mirror of
https://github.com/facebookresearch/pytorch3d.git
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b19fe1de2f
Summary: This diff integrates the pulsar renderer source code into PyTorch3D as an alternative backend for the PyTorch3D point renderer. This diff is the first of a series of three diffs to complete that migration and focuses on the packaging and integration of the source code. For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder `docs/examples`. Tasks addressed in the following diffs: * Add the PyTorch3D interface, * Add notebook examples and documentation (or adapt the existing ones to feature both interfaces). Reviewed By: nikhilaravi Differential Revision: D23947736 fbshipit-source-id: a5e77b53e6750334db22aefa89b4c079cda1b443
117 lines
3.3 KiB
Python
117 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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if __name__ == "__main__":
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bm_barycentric_clip()
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