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https://github.com/facebookresearch/pytorch3d.git
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34f648ede0
Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS. Reviewed By: shapovalov Differential Revision: D36186940 fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b
90 lines
3.3 KiB
Python
90 lines
3.3 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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import unittest
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import numpy as np
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import torch
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from pytorch3d.ops import utils as oputil
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from .common_testing import TestCaseMixin
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class TestOpsUtils(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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super().setUp()
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torch.manual_seed(42)
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np.random.seed(42)
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def test_wmean(self):
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device = torch.device("cuda:0")
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n_points = 20
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x = torch.rand(n_points, 3, device=device)
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weight = torch.rand(n_points, device=device)
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x_np = x.cpu().data.numpy()
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weight_np = weight.cpu().data.numpy()
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# test unweighted
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mean = oputil.wmean(x, keepdim=False)
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mean_gt = np.average(x_np, axis=-2)
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self.assertClose(mean.cpu().data.numpy(), mean_gt)
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# test weighted
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mean = oputil.wmean(x, weight=weight, keepdim=False)
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mean_gt = np.average(x_np, axis=-2, weights=weight_np)
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self.assertClose(mean.cpu().data.numpy(), mean_gt)
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# test keepdim
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mean = oputil.wmean(x, weight=weight, keepdim=True)
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self.assertClose(mean[0].cpu().data.numpy(), mean_gt)
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# test binary weigths
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mean = oputil.wmean(x, weight=weight > 0.5, keepdim=False)
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mean_gt = np.average(x_np, axis=-2, weights=weight_np > 0.5)
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self.assertClose(mean.cpu().data.numpy(), mean_gt)
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# test broadcasting
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x = torch.rand(10, n_points, 3, device=device)
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x_np = x.cpu().data.numpy()
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mean = oputil.wmean(x, weight=weight, keepdim=False)
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mean_gt = np.average(x_np, axis=-2, weights=weight_np)
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self.assertClose(mean.cpu().data.numpy(), mean_gt)
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weight = weight[None, None, :].repeat(3, 1, 1)
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mean = oputil.wmean(x, weight=weight, keepdim=False)
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self.assertClose(mean[0].cpu().data.numpy(), mean_gt)
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# test failing broadcasting
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weight = torch.rand(x.shape[0], device=device)
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with self.assertRaises(ValueError) as context:
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oputil.wmean(x, weight=weight, keepdim=False)
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self.assertTrue("weights are not compatible" in str(context.exception))
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# test dim
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weight = torch.rand(x.shape[0], n_points, device=device)
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weight_np = np.tile(
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weight[:, :, None].cpu().data.numpy(), (1, 1, x_np.shape[-1])
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)
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mean = oputil.wmean(x, dim=0, weight=weight, keepdim=False)
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mean_gt = np.average(x_np, axis=0, weights=weight_np)
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self.assertClose(mean.cpu().data.numpy(), mean_gt)
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# test dim tuple
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mean = oputil.wmean(x, dim=(0, 1), weight=weight, keepdim=False)
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mean_gt = np.average(x_np, axis=(0, 1), weights=weight_np)
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self.assertClose(mean.cpu().data.numpy(), mean_gt)
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def test_masked_gather_errors(self):
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idx = torch.randint(0, 10, size=(5, 10, 4, 2))
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points = torch.randn(size=(5, 10, 3))
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with self.assertRaisesRegex(ValueError, "format is not supported"):
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oputil.masked_gather(points, idx)
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points = torch.randn(size=(2, 10, 3))
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with self.assertRaisesRegex(ValueError, "same batch dimension"):
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oputil.masked_gather(points, idx)
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