{"cells": [{"cell_type": "markdown", "id": "fcc3a1f1", "metadata": {"papermill": {"duration": 0.010714, "end_time": "2023-03-14T15:58:52.555574", "exception": false, "start_time": "2023-03-14T15:58:52.544860", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 7: Deep Energy-Based Generative Models\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-03-14T15:57:19.480351\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": "818fed51", "metadata": {"papermill": {"duration": 0.007631, "end_time": "2023-03-14T15:58:52.570190", "exception": false, "start_time": "2023-03-14T15:58:52.562559", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "3c52da86", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-03-14T15:58:52.614200Z", "iopub.status.busy": "2023-03-14T15:58:52.613288Z", "iopub.status.idle": "2023-03-14T15:58:56.051567Z", "shell.execute_reply": "2023-03-14T15:58:56.050328Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.450241, "end_time": "2023-03-14T15:58:56.055161", "exception": false, "start_time": "2023-03-14T15:58:52.604920", "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 \"torch>=1.8.1, <1.14.0\" \"pytorch-lightning>=1.4, <2.0.0\" \"lightning>=2.0.0rc0\" \"setuptools==67.4.0\" \"tensorboard\" \"torchvision\" \"matplotlib\" \"ipython[notebook]>=8.0.0, <8.12.0\" \"torchmetrics>=0.7, <0.12\""]}, {"cell_type": "markdown", "id": "4d022ac7", "metadata": {"papermill": {"duration": 0.006836, "end_time": "2023-03-14T15:58:56.074281", "exception": false, "start_time": "2023-03-14T15:58:56.067445", "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": "735629d7", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:58:56.090307Z", "iopub.status.busy": "2023-03-14T15:58:56.089916Z", "iopub.status.idle": "2023-03-14T15:58:58.658854Z", "shell.execute_reply": "2023-03-14T15:58:58.657511Z"}, "papermill": {"duration": 2.580302, "end_time": "2023-03-14T15:58:58.661595", "exception": false, "start_time": "2023-03-14T15:58:56.081293", "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": "d0166f69", "metadata": {"papermill": {"duration": 0.006971, "end_time": "2023-03-14T15:58:58.681582", "exception": false, "start_time": "2023-03-14T15:58:58.674611", "status": "completed"}, "tags": []}, "source": ["We also have pre-trained models that we download below."]}, {"cell_type": "code", "execution_count": 3, "id": "fd98fb91", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:58:58.698105Z", "iopub.status.busy": "2023-03-14T15:58:58.697216Z", "iopub.status.idle": "2023-03-14T15:58:59.185872Z", "shell.execute_reply": "2023-03-14T15:58:59.184594Z"}, "papermill": {"duration": 0.50016, "end_time": "2023-03-14T15:58:59.188692", "exception": false, "start_time": "2023-03-14T15:58:58.688532", "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": "69845468", "metadata": {"papermill": {"duration": 0.007111, "end_time": "2023-03-14T15:58:59.206990", "exception": false, "start_time": "2023-03-14T15:58:59.199879", "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": "e52ad8f5", "metadata": {"papermill": {"duration": 0.007085, "end_time": "2023-03-14T15:58:59.221198", "exception": false, "start_time": "2023-03-14T15:58:59.214113", "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\n", "- [Stefano Ermon and Aditya Grover](https://deepgenerativemodels.github.io/assets/slides/cs236_lecture11.pdf)):\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": "f3961daa", "metadata": {"papermill": {"duration": 0.007064, "end_time": "2023-03-14T15:58:59.235361", "exception": false, "start_time": "2023-03-14T15:58:59.228297", "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": "a42b906f", "metadata": {"papermill": {"duration": 0.007051, "end_time": "2023-03-14T15:58:59.249505", "exception": false, "start_time": "2023-03-14T15:58:59.242454", "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\n", "- [Stefano Ermon and Aditya Grover](https://deepgenerativemodels.github.io/assets/slides/cs236_lecture11.pdf)).\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": "be25221c", "metadata": {"papermill": {"duration": 0.007079, "end_time": "2023-03-14T15:58:59.263669", "exception": false, "start_time": "2023-03-14T15:58:59.256590", "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": "bb63c3f8", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:58:59.279890Z", "iopub.status.busy": "2023-03-14T15:58:59.279502Z", "iopub.status.idle": "2023-03-14T15:59:00.345049Z", "shell.execute_reply": "2023-03-14T15:59:00.344434Z"}, "papermill": {"duration": 1.075461, "end_time": "2023-03-14T15:59:00.346230", "exception": false, "start_time": "2023-03-14T15:58:59.270769", "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/6/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e4a7ceab14c749b38562cb17e22a7ec7", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/9912422 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz to /__w/6/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/6/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "bb9d5297216d46668b2e4df4fb0cc3f8", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/28881 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz to /__w/6/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/6/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "1259d770c1e64016bef07f0ff90172e4", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/1648877 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to /__w/6/s/.datasets/MNIST/raw\n"]}, {"name": "stdout", "output_type": "stream", "text": ["\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /__w/6/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "37e9bb9b9b034c2a90e3bf64d01d1d0e", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/4542 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to /__w/6/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": "b0834b2c", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008702, "end_time": "2023-03-14T15:59:00.363502", "exception": false, "start_time": "2023-03-14T15:59:00.354800", "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": "684d2fbb", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.384373Z", "iopub.status.busy": "2023-03-14T15:59:00.383857Z", "iopub.status.idle": "2023-03-14T15:59:00.392769Z", "shell.execute_reply": "2023-03-14T15:59:00.392090Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.020666, "end_time": "2023-03-14T15:59:00.393989", "exception": false, "start_time": "2023-03-14T15:59:00.373323", "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": "beca1320", "metadata": {"papermill": {"duration": 0.007818, "end_time": "2023-03-14T15:59:00.410273", "exception": false, "start_time": "2023-03-14T15:59:00.402455", "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": "0c541514", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007739, "end_time": "2023-03-14T15:59:00.425786", "exception": false, "start_time": "2023-03-14T15:59:00.418047", "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 re-use 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": "8abdf046", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.442322Z", "iopub.status.busy": "2023-03-14T15:59:00.442131Z", "iopub.status.idle": "2023-03-14T15:59:00.454001Z", "shell.execute_reply": "2023-03-14T15:59:00.453494Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.021653, "end_time": "2023-03-14T15:59:00.455205", "exception": false, "start_time": "2023-03-14T15:59:00.433552", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Sampler:\n", "    def __init__(self, model, img_shape, sample_size, max_len=8192):\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": "885770f4", "metadata": {"papermill": {"duration": 0.007798, "end_time": "2023-03-14T15:59:00.471002", "exception": false, "start_time": "2023-03-14T15:59:00.463204", "status": "completed"}, "tags": []}, "source": ["The idea of the buffer becomes a bit clearer in the following algorithm."]}, {"cell_type": "markdown", "id": "2a7a817b", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007828, "end_time": "2023-03-14T15:59:00.486660", "exception": false, "start_time": "2023-03-14T15:59:00.478832", "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": "4b7633ac", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.503927Z", "iopub.status.busy": "2023-03-14T15:59:00.503475Z", "iopub.status.idle": "2023-03-14T15:59:00.517238Z", "shell.execute_reply": "2023-03-14T15:59:00.516768Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.023896, "end_time": "2023-03-14T15:59:00.518416", "exception": false, "start_time": "2023-03-14T15:59:00.494520", "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": "ef4f6ecb", "metadata": {"papermill": {"duration": 0.007829, "end_time": "2023-03-14T15:59:00.534095", "exception": false, "start_time": "2023-03-14T15:59:00.526266", "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": "7c2ff0dc", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007828, "end_time": "2023-03-14T15:59:00.549738", "exception": false, "start_time": "2023-03-14T15:59:00.541910", "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": "d1f88300", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.566784Z", "iopub.status.busy": "2023-03-14T15:59:00.566433Z", "iopub.status.idle": "2023-03-14T15:59:00.577891Z", "shell.execute_reply": "2023-03-14T15:59:00.577424Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.021533, "end_time": "2023-03-14T15:59:00.579079", "exception": false, "start_time": "2023-03-14T15:59:00.557546", "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": "5a220acb", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007884, "end_time": "2023-03-14T15:59:00.595005", "exception": false, "start_time": "2023-03-14T15:59:00.587121", "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": "3bb9b5cd", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.611793Z", "iopub.status.busy": "2023-03-14T15:59:00.611583Z", "iopub.status.idle": "2023-03-14T15:59:00.615786Z", "shell.execute_reply": "2023-03-14T15:59:00.615390Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.014003, "end_time": "2023-03-14T15:59:00.616913", "exception": false, "start_time": "2023-03-14T15:59:00.602910", "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": "c1d7d2ea", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007866, "end_time": "2023-03-14T15:59:00.632726", "exception": false, "start_time": "2023-03-14T15:59:00.624860", "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": "0a3896de", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.649822Z", "iopub.status.busy": "2023-03-14T15:59:00.649494Z", "iopub.status.idle": "2023-03-14T15:59:00.655811Z", "shell.execute_reply": "2023-03-14T15:59:00.654972Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.016431, "end_time": "2023-03-14T15:59:00.657089", "exception": false, "start_time": "2023-03-14T15:59:00.640658", "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": "96dcfad1", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007907, "end_time": "2023-03-14T15:59:00.672925", "exception": false, "start_time": "2023-03-14T15:59:00.665018", "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": "811784b0", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.689866Z", "iopub.status.busy": "2023-03-14T15:59:00.689599Z", "iopub.status.idle": "2023-03-14T15:59:00.696434Z", "shell.execute_reply": "2023-03-14T15:59:00.695810Z"}, "papermill": {"duration": 0.017051, "end_time": "2023-03-14T15:59:00.697938", "exception": false, "start_time": "2023-03-14T15:59:00.680887", "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": "31112329", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:00.715390Z", "iopub.status.busy": "2023-03-14T15:59:00.715122Z", "iopub.status.idle": "2023-03-14T15:59:01.027306Z", "shell.execute_reply": "2023-03-14T15:59:01.026766Z"}, "papermill": {"duration": 0.322552, "end_time": "2023-03-14T15:59:01.028585", "exception": false, "start_time": "2023-03-14T15:59:00.706033", "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": "stderr", "output_type": "stream", "text": ["Lightning automatically upgraded your loaded checkpoint from v1.0.2 to v2.0.0rc0. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file saved_models/tutorial8/MNIST.ckpt`\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\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": "a1000d69", "metadata": {"papermill": {"duration": 0.008431, "end_time": "2023-03-14T15:59:01.046710", "exception": false, "start_time": "2023-03-14T15:59:01.038279", "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": "262b8ce3", "metadata": {"papermill": {"duration": 0.008402, "end_time": "2023-03-14T15:59:01.063598", "exception": false, "start_time": "2023-03-14T15:59:01.055196", "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": "e16cdd1e", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:01.081930Z", "iopub.status.busy": "2023-03-14T15:59:01.081619Z", "iopub.status.idle": "2023-03-14T15:59:02.118370Z", "shell.execute_reply": "2023-03-14T15:59:02.117290Z"}, "papermill": {"duration": 1.049019, "end_time": "2023-03-14T15:59:02.121054", "exception": false, "start_time": "2023-03-14T15:59:01.072035", "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": "f129c46c", "metadata": {"papermill": {"duration": 0.008826, "end_time": "2023-03-14T15:59:02.142935", "exception": false, "start_time": "2023-03-14T15:59:02.134109", "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": "8341c5a8", "metadata": {"papermill": {"duration": 0.008711, "end_time": "2023-03-14T15:59:02.160080", "exception": false, "start_time": "2023-03-14T15:59:02.151369", "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": "63d236e0", "metadata": {"papermill": {"duration": 0.008387, "end_time": "2023-03-14T15:59:02.176893", "exception": false, "start_time": "2023-03-14T15:59:02.168506", "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 truely 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": "935605ad", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:02.195772Z", "iopub.status.busy": "2023-03-14T15:59:02.195307Z", "iopub.status.idle": "2023-03-14T15:59:05.920205Z", "shell.execute_reply": "2023-03-14T15:59:05.919084Z"}, "papermill": {"duration": 3.737993, "end_time": "2023-03-14T15:59:05.923333", "exception": false, "start_time": "2023-03-14T15:59:02.185340", "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": "717b42f0", "metadata": {"papermill": {"duration": 0.008651, "end_time": "2023-03-14T15:59:05.948233", "exception": false, "start_time": "2023-03-14T15:59:05.939582", "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": "cd80d452", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:05.970400Z", "iopub.status.busy": "2023-03-14T15:59:05.970158Z", "iopub.status.idle": "2023-03-14T15:59:06.741929Z", "shell.execute_reply": "2023-03-14T15:59:06.741106Z"}, "papermill": {"duration": 0.788627, "end_time": "2023-03-14T15:59:06.745470", "exception": false, "start_time": "2023-03-14T15:59:05.956843", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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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": "d4188004", "metadata": {"papermill": {"duration": 0.013177, "end_time": "2023-03-14T15:59:06.776523", "exception": false, "start_time": "2023-03-14T15:59:06.763346", "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": "76ce862b", "metadata": {"papermill": {"duration": 0.013438, "end_time": "2023-03-14T15:59:06.803239", "exception": false, "start_time": "2023-03-14T15:59:06.789801", "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": "42be0f85", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:06.832318Z", "iopub.status.busy": "2023-03-14T15:59:06.831748Z", "iopub.status.idle": "2023-03-14T15:59:06.839196Z", "shell.execute_reply": "2023-03-14T15:59:06.838501Z"}, "papermill": {"duration": 0.024695, "end_time": "2023-03-14T15:59:06.841430", "exception": false, "start_time": "2023-03-14T15:59:06.816735", "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": "586b4c58", "metadata": {"papermill": {"duration": 0.013286, "end_time": "2023-03-14T15:59:06.871464", "exception": false, "start_time": "2023-03-14T15:59:06.858178", "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": "97d01c5a", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:06.899469Z", "iopub.status.busy": "2023-03-14T15:59:06.899102Z", "iopub.status.idle": "2023-03-14T15:59:07.455693Z", "shell.execute_reply": "2023-03-14T15:59:07.454817Z"}, "papermill": {"duration": 0.573156, "end_time": "2023-03-14T15:59:07.458027", "exception": false, "start_time": "2023-03-14T15:59:06.884871", "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": "08252820", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.013507, "end_time": "2023-03-14T15:59:07.490940", "exception": false, "start_time": "2023-03-14T15:59:07.477433", "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": "318af4cc", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:07.520192Z", "iopub.status.busy": "2023-03-14T15:59:07.519569Z", "iopub.status.idle": "2023-03-14T15:59:07.530363Z", "shell.execute_reply": "2023-03-14T15:59:07.529836Z"}, "papermill": {"duration": 0.027233, "end_time": "2023-03-14T15:59:07.531846", "exception": false, "start_time": "2023-03-14T15:59:07.504613", "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": "05f5cc25", "metadata": {"papermill": {"duration": 0.013362, "end_time": "2023-03-14T15:59:07.561894", "exception": false, "start_time": "2023-03-14T15:59:07.548532", "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": "523d81e3", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:07.589832Z", "iopub.status.busy": "2023-03-14T15:59:07.589625Z", "iopub.status.idle": "2023-03-14T15:59:08.056637Z", "shell.execute_reply": "2023-03-14T15:59:08.055388Z"}, "papermill": {"duration": 0.484654, "end_time": "2023-03-14T15:59:08.059991", "exception": false, "start_time": "2023-03-14T15:59:07.575337", "status": "completed"}, "tags": []}, "outputs": [], "source": ["test_imgs, _ = next(iter(test_loader))\n", "exmp_img = test_imgs[0].to(model.device)"]}, {"cell_type": "markdown", "id": "6f8a648f", "metadata": {"papermill": {"duration": 0.013464, "end_time": "2023-03-14T15:59:08.094097", "exception": false, "start_time": "2023-03-14T15:59:08.080633", "status": "completed"}, "tags": []}, "source": ["The first transformation is to add some random noise to the image:"]}, {"cell_type": "code", "execution_count": 20, "id": "35b1aad4", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:08.123319Z", "iopub.status.busy": "2023-03-14T15:59:08.122895Z", "iopub.status.idle": "2023-03-14T15:59:08.222527Z", "shell.execute_reply": "2023-03-14T15:59:08.221947Z"}, "papermill": {"duration": 0.118971, "end_time": "2023-03-14T15:59:08.226698", "exception": false, "start_time": "2023-03-14T15:59:08.107727", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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pZxaLS6b/9Ej59iLRFkVSKjWZkikvdZQlWVPeOnyWxA55huVuZDYlKkUvk7Al99AK8X2J5hT33dWWs0M0l2g5fgszKqobHdNLNppwKhO6oNzDM/oNbXQDVocesVsg0KRg17YgcscPGAzBmROLIgxKTQb/rnKPn16LGz7D8UMUkZIO5jX/WP3ycw2vU48nkW5vvuJenKkOAxEckpq8I11YsS4SEWk1QU3PwFotgLu3Xv4btCO6DED2icRxmlKOob9rcKXPL+UnU9gKZW5kc3RyZWFtCmVuZG9iagozMCAwIG9iago8PCAvTGVuZ3RoIDE2MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkDkSAzEIBHO9gidIXIL3rMu1wfr/qQfWR6LpAjQcuhZNynoUaD7psUahutBr6CxKkkTBFpIdUKdjiDsoSExIY5JIth6DI5pYs12YmVQqs1LhtGnFwr/ZWtXIRI1wjfyJ6QZU/E/qXJTwTYOvkjH6GFS8O4OMSfheRdxaMe3+RDCxGfYJb0UmBYSJsanZvs9ghsz3Ctc4x/MNTII36wplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9MZW5ndGggMzM0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nC1SS3LFIAzbcwpdoDP4B+Q86XS6eL3/tpKTRUYOYPQx5YaJSnxZILej1sS3jcxAheGvq8yFz0jbyDqIy5CLuJIthXtELOQxxDzEgu+r8R4e+azMybMHxi/Zdw8r9tSEZSHjxRnaYRXHYRXkWLB1Iap7eFOkw6kk2OOL/z7Fcy0ELXxG0IBf5J+vjuD5khZp95ht0656sEw7qqSwHGxPc14mX1pnuToezwfJ9q7YEVK7AhSFuTPOc+Eo01ZGtBZ2NkhqXGxvjv1YStCFblxGiiOQn6kiPKCkycwmCuKPnB5yKgNh6pqudHIbVXGnnsw1m4u3M0lm675IsZnCeV04s/4MU2a1eSfPcqLUqQjvsWdL0NA5rp69lllodJsTvKSEz8ZOT06+VzPrITkVCaliWlfBaRSZYgnbEl9TUVOaehn++/Lu8Tt+/gEsc3xzCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCAxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNrRQMIDDFEOuNAAd5gNSCmVuZHN0cmVhbQplbmRvYmoKMTYgMCBvYmoKPDwgL1R5cGUgL0ZvbnQgL0Jhc2VGb250IC9CTVFRRFYrRGVqYVZ1U2FucyAvRmlyc3RDaGFyIDAgL0xhc3RDaGFyIDI1NQovRm9udERlc2NyaXB0b3IgMTUgMCBSIC9TdWJ0eXBlIC9UeXBlMyAvTmFtZSAvQk1RUURWK0RlamFWdVNhbnMKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXQovQ2hhclByb2NzIDE3IDAgUgovRW5jb2RpbmcgPDwgL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDMyIC9zcGFjZSA3OSAvTyA4NCAvVCA5NyAvYSAxMDAgL2QgL2UgL2YgL2cgMTA1IC9pIDEwOCAvbCAvbSAvbiAvbyAxMTQKL3IgL3MgXQo+PgovV2lkdGhzIDE0IDAgUiA+PgplbmRvYmoKMTUgMCBvYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9Gb250TmFtZSAvQk1RUURWK0RlamFWdVNhbnMgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0FzY2VudCA5MjkgL0Rlc2NlbnQgLTIzNiAvQ2FwSGVpZ2h0IDAKL1hIZWlnaHQgMCAvSXRhbGljQW5nbGUgMCAvU3RlbVYgMCAvTWF4V2lkdGggMTM0MiA+PgplbmRvYmoKMTQgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTcgMCBvYmoKPDwgL08gMTggMCBSIC9UIDE5IDAgUiAvYSAyMCAwIFIgL2QgMjEgMCBSIC9lIDIyIDAgUiAvZiAyMyAwIFIgL2cgMjQgMCBSCi9pIDI1IDAgUiAvbCAyNiAwIFIgL20gMjcgMCBSIC9uIDI4IDAgUiAvbyAyOSAwIFIgL3IgMzAgMCBSIC9zIDMxIDAgUgovc3BhY2UgMzIgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNiAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDAgL2NhIDEgPj4KL0EyIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDEgPj4gPj4KZW5kb2JqCjUgMCBvYmoKPDwgPj4KZW5kb2JqCjYgMCBvYmoKPDwgPj4KZW5kb2JqCjcgMCBvYmoKPDwgL0kxIDEzIDAgUiA+PgplbmRvYmoKMTMgMCBvYmoKPDwgL1R5cGUgL1hPYmplY3QgL1N1YnR5cGUgL0ltYWdlIC9XaWR0aCAzMTAgL0hlaWdodCAxNjEKL0NvbG9yU3BhY2UgWy9JbmRleGVkIC9EZXZpY2VSR0IgMTYwICj////+/v79/f38/Pz7+/v6+vr5+fn4+Pjy8vLx8fHw8PDu7u7s7Ozq6urp6enn5+fl5eXj4+Ph4eHg4ODf39/e3t7d3d3b29va2trY2NjU1NTR0dHPz8/Ozs7Nzc3Ly8vJycnGxsbCwsLAwMC/v7++vr67u7u6urq5ubm2tra1tbWysrKxsbGtra2qqqqnp6empqajo6OioqKhoaGfn5+bm5uampqZmZmXl5ePj4+Ojo6MjIyLi4uGhoaFhYWDg4OCgoKBgYF/f39+fn57e3t5eXl3d3dzc3NycnJubm5tbW1ra2tqampkZGRgYGBcXFxcXFxXV1dVVVVUVFRTU1NPT09OTk5NTU1LS0tJSUlISEhHR0dGRkZFRUVERERDQ0NCQkJBQUE/Pz8+Pj49PT08PDw7Ozs6Ojo5OTk4ODg3Nzc2NjY1NTU0NDQzMzMyMjIxMTEwMDAvLy8uLi4tLS0sLCwrKysqKipcKVwpXClcKFwoXCgnJycmJiYlJSUkJCQjIyMiIiIhISEgICAfHx8eHh4dHR0cHBwbGxsaGhoZGRkYGBgXFxcWFhYVFRUUFBQTExMSEhIREREQEBAPDw8ODg5cclxyXHIMDAwLCwtcblxuXG4JCQkICAgHBwcGBgYFBQUEBAQDAwMCAgIBAQEAAAApXQovQml0c1BlckNvbXBvbmVudCA4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCi9EZWNvZGVQYXJtcyA8PCAvUHJlZGljdG9yIDEwIC9Db2xvcnMgMSAvQ29sdW1ucyAzMTAgPj4gL0xlbmd0aCAzMyAwIFIgPj4Kc3RyZWFtCnic7Z1ls9xGEEXtMDM6zMzMzMzsMDMzO8zMyf7U1D2up/vcpV1L43Il3nfPJ2mlnW2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"</svg>\n"], "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": "ed24f660", "metadata": {"papermill": {"duration": 0.014218, "end_time": "2023-03-14T15:59:08.264888", "exception": false, "start_time": "2023-03-14T15:59:08.250670", "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": "f7ab7e9a", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:08.294357Z", "iopub.status.busy": "2023-03-14T15:59:08.294180Z", "iopub.status.idle": "2023-03-14T15:59:08.371407Z", "shell.execute_reply": "2023-03-14T15:59:08.370938Z"}, "papermill": {"duration": 0.095852, "end_time": "2023-03-14T15:59:08.374857", "exception": false, "start_time": "2023-03-14T15:59:08.279005", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"</svg>\n"], "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": "54f446b8", "metadata": {"papermill": {"duration": 0.014813, "end_time": "2023-03-14T15:59:08.409672", "exception": false, "start_time": "2023-03-14T15:59:08.394859", "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": "d48afe5b", "metadata": {"execution": {"iopub.execute_input": "2023-03-14T15:59:08.441543Z", "iopub.status.busy": "2023-03-14T15:59:08.440977Z", "iopub.status.idle": "2023-03-14T15:59:08.523677Z", "shell.execute_reply": "2023-03-14T15:59:08.522985Z"}, "papermill": {"duration": 0.102139, "end_time": "2023-03-14T15:59:08.526830", "exception": false, "start_time": "2023-03-14T15:59:08.424691", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"</svg>\n"], "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": "29f3c0f8", "metadata": {"papermill": {"duration": 0.015467, "end_time": "2023-03-14T15:59:08.562835", "exception": false, "start_time": "2023-03-14T15:59:08.547368", "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": "d5f64a16", "metadata": {"papermill": {"duration": 0.016615, "end_time": "2023-03-14T15:59:08.606471", "exception": false, "start_time": "2023-03-14T15:59:08.589856", "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": "d58a2c18", "metadata": {"papermill": {"duration": 0.015475, "end_time": "2023-03-14T15:59:08.637639", "exception": false, "start_time": "2023-03-14T15:59:08.622164", "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": "077d2fa3", "metadata": {"papermill": {"duration": 0.015393, "end_time": "2023-03-14T15:59:08.668614", "exception": false, "start_time": "2023-03-14T15:59:08.653221", "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,colab_type,id,-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": "3.9.16"}, "papermill": {"default_parameters": {}, "duration": 20.325164, "end_time": "2023-03-14T15:59:11.709038", "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": "2023-03-14T15:58:51.383874", "version": "2.4.0"}, "widgets": 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