From b44700848e54f35fcccef9aa14953a116bcc716e Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Sun, 4 Feb 2024 04:10:12 +0000 Subject: [PATCH 01/27] c2f notebook --- bayes3d/genjax/model.py | 13 +- bayes3d/viser.py | 58 +++ demo_c2f.ipynb | 484 ++++++++++++++++++ .../experiments/slam/slam_with_room_obj.ipynb | 286 +++++++---- 4 files changed, 735 insertions(+), 106 deletions(-) create mode 100644 bayes3d/viser.py create mode 100644 demo_c2f.ipynb diff --git a/bayes3d/genjax/model.py b/bayes3d/genjax/model.py index 6f667197..d49b7d3f 100644 --- a/bayes3d/genjax/model.py +++ b/bayes3d/genjax/model.py @@ -7,6 +7,7 @@ from genjax.incremental import Diff, NoChange, UnknownChange import bayes3d as b +import bayes3d.scene_graph from .genjax_distributions import ( contact_params_uniform, @@ -127,14 +128,14 @@ def get_far_plane(trace): def add_object(trace, key, obj_id, parent, face_parent, face_child): - N = b.get_indices(trace).shape[0] + 1 + N = get_indices(trace).shape[0] + 1 choices = trace.get_choices() choices[f"parent_{N-1}"] = parent choices[f"id_{N-1}"] = obj_id choices[f"face_parent_{N-1}"] = face_parent choices[f"face_child_{N-1}"] = face_child choices[f"contact_params_{N-1}"] = jnp.zeros(3) - return model.importance(key, choices, (jnp.arange(N), *trace.get_args()[1:]))[1] + return model.importance(key, choices, (jnp.arange(N), *trace.get_args()[1:]))[0] add_object_jit = jax.jit(add_object) @@ -151,7 +152,7 @@ def print_trace(trace): def viz_trace_meshcat(trace, colors=None): - b.clear() + b.clear_visualizer() b.show_cloud( "1", b.apply_transform_jit(trace["image"].reshape(-1, 3), trace["camera_pose"]) ) @@ -223,14 +224,14 @@ def enumerator(trace, key, *args): key, chm_builder(addresses, args, chm_args), argdiff_f(trace), - )[2] + )[0] def enumerator_with_weight(trace, key, *args): return trace.update( key, chm_builder(addresses, args, chm_args), argdiff_f(trace), - )[1:3] + )[0:2] def enumerator_score(trace, key, *args): return enumerator(trace, key, *args).get_score() @@ -301,4 +302,4 @@ def update_address(trace, key, address, value): key, genjax.choice_map({address: value}), tuple(map(lambda v: Diff(v, UnknownChange), trace.args)), - )[2] + )[0] \ No newline at end of file diff --git a/bayes3d/viser.py b/bayes3d/viser.py new file mode 100644 index 00000000..ca9e8a33 --- /dev/null +++ b/bayes3d/viser.py @@ -0,0 +1,58 @@ +import viser +import random +import time + +import imageio.v3 as iio +import numpy as onp + +server.add_frame( + "/tree", + wxyz=(1.0, 0.0, 0.0, 0.0), + position=(random.random() * 2.0, 2.0, 0.2), +) +server.add_frame( + "/tree/branch", + wxyz=(1.0, 0.0, 0.0, 0.0), + position=(random.random() * 2.0, 2.0, 0.2), +) + +client_handle = list(server.get_clients().values())[0] + +p,q = client_handle.camera.position, client_handle.camera.wxyz + +client_handle.camera.position = p +client_handle.camera.wxyz = q + +img = client_handle.camera.get_render(100,100) + + + +server = viser.ViserServer() + +import os +import trimesh +i = 9 +model_dir = os.path.join(b.utils.get_assets_dir(), "ycb_video_models/models") +mesh_path = os.path.join(model_dir, b.utils.ycb_loader.MODEL_NAMES[i],"textured.obj") +mesh = trimesh.load(mesh_path) + +server.add_mesh_trimesh( + name="/trimesh", + mesh=mesh, +) + +server.reset_scene() + + +server.add_mesh( + name="/trimesh", + vertices=mesh.vertices, + faces=mesh.faces, +) + +sphere = trimesh.creation.uv_sphere(0.1, (10,10,)) +server.add_mesh( + name="/trimesh2", + vertices=sphere.vertices * np.array([1.0, 2.0, 3.0]), + faces=sphere.faces, +) \ No newline at end of file diff --git a/demo_c2f.ipynb b/demo_c2f.ipynb new file mode 100644 index 00000000..c443f647 --- /dev/null +++ b/demo_c2f.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5300d4b8-7b89-492c-950f-3e56fa9d46f2", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import jax\n", + "import bayes3d as b\n", + "import time\n", + "from PIL import Image\n", + "from scipy.spatial.transform import Rotation as R\n", + "import matplotlib.pyplot as plt\n", + "import cv2\n", + "import trimesh\n", + "import os\n", + "import glob\n", + "import bayes3d.neural\n", + "import pickle\n", + "# Can be helpful for debugging:\n", + "# jax.config.update('jax_enable_checks', True) \n", + "# from bayes3d.neural.segmentation import carvekit_get_foreground_mask\n", + "import genjax\n", + "import bayes3d.genjax" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2448882a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
╭─────────────── viser ───────────────╮\n",
+       "│             ╷                       │\n",
+       "│   HTTP      │ http://0.0.0.0:8081   │\n",
+       "│   Websocket │ ws://0.0.0.0:8081     │\n",
+       "│             ╵                       │\n",
+       "╰─────────────────────────────────────╯\n",
+       "
\n" + ], + "text/plain": [ + "╭─────────────── \u001b[1mviser\u001b[0m ───────────────╮\n", + "│ ╷ │\n", + "│ HTTP │ http://0.0.0.0:8081 │\n", + "│ Websocket │ ws://0.0.0.0:8081 │\n", + "│ ╵ │\n", + "╰─────────────────────────────────────╯\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import viser\n", + "server = viser.ViserServer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "57e1fa42-9f39-437f-b408-7c9760a86413", + "metadata": {}, + "outputs": [], + "source": [ + "importance_jit = jax.jit(b.genjax.model.importance)\n", + "key = jax.random.PRNGKey(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ed42e5c3-be5d-420b-9a21-759247e5d7b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['rgbPixels', 'depthPixels', 'segmentationMaskBuffer', 'camera_pose', 'camera_matrix'])\n" + ] + }, + { + "data": { + "image/jpeg": 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", + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file = os.path.join(b.utils.get_assets_dir(),\"tutorial_mug_image.pkl\")\n", + "all_data = pickle.load(open(file, \"rb\"))\n", + "IDX = 0\n", + "data = all_data[IDX]\n", + "print(data[\"camera_image\"].keys())\n", + "K = data[\"camera_image\"]['camera_matrix'][0]\n", + "rgb = data[\"camera_image\"]['rgbPixels']\n", + "depth = data[\"camera_image\"]['depthPixels']\n", + "camera_pose = data[\"camera_image\"]['camera_pose']\n", + "camera_pose = b.t3d.pybullet_pose_to_transform(camera_pose)\n", + "fx, fy, cx, cy = K[0,0],K[1,1],K[0,2],K[1,2]\n", + "h,w = depth.shape\n", + "near = 0.001\n", + "rgbd_original = b.RGBD(rgb, depth, camera_pose, b.Intrinsics(h,w,fx,fy,cx,cy,0.001,10000.0))\n", + "scaling_factor = 0.2\n", + "rgbd_scaled_down = b.RGBD.scale_rgbd(rgbd_original, scaling_factor)\n", + "b.hstack_images([b.get_rgb_image(rgbd_scaled_down.rgb), b.get_depth_image(rgbd_scaled_down.depth,max_val=2.5)])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "432c5da8-eb91-408f-b2e4-501eef3cc221", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
(viser) Connection opened (0, 1 total), 3 persistent messages\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m(\u001b[0m\u001b[1mviser\u001b[0m\u001b[1m)\u001b[0m Connection opened \u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m1\u001b[0m total\u001b[1m)\u001b[0m, \u001b[1;36m3\u001b[0m persistent messages\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "table_pose, plane_dims = b.utils.infer_table_plane(\n", + " b.unproject_depth(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics),\n", + " jnp.eye(4), rgbd_scaled_down.intrinsics, \n", + " ransac_threshold=0.001, inlier_threshold=0.001, segmentation_threshold=0.1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "52b57c87", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PointCloudHandle(_impl=_SceneNodeHandleState(name='/cloud', api=, wxyz=array([1., 0., 0., 0.]), position=array([0., 0., 0.]), visible=True, click_cb=None))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "server.add_point_cloud(\n", + " \"/cloud\",\n", + " points=np.array(b.unproject_depth(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics).reshape(-1,3)),\n", + " colors=np.array(rgbd_scaled_down.rgb.reshape(-1,3)),\n", + " point_size=0.01\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ce8e080e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FrameHandle(_impl=_SceneNodeHandleState(name='/table', api=, wxyz=array([ 0.10174274, 0.1909879 , 0.79682994, -0.56346023], dtype=float32), position=array([0.13303192, 0.06902084, 0.7311834 ], dtype=float32), visible=True, click_cb=None))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "server.add_frame(\n", + " \"/table\",\n", + " position=np.array(table_pose[:3,3]),\n", + " wxyz=b.rotation_matrix_to_quaternion(table_pose[:3,:3]),\n", + " axes_length=0.1,\n", + " axes_radius=0.005\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "89d55282-eb41-4c8e-97fe-1b9528f52481", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[E rasterize_gl.cpp:121] OpenGL version reported as 4.6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Increasing frame buffer size to (width, height, depth) = (192, 96, 1024)\n" + ] + } + ], + "source": [ + "b.setup_renderer(rgbd_scaled_down.intrinsics)\n", + "model_dir = os.path.join(b.utils.get_assets_dir(),\"bop/ycbv/models\")\n", + "mesh_path = os.path.join(model_dir,\"obj_\" + \"{}\".format(13+1).rjust(6, '0') + \".ply\")\n", + "b.RENDERER.add_mesh_from_file(mesh_path, scaling_factor=1.0/1000.0)\n", + "mesh_path = os.path.join(model_dir,\"obj_\" + \"{}\".format(10+1).rjust(6, '0') + \".ply\")\n", + "b.RENDERER.add_mesh_from_file(mesh_path, scaling_factor=1.0/1000.0)\n", + "b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=1.0/1000000000.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "width = 0.03\n", + "ang = jnp.pi\n", + "num_position_grids = 51\n", + "num_angle_grids = 51\n", + "contact_param_deltas = b.utils.make_translation_grid_enumeration_3d(\n", + " -width, -width, -ang,\n", + " width, width, ang,\n", + " num_position_grids,num_position_grids,num_angle_grids\n", + ")\n", + "\n", + "grid_params = [\n", + " (0.5, jnp.pi, (15,15,15)), (0.2, jnp.pi, (15,15,15)), (0.1, jnp.pi, (15,15,15)),\n", + " (0.05, jnp.pi/3, (15,15,15)),\n", + " (0.02, jnp.pi, (9,9,51))\n", + " , (0.01, jnp.pi/5, (15,15,15)),\n", + " (0.01, 0.0, (31,31,1)),(0.05, 0.0, (31,31,1))\n", + "]\n", + "contact_param_gridding_schedule = [\n", + " b.utils.make_translation_grid_enumeration_3d(\n", + " -x, -x, -ang,\n", + " x, x, ang,\n", + " *nums\n", + " )\n", + " for (x,ang,nums) in grid_params\n", + "]\n", + "\n", + "OBJECT_NUMBER = 1\n", + "address = f\"contact_params_{OBJECT_NUMBER}\"\n", + "enumerators = b.genjax.make_enumerator([address])\n", + "\n", + "def c2f_(potential_trace, contact_param_gridding_schedule):\n", + " cp = potential_trace[address]\n", + " for cp_grid in contact_param_gridding_schedule:\n", + " cps = cp + cp_grid\n", + " scores = enumerators.enumerate_choices_get_scores(potential_trace, key, cps)\n", + " cp = cps[scores.argmax()]\n", + " potential_trace = enumerators.update_choices(potential_trace, key, cp)\n", + " return potential_trace, scores.argmax()\n", + "c2f = jax.jit(c2f_)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5a628704", + "metadata": {}, + "outputs": [], + "source": [ + "def viz_trace_viser(server, trace, colors=None):\n", + " server.reset_scene()\n", + " indices = b.genjax.get_indices(trace)\n", + " poses = b.genjax.get_poses(trace)\n", + " for i in range(len(poses)):\n", + " server.add_mesh_trimesh(\n", + " name=\"/trimesh\",\n", + " mesh=b.RENDERER.meshes[indices[i]],\n", + " position=np.array(poses[i][:3,3]),\n", + " wxyz=b.rotation_matrix_to_quaternion(poses[i][:3,:3]),\n", + " )\n", + " server.add_point_cloud(\n", + " \"/cloud\",\n", + " points=np.array(trace[\"image\"].reshape(-1,3)),\n", + " colors=np.array([1.0, 0.0, 0.0]),\n", + " point_size=0.01\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-57.049133\n" + ] + } + ], + "source": [ + "obs_img = b.unproject_depth_jit(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics)\n", + "trace, weight = importance_jit(key, genjax.choice_map({\n", + " \"parent_0\": -1,\n", + " \"parent_1\": 0,\n", + " \"id_0\": jnp.int32(2),\n", + " \"id_1\": jnp.int32(0),\n", + " \"camera_pose\": jnp.eye(4),\n", + " \"root_pose_0\": table_pose,\n", + " \"face_parent_1\": 2,\n", + " \"face_child_1\": 3,\n", + " \"image\": obs_img,\n", + " \"variance\": 0.03,\n", + " \"outlier_prob\": 0.0001,\n", + "}), (\n", + " jnp.arange(2),\n", + " jnp.arange(22),\n", + " jnp.array([-jnp.ones(3)*100.0, jnp.ones(3)*100.0]),\n", + " jnp.array([jnp.array([-0.3, -0.3, -22*jnp.pi]), jnp.array([0.3, 0.3, 22*jnp.pi])]),\n", + " b.RENDERER.model_box_dims)\n", + ")\n", + "print(trace.get_score())\n", + "viz_trace_viser(server, trace)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9170b633", + "metadata": {}, + "outputs": [], + "source": [ + "potential_trace = trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64255e5f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1dd46342", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "cp = potential_trace[address]\n", + "new_potential_trace = potential_trace\n", + "for cp_grid in contact_param_gridding_schedule:\n", + " cps = cp + cp_grid\n", + " scores = enumerators.enumerate_choices_get_scores(new_potential_trace, key, cps)\n", + " cp = cps[scores.argmax()]\n", + " new_potential_trace = enumerators.update_choices(new_potential_trace, key, cp)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8c4398d2", + "metadata": {}, + "outputs": [], + "source": [ + "viz_trace_viser(server, new_potential_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "955c1d6d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.10350819 0.08779351 1.7163045 ]\n", + "CPU times: user 8.78 s, sys: 123 ms, total: 8.91 s\n", + "Wall time: 7.76 s\n" + ] + } + ], + "source": [ + "%%time\n", + "key = jax.random.split(key,2)[0]\n", + "new_potential_trace = c2f(potential_trace, contact_param_gridding_schedule)[0]\n", + "print(new_potential_trace[\"contact_params_1\"])\n", + "b.genjax.viz_trace_meshcat(new_potential_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "32ce4392", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaling_factor = 3.0\n", + "img = b.scale_image(b.get_depth_image(\n", + " b.genjax.get_rendered_image(new_potential_trace)[...,2],\n", + "), scaling_factor)\n", + "rgb = b.scale_image(b.get_rgb_image(\n", + " rgbd_scaled_down.rgb\n", + "),scaling_factor)\n", + "\n", + "b.hstack_images([rgb, img, b.overlay_image(rgb, img, alpha=0.4)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ec593ba", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "561bd492", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/experiments/slam/slam_with_room_obj.ipynb b/scripts/experiments/slam/slam_with_room_obj.ipynb index 5f1e5ce2..ed10b320 100644 --- a/scripts/experiments/slam/slam_with_room_obj.ipynb +++ b/scripts/experiments/slam/slam_with_room_obj.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ "output_type": "stream", "text": [ "You can open the visualizer by visiting the following URL:\n", - "http://127.0.0.1:7050/static/\n" + "http://127.0.0.1:7001/static/\n" ] } ], @@ -35,7 +35,17 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import viser\n", + "server = viser.ViserServer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -98,7 +108,30 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GlbHandle(_impl=_SceneNodeHandleState(name='/trimesh', api=, wxyz=array([1., 0., 0., 0.]), position=array([0., 0., 0.]), visible=True, click_cb=None))" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "server.add_mesh_trimesh(\n", + " name=\"/trimesh\",\n", + " mesh=mesh,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -157,11 +190,11 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "b.clear()\n", + "b.clear_visualizer()\n", "b.show_pose(\"actual\", camera_poses[1])\n", "tr,q = b.pose_matrix_to_translation_and_quaternion(camera_poses[0])\n", "b.show_pose(\"inferred\", b.translation_and_quaternion_to_pose_matrix(tr,q), size=0.1)" @@ -169,21 +202,21 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "start (Array(0.48633558, dtype=float32), (Array([-0.01954 , 0.06823298, -0.4547913 ], dtype=float32), Array([ 0. , 1.0255171 , -0.06746437, -0.06139849], dtype=float32)))\n" + "start (Array(0.48633558, dtype=float32), (Array([-0.01954 , 0.06823303, -0.45479128], dtype=float32), Array([ 0. , 1.025517 , -0.06746437, -0.06139864], dtype=float32)))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "0.6546157002449036: 100%|██████████| 200/200 [00:00<00:00, 387.57it/s] \n" + "0.11390623450279236: 100%|██████████| 200/200 [00:00<00:00, 366.14it/s] \n" ] } ], @@ -210,72 +243,79 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "0.5627371072769165: 100%|██████████| 50/50 [00:00<00:00, 229.39it/s]\n", - "0.0884014368057251: 100%|██████████| 50/50 [00:00<00:00, 249.63it/s]\n", - "0.5697894096374512: 100%|██████████| 50/50 [00:00<00:00, 229.71it/s]\n", - "0.10592619329690933: 100%|██████████| 50/50 [00:00<00:00, 279.70it/s]\n", - "0.22984321415424347: 100%|██████████| 50/50 [00:00<00:00, 283.45it/s]\n", - "0.24738705158233643: 100%|██████████| 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"outputs": [ { @@ -382,9 +422,9 @@ "Stream mapping:\n", " Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n", "Press [q] to stop, [?] for help\n", - "[libx264 @ 0x555a26780740] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", - "[libx264 @ 0x555a26780740] profile High 4:4:4 Predictive, level 3.2, 4:4:4, 8-bit\n", - "[libx264 @ 0x555a26780740] 264 - core 160 r3011 cde9a93 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=3 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "[libx264 @ 0x564a196c9700] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0x564a196c9700] profile High 4:4:4 Predictive, level 3.2, 4:4:4, 8-bit\n", + "[libx264 @ 0x564a196c9700] 264 - core 160 r3011 cde9a93 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=3 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", "Output #0, mp4, to 'localization_with_gradients.mp4':\n", " Metadata:\n", " encoder : Lavf58.45.100\n", @@ -393,24 +433,24 @@ " encoder : Lavc58.91.100 libx264\n", " Side data:\n", " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", - "frame= 53 fps=0.0 q=-1.0 Lsize= 258kB time=00:00:16.66 bitrate= 126.7kbits/s speed=19.3x \n", - "video:257kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.479635%\n", - "[libx264 @ 0x555a26780740] frame I:1 Avg QP:12.62 size: 42569\n", - "[libx264 @ 0x555a26780740] frame P:40 Avg QP:12.86 size: 3571\n", - "[libx264 @ 0x555a26780740] frame B:12 Avg QP:15.89 size: 6382\n", - "[libx264 @ 0x555a26780740] consecutive B-frames: 60.4% 22.6% 17.0% 0.0%\n", - "[libx264 @ 0x555a26780740] mb I I16..4: 34.1% 43.3% 22.6%\n", - "[libx264 @ 0x555a26780740] mb P I16..4: 11.2% 5.2% 1.1% P16..4: 2.0% 1.4% 0.4% 0.0% 0.0% skip:78.7%\n", - "[libx264 @ 0x555a26780740] mb B I16..4: 3.8% 1.8% 1.4% B16..8: 4.0% 1.8% 1.1% direct: 4.2% skip:81.9% L0:52.4% L1:34.6% BI:13.0%\n", - "[libx264 @ 0x555a26780740] 8x8 transform intra:31.0% inter:68.5%\n", - "[libx264 @ 0x555a26780740] coded y,u,v intra: 6.6% 6.5% 6.3% inter: 1.3% 1.9% 2.3%\n", - "[libx264 @ 0x555a26780740] i16 v,h,dc,p: 85% 13% 1% 1%\n", - "[libx264 @ 0x555a26780740] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 64% 11% 23% 0% 0% 0% 0% 0% 0%\n", - "[libx264 @ 0x555a26780740] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 49% 26% 16% 2% 2% 2% 2% 1% 1%\n", - "[libx264 @ 0x555a26780740] Weighted P-Frames: Y:0.0% UV:0.0%\n", - "[libx264 @ 0x555a26780740] ref P L0: 64.5% 6.9% 18.1% 10.5%\n", - "[libx264 @ 0x555a26780740] ref B L0: 78.6% 19.1% 2.3%\n", - "[libx264 @ 0x555a26780740] kb/s:118.65\n" + "frame= 53 fps=0.0 q=-1.0 Lsize= 259kB time=00:00:16.66 bitrate= 127.1kbits/s speed=19.6x \n", + "video:257kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.484197%\n", + "[libx264 @ 0x564a196c9700] frame I:1 Avg QP:12.64 size: 42550\n", + "[libx264 @ 0x564a196c9700] frame P:38 Avg QP:12.79 size: 3663\n", + "[libx264 @ 0x564a196c9700] frame B:14 Avg QP:16.70 size: 5792\n", + "[libx264 @ 0x564a196c9700] consecutive B-frames: 54.7% 22.6% 22.6% 0.0%\n", + "[libx264 @ 0x564a196c9700] mb I I16..4: 34.1% 43.3% 22.6%\n", + "[libx264 @ 0x564a196c9700] mb P I16..4: 11.2% 5.6% 1.1% P16..4: 2.0% 1.3% 0.4% 0.0% 0.0% skip:78.4%\n", + "[libx264 @ 0x564a196c9700] mb B I16..4: 4.0% 2.0% 1.2% B16..8: 4.1% 1.8% 0.9% direct: 3.7% skip:82.3% L0:53.3% L1:34.9% BI:11.8%\n", + "[libx264 @ 0x564a196c9700] 8x8 transform intra:32.2% inter:69.4%\n", + "[libx264 @ 0x564a196c9700] coded y,u,v intra: 6.7% 6.6% 6.5% inter: 1.2% 1.9% 2.4%\n", + "[libx264 @ 0x564a196c9700] i16 v,h,dc,p: 85% 13% 1% 1%\n", + "[libx264 @ 0x564a196c9700] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 65% 10% 22% 0% 0% 0% 0% 0% 0%\n", + "[libx264 @ 0x564a196c9700] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 48% 26% 16% 2% 2% 2% 2% 1% 1%\n", + "[libx264 @ 0x564a196c9700] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0x564a196c9700] ref P L0: 64.9% 6.9% 17.6% 10.6%\n", + "[libx264 @ 0x564a196c9700] ref B L0: 77.7% 19.2% 3.2%\n", + "[libx264 @ 0x564a196c9700] kb/s:119.02\n" ] }, { @@ -419,7 +459,7 @@ "0" ] }, - "execution_count": 91, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -435,11 +475,55 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
╭─────────────── viser ───────────────╮\n",
+       "│             ╷                       │\n",
+       "│   HTTP      │ http://0.0.0.0:8081   │\n",
+       "│   Websocket │ ws://0.0.0.0:8081     │\n",
+       "│             ╵                       │\n",
+       "╰─────────────────────────────────────╯\n",
+       "
\n" + ], + "text/plain": [ + "╭─────────────── \u001b[1mviser\u001b[0m ───────────────╮\n", + "│ ╷ │\n", + "│ HTTP │ http://0.0.0.0:8081 │\n", + "│ Websocket │ ws://0.0.0.0:8081 │\n", + "│ ╵ │\n", + "╰─────────────────────────────────────╯\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
(viser) Connection opened (0, 1 total), 3 persistent messages\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m(\u001b[0m\u001b[1mviser\u001b[0m\u001b[1m)\u001b[0m Connection opened \u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m1\u001b[0m total\u001b[1m)\u001b[0m, \u001b[1;36m3\u001b[0m persistent messages\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "VISUALIZER = b.get_visualizer()" + "server.add_tr" ] }, { @@ -468,7 +552,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + " " + ] } ], "metadata": { @@ -487,7 +573,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.11.7" } }, "nbformat": 4, From ec3368cada4c243003378edc30a5de45b1076e5a Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Mon, 5 Feb 2024 19:30:28 +0000 Subject: [PATCH 02/27] c2f --- demo_c2f.ipynb | 193 +++++++++++-------------------------------------- 1 file changed, 41 insertions(+), 152 deletions(-) diff --git a/demo_c2f.ipynb b/demo_c2f.ipynb index c443f647..22b06f30 100644 --- a/demo_c2f.ipynb +++ b/demo_c2f.ipynb @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "id": "ed42e5c3-be5d-420b-9a21-759247e5d7b7", "metadata": {}, "outputs": [ @@ -95,7 +95,7 @@ "" ] }, - "execution_count": 4, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -114,7 +114,7 @@ "fx, fy, cx, cy = K[0,0],K[1,1],K[0,2],K[1,2]\n", "h,w = depth.shape\n", "near = 0.001\n", - "rgbd_original = b.RGBD(rgb, depth, camera_pose, b.Intrinsics(h,w,fx,fy,cx,cy,0.001,10000.0))\n", + "rgbd_original = b.RGBD(rgb, depth, camera_pose, b.Intrinsics(h,w,fx,fy,cx,cy,0.001,10.0))\n", "scaling_factor = 0.2\n", "rgbd_scaled_down = b.RGBD.scale_rgbd(rgbd_original, scaling_factor)\n", "b.hstack_images([b.get_rgb_image(rgbd_scaled_down.rgb), b.get_depth_image(rgbd_scaled_down.depth,max_val=2.5)])" @@ -122,45 +122,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 42, "id": "432c5da8-eb91-408f-b2e4-501eef3cc221", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
(viser) Connection opened (0, 1 total), 3 persistent messages\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m(\u001b[0m\u001b[1mviser\u001b[0m\u001b[1m)\u001b[0m Connection opened \u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m1\u001b[0m total\u001b[1m)\u001b[0m, \u001b[1;36m3\u001b[0m persistent messages\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "table_pose, plane_dims = b.utils.infer_table_plane(\n", " b.unproject_depth(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics),\n", " jnp.eye(4), rgbd_scaled_down.intrinsics, \n", - " ransac_threshold=0.001, inlier_threshold=0.001, segmentation_threshold=0.1\n", + " ransac_threshold=0.001, inlier_threshold=0.005, segmentation_threshold=0.2\n", ")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "52b57c87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PointCloudHandle(_impl=_SceneNodeHandleState(name='/cloud', api=, wxyz=array([1., 0., 0., 0.]), position=array([0., 0., 0.]), visible=True, click_cb=None))" + "FrameHandle(_impl=_SceneNodeHandleState(name='/table', api=, wxyz=array([-0.3109156 , -0.40532318, 0.71136296, -0.48178506], dtype=float32), position=array([0.13536738, 0.06300807, 0.7492305 ], dtype=float32), visible=True, click_cb=None))" ] }, - "execution_count": 18, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -171,54 +157,34 @@ " points=np.array(b.unproject_depth(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics).reshape(-1,3)),\n", " colors=np.array(rgbd_scaled_down.rgb.reshape(-1,3)),\n", " point_size=0.01\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ce8e080e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FrameHandle(_impl=_SceneNodeHandleState(name='/table', api=, wxyz=array([ 0.10174274, 0.1909879 , 0.79682994, -0.56346023], dtype=float32), position=array([0.13303192, 0.06902084, 0.7311834 ], dtype=float32), visible=True, click_cb=None))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ + ")\n", "server.add_frame(\n", " \"/table\",\n", " position=np.array(table_pose[:3,3]),\n", " wxyz=b.rotation_matrix_to_quaternion(table_pose[:3,:3]),\n", - " axes_length=0.1,\n", + " axes_length=0.2,\n", " axes_radius=0.005\n", ")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "id": "89d55282-eb41-4c8e-97fe-1b9528f52481", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "[E rasterize_gl.cpp:121] OpenGL version reported as 4.6\n" + "Increasing frame buffer size to (width, height, depth) = (192, 96, 1024)\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Increasing frame buffer size to (width, height, depth) = (192, 96, 1024)\n" + "[E rasterize_gl.cpp:121] OpenGL version reported as 4.6\n" ] } ], @@ -227,14 +193,12 @@ "model_dir = os.path.join(b.utils.get_assets_dir(),\"bop/ycbv/models\")\n", "mesh_path = os.path.join(model_dir,\"obj_\" + \"{}\".format(13+1).rjust(6, '0') + \".ply\")\n", "b.RENDERER.add_mesh_from_file(mesh_path, scaling_factor=1.0/1000.0)\n", - "mesh_path = os.path.join(model_dir,\"obj_\" + \"{}\".format(10+1).rjust(6, '0') + \".ply\")\n", - "b.RENDERER.add_mesh_from_file(mesh_path, scaling_factor=1.0/1000.0)\n", - "b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=1.0/1000000000.0)\n" + "b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=jnp.array([0.5,0.5,0.001]))" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -281,49 +245,56 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 49, "id": "5a628704", "metadata": {}, "outputs": [], "source": [ "def viz_trace_viser(server, trace, colors=None):\n", - " server.reset_scene()\n", " indices = b.genjax.get_indices(trace)\n", " poses = b.genjax.get_poses(trace)\n", " for i in range(len(poses)):\n", + " mesh = b.RENDERER.meshes[indices[i]]\n", " server.add_mesh_trimesh(\n", - " name=\"/trimesh\",\n", - " mesh=b.RENDERER.meshes[indices[i]],\n", + " name=f\"/trimesh/{i}\",\n", + " mesh=trimesh.Trimesh(mesh.vertices, mesh.faces),\n", " position=np.array(poses[i][:3,3]),\n", " wxyz=b.rotation_matrix_to_quaternion(poses[i][:3,:3]),\n", " )\n", " server.add_point_cloud(\n", - " \"/cloud\",\n", + " \"/observed_cloud\",\n", " points=np.array(trace[\"image\"].reshape(-1,3)),\n", + " colors=np.array([0.0, 0.0, 0.0]),\n", + " point_size=0.005\n", + " )\n", + " server.add_point_cloud(\n", + " \"/rendered_cloud\",\n", + " points=np.array(b.genjax.get_rendered_image(trace).reshape(-1,3)),\n", " colors=np.array([1.0, 0.0, 0.0]),\n", - " point_size=0.01\n", + " point_size=0.005\n", " )" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-57.049133\n" + "-54.53688\n" ] } ], "source": [ + "key = jax.random.split(key)[0]\n", "obs_img = b.unproject_depth_jit(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics)\n", "trace, weight = importance_jit(key, genjax.choice_map({\n", " \"parent_0\": -1,\n", " \"parent_1\": 0,\n", - " \"id_0\": jnp.int32(2),\n", + " \"id_0\": jnp.int32(1),\n", " \"id_1\": jnp.int32(0),\n", " \"camera_pose\": jnp.eye(4),\n", " \"root_pose_0\": table_pose,\n", @@ -345,108 +316,26 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 58, "id": "9170b633", "metadata": {}, "outputs": [], "source": [ - "potential_trace = trace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64255e5f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "1dd46342", - "metadata": {}, - "outputs": [], - "source": [ + "potential_trace = trace\n", "import time\n", "cp = potential_trace[address]\n", - "new_potential_trace = potential_trace\n", "for cp_grid in contact_param_gridding_schedule:\n", " cps = cp + cp_grid\n", - " scores = enumerators.enumerate_choices_get_scores(new_potential_trace, key, cps)\n", + " scores = enumerators.enumerate_choices_get_scores(potential_trace, key, cps)\n", " cp = cps[scores.argmax()]\n", - " new_potential_trace = enumerators.update_choices(new_potential_trace, key, cp)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8c4398d2", - "metadata": {}, - "outputs": [], - "source": [ - "viz_trace_viser(server, new_potential_trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "955c1d6d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.10350819 0.08779351 1.7163045 ]\n", - "CPU times: user 8.78 s, sys: 123 ms, total: 8.91 s\n", - "Wall time: 7.76 s\n" - ] - } - ], - "source": [ - "%%time\n", - "key = jax.random.split(key,2)[0]\n", - "new_potential_trace = c2f(potential_trace, contact_param_gridding_schedule)[0]\n", - "print(new_potential_trace[\"contact_params_1\"])\n", - "b.genjax.viz_trace_meshcat(new_potential_trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "32ce4392", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scaling_factor = 3.0\n", - "img = b.scale_image(b.get_depth_image(\n", - " b.genjax.get_rendered_image(new_potential_trace)[...,2],\n", - "), scaling_factor)\n", - "rgb = b.scale_image(b.get_rgb_image(\n", - " rgbd_scaled_down.rgb\n", - "),scaling_factor)\n", - "\n", - "b.hstack_images([rgb, img, b.overlay_image(rgb, img, alpha=0.4)])" + " potential_trace = enumerators.update_choices(potential_trace, key, cp)\n", + "viz_trace_viser(server, potential_trace)" ] }, { "cell_type": "code", "execution_count": null, - "id": "8ec593ba", + "id": "1dd46342", "metadata": {}, "outputs": [], "source": [] @@ -454,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "561bd492", + "id": "8c4398d2", "metadata": {}, "outputs": [], "source": [] From 208a68d9e61194dbc7e06633256db3e0eecd9f8e Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Fri, 16 Feb 2024 02:39:41 +0000 Subject: [PATCH 03/27] WTF --- bayes3d/viz/viz.py | 29 ++-- likelihood_debug.ipynb | 339 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 350 insertions(+), 18 deletions(-) create mode 100644 likelihood_debug.ipynb diff --git a/bayes3d/viz/viz.py b/bayes3d/viz/viz.py index c611825f..ffb98da2 100644 --- a/bayes3d/viz/viz.py +++ b/bayes3d/viz/viz.py @@ -45,13 +45,12 @@ def preprocess_for_viz(img): return depth_np -cmap = copy.copy(plt.get_cmap("turbo")) +cmap = copy.copy(plt.get_cmap('turbo')) cmap.set_bad(color=(1.0, 1.0, 1.0, 1.0)) - -def get_depth_image(image, min_val=None, max_val=None, remove_max=True): +def get_depth_image(image, max=None): """Convert a depth image to a PIL image. - + Args: image (np.ndarray): Depth image. Shape (H, W). min (float): Minimum depth value for colormap. @@ -60,28 +59,22 @@ def get_depth_image(image, min_val=None, max_val=None, remove_max=True): Returns: PIL.Image: Depth image visualized as a PIL image. """ - if len(image.shape) > 2: - depth = np.array(image[:, :, -1]) + depth = np.array(image) + if max is None: + maxim = depth.max() else: - depth = np.array(image) - - if max_val is None: - max_val = depth.max() - if not remove_max: - max_val += 1 - if min_val is None: - min_val = depth.min() - - mask = (depth < max_val) * (depth > min_val) + maxim = max + mask = depth < maxim depth[np.logical_not(mask)] = np.nan - depth = (depth - min_val) / (max_val - min_val + 1e-10) + vmin = depth[mask].min() + vmax = depth[mask].max() + depth = (depth - vmin) / (vmax - vmin) img = Image.fromarray( np.rint(cmap(depth) * 255.0).astype(np.int8), mode="RGBA" ).convert("RGB") return img - def get_rgb_image(image, max=255.0): """Convert an RGB image to a PIL image. diff --git a/likelihood_debug.ipynb b/likelihood_debug.ipynb new file mode 100644 index 00000000..445f639f --- /dev/null +++ b/likelihood_debug.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c9a75992-9ded-4c10-bcbe-a68d4e817125", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import bayes3d as b\n", + "import os\n", + "import jax\n", + "import functools\n", + "from jax.scipy.special import logsumexp\n", + "from functools import partial\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "import bayes3d.genjax\n", + "import genjax\n", + "import pathlib\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1e9cc139-2449-4532-acf4-af71ccd6a24d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can open the visualizer by visiting the following URL:\n", + "http://127.0.0.1:7000/static/\n" + ] + } + ], + "source": [ + "b.setup_visualizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dd797e9e", + "metadata": {}, + "outputs": [], + "source": [ + "intrinsics = b.Intrinsics(\n", + " height=100,\n", + " width=100,\n", + " fx=200.0, fy=200.0,\n", + " cx=50.0, cy=50.0,\n", + " near=0.0001, far=2.0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "211f5ade", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[E rasterize_gl.cpp:121] OpenGL version reported as 4.6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Increasing frame buffer size to (width, height, depth) = (128, 128, 1024)\n" + ] + } + ], + "source": [ + "\n", + "b.setup_renderer(intrinsics)\n", + "model_dir = os.path.join(b.utils.get_assets_dir(),\"bop/ycbv/models\")\n", + "meshes = []\n", + "for idx in range(1,22):\n", + " mesh_path = os.path.join(model_dir,\"obj_\" + \"{}\".format(idx).rjust(6, '0') + \".ply\")\n", + " b.RENDERER.add_mesh_from_file(mesh_path, scaling_factor=1.0/1000.0)\n", + "# b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=1.0/10.0)\n", + "b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=1.0/1000000000.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ceed122b", + "metadata": {}, + "outputs": [], + "source": [ + "width = 0.03\n", + "ang = jnp.pi\n", + "num_position_grids = 15\n", + "num_angle_grids = 15\n", + "contact_param_deltas = b.utils.make_translation_grid_enumeration_3d(\n", + " -width, -width, -ang,\n", + " width, width, ang,\n", + " num_position_grids,num_position_grids,num_angle_grids\n", + ")\n", + "\n", + "grid_params = [\n", + " (0.3, jnp.pi, (15,15,15)), (0.2, jnp.pi, (15,15,15)), (0.1, jnp.pi, (15,15,15)),\n", + " (0.05, jnp.pi/3, (15,15,15)), (0.02, jnp.pi, (9,9,51)), (0.01, jnp.pi/5, (15,15,15)), (0.01, 0.0, (31,31,1)),(0.05, 0.0, (31,31,1))\n", + "]\n", + "contact_param_gridding_schedule = [\n", + " b.utils.make_translation_grid_enumeration_3d(\n", + " -x, -x, -ang,\n", + " x, x, ang,\n", + " *nums\n", + " )\n", + " for (x,ang,nums) in grid_params\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f4648f31-caf1-4792-bd83-9d652a8c5e4b", + "metadata": {}, + "outputs": [], + "source": [ + "table_pose = b.t3d.inverse_pose(\n", + " b.t3d.transform_from_pos_target_up(\n", + " jnp.array([0.0, 0.8, .15]),\n", + " jnp.array([0.0, 0.0, 0.0]),\n", + " jnp.array([0.0, 0.0, 1.0]),\n", + " )\n", + ")\n", + "face_child = 3\n", + "cp_to_pose = lambda cp: table_pose@ b.scene_graph.relative_pose_from_edge(cp, face_child, b.RENDERER.model_box_dims[13])\n", + "cp_to_pose_jit = jax.jit(cp_to_pose)\n", + "cp_to_pose_parallel = jax.jit(jax.vmap(cp_to_pose, in_axes=(0,)))\n", + "\n", + "key = jax.random.PRNGKey(30)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "id": "53c182df", + "metadata": {}, + "outputs": [], + "source": [ + "def score_images(rendered, observed):\n", + " return -jnp.linalg.norm(observed - rendered, axis=-1).mean()\n", + "\n", + "def score_images(rendered, observed):\n", + " mask = observed[...,2] < intrinsics.far\n", + " return (jnp.linalg.norm(observed - rendered, axis=-1)* (1.0 * mask)).sum() / mask.sum()\n", + "\n", + "\n", + "# def score_images(rendered, observed):\n", + "# return -jnp.linalg.norm(observed - rendered, axis=-1).mean()\n", + "\n", + "\n", + "\n", + "# def score_images(rendered, observed):\n", + "# distances = jnp.linalg.norm(observed - rendered, axis=-1)\n", + "# width = 0.01\n", + "# outlier_probability = 0.001\n", + "# probabilities_per_pixel = (1.0 - outlier_probability) * (distances < width/2) / width + outlier_probability * (1/10000.0)\n", + "# return jnp.log(probabilities_per_pixel).sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "5253e2ee", + "metadata": {}, + "outputs": [], + "source": [ + "key = jax.random.split(key,2)[0]\n", + "key = jnp.array([2755247810, 1586593754], dtype=np.uint32)\n", + "low, high = jnp.array([-0.2, -0.2, -jnp.pi]), jnp.array([0.2, 0.2, jnp.pi])\n", + "gt_cp = jax.random.uniform(key, shape=(3,),minval=low, maxval=high)\n", + "gt_pose = cp_to_pose_jit(gt_cp)\n", + "obs_img = b.RENDERER.render(gt_pose[None,...], jnp.array([13]))[...,:3]\n", + "# b.viz.scale_image(b.get_depth_image(obs_img[...,2]),3.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "id": "00c00a84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[43792.79 43497.336 43450.188 43235.336 43193.297 43193.297 43164.523\n", + " 43088.688 43088.688 43032.523]\n" + ] + }, + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "def score_images(rendered, observed):\n", + " distances = jnp.linalg.norm(observed - rendered, axis=-1)\n", + " log_probabilities_per_pixel = jax.scipy.stats.norm.logpdf(\n", + " distances,\n", + " loc=0.0, \n", + " scale=0.005\n", + " )\n", + " outlier_probability = 0.0001\n", + " log_probabilities_per_pixel = jnp.logaddexp(\n", + " jnp.log(1.0 - outlier_probability) + log_probabilities_per_pixel,\n", + " (jnp.log(outlier_probability) + jnp.log(1/10000.0)) * jnp.ones_like(log_probabilities_per_pixel)\n", + " )\n", + " return log_probabilities_per_pixel.sum()\n", + "score_vmap = jax.jit(jax.vmap(score_images, in_axes=(0, None)))\n", + "\n", + "contact_param_grid = gt_cp + contact_param_deltas\n", + "scores = jnp.concatenate([\n", + " score_vmap(b.RENDERER.render_many(cp_to_pose_parallel(cps)[:,None,...], jnp.array([13]))[...,:3], obs_img)\n", + " for cps in jnp.array_split(contact_param_grid, 15)\n", + "],axis=0)\n", + "\n", + "sort_order = jnp.argsort(-scores)\n", + "sorted_scores = scores[sort_order]\n", + "k = 10\n", + "# print(\"GT CP: \", gt_cp)\n", + "# print(sorted_scores[:k])\n", + "# print(contact_param_grid[sort_order[:k]])\n", + "poses = cp_to_pose_parallel(contact_param_grid[sort_order[:k]])[:,None,...]\n", + "rendered_top_k = b.RENDERER.render_many(poses, jnp.array([13]))[...,:3]\n", + "\n", + "print(sorted_scores[:k])\n", + "b.viz.scale_image(b.hstack_images([b.get_depth_image(obs_img[...,2]), *[b.get_depth_image(i[...,2]) for i in rendered_top_k]]),3.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "566c9480", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "647aa535", + "metadata": {}, + "outputs": [], + "source": [ + "log_probabilities_per_pixel = jnp.log(jnp.ones((100,100))* 0.2)\n", + "outlier_probability = 0.001\n", + "probabilities_per_pixel = jnp.logaddexp(\n", + " (1.0 - outlier_probability) + log_probabilities_per_pixel,\n", + " (jnp.log(outlier_probability) + jnp.log(1/10000.0)) * jnp.ones_like(log_probabilities_per_pixel)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "b7604635", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([[-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " ...,\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775]], dtype=float32)" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probabilities_per_pixel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcdf5636", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 73aa40109ad38366667dcc16975b4514d192d210 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Tue, 13 Feb 2024 19:29:57 +0000 Subject: [PATCH 04/27] Successfully rendering many images. but ignorning the actual poses --- README.md | 2 +- bayes3d/__init__.py | 1 - bayes3d/renderer.py | 422 ---------- bayes3d/rendering/nvdiffrast/__init__.py | 9 - .../rendering/nvdiffrast/common/__init__.py | 11 - .../rendering/nvdiffrast/common/common.cpp | 60 -- bayes3d/rendering/nvdiffrast/common/common.h | 253 ------ .../rendering/nvdiffrast/common/framework.h | 49 -- .../rendering/nvdiffrast/common/glutil.cpp | 403 ---------- bayes3d/rendering/nvdiffrast/common/glutil.h | 117 --- .../nvdiffrast/common/glutil_extlist.h | 59 -- bayes3d/rendering/nvdiffrast/common/ops.py | 82 -- .../nvdiffrast/common/rasterize_gl.cpp | 720 ------------------ .../nvdiffrast/common/rasterize_gl.h | 129 ---- .../nvdiffrast/common/torch_common.inl | 29 - .../rendering/nvdiffrast/common/torch_types.h | 65 -- bayes3d/rendering/nvdiffrast/lib/setgpu.lib | Bin 7254 -> 0 bytes .../rendering/nvdiffrast_jax/jax_renderer.py | 65 +- .../nvdiffrast/common/rasterize_gl.cpp | 37 +- .../nvdiffrast/common/rasterize_gl.h | 4 +- .../jax/{jax_binding_ops.h => bindings.h} | 5 +- .../nvdiffrast/jax/jax_bindings.cpp | 24 +- .../nvdiffrast/jax/jax_bindings.h | 5 + .../nvdiffrast/jax/jax_interpolate.cpp | 5 +- .../nvdiffrast/jax/jax_interpolate.h | 4 + .../nvdiffrast/jax/jax_rasterize_gl.cpp | 75 +- .../nvdiffrast/jax/jax_rasterize_gl.h | 62 +- .../nvdiffrast/jax/torch_common.inl | 29 - .../nvdiffrast/jax/torch_types.h | 65 -- .../nvdiffrast_jax/test_jax_renderer.py | 69 ++ .../slam/localization_with_gradients.mp4 | Bin 0 -> 264487 bytes 31 files changed, 217 insertions(+), 2643 deletions(-) delete mode 100644 bayes3d/renderer.py delete mode 100644 bayes3d/rendering/nvdiffrast/__init__.py delete mode 100644 bayes3d/rendering/nvdiffrast/common/__init__.py delete mode 100644 bayes3d/rendering/nvdiffrast/common/common.cpp delete mode 100644 bayes3d/rendering/nvdiffrast/common/common.h delete mode 100644 bayes3d/rendering/nvdiffrast/common/framework.h delete mode 100644 bayes3d/rendering/nvdiffrast/common/glutil.cpp delete mode 100644 bayes3d/rendering/nvdiffrast/common/glutil.h delete mode 100644 bayes3d/rendering/nvdiffrast/common/glutil_extlist.h delete mode 100644 bayes3d/rendering/nvdiffrast/common/ops.py delete mode 100644 bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp delete mode 100644 bayes3d/rendering/nvdiffrast/common/rasterize_gl.h delete mode 100644 bayes3d/rendering/nvdiffrast/common/torch_common.inl delete mode 100644 bayes3d/rendering/nvdiffrast/common/torch_types.h delete mode 100644 bayes3d/rendering/nvdiffrast/lib/setgpu.lib rename bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/{jax_binding_ops.h => bindings.h} (96%) create mode 100644 bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.h delete mode 100755 bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_common.inl delete mode 100755 bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_types.h create mode 100644 bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py create mode 100644 scripts/experiments/slam/localization_with_gradients.mp4 diff --git a/README.md b/README.md index 9ad6273d..672a7deb 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ Install compatible versions JAX and Torch: ```bash pip install --upgrade torch==2.2.0 torchvision==0.17.0+cu118 --index-url https://download.pytorch.org/whl/cu118 -pip install --upgrade jax[cuda11_local]==0.4.20 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html +pip install --upgrade jax[cuda11_local] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html ``` Bayes3D is built on top of GenJAX, which is currently hosted in a private Python diff --git a/bayes3d/__init__.py b/bayes3d/__init__.py index 6eccde99..4d6726d4 100644 --- a/bayes3d/__init__.py +++ b/bayes3d/__init__.py @@ -7,7 +7,6 @@ from . import colmap, distributions, scene_graph, utils from .camera import * from .likelihood import * -from .renderer import * from .rgbd import * from .transforms_3d import * from .viz import * diff --git a/bayes3d/renderer.py b/bayes3d/renderer.py deleted file mode 100644 index b3e6fce1..00000000 --- a/bayes3d/renderer.py +++ /dev/null @@ -1,422 +0,0 @@ -import functools -import gc - -import jax -import jax.numpy as jnp -import numpy as np -import trimesh -from jax import core, dtypes -from jax.core import ShapedArray -from jax.interpreters import batching, mlir, xla -from jax.lib import xla_client -from jaxlib.hlo_helpers import custom_call - -import bayes3d as b -import bayes3d as j -import bayes3d.camera -import bayes3d.rendering.nvdiffrast.common as dr - - -def _transform_image_zeros(image_jnp, intrinsics): - image_jnp_2 = jnp.concatenate( - [j.t3d.unproject_depth(image_jnp[:, :, 2], intrinsics), image_jnp[:, :, 3:]], - axis=-1, - ) - return image_jnp_2 - - -_transform_image_zeros_jit = jax.jit(_transform_image_zeros) -_transform_image_zeros_parallel = jax.vmap(_transform_image_zeros, in_axes=(0, None)) -_transform_image_zeros_parallel_jit = jax.jit(_transform_image_zeros_parallel) - - -def setup_renderer(intrinsics, num_layers=1024): - """Setup the renderer. - Args: - intrinsics (bayes3d.camera.Intrinsics): The camera intrinsics. - """ - b.RENDERER = Renderer(intrinsics, num_layers=num_layers) - - -class Renderer(object): - def __init__(self, intrinsics, num_layers=1024): - """A renderer for rendering meshes. - - Args: - intrinsics (bayes3d.camera.Intrinsics): The camera intrinsics. - num_layers (int, optional): The number of scenes to render in parallel. Defaults to 1024. - """ - self.height = intrinsics.height - self.width = intrinsics.width - self.intrinsics = intrinsics - - self.proj_matrix = b.camera._open_gl_projection_matrix( - intrinsics.height, - intrinsics.width, - intrinsics.fx, - intrinsics.fy, - intrinsics.cx, - intrinsics.cy, - intrinsics.near, - intrinsics.far, - ) - - self.renderer_env = dr.RasterizeGLContext( - self.height, self.width, output_db=False - ) - build_setup_primitive(self, self.height, self.width, num_layers).bind() - - self.meshes = [] - self.mesh_names = [] - self.model_box_dims = jnp.zeros((0, 3)) - - def clear_gpu_meshmem(self): - """ - Forcefully deallocate/clear any GPU memory used for mesh data. - NOTE: INITIALZE NEW renderer instance if using this function. - """ - # cpp files are modified so that the destructors deallocate GPU memory - self.renderer_env = None - # Release the meshes - self.meshes.clear() - self.mesh_names.clear() - self.model_box_dims = jnp.zeros((0, 3)) - # Force the garbage collector to run to reclaim memory - gc.collect() - - def add_mesh_from_file( - self, - mesh_filename, - mesh_name=None, - scaling_factor=1.0, - force=None, - center_mesh=True, - ): - """Add a mesh to the renderer from a file. - - Args: - mesh_filename (str): The filename of the mesh. - mesh_name (str, optional): The name of the mesh. Defaults to None. - scaling_factor (float, optional): The scaling factor to apply to the mesh. Defaults to 1.0. - force (str, optional): The file format to force. Defaults to None. - center_mesh (bool, optional): Whether to center the mesh. Defaults to True. - """ - mesh = trimesh.load(mesh_filename, force=force) - self.add_mesh( - mesh, - mesh_name=mesh_name, - scaling_factor=scaling_factor, - center_mesh=center_mesh, - ) - - def add_mesh(self, mesh, mesh_name=None, scaling_factor=1.0, center_mesh=True): - """Add a mesh to the renderer. - - Args: - mesh (trimesh.Trimesh): The mesh to add. - mesh_name (str, optional): The name of the mesh. Defaults to None. - scaling_factor (float, optional): The scaling factor to apply to the mesh. Defaults to 1.0. - center_mesh (bool, optional): Whether to center the mesh. Defaults to True. - """ - if mesh_name is None: - mesh_name = f"object_{len(self.meshes)}" - - mesh.vertices = mesh.vertices * scaling_factor - - bounding_box_dims, bounding_box_pose = bayes3d.utils.aabb(mesh.vertices) - if center_mesh: - if not jnp.isclose(bounding_box_pose[:3, 3], 0.0).all(): - print(f"Centering mesh with translation {bounding_box_pose[:3,3]}") - mesh.vertices = mesh.vertices - bounding_box_pose[:3, 3] - - self.meshes.append(mesh) - self.mesh_names.append(mesh_name) - - self.model_box_dims = jnp.vstack([self.model_box_dims, bounding_box_dims]) - - vertices = np.array(mesh.vertices) - vertices = np.concatenate( - [vertices, np.ones((*vertices.shape[:-1], 1))], axis=-1 - ) - triangles = np.array(mesh.faces) - prim = build_load_vertices_primitive(self) - prim.bind(jnp.float32(vertices), jnp.int32(triangles)) - - def render_many_custom_intrinsics(self, poses, indices, intrinsics): - proj_matrix = b.camera._open_gl_projection_matrix( - intrinsics.height, - intrinsics.width, - intrinsics.fx, - intrinsics.fy, - intrinsics.cx, - intrinsics.cy, - intrinsics.near, - intrinsics.far, - ) - images_jnp = _render_custom_call(self, poses, indices, proj_matrix)[0] - return _transform_image_zeros_parallel(images_jnp, intrinsics) - - def render_many(self, poses, indices): - """Render many scenes in parallel. - - Args: - poses (jnp.ndarray): The poses of the objects in the scene. Shape (N, M, 4, 4) - where N is the number of scenes and M is the number of objects. - and the last two dimensions are the 4x4 poses. - indices (jnp.ndarray): The indices of the objects to render. Shape (M,) - - Outputs: - jnp.ndarray: The rendered images. Shape (N, H, W, 4) where N is the number of scenes - the final dimension is the segmentation image. - """ - return self.render_many_custom_intrinsics(poses, indices, self.intrinsics) - - def render(self, poses, indices): - return self.render_many(poses[None, ...], indices)[0] - - def render_custom_intrinsics(self, poses, indices, intrinsics): - return self.render_many_custom_intrinsics( - poses[None, ...], indices, intrinsics - )[0] - - -# Useful reference for understanding the custom calls setup: -# https://github.com/dfm/extending-jax - - -@functools.lru_cache -def _register_custom_calls(): - for _name, _value in dr._get_plugin(gl=True).registrations().items(): - xla_client.register_custom_call_target(_name, _value, platform="gpu") - - -@functools.partial(jax.jit, static_argnums=(0,)) -def _render_custom_call(r: "Renderer", poses, indices, intrinsics_matrix): - return _build_render_primitive(r).bind(poses, indices, intrinsics_matrix) - - -@functools.lru_cache(maxsize=None) -def _build_render_primitive(r: "Renderer"): - _register_custom_calls() - - # For JIT compilation we need a function to evaluate the shape and dtype of the - # outputs of our op for some given inputs - def _render_abstract(poses, indices, intrinsics_matrix): - num_images = poses.shape[0] - if poses.shape[1] != indices.shape[0]: - raise ValueError( - f"Poses Shape: {poses.shape} Indices Shape: {indices.shape}" - ) - dtype = dtypes.canonicalize_dtype(poses.dtype) - return [ - ShapedArray((num_images, r.height, r.width, 4), dtype), - ShapedArray((), dtype), - ] - - # Provide an MLIR "lowering" of the render primitive. - def _render_lowering(ctx, poses, indices, intrinsics_matrix): - # Extract the numpy type of the inputs - poses_aval, indices_aval, intrinsics_matrix_aval = ctx.avals_in - if poses_aval.ndim != 4: - raise NotImplementedError( - f"Only 4D inputs supported: got {poses_aval.shape}" - ) - if indices_aval.ndim != 1: - raise NotImplementedError( - f"Only 1D inputs supported: got {indices_aval.shape}" - ) - - np_dtype = np.dtype(poses_aval.dtype) - if np_dtype != np.float32: - raise NotImplementedError(f"Unsupported poses dtype {np_dtype}") - if np.dtype(indices_aval.dtype) != np.int32: - raise NotImplementedError(f"Unsupported indices dtype {indices_aval.dtype}") - - num_images, num_objects = poses_aval.shape[:2] - out_shp_dtype = mlir.ir.RankedTensorType.get( - [num_images, r.height, r.width, 4], mlir.dtype_to_ir_type(poses_aval.dtype) - ) - - if num_objects != indices_aval.shape[0]: - raise ValueError( - f"Poses Shape: {poses_aval.shape} Indices Shape: {indices_aval.shape}" - ) - opaque = dr._get_plugin(gl=True).build_rasterize_descriptor( - r.renderer_env.cpp_wrapper, [num_objects, num_images] - ) - - scalar_dummy = mlir.ir.RankedTensorType.get( - [], mlir.dtype_to_ir_type(poses_aval.dtype) - ) - op_name = "jax_rasterize_fwd_gl" - return custom_call( - op_name, - # Output types - result_types=[out_shp_dtype, scalar_dummy], - # The inputs: - operands=[poses, indices, intrinsics_matrix], - # Layout specification: - operand_layouts=[ - (3, 2, 0, 1), - (0,), - ( - 1, - 0, - ), - ], - result_layouts=[(3, 2, 1, 0), ()], - # GPU specific additional data - backend_config=opaque, - ).results - - # ************************************ - # * SUPPORT FOR BATCHING WITH VMAP * - # ************************************ - def _render_batch(args, axes): - poses, indices, intrinsics_matrix = args - if poses.ndim != 5: - raise NotImplementedError("Underlying primitive must operate on 4D poses.") - - original_shape = poses.shape - poses = jnp.moveaxis(poses, axes[0], 0) - size_1 = poses.shape[0] - size_2 = poses.shape[1] - num_objects = poses.shape[2] - poses = poses.reshape(size_1 * size_2, num_objects, 4, 4) - - if poses.shape[1] != indices.shape[0]: - raise ValueError( - f"Poses Original Shape: {original_shape} Poses Shape: {poses.shape} Indices Shape: {indices.shape}" - ) - if poses.shape[-2:] != (4, 4): - raise ValueError( - f"Poses Original Shape: {original_shape} Poses Shape: {poses.shape} Indices Shape: {indices.shape}" - ) - renders, dummy = _render_custom_call(r, poses, indices, intrinsics_matrix) - - renders = renders.reshape(size_1, size_2, *renders.shape[1:]) - out_axes = 0, None - return (renders, dummy), out_axes - - # ********************************************* - # * BOILERPLATE TO REGISTER THE OP WITH JAX * - # ********************************************* - _render_prim = core.Primitive(f"render_multiple_{id(r)}") - _render_prim.multiple_results = True - _render_prim.def_impl(functools.partial(xla.apply_primitive, _render_prim)) - _render_prim.def_abstract_eval(_render_abstract) - - # Connect the XLA translation rules for JIT compilation - mlir.register_lowering(_render_prim, _render_lowering, platform="gpu") - batching.primitive_batchers[_render_prim] = _render_batch - - return _render_prim - - -@functools.lru_cache(maxsize=None) -def build_setup_primitive(r: "Renderer", h, w, num_layers): - _register_custom_calls() - # print('build_setup_primitive') - - # For JIT compilation we need a function to evaluate the shape and dtype of the - # outputs of our op for some given inputs - def _setup_abstract(): - # print('setup abstract eval') - dtype = dtypes.canonicalize_dtype(np.float32) - return [ShapedArray((), dtype), ShapedArray((), dtype)] - - # Provide an MLIR "lowering" of the load_vertices primitive. - def _setup_lowering(ctx): - # print('lowering setup!') - - opaque = dr._get_plugin(gl=True).build_setup_descriptor( - r.renderer_env.cpp_wrapper, h, w, num_layers - ) - - scalar_dummy = mlir.ir.RankedTensorType.get( - [], mlir.dtype_to_ir_type(np.dtype(np.float32)) - ) - op_name = "jax_setup" - return custom_call( - op_name, - # Output types - result_types=[scalar_dummy, scalar_dummy], - # The inputs: - operands=[], - # Layout specification: - operand_layouts=[], - result_layouts=[(), ()], - # GPU specific additional data - backend_config=opaque, - ).results - - # ********************************************* - # * BOILERPLATE TO REGISTER THE OP WITH JAX * - # ********************************************* - _prim = core.Primitive(f"setup__{id(r)}") - _prim.multiple_results = True - _prim.def_impl(functools.partial(xla.apply_primitive, _prim)) - _prim.def_abstract_eval(_setup_abstract) - - # Connect the XLA translation rules for JIT compilation - mlir.register_lowering(_prim, _setup_lowering, platform="gpu") - - return _prim - - -@functools.lru_cache(maxsize=None) -def build_load_vertices_primitive(r: "Renderer"): - _register_custom_calls() - # print('build_load_vertices_primitive') - - # For JIT compilation we need a function to evaluate the shape and dtype of the - # outputs of our op for some given inputs - def _load_vertices_abstract(vertices, triangles): - # print('load_vertices abstract eval:', vertices, triangles) - dtype = dtypes.canonicalize_dtype(np.float32) - return [ShapedArray((), dtype), ShapedArray((), dtype)] - - # Provide an MLIR "lowering" of the load_vertices primitive. - def _load_vertices_lowering(ctx, vertices, triangles): - # print('lowering load_vertices!') - # Extract the numpy type of the inputs - vertices_aval, triangles_aval = ctx.avals_in - - if (dt := np.dtype(vertices_aval.dtype)) != np.float32: - raise NotImplementedError(f"Unsupported vertices dtype {dt}") - if (dt := np.dtype(triangles_aval.dtype)) != np.int32: - raise NotImplementedError(f"Unsupported triangles dtype {dt}") - - opaque = dr._get_plugin(gl=True).build_load_vertices_descriptor( - r.renderer_env.cpp_wrapper, vertices_aval.shape[0], triangles_aval.shape[0] - ) - - scalar_dummy = mlir.ir.RankedTensorType.get( - [], mlir.dtype_to_ir_type(np.dtype(np.float32)) - ) - op_name = "jax_load_vertices" - return custom_call( - op_name, - # Output types - result_types=[scalar_dummy, scalar_dummy], - # The inputs: - operands=[vertices, triangles], - # Layout specification: - operand_layouts=[(1, 0), (1, 0)], - result_layouts=[(), ()], - # GPU specific additional data - backend_config=opaque, - ).results - - # ********************************************* - # * BOILERPLATE TO REGISTER THE OP WITH JAX * - # ********************************************* - _prim = core.Primitive(f"load_vertices__{id(r)}") - _prim.multiple_results = True - _prim.def_impl(functools.partial(xla.apply_primitive, _prim)) - _prim.def_abstract_eval(_load_vertices_abstract) - - # Connect the XLA translation rules for JIT compilation - mlir.register_lowering(_prim, _load_vertices_lowering, platform="gpu") - - return _prim diff --git a/bayes3d/rendering/nvdiffrast/__init__.py b/bayes3d/rendering/nvdiffrast/__init__.py deleted file mode 100644 index 53d2ea76..00000000 --- a/bayes3d/rendering/nvdiffrast/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -__version__ = "0.3.0" diff --git a/bayes3d/rendering/nvdiffrast/common/__init__.py b/bayes3d/rendering/nvdiffrast/common/__init__.py deleted file mode 100644 index 2d9e624d..00000000 --- a/bayes3d/rendering/nvdiffrast/common/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -from .ops import RasterizeGLContext, _get_plugin - -__all__ = ["RasterizeGLContext", "_get_plugin"] diff --git a/bayes3d/rendering/nvdiffrast/common/common.cpp b/bayes3d/rendering/nvdiffrast/common/common.cpp deleted file mode 100644 index e566c035..00000000 --- a/bayes3d/rendering/nvdiffrast/common/common.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include - -//------------------------------------------------------------------------ -// Block and grid size calculators for kernel launches. - -dim3 getLaunchBlockSize(int maxWidth, int maxHeight, int width, int height) -{ - int maxThreads = maxWidth * maxHeight; - if (maxThreads <= 1 || (width * height) <= 1) - return dim3(1, 1, 1); // Degenerate. - - // Start from max size. - int bw = maxWidth; - int bh = maxHeight; - - // Optimizations for weirdly sized buffers. - if (width < bw) - { - // Decrease block width to smallest power of two that covers the buffer width. - while ((bw >> 1) >= width) - bw >>= 1; - - // Maximize height. - bh = maxThreads / bw; - if (bh > height) - bh = height; - } - else if (height < bh) - { - // Halve height and double width until fits completely inside buffer vertically. - while (bh > height) - { - bh >>= 1; - if (bw < width) - bw <<= 1; - } - } - - // Done. - return dim3(bw, bh, 1); -} - -dim3 getLaunchGridSize(dim3 blockSize, int width, int height, int depth) -{ - dim3 gridSize; - gridSize.x = (width - 1) / blockSize.x + 1; - gridSize.y = (height - 1) / blockSize.y + 1; - gridSize.z = (depth - 1) / blockSize.z + 1; - return gridSize; -} - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/common.h b/bayes3d/rendering/nvdiffrast/common/common.h deleted file mode 100644 index 8df48ed7..00000000 --- a/bayes3d/rendering/nvdiffrast/common/common.h +++ /dev/null @@ -1,253 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#pragma once -#include -#include - -//------------------------------------------------------------------------ -// C++ helper function prototypes. - -dim3 getLaunchBlockSize(int maxWidth, int maxHeight, int width, int height); -dim3 getLaunchGridSize(dim3 blockSize, int width, int height, int depth); - -//------------------------------------------------------------------------ -// The rest is CUDA device code specific stuff. - -#ifdef __CUDACC__ - -//------------------------------------------------------------------------ -// Helpers for CUDA vector types. - -static __device__ __forceinline__ float2& operator*= (float2& a, const float2& b) { a.x *= b.x; a.y *= b.y; return a; } -static __device__ __forceinline__ float2& operator+= (float2& a, const float2& b) { a.x += b.x; a.y += b.y; return a; } -static __device__ __forceinline__ float2& operator-= (float2& a, const float2& b) { a.x -= b.x; a.y -= b.y; return a; } -static __device__ __forceinline__ float2& operator*= (float2& a, float b) { a.x *= b; a.y *= b; return a; } -static __device__ __forceinline__ float2& operator+= (float2& a, float b) { a.x += b; a.y += b; return a; } -static __device__ __forceinline__ float2& operator-= (float2& a, float b) { a.x -= b; a.y -= b; return a; } -static __device__ __forceinline__ float2 operator* (const float2& a, const float2& b) { return make_float2(a.x * b.x, a.y * b.y); } -static __device__ __forceinline__ float2 operator+ (const float2& a, const float2& b) { return make_float2(a.x + b.x, a.y + b.y); } -static __device__ __forceinline__ float2 operator- (const float2& a, const float2& b) { return make_float2(a.x - b.x, a.y - b.y); } -static __device__ __forceinline__ float2 operator* (const float2& a, float b) { return make_float2(a.x * b, a.y * b); } -static __device__ __forceinline__ float2 operator+ (const float2& a, float b) { return make_float2(a.x + b, a.y + b); } -static __device__ __forceinline__ float2 operator- (const float2& a, float b) { return make_float2(a.x - b, a.y - b); } -static __device__ __forceinline__ float2 operator* (float a, const float2& b) { return make_float2(a * b.x, a * b.y); } -static __device__ __forceinline__ float2 operator+ (float a, const float2& b) { return make_float2(a + b.x, a + b.y); } -static __device__ __forceinline__ float2 operator- (float a, const float2& b) { return make_float2(a - b.x, a - b.y); } -static __device__ __forceinline__ float2 operator- (const float2& a) { return make_float2(-a.x, -a.y); } -static __device__ __forceinline__ float3& operator*= (float3& a, const float3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } -static __device__ __forceinline__ float3& operator+= (float3& a, const float3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } -static __device__ __forceinline__ float3& operator-= (float3& a, const float3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } -static __device__ __forceinline__ float3& operator*= (float3& a, float b) { a.x *= b; a.y *= b; a.z *= b; return a; } -static __device__ __forceinline__ float3& operator+= (float3& a, float b) { a.x += b; a.y += b; a.z += b; return a; } -static __device__ __forceinline__ float3& operator-= (float3& a, float b) { a.x -= b; a.y -= b; a.z -= b; return a; } -static __device__ __forceinline__ float3 operator* (const float3& a, const float3& b) { return make_float3(a.x * b.x, a.y * b.y, a.z * b.z); } -static __device__ __forceinline__ float3 operator+ (const float3& a, const float3& b) { return make_float3(a.x + b.x, a.y + b.y, a.z + b.z); } -static __device__ __forceinline__ float3 operator- (const float3& a, const float3& b) { return make_float3(a.x - b.x, a.y - b.y, a.z - b.z); } -static __device__ __forceinline__ float3 operator* (const float3& a, float b) { return make_float3(a.x * b, a.y * b, a.z * b); } -static __device__ __forceinline__ float3 operator+ (const float3& a, float b) { return make_float3(a.x + b, a.y + b, a.z + b); } -static __device__ __forceinline__ float3 operator- (const float3& a, float b) { return make_float3(a.x - b, a.y - b, a.z - b); } -static __device__ __forceinline__ float3 operator* (float a, const float3& b) { return make_float3(a * b.x, a * b.y, a * b.z); } -static __device__ __forceinline__ float3 operator+ (float a, const float3& b) { return make_float3(a + b.x, a + b.y, a + b.z); } -static __device__ __forceinline__ float3 operator- (float a, const float3& b) { return make_float3(a - b.x, a - b.y, a - b.z); } -static __device__ __forceinline__ float3 operator- (const float3& a) { return make_float3(-a.x, -a.y, -a.z); } -static __device__ __forceinline__ float4& operator*= (float4& a, const float4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } -static __device__ __forceinline__ float4& operator+= (float4& a, const float4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } -static __device__ __forceinline__ float4& operator-= (float4& a, const float4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } -static __device__ __forceinline__ float4& operator*= (float4& a, float b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } -static __device__ __forceinline__ float4& operator+= (float4& a, float b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } -static __device__ __forceinline__ float4& operator-= (float4& a, float b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } -static __device__ __forceinline__ float4 operator* (const float4& a, const float4& b) { return make_float4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } -static __device__ __forceinline__ float4 operator+ (const float4& a, const float4& b) { return make_float4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } -static __device__ __forceinline__ float4 operator- (const float4& a, const float4& b) { return make_float4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } -static __device__ __forceinline__ float4 operator* (const float4& a, float b) { return make_float4(a.x * b, a.y * b, a.z * b, a.w * b); } -static __device__ __forceinline__ float4 operator+ (const float4& a, float b) { return make_float4(a.x + b, a.y + b, a.z + b, a.w + b); } -static __device__ __forceinline__ float4 operator- (const float4& a, float b) { return make_float4(a.x - b, a.y - b, a.z - b, a.w - b); } -static __device__ __forceinline__ float4 operator* (float a, const float4& b) { return make_float4(a * b.x, a * b.y, a * b.z, a * b.w); } -static __device__ __forceinline__ float4 operator+ (float a, const float4& b) { return make_float4(a + b.x, a + b.y, a + b.z, a + b.w); } -static __device__ __forceinline__ float4 operator- (float a, const float4& b) { return make_float4(a - b.x, a - b.y, a - b.z, a - b.w); } -static __device__ __forceinline__ float4 operator- (const float4& a) { return make_float4(-a.x, -a.y, -a.z, -a.w); } -static __device__ __forceinline__ int2& operator*= (int2& a, const int2& b) { a.x *= b.x; a.y *= b.y; return a; } -static __device__ __forceinline__ int2& operator+= (int2& a, const int2& b) { a.x += b.x; a.y += b.y; return a; } -static __device__ __forceinline__ int2& operator-= (int2& a, const int2& b) { a.x -= b.x; a.y -= b.y; return a; } -static __device__ __forceinline__ int2& operator*= (int2& a, int b) { a.x *= b; a.y *= b; return a; } -static __device__ __forceinline__ int2& operator+= (int2& a, int b) { a.x += b; a.y += b; return a; } -static __device__ __forceinline__ int2& operator-= (int2& a, int b) { a.x -= b; a.y -= b; return a; } -static __device__ __forceinline__ int2 operator* (const int2& a, const int2& b) { return make_int2(a.x * b.x, a.y * b.y); } -static __device__ __forceinline__ int2 operator+ (const int2& a, const int2& b) { return make_int2(a.x + b.x, a.y + b.y); } -static __device__ __forceinline__ int2 operator- (const int2& a, const int2& b) { return make_int2(a.x - b.x, a.y - b.y); } -static __device__ __forceinline__ int2 operator* (const int2& a, int b) { return make_int2(a.x * b, a.y * b); } -static __device__ __forceinline__ int2 operator+ (const int2& a, int b) { return make_int2(a.x + b, a.y + b); } -static __device__ __forceinline__ int2 operator- (const int2& a, int b) { return make_int2(a.x - b, a.y - b); } -static __device__ __forceinline__ int2 operator* (int a, const int2& b) { return make_int2(a * b.x, a * b.y); } -static __device__ __forceinline__ int2 operator+ (int a, const int2& b) { return make_int2(a + b.x, a + b.y); } -static __device__ __forceinline__ int2 operator- (int a, const int2& b) { return make_int2(a - b.x, a - b.y); } -static __device__ __forceinline__ int2 operator- (const int2& a) { return make_int2(-a.x, -a.y); } -static __device__ __forceinline__ int3& operator*= (int3& a, const int3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } -static __device__ __forceinline__ int3& operator+= (int3& a, const int3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } -static __device__ __forceinline__ int3& operator-= (int3& a, const int3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } -static __device__ __forceinline__ int3& operator*= (int3& a, int b) { a.x *= b; a.y *= b; a.z *= b; return a; } -static __device__ __forceinline__ int3& operator+= (int3& a, int b) { a.x += b; a.y += b; a.z += b; return a; } -static __device__ __forceinline__ int3& operator-= (int3& a, int b) { a.x -= b; a.y -= b; a.z -= b; return a; } -static __device__ __forceinline__ int3 operator* (const int3& a, const int3& b) { return make_int3(a.x * b.x, a.y * b.y, a.z * b.z); } -static __device__ __forceinline__ int3 operator+ (const int3& a, const int3& b) { return make_int3(a.x + b.x, a.y + b.y, a.z + b.z); } -static __device__ __forceinline__ int3 operator- (const int3& a, const int3& b) { return make_int3(a.x - b.x, a.y - b.y, a.z - b.z); } -static __device__ __forceinline__ int3 operator* (const int3& a, int b) { return make_int3(a.x * b, a.y * b, a.z * b); } -static __device__ __forceinline__ int3 operator+ (const int3& a, int b) { return make_int3(a.x + b, a.y + b, a.z + b); } -static __device__ __forceinline__ int3 operator- (const int3& a, int b) { return make_int3(a.x - b, a.y - b, a.z - b); } -static __device__ __forceinline__ int3 operator* (int a, const int3& b) { return make_int3(a * b.x, a * b.y, a * b.z); } -static __device__ __forceinline__ int3 operator+ (int a, const int3& b) { return make_int3(a + b.x, a + b.y, a + b.z); } -static __device__ __forceinline__ int3 operator- (int a, const int3& b) { return make_int3(a - b.x, a - b.y, a - b.z); } -static __device__ __forceinline__ int3 operator- (const int3& a) { return make_int3(-a.x, -a.y, -a.z); } -static __device__ __forceinline__ int4& operator*= (int4& a, const int4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } -static __device__ __forceinline__ int4& operator+= (int4& a, const int4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } -static __device__ __forceinline__ int4& operator-= (int4& a, const int4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } -static __device__ __forceinline__ int4& operator*= (int4& a, int b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } -static __device__ __forceinline__ int4& operator+= (int4& a, int b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } -static __device__ __forceinline__ int4& operator-= (int4& a, int b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } -static __device__ __forceinline__ int4 operator* (const int4& a, const int4& b) { return make_int4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } -static __device__ __forceinline__ int4 operator+ (const int4& a, const int4& b) { return make_int4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } -static __device__ __forceinline__ int4 operator- (const int4& a, const int4& b) { return make_int4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } -static __device__ __forceinline__ int4 operator* (const int4& a, int b) { return make_int4(a.x * b, a.y * b, a.z * b, a.w * b); } -static __device__ __forceinline__ int4 operator+ (const int4& a, int b) { return make_int4(a.x + b, a.y + b, a.z + b, a.w + b); } -static __device__ __forceinline__ int4 operator- (const int4& a, int b) { return make_int4(a.x - b, a.y - b, a.z - b, a.w - b); } -static __device__ __forceinline__ int4 operator* (int a, const int4& b) { return make_int4(a * b.x, a * b.y, a * b.z, a * b.w); } -static __device__ __forceinline__ int4 operator+ (int a, const int4& b) { return make_int4(a + b.x, a + b.y, a + b.z, a + b.w); } -static __device__ __forceinline__ int4 operator- (int a, const int4& b) { return make_int4(a - b.x, a - b.y, a - b.z, a - b.w); } -static __device__ __forceinline__ int4 operator- (const int4& a) { return make_int4(-a.x, -a.y, -a.z, -a.w); } -static __device__ __forceinline__ uint2& operator*= (uint2& a, const uint2& b) { a.x *= b.x; a.y *= b.y; return a; } -static __device__ __forceinline__ uint2& operator+= (uint2& a, const uint2& b) { a.x += b.x; a.y += b.y; return a; } -static __device__ __forceinline__ uint2& operator-= (uint2& a, const uint2& b) { a.x -= b.x; a.y -= b.y; return a; } -static __device__ __forceinline__ uint2& operator*= (uint2& a, unsigned int b) { a.x *= b; a.y *= b; return a; } -static __device__ __forceinline__ uint2& operator+= (uint2& a, unsigned int b) { a.x += b; a.y += b; return a; } -static __device__ __forceinline__ uint2& operator-= (uint2& a, unsigned int b) { a.x -= b; a.y -= b; return a; } -static __device__ __forceinline__ uint2 operator* (const uint2& a, const uint2& b) { return make_uint2(a.x * b.x, a.y * b.y); } -static __device__ __forceinline__ uint2 operator+ (const uint2& a, const uint2& b) { return make_uint2(a.x + b.x, a.y + b.y); } -static __device__ __forceinline__ uint2 operator- (const uint2& a, const uint2& b) { return make_uint2(a.x - b.x, a.y - b.y); } -static __device__ __forceinline__ uint2 operator* (const uint2& a, unsigned int b) { return make_uint2(a.x * b, a.y * b); } -static __device__ __forceinline__ uint2 operator+ (const uint2& a, unsigned int b) { return make_uint2(a.x + b, a.y + b); } -static __device__ __forceinline__ uint2 operator- (const uint2& a, unsigned int b) { return make_uint2(a.x - b, a.y - b); } -static __device__ __forceinline__ uint2 operator* (unsigned int a, const uint2& b) { return make_uint2(a * b.x, a * b.y); } -static __device__ __forceinline__ uint2 operator+ (unsigned int a, const uint2& b) { return make_uint2(a + b.x, a + b.y); } -static __device__ __forceinline__ uint2 operator- (unsigned int a, const uint2& b) { return make_uint2(a - b.x, a - b.y); } -static __device__ __forceinline__ uint3& operator*= (uint3& a, const uint3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } -static __device__ __forceinline__ uint3& operator+= (uint3& a, const uint3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } -static __device__ __forceinline__ uint3& operator-= (uint3& a, const uint3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } -static __device__ __forceinline__ uint3& operator*= (uint3& a, unsigned int b) { a.x *= b; a.y *= b; a.z *= b; return a; } -static __device__ __forceinline__ uint3& operator+= (uint3& a, unsigned int b) { a.x += b; a.y += b; a.z += b; return a; } -static __device__ __forceinline__ uint3& operator-= (uint3& a, unsigned int b) { a.x -= b; a.y -= b; a.z -= b; return a; } -static __device__ __forceinline__ uint3 operator* (const uint3& a, const uint3& b) { return make_uint3(a.x * b.x, a.y * b.y, a.z * b.z); } -static __device__ __forceinline__ uint3 operator+ (const uint3& a, const uint3& b) { return make_uint3(a.x + b.x, a.y + b.y, a.z + b.z); } -static __device__ __forceinline__ uint3 operator- (const uint3& a, const uint3& b) { return make_uint3(a.x - b.x, a.y - b.y, a.z - b.z); } -static __device__ __forceinline__ uint3 operator* (const uint3& a, unsigned int b) { return make_uint3(a.x * b, a.y * b, a.z * b); } -static __device__ __forceinline__ uint3 operator+ (const uint3& a, unsigned int b) { return make_uint3(a.x + b, a.y + b, a.z + b); } -static __device__ __forceinline__ uint3 operator- (const uint3& a, unsigned int b) { return make_uint3(a.x - b, a.y - b, a.z - b); } -static __device__ __forceinline__ uint3 operator* (unsigned int a, const uint3& b) { return make_uint3(a * b.x, a * b.y, a * b.z); } -static __device__ __forceinline__ uint3 operator+ (unsigned int a, const uint3& b) { return make_uint3(a + b.x, a + b.y, a + b.z); } -static __device__ __forceinline__ uint3 operator- (unsigned int a, const uint3& b) { return make_uint3(a - b.x, a - b.y, a - b.z); } -static __device__ __forceinline__ uint4& operator*= (uint4& a, const uint4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } -static __device__ __forceinline__ uint4& operator+= (uint4& a, const uint4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } -static __device__ __forceinline__ uint4& operator-= (uint4& a, const uint4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } -static __device__ __forceinline__ uint4& operator*= (uint4& a, unsigned int b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } -static __device__ __forceinline__ uint4& operator+= (uint4& a, unsigned int b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } -static __device__ __forceinline__ uint4& operator-= (uint4& a, unsigned int b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } -static __device__ __forceinline__ uint4 operator* (const uint4& a, const uint4& b) { return make_uint4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } -static __device__ __forceinline__ uint4 operator+ (const uint4& a, const uint4& b) { return make_uint4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } -static __device__ __forceinline__ uint4 operator- (const uint4& a, const uint4& b) { return make_uint4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } -static __device__ __forceinline__ uint4 operator* (const uint4& a, unsigned int b) { return make_uint4(a.x * b, a.y * b, a.z * b, a.w * b); } -static __device__ __forceinline__ uint4 operator+ (const uint4& a, unsigned int b) { return make_uint4(a.x + b, a.y + b, a.z + b, a.w + b); } -static __device__ __forceinline__ uint4 operator- (const uint4& a, unsigned int b) { return make_uint4(a.x - b, a.y - b, a.z - b, a.w - b); } -static __device__ __forceinline__ uint4 operator* (unsigned int a, const uint4& b) { return make_uint4(a * b.x, a * b.y, a * b.z, a * b.w); } -static __device__ __forceinline__ uint4 operator+ (unsigned int a, const uint4& b) { return make_uint4(a + b.x, a + b.y, a + b.z, a + b.w); } -static __device__ __forceinline__ uint4 operator- (unsigned int a, const uint4& b) { return make_uint4(a - b.x, a - b.y, a - b.z, a - b.w); } - -template static __device__ __forceinline__ T zero_value(void); -template<> __device__ __forceinline__ float zero_value (void) { return 0.f; } -template<> __device__ __forceinline__ float2 zero_value(void) { return make_float2(0.f, 0.f); } -template<> __device__ __forceinline__ float4 zero_value(void) { return make_float4(0.f, 0.f, 0.f, 0.f); } -static __device__ __forceinline__ float3 make_float3(const float2& a, float b) { return make_float3(a.x, a.y, b); } -static __device__ __forceinline__ float4 make_float4(const float3& a, float b) { return make_float4(a.x, a.y, a.z, b); } -static __device__ __forceinline__ float4 make_float4(const float2& a, const float2& b) { return make_float4(a.x, a.y, b.x, b.y); } -static __device__ __forceinline__ int3 make_int3(const int2& a, int b) { return make_int3(a.x, a.y, b); } -static __device__ __forceinline__ int4 make_int4(const int3& a, int b) { return make_int4(a.x, a.y, a.z, b); } -static __device__ __forceinline__ int4 make_int4(const int2& a, const int2& b) { return make_int4(a.x, a.y, b.x, b.y); } -static __device__ __forceinline__ uint3 make_uint3(const uint2& a, unsigned int b) { return make_uint3(a.x, a.y, b); } -static __device__ __forceinline__ uint4 make_uint4(const uint3& a, unsigned int b) { return make_uint4(a.x, a.y, a.z, b); } -static __device__ __forceinline__ uint4 make_uint4(const uint2& a, const uint2& b) { return make_uint4(a.x, a.y, b.x, b.y); } - -template static __device__ __forceinline__ void swap(T& a, T& b) { T temp = a; a = b; b = temp; } - -//------------------------------------------------------------------------ -// Coalesced atomics. These are all done via macros. - -#if __CUDA_ARCH__ >= 700 // Warp match instruction __match_any_sync() is only available on compute capability 7.x and higher - -#define CA_TEMP _ca_temp -#define CA_TEMP_PARAM float* CA_TEMP -#define CA_DECLARE_TEMP(threads_per_block) \ - __shared__ float CA_TEMP[(threads_per_block)] - -#define CA_SET_GROUP_MASK(group, thread_mask) \ - bool _ca_leader; \ - float* _ca_ptr; \ - do { \ - int tidx = threadIdx.x + blockDim.x * threadIdx.y; \ - int lane = tidx & 31; \ - int warp = tidx >> 5; \ - int tmask = __match_any_sync((thread_mask), (group)); \ - int leader = __ffs(tmask) - 1; \ - _ca_leader = (leader == lane); \ - _ca_ptr = &_ca_temp[((warp << 5) + leader)]; \ - } while(0) - -#define CA_SET_GROUP(group) \ - CA_SET_GROUP_MASK((group), 0xffffffffu) - -#define caAtomicAdd(ptr, value) \ - do { \ - if (_ca_leader) \ - *_ca_ptr = 0.f; \ - atomicAdd(_ca_ptr, (value)); \ - if (_ca_leader) \ - atomicAdd((ptr), *_ca_ptr); \ - } while(0) - -#define caAtomicAdd3_xyw(ptr, x, y, w) \ - do { \ - caAtomicAdd((ptr), (x)); \ - caAtomicAdd((ptr)+1, (y)); \ - caAtomicAdd((ptr)+3, (w)); \ - } while(0) - -#define caAtomicAddTexture(ptr, level, idx, value) \ - do { \ - CA_SET_GROUP((idx) ^ ((level) << 27)); \ - caAtomicAdd((ptr)+(idx), (value)); \ - } while(0) - -//------------------------------------------------------------------------ -// Disable atomic coalescing for compute capability lower than 7.x - -#else // __CUDA_ARCH__ >= 700 -#define CA_TEMP _ca_temp -#define CA_TEMP_PARAM float CA_TEMP -#define CA_DECLARE_TEMP(threads_per_block) CA_TEMP_PARAM -#define CA_SET_GROUP_MASK(group, thread_mask) -#define CA_SET_GROUP(group) -#define caAtomicAdd(ptr, value) atomicAdd((ptr), (value)) -#define caAtomicAdd3_xyw(ptr, x, y, w) \ - do { \ - atomicAdd((ptr), (x)); \ - atomicAdd((ptr)+1, (y)); \ - atomicAdd((ptr)+3, (w)); \ - } while(0) -#define caAtomicAddTexture(ptr, level, idx, value) atomicAdd((ptr)+(idx), (value)) -#endif // __CUDA_ARCH__ >= 700 - -//------------------------------------------------------------------------ -#endif // __CUDACC__ diff --git a/bayes3d/rendering/nvdiffrast/common/framework.h b/bayes3d/rendering/nvdiffrast/common/framework.h deleted file mode 100644 index 75125ac5..00000000 --- a/bayes3d/rendering/nvdiffrast/common/framework.h +++ /dev/null @@ -1,49 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#pragma once - -// Framework-specific macros to enable code sharing. - -//------------------------------------------------------------------------ -// Tensorflow. - -#ifdef NVDR_TENSORFLOW -#define EIGEN_USE_GPU -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/framework/shape_inference.h" -#include "tensorflow/core/platform/default/logging.h" -using namespace tensorflow; -using namespace tensorflow::shape_inference; -#define NVDR_CTX_ARGS OpKernelContext* _nvdr_ctx -#define NVDR_CTX_PARAMS _nvdr_ctx -#define NVDR_CHECK(COND, ERR) OP_REQUIRES(_nvdr_ctx, COND, errors::Internal(ERR)) -#define NVDR_CHECK_CUDA_ERROR(CUDA_CALL) OP_CHECK_CUDA_ERROR(_nvdr_ctx, CUDA_CALL) -#define NVDR_CHECK_GL_ERROR(GL_CALL) OP_CHECK_GL_ERROR(_nvdr_ctx, GL_CALL) -#endif - -//------------------------------------------------------------------------ -// PyTorch. - -#ifdef NVDR_TORCH -#ifndef __CUDACC__ -#include -#include -#include -#include -#include -#endif -#define NVDR_CTX_ARGS int _nvdr_ctx_dummy -#define NVDR_CTX_PARAMS 0 -#define NVDR_CHECK(COND, ERR) do { TORCH_CHECK(COND, ERR) } while(0) -#define NVDR_CHECK_CUDA_ERROR(CUDA_CALL) do { cudaError_t err = CUDA_CALL; TORCH_CHECK(!err, "Cuda error: ", cudaGetLastError(), "[", #CUDA_CALL, ";]"); } while(0) -#define NVDR_CHECK_GL_ERROR(GL_CALL) do { GL_CALL; GLenum err = glGetError(); TORCH_CHECK(err == GL_NO_ERROR, "OpenGL error: ", getGLErrorString(err), " ", err, "[", #GL_CALL, ";]"); } while(0) -#endif - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/glutil.cpp b/bayes3d/rendering/nvdiffrast/common/glutil.cpp deleted file mode 100644 index 2af3e931..00000000 --- a/bayes3d/rendering/nvdiffrast/common/glutil.cpp +++ /dev/null @@ -1,403 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -//------------------------------------------------------------------------ -// Common. -//------------------------------------------------------------------------ - -#include "framework.h" -#include "glutil.h" -#include -#include - -// Create the function pointers. -#define GLUTIL_EXT(return_type, name, ...) return_type (GLAPIENTRY* name)(__VA_ARGS__) = 0; -#include "glutil_extlist.h" -#undef GLUTIL_EXT - -// Track initialization status. -static volatile bool s_glExtInitialized = false; - -// Error strings. -const char* getGLErrorString(GLenum err) -{ - switch(err) - { - case GL_NO_ERROR: return "GL_NO_ERROR"; - case GL_INVALID_ENUM: return "GL_INVALID_ENUM"; - case GL_INVALID_VALUE: return "GL_INVALID_VALUE"; - case GL_INVALID_OPERATION: return "GL_INVALID_OPERATION"; - case GL_STACK_OVERFLOW: return "GL_STACK_OVERFLOW"; - case GL_STACK_UNDERFLOW: return "GL_STACK_UNDERFLOW"; - case GL_OUT_OF_MEMORY: return "GL_OUT_OF_MEMORY"; - case GL_INVALID_FRAMEBUFFER_OPERATION: return "GL_INVALID_FRAMEBUFFER_OPERATION"; - case GL_TABLE_TOO_LARGE: return "GL_TABLE_TOO_LARGE"; - case GL_CONTEXT_LOST: return "GL_CONTEXT_LOST"; - } - return "Unknown error"; -} - -//------------------------------------------------------------------------ -// Windows. -//------------------------------------------------------------------------ - -#ifdef _WIN32 - -static CRITICAL_SECTION getInitializedCriticalSection(void) -{ - CRITICAL_SECTION cs; - InitializeCriticalSection(&cs); - return cs; -} - -static CRITICAL_SECTION s_getProcAddressMutex = getInitializedCriticalSection(); - -static void safeGetProcAddress(const char* name, PROC* pfn) -{ - PROC result = wglGetProcAddress(name); - if (!result) - { - LeaveCriticalSection(&s_getProcAddressMutex); // Prepare for thread exit. - LOG(FATAL) << "wglGetProcAddress() failed for '" << name << "'"; - exit(1); // Should never get here but make sure we exit. - } - *pfn = result; -} - -static void initializeGLExtensions(void) -{ - // Use critical section for thread safety. - EnterCriticalSection(&s_getProcAddressMutex); - - // Only dig function pointers if not done already. - if (!s_glExtInitialized) - { - // Generate code to populate the function pointers. -#define GLUTIL_EXT(return_type, name, ...) safeGetProcAddress(#name, (PROC*)&name); -#include "glutil_extlist.h" -#undef GLUTIL_EXT - - // Mark as initialized. - s_glExtInitialized = true; - } - - // Done. - LeaveCriticalSection(&s_getProcAddressMutex); - return; -} - -void setGLContext(GLContext& glctx) -{ - if (!glctx.hglrc) - LOG(FATAL) << "setGLContext() called with null gltcx"; - if (!wglMakeCurrent(glctx.hdc, glctx.hglrc)) - LOG(FATAL) << "wglMakeCurrent() failed when setting GL context"; - - if (glctx.extInitialized) - return; - initializeGLExtensions(); - glctx.extInitialized = 1; -} - -void releaseGLContext(void) -{ - if (!wglMakeCurrent(NULL, NULL)) - LOG(FATAL) << "wglMakeCurrent() failed when releasing GL context"; -} - -extern "C" int set_gpu(const char*); // In setgpu.lib -GLContext createGLContext(int cudaDeviceIdx) -{ - if (cudaDeviceIdx >= 0) - { - char pciBusId[256] = ""; - LOG(INFO) << "Creating GL context for Cuda device " << cudaDeviceIdx; - if (cudaDeviceGetPCIBusId(pciBusId, 255, cudaDeviceIdx)) - { - LOG(INFO) << "PCI bus id query failed"; - } - else - { - int res = set_gpu(pciBusId); - LOG(INFO) << "Selecting device with PCI bus id " << pciBusId << " - " << (res ? "failed, expect crash or major slowdown" : "success"); - } - } - - HINSTANCE hInstance = GetModuleHandle(NULL); - WNDCLASS wc = {}; - wc.style = CS_OWNDC; - wc.lpfnWndProc = DefWindowProc; - wc.hInstance = hInstance; - wc.lpszClassName = "__DummyGLClassCPP"; - int res = RegisterClass(&wc); - - HWND hwnd = CreateWindow( - "__DummyGLClassCPP", // lpClassName - "__DummyGLWindowCPP", // lpWindowName - WS_OVERLAPPEDWINDOW, // dwStyle - CW_USEDEFAULT, // x - CW_USEDEFAULT, // y - 0, 0, // nWidth, nHeight - NULL, NULL, // hWndParent, hMenu - hInstance, // hInstance - NULL // lpParam - ); - - PIXELFORMATDESCRIPTOR pfd = {}; - pfd.dwFlags = PFD_SUPPORT_OPENGL; - pfd.iPixelType = PFD_TYPE_RGBA; - pfd.iLayerType = PFD_MAIN_PLANE; - pfd.cColorBits = 32; - pfd.cDepthBits = 24; - pfd.cStencilBits = 8; - - HDC hdc = GetDC(hwnd); - int pixelformat = ChoosePixelFormat(hdc, &pfd); - SetPixelFormat(hdc, pixelformat, &pfd); - - HGLRC hglrc = wglCreateContext(hdc); - LOG(INFO) << std::hex << std::setfill('0') - << "WGL OpenGL context created (hdc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)hdc - << ", hglrc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)hglrc << ")"; - - GLContext glctx = {hdc, hglrc, 0}; - return glctx; -} - -void destroyGLContext(GLContext& glctx) -{ - if (!glctx.hglrc) - LOG(FATAL) << "destroyGLContext() called with null gltcx"; - - // If this is the current context, release it. - if (wglGetCurrentContext() == glctx.hglrc) - releaseGLContext(); - - HWND hwnd = WindowFromDC(glctx.hdc); - if (!hwnd) - LOG(FATAL) << "WindowFromDC() failed"; - if (!ReleaseDC(hwnd, glctx.hdc)) - LOG(FATAL) << "ReleaseDC() failed"; - if (!wglDeleteContext(glctx.hglrc)) - LOG(FATAL) << "wglDeleteContext() failed"; - if (!DestroyWindow(hwnd)) - LOG(FATAL) << "DestroyWindow() failed"; - - LOG(INFO) << std::hex << std::setfill('0') - << "WGL OpenGL context destroyed (hdc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)glctx.hdc - << ", hglrc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)glctx.hglrc << ")"; - - memset(&glctx, 0, sizeof(GLContext)); -} - -#endif // _WIN32 - -//------------------------------------------------------------------------ -// Linux. -//------------------------------------------------------------------------ - -#ifdef __linux__ - -static pthread_mutex_t s_getProcAddressMutex; - -typedef void (*PROCFN)(); - -static void safeGetProcAddress(const char* name, PROCFN* pfn) -{ - PROCFN result = eglGetProcAddress(name); - if (!result) - { - pthread_mutex_unlock(&s_getProcAddressMutex); // Prepare for thread exit. - LOG(FATAL) << "wglGetProcAddress() failed for '" << name << "'"; - exit(1); // Should never get here but make sure we exit. - } - *pfn = result; -} - -static void initializeGLExtensions(void) -{ - pthread_mutex_lock(&s_getProcAddressMutex); - - // Only dig function pointers if not done already. - if (!s_glExtInitialized) - { - // Generate code to populate the function pointers. -#define GLUTIL_EXT(return_type, name, ...) safeGetProcAddress(#name, (PROCFN*)&name); -#include "glutil_extlist.h" -#undef GLUTIL_EXT - - // Mark as initialized. - s_glExtInitialized = true; - } - - pthread_mutex_unlock(&s_getProcAddressMutex); - return; -} - -void setGLContext(GLContext& glctx) -{ - if (!glctx.context) - LOG(FATAL) << "setGLContext() called with null gltcx"; - - if (!eglMakeCurrent(glctx.display, EGL_NO_SURFACE, EGL_NO_SURFACE, glctx.context)) - LOG(ERROR) << "eglMakeCurrent() failed when setting GL context"; - - if (glctx.extInitialized) - return; - initializeGLExtensions(); - glctx.extInitialized = 1; -} - -void releaseGLContext(void) -{ - EGLDisplay display = eglGetCurrentDisplay(); - if (display == EGL_NO_DISPLAY) - LOG(WARNING) << "releaseGLContext() called with no active display"; - if (!eglMakeCurrent(display, EGL_NO_SURFACE, EGL_NO_SURFACE, EGL_NO_CONTEXT)) - LOG(FATAL) << "eglMakeCurrent() failed when releasing GL context"; -} - -static EGLDisplay getCudaDisplay(int cudaDeviceIdx) -{ - typedef EGLBoolean (*eglQueryDevicesEXT_t)(EGLint, EGLDeviceEXT, EGLint*); - typedef EGLBoolean (*eglQueryDeviceAttribEXT_t)(EGLDeviceEXT, EGLint, EGLAttrib*); - typedef EGLDisplay (*eglGetPlatformDisplayEXT_t)(EGLenum, void*, const EGLint*); - - eglQueryDevicesEXT_t eglQueryDevicesEXT = (eglQueryDevicesEXT_t)eglGetProcAddress("eglQueryDevicesEXT"); - if (!eglQueryDevicesEXT) - { - LOG(INFO) << "eglGetProcAddress(\"eglQueryDevicesEXT\") failed"; - return 0; - } - - eglQueryDeviceAttribEXT_t eglQueryDeviceAttribEXT = (eglQueryDeviceAttribEXT_t)eglGetProcAddress("eglQueryDeviceAttribEXT"); - if (!eglQueryDeviceAttribEXT) - { - LOG(INFO) << "eglGetProcAddress(\"eglQueryDeviceAttribEXT\") failed"; - return 0; - } - - eglGetPlatformDisplayEXT_t eglGetPlatformDisplayEXT = (eglGetPlatformDisplayEXT_t)eglGetProcAddress("eglGetPlatformDisplayEXT"); - if (!eglGetPlatformDisplayEXT) - { - LOG(INFO) << "eglGetProcAddress(\"eglGetPlatformDisplayEXT\") failed"; - return 0; - } - - int num_devices = 0; - eglQueryDevicesEXT(0, 0, &num_devices); - if (!num_devices) - return 0; - - EGLDisplay display = 0; - EGLDeviceEXT* devices = (EGLDeviceEXT*)malloc(num_devices * sizeof(void*)); - eglQueryDevicesEXT(num_devices, devices, &num_devices); - for (int i=0; i < num_devices; i++) - { - EGLDeviceEXT device = devices[i]; - intptr_t value = -1; - if (eglQueryDeviceAttribEXT(device, EGL_CUDA_DEVICE_NV, &value) && value == cudaDeviceIdx) - { - display = eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, device, 0); - break; - } - } - - free(devices); - return display; -} - -GLContext createGLContext(int cudaDeviceIdx) -{ - EGLDisplay display = 0; - - if (cudaDeviceIdx >= 0) - { - char pciBusId[256] = ""; - LOG(INFO) << "Creating GL context for Cuda device " << cudaDeviceIdx; - display = getCudaDisplay(cudaDeviceIdx); - if (!display) - LOG(INFO) << "Failed, falling back to default display"; - } - - if (!display) - { - display = eglGetDisplay(EGL_DEFAULT_DISPLAY); - if (display == EGL_NO_DISPLAY) - LOG(FATAL) << "eglGetDisplay() failed"; - } - - EGLint major; - EGLint minor; - if (!eglInitialize(display, &major, &minor)) - LOG(FATAL) << "eglInitialize() failed"; - - // Choose configuration. - - const EGLint context_attribs[] = { - EGL_RED_SIZE, 8, - EGL_GREEN_SIZE, 8, - EGL_BLUE_SIZE, 8, - EGL_ALPHA_SIZE, 8, - EGL_DEPTH_SIZE, 24, - EGL_STENCIL_SIZE, 8, - EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT, - EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, - EGL_NONE - }; - - EGLConfig config; - EGLint num_config; - if (!eglChooseConfig(display, context_attribs, &config, 1, &num_config)) - LOG(FATAL) << "eglChooseConfig() failed"; - - // Create GL context. - - if (!eglBindAPI(EGL_OPENGL_API)) - LOG(FATAL) << "eglBindAPI() failed"; - - EGLContext context = eglCreateContext(display, config, EGL_NO_CONTEXT, NULL); - if (context == EGL_NO_CONTEXT) - LOG(FATAL) << "eglCreateContext() failed"; - - // Done. - - LOG(INFO) << "EGL " << (int)minor << "." << (int)major << " OpenGL context created (disp: 0x" - << std::hex << std::setfill('0') - << std::setw(16) << (uintptr_t)display - << ", ctx: 0x" << std::setw(16) << (uintptr_t)context << ")"; - - GLContext glctx = {display, context, 0}; - return glctx; -} - -void destroyGLContext(GLContext& glctx) -{ - if (!glctx.context) - LOG(FATAL) << "destroyGLContext() called with null gltcx"; - - // If this is the current context, release it. - if (eglGetCurrentContext() == glctx.context) - releaseGLContext(); - - if (!eglDestroyContext(glctx.display, glctx.context)) - LOG(ERROR) << "eglDestroyContext() failed"; - - LOG(INFO) << "EGL OpenGL context destroyed (disp: 0x" - << std::hex << std::setfill('0') - << std::setw(16) << (uintptr_t)glctx.display - << ", ctx: 0x" << std::setw(16) << (uintptr_t)glctx.context << ")"; - - memset(&glctx, 0, sizeof(GLContext)); -} - -//------------------------------------------------------------------------ - -#endif // __linux__ - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/glutil.h b/bayes3d/rendering/nvdiffrast/common/glutil.h deleted file mode 100644 index 19e12b21..00000000 --- a/bayes3d/rendering/nvdiffrast/common/glutil.h +++ /dev/null @@ -1,117 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - - -#pragma once - - -//------------------------------------------------------------------------ -// Windows-specific headers and types. -//------------------------------------------------------------------------ - -#ifdef _WIN32 -#define NOMINMAX -#include // Required by gl.h in Windows. -#define GLAPIENTRY APIENTRY - -struct GLContext -{ - HDC hdc; - HGLRC hglrc; - int extInitialized; -}; - -#endif // _WIN32 - -//------------------------------------------------------------------------ -// Linux-specific headers and types. -//------------------------------------------------------------------------ - -#ifdef __linux__ -#define EGL_NO_X11 // X11/Xlib.h has "#define Status int" which breaks Tensorflow. Avoid it. -#define MESA_EGL_NO_X11_HEADERS -#include -#include -#define GLAPIENTRY - -struct GLContext -{ - EGLDisplay display; - EGLContext context; - int extInitialized; -}; - -#endif // __linux__ - -//------------------------------------------------------------------------ -// OpenGL, CUDA interop, GL extensions. -//------------------------------------------------------------------------ -#define GL_GLEXT_LEGACY -#include -#include -#include - -// Constants. -#ifndef GL_VERSION_1_2 -#define GL_CLAMP_TO_EDGE 0x812F -#define GL_TEXTURE_3D 0x806F -#endif -#ifndef GL_VERSION_1_5 -#define GL_ARRAY_BUFFER 0x8892 -#define GL_DYNAMIC_DRAW 0x88E8 -#define GL_ELEMENT_ARRAY_BUFFER 0x8893 -#endif -#ifndef GL_VERSION_2_0 -#define GL_FRAGMENT_SHADER 0x8B30 -#define GL_INFO_LOG_LENGTH 0x8B84 -#define GL_LINK_STATUS 0x8B82 -#define GL_VERTEX_SHADER 0x8B31 -#endif -#ifndef GL_VERSION_3_0 -#define GL_MAJOR_VERSION 0x821B -#define GL_MINOR_VERSION 0x821C -#define GL_RGBA32F 0x8814 -#define GL_R32F 0x822E -#define GL_TEXTURE_2D_ARRAY 0x8C1A -#endif -#ifndef GL_VERSION_3_2 -#define GL_GEOMETRY_SHADER 0x8DD9 -#endif -#ifndef GL_ARB_framebuffer_object -#define GL_COLOR_ATTACHMENT0 0x8CE0 -#define GL_COLOR_ATTACHMENT1 0x8CE1 -#define GL_DEPTH_STENCIL 0x84F9 -#define GL_DEPTH_STENCIL_ATTACHMENT 0x821A -#define GL_DEPTH24_STENCIL8 0x88F0 -#define GL_FRAMEBUFFER 0x8D40 -#define GL_INVALID_FRAMEBUFFER_OPERATION 0x0506 -#define GL_UNSIGNED_INT_24_8 0x84FA -#endif -#ifndef GL_ARB_imaging -#define GL_TABLE_TOO_LARGE 0x8031 -#endif -#ifndef GL_KHR_robustness -#define GL_CONTEXT_LOST 0x0507 -#endif - -// Declare function pointers to OpenGL extension functions. -#define GLUTIL_EXT(return_type, name, ...) extern return_type (GLAPIENTRY* name)(__VA_ARGS__); -#include "glutil_extlist.h" -#undef GLUTIL_EXT - -//------------------------------------------------------------------------ -// Common functions. -//------------------------------------------------------------------------ - -void setGLContext (GLContext& glctx); -void releaseGLContext (void); -GLContext createGLContext (int cudaDeviceIdx); -void destroyGLContext (GLContext& glctx); -const char* getGLErrorString (GLenum err); - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/glutil_extlist.h b/bayes3d/rendering/nvdiffrast/common/glutil_extlist.h deleted file mode 100644 index 457dbe47..00000000 --- a/bayes3d/rendering/nvdiffrast/common/glutil_extlist.h +++ /dev/null @@ -1,59 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -// ... (previous declarations) - -#ifndef GL_VERSION_1_2 -GLUTIL_EXT(void, glTexImage3D, GLenum target, GLint level, GLint internalFormat, GLsizei width, GLsizei height, GLsizei depth, GLint border, GLenum format, GLenum type, const void *pixels); -#endif -#ifndef GL_VERSION_1_5 -GLUTIL_EXT(void, glBindBuffer, GLenum target, GLuint buffer); -GLUTIL_EXT(void, glBufferData, GLenum target, ptrdiff_t size, const void* data, GLenum usage); -GLUTIL_EXT(void, glGenBuffers, GLsizei n, GLuint* buffers); -GLUTIL_EXT(void, glDeleteBuffers, GLsizei n, const GLuint* buffers); // <-- Add this line -#endif -#ifndef GL_VERSION_2_0 -GLUTIL_EXT(void, glAttachShader, GLuint program, GLuint shader); -GLUTIL_EXT(void, glCompileShader, GLuint shader); -GLUTIL_EXT(GLuint, glCreateProgram, void); -GLUTIL_EXT(GLuint, glCreateShader, GLenum type); -GLUTIL_EXT(void, glDeleteProgram, GLuint program); // <-- Add this line -GLUTIL_EXT(void, glDeleteShader, GLuint shader); // <-- Add this line -GLUTIL_EXT(void, glDrawBuffers, GLsizei n, const GLenum* bufs); -GLUTIL_EXT(void, glEnableVertexAttribArray, GLuint index); -GLUTIL_EXT(void, glGetProgramInfoLog, GLuint program, GLsizei bufSize, GLsizei* length, char* infoLog); -GLUTIL_EXT(void, glGetProgramiv, GLuint program, GLenum pname, GLint* param); -GLUTIL_EXT(void, glLinkProgram, GLuint program); -GLUTIL_EXT(void, glShaderSource, GLuint shader, GLsizei count, const char *const* string, const GLint* length); -GLUTIL_EXT(void, glUniform1f, GLint location, GLfloat v0); -GLUTIL_EXT(void, glUniform1i, GLint location, GLint v0); -GLUTIL_EXT(void, glUniform2f, GLint location, GLfloat v0, GLfloat v1); -GLUTIL_EXT(void, glUniformMatrix4fv, GLint location, GLsizei count, GLboolean transpose, const GLfloat *value); -GLUTIL_EXT(void, glUseProgram, GLuint program); -GLUTIL_EXT(void, glVertexAttribPointer, GLuint index, GLint size, GLenum type, GLboolean normalized, GLsizei stride, const void* pointer); -#endif -#ifndef GL_VERSION_3_0 -GLUTIL_EXT(void, glDeleteVertexArrays, GLsizei n, const GLuint* arrays); // <-- Add this line -#endif -#ifndef GL_VERSION_3_2 -GLUTIL_EXT(void, glFramebufferTexture, GLenum target, GLenum attachment, GLuint texture, GLint level); -GLUTIL_EXT(void, glDeleteFramebuffers, GLsizei n, const GLuint* framebuffers); // <-- Add this line -#endif -#ifndef GL_ARB_framebuffer_object -GLUTIL_EXT(void, glBindFramebuffer, GLenum target, GLuint framebuffer); -GLUTIL_EXT(void, glGenFramebuffers, GLsizei n, GLuint* framebuffers); -#endif -#ifndef GL_ARB_vertex_array_object -GLUTIL_EXT(void, glBindVertexArray, GLuint array); -GLUTIL_EXT(void, glGenVertexArrays, GLsizei n, GLuint* arrays); -#endif -#ifndef GL_ARB_multi_draw_indirect -GLUTIL_EXT(void, glMultiDrawElementsIndirect, GLenum mode, GLenum type, const void *indirect, GLsizei primcount, GLsizei stride); -#endif - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/ops.py b/bayes3d/rendering/nvdiffrast/common/ops.py deleted file mode 100644 index 3978116d..00000000 --- a/bayes3d/rendering/nvdiffrast/common/ops.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - - -import torch - -import bayes3d.rendering.nvdiffrast.nvdiffrast_plugin_gl as plugin_gl - -# ---------------------------------------------------------------------------- -# C++/Cuda plugin loader. - - -def _get_plugin(gl=True): - # the gl flag is left here for backward compatibility - assert gl is True - return plugin_gl - - -# ---------------------------------------------------------------------------- -# GL state wrapper. -# ---------------------------------------------------------------------------- - - -class RasterizeGLContext: - def __init__(self, height, width, output_db=False, mode="automatic", device=None): - """Create a new OpenGL rasterizer context. - - Creating an OpenGL context is a slow operation so you should usually reuse the same - context in all calls to `rasterize()` on the same CPU thread. The OpenGL context - is deleted when the object is destroyed. - - Side note: When using the OpenGL context in a rasterization operation, the - context's internal framebuffer object is automatically enlarged to accommodate the - rasterization operation's output shape, but it is never shrunk in size until the - context is destroyed. Thus, if you need to rasterize, say, deep low-resolution - tensors and also shallow high-resolution tensors, you can conserve GPU memory by - creating two separate OpenGL contexts for these tasks. In this scenario, using the - same OpenGL context for both tasks would end up reserving GPU memory for a deep, - high-resolution output tensor. - - Args: - output_db (bool): Compute and output image-space derivates of barycentrics. - mode: OpenGL context handling mode. Valid values are 'manual' and 'automatic'. - device (Optional): Cuda device on which the context is created. Type can be - `torch.device`, string (e.g., `'cuda:1'`), or int. If not - specified, context will be created on currently active Cuda - device. - Returns: - The newly created OpenGL rasterizer context. - """ - assert output_db is True or output_db is False - assert mode in ["automatic", "manual"] - self.output_db = output_db - self.mode = mode - if device is None: - cuda_device_idx = torch.cuda.current_device() - else: - with torch.cuda.device(device): - cuda_device_idx = torch.cuda.current_device() - self.cpp_wrapper = _get_plugin(gl=True).RasterizeGLStateWrapper( - output_db, mode == "automatic", cuda_device_idx - ) - self.active_depth_peeler = None # For error checking only. - - def set_context(self): - """Set (activate) OpenGL context in the current CPU thread. - Only available if context was created in manual mode. - """ - assert self.mode == "manual" - self.cpp_wrapper.set_context() - - def release_context(self): - """Release (deactivate) currently active OpenGL context. - Only available if context was created in manual mode. - """ - assert self.mode == "manual" - self.cpp_wrapper.release_context() diff --git a/bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp deleted file mode 100644 index 41f4c2f0..00000000 --- a/bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp +++ /dev/null @@ -1,720 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include "rasterize_gl.h" -#include "glutil.h" -// #include "torch_common.inl" -#include "torch_types.h" -#include "common.h" -#include -#include -#include -#include - -#include - -#include -#include -#define STRINGIFY_SHADER_SOURCE(x) #x - -//------------------------------------------------------------------------ -// Helpers. - -#define ROUND_UP(x, y) ((((x) + ((y) - 1)) / (y)) * (y)) -static int ROUND_UP_BITS(uint32_t x, uint32_t y) -{ - // Round x up so that it has at most y bits of mantissa. - if (x < (1u << y)) - return x; - uint32_t m = 0; - while (x & ~m) - m = (m << 1) | 1u; - m >>= y; - if (!(x & m)) - return x; - return (x | m) + 1u; -} - -//------------------------------------------------------------------------ -// Draw command struct used by rasterizer. - -struct GLDrawCmd -{ - uint32_t count; - uint32_t instanceCount; - uint32_t firstIndex; - uint32_t baseVertex; - uint32_t baseInstance; -}; - -//------------------------------------------------------------------------ -// GL helpers. - -static void compileGLShader(NVDR_CTX_ARGS, const RasterizeGLState& s, GLuint* pShader, GLenum shaderType, const char* src_buf) -{ - std::string src(src_buf); - - // Set preprocessor directives. - int n = src.find('\n') + 1; // After first line containing #version directive. - if (s.enableZModify) - src.insert(n, "#define IF_ZMODIFY(x) x\n"); - else - src.insert(n, "#define IF_ZMODIFY(x)\n"); - - const char *cstr = src.c_str(); - *pShader = 0; - NVDR_CHECK_GL_ERROR(*pShader = glCreateShader(shaderType)); - NVDR_CHECK_GL_ERROR(glShaderSource(*pShader, 1, &cstr, 0)); - NVDR_CHECK_GL_ERROR(glCompileShader(*pShader)); -} - -static void constructGLProgram(NVDR_CTX_ARGS, GLuint* pProgram, GLuint glVertexShader, GLuint glFragmentShader) -{ - *pProgram = 0; - - GLuint glProgram = 0; - NVDR_CHECK_GL_ERROR(glProgram = glCreateProgram()); - NVDR_CHECK_GL_ERROR(glAttachShader(glProgram, glVertexShader)); - NVDR_CHECK_GL_ERROR(glAttachShader(glProgram, glFragmentShader)); - NVDR_CHECK_GL_ERROR(glLinkProgram(glProgram)); - - GLint linkStatus = 0; - NVDR_CHECK_GL_ERROR(glGetProgramiv(glProgram, GL_LINK_STATUS, &linkStatus)); - if (!linkStatus) - { - GLint infoLen = 0; - NVDR_CHECK_GL_ERROR(glGetProgramiv(glProgram, GL_INFO_LOG_LENGTH, &infoLen)); - if (infoLen) - { - const char* hdr = "glLinkProgram() failed:\n"; - std::vector info(strlen(hdr) + infoLen); - strcpy(&info[0], hdr); - NVDR_CHECK_GL_ERROR(glGetProgramInfoLog(glProgram, infoLen, &infoLen, &info[strlen(hdr)])); - NVDR_CHECK(0, &info[0]); - } - NVDR_CHECK(0, "glLinkProgram() failed"); - } - - *pProgram = glProgram; -} - -//------------------------------------------------------------------------ -// Shared C++ functions. - -void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceIdx) -{ - // Create GL context and set it current. - s.glctx = createGLContext(cudaDeviceIdx); - setGLContext(s.glctx); - - // Version check. - GLint vMajor = 0; - GLint vMinor = 0; - glGetIntegerv(GL_MAJOR_VERSION, &vMajor); - glGetIntegerv(GL_MINOR_VERSION, &vMinor); - glGetError(); // Clear possible GL_INVALID_ENUM error in version query. - LOG(ERROR) << "OpenGL version reported as " << vMajor << "." << vMinor; - NVDR_CHECK((vMajor == 4 && vMinor >= 4) || vMajor > 4, "OpenGL 4.4 or later is required"); - - // Enable depth modification workaround on A100 and later. - int capMajor = 0; - NVDR_CHECK_CUDA_ERROR(cudaDeviceGetAttribute(&capMajor, cudaDevAttrComputeCapabilityMajor, cudaDeviceIdx)); - s.enableZModify = (capMajor >= 8); - - // Number of output buffers. - int num_outputs = s.enableDB ? 2 : 1; - - // Set up vertex shader. - compileGLShader(NVDR_CTX_PARAMS, s, &s.glVertexShader, GL_VERTEX_SHADER, - "#version 330\n" - "#extension GL_ARB_shader_draw_parameters : enable\n" - "#extension GL_ARB_explicit_uniform_location : enable\n" - "#extension GL_AMD_vertex_shader_layer : enable\n" - STRINGIFY_SHADER_SOURCE( - layout(location = 0) uniform mat4 mvp; - layout(location = 1) uniform float seg_id; - in vec4 in_vert; - out vec4 vertex_on_object; - out float seg_id_out; - uniform sampler2D texture; - void main() - { - gl_Layer = gl_DrawIDARB; - vec4 v1 = texelFetch(texture, ivec2(0, gl_Layer), 0); - vec4 v2 = texelFetch(texture, ivec2(1, gl_Layer), 0); - vec4 v3 = texelFetch(texture, ivec2(2, gl_Layer), 0); - vec4 v4 = texelFetch(texture, ivec2(3, gl_Layer), 0); - mat4 pose_mat = transpose(mat4(v1,v2,v3,v4)); - vertex_on_object = pose_mat * in_vert; - gl_Position = mvp * vertex_on_object; - seg_id_out = seg_id; - } - ) - ); - - // Fragment shader without bary differential output. - compileGLShader(NVDR_CTX_PARAMS, s, &s.glFragmentShader, GL_FRAGMENT_SHADER, - "#version 430\n" - STRINGIFY_SHADER_SOURCE( - in vec4 vertex_on_object; - in float seg_id_out; - out vec4 fragColor; - void main() - { - fragColor = vec4(vertex_on_object[0],vertex_on_object[1],vertex_on_object[2], seg_id_out); - } - ) - ); - - // Finalize programs. - constructGLProgram(NVDR_CTX_PARAMS, &s.glProgram, s.glVertexShader, s.glFragmentShader); - constructGLProgram(NVDR_CTX_PARAMS, &s.glProgramDP, s.glVertexShader, s.glFragmentShader); - - // Construct main fbo and bind permanently. - NVDR_CHECK_GL_ERROR(glGenFramebuffers(1, &s.glFBO)); - NVDR_CHECK_GL_ERROR(glBindFramebuffer(GL_FRAMEBUFFER, s.glFBO)); - - // Enable two color attachments. - GLenum draw_buffers[2] = { GL_COLOR_ATTACHMENT0, GL_COLOR_ATTACHMENT1 }; - NVDR_CHECK_GL_ERROR(glDrawBuffers(num_outputs, draw_buffers)); - - // Set up depth test. - NVDR_CHECK_GL_ERROR(glEnable(GL_DEPTH_TEST)); - NVDR_CHECK_GL_ERROR(glDepthFunc(GL_LESS)); - NVDR_CHECK_GL_ERROR(glClearDepth(1.0)); - - // Create and bind output buffers. Storage is allocated later. - NVDR_CHECK_GL_ERROR(glGenTextures(num_outputs, s.glColorBuffer)); - for (int i=0; i < num_outputs; i++) - { - NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); - NVDR_CHECK_GL_ERROR(glFramebufferTexture(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0 + i, s.glColorBuffer[i], 0)); - } - - // Create and bind depth/stencil buffer. Storage is allocated later. - NVDR_CHECK_GL_ERROR(glGenTextures(1, &s.glDepthStencilBuffer)); - NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glDepthStencilBuffer)); - NVDR_CHECK_GL_ERROR(glFramebufferTexture(GL_FRAMEBUFFER, GL_DEPTH_STENCIL_ATTACHMENT, s.glDepthStencilBuffer, 0)); - - // Create texture name for previous output buffer (depth peeling). - NVDR_CHECK_GL_ERROR(glGenTextures(1, &s.glPrevOutBuffer)); -} -void rasterizeReleaseBuffers(NVDR_CTX_ARGS, RasterizeGLState& s) -{ - int num_outputs = s.enableDB ? 2 : 1; - - if (s.cudaPosBuffer) - { - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPosBuffer)); - s.cudaPosBuffer = 0; - } - - if (s.cudaTriBuffer) - { - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaTriBuffer)); - s.cudaTriBuffer = 0; - } - - for (int i=0; i < num_outputs; i++) - { - if (s.cudaColorBuffer[i]) - { - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaColorBuffer[i])); - s.cudaColorBuffer[i] = 0; - } - } - - if (s.cudaPrevOutBuffer) - { - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPrevOutBuffer)); - s.cudaPrevOutBuffer = 0; - } -} - -void rasterizeReleaseGLResources(RasterizeGLState& s) { - // Release OpenGL resources here. - // For example, you can use glDeleteVertexArrays, glDeleteBuffers, glDeleteTextures, etc. - // For every resource created with glGen* functions, you need to call the corresponding glDelete* function. - // Make sure to properly delete all resources to avoid memory leaks. - - // Example: - if (s.glProgram) { - glDeleteProgram(s.glProgram); - s.glProgram = 0; - } - if (s.glProgramDP) { - glDeleteProgram(s.glProgramDP); - s.glProgramDP = 0; - } - if (s.glVertexShader) { - glDeleteShader(s.glVertexShader); - s.glVertexShader = 0; - } - if (s.glFragmentShader) { - glDeleteShader(s.glFragmentShader); - s.glFragmentShader = 0; - } - - for (int i = 0; i < s.model_counter; i++) { - if (s.glVAOs[i]) { - glDeleteVertexArrays(1, &s.glVAOs[i]); - s.glVAOs[i] = 0; - } - } - if (s.glPosBuffer) { - glDeleteBuffers(1, &s.glPosBuffer); - s.glPosBuffer = 0; - } - if (s.glTriBuffer) { - glDeleteBuffers(1, &s.glTriBuffer); - s.glTriBuffer = 0; - } - if (s.glFBO) { - glDeleteFramebuffers(1, &s.glFBO); - s.glFBO = 0; - } - if (s.glDepthStencilBuffer) { - glDeleteTextures(1, &s.glDepthStencilBuffer); - s.glDepthStencilBuffer = 0; - } - if (s.glPoseTexture) { - glDeleteTextures(1, &s.glPoseTexture); - s.glPoseTexture = 0; - } - if (s.glPrevOutBuffer) { - glDeleteTextures(1, &s.glPrevOutBuffer); - s.glPrevOutBuffer = 0; - } - for (int i = 0; i < 2; i++) { - if (s.glColorBuffer[i]) { - glDeleteTextures(1, &s.glColorBuffer[i]); - s.glColorBuffer[i] = 0; - } - } - - s.model_counter = 0; // Reset the model counter to allow restarting the renderer. - // No need to delete the OpenGL context here, as it will be reused for the next initialization. -} - -RasterizeGLStateWrapper::RasterizeGLStateWrapper(bool enableDB, bool automatic_, int cudaDeviceIdx_) -{ - pState = new RasterizeGLState(); - automatic = automatic_; - cudaDeviceIdx = cudaDeviceIdx_; - memset(pState, 0, sizeof(RasterizeGLState)); - pState->enableDB = enableDB ? 1 : 0; - rasterizeInitGLContext(NVDR_CTX_PARAMS, *pState, cudaDeviceIdx_); - releaseGLContext(); -} - -RasterizeGLStateWrapper::~RasterizeGLStateWrapper(void) -{ - setGLContext(pState->glctx); - rasterizeReleaseBuffers(NVDR_CTX_PARAMS, *pState); - rasterizeReleaseGLResources(*pState); // Call the function to release OpenGL resources. - releaseGLContext(); - destroyGLContext(pState->glctx); - delete pState; -} - -void RasterizeGLStateWrapper::setContext(void) -{ - setGLContext(pState->glctx); -} - -void RasterizeGLStateWrapper::releaseContext(void) -{ - releaseGLContext(); -} - -//------------------------------------------------------------------------ -// Forward op (OpenGL). - -void threedp3_likelihood(float *pos, float *latent_points, float *likelihoods, float *obs_image, float r, int width, int height, int depth); - -void _setup(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, int height, int width, int num_layers) -{ - - // const at::cuda::OptionalCUDAGuard device_guard(device_of(pos)); - // cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - RasterizeGLState& s = *stateWrapper.pState; - s.model_counter = 0; - s.img_width = width; - s.img_height = height; - s.num_layers = num_layers; - - // std::cout << "" << "OpenGL Version: " << glGetString(GL_VERSION) << std::endl; - - // Check that GL context was created for the correct GPU. - // NVDR_CHECK(pos.get_device() == stateWrapper.cudaDeviceIdx, "GL context must must reside on the same device as input tensors"); - - // Determine number of outputs - - // Get output shape. - NVDR_CHECK(height > 0 && width > 0, "resolution must be [>0, >0]"); - - // Set the GL context unless manual context. - if (stateWrapper.automatic) - setGLContext(s.glctx); - - // Resize all buffers. - int num_outputs = 1; - if (s.cudaColorBuffer[0]) - for (int i=0; i < num_outputs; i++) - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaColorBuffer[i])); - - if (s.cudaPrevOutBuffer) - { - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPrevOutBuffer)); - s.cudaPrevOutBuffer = 0; - } - - s.width = ROUND_UP(s.img_width, 32); - s.height = ROUND_UP(s.img_height, 32); - std::cout << "Increasing frame buffer size to (width, height, depth) = (" << s.width << ", " << s.height << ", " << s.num_layers << ")" << std::endl; - - // Allocate color buffers. - for (int i=0; i < num_outputs; i++) - { - NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); - NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, s.num_layers, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)); - } - - // Allocate depth/stencil buffer. - NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glDepthStencilBuffer)); - NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_DEPTH24_STENCIL8, s.width, s.height, s.num_layers, 0, GL_DEPTH_STENCIL, GL_UNSIGNED_INT_24_8, 0)); - - // (Re-)register all GL buffers into Cuda. - for (int i=0; i < num_outputs; i++) - NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaColorBuffer[i], s.glColorBuffer[i], GL_TEXTURE_3D, cudaGraphicsRegisterFlagsReadOnly)); - - // if (s.cudaPoseTexture) - // NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPoseTexture)); - NVDR_CHECK_GL_ERROR(glGenTextures(1, &s.glPoseTexture)); - NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D, s.glPoseTexture)); - NVDR_CHECK_GL_ERROR(glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA32F, 4, s.num_layers, 0, GL_RGBA, GL_FLOAT, 0)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)); - - return; -} - -void setup(RasterizeGLStateWrapper& stateWrapper, int height, int width, int num_layers) -{ - _setup(at::cuda::getCurrentCUDAStream(), stateWrapper, height, width, num_layers); -} - - -void jax_setup(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len) { - const SetUpCustomCallDescriptor &d = - *UnpackDescriptor(opaque, opaque_len); - RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; - _setup(stream, stateWrapper, d.height, d.width, d.num_layers); -} - - -void _load_vertices_fwd(cudaStream_t stream, - RasterizeGLStateWrapper& stateWrapper, const float * pos, uint num_vertices, const int * tri, uint num_triangles) -{ - // const at::cuda::OptionalCUDAGuard device_guard(device_of(pos)); - // cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - RasterizeGLState& s = *stateWrapper.pState; - - // // Check inputs. - // NVDR_CHECK_DEVICE(pos, tri); - // NVDR_CHECK_CONTIGUOUS(pos, tri); - // NVDR_CHECK_F32(pos); - // NVDR_CHECK_I32(tri); - - // Check that GL context was created for the correct GPU. - // NVDR_CHECK(pos.get_device() == stateWrapper.cudaDeviceIdx, "GL context must must reside on the same device as input tensors"); - - // Determine number of outputs - - // Determine instance mode and check input dimensions. - // NVDR_CHECK(pos.sizes().size() == 2 && pos.size(0) > 0 && pos.size(1) == 4, "range mode - pos must have shape [>0, 4]"); - // NVDR_CHECK(tri.sizes().size() == 2 && tri.size(0) > 0 && tri.size(1) == 3, "tri must have shape [>0, 3]"); - - - // Get position and triangle buffer sizes in int32/float32. - int posCount = 4 * num_vertices; - int triCount = 3 * num_triangles; - - // Set the GL context unless manual context. - if (stateWrapper.automatic) - setGLContext(s.glctx); - - - // Construct vertex array object. - NVDR_CHECK_GL_ERROR(glGenVertexArrays(1, &s.glVAOs[s.model_counter])); - NVDR_CHECK_GL_ERROR(glBindVertexArray(s.glVAOs[s.model_counter])); - - // Construct position buffer, bind permanently, enable, set ptr. - NVDR_CHECK_GL_ERROR(glGenBuffers(1, &s.glPosBuffer)); - NVDR_CHECK_GL_ERROR(glBindBuffer(GL_ARRAY_BUFFER, s.glPosBuffer)); - NVDR_CHECK_GL_ERROR(glEnableVertexAttribArray(0)); - NVDR_CHECK_GL_ERROR(glVertexAttribPointer(0, 4, GL_FLOAT, GL_FALSE, 0, 0)); - - // Construct index buffer and bind permanently. - NVDR_CHECK_GL_ERROR(glGenBuffers(1, &s.glTriBuffer)); - NVDR_CHECK_GL_ERROR(glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, s.glTriBuffer)); - - // Resize all buffers. - - // Resize vertex buffer? - if (s.cudaPosBuffer) - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPosBuffer)); - s.posCount = (posCount > 64) ? ROUND_UP_BITS(posCount, 2) : 64; - LOG(INFO) << "Increasing position buffer size to " << s.posCount << " float32"; - NVDR_CHECK_GL_ERROR(glBufferData(GL_ARRAY_BUFFER, s.posCount * sizeof(float), NULL, GL_DYNAMIC_DRAW)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterBuffer(&s.cudaPosBuffer, s.glPosBuffer, cudaGraphicsRegisterFlagsWriteDiscard)); - - // Resize triangle buffer? - if (s.cudaTriBuffer) - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaTriBuffer)); - s.triCounts[s.model_counter] = (triCount > 64) ? ROUND_UP_BITS(triCount, 2) : 64; - LOG(INFO) << "Increasing triangle buffer size to " << s.triCounts[s.model_counter] << " int32"; - NVDR_CHECK_GL_ERROR(glBufferData(GL_ELEMENT_ARRAY_BUFFER, s.triCounts[s.model_counter] * sizeof(int32_t), NULL, GL_DYNAMIC_DRAW)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterBuffer(&s.cudaTriBuffer, s.glTriBuffer, cudaGraphicsRegisterFlagsWriteDiscard)); - - const float* posPtr = pos; - const int32_t* triPtr = tri; - - // Copy both position and triangle buffers. - void* glPosPtr = NULL; - void* glTriPtr = NULL; - size_t posBytes = 0; - size_t triBytes = 0; - NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(2, &s.cudaPosBuffer, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsResourceGetMappedPointer(&glPosPtr, &posBytes, s.cudaPosBuffer)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsResourceGetMappedPointer(&glTriPtr, &triBytes, s.cudaTriBuffer)); - NVDR_CHECK(posBytes >= posCount * sizeof(float), "mapped GL position buffer size mismatch"); - NVDR_CHECK(triBytes >= triCount * sizeof(int32_t), "mapped GL triangle buffer size mismatch"); - NVDR_CHECK_CUDA_ERROR(cudaMemcpyAsync(glPosPtr, posPtr, posCount * sizeof(float), cudaMemcpyDeviceToDevice, stream)); - NVDR_CHECK_CUDA_ERROR(cudaMemcpyAsync(glTriPtr, triPtr, triCount * sizeof(int32_t), cudaMemcpyDeviceToDevice, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(2, &s.cudaPosBuffer, stream)); - - - s.model_counter = s.model_counter + 1; -} - -void jax_load_vertices(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len) -{ - const LoadVerticesCustomCallDescriptor &d = - *UnpackDescriptor(opaque, opaque_len); - RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; - // std::cerr << "load_vertices: " << d.num_vertices << "," << d.num_triangles << "\n"; - _load_vertices_fwd(stream, stateWrapper, reinterpret_cast(buffers[0]), d.num_vertices, reinterpret_cast(buffers[1]), d.num_triangles); -} - - -void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, const float* pose, uint num_objects, uint num_images, const std::vector& proj, const std::vector& indices, void* out = nullptr) -{ - // NVDR_CHECK_DEVICE(pose); - // NVDR_CHECK_CONTIGUOUS(pose); - // NVDR_CHECK_F32(pose); - - auto start = std::chrono::high_resolution_clock::now(); - - // const at::cuda::OptionalCUDAGuard device_guard(device_of(pos)); - RasterizeGLState& s = *stateWrapper.pState; - - // Set the GL context unless manual context. - if (stateWrapper.automatic) - setGLContext(s.glctx); - - - // uint num_objects = pose.size(0); - // uint num_images = pose.size(1); - - // Set the GL context unless manual context. - if (stateWrapper.automatic) - setGLContext(s.glctx); - - NVDR_CHECK_GL_ERROR(glUseProgram(s.glProgram)); - glUniformMatrix4fv(0, 1, GL_TRUE, &proj[0]); - - // Copy color buffers to output tensors. - cudaArray_t array = 0; - NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, s.cudaColorBuffer, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[0], 0, 0)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, s.cudaColorBuffer, stream)); - - NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaPoseTexture, s.glPoseTexture, GL_TEXTURE_2D, cudaGraphicsRegisterFlagsReadOnly)); - cudaArray_t pose_array = 0; - NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); - - const float* posePtr = pose; - for(int start_pose_idx=0; start_pose_idx < num_images; start_pose_idx+=s.num_layers) - { - int poses_on_this_iter = std::min(num_images-start_pose_idx, s.num_layers); - // Set viewport, clear color buffer(s) and depth/stencil buffer. - NVDR_CHECK_GL_ERROR(glViewport(0, 0, s.img_width, s.img_height)); - NVDR_CHECK_GL_ERROR(glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT)); - - for(int object_idx=0; object_idx < indices.size(); object_idx++){ - if (indices[object_idx] < 0){ - continue; - } - NVDR_CHECK_GL_ERROR(glBindVertexArray(s.glVAOs[indices[object_idx]])); - std::vector drawCmdBuffer(poses_on_this_iter); - for (int i=0; i < poses_on_this_iter; i++) - { - GLDrawCmd& cmd = drawCmdBuffer[i]; - cmd.firstIndex = 0; - cmd.count = s.triCounts[indices[object_idx]]; - cmd.baseVertex = 0; - cmd.baseInstance = 0; - cmd.instanceCount = 1; - } - - NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); - NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( - pose_array, 0, 0, posePtr + num_images*16*object_idx + start_pose_idx*16, - poses_on_this_iter*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); - glUniform1f(1, object_idx+1.0); - - NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], poses_on_this_iter, sizeof(GLDrawCmd))); - } - - - - // Draw! - NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, s.cudaColorBuffer, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[0], 0, 0)); - cudaMemcpy3DParms p = {0}; - p.srcArray = array; - p.dstPtr.ptr = ((float * )out) + start_pose_idx*s.img_height*s.img_width*4; - p.dstPtr.pitch = s.img_width * 4 * sizeof(float); - p.dstPtr.xsize = s.img_width; - p.dstPtr.ysize = s.img_height; - p.extent.width = s.img_width; - p.extent.height = s.img_height; - p.extent.depth = poses_on_this_iter; - p.kind = cudaMemcpyDeviceToDevice; - NVDR_CHECK_CUDA_ERROR(cudaMemcpy3DAsync(&p, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, s.cudaColorBuffer, stream)); - } - - // Done. Release GL context and return. - if (stateWrapper.automatic) - releaseGLContext(); - - return; -} - - -// void rasterize_fwd_gl(RasterizeGLStateWrapper& stateWrapper, cudaArray_t pose, const std::vector& proj, const std::vector& indices) { -// return _rasterize_fwd_gl(at::cuda::getCurrentCUDAStream(), stateWrapper, pose, proj, indices, nullptr); -// } - - -void jax_rasterize_fwd_gl(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len) { - - const RasterizeCustomCallDescriptor &d = - *UnpackDescriptor(opaque, opaque_len); - RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; - - void *pose = buffers[0]; - void *obj_idx = buffers[1]; - void *proj_list = buffers[2]; - void *out = buffers[3]; - auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); - - std::vector indices; - indices.resize(d.num_objects); - - std::vector proj_list_cpu; - proj_list_cpu.resize(16); - - - cudaStreamSynchronize(stream); - - NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&indices[0], obj_idx, d.num_objects * sizeof(int), cudaMemcpyDeviceToHost)); - NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&proj_list_cpu[0], proj_list, 16 * sizeof(float), cudaMemcpyDeviceToHost)); - - _rasterize_fwd_gl(stream, - stateWrapper, - reinterpret_cast(pose), - d.num_objects, - d.num_images, - /*proj=*/proj_list_cpu, - /*indices=*/indices, - /*out=*/out); - cudaStreamSynchronize(stream); -} - - -template -pybind11::capsule EncapsulateFunction(T* fn) { - return pybind11::capsule((void*)fn, "xla._CUSTOM_CALL_TARGET"); -} - -pybind11::dict Registrations() { - pybind11::dict dict; - dict["jax_setup"] = EncapsulateFunction(jax_setup); - dict["jax_load_vertices"] = EncapsulateFunction(jax_load_vertices); - dict["jax_rasterize_fwd_gl"] = EncapsulateFunction(jax_rasterize_fwd_gl); - return dict; -} - - - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - // State classes. - pybind11::class_(m, "RasterizeGLStateWrapper").def(pybind11::init()) - .def("set_context", &RasterizeGLStateWrapper::setContext) - .def("release_context", &RasterizeGLStateWrapper::releaseContext); - - // Ops. - // m.def("setup", &setup, "rasterize forward op (opengl)"); - // m.def("load_vertices_fwd", &load_vertices_fwd, "rasterize forward op (opengl)"); - // m.def("rasterize_fwd_gl", &rasterize_fwd_gl, "rasterize forward op (opengl)"); - m.def("registrations", &Registrations, "custom call registrations"); - m.def("build_setup_descriptor", - [](RasterizeGLStateWrapper& stateWrapper, - int h, int w, int num_layers) { - // std::cout << h << " " << w << " " << num_layers << "\n"; - return PackDescriptor(SetUpCustomCallDescriptor{&stateWrapper, h, w, num_layers}); - }); - m.def("build_load_vertices_descriptor", - [](RasterizeGLStateWrapper& stateWrapper, - long num_vertices, - long num_triangles) { - return PackDescriptor( - LoadVerticesCustomCallDescriptor{&stateWrapper, num_vertices, num_triangles}); - }); - m.def("build_rasterize_descriptor", - [](RasterizeGLStateWrapper& stateWrapper, - std::vector objs_images) { - RasterizeCustomCallDescriptor d; - d.gl_state_wrapper = &stateWrapper; - // NVDR_CHECK(proj.size() == 4 * 4); - d.num_objects = objs_images[0]; - d.num_images = objs_images[1]; - return PackDescriptor(d); - }); -} - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/rasterize_gl.h b/bayes3d/rendering/nvdiffrast/common/rasterize_gl.h deleted file mode 100644 index b4c84ca4..00000000 --- a/bayes3d/rendering/nvdiffrast/common/rasterize_gl.h +++ /dev/null @@ -1,129 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#pragma once - -//------------------------------------------------------------------------ -// Do not try to include OpenGL stuff when compiling CUDA kernels for torch. - -#if !(defined(NVDR_TORCH) && defined(__CUDACC__)) -#include "framework.h" -#include "glutil.h" - -//------------------------------------------------------------------------ -// OpenGL-related persistent state for forward op. - -struct RasterizeGLState // Must be initializable by memset to zero. -{ - int width; // Allocated frame buffer width. - int height; // Allocated frame buffer height. - int depth; // Allocated frame buffer depth. - int img_width; // Allocated frame buffer depth. - int img_height; // Allocated frame buffer depth. - uint num_layers; // Allocated frame buffer depth. - std::vector proj; - int posCount; // Allocated position buffer in floats. - int triCounts[1000]; // Allocated triangle buffer in ints. - int model_counter; // Allocated triangle buffer in ints. - GLContext glctx; - GLuint glFBO; - GLuint glColorBuffer[2]; - GLuint glPrevOutBuffer; - GLuint glDepthStencilBuffer; - GLuint glVAOs[100]; - GLuint glTriBuffer; - GLuint glPosBuffer; - GLuint glPoseTexture; - GLuint glProgram; - GLuint glProgramDP; - GLuint glVertexShader; - GLuint glGeometryShader; - GLuint glFragmentShader; - GLuint glFragmentShaderDP; - cudaGraphicsResource_t cudaColorBuffer[2]; - cudaGraphicsResource_t cudaPrevOutBuffer; - cudaGraphicsResource_t cudaPosBuffer; - cudaGraphicsResource_t cudaTriBuffer; - cudaGraphicsResource_t cudaPoseTexture; - cudaArray_t cuda_color_buffer; - cudaArray_t cuda_pose_buffer; - float* obs_image; - int enableDB; - int enableZModify; // Modify depth in shader, workaround for a rasterization issue on A100. -}; - - -class RasterizeGLStateWrapper; - -struct SetUpCustomCallDescriptor { - RasterizeGLStateWrapper* gl_state_wrapper; - - int height; - int width; - int num_layers; -}; - -struct LoadVerticesCustomCallDescriptor { - RasterizeGLStateWrapper* gl_state_wrapper; - long num_vertices; - long num_triangles; -}; - -struct RasterizeCustomCallDescriptor { - RasterizeGLStateWrapper* gl_state_wrapper; - float proj[16]; - int num_objects; - int num_images; - int on_object; -}; - - -#include - -// https://en.cppreference.com/w/cpp/numeric/bit_cast -template -typename std::enable_if::value && - std::is_trivially_copyable::value, - To>::type -bit_cast(const From& src) noexcept { - static_assert( - std::is_trivially_constructible::value, - "This implementation additionally requires destination type to be trivially constructible"); - - To dst; - memcpy(&dst, &src, sizeof(To)); - return dst; -} - -// Note that bit_cast is only available in recent C++ standards so you might need -// to provide a shim like the one in lib/kernel_helpers.h -template -std::string PackDescriptorAsString(const T& descriptor) { - return std::string(bit_cast(&descriptor), sizeof(T)); -} - -#include - -template -pybind11::bytes PackDescriptor(const T& descriptor) { - return pybind11::bytes(PackDescriptorAsString(descriptor)); -} - -template -const T* UnpackDescriptor(const char* opaque, std::size_t opaque_len) { - if (opaque_len != sizeof(T)) { - throw std::runtime_error("Invalid opaque object size"); - } - return bit_cast(opaque); -} - -//------------------------------------------------------------------------ -// Shared C++ code prototypes. - -//------------------------------------------------------------------------ -#endif // !(defined(NVDR_TORCH) && defined(__CUDACC__)) diff --git a/bayes3d/rendering/nvdiffrast/common/torch_common.inl b/bayes3d/rendering/nvdiffrast/common/torch_common.inl deleted file mode 100644 index 74dea415..00000000 --- a/bayes3d/rendering/nvdiffrast/common/torch_common.inl +++ /dev/null @@ -1,29 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#pragma once -#include "../common/framework.h" - -//------------------------------------------------------------------------ -// Input check helpers. -//------------------------------------------------------------------------ - -#ifdef _MSC_VER -#define __func__ __FUNCTION__ -#endif - -#define NVDR_CHECK_DEVICE(...) do { TORCH_CHECK(at::cuda::check_device({__VA_ARGS__}), __func__, "(): Inputs " #__VA_ARGS__ " must reside on the same GPU device") } while(0) -#define NVDR_CHECK_CPU(...) do { nvdr_check_cpu({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must reside on CPU"); } while(0) -#define NVDR_CHECK_CONTIGUOUS(...) do { nvdr_check_contiguous({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be contiguous tensors"); } while(0) -#define NVDR_CHECK_F32(...) do { nvdr_check_f32({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be float32 tensors"); } while(0) -#define NVDR_CHECK_I32(...) do { nvdr_check_i32({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be int32 tensors"); } while(0) -inline void nvdr_check_cpu(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.device().type() == c10::DeviceType::CPU, func, err_msg); } -inline void nvdr_check_contiguous(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.is_contiguous(), func, err_msg); } -inline void nvdr_check_f32(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.dtype() == torch::kFloat32, func, err_msg); } -inline void nvdr_check_i32(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.dtype() == torch::kInt32, func, err_msg); } -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/torch_types.h b/bayes3d/rendering/nvdiffrast/common/torch_types.h deleted file mode 100644 index 8e389582..00000000 --- a/bayes3d/rendering/nvdiffrast/common/torch_types.h +++ /dev/null @@ -1,65 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include "torch_common.inl" - -//------------------------------------------------------------------------ -// Python GL state wrapper. - -class RasterizeGLState; -class RasterizeGLStateWrapper -{ -public: - RasterizeGLStateWrapper (bool enableDB, bool automatic, int cudaDeviceIdx); - ~RasterizeGLStateWrapper (void); - - void setContext (void); - void releaseContext (void); - - RasterizeGLState* pState; - bool automatic; - int cudaDeviceIdx; -}; - -//------------------------------------------------------------------------ -// Python CudaRaster state wrapper. - -namespace CR { class CudaRaster; } -class RasterizeCRStateWrapper -{ -public: - RasterizeCRStateWrapper (int cudaDeviceIdx); - ~RasterizeCRStateWrapper (void); - - CR::CudaRaster* cr; - int cudaDeviceIdx; -}; - -//------------------------------------------------------------------------ -// Mipmap wrapper to prevent intrusion from Python side. - -class TextureMipWrapper -{ -public: - torch::Tensor mip; - int max_mip_level; - std::vector texture_size; // For error checking. - bool cube_mode; // For error checking. -}; - - -//------------------------------------------------------------------------ -// Antialias topology hash wrapper to prevent intrusion from Python side. - -class TopologyHashWrapper -{ -public: - torch::Tensor ev_hash; -}; - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/lib/setgpu.lib b/bayes3d/rendering/nvdiffrast/lib/setgpu.lib deleted file mode 100644 index add9a0c4f631cb56dbee31a05ed97339930301e2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7254 zcmd^EeQ*=U6<=9`*bX2Y>Jply7DR36K#YupF__jmEZOJyYzwe~q$aYE&LI3DS06US znTFI>2KUfFr=gjo(9#duFl~})C#66Vupu#c+;I&w4QZxRCesfX(i8$s1JkMc-tOtX zBG<|EpU!kWTD^Vy`~BYT+r7QhdH$+EG`RIk`Acm2Qd+jOtbE1trKQXCeuvy#TIQ6k z)_g*Ug^--R+D~Przsl`*tgd!9R{N^G^>q#IuAV@5*uN$rMt9V9#l>h_AShPaInGUF zaJ}2>ebDO>JRHN8xhh?ujt(uR)Z=xp=BjFZt3B0j>}bTQ1}gz8KUN;ByjWFZ#bMQq z6@@gRMR9AA9heZG~6U6#{FBm6Xd_jp$U?>H-{#YnB>3z z#~ea3A(thQ&D+?HoNOPKIvizXWj0&cGUsx(5nJ;^PZpEPz7Jd9n?*=FK{zW{LJ=EN5Jx{UuI823)gwAidyu9)sNuZ?vl8; zJ#O#p${>%J33(lGeR<4N1YfoSUu(&Bz22w5Uk_JQ0Iw=2xG&rV4tGhn9ybI0?SSc( zaUjodS@iY+=CBc$Meht?E*NoH^sWPD+MN&(iV`=A-hF^sV#FEr?gEW^z=%d%7QKGJ z>@ngDdYHfG0W)mGWzqWsV6GZ*2E78%%Y$Uk!-PZmPxD<4m?|SKi(UXQUoqmcjBgKM zzHP*1(K`m1Q6nyk-d_N7!-&hGS2&Z9`S6))zLkKfGvW+-b`S{zCS}BB8Q)I;^GhSn zpl1WUHvsc@BQA^H95@Gx;e$n4H-jGTmoEZljS-hcF9Mi8BhGLhV0xdCF&Gz%y8zAu z0}ikMLmva@0^IQ|I5_i`c)ZnIv(O~eu3otykqC!MI>MV5Oyc;*g3o$lDugdm zwX&t#6%}J5J_EEUMyw+c?h;$NdP3p0wrC(0Z|RPPdjfH>r8nFaiuA@FfFn94cEG_J ziMFUGHd#ql6_U+_OprOC{`4az<-0x{j7DOSwzzHK+Ar7|yW`=`@T1|bEw-viXLqE{?KbW*VG@S_2FrOSvfs_8_SrNCptWTZ-9-lhkw!&Bcc-mSFt$j9Bk;d9QE1dD!)*~i9RhzRb2l4`* z(!_h7T}$~?3PHzLLR7XQIIclx9bb%Tk9ln(N~@ zXnxUfy)-VQHk#V#dhn?vcGjAQA^$VX9_tZeJ#j9@om@4o*W7=~#--dRIqQkDv^i|6 zO%HR@NX0qM>N(|+CTd}w9(x5cepmVo|HNqv9M=_(!9>6IxpX>x$#A|qIZ8}|V z9^xg~cuW<(rHdK;az%8Q_XZbhprCL0bEV8uWw;S@hG*B&p^G6THk~^ zNUdj}#`D@j4ny5Wt>aMRVQC@DVJdT}^?k7LerzEq%dB;<`NPx~Qmh?{^|)f41q(-E zA(z2|{S8(wT&c`w0}D@43-N=6*RF;1fc0r=?NoXXfQ3h+h0I%MCUZ^vSdI^xP;8Jf z950@10D@9MYgXZ7j2T#jFe?|VyQyz}ZVt))W6c;e6bAM(94ayk$H^F|`#gLyC7bqq z=rj|m?Rs!b%a}}?F;I6rBMjf!wC`uyMHW(YvwAEyE{the`Vliet~_I*_FP!IOv$GI z0CdV@57mun8Iz5jF;LgVQcvZxX*-!V)LLErx-cHjgXyBCXA0dFimo;nlj*Wtpr$Z| zZl$8DUBk(A*&3o|(-gX1MHdWZ-%X~=_8n?=OrcvR>%uPp*c$}*HS7-p>quZ9YGYxR zS*Gt%EE`xX7RGsQidbs^4f}+^uF%FZ#KFDC;^-r?I2hx_DdKF9V&B@bF!{y)MFj+s5MX=&3yY*$9q+EhsghVjjFy zGbM+#MMW{wf}*yNHF8e=(sZuiophMU{(;5d3A#xi-K7%Bbc(^AKcm=i?^#R zw6^FlonmLOvpeIW;}JVSTf{Y;bz8l+W)tkH!XxdxIpW1 z_+X(JqJ0wpkGR0XqYN{?sPV@aRn{KxV4)aYtUJKVVj*i~-RnD5)_0Hsg<^EEaK+VF z8(MEPsjL?%hv20OBhk(aUw5dno+61pl{F-@C`K0xUqYy{Zd^(Xs;qM|i(+)K?xZYs z84~hxVPLz;dPinaj4l?`b>xRr?8P?8gCXI#bg*mit+TEJhT7R4*`y7ub(vsD&8a$#Yb z+Iiu_j~c5ue}O|~@iL2Ibg^brR%Rdm?EX!EQdtklEQ-<1i#|#z^LqK~Km4o8+9tCo zMi-0bT$$IoKC7Uz`eYVc7wx?8)KTYk=Fqi(%GxcnC`K0x9}m^_y8Ah2fyz1{vnWP4 zFMOCao9tM_r=tAa+bZh?nMExkv8o>^a3S*v6gV)U$H z@R&_5q(FJp^!|M+D=f1R!%QUKy6~NhJ#! s.posCount) { + cudaGraphicsResource_t test; + std::cout << "Before 1111" << std::endl; + std::cout << s.cudaPosBuffer << std::endl; + std::cout << test << std::endl; + std::cout << "Before 111231211" << std::endl; + + if (s.cudaPosBuffer) NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPosBuffer)); + std::cout << "Before 111" << std::endl; s.posCount = (posCount > 64) ? ROUND_UP_BITS(posCount, 2) : 64; LOG(INFO) << "Increasing position buffer size to " << s.posCount << " float32"; + std::cout << "Before 11" << std::endl; NVDR_CHECK_GL_ERROR(glBufferData(GL_ARRAY_BUFFER, s.posCount * sizeof(float), NULL, GL_DYNAMIC_DRAW)); + std::cout << "Before 12" << std::endl; NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterBuffer(&s.cudaPosBuffer, s.glPosBuffer, cudaGraphicsRegisterFlagsWriteDiscard)); changes = true; } // Resize triangle buffer? + std::cout << "Before 2" << std::endl; if (triCount > s.triCount) { if (s.cudaTriBuffer) @@ -385,6 +406,7 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i } // Resize framebuffer? + std::cout << "Before 3" << std::endl; if (width > s.width || height > s.height || depth > s.depth) { int num_outputs = s.enableDB ? 2 : 1; @@ -429,7 +451,7 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i } } -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx) +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx) { // Only copy inputs if we are on first iteration of depth peeling or not doing it at all. if (peeling_idx < 1) @@ -516,6 +538,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, co if (s.enableZModify) NVDR_CHECK_GL_ERROR(glUniform1f(1, 0.f)); + // Render the meshes. if (depth == 1 && !rangesPtr) { @@ -526,6 +549,8 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, co { // Populate a buffer for draw commands and execute it. std::vector drawCmdBuffer(depth); + cudaArray_t pose_array = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaPoseTexture, s.glPoseTexture, GL_TEXTURE_2D, cudaGraphicsRegisterFlagsReadOnly)); if (!rangesPtr) { @@ -537,10 +562,18 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, co GLDrawCmd& cmd = drawCmdBuffer[i]; cmd.firstIndex = 0; cmd.count = triCount; - cmd.baseVertex = vtxPerInstance * i; + cmd.baseVertex = 0; cmd.baseInstance = 0; cmd.instanceCount = 1; } + + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( + pose_array, 0, 0, posePtr, + depth*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); + } else { diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h index 27537c56..63b126bf 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h @@ -33,12 +33,14 @@ struct RasterizeGLState // Must be initializable by memset to zero. GLuint glVAO; GLuint glTriBuffer; GLuint glPosBuffer; + GLuint glPoseTexture; GLuint glProgram; GLuint glProgramDP; GLuint glVertexShader; GLuint glGeometryShader; GLuint glFragmentShader; GLuint glFragmentShaderDP; + cudaGraphicsResource_t cudaPoseTexture; cudaGraphicsResource_t cudaColorBuffer[2]; cudaGraphicsResource_t cudaPrevOutBuffer; cudaGraphicsResource_t cudaPosBuffer; @@ -52,7 +54,7 @@ struct RasterizeGLState // Must be initializable by memset to zero. void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceIdx); void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, int posCount, int triCount, int width, int height, int depth); -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth); void rasterizeReleaseBuffers(NVDR_CTX_ARGS, RasterizeGLState& s); diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_binding_ops.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/bindings.h similarity index 96% rename from bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_binding_ops.h rename to bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/bindings.h index daee555a..f3c56397 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_binding_ops.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/bindings.h @@ -1,5 +1,5 @@ -#include -#include +#ifndef INV_TREE_H +#define INV_TREE_H // https://en.cppreference.com/w/cpp/numeric/bit_cast template @@ -35,3 +35,4 @@ const T* UnpackDescriptor(const char* opaque, std::size_t opaque_len) { } return bit_cast(opaque); } +#endif diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp index 18b88051..28216110 100755 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp @@ -1,29 +1,7 @@ -#include "torch_types.h" -#include "jax_binding_ops.h" -#include "jax_rasterize_gl.h" -#include "jax_interpolate.h" +#include "jax_bindings.h" #include #include -//------------------------------------------------------------------------ -// Op prototypes. - -void jax_rasterize_fwd_gl(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len); - -void jax_rasterize_bwd(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len); - -void jax_interpolate_fwd(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len); - -void jax_interpolate_bwd(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len); - //--------------------------------------------------- template diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.h new file mode 100644 index 00000000..f51d3ead --- /dev/null +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.h @@ -0,0 +1,5 @@ +#include +#include +#include "jax_rasterize_gl.h" +#include "jax_interpolate.h" + diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.cpp index 3941ebfc..f1d0c016 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.cpp @@ -6,11 +6,8 @@ // distribution of this software and related documentation without an express // license agreement from NVIDIA CORPORATION is strictly prohibited. -#include "torch_common.inl" -#include "jax_binding_ops.h" #include "jax_interpolate.h" -#include "../common/interpolate.h" -#include "../common/common.h" + //------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.h index f199fef6..c8e7bbcf 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_interpolate.h @@ -11,6 +11,10 @@ #if !(defined(NVDR_TORCH) && defined(__CUDACC__)) #include "../common/framework.h" #include "../common/glutil.h" +#include "../common/torch_common.inl" +#include "../common/interpolate.h" +#include "../common/common.h" +#include "bindings.h" struct DiffInterpolateCustomCallDescriptor { int num_images; // attr[0] diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index 6b6cd0ad..4270a6b2 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -6,17 +6,11 @@ // distribution of this software and related documentation without an express // license agreement from NVIDIA CORPORATION is strictly prohibited. -#include "torch_common.inl" -#include "torch_types.h" -#include "../common/common.h" -#include "../common/rasterize.h" #include "jax_rasterize_gl.h" -#include "jax_binding_ops.h" #include //------------------------------------------------------------------------ -// Python GL state wrapper methods. - +// Forward op (OpenGL). RasterizeGLStateWrapper::RasterizeGLStateWrapper(bool enableDB, bool automatic_, int cudaDeviceIdx_) { pState = new RasterizeGLState(); @@ -47,13 +41,11 @@ void RasterizeGLStateWrapper::releaseContext(void) releaseGLContext(); } -//------------------------------------------------------------------------ -// Forward op (OpenGL). // void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, torch::Tensor pos, torch::Tensor tri, std::tuple resolution, torch::Tensor ranges, int peeling_idx) void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, - const float* pos, const int* tri, - std::vector dims, + const float* pose, const float* pos, const int* tri, + int num_images, int num_vertices, int num_triangles, std::vector resolution, float* out, float* out_db) @@ -67,13 +59,14 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe // Get output shape. int height = resolution[0]; int width = resolution[1]; - int depth = dims[0]; + int depth = num_images; // int depth = instance_mode ? pos.size(0) : ranges.size(0); NVDR_CHECK(height > 0 && width > 0, "resolution must be [>0, >0];"); + std::cout << "hwd" << height << " " << width << " " << depth << std::endl; // Get position and triangle buffer sizes in int32/float32. - int posCount = 4 * dims[0] * dims[1]; - int triCount = 3 * dims[2]; + int posCount = 4 * num_vertices; + int triCount = 3 * num_triangles; // Set the GL context unless manual context. if (stateWrapper.automatic) @@ -81,7 +74,9 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe // Resize all buffers. bool changes = false; + std::cout << "Before rasterizeResizeBuffers" << std::endl; rasterizeResizeBuffers(NVDR_CTX_PARAMS, s, changes, posCount, triCount, width, height, depth); + std::cout << "after rasterizeResizeBuffers" << std::endl; if (changes) { #ifdef _WIN32 @@ -93,11 +88,13 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe // // Copy input data to GL and render. int peeling_idx = -1; + const float* posePtr = pose; const float* posPtr = pos; const int32_t* rangesPtr = 0; // This is in CPU memory. const int32_t* triPtr = tri; - int vtxPerInstance = dims[1]; - rasterizeRender(NVDR_CTX_PARAMS, s, stream, posPtr, posCount, vtxPerInstance, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); + std::cout << "Before rasterizeRender" << std::endl; + rasterizeRender(NVDR_CTX_PARAMS, s, stream, posePtr, posPtr, posCount, num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); + std::cout << "after rasterizeRender" << std::endl; // Allocate output tensors. float* outputPtr[2]; @@ -105,7 +102,9 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe outputPtr[1] = s.enableDB ? out_db : NULL; // Copy rasterized results into CUDA buffers. + std::cout << "bef rasterizeCopyResults" << std::endl; rasterizeCopyResults(NVDR_CTX_PARAMS, s, stream, outputPtr, width, height, depth); + std::cout << "after rasterizeCopyResults" << std::endl; // Done. Release GL context and return. if (stateWrapper.automatic) @@ -115,40 +114,42 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe void jax_rasterize_fwd_gl(cudaStream_t stream, void **buffers, const char *opaque, std::size_t opaque_len) { - const DiffRasterizeCustomCallDescriptor &d = *UnpackDescriptor(opaque, opaque_len); RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; - const float *pos = reinterpret_cast (buffers[0]); - const int *tri = reinterpret_cast (buffers[1]); - const int *_resolution = reinterpret_cast (buffers[2]); + const float *pose = reinterpret_cast (buffers[0]); + const float *pos = reinterpret_cast (buffers[1]); + const int *tri = reinterpret_cast (buffers[2]); + const int *_resolution = reinterpret_cast (buffers[3]); - float *out = reinterpret_cast (buffers[3]); - float *out_db = reinterpret_cast (buffers[4]); + float *out = reinterpret_cast (buffers[4]); + float *out_db = reinterpret_cast (buffers[5]); auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); std::vector resolution; resolution.resize(2); - std::vector pos_dim; - pos_dim.resize(3); cudaStreamSynchronize(stream); NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&resolution[0], _resolution, 2 * sizeof(int), cudaMemcpyDeviceToHost)); - pos_dim[0] = d.num_images; - pos_dim[1] = d.num_vertices; - pos_dim[2] = d.num_triangles; - - _rasterize_fwd_gl(stream, - stateWrapper, - pos, - tri, - pos_dim, - resolution, - out, - out_db - ); + std::cout << "Before rasterize_fwd_gl" << std::endl; + + _rasterize_fwd_gl( + stream, + stateWrapper, + pose, + pos, + tri, + d.num_images, + d.num_vertices, + d.num_triangles, + resolution, + out, + out_db + ); + + std::cout << "Done with rasterize_fwd_gl" << std::endl; cudaStreamSynchronize(stream); } diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h index 7193c003..dfe86638 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h @@ -14,41 +14,12 @@ #if !(defined(NVDR_TORCH) && defined(__CUDACC__)) #include "../common/framework.h" #include "../common/glutil.h" - -//------------------------------------------------------------------------ -// OpenGL-related persistent state for forward op. - -struct RasterizeGLState // Must be initializable by memset to zero. -{ - int width; // Allocated frame buffer width. - int height; // Allocated frame buffer height. - int depth; // Allocated frame buffer depth. - int posCount; // Allocated position buffer in floats. - int triCount; // Allocated triangle buffer in ints. - GLContext glctx; - GLuint glFBO; - GLuint glColorBuffer[2]; - GLuint glPrevOutBuffer; - GLuint glDepthStencilBuffer; - GLuint glVAO; - GLuint glTriBuffer; - GLuint glPosBuffer; - GLuint glProgram; - GLuint glProgramDP; - GLuint glVertexShader; - GLuint glGeometryShader; - GLuint glFragmentShader; - GLuint glFragmentShaderDP; - cudaGraphicsResource_t cudaColorBuffer[2]; - cudaGraphicsResource_t cudaPrevOutBuffer; - cudaGraphicsResource_t cudaPosBuffer; - cudaGraphicsResource_t cudaTriBuffer; - int enableDB; - int enableZModify; // Modify depth in shader, workaround for a rasterization issue on A100. -}; - - -class RasterizeGLStateWrapper; +#include "../common/torch_types.h" +#include "../common/torch_common.inl" +#include "../common/common.h" +#include "../common/rasterize.h" +#include "../common/rasterize_gl.h" +#include "bindings.h" struct DiffRasterizeCustomCallDescriptor { RasterizeGLStateWrapper* gl_state_wrapper; @@ -68,12 +39,23 @@ struct DiffRasterizeBwdCustomCallDescriptor { //------------------------------------------------------------------------ // Shared C++ code prototypes. -void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceIdx); -void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, int posCount, int triCount, int width, int height, int depth); -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); -void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth); -void rasterizeReleaseBuffers(NVDR_CTX_ARGS, RasterizeGLState& s); //------------------------------------------------------------------------ +// Op prototypes. + +void jax_rasterize_fwd_gl(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len); + +void jax_rasterize_bwd(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len); + +void jax_interpolate_fwd(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len); +void jax_interpolate_bwd(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len); #endif diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_common.inl b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_common.inl deleted file mode 100755 index 74dea415..00000000 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_common.inl +++ /dev/null @@ -1,29 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#pragma once -#include "../common/framework.h" - -//------------------------------------------------------------------------ -// Input check helpers. -//------------------------------------------------------------------------ - -#ifdef _MSC_VER -#define __func__ __FUNCTION__ -#endif - -#define NVDR_CHECK_DEVICE(...) do { TORCH_CHECK(at::cuda::check_device({__VA_ARGS__}), __func__, "(): Inputs " #__VA_ARGS__ " must reside on the same GPU device") } while(0) -#define NVDR_CHECK_CPU(...) do { nvdr_check_cpu({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must reside on CPU"); } while(0) -#define NVDR_CHECK_CONTIGUOUS(...) do { nvdr_check_contiguous({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be contiguous tensors"); } while(0) -#define NVDR_CHECK_F32(...) do { nvdr_check_f32({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be float32 tensors"); } while(0) -#define NVDR_CHECK_I32(...) do { nvdr_check_i32({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be int32 tensors"); } while(0) -inline void nvdr_check_cpu(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.device().type() == c10::DeviceType::CPU, func, err_msg); } -inline void nvdr_check_contiguous(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.is_contiguous(), func, err_msg); } -inline void nvdr_check_f32(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.dtype() == torch::kFloat32, func, err_msg); } -inline void nvdr_check_i32(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.dtype() == torch::kInt32, func, err_msg); } -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_types.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_types.h deleted file mode 100755 index 8e389582..00000000 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/torch_types.h +++ /dev/null @@ -1,65 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include "torch_common.inl" - -//------------------------------------------------------------------------ -// Python GL state wrapper. - -class RasterizeGLState; -class RasterizeGLStateWrapper -{ -public: - RasterizeGLStateWrapper (bool enableDB, bool automatic, int cudaDeviceIdx); - ~RasterizeGLStateWrapper (void); - - void setContext (void); - void releaseContext (void); - - RasterizeGLState* pState; - bool automatic; - int cudaDeviceIdx; -}; - -//------------------------------------------------------------------------ -// Python CudaRaster state wrapper. - -namespace CR { class CudaRaster; } -class RasterizeCRStateWrapper -{ -public: - RasterizeCRStateWrapper (int cudaDeviceIdx); - ~RasterizeCRStateWrapper (void); - - CR::CudaRaster* cr; - int cudaDeviceIdx; -}; - -//------------------------------------------------------------------------ -// Mipmap wrapper to prevent intrusion from Python side. - -class TextureMipWrapper -{ -public: - torch::Tensor mip; - int max_mip_level; - std::vector texture_size; // For error checking. - bool cube_mode; // For error checking. -}; - - -//------------------------------------------------------------------------ -// Antialias topology hash wrapper to prevent intrusion from Python side. - -class TopologyHashWrapper -{ -public: - torch::Tensor ev_hash; -}; - -//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py new file mode 100644 index 00000000..5f7324f8 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -0,0 +1,69 @@ +import bayes3d as b +import jax.numpy as jnp +import jax +from tqdm import tqdm +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +import numpy as np +import os +import trimesh + +intrinsics = b.Intrinsics( + height=100, + width=100, + fx=75.0, fy=75.0, + cx=50.0, cy=50.0, + near=0.001, far=16.0 +) + +projection_matrix = b.camera._open_gl_projection_matrix( + intrinsics.height, + intrinsics.width, + intrinsics.fx, + intrinsics.fy, + intrinsics.cx, + intrinsics.cy, + intrinsics.near, + intrinsics.far, +) + +from bayes3d.rendering.nvdiffrast_jax.jax_renderer import Renderer as JaxRenderer +jax_renderer = JaxRenderer(intrinsics) + +path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") +mesh =trimesh.load(path) +mesh.vertices = mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) +vertices = mesh.vertices +faces = mesh.faces + +vertices_h = jnp.hstack([vertices, jnp.ones((vertices.shape[0], 1))]) + +poses =jnp.array([jnp.eye(4)]*1000) + +def xfm_points(points, matrix): + points2 = jnp.concatenate([points, jnp.ones((*points.shape[:-1], 1))], axis=-1) + return jnp.matmul(points2, matrix.T) + +object_pose = b.transform_from_pos(jnp.array([0.0, 0.0, 3.0])) +final_mtx_proj = projection_matrix @ object_pose +posw = jnp.concatenate([vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1) +pos_clip_ja = xfm_points(vertices, final_mtx_proj) +rast_out, rast_out_db = jax_renderer.rasterize( + poses, + pos_clip_ja, + faces, + jnp.array([intrinsics.height, intrinsics.width]), +) +img = rast_out[150,...,3] +b.get_depth_image(img).save("test.png") + + + +shape_keep = gb_pos.shape + +gb_pos, _ = jax_renderer.interpolate( + posw[None, ...], rast_out, faces, rast_out_db, jnp.array([0, 1, 2, 3]) +) +gb_pos = gb_pos[..., :3] +depth = xfm_points(gb_pos, object_pose) +depth = depth.reshape(shape_keep)[..., 2] * -1 \ No newline at end of file diff --git a/scripts/experiments/slam/localization_with_gradients.mp4 b/scripts/experiments/slam/localization_with_gradients.mp4 new file mode 100644 index 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z&6Wm|f74(1|7iS+{U3j^KY^ht0h~aqVjy<_;?G?Gjuj9m=ThT3`ub|8Lqc`)^&`fANTc z=&uae$^YPg$^!GkfAc`E|Ia*-PyAQ@zxDou|G(}1m;V3YL;ts(|H1$Nv-2PN!hik$ zSN)*=Z3iFNX0yKnAOP$3PXhMGKllhJ^ZhIMHvst+F93k7Apk(10s!y;E5PCp0MI4? z05na&{zwDj9RL828vsDv1#Wcf1Mw{IUGyp7NSpzuHgL{>4FUj=Kt7lxFabvp8U`5m zs|$F`_`gf;>upboSE z&|el%_63;ufpiYATpW-e4z&LU#K5@-(+9-B`a=UR02p9hU{QgX7uYu7xSKisyN!SQ z;BUv8dYHQd{Q-0xEdIfN#U%fEAptKwR}*LFzjOWn0)x%Ju2zalcc;Gw3CvCYDg4(c TK;FdDl7pL(or95unfd Date: Tue, 13 Feb 2024 20:33:06 +0000 Subject: [PATCH 05/27] working --- .../rendering/nvdiffrast_jax/jax_renderer.py | 145 +++++++++--------- .../nvdiffrast/common/rasterize_gl.cpp | 31 ++-- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 9 -- .../nvdiffrast_jax/test_jax_renderer.py | 30 ++-- 4 files changed, 110 insertions(+), 105 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py index 7e286a09..daed4b0a 100644 --- a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py @@ -87,74 +87,74 @@ def _interpolate_bwd(self, saved_tensors, diffs): _interpolate.defvjp(_interpolate_fwd, _interpolate_bwd) - def render_many(self, vertices, faces, poses, intrinsics): - jax_renderer = self - projection_matrix = b.camera._open_gl_projection_matrix( - intrinsics.height, - intrinsics.width, - intrinsics.fx, - intrinsics.fy, - intrinsics.cx, - intrinsics.cy, - intrinsics.near, - intrinsics.far, - ) - composed_projection = projection_matrix @ poses - vertices_homogenous = jnp.concatenate( - [vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1 - ) - clip_spaces_projected_vertices = jnp.einsum( - "nij,mj->nmi", composed_projection, vertices_homogenous - ) - rast_out, rast_out_db = jax_renderer.rasterize( - clip_spaces_projected_vertices, - faces, - jnp.array([intrinsics.height, intrinsics.width]), - ) - interpolated_collided_vertices_clip, _ = jax_renderer.interpolate( - jnp.tile(vertices_homogenous[None, ...], (poses.shape[0], 1, 1)), - rast_out, - faces, - rast_out_db, - jnp.array([0, 1, 2, 3]), - ) - interpolated_collided_vertices = jnp.einsum( - "a...ij,a...j->a...i", poses, interpolated_collided_vertices_clip - ) - mask = rast_out[..., -1] > 0 - depth = interpolated_collided_vertices[..., 2] * mask - return depth - - def render(self, vertices, faces, object_pose, intrinsics): - jax_renderer = self - projection_matrix = b.camera._open_gl_projection_matrix( - intrinsics.height, - intrinsics.width, - intrinsics.fx, - intrinsics.fy, - intrinsics.cx, - intrinsics.cy, - intrinsics.near, - intrinsics.far, - ) - final_mtx_proj = projection_matrix @ object_pose - posw = jnp.concatenate([vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1) - pos_clip_ja = xfm_points(vertices, final_mtx_proj) - rast_out, rast_out_db = jax_renderer.rasterize( - pos_clip_ja[None, ...], - faces, - jnp.array([intrinsics.height, intrinsics.width]), - ) - gb_pos, _ = jax_renderer.interpolate( - posw[None, ...], rast_out, faces, rast_out_db, jnp.array([0, 1, 2, 3]) - ) - mask = rast_out[..., -1] > 0 - shape_keep = gb_pos.shape - gb_pos = gb_pos.reshape(shape_keep[0], -1, shape_keep[-1]) - gb_pos = gb_pos[..., :3] - depth = xfm_points(gb_pos, object_pose) - depth = depth.reshape(shape_keep)[..., 2] * -1 - return -(depth * mask), mask + # def render_many(self, vertices, faces, poses, intrinsics): + # jax_renderer = self + # projection_matrix = b.camera._open_gl_projection_matrix( + # intrinsics.height, + # intrinsics.width, + # intrinsics.fx, + # intrinsics.fy, + # intrinsics.cx, + # intrinsics.cy, + # intrinsics.near, + # intrinsics.far, + # ) + # composed_projection = projection_matrix @ poses + # vertices_homogenous = jnp.concatenate( + # [vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1 + # ) + # clip_spaces_projected_vertices = jnp.einsum( + # "nij,mj->nmi", composed_projection, vertices_homogenous + # ) + # rast_out, rast_out_db = jax_renderer.rasterize( + # clip_spaces_projected_vertices, + # faces, + # jnp.array([intrinsics.height, intrinsics.width]), + # ) + # interpolated_collided_vertices_clip, _ = jax_renderer.interpolate( + # jnp.tile(vertices_homogenous[None, ...], (poses.shape[0], 1, 1)), + # rast_out, + # faces, + # rast_out_db, + # jnp.array([0, 1, 2, 3]), + # ) + # interpolated_collided_vertices = jnp.einsum( + # "a...ij,a...j->a...i", poses, interpolated_collided_vertices_clip + # ) + # mask = rast_out[..., -1] > 0 + # depth = interpolated_collided_vertices[..., 2] * mask + # return depth + + # def render(self, vertices, faces, object_pose, intrinsics): + # jax_renderer = self + # projection_matrix = b.camera._open_gl_projection_matrix( + # intrinsics.height, + # intrinsics.width, + # intrinsics.fx, + # intrinsics.fy, + # intrinsics.cx, + # intrinsics.cy, + # intrinsics.near, + # intrinsics.far, + # ) + # final_mtx_proj = projection_matrix @ object_pose + # posw = jnp.concatenate([vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1) + # pos_clip_ja = xfm_points(vertices, final_mtx_proj) + # rast_out, rast_out_db = jax_renderer.rasterize( + # pos_clip_ja[None, ...], + # faces, + # jnp.array([intrinsics.height, intrinsics.width]), + # ) + # gb_pos, _ = jax_renderer.interpolate( + # posw[None, ...], rast_out, faces, rast_out_db, jnp.array([0, 1, 2, 3]) + # ) + # mask = rast_out[..., -1] > 0 + # shape_keep = gb_pos.shape + # gb_pos = gb_pos.reshape(shape_keep[0], -1, shape_keep[-1]) + # gb_pos = gb_pos[..., :3] + # depth = xfm_points(gb_pos, object_pose) + # depth = depth.reshape(shape_keep)[..., 2] * -1 + # return -(depth * mask), mask # ================================================================================================ @@ -266,9 +266,12 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, resolution): # The inputs: operands=[poses, pos, tri, resolution], backend_config=opaque, - operand_layouts=default_layouts( - poses_aval.shape, pos_aval.shape, tri_aval.shape, resolution_aval.shape - ), + operand_layouts=[ + (2, 1, 0), + *default_layouts( + pos_aval.shape, tri_aval.shape, resolution_aval.shape + ) + ], result_layouts=default_layouts( ( num_images, diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index ea660865..d6d18df0 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -123,14 +123,22 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId compileGLShader(NVDR_CTX_PARAMS, s, &s.glVertexShader, GL_VERTEX_SHADER, "#version 330\n" "#extension GL_ARB_shader_draw_parameters : enable\n" + "#extension GL_ARB_explicit_uniform_location : enable\n" + "#extension GL_AMD_vertex_shader_layer : enable\n" STRINGIFY_SHADER_SOURCE( layout(location = 0) in vec4 in_pos; out int v_layer; out int v_offset; + uniform sampler2D texture; void main() { int layer = gl_DrawIDARB; - gl_Position = in_pos; + vec4 v1 = texelFetch(texture, ivec2(0, layer), 0); + vec4 v2 = texelFetch(texture, ivec2(1, layer), 0); + vec4 v3 = texelFetch(texture, ivec2(2, layer), 0); + vec4 v4 = texelFetch(texture, ivec2(3, layer), 0); + mat4 pose_mat = transpose(mat4(v1,v2,v3,v4)); + gl_Position = pose_mat * in_pos; v_layer = layer; v_offset = gl_BaseInstanceARB; // Sneak in TriID offset here. } @@ -370,30 +378,18 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i changes = false; // Resize vertex buffer? - std::cout << "Before 1" << std::endl; if (posCount > s.posCount) { - cudaGraphicsResource_t test; - std::cout << "Before 1111" << std::endl; - std::cout << s.cudaPosBuffer << std::endl; - std::cout << test << std::endl; - std::cout << "Before 111231211" << std::endl; - - if (s.cudaPosBuffer) NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPosBuffer)); - std::cout << "Before 111" << std::endl; s.posCount = (posCount > 64) ? ROUND_UP_BITS(posCount, 2) : 64; LOG(INFO) << "Increasing position buffer size to " << s.posCount << " float32"; - std::cout << "Before 11" << std::endl; NVDR_CHECK_GL_ERROR(glBufferData(GL_ARRAY_BUFFER, s.posCount * sizeof(float), NULL, GL_DYNAMIC_DRAW)); - std::cout << "Before 12" << std::endl; NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterBuffer(&s.cudaPosBuffer, s.glPosBuffer, cudaGraphicsRegisterFlagsWriteDiscard)); changes = true; } // Resize triangle buffer? - std::cout << "Before 2" << std::endl; if (triCount > s.triCount) { if (s.cudaTriBuffer) @@ -406,7 +402,6 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i } // Resize framebuffer? - std::cout << "Before 3" << std::endl; if (width > s.width || height > s.height || depth > s.depth) { int num_outputs = s.enableDB ? 2 : 1; @@ -549,14 +544,19 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, co { // Populate a buffer for draw commands and execute it. std::vector drawCmdBuffer(depth); - cudaArray_t pose_array = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaPoseTexture, s.glPoseTexture, GL_TEXTURE_2D, cudaGraphicsRegisterFlagsReadOnly)); + cudaArray_t pose_array = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); if (!rangesPtr) { // Fill in range array to instantiate the same triangles for each output layer. // Triangle IDs starts at zero (i.e., one) for each layer, so they correspond to // the first dimension in addressing the triangle array. + std::cout << "depth " << depth << std::endl; for (int i=0; i < depth; i++) { GLDrawCmd& cmd = drawCmdBuffer[i]; @@ -598,6 +598,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, co // Draw! NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], depth, sizeof(GLDrawCmd))); } + } void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index 4270a6b2..df2a9597 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -62,7 +62,6 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe int depth = num_images; // int depth = instance_mode ? pos.size(0) : ranges.size(0); NVDR_CHECK(height > 0 && width > 0, "resolution must be [>0, >0];"); - std::cout << "hwd" << height << " " << width << " " << depth << std::endl; // Get position and triangle buffer sizes in int32/float32. int posCount = 4 * num_vertices; @@ -74,9 +73,7 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe // Resize all buffers. bool changes = false; - std::cout << "Before rasterizeResizeBuffers" << std::endl; rasterizeResizeBuffers(NVDR_CTX_PARAMS, s, changes, posCount, triCount, width, height, depth); - std::cout << "after rasterizeResizeBuffers" << std::endl; if (changes) { #ifdef _WIN32 @@ -92,9 +89,7 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe const float* posPtr = pos; const int32_t* rangesPtr = 0; // This is in CPU memory. const int32_t* triPtr = tri; - std::cout << "Before rasterizeRender" << std::endl; rasterizeRender(NVDR_CTX_PARAMS, s, stream, posePtr, posPtr, posCount, num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); - std::cout << "after rasterizeRender" << std::endl; // Allocate output tensors. float* outputPtr[2]; @@ -102,9 +97,7 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe outputPtr[1] = s.enableDB ? out_db : NULL; // Copy rasterized results into CUDA buffers. - std::cout << "bef rasterizeCopyResults" << std::endl; rasterizeCopyResults(NVDR_CTX_PARAMS, s, stream, outputPtr, width, height, depth); - std::cout << "after rasterizeCopyResults" << std::endl; // Done. Release GL context and return. if (stateWrapper.automatic) @@ -133,7 +126,6 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, cudaStreamSynchronize(stream); NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&resolution[0], _resolution, 2 * sizeof(int), cudaMemcpyDeviceToHost)); - std::cout << "Before rasterize_fwd_gl" << std::endl; _rasterize_fwd_gl( stream, @@ -149,7 +141,6 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, out_db ); - std::cout << "Done with rasterize_fwd_gl" << std::endl; cudaStreamSynchronize(stream); } diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index 5f7324f8..fec94cb8 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -38,7 +38,6 @@ vertices_h = jnp.hstack([vertices, jnp.ones((vertices.shape[0], 1))]) -poses =jnp.array([jnp.eye(4)]*1000) def xfm_points(points, matrix): points2 = jnp.concatenate([points, jnp.ones((*points.shape[:-1], 1))], axis=-1) @@ -48,22 +47,33 @@ def xfm_points(points, matrix): final_mtx_proj = projection_matrix @ object_pose posw = jnp.concatenate([vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1) pos_clip_ja = xfm_points(vertices, final_mtx_proj) + + +poses =jnp.array([jnp.eye(4)]*1000) rast_out, rast_out_db = jax_renderer.rasterize( poses, pos_clip_ja, faces, jnp.array([intrinsics.height, intrinsics.width]), ) -img = rast_out[150,...,3] -b.get_depth_image(img).save("test.png") - +assert jnp.all(rast_out[0] == rast_out[100]) -shape_keep = gb_pos.shape -gb_pos, _ = jax_renderer.interpolate( - posw[None, ...], rast_out, faces, rast_out_db, jnp.array([0, 1, 2, 3]) +poses = poses.at[:, 1,3].set(jnp.linspace(-0.1, 0.1, 1000)) +rast_out, rast_out_db = jax_renderer.rasterize( + poses, + pos_clip_ja, + faces, + jnp.array([intrinsics.height, intrinsics.width]), ) -gb_pos = gb_pos[..., :3] -depth = xfm_points(gb_pos, object_pose) -depth = depth.reshape(shape_keep)[..., 2] * -1 \ No newline at end of file +b.hstack_images( + [ + b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False) + for i in [1, 500, 999] + ] +).save("test.png") + +import viser +server = viser.ViserServer() +server.add_point_cloud("bunny", points=np.array(vertices), colors=np.zeros_like(vertices)) \ No newline at end of file From cbd341e4d71c9f9f3dd3396d0fbca48c02bfb8d6 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Tue, 13 Feb 2024 23:20:24 +0000 Subject: [PATCH 06/27] Projection matrix is through --- .../rendering/nvdiffrast_jax/jax_renderer.py | 22 ++--- .../nvdiffrast/common/rasterize_gl.cpp | 8 +- .../nvdiffrast/common/rasterize_gl.h | 2 +- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 86 ++++++++----------- .../nvdiffrast_jax/test_jax_renderer.py | 43 +++------- 5 files changed, 67 insertions(+), 94 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py index daed4b0a..23cc3651 100644 --- a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py @@ -34,11 +34,11 @@ def __init__(self, intrinsics, num_layers=1024): # ------------------ @functools.partial(jax.custom_vjp, nondiff_argnums=(0,)) - def _rasterize(self, pose, pos, tri, resolution): - return _rasterize_fwd_custom_call(self, pose, pos, tri, resolution) + def _rasterize(self, pose, pos, tri, projMatrix, resolution): + return _rasterize_fwd_custom_call(self, pose, pos, tri, projMatrix, resolution) - def _rasterize_fwd(self, pose, pos, tri, resolution): - rast_out, rast_out_db = _rasterize_fwd_custom_call(self, pose, pos, tri, resolution) + def _rasterize_fwd(self, pose, pos, tri, projMatrix, resolution): + rast_out, rast_out_db = _rasterize_fwd_custom_call(self, pose, pos, tri, projMatrix, resolution) saved_tensors = (pose, pos, tri, rast_out) return (rast_out, rast_out_db), saved_tensors @@ -185,8 +185,8 @@ def _register_custom_calls(): # @functools.partial(jax.jit, static_argnums=(0,)) -def _rasterize_fwd_custom_call(r: "Renderer", pose, pos, tri, resolution): - return _build_rasterize_fwd_primitive(r).bind(pose, pos, tri, resolution) +def _rasterize_fwd_custom_call(r: "Renderer", pose, pos, tri, projMatrix, resolution): + return _build_rasterize_fwd_primitive(r).bind(pose, pos, tri, projMatrix, resolution) @functools.lru_cache(maxsize=None) @@ -195,7 +195,7 @@ def _build_rasterize_fwd_primitive(r: "Renderer"): # For JIT compilation we need a function to evaluate the shape and dtype of the # outputs of our op for some given inputs - def _rasterize_fwd_abstract(pose, pos, tri, resolution): + def _rasterize_fwd_abstract(pose, pos, tri, projection_matrix, resolution): if len(pos.shape) != 2 or pos.shape[-1] != 4: raise ValueError( "Pass in pos aa [num_vertices, 4] sized input" @@ -218,14 +218,14 @@ def _rasterize_fwd_abstract(pose, pos, tri, resolution): ] # Provide an MLIR "lowering" of the rasterize primitive. - def _rasterize_fwd_lowering(ctx, poses, pos, tri, resolution): + def _rasterize_fwd_lowering(ctx, poses, pos, tri, projection_matrix, resolution): """ Single-object (one obj represented by tri) rasterization with multiple poses (first dimension fo pos) dr.rasterize(glctx, pos, tri, resolution=resolution) """ # Extract the numpy type of the inputs - poses_aval, pos_aval, tri_aval, resolution_aval = ctx.avals_in + poses_aval, pos_aval, tri_aval, projection_matrix_aval, resolution_aval = ctx.avals_in # if poses_aval.ndim != 3: # raise NotImplementedError( # f"Only 3D vtx position inputs supported: got {poses_aval.shape}" @@ -264,12 +264,12 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, resolution): # Output types result_types=[out_shp_dtype, out_shp_dtype], # The inputs: - operands=[poses, pos, tri, resolution], + operands=[poses, pos, tri, projection_matrix, resolution], backend_config=opaque, operand_layouts=[ (2, 1, 0), *default_layouts( - pos_aval.shape, tri_aval.shape, resolution_aval.shape + pos_aval.shape, tri_aval.shape, projection_matrix_aval.shape, resolution_aval.shape ) ], result_layouts=default_layouts( diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index d6d18df0..a6bc5423 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -127,6 +127,7 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId "#extension GL_AMD_vertex_shader_layer : enable\n" STRINGIFY_SHADER_SOURCE( layout(location = 0) in vec4 in_pos; + layout(location = 3) uniform mat4 projection_matrix; out int v_layer; out int v_offset; uniform sampler2D texture; @@ -138,7 +139,7 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId vec4 v3 = texelFetch(texture, ivec2(2, layer), 0); vec4 v4 = texelFetch(texture, ivec2(3, layer), 0); mat4 pose_mat = transpose(mat4(v1,v2,v3,v4)); - gl_Position = pose_mat * in_pos; + gl_Position = projection_matrix * pose_mat * in_pos; v_layer = layer; v_offset = gl_BaseInstanceARB; // Sneak in TriID offset here. } @@ -446,8 +447,9 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i } } -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx) +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx) { + // Only copy inputs if we are on first iteration of depth peeling or not doing it at all. if (peeling_idx < 1) { @@ -550,13 +552,13 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, co NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); + glUniformMatrix4fv(3, 1, GL_TRUE, &projMatrix[0]); if (!rangesPtr) { // Fill in range array to instantiate the same triangles for each output layer. // Triangle IDs starts at zero (i.e., one) for each layer, so they correspond to // the first dimension in addressing the triangle array. - std::cout << "depth " << depth << std::endl; for (int i=0; i < depth; i++) { GLDrawCmd& cmd = drawCmdBuffer[i]; diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h index 63b126bf..63533517 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h @@ -54,7 +54,7 @@ struct RasterizeGLState // Must be initializable by memset to zero. void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceIdx); void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, int posCount, int triCount, int width, int height, int depth); -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth); void rasterizeReleaseBuffers(NVDR_CTX_ARGS, RasterizeGLState& s); diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index df2a9597..0b96c844 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -42,14 +42,31 @@ void RasterizeGLStateWrapper::releaseContext(void) } -// void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, torch::Tensor pos, torch::Tensor tri, std::tuple resolution, torch::Tensor ranges, int peeling_idx) -void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, - const float* pose, const float* pos, const int* tri, - int num_images, int num_vertices, int num_triangles, - std::vector resolution, - float* out, - float* out_db) -{ + +void jax_rasterize_fwd_gl(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len) { + const DiffRasterizeCustomCallDescriptor &d = + *UnpackDescriptor(opaque, opaque_len); + RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; + + const float *pose = reinterpret_cast (buffers[0]); + const float *pos = reinterpret_cast (buffers[1]); + const int *tri = reinterpret_cast (buffers[2]); + const float *projectionMatrix = reinterpret_cast (buffers[3]); + const int *_resolution = reinterpret_cast (buffers[4]); + + float *out = reinterpret_cast (buffers[5]); + float *out_db = reinterpret_cast (buffers[6]); + + auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); + + std::vector resolution; + resolution.resize(2); + + cudaStreamSynchronize(stream); + NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&resolution[0], _resolution, 2 * sizeof(int), cudaMemcpyDeviceToHost)); + // const at::cuda::OptionalCUDAGuard device_guard(at::device_of(pos)); RasterizeGLState& s = *stateWrapper.pState; @@ -59,13 +76,13 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe // Get output shape. int height = resolution[0]; int width = resolution[1]; - int depth = num_images; + int depth = d.num_images; // int depth = instance_mode ? pos.size(0) : ranges.size(0); NVDR_CHECK(height > 0 && width > 0, "resolution must be [>0, >0];"); // Get position and triangle buffer sizes in int32/float32. - int posCount = 4 * num_vertices; - int triCount = 3 * num_triangles; + int posCount = 4 * d.num_vertices; + int triCount = 3 * d.num_triangles; // Set the GL context unless manual context. if (stateWrapper.automatic) @@ -83,13 +100,20 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe #endif } + cudaStreamSynchronize(stream); + std::vector projMatrix; + projMatrix.resize(16); + NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&projMatrix[0], projectionMatrix, 16 * sizeof(int), cudaMemcpyDeviceToHost)); + cudaStreamSynchronize(stream); + + // // Copy input data to GL and render. int peeling_idx = -1; const float* posePtr = pose; const float* posPtr = pos; const int32_t* rangesPtr = 0; // This is in CPU memory. const int32_t* triPtr = tri; - rasterizeRender(NVDR_CTX_PARAMS, s, stream, posePtr, posPtr, posCount, num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); + rasterizeRender(NVDR_CTX_PARAMS, s, stream, projMatrix, posePtr, posPtr, posCount, d.num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); // Allocate output tensors. float* outputPtr[2]; @@ -102,44 +126,6 @@ void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrappe // Done. Release GL context and return. if (stateWrapper.automatic) releaseGLContext(); -} - -void jax_rasterize_fwd_gl(cudaStream_t stream, - void **buffers, - const char *opaque, std::size_t opaque_len) { - const DiffRasterizeCustomCallDescriptor &d = - *UnpackDescriptor(opaque, opaque_len); - RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; - - const float *pose = reinterpret_cast (buffers[0]); - const float *pos = reinterpret_cast (buffers[1]); - const int *tri = reinterpret_cast (buffers[2]); - const int *_resolution = reinterpret_cast (buffers[3]); - - float *out = reinterpret_cast (buffers[4]); - float *out_db = reinterpret_cast (buffers[5]); - - auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); - - std::vector resolution; - resolution.resize(2); - - cudaStreamSynchronize(stream); - NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&resolution[0], _resolution, 2 * sizeof(int), cudaMemcpyDeviceToHost)); - - _rasterize_fwd_gl( - stream, - stateWrapper, - pose, - pos, - tri, - d.num_images, - d.num_vertices, - d.num_triangles, - resolution, - out, - out_db - ); cudaStreamSynchronize(stream); } diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index fec94cb8..c43df9d9 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -11,7 +11,7 @@ intrinsics = b.Intrinsics( height=100, width=100, - fx=75.0, fy=75.0, + fx=200.0, fy=200.0, cx=50.0, cy=50.0, near=0.001, far=16.0 ) @@ -34,37 +34,16 @@ mesh =trimesh.load(path) mesh.vertices = mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) vertices = mesh.vertices +vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = mesh.faces -vertices_h = jnp.hstack([vertices, jnp.ones((vertices.shape[0], 1))]) - - -def xfm_points(points, matrix): - points2 = jnp.concatenate([points, jnp.ones((*points.shape[:-1], 1))], axis=-1) - return jnp.matmul(points2, matrix.T) - -object_pose = b.transform_from_pos(jnp.array([0.0, 0.0, 3.0])) -final_mtx_proj = projection_matrix @ object_pose -posw = jnp.concatenate([vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1) -pos_clip_ja = xfm_points(vertices, final_mtx_proj) - - -poses =jnp.array([jnp.eye(4)]*1000) -rast_out, rast_out_db = jax_renderer.rasterize( - poses, - pos_clip_ja, - faces, - jnp.array([intrinsics.height, intrinsics.width]), -) -assert jnp.all(rast_out[0] == rast_out[100]) - - - -poses = poses.at[:, 1,3].set(jnp.linspace(-0.1, 0.1, 1000)) +poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*1000) +poses = poses.at[:, 1,3].set(jnp.linspace(-0.4, 0.4, len(poses))) rast_out, rast_out_db = jax_renderer.rasterize( poses, - pos_clip_ja, + vertices, faces, + projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) b.hstack_images( @@ -72,8 +51,14 @@ def xfm_points(points, matrix): b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False) for i in [1, 500, 999] ] -).save("test.png") +).save("test2.png") + + +from IPython import embed; embed() + +T = 0 +points_transformed = b.apply_transform(vertices[:,:3], poses[T]) import viser server = viser.ViserServer() -server.add_point_cloud("bunny", points=np.array(vertices), colors=np.zeros_like(vertices)) \ No newline at end of file +server.add_point_cloud("bunny", points=np.array(points_transformed)[:,:3], colors=np.zeros_like(points_transformed)[:,:3]) From c4e36a217afe90428119d0fe8820549ca68c1f91 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Wed, 14 Feb 2024 02:19:18 +0000 Subject: [PATCH 07/27] interpolation --- .../nvdiffrast_jax/test_jax_renderer.py | 49 +++++++++++++++++-- 1 file changed, 44 insertions(+), 5 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index c43df9d9..ed55011b 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -8,6 +8,9 @@ import os import trimesh +import viser +server = viser.ViserServer() + intrinsics = b.Intrinsics( height=100, width=100, @@ -35,7 +38,7 @@ mesh.vertices = mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) vertices = mesh.vertices vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) -faces = mesh.faces +faces = jnp.array(mesh.faces) poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*1000) poses = poses.at[:, 1,3].set(jnp.linspace(-0.4, 0.4, len(poses))) @@ -52,13 +55,49 @@ for i in [1, 500, 999] ] ).save("test2.png") +uvs = rast_out[...,:2] +triangle_ids = rast_out[...,3:4].astype(jnp.int32) + +mask = rast_out[...,2] > 0 +b.get_depth_image((mask[0]) *1.0, remove_max=False).save("mask.png") + +import functools +@functools.partial( + jnp.vectorize, + signature="(2),(1),(4,4)->(3)", + excluded=( + 3, + 4, + ), +) +def interpolate_(uv, triangle_id, pose, vertices, faces): + u,v = uv + relevant_vertices = vertices[faces[triangle_id-1][0]] + relevant_vertices_transformed = relevant_vertices @ pose.T + barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) + interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) + return interpolated_value + +interpolated_values = interpolate_(uvs, triangle_ids, poses[...,None, None,:,:], vertices, faces) +image = interpolated_values * mask[...,None] -from IPython import embed; embed() T = 0 points_transformed = b.apply_transform(vertices[:,:3], poses[T]) -import viser -server = viser.ViserServer() -server.add_point_cloud("bunny", points=np.array(points_transformed)[:,:3], colors=np.zeros_like(points_transformed)[:,:3]) + +server.add_point_cloud( + "bunny", + points=np.array(points_transformed)[:,:3], + colors=np.zeros_like(points_transformed)[:,:3] +) + +server.add_point_cloud( + "image", + points=np.array(image[T]).reshape(-1,3), + colors=np.array([1.0, 0.0, 0.0]) +) + + +from IPython import embed; embed() From 1dae43a13957aa427bc492b6ba97597afef55882 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Wed, 14 Feb 2024 05:30:16 +0000 Subject: [PATCH 08/27] bug where rendering 5 images and rendering 1 produces different results --- .../nvdiffrast/common/rasterize_gl.cpp | 3 +- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 22 ++++++++- .../nvdiffrast_jax/test_jax_renderer.py | 49 ++++++++++++++++--- 3 files changed, 65 insertions(+), 9 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index a6bc5423..cd537ec3 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -141,7 +141,7 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId mat4 pose_mat = transpose(mat4(v1,v2,v3,v4)); gl_Position = projection_matrix * pose_mat * in_pos; v_layer = layer; - v_offset = gl_BaseInstanceARB; // Sneak in TriID offset here. + v_offset = 0; // Sneak in TriID offset here. } ) ); @@ -556,6 +556,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, st if (!rangesPtr) { + std::cout << "No rangesPtr" << std::endl; // Fill in range array to instantiate the same triangles for each output layer. // Triangle IDs starts at zero (i.e., one) for each layer, so they correspond to // the first dimension in addressing the triangle array. diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index 0b96c844..bbb2afff 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -66,6 +66,7 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, cudaStreamSynchronize(stream); NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&resolution[0], _resolution, 2 * sizeof(int), cudaMemcpyDeviceToHost)); + cudaStreamSynchronize(stream); // const at::cuda::OptionalCUDAGuard device_guard(at::device_of(pos)); RasterizeGLState& s = *stateWrapper.pState; @@ -90,7 +91,11 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, // Resize all buffers. bool changes = false; + cudaStreamSynchronize(stream); + rasterizeResizeBuffers(NVDR_CTX_PARAMS, s, changes, posCount, triCount, width, height, depth); + cudaStreamSynchronize(stream); + if (changes) { #ifdef _WIN32 @@ -107,13 +112,25 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, cudaStreamSynchronize(stream); + cudaStreamSynchronize(stream); + std::vector firstPose; + firstPose.resize(16); + NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&firstPose[0], pose, 16 * sizeof(int), cudaMemcpyDeviceToHost)); + cudaStreamSynchronize(stream); + for(int i = 0; i < 16; i++) { + std::cout << firstPose[i] << " "; + } + std::cout << std::endl; + // // Copy input data to GL and render. int peeling_idx = -1; const float* posePtr = pose; const float* posPtr = pos; const int32_t* rangesPtr = 0; // This is in CPU memory. const int32_t* triPtr = tri; + cudaStreamSynchronize(stream); rasterizeRender(NVDR_CTX_PARAMS, s, stream, projMatrix, posePtr, posPtr, posCount, d.num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); + cudaStreamSynchronize(stream); // Allocate output tensors. float* outputPtr[2]; @@ -121,7 +138,9 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, outputPtr[1] = s.enableDB ? out_db : NULL; // Copy rasterized results into CUDA buffers. + cudaStreamSynchronize(stream); rasterizeCopyResults(NVDR_CTX_PARAMS, s, stream, outputPtr, width, height, depth); + cudaStreamSynchronize(stream); // Done. Release GL context and return. if (stateWrapper.automatic) @@ -146,7 +165,8 @@ void _rasterize_grad_db(cudaStream_t stream, std::vector pos_shape, std::vector tri_shape, std::vector rast_out_shape, - float* grad) + float* grad +) { RasterizeGradParams p; bool enable_db = true; diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index ed55011b..91db4b73 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -40,8 +40,8 @@ vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = jnp.array(mesh.faces) -poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*1000) -poses = poses.at[:, 1,3].set(jnp.linspace(-0.4, 0.4, len(poses))) +poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*10) +poses = poses.at[:, 1,3].set(jnp.linspace(-1.4, 0.1, len(poses))) rast_out, rast_out_db = jax_renderer.rasterize( poses, vertices, @@ -49,12 +49,47 @@ projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) +images = [] +for i in [1, int(len(poses)/2), len(poses)-1]: + images.append(b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False)) b.hstack_images( - [ - b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False) - for i in [1, 500, 999] - ] -).save("test2.png") + images +).save("sweep.png") + +i = 1 +rast_out_individual, rast_out_db = jax_renderer.rasterize( + poses[i:i+2], + vertices, + faces, + projection_matrix, + jnp.array([intrinsics.height, intrinsics.width]), +) +for j in tqdm(range(len(poses))): + print(jnp.allclose(rast_out[j], rast_out_individual[0])) + + +original = b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False) +individual = b.get_depth_image((rast_out_individual[0,...,3]) *1.0, remove_max=False) +b.hstack_images([original, individual, b.overlay_image(original, individual)]).save(f"compare.png") + +from IPython import embed; embed() + +jnp.abs(rast_out[i,...,3]- rast_out_individual[0,...,3]).max() + +for i in test_indices: + print(i) + rast_out_individual, rast_out_db = jax_renderer.rasterize( + poses[i:i+1], + vertices, + faces, + projection_matrix, + jnp.array([intrinsics.height, intrinsics.width]), + ) + assert jnp.allclose(rast_out[i], rast_out_individual[0]) + + + + uvs = rast_out[...,:2] triangle_ids = rast_out[...,3:4].astype(jnp.int32) From 7903766f854fa5e9ed135c306c68c16f2cd41526 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Wed, 14 Feb 2024 05:47:53 +0000 Subject: [PATCH 09/27] stupid bug fixed --- .../nvdiffrast/common/rasterize_gl.cpp | 113 +++++++------- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 2 + .../nvdiffrast_jax/test_jax_renderer.py | 146 ++++++++++-------- 3 files changed, 143 insertions(+), 118 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index cd537ec3..412a9148 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -536,72 +536,71 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, st NVDR_CHECK_GL_ERROR(glUniform1f(1, 0.f)); - // Render the meshes. - if (depth == 1 && !rangesPtr) + // // Render the meshes. + // if (depth == 1 && !rangesPtr) + // { + // // Trivial case. + // NVDR_CHECK_GL_ERROR(glDrawElements(GL_TRIANGLES, triCount, GL_UNSIGNED_INT, 0)); + // } + // else + // { + // Populate a buffer for draw commands and execute it. + std::vector drawCmdBuffer(depth); + + NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaPoseTexture, s.glPoseTexture, GL_TEXTURE_2D, cudaGraphicsRegisterFlagsReadOnly)); + cudaArray_t pose_array = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); + glUniformMatrix4fv(3, 1, GL_TRUE, &projMatrix[0]); + + if (!rangesPtr) { - // Trivial case. - NVDR_CHECK_GL_ERROR(glDrawElements(GL_TRIANGLES, triCount, GL_UNSIGNED_INT, 0)); - } - else - { - // Populate a buffer for draw commands and execute it. - std::vector drawCmdBuffer(depth); - - NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaPoseTexture, s.glPoseTexture, GL_TEXTURE_2D, cudaGraphicsRegisterFlagsReadOnly)); - cudaArray_t pose_array = 0; + std::cout << "depth " << depth << std::endl; + // Fill in range array to instantiate the same triangles for each output layer. + // Triangle IDs starts at zero (i.e., one) for each layer, so they correspond to + // the first dimension in addressing the triangle array. + for (int i=0; i < depth; i++) + { + GLDrawCmd& cmd = drawCmdBuffer[i]; + cmd.firstIndex = 0; + cmd.count = triCount; + cmd.baseVertex = 0; + cmd.baseInstance = 0; + cmd.instanceCount = 1; + } + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( + pose_array, 0, 0, posePtr, + depth*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); - glUniformMatrix4fv(3, 1, GL_TRUE, &projMatrix[0]); - if (!rangesPtr) - { - std::cout << "No rangesPtr" << std::endl; - // Fill in range array to instantiate the same triangles for each output layer. - // Triangle IDs starts at zero (i.e., one) for each layer, so they correspond to - // the first dimension in addressing the triangle array. - for (int i=0; i < depth; i++) - { - GLDrawCmd& cmd = drawCmdBuffer[i]; - cmd.firstIndex = 0; - cmd.count = triCount; - cmd.baseVertex = 0; - cmd.baseInstance = 0; - cmd.instanceCount = 1; - } - - NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); - NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( - pose_array, 0, 0, posePtr, - depth*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); - - } - else + } + else + { + // Fill in the range array according to user-given ranges. Triangle IDs point + // to the input triangle array, NOT index within range, so they correspond to + // the first dimension in addressing the triangle array. + for (int i=0, j=0; i < depth; i++) { - // Fill in the range array according to user-given ranges. Triangle IDs point - // to the input triangle array, NOT index within range, so they correspond to - // the first dimension in addressing the triangle array. - for (int i=0, j=0; i < depth; i++) - { - GLDrawCmd& cmd = drawCmdBuffer[i]; - int first = rangesPtr[j++]; - int count = rangesPtr[j++]; - NVDR_CHECK(first >= 0 && count >= 0, "range contains negative values"); - NVDR_CHECK((first + count) * 3 <= triCount, "range extends beyond end of triangle buffer"); - cmd.firstIndex = first * 3; - cmd.count = count * 3; - cmd.baseVertex = 0; - cmd.baseInstance = first; - cmd.instanceCount = 1; - } + GLDrawCmd& cmd = drawCmdBuffer[i]; + int first = rangesPtr[j++]; + int count = rangesPtr[j++]; + NVDR_CHECK(first >= 0 && count >= 0, "range contains negative values"); + NVDR_CHECK((first + count) * 3 <= triCount, "range extends beyond end of triangle buffer"); + cmd.firstIndex = first * 3; + cmd.count = count * 3; + cmd.baseVertex = 0; + cmd.baseInstance = first; + cmd.instanceCount = 1; } - - // Draw! - NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], depth, sizeof(GLDrawCmd))); } + // Draw! + NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], depth, sizeof(GLDrawCmd))); + } void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index bbb2afff..f9c89842 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -136,6 +136,8 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, float* outputPtr[2]; outputPtr[0] = out; outputPtr[1] = s.enableDB ? out_db : NULL; + cudaMemset(out, 0, d.num_images*width*height*4*sizeof(float)); + cudaMemset(out_db, 0, d.num_images*width*height*4*sizeof(float)); // Copy rasterized results into CUDA buffers. cudaStreamSynchronize(stream); diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index 91db4b73..66f80e3d 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -42,97 +42,121 @@ poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*10) poses = poses.at[:, 1,3].set(jnp.linspace(-1.4, 0.1, len(poses))) -rast_out, rast_out_db = jax_renderer.rasterize( - poses, + +images = [] +i = 3 +individual, _ = jax_renderer.rasterize( + poses[i:i+1], vertices, faces, projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) -images = [] -for i in [1, int(len(poses)/2), len(poses)-1]: - images.append(b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False)) -b.hstack_images( - images -).save("sweep.png") +individual = jnp.array(individual) +images.append(b.get_depth_image((individual[0,...,3]) *1.0, remove_max=False)) -i = 1 -rast_out_individual, rast_out_db = jax_renderer.rasterize( - poses[i:i+2], +full, _ = jax_renderer.rasterize( + poses, vertices, faces, projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) for j in tqdm(range(len(poses))): - print(jnp.allclose(rast_out[j], rast_out_individual[0])) + print(jnp.allclose(full[j], individual[0])) + +for i in [0, int(len(poses)/2), len(poses)-1]: + images.append(b.get_depth_image((full[i,...,3]) *1.0, remove_max=False)) +b.hstack_images( + images +).save("sweep.png") -original = b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False) -individual = b.get_depth_image((rast_out_individual[0,...,3]) *1.0, remove_max=False) -b.hstack_images([original, individual, b.overlay_image(original, individual)]).save(f"compare.png") -from IPython import embed; embed() +# images = [] +# for i in [1, int(len(poses)/2), len(poses)-1]: +# images.append(b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False)) +# b.hstack_images( +# images +# ).save("sweep.png") -jnp.abs(rast_out[i,...,3]- rast_out_individual[0,...,3]).max() +# i = 1 +# rast_out_individual, rast_out_db = jax_renderer.rasterize( +# poses[i:i+2], +# vertices, +# faces, +# projection_matrix, +# jnp.array([intrinsics.height, intrinsics.width]), +# ) +# for j in tqdm(range(len(poses))): +# print(jnp.allclose(rast_out[j], rast_out_individual[0])) -for i in test_indices: - print(i) - rast_out_individual, rast_out_db = jax_renderer.rasterize( - poses[i:i+1], - vertices, - faces, - projection_matrix, - jnp.array([intrinsics.height, intrinsics.width]), - ) - assert jnp.allclose(rast_out[i], rast_out_individual[0]) +# original = b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False) +# individual = b.get_depth_image((rast_out_individual[0,...,3]) *1.0, remove_max=False) +# b.hstack_images([original, individual, b.overlay_image(original, individual)]).save(f"compare.png") +# from IPython import embed; embed() +# jnp.abs(rast_out[i,...,3]- rast_out_individual[0,...,3]).max() -uvs = rast_out[...,:2] -triangle_ids = rast_out[...,3:4].astype(jnp.int32) +# for i in test_indices: +# print(i) +# rast_out_individual, rast_out_db = jax_renderer.rasterize( +# poses[i:i+1], +# vertices, +# faces, +# projection_matrix, +# jnp.array([intrinsics.height, intrinsics.width]), +# ) +# assert jnp.allclose(rast_out[i], rast_out_individual[0]) -mask = rast_out[...,2] > 0 -b.get_depth_image((mask[0]) *1.0, remove_max=False).save("mask.png") -import functools -@functools.partial( - jnp.vectorize, - signature="(2),(1),(4,4)->(3)", - excluded=( - 3, - 4, - ), -) -def interpolate_(uv, triangle_id, pose, vertices, faces): - u,v = uv - relevant_vertices = vertices[faces[triangle_id-1][0]] - relevant_vertices_transformed = relevant_vertices @ pose.T - barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) - interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) - return interpolated_value -interpolated_values = interpolate_(uvs, triangle_ids, poses[...,None, None,:,:], vertices, faces) -image = interpolated_values * mask[...,None] +# uvs = rast_out[...,:2] +# triangle_ids = rast_out[...,3:4].astype(jnp.int32) +# mask = rast_out[...,2] > 0 +# b.get_depth_image((mask[0]) *1.0, remove_max=False).save("mask.png") -T = 0 -points_transformed = b.apply_transform(vertices[:,:3], poses[T]) +# import functools +# @functools.partial( +# jnp.vectorize, +# signature="(2),(1),(4,4)->(3)", +# excluded=( +# 3, +# 4, +# ), +# ) +# def interpolate_(uv, triangle_id, pose, vertices, faces): +# u,v = uv +# relevant_vertices = vertices[faces[triangle_id-1][0]] +# relevant_vertices_transformed = relevant_vertices @ pose.T +# barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) +# interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) +# return interpolated_value +# interpolated_values = interpolate_(uvs, triangle_ids, poses[...,None, None,:,:], vertices, faces) +# image = interpolated_values * mask[...,None] -server.add_point_cloud( - "bunny", - points=np.array(points_transformed)[:,:3], - colors=np.zeros_like(points_transformed)[:,:3] -) -server.add_point_cloud( - "image", - points=np.array(image[T]).reshape(-1,3), - colors=np.array([1.0, 0.0, 0.0]) -) + +# T = 0 +# points_transformed = b.apply_transform(vertices[:,:3], poses[T]) + + +# server.add_point_cloud( +# "bunny", +# points=np.array(points_transformed)[:,:3], +# colors=np.zeros_like(points_transformed)[:,:3] +# ) + +# server.add_point_cloud( +# "image", +# points=np.array(image[T]).reshape(-1,3), +# colors=np.array([1.0, 0.0, 0.0]) +# ) from IPython import embed; embed() From 0e749db6d52e6959d095391630eac60604334b1b Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Wed, 14 Feb 2024 05:50:27 +0000 Subject: [PATCH 10/27] update test --- .../nvdiffrast_jax/test_jax_renderer.py | 71 ++++--------------- 1 file changed, 15 insertions(+), 56 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index 66f80e3d..a225a6ef 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -40,77 +40,36 @@ vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = jnp.array(mesh.faces) -poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*10) -poses = poses.at[:, 1,3].set(jnp.linspace(-1.4, 0.1, len(poses))) +poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*1000) +poses = poses.at[:, 1,3].set(jnp.linspace(-1.4, 0.2, len(poses))) -images = [] -i = 3 -individual, _ = jax_renderer.rasterize( - poses[i:i+1], - vertices, - faces, - projection_matrix, - jnp.array([intrinsics.height, intrinsics.width]), -) -individual = jnp.array(individual) -images.append(b.get_depth_image((individual[0,...,3]) *1.0, remove_max=False)) - -full, _ = jax_renderer.rasterize( +parallel_render, _ = jax_renderer.rasterize( poses, vertices, faces, projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) -for j in tqdm(range(len(poses))): - print(jnp.allclose(full[j], individual[0])) +images = [] for i in [0, int(len(poses)/2), len(poses)-1]: - images.append(b.get_depth_image((full[i,...,3]) *1.0, remove_max=False)) + images.append(b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False)) b.hstack_images( images ).save("sweep.png") +test_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] +for i in test_indices: + individual, rast_out_db = jax_renderer.rasterize( + poses[i:i+1], + vertices, + faces, + projection_matrix, + jnp.array([intrinsics.height, intrinsics.width]), + ) + assert jnp.allclose(parallel_render[i], individual[0]) -# images = [] -# for i in [1, int(len(poses)/2), len(poses)-1]: -# images.append(b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False)) -# b.hstack_images( -# images -# ).save("sweep.png") - -# i = 1 -# rast_out_individual, rast_out_db = jax_renderer.rasterize( -# poses[i:i+2], -# vertices, -# faces, -# projection_matrix, -# jnp.array([intrinsics.height, intrinsics.width]), -# ) -# for j in tqdm(range(len(poses))): -# print(jnp.allclose(rast_out[j], rast_out_individual[0])) - - -# original = b.get_depth_image((rast_out[i,...,3]) *1.0, remove_max=False) -# individual = b.get_depth_image((rast_out_individual[0,...,3]) *1.0, remove_max=False) -# b.hstack_images([original, individual, b.overlay_image(original, individual)]).save(f"compare.png") - -# from IPython import embed; embed() - -# jnp.abs(rast_out[i,...,3]- rast_out_individual[0,...,3]).max() - -# for i in test_indices: -# print(i) -# rast_out_individual, rast_out_db = jax_renderer.rasterize( -# poses[i:i+1], -# vertices, -# faces, -# projection_matrix, -# jnp.array([intrinsics.height, intrinsics.width]), -# ) -# assert jnp.allclose(rast_out[i], rast_out_individual[0]) - From 3c20e2e1f67a2849c1aa7765be5b549e0bf420b6 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Thu, 15 Feb 2024 07:26:06 +0000 Subject: [PATCH 11/27] larger than 2048 poses done --- .../nvdiffrast/common/rasterize_gl.cpp | 106 ++++++++++++------ .../nvdiffrast/common/rasterize_gl.h | 2 +- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 33 +++--- .../nvdiffrast_jax/test_jax_renderer.py | 95 +++++++--------- 4 files changed, 134 insertions(+), 102 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index 412a9148..1adb5e14 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -62,6 +62,8 @@ static void compileGLShader(NVDR_CTX_ARGS, const RasterizeGLState& s, GLuint* pS NVDR_CHECK_GL_ERROR(glCompileShader(*pShader)); } +int NUM_LAYERS = 2048; + static void constructGLProgram(NVDR_CTX_ARGS, GLuint* pProgram, GLuint glVertexShader, GLuint glGeometryShader, GLuint glFragmentShader) { *pProgram = 0; @@ -345,8 +347,7 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId NVDR_CHECK_GL_ERROR(glGenTextures(1, &s.glPoseTexture)); NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D, s.glPoseTexture)); - int num_layers = 1024; - NVDR_CHECK_GL_ERROR(glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA32F, 4, num_layers, 0, GL_RGBA, GL_FLOAT, 0)); + NVDR_CHECK_GL_ERROR(glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA32F, 4, NUM_LAYERS, 0, GL_RGBA, GL_FLOAT, 0)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); @@ -403,7 +404,7 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i } // Resize framebuffer? - if (width > s.width || height > s.height || depth > s.depth) + if (width > s.width || height > s.height) // || depth > s.depth) { int num_outputs = s.enableDB ? 2 : 1; if (s.cudaColorBuffer[0]) @@ -428,7 +429,7 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i for (int i=0; i < num_outputs; i++) { NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); - NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, s.depth, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); + NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, NUM_LAYERS, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); @@ -437,7 +438,7 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i // Allocate depth/stencil buffer. NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glDepthStencilBuffer)); - NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_DEPTH24_STENCIL8, s.width, s.height, s.depth, 0, GL_DEPTH_STENCIL, GL_UNSIGNED_INT_24_8, 0)); + NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_DEPTH24_STENCIL8, s.width, s.height, NUM_LAYERS, 0, GL_DEPTH_STENCIL, GL_UNSIGNED_INT_24_8, 0)); // (Re-)register all GL buffers into Cuda. for (int i=0; i < num_outputs; i++) @@ -447,7 +448,7 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i } } -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx) +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx) { // Only copy inputs if we are on first iteration of depth peeling or not doing it at all. @@ -494,7 +495,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, st if (!s.cudaPrevOutBuffer) { NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glPrevOutBuffer)); - NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, s.depth, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); + NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, NUM_LAYERS, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); @@ -545,7 +546,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, st // else // { // Populate a buffer for draw commands and execute it. - std::vector drawCmdBuffer(depth); + std::vector drawCmdBuffer(NUM_LAYERS); NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaPoseTexture, s.glPoseTexture, GL_TEXTURE_2D, cudaGraphicsRegisterFlagsReadOnly)); cudaArray_t pose_array = 0; @@ -554,13 +555,20 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, st NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); glUniformMatrix4fv(3, 1, GL_TRUE, &projMatrix[0]); - if (!rangesPtr) + // if (!rangesPtr) + // { + + // std::cout << "depth " << depth << std::endl; + // Fill in range array to instantiate the same triangles for each output layer. + // Triangle IDs starts at zero (i.e., one) for each layer, so they correspond to + // the first dimension in addressing the triangle array. + for(int start_pose_idx=0; start_pose_idx < depth; start_pose_idx+=NUM_LAYERS) { - std::cout << "depth " << depth << std::endl; - // Fill in range array to instantiate the same triangles for each output layer. - // Triangle IDs starts at zero (i.e., one) for each layer, so they correspond to - // the first dimension in addressing the triangle array. - for (int i=0; i < depth; i++) + int poses_on_this_iter = std::min(depth-start_pose_idx, NUM_LAYERS); + NVDR_CHECK_GL_ERROR(glViewport(0, 0, width, height)); + NVDR_CHECK_GL_ERROR(glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT)); + + for (int i=0; i < poses_on_this_iter; i++) { GLDrawCmd& cmd = drawCmdBuffer[i]; cmd.firstIndex = 0; @@ -573,33 +581,63 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, st NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( - pose_array, 0, 0, posePtr, - depth*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); + pose_array, 0, 0, posePtr + start_pose_idx*16, + poses_on_this_iter*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); - } - else - { - // Fill in the range array according to user-given ranges. Triangle IDs point - // to the input triangle array, NOT index within range, so they correspond to - // the first dimension in addressing the triangle array. - for (int i=0, j=0; i < depth; i++) + NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], poses_on_this_iter, sizeof(GLDrawCmd))); + + // Copy color buffers to output tensors. + cudaArray_t array = 0; + cudaChannelFormatDesc arrayDesc = {}; // For error checking. + cudaExtent arrayExt = {}; // For error checking. + int num_outputs = s.enableDB ? 2 : 1; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(num_outputs, s.cudaColorBuffer, stream)); + for (int i=0; i < num_outputs; i++) { - GLDrawCmd& cmd = drawCmdBuffer[i]; - int first = rangesPtr[j++]; - int count = rangesPtr[j++]; - NVDR_CHECK(first >= 0 && count >= 0, "range contains negative values"); - NVDR_CHECK((first + count) * 3 <= triCount, "range extends beyond end of triangle buffer"); - cmd.firstIndex = first * 3; - cmd.count = count * 3; - cmd.baseVertex = 0; - cmd.baseInstance = first; - cmd.instanceCount = 1; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[i], 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaArrayGetInfo(&arrayDesc, &arrayExt, NULL, array)); + NVDR_CHECK(arrayDesc.f == cudaChannelFormatKindFloat, "CUDA mapped array data kind mismatch"); + NVDR_CHECK(arrayDesc.x == 32 && arrayDesc.y == 32 && arrayDesc.z == 32 && arrayDesc.w == 32, "CUDA mapped array data width mismatch"); + // NVDR_CHECK(arrayExt.width >= width && arrayExt.height >= height && arrayExt.depth >= depth, "CUDA mapped array extent mismatch"); + cudaMemcpy3DParms p = {0}; + p.srcArray = array; + p.dstPtr.ptr = ((float * ) outputPtr[i]) + start_pose_idx * width * height * 4;; + p.dstPtr.pitch = width * 4 * sizeof(float); + p.dstPtr.xsize = width; + p.dstPtr.ysize = height; + p.extent.width = width; + p.extent.height = height; + p.extent.depth = poses_on_this_iter; + p.kind = cudaMemcpyDeviceToDevice; + NVDR_CHECK_CUDA_ERROR(cudaMemcpy3DAsync(&p, stream)); } + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(num_outputs, s.cudaColorBuffer, stream)); + } + // } + // else + // { + // // Fill in the range array according to user-given ranges. Triangle IDs point + // // to the input triangle array, NOT index within range, so they correspond to + // // the first dimension in addressing the triangle array. + // for (int i=0, j=0; i < depth; i++) + // { + // GLDrawCmd& cmd = drawCmdBuffer[i]; + // int first = rangesPtr[j++]; + // int count = rangesPtr[j++]; + // NVDR_CHECK(first >= 0 && count >= 0, "range contains negative values"); + // NVDR_CHECK((first + count) * 3 <= triCount, "range extends beyond end of triangle buffer"); + // cmd.firstIndex = first * 3; + // cmd.count = count * 3; + // cmd.baseVertex = 0; + // cmd.baseInstance = first; + // cmd.instanceCount = 1; + // } + // } + // Draw! - NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], depth, sizeof(GLDrawCmd))); } diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h index 63533517..2627b10c 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h @@ -54,7 +54,7 @@ struct RasterizeGLState // Must be initializable by memset to zero. void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceIdx); void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, int posCount, int triCount, int width, int height, int depth); -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth); void rasterizeReleaseBuffers(NVDR_CTX_ARGS, RasterizeGLState& s); diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index f9c89842..a999a1a1 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -78,6 +78,7 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, int height = resolution[0]; int width = resolution[1]; int depth = d.num_images; + int max_parallel_images = 1024; // int depth = instance_mode ? pos.size(0) : ranges.size(0); NVDR_CHECK(height > 0 && width > 0, "resolution must be [>0, >0];"); @@ -105,6 +106,14 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, #endif } + // Allocate output tensors. + float* outputPtr[2]; + outputPtr[0] = out; + outputPtr[1] = s.enableDB ? out_db : NULL; + cudaMemset(out, 0, d.num_images*width*height*4*sizeof(float)); + cudaMemset(out_db, 0, d.num_images*width*height*4*sizeof(float)); + + cudaStreamSynchronize(stream); std::vector projMatrix; projMatrix.resize(16); @@ -117,10 +126,10 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, firstPose.resize(16); NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&firstPose[0], pose, 16 * sizeof(int), cudaMemcpyDeviceToHost)); cudaStreamSynchronize(stream); - for(int i = 0; i < 16; i++) { - std::cout << firstPose[i] << " "; - } - std::cout << std::endl; + // for(int i = 0; i < 16; i++) { + // std::cout << firstPose[i] << " "; + // } + // std::cout << std::endl; // // Copy input data to GL and render. int peeling_idx = -1; @@ -129,20 +138,14 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, const int32_t* rangesPtr = 0; // This is in CPU memory. const int32_t* triPtr = tri; cudaStreamSynchronize(stream); - rasterizeRender(NVDR_CTX_PARAMS, s, stream, projMatrix, posePtr, posPtr, posCount, d.num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); + rasterizeRender(NVDR_CTX_PARAMS, s, stream, outputPtr, projMatrix, posePtr, posPtr, posCount, d.num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); cudaStreamSynchronize(stream); - // Allocate output tensors. - float* outputPtr[2]; - outputPtr[0] = out; - outputPtr[1] = s.enableDB ? out_db : NULL; - cudaMemset(out, 0, d.num_images*width*height*4*sizeof(float)); - cudaMemset(out_db, 0, d.num_images*width*height*4*sizeof(float)); - // Copy rasterized results into CUDA buffers. - cudaStreamSynchronize(stream); - rasterizeCopyResults(NVDR_CTX_PARAMS, s, stream, outputPtr, width, height, depth); - cudaStreamSynchronize(stream); + // // Copy rasterized results into CUDA buffers. + // cudaStreamSynchronize(stream); + // rasterizeCopyResults(NVDR_CTX_PARAMS, s, stream, outputPtr, width, height, depth); + // cudaStreamSynchronize(stream); // Done. Release GL context and return. if (stateWrapper.automatic) diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index a225a6ef..b5f2b5d5 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -40,8 +40,8 @@ vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = jnp.array(mesh.faces) -poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*1000) -poses = poses.at[:, 1,3].set(jnp.linspace(-1.4, 0.2, len(poses))) +poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*6000) +poses = poses.at[:, 1,3].set(jnp.linspace(-1.9, 0.5, len(poses))) parallel_render, _ = jax_renderer.rasterize( poses, @@ -58,7 +58,7 @@ images ).save("sweep.png") -test_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] +test_indices = jax.random.randint(jax.random.PRNGKey(0), (100,), 0, len(poses)) for i in test_indices: individual, rast_out_db = jax_renderer.rasterize( poses[i:i+1], @@ -67,55 +67,46 @@ projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) - assert jnp.allclose(parallel_render[i], individual[0]) - - - - - -# uvs = rast_out[...,:2] -# triangle_ids = rast_out[...,3:4].astype(jnp.int32) - -# mask = rast_out[...,2] > 0 -# b.get_depth_image((mask[0]) *1.0, remove_max=False).save("mask.png") - -# import functools -# @functools.partial( -# jnp.vectorize, -# signature="(2),(1),(4,4)->(3)", -# excluded=( -# 3, -# 4, -# ), -# ) -# def interpolate_(uv, triangle_id, pose, vertices, faces): -# u,v = uv -# relevant_vertices = vertices[faces[triangle_id-1][0]] -# relevant_vertices_transformed = relevant_vertices @ pose.T -# barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) -# interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) -# return interpolated_value - -# interpolated_values = interpolate_(uvs, triangle_ids, poses[...,None, None,:,:], vertices, faces) -# image = interpolated_values * mask[...,None] - - - -# T = 0 -# points_transformed = b.apply_transform(vertices[:,:3], poses[T]) - - -# server.add_point_cloud( -# "bunny", -# points=np.array(points_transformed)[:,:3], -# colors=np.zeros_like(points_transformed)[:,:3] -# ) - -# server.add_point_cloud( -# "image", -# points=np.array(image[T]).reshape(-1,3), -# colors=np.array([1.0, 0.0, 0.0]) -# ) + assert jnp.allclose(parallel_render[i], individual[0]), f"Failed at {i}" + + +uvs = parallel_render[...,:2] +triangle_ids = parallel_render[...,3:4].astype(jnp.int32) +mask = parallel_render[...,2] > 0 + + +import functools +@functools.partial( + jnp.vectorize, + signature="(2),(1),(4,4)->(3)", + excluded=( + 3, + 4, + ), +) +def interpolate_(uv, triangle_id, pose, vertices, faces): + u,v = uv + relevant_vertices = vertices[faces[triangle_id-1][0]] + relevant_vertices_transformed = relevant_vertices @ pose.T + barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) + interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) + return interpolated_value + +interpolated_values = interpolate_(uvs, triangle_ids, poses[...,None, None,:,:], vertices, faces) +image = interpolated_values * mask[...,None] + +T = 3000 +points_transformed = b.apply_transform(vertices[:,:3], poses[T]) +server.add_point_cloud( + "bunny", + points=np.array(points_transformed)[:,:3], + colors=np.zeros_like(points_transformed)[:,:3] +) +server.add_point_cloud( + "image", + points=np.array(image[T]).reshape(-1,3), + colors=np.array([1.0, 0.0, 0.0]) +) from IPython import embed; embed() From 6bf3608b20e3a2f4ab2cba588801ed0358d055a9 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Thu, 15 Feb 2024 17:23:45 +0000 Subject: [PATCH 12/27] interpolate working in test --- .../rendering/nvdiffrast_jax/jax_renderer.py | 36 ++++++++++--------- .../nvdiffrast/jax/jax_bindings.cpp | 9 ++--- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 29 +++++++++------ .../nvdiffrast/jax/jax_rasterize_gl.h | 2 ++ .../nvdiffrast_jax/test_jax_renderer.py | 16 ++++++--- 5 files changed, 57 insertions(+), 35 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py index 23cc3651..ec631a02 100644 --- a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py @@ -34,11 +34,11 @@ def __init__(self, intrinsics, num_layers=1024): # ------------------ @functools.partial(jax.custom_vjp, nondiff_argnums=(0,)) - def _rasterize(self, pose, pos, tri, projMatrix, resolution): - return _rasterize_fwd_custom_call(self, pose, pos, tri, projMatrix, resolution) + def _rasterize(self, pose, pos, tri, ranges, projMatrix, resolution): + return _rasterize_fwd_custom_call(self, pose, pos, tri, ranges, projMatrix, resolution) - def _rasterize_fwd(self, pose, pos, tri, projMatrix, resolution): - rast_out, rast_out_db = _rasterize_fwd_custom_call(self, pose, pos, tri, projMatrix, resolution) + def _rasterize_fwd(self, pose, pos, tri, ranges, projMatrix, resolution): + rast_out, rast_out_db = _rasterize_fwd_custom_call(self, pose, pos, tri, ranges, projMatrix, resolution) saved_tensors = (pose, pos, tri, rast_out) return (rast_out, rast_out_db), saved_tensors @@ -185,8 +185,8 @@ def _register_custom_calls(): # @functools.partial(jax.jit, static_argnums=(0,)) -def _rasterize_fwd_custom_call(r: "Renderer", pose, pos, tri, projMatrix, resolution): - return _build_rasterize_fwd_primitive(r).bind(pose, pos, tri, projMatrix, resolution) +def _rasterize_fwd_custom_call(r: "Renderer", pose, pos, tri, ranges, projMatrix, resolution): + return _build_rasterize_fwd_primitive(r).bind(pose, pos, tri, ranges, projMatrix, resolution) @functools.lru_cache(maxsize=None) @@ -195,15 +195,15 @@ def _build_rasterize_fwd_primitive(r: "Renderer"): # For JIT compilation we need a function to evaluate the shape and dtype of the # outputs of our op for some given inputs - def _rasterize_fwd_abstract(pose, pos, tri, projection_matrix, resolution): + def _rasterize_fwd_abstract(pose, pos, tri, ranges, projection_matrix, resolution): if len(pos.shape) != 2 or pos.shape[-1] != 4: raise ValueError( "Pass in pos aa [num_vertices, 4] sized input" ) - if len(pose.shape) != 3 or pose.shape[-1] != 4: - raise ValueError( - "Pass in pose aa [num_images, 4, 4] sized input" - ) + # if len(pose.shape) != 3 or pose.shape[-1] != 4: + # raise ValueError( + # "Pass in pose aa [num_images, 4, 4] sized input" + # ) num_images = pose.shape[0] dtype = dtypes.canonicalize_dtype(pose.dtype) @@ -218,14 +218,14 @@ def _rasterize_fwd_abstract(pose, pos, tri, projection_matrix, resolution): ] # Provide an MLIR "lowering" of the rasterize primitive. - def _rasterize_fwd_lowering(ctx, poses, pos, tri, projection_matrix, resolution): + def _rasterize_fwd_lowering(ctx, poses, pos, tri, ranges, projection_matrix, resolution): """ Single-object (one obj represented by tri) rasterization with multiple poses (first dimension fo pos) dr.rasterize(glctx, pos, tri, resolution=resolution) """ # Extract the numpy type of the inputs - poses_aval, pos_aval, tri_aval, projection_matrix_aval, resolution_aval = ctx.avals_in + poses_aval, pos_aval, tri_aval, ranges_aval, projection_matrix_aval, resolution_aval = ctx.avals_in # if poses_aval.ndim != 3: # raise NotImplementedError( # f"Only 3D vtx position inputs supported: got {poses_aval.shape}" @@ -246,6 +246,8 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, projection_matrix, resolution) raise NotImplementedError(f"Unsupported triangles dtype {tri_aval.dtype}") num_images = poses_aval.shape[0] + num_objects = ranges_aval.shape[0] + assert num_objects == poses_aval.shape[1], f"Number of poses {poses_aval.shape[1]} should match number of objects {num_objects}" num_vertices = pos_aval.shape[0] num_triangles = tri_aval.shape[0] out_shp_dtype = mlir.ir.RankedTensorType.get( @@ -254,7 +256,7 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, projection_matrix, resolution) ) opaque = dr._get_plugin(gl=True).build_diff_rasterize_fwd_descriptor( - r.renderer_env.cpp_wrapper, [num_images, num_vertices, num_triangles] + r.renderer_env.cpp_wrapper, [num_images, num_objects, num_vertices, num_triangles] ) op_name = "jax_rasterize_fwd_gl" @@ -264,12 +266,12 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, projection_matrix, resolution) # Output types result_types=[out_shp_dtype, out_shp_dtype], # The inputs: - operands=[poses, pos, tri, projection_matrix, resolution], + operands=[poses, pos, tri, ranges, projection_matrix, resolution], backend_config=opaque, operand_layouts=[ - (2, 1, 0), + (3, 2, 0, 1), *default_layouts( - pos_aval.shape, tri_aval.shape, projection_matrix_aval.shape, resolution_aval.shape + pos_aval.shape, tri_aval.shape, ranges_aval.shape, projection_matrix_aval.shape, resolution_aval.shape ) ], result_layouts=default_layouts( diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp index 28216110..0b5bfb1f 100755 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp @@ -28,12 +28,13 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("registrations", &Registrations, "custom call registrations"); m.def("build_diff_rasterize_fwd_descriptor", [](RasterizeGLStateWrapper& stateWrapper, - std::vector images_vertices_triangles) { + std::vector images_objects_vertices_triangles) { DiffRasterizeCustomCallDescriptor d; d.gl_state_wrapper = &stateWrapper; - d.num_images = images_vertices_triangles[0]; - d.num_vertices = images_vertices_triangles[1]; - d.num_triangles = images_vertices_triangles[2]; + d.num_images = images_objects_vertices_triangles[0]; + d.num_objects = images_objects_vertices_triangles[1]; + d.num_vertices = images_objects_vertices_triangles[2]; + d.num_triangles = images_objects_vertices_triangles[3]; return PackDescriptor(d); }); m.def("build_diff_interpolate_descriptor", diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index a999a1a1..2c8eba71 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -53,19 +53,32 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, const float *pose = reinterpret_cast (buffers[0]); const float *pos = reinterpret_cast (buffers[1]); const int *tri = reinterpret_cast (buffers[2]); - const float *projectionMatrix = reinterpret_cast (buffers[3]); - const int *_resolution = reinterpret_cast (buffers[4]); + const int *_ranges = reinterpret_cast (buffers[3]); + const float *projectionMatrix = reinterpret_cast (buffers[4]); + const int *_resolution = reinterpret_cast (buffers[5]); - float *out = reinterpret_cast (buffers[5]); - float *out_db = reinterpret_cast (buffers[6]); + float *out = reinterpret_cast (buffers[6]); + float *out_db = reinterpret_cast (buffers[7]); auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); std::vector resolution; resolution.resize(2); + int ranges[2*d.num_objects]; + + // std::cout << "num_images: " << d.num_images << std::endl; + // std::cout << "num_objects: " << d.num_objects << std::endl; + // std::cout << "num_vertices: " << d.num_vertices << std::endl; + // std::cout << "num_triangles: " << d.num_triangles << std::endl; cudaStreamSynchronize(stream); NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&resolution[0], _resolution, 2 * sizeof(int), cudaMemcpyDeviceToHost)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&ranges[0], _ranges, 2 * d.num_objects * sizeof(int), cudaMemcpyDeviceToHost)); + cudaStreamSynchronize(stream); + + // Allocate output tensors. + cudaStreamSynchronize(stream); + cudaStreamSynchronize(stream); // const at::cuda::OptionalCUDAGuard device_guard(at::device_of(pos)); @@ -121,11 +134,7 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, cudaStreamSynchronize(stream); - cudaStreamSynchronize(stream); - std::vector firstPose; - firstPose.resize(16); - NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&firstPose[0], pose, 16 * sizeof(int), cudaMemcpyDeviceToHost)); - cudaStreamSynchronize(stream); + // for(int i = 0; i < 16; i++) { // std::cout << firstPose[i] << " "; // } @@ -135,7 +144,7 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, int peeling_idx = -1; const float* posePtr = pose; const float* posPtr = pos; - const int32_t* rangesPtr = 0; // This is in CPU memory. + const int32_t* rangesPtr = ranges; // This is in CPU memory. const int32_t* triPtr = tri; cudaStreamSynchronize(stream); rasterizeRender(NVDR_CTX_PARAMS, s, stream, outputPtr, projMatrix, posePtr, posPtr, posCount, d.num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h index dfe86638..1a648b2b 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h @@ -24,9 +24,11 @@ struct DiffRasterizeCustomCallDescriptor { RasterizeGLStateWrapper* gl_state_wrapper; int num_images; + int num_objects; int num_vertices; int num_triangles; }; + struct DiffRasterizeBwdCustomCallDescriptor { int num_images; // pos[0] int num_vertices; // pos[1] diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py index b5f2b5d5..500f7f39 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py @@ -39,14 +39,19 @@ vertices = mesh.vertices vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = jnp.array(mesh.faces) +ranges = jnp.array([[0, faces.shape[0]]]) + poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*6000) poses = poses.at[:, 1,3].set(jnp.linspace(-1.9, 0.5, len(poses))) +poses2 = poses.at[:, 1,3].set(jnp.linspace(-1.0, 1.5, len(poses))) +poses = poses[:,None,...] parallel_render, _ = jax_renderer.rasterize( poses, vertices, faces, + ranges, projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) @@ -64,6 +69,7 @@ poses[i:i+1], vertices, faces, + ranges, projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) @@ -92,20 +98,22 @@ def interpolate_(uv, triangle_id, pose, vertices, faces): interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) return interpolated_value -interpolated_values = interpolate_(uvs, triangle_ids, poses[...,None, None,:,:], vertices, faces) +interpolated_values = interpolate_(uvs, triangle_ids, poses[...,0,None, None,:,:], vertices, faces) image = interpolated_values * mask[...,None] T = 3000 -points_transformed = b.apply_transform(vertices[:,:3], poses[T]) +points_transformed = b.apply_transform(vertices[:,:3], poses[T,0]) server.add_point_cloud( "bunny", points=np.array(points_transformed)[:,:3], - colors=np.zeros_like(points_transformed)[:,:3] + colors=np.zeros_like(points_transformed)[:,:3], + point_size=0.005 ) server.add_point_cloud( "image", points=np.array(image[T]).reshape(-1,3), - colors=np.array([1.0, 0.0, 0.0]) + colors=np.array([1.0, 0.0, 0.0]), + point_size=0.005 ) From 40872a167954fa15a022902fd69e8ee434baacdc Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Thu, 15 Feb 2024 21:52:08 +0000 Subject: [PATCH 13/27] multiobject rendering working --- .../nvdiffrast/common/rasterize_gl.cpp | 59 +++++++++++-------- .../nvdiffrast/common/rasterize_gl.h | 2 +- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 2 +- ...st_jax_renderer.py => test_jax_renderer.py | 17 +++--- 4 files changed, 47 insertions(+), 33 deletions(-) rename bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py => test_jax_renderer.py (88%) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index 1adb5e14..b401a9cf 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -130,8 +130,10 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId STRINGIFY_SHADER_SOURCE( layout(location = 0) in vec4 in_pos; layout(location = 3) uniform mat4 projection_matrix; + layout(location = 5) uniform float seg_id; out int v_layer; out int v_offset; + out float seg_id_out; uniform sampler2D texture; void main() { @@ -143,7 +145,8 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId mat4 pose_mat = transpose(mat4(v1,v2,v3,v4)); gl_Position = projection_matrix * pose_mat * in_pos; v_layer = layer; - v_offset = 0; // Sneak in TriID offset here. + v_offset = gl_BaseInstanceARB; // Sneak in TriID offset here. + seg_id_out = seg_id; } ) ); @@ -164,8 +167,10 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId layout(location = 0) uniform vec2 vp_scale; in int v_layer[]; in int v_offset[]; + in float seg_id_out[]; out vec4 var_uvzw; out vec4 var_db; + out float seg_id; void main() { // Plane equations for bary differentials. @@ -204,9 +209,9 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId int layer_id = v_layer[0]; int prim_id = gl_PrimitiveIDIn + v_offset[0]; - gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[0].gl_Position.x, gl_in[0].gl_Position.y, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); var_uvzw = vec4(1.f, 0.f, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); var_db = db0; EmitVertex(); - gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[1].gl_Position.x, gl_in[1].gl_Position.y, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); var_uvzw = vec4(0.f, 1.f, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); var_db = db1; EmitVertex(); - gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[2].gl_Position.x, gl_in[2].gl_Position.y, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); var_uvzw = vec4(0.f, 0.f, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); var_db = db2; EmitVertex(); + gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[0].gl_Position.x, gl_in[0].gl_Position.y, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); var_uvzw = vec4(1.f, 0.f, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); var_db = db0; seg_id = seg_id_out[0]; EmitVertex(); + gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[1].gl_Position.x, gl_in[1].gl_Position.y, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); var_uvzw = vec4(0.f, 1.f, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); var_db = db1; seg_id = seg_id_out[1]; EmitVertex(); + gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[2].gl_Position.x, gl_in[2].gl_Position.y, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); var_uvzw = vec4(0.f, 0.f, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); var_db = db2; seg_id = seg_id_out[2]; EmitVertex(); } ) ); @@ -217,6 +222,7 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId STRINGIFY_SHADER_SOURCE( in vec4 var_uvzw; in vec4 var_db; + in float seg_id; layout(location = 0) out vec4 out_raster; layout(location = 1) out vec4 out_db; IF_ZMODIFY( @@ -224,7 +230,7 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId ) void main() { - out_raster = vec4(var_uvzw.x, var_uvzw.y, var_uvzw.z / var_uvzw.w, float(gl_PrimitiveID + 1)); + out_raster = vec4(var_uvzw.x, var_uvzw.y, seg_id, float(gl_PrimitiveID + 1)); out_db = var_db * var_uvzw.w; IF_ZMODIFY(gl_FragDepth = gl_FragCoord.z + in_dummy;) } @@ -448,7 +454,7 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i } } -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx) +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int num_objects, int width, int height, int depth, int peeling_idx) { // Only copy inputs if we are on first iteration of depth peeling or not doing it at all. @@ -568,24 +574,30 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl NVDR_CHECK_GL_ERROR(glViewport(0, 0, width, height)); NVDR_CHECK_GL_ERROR(glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT)); - for (int i=0; i < poses_on_this_iter; i++) + for(int object_idx=0; object_idx < num_objects; object_idx++) { - GLDrawCmd& cmd = drawCmdBuffer[i]; - cmd.firstIndex = 0; - cmd.count = triCount; - cmd.baseVertex = 0; - cmd.baseInstance = 0; - cmd.instanceCount = 1; + for (int i=0; i < poses_on_this_iter; i++) + { + int first = rangesPtr[2*object_idx]; + int count = rangesPtr[2*object_idx+1]; + GLDrawCmd& cmd = drawCmdBuffer[i]; + cmd.firstIndex = first * 3; + cmd.count = count * 3; + cmd.baseVertex = 0; + cmd.baseInstance = first; + cmd.instanceCount = 1; + } + + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( + pose_array, 0, 0, posePtr + depth * 16 * object_idx + start_pose_idx * 16, + poses_on_this_iter * 16 * sizeof(float), cudaMemcpyDeviceToDevice, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); + glUniform1f(5, object_idx+1.0); + + NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], poses_on_this_iter, sizeof(GLDrawCmd))); } - - NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); - NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( - pose_array, 0, 0, posePtr + start_pose_idx*16, - poses_on_this_iter*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); - NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); - - NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], poses_on_this_iter, sizeof(GLDrawCmd))); // Copy color buffers to output tensors. cudaArray_t array = 0; @@ -613,7 +625,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl NVDR_CHECK_CUDA_ERROR(cudaMemcpy3DAsync(&p, stream)); } NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(num_outputs, s.cudaColorBuffer, stream)); - + } // } @@ -638,7 +650,6 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl // } // Draw! - } void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h index 2627b10c..0e3d5e7e 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.h @@ -54,7 +54,7 @@ struct RasterizeGLState // Must be initializable by memset to zero. void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceIdx); void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, int posCount, int triCount, int width, int height, int depth); -void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int width, int height, int depth, int peeling_idx); +void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, std::vector& projMatrix, const float* posePtr, const float* posPtr, int posCount, int vtxPerInstance, const int32_t* triPtr, int triCount, const int32_t* rangesPtr, int num_objects, int width, int height, int depth, int peeling_idx); void rasterizeCopyResults(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, float** outputPtr, int width, int height, int depth); void rasterizeReleaseBuffers(NVDR_CTX_ARGS, RasterizeGLState& s); diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index 2c8eba71..73b2b9af 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -147,7 +147,7 @@ void jax_rasterize_fwd_gl(cudaStream_t stream, const int32_t* rangesPtr = ranges; // This is in CPU memory. const int32_t* triPtr = tri; cudaStreamSynchronize(stream); - rasterizeRender(NVDR_CTX_PARAMS, s, stream, outputPtr, projMatrix, posePtr, posPtr, posCount, d.num_vertices, triPtr, triCount, rangesPtr, width, height, depth, peeling_idx); + rasterizeRender(NVDR_CTX_PARAMS, s, stream, outputPtr, projMatrix, posePtr, posPtr, posCount, d.num_vertices, triPtr, triCount, rangesPtr, d.num_objects, width, height, depth, peeling_idx); cudaStreamSynchronize(stream); diff --git a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py b/test_jax_renderer.py similarity index 88% rename from bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py rename to test_jax_renderer.py index 500f7f39..d607bf47 100644 --- a/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer.py +++ b/test_jax_renderer.py @@ -39,13 +39,13 @@ vertices = mesh.vertices vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = jnp.array(mesh.faces) -ranges = jnp.array([[0, faces.shape[0]]]) +ranges = jnp.array([[0, faces.shape[0]], [0, faces.shape[0]]]) poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*6000) poses = poses.at[:, 1,3].set(jnp.linspace(-1.9, 0.5, len(poses))) poses2 = poses.at[:, 1,3].set(jnp.linspace(-1.0, 1.5, len(poses))) -poses = poses[:,None,...] +poses = jnp.stack([poses, poses2], axis=1) parallel_render, _ = jax_renderer.rasterize( poses, @@ -56,13 +56,16 @@ jnp.array([intrinsics.height, intrinsics.width]), ) -images = [] -for i in [0, int(len(poses)/2), len(poses)-1]: - images.append(b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False)) +images = [b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False) for i in [0, int(len(poses)/2), len(poses)-1]] +b.hstack_images( + images +).save("sweep.png") +images = [b.get_depth_image((parallel_render[i,...,2]) *1.0, remove_max=False) for i in [0, int(len(poses)/2), len(poses)-1]] b.hstack_images( images ).save("sweep.png") + test_indices = jax.random.randint(jax.random.PRNGKey(0), (100,), 0, len(poses)) for i in test_indices: individual, rast_out_db = jax_renderer.rasterize( @@ -79,7 +82,7 @@ uvs = parallel_render[...,:2] triangle_ids = parallel_render[...,3:4].astype(jnp.int32) mask = parallel_render[...,2] > 0 - + import functools @functools.partial( @@ -101,7 +104,7 @@ def interpolate_(uv, triangle_id, pose, vertices, faces): interpolated_values = interpolate_(uvs, triangle_ids, poses[...,0,None, None,:,:], vertices, faces) image = interpolated_values * mask[...,None] -T = 3000 +T = 0 points_transformed = b.apply_transform(vertices[:,:3], poses[T,0]) server.add_point_cloud( "bunny", From 65951deaf1c61469f6cf0c2421d5386c43324d45 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Fri, 16 Feb 2024 05:04:01 +0000 Subject: [PATCH 14/27] multiobejct seems to be working --- .../nvdiffrast/common/rasterize_gl.cpp | 7 +- test_jax_renderer.py | 197 +++++++++++++----- 2 files changed, 153 insertions(+), 51 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index b401a9cf..a7a854cc 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -573,18 +573,21 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl int poses_on_this_iter = std::min(depth-start_pose_idx, NUM_LAYERS); NVDR_CHECK_GL_ERROR(glViewport(0, 0, width, height)); NVDR_CHECK_GL_ERROR(glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT)); - + std::cout << "start_pose_idx " << start_pose_idx << std::endl; for(int object_idx=0; object_idx < num_objects; object_idx++) { + std::cout << "object_idx " << object_idx << std::endl; for (int i=0; i < poses_on_this_iter; i++) { int first = rangesPtr[2*object_idx]; int count = rangesPtr[2*object_idx+1]; + std::cout << "first " << first << std::endl; + std::cout << "count " << count << std::endl; GLDrawCmd& cmd = drawCmdBuffer[i]; cmd.firstIndex = first * 3; cmd.count = count * 3; cmd.baseVertex = 0; - cmd.baseInstance = first; + cmd.baseInstance = 0; cmd.instanceCount = 1; } diff --git a/test_jax_renderer.py b/test_jax_renderer.py index d607bf47..c8d7cd55 100644 --- a/test_jax_renderer.py +++ b/test_jax_renderer.py @@ -33,18 +33,40 @@ from bayes3d.rendering.nvdiffrast_jax.jax_renderer import Renderer as JaxRenderer jax_renderer = JaxRenderer(intrinsics) +meshes = [] + + +path = os.path.join(b.utils.get_assets_dir(), "sample_objs/cube.obj") +mesh = trimesh.load(path) +mesh.vertices = mesh.vertices * jnp.array([1.0, 1.0, 1.0]) * 0.7 +meshes.append(mesh) + path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") -mesh =trimesh.load(path) -mesh.vertices = mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) -vertices = mesh.vertices +bunny_mesh = trimesh.load(path) +bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) +meshes.append(bunny_mesh) + + +all_vertices = [jnp.array(mesh.vertices) for mesh in meshes] +all_faces = [jnp.array(mesh.faces) for mesh in meshes] +vertices_lens = jnp.array([len(verts) for verts in all_vertices]) +vertices_lens_cumsum = jnp.pad(jnp.cumsum(vertices_lens),(1,0)) +faces_lens = jnp.array([len(faces) for faces in all_faces]) +faces_lens_cumsum = jnp.pad(jnp.cumsum(faces_lens),(1,0)) + +vertices = jnp.concatenate(all_vertices, axis=0) vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) -faces = jnp.array(mesh.faces) -ranges = jnp.array([[0, faces.shape[0]], [0, faces.shape[0]]]) +faces = jnp.concatenate([faces + vertices_lens_cumsum[i] for (i,faces) in enumerate(all_faces)]) + +object_indices = jnp.array([0, 1]) +ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) -poses =jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*6000) -poses = poses.at[:, 1,3].set(jnp.linspace(-1.9, 0.5, len(poses))) -poses2 = poses.at[:, 1,3].set(jnp.linspace(-1.0, 1.5, len(poses))) + +poses = jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*1000) +poses = poses.at[:, 1,3].set(jnp.linspace(-0.2, 0.5, len(poses))) +poses2 = poses.at[:, 1,3].set(jnp.linspace(-0.0, 1.5, len(poses))) +poses2 = poses2.at[:, 0,3].set(-0.5) poses = jnp.stack([poses, poses2], axis=1) parallel_render, _ = jax_renderer.rasterize( @@ -55,69 +77,146 @@ projection_matrix, jnp.array([intrinsics.height, intrinsics.width]), ) +uvs = parallel_render[...,:2] +triangle_ids = jnp.rint(parallel_render[...,3]).astype(jnp.int32) +object_ids = jnp.rint(parallel_render[...,2]).astype(jnp.int32) +mask = parallel_render[...,2] > 0 -images = [b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False) for i in [0, int(len(poses)/2), len(poses)-1]] -b.hstack_images( - images -).save("sweep.png") -images = [b.get_depth_image((parallel_render[i,...,2]) *1.0, remove_max=False) for i in [0, int(len(poses)/2), len(poses)-1]] -b.hstack_images( - images -).save("sweep.png") -test_indices = jax.random.randint(jax.random.PRNGKey(0), (100,), 0, len(poses)) -for i in test_indices: - individual, rast_out_db = jax_renderer.rasterize( - poses[i:i+1], - vertices, - faces, - ranges, - projection_matrix, - jnp.array([intrinsics.height, intrinsics.width]), +server.reset_scene() +T=0 +for i in range(len(object_indices)): + server.add_mesh_trimesh( + f"mesh/{i}", + mesh=meshes[object_indices[i]], + position=poses[T, i][:3,3], + wxyz=b.rotation_matrix_to_quaternion(poses[T, i][:3,:3]), ) - assert jnp.allclose(parallel_render[i], individual[0]), f"Failed at {i}" - - -uvs = parallel_render[...,:2] -triangle_ids = parallel_render[...,3:4].astype(jnp.int32) -mask = parallel_render[...,2] > 0 - import functools @functools.partial( jnp.vectorize, - signature="(2),(1),(4,4)->(3)", + signature="(2),(),(m,4,4),()->(3)", excluded=( - 3, 4, + 5, + 6, ), ) -def interpolate_(uv, triangle_id, pose, vertices, faces): - u,v = uv - relevant_vertices = vertices[faces[triangle_id-1][0]] - relevant_vertices_transformed = relevant_vertices @ pose.T +def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): + relevant_vertices = vertices[faces[triangle_id-1]] + pose_of_object = poses[object_id-1] + relevant_vertices_transformed = relevant_vertices @ pose_of_object.T barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) return interpolated_value -interpolated_values = interpolate_(uvs, triangle_ids, poses[...,0,None, None,:,:], vertices, faces) +interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) image = interpolated_values * mask[...,None] - -T = 0 -points_transformed = b.apply_transform(vertices[:,:3], poses[T,0]) -server.add_point_cloud( - "bunny", - points=np.array(points_transformed)[:,:3], - colors=np.zeros_like(points_transformed)[:,:3], - point_size=0.005 -) server.add_point_cloud( "image", points=np.array(image[T]).reshape(-1,3), colors=np.array([1.0, 0.0, 0.0]), - point_size=0.005 + point_size=0.01 ) + +images = [] +images2 = [] +for i in [0, int(len(poses)/2), len(poses)-1]: + images.append( + b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False) + ) + images2.append( + b.get_depth_image((parallel_render[i,...,2]) *1.0, remove_max=False) + ) +b.vstack_images( + [ + b.hstack_images(images), + b.hstack_images(images2), + ] +).save("sweep2.png") +print(triangle_ids.min(), triangle_ids.max()) + + + +test_indices = jax.random.randint(jax.random.PRNGKey(0), (100,), 0, len(poses)) +for i in test_indices: + individual, rast_out_db = jax_renderer.rasterize( + poses[i:i+1], + vertices, + faces, + ranges, + projection_matrix, + jnp.array([intrinsics.height, intrinsics.width]), + ) + assert jnp.allclose(parallel_render[i], individual[0]), f"Failed at {i}" + + + +# server.reset_scene() +# server.add_point_cloud( +# "image", +# points=np.array( +# image[T] +# ).reshape(-1,3), +# colors=np.array([1.0, 0.0, 0.0]), +# point_size=0.01 +# ) + + +# T = 0 +# b.get_depth_image((image[T,...,2]) *1.0).save("sweep.png") + + + +# server.add_point_cloud( +# "image", +# points=np.array(image[T]).reshape(-1,3), +# colors=np.array([1.0, 0.0, 0.0]), +# point_size=0.01 +# ) + + +# T = 0 +# for i in range(intrinsics.height): +# for j in range(intrinsics.width): +# object_id = object_ids[T, i, j] +# triangle_id = triangle_ids[T, i, j] +# uv = uvs[T, i, j] + +# pose_of_object = poses[T,object_id-1] +# relevant_vertices = vertices[faces[triangle_id-1]] +# relevant_vertices_transformed = relevant_vertices @ pose_of_object.T + +# barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) +# interpolated_value = (barycentric.reshape(1,3) @ relevant_vertices_transformed[:,:3]) +# if object_id > 0: +# assert jnp.allclose(interpolated_value, interpolated_values[T, i, j]), f"Failed at {i}, {j}" + + + + +# T = 0 + + +# server.reset_scene() +# for i in range(len(object_indices)): +# server.add_mesh_trimesh( +# f"mesh/{i}", +# mesh=meshes[object_indices[i]], +# position=poses[T, i][:3,3], +# wxyz=b.rotation_matrix_to_quaternion(poses[T, i][:3,:3]), +# ) + +# server.add_point_cloud( +# "image", +# points=np.array(image[T]).reshape(-1,3), +# colors=np.array([1.0, 0.0, 0.0]), +# point_size=0.01 +# ) + + from IPython import embed; embed() From c80b897b53e946d00cfc706dc78f1136821c810f Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Fri, 16 Feb 2024 22:24:25 +0000 Subject: [PATCH 15/27] Intergers output --- .../rendering/nvdiffrast_jax/jax_renderer.py | 10 +- .../nvdiffrast_jax/nvdiffrast/common/glutil.h | 2 + .../nvdiffrast/common/rasterize_gl.cpp | 234 ++++++++++-------- .../nvdiffrast/jax/jax_bindings.cpp | 12 +- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 154 +++++------- .../nvdiffrast/jax/jax_rasterize_gl.h | 12 +- test_jax_renderer.py | 118 +++++---- 7 files changed, 278 insertions(+), 264 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py index ec631a02..26773040 100644 --- a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py @@ -207,13 +207,14 @@ def _rasterize_fwd_abstract(pose, pos, tri, ranges, projection_matrix, resolutio num_images = pose.shape[0] dtype = dtypes.canonicalize_dtype(pose.dtype) + int_dtype = dtypes.canonicalize_dtype(np.int32) return [ ShapedArray( (num_images, r.intrinsics.height, r.intrinsics.width, 4), dtype ), ShapedArray( - (num_images, r.intrinsics.height, r.intrinsics.width, 4), dtype + (num_images, r.intrinsics.height, r.intrinsics.width, 4), int_dtype ), ] @@ -254,7 +255,10 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, ranges, projection_matrix, res [num_images, r.intrinsics.height, r.intrinsics.width, 4], mlir.dtype_to_ir_type(np_dtype), ) - + out_shp_dtype_int = mlir.ir.RankedTensorType.get( + [num_images, r.intrinsics.height, r.intrinsics.width, 4], + mlir.dtype_to_ir_type(np.dtype(np.int32)), + ) opaque = dr._get_plugin(gl=True).build_diff_rasterize_fwd_descriptor( r.renderer_env.cpp_wrapper, [num_images, num_objects, num_vertices, num_triangles] ) @@ -264,7 +268,7 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, ranges, projection_matrix, res return custom_call( op_name, # Output types - result_types=[out_shp_dtype, out_shp_dtype], + result_types=[out_shp_dtype, out_shp_dtype_int], # The inputs: operands=[poses, pos, tri, ranges, projection_matrix, resolution], backend_config=opaque, diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/glutil.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/glutil.h index 19e12b21..45043509 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/glutil.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/glutil.h @@ -77,6 +77,8 @@ struct GLContext #define GL_MINOR_VERSION 0x821C #define GL_RGBA32F 0x8814 #define GL_R32F 0x822E +#define GL_RGBA32UI 0x8D70 +#define GL_RGBA_INTEGER 0x8D99 #define GL_TEXTURE_2D_ARRAY 0x8C1A #endif #ifndef GL_VERSION_3_2 diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index a7a854cc..1c2b7b54 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -130,10 +130,10 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId STRINGIFY_SHADER_SOURCE( layout(location = 0) in vec4 in_pos; layout(location = 3) uniform mat4 projection_matrix; - layout(location = 5) uniform float seg_id; + layout(location = 5) uniform int seg_id; out int v_layer; out int v_offset; - out float seg_id_out; + flat out int seg_id_out; uniform sampler2D texture; void main() { @@ -167,10 +167,10 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId layout(location = 0) uniform vec2 vp_scale; in int v_layer[]; in int v_offset[]; - in float seg_id_out[]; + flat in int seg_id_out[]; out vec4 var_uvzw; out vec4 var_db; - out float seg_id; + flat out int seg_id; void main() { // Plane equations for bary differentials. @@ -222,16 +222,16 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId STRINGIFY_SHADER_SOURCE( in vec4 var_uvzw; in vec4 var_db; - in float seg_id; + flat in int seg_id; layout(location = 0) out vec4 out_raster; - layout(location = 1) out vec4 out_db; + layout(location = 1) out ivec4 out_db; IF_ZMODIFY( layout(location = 1) uniform float in_dummy; ) void main() - { - out_raster = vec4(var_uvzw.x, var_uvzw.y, seg_id, float(gl_PrimitiveID + 1)); - out_db = var_db * var_uvzw.w; + { + out_raster = vec4(var_uvzw.x, var_uvzw.y, var_uvzw.z / var_uvzw.w, float(gl_PrimitiveID + 1)); + out_db = ivec4(seg_id, gl_PrimitiveID + 1, 0.0, 0.0); IF_ZMODIFY(gl_FragDepth = gl_FragCoord.z + in_dummy;) } ) @@ -262,72 +262,72 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId ) ); } - else - { - // Geometry shader without bary differential output. - compileGLShader(NVDR_CTX_PARAMS, s, &s.glGeometryShader, GL_GEOMETRY_SHADER, - "#version 330\n" - STRINGIFY_SHADER_SOURCE( - layout(triangles) in; - layout(triangle_strip, max_vertices=3) out; - in int v_layer[]; - in int v_offset[]; - out vec4 var_uvzw; - void main() - { - int layer_id = v_layer[0]; - int prim_id = gl_PrimitiveIDIn + v_offset[0]; - - gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[0].gl_Position.x, gl_in[0].gl_Position.y, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); var_uvzw = vec4(1.f, 0.f, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); EmitVertex(); - gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[1].gl_Position.x, gl_in[1].gl_Position.y, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); var_uvzw = vec4(0.f, 1.f, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); EmitVertex(); - gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[2].gl_Position.x, gl_in[2].gl_Position.y, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); var_uvzw = vec4(0.f, 0.f, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); EmitVertex(); - } - ) - ); - - // Fragment shader without bary differential output. - compileGLShader(NVDR_CTX_PARAMS, s, &s.glFragmentShader, GL_FRAGMENT_SHADER, - "#version 430\n" - STRINGIFY_SHADER_SOURCE( - in vec4 var_uvzw; - layout(location = 0) out vec4 out_raster; - IF_ZMODIFY( - layout(location = 1) uniform float in_dummy; - ) - void main() - { - out_raster = vec4(var_uvzw.x, var_uvzw.y, var_uvzw.z / var_uvzw.w, float(gl_PrimitiveID + 1)); - IF_ZMODIFY(gl_FragDepth = gl_FragCoord.z + in_dummy;) - } - ) - ); - - // Depth peeling variant of fragment shader. - compileGLShader(NVDR_CTX_PARAMS, s, &s.glFragmentShaderDP, GL_FRAGMENT_SHADER, - "#version 430\n" - STRINGIFY_SHADER_SOURCE( - in vec4 var_uvzw; - layout(binding = 0) uniform sampler2DArray out_prev; - layout(location = 0) out vec4 out_raster; - IF_ZMODIFY( - layout(location = 1) uniform float in_dummy; - ) - void main() - { - vec4 prev = texelFetch(out_prev, ivec3(gl_FragCoord.x, gl_FragCoord.y, gl_Layer), 0); - float depth_new = var_uvzw.z / var_uvzw.w; - if (prev.w == 0 || depth_new <= prev.z) - discard; - out_raster = vec4(var_uvzw.x, var_uvzw.y, var_uvzw.z / var_uvzw.w, float(gl_PrimitiveID + 1)); - IF_ZMODIFY(gl_FragDepth = gl_FragCoord.z + in_dummy;) - } - ) - ); - } + // else + // { + // // Geometry shader without bary differential output. + // compileGLShader(NVDR_CTX_PARAMS, s, &s.glGeometryShader, GL_GEOMETRY_SHADER, + // "#version 330\n" + // STRINGIFY_SHADER_SOURCE( + // layout(triangles) in; + // layout(triangle_strip, max_vertices=3) out; + // in int v_layer[]; + // in int v_offset[]; + // out vec4 var_uvzw; + // void main() + // { + // int layer_id = v_layer[0]; + // int prim_id = gl_PrimitiveIDIn + v_offset[0]; + + // gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[0].gl_Position.x, gl_in[0].gl_Position.y, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); var_uvzw = vec4(1.f, 0.f, gl_in[0].gl_Position.z, gl_in[0].gl_Position.w); EmitVertex(); + // gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[1].gl_Position.x, gl_in[1].gl_Position.y, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); var_uvzw = vec4(0.f, 1.f, gl_in[1].gl_Position.z, gl_in[1].gl_Position.w); EmitVertex(); + // gl_Layer = layer_id; gl_PrimitiveID = prim_id; gl_Position = vec4(gl_in[2].gl_Position.x, gl_in[2].gl_Position.y, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); var_uvzw = vec4(0.f, 0.f, gl_in[2].gl_Position.z, gl_in[2].gl_Position.w); EmitVertex(); + // } + // ) + // ); + + // // Fragment shader without bary differential output. + // compileGLShader(NVDR_CTX_PARAMS, s, &s.glFragmentShader, GL_FRAGMENT_SHADER, + // "#version 430\n" + // STRINGIFY_SHADER_SOURCE( + // in vec4 var_uvzw; + // layout(location = 0) out vec4 out_raster; + // IF_ZMODIFY( + // layout(location = 1) uniform float in_dummy; + // ) + // void main() + // { + // out_raster = vec4(var_uvzw.x, var_uvzw.y, var_uvzw.z / var_uvzw.w, float(gl_PrimitiveID + 1)); + // IF_ZMODIFY(gl_FragDepth = gl_FragCoord.z + in_dummy;) + // } + // ) + // ); + + // // Depth peeling variant of fragment shader. + // compileGLShader(NVDR_CTX_PARAMS, s, &s.glFragmentShaderDP, GL_FRAGMENT_SHADER, + // "#version 430\n" + // STRINGIFY_SHADER_SOURCE( + // in vec4 var_uvzw; + // layout(binding = 0) uniform sampler2DArray out_prev; + // layout(location = 0) out vec4 out_raster; + // IF_ZMODIFY( + // layout(location = 1) uniform float in_dummy; + // ) + // void main() + // { + // vec4 prev = texelFetch(out_prev, ivec3(gl_FragCoord.x, gl_FragCoord.y, gl_Layer), 0); + // float depth_new = var_uvzw.z / var_uvzw.w; + // if (prev.w == 0 || depth_new <= prev.z) + // discard; + // out_raster = vec4(var_uvzw.x, var_uvzw.y, var_uvzw.z / var_uvzw.w, float(gl_PrimitiveID + 1)); + // IF_ZMODIFY(gl_FragDepth = gl_FragCoord.z + in_dummy;) + // } + // ) + // ); + // } // Finalize programs. constructGLProgram(NVDR_CTX_PARAMS, &s.glProgram, s.glVertexShader, s.glGeometryShader, s.glFragmentShader); - constructGLProgram(NVDR_CTX_PARAMS, &s.glProgramDP, s.glVertexShader, s.glGeometryShader, s.glFragmentShaderDP); + // constructGLProgram(NVDR_CTX_PARAMS, &s.glProgramDP, s.glVertexShader, s.glGeometryShader, s.glFragmentShaderDP); // Construct main fbo and bind permanently. NVDR_CHECK_GL_ERROR(glGenFramebuffers(1, &s.glFBO)); @@ -431,16 +431,20 @@ void rasterizeResizeBuffers(NVDR_CTX_ARGS, RasterizeGLState& s, bool& changes, i s.height = ROUND_UP(s.height, 32); LOG(INFO) << "Increasing frame buffer size to (width, height, depth) = (" << s.width << ", " << s.height << ", " << s.depth << ")"; - // Allocate color buffers. - for (int i=0; i < num_outputs; i++) - { - NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); - NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, NUM_LAYERS, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); - NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)); - } + int i =0; + NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); + NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, NUM_LAYERS, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)); + i =1; + NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); + NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32UI, s.width, s.height, NUM_LAYERS, 0, GL_RGBA_INTEGER, GL_UNSIGNED_BYTE, 0)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)); // Allocate depth/stencil buffer. NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glDepthStencilBuffer)); @@ -573,16 +577,16 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl int poses_on_this_iter = std::min(depth-start_pose_idx, NUM_LAYERS); NVDR_CHECK_GL_ERROR(glViewport(0, 0, width, height)); NVDR_CHECK_GL_ERROR(glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT)); - std::cout << "start_pose_idx " << start_pose_idx << std::endl; + // std::cout << "start_pose_idx " << start_pose_idx << std::endl; for(int object_idx=0; object_idx < num_objects; object_idx++) { - std::cout << "object_idx " << object_idx << std::endl; + // std::cout << "object_idx " << object_idx << std::endl; for (int i=0; i < poses_on_this_iter; i++) { int first = rangesPtr[2*object_idx]; int count = rangesPtr[2*object_idx+1]; - std::cout << "first " << first << std::endl; - std::cout << "count " << count << std::endl; + // std::cout << "first " << first << std::endl; + // std::cout << "count " << count << std::endl; GLDrawCmd& cmd = drawCmdBuffer[i]; cmd.firstIndex = first * 3; cmd.count = count * 3; @@ -597,7 +601,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl pose_array, 0, 0, posePtr + depth * 16 * object_idx + start_pose_idx * 16, poses_on_this_iter * 16 * sizeof(float), cudaMemcpyDeviceToDevice, stream)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); - glUniform1f(5, object_idx+1.0); + glUniform1i(5, object_idx+1); NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], poses_on_this_iter, sizeof(GLDrawCmd))); } @@ -608,27 +612,43 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl cudaExtent arrayExt = {}; // For error checking. int num_outputs = s.enableDB ? 2 : 1; NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(num_outputs, s.cudaColorBuffer, stream)); - for (int i=0; i < num_outputs; i++) - { - NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[i], 0, 0)); - NVDR_CHECK_CUDA_ERROR(cudaArrayGetInfo(&arrayDesc, &arrayExt, NULL, array)); - NVDR_CHECK(arrayDesc.f == cudaChannelFormatKindFloat, "CUDA mapped array data kind mismatch"); - NVDR_CHECK(arrayDesc.x == 32 && arrayDesc.y == 32 && arrayDesc.z == 32 && arrayDesc.w == 32, "CUDA mapped array data width mismatch"); - // NVDR_CHECK(arrayExt.width >= width && arrayExt.height >= height && arrayExt.depth >= depth, "CUDA mapped array extent mismatch"); - cudaMemcpy3DParms p = {0}; - p.srcArray = array; - p.dstPtr.ptr = ((float * ) outputPtr[i]) + start_pose_idx * width * height * 4;; - p.dstPtr.pitch = width * 4 * sizeof(float); - p.dstPtr.xsize = width; - p.dstPtr.ysize = height; - p.extent.width = width; - p.extent.height = height; - p.extent.depth = poses_on_this_iter; - p.kind = cudaMemcpyDeviceToDevice; - NVDR_CHECK_CUDA_ERROR(cudaMemcpy3DAsync(&p, stream)); - } + int i = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[i], 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaArrayGetInfo(&arrayDesc, &arrayExt, NULL, array)); + NVDR_CHECK(arrayDesc.f == cudaChannelFormatKindFloat, "CUDA mapped array data kind mismatch"); + NVDR_CHECK(arrayDesc.x == 32 && arrayDesc.y == 32 && arrayDesc.z == 32 && arrayDesc.w == 32, "CUDA mapped array data width mismatch"); + // NVDR_CHECK(arrayExt.width >= width && arrayExt.height >= height && arrayExt.depth >= depth, "CUDA mapped array extent mismatch"); + cudaMemcpy3DParms p = {0}; + p.srcArray = array; + p.dstPtr.ptr = ((float * ) outputPtr[i]) + start_pose_idx * width * height * 4;; + p.dstPtr.pitch = width * 4 * sizeof(float); + p.dstPtr.xsize = width; + p.dstPtr.ysize = height; + p.extent.width = width; + p.extent.height = height; + p.extent.depth = poses_on_this_iter; + p.kind = cudaMemcpyDeviceToDevice; + NVDR_CHECK_CUDA_ERROR(cudaMemcpy3DAsync(&p, stream)); + + i = 1; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[i], 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaArrayGetInfo(&arrayDesc, &arrayExt, NULL, array)); + // NVDR_CHECK(arrayDesc.f == cudaChannelFormatKindFloat, "CUDA mapped array data kind mismatch"); + // NVDR_CHECK(arrayDesc.x == 32 && arrayDesc.y == 32 && arrayDesc.z == 32 && arrayDesc.w == 32, "CUDA mapped array data width mismatch"); + // NVDR_CHECK(arrayExt.width >= width && arrayExt.height >= height && arrayExt.depth >= depth, "CUDA mapped array extent mismatch"); + p = {0}; + p.srcArray = array; + p.dstPtr.ptr = ((int * ) outputPtr[i]) + start_pose_idx * width * height * 4;; + p.dstPtr.pitch = width * 4 * sizeof(int); + p.dstPtr.xsize = width; + p.dstPtr.ysize = height; + p.extent.width = width; + p.extent.height = height; + p.extent.depth = poses_on_this_iter; + p.kind = cudaMemcpyDeviceToDevice; + NVDR_CHECK_CUDA_ERROR(cudaMemcpy3DAsync(&p, stream)); NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(num_outputs, s.cudaColorBuffer, stream)); - + } // } diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp index 0b5bfb1f..423af4f9 100755 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp @@ -57,12 +57,12 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("build_diff_rasterize_bwd_descriptor", [](std::vector pos_shape, std::vector tri_shape, std::vector rast_shape) { DiffRasterizeBwdCustomCallDescriptor d; - d.num_images = pos_shape[0]; - d.num_vertices = pos_shape[1]; - d.num_triangles = tri_shape[0]; - d.rast_height = rast_shape[1]; - d.rast_width = rast_shape[2]; - d.rast_depth = rast_shape[0]; + // d.num_images = pos_shape[0]; + // d.num_vertices = pos_shape[1]; + // d.num_triangles = tri_shape[0]; + // d.rast_height = rast_shape[1]; + // d.rast_width = rast_shape[2]; + // d.rast_depth = rast_shape[0]; return PackDescriptor(d); }); } diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index 73b2b9af..b88b905b 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -173,104 +173,80 @@ void RasterizeGradKernel(const RasterizeGradParams p); void RasterizeGradKernelDb(const RasterizeGradParams p); //------------------------------------------------------------------------ -void _rasterize_grad_db(cudaStream_t stream, - const float* pos, const int* tri, const float* rast_out, - const float* dy, const float* ddb, - std::vector pos_shape, - std::vector tri_shape, - std::vector rast_out_shape, - float* grad -) -{ - RasterizeGradParams p; - bool enable_db = true; - - // Determine instance mode. - p.instance_mode = 1; - NVDR_CHECK(p.instance_mode == 1, "Should be in instance mode; check input sizes"); - - // Shape is taken from the rasterizer output tensor. - p.depth = rast_out_shape[0]; - p.height = rast_out_shape[1]; - p.width = rast_out_shape[2]; - NVDR_CHECK(p.depth > 0 && p.height > 0 && p.width > 0, "resolution must be [>0, >0, >0]"); - - // Populate parameters. - p.numTriangles = tri_shape[0]; - p.numVertices = p.instance_mode ? pos_shape[1] : pos_shape[0]; - p.pos = pos; - p.tri = tri; - p.out = rast_out; - p.dy = dy; - p.ddb = enable_db ? ddb : NULL; - - // Set up pixel position to clip space x, y transform. - p.xs = 2.f / (float)p.width; - p.xo = 1.f / (float)p.width - 1.f; - p.ys = 2.f / (float)p.height; - p.yo = 1.f / (float)p.height - 1.f; - - // Output tensor for position gradients. - p.grad = grad; - - // Verify that buffers are aligned to allow float2/float4 operations. - NVDR_CHECK(!((uintptr_t)p.pos & 15), "pos input tensor not aligned to float4"); - NVDR_CHECK(!((uintptr_t)p.dy & 7), "dy input tensor not aligned to float2"); - NVDR_CHECK(!((uintptr_t)p.ddb & 15), "ddb input tensor not aligned to float4"); - - // Choose launch parameters. - dim3 blockSize = getLaunchBlockSize(RAST_GRAD_MAX_KERNEL_BLOCK_WIDTH, RAST_GRAD_MAX_KERNEL_BLOCK_HEIGHT, p.width, p.height); - dim3 gridSize = getLaunchGridSize(blockSize, p.width, p.height, p.depth); - - // Launch CUDA kernel to populate gradient values. - void* args[] = {&p}; - void* func = enable_db ? (void*)RasterizeGradKernelDb : (void*)RasterizeGradKernel; - NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); -} void jax_rasterize_bwd(cudaStream_t stream, void **buffers, const char *opaque, std::size_t opaque_len) { - const DiffRasterizeBwdCustomCallDescriptor &d = - *UnpackDescriptor(opaque, opaque_len); + // const DiffRasterizeBwdCustomCallDescriptor &d = + // *UnpackDescriptor(opaque, opaque_len); - const float *pos = reinterpret_cast (buffers[0]); - const int *tri = reinterpret_cast (buffers[1]); - const float *rast_out = reinterpret_cast (buffers[2]); - const float *dy = reinterpret_cast (buffers[3]); - const float *ddb = reinterpret_cast (buffers[4]); + // const float *pose = reinterpret_cast (buffers[0]); + // const float *pos = reinterpret_cast (buffers[1]); + // const int *tri = reinterpret_cast (buffers[2]); + // const int *_ranges = reinterpret_cast (buffers[3]); + // const float *projectionMatrix = reinterpret_cast (buffers[4]); + // const int *_resolution = reinterpret_cast (buffers[5]); + + // float *out = reinterpret_cast (buffers[6]); + // float *out_db = reinterpret_cast (buffers[7]); - float *grad = reinterpret_cast (buffers[5]); // output - cudaMemset(grad, 0, d.num_images*d.num_vertices*4*sizeof(float)); + // const float *dy = reinterpret_cast (buffers[8]); + // const float *ddb = reinterpret_cast (buffers[9]); - auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); + // float *grad = reinterpret_cast (buffers[10]); // output + // cudaMemset(grad, 0, d.num_images*d.num_vertices*4*sizeof(float)); + + // auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); - std::vector pos_shape; - pos_shape.resize(2); - std::vector tri_shape; - tri_shape.resize(1); - std::vector rast_out_shape; - rast_out_shape.resize(3); + // cudaStreamSynchronize(stream); - pos_shape[0] = d.num_images; - pos_shape[1] = d.num_vertices; - tri_shape[0] = d.num_triangles; - rast_out_shape[0] = d.rast_depth; - rast_out_shape[1] = d.rast_height; - rast_out_shape[2] = d.rast_width; + // RasterizeGradParams p; + // bool enable_db = true; + + // // Determine instance mode. + // p.instance_mode = 1; + // NVDR_CHECK(p.instance_mode == 1, "Should be in instance mode; check input sizes"); + + // // Shape is taken from the rasterizer output tensor. + // p.depth = d.num_images; + // p.height = d.height; + // p.width = d.width + // NVDR_CHECK(p.depth > 0 && p.height > 0 && p.width > 0, "resolution must be [>0, >0, >0]"); + + // // Populate parameters. + // p.numTriangles = d.num_triangles + // p.numVertices = d.num_vertices; + // p.pose = pose; + // p.pos = pos; + // p.tri = tri; + // p.out = rast_out; + // p.dy = dy; + // p.ddb = enable_db ? ddb : NULL; + + // // Set up pixel position to clip space x, y transform. + // p.xs = 2.f / (float)p.width; + // p.xo = 1.f / (float)p.width - 1.f; + // p.ys = 2.f / (float)p.height; + // p.yo = 1.f / (float)p.height - 1.f; + + // // Output tensor for position gradients. + // p.grad = grad; + + // // Verify that buffers are aligned to allow float2/float4 operations. + // NVDR_CHECK(!((uintptr_t)p.pos & 15), "pos input tensor not aligned to float4"); + // NVDR_CHECK(!((uintptr_t)p.dy & 7), "dy input tensor not aligned to float2"); + // NVDR_CHECK(!((uintptr_t)p.ddb & 15), "ddb input tensor not aligned to float4"); + + // // Choose launch parameters. + // dim3 blockSize = getLaunchBlockSize(RAST_GRAD_MAX_KERNEL_BLOCK_WIDTH, RAST_GRAD_MAX_KERNEL_BLOCK_HEIGHT, p.width, p.height); + // dim3 gridSize = getLaunchGridSize(blockSize, p.width, p.height, p.depth); + + // // Launch CUDA kernel to populate gradient values. + // void* args[] = {&p}; + // enable_db = false; + // void* func = enable_db ? (void*)RasterizeGradKernelDb : (void*)RasterizeGradKernel; + // NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); - cudaStreamSynchronize(stream); - _rasterize_grad_db(stream, - pos, - tri, - rast_out, - dy, - ddb, - pos_shape, - tri_shape, - rast_out_shape, - grad - ); - cudaStreamSynchronize(stream); + // cudaStreamSynchronize(stream); } diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h index 1a648b2b..524a4946 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.h @@ -30,12 +30,12 @@ struct DiffRasterizeCustomCallDescriptor { }; struct DiffRasterizeBwdCustomCallDescriptor { - int num_images; // pos[0] - int num_vertices; // pos[1] - int num_triangles; // tri[0] - int rast_height; // rast[1] - int rast_width; // rast[2] - int rast_depth; // rast[0] + int num_images; + int num_objects; + int num_vertices; + int num_triangles; + int height; + int width; }; //------------------------------------------------------------------------ diff --git a/test_jax_renderer.py b/test_jax_renderer.py index c8d7cd55..0443efeb 100644 --- a/test_jax_renderer.py +++ b/test_jax_renderer.py @@ -34,13 +34,10 @@ jax_renderer = JaxRenderer(intrinsics) meshes = [] - - path = os.path.join(b.utils.get_assets_dir(), "sample_objs/cube.obj") mesh = trimesh.load(path) mesh.vertices = mesh.vertices * jnp.array([1.0, 1.0, 1.0]) * 0.7 meshes.append(mesh) - path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") bunny_mesh = trimesh.load(path) bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) @@ -58,18 +55,17 @@ vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = jnp.concatenate([faces + vertices_lens_cumsum[i] for (i,faces) in enumerate(all_faces)]) - object_indices = jnp.array([0, 1]) ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) -poses = jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*1000) +poses = jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*5) poses = poses.at[:, 1,3].set(jnp.linspace(-0.2, 0.5, len(poses))) poses2 = poses.at[:, 1,3].set(jnp.linspace(-0.0, 1.5, len(poses))) poses2 = poses2.at[:, 0,3].set(-0.5) poses = jnp.stack([poses, poses2], axis=1) -parallel_render, _ = jax_renderer.rasterize( +parallel_render, parallel_render_2 = jax_renderer.rasterize( poses, vertices, faces, @@ -78,59 +74,18 @@ jnp.array([intrinsics.height, intrinsics.width]), ) uvs = parallel_render[...,:2] -triangle_ids = jnp.rint(parallel_render[...,3]).astype(jnp.int32) -object_ids = jnp.rint(parallel_render[...,2]).astype(jnp.int32) +object_ids =parallel_render_2[...,0] +triangle_ids =parallel_render_2[...,1] mask = parallel_render[...,2] > 0 - - -server.reset_scene() -T=0 -for i in range(len(object_indices)): - server.add_mesh_trimesh( - f"mesh/{i}", - mesh=meshes[object_indices[i]], - position=poses[T, i][:3,3], - wxyz=b.rotation_matrix_to_quaternion(poses[T, i][:3,:3]), - ) - -import functools -@functools.partial( - jnp.vectorize, - signature="(2),(),(m,4,4),()->(3)", - excluded=( - 4, - 5, - 6, - ), -) -def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): - relevant_vertices = vertices[faces[triangle_id-1]] - pose_of_object = poses[object_id-1] - relevant_vertices_transformed = relevant_vertices @ pose_of_object.T - barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) - interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) - return interpolated_value - -interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) -image = interpolated_values * mask[...,None] -server.add_point_cloud( - "image", - points=np.array(image[T]).reshape(-1,3), - colors=np.array([1.0, 0.0, 0.0]), - point_size=0.01 -) - - - images = [] images2 = [] for i in [0, int(len(poses)/2), len(poses)-1]: images.append( - b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False) + b.get_depth_image(object_ids[i] *1.0, remove_max=False) ) images2.append( - b.get_depth_image((parallel_render[i,...,2]) *1.0, remove_max=False) + b.get_depth_image(triangle_ids[i] *1.0, remove_max=False) ) b.vstack_images( [ @@ -138,8 +93,6 @@ def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): b.hstack_images(images2), ] ).save("sweep2.png") -print(triangle_ids.min(), triangle_ids.max()) - test_indices = jax.random.randint(jax.random.PRNGKey(0), (100,), 0, len(poses)) @@ -155,6 +108,65 @@ def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): assert jnp.allclose(parallel_render[i], individual[0]), f"Failed at {i}" +# server.reset_scene() +# T=0 +# for i in range(len(object_indices)): +# server.add_mesh_trimesh( +# f"mesh/{i}", +# mesh=meshes[object_indices[i]], +# position=poses[T, i][:3,3], +# wxyz=b.rotation_matrix_to_quaternion(poses[T, i][:3,:3]), +# ) + +# import functools +# @functools.partial( +# jnp.vectorize, +# signature="(2),(),(m,4,4),()->(3)", +# excluded=( +# 4, +# 5, +# 6, +# ), +# ) +# def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): +# relevant_vertices = vertices[faces[triangle_id-1]] +# pose_of_object = poses[object_id-1] +# relevant_vertices_transformed = relevant_vertices @ pose_of_object.T +# barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) +# interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) +# return interpolated_value + +# interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) +# image = interpolated_values * mask[...,None] +# server.add_point_cloud( +# "image", +# points=np.array(image[T]).reshape(-1,3), +# colors=np.array([1.0, 0.0, 0.0]), +# point_size=0.01 +# ) + +# image[T] + +# images = [] +# images2 = [] +# for i in [0, int(len(poses)/2), len(poses)-1]: +# images.append( +# b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False) +# ) +# images2.append( +# b.get_depth_image((parallel_render[i,...,2]) *1.0, remove_max=False) +# ) +# b.vstack_images( +# [ +# b.hstack_images(images), +# b.hstack_images(images2), +# ] +# ).save("sweep2.png") +# print(triangle_ids.min(), triangle_ids.max()) + + + + # server.reset_scene() # server.add_point_cloud( From 1e0aab412171c3fd54f446f452264d4d0437ccdf Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Sat, 17 Feb 2024 17:34:51 +0000 Subject: [PATCH 16/27] fix rendering --- .../nvdiffrast/common/rasterize_gl.cpp | 2 +- test_jax_renderer.py | 212 +++++------------- 2 files changed, 53 insertions(+), 161 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index 1c2b7b54..d6bb5493 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -591,7 +591,7 @@ void rasterizeRender(NVDR_CTX_ARGS, RasterizeGLState& s, cudaStream_t stream, fl cmd.firstIndex = first * 3; cmd.count = count * 3; cmd.baseVertex = 0; - cmd.baseInstance = 0; + cmd.baseInstance = first; cmd.instanceCount = 1; } diff --git a/test_jax_renderer.py b/test_jax_renderer.py index 0443efeb..b69050f3 100644 --- a/test_jax_renderer.py +++ b/test_jax_renderer.py @@ -34,10 +34,13 @@ jax_renderer = JaxRenderer(intrinsics) meshes = [] + + path = os.path.join(b.utils.get_assets_dir(), "sample_objs/cube.obj") mesh = trimesh.load(path) mesh.vertices = mesh.vertices * jnp.array([1.0, 1.0, 1.0]) * 0.7 meshes.append(mesh) + path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") bunny_mesh = trimesh.load(path) bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) @@ -55,6 +58,31 @@ vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) faces = jnp.concatenate([faces + vertices_lens_cumsum[i] for (i,faces) in enumerate(all_faces)]) + + +resolution = jnp.array([intrinsics.height, intrinsics.width]) + + + +import functools +@functools.partial( + jnp.vectorize, + signature="(2),(),(m,4,4),()->(3)", + excluded=( + 4, + 5, + 6, + ), +) +def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): + relevant_vertices = vertices[faces[triangle_id-1]] + pose_of_object = poses[object_id-1] + relevant_vertices_transformed = relevant_vertices @ pose_of_object.T + barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) + interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) + return interpolated_value + + object_indices = jnp.array([0, 1]) ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) @@ -65,170 +93,34 @@ poses2 = poses2.at[:, 0,3].set(-0.5) poses = jnp.stack([poses, poses2], axis=1) -parallel_render, parallel_render_2 = jax_renderer.rasterize( +rast_out, rast_out_aux = jax_renderer.rasterize( poses, vertices, faces, ranges, projection_matrix, - jnp.array([intrinsics.height, intrinsics.width]), + resolution +) +uvs = rast_out[...,:2] +object_ids = jnp.rint(rast_out_aux[...,0]).astype(jnp.int32) +triangle_ids = jnp.rint(rast_out_aux[...,1]).astype(jnp.int32) +mask = object_ids > 0 + +interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) +image = interpolated_values * mask[...,None] + +server.reset_scene() +server.add_point_cloud( + "image1", + points=np.array(image[0]).reshape(-1,3), + colors=np.array([1.0, 0.0, 0.0]), + point_size=0.01 +) +server.add_point_cloud( + "image2", + points=np.array(image[1]).reshape(-1,3), + colors=np.array([0.0, 0.0, 0.0]), + point_size=0.01 ) -uvs = parallel_render[...,:2] -object_ids =parallel_render_2[...,0] -triangle_ids =parallel_render_2[...,1] -mask = parallel_render[...,2] > 0 - -images = [] -images2 = [] -for i in [0, int(len(poses)/2), len(poses)-1]: - images.append( - b.get_depth_image(object_ids[i] *1.0, remove_max=False) - ) - images2.append( - b.get_depth_image(triangle_ids[i] *1.0, remove_max=False) - ) -b.vstack_images( - [ - b.hstack_images(images), - b.hstack_images(images2), - ] -).save("sweep2.png") - - -test_indices = jax.random.randint(jax.random.PRNGKey(0), (100,), 0, len(poses)) -for i in test_indices: - individual, rast_out_db = jax_renderer.rasterize( - poses[i:i+1], - vertices, - faces, - ranges, - projection_matrix, - jnp.array([intrinsics.height, intrinsics.width]), - ) - assert jnp.allclose(parallel_render[i], individual[0]), f"Failed at {i}" - - -# server.reset_scene() -# T=0 -# for i in range(len(object_indices)): -# server.add_mesh_trimesh( -# f"mesh/{i}", -# mesh=meshes[object_indices[i]], -# position=poses[T, i][:3,3], -# wxyz=b.rotation_matrix_to_quaternion(poses[T, i][:3,:3]), -# ) - -# import functools -# @functools.partial( -# jnp.vectorize, -# signature="(2),(),(m,4,4),()->(3)", -# excluded=( -# 4, -# 5, -# 6, -# ), -# ) -# def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): -# relevant_vertices = vertices[faces[triangle_id-1]] -# pose_of_object = poses[object_id-1] -# relevant_vertices_transformed = relevant_vertices @ pose_of_object.T -# barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) -# interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) -# return interpolated_value - -# interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) -# image = interpolated_values * mask[...,None] -# server.add_point_cloud( -# "image", -# points=np.array(image[T]).reshape(-1,3), -# colors=np.array([1.0, 0.0, 0.0]), -# point_size=0.01 -# ) - -# image[T] - -# images = [] -# images2 = [] -# for i in [0, int(len(poses)/2), len(poses)-1]: -# images.append( -# b.get_depth_image((parallel_render[i,...,3]) *1.0, remove_max=False) -# ) -# images2.append( -# b.get_depth_image((parallel_render[i,...,2]) *1.0, remove_max=False) -# ) -# b.vstack_images( -# [ -# b.hstack_images(images), -# b.hstack_images(images2), -# ] -# ).save("sweep2.png") -# print(triangle_ids.min(), triangle_ids.max()) - - - - - -# server.reset_scene() -# server.add_point_cloud( -# "image", -# points=np.array( -# image[T] -# ).reshape(-1,3), -# colors=np.array([1.0, 0.0, 0.0]), -# point_size=0.01 -# ) - - -# T = 0 -# b.get_depth_image((image[T,...,2]) *1.0).save("sweep.png") - - - -# server.add_point_cloud( -# "image", -# points=np.array(image[T]).reshape(-1,3), -# colors=np.array([1.0, 0.0, 0.0]), -# point_size=0.01 -# ) - - -# T = 0 -# for i in range(intrinsics.height): -# for j in range(intrinsics.width): -# object_id = object_ids[T, i, j] -# triangle_id = triangle_ids[T, i, j] -# uv = uvs[T, i, j] - -# pose_of_object = poses[T,object_id-1] -# relevant_vertices = vertices[faces[triangle_id-1]] -# relevant_vertices_transformed = relevant_vertices @ pose_of_object.T - -# barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) -# interpolated_value = (barycentric.reshape(1,3) @ relevant_vertices_transformed[:,:3]) -# if object_id > 0: -# assert jnp.allclose(interpolated_value, interpolated_values[T, i, j]), f"Failed at {i}, {j}" - - - - -# T = 0 - - -# server.reset_scene() -# for i in range(len(object_indices)): -# server.add_mesh_trimesh( -# f"mesh/{i}", -# mesh=meshes[object_indices[i]], -# position=poses[T, i][:3,3], -# wxyz=b.rotation_matrix_to_quaternion(poses[T, i][:3,:3]), -# ) - -# server.add_point_cloud( -# "image", -# points=np.array(image[T]).reshape(-1,3), -# colors=np.array([1.0, 0.0, 0.0]), -# point_size=0.01 -# ) - -from IPython import embed; embed() +from IPython import embed; embed() \ No newline at end of file From 34eba7cbbe51bf2b85331974a0d6d811e9ae8f5a Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Sat, 17 Feb 2024 17:38:43 +0000 Subject: [PATCH 17/27] fwd fixed --- ...ax_renderer.py => test_jax_renderer_fwd.py | 46 ++++++++++--------- 1 file changed, 25 insertions(+), 21 deletions(-) rename test_jax_renderer.py => test_jax_renderer_fwd.py (81%) diff --git a/test_jax_renderer.py b/test_jax_renderer_fwd.py similarity index 81% rename from test_jax_renderer.py rename to test_jax_renderer_fwd.py index b69050f3..955e0f8b 100644 --- a/test_jax_renderer.py +++ b/test_jax_renderer_fwd.py @@ -62,8 +62,6 @@ resolution = jnp.array([intrinsics.height, intrinsics.width]) - - import functools @functools.partial( jnp.vectorize, @@ -82,18 +80,36 @@ def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) return interpolated_value - -object_indices = jnp.array([0, 1]) +def render(poses, vertices, faces, ranges, projection_matrix, resolution): + rast_out, rast_out_aux = jax_renderer.rasterize( + poses, + vertices, + faces, + ranges, + projection_matrix, + resolution + ) + uvs = rast_out[...,:2] + object_ids = rast_out_aux[...,0] + triangle_ids = rast_out_aux[...,1] + mask = object_ids > 0 + + interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) + image = interpolated_values * mask[...,None] + return image + +render_jit = jax.jit(render) + +object_indices = jnp.array([1, 0]) ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) - -poses = jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*5) +poses = jnp.array([b.transform_from_pos(jnp.array([0.0, 0.0, 5.0]))]*100) poses = poses.at[:, 1,3].set(jnp.linspace(-0.2, 0.5, len(poses))) poses2 = poses.at[:, 1,3].set(jnp.linspace(-0.0, 1.5, len(poses))) poses2 = poses2.at[:, 0,3].set(-0.5) poses = jnp.stack([poses, poses2], axis=1) -rast_out, rast_out_aux = jax_renderer.rasterize( +image = render_jit( poses, vertices, faces, @@ -101,26 +117,14 @@ def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): projection_matrix, resolution ) -uvs = rast_out[...,:2] -object_ids = jnp.rint(rast_out_aux[...,0]).astype(jnp.int32) -triangle_ids = jnp.rint(rast_out_aux[...,1]).astype(jnp.int32) -mask = object_ids > 0 - -interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) -image = interpolated_values * mask[...,None] server.reset_scene() + server.add_point_cloud( "image1", - points=np.array(image[0]).reshape(-1,3), + points=np.array(image[10]).reshape(-1,3), colors=np.array([1.0, 0.0, 0.0]), point_size=0.01 ) -server.add_point_cloud( - "image2", - points=np.array(image[1]).reshape(-1,3), - colors=np.array([0.0, 0.0, 0.0]), - point_size=0.01 -) from IPython import embed; embed() \ No newline at end of file From be74febcab8616866f9a4f03a47dd6fb6ac77f2e Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Sat, 17 Feb 2024 18:47:50 +0000 Subject: [PATCH 18/27] save but bwd not working --- .../rendering/nvdiffrast_jax/jax_renderer.py | 685 +++++++++--------- .../nvdiffrast/common/rasterize.cu | 152 ++-- .../nvdiffrast/common/rasterize.h | 14 +- .../nvdiffrast/common/rasterize_gl.cpp | 1 + .../nvdiffrast/jax/jax_bindings.cpp | 23 +- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 146 ++-- test_jax_renderer_bwd.py | 163 +++++ 7 files changed, 690 insertions(+), 494 deletions(-) create mode 100644 test_jax_renderer_bwd.py diff --git a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py index 26773040..f73d17ab 100644 --- a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py @@ -39,15 +39,15 @@ def _rasterize(self, pose, pos, tri, ranges, projMatrix, resolution): def _rasterize_fwd(self, pose, pos, tri, ranges, projMatrix, resolution): rast_out, rast_out_db = _rasterize_fwd_custom_call(self, pose, pos, tri, ranges, projMatrix, resolution) - saved_tensors = (pose, pos, tri, rast_out) + saved_tensors = (pose, pos, tri, ranges, projMatrix, resolution, rast_out, rast_out_db) return (rast_out, rast_out_db), saved_tensors def _rasterize_bwd(self, saved_tensors, diffs): - pose, pos, tri, rast_out = saved_tensors + pose, pos, tri, ranges, projMatrix, resolution, rast_out, rast_out_db = saved_tensors dy, ddb = diffs - grads = _rasterize_bwd_custom_call(self, pos, tri, rast_out, dy, ddb) - return grads[0], None, None + grads = _rasterize_bwd_custom_call(self, pose, pos, tri, ranges, projMatrix, resolution, rast_out, rast_out_db, dy, ddb) + return grads[0], None, None, None, None, None _rasterize.defvjp(_rasterize_fwd, _rasterize_bwd) @@ -312,8 +312,8 @@ def _rasterize_fwd_lowering(ctx, poses, pos, tri, ranges, projection_matrix, res # @functools.partial(jax.jit, static_argnums=(0,)) -def _rasterize_bwd_custom_call(r: "Renderer", pos, tri, rast_out, dy, ddb): - return _build_rasterize_bwd_primitive(r).bind(pos, tri, rast_out, dy, ddb) +def _rasterize_bwd_custom_call(r: "Renderer", pose, pos, tri, ranges, projection_matrix, resolution, rast_out, rast_out2, dy, ddb): + return _build_rasterize_bwd_primitive(r).bind(pose, pos, tri, ranges, projection_matrix, resolution, rast_out, rast_out2, dy, ddb) @functools.lru_cache(maxsize=None) @@ -322,44 +322,42 @@ def _build_rasterize_bwd_primitive(r: "Renderer"): # For JIT compilation we need a function to evaluate the shape and dtype of the # outputs of our op for some given inputs - def _rasterize_bwd_abstract(pos, tri, rast_out, dy, ddb): - if len(pos.shape) != 3: - raise ValueError( - "Pass in a [num_images, num_vertices, 4] sized first input" - ) - out_shp = pos.shape - dtype = dtypes.canonicalize_dtype(pos.dtype) + def _rasterize_bwd_abstract(pose, pos, tri, ranges, projection_matrix, resolution, rast_out, rast_out2, dy, ddb): + # if len(pos.shape) != 3: + # raise ValueError( + # "Pass in a [num_images, num_vertices, 4] sized first input" + # ) + out_shp = pose.shape + dtype = dtypes.canonicalize_dtype(pose.dtype) return [ShapedArray(out_shp, dtype)] # Provide an MLIR "lowering" of the rasterize primitive. - def _rasterize_bwd_lowering(ctx, pos, tri, rast_out, dy, ddb): + def _rasterize_bwd_lowering(ctx, pose, pos, tri, ranges, projection_matrix, resolution, rast_out, rast_out2, dy, ddb): # Extract the numpy type of the inputs - pos_aval, tri_aval, rast_aval, dy_aval, ddb_aval = ctx.avals_in + ( + poses_aval, pos_aval, tri_aval, ranges_aval, + projection_matrix_aval, resolution_aval, rast_aval, + rast_aval2, dy_aval, ddb_aval + ) = ctx.avals_in - num_images, num_vertices = pos_aval.shape[:2] + num_images = poses_aval.shape[0] + num_objects = ranges_aval.shape[0] + assert num_objects == poses_aval.shape[1], f"Number of poses {poses_aval.shape[1]} should match number of objects {num_objects}" + num_vertices = pos_aval.shape[0] num_triangles = tri_aval.shape[0] depth, height, width = rast_aval.shape[:3] - if rast_aval.ndim != 4: - raise NotImplementedError( - f"Rasterization output should be 4D: got {rast_aval.shape}" - ) - if dy_aval.ndim != 4 or ddb_aval.ndim != 4: - raise NotImplementedError( - f"Grad outputs from rasterize should be 4D: got dy={dy_aval.shape} and ddb={ddb_aval.shape}" - ) - - np_dtype = np.dtype(rast_aval.dtype) - if np_dtype != np.float32: - raise NotImplementedError(f"Unsupported dtype {np_dtype}") - - out_shp_dtype = mlir.ir.RankedTensorType.get( - [num_images, num_vertices, 4], mlir.dtype_to_ir_type(np_dtype) - ) # gradients have same size as the positions + print("depth height width", depth, " ", height, " ", width) opaque = dr._get_plugin(gl=True).build_diff_rasterize_bwd_descriptor( - [num_images, num_vertices], [num_triangles], [depth, height, width] + [num_images, num_objects, num_vertices, num_triangles, height, width] + ) + + np_dtype = np.dtype(poses_aval.dtype) + out_shp_dtype = mlir.ir.RankedTensorType.get( + [num_images, num_objects, 4, 4], + mlir.dtype_to_ir_type(np_dtype), ) op_name = "jax_rasterize_bwd" @@ -369,22 +367,21 @@ def _rasterize_bwd_lowering(ctx, pos, tri, rast_out, dy, ddb): # Output types result_types=[out_shp_dtype], # The inputs: - operands=[pos, tri, rast_out, dy, ddb], + operands=[pose, pos, tri, ranges, projection_matrix, resolution, rast_out, rast_out2, dy, ddb], backend_config=opaque, operand_layouts=default_layouts( + poses_aval.shape, pos_aval.shape, tri_aval.shape, + ranges_aval.shape, + projection_matrix_aval.shape, + resolution_aval.shape, rast_aval.shape, + rast_aval2.shape, dy_aval.shape, ddb_aval.shape, ), - result_layouts=default_layouts( - ( - num_images, - num_vertices, - 4, - ) - ), + result_layouts=[(3,2,1,0)], ).results # ********************************************* @@ -408,306 +405,306 @@ def _rasterize_bwd_lowering(ctx, pos, tri, rast_out, dy, ddb): #### FORWARD #### -# @functools.partial(jax.jit, static_argnums=(0,)) -def _interpolate_fwd_custom_call( - r: "Renderer", attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs -): - return _build_interpolate_fwd_primitive(r).bind( - attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs - ) - - -# @functools.lru_cache(maxsize=None) -def _build_interpolate_fwd_primitive(r: "Renderer"): - _register_custom_calls() - # For JIT compilation we need a function to evaluate the shape and dtype of the - # outputs of our op for some given inputs - - def _interpolate_fwd_abstract( - attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs - ): - if len(attr.shape) != 3: - raise ValueError( - "Pass in a [num_images, num_vertices, num_attributes] sized first input" - ) - num_images, num_vertices, num_attributes = attr.shape - _, height, width, _ = rast_out.shape - num_tri, _ = tri.shape - num_diff_attrs = diff_attrs.shape[0] - - dtype = dtypes.canonicalize_dtype(attr.dtype) - - out_abstract = ShapedArray((num_images, height, width, num_attributes), dtype) - out_db_abstract = ShapedArray( - (num_images, height, width, 2 * num_diff_attrs), dtype - ) # empty tensor - return [out_abstract, out_db_abstract] - - # Provide an MLIR "lowering" of the interpolate primitive. - def _interpolate_fwd_lowering( - ctx, attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs - ): - # Extract the numpy type of the inputs - ( - attr_aval, - rast_out_aval, - tri_aval, - rast_db_aval, - _, - diff_attr_aval, - ) = ctx.avals_in - - if attr_aval.ndim != 3: - raise NotImplementedError( - f"Only 3D attribute inputs supported: got {attr_aval.shape}" - ) - if rast_out_aval.ndim != 4: - raise NotImplementedError( - f"Only 4D rast inputs supported: got {rast_out_aval.shape}" - ) - if tri_aval.ndim != 2: - raise NotImplementedError( - f"Only 2D triangle tensors supported: got {tri_aval.shape}" - ) - - np_dtype = np.dtype(attr_aval.dtype) - if np_dtype != np.float32: - raise NotImplementedError(f"Unsupported attributes dtype {np_dtype}") - if np.dtype(tri_aval.dtype) != np.int32: - raise NotImplementedError(f"Unsupported triangle dtype {tri_aval.dtype}") - if np.dtype(diff_attr_aval.dtype) != np.int32: - raise NotImplementedError( - f"Unsupported diff attribute dtype {diff_attr_aval.dtype}" - ) - - num_images, num_vertices, num_attributes = attr_aval.shape - depth, height, width = rast_out_aval.shape[:3] - num_triangles = tri_aval.shape[0] - num_diff_attrs = diff_attr_aval.shape[0] - - if num_diff_attrs > 0 and rast_db_aval.shape[-1] < num_diff_attrs: - raise NotImplementedError( - f"Attempt to propagate bary gradients through {num_diff_attrs} attributes: got {rast_db_aval.shape}" - ) - - out_shp_dtype = mlir.ir.RankedTensorType.get( - [num_images, height, width, num_attributes], mlir.dtype_to_ir_type(np_dtype) - ) - out_db_shp_dtype = mlir.ir.RankedTensorType.get( - [num_images, height, width, 2 * num_diff_attrs], - mlir.dtype_to_ir_type(np_dtype), - ) - - opaque = dr._get_plugin(gl=True).build_diff_interpolate_descriptor( - [num_images, num_vertices, num_attributes], - [depth, height, width], - [num_triangles], - num_diff_attrs, # diff wrt all attributes (TODO) - ) - - op_name = "jax_interpolate_fwd" - - return custom_call( - op_name, - # Output types - result_types=[out_shp_dtype, out_db_shp_dtype], - # The inputs: - operands=[attr, rast_out, tri, rast_db, diff_attrs], - backend_config=opaque, - operand_layouts=default_layouts( - attr_aval.shape, - rast_out_aval.shape, - tri_aval.shape, - rast_db_aval.shape, - diff_attr_aval.shape, - ), - result_layouts=default_layouts( - ( - num_images, - height, - width, - num_attributes, - ), - ( - num_images, - height, - width, - num_attributes, - ), - ), - ).results - - # ********************************************* - # * REGISTER THE OP WITH JAX * - # ********************************************* - _interpolate_prim = core.Primitive(f"interpolate_multiple_fwd_{id(r)}") - _interpolate_prim.multiple_results = True - _interpolate_prim.def_impl( - functools.partial(xla.apply_primitive, _interpolate_prim) - ) - _interpolate_prim.def_abstract_eval(_interpolate_fwd_abstract) - - # # Connect the XLA translation rules for JIT compilation - mlir.register_lowering(_interpolate_prim, _interpolate_fwd_lowering, platform="gpu") - - return _interpolate_prim - - -#### BACKWARD #### - - -# @functools.partial(jax.jit, static_argnums=(0,)) -def _interpolate_bwd_custom_call( - r: "Renderer", - attr, - rast_out, - tri, - dy, - rast_db, - dda, - diff_attrs_all, - diff_attrs_list, -): - return _build_interpolate_bwd_primitive(r).bind( - attr, rast_out, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list - ) - - -# @functools.lru_cache(maxsize=None) -def _build_interpolate_bwd_primitive(r: "Renderer"): - _register_custom_calls() - # For JIT compilation we need a function to evaluate the shape and dtype of the - # outputs of our op for some given inputs - - def _interpolate_bwd_abstract( - attr, rast_out, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list - ): - if len(attr.shape) != 3: - raise ValueError( - "Pass in a [num_images, num_vertices, num_attributes] sized first input" - ) - num_images, num_vertices, num_attributes = attr.shape - depth, height, width, rast_channels = rast_out.shape - depth_db, height_db, width_db, rast_channels_db = rast_db.shape - - dtype = dtypes.canonicalize_dtype(attr.dtype) - - g_attr_abstract = ShapedArray((num_images, num_vertices, num_attributes), dtype) - g_rast_abstract = ShapedArray((depth, height, width, rast_channels), dtype) - g_rast_db_abstract = ShapedArray( - (depth_db, height_db, width_db, rast_channels_db), dtype - ) - return [g_attr_abstract, g_rast_abstract, g_rast_db_abstract] - - # Provide an MLIR "lowering" of the interpolate primitive. - def _interpolate_bwd_lowering( - ctx, attr, rast_out, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list - ): - # Extract the numpy type of the inputs - ( - attr_aval, - rast_out_aval, - tri_aval, - dy_aval, - rast_db_aval, - dda_aval, - _, - diff_attr_aval, - ) = ctx.avals_in - - if attr_aval.ndim != 3: - raise NotImplementedError( - f"Only 3D attribute inputs supported: got {attr_aval.shape}" - ) - if rast_out_aval.ndim != 4: - raise NotImplementedError( - f"Only 4D rast inputs supported: got {rast_out_aval.shape}" - ) - if tri_aval.ndim != 2: - raise NotImplementedError( - f"Only 2D triangle tensors supported: got {tri_aval.shape}" - ) - - np_dtype = np.dtype(attr_aval.dtype) - if np_dtype != np.float32: - raise NotImplementedError(f"Unsupported attributes dtype {np_dtype}") - if np.dtype(tri_aval.dtype) != np.int32: - raise NotImplementedError(f"Unsupported triangle dtype {tri_aval.dtype}") - - num_images, num_vertices, num_attributes = attr_aval.shape - depth, height, width, rast_channels = rast_out_aval.shape - depth_db, height_db, width_db, rast_channels_db = rast_db_aval.shape - num_triangles = tri_aval.shape[0] - num_diff_attrs = diff_attr_aval.shape[0] - - g_attr_shp_dtype = mlir.ir.RankedTensorType.get( - [num_images, num_vertices, num_attributes], mlir.dtype_to_ir_type(np_dtype) - ) - g_rast_shp_dtype = mlir.ir.RankedTensorType.get( - [depth, height, width, rast_channels], mlir.dtype_to_ir_type(np_dtype) - ) - g_rast_db_shp_dtype = mlir.ir.RankedTensorType.get( - [depth_db, height_db, width_db, rast_channels_db], - mlir.dtype_to_ir_type(np_dtype), - ) - - opaque = dr._get_plugin(gl=True).build_diff_interpolate_descriptor( - [num_images, num_vertices, num_attributes], - [depth, height, width], - [num_triangles], - num_diff_attrs, - ) - - op_name = "jax_interpolate_bwd" - - return custom_call( - op_name, - # Output types - result_types=[g_attr_shp_dtype, g_rast_shp_dtype, g_rast_db_shp_dtype], - # The inputs: - operands=[attr, rast_out, tri, dy, rast_db, dda, diff_attrs_list], - backend_config=opaque, - operand_layouts=default_layouts( - attr_aval.shape, - rast_out_aval.shape, - tri_aval.shape, - dy_aval.shape, - rast_db_aval.shape, - dda_aval.shape, - diff_attr_aval.shape, - ), - result_layouts=default_layouts( - ( - num_images, - num_vertices, - num_attributes, - ), - ( - depth, - height, - width, - rast_channels, - ), - ( - depth_db, - height_db, - width_db, - rast_channels_db, - ), - ), - ).results - - # ********************************************* - # * REGISTER THE OP WITH JAX * - # ********************************************* - _interpolate_prim = core.Primitive(f"interpolate_multiple_bwd_{id(r)}") - _interpolate_prim.multiple_results = True - _interpolate_prim.def_impl( - functools.partial(xla.apply_primitive, _interpolate_prim) - ) - _interpolate_prim.def_abstract_eval(_interpolate_bwd_abstract) - - # # Connect the XLA translation rules for JIT compilation - mlir.register_lowering(_interpolate_prim, _interpolate_bwd_lowering, platform="gpu") - - return _interpolate_prim +# # @functools.partial(jax.jit, static_argnums=(0,)) +# def _interpolate_fwd_custom_call( +# r: "Renderer", attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs +# ): +# return _build_interpolate_fwd_primitive(r).bind( +# attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs +# ) + + +# # @functools.lru_cache(maxsize=None) +# def _build_interpolate_fwd_primitive(r: "Renderer"): +# _register_custom_calls() +# # For JIT compilation we need a function to evaluate the shape and dtype of the +# # outputs of our op for some given inputs + +# def _interpolate_fwd_abstract( +# attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs +# ): +# if len(attr.shape) != 3: +# raise ValueError( +# "Pass in a [num_images, num_vertices, num_attributes] sized first input" +# ) +# num_images, num_vertices, num_attributes = attr.shape +# _, height, width, _ = rast_out.shape +# num_tri, _ = tri.shape +# num_diff_attrs = diff_attrs.shape[0] + +# dtype = dtypes.canonicalize_dtype(attr.dtype) + +# out_abstract = ShapedArray((num_images, height, width, num_attributes), dtype) +# out_db_abstract = ShapedArray( +# (num_images, height, width, 2 * num_diff_attrs), dtype +# ) # empty tensor +# return [out_abstract, out_db_abstract] + +# # Provide an MLIR "lowering" of the interpolate primitive. +# def _interpolate_fwd_lowering( +# ctx, attr, rast_out, tri, rast_db, diff_attrs_all, diff_attrs +# ): +# # Extract the numpy type of the inputs +# ( +# attr_aval, +# rast_out_aval, +# tri_aval, +# rast_db_aval, +# _, +# diff_attr_aval, +# ) = ctx.avals_in + +# if attr_aval.ndim != 3: +# raise NotImplementedError( +# f"Only 3D attribute inputs supported: got {attr_aval.shape}" +# ) +# if rast_out_aval.ndim != 4: +# raise NotImplementedError( +# f"Only 4D rast inputs supported: got {rast_out_aval.shape}" +# ) +# if tri_aval.ndim != 2: +# raise NotImplementedError( +# f"Only 2D triangle tensors supported: got {tri_aval.shape}" +# ) + +# np_dtype = np.dtype(attr_aval.dtype) +# if np_dtype != np.float32: +# raise NotImplementedError(f"Unsupported attributes dtype {np_dtype}") +# if np.dtype(tri_aval.dtype) != np.int32: +# raise NotImplementedError(f"Unsupported triangle dtype {tri_aval.dtype}") +# if np.dtype(diff_attr_aval.dtype) != np.int32: +# raise NotImplementedError( +# f"Unsupported diff attribute dtype {diff_attr_aval.dtype}" +# ) + +# num_images, num_vertices, num_attributes = attr_aval.shape +# depth, height, width = rast_out_aval.shape[:3] +# num_triangles = tri_aval.shape[0] +# num_diff_attrs = diff_attr_aval.shape[0] + +# if num_diff_attrs > 0 and rast_db_aval.shape[-1] < num_diff_attrs: +# raise NotImplementedError( +# f"Attempt to propagate bary gradients through {num_diff_attrs} attributes: got {rast_db_aval.shape}" +# ) + +# out_shp_dtype = mlir.ir.RankedTensorType.get( +# [num_images, height, width, num_attributes], mlir.dtype_to_ir_type(np_dtype) +# ) +# out_db_shp_dtype = mlir.ir.RankedTensorType.get( +# [num_images, height, width, 2 * num_diff_attrs], +# mlir.dtype_to_ir_type(np_dtype), +# ) + +# opaque = dr._get_plugin(gl=True).build_diff_interpolate_descriptor( +# [num_images, num_vertices, num_attributes], +# [depth, height, width], +# [num_triangles], +# num_diff_attrs, # diff wrt all attributes (TODO) +# ) + +# op_name = "jax_interpolate_fwd" + +# return custom_call( +# op_name, +# # Output types +# result_types=[out_shp_dtype, out_db_shp_dtype], +# # The inputs: +# operands=[attr, rast_out, tri, rast_db, diff_attrs], +# backend_config=opaque, +# operand_layouts=default_layouts( +# attr_aval.shape, +# rast_out_aval.shape, +# tri_aval.shape, +# rast_db_aval.shape, +# diff_attr_aval.shape, +# ), +# result_layouts=default_layouts( +# ( +# num_images, +# height, +# width, +# num_attributes, +# ), +# ( +# num_images, +# height, +# width, +# num_attributes, +# ), +# ), +# ).results + +# # ********************************************* +# # * REGISTER THE OP WITH JAX * +# # ********************************************* +# _interpolate_prim = core.Primitive(f"interpolate_multiple_fwd_{id(r)}") +# _interpolate_prim.multiple_results = True +# _interpolate_prim.def_impl( +# functools.partial(xla.apply_primitive, _interpolate_prim) +# ) +# _interpolate_prim.def_abstract_eval(_interpolate_fwd_abstract) + +# # # Connect the XLA translation rules for JIT compilation +# mlir.register_lowering(_interpolate_prim, _interpolate_fwd_lowering, platform="gpu") + +# return _interpolate_prim + + +# #### BACKWARD #### + + +# # @functools.partial(jax.jit, static_argnums=(0,)) +# def _interpolate_bwd_custom_call( +# r: "Renderer", +# attr, +# rast_out, +# tri, +# dy, +# rast_db, +# dda, +# diff_attrs_all, +# diff_attrs_list, +# ): +# return _build_interpolate_bwd_primitive(r).bind( +# attr, rast_out, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list +# ) + + +# # @functools.lru_cache(maxsize=None) +# def _build_interpolate_bwd_primitive(r: "Renderer"): +# _register_custom_calls() +# # For JIT compilation we need a function to evaluate the shape and dtype of the +# # outputs of our op for some given inputs + +# def _interpolate_bwd_abstract( +# attr, rast_out, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list +# ): +# if len(attr.shape) != 3: +# raise ValueError( +# "Pass in a [num_images, num_vertices, num_attributes] sized first input" +# ) +# num_images, num_vertices, num_attributes = attr.shape +# depth, height, width, rast_channels = rast_out.shape +# depth_db, height_db, width_db, rast_channels_db = rast_db.shape + +# dtype = dtypes.canonicalize_dtype(attr.dtype) + +# g_attr_abstract = ShapedArray((num_images, num_vertices, num_attributes), dtype) +# g_rast_abstract = ShapedArray((depth, height, width, rast_channels), dtype) +# g_rast_db_abstract = ShapedArray( +# (depth_db, height_db, width_db, rast_channels_db), dtype +# ) +# return [g_attr_abstract, g_rast_abstract, g_rast_db_abstract] + +# # Provide an MLIR "lowering" of the interpolate primitive. +# def _interpolate_bwd_lowering( +# ctx, attr, rast_out, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list +# ): +# # Extract the numpy type of the inputs +# ( +# attr_aval, +# rast_out_aval, +# tri_aval, +# dy_aval, +# rast_db_aval, +# dda_aval, +# _, +# diff_attr_aval, +# ) = ctx.avals_in + +# if attr_aval.ndim != 3: +# raise NotImplementedError( +# f"Only 3D attribute inputs supported: got {attr_aval.shape}" +# ) +# if rast_out_aval.ndim != 4: +# raise NotImplementedError( +# f"Only 4D rast inputs supported: got {rast_out_aval.shape}" +# ) +# if tri_aval.ndim != 2: +# raise NotImplementedError( +# f"Only 2D triangle tensors supported: got {tri_aval.shape}" +# ) + +# np_dtype = np.dtype(attr_aval.dtype) +# if np_dtype != np.float32: +# raise NotImplementedError(f"Unsupported attributes dtype {np_dtype}") +# if np.dtype(tri_aval.dtype) != np.int32: +# raise NotImplementedError(f"Unsupported triangle dtype {tri_aval.dtype}") + +# num_images, num_vertices, num_attributes = attr_aval.shape +# depth, height, width, rast_channels = rast_out_aval.shape +# depth_db, height_db, width_db, rast_channels_db = rast_db_aval.shape +# num_triangles = tri_aval.shape[0] +# num_diff_attrs = diff_attr_aval.shape[0] + +# g_attr_shp_dtype = mlir.ir.RankedTensorType.get( +# [num_images, num_vertices, num_attributes], mlir.dtype_to_ir_type(np_dtype) +# ) +# g_rast_shp_dtype = mlir.ir.RankedTensorType.get( +# [depth, height, width, rast_channels], mlir.dtype_to_ir_type(np_dtype) +# ) +# g_rast_db_shp_dtype = mlir.ir.RankedTensorType.get( +# [depth_db, height_db, width_db, rast_channels_db], +# mlir.dtype_to_ir_type(np_dtype), +# ) + +# opaque = dr._get_plugin(gl=True).build_diff_interpolate_descriptor( +# [num_images, num_vertices, num_attributes], +# [depth, height, width], +# [num_triangles], +# num_diff_attrs, +# ) + +# op_name = "jax_interpolate_bwd" + +# return custom_call( +# op_name, +# # Output types +# result_types=[g_attr_shp_dtype, g_rast_shp_dtype, g_rast_db_shp_dtype], +# # The inputs: +# operands=[attr, rast_out, tri, dy, rast_db, dda, diff_attrs_list], +# backend_config=opaque, +# operand_layouts=default_layouts( +# attr_aval.shape, +# rast_out_aval.shape, +# tri_aval.shape, +# dy_aval.shape, +# rast_db_aval.shape, +# dda_aval.shape, +# diff_attr_aval.shape, +# ), +# result_layouts=default_layouts( +# ( +# num_images, +# num_vertices, +# num_attributes, +# ), +# ( +# depth, +# height, +# width, +# rast_channels, +# ), +# ( +# depth_db, +# height_db, +# width_db, +# rast_channels_db, +# ), +# ), +# ).results + +# # ********************************************* +# # * REGISTER THE OP WITH JAX * +# # ********************************************* +# _interpolate_prim = core.Primitive(f"interpolate_multiple_bwd_{id(r)}") +# _interpolate_prim.multiple_results = True +# _interpolate_prim.def_impl( +# functools.partial(xla.apply_primitive, _interpolate_prim) +# ) +# _interpolate_prim.def_abstract_eval(_interpolate_bwd_abstract) + +# # # Connect the XLA translation rules for JIT compilation +# mlir.register_lowering(_interpolate_prim, _interpolate_bwd_lowering, platform="gpu") + +# return _interpolate_prim diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu index cf6ca209..9748f591 100755 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu @@ -109,6 +109,19 @@ __global__ void RasterizeCudaFwdShaderKernel(const RasterizeCudaFwdShaderParams ((float4*)p.out_db)[pidx] = make_float4(dudx, dudy, dvdx, dvdy); } + +__device__ inline void mv_multiply_4(float* matrix, float* vector, float* res) +{ + for (int i = 0; i < 4; i++) + { + for (int j = 0; j < 4; j++) + { + res[i] += matrix[4*i + j] * vector[j]; + } + } +} + + //------------------------------------------------------------------------ // Gradient Cuda kernel. @@ -131,8 +144,13 @@ static __forceinline__ __device__ void RasterizeGradKernelTemplate(const Rasteri // Read triangle idx and dy. float2 dy = ((float2*)p.dy)[pidx * 2]; float4 ddb = ENABLE_DB ? ((float4*)p.ddb)[pidx] : make_float4(0.f, 0.f, 0.f, 0.f); + int triIdx = (int)(((float*)p.out)[pidx * 4 + 3]) - 1; + int object_idx = (int)(((int*)p.out2)[pidx * 4 + 0]) - 1; + int depth = p.depth; + float* pose = p.pose + ( depth * 16 * object_idx + pz * 16); + // Exit if nothing to do. if (triIdx < 0 || triIdx >= p.numTriangles) return; // No or corrupt triangle. @@ -154,21 +172,46 @@ static __forceinline__ __device__ void RasterizeGradKernelTemplate(const Rasteri vi2 < 0 || vi2 >= p.numVertices) return; - // In instance mode, adjust vertex indices by minibatch index. - if (p.instance_mode) - { - vi0 += pz * p.numVertices; - vi1 += pz * p.numVertices; - vi2 += pz * p.numVertices; - } + // // In instance mode, adjust vertex indices by minibatch index. + // if (p.instance_mode) + // { + // vi0 += pz * p.numVertices; + // vi1 += pz * p.numVertices; + // vi2 += pz * p.numVertices; + // } + + float* proj = p.proj; // Initialize coalesced atomics. CA_SET_GROUP(triIdx); + // Apply projection_matrix * pose[frame_idx, object_index] * p.pos (which is in object space) to get the clip vertex positions. + // The following computations assume p.pos is in clip space. + float* vertex_1_object_frame = p.pos + vi0 * 4; + float* vertex_2_object_frame = p.pos + vi1 * 4; + float* vertex_3_object_frame = p.pos + vi2 * 4; + + float vertex_1_camera_frame[4] = {0}; + float vertex_2_camera_frame[4] = {0}; + float vertex_3_camera_frame[4] = {0}; + mv_multiply_4(pose, vertex_1_object_frame, vertex_1_camera_frame); + mv_multiply_4(pose, vertex_2_object_frame, vertex_2_camera_frame); + mv_multiply_4(pose, vertex_3_object_frame, vertex_3_camera_frame); + + float vertex_1_clip_space[4] = {0}; + float vertex_2_clip_space[4] = {0}; + float vertex_3_clip_space[4] = {0}; + mv_multiply_4(proj, vertex_1_object_frame, vertex_1_clip_space); + mv_multiply_4(proj, vertex_2_object_frame, vertex_2_clip_space); + mv_multiply_4(proj, vertex_3_object_frame, vertex_3_clip_space); + // Fetch vertex positions. - float4 p0 = ((float4*)p.pos)[vi0]; - float4 p1 = ((float4*)p.pos)[vi1]; - float4 p2 = ((float4*)p.pos)[vi2]; + // float4 p0 = ((float4*)p.pos)[vi0]; + // float4 p1 = ((float4*)p.pos)[vi1]; + // float4 p2 = ((float4*)p.pos)[vi2]; + float4 p0 = ((float4*)vertex_1_clip_space)[0]; + float4 p1 = ((float4*)vertex_2_clip_space)[0]; + float4 p2 = ((float4*)vertex_3_clip_space)[0]; // Evaluate edge functions. float fx = p.xs * (float)px + p.xo; @@ -206,66 +249,37 @@ static __forceinline__ __device__ void RasterizeGradKernelTemplate(const Rasteri float gp1w = -fx * gp1x - fy * gp1y; float gp2w = -fx * gp2x - fy * gp2y; - // Bary differential gradients. - if (ENABLE_DB && ((grad_all_ddb) << 1) != 0) - { - float dfxdX = p.xs * iw; - float dfydY = p.ys * iw; - ddb.x *= dfxdX; - ddb.y *= dfydY; - ddb.z *= dfxdX; - ddb.w *= dfydY; - - float da0dX = p1.y * p2.w - p2.y * p1.w; - float da1dX = p2.y * p0.w - p0.y * p2.w; - float da2dX = p0.y * p1.w - p1.y * p0.w; - float da0dY = p2.x * p1.w - p1.x * p2.w; - float da1dY = p0.x * p2.w - p2.x * p0.w; - float da2dY = p1.x * p0.w - p0.x * p1.w; - float datdX = da0dX + da1dX + da2dX; - float datdY = da0dY + da1dY + da2dY; - - float x01 = p0.x - p1.x; - float x12 = p1.x - p2.x; - float x20 = p2.x - p0.x; - float y01 = p0.y - p1.y; - float y12 = p1.y - p2.y; - float y20 = p2.y - p0.y; - float w01 = p0.w - p1.w; - float w12 = p1.w - p2.w; - float w20 = p2.w - p0.w; - - float a0p1 = fy * p2.x - fx * p2.y; - float a0p2 = fx * p1.y - fy * p1.x; - float a1p0 = fx * p2.y - fy * p2.x; - float a1p2 = fy * p0.x - fx * p0.y; - - float wdudX = 2.f * b0 * datdX - da0dX; - float wdudY = 2.f * b0 * datdY - da0dY; - float wdvdX = 2.f * b1 * datdX - da1dX; - float wdvdY = 2.f * b1 * datdY - da1dY; - - float c0 = iw * (ddb.x * wdudX + ddb.y * wdudY + ddb.z * wdvdX + ddb.w * wdvdY); - float cx = c0 * fx - ddb.x * b0 - ddb.z * b1; - float cy = c0 * fy - ddb.y * b0 - ddb.w * b1; - float cxy = iw * (ddb.x * datdX + ddb.y * datdY); - float czw = iw * (ddb.z * datdX + ddb.w * datdY); - - gp0x += c0 * y12 - cy * w12 + czw * p2y + ddb.w * p2.w; - gp1x += c0 * y20 - cy * w20 - cxy * p2y - ddb.y * p2.w; - gp2x += c0 * y01 - cy * w01 + cxy * p1y - czw * p0y + ddb.y * p1.w - ddb.w * p0.w; - gp0y += cx * w12 - c0 * x12 - czw * p2x - ddb.z * p2.w; - gp1y += cx * w20 - c0 * x20 + cxy * p2x + ddb.x * p2.w; - gp2y += cx * w01 - c0 * x01 - cxy * p1x + czw * p0x - ddb.x * p1.w + ddb.z * p0.w; - gp0w += cy * x12 - cx * y12 - czw * a1p0 + ddb.z * p2.y - ddb.w * p2.x; - gp1w += cy * x20 - cx * y20 - cxy * a0p1 - ddb.x * p2.y + ddb.y * p2.x; - gp2w += cy * x01 - cx * y01 - cxy * a0p2 - czw * a1p2 + ddb.x * p1.y - ddb.y * p1.x - ddb.z * p0.y + ddb.w * p0.x; + float loss_grad_v1_clip_space[4] = {gp0x, gp0y, 0.f, gp0w}; + float loss_grad_v2_clip_space[4] = {gp1x, gp1y, 0.f, gp1w}; + float loss_grad_v3_clip_space[4] = {gp2x, gp2y, 0.f, gp2w}; + + for (int i = 0; i < 16; i++) { + int row=i/4, col=i%4; + //XXX somehow get the xyzw of the vertices + //col-th coordinate of vi0-, vi1-, vi2-th vertices + float vertex_term1 = ((float*) vertex_1_object_frame)[col]; + float vertex_term2 = ((float*) vertex_2_object_frame)[col]; + float vertex_term3 = ((float*) vertex_3_object_frame)[col]; + + float accumulated_gradient = 0.0; + accumulated_gradient += loss_grad_v1_clip_space[0] * proj[row] * vertex_term1; + accumulated_gradient += loss_grad_v2_clip_space[0] * proj[row] * vertex_term2; + accumulated_gradient += loss_grad_v3_clip_space[0] * proj[row] * vertex_term3; + + accumulated_gradient += loss_grad_v1_clip_space[1] * proj[4 + row] * vertex_term1; + accumulated_gradient += loss_grad_v2_clip_space[1] * proj[4 + row]* vertex_term2; + accumulated_gradient += loss_grad_v3_clip_space[1] * proj[4 + row]* vertex_term3; + + accumulated_gradient += loss_grad_v1_clip_space[3] * proj[12 + row] * vertex_term1; + accumulated_gradient += loss_grad_v2_clip_space[3] * proj[12 + row]* vertex_term2; + accumulated_gradient += loss_grad_v3_clip_space[3] * proj[12 + row]* vertex_term3; + + // Fix this; + caAtomicAdd( + p.grad + (depth * 16 * object_idx + pz * 16 + i), + accumulated_gradient + ); } - - // Accumulate using coalesced atomics. - caAtomicAdd3_xyw(p.grad + 4 * vi0, gp0x, gp0y, gp0w); - caAtomicAdd3_xyw(p.grad + 4 * vi1, gp1x, gp1y, gp1w); - caAtomicAdd3_xyw(p.grad + 4 * vi2, gp2x, gp2y, gp2w); } // Template specializations. diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.h b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.h index dd2d101d..10cc24f8 100755 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.h +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.h @@ -40,14 +40,18 @@ struct RasterizeCudaFwdShaderParams struct RasterizeGradParams { - const float* pos; // Incoming position buffer. - const int* tri; // Incoming triangle buffer. - const float* out; // Rasterizer output buffer. - const float* dy; // Incoming gradients of rasterizer output buffer. - const float* ddb; // Incoming gradients of bary diff output buffer. + float* pose; // Incoming position buffer. + float* pos; // Incoming position buffer. + float* proj; // Incoming position buffer. + int* tri; // Incoming triangle buffer. + float* out; // Rasterizer output buffer. + float* out2; // Rasterizer output buffer. + float* dy; // Incoming gradients of rasterizer output buffer. + float* ddb; // Incoming gradients of bary diff output buffer. float* grad; // Outgoing position gradients. int numTriangles; // Number of triangles. int numVertices; // Number of vertices. + int num_objects; // Number of vertices. int width; // Image width. int height; // Image height. int depth; // Size of minibatch. diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp index d6bb5493..fcd7956d 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize_gl.cpp @@ -262,6 +262,7 @@ void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceId ) ); } + // else // { // // Geometry shader without bary differential output. diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp index 423af4f9..c233ef66 100755 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_bindings.cpp @@ -37,6 +37,17 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { d.num_triangles = images_objects_vertices_triangles[3]; return PackDescriptor(d); }); + m.def("build_diff_rasterize_bwd_descriptor", + [](std::vector all_info) { + DiffRasterizeBwdCustomCallDescriptor d; + d.num_images = all_info[0]; + d.num_objects = all_info[1]; + d.num_vertices = all_info[2]; + d.num_triangles = all_info[3]; + d.height = all_info[4]; + d.width = all_info[5]; + return PackDescriptor(d); + }); m.def("build_diff_interpolate_descriptor", [](std::vector attr_shape, std::vector rast_shape, @@ -54,17 +65,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { d.num_diff_attributes = num_diff_attrs; return PackDescriptor(d); }); - m.def("build_diff_rasterize_bwd_descriptor", - [](std::vector pos_shape, std::vector tri_shape, std::vector rast_shape) { - DiffRasterizeBwdCustomCallDescriptor d; - // d.num_images = pos_shape[0]; - // d.num_vertices = pos_shape[1]; - // d.num_triangles = tri_shape[0]; - // d.rast_height = rast_shape[1]; - // d.rast_width = rast_shape[2]; - // d.rast_depth = rast_shape[0]; - return PackDescriptor(d); - }); + } diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index b88b905b..4e7b6068 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -178,75 +178,91 @@ void jax_rasterize_bwd(cudaStream_t stream, void **buffers, const char *opaque, std::size_t opaque_len) { - // const DiffRasterizeBwdCustomCallDescriptor &d = - // *UnpackDescriptor(opaque, opaque_len); - - // const float *pose = reinterpret_cast (buffers[0]); - // const float *pos = reinterpret_cast (buffers[1]); - // const int *tri = reinterpret_cast (buffers[2]); - // const int *_ranges = reinterpret_cast (buffers[3]); - // const float *projectionMatrix = reinterpret_cast (buffers[4]); - // const int *_resolution = reinterpret_cast (buffers[5]); - - // float *out = reinterpret_cast (buffers[6]); - // float *out_db = reinterpret_cast (buffers[7]); + const DiffRasterizeBwdCustomCallDescriptor &d = + *UnpackDescriptor(opaque, opaque_len); + + float *pose = reinterpret_cast (buffers[0]); + float *pos = reinterpret_cast (buffers[1]); + int *tri = reinterpret_cast (buffers[2]); + int *_ranges = reinterpret_cast (buffers[3]); + float *projectionMatrix = reinterpret_cast (buffers[4]); + int *_resolution = reinterpret_cast (buffers[5]); + float *rast_out = reinterpret_cast (buffers[6]); + float *rast_out2 = reinterpret_cast (buffers[7]); + + float *dy = reinterpret_cast (buffers[8]); + float *ddb = reinterpret_cast (buffers[9]); + + float *grad = reinterpret_cast (buffers[10]); // output + std::cout << "num_images: " << d.num_images << std::endl; + std::cout << "num_objects: " << d.num_objects << std::endl; + cudaMemset(grad, 0, d.num_images*d.num_objects*4*4*sizeof(float)); + cudaStreamSynchronize(stream); - // const float *dy = reinterpret_cast (buffers[8]); - // const float *ddb = reinterpret_cast (buffers[9]); + cudaStreamSynchronize(stream); + std::vector grad_cpu; + grad_cpu.resize(16); + NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&grad_cpu[0], grad, 16 * sizeof(int), cudaMemcpyDeviceToHost)); + cudaStreamSynchronize(stream); - // float *grad = reinterpret_cast (buffers[10]); // output - // cudaMemset(grad, 0, d.num_images*d.num_vertices*4*sizeof(float)); + for(int i = 0; i < 16; i++) { + std::cout << grad_cpu[i] << " "; + } - // auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); + auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); - // cudaStreamSynchronize(stream); + cudaStreamSynchronize(stream); - // RasterizeGradParams p; - // bool enable_db = true; - - // // Determine instance mode. - // p.instance_mode = 1; - // NVDR_CHECK(p.instance_mode == 1, "Should be in instance mode; check input sizes"); - - // // Shape is taken from the rasterizer output tensor. - // p.depth = d.num_images; - // p.height = d.height; - // p.width = d.width - // NVDR_CHECK(p.depth > 0 && p.height > 0 && p.width > 0, "resolution must be [>0, >0, >0]"); - - // // Populate parameters. - // p.numTriangles = d.num_triangles - // p.numVertices = d.num_vertices; - // p.pose = pose; - // p.pos = pos; - // p.tri = tri; - // p.out = rast_out; - // p.dy = dy; - // p.ddb = enable_db ? ddb : NULL; - - // // Set up pixel position to clip space x, y transform. - // p.xs = 2.f / (float)p.width; - // p.xo = 1.f / (float)p.width - 1.f; - // p.ys = 2.f / (float)p.height; - // p.yo = 1.f / (float)p.height - 1.f; - - // // Output tensor for position gradients. - // p.grad = grad; - - // // Verify that buffers are aligned to allow float2/float4 operations. - // NVDR_CHECK(!((uintptr_t)p.pos & 15), "pos input tensor not aligned to float4"); - // NVDR_CHECK(!((uintptr_t)p.dy & 7), "dy input tensor not aligned to float2"); - // NVDR_CHECK(!((uintptr_t)p.ddb & 15), "ddb input tensor not aligned to float4"); - - // // Choose launch parameters. - // dim3 blockSize = getLaunchBlockSize(RAST_GRAD_MAX_KERNEL_BLOCK_WIDTH, RAST_GRAD_MAX_KERNEL_BLOCK_HEIGHT, p.width, p.height); - // dim3 gridSize = getLaunchGridSize(blockSize, p.width, p.height, p.depth); - - // // Launch CUDA kernel to populate gradient values. - // void* args[] = {&p}; - // enable_db = false; - // void* func = enable_db ? (void*)RasterizeGradKernelDb : (void*)RasterizeGradKernel; - // NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); + RasterizeGradParams p; + bool enable_db = true; + + // Determine instance mode. + p.instance_mode = 1; + NVDR_CHECK(p.instance_mode == 1, "Should be in instance mode; check input sizes"); + + // Shape is taken from the rasterizer output tensor. + p.depth = d.num_images; + p.height = d.height; + p.width = d.width; + NVDR_CHECK(p.depth > 0 && p.height > 0 && p.width > 0, "resolution must be [>0, >0, >0]"); + + // Populate parameters. + p.numTriangles = d.num_triangles; + p.numVertices = d.num_vertices; + p.num_objects = d.num_objects; + p.pose = pose; + p.pos = pos; + p.proj = projectionMatrix; + p.tri = tri; + p.out = rast_out; + p.out2 = rast_out2; + p.dy = dy; + p.ddb = enable_db ? ddb : NULL; + + // Set up pixel position to clip space x, y transform. + p.xs = 2.f / (float)p.width; + p.xo = 1.f / (float)p.width - 1.f; + p.ys = 2.f / (float)p.height; + p.yo = 1.f / (float)p.height - 1.f; + + // Output tensor for position gradients. + p.grad = grad; + + // Verify that buffers are aligned to allow float2/float4 operations. + NVDR_CHECK(!((uintptr_t)p.pos & 15), "pos input tensor not aligned to float4"); + NVDR_CHECK(!((uintptr_t)p.dy & 7), "dy input tensor not aligned to float2"); + NVDR_CHECK(!((uintptr_t)p.ddb & 15), "ddb input tensor not aligned to float4"); + + // Choose launch parameters. + dim3 blockSize = getLaunchBlockSize(RAST_GRAD_MAX_KERNEL_BLOCK_WIDTH, RAST_GRAD_MAX_KERNEL_BLOCK_HEIGHT, p.width, p.height); + dim3 gridSize = getLaunchGridSize(blockSize, p.width, p.height, p.depth); + + // Launch CUDA kernel to populate gradient values. + void* args[] = {&p}; + enable_db = false; + void* func = enable_db ? (void*)RasterizeGradKernelDb : (void*)RasterizeGradKernel; + // std::cout << "not calling: "<< std::endl; + NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); - // cudaStreamSynchronize(stream); + cudaStreamSynchronize(stream); } diff --git a/test_jax_renderer_bwd.py b/test_jax_renderer_bwd.py new file mode 100644 index 00000000..55d1177c --- /dev/null +++ b/test_jax_renderer_bwd.py @@ -0,0 +1,163 @@ +import bayes3d as b +import jax.numpy as jnp +import jax +from tqdm import tqdm +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +import numpy as np +import os +import trimesh + +import viser +server = viser.ViserServer() + + +intrinsics = b.Intrinsics( + height=100, + width=100, + fx=200.0, fy=200.0, + cx=50.0, cy=50.0, + near=0.001, far=16.0 +) + +projection_matrix = b.camera._open_gl_projection_matrix( + intrinsics.height, + intrinsics.width, + intrinsics.fx, + intrinsics.fy, + intrinsics.cx, + intrinsics.cy, + intrinsics.near, + intrinsics.far, +) + +from bayes3d.rendering.nvdiffrast_jax.jax_renderer import Renderer as JaxRenderer +jax_renderer = JaxRenderer(intrinsics) + +meshes = [] + +# path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") +# bunny_mesh = trimesh.load(path) +# bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) +# meshes.append(bunny_mesh) + +path = os.path.join(b.utils.get_assets_dir(), "sample_objs/cube.obj") +mesh = trimesh.load(path) +mesh.vertices = mesh.vertices * jnp.array([1.0, 1.0, 1.0]) * 0.7 +meshes.append(mesh) + +all_vertices = [jnp.array(mesh.vertices) for mesh in meshes] +all_faces = [jnp.array(mesh.faces) for mesh in meshes] +vertices_lens = jnp.array([len(verts) for verts in all_vertices]) +vertices_lens_cumsum = jnp.pad(jnp.cumsum(vertices_lens),(1,0)) +faces_lens = jnp.array([len(faces) for faces in all_faces]) +faces_lens_cumsum = jnp.pad(jnp.cumsum(faces_lens),(1,0)) + +vertices = jnp.concatenate(all_vertices, axis=0) +vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) +faces = jnp.concatenate([faces + vertices_lens_cumsum[i] for (i,faces) in enumerate(all_faces)]) + +resolution = jnp.array([intrinsics.height, intrinsics.width]) + +object_indices = jnp.array([0]) +ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) + + +import functools +@functools.partial( + jnp.vectorize, + signature="(2),(),(m,4,4),()->(3)", + excluded=( + 4, + 5, + 6, + ), +) +def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): + relevant_vertices = vertices[faces[triangle_id-1]] + pose_of_object = poses[object_id-1] + relevant_vertices_transformed = relevant_vertices @ pose_of_object.T + barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) + interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) + return interpolated_value + +def render(poses, vertices, faces, ranges, projection_matrix, resolution): + rast_out, rast_out_aux = jax_renderer.rasterize( + poses, + vertices, + faces, + ranges, + projection_matrix, + resolution + ) + uvs = rast_out[...,:2] + object_ids = rast_out_aux[...,0] + triangle_ids = rast_out_aux[...,1] + mask = object_ids > 0 + + interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) + image = interpolated_values * mask[...,None] + return image + + +pose_gt = b.transform_from_pos(jnp.array([0.0, 0.0, 3.0])) +pose_estim = b.transform_from_pos(jnp.array([0.0, 0.0, 2.0])) +image_gt = render( + pose_gt[None, None,...], + vertices, + faces, + ranges, + projection_matrix, + resolution +) +b.get_depth_image(image_gt[0,...,2]).save("sweep2.png") + +def loss(pose_estim, image_gt): + image_estim = render( + pose_estim[None, None,...], + vertices, + faces, + ranges, + projection_matrix, + resolution + ) + return ((image_gt[...,2] - image_estim[...,2])**2).mean() + +grad = jax.grad(loss, argnums=0) +for _ in range(5): + print(pose_estim[:3,3]) + pose_estim = pose_estim - 1.0 * grad(pose_estim, image_gt) + pose_estim = pose_estim.at[:3,:3].set(jnp.eye(3)) + print(pose_estim[:3,3]) + + + + + + + + +# poses = pose_gt[None, None,...] +# (out1, out2), saved_tensors = jax_renderer._rasterize_fwd( +# pose_gt[None, None,...], +# vertices, +# faces, +# ranges, +# projection_matrix, +# resolution +# ) +# print(out1) + +# dout1, dout2 = jnp.zeros_like(out1), jnp.zeros_like(out2) +# dout1 = dout1.at[:,:,:,:2].set(0.333) +# grads = jax_renderer._rasterize_bwd( +# saved_tensors, +# (dout1, dout2), +# )[0] +# print(grads) + + + + + +from IPython import embed; embed() \ No newline at end of file From fb7e53a82f1033c1ed99b17b5fe20292048320cf Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Sat, 17 Feb 2024 20:24:32 +0000 Subject: [PATCH 19/27] gradient seem to be working --- .../nvdiffrast/common/rasterize.cu | 4 +++ test_jax_renderer_bwd.py | 29 ++++++++++--------- 2 files changed, 19 insertions(+), 14 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu index 9748f591..adc9335e 100755 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/common/rasterize.cu @@ -270,6 +270,10 @@ static __forceinline__ __device__ void RasterizeGradKernelTemplate(const Rasteri accumulated_gradient += loss_grad_v2_clip_space[1] * proj[4 + row]* vertex_term2; accumulated_gradient += loss_grad_v3_clip_space[1] * proj[4 + row]* vertex_term3; + accumulated_gradient += loss_grad_v1_clip_space[2] * proj[8 + row] * vertex_term1; + accumulated_gradient += loss_grad_v2_clip_space[2] * proj[8 + row]* vertex_term2; + accumulated_gradient += loss_grad_v3_clip_space[2] * proj[8 + row]* vertex_term3; + accumulated_gradient += loss_grad_v1_clip_space[3] * proj[12 + row] * vertex_term1; accumulated_gradient += loss_grad_v2_clip_space[3] * proj[12 + row]* vertex_term2; accumulated_gradient += loss_grad_v3_clip_space[3] * proj[12 + row]* vertex_term3; diff --git a/test_jax_renderer_bwd.py b/test_jax_renderer_bwd.py index 55d1177c..14881665 100644 --- a/test_jax_renderer_bwd.py +++ b/test_jax_renderer_bwd.py @@ -16,7 +16,7 @@ height=100, width=100, fx=200.0, fy=200.0, - cx=50.0, cy=50.0, + cx=50., cy=50., near=0.001, far=16.0 ) @@ -36,10 +36,10 @@ meshes = [] -# path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") -# bunny_mesh = trimesh.load(path) -# bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) -# meshes.append(bunny_mesh) +path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") +bunny_mesh = trimesh.load(path) +bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) +meshes.append(bunny_mesh) path = os.path.join(b.utils.get_assets_dir(), "sample_objs/cube.obj") mesh = trimesh.load(path) @@ -59,9 +59,6 @@ resolution = jnp.array([intrinsics.height, intrinsics.width]) -object_indices = jnp.array([0]) -ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) - import functools @functools.partial( @@ -99,9 +96,11 @@ def render(poses, vertices, faces, ranges, projection_matrix, resolution): image = interpolated_values * mask[...,None] return image +object_indices = jnp.array([1]) +ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) + pose_gt = b.transform_from_pos(jnp.array([0.0, 0.0, 3.0])) -pose_estim = b.transform_from_pos(jnp.array([0.0, 0.0, 2.0])) image_gt = render( pose_gt[None, None,...], vertices, @@ -121,14 +120,16 @@ def loss(pose_estim, image_gt): projection_matrix, resolution ) - return ((image_gt[...,2] - image_estim[...,2])**2).mean() + mask = (image_gt[...,2] > 0) * (image_estim[...,2] > 0) + return (((image_gt[...,2] - image_estim[...,2]) * mask ) **2).mean() grad = jax.grad(loss, argnums=0) -for _ in range(5): - print(pose_estim[:3,3]) - pose_estim = pose_estim - 1.0 * grad(pose_estim, image_gt) +pose_estim = b.transform_from_pos(jnp.array([0.0, 0.0, 2.0])) + +for _ in range(100): + print("estim ", pose_estim[:3,3]) + pose_estim = pose_estim - 0.1 * grad(pose_estim, image_gt) pose_estim = pose_estim.at[:3,:3].set(jnp.eye(3)) - print(pose_estim[:3,3]) From 65b5d6f709d005881b8dbc6173c86eafaa56e010 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Mon, 19 Feb 2024 18:38:48 +0000 Subject: [PATCH 20/27] savE --- .../nvdiffrast/jax/jax_rasterize_gl.cpp | 2 +- nvdiffrast_test.py | 54 ++++++++ pytorch_check.py | 130 ++++++++++++++++++ test_jax_renderer_bwd.py | 85 +++++++++--- 4 files changed, 248 insertions(+), 23 deletions(-) create mode 100644 nvdiffrast_test.py create mode 100644 pytorch_check.py diff --git a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp index 4e7b6068..16d6a72c 100644 --- a/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp +++ b/bayes3d/rendering/nvdiffrast_jax/nvdiffrast/jax/jax_rasterize_gl.cpp @@ -262,7 +262,7 @@ void jax_rasterize_bwd(cudaStream_t stream, enable_db = false; void* func = enable_db ? (void*)RasterizeGradKernelDb : (void*)RasterizeGradKernel; // std::cout << "not calling: "<< std::endl; - NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); + // NVDR_CHECK_CUDA_ERROR(cudaLaunchKernel(func, gridSize, blockSize, args, 0, stream)); cudaStreamSynchronize(stream); } diff --git a/nvdiffrast_test.py b/nvdiffrast_test.py new file mode 100644 index 00000000..60477681 --- /dev/null +++ b/nvdiffrast_test.py @@ -0,0 +1,54 @@ +import bayes3d as b +import jax.numpy as jnp +import jax +from tqdm import tqdm +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +import numpy as np +import os +import trimesh +from dcolmap.hgps.pose import Pose +import viser +server = viser.ViserServer() + + +intrinsics = b.Intrinsics( + height=100, + width=100, + fx=200.0, fy=200.0, + cx=50., cy=50., + near=0.001, far=16.0 +) + +projection_matrix = b.camera._open_gl_projection_matrix( + intrinsics.height, + intrinsics.width, + intrinsics.fx, + intrinsics.fy, + intrinsics.cx, + intrinsics.cy, + intrinsics.near, + intrinsics.far, +) + + +meshes = [] + +path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") +bunny_mesh = trimesh.load(path) +bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) +meshes.append(bunny_mesh) + + +all_vertices = [jnp.array(mesh.vertices) for mesh in meshes] +all_faces = [jnp.array(mesh.faces) for mesh in meshes] +vertices_lens = jnp.array([len(verts) for verts in all_vertices]) +vertices_lens_cumsum = jnp.pad(jnp.cumsum(vertices_lens),(1,0)) +faces_lens = jnp.array([len(faces) for faces in all_faces]) +faces_lens_cumsum = jnp.pad(jnp.cumsum(faces_lens),(1,0)) + +vertices = jnp.concatenate(all_vertices, axis=0) +vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) +faces = jnp.concatenate([faces + vertices_lens_cumsum[i] for (i,faces) in enumerate(all_faces)]) + +resolution = jnp.array([intrinsics.height, intrinsics.width]) diff --git a/pytorch_check.py b/pytorch_check.py new file mode 100644 index 00000000..51f97f85 --- /dev/null +++ b/pytorch_check.py @@ -0,0 +1,130 @@ +import argparse +import os +import time +from collections import namedtuple + +import jax +import jax.numpy as jnp +import numpy as np +import torch +import nvdiffrast +import bayes3d as b +import nvdiffrast.torch as dr # modified nvdiffrast to expose backward fn call api +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +import pytorch3d.transforms + +# -------------------- +# transform points op +# -------------------- +def xfm_points(points, matrix): + """Transform points. + Args: + points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3] + matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4] + use_python: Use PyTorch's torch.matmul (for validation) + Returns: + Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4]. + """ + out = torch.matmul( + torch.nn.functional.pad(points, pad=(0, 1), mode="constant", value=1.0), + torch.transpose(matrix, 1, 2), + ) + return out + + +def posevec_to_matrix(position, quat): + return torch.cat( + ( + torch.cat((pytorch3d.transforms.quaternion_to_matrix(quat), position.unsqueeze(1)), 1), + torch.tensor([[0.0, 0.0, 0.0, 1.0]],device=device), + ), + 0, + ) + +intrinsics = b.Intrinsics( + height=100, + width=100, + fx=200.0, fy=200.0, + cx=50., cy=50., + near=0.001, far=16.0 +) +proj_cam = torch.from_numpy( + np.array( + b.camera._open_gl_projection_matrix( + intrinsics.height, + intrinsics.width, + intrinsics.fx, + intrinsics.fy, + intrinsics.cx, + intrinsics.cy, + intrinsics.near, + intrinsics.far, + ) + ) +).cuda() + + +torch_glctx = dr.RasterizeGLContext() + +import trimesh +meshes = [] + +path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") +bunny_mesh = trimesh.load(path) +bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) +meshes.append(bunny_mesh) + +m = meshes[0] +vertices = torch.from_numpy(m.vertices.astype(np.float32)).cuda()[None,...].contiguous() +faces = torch.from_numpy(m.faces.astype(np.int32)).cuda().contiguous() + +pos_gt, quat_gt = ( + torch.from_numpy(np.array([0.0, 0.0, 7.0]).astype(np.float32)).cuda(), + torch.from_numpy(np.array([0.2, 0.4, 1.0, 0.1]).astype(np.float32)).cuda() +) + +def render(pos, quat): + vertices_camera = xfm_points(vertices, posevec_to_matrix(pos, quat)[None,...]) + vertices_clip = xfm_points(vertices_camera[...,:3], proj_cam[None,...]).contiguous() + rast_out, _ = dr.rasterize(torch_glctx, vertices_clip, faces, resolution=[intrinsics.height, intrinsics.width]) + color , _ = dr.interpolate(vertices_camera[0,...,2:3].contiguous(), rast_out, faces) + return color + + +gt_color = render(pos_gt, quat_gt) + + + +pos, quat = ( + torch.from_numpy(np.array([-0.3, 0.1, 6.5]).astype(np.float32)).cuda(), + torch.from_numpy(np.array([0.5, 0.4, 1.0, 0.1]).astype(np.float32)).cuda() +) +pos.requires_grad = True +quat.requires_grad = True + +b.hstack_images( + [ + b.get_depth_image(gt_color[0,...,0].detach().cpu().numpy()), + b.get_depth_image(render(pos, quat)[0,...,0].detach().cpu().numpy()) + ] +).save("depth.png") + + +for _ in range(100): + color = render(pos, quat) + mask = (color[...,-1] > 0.0) * (gt_color[...,-1] > 0.0) + loss = (torch.abs(gt_color - color) * mask[...,None]).mean() + loss.backward() + print(loss) + pos.data -= 0.1 * pos.grad + quat.data -= 0.1 * quat.grad + pos.grad.zero_() + quat.grad.zero_() + +print(quat_gt / torch.linalg.norm(quat_gt)) +print(quat / torch.linalg.norm(quat)) + + + + +from IPython import embed; embed() \ No newline at end of file diff --git a/test_jax_renderer_bwd.py b/test_jax_renderer_bwd.py index 14881665..923cc2a6 100644 --- a/test_jax_renderer_bwd.py +++ b/test_jax_renderer_bwd.py @@ -7,7 +7,7 @@ import numpy as np import os import trimesh - +from dcolmap.hgps.pose import Pose import viser server = viser.ViserServer() @@ -70,7 +70,7 @@ 6, ), ) -def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): +def interpolate_(uv, triangle_id, poses, object_id, vertices, faces): relevant_vertices = vertices[faces[triangle_id-1]] pose_of_object = poses[object_id-1] relevant_vertices_transformed = relevant_vertices @ pose_of_object.T @@ -78,7 +78,21 @@ def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) return interpolated_value -def render(poses, vertices, faces, ranges, projection_matrix, resolution): +value = interpolate_( + jnp.array([0.15, 0.25]), + jnp.array([1]), + b.transform_from_posevec(jnp.array([0.2, 0.4, 1.0, 0.1, 0.3, 0.5]))[None,...], + jnp.array([1]), + jnp.array([[0.0, 0.3, 0.7, 1.0], [1.0, 0.3, 0.3, 1.0], [0.3, 1.0, 0.1, 1.0]]), + jnp.array([[0, 1, 2]]), +) +print(value) + +from IPython import embed; embed() + +def render(pos, quat, vertices, faces, ranges, projection_matrix, resolution): + pose = Pose(pos, quat) + poses = pose.as_matrix()[None, None,...] rast_out, rast_out_aux = jax_renderer.rasterize( poses, vertices, @@ -92,47 +106,76 @@ def render(poses, vertices, faces, ranges, projection_matrix, resolution): triangle_ids = rast_out_aux[...,1] mask = object_ids > 0 - interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) - image = interpolated_values * mask[...,None] + interpolated_values = interpolate_(uvs, triangle_ids, poses, object_ids, vertices, faces) + image = interpolated_values * mask[...,None] + (1.0 - mask[...,None]) * intrinsics.far return image -object_indices = jnp.array([1]) +object_indices = jnp.array([0]) ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) - -pose_gt = b.transform_from_pos(jnp.array([0.0, 0.0, 3.0])) +pos_gt, quat_gt =jnp.array([-.5, 0.0, 6.0]), jnp.array([1.0, 2.0, -1.0, 1.0]) image_gt = render( - pose_gt[None, None,...], + pos_gt, quat_gt, vertices, faces, ranges, projection_matrix, resolution ) -b.get_depth_image(image_gt[0,...,2]).save("sweep2.png") -def loss(pose_estim, image_gt): +def loss(pos, quat, image_gt): image_estim = render( - pose_estim[None, None,...], + pos,quat, vertices, faces, ranges, projection_matrix, resolution ) - mask = (image_gt[...,2] > 0) * (image_estim[...,2] > 0) - return (((image_gt[...,2] - image_estim[...,2]) * mask ) **2).mean() + mask = (image_estim[...,2] < intrinsics.far) * (image_gt[...,2] < intrinsics.far) + return (jnp.abs((image_gt[...,2] - image_estim[...,2]) * mask[...,None] )).mean() + +grad_func = jax.value_and_grad(loss, argnums=(0,1,)) + + -grad = jax.grad(loss, argnums=0) -pose_estim = b.transform_from_pos(jnp.array([0.0, 0.0, 2.0])) +import optax +optimizer = optax.adam(1e-2) +pos_estim, quat_estim = (jnp.array([0.0, 0.0, 6.5]), jnp.array([1.3, 2.5, -0.6, 1.0])) +opt_state = optimizer.init((pos_gt, quat_gt)) +# Optimize the initial scene. +progress_bar = tqdm(range(1000)) -for _ in range(100): - print("estim ", pose_estim[:3,3]) - pose_estim = pose_estim - 0.1 * grad(pose_estim, image_gt) - pose_estim = pose_estim.at[:3,:3].set(jnp.eye(3)) +image = render( + pos_estim, quat_estim, + vertices, + faces, + ranges, + projection_matrix, + resolution +) +b.hstack_images( +[ + b.get_depth_image(image_gt[0,...,2]), + b.get_depth_image(image[0,...,2]), + b.overlay_image( + b.get_depth_image(image_gt[0,...,2]), + b.get_depth_image(image[0,...,2]), + alpha=0.5 + ) +] +).save("sweep2.png") +from IPython import embed; embed() +progress_bar = tqdm(range(100)) +for _ in progress_bar: + print("estim ", pos_estim, quat_estim) + loss, grads = grad_func(pos_estim, quat_estim, image_gt) + updates, opt_state = optimizer.update(grads, opt_state) + (pos_estim, quat_estim) = optax.apply_updates((pos_estim, quat_estim), updates) + progress_bar.set_description(f"loss: {loss}") @@ -160,5 +203,3 @@ def loss(pose_estim, image_gt): - -from IPython import embed; embed() \ No newline at end of file From 5b049e225c7c546ea81c205a50a2f542e23e2212 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Mon, 19 Feb 2024 19:28:43 +0000 Subject: [PATCH 21/27] pytorch stop gradients shows that we need rasterize gradients --- pytorch_check.py | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/pytorch_check.py b/pytorch_check.py index 51f97f85..b017fd24 100644 --- a/pytorch_check.py +++ b/pytorch_check.py @@ -83,35 +83,30 @@ def posevec_to_matrix(position, quat): torch.from_numpy(np.array([0.2, 0.4, 1.0, 0.1]).astype(np.float32)).cuda() ) -def render(pos, quat): + +def render(pos, quat, stop_grad=False): vertices_camera = xfm_points(vertices, posevec_to_matrix(pos, quat)[None,...]) vertices_clip = xfm_points(vertices_camera[...,:3], proj_cam[None,...]).contiguous() rast_out, _ = dr.rasterize(torch_glctx, vertices_clip, faces, resolution=[intrinsics.height, intrinsics.width]) + if stop_grad: + rast_out = rast_out.detach() color , _ = dr.interpolate(vertices_camera[0,...,2:3].contiguous(), rast_out, faces) return color - gt_color = render(pos_gt, quat_gt) - -pos, quat = ( +init_pos, init_quat = ( torch.from_numpy(np.array([-0.3, 0.1, 6.5]).astype(np.float32)).cuda(), torch.from_numpy(np.array([0.5, 0.4, 1.0, 0.1]).astype(np.float32)).cuda() ) +pos,quat = init_pos.clone(), init_quat.clone() pos.requires_grad = True quat.requires_grad = True -b.hstack_images( - [ - b.get_depth_image(gt_color[0,...,0].detach().cpu().numpy()), - b.get_depth_image(render(pos, quat)[0,...,0].detach().cpu().numpy()) - ] -).save("depth.png") - -for _ in range(100): - color = render(pos, quat) +for _ in range(500): + color = render(pos, quat, stop_grad=False) mask = (color[...,-1] > 0.0) * (gt_color[...,-1] > 0.0) loss = (torch.abs(gt_color - color) * mask[...,None]).mean() loss.backward() @@ -120,11 +115,19 @@ def render(pos, quat): quat.data -= 0.1 * quat.grad pos.grad.zero_() quat.grad.zero_() - print(quat_gt / torch.linalg.norm(quat_gt)) print(quat / torch.linalg.norm(quat)) +b.hstack_images( + [ + b.get_depth_image(gt_color[0,...,0].detach().cpu().numpy()), + b.get_depth_image(render(init_pos, init_quat)[0,...,0].detach().cpu().numpy()), + b.get_depth_image(render(pos, quat)[0,...,0].detach().cpu().numpy()) + ] +).save("depth.png") + + from IPython import embed; embed() \ No newline at end of file From f5fa82b7432517faec339e206c0723d5703b68e1 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Tue, 20 Feb 2024 20:07:02 +0000 Subject: [PATCH 22/27] Bring back old renderer --- bayes3d/renderer.py | 422 ++++++++++ bayes3d/rendering/nvdiffrast/__init__.py | 9 + .../rendering/nvdiffrast/common/__init__.py | 11 + .../rendering/nvdiffrast/common/common.cpp | 60 ++ bayes3d/rendering/nvdiffrast/common/common.h | 253 ++++++ .../rendering/nvdiffrast/common/framework.h | 49 ++ .../rendering/nvdiffrast/common/glutil.cpp | 403 ++++++++++ bayes3d/rendering/nvdiffrast/common/glutil.h | 117 +++ .../nvdiffrast/common/glutil_extlist.h | 59 ++ bayes3d/rendering/nvdiffrast/common/ops.py | 82 ++ .../nvdiffrast/common/rasterize_gl.cpp | 720 ++++++++++++++++++ .../nvdiffrast/common/rasterize_gl.h | 129 ++++ .../nvdiffrast/common/torch_common.inl | 29 + .../rendering/nvdiffrast/common/torch_types.h | 65 ++ bayes3d/rendering/nvdiffrast/lib/setgpu.lib | Bin 0 -> 7254 bytes test_jax_renderer_fwd.py | 4 +- 16 files changed, 2410 insertions(+), 2 deletions(-) create mode 100644 bayes3d/renderer.py create mode 100644 bayes3d/rendering/nvdiffrast/__init__.py create mode 100644 bayes3d/rendering/nvdiffrast/common/__init__.py create mode 100644 bayes3d/rendering/nvdiffrast/common/common.cpp create mode 100644 bayes3d/rendering/nvdiffrast/common/common.h create mode 100644 bayes3d/rendering/nvdiffrast/common/framework.h create mode 100644 bayes3d/rendering/nvdiffrast/common/glutil.cpp create mode 100644 bayes3d/rendering/nvdiffrast/common/glutil.h create mode 100644 bayes3d/rendering/nvdiffrast/common/glutil_extlist.h create mode 100644 bayes3d/rendering/nvdiffrast/common/ops.py create mode 100644 bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp create mode 100644 bayes3d/rendering/nvdiffrast/common/rasterize_gl.h create mode 100644 bayes3d/rendering/nvdiffrast/common/torch_common.inl create mode 100644 bayes3d/rendering/nvdiffrast/common/torch_types.h create mode 100644 bayes3d/rendering/nvdiffrast/lib/setgpu.lib diff --git a/bayes3d/renderer.py b/bayes3d/renderer.py new file mode 100644 index 00000000..b3e6fce1 --- /dev/null +++ b/bayes3d/renderer.py @@ -0,0 +1,422 @@ +import functools +import gc + +import jax +import jax.numpy as jnp +import numpy as np +import trimesh +from jax import core, dtypes +from jax.core import ShapedArray +from jax.interpreters import batching, mlir, xla +from jax.lib import xla_client +from jaxlib.hlo_helpers import custom_call + +import bayes3d as b +import bayes3d as j +import bayes3d.camera +import bayes3d.rendering.nvdiffrast.common as dr + + +def _transform_image_zeros(image_jnp, intrinsics): + image_jnp_2 = jnp.concatenate( + [j.t3d.unproject_depth(image_jnp[:, :, 2], intrinsics), image_jnp[:, :, 3:]], + axis=-1, + ) + return image_jnp_2 + + +_transform_image_zeros_jit = jax.jit(_transform_image_zeros) +_transform_image_zeros_parallel = jax.vmap(_transform_image_zeros, in_axes=(0, None)) +_transform_image_zeros_parallel_jit = jax.jit(_transform_image_zeros_parallel) + + +def setup_renderer(intrinsics, num_layers=1024): + """Setup the renderer. + Args: + intrinsics (bayes3d.camera.Intrinsics): The camera intrinsics. + """ + b.RENDERER = Renderer(intrinsics, num_layers=num_layers) + + +class Renderer(object): + def __init__(self, intrinsics, num_layers=1024): + """A renderer for rendering meshes. + + Args: + intrinsics (bayes3d.camera.Intrinsics): The camera intrinsics. + num_layers (int, optional): The number of scenes to render in parallel. Defaults to 1024. + """ + self.height = intrinsics.height + self.width = intrinsics.width + self.intrinsics = intrinsics + + self.proj_matrix = b.camera._open_gl_projection_matrix( + intrinsics.height, + intrinsics.width, + intrinsics.fx, + intrinsics.fy, + intrinsics.cx, + intrinsics.cy, + intrinsics.near, + intrinsics.far, + ) + + self.renderer_env = dr.RasterizeGLContext( + self.height, self.width, output_db=False + ) + build_setup_primitive(self, self.height, self.width, num_layers).bind() + + self.meshes = [] + self.mesh_names = [] + self.model_box_dims = jnp.zeros((0, 3)) + + def clear_gpu_meshmem(self): + """ + Forcefully deallocate/clear any GPU memory used for mesh data. + NOTE: INITIALZE NEW renderer instance if using this function. + """ + # cpp files are modified so that the destructors deallocate GPU memory + self.renderer_env = None + # Release the meshes + self.meshes.clear() + self.mesh_names.clear() + self.model_box_dims = jnp.zeros((0, 3)) + # Force the garbage collector to run to reclaim memory + gc.collect() + + def add_mesh_from_file( + self, + mesh_filename, + mesh_name=None, + scaling_factor=1.0, + force=None, + center_mesh=True, + ): + """Add a mesh to the renderer from a file. + + Args: + mesh_filename (str): The filename of the mesh. + mesh_name (str, optional): The name of the mesh. Defaults to None. + scaling_factor (float, optional): The scaling factor to apply to the mesh. Defaults to 1.0. + force (str, optional): The file format to force. Defaults to None. + center_mesh (bool, optional): Whether to center the mesh. Defaults to True. + """ + mesh = trimesh.load(mesh_filename, force=force) + self.add_mesh( + mesh, + mesh_name=mesh_name, + scaling_factor=scaling_factor, + center_mesh=center_mesh, + ) + + def add_mesh(self, mesh, mesh_name=None, scaling_factor=1.0, center_mesh=True): + """Add a mesh to the renderer. + + Args: + mesh (trimesh.Trimesh): The mesh to add. + mesh_name (str, optional): The name of the mesh. Defaults to None. + scaling_factor (float, optional): The scaling factor to apply to the mesh. Defaults to 1.0. + center_mesh (bool, optional): Whether to center the mesh. Defaults to True. + """ + if mesh_name is None: + mesh_name = f"object_{len(self.meshes)}" + + mesh.vertices = mesh.vertices * scaling_factor + + bounding_box_dims, bounding_box_pose = bayes3d.utils.aabb(mesh.vertices) + if center_mesh: + if not jnp.isclose(bounding_box_pose[:3, 3], 0.0).all(): + print(f"Centering mesh with translation {bounding_box_pose[:3,3]}") + mesh.vertices = mesh.vertices - bounding_box_pose[:3, 3] + + self.meshes.append(mesh) + self.mesh_names.append(mesh_name) + + self.model_box_dims = jnp.vstack([self.model_box_dims, bounding_box_dims]) + + vertices = np.array(mesh.vertices) + vertices = np.concatenate( + [vertices, np.ones((*vertices.shape[:-1], 1))], axis=-1 + ) + triangles = np.array(mesh.faces) + prim = build_load_vertices_primitive(self) + prim.bind(jnp.float32(vertices), jnp.int32(triangles)) + + def render_many_custom_intrinsics(self, poses, indices, intrinsics): + proj_matrix = b.camera._open_gl_projection_matrix( + intrinsics.height, + intrinsics.width, + intrinsics.fx, + intrinsics.fy, + intrinsics.cx, + intrinsics.cy, + intrinsics.near, + intrinsics.far, + ) + images_jnp = _render_custom_call(self, poses, indices, proj_matrix)[0] + return _transform_image_zeros_parallel(images_jnp, intrinsics) + + def render_many(self, poses, indices): + """Render many scenes in parallel. + + Args: + poses (jnp.ndarray): The poses of the objects in the scene. Shape (N, M, 4, 4) + where N is the number of scenes and M is the number of objects. + and the last two dimensions are the 4x4 poses. + indices (jnp.ndarray): The indices of the objects to render. Shape (M,) + + Outputs: + jnp.ndarray: The rendered images. Shape (N, H, W, 4) where N is the number of scenes + the final dimension is the segmentation image. + """ + return self.render_many_custom_intrinsics(poses, indices, self.intrinsics) + + def render(self, poses, indices): + return self.render_many(poses[None, ...], indices)[0] + + def render_custom_intrinsics(self, poses, indices, intrinsics): + return self.render_many_custom_intrinsics( + poses[None, ...], indices, intrinsics + )[0] + + +# Useful reference for understanding the custom calls setup: +# https://github.com/dfm/extending-jax + + +@functools.lru_cache +def _register_custom_calls(): + for _name, _value in dr._get_plugin(gl=True).registrations().items(): + xla_client.register_custom_call_target(_name, _value, platform="gpu") + + +@functools.partial(jax.jit, static_argnums=(0,)) +def _render_custom_call(r: "Renderer", poses, indices, intrinsics_matrix): + return _build_render_primitive(r).bind(poses, indices, intrinsics_matrix) + + +@functools.lru_cache(maxsize=None) +def _build_render_primitive(r: "Renderer"): + _register_custom_calls() + + # For JIT compilation we need a function to evaluate the shape and dtype of the + # outputs of our op for some given inputs + def _render_abstract(poses, indices, intrinsics_matrix): + num_images = poses.shape[0] + if poses.shape[1] != indices.shape[0]: + raise ValueError( + f"Poses Shape: {poses.shape} Indices Shape: {indices.shape}" + ) + dtype = dtypes.canonicalize_dtype(poses.dtype) + return [ + ShapedArray((num_images, r.height, r.width, 4), dtype), + ShapedArray((), dtype), + ] + + # Provide an MLIR "lowering" of the render primitive. + def _render_lowering(ctx, poses, indices, intrinsics_matrix): + # Extract the numpy type of the inputs + poses_aval, indices_aval, intrinsics_matrix_aval = ctx.avals_in + if poses_aval.ndim != 4: + raise NotImplementedError( + f"Only 4D inputs supported: got {poses_aval.shape}" + ) + if indices_aval.ndim != 1: + raise NotImplementedError( + f"Only 1D inputs supported: got {indices_aval.shape}" + ) + + np_dtype = np.dtype(poses_aval.dtype) + if np_dtype != np.float32: + raise NotImplementedError(f"Unsupported poses dtype {np_dtype}") + if np.dtype(indices_aval.dtype) != np.int32: + raise NotImplementedError(f"Unsupported indices dtype {indices_aval.dtype}") + + num_images, num_objects = poses_aval.shape[:2] + out_shp_dtype = mlir.ir.RankedTensorType.get( + [num_images, r.height, r.width, 4], mlir.dtype_to_ir_type(poses_aval.dtype) + ) + + if num_objects != indices_aval.shape[0]: + raise ValueError( + f"Poses Shape: {poses_aval.shape} Indices Shape: {indices_aval.shape}" + ) + opaque = dr._get_plugin(gl=True).build_rasterize_descriptor( + r.renderer_env.cpp_wrapper, [num_objects, num_images] + ) + + scalar_dummy = mlir.ir.RankedTensorType.get( + [], mlir.dtype_to_ir_type(poses_aval.dtype) + ) + op_name = "jax_rasterize_fwd_gl" + return custom_call( + op_name, + # Output types + result_types=[out_shp_dtype, scalar_dummy], + # The inputs: + operands=[poses, indices, intrinsics_matrix], + # Layout specification: + operand_layouts=[ + (3, 2, 0, 1), + (0,), + ( + 1, + 0, + ), + ], + result_layouts=[(3, 2, 1, 0), ()], + # GPU specific additional data + backend_config=opaque, + ).results + + # ************************************ + # * SUPPORT FOR BATCHING WITH VMAP * + # ************************************ + def _render_batch(args, axes): + poses, indices, intrinsics_matrix = args + if poses.ndim != 5: + raise NotImplementedError("Underlying primitive must operate on 4D poses.") + + original_shape = poses.shape + poses = jnp.moveaxis(poses, axes[0], 0) + size_1 = poses.shape[0] + size_2 = poses.shape[1] + num_objects = poses.shape[2] + poses = poses.reshape(size_1 * size_2, num_objects, 4, 4) + + if poses.shape[1] != indices.shape[0]: + raise ValueError( + f"Poses Original Shape: {original_shape} Poses Shape: {poses.shape} Indices Shape: {indices.shape}" + ) + if poses.shape[-2:] != (4, 4): + raise ValueError( + f"Poses Original Shape: {original_shape} Poses Shape: {poses.shape} Indices Shape: {indices.shape}" + ) + renders, dummy = _render_custom_call(r, poses, indices, intrinsics_matrix) + + renders = renders.reshape(size_1, size_2, *renders.shape[1:]) + out_axes = 0, None + return (renders, dummy), out_axes + + # ********************************************* + # * BOILERPLATE TO REGISTER THE OP WITH JAX * + # ********************************************* + _render_prim = core.Primitive(f"render_multiple_{id(r)}") + _render_prim.multiple_results = True + _render_prim.def_impl(functools.partial(xla.apply_primitive, _render_prim)) + _render_prim.def_abstract_eval(_render_abstract) + + # Connect the XLA translation rules for JIT compilation + mlir.register_lowering(_render_prim, _render_lowering, platform="gpu") + batching.primitive_batchers[_render_prim] = _render_batch + + return _render_prim + + +@functools.lru_cache(maxsize=None) +def build_setup_primitive(r: "Renderer", h, w, num_layers): + _register_custom_calls() + # print('build_setup_primitive') + + # For JIT compilation we need a function to evaluate the shape and dtype of the + # outputs of our op for some given inputs + def _setup_abstract(): + # print('setup abstract eval') + dtype = dtypes.canonicalize_dtype(np.float32) + return [ShapedArray((), dtype), ShapedArray((), dtype)] + + # Provide an MLIR "lowering" of the load_vertices primitive. + def _setup_lowering(ctx): + # print('lowering setup!') + + opaque = dr._get_plugin(gl=True).build_setup_descriptor( + r.renderer_env.cpp_wrapper, h, w, num_layers + ) + + scalar_dummy = mlir.ir.RankedTensorType.get( + [], mlir.dtype_to_ir_type(np.dtype(np.float32)) + ) + op_name = "jax_setup" + return custom_call( + op_name, + # Output types + result_types=[scalar_dummy, scalar_dummy], + # The inputs: + operands=[], + # Layout specification: + operand_layouts=[], + result_layouts=[(), ()], + # GPU specific additional data + backend_config=opaque, + ).results + + # ********************************************* + # * BOILERPLATE TO REGISTER THE OP WITH JAX * + # ********************************************* + _prim = core.Primitive(f"setup__{id(r)}") + _prim.multiple_results = True + _prim.def_impl(functools.partial(xla.apply_primitive, _prim)) + _prim.def_abstract_eval(_setup_abstract) + + # Connect the XLA translation rules for JIT compilation + mlir.register_lowering(_prim, _setup_lowering, platform="gpu") + + return _prim + + +@functools.lru_cache(maxsize=None) +def build_load_vertices_primitive(r: "Renderer"): + _register_custom_calls() + # print('build_load_vertices_primitive') + + # For JIT compilation we need a function to evaluate the shape and dtype of the + # outputs of our op for some given inputs + def _load_vertices_abstract(vertices, triangles): + # print('load_vertices abstract eval:', vertices, triangles) + dtype = dtypes.canonicalize_dtype(np.float32) + return [ShapedArray((), dtype), ShapedArray((), dtype)] + + # Provide an MLIR "lowering" of the load_vertices primitive. + def _load_vertices_lowering(ctx, vertices, triangles): + # print('lowering load_vertices!') + # Extract the numpy type of the inputs + vertices_aval, triangles_aval = ctx.avals_in + + if (dt := np.dtype(vertices_aval.dtype)) != np.float32: + raise NotImplementedError(f"Unsupported vertices dtype {dt}") + if (dt := np.dtype(triangles_aval.dtype)) != np.int32: + raise NotImplementedError(f"Unsupported triangles dtype {dt}") + + opaque = dr._get_plugin(gl=True).build_load_vertices_descriptor( + r.renderer_env.cpp_wrapper, vertices_aval.shape[0], triangles_aval.shape[0] + ) + + scalar_dummy = mlir.ir.RankedTensorType.get( + [], mlir.dtype_to_ir_type(np.dtype(np.float32)) + ) + op_name = "jax_load_vertices" + return custom_call( + op_name, + # Output types + result_types=[scalar_dummy, scalar_dummy], + # The inputs: + operands=[vertices, triangles], + # Layout specification: + operand_layouts=[(1, 0), (1, 0)], + result_layouts=[(), ()], + # GPU specific additional data + backend_config=opaque, + ).results + + # ********************************************* + # * BOILERPLATE TO REGISTER THE OP WITH JAX * + # ********************************************* + _prim = core.Primitive(f"load_vertices__{id(r)}") + _prim.multiple_results = True + _prim.def_impl(functools.partial(xla.apply_primitive, _prim)) + _prim.def_abstract_eval(_load_vertices_abstract) + + # Connect the XLA translation rules for JIT compilation + mlir.register_lowering(_prim, _load_vertices_lowering, platform="gpu") + + return _prim diff --git a/bayes3d/rendering/nvdiffrast/__init__.py b/bayes3d/rendering/nvdiffrast/__init__.py new file mode 100644 index 00000000..53d2ea76 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +__version__ = "0.3.0" diff --git a/bayes3d/rendering/nvdiffrast/common/__init__.py b/bayes3d/rendering/nvdiffrast/common/__init__.py new file mode 100644 index 00000000..2d9e624d --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +from .ops import RasterizeGLContext, _get_plugin + +__all__ = ["RasterizeGLContext", "_get_plugin"] diff --git a/bayes3d/rendering/nvdiffrast/common/common.cpp b/bayes3d/rendering/nvdiffrast/common/common.cpp new file mode 100644 index 00000000..e566c035 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/common.cpp @@ -0,0 +1,60 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include + +//------------------------------------------------------------------------ +// Block and grid size calculators for kernel launches. + +dim3 getLaunchBlockSize(int maxWidth, int maxHeight, int width, int height) +{ + int maxThreads = maxWidth * maxHeight; + if (maxThreads <= 1 || (width * height) <= 1) + return dim3(1, 1, 1); // Degenerate. + + // Start from max size. + int bw = maxWidth; + int bh = maxHeight; + + // Optimizations for weirdly sized buffers. + if (width < bw) + { + // Decrease block width to smallest power of two that covers the buffer width. + while ((bw >> 1) >= width) + bw >>= 1; + + // Maximize height. + bh = maxThreads / bw; + if (bh > height) + bh = height; + } + else if (height < bh) + { + // Halve height and double width until fits completely inside buffer vertically. + while (bh > height) + { + bh >>= 1; + if (bw < width) + bw <<= 1; + } + } + + // Done. + return dim3(bw, bh, 1); +} + +dim3 getLaunchGridSize(dim3 blockSize, int width, int height, int depth) +{ + dim3 gridSize; + gridSize.x = (width - 1) / blockSize.x + 1; + gridSize.y = (height - 1) / blockSize.y + 1; + gridSize.z = (depth - 1) / blockSize.z + 1; + return gridSize; +} + +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/common.h b/bayes3d/rendering/nvdiffrast/common/common.h new file mode 100644 index 00000000..8df48ed7 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/common.h @@ -0,0 +1,253 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#pragma once +#include +#include + +//------------------------------------------------------------------------ +// C++ helper function prototypes. + +dim3 getLaunchBlockSize(int maxWidth, int maxHeight, int width, int height); +dim3 getLaunchGridSize(dim3 blockSize, int width, int height, int depth); + +//------------------------------------------------------------------------ +// The rest is CUDA device code specific stuff. + +#ifdef __CUDACC__ + +//------------------------------------------------------------------------ +// Helpers for CUDA vector types. + +static __device__ __forceinline__ float2& operator*= (float2& a, const float2& b) { a.x *= b.x; a.y *= b.y; return a; } +static __device__ __forceinline__ float2& operator+= (float2& a, const float2& b) { a.x += b.x; a.y += b.y; return a; } +static __device__ __forceinline__ float2& operator-= (float2& a, const float2& b) { a.x -= b.x; a.y -= b.y; return a; } +static __device__ __forceinline__ float2& operator*= (float2& a, float b) { a.x *= b; a.y *= b; return a; } +static __device__ __forceinline__ float2& operator+= (float2& a, float b) { a.x += b; a.y += b; return a; } +static __device__ __forceinline__ float2& operator-= (float2& a, float b) { a.x -= b; a.y -= b; return a; } +static __device__ __forceinline__ float2 operator* (const float2& a, const float2& b) { return make_float2(a.x * b.x, a.y * b.y); } +static __device__ __forceinline__ float2 operator+ (const float2& a, const float2& b) { return make_float2(a.x + b.x, a.y + b.y); } +static __device__ __forceinline__ float2 operator- (const float2& a, const float2& b) { return make_float2(a.x - b.x, a.y - b.y); } +static __device__ __forceinline__ float2 operator* (const float2& a, float b) { return make_float2(a.x * b, a.y * b); } +static __device__ __forceinline__ float2 operator+ (const float2& a, float b) { return make_float2(a.x + b, a.y + b); } +static __device__ __forceinline__ float2 operator- (const float2& a, float b) { return make_float2(a.x - b, a.y - b); } +static __device__ __forceinline__ float2 operator* (float a, const float2& b) { return make_float2(a * b.x, a * b.y); } +static __device__ __forceinline__ float2 operator+ (float a, const float2& b) { return make_float2(a + b.x, a + b.y); } +static __device__ __forceinline__ float2 operator- (float a, const float2& b) { return make_float2(a - b.x, a - b.y); } +static __device__ __forceinline__ float2 operator- (const float2& a) { return make_float2(-a.x, -a.y); } +static __device__ __forceinline__ float3& operator*= (float3& a, const float3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } +static __device__ __forceinline__ float3& operator+= (float3& a, const float3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } +static __device__ __forceinline__ float3& operator-= (float3& a, const float3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } +static __device__ __forceinline__ float3& operator*= (float3& a, float b) { a.x *= b; a.y *= b; a.z *= b; return a; } +static __device__ __forceinline__ float3& operator+= (float3& a, float b) { a.x += b; a.y += b; a.z += b; return a; } +static __device__ __forceinline__ float3& operator-= (float3& a, float b) { a.x -= b; a.y -= b; a.z -= b; return a; } +static __device__ __forceinline__ float3 operator* (const float3& a, const float3& b) { return make_float3(a.x * b.x, a.y * b.y, a.z * b.z); } +static __device__ __forceinline__ float3 operator+ (const float3& a, const float3& b) { return make_float3(a.x + b.x, a.y + b.y, a.z + b.z); } +static __device__ __forceinline__ float3 operator- (const float3& a, const float3& b) { return make_float3(a.x - b.x, a.y - b.y, a.z - b.z); } +static __device__ __forceinline__ float3 operator* (const float3& a, float b) { return make_float3(a.x * b, a.y * b, a.z * b); } +static __device__ __forceinline__ float3 operator+ (const float3& a, float b) { return make_float3(a.x + b, a.y + b, a.z + b); } +static __device__ __forceinline__ float3 operator- (const float3& a, float b) { return make_float3(a.x - b, a.y - b, a.z - b); } +static __device__ __forceinline__ float3 operator* (float a, const float3& b) { return make_float3(a * b.x, a * b.y, a * b.z); } +static __device__ __forceinline__ float3 operator+ (float a, const float3& b) { return make_float3(a + b.x, a + b.y, a + b.z); } +static __device__ __forceinline__ float3 operator- (float a, const float3& b) { return make_float3(a - b.x, a - b.y, a - b.z); } +static __device__ __forceinline__ float3 operator- (const float3& a) { return make_float3(-a.x, -a.y, -a.z); } +static __device__ __forceinline__ float4& operator*= (float4& a, const float4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } +static __device__ __forceinline__ float4& operator+= (float4& a, const float4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } +static __device__ __forceinline__ float4& operator-= (float4& a, const float4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } +static __device__ __forceinline__ float4& operator*= (float4& a, float b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } +static __device__ __forceinline__ float4& operator+= (float4& a, float b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } +static __device__ __forceinline__ float4& operator-= (float4& a, float b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } +static __device__ __forceinline__ float4 operator* (const float4& a, const float4& b) { return make_float4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } +static __device__ __forceinline__ float4 operator+ (const float4& a, const float4& b) { return make_float4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } +static __device__ __forceinline__ float4 operator- (const float4& a, const float4& b) { return make_float4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } +static __device__ __forceinline__ float4 operator* (const float4& a, float b) { return make_float4(a.x * b, a.y * b, a.z * b, a.w * b); } +static __device__ __forceinline__ float4 operator+ (const float4& a, float b) { return make_float4(a.x + b, a.y + b, a.z + b, a.w + b); } +static __device__ __forceinline__ float4 operator- (const float4& a, float b) { return make_float4(a.x - b, a.y - b, a.z - b, a.w - b); } +static __device__ __forceinline__ float4 operator* (float a, const float4& b) { return make_float4(a * b.x, a * b.y, a * b.z, a * b.w); } +static __device__ __forceinline__ float4 operator+ (float a, const float4& b) { return make_float4(a + b.x, a + b.y, a + b.z, a + b.w); } +static __device__ __forceinline__ float4 operator- (float a, const float4& b) { return make_float4(a - b.x, a - b.y, a - b.z, a - b.w); } +static __device__ __forceinline__ float4 operator- (const float4& a) { return make_float4(-a.x, -a.y, -a.z, -a.w); } +static __device__ __forceinline__ int2& operator*= (int2& a, const int2& b) { a.x *= b.x; a.y *= b.y; return a; } +static __device__ __forceinline__ int2& operator+= (int2& a, const int2& b) { a.x += b.x; a.y += b.y; return a; } +static __device__ __forceinline__ int2& operator-= (int2& a, const int2& b) { a.x -= b.x; a.y -= b.y; return a; } +static __device__ __forceinline__ int2& operator*= (int2& a, int b) { a.x *= b; a.y *= b; return a; } +static __device__ __forceinline__ int2& operator+= (int2& a, int b) { a.x += b; a.y += b; return a; } +static __device__ __forceinline__ int2& operator-= (int2& a, int b) { a.x -= b; a.y -= b; return a; } +static __device__ __forceinline__ int2 operator* (const int2& a, const int2& b) { return make_int2(a.x * b.x, a.y * b.y); } +static __device__ __forceinline__ int2 operator+ (const int2& a, const int2& b) { return make_int2(a.x + b.x, a.y + b.y); } +static __device__ __forceinline__ int2 operator- (const int2& a, const int2& b) { return make_int2(a.x - b.x, a.y - b.y); } +static __device__ __forceinline__ int2 operator* (const int2& a, int b) { return make_int2(a.x * b, a.y * b); } +static __device__ __forceinline__ int2 operator+ (const int2& a, int b) { return make_int2(a.x + b, a.y + b); } +static __device__ __forceinline__ int2 operator- (const int2& a, int b) { return make_int2(a.x - b, a.y - b); } +static __device__ __forceinline__ int2 operator* (int a, const int2& b) { return make_int2(a * b.x, a * b.y); } +static __device__ __forceinline__ int2 operator+ (int a, const int2& b) { return make_int2(a + b.x, a + b.y); } +static __device__ __forceinline__ int2 operator- (int a, const int2& b) { return make_int2(a - b.x, a - b.y); } +static __device__ __forceinline__ int2 operator- (const int2& a) { return make_int2(-a.x, -a.y); } +static __device__ __forceinline__ int3& operator*= (int3& a, const int3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } +static __device__ __forceinline__ int3& operator+= (int3& a, const int3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } +static __device__ __forceinline__ int3& operator-= (int3& a, const int3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } +static __device__ __forceinline__ int3& operator*= (int3& a, int b) { a.x *= b; a.y *= b; a.z *= b; return a; } +static __device__ __forceinline__ int3& operator+= (int3& a, int b) { a.x += b; a.y += b; a.z += b; return a; } +static __device__ __forceinline__ int3& operator-= (int3& a, int b) { a.x -= b; a.y -= b; a.z -= b; return a; } +static __device__ __forceinline__ int3 operator* (const int3& a, const int3& b) { return make_int3(a.x * b.x, a.y * b.y, a.z * b.z); } +static __device__ __forceinline__ int3 operator+ (const int3& a, const int3& b) { return make_int3(a.x + b.x, a.y + b.y, a.z + b.z); } +static __device__ __forceinline__ int3 operator- (const int3& a, const int3& b) { return make_int3(a.x - b.x, a.y - b.y, a.z - b.z); } +static __device__ __forceinline__ int3 operator* (const int3& a, int b) { return make_int3(a.x * b, a.y * b, a.z * b); } +static __device__ __forceinline__ int3 operator+ (const int3& a, int b) { return make_int3(a.x + b, a.y + b, a.z + b); } +static __device__ __forceinline__ int3 operator- (const int3& a, int b) { return make_int3(a.x - b, a.y - b, a.z - b); } +static __device__ __forceinline__ int3 operator* (int a, const int3& b) { return make_int3(a * b.x, a * b.y, a * b.z); } +static __device__ __forceinline__ int3 operator+ (int a, const int3& b) { return make_int3(a + b.x, a + b.y, a + b.z); } +static __device__ __forceinline__ int3 operator- (int a, const int3& b) { return make_int3(a - b.x, a - b.y, a - b.z); } +static __device__ __forceinline__ int3 operator- (const int3& a) { return make_int3(-a.x, -a.y, -a.z); } +static __device__ __forceinline__ int4& operator*= (int4& a, const int4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } +static __device__ __forceinline__ int4& operator+= (int4& a, const int4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } +static __device__ __forceinline__ int4& operator-= (int4& a, const int4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } +static __device__ __forceinline__ int4& operator*= (int4& a, int b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } +static __device__ __forceinline__ int4& operator+= (int4& a, int b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } +static __device__ __forceinline__ int4& operator-= (int4& a, int b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } +static __device__ __forceinline__ int4 operator* (const int4& a, const int4& b) { return make_int4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } +static __device__ __forceinline__ int4 operator+ (const int4& a, const int4& b) { return make_int4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } +static __device__ __forceinline__ int4 operator- (const int4& a, const int4& b) { return make_int4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } +static __device__ __forceinline__ int4 operator* (const int4& a, int b) { return make_int4(a.x * b, a.y * b, a.z * b, a.w * b); } +static __device__ __forceinline__ int4 operator+ (const int4& a, int b) { return make_int4(a.x + b, a.y + b, a.z + b, a.w + b); } +static __device__ __forceinline__ int4 operator- (const int4& a, int b) { return make_int4(a.x - b, a.y - b, a.z - b, a.w - b); } +static __device__ __forceinline__ int4 operator* (int a, const int4& b) { return make_int4(a * b.x, a * b.y, a * b.z, a * b.w); } +static __device__ __forceinline__ int4 operator+ (int a, const int4& b) { return make_int4(a + b.x, a + b.y, a + b.z, a + b.w); } +static __device__ __forceinline__ int4 operator- (int a, const int4& b) { return make_int4(a - b.x, a - b.y, a - b.z, a - b.w); } +static __device__ __forceinline__ int4 operator- (const int4& a) { return make_int4(-a.x, -a.y, -a.z, -a.w); } +static __device__ __forceinline__ uint2& operator*= (uint2& a, const uint2& b) { a.x *= b.x; a.y *= b.y; return a; } +static __device__ __forceinline__ uint2& operator+= (uint2& a, const uint2& b) { a.x += b.x; a.y += b.y; return a; } +static __device__ __forceinline__ uint2& operator-= (uint2& a, const uint2& b) { a.x -= b.x; a.y -= b.y; return a; } +static __device__ __forceinline__ uint2& operator*= (uint2& a, unsigned int b) { a.x *= b; a.y *= b; return a; } +static __device__ __forceinline__ uint2& operator+= (uint2& a, unsigned int b) { a.x += b; a.y += b; return a; } +static __device__ __forceinline__ uint2& operator-= (uint2& a, unsigned int b) { a.x -= b; a.y -= b; return a; } +static __device__ __forceinline__ uint2 operator* (const uint2& a, const uint2& b) { return make_uint2(a.x * b.x, a.y * b.y); } +static __device__ __forceinline__ uint2 operator+ (const uint2& a, const uint2& b) { return make_uint2(a.x + b.x, a.y + b.y); } +static __device__ __forceinline__ uint2 operator- (const uint2& a, const uint2& b) { return make_uint2(a.x - b.x, a.y - b.y); } +static __device__ __forceinline__ uint2 operator* (const uint2& a, unsigned int b) { return make_uint2(a.x * b, a.y * b); } +static __device__ __forceinline__ uint2 operator+ (const uint2& a, unsigned int b) { return make_uint2(a.x + b, a.y + b); } +static __device__ __forceinline__ uint2 operator- (const uint2& a, unsigned int b) { return make_uint2(a.x - b, a.y - b); } +static __device__ __forceinline__ uint2 operator* (unsigned int a, const uint2& b) { return make_uint2(a * b.x, a * b.y); } +static __device__ __forceinline__ uint2 operator+ (unsigned int a, const uint2& b) { return make_uint2(a + b.x, a + b.y); } +static __device__ __forceinline__ uint2 operator- (unsigned int a, const uint2& b) { return make_uint2(a - b.x, a - b.y); } +static __device__ __forceinline__ uint3& operator*= (uint3& a, const uint3& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; return a; } +static __device__ __forceinline__ uint3& operator+= (uint3& a, const uint3& b) { a.x += b.x; a.y += b.y; a.z += b.z; return a; } +static __device__ __forceinline__ uint3& operator-= (uint3& a, const uint3& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; return a; } +static __device__ __forceinline__ uint3& operator*= (uint3& a, unsigned int b) { a.x *= b; a.y *= b; a.z *= b; return a; } +static __device__ __forceinline__ uint3& operator+= (uint3& a, unsigned int b) { a.x += b; a.y += b; a.z += b; return a; } +static __device__ __forceinline__ uint3& operator-= (uint3& a, unsigned int b) { a.x -= b; a.y -= b; a.z -= b; return a; } +static __device__ __forceinline__ uint3 operator* (const uint3& a, const uint3& b) { return make_uint3(a.x * b.x, a.y * b.y, a.z * b.z); } +static __device__ __forceinline__ uint3 operator+ (const uint3& a, const uint3& b) { return make_uint3(a.x + b.x, a.y + b.y, a.z + b.z); } +static __device__ __forceinline__ uint3 operator- (const uint3& a, const uint3& b) { return make_uint3(a.x - b.x, a.y - b.y, a.z - b.z); } +static __device__ __forceinline__ uint3 operator* (const uint3& a, unsigned int b) { return make_uint3(a.x * b, a.y * b, a.z * b); } +static __device__ __forceinline__ uint3 operator+ (const uint3& a, unsigned int b) { return make_uint3(a.x + b, a.y + b, a.z + b); } +static __device__ __forceinline__ uint3 operator- (const uint3& a, unsigned int b) { return make_uint3(a.x - b, a.y - b, a.z - b); } +static __device__ __forceinline__ uint3 operator* (unsigned int a, const uint3& b) { return make_uint3(a * b.x, a * b.y, a * b.z); } +static __device__ __forceinline__ uint3 operator+ (unsigned int a, const uint3& b) { return make_uint3(a + b.x, a + b.y, a + b.z); } +static __device__ __forceinline__ uint3 operator- (unsigned int a, const uint3& b) { return make_uint3(a - b.x, a - b.y, a - b.z); } +static __device__ __forceinline__ uint4& operator*= (uint4& a, const uint4& b) { a.x *= b.x; a.y *= b.y; a.z *= b.z; a.w *= b.w; return a; } +static __device__ __forceinline__ uint4& operator+= (uint4& a, const uint4& b) { a.x += b.x; a.y += b.y; a.z += b.z; a.w += b.w; return a; } +static __device__ __forceinline__ uint4& operator-= (uint4& a, const uint4& b) { a.x -= b.x; a.y -= b.y; a.z -= b.z; a.w -= b.w; return a; } +static __device__ __forceinline__ uint4& operator*= (uint4& a, unsigned int b) { a.x *= b; a.y *= b; a.z *= b; a.w *= b; return a; } +static __device__ __forceinline__ uint4& operator+= (uint4& a, unsigned int b) { a.x += b; a.y += b; a.z += b; a.w += b; return a; } +static __device__ __forceinline__ uint4& operator-= (uint4& a, unsigned int b) { a.x -= b; a.y -= b; a.z -= b; a.w -= b; return a; } +static __device__ __forceinline__ uint4 operator* (const uint4& a, const uint4& b) { return make_uint4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w); } +static __device__ __forceinline__ uint4 operator+ (const uint4& a, const uint4& b) { return make_uint4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w); } +static __device__ __forceinline__ uint4 operator- (const uint4& a, const uint4& b) { return make_uint4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w); } +static __device__ __forceinline__ uint4 operator* (const uint4& a, unsigned int b) { return make_uint4(a.x * b, a.y * b, a.z * b, a.w * b); } +static __device__ __forceinline__ uint4 operator+ (const uint4& a, unsigned int b) { return make_uint4(a.x + b, a.y + b, a.z + b, a.w + b); } +static __device__ __forceinline__ uint4 operator- (const uint4& a, unsigned int b) { return make_uint4(a.x - b, a.y - b, a.z - b, a.w - b); } +static __device__ __forceinline__ uint4 operator* (unsigned int a, const uint4& b) { return make_uint4(a * b.x, a * b.y, a * b.z, a * b.w); } +static __device__ __forceinline__ uint4 operator+ (unsigned int a, const uint4& b) { return make_uint4(a + b.x, a + b.y, a + b.z, a + b.w); } +static __device__ __forceinline__ uint4 operator- (unsigned int a, const uint4& b) { return make_uint4(a - b.x, a - b.y, a - b.z, a - b.w); } + +template static __device__ __forceinline__ T zero_value(void); +template<> __device__ __forceinline__ float zero_value (void) { return 0.f; } +template<> __device__ __forceinline__ float2 zero_value(void) { return make_float2(0.f, 0.f); } +template<> __device__ __forceinline__ float4 zero_value(void) { return make_float4(0.f, 0.f, 0.f, 0.f); } +static __device__ __forceinline__ float3 make_float3(const float2& a, float b) { return make_float3(a.x, a.y, b); } +static __device__ __forceinline__ float4 make_float4(const float3& a, float b) { return make_float4(a.x, a.y, a.z, b); } +static __device__ __forceinline__ float4 make_float4(const float2& a, const float2& b) { return make_float4(a.x, a.y, b.x, b.y); } +static __device__ __forceinline__ int3 make_int3(const int2& a, int b) { return make_int3(a.x, a.y, b); } +static __device__ __forceinline__ int4 make_int4(const int3& a, int b) { return make_int4(a.x, a.y, a.z, b); } +static __device__ __forceinline__ int4 make_int4(const int2& a, const int2& b) { return make_int4(a.x, a.y, b.x, b.y); } +static __device__ __forceinline__ uint3 make_uint3(const uint2& a, unsigned int b) { return make_uint3(a.x, a.y, b); } +static __device__ __forceinline__ uint4 make_uint4(const uint3& a, unsigned int b) { return make_uint4(a.x, a.y, a.z, b); } +static __device__ __forceinline__ uint4 make_uint4(const uint2& a, const uint2& b) { return make_uint4(a.x, a.y, b.x, b.y); } + +template static __device__ __forceinline__ void swap(T& a, T& b) { T temp = a; a = b; b = temp; } + +//------------------------------------------------------------------------ +// Coalesced atomics. These are all done via macros. + +#if __CUDA_ARCH__ >= 700 // Warp match instruction __match_any_sync() is only available on compute capability 7.x and higher + +#define CA_TEMP _ca_temp +#define CA_TEMP_PARAM float* CA_TEMP +#define CA_DECLARE_TEMP(threads_per_block) \ + __shared__ float CA_TEMP[(threads_per_block)] + +#define CA_SET_GROUP_MASK(group, thread_mask) \ + bool _ca_leader; \ + float* _ca_ptr; \ + do { \ + int tidx = threadIdx.x + blockDim.x * threadIdx.y; \ + int lane = tidx & 31; \ + int warp = tidx >> 5; \ + int tmask = __match_any_sync((thread_mask), (group)); \ + int leader = __ffs(tmask) - 1; \ + _ca_leader = (leader == lane); \ + _ca_ptr = &_ca_temp[((warp << 5) + leader)]; \ + } while(0) + +#define CA_SET_GROUP(group) \ + CA_SET_GROUP_MASK((group), 0xffffffffu) + +#define caAtomicAdd(ptr, value) \ + do { \ + if (_ca_leader) \ + *_ca_ptr = 0.f; \ + atomicAdd(_ca_ptr, (value)); \ + if (_ca_leader) \ + atomicAdd((ptr), *_ca_ptr); \ + } while(0) + +#define caAtomicAdd3_xyw(ptr, x, y, w) \ + do { \ + caAtomicAdd((ptr), (x)); \ + caAtomicAdd((ptr)+1, (y)); \ + caAtomicAdd((ptr)+3, (w)); \ + } while(0) + +#define caAtomicAddTexture(ptr, level, idx, value) \ + do { \ + CA_SET_GROUP((idx) ^ ((level) << 27)); \ + caAtomicAdd((ptr)+(idx), (value)); \ + } while(0) + +//------------------------------------------------------------------------ +// Disable atomic coalescing for compute capability lower than 7.x + +#else // __CUDA_ARCH__ >= 700 +#define CA_TEMP _ca_temp +#define CA_TEMP_PARAM float CA_TEMP +#define CA_DECLARE_TEMP(threads_per_block) CA_TEMP_PARAM +#define CA_SET_GROUP_MASK(group, thread_mask) +#define CA_SET_GROUP(group) +#define caAtomicAdd(ptr, value) atomicAdd((ptr), (value)) +#define caAtomicAdd3_xyw(ptr, x, y, w) \ + do { \ + atomicAdd((ptr), (x)); \ + atomicAdd((ptr)+1, (y)); \ + atomicAdd((ptr)+3, (w)); \ + } while(0) +#define caAtomicAddTexture(ptr, level, idx, value) atomicAdd((ptr)+(idx), (value)) +#endif // __CUDA_ARCH__ >= 700 + +//------------------------------------------------------------------------ +#endif // __CUDACC__ diff --git a/bayes3d/rendering/nvdiffrast/common/framework.h b/bayes3d/rendering/nvdiffrast/common/framework.h new file mode 100644 index 00000000..75125ac5 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/framework.h @@ -0,0 +1,49 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#pragma once + +// Framework-specific macros to enable code sharing. + +//------------------------------------------------------------------------ +// Tensorflow. + +#ifdef NVDR_TENSORFLOW +#define EIGEN_USE_GPU +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/platform/default/logging.h" +using namespace tensorflow; +using namespace tensorflow::shape_inference; +#define NVDR_CTX_ARGS OpKernelContext* _nvdr_ctx +#define NVDR_CTX_PARAMS _nvdr_ctx +#define NVDR_CHECK(COND, ERR) OP_REQUIRES(_nvdr_ctx, COND, errors::Internal(ERR)) +#define NVDR_CHECK_CUDA_ERROR(CUDA_CALL) OP_CHECK_CUDA_ERROR(_nvdr_ctx, CUDA_CALL) +#define NVDR_CHECK_GL_ERROR(GL_CALL) OP_CHECK_GL_ERROR(_nvdr_ctx, GL_CALL) +#endif + +//------------------------------------------------------------------------ +// PyTorch. + +#ifdef NVDR_TORCH +#ifndef __CUDACC__ +#include +#include +#include +#include +#include +#endif +#define NVDR_CTX_ARGS int _nvdr_ctx_dummy +#define NVDR_CTX_PARAMS 0 +#define NVDR_CHECK(COND, ERR) do { TORCH_CHECK(COND, ERR) } while(0) +#define NVDR_CHECK_CUDA_ERROR(CUDA_CALL) do { cudaError_t err = CUDA_CALL; TORCH_CHECK(!err, "Cuda error: ", cudaGetLastError(), "[", #CUDA_CALL, ";]"); } while(0) +#define NVDR_CHECK_GL_ERROR(GL_CALL) do { GL_CALL; GLenum err = glGetError(); TORCH_CHECK(err == GL_NO_ERROR, "OpenGL error: ", getGLErrorString(err), " ", err, "[", #GL_CALL, ";]"); } while(0) +#endif + +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/glutil.cpp b/bayes3d/rendering/nvdiffrast/common/glutil.cpp new file mode 100644 index 00000000..2af3e931 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/glutil.cpp @@ -0,0 +1,403 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ +// Common. +//------------------------------------------------------------------------ + +#include "framework.h" +#include "glutil.h" +#include +#include + +// Create the function pointers. +#define GLUTIL_EXT(return_type, name, ...) return_type (GLAPIENTRY* name)(__VA_ARGS__) = 0; +#include "glutil_extlist.h" +#undef GLUTIL_EXT + +// Track initialization status. +static volatile bool s_glExtInitialized = false; + +// Error strings. +const char* getGLErrorString(GLenum err) +{ + switch(err) + { + case GL_NO_ERROR: return "GL_NO_ERROR"; + case GL_INVALID_ENUM: return "GL_INVALID_ENUM"; + case GL_INVALID_VALUE: return "GL_INVALID_VALUE"; + case GL_INVALID_OPERATION: return "GL_INVALID_OPERATION"; + case GL_STACK_OVERFLOW: return "GL_STACK_OVERFLOW"; + case GL_STACK_UNDERFLOW: return "GL_STACK_UNDERFLOW"; + case GL_OUT_OF_MEMORY: return "GL_OUT_OF_MEMORY"; + case GL_INVALID_FRAMEBUFFER_OPERATION: return "GL_INVALID_FRAMEBUFFER_OPERATION"; + case GL_TABLE_TOO_LARGE: return "GL_TABLE_TOO_LARGE"; + case GL_CONTEXT_LOST: return "GL_CONTEXT_LOST"; + } + return "Unknown error"; +} + +//------------------------------------------------------------------------ +// Windows. +//------------------------------------------------------------------------ + +#ifdef _WIN32 + +static CRITICAL_SECTION getInitializedCriticalSection(void) +{ + CRITICAL_SECTION cs; + InitializeCriticalSection(&cs); + return cs; +} + +static CRITICAL_SECTION s_getProcAddressMutex = getInitializedCriticalSection(); + +static void safeGetProcAddress(const char* name, PROC* pfn) +{ + PROC result = wglGetProcAddress(name); + if (!result) + { + LeaveCriticalSection(&s_getProcAddressMutex); // Prepare for thread exit. + LOG(FATAL) << "wglGetProcAddress() failed for '" << name << "'"; + exit(1); // Should never get here but make sure we exit. + } + *pfn = result; +} + +static void initializeGLExtensions(void) +{ + // Use critical section for thread safety. + EnterCriticalSection(&s_getProcAddressMutex); + + // Only dig function pointers if not done already. + if (!s_glExtInitialized) + { + // Generate code to populate the function pointers. +#define GLUTIL_EXT(return_type, name, ...) safeGetProcAddress(#name, (PROC*)&name); +#include "glutil_extlist.h" +#undef GLUTIL_EXT + + // Mark as initialized. + s_glExtInitialized = true; + } + + // Done. + LeaveCriticalSection(&s_getProcAddressMutex); + return; +} + +void setGLContext(GLContext& glctx) +{ + if (!glctx.hglrc) + LOG(FATAL) << "setGLContext() called with null gltcx"; + if (!wglMakeCurrent(glctx.hdc, glctx.hglrc)) + LOG(FATAL) << "wglMakeCurrent() failed when setting GL context"; + + if (glctx.extInitialized) + return; + initializeGLExtensions(); + glctx.extInitialized = 1; +} + +void releaseGLContext(void) +{ + if (!wglMakeCurrent(NULL, NULL)) + LOG(FATAL) << "wglMakeCurrent() failed when releasing GL context"; +} + +extern "C" int set_gpu(const char*); // In setgpu.lib +GLContext createGLContext(int cudaDeviceIdx) +{ + if (cudaDeviceIdx >= 0) + { + char pciBusId[256] = ""; + LOG(INFO) << "Creating GL context for Cuda device " << cudaDeviceIdx; + if (cudaDeviceGetPCIBusId(pciBusId, 255, cudaDeviceIdx)) + { + LOG(INFO) << "PCI bus id query failed"; + } + else + { + int res = set_gpu(pciBusId); + LOG(INFO) << "Selecting device with PCI bus id " << pciBusId << " - " << (res ? "failed, expect crash or major slowdown" : "success"); + } + } + + HINSTANCE hInstance = GetModuleHandle(NULL); + WNDCLASS wc = {}; + wc.style = CS_OWNDC; + wc.lpfnWndProc = DefWindowProc; + wc.hInstance = hInstance; + wc.lpszClassName = "__DummyGLClassCPP"; + int res = RegisterClass(&wc); + + HWND hwnd = CreateWindow( + "__DummyGLClassCPP", // lpClassName + "__DummyGLWindowCPP", // lpWindowName + WS_OVERLAPPEDWINDOW, // dwStyle + CW_USEDEFAULT, // x + CW_USEDEFAULT, // y + 0, 0, // nWidth, nHeight + NULL, NULL, // hWndParent, hMenu + hInstance, // hInstance + NULL // lpParam + ); + + PIXELFORMATDESCRIPTOR pfd = {}; + pfd.dwFlags = PFD_SUPPORT_OPENGL; + pfd.iPixelType = PFD_TYPE_RGBA; + pfd.iLayerType = PFD_MAIN_PLANE; + pfd.cColorBits = 32; + pfd.cDepthBits = 24; + pfd.cStencilBits = 8; + + HDC hdc = GetDC(hwnd); + int pixelformat = ChoosePixelFormat(hdc, &pfd); + SetPixelFormat(hdc, pixelformat, &pfd); + + HGLRC hglrc = wglCreateContext(hdc); + LOG(INFO) << std::hex << std::setfill('0') + << "WGL OpenGL context created (hdc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)hdc + << ", hglrc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)hglrc << ")"; + + GLContext glctx = {hdc, hglrc, 0}; + return glctx; +} + +void destroyGLContext(GLContext& glctx) +{ + if (!glctx.hglrc) + LOG(FATAL) << "destroyGLContext() called with null gltcx"; + + // If this is the current context, release it. + if (wglGetCurrentContext() == glctx.hglrc) + releaseGLContext(); + + HWND hwnd = WindowFromDC(glctx.hdc); + if (!hwnd) + LOG(FATAL) << "WindowFromDC() failed"; + if (!ReleaseDC(hwnd, glctx.hdc)) + LOG(FATAL) << "ReleaseDC() failed"; + if (!wglDeleteContext(glctx.hglrc)) + LOG(FATAL) << "wglDeleteContext() failed"; + if (!DestroyWindow(hwnd)) + LOG(FATAL) << "DestroyWindow() failed"; + + LOG(INFO) << std::hex << std::setfill('0') + << "WGL OpenGL context destroyed (hdc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)glctx.hdc + << ", hglrc: 0x" << std::setw(8) << (uint32_t)(uintptr_t)glctx.hglrc << ")"; + + memset(&glctx, 0, sizeof(GLContext)); +} + +#endif // _WIN32 + +//------------------------------------------------------------------------ +// Linux. +//------------------------------------------------------------------------ + +#ifdef __linux__ + +static pthread_mutex_t s_getProcAddressMutex; + +typedef void (*PROCFN)(); + +static void safeGetProcAddress(const char* name, PROCFN* pfn) +{ + PROCFN result = eglGetProcAddress(name); + if (!result) + { + pthread_mutex_unlock(&s_getProcAddressMutex); // Prepare for thread exit. + LOG(FATAL) << "wglGetProcAddress() failed for '" << name << "'"; + exit(1); // Should never get here but make sure we exit. + } + *pfn = result; +} + +static void initializeGLExtensions(void) +{ + pthread_mutex_lock(&s_getProcAddressMutex); + + // Only dig function pointers if not done already. + if (!s_glExtInitialized) + { + // Generate code to populate the function pointers. +#define GLUTIL_EXT(return_type, name, ...) safeGetProcAddress(#name, (PROCFN*)&name); +#include "glutil_extlist.h" +#undef GLUTIL_EXT + + // Mark as initialized. + s_glExtInitialized = true; + } + + pthread_mutex_unlock(&s_getProcAddressMutex); + return; +} + +void setGLContext(GLContext& glctx) +{ + if (!glctx.context) + LOG(FATAL) << "setGLContext() called with null gltcx"; + + if (!eglMakeCurrent(glctx.display, EGL_NO_SURFACE, EGL_NO_SURFACE, glctx.context)) + LOG(ERROR) << "eglMakeCurrent() failed when setting GL context"; + + if (glctx.extInitialized) + return; + initializeGLExtensions(); + glctx.extInitialized = 1; +} + +void releaseGLContext(void) +{ + EGLDisplay display = eglGetCurrentDisplay(); + if (display == EGL_NO_DISPLAY) + LOG(WARNING) << "releaseGLContext() called with no active display"; + if (!eglMakeCurrent(display, EGL_NO_SURFACE, EGL_NO_SURFACE, EGL_NO_CONTEXT)) + LOG(FATAL) << "eglMakeCurrent() failed when releasing GL context"; +} + +static EGLDisplay getCudaDisplay(int cudaDeviceIdx) +{ + typedef EGLBoolean (*eglQueryDevicesEXT_t)(EGLint, EGLDeviceEXT, EGLint*); + typedef EGLBoolean (*eglQueryDeviceAttribEXT_t)(EGLDeviceEXT, EGLint, EGLAttrib*); + typedef EGLDisplay (*eglGetPlatformDisplayEXT_t)(EGLenum, void*, const EGLint*); + + eglQueryDevicesEXT_t eglQueryDevicesEXT = (eglQueryDevicesEXT_t)eglGetProcAddress("eglQueryDevicesEXT"); + if (!eglQueryDevicesEXT) + { + LOG(INFO) << "eglGetProcAddress(\"eglQueryDevicesEXT\") failed"; + return 0; + } + + eglQueryDeviceAttribEXT_t eglQueryDeviceAttribEXT = (eglQueryDeviceAttribEXT_t)eglGetProcAddress("eglQueryDeviceAttribEXT"); + if (!eglQueryDeviceAttribEXT) + { + LOG(INFO) << "eglGetProcAddress(\"eglQueryDeviceAttribEXT\") failed"; + return 0; + } + + eglGetPlatformDisplayEXT_t eglGetPlatformDisplayEXT = (eglGetPlatformDisplayEXT_t)eglGetProcAddress("eglGetPlatformDisplayEXT"); + if (!eglGetPlatformDisplayEXT) + { + LOG(INFO) << "eglGetProcAddress(\"eglGetPlatformDisplayEXT\") failed"; + return 0; + } + + int num_devices = 0; + eglQueryDevicesEXT(0, 0, &num_devices); + if (!num_devices) + return 0; + + EGLDisplay display = 0; + EGLDeviceEXT* devices = (EGLDeviceEXT*)malloc(num_devices * sizeof(void*)); + eglQueryDevicesEXT(num_devices, devices, &num_devices); + for (int i=0; i < num_devices; i++) + { + EGLDeviceEXT device = devices[i]; + intptr_t value = -1; + if (eglQueryDeviceAttribEXT(device, EGL_CUDA_DEVICE_NV, &value) && value == cudaDeviceIdx) + { + display = eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, device, 0); + break; + } + } + + free(devices); + return display; +} + +GLContext createGLContext(int cudaDeviceIdx) +{ + EGLDisplay display = 0; + + if (cudaDeviceIdx >= 0) + { + char pciBusId[256] = ""; + LOG(INFO) << "Creating GL context for Cuda device " << cudaDeviceIdx; + display = getCudaDisplay(cudaDeviceIdx); + if (!display) + LOG(INFO) << "Failed, falling back to default display"; + } + + if (!display) + { + display = eglGetDisplay(EGL_DEFAULT_DISPLAY); + if (display == EGL_NO_DISPLAY) + LOG(FATAL) << "eglGetDisplay() failed"; + } + + EGLint major; + EGLint minor; + if (!eglInitialize(display, &major, &minor)) + LOG(FATAL) << "eglInitialize() failed"; + + // Choose configuration. + + const EGLint context_attribs[] = { + EGL_RED_SIZE, 8, + EGL_GREEN_SIZE, 8, + EGL_BLUE_SIZE, 8, + EGL_ALPHA_SIZE, 8, + EGL_DEPTH_SIZE, 24, + EGL_STENCIL_SIZE, 8, + EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT, + EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, + EGL_NONE + }; + + EGLConfig config; + EGLint num_config; + if (!eglChooseConfig(display, context_attribs, &config, 1, &num_config)) + LOG(FATAL) << "eglChooseConfig() failed"; + + // Create GL context. + + if (!eglBindAPI(EGL_OPENGL_API)) + LOG(FATAL) << "eglBindAPI() failed"; + + EGLContext context = eglCreateContext(display, config, EGL_NO_CONTEXT, NULL); + if (context == EGL_NO_CONTEXT) + LOG(FATAL) << "eglCreateContext() failed"; + + // Done. + + LOG(INFO) << "EGL " << (int)minor << "." << (int)major << " OpenGL context created (disp: 0x" + << std::hex << std::setfill('0') + << std::setw(16) << (uintptr_t)display + << ", ctx: 0x" << std::setw(16) << (uintptr_t)context << ")"; + + GLContext glctx = {display, context, 0}; + return glctx; +} + +void destroyGLContext(GLContext& glctx) +{ + if (!glctx.context) + LOG(FATAL) << "destroyGLContext() called with null gltcx"; + + // If this is the current context, release it. + if (eglGetCurrentContext() == glctx.context) + releaseGLContext(); + + if (!eglDestroyContext(glctx.display, glctx.context)) + LOG(ERROR) << "eglDestroyContext() failed"; + + LOG(INFO) << "EGL OpenGL context destroyed (disp: 0x" + << std::hex << std::setfill('0') + << std::setw(16) << (uintptr_t)glctx.display + << ", ctx: 0x" << std::setw(16) << (uintptr_t)glctx.context << ")"; + + memset(&glctx, 0, sizeof(GLContext)); +} + +//------------------------------------------------------------------------ + +#endif // __linux__ + +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/glutil.h b/bayes3d/rendering/nvdiffrast/common/glutil.h new file mode 100644 index 00000000..19e12b21 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/glutil.h @@ -0,0 +1,117 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + + +#pragma once + + +//------------------------------------------------------------------------ +// Windows-specific headers and types. +//------------------------------------------------------------------------ + +#ifdef _WIN32 +#define NOMINMAX +#include // Required by gl.h in Windows. +#define GLAPIENTRY APIENTRY + +struct GLContext +{ + HDC hdc; + HGLRC hglrc; + int extInitialized; +}; + +#endif // _WIN32 + +//------------------------------------------------------------------------ +// Linux-specific headers and types. +//------------------------------------------------------------------------ + +#ifdef __linux__ +#define EGL_NO_X11 // X11/Xlib.h has "#define Status int" which breaks Tensorflow. Avoid it. +#define MESA_EGL_NO_X11_HEADERS +#include +#include +#define GLAPIENTRY + +struct GLContext +{ + EGLDisplay display; + EGLContext context; + int extInitialized; +}; + +#endif // __linux__ + +//------------------------------------------------------------------------ +// OpenGL, CUDA interop, GL extensions. +//------------------------------------------------------------------------ +#define GL_GLEXT_LEGACY +#include +#include +#include + +// Constants. +#ifndef GL_VERSION_1_2 +#define GL_CLAMP_TO_EDGE 0x812F +#define GL_TEXTURE_3D 0x806F +#endif +#ifndef GL_VERSION_1_5 +#define GL_ARRAY_BUFFER 0x8892 +#define GL_DYNAMIC_DRAW 0x88E8 +#define GL_ELEMENT_ARRAY_BUFFER 0x8893 +#endif +#ifndef GL_VERSION_2_0 +#define GL_FRAGMENT_SHADER 0x8B30 +#define GL_INFO_LOG_LENGTH 0x8B84 +#define GL_LINK_STATUS 0x8B82 +#define GL_VERTEX_SHADER 0x8B31 +#endif +#ifndef GL_VERSION_3_0 +#define GL_MAJOR_VERSION 0x821B +#define GL_MINOR_VERSION 0x821C +#define GL_RGBA32F 0x8814 +#define GL_R32F 0x822E +#define GL_TEXTURE_2D_ARRAY 0x8C1A +#endif +#ifndef GL_VERSION_3_2 +#define GL_GEOMETRY_SHADER 0x8DD9 +#endif +#ifndef GL_ARB_framebuffer_object +#define GL_COLOR_ATTACHMENT0 0x8CE0 +#define GL_COLOR_ATTACHMENT1 0x8CE1 +#define GL_DEPTH_STENCIL 0x84F9 +#define GL_DEPTH_STENCIL_ATTACHMENT 0x821A +#define GL_DEPTH24_STENCIL8 0x88F0 +#define GL_FRAMEBUFFER 0x8D40 +#define GL_INVALID_FRAMEBUFFER_OPERATION 0x0506 +#define GL_UNSIGNED_INT_24_8 0x84FA +#endif +#ifndef GL_ARB_imaging +#define GL_TABLE_TOO_LARGE 0x8031 +#endif +#ifndef GL_KHR_robustness +#define GL_CONTEXT_LOST 0x0507 +#endif + +// Declare function pointers to OpenGL extension functions. +#define GLUTIL_EXT(return_type, name, ...) extern return_type (GLAPIENTRY* name)(__VA_ARGS__); +#include "glutil_extlist.h" +#undef GLUTIL_EXT + +//------------------------------------------------------------------------ +// Common functions. +//------------------------------------------------------------------------ + +void setGLContext (GLContext& glctx); +void releaseGLContext (void); +GLContext createGLContext (int cudaDeviceIdx); +void destroyGLContext (GLContext& glctx); +const char* getGLErrorString (GLenum err); + +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/glutil_extlist.h b/bayes3d/rendering/nvdiffrast/common/glutil_extlist.h new file mode 100644 index 00000000..457dbe47 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/glutil_extlist.h @@ -0,0 +1,59 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +// ... (previous declarations) + +#ifndef GL_VERSION_1_2 +GLUTIL_EXT(void, glTexImage3D, GLenum target, GLint level, GLint internalFormat, GLsizei width, GLsizei height, GLsizei depth, GLint border, GLenum format, GLenum type, const void *pixels); +#endif +#ifndef GL_VERSION_1_5 +GLUTIL_EXT(void, glBindBuffer, GLenum target, GLuint buffer); +GLUTIL_EXT(void, glBufferData, GLenum target, ptrdiff_t size, const void* data, GLenum usage); +GLUTIL_EXT(void, glGenBuffers, GLsizei n, GLuint* buffers); +GLUTIL_EXT(void, glDeleteBuffers, GLsizei n, const GLuint* buffers); // <-- Add this line +#endif +#ifndef GL_VERSION_2_0 +GLUTIL_EXT(void, glAttachShader, GLuint program, GLuint shader); +GLUTIL_EXT(void, glCompileShader, GLuint shader); +GLUTIL_EXT(GLuint, glCreateProgram, void); +GLUTIL_EXT(GLuint, glCreateShader, GLenum type); +GLUTIL_EXT(void, glDeleteProgram, GLuint program); // <-- Add this line +GLUTIL_EXT(void, glDeleteShader, GLuint shader); // <-- Add this line +GLUTIL_EXT(void, glDrawBuffers, GLsizei n, const GLenum* bufs); +GLUTIL_EXT(void, glEnableVertexAttribArray, GLuint index); +GLUTIL_EXT(void, glGetProgramInfoLog, GLuint program, GLsizei bufSize, GLsizei* length, char* infoLog); +GLUTIL_EXT(void, glGetProgramiv, GLuint program, GLenum pname, GLint* param); +GLUTIL_EXT(void, glLinkProgram, GLuint program); +GLUTIL_EXT(void, glShaderSource, GLuint shader, GLsizei count, const char *const* string, const GLint* length); +GLUTIL_EXT(void, glUniform1f, GLint location, GLfloat v0); +GLUTIL_EXT(void, glUniform1i, GLint location, GLint v0); +GLUTIL_EXT(void, glUniform2f, GLint location, GLfloat v0, GLfloat v1); +GLUTIL_EXT(void, glUniformMatrix4fv, GLint location, GLsizei count, GLboolean transpose, const GLfloat *value); +GLUTIL_EXT(void, glUseProgram, GLuint program); +GLUTIL_EXT(void, glVertexAttribPointer, GLuint index, GLint size, GLenum type, GLboolean normalized, GLsizei stride, const void* pointer); +#endif +#ifndef GL_VERSION_3_0 +GLUTIL_EXT(void, glDeleteVertexArrays, GLsizei n, const GLuint* arrays); // <-- Add this line +#endif +#ifndef GL_VERSION_3_2 +GLUTIL_EXT(void, glFramebufferTexture, GLenum target, GLenum attachment, GLuint texture, GLint level); +GLUTIL_EXT(void, glDeleteFramebuffers, GLsizei n, const GLuint* framebuffers); // <-- Add this line +#endif +#ifndef GL_ARB_framebuffer_object +GLUTIL_EXT(void, glBindFramebuffer, GLenum target, GLuint framebuffer); +GLUTIL_EXT(void, glGenFramebuffers, GLsizei n, GLuint* framebuffers); +#endif +#ifndef GL_ARB_vertex_array_object +GLUTIL_EXT(void, glBindVertexArray, GLuint array); +GLUTIL_EXT(void, glGenVertexArrays, GLsizei n, GLuint* arrays); +#endif +#ifndef GL_ARB_multi_draw_indirect +GLUTIL_EXT(void, glMultiDrawElementsIndirect, GLenum mode, GLenum type, const void *indirect, GLsizei primcount, GLsizei stride); +#endif + +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/ops.py b/bayes3d/rendering/nvdiffrast/common/ops.py new file mode 100644 index 00000000..3978116d --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/ops.py @@ -0,0 +1,82 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + + +import torch + +import bayes3d.rendering.nvdiffrast.nvdiffrast_plugin_gl as plugin_gl + +# ---------------------------------------------------------------------------- +# C++/Cuda plugin loader. + + +def _get_plugin(gl=True): + # the gl flag is left here for backward compatibility + assert gl is True + return plugin_gl + + +# ---------------------------------------------------------------------------- +# GL state wrapper. +# ---------------------------------------------------------------------------- + + +class RasterizeGLContext: + def __init__(self, height, width, output_db=False, mode="automatic", device=None): + """Create a new OpenGL rasterizer context. + + Creating an OpenGL context is a slow operation so you should usually reuse the same + context in all calls to `rasterize()` on the same CPU thread. The OpenGL context + is deleted when the object is destroyed. + + Side note: When using the OpenGL context in a rasterization operation, the + context's internal framebuffer object is automatically enlarged to accommodate the + rasterization operation's output shape, but it is never shrunk in size until the + context is destroyed. Thus, if you need to rasterize, say, deep low-resolution + tensors and also shallow high-resolution tensors, you can conserve GPU memory by + creating two separate OpenGL contexts for these tasks. In this scenario, using the + same OpenGL context for both tasks would end up reserving GPU memory for a deep, + high-resolution output tensor. + + Args: + output_db (bool): Compute and output image-space derivates of barycentrics. + mode: OpenGL context handling mode. Valid values are 'manual' and 'automatic'. + device (Optional): Cuda device on which the context is created. Type can be + `torch.device`, string (e.g., `'cuda:1'`), or int. If not + specified, context will be created on currently active Cuda + device. + Returns: + The newly created OpenGL rasterizer context. + """ + assert output_db is True or output_db is False + assert mode in ["automatic", "manual"] + self.output_db = output_db + self.mode = mode + if device is None: + cuda_device_idx = torch.cuda.current_device() + else: + with torch.cuda.device(device): + cuda_device_idx = torch.cuda.current_device() + self.cpp_wrapper = _get_plugin(gl=True).RasterizeGLStateWrapper( + output_db, mode == "automatic", cuda_device_idx + ) + self.active_depth_peeler = None # For error checking only. + + def set_context(self): + """Set (activate) OpenGL context in the current CPU thread. + Only available if context was created in manual mode. + """ + assert self.mode == "manual" + self.cpp_wrapper.set_context() + + def release_context(self): + """Release (deactivate) currently active OpenGL context. + Only available if context was created in manual mode. + """ + assert self.mode == "manual" + self.cpp_wrapper.release_context() diff --git a/bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp b/bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp new file mode 100644 index 00000000..41f4c2f0 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/rasterize_gl.cpp @@ -0,0 +1,720 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "rasterize_gl.h" +#include "glutil.h" +// #include "torch_common.inl" +#include "torch_types.h" +#include "common.h" +#include +#include +#include +#include + +#include + +#include +#include +#define STRINGIFY_SHADER_SOURCE(x) #x + +//------------------------------------------------------------------------ +// Helpers. + +#define ROUND_UP(x, y) ((((x) + ((y) - 1)) / (y)) * (y)) +static int ROUND_UP_BITS(uint32_t x, uint32_t y) +{ + // Round x up so that it has at most y bits of mantissa. + if (x < (1u << y)) + return x; + uint32_t m = 0; + while (x & ~m) + m = (m << 1) | 1u; + m >>= y; + if (!(x & m)) + return x; + return (x | m) + 1u; +} + +//------------------------------------------------------------------------ +// Draw command struct used by rasterizer. + +struct GLDrawCmd +{ + uint32_t count; + uint32_t instanceCount; + uint32_t firstIndex; + uint32_t baseVertex; + uint32_t baseInstance; +}; + +//------------------------------------------------------------------------ +// GL helpers. + +static void compileGLShader(NVDR_CTX_ARGS, const RasterizeGLState& s, GLuint* pShader, GLenum shaderType, const char* src_buf) +{ + std::string src(src_buf); + + // Set preprocessor directives. + int n = src.find('\n') + 1; // After first line containing #version directive. + if (s.enableZModify) + src.insert(n, "#define IF_ZMODIFY(x) x\n"); + else + src.insert(n, "#define IF_ZMODIFY(x)\n"); + + const char *cstr = src.c_str(); + *pShader = 0; + NVDR_CHECK_GL_ERROR(*pShader = glCreateShader(shaderType)); + NVDR_CHECK_GL_ERROR(glShaderSource(*pShader, 1, &cstr, 0)); + NVDR_CHECK_GL_ERROR(glCompileShader(*pShader)); +} + +static void constructGLProgram(NVDR_CTX_ARGS, GLuint* pProgram, GLuint glVertexShader, GLuint glFragmentShader) +{ + *pProgram = 0; + + GLuint glProgram = 0; + NVDR_CHECK_GL_ERROR(glProgram = glCreateProgram()); + NVDR_CHECK_GL_ERROR(glAttachShader(glProgram, glVertexShader)); + NVDR_CHECK_GL_ERROR(glAttachShader(glProgram, glFragmentShader)); + NVDR_CHECK_GL_ERROR(glLinkProgram(glProgram)); + + GLint linkStatus = 0; + NVDR_CHECK_GL_ERROR(glGetProgramiv(glProgram, GL_LINK_STATUS, &linkStatus)); + if (!linkStatus) + { + GLint infoLen = 0; + NVDR_CHECK_GL_ERROR(glGetProgramiv(glProgram, GL_INFO_LOG_LENGTH, &infoLen)); + if (infoLen) + { + const char* hdr = "glLinkProgram() failed:\n"; + std::vector info(strlen(hdr) + infoLen); + strcpy(&info[0], hdr); + NVDR_CHECK_GL_ERROR(glGetProgramInfoLog(glProgram, infoLen, &infoLen, &info[strlen(hdr)])); + NVDR_CHECK(0, &info[0]); + } + NVDR_CHECK(0, "glLinkProgram() failed"); + } + + *pProgram = glProgram; +} + +//------------------------------------------------------------------------ +// Shared C++ functions. + +void rasterizeInitGLContext(NVDR_CTX_ARGS, RasterizeGLState& s, int cudaDeviceIdx) +{ + // Create GL context and set it current. + s.glctx = createGLContext(cudaDeviceIdx); + setGLContext(s.glctx); + + // Version check. + GLint vMajor = 0; + GLint vMinor = 0; + glGetIntegerv(GL_MAJOR_VERSION, &vMajor); + glGetIntegerv(GL_MINOR_VERSION, &vMinor); + glGetError(); // Clear possible GL_INVALID_ENUM error in version query. + LOG(ERROR) << "OpenGL version reported as " << vMajor << "." << vMinor; + NVDR_CHECK((vMajor == 4 && vMinor >= 4) || vMajor > 4, "OpenGL 4.4 or later is required"); + + // Enable depth modification workaround on A100 and later. + int capMajor = 0; + NVDR_CHECK_CUDA_ERROR(cudaDeviceGetAttribute(&capMajor, cudaDevAttrComputeCapabilityMajor, cudaDeviceIdx)); + s.enableZModify = (capMajor >= 8); + + // Number of output buffers. + int num_outputs = s.enableDB ? 2 : 1; + + // Set up vertex shader. + compileGLShader(NVDR_CTX_PARAMS, s, &s.glVertexShader, GL_VERTEX_SHADER, + "#version 330\n" + "#extension GL_ARB_shader_draw_parameters : enable\n" + "#extension GL_ARB_explicit_uniform_location : enable\n" + "#extension GL_AMD_vertex_shader_layer : enable\n" + STRINGIFY_SHADER_SOURCE( + layout(location = 0) uniform mat4 mvp; + layout(location = 1) uniform float seg_id; + in vec4 in_vert; + out vec4 vertex_on_object; + out float seg_id_out; + uniform sampler2D texture; + void main() + { + gl_Layer = gl_DrawIDARB; + vec4 v1 = texelFetch(texture, ivec2(0, gl_Layer), 0); + vec4 v2 = texelFetch(texture, ivec2(1, gl_Layer), 0); + vec4 v3 = texelFetch(texture, ivec2(2, gl_Layer), 0); + vec4 v4 = texelFetch(texture, ivec2(3, gl_Layer), 0); + mat4 pose_mat = transpose(mat4(v1,v2,v3,v4)); + vertex_on_object = pose_mat * in_vert; + gl_Position = mvp * vertex_on_object; + seg_id_out = seg_id; + } + ) + ); + + // Fragment shader without bary differential output. + compileGLShader(NVDR_CTX_PARAMS, s, &s.glFragmentShader, GL_FRAGMENT_SHADER, + "#version 430\n" + STRINGIFY_SHADER_SOURCE( + in vec4 vertex_on_object; + in float seg_id_out; + out vec4 fragColor; + void main() + { + fragColor = vec4(vertex_on_object[0],vertex_on_object[1],vertex_on_object[2], seg_id_out); + } + ) + ); + + // Finalize programs. + constructGLProgram(NVDR_CTX_PARAMS, &s.glProgram, s.glVertexShader, s.glFragmentShader); + constructGLProgram(NVDR_CTX_PARAMS, &s.glProgramDP, s.glVertexShader, s.glFragmentShader); + + // Construct main fbo and bind permanently. + NVDR_CHECK_GL_ERROR(glGenFramebuffers(1, &s.glFBO)); + NVDR_CHECK_GL_ERROR(glBindFramebuffer(GL_FRAMEBUFFER, s.glFBO)); + + // Enable two color attachments. + GLenum draw_buffers[2] = { GL_COLOR_ATTACHMENT0, GL_COLOR_ATTACHMENT1 }; + NVDR_CHECK_GL_ERROR(glDrawBuffers(num_outputs, draw_buffers)); + + // Set up depth test. + NVDR_CHECK_GL_ERROR(glEnable(GL_DEPTH_TEST)); + NVDR_CHECK_GL_ERROR(glDepthFunc(GL_LESS)); + NVDR_CHECK_GL_ERROR(glClearDepth(1.0)); + + // Create and bind output buffers. Storage is allocated later. + NVDR_CHECK_GL_ERROR(glGenTextures(num_outputs, s.glColorBuffer)); + for (int i=0; i < num_outputs; i++) + { + NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); + NVDR_CHECK_GL_ERROR(glFramebufferTexture(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0 + i, s.glColorBuffer[i], 0)); + } + + // Create and bind depth/stencil buffer. Storage is allocated later. + NVDR_CHECK_GL_ERROR(glGenTextures(1, &s.glDepthStencilBuffer)); + NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glDepthStencilBuffer)); + NVDR_CHECK_GL_ERROR(glFramebufferTexture(GL_FRAMEBUFFER, GL_DEPTH_STENCIL_ATTACHMENT, s.glDepthStencilBuffer, 0)); + + // Create texture name for previous output buffer (depth peeling). + NVDR_CHECK_GL_ERROR(glGenTextures(1, &s.glPrevOutBuffer)); +} +void rasterizeReleaseBuffers(NVDR_CTX_ARGS, RasterizeGLState& s) +{ + int num_outputs = s.enableDB ? 2 : 1; + + if (s.cudaPosBuffer) + { + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPosBuffer)); + s.cudaPosBuffer = 0; + } + + if (s.cudaTriBuffer) + { + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaTriBuffer)); + s.cudaTriBuffer = 0; + } + + for (int i=0; i < num_outputs; i++) + { + if (s.cudaColorBuffer[i]) + { + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaColorBuffer[i])); + s.cudaColorBuffer[i] = 0; + } + } + + if (s.cudaPrevOutBuffer) + { + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPrevOutBuffer)); + s.cudaPrevOutBuffer = 0; + } +} + +void rasterizeReleaseGLResources(RasterizeGLState& s) { + // Release OpenGL resources here. + // For example, you can use glDeleteVertexArrays, glDeleteBuffers, glDeleteTextures, etc. + // For every resource created with glGen* functions, you need to call the corresponding glDelete* function. + // Make sure to properly delete all resources to avoid memory leaks. + + // Example: + if (s.glProgram) { + glDeleteProgram(s.glProgram); + s.glProgram = 0; + } + if (s.glProgramDP) { + glDeleteProgram(s.glProgramDP); + s.glProgramDP = 0; + } + if (s.glVertexShader) { + glDeleteShader(s.glVertexShader); + s.glVertexShader = 0; + } + if (s.glFragmentShader) { + glDeleteShader(s.glFragmentShader); + s.glFragmentShader = 0; + } + + for (int i = 0; i < s.model_counter; i++) { + if (s.glVAOs[i]) { + glDeleteVertexArrays(1, &s.glVAOs[i]); + s.glVAOs[i] = 0; + } + } + if (s.glPosBuffer) { + glDeleteBuffers(1, &s.glPosBuffer); + s.glPosBuffer = 0; + } + if (s.glTriBuffer) { + glDeleteBuffers(1, &s.glTriBuffer); + s.glTriBuffer = 0; + } + if (s.glFBO) { + glDeleteFramebuffers(1, &s.glFBO); + s.glFBO = 0; + } + if (s.glDepthStencilBuffer) { + glDeleteTextures(1, &s.glDepthStencilBuffer); + s.glDepthStencilBuffer = 0; + } + if (s.glPoseTexture) { + glDeleteTextures(1, &s.glPoseTexture); + s.glPoseTexture = 0; + } + if (s.glPrevOutBuffer) { + glDeleteTextures(1, &s.glPrevOutBuffer); + s.glPrevOutBuffer = 0; + } + for (int i = 0; i < 2; i++) { + if (s.glColorBuffer[i]) { + glDeleteTextures(1, &s.glColorBuffer[i]); + s.glColorBuffer[i] = 0; + } + } + + s.model_counter = 0; // Reset the model counter to allow restarting the renderer. + // No need to delete the OpenGL context here, as it will be reused for the next initialization. +} + +RasterizeGLStateWrapper::RasterizeGLStateWrapper(bool enableDB, bool automatic_, int cudaDeviceIdx_) +{ + pState = new RasterizeGLState(); + automatic = automatic_; + cudaDeviceIdx = cudaDeviceIdx_; + memset(pState, 0, sizeof(RasterizeGLState)); + pState->enableDB = enableDB ? 1 : 0; + rasterizeInitGLContext(NVDR_CTX_PARAMS, *pState, cudaDeviceIdx_); + releaseGLContext(); +} + +RasterizeGLStateWrapper::~RasterizeGLStateWrapper(void) +{ + setGLContext(pState->glctx); + rasterizeReleaseBuffers(NVDR_CTX_PARAMS, *pState); + rasterizeReleaseGLResources(*pState); // Call the function to release OpenGL resources. + releaseGLContext(); + destroyGLContext(pState->glctx); + delete pState; +} + +void RasterizeGLStateWrapper::setContext(void) +{ + setGLContext(pState->glctx); +} + +void RasterizeGLStateWrapper::releaseContext(void) +{ + releaseGLContext(); +} + +//------------------------------------------------------------------------ +// Forward op (OpenGL). + +void threedp3_likelihood(float *pos, float *latent_points, float *likelihoods, float *obs_image, float r, int width, int height, int depth); + +void _setup(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, int height, int width, int num_layers) +{ + + // const at::cuda::OptionalCUDAGuard device_guard(device_of(pos)); + // cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + RasterizeGLState& s = *stateWrapper.pState; + s.model_counter = 0; + s.img_width = width; + s.img_height = height; + s.num_layers = num_layers; + + // std::cout << "" << "OpenGL Version: " << glGetString(GL_VERSION) << std::endl; + + // Check that GL context was created for the correct GPU. + // NVDR_CHECK(pos.get_device() == stateWrapper.cudaDeviceIdx, "GL context must must reside on the same device as input tensors"); + + // Determine number of outputs + + // Get output shape. + NVDR_CHECK(height > 0 && width > 0, "resolution must be [>0, >0]"); + + // Set the GL context unless manual context. + if (stateWrapper.automatic) + setGLContext(s.glctx); + + // Resize all buffers. + int num_outputs = 1; + if (s.cudaColorBuffer[0]) + for (int i=0; i < num_outputs; i++) + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaColorBuffer[i])); + + if (s.cudaPrevOutBuffer) + { + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPrevOutBuffer)); + s.cudaPrevOutBuffer = 0; + } + + s.width = ROUND_UP(s.img_width, 32); + s.height = ROUND_UP(s.img_height, 32); + std::cout << "Increasing frame buffer size to (width, height, depth) = (" << s.width << ", " << s.height << ", " << s.num_layers << ")" << std::endl; + + // Allocate color buffers. + for (int i=0; i < num_outputs; i++) + { + NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glColorBuffer[i])); + NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_RGBA32F, s.width, s.height, s.num_layers, 0, GL_RGBA, GL_UNSIGNED_BYTE, 0)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D_ARRAY, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)); + } + + // Allocate depth/stencil buffer. + NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D_ARRAY, s.glDepthStencilBuffer)); + NVDR_CHECK_GL_ERROR(glTexImage3D(GL_TEXTURE_2D_ARRAY, 0, GL_DEPTH24_STENCIL8, s.width, s.height, s.num_layers, 0, GL_DEPTH_STENCIL, GL_UNSIGNED_INT_24_8, 0)); + + // (Re-)register all GL buffers into Cuda. + for (int i=0; i < num_outputs; i++) + NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaColorBuffer[i], s.glColorBuffer[i], GL_TEXTURE_3D, cudaGraphicsRegisterFlagsReadOnly)); + + // if (s.cudaPoseTexture) + // NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPoseTexture)); + NVDR_CHECK_GL_ERROR(glGenTextures(1, &s.glPoseTexture)); + NVDR_CHECK_GL_ERROR(glBindTexture(GL_TEXTURE_2D, s.glPoseTexture)); + NVDR_CHECK_GL_ERROR(glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA32F, 4, s.num_layers, 0, GL_RGBA, GL_FLOAT, 0)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)); + NVDR_CHECK_GL_ERROR(glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)); + + return; +} + +void setup(RasterizeGLStateWrapper& stateWrapper, int height, int width, int num_layers) +{ + _setup(at::cuda::getCurrentCUDAStream(), stateWrapper, height, width, num_layers); +} + + +void jax_setup(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len) { + const SetUpCustomCallDescriptor &d = + *UnpackDescriptor(opaque, opaque_len); + RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; + _setup(stream, stateWrapper, d.height, d.width, d.num_layers); +} + + +void _load_vertices_fwd(cudaStream_t stream, + RasterizeGLStateWrapper& stateWrapper, const float * pos, uint num_vertices, const int * tri, uint num_triangles) +{ + // const at::cuda::OptionalCUDAGuard device_guard(device_of(pos)); + // cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + RasterizeGLState& s = *stateWrapper.pState; + + // // Check inputs. + // NVDR_CHECK_DEVICE(pos, tri); + // NVDR_CHECK_CONTIGUOUS(pos, tri); + // NVDR_CHECK_F32(pos); + // NVDR_CHECK_I32(tri); + + // Check that GL context was created for the correct GPU. + // NVDR_CHECK(pos.get_device() == stateWrapper.cudaDeviceIdx, "GL context must must reside on the same device as input tensors"); + + // Determine number of outputs + + // Determine instance mode and check input dimensions. + // NVDR_CHECK(pos.sizes().size() == 2 && pos.size(0) > 0 && pos.size(1) == 4, "range mode - pos must have shape [>0, 4]"); + // NVDR_CHECK(tri.sizes().size() == 2 && tri.size(0) > 0 && tri.size(1) == 3, "tri must have shape [>0, 3]"); + + + // Get position and triangle buffer sizes in int32/float32. + int posCount = 4 * num_vertices; + int triCount = 3 * num_triangles; + + // Set the GL context unless manual context. + if (stateWrapper.automatic) + setGLContext(s.glctx); + + + // Construct vertex array object. + NVDR_CHECK_GL_ERROR(glGenVertexArrays(1, &s.glVAOs[s.model_counter])); + NVDR_CHECK_GL_ERROR(glBindVertexArray(s.glVAOs[s.model_counter])); + + // Construct position buffer, bind permanently, enable, set ptr. + NVDR_CHECK_GL_ERROR(glGenBuffers(1, &s.glPosBuffer)); + NVDR_CHECK_GL_ERROR(glBindBuffer(GL_ARRAY_BUFFER, s.glPosBuffer)); + NVDR_CHECK_GL_ERROR(glEnableVertexAttribArray(0)); + NVDR_CHECK_GL_ERROR(glVertexAttribPointer(0, 4, GL_FLOAT, GL_FALSE, 0, 0)); + + // Construct index buffer and bind permanently. + NVDR_CHECK_GL_ERROR(glGenBuffers(1, &s.glTriBuffer)); + NVDR_CHECK_GL_ERROR(glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, s.glTriBuffer)); + + // Resize all buffers. + + // Resize vertex buffer? + if (s.cudaPosBuffer) + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaPosBuffer)); + s.posCount = (posCount > 64) ? ROUND_UP_BITS(posCount, 2) : 64; + LOG(INFO) << "Increasing position buffer size to " << s.posCount << " float32"; + NVDR_CHECK_GL_ERROR(glBufferData(GL_ARRAY_BUFFER, s.posCount * sizeof(float), NULL, GL_DYNAMIC_DRAW)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterBuffer(&s.cudaPosBuffer, s.glPosBuffer, cudaGraphicsRegisterFlagsWriteDiscard)); + + // Resize triangle buffer? + if (s.cudaTriBuffer) + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnregisterResource(s.cudaTriBuffer)); + s.triCounts[s.model_counter] = (triCount > 64) ? ROUND_UP_BITS(triCount, 2) : 64; + LOG(INFO) << "Increasing triangle buffer size to " << s.triCounts[s.model_counter] << " int32"; + NVDR_CHECK_GL_ERROR(glBufferData(GL_ELEMENT_ARRAY_BUFFER, s.triCounts[s.model_counter] * sizeof(int32_t), NULL, GL_DYNAMIC_DRAW)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterBuffer(&s.cudaTriBuffer, s.glTriBuffer, cudaGraphicsRegisterFlagsWriteDiscard)); + + const float* posPtr = pos; + const int32_t* triPtr = tri; + + // Copy both position and triangle buffers. + void* glPosPtr = NULL; + void* glTriPtr = NULL; + size_t posBytes = 0; + size_t triBytes = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(2, &s.cudaPosBuffer, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsResourceGetMappedPointer(&glPosPtr, &posBytes, s.cudaPosBuffer)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsResourceGetMappedPointer(&glTriPtr, &triBytes, s.cudaTriBuffer)); + NVDR_CHECK(posBytes >= posCount * sizeof(float), "mapped GL position buffer size mismatch"); + NVDR_CHECK(triBytes >= triCount * sizeof(int32_t), "mapped GL triangle buffer size mismatch"); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyAsync(glPosPtr, posPtr, posCount * sizeof(float), cudaMemcpyDeviceToDevice, stream)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyAsync(glTriPtr, triPtr, triCount * sizeof(int32_t), cudaMemcpyDeviceToDevice, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(2, &s.cudaPosBuffer, stream)); + + + s.model_counter = s.model_counter + 1; +} + +void jax_load_vertices(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len) +{ + const LoadVerticesCustomCallDescriptor &d = + *UnpackDescriptor(opaque, opaque_len); + RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; + // std::cerr << "load_vertices: " << d.num_vertices << "," << d.num_triangles << "\n"; + _load_vertices_fwd(stream, stateWrapper, reinterpret_cast(buffers[0]), d.num_vertices, reinterpret_cast(buffers[1]), d.num_triangles); +} + + +void _rasterize_fwd_gl(cudaStream_t stream, RasterizeGLStateWrapper& stateWrapper, const float* pose, uint num_objects, uint num_images, const std::vector& proj, const std::vector& indices, void* out = nullptr) +{ + // NVDR_CHECK_DEVICE(pose); + // NVDR_CHECK_CONTIGUOUS(pose); + // NVDR_CHECK_F32(pose); + + auto start = std::chrono::high_resolution_clock::now(); + + // const at::cuda::OptionalCUDAGuard device_guard(device_of(pos)); + RasterizeGLState& s = *stateWrapper.pState; + + // Set the GL context unless manual context. + if (stateWrapper.automatic) + setGLContext(s.glctx); + + + // uint num_objects = pose.size(0); + // uint num_images = pose.size(1); + + // Set the GL context unless manual context. + if (stateWrapper.automatic) + setGLContext(s.glctx); + + NVDR_CHECK_GL_ERROR(glUseProgram(s.glProgram)); + glUniformMatrix4fv(0, 1, GL_TRUE, &proj[0]); + + // Copy color buffers to output tensors. + cudaArray_t array = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, s.cudaColorBuffer, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[0], 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, s.cudaColorBuffer, stream)); + + NVDR_CHECK_CUDA_ERROR(cudaGraphicsGLRegisterImage(&s.cudaPoseTexture, s.glPoseTexture, GL_TEXTURE_2D, cudaGraphicsRegisterFlagsReadOnly)); + cudaArray_t pose_array = 0; + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); + + const float* posePtr = pose; + for(int start_pose_idx=0; start_pose_idx < num_images; start_pose_idx+=s.num_layers) + { + int poses_on_this_iter = std::min(num_images-start_pose_idx, s.num_layers); + // Set viewport, clear color buffer(s) and depth/stencil buffer. + NVDR_CHECK_GL_ERROR(glViewport(0, 0, s.img_width, s.img_height)); + NVDR_CHECK_GL_ERROR(glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT)); + + for(int object_idx=0; object_idx < indices.size(); object_idx++){ + if (indices[object_idx] < 0){ + continue; + } + NVDR_CHECK_GL_ERROR(glBindVertexArray(s.glVAOs[indices[object_idx]])); + std::vector drawCmdBuffer(poses_on_this_iter); + for (int i=0; i < poses_on_this_iter; i++) + { + GLDrawCmd& cmd = drawCmdBuffer[i]; + cmd.firstIndex = 0; + cmd.count = s.triCounts[indices[object_idx]]; + cmd.baseVertex = 0; + cmd.baseInstance = 0; + cmd.instanceCount = 1; + } + + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, &s.cudaPoseTexture, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&pose_array, s.cudaPoseTexture, 0, 0)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpyToArrayAsync( + pose_array, 0, 0, posePtr + num_images*16*object_idx + start_pose_idx*16, + poses_on_this_iter*16*sizeof(float), cudaMemcpyDeviceToDevice, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, &s.cudaPoseTexture, stream)); + glUniform1f(1, object_idx+1.0); + + NVDR_CHECK_GL_ERROR(glMultiDrawElementsIndirect(GL_TRIANGLES, GL_UNSIGNED_INT, &drawCmdBuffer[0], poses_on_this_iter, sizeof(GLDrawCmd))); + } + + + + // Draw! + NVDR_CHECK_CUDA_ERROR(cudaGraphicsMapResources(1, s.cudaColorBuffer, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsSubResourceGetMappedArray(&array, s.cudaColorBuffer[0], 0, 0)); + cudaMemcpy3DParms p = {0}; + p.srcArray = array; + p.dstPtr.ptr = ((float * )out) + start_pose_idx*s.img_height*s.img_width*4; + p.dstPtr.pitch = s.img_width * 4 * sizeof(float); + p.dstPtr.xsize = s.img_width; + p.dstPtr.ysize = s.img_height; + p.extent.width = s.img_width; + p.extent.height = s.img_height; + p.extent.depth = poses_on_this_iter; + p.kind = cudaMemcpyDeviceToDevice; + NVDR_CHECK_CUDA_ERROR(cudaMemcpy3DAsync(&p, stream)); + NVDR_CHECK_CUDA_ERROR(cudaGraphicsUnmapResources(1, s.cudaColorBuffer, stream)); + } + + // Done. Release GL context and return. + if (stateWrapper.automatic) + releaseGLContext(); + + return; +} + + +// void rasterize_fwd_gl(RasterizeGLStateWrapper& stateWrapper, cudaArray_t pose, const std::vector& proj, const std::vector& indices) { +// return _rasterize_fwd_gl(at::cuda::getCurrentCUDAStream(), stateWrapper, pose, proj, indices, nullptr); +// } + + +void jax_rasterize_fwd_gl(cudaStream_t stream, + void **buffers, + const char *opaque, std::size_t opaque_len) { + + const RasterizeCustomCallDescriptor &d = + *UnpackDescriptor(opaque, opaque_len); + RasterizeGLStateWrapper& stateWrapper = *d.gl_state_wrapper; + + void *pose = buffers[0]; + void *obj_idx = buffers[1]; + void *proj_list = buffers[2]; + void *out = buffers[3]; + auto opts = torch::dtype(torch::kFloat32).device(torch::kCUDA); + + std::vector indices; + indices.resize(d.num_objects); + + std::vector proj_list_cpu; + proj_list_cpu.resize(16); + + + cudaStreamSynchronize(stream); + + NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&indices[0], obj_idx, d.num_objects * sizeof(int), cudaMemcpyDeviceToHost)); + NVDR_CHECK_CUDA_ERROR(cudaMemcpy(&proj_list_cpu[0], proj_list, 16 * sizeof(float), cudaMemcpyDeviceToHost)); + + _rasterize_fwd_gl(stream, + stateWrapper, + reinterpret_cast(pose), + d.num_objects, + d.num_images, + /*proj=*/proj_list_cpu, + /*indices=*/indices, + /*out=*/out); + cudaStreamSynchronize(stream); +} + + +template +pybind11::capsule EncapsulateFunction(T* fn) { + return pybind11::capsule((void*)fn, "xla._CUSTOM_CALL_TARGET"); +} + +pybind11::dict Registrations() { + pybind11::dict dict; + dict["jax_setup"] = EncapsulateFunction(jax_setup); + dict["jax_load_vertices"] = EncapsulateFunction(jax_load_vertices); + dict["jax_rasterize_fwd_gl"] = EncapsulateFunction(jax_rasterize_fwd_gl); + return dict; +} + + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + // State classes. + pybind11::class_(m, "RasterizeGLStateWrapper").def(pybind11::init()) + .def("set_context", &RasterizeGLStateWrapper::setContext) + .def("release_context", &RasterizeGLStateWrapper::releaseContext); + + // Ops. + // m.def("setup", &setup, "rasterize forward op (opengl)"); + // m.def("load_vertices_fwd", &load_vertices_fwd, "rasterize forward op (opengl)"); + // m.def("rasterize_fwd_gl", &rasterize_fwd_gl, "rasterize forward op (opengl)"); + m.def("registrations", &Registrations, "custom call registrations"); + m.def("build_setup_descriptor", + [](RasterizeGLStateWrapper& stateWrapper, + int h, int w, int num_layers) { + // std::cout << h << " " << w << " " << num_layers << "\n"; + return PackDescriptor(SetUpCustomCallDescriptor{&stateWrapper, h, w, num_layers}); + }); + m.def("build_load_vertices_descriptor", + [](RasterizeGLStateWrapper& stateWrapper, + long num_vertices, + long num_triangles) { + return PackDescriptor( + LoadVerticesCustomCallDescriptor{&stateWrapper, num_vertices, num_triangles}); + }); + m.def("build_rasterize_descriptor", + [](RasterizeGLStateWrapper& stateWrapper, + std::vector objs_images) { + RasterizeCustomCallDescriptor d; + d.gl_state_wrapper = &stateWrapper; + // NVDR_CHECK(proj.size() == 4 * 4); + d.num_objects = objs_images[0]; + d.num_images = objs_images[1]; + return PackDescriptor(d); + }); +} + +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/rasterize_gl.h b/bayes3d/rendering/nvdiffrast/common/rasterize_gl.h new file mode 100644 index 00000000..b4c84ca4 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/rasterize_gl.h @@ -0,0 +1,129 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#pragma once + +//------------------------------------------------------------------------ +// Do not try to include OpenGL stuff when compiling CUDA kernels for torch. + +#if !(defined(NVDR_TORCH) && defined(__CUDACC__)) +#include "framework.h" +#include "glutil.h" + +//------------------------------------------------------------------------ +// OpenGL-related persistent state for forward op. + +struct RasterizeGLState // Must be initializable by memset to zero. +{ + int width; // Allocated frame buffer width. + int height; // Allocated frame buffer height. + int depth; // Allocated frame buffer depth. + int img_width; // Allocated frame buffer depth. + int img_height; // Allocated frame buffer depth. + uint num_layers; // Allocated frame buffer depth. + std::vector proj; + int posCount; // Allocated position buffer in floats. + int triCounts[1000]; // Allocated triangle buffer in ints. + int model_counter; // Allocated triangle buffer in ints. + GLContext glctx; + GLuint glFBO; + GLuint glColorBuffer[2]; + GLuint glPrevOutBuffer; + GLuint glDepthStencilBuffer; + GLuint glVAOs[100]; + GLuint glTriBuffer; + GLuint glPosBuffer; + GLuint glPoseTexture; + GLuint glProgram; + GLuint glProgramDP; + GLuint glVertexShader; + GLuint glGeometryShader; + GLuint glFragmentShader; + GLuint glFragmentShaderDP; + cudaGraphicsResource_t cudaColorBuffer[2]; + cudaGraphicsResource_t cudaPrevOutBuffer; + cudaGraphicsResource_t cudaPosBuffer; + cudaGraphicsResource_t cudaTriBuffer; + cudaGraphicsResource_t cudaPoseTexture; + cudaArray_t cuda_color_buffer; + cudaArray_t cuda_pose_buffer; + float* obs_image; + int enableDB; + int enableZModify; // Modify depth in shader, workaround for a rasterization issue on A100. +}; + + +class RasterizeGLStateWrapper; + +struct SetUpCustomCallDescriptor { + RasterizeGLStateWrapper* gl_state_wrapper; + + int height; + int width; + int num_layers; +}; + +struct LoadVerticesCustomCallDescriptor { + RasterizeGLStateWrapper* gl_state_wrapper; + long num_vertices; + long num_triangles; +}; + +struct RasterizeCustomCallDescriptor { + RasterizeGLStateWrapper* gl_state_wrapper; + float proj[16]; + int num_objects; + int num_images; + int on_object; +}; + + +#include + +// https://en.cppreference.com/w/cpp/numeric/bit_cast +template +typename std::enable_if::value && + std::is_trivially_copyable::value, + To>::type +bit_cast(const From& src) noexcept { + static_assert( + std::is_trivially_constructible::value, + "This implementation additionally requires destination type to be trivially constructible"); + + To dst; + memcpy(&dst, &src, sizeof(To)); + return dst; +} + +// Note that bit_cast is only available in recent C++ standards so you might need +// to provide a shim like the one in lib/kernel_helpers.h +template +std::string PackDescriptorAsString(const T& descriptor) { + return std::string(bit_cast(&descriptor), sizeof(T)); +} + +#include + +template +pybind11::bytes PackDescriptor(const T& descriptor) { + return pybind11::bytes(PackDescriptorAsString(descriptor)); +} + +template +const T* UnpackDescriptor(const char* opaque, std::size_t opaque_len) { + if (opaque_len != sizeof(T)) { + throw std::runtime_error("Invalid opaque object size"); + } + return bit_cast(opaque); +} + +//------------------------------------------------------------------------ +// Shared C++ code prototypes. + +//------------------------------------------------------------------------ +#endif // !(defined(NVDR_TORCH) && defined(__CUDACC__)) diff --git a/bayes3d/rendering/nvdiffrast/common/torch_common.inl b/bayes3d/rendering/nvdiffrast/common/torch_common.inl new file mode 100644 index 00000000..74dea415 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/torch_common.inl @@ -0,0 +1,29 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#pragma once +#include "../common/framework.h" + +//------------------------------------------------------------------------ +// Input check helpers. +//------------------------------------------------------------------------ + +#ifdef _MSC_VER +#define __func__ __FUNCTION__ +#endif + +#define NVDR_CHECK_DEVICE(...) do { TORCH_CHECK(at::cuda::check_device({__VA_ARGS__}), __func__, "(): Inputs " #__VA_ARGS__ " must reside on the same GPU device") } while(0) +#define NVDR_CHECK_CPU(...) do { nvdr_check_cpu({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must reside on CPU"); } while(0) +#define NVDR_CHECK_CONTIGUOUS(...) do { nvdr_check_contiguous({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be contiguous tensors"); } while(0) +#define NVDR_CHECK_F32(...) do { nvdr_check_f32({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be float32 tensors"); } while(0) +#define NVDR_CHECK_I32(...) do { nvdr_check_i32({__VA_ARGS__}, __func__, "(): Inputs " #__VA_ARGS__ " must be int32 tensors"); } while(0) +inline void nvdr_check_cpu(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.device().type() == c10::DeviceType::CPU, func, err_msg); } +inline void nvdr_check_contiguous(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.is_contiguous(), func, err_msg); } +inline void nvdr_check_f32(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.dtype() == torch::kFloat32, func, err_msg); } +inline void nvdr_check_i32(at::ArrayRef ts, const char* func, const char* err_msg) { for (const at::Tensor& t : ts) TORCH_CHECK(t.dtype() == torch::kInt32, func, err_msg); } +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/common/torch_types.h b/bayes3d/rendering/nvdiffrast/common/torch_types.h new file mode 100644 index 00000000..8e389582 --- /dev/null +++ b/bayes3d/rendering/nvdiffrast/common/torch_types.h @@ -0,0 +1,65 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "torch_common.inl" + +//------------------------------------------------------------------------ +// Python GL state wrapper. + +class RasterizeGLState; +class RasterizeGLStateWrapper +{ +public: + RasterizeGLStateWrapper (bool enableDB, bool automatic, int cudaDeviceIdx); + ~RasterizeGLStateWrapper (void); + + void setContext (void); + void releaseContext (void); + + RasterizeGLState* pState; + bool automatic; + int cudaDeviceIdx; +}; + +//------------------------------------------------------------------------ +// Python CudaRaster state wrapper. + +namespace CR { class CudaRaster; } +class RasterizeCRStateWrapper +{ +public: + RasterizeCRStateWrapper (int cudaDeviceIdx); + ~RasterizeCRStateWrapper (void); + + CR::CudaRaster* cr; + int cudaDeviceIdx; +}; + +//------------------------------------------------------------------------ +// Mipmap wrapper to prevent intrusion from Python side. + +class TextureMipWrapper +{ +public: + torch::Tensor mip; + int max_mip_level; + std::vector texture_size; // For error checking. + bool cube_mode; // For error checking. +}; + + +//------------------------------------------------------------------------ +// Antialias topology hash wrapper to prevent intrusion from Python side. + +class TopologyHashWrapper +{ +public: + torch::Tensor ev_hash; +}; + +//------------------------------------------------------------------------ diff --git a/bayes3d/rendering/nvdiffrast/lib/setgpu.lib b/bayes3d/rendering/nvdiffrast/lib/setgpu.lib new file mode 100644 index 0000000000000000000000000000000000000000..add9a0c4f631cb56dbee31a05ed97339930301e2 GIT binary patch literal 7254 zcmd^EeQ*=U6<=9`*bX2Y>Jply7DR36K#YupF__jmEZOJyYzwe~q$aYE&LI3DS06US znTFI>2KUfFr=gjo(9#duFl~})C#66Vupu#c+;I&w4QZxRCesfX(i8$s1JkMc-tOtX zBG<|EpU!kWTD^Vy`~BYT+r7QhdH$+EG`RIk`Acm2Qd+jOtbE1trKQXCeuvy#TIQ6k z)_g*Ug^--R+D~Przsl`*tgd!9R{N^G^>q#IuAV@5*uN$rMt9V9#l>h_AShPaInGUF zaJ}2>ebDO>JRHN8xhh?ujt(uR)Z=xp=BjFZt3B0j>}bTQ1}gz8KUN;ByjWFZ#bMQq z6@@gRMR9AA9heZG~6U6#{FBm6Xd_jp$U?>H-{#YnB>3z z#~ea3A(thQ&D+?HoNOPKIvizXWj0&cGUsx(5nJ;^PZpEPz7Jd9n?*=FK{zW{LJ=EN5Jx{UuI823)gwAidyu9)sNuZ?vl8; zJ#O#p${>%J33(lGeR<4N1YfoSUu(&Bz22w5Uk_JQ0Iw=2xG&rV4tGhn9ybI0?SSc( zaUjodS@iY+=CBc$Meht?E*NoH^sWPD+MN&(iV`=A-hF^sV#FEr?gEW^z=%d%7QKGJ z>@ngDdYHfG0W)mGWzqWsV6GZ*2E78%%Y$Uk!-PZmPxD<4m?|SKi(UXQUoqmcjBgKM zzHP*1(K`m1Q6nyk-d_N7!-&hGS2&Z9`S6))zLkKfGvW+-b`S{zCS}BB8Q)I;^GhSn zpl1WUHvsc@BQA^H95@Gx;e$n4H-jGTmoEZljS-hcF9Mi8BhGLhV0xdCF&Gz%y8zAu z0}ikMLmva@0^IQ|I5_i`c)ZnIv(O~eu3otykqC!MI>MV5Oyc;*g3o$lDugdm zwX&t#6%}J5J_EEUMyw+c?h;$NdP3p0wrC(0Z|RPPdjfH>r8nFaiuA@FfFn94cEG_J ziMFUGHd#ql6_U+_OprOC{`4az<-0x{j7DOSwzzHK+Ar7|yW`=`@T1|bEw-viXLqE{?KbW*VG@S_2FrOSvfs_8_SrNCptWTZ-9-lhkw!&Bcc-mSFt$j9Bk;d9QE1dD!)*~i9RhzRb2l4`* z(!_h7T}$~?3PHzLLR7XQIIclx9bb%Tk9ln(N~@ zXnxUfy)-VQHk#V#dhn?vcGjAQA^$VX9_tZeJ#j9@om@4o*W7=~#--dRIqQkDv^i|6 zO%HR@NX0qM>N(|+CTd}w9(x5cepmVo|HNqv9M=_(!9>6IxpX>x$#A|qIZ8}|V z9^xg~cuW<(rHdK;az%8Q_XZbhprCL0bEV8uWw;S@hG*B&p^G6THk~^ zNUdj}#`D@j4ny5Wt>aMRVQC@DVJdT}^?k7LerzEq%dB;<`NPx~Qmh?{^|)f41q(-E zA(z2|{S8(wT&c`w0}D@43-N=6*RF;1fc0r=?NoXXfQ3h+h0I%MCUZ^vSdI^xP;8Jf z950@10D@9MYgXZ7j2T#jFe?|VyQyz}ZVt))W6c;e6bAM(94ayk$H^F|`#gLyC7bqq z=rj|m?Rs!b%a}}?F;I6rBMjf!wC`uyMHW(YvwAEyE{the`Vliet~_I*_FP!IOv$GI z0CdV@57mun8Iz5jF;LgVQcvZxX*-!V)LLErx-cHjgXyBCXA0dFimo;nlj*Wtpr$Z| zZl$8DUBk(A*&3o|(-gX1MHdWZ-%X~=_8n?=OrcvR>%uPp*c$}*HS7-p>quZ9YGYxR zS*Gt%EE`xX7RGsQidbs^4f}+^uF%FZ#KFDC;^-r?I2hx_DdKF9V&B@bF!{y)MFj+s5MX=&3yY*$9q+EhsghVjjFy zGbM+#MMW{wf}*yNHF8e=(sZuiophMU{(;5d3A#xi-K7%Bbc(^AKcm=i?^#R zw6^FlonmLOvpeIW;}JVSTf{Y;bz8l+W)tkH!XxdxIpW1 z_+X(JqJ0wpkGR0XqYN{?sPV@aRn{KxV4)aYtUJKVVj*i~-RnD5)_0Hsg<^EEaK+VF z8(MEPsjL?%hv20OBhk(aUw5dno+61pl{F-@C`K0xUqYy{Zd^(Xs;qM|i(+)K?xZYs z84~hxVPLz;dPinaj4l?`b>xRr?8P?8gCXI#bg*mit+TEJhT7R4*`y7ub(vsD&8a$#Yb z+Iiu_j~c5ue}O|~@iL2Ibg^brR%Rdm?EX!EQdtklEQ-<1i#|#z^LqK~Km4o8+9tCo zMi-0bT$$IoKC7Uz`eYVc7wx?8)KTYk=Fqi(%GxcnC`K0x9}m^_y8Ah2fyz1{vnWP4 zFMOCao9tM_r=tAa+bZh?nMExkv8o>^a3S*v6gV)U$H z@R&_5q(FJp^!|M+D=f1R!%QUKy6~NhJ#! Date: Tue, 20 Feb 2024 20:15:38 +0000 Subject: [PATCH 23/27] renderer --- .../rendering/nvdiffrast_jax/jax_renderer.py | 149 +++++------------- test_jax_renderer_fwd.py | 40 +---- 2 files changed, 38 insertions(+), 151 deletions(-) diff --git a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py index f73d17ab..90d8e028 100644 --- a/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py +++ b/bayes3d/rendering/nvdiffrast_jax/jax_renderer.py @@ -8,14 +8,33 @@ from jax.interpreters import mlir, xla from jax.lib import xla_client from jaxlib.hlo_helpers import custom_call +import functools import bayes3d as b import bayes3d.camera -# import bayes3d._rendering.nvdiffrast.common as dr import bayes3d.rendering.nvdiffrast_jax.nvdiffrast.jax as dr +@staticmethod +@functools.partial( + jnp.vectorize, + signature="(2),(),(m,4,4),()->(3)", + excluded=( + 4, + 5, + 6, + ), +) +def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): + relevant_vertices = vertices[faces[triangle_id-1]] + pose_of_object = poses[object_id-1] + relevant_vertices_transformed = relevant_vertices @ pose_of_object.T + barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) + interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) + return interpolated_value + + class Renderer(object): def __init__(self, intrinsics, num_layers=1024): """A renderer for rendering meshes. @@ -27,7 +46,6 @@ def __init__(self, intrinsics, num_layers=1024): self.intrinsics = intrinsics self.renderer_env = dr.RasterizeGLContext(output_db=True) self.rasterize = jax.tree_util.Partial(self._rasterize, self) - self.interpolate = jax.tree_util.Partial(self._interpolate, self) # ------------------ # Rasterization @@ -51,118 +69,23 @@ def _rasterize_bwd(self, saved_tensors, diffs): _rasterize.defvjp(_rasterize_fwd, _rasterize_bwd) - # ------------------ - # Interpolation - # ------------------ - - @functools.partial(jax.custom_vjp, nondiff_argnums=(0,)) - def _interpolate(self, attr, rast, tri, rast_db, diff_attrs): - num_total_attrs = attr.shape[-1] - diff_attrs_all = jax.lax.cond( - diff_attrs.shape[0] == num_total_attrs, lambda: True, lambda: False - ) - return _interpolate_fwd_custom_call( - self, attr, rast, tri, rast_db, diff_attrs_all, diff_attrs - ) - - def _interpolate_fwd(self, attr, rast, tri, rast_db, diff_attrs): - num_total_attrs = attr.shape[-1] - diff_attrs_all = jax.lax.cond( - diff_attrs.shape[0] == num_total_attrs, lambda: True, lambda: False - ) - out, out_da = _interpolate_fwd_custom_call( - self, attr, rast, tri, rast_db, diff_attrs_all, diff_attrs - ) - saved_tensors = (attr, rast, tri, rast_db, diff_attrs_all, diff_attrs) - - return (out, out_da), saved_tensors - - def _interpolate_bwd(self, saved_tensors, diffs): - attr, rast, tri, rast_db, diff_attrs_all, diff_attrs_list = saved_tensors - dy, dda = diffs - g_attr, g_rast, g_rast_db = _interpolate_bwd_custom_call( - self, attr, rast, tri, dy, rast_db, dda, diff_attrs_all, diff_attrs_list + def render(self, poses, vertices, faces, ranges, projection_matrix, resolution): + rast_out, rast_out_aux = self.rasterize( + poses, + vertices, + faces, + ranges, + projection_matrix, + resolution ) - return g_attr, g_rast, None, g_rast_db, None - - _interpolate.defvjp(_interpolate_fwd, _interpolate_bwd) - - # def render_many(self, vertices, faces, poses, intrinsics): - # jax_renderer = self - # projection_matrix = b.camera._open_gl_projection_matrix( - # intrinsics.height, - # intrinsics.width, - # intrinsics.fx, - # intrinsics.fy, - # intrinsics.cx, - # intrinsics.cy, - # intrinsics.near, - # intrinsics.far, - # ) - # composed_projection = projection_matrix @ poses - # vertices_homogenous = jnp.concatenate( - # [vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1 - # ) - # clip_spaces_projected_vertices = jnp.einsum( - # "nij,mj->nmi", composed_projection, vertices_homogenous - # ) - # rast_out, rast_out_db = jax_renderer.rasterize( - # clip_spaces_projected_vertices, - # faces, - # jnp.array([intrinsics.height, intrinsics.width]), - # ) - # interpolated_collided_vertices_clip, _ = jax_renderer.interpolate( - # jnp.tile(vertices_homogenous[None, ...], (poses.shape[0], 1, 1)), - # rast_out, - # faces, - # rast_out_db, - # jnp.array([0, 1, 2, 3]), - # ) - # interpolated_collided_vertices = jnp.einsum( - # "a...ij,a...j->a...i", poses, interpolated_collided_vertices_clip - # ) - # mask = rast_out[..., -1] > 0 - # depth = interpolated_collided_vertices[..., 2] * mask - # return depth - - # def render(self, vertices, faces, object_pose, intrinsics): - # jax_renderer = self - # projection_matrix = b.camera._open_gl_projection_matrix( - # intrinsics.height, - # intrinsics.width, - # intrinsics.fx, - # intrinsics.fy, - # intrinsics.cx, - # intrinsics.cy, - # intrinsics.near, - # intrinsics.far, - # ) - # final_mtx_proj = projection_matrix @ object_pose - # posw = jnp.concatenate([vertices, jnp.ones((*vertices.shape[:-1], 1))], axis=-1) - # pos_clip_ja = xfm_points(vertices, final_mtx_proj) - # rast_out, rast_out_db = jax_renderer.rasterize( - # pos_clip_ja[None, ...], - # faces, - # jnp.array([intrinsics.height, intrinsics.width]), - # ) - # gb_pos, _ = jax_renderer.interpolate( - # posw[None, ...], rast_out, faces, rast_out_db, jnp.array([0, 1, 2, 3]) - # ) - # mask = rast_out[..., -1] > 0 - # shape_keep = gb_pos.shape - # gb_pos = gb_pos.reshape(shape_keep[0], -1, shape_keep[-1]) - # gb_pos = gb_pos[..., :3] - # depth = xfm_points(gb_pos, object_pose) - # depth = depth.reshape(shape_keep)[..., 2] * -1 - # return -(depth * mask), mask - - -# ================================================================================================ -# Register custom call targets helpers -# ================================================================================================ -def xfm_points(points, matrix): - points2 = jnp.concatenate([points, jnp.ones((*points.shape[:-1], 1))], axis=-1) - return jnp.matmul(points2, matrix.T) + uvs = rast_out[...,:2] + object_ids = rast_out_aux[...,0] + triangle_ids = rast_out_aux[...,1] + mask = object_ids > 0 + + interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) + image = interpolated_values * mask[...,None] + return image # XLA array layout in memory diff --git a/test_jax_renderer_fwd.py b/test_jax_renderer_fwd.py index 847c88a2..9bf1911f 100644 --- a/test_jax_renderer_fwd.py +++ b/test_jax_renderer_fwd.py @@ -62,44 +62,6 @@ resolution = jnp.array([intrinsics.height, intrinsics.width]) -import functools -@functools.partial( - jnp.vectorize, - signature="(2),(),(m,4,4),()->(3)", - excluded=( - 4, - 5, - 6, - ), -) -def interpolate_(uv, triangle_id, poses, object_id, vertices, faces, ranges): - relevant_vertices = vertices[faces[triangle_id-1]] - pose_of_object = poses[object_id-1] - relevant_vertices_transformed = relevant_vertices @ pose_of_object.T - barycentric = jnp.concatenate([uv, jnp.array([1.0 - uv.sum()])]) - interpolated_value = (relevant_vertices_transformed[:,:3] * barycentric.reshape(3,1)).sum(0) - return interpolated_value - -def render(poses, vertices, faces, ranges, projection_matrix, resolution): - rast_out, rast_out_aux = jax_renderer.rasterize( - poses, - vertices, - faces, - ranges, - projection_matrix, - resolution - ) - uvs = rast_out[...,:2] - object_ids = rast_out_aux[...,0] - triangle_ids = rast_out_aux[...,1] - mask = object_ids > 0 - - interpolated_values = interpolate_(uvs, triangle_ids, poses[:,None, None,:,:], object_ids, vertices, faces, ranges) - image = interpolated_values * mask[...,None] - return image - -render_jit = jax.jit(render) - object_indices = jnp.array([1, 0]) ranges = jnp.hstack([faces_lens_cumsum[object_indices].reshape(-1,1), faces_lens[object_indices].reshape(-1,1)]) @@ -109,6 +71,8 @@ def render(poses, vertices, faces, ranges, projection_matrix, resolution): poses2 = poses2.at[:, 0,3].set(-0.5) poses = jnp.stack([poses, poses2], axis=1) +render_jit = jax.jit(jax_renderer.render) + image = render_jit( poses, vertices, From fb2fa5b2724165141ca0cecaa5f655eeb4ad5f0e Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Tue, 20 Feb 2024 20:19:34 +0000 Subject: [PATCH 24/27] save --- README.md | 2 +- .../rendering/nvdiffrast_jax/pytorch_check.py | 0 .../rendering/nvdiffrast_jax/test_jax_renderer_bwd.py | 0 .../rendering/nvdiffrast_jax/test_jax_renderer_fwd.py | 0 bayes3d/viz/__init__.py | 2 +- 5 files changed, 2 insertions(+), 2 deletions(-) rename pytorch_check.py => bayes3d/rendering/nvdiffrast_jax/pytorch_check.py (100%) rename test_jax_renderer_bwd.py => bayes3d/rendering/nvdiffrast_jax/test_jax_renderer_bwd.py (100%) rename test_jax_renderer_fwd.py => bayes3d/rendering/nvdiffrast_jax/test_jax_renderer_fwd.py (100%) diff --git a/README.md b/README.md index 672a7deb..9ad6273d 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ Install compatible versions JAX and Torch: ```bash pip install --upgrade torch==2.2.0 torchvision==0.17.0+cu118 --index-url https://download.pytorch.org/whl/cu118 -pip install --upgrade jax[cuda11_local] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html +pip install --upgrade jax[cuda11_local]==0.4.20 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html ``` Bayes3D is built on top of GenJAX, which is currently hosted in a private Python diff --git a/pytorch_check.py b/bayes3d/rendering/nvdiffrast_jax/pytorch_check.py similarity index 100% rename from pytorch_check.py rename to bayes3d/rendering/nvdiffrast_jax/pytorch_check.py diff --git a/test_jax_renderer_bwd.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer_bwd.py similarity index 100% rename from test_jax_renderer_bwd.py rename to bayes3d/rendering/nvdiffrast_jax/test_jax_renderer_bwd.py diff --git a/test_jax_renderer_fwd.py b/bayes3d/rendering/nvdiffrast_jax/test_jax_renderer_fwd.py similarity index 100% rename from test_jax_renderer_fwd.py rename to bayes3d/rendering/nvdiffrast_jax/test_jax_renderer_fwd.py diff --git a/bayes3d/viz/__init__.py b/bayes3d/viz/__init__.py index 10113e08..fed93966 100644 --- a/bayes3d/viz/__init__.py +++ b/bayes3d/viz/__init__.py @@ -1,2 +1,2 @@ -from .meshcatviz import * +# from .meshcatviz import * from .viz import * From fa1effbf9368048052b203536ed73949fee77f71 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Tue, 20 Feb 2024 20:20:03 +0000 Subject: [PATCH 25/27] save --- bayes3d/viz/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bayes3d/viz/__init__.py b/bayes3d/viz/__init__.py index fed93966..10113e08 100644 --- a/bayes3d/viz/__init__.py +++ b/bayes3d/viz/__init__.py @@ -1,2 +1,2 @@ -# from .meshcatviz import * +from .meshcatviz import * from .viz import * From 269e40ffab9d033d2252e739096fc19f1d2a5c32 Mon Sep 17 00:00:00 2001 From: Nishad Gothoskar Date: Tue, 20 Feb 2024 20:20:49 +0000 Subject: [PATCH 26/27] cleanup --- nvdiffrast_test.py | 54 ------------------ .../slam/localization_with_gradients.mp4 | Bin 264487 -> 0 bytes 2 files changed, 54 deletions(-) delete mode 100644 nvdiffrast_test.py delete mode 100644 scripts/experiments/slam/localization_with_gradients.mp4 diff --git a/nvdiffrast_test.py b/nvdiffrast_test.py deleted file mode 100644 index 60477681..00000000 --- a/nvdiffrast_test.py +++ /dev/null @@ -1,54 +0,0 @@ -import bayes3d as b -import jax.numpy as jnp -import jax -from tqdm import tqdm -import matplotlib.pyplot as plt -import matplotlib.gridspec as gridspec -import numpy as np -import os -import trimesh -from dcolmap.hgps.pose import Pose -import viser -server = viser.ViserServer() - - -intrinsics = b.Intrinsics( - height=100, - width=100, - fx=200.0, fy=200.0, - cx=50., cy=50., - near=0.001, far=16.0 -) - -projection_matrix = b.camera._open_gl_projection_matrix( - intrinsics.height, - intrinsics.width, - intrinsics.fx, - intrinsics.fy, - intrinsics.cx, - intrinsics.cy, - intrinsics.near, - intrinsics.far, -) - - -meshes = [] - -path = os.path.join(b.utils.get_assets_dir(), "sample_objs/bunny.obj") -bunny_mesh = trimesh.load(path) -bunny_mesh.vertices = bunny_mesh.vertices * jnp.array([1.0, -1.0, 1.0]) + jnp.array([0.0, 1.0, 0.0]) -meshes.append(bunny_mesh) - - -all_vertices = [jnp.array(mesh.vertices) for mesh in meshes] -all_faces = [jnp.array(mesh.faces) for mesh in meshes] -vertices_lens = jnp.array([len(verts) for verts in all_vertices]) -vertices_lens_cumsum = jnp.pad(jnp.cumsum(vertices_lens),(1,0)) -faces_lens = jnp.array([len(faces) for faces in all_faces]) -faces_lens_cumsum = jnp.pad(jnp.cumsum(faces_lens),(1,0)) - -vertices = jnp.concatenate(all_vertices, axis=0) -vertices = jnp.concatenate([vertices, jnp.ones((vertices.shape[0], 1))], axis=-1) -faces = jnp.concatenate([faces + vertices_lens_cumsum[i] for (i,faces) in enumerate(all_faces)]) - -resolution 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z&6Wm|f74(1|7iS+{U3j^KY^ht0h~aqVjy<_;?G?Gjuj9m=ThT3`ub|8Lqc`)^&`fANTc z=&uae$^YPg$^!GkfAc`E|Ia*-PyAQ@zxDou|G(}1m;V3YL;ts(|H1$Nv-2PN!hik$ zSN)*=Z3iFNX0yKnAOP$3PXhMGKllhJ^ZhIMHvst+F93k7Apk(10s!y;E5PCp0MI4? z05na&{zwDj9RL828vsDv1#Wcf1Mw{IUGyp7NSpzuHgL{>4FUj=Kt7lxFabvp8U`5m zs|$F`_`gf;>upboSE z&|el%_63;ufpiYATpW-e4z&LU#K5@-(+9-B`a=UR02p9hU{QgX7uYu7xSKisyN!SQ z;BUv8dYHQd{Q-0xEdIfN#U%fEAptKwR}*LFzjOWn0)x%Ju2zalcc;Gw3CvCYDg4(c TK;FdDl7pL(or95unfd Date: Tue, 20 Feb 2024 20:26:08 +0000 Subject: [PATCH 27/27] revert --- bayes3d/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/bayes3d/__init__.py b/bayes3d/__init__.py index 4d6726d4..6eccde99 100644 --- a/bayes3d/__init__.py +++ b/bayes3d/__init__.py @@ -7,6 +7,7 @@ from . import colmap, distributions, scene_graph, utils from .camera import * from .likelihood import * +from .renderer import * from .rgbd import * from .transforms_3d import * from .viz import *

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