mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 20:02:49 +08:00
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
122 lines
3.7 KiB
Python
Executable File
122 lines
3.7 KiB
Python
Executable File
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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"""Test render speed."""
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import logging
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import sys
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from os import path
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import torch
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from fvcore.common.benchmark import benchmark
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from pytorch3d.renderer.points.pulsar import Renderer
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from torch.autograd import Variable
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# Making sure you can run this, even if pulsar hasn't been installed yet.
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sys.path.insert(0, path.join(path.dirname(__file__), ".."))
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LOGGER = logging.getLogger(__name__)
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"""Measure the execution speed of the rendering.
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This measures a very pessimistic upper bound on speed, because synchronization
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points have to be introduced in Python. On a pure PyTorch execution pipeline,
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results should be significantly faster. You can get pure CUDA timings through
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C++ by activating `PULSAR_TIMINGS_BATCHED_ENABLED` in the file
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`pytorch3d/csrc/pulsar/logging.h` or defining it for your compiler.
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"""
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def _bm_pulsar():
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n_points = 1_000_000
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width = 1_000
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height = 1_000
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renderer = Renderer(width, height, n_points)
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# Generate sample data.
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torch.manual_seed(1)
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vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
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vert_pos[:, 2] += 25.0
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vert_pos[:, :2] -= 5.0
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vert_col = torch.rand(n_points, 3, dtype=torch.float32)
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vert_rad = torch.rand(n_points, dtype=torch.float32)
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cam_params = torch.tensor(
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
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)
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device = torch.device("cuda")
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vert_pos = vert_pos.to(device)
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vert_col = vert_col.to(device)
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vert_rad = vert_rad.to(device)
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cam_params = cam_params.to(device)
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renderer = renderer.to(device)
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vert_pos_var = Variable(vert_pos, requires_grad=False)
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vert_col_var = Variable(vert_col, requires_grad=False)
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vert_rad_var = Variable(vert_rad, requires_grad=False)
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cam_params_var = Variable(cam_params, requires_grad=False)
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def bm_closure():
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renderer.forward(
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vert_pos_var,
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vert_col_var,
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vert_rad_var,
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cam_params_var,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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)
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torch.cuda.synchronize()
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return bm_closure
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def _bm_pulsar_backward():
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n_points = 1_000_000
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width = 1_000
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height = 1_000
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renderer = Renderer(width, height, n_points)
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# Generate sample data.
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torch.manual_seed(1)
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vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
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vert_pos[:, 2] += 25.0
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vert_pos[:, :2] -= 5.0
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vert_col = torch.rand(n_points, 3, dtype=torch.float32)
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vert_rad = torch.rand(n_points, dtype=torch.float32)
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cam_params = torch.tensor(
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
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)
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device = torch.device("cuda")
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vert_pos = vert_pos.to(device)
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vert_col = vert_col.to(device)
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vert_rad = vert_rad.to(device)
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cam_params = cam_params.to(device)
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renderer = renderer.to(device)
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vert_pos_var = Variable(vert_pos, requires_grad=True)
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vert_col_var = Variable(vert_col, requires_grad=True)
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vert_rad_var = Variable(vert_rad, requires_grad=True)
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cam_params_var = Variable(cam_params, requires_grad=True)
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res = renderer.forward(
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vert_pos_var,
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vert_col_var,
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vert_rad_var,
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cam_params_var,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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)
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loss = res.sum()
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def bm_closure():
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loss.backward(retain_graph=True)
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torch.cuda.synchronize()
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return bm_closure
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def bm_pulsar() -> None:
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if not torch.cuda.is_available():
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return
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benchmark(_bm_pulsar, "PULSAR_FORWARD", [{}], warmup_iters=3)
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benchmark(_bm_pulsar_backward, "PULSAR_BACKWARD", [{}], warmup_iters=3)
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if __name__ == "__main__":
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bm_pulsar()
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