{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 11. Using External Models\n", "\n", "In this tutorial, we show how to load and use an existing model in a molecular dynamics simulation. Let's import a few packages and then choose our particle number. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "# uncomment to use GPUs\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"-1\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.3.0\n" ] } ], "source": [ "import hoomd\n", "import hoomd.htf as htf\n", "import tensorflow as tf\n", "import hoomd.md\n", "import hoomd.dump\n", "import hoomd.group\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "print(htf.__version__)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# number of particles\n", "N = 2**10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first create a function that maps positions to an image. It does this by doing kernel density estimation in 2D so that pixels are higher valued when more particles are near them. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def pos2img(positions, low, high, imgw):\n", " rbf = htf.RBFExpansion(low, high, imgw)\n", " # histogram in xy dimensions\n", " x = rbf(positions[...,0])\n", " y = rbf(positions[...,1]) \n", " xy = tf.reduce_sum(x[...,None] * y[:,None], 0)\n", " # now add channel information\n", " # preprocess -> -1 to 1\n", " xy -= tf.reduce_mean(xy)\n", " xy /= tf.math.sqrt(tf.reduce_mean(xy**2))\n", " # add channels\n", " img = xy[...,None] * [1., 1., 1.]\n", " \n", " return xy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's view what this looks like with random particles. The specific size of `224x224` that appears here is because the neural network (mobile net) used below requires images to be in that dimension." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.random.uniform(-1, 1, size=(N, 3))\n", "# make a few dense points\n", "x[0:10, :] = np.random.uniform(0.01, 0.01, size=(10, 3))\n", "x[10:20, :] = np.random.uniform(-0.9, -0.95, size=(10, 3))\n", "i = pos2img(x, -1, 1, 224)\n", "plt.gca().set_aspect('equal')\n", "plt.scatter(x[:,0], x[:,1], s=1)\n", "plt.xlim(-1, 1)\n", "plt.ylim(-1,1)\n", "plt.figure()\n", "plt.gca().set_aspect('equal')\n", "plt.imshow(i.numpy().T, origin='upper', interpolation='none')\n", "plt.colorbar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll define our model. Our potential will use similar code to what is above, except we need to create multiple channels because the NN requires channels. We will also do some preprocessing to get the pixel values looking like a standard normal distribution, which again is required for the NN. \n", "\n", "The actual potential energy is the negative log probability of a specific class from the underlying NN. Mobilenet predicts classes from the ImageNet database and so our potential will drive particles to maximize the chosen class. For example, it could drive the simulation to look like a dog or spider web or bulldozer. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224.h5\n", "14540800/14536120 [==============================] - 1s 0us/step\n" ] } ], "source": [ "mobilenet = tf.keras.applications.MobileNetV2(classifier_activation=None)\n", "\n", "class MobileModel(htf.SimModel):\n", " def setup(self, low, high, img_shape, target_class):\n", " self.img_shape = img_shape\n", " self.rbf = htf.RBFExpansion(low, high, img_shape[0])\n", " self.target_class = target_class\n", " self.mean, self.std = None, None\n", " def compute(self, nlist, positions):\n", " # histogram in xy dimensions\n", " x = self.rbf(positions[...,0])\n", " y = self.rbf(positions[...,1])\n", " xy = x[...,None] * y[:,None]\n", " xy = tf.reduce_mean(xy, axis=0)\n", " img = tf.stack((xy, xy, xy), axis=-1)\n", " # preprocess -> -1 to 1\n", " if self.mean is None:\n", " self.mean = tf.reduce_mean(img)\n", " self.std = tf.math.sqrt(tf.reduce_mean((img - self.mean)**2))\n", " img -= self.mean\n", " img /= self.std\n", " # get prediction\n", " logits = mobilenet(img[None])[0]\n", " energy = -(logits[self.target_class] - tf.reduce_sum(logits))\n", " forces = htf.compute_positions_forces(positions, energy) \n", " return forces, energy, tf.math.softmax(logits)[self.target_class], img, positions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we set-up the system to be a grid of particles from a lattice." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HOOMD-blue 2.5.2 DOUBLE HPMC_MIXED TBB SSE SSE2 SSE3 \n", "Compiled: 04/30/2019\n", "Copyright (c) 2009-2019 The Regents of the University of Michigan.\n", "-----\n", "You are using HOOMD-blue. Please cite the following:\n", "* J A Anderson, C D Lorenz, and A Travesset. \"General purpose molecular dynamics\n", " simulations fully implemented on graphics processing units\", Journal of\n", " Computational Physics 227 (2008) 5342--5359\n", "* J Glaser, T D Nguyen, J A Anderson, P Liu, F Spiga, J A Millan, D C Morse, and\n", " S C Glotzer. \"Strong scaling of general-purpose molecular dynamics simulations\n", " on GPUs\", Computer Physics Communications 192 (2015) 97--107\n", "-----\n", "HOOMD-blue is running on the CPU\n", "notice(2): Group \"all\" created containing 1024 particles\n" ] } ], "source": [ "hoomd.context.initialize('')\n", "sqrt_N = int(np.sqrt(N))\n", "N = sqrt_N**2\n", "system = hoomd.init.create_lattice(unitcell=hoomd.lattice.sq(a=0.1),\n", " n=[sqrt_N, sqrt_N])\n", "\n", "# add margin for pbc?\n", "L = system.box.Lx / 2 * 0.95\n", "model = MobileModel(0, low=-L, high=L, img_shape=(224, 224, 3),\n", " target_class = 134) #134: 'crane', #883 vase #815 spider web #937 broccoli\n", "\n", "tfcompute = htf.tfcompute(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lattice gives a high energy, since it is far away from looking like a particular image. Thus we first energy minimize to get to a lower potential energy" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "notice(2): Force mode is FORCE_MODE.tf2hoomd \n", "notice(2): Starting TensorflowCompute \n", "notice(2): completed reallocate\n", "notice(2): Setting flag indicating virial modification will occur\n", "** starting run **\n", "Time 00:00:04 | Step 0 / 0 | TPS 0 | ETA -2562047:-47:-16\n", "Average TPS: 0\n", "---------\n", "** run complete **\n" ] } ], "source": [ "nve = hoomd.md.integrate.nve(group=hoomd.group.all())\n", "fire=hoomd.md.integrate.mode_minimize_fire(dt=0.01, ftol=1e-5, Etol=1e-9)\n", "tfcompute.attach()\n", "hoomd.run(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is some code to check how our potential energy and temperature are looking. Remember low potential means a high probability of our target class. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log = hoomd.analyze.log(filename=None, quantities=['potential_energy', 'kinetic_energy', 'temperature'], period=1)\n", "\n", "def total_energy(timestep):\n", " U = log.query('potential_energy')\n", " TE = log.query('kinetic_energy')\n", " T = log.query('temperature')\n", " print('Total energy\\t', timestep,'\\t', f'{U + TE:.2f}', '\\tPotential\\t', f'{U:.2f}\\tTemperature\\t{T:3f}', )\n", "hoomd.analyze.callback(total_energy, 100)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "** starting run **\n", "Total energy\t 0 \t -7812.82 \tPotential\t -7812.82\tTemperature\t0.000000\n", "Time 00:00:53 | Step 12 / 250 | TPS 1.15022 | ETA 00:03:26\n", "Time 00:01:03 | Step 24 / 250 | TPS 1.17072 | ETA 00:03:13\n", "Time 00:01:13 | Step 36 / 250 | TPS 1.18075 | ETA 00:03:01\n", "Time 00:01:23 | Step 48 / 250 | TPS 1.17284 | ETA 00:02:52\n", "Time 00:01:34 | Step 60 / 250 | TPS 1.16316 | ETA 00:02:43\n", "Time 00:01:44 | Step 72 / 250 | TPS 1.17958 | ETA 00:02:30\n", "Time 00:01:54 | Step 84 / 250 | TPS 1.16657 | ETA 00:02:22\n", "Time 00:02:04 | Step 96 / 250 | TPS 1.1736 | ETA 00:02:11\n", "Total energy\t 100 \t -87519.09 \tPotential\t -87545.20\tTemperature\t0.025525\n", "Time 00:02:15 | Step 108 / 250 | TPS 1.17455 | ETA 00:02:00\n", "Time 00:02:25 | Step 120 / 250 | TPS 1.17813 | ETA 00:01:50\n", "Time 00:02:35 | Step 132 / 250 | TPS 1.17627 | ETA 00:01:40\n", "Time 00:02:45 | Step 144 / 250 | TPS 1.18636 | ETA 00:01:29\n", "Time 00:02:55 | Step 156 / 250 | TPS 1.17691 | ETA 00:01:19\n", "Time 00:03:05 | Step 168 / 250 | TPS 1.18729 | ETA 00:01:09\n", "Time 00:03:16 | Step 180 / 250 | TPS 1.18802 | ETA 00:00:58\n", "Time 00:03:26 | Step 192 / 250 | TPS 1.1909 | ETA 00:00:48\n", "Total energy\t 200 \t -125184.19 \tPotential\t -125184.57\tTemperature\t0.000372\n", "Time 00:03:36 | Step 204 / 250 | TPS 1.18626 | ETA 00:00:38\n", "Time 00:03:46 | Step 216 / 250 | TPS 1.19894 | ETA 00:00:28\n", "Time 00:03:56 | Step 228 / 250 | TPS 1.1357 | ETA 00:00:19\n", "Time 00:04:07 | Step 239 / 250 | TPS 1.07536 | ETA 00:00:10\n", "Time 00:04:16 | Step 250 / 250 | TPS 1.11402 | ETA 00:00:00\n", "Average TPS: 1.16783\n", "---------\n", "** run complete **\n" ] } ], "source": [ "hoomd.run(250)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run an NVT simulation. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "*Warning*: Ignoring command to disable an integration method that is already disabled" ] }, { "name": "stdout", "output_type": "stream", "text": [ "notice(2): integrate.langevin/bd is using specified gamma values\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nve.disable()\n", "nvt = hoomd.md.integrate.langevin(group=hoomd.group.all(), kT=0.01, seed=0)\n", "hoomd.md.integrate.mode_standard(dt=0.005)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "notice(2): Force mode is FORCE_MODE.tf2hoomd \n", "notice(2): Starting TensorflowCompute \n", "notice(2): completed reallocate\n", "notice(2): Setting flag indicating virial modification will occur\n", "** starting run **\n", "Time 00:05:06 | Step 261 / 650 | TPS 1.08523 | ETA 00:05:58\n", "Time 00:05:17 | Step 273 / 650 | TPS 1.17946 | ETA 00:05:19\n", "Time 00:05:27 | Step 285 / 650 | TPS 1.15693 | ETA 00:05:15\n", "Time 00:05:37 | Step 297 / 650 | TPS 1.15444 | ETA 00:05:05\n", "Total energy\t 300 \t -195356.61 \tPotential\t -195427.64\tTemperature\t0.069433\n", "Time 00:05:48 | Step 308 / 650 | TPS 1.06883 | ETA 00:05:19\n", "Time 00:05:58 | Step 320 / 650 | TPS 1.18357 | ETA 00:04:38\n", "Time 00:06:08 | Step 332 / 650 | TPS 1.1585 | ETA 00:04:34\n", "Time 00:06:18 | Step 344 / 650 | TPS 1.182 | ETA 00:04:18\n", "Time 00:06:29 | Step 356 / 650 | TPS 1.17866 | ETA 00:04:09\n", "Time 00:06:39 | Step 368 / 650 | TPS 1.19082 | ETA 00:03:56\n", "Time 00:06:49 | Step 380 / 650 | TPS 1.18946 | ETA 00:03:46\n", "Time 00:06:59 | Step 392 / 650 | TPS 1.18352 | ETA 00:03:37\n", "Total energy\t 400 \t -197427.13 \tPotential\t -197558.92\tTemperature\t0.128831\n", "Time 00:07:09 | Step 403 / 650 | TPS 1.05729 | ETA 00:03:53\n", "Time 00:07:20 | Step 415 / 650 | TPS 1.14645 | ETA 00:03:24\n", "Time 00:07:30 | Step 427 / 650 | TPS 1.19881 | ETA 00:03:06\n", "Time 00:07:40 | Step 440 / 650 | TPS 1.20428 | ETA 00:02:54\n", "Time 00:07:51 | Step 452 / 650 | TPS 1.1966 | ETA 00:02:45\n", "Time 00:08:01 | Step 464 / 650 | TPS 1.15394 | ETA 00:02:41\n", "Time 00:08:12 | Step 476 / 650 | TPS 1.12163 | ETA 00:02:35\n", "Time 00:08:22 | Step 488 / 650 | TPS 1.14299 | ETA 00:02:21\n", "Time 00:08:33 | Step 500 / 650 | TPS 1.13584 | ETA 00:02:12\n", "Total energy\t 500 \t -198181.77 \tPotential\t -198340.27\tTemperature\t0.154932\n", "Time 00:08:43 | Step 511 / 650 | TPS 1.05863 | ETA 00:02:11\n", "Time 00:08:53 | Step 523 / 650 | TPS 1.17337 | ETA 00:01:48\n", "Time 00:09:04 | Step 535 / 650 | TPS 1.16411 | ETA 00:01:38\n", "Time 00:09:14 | Step 547 / 650 | TPS 1.17125 | ETA 00:01:27\n", "Time 00:09:24 | Step 559 / 650 | TPS 1.15755 | ETA 00:01:18\n", "Time 00:09:34 | Step 571 / 650 | TPS 1.19048 | ETA 00:01:06\n", "Time 00:09:45 | Step 583 / 650 | TPS 1.13422 | ETA 00:00:59\n", "Time 00:09:55 | Step 595 / 650 | TPS 1.15032 | ETA 00:00:47\n", "Total energy\t 600 \t -216367.52 \tPotential\t -216539.64\tTemperature\t0.168252\n", "Time 00:10:06 | Step 606 / 650 | TPS 1.07866 | ETA 00:00:40\n", "Time 00:10:16 | Step 618 / 650 | TPS 1.15575 | ETA 00:00:27\n", "Time 00:10:26 | Step 630 / 650 | TPS 1.18886 | ETA 00:00:16\n", "Time 00:10:36 | Step 642 / 650 | TPS 1.18061 | ETA 00:00:06\n", "Time 00:10:43 | Step 650 / 650 | TPS 1.18242 | ETA 00:00:00\n", "Average TPS: 1.15405\n", "---------\n", "** run complete **\n" ] } ], "source": [ "tfcompute.attach(save_output_period=20)\n", "hoomd.run(400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's view how the potential -- probability of the class -- changes over time. " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(tfcompute.outputs[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It decreases some, since we started from an energy minimum. We can also view the probability of the given class. You'll notice the classifier sometimes gives relatively high probabilities that it's looking at the target class. You can experiment with different classes and increasing `N` should improve this agreement." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(tfcompute.outputs[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To look at the generated images, we need to convert from the standard normal pixels to normal pixels that can be plotted. That is what this function does. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 224, 224, 3)\n" ] } ], "source": [ "scale = np.max(tfcompute.outputs[-2] , axis=(0,1,2)) - np.min(tfcompute.outputs[-2] , axis=(0,1,2))\n", "processed_outs = (tfcompute.outputs[-2] - np.min(tfcompute.outputs[-2] , axis=(0,1,2))) / scale\n", "print(processed_outs.shape)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot most certain frame\n", "frame = np.argmax(tfcompute.outputs[1])\n", "\n", "fig, axs = plt.subplots(ncols=2, figsize=(1920 //180, 1080 // 180), dpi=180, squeeze=2)\n", "\n", "scat = axs[0].scatter(tfcompute.outputs[-1][frame,:,0], tfcompute.outputs[-1][frame,:,1], marker='.', s=3, alpha=0.25, color='#f5900a')\n", "ims = axs[1].imshow(processed_outs[frame,...,0].T, interpolation='None', cmap='copper', origin='lower')\n", "\n", "\n", "axs[0].axis('off')\n", "axs[0].set_aspect('equal')\n", "axs[0].set_xlim(-system.box.Lx / 2, system.box.Lx / 2)\n", "axs[0].set_ylim(-system.box.Ly / 2, system.box.Ly / 2)\n", "\n", "axs[1].axis('off')\n", "axs[1].set_aspect('equal')\n", "\n", "fig.patch.set_facecolor('black')\n", "fig.tight_layout(pad=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see this code animated as a [video here](https://twitter.com/andrewwhite01/status/1420010598755094528)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 5 }