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https://github.com/facebookresearch/pytorch3d.git
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separate multigpu tests
Reviewed By: MichaelRamamonjisoa Differential Revision: D83477594 fbshipit-source-id: 5ea67543e288e9a06ee5141f436e879aa5cfb7f3
This commit is contained in:
Jeremy Reizenstein
meta-codesync[bot]
parent
7711bf34a8
commit
fc6a6b8951
@ -17,7 +17,7 @@ from pytorch3d.structures.pointclouds import (
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Pointclouds,
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)
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from .common_testing import needs_multigpu, TestCaseMixin
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from .common_testing import TestCaseMixin
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class TestPointclouds(TestCaseMixin, unittest.TestCase):
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@ -703,82 +703,6 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
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self.assertEqual(cuda_device, cloud.device)
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self.assertIsNot(cloud, converted_cloud)
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@needs_multigpu
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def test_to_list(self):
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cloud = self.init_cloud(5, 100, 10)
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device = torch.device("cuda:1")
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new_cloud = cloud.to(device)
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self.assertTrue(new_cloud.device == device)
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self.assertTrue(cloud.device == torch.device("cuda:0"))
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for attrib in [
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"points_padded",
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"points_packed",
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"normals_padded",
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"normals_packed",
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"features_padded",
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"features_packed",
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"num_points_per_cloud",
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"cloud_to_packed_first_idx",
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"padded_to_packed_idx",
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]:
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self.assertClose(
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getattr(new_cloud, attrib)().cpu(), getattr(cloud, attrib)().cpu()
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)
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for i in range(len(cloud)):
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self.assertClose(
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cloud.points_list()[i].cpu(), new_cloud.points_list()[i].cpu()
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)
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self.assertClose(
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cloud.normals_list()[i].cpu(), new_cloud.normals_list()[i].cpu()
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)
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self.assertClose(
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cloud.features_list()[i].cpu(), new_cloud.features_list()[i].cpu()
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)
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self.assertTrue(all(cloud.valid.cpu() == new_cloud.valid.cpu()))
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self.assertTrue(cloud.equisized == new_cloud.equisized)
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self.assertTrue(cloud._N == new_cloud._N)
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self.assertTrue(cloud._P == new_cloud._P)
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self.assertTrue(cloud._C == new_cloud._C)
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@needs_multigpu
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def test_to_tensor(self):
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cloud = self.init_cloud(5, 100, 10, lists_to_tensors=True)
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device = torch.device("cuda:1")
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new_cloud = cloud.to(device)
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self.assertTrue(new_cloud.device == device)
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self.assertTrue(cloud.device == torch.device("cuda:0"))
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for attrib in [
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"points_padded",
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"points_packed",
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"normals_padded",
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"normals_packed",
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"features_padded",
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"features_packed",
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"num_points_per_cloud",
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"cloud_to_packed_first_idx",
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"padded_to_packed_idx",
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]:
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self.assertClose(
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getattr(new_cloud, attrib)().cpu(), getattr(cloud, attrib)().cpu()
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)
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for i in range(len(cloud)):
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self.assertClose(
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cloud.points_list()[i].cpu(), new_cloud.points_list()[i].cpu()
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)
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self.assertClose(
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cloud.normals_list()[i].cpu(), new_cloud.normals_list()[i].cpu()
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)
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self.assertClose(
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cloud.features_list()[i].cpu(), new_cloud.features_list()[i].cpu()
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)
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self.assertTrue(all(cloud.valid.cpu() == new_cloud.valid.cpu()))
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self.assertTrue(cloud.equisized == new_cloud.equisized)
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self.assertTrue(cloud._N == new_cloud._N)
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self.assertTrue(cloud._P == new_cloud._P)
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self.assertTrue(cloud._C == new_cloud._C)
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def test_split(self):
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clouds = self.init_cloud(5, 100, 10)
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split_sizes = [2, 3]
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166
tests/test_pointclouds_multigpu.py
Normal file
166
tests/test_pointclouds_multigpu.py
Normal file
@ -0,0 +1,166 @@
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# 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.structures.pointclouds import Pointclouds
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from .common_testing import needs_multigpu, TestCaseMixin
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class TestPointclouds(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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np.random.seed(42)
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torch.manual_seed(42)
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@staticmethod
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def init_cloud(
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num_clouds: int = 3,
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max_points: int = 100,
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channels: int = 4,
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lists_to_tensors: bool = False,
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with_normals: bool = True,
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with_features: bool = True,
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min_points: int = 0,
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requires_grad: bool = False,
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):
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"""
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Function to generate a Pointclouds object of N meshes with
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random number of points.
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Args:
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num_clouds: Number of clouds to generate.
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channels: Number of features.
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max_points: Max number of points per cloud.
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lists_to_tensors: Determines whether the generated clouds should be
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constructed from lists (=False) or
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tensors (=True) of points/normals/features.
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with_normals: bool whether to include normals
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with_features: bool whether to include features
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min_points: Min number of points per cloud
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Returns:
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Pointclouds object.
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"""
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device = torch.device("cuda:0")
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p = torch.randint(low=min_points, high=max_points, size=(num_clouds,))
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if lists_to_tensors:
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p.fill_(p[0])
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points_list = [
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torch.rand(
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(i, 3), device=device, dtype=torch.float32, requires_grad=requires_grad
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)
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for i in p
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]
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normals_list, features_list = None, None
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if with_normals:
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normals_list = [
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torch.rand(
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(i, 3),
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device=device,
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dtype=torch.float32,
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requires_grad=requires_grad,
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)
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for i in p
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]
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if with_features:
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features_list = [
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torch.rand(
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(i, channels),
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device=device,
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dtype=torch.float32,
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requires_grad=requires_grad,
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)
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for i in p
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]
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if lists_to_tensors:
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points_list = torch.stack(points_list)
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if with_normals:
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normals_list = torch.stack(normals_list)
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if with_features:
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features_list = torch.stack(features_list)
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return Pointclouds(points_list, normals=normals_list, features=features_list)
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@needs_multigpu
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def test_to_list(self):
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cloud = self.init_cloud(5, 100, 10)
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device = torch.device("cuda:1")
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new_cloud = cloud.to(device)
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self.assertTrue(new_cloud.device == device)
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self.assertTrue(cloud.device == torch.device("cuda:0"))
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for attrib in [
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"points_padded",
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"points_packed",
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"normals_padded",
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"normals_packed",
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"features_padded",
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"features_packed",
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"num_points_per_cloud",
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"cloud_to_packed_first_idx",
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"padded_to_packed_idx",
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]:
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self.assertClose(
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getattr(new_cloud, attrib)().cpu(), getattr(cloud, attrib)().cpu()
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)
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for i in range(len(cloud)):
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self.assertClose(
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cloud.points_list()[i].cpu(), new_cloud.points_list()[i].cpu()
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)
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self.assertClose(
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cloud.normals_list()[i].cpu(), new_cloud.normals_list()[i].cpu()
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)
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self.assertClose(
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cloud.features_list()[i].cpu(), new_cloud.features_list()[i].cpu()
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)
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self.assertTrue(all(cloud.valid.cpu() == new_cloud.valid.cpu()))
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self.assertTrue(cloud.equisized == new_cloud.equisized)
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self.assertTrue(cloud._N == new_cloud._N)
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self.assertTrue(cloud._P == new_cloud._P)
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self.assertTrue(cloud._C == new_cloud._C)
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@needs_multigpu
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def test_to_tensor(self):
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cloud = self.init_cloud(5, 100, 10, lists_to_tensors=True)
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device = torch.device("cuda:1")
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new_cloud = cloud.to(device)
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self.assertTrue(new_cloud.device == device)
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self.assertTrue(cloud.device == torch.device("cuda:0"))
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for attrib in [
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"points_padded",
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"points_packed",
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"normals_padded",
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"normals_packed",
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"features_padded",
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"features_packed",
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"num_points_per_cloud",
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"cloud_to_packed_first_idx",
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"padded_to_packed_idx",
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]:
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self.assertClose(
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getattr(new_cloud, attrib)().cpu(), getattr(cloud, attrib)().cpu()
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)
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for i in range(len(cloud)):
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self.assertClose(
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cloud.points_list()[i].cpu(), new_cloud.points_list()[i].cpu()
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)
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self.assertClose(
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cloud.normals_list()[i].cpu(), new_cloud.normals_list()[i].cpu()
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)
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self.assertClose(
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cloud.features_list()[i].cpu(), new_cloud.features_list()[i].cpu()
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)
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self.assertTrue(all(cloud.valid.cpu() == new_cloud.valid.cpu()))
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self.assertTrue(cloud.equisized == new_cloud.equisized)
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self.assertTrue(cloud._N == new_cloud._N)
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self.assertTrue(cloud._P == new_cloud._P)
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self.assertTrue(cloud._C == new_cloud._C)
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