| from common_utils import TestCase, run_tests |
| import torch |
| from torch import tensor |
| import unittest |
| import warnings |
| |
| class TestIndexing(TestCase): |
| def test_single_int(self): |
| v = torch.randn(5, 7, 3) |
| self.assertEqual(v[4].shape, (7, 3)) |
| |
| def test_multiple_int(self): |
| v = torch.randn(5, 7, 3) |
| self.assertEqual(v[4].shape, (7, 3)) |
| self.assertEqual(v[4, :, 1].shape, (7,)) |
| |
| def test_none(self): |
| v = torch.randn(5, 7, 3) |
| self.assertEqual(v[None].shape, (1, 5, 7, 3)) |
| self.assertEqual(v[:, None].shape, (5, 1, 7, 3)) |
| self.assertEqual(v[:, None, None].shape, (5, 1, 1, 7, 3)) |
| self.assertEqual(v[..., None].shape, (5, 7, 3, 1)) |
| |
| def test_step(self): |
| v = torch.arange(10) |
| self.assertEqual(v[::1], v) |
| self.assertEqual(v[::2].tolist(), [0, 2, 4, 6, 8]) |
| self.assertEqual(v[::3].tolist(), [0, 3, 6, 9]) |
| self.assertEqual(v[::11].tolist(), [0]) |
| self.assertEqual(v[1:6:2].tolist(), [1, 3, 5]) |
| |
| def test_step_assignment(self): |
| v = torch.zeros(4, 4) |
| v[0, 1::2] = torch.tensor([3., 4.]) |
| self.assertEqual(v[0].tolist(), [0, 3, 0, 4]) |
| self.assertEqual(v[1:].sum(), 0) |
| |
| def test_bool_indices(self): |
| v = torch.randn(5, 7, 3) |
| boolIndices = torch.tensor([True, False, True, True, False], dtype=torch.bool) |
| self.assertEqual(v[boolIndices].shape, (3, 7, 3)) |
| self.assertEqual(v[boolIndices], torch.stack([v[0], v[2], v[3]])) |
| |
| v = torch.tensor([True, False, True], dtype=torch.bool) |
| boolIndices = torch.tensor([True, False, False], dtype=torch.bool) |
| uint8Indices = torch.tensor([1, 0, 0], dtype=torch.uint8) |
| with warnings.catch_warnings(record=True) as w: |
| self.assertEqual(v[boolIndices].shape, v[uint8Indices].shape) |
| self.assertEqual(v[boolIndices], v[uint8Indices]) |
| self.assertEqual(v[boolIndices], tensor([True], dtype=torch.bool)) |
| self.assertEquals(len(w), 2) |
| |
| def test_bool_indices_accumulate(self): |
| mask = torch.zeros(size=(10, ), dtype=torch.bool) |
| y = torch.ones(size=(10, 10)) |
| y.index_put_((mask, ), y[mask], accumulate=True) |
| self.assertEqual(y, torch.ones(size=(10, 10))) |
| |
| def test_multiple_bool_indices(self): |
| v = torch.randn(5, 7, 3) |
| # note: these broadcast together and are transposed to the first dim |
| mask1 = torch.tensor([1, 0, 1, 1, 0], dtype=torch.bool) |
| mask2 = torch.tensor([1, 1, 1], dtype=torch.bool) |
| self.assertEqual(v[mask1, :, mask2].shape, (3, 7)) |
| |
| def test_byte_mask(self): |
| v = torch.randn(5, 7, 3) |
| mask = torch.ByteTensor([1, 0, 1, 1, 0]) |
| with warnings.catch_warnings(record=True) as w: |
| self.assertEqual(v[mask].shape, (3, 7, 3)) |
| self.assertEqual(v[mask], torch.stack([v[0], v[2], v[3]])) |
| self.assertEquals(len(w), 2) |
| |
| v = torch.tensor([1.]) |
| self.assertEqual(v[v == 0], torch.tensor([])) |
| |
| def test_byte_mask_accumulate(self): |
| mask = torch.zeros(size=(10, ), dtype=torch.uint8) |
| y = torch.ones(size=(10, 10)) |
| with warnings.catch_warnings(record=True) as w: |
| y.index_put_((mask, ), y[mask], accumulate=True) |
| self.assertEqual(y, torch.ones(size=(10, 10))) |
| self.assertEquals(len(w), 2) |
| |
| def test_multiple_byte_mask(self): |
| v = torch.randn(5, 7, 3) |
| # note: these broadcast together and are transposed to the first dim |
| mask1 = torch.ByteTensor([1, 0, 1, 1, 0]) |
| mask2 = torch.ByteTensor([1, 1, 1]) |
| with warnings.catch_warnings(record=True) as w: |
| self.assertEqual(v[mask1, :, mask2].shape, (3, 7)) |
| self.assertEquals(len(w), 2) |
| |
| def test_byte_mask2d(self): |
| v = torch.randn(5, 7, 3) |
| c = torch.randn(5, 7) |
| num_ones = (c > 0).sum() |
| r = v[c > 0] |
| self.assertEqual(r.shape, (num_ones, 3)) |
| |
| def test_int_indices(self): |
| v = torch.randn(5, 7, 3) |
| self.assertEqual(v[[0, 4, 2]].shape, (3, 7, 3)) |
| self.assertEqual(v[:, [0, 4, 2]].shape, (5, 3, 3)) |
| self.assertEqual(v[:, [[0, 1], [4, 3]]].shape, (5, 2, 2, 3)) |
| |
| def test_int_indices2d(self): |
| # From the NumPy indexing example |
| x = torch.arange(0, 12).view(4, 3) |
| rows = torch.tensor([[0, 0], [3, 3]]) |
| columns = torch.tensor([[0, 2], [0, 2]]) |
| self.assertEqual(x[rows, columns].tolist(), [[0, 2], [9, 11]]) |
| |
| def test_int_indices_broadcast(self): |
| # From the NumPy indexing example |
| x = torch.arange(0, 12).view(4, 3) |
| rows = torch.tensor([0, 3]) |
| columns = torch.tensor([0, 2]) |
| result = x[rows[:, None], columns] |
| self.assertEqual(result.tolist(), [[0, 2], [9, 11]]) |
| |
| def test_empty_index(self): |
| x = torch.arange(0, 12).view(4, 3) |
| idx = torch.tensor([], dtype=torch.long) |
| self.assertEqual(x[idx].numel(), 0) |
| |
| # empty assignment should have no effect but not throw an exception |
| y = x.clone() |
| y[idx] = -1 |
| self.assertEqual(x, y) |
| |
| mask = torch.zeros(4, 3).bool() |
| y[mask] = -1 |
| self.assertEqual(x, y) |
| |
| def test_empty_ndim_index(self): |
| devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda'] |
| for device in devices: |
| x = torch.randn(5, device=device) |
| self.assertEqual(torch.empty(0, 2, device=device), x[torch.empty(0, 2, dtype=torch.int64, device=device)]) |
| |
| x = torch.randn(2, 3, 4, 5, device=device) |
| self.assertEqual(torch.empty(2, 0, 6, 4, 5, device=device), |
| x[:, torch.empty(0, 6, dtype=torch.int64, device=device)]) |
| |
| x = torch.empty(10, 0) |
| self.assertEqual(x[[1, 2]].shape, (2, 0)) |
| self.assertEqual(x[[], []].shape, (0,)) |
| with self.assertRaisesRegex(IndexError, 'for dimension with size 0'): |
| x[:, [0, 1]] |
| |
| def test_empty_ndim_index_bool(self): |
| devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda'] |
| for device in devices: |
| x = torch.randn(5, device=device) |
| self.assertRaises(IndexError, lambda: x[torch.empty(0, 2, dtype=torch.uint8, device=device)]) |
| |
| def test_empty_slice(self): |
| devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda'] |
| for device in devices: |
| x = torch.randn(2, 3, 4, 5, device=device) |
| y = x[:, :, :, 1] |
| z = y[:, 1:1, :] |
| self.assertEqual((2, 0, 4), z.shape) |
| # this isn't technically necessary, but matches NumPy stride calculations. |
| self.assertEqual((60, 20, 5), z.stride()) |
| self.assertTrue(z.is_contiguous()) |
| |
| def test_index_getitem_copy_bools_slices(self): |
| true = torch.tensor(1, dtype=torch.uint8) |
| false = torch.tensor(0, dtype=torch.uint8) |
| |
| tensors = [torch.randn(2, 3), torch.tensor(3)] |
| |
| for a in tensors: |
| self.assertNotEqual(a.data_ptr(), a[True].data_ptr()) |
| self.assertEqual(torch.empty(0, *a.shape), a[False]) |
| self.assertNotEqual(a.data_ptr(), a[true].data_ptr()) |
| self.assertEqual(torch.empty(0, *a.shape), a[false]) |
| self.assertEqual(a.data_ptr(), a[None].data_ptr()) |
| self.assertEqual(a.data_ptr(), a[...].data_ptr()) |
| |
| def test_index_setitem_bools_slices(self): |
| true = torch.tensor(1, dtype=torch.uint8) |
| false = torch.tensor(0, dtype=torch.uint8) |
| |
| tensors = [torch.randn(2, 3), torch.tensor(3)] |
| |
| for a in tensors: |
| # prefix with a 1,1, to ensure we are compatible with numpy which cuts off prefix 1s |
| # (some of these ops already prefix a 1 to the size) |
| neg_ones = torch.ones_like(a) * -1 |
| neg_ones_expanded = neg_ones.unsqueeze(0).unsqueeze(0) |
| a[True] = neg_ones_expanded |
| self.assertEqual(a, neg_ones) |
| a[False] = 5 |
| self.assertEqual(a, neg_ones) |
| a[true] = neg_ones_expanded * 2 |
| self.assertEqual(a, neg_ones * 2) |
| a[false] = 5 |
| self.assertEqual(a, neg_ones * 2) |
| a[None] = neg_ones_expanded * 3 |
| self.assertEqual(a, neg_ones * 3) |
| a[...] = neg_ones_expanded * 4 |
| self.assertEqual(a, neg_ones * 4) |
| if a.dim() == 0: |
| with self.assertRaises(IndexError): |
| a[:] = neg_ones_expanded * 5 |
| |
| def test_index_scalar_with_bool_mask(self): |
| for device in torch.testing.get_all_device_types(): |
| a = torch.tensor(1, device=device) |
| uintMask = torch.tensor(True, dtype=torch.uint8, device=device) |
| boolMask = torch.tensor(True, dtype=torch.bool, device=device) |
| self.assertEqual(a[uintMask], a[boolMask]) |
| self.assertEqual(a[uintMask].dtype, a[boolMask].dtype) |
| |
| a = torch.tensor(True, dtype=torch.bool, device=device) |
| self.assertEqual(a[uintMask], a[boolMask]) |
| self.assertEqual(a[uintMask].dtype, a[boolMask].dtype) |
| |
| def test_setitem_expansion_error(self): |
| true = torch.tensor(True) |
| a = torch.randn(2, 3) |
| # check prefix with non-1s doesn't work |
| a_expanded = a.expand(torch.Size([5, 1]) + a.size()) |
| # NumPy: ValueError |
| with self.assertRaises(RuntimeError): |
| a[True] = a_expanded |
| with self.assertRaises(RuntimeError): |
| a[true] = a_expanded |
| |
| def test_getitem_scalars(self): |
| zero = torch.tensor(0, dtype=torch.int64) |
| one = torch.tensor(1, dtype=torch.int64) |
| |
| # non-scalar indexed with scalars |
| a = torch.randn(2, 3) |
| self.assertEqual(a[0], a[zero]) |
| self.assertEqual(a[0][1], a[zero][one]) |
| self.assertEqual(a[0, 1], a[zero, one]) |
| self.assertEqual(a[0, one], a[zero, 1]) |
| |
| # indexing by a scalar should slice (not copy) |
| self.assertEqual(a[0, 1].data_ptr(), a[zero, one].data_ptr()) |
| self.assertEqual(a[1].data_ptr(), a[one.int()].data_ptr()) |
| self.assertEqual(a[1].data_ptr(), a[one.short()].data_ptr()) |
| |
| # scalar indexed with scalar |
| r = torch.randn(()) |
| with self.assertRaises(IndexError): |
| r[:] |
| with self.assertRaises(IndexError): |
| r[zero] |
| self.assertEqual(r, r[...]) |
| |
| def test_setitem_scalars(self): |
| zero = torch.tensor(0, dtype=torch.int64) |
| |
| # non-scalar indexed with scalars |
| a = torch.randn(2, 3) |
| a_set_with_number = a.clone() |
| a_set_with_scalar = a.clone() |
| b = torch.randn(3) |
| |
| a_set_with_number[0] = b |
| a_set_with_scalar[zero] = b |
| self.assertEqual(a_set_with_number, a_set_with_scalar) |
| a[1, zero] = 7.7 |
| self.assertEqual(7.7, a[1, 0]) |
| |
| # scalar indexed with scalars |
| r = torch.randn(()) |
| with self.assertRaises(IndexError): |
| r[:] = 8.8 |
| with self.assertRaises(IndexError): |
| r[zero] = 8.8 |
| r[...] = 9.9 |
| self.assertEqual(9.9, r) |
| |
| def test_basic_advanced_combined(self): |
| # From the NumPy indexing example |
| x = torch.arange(0, 12).view(4, 3) |
| self.assertEqual(x[1:2, 1:3], x[1:2, [1, 2]]) |
| self.assertEqual(x[1:2, 1:3].tolist(), [[4, 5]]) |
| |
| # Check that it is a copy |
| unmodified = x.clone() |
| x[1:2, [1, 2]].zero_() |
| self.assertEqual(x, unmodified) |
| |
| # But assignment should modify the original |
| unmodified = x.clone() |
| x[1:2, [1, 2]] = 0 |
| self.assertNotEqual(x, unmodified) |
| |
| def test_int_assignment(self): |
| x = torch.arange(0, 4).view(2, 2) |
| x[1] = 5 |
| self.assertEqual(x.tolist(), [[0, 1], [5, 5]]) |
| |
| x = torch.arange(0, 4).view(2, 2) |
| x[1] = torch.arange(5, 7) |
| self.assertEqual(x.tolist(), [[0, 1], [5, 6]]) |
| |
| def test_byte_tensor_assignment(self): |
| x = torch.arange(0., 16).view(4, 4) |
| b = torch.ByteTensor([True, False, True, False]) |
| value = torch.tensor([3., 4., 5., 6.]) |
| |
| with warnings.catch_warnings(record=True) as w: |
| x[b] = value |
| self.assertEquals(len(w), 1) |
| |
| self.assertEqual(x[0], value) |
| self.assertEqual(x[1], torch.arange(4, 8)) |
| self.assertEqual(x[2], value) |
| self.assertEqual(x[3], torch.arange(12, 16)) |
| |
| def test_variable_slicing(self): |
| x = torch.arange(0, 16).view(4, 4) |
| indices = torch.IntTensor([0, 1]) |
| i, j = indices |
| self.assertEqual(x[i:j], x[0:1]) |
| |
| def test_ellipsis_tensor(self): |
| x = torch.arange(0, 9).view(3, 3) |
| idx = torch.tensor([0, 2]) |
| self.assertEqual(x[..., idx].tolist(), [[0, 2], |
| [3, 5], |
| [6, 8]]) |
| self.assertEqual(x[idx, ...].tolist(), [[0, 1, 2], |
| [6, 7, 8]]) |
| |
| def test_invalid_index(self): |
| x = torch.arange(0, 16).view(4, 4) |
| self.assertRaisesRegex(TypeError, 'slice indices', lambda: x["0":"1"]) |
| |
| def test_out_of_bound_index(self): |
| x = torch.arange(0, 100).view(2, 5, 10) |
| self.assertRaisesRegex(IndexError, 'index 5 is out of bounds for dimension 1 with size 5', lambda: x[0, 5]) |
| self.assertRaisesRegex(IndexError, 'index 4 is out of bounds for dimension 0 with size 2', lambda: x[4, 5]) |
| self.assertRaisesRegex(IndexError, 'index 15 is out of bounds for dimension 2 with size 10', |
| lambda: x[0, 1, 15]) |
| self.assertRaisesRegex(IndexError, 'index 12 is out of bounds for dimension 2 with size 10', |
| lambda: x[:, :, 12]) |
| |
| def test_zero_dim_index(self): |
| x = torch.tensor(10) |
| self.assertEqual(x, x.item()) |
| |
| def runner(): |
| print(x[0]) |
| return x[0] |
| |
| self.assertRaisesRegex(IndexError, 'invalid index', runner) |
| |
| |
| # The tests below are from NumPy test_indexing.py with some modifications to |
| # make them compatible with PyTorch. It's licensed under the BDS license below: |
| # |
| # Copyright (c) 2005-2017, NumPy Developers. |
| # All rights reserved. |
| # |
| # Redistribution and use in source and binary forms, with or without |
| # modification, are permitted provided that the following conditions are |
| # met: |
| # |
| # * Redistributions of source code must retain the above copyright |
| # notice, this list of conditions and the following disclaimer. |
| # |
| # * Redistributions in binary form must reproduce the above |
| # copyright notice, this list of conditions and the following |
| # disclaimer in the documentation and/or other materials provided |
| # with the distribution. |
| # |
| # * Neither the name of the NumPy Developers nor the names of any |
| # contributors may be used to endorse or promote products derived |
| # from this software without specific prior written permission. |
| # |
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS |
| # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT |
| # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR |
| # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT |
| # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, |
| # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT |
| # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, |
| # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY |
| # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT |
| # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE |
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| |
| class NumpyTests(TestCase): |
| def test_index_no_floats(self): |
| a = torch.tensor([[[5.]]]) |
| |
| self.assertRaises(IndexError, lambda: a[0.0]) |
| self.assertRaises(IndexError, lambda: a[0, 0.0]) |
| self.assertRaises(IndexError, lambda: a[0.0, 0]) |
| self.assertRaises(IndexError, lambda: a[0.0, :]) |
| self.assertRaises(IndexError, lambda: a[:, 0.0]) |
| self.assertRaises(IndexError, lambda: a[:, 0.0, :]) |
| self.assertRaises(IndexError, lambda: a[0.0, :, :]) |
| self.assertRaises(IndexError, lambda: a[0, 0, 0.0]) |
| self.assertRaises(IndexError, lambda: a[0.0, 0, 0]) |
| self.assertRaises(IndexError, lambda: a[0, 0.0, 0]) |
| self.assertRaises(IndexError, lambda: a[-1.4]) |
| self.assertRaises(IndexError, lambda: a[0, -1.4]) |
| self.assertRaises(IndexError, lambda: a[-1.4, 0]) |
| self.assertRaises(IndexError, lambda: a[-1.4, :]) |
| self.assertRaises(IndexError, lambda: a[:, -1.4]) |
| self.assertRaises(IndexError, lambda: a[:, -1.4, :]) |
| self.assertRaises(IndexError, lambda: a[-1.4, :, :]) |
| self.assertRaises(IndexError, lambda: a[0, 0, -1.4]) |
| self.assertRaises(IndexError, lambda: a[-1.4, 0, 0]) |
| self.assertRaises(IndexError, lambda: a[0, -1.4, 0]) |
| # self.assertRaises(IndexError, lambda: a[0.0:, 0.0]) |
| # self.assertRaises(IndexError, lambda: a[0.0:, 0.0,:]) |
| |
| def test_none_index(self): |
| # `None` index adds newaxis |
| a = tensor([1, 2, 3]) |
| self.assertEqual(a[None].dim(), a.dim() + 1) |
| |
| def test_empty_tuple_index(self): |
| # Empty tuple index creates a view |
| a = tensor([1, 2, 3]) |
| self.assertEqual(a[()], a) |
| self.assertEqual(a[()].data_ptr(), a.data_ptr()) |
| |
| def test_empty_fancy_index(self): |
| # Empty list index creates an empty array |
| a = tensor([1, 2, 3]) |
| self.assertEqual(a[[]], torch.tensor([])) |
| |
| b = tensor([]).long() |
| self.assertEqual(a[[]], torch.tensor([], dtype=torch.long)) |
| |
| b = tensor([]).float() |
| self.assertRaises(IndexError, lambda: a[b]) |
| |
| def test_ellipsis_index(self): |
| a = tensor([[1, 2, 3], |
| [4, 5, 6], |
| [7, 8, 9]]) |
| self.assertIsNot(a[...], a) |
| self.assertEqual(a[...], a) |
| # `a[...]` was `a` in numpy <1.9. |
| self.assertEqual(a[...].data_ptr(), a.data_ptr()) |
| |
| # Slicing with ellipsis can skip an |
| # arbitrary number of dimensions |
| self.assertEqual(a[0, ...], a[0]) |
| self.assertEqual(a[0, ...], a[0, :]) |
| self.assertEqual(a[..., 0], a[:, 0]) |
| |
| # In NumPy, slicing with ellipsis results in a 0-dim array. In PyTorch |
| # we don't have separate 0-dim arrays and scalars. |
| self.assertEqual(a[0, ..., 1], torch.tensor(2)) |
| |
| # Assignment with `(Ellipsis,)` on 0-d arrays |
| b = torch.tensor(1) |
| b[(Ellipsis,)] = 2 |
| self.assertEqual(b, 2) |
| |
| def test_single_int_index(self): |
| # Single integer index selects one row |
| a = tensor([[1, 2, 3], |
| [4, 5, 6], |
| [7, 8, 9]]) |
| |
| self.assertEqual(a[0], [1, 2, 3]) |
| self.assertEqual(a[-1], [7, 8, 9]) |
| |
| # Index out of bounds produces IndexError |
| self.assertRaises(IndexError, a.__getitem__, 1 << 30) |
| # Index overflow produces Exception NB: different exception type |
| self.assertRaises(Exception, a.__getitem__, 1 << 64) |
| |
| def test_single_bool_index(self): |
| # Single boolean index |
| a = tensor([[1, 2, 3], |
| [4, 5, 6], |
| [7, 8, 9]]) |
| |
| self.assertEqual(a[True], a[None]) |
| self.assertEqual(a[False], a[None][0:0]) |
| |
| def test_boolean_shape_mismatch(self): |
| arr = torch.ones((5, 4, 3)) |
| |
| index = tensor([True]) |
| self.assertRaisesRegex(IndexError, 'mask', lambda: arr[index]) |
| |
| index = tensor([False] * 6) |
| self.assertRaisesRegex(IndexError, 'mask', lambda: arr[index]) |
| |
| with warnings.catch_warnings(record=True) as w: |
| index = torch.ByteTensor(4, 4).zero_() |
| self.assertRaisesRegex(IndexError, 'mask', lambda: arr[index]) |
| self.assertRaisesRegex(IndexError, 'mask', lambda: arr[(slice(None), index)]) |
| self.assertEquals(len(w), 2) |
| |
| def test_boolean_indexing_onedim(self): |
| # Indexing a 2-dimensional array with |
| # boolean array of length one |
| a = tensor([[0., 0., 0.]]) |
| b = tensor([True]) |
| self.assertEqual(a[b], a) |
| # boolean assignment |
| a[b] = 1. |
| self.assertEqual(a, tensor([[1., 1., 1.]])) |
| |
| def test_boolean_assignment_value_mismatch(self): |
| # A boolean assignment should fail when the shape of the values |
| # cannot be broadcast to the subscription. (see also gh-3458) |
| a = torch.arange(0, 4) |
| |
| def f(a, v): |
| a[a > -1] = tensor(v) |
| |
| self.assertRaisesRegex(Exception, 'shape mismatch', f, a, []) |
| self.assertRaisesRegex(Exception, 'shape mismatch', f, a, [1, 2, 3]) |
| self.assertRaisesRegex(Exception, 'shape mismatch', f, a[:1], [1, 2, 3]) |
| |
| def test_boolean_indexing_twodim(self): |
| # Indexing a 2-dimensional array with |
| # 2-dimensional boolean array |
| a = tensor([[1, 2, 3], |
| [4, 5, 6], |
| [7, 8, 9]]) |
| b = tensor([[True, False, True], |
| [False, True, False], |
| [True, False, True]]) |
| self.assertEqual(a[b], tensor([1, 3, 5, 7, 9])) |
| self.assertEqual(a[b[1]], tensor([[4, 5, 6]])) |
| self.assertEqual(a[b[0]], a[b[2]]) |
| |
| # boolean assignment |
| a[b] = 0 |
| self.assertEqual(a, tensor([[0, 2, 0], |
| [4, 0, 6], |
| [0, 8, 0]])) |
| |
| def test_boolean_indexing_weirdness(self): |
| # Weird boolean indexing things |
| a = torch.ones((2, 3, 4)) |
| self.assertEqual((0, 2, 3, 4), a[False, True, ...].shape) |
| self.assertEqual(torch.ones(1, 2), a[True, [0, 1], True, True, [1], [[2]]]) |
| self.assertRaises(IndexError, lambda: a[False, [0, 1], ...]) |
| |
| def test_boolean_indexing_weirdness_tensors(self): |
| # Weird boolean indexing things |
| false = torch.tensor(False) |
| true = torch.tensor(True) |
| a = torch.ones((2, 3, 4)) |
| self.assertEqual((0, 2, 3, 4), a[False, True, ...].shape) |
| self.assertEqual(torch.ones(1, 2), a[true, [0, 1], true, true, [1], [[2]]]) |
| self.assertRaises(IndexError, lambda: a[false, [0, 1], ...]) |
| |
| def test_boolean_indexing_alldims(self): |
| true = torch.tensor(True) |
| a = torch.ones((2, 3)) |
| self.assertEqual((1, 2, 3), a[True, True].shape) |
| self.assertEqual((1, 2, 3), a[true, true].shape) |
| |
| def test_boolean_list_indexing(self): |
| # Indexing a 2-dimensional array with |
| # boolean lists |
| a = tensor([[1, 2, 3], |
| [4, 5, 6], |
| [7, 8, 9]]) |
| b = [True, False, False] |
| c = [True, True, False] |
| self.assertEqual(a[b], tensor([[1, 2, 3]])) |
| self.assertEqual(a[b, b], tensor([1])) |
| self.assertEqual(a[c], tensor([[1, 2, 3], [4, 5, 6]])) |
| self.assertEqual(a[c, c], tensor([1, 5])) |
| |
| def test_everything_returns_views(self): |
| # Before `...` would return a itself. |
| a = tensor([5]) |
| |
| self.assertIsNot(a, a[()]) |
| self.assertIsNot(a, a[...]) |
| self.assertIsNot(a, a[:]) |
| |
| def test_broaderrors_indexing(self): |
| a = torch.zeros(5, 5) |
| self.assertRaisesRegex(IndexError, 'shape mismatch', a.__getitem__, ([0, 1], [0, 1, 2])) |
| self.assertRaisesRegex(IndexError, 'shape mismatch', a.__setitem__, ([0, 1], [0, 1, 2]), 0) |
| |
| def test_trivial_fancy_out_of_bounds(self): |
| a = torch.zeros(5) |
| ind = torch.ones(20, dtype=torch.int64) |
| if a.is_cuda: |
| raise unittest.SkipTest('CUDA asserts instead of raising an exception') |
| ind[-1] = 10 |
| self.assertRaises(IndexError, a.__getitem__, ind) |
| self.assertRaises(IndexError, a.__setitem__, ind, 0) |
| ind = torch.ones(20, dtype=torch.int64) |
| ind[0] = 11 |
| self.assertRaises(IndexError, a.__getitem__, ind) |
| self.assertRaises(IndexError, a.__setitem__, ind, 0) |
| |
| def test_index_is_larger(self): |
| # Simple case of fancy index broadcasting of the index. |
| a = torch.zeros((5, 5)) |
| a[[[0], [1], [2]], [0, 1, 2]] = tensor([2., 3., 4.]) |
| |
| self.assertTrue((a[:3, :3] == tensor([2., 3., 4.])).all()) |
| |
| def test_broadcast_subspace(self): |
| a = torch.zeros((100, 100)) |
| v = torch.arange(0., 100)[:, None] |
| b = torch.arange(99, -1, -1).long() |
| a[b] = v |
| expected = b.double().unsqueeze(1).expand(100, 100) |
| self.assertEqual(a, expected) |
| |
| if __name__ == '__main__': |
| run_tests() |
