mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-03 04:12:48 +08:00
88f579389f
Summary: Fixes the default setting of `max_points_per_bin` in `rasterize_points.py`. For large batches with large size pointclouds this was a causing the rasterizer to be very slow. Expanded the pointcloud rendering benchmarks to include larger size pointclouds and fixed cuda synchronization issue in benchmark. Reviewed By: gkioxari Differential Revision: D22301185 fbshipit-source-id: 5077c1ba2c43d73efc1c659f0ec75959ceddf893
62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
|
|
|
|
|
|
import torch
|
|
from fvcore.common.benchmark import benchmark
|
|
from pytorch3d.renderer.points.rasterize_points import (
|
|
rasterize_points,
|
|
rasterize_points_python,
|
|
)
|
|
from pytorch3d.structures.pointclouds import Pointclouds
|
|
|
|
|
|
def _bm_python_with_init(N, P, img_size=32, radius=0.1, pts_per_pxl=3):
|
|
torch.manual_seed(231)
|
|
points = torch.randn(N, P, 3)
|
|
pointclouds = Pointclouds(points=points)
|
|
args = (pointclouds, img_size, radius, pts_per_pxl)
|
|
return lambda: rasterize_points_python(*args)
|
|
|
|
|
|
def _bm_cpu_with_init(N, P, img_size=32, radius=0.1, pts_per_pxl=3):
|
|
torch.manual_seed(231)
|
|
points = torch.randn(N, P, 3)
|
|
pointclouds = Pointclouds(points=points)
|
|
args = (pointclouds, img_size, radius, pts_per_pxl)
|
|
return lambda: rasterize_points(*args)
|
|
|
|
|
|
def _bm_cuda_with_init(N, P, img_size=32, radius=0.1, pts_per_pxl=3):
|
|
torch.manual_seed(231)
|
|
device = torch.device("cuda:0")
|
|
points = torch.randn(N, P, 3, device=device)
|
|
pointclouds = Pointclouds(points=points)
|
|
args = (pointclouds, img_size, radius, pts_per_pxl)
|
|
torch.cuda.synchronize(device)
|
|
|
|
def fn():
|
|
rasterize_points(*args)
|
|
torch.cuda.synchronize(device)
|
|
|
|
return fn
|
|
|
|
|
|
def bm_python_vs_cpu() -> None:
|
|
kwargs_list = [
|
|
{"N": 1, "P": 32, "img_size": 32, "radius": 0.1, "pts_per_pxl": 3},
|
|
{"N": 2, "P": 32, "img_size": 32, "radius": 0.1, "pts_per_pxl": 3},
|
|
]
|
|
benchmark(_bm_python_with_init, "RASTERIZE_PYTHON", kwargs_list, warmup_iters=1)
|
|
benchmark(_bm_cpu_with_init, "RASTERIZE_CPU", kwargs_list, warmup_iters=1)
|
|
kwargs_list = [
|
|
{"N": 2, "P": 32, "img_size": 32, "radius": 0.1, "pts_per_pxl": 3},
|
|
{"N": 4, "P": 1024, "img_size": 128, "radius": 0.05, "pts_per_pxl": 5},
|
|
]
|
|
benchmark(_bm_cpu_with_init, "RASTERIZE_CPU", kwargs_list, warmup_iters=1)
|
|
kwargs_list += [
|
|
{"N": 32, "P": 10000, "img_size": 128, "radius": 0.01, "pts_per_pxl": 50},
|
|
{"N": 32, "P": 100000, "img_size": 128, "radius": 0.01, "pts_per_pxl": 50},
|
|
{"N": 8, "P": 200000, "img_size": 512, "radius": 0.01, "pts_per_pxl": 50},
|
|
]
|
|
benchmark(_bm_cuda_with_init, "RASTERIZE_CUDA", kwargs_list, warmup_iters=1)
|