{"cells": [{"cell_type": "markdown", "id": "e7c29085", "metadata": {"papermill": {"duration": 0.012321, "end_time": "2023-10-11T16:06:20.651062", "exception": false, "start_time": "2023-10-11T16:06:20.638741", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 7: Deep Energy-Based Generative Models\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-10-11T16:04:04.126072\n", "\n", "In this tutorial, we will look at energy-based deep learning models, and focus on their application as generative models.\n", "Energy models have been a popular tool before the huge deep learning hype around 2012 hit.\n", "However, in recent years, energy-based models have gained increasing attention because of improved training methods and tricks being proposed.\n", "Although they are still in a research stage, they have shown to outperform strong Generative Adversarial Networks\n", "in certain cases which have been the state of the art of generating images\n", "([blog post](https://ajolicoeur.wordpress.com/the-new-contender-to-gans-score-matching-with-langevin-sampling/)about strong energy-based models,\n", "[blog post](https://medium.com/syncedreview/nvidia-open-sources-hyper-realistic-face-generator-stylegan-f346e1a73826) about the power of GANs).\n", "Hence, it is important to be aware of energy-based models, and as the theory can be abstract sometimes,\n", "we will show the idea of energy-based models with a lot of examples.\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/07-deep-energy-based-generative-models.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": "8c47b1ee", "metadata": {"papermill": {"duration": 0.008095, "end_time": "2023-10-11T16:06:20.667558", "exception": false, "start_time": "2023-10-11T16:06:20.659463", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "5a8ec6b2", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-10-11T16:06:20.686406Z", "iopub.status.busy": "2023-10-11T16:06:20.685655Z", "iopub.status.idle": "2023-10-11T16:08:04.396471Z", "shell.execute_reply": "2023-10-11T16:08:04.395355Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 103.723648, "end_time": "2023-10-11T16:08:04.399332", "exception": false, "start_time": "2023-10-11T16:06:20.675684", "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 \"pytorch-lightning>=1.4, <2.1.0\" \"setuptools>=68.0.0, <68.3.0\" \"torchvision\" \"torch>=1.8.1, <2.1.0\" \"tensorboard\" \"ipython[notebook]>=8.0.0, <8.17.0\" \"torchmetrics>=0.7, <1.3\" \"urllib3\" \"matplotlib\" \"matplotlib>=3.0.0, <3.9.0\" \"lightning>=2.0.0\""]}, {"cell_type": "markdown", "id": "d1a391e6", "metadata": {"papermill": {"duration": 0.00843, "end_time": "2023-10-11T16:08:04.418583", "exception": false, "start_time": "2023-10-11T16:08:04.410153", "status": "completed"}, "tags": []}, "source": ["<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/E6PDwquBBQc\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "First, let's import our standard libraries below."]}, {"cell_type": "code", "execution_count": 2, "id": "0d3ee052", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:04.436628Z", "iopub.status.busy": "2023-10-11T16:08:04.436183Z", "iopub.status.idle": "2023-10-11T16:08:08.539921Z", "shell.execute_reply": "2023-10-11T16:08:08.538987Z"}, "papermill": {"duration": 4.11557, "end_time": "2023-10-11T16:08:08.542238", "exception": false, "start_time": "2023-10-11T16:08:04.426668", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}], "source": ["# Standard libraries\n", "import os\n", "import random\n", "import urllib.request\n", "from urllib.error import HTTPError\n", "\n", "# PyTorch Lightning\n", "import lightning as L\n", "\n", "# Plotting\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "import matplotlib_inline.backend_inline\n", "import numpy as np\n", "\n", "# PyTorch\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import torch.utils.data as data\n", "\n", "# Torchvision\n", "import torchvision\n", "from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint\n", "from torchvision import transforms\n", "from torchvision.datasets import MNIST\n", "\n", "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\", \"pdf\")  # For export\n", "matplotlib.rcParams[\"lines.linewidth\"] = 2.0\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/tutorial8\")\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\")"]}, {"cell_type": "markdown", "id": "8a33d25d", "metadata": {"papermill": {"duration": 0.010176, "end_time": "2023-10-11T16:08:08.561587", "exception": false, "start_time": "2023-10-11T16:08:08.551411", "status": "completed"}, "tags": []}, "source": ["We also have pre-trained models that we download below."]}, {"cell_type": "code", "execution_count": 3, "id": "4c85ff5c", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:08.634156Z", "iopub.status.busy": "2023-10-11T16:08:08.630487Z", "iopub.status.idle": "2023-10-11T16:08:09.046115Z", "shell.execute_reply": "2023-10-11T16:08:09.045244Z"}, "papermill": {"duration": 0.428468, "end_time": "2023-10-11T16:08:09.048230", "exception": false, "start_time": "2023-10-11T16:08:08.619762", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial8/MNIST.ckpt...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial8/tensorboards/events.out.tfevents.MNIST...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial8/\"\n", "# Files to download\n", "pretrained_files = [\"MNIST.ckpt\", \"tensorboards/events.out.tfevents.MNIST\"]\n", "\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 \"/\" in file_name:\n", "        os.makedirs(file_path.rsplit(\"/\", 1)[0], exist_ok=True)\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": "3ecc9a9f", "metadata": {"papermill": {"duration": 0.010196, "end_time": "2023-10-11T16:08:09.067680", "exception": false, "start_time": "2023-10-11T16:08:09.057484", "status": "completed"}, "tags": []}, "source": ["## Energy Models\n", "\n", "In the first part of this tutorial, we will review the theory of the energy-based models\n", "(the same theory has been discussed in Lecture 8).\n", "While most of the previous models had the goal of classification or regression,\n", "energy-based models are motivated from a different perspective: density estimation.\n", "Given a dataset with a lot of elements, we want to estimate the probability distribution over the whole data space.\n", "As an example, if we model images from CIFAR10, our goal would be to have a probability distribution\n", "over all possible images of size $32\\times32\\times3$ where those images have a high likelihood\n", "that look realistic and are one of the 10 CIFAR classes.\n", "Simple methods like interpolation between images don't work because images are extremely high-dimensional\n", "(especially for large HD images).\n", "Hence, we turn to deep learning methods that have performed well on complex data.\n", "\n", "However, how do we predict a probability distribution $p(\\mathbf{x})$ over so many dimensions using a simple neural network?\n", "The problem is that we cannot just predict a score between 0 and 1,\n", "because a probability distribution over data needs to fulfill two properties:\n", "\n", "1.\n", "The probability distribution needs to assign any possible value of\n", "$\\mathbf{x}$ a non-negative value: $p(\\mathbf{x}) \\geq 0$.\n", "2.\n", "The probability density must sum/integrate to 1 over **all** possible inputs:\n", "$\\int_{\\mathbf{x}} p(\\mathbf{x}) d\\mathbf{x} = 1$.\n", "\n", "Luckily, there are actually many approaches for this, and one of them are energy-based models.\n", "The fundamental idea of energy-based models is that you can turn any function\n", "that predicts values larger than zero into a probability distribution by dviding by its volume.\n", "Imagine we have a neural network, which has as output a single neuron, like in regression.\n", "We can call this network $E_{\\theta}(\\mathbf{x})$, where $\\theta$ are our parameters of the network,\n", "and $\\mathbf{x}$ the input data (e.g. an image).\n", "The output of $E_{\\theta}$ is a scalar value between $-\\infty$ and $\\infty$.\n", "Now, we can use basic probability theory to *normalize* the scores of all possible inputs:\n", "\n", "$$\n", "q_{\\theta}(\\mathbf{x}) = \\frac{\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right)}{Z_{\\theta}} \\hspace{5mm}\\text{where}\\hspace{5mm}\n", "Z_{\\theta} = \\begin{cases}\n", "    \\int_{\\mathbf{x}}\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right) d\\mathbf{x} & \\text{if }x\\text{ is continuous}\\\\\n", "    \\sum_{\\mathbf{x}}\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right) & \\text{if }x\\text{ is discrete}\n", "\\end{cases}\n", "$$\n", "\n", "The $\\exp$-function ensures that we assign a probability greater than zero to any possible input.\n", "We use a negative sign in front of $E$ because we call $E_{\\theta}$ to be the energy function:\n", "data points with high likelihood have a low energy, while data points with low likelihood have a high energy.\n", "$Z_{\\theta}$ is our normalization terms that ensures that the density integrates/sums to 1.\n", "We can show this by integrating over $q_{\\theta}(\\mathbf{x})$:\n", "\n", "$$\n", "\\int_{\\mathbf{x}}q_{\\theta}(\\mathbf{x})d\\mathbf{x} =\n", "\\int_{\\mathbf{x}}\\frac{\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right)}{\\int_{\\mathbf{\\tilde{x}}}\\exp\\left(-E_{\\theta}(\\mathbf{\\tilde{x}})\\right) d\\mathbf{\\tilde{x}}}d\\mathbf{x} =\n", "\\frac{\\int_{\\mathbf{x}}\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right)d\\mathbf{x}}{\\int_{\\mathbf{\\tilde{x}}}\\exp\\left(-E_{\\theta}(\\mathbf{\\tilde{x}})\\right) d\\mathbf{\\tilde{x}}} = 1\n", "$$\n", "\n", "Note that we call the probability distribution $q_{\\theta}(\\mathbf{x})$ because this is the learned distribution by the model,\n", "and is trained to be as close as possible to the *true*, unknown distribution $p(\\mathbf{x})$.\n", "\n", "The main benefit of this formulation of the probability distribution is its great flexibility as we can choose\n", "$E_{\\theta}$ in whatever way we like, without any constraints.\n", "Nevertheless, when looking at the equation above, we can see a fundamental issue: How do we calculate $Z_{\\theta}$?\n", "There is no chance that we can calculate $Z_{\\theta}$ analytically for high-dimensional input\n", "and/or larger neural networks, but the task requires us to know $Z_{\\theta}$.\n", "Although we can't determine the exact likelihood of a point, there exist methods with which we can train energy-based models.\n", "Thus, we will look next at \"Contrastive Divergence\" for training the model."]}, {"cell_type": "markdown", "id": "8293bdcf", "metadata": {"papermill": {"duration": 0.00834, "end_time": "2023-10-11T16:08:09.084298", "exception": false, "start_time": "2023-10-11T16:08:09.075958", "status": "completed"}, "tags": []}, "source": ["### Contrastive Divergence\n", "\n", "When we train a model on generative modeling, it is usually done by maximum likelihood estimation.\n", "In other words, we try to maximize the likelihood of the examples in the training set.\n", "As the exact likelihood of a point cannot be determined due to the unknown normalization constant $Z_{\\theta}$,\n", "we need to train energy-based models slightly different.\n", "We cannot just maximize the un-normalized probability $\\exp(-E_{\\theta}(\\mathbf{x}_{\\text{train}}))$\n", "because there is no guarantee that $Z_{\\theta}$ stays constant, or that $\\mathbf{x}_{\\text{train}}$\n", "is becoming more likely than the others.\n", "However, if we base our training on comparing the likelihood of points, we can create a stable objective.\n", "Namely, we can re-write our maximum likelihood objective where we maximize the probability\n", "of $\\mathbf{x}_{\\text{train}}$ compared to a randomly sampled data point of our model:\n", "\n", "$$\n", "\\begin{split}\n", "    \\nabla_{\\theta}\\mathcal{L}_{\\text{MLE}}(\\mathbf{\\theta};p) & = -\\mathbb{E}_{p(\\mathbf{x})}\\left[\\nabla_{\\theta}\\log q_{\\theta}(\\mathbf{x})\\right]\\\\[5pt]\n", "    & = \\mathbb{E}_{p(\\mathbf{x})}\\left[\\nabla_{\\theta}E_{\\theta}(\\mathbf{x})\\right] - \\mathbb{E}_{q_{\\theta}(\\mathbf{x})}\\left[\\nabla_{\\theta}E_{\\theta}(\\mathbf{x})\\right]\n", "\\end{split}\n", "$$\n", "\n", "Note that the loss is still an objective we want to minimize.\n", "Thus, we try to minimize the energy for data points from the dataset, while maximizing the energy for randomly\n", "sampled data points from our model (how we sample will be explained below).\n", "Although this objective sounds intuitive, how is it actually derived from our original distribution $q_{\\theta}(\\mathbf{x})$?\n", "The trick is that we approximate $Z_{\\theta}$ by a single Monte-Carlo sample.\n", "This gives us the exact same objective as written above.\n", "\n", "Visually, we can look at the objective as follows (figure credit - Stefano Ermon and Aditya Grover: lecture cs236/11):\n", "\n", "<center width=\"100%\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/contrastive_divergence.svg\" width=\"700px\"></center>\n", "\n", "$f_{\\theta}$ represents $\\exp(-E_{\\theta}(\\mathbf{x}))$ in our case.\n", "The point on the right, called \"correct answer\", represents a data point from the dataset\n", "(i.e. $x_{\\text{train}}$), and the left point, \"wrong answer\", a sample from our model (i.e. $x_{\\text{sample}}$).\n", "Thus, we try to \"pull up\" the probability of the data points in the dataset,\n", "while \"pushing down\" randomly sampled points.\n", "The two forces for pulling and pushing are in balance iff $q_{\\theta}(\\mathbf{x})=p(\\mathbf{x})$."]}, {"cell_type": "markdown", "id": "b76d6691", "metadata": {"papermill": {"duration": 0.008606, "end_time": "2023-10-11T16:08:09.101095", "exception": false, "start_time": "2023-10-11T16:08:09.092489", "status": "completed"}, "tags": []}, "source": ["### Sampling from Energy-Based Models\n", "\n", "For sampling from an energy-based model, we can apply a Markov Chain Monte Carlo using Langevin Dynamics.\n", "The idea of the algorithm is to start from a random point, and slowly move towards the direction\n", "of higher probability using the gradients of $E_{\\theta}$.\n", "Nevertheless, this is not enough to fully capture the probability distribution.\n", "We need to add noise $\\omega$ at each gradient step to the current sample.\n", "Under certain conditions such as that we perform the gradient steps an infinite amount of times,\n", "we would be able to create an exact sample from our modeled distribution.\n", "However, as this is not practically possible, we usually limit the chain to $K$ steps\n", "($K$ a hyperparameter that needs to be finetuned).\n", "Overall, the sampling procedure can be summarized in the following algorithm:\n", "\n", "<center width=\"100%\" style=\"padding:15px\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/sampling.svg\" width=\"750px\"></center>"]}, {"cell_type": "markdown", "id": "cb7c87dc", "metadata": {"papermill": {"duration": 0.008221, "end_time": "2023-10-11T16:08:09.117576", "exception": false, "start_time": "2023-10-11T16:08:09.109355", "status": "completed"}, "tags": []}, "source": ["### Applications of Energy-based models beyond generation\n", "\n", "Modeling the probability distribution for sampling new data is not the only application of energy-based models.\n", "Any application which requires us to compare two elements is much simpler to learn\n", "because we just need to go for the higher energy.\n", "A couple of examples are shown below (figure credit - Stefano Ermon and Aditya Grover: lecture cs236/11).\n", "A classification setup like object recognition or sequence labeling can be considered as an energy-based\n", "task as we just need to find the $Y$ input that minimizes the output $E(X, Y)$ (hence maximizes probability).\n", "Similarly, a popular application of energy-based models is denoising of images.\n", "Given an image $X$ with a lot of noise, we try to minimize the energy by finding the true input image $Y$.\n", "\n", "<center width=\"100%\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/energy_models_application.png\" width=\"600px\"></center>\n", "\n", "Nonetheless, we will focus on generative modeling here as in the next couple of lectures,\n", "we will discuss more generative deep learning approaches."]}, {"cell_type": "markdown", "id": "87ee45bc", "metadata": {"papermill": {"duration": 0.008193, "end_time": "2023-10-11T16:08:09.134015", "exception": false, "start_time": "2023-10-11T16:08:09.125822", "status": "completed"}, "tags": []}, "source": ["## Image generation\n", "\n", "<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/QJ94zuSQoP4\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "\n", "As an example for energy-based models, we will train a model on image generation.\n", "Specifically, we will look at how we can generate MNIST digits with a very simple CNN model.\n", "However, it should be noted that energy models are not easy to train and often diverge\n", "if the hyperparameters are not well tuned.\n", "We will rely on training tricks proposed in the paper\n", "[Implicit Generation and Generalization in Energy-Based Models](https://arxiv.org/abs/1903.08689)\n", "by Yilun Du and Igor Mordatch ([blog](https://openai.com/blog/energy-based-models/)).\n", "The important part of this notebook is however to see how the theory above can actually be used in a model.\n", "\n", "### Dataset\n", "\n", "First, we can load the MNIST dataset below.\n", "Note that we need to normalize the images between -1 and 1 instead of mean 0 and std 1 because during sampling,\n", "we have to limit the input space.\n", "Scaling between -1 and 1 makes it easier to implement it."]}, {"cell_type": "code", "execution_count": 4, "id": "cc0a4b48", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:09.152200Z", "iopub.status.busy": "2023-10-11T16:08:09.151743Z", "iopub.status.idle": "2023-10-11T16:08:10.521946Z", "shell.execute_reply": "2023-10-11T16:08:10.521248Z"}, "papermill": {"duration": 1.384388, "end_time": "2023-10-11T16:08:10.526629", "exception": false, "start_time": "2023-10-11T16:08:09.142241", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /__w/15/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/9912422 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 38%|\u2588\u2588\u2588\u258a      | 3801088/9912422 [00:00<00:00, 37860352.23it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 77%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b  | 7602176/9912422 [00:00<00:00, 29686760.72it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9912422/9912422 [00:00<00:00, 32101871.22it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz to /__w/15/s/.datasets/MNIST/raw\n"]}, {"name": "stdout", "output_type": "stream", "text": ["\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to /__w/15/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/28881 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 28881/28881 [00:00<00:00, 70061130.03it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz to /__w/15/s/.datasets/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to /__w/15/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/1648877 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1648877/1648877 [00:00<00:00, 22784268.84it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to /__w/15/s/.datasets/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /__w/15/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/4542 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4542/4542 [00:00<00:00, 11232623.09it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to /__w/15/s/.datasets/MNIST/raw\n", "\n"]}], "source": ["# Transformations applied on each image => make them a tensor and normalize between -1 and 1\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_set = MNIST(root=DATASET_PATH, train=True, transform=transform, download=True)\n", "\n", "# Loading the test set\n", "test_set = MNIST(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", "# Note that for actually training a model, we will use different data loaders\n", "# with a lower batch size.\n", "train_loader = data.DataLoader(train_set, batch_size=128, shuffle=True, drop_last=True, num_workers=4, pin_memory=True)\n", "test_loader = data.DataLoader(test_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)"]}, {"cell_type": "markdown", "id": "6dee6dc6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.018508, "end_time": "2023-10-11T16:08:10.565644", "exception": false, "start_time": "2023-10-11T16:08:10.547136", "status": "completed"}, "tags": []}, "source": ["### CNN Model\n", "\n", "First, we implement our CNN model.\n", "The MNIST images are of size 28x28, hence we only need a small model.\n", "As an example, we will apply several convolutions with stride 2 that downscale the images.\n", "If you are interested, you can also use a deeper model such as a small ResNet, but for simplicity,\n", "we will stick with the tiny network.\n", "\n", "It is a good practice to use a smooth activation function like Swish instead of ReLU in the energy model.\n", "This is because we will rely on the gradients we get back with respect to the input image, which should not be sparse."]}, {"cell_type": "code", "execution_count": 5, "id": "33914651", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:10.587189Z", "iopub.status.busy": "2023-10-11T16:08:10.586873Z", "iopub.status.idle": "2023-10-11T16:08:10.597916Z", "shell.execute_reply": "2023-10-11T16:08:10.597240Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.024372, "end_time": "2023-10-11T16:08:10.599573", "exception": false, "start_time": "2023-10-11T16:08:10.575201", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class CNNModel(nn.Module):\n", "    def __init__(self, hidden_features=32, out_dim=1, **kwargs):\n", "        super().__init__()\n", "        # We increase the hidden dimension over layers. Here pre-calculated for simplicity.\n", "        c_hid1 = hidden_features // 2\n", "        c_hid2 = hidden_features\n", "        c_hid3 = hidden_features * 2\n", "\n", "        # Series of convolutions and Swish activation functions\n", "        self.cnn_layers = nn.Sequential(\n", "            nn.Conv2d(1, c_hid1, kernel_size=5, stride=2, padding=4),  # [16x16] - Larger padding to get 32x32 image\n", "            nn.SiLU(),\n", "            nn.Conv2d(c_hid1, c_hid2, kernel_size=3, stride=2, padding=1),  # [8x8]\n", "            nn.SiLU(),\n", "            nn.Conv2d(c_hid2, c_hid3, kernel_size=3, stride=2, padding=1),  # [4x4]\n", "            nn.SiLU(),\n", "            nn.Conv2d(c_hid3, c_hid3, kernel_size=3, stride=2, padding=1),  # [2x2]\n", "            nn.SiLU(),\n", "            nn.Flatten(),\n", "            nn.Linear(c_hid3 * 4, c_hid3),\n", "            nn.SiLU(),\n", "            nn.Linear(c_hid3, out_dim),\n", "        )\n", "\n", "    def forward(self, x):\n", "        x = self.cnn_layers(x).squeeze(dim=-1)\n", "        return x"]}, {"cell_type": "markdown", "id": "e39b1aef", "metadata": {"papermill": {"duration": 0.009443, "end_time": "2023-10-11T16:08:10.618513", "exception": false, "start_time": "2023-10-11T16:08:10.609070", "status": "completed"}, "tags": []}, "source": ["In the rest of the notebook, the output of the model will actually not represent\n", "$E_{\\theta}(\\mathbf{x})$, but $-E_{\\theta}(\\mathbf{x})$.\n", "This is a standard implementation practice for energy-based models, as some people also write the energy probability\n", "density as $q_{\\theta}(\\mathbf{x}) = \\frac{\\exp\\left(f_{\\theta}(\\mathbf{x})\\right)}{Z_{\\theta}}$.\n", "In that case, the model would actually represent $f_{\\theta}(\\mathbf{x})$.\n", "In the training loss etc., we need to be careful to not switch up the signs."]}, {"cell_type": "markdown", "id": "1f724be4", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009237, "end_time": "2023-10-11T16:08:10.637335", "exception": false, "start_time": "2023-10-11T16:08:10.628098", "status": "completed"}, "tags": []}, "source": ["### Sampling buffer\n", "\n", "In the next part, we look at the training with sampled elements.\n", "To use the contrastive divergence objective, we need to generate samples during training.\n", "Previous work has shown that due to the high dimensionality of images, we need a lot of iterations\n", "inside the MCMC sampling to obtain reasonable samples.\n", "However, there is a training trick that significantly reduces the sampling cost: using a sampling buffer.\n", "The idea is that we store the samples of the last couple of batches in a buffer,\n", "and reuse those as the starting point of the MCMC algorithm for the next batches.\n", "This reduces the sampling cost because the model requires a significantly\n", "lower number of steps to converge to reasonable samples.\n", "However, to not solely rely on previous samples and allow novel samples as well,\n", "we re-initialize 5% of our samples from scratch (random noise between -1 and 1).\n", "\n", "Below, we implement the sampling buffer.\n", "The function `sample_new_exmps` returns a new batch of \"fake\" images.\n", "We refer to those as fake images because they have been generated, but are not actually part of the dataset.\n", "As mentioned before, we use initialize 5% randomly, and 95% are randomly picked from our buffer.\n", "On this initial batch, we perform MCMC for 60 iterations to improve the image quality\n", "and come closer to samples from $q_{\\theta}(\\mathbf{x})$.\n", "In the function `generate_samples`, we implemented the MCMC for images.\n", "Note that the hyperparameters of `step_size`, `steps`, the noise standard deviation\n", "$\\sigma$ are specifically set for MNIST, and need to be finetuned for a different dataset if you want to use such."]}, {"cell_type": "code", "execution_count": 6, "id": "8d685ef8", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:10.658904Z", "iopub.status.busy": "2023-10-11T16:08:10.658622Z", "iopub.status.idle": "2023-10-11T16:08:10.674150Z", "shell.execute_reply": "2023-10-11T16:08:10.673559Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.02897, "end_time": "2023-10-11T16:08:10.675835", "exception": false, "start_time": "2023-10-11T16:08:10.646865", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Sampler:\n", "    def __init__(self, model, img_shape, sample_size, max_len=8192):\n", "        \"\"\"Sampler.\n", "\n", "        Args:\n", "            model: Neural network to use for modeling E_theta\n", "            img_shape: Shape of the images to model\n", "            sample_size: Batch size of the samples\n", "            max_len: Maximum number of data points to keep in the buffer\n", "        \"\"\"\n", "        super().__init__()\n", "        self.model = model\n", "        self.img_shape = img_shape\n", "        self.sample_size = sample_size\n", "        self.max_len = max_len\n", "        self.examples = [(torch.rand((1,) + img_shape) * 2 - 1) for _ in range(self.sample_size)]\n", "\n", "    def sample_new_exmps(self, steps=60, step_size=10):\n", "        \"\"\"Function for getting a new batch of \"fake\" images.\n", "\n", "        Args:\n", "            steps: Number of iterations in the MCMC algorithm\n", "            step_size: Learning rate nu in the algorithm above\n", "        \"\"\"\n", "        # Choose 95% of the batch from the buffer, 5% generate from scratch\n", "        n_new = np.random.binomial(self.sample_size, 0.05)\n", "        rand_imgs = torch.rand((n_new,) + self.img_shape) * 2 - 1\n", "        old_imgs = torch.cat(random.choices(self.examples, k=self.sample_size - n_new), dim=0)\n", "        inp_imgs = torch.cat([rand_imgs, old_imgs], dim=0).detach().to(device)\n", "\n", "        # Perform MCMC sampling\n", "        inp_imgs = Sampler.generate_samples(self.model, inp_imgs, steps=steps, step_size=step_size)\n", "\n", "        # Add new images to the buffer and remove old ones if needed\n", "        self.examples = list(inp_imgs.to(torch.device(\"cpu\")).chunk(self.sample_size, dim=0)) + self.examples\n", "        self.examples = self.examples[: self.max_len]\n", "        return inp_imgs\n", "\n", "    @staticmethod\n", "    def generate_samples(model, inp_imgs, steps=60, step_size=10, return_img_per_step=False):\n", "        \"\"\"Function for sampling images for a given model.\n", "\n", "        Args:\n", "            model: Neural network to use for modeling E_theta\n", "            inp_imgs: Images to start from for sampling. If you want to generate new images, enter noise between -1 and 1.\n", "            steps: Number of iterations in the MCMC algorithm.\n", "            step_size: Learning rate nu in the algorithm above\n", "            return_img_per_step: If True, we return the sample at every iteration of the MCMC\n", "        \"\"\"\n", "        # Before MCMC: set model parameters to \"required_grad=False\"\n", "        # because we are only interested in the gradients of the input.\n", "        is_training = model.training\n", "        model.eval()\n", "        for p in model.parameters():\n", "            p.requires_grad = False\n", "        inp_imgs.requires_grad = True\n", "\n", "        # Enable gradient calculation if not already the case\n", "        had_gradients_enabled = torch.is_grad_enabled()\n", "        torch.set_grad_enabled(True)\n", "\n", "        # We use a buffer tensor in which we generate noise each loop iteration.\n", "        # More efficient than creating a new tensor every iteration.\n", "        noise = torch.randn(inp_imgs.shape, device=inp_imgs.device)\n", "\n", "        # List for storing generations at each step (for later analysis)\n", "        imgs_per_step = []\n", "\n", "        # Loop over K (steps)\n", "        for _ in range(steps):\n", "            # Part 1: Add noise to the input.\n", "            noise.normal_(0, 0.005)\n", "            inp_imgs.data.add_(noise.data)\n", "            inp_imgs.data.clamp_(min=-1.0, max=1.0)\n", "\n", "            # Part 2: calculate gradients for the current input.\n", "            out_imgs = -model(inp_imgs)\n", "            out_imgs.sum().backward()\n", "            inp_imgs.grad.data.clamp_(-0.03, 0.03)  # For stabilizing and preventing too high gradients\n", "\n", "            # Apply gradients to our current samples\n", "            inp_imgs.data.add_(-step_size * inp_imgs.grad.data)\n", "            inp_imgs.grad.detach_()\n", "            inp_imgs.grad.zero_()\n", "            inp_imgs.data.clamp_(min=-1.0, max=1.0)\n", "\n", "            if return_img_per_step:\n", "                imgs_per_step.append(inp_imgs.clone().detach())\n", "\n", "        # Reactivate gradients for parameters for training\n", "        for p in model.parameters():\n", "            p.requires_grad = True\n", "        model.train(is_training)\n", "\n", "        # Reset gradient calculation to setting before this function\n", "        torch.set_grad_enabled(had_gradients_enabled)\n", "\n", "        if return_img_per_step:\n", "            return torch.stack(imgs_per_step, dim=0)\n", "        else:\n", "            return inp_imgs"]}, {"cell_type": "markdown", "id": "e56583c6", "metadata": {"papermill": {"duration": 0.009516, "end_time": "2023-10-11T16:08:10.695678", "exception": false, "start_time": "2023-10-11T16:08:10.686162", "status": "completed"}, "tags": []}, "source": ["The idea of the buffer becomes a bit clearer in the following algorithm."]}, {"cell_type": "markdown", "id": "169c04cf", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009423, "end_time": "2023-10-11T16:08:10.714813", "exception": false, "start_time": "2023-10-11T16:08:10.705390", "status": "completed"}, "tags": []}, "source": ["### Training algorithm\n", "\n", "With the sampling buffer being ready, we can complete our training algorithm.\n", "Below is shown a summary of the full training algorithm of an energy model on image modeling:\n", "\n", "<center width=\"100%\" style=\"padding: 15px\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/training_algorithm.svg\" width=\"700px\"></center>\n", "\n", "The first few statements in each training iteration concern the sampling of the real and fake data,\n", "as we have seen above with the sample buffer.\n", "Next, we calculate the contrastive divergence objective using our energy model $E_{\\theta}$.\n", "However, one additional training trick we need is to add a regularization loss on the output of $E_{\\theta}$.\n", "As the output of the network is not constrained and adding a large bias or not to the output\n", "doesn't change the contrastive divergence loss, we need to ensure somehow else that the output values are in a reasonable range.\n", "Without the regularization loss, the output values will fluctuate in a very large range.\n", "With this, we ensure that the values for the real data are around 0, and the fake data likely slightly lower\n", "(for noise or outliers the score can be still significantly lower).\n", "As the regularization loss is less important than the Contrastive Divergence, we have a weight factor\n", "$\\alpha$ which is usually quite some smaller than 1.\n", "Finally, we perform an update step with an optimizer on the combined loss and add the new samples to the buffer.\n", "\n", "Below, we put this training dynamic into a PyTorch Lightning module:"]}, {"cell_type": "code", "execution_count": 7, "id": "a1af6f92", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:10.745474Z", "iopub.status.busy": "2023-10-11T16:08:10.744824Z", "iopub.status.idle": "2023-10-11T16:08:10.763035Z", "shell.execute_reply": "2023-10-11T16:08:10.762239Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.034255, "end_time": "2023-10-11T16:08:10.765236", "exception": false, "start_time": "2023-10-11T16:08:10.730981", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class DeepEnergyModel(L.LightningModule):\n", "    def __init__(self, img_shape, batch_size, alpha=0.1, lr=1e-4, beta1=0.0, **CNN_args):\n", "        super().__init__()\n", "        self.save_hyperparameters()\n", "\n", "        self.cnn = CNNModel(**CNN_args)\n", "        self.sampler = Sampler(self.cnn, img_shape=img_shape, sample_size=batch_size)\n", "        self.example_input_array = torch.zeros(1, *img_shape)\n", "\n", "    def forward(self, x):\n", "        z = self.cnn(x)\n", "        return z\n", "\n", "    def configure_optimizers(self):\n", "        # Energy models can have issues with momentum as the loss surfaces changes with its parameters.\n", "        # Hence, we set it to 0 by default.\n", "        optimizer = optim.Adam(self.parameters(), lr=self.hparams.lr, betas=(self.hparams.beta1, 0.999))\n", "        scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.97)  # Exponential decay over epochs\n", "        return [optimizer], [scheduler]\n", "\n", "    def training_step(self, batch, batch_idx):\n", "        # We add minimal noise to the original images to prevent the model from focusing on purely \"clean\" inputs\n", "        real_imgs, _ = batch\n", "        small_noise = torch.randn_like(real_imgs) * 0.005\n", "        real_imgs.add_(small_noise).clamp_(min=-1.0, max=1.0)\n", "\n", "        # Obtain samples\n", "        fake_imgs = self.sampler.sample_new_exmps(steps=60, step_size=10)\n", "\n", "        # Predict energy score for all images\n", "        inp_imgs = torch.cat([real_imgs, fake_imgs], dim=0)\n", "        real_out, fake_out = self.cnn(inp_imgs).chunk(2, dim=0)\n", "\n", "        # Calculate losses\n", "        reg_loss = self.hparams.alpha * (real_out**2 + fake_out**2).mean()\n", "        cdiv_loss = fake_out.mean() - real_out.mean()\n", "        loss = reg_loss + cdiv_loss\n", "\n", "        # Logging\n", "        self.log(\"loss\", loss)\n", "        self.log(\"loss_regularization\", reg_loss)\n", "        self.log(\"loss_contrastive_divergence\", cdiv_loss)\n", "        self.log(\"metrics_avg_real\", real_out.mean())\n", "        self.log(\"metrics_avg_fake\", fake_out.mean())\n", "        return loss\n", "\n", "    def validation_step(self, batch, batch_idx):\n", "        # For validating, we calculate the contrastive divergence between purely random images and unseen examples\n", "        # Note that the validation/test step of energy-based models depends on what we are interested in the model\n", "        real_imgs, _ = batch\n", "        fake_imgs = torch.rand_like(real_imgs) * 2 - 1\n", "\n", "        inp_imgs = torch.cat([real_imgs, fake_imgs], dim=0)\n", "        real_out, fake_out = self.cnn(inp_imgs).chunk(2, dim=0)\n", "\n", "        cdiv = fake_out.mean() - real_out.mean()\n", "        self.log(\"val_contrastive_divergence\", cdiv)\n", "        self.log(\"val_fake_out\", fake_out.mean())\n", "        self.log(\"val_real_out\", real_out.mean())"]}, {"cell_type": "markdown", "id": "95fa74ec", "metadata": {"papermill": {"duration": 0.012163, "end_time": "2023-10-11T16:08:10.790352", "exception": false, "start_time": "2023-10-11T16:08:10.778189", "status": "completed"}, "tags": []}, "source": ["We do not implement a test step because energy-based, generative models are usually not evaluated on a test set.\n", "The validation step however is used to get an idea of the difference between ennergy/likelihood\n", "of random images to unseen examples of the dataset."]}, {"cell_type": "markdown", "id": "f137b417", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.012123, "end_time": "2023-10-11T16:08:10.815391", "exception": false, "start_time": "2023-10-11T16:08:10.803268", "status": "completed"}, "tags": []}, "source": ["### Callbacks\n", "\n", "To track the performance of our model during training, we will make extensive use of PyTorch Lightning's callback framework.\n", "Remember that callbacks can be used for running small functions at any point of the training,\n", "for instance after finishing an epoch.\n", "Here, we will use three different callbacks we define ourselves.\n", "\n", "The first callback, called `GenerateCallback`, is used for adding image generations to the model during training.\n", "After every $N$ epochs (usually $N=5$ to reduce output to TensorBoard), we take a small batch\n", "of random images and perform many MCMC iterations until the model's generation converges.\n", "Compared to the training that used 60 iterations, we use 256 here because\n", "(1) we only have to do it once compared to the training that has to do it every iteration, and\n", "(2) we do not start from a buffer here, but from scratch.\n", "It is implemented as follows:"]}, {"cell_type": "code", "execution_count": 8, "id": "cef2254e", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:10.842119Z", "iopub.status.busy": "2023-10-11T16:08:10.841807Z", "iopub.status.idle": "2023-10-11T16:08:10.858684Z", "shell.execute_reply": "2023-10-11T16:08:10.857488Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.032652, "end_time": "2023-10-11T16:08:10.860595", "exception": false, "start_time": "2023-10-11T16:08:10.827943", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class GenerateCallback(Callback):\n", "    def __init__(self, batch_size=8, vis_steps=8, num_steps=256, every_n_epochs=5):\n", "        super().__init__()\n", "        self.batch_size = batch_size  # Number of images to generate\n", "        self.vis_steps = vis_steps  # Number of steps within generation to visualize\n", "        self.num_steps = num_steps  # Number of steps to take during generation\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_epoch_end(self, trainer, pl_module):\n", "        # Skip for all other epochs\n", "        if trainer.current_epoch % self.every_n_epochs == 0:\n", "            # Generate images\n", "            imgs_per_step = self.generate_imgs(pl_module)\n", "            # Plot and add to tensorboard\n", "            for i in range(imgs_per_step.shape[1]):\n", "                step_size = self.num_steps // self.vis_steps\n", "                imgs_to_plot = imgs_per_step[step_size - 1 :: step_size, i]\n", "                grid = torchvision.utils.make_grid(\n", "                    imgs_to_plot, nrow=imgs_to_plot.shape[0], normalize=True, range=(-1, 1)\n", "                )\n", "                trainer.logger.experiment.add_image(\"generation_%i\" % i, grid, global_step=trainer.current_epoch)\n", "\n", "    def generate_imgs(self, pl_module):\n", "        pl_module.eval()\n", "        start_imgs = torch.rand((self.batch_size,) + pl_module.hparams[\"img_shape\"]).to(pl_module.device)\n", "        start_imgs = start_imgs * 2 - 1\n", "        imgs_per_step = Sampler.generate_samples(\n", "            pl_module.cnn, start_imgs, steps=self.num_steps, step_size=10, return_img_per_step=True\n", "        )\n", "        pl_module.train()\n", "        return imgs_per_step"]}, {"cell_type": "markdown", "id": "2365c926", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.012059, "end_time": "2023-10-11T16:08:10.885582", "exception": false, "start_time": "2023-10-11T16:08:10.873523", "status": "completed"}, "tags": []}, "source": ["The second callback is called `SamplerCallback`, and simply adds a randomly picked subset of images\n", "in the sampling buffer to the TensorBoard.\n", "This helps to understand what images are currently shown to the model as \"fake\"."]}, {"cell_type": "code", "execution_count": 9, "id": "a3257948", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:10.911115Z", "iopub.status.busy": "2023-10-11T16:08:10.910868Z", "iopub.status.idle": "2023-10-11T16:08:10.916861Z", "shell.execute_reply": "2023-10-11T16:08:10.915989Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.020848, "end_time": "2023-10-11T16:08:10.918458", "exception": false, "start_time": "2023-10-11T16:08:10.897610", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SamplerCallback(Callback):\n", "    def __init__(self, num_imgs=32, every_n_epochs=5):\n", "        super().__init__()\n", "        self.num_imgs = num_imgs  # Number of images to plot\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_epoch_end(self, trainer, pl_module):\n", "        if trainer.current_epoch % self.every_n_epochs == 0:\n", "            exmp_imgs = torch.cat(random.choices(pl_module.sampler.examples, k=self.num_imgs), dim=0)\n", "            grid = torchvision.utils.make_grid(exmp_imgs, nrow=4, normalize=True, range=(-1, 1))\n", "            trainer.logger.experiment.add_image(\"sampler\", grid, global_step=trainer.current_epoch)"]}, {"cell_type": "markdown", "id": "276f4a66", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.014339, "end_time": "2023-10-11T16:08:10.944636", "exception": false, "start_time": "2023-10-11T16:08:10.930297", "status": "completed"}, "tags": []}, "source": ["Finally, our last callback is `OutlierCallback`.\n", "This callback evaluates the model by recording the (negative) energy assigned to random noise.\n", "While our training loss is almost constant across iterations,\n", "this score is likely showing the progress of the model to detect \"outliers\"."]}, {"cell_type": "code", "execution_count": 10, "id": "79f797b5", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:10.965924Z", "iopub.status.busy": "2023-10-11T16:08:10.965640Z", "iopub.status.idle": "2023-10-11T16:08:10.971304Z", "shell.execute_reply": "2023-10-11T16:08:10.970459Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.017447, "end_time": "2023-10-11T16:08:10.972600", "exception": false, "start_time": "2023-10-11T16:08:10.955153", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class OutlierCallback(Callback):\n", "    def __init__(self, batch_size=1024):\n", "        super().__init__()\n", "        self.batch_size = batch_size\n", "\n", "    def on_epoch_end(self, trainer, pl_module):\n", "        with torch.no_grad():\n", "            pl_module.eval()\n", "            rand_imgs = torch.rand((self.batch_size,) + pl_module.hparams[\"img_shape\"]).to(pl_module.device)\n", "            rand_imgs = rand_imgs * 2 - 1.0\n", "            rand_out = pl_module.cnn(rand_imgs).mean()\n", "            pl_module.train()\n", "\n", "        trainer.logger.experiment.add_scalar(\"rand_out\", rand_out, global_step=trainer.current_epoch)"]}, {"cell_type": "markdown", "id": "de964556", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009684, "end_time": "2023-10-11T16:08:10.992029", "exception": false, "start_time": "2023-10-11T16:08:10.982345", "status": "completed"}, "tags": []}, "source": ["### Running the model\n", "\n", "Finally, we can add everything together to create our final training function.\n", "The function is very similar to any other PyTorch Lightning training function we have seen so far.\n", "However, there is the small difference of that we do not test the model on a test set\n", "because we will analyse the model afterward by checking its prediction and ability to perform outlier detection."]}, {"cell_type": "code", "execution_count": 11, "id": "ce209d76", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:11.012659Z", "iopub.status.busy": "2023-10-11T16:08:11.012431Z", "iopub.status.idle": "2023-10-11T16:08:11.019008Z", "shell.execute_reply": "2023-10-11T16:08:11.017479Z"}, "papermill": {"duration": 0.018448, "end_time": "2023-10-11T16:08:11.020288", "exception": false, "start_time": "2023-10-11T16:08:11.001840", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_model(**kwargs):\n", "    # Create a PyTorch Lightning trainer with the generation callback\n", "    trainer = L.Trainer(\n", "        default_root_dir=os.path.join(CHECKPOINT_PATH, \"MNIST\"),\n", "        accelerator=\"auto\",\n", "        devices=1,\n", "        max_epochs=60,\n", "        gradient_clip_val=0.1,\n", "        callbacks=[\n", "            ModelCheckpoint(save_weights_only=True, mode=\"min\", monitor=\"val_contrastive_divergence\"),\n", "            GenerateCallback(every_n_epochs=5),\n", "            SamplerCallback(every_n_epochs=5),\n", "            OutlierCallback(),\n", "            LearningRateMonitor(\"epoch\"),\n", "        ],\n", "    )\n", "    # Check whether pretrained model exists. If yes, load it and skip training\n", "    pretrained_filename = os.path.join(CHECKPOINT_PATH, \"MNIST.ckpt\")\n", "    if os.path.isfile(pretrained_filename):\n", "        print(\"Found pretrained model, loading...\")\n", "        model = DeepEnergyModel.load_from_checkpoint(pretrained_filename)\n", "    else:\n", "        L.seed_everything(42)\n", "        model = DeepEnergyModel(**kwargs)\n", "        trainer.fit(model, train_loader, test_loader)\n", "        model = DeepEnergyModel.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n", "    # No testing as we are more interested in other properties\n", "    return model"]}, {"cell_type": "code", "execution_count": 12, "id": "672d4ba2", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:11.040733Z", "iopub.status.busy": "2023-10-11T16:08:11.040526Z", "iopub.status.idle": "2023-10-11T16:08:11.887107Z", "shell.execute_reply": "2023-10-11T16:08:11.886250Z"}, "papermill": {"duration": 0.85863, "end_time": "2023-10-11T16:08:11.888616", "exception": false, "start_time": "2023-10-11T16:08:11.029986", "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": ["Lightning automatically upgraded your loaded checkpoint from v1.0.2 to v2.0.9.post0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/tutorial8/MNIST.ckpt`\n"]}], "source": ["model = train_model(img_shape=(1, 28, 28), batch_size=train_loader.batch_size, lr=1e-4, beta1=0.0)"]}, {"cell_type": "markdown", "id": "04f43c1a", "metadata": {"papermill": {"duration": 0.010113, "end_time": "2023-10-11T16:08:11.910277", "exception": false, "start_time": "2023-10-11T16:08:11.900164", "status": "completed"}, "tags": []}, "source": ["## Analysis\n", "\n", "In the last part of the notebook, we will try to take the trained energy-based generative model,\n", "and analyse its properties."]}, {"cell_type": "markdown", "id": "2f9f8ae5", "metadata": {"papermill": {"duration": 0.010064, "end_time": "2023-10-11T16:08:11.934631", "exception": false, "start_time": "2023-10-11T16:08:11.924567", "status": "completed"}, "tags": []}, "source": ["### TensorBoard\n", "\n", "The first thing we can look at is the TensorBoard generate during training.\n", "This can help us to understand the training dynamic even better, and shows potential issues.\n", "Let's load the TensorBoard below:"]}, {"cell_type": "code", "execution_count": 13, "id": "c5740701", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:11.957262Z", "iopub.status.busy": "2023-10-11T16:08:11.956654Z", "iopub.status.idle": "2023-10-11T16:08:13.982321Z", "shell.execute_reply": "2023-10-11T16:08:13.981562Z"}, "papermill": {"duration": 2.038866, "end_time": "2023-10-11T16:08:13.984015", "exception": false, "start_time": "2023-10-11T16:08:11.945149", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["\n", "      <iframe id=\"tensorboard-frame-1c80317fa3b1799d\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", "      </iframe>\n", "      <script>\n", "        (function() {\n", "          const frame = document.getElementById(\"tensorboard-frame-1c80317fa3b1799d\");\n", "          const url = new URL(\"/\", window.location);\n", "          const port = 6006;\n", "          if (port) {\n", "            url.port = port;\n", "          }\n", "          frame.src = url;\n", "        })();\n", "      </script>\n", "    "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Uncomment the following two lines to open a tensorboard in the notebook.\n", "# Adjust the path to your CHECKPOINT_PATH if needed.\n", "%load_ext tensorboard\n", "%tensorboard --logdir ../saved_models/tutorial8/tensorboards/"]}, {"cell_type": "markdown", "id": "04ff3444", "metadata": {"papermill": {"duration": 0.010275, "end_time": "2023-10-11T16:08:14.005577", "exception": false, "start_time": "2023-10-11T16:08:13.995302", "status": "completed"}, "tags": []}, "source": ["<center width=\"100%\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/tensorboard_screenshot.png\" width=\"1000px\"></center>"]}, {"cell_type": "markdown", "id": "ef9a8311", "metadata": {"papermill": {"duration": 0.012552, "end_time": "2023-10-11T16:08:14.028446", "exception": false, "start_time": "2023-10-11T16:08:14.015894", "status": "completed"}, "tags": []}, "source": ["We see that the contrastive divergence as well as the regularization converge quickly to 0.\n", "However, the training continues although the loss is always close to zero.\n", "This is because our \"training\" data changes with the model by sampling.\n", "The progress of training can be best measured by looking at the samples across iterations,\n", "and the score for random images that decreases constantly over time."]}, {"cell_type": "markdown", "id": "d7bf188d", "metadata": {"papermill": {"duration": 0.011746, "end_time": "2023-10-11T16:08:14.053878", "exception": false, "start_time": "2023-10-11T16:08:14.042132", "status": "completed"}, "tags": []}, "source": ["### Image Generation\n", "\n", "Another way of evaluating generative models is by sampling a few generated images.\n", "Generative models need to be good at generating realistic images as this truly shows that they have modeled the true data distribution.\n", "Thus, let's sample a few images of the model below:"]}, {"cell_type": "code", "execution_count": 14, "id": "a04051e4", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:14.080878Z", "iopub.status.busy": "2023-10-11T16:08:14.080680Z", "iopub.status.idle": "2023-10-11T16:08:15.277516Z", "shell.execute_reply": "2023-10-11T16:08:15.274959Z"}, "papermill": {"duration": 1.212781, "end_time": "2023-10-11T16:08:15.279444", "exception": false, "start_time": "2023-10-11T16:08:14.066663", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 43\n"]}], "source": ["model.to(device)\n", "L.seed_everything(43)\n", "callback = GenerateCallback(batch_size=4, vis_steps=8, num_steps=256)\n", "imgs_per_step = callback.generate_imgs(model)\n", "imgs_per_step = imgs_per_step.cpu()"]}, {"cell_type": "markdown", "id": "3278d60d", "metadata": {"papermill": {"duration": 0.010299, "end_time": "2023-10-11T16:08:15.300852", "exception": false, "start_time": "2023-10-11T16:08:15.290553", "status": "completed"}, "tags": []}, "source": ["The characteristic of sampling with energy-based models is that they require the iterative MCMC algorithm.\n", "To gain an insight in how the images change over iterations, we plot a few intermediate samples in the MCMC as well:"]}, {"cell_type": "code", "execution_count": 15, "id": "80ee3cda", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:15.323294Z", "iopub.status.busy": "2023-10-11T16:08:15.322764Z", "iopub.status.idle": "2023-10-11T16:08:15.973815Z", "shell.execute_reply": "2023-10-11T16:08:15.973222Z"}, "papermill": {"duration": 0.671307, "end_time": "2023-10-11T16:08:15.982474", "exception": false, "start_time": "2023-10-11T16:08:15.311167", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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HR0cH+9bSZefTfAg9Ttlcr9djcHAQ9Xod09PTn1nfWoJMJkN3dzeMRiN8Ph9WV1d3+5F2HaQakc1m4fV6W4LYT+u6WRewKZVKmM1mGI1GKJVK1Ot1mM1m2Gw2aLXaFp6YUqmERCJBo9FAJpNBJpOBxWJBoVDgrJpIJEKlUkGxWEQikUClUoHJZILD4UCtVkMqleKbNFlMyeVyiEQiFAoFvkWJRCIW9aWsXrPZhMFggFgshkqlglwu50aG9vZ2aDQaaDQa3hhIdLdQKLCTQjAYhEQiQS6X40zd40CpVEKlUsHpdKK/vx86nQ5ms/n+YP//rbsUCgV30JJ5PWUcJRIJxGIxZyebzSY0Gs1jZV3EYjF7ulLwTR6xO3UoS6VSmEwm9njt6elhPiFp5OXzeRSLRcjlcl5Qwn9TFpWyco8LrVYLp9PJv3On7YNkMhlkMhmMRiOcTidnk/L5PBqNxrpxEJogCy3Ttsrih/QUAfAYb3Zj28x/J5FIIJfLodPpYLfbodfrUS6XIRKJUCwWOQtKn03PR+NCEkGb/f0bgXQmSZB7JyESiSCXyzkD73K5eL1UKhWUSqWW753mgfCfx/kON4JQPH03AnpaMwaDAU6nE/V6HdFoFBKJZF3WUfj+a8dmKyH0Wd7pQIAqFyqVih18SAt17VjQM9K4CNfPVj+TMOmw0yBRfdpXVSoVW2sK90/hHkrzebvswcRi8ZZkxdcFbDdv3sTbb7+Ner2Op556CgqFAgcOHODMUzAYxPLyMuukURnTZDKhVCpBJpMhlUohmUwimUwik8lwGezChQsYHR3l0ku5XEY0GuWSUKlUYl20YrGIWq2GaDSKf/3Xf4XJZIJIJMK5c+dgMBi4MYFcDTo6OqDRaDA3Nwefz8dlR6lUioMHDyIWi+H27dt45513UCgUYDQaUSgU8Jvf/AZarRZnz56F3W5/rHSyTCbDc889h2PHjuHQoUPo6uqCXC6HQqG4P9hSKarVKjweD78fTSIqRVGQW6vVUK1Wsby8DKVSiUgkghs3bnDTxcNCqVTCYrGgvb0dXV1d6O3txcmTJ5HP5+Hz+TA5Obkji8pkMuHFF19EZ2cnBgcH4XA4UCwWuUOX/i30rK1UKjwOpVIJ4XAY2WwWly5dwtLS0mNNfpFIhL6+Pjz77LOYnJxEIpFg2ZpSqbSFb74xJBIJTp48iaGhIfT29uLYsWPsxlGv17lBhTLHFNxXq1XkcjlUKhXOPkWjUfj9/sfeDOx2O55//nkAwHvvvYdgMIhyubwjB7NIJEJHRwfOnDkDh8OBJ598EiaTiedBqVRiizvaJ4j6kEqlkM1mEYvFsLi4iHK5zB7BjwO5XI6jR4/C5XJhZmbmsYy5N/PdqNVqXLhwAR6PB6Ojozhw4ABKpRKy2Syq1Sqy2SxqtRoH15FIBOFwGLlcjsdmYWEB2Wx208+9Fk6nE729vchkMpiZmdmRtSLEoUOH8MQTT8DtduPUqVOQSqW8Z+Tzefa6rtVqKBQKWFlZQT6fx9LSElKpFCKRCCKRyJY9D3GUZTIZVlZW1lGDtjuAs9vtePbZZ2G32/HEE0/A5XIhnU7z3KCgvlwuo1qtwu/3I5lMwu/3Y2lpCYVCAZFIZMsuqiKRCBaLBQaDAZlMBtFodEeDWJlMhrNnz6K/vx8HDhzAkSNHUK1Wuakvn8+zJ3mtVkM8HofP52Pd2Fwuh2QyuaXZfKJ1NZvNx24uXBewraysYGxsDG1tbXjqqadgMpnQ3d3N8h7pdBqJRALBYBBarRaHDx+GWq2GRqNhAdx0Os1RfqlU4gxPf38/zpw5g1KphEKhgGKxCK1WyzfJSqXCGzRl3AqFAu7cuQONRsMBB/mIVioVxONxKBQKnDx5ElarFfl8HgsLC3zQicViOBwOSCQSXLt2DePj47DZbHA6nYjFYpiamoLRaMQLL7yA9vZ2hMNhJBKJTQ2mRCLBwMAAzp07B7fbDYvFwguXOAYbYaMJXa1WUS6Xodfrsbq6CoPBgKmpqUcO2CgjajQaYTabYbFYOGgql8uYnp7e9oBNJBJBpVJhaGgIg4ODcLvdMBqNLcKIIpGIG1xoYRWLRQ7WcrkcvF4vYrEYJiYmsLy8DGDzG6JIJILNZsPg4CCKxSIcDgfEYjHC4fCWvffHQSwWo6OjA8ePH0dPTw+OHTvGFxfKShP3olwuI5VKYXl5GaVSCYlEAsViEQqFgnlNgUDgsTdGnU6HoaEhAMDY2Bji8fiOZJZoHphMJhw9ehRtbW04deoUrFYrZ7hozyBOJ7mxJBIJhMNhRKNRKBQKhMNhiESiddmnzUAikcDj8WBgYADJZBKzs7MP9ZkfVep71O9HLpdjcHAQIyMjOH78OA4ePMh/VqvVkE6nWy47S0tLWFhYQCKRgFwuRzKZxOrq6pYGbKQIEI1G4fV6ty1g26h8LBKJ0NbWhpMnT3JwTxWYZrOJVCrFQX2lUkEqlcLk5CQHUaFQCIVCYUuDCJlMBpvNBpVKhXg8jmQy+Vjl70eFTqfDkSNHeDzcbjdn0CiIpeCV9vtAIACVSsWXHRKV3wqIRCJoNBpYLBY0Go0t/eyHgUQiQU9PD06cOIFDhw7hxIkTnN2q1+sc3NNlmOKdcDiMeDwOsVjM+8tWQSqVcpWMvo9Nf9baHxw+fBjHjx+HTqeD2+1GvV7H5OQkMpkMVCoVOw+cPXsWEokEMpkMANi70+/3Y2Fhgd0NVCoVuru7oVKpcPDgQVgsFpRKJSiVSkSjUayurqJQKGB5eZlLk/l8HnNzc8jn81AqlRgaGoLZbOZbZTabxcLCAiwWC44cOQK1Ws0cOSLzF4tF3LlzhzswM5kMxGIx2tvbYTQaYTAYUKvVYLFYYDKZYLPZYLPZNkXGp4CTyrJ0yDwOyuUyvxNlEAC0eLl+HGQyGRQKBTo6OnD+/Hl4PB6YTCZIJBK0tbVx1nG75RekUim7SNA/crm8JaNIEIlEPHYikailbKvValGtVqHRaNDZ2ckZsVQq9UjPo1AocPz4cbjdbhw7dgw2mw0DAwP43Oc+h5WVFfj9fuRyua0cgo+EwWBAW1sby9ZQWZzK/zQOtLYoQ0sZNp1Oh66uLqhUKvj9fuZ5PepB0dHRgd7eXgwMDODo0aOQSCTI5/NYXV3Fhx9+iPHx8W0agfug51Wr1cxXVavVkEqlPD9oHlFJjC56hUIBmUwG2WwWwWAQbrcbkUgE77//PpLJJJc5HgUajQZ9fX0wm804c+YMVxg6OzuxsrKCq1evfiyJeSsOaip1eTwe9Pf3w2Qy8Z9RhYL2Y1onYrEYBoMBhUIBvb29yGaz6OzsRCwWw507dzA/P7+pZxOLxXC5XDCbzRgdHcWFCxeQyWTQ3t6OeDyOq1evIhgMbmmAstFn0SVreHgYZrOZS5E0R1QqFSQSCZf71Go1JBIJisUi2tvbkclksLCwAK/Xi9XVVdy8eXPT1RSdTgebzQaHw4HnnnsOJpMJBw4cQCQSwczMDMbHx7n8uJ2Bm1qtxsDAALq6uqDT6fjntG9QM5NSqeRAoa2tDZ2dnTh06BDi8TgmJyeRSqVw69YtRKPRTT2HRCKB2WyGWq3GU089hUOHDiGRSMDv9yMajeL69euc9dvO8ZBIJOjs7MSRI0fgdDpbgrVGowGlUsmNCPV6HRKJBEqlErlcDl1dXchms5iZmUE0GsXc3NxjZdUpkdXT04Nz585BIpEwN/n27dtYWlp65AvxuoDt2LFjePrppzm7FQwG8eMf/xjj4+Po6emB2+2Gy+XChQsXOGKn2z51Qc7MzDAXzmQy4ezZs+xHarVaUSqVoFarkclk4PP5EAqFEAgEkMlk+CCmmrJer8ehQ4fQ1taGe/fuYWVlBaFQCCsrKzh69Ci+/vWvQ6fT4datW4hEIiiVSjCZTCgUCrh+/ToKhQIWFhZQKpU4u6RSqZhcbbPZmFPncDgQCAQe+YuhDKJGo4FCoYBMJuOAZLMolUpIJpM8HkSqlclkD8VlUygUfKC/+OKLcDqdMJvNnDVQqVTwer2P/ZyfBJlMxoEaBcoKhWLDQJE2GXp+IUqlEvMSuru7kUqluMzxKFCpVLhw4QJOnDiBoaEhDg4MBgNmZ2fxxhtvbPnhsxEoo9TR0cEdwrS5AOBxIB4b8UabzSan9Nva2pgXevv2bWSzWS4JPQq6u7vx4osvoru7GydPnoRcLofNZmMx6+0O2IAHXdw9PT2w2+3QaDR8IAPggFUIh8PBtIharYZYLIaDBw9ifn4e09PTXBZ91IBNp9NxY8znPvc5DA8P48CBAzh9+jQuX76MsbExpjJsByggUygU6OzsxNDQEFQqFf8Z0Mo3JFitVvT39zPftVgs4ujRo4jFYiiXy/B6vZviFYnFYnR2dqKvrw9PPfUUXn75ZZTLZRw/fhyBQACBQAChUAjA9pUA6SLjcDhw+PBhvsQI/5y6voVob29v4SzNz89jYWEBly9fxvj4+KYDNmp66O7uxhe/+EW4XC4Wgf/JT36CmZkZAK0c3Id5x0eFVqvFwYMH0dPT0/I5pEpAfHD6bIfDwc/TbDYRjUZx48YNvqxuNmCTSqVwOBywWq343Oc+h89//vNIpVIIh8OYnp7G4uIiZ723M+MmlUrR3d2NEydOtJSlKShSqVTMsQPurxkaO+JHX7t2DT6fD7/61a8eOqu+ESioP3r0KP70T/+Us7DpdBp/8zd/g3A4zBWlh36/tT+gL7tUKrF1U1tbGxqNBsxmM/R6PQqFAu7du8elGypnUXOATCbjllqyqFIoFIjFYlwfzuVySCQScDgcfDNKp9OQSqUtC8xkMkGr1UKj0cBms7H0QzQaRblcxtTUFPR6PctWaDQaGI1G/vx6vQ6bzYZms8mNE2q1mnl3jUaDbyHEDXlU0ISgUq6Q9/Nxi1BIQhQuqmazyR2wdrsdw8PDsFqtCAQCqNfr0Gg0UKlUnPKmrAtwfwJqNBouG/f19cHhcMBoNLYEQxqNBiaTCW63G5lMBslkclsWEh0elAGgrIlwXD5uMxMeUGRVRtZnJNsilUr5z1KpFHOeqtUqjEYjN8iYTCYYjUYMDg7C6XRylobKxgaDgYNKKr9tFyiDqNFoeFMVvu9GY0D/mzJvGo0GzWYTfX19OH/+PGKxGCYnJ1EqlVhCJZ1Os/B1uVyGQqGA2+2GSqWCyWRie7ne3l44nU7OcOr1etYKVKlUzCXcTtB3TPvBJ4HmEY2HWq2G1WpFpVLBuXPn0Nvbi+XlZcTjcQ74isUiYrEYcwUBMOdGp9PBYrHAYrFgZGQENpsNer2egyP6e3Qp2+4uTeFeSuNBe8baOSH8N60zuVwOk8kEsViM48ePo16vI5lM8oWEGsai0WgLSZ3sCJVKJWw2GzQaDYaHh+HxeODxePhybjabuZJCma3tGg+xWMwd+NSYRVhL5l5LtRDuqzqdDg6HAwcOHMDzzz/P40FaoyKRiGk/9JlisRgmk6mlIc/hcGBgYAAulwsGgwFKpRJGo5HLX8Ln2s4xofNa+I50SaH3Ef6dtXOFtEslEglTeSKRCNOC6IyPRqMt61+j0fBZSnOkq6uLzxSVSsXPEQqFWmKE7cZG54swPqG5tHZMgPsXZavVimaziWPHjnEG3+/3tzQ0JZNJ5PN5/u+kUin0ej3kcjlfOK1WK6xWKwYHB6HVajkTTs1Vm1kv6wI2Sh8mEgncuHEDlUoFZ86cgVKpRDKZRDabRTgcxt/+7d/CbDbjmWee4fJio9HgB6cymFar5U3/1q1bmJ+fR7FYRDabZW9RmUyGxcVF5oksLCzwRKe0M/EE+vv7oVKpkEgkkMvl8KMf/QgWiwV//Md/zLysSqWCyclJLC0tQS6X48iRI9Dr9XA4HNDpdHwwp9NpLCwscNnN5/Ntiu8h7FolDp/JZPrEL4PGGsC67AEtBpfLhaGhIUQiEaTTaeh0Ohw+fBh9fX2IRCIsxjs3N4dGo4Hz58+jp6cHhw4dwtGjR5kXJQyWtFotpFIpent7cfr0aQSDQVy/fn1bSoH1eh3FYhHlcpnLtBsdxsLAdSNQp5zJZMLv/d7voVKp4De/+Q30ej30ej06OztRLpdx+/btFm/aI0eO4LnnnoPL5cLp06eh0+k4SKIxp9JSoVBAV1cXisUifD7fprmMDwutVgubzdYSsD0M6FJjs9lgsVjgdDrx3HPPYX5+Hj/60Y+QSqVgs9mgVCoxNjaGmZkZJuUbjUa8/PLL6OjowMmTJ9Hd3Q2lUsklSMp+ulwuWCwWLtmS1uJ2brgKhYK/440yvx81R2jzNZlM0Ov16OnpwejoKHK5HH7605/izp07vA/5/X68++67TL1oNpsYGRnB6OgoBgcHcfbsWahUKuh0Oi4picViDtYWFhag1+u5o3y7+H10kCgUihZ9RmF3pvAwFo4XBbEqlQodHR1oNBro6enBd7/7Xdy8eROvvvoqGo0GX/ouXryIxcVFPmDtdjvOnj0Lp9OJZ599liWVKJimgESr1UKlUvFBtZ0aikS/IZ9o4fvSwScMsCiIXzumlAUaGBjA888/j0gkgp///OesFiAWi3Hv3j188MEHvDcrFAocPHiQ95DR0VHOnsjl8pZArV6vw2q1tnT5Pywe5e/Sfr723BCJRMxfE+759G9hBydwPwt06NAh1Ot1HD9+HOVyGW+88QauXLnC8ygWi+Htt99mLiAAuFwuLsU+++yzMBgM7BZksVig1+t5fyMlCNJT3C5sdOkVdo9T8gAA05eEf58uMf39/ejt7cXRo0fxne98B9PT0/jJT37CElz1eh3Xr1/H3Nwc/x6NRoOBgQFYrVa88MIL6OnpgcFggF6v57kikUhgNBqZ6rWZasi6gI1SloVCAeFwGNVqlTcvSrFSYCKRSJhUSBu+Wq2GxWKBTCbjgzGZTKJSqbCLQKFQQC6X4yhTJpPxLZ5KptTppVar+XClTBYtEiqF0POS8G2tVuMNXCiTATxor63X6xCJRNDpdC1dI5vdcOgz0+k0wuEwTCYTfxn0O+kLyufzXK6h5yDOkslkYuK5VCqFTCZjsV+32418Po/29na0t7dDoVC0kI/r9Trfgtva2mC329cdfDQpKXiimv52cdloXEgct1Kp8HdCmTAikq/NNlJwSQc43a5lMhkajQacTidru3k8HpTLZcTjcRZINhgM6OjogMfjgcPhgMvl2lBjjzYm4e19u7l9AHhMKICleUxzRJjho0NKJpPBbDa3rAmSwsjlcvB4PNDr9bBarVAqlewDnM/nodPpYLVa4fF40N7ezvQGGlshiCtJ82cnbsaUPa9Wq7zf0Pygd6BbMn1nxNvSarX8/QH3N2StVov29nYkEgloNBrodDqIxWJ0dXUhl8vxJauzs5PXVFtbG/NQ6XfQ76RgYSfGgwKQer2OarXK70Ydw/RvylwL1wdle4T7tUqlgkgkgtvt5iCODo2uri4e/0ajwXuI0+mE2+3mg5gyawT6fZsl2G/mv6HxAB7MUWpOIrK4MIMi3FdJWJYyHDRnPB4P/5xoPST7RGPX0dEBl8vFc0SlUsFgMLS8vzDbuxPrRThHaB5QRozOQmoKWTseWq0Wer2eS+/Afc5VvV5He3s7PB4PzyuNRoPu7m4YDAb+3R0dHby3ut1u6HQ6mEwmKBQKlkgS/t7tzjYKQeNB+wTtq2RhWavV+DklEklLNprOXJFIxOsok8nA4/FwQoPUK4QBKCUNLBYL3G43W26SMD4FjsKzZTMZx3UBG018n8+H119/HZlMhr+QAwcOsGcoEft/+tOfQqlU4itf+QqGh4cxOjqK4eFh7l5KJBL4p3/6J4TDYS7NlUolFItFRCIRXLlyBSqVinWXzp49C51Oh8XFRVy5cgUajYaDwbm5OczPz8NgMODcuXP88rVaDVevXsXbb7/Nm0hfXx/+8A//EMlkEq+88gqSySRrkNHGpNVqeYOmYO1xJlS1WsV7772H6elpfOlLX0J/fz/fSmu1GpMZL168iA8++IAnMCm7a7Va/Pt//+/x1FNPcUBF0Ov1+Na3voVCocDZN6H8CYms0k1YqD+3FsLGCKGS/HaANr1cLoelpSXm5ZhMJni9Xty7dw9LS0t4++23OcVMmQWFQoHnn38e/+E//Id13BSRSIRjx46hs7OTCemNRgNnz55lUdFqtQq9Xs8BztrPEII2vq1yvPgkNJtNrK6u4u7du3C73Thw4ABSqRQ++OADRCIRvPvuu1haWuK/T76zHo8Hf/mXf4m+vj4eB4LT6cTv//7vs9SNWCzG5z73OT7YK5UKp+yVSiX0en3LDV04B+hWSm3u200WBoBMJoPp6Wk4nU50d3dDLpdjYmICS0tLuHPnDi5dusSXIIlEAqvVCq1Wi29961t4+eWX132eXC7H+fPncezYMd6Yy+Uyvv71r/OmDoADPirz0IFLlyz639TlRbIJ28XFod9F3b+Li4uw2+0wmUwIh8O4c+cOAoEAXn/99ZbGIYPBAJPJhCNHjuC73/0uDAZDSzABAH19fZz9p4vs5z//eW5WoUCOSr9ms5m1L2lO0HvTui4Wi9uuJE/zL5VKIRAIQKvV8uX+7t27iEQiePvtt3H37l3+bxQKBaxWKywWC/7sz/4MR44cAdCamTQajXjxxRdZ7w8AXn75ZaRSqZZDVq/XQ6FQwGg0Ml2A+KSkm0iXBbp0bWf3PZ1VxHWm5yJ5ESLMv/nmmxy0URlbo9Hg85//PH7v936vpXGDAs4TJ06gv7+ff16tVrmqQecknVdEQxBe+IrFIjf5SSQSDpK2W42Avq9CoYBUKsWcxkwmg3v37iEcDuM3v/kNfD4fxw5qtRpGoxG9vb34i7/4C+6ypfESi8XweDz4/d///ZZM7je+8Q0WF6d9ldaMzWbjrDgALikD9+ckyVltZk9dF7AR1yWXyyESifDEpeCGauN021tZWYFUKmUNNsqIkVp7JpOB1+uF1+uFVCqFwWDgUkK5XEYymUSpVILVaoVcLofD4WC7qLGxMQ5aarUaW1KRaCJFw/l8HhMTE1hdXWV+SXt7Ozo7O7nUlUgk2BKrWCyiWCzCYrHAZrMxt+txsyrkTJBKpRAKhThzB4D5I+FwGBMTE3j//ff5vyOOjMFgwBe/+EW+UQshl8vR3d39WM9HEHZobiZ1/6igw2dtMJTJZLCysoLZ2VlcuXKlRa1crVZDqVSiu7ubF/taDgbxjR4GH/d+wgOZgr2d2FyozEgHaLlcRjAYhM/nw82bNzE1NcV/nzhniUQC2Wy2JdNEUKvVDz1H1o6HkBxNmWzirZXL5R3R6qtUKkin03zTJ66I3+/H1NQULl++zOtJKpUyf+jcuXPreF0AWNKHiNYfhbW8MHp/+rdQhJayxNstdUIXCOL7Go1GAGBusc/nw/Xr11uapGg/I7u1jeYINQARhO8szIAIy4sAuDpAckn0d4krulOHcaVSQaFQ4D1buK+Oj4+3XIRVKhVcLhecTidzdNdWHORyOdra2jb8fcCDwEi471B5jy7LZI0lzHDtRDaJ5ohQBgsAn71LS0u4evUqZ6bFYjEnRoaGhrgSRRkwGhuLxcI8rrUguSEhf44y/USgp+oY/dlOW4fRHk6ZMhLoD4fDTBGh75WqDiQbJBQnp+9crVZzFhpoXSv0viKRiCtVlImnpAElAprNZgu/ejPzY13AFo1Gce/ePZRKJXzhC19AvV5nwno4HMZbb72FYrHI8hfUZVetVhEOh5kk6/f7cePGDSQSCUilUrjdbvT392NoaAgrKytIp9MwGAz43Oc+B71ezxuL0WhkheKBgQGk02m88sornLp/7rnnuJSn1WrhdrtRKBQwNjbGz0TE0ampKVQqFRw9ehQDAwP8jlQ6SyaT8Pl80Ov1eOaZZ2C32/nQ2CxoY6vX6y2lFXJVmJ+fX9fZSPpjMpkM2WyWf//HZYQeF3Q7zGazXBraTlDqnQjKAFjgk5pDhKANiIJfmhe0UT8O6F3peyKByUQigXg8jkQigXK5/Ni/55NAVAB6J9pYqHFCiGq1inQ6zYdTKBRiL9rHBWVOaD6QgDGJ9e4kWVjIT6IglnTGaI+gwzudTqNSqSASiSAUCrHX8WYuXsL/RliSjsfjLdIhU1NTO2Jh9nElJGE2RCjzQyKo0WgUkUiEM0MPu2bWBm/C/5/L5ZDNZll8ljI7sVhs052FjwLheAjlMoRK/0qlkuUr6LBMJBKQSCSIRqOIxWIs9L4287j2d9G/hWU1opMA94OiQCCAfD6PlZUVFItFpFIpFItF3Lt3b8cU/imIFQZslCEl7jitb/oeSbA+Go1Cq9XCaDSuSxBsNO/o/cViMe+bwiB1eXkZ2WwWgUAAsViMs/Orq6s75l0tDGJJ91Qul3NQRuNCQVO1WkUymUQ8Hkc4HGaOu1KpbCltr23yoT+jDCUFbwTS9CR9N6/Xy+szl8thcnJyU++3LmBLJpMoFArQaDQ4c+YM5HI5Hwo/+clPcOvWLX5g0ivS6/Wo1WpIJBIcsPl8Ply9ehWFQoFlAsiSiKJNrVaL48ePw2w2Y3V1FblcjhsVzGYzPB4ParUaPvjgA0SjUfzlX/4lTpw4wV2NWq2WXQOoFEiZgXw+j8XFRajVavT390MkEmF1dRXJZJKVj3O5HMLhMOx2Oz7/+c/D7XY/tgebkFcnJIXW63XEYjH4/f51jQ3CyJsEh7c7WAPAZeutEBn9JIhEDzre6EAuFouIx+PIZDLrfj+NI3X7ikQi6PX6LXseIT+oXC4jm81yw8hO+BLSTYy4aHQo5PN5LiEIQeNB0jfxeJylZB4XlD2iQ7hUKrH/L0ns7ATWdrwBaMlkrLW8IT06clVpNBrQ6XSPHLCt5RxRZkvYKU8K+aSdtBP4uCBZyLukwIK4v8lkkktCarX6kS45wrEQ/n66pAeDQXZP8Pl8m9JC3Czoe197gaB9haor9Ge0XsgNIZ1Os8yQ8DOFganw58CD8RCWR4H7mc5YLIZUKoWZmRmk02l2OiD5lJ0ABQrCLCed2VSloAsG8f1IMJe+Nyqdf9LvAdDCtxUGsZSwiUajmJ+fx+rqKq+XTCbT0lFJ2GjctwIUTNJ4kP1fPp/nMi7teVS5o87gZDIJjUbTYnX2UWMj5CzSWUJzhKoDS0tLCAaDGB8fRzKZxMTEBM/FzWBdwEaT0OFwwGKxsP9lrVbjjcDlcrGY48GDB6FUKrlTsb+/H93d3ahWqzhy5AibnZOVDGXfiChKE4DEDKn+XyqV1vk8+v1+jI+Ps7p5d3c3RkZGuBTUbDZZp41ss6RSKQcE1KUhlUq5e2V5eRkqlYpb3ePx+KYGkkAkfvLJJMjlcgwNDUGr1bI2DU0YiUTCkT1p160N2D7qpv1JoOwRpe7p1pnJZDA2NoZQKIR0Or3tKWsKkIStzO3t7Thz5gzGx8dx8+bNFnVp6uIxmUwwmUxMGN/sOFBGsV6vIx6PM4cyFoshm80iFAohGAzu6OFDNkPE/9FqtRgdHYXNZsP4+Di7OQD3N0rS1jMajTCZTC1eow8L4SZJWaRIJMIaUtTFTdmCnTSRpqyFSCRCb28vVCoVOjs7IRKJEIlEOPCgTZQOaXLyoGadRwWNIV0OUqkUxsfHkclkODtAAX0wGNyxgI061w0GA9RqNRwOBwwGAwYGBvjCTFzURqPR4k1LmYS1XYQPC8pSkOTH7du3uZM/Go1yRpoumDuFcDiMu3fvchOATCaDy+VifplQd4yyb9R5TP7YwPpOQuHP1pZAhUGR3+9HIpHA8vIybt++jVwux+cZjUc6nd6xgC2bzXJnPDk/6HQ6tLe3Y2FhgaW16JylS4/VamXJrIfR4ly7ruiMpoxaOp3G9evX+WJDihKxWOwjS6LbMUa1Wg1TU1MwGAw4ePAgDAYDpFIpLBYLyuUyX2BIZoPmCo2HwWBoueBstJ9sNHeoSlYul3nPmJ6e5mDe7/czt474a5vButXs8/lw69YttkSRy+VcHgkGgwgGg+jv78ezzz4Ll8uFkydPAgD+9m//Fjdu3IDH48GBAwdgsVjYKua1115DKBTC/Pw8fD4fZxFI6gEAbt26hZs3b/JharfbOTNGJvTkuEB+pqdPn8Y3vvENGI1GjI6Oor29HW+99RZ8Ph8rL9frdXi9XpTLZdjtdpjNZvT398NoNCIajWJsbAylUglzc3OYmZlZJ8j4KKCuU/qHyjv0DufOnWMByw8++IClAUhDzmazcefeVqFSqSCRSHDWJJ/PY3x8HEtLS5iamsLc3Ny2t1sDG98Eh4aG0N3dDavVih//+MctJW3qOnI6nWhra9v0YUygBVUsFjEzM4NIJIK7d+9icnKSA2gqLewUqORIjTAWiwUvvfQSIpEI3njjDdy+fZv/LnVGm81muFwutLW1PdZ40A0wmUzizp07uHLlCvx+P65cudKiybWTBt+kMl4qlXDs2DFYLBYcPnwYBw8exOrqaovoJV3I1Go17HY7nE7nOr7Wo6DZbCKRSGB8fBw+nw8/+clPuPRMHcyUZdkpPk65XMbY2BgymQwcDgeGhoZgt9thsVh4fYTDYf6uqBPW6XTy39uMMDaV0LLZLG7duoVAIICf/exnuHr1aktpUjgmO4Fmswmv14s33ngDx48fx4kTJziALRaLcDqd3P1HVAODwQCbzcZcNmC9/MPavW+jOUQNXnfu3MHNmzcxPT2Nt956q4XfufbfO4F4PI7XXnsNbrebXUKsVivsdjuWlpbYMpLWjU6ng1KphMfj4YB3M2uGOi69Xi9effVVhMNhXLp0CZFIpIUH9nGl/e1AtVrF+++/j5mZGXzzm9/EoUOHIJfL0dHRwZJj1EEuVB2g8SBNOuDhOe3EBc/n80ilUnj99dcxMzODqakpTE9P8xoRrpXNjseGTQelUgmpVAoLCwvsOUblOrrBUGMCieIpFArY7XYOVOgFisUit1FThkStVrP8B7UZW61WuN1uKJVKJk2KxWKsrKwAAHMT8vk836r0ej1SqRTq9TqnXUkyIx6PY3FxkW+fxPWoVqvMXysUCqw9RSWnx208IKxN31NJUCQSob29HYcPH24RM21ra+NskrDsQ/Iqwo5QAkl+qFQqtLW1rXMHAO6XMqgMu7S0xIuMuGM70f0HPEjHC73UpFIpVCoVbDYbRkdHueNNJBKxphYtNOIz0S13o7IlNW+Qv5+wDR14sLD8fj8WFxexsrLCIpGU2d0pNJv3pXHC4TCcTifK5TKXR9VqNQYHB1tMqhUKBQwGAzweD3NwaDyI17b2oKAuKOoatlqt6w5wkUiEXC6HQCCASCTC/r+7gWKxiEAgAJlMhkQiwbdgUlE/duwYl3eos1qpVLIPLO1dpVIJgUCAxVCF70rjKJfLuXuY/gx4cLHIZDJ8G94JPuNGID1MuVzO5SYKRsibV9iNRiXQnp4efi/aM8LhMOto0X5EXXzU3UYXTWFpWtggtlvzQoh8Ps+cxWAwiHK5zMba7e3tGB0d5QBBKpWyLJDBYGAiOAUba7OldBZR4Eudf8AD3TPggbQISc7sJqhiIpPJEAgEWGuU/hkZGUE+n+fvVK1WQ6FQsJwP7auVSgWrq6stlzXiSMpkMs7o05oUnunEGSyXy7u2VgjE05NIJAgGg1hcXIRWq+XmtO7ubpTLZebi0Tt2dnay5iJVYogLSp9LWX1aM1qtlnVOgQfZS1ov2zEeGwZsZKxM7a9CRWCn04lms4mZmRl4vV7cunULcrkcfX19GB4eRk9PD99W7969i3Q6DYlE0mKE7na70dvbC5fLxZvuCy+8gJMnT8Lv9yMYDGJiYgLz8/N8kNrtdi5nDQ0NYXR0FAqFAvfu3UO9XkcgEEA2m8Xs7CyWl5cRjUYxOTkJh8OBF154gVW58/k8xsbGcOfOHTa4p0lIWZjHIRXTl0WkV5rwAFhH6+WXX8bo6CiTzkkvibKC9DmVSgUzMzP40Y9+hHA4jIWFhZbat9VqRUdHB/r6+vDnf/7ncLvd654nEAjgl7/8JYLBIKfO6aDfiW5IQqVSgd/v59sOjYdIJMLRo0fxv//3/24JmKnThjbOcrmMUCiEWCyG73//+7hy5cq636FWq3Ho0CHYbDZ8/etfx5kzZ/jPqDySTqfxxhtv4O233+YUtlDiYafQaDTYL1ckEmF0dJRlJTQaDf7jf/yP+O53v8t/n/gSJMtBHcnxeBy//e1v8cMf/nDdvBWJRBgZGcHAwAAOHz6ML3/5y1wSAsCXlXA4jMuXL/N47BaCwSB++9vforOzE8PDw8hms3C5XGxvd/To0ZbLBfFHaM1ks1msrKxgcXER3//+9+Hz+VrKYxKJBG63G2fOnIHT6cSLL74It9vNc47KziKRCPF4HJFIZEczjGtBGba5uTnuZic6itFoxH/9r/+V9aaABzIEVBar1Wpc6v7hD3+IN954gz+b3lWtVuPs2bPo7OzEyZMncfz4cf4zCpY3EqHdLQSDQW4EcbvdcDqdOHLkCIxGI770pS/hpZde4r8r7GKkAzuVSiEajeLDDz/E3/zN3yCXy/E+RNmX4eFh9ht+8sknmQ9GhzONy1aPyWY+r1AoYG5uDqFQCK+99hq8Xi9GRkYwNDSEzs5O/NVf/VXL59McoXJ5sVhEKBRCKBTC//k//wf37t3jNaZWq2E2m2Gz2XDhwgU4HA6uZFHzGEkD7ZR25Seh0WggEokgHo/jvffeY125c+fOQaPR4N/9u38HoDXLSucvzRHqxP+3f/s3/PM//zPTeCgRoNVq8fTTT2NkZAQejweDg4MsVF0qlR7KpWWzWBewkeBtqVRCNBrlNCrVfSnVTC9BNkxkh0SEPiodUHaLMkzEI7Pb7TAajcxt0ul0rKpMumKxWAyVSoXT3ETMVyqVbEyfyWS4k4xEEyniTaVSLA5JHSHUcBAMBlkShAjgRJ58nICNpE0+iugrEonYO5KsiYQTnQK1XC6HTCaDUCiExcVFBINBrocTEokEqtUqlErlR970KAjN5/MIhUItWZudxkelxzUaDXp7ezckO1PjAXXYUOBKXn1rP0etVnPJfS1oUyHuxU6XQIWgtUNZ6HK53NKEQFmjtURo+j6z2Sx3BC4vL2NmZmbDgI04lS6Xa11wTrdL4tNtt9XSJ4G6U3U6HZdr6RarUqlgNps35I9QNyc5XKyurmJubg5LS0stwRgdUKS1tDZjRIGKRCLZESuuT0Kz2eQMXywWQygUglQqZSFbm83WclgKy9jpdBqlUgnhcBiJRAJerxfT09PrglONRoP29nZIJBIMDg6uy76t5UDtNmjNkrF4vV7noJtKxWtL4zS/8/k8otEowuEwVlZW2AFEKMBM2Uun08nC7gCYhE78ZMpg7jaociESiRAKhaBSqbgkSu4+QsFn4IHdVDweRz6fRzgc5mYSMjsnTq3FYkEmk0F/fz+PI/Cg+UBoPbmZ8vt2gM7weDzOPOBwOAyz2YyOjg7m7QmDtnq9zh3y5Cq0vLyM6elprpLJZDLen7q7u1uqOLReqAGGRJq3Gus+8ejRozh27BhWV1dx6dIlSKVSPP/883C5XFhZWUE4HEZbWxsOHDgAnU6Hjo4OnuQk1pnNZqHT6XDhwgWkUincvHkTmUwGIyMj6Orqglqt5oXl9Xq59EcaO3K5HE6nk1uSgfsL9YMPPsDi4iLOnDmDwcFBbmQgUqNGo8HXv/51aDQaRCIRzM/Po1Qq4ebNmwCAgYEBjpBdLhdKpRJ+/vOf87M6nc7HmnRSqRQnTpzAkSNHcPLkSS7Prb150A1trWp6vV7H8vIyYrEYrl69irfeeou7jkhsT4hMJoP5+XnodLqPDDw8Hg++/e1v84a9WwEb3VwHBwc5I0IdmsCDYIS+S1ok8/PzLA8zOTnJXVkboVQqYWZmhjNxQlCGSqfT4aWXXkJnZycuX76Ma9eube+LfwREIhHzNPV6PZLJJHctU6BFwsYUSJHMyZtvvolQKIR4PM6k340yQc1mE0tLS0gkEi3OGwD4gKIS2tGjR7n9fLcDFfoe8/k8fD4ftFotDAYDc7JoPVEW++rVq7hz5w53wFEwC7SurUbjvq/hu+++i87OznViuzqdDj09PchkMpxp280AltBoNDA/P49mswmr1YqJiQk+NOhyKJVKOVO8vLyMixcvIpvNcmOJ1+sF0DoelF2+efMmFhYW0NXVhWeeeYZ/r0KhwMDAAMxmM15//fXdePWPRCgUwiuvvAKDwYDp6WlYLBb09PRwAwI1bdHZQvZbVDKPRqPc5ER7DUkcUWPb8ePHWXYKAIuoCstuW4nHmWvlchm3bt3C7Owspqam8N577zHvkezDqIO22Wzi3r17uHLlClNtisUiW0IShOP05ptvwuFwYHBwEH19fbyfWq1WnD59Gj6fDzdu3EAwGNyKodgSBAIBFAoFTExMYGJiAnq9npu6yBedkkuRSAQXL15EIpHg5rzl5eWWjmRSwshms7h06RLm5+fx0ksvcbVMo9FALBbj8OHDMBgM3Ly0lVgXsLlcLnR0dMBoNGJqagpyuRxHjx5Fb28vbt26hUajwWKUFosFhw4dgkqlQiqV4m63UqkEuVyOnp4eJJNJTE9P8812aGiIJ02xWEQwGGwxTKf6MllnAA9uSCLRfX016uogOQayzxCLxRgeHkZ/fz+8Xi8kEglCoRAmJydRKBT4mSlbGI/HMTs7C71ej9OnTz92WlcsFsPtduPgwYNwu90tEhZCCNvxhRknmhCBQAC3b9/GK6+88rGlOqqRp1KpFi6a8B0MBgMOHz7MytS7BSrluVwu3kzp+xYeInTLoYW0vLyMq1evIhKJ4M6dOx/bkUbSKdS5Jtx86NYtl8sxMDAAtVqNxcXF7X3pjwF1+ZnNZuZt0s0XuB/gUkaoWCzyeFDA4fV6mVv6caD2/Vgstm4uUebaZDKxeOjy8vKuB2zVapU5rNlslrNrhUKhxfibMpNXrlzBG2+88bENAbTOcrkcFhcXef8R/rlCoWC+E12odkqD7pMQj8chFosRj8dZw5LcWug2TzzcsbEx/PrXv/5YeRri6tXrdfj9fsRiMTY8F9ISrFYr8yr3Eoj+olKpUK1WYTKZ2KOa9PgAcBXmrbfeanFBEILmBl0ew+EwMpkMbDZby0VYJLqvE9poNGA2m/dM1hEA63wC96W5VlZW4PF40Gg0uPpFXeXNZhPXrl3DL3/5y48t+QvFs4mOQ3OK5oharYbH40G9Xt+QQ72byGazyGazzO+jJBE1stlsNr7kLC0t4bXXXvvYgJMy3mKxGEtLS4jFYjh8+DCLulPWlZpbhE4jW4UNS6JUr280Gshms/jwww/Z6sLn86FSqaC3txfpdBq3bt1Cs9lEOBxGNpvlG67H48GTTz7Z4m5An0m6RqTP1mg0MD4+ziS/WCwGs9mM7u5u5nGl02nI5XIMDw9zd0sikcDY2FiLpdHc3BzC4TCA+7IRxBurVCoYGxuD1+ttKbc8//zzvOlRmWyzIB6E0+lcJ+BJG4LQS5TkE3K5HGZmZpDJZOD3+znIfVh+mVC/SyjESs8k9EujBoud4mxRWr2trY1Jv/F4nKUaSHwyFAqxthEF/s1mE4FAgLMtD/vdNJtNpNPpFjFVgkQi4TLBdiyohwXd6KPRKHvdkdMBcR+J0CtUlicZgUdtDS8UCvD7/SiXyzCbzS3uHk6nE6Ojo9BqtRgfH991cnmpVML09DRWVlZYfJo6u4TlLiIHz83NtcjFfBwoU0lkbRIPValUXAK0WCw4c+YMXC4XJiYmdpVGQM+cy+VYLDiZTEKhUGB1dZVL6NRcRdzGh+UiNptNnl+kz0XcNuLDNRoNdHd34/Dhw4jFYiyBtNugZydponw+j9nZWW7GAu5nz8rlMp8JDwNqXiF/6lwuxxlv6lYnRx4SXd3tS44QJIRO70G6bJQkAICFhYVHOl/I+5uytkLtO5vNhkKhAKPR2CJKu1dAuooUZywvL0On00GtVrdULjai0WwEuuzRuBBvjShfdMkR6v1tFT6Sw6ZQKDjjceXKFe7YjEQi0Gq1eOaZZ1CtVjE1NYVCoYDl5WUkk0kEAgEEAgE89dRTOHbsGLdWA+CALZFIYGZmBjabDUNDQxCLxfjwww/h8/mwuLgIn8+Hvr4+dHd3s1dcLBZDZ2cnDh48iLa2NshkMnY4IL9TrVbLKdyenh6cOnWKuXPVahV3797lVLZEIsGhQ4fw4osvQqVSIRQKIZPJPJY0gEgkgsFg4E4dIYQ8oXA4zDfalZUVBINB/PKXv0Q4HEaxWOTA5GE3RZo42Wy2pcmBnokCNiISE29wJ6DVatHd3Q23280BG3Uizs/PY2pqiiU2stksa/ERNtMSTgFbMBiE1WptEVOlgI1073YT2WyWGwcWFxeRTqcxPj6+rvS99v03c1hSwFar1ZiTSnA4HDh+/DgA7AluTqlUwuTk5EOvw0cdD2HARo4A1PxDfotnzpxBR0dHS6fYboKyBYRPGptHGRMKfClgowCW+MBisRg9PT04cuQIUw72QsAG3H92yopslDGncXqU5irKLJHDRS6X47KzRqOBRqOBw+FAR0cHFAoFMpnMngpQCoVCi1LCRniU748UH2QyGQewGo2GAza73c7i9TqdjtfXXgHxVUulEsbGxj5y7TzsmFDARhQMYYe/0ON4K4XeCesCtkwmA5/Ph1wuh66uLuRyOeTzeVSrVW53JoFX8ujM5/NwOp3o6OiA3W6H3W5Hd3c3JBIJbwSZTIbV/8mFIJ1OsxG1RqNBd3c320UVCgWsrKygVquxZ6BUKmVlfmqrJlFL6pyjf0jYMZPJ8A2dshZ6vZ5LH2TiTN2qj9MpR9wjvV7fYm1BPIHbt28zCZg4S9FolOviVBZ+1M2wVCoxR4VMe2mDEZY3LBYLHA4HIpHIjpHttVotOjs70d7ezilzkvcIhUKYm5tDMpls4Q48budqo9HA4uIiPvzwQ4yMjHB2D3jguEA8wt2AUDYBAAfy5LO6HZ271BlHekPkkEAyOzabDUajcc8Qh4HtEdYk0EWwVCrh7NmzLV3sxKGt1+vb6jjyONjqsWk2m1hcXMQ777yDrq4unD17lterWCyG1WpFV1cXN6LtRWw0Jo8zTslkElevXkUgEMCTTz7JWn9EyCfJoaWlpXWXrL2CrZwn1WoVk5OTMBqNOHDgAEZGRlqoJi6XC52dnVhcXNxRMeVHxVaOSTAYxPvvvw+Xy4XR0VGo1WquZlGTQz6fRyKR2JJ9fd2JtbKygvn5+ZYs2p07dxCPx9FsNll9vKenB8FgEF6vF9lsFt/97ndx8uRJbhGm7i7qyIxGozhy5AjUajXC4TDefvttqNVq+Hw+WK1WXLhwAceOHUM6ncbk5CQSiQQuX74Mo9GIJ598ElqtFlevXoXX62UV9nQ6zZZWbrebo3tKcVIXEGm+pdNplMtllg2gsoJGo8GhQ4dgMBjg8/k2faMmjoNQ1JQEKMPhMP7u7/6O7boobU1kaJLY2MxkSiaT+O1vf4u2tja8/PLLGBoagk6na4nwFQoF+vv7UavVcPv27R3zdnM4HHj66af51kHZr2g0irt37+I3v/kN32i3KvNXrVbx5ptv4vr16/j2t7+NJ554goMzkUjEZePdyCZRR7Kwu49K4USi3w5MTU3hf/7P/4mRkRGcPHmyxRDdbDZDp9MhEAjsWhC700ilUvjBD34ArVaL733vezh06BD/mUajwcjICOx2O0wm0y4+5c6h2WzirbfewvXr1/Hiiy+ybBIA7iAl3cuLFy/uuAzObmBpaQn/63/9L7S3t8PhcDA3CXiwry0sLODu3bs75pCymygWi/jJT36CN998E9/97ncxPDzM1SqNRoNjx47BYDCgUqkgFArt9uPuCKjRYnR0FH/1V3/F6g8ymQx9fX146qmn2KZzK5IkH7k7k2FqrVbjL4F4aFqttsVQlvhZ1OFJmmmhUIj5Yul0mrWNstks/3f0OcSdICFZAllgEPHVZrOx2Tzxe8gTk4RwySS8UqmgVqtxl4zT6YRGo4HJZOLuTQrwKCreDJGUSiqUWVt76NGtLJPJIBqNbrmgXq1WY9HZ1dVVqNVqtLe3r+NuWa1WOJ3OHSUQk3glpdAp80ddbR9lW/I4aDabLNAbCoWwvLwMvV4Pq9XK3zs1qpApMKnZ7wRoHKj7sVKpcAlKSITfSpB5fDwex+rqKrsmqNVq1tkiCQeZTLZjgspC0PcCYNsDgkajgUwmwxQFv98PjUbDAqskSL2XiOXbDaJjRKNRhEIh1Ot1GAwGSCQSKJVK1qv8rIDK5gqFgs3SNRoNX/jI93ovZaW3E3SGkWRGIpGAUqnkPYTWz16gVewUqJM2FoshnU6zrSTFBEajkc/mrcC6gM1kMkGj0cDpdOLEiRNMXIxEItyhZTKZsLi4yHIBUqkUly5dwq1bt3D27Fk89dRTuHXrFv6//+//Y54IdQTeunUL2WwWvb29MBqNnFb92c9+Br/fz9YqFECVy2W8+eabaDab+OIXv4ivfOUrMBgM7GpA2anFxUVEo1E888wzeOKJJxAOhzE7OwsA6OnpgVgsZieG2dlZTE5OQqVSMV+P/tnMBq1Wq3H8+HG4XK51tlJkh5HL5dipYatLXiQGrFQqsbq6CpPJhG9961v4oz/6I34frVaLc+fOYXBwEMvLyxgbG9vSZ/go0MZGsgwKhQKVSgVarZblPbYD1EH41ltvIRqNYnh4GH/5l3/JmSWRSIRDhw7hW9/6Fubn5/Huu+/uiGgslaZ1Oh2OHTuGkZERhMNhLC4uIhKJ4OrVqw9Nft0MQqEQvv/978PtduM73/kOTp8+zTxHpVIJu93OjSE7LaJLc4WanbZbtJb2jjfeeAOhUAinT5/GN7/5TRbB/KwFbNS4MT8/j7//+79HR0cHvv71r7M1HAX4e7UkutWgZq5oNIrXX38dy8vLOHv2LI4fP846eIlE4jOTlSYedrVaxfj4OH7961+jo6MDZ86cgUwmg91uZ+rUZwXk072ysoJr164hHA7j8OHDTDHp6Ohg84CtwIZNB0SYM5lMTFLO5/PQ6/XQaDTcrZTP56FQKFCtVhGNRlGtVnHw4EHIZDIUi0XMz89zq7hYLEY4HEatVmMbFIPBAKPRiGq1ipWVFYyPj2NwcJAzZCqVCrVaDdFolLMQXV1d7MZA3YTCDZ5sNyhzQ3wDhUKBrq4u2Gw2xGIxVKtVzrZQpu1hO83WDaJUCrPZzIKWQhBnijTq6PdtZfaCMpSUtaKmhrXPSK3dO7mgSGmceGPEV6zVaszz2w4I9XXu3bvHwssEkei+/VVXVxfS6fSO3ZLJHkmtVsNkMsHhcLCYL2V6txPU7JFOp5FKpVrGn0jVarV6V0o81NFN40BrezszfbRepqen0dnZyb+bSj1rRWl/lyHcS4msTh3DtIY/K8EJAKZoUJcpOakAD5xTyKrxs4JarcbZ6WAwyNU2Gg/iin9WQLIgxOcXaqKS4sZWZqXXjSzpemk0Gs5iXbp0CXNzc/ja176GM2fOYHx8HO+//z6azSaMRiMMBgNWVlaQyWRw8+ZNhMNhRCIRWCwWNog3Go2sIAw88DmkIIMU/qnbVK/Xw+VyQSQSwePxQCwWY3l5Ga+++ip7uZGnJKnE06FLh3M4HGaxVNJiIQ86KqdShPzuu+9yGvNRU7pUXlvbfSeEQqHAkSNHUKlUMD8/j6WlpUf8qj4atLGIxWLYbLYNuTfE3arVah/5jNsBejbi6gFgRXGLxQKDwcAt9NtxKGo0GrS1tcFqta675eh0OrhcLrZg2wlQAE8bfb1eh9lshtFohFarxTvvvLOtv18mk8FisXCzjxBarRaDg4NQq9VIp9O7QqQmPTSye/H5fOsuH1sJcnJxOBws8wKAtZUsFgva29uRyWR2jPe5EUhNnhqYtgvNZpP9WW02G++FtEd/FrhrQhDn1Gq1oq2tjbUsiaoj1JH8rKDZbEKj0bBtnFgsZnqL0Cv6swLa081mMywWC68Z8mRfqwn6OFgXsBFXLZ/PszDtxMQExsbG8OUvfxnd3d2YmprC1NQUm46rVCpEo1FkMhksLCxgfHwcMpkMer0edrsdzz77LNxuNz788EPMzMxwcEbaNpThokxdOByG1Wpl7zaHw8E8glAoxCT1dDrN5sakXQWAu1NTqRSL10okEvagSyQSLUFisVjE6uoqSqUSDhw4AI/H80iDSFkTul0IBWzpAJDJZCxTkk6ntzRgAx6odZMdCWlWEegGJBynnQDd2oWZEjIkp4wttUlvx8anVCphNpuZi0PPRM9hNpvX2YNtJ4QyKzTvdTodLBYLisXitgfT5CO4lmtCBzXZNq0N5nYCNFdkMhn7FSaTyW0P2IhrQuU+4TzU6XTM5d3NgI32QsrGbmeQIJPJYDQaodfrec0IrQY/SxA6pJDINfCgnE4adp8VkLCyUqmEyWSCTqdrMYCnBMhnDcTVFiZthBJW2xawUZdnJBJhVeeBgQH+coLBIORyOU6cOMEbbLlcxoEDBzA8PIxIJMJ8N/Jcu3r1Kut/kR8gHVZkH2GxWDA8PAybzQaLxcIBnF6vx4kTJ2AwGFikjvSSisUijhw5gnw+j7m5ORYinZiYYPFdktmQy+WYm5tDIBCAw+HAc889h1gshomJCeYpVKtVNiZ/FFDTBQWPNKmFquFyuRxDQ0MwGAxIp9PsA0o+p5lM5pEnulQqZTJwX18fjEYjTp48iY6ODvT09LT8XbJoyeVyO+qfmc1m2VqL9PNIYPDgwYP4oz/6I/j9fuZubQV3SiS6bwiu1Wpx+PBhXLhwgQMA4cKhLuOtarl+GJCbBdmwUbBvMpnQ19eH3//930c4HMbY2Bg36GwFp40uLUajEceOHWMvPGHzTyaTwdzcHFZWVnYlu0auCyaTCd3d3TAYDDAYDBgdHYXf78fi4iJfIrfq+xKJROjq6sLp06eZ60qCrJVKBel0GrFYbNdkCmivdDqd8Hg8/P/z+Ty8Xi8fCFvZXWwwGFgOhy5UNF/3ir4W8ayJXiORSNixgYKHrTgkm80mpFIpOjo62FaPzibyAd4LQSxxy4mXrdFokEqlWHtxravOZkHnmdFoRFdXF2eliZ9NTjV7AdQ4pFAo2BmERMe3CtQoRQLCdrudvdYpObSVDW3rAralpSW8//776OjoYBHJ48ePM6/N6/VCqVTihRdeQCaTwb1791AqlXDu3Dn09fVx9g24n+lKJpP49a9/jVgshpdffhknTpzgzpJIJIL3338fuVwObW1t6OjowMmTJzE6Ogqv14v3338fZrMZX/ziF9HW1oZQKIRUKgW1Ws08rHq9jng8jr/927/F1NQUVldXcfnyZUgkEu6ItFqtaDabWF1dxa1bt/Anf/In+OM//mNcuXIFr776Krs0AMDg4OAjD+LaYEiYYSMolUqcPn2aO1pTqRT/mwLRRw3YFAoFjEYjPB4PvvGNb6C9vR3Hjh1jhwfh7yeLlmQyuaNk8mQyibt37yKZTGJoaIizqlKpFE899RROnTqF27dvc4mbmgUeB2KxGG1tbfB4PHj66afxB3/wB1CpVC3dsc1mE7FYDHNzcwgGgztW6qnVaggEAix5A4CzyC6XCyMjI0in0/h//+//4fr161heXt6ygE2pVMJms+H555/H8PAwizvTARSPx3Hz5k34/X7kcrnH/p2PCqVSCafTCYfDgcOHD7OfsEKhwLvvvotf/OIXLOmzVZcOujh84Qtf4C5xYbYgGo1idXV117IoVJLr6urCE088wcLc4XAYP/nJTxAKhfiyuVWwWq146qmnWkqipVIJmUxm27N7Dwuj0YjR0VEmdisUCrzzzju4d+9eS2D5uM9KAdvg4CCeeOKJliRFJpNhHc/dhlQqhdvthtVqhdvthsvlwvz8PJLJZEsiYSs4oaTLR7Ie5KpAiYjddkohkCWX0WhEX18fa13SRZmSKo8zHhSwqVQqeDwedHR0cPWO3HZSqdSWBfXrAjar1YqDBw/CYDCgXC6zJRRpZ8lkMthsNnR1dUEul6OtrY1LKMQ/IVsmKvu0t7ezFhq1QdMgEbmXTK5JXJfIr2KxmDN2FOTQLZtIfbRgKGtHmmbEMzCZTPwFqdVq5HI5zM/PI5VK8S2Som6j0fjIg1ir1RCPxyGTyVgQVy6Xr8voUPnX5XJheHgYxWIRmUwG+XweWq0W+XyegzdyLqCsg0gk4vQ72WqQF6XL5UJbWxvsdjvr361FpVLBwsICAoEAEonEI7/jZlEoFBAMBiGTyRCLxVijhg5HiUQCs9mMAwcOwGw281yjppZ8Po90Ov2xi4oshYg0r1Ao0NnZia6uLjgcDuYlCi2NqGV/pzNswANeXyKRgM/n4ywGvUez2URvby/PXZvNxsbw1Wr1kaRQaKyp+7qrqwt6vb6lA5I0DckPmNbPToM0C2mPINcVlUqF9vZ2HD58mD0eyYeYqBuPErDQ3mS322EwGGC1WrlDvNFo8GeHQqEt5Z9sBnSgUBOWWq2GTqdDvV7nLFg8Hkc+n0csFuOO/M0EtJTdbGtr4/Eg+kkwGMTCwgKi0eieCNgkEgk3MdF+2NfXx/67ZKG0srLCGchHndN0wWlvb4dWq4VEIkG5XOYu6vn5eaysrOx4N/VHgS7CKpUKOp0ObW1tGB0dZXmrWq3G1of0/x/180lpwWKxcLBGe3QwGGRd1L0EOhe0Wi0OHjyI9vZ2PgvInWmzWVmifbndbigUCohEIradJNsykjHbkndZ+4Pz58/jT//0T7G6uop33nkHkUgE165dY44RyWs8/fTTkMlk6O3tZekP0qzp6uriBVWr1eB0OlGpVGCz2aDX65HNZvmQNJlMkEgkmJycRDKZRF9fH/L5PMLhMCYmJviwsVqtUCqVkMlkSKVSiMfjMJlMOHjwIAd7ZNZcq9UQiUSwsLAAtVrNHptKpRK9vb2Ym5vD//gf/wN2ux1f+cpXuFxLZaFHTZkWCgXcuHGDS3Aej4dJqmu5UWKxGGfOnMGRI0daOHSBQADZbBZ37tzBysoKJiYmcOPGDVitVpw7dw4KhQLxeByVSgVHjx7F4OAgNBoNk9W7u7tbMo9rkUgk8A//8A+4devWjlrthMNhvPfee/B4POjp6UF3dzdOnDjRYkTf1dWFP//zP+cSVKFQwNWrVzExMYHp6Wlcvnz5YzcXlUoFp9MJnU6HgwcPwmw249ixY+jt7eUsDWUchR3Od+7cwWuvvcYb2k6iXq/j1q1bWFlZwUsvvYSTJ0+2+PN961vfwpe+9CUOXsfGxvCv//qvfFA8bInOZDLBYrFgcHAQFy5cgMvl4gsU3TCDwSAmJiYwOTnJvNDdOJQzmQympqaYuKxQKDizf/78eTz99NOoVCrIZDKIx+P4wQ9+gMnJSc4yPQwoWDOZTPjyl7+M3t5eHDlyhK34yuUyIpEIfvvb32J1dRWBQGCb3/rjQfsSXYQpgG9vb8fp06chFouRzWZRKpXw61//Gj/96U+RzWYRiUQeKfMjEolw7NgxPPPMMxgYGIBSqeRLcz6fx1tvvYW33nprR7PRHwfS/aID02Qy4ciRI9DpdMxpnp6exl//9V9zqfRRmzWcTie+9rWvsTsIAK5S3LhxA//4j/+IVCrFmfLdBHHtyAqSkgLf+c53IBaLubLz93//93jvvfeQzWaRSqU+cp1v1Bktl8vx9NNPY2RkBKOjoxCJRMjlclheXobP58PFixcxNja2rU0xjwKqHJAdocViwVe/+tWWpMx7772Hv//7v2fqw6NedAYGBvDFL34RXV1dPPeWl5dZneDOnTubCo4/CusCNqVSCYPBwDIRlA2jmyelgmlTpxQrlRAoiyWsmRMZnz6jWq3yxkltwZRBorZhEvKUSCRIp9MQiURs0p5KpRCLxThLQYK+BoOBxQzJVxMAW/7UajWOgPP5PAwGA+x2O8tMUCrzUQM28lqj56FMEnVuri1P6nQ6viUTj00ulyOXy7EMSjKZhNPp5AOWbpOVSgUdHR3o7OyEWq2GwWBo4emt7YSkRUdZwFAotKMLirJZqVQKkUgEarWaD2QaG/KjazabMJlMKJfL7O2azWY54N+o1Aw86PbU6/Vob2/n7mS73Q69Xt8y/sRPKpVKD5W92y40m02+iSaTSeTzedb/kkgksFgsAMCag9lsFm63m+cJlWJorXxUlyt1+1GJmC4+dLmhNZlMJvkzdyuDQt3E1G1WKBRYMkCr1UKr1XJDFH3XyWQSALjjljZG4XcuLH0IDd7b29vR0dHBc4QuUIVCgT1E90r2pFwu83sTP9jlckGhUHDVoaOjo8V2jNYecWk3Gg/gQZWDrIVsNhtnG2mdkOn7bjZeCEFlyXK53NLJ6XA4+NJeKpXQ1tbG+zDtO3QOCO3q6DNpjtAh7/F44Ha7uVpC518ymUQkEtkzFkxUlqTvmxolyD6LzrX29na4XC4+k+mSAjxYM0KOOe0vlO0m6pLBYOBzP5VKsUD+XnJ8oHnQaDQ4geRyudixQiQSYWlpiRsnRSIRzyk6n4TjIYxpKDaiOeJwODgjTcEfJR+2cj9dF7BNTEzg9u3bUCqVGBwc5JKcVCrFrVu32O7p7/7u7zhbIRKJMDAwAJvNBo/HA4/Hg9XVVVy5cgW5XI6J5FS6GB4expNPPgmTyQSXy4VsNotkMgmJRMIb0oEDB/AXf/EXyOVy8Hq9TNSmzjFyTwgEAtBoNHjiiSfgcDhgMpmg1+uRTCbh9/sRj8fxwQcfcIkjm80ygZQyPlKpFAsLCywRslnUajV8+OGHiMfjOHv2LJNAKZ2+FlT2Ib5dvV6H0WhEoVDASy+9hEQiwQKNZJzeaDRaOrhI302hUHAAtBa0YdONlALFnUQymcSrr77KgoIUtAoN2Kn7SCaT4cSJE+jv78dzzz2HP/zDP+RSYLPZbAlMaTMh9XGTycQkU7VazVp7NA71ep0X1G77/1H5e2ZmBhcvXkRbWxtOnz4NvV7P3yuNx5EjRziYJYs16swmXTdqLAAeOAWQ7I7BYOBSl0qlQrPZ5MxMMBjE8vIy6yTuNjKZDC5evIjJyUm89NJLGB0d5U2SDlOFQoE/+ZM/4WwpmV0vLS1BJBJBr9ezowaVssrlMq8njUaDvr4+1oIE7n8fq6ur8Hq9mJmZgd/v31YR44dFo9HArVu34PP5cPr0aXg8HtZ7JIN2lUqFF154AUNDQygWi0wfWVhYQC6XY69k0kWkwFgkEjGVoqenBz09PUwhKBaLWFpaQjgchs/nQygU2jNdkdFoFO+88w4cDgfcbjdTY+iSTtIw/+W//Bfk83nE43F+H5/PxxlL2kNFIhFnl+mCY7VaMTIywt1/5XIZy8vLuHfvHrxe757INBJqtRoWFxcRCoVgMpng8XjYH5ea09RqNb797W/jueee46AzFovh3r17qFQqvHfSmiHeokqlQk9PD/R6PQ4fPgy32w2VSsV7x6VLl+D3+7c8WBNeMjYD8kBXq9VIJpMsawWANfTII5Yuafl8Hrdv30YgEGA1A5lMBqVSyTzwer3OfMG+vj62caNEy7Vr1zA+Po6FhYUtXyvrArZYLAafzwe3243+/n7uXqPSYiKRQLlcxp07d7iWT3VzqVTKnLB6vQ6fz8fZMJKzyOfzPDGUSiVzEMxmM2emqMuvra2NeT6ktUZqy8S1oc9ra2vjlDg9a1tbG5aWlnDp0iWeoOl0mjMNZrOZ7ZtIjPdxDqxGo4HV1VWUy2V4PB4Ui0Wu82+UHaI0Nm0awOY4dGshnCTCLKiQO7bTKJVKzIEJhUJIp9Ms/SIcE+Ftn8oQdHvMZDJoNBo819ZmDWg8PypopYBtN7plNwJlQGKxGB8A5LdL70FBh8PhgN1uBwDOBpNGGUmk0AWA/g6JRtOFQSgZQhmKfD7PayOXy+2JjrdyuYzFxUWk02mcOnWqJetOQalcLsehQ4d4btTrdfj9foyPj7N+GlltEa+JuKKUsdZoNDyP6Pcmk0kkEgnE4/FdcXvYCFS2DgaDaGtrY64vrXMK7ukCSuTqXC4Hs9mMRCLBEjokgFur1ZDL5SASidDZ2QmDwQCTycT7D3GAyamG9KT2CgqFApaXl1EqlZDNZlt4l7SHWCwWVhwg8v34+DifEWazmQW9RSIRYrEYX+j7+/u5E5U41tVqlRMByWRyTwSuBEqeZLNZpNNpPh/psi7szD948CCXRFdXV7k65nA4oFarec1QskWn0+HIkSMwGo1ob29n7Uwae5/Ph0AgsCfWihCUTaVu3lKpxOuG9pD29na0t7ejUqnw90+i7nq9nitXZBgQjUZRq9UwODjIHrMdHR1oNBrI5XIoFovw+/1sGrDVWBewpdNp9l4ksuXs7CxyuRzu3r2L+fl55k5ZLBacOnWqJYtUKpUQj8eRy+UgFothNBpx5MgRqFQqLC0tIRKJwGq1su6Z3+/ntHZ/fz9sNhtrrs3NzaFSqcBgMLRwnqilWiwWw+12M69Dq9XyoUTNBwqFgrM1VEKi0snc3Bymp6dRrVYRCoW4rCA0xn4UNJtNpFIpVCoV+P1+9mykTWEngyUKUPL5PPtphsPhHe8SFT4P8Sg+/PBD5HI5jI6O4tSpU3x4ri0dEygQo1K70Dhd+HeE/16LQqHADQa/+c1vMDc3h3v37u2JTTcUCuHSpUtYXV3lQNXtdsNgMLBlGvDg3ah5xWq18g2QmiqEXD0AvPlSEEtBa7lc5kzB2NgYrl+/znN3t0Ec1EKhgPHxce7A6u/vZ8qG8Pun8TCZTDhw4AAAMG+R3p02b/JCpkOdyiCVSgVTU1P41a9+hVAohNXVVaTT6T0jUUCIRqO4evUql3Vo7xNyV6lkKhaLOeNGFwB6b5oHIpEIRqORL890IU6lUggGg3jzzTcxPz+P5eXl3Xrlj0WxWMTt27eZs1epVGA2m+F0OlsubnTJ6+3tZe0wyqxR0E4e2GSjJ5PJuOxHFI3bt2/jvffe4+aOvYZms4nFxUVu1iPP4J6eHs7Ck5SQ0WiESCTC2bNnUa1W+c/I0YJKilR+J5kuUmbw+/2Ynp7G+Pg4Z6e2+l22Yn9OpVK4fv06lpaWYLPZmCtPagq0LnQ6HeRyOY4dO4aOjo6W8idpdfb29gIAbDYbC8BXq1Vks1nMzs4iEolgfn4ePp9vW+gD6wK2TCaDlZUVdHd3cwnl0qVL8Hq9mJiYwMLCAtrb25lf9cILL0Cv12NhYYGzb8TJIe7J+fPn4XQ6ce/ePSwuLqJeryMQCCAcDuPSpUuo1+u4cOECuru7WTA3kUjg0qVLkMvlOHXqFIxGI3eElctl5HI5qFQqOBwOmM1mFkAFHnSLVqtVKBQKLqlYLBZoNBrcuHEDH374IRYXF3Hx4kUujUmlUrzwwguPFbARSTwQCMDv96NSqTAfiVLNOwWK+peWljhYJs7PboDKMDdu3IDX60Wz2URfXx90Oh3bVH1UwEWt05tFsViE1+uF3+/HK6+8gtu3b++JYA2435gRDocRDAbR39/P/oRU2ltrbUKbjNlsZkeLtSThjyslEL9ncnKSxazv3r27J7JrwAO+ZSaTwfT0NGsu6vV6vrwJ+Ud00FCGaKNxWJuNFYJ4WrOzs3jllVeQTqe5k26vIRaL4ebNm2hra4PNZkOpVIJWq23Z+yhgUyqVnDGjTkkaBwpYhBxbOqDIO3ppaQnvvPMOxsfHd/GNPx6lUgn37t3D0tISZ0OoOxx4UG0gpwi1Wo2Ojo4WigWNCZWCaUwoe0vWVNFoFPfu3cPly5e3Ze94nPIfodlsYmlpCcFgELlcDp2dnczbEu6xdBHUarWw2Wxsb0iVLPLy1mg064S+G40GEokEFhYW2Jd7L3HX1iKdTuPmzZtsRVgoFJgiAjzQqCRbviNHjqBarXLnPFFuyBecAlqJRNKi10g0Cq/Xi5WVlW15l3UBm8vlwvHjx9HT08O2GzabjUVuiSB+8OBB6PV6Tr9S6UUul3OwRAKXlMInXZREIoFkMgmxWIyRkRGIxWIuYZAMBHGMhIdIPB5HLBaDXq/HyMgI35ZlMhkmJycxPz8Pm80Gk8mERqP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8))\n", "    plt.imshow(grid)\n", "    plt.xlabel(\"Generation iteration\")\n", "    plt.xticks(\n", "        [(imgs_per_step.shape[-1] + 2) * (0.5 + j) for j in range(callback.vis_steps + 1)],\n", "        labels=[1] + list(range(step_size, imgs_per_step.shape[0] + 1, step_size)),\n", "    )\n", "    plt.yticks([])\n", "    plt.show()"]}, {"cell_type": "markdown", "id": "b079b0bd", "metadata": {"papermill": {"duration": 0.020562, "end_time": "2023-10-11T16:08:16.032763", "exception": false, "start_time": "2023-10-11T16:08:16.012201", "status": "completed"}, "tags": []}, "source": ["We see that although starting from noise in the very first step, the sampling algorithm obtains reasonable shapes after only 32 steps.\n", "Over the next 200 steps, the shapes become clearer and changed towards realistic digits.\n", "The specific samples can differ when you run the code on Colab, hence the following description is specific to the plots shown on the website.\n", "The first row shows an 8, where we remove unnecessary white parts over iterations.\n", "The transformation across iterations can be seen at best for the second sample, which creates a digit of 2.\n", "While the first sample after 32 iterations looks a bit like a digit, but not really,\n", "the sample is transformed more and more to a typical image of the digit 2."]}, {"cell_type": "markdown", "id": "b776b4f4", "metadata": {"papermill": {"duration": 0.021021, "end_time": "2023-10-11T16:08:16.074587", "exception": false, "start_time": "2023-10-11T16:08:16.053566", "status": "completed"}, "tags": []}, "source": ["### Out-of-distribution detection\n", "\n", "A very common and strong application of energy-based models is out-of-distribution detection\n", "(sometimes referred to as \"anomaly\" detection).\n", "As more and more deep learning models are applied in production and applications,\n", "a crucial aspect of these models is to know what the models don't know.\n", "Deep learning models are usually overconfident, meaning that they classify even random images sometimes with 100% probability.\n", "Clearly, this is not something that we want to see in applications.\n", "Energy-based models can help with this problem because they are trained to detect images that do not fit the training dataset distribution.\n", "Thus, in those applications, you could train an energy-based model along with the classifier,\n", "and only output predictions if the energy-based models assign a (unnormalized) probability higher than $\\delta$ to the image.\n", "You can actually combine classifiers and energy-based objectives in a single model,\n", "as proposed in this [paper](https://arxiv.org/abs/1912.03263).\n", "\n", "In this part of the analysis, we want to test the out-of-distribution capability of our energy-based model.\n", "Remember that a lower output of the model denotes a low probability.\n", "Thus, we hope to see low scores if we enter random noise to the model:"]}, {"cell_type": "code", "execution_count": 16, "id": "331c3b01", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:16.117920Z", "iopub.status.busy": "2023-10-11T16:08:16.117085Z", "iopub.status.idle": "2023-10-11T16:08:16.134285Z", "shell.execute_reply": "2023-10-11T16:08:16.133721Z"}, "papermill": {"duration": 0.040849, "end_time": "2023-10-11T16:08:16.135550", "exception": false, "start_time": "2023-10-11T16:08:16.094701", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Average score for random images: -17.88\n"]}], "source": ["with torch.no_grad():\n", "    rand_imgs = torch.rand((128,) + model.hparams.img_shape).to(model.device)\n", "    rand_imgs = rand_imgs * 2 - 1.0\n", "    rand_out = model.cnn(rand_imgs).mean()\n", "    print(\"Average score for random images: %4.2f\" % (rand_out.item()))"]}, {"cell_type": "markdown", "id": "028b0669", "metadata": {"papermill": {"duration": 0.017763, "end_time": "2023-10-11T16:08:16.171701", "exception": false, "start_time": "2023-10-11T16:08:16.153938", "status": "completed"}, "tags": []}, "source": ["As we hoped, the model assigns very low probability to those noisy images.\n", "As another reference, let's look at predictions for a batch of images from the training set:"]}, {"cell_type": "code", "execution_count": 17, "id": "3db38094", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:16.215211Z", "iopub.status.busy": "2023-10-11T16:08:16.214233Z", "iopub.status.idle": "2023-10-11T16:08:16.949913Z", "shell.execute_reply": "2023-10-11T16:08:16.949014Z"}, "papermill": {"duration": 0.760522, "end_time": "2023-10-11T16:08:16.951394", "exception": false, "start_time": "2023-10-11T16:08:16.190872", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Average score for training images: -0.01\n"]}], "source": ["with torch.no_grad():\n", "    train_imgs, _ = next(iter(train_loader))\n", "    train_imgs = train_imgs.to(model.device)\n", "    train_out = model.cnn(train_imgs).mean()\n", "    print(\"Average score for training images: %4.2f\" % (train_out.item()))"]}, {"cell_type": "markdown", "id": "299f1d1c", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017675, "end_time": "2023-10-11T16:08:16.987482", "exception": false, "start_time": "2023-10-11T16:08:16.969807", "status": "completed"}, "tags": []}, "source": ["The scores are close to 0 because of the regularization objective that was added to the training.\n", "So clearly, the model can distinguish between noise and real digits.\n", "However, what happens if we change the training images a little, and see which ones gets a very low score?"]}, {"cell_type": "code", "execution_count": 18, "id": "f323aa34", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:17.024498Z", "iopub.status.busy": "2023-10-11T16:08:17.024011Z", "iopub.status.idle": "2023-10-11T16:08:17.035503Z", "shell.execute_reply": "2023-10-11T16:08:17.034827Z"}, "papermill": {"duration": 0.032008, "end_time": "2023-10-11T16:08:17.036838", "exception": false, "start_time": "2023-10-11T16:08:17.004830", "status": "completed"}, "tags": []}, "outputs": [], "source": ["@torch.no_grad()\n", "def compare_images(img1, img2):\n", "    imgs = torch.stack([img1, img2], dim=0).to(model.device)\n", "    score1, score2 = model.cnn(imgs).cpu().chunk(2, dim=0)\n", "    grid = torchvision.utils.make_grid(\n", "        [img1.cpu(), img2.cpu()], nrow=2, normalize=True, range=(-1, 1), pad_value=0.5, padding=2\n", "    )\n", "    grid = grid.permute(1, 2, 0)\n", "    plt.figure(figsize=(4, 4))\n", "    plt.imshow(grid)\n", "    plt.xticks([(img1.shape[2] + 2) * (0.5 + j) for j in range(2)], labels=[\"Original image\", \"Transformed image\"])\n", "    plt.yticks([])\n", "    plt.show()\n", "    print(\"Score original image: %4.2f\" % score1)\n", "    print(\"Score transformed image: %4.2f\" % score2)"]}, {"cell_type": "markdown", "id": "2da181cf", "metadata": {"papermill": {"duration": 0.017785, "end_time": "2023-10-11T16:08:17.072555", "exception": false, "start_time": "2023-10-11T16:08:17.054770", "status": "completed"}, "tags": []}, "source": ["We use a random test image for this. Feel free to change it to experiment with the model yourself."]}, {"cell_type": "code", "execution_count": 19, "id": "4eea2a4e", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:17.110214Z", "iopub.status.busy": "2023-10-11T16:08:17.109648Z", "iopub.status.idle": "2023-10-11T16:08:17.682636Z", "shell.execute_reply": "2023-10-11T16:08:17.681648Z"}, "papermill": {"duration": 0.59446, "end_time": "2023-10-11T16:08:17.684687", "exception": false, "start_time": "2023-10-11T16:08:17.090227", "status": "completed"}, "tags": []}, "outputs": [], "source": ["test_imgs, _ = next(iter(test_loader))\n", "exmp_img = test_imgs[0].to(model.device)"]}, {"cell_type": "markdown", "id": "1a664126", "metadata": {"papermill": {"duration": 0.017809, "end_time": "2023-10-11T16:08:17.720830", "exception": false, "start_time": "2023-10-11T16:08:17.703021", "status": "completed"}, "tags": []}, "source": ["The first transformation is to add some random noise to the image:"]}, {"cell_type": "code", "execution_count": 20, "id": "0f83edba", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:17.761045Z", "iopub.status.busy": "2023-10-11T16:08:17.760825Z", "iopub.status.idle": "2023-10-11T16:08:17.883793Z", "shell.execute_reply": "2023-10-11T16:08:17.882836Z"}, "papermill": {"duration": 0.157188, "end_time": "2023-10-11T16:08:17.898248", "exception": false, "start_time": "2023-10-11T16:08:17.741060", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"text/plain": ["<Figure size 400x400 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Score original image: 0.03\n", "Score transformed image: -0.07\n"]}], "source": ["img_noisy = exmp_img + torch.randn_like(exmp_img) * 0.3\n", "img_noisy.clamp_(min=-1.0, max=1.0)\n", "compare_images(exmp_img, img_noisy)"]}, {"cell_type": "markdown", "id": "ce5dc06e", "metadata": {"papermill": {"duration": 0.031228, "end_time": "2023-10-11T16:08:17.981963", "exception": false, "start_time": "2023-10-11T16:08:17.950735", "status": "completed"}, "tags": []}, "source": ["We can see that the score considerably drops.\n", "Hence, the model can detect random Gaussian noise on the image.\n", "This is also to expect as initially, the \"fake\" samples are pure noise images.\n", "\n", "Next, we flip an image and check how this influences the score:"]}, {"cell_type": "code", "execution_count": 21, "id": "8bc3c6c5", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:18.052520Z", "iopub.status.busy": "2023-10-11T16:08:18.052150Z", "iopub.status.idle": "2023-10-11T16:08:18.143526Z", "shell.execute_reply": "2023-10-11T16:08:18.142931Z"}, "papermill": {"duration": 0.125461, "end_time": "2023-10-11T16:08:18.146934", "exception": false, "start_time": "2023-10-11T16:08:18.021473", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"text/plain": ["<Figure size 400x400 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Score original image: 0.03\n", "Score transformed image: -0.00\n"]}], "source": ["img_flipped = exmp_img.flip(dims=(1, 2))\n", "compare_images(exmp_img, img_flipped)"]}, {"cell_type": "markdown", "id": "493da1ad", "metadata": {"papermill": {"duration": 0.019061, "end_time": "2023-10-11T16:08:18.191207", "exception": false, "start_time": "2023-10-11T16:08:18.172146", "status": "completed"}, "tags": []}, "source": ["If the digit can only be read in this way, for example, the 7, then we can see that the score drops.\n", "However, the score only drops slightly.\n", "This is likely because of the small size of our model.\n", "Keep in mind that generative modeling is a much harder task than classification,\n", "as we do not only need to distinguish between classes but learn **all** details/characteristics of the digits.\n", "With a deeper model, this could eventually be captured better (but at the cost of greater training instability).\n", "\n", "Finally, we check what happens if we reduce the digit significantly in size:"]}, {"cell_type": "code", "execution_count": 22, "id": "b84617d5", "metadata": {"execution": {"iopub.execute_input": "2023-10-11T16:08:18.238548Z", "iopub.status.busy": "2023-10-11T16:08:18.238069Z", "iopub.status.idle": "2023-10-11T16:08:18.341662Z", "shell.execute_reply": "2023-10-11T16:08:18.341062Z"}, "papermill": {"duration": 0.130581, "end_time": "2023-10-11T16:08:18.343790", "exception": false, "start_time": "2023-10-11T16:08:18.213209", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"text/plain": ["<Figure size 400x400 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Score original image: 0.03\n", "Score transformed image: -0.02\n"]}], "source": ["img_tiny = torch.zeros_like(exmp_img) - 1\n", "img_tiny[:, exmp_img.shape[1] // 2 :, exmp_img.shape[2] // 2 :] = exmp_img[:, ::2, ::2]\n", "compare_images(exmp_img, img_tiny)"]}, {"cell_type": "markdown", "id": "7a179daa", "metadata": {"papermill": {"duration": 0.020612, "end_time": "2023-10-11T16:08:18.389685", "exception": false, "start_time": "2023-10-11T16:08:18.369073", "status": "completed"}, "tags": []}, "source": ["The score again drops but not by a large margin, although digits in the MNIST dataset usually are much larger.\n", "\n", "Overall, we can conclude that our model is good for detecting Gaussian noise and smaller transformations to existing digits.\n", "Nonetheless, to obtain a very good out-of-distribution model, we would need to train deeper models and for more iterations."]}, {"cell_type": "markdown", "id": "2ac68ff3", "metadata": {"papermill": {"duration": 0.019538, "end_time": "2023-10-11T16:08:18.428887", "exception": false, "start_time": "2023-10-11T16:08:18.409349", "status": "completed"}, "tags": []}, "source": ["### Instability\n", "\n", "Finally, we should discuss the possible instabilities of energy-based models,\n", "in particular for the example of image generation that we have implemented in this notebook.\n", "In the process of hyperparameter search for this notebook, there have been several models that diverged.\n", "Divergence in energy-based models means that the models assign a high probability to examples of the training set which is a good thing.\n", "However, at the same time, the sampling algorithm fails and only generates noise images that obtain minimal probability scores.\n", "This happens because the model has created many local maxima in which the generated noise images fall.\n", "The energy surface over which we calculate the gradients to reach data points with high probability has \"diverged\" and is not useful for our MCMC sampling.\n", "\n", "Besides finding the optimal hyperparameters, a common trick in energy-based models is to reload stable checkpoints.\n", "If we detect that the model is diverging, we stop the training, load the model from one epoch ago where it did not diverge yet.\n", "Afterward, we continue training and hope that with a different seed the model is not diverging again.\n", "Nevertheless, this should be considered as the \"last hope\" for stabilizing the models,\n", "and careful hyperparameter tuning is the better way to do so.\n", "Sensitive hyperparameters include `step_size`, `steps` and the noise standard deviation in the sampler,\n", "and the learning rate and feature dimensionality in the CNN model."]}, {"cell_type": "markdown", "id": "8a8e6e7e", "metadata": {"papermill": {"duration": 0.020289, "end_time": "2023-10-11T16:08:18.469031", "exception": false, "start_time": "2023-10-11T16:08:18.448742", "status": "completed"}, "tags": []}, "source": ["## Conclusion\n", "\n", "In this tutorial, we have discussed energy-based models for generative modeling.\n", "The concept relies on the idea that any strictly positive function can be turned into a probability\n", "distribution by normalizing over the whole dataset.\n", "As this is not reasonable to calculate for high dimensional data like images,\n", "we train the model using contrastive divergence and sampling via MCMC.\n", "While the idea allows us to turn any neural network into an energy-based model,\n", "we have seen that there are multiple training tricks needed to stabilize the training.\n", "Furthermore, the training time of these models is relatively long as, during every training iteration,\n", "we need to sample new \"fake\" images, even with a sampling buffer.\n", "In the next lectures and assignment, we will see different generative models (e.g. VAE, GAN, NF)\n", "that allow us to do generative modeling more stably, but with the cost of more parameters."]}, {"cell_type": "markdown", "id": "e3cac67d", "metadata": {"papermill": {"duration": 0.018854, "end_time": "2023-10-11T16:08:18.507903", "exception": false, "start_time": "2023-10-11T16:08:18.489049", "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 7: Deep Energy-Based Generative Models\n", "   :card_description: In this tutorial, we will look at energy-based deep learning models, and focus on their application as generative models. Energy models have been a popular tool before the...\n", "   :tags: Image,GPU/TPU,UvA-DL-Course\n", "   :image: _static/images/course_UvA-DL/07-deep-energy-based-generative-models.jpg"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab_type,id,colab,-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": 121.832756, "end_time": "2023-10-11T16:08:21.450188", "environment_variables": {}, "exception": null, "input_path": "course_UvA-DL/07-deep-energy-based-generative-models/Deep_Energy_Models.ipynb", "output_path": ".notebooks/course_UvA-DL/07-deep-energy-based-generative-models.ipynb", "parameters": {}, "start_time": "2023-10-11T16:06:19.617432", "version": "2.4.0"}}, "nbformat": 4, "nbformat_minor": 5}