mirror of
https://github.com/facebookresearch/pytorch3d.git
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57a22e7306
Summary: Refactor cameras * CamerasBase was enhanced with `transform_points_screen` that transforms projected points from NDC to screen space * OpenGLPerspective, OpenGLOrthographic -> FoVPerspective, FoVOrthographic * SfMPerspective, SfMOrthographic -> Perspective, Orthographic * PerspectiveCamera can optionally be constructred with screen space parameters * Note on Cameras and coordinate systems was added Reviewed By: nikhilaravi Differential Revision: D23168525 fbshipit-source-id: dd138e2b2cc7e0e0d9f34c45b8251c01266a2063
113 lines
3.3 KiB
Python
113 lines
3.3 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from itertools import product
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import torch
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from fvcore.common.benchmark import benchmark
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from pytorch3d.renderer.cameras import FoVPerspectiveCameras, look_at_view_transform
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from pytorch3d.renderer.mesh.rasterizer import (
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Fragments,
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MeshRasterizer,
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RasterizationSettings,
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)
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from pytorch3d.renderer.mesh.utils import (
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_clip_barycentric_coordinates,
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_interpolate_zbuf,
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)
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from pytorch3d.utils.ico_sphere import ico_sphere
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def baryclip_cuda(
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num_meshes: int = 8,
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ico_level: int = 5,
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image_size: int = 64,
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faces_per_pixel: int = 50,
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device="cuda",
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):
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# Init meshes
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sphere_meshes = ico_sphere(ico_level, device).extend(num_meshes)
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# Init transform
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R, T = look_at_view_transform(1.0, 0.0, 0.0)
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cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
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# Init rasterizer
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raster_settings = RasterizationSettings(
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image_size=image_size,
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blur_radius=1e-4,
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faces_per_pixel=faces_per_pixel,
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clip_barycentric_coords=True,
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)
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rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
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torch.cuda.synchronize()
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def raster_fn():
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rasterizer(sphere_meshes)
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torch.cuda.synchronize()
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return raster_fn
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def baryclip_pytorch(
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num_meshes: int = 8,
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ico_level: int = 5,
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image_size: int = 64,
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faces_per_pixel: int = 50,
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device="cuda",
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):
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# Init meshes
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sphere_meshes = ico_sphere(ico_level, device).extend(num_meshes)
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# Init transform
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R, T = look_at_view_transform(1.0, 0.0, 0.0)
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cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
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# Init rasterizer
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raster_settings = RasterizationSettings(
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image_size=image_size,
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blur_radius=1e-4,
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faces_per_pixel=faces_per_pixel,
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clip_barycentric_coords=False,
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)
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rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
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torch.cuda.synchronize()
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def raster_fn():
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fragments = rasterizer(sphere_meshes)
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# Clip bary and reinterpolate
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clipped_bary_coords = _clip_barycentric_coordinates(fragments.bary_coords)
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clipped_zbuf = _interpolate_zbuf(
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fragments.pix_to_face, clipped_bary_coords, sphere_meshes
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)
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fragments = Fragments(
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bary_coords=clipped_bary_coords,
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zbuf=clipped_zbuf,
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dists=fragments.dists,
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pix_to_face=fragments.pix_to_face,
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)
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torch.cuda.synchronize()
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return raster_fn
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def bm_barycentric_clip() -> None:
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if torch.cuda.is_available():
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kwargs_list = []
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num_meshes = [1, 8]
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ico_level = [0, 4]
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image_size = [64, 128, 256]
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faces_per_pixel = [10, 75, 100]
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test_cases = product(num_meshes, ico_level, image_size, faces_per_pixel)
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for case in test_cases:
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n, ic, im, nf = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"ico_level": ic,
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"image_size": im,
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"faces_per_pixel": nf,
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}
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)
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benchmark(baryclip_cuda, "BARY_CLIP_CUDA", kwargs_list, warmup_iters=1)
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benchmark(baryclip_pytorch, "BARY_CLIP_PYTORCH", kwargs_list, warmup_iters=1)
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