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# 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 numpy as np
import torch
from .common_testing import TestCaseMixin
class TestOpsUtils(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
torch.manual_seed(42)
np.random.seed(42)
def test_all_close(self):
device = torch.device("cuda:0")
n_points = 20
noise_std = 1e-3
msg = "tratata"
# test absolute tolerance
x = torch.rand(n_points, 3, device=device)
x_noise = x + noise_std * torch.rand(n_points, 3, device=device)
assert torch.allclose(x, x_noise, atol=10 * noise_std)
assert not torch.allclose(x, x_noise, atol=0.1 * noise_std)
self.assertClose(x, x_noise, atol=10 * noise_std)
with self.assertRaises(AssertionError) as context:
self.assertClose(x, x_noise, atol=0.1 * noise_std, msg=msg)
self.assertTrue(msg in str(context.exception))
# test numpy
def to_np(t):
return t.data.cpu().numpy()
self.assertClose(to_np(x), to_np(x_noise), atol=10 * noise_std)
with self.assertRaises(AssertionError) as context:
self.assertClose(to_np(x), to_np(x_noise), atol=0.1 * noise_std, msg=msg)
self.assertIn(msg, str(context.exception))
self.assertIn("Not close", str(context.exception))
# test relative tolerance
assert torch.allclose(x, x_noise, rtol=100 * noise_std)
assert not torch.allclose(x, x_noise, rtol=noise_std)
self.assertClose(x, x_noise, rtol=100 * noise_std)
with self.assertRaises(AssertionError) as context:
self.assertClose(x, x_noise, rtol=noise_std, msg=msg)
self.assertTrue(msg in str(context.exception))
# test norm aggregation
# if one of the spatial dimensions is small, norm aggregation helps
x_noise[:, 0] = x_noise[:, 0] - x[:, 0]
x[:, 0] = 0.0
assert not torch.allclose(x, x_noise, rtol=100 * noise_std)
self.assertNormsClose(
x, x_noise, rtol=100 * noise_std, norm_fn=lambda t: t.norm(dim=-1)
)
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