{"cells": [{"cell_type": "markdown", "id": "0a656fd6", "metadata": {"papermill": {"duration": 0.011815, "end_time": "2023-10-11T16:11:21.214413", "exception": false, "start_time": "2023-10-11T16:11:21.202598", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 8: Deep Autoencoders\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-10-11T16:09:06.704365\n", "\n", "In this tutorial, we will take a closer look at autoencoders (AE).\n", "Autoencoders are trained on encoding input data such as images into a smaller feature vector,\n", "and afterward, reconstruct it by a second neural network, called a decoder.\n", "The feature vector is called the \"bottleneck\" of the network as we aim to compress the input data into a smaller amount of features.\n", "This property is useful in many applications, in particular in compressing data or comparing images on a metric beyond pixel-level comparisons.\n", "Besides learning about the autoencoder framework, we will also see the \"deconvolution\"\n", "(or transposed convolution) operator in action for scaling up feature maps in height and width.\n", "Such deconvolution networks are necessary wherever we start from a small feature vector\n", "and need to output an image of full size (e.g. in VAE, GANs, or super-resolution applications).\n", "This notebook is part of a lecture series on Deep Learning at the University of Amsterdam.\n", "The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.\n", "\n", "\n", "---\n", "Open in [![Open In Colab](data:image/png;base64,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){height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/course_UvA-DL/08-deep-autoencoders.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://www.pytorchlightning.ai/community)"]}, {"cell_type": "markdown", "id": "97a275e9", "metadata": {"papermill": {"duration": 0.009291, "end_time": "2023-10-11T16:11:21.233282", "exception": false, "start_time": "2023-10-11T16:11:21.223991", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "de4ed19b", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-10-11T16:11:21.253221Z", "iopub.status.busy": "2023-10-11T16:11:21.253004Z", "iopub.status.idle": "2023-10-11T16:13:06.778298Z", "shell.execute_reply": "2023-10-11T16:13:06.774117Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 105.538324, "end_time": "2023-10-11T16:13:06.780905", "exception": false, "start_time": "2023-10-11T16:11:21.242581", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}], "source": ["! pip install --quiet \"torchvision\" \"matplotlib\" \"seaborn\" \"torchmetrics>=0.7, <1.3\" \"torch>=1.8.1, <2.1.0\" \"pytorch-lightning>=1.4, <2.1.0\" \"urllib3\" \"tensorboard\" \"lightning>=2.0.0\" \"matplotlib>=3.0.0, <3.9.0\" \"setuptools>=68.0.0, <68.3.0\" \"ipython[notebook]>=8.0.0, <8.17.0\""]}, {"cell_type": "markdown", "id": "875907c8", "metadata": {"papermill": {"duration": 0.007916, "end_time": "2023-10-11T16:13:06.798202", "exception": false, "start_time": "2023-10-11T16:13:06.790286", "status": "completed"}, "tags": []}, "source": ["
"]}, {"cell_type": "code", "execution_count": 2, "id": "6a439f15", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:06.815535Z", "iopub.status.busy": "2023-10-11T16:13:06.814915Z", "iopub.status.idle": "2023-10-11T16:13:11.485209Z", "shell.execute_reply": "2023-10-11T16:13:11.484141Z"}, "papermill": {"duration": 4.68102, "end_time": "2023-10-11T16:13:11.487076", "exception": false, "start_time": "2023-10-11T16:13:06.806056", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Device: cuda:0\n"]}], "source": ["import os\n", "import urllib.request\n", "from urllib.error import HTTPError\n", "\n", "import lightning as L\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import matplotlib_inline.backend_inline\n", "import seaborn as sns\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "import torch.utils.data as data\n", "import torchvision\n", "from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint\n", "from torch.utils.tensorboard import SummaryWriter\n", "from torchvision import transforms\n", "from torchvision.datasets import CIFAR10\n", "from tqdm.notebook import tqdm\n", "\n", "%matplotlib inline\n", "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "matplotlib.rcParams[\"lines.linewidth\"] = 2.0\n", "sns.reset_orig()\n", "sns.set()\n", "\n", "# Tensorboard extension (for visualization purposes later)\n", "%load_ext tensorboard\n", "\n", "# Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)\n", "DATASET_PATH = os.environ.get(\"PATH_DATASETS\", \"data\")\n", "# Path to the folder where the pretrained models are saved\n", "CHECKPOINT_PATH = os.environ.get(\"PATH_CHECKPOINT\", \"saved_models/tutorial9\")\n", "\n", "# Setting the seed\n", "L.seed_everything(42)\n", "\n", "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n", "torch.backends.cudnn.deterministic = True\n", "torch.backends.cudnn.benchmark = False\n", "\n", "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "print(\"Device:\", device)"]}, {"cell_type": "markdown", "id": "640b9a93", "metadata": {"papermill": {"duration": 0.017522, "end_time": "2023-10-11T16:13:11.515989", "exception": false, "start_time": "2023-10-11T16:13:11.498467", "status": "completed"}, "tags": []}, "source": ["We have 4 pretrained models that we have to download.\n", "Remember the adjust the variables `DATASET_PATH` and `CHECKPOINT_PATH` if needed."]}, {"cell_type": "code", "execution_count": 3, "id": "48708914", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:11.582293Z", "iopub.status.busy": "2023-10-11T16:13:11.581695Z", "iopub.status.idle": "2023-10-11T16:13:13.120216Z", "shell.execute_reply": "2023-10-11T16:13:13.119147Z"}, "papermill": {"duration": 1.549981, "end_time": "2023-10-11T16:13:13.122797", "exception": false, "start_time": "2023-10-11T16:13:11.572816", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_64.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_128.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_256.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_384.ckpt...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/\"\n", "# Files to download\n", "pretrained_files = [\"cifar10_64.ckpt\", \"cifar10_128.ckpt\", \"cifar10_256.ckpt\", \"cifar10_384.ckpt\"]\n", "# Create checkpoint path if it doesn't exist yet\n", "os.makedirs(CHECKPOINT_PATH, exist_ok=True)\n", "\n", "# For each file, check whether it already exists. If not, try downloading it.\n", "for file_name in pretrained_files:\n", " file_path = os.path.join(CHECKPOINT_PATH, file_name)\n", " if not os.path.isfile(file_path):\n", " file_url = base_url + file_name\n", " print(\"Downloading %s...\" % file_url)\n", " try:\n", " urllib.request.urlretrieve(file_url, file_path)\n", " except HTTPError as e:\n", " print(\n", " \"Something went wrong. Please try to download the files manually,\"\n", " \" or contact the author with the full output including the following error:\\n\",\n", " e,\n", " )"]}, {"cell_type": "markdown", "id": "840bbb63", "metadata": {"papermill": {"duration": 0.015951, "end_time": "2023-10-11T16:13:13.156017", "exception": false, "start_time": "2023-10-11T16:13:13.140066", "status": "completed"}, "tags": []}, "source": ["In this tutorial, we work with the CIFAR10 dataset.\n", "In CIFAR10, each image has 3 color channels and is 32x32 pixels large.\n", "As autoencoders do not have the constrain of modeling images probabilistic, we can work on more complex image data\n", "(i.e. 3 color channels instead of black-and-white) much easier than for VAEs.\n", "In case you have downloaded CIFAR10 already in a different directory, make sure to set DATASET_PATH\n", "accordingly to prevent another download.\n", "\n", "In contrast to previous tutorials on CIFAR10 like\n", "[Tutorial 5](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial5/Inception_ResNet_DenseNet.html)\n", "(CNN classification), we do not normalize the data explicitly with a mean of 0 and std of 1,\n", "but roughly estimate it scaling the data between -1 and 1.\n", "This is because limiting the range will make our task of predicting/reconstructing images easier."]}, {"cell_type": "code", "execution_count": 4, "id": "ff2340f5", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:13.184489Z", "iopub.status.busy": "2023-10-11T16:13:13.184137Z", "iopub.status.idle": "2023-10-11T16:13:20.807579Z", "shell.execute_reply": "2023-10-11T16:13:20.806392Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 7.638335, "end_time": "2023-10-11T16:13:20.810244", "exception": false, "start_time": "2023-10-11T16:13:13.171909", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /__w/15/s/.datasets/cifar-10-python.tar.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 0%| | 0/170498071 [00:00 only make them a tensor\n", "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n", "\n", "# Loading the training dataset. We need to split it into a training and validation part\n", "train_dataset = CIFAR10(root=DATASET_PATH, train=True, transform=transform, download=True)\n", "L.seed_everything(42)\n", "train_set, val_set = torch.utils.data.random_split(train_dataset, [45000, 5000])\n", "\n", "# Loading the test set\n", "test_set = CIFAR10(root=DATASET_PATH, train=False, transform=transform, download=True)\n", "\n", "# We define a set of data loaders that we can use for various purposes later.\n", "train_loader = data.DataLoader(train_set, batch_size=256, shuffle=True, drop_last=True, pin_memory=True, num_workers=4)\n", "val_loader = data.DataLoader(val_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)\n", "test_loader = data.DataLoader(test_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)\n", "\n", "\n", "def get_train_images(num):\n", " return torch.stack([train_dataset[i][0] for i in range(num)], dim=0)"]}, {"cell_type": "markdown", "id": "c2fe5865", "metadata": {"papermill": {"duration": 0.021098, "end_time": "2023-10-11T16:13:20.853753", "exception": false, "start_time": "2023-10-11T16:13:20.832655", "status": "completed"}, "tags": []}, "source": ["## Building the autoencoder\n", "\n", "In general, an autoencoder consists of an **encoder** that maps the input $x$ to a lower-dimensional feature vector $z$,\n", "and a **decoder** that reconstructs the input $\\hat{x}$ from $z$.\n", "We train the model by comparing $x$ to $\\hat{x}$ and optimizing the parameters to increase the similarity between $x$ and $\\hat{x}$.\n", "See below for a small illustration of the autoencoder framework."]}, {"cell_type": "markdown", "id": "551e1193", "metadata": {"papermill": {"duration": 0.017204, "end_time": "2023-10-11T16:13:20.892663", "exception": false, "start_time": "2023-10-11T16:13:20.875459", "status": "completed"}, "tags": []}, "source": ["
"]}, {"cell_type": "markdown", "id": "f74dc681", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009391, "end_time": "2023-10-11T16:13:20.912447", "exception": false, "start_time": "2023-10-11T16:13:20.903056", "status": "completed"}, "tags": []}, "source": ["We first start by implementing the encoder.\n", "The encoder effectively consists of a deep convolutional network, where we scale down the image layer-by-layer using strided convolutions.\n", "After downscaling the image three times, we flatten the features and apply linear layers.\n", "The latent representation $z$ is therefore a vector of size *d* which can be flexibly selected."]}, {"cell_type": "code", "execution_count": 5, "id": "cf346a22", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:20.933950Z", "iopub.status.busy": "2023-10-11T16:13:20.933758Z", "iopub.status.idle": "2023-10-11T16:13:20.940641Z", "shell.execute_reply": "2023-10-11T16:13:20.940087Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.019984, "end_time": "2023-10-11T16:13:20.941951", "exception": false, "start_time": "2023-10-11T16:13:20.921967", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Encoder(nn.Module):\n", " def __init__(self, num_input_channels: int, base_channel_size: int, latent_dim: int, act_fn: object = nn.GELU):\n", " \"\"\"Encoder.\n", "\n", " Args:\n", " num_input_channels : Number of input channels of the image. For CIFAR, this parameter is 3\n", " base_channel_size : Number of channels we use in the first convolutional layers. Deeper layers might use a duplicate of it.\n", " latent_dim : Dimensionality of latent representation z\n", " act_fn : Activation function used throughout the encoder network\n", " \"\"\"\n", " super().__init__()\n", " c_hid = base_channel_size\n", " self.net = nn.Sequential(\n", " nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2), # 32x32 => 16x16\n", " act_fn(),\n", " nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.Conv2d(c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2), # 16x16 => 8x8\n", " act_fn(),\n", " nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2), # 8x8 => 4x4\n", " act_fn(),\n", " nn.Flatten(), # Image grid to single feature vector\n", " nn.Linear(2 * 16 * c_hid, latent_dim),\n", " )\n", "\n", " def forward(self, x):\n", " return self.net(x)"]}, {"cell_type": "markdown", "id": "2e80190b", "metadata": {"papermill": {"duration": 0.00972, "end_time": "2023-10-11T16:13:20.961630", "exception": false, "start_time": "2023-10-11T16:13:20.951910", "status": "completed"}, "tags": []}, "source": ["Note that we do not apply Batch Normalization here.\n", "This is because we want the encoding of each image to be independent of all the other images.\n", "Otherwise, we might introduce correlations into the encoding or decoding that we do not want to have.\n", "In some implementations, you still can see Batch Normalization being used, because it can also serve as a form of regularization.\n", "Nevertheless, the better practice is to go with other normalization techniques if necessary like Instance Normalization or Layer Normalization.\n", "Given the small size of the model, we can neglect normalization for now."]}, {"cell_type": "markdown", "id": "ba11da37", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009437, "end_time": "2023-10-11T16:13:20.980516", "exception": false, "start_time": "2023-10-11T16:13:20.971079", "status": "completed"}, "tags": []}, "source": ["The decoder is a mirrored, flipped version of the encoder.\n", "The only difference is that we replace strided convolutions by transposed convolutions\n", "(i.e. deconvolutions) to upscale the features.\n", "Transposed convolutions can be imagined as adding the stride to the input instead of the output,\n", "and can thus upscale the input.\n", "For an illustration of a `nn.ConvTranspose2d` layer with kernel size 3, stride 2, and padding 1,\n", "see below (figure credit - [Vincent Dumoulin and Francesco Visin](https://arxiv.org/abs/1603.07285)):\n", "\n", "
\n", "\n", "You see that for an input of size $3\\times3$, we obtain an output of $5\\times5$.\n", "However, to truly have a reverse operation of the convolution,\n", "we need to ensure that the layer scales the input shape by a factor of 2 (e.g. $4\\times4\\to8\\times8$).\n", "For this, we can specify the parameter `output_padding` which adds additional values to the output shape.\n", "Note that we do not perform zero-padding with this, but rather increase the output shape for calculation.\n", "\n", "Overall, the decoder can be implemented as follows:"]}, {"cell_type": "code", "execution_count": 6, "id": "24644eda", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:21.000529Z", "iopub.status.busy": "2023-10-11T16:13:21.000300Z", "iopub.status.idle": "2023-10-11T16:13:21.014993Z", "shell.execute_reply": "2023-10-11T16:13:21.013688Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.026477, "end_time": "2023-10-11T16:13:21.016438", "exception": false, "start_time": "2023-10-11T16:13:20.989961", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Decoder(nn.Module):\n", " def __init__(self, num_input_channels: int, base_channel_size: int, latent_dim: int, act_fn: object = nn.GELU):\n", " \"\"\"Decoder.\n", "\n", " Args:\n", " num_input_channels : Number of channels of the image to reconstruct. For CIFAR, this parameter is 3\n", " base_channel_size : Number of channels we use in the last convolutional layers. Early layers might use a duplicate of it.\n", " latent_dim : Dimensionality of latent representation z\n", " act_fn : Activation function used throughout the decoder network\n", " \"\"\"\n", " super().__init__()\n", " c_hid = base_channel_size\n", " self.linear = nn.Sequential(nn.Linear(latent_dim, 2 * 16 * c_hid), act_fn())\n", " self.net = nn.Sequential(\n", " nn.ConvTranspose2d(\n", " 2 * c_hid, 2 * c_hid, kernel_size=3, output_padding=1, padding=1, stride=2\n", " ), # 4x4 => 8x8\n", " act_fn(),\n", " nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), # 8x8 => 16x16\n", " act_fn(),\n", " nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.ConvTranspose2d(\n", " c_hid, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2\n", " ), # 16x16 => 32x32\n", " nn.Tanh(), # The input images is scaled between -1 and 1, hence the output has to be bounded as well\n", " )\n", "\n", " def forward(self, x):\n", " x = self.linear(x)\n", " x = x.reshape(x.shape[0], -1, 4, 4)\n", " x = self.net(x)\n", " return x"]}, {"cell_type": "markdown", "id": "12de3c0a", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009482, "end_time": "2023-10-11T16:13:21.035434", "exception": false, "start_time": "2023-10-11T16:13:21.025952", "status": "completed"}, "tags": []}, "source": ["The encoder and decoder networks we chose here are relatively simple.\n", "Usually, more complex networks are applied, especially when using a ResNet-based architecture.\n", "For example, see [VQ-VAE](https://arxiv.org/abs/1711.00937) and\n", "[NVAE](https://arxiv.org/abs/2007.03898) (although the papers discuss architectures for VAEs,\n", "they can equally be applied to standard autoencoders).\n", "\n", "In a final step, we add the encoder and decoder together into the autoencoder architecture.\n", "We define the autoencoder as PyTorch Lightning Module to simplify the needed training code:"]}, {"cell_type": "code", "execution_count": 7, "id": "55c87e75", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:21.055887Z", "iopub.status.busy": "2023-10-11T16:13:21.055537Z", "iopub.status.idle": "2023-10-11T16:13:21.071433Z", "shell.execute_reply": "2023-10-11T16:13:21.070426Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.027945, "end_time": "2023-10-11T16:13:21.072875", "exception": false, "start_time": "2023-10-11T16:13:21.044930", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Autoencoder(L.LightningModule):\n", " def __init__(\n", " self,\n", " base_channel_size: int,\n", " latent_dim: int,\n", " encoder_class: object = Encoder,\n", " decoder_class: object = Decoder,\n", " num_input_channels: int = 3,\n", " width: int = 32,\n", " height: int = 32,\n", " ):\n", " super().__init__()\n", " # Saving hyperparameters of autoencoder\n", " self.save_hyperparameters()\n", " # Creating encoder and decoder\n", " self.encoder = encoder_class(num_input_channels, base_channel_size, latent_dim)\n", " self.decoder = decoder_class(num_input_channels, base_channel_size, latent_dim)\n", " # Example input array needed for visualizing the graph of the network\n", " self.example_input_array = torch.zeros(2, num_input_channels, width, height)\n", "\n", " def forward(self, x):\n", " \"\"\"The forward function takes in an image and returns the reconstructed image.\"\"\"\n", " z = self.encoder(x)\n", " x_hat = self.decoder(z)\n", " return x_hat\n", "\n", " def _get_reconstruction_loss(self, batch):\n", " \"\"\"Given a batch of images, this function returns the reconstruction loss (MSE in our case).\"\"\"\n", " x, _ = batch # We do not need the labels\n", " x_hat = self.forward(x)\n", " loss = F.mse_loss(x, x_hat, reduction=\"none\")\n", " loss = loss.sum(dim=[1, 2, 3]).mean(dim=[0])\n", " return loss\n", "\n", " def configure_optimizers(self):\n", " optimizer = optim.Adam(self.parameters(), lr=1e-3)\n", " # Using a scheduler is optional but can be helpful.\n", " # The scheduler reduces the LR if the validation performance hasn't improved for the last N epochs\n", " scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"min\", factor=0.2, patience=20, min_lr=5e-5)\n", " return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler, \"monitor\": \"val_loss\"}\n", "\n", " def training_step(self, batch, batch_idx):\n", " loss = self._get_reconstruction_loss(batch)\n", " self.log(\"train_loss\", loss)\n", " return loss\n", "\n", " def validation_step(self, batch, batch_idx):\n", " loss = self._get_reconstruction_loss(batch)\n", " self.log(\"val_loss\", loss)\n", "\n", " def test_step(self, batch, batch_idx):\n", " loss = self._get_reconstruction_loss(batch)\n", " self.log(\"test_loss\", loss)"]}, {"cell_type": "markdown", "id": "8414e9fd", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009535, "end_time": "2023-10-11T16:13:21.092043", "exception": false, "start_time": "2023-10-11T16:13:21.082508", "status": "completed"}, "tags": []}, "source": ["For the loss function, we use the mean squared error (MSE).\n", "The mean squared error pushes the network to pay special attention to those pixel values its estimate is far away.\n", "Predicting 127 instead of 128 is not important when reconstructing, but confusing 0 with 128 is much worse.\n", "Note that in contrast to VAEs, we do not predict the probability per pixel value, but instead use a distance measure.\n", "This saves a lot of parameters and simplifies training.\n", "To get a better intuition per pixel, we report the summed squared error averaged over the batch dimension\n", "(any other mean/sum leads to the same result/parameters).\n", "\n", "However, MSE has also some considerable disadvantages.\n", "Usually, MSE leads to blurry images where small noise/high-frequent patterns are removed as those cause a very low error.\n", "To ensure realistic images to be reconstructed, one could combine Generative Adversarial Networks\n", "(lecture 10) with autoencoders as done in several works (e.g. see [here](https://arxiv.org/abs/1704.02304),\n", "[here](https://arxiv.org/abs/1511.05644) or these [slides](http://elarosca.net/slides/iccv_autoencoder_gans.pdf)).\n", "Additionally, comparing two images using MSE does not necessarily reflect their visual similarity.\n", "For instance, suppose the autoencoder reconstructs an image shifted by one pixel to the right and bottom.\n", "Although the images are almost identical, we can get a higher loss than predicting a constant pixel value for half of the image (see code below).\n", "An example solution for this issue includes using a separate, pre-trained CNN,\n", "and use a distance of visual features in lower layers as a distance measure instead of the original pixel-level comparison."]}, {"cell_type": "code", "execution_count": 8, "id": "f6511b7f", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:21.112719Z", "iopub.status.busy": "2023-10-11T16:13:21.112064Z", "iopub.status.idle": "2023-10-11T16:13:22.038754Z", "shell.execute_reply": "2023-10-11T16:13:22.037935Z"}, "papermill": {"duration": 0.938648, "end_time": "2023-10-11T16:13:22.040167", "exception": false, "start_time": "2023-10-11T16:13:21.101519", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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", 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", 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qpLAcbE9zXiZfWme5Oh7PB8n2rtgRUrsCFIW5M85z4SjTVka0FnY2SGpcbG+O/VhK0IVuXEaKI5CfqSI8oKTJzCYK4o+cHnIqA2Hqmq50chtVcaeezDWbi7czSWbrvkixmcJ5XTiz/gxTZrV5J89yotSpCO+xZ0vQ0Dmunr2WWWh0mxO8pITPxk5PTr5XM+shORUJqWJaV8FpFJliCdsSX1NRU5p6Gf778u7xO37+ASxzfHMKZW5kc3RyZWFtCmVuZG9iagozMSAwIG9iago8PCAvTGVuZ3RoIDE4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM2tFAwgMMUQ640AB3mA1IKZW5kc3RyZWFtCmVuZG9iagozMiAwIG9iago8PCAvTGVuZ3RoIDI1MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwtUUlyA0EIu88r9IRmp99jlyuH5P/XCMoHBg2LQHRa4qCMnyAsV7zlkatow98zMYLfBYd+K9dtWORAVCBJY1A1oXbxevQe2HGYCcyT1rAMZqwP/Iwp3OjF4TEZZ7fXZdQQ7F2vPZlByaxcxCUTF0zVYSNnDj+ZMi60cz03IOdGWJdhkG5WGjMSjjSFSCGFqpukzgRBEoyuRo02chT7pS+PdIZVjagx7HMtbV/PTThr0OxYrPLklB5dcS4nFy+sHPT1NgMXUWms8kBIwP1uD/VzspPfeEvnzhbT43vNyfLCVGDFm9duQDbV4t+8iOP7jK/n5/n8A19gW4gKZW5kc3RyZWFtCmVuZG9iagozMyAwIG9iago8PCAvTGVuZ3RoIDIxNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNiAwIG9iago8PCAvVHlwZSAvRm9udCAvQmFzZUZvbnQgL0JNUVFEVitEZWphVnVTYW5zIC9GaXJzdENoYXIgMCAvTGFzdENoYXIgMjU1Ci9Gb250RGVzY3JpcHRvciAxNSAwIFIgL1N1YnR5cGUgL1R5cGUzIC9OYW1lIC9CTVFRRFYrRGVqYVZ1U2FucwovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdCi9DaGFyUHJvY3MgMTcgMCBSCi9FbmNvZGluZyA8PCAvVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ1IC9oeXBoZW4gL3BlcmlvZCA0OCAvemVybyA1MCAvdHdvIDUzIC9maXZlIDU3IC9uaW5lIC9jb2xvbgo3NiAvTCAvTSA5NyAvYSAxMDAgL2QgL2UgMTA3IC9rIDExMSAvbyAxMTUgL3MgXQo+PgovV2lkdGhzIDE0IDAgUiA+PgplbmRvYmoKMTUgMCBvYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9Gb250TmFtZSAvQk1RUURWK0RlamFWdVNhbnMgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0FzY2VudCA5MjkgL0Rlc2NlbnQgLTIzNiAvQ2FwSGVpZ2h0IDAKL1hIZWlnaHQgMCAvSXRhbGljQW5nbGUgMCAvU3RlbVYgMCAvTWF4V2lkdGggMTM0MiA+PgplbmRvYmoKMTQgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTcgMCBvYmoKPDwgL0wgMTggMCBSIC9NIDE5IDAgUiAvYSAyMCAwIFIgL2NvbG9uIDIxIDAgUiAvZCAyMiAwIFIgL2UgMjMgMCBSCi9maXZlIDI0IDAgUiAvaHlwaGVuIDI1IDAgUiAvayAyNiAwIFIgL25pbmUgMjcgMCBSIC9vIDI4IDAgUiAvcGVyaW9kIDI5IDAgUgovcyAzMCAwIFIgL3NwYWNlIDMxIDAgUiAvdHdvIDMyIDAgUiAvemVybyAzMyAwIFIgPj4KZW5kb2JqCjMgMCBvYmoKPDwgL0YxIDE2IDAgUiA+PgplbmRvYmoKNCAwIG9iago8PCAvQTEgPDwgL1R5cGUgL0V4dEdTdGF0ZSAvQ0EgMSAvY2EgMSA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCAvSTEgMTMgMCBSID4+CmVuZG9iagoxMyAwIG9iago8PCAvVHlwZSAvWE9iamVjdCAvU3VidHlwZSAvSW1hZ2UgL1dpZHRoIDMwMCAvSGVpZ2h0IDE1NAovQ29sb3JTcGFjZSAvRGV2aWNlUkdCIC9CaXRzUGVyQ29tcG9uZW50IDggL0ZpbHRlciAvRmxhdGVEZWNvZGUKL0RlY29kZVBhcm1zIDw8IC9QcmVkaWN0b3IgMTAgL0NvbG9ycyAzIC9Db2x1bW5zIDMwMCA+PiAvTGVuZ3RoIDM0IDAgUiA+PgpzdHJlYW0KeJztndlvXFdyxk/vG9nd3MVFFEnREi1Lsi2PLY819tjATCaGgQkmwSRB8pQ/IEBe8u4/I49JHpKHAI5jw0nGiMcLDHsce2TLki1RC0Vq4042m+y97715O99Xgm/jAgTmAEz9norkOZe3+0P17TpVp44xiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqL8QYn1+Ns/vP2+te9f/8ram0vfW9vzkjxl7MST1j5x8oy1B8eneVg2l7L24rVPrb1864q1O/sHPCVB/6g4ULJ2MpvnYS++/Kq1nziNm2lUtq197eplnuL7bWu3O01rf3ftWx5WrWxau9Vu4T7bCWtvb9V5ykEdV+t6mDIyMsjDBof6rO0F+7hyB2Oa9YCnvPfub80P8eabb/7g7xXn9JAm/ge8DUVRfgB1QkVxjDqhojgm2eNv1R3EUcNlhDHByBjsZImnTEyftLbnI6aJ+yJY8utdazd38V+CBoKoqeFRnjI9/QTsJ2asPTl1nIeNjuLeUqmMtbtlhI7Tx8d5SreLmLDZbFi7siuC0q0t3GcyncUfYogJB4czPCXbwNX2qrvWzmTF2+4HeDdSSVxhr1KxdrslYkLlKKFPQkVxjDqhojhGnVBRHNMrJuREVbsFu15HEDV7epJnHNQQR3HObXBEhI7JFJz/1KnT1r704+etPXVMBHul0gjuK+lZO58VYViSQqdYF7FWg26sxQk4Y/I5hIsDZQSi8yef4mHff3+DLo0rtFo13GRRJABTadh71XVrB6bNw3wfN72zg6s16kgtBhoSHl30SagojlEnVBTHqBMqimN6xYRdSprFugjDMumctfe2tnjK0LEpa584i8ze6LQIHVMcLXURX3W6CCOvPxJXrt3ZwLA4Yqob337Dwy6eQSD304svWDugoGpvr8JTVpYfWjudQgIwnS7ysOERvIR7929iWLZg7YNGjafs7eElJFMo0y2VRL1ro4Ekqocw1nS7vrUzmbRRjij6JFQUx6gTKopj1AkVxTHqhIrimF4LM806lhn6c1ixKA0ho/3cM8/ylOmTp6xdpVz5jTv3eFi1jqWIg11UNm/R1tvV1V2eUiwjWW/iyGK/86//xsPSf4mPlVdfetnaqRSWf8bHxSqRCbB8UtmtWvv3l6/wqCSt2RT6UXvQ9bDk0z4Q95ygj7jRUeTxPU8k67e2cQNxgzWbZBLqlAdEtYNylNAnoaI4Rp1QURyjTqgojukVE2YzaMfUSfRbu55DY6KlaoOnfP3JF9be3kbPoocP13lYKoHMdSqOlHSL9tc2mi2eMj6KW91YXbZ2UWaxqxTULS4tWXtiAiFlKiVe9cT0MWtPkr2yep+H3fgWYe0oXW15hYoK2j5P8enHbhPVDtmiKDrPJPE+N2hYsYhqAd7sqxwx9EmoKI5RJ1QUx6gTKopjesWE+TwCpPUK8my37yFY+u7aVZ4Sp3DLo33AjX1R2ZygOLDRRBRX2d+zdrUm+iwt30fH4UIewdLC/IK46S4iyU8/+dDaM3Nz1j69cJpnDA0hBcctmMolEYbFu7i32h4+vOq09bZR2ecpnod6dO53vL8nhpUo9stk0Taq3cYbWKuJTlnKUUKfhIriGHVCRXGMOqGiOKZXTFgeGrb2rXuL1n50F/m3fEpk8/ZqO9ber2IbbtwXCbTdKoKiCjX8TVJmcvjYGE/JFcvWnpp5xtrHcwkedvfrz6ydiCHr2PGQf9vYFNuFz5/HwTVPnELz4uPjIzys76UL1r5yfcXarSZqSlspmSc0CPa4w+/q6iMels4g+CwPoi734ICaPjVEPlY5SuiTUFEco06oKI5RJ1QUx6gTKopjei3M3L6Nauzrd25Z++HqbWt7VZGFL5bRemzh1Ky1z505x8NWN5B6Xt7EFUaPoTzgxPwcTynSTuK1XUwJtpZ42MoKlkw2KliAoSZs5henz/CU2gHWPHws35igLbbeXvsMSz6nFrCVeWyybO3Pv/iYp6ytoQ6h3aGDqBriyrtUdJ7vx9X8AMs8tbp4n5WjhD4JFcUx6oSK4hh1QkVxTK+Y8POPfoNxx1AnPX/mvLVzLZGePvMUGj0tnMaxSl5TpNSDOGLCmuE21Uh8JxJlntLpIqNd20dJQKnV5WHcdmllA22Xsn1os10qDvCUk/OIXQP6VGpURM309d9dxrAGXvW511+39vmnT/KURhs137dvLVs7n+/jYaXyEP2EqLRKh/vy2U/KEUOfhIriGHVCRXGMOqGiOKZXTLhxD9HahWdwxFImg8rmQXmBiQlskN2pIP1179YOD2v7CPDiMURBiSRiLS8QpeHtLm8XRrQWeCIo7Suh6HyLCqDjGSQw/cePvaUf6WJ9WXEq0+zktLWzCUyJG2w+Pn9ulqcMDJSt/Xbjv629+kj0CJ4aQzNiL4ak5eQkXvLeXtUIbhvlqKBPQkVxjDqhojhGnVBRHKNOqCiO6dltrR8HCaVo8aJSwZb5zFCZp9TphOcmdsyb3GA/D8v46MBtGliYCeh2mh2RK8/m8Lc4bZn34+Il9A1hkSMdYDUokUOCPkiLygE/hn8U87B+k0iIK6cKaPWd64PdbaFLwPbDNZ4yVEDR+a/eQE7/f7+5y8MOqJ67SbUHLdpNP0CNBZQjhj4JFcUx6oSK4hh1QkVxTK+YcPwEttXG4lTZTD2z1/fEFdJ0ni6XXMdS4uykxj7iqE6AKydTmNJNiAbYhRJS56MNJLuDHdGGjPfOxnxcOZ/LWTsuQkLRB82jpmzxlCw6p3N3D2p4B2LUSC4jL13dQIiYK6BK+9VLT/OwG7exEfnqd6vW3t9DsUGaStuVI4Y+CRXFMeqEiuIYdUJFcUyvmDCIIcLpUKxV30dElKVYyxizX922druBCux6VZxDlKI0YX8Bsd/IAAKn0mCBp4yU8Y+8ZNnajYzY1LszM2Htlof4ylDW0euKPks+JS09Oi4qJmPC8iCypr5HV2vjBkpl8W6kYwjkKrRDN2iLA6eePYP2VuV+vBvvvIOa78110TVcOUrok1BRHKNOqCiOUSdUFMf0iglNB7FT0oddopTV8VKMZzw5hxLNPgoXkzHh7Qd7FWs367BzBfyXhVODNMNMz6BtVDw1g0tVKmLYBGLChaV1axcHcTODgyWekkwih+lTiWwg04nZvry1uw3EgXGakoqLl9k0iIqHRlA9e1AXZbG1XaQTp0aRaP3TP/lja7/17vtGOaLok1BRHKNOqCiOUSdUFMeoEyqKY3otzLx26UfWPvnUM9Z++OCBtacmuXu0OX1q3trHRrClNRGI9Zv9/Yq1W5RGj8UxrK8gkvV9fVgOSqaxypLyRea9UcOG4+fOzVh79jT6oHX8Dk/hrttdHysuQVLcczKF96rTwGqM38HV4klxz7EsXSGJ/9KS5z3xypDXrlh7dASNul/56Qs85a3/+NAoRwV9EiqKY9QJFcUx6oSK4pheMeGPnn7S2mcvPGPtxjkEfrzX1ogG1iaIISKKJ1I8bLCAkmXa0ys+EnxftNbuUgW5oTCs1RKbek/On7B2nrpuN2o4ICmQvaFMDD8GMQR7nmzU3aWX41NSv03tmDxfHLcUp6gyTi+uui2S9StLN6z9k1ees3a9g6r3fFYEqGH87NIl/vHshYvWbjRQNc6qiXfZmDi9zDSplkiKz+soqnkdUVufpEqGTapPWF17xMNYtUQG5exCtZhQkFXrHKBQ/tp//jMPa+1uWntkGkUd4ydRINGgU7GMMUmDlYg7N+5Ze2XpHg8TqlFL+JUNUakfhj4JFcUx6oSK4hh1QkVxTK+YMEeZur4svp0X8jQrKRvpUhgV45gwFpPD8L3Z75BNYVhMFkN3KXihbKIJZGl4P20L7tK3c8+n+/TFzQQUBsT50l0xzEumaAq9TtoiHPNFRJGhf5rycJ99j51bvI6ocvMOFXMvoGZ9My52RYeReyy5GkE1Xx5RFaYaS2aiqdaV8WaYaiyZiaZaICO3MNVYMhNNNZbMhKvGkplDq6ZPQkVxjDqhojhGnVBRHKNOqCiO6bUwUywjYg4SKDKut5CCDFriUOtWC/FujU6rbndEyXKziR+7dJBTh7LwHTmlTrvR6zXEu12Z0++nXfP9pbK1B4pIyGbToh24xyXgMeSXE0bkmov9yNtureNVNykJ7stq8pihPfsephT7RXPxE9MoXajX8aYFVE1e7hdlAGGwZCaaaiyZCVeNJTPRVKvLBgJhqvXLRgdRVPPk+xymGktmoqnGkplw1Vgyc2jV9EmoKI5RJ1QUx6gTKopjesWEb739nrW91MfW3tlBF7ODvU2ewq3HONhYWxNH2HqUIR4aHbP2wDBCmow8KLe2U7H24s3vrV09EDWyx2dnrJ1II1db6seV5+ZO8JSp4/h+P3dyCjeWEcn6/iwFeGUKYxLUp9wT24W56DlBVzs2O8LDskWELp0AiWMK6MzgkCiUD4MlM9FUi8tkfZhqnkzqR1GNJTPhqrFkJppqLJkJV40lM9FUe6xOPUw1lswcWjV9EiqKY9QJFcUx6oSK4pheMeFvPvjU2uXjC9YOPHyhv/zpb3nKzHF8Ox8eGrb2wwciJuxSyWx+CE27C3Qo0vqD+zzlZxdfsvazT5+1dr3V5GFxase0tLJs7cWbt6397dXLPKVcQjLn13/+Z9Z++expHpamfaxT49PWblN0wY2qjKxs7nCZeFLWeQ8gwMhRAbSfQHgmKpHDYclMNNVYMhOuWlfWpkdRjSUz4aqxZCaaaiyZCVeNJTPRVPPlTu4w1Vgyc2jV9EmoKI5RJ1QUx6gTKopjesWEf/HXf2PtzNgpa9erOAH31rff8JQJ+todpy/KuaxImLR97Ik8fQ5XHhxHv+D6iDiV6Zdv/JG18/1o/luTMSHv1+3SPtRmF8PW17d5yvISGg0VCrjP1Qdi2N1rN60db+Jqd9aQf3vxF8/zlBOzk9bueFTfKPNXJoVgI0aVhyaG36djjzVk+mFYMhNNNZbMhKvGkploqrFkJlw1ucU6kmosmQlXjSUz0VRjyUwP1VIiQj6kavokVBTHqBMqimPUCRXFMeqEiuKYXgszmTRcdPH6VWtXKwjxA5ncbFP57wFtD43JbmvZDNKYnVrV2pVNXG19RTQ5fu+/UJq8u4/toRU64MkYUyyhSLc0gEWCQhE7Mh88EGH96DBi8WwJawwfvyuKoXduXrG210bJ761VZLQfHIjuWqfOYPWiVMJp26UBsY01n0fmt9SH6D+VTdAYsQ84DJbMRFOtLTf1hqnGkploqrFkJlw1lsxEU40lM+GqsWQmmmosmQlXjSUzh1ZNn4SK4hh1QkVxjDqhojimV0y4v40o4oN/f9fa91ZxUm+8I5r5XLmCUMFQRNHtiqZJnNB8/90PrJ1O4Tv0sxee4xntdL+195r4p0v3NnjY1hZ2jrabSJU+XF229t3l73nK8xdwIPHf/e3fW/uLzz/jYd3KlrWr1CipQX2d73wp4tiPv0IY05dEQJJKizbPfPZQfx9Cr+MncLrwr379VyYCLJmJppqQzISrFhPp6SiqsWQmXDWWzERTjSUz4aqxZCaaaiyZCVeNJTOHVk2fhIriGHVCRXGMOqGiOKZXTDgxhtNMT83OWTug03aScVGimhCn88LDA9kmKJ2lTZkppFwmJ5H/ee3113lKMU8ZmyxSSd9d/YaHLd66Y+3xqRlrN2l/ZyKX5ylXF6/jaouL1s7PnOFhjx5hG+tAGTcwSk1pC1SjbIzZppBm+8Eta29srfOwpkd7f6mc+dEu1Ln080gn9bJkJppqCZnCDVN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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["def compare_imgs(img1, img2, title_prefix=\"\"):\n", " # Calculate MSE loss between both images\n", " loss = F.mse_loss(img1, img2, reduction=\"sum\")\n", " # Plot images for visual comparison\n", " grid = torchvision.utils.make_grid(torch.stack([img1, img2], dim=0), nrow=2, normalize=True, range=(-1, 1))\n", " grid = grid.permute(1, 2, 0)\n", " plt.figure(figsize=(4, 2))\n", " plt.title(f\"{title_prefix} Loss: {loss.item():4.2f}\")\n", " plt.imshow(grid)\n", " plt.axis(\"off\")\n", " plt.show()\n", "\n", "\n", "for i in range(2):\n", " # Load example image\n", " img, _ = train_dataset[i]\n", " img_mean = img.mean(dim=[1, 2], keepdims=True)\n", "\n", " # Shift image by one pixel\n", " SHIFT = 1\n", " img_shifted = torch.roll(img, shifts=SHIFT, dims=1)\n", " img_shifted = torch.roll(img_shifted, shifts=SHIFT, dims=2)\n", " img_shifted[:, :1, :] = img_mean\n", " img_shifted[:, :, :1] = img_mean\n", " compare_imgs(img, img_shifted, \"Shifted -\")\n", "\n", " # Set half of the image to zero\n", " img_masked = img.clone()\n", " img_masked[:, : img_masked.shape[1] // 2, :] = img_mean\n", " compare_imgs(img, img_masked, \"Masked -\")"]}, {"cell_type": "markdown", "id": "6d57cfd4", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.013427, "end_time": "2023-10-11T16:13:22.070853", "exception": false, "start_time": "2023-10-11T16:13:22.057426", "status": "completed"}, "tags": []}, "source": ["### Training the model\n", "\n", "During the training, we want to keep track of the learning progress by seeing reconstructions made by our model.\n", "For this, we implement a callback object in PyTorch Lightning which will add reconstructions every $N$ epochs to our tensorboard:"]}, {"cell_type": "code", "execution_count": 9, "id": "eef06bb1", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:22.105601Z", "iopub.status.busy": "2023-10-11T16:13:22.105423Z", "iopub.status.idle": "2023-10-11T16:13:22.110773Z", "shell.execute_reply": "2023-10-11T16:13:22.110253Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.02265, "end_time": "2023-10-11T16:13:22.111943", "exception": false, "start_time": "2023-10-11T16:13:22.089293", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class GenerateCallback(Callback):\n", " def __init__(self, input_imgs, every_n_epochs=1):\n", " super().__init__()\n", " self.input_imgs = input_imgs # Images to reconstruct during training\n", " # Only save those images every N epochs (otherwise tensorboard gets quite large)\n", " self.every_n_epochs = every_n_epochs\n", "\n", " def on_train_epoch_end(self, trainer, pl_module):\n", " if trainer.current_epoch % self.every_n_epochs == 0:\n", " # Reconstruct images\n", " input_imgs = self.input_imgs.to(pl_module.device)\n", " with torch.no_grad():\n", " pl_module.eval()\n", " reconst_imgs = pl_module(input_imgs)\n", " pl_module.train()\n", " # Plot and add to tensorboard\n", " imgs = torch.stack([input_imgs, reconst_imgs], dim=1).flatten(0, 1)\n", " grid = torchvision.utils.make_grid(imgs, nrow=2, normalize=True, range=(-1, 1))\n", " trainer.logger.experiment.add_image(\"Reconstructions\", grid, global_step=trainer.global_step)"]}, {"cell_type": "markdown", "id": "4c0afd63", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.012321, "end_time": "2023-10-11T16:13:22.136561", "exception": false, "start_time": "2023-10-11T16:13:22.124240", "status": "completed"}, "tags": []}, "source": ["We will now write a training function that allows us to train the autoencoder with different latent dimensionality\n", "and returns both the test and validation score.\n", "We provide pre-trained models and recommend you using those, especially when you work on a computer without GPU.\n", "Of course, feel free to train your own models on Lisa."]}, {"cell_type": "code", "execution_count": 10, "id": "ff9ecb48", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:22.162297Z", "iopub.status.busy": "2023-10-11T16:13:22.162131Z", "iopub.status.idle": "2023-10-11T16:13:22.167574Z", "shell.execute_reply": "2023-10-11T16:13:22.166976Z"}, "papermill": {"duration": 0.019835, "end_time": "2023-10-11T16:13:22.168749", "exception": false, "start_time": "2023-10-11T16:13:22.148914", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_cifar(latent_dim):\n", " # Create a PyTorch Lightning trainer with the generation callback\n", " trainer = L.Trainer(\n", " default_root_dir=os.path.join(CHECKPOINT_PATH, \"cifar10_%i\" % latent_dim),\n", " accelerator=\"auto\",\n", " devices=1,\n", " max_epochs=500,\n", " callbacks=[\n", " ModelCheckpoint(save_weights_only=True),\n", " GenerateCallback(get_train_images(8), every_n_epochs=10),\n", " LearningRateMonitor(\"epoch\"),\n", " ],\n", " )\n", " trainer.logger._log_graph = True # If True, we plot the computation graph in tensorboard\n", " trainer.logger._default_hp_metric = None # Optional logging argument that we don't need\n", "\n", " # Check whether pretrained model exists. If yes, load it and skip training\n", " pretrained_filename = os.path.join(CHECKPOINT_PATH, \"cifar10_%i.ckpt\" % latent_dim)\n", " if os.path.isfile(pretrained_filename):\n", " print(\"Found pretrained model, loading...\")\n", " model = Autoencoder.load_from_checkpoint(pretrained_filename)\n", " else:\n", " model = Autoencoder(base_channel_size=32, latent_dim=latent_dim)\n", " trainer.fit(model, train_loader, val_loader)\n", " # Test best model on validation and test set\n", " val_result = trainer.test(model, dataloaders=val_loader, verbose=False)\n", " test_result = trainer.test(model, dataloaders=test_loader, verbose=False)\n", " result = {\"test\": test_result, \"val\": val_result}\n", " return model, result"]}, {"cell_type": "markdown", "id": "7babc0d7", "metadata": {"papermill": {"duration": 0.012319, "end_time": "2023-10-11T16:13:22.193575", "exception": false, "start_time": "2023-10-11T16:13:22.181256", "status": "completed"}, "tags": []}, "source": ["### Comparing latent dimensionality\n", "\n", "
\n", "\n", "When training an autoencoder, we need to choose a dimensionality for the latent representation $z$.\n", "The higher the latent dimensionality, the better we expect the reconstruction to be.\n", "However, the idea of autoencoders is to *compress* data.\n", "Hence, we are also interested in keeping the dimensionality low.\n", "To find the best tradeoff, we can train multiple models with different latent dimensionalities.\n", "The original input has $32\\times 32\\times 3 = 3072$ pixels.\n", "Keeping this in mind, a reasonable choice for the latent dimensionality might be between 64 and 384:"]}, {"cell_type": "code", "execution_count": 11, "id": "6ec15ec2", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:22.219471Z", "iopub.status.busy": "2023-10-11T16:13:22.219124Z", "iopub.status.idle": "2023-10-11T16:13:31.191034Z", "shell.execute_reply": "2023-10-11T16:13:31.190330Z"}, "papermill": {"duration": 8.986923, "end_time": "2023-10-11T16:13:31.192988", "exception": false, "start_time": "2023-10-11T16:13:22.206065", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/migration/utils.py:195: UserWarning: Redirecting import of pytorch_lightning.utilities.parsing.AttributeDict to lightning.pytorch.utilities.parsing.AttributeDict\n", " warnings.warn(f\"Redirecting import of {module}.{name} to {new_module}.{name}\")\n", "Lightning automatically upgraded your loaded checkpoint from v0.9.0 to v2.0.9.post0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/tutorial9/cifar10_64.ckpt`\n"]}, {"name": "stderr", "output_type": "stream", "text": ["You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: saved_models/tutorial9/cifar10_64/lightning_logs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b4c1090e1cdb4ffe8f93c98c632fe49e", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "10b629a0688b4c508a01d56ea538b6d6", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/migration/utils.py:195: UserWarning: Redirecting import of pytorch_lightning.utilities.parsing.AttributeDict to lightning.pytorch.utilities.parsing.AttributeDict\n", " warnings.warn(f\"Redirecting import of {module}.{name} to {new_module}.{name}\")\n", "Lightning automatically upgraded your loaded checkpoint from v0.9.0 to v2.0.9.post0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/tutorial9/cifar10_128.ckpt`\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: saved_models/tutorial9/cifar10_128/lightning_logs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "6b5f67bde7e24279b6a9d7efe87e9962", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "6676abff179c40fdb27bc1125e866788", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Lightning automatically upgraded your loaded checkpoint from v0.9.0 to v2.0.9.post0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/tutorial9/cifar10_256.ckpt`\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: saved_models/tutorial9/cifar10_256/lightning_logs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "cfd7819890314627b5bb56b3f58affdb", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "cef0914a875c4289a81d66a6907d55bf", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Lightning automatically upgraded your loaded checkpoint from v0.9.0 to v2.0.9.post0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/tutorial9/cifar10_384.ckpt`\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: saved_models/tutorial9/cifar10_384/lightning_logs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "17f6ca9988cd43cd99455c263c1ff2c2", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0b2fe11c15b24139a37f434c211e01d2", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model_dict = {}\n", "for latent_dim in [64, 128, 256, 384]:\n", " model_ld, result_ld = train_cifar(latent_dim)\n", " model_dict[latent_dim] = {\"model\": model_ld, \"result\": result_ld}"]}, {"cell_type": "markdown", "id": "10020f99", "metadata": {"papermill": {"duration": 0.01481, "end_time": "2023-10-11T16:13:31.224227", "exception": false, "start_time": "2023-10-11T16:13:31.209417", "status": "completed"}, "tags": []}, "source": ["After training the models, we can plot the reconstruction loss over the latent dimensionality to get an intuition\n", "how these two properties are correlated:"]}, {"cell_type": "code", "execution_count": 12, "id": "59118abf", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:31.254980Z", "iopub.status.busy": "2023-10-11T16:13:31.254714Z", "iopub.status.idle": "2023-10-11T16:13:31.597913Z", "shell.execute_reply": "2023-10-11T16:13:31.597354Z"}, "papermill": {"duration": 0.360243, "end_time": "2023-10-11T16:13:31.599194", "exception": false, "start_time": "2023-10-11T16:13:31.238951", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["latent_dims = sorted(k for k in model_dict)\n", "val_scores = [model_dict[k][\"result\"][\"val\"][0][\"test_loss\"] for k in latent_dims]\n", "\n", "fig = plt.figure(figsize=(6, 4))\n", "plt.plot(\n", " latent_dims, val_scores, \"--\", color=\"#000\", marker=\"*\", markeredgecolor=\"#000\", markerfacecolor=\"y\", markersize=16\n", ")\n", "plt.xscale(\"log\")\n", "plt.xticks(latent_dims, labels=latent_dims)\n", "plt.title(\"Reconstruction error over latent dimensionality\", fontsize=14)\n", "plt.xlabel(\"Latent dimensionality\")\n", "plt.ylabel(\"Reconstruction error\")\n", "plt.minorticks_off()\n", "plt.ylim(0, 100)\n", "plt.show()"]}, {"cell_type": "markdown", "id": "8eb874be", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.01647, "end_time": "2023-10-11T16:13:31.633089", "exception": false, "start_time": "2023-10-11T16:13:31.616619", "status": "completed"}, "tags": []}, "source": ["As we initially expected, the reconstruction loss goes down with increasing latent dimensionality.\n", "For our model and setup, the two properties seem to be exponentially (or double exponentially) correlated.\n", "To understand what these differences in reconstruction error mean, we can visualize example reconstructions of the four models:"]}, {"cell_type": "code", "execution_count": 13, "id": "e1f153b2", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:31.665754Z", "iopub.status.busy": "2023-10-11T16:13:31.665558Z", "iopub.status.idle": "2023-10-11T16:13:31.671142Z", "shell.execute_reply": "2023-10-11T16:13:31.670633Z"}, "papermill": {"duration": 0.023449, "end_time": "2023-10-11T16:13:31.672304", "exception": false, "start_time": "2023-10-11T16:13:31.648855", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def visualize_reconstructions(model, input_imgs):\n", " # Reconstruct images\n", " model.eval()\n", " with torch.no_grad():\n", " reconst_imgs = model(input_imgs.to(model.device))\n", " reconst_imgs = reconst_imgs.cpu()\n", "\n", " # Plotting\n", " imgs = torch.stack([input_imgs, reconst_imgs], dim=1).flatten(0, 1)\n", " grid = torchvision.utils.make_grid(imgs, nrow=4, normalize=True, range=(-1, 1))\n", " grid = grid.permute(1, 2, 0)\n", " plt.figure(figsize=(7, 4.5))\n", " plt.title(\"Reconstructed from %i latents\" % (model.hparams.latent_dim))\n", " plt.imshow(grid)\n", " plt.axis(\"off\")\n", " plt.show()"]}, {"cell_type": "code", "execution_count": 14, "id": "d7c35b89", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:31.705369Z", "iopub.status.busy": "2023-10-11T16:13:31.705177Z", "iopub.status.idle": "2023-10-11T16:13:32.560824Z", "shell.execute_reply": "2023-10-11T16:13:32.560132Z"}, "papermill": {"duration": 0.874188, "end_time": "2023-10-11T16:13:32.562414", "exception": false, "start_time": "2023-10-11T16:13:31.688226", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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", 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", 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["input_imgs = get_train_images(4)\n", "for latent_dim in model_dict:\n", " visualize_reconstructions(model_dict[latent_dim][\"model\"], input_imgs)"]}, {"cell_type": "markdown", "id": "93c9dc1d", "metadata": {"papermill": {"duration": 0.032447, "end_time": "2023-10-11T16:13:32.629250", "exception": false, "start_time": "2023-10-11T16:13:32.596803", "status": "completed"}, "tags": []}, "source": ["Clearly, the smallest latent dimensionality can only save information about the rough shape and color of the object,\n", "but the reconstructed image is extremely blurry and it is hard to recognize the original object in the reconstruction.\n", "With 128 features, we can recognize some shapes again although the picture remains blurry.\n", "The models with the highest two dimensionalities reconstruct the images quite well.\n", "The difference between 256 and 384 is marginal at first sight but can be noticed when comparing, for instance,\n", "the backgrounds of the first image (the 384 features model more of the pattern than 256)."]}, {"cell_type": "markdown", "id": "3584eb6c", "metadata": {"papermill": {"duration": 0.028859, "end_time": "2023-10-11T16:13:32.685921", "exception": false, "start_time": "2023-10-11T16:13:32.657062", "status": "completed"}, "tags": []}, "source": ["### Out-of-distribution images\n", "\n", "Before continuing with the applications of autoencoder, we can actually explore some limitations of our autoencoder.\n", "For example, what happens if we try to reconstruct an image that is clearly out of the distribution of our dataset?\n", "We expect the decoder to have learned some common patterns in the dataset,\n", "and thus might in particular fail to reconstruct images that do not follow these patterns.\n", "\n", "The first experiment we can try is to reconstruct noise.\n", "We, therefore, create two images whose pixels are randomly sampled from a uniform distribution over pixel values,\n", "and visualize the reconstruction of the model (feel free to test different latent dimensionalities):"]}, {"cell_type": "code", "execution_count": 15, "id": "3484bc6c", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:32.740632Z", "iopub.status.busy": "2023-10-11T16:13:32.740449Z", "iopub.status.idle": "2023-10-11T16:13:32.930872Z", "shell.execute_reply": "2023-10-11T16:13:32.930247Z"}, "papermill": {"duration": 0.218186, "end_time": "2023-10-11T16:13:32.932145", "exception": false, "start_time": "2023-10-11T16:13:32.713959", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["rand_imgs = torch.rand(2, 3, 32, 32) * 2 - 1\n", "visualize_reconstructions(model_dict[256][\"model\"], rand_imgs)"]}, {"cell_type": "markdown", "id": "7de82e17", "metadata": {"papermill": {"duration": 0.024627, "end_time": "2023-10-11T16:13:32.982083", "exception": false, "start_time": "2023-10-11T16:13:32.957456", "status": "completed"}, "tags": []}, "source": ["The reconstruction of the noise is quite poor, and seems to introduce some rough patterns.\n", "As the input does not follow the patterns of the CIFAR dataset, the model has issues reconstructing it accurately.\n", "\n", "We can also check how well the model can reconstruct other manually-coded patterns:"]}, {"cell_type": "code", "execution_count": 16, "id": "095d2315", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:33.030613Z", "iopub.status.busy": "2023-10-11T16:13:33.030437Z", "iopub.status.idle": "2023-10-11T16:13:33.255164Z", "shell.execute_reply": "2023-10-11T16:13:33.254400Z"}, "papermill": {"duration": 0.250531, "end_time": "2023-10-11T16:13:33.256506", "exception": false, "start_time": "2023-10-11T16:13:33.005975", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n", " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"]}, {"data": {"application/pdf": 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["plain_imgs = torch.zeros(4, 3, 32, 32)\n", "\n", "# Single color channel\n", "plain_imgs[1, 0] = 1\n", "# Checkboard pattern\n", "plain_imgs[2, :, :16, :16] = 1\n", "plain_imgs[2, :, 16:, 16:] = -1\n", "# Color progression\n", "xx, yy = torch.meshgrid(torch.linspace(-1, 1, 32), torch.linspace(-1, 1, 32))\n", "plain_imgs[3, 0, :, :] = xx\n", "plain_imgs[3, 1, :, :] = yy\n", "\n", "visualize_reconstructions(model_dict[256][\"model\"], plain_imgs)"]}, {"cell_type": "markdown", "id": "727ec341", "metadata": {"papermill": {"duration": 0.02584, "end_time": "2023-10-11T16:13:33.309893", "exception": false, "start_time": "2023-10-11T16:13:33.284053", "status": "completed"}, "tags": []}, "source": ["The plain, constant images are reconstructed relatively good although the single color channel contains some noticeable noise.\n", "The hard borders of the checkboard pattern are not as sharp as intended, as well as the color progression,\n", "both because such patterns never occur in the real-world pictures of CIFAR.\n", "\n", "In general, autoencoders tend to fail reconstructing high-frequent noise (i.e. sudden, big changes across few pixels)\n", "due to the choice of MSE as loss function (see our previous discussion about loss functions in autoencoders).\n", "Small misalignments in the decoder can lead to huge losses so that the model settles for the expected value/mean in these regions.\n", "For low-frequent noise, a misalignment of a few pixels does not result in a big difference to the original image.\n", "However, the larger the latent dimensionality becomes, the more of this high-frequent noise can be accurately reconstructed."]}, {"cell_type": "markdown", "id": "78cd55de", "metadata": {"papermill": {"duration": 0.026985, "end_time": "2023-10-11T16:13:33.361753", "exception": false, "start_time": "2023-10-11T16:13:33.334768", "status": "completed"}, "tags": []}, "source": ["### Generating new images\n", "\n", "Variational autoencoders are a generative version of the autoencoders because we regularize the latent space to follow a Gaussian distribution.\n", "However, in vanilla autoencoders, we do not have any restrictions on the latent vector.\n", "So what happens if we would actually input a randomly sampled latent vector into the decoder?\n", "Let's find it out below:"]}, {"cell_type": "code", "execution_count": 17, "id": "1af8f122", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:33.413131Z", "iopub.status.busy": "2023-10-11T16:13:33.412773Z", "iopub.status.idle": "2023-10-11T16:13:33.604453Z", "shell.execute_reply": "2023-10-11T16:13:33.603684Z"}, "papermill": {"duration": 0.218658, "end_time": "2023-10-11T16:13:33.606047", "exception": false, "start_time": "2023-10-11T16:13:33.387389", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model = model_dict[256][\"model\"]\n", "latent_vectors = torch.randn(8, model.hparams.latent_dim, device=model.device)\n", "with torch.no_grad():\n", " imgs = model.decoder(latent_vectors)\n", " imgs = imgs.cpu()\n", "\n", "grid = torchvision.utils.make_grid(imgs, nrow=4, normalize=True, range=(-1, 1), pad_value=0.5)\n", "grid = grid.permute(1, 2, 0)\n", "plt.figure(figsize=(8, 5))\n", "plt.imshow(grid)\n", "plt.axis(\"off\")\n", "plt.show()"]}, {"cell_type": "markdown", "id": "115e3357", "metadata": {"papermill": {"duration": 0.026271, "end_time": "2023-10-11T16:13:33.659520", "exception": false, "start_time": "2023-10-11T16:13:33.633249", "status": "completed"}, "tags": []}, "source": ["As we can see, the generated images more look like art than realistic images.\n", "As the autoencoder was allowed to structure the latent space in whichever way it suits the reconstruction best,\n", "there is no incentive to map every possible latent vector to realistic images.\n", "Furthermore, the distribution in latent space is unknown to us and doesn't necessarily follow a multivariate normal distribution.\n", "Thus, we can conclude that vanilla autoencoders are indeed not generative."]}, {"cell_type": "markdown", "id": "99fe94c3", "metadata": {"papermill": {"duration": 0.025642, "end_time": "2023-10-11T16:13:33.710862", "exception": false, "start_time": "2023-10-11T16:13:33.685220", "status": "completed"}, "tags": []}, "source": ["## Finding visually similar images\n", "\n", "One application of autoencoders is to build an image-based search engine to retrieve visually similar images.\n", "This can be done by representing all images as their latent dimensionality, and find the closest $K$ images in this domain.\n", "The first step to such a search engine is to encode all images into $z$.\n", "In the following, we will use the training set as a search corpus, and the test set as queries to the system.\n", "\n", "(Warning: the following cells can be computationally heavy for a weak CPU-only system.\n", "If you do not have a strong computer and are not on Google Colab,\n", "you might want to skip the execution of the following cells and rely on the results shown in the filled notebook)"]}, {"cell_type": "code", "execution_count": 18, "id": "8f10cc8f", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:33.765548Z", "iopub.status.busy": "2023-10-11T16:13:33.765249Z", "iopub.status.idle": "2023-10-11T16:13:33.769384Z", "shell.execute_reply": "2023-10-11T16:13:33.768438Z"}, "papermill": {"duration": 0.032361, "end_time": "2023-10-11T16:13:33.770737", "exception": false, "start_time": "2023-10-11T16:13:33.738376", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# We use the following model throughout this section.\n", "# If you want to try a different latent dimensionality, change it here!\n", "model = model_dict[128][\"model\"]"]}, {"cell_type": "code", "execution_count": 19, "id": "7f8e099f", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:33.823556Z", "iopub.status.busy": "2023-10-11T16:13:33.823104Z", "iopub.status.idle": "2023-10-11T16:13:37.950834Z", "shell.execute_reply": "2023-10-11T16:13:37.950115Z"}, "papermill": {"duration": 4.15607, "end_time": "2023-10-11T16:13:37.952540", "exception": false, "start_time": "2023-10-11T16:13:33.796470", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "ffd889b2d92643bbbf3a3fed13b411e0", "version_major": 2, "version_minor": 0}, "text/plain": ["Encoding images: 0%| | 0/175 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " 2023-10-11T16:13:38.279312\n", " image/svg+xml\n", " \n", " \n", " Matplotlib v3.8.0, https://matplotlib.org/\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n"], "text/plain": ["
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", 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", 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", 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", 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", 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4HsvNWOm9XZHgOdQ+RZ5u7UFi0XAB73rS0pwNVYEx23KefaDD0yRk/a7lIu6lfvgAk58hRiKIhrwZ10lV1PlX2MNHHR306Exh+2P8EGY0cDLR6RlMVMbUZ8po8GhNDCgmyP/6Eb1BNy7jUmWLNaSjsgx0KpluJ94ncXbCRkdLB6bQY1YM7EmaNdWczlqq1dWKtcTcxpRF2ZyG2HyZRlg2BSyu0xNpDBjnRHKZQc4xQJM3eqY9qkfP3v2xDHzKFaR0CMbMm+uLX5Rpyckzia+uXJWUttPXdUzb02ekljYWPjfd11wHBwcHBwcHB4dLCPea6+Dg4ODg4ODgcAnxHE755IzoRSjCkBC2AfysB6/abouIDE1jAEExgXOZwgENcCroIwMwO+UM1MCE7BIj6Mgh5gvmeGDu9HgGeDj9ey1CvwPiNAfZ5FP207E+1IdNcQprWGcvQR5V+ICfMUIWqij0F/6rICBbwQiSsVwRWZmHIqnXRXGmCWY3Ysi+/xtLnpiWW5g8o2Cc2oz+7OLPPEVNMoHi7GIvv1RXfHcP5/keOSl6VCPiysWCGNsRaooc/HJ5SR3bPCM/AmoKC+cswpAGi/n3jOU1wPogzd1zBBRPoFrMkSOVIkfGOS2KJSDp4Lc/hHoz0YI1rQBDdLSnSM9hS0OWwhNgRNC08VLmiTEdDvmFTov0k0c3jvvIMMo6+HUs0z842IkLx0cKFDWq19J/pKnhPELixLM2OWlgIZ2nFfj/M10tlnxEN0awYj6MT4+fsikNWRn5TRIjn0S7Y57AtJ/A1lVWNcknjE8fG/PlBmHmNR1jBGv63CJNhDsQ95ajJGQHMFo8jSomlVkYZhvRaRHrZcR6KUHcn53Je2E4MPFDj1poCXzmlduqP9qntKcVlMEtJByTGQTH+M11NTaX1QIcI2d6tvssLuQrZFpJa9No4Upj69csaBrrCjo+QAT23scf62A0LZ//7GuqGOTp9EyKi6EHHVkkRc4cUpbfZDFZP59DwVQE/nyeBu8TFgkXL5XoB+ZvE1z8aaGtghdFc1eec2MIvIuz2ntOCgxTOFw8Zu4OCfJMRbveSw0t5DReNBauvkJOmdVlzYoJz8RmR0NWYDSHFolPpet5DeISCXEKfd1ryi1sD0uVVDHzpnlyLPa/jVTADBOquNb0UbLN2OhyGAR5nlde0gMiCrXt9NsczwTLYZ8yOCcwu4B2Sf4Mw0jLLc0jrIQ1QetUtR0jWhip0d4jX9V4b6ybZpAobBfYBjO8USANWqMd68gY+rzqdFj+Z6aL4FHY8y8+jJbMKAnWv1Cux4WI14njMb2H9jIfsqK5sod1UoTCcMSqn8cS7i4TXksipAJDui70eBKR52hvX94UW6tX1fa1il2z00HkUFTPLq+qIQWezrYxjqeaYPv7cp7Jp9X5yys668FDvRtg5uTNeEY/+/DtuPArv6Xt1DJQrGxqMhyRDGJzTeqFYU+jefdDaR6wM/E65M2pby5+PVv0g4ODg4ODg4ODg8N/unCvuQ4ODg4ODg4ODpcQzxEt7PqiLbpGX5ILezwk2revQr/Ll3O+yQ+RHwzNGAEyaAzlM7I4eLikLAUL9B4HF88ymmkCd5mGH0pjazxa2Y4LuRUlc2/t7lrTojNFg5rpcQeHhBtkK8gEqC7gFALIwXC2kHyxiFdzvM/j2GCuBbW6OvbgWAzpBJPtSl09nCHY0Ag4c0iwvyyviPx9tqc4xON9feTfvirH5k5T/M4BjM8UW/g+Lv1jMizMjAKGfZgSTNomt8UEKiqTn1AxAvmZRQV8vKsFNbnZW0i+TDFqyBSQoBRJ00BVWxjgh1hqFIrqhBph+J7npU1s0CUDuDmKJ6SnEaP6S4/0FuM+KRuIc+/BOKfJETLowloyZKkUxyCz8QYEVkP8BRamGuo6KxXxMulnmorhEGuRFBz9Yt9+cz/IwGHDrXmBRzXojRShuAUI/UyySCHg6J8xrehPND2WWf7ZOU7ZUq5MC5q3hQ21K7usaG5vpluUC+qNyDeTClhD8pvUl1asjcafDnqqidkO9IgZH+M24Js6JVhIJ+ewFillyLkw0QI0d/1uTzMnxRKIcO2o1QiRXpLCIeWTeh5/ieFAc7hWkYvCdKYLpnGjCCB/JyRYuX5b4cNN5u0JaR0sY4tHSoscuohSWWIDP5BRg62XDr4xnWNdcKUsFrIIZd0Z6ZghnTmPYD49xHOSOlwULTzHA+Gc7MGf82G4aIJwzr3hU2DZMSIuaCYwtsZDWH4zC7KMD+dEDBfzW9jDKDKJQlK4qJaZh2mNcj4ZCliJDLg3RoU1KpL7AK1CyHzLIF6qlXn+VtlgK9C7mDk06vW4YMl6Tg61saTyF3d1y8/SgRMf8WRPc9MC827G2pzZsj2nC8qzpro9rcRCieaz7ZjkLJ9a2GlTGPBWu6nmk12igCSxivxpghlFly1id6C67THiN/FeKLBwliHni5hRFBnNJV4kDqDAtyvqhxMSPfRQTPm8Y5jzTDqln1aqGo5RXp0Q8j4QleD6T7o0Qz8ddvTT8YkW8goNLD7HM0TIIMawp5WHoUeZyteX63GhglLx5BgVAVlI/MDUWV5nqj0hX5Y/TK6r8b35oi61syPZQ7ujIWs05NfRPlJNzJNqY52fmmg4GY5+iBblTIUiT5OdJ8oKMUaydWPjhbjw9Jne4rq8jHVbZOsgu1Ovs7DT3NdcBwcHBwcHBweHSwj3muvg4ODg4ODg4HAJ8Rx6dC/QF/geIeTRsb4hD4mmHIygbjkrRRSzhYWOYATM09pYS7MI8BM3BribhFW6GJmbttO5a8qiMlOqc/6Nz8eFj319wD8aWoCp14jg4tti+VfK+uJ9rSpqtUzdIryahyN9XfcnC0ULIVdegk7iMt4Y9mdvX1TpaKgv+fXlMq02B/uL3/+zxFH2zXshJVrn6ETGAi08EwoM0Cmf/Q/xzdg9lgP2ekMtbZTJd09kf29IqCZeFkZmZbJYDRA2W0Q2UCNSMs2ItzsiaI6bC4nRMnYTY7Js56DkMuaScaILDqlYqaTxurq+nlzLH9BYESt9SO0hxFkKrvOwrVFIW76MJdFaKSaheZ4zl72DHbWoCin22gvi2XPY/p8hd6nirZ0hfPg+sfCzghjn1aJOT9PYY5jEGUk65lGZ02AYv+xZEhPUCymIM3wvEpp2xFwyNtY2gpxFDSe3gHtlVnSYDJNtiWRy67J5nzJhhq2mqpE2lQt8GaIFM8APsDH3PC8Dx5o0aGY8NdIgfvGRRkSLRQsjJkMxr86PxmpIb6Y+j5gM05nWS4U872lSLYww2SgXLtKXx21Nj3ZPS9Ij93q1rFndqKFOwedhTI6ACSqIEeKcgE6oIVEYcvcD+LsIScwWHOWgr53h8WMt9lZJFdtq5LgyuX7akJ5zSMM7R4sTLvhzFP9zDRPOpYeYkw/NqR0M8/e1vxgFny2S5wKbjl5Hu98IDjqLJ78ZLMx4ZCQ6hLlbGF//vPrMvAUYkQ/FLHFC/jLEP8c8dnp91dBy9JQq2kaqJU2YLAvnDlK0UpWdCp43Qyh7mtwNlbqOsaxMJh7okxSp3VVHPT1tckE2Qx/HBuRAE5RO43HS9pND2f53R2raNVJObJCYwUfPcOQtxLMjkenH+3IduVnAtCRSTQI8I5aq6pkDdIW7SAvScOJt9ooPTEeU1tHlIhmsclpTI942Ur46pMyG8HJVe9ROV5PhhJQHGcvRw7NgLW1izmZciEq6BbYZ3jGCoixTcYDLU6unBpZz5HuqJPvhBfRaOv2Mt7IgQFOK1c+gQtosEnEdH+rgPBvCjZcT1V9U133X0dR9dPe+2ljQc/bOGzfjwocffBAXLEFDsa+GDHpq4+a6xBItUkcNpzq4Uiet0kjz5PEHeiI3j3X6lVWpSoc9Vazd1HXyGAp1A5OrqUM6nUSGcQHua66Dg4ODg4ODg8MlhHvNdXBwcHBwcHBwuIR4jmjhLrb5k7FZGxjjo2/gARShBelnzUUBMsjCZgOo3yBlHAGsH2SQccoWyZoiftAzDpeAYstsnzEGqiEP9ocwLN//WMnc26dJVuiXCQOvwoPcRP9QgjdM4SbhwTRFUf9Cq+dRJvLUmNkedhKtHhzlSLeoQjD1IFaeHSs8djyGMkZJMItgaKD29rsKx+5y5Q7hh0+aSjqQqEGQbFTKuuCQatSu1ePCrdviCD78QHxomomRhxQL6epxyoae0cwhbsGgIIPtfwn/inks59X1HQQwdZiapbJGs4tp/4hm5GDEqnnTy3geCRFO+viot8XeplGepGF2jk5Ff9SIMq6T2mA6EbNThO/LQIFZ8oUrKByuVkVL5aqYJ0w0Lv1TFepbakiY1vTY2dfd6zn0Kgh/Tnv4b5xr2QUU5iK+E7t7rmNKgNR85DiUa4B3QYbFtZwyJZAmcBoX9BE0U5/cHMOS1tFsS14BY1zZ2201MIcZez5v/gZkeSCfSMqyh5zjqI0/nUbGL1NbXPED3GAiJkboz9PrHo2lxCFp5BxjrpzOQ3yjadmsSFVSzNfjwtmRfppiuj5CEHVGXpI+QfU28YpIv8aE0o9ZXDNC1+t4ODR72qwCttwpuSRy8O8ZHDCubJhdP/TfM1VsHyq8i84qlUHWhUNIFzOWeaSZXTPmm1H7JjlIEq8ksgQ75vzXkzmJwvz9GBdLvmAHz6sXLCFIm5D8YV/LbUxjTS1gGUlCGjIeo2dAP5DUZy73w7zlwqc4QZilfwbRwoyNJZNlN8ZjP2Se1GraRjbWtaayxO9POaaEfqZcYtoTXR6RocBy65gXTYgayiq/iZyp09R07bwrAnpIUgmPK1tSpyQpEq4InueF6G3G7N77rI7Gmiq5hptED1OdeTzb04Mvhx6ph8QiN9MFKyRPOeORukOU/Zi1ncGm5iBNEhYkFi9jLRLBm/8Mr4A+4pZNvHE2eayU8ppmIQKtUzIlTfp6Qcp7uuCMNV7HMWmKcc3UbJp4Jo7J2JLmXjm2wQIP2d7ieTbltaSEPCzwyNCBd4EJe0aMzoSlcdxsxoUrs5JdE4MHr57X8S/d0ENtcKSmFdZ0l9tX78SFp0+V6qh9ZvIJXCnkN+OFKBIzSL+u3SKhyZIm/DtvvaMWoSrZ2pRN1s5TvZBMyakxILlVzoxW2AWLtYUORe5rroODg4ODg4ODwyWEe811cHBwcHBwcHC4hHjOZ96DM33Jz8GFkQfA82Htc9DEoUWwWhr0efmBBcnyJ3hvL8WX53zClZCAHlJplLHIcShLItl9Emof10Tr3N1TrP2Dj0THpOEsPM/Lh+KwXkjpLqWBfjV7/OnI+FxLfzCjjQvpZOMopxAZHeKg946acSE0AUFaQalrdZHa6QIR1ggASoTk7+6be4CqarRdGOrgFk0cwHpgDe5dW1XPfP4NOS0/e6aY1q2Nelx48/Ovq85Eg7ZOcctPouzhIxi7xBMDeUkBg/Q0bZ+e47kuoFFWZzbqGpTGtoiSPAM92FT/WEKQKb4TJ+395FJV3beMTfeyB1/Wx1GcAFXLJJJn6lpEs/EgSxVSexPabIKKFOzJ4w/EL0dVZv5MC2e5pOusb2ugw0CihXZLhNcO6SEmJMUY0bRRbuE/PjNGnvo2OS9yxyEuCiGChBGR42N0CDP6M89cShlPTYR1CMVZeUkzJ79+LS50ThD2FNQ/ncPHag6kf32pHhfMVX5sqRyQgqRtIzinWrCsFil2gCHR3IGJnXBmmJqWxi4+Bzvr4HCPeyD5gKfOZTRAlTXlfK9UNPdMNgBJ6A0RZkTcdI0w+SniFjP5b7W1lA45q1BDysIAjQeaOWly64x7JEZhi/7Vb/wNteJE1ORbP/t5XHj51Vd1FpPh7r2P4sIEvriS1U031sQVDqv1uPDDe0oTb0gyOCRaBfvtomFC8t/nKQ38OZ4/8VVItDRWsF8u/mS3mTKZLV9Gz9KIoExYXRNpWsWnonmmzrfUFc/xkFhs7/AXQdoayrBOMMfIIgSyBDpDnHmu39J8W13WJtZtaWPpkJLGXG7SiB+8DFoyHje+rSkSDBlrP8DZI01ofI7pur6pOPp9Mg01kbtEPNCLRR08w4TEO5fUpsQGkmW7GDKZ2+m6TrR0MHNIXgksqYRty0j7Dsh8sc9mHvC0bWCUZOlUMsgYfPyRjqDkx1UtgUPGJbQkO2My6axpwhygfxu01XvbPkYresh4M14MBogWfC44Gujux6cYLDCINZw0ImRdUzJQBOxL7ezCHhuZiqmo/hm1dJ1+F1+juUQtRRI22Trqd845UJX0a4OUHC/cwD4I84QR25dtnqW0+mqH948JgoqVBoMY6Mq2NpdXVO3+SGNX5Dqba7ryzo7MOQ4O9TRZ21Cnz1B1to7xz6lp4dx4UW4/3h81vU/Cfc11cHBwcHBwcHC4hHCvuQ4ODg4ODg4ODpcQzxEtGE3mE/yYNh0ChILJGIzUyRgZxN/sg3mGv6ShU1OIH6ZFuP5lEQqFCamKCVPt8m0/TVj0hED4ASKCzkR/OcBu2ocVqmQS4mlzqK/ra5i3W8ToDP50lORM11kBNs7hYgqrB0N0SMz1CWGP1Ya+229uipSPiOjPQ9yvpMSSn56ohvvHIlirxA9eI6GAMWm7+0gLiK71idncIvFEOaUGNncfxgU8rb0GSo/2qTiCPlRLE7uJfB4HibxYjAB1R4FBLOHTXi4wZGgVqoudFlavqDlZgtw9wlRbGCzU6yI4tmBhDobq3setU7vU+ro6djtTVxsnasiHT6TQmA01QJvwmCljWCrqh0pVDamWdJ2sEe4jhXX3zjSvHj1S4WFLnh6v3NYg/uJvyEl7eUm9Z3HMr8BRDrDyvrcvutBfJzE6scbzCBELJcxvEhSvP1heEjtkAotqgfMZqEY7bWJ5FlhlHuYh5RdejgvjZXHBlUCKi3ZX3Ppg71FcCGA2owpJR3KE9CZUOIYtZhxhEf2eZ7RaAYv1HLTpeEwQOloj8+TvL+60Ktdp9yR0ySNuOTkRE3dwoHl17YqCfHsYuw+GOma5jHCFBDq4s3ijUPNkt6kOOSUBTZRD34Vi5GRXEcorGL9c3boVFw53FFncSRFQXOJe8NQfPVGqEbMo+cqrGqCTljaEe/d0jG3UK2S3v3Xttu51qInneXOihSSVA3+ZS+Vg+/x8cofzSA6b8/04d8z8WYLNWMvE4fMQ8ZP9RxN1wvQo25aCTmzGlmss+XPuOod5GcannJQmS0Wnq7k0goye+qrhiOu88YZG4eYthEAtbcIhD54JB/dJJ5RFi5bJkicFpwV72vbQDPjIzBrFi8+vNOYnS2vy2Jkh2skw3zqnpNHhgTHOJUx6EauWLPKnFE/JNH4sAVqFVKHsLYCldZhOEfDAiT9DNnAEFT/CYmkzry6qU+hw05sVTFQGqmGL1FED5lKEHVMxRe4VOraVrceFJ4dayB0MPZbKaD9ITGDmCS18DCboK3zsUJqezpphsrE0VQ2XmaU5ruNlyb8TPefFLEY/1PjW6hq7I3OrwB3CJkPzTK0wcVeK3FphL9lyGw1tREXsXDY2tOUW2mz1pKCakrYjZN2trWpvaZMbJ4uvQrWs63R72pqePtG86uNMUi8jvKGSH36od5Ugrb9cuS51TaWGQVML8Ua5rtODhZ3mvuY6ODg4ODg4ODhcQrjXXAcHBwcHBwcHh0uI53zmTWF6EBjHkXA1sI1m+g3pCZWUKBOM9Q/MyBcWo7QEWVAh/TfhchEBdCFe0JaYoAcLM0tD7+Lq3MRSv1yox4Ub1/VFvTBtJ62FvW0RrzfrY1+AefsUjUSIZsOPLuox5nFE5utD8qrXiSjcIHjz5k1FAh4d7F5sWltf4Pd2JbqwrOV58hF4xPibdGSpCvkC51KD3i1Y9gRIZCPrlms6Jgth9OD+Pd0Blm0GJ2U25mnsFIzUrhRr3F2kQxa+KQcnngkSD+oLmMBHpImYnkAGneC/3cD4ugKv8T5p2VOzZCye7YkgDtuaPMOBfl0vkRidiuTp2CTjA5oH43pqS5LQDFC5TDFmLxYtxlltnOxoyL7wpojI2zfUM0cnkC+hDr59VfR0FxnD/RNGnOja1OQ8g/8JQN95lgzBEqz4iQsIixRCP8fBeZsEBCYnhvx0i7nBz8yUflvt6mqxeqlQE+YIAj3si9vyGc0ZZH2WDCNp83DAzMS8+v3MOT7UxE4waB5WCRlMV9pjskKwEWWyC+UxGWLg0/RwsSIHjDTKhHtYE7RO9Zc3Py9/iRkO52183bcb9bgQeeoRC1uesKIzaK48/CIqWE/UyKJSYPuy7bReU8WyBW1fRabizNe9sjDOL77ySlyYMgeObS6x5dYYRJ/T335X2eqPThPZzwXMSxTmEQS2IXwa/T+fYeFTYKHrkX/x4mnkQynUC52ONvY2Uo3bN7W4TA3yEcM6b/jw/zlCniAR5vwZHIF2DlXDlVU9C37pa1+KC0b69zrSxuTZVzv2BKQTBgPNnIAlnWJuBxRsX8xh72BbVsdO5/NWCTnBgHQkBTRdXdLfnOyRM+jcN7FKiRQnrOUcJ6bhsmecMOkvHPrI7IyQ23VRaLQwiDBBhakuBqF65oihz2Jhkc9rBWV8Ldtj7JgeIQj0elrI68gwbuBaEJ0040IRb5wD3gd2kUW1pmR1IcGQJYXJIJXsjTCFYBQsh4t5F2RQ3eTJerNS16PZHy3ssWJDN02RfWNgVjA5TITwVTg+VHMCHg/29F8+pyRJT1Xbo0NNwnQOkcNEdzHJlslaiiWN1FJV8onT0zwt0miurm3HhWZHP51hefT4keZVKa97DfoIKnhyNXiJaqOH6KCdaGzwDonu9P13H3kL4L7mOjg4ODg4ODg4XEK411wHBwcHBwcHB4dLiOeIFnLQO2m4EjNxn8FxnnML13/PkVP8BdFDyOf6qRETqBcOIQ3zGUyDM/omn2/o63r12lZcuH7zelzYvCo79BQRgv1vfycujI51wYOnT+PCzns/scrur9fjQjsDw3IgvrvewY89Mj4XPmhmfO5CKuGEb+nFmhryy1/7Ylx4+vGjuPDwoydxoVJRP5wcKIrzyZNmXMgE6pkV0txbELoP5RoiG8hVjA5WNYoQxGdn+shv+SYKmB4M93Wv/QJkLlzwFbwgGkukCMfMIU/oqI+Pd5H6BFBIQ8vEYVTjYio5gszZPSJRwlBd1+oTrU+hCuFbxhUhXzIyyBtCGRcJI83nVSVzxQ4iHbO+JlZrY0NWD/0OaRQGcCUQQ093FI1exZUit0Ju7tuany9N5atQJhn9wweiSs2oYXlFHduEZfv4sSaDlyEbAi4K6WDhNJuin8nMBbxPoMIjlDllyNMCy82UOSb5GAQXZ3t6RqKWVQW3lpfUUcM+7gEDcVthH5bc0/z3I8QTMIxJDPVYmoss7h8ZZDMhvJvneRFcXgiFPR7qxEQrxU6Sz2tcur0TbwHMJWNG858+U0MyOVGTjYZMJA72tXCOjzRSVzY1LQvQl8ckTznDkGS/qZlvkp47t5X5/exUFSsYb0gyd9aNt/tM/N3Lr3wmLpzAbK5eUSR+hbNeeenFuNAbqF1Pd5Wb4+iYdsHsZxn6ZlsD9M770pn0x9oqn4MkC8lCrv85hgn/b/QJnudFJHpIfBXsQYNoKpW24HH8B7BTqJRE7tex/S/ktGzH8N22pkyOZfh0g4hFB3/KOeG4zzGkToDHv8pz83Nvanzr0NNPHsmnJWTnN/+O1ZV6XCiTT2TKkKVRwgQsljTkeIUcPbbuWmxrJlxJ489QwIYoywVtH7h+VZYjBaRop4cH1tgqG3sQIbzJk5JjSRusyTCC/sLULRU26B6SAMv9VMQlJAhsi9NZKfqzTCC/PSU/PhWTnqJDJtj+TDHbmTFP9rCXydn6LSLnqOJZhNNRMSIdyVC9N2ySjKlg9VFzhiP9JUt2CS9xl9LpM1MdUNUmSTdy/sLvj5Wcdjzza0Lf5GV5/JlSojdUDcuowm5c0TBtb9WTi6LhLBc1dqmsurGDicSM/bzP1tTtarfJZjXPt7f1yMjxHmLb8o0bUr5ZlokMQ9ZqNePCGcrPLJLLKsqZaIZi0x9wvqqRD9WQ/kIRlvua6+Dg4ODg4ODgcBnhXnMdHBwcHBwcHBwuIZ4jWshj3p6GokkR6ZzkHE9dfD/G+9ybWXoFO5iIyzasw5S4ztKLb8SFl3/9G3FheVtcSUBK61xNH66N+ZiGIh1O+G5/60tfiQtfuyau8L3v/yAu/P0ffs8q+Z1HYvcqFRFev3pL+ofoMeTRiRIKmETBgtnDxWRcgcBq+1wfTa2xImQH+CGPCSZtwpWkybu9saLGlvhuP4ZzsaDakODHAZy+5bnOQM2vNMrUWTXsdXXT9RWxny+9JFf542M5k088nV7H+KKItf5HD+VpX6sTpQ4PYqnbR5DLaWQuk3Np0C/AjM1HY1VxRrwneRu8k1NdsFQTb75aUuUH+wlJXSCxewXOKJ+B7+6KybiyqWp/8Suacua+/tOffBAXcllNuUZDPFAup0BRnyWQImf6IBRlk1/VMdkZvhC1m7RIZ+0SwfrkQCqIMYqCZdLKB3BYYf8iwXqupYIZExiZG2KO7RthbYb8WBwMA1vRJHNn2XaJ+D5G3FK/pXbl0Nik2hroiPk2Y9lHCcvGLaDki2kVshVVf3lZqy+XVY/t7iWE07RvCVY0xB1SrpSrYtyyRKPbgkwtppOHA50+C9WQn76lhAgvvSJJwJe/9Lm4cPfnEpw0TyQkeOmGDA1mQ93j0Y7Wwpj6t1nstn5DVqIP5RpQ2QYc9AHGCKWKuNelNTVw3JKGqgQjf3Csg3s9NcfI8Qcfa1s7JQX81Q0mFeqUZlsX7JPIZuJbmvuu9x9D9BeQMSw4caFtyLkfLgreLGvAUkPL1rRSUzQ5GZjZMjR9OLGfdHoN0nP/AIeWlD3yLm7m8017zl8WNcbzKmUtyh5Slghu/cVbeqi99qrsO/Z29ZQ5PNLea3Pb82Bjoe+t84Mkv5IONfXCCC3HDClIDjJ7PCaSncwR3b7JhzDtYW2GPDCqeMtkSJiSy9v241Up5qjSIaqY42YzLjz+WD4zlfrC9BAVQvIn3HdC/YvkSqiSPYGR9zAW8hpoP3pscWNfo3DQQtpnBillndalQ07xtPmgRZYZX3NphVFYZs/sc4vWRF1d4Flgm3CwLDMle4ZN8F7wE3McdkhMabLIb/ZYv9HIPHUuwie7RPu4qb9Epp1gVJimnY6us7mh5+ZnXv2sauGhHvC85okmRht5yRYirsEkze2Ur8r8HAqBOt+Wm0lo7JXA5wGRQgsaYBf0+qvaew8O1fx8RZWcoq55+EAKq0GfZ1nI8y7x7SFJVuo5b7Pc1MHBwcHBwcHBweHSwb3mOjg4ODg4ODg4XEI8z2nBAmb5BG3Zwy1RgvlL2/lT6A+jLy0deR96unRbMcIrb+jLee4GydzT+nD9zj0RgofQuwNMAzrdZlw4PROhYE7LX/zqL8SFX/wffjUulL+mavzkq1+1pn3zT38/Lhy39QV+DaP4L18XndSH3QsmXdqoz+PTxaKFPIYGHRwb3vrpz/QTvFKKAknCk2zsoU+YPBGFIbqOiVH4xLBPyIVBOLJXK+oj/7VNdaPP6UdH4kGyfNJ/7Uu/GBcOYVqH8KpfeF1cZ7+rVvhZXfBn74sYnQUEeJIDYjrpUx+88fHNtrvPo3msOPEuyamzeHwEM0wh4JsePbGE7+KAMscJrVPE/cCHH5/ARl2/Iv3A517X3Cvl6nHh7kN5cfSwYa/UcCRAE7AGnZSF4e1hlbBUvxoXcuuKIT07EK28uyO+u09e9Sl51Z+eERGMcXdhWTfbgAi8sr5By/a8T8JmTtaYL/hLUxYZGWzJR4wMzjJ/x0RYj5AWTCCDuksau8oKces9VXXSUgOHZM2III5DEiXATXkjtAfG4y9tqF2FSl0HYT6w9/SZtfEplv5d/P8zxMtPSRjRw5TeuLBcLmFUL6CI+3oYqEVlkoak8ShZX4Hhva7azqAmQ4waOl119TTQZLCsFlvL4u/uXBfDO0IpMUDqMEN0dIjV+fsfK1PDr/3Gb6pdJnAaojHoqxNOmiIZs7DtnTP99OiuBFfLSKc2EB11usiikKLZlm1x65+CT7FTMEL/L2qwwK8z6h8mv9g1yS9DCgwbVru0dazvE5tvfiaoQSKY9DTL5BgPCrupuXaYr4kfXBTePAeLzXa8RDUEdcssrZYL3FR1HnS1fTVq9bhQqavyfajwyB6pML9Z+OKUKQOnmjDtJokS6I0Kk3zc5wkCzLAlR4sLOOKMSRwz5cop5CKl2rJdIfRNJIDVQ1nH7z/Qvnqwr037pL1QFTPkGeSRlGpKaH9voL7KcosiLH85f1F5kqHV9oqS4elYy+r0SkmnTxEUlbD2yNo0Y888QC9nT//VhnaGbIr+ZMTzJGEZTnTB3pD8NfSeyUs8VuKApBIj0nb4+FekMwu/P6ZS2nwmEykN8iyWQhlTiBKWC7yDnRzp4Epxnb8kqr8///aP4kKuIHHgK5OX4sIv8rbQaUjoVUXrdHysYe1ONPfGpo7jwTfoqY1tNvMIbx97POULqFMqSJVWpP0okGPo9LQZF95+Vw3pd3Xl+hKOT9HCHc19zXVwcHBwcHBwcLiEcK+5Dg4ODg4ODg4OlxDPES2kyeYwtnfg5HM7VCkxpClelJuwGBkYH4vkreF5PrkueveHRyKemo++Hxdm5KR+92PF1j15IEavyLfo1SUxg3snosJHhFX+yq9+PS70euSLL+nz+6/8jb9tTfvue+/FhUdPdZf3dsSw5ArQ/TkVKiMx5kv+f1y0cAaBmIGaLEIgmjJhiJ27mZ8bwZxFABDAaKyuSk1hpFoBxuoEsiBExtAglcZaTX1++ETteuW24uWrW6Lve3Al/+Rf/Pu48Hd+6y/HhZtXb8QFyGTv6cHFe3W66pYp98pD9g3RVwTYCEzDhXHWgy7UD1RDBF0VjfRTtap2HSNcGePM/wr+G57nLcEJhnk1bbUifvxzr2nuWYqBH/zw/biwz6WKFcUU15lgaVI2WDjnCL+LIiqOI2K39x+IzSFdhleuauyydQ3ZvcfNuPDj96TJ2eloMtzGVr04ILg+XfQWoItmYwI3l2ZcykSnJmkzLJkCwoYspHDKhoy45gHTNb+ihVPjrOZjqQgOdx5ydw1rGTuUFOxhGoYxg2tHakbkOBR/FofzLF3W67Ss0qeHEjAYEZkt058sE/MPySKIiqKF9hQWlj6AONtaFUGcokP29/Y5GIoTb3+LPTd7mUweE3jmrmmW0sz8MfYylm4mTy74M3RNRcztt65IAPPRg0dx4fhUW9x4puusYujROlRVf/K978aFGozza69IeZWBIe1BMc4Yl52m7j5uL4zmntchzHsOWF6SWXLwLPkxuZR34Y/mV5OcyE+rK1oLGaaKedEnGWd44tSxAqgUVChjBWCahxZJbQr8pc0qC2yPS/AXMZFYeEyuqGpE2LyM+nQ1m88RPH4GF4V1FEq7SJ7GI9Gy9VXSsnSlVynSwGJJhVSGZAFzG22Z50WPSHyfrltf0YIadHTTAcItG+jJxB5S+ssAgyDvXO6npSUlvlnHPuXZE0mttrdZ1NHCTpvybjEJyejBkgxJdjDkgVUid4OPEccEI5EAbYBpjcr4MzSKbBqmFaxiIkFOivFIm1UetUCPt5c+Pw3MHgpnjxlVHbP3tnimWH2qy9pGMkinCmTEiCwRFYIN8wqwang/1sPFYFtNq6lJVSvw4DHnBwr1MgthXwf/g//ln8WFX/+11+2aV29o37hxW4/LZRJpjXGc8MghtXMoSV5jSas1GmkfO3ump1shr79sbEjENRirZyZTXD6YFGUyPY3IJXHE0/baFYlar11TarDDY02q3T2JWsOJDp4tfDtzX3MdHBwcHBwcHBwuI9xrroODg4ODg4ODwyXEc0QLxghYnmjLDG5xiJYDwr7bD8zkn/DDzC19cD4lF8N7b78TF5pnYmwbK4r7mzZ0TEhCgRQZE/rQu15BVGOmJtLhldc/Hxe+/JckWhgSP5wmEO+NLyROC7/2G381LvzTf/KP1CKcon9+/25cqEA7rloUORqDgreQGDVWxpQVaVjLg0N9XTcv7hwBrgExuWXY2+0NcRyb6yrkiCrNQDSX8iKqRtAoFcy9I9whrl8Vl3T9RTkMRCX1np9TDf/6b6hnPvfijbgwGYihKC0Tj3kmhqIIRXhyLDr1rKkm13BIyBEJW8Cneg3y0fOSUPoYzVMNa5Whr5PSohTivUAahAkm1RH5xHPZ5F9olbqomfqqCut1NX//WPX/+JFUHKPxRaHIzS2RL1skkS/Bqp/1VMlTLCMmfQJFCapdQj5RqashI0bqnY+exIU/+vOfqxrPxD8O4dQGvvrqFLot7RtdeBGjxEUBt21zy88QOW5sbHCRKxyybFN0wpTZPry2petsiUUNCWk/7h5wU9FDlYqaPA7FQFXg8bPcgiwTidimXBNVWlhi1RMs3LQ1fo4lrOQQk2wo50sb13Emmhew/xjxN482IoGjI/xD0mSc0fW8o4E4NZ+MMyk0OWPcD9pjrY5cMc/diZVG4PTw0SP9hIdDDaN1kyjsnzTjwsuviiI8ZE0dHMoQ4LXXRSn2GfI60+xbf/AH+qmps375C1rjBYtinpnYQH/pM3OWVzS3fYjR1llCRi9C4qLAnDonULAUIRezPHzyjxclCrM5l4wSvP8JiTO6bY3Lcl1h/qvLKhTZZCzJiA93nCUavVTA3MY2InI3eElCk3n1wkUYlT+3pBJYjp4+aYDqPKdMpZZOWUIBUiaRB6SKpmVikxvZQMiK6EJqTwPtFfkcIj0Lt4fUXlqq62CUY7vvS32U40m0hfnJ/on2pX2eVvkM3DpP/5LZMXheybxKcioMEAAUEOmtvqRl649ZZn/6rvdJWH4Ze9lA9OdlyRxh88RMnEKqNCYpRkAPJ2kyzF6moBoOZ3YLXcd2rbNTNd8mRZnN3GZzBfGDb64ONqmoTw6RWxXvhVR2fl+ytckfUDgk02yxaUAak5xqHdEg83cyxceDBEMT9iUzCHn7PT2IW53kZeYbv/mluPDLv/jrceHgWKrO07aEarW83h9OehKl9FPqtFvXlGNrD/ekkCqVyc+SnamvplMNR5NNL2OyTNQLJy39dHyoOXDlxmtx4bf+6m/FhXfflfjwu9/+oVqNPdQ83NdcBwcHBwcHBweHSwj3muvg4ODg4ODg4HAJ8RzRwsyIGbjTILpI2Yx9ff8fE4/cWJef8HCon5pr8vj9yQNlFsgih2jgtLyCdfwOgZZjPrzb5+6gpI/zq9flFfDrb34xLvyl3/zr+mlbgXhjOL40kePDEaGCnpeFRfrMa8pPsX9f3gsnROD2lkStfuYzSjmxSgbws3d+6C2AUXIZwrEHfd3XEl4vEYs6g/wdQrVY5ohqRZ/0l4nQ7BGuOxzqrDyhmm18+3NTdWwfN/hbr6ivUmSOiCo6a6moe/0Xf0tKj9kAWrlMOPaJuMKfvyNqoAiFdIxt+CmJsOvbigg2GtFnepTKJlq4CPNO7/VErCzTCVlou7AJSQ2hny+qhiurJbvU1WvKjm0MqXHHB4gNzNv8yhVNyyU4uOtb9bjQaOiaFnccQUSaDGOprrMiBtGyGNz7WEzr2w+bceH770sp8fCZIqzT8N3liq7T7GjIyitQV/mF9hS2Ei0e2QzAp/DUE3hqyzCe4V+zPvRlH2XRGVqF8NYNtZSE8c0+iUWYb9tXFOyf3Re3boljUhg8DKD/0qmLpGGeiPhllthTPBw6rSTBuhGHflbHL12RQ0tAfopBh7tA0U5nC2m+aaCuzpd05U4Pct+c580mAwq+z7S0tA5Z/EyMbjR/iUpVHKVl5JhFiB/QPu08E9mXgaO/dUdiA5NavfKatApb25rSzZYF6YtNbp9oLr1yWzN5a1l375zqmH5fa9OcZw7a+kuV3Bw+4paPvEPvkzBdQWKeYJID2o4DRHLw/Ome54V2KVMvGHcMBb++JhHLk6cS+bRxVvncZ7RRr2MAYkHxUxwwhhZuz/TO81M2gyOB6e6gg33SqVieCKu0ccfz/hLBYtXCJMmnQI6Arvo8ZD95480V6qNj8ux1pdLFzfOj+8r6YbkkcuisTo41ZJmC5qRPK8oVTYauiW2w1Fihn2s8B7vwvGbNUcFGIOT0EFOX6TktlY/So9fT7neE19BSXVW6sqy95e4773gLEBJTn0XjVyYFjOVTSNOfKY7hFcObIlWKMPkZJ9sgHg4ckyH5Qp6kA1nkYRWkhiFbZYiJUxndS6Gov5ivwozcFjPmkg1rGg8WP1lKF7UKlgwlWS2skWDx98cefk1ba3qi1XFT+YiERyMEJGYykyIFQzmtTnhytG/X/Obv/Tv9ij/V57+gLbdY0F49CzWvrl7Xe1F3oiuk87wyffYrcaHd0R416GsWnWAdU0BtmPY10yZTPQHt3Q9BX5J56tFDPSmWVq9wllrU66iQS7M/e8krXwz3NdfBwcHBwcHBweESwr3mOjg4ODg4ODg4XEI8R7QQmZkzGZ/PRc5CBEBhn5KJu/GCHIb7mDnfP9HX9fVX34gLTx7J6zgkKjzyiU4lQPL1zyj6+Dd/U3neX4BO3d7W9+rGmuJDZ7ymH5/iRI2x/3Qswuh3/uH/YbX/1u9+My68sSZOfzjVFc4m+vT9KuHPv/wbiulLHyjS/Dvvvc2VCB0FKysWmElQLVxnmRwH5uk9nVq+DR3cOhV5ZCHGt28oQDubhctmOMw3e0DgOSkCvK0t8ZgpuKcAtr2yQkbyocZl00zyA5EO792/Fxd+9/d+Py50WqKiGrSiB+HV6zbVUkjYHoM4HYgmKwdiOuaxgirjGNZ+Z09Mh0eO70JL1M+VYl0/hWKrX7yzZZcKoODfvyt5zGikateX1Y1rqyLsrpB3Y71KUokh6bbHpFoghDaADg6yFAizbTY1KwaQlcddXefuI1GKu2eYBuDHXlvBEwPirD2CzSnqmN6w6S2Ab6wWhbStTVZpAD8bmCE/ZG6S44NOmLzwSlzIQDVOO490TFfzZNrT6Z95VSR7Fvvx6yXlNdjb07h0uGkfP3CTE0yHmuTTrib54w8Vee2PklQFVSK1LctGilD6JVwLBjhg5In4DlILnRZaE63xEcYdPWiyKb79hYZ6pkb8ctboenQRE3Yts7mw7CFZ4qlruP2P8TxJ5Eyk23nhFUUNW5KLxoqMQTa31Z/PdqRwqCG2uf+B0uWMUEOt3lJv9EgfYIx8Eebap897ffLXsOeM+hc3McOMR0CinvEvMvvJU2EuAUR07oFxjuMPuLj+f6mhJWmJCHzu+4XPvRkXNtjqxwPVdgRZOeF2KUYhRCjiB5aIhPtjfpJ810GPYXU19UIiWrDq22X8hR+GfCpmgqIhaW42aUV/glkBq6NGapscSSUOD7WxvP+RdrMmbgxf/awO3lgTueyX8AxBLmNP0oM9mcx0mlosn6VXr2Cw8OMfKT1Tb6jTNze0r7bOFJIfoS0ZDpO2H+EWkkVSaA4PMwQzTx8rSH84WWjlYSx/jn3Vs3QqZIyqY3pgWQ+GCFfMQKDLTQe4OizzvMuiAyxxwTyrNZUipxWvDT75RAKMJtK4l0wR24S8KgRJ5S27ExOFLcJsb0zmcS6NgVmUkLWHu/tzQiDDzPLgjMlE00N6sVzXBbn7DE3I8hV1QiNPPqBW8mg+2lF//uPf+Se65vRvxoUvfUUC0XpVbwt9dpsaMoY22sJ6XX9pLGkWZVfYPHlKlnKq9ib+SIeYe5y2pIUL2bXGoYZ1imqx/UC73/e/J2usPqrOCm8U83Bfcx0cHBwcHBwcHC4h3Guug4ODg4ODg4PDJYR7zXVwcHBwcHBwcLiEeF4WNIRIpkOaoLWaINM5I03UOwNpJrrvStZZXJKGo7osAVC7JznF4z05UETcNn/ajAu9M4nk/sf/6W/Hhb/7278dF8bIMiJUv/2udB4jxE+mUEzjxvJvv/kv4sL3fuefWdMKJN4YdFWDrXU5kW1d+UJc+PLXfi0urGGIlkX/lKtJDOdRbUOlrAtOUIrU69Kwmk1Gu6c25rFYSpHhrFxFzkVWtv0jKWAyafK6YXFiat0yqqMZ47i6Jk3MAOVZNYvjW1HiS5Pt5slr8va7kv1981/+y7hwdCD15Ka5lmANlr8hHeG7dyX5MresummDMEoL+gsTelmynNGJeqx5oCZfWVbFbl3RvZZNq5RGuFlJDMU+/FApW8YjdePqqubeypr0nZbzzAslJMpjb9eeaFxIgOVl8uS946wIm60WDimzwAzvLI2Zpncb6fbGLfXel74qefpHJNsbke2vzHV8pFrtzkIpm6UISrFI8dhJRJDY73g5hGJD5G7dmlRuwxvSgKa3pI80o6RhS5XPlkg1xALsYWlXqdfjwsqq1khziA4Y2VyAYCvA0a/f0jzZG+uYD+9Km3teiVYu6+JLdZabZUhCgJvPa/SnCCJrpWQ+XMDBmSrQOlX9mySBC5gM/bzav7yk4UgjhW+PNBw+tzCHtRrZ4Aa4jz1+JD1iHfexLDZGr7wm0f8GMQY/+PFP48KdlzTN0rSrXFUnpPAqevbkUVxYR4JWRA3Zb2PHhgw6h89UOqeZHCHoH6HCrBTMf+ciEr33/HeQRGw7m/uLSVrPp0EzgTjp8bgvinFv2NEC/Mxrb3CM9qgOQuQBSuIJe4uP7ZDJUsf4spm2OCCtYz+xd7QWUW8qa1JL/7yi+MIx8z+BYl77gOlE80yqW3duxIU//96P4kKJ5b+3pidpJq+5/f49uar97KOduDBg7NYQzm5dl7VfhHI0YCH0UTqvLWmxr9XIyBVp/p/iR9YgU9qIx80eO3+rrUIWG77euX3p9EQyys0ralq1pHk+ZmJkGaDPva4AAO+fftf7JIplVXI6wRGMaZG2pGvs3JbhzK5sWtiMp+us5/TsWF5T08ydqkjGR5+sY/bcNAmthV5EKXv5MU05SUzR6KdQ9JrC2NLEtnu8meCflctoSiepzsj+ZalkzZjsU5LtWdvbe5rSu08UmFS9qoEuEFhQ59m0hOnYJKOJN2kli3RzXc9ZL+KaCLsLJUJ6sGWMfE2DFO8hKzXdZTLVQj48eKQKVDUuX3jzL8eFHUwk93a0+ad5dxq16POZbe9EOKC/n41JnopFWnadsJlzoQQX4L7mOjg4ODg4ODg4XEK411wHBwcHBwcHB4dLiOeIFsznxjLgTFACzKDSNr/01bjwDhxHZ08OKeOmvop7WX1w/vieiNpxxwywRA0s10QxZJb0Sb9Wk1XK3r4+xZ92VBjgKUN1vKWa+LsytibGkW1siNZ54/XPWcv6pGhauyX7s5UXX44LVSyWLHGIGSotkblqhh7De+hdwAy6MIWXzTDh67HZCskrM9bBVezYrr8oHnP/mS795MkebdQ3efOZWiKHnGWlOiFpWYucSSur9bgQpEQoVElwMpuJT/mzb/0gLvybP/jjuHBEMqoaye0C7IdSzIZGzZJa6coHTd29VNdZ+bQ6YYQ12Dy6EMfYsyTZbspGV1nGtTONRamhmfPO+/ftUu2mWnRtW7lb8jld1J+p82ewHr6vakNheRNTlbTVkHDMuqAhAemUdrF+e/BYBz/cE3/34Z6cgHysWl56ReN79Y467ayjC54eQb7Qnz20PSfthZ1myzWDQVLOnJLwETPTsSwEaw8mbriJE9+WZk6AV1eW8S3UtBCuXxXf9OiJKM7jx1Jl3LopzcMH9+U69OOPNW9TtGIb45gIurl3po6adtT2M4hRL5VsREEWjzCM6vrkasohACgzP/tkKIwWs8kB9jr5QHRbgUxLIZ0WMtPaTbzwEB15FTWkAgtse5Spr1bXRcRHsOQt+uEUEcjn1tTnT3ZECLZa2tkqULezCfIwvJMePlSaxnKgC77xeW1ZIa5qHRIK2kzOlfSXIv1ayMFaws9m/fNSkU8gmiuc61xzFrO8YhyVmI4lVzYeMZUJLhxvDO/1q9r9pggSTlpSl41JNDUYaI1PzLEx1LhkeZpM4etDbtttqof7JLkMkplGsr3FDPF8FrRPwYzta0YryvTD2+/JSfM+OoTXkTE85gH6/seaFd2BKn97TVRyh6RW77HpbayoyS++JI+/1RXJh8Jd7UIFNrgZWbtSCUvOZkjbq3lNjyZr84MPZGf25FSFZRJAep5XyegK9YbWQq2KaAFzwN0D7RtrV1/wFmA4QiiGsKqMRaCpzLyZjinkNGR1nok5kheamITt8JwExczgOD2FX6RlwktFuKHxbhFaVsXEcI4NliubxdjE0qHxlEnZg4PTpyxtu6klEpxZyjRWfSb9vBezuMks5L2uNGCHB+QKXdGWlccorYZA0RuitbMkgr1kkU5GyB5qatpPfqx3gy9++VfiwqtorgY8sAtF7M/QYyw36nGhO+BtkC46QudZJ/Ht7oEq0MSwMvT16BlNdPBs6tMQNa090vx8+Y7kdnv7WkH7h21vAdzXXAcHBwcHBwcHh0sI95rr4ODg4ODg4OBwCfFc0QJsO5/i+8tiT778d//zuJB/88tx4U/+r9+NC92P9QndvttnCvpu320148KENFE5ov6LBbEPKxvirVI5fZ0+IM9Kl5RaFpm7RPjeiHu1D0SnGuXx+V+VYUKWgz3P29kTx5ohscrIYnIJJ58NRQ7OQv3l6b6YpoNBx1uASkUtGrVFrrXbIgI21szZQB/5B9gpXN8Wj1kkTDWICF0/FsNLdLs3S4mtGBMDftaUxiCAB8lA+BqlmOKs3V3Vp9kWq/WtP/t2XGh11cMVNCSFgi6YS+OZAFcYeapqCa+DXF9VzBDBWswRBE2etnnA5XprWwofzpDE5DYk5vi+ev4UErM10U3Dc5TrCtHx5aJqUoLM6vfU6hzGDoWS2jiciL7pwuP022I2NyFWKo2LCd7uPVPv/Yfvi00OCXiv4QuxCs09maja9+79UG3MaE197rVrcWFGHr7v/fj9uDCyiT6HjEUWw8RlTbRgAbz0zIwY4S5WIZMtdWy6qvjZKIRKTnhpJDoMawrNwOEJaepINfSATIddyKmlnGZgmiDf1Finh4h2nu5rtRplmUYT4nlehkj8FH/MQjTb6p6F+ssYMtpMBuZRReqw3FBjC4hSzjA/KRU1iJYDMluGGC1biLGmd4WEQEP4ZatzH++XAkvpF15XwsVdkinu70mP9Mqrr8aFeq3O3VXDI9j2nUdyEamXjKPX+I4hT02QkKGlaaRX2xtyq+iM1OSHTODlZWKo5zCDqZ3B/p8j79EqPCcvGoVzqcJ85mevp5GqltSxN69pCQzZco/PmnEhNG8f5lWfRRpGViWLfFerC3lNjxHiq+OjPQ4m41R0cb0kmQUT8cVFrYK14lNEDKbBKBGB3iTc/uhA5glpMmBlEQnYdlQ/0VrYZpZuklNqwpxskyaquauVmHpJ3hS283uBqjHkyeFbWkR2RaPUU+zYaUt1huqmiMikGqAVHCVOC2ubek5lsKAZMr7Hh9r0Hj3U07Y1+La3ANfX66oJk7lc1g5fpBvJF5lkSkvNCRIsD2iQZBTTwWkaa4PoeYTtI2mLbHokz02uGAUXTrcsZjOUTjbzZ/xm2+lkTr+TQplwrvZcGbuMKe8e88ikNXNCKh+kEGWhIuigPPE6OqaG2M9SUXq8hHieN0ZXNkAsUSpoFP7o938vLlzdRhUTal6dIAFdZVxOHkumkk1r6l7HiON73/13cQHll1ev6YJHR01Vg1eCPi8kjSVVo7bEGyNytVZXE7WLy83RQdNbAPc118HBwcHBwcHB4RLCveY6ODg4ODg4ODhcQjxHtGCE0Qi37Su/+o248MX/8r+NCz96Ija5ij98piTX3yjSd/LJWB+3+/YJ3X4a6cv5Rw8UOnr1jvi7AAp4CCNg7vQFvNN7HZ3+R//2X8WFt9+R0frquki6v/KNvxYXbr/0etLadfHjHej+Pt71I4gzrOsTCvs7f/6nceHp3o63AF0+sxPDmsRjFqHLs7hJj4kI7vcvhtuvr6s/r1DVKaPweF+huEWMxD1CyNc3FPl+dCpZCMG13h5yjikEer/fVAHG1hynC7iyl/JiT0oJYaTSWVfDmqddRWzz+/AI1byYiUIR04E5jKniSlU0RKcvOrWCpfbyyzfiws9wvaB9XqncsEttbqncqKgCm0siZHt9YmCJOy5VcK7oixcuko+gSLXDUD2TwohgHy+LpzAslW01//oriiNeuyp6OpcTefTD7/wHVbsl1nJ9RRzl7Wsa6A/f06SadVXVq9clfviZd3G+FYgRTsGTpROtAoHe/DSGvxuvKih1tqZCkbwGEcYgUyQcpQYJI3BKsRwB47GufHSikXr15g0Vqgq4Dsjh8vi+NBimfSoxGbpMvFRApHMqCeLOsswDhixjVu14yE9J62C2Ib3FgqKAFZTiOgX6qoU1QSatmV+HVFtd1QI8GWqeZNEGbGxo7E6OddP7Hz9SxVgvf+Ub2jNnLNLv/Jv/Oy68/rqI5tU1GV/s7DyNCz6cqQ3iAP69Anebz5oPv2Z7CqpxylZpngOrG2rOi0RDt5DxbG7iFzGHEJY8SnQIF/6bFBJfBX/uLM87hdk0P4PVW9qim11kVGc6xnKmTJCgDBGBmHrBLp5JmHcULDxx2i2tViN/Le7elAlJxgdjnL2LsGwsPnc9l13iIsY8lXwmfJDVTN7YFOU66Yr3z7KxtJo6a0hSiS3GpUtzeiOceZC9dTnrEJuXEtq8MZYF4xHqDv4y9bXuRkn/qF0nB3o62Mz54uc1Sx/vSPgRnWt7rU6Go4L++PgjPcp3nur4LPqlo+MTbwF+7eu/pOYP1PwxD+IQndiMKplMxTYEq1E4pwTwPVOnoMsygRbTe2Y6hLQ6dhLg7oJ7yQiPgjFvBjOmon0k9M2mhi3CKhagnUghLZh6JnVQwUeFaPthdH4JfRKVih5t6xsa+gwbdYk0DYfoRsan7IoYLpVqammjylbleSH7qnVakU14Ntba/KPf/2Zc+PIvymWrTp6aflfHdPFcMvOTblGDuFTSQ+3JRxL75TDS6fV1ur0p3X5DZjLtvtr40UPNris8ZGsNXTCFUiLjJy26APc118HBwcHBwcHB4RLCveY6ODg4ODg4ODhcQjxHtNDHkndWJO7+ujyo//AH0gbsQ6XVl8QX5wgd9eFB9p/JVnqIX3QWMjebF31ZJN+ExQgHRDgaRzA16wa+Sf/r3/uXceEf/+//a1yIzO86rRf3937+Tlz4b/67/96a9iICBiMHT0/0eX/Q0/f2CaTet/74D+PCz7//nbjQSC+kEiyzwG3Ch3N59Uw2HV44ZkBIYTqrb/t5OsTiVUsEnAYkIs/AlWTsHydQvW+9pQQcR2eq/C9+/bW4MOJe0bRIAzUck6GJFkQWzEZGg2oOVPCXbqFVODxF3WG5y0kEMCO2N4SozZJCYh6VZSN6xGs0iuqx0yNRFRN4jSEpuU+H8COTJOj5+lCE3eYSWUJSqslGQ92Iw7oXQeGdNbHUaIsvW22ITk0XyVFypnn11ruP4kJtWfdqvCTGOYNmo9NtqrYDcZQ7T3XXIVHYKxuq4T1c3CeBqvr6dV3w1gs34sK/nhMtZI1pTcztYeJg69IT/aWNFcaQIFkfwYaP93qaofdRCGRxyXjx1c+pXT39NB4/iAsvvSziLAPvVkZ1s0sMeA9/hhaRsBWGoN0SlRbgqO9lk3kS4LWSTnJzqOCliYOGps8WTbTQ9xZgzLScMmMnsITVPFcmxrlPbYekL5l5an4R0UW9rok6m+qnal0tuv7qS3Fh++atuPD2Wz+JC7/8KzJav3ZNP51hmT4ZkwGHTmubicpUlV8mf80UMvfoUGxdQMx1mfHtDgdcWc1pMBlWiVnuohmYxyzRIRgPOKdWMOqWuHU/UG8cH5/ZpTqouV7/jHpmZ1/PhVO47DRWCcWKDT06BLQ0vvn/wwf7GJL0oOBb9IxF4ic5AqhPQNMC44UphMY0WwILY8ItTD698MPQGNsQP8D4gqdSqWA6E9Mq6LljajezV9knxU/gkxoDAn2aLNs8NVRVK+yQ6SMd82ffeTsuHHPBNz4jeZWPbc4+u9CUW2S5xfVrcgG6cVNLex9hg+d5Y5ZbONRe3cNU5xGyru1lvT9YEod5hPgGpEs6Jo0Lk3mnRDxNIv5i6gUbIbNFCtlpZ0l+B0bTdhufzC9p0e7tsRbOMxOn7WrvHSAmSeY7O+2AFxvL5mAOJxmENKvI59aW1dIqLkapaMBZuvB0Zq1YaLaT5Zm4sbXK30gGgTApZcKeJcSLVR7oa+rnYiWh+NN59cNooj8eHGtvebmqp9KjR3qhWltXX325IcGJ5ZB6+lC2MK0zNe3Bh5JjFYtYT5AUI887hu+r1QMmVfm6mjZLa6TGlsKpq02jWtZfepZ0bEJj5+C+5jo4ODg4ODg4OFxCuNdcBwcHBwcHBweHS4jniBbGiATyq+Jnv/3Wz+LCv/rfficufPbNz8WFO5/9bFyw6PLpQN+Q+8gA0nyUt2z1r5Nd4sYdhdRZjL/ZMidaBcyHjw5lNfAH//pfqIZYWDeWFR4+GPO1/P4HceH3/vn/aU37m3/rt+NCp6PDTpriXDxit7/7J/IxfvsH0irkYGYLGJt7Hh7LwPztcyTFyGZJrMC/JYxuqEPLWi77YzKbn+zLUvsZjFVIPxh3M+zANiJaePyEtBd0Y4HhqFb1l2FPHfvePXlijDGIzmcJPCfkuTBTK3YJgA1JM5HFOHo21U/5vJlj6IJHsJYbuYV01fWrIsUKcNkpOMcp0fonbfX8zVuKr2wPSZbdIW2G52WRoBhff3ak/swUzPlCHXLcETMCVeL5ECLNji5+cqrJ8B/ekl3AjADV5Q2RUD/9ofLUmxFBA4FEv4O5PbwZTLjX74uNyptNPaKU5YYW42yS2LBfQDoyosfM+Y2WhRXCwqK3LJp7UhMPmL3I0yawVTYY49VP52cqWlxv/sr1uFDO6Zj9Zw/jwhnM0REx4H5GvdEiKczOvqJl+0wqP6sFtYStged5S6xl0waYack40S/hDVIiLQuU8TzMOt6sxVPM/BF2LhZKb8Ymk1A3feHl17iSznr75++qqnVV9ctf0oY25pif/Vj6rilx96++JsXUEII4jVKrgkJp/8kjnUW8eYktrnMmytj2kww6kzyijnJZvTGjx8znYe+JpAIhYrD7T/a8RUjB/DKBg+jiQp55eJjYXJrprOX1xAVlfVvM9eGJmpaGrKxC405x8IjgasdmvG814rkQcpc+2QrS6HZSpmkxJQ8X9GeaMAEyDMsKEdGQyOdgTk9kDNDdU3PSmYPpf9I8JnIBHiMMYjGlbuygKslzwY1ljV2rAxVOfa5jH5Ql0YPR3MORmQ+YKEUdfkZWpj/9wb248METue6sN/TcOTs2+x2WJM+dt6Gbx6h3wnM5C6pVNXbKcBwijWhS/yurioX3Zwsp+MHEtvGLqhjLWJFia0oy4FjWEtsPocIjrFdsNs6QMYwYzTCl3aZNsqH39yXeaCF9apG1Z8wUyvL2kiULyZil7SHbO2zp0ZzC2aDNEmrj83BzrR4XqsjDhj29hOzv6jlum8Y8nj7VW1CN/DulGnmVppJOpQNtwms3NRk2NrBVmWh0jprk+vG8GQrAdlNDPEKCYs/E6opqu7uvPX/nifydWj097iOcUr715yQEYfO9cpXcSeua5595Qyqmpbo0dbazff/HcmMoFjVA29vS2ywzdX/wre/GhcNd9Z45Wc3Dfc11cHBwcHBwcHC4hHCvuQ4ODg4ODg4ODpcQz0sPAe82JO7vyVN9pk5jpd0m40OWIMqlpXpcuLf7KC5MIJ5yRCgXl0S+VOu49/dEEjQaBCSurV2sInTVvffeigutlqiWekUfwM9g9CzjeRUe8F0inT3Pe/HFV+LCxpVbF+r/4ENx0PfeFyOZw3F6FS/9Em4SnnfR77rbU1/tw9ffuS6aYAIXXymLQbD845MhmTiIt5321LGWZTvHd3uzZYjo2CBDHnDCVD0yR1TpmQqWy1NosjGx2wNst7dIH3Dzlu6VIZFHiGG1R2+cnYgjGHcscFUMSwemYwRJn88uNAb3+sRFQnr26cO374rd/uxLoipGqCkyUK5rS+t2pQL8o83mVErURrtFvu+SatvBy2KDSRjCqbWY8N/+qbQK370rYveVr38+LvzsLdXt3rsKxa2UNJpf/7pY6Uy2GRdu3dKI5zFvf3hXeSJeuiMBwAuvi7L5/k9/GBeCNLHGixGYsbnZoUMMnsGFdVcwBIDUDqHLJ1lTiiCtgQe1gPFj/C5u3L6tYzyddXqqtjdDrYgUXPAAbu64pfnW7UHbsWlEsO1bW7Jwaaxft6aV6SsLt+91mnGhg1wnbZkjoIMzi//BHpD9pM0SKJVYyEN4wwFxu3TRlQzH+Dqmj/pigrDBTP7HA62pt376vbgwwyD9za98LS6cNnX3Fjxglu307BRzfjICNMqaVH5KFeseiaw8JpK9hMVHpaANyozrV1a0004RLcwm/LSkDXZ3N/FDuAgWUhLkjsQn8Sdg0WXohBEEdH/QTC5F/XMV0/ZoUZjdTfNMSzIVScWRQ85hgogxeSIKCDP6I00nm3sW8B5AfHdauvJsSD9gBMSoJjoEWwJFNk+T201pWpBfOM/y5G5IEk0gTBoxqUwFUsrqylt19UYP+UGjqNWaQ3liDgHU3aM13pNnh9yCtqd1wTtXSTCEQu8ZyXWODo9pl7XdrC1U559/rDXeH3Ozc+khijmVS4UULVLzr7LtFLOmwkoEZheAM4E3xerElrbHowcJm5c25wvmhQ9LbttfhBVGSKKHcYhRjKcNf+8USV5bj92oiJxpZtoJHTPjLwEiilIFTY5PggM8dip5HZNBTxhgJXHUQZAz1pzcxnrGR+c5nuqmo/FCbczxoR6pwYbGt7JcV5N70iHUKqpGkbX54K52j3BgrxyJLqKQ0mtYkWlgDhgZxrdCMojjlipwcKJrrq1rypkxy61b8pv60Q9l9zHk3eD+fdSYuDpsbekJ+OSpNBu7e9oPX3gBKQ479s5T/XR8oP4s5n2qwXPzZ4klSAz3NdfBwcHBwcHBweESwr3mOjg4ODg4ODg4XEI8R7TQ5d3XmPTZqj5037yub9FhEuitnwokArDUAClYjBqu+0sbN3QWAZIDUhVcuXIlLhgb2+8TEc+9Dg4UDpkm9LKEnKBYxhedC5q/eqeTfMG+/4EECZvXbnBx1f/Jo0dxYYpHcR0DgbwxK4sjRvNYCgx6ImgO9lWBLGKDel2VLOGAPSRfdh77gtVVMhQgHbF8GUYZGQU0huixHN896FSfcHuLBh1O1K4vf/HVuPD06VMqpspvb4vQLEHbHRypFU9wfu7jYD+l8hG3KBKC2oOwNs+EeZQsGNxyhUM81ddEWw88MR2P9w/5qR4XCikTkHjtqQb9dEr+kZS6OoPz/AA+ZQrpOYKgKeKAcbWm+16/qr662sWEoa2/7Ow040KtLlHK2iqUMSxQtaL+XF9Xh0RZNa1RRcADAVeCZdu6Le7m/uFFH49PQZQEuhMtW1ST20Tg5ogBD4jUHo0Yuwx2CmwFPs3IsDSyOVX1lOQjR+QuH84s5FsdNaY+7aEm8CkmDDNI6jJJYbZvSJTSGSRB3L2+RnOcTDB+pW5dEkxkIEZt25lHhtD+Bo4TFQtSLugvAyrw8RMxcWf4umxd07gM+mrR6trWhYbsHR5xsHbI1Q1taMvLWlNTgo57SHqmRIWPCAbPsNgHba27Ku4YaeZJxC40ZBBP8bRZLmj3qGGy0etpJW7f1tzuwUFfZ6v03pKQxmDSrynipWpJkypkzwkZeqN5R1N82s/ZEVSXNeHT0P0hAdGpQMuktqalNLPti8lcKORpCE+c4KJYImR6jKmtheTnl6g2FG00YA+nY/Nl6GmeZaOUOm1ga6Gg7TQMFm5oGXb1GfoKe8xNce1IsQCtgVl7kiK7mbCC8sztLp4JadRHKVZr+0gTr9+3TDQ6xrzy37wlh//Niqb9ETPHDPmzlp4prWN2j3XTQ4Qf3rklRn94dfLCFPGXKEPuVzmoM01W9wX0Se3R7eguFlyfpVBAFWaahclMV+72zAVCNx0hRunxLGgPWFP4tLR5hE1wJChVda8S3RYVpXDwyNy0hLykVNFS8snP0O/rOkFkc1uTM+AtYsL6zZu1zkDsf48MQX1raWvhI6CK1UCB3eyspfWb6mlYGyXtOUeH6vmDJ1qb2bRuUTPphecVyKZUROK4Zk8K9AxtkigNEWiZlnUw0iCaZ8LLr8hF4f79RxfOClHe/OhH0jOsrDymIdrVV9brcWFrW42d4v/z6IEctDw6rd7QkJmDzTzc11wHBwcHBwcHB4dLCPea6+Dg4ODg4ODgcAnxHNHCIbHS4xFJw0cwPnl9cJ6lIc4IoDPudDTRd/I0RHxtVZ+yr96Qv8EKiZ4tOXUBE4O9PXGFxsbmiGC1iHifYMwUWe9r9XpcmM5E4kwH+oDfJ67Z87xHD5QZ4c6evpN3IRCfPXlCq/WXCUHFfawJPMjfeRRy+sg/gYI/PtJ9GwRCGufSQ48xgJ/NwquWED9k+RdIOFF9iqR+CLBTeLJzyDEkPYcR+OhDaQzWNkS1WGS0Rc528VevwsQ93FEn1Oriu4/2FZObt/QBZK4fD9RAi/ieEh8aEGJsDvbzWCbd9v4z4kNruk5xRSzbg0cyOvDh78p1HTMKk6m7Dy2egnLKMnkCiLbWmWqbgn49g+J5rVKPC3Wadm1FnXZrrRkXvn9fzuqFMcnB66h0iN8ftHWL+qZ+Go3E8p+ciISqQHOP4UN/8DP5h1RuimlqDRdyfCbgOQe11KzsZzgVZBsyo6guiRROWTZ2JozZgAQWA848yafUP8OuAmC9KRQnhNEUk40Rc8l0BWOmYndCNLRFTNOHnZ76Z3CuySZRKJM8xVJXZHME9dPnE/NRh+6fxxLzamUZUco1KQqG7BK7zPMXX5TnQwoVVoQ1xwBDgF5W5NoarKVloMgylwKUMCenkh8ETM40q2OYbHFQ8+wRSybHQu6SJtdAvaa7NuF5D1raB2oIgeyniFmRN4pzV2tqjdDpeWRJQJ9HDJbLM6VRbpxh+BCiLFpCvZOFvvc8L2BnC7HZ7/WM08c8AWXCkKmSQlDUR3RhuQlMNWGeDzY9Rkxm2w8zUNhBCu0Hhg9pdtEpxghjNsYpDzXzqagg4gqjhWszR7T+MFLn23TN8jUpQ3qICQKPJs8Cz+x3uGAHxdQAQcL2Co8AqmqZLA52pUC7/tpn4kJ9RY/d04/F8xYZxBrP/ZxnChBSjZTr+gtDsFLRTXPROV0QyzwgD5FtaBn8ajySDoTjhZ027WvPJKDfC8ZIJUOI+0BVGoSajUdDVWm/zXPqxNJbqEXlolq0jGaysaqqvlKl+XndooC0wKwJpmNVLIrI30G2iwJLO8vE65NL4gBjn+5At8gj5EsX1cIln8wgDzUuP72vJh+QiKpua1M5EBK8/Dm50xQwH/jBHyv9R9BU21Ns3c1TVBm8uvi4M6VKyaO5eaIKTEgwUVvRxU1SeLKPHQoOGIOKGlK4qnGp13DQ6uiZXsbzwfNZU8yKDCqO5RUtri/8wmv8RRv1zkOpTI/3JFhtH+imPdZ4DZHe8SFps+bgvuY6ODg4ODg4ODhcQrjXXAcHBwcHBwcHh0uI54gW2nxMNu/mNCHPk7K5hfO5HtK/TH6HrZsKsquuKpTvxZeVlOGlF/VR+sqG6FS7VY4vzzlLgmARvMSrWlh0wN1DXtM3t7fjwuq6+O73f644vv4oCVrcP5Ai4t57+rVLLu+jQzGzY+Ixe15CNamQXfivggzHlHL6gG9u0jO+rrcwgV/BQLtRK3OMqKsJtJ1dMAMrVKqKyAgi43ylMWiTRrxUVRd1oEpX+f7fHuqYEaM5Is/7jOjaJuxnFwvokADJsS9ap8u9LBy7gyPH4QFUaWTsibcIL7woBUubbtndlTCgtlqPC0srmicjTBiGBH5GkySs8ph06h049FqJFCdNHX9MJaOMWl0faDTrd4yw03CsrGmA7nQ1Pd59F/kE/PRqmRBpGpmF3qpyncmsGRdaBBS3iSXvw2z2B6LLry1ppJbL5B+fg4kWfMhoS+8eQlhnlnV6bhnRggXVMoEDlDkDONMkXJ31MjM9AwRrdqZCAQbq2T55IjA2OSEJQsccOVg0fqR+brb1089/rowYhXzJ2njntpxAVlfVkBCLhiEiB9MvmUQqSC1cmxapPWVpHx1optXRGGysqJAhYHyG3sasTjpMwscfvKNjsEpYvyapg7lJdGijJUEw/n2ExcEMjrLT1Loro1lqwLTm05owBUj2flvE4vBEY5ej9yYTbPOR3xTgqaeoSnySy9QtR8YcMuWAyiMeQDp11MSeHduN67dEjqe44JBbeJ4XYDIwHmEFgAWHTeYhP02nxsVzuj2LGIXUnOOEXblNfHqIwUsRwVsFCcoMbt2HlE/4d3NRYKBzOR40KHCCxTtar3164ZgxyQsitBMhAewBcrtwxk7FTms2EX3+0kc0OLJUBbamyNrzzjs/jwvVNaLdX9Q6+sFTKfTGPS3SFI+2PCKKHGmSsnRLBe+FAlqFXCZRTKXp2JMmSTroonyBNBk0LY9iZB75mramAs/9GYs0DIe0UZ12dqY5fDzWY84v63E/OL0bF5pHeiZuICr7+lelULp2VWtqNNB1AhJvpJI9SlWNPE3mCWqZ2bgZF8ZjdWPI6bO0mvzkoe6+f6zKry2rhi+/IrFBFvVRC9VfDxsBP4v/xuJsN401vWi1SZI17eF50tVZ2YJaUSmrE/LsHqWG9rcolYgW7uMKNRyqJoW6Krm2ok149+CU+2panmW1j/U66qLMFcuQwpwpoNRE6nCwx4RBzrSxxT4falzOjnT6x/ckxWkdkppnpFtMyYI0mfLUXi9z94s+Fe5rroODg4ODg4ODwyWEe811cHBwcHBwcHC4hHiOaMHLW3AcYZi4HyeGz1A3Y2zMT09Fc3twHANo4g/vilDYf6JP0GUoJIvEzxREmlg68hkElv2ldSxqbwZTmSUA/949BRua//bhkXjJ0bmE2p2OiLYffvvP9etY5MIIo+w0BMYQbUBEZHQ6WMhYZZAo1NEhWPB4Gno9y5d8b4LFAWzjGH/4COP6LOGcsymyCujCPkbNx2foB+iizVV87yGY9p+KVu7CjORIt10iqvRkR1qOdrupu4c6eIlkCqctDfQUW/gcHOX+vip2Sorw5aqqsblpHtQPvU8inGnET06MzNV8+8wvfS4uvPNI8ZXNMw39cl31efpszy4VcalMTr+OLT70RNX28alonmlciqEq+eFjTZUuuRKu3RTPtXlV9f+Fz96ICy1cHW6/Lp46W9JN3/tIPh4jc9IINfcqGLPvPFMXPTjGtx9bhgEiipUCKcKfA0vLAowUgkHLYaveZea3WYklHE6yxiuyxkc9Tc5JR5U/OsQiYCa1TLfZ1AWbJh2BaeXuIwhrsxPxjdqD1gxR5vg9EVgDCp7nPWCZFGA/166I70th3pKFsM6wcIyInEdIb1XxoBgwmbMcUyyrjXlz6YehDajP9Q2Ra2dNxRH//IcKhL6OgX99TbNie1tTKE8Q9+4TTbNnZFpJIfvZfyZa+c4NZZeopjQrKuzGKetG34Zcf1lpKF2F5dY5a2rEG/iZRKH63Oj7lXriD38BPRQOqRS2BnwQqeBOH8Ayp6hhnyGw/Bee52WhxUPkOpYrIAxNvYCBD7KfLGqBFHfxoPLH2A7MsEEw0cIY34w8bbT8OwX2Og/Dh8FQU9eWgD1xMuiaTFYRMVLRbOGHoWFXc9h2vymPgAmdMEZ1MBro4BILOG3WOogoKmmevySwKDcaVENn3XvwKC4c3Jfo7gn07q9+7Wtx4c5rb8SFH3zvW7o7CSyqZS2oFJ0Q8hxMTBhocWjPr3M5QQIT1yE0KfLImLEVzLxExHIB6RVN+KlJR3h/8ClMyezT3tVToIlSouyr/sOD99TYTd29efogLnz/29+MC7Vv/CUd3McWickwtqoihbNxNm1eLmVKD1wUippUz3b1hvPj730/LqxgFnT3sSpWL6g54apW691TdfXhUD/lkzmwUObx4K4efKOBztpclTrFX9J8K1S0c5p1Sb6sOq9vaP0+fvyhXRM1qDcb89ZhypmJplOQxkbJVgA6xjSaDcs61GI/jNBljVgLG8hKb9/RVsk25vnsbB/ee193H+pmtXXNkyF91RlrDu+1UcvkFnaa+5rr4ODg4ODg4OBwCeFecx0cHBwcHBwcHC4hniNasCDQfEZfsHsQK3uP78eFAeH/z57I4nj/UPxyj8TEER/57VOyKQqSl2v4CB8iIID7sJhH8wEPPPO01/fq29dlsOATynp0pLj17U0FnN794MDuZjRK6+yEunFxWBtLo+AZS2jM3XPM+YUqSbEtXDeyYEMyRxSIc89xTDgSt+jTw92eiIzdw2Zc6LQktDBH8WHvYgSumVHAtnlDSDGLcCwhC/FDU1PokGaiBsFyGXvqLIM4IDzcK4j16HRVje7oYuKJKiP8cEf07jyOII4jCMqXXpMRR6Mmdvjw6Y9Vw34zLqzX5B5wvN+0S22uiQepYAneOpKOolon+hvGfLUuVYnf1rjcO1BNDtLEsBeJEfa0BF5/6Yba2CYWtawrN6fqmSCDnTtpFFoDLLWZA0PUFDaVaviHRzBxvaOFbuqM8znRgl0Ici2490i/ZLRIB1taCxM4uQLamDSh/bPsRWOBR49Fgw5oheUuOTrRKuuTQaZPu3oDzdIALioVmWhB08NERx4bQjqdyIE6rM2f/dgkASLOrt+WesFWq09tw/Tz9Fee53leHaq3TB4TM5qwXAND6LYRDglNGjImVXohp5EqFzWFsqfNuLD3QJqcxx8p1PpwW9Tk5qZc5T0IxDK5dTonamkexvbxQzGtBzvatSos25de1AUtUcvV63fiQpQp0grV+T76mRa+KEsVtb2Meue0mQhFLqDVMqEXye7J4FAlv0kK8nKIhUvAZp6eJWPRwwUlE6ghxvtPBnCsZGzpoo7LoTGIMGoImCrm7WM/TfFVqDC+Bdjks06HxraoBsHsKJ3SNvMvdoM3YaLmyd/RZ3+ex4Ddb0hCogo2AqGtWwY6azOfx0SawPP+gJRJWcwloLBb+Jl0EWOctkgz0VNVf/CWLBeOmcm//bf/s7iwfkU08cnHmmZn1LlIt5jWyFJspKCSTZ/ged5kiJEIbwtpIt9DNH72IhFhBzGPfqShN1lIQmEzwWYoJB4eiGffQeSz0tYyyQaqz29+/Rtq/okePX/4J38aFz58IP1eMa+n24h8RkMULKZ2M+nIjIpZvgxLZVUqaind/VDvRZubWqRf+OybceHPv6NN7E/+/R/HhY3tG3Fhd1+P3To5FNK4spiTzDwmiA9PDjBq2JdY6Pp1adLqpBEZTVDLkMNl7KE3yyQSlHwdFxcWvp8jRxjGFzXyy9SqyFTIXDOcdWmReniHZFudY92OCett3a5zHVyhunpYm2jBxiXiBfWUt8oB9iNH5Hsa8/pRayzMReW+5jo4ODg4ODg4OFxCuNdcBwcHBwcHBweHS4jnMH0Zo+QIoJtBfFuE5j4W5T1CrbPIGFZRC/QIvLUgyuc5FcC+cowVzCbaClOi9szi+913RdC89OLrcWFrQ3d//Fi0nTkee57nczuzH/c940j4L1oFnxDTLBSYn174r4JSSezAlPDhYlG8cCFDJC8GCynPQozhfAnyPT7Vp/gDEjTTes+HPRnzSb8CEZl0IyNlkbAV6OmIwNU+V7QEFqOhKmbRshEUf5dB9KFKex39Ze+YqkKcDRBRtImGLvYX9lhnIqpinNawPj2S4UPpoe7VqBF5Ss+P+8y3cyR1Bha4eSoaqFLRkBkbfkhtN5YluoiWxOycnRWoiap01BObnBrqLreXRBC//pKyWjw+Fl/W6TbV2BoCANKXz+D0T4/IFV7QBddgAJdZE7dIihENFv/j00K/bcSh740YTB0oFLc4EF88fUGVb10nl8SK6K2yL+YoC1lYhpw6bqoPH+/ognlY1CkzcEw+ggGe/7aOA2poaSaixDsF2tpyfJyLLM7iZB6SqOL+3Z/EhR5dfeXqjbiQyaiSn+Lbb1T+IbHwHklhjLeLqP/Y0zFteMyjE920Qn6WlYZ0CLViPS50cTaoleDWD+Qq8/hE/N0Ur4PlVe1R1zd0nU5OrTjuaZYeHWsqpgLtxh2yS1TWRIw+eChVSZsersIDMj7esz2tiNFYI37nlhRBO4+fegsw7MOts+rbyKumQxNKwU1b4gYOrrL6PM8L2In6OHh4IT4GqBf6bZhiIt+nmCeYSsyyJwxh5816wkdUNuEBYfYOK2v1uNBCtGD5Mmz3S8AMmnmmUtO0tC23g5nMPLI8N40gPkPuYpl9QubAhG5J8dw0i49xkmcBORNajq5l1ohU+eUl6SKqS6r9APnN8Ymm0IOn2ldDOjyf04OjiaijWsdppKDrtAixL5IwJXVOsjfDkCFkceVZCzWS4wwRNizXi94CDIdqrGUGidgKZlF44WAfTd54qmo/RduzzjZq2ZTS6F76zKU/+TPpB8zfaTKZvylvCLbT2tbEX4JkVqgTTk9VjV/+6i/FhTbajwjphc3tblfKk80NLckSLxheYuixWB5J6pYxGbWq22pOpcYT7VT9k0ZnFZEv5vhI20irc+4WbDItpImNSDOkc4S+i9wNlZruu7ml3WbripSEH9/TXnd6KFVbFjsj+6D65JEEJ8eHmpbVmo6xXD+Hh6p/Jk1mn4k9X8jBxIvNBH3Fjds6+KN7F1133NdcBwcHBwcHBweHSwj3muvg4ODg4ODg4HAJ8RzRwgjiO4IyGvOXPN7gm0UVfMzPy/BlRk0+eqBwzgGcuLl2JwnKYe19CP1wcpHinELJhTN9P59YWDfE4vsfKgYzoKrtlgIto3Pf5jM5i/aF8PIvihasbhaBm4VSNEZjHsap2TF1y9aNa3HeFzWwvqTe6+GHYB4UmxvKu12rmaGBemZtTRecYpVQIhGAESJPnijUMcSEfXlZ4zIaWsyjCtlsItRQAYal11dXT+GyB2Pd4vCEUPSWKjaB0VvfqMeF114Ws78COf6H333kfRLm194i0DIgLLRQEsneIC956GNuXxK5VrgNG+J52TzVJml7iVzeFh6bprC1IXeOd98We/LTd6Vv+dLXbsSF2ppG6vRpMy7snYqFuX1D0crLgUaqlVJXt5s65uEuqSt8LAWgLxtlESsBMc4TWL8AI440EcrzSGZgYlyvP5hIIOXrXmun6o2TR7CWa8pZ0O9gAcHEq8GFNUjAsZx7IS50OqfcS3U+PRUF/OiBiO/TFhkxLKTWBEJmBUFVM3hrTDE66PYTminwdZd6TdN7xGR+dF827BO2lKs3NFVyGIDMo9+zixPDDrNZLCsTR47lZrH56TIp1zE0MK/1hwRWl+DUzK5/OsIUAhMYE9dk2U9mngrY/3sjzArSbD5f/sqX4kLIxphlp+1jtP5sX/kmTkjbkYWNLVc1gWdUzPQhJyd4DuQXUsm9M83JGYSgydW8CrOUvRQvFi/EAGeWP8+HstOiO7Io8vHMdni4Y9+0SWR8YKsfWXYJNqIQvVxjXXuC2ZiMLX0AHg6WvmTCXJpxsKkXLBEAngfeCIlUMEMp4T/niRkjn1WHDHCQ8KhhMY/0C9FCnqUUIOGY8skpeeyaLATLowzCpDyyioJdB/WOX9dMnvIoPNqRuOUKOQLWUCaEuDpENgNZpcs4yWRMqjRJVAS5tClVVMmCWS7wMCoymQvlhTOtgDBjatJECmObTsyTW7e095qv0b0PlUfg5FSP+299D4ETsoFGQ5R6zvQqiXgSWyeegOYfYplWAuZ3NkMGK+bSR/d190xfq/7xM3Za1ngWN4Y3XtB2msuZpIfZzhvOjP1w8YuGF0QYJTEZxrwXzWZa9eFUA/3e+0qb5fOsN9OP80Yrv/DmZ/XrSLV9tqOHY2qiS5mQqdvW7ba3tO72nkoZ1T7TkyLD9GizWkcMayqH9oM29rAEMScrU5X0uvgakSrIDB96ZBSyJ3sqc1HlYnBfcx0cHBwcHBwcHC4h3Guug4ODg4ODg4PDJcRzKJgpjF5k5NqSIoI3roo1KK6Jkpvwntwj9PLsWFHq2VI9LpRhDTLwbhFMVgZGII1xvdF/EdzHxHzaidw0Q3IzUc5mobA5K+Tb/micfJy3dMwBgYfGDhgzkrGM1ZBQKRiNWWSE7EUYUTVKjKbVonIZJhHaLoByGsNIFnDy39hUpoMxXvpPnooXnsCZGq0zSuleHb7t7++LkVxerseFbs9SUasaa+sK9C7kSP+BecIUAn1CtG+TIGizUzhEqzCF/rh+TRe8cVOFxpLojExqIftSghRbxWEgNSWHwlBDlqPHXrh2Q83pkBk8SEKer2DucXRiZhQajpvXNWP7+OQX4I77ZD3IQ0+XltUhOWI/szQfLtq791TBpO0e4o1QBVPXBIxdvU4+AqpdIQZ8SnKOHn4IT4+leagXSTE+h4j5ak4LFghs8pIM3GuIW3a0LrlLqSENibltWLcUoeT6oaQOmZHmkgdja+Ty0YGq2kFsMOMfzDl2j5lZQCBjiAj/n0QXaWs/ndDcJSjOTNa2C06caBLuPhWnls2rhzevmevIRayyfaVmROIz84OCzppgupAlHYM3UpVW8VVo4/xQKKmGtYo69klbAq2bW/IxWEZi5DNAlpzC4tyf7oji7CAosvwXRSp299EjVSwLNwfjGqVV5+tXWXcVEdYh82Qw1RYxGZJVgaHPFRI/hAuYdKHCEdvYGMxQWlgg/JRxNcXFbJys+i6x22kmWEIyolhLQ+OO4KmHAx4HyH4sQUNgpivID8wRaIwwo3mqkbKMJEVIzyCZaTw8LP0Q+SZGmBXMEgGOrjMYXAziNoz7pMmAsa3kSYc01k/L6HDK2ImMuGmIHGJGBoqOyZnYabPoBwosnADqug9zncHCZTDRdR6RuyS1reXvsfWVTVmECcMymoc0q6/JFmFPbc9LKPMpD26bYEMewd6UJ8V0IZvss0ukGdYM8ySX1ZCFyQrSlZfwl7h69aqaT0oO5pRXruuC1/AxMB2Cz2aVStw2UIzMOS2Y0ik5nZ/Wt3X3Kc/xNEKRwDcmnaxMgd3iYuoHW9ozVvRCnwXPy7F8lkoaqRPWuPH4flSPCyUeJau8V5gTxZOdh3bN0yOpDlYw3JiMmGncrmVvQQg+p+yQzX3ddxfvF0u7E6VMokAiMNadrc3kdcjeOpqsspnlDyJhE1ZFtaoactbW6T1m4Dzc11wHBwcHBwcHB4dLCPea6+Dg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODwnzD+H0aElacKZW5kc3RyZWFtCmVuZG9iagoxNCAwIG9iagozMDg2NgplbmRvYmoKMiAwIG9iago8PCAvVHlwZSAvUGFnZXMgL0tpZHMgWyAxMSAwIFIgXSAvQ291bnQgMSA+PgplbmRvYmoKMTUgMCBvYmoKPDwgL0NyZWF0b3IgKE1hdHBsb3RsaWIgdjMuOC4wLCBodHRwczovL21hdHBsb3RsaWIub3JnKQovUHJvZHVjZXIgKE1hdHBsb3RsaWIgcGRmIGJhY2tlbmQgdjMuOC4wKSAvQ3JlYXRpb25EYXRlIChEOjIwMjMxMDExMTYxMzM5WikKPj4KZW5kb2JqCnhyZWYKMCAxNgowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAzMTg3MiAwMDAwMCBuIAowMDAwMDAwNjAwIDAwMDAwIG4gCjAwMDAwMDA2MjEgMDAwMDAgbiAKMDAwMDAwMDY4MSAwMDAwMCBuIAowMDAwMDAwNzAyIDAwMDAwIG4gCjAwMDAwMDA3MjMgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzQwIDAwMDAwIG4gCjAwMDAwMDA1ODAgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAowMDAwMDAwNTYwIDAwMDAwIG4gCjAwMDAwMDA3NTUgMDAwMDAgbiAKMDAwMDAzMTg1MCAwMDAwMCBuIAowMDAwMDMxOTMyIDAwMDAwIG4gCnRyYWlsZXIKPDwgL1NpemUgMTYgL1Jvb3QgMSAwIFIgL0luZm8gMTUgMCBSID4+CnN0YXJ0eHJlZgozMjA4MwolJUVPRgo=", 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Plot the closest images for the first N test images as example\n", "for i in range(8):\n", " find_similar_images(test_img_embeds[0][i], test_img_embeds[1][i], key_embeds=train_img_embeds)"]}, {"cell_type": "markdown", "id": "1b3e52f5", "metadata": {"papermill": {"duration": 0.038938, "end_time": "2023-10-11T16:13:39.583079", "exception": false, "start_time": "2023-10-11T16:13:39.544141", "status": "completed"}, "tags": []}, "source": ["Based on our autoencoder, we see that we are able to retrieve many similar images to the test input.\n", "In particular, in row 4, we can spot that some test images might not be that different\n", "from the training set as we thought (same poster, just different scaling/color scaling).\n", "We also see that although we haven't given the model any labels,\n", "it can cluster different classes in different parts of the latent space (airplane + ship, animals, etc.).\n", "This is why autoencoders can also be used as a pre-training strategy for deep networks,\n", "especially when we have a large set of unlabeled images (often the case).\n", "However, it should be noted that the background still plays a big role in autoencoders while it doesn't for classification.\n", "Hence, we don't get \"perfect\" clusters and need to finetune such models for classification."]}, {"cell_type": "markdown", "id": "3a33abf9", "metadata": {"papermill": {"duration": 0.035247, "end_time": "2023-10-11T16:13:39.653859", "exception": false, "start_time": "2023-10-11T16:13:39.618612", "status": "completed"}, "tags": []}, "source": ["### Tensorboard clustering\n", "\n", "Another way of exploring the similarity of images in the latent space is by dimensionality-reduction methods like PCA or T-SNE.\n", "Luckily, Tensorboard provides a nice interface for this and we can make use of it in the following:"]}, {"cell_type": "code", "execution_count": 22, "id": "cfcea1a9", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:39.727434Z", "iopub.status.busy": "2023-10-11T16:13:39.727242Z", "iopub.status.idle": "2023-10-11T16:13:39.730128Z", "shell.execute_reply": "2023-10-11T16:13:39.729648Z"}, "papermill": {"duration": 0.040872, "end_time": "2023-10-11T16:13:39.731295", "exception": false, "start_time": "2023-10-11T16:13:39.690423", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# We use the following model throughout this section.\n", "# If you want to try a different latent dimensionality, change it here!\n", "model = model_dict[128][\"model\"]"]}, {"cell_type": "code", "execution_count": 23, "id": "d67d8695", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:39.803540Z", "iopub.status.busy": "2023-10-11T16:13:39.802998Z", "iopub.status.idle": "2023-10-11T16:13:39.809510Z", "shell.execute_reply": "2023-10-11T16:13:39.808408Z"}, "papermill": {"duration": 0.044024, "end_time": "2023-10-11T16:13:39.810940", "exception": false, "start_time": "2023-10-11T16:13:39.766916", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# Create a summary writer\n", "writer = SummaryWriter(\"tensorboard/\")"]}, {"cell_type": "markdown", "id": "ef93c71b", "metadata": {"papermill": {"duration": 0.035419, "end_time": "2023-10-11T16:13:39.881813", "exception": false, "start_time": "2023-10-11T16:13:39.846394", "status": "completed"}, "tags": []}, "source": ["The function `add_embedding` allows us to add high-dimensional feature vectors to TensorBoard on which we can perform clustering.\n", "What we have to provide in the function are the feature vectors, additional metadata such as the labels,\n", "and the original images so that we can identify a specific image in the clustering."]}, {"cell_type": "code", "execution_count": 24, "id": "d2701bf7", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:39.955637Z", "iopub.status.busy": "2023-10-11T16:13:39.955041Z", "iopub.status.idle": "2023-10-11T16:13:39.959766Z", "shell.execute_reply": "2023-10-11T16:13:39.958675Z"}, "papermill": {"duration": 0.043216, "end_time": "2023-10-11T16:13:39.961239", "exception": false, "start_time": "2023-10-11T16:13:39.918023", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# In case you obtain the following error in the next cell, execute the import statements and last line in this cell\n", "# AttributeError: module 'tensorflow._api.v2.io.gfile' has no attribute 'get_filesystem'\n", "\n", "# import tensorflow as tf\n", "# import tensorboard as tb\n", "# tf.io.gfile = tb.compat.tensorflow_stub.io.gfile"]}, {"cell_type": "code", "execution_count": 25, "id": "63da69f0", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:40.033889Z", "iopub.status.busy": "2023-10-11T16:13:40.033494Z", "iopub.status.idle": "2023-10-11T16:13:46.812606Z", "shell.execute_reply": "2023-10-11T16:13:46.811745Z"}, "papermill": {"duration": 6.818604, "end_time": "2023-10-11T16:13:46.815351", "exception": false, "start_time": "2023-10-11T16:13:39.996747", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# Note: the embedding projector in tensorboard is computationally heavy.\n", "# Reduce the image amount below if your computer struggles with visualizing all 10k points\n", "NUM_IMGS = len(test_set)\n", "\n", "writer.add_embedding(\n", " test_img_embeds[1][:NUM_IMGS], # Encodings per image\n", " metadata=[test_set[i][1] for i in range(NUM_IMGS)], # Adding the labels per image to the plot\n", " label_img=(test_img_embeds[0][:NUM_IMGS] + 1) / 2.0,\n", ") # Adding the original images to the plot"]}, {"cell_type": "markdown", "id": "1035373a", "metadata": {"papermill": {"duration": 0.039096, "end_time": "2023-10-11T16:13:46.903929", "exception": false, "start_time": "2023-10-11T16:13:46.864833", "status": "completed"}, "tags": []}, "source": ["Finally, we can run tensorboard to explore similarities among images:"]}, {"cell_type": "code", "execution_count": 26, "id": "438185fc", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:46.976438Z", "iopub.status.busy": "2023-10-11T16:13:46.976232Z", "iopub.status.idle": "2023-10-11T16:13:47.988900Z", "shell.execute_reply": "2023-10-11T16:13:47.987720Z"}, "papermill": {"duration": 1.05158, "end_time": "2023-10-11T16:13:47.990771", "exception": false, "start_time": "2023-10-11T16:13:46.939191", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["\n", " \n", " \n", " "], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Uncomment the next line to start the tensorboard\n", "%tensorboard --logdir tensorboard/"]}, {"cell_type": "markdown", "id": "c6e6e26e", "metadata": {"papermill": {"duration": 0.040454, "end_time": "2023-10-11T16:13:48.080897", "exception": false, "start_time": "2023-10-11T16:13:48.040443", "status": "completed"}, "tags": []}, "source": ["You should be able to see something similar as in the following image.\n", "In case the projector stays empty, try to start the TensorBoard outside of the Jupyter notebook.\n", "\n", "
\n", "\n", "Overall, we can see that the model indeed clustered images together that are visually similar.\n", "Especially the background color seems to be a crucial factor in the encoding.\n", "This correlates to the chosen loss function, here Mean Squared Error on pixel-level\n", "because the background is responsible for more than half of the pixels in an average image.\n", "Hence, the model learns to focus on it.\n", "Nevertheless, we can see that the encodings also separate a couple of classes in the latent space although it hasn't seen any labels.\n", "This shows again that autoencoding can also be used as a \"pre-training\"/transfer learning task before classification."]}, {"cell_type": "code", "execution_count": 27, "id": "93c9095c", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:13:48.154402Z", "iopub.status.busy": "2023-10-11T16:13:48.154060Z", "iopub.status.idle": "2023-10-11T16:13:48.159137Z", "shell.execute_reply": "2023-10-11T16:13:48.158037Z"}, "papermill": {"duration": 0.043747, "end_time": "2023-10-11T16:13:48.160593", "exception": false, "start_time": "2023-10-11T16:13:48.116846", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# Closing the summary writer\n", "writer.close()"]}, {"cell_type": "markdown", "id": "a739da26", "metadata": {"papermill": {"duration": 0.035735, "end_time": "2023-10-11T16:13:48.232141", "exception": false, "start_time": "2023-10-11T16:13:48.196406", "status": "completed"}, "tags": []}, "source": ["## Conclusion\n", "\n", "In this tutorial, we have implemented our own autoencoder on small RGB images and explored various properties of the model.\n", "In contrast to variational autoencoders, vanilla AEs are not generative and can work on MSE loss functions.\n", "This makes them often easier to train.\n", "Both versions of AE can be used for dimensionality reduction, as we have seen for finding visually similar images beyond pixel distances.\n", "Despite autoencoders gaining less interest in the research community due to their more \"theoretically\"\n", "challenging counterpart of VAEs, autoencoders still find usage in a lot of applications like denoising and compression.\n", "Hence, AEs are an essential tool that every Deep Learning engineer/researcher should be familiar with."]}, {"cell_type": "markdown", "id": "d69261d2", "metadata": {"papermill": {"duration": 0.036424, "end_time": "2023-10-11T16:13:48.304288", "exception": false, "start_time": "2023-10-11T16:13:48.267864", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/Lightning-AI/lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Slack](https://www.pytorchlightning.ai/community)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/Lightning-AI/lightning) or [Bolt](https://github.com/Lightning-AI/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/Lightning-AI/lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/Lightning-AI/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[![Pytorch Lightning](data:image/png;base64,NDA0OiBOb3QgRm91bmQ=){height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", " :header: Tutorial 8: Deep Autoencoders\n", " :card_description: In this tutorial, we will take a closer look at autoencoders (AE). Autoencoders are trained on encoding input data such as images into a smaller feature vector, and afterward,...\n", " :tags: Image,GPU/TPU,UvA-DL-Course\n", " :image: _static/images/course_UvA-DL/08-deep-autoencoders.jpg"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab,id,colab_type,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12"}, "papermill": {"default_parameters": {}, "duration": 151.145106, "end_time": "2023-10-11T16:13:51.464842", "environment_variables": {}, "exception": null, "input_path": "course_UvA-DL/08-deep-autoencoders/Deep_Autoencoders.ipynb", "output_path": ".notebooks/course_UvA-DL/08-deep-autoencoders.ipynb", "parameters": {}, "start_time": "2023-10-11T16:11:20.319736", "version": "2.4.0"}, "widgets": 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