diff --git a/examples/benchmarks/ladle_furnace.ipynb b/examples/benchmarks/ladle_furnace.ipynb new file mode 100644 index 00000000..d05b1e1b --- /dev/null +++ b/examples/benchmarks/ladle_furnace.ipynb @@ -0,0 +1,3441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/giles/anaconda3/envs/tomopt/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import torch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ladle furnace benchmark\n", + "\n", + "In this example, we'll be optimising a detector for the task described in our initial [TomOpt paper](https://arxiv.org/abs/2309.14027)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "NAME = 'ladle_furnace'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "DEVICE = torch.device('cpu')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup volume\n", + "\n", + "We'll begin by defining our volume, consisting of the detectors and passive volume. The `ladle_furnace` benchmark module has a function to construct our initial detector:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/giles/anaconda3/envs/tomopt/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.0)\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n" + ] + } + ], + "source": [ + "from tomopt.benchmarks.ladle_furnace import get_initial_detector\n", + "from tomopt.volume import Volume" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Volume(\n", + " (layers): ModuleList(\n", + " (0): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0., 0.]), z=tensor([1.4800]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " (1): located at xy=tensor([0., 0.]), z=tensor([1.4700]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " (2): located at xy=tensor([0., 0.]), z=tensor([1.4600]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " (3): located at xy=tensor([0., 0.]), z=tensor([1.4500]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " )\n", + " )\n", + " (1): PassiveLayer located at z=tensor([1.4000])\n", + " (2): PassiveLayer located at z=tensor([1.3000])\n", + " (3): PassiveLayer located at z=tensor([1.2000])\n", + " (4): PassiveLayer located at z=tensor([1.1000])\n", + " (5): PassiveLayer located at z=tensor([1.])\n", + " (6): PassiveLayer located at z=tensor([0.9000])\n", + " (7): PassiveLayer located at z=tensor([0.8000])\n", + " (8): PassiveLayer located at z=tensor([0.7000])\n", + " (9): PassiveLayer located at z=tensor([0.6000])\n", + " (10): PassiveLayer located at z=tensor([0.5000])\n", + " (11): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0., 0.]), z=tensor([0.3500]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " (1): located at xy=tensor([0., 0.]), z=tensor([0.3400]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " (2): located at xy=tensor([0., 0.]), z=tensor([0.3300]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " (3): located at xy=tensor([0., 0.]), z=tensor([0.3200]), and xy span tensor([0.8660, 0.8660]) with budget scale 1.0825316905975342\n", + " )\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "volume = Volume(get_initial_detector(device=DEVICE), budget=6)\n", + "volume" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualise the layout of this in 3D. Note the closeness of the panels, and their offset from the passive volume. We do not expect this detector to perform well, but we can optimise it to a better configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. 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