From c49ebad249e483008a802ddf8813d0df50b4aed5 Mon Sep 17 00:00:00 2001 From: Jeremy Reizenstein Date: Tue, 9 Aug 2022 20:48:51 -0700 Subject: [PATCH] tutorial fix: add visdom Summary: need to pip install visdom in new volumes tutorial. Reviewed By: kjchalup Differential Revision: D38501905 fbshipit-source-id: 534bf097e41f05b3389e9420e6dd2b61a4517861 --- docs/tutorials/implicitron_volumes.ipynb | 640 +++++++++++------------ 1 file changed, 317 insertions(+), 323 deletions(-) diff --git a/docs/tutorials/implicitron_volumes.ipynb b/docs/tutorials/implicitron_volumes.ipynb index 752334db..69a364d4 100644 --- a/docs/tutorials/implicitron_volumes.ipynb +++ b/docs/tutorials/implicitron_volumes.ipynb @@ -1,71 +1,30 @@ { - "metadata": { - "dataExplorerConfig": {}, - "bento_stylesheets": { - "bento/extensions/flow/main.css": true, - "bento/extensions/kernel_selector/main.css": true, - "bento/extensions/kernel_ui/main.css": true, - "bento/extensions/new_kernel/main.css": true, - "bento/extensions/system_usage/main.css": true, - "bento/extensions/theme/main.css": true - }, - "kernelspec": { - "display_name": "pytorch3d", - "language": "python", - "name": "bento_kernel_pytorch3d", - "metadata": { - "kernel_name": "bento_kernel_pytorch3d", - "nightly_builds": true, - "fbpkg_supported": true, - "cinder_runtime": false, - "is_prebuilt": true - } - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" - }, - "last_server_session_id": "2944b203-9ea8-4c0e-9634-645dfea5f26b", - "last_kernel_id": "bb33cd83-7924-489a-8bd8-2d9d62eb0126", - "last_base_url": "https://9177.od.fbinfra.net:443/", - "last_msg_id": "99f7088e-d22b355b859660479ef0574e_5743", - "captumWidgetMessage": {}, - "outputWidgetContext": {} - }, - "nbformat": 4, - "nbformat_minor": 2, "cells": [ { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "d38652e8-200a-413c-a36a-f4d349b78a9d", - "showInput": true, - "customInput": null, "collapsed": false, - "requestMsgId": "641de8aa-0e42-4446-9304-c160a2d226bf", + "customInput": null, "customOutput": null, "executionStartTime": 1659619824914, - "executionStopTime": 1659619825485 + "executionStopTime": 1659619825485, + "originalKey": "d38652e8-200a-413c-a36a-f4d349b78a9d", + "requestMsgId": "641de8aa-0e42-4446-9304-c160a2d226bf", + "showInput": true }, + "outputs": [], "source": [ "# Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved." - ], - "execution_count": null, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "customInput": null, "originalKey": "a48a9dcf-e80f-474b-a0c4-2c9a765b15c5", - "showInput": false, - "customInput": null + "showInput": false }, "source": [ "# A simple model using Implicitron\n", @@ -73,36 +32,36 @@ "In this demo, we use the VolumeRenderer from PyTorch3D as a custom implicit function in Implicitron. We will see\n", "* some of the main objects in Implicitron\n", "* how to plug in a custom part of a model" - ], - "attachments": {} + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "customInput": null, "originalKey": "51337c0e-ad27-4b75-ad6a-737dca5d7b95", - "showInput": false, - "customInput": null + "showInput": false }, "source": [ "## 0. Install and import modules\n", "\n", - "Ensure `torch` and `torchvision` are installed. If `pytorch3d` is not installed, install it using the following cell:\n", - "" - ], - "attachments": {} + "Ensure `torch` and `torchvision` are installed. If `pytorch3d` is not installed, install it using the following cell:\n" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "76f1ecd4-6b73-4214-81b0-118ef8d86872", - "showInput": true, - "customInput": null, "collapsed": false, - "requestMsgId": "deb6a860-6923-4227-abef-d31388b5142d", + "customInput": null, "customOutput": null, "executionStartTime": 1659619898147, - "executionStopTime": 1659619898274 + "executionStopTime": 1659619898274, + "originalKey": "76f1ecd4-6b73-4214-81b0-118ef8d86872", + "requestMsgId": "deb6a860-6923-4227-abef-d31388b5142d", + "showInput": true }, + "outputs": [], "source": [ "import os\n", "import sys\n", @@ -129,48 +88,48 @@ " !tar xzf 1.10.0.tar.gz\n", " os.environ[\"CUB_HOME\"] = os.getcwd() + \"/cub-1.10.0\"\n", " !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'" - ], - "execution_count": null, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { - "originalKey": "2c1020e6-eb4a-4644-9719-9147500d8e4f", - "showInput": false, - "customInput": null - }, - "source": [ - "Ensure omegaconf is installed. If not, run this cell. (It should not be necessary to restart the runtime.)" - ], - "attachments": {} - }, - { - "cell_type": "code", - "metadata": { - "originalKey": "9e751931-a38d-44c9-9ff1-ac2f7d3a3f99", - "showInput": true, "customInput": null, - "customOutput": null + "originalKey": "2c1020e6-eb4a-4644-9719-9147500d8e4f", + "showInput": false }, "source": [ - "!pip install omegaconf" - ], - "execution_count": null, - "outputs": [] + "Ensure omegaconf and visdom are installed. If not, run this cell. (It should not be necessary to restart the runtime.)" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { + "customInput": null, + "customOutput": null, + "originalKey": "9e751931-a38d-44c9-9ff1-ac2f7d3a3f99", + "showInput": true + }, + "outputs": [], + "source": [ + "!pip install omegaconf visdom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], "collapsed": false, - "originalKey": "86807e4a-1675-4520-a033-c7af85b233ec", - "requestMsgId": "880a7e20-4a90-4b37-a5eb-bccc0b23cac6", "customOutput": null, "executionStartTime": 1659612480556, "executionStopTime": 1659612480644, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "86807e4a-1675-4520-a033-c7af85b233ec", + "requestMsgId": "880a7e20-4a90-4b37-a5eb-bccc0b23cac6" }, + "outputs": [], "source": [ "import logging\n", "from typing import Tuple\n", @@ -193,56 +152,55 @@ "from pytorch3d.renderer.implicit.renderer import VolumeSampler\n", "from pytorch3d.structures import Volumes\n", "from pytorch3d.vis.plotly_vis import plot_batch_individually, plot_scene" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "b2d9f5bd-a9d4-4f78-b21e-92f2658e0fe9", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "7e43e623-4030-438b-af4e-b96170c9a052", + "customInput": null, "customOutput": null, "executionStartTime": 1659610929375, "executionStopTime": 1659610929383, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "b2d9f5bd-a9d4-4f78-b21e-92f2658e0fe9", + "requestMsgId": "7e43e623-4030-438b-af4e-b96170c9a052", + "showInput": true }, + "outputs": [], "source": [ "output_resolution = 80" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "0b0c2087-4c86-4c57-b0ee-6f48a70a9c78", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "46883aad-f00b-4fd4-ac17-eec0b2ac272a", + "customInput": null, "customOutput": null, "executionStartTime": 1659610930042, "executionStopTime": 1659610930050, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "0b0c2087-4c86-4c57-b0ee-6f48a70a9c78", + "requestMsgId": "46883aad-f00b-4fd4-ac17-eec0b2ac272a", + "showInput": true }, + "outputs": [], "source": [ "torch.set_printoptions(sci_mode=False)" - ], - "execution_count": null, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "customInput": null, "originalKey": "37809d0d-b02e-42df-85b6-cdd038373653", - "showInput": false, - "customInput": null + "showInput": false }, "source": [ "## 1. Load renders of a mesh (the cow mesh) as a dataset\n", @@ -250,50 +208,49 @@ "A dataset's train, val andĀ test parts in Implicitron are represented as a `dataset_map`, and provided by an implementation of `DatasetMapProvider`. \n", "`RenderedMeshDatasetMapProvider` is one which generates a single-scene dataset with only a train component by taking a mesh and rendering it.\n", "We use it with the cow mesh." - ], - "attachments": {} + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { - "originalKey": "cc68cb9c-b8bf-4e9e-bef1-2cfafdf6caa2", - "showInput": false, - "customInput": null, "collapsed": false, - "requestMsgId": "398cfcae-5d43-4b6f-9c75-db3d297364d4", + "customInput": null, "customOutput": null, "executionStartTime": 1659620739780, - "executionStopTime": 1659620739914 + "executionStopTime": 1659620739914, + "originalKey": "cc68cb9c-b8bf-4e9e-bef1-2cfafdf6caa2", + "requestMsgId": "398cfcae-5d43-4b6f-9c75-db3d297364d4", + "showInput": false }, "source": [ "If running this notebook using **Google Colab**, run the following cell to fetch the mesh obj and texture files and save it at the path data/cow_mesh.\n", "If running locally, the data is already available at the correct path." - ], - "attachments": {} + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "2c55e002-a885-4169-8fdc-af9078b05968", - "showInput": true, "customInput": null, - "customOutput": null + "customOutput": null, + "originalKey": "2c55e002-a885-4169-8fdc-af9078b05968", + "showInput": true }, + "outputs": [], "source": [ "!mkdir -p data/cow_mesh\n", "!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.obj\n", "!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.mtl\n", "!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow_texture.png" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { + "customInput": null, "originalKey": "2a976be8-01bf-4a1c-a6e7-61d5d08c3dbd", - "showInput": false, - "customInput": null + "showInput": false }, "source": [ "If we want to instantiate one of Implicitron's configurable objects, such as `RenderedMeshDatasetMapProvider`, without using the OmegaConf initialisation (get_default_args), we need to call `expand_args_fields` on the class first." @@ -301,17 +258,19 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "eb77aaec-048c-40bd-bd69-0e66b6ab60b1", - "showInput": true, + "code_folding": [], "collapsed": false, - "requestMsgId": "09b9975c-ff86-41c9-b4a9-975d23afc562", "customOutput": null, "executionStartTime": 1659621652237, "executionStopTime": 1659621652903, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "eb77aaec-048c-40bd-bd69-0e66b6ab60b1", + "requestMsgId": "09b9975c-ff86-41c9-b4a9-975d23afc562", + "showInput": true }, + "outputs": [], "source": [ "expand_args_fields(RenderedMeshDatasetMapProvider)\n", "cow_provider = RenderedMeshDatasetMapProvider(\n", @@ -319,83 +278,82 @@ " use_point_light=False,\n", " resolution=output_resolution,\n", ")" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "8210e15b-da48-4306-a49a-41c4e7e7d42f", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "c243edd2-a106-4fba-8471-dfa4f99a2088", + "customInput": null, "customOutput": null, "executionStartTime": 1659610966145, "executionStopTime": 1659610966255, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "8210e15b-da48-4306-a49a-41c4e7e7d42f", + "requestMsgId": "c243edd2-a106-4fba-8471-dfa4f99a2088", + "showInput": true }, + "outputs": [], "source": [ "dataset_map = cow_provider.get_dataset_map()\n", "tr_cameras = [training_frame.camera for training_frame in dataset_map.train]" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "458d72ad-d9a7-4f13-b5b7-90d2aec61c16", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "7f9431f3-8717-4d89-a7fe-1420dd0e00c4", + "customInput": null, "customOutput": null, "executionStartTime": 1659610967703, "executionStopTime": 1659610967848, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "458d72ad-d9a7-4f13-b5b7-90d2aec61c16", + "requestMsgId": "7f9431f3-8717-4d89-a7fe-1420dd0e00c4", + "showInput": true }, + "outputs": [], "source": [ "# The cameras are all in the XZ plane, in a circle about 2.7 from the origin\n", "centers = torch.cat([i.get_camera_center() for i in tr_cameras])\n", "print(centers.min(0).values)\n", "print(centers.max(0).values)" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "931e712b-b141-437a-97fb-dc2a07ce3458", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "931e712b-b141-437a-97fb-dc2a07ce3458", + "customInput": null, "customOutput": null, "executionStartTime": 1659552920194, "executionStopTime": 1659552923122, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "931e712b-b141-437a-97fb-dc2a07ce3458", + "requestMsgId": "931e712b-b141-437a-97fb-dc2a07ce3458", + "showInput": true }, + "outputs": [], "source": [ "# visualization of the cameras\n", "plot = plot_scene({\"k\": {i: camera for i, camera in enumerate(tr_cameras)}}, camera_scale=0.25)\n", "plot.layout.scene.aspectmode = \"data\"\n", "plot" - ], - "execution_count": null, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "customInput": null, "originalKey": "afa9c02d-f76b-4f68-83e9-9733c615406b", - "showInput": false, - "customInput": null + "showInput": false }, "source": [ "## 2. Custom implicit function šŸ§Š\n", @@ -409,23 +367,24 @@ "We do this by subclassing `ImplicitFunctionBase`.\n", "We need to register our subclass with a special decorator.\n", "We use Python's dataclass annotations for configuring the module." - ], - "attachments": {} + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "61b55043-dc52-4de7-992e-e2195edd2123", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "dfaace3c-098c-4ffe-9240-6a7ae0ff271e", + "customInput": null, "customOutput": null, "executionStartTime": 1659613575850, "executionStopTime": 1659613575940, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "61b55043-dc52-4de7-992e-e2195edd2123", + "requestMsgId": "dfaace3c-098c-4ffe-9240-6a7ae0ff271e", + "showInput": true }, + "outputs": [], "source": [ "@registry.register\n", "class MyVolumes(ImplicitFunctionBase, torch.nn.Module):\n", @@ -463,39 +422,38 @@ "\n", " # When an implicit function is used for raymarching, i.e. for MultiPassEmissionAbsorptionRenderer,\n", " # it must return (densities, features, an auxiliary tuple)\n", - " return densities, features, {}\n", - "" - ], - "execution_count": null, - "outputs": [] + " return densities, features, {}\n" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "customInput": null, "originalKey": "abaf2cd6-1b68-400e-a142-8fb9f49953f3", - "showInput": false, - "customInput": null + "showInput": false }, "source": [ "## 3. Construct the model object.\n", "\n", "The main model object in PyTorch3D is `GenericModel`, which has pluggable components for the major steps, including the renderer and the implicit function(s).\n", "There are two ways to construct it which are equivalent here." - ], - "attachments": {} + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "f26c3dce-fbae-4592-bd0e-e4a8abc57c2c", - "showInput": true, - "customInput": null, "collapsed": false, - "requestMsgId": "9213687e-1caf-46a8-a4e5-a9c531530092", + "customInput": null, "customOutput": null, "executionStartTime": 1659621267561, - "executionStopTime": 1659621267938 + "executionStopTime": 1659621267938, + "originalKey": "f26c3dce-fbae-4592-bd0e-e4a8abc57c2c", + "requestMsgId": "9213687e-1caf-46a8-a4e5-a9c531530092", + "showInput": true }, + "outputs": [], "source": [ "CONSTRUCT_MODEL_FROM_CONFIG = True\n", "if CONSTRUCT_MODEL_FROM_CONFIG:\n", @@ -521,82 +479,79 @@ " )\n", "\n", " # In this case we can get the equivalent DictConfig cfg object to the way gm is configured as follows\n", - " cfg = OmegaConf.structured(gm)\n", - "" - ], - "execution_count": null, - "outputs": [] + " cfg = OmegaConf.structured(gm)\n" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { - "originalKey": "4e659f7d-ce66-4999-83de-005eb09d7705", - "showInput": false, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "7b815b2b-cf19-44d0-ae89-76fde6df35ec", + "customInput": null, "customOutput": null, "executionStartTime": 1659611214689, "executionStopTime": 1659611214748, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "4e659f7d-ce66-4999-83de-005eb09d7705", + "requestMsgId": "7b815b2b-cf19-44d0-ae89-76fde6df35ec", + "showInput": false }, "source": [ " The default renderer is an emission-absorbtion raymarcher. We keep that default." - ], - "attachments": {} + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "d37ae488-c57c-44d3-9def-825dc1a6495b", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "71143ec1-730f-4876-8a14-e46eea9d6dd1", + "customInput": null, "customOutput": null, "executionStartTime": 1659621268007, "executionStopTime": 1659621268190, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "d37ae488-c57c-44d3-9def-825dc1a6495b", + "requestMsgId": "71143ec1-730f-4876-8a14-e46eea9d6dd1", + "showInput": true }, + "outputs": [], "source": [ "# We can display the configuration in use as follows.\n", "remove_unused_components(cfg)\n", "yaml = OmegaConf.to_yaml(cfg, sort_keys=False)\n", "%page -r yaml" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "52e53179-3c6e-4c1f-a38a-3a6d803687bb", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "05de9bc3-3f74-4a6f-851c-9ec919b59506", + "customInput": null, "customOutput": null, "executionStartTime": 1659621268727, "executionStopTime": 1659621268776, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "52e53179-3c6e-4c1f-a38a-3a6d803687bb", + "requestMsgId": "05de9bc3-3f74-4a6f-851c-9ec919b59506", + "showInput": true }, + "outputs": [], "source": [ "device = torch.device(\"cuda:0\")\n", "gm.to(device)\n", "assert next(gm.parameters()).is_cuda" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { + "customInput": null, "originalKey": "528a7d53-c645-49c2-9021-09adbb18cd23", - "showInput": false, - "customInput": null + "showInput": false }, "source": [ "## 4. train the model " @@ -604,58 +559,60 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "953280bd-3161-42ba-8dcb-0c8ef2d5cc25", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "9bba424b-7bfd-4e5a-9d79-ae316e20bab0", + "customInput": null, "customOutput": null, "executionStartTime": 1659621270236, "executionStopTime": 1659621270446, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "953280bd-3161-42ba-8dcb-0c8ef2d5cc25", + "requestMsgId": "9bba424b-7bfd-4e5a-9d79-ae316e20bab0", + "showInput": true }, + "outputs": [], "source": [ "train_data_collated = [FrameData.collate([frame.to(device)]) for frame in dataset_map.train]" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "2fcf07f0-0c28-49c7-8c76-1c9a9d810167", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "821deb43-6084-4ece-83c3-dee214562c47", + "customInput": null, "customOutput": null, "executionStartTime": 1659621270815, "executionStopTime": 1659621270948, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "2fcf07f0-0c28-49c7-8c76-1c9a9d810167", + "requestMsgId": "821deb43-6084-4ece-83c3-dee214562c47", + "showInput": true }, + "outputs": [], "source": [ "gm.train()\n", "optimizer = torch.optim.Adam(gm.parameters(), lr=0.1)" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "105099f7-ed0c-4e7f-a976-61a93fd0a8fe", - "showInput": true, + "code_folding": [], "collapsed": false, - "requestMsgId": "0c87c108-83e3-4129-ad02-85e0140f1368", "customOutput": null, "executionStartTime": 1659621271875, "executionStopTime": 1659621298146, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "105099f7-ed0c-4e7f-a976-61a93fd0a8fe", + "requestMsgId": "0c87c108-83e3-4129-ad02-85e0140f1368", + "showInput": true }, + "outputs": [], "source": [ "iterator = tqdm.tqdm(range(2000))\n", "for n_batch in iterator:\n", @@ -667,43 +624,43 @@ " if n_batch % 100 == 0:\n", " iterator.set_postfix_str(f\"loss: {float(out['objective']):.5f}\")\n", " optimizer.step()" - ], - "execution_count": null, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { - "originalKey": "e3cd494a-536b-48bc-8290-c048118c82eb", - "showInput": false, - "customInput": null, "collapsed": false, - "requestMsgId": "e3cd494a-536b-48bc-8290-c048118c82eb", + "customInput": null, "customOutput": null, "executionStartTime": 1659535024768, - "executionStopTime": 1659535024906 + "executionStopTime": 1659535024906, + "originalKey": "e3cd494a-536b-48bc-8290-c048118c82eb", + "requestMsgId": "e3cd494a-536b-48bc-8290-c048118c82eb", + "showInput": false }, "source": [ "## 5. Evaluate the module\n", "\n", "We generate complete images from all the viewpoints to see how they look." - ], - "attachments": {} + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "fbe1b2ea-cc24-4b20-a2d7-0249185e34a5", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "771ef1f8-5eee-4932-9e81-33604bf0512a", + "customInput": null, "customOutput": null, "executionStartTime": 1659621299859, "executionStopTime": 1659621311133, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "fbe1b2ea-cc24-4b20-a2d7-0249185e34a5", + "requestMsgId": "771ef1f8-5eee-4932-9e81-33604bf0512a", + "showInput": true }, + "outputs": [], "source": [ "def to_numpy_image(image):\n", " # Takes an image of shape (C, H, W) in [0,1], where C=3 or 1\n", @@ -731,21 +688,20 @@ " masks.append(mask)\n", " expected.append(expd)\n", " masks_expected.append(mask_expected)" - ], - "execution_count": null, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { - "originalKey": "24953039-9780-40fd-bd81-5d63e9f40069", - "showInput": false, - "customInput": null, "collapsed": false, - "requestMsgId": "7af895a3-dfe4-4c28-ac3b-4ff0fbb40c7f", + "customInput": null, "customOutput": null, "executionStartTime": 1659614622542, - "executionStopTime": 1659614622757 + "executionStopTime": 1659614622757, + "originalKey": "24953039-9780-40fd-bd81-5d63e9f40069", + "requestMsgId": "7af895a3-dfe4-4c28-ac3b-4ff0fbb40c7f", + "showInput": false }, "source": [ "We draw a grid showing predicted image and expected image, followed by predicted mask and expected mask, from each viewpoint. \n", @@ -788,23 +744,24 @@ " ...\n", "```\n", "" - ], - "attachments": {} + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "c488a34a-e46d-4649-93fb-4b1bb5a0e439", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "4221e632-fca1-4fe5-b2e3-f92c37aa40e4", + "customInput": null, "customOutput": null, "executionStartTime": 1659621313894, "executionStopTime": 1659621314042, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "c488a34a-e46d-4649-93fb-4b1bb5a0e439", + "requestMsgId": "4221e632-fca1-4fe5-b2e3-f92c37aa40e4", + "showInput": true }, + "outputs": [], "source": [ "images_to_display = [images.copy(), expected.copy(), masks.copy(), masks_expected.copy()]\n", "n_rows = 4\n", @@ -821,47 +778,46 @@ " for group in images_to_display_listed:\n", " split.append(group[row*n_per_row:(row+1)*n_per_row]) \n", "\n", - "Image.fromarray(np.block(split))\n", - "" - ], - "execution_count": null, - "outputs": [] + "Image.fromarray(np.block(split))\n" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "49eab9e1-4fe2-4fbe-b4f3-7b6953340170", - "showInput": true, - "customInput": null, + "code_folding": [], "collapsed": false, - "requestMsgId": "85b402ad-f903-431f-a13e-c2d697e869bb", + "customInput": null, "customOutput": null, "executionStartTime": 1659621323795, "executionStopTime": 1659621323820, - "code_folding": [], - "hidden_ranges": [] + "hidden_ranges": [], + "originalKey": "49eab9e1-4fe2-4fbe-b4f3-7b6953340170", + "requestMsgId": "85b402ad-f903-431f-a13e-c2d697e869bb", + "showInput": true }, + "outputs": [], "source": [ "# Print the maximum channel intensity in the first image.\n", "print(images[1].max()/255)" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "137d2c43-d39d-4266-ac5e-2b714da5e0ee", - "showInput": true, + "code_folding": [], + "collapsed": false, "customInput": null, "customOutput": null, - "code_folding": [], - "hidden_ranges": [], - "collapsed": false, - "requestMsgId": "8e27ec57-c2d6-4ae0-be69-b63b6af929ff", "executionStartTime": 1659621408642, - "executionStopTime": 1659621409559 + "executionStopTime": 1659621409559, + "hidden_ranges": [], + "originalKey": "137d2c43-d39d-4266-ac5e-2b714da5e0ee", + "requestMsgId": "8e27ec57-c2d6-4ae0-be69-b63b6af929ff", + "showInput": true }, + "outputs": [], "source": [ "plt.ioff()\n", "fig, ax = plt.subplots(figsize=(3,3))\n", @@ -869,51 +825,89 @@ "ax.grid(None)\n", "ims = [[ax.imshow(im, animated=True)] for im in images]\n", "ani = animation.ArtistAnimation(fig, ims, interval=80, blit=True)\n", - "ani_html = ani.to_jshtml()\n", - "" - ], - "execution_count": null, - "outputs": [] + "ani_html = ani.to_jshtml()\n" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "783e70d6-7cf1-4d76-a126-ba11ffc2f5be", - "showInput": true, - "customInput": null, "collapsed": false, - "requestMsgId": "b6843506-c5fa-4508-80fc-8ecae51a934a", + "customInput": null, "customOutput": null, "executionStartTime": 1659621409620, - "executionStopTime": 1659621409725 + "executionStopTime": 1659621409725, + "originalKey": "783e70d6-7cf1-4d76-a126-ba11ffc2f5be", + "requestMsgId": "b6843506-c5fa-4508-80fc-8ecae51a934a", + "showInput": true }, + "outputs": [], "source": [ "HTML(ani_html)" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "0286c350-2362-4f47-8181-2fc2ba51cfcf", - "showInput": true, - "customInput": null, "collapsed": false, - "requestMsgId": "976f4db9-d4c7-466c-bcfd-218234400226", + "customInput": null, "customOutput": null, "executionStartTime": 1659614670081, - "executionStopTime": 1659614670168 + "executionStopTime": 1659614670168, + "originalKey": "0286c350-2362-4f47-8181-2fc2ba51cfcf", + "requestMsgId": "976f4db9-d4c7-466c-bcfd-218234400226", + "showInput": true }, + "outputs": [], "source": [ "# If you want to see the output of the model with the volume forced to opaque white, run this and re-evaluate\n", "# with torch.no_grad():\n", "# gm._implicit_functions[0]._fn.density.fill_(9.0)\n", - "# gm._implicit_functions[0]._fn.color.fill_(9.0)\n", - "" - ], - "execution_count": null, - "outputs": [] + "# gm._implicit_functions[0]._fn.color.fill_(9.0)\n" + ] } - ] + ], + "metadata": { + "bento_stylesheets": { + "bento/extensions/flow/main.css": true, + "bento/extensions/kernel_selector/main.css": true, + "bento/extensions/kernel_ui/main.css": true, + "bento/extensions/new_kernel/main.css": true, + "bento/extensions/system_usage/main.css": true, + "bento/extensions/theme/main.css": true + }, + "captumWidgetMessage": {}, + "dataExplorerConfig": {}, + "kernelspec": { + "display_name": "pytorch3d", + "language": "python", + "metadata": { + "cinder_runtime": false, + "fbpkg_supported": true, + "is_prebuilt": true, + "kernel_name": "bento_kernel_pytorch3d", + "nightly_builds": true + }, + "name": "bento_kernel_pytorch3d" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + }, + "last_base_url": "https://9177.od.fbinfra.net:443/", + "last_kernel_id": "bb33cd83-7924-489a-8bd8-2d9d62eb0126", + "last_msg_id": "99f7088e-d22b355b859660479ef0574e_5743", + "last_server_session_id": "2944b203-9ea8-4c0e-9634-645dfea5f26b", + "outputWidgetContext": {} + }, + "nbformat": 4, + "nbformat_minor": 2 }