{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from PDE3D import PATH\n", "import mitsuba as mi \n", "mi.set_variant(\"cuda_ad_rgb\")\n", "import os\n", "from PDE3D.utils import *\n", "root_directory = os.path.join(PATH, \"output\", \"optimizations\")\n", "from PDE3D.BoundaryShape import *\n", "from PDE3D.Coefficient import *\n", "import matplotlib.pyplot as plt\n", "from matplotlib import gridspec\n", "import matplotlib\n", "import matplotlib.ticker as ticker\n", "from python.optimization.textures import load_boundary_data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "\n", "seed = 243\n", "spp = 11\n", "spp_primal = 13" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "conf = 1 \n", "parameter = \"source\"\n", "bias = 0.0\n", "scale=10.0\n", "screen_s1 = 0\n", "folder0 = f\"conf{conf}-{parameter}-constDirichlet\"\n", "folder1 = f\"res16-scale{scale}-bias{bias}-centered-screen{screen_s1}\"\n", "folder2 = f\"spp{spp_primal}_{spp}-seed{seed}-epsilon0.01\"\n", "folder3 = \"restensor16\"\n", "recordpath = os.path.join(root_directory, folder0, folder1, \n", " folder2, folder3, \"npy\")\n", "max_range_s1 = [bias, bias+scale * 1.1]\n", "s1_primal = np.load(os.path.join(recordpath, \"primal_time.npy\"))\n", "s1_grad = np.load(os.path.join(recordpath, \"grad_time.npy\"))\n", "\n", "\n", "\n", "conf = 2 \n", "parameter = \"source\"\n", "bias = 0.0\n", "scale=10.0\n", "screen_s2 = 10.0\n", "folder0 = f\"conf{conf}-{parameter}-constDirichlet\"\n", "folder1 = f\"res16-scale{scale}-bias{bias}-centered-screen{screen_s2}\"\n", "folder2 = f\"spp{spp_primal}_{spp}-seed{seed}-epsilon0.01\"\n", "folder3 = \"restensor16\"\n", "recordpath = os.path.join(root_directory, folder0, folder1, \n", " folder2, folder3, \"npy\")\n", "max_range_s2 = [bias, bias+scale * 1.1]\n", "s2_primal = np.load(os.path.join(recordpath, \"primal_time.npy\"))\n", "s2_grad = np.load(os.path.join(recordpath, \"grad_time.npy\"))\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "conf = 3\n", "parameter = \"screening\"\n", "bias = 0.0\n", "scale=20.0\n", "folder0 = f\"conf{conf}-{parameter}\"\n", "folder1 = f\"res16-scale{scale}-bias{bias}-centered\"\n", "folder2 = f\"spp{spp_primal}_{spp}-seed{seed}-epsilon0.01\"\n", "folder3 = \"restensor16\"\n", "recordpath = os.path.join(root_directory, folder0, folder1, \n", " folder2, folder3, \"npy\")\n", "max_range_sc1 = [bias, bias+scale * 1.1]\n", "sc1_primal = np.load(os.path.join(recordpath, \"primal_time.npy\"))\n", "sc1_grad = np.load(os.path.join(recordpath, \"grad_time.npy\"))\n", "\n", "conf = 4\n", "parameter = \"screening\"\n", "bias = 10.0\n", "scale=20.0\n", "folder0 = f\"conf{conf}-{parameter}\"\n", "folder1 = f\"res16-scale{scale}-bias{bias}-centered\"\n", "folder2 = f\"spp{spp_primal}_{spp}-seed{seed}-epsilon0.01\"\n", "folder3 = \"restensor16\"\n", "recordpath = os.path.join(root_directory, folder0, folder1, \n", " folder2, folder3, \"npy\")\n", "max_range_sc2 = [bias, bias+scale * 1.1]\n", "\n", "sc2_primal = np.load(os.path.join(recordpath, \"primal_time.npy\"))\n", "sc2_grad = np.load(os.path.join(recordpath, \"grad_time.npy\"))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "conf = 5\n", "parameter = \"diffusion\"\n", "bias = 1.0\n", "scale= 10.0\n", "screen_d1 = 0\n", "folder0 = f\"conf{conf}-{parameter}\"\n", "folder1 = f\"res16-scale{scale}-bias{bias}-centered-screen{screen_d1}\"\n", "folder2 = f\"spp{spp_primal}_{spp}-seed{seed}-epsilon0.01\"\n", "folder3 = \"restensor16\"\n", "recordpath = os.path.join(root_directory, folder0, folder1, \n", " folder2, folder3, \"npy\")\n", "max_range_d1 = [bias, bias+scale * 1.1]\n", "d1_primal = np.load(os.path.join(recordpath, \"primal_time.npy\"))\n", "d1_grad = np.load(os.path.join(recordpath, \"grad_time.npy\"))\n", "\n", "conf = 6\n", "parameter = \"diffusion\"\n", "bias = 1.0\n", "scale=10.0\n", "screen_d2 = 10.0\n", "folder0 = f\"conf{conf}-{parameter}\"\n", "folder1 = f\"res16-scale{scale}-bias{bias}-centered-screen{screen_d2}\"\n", "folder2 = f\"spp{spp_primal}_{spp}-seed{seed}-epsilon0.01\"\n", "folder3 = \"restensor16\"\n", "recordpath = os.path.join(root_directory, folder0, folder1, \n", " folder2, folder3, \"npy\")\n", "d2_primal = np.load(os.path.join(recordpath, \"primal_time.npy\"))\n", "d2_grad = np.load(os.path.join(recordpath, \"grad_time.npy\"))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opt 1\n", "Primal = 0.06 s\n", "Grad = 0.28 s\n", "Opt 2\n", "Primal = 0.05 s\n", "Grad = 0.15 s\n", "Opt 3\n", "Primal = 0.36 s\n", "Grad = 0.37 s\n", "Opt 4\n", "Primal = 0.25 s\n", "Grad = 0.26 s\n", "Opt 5\n", "Primal = 0.70 s\n", "Grad = 2.98 s\n", "Opt 6\n", "Primal = 0.42 s\n", "Grad = 1.00 s\n" ] } ], "source": [ "primals = [s1_primal, s2_primal, sc1_primal, sc2_primal, d1_primal, d2_primal]\n", "grads = [s1_grad, s2_grad, sc1_grad, sc2_grad, d1_grad, d2_grad]\n", "num_iters = [4096, 4096, 4096, 4096, 512, 512]\n", "\n", "for i, (primal, grad, num_iter) in enumerate(zip(primals, grads, num_iters)):\n", " primal_time = 0\n", " grad_time = 0\n", " for j in range(num_iter):\n", " primal_time += primal[i]\n", " grad_time += grad[i] \n", " primal_time /= num_iter\n", " grad_time /= num_iter\n", " print(f\"Opt {i + 1}\")\n", " print(f\"Primal = {primal_time:.2f} s\")\n", " print(f\"Grad = {grad_time:.2f} s\")" ] } ], "metadata": { "kernelspec": { "display_name": "inv-pde-new", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }