| import torch | |
| import numpy as np | |
| from itertools import product | |
| from torch.testing._internal.common_utils import \ | |
| (TestCase, run_tests) | |
| from torch.testing._internal.common_device_type import \ | |
| (instantiate_device_type_tests, onlyCPU, dtypes) | |
| # For testing handling NumPy objects and sending tensors to / accepting | |
| # arrays from NumPy. | |
| class TestNumPyInterop(TestCase): | |
| # Note: the warning this tests for only appears once per program, so | |
| # other instances of this warning should be addressed to avoid | |
| # the tests depending on the order in which they're run. | |
| @onlyCPU | |
| def test_numpy_non_writeable(self, device): | |
| arr = np.zeros(5) | |
| arr.flags['WRITEABLE'] = False | |
| self.assertWarns(UserWarning, lambda: torch.from_numpy(arr)) | |
| @onlyCPU | |
| def test_numpy_unresizable(self, device) -> None: | |
| x = np.zeros((2, 2)) | |
| y = torch.from_numpy(x) | |
| with self.assertRaises(ValueError): | |
| x.resize((5, 5)) | |
| z = torch.randn(5, 5) | |
| w = z.numpy() | |
| with self.assertRaises(RuntimeError): | |
| z.resize_(10, 10) | |
| with self.assertRaises(ValueError): | |
| w.resize((10, 10)) | |
| @onlyCPU | |
| def test_to_numpy(self, device) -> None: | |
| def get_castable_tensor(shape, dtype): | |
| if dtype.is_floating_point: | |
| dtype_info = torch.finfo(dtype) | |
| # can't directly use min and max, because for double, max - min | |
| # is greater than double range and sampling always gives inf. | |
| low = max(dtype_info.min, -1e10) | |
| high = min(dtype_info.max, 1e10) | |
| t = torch.empty(shape, dtype=torch.float64).uniform_(low, high) | |
| else: | |
| # can't directly use min and max, because for int64_t, max - min | |
| # is greater than int64_t range and triggers UB. | |
| dtype_info = torch.iinfo(dtype) | |
| low = max(dtype_info.min, int(-1e10)) | |
| high = min(dtype_info.max, int(1e10)) | |
| dtype_info = torch.iinfo(dtype) | |
| t = torch.empty(shape, dtype=torch.int64).random_(low, high) | |
| return t.to(dtype) | |
| dtypes = [ | |
| torch.uint8, | |
| torch.int8, | |
| torch.short, | |
| torch.int, | |
| torch.half, | |
| torch.float, | |
| torch.double, | |
| torch.long, | |
| ] | |
| for dtp in dtypes: | |
| # 1D | |
| sz = 10 | |
| x = get_castable_tensor(sz, dtp) | |
| y = x.numpy() | |
| for i in range(sz): | |
| self.assertEqual(x[i], y[i]) | |
| # 1D > 0 storage offset | |
| xm = get_castable_tensor(sz * 2, dtp) | |
| x = xm.narrow(0, sz - 1, sz) | |
| self.assertTrue(x.storage_offset() > 0) | |
| y = x.numpy() | |
| for i in range(sz): | |
| self.assertEqual(x[i], y[i]) | |
| def check2d(x, y): | |
| for i in range(sz1): | |
| for j in range(sz2): | |
| self.assertEqual(x[i][j], y[i][j]) | |
| # empty | |
| x = torch.Tensor().to(dtp) | |
| y = x.numpy() | |
| self.assertEqual(y.size, 0) | |
| # contiguous 2D | |
| sz1 = 3 | |
| sz2 = 5 | |
| x = get_castable_tensor((sz1, sz2), dtp) | |
| y = x.numpy() | |
| check2d(x, y) | |
| self.assertTrue(y.flags['C_CONTIGUOUS']) | |
| # with storage offset | |
| xm = get_castable_tensor((sz1 * 2, sz2), dtp) | |
| x = xm.narrow(0, sz1 - 1, sz1) | |
| y = x.numpy() | |
| self.assertTrue(x.storage_offset() > 0) | |
| check2d(x, y) | |
| self.assertTrue(y.flags['C_CONTIGUOUS']) | |
| # non-contiguous 2D | |
| x = get_castable_tensor((sz2, sz1), dtp).t() | |
| y = x.numpy() | |
| check2d(x, y) | |
| self.assertFalse(y.flags['C_CONTIGUOUS']) | |
| # with storage offset | |
| xm = get_castable_tensor((sz2 * 2, sz1), dtp) | |
| x = xm.narrow(0, sz2 - 1, sz2).t() | |
| y = x.numpy() | |
| self.assertTrue(x.storage_offset() > 0) | |
| check2d(x, y) | |
| # non-contiguous 2D with holes | |
| xm = get_castable_tensor((sz2 * 2, sz1 * 2), dtp) | |
| x = xm.narrow(0, sz2 - 1, sz2).narrow(1, sz1 - 1, sz1).t() | |
| y = x.numpy() | |
| self.assertTrue(x.storage_offset() > 0) | |
| check2d(x, y) | |
| if dtp != torch.half: | |
| # check writeable | |
| x = get_castable_tensor((3, 4), dtp) | |
| y = x.numpy() | |
| self.assertTrue(y.flags.writeable) | |
| y[0][1] = 3 | |
| self.assertTrue(x[0][1] == 3) | |
| y = x.t().numpy() | |
| self.assertTrue(y.flags.writeable) | |
| y[0][1] = 3 | |
| self.assertTrue(x[0][1] == 3) | |
| def test_to_numpy_bool(self, device) -> None: | |
| x = torch.tensor([True, False], dtype=torch.bool) | |
| self.assertEqual(x.dtype, torch.bool) | |
| y = x.numpy() | |
| self.assertEqual(y.dtype, np.bool) | |
| for i in range(len(x)): | |
| self.assertEqual(x[i], y[i]) | |
| x = torch.tensor([True], dtype=torch.bool) | |
| self.assertEqual(x.dtype, torch.bool) | |
| y = x.numpy() | |
| self.assertEqual(y.dtype, np.bool) | |
| self.assertEqual(x[0], y[0]) | |
| def test_from_numpy(self, device) -> None: | |
| dtypes = [ | |
| np.double, | |
| np.float, | |
| np.float16, | |
| np.complex64, | |
| np.complex128, | |
| np.int64, | |
| np.int32, | |
| np.int16, | |
| np.int8, | |
| np.uint8, | |
| np.longlong, | |
| np.bool, | |
| ] | |
| complex_dtypes = [ | |
| np.complex64, | |
| np.complex128, | |
| ] | |
| for dtype in dtypes: | |
| array = np.array([1, 2, 3, 4], dtype=dtype) | |
| tensor_from_array = torch.from_numpy(array) | |
| # TODO: change to tensor equality check once HalfTensor | |
| # implements `==` | |
| for i in range(len(array)): | |
| self.assertEqual(tensor_from_array[i], array[i]) | |
| # ufunc 'remainder' not supported for complex dtypes | |
| if dtype not in complex_dtypes: | |
| # This is a special test case for Windows | |
| # https://github.com/pytorch/pytorch/issues/22615 | |
| array2 = array % 2 | |
| tensor_from_array2 = torch.from_numpy(array2) | |
| for i in range(len(array2)): | |
| self.assertEqual(tensor_from_array2[i], array2[i]) | |
| # Test unsupported type | |
| array = np.array([1, 2, 3, 4], dtype=np.uint16) | |
| with self.assertRaises(TypeError): | |
| tensor_from_array = torch.from_numpy(array) | |
| # check storage offset | |
| x = np.linspace(1, 125, 125) | |
| x.shape = (5, 5, 5) | |
| x = x[1] | |
| expected = torch.arange(1, 126, dtype=torch.float64).view(5, 5, 5)[1] | |
| self.assertEqual(torch.from_numpy(x), expected) | |
| # check noncontiguous | |
| x = np.linspace(1, 25, 25) | |
| x.shape = (5, 5) | |
| expected = torch.arange(1, 26, dtype=torch.float64).view(5, 5).t() | |
| self.assertEqual(torch.from_numpy(x.T), expected) | |
| # check noncontiguous with holes | |
| x = np.linspace(1, 125, 125) | |
| x.shape = (5, 5, 5) | |
| x = x[:, 1] | |
| expected = torch.arange(1, 126, dtype=torch.float64).view(5, 5, 5)[:, 1] | |
| self.assertEqual(torch.from_numpy(x), expected) | |
| # check zero dimensional | |
| x = np.zeros((0, 2)) | |
| self.assertEqual(torch.from_numpy(x).shape, (0, 2)) | |
| x = np.zeros((2, 0)) | |
| self.assertEqual(torch.from_numpy(x).shape, (2, 0)) | |
| # check ill-sized strides raise exception | |
| x = np.array([3., 5., 8.]) | |
| x.strides = (3,) | |
| self.assertRaises(ValueError, lambda: torch.from_numpy(x)) | |
| @onlyCPU | |
| def test_ctor_with_numpy_scalar_ctor(self, device) -> None: | |
| dtypes = [ | |
| np.double, | |
| np.float, | |
| np.float16, | |
| np.int64, | |
| np.int32, | |
| np.int16, | |
| np.uint8, | |
| np.bool, | |
| ] | |
| for dtype in dtypes: | |
| self.assertEqual(dtype(42), torch.tensor(dtype(42)).item()) | |
| @onlyCPU | |
| def test_numpy_index(self, device): | |
| i = np.int32([0, 1, 2]) | |
| x = torch.randn(5, 5) | |
| for idx in i: | |
| self.assertFalse(isinstance(idx, int)) | |
| self.assertEqual(x[idx], x[int(idx)]) | |
| @onlyCPU | |
| def test_numpy_array_interface(self, device): | |
| types = [ | |
| torch.DoubleTensor, | |
| torch.FloatTensor, | |
| torch.HalfTensor, | |
| torch.LongTensor, | |
| torch.IntTensor, | |
| torch.ShortTensor, | |
| torch.ByteTensor, | |
| ] | |
| dtypes = [ | |
| np.float64, | |
| np.float32, | |
| np.float16, | |
| np.int64, | |
| np.int32, | |
| np.int16, | |
| np.uint8, | |
| ] | |
| for tp, dtype in zip(types, dtypes): | |
| if np.dtype(dtype).kind == 'u': | |
| x = torch.Tensor([1, 2, 3, 4]).type(tp) | |
| array = np.array([1, 2, 3, 4], dtype=dtype) | |
| else: | |
| x = torch.Tensor([1, -2, 3, -4]).type(tp) | |
| array = np.array([1, -2, 3, -4], dtype=dtype) | |
| # Test __array__ w/o dtype argument | |
| asarray = np.asarray(x) | |
| self.assertIsInstance(asarray, np.ndarray) | |
| self.assertEqual(asarray.dtype, dtype) | |
| for i in range(len(x)): | |
| self.assertEqual(asarray[i], x[i]) | |
| # Test __array_wrap__, same dtype | |
| abs_x = np.abs(x) | |
| abs_array = np.abs(array) | |
| self.assertIsInstance(abs_x, tp) | |
| for i in range(len(x)): | |
| self.assertEqual(abs_x[i], abs_array[i]) | |
| # Test __array__ with dtype argument | |
| for dtype in dtypes: | |
| x = torch.IntTensor([1, -2, 3, -4]) | |
| asarray = np.asarray(x, dtype=dtype) | |
| self.assertEqual(asarray.dtype, dtype) | |
| if np.dtype(dtype).kind == 'u': | |
| wrapped_x = np.array([1, -2, 3, -4], dtype=dtype) | |
| for i in range(len(x)): | |
| self.assertEqual(asarray[i], wrapped_x[i]) | |
| else: | |
| for i in range(len(x)): | |
| self.assertEqual(asarray[i], x[i]) | |
| # Test some math functions with float types | |
| float_types = [torch.DoubleTensor, torch.FloatTensor] | |
| float_dtypes = [np.float64, np.float32] | |
| for tp, dtype in zip(float_types, float_dtypes): | |
| x = torch.Tensor([1, 2, 3, 4]).type(tp) | |
| array = np.array([1, 2, 3, 4], dtype=dtype) | |
| for func in ['sin', 'sqrt', 'ceil']: | |
| ufunc = getattr(np, func) | |
| res_x = ufunc(x) | |
| res_array = ufunc(array) | |
| self.assertIsInstance(res_x, tp) | |
| for i in range(len(x)): | |
| self.assertEqual(res_x[i], res_array[i]) | |
| # Test functions with boolean return value | |
| for tp, dtype in zip(types, dtypes): | |
| x = torch.Tensor([1, 2, 3, 4]).type(tp) | |
| array = np.array([1, 2, 3, 4], dtype=dtype) | |
| geq2_x = np.greater_equal(x, 2) | |
| geq2_array = np.greater_equal(array, 2).astype('uint8') | |
| self.assertIsInstance(geq2_x, torch.ByteTensor) | |
| for i in range(len(x)): | |
| self.assertEqual(geq2_x[i], geq2_array[i]) | |
| @onlyCPU | |
| def test_multiplication_numpy_scalar(self, device) -> None: | |
| for np_dtype in [np.float32, np.float64, np.int32, np.int64, np.int16, np.uint8]: | |
| for t_dtype in [torch.float, torch.double]: | |
| np_sc = np_dtype(2.0) | |
| t = torch.ones(2, requires_grad=True, dtype=t_dtype) | |
| r1 = t * np_sc | |
| self.assertIsInstance(r1, torch.Tensor) | |
| self.assertTrue(r1.dtype == t_dtype) | |
| self.assertTrue(r1.requires_grad) | |
| r2 = np_sc * t | |
| self.assertIsInstance(r2, torch.Tensor) | |
| self.assertTrue(r2.dtype == t_dtype) | |
| self.assertTrue(r2.requires_grad) | |
| @onlyCPU | |
| def test_parse_numpy_int(self, device): | |
| self.assertRaisesRegex(RuntimeError, "Overflow", | |
| lambda: torch.mean(torch.randn(1, 1), np.uint64(-1))) | |
| # https://github.com/pytorch/pytorch/issues/29252 | |
| for nptype in [np.int16, np.int8, np.uint8, np.int32, np.int64]: | |
| scalar = 3 | |
| np_arr = np.array([scalar], dtype=nptype) | |
| np_val = np_arr[0] | |
| # np integral type can be treated as a python int in native functions with | |
| # int parameters: | |
| self.assertEqual(torch.ones(5).diag(scalar), torch.ones(5).diag(np_val)) | |
| self.assertEqual(torch.ones([2, 2, 2, 2]).mean(scalar), torch.ones([2, 2, 2, 2]).mean(np_val)) | |
| # numpy integral type parses like a python int in custom python bindings: | |
| self.assertEqual(torch.Storage(np_val).size(), scalar) | |
| tensor = torch.tensor([2], dtype=torch.int) | |
| tensor[0] = np_val | |
| self.assertEqual(tensor[0], np_val) | |
| # Original reported issue, np integral type parses to the correct | |
| # PyTorch integral type when passed for a `Scalar` parameter in | |
| # arithmetic operations: | |
| t = torch.from_numpy(np_arr) | |
| self.assertEqual((t + np_val).dtype, t.dtype) | |
| self.assertEqual((np_val + t).dtype, t.dtype) | |
| def test_has_storage_numpy(self, device): | |
| for dtype in [np.float32, np.float64, np.int64, | |
| np.int32, np.int16, np.uint8]: | |
| arr = np.array([1], dtype=dtype) | |
| self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.float32).storage()) | |
| self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.double).storage()) | |
| self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.int).storage()) | |
| self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.long).storage()) | |
| self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.uint8).storage()) | |
| @dtypes(*torch.testing.get_all_dtypes()) | |
| def test_numpy_scalar_cmp(self, device, dtype): | |
| if dtype.is_complex: | |
| tensors = (torch.tensor(complex(1, 3), dtype=dtype, device=device), | |
| torch.tensor([complex(1, 3), 0, 2j], dtype=dtype, device=device), | |
| torch.tensor([[complex(3, 1), 0], [-1j, 5]], dtype=dtype, device=device)) | |
| else: | |
| tensors = (torch.tensor(3, dtype=dtype, device=device), | |
| torch.tensor([1, 0, -3], dtype=dtype, device=device), | |
| torch.tensor([[3, 0, -1], [3, 5, 4]], dtype=dtype, device=device)) | |
| for tensor in tensors: | |
| if dtype == torch.bfloat16: | |
| with self.assertRaises(TypeError): | |
| np_array = tensor.cpu().numpy() | |
| continue | |
| np_array = tensor.cpu().numpy() | |
| for t, a in product((tensor.flatten()[0], tensor.flatten()[0].item()), | |
| (np_array.flatten()[0], np_array.flatten()[0].item())): | |
| self.assertEqual(t, a) | |
| if dtype == torch.complex64 and torch.is_tensor(t) and type(a) == np.complex64: | |
| # TODO: Imaginary part is dropped in this case. Need fix. | |
| # https://github.com/pytorch/pytorch/issues/43579 | |
| self.assertFalse(t == a) | |
| else: | |
| self.assertTrue(t == a) | |
| instantiate_device_type_tests(TestNumPyInterop, globals()) | |
| if __name__ == '__main__': | |
| run_tests() |