mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
9eeb456e82
Summary: Update all FB license strings to the new format. Reviewed By: patricklabatut Differential Revision: D33403538 fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the BSD-style license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
import unittest
|
|
|
|
import torch
|
|
from common_testing import TestCaseMixin
|
|
from pytorch3d.renderer.implicit import HarmonicEmbedding
|
|
|
|
|
|
class TestHarmonicEmbedding(TestCaseMixin, unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
super().setUp()
|
|
torch.manual_seed(1)
|
|
|
|
def test_correct_output_dim(self):
|
|
embed_fun = HarmonicEmbedding(n_harmonic_functions=2, append_input=False)
|
|
# input_dims * (2 * n_harmonic_functions + int(append_input))
|
|
output_dim = 3 * (2 * 2 + int(False))
|
|
self.assertEqual(
|
|
output_dim,
|
|
embed_fun.get_output_dim_static(
|
|
input_dims=3, n_harmonic_functions=2, append_input=False
|
|
),
|
|
)
|
|
self.assertEqual(output_dim, embed_fun.get_output_dim())
|
|
|
|
def test_correct_frequency_range(self):
|
|
embed_fun_log = HarmonicEmbedding(n_harmonic_functions=3)
|
|
embed_fun_lin = HarmonicEmbedding(n_harmonic_functions=3, logspace=False)
|
|
self.assertClose(embed_fun_log._frequencies, torch.FloatTensor((1.0, 2.0, 4.0)))
|
|
self.assertClose(embed_fun_lin._frequencies, torch.FloatTensor((1.0, 2.5, 4.0)))
|
|
|
|
def test_correct_embed_out(self):
|
|
embed_fun = HarmonicEmbedding(n_harmonic_functions=2, append_input=False)
|
|
x = torch.randn((1, 5))
|
|
D = 5 * 4
|
|
embed_out = embed_fun(x)
|
|
self.assertEqual(embed_out.shape, (1, D))
|
|
# Sum the squares of the respective frequencies
|
|
sum_squares = embed_out[0, : D // 2] ** 2 + embed_out[0, D // 2 :] ** 2
|
|
self.assertClose(sum_squares, torch.ones((D // 2)))
|
|
embed_fun = HarmonicEmbedding(n_harmonic_functions=2, append_input=True)
|
|
embed_out = embed_fun(x)
|
|
self.assertClose(embed_out.shape, torch.tensor((1, 5 * 5)))
|
|
# Last plane in output is the input
|
|
self.assertClose(embed_out[..., -5:], x)
|