mirror of
https://github.com/pytorch/pytorch.git
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0509022450
References: https://github.com/pytorch/pytorch/issues/13918 Add more test cases for list of numpy array inputs Pull Request resolved: https://github.com/pytorch/pytorch/pull/72249 Approved by: https://github.com/mruberry
454 lines
18 KiB
Python
454 lines
18 KiB
Python
# Owner(s): ["module: numpy"]
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import torch
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import numpy as np
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from itertools import product
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from torch.testing._internal.common_utils import \
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(TestCase, run_tests)
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from torch.testing._internal.common_device_type import \
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(instantiate_device_type_tests, onlyCPU, dtypes, skipMeta)
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from torch.testing._internal.common_dtype import all_types_and_complex_and
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# For testing handling NumPy objects and sending tensors to / accepting
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# arrays from NumPy.
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class TestNumPyInterop(TestCase):
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# Note: the warning this tests for only appears once per program, so
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# other instances of this warning should be addressed to avoid
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# the tests depending on the order in which they're run.
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@onlyCPU
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def test_numpy_non_writeable(self, device):
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arr = np.zeros(5)
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arr.flags['WRITEABLE'] = False
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self.assertWarns(UserWarning, lambda: torch.from_numpy(arr))
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@onlyCPU
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def test_numpy_unresizable(self, device) -> None:
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x = np.zeros((2, 2))
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y = torch.from_numpy(x)
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with self.assertRaises(ValueError):
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x.resize((5, 5))
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z = torch.randn(5, 5)
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w = z.numpy()
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with self.assertRaises(RuntimeError):
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z.resize_(10, 10)
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with self.assertRaises(ValueError):
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w.resize((10, 10))
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@onlyCPU
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def test_to_numpy(self, device) -> None:
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def get_castable_tensor(shape, dtype):
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if dtype.is_floating_point:
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dtype_info = torch.finfo(dtype)
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# can't directly use min and max, because for double, max - min
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# is greater than double range and sampling always gives inf.
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low = max(dtype_info.min, -1e10)
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high = min(dtype_info.max, 1e10)
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t = torch.empty(shape, dtype=torch.float64).uniform_(low, high)
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else:
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# can't directly use min and max, because for int64_t, max - min
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# is greater than int64_t range and triggers UB.
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low = max(torch.iinfo(dtype).min, int(-1e10))
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high = min(torch.iinfo(dtype).max, int(1e10))
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t = torch.empty(shape, dtype=torch.int64).random_(low, high)
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return t.to(dtype)
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dtypes = [
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torch.uint8,
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torch.int8,
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torch.short,
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torch.int,
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torch.half,
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torch.float,
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torch.double,
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torch.long,
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]
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for dtp in dtypes:
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# 1D
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sz = 10
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x = get_castable_tensor(sz, dtp)
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y = x.numpy()
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for i in range(sz):
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self.assertEqual(x[i], y[i])
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# 1D > 0 storage offset
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xm = get_castable_tensor(sz * 2, dtp)
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x = xm.narrow(0, sz - 1, sz)
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self.assertTrue(x.storage_offset() > 0)
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y = x.numpy()
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for i in range(sz):
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self.assertEqual(x[i], y[i])
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def check2d(x, y):
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for i in range(sz1):
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for j in range(sz2):
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self.assertEqual(x[i][j], y[i][j])
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# empty
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x = torch.tensor([]).to(dtp)
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y = x.numpy()
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self.assertEqual(y.size, 0)
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# contiguous 2D
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sz1 = 3
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sz2 = 5
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x = get_castable_tensor((sz1, sz2), dtp)
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y = x.numpy()
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check2d(x, y)
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self.assertTrue(y.flags['C_CONTIGUOUS'])
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# with storage offset
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xm = get_castable_tensor((sz1 * 2, sz2), dtp)
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x = xm.narrow(0, sz1 - 1, sz1)
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y = x.numpy()
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self.assertTrue(x.storage_offset() > 0)
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check2d(x, y)
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self.assertTrue(y.flags['C_CONTIGUOUS'])
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# non-contiguous 2D
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x = get_castable_tensor((sz2, sz1), dtp).t()
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y = x.numpy()
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check2d(x, y)
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self.assertFalse(y.flags['C_CONTIGUOUS'])
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# with storage offset
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xm = get_castable_tensor((sz2 * 2, sz1), dtp)
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x = xm.narrow(0, sz2 - 1, sz2).t()
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y = x.numpy()
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self.assertTrue(x.storage_offset() > 0)
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check2d(x, y)
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# non-contiguous 2D with holes
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xm = get_castable_tensor((sz2 * 2, sz1 * 2), dtp)
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x = xm.narrow(0, sz2 - 1, sz2).narrow(1, sz1 - 1, sz1).t()
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y = x.numpy()
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self.assertTrue(x.storage_offset() > 0)
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check2d(x, y)
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if dtp != torch.half:
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# check writeable
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x = get_castable_tensor((3, 4), dtp)
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y = x.numpy()
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self.assertTrue(y.flags.writeable)
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y[0][1] = 3
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self.assertTrue(x[0][1] == 3)
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y = x.t().numpy()
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self.assertTrue(y.flags.writeable)
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y[0][1] = 3
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self.assertTrue(x[0][1] == 3)
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def test_to_numpy_bool(self, device) -> None:
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x = torch.tensor([True, False], dtype=torch.bool)
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self.assertEqual(x.dtype, torch.bool)
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y = x.numpy()
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self.assertEqual(y.dtype, np.bool_)
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for i in range(len(x)):
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self.assertEqual(x[i], y[i])
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x = torch.tensor([True], dtype=torch.bool)
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self.assertEqual(x.dtype, torch.bool)
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y = x.numpy()
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self.assertEqual(y.dtype, np.bool_)
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self.assertEqual(x[0], y[0])
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def test_from_numpy(self, device) -> None:
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dtypes = [
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np.double,
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np.float64,
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np.float16,
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np.complex64,
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np.complex128,
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np.int64,
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np.int32,
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np.int16,
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np.int8,
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np.uint8,
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np.longlong,
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np.bool_,
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]
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complex_dtypes = [
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np.complex64,
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np.complex128,
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]
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for dtype in dtypes:
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array = np.array([1, 2, 3, 4], dtype=dtype)
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tensor_from_array = torch.from_numpy(array)
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# TODO: change to tensor equality check once HalfTensor
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# implements `==`
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for i in range(len(array)):
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self.assertEqual(tensor_from_array[i], array[i])
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# ufunc 'remainder' not supported for complex dtypes
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if dtype not in complex_dtypes:
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# This is a special test case for Windows
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# https://github.com/pytorch/pytorch/issues/22615
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array2 = array % 2
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tensor_from_array2 = torch.from_numpy(array2)
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for i in range(len(array2)):
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self.assertEqual(tensor_from_array2[i], array2[i])
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# Test unsupported type
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array = np.array([1, 2, 3, 4], dtype=np.uint16)
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with self.assertRaises(TypeError):
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tensor_from_array = torch.from_numpy(array)
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# check storage offset
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x = np.linspace(1, 125, 125)
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x.shape = (5, 5, 5)
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x = x[1]
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expected = torch.arange(1, 126, dtype=torch.float64).view(5, 5, 5)[1]
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self.assertEqual(torch.from_numpy(x), expected)
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# check noncontiguous
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x = np.linspace(1, 25, 25)
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x.shape = (5, 5)
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expected = torch.arange(1, 26, dtype=torch.float64).view(5, 5).t()
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self.assertEqual(torch.from_numpy(x.T), expected)
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# check noncontiguous with holes
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x = np.linspace(1, 125, 125)
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x.shape = (5, 5, 5)
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x = x[:, 1]
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expected = torch.arange(1, 126, dtype=torch.float64).view(5, 5, 5)[:, 1]
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self.assertEqual(torch.from_numpy(x), expected)
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# check zero dimensional
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x = np.zeros((0, 2))
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self.assertEqual(torch.from_numpy(x).shape, (0, 2))
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x = np.zeros((2, 0))
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self.assertEqual(torch.from_numpy(x).shape, (2, 0))
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# check ill-sized strides raise exception
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x = np.array([3., 5., 8.])
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x.strides = (3,)
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self.assertRaises(ValueError, lambda: torch.from_numpy(x))
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@skipMeta
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def test_from_list_of_ndarray_warning(self, device):
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warning_msg = r"Creating a tensor from a list of numpy.ndarrays is extremely slow"
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with self.assertWarnsOnceRegex(UserWarning, warning_msg):
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torch.tensor([np.array([0]), np.array([1])], device=device)
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def test_ctor_with_invalid_numpy_array_sequence(self, device):
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# Invalid list of numpy array
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with self.assertRaisesRegex(ValueError, "expected sequence of length"):
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torch.tensor([np.random.random(size=(3, 3)), np.random.random(size=(3, 0))], device=device)
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# Invalid list of list of numpy array
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with self.assertRaisesRegex(ValueError, "expected sequence of length"):
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torch.tensor([[np.random.random(size=(3, 3)), np.random.random(size=(3, 2))]], device=device)
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with self.assertRaisesRegex(ValueError, "expected sequence of length"):
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torch.tensor([[np.random.random(size=(3, 3)), np.random.random(size=(3, 3))],
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[np.random.random(size=(3, 3)), np.random.random(size=(3, 2))]], device=device)
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# expected shape is `[1, 2, 3]`, hence we try to iterate over 0-D array
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# leading to type error : not a sequence.
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with self.assertRaisesRegex(TypeError, "not a sequence"):
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torch.tensor([[np.random.random(size=(3)), np.random.random()]], device=device)
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# list of list or numpy array.
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with self.assertRaisesRegex(ValueError, "expected sequence of length"):
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torch.tensor([[1, 2, 3], np.random.random(size=(2,)), ], device=device)
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@onlyCPU
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def test_ctor_with_numpy_scalar_ctor(self, device) -> None:
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dtypes = [
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np.double,
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np.float64,
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np.float16,
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np.int64,
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np.int32,
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np.int16,
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np.uint8,
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np.bool_,
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]
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for dtype in dtypes:
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self.assertEqual(dtype(42), torch.tensor(dtype(42)).item())
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@onlyCPU
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def test_numpy_index(self, device):
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i = np.array([0, 1, 2], dtype=np.int32)
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x = torch.randn(5, 5)
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for idx in i:
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self.assertFalse(isinstance(idx, int))
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self.assertEqual(x[idx], x[int(idx)])
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@onlyCPU
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def test_numpy_array_interface(self, device):
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types = [
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torch.DoubleTensor,
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torch.FloatTensor,
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torch.HalfTensor,
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torch.LongTensor,
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torch.IntTensor,
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torch.ShortTensor,
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torch.ByteTensor,
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]
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dtypes = [
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np.float64,
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np.float32,
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np.float16,
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np.int64,
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np.int32,
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np.int16,
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np.uint8,
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]
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for tp, dtype in zip(types, dtypes):
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# Only concrete class can be given where "Type[number[_64Bit]]" is expected
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if np.dtype(dtype).kind == 'u': # type: ignore[misc]
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# .type expects a XxxTensor, which have no type hints on
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# purpose, so ignore during mypy type checking
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x = torch.tensor([1, 2, 3, 4]).type(tp) # type: ignore[call-overload]
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array = np.array([1, 2, 3, 4], dtype=dtype)
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else:
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x = torch.tensor([1, -2, 3, -4]).type(tp) # type: ignore[call-overload]
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array = np.array([1, -2, 3, -4], dtype=dtype)
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# Test __array__ w/o dtype argument
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asarray = np.asarray(x)
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self.assertIsInstance(asarray, np.ndarray)
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self.assertEqual(asarray.dtype, dtype)
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for i in range(len(x)):
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self.assertEqual(asarray[i], x[i])
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# Test __array_wrap__, same dtype
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abs_x = np.abs(x)
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abs_array = np.abs(array)
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self.assertIsInstance(abs_x, tp)
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for i in range(len(x)):
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self.assertEqual(abs_x[i], abs_array[i])
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# Test __array__ with dtype argument
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for dtype in dtypes:
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x = torch.IntTensor([1, -2, 3, -4])
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asarray = np.asarray(x, dtype=dtype)
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self.assertEqual(asarray.dtype, dtype)
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# Only concrete class can be given where "Type[number[_64Bit]]" is expected
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if np.dtype(dtype).kind == 'u': # type: ignore[misc]
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wrapped_x = np.array([1, -2, 3, -4], dtype=dtype)
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for i in range(len(x)):
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self.assertEqual(asarray[i], wrapped_x[i])
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else:
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for i in range(len(x)):
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self.assertEqual(asarray[i], x[i])
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# Test some math functions with float types
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float_types = [torch.DoubleTensor, torch.FloatTensor]
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float_dtypes = [np.float64, np.float32]
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for tp, dtype in zip(float_types, float_dtypes):
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x = torch.tensor([1, 2, 3, 4]).type(tp) # type: ignore[call-overload]
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array = np.array([1, 2, 3, 4], dtype=dtype)
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for func in ['sin', 'sqrt', 'ceil']:
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ufunc = getattr(np, func)
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res_x = ufunc(x)
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res_array = ufunc(array)
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self.assertIsInstance(res_x, tp)
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for i in range(len(x)):
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self.assertEqual(res_x[i], res_array[i])
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# Test functions with boolean return value
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for tp, dtype in zip(types, dtypes):
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x = torch.tensor([1, 2, 3, 4]).type(tp) # type: ignore[call-overload]
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array = np.array([1, 2, 3, 4], dtype=dtype)
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geq2_x = np.greater_equal(x, 2)
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geq2_array = np.greater_equal(array, 2).astype('uint8')
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self.assertIsInstance(geq2_x, torch.ByteTensor)
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for i in range(len(x)):
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self.assertEqual(geq2_x[i], geq2_array[i])
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@onlyCPU
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def test_multiplication_numpy_scalar(self, device) -> None:
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for np_dtype in [np.float32, np.float64, np.int32, np.int64, np.int16, np.uint8]:
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for t_dtype in [torch.float, torch.double]:
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# mypy raises an error when np.floatXY(2.0) is called
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# even though this is valid code
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np_sc = np_dtype(2.0) # type: ignore[abstract, arg-type]
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t = torch.ones(2, requires_grad=True, dtype=t_dtype)
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r1 = t * np_sc
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self.assertIsInstance(r1, torch.Tensor)
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self.assertTrue(r1.dtype == t_dtype)
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self.assertTrue(r1.requires_grad)
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r2 = np_sc * t
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self.assertIsInstance(r2, torch.Tensor)
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self.assertTrue(r2.dtype == t_dtype)
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self.assertTrue(r2.requires_grad)
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@onlyCPU
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def test_parse_numpy_int(self, device):
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# Only concrete class can be given where "Type[number[_64Bit]]" is expected
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self.assertRaisesRegex(RuntimeError, "Overflow",
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lambda: torch.mean(torch.randn(1, 1), np.uint64(-1))) # type: ignore[call-overload]
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# https://github.com/pytorch/pytorch/issues/29252
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for nptype in [np.int16, np.int8, np.uint8, np.int32, np.int64]:
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scalar = 3
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np_arr = np.array([scalar], dtype=nptype)
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np_val = np_arr[0]
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# np integral type can be treated as a python int in native functions with
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# int parameters:
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self.assertEqual(torch.ones(5).diag(scalar), torch.ones(5).diag(np_val))
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self.assertEqual(torch.ones([2, 2, 2, 2]).mean(scalar), torch.ones([2, 2, 2, 2]).mean(np_val))
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# numpy integral type parses like a python int in custom python bindings:
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self.assertEqual(torch.Storage(np_val).size(), scalar) # type: ignore[attr-defined]
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tensor = torch.tensor([2], dtype=torch.int)
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tensor[0] = np_val
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self.assertEqual(tensor[0], np_val)
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# Original reported issue, np integral type parses to the correct
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# PyTorch integral type when passed for a `Scalar` parameter in
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# arithmetic operations:
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t = torch.from_numpy(np_arr)
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self.assertEqual((t + np_val).dtype, t.dtype)
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self.assertEqual((np_val + t).dtype, t.dtype)
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def test_has_storage_numpy(self, device):
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for dtype in [np.float32, np.float64, np.int64,
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np.int32, np.int16, np.uint8]:
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arr = np.array([1], dtype=dtype)
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.float32).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.double).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.int).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.long).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.uint8).storage())
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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def test_numpy_scalar_cmp(self, device, dtype):
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if dtype.is_complex:
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tensors = (torch.tensor(complex(1, 3), dtype=dtype, device=device),
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torch.tensor([complex(1, 3), 0, 2j], dtype=dtype, device=device),
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torch.tensor([[complex(3, 1), 0], [-1j, 5]], dtype=dtype, device=device))
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else:
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tensors = (torch.tensor(3, dtype=dtype, device=device),
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torch.tensor([1, 0, -3], dtype=dtype, device=device),
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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()
|