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https://github.com/facebookresearch/pytorch3d.git
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ebe2693b11
Summary: Support variable size pointclouds in the renderer API to allow compatibility with Pulsar rasterizer. If radius is provided as a float, it is converted to a tensor of shape (P). Otherwise radius is expected to be an (N, P_padded) dimensional tensor where P_padded is the max number of points in the batch (following the convention from pulsar: https://our.intern.facebook.com/intern/diffusion/FBS/browse/master/fbcode/frl/gemini/pulsar/pulsar/renderer.py?commit=ee0342850210e5df441e14fd97162675c70d147c&lines=50) Reviewed By: jcjohnson, gkioxari Differential Revision: D21429400 fbshipit-source-id: 65de7d9cd2472b27fc29f96160c33687e88098a2
83 lines
2.4 KiB
Python
83 lines
2.4 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.points.rasterize_points import (
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rasterize_points,
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rasterize_points_python,
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)
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from pytorch3d.structures.pointclouds import Pointclouds
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def _bm_python_with_init(N, P, img_size=32, radius=0.1, pts_per_pxl=3):
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torch.manual_seed(231)
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points = torch.randn(N, P, 3)
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pointclouds = Pointclouds(points=points)
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args = (pointclouds, img_size, radius, pts_per_pxl)
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return lambda: rasterize_points_python(*args)
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def _bm_rasterize_points_with_init(
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N, P, img_size=32, radius=0.1, pts_per_pxl=3, device="cpu", expand_radius=False
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):
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torch.manual_seed(231)
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device = torch.device(device)
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points = torch.randn(N, P, 3, device=device)
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pointclouds = Pointclouds(points=points)
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if expand_radius:
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points_padded = pointclouds.points_padded()
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radius = torch.full((N, P), fill_value=radius).type_as(points_padded)
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args = (pointclouds, img_size, radius, pts_per_pxl)
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if device == "cuda":
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torch.cuda.synchronize(device)
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def fn():
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rasterize_points(*args)
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if device == "cuda":
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torch.cuda.synchronize(device)
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return fn
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def bm_python_vs_cpu_vs_cuda() -> None:
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kwargs_list = []
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num_meshes = [1]
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num_points = [10000, 2000]
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image_size = [128, 256]
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radius = [1e-3, 0.01]
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pts_per_pxl = [50, 100]
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expand = [True, False]
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test_cases = product(
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num_meshes, num_points, image_size, radius, pts_per_pxl, expand
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)
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for case in test_cases:
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n, p, im, r, pts, e = case
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kwargs_list.append(
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{
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"N": n,
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"P": p,
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"img_size": im,
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"radius": r,
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"pts_per_pxl": pts,
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"device": "cpu",
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"expand_radius": e,
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}
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)
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benchmark(
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_bm_rasterize_points_with_init, "RASTERIZE_CPU", kwargs_list, warmup_iters=1
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)
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kwargs_list += [
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{"N": 32, "P": 100000, "img_size": 128, "radius": 0.01, "pts_per_pxl": 50},
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{"N": 8, "P": 200000, "img_size": 512, "radius": 0.01, "pts_per_pxl": 50},
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]
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for k in kwargs_list:
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k["device"] = "cuda"
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benchmark(
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_bm_rasterize_points_with_init, "RASTERIZE_CUDA", kwargs_list, warmup_iters=1
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)
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