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# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import unittest
from typing import Any
import numpy as np
import warp as wp
from warp.tests.test_array import FillStruct
from warp.tests.unittest_utils import *
wp.init()
@wp.kernel
def kernel_1d(a: wp.indexedarray(dtype=float), expected: wp.array(dtype=float)):
i = wp.tid()
wp.expect_eq(a[i], expected[i])
a[i] = 2.0 * a[i]
wp.atomic_add(a, i, 1.0)
wp.expect_eq(a[i], 2.0 * expected[i] + 1.0)
def test_indexedarray_1d(test, device):
values = np.arange(10, dtype=np.float32)
arr = wp.array(data=values, device=device)
indices = wp.array([1, 3, 5, 7, 9], dtype=int, device=device)
iarr = wp.indexedarray1d(arr, [indices])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 1)
test.assertEqual(iarr.shape, (5,))
test.assertEqual(iarr.size, 5)
expected_arr = wp.array(data=[1, 3, 5, 7, 9], dtype=float, device=device)
wp.launch(kernel_1d, dim=iarr.size, inputs=[iarr, expected_arr], device=device)
@wp.kernel
def kernel_2d(a: wp.indexedarray2d(dtype=float), expected: wp.array2d(dtype=float)):
i, j = wp.tid()
# check expected values
wp.expect_eq(a[i, j], expected[i, j])
# test wp.view()
wp.expect_eq(a[i][j], a[i, j])
a[i, j] = 2.0 * a[i, j]
wp.atomic_add(a, i, j, 1.0)
wp.expect_eq(a[i, j], 2.0 * expected[i, j] + 1.0)
def test_indexedarray_2d(test, device):
values = np.arange(100, dtype=np.float32).reshape((10, 10))
arr = wp.array(data=values, device=device)
indices0 = wp.array([1, 3], dtype=int, device=device)
indices1 = wp.array([2, 4, 8], dtype=int, device=device)
iarr = wp.indexedarray2d(arr, [indices0, indices1])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 2)
test.assertEqual(iarr.shape, (2, 3))
test.assertEqual(iarr.size, 6)
expected_values = [[12, 14, 18], [32, 34, 38]]
expected_arr = wp.array(data=expected_values, dtype=float, device=device)
wp.launch(kernel_2d, dim=iarr.shape, inputs=[iarr, expected_arr], device=device)
@wp.kernel
def kernel_3d(a: wp.indexedarray3d(dtype=float), expected: wp.array3d(dtype=float)):
i, j, k = wp.tid()
# check expected values
wp.expect_eq(a[i, j, k], expected[i, j, k])
# test wp.view()
wp.expect_eq(a[i][j][k], a[i, j, k])
wp.expect_eq(a[i, j][k], a[i, j, k])
wp.expect_eq(a[i][j, k], a[i, j, k])
a[i, j, k] = 2.0 * a[i, j, k]
wp.atomic_add(a, i, j, k, 1.0)
wp.expect_eq(a[i, j, k], 2.0 * expected[i, j, k] + 1.0)
def test_indexedarray_3d(test, device):
values = np.arange(1000, dtype=np.float32).reshape((10, 10, 10))
arr = wp.array(data=values, device=device)
indices0 = wp.array([1, 3], dtype=int, device=device)
indices1 = wp.array([2, 4, 8], dtype=int, device=device)
indices2 = wp.array([0, 5], dtype=int, device=device)
iarr = wp.indexedarray3d(arr, [indices0, indices1, indices2])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 3)
test.assertEqual(iarr.shape, (2, 3, 2))
test.assertEqual(iarr.size, 12)
expected_values = [
[[120, 125], [140, 145], [180, 185]],
[[320, 325], [340, 345], [380, 385]],
]
expected_arr = wp.array(data=expected_values, dtype=float, device=device)
wp.launch(kernel_3d, dim=iarr.shape, inputs=[iarr, expected_arr], device=device)
@wp.kernel
def kernel_4d(a: wp.indexedarray4d(dtype=float), expected: wp.array4d(dtype=float)):
i, j, k, l = wp.tid()
# check expected values
wp.expect_eq(a[i, j, k, l], expected[i, j, k, l])
# test wp.view()
wp.expect_eq(a[i][j][k][l], a[i, j, k, l])
wp.expect_eq(a[i][j, k, l], a[i, j, k, l])
wp.expect_eq(a[i, j][k, l], a[i, j, k, l])
wp.expect_eq(a[i, j, k][l], a[i, j, k, l])
a[i, j, k, l] = 2.0 * a[i, j, k, l]
wp.atomic_add(a, i, j, k, l, 1.0)
wp.expect_eq(a[i, j, k, l], 2.0 * expected[i, j, k, l] + 1.0)
def test_indexedarray_4d(test, device):
values = np.arange(10000, dtype=np.float32).reshape((10, 10, 10, 10))
arr = wp.array(data=values, device=device)
indices0 = wp.array([1, 3], dtype=int, device=device)
indices1 = wp.array([2, 4, 8], dtype=int, device=device)
indices2 = wp.array([0, 5], dtype=int, device=device)
indices3 = wp.array([6, 7, 9], dtype=int, device=device)
iarr = wp.indexedarray4d(arr, [indices0, indices1, indices2, indices3])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 4)
test.assertEqual(iarr.shape, (2, 3, 2, 3))
test.assertEqual(iarr.size, 36)
expected_values = [
[
[[1206, 1207, 1209], [1256, 1257, 1259]],
[[1406, 1407, 1409], [1456, 1457, 1459]],
[[1806, 1807, 1809], [1856, 1857, 1859]],
],
[
[[3206, 3207, 3209], [3256, 3257, 3259]],
[[3406, 3407, 3409], [3456, 3457, 3459]],
[[3806, 3807, 3809], [3856, 3857, 3859]],
],
]
expected_arr = wp.array(data=expected_values, dtype=float, device=device)
wp.launch(kernel_4d, dim=iarr.shape, inputs=[iarr, expected_arr], device=device)
def test_indexedarray_mixed(test, device):
# [[[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11],
# [12, 13, 14, 15]],
# [[16, 17, 18, 19],
# [20, 21, 22, 23],
# [24, 25, 26, 27],
# [28, 29, 30, 31]],
# [[32, 33, 34, 35],
# [36, 37, 38, 39],
# [40, 41, 42, 43],
# [44, 45, 46, 47],
# [[48, 49, 50, 51],
# [52, 53, 54, 55],
# [56, 57, 58, 59],
# [60, 61, 62, 63]]]]
values = np.arange(64, dtype=np.float32).reshape((4, 4, 4))
indices = wp.array([0, 3], dtype=int, device=device)
# -----
arr = wp.array(data=values, device=device)
iarr = wp.indexedarray(arr, [indices, None, None])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 3)
test.assertEqual(iarr.shape, (2, 4, 4))
test.assertEqual(iarr.size, 32)
expected_values = [
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]],
[[48, 49, 50, 51], [52, 53, 54, 55], [56, 57, 58, 59], [60, 61, 62, 63]],
]
expected_arr = wp.array(data=expected_values, dtype=float, device=device)
wp.launch(kernel_3d, dim=iarr.shape, inputs=[iarr, expected_arr], device=device)
# -----
arr = wp.array(data=values, device=device)
iarr = wp.indexedarray(arr, [indices, indices, None])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 3)
test.assertEqual(iarr.shape, (2, 2, 4))
test.assertEqual(iarr.size, 16)
expected_values = [[[0, 1, 2, 3], [12, 13, 14, 15]], [[48, 49, 50, 51], [60, 61, 62, 63]]]
expected_arr = wp.array(data=expected_values, dtype=float, device=device)
wp.launch(kernel_3d, dim=iarr.shape, inputs=[iarr, expected_arr], device=device)
# -----
arr = wp.array(data=values, device=device)
iarr = wp.indexedarray(arr, [indices, None, indices])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 3)
test.assertEqual(iarr.shape, (2, 4, 2))
test.assertEqual(iarr.size, 16)
expected_values = [[[0, 3], [4, 7], [8, 11], [12, 15]], [[48, 51], [52, 55], [56, 59], [60, 63]]]
expected_arr = wp.array(data=expected_values, dtype=float, device=device)
wp.launch(kernel_3d, dim=iarr.shape, inputs=[iarr, expected_arr], device=device)
# -----
arr = wp.array(data=values, device=device)
iarr = wp.indexedarray(arr, [None, indices, indices])
test.assertEqual(iarr.dtype, arr.dtype)
test.assertEqual(iarr.ndim, 3)
test.assertEqual(iarr.shape, (4, 2, 2))
test.assertEqual(iarr.size, 16)
expected_values = [[[0, 3], [12, 15]], [[16, 19], [28, 31]], [[32, 35], [44, 47]], [[48, 51], [60, 63]]]
expected_arr = wp.array(data=expected_values, dtype=float, device=device)
wp.launch(kernel_3d, dim=iarr.shape, inputs=[iarr, expected_arr], device=device)
vec2i = wp.types.vector(length=2, dtype=wp.int32)
vec3i = wp.types.vector(length=3, dtype=wp.int32)
vec4i = wp.types.vector(length=4, dtype=wp.int32)
@wp.kernel
def shape_kernel_1d(arr: wp.indexedarray1d(dtype=float), expected: int):
wp.expect_eq(arr.shape[0], expected)
@wp.kernel
def shape_kernel_2d(arr: wp.indexedarray2d(dtype=float), expected: vec2i):
wp.expect_eq(arr.shape[0], expected[0])
wp.expect_eq(arr.shape[1], expected[1])
# 1d slice
view = arr[0]
wp.expect_eq(view.shape[0], expected[1])
@wp.kernel
def shape_kernel_3d(arr: wp.indexedarray3d(dtype=float), expected: vec3i):
wp.expect_eq(arr.shape[0], expected[0])
wp.expect_eq(arr.shape[1], expected[1])
wp.expect_eq(arr.shape[2], expected[2])
# 2d slice
view2 = arr[0]
wp.expect_eq(view2.shape[0], expected[1])
wp.expect_eq(view2.shape[1], expected[2])
# 1d slice
view1 = arr[0, 0]
wp.expect_eq(view1.shape[0], expected[2])
@wp.kernel
def shape_kernel_4d(arr: wp.indexedarray4d(dtype=float), expected: vec4i):
wp.expect_eq(arr.shape[0], expected[0])
wp.expect_eq(arr.shape[1], expected[1])
wp.expect_eq(arr.shape[2], expected[2])
wp.expect_eq(arr.shape[3], expected[3])
# 3d slice
view3 = arr[0]
wp.expect_eq(view3.shape[0], expected[1])
wp.expect_eq(view3.shape[1], expected[2])
wp.expect_eq(view3.shape[2], expected[3])
# 2d slice
view2 = arr[0, 0]
wp.expect_eq(view2.shape[0], expected[2])
wp.expect_eq(view2.shape[1], expected[3])
# 1d slice
view1 = arr[0, 0, 0]
wp.expect_eq(view1.shape[0], expected[3])
def test_indexedarray_shape(test, device):
with wp.ScopedDevice(device):
data1 = wp.zeros(10, dtype=float)
data2 = wp.zeros((10, 20), dtype=float)
data3 = wp.zeros((10, 20, 30), dtype=float)
data4 = wp.zeros((10, 20, 30, 40), dtype=float)
indices1 = wp.array(data=[2, 7], dtype=int)
indices2 = wp.array(data=[2, 7, 12, 17], dtype=int)
indices3 = wp.array(data=[2, 7, 12, 17, 22, 27], dtype=int)
indices4 = wp.array(data=[2, 7, 12, 17, 22, 27, 32, 37], dtype=int)
ia1 = wp.indexedarray(data1, [indices1])
wp.launch(shape_kernel_1d, dim=1, inputs=[ia1, 2])
ia2_1 = wp.indexedarray(data2, [indices1, None])
ia2_2 = wp.indexedarray(data2, [None, indices2])
ia2_3 = wp.indexedarray(data2, [indices1, indices2])
wp.launch(shape_kernel_2d, dim=1, inputs=[ia2_1, vec2i(2, 20)])
wp.launch(shape_kernel_2d, dim=1, inputs=[ia2_2, vec2i(10, 4)])
wp.launch(shape_kernel_2d, dim=1, inputs=[ia2_3, vec2i(2, 4)])
ia3_1 = wp.indexedarray(data3, [indices1, None, None])
ia3_2 = wp.indexedarray(data3, [None, indices2, None])
ia3_3 = wp.indexedarray(data3, [None, None, indices3])
ia3_4 = wp.indexedarray(data3, [indices1, indices2, None])
ia3_5 = wp.indexedarray(data3, [indices1, None, indices3])
ia3_6 = wp.indexedarray(data3, [None, indices2, indices3])
ia3_7 = wp.indexedarray(data3, [indices1, indices2, indices3])
wp.launch(shape_kernel_3d, dim=1, inputs=[ia3_1, vec3i(2, 20, 30)])
wp.launch(shape_kernel_3d, dim=1, inputs=[ia3_2, vec3i(10, 4, 30)])
wp.launch(shape_kernel_3d, dim=1, inputs=[ia3_3, vec3i(10, 20, 6)])
wp.launch(shape_kernel_3d, dim=1, inputs=[ia3_4, vec3i(2, 4, 30)])
wp.launch(shape_kernel_3d, dim=1, inputs=[ia3_5, vec3i(2, 20, 6)])
wp.launch(shape_kernel_3d, dim=1, inputs=[ia3_6, vec3i(10, 4, 6)])
wp.launch(shape_kernel_3d, dim=1, inputs=[ia3_7, vec3i(2, 4, 6)])
ia4_1 = wp.indexedarray(data4, [indices1, None, None, None])
ia4_2 = wp.indexedarray(data4, [indices1, None, None, indices4])
ia4_3 = wp.indexedarray(data4, [None, indices2, indices3, None])
ia4_4 = wp.indexedarray(data4, [indices1, indices2, indices3, indices4])
wp.launch(shape_kernel_4d, dim=1, inputs=[ia4_1, vec4i(2, 20, 30, 40)])
wp.launch(shape_kernel_4d, dim=1, inputs=[ia4_2, vec4i(2, 20, 30, 8)])
wp.launch(shape_kernel_4d, dim=1, inputs=[ia4_3, vec4i(10, 4, 6, 40)])
wp.launch(shape_kernel_4d, dim=1, inputs=[ia4_4, vec4i(2, 4, 6, 8)])
wp.synchronize_device(device)
def test_indexedarray_getitem(test, device):
with wp.ScopedDevice(device):
data = wp.array(data=np.arange(1000, dtype=np.int32).reshape((10, 10, 10)))
I = wp.array(data=[0, 1, 2], dtype=int)
# use constructor
a1 = wp.indexedarray(data, [None, None, I])
a2 = wp.indexedarray(data, [None, I])
a3 = wp.indexedarray(data, [None, I, I])
a4 = wp.indexedarray(data, [I])
a5 = wp.indexedarray(data, [I, None, I])
a6 = wp.indexedarray(data, [I, I])
a7 = wp.indexedarray(data, [I, I, I])
# use array.__getitem__()
b1 = data[:, :, I]
b2 = data[:, I]
b3 = data[:, I, I]
b4 = data[I]
b5 = data[I, :, I]
b6 = data[I, I]
b7 = data[I, I, I]
test.assertEqual(type(a1), type(b1))
test.assertEqual(type(a2), type(b2))
test.assertEqual(type(a3), type(b3))
test.assertEqual(type(a4), type(b4))
test.assertEqual(type(a5), type(b5))
test.assertEqual(type(a6), type(b6))
test.assertEqual(type(a7), type(b7))
assert_np_equal(a1.numpy(), b1.numpy())
assert_np_equal(a2.numpy(), b2.numpy())
assert_np_equal(a3.numpy(), b3.numpy())
assert_np_equal(a4.numpy(), b4.numpy())
assert_np_equal(a5.numpy(), b5.numpy())
assert_np_equal(a6.numpy(), b6.numpy())
assert_np_equal(a7.numpy(), b7.numpy())
def test_indexedarray_slicing(test, device):
with wp.ScopedDevice(device):
data = wp.array(data=np.arange(1000, dtype=np.int32).reshape((10, 10, 10)))
# test equivalence of slicing and indexing the same range
s = slice(0, 3)
I = wp.array(data=[0, 1, 2], dtype=int)
a0 = data[s, s, s]
test.assertEqual(type(a0), wp.array)
a1 = data[s, s, I]
test.assertEqual(type(a1), wp.indexedarray)
a2 = data[s, I, s]
test.assertEqual(type(a2), wp.indexedarray)
a3 = data[s, I, I]
test.assertEqual(type(a3), wp.indexedarray)
a4 = data[I, s, s]
test.assertEqual(type(a4), wp.indexedarray)
a5 = data[I, s, I]
test.assertEqual(type(a5), wp.indexedarray)
a6 = data[I, I, s]
test.assertEqual(type(a6), wp.indexedarray)
a7 = data[I, I, I]
test.assertEqual(type(a7), wp.indexedarray)
expected = a0.numpy()
assert_np_equal(a1.numpy(), expected)
assert_np_equal(a2.numpy(), expected)
assert_np_equal(a3.numpy(), expected)
assert_np_equal(a4.numpy(), expected)
assert_np_equal(a5.numpy(), expected)
assert_np_equal(a6.numpy(), expected)
assert_np_equal(a7.numpy(), expected)
# generic increment kernels that work with any array (regular or indexed)
@wp.kernel
def inc_1d(a: Any):
i = wp.tid()
a[i] = a[i] + 1
@wp.kernel
def inc_2d(a: Any):
i, j = wp.tid()
a[i, j] = a[i, j] + 1
@wp.kernel
def inc_3d(a: Any):
i, j, k = wp.tid()
a[i, j, k] = a[i, j, k] + 1
@wp.kernel
def inc_4d(a: Any):
i, j, k, l = wp.tid()
a[i, j, k, l] = a[i, j, k, l] + 1
# optional overloads to avoid module reloading
wp.overload(inc_1d, [wp.array1d(dtype=int)])
wp.overload(inc_2d, [wp.array2d(dtype=int)])
wp.overload(inc_3d, [wp.array3d(dtype=int)])
wp.overload(inc_4d, [wp.array4d(dtype=int)])
wp.overload(inc_1d, [wp.indexedarray1d(dtype=int)])
wp.overload(inc_2d, [wp.indexedarray2d(dtype=int)])
wp.overload(inc_3d, [wp.indexedarray3d(dtype=int)])
wp.overload(inc_4d, [wp.indexedarray4d(dtype=int)])
def test_indexedarray_generics(test, device):
with wp.ScopedDevice(device):
data1 = wp.zeros((5,), dtype=int)
data2 = wp.zeros((5, 5), dtype=int)
data3 = wp.zeros((5, 5, 5), dtype=int)
data4 = wp.zeros((5, 5, 5, 5), dtype=int)
indices = wp.array(data=[0, 4], dtype=int)
ia1 = wp.indexedarray(data1, [indices])
ia2 = wp.indexedarray(data2, [indices, indices])
ia3 = wp.indexedarray(data3, [indices, indices, indices])
ia4 = wp.indexedarray(data4, [indices, indices, indices, indices])
wp.launch(inc_1d, dim=data1.shape, inputs=[data1])
wp.launch(inc_2d, dim=data2.shape, inputs=[data2])
wp.launch(inc_3d, dim=data3.shape, inputs=[data3])
wp.launch(inc_4d, dim=data4.shape, inputs=[data4])
wp.launch(inc_1d, dim=ia1.shape, inputs=[ia1])
wp.launch(inc_2d, dim=ia2.shape, inputs=[ia2])
wp.launch(inc_3d, dim=ia3.shape, inputs=[ia3])
wp.launch(inc_4d, dim=ia4.shape, inputs=[ia4])
expected1 = np.ones(5, dtype=np.int32)
expected1[0] = 2
expected1[4] = 2
expected2 = np.ones((5, 5), dtype=np.int32)
expected2[0, 0] = 2
expected2[0, 4] = 2
expected2[4, 0] = 2
expected2[4, 4] = 2
expected3 = np.ones((5, 5, 5), dtype=np.int32)
expected3[0, 0, 0] = 2
expected3[0, 0, 4] = 2
expected3[0, 4, 0] = 2
expected3[0, 4, 4] = 2
expected3[4, 0, 0] = 2
expected3[4, 0, 4] = 2
expected3[4, 4, 0] = 2
expected3[4, 4, 4] = 2
expected4 = np.ones((5, 5, 5, 5), dtype=np.int32)
expected4[0, 0, 0, 0] = 2
expected4[0, 0, 0, 4] = 2
expected4[0, 0, 4, 0] = 2
expected4[0, 0, 4, 4] = 2
expected4[0, 4, 0, 0] = 2
expected4[0, 4, 0, 4] = 2
expected4[0, 4, 4, 0] = 2
expected4[0, 4, 4, 4] = 2
expected4[4, 0, 0, 0] = 2
expected4[4, 0, 0, 4] = 2
expected4[4, 0, 4, 0] = 2
expected4[4, 0, 4, 4] = 2
expected4[4, 4, 0, 0] = 2
expected4[4, 4, 0, 4] = 2
expected4[4, 4, 4, 0] = 2
expected4[4, 4, 4, 4] = 2
assert_np_equal(data1.numpy(), expected1)
assert_np_equal(data2.numpy(), expected2)
assert_np_equal(data3.numpy(), expected3)
assert_np_equal(data4.numpy(), expected4)
assert_np_equal(ia1.numpy(), np.full((2,), 2, dtype=np.int32))
assert_np_equal(ia2.numpy(), np.full((2, 2), 2, dtype=np.int32))
assert_np_equal(ia3.numpy(), np.full((2, 2, 2), 2, dtype=np.int32))
assert_np_equal(ia4.numpy(), np.full((2, 2, 2, 2), 2, dtype=np.int32))
def test_indexedarray_empty(test, device):
# Test whether common operations work with empty (zero-sized) indexed arrays
# without throwing exceptions.
def test_empty_ops(ndim, nrows, ncols, wptype, nptype):
data_shape = (1,) * ndim
dtype_shape = ()
if wptype in wp.types.scalar_types:
# scalar, vector, or matrix
if ncols > 0:
if nrows > 0:
wptype = wp.types.matrix((nrows, ncols), wptype)
else:
wptype = wp.types.vector(ncols, wptype)
dtype_shape = wptype._shape_
fill_value = wptype(42)
else:
# struct
fill_value = wptype()
# create a data array
data = wp.empty(data_shape, dtype=wptype, device=device, requires_grad=True)
# create a zero-sized array of indices
indices = wp.empty(0, dtype=int, device=device)
a = data[indices]
# we expect dim to be zero for the empty indexed array, unchanged otherwise
expected_shape = (0, *data_shape[1:])
test.assertEqual(a.size, 0)
test.assertEqual(a.shape, expected_shape)
# all of these methods should succeed with zero-sized arrays
a.zero_()
a.fill_(fill_value)
b = a.contiguous()
b = wp.empty_like(a)
b = wp.zeros_like(a)
b = wp.full_like(a, fill_value)
b = wp.clone(a)
wp.copy(a, b)
a.assign(b)
na = a.numpy()
test.assertEqual(na.size, 0)
test.assertEqual(na.shape, (*expected_shape, *dtype_shape))
test.assertEqual(na.dtype, nptype)
test.assertEqual(a.list(), [])
for ndim in range(1, 5):
# test with scalars, vectors, and matrices
for nptype, wptype in wp.types.np_dtype_to_warp_type.items():
# scalars
test_empty_ops(ndim, 0, 0, wptype, nptype)
for ncols in [2, 3, 4, 5]:
# vectors
test_empty_ops(ndim, 0, ncols, wptype, nptype)
# square matrices
test_empty_ops(ndim, ncols, ncols, wptype, nptype)
# non-square matrices
test_empty_ops(ndim, 2, 3, wptype, nptype)
test_empty_ops(ndim, 3, 2, wptype, nptype)
test_empty_ops(ndim, 3, 4, wptype, nptype)
test_empty_ops(ndim, 4, 3, wptype, nptype)
# test with structs
test_empty_ops(ndim, 0, 0, FillStruct, FillStruct.numpy_dtype())
def test_indexedarray_fill_scalar(test, device):
dim_x = 4
for nptype, wptype in wp.types.np_dtype_to_warp_type.items():
data1 = wp.zeros(dim_x, dtype=wptype, device=device)
data2 = wp.zeros((dim_x, dim_x), dtype=wptype, device=device)
data3 = wp.zeros((dim_x, dim_x, dim_x), dtype=wptype, device=device)
data4 = wp.zeros((dim_x, dim_x, dim_x, dim_x), dtype=wptype, device=device)
indices = wp.array(np.arange(0, dim_x, 2, dtype=np.int32), device=device)
a1 = data1[indices]
a2 = data2[indices]
a3 = data3[indices]
a4 = data4[indices]
assert_np_equal(a1.numpy(), np.zeros(a1.shape, dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros(a2.shape, dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros(a3.shape, dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros(a4.shape, dtype=nptype))
# fill with int value
fill_value = 42
a1.fill_(fill_value)
a2.fill_(fill_value)
a3.fill_(fill_value)
a4.fill_(fill_value)
assert_np_equal(a1.numpy(), np.full(a1.shape, fill_value, dtype=nptype))
assert_np_equal(a2.numpy(), np.full(a2.shape, fill_value, dtype=nptype))
assert_np_equal(a3.numpy(), np.full(a3.shape, fill_value, dtype=nptype))
assert_np_equal(a4.numpy(), np.full(a4.shape, fill_value, dtype=nptype))
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
assert_np_equal(a1.numpy(), np.zeros(a1.shape, dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros(a2.shape, dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros(a3.shape, dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros(a4.shape, dtype=nptype))
if wptype in wp.types.float_types:
# fill with float value
fill_value = 13.37
a1.fill_(fill_value)
a2.fill_(fill_value)
a3.fill_(fill_value)
a4.fill_(fill_value)
assert_np_equal(a1.numpy(), np.full(a1.shape, fill_value, dtype=nptype))
assert_np_equal(a2.numpy(), np.full(a2.shape, fill_value, dtype=nptype))
assert_np_equal(a3.numpy(), np.full(a3.shape, fill_value, dtype=nptype))
assert_np_equal(a4.numpy(), np.full(a4.shape, fill_value, dtype=nptype))
# fill with Warp scalar value
fill_value = wptype(17)
a1.fill_(fill_value)
a2.fill_(fill_value)
a3.fill_(fill_value)
a4.fill_(fill_value)
assert_np_equal(a1.numpy(), np.full(a1.shape, fill_value.value, dtype=nptype))
assert_np_equal(a2.numpy(), np.full(a2.shape, fill_value.value, dtype=nptype))
assert_np_equal(a3.numpy(), np.full(a3.shape, fill_value.value, dtype=nptype))
assert_np_equal(a4.numpy(), np.full(a4.shape, fill_value.value, dtype=nptype))
def test_indexedarray_fill_vector(test, device):
# test filling a vector array with scalar or vector values (vec_type, list, or numpy array)
dim_x = 4
for nptype, wptype in wp.types.np_dtype_to_warp_type.items():
# vector types
vector_types = [
wp.types.vector(2, wptype),
wp.types.vector(3, wptype),
wp.types.vector(4, wptype),
wp.types.vector(5, wptype),
]
for vec_type in vector_types:
vec_len = vec_type._length_
data1 = wp.zeros(dim_x, dtype=vec_type, device=device)
data2 = wp.zeros((dim_x, dim_x), dtype=vec_type, device=device)
data3 = wp.zeros((dim_x, dim_x, dim_x), dtype=vec_type, device=device)
data4 = wp.zeros((dim_x, dim_x, dim_x, dim_x), dtype=vec_type, device=device)
indices = wp.array(np.arange(0, dim_x, 2, dtype=np.int32), device=device)
a1 = data1[indices]
a2 = data2[indices]
a3 = data3[indices]
a4 = data4[indices]
assert_np_equal(a1.numpy(), np.zeros((*a1.shape, vec_len), dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros((*a2.shape, vec_len), dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros((*a3.shape, vec_len), dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros((*a4.shape, vec_len), dtype=nptype))
# fill with int scalar
fill_value = 42
a1.fill_(fill_value)
a2.fill_(fill_value)
a3.fill_(fill_value)
a4.fill_(fill_value)
assert_np_equal(a1.numpy(), np.full((*a1.shape, vec_len), fill_value, dtype=nptype))
assert_np_equal(a2.numpy(), np.full((*a2.shape, vec_len), fill_value, dtype=nptype))
assert_np_equal(a3.numpy(), np.full((*a3.shape, vec_len), fill_value, dtype=nptype))
assert_np_equal(a4.numpy(), np.full((*a4.shape, vec_len), fill_value, dtype=nptype))
# test zeroing
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
assert_np_equal(a1.numpy(), np.zeros((*a1.shape, vec_len), dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros((*a2.shape, vec_len), dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros((*a3.shape, vec_len), dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros((*a4.shape, vec_len), dtype=nptype))
# vector values can be passed as a list, numpy array, or Warp vector instance
fill_list = [17, 42, 99, 101, 127][:vec_len]
fill_arr = np.array(fill_list, dtype=nptype)
fill_vec = vec_type(fill_list)
expected1 = np.tile(fill_arr, a1.size).reshape((*a1.shape, vec_len))
expected2 = np.tile(fill_arr, a2.size).reshape((*a2.shape, vec_len))
expected3 = np.tile(fill_arr, a3.size).reshape((*a3.shape, vec_len))
expected4 = np.tile(fill_arr, a4.size).reshape((*a4.shape, vec_len))
# fill with list of vector length
a1.fill_(fill_list)
a2.fill_(fill_list)
a3.fill_(fill_list)
a4.fill_(fill_list)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
# clear
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
# fill with numpy array of vector length
a1.fill_(fill_arr)
a2.fill_(fill_arr)
a3.fill_(fill_arr)
a4.fill_(fill_arr)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
# clear
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
# fill with vec instance
a1.fill_(fill_vec)
a2.fill_(fill_vec)
a3.fill_(fill_vec)
a4.fill_(fill_vec)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
if wptype in wp.types.float_types:
# fill with float scalar
fill_value = 13.37
a1.fill_(fill_value)
a2.fill_(fill_value)
a3.fill_(fill_value)
a4.fill_(fill_value)
assert_np_equal(a1.numpy(), np.full((*a1.shape, vec_len), fill_value, dtype=nptype))
assert_np_equal(a2.numpy(), np.full((*a2.shape, vec_len), fill_value, dtype=nptype))
assert_np_equal(a3.numpy(), np.full((*a3.shape, vec_len), fill_value, dtype=nptype))
assert_np_equal(a4.numpy(), np.full((*a4.shape, vec_len), fill_value, dtype=nptype))
# fill with float list of vector length
fill_list = [-2.5, -1.25, 1.25, 2.5, 5.0][:vec_len]
a1.fill_(fill_list)
a2.fill_(fill_list)
a3.fill_(fill_list)
a4.fill_(fill_list)
expected1 = np.tile(np.array(fill_list, dtype=nptype), a1.size).reshape((*a1.shape, vec_len))
expected2 = np.tile(np.array(fill_list, dtype=nptype), a2.size).reshape((*a2.shape, vec_len))
expected3 = np.tile(np.array(fill_list, dtype=nptype), a3.size).reshape((*a3.shape, vec_len))
expected4 = np.tile(np.array(fill_list, dtype=nptype), a4.size).reshape((*a4.shape, vec_len))
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
def test_indexedarray_fill_matrix(test, device):
# test filling a matrix array with scalar or matrix values (mat_type, nested list, or 2d numpy array)
dim_x = 4
for nptype, wptype in wp.types.np_dtype_to_warp_type.items():
# matrix types
matrix_types = [
# square matrices
wp.types.matrix((2, 2), wptype),
wp.types.matrix((3, 3), wptype),
wp.types.matrix((4, 4), wptype),
wp.types.matrix((5, 5), wptype),
# non-square matrices
wp.types.matrix((2, 3), wptype),
wp.types.matrix((3, 2), wptype),
wp.types.matrix((3, 4), wptype),
wp.types.matrix((4, 3), wptype),
]
for mat_type in matrix_types:
mat_len = mat_type._length_
mat_shape = mat_type._shape_
data1 = wp.zeros(dim_x, dtype=mat_type, device=device)
data2 = wp.zeros((dim_x, dim_x), dtype=mat_type, device=device)
data3 = wp.zeros((dim_x, dim_x, dim_x), dtype=mat_type, device=device)
data4 = wp.zeros((dim_x, dim_x, dim_x, dim_x), dtype=mat_type, device=device)
indices = wp.array(np.arange(0, dim_x, 2, dtype=np.int32), device=device)
a1 = data1[indices]
a2 = data2[indices]
a3 = data3[indices]
a4 = data4[indices]
assert_np_equal(a1.numpy(), np.zeros((*a1.shape, *mat_shape), dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros((*a2.shape, *mat_shape), dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros((*a3.shape, *mat_shape), dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros((*a4.shape, *mat_shape), dtype=nptype))
# fill with scalar
fill_value = 42
a1.fill_(fill_value)
a2.fill_(fill_value)
a3.fill_(fill_value)
a4.fill_(fill_value)
assert_np_equal(a1.numpy(), np.full((*a1.shape, *mat_shape), fill_value, dtype=nptype))
assert_np_equal(a2.numpy(), np.full((*a2.shape, *mat_shape), fill_value, dtype=nptype))
assert_np_equal(a3.numpy(), np.full((*a3.shape, *mat_shape), fill_value, dtype=nptype))
assert_np_equal(a4.numpy(), np.full((*a4.shape, *mat_shape), fill_value, dtype=nptype))
# test zeroing
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
assert_np_equal(a1.numpy(), np.zeros((*a1.shape, *mat_shape), dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros((*a2.shape, *mat_shape), dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros((*a3.shape, *mat_shape), dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros((*a4.shape, *mat_shape), dtype=nptype))
# matrix values can be passed as a 1d numpy array, 2d numpy array, flat list, nested list, or Warp matrix instance
if wptype != wp.bool:
fill_arr1 = np.arange(mat_len, dtype=nptype)
else:
fill_arr1 = np.ones(mat_len, dtype=nptype)
fill_arr2 = fill_arr1.reshape(mat_shape)
fill_list1 = list(fill_arr1)
fill_list2 = [list(row) for row in fill_arr2]
fill_mat = mat_type(fill_arr1)
expected1 = np.tile(fill_arr1, a1.size).reshape((*a1.shape, *mat_shape))
expected2 = np.tile(fill_arr1, a2.size).reshape((*a2.shape, *mat_shape))
expected3 = np.tile(fill_arr1, a3.size).reshape((*a3.shape, *mat_shape))
expected4 = np.tile(fill_arr1, a4.size).reshape((*a4.shape, *mat_shape))
# fill with 1d numpy array
a1.fill_(fill_arr1)
a2.fill_(fill_arr1)
a3.fill_(fill_arr1)
a4.fill_(fill_arr1)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
# clear
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
# fill with 2d numpy array
a1.fill_(fill_arr2)
a2.fill_(fill_arr2)
a3.fill_(fill_arr2)
a4.fill_(fill_arr2)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
# clear
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
# fill with flat list
a1.fill_(fill_list1)
a2.fill_(fill_list1)
a3.fill_(fill_list1)
a4.fill_(fill_list1)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
# clear
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
# fill with nested list
a1.fill_(fill_list2)
a2.fill_(fill_list2)
a3.fill_(fill_list2)
a4.fill_(fill_list2)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
# clear
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
# fill with mat instance
a1.fill_(fill_mat)
a2.fill_(fill_mat)
a3.fill_(fill_mat)
a4.fill_(fill_mat)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
def test_indexedarray_fill_struct(test, device):
dim_x = 8
nptype = FillStruct.numpy_dtype()
data1 = wp.zeros(dim_x, dtype=FillStruct, device=device)
data2 = wp.zeros((dim_x, dim_x), dtype=FillStruct, device=device)
data3 = wp.zeros((dim_x, dim_x, dim_x), dtype=FillStruct, device=device)
data4 = wp.zeros((dim_x, dim_x, dim_x, dim_x), dtype=FillStruct, device=device)
indices = wp.array(np.arange(0, dim_x, 2, dtype=np.int32), device=device)
a1 = data1[indices]
a2 = data2[indices]
a3 = data3[indices]
a4 = data4[indices]
assert_np_equal(a1.numpy(), np.zeros(a1.shape, dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros(a2.shape, dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros(a3.shape, dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros(a4.shape, dtype=nptype))
s = FillStruct()
# fill with default struct value (should be all zeros)
a1.fill_(s)
a2.fill_(s)
a3.fill_(s)
a4.fill_(s)
assert_np_equal(a1.numpy(), np.zeros(a1.shape, dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros(a2.shape, dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros(a3.shape, dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros(a4.shape, dtype=nptype))
# scalars
s.i1 = -17
s.i2 = 42
s.i4 = 99
s.i8 = 101
s.f2 = -1.25
s.f4 = 13.37
s.f8 = 0.125
# vectors
s.v2 = [21, 22]
s.v3 = [31, 32, 33]
s.v4 = [41, 42, 43, 44]
s.v5 = [51, 52, 53, 54, 55]
# matrices
s.m2 = [[61, 62]] * 2
s.m3 = [[71, 72, 73]] * 3
s.m4 = [[81, 82, 83, 84]] * 4
s.m5 = [[91, 92, 93, 94, 95]] * 5
# arrays
s.a1 = wp.zeros((2,) * 1, dtype=float, device=device)
s.a2 = wp.zeros((2,) * 2, dtype=float, device=device)
s.a3 = wp.zeros((2,) * 3, dtype=float, device=device)
s.a4 = wp.zeros((2,) * 4, dtype=float, device=device)
# fill with custom struct value
a1.fill_(s)
a2.fill_(s)
a3.fill_(s)
a4.fill_(s)
ns = s.numpy_value()
expected1 = np.empty(a1.shape, dtype=nptype)
expected2 = np.empty(a2.shape, dtype=nptype)
expected3 = np.empty(a3.shape, dtype=nptype)
expected4 = np.empty(a4.shape, dtype=nptype)
expected1.fill(ns)
expected2.fill(ns)
expected3.fill(ns)
expected4.fill(ns)
assert_np_equal(a1.numpy(), expected1)
assert_np_equal(a2.numpy(), expected2)
assert_np_equal(a3.numpy(), expected3)
assert_np_equal(a4.numpy(), expected4)
# test clearing
a1.zero_()
a2.zero_()
a3.zero_()
a4.zero_()
assert_np_equal(a1.numpy(), np.zeros(a1.shape, dtype=nptype))
assert_np_equal(a2.numpy(), np.zeros(a2.shape, dtype=nptype))
assert_np_equal(a3.numpy(), np.zeros(a3.shape, dtype=nptype))
assert_np_equal(a4.numpy(), np.zeros(a4.shape, dtype=nptype))
devices = get_test_devices()
class TestIndexedArray(unittest.TestCase):
pass
add_function_test(TestIndexedArray, "test_indexedarray_1d", test_indexedarray_1d, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_2d", test_indexedarray_2d, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_3d", test_indexedarray_3d, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_4d", test_indexedarray_4d, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_mixed", test_indexedarray_mixed, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_shape", test_indexedarray_shape, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_getitem", test_indexedarray_getitem, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_slicing", test_indexedarray_slicing, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_generics", test_indexedarray_generics, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_empty", test_indexedarray_empty, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_fill_scalar", test_indexedarray_fill_scalar, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_fill_vector", test_indexedarray_fill_vector, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_fill_matrix", test_indexedarray_fill_matrix, devices=devices)
add_function_test(TestIndexedArray, "test_indexedarray_fill_struct", test_indexedarray_fill_struct, devices=devices)
if __name__ == "__main__":
wp.build.clear_kernel_cache()
unittest.main(verbosity=2)
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