN0JxOJx+EZrMJg+Fpj2nya6h7RTabfaZX1HFI7fvS5qTfK5UKH9hKpbLPzKO86MXFRa4++tnPfsadMxcWFjA/P4/r168jn8/zIr7xxhtYXV1FKpXi3NmLFy9iZmYG3W4X//AP/8AWCOUPaxf/NMJn2mbkapQYeJq84vf7sbi4CJ/PB4/Hg2g0yq2EHj9+jEePHnFRCvGnjhur55V+FgQBDx48wOrqKgyGp0Xv1EydDvJJfH6yjigyQo0I6K0ZlAVGY6F+VjabDffv38fDhw+xtbWFyclJTExMIJVKcank5OQkpqam4Ha7sbOzg5WVFXYn4/E4JEnCxsYGA1bkj2tLKI8URTj2TBxA2gUBvkYsKY7r8/lgt9s5ha5er8PtdkNRFFQqFfT7fTidTszMzCAUCmF3d5fri/v9PndCIDOOMq+8Xu8ztZ4njZWqeVJrJsqyokwwdWyWFo5QWIfDgWAwCKfTyR02KVTRbDa51xd9hkxZahQAPDVz8/k8ut0unE4nf047zpP6iAf50cSjzWaDz+fD5OQkt0ey2+0AAL/fj1Qqtc/1UK+J+jnaZ1O2kiiKDNpRnjkh0kflT48X0sbk6pH5q+5CabFYGKRyOp3clO/hw4e8b8vlMlsJRqMRPp8P8Xic3T9SSuowpPr5WmH2cw8j6ZlatIGbzSZarRanjK2srGB0dBTRaBRvvfUWIpEIJiYmsLKygs3NTZw5cwayLGNpaQmjo6NYXFyE0+nkBAmSyCaTCSMjI/iTP/kTVCoV5PN5fPrpp1xfSkUQhExr/a6jTJRaEGgnm9qjhsPhfc3XyJdrtVr4n//5Hz7Ev/u7v8um140bN/CP//iPWFxcxMjICM6dO4dsNou9vT28/PLLMBgM+Oijj7C8vIw7d+6gVqtxKioAfPHFF7qVK2qheRJSv0lBu+nIJKaGBNRN5J//+Z/RaDQwOzuLRCLBmXfqBvZqIacFG8n0puerTfp+v6/b4O6opJ4vKl3c3t7meVM3og+FQvjt3/5thEIhuN1u/PSnP+X68t3dXdTrdezs7KDX6yEQCAB4anWeP38ely5dwquvvoqNjQ3cv38fW1tbaLVaDFgZDF/XT59kvb6RODAtMk02mQyUwCHLMra2tpDP57G9vY1CoYBKpYKPP/4YAFibOhwO9kVKpRK3yPnqq68giiJGR0chiiJLUIoTq01ro9GITCazz389DgpNvGr/3u12USgUuJNmo9FAu93mhSKQol6v49GjR8hkMlhZWUGpVEKlUsH4+DhX71AMcG9vD/V6HalUCp1OB+FwGJVKhf0s7cE6DaJN1e/3YbPZ4Pf7+fA5nU70ej3GIxTlaRKNzWZDKpVCvV5ni0hRFCwtLSGbzaLRaDwDsp2ETuMeatIKJlIOlKs8NTWF0dFRztT6t3/7N6TTaQ5xUUjTbrdjbm4Oo6OjcLvdCIVC7MtTOiqBZZTzTtiAlq+jrumpm9A0ALVpR+EH0oyyLOPBgwdot9uo1+tsoty7dw+CIDBgRZ0pqF7W5/Oh3+/jv//7v1EoFPD222/zZNAbGqiTo9FoxMjICGw2G3K53LFKtQD9Khi1+UNj83q9mJubY2ScNrggCNje3ka5XMbNmzchyzLq9TqCwSDC4TCKxSJLeIo3fvTRR9jd3cXW1hbnF+/s7OzjYdChOI6AUvt7sixzQQK1QiULg1IgDYanTRScTifW19e5lVA8Hocsy3j//fd5/QeNUW8MgzbvaQkA7b20a6puGLG4uIiZmRmMj49jdnYWwWCQ14Sq4NRN7K9evcrhUGryrigKotEo5ufnsbOzA1mWEQwGuUEA8X0SDONUTGj1QwctmCRJyGQyKJfLnI1EaDWFUaikMJFIwOVyIRqNYmlpCZVKBXfv3sX09DTOnz+Pb33rW5x15fP5OAkim82iVqsxoFIoFJDJZHh8J/UN6TttNrWwIhSX+iH/4Ac/gN1ux/b2Nr766ivOb6bURYohr6ysIJlMcizb4/HgJz/5CSfMEwJKLYP0SG3uDlqDQaReN3I3yLQj5Jh8UwB45ZVXcO7cObz22mucsZVMJrG3t4dHjx4hm83u6xJCRQOUUUfhGT1MQW9c2p9PsoYHCT3gayuELERyx8ii+83f/E2Uy2WEQiE0m000Gg2Ew2H4/X5cuHCB00drtRrK5TLW19ext7eHra0tLC8vo1qtMso9yLI7Kn0jYSTg2UH1+30OZtMkERPUgsfj8XBTbCp4UCPVhEaHw2EYDE8bBlATcXoTA/Vo9nq9yGQyupLuNA6zGpSghHfKeyVEkpLeR0dHuVCe8r3j8TgikQi++OIL5HI5bGxsIBQKIRQKsb9EnT2KxeIz2ldLBwnQw5D6EFFPrGAwyG9SLJfLSKfTGBsbw+XLlzE9PQ2r1Yput8vVQvR6FHXLVrK66H1W2rGrc8QP0kSnqYX1iEJkVKZKTRVoPmRZxrlz59But+H3+1Eul1GpVDA2Ngav14tYLMaRAUpOoWSktbU1VCoVfoPh8/j4uaLQWumpt8nUoRf1z7R4sVgM4XCYe0WfP3+eNUIsFoPT6eQspkAgAFmW4fF48J3vfIf9TIfDgd3dXXzwwQf8EjHqyqGtZz2OhtL+rigK+0tzc3OYmprCr/3ar2FychKhUAi3bt1Ct9uF1+vF4uIid6kkNDWRSODBgwdIp9PsQ+7s7ODhw4cAniK5r732Gra3t/lNBepkFbUPrC0yOCoR4qquNc5kMvjLv/xLvPfee9jc3MTdu3fx13/917h06RLefPNNBuk2Nze548Zrr72GbDaLzz77jLXw1NQUAGBjYwMAuK0QfVFkQd1OVm/OT0NbDVp3s9nM7WGpOul73/sePB4Pv1SAGi4Qr1tbW9je3uYIBGWwmc1mJJNJFAoFPHr0iFMutSj8QWP8hYaRDtIOaqL42vj4OIMF9EY7k8nEPXUzmQyq1SoymQxvtEAgwH2xqNSLTBrSHuQTn9Z7WNW8qCdYDdhRPXKr1UI6nWYAZ2RkhDtNUGgoGo3CbDZjfX0dADiGTX4/vbKS3Ap1aE7PhzwJkTCiohOn08mZUNRhJBqN4u2338b09DR3bOx2uxAEAfl8Hqurq4hGo4hGo3j33Xd5I7rdbu7oWC6XUSqVUCwW9+UBqxu/qfk5qVWhR1rgCvjaBSFTmXKk6S0bZGWsrKxw7XkikUAikeA9m06nWQgmk0kkk0lkMhkGWEmL67liJ1nPUw8jEelJTfXvFGO7du0azp49i+npadjtdrRaLTx+/Bi1Wg2FQgHr6+soFotIJBKcuPDSSy/BZrNhdXUVkUgEo6OjnAlFC0G+xkmC/jRW7SSr0Wzy79vtNrrdLjY3N7kmllqwUMuVZDLJ2UrT09O4ePEi7t27x0UY1JeK0PpyuYxGozEwjqk3r8cBsQBwdpDJZEI4HMaVK1fQ6/WwtrYGj8eDWCyGP/zDP0QoFILRaOSwlslkwvb2Nm7cuIHvfve7mJ2dxeuvv85CJ5/Po9lsolQqYXNzE2tra3j48CG3QKK4LwGdByGzx+FvEBHiToKXXvlDfcnMZjPH77vdLnZ2dvCf//mfKBaLnH23sbGBN998Ex6PB7VaDWfOnMG5c+ewurqKtbU1pFKpZw4u8UDYgHb9jkqnYkKrB0GklTQ08eQPXb16FbFYjH3Fer2OxcVFSJKEJ0+ecBoeFSnQW+02NzehKArC4TBefvll7O7u4smTJ/jyyy+RzWaRy+U4r3fQQh/Fn9KGbNS80OIIgoB0Oo1/+qd/4kXJZDLc7D6fz/PrWGq1Gj7++GN4PB643W7Og93d3eW+S9euXYPP50O9XmdfTFs8oM6U0gJCx4krUuI+vZxNURR89tlnqNfriMfjnGxPJZTUNYQSVC5evIjx8XHY7Xbcvn0bDocDTqcTi4uLMBqNWF5ehsFg4EZ3yWSS62vVVoyaRy1v2v8dlgatH81Tu93mxJsnT55waCwajXIGnCRJ3HyeIh3hcJg7zNjtdm6lRM0eBgGLFGfWrp+W58PQN/p+4EGDF4SvX0pGuaMk+RRFYVOOkhZIIpKZvLe3h263i+npaX5p2d7eHld56AEip0F6oSTKMqK2o4Q2k99TqVS4iwa9M2hra4s3OIXA6vU6++rNZpOzgrSIpXosB5nVRyVKNrHb7ZwZR2+aL5fLAMBuCYX+qPCAWup2Oh3OJ6YKHgIo6ZB4vV52E9RzOEjjqum01lRrHapj1eVyGcViEW63G7lcjl2FTqfDsfu9vT1+f5coitxFpFarcY3v8yy/03IPDMox70DaUbuRiAYhihRvDAaDmJ+fxx//8R9zEgZtCo/Hg+vXr+P69evclzeXy+3TeIQaEuhCm0Bv4tQSmH5XFAXVapXTFAfxR+l8xCeBEerQDaGuJNXVm4LMYUEQMDs7y6+kJGFEfrF6E9Mz1e1G6f/a55IpqOWNuj88jz+ay8nJSe7dTBlQZMmotf3Fixc5e46SaGj8lKs+OTmJX/qlX8JLL70EQRBQKpXwox/9iPt637lzB9lsFslk8sDKJ7UC0P4sy/Kh+KO4rnYfqjWxeg/Tc+hzatCNMrfUqZ3U9nhhYQGlUoljvOq3hmjBW+1aaZ9Pa34Qf0SnqoHV5vKg/xHj9EWACPUYohS2bDbLHQLb7fa+tjzqidTzH7R/OwlyqR673nNoPERqaa4oyr7Wr+l0GiaTiatd6PBq/Vkq8tdq3tMMhanvCYAtAELt1S6COtm+UChwyiOlyRLRmlitVjx+/BjdbpebBaRSKeaZ3it00PgHmZ/HIfX8agsH1NeoieLh6nf5audefUiz2Sx3JKV9edA50D5z0B57Hn1jceBBRB34vF4vm1PUBYFeReH1erGysoLd3V3dtyoM0u7q30/bhFb7nvS7VoLqaQz1+Oh1Jepr1G9N1D7reeCG2vxUWwVHJYPBsK8RoPp5WuCIEFa959CG3t7eRiaTwa1btziNFniaDpvJZBin0D5HzbN6jrT8HZVHvXuqtbLe/fQAQj3MQVGegpiJRILXQ4tXaO+jPcQnMae/UR9YS4rydV8sitNmMpl9vhWFWQihHDS5WrNY/Qz1NerPnHTsehOvt9kHjUnvQB4WJT9ocU8K9Oht8IOEht7vWhOQUHm1yUuF8s+r69Wu2yC0/bCkJygO4kl9f21Rx/OUh1YwDDLR9T6vt6eeR8c+wIfdLNrNQFK60+nse7EV8CyaetB9Bj1bT+qpf9ZqysPwp/cZreY9yH04aDxa3+h5G+x5Qumwpuegaw7DBz2bDqbeumj5Ir9ObZLqjWfQs/TGfJz1O0jY6fGuxjcOIzTU91DPgfZgDlpTPR4OomMfYHqL+mEeNEhqH3Td8w7n8+gwz6AXOusR8Tdow70IdBj+ThonB04PHT4qHZa/k/B4Gi9bO47ZDxzMH9GxUWhZlvf1bX6RSFEUiKLI8U09GvL3f5eG/H1Nxz7AQxrSkH7xdPLWDUMa0pB+YTQ8wEMa0gtMwwM8pCG9wDQ8wEMa0gtMwwM8pCG9wDQ8wEMa0gtMwwM8pCG9wDQ8wEMa0gtMwwM8pCG9wDQ8wEMa0gtMwwM8pCG9wDQ8wEMa0gtM/wtMYGWPgigcmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                          \n",
      "Epoch 17:\n",
      "Average Training Loss: 0.08917840000391007\n",
      "Average Test Loss: 0.08868853800762445\n",
      "\n",
      "                                                                                          \n",
      "Epoch 18:\n",
      "Average Training Loss: 0.08932638048022054\n",
      "Average Test Loss: 0.0928728468183428\n",
      "\n",
      "                                                                                          \n",
      "Epoch 19:\n",
      "Average Training Loss: 0.08729605610095896\n",
      "Average Test Loss: 0.09065250429287552\n",
      "\n",
      "                                                                                          \n",
      "Epoch 20:\n",
      "Average Training Loss: 0.08801267652289632\n",
      "Average Test Loss: 0.08832227495703847\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 20\n",
    "\n",
    "train_losses = []\n",
    "test_losses = []\n",
    "    \n",
    "for epoch_index in range(epochs):\n",
    "    if epoch_index % 4 == 0:\n",
    "        evaluate_model_results()\n",
    "        \n",
    "    batch_train_losses = []\n",
    "    for batch_index, train_data_batch in enumerate(train_loader):\n",
    "        start_time_batch = time.time()\n",
    "\n",
    "        images, labels = train_data_batch\n",
    "        images = images.to(device)\n",
    "        noisy_images, timesteps, expected_noise = get_training_data_single_timestep(images, variances)\n",
    "        predicted_noise = model(noisy_images, timesteps, labels)\n",
    "        optimizer.zero_grad()\n",
    "        loss = get_loss(predicted_noise, expected_noise)\n",
    "        batch_train_losses.append(loss.item())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        del images, noisy_images, timesteps, expected_noise, predicted_noise, loss\n",
    "\n",
    "        if batch_index % 100 == 0:\n",
    "            end_time = time.time()\n",
    "            batch_time_delta = end_time - start_time_batch\n",
    "\n",
    "            estimated_remaining_time = batch_time_delta * (amount_batches_train - (batch_index + 1))\n",
    "            estimated_remaining_time = round(estimated_remaining_time)\n",
    "            time_delta_formatted = datetime.timedelta(seconds=estimated_remaining_time)\n",
    "\n",
    "            print(\"\\r\", end=f\"Batch {batch_index + 1} | {amount_batches_train} ({time_delta_formatted})\")\n",
    "\n",
    "    scheduler.step()\n",
    "\n",
    "    mean_train_loss = np.mean(batch_train_losses)\n",
    "    print(\"\\r                                                                                          \")\n",
    "    print(f\"Epoch {epoch_index + 1}:\")\n",
    "    print(f\"Average Training Loss: {mean_train_loss}\")\n",
    "    train_losses.append(mean_train_loss)\n",
    "\n",
    "    mean_test_loss = get_test_loss()\n",
    "    test_losses.append(mean_test_loss)\n",
    "\n",
    "evaluate_model_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b89a93",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a015c826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fddc733b610>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(range(len(train_losses))) + 0.5, train_losses, linestyle=\"dashed\", label=\"Training\")\n",
    "plt.plot(range(len(test_losses)), test_losses, linestyle=\"dashed\", label=\"Validation\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Mean Loss\")\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "410fd34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_results(image, label=None, amount_columns=10):\n",
    "    images = image.unsqueeze(0).to(device)\n",
    "    amount_variances = variances.shape[0]\n",
    "    selected_timesteps = np.linspace(1, amount_variances, amount_columns, dtype=int)\n",
    "\n",
    "    for i, timestep in enumerate(selected_timesteps):\n",
    "        selected_variances = variances[:timestep]\n",
    "        plt.subplot(2, amount_columns, i + 1)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        noisy_images = add_noise(images, selected_variances).to(device)\n",
    "        noisy_image_to_show = noisy_images.cpu()[0][0]\n",
    "        plt.imshow(noisy_image_to_show, cmap=\"gray\")\n",
    "\n",
    "        plt.subplot(2, amount_columns, i + 1 + amount_columns)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        predicted_noises = model(noisy_images, torch.ones(noisy_images.shape[0]).to(device) * timestep, label)\n",
    "        predicted_noise_to_show = predicted_noises.cpu()[0][0]\n",
    "        plt.imshow(predicted_noise_to_show, cmap=\"gray\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f2a8244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_batch, labels = next(iter(train_loader))\n",
    "image = data_batch[0].to(device)\n",
    "label = labels[0].to(device)\n",
    "with torch.no_grad():\n",
    "    plot_model_results(image, label, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c6d740a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_variances = variances[:250]\n",
    "\n",
    "sample_images, labels = next(iter(test_loader))\n",
    "sample_images = sample_images[:9]\n",
    "labels = labels[:9]\n",
    "\n",
    "plot_images(sample_images.permute(0, 2, 3, 1).numpy())\n",
    "\n",
    "noisy_images = add_noise(sample_images.to(device), selected_variances)\n",
    "plot_images(noisy_images.permute(0, 2, 3, 1).cpu().numpy())\n",
    "\n",
    "denoised_images = denoising_process(noisy_images, selected_variances, model)\n",
    "plot_images(denoised_images.permute(0, 2, 3, 1).cpu().numpy())\n",
    "\n",
    "denoised_images = denoising_process(noisy_images, selected_variances, model, labels)\n",
    "plot_images(denoised_images.permute(0, 2, 3, 1).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "deeb285a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_variances = variances[:500]\n",
    "\n",
    "sample_images, labels = next(iter(test_loader))\n",
    "sample_images = sample_images[:9]\n",
    "labels = labels[:9]\n",
    "\n",
    "plot_images(sample_images.permute(0, 2, 3, 1).numpy())\n",
    "\n",
    "noisy_images = add_noise(sample_images.to(device), selected_variances)\n",
    "plot_images(noisy_images.permute(0, 2, 3, 1).cpu().numpy())\n",
    "\n",
    "denoised_images = denoising_process(noisy_images, selected_variances, model)\n",
    "plot_images(denoised_images.permute(0, 2, 3, 1).cpu().numpy())\n",
    "\n",
    "denoised_images = denoising_process(noisy_images, selected_variances, model, labels)\n",
    "plot_images(denoised_images.permute(0, 2, 3, 1).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c6d72fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_images, labels = next(iter(test_loader))\n",
    "sample_images = sample_images[:9]\n",
    "labels = labels[:9]\n",
    "\n",
    "plot_images(sample_images.permute(0, 2, 3, 1).numpy())\n",
    "\n",
    "noisy_images = add_noise(sample_images.to(device), variances)\n",
    "plot_images(noisy_images.permute(0, 2, 3, 1).cpu().numpy())\n",
    "\n",
    "denoised_images = denoising_process(noisy_images, variances, model)\n",
    "plot_images(denoised_images.permute(0, 2, 3, 1).cpu().numpy())\n",
    "\n",
    "denoised_images = denoising_process(noisy_images, variances, model, labels)\n",
    "plot_images(denoised_images.permute(0, 2, 3, 1).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "488eb655",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IISJTK52jkVhSUmIKCgpMnTp1MvPjPt+NSyra5bTmz58PbX/6059AczjH4sWLQdetWxc0fy0ObeJ2O73m6NGjoY3TEXIaNeamm24CXVhYCJrTZ/L+2D+3PWk7jMWY8ueNYc84lUqZ4uLich57TaKs79auXbvSkIWQsnRJy6mxL8dl5rg/nHjiiaDt/vP5559D2w477ACaQ5vYI2YPke8z9qA5zI+9NDvF4QEHHABtHBbjKqmYTqfNunXr1Hf/3XeNqbyPJum77Mtyfzj66KNBc6laXjPw+OOPg+aSnFdeeWVmm0vXbr/99qDbtWsHeu7cuaC573E/eeWVV0CPGjUK9HHHHQf6mWeeyWzzeeDv6brHy7Z9fVf/jIUQQojIaDAWQgghIqPBWAghhIhMYs/Y9t1CYzXtuC4ub8XeROfOnUE/8MADoD/88EPQXI7rtttuA/3222+D/uyzzzLbHNPMsZm//vor6NNOOw00pzc88sgjQbN/PW7cONDsUZ9++ummMvicuvwi+W6/k41nzPGWrr7t6+fsN/H15bj1tWvXOt/PpeLsNQjs6XIcMMdynnDCCaAffPBB0G+++SZojnnme8WVGpTvSTtVpjHudLnpdNqk02n13Qo84xCPmF/P7+W1N7zmgPM7cOrfb7/9FjTHpfM1t5+V/Nm8VuLggw8GzeU+zz77bNBDhgwBzespBg0aBPqNN94AbZcy5ZjlEOQZCyGEENUEDcZCCCFEZDQYCyGEEJFJnJu6Vq1aWXsXnJva9pBat24Nbew1FBcXg7ZzSRtT3gPm8mycd9SObzMGYz1LSkqgzc4NbUz5EnfsKXPea/7eZ555JugjjjgCdMuWLUHb5zWkPFpIm0DY++S+bV8Hn0fMmssc8pqC+++/H3S/fv1Ac2yvvQaB107w+obBgweDZg+Q1ztwXCj7vtwfOU7VLpnH+0oany1+x7duJJsY2DJ4PQs/h9m35VhxjvXlvP3sC9teLPclXp/w/PPPg+bX81oLXi/B52Ho0KEmW0J9+VzQP2MhhBAiMhqMhRBCiMhoMBZCCCEik9d6xj4/k+fd7bzK7CedfPLJoNn7eu6555yfbccNG1O+ZjD7wE2aNMlsT5s2Ddo4LpR9sYkTJ4K+7777QP/9738H/cQTT4AeP3486DPOOAO0Ky4wJMY1nU5763CK3+Hzyr7/Zpttltnm/sDrH77++mvQ3Je7desGmnOb77LLLqDZp7O9r6ZNm0Ibe4ALFy4E7atPy+sfJk+eDHrChAmg+VzYx8P5fOUB5wefZ+w6z/xajgPm9Qxci53XHHBf5/uGj8XW3Hd4/YLv2cX1sX33MI8ZvtrOVY3+GQshhBCR0WAshBBCREaDsRBCCBGZvHrGvthMV0wbexV33XUXaM5NzV4Fx6/x/rgu6yOPPAJ65syZmW2OrWPvgGPtOD/vueeeC/rVV18FfcUVV4DmvKfs89n44t0Y22eRR4ekUqlKY+TZG+Ocz3Z/nDp1KrQNHDgQNOfIPf/880HzZ/fv3x90+/btQV933XWg7b7P++Z7kuOE7fy7xpSPvWR/m/Ni8/445p5rd4v8EJLfIeSZwc+IRx99FDSvZ+C1Puz7cuwv+8B23+XjdOU5N8bvATPsOfPncTuvt3Edi2stT1kdeR/6ZyyEEEJERoOxEEIIERkNxkIIIURk8uoZM755djtPbvfu3aHt9ttvB835mtnXZS+Cueiii0BzPeRFixZltn1xxRzLyb4I58m2921MeT+S3+/zH1yEeso1GVc9Y+5Pbdq0Ab3ffvtltnfaaSdomzJlCmiOO2dvqkuXLqA5p+5VV10FmmOF7XrId9xxB7Rx/WJeazFp0iTQXL+W11pwTVg+L5wT3v6u9957L7Spr+ZOyPoPV151Xx51zoveoUMH0BdccAHoPffcEzTXkeeawXacO6+d8cUor1u3znnsSXJ2sw5db+PKXV8Z+mcshBBCREaDsRBCCBEZDcZCCCFEZKrUM+Y5eY6fffDBBzPbe+yxB7Sx98Rxw+zTsZfBccec65rz5DZs2DCzzR4ux0qyz2bntTbGmK233hr0nXfeCZpjN/lYfV67DfuPfI5tX0Vxxoh9Pnx+Eudktr0wzi3+P//zP6A5dpdjM3mNQosWLUDz+gmOc//qq68y2+eccw60cd+aM2cOaO4T06dPB83xkXxf8tqNl19+GXTjxo0rPRb2CBl5yrkRmq/e9V6uKcxw/D3v77jjjgN9yimngF66dGlm++qrr4Y2rivPnjCPCb7nW8hzlQnxl3NF/4yFEEKIyGgwFkIIISKjwVgIIYSIzAaNM2av1PZpuSYw+7IjRowAzbG+Z555JuixY8eCZr/hmWeeAX3eeedltu081caUj5UbMGAA6HHjxoHu27cvaM5d7fPC2AfmeDubkPytqmeM2Pl9+bxxPmiOS7e919GjR0Mb5z0/9thjQXft2hU0r3/gvOydOnUCfeihh4K281Fzrmn2gLnebGh/+P77753tS5YsAW0fj89X41wCvC+RHSH1jX1rJXr37g169uzZoHv16gWaPeSPPvoIdMeOHUHb9bLHjBkDbZyvgWtnv/vuu6D5OesjpO6z75z6Xp8N+mcshBBCREaDsRBCCBEZDcZCCCFEZDZonDHHGbpiYHv06AHajlc0prxX8fe//x30qFGjQNs5UI0pH0u8fPnyzPb8+fOhrbCwEPRjjz0Gmr/nE088YVy4csUa465nLKoGvibcP+6++27QttfFXhXnYOZ6x4MGDQLNXpgde2mMMXPnzgV9yy23gLbjkLmvsu/KdcE5zzV/NsMx0pttthlovm+/+OKLzLYvBzvH8yvOuHLs9Q5MaO1d12vPOOMM0Jzjn5+Vu+66K2juT3vvvTdo+97o06cPtPFzkNdmhPaPqqz7nA/0z1gIIYSIjAZjIYQQIjKJ50Nd4SE8/cphODYcYjFjxgzQQ4YMAc1L5DmlIIdgrF69GnTz5s1B28vkOZSIpy98Zcf4e/umiXyflyRtm8gNnr7lUoL2NWnQoAG0cShSQUEB6FmzZoHmvsyhdXxNubzjwIEDM9vdunWDNu5bnA6T98V9uXXr1qC5HCj3zU8//RQ03ws2+Zw2rGm4nglJplt9z6qHHnrI+frTTz8dNFuTTz/9NGg7zI8tELZ/GjVq5GzPNxs6fbD+GQshhBCR0WAshBBCREaDsRBCCBGZWukcJ8ZLSkpMQUGBqVOnTsZX8KVWS7LEPmnJKpdf7dtfqIfLnxWS3tJ3bD5/2hU2VZYOs7i42GyxxRZBx/BHoqzv1q5dO2t/zfW63XbbDTSnq7TDe4wx5oorrgDN6xtOPPFE0OzrJvGyfGF1SdYrVPT+JPe4/d50Om1SqZT67r/7rjGm0ueuD1c6TN/1Dn2mu8o3+j4v355tPtfihHyvdDpt0um0t+/qn7EQQggRGQ3GQgghRGQ0GAshhBCRyWvexdCYNdtL9XkJoTGJPm8sZF+h7b6ydNzu87Pt1/t8jpDY7ppOknUCtp4yZQq0sa5fvz5oTvv42WefgbbTxFZEyHEnuQ8qen8o7J259q244twI9W2TnOek+/KtC3CR77KFSdYwhdxX2R6XntRCCCFEZDQYCyGEEJHRYCyEEEJEJrFn7IpZc73WGPQzffPqSfxo/izf+5PGViaJaa6oPcT3dflFGzrX6saOK696Nu8tw1ca8LfffgP98ccfO/edT2811E9M6ttV1XtF9oRecxdJ+4vv2ELWP/jyN4T2r6pcT8NjWzZrNfTPWAghhIiMBmMhhBAiMjlPU5dNL+Rr6jPf0x1J0mlW9XRu6DR1vj+3pk9XV3QekpyTfJ/PDXl9qktfUN/9nXz02Y2pryf57I2pL7iOLdu+m/NgXFpamvmAjeGk+ObkQ+Mr/8iUlpaWq7Fbk8im725MD6yN4f4qI/axqO+WBr9nY+rLST7bl78hdt+0qWi88fXdnAtFpFIpU1RUZBo1aqSFGdWEdDptSktLTWFhYY1OBqK+W/1Q3/0d9d3qR7Z9N+fBWAghhBD5oeb+xBRCCCE2EjQYCyGEEJHRYCyEEEJERoOxEEIIERkNxkIIIURkNBgLIYQQkdFgLIQQQkRGg7EQQggRGQ3GQgghRGQ0GAshhBCR0WAshBBCREaDsRBCCBEZDcZCCCFEZDQYCyGEEJHRYCyEEEJERoOxEEIIERkNxkIIIURkNBgLIYQQkdkk1zemUilTVFRkGjVqZGrVqpXPYxJVRDqdNqWlpaawsNDUrl1zf4ep71Y/1Hd/R323+pFt3815MC4qKjKtW7fO9e0iIgsXLjTbbLNN7MOIhvpu9UV9V323uuLruzkPxo0aNTLGGFO3bt1Kf6GlUinQ/DrXLzvfr750Ou18Pbf79m+/3rfvkH3lcmyu9tBfw/y91q1bl7l2NZWy71+rVq2szyf/ol2/fn1mO/T6h34W78/+bH596H3j075/oSF9O8l5SafTJp1Oq+9W8P03pn/ISa9xru/NB1X1+WX79fXdnAfjsgN1PdB8g2+SwXhDvj50ME7yWUmPZUPtozqTTd+t7D0V6Xxf/9D9hRyLj6Q/REPI5Yel+m75a70xn5MN2T/yTT4/P5u+W3PNFyGEEGIjIed/xtngm+Kyf4XzrwbfFLdvei10ui5f7zWm/LHXqVPH+XrGN00ZQuxflxszrv7H8DWtbD8V7Su0b/I0NL+e+5O9f9dxGuO/J3nfofddkvsq1M4RvxNqybnem9QezKdl47NMko4BTD6flb77sCL0z1gIIYSIjAZjIYQQIjIajIUQQojIJPaMQ3w3xvYAfPP9PAfP/kGSkB8myWrWio7N5wGGHJsPecT5IYl/GRI1YIy/byfxxnz3EZN0vUKS96vvZo8dCZDEK016zqvS50+6Tiiph5xkbLNfn+050T9jIYQQIjIajIUQQojIaDAWQgghIpNXz9gV/2iMO3Y41GcLhffXuXNn0LNmzar0vfy9jj32WNCffPIJ6O7du4Nevnw56AkTJoD2eQpr1qzJbNevXx/a+Httsgle0t9++y3rz6nJhMZLuuJ1Q72rUN+tYcOGoFetWlXpcfG+fPdovvuIyzsLWVuhvotUVdpRHzF93A3dB0L6Xz7Osf4ZCyGEEJHRYCyEEEJERoOxEEIIEZnEnrEd7+bLJ81+pg2/d926daBDva527dqB7tevX6WfbQz6V0uWLIG2U045BXTv3r1B77zzzqDbt28P+sUXX3Qeq4969epltn3exNq1a0HX5ELsPlx91xfHbvdH7qt8fbnf+3wz9k633HJL0H369AH9/vvvZ7b5eyxdujTos0O9ci4Lt3LlykrfP3DgQGj75ZdfQI8YMcJ5LAIpO7chNQB8+PqDL07d95x27S9pGVxfuy8fBN/HIXkwkpbNNUb/jIUQQojoaDAWQgghIpPXaWofdpiNMTil4QvBYHi6pEWLFqB56njOnDnO9quuuiqzfc0110AbTxPy9NoHH3wA+qOPPgL9yiuvgO7QoYPz2ELCvKoyjOGPTiqVypyv0Kk+nkq24WvA0188bc22xlZbbQW6V69eoPv37w+6sLAwsz1u3Dho47A613EbU/571q1bF3TLli1B//rrr6Avvvhi0KNGjcpsf/3119DWt29f0DzlbYdsifKU9bN8pm4tKChw7qu4uBh0x44dQRcVFYG2LTZjytsYbKu5js0Hv75Bgwagefzhe+Gpp54Cfeutt2a2+Rnt++xc0D9jIYQQIjIajIUQQojIaDAWQgghIpPXdJih/mVISjeGfdxly5aBZs+ZPeI2bdqAbtKkSWZ7/Pjx0Pb999+D3n333UEvWrQI9IoVK0BPnz4d9Oabbw66efPmoPm72L4deyzyiHOndu3alZ4/XpPA/cn2m3whEk2bNgW9yy67gD7qqKNA33DDDaCvvfZa0OzD2T5dqEfM8Plgn419Xl6rwWGBzz//fGab/ewbb7wRNJ83+xooHWbl+MKNGNczgz3dPffcEzSvbxg0aBDoyZMng+b1NUOHDgXN6whC4HuSv9eZZ54Jmo/1q6++Ar311ltX+vovv/wS2h5//HHQ+eif+mcshBBCREaDsRBCCBEZDcZCCCFEZGqlc5zsLikpMQUFBaZu3bqVxrv5/ExXKrTQ2Dlu53i5//7v/wbNPt+3336b2e7Zsye0sSfM6TA59u6AAw4Avf3224O+6aabQL/zzjug2RN84YUXKv0sH7ZnmE6nzfr1601xcbHZYostgvbzR6Ks79ox8q50l8a4069uuumm0NajRw/QHD97yy23gOY1AuzDsg/Msb/z58/PbF9yySXQ9vPPP4P2paxl9tprL2c7H8u2224L2o6BPuuss6CN/UL29ObNm5fZTqfTJp1Oq+/+u+8aU3mcMeNKacrvveiii0DzueY0w926dQPNvv/w4cNB22sIjMH1Dr7vwfckP8P52Lid061yKdIFCxaAtvsfjyd8z15++eWgbe89276rf8ZCCCFEZDQYCyGEEJHRYCyEEEJEJnGccRJsj4C9J9Y77bQTaI4zZt/1rrvuAs0xZDNnzgR9wgknZLY5TphjJ0tLS0Gff/75oDn3MHuIe+yxB2iOOx42bBjo1q1bZ7bZk/Hl7+UyYKFxpzWF0JJoBx98cGb7iiuugLZJkyaBZt+WYxZ33HFH0J07dwbNvhtrO343tOQdr2f48ccfQbNn3KlTJ9D2fWOMMf/6179A23Hx999/P7TxPcyenaickJoAjGv9zT/+8Q/Ql156KeipU6eC5r589913g27VqhVofpaGfAd+9vF77bUTxpTv+5wfgu8jXsNgr93geGv2fjmXxAMPPJDZznZZlv4ZCyGEEJHRYCyEEEJERoOxEEIIEZm85qYOjQ224zM5JoxjbRcvXgya8/vusMMOoLnGcJcuXUDfc889oO26rHfeeSe08fd67bXXQNu1kI0pH6t37rnngmZv49lnnwXNcaZ2ftdZs2Y5jy00T21NxvbdXLGYxmDucmOMmTt3bmab1x+89dZboA866CDQ7NO++eaboHkdAMe5c4yjfeyhccQLFy4EfcQRR4DmvjtlyhTQL7/8Mmiu5W3HWPN5+uabb5zHJionyXPXVU+A17vY/dwYY/bZZx/QI0eOBM1rUngdgOtYQ78Hv54/m9u59va9994Lmtfy2Pdh9+7doY3HE953LuifsRBCCBEZDcZCCCFEZDQYCyGEEJGp0tzU7D+wD2fPszdo0ADaOCaM/SaO9bXjPo0pn3fUzu9sTHk/Yquttspsc83g+vXrg37wwQdBcw3QiRMngmaPr3///qC32WYb0Jzv1Y714xhWO6e2j3Q6bdauXav8vv/uu656xpwHl+v62v4S90WOV7zmmmtAc6zukCFDQHN9Y45DP/vss0FzLLkL/l4cz//nP/8ZNHvG7MtxXPKVV14J2vagH330UeexuPJ/p9Npk0ql1HdzyE2dT/ia8dqeNWvWgOY1DNzf7Jj62bNnQxs/h0OPjfvy9ddfD5rz/D/00EOg7XVH/Iy3cz8YU379g31NlJtaCCGEqCZoMBZCCCEio8FYCCGEiExec1Oz59OsWTPQAwYMAG3n72RvoV+/fqDZV+WYsPvuuw80e9DsVXAN2n333Tez3bFjR2h75ZVXQNs1Wo0pH0vHMWlcz7Zx48ag2YdzedI//PADtPli81wxhaJy+Jrwegf7XC5duhTabr75ZtBNmzYFzWsI+vbtC3rnnXcG/d5774FmD9mOh+RrzD4a54MeOHAg6DZt2oDm+EmO7//kk09At2/fHvRjjz2W2eZzGJInXX03e3x51XN9bUXt3D94f0ceeSRors19xx13ZLZnzJgBbVz32+XLGlO+P9k5240pnz+a+/JPP/0E2l4LwuOTL0Y+l+eu/hkLIYQQkdFgLIQQQkRGg7EQQggRmSqtZ8w1INlbs+OQ2cvi2Euez2ev4rvvvgPN/gHrMWPGgLZ93549e0Ibe3qrV68G3bVrV9Acm3nTTTeB5ng6rgk6fvx40LZn7PNoXDmW5bshrry4Pj/KjvfmWtvsL/Gag2uvvRY0X3+uKcsx9Hyv2LnLOf6RPTr2dLnvcj3iAw88EPTQoUNBjxgxAvS8efNAs09sw+eYX2vXr1XfrRxfXnVXvvrQGGX+LL4veM3BSSedBJrHAHs9zYQJE6CN82K7+lJF8Hdr2bIlaM7RMGfOHND2dwv11pls+q/+GQshhBCR0WAshBBCREaDsRBCCBGZKvWM2R/g2Czb32KvyTcn/3//93+gd911V9B/+ctfQHNN2bFjx4L+29/+ltl+/fXXoY2PjeNGOY821xzmfKTsfz/55JOgOSer7Tf44l8ZxRlXjl3P2Afn4LXhuOFDDjkENPdFvi94DQJ70G3btgXNNYdt39bOsW6MMcOGDQPN+Xg5lpOP7bzzzgPN9Y9PPvlk0Lw+wvYrfX2VvU1e76Ba3dkRUtM69JnAzx9fDvfPP/8c9HPPPQf6ggsuyGzzPeaK7a9I+/rX5MmTQXPtBHuNgjHJcn4rzlgIIYSohmgwFkIIISKTuIRivXr1Mn/nfbvilJR2ykouQ+eDpxhOOOEE0Dzl0Lt3b9CcDtOezuMp7MMPPxw0l0BktttuO9A8DclTPWeccQZonkq02WyzzUBzaS/flJRKKFZcho6nuHjKjKf+7KlntkDYxuBwNJ7WZhuE06GedtppoLlvv/zyy5ntG2+8Edo4feptt93m3NfgwYNB9+jRA/Q555wDmkOp2IKx+75v2tGV2jXbMnR/dMr6rm2x+J67vpS5Ie/17YvTX3J/4Hvl448/zmxzGB2nNOaQQLZ/Hn/8ceMinzYHnweXNaASikIIIUQ1QYOxEEIIERkNxkIIIURkEoc2uVIK8jw6L3sPSTfGfhN7VZze8qqrrgK9YsUKZ7v9efxZo0aNAm2nQjSmfLgHl+bi0BT23bjEIh+rfTx26kNjypeWXLRoEegky/P/6NSuXbvS88MhO9tvvz3ozp07Z7a55CaXattxxx1Bc3pMDqVr0qQJaE5Z+eyzz4L+85//nNm2Q/SMKd+3Dj30UNDsKXO5Ri6RyJ4Xr2/gY7XDSXxpGn1hMwrN+w+ucxFyz4c+H/ia9erVC/SJJ54IesmSJaA5JeXll1+e2bbXPhhTvkwuP/O5FO1+++0H+t577wX92WefgfZ9d/u7+rxy39iXDfpnLIQQQkRGg7EQQggRGQ3GQgghRGSqNB2mL0bNnldn75PjiNmr4rhh9geuvvpq0JxiskWLFqBtb8OXVq2oqAg0e1vsfXHJu6effhr0jz/+CJo/f6+99spsf/3119DGccZ8LEohWDmu9Q4NGzYEzb6vHafOafbmz5/vfC/H7nL8Pfu43F5YWAjaLh9aFj9dBq8xeOGFF0BvvfXWoLlvc6ynLz0mp6m1z6uvL7r6rvxiJCTOOASfF8p9sUuXLqDfeOMN0Jz+krF93ksvvRTa+Lm4ePFi0PZz0RhjPv30U9C8XsIX586ElGz0la5VCUUhhBCiGqDBWAghhIiMBmMhhBAiMlXqGfv8S9sn5tJt7Lu2adMGNMeUcTybnfPUmPLeFucH5jl+G5/3zfTp0wf0nnvuCZpj8Rj+rpdccklmm711jgNlj8b2UeS7Ibbvxtf0/vvvB80+v+2VsWd8/PHHgx45ciToK6+8EvS//vUv0MOHDwd9++23gz711FNB23HHa9asgTb2zR544AHQ7D9z/+LXd+rUCTSXYOSc73Zebt99xSTJqVyT8J3HkPPuiwXndnu9gjHGjB8/HjSv1eHPtj1mLrc4aNAg0Ntuuy3o2bNng+ZnPHvKH374IWi+V0KoivwN+mcshBBCREaDsRBCCBEZDcZCCCFEZBLXM65bt26lNWEZjt20YxyPOOIIaOPalpwzl2sKc3wlx7tdccUVoF0xj77vwTHQ7MtxDlWO5WQ//M0333Qem52jlT+L46+/+OIL0Hau6nQ6bVavXq2asP/uu67c1IwrRp59Uq5nzfGTvCbgtddeA835xbkeMudVLykpyWxzXDDvm+OIOe861xXn/XHf5DzsvFbDfrz47itfPeNUKqW+W0Et7iSeMT/LOCeC3beMMaZZs2ag2TNO4qXysXD94oMPPhj0l19+CZqfjby/Y489FjT72S5C1ztw3zXGqJ6xEEIIsbGjwVgIIYSIjAZjIYQQIjJ5jTPmeXXOyevKJ801gjkG7C9/+QvouXPngvbN4bNPG5KnlF/LtW25/jDn7+V8wOwxuuKvjTHmpJNOymy/9NJLzn2xh2OjOE3Eld/X15/sPsF9lT1fjm+sV68eaK5/vGrVKtDs806dOhW0nQt7ypQp0MYx7QMHDgTdvXt30N988w1oXs/A/eujjz4CzT5dSE1YX0yr+A923/XB59HO/8B5Cdq1awf622+/BT1x4kTQnOOf4/G5b7uuKfeHDh06gOb+wXXEWXNf57UXvv7lyl3vI5cYefV2IYQQIjIajIUQQojIaDAWQgghIpNXz5jnydnXZQ/Ajg2uX78+tL3yyiug77nnHtAca9mtWzfQnKeUYz/ZP7BzY69btw7a2FdhX2zAgAGgL7vsMtCcR5tzsHIO71NOOQW0XUN0p512gratttrKua/ffvstsy3PGHGdj5Bap/za4uJi0Jx3na8/+7r8/hkzZoBm74xzV7to27Yt6NWrV4Nmz2+33XYDzXGSnDvgq6++As3rKVz44oxFbvC5s/Xrr78ObS1btgTdq1cv0KNHjwa9YMEC0HYefWPKP0vnzJkD2n5e8XGy39yqVSvQvL6Bn8Pc17mv8hjA2P0xH3HGPvTPWAghhIiMBmMhhBAiMhqMhRBCiMgk9oxdsZocA7v55puDtmuhcvwsx1YecsghoLmG7A477ACaPeOdd94ZNOc1bd++fWab81537doVNMensZdx3HHHgea44TFjxoDeddddQdv1aRn+bDvG1JjyXobL26zppFKprPOqh8RHcn/gesWcR92OtzemfOxvkyZNQHOOXvv9nJOdfbdHH30U9NixY0Gzh/zTTz+B7tGjh/PYOA7VRUhuAPVdxBUD6ztX9poDzmXP9Yj5OTtr1izQt956K2hew/L999+Dbt68OWj7XuH4eY7HnzBhAmiO52e41nuSPhSa/zsX9M9YCCGEiIwGYyGEECIyGoyFEEKIyOQ1zpjhGEP2hW3vYvDgwdDWuHFj0B988AFo9lm59iW/n70trmVpxzlzzDLXI+ZYTPb4uDbz8OHDQe+9996g2dfjY7P9Svb0fF6FYjVzI8R349juJUuWOPfF9wW3r1y5EvT5558Pmr00OxaUPb6LLroI9MMPPwyaa8Bec801oLfddlvQdty6MeXrjIfEY/pyUfN6B1cNclE5rmvAccX//Oc/Qe+7776gR44cCZqfs7wuaLPNNgPN62fstUCnn346tPE6H15b8eKLL4Lm+zD0ecevD1lvk0suakb/jIUQQojIaDAWQgghIqPBWAghhIhMYs84JFaT85TaeWw55ynXiOXc1e+++y5ozqG7bNky0Oy7sY9r12196623oI3jOjnumHOgss/CXkZozJorptB3zl35VWs6IfWMXeeOa2X7rpHv+nPOXY715fYDDjggs3355ZdD24gRI0CPGjUK9Lhx40Bz7WW+F3755RfQnA/AhcuTq6hd6x1yw9d3bf3DDz9AG6+Xadq0KWi+3n379gXN/Yfz8nONe3vNAud3YH+ac0lw307qEYe2u14rz1gIIYSohmgwFkIIISJTK53j/E9JSYkpKCgwdevWzUyL+Kbfyn249Xrfezncxy4rWNH7TzrpJNAzZ84EzeEl8+bNy2xzmTgOJ+Jj4devWrUKNIdk8LGy5mlPF6EpBdetW2eKi4vLHXNNoqzv1q5du9LzF9KXfSE3vulY32exLXLggQeCtsuNcsgflzQ899xzQXNqVg5V4tSdnIKQy9Jx33X1ZT4vrteWhTap75Zkrkk+nrt8Lnv37g2aQ05nz54NurS0FPQLL7wA+vjjjwd9//33gz700EMz2xwmdffdd4Nmm5PD7ELtv9DzFkJFFouv7+qfsRBCCBEZDcZCCCFEZDQYCyGEEJGpUs84HynCsoX3zWn+2I9iHzgmPu/ClXoxJEVgOp02a9eule9m+W5lbLIJRvn5/Ey+Djbsbfnui1B4vYQdBhjzHjTGnZIw9LPtvp1Op006nVbfrcAzZpL0rwEDBoDu2LEjaF5jMG3aNNB8H/G9wH3Xvq/yuZbGmOQesSukNHQ/2fRd/TMWQgghIqPBWAghhIiMBmMhhBAiMlVaQtGXJjCfcV68L45/C/ks9gtDS7eFehW+VGr2+33n1LVvpRRE7PUOvvjYEP/Kl/6S4yNd/nNF7+dUsa54/dBr7vuevlSfrteHHguXsAv1DP/IuFK5+rBfz32PU1Cyx+tLvxt639jH4svHEJqvIZ/3QpISitl+rv4ZCyGEEJHRYCyEEEJERoOxEEIIEZnEnnGS8n6uufRQP9nn64bE8jJJyutl836mKnOmiv9ge7c+39bnV4W81ndfMPks5ebru3zsvvUTvvvO9XzwfS+OMxb/Icn5sK8DxwFznLBrfUJF2vVZxoQ98/m1fGyha3mSPFfz+T0rQ/+MhRBCiMhoMBZCCCEik/M0ddnf8CShM/mcegqdKna1+6b6QvedT5Kc44quWU2kou+fz2tc1f2lKo8lVCchl/Okvlu1oYobc9+tyr6Yb3LpuzkPxmVxvIr9q36UlpaWy81ck+AYdGOSx5Inee3GNDgzIZ5wvj+7ItR3y/ddJsl5Tpr/OZ/HwmzMY00239PXd3MuFJFKpUxRUZFp1KiRFhxVE9LptCktLTWFhYXBi4j+SKjvVj/Ud39Hfbf6kW3fzXkwFkIIIUR+qLk/MYUQQoiNBA3GQgghRGQ0GAshhBCR0WAshBBCREaDsRBCCBEZDcZCCCFEZDQYCyGEEJHRYCyEEEJERoOxEEIIERkNxkIIIURkNBgLIYQQkdFgLIQQQkRGg7EQQggRGQ3GQgghRGQ0GAshhBCR0WAshBBCREaDsRBCCBGZTXJ9YyqVMkVFRaZRo0amVq1a+TwmUUWk02lTWlpqCgsLTe3aNfd3mPpu9UN993fUd6sf2fbdnAfjoqIi07p161zfLiKycOFCs80228Q+jGio71Zf1HfVd6srvr6b82DcqFGjzHZlv9DS6TTofP6i5X3zMSRtt0mlUs7Xhv5C9X02f17IZ7na0+m0SafTcO1qImXfv3v37qZOnTrGGGOmTp3qfA9fsw2Jr7/Yml/ruwdD7oOKXu/Dfn3ofWIfazqdNqlUSn03i+duzH/Moc8215jge2b7PjsU17EmeeZn+9zNeTAuO5hatWplfWBV2UmSDFK+dt+FSPq9QvaX9Hum0+kaP71V9v3r1KmTGYyr0zlJ0v+S9p8khO67otdXp+tUFWTz3N2YzlE+n7tVjeu+yscfMN97aq75IoQQQmwk5PzPuAx7aoCnHEKmvPi9rqnaivadz19RIdOCuezPR8ivRd++7fMYc6p1Y2Tq1KmZ87kxn5skU8eh/SVk39mQ5L60P3tjvj4xSDIj6TqXobZFaP8qm4mq6P2+Z35VP5ddlo5vut11HrLtu/pnLIQQQkRGg7EQQggRGQ3GQgghRGQSe8Yhq/pcc/6hXoWPTTbBr7Zu3bqs2+vVq+fcN/vba9asAc2+yKWXXgp69OjRoGfOnAm6KlfHynvb8OS7b29I8nnsoWFWonJq166duRahXquL0OcFv75u3bqgfaFMdnvTpk2h7aeffnJ+Vr5Jcp587dnsW/+MhRBCiMhoMBZCCCEio8FYCCGEiExiz9hFSErKpF4Fv/7iiy8G3atXL9ANGzYEvXLlysx2SUkJtHE+0SFDhoBu27Yt6K+//hr0AQccAPqdd94B7YuXc8WshcSgypNDUqkUZDSqKjak15X0s6rS306S3lB9F7H7ro8k60ZCY3lbtmwJ+rDDDgP9448/gt5ll10y28uXL4e2YcOGgV60aJH7YAOpylwVuaB/xkIIIURkNBgLIYQQkdFgLIQQQkQmsWdsx7slKXHli5Xj+DXeV0FBAehPPvkE9KpVq0AfddRRoIuLizPbv/32G7Sxl3HBBReAPvjgg0GzXz1x4kTQkyZNMi5c8Zi+88QozrhyXDHySbzTpGUJk1Q38u2L7yPXvowxZu3atUHHUp1jqqsTIesEfGVVs32tMeVzKPBz97LLLgO93377gf7hhx9A27HE06ZNcx5LYWEhaM7vYD/DjSmfW4IJyaPNz92q6Of6ZyyEEEJERoOxEEIIEZla6RznLUtKSkxBQQFM9eUzRZhv2XmjRo1A8zT0CSecAJpTrfE09qabbprZPv3006GtVatWoDlMikOflixZAppTb/L35mlxJsk0dUUlFIuLi80WW2wRtJ8/EmV9N8RiySe++yB0CszuHy1atIA2nibkacbvv/8eNPf1jz76CPS8efOcx8IkCbvi6fdUKqW+m8VzN7Qcreu93bt3B92/f3/Q/NmfffYZaA4hHTNmDOjNNtsss33WWWdB27bbbguaw6K23HJL0FdffTXoX3/9FbTvHs9nelYOy0un096+q3/GQgghRGQ0GAshhBCR0WAshBBCRCav6TBDloobg14pz9f79l1aWgqa3//GG29U+lnGoFdhjDGNGzeudF9FRUWgzz33XNCvvvoqaPZo+LO5PTT0JeS1tl+dTqe9y/1rEhvKJw5N7RrqKf/pT3/KbP/1r3+FNvaAO3ToAPr5558HzZ7WdtttB/rGG280IYSEXSksKntcYXnr168v91qbEB9/8803B83rY3gNQe/evUHfddddoPn506NHj8z25MmToW3//fcH7UqlaYwxW221FejFixeD5nuB02vyd7Of+3zcvnu2orU6PvTPWAghhIiMBmMhhBAiMhqMhRBCiMjk1TP2+Q/sZbh84lD/mX3YZcuWOfd38skng7a9tjPOOAPaOBaTU7r5POCkpbpC4gR53/Y5VypMxI4z9p3jJH4mx5n7PD3fsfB9Y8fQc5wxr1c4//zzQQ8aNAg0e8Svvfaa89h8az1sfOcwpHRoTcfl+4bEpfN5ZW+0QYMGoDm2l5+NCxYsAM0pK/lesH3dm266Cdqee+4557E8/PDDoPl78zP+qaeeAs0x0N26dQNtryvisrh8nvi+yGWtjv4ZCyGEEJHRYCyEEEJERoOxEEIIEZkNGmecJG6Q3+ubs2c/gL2Ldu3agf7uu+8y2507d4a2GTNmBB1bvssWukp5hcSkyndDQs6HyxtlL2v33Xd36qeffhr0ihUrQIfmHx81alRm++WXX4Y2LnF31VVXgeY86pwPmHMN+zxi1z3uy/1b1ffRH4mQvOqua8Jrb+wc/cYYs3DhQtBTpkwBfeedd4LmuGTOB8ElPE899dTMNj/Tzz77bNC33HILaP7enTp1As1xyQzH1POxtWzZMrPNuSZKSkpAu8YnxRkLIYQQ1QQNxkIIIURkNBgLIYQQkdmgccaueMqkfhH7UYMHDwZ9yCGHgJ44cSLot99+O7M9bNgw52eF5pIO9cpcsX+hNTflu1VOKpXKnB/feWVdr169zPall14KbbvtthvoESNGgGaflvOk8/oGX31a+xrz9eaaruwZd+zYETTn533rrbcq/ayKCOlfIftSv0WS5Kt3vZfrwHN87eOPPw66efPmoJcuXQqafdijjjoKtF2X/v7774e2SZMmgebjbtu2LeiLLroINOemPvbYY0FzvD/75/aYwHW9fc8LjuXOZh2I/hkLIYQQkdFgLIQQQkRGg7EQQggRmbx6xj5C4idDPWL23fr37w+aPWJ+vx0LWlhYCG12vJkx5WPtQuvPMkniBH1+ony37Ag9N7aPzzl1ua4qx7TfcccdoP/3f/8XNMc/Ll++HPTatWsrPS7uK5ybmpkzZ45Tb8iawnwNmjVrltlOpVLeuFHxOyG5zu21D8YYM23aNNDFxcWgV61aBdpXq53XTxx00EGg7fUT/Fxl9t57b9Acv7969WrQTZo0Af3EE0+AnjBhAuhHHnkEtF0v+c0334Q2zsnN5PLc1T9jIYQQIjIajIUQQojIaDAWQgghIpPYM06n05k5ca5VGRpP64K9Ud4X50QdO3Ys6EWLFoHu2bMnaNtz5jhjjllu1aoVaM7fGhoLzITUjPX58Iozrpw6depkXc+YccXIs6d74403guYcvByL2bVrV9Dz588HPW/evEqPK+n1TVK3Od+fZXvE6reI63yE1Dfm5yavb+A489NOOw309OnTne/n2u/s+9t9/5hjjoE2e82AMRiTbIwxc+fOBc1+NscRs7/N+zv33HOd77epiv6of8ZCCCFEZDQYCyGEEJHRYCyEEEJEJrFnXKtWrUrraob4T6GxuBwLzLGYnEv0m2++Ac2+Xd++fTPb77//vnPfe+21F2j2/DgGrUuXLqC//PJL57FsyNjOmozddytqs/HlG7fp0aMHaK6tzf2J6xl/8MEHoDlXdVX2j5A82NmQ5J4X2cFrdVxeJ/Pzzz+D/tOf/gTajrU1pnxtd67lfdhhh4Hm9TQtWrQAveWWW2a22dNt2rQp6GeeecZ5LHwf+Z6rnIvadd5CfPiKyKav65+xEEIIERkNxkIIIURkNBgLIYQQkamVztG4KSkpMQUFBaZ27dqVesY+3801785t7IuwNzF69GjQBQUFoK+99lrQLp+X62qyz7Fy5UrQO+ywg/PYuCYoexNPP/00aPZx7PMWek5tz6/stcXFxeXyH9ckyvquyzMOgfex4447gj7iiCNAcy7rkFzCFb3e7gOtW7eGNvbsNmQcMe8/yWeX1YRV3/U/d0Pqp/vW+fBzkvfNedXZh2Vfd7/99gNt50LfaaedoI2fo9xfGjduDJr9bX6OTp482bm/kNhtH5ybOp1Oe/uu/hkLIYQQkdFgLIQQQkQmcWhTKpXK/IV3hXsY454W4OkQXtbOoUw85dChQwfQnLKSSygeffTRoO2pZp5GHj9+PGj+Hm+88QZonh4ZMmQI6G+//Rb0zJkzQfO0NqdDdOELRVE4yX9wTf+HwOd88eLFoO++++6g9zO+sAo7XWunTp2g7Z577gHNfauq+4Nr/0lKh4r/4HvuMiHTsRxuxNPMXFqQyxpyKdvDDz8c9MEHH5zZHjlypPNY+LhLS0ud+549ezZoLtEYYvElDelTaJMQQghRDdBgLIQQQkRGg7EQQggRmbymw/ThWzZvc+mll4LeeeedQXN5rc8//xz0zTffDPqcc84BzWXobJ+Y01UWFRWB5nCRbt26gWa/m49t6623Bn3hhReCPvPMM0Hb3gWHDmzoUJU/EvkKbWI43aXPx/f5S6y59Ns777yT2ebyjf/1X/8F+t577wX93XffmRCSrEHw9VXWdlnTdDpdziMUvxPqvdthor7yn/vuuy/o/fffHzSvSTj11FNBH3LIIaA7duwIunnz5pnt119/HdrYC+dnH/vX/Nzl88LlIn39aUOvr9E/YyGEECIyGoyFEEKIyGgwFkIIISKzQT1jxvaM7VJaxpT3LjiNGKe75LJzY8aMAT137lzQe++9N2i7zN1bb70FbWeccQZo9hI4Fq9r166g2SOuX78+aNs3MaZ8uk1OBWrjO/eclk3khssrDY2vT+pTs9dlr2HgGHmOvbzssssSfXZoicWQWE3un/Z9oL6LJEmRaz9bmzRpAm3cf3r37g2an228BoH7B/vAnGr4tNNOy2xzOcZffvkFNPe1TTfdFPTFF18M+qeffjIh8Hljj9r1Wtc1yLbv6p+xEEIIERkNxkIIIURkNBgLIYQQkUlcQtGY/8yX+7wxnvO3fVqOAWPvYd26daA5vs2OtTSmfP5n9mEHDBgAesGCBZltjoXjvNfvvfce6COPPBI0l/b64osvQHOpr6FDh4Jm7+TVV1+t8DiNKe+r+MqlZVPK649OLn3XFftb1Xlrffu31160bNkS2l566SXQw4YNA82eHx8Lr1ewcwkbY8z06dNB26VIjTFmzZo1lR120PdOp9Nm/fr16rtZlFBkXP2Nn1UPPvgg6LFjx4LmvPsnnXQSaI71/eqrr0BPnToV9LPPPpvZ5nVCvnh7xjfeJCk1GXrPqoSiEEIIUQ3RYCyEEEJERoOxEEIIEZnEccZJvAs7VviAAw6ANo5P49jKUaNGgR4xYgRo9n1t39UYY95//33Qq1atymxzfBr7Khyrybmn2RfZZpttQHN9Yq53+9BDD4G2/XQ7X68xxqxevRq06hdnT5IYedf7QnNN+/DF8tq+LOdNf+yxx0DbHl1FsI9Wr1490JzjvXXr1qAffvhh0A888EBmm+M2OaY1H7GaNQVXHXnuL3ze7fM8cOBAaOO1O88//zxo7g/bbbcdaI4j5mvK19w+ttD7xFfroHPnzqC55oDvvNnwcfv8Zru9bL2DD/0zFkIIISKjwVgIIYSIjAZjIYQQIjKJ44xt383nlbF3YfsPzzzzDLQtWrQI9LRp00BzTVeOUWvfvr3z/Zx/2n4/5zzlWsrsCffr1w/06NGjQXMu6qeeegr09ttvD/q6664DPX78+Mz2smXLoO3XX3812ZJOp00qlVKsZg6xmvkkNN6RcfnVITlzs4Hr0R511FGgOf5/+PDhoPk+c+GLkVffrfi56+tPjB07zteP+wv7sOwZM5zPgd/Pz2m+xq5j4c/edtttQXP+Bh4jpkyZArpNmzaged2R6zyG5qZWnLEQQghRDdBgLIQQQkRGg7EQQggRmcRxxi5CcoVyDl2uN7zXXnuBHjx4MOjQvKbNmjUD/dxzz2W2CwsLoY1jlPlYOP8u77tnz56gf/jhB6fm95eUlFT6WT7vwj7nitXMH3bs+I8//ghtXJubvSeOBWaS5BpOCu+b70Puq3wvcC7iJMemOOPKsdc7+OBzZ8e8umKQjSlfq53XqHDucvZ1+bnMuavt19vPOWPK51C45ZZbQHMtZva/uZ2PndcRca4L+75mvzm0hnQ2/Vf/jIUQQojIaDAWQgghIqPBWAghhIhMlXrGPmw/84MPPoC2Hj16gPbFGbNv5/OMef92nVaOjWNfhL2FCy+8EDR7go8//jjod999FzR7jEuXLgVt5+UO9SaUq7pyQs4Fx4r36dMns923b19oa9GiBWiOveU1CFyTmuHYTddxsz/NcY3Lly93vn733XcH3apVK9Bt27YFzZ4ix9AfeuihmW327Fy5gI1BfzGVSpXLGV+TcfnpfM+74thdfnJF7b58zscccwzodu3agWYP2a6P/MQTT0CbL4b9oosuAj1y5EjQXNOe685PnDgR9Lhx40CH1DPm82Kfx2yfM/pnLIQQQkRGg7EQQggRGQ3GQgghRGQ2qGfs8nEXL14Mbddee61zXxwfFxqb6fIH2CNu0KABaK4p/Pbbbztfz7WVuZ19GvbWXLUw+XvJI84PfB7Z67K9WF+O3AULFoDu2rUraO4PnKucawh/8803lR4re3i2J2eMP28x197ed999Qc+ZMwf0brvtBpp9O9sPD60dveeee2a2f/vtN/PGG28Evf+PjOu+9tUQduWDTpo3fdiwYaB5jcGtt94K2o5L55z+M2fOBM3rNrjG/T777AO6f//+oDnu+LTTTgN91113gbbvHf7efJ+x5nrGvvURxuifsRBCCBEdDcZCCCFEZDQYCyGEEJGp0nrG5T4swM/05bVmz9jlq2ZzbPax+F7LnnLosbk8nIq0vf8knnC2dTX/6FTUd0PZfPPNM9utW7eGtieffBI0e8LvvPMO6FmzZoFesWIFaI6v9MWVJoH3xXnaObcwx4bydx8yZEil+/b5aHYugXQ6bUpKStR3c3juuvB5wnyNfM8f1p06dQLN6yfsZ2njxo2h7eijjwbNedCnT58OuqCgAPSoUaNAcz2DZ599FjTHJdvrI7gmQMh4onrGQgghRDVBg7EQQggRmY0mtCk0raNvqtdHyOt5qoZTAPK0NLf7pqGZ0KnnkH2Ligm9JnbIzuzZs6GN06XecccdoL/++mvQDRs2BP3QQw85j60qrzGnguXv/cgjj4DmaU4OLxk6dGhmO/SedaWBFfnBZxWE9jV+FnIoHFt89vQvl0y0+44x5cP0WrZsCfq6664DfdBBB4GeNGkSaP7uXP7TbvelAc3HPal/xkIIIURkNBgLIYQQkdFgLIQQQkQmsWdctmzbGH+KSldpr9A5+KRz+C4PyrevUP86qd/l+i7yiPODLw2g6/X8Xk6duWjRItD33Xcf6P333x/0hrym/D15vYMvNeyIESNA77jjjqDtFIbsCfpQatfKsUObQteNuNbqhJ7z0NeHpObk8eT1118HvWzZMtC83mHs2LGgzzvvPNDz588HzSUbXeQzjLcM/TMWQgghIqPBWAghhIiMBmMhhBAiMnlNh1lu5570dz5fzibUF0nyWRsa33cLuUSu9yod5u9kk1Iw1CtzvZe9LE6t50uvGtI/kvrNfJ+w5vKQvXr1Av3aa6+BtsvghZ5jLkO3fv169d1/993atWtX6hmHpLgMfS4mvU9C1vaEPsP59Xxf1atXDzT717xeIuS+cp0HpcMUQgghqgkajIUQQojIaDAWQgghIlOlualDvIykcVuuGOZscJUp9OE7Np+HE/rdXLjyfytOEwkpocivs2MgfeU7fbG7TJL+ElqmkGM5fX71jBkzQNuesDHlY4ldx8ZeusuzU99FkuRJYC/eRdJY76rMne97zvJ96eqbFb3fFQPtG298pSUrQv+MhRBCiMhoMBZCCCEik/M0ddnf7iTTR7HeW9X7801R+D4rn+elon3V9Cm/bPpuyDWq6uubZH+h78133w05T9l8D/Xd/J6Hjfl8VvVzNJf+ly0V9XvfPnIejHOpNZrPL5vv9yfZV2g+1lDyfcOUlpaagoKCvO6zOlHWd+286j5csZqhJO0P+cSXj5e/58qVK3P+LN4X5/BmKro26rv5fe5W5Xvzje+eS3qsIfd0Lp/l67s5J/1IpVKmqKjINGrUSMUKqgnpdNqUlpaawsLCjToJSlWjvlv9UN/9HfXd6ke2fTfnwVgIIYQQ+aHm/sQUQgghNhI0GAshhBCR0WAshBBCREaDsRBCCBEZDcZCCCFEZDQYCyGEEJHRYCyEEEJERoOxEEIIERkNxkIIIURkNBgLIYQQkdFgLIQQQkRGg7EQQggRmf8HGo9Ia2hd/I8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_images_size = [9, 1, 28, 28]\n",
    "sample_images = torch.randn(sample_images_size).to(device)\n",
    "labels = torch.tensor(range(9))\n",
    "samples = denoising_process(sample_images, variances, model, labels).cpu().permute(0, 2, 3, 1).numpy()\n",
    "\n",
    "plot_images(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133e675e",
   "metadata": {},
   "source": [
    "## FID & Inception Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d6fe0dc",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "def get_image_generation_metrics():\n",
    "    test_data = test_dataset.data\n",
    "    \n",
    "    if len(test_data.shape) == 3:\n",
    "        test_data = test_data.reshape(test_data.shape[0], 1, test_data.shape[1], test_data.shape[2])\n",
    "    \n",
    "    data_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False)\n",
    "    \n",
    "    def get_generated_and_actual_images(engine, data_batch):\n",
    "        with torch.no_grad():\n",
    "            sample_images_size = data_batch.shape\n",
    "            sample_images = torch.randn(sample_images_size).to(device)\n",
    "            samples = denoising_process(sample_images, variances, model).cpu()\n",
    "        \n",
    "            samples = torch.cat([samples, samples, samples], dim=1)\n",
    "            data_batch = torch.cat([data_batch, data_batch, data_batch], dim=1)\n",
    "            \n",
    "            # Resize images to 299x299\n",
    "            transform = transforms.Resize(size = (299, 299))\n",
    "            samples = transform(samples)\n",
    "            data_batch = transform(data_batch)\n",
    "        return samples, data_batch\n",
    "\n",
    "    engine = Engine(get_generated_and_actual_images)\n",
    "    \n",
    "    fid = FID(device=device)\n",
    "    fid.attach(engine, \"fid\")\n",
    "    \n",
    "    inception_score = InceptionScore(device=device, output_transform=lambda output: output[0])\n",
    "    inception_score.attach(engine, \"is\")\n",
    "    \n",
    "    engine.run(data_loader, max_epochs=1)\n",
    "    metrics = engine.state.metrics\n",
    "\n",
    "    fid_score = metrics[\"fid\"]\n",
    "    is_score = metrics[\"is\"]\n",
    "    \n",
    "    return fid_score, is_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c92dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: This takes too long to run, thus it had to be interrupted. \n",
    "fid_score, is_score = get_image_generation_metrics()\n",
    "print(f\"   FID: {fid_score:.3e}\")\n",
    "print(f\"   IS:  {is_score:.3e}\")"
   ]
  }
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