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allow-large-files
Reviewed By: bottler
Differential Revision: D66472063
fbshipit-source-id: 35841cb397e4f8e066e2159550d2f56b403b1bef
133 lines
4.0 KiB
Python
133 lines
4.0 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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"""Tests for the orthogonal projection."""
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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 numpy as np
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import torch
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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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devices = [torch.device("cuda"), torch.device("cpu")]
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class TestOrtho(unittest.TestCase):
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"""Test the orthogonal projection."""
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def test_basic(self):
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"""Basic forward test of the orthogonal projection."""
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from pytorch3d.renderer.points.pulsar import Renderer
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n_points = 10
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width = 1000
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height = 1000
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renderer_left = Renderer(
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width,
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height,
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n_points,
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right_handed_system=False,
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orthogonal_projection=True,
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)
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renderer_right = Renderer(
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width,
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height,
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n_points,
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right_handed_system=True,
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orthogonal_projection=True,
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)
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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_pos_neg = vert_pos.clone()
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vert_pos_neg[:, 2] *= -1.0
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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, 0.0, 20.0], 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_pos_neg = vert_pos_neg.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_left = renderer_left.to(device)
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renderer_right = renderer_right.to(device)
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result_left = (
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renderer_left.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_left = (
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renderer_left.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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result_right = (
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renderer_right.forward(
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vert_pos_neg,
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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_right = (
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renderer_right.forward(
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vert_pos_neg,
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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.assertTrue(np.allclose(result_left, result_right))
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self.assertTrue(np.allclose(hits_left, hits_right))
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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logging.getLogger("pulsar.renderer").setLevel(logging.WARN)
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unittest.main()
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