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https://github.com/facebookresearch/pytorch3d.git
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Summary: The shebang line `#!<path to interpreter>` is only required for Python scripts, so remove it on source files for class or function definitions. Additionally explicitly mark as executable the actual Python scripts in the codebase. Reviewed By: nikhilaravi Differential Revision: D20095778 fbshipit-source-id: d312599fba485e978a243292f88a180d71e1b55a
126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import unittest
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import torch
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from pytorch3d.structures import utils as struct_utils
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from common_testing import TestCaseMixin
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class TestStructUtils(TestCaseMixin, unittest.TestCase):
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def test_list_to_padded(self):
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device = torch.device("cuda:0")
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N = 5
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K = 20
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ndim = 2
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x = []
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for _ in range(N):
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dims = torch.randint(K, size=(ndim,)).tolist()
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x.append(torch.rand(dims, device=device))
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pad_size = [K] * ndim
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x_padded = struct_utils.list_to_padded(
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x, pad_size=pad_size, pad_value=0.0, equisized=False
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)
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self.assertEqual(x_padded.shape[1], K)
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self.assertEqual(x_padded.shape[2], K)
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for i in range(N):
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self.assertClose(
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x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i]
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)
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# check for no pad size (defaults to max dimension)
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x_padded = struct_utils.list_to_padded(
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x, pad_value=0.0, equisized=False
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)
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max_size0 = max(y.shape[0] for y in x)
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max_size1 = max(y.shape[1] for y in x)
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self.assertEqual(x_padded.shape[1], max_size0)
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self.assertEqual(x_padded.shape[2], max_size1)
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for i in range(N):
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self.assertClose(
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x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i]
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)
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# check for equisized
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x = [torch.rand((K, 10), device=device) for _ in range(N)]
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x_padded = struct_utils.list_to_padded(x, equisized=True)
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self.assertClose(x_padded, torch.stack(x, 0))
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# catch ValueError for invalid dimensions
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with self.assertRaisesRegex(ValueError, "Pad size must"):
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pad_size = [K] * 4
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struct_utils.list_to_padded(
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x, pad_size=pad_size, pad_value=0.0, equisized=False
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)
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# invalid input tensor dimensions
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x = []
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ndim = 3
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for _ in range(N):
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dims = torch.randint(K, size=(ndim,)).tolist()
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x.append(torch.rand(dims, device=device))
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pad_size = [K] * 2
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with self.assertRaisesRegex(ValueError, "Supports only"):
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x_padded = struct_utils.list_to_padded(
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x, pad_size=pad_size, pad_value=0.0, equisized=False
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)
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def test_padded_to_list(self):
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device = torch.device("cuda:0")
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N = 5
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K = 20
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ndim = 2
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dims = [K] * ndim
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x = torch.rand([N] + dims, device=device)
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x_list = struct_utils.padded_to_list(x)
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for i in range(N):
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self.assertClose(x_list[i], x[i])
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split_size = torch.randint(1, K, size=(N,)).tolist()
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x_list = struct_utils.padded_to_list(x, split_size)
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for i in range(N):
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self.assertClose(x_list[i], x[i, : split_size[i]])
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split_size = torch.randint(1, K, size=(2 * N,)).view(N, 2).unbind(0)
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x_list = struct_utils.padded_to_list(x, split_size)
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for i in range(N):
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self.assertClose(
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x_list[i], x[i, : split_size[i][0], : split_size[i][1]]
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)
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with self.assertRaisesRegex(ValueError, "Supports only"):
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x = torch.rand((N, K, K, K, K), device=device)
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split_size = torch.randint(1, K, size=(N,)).tolist()
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struct_utils.padded_to_list(x, split_size)
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def test_list_to_packed(self):
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device = torch.device("cuda:0")
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N = 5
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K = 20
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x, x_dims = [], []
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dim2 = torch.randint(K, size=(1,)).item()
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for _ in range(N):
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dim1 = torch.randint(K, size=(1,)).item()
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x_dims.append(dim1)
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x.append(torch.rand([dim1, dim2], device=device))
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out = struct_utils.list_to_packed(x)
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x_packed = out[0]
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num_items = out[1]
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item_packed_first_idx = out[2]
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item_packed_to_list_idx = out[3]
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cur = 0
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for i in range(N):
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self.assertTrue(num_items[i] == x_dims[i])
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self.assertTrue(item_packed_first_idx[i] == cur)
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self.assertTrue(
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item_packed_to_list_idx[cur : cur + x_dims[i]].eq(i).all()
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)
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self.assertClose(x_packed[cur : cur + x_dims[i]], x[i])
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cur += x_dims[i]
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