diff --git a/supervised-airfoils.ipynb b/supervised-airfoils.ipynb index b14b128..b0410a4 100644 --- a/supervised-airfoils.ipynb +++ b/supervised-airfoils.ipynb @@ -8,7 +8,7 @@ "source": [ "# Supervised training for RANS flows around airfoils\n", "\n", - "## Overview \n", + "## Overview\n", "\n", "For this example of supervised training\n", "we have a turbulent airflow around wing profiles, and we'd like to know the average motion\n", @@ -33,7 +33,7 @@ "## Formulation\n", "\n", "\n", - "With the supervised formulation from {doc}`supervised`, our learning task is pretty straight-forward, and can be written as \n", + "With the supervised formulation from {doc}`supervised`, our learning task is pretty straight-forward, and can be written as\n", "\n", "$$\\begin{aligned}\n", "\\text{arg min}_{\\theta} \\sum_i ( f(x_i ; \\theta)-y^*_i )^2 ,\n", @@ -43,12 +43,12 @@ "and the index $i$ evaluates the difference across all discretization points in our data sets.\n", "\n", "The goal is to infer velocity $\\mathbf{u} = u_x,u_y$ and a pressure field $p$ in a computational domain $\\Omega$\n", - "around the airfoil in the center of $\\Omega$. \n", + "around the airfoil in the center of $\\Omega$.\n", "$u_x,u_y$ and $p$ each have a dimension of $128^2$.\n", "As inputs we have the Reynolds number $\\text{Re} \\in \\mathbb{R}$, the angle of attack\n", "$\\alpha \\in \\mathbb{R}$, and the airfoil shape $\\mathbf{s}$ encoded as a rasterized grid with $128^2$.\n", " $\\text{Re}$ and $\\alpha$ are provided in terms of the freestream flow velocity $\\mathbf{f}$, the x and y components of which are represented as constant fields of the same size, and contain zeros in the airfoil region.\n", - "Thus, put together, both input and output have the same dimensions: $x,y^* \\in \\mathbb{R}^{3\\times128\\times128}$. The inputs contain \n", + "Thus, put together, both input and output have the same dimensions: $x,y^* \\in \\mathbb{R}^{3\\times128\\times128}$. The inputs contain\n", "$[f_x,f_y,\\text{mask}]$, while the outputs store the channels $[p,u_x,u_y]$.\n", "This is exactly what we'll specify as input and output dimensions for the NN below.\n", "\n", @@ -63,7 +63,7 @@ "source": [ "## Code coming up...\n", "\n", - "Let's get started with the implementation. Note that we'll skip the data generation process here. The code below is adapted from {cite}`thuerey2020dfp` and [this codebase](https://github.com/thunil/Deep-Flow-Prediction), which you can check out for details. Here, we'll simply download a small set of training data generated with a Spalart-Almaras RANS simulation in [OpenFOAM](https://openfoam.org/)." + "Let's get started with the implementation. Note that we'll skip the data generation process here. The code below is adapted from {cite}`thuerey2020dfp` and [this codebase](https://github.com/thunil/Deep-Flow-Prediction), which you can check out for details. Below, we'll simply use a small set of training data generated with a Spalart-Almaras RANS simulation in [OpenFOAM](https://openfoam.org/). First, let's import the required module, and install the dataloader from git." ] }, { @@ -74,63 +74,60 @@ "base_uri": "https://localhost:8080/" }, "id": "JwZudtWauiGa", - "outputId": "ac464e27-1aad-4a64-d748-6bb1b6b566a5" + "outputId": "60e43840-2888-46aa-a750-8453b3b606aa" + }, + "outputs": [], + "source": [ + "import os, sys, random\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "\n", + "!pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset@develop\n", + "from pbdl.torch.loader import Dataloader" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cghhfbQjiPIB" + }, + "source": [ + "The next cell will download the training data from HuggingFace, which can take a moment... The PBDL dataloader call below directly splits it into 320 samples for training, and 80 samples for validation. These validation samples are using the same airfoil shapes as the training samples, but different conditions (later on we'll download new shapes for testing)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OmYUCtpeiPIB", + "outputId": "f5427adb-d15b-4470-ac18-be4859831a53" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Torch version 1.11.0+cu113\n", - "Downloading training data (300MB), this can take a few minutes the first time...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded data, 320 training, 80 validation samples\n", - "Size of the inputs array: (320, 3, 128, 128)\n" + "\u001b[93m\u001b[1mWarning:\u001b[22m `airfoils` is stored in single-file format. The download might take some time.\u001b[0m\n", + "\u001b[96m\u001b[1mSuccess:\u001b[22m Loaded airfoils with 400 simulations and 1 samples each.\u001b[0m\n" ] } ], "source": [ - "import numpy as np\n", - "import os.path, random\n", - "import torch\n", - "from torch.utils.data import Dataset\n", - "print(\"Torch version {}\".format(torch.__version__))\n", + "BATCH_SIZE = 10\n", "\n", - "# get training data\n", - "dir = \"./\"\n", - "if True:\n", - " # download\n", - " if not os.path.isfile('data-airfoils.npz'):\n", - " import requests\n", - " print(\"Downloading training data (300MB), this can take a few minutes the first time...\")\n", - " with open(\"data-airfoils.npz\", 'wb') as datafile:\n", - " resp = requests.get('https://dataserv.ub.tum.de/s/m1615239/download?path=%2F&files=dfp-data-400.npz', verify=False)\n", - " datafile.write(resp.content)\n", - "else: \n", - " # alternative: load from google drive (upload there beforehand):\n", - " from google.colab import drive\n", - " drive.mount('/content/gdrive')\n", - " dir = \"./gdrive/My Drive/\"\n", - "\n", - "npfile=np.load(dir+'data-airfoils.npz')\n", - " \n", - "print(\"Loaded data, {} training, {} validation samples\".format(len(npfile[\"inputs\"]),len(npfile[\"vinputs\"])))\n", - "\n", - "print(\"Size of the inputs array: \"+format(npfile[\"inputs\"].shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u488C6ybnMCN" - }, - "source": [ - "If you run this notebook in colab, the `else` statement above (which is deactivated by default) might be interesting for you: instead of downloading the training data anew every time, you can manually download it once and store it in your google drive. We assume it's stored in the root directory as `data-airfoils.npz`. Afterwards, you can use the code above to load the file from your google drive, which is typically much faster. This is highly recommended if you want to experiment more extensively via colab." + "loader_train, loader_val = Dataloader.new_split(\n", + " [320, 80],\n", + " \"airfoils\",\n", + " batch_size=BATCH_SIZE, normalize_data=None,\n", + ")" ] }, { @@ -141,119 +138,59 @@ "source": [ "## RANS training data\n", "\n", - "Now we have some training data. In general it's very important to understand the data we're working with as much as possible (for any ML task the _garbage-in-gargabe-out_ principle definitely holds). We should at least understand the data in terms of dimensions and rough statistics, but ideally also in terms of content. Otherwise we'll have a very hard time interpreting the results of a training run. And despite all the DL magic: if you can't make out any patterns in your data, NNs surely won't find any useful ones.\n", + "Now we have the training and validation data. In general it's very important to understand the data we're working with as much as possible (for any ML task the _garbage-in-gargabe-out_ principle definitely holds). We should at least understand the data in terms of dimensions and rough statistics, but ideally also in terms of content. Otherwise we'll have a very hard time interpreting the results of a training run. And despite all the DL magic: if you can't make out any patterns in your data, NNs surely won't find any useful ones.\n", "\n", "Hence, let's look at one of the training samples... The following is just some helper code to show images side by side." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 353 + "height": 505 }, "id": "6y6YGxMeNECD", - "outputId": "ac8e0746-a975-4dc9-de80-cc8202b36f6e" + "outputId": "5ef5f729-de46-4312-9f97-72bdb3da0ea4" }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "import pylab\n", + "def plot(a1, a2, mask=None, stats=False, bottom=\"NN Output\", top=\"Reference\", title=None):\n", + " c = []\n", + " if mask is not None: mask = np.asarray(mask)\n", + " for i in range(3):\n", + " a2i = np.asarray(a2[i])\n", + " if mask is not None: a2i = a2i - mask*a2i # optionally mask out inner region\n", + " b = np.flipud(np.concatenate((a2i, a1[i]), axis=1).transpose())\n", + " min, mean, max = np.min(b), np.mean(b), np.max(b)\n", + " if stats:\n", + " print(\"Stats %d: \" % i + format([min, mean, max]))\n", + " b -= min\n", + " b /= max - min\n", + " c.append(b)\n", + " fig, axes = plt.subplots(1, 1, figsize=(16, 5))\n", + " axes.set_xticks([]) ; axes.set_yticks([])\n", + " im = axes.imshow(np.concatenate(c, axis=1), origin=\"upper\", cmap=\"magma\")\n", + " fig.colorbar(im, ax=axes)\n", + " axes.set_xlabel(\"p, ux, uy\")\n", + " axes.set_ylabel(\"%s %s\" % (bottom, top))\n", + " if title is not None: plt.title(title)\n", + " plt.show()\n", "\n", - "# helper to show three target channels: normalized, with colormap, side by side\n", - "def showSbs(a1,a2, stats=False, bottom=\"NN Output\", top=\"Reference\", title=None): \n", - " c=[]\n", - " for i in range(3):\n", - " b = np.flipud( np.concatenate((a2[i],a1[i]),axis=1).transpose())\n", - " min, mean, max = np.min(b), np.mean(b), np.max(b); \n", - " if stats: print(\"Stats %d: \"%i + format([min,mean,max]))\n", - " b -= min; b /= (max-min)\n", - " c.append(b)\n", - " fig, axes = pylab.subplots(1, 1, figsize=(16, 5))\n", - " axes.set_xticks([]); axes.set_yticks([]); \n", - " im = axes.imshow(np.concatenate(c,axis=1), origin='upper', cmap='magma')\n", - "\n", - " pylab.colorbar(im); pylab.xlabel('p, ux, uy'); pylab.ylabel('%s %s'%(bottom,top))\n", - " if title is not None: pylab.title(title)\n", - "\n", - "NUM=72\n", - "showSbs(npfile[\"inputs\"][NUM],npfile[\"targets\"][NUM], stats=False, bottom=\"Target Output\", top=\"Inputs\", title=\"3 inputs are shown at the top (free-ux, free-uy, mask), with the 3 output channels (p,ux,uy) at the bottom\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TVHhr8zCPUfN" - }, - "source": [ - "Next, let's define a small helper class `DfpDataset` to organize inputs and targets. We'll transfer the corresponding data to the pytorch `DataLoader` class. \n", - "\n", - "We also set up some globals to control training parameters, maybe most importantly: the learning rate `LR`, i.e. $\\eta$ from the previous sections. When your training run doesn't converge this is the first parameter to experiment with.\n", - "\n", - "Here, we'll keep it relatively small throughout. (Using _learning rate decay_ would be better, i.e. potentially give an improved convergence, but is omitted here for clarity.) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BGN4GqxkIueM", - "outputId": "edafc7f9-9f88-4d6a-ebea-77500412ef1e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training & validation batches: 32 , 8\n" - ] - } - ], - "source": [ - "# some global training constants\n", - "\n", - "# number of training epochs\n", - "EPOCHS = 100\n", - "# batch size\n", - "BATCH_SIZE = 10\n", - "# learning rate\n", - "LR = 0.00002\n", - "\n", - "class DfpDataset():\n", - " def __init__(self, inputs,targets): \n", - " self.inputs = inputs\n", - " self.targets = targets\n", - "\n", - " def __len__(self):\n", - " return len(self.inputs)\n", - "\n", - " def __getitem__(self, idx):\n", - " return self.inputs[idx], self.targets[idx]\n", - "\n", - "tdata = DfpDataset(npfile[\"inputs\"],npfile[\"targets\"])\n", - "vdata = DfpDataset(npfile[\"vinputs\"],npfile[\"vtargets\"])\n", - "\n", - "trainLoader = torch.utils.data.DataLoader(tdata, batch_size=BATCH_SIZE, shuffle=True , drop_last=True) \n", - "valiLoader = torch.utils.data.DataLoader(vdata, batch_size=BATCH_SIZE, shuffle=False, drop_last=True) \n", - "\n", - "print(\"Training & validation batches: {} , {}\".format(len(trainLoader),len(valiLoader) ))" + "inputs, targets = next(iter(loader_train))\n", + "plot(inputs[0], targets[0], stats=False, bottom=\"Target Output\", top=\"Inputs\", title=\"3 inputs are shown at the top (free-ux, free-uy, mask),\\n with the 3 output channels (p,ux,uy) at the bottom\")\n" ] }, { @@ -275,69 +212,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "id": "PVNjz4FjGhdY" }, "outputs": [], "source": [ - "import os, sys, random\n", - "import numpy as np\n", + "def blockUNet( in_c, out_c, name, size=4, pad=1, transposed=False, bn=True, activation=True, relu=True, dropout=0.0 ):\n", "\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torch.autograd \n", - "import torch.utils.data \n", - "\n", - "def blockUNet(in_c, out_c, name, size=4, pad=1, transposed=False, bn=True, activation=True, relu=True, dropout=0. ):\n", " block = nn.Sequential()\n", "\n", " if not transposed:\n", - " block.add_module('%s_conv' % name, nn.Conv2d(in_c, out_c, kernel_size=size, stride=2, padding=pad, bias=True))\n", + " block.add_module(\n", + " \"%s_conv\" % name,\n", + " nn.Conv2d(in_c, out_c, kernel_size=size, stride=2, padding=pad, bias=True),\n", + " )\n", " else:\n", - " block.add_module('%s_upsam' % name, nn.Upsample(scale_factor=2, mode='bilinear'))\n", + " block.add_module(\n", + " \"%s_upsam\" % name, nn.Upsample(scale_factor=2, mode=\"bilinear\")\n", + " )\n", " # reduce kernel size by one for the upsampling (ie decoder part)\n", - " block.add_module('%s_tconv' % name, nn.Conv2d(in_c, out_c, kernel_size=(size-1), stride=1, padding=pad, bias=True))\n", + " block.add_module(\n", + " \"%s_tconv\" % name,\n", + " nn.Conv2d( in_c, out_c, kernel_size=(size - 1), stride=1, padding=pad, bias=True ),\n", + " )\n", "\n", " if bn:\n", - " block.add_module('%s_bn' % name, nn.BatchNorm2d(out_c))\n", - " if dropout>0.:\n", - " block.add_module('%s_dropout' % name, nn.Dropout2d( dropout, inplace=True))\n", + " block.add_module(\"%s_bn\" % name, nn.BatchNorm2d(out_c))\n", + " if dropout > 0.0:\n", + " block.add_module(\"%s_dropout\" % name, nn.Dropout2d(dropout, inplace=True))\n", "\n", " if activation:\n", " if relu:\n", - " block.add_module('%s_relu' % name, nn.ReLU(inplace=True))\n", + " block.add_module(\"%s_relu\" % name, nn.ReLU(inplace=True))\n", " else:\n", - " block.add_module('%s_leakyrelu' % name, nn.LeakyReLU(0.2, inplace=True))\n", + " block.add_module(\"%s_leakyrelu\" % name, nn.LeakyReLU(0.2, inplace=True))\n", "\n", " return block\n", - " \n", + "\n", + "\n", "class DfpNet(nn.Module):\n", - " def __init__(self, channelExponent=6, dropout=0.):\n", + " def __init__(self, channelExponent=6, dropout=0.0):\n", " super(DfpNet, self).__init__()\n", - " channels = int(2 ** channelExponent + 0.5)\n", + " channels = int(2**channelExponent + 0.5)\n", + "\n", + " self.layer1 = blockUNet( 3, channels * 1, \"enc_layer1\", transposed=False, bn=True, relu=False, dropout=dropout, )\n", + " self.layer2 = blockUNet( channels, channels * 2, \"enc_layer2\", transposed=False, bn=True, relu=False, dropout=dropout, )\n", + " self.layer3 = blockUNet( channels * 2, channels * 2, \"enc_layer3\", transposed=False, bn=True, relu=False, dropout=dropout, )\n", + " self.layer4 = blockUNet( channels * 2, channels * 4, \"enc_layer4\", transposed=False, bn=True, relu=False, dropout=dropout, )\n", + " self.layer5 = blockUNet( channels * 4, channels * 8, \"enc_layer5\", transposed=False, bn=True, relu=False, dropout=dropout, )\n", + " self.layer6 = blockUNet( channels * 8, channels * 8, \"enc_layer6\", transposed=False, bn=True, relu=False, dropout=dropout, size=2, pad=0, )\n", + " self.layer7 = blockUNet( channels * 8, channels * 8, \"enc_layer7\", transposed=False, bn=True, relu=False, dropout=dropout, size=2, pad=0, )\n", "\n", - " self.layer1 = blockUNet(3 , channels*1, 'enc_layer1', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer2 = blockUNet(channels , channels*2, 'enc_layer2', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer3 = blockUNet(channels*2, channels*2, 'enc_layer3', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer4 = blockUNet(channels*2, channels*4, 'enc_layer4', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer5 = blockUNet(channels*4, channels*8, 'enc_layer5', transposed=False, bn=True, relu=False, dropout=dropout ) \n", - " self.layer6 = blockUNet(channels*8, channels*8, 'enc_layer6', transposed=False, bn=True, relu=False, dropout=dropout , size=2,pad=0)\n", - " self.layer7 = blockUNet(channels*8, channels*8, 'enc_layer7', transposed=False, bn=True, relu=False, dropout=dropout , size=2,pad=0)\n", - " \n", " # note, kernel size is internally reduced by one for the decoder part\n", - " self.dlayer7 = blockUNet(channels*8, channels*8, 'dec_layer7', transposed=True, bn=True, relu=True, dropout=dropout , size=2,pad=0)\n", - " self.dlayer6 = blockUNet(channels*16,channels*8, 'dec_layer6', transposed=True, bn=True, relu=True, dropout=dropout , size=2,pad=0)\n", - " self.dlayer5 = blockUNet(channels*16,channels*4, 'dec_layer5', transposed=True, bn=True, relu=True, dropout=dropout ) \n", - " self.dlayer4 = blockUNet(channels*8, channels*2, 'dec_layer4', transposed=True, bn=True, relu=True, dropout=dropout )\n", - " self.dlayer3 = blockUNet(channels*4, channels*2, 'dec_layer3', transposed=True, bn=True, relu=True, dropout=dropout )\n", - " self.dlayer2 = blockUNet(channels*4, channels , 'dec_layer2', transposed=True, bn=True, relu=True, dropout=dropout )\n", - " self.dlayer1 = blockUNet(channels*2, 3 , 'dec_layer1', transposed=True, bn=False, activation=False, dropout=dropout )\n", + " self.dlayer7 = blockUNet( channels * 8, channels * 8, \"dec_layer7\", transposed=True, bn=True, relu=True, dropout=dropout, size=2, pad=0, )\n", + " self.dlayer6 = blockUNet( channels * 16, channels * 8, \"dec_layer6\", transposed=True, bn=True, relu=True, dropout=dropout, size=2, pad=0, )\n", + " self.dlayer5 = blockUNet( channels * 16, channels * 4, \"dec_layer5\", transposed=True, bn=True, relu=True, dropout=dropout, )\n", + " self.dlayer4 = blockUNet( channels * 8, channels * 2, \"dec_layer4\", transposed=True, bn=True, relu=True, dropout=dropout, )\n", + " self.dlayer3 = blockUNet( channels * 4, channels * 2, \"dec_layer3\", transposed=True, bn=True, relu=True, dropout=dropout, )\n", + " self.dlayer2 = blockUNet( channels * 4, channels, \"dec_layer2\", transposed=True, bn=True, relu=True, dropout=dropout, )\n", + " self.dlayer1 = blockUNet( channels * 2, 3, \"dec_layer1\", transposed=True, bn=False, activation=False, dropout=dropout, )\n", "\n", - " def forward(self, x):\n", - " # note, this Unet stack could be allocated with a loop, of course... \n", - " out1 = self.layer1(x)\n", + " def forward(self, input):\n", + " # note, this Unet stack could be allocated with a loop, of course...\n", + " out1 = self.layer1(input)\n", " out2 = self.layer2(out1)\n", " out3 = self.layer3(out2)\n", " out4 = self.layer4(out3)\n", @@ -362,9 +300,9 @@ "\n", "def weights_init(m):\n", " classname = m.__class__.__name__\n", - " if classname.find('Conv') != -1:\n", + " if classname.find(\"Conv\") != -1:\n", " m.weight.data.normal_(0.0, 0.02)\n", - " elif classname.find('BatchNorm') != -1:\n", + " elif classname.find(\"BatchNorm\") != -1:\n", " m.weight.data.normal_(1.0, 0.02)\n", " m.bias.data.fill_(0)\n" ] @@ -377,49 +315,46 @@ "source": [ "Next, we can initialize an instance of the `DfpNet`.\n", "\n", - "Below, the `EXPO` parameter here controls the exponent for the feature maps of our Unet: this directly scales the network size (3 gives a network with ca. 150k parameters). This is relatively small for a generative NN with $3 \\times 128^2 = \\text{ca. }49k$ outputs, but yields fast training times and prevents overfitting given the relatively small data set we're using here. Hence it's a good starting point." + "Below, the `EXPO` parameter here controls the exponent for the feature maps of our Unet: this directly scales the network size (an exponent of 4 gives a network with ca. 585k parameters). This is a medium sized network for a generative NN with $3 \\times 128^2 = \\text{ca. }49k$ outputs, and still yields fast training times. Hence it's a good starting point. The `weights_init` function initializes the conv net to a reasonable initial value range, so that we can directly train with a fixed learning rate (otherwise learning rate schedules are highly recommended)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "N1uV8k5VIoqT", - "outputId": "f91c9beb-5ca9-4516-ae74-7ba87b2a2b0e" + "outputId": "bf19c644-7d34-4879-c538-3ffd802d3c4c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Trainable params: 147363 -> crucial! always keep in view... \n" + "Trainable params: 585027 -> crucial! always keep in view... \n" ] } ], "source": [ "# channel exponent to control network size\n", - "EXPO = 3\n", + "EXPO = 4\n", + "\n", + "torch.set_default_device(\"cuda:0\")\n", + "device = torch.get_default_device()\n", "\n", - "# setup network\n", "net = DfpNet(channelExponent=EXPO)\n", - "#print(net) # to double check the details...\n", - "\n", - "nn_parameters = filter(lambda p: p.requires_grad, net.parameters())\n", - "params = sum([np.prod(p.size()) for p in nn_parameters])\n", - "\n", - "# crucial parameter to keep in view: how many parameters do we have?\n", - "print(\"Trainable params: {} -> crucial! always keep in view... \".format(params)) \n", - "\n", "net.apply(weights_init)\n", "\n", - "criterionL1 = nn.L1Loss()\n", - "optimizerG = optim.Adam(net.parameters(), lr=LR, betas=(0.5, 0.999), weight_decay=0.0)\n", + "# crucial parameter to keep in view: how many parameters do we have?\n", + "nn_parameters = filter(lambda p: p.requires_grad, net.parameters())\n", + "print(\"Trainable params: {} -> crucial! always keep in view... \".format( sum([np.prod(p.size()) for p in nn_parameters]) ))\n", "\n", - "targets = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n", - "inputs = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n" + "LR = 0.0002 # learning rate\n", + "\n", + "loss = nn.L1Loss()\n", + "optimizer = optim.Adam(net.parameters(), lr=LR, betas=(0.5, 0.999), weight_decay=0.0)" ] }, { @@ -428,7 +363,8 @@ "id": "UNjBAvfWJMTR" }, "source": [ - "With an exponent of 3, this network has 147555 trainable parameters. As the subtle hint in the print statement indicates, this is a crucial number to always have in view when training NNs. It's easy to change settings, and get a network that has millions of parameters, and as a result probably all kinds of convergence and overfitting problems. The number of parameters definitely has to be matched with the amount of training data, and should also scale with the depth of the network. How these three relate to each other exactly is problem dependent, though." + "As the subtle hint in the print statement indicates, the parameter count is a crucial number to have in view when training NNs. It's easy to change settings, and get a network that has millions of parameters, and as a result can cause wasting resources at training time (and potentially training instabilities).\n", + "The number of parameters definitely has to be matched with the amount of training data, and should also scale with the depth of the network. How exactly these three relate to each other is problem dependent, though." ] }, { @@ -450,74 +386,74 @@ "base_uri": "https://localhost:8080/" }, "id": "kkOYvwxqKa8n", - "outputId": "5daf4281-1151-4fc4-fa3a-74a9ff8435d9" + "outputId": "ee34c1ec-7b83-41e8-eafb-15814fa4cd64" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Training from scratch\n", - "Epoch: 0, L1 train: 0.29219, L1 vali: 0.23295\n", - "Epoch: 1, L1 train: 0.25406, L1 vali: 0.22507\n", - "Epoch: 2, L1 train: 0.22487, L1 vali: 0.21019\n", - "Epoch: 20, L1 train: 0.05228, L1 vali: 0.04134\n", - "Epoch: 40, L1 train: 0.03730, L1 vali: 0.03020\n", - "Epoch: 60, L1 train: 0.03236, L1 vali: 0.02523\n", - "Epoch: 80, L1 train: 0.03364, L1 vali: 0.02302\n", - "Training done, saved network\n" + "Training from scratch...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loss train: 0.02743, loss val: 0.03572: 7%|█▎ | 14/200 [00:23<05:01, 1.62s/it]" ] } ], "source": [ - "history_L1 = []\n", - "history_L1val = []\n", + "EPOCHS = 200 # number of training epochs\n", "\n", - "if os.path.isfile(\"network\"):\n", - " print(\"Found existing network, loading & skipping training\")\n", - " net.load_state_dict(torch.load(\"network\")) # optionally, load existing network\n", + "loss_hist = []\n", + "loss_hist_val = []\n", + "\n", + "if os.path.isfile(\"dfpnet\"): # NT_DEBUG\n", + " print(\"Found existing network, loading & skipping training\")\n", + " net.load_state_dict(torch.load(\"dfpnet\"))\n", "\n", "else:\n", - " print(\"Training from scratch\")\n", - " for epoch in range(EPOCHS):\n", - " net.train()\n", - " L1_accum = 0.0\n", - " for i, traindata in enumerate(trainLoader, 0):\n", - " inputs_curr, targets_curr = traindata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", + " print(\"Training from scratch...\")\n", + " pbar = tqdm(initial=0, total=EPOCHS, ncols=96)\n", + " for epoch in range(EPOCHS):\n", "\n", - " net.zero_grad()\n", - " gen_out = net(inputs)\n", + " # training\n", + " net.train()\n", + " loss_acc = 0\n", + " for i, (inputs, targets) in enumerate(loader_train):\n", + " inputs = inputs.float()\n", + " targets = targets.float() \n", "\n", - " lossL1 = criterionL1(gen_out, targets)\n", - " lossL1.backward()\n", - " optimizerG.step()\n", - " L1_accum += lossL1.item()\n", + " net.zero_grad()\n", + " outputs = net(inputs)\n", + " lossL1 = loss(outputs, targets)\n", + " lossL1.backward()\n", + " optimizer.step()\n", + " loss_acc += lossL1.item()\n", "\n", - " # validation\n", - " net.eval()\n", - " L1val_accum = 0.0\n", - " for i, validata in enumerate(valiLoader, 0):\n", - " inputs_curr, targets_curr = validata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", + " loss_hist.append(loss_acc / len(loader_train))\n", "\n", - " outputs = net(inputs)\n", - " outputs_curr = outputs.data.cpu().numpy()\n", + " # evaluate validation samples\n", + " net.eval()\n", + " loss_acc_v = 0\n", + " with torch.no_grad():\n", + " for i, (inputs, targets) in enumerate(loader_val):\n", + " inputs = inputs.float()\n", + " targets = targets.float()\n", "\n", - " lossL1val = criterionL1(outputs, targets)\n", - " L1val_accum += lossL1val.item()\n", + " outputs = net(inputs)\n", + " loss_acc_v += loss(outputs, targets).item()\n", "\n", - " # data for graph plotting\n", - " history_L1.append( L1_accum / len(trainLoader) )\n", - " history_L1val.append( L1val_accum / len(valiLoader) )\n", + " loss_hist_val.append(loss_acc_v / len(loader_val))\n", + " pbar.set_description(\"loss train: {:7.5f}, loss val: {:7.5f}\".format( loss_hist[-1], loss_hist_val[-1] ) , refresh=False); pbar.update(1)\n", "\n", - " if epoch<3 or epoch%20==0:\n", - " print( \"Epoch: {}, L1 train: {:7.5f}, L1 vali: {:7.5f}\".format(epoch, history_L1[-1], history_L1val[-1]) )\n", + " torch.save(net.state_dict(), \"dfpnet\")\n", + " print(\"training done, saved network weights\")\n", "\n", - " torch.save(net.state_dict(), \"network\" )\n", - " print(\"Training done, saved network\")\n" + "loss_hist = np.asarray(loss_hist)\n", + "loss_hist_val = np.asarray(loss_hist_val)" ] }, { @@ -526,9 +462,9 @@ "id": "4KuUpJsSL3Jv" }, "source": [ - "The NN is finally trained! The losses should have nicely gone down in terms of absolute values: With the standard settings from an initial value of around 0.2 for the validation loss, to ca. 0.02 after 100 epochs. \n", + "The NN is finally trained! The losses should have nicely gone down in terms of absolute values: With the standard settings from an initial value of around 0.2 for the validation loss, to ca. 0.01 after all epochs.\n", "\n", - "Let's look at the graphs to get some intuition for how the training progressed over time. This is typically important to identify longer-term trends in the training. In practice it's tricky to spot whether the overall trend of 100 or so noisy numbers in a command line log is going slightly up or down - this is much easier to spot in a visualization." + "Let's look at the graphs to get some intuition for how the training progressed over time. This is typically important to identify longer-term trends in the training. In practice it's tricky to spot whether the overall trend of hundreds of noisy numbers in a command line log is going slightly up or down - this is much easier to spot in a visualization." ] }, { @@ -537,35 +473,17 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 286 + "height": 450 }, "id": "Hlki3bU8JMTT", - "outputId": "fa4b4911-85f2-478d-87e2-44b90bf5b83b" + "outputId": "3a9fa5a9-6682-4857-d9e4-656b252bd78b" }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "l1train = np.asarray(history_L1)\n", - "l1vali = np.asarray(history_L1val)\n", - "\n", - "plt.plot(np.arange(l1train.shape[0]),l1train,'b',label='Training loss')\n", - "plt.plot(np.arange(l1vali.shape[0] ),l1vali ,'g',label='Validation loss')\n", + "plt.plot(np.arange(loss_hist.shape[0]), loss_hist, \"b\", label=\"Training loss\")\n", + "plt.plot(np.arange(loss_hist_val.shape[0]), loss_hist_val, \"g\", label=\"Validation loss\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -579,12 +497,12 @@ "## Training progress and validation\n", "\n", "If you look closely at this graph, you should spot something peculiar:\n", - "_Why is the validation loss lower than the training loss_?\n", + "_Why is the validation loss lower than the training loss in the initial phase_?\n", "The data is similar to the training data of course, but in a way it's slightly \"tougher\", because the network certainly never received any validation samples during training. It is natural that the validation loss slightly deviates from the training loss, but how can the L1 loss be _lower_ for these inputs?\n", "\n", - "This is caused by the way the the training loop above is implemented in pytorch: while the training loss is evaluated in training mode via `net.train()`, the evaluation takes place after a call to `net.eval()`. This turns off batch normalization, and would disable features like dropout (if active). This slightly changes the evaluation. The code also runs a training step, and the loss for each point in the graph is measured with the evolving state of the network in an epoch. The network is updated, and afterwards runs through the validation samples. Thus all validation samples are using a state that is slightly different (and hopefully a bit better) than the initial states of the epoch. Due to both reasons, the validation loss can deviate, and in this example it's typically slightly lower.\n", + "This is caused by the way the the training loop above is implemented in pytorch: the training loss for each point in the graph is measured with the evolving state of the network in an epoch. The network is updated, and afterwards runs through the validation samples. Thus all validation samples are using a state that is slightly different (and hopefully a bit better) than the initial states of the epoch. Due to both reasons, the validation loss can deviate, and in this example it's slightly lower initially, while later on the network starts to slow down in terms of improving the new conditions of the validation samples. This indicates a slight overfitting for the training data, but nonetheless the validation loss still increases.\n", "\n", - "A general word of caution here: never evaluate your network with training data! That won't tell you much because overfitting is a very common problem. At least use data the network hasn't seen before, i.e. validation data, and if that looks good, try some more different (at least slightly out-of-distribution) inputs, i.e., _test data_. The next cell runs the trained network over the validation data, and displays one of them with the `showSbs` function.\n", + "A general word of caution here: never evaluate your network with training data! That won't tell you much because overfitting is a very common problem. At least use data the network hasn't seen before, i.e. validation data, and if that looks good, try some more different (at least slightly out-of-distribution) inputs, i.e., _test data_. The next cell runs the trained network on a batch of samples from the validation data, and displays one with the `plot` function.\n", "\n" ] }, @@ -594,36 +512,25 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 353 + "height": 483 }, "id": "N6OONK0bL4ev", - "outputId": "99dcb709-c2c3-4407-f0d4-9e9a91cee3bb", + "outputId": "1a473bd9-b3de-463a-cff9-3d886afeda93", "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "net.eval()\n", - "for i, validata in enumerate(valiLoader, 0):\n", - " inputs_curr, targets_curr = validata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", - " \n", - " outputs = net(inputs)\n", - " outputs_curr = outputs.data.cpu().numpy()\n", - " if i<1: showSbs(targets_curr[0] , outputs_curr[0], title=\"Validation sample %d\"%(i*BATCH_SIZE))\n" + "inputs, targets = next(iter(loader_val))\n", + "inputs = inputs.float()\n", + "targets = targets.float()\n", + "\n", + "outputs = net(inputs)\n", + "\n", + "outputs = outputs.data.cpu().numpy()\n", + "inputs = inputs.cpu()\n", + "targets = targets.cpu()\n", + "plot(targets[0], outputs[0], mask=inputs[0][2], title=\"Validation sample\")" ] }, { @@ -632,7 +539,7 @@ "id": "TJtxXgGoJMTV" }, "source": [ - "Visually, there should at least be a rough resemblance here between input out network output. We'll save the more detailed evaluation for the test data, though." + "This shows a good resemblance here between input out network output. The region around the airfoil is typically still a bit noisy (this is caused by the Dirichlet boundary, and could be alleviated with a modified loss and larger networks). The pressure values are typically the most difficult ones to learn. We'll save the more detailed evaluation for the test data, though." ] }, { @@ -659,85 +566,30 @@ "height": 1000 }, "id": "dD5O58L9HLE-", - "outputId": "89c56574-3010-4069-9664-a59894f5dec1" + "outputId": "25a66fab-38e3-438f-c2b7-71fbcbf2e3f6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading test data, this should be fast...\n", - "Loaded 10/10 test samples\n", - "\n", - "\n", - "Average test error: 0.028802116494625808\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "if not os.path.isfile('data-airfoils-test.npz'):\n", - " import urllib.request\n", - " url=\"https://physicsbaseddeeplearning.org/data/data_test.npz\"\n", - " print(\"Downloading test data, this should be fast...\")\n", - " urllib.request.urlretrieve(url, 'data-airfoils-test.npz')\n", - "\n", - "nptfile=np.load('data-airfoils-test.npz')\n", - "print(\"Loaded {}/{} test samples\\n\".format(len(nptfile[\"test_inputs\"]),len(nptfile[\"test_targets\"])))\n", - "\n", - "testdata = DfpDataset(nptfile[\"test_inputs\"],nptfile[\"test_targets\"])\n", - "testLoader = torch.utils.data.DataLoader(testdata, batch_size=1, shuffle=False, drop_last=True) \n", + "loader_test = Dataloader( \"airfoils-test\", batch_size=1, normalize_data=None, shuffle=False )\n", + "loss = nn.L1Loss()\n", "\n", "net.eval()\n", "L1t_accum = 0.\n", - "for i, validata in enumerate(testLoader, 0):\n", - " inputs_curr, targets_curr = validata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", + "for i, testdata in enumerate(loader_test, 0):\n", + " inputs_curr, targets_curr = testdata\n", + " inputs = inputs_curr.float()\n", + " targets = targets_curr.float()\n", "\n", " outputs = net(inputs)\n", + " \n", " outputs_curr = outputs.data.cpu().numpy()\n", + " inputs_curr = inputs_curr.cpu()\n", + " targets_curr = targets_curr.cpu()\n", + " \n", + " L1t_accum += loss(outputs, targets).item()\n", + " if i<3: plot(targets_curr[0] , outputs_curr[0], mask=inputs_curr[0][2], title=\"Test sample %d\"%(i))\n", "\n", - " lossL1t = criterionL1(outputs, targets)\n", - " L1t_accum += lossL1t.item()\n", - " if i<3: showSbs(targets_curr[0] , outputs_curr[0], title=\"Test sample %d\"%(i))\n", - "\n", - "print(\"\\nAverage test error: {}\".format( L1t_accum/len(testLoader) ))" + "print(\"\\nAverage relative test error: {}\".format( L1t_accum/len(loader_test) ))" ] }, { @@ -746,11 +598,11 @@ "id": "By722sgwFbwG" }, "source": [ - "The average test error with the default settings should be ca. 0.03. As the inputs are normalized, this means the average error across all three fields is 3% w.r.t. the maxima of each quantity. This is not too bad for new shapes, but clearly leaves room for improvement.\n", + "The average test error with the default settings should be below 0.025. As the inputs are normalized, this means the average relative error across all three fields is around 2.5% w.r.t. the maxima of each quantity. This is not too bad for new shapes, but clearly leaves room for improvement.\n", "\n", "Looking at the visualizations, you'll notice that especially high-pressure peaks and pockets of larger y-velocities are missing in the outputs. This is primarily caused by the small network, which does not have enough resources to reconstruct details.\n", "\n", - "Nonetheless, we have successfully replaced a fairly sophisticated RANS solver with a very small and fast neural network architecture. It has GPU support \"out-of-the-box\" (via pytorch), is differentiable, and introduces an error of only a few per-cent. With additional changes and more data, this setup can be made highly accurate {cite}`chen2021highacc`. \n", + "Nonetheless, we have successfully replaced a fairly sophisticated RANS solver with a small and fast neural network architecture. It has GPU support \"out-of-the-box\" (via pytorch), is differentiable, and introduces an error of only a few per-cent. With additional changes and more data, this setup can be made highly accurate {cite}`chen2021highacc`.\n", "\n", "---\n", "\n", @@ -765,7 +617,7 @@ "source": [ "## Next steps\n", "\n", - "There are many obvious things to try here (see the suggestions below), e.g. longer training, larger data sets, larger networks etc. \n", + "There are many obvious things to try here (see the suggestions below), e.g. longer training, larger data sets, larger networks etc.\n", "\n", "* Experiment with learning rate, dropout, and network size to reduce the error on the test set. How small can you make it with the given training data?\n", "\n", @@ -777,12 +629,14 @@ } ], "metadata": { + "accelerator": "GPU", "colab": { - "name": "supervised-airfoils.ipynb", + "gpuType": "A100", + "machine_shape": "hm", "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -796,9 +650,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.12.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 }