{ "cells": [ { "cell_type": "markdown", "id": "2dfbd8da", "metadata": {}, "source": [ "## Checking consistency of serialization\n", "\n", "This test checks the consistency of the internal states between before/after serialization. We render two images. One with normal configuration and the other with deserialized states." ] }, { "cell_type": "code", "execution_count": 1, "id": "08170268", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.294290Z", "iopub.status.busy": "2021-10-22T11:35:38.293599Z", "iopub.status.idle": "2021-10-22T11:35:38.385096Z", "shell.execute_reply": "2021-10-22T11:35:38.385525Z" } }, "outputs": [], "source": [ "import lmenv\n", "env = lmenv.load('.lmenv')" ] }, { "cell_type": "code", "execution_count": 2, "id": "f51ac5d8", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.388701Z", "iopub.status.busy": "2021-10-22T11:35:38.388030Z", "iopub.status.idle": "2021-10-22T11:35:38.873773Z", "shell.execute_reply": "2021-10-22T11:35:38.873347Z" } }, "outputs": [], "source": [ "import os\n", "import imageio\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "import lmscene\n", "import lightmetrica as lm" ] }, { "cell_type": "code", "execution_count": 3, "id": "7439bad8", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.879668Z", "iopub.status.busy": "2021-10-22T11:35:38.879185Z", "iopub.status.idle": "2021-10-22T11:35:38.884578Z", "shell.execute_reply": "2021-10-22T11:35:38.884202Z" } }, "outputs": [ { "data": { "text/plain": [ "374" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getpid()" ] }, { "cell_type": "code", "execution_count": 4, "id": "481a90be", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.888524Z", "iopub.status.busy": "2021-10-22T11:35:38.888074Z", "iopub.status.idle": "2021-10-22T11:35:38.894712Z", "shell.execute_reply": "2021-10-22T11:35:38.894281Z" } }, "outputs": [], "source": [ "%load_ext lightmetrica_jupyter" ] }, { "cell_type": "code", "execution_count": 5, "id": "4aec9410", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.898994Z", "iopub.status.busy": "2021-10-22T11:35:38.898541Z", "iopub.status.idle": "2021-10-22T11:35:38.902017Z", "shell.execute_reply": "2021-10-22T11:35:38.902356Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[I|0.000] Lightmetrica -- Version 3.0.0 (rev. 70601db) Linux x64\n" ] } ], "source": [ "lm.init()\n", "lm.log.init('jupyter')\n", "lm.progress.init('jupyter')\n", "lm.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9b2ab54f", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.905676Z", "iopub.status.busy": "2021-10-22T11:35:38.905237Z", "iopub.status.idle": "2021-10-22T11:35:38.908053Z", "shell.execute_reply": "2021-10-22T11:35:38.908391Z" } }, "outputs": [], "source": [ "if not lm.Release:\n", " lm.debug.attach_to_debugger()" ] }, { "cell_type": "code", "execution_count": 7, "id": "282183ae", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.912746Z", "iopub.status.busy": "2021-10-22T11:35:38.911480Z", "iopub.status.idle": "2021-10-22T11:35:38.913296Z", "shell.execute_reply": "2021-10-22T11:35:38.913660Z" } }, "outputs": [], "source": [ "scene_names = lmscene.scenes_small()" ] }, { "cell_type": "code", "execution_count": 8, "id": "c3228d27", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:38.917953Z", "iopub.status.busy": "2021-10-22T11:35:38.916877Z", "iopub.status.idle": "2021-10-22T11:35:38.918606Z", "shell.execute_reply": "2021-10-22T11:35:38.918983Z" } }, "outputs": [], "source": [ "def rmse(img1, img2):\n", " return np.sqrt(np.mean((img1 - img2) ** 2))" ] }, { "cell_type": "code", "execution_count": 9, "id": "77f7a2b6", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:39.006633Z", "iopub.status.busy": "2021-10-22T11:35:38.927238Z", "iopub.status.idle": "2021-10-22T11:35:58.000392Z", "shell.execute_reply": "2021-10-22T11:35:57.999932Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing [scene='fireplace_room']\n", "w/o serialization\n", "[I|0.031] Loading asset [name='film_output']\n", "[I|0.109] Loading asset [name='accel']\n", "[I|0.109] Loading asset [name='scene']\n", "[I|0.109] Loading asset [name='camera_main']\n", "[I|0.109] Loading asset [name='model_obj']\n", "[I|0.109] .. Loading OBJ file [path='fireplace_room.obj']\n", "[I|0.109] .. Loading MTL file [path='fireplace_room.mtl']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/miniconda/envs/lm3_dev/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[I|0.436] .. Loading texture [path='wood.ppm']\n", "[I|0.526] .. Loading texture [path='leaf.ppm']\n", "[I|0.528] .. Loading texture [path='picture8.ppm']\n", "[I|0.560] .. Loading texture [path='wood5.ppm']\n", "[I|0.616] Building acceleration structure [name='accel']\n", "[I|0.616] .. Flattening scene\n", "[I|0.643] .. Building\n", "[I|1.285] Loading asset [name='renderer']\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9e9e3ac0ddbe4e7fa7cabf01c8c60dd3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2073600 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "w/ serialization\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ee646bf74d2f4352ba966b5bbd7520e0", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2073600 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Testing [scene='cornell_box_sphere']\n", "w/o serialization\n", "[I|10.028] Loading asset [name='film_output']\n", "[I|10.090] Loading asset [name='accel']\n", "[I|10.090] Loading asset [name='scene']\n", "[I|10.091] Loading asset [name='camera_main']\n", "[I|10.091] Loading asset [name='model_obj']\n", "[I|10.091] .. Loading OBJ file [path='CornellBox-Sphere.obj']\n", "[I|10.107] .. Loading MTL file [path='CornellBox-Sphere.mtl']\n", "[I|10.109] Building acceleration structure [name='accel']\n", "[I|10.109] .. Flattening scene\n", "[I|10.109] .. Building\n", "[I|10.113] Loading asset [name='renderer']\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "206baa66b07a44b2b78c3797145abbed", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2073600 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "w/ serialization\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e261cfe1f4e947bb8c82f0ceb22ddaac", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2073600 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Testing [scene='cube']\n", "w/o serialization\n", "[I|14.668] Loading asset [name='film_output']\n", "[I|14.730] Loading asset [name='accel']\n", "[I|14.730] Loading asset [name='scene']\n", "[I|14.730] Loading asset [name='camera_main']\n", "[I|14.731] Loading asset [name='model_obj']\n", "[I|14.731] .. Loading OBJ file [path='cube.obj']\n", "[I|14.731] .. Loading MTL file [path='default.mtl']\n", "[I|14.731] .. Loading texture [path='default.png']\n", "[I|14.742] Building acceleration structure [name='accel']\n", "[I|14.742] .. Flattening scene\n", "[I|14.742] .. Building\n", "[I|14.743] Loading asset [name='renderer']\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0c0a088e90a7491c80af442234a54931", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2073600 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "w/ serialization\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ab8d7f5294514a4e85c1e4a1298da5db", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2073600 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rmse_series = pd.Series(index=scene_names)\n", "for scene_name in scene_names:\n", " print(\"Testing [scene='{}']\".format(scene_name))\n", " \n", " # Load scene and render\n", " print('w/o serialization')\n", " lm.reset()\n", " lm.load_film('film_output', 'bitmap', w=1920, h=1080)\n", " lm.load_accel('accel', 'sahbvh')\n", " scene = lm.load_scene('scene', 'default', accel='$.assets.accel')\n", " lmscene.load(scene, env.scene_path, scene_name)\n", " scene.build()\n", " lm.load_renderer('renderer', 'raycast',\n", " scene='$.assets.scene',\n", " output='$.assets.film_output')\n", " \n", " renderer = lm.get_renderer('$.assets.renderer')\n", " renderer.render()\n", " film = lm.get_film('$.assets.film_output')\n", " img_orig = np.copy(film.buffer())\n", " \n", " # Visualize\n", " f = plt.figure(figsize=(15,15))\n", " ax = f.add_subplot(111)\n", " ax.imshow(np.clip(np.power(img_orig,1/2.2),0,1), origin='lower')\n", " plt.show()\n", " \n", " # Serialize, reset, deserialize, and render\n", " print('w/ serialization')\n", " lm.save_state_to_file('lm.serialized')\n", " lm.reset()\n", " lm.load_state_from_file('lm.serialized')\n", " \n", " renderer = lm.get_renderer('$.assets.renderer')\n", " renderer.render()\n", " film = lm.get_film('$.assets.film_output')\n", " img_serial = np.copy(film.buffer())\n", " \n", " # Visualize\n", " f = plt.figure(figsize=(15,15))\n", " ax = f.add_subplot(111)\n", " ax.imshow(np.clip(np.power(img_serial,1/2.2),0,1), origin='lower')\n", " plt.show()\n", " \n", " # Compare two images\n", " e = rmse(img_orig, img_serial)\n", " rmse_series[scene_name] = e" ] }, { "cell_type": "code", "execution_count": 10, "id": "4f6753b0", "metadata": { "execution": { "iopub.execute_input": "2021-10-22T11:35:58.006219Z", "iopub.status.busy": "2021-10-22T11:35:58.005645Z", "iopub.status.idle": "2021-10-22T11:35:58.008386Z", "shell.execute_reply": "2021-10-22T11:35:58.008772Z" } }, "outputs": [ { "data": { "text/plain": [ "fireplace_room 0.0\n", "cornell_box_sphere 0.0\n", "cube 0.0\n", "dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rmse_series" ] } ], "metadata": { "jupytext": { "formats": "ipynb,py:light", "text_representation": { "extension": ".py", "format_name": "light", 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