probmodels-ddpm-fm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q4kVgOM2pyzJ"
   },
   "source": [
    "# From DDPM to Flow Matching for Airfoil RANS Flows\n",
    "\n",
    "Ever wondered how to turn your existing _denoising diffusion code_ into a _flow matching_ approach? 🤔 Or what all the fuss regarding diffusion models was about in the first place? 🧐 That's exactly what this notebook is focusing on 😎\n",
    "\n",
    "We'll be using a learning task where we can reliably generate arbitrary amounts of ground truth data, to make sure we can quantify how well the target distribution was learned. Specifically, we'll focus on Reynolds-averaged Navier-Stokes simulations around airfoils, which have the interesting characteristic that typical solvers (such as OpenFoam) transition from steady solutions to oscillating ones for larger Reynolds numbers. This transition is exactly what we'll give as a task to diffusion models below. (Details can be found in our [diffuion-based flow prediction repository](https://github.com/tum-pbs/Diffusion-based-Flow-Prediction/).)\n",
    "\n",
    "# Intro\n",
    "\n",
    "Diffusion models have been rising stars in the deep learning field in the past years, and have made it possible to train powerful generative models with surprisingly simple and robust training setups. Within this sub-field of deep learning, a very promising new development are flow-based approaches, typically going under names such as _flow matching_ {cite}`lipman2022flow` and _rectified flows_ {cite}`liu2022rect` . We'll stick to the former here for simplicity, and denote this class of models with _FM_.\n",
    "\n",
    "For the original diffusion models, especially the _denoising_ tasks were extremely successful: a neural network learns to restore a signal from pure noise. Score functions provided an alternate viewpoint, but ultimately also resulted in denoising tasks. Instead, flow-based approaches aim for transforming distributions. The goal is to transform a known one, such as gaussian noise, into one that represents the distribution of the signal or target function we're interested in. Despite these seemingly different viewpoints, all viewpoints above effectively do the same: starting with noise, they step by step turn it into samples for our target signal. Interestingly, the FM-perspective is not only more stable at training time, it also speeds up inference by orders of magnitude thanks to yielding straighter paths. And even better: if you have a working DM setup, it's surprisingly simple to turn it into an FM one.\n",
    "\n",
    "Below, we'll highlight the simiarities and differences, and evaluate both methods with the RANS-based flow setup outlined above.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "32daES8LGs6v"
   },
   "source": [
    "# Problem statement\n",
    "\n",
    "Instead of the previous supervised learning tasks, we'll need to consider distributions. For \"classic\" supervised tasks, we have unique input-output pairs $(x,y)$ and train a model to provide $y$ given $x$ based on internal parameters $\\theta$, i.e. $y=f(x;\\theta)$.\n",
    "\n",
    "In contrast, let's assume there is _some_ hidden state $\\psi$, that varies for a single $x$. This could e.g. be measurement noise, the starting point of an optimization for inverse problems, or the non-converging solution of a RANS solver (our scenario here). Now we can phrase our problem in terms of random variable $Y$, and our solution is drawn from the distribution $y \\sim P_Y(Y)$ that we typically specify as a marginalized distribution, in terms of samples with varying $\\psi$ for any given $x$. From a probabilistic perspective, it is important to capture the conditional probabilty of our solutions, i.e. $p(y|x)$, where we marginalize over $\\psi$. (We don't need to know about the specifics of $\\psi$ in practice.)\n",
    "\n",
    "This conditional distribution $p(y|x)$, the _posterior_, is exactly what our generative model should learn: when we repeatedly evaluate our model, it should give us samples from the posterior with the right probabilities. And it should do so efficiently, without wasting computations...\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AVmsZmUsGwkR"
   },
   "source": [
    "# Implementation and Setup\n",
    "\n",
    "First, we need to install the required packages and clone the repository:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KtHsiFscpyzN",
    "outputId": "980dbf3a-54ab-441c-de05-3f95729a88e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/thuerey/jupyter/Diffusion-based-Flow-Prediction\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thuerey/anaconda3/envs/torch24/lib/python3.12/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n",
      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import google.colab  # to ensure that we are inside colab\n",
    "    !pip install --upgrade --quiet einops bayesian_torch\n",
    "except ImportError:\n",
    "    print('This notebook is running locally, please make sure the necessary pip packages are installed.')\n",
    "    pass\n",
    "!git clone https://github.com/tum-pbs/Diffusion-based-Flow-Prediction.git\n",
    "%cd Diffusion-based-Flow-Prediction/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "geSEvam6vDxA"
   },
   "source": [
    "We also need to prepare the training dataset. The one below can be generated with [OpenFoam](https://www.openfoam.com/\n",
    "), but is downloaded below for convenience. The data structure and the `DataFiles` class that are used to organize the dataset come from the diffusion-based flow prediction repository, details can be found [here](https://github.com/tum-pbs/Diffusion-based-Flow-Prediction/blob/main/generate_dataset.ipynb) if you're interested.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cd-7RLz7pyzO",
    "outputId": "ae8d07ab-8d2f-4d6e-90d1-4eb5d8bf2693"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda:0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading data: 100%|██████████| 125/125 [00:00<00:00, 341.19it/s]\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "from airfoil_diffusion.airfoil_datasets import *\n",
    "from airfoil_diffusion.networks import *\n",
    "from airfoil_diffusion.trainer import *\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Using device: \"+str(device))\n",
    "\n",
    "if not os.path.exists(\"./datasets/1_parameter/data/\"):\n",
    "    files=[file for file in os.listdir(\"./datasets/1_parameter/\") if file.endswith(\".zip\")]\n",
    "    for file in tqdm(files):\n",
    "        f=zipfile.ZipFile(\"./datasets/1_parameter/\"+file,'r')\n",
    "        for file in f.namelist():\n",
    "            f.extract(file,\"./datasets/1_parameter/data/\")\n",
    "        f.close()\n",
    "\n",
    "df_train=FileDataFiles(\"./datasets/1_parameter/train_cases.txt\",base_path=\"./datasets/1_parameter/data/\")\n",
    "train_dataset = AirfoilDataset(df_train,data_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I913Pn0opyzO"
   },
   "source": [
    "Next, we'll implement the denoising diffusion model, so that we can compare with flow matching afterwards.\n",
    "\n",
    "------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PGf_5o2CFNC0"
   },
   "source": [
    "## Denoising with Diffusion Models\n",
    "\n",
    "At it's core, the denoising task is as simple as the name implies: we learn to estimate the noise $\\epsilon$ by minimizing\n",
    "$\n",
    "    \\parallel \\epsilon - f_\\theta(x,t) \\parallel^2\n",
    "$,\n",
    "the trick is primarily to carefully control the noise so that it can be learned easily.\n",
    "\n",
    "Note that for all equations here, we'll omit the $i$ subscript that previously denoted the different samples of a batch or dataset in {doc}`overview-equations`. Thus, we'll e.g. shorten $y_{i}$ to $y$, and leave out summations over $i$.\n",
    "\n",
    "To get started with denoising, we'll define a forward process that\n",
    "adds noise to give a perfect\n",
    "Gaussian distribution $\\mathcal{N}\\left(\\mathbf{0}, \\mathbf{I}\\right)$ at $t=1$.\n",
    "It starts with a data sample $y$ at $t=0$, i.e. $\\mathcal{N}\\left(y, 0\\right)$ and then turns it into noise as $t$ increases. Note that the noising / denoising time $t$ is a _virtual_ one, it has no physical meaning.\n",
    "The change of mean and standard deviation over time $t$ is controlled by a\n",
    " _noise schedule_  $\\beta^t \\in (0,1)$, that gradually increases from $0$ to $\\beta^t=1$ at the end of the chain, where $t=1$.\n",
    "\n",
    "By choosing a Gaussian distribution, we can decouple the steps to give a Markov chain for the distribution $q$ of the form \n",
    "$$\n",
    "    q\\left(y^{0:T}\\right) =\n",
    "    q(y^0) \\prod_{t=1}^{T}  q\\left(y^{t} \\mid y^{t-1}\\right) ,\n",
    "$$\n",
    "where\n",
    "$$\n",
    "    q\\left(y^{t} \\mid y^{t-1}\\right) = \\mathcal{N}\n",
    "    \\left( \\sqrt{1-\\beta^{t}} y^{t-1}, \\beta^{t} \\mathbf{I}\\right) .\n",
    "$$\n",
    "\n",
    "It's fairly obvious that we can destroy any input signal $y$ by accumulating more and more noise. What's more interesting is\n",
    "the reverse process that removes the noise, i.e. the denoising. We can likewise formulate a\n",
    "reverse Markov chain for the distribution $p_\\theta$. The subscript already indicates that we'll learn the transition and parameterize it by a set or parameters $\\theta$:\n",
    "$$\n",
    "    p_\\theta\\left(y^{0:T}\\right)\n",
    "    =\n",
    "    p(y^T)\n",
    "    \\prod_{t=1}^{T}\n",
    "    p_\\theta \\left(y^{t-1} \\mid y^{t}\\right)\n",
    "$$\n",
    "with\n",
    "$$\n",
    "    y^{t} = \\sqrt{\\bar{\\alpha}^t} y^{0}\n",
    "    +\\sqrt{\\left(1-\\bar{\\alpha}^t\\right)}\\epsilon .\n",
    "$$ (eq-yt)\n",
    "\n",
    "We can calculate the correct coefficients $\\alpha$ from the noise schedule of the forward chain via\n",
    "$\\alpha^t = 1 - \\beta^t$ and $\\bar{\\alpha}^t = \\prod_{i=1}^t \\alpha^i$.\n",
    "\n",
    "Each step $p_\\theta \\left(y^{t-1} \\mid y^{t}\\right)$ along the way now has the specific form\n",
    "$$\n",
    "    p_\\theta \\left(y^{t-1} \\mid y^{t}\\right) =\n",
    "    \\mathcal{N} \\left( \\mu(f_{\\theta}) , \\sigma_{\\theta} \\right)\n",
    "$$ (eq-denoising-step)  \n",
    "where we're employing a neural network $f_\\theta$ to predict the noise. We could also call the network $\\epsilon_\\theta$ here, but for consistency we'll stick to $f_\\theta$. The noise inferred by our network parametrizes the mean\n",
    "$$\n",
    "    \\mu(\\epsilon) =\n",
    "    \\frac{1}{\\sqrt{\\alpha^t}}\n",
    "    \\Big(\n",
    "        y^t - \\frac{\\beta^t}{\\sqrt{1 - \\bar{\\alpha}^t}} \\epsilon\n",
    "    \\Big) .\n",
    "$$\n",
    "The standard deviation interestingly does not depend on the noise (and our network), but evolves over time with\n",
    "$$\n",
    "    \\sigma=\n",
    "    \\frac{1-\\bar{\\alpha}^{t-1}}\n",
    "    {1-\\bar{\\alpha}^{t}}\\beta^{t} \\mathbf{I}.\n",
    "$$\n",
    "\n",
    "Thus, given a pair $x,y$, we can directly compute the right amount of noise for time $t-1$ and $t$, and generate $y^t$ and $y^{t-1}$. In practice, we're not only interested in retrieving an arbitrary $y$, but the one that corresponds to some global paramters like a chosen Reynolds number. These conditions, together with e.g. the shape of an airfoil are actually our $x$ from $f(x)=y$ at the top.\n",
    "So, we'll also condition $f$ on $x$ to have the form $f_\\theta (y^t,x,t)$.\n",
    "\n",
    "In practice, we simply choose a time $t$, compute the right amount of noise $\\epsilon$, and let our network predict the noise given $y^t$ computed by linear interpolation with $\\bar{\\alpha}^t$, as given above in equation `ref`(eq-yt).\n",
    "This gives the loss function:\n",
    "$$\n",
    "    \\mathcal{L}_{\\text{DM}}(\\theta) = \\mathbb{E}_{x,\\epsilon \\sim\\mathcal{N}(\\mathbf{0},\\mathbf{I}),t  }\n",
    "    \\left[   \\parallel \\epsilon - f_\\theta(y^t,x,t) \\parallel^2   \\right]\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wQBjN33OWsP_"
   },
   "source": [
    "## Implementing DDPM\n",
    "\n",
    "We'll split the implementation into a helper class that computes the coefficients for the denoising schedule, and a trainer class that takes care of the training loop.\n",
    "\n",
    "The helper class is callsed `MyDiffuser` below, and starts by computing the beta coefficients for a so called _cosine-beta_ schedule.\n",
    "It has the form $\\beta = cos\\big( (t/T+s)~/~(1+s) * \\pi/2 \\big)^2$.\n",
    "The offset $s$ below is chosen such that the standard deviation of the $\\sqrt{\\beta}$ is smaller than the color step of $1/256$ for a typical RGB image. The `generate_parameters_from_beta()` function takes the precomputed list of `beta` coefficients, and turns them into PyTorch tensors, computing the correct `alpha` and `alpha_bar` values along the way.\n",
    "\n",
    "The class also implements a function `forward_diffusion()` that calculates forward diffusion step using the precomputed `alpha_bar` values, given an input $y$ and $t$. Hence, this function computes $y^t$ from above.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "yxF4ePQhW4Ol"
   },
   "outputs": [],
   "source": [
    "class MyDiffuser():\n",
    "    def __init__(self, steps, device):\n",
    "        self.device = device\n",
    "        self.steps = steps\n",
    "        self.name = \"Cos2ParamsDiffuser\"\n",
    "\n",
    "        s = 0.008\n",
    "        tlist = torch.arange(1, self.steps+1, 1)\n",
    "        temp1 = torch.cos((tlist/self.steps+s)/(1+s)*np.pi/2)\n",
    "        temp1 = temp1*temp1\n",
    "        temp2 = np.cos(((tlist-1)/self.steps+s)/(1+s)*np.pi/2)\n",
    "        temp2 = temp2*temp2\n",
    "        self.beta_source = 1-(temp1/temp2)\n",
    "        self.beta_source[self.beta_source > 0.999] = 0.999\n",
    "        self.generate_parameters_from_beta()\n",
    "\n",
    "\n",
    "    def generate_parameters_from_beta(self):\n",
    "        self.betas = torch.cat( (torch.tensor([0]), self.beta_source), dim=0)\n",
    "        self.betas = self.betas.view(self.steps+1, 1, 1, 1)\n",
    "        self.betas = self.betas.to(self.device)\n",
    "\n",
    "        self.alphas = 1-self.betas\n",
    "        self.alphas_bar = torch.cumprod(self.alphas, 0)\n",
    "        self.one_minus_alphas_bar = 1 - self.alphas_bar\n",
    "        self.sqrt_alphas = torch.sqrt(self.alphas)\n",
    "        self.sqrt_alphas_bar = torch.sqrt(self.alphas_bar)\n",
    "        self.sqrt_one_minus_alphas_bar = torch.sqrt(self.one_minus_alphas_bar)\n",
    "\n",
    "\n",
    "    def forward_diffusion(self, y0, t, noise):\n",
    "        yt = self.sqrt_alphas_bar[t]*y0+self.sqrt_one_minus_alphas_bar[t]*noise\n",
    "        return yt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cux8zWnzan7G"
   },
   "source": [
    "Now we're ready to start training. The `MyDiffusionTrainer` class derives from a `Trainer` base class from the airfoil diffusion repository. This base class primarily handles parameters, book-keeping and a few other mundane tasks. Effectively, it makes sure we have a batch to train with, and then calls `train_step()`, which is the most interesting function below.\n",
    "\n",
    "It implements exactly the procedure outlines above: given a $y$, we compute noise, and $y^t$ with a forward step for a random $t$. Then we let our network predict the noise $\\epsilon$ from $y^t$, the condition $x$, and $t$. All that's left afterwards is to compute an MSE loss on the true noise versus the precicted one, and let PyTorch backpropagate the gradient to update the weights of our neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "nwVvxlbsFIeO"
   },
   "outputs": [],
   "source": [
    "class MyDiffusionTrainer(TrainerStepLr):\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        super().__init__()\n",
    "\n",
    "    def set_configs_type(self):\n",
    "        super().set_configs_type()\n",
    "        self.configs_handler.add_config_item(\"diffusion_step\",value_type=int,default_value=200,description=\"The number of diffusion steps.\")\n",
    "\n",
    "    def event_before_training(self,network):\n",
    "        self.diffuser = MyDiffuser(steps=self.configs.diffusion_step,device=self.configs.device)\n",
    "\n",
    "    def train_step(self, network: torch.nn.Module, batched_data, idx_batch: int, num_batches: int, idx_epoch: int, num_epoch: int):\n",
    "        condition = batched_data[0].to(device=self.configs.device)\n",
    "        targets = batched_data[1].to(device=self.configs.device)\n",
    "        t = torch.randint(1, self.diffuser.steps+1,\n",
    "                          size=(targets.shape[0],), device=self.configs.device)\n",
    "        noise = torch.randn_like(targets, device=self.configs.device)\n",
    "        yt = self.diffuser.forward_diffusion(targets, t, noise)\n",
    "        predicted_noise = network(yt, t, condition)\n",
    "        loss=torch.nn.functional.mse_loss(predicted_noise, noise)\n",
    "        return loss\n",
    "\n",
    "diffusion_trainer = MyDiffusionTrainer()\n",
    "\n",
    "dif_network = AifNet(\"./pre_trained/single_parameter/32/diffusion/network_configs.yaml\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_36M6WQyFSff"
   },
   "source": [
    "At the end of the cell above we directly instantiate a trainer object, and initialiaze a neural network. The `AifNet` class implements a _state-of-the-art_ U-net with all the latest tricks for diffusion modeling, but in the end it's \"just a U-net\", and we'll skip the details here.\n",
    "\n",
    "More importantly, we can finally starting training our DDPM model. For that we can use the `train_from_scratch()` function of the trainer class, which we'll call in the next cell. The training with a default of 10000 steps can take a while, but shouldn't take much longer than half an hour on current GPUS. If you're not patient enough, feel free to skip this step and load one of the pre-trained models from our DBFP repository with the commented-out code at the bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fb8uPp9qFRvi",
    "outputId": "7f82b6a0-26fa-488b-f996-ad266b4ad7a9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer created at 2024-11-05-03_25_26\n",
      "Working path:./training/single_parameter/32/diffusion/2024-11-05-03_25_26/\n",
      "Random seed: 1730773526\n",
      "Training configurations saved to ./training/single_parameter/32/diffusion/2024-11-05-03_25_26/configs.yaml\n",
      "Network has 1185218 trainable parameters\n",
      "There are 5 training batches in each epoch\n",
      "Batch size for training:25\n",
      "Training epochs:10000\n",
      "Total training iterations:50000\n",
      "learning rate:0.0001\n",
      "Optimizer:AdamW\n",
      "Learning rate scheduler:step\n",
      "Training start!\n",
      "  0%|          | 0/10000 [00:00<?, ?it/s]/tmp/ipykernel_65435/3287631965.py:11: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n",
      "  temp2 = np.cos(((tlist-1)/self.steps+s)/(1+s)*np.pi/2)\n",
      "lr:1.000e-05 train loss:0.00100: 100%|██████████| 10000/10000 [30:40<00:00,  5.43it/s]\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "diffusion_trainer.train_from_scratch(name=\"diffusion\", #device=\"cpu\",\n",
    "                                     network=dif_network,\n",
    "                                     train_dataset=train_dataset,\n",
    "                                     path_config_file=\"./pre_trained/train_configs.yaml\",\n",
    "                                     save_path=\"./training/single_parameter/32/\", epochs=10000)\n",
    "\n",
    "# alternative load\n",
    "#dif_network.load_state_dict(torch.load(\"./pre_trained/single_parameter/32/diffusion/weights_0.pt\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1OgWWVnfcoEW"
   },
   "source": [
    "Before we investigate the capabilities of this model, let's directly train a flow matching version, so that we can compare.\n",
    "\n",
    "---------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uR27s9bZc728"
   },
   "source": [
    "# Flow Matching\n",
    "\n",
    "Instead of adding and removing noise,\n",
    "flow matching transforms probability distributions.\n",
    "Let's consider\n",
    "a time-dependent differentiable mapping $\\phi:[0,1]\\times \\mathbb{R}^d \\rightarrow \\mathbb{R}^d$ that transforms samples $x_0 \\in \\mathbb{R}^d$ from an initial distribution $p(x_0)$ to\n",
    "samples $x_t$ from a distribution $p(x_t)$.\n",
    "In short: $x_t=\\phi(x_0)$.\n",
    "\n",
    "Later on, $x_0$ will represent samples from a simple distribution, such as a Gaussian distribution, similar to what we used for the diffusion models previously. On the other hand, $x_1$ corresponds to samples from the target distribution, i.e., samples from our training dataset ($y$ in the notation above). For the transformation of distributions, it's convenient to consider continuously changing distributions $p(x_t)$ for varying $t$. Just keep in mind that for $t=1$, we're at $x_1$ which is identical to $y$. I.e. $p(x_t)|_{t=1} = p(x_1) = p(y)$.\n",
    "\n",
    "Flow matching learns the time derivative of this transformation, the _flow_, as a time-dependent vector field $u:[0,1]\\times \\mathbb{R}^d \\rightarrow \\mathbb{R}^d$, where\n",
    "$u_t(x_t)=\\frac{d}{dt}x_t$. For a neural network $f_{\\theta}(x,t)$ the\n",
    "loss function is simply an $L^2$ between predicted and target velocities:\n",
    "$$\n",
    "    \\mathcal{L}_{\\text{FM} }(\\theta) = \\mathbb{E}_{t \\sim [0,1], ~ x_t \\sim p(x_t)}\\| f_{\\theta}(x_t,t)-u_t(x_t) \\|^2 ,\n",
    "$$\n",
    "where $p_t$ denotes the intermediate distributions at time $t$ with  $t \\sim [0,1]$.\n",
    "\n",
    "Looks surprisingly simple so far, and a lot like the loss for our noise estimation problem above. However, tithout additional tricks this loss function is intractable since we don't know the distributions $p(x_t)$ and the correct velocities $u_t$.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qd9x0PnlSP-D"
   },
   "source": [
    "\n",
    "Luckily, it was shown in previous work that we can make use of\n",
    "the known samples $x_1$ to drive the procedure without distorting the distributions.\n",
    "We can construct a conditional vector field $u_t(x_t \\vert x_1) $ based on  the $x_1$ samples.\n",
    "Then the intermediate probability density and vector fields can be marginalized by integrating over $x_1$ as follows:\n",
    "$\n",
    "    p(x_t)=\\int p(x_t|x_1)p(x_1)dx_1 ~ ,\n",
    "$\n",
    "and\n",
    "$\n",
    "u_t(x_t)\n",
    "    = \\int u_t(x_t\\vert x_1) \\frac{p(x_t\\vert x_1)p(x_1)}{p(x_t)}dx_1  ~.\n",
    "$\n",
    "With this marginalization, it was demonstrated that learning this conditional flow is mathematically equivalent to learning the original flow.\n",
    "\n",
    "We now have some freedom to prescribe flows, and it turns\n",
    "out that straight, rectified motions are particularly interesting.\n",
    "They can be derived from\n",
    "optimal transport, which to define a **linear** mapping between samples from $p(x_0)$ and $p(x_1)$.\n",
    "Starting with a normalized Gaussian distribution at $t=0$, we then want the standard deviations $\\sigma_t$ to linearly decrease with $1-t$, so that we're left with no randomness at $t=1$. At the same time, the mean $\\mu_t$ should change from zero to $x_1$, i.e. $\\mu_t(x_1)=t ~ x_1$.\n",
    "\n",
    "This gives the mapping:\n",
    "$$\n",
    "    \\phi_t(x_0) = \\sigma_t(x_t)x_0 + \\mu_t(x_t),\n",
    "$$\n",
    "with it's time derivative being the velocity:\n",
    "$$\n",
    "    u_t(x_t \\vert x_1)=\\frac{d}{dt}\\phi_t(x_0)=\\sigma_t'(x_1)x_0 + \\mu_t'(x_1)\n",
    "    ~.\n",
    "$$\n",
    "\n",
    "In practice, we also introduce a threshold $\\sigma_\\text{min}$, to ensure that the standard deviation stays above zero.\n",
    "In practice, $\\sigma_\\text{min}$ is chosen sufficiently small so that $p(x_1 \\vert x_1)$ is representing a very concentrated Gaussian distribution centered at $x_1$. The time evolution of the standard deviation is then computed with\n",
    "$\\sigma_t(x_1)=1-(1-\\sigma_\\text{min})t$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cvAjYC5C3W_i"
   },
   "source": [
    "The great advantage of this setup is that it actually provides a \"straight\" motions with a constant vector field. The vector field is independent of time $t$! If things work out as planned, that means that we can compute the result of the transformation in a single Euler step, directly from $x_0$ to $x_1$. This is in stark contrast to the denoising above, where the network can learn arbitrary paths, and correspondingly requires a larger number of steps to arrive at the target. In practice, the _single-step_ inference requires a few more tricks, but we'll see below that flow matching works with much fewer steps than denoising, even with this simple, basic formulation.\n",
    "\n",
    "Now we have all necessary ingredients to compute the target velocities $u_t$\n",
    "as\n",
    "$$\n",
    "    u_t(x_t \\vert x_1) = x_1 - (1-\\sigma_\\text{min}) x_0\n",
    "    ~,\n",
    "$$\n",
    " and we can formulate the conditional version of the loss function above:\n",
    "$$\n",
    "    \\mathcal{L}_{\\text{CFM}}(\\theta) = \\mathbb{E}_{t\\sim [0,1], ~ x_1\\sim p(x_1), ~ x_t \\sim p(x_t\\vert x_1)} \\big\\| f_{\\theta}(x_t,t, x_1) - u_t(x_t\\vert x_1) \\big\\|^2\n",
    "$$\n",
    "\n",
    "And once trained, we can query our network for the vector field to  generate  $x_1$ samples from $x_0$ via integration in time:\n",
    "$x_1=x_0 + \\int_0^1 f_{\\theta}(x_t,t) dt$. To integrate this ODE, any ODE solver can be used. For simplicity, we'll use Euler steps below, but you can try a variety of higher-order methods in the DBFP code.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qnIpelg_3PR7"
   },
   "source": [
    "# Implementing Flow Matching\n",
    "\n",
    "For the implementation, we'll again split the core functionality and the training code. The former is handled by the helper class `MyFlowMatcher`. It's even simpler than the previous one for denoising: `phi_t()` computes the linear forward step by interpolating two samples `x_0` and `x_1`. `u_t()` instead computes the time derivative, as explained above. Because we're aiming for a straight motion, it's constant in time, and `u_t` does not depend on `t` anymore.\n",
    "\n",
    "The last function `cfm_loss()` computes the target velocity and evaluates the conditional flow matching loss for a training step.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NlfE6KAEpyzP"
   },
   "outputs": [],
   "source": [
    "class MyFlowMatcher():\n",
    "\n",
    "    def __init__(self):\n",
    "        self._unifrom_sampler = torch.distributions.uniform.Uniform(0.,1.)\n",
    "        self.sig_min = 0.001\n",
    "\n",
    "    def phi_t(self, x_0, x_1, t):\n",
    "        return (1-(1-self.sig_min)*t)*x_0 + t*x_1\n",
    "\n",
    "    def u_t(self, x_0, x_1): # note - linear flow, does not depend on t anymore!\n",
    "        return x_1-(1-self.sig_min)*x_0\n",
    "\n",
    "    def cfm_loss(self, network, x_1, x_0=None, *args,**kwargs):\n",
    "        x_0 = torch.randn_like(x_1) if x_0 is None else x_0\n",
    "        t = self._unifrom_sampler.sample([x_1.shape[0]]+[1]*(x_1.dim()-1)).to(x_1.device)\n",
    "        x_t = self.phi_t(x_0,x_1,t)\n",
    "        v_t = self.u_t(x_0,x_1)\n",
    "        return torch.mean( (network(x_t,t.view(t.shape[0]),*args,**kwargs) - v_t) **2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ksXTgRrjqxaw"
   },
   "source": [
    "The flow matching trainer relies on the `trainer` base class, and primarily has the job transfer targets and conditioning data to the PyTorch device, and call the `cfm_loss()` function. Not much left to do here...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "wuBx5HzBqziO"
   },
   "outputs": [],
   "source": [
    "class MyFMTrainer(TrainerStepLr):\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        super().__init__()\n",
    "\n",
    "    def event_before_training(self,network):\n",
    "        self.flow_matcher = MyFlowMatcher()\n",
    "\n",
    "    def train_step(self, network: torch.nn.Module, batched_data, idx_batch: int, num_batches: int, idx_epoch: int, num_epoch: int):\n",
    "        condition = batched_data[0].to(device=self.configs.device)\n",
    "        targets = batched_data[1].to(device=self.configs.device)\n",
    "        loss=self.flow_matcher.cfm_loss(network=network,x_1=targets,condition=condition)\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6klh2FfQFZCv"
   },
   "source": [
    "Next we can instantiate a trainer object, and allocate a network. We're using a U-net that's identical to the one previously used for denoising, so that we can make a fair comparison between the two training methodologies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "uY6BS0IRFYYr"
   },
   "outputs": [],
   "source": [
    "fmatching_trainer=MyFMTrainer()\n",
    "\n",
    "network = AifNet(\"./pre_trained/single_parameter/32/diffusion/network_configs.yaml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t10nHfJLyLK3"
   },
   "source": [
    "\n",
    "Now we can start training. Similar to before, this should take around half an hour for 10000 epochs, but if you want to skip this step, you can find code for loading one of the models from the github repo below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sQ-lNvZSsGj9",
    "outputId": "c3555bab-c5c3-4da3-c032-515aa1880b93"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer created at 2024-11-05-03_56_06\n",
      "Working path:./training/single_parameter/32/flowmatching/2024-11-05-03_56_06/\n",
      "Random seed: 1730775366\n",
      "Training configurations saved to ./training/single_parameter/32/flowmatching/2024-11-05-03_56_06/configs.yaml\n",
      "Network has 1185218 trainable parameters\n",
      "There are 5 training batches in each epoch\n",
      "Batch size for training:25\n",
      "Training epochs:10000\n",
      "Total training iterations:50000\n",
      "learning rate:0.0001\n",
      "Optimizer:AdamW\n",
      "Learning rate scheduler:step\n",
      "Training start!\n",
      "lr:1.000e-05 train loss:0.00430: 100%|██████████| 10000/10000 [30:44<00:00,  5.42it/s]\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "fmatching_trainer.train_from_scratch(name=\"flowmatching\",\n",
    "                                     network=network,\n",
    "                                     train_dataset=train_dataset,\n",
    "                                     path_config_file=\"./pre_trained/train_configs.yaml\",\n",
    "                                     save_path=\"./training/single_parameter/32/\", epochs=10000)\n",
    "\n",
    "# uncomment to load the checked in model\n",
    "#network.load_state_dict(torch.load(\"./pre_trained/single_parameter/32/flow_matching/weight.pt\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ezZSMjpvgjbF"
   },
   "source": [
    "We finally have to trained models that we can evaluate side by side.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZCGAEnlFpyzP"
   },
   "source": [
    "# Test Evaluation\n",
    "\n",
    "To evaluate the trained models on inputs that weren't used for training we first need to download some more data. This is what happens in the next cell. \n",
    "The `scale_factor=0.25` parameters of the `read_single_file()` function below make sure that we get fields of size $32 \\times 32$ like the ones in the training data set. However, the test set has previously unseen Reynolds numbers, so that we can check how well the model generalizes. While loading the data, the code also computes statistics for the ground truth mean and standard deviations (`mean_field_test_gd` and `std_field_test_gd`). This data will be used to quantify differences between the trained models later on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Skfwn5_-pyzP"
   },
   "outputs": [],
   "source": [
    "df_test=FileDataFiles(\"./datasets/1_parameter/test_cases.txt\",base_path=\"./datasets/1_parameter/data/\")\n",
    "df_test.sort()\n",
    "std_field_test_gd=[]\n",
    "mean_field_test_gd=[]\n",
    "inputs_test=[]\n",
    "samples_gd=[]\n",
    "for case in df_test.get_simulation_cases():\n",
    "    datas=[]\n",
    "    selected_cases=df_test.select_simulation_cases([case])\n",
    "    for case in selected_cases:\n",
    "        raw_data=read_single_file(case['path']+case['file_name'],model=\"dimless\",scale_factor=0.25)\n",
    "        datas.append(\n",
    "            raw_data[3:]\n",
    "            )\n",
    "    # scale factor is 0.25 to get 32x32 data\n",
    "    inputs_test.append(read_single_file(case['path']+case['file_name'],model=\"normalized\",scale_factor=0.25)[0:3])\n",
    "    samples_gd.append(np.stack(datas,axis=0))\n",
    "    std_field_test_gd.append(samples_gd[-1].std(axis=0))\n",
    "    mean_field_test_gd.append(samples_gd[-1].mean(axis=0))\n",
    "std_field_test_gd=np.stack(std_field_test_gd,axis=0)\n",
    "mean_field_test_gd=np.stack(mean_field_test_gd,axis=0)\n",
    "\n",
    "df_all=DataFiles(df_train.case_list+df_test.case_list)\n",
    "df_all.sort()\n",
    "std_value_gd=[]\n",
    "for case in df_all.get_simulation_cases():\n",
    "    datas=[]\n",
    "    selected_cases=df_all.select_simulation_cases([case])\n",
    "    for case in selected_cases:\n",
    "        datas.append(\n",
    "            read_single_file(case['path']+case['file_name'],model=\"dimless\",scale_factor=0.25)[3:] \n",
    "            )\n",
    "    std_value_gd.append(np.stack(datas,axis=0).std(axis=0).mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3AUNl4MEpyzP"
   },
   "source": [
    "The next cell defines two helper functions to compute the Euler integration for a chosen number of steps of a trained FM model. It simply evalutes the target samples via ODE integration steps to compute \n",
    "$$\n",
    "x_1 = x_0+\\int_0^1 v_\\theta(x,t) dt ~.\n",
    "$$\n",
    "\n",
    "It does so using Euler steps (for simplicity) for a whole batch of 25 samples, while the main `sample_flowmatching()` function breaks down larger inputs into chunks of 25. More advanced ODE integration methods are interesting to try here, of course, and a variety of integrators can be found in the accompanying github repository.\n",
    "\n",
    "It's worth explicitly pointing out a key difference between denoising and flow matching here that is not so obvious from the equations above: for denoising, we repeatedly add noise again over the course of the denoising steps. Flow matching, in contrast, only works with the initial noise, and follows the trajectory prescribed by the learned vector field. Hence, _no noise is added_ over the course of the flow matching steps at inference time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "HEWdmjANpyzP"
   },
   "outputs": [],
   "source": [
    "def integrate_euler( f, x_0, t_0, t_1, dt):\n",
    "    t_0 = torch.as_tensor(t_0, dtype=x_0.dtype, device=x_0.device)\n",
    "    t_1 = torch.as_tensor(t_1, dtype=x_0.dtype, device=x_0.device)\n",
    "    dt  = torch.as_tensor(dt,  dtype=x_0.dtype, device=x_0.device)\n",
    "    with torch.no_grad():\n",
    "        t=t_0\n",
    "        x=x_0\n",
    "        while (t_1 - t) > 0:\n",
    "            dt = torch.min(abs(dt), abs(t_1 - t))\n",
    "            x, t = x + dt * f(t,x), t + dt\n",
    "    return x\n",
    "\n",
    "def fm_sample( network, x_0, dt, condition):\n",
    "    with torch.no_grad():\n",
    "\n",
    "        def wrapper(t,x):\n",
    "            return network(x,\n",
    "                        t*torch.ones((x.shape[0],)).to(x_0.device),\n",
    "                        condition=condition)\n",
    "\n",
    "        return integrate_euler( f=wrapper, x_0=x_0,  t_0=0.,  t_1=1., dt=dt)\n",
    "\n",
    "def sample_flowmatching(network, input_field, dt, num_sample=100):\n",
    "    network.eval();network.to(device);predictions=[]\n",
    "    batch_size=25;N_all=num_sample\n",
    "\n",
    "    while N_all>0:\n",
    "        batch_size_now=min(batch_size,N_all)\n",
    "        N_all-=batch_size\n",
    "        condition=input_field.to(device).repeat(batch_size_now,1,1,1)\n",
    "        noise=torch.randn_like(condition)\n",
    "        prediction_batch=normalized2dimless( \n",
    "            fm_sample(x_0=noise,\n",
    "                      network=network, dt=dt, \n",
    "                      condition=condition)\n",
    "            )\n",
    "        predictions.append(prediction_batch.detach().cpu().numpy())\n",
    "    predictions=np.concatenate(predictions,axis=0)\n",
    "    return np.mean(predictions,axis=0), np.std(predictions,axis=0), predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YLOQ9i9_pyzQ"
   },
   "source": [
    "We'll directly test our FM model with a varying number of integration steps. As promised above, FM can produce results in very few steps, so this is an interesting hyperparameter to vary. The next cell collects results via `sample_flowmatching()` with `step` varying from `1` to `100`. For qualitative comparison, we'll only do this for a single test sample, so that we can visualize the results next to the ground truth.\n",
    "\n",
    "For each integration step count, we'll collect 100 samples produces with different noise as starting point, in order to gather the mean and standard deviation statistics.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n6K7JVTkpyzQ",
    "outputId": "a1315a4a-dbca-4fd7-bd7b-68c6f2d11ff2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4/4 [00:05<00:00,  1.36s/it]\n"
     ]
    }
   ],
   "source": [
    "index=3\n",
    "input_field=inputs_test[index].unsqueeze(0)\n",
    "\n",
    "titles=[]\n",
    "result_fms=[]\n",
    "for step in tqdm([1,5,20,100]):\n",
    "    mean_fm,std_fm,samples_fm=sample_flowmatching(network,input_field,dt=1/step)\n",
    "    titles.append(\"Flow Matching {}\".format(step))\n",
    "    result_fms.append(np.concatenate([mean_fm,std_fm],axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69f5EdBNtK7K"
   },
   "source": [
    "Next, we repeat this process and define helper functions to produce samples with the diffusion model.\n",
    "\n",
    "As mentioned just above, DDPM adds the correct amount of noise for the current noise schedule in the `DDPM_sample_step()` calls.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "cIi4AAmuA5ma"
   },
   "outputs": [],
   "source": [
    "def diffusion_sample_from_noise(diffuser, model, condition):\n",
    "    with torch.no_grad():\n",
    "        x_t=torch.randn_like(condition)\n",
    "        t_now = torch.tensor([diffuser.steps], device=diffuser.device).repeat(x_t.shape[0])\n",
    "        t_pre = t_now-1\n",
    "        for t in range(diffuser.steps):\n",
    "            predicted_noise=model(x_t,t_now,condition)\n",
    "            x_t=DDPM_sample_step(diffuser, x_t,t_now,t_pre,predicted_noise)\n",
    "            t_now=t_pre\n",
    "            t_pre=t_pre-1\n",
    "        return x_t\n",
    "\n",
    "def DDPM_sample_step(d, x_t, t, t_pre, noise):\n",
    "    coef1 = 1/d.sqrt_alphas[t]\n",
    "    coef2 = d.betas[t]/d.sqrt_one_minus_alphas_bar[t]\n",
    "    sig = torch.sqrt(d.betas[t]) *d.sqrt_one_minus_alphas_bar[t_pre] /d.sqrt_one_minus_alphas_bar[t]\n",
    "    return coef1*(x_t-coef2*noise) + sig*torch.randn_like(x_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J-bBAWGGpyzQ"
   },
   "source": [
    "Note that these snippest closely follow the sampling of the original airfoil paper, e.g. [sample.ipynb](https://github.com/tum-pbs/Diffusion-based-Flow-Prediction/blob/main/sample.ipynb). Below we compute a single batch of outputs for the diffusion model in `result_diffusion`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "LzsUIt0KpyzR"
   },
   "outputs": [],
   "source": [
    "def sample_diffusion(diffuser, network,input_field,num_diffusion_sample=100):\n",
    "    network.eval();network.to(device);predictions=[]\n",
    "    batch_size=25;N_all=num_diffusion_sample\n",
    "    while N_all>0:\n",
    "        batch_size_now=min(batch_size,N_all)\n",
    "        N_all-=batch_size\n",
    "        prediction_batch=normalized2dimless(\n",
    "            diffusion_sample_from_noise(diffuser, network,\n",
    "                                       input_field.to(device).repeat(batch_size_now,1,1,1) ))\n",
    "        predictions.append(prediction_batch.detach().cpu().numpy())\n",
    "    predictions=np.concatenate(predictions,axis=0)\n",
    "    return np.mean(predictions,axis=0),np.std(predictions,axis=0),predictions\n",
    "\n",
    "mean,std,samples_diffusion=sample_diffusion(diffusion_trainer.diffuser,dif_network,input_field)\n",
    "result_diffusion=np.concatenate([mean,std],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0GkKGQv6pyzR"
   },
   "source": [
    "Let's first do some qualitative comparisons first by plotting the mean and standard deviation fields of a single test case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ngLHnm8ZpyzR",
    "outputId": "61e2ad43-6357-4bdb-dba4-651fcfe43a61"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2100x2100 with 72 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_ground_truth=np.concatenate([mean_field_test_gd[index],std_field_test_gd[index]],axis=0)\n",
    "\n",
    "CHANNEL_NAME_MEAN=[r\"$\\mu_p^*$\",r\"$\\mu_{u_x^*}$\",r\"$\\mu_{u_y^*}$\"]\n",
    "CHANNEL_NAME_STD=[r\"$\\sigma_{p^*}$\",r\"$\\sigma_{u_x^*}$\",r\"$\\sigma_{u_y^*}$\"]\n",
    "\n",
    "from airfoil_diffusion.plotter import *\n",
    "show_each_channel([result_ground_truth,result_diffusion]+result_fms,\n",
    "                  channel_names=CHANNEL_NAME_MEAN+CHANNEL_NAME_STD,\n",
    "                  case_names=[\"Ground truth\",\"Diffusion 200\"]+titles,transpose=True,inverse_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "whqPXFippyzR"
   },
   "source": [
    "The code above produced 100 samples with each method, and the image of the previous cell shows the mean and standard deviation for each spatial point in the regular grid. This illustrates that the mean is relatively easy to get right for all methods. The corresponding fields don't vary too much, and even the 1-step FM variant gets this mostly right (with a bit of noise). \n",
    "\n",
    "The standard deviation across the samples is more difficult: 1-step FM completely fails here, but, e.g., the 20-step FM version already does very well. This version only uses one tenth of steps compared to the DDPM version. The latter uses 200 steps, so the FM version is effectively 10x faster, while yielding a comparable accuracy here.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantified Results\n",
    "\n",
    "So far, we've focused on a single test case, and this could have been a \"lucky\" one for FM. Hence, below we'll repeat the evaluation for different cases across different Reynolds numbers to obtain quantified results. In total, the test set has six different Reynolds numbers, the middle four being interpolations of the training parameters, the first and last being extrapolations.\n",
    "\n",
    "The `do_test()` helper function defined in the next cell directly computes the statistics for a given network over the whole test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "oE0ObxRx_jdD"
   },
   "outputs": [],
   "source": [
    "def do_test(sample_func):\n",
    "    mean_predictions=[]\n",
    "    std_predictions=[]\n",
    "    std_a_predictions=[]\n",
    "    for input_field in tqdm(inputs_test):\n",
    "        mean_fields,std_fields,_=sample_func(input_field.unsqueeze(0))\n",
    "        mean_predictions.append(mean_fields)\n",
    "        std_predictions.append(std_fields)\n",
    "        std_a_predictions.append(np.mean(std_fields))\n",
    "    return mean_predictions,std_predictions,std_a_predictions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the larger number of test cases, the following cells can take a bit longer, especially for the diffusion model with its 200 steps.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ld8sTk1qpyzR",
    "outputId": "e44918f5-8436-4f9f-bab0-59ccaed4c6d2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6/6 [00:01<00:00,  4.35it/s]\n",
      "100%|██████████| 6/6 [00:06<00:00,  1.08s/it]\n",
      "100%|██████████| 6/6 [00:25<00:00,  4.28s/it]\n",
      "100%|██████████| 6/6 [02:09<00:00, 21.59s/it]\n"
     ]
    }
   ],
   "source": [
    "std_a_fms=[]\n",
    "labels=[]\n",
    "for step in [1,5,20,100]:\n",
    "    _,_,std_a_fm_i = do_test(lambda x:sample_flowmatching( network, x, dt=1/step, num_sample=500))\n",
    "    std_a_fms.append(std_a_fm_i)\n",
    "    labels.append(\"Flow Matching {}\".format(step))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jjoAQ8u0pyzR",
    "outputId": "2fe75d92-8f35-4426-9b21-3fa555613c06"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6/6 [03:40<00:00, 36.68s/it]\n"
     ]
    }
   ],
   "source": [
    "_,_,std_a_predictions_dif = do_test(lambda x:sample_diffusion(diffusion_trainer.diffuser, dif_network, x, num_diffusion_sample=500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have all the numbers, and the next cell produces a graph plotting the ground truth standard deviation per Re (blue line) next to the esimates from the different trained NNs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "UFlXoT1KpyzS",
    "outputId": "c28f9035-7cc3-4812-db24-a964dc0e8172"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[0.5 +2*i for i in range(6)]\n",
    "plt.plot([0.5 +i for i in range(11)],std_value_gd,label=\"Ground truth\")\n",
    "plt.scatter(x,std_a_predictions_dif,label=\"Diffusion 200\",marker=\"o\")\n",
    "for i in range(len(std_a_fms)):\n",
    "    plt.scatter(x,std_a_fms[i],label=labels[i],marker=\"x\")\n",
    "plt.legend(bbox_to_anchor=(1.0,1.0))\n",
    "plt.xlabel(r\"$Re \\times 10^{-6}$\")\n",
    "plt.ylabel(r\"$\\sigma_{y,a}$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E_hDWNLppyzS"
   },
   "source": [
    "The graph shows that the single result visualized above was not an outlier: 1-step FM doesn't do well, but 5- or 20-step FM are already very good, and largely on-par with the DDPM variant. In general, the accuracy of the NNs is very good. Even the last and first dots, the extrapolation regions, are captured reasonably well. The networks over-estimate the variance for the low Re cases because they haven't really seen static cases in training data, but the potentially more difficult high-Re case on the right is handled very well.\n",
    "\n",
    "Overall, this setup is a non-trivial case: the networks had to learn the posterior distributions from almost constant to strongly varying across the differen Reynolds numbers. The _flow matching_ NNs really excel here: they yield excellent estimates and posterior samples with a very small number of integration steps. This is important for practical applications: these surrogate models have to compete with classical numerical simulations, and many fields have highly optimized simulation codes. Hence, it's important the trained NNs provide estimates quickly, with reasonable hardware requirements (i.e. not overly large parameter counts). These results indicate that _flow matching_ is a highly interesting contender for probabilistic simulations. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "- For this setup it is interesting to try higher order integration methods. Can you observe any gains over Euler? (Make sure to count all NFEs within the integrator, e.g., the four NN calls of the RK4 method.) \n",
    "- Improve the overall accuracy of the trained models by increasing the number of epochs, and the feature count of the U-net architecture.\n",
    "- The implementation above uses _basic_ denoising and flow matching. It's worth trying improvements, e.g., additional rectification steps from the paper by Liu et al. This could potentially reduce the number of required steps even further.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------\n",
    "[-> Back to PBDL main page](https://www.physicsbaseddeeplearning.org/)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "A100",
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}