mirror of
https://github.com/facebookresearch/pytorch3d.git
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9eeb456e82
Summary: Update all FB license strings to the new format. Reviewed By: patricklabatut Differential Revision: D33403538 fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
99 lines
3.3 KiB
Python
99 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 common_testing import TestCaseMixin
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from pytorch3d.renderer.utils import TensorProperties
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# Example class for testing
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class TensorPropertiesTestClass(TensorProperties):
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def __init__(self, x=None, y=None, device="cpu"):
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super().__init__(device=device, x=x, y=y)
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def clone(self):
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other = TensorPropertiesTestClass()
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return super().clone(other)
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class TestTensorProperties(TestCaseMixin, unittest.TestCase):
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def test_init(self):
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example = TensorPropertiesTestClass(x=10.0, y=(100.0, 200.0))
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# Check kwargs set as attributes + converted to tensors
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self.assertTrue(torch.is_tensor(example.x))
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self.assertTrue(torch.is_tensor(example.y))
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# Check broadcasting
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self.assertTrue(example.x.shape == (2,))
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self.assertTrue(example.y.shape == (2,))
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self.assertTrue(len(example) == 2)
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def test_to(self):
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# Check to method
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example = TensorPropertiesTestClass(x=10.0, y=(100.0, 200.0))
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device = torch.device("cuda:0")
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new_example = example.to(device=device)
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self.assertEqual(new_example.device, device)
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example_cpu = example.cpu()
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self.assertEqual(example_cpu.device, torch.device("cpu"))
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example_gpu = example.cuda()
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self.assertEqual(example_gpu.device.type, "cuda")
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self.assertIsNotNone(example_gpu.device.index)
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example_gpu1 = example.cuda(1)
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self.assertEqual(example_gpu1.device, torch.device("cuda:1"))
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def test_clone(self):
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# Check clone method
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example = TensorPropertiesTestClass(x=10.0, y=(100.0, 200.0))
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new_example = example.clone()
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self.assertSeparate(example.x, new_example.x)
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self.assertSeparate(example.y, new_example.y)
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def test_get_set(self):
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# Test getitem returns an accessor which can be used to modify
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# attributes at a particular index
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example = TensorPropertiesTestClass(x=10.0, y=(100.0, 200.0, 300.0))
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# update y1
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example[1].y = 5.0
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self.assertTrue(example.y[1] == 5.0)
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# Get item and get value
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ex0 = example[0]
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self.assertTrue(ex0.y == 100.0)
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def test_empty_input(self):
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example = TensorPropertiesTestClass(x=(), y=())
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self.assertTrue(len(example) == 0)
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self.assertTrue(example.isempty())
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def test_gather_props(self):
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N = 4
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x = torch.randn((N, 3, 4))
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y = torch.randn((N, 5))
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test_class = TensorPropertiesTestClass(x=x, y=y)
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S = 15
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idx = torch.tensor(np.random.choice(N, S))
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test_class_gathered = test_class.gather_props(idx)
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self.assertTrue(test_class_gathered.x.shape == (S, 3, 4))
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self.assertTrue(test_class_gathered.y.shape == (S, 5))
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for i in range(N):
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inds = idx == i
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if inds.sum() > 0:
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# Check the gathered points in the output have the same value from
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# the input.
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self.assertClose(test_class_gathered.x[inds].mean(dim=0), x[i, ...])
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self.assertClose(test_class_gathered.y[inds].mean(dim=0), y[i, ...])
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