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
150 lines
4.5 KiB
Python
150 lines
4.5 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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"""Test number of channels."""
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import logging
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import sys
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import unittest
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from os import path
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import torch
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# fmt: off
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# Make the mixin available.
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sys.path.insert(0, path.join(path.dirname(__file__), ".."))
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from common_testing import TestCaseMixin # isort:skip # noqa: E402
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# fmt: on
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sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
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devices = [torch.device("cuda"), torch.device("cpu")]
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class TestChannels(TestCaseMixin, unittest.TestCase):
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"""Test different numbers of channels."""
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def test_basic(self):
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"""Basic forward test."""
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from pytorch3d.renderer.points.pulsar import Renderer
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import torch
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n_points = 10
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width = 1_000
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height = 1_000
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renderer_1 = Renderer(width, height, n_points, n_channels=1)
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renderer_3 = Renderer(width, height, n_points, n_channels=3)
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renderer_8 = Renderer(width, height, n_points, n_channels=8)
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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, 8, 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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for device in devices:
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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_1 = renderer_1.to(device)
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renderer_3 = renderer_3.to(device)
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renderer_8 = renderer_8.to(device)
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result_1 = (
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renderer_1.forward(
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vert_pos,
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vert_col[:, :1],
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vert_rad,
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cam_params,
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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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.cpu()
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.detach()
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.numpy()
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)
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hits_1 = (
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renderer_1.forward(
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vert_pos,
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vert_col[:, :1],
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vert_rad,
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cam_params,
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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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mode=1,
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)
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.cpu()
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.detach()
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.numpy()
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)
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result_3 = (
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renderer_3.forward(
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vert_pos,
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vert_col[:, :3],
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vert_rad,
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cam_params,
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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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.cpu()
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.detach()
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.numpy()
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)
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hits_3 = (
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renderer_3.forward(
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vert_pos,
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vert_col[:, :3],
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vert_rad,
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cam_params,
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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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mode=1,
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)
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.cpu()
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.detach()
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.numpy()
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)
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result_8 = (
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renderer_8.forward(
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vert_pos,
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vert_col,
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vert_rad,
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cam_params,
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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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.cpu()
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.detach()
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.numpy()
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)
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hits_8 = (
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renderer_8.forward(
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vert_pos,
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vert_col,
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vert_rad,
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cam_params,
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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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mode=1,
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)
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.cpu()
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.detach()
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.numpy()
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)
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self.assertClose(result_1, result_3[:, :, :1])
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self.assertClose(result_3, result_8[:, :, :3])
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self.assertClose(hits_1, hits_3)
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self.assertClose(hits_8, hits_3)
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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unittest.main()
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