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rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure
|
rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure/tests/test_polygon.py
|
import cupy as cp
import pytest
from cupy.testing import assert_array_equal
from numpy.testing import assert_equal
from cucim.skimage.measure import approximate_polygon, subdivide_polygon
from cucim.skimage.measure._polygon import _SUBDIVISION_MASKS
_square = cp.array(
[
[0, 0],
[0, 1],
[0, 2],
[0, 3],
[1, 3],
[2, 3],
[3, 3],
[3, 2],
[3, 1],
[3, 0],
[2, 0],
[1, 0],
[0, 0],
]
)
@pytest.mark.parametrize("dtype", [cp.float32, cp.float64])
def test_approximate_polygon(dtype):
square = _square.astype(dtype, copy=False)
out = approximate_polygon(square, 0.1)
assert out.dtype == dtype
assert_array_equal(out, square[cp.asarray((0, 3, 6, 9, 12)), :])
out = approximate_polygon(square, 2.2)
assert_array_equal(out, square[cp.asarray((0, 6, 12)), :])
out = approximate_polygon(
square[cp.asarray((0, 1, 3, 4, 5, 6, 7, 9, 11, 12)), :], 0.1
)
assert_array_equal(out, square[cp.asarray((0, 3, 6, 9, 12)), :])
out = approximate_polygon(square, -1)
assert_array_equal(out, square)
out = approximate_polygon(square, 0)
assert_array_equal(out, square)
@pytest.mark.parametrize("dtype", [cp.float32, cp.float64])
def test_subdivide_polygon(dtype):
square = _square.astype(dtype, copy=False)
new_square1 = square
new_square2 = square[:-1]
new_square3 = square[:-1]
# test iterative subdvision
for _ in range(10):
square1, square2, square3 = new_square1, new_square2, new_square3
# test different B-Spline degrees
for degree in range(1, 7):
mask_len = len(_SUBDIVISION_MASKS[degree][0])
# test circular
new_square1 = subdivide_polygon(square1, degree)
assert new_square1.dtype == dtype
assert_array_equal(new_square1[-1], new_square1[0])
assert_equal(new_square1.shape[0], 2 * square1.shape[0] - 1)
# test non-circular
new_square2 = subdivide_polygon(square2, degree)
assert new_square3.dtype == dtype
assert_equal(
new_square2.shape[0], 2 * (square2.shape[0] - mask_len + 1)
)
# test non-circular, preserve_ends
new_square3 = subdivide_polygon(square3, degree, True)
assert new_square3.dtype == dtype
assert_array_equal(new_square3[0], square3[0])
assert_array_equal(new_square3[-1], square3[-1])
assert_equal(
new_square3.shape[0], 2 * (square3.shape[0] - mask_len + 2)
)
# not supported B-Spline degree
with pytest.raises(ValueError):
subdivide_polygon(square, 0)
with pytest.raises(ValueError):
subdivide_polygon(square, 8)
| 0 |
rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure
|
rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure/tests/test_entropy.py
|
import cupy as cp
import numpy as np
from numpy.testing import assert_almost_equal
from cucim.skimage.measure import shannon_entropy
def test_shannon_ones():
img = cp.ones((10, 10))
res = shannon_entropy(img, base=np.e)
assert_almost_equal(float(res), 0.0)
def test_shannon_all_unique():
img = cp.arange(64)
res = shannon_entropy(img, base=2)
assert_almost_equal(float(res), np.log(64) / np.log(2))
| 0 |
rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure
|
rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure/tests/test_block.py
|
import cupy as cp
import pytest
from cupy.testing import assert_array_equal
from cucim.skimage.measure import block_reduce
def test_block_reduce_sum():
image1 = cp.arange(4 * 6).reshape(4, 6)
out1 = block_reduce(image1, (2, 3))
# fmt: off
expected1 = cp.array([[24, 42], # noqa
[96, 114]])
# fmt: on
assert_array_equal(expected1, out1)
image2 = cp.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (3, 3))
# fmt: off
expected2 = cp.array([[ 81, 108, 87], # noqa
[174, 192, 138]])
# fmt: on
assert_array_equal(expected2, out2)
def test_block_reduce_mean():
image1 = cp.arange(4 * 6).reshape(4, 6)
out1 = block_reduce(image1, (2, 3), func=cp.mean)
# fmt: off
expected1 = cp.array([[ 4., 7.], # noqa
[16., 19.]])
# fmt: on
assert_array_equal(expected1, out1)
image2 = cp.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=cp.mean)
# fmt: off
expected2 = cp.array([[14. , 10.8],
[ 8.5, 5.7]]) # noqa
# fmt: on
assert_array_equal(expected2, out2)
def test_block_reduce_median():
image1 = cp.arange(4 * 6).reshape(4, 6)
out1 = block_reduce(image1, (2, 3), func=cp.median)
# fmt: off
expected1 = cp.array([[ 4., 7.], # noqa
[16., 19.]])
# fmt: on
assert_array_equal(expected1, out1)
image2 = cp.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=cp.median)
# fmt: off
expected2 = cp.array([[14., 6.5], # noqa
[ 0., 0. ]]) # noqa
# fmt: on
assert_array_equal(expected2, out2)
image3 = cp.array([[1, 5, 5, 5], [5, 5, 5, 1000]])
out3 = block_reduce(image3, (2, 4), func=cp.median)
assert_array_equal(5, out3)
def test_block_reduce_min():
image1 = cp.arange(4 * 6).reshape(4, 6)
out1 = block_reduce(image1, (2, 3), func=cp.min)
# fmt: off
expected1 = cp.array([[ 0, 3], # noqa
[12, 15]])
# fmt: on
assert_array_equal(expected1, out1)
image2 = cp.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=cp.min)
# fmt: off
expected2 = cp.array([[0, 0],
[0, 0]])
# fmt: on
assert_array_equal(expected2, out2)
def test_block_reduce_max():
image1 = cp.arange(4 * 6).reshape(4, 6)
out1 = block_reduce(image1, (2, 3), func=cp.max)
# fmt: off
expected1 = cp.array([[ 8, 11], # noqa
[20, 23]])
# fmt: on
assert_array_equal(expected1, out1)
image2 = cp.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=cp.max)
# fmt: off
expected2 = cp.array([[28, 31],
[36, 39]])
# fmt: on
assert_array_equal(expected2, out2)
def test_invalid_block_size():
image = cp.arange(4 * 6).reshape(4, 6)
with pytest.raises(ValueError):
block_reduce(image, [1, 2, 3])
with pytest.raises(ValueError):
block_reduce(image, [1, 0.5])
def test_default_block_size():
image = cp.arange(4 * 6).reshape(4, 6)
out = block_reduce(image, func=cp.min)
expected = cp.array([[0, 2, 4], [12, 14, 16]])
assert_array_equal(expected, out)
def test_scalar_block_size():
image = cp.arange(6 * 6).reshape(6, 6)
out = block_reduce(image, 3, func=cp.min)
expected1 = cp.array([[0, 3], [18, 21]])
assert_array_equal(expected1, out)
expected2 = block_reduce(image, (3, 3), func=cp.min)
assert_array_equal(expected2, out)
@pytest.mark.skip(reason="cupy.mean doesn't support setting dtype=cupy.uint8")
def test_func_kwargs_same_dtype():
# fmt: off
image = cp.array([[97, 123, 173, 227],
[217, 241, 221, 214],
[211, 11, 170, 53], # noqa
[214, 205, 101, 57]], dtype=cp.uint8) # noqa
# fmt: on
out = block_reduce(
image, (2, 2), func=cp.mean, func_kwargs={"dtype": cp.uint8}
)
expected = cp.array([[41, 16], [32, 31]], dtype=cp.uint8)
assert_array_equal(out, expected)
assert out.dtype == expected.dtype
def test_func_kwargs_different_dtype():
# fmt: off
image = cp.array([[0.45745366, 0.67479345, 0.20949775, 0.3147348],
[0.7209286, 0.88915504, 0.66153409, 0.07919526],
[0.04640037, 0.54008495, 0.34664343, 0.56152301],
[0.58085003, 0.80144708, 0.87844473, 0.29811511]],
dtype=cp.float64)
# fmt: on
out = block_reduce(
image, (2, 2), func=cp.mean, func_kwargs={"dtype": cp.float16}
)
expected = cp.array([[0.6855, 0.3164], [0.4922, 0.521]], dtype=cp.float16)
# Note: had to set decimal=3 for float16 to pass here when using CuPy
cp.testing.assert_array_almost_equal(out, expected, decimal=3)
assert out.dtype == expected.dtype
| 0 |
rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure
|
rapidsai_public_repos/cucim/python/cucim/src/cucim/skimage/measure/tests/test_moments.py
|
import itertools
import cupy as cp
import numpy as np
import pytest
from cupy.testing import (
assert_allclose,
assert_array_almost_equal,
assert_array_equal,
)
from cupyx.scipy import ndimage as ndi
from numpy.testing import assert_almost_equal
from skimage import draw
from cucim.skimage._shared.utils import _supported_float_type
from cucim.skimage.measure import (
centroid,
inertia_tensor,
inertia_tensor_eigvals,
moments,
moments_central,
moments_coords,
moments_coords_central,
moments_hu,
moments_normalized,
)
def compare_moments(m1, m2, thresh=1e-8):
"""Compare two moments arrays.
Compares only values in the upper-left triangle of m1, m2 since
values below the diagonal exceed the specified order and are not computed
when the analytical computation is used.
Also, there the first-order central moments will be exactly zero with the
analytical calculation, but will not be zero due to limited floating point
precision when using a numerical computation. Here we just specify the
tolerance as a fraction of the maximum absolute value in the moments array.
"""
m1 = cp.asnumpy(m1)
m2 = cp.asnumpy(m2)
# make sure location of any NaN values match and then ignore the NaN values
# in the subsequent comparisons
nan_idx1 = np.where(np.isnan(m1.ravel()))[0]
nan_idx2 = np.where(np.isnan(m2.ravel()))[0]
assert len(nan_idx1) == len(nan_idx2)
assert np.all(nan_idx1 == nan_idx2)
m1[np.isnan(m1)] = 0
m2[np.isnan(m2)] = 0
max_val = np.abs(m1[m1 != 0]).max()
for orders in itertools.product(*((range(m1.shape[0]),) * m1.ndim)):
if sum(orders) > m1.shape[0] - 1:
m1[orders] = 0
m2[orders] = 0
continue
abs_diff = abs(m1[orders] - m2[orders])
rel_diff = abs_diff / max_val
assert rel_diff < thresh
@pytest.mark.parametrize("dtype", [cp.float32, cp.float64])
@pytest.mark.parametrize("anisotropic", [False, True, None])
def test_moments(anisotropic, dtype):
image = cp.zeros((20, 20), dtype=dtype)
image[14, 14] = 1
image[15, 15] = 1
image[14, 15] = 0.5
image[15, 14] = 0.5
if anisotropic:
spacing = (1.4, 2)
else:
spacing = (1, 1)
if anisotropic is None:
m = moments(image)
else:
m = moments(image, spacing=spacing)
assert m.dtype == dtype
assert_array_equal(m[0, 0], 3)
decimal = 5 if dtype == np.float32 else 12
assert_almost_equal(m[1, 0] / m[0, 0], 14.5 * spacing[0], decimal=decimal)
assert_almost_equal(m[0, 1] / m[0, 0], 14.5 * spacing[1], decimal=decimal)
@pytest.mark.parametrize("dtype", [cp.float32, cp.float64])
@pytest.mark.parametrize("anisotropic", [False, True, None])
def test_moments_central(anisotropic, dtype):
image = cp.zeros((20, 20), dtype=dtype)
image[14, 14] = 1
image[15, 15] = 1
image[14, 15] = 0.5
image[15, 14] = 0.5
if anisotropic:
spacing = (2, 1)
else:
spacing = (1, 1)
if anisotropic is None:
mu = moments_central(image, (14.5, 14.5))
# check for proper centroid computation
mu_calc_centroid = moments_central(image)
else:
mu = moments_central(
image, (14.5 * spacing[0], 14.5 * spacing[1]), spacing=spacing
)
# check for proper centroid computation
mu_calc_centroid = moments_central(image, spacing=spacing)
assert mu.dtype == dtype
thresh = 1e-6 if dtype == np.float32 else 1e-14
compare_moments(mu, mu_calc_centroid, thresh=thresh)
# shift image by dx=2, dy=2
image2 = cp.zeros((20, 20), dtype=dtype)
image2[16, 16] = 1
image2[17, 17] = 1
image2[16, 17] = 0.5
image2[17, 16] = 0.5
if anisotropic is None:
mu2 = moments_central(image2, (14.5 + 2, 14.5 + 2))
else:
mu2 = moments_central(
image2,
((14.5 + 2) * spacing[0], (14.5 + 2) * spacing[1]),
spacing=spacing,
)
assert mu2.dtype == dtype
# central moments must be translation invariant
compare_moments(mu, mu2, thresh=thresh)
def test_moments_coords():
image = cp.zeros((20, 20), dtype=cp.float64)
image[13:17, 13:17] = 1
mu_image = moments(image)
coords = cp.array(
[[r, c] for r in range(13, 17) for c in range(13, 17)], dtype=cp.float64
)
mu_coords = moments_coords(coords)
assert_array_almost_equal(mu_coords, mu_image)
@pytest.mark.parametrize("dtype", [cp.float16, cp.float32, cp.float64])
def test_moments_coords_dtype(dtype):
image = cp.zeros((20, 20), dtype=dtype)
image[13:17, 13:17] = 1
expected_dtype = _supported_float_type(dtype)
mu_image = moments(image)
assert mu_image.dtype == expected_dtype
coords = cp.asarray(
np.array(
[[r, c] for r in range(13, 17) for c in range(13, 17)], dtype=dtype
)
)
mu_coords = moments_coords(coords)
assert mu_coords.dtype == expected_dtype
assert_array_almost_equal(mu_coords, mu_image)
def test_moments_central_coords():
image = cp.zeros((20, 20), dtype=float)
image[13:17, 13:17] = 1
mu_image = moments_central(image, (14.5, 14.5))
coords = cp.asarray(
np.array(
[[r, c] for r in range(13, 17) for c in range(13, 17)],
dtype=float,
)
)
mu_coords = moments_coords_central(coords, (14.5, 14.5))
assert_array_almost_equal(mu_coords, mu_image)
# ensure that center is being calculated normally
mu_coords_calc_centroid = moments_coords_central(coords)
assert_array_almost_equal(mu_coords_calc_centroid, mu_coords)
# shift image by dx=3 dy=3
image = cp.zeros((20, 20), dtype=float)
image[16:20, 16:20] = 1
mu_image = moments_central(image, (14.5, 14.5))
coords = cp.asarray(
np.array(
[[r, c] for r in range(16, 20) for c in range(16, 20)], dtype=float
)
)
mu_coords = moments_coords_central(coords, (14.5, 14.5))
decimal = 6
assert_array_almost_equal(mu_coords, mu_image, decimal=decimal)
def test_moments_normalized():
image = cp.zeros((20, 20), dtype=float)
image[13:17, 13:17] = 1
mu = moments_central(image, (14.5, 14.5))
nu = moments_normalized(mu)
# shift image by dx=-2, dy=-2 and scale non-zero extent by 0.5
image2 = cp.zeros((20, 20), dtype=float)
# scale amplitude by 0.7
image2[11:13, 11:13] = 0.7
mu2 = moments_central(image2, (11.5, 11.5))
nu2 = moments_normalized(mu2)
# central moments must be translation and scale invariant
assert_array_almost_equal(nu, nu2, decimal=1)
@pytest.mark.parametrize("anisotropic", [False, True])
def test_moments_normalized_spacing(anisotropic):
image = cp.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
if not anisotropic:
spacing1 = (1, 1)
spacing2 = (3, 3)
else:
spacing1 = (1, 2)
spacing2 = (2, 4)
mu = moments_central(image, spacing=spacing1)
nu = moments_normalized(mu, spacing=spacing1)
mu2 = moments_central(image, spacing=spacing2)
nu2 = moments_normalized(mu2, spacing=spacing2)
# result should be invariant to absolute scale of spacing
compare_moments(nu, nu2)
def test_moments_normalized_3d():
image = cp.asarray(draw.ellipsoid(1, 1, 10))
mu_image = moments_central(image)
nu = moments_normalized(mu_image)
assert nu[0, 0, 2] > nu[0, 2, 0]
assert_almost_equal(nu[0, 2, 0], nu[2, 0, 0])
coords = cp.where(image)
mu_coords = moments_coords_central(coords)
assert_array_almost_equal(mu_coords, mu_image)
@pytest.mark.parametrize("dtype", [np.uint8, np.int32, np.float32, np.float64])
@pytest.mark.parametrize("order", [1, 2, 3, 4])
@pytest.mark.parametrize("ndim", [2, 3, 4])
def test_analytical_moments_calculation(dtype, order, ndim):
if ndim == 2:
shape = (256, 256)
elif ndim == 3:
shape = (64, 64, 64)
else:
shape = (16,) * ndim
rng = np.random.default_rng(1234)
if np.dtype(dtype).kind in "iu":
x = rng.integers(0, 256, shape, dtype=dtype)
else:
x = rng.standard_normal(shape, dtype=dtype)
x = cp.asarray(x)
# setting center=None will use the analytical expressions
m1 = moments_central(x, center=None, order=order)
# providing explicit centroid will bypass the analytical code path
m2 = moments_central(x, center=centroid(x), order=order)
# ensure numeric and analytical central moments are close
thresh = 5e-4 if _supported_float_type(x.dtype) == np.float32 else 1e-11
compare_moments(m1, m2, thresh=thresh)
def test_moments_normalized_invalid():
with pytest.raises(ValueError):
moments_normalized(cp.zeros((3, 3)), 3)
with pytest.raises(ValueError):
moments_normalized(cp.zeros((3, 3)), 4)
def test_moments_hu():
image = cp.zeros((20, 20), dtype=float)
image[13:15, 13:17] = 1
mu = moments_central(image, (13.5, 14.5))
nu = moments_normalized(mu)
hu = moments_hu(nu)
# shift image by dx=2, dy=3, scale by 0.5 and rotate by 90deg
image2 = cp.zeros((20, 20), dtype=float)
image2[11, 11:13] = 1
image2 = image2.T
mu2 = moments_central(image2, (11.5, 11))
nu2 = moments_normalized(mu2)
hu2 = moments_hu(nu2)
# central moments must be translation and scale invariant
assert_array_almost_equal(hu, hu2, decimal=1)
@pytest.mark.parametrize("dtype", [cp.float16, cp.float32, cp.float64])
def test_moments_dtype(dtype):
image = cp.zeros((20, 20), dtype=dtype)
image[13:15, 13:17] = 1
expected_dtype = _supported_float_type(dtype)
mu = moments_central(image, (13.5, 14.5))
assert mu.dtype == expected_dtype
nu = moments_normalized(mu)
assert nu.dtype == expected_dtype
hu = moments_hu(nu)
assert hu.dtype == expected_dtype
@pytest.mark.parametrize("dtype", [cp.float16, cp.float32, cp.float64])
def test_centroid(dtype):
image = cp.zeros((20, 20), dtype=dtype)
image[14, 14:16] = 1
image[15, 14:16] = 1 / 3
image_centroid = centroid(image)
if dtype == cp.float16:
rtol = 1e-3
elif dtype == cp.float32:
rtol = 1e-5
else:
rtol = 1e-7
assert_allclose(image_centroid, (14.25, 14.5), rtol=rtol)
@pytest.mark.parametrize("dtype", [cp.float16, cp.float32, cp.float64])
def test_inertia_tensor_2d(dtype):
image = cp.zeros((40, 40), dtype=dtype)
image[15:25, 5:35] = 1 # big horizontal rectangle (aligned with axis 1)
expected_dtype = _supported_float_type(image.dtype)
T = inertia_tensor(image)
assert T.dtype == expected_dtype
assert T[0, 0] > T[1, 1]
cp.testing.assert_allclose(T[0, 1], 0)
v0, v1 = inertia_tensor_eigvals(image, T=T)
assert v0.dtype == expected_dtype
assert v1.dtype == expected_dtype
cp.testing.assert_allclose(cp.sqrt(v0 / v1), 3, rtol=0.01, atol=0.05)
def test_inertia_tensor_3d():
image = cp.asarray(draw.ellipsoid(10, 5, 3))
T0 = inertia_tensor(image)
eig0, V0 = np.linalg.eig(cp.asnumpy(T0))
# principal axis of ellipse = eigenvector of smallest eigenvalue
v0 = cp.asarray(V0[:, np.argmin(eig0)])
assert cp.allclose(v0, [1, 0, 0]) or cp.allclose(-v0, [1, 0, 0])
imrot = ndi.rotate(image.astype(float), 30, axes=(0, 1), order=1)
Tr = inertia_tensor(imrot)
eigr, Vr = np.linalg.eig(cp.asnumpy(Tr))
vr = cp.asarray(Vr[:, np.argmin(eigr)])
# Check that axis has rotated by expected amount
pi, cos, sin = np.pi, np.cos, np.sin
# fmt: off
R = cp.array([[cos(pi/6), -sin(pi/6), 0], # noqa
[sin(pi/6), cos(pi/6), 0], # noqa
[ 0, 0, 1]]) # noqa
# fmt: on
expected_vr = R @ v0
assert cp.allclose(vr, expected_vr, atol=1e-3, rtol=0.01) or cp.allclose(
-vr, expected_vr, atol=1e-3, rtol=0.01
)
def test_inertia_tensor_eigvals():
# Floating point precision problems could make a positive
# semidefinite matrix have an eigenvalue that is very slightly
# negative. Check that we have caught and fixed this problem.
# fmt: off
image = cp.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]])
# fmt: on
# mu = np.array([[3, 0, 98], [0, 14, 0], [2, 0, 98]])
eigvals = inertia_tensor_eigvals(image=image)
assert min(eigvals) >= 0
| 0 |
rapidsai_public_repos/cucim/python
|
rapidsai_public_repos/cucim/python/pybind11/macros.h
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_MACRO_H
#define PYCUCIM_MACRO_H
#include <string>
#include <fmt/format.h>
constexpr const char* remove_leading_spaces(const char* str)
{
return *str == '\0' ? str : ((*str == ' ' || *str == '\n') ? remove_leading_spaces(str + 1) : str);
}
#define PYDOC(method, doc) static constexpr const char* doc_##method = remove_leading_spaces(doc);
#endif // PYCUCIM_MACRO_H
| 0 |
rapidsai_public_repos/cucim/python
|
rapidsai_public_repos/cucim/python/pybind11/cucim_pydoc.h
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CUCIM_PYDOC_H
#define PYCUCIM_CUCIM_PYDOC_H
#include <string>
#include "macros.h"
namespace cucim::doc
{
namespace DLDataType
{
// Constructor
PYDOC(DLDataType, R"doc(
Constructor for `DLDataType`.
)doc")
// uint8_t code;
PYDOC(code, R"doc(
Type code of base types.
)doc")
// uint8_t bits;
PYDOC(bits, R"doc(
Number of bits, common choices are 8, 16, 32.
)doc")
// uint16_t lanes;
PYDOC(lanes, R"doc(
Number of lanes in the type, used for vector types.
)doc")
} // namespace DLDataType
namespace CuImage
{
// CuImage(const filesystem::Path& path);
PYDOC(CuImage, R"doc(
Constructor of CuImage.
)doc")
// CuImage(const filesystem::Path& path, const std::string& plugin_name);
// CuImage(const CuImage& cuimg) = delete;
// CuImage(CuImage&& cuimg);
//
// ~CuImage();
// std::shared_ptr<cache::ImageCache> CuImage::cache()
PYDOC(cache, R"doc(
Get cache object.
)doc")
// std::shared_ptr<profiler::Profiler> CuImage::profiler()
PYDOC(profiler, R"doc(
Get profiler object.
)doc")
// bool CuImage::is_trace_enabled()
PYDOC(is_trace_enabled, R"doc(
Return whether if the tracing is enabled or not.
)doc")
// filesystem::Path path() const;
PYDOC(path, R"doc(
Underlying file path for this object.
)doc")
// bool is_loaded() const;
PYDOC(is_loaded, R"doc(
True if image data is loaded & available.
)doc")
// io::Device device() const;
PYDOC(device, R"doc(
A device type.
By default t is `cpu` (It will be changed since v0.19.0).
)doc")
// Metadata metadata() const;
PYDOC(raw_metadata, R"doc(
A raw metadata string.
)doc")
// Metadata metadata() const;
PYDOC(metadata, R"doc(
A metadata object as `dict`.
It would be a dictionary(key-value pair) in general but can be a complex object (e.g., OME-TIFF metadata).
)doc")
// uint16_t ndim() const;
PYDOC(ndim, R"doc(
The number of dimensions.
)doc")
// std::string dims() const;
PYDOC(dims, R"doc(
A string containing a list of dimensions being requested.
The default is to return the six standard dims ('STCZYX') unless it is a DP multi-resolution image.
[sites, time, channel(or wavelength), z, y, x]. S - Sites or multiposition locations.
NOTE: in OME-TIFF's metadata, dimension order would be specified as 'XYZCTS' (first one is fast-iterating dimension).
)doc")
// Shape shape() const;
PYDOC(shape, R"doc(
A tuple of dimension sizes (in the order of `dims`)
)doc")
// std::vector<int64_t> size(std::string dim_order) const;
PYDOC(size, R"doc(
Returns size as a tuple for the given dimension order.
)doc")
// DLDataType dtype() const;
PYDOC(dtype, R"doc(
The data type of the image.
)doc")
// DLDataType typestr() const;
PYDOC(typestr, R"doc(
The data type of the image in string format.
The value can be converted to NumPy's dtype using `numpy.dtype()`.
(e.g., `numpy.dtype(img.typestr)`).
)doc")
// std::vector<std::string> channel_names() const;
PYDOC(channel_names, R"doc(
A channel name list.
)doc")
// std::vector<float> spacing(std::string dim_order = std::string{}) const;
PYDOC(spacing, R"doc(
Returns physical size in tuple.
If `dim_order` is specified, it returns physical size for the dimensions.
If a dimension given by the `dim_order` doesn't exist, it returns 1.0 by default for the missing dimension.
Args:
dim_order: A dimension string (e.g., 'XYZ')
Returns:
A tuple with physical size for each dimension
)doc")
// std::vector<std::string> spacing_units(std::string dim_order = std::string{}) const;
PYDOC(spacing_units, R"doc(
Units for each spacing element (size is same with `ndim`).
)doc")
// std::array<float, 3> origin() const;
PYDOC(origin, R"doc(
Physical location of (0, 0, 0) (size is always 3).
)doc")
// std::array<std::array<float, 3>, 3> direction() const;
PYDOC(direction, R"doc(
Direction cosines (size is always 3x3).
)doc")
// std::string coord_sys() const;
PYDOC(coord_sys, R"doc(
Coordinate frame in which the direction cosines are measured.
Available Coordinate frame names are not finalized yet.
)doc") // (either `LPS`(ITK/DICOM) or `RAS`(NIfTI/3D Slicer)). (either `LPS`(ITK/DICOM) or `RAS`(NIfTI/3D Slicer)).
// ResolutionInfo resolutions() const;
PYDOC(resolutions, R"doc(
Returns a dict that includes resolution information.
- level_count: The number of levels
- level_dimensions: A tuple of dimension tuples (width, height)
- level_downsamples: A tuple of down-sample factors
- level_tile_sizes: A tuple of tile size tuple (tile width, tile_height)
)doc")
// dlpack::DLTContainer container() const;
// CuImage read_region(std::vector<int64_t> location,
// std::vector<int64_t> size,
// uint16_t level=0,
// DimIndices region_dim_indices={},
// io::Device device="cpu",
// DLTensor* buf=nullptr,
// std::string shm_name="");
PYDOC(read_region, R"doc(
Returns a subresolution image.
- `location` and `size`'s dimension order is reverse of image's dimension order.
- Need to specify (X,Y) and (Width, Height) instead of (Y,X) and (Height, Width).
- If location is not specified, location would be (0, 0) if Z=0. Otherwise, location would be (0, 0, 0)
- Like OpenSlide, location is level-0 based coordinates (using the level-0 reference frame)
- If `size` is not specified, size would be (width, height) of the image at the specified `level`.
- `<not supported yet>` Additional parameters (S,T,C,Z) are similar to <https://allencellmodeling.github.io/aicsimageio/aicsimageio.html#aicsimageio.aics_image.AICSImage.get_image_data>
- Do not yet support indices/ranges for (S,T,C,Z).
- Default value for level, S, T, Z are zero.
- Default value for C is -1 (whole channels)
- `<not supported yet>` `device` could be one of the following strings or Device object: e.g., `'cpu'`, `'cuda'`, `'cuda:0'` (use index 0), `cucim.clara.io.Device(cucim.clara.io.CUDA,0)`.
- `<not supported yet>` If `buf` is specified (buf's type can be either numpy object that implements `__array_interface__`, or cupy-compatible object that implements `__cuda_array_interface__`), the read image would be saved into buf object without creating CPU/GPU memory.
- `<not supported yet>` If `shm_name` is specified, shared memory would be created and data would be read in the shared memory.
)doc")
// std::set<std::string> associated_images() const;
PYDOC(associated_images, R"doc(
Returns a set of associated image names.
Digital Pathology image usually has a label/thumbnail or a macro image(low-power snapshot of the entire glass slide).
Names of those images (such as 'macro' and 'label') are in `associated_images`.
)doc")
// CuImage associated_image(const std::string& name) const;
PYDOC(associated_image, R"doc(
Returns an associated image for the given name, as a CuImage object.
)doc")
// void save(std::string file_path) const;
PYDOC(save, R"doc(
Saves image data to the file path.
Currently it supports only .ppm file format that can be viewed by `eog` command in Ubuntu.
)doc")
// void close();
PYDOC(close, R"doc(
Closes the file handle.
Once the file handle is closed, the image object (if loaded before) still exists but cannot read additional images
from the file.
)doc")
// void _set_array_interface(const CuImage& cuimg);
PYDOC(_set_array_interface, R"doc(
Add `__array_interface__` or `__cuda_array_interface__` depending on the memory type.
Args:
cuimg: CuImage object
Returns:
None
)doc")
}; // namespace CuImage
namespace CuImageIterator
{
// CuImageIterator(std::shared_ptr<DataType> cuimg, bool ending = false);
PYDOC(CuImageIterator, R"doc(
Constructor of CuImageIterator.
)doc")
} // namespace CuImageIterator
} // namespace cucim::doc
#endif // PYCUCIM_CUCIM_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python
|
rapidsai_public_repos/cucim/python/pybind11/cucim_py.h
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CUIMAGE_PY_H
#define PYCUCIM_CUIMAGE_PY_H
#include <vector>
#include <nlohmann/json.hpp>
#include <pybind11_json/pybind11_json.hpp>
using json = nlohmann::json;
namespace cucim
{
// Forward declarations
class CuImage;
template <typename DataType = CuImage>
class CuImageIterator;
namespace io
{
class Device;
}
namespace cache
{
class ImageCache;
}
namespace profiler
{
class Profiler;
}
std::string get_plugin_root();
void set_plugin_root(std::string path);
/**
* Converts an object with std::vector type to one with pybind11::tuple type.
*
* The code is derived from `make_tuple()` method in pybind11/cast.h which is under BSD-3-Clause License.
* Please see LICENSE-3rdparty.md for the detail.
* (https://github.com/pybind/pybind11/blob/993495c96c869c5d3f3266c3ed3b1b8439340fd2/include/pybind11/cast.h#L1817)
*
* @tparam PT Python type
* @tparam T Vector type
* @param vec A vector object to convert
* @return An object of pybind11::tuple type to which `vec` is converted
*/
template<typename PT, typename T>
pybind11::tuple vector2pytuple(const std::vector<T>& vec);
std::shared_ptr<cucim::cache::ImageCache> py_cache(const py::object& ctype, const py::kwargs& kwargs);
std::shared_ptr<cucim::profiler::Profiler> py_profiler(const py::kwargs& kwargs);
bool py_is_trace_enabled(py::object /* self */);
json py_metadata(const CuImage& cuimg);
py::dict py_resolutions(const CuImage& cuimg);
py::object py_read_region(const CuImage& cuimg,
const py::iterable& location,
std::vector<int64_t>&& size,
int16_t level,
uint32_t num_workers,
uint32_t batch_size,
bool drop_last,
uint32_t prefetch_factor,
bool shuffle,
uint64_t seed,
const io::Device& device,
const py::object& buf,
const std::string& shm_name,
const py::kwargs& kwargs);
py::object py_associated_image(const CuImage& cuimg, const std::string& name, const io::Device& device);
py::object py_cuimage_iterator_next(CuImageIterator<CuImage>& it);
void _set_array_interface(const py::object& cuimg_obj);
} // namespace cucim
#endif // PYCUCIM_CUIMAGE_PY_H
| 0 |
rapidsai_public_repos/cucim/python
|
rapidsai_public_repos/cucim/python/pybind11/cucim_py.cpp
|
/*
* Copyright (c) 2020-2023, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "cucim_py.h"
#include "cucim_pydoc.h"
#include <fmt/format.h>
#include <fmt/ranges.h>
#include <pybind11/numpy.h>
#include <pybind11/operators.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <cucim/cuimage.h>
#include "cache/cache_py.h"
#include "filesystem/filesystem_py.h"
#include "io/io_py.h"
#include "profiler/profiler_py.h"
using namespace pybind11::literals;
namespace py = pybind11;
namespace cucim
{
static const std::unordered_map<uint8_t, const char*> g_dldata_typecode{
{ kDLInt, "DLInt" },
{ kDLUInt, "DLUInt" },
{ kDLFloat, "DLFloat" },
{ kDLBfloat, "DLBfloat" },
};
PYBIND11_MODULE(_cucim, m)
{
// clang-format off
#ifdef CUCIM_VERSION
# define XSTR(x) STR(x)
# define STR(x) #x
// Set version
m.attr("__version__") = XSTR(CUCIM_VERSION);
#endif
// clang-format on
// Get/set plugin root path
m.def("_get_plugin_root", &get_plugin_root);
m.def("_set_plugin_root", &set_plugin_root);
// Submodule: io
auto m_io = m.def_submodule("io");
io::init_io(m_io);
// Submodule: filesystem
auto m_fs = m.def_submodule("filesystem");
filesystem::init_filesystem(m_fs);
// Submodule: cache
auto m_cache = m.def_submodule("cache");
cache::init_cache(m_cache);
// Submodule: profiler
auto m_profiler = m.def_submodule("profiler");
profiler::init_profiler(m_profiler);
// Data structures
py::enum_<DLDataTypeCode>(m, "DLDataTypeCode") //
.value("DLInt", kDLInt) //
.value("DLUInt", kDLUInt) //
.value("DLFloat", kDLFloat) //
.value("DLBfloat", kDLBfloat);
py::class_<DLDataType>(m, "DLDataType") //
.def(py::init([](DLDataTypeCode code, uint8_t bits, uint16_t lanes) {
auto ctr = std::make_unique<DLDataType>();
ctr->code = static_cast<uint8_t>(code);
ctr->bits = bits;
ctr->lanes = lanes;
return ctr;
}),
doc::DLDataType::doc_DLDataType, py::call_guard<py::gil_scoped_release>())
.def_readonly("code", &DLDataType::code, doc::DLDataType::doc_code, py::call_guard<py::gil_scoped_release>()) //
.def_readonly("bits", &DLDataType::bits, doc::DLDataType::doc_bits, py::call_guard<py::gil_scoped_release>()) //
.def_readonly("lanes", &DLDataType::lanes, doc::DLDataType::doc_lanes, py::call_guard<py::gil_scoped_release>()) //
.def(py::self == py::self) //
.def(py::self != py::self) //
.def(
"__repr__",
[](const DLDataType& dtype) {
return fmt::format("<cucim.clara.DLDataType code:{}({}) bits:{} lanes:{}>",
g_dldata_typecode.at(dtype.code), dtype.code, dtype.bits, dtype.lanes);
},
py::call_guard<py::gil_scoped_release>());
py::class_<CuImage, std::shared_ptr<CuImage>>(m, "CuImage", py::dynamic_attr()) //
.def(py::init<const std::string&>(), doc::CuImage::doc_CuImage, py::call_guard<py::gil_scoped_release>(), //
py::arg("path")) //
.def_static("cache", &py_cache, doc::CuImage::doc_cache, //
py::arg("type") = py::none()) //
.def_static("profiler", &py_profiler, doc::CuImage::doc_profiler, py::call_guard<py::gil_scoped_release>()) //
.def_property_readonly_static("is_trace_enabled", &py_is_trace_enabled, doc::CuImage::doc_is_trace_enabled,
py::call_guard<py::gil_scoped_release>()) //);
// Do not release GIL
.def_static("_set_array_interface", &_set_array_interface, doc::CuImage::doc__set_array_interface, //
py::arg("cuimg") = py::none()) //
.def_property("path", &CuImage::path, nullptr, doc::CuImage::doc_path, py::call_guard<py::gil_scoped_release>()) //
.def_property("is_loaded", &CuImage::is_loaded, nullptr, doc::CuImage::doc_is_loaded,
py::call_guard<py::gil_scoped_release>()) //
.def_property(
"device", &CuImage::device, nullptr, doc::CuImage::doc_device, py::call_guard<py::gil_scoped_release>()) //
.def_property("raw_metadata", &CuImage::raw_metadata, nullptr, doc::CuImage::doc_raw_metadata,
py::call_guard<py::gil_scoped_release>()) //
.def_property(
"metadata", &py_metadata, nullptr, doc::CuImage::doc_metadata, py::call_guard<py::gil_scoped_release>()) //
.def_property("ndim", &CuImage::ndim, nullptr, doc::CuImage::doc_ndim, py::call_guard<py::gil_scoped_release>()) //
.def_property("dims", &CuImage::dims, nullptr, doc::CuImage::doc_dims, py::call_guard<py::gil_scoped_release>()) //
.def_property(
"shape", &CuImage::shape, nullptr, doc::CuImage::doc_shape, py::call_guard<py::gil_scoped_release>()) //
.def("size", &CuImage::size, doc::CuImage::doc_size, py::call_guard<py::gil_scoped_release>(), //
py::arg("dim_order") = "" //
) //
.def_property(
"dtype", &CuImage::dtype, nullptr, doc::CuImage::doc_dtype, py::call_guard<py::gil_scoped_release>()) //
.def_property("typestr", &CuImage::typestr, nullptr, doc::CuImage::doc_typestr,
py::call_guard<py::gil_scoped_release>()) //
.def_property("channel_names", &CuImage::channel_names, nullptr, doc::CuImage::doc_channel_names,
py::call_guard<py::gil_scoped_release>()) //
.def("spacing", &CuImage::spacing, doc::CuImage::doc_spacing, py::call_guard<py::gil_scoped_release>(), //
py::arg("dim_order") = "" //
) //
.def("spacing_units", &CuImage::spacing_units, doc::CuImage::doc_spacing_units,
py::call_guard<py::gil_scoped_release>(), //
py::arg("dim_order") = "" //
) //
.def_property(
"origin", &CuImage::origin, nullptr, doc::CuImage::doc_origin, py::call_guard<py::gil_scoped_release>()) //
.def_property("direction", &CuImage::direction, nullptr, doc::CuImage::doc_direction,
py::call_guard<py::gil_scoped_release>()) //
.def_property("coord_sys", &CuImage::coord_sys, nullptr, doc::CuImage::doc_coord_sys,
py::call_guard<py::gil_scoped_release>()) //
.def_property("resolutions", &py_resolutions, nullptr, doc::CuImage::doc_resolutions,
py::call_guard<py::gil_scoped_release>()) //
.def("read_region", &py_read_region, doc::CuImage::doc_read_region, py::call_guard<py::gil_scoped_release>(), //
py::arg("location") = py::tuple{}, //
py::arg("size") = py::tuple{}, //
py::arg("level") = 0, //
py::arg("num_workers") = 0, //
py::arg("batch_size") = 1, //
py::arg("drop_last") = py::bool_(false), //
py::arg("prefetch_factor") = 2, //
py::arg("shuffle") = py::bool_(false), //
py::arg("seed") = py::int_(0), //
py::arg("device") = io::Device(), //
py::arg("buf") = py::none(), //
py::arg("shm_name") = "") //
.def_property("associated_images", &CuImage::associated_images, nullptr, doc::CuImage::doc_associated_images,
py::call_guard<py::gil_scoped_release>()) //
.def("associated_image", &py_associated_image, doc::CuImage::doc_associated_image,
py::call_guard<py::gil_scoped_release>(), //
py::arg("name") = "", //
py::arg("device") = io::Device()) //
.def("save", &CuImage::save, doc::CuImage::doc_save, py::call_guard<py::gil_scoped_release>()) //
.def("close", &CuImage::close, doc::CuImage::doc_close, py::call_guard<py::gil_scoped_release>()) //
.def("__bool__", &CuImage::operator bool, py::call_guard<py::gil_scoped_release>()) //
.def(
"__iter__", //
[](const std::shared_ptr<CuImage>& cuimg) { //
return cuimg->begin(); //
}, //
py::call_guard<py::gil_scoped_release>())
.def(
"__enter__",
[](const std::shared_ptr<CuImage>& cuimg) { //
return cuimg; //
}, //
py::call_guard<py::gil_scoped_release>())
.def(
"__exit__",
[](const std::shared_ptr<CuImage>& cuimg, const py::object& type, const py::object& value,
const py::object& traceback) { //
cuimg->close(); //
}, //
py::call_guard<py::gil_scoped_release>())
.def(
"__repr__", //
[](const CuImage& cuimg) { //
return fmt::format("<cucim.CuImage path:{}>", cuimg.path());
},
py::call_guard<py::gil_scoped_release>());
py::class_<CuImageIterator<CuImage>, std::shared_ptr<CuImageIterator<CuImage>>>(m, "CuImageIterator") //
.def(py::init<std::shared_ptr<CuImage>, bool>(), doc::CuImageIterator::doc_CuImageIterator,
py::arg("cuimg"), //
py::arg("ending") = false, py::call_guard<py::gil_scoped_release>())
.def(
"__len__",
[](const CuImageIterator<CuImage>& it) { //
return it.size(); //
}, //
py::call_guard<py::gil_scoped_release>())
.def(
"__iter__", //
[](CuImageIterator<CuImage>* it) { //
return it; //
}, //
py::call_guard<py::gil_scoped_release>())
.def("__next__", &py_cuimage_iterator_next, py::call_guard<py::gil_scoped_release>())
.def(
"__repr__", //
[](CuImageIterator<CuImage>& it) { //
return fmt::format("<cucim.CuImageIterator index:{}>", it.index());
},
py::call_guard<py::gil_scoped_release>());
// We can use `"cpu"` instead of `Device("cpu")`
py::implicitly_convertible<const char*, io::Device>();
}
std::string get_plugin_root()
{
return CuImage::get_framework()->get_plugin_root();
}
void set_plugin_root(std::string path)
{
CuImage::get_framework()->set_plugin_root(path.c_str());
}
template <typename PT, typename T>
pybind11::tuple vector2pytuple(const std::vector<T>& vec)
{
py::tuple result(vec.size());
std::vector<pybind11::object> args;
args.reserve(vec.size());
for (auto& arg_value : vec)
{
args.emplace_back(pybind11::reinterpret_steal<pybind11::object>(pybind11::detail::make_caster<PT>::cast(
std::forward<PT>(arg_value), pybind11::return_value_policy::automatic_reference, nullptr)));
}
int counter = 0;
for (auto& arg_value : args)
{
PyTuple_SET_ITEM(result.ptr(), counter++, arg_value.release().ptr());
}
return result;
}
std::shared_ptr<cucim::cache::ImageCache> py_cache(const py::object& type, const py::kwargs& kwargs)
{
if (py::isinstance<py::str>(type))
{
std::string ctype = std::string(py::cast<py::str>(type));
cucim::cache::CacheType cache_type = cucim::cache::lookup_cache_type(ctype);
// Copy default cache config to local
cucim::cache::ImageCacheConfig config = cucim::CuImage::get_config()->cache();
config.type = cache_type;
if (kwargs.contains("memory_capacity"))
{
config.memory_capacity = py::cast<uint32_t>(kwargs["memory_capacity"]);
}
if (kwargs.contains("capacity"))
{
config.capacity = py::cast<uint32_t>(kwargs["capacity"]);
}
else
{
// Update capacity depends on memory_capacity.
config.capacity = cucim::cache::calc_default_cache_capacity(cucim::cache::kOneMiB * config.memory_capacity);
}
if (kwargs.contains("mutex_pool_capacity"))
{
config.mutex_pool_capacity = py::cast<uint32_t>(kwargs["mutex_pool_capacity"]);
}
if (kwargs.contains("list_padding"))
{
config.list_padding = py::cast<uint32_t>(kwargs["list_padding"]);
}
if (kwargs.contains("extra_shared_memory_size"))
{
config.extra_shared_memory_size = py::cast<uint32_t>(kwargs["extra_shared_memory_size"]);
}
if (kwargs.contains("record_stat"))
{
config.record_stat = py::cast<bool>(kwargs["record_stat"]);
}
return CuImage::cache(config);
}
else if (type.is_none())
{
return CuImage::cache();
}
throw std::invalid_argument(
fmt::format("The first argument should be one of ['nocache', 'per_process', 'shared_memory']."));
}
std::shared_ptr<cucim::profiler::Profiler> py_profiler(const py::kwargs& kwargs)
{
if (kwargs.empty())
{
return CuImage::profiler();
}
else
{
// Copy default profiler config to local
cucim::profiler::ProfilerConfig config = cucim::CuImage::get_config()->profiler();
if (kwargs.contains("trace"))
{
config.trace = py::cast<bool>(kwargs["trace"]);
}
return CuImage::profiler(config);
}
}
bool py_is_trace_enabled(py::object /* self */)
{
return CuImage::is_trace_enabled();
}
json py_metadata(const CuImage& cuimg)
{
auto metadata = cuimg.metadata();
auto json_obj = json::parse(metadata.empty() ? "{}" : metadata);
// Append basic metadata for the image
auto item_iter = json_obj.emplace("cucim", json::object());
json& cucim_metadata = *(item_iter.first);
cucim_metadata.emplace("path", cuimg.path());
cucim_metadata.emplace("ndim", cuimg.ndim());
cucim_metadata.emplace("dims", cuimg.dims());
cucim_metadata.emplace("shape", cuimg.shape());
{
const auto& dtype = cuimg.dtype();
cucim_metadata.emplace(
"dtype", json::object({ { "code", dtype.code }, { "bits", dtype.bits }, { "lanes", dtype.lanes } }));
}
cucim_metadata.emplace("typestr", cuimg.typestr());
cucim_metadata.emplace("channel_names", cuimg.channel_names());
cucim_metadata.emplace("spacing", cuimg.spacing());
cucim_metadata.emplace("spacing_units", cuimg.spacing_units());
cucim_metadata.emplace("origin", cuimg.origin());
cucim_metadata.emplace("direction", cuimg.direction());
cucim_metadata.emplace("coord_sys", cuimg.coord_sys());
{
const auto& resolutions = cuimg.resolutions();
auto resolutions_iter = cucim_metadata.emplace("resolutions", json::object());
json& resolutions_metadata = *(resolutions_iter.first);
auto level_count = resolutions.level_count();
resolutions_metadata.emplace("level_count", level_count);
std::vector<std::vector<int64_t>> level_dimensions_vec;
level_dimensions_vec.reserve(level_count);
for (int level = 0; level < level_count; ++level)
{
level_dimensions_vec.emplace_back(resolutions.level_dimension(level));
}
resolutions_metadata.emplace("level_dimensions", level_dimensions_vec);
resolutions_metadata.emplace("level_downsamples", resolutions.level_downsamples());
std::vector<std::vector<uint32_t>> level_tile_sizes_vec;
level_tile_sizes_vec.reserve(level_count);
for (int level = 0; level < level_count; ++level)
{
level_tile_sizes_vec.emplace_back(resolutions.level_tile_size(level));
}
resolutions_metadata.emplace("level_tile_sizes", level_tile_sizes_vec);
}
cucim_metadata.emplace("associated_images", cuimg.associated_images());
return json_obj;
}
py::dict py_resolutions(const CuImage& cuimg)
{
const auto& resolutions = cuimg.resolutions();
auto level_count = resolutions.level_count();
if (resolutions.level_count() == 0)
{
return py::dict{
"level_count"_a = pybind11::int_(0), //
"level_dimensions"_a = pybind11::tuple(), //
"level_downsamples"_a = pybind11::tuple(), //
"level_tile_sizes"_a = pybind11::tuple() //
};
}
std::vector<py::tuple> level_dimensions_vec;
level_dimensions_vec.reserve(level_count);
std::vector<py::tuple> level_tile_sizes_vec;
level_tile_sizes_vec.reserve(level_count);
for (int level = 0; level < level_count; ++level)
{
level_dimensions_vec.emplace_back(vector2pytuple<pybind11::int_>(resolutions.level_dimension(level)));
level_tile_sizes_vec.emplace_back(vector2pytuple<pybind11::int_>(resolutions.level_tile_size(level)));
}
py::tuple level_dimensions = vector2pytuple<const pybind11::tuple&>(level_dimensions_vec);
py::tuple level_downsamples = vector2pytuple<pybind11::float_>(resolutions.level_downsamples());
py::tuple level_tile_sizes = vector2pytuple<const pybind11::tuple&>(level_tile_sizes_vec);
return py::dict{
"level_count"_a = pybind11::int_(level_count), //
"level_dimensions"_a = level_dimensions, //
"level_downsamples"_a = level_downsamples, //
"level_tile_sizes"_a = level_tile_sizes //
};
}
py::object py_read_region(const CuImage& cuimg,
const py::iterable& location,
std::vector<int64_t>&& size,
int16_t level,
uint32_t num_workers,
uint32_t batch_size,
bool drop_last,
uint32_t prefetch_factor,
bool shuffle,
uint64_t seed,
const io::Device& device,
const py::object& buf,
const std::string& shm_name,
const py::kwargs& kwargs)
{
if (!size.empty() && size.size() != 2)
{
throw std::runtime_error("size (patch size) should be 2!");
}
cucim::DimIndices indices;
std::vector<int64_t> locations;
{
py::gil_scoped_acquire scope_guard;
auto arr = pybind11::array_t<int64_t, py::array::c_style | py::array::forcecast>::ensure(location);
if (arr) // fast copy
{
py::buffer_info buf = arr.request();
int64_t* data_array = static_cast<int64_t*>(buf.ptr);
ssize_t data_size = buf.size;
locations.reserve(data_size);
locations.insert(locations.end(), &data_array[0], &data_array[data_size]);
}
else
{
auto iter = py::iter(location);
while (iter != py::iterator::sentinel())
{
if (py::isinstance<py::iterable>(*iter))
{
auto iter2 = py::iter(*iter);
while (iter2 != py::iterator::sentinel())
{
locations.emplace_back(py::cast<int64_t>(*iter2));
++iter2;
}
}
else
{
locations.emplace_back(py::cast<int64_t>(*iter));
}
++iter;
}
}
}
if (kwargs)
{
std::vector<std::pair<char, int64_t>> indices_args;
{
py::gil_scoped_acquire scope_guard;
for (auto item : kwargs)
{
auto key = std::string(py::str(item.first));
auto value = py::cast<int>(item.second);
if (key.size() != 1)
{
throw std::invalid_argument(
fmt::format("Argument name for Dimension should be a single character but '{}' is used.", key));
}
char key_char = key[0] & ~32;
if (key_char < 'A' || key_char > 'Z')
{
throw std::invalid_argument(
fmt::format("Dimension character should be an alphabet but '{}' is used.", key));
}
indices_args.emplace_back(std::make_pair(key_char, value));
}
}
indices = cucim::DimIndices(indices_args);
}
else
{
indices = cucim::DimIndices{};
}
auto region_ptr = std::make_shared<cucim::CuImage>(
std::move(cuimg.read_region(std::move(locations), std::move(size), level, num_workers, batch_size, drop_last,
prefetch_factor, shuffle, seed, indices, device, nullptr, "")));
auto loader = region_ptr->loader();
if (batch_size > 1 || (loader && loader->size() > 1))
{
auto iter_ptr = std::make_shared<CuImageIterator<CuImage>>(region_ptr->shared_from_this());
py::gil_scoped_acquire scope_guard;
py::object iter = py::cast(iter_ptr, py::return_value_policy::take_ownership);
return iter;
}
else
{
py::gil_scoped_acquire scope_guard;
py::object region = py::cast(region_ptr);
// Add `__array_interface__` or `__cuda_array_interface__` in runtime.
_set_array_interface(region);
return region;
}
}
py::object py_associated_image(const CuImage& cuimg, const std::string& name, const io::Device& device)
{
auto image_ptr = std::make_shared<cucim::CuImage>(cuimg.associated_image(name, device));
{
py::gil_scoped_acquire scope_guard;
py::object image = py::cast(image_ptr);
// Add `__array_interface__` or `__cuda_array_interface__` in runtime.
_set_array_interface(image);
return image;
}
}
py::object py_cuimage_iterator_next(CuImageIterator<CuImage>& it)
{
bool stop_iteration = (it.index() == it.size());
if (stop_iteration)
{
throw py::stop_iteration();
}
// Get the next batch of images.
++it;
auto cuimg = *it;
memory::DLTContainer container = cuimg->container();
DLTensor* tensor = static_cast<DLTensor*>(container);
cucim::loader::ThreadBatchDataLoader* loader = cuimg->loader();
{
py::gil_scoped_acquire scope_guard;
py::object cuimg_obj = py::cast(cuimg);
if (loader)
{
_set_array_interface(cuimg_obj);
}
return cuimg_obj;
}
}
void _set_array_interface(const py::object& cuimg_obj)
{
const auto& cuimg = cuimg_obj.cast<const CuImage&>();
// TODO: using __array_struct__, access to array interface could be faster
// (https://numpy.org/doc/stable/reference/arrays.interface.html#c-struct-access)
// TODO: check the performance difference between python int vs python long later.
loader::ThreadBatchDataLoader* loader = cuimg.loader();
memory::DLTContainer container = cuimg.container();
DLTensor* tensor = static_cast<DLTensor*>(container);
if (!tensor)
{
return;
}
if (loader)
{
// Get the last available (batch) image.
tensor->data = loader->data();
}
if (tensor->data)
{
const char* type_str = container.numpy_dtype();
py::str typestr = py::str(type_str);
py::tuple data = pybind11::make_tuple(py::int_(reinterpret_cast<uint64_t>(tensor->data)), py::bool_(false));
py::list descr;
descr.append(py::make_tuple(""_s, typestr));
py::tuple shape = vector2pytuple<pybind11::int_>(cuimg.shape());
// Depending on container's memory type, expose either array_interface or cuda_array_interface
switch (tensor->device.device_type)
{
case kDLCPU: {
// Reference: https://numpy.org/doc/stable/reference/arrays.interface.html
cuimg_obj.attr("__array_interface__") =
py::dict{ "data"_a = data, "strides"_a = py::none(), "descr"_a = descr,
"typestr"_a = typestr, "shape"_a = shape, "version"_a = py::int_(3) };
}
break;
case kDLCUDA: {
// Reference: https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html
cuimg_obj.attr("__cuda_array_interface__") =
py::dict{ "data"_a = data, "strides"_a = py::none(), "descr"_a = descr, "typestr"_a = typestr,
"shape"_a = shape, "version"_a = py::int_(3), "mask"_a = py::none(), "stream"_a = 1 };
}
break;
default:
break;
}
}
else
{
switch (tensor->device.device_type)
{
case kDLCPU: {
if (py::hasattr(cuimg_obj, "__array_interface__"))
{
py::delattr(cuimg_obj, "__array_interface__");
}
}
break;
case kDLCUDA: {
if (py::hasattr(cuimg_obj, "__cuda_array_interface__"))
{
py::delattr(cuimg_obj, "__cuda_array_interface__");
}
}
break;
default:
break;
}
}
}
} // namespace cucim
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/cache/cache_py.h
|
/*
* Copyright (c) 2021, NVIDIA CORPORATcacheN.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CACHE_INIT_H
#define PYCUCIM_CACHE_INIT_H
#include <optional>
#include <vector>
#include <pybind11/pybind11.h>
namespace py = pybind11;
namespace cucim::cache
{
// Forward declaration
class ImageCache;
void init_cache(py::module& m);
bool py_record(ImageCache& cache, py::object value);
py::dict py_config(ImageCache& cache);
void py_image_cache_reserve(ImageCache& cache, uint32_t memory_capacity, py::kwargs kwargs);
uint32_t py_preferred_memory_capacity(const py::object& img,
const std::optional<const std::vector<uint64_t>>& image_size,
const std::optional<const std::vector<uint32_t>>& tile_size,
const std::optional<const std::vector<uint32_t>>& patch_size,
uint32_t bytes_per_pixel);
} // namespace cucim::cache
#endif // PYCUCIM_CACHE_INIT_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/cache/image_cache_pydoc.h
|
/*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CACHE_IMAGE_CACHE_PYDOC_H
#define PYCUCIM_CACHE_IMAGE_CACHE_PYDOC_H
#include "../macros.h"
namespace cucim::cache::doc::ImageCache
{
// PYDOC(ImageCache, R"doc(
// Constructor of ImageCache.
// Args:
// )doc")
// virtual CacheType type() const;
PYDOC(type, R"doc(
A Cache type.
)doc")
PYDOC(config, R"doc(
Returns the dictionary of configuration.
)doc")
// virtual uint32_t size() const = 0;
PYDOC(size, R"doc(
A size of list/hashmap.
)doc")
// virtual uint64_t memory_size() const = 0;
PYDOC(memory_size, R"doc(
A size of cache memory used.
)doc")
// virtual uint32_t capacity() const = 0;
PYDOC(capacity, R"doc(
A capacity of list/hashmap.
)doc")
// virtual uint64_t memory_capacity() const = 0;
PYDOC(memory_capacity, R"doc(
A capacity of cache memory.
)doc")
// virtual uint64_t free_memory() const = 0;
PYDOC(free_memory, R"doc(
A cache memory size available in the cache memory.
)doc")
// virtual void record(bool value) = 0;
// virtual bool record() const = 0;
PYDOC(record, R"doc(
Records the cache statistics.
)doc")
// virtual uint64_t hit_count() const = 0;
PYDOC(hit_count, R"doc(
A cache hit count.
)doc")
// virtual uint64_t miss_count() const = 0;
PYDOC(miss_count, R"doc(
A cache miss count.
)doc")
// virtual void reserve(const ImageCacheConfig& config) = 0;
PYDOC(reserve, R"doc(
Reserves more memory if possible.
)doc")
} // namespace cucim::cache::doc::ImageCache
#endif // PYCUCIM_CACHE_IMAGE_CACHE_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/cache/cache_py.cpp
|
/*
* Copyright (c) 2021-2023, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "cache_py.h"
#include "cache_pydoc.h"
#include <pybind11/stl.h>
#include <cucim/cache/image_cache.h>
#include <cucim/cuimage.h>
#include "image_cache_py.h"
#include "image_cache_pydoc.h"
using namespace pybind11::literals;
namespace py = pybind11;
namespace cucim::cache
{
void init_cache(py::module& cache)
{
py::enum_<CacheType>(cache, "CacheType") //
.value("NoCache", CacheType::kNoCache) //
.value("PerProcess", CacheType::kPerProcess) //
.value("SharedMemory", CacheType::kSharedMemory);
py::class_<ImageCache, std::shared_ptr<ImageCache>>(cache, "ImageCache")
.def_property(
"type", &ImageCache::type, nullptr, doc::ImageCache::doc_type, py::call_guard<py::gil_scoped_release>())
.def_property(
"config", &py_config, nullptr, doc::ImageCache::doc_config, py::call_guard<py::gil_scoped_release>())
.def_property(
"size", &ImageCache::size, nullptr, doc::ImageCache::doc_size, py::call_guard<py::gil_scoped_release>())
.def_property("memory_size", &ImageCache::memory_size, nullptr, doc::ImageCache::doc_memory_size,
py::call_guard<py::gil_scoped_release>())
.def_property("capacity", &ImageCache::capacity, nullptr, doc::ImageCache::doc_capacity,
py::call_guard<py::gil_scoped_release>())
.def_property("memory_capacity", &ImageCache::memory_capacity, nullptr, doc::ImageCache::doc_memory_capacity,
py::call_guard<py::gil_scoped_release>())
.def_property("free_memory", &ImageCache::free_memory, nullptr, doc::ImageCache::doc_free_memory,
py::call_guard<py::gil_scoped_release>())
.def("record", &py_record, doc::ImageCache::doc_record, //
py::arg("value") = py::none())
.def_property("hit_count", &ImageCache::hit_count, nullptr, doc::ImageCache::doc_hit_count,
py::call_guard<py::gil_scoped_release>())
.def_property("miss_count", &ImageCache::miss_count, nullptr, doc::ImageCache::doc_miss_count,
py::call_guard<py::gil_scoped_release>())
.def("reserve", &py_image_cache_reserve, doc::ImageCache::doc_reserve, //
py::arg("memory_capacity"));
cache.def("preferred_memory_capacity", &py_preferred_memory_capacity, doc::doc_preferred_memory_capacity, //
py::arg("img") = py::none(), //
py::arg("image_size") = std::nullopt, //
py::arg("tile_size") = std::nullopt, //
py::arg("patch_size") = std::nullopt, //
py::arg("bytes_per_pixel") = 3);
}
bool py_record(ImageCache& cache, py::object value)
{
if (value.is_none())
{
return cache.record();
}
else if (py::isinstance<py::bool_>(value))
{
py::bool_ v = value.cast<py::bool_>();
cache.record(v);
return v;
}
else
{
throw std::invalid_argument(fmt::format("Only 'NoneType' or 'bool' is available for the argument"));
}
}
py::dict py_config(ImageCache& cache)
{
ImageCacheConfig& config = cache.config();
return py::dict{
"type"_a = pybind11::str(std::string(lookup_cache_type_str(config.type))), //
"memory_capacity"_a = pybind11::int_(config.memory_capacity), //
"capacity"_a = pybind11::int_(config.capacity), //
"mutex_pool_capacity"_a = pybind11::int_(config.mutex_pool_capacity), //
"list_padding"_a = pybind11::int_(config.list_padding), //
"extra_shared_memory_size"_a = pybind11::int_(config.extra_shared_memory_size), //
"record_stat"_a = pybind11::bool_(config.record_stat) //
};
}
void py_image_cache_reserve(ImageCache& cache, uint32_t memory_capacity, py::kwargs kwargs)
{
cucim::cache::ImageCacheConfig config = cucim::CuImage::get_config()->cache();
config.memory_capacity = memory_capacity;
if (kwargs.contains("capacity"))
{
config.capacity = py::cast<uint32_t>(kwargs["capacity"]);
}
else
{
// Update capacity depends on memory_capacity.
config.capacity = calc_default_cache_capacity(kOneMiB * memory_capacity);
}
cache.reserve(config);
}
uint32_t py_preferred_memory_capacity(const py::object& img,
const std::optional<const std::vector<uint64_t>>& image_size,
const std::optional<const std::vector<uint32_t>>& tile_size,
const std::optional<const std::vector<uint32_t>>& patch_size,
uint32_t bytes_per_pixel)
{
std::vector<uint64_t> param_image;
std::vector<uint32_t> param_tile;
std::vector<uint32_t> param_patch;
if (!img.is_none())
{
const CuImage& cuimg = *img.cast<cucim::CuImage*>();
std::vector<int64_t> image_size_vec = cuimg.size("XY");
param_image.insert(param_image.end(), image_size_vec.begin(), image_size_vec.end());
std::vector<uint32_t> tile_size_vec = cuimg.resolutions().level_tile_size(0);
param_tile.insert(param_tile.end(), tile_size_vec.begin(), tile_size_vec.end());
// Calculate pixel size in bytes
// (For example, if axes == "YXC" or "YXS", calculate [bytes per pixel] * [# items inside dims after 'X'] )
std::string dims = cuimg.dims();
std::size_t pivot = std::max(dims.rfind('X'), dims.rfind('Y'));
if (pivot == std::string::npos)
{
bytes_per_pixel = 3;
}
else
{
if (pivot < dims.size())
{
std::vector<int64_t> size_vec = cuimg.size(&dims.c_str()[pivot + 1]);
int64_t item_count = 1;
for (auto size : size_vec)
{
item_count *= size;
}
bytes_per_pixel = (cuimg.dtype().bits * item_count + 7) / 8;
}
}
}
else
{
if (!image_size || image_size->size() != 2)
{
throw std::invalid_argument(
fmt::format("Please specify 'image_size' parameter (e.g., 'image_size=(100000, 100000)')!"));
}
if (!tile_size || tile_size->size() != 2)
{
param_tile = { kDefaultTileSize, kDefaultTileSize };
}
}
if (!patch_size || patch_size->size() != 2)
{
param_patch = { kDefaultPatchSize, kDefaultPatchSize };
}
return preferred_memory_capacity(!param_image.empty() ? param_image : image_size.value(), //
!param_tile.empty() ? param_tile : tile_size.value(), //
!param_patch.empty() ? param_patch : patch_size.value(), //
bytes_per_pixel);
}
} // namespace cucim::cache
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/cache/cache_pydoc.h
|
/*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CACHE_PYDOC_H
#define PYCUCIM_CACHE_PYDOC_H
#include "../macros.h"
namespace cucim::cache::doc
{
// py::int_ py_preferred_memory_capacity(const py::object& img,
// const std::optional<const std::vector<uint64_t>>& image_size,
// const std::optional<const std::vector<uint32_t>>& tile_size,
// const std::optional<const std::vector<uint32_t>>& patch_size,
// uint32_t bytes_per_pixel);
PYDOC(preferred_memory_capacity, R"doc(
Returns a good cache memory capacity value in MiB for the given conditions.
Please see how the value is calculated: https://godbolt.org/z/8vxnPfKM5
Args:
img: A `CuImage` object that can provide `image_size`, `tile_size`, `bytes_per_pixel` information. If this argument is provided, only `patch_size` from the arguments is used for the calculation.
image_size: A list of values that presents the image size (width, height).
tile_size: A list of values that presents the tile size (width, height). The default value is (256, 256).
patch_size: A list of values that presents the patch size (width, height). The default value is (256, 256).
bytes_per_pixel: The number of bytes that each pixel in the 2D image takes place. The default value is 3.
Returns:
int: The suggested memory capacity in MiB.
)doc")
} // namespace cucim::cache::doc
#endif // PYCUCIM_CACHE_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/cache/image_cache_py.cpp
|
/*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "image_cache_py.h"
#include "image_cache_pydoc.h"
#include <pybind11/pybind11.h>
namespace py = pybind11;
namespace cucim::cache
{
} // namespace cucim::cache
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/cache/image_cache_py.h
|
/*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CACHE_IMAGE_CACHE_PY_H
#define PYCUCIM_CACHE_IMAGE_CACHE_PY_H
#include <cucim/cache/image_cache.h>
#include <pybind11/pytypes.h>
namespace py = pybind11;
namespace cucim::cache
{
} // namespace cucim::cache
#endif // PYCUCIM_CACHE_IMAGE_CACHE_PY_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/profiler/profiler_py.cpp
|
/*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "profiler_py.h"
#include "profiler_pydoc.h"
#include <cucim/profiler/profiler.h>
#include <cucim/cuimage.h>
using namespace pybind11::literals;
namespace py = pybind11;
namespace cucim::profiler
{
void init_profiler(py::module& profiler)
{
py::class_<Profiler, std::shared_ptr<Profiler>>(profiler, "Profiler")
.def_property("config", &py_config, nullptr, doc::Profiler::doc_config, py::call_guard<py::gil_scoped_release>())
.def("trace", &py_trace, doc::Profiler::doc_trace, py::call_guard<py::gil_scoped_release>(), //
py::arg("value") = py::none() //
);
}
bool py_trace(Profiler& profiler, py::object value)
{
if (value.is_none())
{
return profiler.trace();
}
else if (py::isinstance<py::bool_>(value))
{
py::bool_ v = value.cast<py::bool_>();
profiler.trace(v);
return v;
}
else
{
throw std::invalid_argument(fmt::format("Only 'NoneType' or 'bool' is available for the argument"));
}
}
py::dict py_config(Profiler& profiler)
{
ProfilerConfig& config = profiler.config();
return py::dict{
"trace"_a = pybind11::bool_(config.trace) //
};
}
} // namespace cucim::profiler
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/profiler/profiler_pydoc.h
|
/*
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_PROFILER_PROFILER_PYDOC_H
#define PYCUCIM_PROFILER_PROFILER_PYDOC_H
#include "../macros.h"
namespace cucim::profiler::doc::Profiler
{
PYDOC(config, R"doc(
Returns the dictionary of configuration.
)doc")
// void trace(bool value) = 0;
// bool trace() const = 0;
PYDOC(trace, R"doc(
Traces method executions with NVTX.
)doc")
} // namespace cucim::profiler::doc::Profiler
#endif // PYCUCIM_PROFILER_PROFILER_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/profiler/profiler_py.h
|
/*
* Copyright (c) 2021, NVIDIA CORPORATcacheN.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_PROFILER_INIT_H
#define PYCUCIM_PROFILER_INIT_H
#include <pybind11/pybind11.h>
namespace py = pybind11;
namespace cucim::profiler
{
// Forward declaration
class Profiler;
void init_profiler(py::module& m);
bool py_trace(Profiler& profiler, py::object value);
py::dict py_config(Profiler& profiler);
} // namespace cucim::profiler
#endif // PYCUCIM_PROFILER_INIT_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/filesystem/filesystem_pydoc.h
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_FILESYSTEM_PYDOC_H
#define PYCUCIM_FILESYSTEM_PYDOC_H
#include "../macros.h"
namespace cucim::filesystem::doc
{
// bool is_gds_available();
PYDOC(is_gds_available, R"doc(
Check if the GDS is available in the system.
Returns:
True if libcufile.so is loaded and cuFileDriverOpen() API call succeeds.
)doc")
// std::shared_ptr<CuFileDriver> open(const char* file_path, const char* flags = "r", mode_t mode = 0644);
PYDOC(open, R"doc(
Open file with specific flags and mode.
'flags' can be one of the following flag string:
- "r": os.O_RDONLY
- "r+": os.O_RDWR
- "w": os.O_RDWR | os.O_CREAT | os.O_TRUNC
- "a": os.O_RDWR | os.O_CREAT
In addition to above flags, the method append os.O_CLOEXEC and os.O_DIRECT by default.
The following is optional flags that can be added to above string:
- 'p': Use POSIX APIs only (first try to open with O_DIRECT). It does not use GDS.
- 'n': Do not add O_DIRECT flag.
- 'm': Use memory-mapped file. This flag is supported only for the read-only file descriptor.
When 'm' is used, `PROT_READ` and `MAP_SHARED` are used for the parameter of mmap() function.
Args:
file_path: A file path to open.
flags: File flags in string. Default value is "r".
mode: A file mode. Default value is '0o644'.
Returns:
An object of CuFileDriver.
)doc")
// bool close(const std::shared_ptr<CuFileDriver>& fd);
PYDOC(close, R"doc(
Closes the given file driver.
Args:
fd: An CuFileDriver object.
Returns:
True if succeed, False otherwise.
)doc")
// ssize_t pread(void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
PYDOC(pread, R"doc(
Reads up to `count` bytes from file driver `fd` at offset `offset` (from the start of the file) into the buffer
`buf` starting at offset `buf_offset`. The file offset is not changed.
Args:
fd: An object of CuFileDriver.
buf: A buffer where read bytes are stored. Buffer can be either in CPU memory or (CUDA) GPU memory.
count: The number of bytes to read.
file_offset: An offset from the start of the file.
buf_offset: An offset from the start of the buffer. Default value is 0.
Returns:
The number of bytes read if succeed, -1 otherwise.
)doc")
// ssize_t pread(const std::shared_ptr<CuFileDriver>& fd, const void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
PYDOC(pwrite, R"doc(
Write up to `count` bytes from the buffer `buf` starting at offset `buf_offset` to the file driver `fd` at offset
`offset` (from the start of the file). The file offset is not changed.
Args:
fd: An object of CuFileDriver.
buf: A buffer where write bytes come from. Buffer can be either in CPU memory or (CUDA) GPU memory.
count: The number of bytes to write.
file_offset: An offset from the start of the file.
buf_offset: An offset from the start of the buffer. Default value is 0.
Returns:
The number of bytes written if succeed, -1 otherwise.
)doc")
// bool discard_page_cache(const char* file_path);
PYDOC(discard_page_cache, R"doc(
Discards a system (page) cache for the given file path.
Args:
file_path: A file path to drop system cache.
Returns:
True if succeed, False otherwise.
)doc")
} // namespace cucim::filesystem::doc
#endif // PYCUCIM_FILESYSTEM_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/filesystem/filesystem_py.cpp
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "filesystem_py.h"
#include "filesystem_pydoc.h"
#include <pybind11/pybind11.h>
#include <cucim/filesystem/cufile_driver.h>
#include "cufile_py.h"
#include "cufile_pydoc.h"
namespace py = pybind11;
namespace cucim::filesystem
{
void init_filesystem(py::module& fs)
{
py::enum_<FileHandleType>(fs, "FileHandleType") //
.value("Unknown", FileHandleType::kUnknown) //
.value("Posix", FileHandleType::kPosix) //
.value("PosixODirect", FileHandleType::kPosixODirect) //
.value("MemoryMapped", FileHandleType::kMemoryMapped) //
.value("GPUDirect", FileHandleType::kGPUDirect);
py::class_<CuFileDriver, std::shared_ptr<CuFileDriver>>(fs, "CuFileDriver")
.def(py::init<int, bool, bool, const char*>(), doc::CuFileDriver::doc_CuFileDriver, //
py::arg("fd"), //
py::arg("no_gds") = false, //
py::arg("use_mmap") = false, //
py::arg("file_path") = py::str(""), //
py::call_guard<py::gil_scoped_release>())
.def("pread", &fd_pread, doc::CuFileDriver::doc_pread, // Do not release GIL as it would access properties of
// python object
py::arg("buf"), //
py::arg("count"), //
py::arg("file_offset"), //
py::arg("buf_offset") = 0) //
.def("pwrite", &fd_pwrite, doc::CuFileDriver::doc_pwrite, // Do not release GIL as it would access properties of
// python object
py::arg("buf"), //
py::arg("count"), //
py::arg("file_offset"), //
py::arg("buf_offset") = 0) //
.def("close", &CuFileDriver::close, doc::CuFileDriver::doc_close, py::call_guard<py::gil_scoped_release>()) //
.def(
"__enter__",
[](const std::shared_ptr<CuFileDriver>& fd) { //
return fd; //
}, //
py::call_guard<py::gil_scoped_release>())
.def(
"__exit__",
[](const std::shared_ptr<CuFileDriver>& fd, const py::object& type, const py::object& value,
const py::object& traceback) { //
fd->close(); //
}, //
py::call_guard<py::gil_scoped_release>())
.def("__repr__", [](const CuFileDriver& fd) {
return fmt::format("<cucim.clara.filesystem.CuFileDriver path:{}>", fd.path());
});
fs.def("is_gds_available", &is_gds_available, doc::doc_is_gds_available, py::call_guard<py::gil_scoped_release>())
.def("open", &py_open, doc::doc_open,
py::arg("file_path"), //
py::arg("flags"), //
py::arg("mode") = 0644, //
py::call_guard<py::gil_scoped_release>())
.def("pread", &py_pread, doc::doc_pread, // Do not release GIL as it would access properties of python object
py::arg("fd"), //
py::arg("buf"), //
py::arg("count"), //
py::arg("file_offset"), //
py::arg("buf_offset") = 0) //
.def("pwrite", &py_pwrite, doc::doc_pwrite, // Do not release GIL as it would access properties of python object
py::arg("fd"), //
py::arg("buf"), //
py::arg("count"), //
py::arg("file_offset"), //
py::arg("buf_offset") = 0) //
.def("close", &close, doc::doc_close, py::call_guard<py::gil_scoped_release>())
.def("discard_page_cache", &discard_page_cache, doc::doc_discard_page_cache,
py::arg("file_path"), //
py::call_guard<py::gil_scoped_release>());
}
std::shared_ptr<CuFileDriver> py_open(const char* file_path, const char* flags, mode_t mode)
{
return open(file_path, flags, mode);
}
ssize_t py_pread(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset)
{
if (fd != nullptr)
{
return fd_pread(*fd, buf, count, file_offset, buf_offset);
}
else
{
fmt::print(stderr, "fd (CuFileDriver) is None!");
return -1;
}
}
ssize_t py_pwrite(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset)
{
if (fd != nullptr)
{
return fd_pwrite(*fd, buf, count, file_offset, buf_offset);
}
else
{
fmt::print(stderr, "fd (CuFileDriver) is None!");
return -1;
}
}
} // namespace cucim::filesystem
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/filesystem/filesystem_py.h
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_FILESYSTEM_INIT_H
#define PYCUCIM_FILESYSTEM_INIT_H
#include <memory>
#include <pybind11/pybind11.h>
namespace py = pybind11;
namespace cucim::filesystem
{
// Forward declaration
class CuFileDriver;
void init_filesystem(py::module& m);
std::shared_ptr<CuFileDriver> py_open(const char* file_path, const char* flags, mode_t mode);
ssize_t py_pread(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset = 0);
ssize_t py_pwrite(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset = 0);
// bool py_close(const std::shared_ptr<CuFileDriver>& fd);
// bool py_discard_page_cache(const char* file_path);
}
#endif // PYCUCIM_FILESYSTEM_INIT_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/filesystem/cufile_pydoc.h
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CUFILE_PYDOC_H
#define PYCUCIM_CUFILE_PYDOC_H
#include "../macros.h"
namespace cucim::filesystem::doc::CuFileDriver
{
// CuFileDriver(int fd, bool no_gds = false, bool use_mmap = false, const char* file_path = "");
PYDOC(CuFileDriver, R"doc(
Constructor of CuFileDriver.
Args:
fd: A file descriptor (in `int` type) which is available through `os.open()` method.
no_gds: If True, use POSIX APIs only even when GDS can be supported for the file.
use_mmap: If True, use memory-mapped IO. This flag is supported only for the read-only file descriptor. Default value is `False`.
file_path: A file path for the file descriptor. It would retrieve the absolute file path of the file descriptor if not specified.
)doc")
// ssize_t pread(void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
PYDOC(pread, R"doc(
Reads up to `count` bytes from the file driver at offset `file_offset` (from the start of the file) into the buffer
`buf` starting at offset `buf_offset`. The file offset is not changed.
Args:
buf: A buffer where read bytes are stored. Buffer can be either in CPU memory or (CUDA) GPU memory.
count: The number of bytes to read.
file_offset: An offset from the start of the file.
buf_offset: An offset from the start of the buffer. Default value is 0.
Returns:
The number of bytes read if succeed, -1 otherwise.
)doc")
// ssize_t pwrite(const void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
PYDOC(pwrite, R"doc(
Reads up to `count` bytes from the file driver at offset `file_offset` (from the start of the file) into the buffer
`buf` starting at offset `buf_offset`. The file offset is not changed.
Args:
buf: A buffer where write bytes come from. Buffer can be either in CPU memory or (CUDA) GPU memory.
count: The number of bytes to write.
file_offset: An offset from the start of the file.
buf_offset: An offset from the start of the buffer. Default value is 0.
Returns:
The number of bytes written if succeed, -1 otherwise.
)doc")
// bool close();
PYDOC(close, R"doc(
Closes opened file if not closed.
)doc")
} // namespace cucim::filesystem::doc::CuFileDriver
#endif // PYCUCIM_CUFILE_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/filesystem/cufile_py.h
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_CUFILE_PY_H
#define PYCUCIM_CUFILE_PY_H
#include "cucim/filesystem/cufile_driver.h"
#include <cstdio>
#include <pybind11/pytypes.h>
namespace py = pybind11;
namespace cucim::filesystem
{
// Note: there would be name conflict with pread/pwrite in cufile_driver.h so prefixed 'fd_'.
ssize_t fd_pread(const CuFileDriver& fd, const py::object& buf, size_t count, off_t file_offset, off_t buf_offset = 0);
ssize_t fd_pwrite(CuFileDriver& fd, const py::object& buf, size_t count, off_t file_offset, off_t buf_offset = 0);
} // namespace cucim::filesystem
#endif // PYCUCIM_CUFILE_PY_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/filesystem/cufile_py.cpp
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "cufile_py.h"
#include "cufile_pydoc.h"
#include <pybind11/pybind11.h>
#include <cucim/filesystem/cufile_driver.h>
#include "../memory/memory_py.h"
namespace py = pybind11;
namespace cucim::filesystem
{
ssize_t fd_pread(const CuFileDriver& fd, const py::object& obj, size_t count, off_t file_offset, off_t buf_offset)
{
void* buf = nullptr;
size_t memory_size = 0;
bool readonly = false;
cucim::memory::get_memory_info(obj, &buf, nullptr, &memory_size, &readonly);
if (buf == nullptr)
{
throw std::runtime_error("Cannot Recognize the array object!");
}
if (readonly)
{
throw std::runtime_error("The buffer is readonly so cannot be used for pread!");
}
if (memory_size && count > memory_size) {
throw std::runtime_error(fmt::format("[Error] 'count' ({}) is larger than the size of the array object ({})!", count, memory_size));
}
py::call_guard<py::gil_scoped_release>();
return fd.pread(buf, count, file_offset, buf_offset);
}
ssize_t fd_pwrite(CuFileDriver& fd, const py::object& obj, size_t count, off_t file_offset, off_t buf_offset)
{
void* buf = nullptr;
size_t memory_size = 0;
cucim::memory::get_memory_info(obj, &buf, nullptr, &memory_size, nullptr);
if (buf == nullptr)
{
throw std::runtime_error("Cannot Recognize the array object!");
}
if (memory_size && count > memory_size) {
throw std::runtime_error(fmt::format("[Error] 'count' ({}) is larger than the size of the array object ({})!", count, memory_size));
}
py::call_guard<py::gil_scoped_release>();
return fd.pwrite(buf, count, file_offset, buf_offset);
}
} // namespace cucim::filesystem
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/memory/memory_py.cpp
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "memory_py.h"
#include "memory_pydoc.h"
#include <pybind11/pybind11.h>
#include <cucim/memory/memory_manager.h>
namespace py = pybind11;
namespace cucim::memory
{
void init_memory(py::module& memory)
{
}
static size_t calculate_memory_size(const std::vector<size_t>& shape, const char* dtype_str)
{
// TODO: implement method to calculate size
// https://github.com/pytorch/pytorch/blob/master/torch/tensor.py#L733 (we can take digit part)
return 0;
}
void get_memory_info(const py::object& buf_obj,
void** out_buf,
cucim::io::Device* out_device,
size_t* out_memory_size,
bool* out_readonly)
{
if (out_buf == nullptr)
{
throw std::runtime_error("[Error] out_buf shouldn't be nullptr!");
}
void* buf = nullptr;
size_t memory_size = 0;
if (py::hasattr(buf_obj, "__array_interface__"))
{
auto attr = py::getattr(buf_obj, "__array_interface__");
if (py::isinstance<py::dict>(attr))
{
auto dict = py::cast<py::dict>(attr);
if (dict.contains("data"))
{
auto data = dict["data"];
if (py::isinstance<py::tuple>(data))
{
auto data_tuple = data.cast<py::tuple>();
if (data_tuple.size() == 2)
{
if (out_readonly)
{
*out_readonly = data_tuple[1].cast<bool>();
}
buf = reinterpret_cast<void*>(data_tuple[0].cast<uint64_t>());
if (py::hasattr(buf_obj, "nbytes"))
{
memory_size = py::getattr(buf_obj, "nbytes").cast<size_t>();
}
else
{
// TODO: implement method to calculate size
}
}
}
}
}
}
else if (py::hasattr(buf_obj, "__cuda_array_interface__"))
{
auto attr = py::getattr(buf_obj, "__cuda_array_interface__");
if (py::isinstance<py::dict>(attr))
{
auto dict = py::cast<py::dict>(attr);
if (dict.contains("data"))
{
auto data = dict["data"];
if (py::isinstance<py::tuple>(data))
{
auto data_tuple = data.cast<py::tuple>();
if (data_tuple.size() == 2)
{
if (out_readonly)
{
*out_readonly = data_tuple[1].cast<bool>();
}
buf = reinterpret_cast<void*>(data_tuple[0].cast<uint64_t>());
if (py::hasattr(buf_obj, "nbytes"))
{
memory_size = py::getattr(buf_obj, "nbytes").cast<size_t>();
}
else
{
// TODO: implement method to calculate size
}
}
}
}
}
}
else if (py::isinstance<py::int_>(buf_obj))
{
buf = reinterpret_cast<void*>(buf_obj.cast<uint64_t>());
}
*out_buf = buf;
if (out_memory_size)
{
*out_memory_size = memory_size;
}
if (buf == nullptr)
{
return;
}
if (out_device)
{
cucim::memory::PointerAttributes attributes;
cucim::memory::get_pointer_attributes(attributes, buf);
*out_device = attributes.device;
}
}
} // namespace cucim::memory
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/memory/memory_py.h
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_MEMORY_INIT_H
#define PYCUCIM_MEMORY_INIT_H
#include <pybind11/pybind11.h>
#include <cucim/io/device.h>
namespace py = pybind11;
namespace cucim::memory
{
void init_memory(py::module& m);
void get_memory_info(const py::object& buf_obj,
void** out_buf,
cucim::io::Device* out_device = nullptr,
size_t* out_memory_size = 0,
bool* out_readonly = nullptr);
} // namespace cucim::memory
#endif // PYCUCIM_MEMORY_INIT_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/memory/memory_pydoc.h
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_MEMORY_PYDOC_H
#define PYCUCIM_MEMORY_PYDOC_H
#include "../macros.h"
namespace cucim::memory::doc
{
} // namespace cucim::memory::doc
#endif // PYCUCIM_MEMORY_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/io/device_pydoc.h
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_DEVICE_PYDOC_H
#define PYCUCIM_DEVICE_PYDOC_H
#include "../macros.h"
namespace cucim::io::doc::Device
{
// explicit Device();
// explicit Device(const std::string& device_name);
PYDOC(Device, R"doc(
Constructor for `Device`.
)doc")
// Device(const char* device_name);
// Device(DeviceType type, DeviceIndex index);
// Device(DeviceType type, DeviceIndex index, const std::string& param);
// static DeviceType parse_type(const std::string& device_name);
PYDOC(parse_type, R"doc(
Create `DeviceType` object from the device name string.
)doc")
// explicit operator std::string() const;
// DeviceType type() const;
PYDOC(type, R"doc(
Device type.
)doc")
// DeviceIndex index() const;
PYDOC(index, R"doc(
Device index.
)doc")
} // namespace cucim::io::doc::Device
#endif // PYCUCIM_DEVICE_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/io/io_py.h
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_IO_INIT_H
#define PYCUCIM_IO_INIT_H
#include <pybind11/pybind11.h>
namespace py = pybind11;
namespace cucim::io
{
void init_io(py::module& m);
}
#endif // PYCUCIM_IO_INIT_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/io/device_py.cpp
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "device_pydoc.h"
#include <pybind11/pybind11.h>
#include <cucim/io/device.h>
namespace py = pybind11;
namespace cucim::io
{
} // namespace cucim::io
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/io/io_pydoc.h
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef PYCUCIM_IO_PYDOC_H
#define PYCUCIM_IO_PYDOC_H
#include "../macros.h"
namespace cucim::io::doc
{
}
#endif // PYCUCIM_IO_PYDOC_H
| 0 |
rapidsai_public_repos/cucim/python/pybind11
|
rapidsai_public_repos/cucim/python/pybind11/io/io_py.cpp
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "io_pydoc.h"
#include "device_pydoc.h"
#include <pybind11/pybind11.h>
#include <cucim/io/device.h>
namespace py = pybind11;
namespace cucim::io
{
void init_io(py::module& io)
{
py::enum_<DeviceType>(io, "DeviceType") //
.value("CPU", DeviceType::kCPU) //
.value("CUDA", DeviceType::kCUDA) //
.value("CUDAHost", DeviceType::kCUDAHost) //
.value("CUDAManaged", DeviceType::kCUDAManaged) //
.value("CPUShared", DeviceType::kCPUShared) //
.value("CUDAShared", DeviceType::kCUDAShared);
py::class_<Device>(io, "Device") //
.def(py::init<const std::string&>(), doc::Device::doc_Device) //
.def_static("parse_type", &Device::parse_type, doc::Device::doc_parse_type) //
.def_property("type", &Device::type, nullptr, doc::Device::doc_type) //
.def_property("index", &Device::index, nullptr, doc::Device::doc_index)
.def("__repr__", [](const Device& device) { return std::string(device); });
}
} // namespace cucim::io
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/test_data/gen_images.sh
|
#!/bin/bash
SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
TOP="$(git rev-parse --show-toplevel 2> /dev/null || echo "${SCRIPT_DIR}")"
[ -z "${TOP}" ] && >&2 echo "Repository root is not available!" && exit 1
DEST_FOLDER=${TOP}/test_data/generated
mkdir -p ${DEST_FOLDER}
generate_image() {
local recipe="${1:-tiff}"
local check_file="${2:-}"
[ -n "${check_file}" ] && [ -f "${DEST_FOLDER}/${check_file}" ] && return
python3 ${TOP}/python/cucim/tests/util/gen_image.py ${recipe} --dest ${DEST_FOLDER}
}
generate_image tiff::stripe:32x32:16 tiff_stripe_32x32_16.tif
generate_image tiff::stripe:4096x4096:256:deflate tiff_stripe_4096x4096_256.tif
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/test_data/README.md
|
# Test Data Folder (`test_data` folder)
Symbolic links for for this folder is available under the following subfolders:
- `cpp/plugins/cucim.kit.cuslide`
- `python/cucim`
## Generated Test Data (`test_data/generated` folder)
Generated by executing `gen_images.sh`.
- tiff_stripe_32x32_16.tif
- tiff_stripe_4096x4096_256.tif
## Private Test Data (`test_data/private` folder)
- `generic_tiff_000.tif` : 256x256 multi-resolution/tiled TIF conversion of `TUPAC-TR-467.svs` from the dataset
of [Tumor Proliferation Assessment Challenge 2016](http://tupac.tue-image.nl/node/3) (TUPAC16 | MICCAI Grand Challenge) which are publicly
available through [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) under [CC BY 3.0 License](https://creativecommons.org/licenses/by/3.0/)
- `philips_tiff_000.tif` : `normal_042.tif` from the [Camelyon16 challenge data](ftp://parrot.genomics.cn/gigadb/pub/10.5524/100001_101000/100439/CAMELYON16/training/normal/normal_042.tif) (another [link](https://camelyon17.grand-challenge.org/Data/)) which is under [CC0 License](https://choosealicense.com/licenses/cc0-1.0/)
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/test_data/push_testdata.sh
|
#!/bin/bash
SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
docker build -t gigony/svs-testdata:little-big ${SCRIPT_DIR}
docker push gigony/svs-testdata:little-big
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/test_data/Dockerfile
|
FROM scratch
WORKDIR /input
COPY ./input /input
CMD ["dummy"]
| 0 |
rapidsai_public_repos/cucim/test_data
|
rapidsai_public_repos/cucim/test_data/input/README.md
|
# Test Dataset
TUPAC-TR-488.svs and TUPAC-TR-467.svs are breast cancer cases (TCGA-BRCA) from the dataset
of Tumor Proliferation Assessment Challenge 2016 (TUPAC16 | MICCAI Grand Challenge) which are publicly
available through [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) under [CC BY 3.0 License](https://creativecommons.org/licenses/by/3.0/)
- Website: http://tupac.tue-image.nl/node/3
- Data link: https://drive.google.com/drive/u/0/folders/0B--ztKW0d17XYlBqOXppQmw0M2M
- TUPAC-TR-467.svs : https://portal.gdc.cancer.gov/files/575c0465-c4bc-4ea7-ab63-ba48aa5e374b
- TUPAC-TR-488.svs : https://portal.gdc.cancer.gov/files/e27c87c9-e163-4d55-8f27-4cc7dfca08d8
- License: CC BY 3.0 (https://wiki.cancerimagingarchive.net/display/Public/TCGA-BRCA#3539225f58e64731d8e47d588cedd99d300d5d6)
- See LICENSE-3rdparty file
## Converted files
- image.tif : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-467.svs
- image2.tif : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-488.svs
| 0 |
rapidsai_public_repos/cucim/test_data
|
rapidsai_public_repos/cucim/test_data/input/LICENSE-3rdparty
|
Creative Commons Legal Code
Attribution 3.0 Unported
CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE
LEGAL SERVICES. DISTRIBUTION OF THIS LICENSE DOES NOT CREATE AN
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REGARDING THE INFORMATION PROVIDED, AND DISCLAIMS LIABILITY FOR
DAMAGES RESULTING FROM ITS USE.
License
THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS CREATIVE
COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY
COPYRIGHT AND/OR OTHER APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS
AUTHORIZED UNDER THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED.
BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE
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d. to Distribute and Publicly Perform Adaptations.
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License is not intended to restrict the license of any rights under
applicable law.
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| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/gds/CMakeLists.txt
|
#
# Copyright (c) 2020-2021, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
cucim_set_build_shared_libs(OFF)
# Disable visibility to not expose unnecessary symbols
set(CMAKE_CXX_VISIBILITY_PRESET hidden)
set(CMAKE_VISIBILITY_INLINES_HIDDEN YES)
# Set RPATH
if (NOT APPLE)
set(CMAKE_INSTALL_RPATH $ORIGIN)
endif ()
################################################################################
# Add library: cufile_stub
################################################################################
add_library(cufile_stub
include/cufile_stub.h
src/cufile_stub.cpp
)
# Compile options
set_target_properties(cufile_stub
PROPERTIES
CXX_STANDARD 17
CXX_STANDARD_REQUIRED YES
CXX_EXTENSIONS NO
# To prevent the error message: /usr/bin/ld: ../lib/libcufile_stub.a(cufile_stub.cpp.o): relocation R_X86_64_PC32 against symbol `__fatbinwrap_46_tmpxft_00005869_00000000_6_cufile_stub_cpp1_ii_1e2ddd49' can not be used when making a shared object; recompile with -fPIC
POSITION_INDEPENDENT_CODE ON
)
# Note: Looks like the following line causes error on CMake 3.18.4 (it is working on 3.18.2). Keeping it for now.
set(CUCIM_REQUIRED_FEATURES cxx_std_17)
target_compile_features(cufile_stub PRIVATE ${CUCIM_REQUIRED_FEATURES})
# Use generator expression to avoid `nvcc fatal : Value '-std=c++17' is not defined for option 'Werror'`
target_compile_options(cufile_stub
PRIVATE
$<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>
)
## Link libraries
target_link_libraries(cufile_stub
PUBLIC
${CMAKE_DL_LIBS}
PRIVATE
CUDA::cudart
)
# Set GDS include path (cufile.h)
if (DEFINED ENV{CONDA_BUILD} AND EXISTS $ENV{PREFIX}/include/cufile.h)
set(GDS_INCLUDE_PATH "$ENV{PREFIX}/include")
elseif (DEFINED ENV{CONDA_PREFIX} AND EXISTS $ENV{CONDA_PREFIX}/include/cufile.h)
set(GDS_INCLUDE_PATH "$ENV{CONDA_PREFIX}/include")
elseif (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/../temp/cuda/include/cufile.h)
set(GDS_INCLUDE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../temp/cuda/include)
else ()
set(GDS_INCLUDE_PATH /usr/local/cuda/include)
endif ()
message("Set GDS_INCLUDE_PATH to '${GDS_INCLUDE_PATH}'.")
# Enabling CUCIM_STATIC_GDS assumes that lib/libcufile_static.a and include/cufile.h is available
# under ../temp/cuda folder.
if (CUCIM_STATIC_GDS)
add_library(deps::gds_static STATIC IMPORTED GLOBAL)
if (DEFINED ENV{CONDA_BUILD})
set(GDS_STATIC_LIB_PATH "$ENV{PREFIX}/lib/libcufile_static.a")
elseif (DEFINED ENV{CONDA_PREFIX})
set(GDS_STATIC_LIB_PATH "$ENV{CONDA_PREFIX}/lib/libcufile_static.a")
elseif (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/../temp/cuda/lib64/libcufile_static.a)
set(GDS_STATIC_LIB_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../temp/cuda/lib64/libcufile_static.a)
else ()
set(GDS_STATIC_LIB_PATH /usr/local/cuda/lib64/libcufile_static.a)
endif ()
message("Set GDS_STATIC_LIB_PATH to '${GDS_STATIC_LIB_PATH}'.")
set_target_properties(deps::gds_static PROPERTIES
IMPORTED_LOCATION "${GDS_STATIC_LIB_PATH}"
INTERFACE_INCLUDE_DIRECTORIES "${GDS_INCLUDE_PATH}"
)
target_link_libraries(cufile_stub
PUBLIC
${CMAKE_DL_LIBS}
PRIVATE
deps::gds_static
)
endif()
target_compile_definitions(cufile_stub
PUBLIC
CUCIM_SUPPORT_GDS=$<BOOL:${CUCIM_SUPPORT_GDS}>
CUCIM_STATIC_GDS=$<BOOL:${CUCIM_STATIC_GDS}>
)
target_include_directories(cufile_stub
PUBLIC
$<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/include>
$<BUILD_INTERFACE:${GDS_INCLUDE_PATH}>
PRIVATE
# Add path to cufile.h explicitly. ${TOP}/temp/cuda would be available by `./run copy_gds_files_`
${CMAKE_CURRENT_SOURCE_DIR}/../cpp/include # for including helper.h in cucim/dynlib
)
add_library(deps::gds ALIAS cufile_stub)
# Do not generate SONAME as this would be used as a stub library for building on CentOS until cufile has a static library.
# Need to use IMPORTED_NO_SONAME when using this .so file.
# : https://stackoverflow.com/questions/27261288/cmake-linking-shared-c-object-from-externalproject-produces-binaries-with-rel
#set_target_properties(cufile_stub PROPERTIES NO_SONAME 1)
#target_link_options(cufile_stub PRIVATE "LINKER:-soname=cufile.so")
## Build a fake libcufile.so
#set_target_properties(cufile_stub PROPERTIES OUTPUT_NAME "cufile")
#
#################################################################################
## Add tests
#################################################################################
#add_subdirectory(tests)
cucim_restore_build_shared_libs()
| 0 |
rapidsai_public_repos/cucim/gds
|
rapidsai_public_repos/cucim/gds/include/cufile_stub.h
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef CUCIM_CUFILE_STUB_H
#define CUCIM_CUFILE_STUB_H
#include "cufile.h"
#include "cucim/dynlib/helper.h"
class CuFileStub
{
public:
void load();
void unload();
~CuFileStub();
private:
cucim::dynlib::LibraryHandle handle_ = nullptr;
};
#endif // CUCIM_CUFILE_STUB_H
| 0 |
rapidsai_public_repos/cucim/gds
|
rapidsai_public_repos/cucim/gds/src/cufile_stub.cpp
|
/*
* Copyright (c) 2020, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "cufile_stub.h"
#include "cucim/dynlib/helper.h"
#include "cucim/util/platform.h"
#define IMPORT_FUNCTION(handle, name) impl_##name = cucim::dynlib::get_library_symbol<t_##name>(handle, #name);
typedef CUfileError_t (*t_cuFileHandleRegister)(CUfileHandle_t* fh, CUfileDescr_t* descr);
typedef void (*t_cuFileHandleDeregister)(CUfileHandle_t fh);
typedef CUfileError_t (*t_cuFileBufRegister)(const void* devPtr_base, size_t length, int flags);
typedef CUfileError_t (*t_cuFileBufDeregister)(const void* devPtr_base);
typedef ssize_t (*t_cuFileRead)(CUfileHandle_t fh, void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset);
typedef ssize_t (*t_cuFileWrite)(
CUfileHandle_t fh, const void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset);
typedef CUfileError_t (*t_cuFileDriverOpen)(void);
typedef CUfileError_t (*t_cuFileDriverClose)(void);
typedef CUfileError_t (*t_cuFileDriverGetProperties)(CUfileDrvProps_t* props);
typedef CUfileError_t (*t_cuFileDriverSetPollMode)(bool poll, size_t poll_threshold_size);
typedef CUfileError_t (*t_cuFileDriverSetMaxDirectIOSize)(size_t max_direct_io_size);
typedef CUfileError_t (*t_cuFileDriverSetMaxCacheSize)(size_t max_cache_size);
typedef CUfileError_t (*t_cuFileDriverSetMaxPinnedMemSize)(size_t max_pinned_size);
static t_cuFileHandleRegister impl_cuFileHandleRegister = nullptr;
static t_cuFileHandleDeregister impl_cuFileHandleDeregister = nullptr;
static t_cuFileBufRegister impl_cuFileBufRegister = nullptr;
static t_cuFileBufDeregister impl_cuFileBufDeregister = nullptr;
static t_cuFileRead impl_cuFileRead = nullptr;
static t_cuFileWrite impl_cuFileWrite = nullptr;
static t_cuFileDriverOpen impl_cuFileDriverOpen = nullptr;
static t_cuFileDriverClose impl_cuFileDriverClose = nullptr;
static t_cuFileDriverGetProperties impl_cuFileDriverGetProperties = nullptr;
static t_cuFileDriverSetPollMode impl_cuFileDriverSetPollMode = nullptr;
static t_cuFileDriverSetMaxDirectIOSize impl_cuFileDriverSetMaxDirectIOSize = nullptr;
static t_cuFileDriverSetMaxCacheSize impl_cuFileDriverSetMaxCacheSize = nullptr;
static t_cuFileDriverSetMaxPinnedMemSize impl_cuFileDriverSetMaxPinnedMemSize = nullptr;
void CuFileStub::load()
{
#if !CUCIM_SUPPORT_GDS
return;
#endif
#if !CUCIM_STATIC_GDS
if (handle_ == nullptr)
{
// Note: Load the dynamic library with RTLD_NODELETE flag because libcufile.so uses thread_local which can
// cause a segmentation fault if the library is dynamically loaded/unloaded. (See #158)
// CUDA versions before CUDA 11.7.1 did not ship libcufile.so.0, so this is
// a workaround that adds support for all prior versions of libcufile.
handle_ = cucim::dynlib::load_library(
{
"libcufile.so.0",
"libcufile.so.1.3.0" /* 11.7.0 */,
"libcufile.so.1.2.1" /* 11.6.2, 11.6.1 */,
"libcufile.so.1.2.0" /* 11.6.0 */,
"libcufile.so.1.1.1" /* 11.5.1 */,
"libcufile.so.1.1.0" /* 11.5.0 */,
"libcufile.so.1.0.2" /* 11.4.4, 11.4.3, 11.4.2 */,
"libcufile.so.1.0.1" /* 11.4.1 */,
"libcufile.so.1.0.0" /* 11.4.0 */
},
RTLD_LAZY | RTLD_LOCAL | RTLD_NODELETE);
if (handle_ == nullptr)
{
return;
}
IMPORT_FUNCTION(handle_, cuFileDriverOpen);
IMPORT_FUNCTION(handle_, cuFileHandleRegister);
IMPORT_FUNCTION(handle_, cuFileHandleDeregister);
IMPORT_FUNCTION(handle_, cuFileBufRegister);
IMPORT_FUNCTION(handle_, cuFileBufDeregister);
IMPORT_FUNCTION(handle_, cuFileRead);
IMPORT_FUNCTION(handle_, cuFileWrite);
IMPORT_FUNCTION(handle_, cuFileDriverOpen);
IMPORT_FUNCTION(handle_, cuFileDriverClose);
IMPORT_FUNCTION(handle_, cuFileDriverGetProperties);
IMPORT_FUNCTION(handle_, cuFileDriverSetPollMode);
IMPORT_FUNCTION(handle_, cuFileDriverSetMaxDirectIOSize);
IMPORT_FUNCTION(handle_, cuFileDriverSetMaxCacheSize);
IMPORT_FUNCTION(handle_, cuFileDriverSetMaxPinnedMemSize);
}
#endif
}
void CuFileStub::unload()
{
#if !CUCIM_SUPPORT_GDS
return;
#endif
#if !CUCIM_STATIC_GDS
if (handle_)
{
cucim::dynlib::unload_library(handle_);
handle_ = nullptr;
impl_cuFileDriverOpen = nullptr;
impl_cuFileHandleRegister = nullptr;
impl_cuFileHandleDeregister = nullptr;
impl_cuFileBufRegister = nullptr;
impl_cuFileBufDeregister = nullptr;
impl_cuFileRead = nullptr;
impl_cuFileWrite = nullptr;
impl_cuFileDriverOpen = nullptr;
impl_cuFileDriverClose = nullptr;
impl_cuFileDriverGetProperties = nullptr;
impl_cuFileDriverSetPollMode = nullptr;
impl_cuFileDriverSetMaxDirectIOSize = nullptr;
impl_cuFileDriverSetMaxCacheSize = nullptr;
impl_cuFileDriverSetMaxPinnedMemSize = nullptr;
}
#endif
}
CuFileStub::~CuFileStub()
{
// Note: unload() would be called explicitly by CuFileDriverInitializer to unload the shared library after
// calling cuFileDriverClose() in CuFileDriverInitializer::~CuFileDriverInitializer()
// unload();
}
#if __cplusplus
extern "C"
{
#endif
#if !CUCIM_STATIC_GDS
CUfileError_t cuFileHandleRegister(CUfileHandle_t* fh, CUfileDescr_t* descr)
{
if (impl_cuFileHandleRegister)
{
return impl_cuFileHandleRegister(fh, descr);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
void cuFileHandleDeregister(CUfileHandle_t fh)
{
if (impl_cuFileHandleDeregister)
{
impl_cuFileHandleDeregister(fh);
}
}
CUfileError_t cuFileBufRegister(const void* devPtr_base, size_t length, int flags)
{
if (impl_cuFileBufRegister)
{
return impl_cuFileBufRegister(devPtr_base, length, flags);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
CUfileError_t cuFileBufDeregister(const void* devPtr_base)
{
if (impl_cuFileBufDeregister)
{
return impl_cuFileBufDeregister(devPtr_base);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
ssize_t cuFileRead(CUfileHandle_t fh, void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset)
{
if (impl_cuFileRead)
{
return impl_cuFileRead(fh, devPtr_base, size, file_offset, devPtr_offset);
}
return -1;
}
ssize_t cuFileWrite(CUfileHandle_t fh, const void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset)
{
if (impl_cuFileWrite)
{
return impl_cuFileWrite(fh, devPtr_base, size, file_offset, devPtr_offset);
}
return -1;
}
CUfileError_t cuFileDriverOpen(void)
{
// GDS v1.0.0 does not support WSL and executing this can cause the following error:
// Assertion failure, file index :cufio-udev line :143
// So we do not call impl_cuFileDriverOpen() here if the current platform is WSL.
if (impl_cuFileDriverOpen)
{
// If not in WSL, call impl_cuFileDriverOpen()
if (!cucim::util::is_in_wsl())
{
return impl_cuFileDriverOpen();
}
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
CUfileError_t cuFileDriverClose(void)
{
if (impl_cuFileDriverClose)
{
return impl_cuFileDriverClose();
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
CUfileError_t cuFileDriverGetProperties(CUfileDrvProps_t* props)
{
if (impl_cuFileDriverGetProperties)
{
return impl_cuFileDriverGetProperties(props);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
CUfileError_t cuFileDriverSetPollMode(bool poll, size_t poll_threshold_size)
{
if (impl_cuFileDriverSetPollMode)
{
return impl_cuFileDriverSetPollMode(poll, poll_threshold_size);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
CUfileError_t cuFileDriverSetMaxDirectIOSize(size_t max_direct_io_size)
{
if (impl_cuFileDriverSetMaxDirectIOSize)
{
return impl_cuFileDriverSetMaxDirectIOSize(max_direct_io_size);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
CUfileError_t cuFileDriverSetMaxCacheSize(size_t max_cache_size)
{
if (impl_cuFileDriverSetMaxCacheSize)
{
return impl_cuFileDriverSetMaxCacheSize(max_cache_size);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
CUfileError_t cuFileDriverSetMaxPinnedMemSize(size_t max_pinned_size)
{
if (impl_cuFileDriverSetMaxPinnedMemSize)
{
return impl_cuFileDriverSetMaxPinnedMemSize(max_pinned_size);
}
return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
}
#endif
#if __cplusplus
}
#endif
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.abseil-cpp
|
Apache License
Version 2.0, January 2004
https://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
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rapidsai_public_repos/cucim/3rdparty/LICENSE.googletest
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Copyright 2008, Google Inc.
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rapidsai_public_repos/cucim/3rdparty/LICENSE.fmt
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rapidsai_public_repos/cucim/3rdparty/LICENSE.StainTools
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MIT License
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rapidsai_public_repos/cucim
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rapidsai_public_repos/cucim/3rdparty/LICENSE.pytorch
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From PyTorch:
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rapidsai_public_repos/cucim/3rdparty/LICENSE.dockcross
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rapidsai_public_repos/cucim/3rdparty/LICENSE.pugixml
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rapidsai_public_repos/cucim
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rapidsai_public_repos/cucim/3rdparty/LICENSE.libcuckoo
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Copyright (C) 2013, Carnegie Mellon University and Intel Corporation
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rapidsai_public_repos/cucim/3rdparty/LICENSE.dask
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BSD 3-Clause License
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rapidsai_public_repos/cucim/3rdparty/LICENSE.tifffile
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BSD 3-Clause License
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rapidsai_public_repos/cucim/3rdparty/LICENSE.libtiff
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rapidsai_public_repos/cucim/3rdparty/LICENSE.pytest
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The MIT License (MIT)
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rapidsai_public_repos/cucim/3rdparty/LICENSE.aicsimageio
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Copyright 2020 Allen Institute for Cell Science
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rapidsai_public_repos/cucim/3rdparty/LICENSE.openslide
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OpenSlide
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rapidsai_public_repos/cucim/3rdparty/LICENSE.click
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Copyright 2014 Pallets
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You may add Your own copyright statement to Your modifications and
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5. Submission of Contributions. Unless You explicitly state otherwise,
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this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
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origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
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of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
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8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
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Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
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Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
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Unless required by applicable law or agreed to in writing, software
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rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.spdlog
|
The MIT License (MIT)
Copyright (c) 2016 Gabi Melman.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
-- NOTE: Third party dependency used by this software --
This software depends on the fmt lib (MIT License),
and users must comply to its license: https://github.com/fmtlib/fmt/blob/master/LICENSE.rst
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rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.pybind11
|
Copyright (c) 2016 Wenzel Jakob <[email protected]>, All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. 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.
3. Neither the name of the copyright holder nor the names of its 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 HOLDER 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.
Please also refer to the file .github/CONTRIBUTING.md, which clarifies licensing of
external contributions to this project including patches, pull requests, etc.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.gputil
|
MIT License
Copyright (c) 2017 anderskm
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.taskflow
|
TASKFLOW MIT LICENSE
Copyright (c) 2018-2021 Dr. Tsung-Wei Huang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.psutil
|
BSD 3-Clause License
Copyright (c) 2009, Jay Loden, Dave Daeschler, Giampaolo Rodola'
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.
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this list of conditions and the following disclaimer in the documentation
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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(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.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.libdeflate
|
Copyright 2016 Eric Biggers
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation files
(the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge,
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and to permit persons to whom the Software is furnished to do so,
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The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.Catch2
|
Boost Software License - Version 1.0 - August 17th, 2003
Permission is hereby granted, free of charge, to any person or organization
obtaining a copy of the software and accompanying documentation covered by
this license (the "Software") to use, reproduce, display, distribute,
execute, and transmit the Software, and to prepare derivative works of the
Software, and to permit third-parties to whom the Software is furnished to
do so, all subject to the following:
The copyright notices in the Software and this entire statement, including
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.pybind11_json
|
BSD 3-Clause License
Copyright (c) 2019,
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.
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this list of conditions and the following disclaimer in the documentation
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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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.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.NVTX
|
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
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Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
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origin of the Work and reproducing the content of the NOTICE file.
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whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
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liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
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defend, and hold each Contributor harmless for any liability
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END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
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rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.cuda
|
End User License Agreement
--------------------------
The CUDA Toolkit End User License Agreement applies to the
NVIDIA CUDA Toolkit, the NVIDIA CUDA Samples, the NVIDIA
Display Driver, NVIDIA Nsight tools (Visual Studio Edition),
and the associated documentation on CUDA APIs, programming
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terms and conditions of the license agreement, then do not
download or use the software.
Last updated: Mar 24, 2021.
Preface
-------
The Software License Agreement in Chapter 1 and the Supplement
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NVIDIA Driver
Description
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and
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Default Install Location of Nsight Visual Studio Edition
Windows platform:
%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
1. License Agreement for NVIDIA Software Development Kits
---------------------------------------------------------
Important Notice—Read before downloading, installing,
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SDK.
1.1.3. Authorized Users
You may allow employees and contractors of your entity or of
your subsidiary(ies) to access and use the SDK from your
secure network to perform work on your behalf.
If you are an academic institution you may allow users
enrolled or employed by the academic institution to access and
use the SDK from your secure network.
You are responsible for the compliance with the terms of this
Agreement by your authorized users. If you become aware that
your authorized users didn’t follow the terms of this
Agreement, you agree to take reasonable steps to resolve the
non-compliance and prevent new occurrences.
1.1.4. Pre-Release SDK
The SDK versions identified as alpha, beta, preview or
otherwise as pre-release, may not be fully functional, may
contain errors or design flaws, and may have reduced or
different security, privacy, accessibility, availability, and
reliability standards relative to commercial versions of
NVIDIA software and materials. Use of a pre-release SDK may
result in unexpected results, loss of data, project delays or
other unpredictable damage or loss.
You may use a pre-release SDK at your own risk, understanding
that pre-release SDKs are not intended for use in production
or business-critical systems.
NVIDIA may choose not to make available a commercial version
of any pre-release SDK. NVIDIA may also choose to abandon
development and terminate the availability of a pre-release
SDK at any time without liability.
1.1.5. Updates
NVIDIA may, at its option, make available patches, workarounds
or other updates to this SDK. Unless the updates are provided
with their separate governing terms, they are deemed part of
the SDK licensed to you as provided in this Agreement. You
agree that the form and content of the SDK that NVIDIA
provides may change without prior notice to you. While NVIDIA
generally maintains compatibility between versions, NVIDIA may
in some cases make changes that introduce incompatibilities in
future versions of the SDK.
1.1.6. Third Party Licenses
The SDK may come bundled with, or otherwise include or be
distributed with, third party software licensed by a NVIDIA
supplier and/or open source software provided under an open
source license. Use of third party software is subject to the
third-party license terms, or in the absence of third party
terms, the terms of this Agreement. Copyright to third party
software is held by the copyright holders indicated in the
third-party software or license.
Subject to the other terms of this Agreement, you may use the
SDK to develop and test applications released under Open
Source Initiative (OSI) approved open source software
licenses.
1.1.7. Reservation of Rights
NVIDIA reserves all rights, title, and interest in and to the
SDK, not expressly granted to you under this Agreement.
1.2. Limitations
The following license limitations apply to your use of the
SDK:
1. You may not reverse engineer, decompile or disassemble,
or remove copyright or other proprietary notices from any
portion of the SDK or copies of the SDK.
2. Except as expressly provided in this Agreement, you may
not copy, sell, rent, sublicense, transfer, distribute,
modify, or create derivative works of any portion of the
SDK. For clarity, you may not distribute or sublicense the
SDK as a stand-alone product.
3. Unless you have an agreement with NVIDIA for this
purpose, you may not indicate that an application created
with the SDK is sponsored or endorsed by NVIDIA.
4. You may not bypass, disable, or circumvent any
encryption, security, digital rights management or
authentication mechanism in the SDK.
5. You may not use the SDK in any manner that would cause it
to become subject to an open source software license. As
examples, licenses that require as a condition of use,
modification, and/or distribution that the SDK be:
a. Disclosed or distributed in source code form;
b. Licensed for the purpose of making derivative works;
or
c. Redistributable at no charge.
6. Unless you have an agreement with NVIDIA for this
purpose, you may not use the SDK with any system or
application where the use or failure of the system or
application can reasonably be expected to threaten or
result in personal injury, death, or catastrophic loss.
Examples include use in avionics, navigation, military,
medical, life support or other life critical applications.
NVIDIA does not design, test or manufacture the SDK for
these critical uses and NVIDIA shall not be liable to you
or any third party, in whole or in part, for any claims or
damages arising from such uses.
7. You agree to defend, indemnify and hold harmless NVIDIA
and its affiliates, and their respective employees,
contractors, agents, officers and directors, from and
against any and all claims, damages, obligations, losses,
liabilities, costs or debt, fines, restitutions and
expenses (including but not limited to attorney’s fees
and costs incident to establishing the right of
indemnification) arising out of or related to your use of
the SDK outside of the scope of this Agreement, or not in
compliance with its terms.
1.3. Ownership
1. NVIDIA or its licensors hold all rights, title and
interest in and to the SDK and its modifications and
derivative works, including their respective intellectual
property rights, subject to your rights under Section
1.3.2. This SDK may include software and materials from
NVIDIA’s licensors, and these licensors are intended
third party beneficiaries that may enforce this Agreement
with respect to their intellectual property rights.
2. You hold all rights, title and interest in and to your
applications and your derivative works of the sample
source code delivered in the SDK, including their
respective intellectual property rights, subject to
NVIDIA’s rights under Section 1.3.1.
3. You may, but don’t have to, provide to NVIDIA
suggestions, feature requests or other feedback regarding
the SDK, including possible enhancements or modifications
to the SDK. For any feedback that you voluntarily provide,
you hereby grant NVIDIA and its affiliates a perpetual,
non-exclusive, worldwide, irrevocable license to use,
reproduce, modify, license, sublicense (through multiple
tiers of sublicensees), and distribute (through multiple
tiers of distributors) it without the payment of any
royalties or fees to you. NVIDIA will use feedback at its
choice. NVIDIA is constantly looking for ways to improve
its products, so you may send feedback to NVIDIA through
the developer portal at https://developer.nvidia.com.
1.4. No Warranties
THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
DEALING OR COURSE OF TRADE.
1.5. Limitation of Liability
TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
LIMIT.
These exclusions and limitations of liability shall apply
regardless if NVIDIA or its affiliates have been advised of
the possibility of such damages, and regardless of whether a
remedy fails its essential purpose. These exclusions and
limitations of liability form an essential basis of the
bargain between the parties, and, absent any of these
exclusions or limitations of liability, the provisions of this
Agreement, including, without limitation, the economic terms,
would be substantially different.
1.6. Termination
1. This Agreement will continue to apply until terminated by
either you or NVIDIA as described below.
2. If you want to terminate this Agreement, you may do so by
stopping to use the SDK.
3. NVIDIA may, at any time, terminate this Agreement if:
a. (i) you fail to comply with any term of this
Agreement and the non-compliance is not fixed within
thirty (30) days following notice from NVIDIA (or
immediately if you violate NVIDIA’s intellectual
property rights);
b. (ii) you commence or participate in any legal
proceeding against NVIDIA with respect to the SDK; or
c. (iii) NVIDIA decides to no longer provide the SDK in
a country or, in NVIDIA’s sole discretion, the
continued use of it is no longer commercially viable.
4. Upon any termination of this Agreement, you agree to
promptly discontinue use of the SDK and destroy all copies
in your possession or control. Your prior distributions in
accordance with this Agreement are not affected by the
termination of this Agreement. Upon written request, you
will certify in writing that you have complied with your
commitments under this section. Upon any termination of
this Agreement all provisions survive except for the
license grant provisions.
1.7. General
If you wish to assign this Agreement or your rights and
obligations, including by merger, consolidation, dissolution
or operation of law, contact NVIDIA to ask for permission. Any
attempted assignment not approved by NVIDIA in writing shall
be void and of no effect. NVIDIA may assign, delegate or
transfer this Agreement and its rights and obligations, and if
to a non-affiliate you will be notified.
You agree to cooperate with NVIDIA and provide reasonably
requested information to verify your compliance with this
Agreement.
This Agreement will be governed in all respects by the laws of
the United States and of the State of Delaware as those laws
are applied to contracts entered into and performed entirely
within Delaware by Delaware residents, without regard to the
conflicts of laws principles. The United Nations Convention on
Contracts for the International Sale of Goods is specifically
disclaimed. You agree to all terms of this Agreement in the
English language.
The state or federal courts residing in Santa Clara County,
California shall have exclusive jurisdiction over any dispute
or claim arising out of this Agreement. Notwithstanding this,
you agree that NVIDIA shall still be allowed to apply for
injunctive remedies or an equivalent type of urgent legal
relief in any jurisdiction.
If any court of competent jurisdiction determines that any
provision of this Agreement is illegal, invalid or
unenforceable, such provision will be construed as limited to
the extent necessary to be consistent with and fully
enforceable under the law and the remaining provisions will
remain in full force and effect. Unless otherwise specified,
remedies are cumulative.
Each party acknowledges and agrees that the other is an
independent contractor in the performance of this Agreement.
The SDK has been developed entirely at private expense and is
“commercial items” consisting of “commercial computer
software” and “commercial computer software
documentation” provided with RESTRICTED RIGHTS. Use,
duplication or disclosure by the U.S. Government or a U.S.
Government subcontractor is subject to the restrictions in
this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
in subparagraphs (c)(1) and (2) of the Commercial Computer
Software - Restricted Rights clause at FAR 52.227-19, as
applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
Expressway, Santa Clara, CA 95051.
The SDK is subject to United States export laws and
regulations. You agree that you will not ship, transfer or
export the SDK into any country, or use the SDK in any manner,
prohibited by the United States Bureau of Industry and
Security or economic sanctions regulations administered by the
U.S. Department of Treasury’s Office of Foreign Assets
Control (OFAC), or any applicable export laws, restrictions or
regulations. These laws include restrictions on destinations,
end users and end use. By accepting this Agreement, you
confirm that you are not a resident or citizen of any country
currently embargoed by the U.S. and that you are not otherwise
prohibited from receiving the SDK.
Any notice delivered by NVIDIA to you under this Agreement
will be delivered via mail, email or fax. You agree that any
notices that NVIDIA sends you electronically will satisfy any
legal communication requirements. Please direct your legal
notices or other correspondence to NVIDIA Corporation, 2788
San Tomas Expressway, Santa Clara, California 95051, United
States of America, Attention: Legal Department.
This Agreement and any exhibits incorporated into this
Agreement constitute the entire agreement of the parties with
respect to the subject matter of this Agreement and supersede
all prior negotiations or documentation exchanged between the
parties relating to this SDK license. Any additional and/or
conflicting terms on documents issued by you are null, void,
and invalid. Any amendment or waiver under this Agreement
shall be in writing and signed by representatives of both
parties.
2. CUDA Toolkit Supplement to Software License Agreement for
NVIDIA Software Development Kits
------------------------------------------------------------
The terms in this supplement govern your use of the NVIDIA
CUDA Toolkit SDK under the terms of your license agreement
(“Agreement”) as modified by this supplement. Capitalized
terms used but not defined below have the meaning assigned to
them in the Agreement.
This supplement is an exhibit to the Agreement and is
incorporated as an integral part of the Agreement. In the
event of conflict between the terms in this supplement and the
terms in the Agreement, the terms in this supplement govern.
2.1. License Scope
The SDK is licensed for you to develop applications only for
use in systems with NVIDIA GPUs.
2.2. Distribution
The portions of the SDK that are distributable under the
Agreement are listed in Attachment A.
2.3. Operating Systems
Those portions of the SDK designed exclusively for use on the
Linux or FreeBSD operating systems, or other operating systems
derived from the source code to these operating systems, may
be copied and redistributed for use in accordance with this
Agreement, provided that the object code files are not
modified in any way (except for unzipping of compressed
files).
2.4. Audio and Video Encoders and Decoders
You acknowledge and agree that it is your sole responsibility
to obtain any additional third-party licenses required to
make, have made, use, have used, sell, import, and offer for
sale your products or services that include or incorporate any
third-party software and content relating to audio and/or
video encoders and decoders from, including but not limited
to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
under this Agreement any necessary patent or other rights with
respect to any audio and/or video encoders and decoders.
2.5. Licensing
If the distribution terms in this Agreement are not suitable
for your organization, or for any questions regarding this
Agreement, please contact NVIDIA at
[email protected].
2.6. Attachment A
The following CUDA Toolkit files may be distributed with
Licensee Applications developed by you, including certain
variations of these files that have version number or
architecture specific information embedded in the file name -
as an example only, for release version 9.0 of the 64-bit
Windows software, the file cudart64_90.dll is redistributable.
Component
CUDA Runtime
Windows
cudart.dll, cudart_static.lib, cudadevrt.lib
Mac OSX
libcudart.dylib, libcudart_static.a, libcudadevrt.a
Linux
libcudart.so, libcudart_static.a, libcudadevrt.a
Android
libcudart.so, libcudart_static.a, libcudadevrt.a
Component
CUDA FFT Library
Windows
cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
Mac OSX
libcufft.dylib, libcufft_static.a, libcufftw.dylib,
libcufftw_static.a
Linux
libcufft.so, libcufft_static.a, libcufftw.so,
libcufftw_static.a
Android
libcufft.so, libcufft_static.a, libcufftw.so,
libcufftw_static.a
Component
CUDA BLAS Library
Windows
cublas.dll, cublasLt.dll
Mac OSX
libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
libcublasLt_static.a
Linux
libcublas.so, libcublasLt.so, libcublas_static.a,
libcublasLt_static.a
Android
libcublas.so, libcublasLt.so, libcublas_static.a,
libcublasLt_static.a
Component
NVIDIA "Drop-in" BLAS Library
Windows
nvblas.dll
Mac OSX
libnvblas.dylib
Linux
libnvblas.so
Component
CUDA Sparse Matrix Library
Windows
cusparse.dll, cusparse.lib
Mac OSX
libcusparse.dylib, libcusparse_static.a
Linux
libcusparse.so, libcusparse_static.a
Android
libcusparse.so, libcusparse_static.a
Component
CUDA Linear Solver Library
Windows
cusolver.dll, cusolver.lib
Mac OSX
libcusolver.dylib, libcusolver_static.a
Linux
libcusolver.so, libcusolver_static.a
Android
libcusolver.so, libcusolver_static.a
Component
CUDA Random Number Generation Library
Windows
curand.dll, curand.lib
Mac OSX
libcurand.dylib, libcurand_static.a
Linux
libcurand.so, libcurand_static.a
Android
libcurand.so, libcurand_static.a
Component
NVIDIA Performance Primitives Library
Windows
nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
Mac OSX
libnppc.dylib, libnppc_static.a, libnppial.dylib,
libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
libnppidei_static.a, libnppif.dylib, libnppif_static.a,
libnppig.dylib, libnppig_static.a, libnppim.dylib,
libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
libnpps.dylib, libnpps_static.a
Linux
libnppc.so, libnppc_static.a, libnppial.so,
libnppial_static.a, libnppicc.so, libnppicc_static.a,
libnppicom.so, libnppicom_static.a, libnppidei.so,
libnppidei_static.a, libnppif.so, libnppif_static.a
libnppig.so, libnppig_static.a, libnppim.so,
libnppim_static.a, libnppist.so, libnppist_static.a,
libnppisu.so, libnppisu_static.a, libnppitc.so
libnppitc_static.a, libnpps.so, libnpps_static.a
Android
libnppc.so, libnppc_static.a, libnppial.so,
libnppial_static.a, libnppicc.so, libnppicc_static.a,
libnppicom.so, libnppicom_static.a, libnppidei.so,
libnppidei_static.a, libnppif.so, libnppif_static.a
libnppig.so, libnppig_static.a, libnppim.so,
libnppim_static.a, libnppist.so, libnppist_static.a,
libnppisu.so, libnppisu_static.a, libnppitc.so
libnppitc_static.a, libnpps.so, libnpps_static.a
Component
NVIDIA JPEG Library
Linux
libnvjpeg.so, libnvjpeg_static.a
Component
Internal common library required for statically linking to
cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
Mac OSX
libculibos.a
Linux
libculibos.a
Component
NVIDIA Runtime Compilation Library and Header
All
nvrtc.h
Windows
nvrtc.dll, nvrtc-builtins.dll
Mac OSX
libnvrtc.dylib, libnvrtc-builtins.dylib
Linux
libnvrtc.so, libnvrtc-builtins.so
Component
NVIDIA Optimizing Compiler Library
Windows
nvvm.dll
Mac OSX
libnvvm.dylib
Linux
libnvvm.so
Component
NVIDIA Common Device Math Functions Library
Windows
libdevice.10.bc
Mac OSX
libdevice.10.bc
Linux
libdevice.10.bc
Component
CUDA Occupancy Calculation Header Library
All
cuda_occupancy.h
Component
CUDA Half Precision Headers
All
cuda_fp16.h, cuda_fp16.hpp
Component
CUDA Profiling Tools Interface (CUPTI) Library
Windows
cupti.dll
Mac OSX
libcupti.dylib
Linux
libcupti.so
Component
NVIDIA Tools Extension Library
Windows
nvToolsExt.dll, nvToolsExt.lib
Mac OSX
libnvToolsExt.dylib
Linux
libnvToolsExt.so
Component
NVIDIA CUDA Driver Libraries
Linux
libcuda.so, libnvidia-ptxjitcompiler.so
Component
NVIDIA CUDA File IO Libraries and Header
All
cufile.h
Linux
libcufile.so, libcufile_rdma.so, libcufile_static.a,
libcufile_rdma_static.a
The NVIDIA CUDA Driver Libraries are only distributable in
applications that meet this criteria:
1. The application was developed starting from a NVIDIA CUDA
container obtained from Docker Hub or the NVIDIA GPU
Cloud, and
2. The resulting application is packaged as a Docker
container and distributed to users on Docker Hub or the
NVIDIA GPU Cloud only.
In addition to the rights above, for parties that are
developing software intended solely for use on Jetson
development kits or Jetson modules, and running Linux for
Tegra software, the following shall apply:
* The SDK may be distributed in its entirety, as provided by
NVIDIA, and without separation of its components, for you
and/or your licensees to create software development kits
for use only on the Jetson platform and running Linux for
Tegra software.
2.7. Attachment B
Additional Licensing Obligations
The following third party components included in the SOFTWARE
are licensed to Licensee pursuant to the following terms and
conditions:
1. Licensee's use of the GDB third party component is
subject to the terms and conditions of GNU GPL v3:
This product includes copyrighted third-party software licensed
under the terms of the GNU General Public License v3 ("GPL v3").
All third-party software packages are copyright by their respective
authors. GPL v3 terms and conditions are hereby incorporated into
the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt
Consistent with these licensing requirements, the software
listed below is provided under the terms of the specified
open source software licenses. To obtain source code for
software provided under licenses that require
redistribution of source code, including the GNU General
Public License (GPL) and GNU Lesser General Public License
(LGPL), contact [email protected]. This offer is
valid for a period of three (3) years from the date of the
distribution of this product by NVIDIA CORPORATION.
Component License
CUDA-GDB GPL v3
2. Licensee represents and warrants that any and all third
party licensing and/or royalty payment obligations in
connection with Licensee's use of the H.264 video codecs
are solely the responsibility of Licensee.
3. Licensee's use of the Thrust library is subject to the
terms and conditions of the Apache License Version 2.0.
All third-party software packages are copyright by their
respective authors. Apache License Version 2.0 terms and
conditions are hereby incorporated into the Agreement by
this reference.
http://www.apache.org/licenses/LICENSE-2.0.html
In addition, Licensee acknowledges the following notice:
Thrust includes source code from the Boost Iterator,
Tuple, System, and Random Number libraries.
Boost Software License - Version 1.0 - August 17th, 2003
. . . .
Permission is hereby granted, free of charge, to any person or
organization obtaining a copy of the software and accompanying
documentation covered by this license (the "Software") to use,
reproduce, display, distribute, execute, and transmit the Software,
and to prepare derivative works of the Software, and to permit
third-parties to whom the Software is furnished to do so, all
subject to the following:
The copyright notices in the Software and this entire statement,
including the above license grant, this restriction and the following
disclaimer, must be included in all copies of the Software, in whole
or in part, and all derivative works of the Software, unless such
copies or derivative works are solely in the form of machine-executable
object code generated by a source language processor.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
4. Licensee's use of the LLVM third party component is
subject to the following terms and conditions:
======================================================
LLVM Release License
======================================================
University of Illinois/NCSA
Open Source License
Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
All rights reserved.
Developed by:
LLVM Team
University of Illinois at Urbana-Champaign
http://llvm.org
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to
deal with the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimers.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimers in the
documentation and/or other materials provided with the distribution.
* Neither the names of the LLVM Team, University of Illinois at Urbana-
Champaign, nor the names of its contributors may be used to endorse or
promote products derived from this Software without specific prior
written permission.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS WITH THE SOFTWARE.
5. Licensee's use (e.g. nvprof) of the PCRE third party
component is subject to the following terms and
conditions:
------------
PCRE LICENCE
------------
PCRE is a library of functions to support regular expressions whose syntax
and semantics are as close as possible to those of the Perl 5 language.
Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
specified below. The documentation for PCRE, supplied in the "doc"
directory, is distributed under the same terms as the software itself. The
basic library functions are written in C and are freestanding. Also
included in the distribution is a set of C++ wrapper functions, and a just-
in-time compiler that can be used to optimize pattern matching. These are
both optional features that can be omitted when the library is built.
THE BASIC LIBRARY FUNCTIONS
---------------------------
Written by: Philip Hazel
Email local part: ph10
Email domain: cam.ac.uk
University of Cambridge Computing Service,
Cambridge, England.
Copyright (c) 1997-2012 University of Cambridge
All rights reserved.
PCRE JUST-IN-TIME COMPILATION SUPPORT
-------------------------------------
Written by: Zoltan Herczeg
Email local part: hzmester
Emain domain: freemail.hu
Copyright(c) 2010-2012 Zoltan Herczeg
All rights reserved.
STACK-LESS JUST-IN-TIME COMPILER
--------------------------------
Written by: Zoltan Herczeg
Email local part: hzmester
Emain domain: freemail.hu
Copyright(c) 2009-2012 Zoltan Herczeg
All rights reserved.
THE C++ WRAPPER FUNCTIONS
-------------------------
Contributed by: Google Inc.
Copyright (c) 2007-2012, Google Inc.
All rights reserved.
THE "BSD" LICENCE
-----------------
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 University of Cambridge nor the name of Google
Inc. nor the names of their 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"
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6. Some of the cuBLAS library routines were written by or
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follows:
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All rights reserved.
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7. Some of the cuBLAS library routines were written by or
derived from code written by Davide Barbieri and are
subject to the Modified Berkeley Software Distribution
License as follows:
Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
All rights reserved.
Redistribution and use in source and binary forms, with or without
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HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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8. Some of the cuBLAS library routines were derived from
code developed by the University of Tennessee and are
subject to the Modified Berkeley Software Distribution
License as follows:
Copyright (c) 2010 The University of Tennessee.
All rights reserved.
Redistribution and use in source and binary forms, with or without
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met:
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notice, this list of conditions and the following disclaimer.
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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.
9. Some of the cuBLAS library routines were written by or
derived from code written by Jonathan Hogg and are subject
to the Modified Berkeley Software Distribution License as
follows:
Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
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software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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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.
10. Some of the cuBLAS library routines were written by or
derived from code written by Ahmad M. Abdelfattah, David
Keyes, and Hatem Ltaief, and are subject to the Apache
License, Version 2.0, as follows:
-- (C) Copyright 2013 King Abdullah University of Science and Technology
Authors:
Ahmad Abdelfattah ([email protected])
David Keyes ([email protected])
Hatem Ltaief ([email protected])
Redistribution and use in source and binary forms, with or without
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are met:
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* Neither the name of the King Abdullah University of Science and
Technology nor the names of its contributors may be used to endorse
or promote products derived from this software without specific prior
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
11. Some of the cuSPARSE library routines were written by or
derived from code written by Li-Wen Chang and are subject
to the NCSA Open Source License as follows:
Copyright (c) 2012, University of Illinois.
All rights reserved.
Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal with the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
* 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
disclaimers in the documentation and/or other materials provided
with the distribution.
* Neither the names of IMPACT Group, University of Illinois, nor
the names of its contributors may be used to endorse or promote
products derived from this Software without specific prior
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HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
SOFTWARE.
12. Some of the cuRAND library routines were written by or
derived from code written by Mutsuo Saito and Makoto
Matsumoto and are subject to the following license:
Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
University. All rights reserved.
Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
University and University of Tokyo. 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 Hiroshima University nor the names of
its contributors may be used to endorse or promote products
derived from this software without specific prior written
permission.
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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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.
13. Some of the cuRAND library routines were derived from
code developed by D. E. Shaw Research and are subject to
the following license:
Copyright 2010-2011, D. E. Shaw Research.
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
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
14. Some of the Math library routines were written by or
derived from code developed by Norbert Juffa and are
subject to the following license:
Copyright (c) 2015-2017, Norbert Juffa
All rights reserved.
Redistribution and use in source and binary forms, with or without
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are met:
1. Redistributions of source code must retain the above copyright
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15. Licensee's use of the lz4 third party component is
subject to the following terms and conditions:
Copyright (C) 2011-2013, Yann Collet.
BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
Redistribution and use in source and binary forms, with or without
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16. The NPP library uses code from the Boost Math Toolkit,
and is subject to the following license:
Boost Software License - Version 1.0 - August 17th, 2003
. . . .
Permission is hereby granted, free of charge, to any person or
organization obtaining a copy of the software and accompanying
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17. Portions of the Nsight Eclipse Edition is subject to the
following license:
The Eclipse Foundation makes available all content in this plug-in
("Content"). Unless otherwise indicated below, the Content is provided
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If you did not receive this Content directly from the Eclipse
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18. Some of the cuBLAS library routines uses code from
OpenAI, which is subject to the following license:
License URL
https://github.com/openai/openai-gemm/blob/master/LICENSE
License Text
The MIT License
Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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19. Licensee's use of the Visual Studio Setup Configuration
Samples is subject to the following license:
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Permission is hereby granted, free of charge, to any person
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20. Licensee's use of linmath.h header for CPU functions for
GL vector/matrix operations from lunarG is subject to the
Apache License Version 2.0.
21. The DX12-CUDA sample uses the d3dx12.h header, which is
subject to the MIT license .
-----------------
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rapidsai_public_repos/cucim/3rdparty/LICENSE.dask-cuda
|
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rapidsai_public_repos/cucim/3rdparty/LICENSE.scifio
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| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.CLI11
|
CLI11 1.8 Copyright (c) 2017-2019 University of Cincinnati, developed by Henry
Schreiner under NSF AWARD 1414736. All rights reserved.
Redistribution and use in source and binary forms of CLI11, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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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.
| 0 |
rapidsai_public_repos/cucim
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rapidsai_public_repos/cucim/3rdparty/LICENSE.opencv-contrib-python
|
Apache License
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http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
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| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.libjpeg-turbo
|
libjpeg-turbo Licenses
======================
libjpeg-turbo is covered by three compatible BSD-style open source licenses:
- The IJG (Independent JPEG Group) License, which is listed in
[README.ijg](README.ijg)
This license applies to the libjpeg API library and associated programs
(any code inherited from libjpeg, and any modifications to that code.)
- The Modified (3-clause) BSD License, which is listed below
This license covers the TurboJPEG API library and associated programs, as
well as the build system.
- The [zlib License](https://opensource.org/licenses/Zlib)
This license is a subset of the other two, and it covers the libjpeg-turbo
SIMD extensions.
Complying with the libjpeg-turbo Licenses
=========================================
This section provides a roll-up of the libjpeg-turbo licensing terms, to the
best of our understanding.
1. If you are distributing a modified version of the libjpeg-turbo source,
then:
1. You cannot alter or remove any existing copyright or license notices
from the source.
**Origin**
- Clause 1 of the IJG License
- Clause 1 of the Modified BSD License
- Clauses 1 and 3 of the zlib License
2. You must add your own copyright notice to the header of each source
file you modified, so others can tell that you modified that file (if
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simply add a notice stating that you modified the file.)
**Origin**
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- Clause 2 of the zlib License
3. You must include the IJG README file, and you must not alter any of the
copyright or license text in that file.
**Origin**
- Clause 1 of the IJG License
2. If you are distributing only libjpeg-turbo binaries without the source, or
if you are distributing an application that statically links with
libjpeg-turbo, then:
1. Your product documentation must include a message stating:
This software is based in part on the work of the Independent JPEG
Group.
**Origin**
- Clause 2 of the IJG license
2. If your binary distribution includes or uses the TurboJPEG API, then
your product documentation must include the text of the Modified BSD
License (see below.)
**Origin**
- Clause 2 of the Modified BSD License
3. You cannot use the name of the IJG or The libjpeg-turbo Project or the
contributors thereof in advertising, publicity, etc.
**Origin**
- IJG License
- Clause 3 of the Modified BSD License
4. The IJG and The libjpeg-turbo Project do not warrant libjpeg-turbo to be
free of defects, nor do we accept any liability for undesirable
consequences resulting from your use of the software.
**Origin**
- IJG License
- Modified BSD License
- zlib License
The Modified (3-clause) BSD License
===================================
Copyright (C)2009-2020 D. R. Commander. All Rights Reserved.
Copyright (C)2015 Viktor Szathmáry. 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 libjpeg-turbo Project nor the names of its
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 HOLDERS 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.
Why Three Licenses?
===================
The zlib License could have been used instead of the Modified (3-clause) BSD
License, and since the IJG License effectively subsumes the distribution
conditions of the zlib License, this would have effectively placed
libjpeg-turbo binary distributions under the IJG License. However, the IJG
License specifically refers to the Independent JPEG Group and does not extend
attribution and endorsement protections to other entities. Thus, it was
desirable to choose a license that granted us the same protections for new code
that were granted to the IJG for code derived from their software.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.zarr-python
|
The MIT License (MIT)
Copyright (c) 2015-2018 Zarr Developers <https://github.com/zarr-developers>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.pytest-lazy-fixture
|
The MIT License (MIT)
Copyright (c) 2016 Marsel Zaripov
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.folly
|
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
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control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
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not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
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of this License, Derivative Works shall not include works that remain
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the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
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or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
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communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
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2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
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Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
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Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
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the conditions stated in this License.
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any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
Files in folly/external/farmhash licensed as follows
Copyright (c) 2014 Google, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.pba+
|
MIT License
Copyright (c) 2019 School of Computing, National University of Singapore
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/3rdparty/LICENSE.rmm
|
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
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rapidsai_public_repos/cucim/3rdparty/LICENSE.numpy
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rapidsai_public_repos/cucim/3rdparty/LICENSE.cupy
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rapidsai_public_repos/cucim/3rdparty/LICENSE.scikit-image
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Copyright (C) 2019, the scikit-image team
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rapidsai_public_repos/cucim/3rdparty/LICENSE.boost
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rapidsai_public_repos/cucim/3rdparty/LICENSE.json
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rapidsai_public_repos/cucim/3rdparty/LICENSE.nvjpeg2000
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Software License Agreement
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nvJPEG2K SUPPLEMENT TO SOFTWARE LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
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(v. August 27, 2020)
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rapidsai_public_repos/cucim/conda
|
rapidsai_public_repos/cucim/conda/environments/all_cuda-120_arch-x86_64.yaml
|
# This file is generated by `rapids-dependency-file-generator`.
# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
channels:
- rapidsai
- rapidsai-nightly
- conda-forge
- nvidia
dependencies:
- GPUtil>=1.4.0
- c-compiler
- click
- cmake>=3.23.1,!=3.25.0
- cuda-cudart-dev
- cuda-nvcc
- cuda-version=12.0
- cupy>=12.0.0
- cxx-compiler
- gcc_linux-64=11.*
- imagecodecs>=2021.6.8
- ipython
- jbig
- lazy_loader>=0.1
- libcufile-dev
- libnvjpeg-dev
- libnvjpeg-static
- libwebp-base
- matplotlib-base
- nbsphinx
- ninja
- numpy>=1.21.3
- numpydoc
- openslide-python>=1.1.2
- pip
- pooch>=1.6.0
- pre-commit
- psutil>=5.8.0
- pydata-sphinx-theme
- pytest-cov>=2.12.1
- pytest-lazy-fixture>=0.6.3
- pytest-xdist
- pytest>=6.2.4
- python>=3.8,<3.11
- recommonmark
- scikit-image>=0.19.0,<0.22.0a0
- scipy
- sphinx<6
- sysroot_linux-64==2.17
- tifffile>=2022.7.28
- xz
- yasm
- zlib
- zstd
- pip:
- opencv-python-headless>=4.6
name: all_cuda-120_arch-x86_64
| 0 |
rapidsai_public_repos/cucim/conda
|
rapidsai_public_repos/cucim/conda/environments/all_cuda-118_arch-x86_64.yaml
|
# This file is generated by `rapids-dependency-file-generator`.
# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
channels:
- rapidsai
- rapidsai-nightly
- conda-forge
- nvidia
dependencies:
- GPUtil>=1.4.0
- c-compiler
- click
- cmake>=3.23.1,!=3.25.0
- cuda-version=11.8
- cudatoolkit
- cupy>=12.0.0
- cxx-compiler
- gcc_linux-64=11.*
- imagecodecs>=2021.6.8
- ipython
- jbig
- lazy_loader>=0.1
- libcufile-dev=1.4.0.31
- libcufile=1.4.0.31
- libnvjpeg-dev=11.6.0.55
- libnvjpeg=11.6.0.55
- libwebp-base
- matplotlib-base
- nbsphinx
- ninja
- numpy>=1.21.3
- numpydoc
- nvcc_linux-64=11.8
- openslide-python>=1.1.2
- pip
- pooch>=1.6.0
- pre-commit
- psutil>=5.8.0
- pydata-sphinx-theme
- pytest-cov>=2.12.1
- pytest-lazy-fixture>=0.6.3
- pytest-xdist
- pytest>=6.2.4
- python>=3.8,<3.11
- recommonmark
- scikit-image>=0.19.0,<0.22.0a0
- scipy
- sphinx<6
- sysroot_linux-64==2.17
- tifffile>=2022.7.28
- xz
- yasm
- zlib
- zstd
- pip:
- opencv-python-headless>=4.6
name: all_cuda-118_arch-x86_64
| 0 |
rapidsai_public_repos/cucim/conda/recipes
|
rapidsai_public_repos/cucim/conda/recipes/libcucim/conda_build_config.yaml
|
c_compiler_version:
- 11
cxx_compiler_version:
- 11
cuda_compiler:
- cuda-nvcc
cuda11_compiler:
- nvcc
sysroot_version:
- "2.17"
# The CTK libraries below are missing from the conda-forge::cudatoolkit package
# for CUDA 11. The "*_host_*" version specifiers correspond to `11.8` packages
# and the "*_run_*" version specifiers correspond to `11.x` packages.
cuda11_libcufile_host_version:
- "1.4.0.31"
cuda11_libcufile_run_version:
- ">=1.0.0.82,<=1.4.0.31"
cuda11_libnvjpeg_host_version:
- "11.6.0.55"
| 0 |
rapidsai_public_repos/cucim/conda/recipes
|
rapidsai_public_repos/cucim/conda/recipes/libcucim/build.sh
|
# Copyright (c) 2021, NVIDIA CORPORATION.
CUCIM_BUILD_TYPE=${CUCIM_BUILD_TYPE:-release}
echo "CC : ${CC}"
echo "CXX : ${CXX}"
# CUDA needs to include $PREFIX/include as system include path
export CUDAFLAGS="-isystem $BUILD_PREFIX/include -isystem $PREFIX/include "
export LD_LIBRARY_PATH="$BUILD_PREFIX/lib:$PREFIX/lib:$LD_LIBRARY_PATH"
# It is assumed that this script is executed from the root of the repo directory by conda-build
# (https://conda-forge.org/docs/maintainer/knowledge_base.html#using-cmake)
# Build libcucim core
./run build_local libcucim ${CUCIM_BUILD_TYPE} ${PREFIX}
mkdir -p $PREFIX/bin $PREFIX/lib $PREFIX/include
cp -P -r install/bin/* $PREFIX/bin/ || true
cp -P -r install/lib/* $PREFIX/lib/ || true
cp -P -r install/include/* $PREFIX/include/ || true
# Build plugins
for plugin_name in cuslide cumed; do
echo "Building cucim.kit.${plugin_name} ..."
./run build_local ${plugin_name} ${CUCIM_BUILD_TYPE} ${PREFIX}
mkdir -p $PREFIX/bin $PREFIX/lib $PREFIX/include
cp -P -r cpp/plugins/cucim.kit.${plugin_name}/install/bin/* $PREFIX/bin/ || true
cp -P -r cpp/plugins/cucim.kit.${plugin_name}/install/lib/* $PREFIX/lib/ || true
done
| 0 |
rapidsai_public_repos/cucim/conda/recipes
|
rapidsai_public_repos/cucim/conda/recipes/libcucim/meta.yaml
|
# Copyright (c) 2021-2023, NVIDIA CORPORATION.
{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') %}
{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
{% set cuda_major = cuda_version.split('.')[0] %}
{% set date_string = environ['RAPIDS_DATE_STRING'] %}
package:
name: libcucim
version: {{ version }}
source:
path: ../../..
build:
number: {{ GIT_DESCRIBE_NUMBER }}
string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
ignore_run_exports:
- libwebp-base
ignore_run_exports_from:
{% if cuda_major == "11" %}
- {{ compiler('cuda11') }}
{% endif %}
script_env:
- AWS_ACCESS_KEY_ID
- AWS_SECRET_ACCESS_KEY
- AWS_SESSION_TOKEN
- CMAKE_C_COMPILER_LAUNCHER
- CMAKE_CUDA_COMPILER_LAUNCHER
- CMAKE_CXX_COMPILER_LAUNCHER
- CMAKE_GENERATOR
- PARALLEL_LEVEL
- SCCACHE_BUCKET
- SCCACHE_IDLE_TIMEOUT
- SCCACHE_REGION
- SCCACHE_S3_KEY_PREFIX=libcucim-aarch64 # [aarch64]
- SCCACHE_S3_KEY_PREFIX=libcucim-linux64 # [linux64]
- SCCACHE_S3_USE_SSL
- SCCACHE_S3_NO_CREDENTIALS
requirements:
build:
- {{ compiler("c") }}
- {{ compiler("cxx") }}
{% if cuda_major == "11" %}
- {{ compiler('cuda11') }} ={{ cuda_version }}
{% else %}
- {{ compiler('cuda') }}
{% endif %}
- cuda-version ={{ cuda_version }}
- binutils
- cmake >=3.23.1,!=3.25.0
- make
- ninja
- sysroot_{{ target_platform }} {{ sysroot_version }}
- yasm # [x86_64]
host:
- cuda-version ={{ cuda_version }}
{% if cuda_major == "11" %}
- libcufile {{ cuda11_libcufile_host_version }} # [linux64]
- libcufile-dev {{ cuda11_libcufile_host_version }} # [linux64]
- libnvjpeg {{ cuda11_libnvjpeg_host_version }}
- libnvjpeg-dev {{ cuda11_libnvjpeg_host_version }}
{% else %}
- libcufile-dev # [linux64]
- libnvjpeg-dev
- libnvjpeg-static
{% endif %}
- jbig
- libwebp-base
- nvtx-c >=3.1.0
- openslide
- xz
- zlib
- zstd
run:
- {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
{% if cuda_major == "11" %}
- cudatoolkit
- libcufile {{ cuda11_libcufile_run_version }} # [linux64]
{% endif %}
- {{ pin_compatible('libwebp-base', max_pin='x.x') }}
- jbig
# - openslide # skipping here but benchmark binary would need openslide library
- xz
- zlib
- zstd
about:
home: https://developer.nvidia.com/multidimensional-image-processing
summary: libcucim C++ library
license: Apache-2.0
license_family: Apache
license_file: LICENSE
doc_url: https://docs.rapids.ai/api/cucim/stable/
dev_url: https://github.com/rapidsai/cucim
| 0 |
rapidsai_public_repos/cucim/conda/recipes
|
rapidsai_public_repos/cucim/conda/recipes/cucim/conda_build_config.yaml
|
c_compiler_version:
- 11
cxx_compiler_version:
- 11
cuda_compiler:
- cuda-nvcc
cuda11_compiler:
- nvcc
sysroot_version:
- "2.17"
| 0 |
rapidsai_public_repos/cucim/conda/recipes
|
rapidsai_public_repos/cucim/conda/recipes/cucim/build.sh
|
# Copyright (c) 2021, NVIDIA CORPORATION.
CUCIM_BUILD_TYPE=${CUCIM_BUILD_TYPE:-release}
echo "CC : ${CC}"
echo "CXX : ${CXX}"
# CUDA needs to include $PREFIX/include as system include path
export CUDAFLAGS="-isystem $BUILD_PREFIX/include -isystem $PREFIX/include "
export LD_LIBRARY_PATH="$BUILD_PREFIX/lib:$PREFIX/lib:$LD_LIBRARY_PATH"
# It is assumed that this script is executed from the root of the repo directory by conda-build
# (https://conda-forge.org/docs/maintainer/knowledge_base.html#using-cmake)
./run build_local cucim ${CUCIM_BUILD_TYPE}
cp -P python/install/lib/* python/cucim/src/cucim/clara/
pushd python/cucim
echo "PYTHON: ${PYTHON}"
$PYTHON -m pip install . -vv
popd
| 0 |
rapidsai_public_repos/cucim/conda/recipes
|
rapidsai_public_repos/cucim/conda/recipes/cucim/meta.yaml
|
# Copyright (c) 2021-2023, NVIDIA CORPORATION.
{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') %}
{% set py_version = environ['CONDA_PY'] %}
{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
{% set cuda_major = cuda_version.split('.')[0] %}
{% set date_string = environ['RAPIDS_DATE_STRING'] %}
package:
name: cucim
version: {{ version }}
source:
path: ../../..
build:
number: {{ GIT_DESCRIBE_NUMBER }}
string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
ignore_run_exports_from:
{% if cuda_major == "11" %}
- {{ compiler('cuda11') }}
{% endif %}
script_env:
- AWS_ACCESS_KEY_ID
- AWS_SECRET_ACCESS_KEY
- AWS_SESSION_TOKEN
- CMAKE_C_COMPILER_LAUNCHER
- CMAKE_CUDA_COMPILER_LAUNCHER
- CMAKE_CXX_COMPILER_LAUNCHER
- CMAKE_GENERATOR
- PARALLEL_LEVEL
- SCCACHE_BUCKET
- SCCACHE_IDLE_TIMEOUT
- SCCACHE_REGION
- SCCACHE_S3_KEY_PREFIX=cucim-aarch64 # [aarch64]
- SCCACHE_S3_KEY_PREFIX=cucim-linux64 # [linux64]
- SCCACHE_S3_USE_SSL
- SCCACHE_S3_NO_CREDENTIALS
requirements:
build:
- {{ compiler("c") }}
- {{ compiler("cxx") }}
{% if cuda_major == "11" %}
- {{ compiler('cuda11') }} ={{ cuda_version }}
{% else %}
- {{ compiler('cuda') }}
{% endif %}
- cuda-version ={{ cuda_version }}
- cmake >=3.23.1,!=3.25.0
- make
- ninja
- sysroot_{{ target_platform }} {{ sysroot_version }}
host:
- click
- cuda-version ={{ cuda_version }}
- cupy >=12.0.0
- libcucim ={{ version }}
- numpy 1.21
- python
- scikit-image >=0.19.0,<0.22.0a0
- scipy
run:
- {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
- {{ pin_compatible('numpy') }}
- click
- cupy >=12.0.0
- lazy_loader >=0.1
- libcucim ={{ version }}
# - openslide # skipping here but benchmark binary would need openslide library
- python
- scikit-image >=0.19.0,<0.22.0a0
- scipy
tests:
requirements:
- cuda-version ={{ cuda_version }}
imports:
- cucim
about:
home: https://developer.nvidia.com/multidimensional-image-processing
summary: cucim Python package
license: Apache-2.0
license_family: Apache
license_file: LICENSE
doc_url: https://docs.rapids.ai/api/cucim/stable/
dev_url: https://github.com/rapidsai/cucim
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/scripts/run-dist
|
#!/bin/bash
#
# Copyright (c) 2020, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
init_globals() {
if [ "$0" != "/bin/bash" ]; then
SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
export RUN_SCRIPT_FILE="$(readlink -f "$0")"
else
export RUN_SCRIPT_FILE="$(readlink -f "${BASH_SOURCE[0]}")"
fi
export TOP=$(dirname "${RUN_SCRIPT_FILE}")
}
################################################################################
# Utility functions
################################################################################
#######################################
# Get list of available commands from a given input file.
#
# Available commands and command summary are extracted by checking a pattern
# "_desc() { echo '".
# Section title is extracted by checking a pattern "# Section: ".
# This command is used for listing available commands in CLI.
#
# e.g.)
# "# Section: String/IO functions"
# => "# String/IO functions"
# "to_lower_desc() { echo 'Convert to lower case"
# => "to_lower ----------------- Convert to lower case"
#
# Arguments:
# $1 - input file that defines commands
# Returns:
# Print list of available commands from $1
#######################################
get_list_of_available_commands() {
local file_name="$1"
if [ ! -e "$1" ]; then
echo "$1 doesn't exist!"
fi
local line_str='--------------------------------'
local IFS= cmd_lines="$(IFS= cat "$1" | grep -E -e "^(([[:alpha:]_[:digit:]]+)_desc\(\)|# Section: )" | sed "s/_desc() *{ *echo '/ : /")"
local line
while IFS= read -r line; do
local cmd=$(echo "$line" | cut -d":" -f1)
local desc=$(echo "$line" | cut -d":" -f2-)
if [ "$cmd" = "# Section" ]; then
c_echo B "${desc}"
else
# there is no substring operation in 'sh' so use 'cut'
local dash_line="$(echo "${line_str}" | cut -c ${#cmd}-)" # = "${line_str:${#cmd}}"
c_echo Y " ${cmd}" w " ${dash_line} ${desc}"
fi
# use <<EOF, not '<<<"$cmd_lines"' to be executable in sh
done <<EOF
$cmd_lines
EOF
}
my_cat_prefix() {
local IFS
local prefix="$1"
local line
while IFS= read -r line; do
echo "${prefix}${line}" # -e option doesn't work in 'sh' so disallow escaped characters
done <&0
}
c_str() {
local old_color=39
local old_attr=0
local color=39
local attr=0
local text=""
#local no_change=0
for i in "$@"; do
case "$i" in
r|R)
color=31
;;
g|G)
color=32
;;
y|Y)
color=33
;;
b|B)
color=34
;;
p|P)
color=35
;;
c|C)
color=36
;;
w|W)
color=37
;;
z|Z)
color=0
;;
esac
case "$i" in
l|L|R|G|Y|B|P|C|W)
attr=1
;;
n|N|r|g|y|b|p|c|w)
attr=0
;;
z|Z)
attr=0
;;
*)
text="${text}$i"
esac
if [ ${old_color} -ne ${color} ] || [ ${old_attr} -ne ${attr} ]; then
text="${text}\033[${attr};${color}m"
old_color=$color
old_attr=$attr
fi
done
/bin/echo -en "$text"
}
c_echo() {
local old_opt="$(shopt -op xtrace)" # save old xtrace option
set +x # unset xtrace
local text="$(c_str "$@")"
/bin/echo -e "$text\033[0m"
eval "${old_opt}" # restore old xtrace option
}
echo_err() {
>&2 echo "$@"
}
c_echo_err() {
>&2 c_echo "$@"
}
printf_err() {
>&2 printf "$@"
}
get_item_ranges() {
local indexes="$1"
local list="$2"
echo -n "$(echo "${list}" | xargs | cut -d " " -f "${indexes}")"
return $?
}
get_unused_ports() {
local num_of_ports=${1:-1}
local start=${2:-49152}
local end=${3:-61000}
comm -23 \
<(seq ${start} ${end} | sort) \
<(ss -tan | awk '{print $4}' | while read line; do echo ${line##*\:}; done | grep '[0-9]\{1,5\}' | sort -u) \
| shuf | tail -n ${num_of_ports} # use tail instead head to avoid broken pipe in VSCode terminal
}
newline() {
echo
}
info() {
c_echo W "$(date -u '+%Y-%m-%d %H:%M:%S') [INFO] " Z "$@"
}
error() {
echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [ERROR] " Z "$@"
}
fatal() {
echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [FATAL] " Z "$@"
echo
if [ -n "${SCRIPT_DIR}" ]; then
exit 1
fi
}
run_command() {
local status=0
local cmd="$*"
c_echo B "$(date -u '+%Y-%m-%d %H:%M:%S') " W "\$ " G "${cmd}"
[ "$(echo -n "$@")" = "" ] && return 1 # return 1 if there is no command available
"$@"
status=$?
unset IFS
return $status
}
retry() {
local retries=$1
shift
local count=0
until run_command "$@"; do
exit=$?
wait=$((2 ** count))
count=$((count + 1))
if [ $count -lt $retries ]; then
info "Retry $count/$retries. Exit code=$exit, Retrying in $wait seconds..."
sleep $wait
else
fatal "Retry $count/$retries. Exit code=$exit, no more retries left."
return 1
fi
done
return 0
}
#==================================================================================
# Section: Example
#==================================================================================
download_testdata_desc() { echo 'Download test data from Docker Hub
'
}
download_testdata() {
c_echo W "Downloading test data..."
run_command mkdir -p ${TOP}/notebooks/input
if [ ! -e ${TOP}/notebooks/input/README.md ]; then
run_command rm -rf ${TOP}/notebooks/input
id=$(docker create gigony/svs-testdata:little-big)
run_command docker cp $id:/input ${TOP}/notebooks
run_command docker rm -v $id
c_echo G "Test data is downloaded to ${TOP}/notebooks/input!"
else
c_echo G "Test data already exists at ${TOP}/notebooks/input!"
fi
}
copy_gds_files_() {
[ ! -d /usr/local/cuda/gds ] && c_echo_err R "GDS is not available at /usr/local/cuda/gds !" && return 1
rm -rf ${TOP}/temp/gds
mkdir -p ${TOP}/temp/gds/lib64
cp -P -r /usr/local/cuda/gds/* ${TOP}/temp/gds/
cp -P /usr/local/cuda/lib64/cufile.h /usr/local/cuda/lib64/libcufile* ${TOP}/temp/gds/lib64/
}
launch_notebooks_desc() { echo 'Launch jupyter notebooks
Arguments:
-p <port> - port number
-h <host> - hostname to serve documentation on (default: 0.0.0.0)
-g - launch GDS-enabled container
'
}
launch_notebooks() {
local OPTIND
local port=$(get_unused_ports 1 10000 10030)
local host='0.0.0.0'
local gds_postfix=''
local gds_nvme_path=''
while getopts 'p:h:g:' option;
do
case "${option}" in
p)
port="$OPTARG"
;;
h)
host="$OPTARG"
;;
g)
gds_postfix='-gds'
echo "# OPTARG:$OPTARG"
[ -z "$OPTARG" ] && c_echo_err R "Please specify NVMe path!" && return 1
gds_nvme_path=$(readlink -f "$OPTARG")
[ ! -d "$gds_nvme_path" ] && c_echo_err R "Folder $gds_nvme_path doesn't exist!" && return 1
# Copy cufile SDK from host system to temp/gds
copy_gds_files_
;;
*)
return 1
esac
done
download_testdata
run_command cp ${TOP}/*.whl ${TOP}/notebooks
run_command docker build --runtime nvidia -t cucim-jupyter${gds_postfix} -f ${TOP}/docker/Dockerfile-jupyter${gds_postfix} ${TOP}
[ $? -ne 0 ] && return 1
c_echo W "Port " G "$port" W " would be used...(" B "http://$(hostname -I | cut -d' ' -f 1):${port}" W ")"
if [ -z "${gds_postfix}" ]; then
run_command docker run --runtime nvidia --gpus all -it --rm \
-v ${TOP}/notebooks:/notebooks \
-p ${port}:${port} \
cucim-jupyter \
-c "echo -n 'Enter New Password: '; jupyter lab --ServerApp.password=\"\$(python3 -u -c \"from jupyter_server.auth import passwd;pw=input();print(passwd(pw));\" | egrep 'sha|argon')\" --ServerApp.root_dir=/notebooks --allow-root --port=${port} --ip=${host} --no-browser"
else
local MNT_PATH=/nvme
local GDS_IMAGE=cucim-jupyter${gds_postfix}
local BUILD_VER=`uname -r`
local NV_DRIVER=`nvidia-smi -q -i 0 | sed -n 's/Driver Version.*: *\(.*\) *$/\1/p'`
echo "using nvidia driver version $NV_DRIVER on kernel $BUILD_VER"
local ofed_version=$(ofed_info -s | grep MLNX)
if [ $? -eq 0 ]; then
local rdma_core=$(dpkg -s libibverbs-dev | grep "Source: rdma-core")
if [ $? -eq 0 ]; then
local CONFIG_MOFED_VERSION=$(echo $ofed_version | cut -d '-' -f 2)
echo "Found MOFED version $CONFIG_MOFED_VERSION"
fi
local MLNX_SRCS="--volume /usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:/usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:ro"
local MOFED_DEVS="--net=host --volume /sys/class/infiniband_verbs:/sys/class/infiniband_verbs/ "
fi
docker run \
--ipc host \
-it \
--rm \
--gpus all \
-v ${TOP}/notebooks:/notebooks \
-p ${port}:${port} \
--volume /run/udev:/run/udev:ro \
--volume /sys/kernel/config:/sys/kernel/config/ \
--volume /usr/src/nvidia-$NV_DRIVER:/usr/src/nvidia-$NV_DRIVER:ro ${MLNX_SRCS}\
--volume /dev:/dev:ro \
--privileged \
--env NV_DRIVER=${NV_DRIVER} \
--volume /lib/modules/$BUILD_VER/:/lib/modules/$BUILD_VER \
--volume "${MNT_PATH}:/notebooks/nvme:rw" \
${MOFED_DEVS} \
${GDS_IMAGE} \
-c "echo -n 'Enter New Password: '; jupyter lab --ServerApp.password=\"\$(python3 -u -c \"from jupyter_server.auth import passwd;pw=input();print(passwd(pw));\" | egrep 'sha|argon')\" --ServerApp.root_dir=/notebooks --allow-root --port=${port} --ip=${host} --no-browser"
fi
}
#==================================================================================
# Section: Build
#==================================================================================
build_train() {
local image_name=${1:-cucim-train}
run_command docker build -t ${image_name} -f ${TOP}/docker/Dockerfile-claratrain ${TOP}/docker
}
build_train_desc() { echo 'Build Clara Train Docker image with cuCIM (& OpenSlide)
Build image from docker/Dockerfile-claratrain
Arguments:
$1 - docker image name (default:cucim-train)
'
}
build_train() {
local image_name=${1:-cucim-train}
run_command docker build -t ${image_name} -f ${TOP}/docker/Dockerfile-claratrain ${TOP}
}
build_examples_desc() { echo 'Build cuCIM C++ examples
'
}
build_examples() {
local image_name=cucim-cmake
run_command docker build -t ${image_name} -f ${TOP}/docker/Dockerfile-cmake ${TOP}/docker
run_command docker run -it --rm \
-v ${TOP}:/workspace \
${image_name} \
-c "
mkdir -p /workspace/examples/cpp/build;
rm -rf /workspace/examples/cpp/build/*;
cd /workspace/examples/cpp/build;
cmake .. && make"
c_echo W "Copying binary files to ${TOP}/bin folder..."
run_command mkdir -p ${TOP}/bin
run_command cp ${TOP}/examples/cpp/build/bin/* ${TOP}/bin
download_testdata
c_echo W "Execute the binary with the following commands:"
c_echo " # Set library path"
c_echo B " export LD_LIBRARY_PATH=${TOP}/install/lib:\$LD_LIBRARY_PATH"
c_echo " # Execute"
c_echo B " ./bin/tiff_image notebooks/input/image.tif ."
}
parse_args() {
local OPTIND
while getopts 'yh' option;
do
case "${option}" in
y)
ALWAYS_YES=true;
;;
h)
print_usage
exit 1
;;
*)
;;
esac
done
shift $((OPTIND-1))
CMD="$1"
shift
ARGS=("$@")
}
print_usage() {
set +x
echo_err
echo_err "USAGE: $0 [command] [arguments]..."
echo_err ""
c_echo_err W "Global Arguments"
echo_err
c_echo_err W "Command List"
c_echo_err Y " help " w "---------------------------- Print detailed description for a given argument (command name)"
echo_err "$(get_list_of_available_commands "${RUN_SCRIPT_FILE}" | my_cat_prefix " ")"
echo_err
}
print_cmd_help_messages() {
local cmd="$1"
if [ -n "${cmd}" ]; then
if type ${cmd}_desc > /dev/null 2>&1; then
${cmd}_desc
exit 0
else
c_echo_err R "Command '${cmd}' doesn't exist!"
exit 1
fi
fi
print_usage
return 0
}
main() {
local ret=0
parse_args "$@"
case "$CMD" in
help)
print_cmd_help_messages "${ARGS[@]}"
exit 0
;;
build)
build_examples "${ARGS[@]}"
;;
notebooks)
launch_notebooks "${ARGS[@]}"
;;
''|main)
print_usage
;;
*)
if type ${CMD} > /dev/null 2>&1; then
"$CMD" "${ARGS[@]}"
else
print_usage
exit 1
fi
;;
esac
ret=$?
if [ -n "${SCRIPT_DIR}" ]; then
exit $ret
fi
}
init_globals
if [ -n "${SCRIPT_DIR}" ]; then
main "$@"
fi
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/scripts/auditwheel_repair.py
|
# Apache License, Version 2.0
# Copyright 2020 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import re
from unittest.mock import patch
import auditwheel.elfutils
from auditwheel.main import main
from auditwheel.wheeltools import InWheelCtx
# How auditwheel repair works?
#
# From https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/wheel_abi.py#L38
# 1) Find a Python extension libraries(.so)
# if so ==> A, else ==> B
# 2) `needed_libs` <== external libraries needed by A and B
# 3) From b in B,
# if b is not in `needed_libs`, b is added to A
# 4) Only external libraries in A are patched to use locally copied .so files
# - external libraries that exists under wheel path are discarded
# - https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/policy/external_references.py#L61
# - https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/repair.py#L62
#
# With current implementation,
# - `cucim/_cucim.cpython-XX-x86_64-linux-gnu.so` files are in A by 1)
# - `cucim/libcucim.so.??` is in B by 1)
# - `cucim/libcucim.so.??` and `libcudart.so.11.0` are in `needed_libs` by 2)
# - `cucim/cucim.kit.cuslide@??.??.??.so` is in A by 3)
#
# And only libz and libcudart are considered as external libraries.
# To work with cuCIM, we need to
# 1) make `cucim/libcucim.so.??` as Python extension library
# - Patch elf_is_python_extension : https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/elfutils.py#L81
# 2) control how to copy external libraries
# - Patch copylib: https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/repair.py#L108
# - Need for libnvjpeg library
# 3) preprocess wheel metadata
# - patch InWheelCtx.__enter__ : https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/wheeltools.py#L158
# - `Root-Is-Purelib: true` -> `Root-Is-Purelib: false` from WHEEL file
# Parameters
PYTHON_EXTENSION_LIBRARIES = [r"cucim/libcucim\.so\.\d{1,2}"]
# 1) auditwheel.elfutils.elf_is_python_extension replacement
orig_elf_is_python_extension = auditwheel.elfutils.elf_is_python_extension
@functools.wraps(orig_elf_is_python_extension)
def elf_is_python_extension(fn, elf):
if any(map(lambda x: re.fullmatch(x, fn), PYTHON_EXTENSION_LIBRARIES)):
print("[cuCIM] Consider {} as a python extension.".format(fn))
return True, 3
return orig_elf_is_python_extension(fn, elf)
# 3) auditwheel.wheeltools.InWheelCtx.__enter__ replacement
orig_inwheelctx_enter = InWheelCtx.__enter__
@functools.wraps(orig_inwheelctx_enter)
def inwheelctx_enter(self):
rtn = orig_inwheelctx_enter(self)
# `self.path` is a path that extracted files from the wheel file exists
# base_dir = glob.glob(join(self.path, '*.dist-info'))
# wheel_path = join(base_dir[0], 'WHEEL')
# with open(wheel_path, 'r') as f:
# wheel_text = f.read()
# wheel_text = wheel_text.replace('Root-Is-Purelib: true', 'Root-Is-Purelib: false') # noqa: E501
# with open(wheel_path, 'w') as f:
# f.write(wheel_text)
return rtn
# # sys.argv replacement
# testargs = ["auditwheel_repair.py", "repair", "--plat", "manylinux2014_x86_64", "-w", "wherehouse", "cuclara_image-0.1.1-py3-none-manylinux2014_x86_64.whl"] # noqa: E501
# with patch.object(sys, 'argv', testargs):
if __name__ == "__main__":
# Patch
with patch.object(
auditwheel.elfutils, "elf_is_python_extension", elf_is_python_extension
):
with patch.object(InWheelCtx, "__enter__", inwheelctx_enter):
main()
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/scripts/debug_python
|
#!/bin/bash
#
# Copyright (c) 2021, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
if [ -e ${SCRIPT_DIR}/debug_env.sh ]; then
# User can place debug_env.sh in the same directory as this script (scripts/debug_env.sh would be ignored in git repo)
. ${SCRIPT_DIR}/debug_env.sh
elif [ -e ${SCRIPT_DIR}/../.python-version ]; then
# Need to init in script: https://github.com/pyenv/pyenv-virtualenv/issues/204
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"
# Above will do `pyenv activate $(cat ${SCRIPT_DIR}/../.python-version)`.
else
echo "Environment file not found. Exiting."
exit 1
fi
echo "Python: $(python3 -c "import sys;print(sys.executable)")"
exec env python3 "$@"
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/benchmarks/config.h
|
/*
* Apache License, Version 2.0
* Copyright 2020-2021 NVIDIA Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef CUCIM_CONFIG_H
#define CUCIM_CONFIG_H
#include <string>
struct AppConfig
{
std::string test_folder;
std::string test_file;
bool discard_cache = false;
int random_seed = 0;
bool random_start_location = false;
int64_t image_width = 0;
int64_t image_height = 0;
// Pseudo configurations for google benchmark
bool benchmark_list_tests = false;
std::string benchmark_filter; // <regex>
int benchmark_min_time = 0; // <min_time>
int benchmark_repetitions = 0; // <num_repetitions>
bool benchmark_report_aggregates_only = false;
bool benchmark_display_aggregates_only = false;
std::string benchmark_format; // <console|json|csv>
std::string benchmark_out; // <filename>
std::string benchmark_out_format; // <json|console|csv>
std::string benchmark_color; // {auto|true|false}
std::string benchmark_counters_tabular;
std::string v; // <verbosity>
std::string get_input_path(const std::string default_value = "generated/tiff_stripe_4096x4096_256.tif") const
{
// If `test_file` is absolute path
if (!test_folder.empty() && test_file.substr(0, 1) == "/")
{
return test_file;
}
else
{
std::string test_data_folder = test_folder;
if (test_data_folder.empty())
{
if (const char* env_p = std::getenv("CUCIM_TESTDATA_FOLDER"))
{
test_data_folder = env_p;
}
else
{
test_data_folder = "test_data";
}
}
if (test_file.empty())
{
return test_data_folder + "/" + default_value;
}
else
{
return test_data_folder + "/" + test_file;
}
}
}
};
#endif // CUCIM_CONFIG_H
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/benchmarks/CMakeLists.txt
|
#
# Copyright (c) 2020, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
################################################################################
# Add executable: cucim_benchmarks
################################################################################
add_executable(cucim_benchmarks main.cpp config.h)
set_target_properties(cucim_benchmarks
PROPERTIES
CXX_STANDARD 17
CXX_STANDARD_REQUIRED YES
CXX_EXTENSIONS NO
)
target_compile_features(cucim_benchmarks PRIVATE ${CUCIM_REQUIRED_FEATURES})
# Use generator expression to avoid `nvcc fatal : Value '-std=c++17' is not defined for option 'Werror'`
target_compile_options(cucim_benchmarks PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
target_compile_definitions(cucim_benchmarks
PUBLIC
CUCIM_VERSION=${PROJECT_VERSION}
CUCIM_VERSION_MAJOR=${PROJECT_VERSION_MAJOR}
CUCIM_VERSION_MINOR=${PROJECT_VERSION_MINOR}
CUCIM_VERSION_PATCH=${PROJECT_VERSION_PATCH}
CUCIM_VERSION_BUILD=${PROJECT_VERSION_BUILD}
)
target_link_libraries(cucim_benchmarks
PRIVATE
${CUCIM_PACKAGE_NAME}
deps::googlebenchmark
deps::openslide
deps::cli11
)
################################################################################
# Add executable: cucim_primitives_benchmarks
################################################################################
add_executable(cucim_primitives_benchmarks primitives.cpp)
#set_source_files_properties(main.cpp PROPERTIES LANGUAGE CUDA) # failed with CLI11 library
set_target_properties(cucim_primitives_benchmarks
PROPERTIES
CXX_STANDARD 17
CXX_STANDARD_REQUIRED YES
CXX_EXTENSIONS NO
)
target_compile_features(cucim_primitives_benchmarks PRIVATE ${CUCIM_REQUIRED_FEATURES})
# Use generator expression to avoid `nvcc fatal : Value '-std=c++17' is not defined for option 'Werror'`
target_compile_options(cucim_primitives_benchmarks PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
target_link_libraries(cucim_primitives_benchmarks
PRIVATE
${CUCIM_PACKAGE_NAME}
deps::googlebenchmark
)
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/benchmarks/main.cpp
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "config.h"
#include <fcntl.h>
#include <unistd.h>
#include <cstdlib>
#include <cstring>
#include <fmt/format.h>
#include <benchmark/benchmark.h>
#include <openslide/openslide.h>
#include <CLI/CLI.hpp>
#include "cucim/cuimage.h"
static AppConfig g_config;
static void test_cucim(benchmark::State& state)
{
std::string input_path = g_config.get_input_path();
int arg = -1;
for (auto state_item : state)
{
state.PauseTiming();
{
// Use a different start random seed for the different argument
if (arg != state.range())
{
arg = state.range();
srand(g_config.random_seed + arg);
}
if (g_config.discard_cache)
{
int fd = open(input_path.c_str(), O_RDONLY);
fdatasync(fd);
posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED);
close(fd);
}
}
state.ResumeTiming();
int64_t request_location[2] = { 0, 0 };
if (g_config.random_start_location)
{
request_location[0] = rand() % (g_config.image_width - state.range(0));
request_location[1] = rand() % (g_config.image_height - state.range(0));
}
cucim::CuImage image = cucim::CuImage(input_path.c_str());
cucim::CuImage region =
image.read_region({ request_location[0], request_location[1] }, { state.range(0), state.range(0) }, 0);
}
}
static void test_openslide(benchmark::State& state)
{
std::string input_path = g_config.get_input_path();
int arg = -1;
for (auto _ : state)
{
state.PauseTiming();
{
// Use a different start random seed for the different argument
if (arg != state.range())
{
arg = state.range();
srand(g_config.random_seed + arg);
}
if (g_config.discard_cache)
{
int fd = open(input_path.c_str(), O_RDONLY);
fdatasync(fd);
posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED);
close(fd);
}
}
state.ResumeTiming();
openslide_t* slide = openslide_open(input_path.c_str());
uint32_t* buf = static_cast<uint32_t*>(cucim_malloc(state.range(0) * state.range(0) * 4));
int64_t request_location[2] = { 0, 0 };
if (g_config.random_start_location)
{
request_location[0] = rand() % (g_config.image_width - state.range(0));
request_location[1] = rand() % (g_config.image_height - state.range(0));
}
openslide_read_region(slide, buf, request_location[0], request_location[1], 0, state.range(0), state.range(0));
cucim_free(buf);
openslide_close(slide);
}
}
BENCHMARK(test_cucim)->Unit(benchmark::kMicrosecond)->RangeMultiplier(2)->Range(1, 4096);
BENCHMARK(test_openslide)->Unit(benchmark::kMicrosecond)->RangeMultiplier(2)->Range(1, 4096);
static bool remove_help_option(int* argc, char** argv)
{
for (int i = 1; argc && i < *argc; ++i)
{
if (strncmp(argv[i], "-h", 3) == 0 || strncmp(argv[i], "--help", 7) == 0)
{
for (int j = i + 1; argc && j < *argc; ++j)
{
argv[j - 1] = argv[j];
}
--(*argc);
argv[*argc] = nullptr;
return true;
}
}
return false;
}
static bool setup_configuration()
{
std::string input_path = g_config.get_input_path();
openslide_t* slide = openslide_open(input_path.c_str());
if (slide == nullptr)
{
fmt::print("[Error] Cannot load {}!\n", input_path);
return false;
}
int64_t w, h;
openslide_get_level0_dimensions(slide, &w, &h);
g_config.image_width = w;
g_config.image_height = h;
openslide_close(slide);
return true;
}
// BENCHMARK_MAIN();
int main(int argc, char** argv)
{
// Skip processing help option
bool has_help_option = remove_help_option(&argc, argv);
::benchmark::Initialize(&argc, argv);
// if (::benchmark::ReportUnrecognizedArguments(argc, argv))
// return 1;
CLI::App app{ "cuCIM Benchmark" };
app.add_option("--test_folder", g_config.test_folder, "An input test folder path");
app.add_option("--test_file", g_config.test_file, "An input test image file path");
app.add_option("--discard_cache", g_config.discard_cache, "Discard page cache for the input file for each iteration");
app.add_option("--random_seed", g_config.random_seed, "A random seed number");
app.add_option(
"--random_start_location", g_config.random_start_location, "Randomize start location of read_region()");
// Pseudo benchmark options
app.add_option("--benchmark_list_tests", g_config.benchmark_list_tests, "{true|false}");
app.add_option("--benchmark_filter", g_config.benchmark_filter, "<regex>");
app.add_option("--benchmark_min_time", g_config.benchmark_min_time, "<min_time>");
app.add_option("--benchmark_repetitions", g_config.benchmark_repetitions, "<num_repetitions>");
app.add_option("--benchmark_report_aggregates_only", g_config.benchmark_report_aggregates_only, "{true|false}");
app.add_option("--benchmark_display_aggregates_only", g_config.benchmark_display_aggregates_only, "{true|false}");
app.add_option("--benchmark_format", g_config.benchmark_format, "<console|json|csv>");
app.add_option("--benchmark_out", g_config.benchmark_out, "<filename>");
app.add_option("--benchmark_out_format", g_config.benchmark_out_format, "<json|console|csv>");
app.add_option("--benchmark_color", g_config.benchmark_color, "{auto|true|false}");
app.add_option("--benchmark_counters_tabular", g_config.benchmark_counters_tabular, "{true|false}");
app.add_option("--v", g_config.v, "<verbosity>");
// Append help option if exists
if (has_help_option)
{
argv[argc] = const_cast<char*>("--help");
++argc;
}
CLI11_PARSE(app, argc, argv);
if (!setup_configuration())
{
return 1;
}
::benchmark::RunSpecifiedBenchmarks();
}
| 0 |
rapidsai_public_repos/cucim
|
rapidsai_public_repos/cucim/benchmarks/primitives.cpp
|
/*
* Copyright (c) 2020-2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "cucim/memory/memory_manager.h"
#include <vector>
#include <string>
#include <cstring>
#include <memory_resource>
#include <benchmark/benchmark.h>
static void vector_copy_push_back(benchmark::State& state)
{
const int data_count = 50000;
uint64_t data[data_count];
// Code inside this loop is measured repeatedly
for (auto _ : state)
{
std::vector<uint64_t> data_vec;
for (int i = 0; i < data_count; ++i)
{
data_vec.push_back(data[i]);
}
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(data_vec);
}
}
// Register the function as a benchmark
BENCHMARK(vector_copy_push_back);
static void vector_copy_insert(benchmark::State& state)
{
const int data_count = 50000;
uint64_t data[data_count];
// Code before the loop is not measured
for (auto _ : state)
{
std::vector<uint64_t> data_vec;
data_vec.insert(data_vec.end(), &data[0], &data[data_count]);
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(data_vec);
}
}
BENCHMARK(vector_copy_insert);
static void vector_copy_vector_vector(benchmark::State& state)
{
const int data_count = 50000;
uint64_t data[data_count];
// Code before the loop is not measured
for (auto _ : state)
{
std::vector<uint64_t> data_vec;
data_vec.insert(data_vec.end(), &data[0], &data[data_count]);
std::vector<uint64_t> data_vec2(&data[0], &data[data_count]);
data_vec.insert(data_vec.end(), data_vec2.begin(), data_vec2.end());
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(data_vec);
}
}
BENCHMARK(vector_copy_vector_vector);
static void string_memcpy(benchmark::State& state)
{
// Code before the loop is not measured
for (auto _ : state)
{
std::string data = "#########################################################################################################################################################################################";
const int size = data.size();
char * c_str = (char*) malloc(size + 1);
memcpy(c_str, data.data(), size);
c_str[size] = '\0';
free(c_str);
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(c_str);
benchmark::DoNotOptimize(size);
}
}
BENCHMARK(string_memcpy);
static void string_strcpy(benchmark::State& state)
{
// Code before the loop is not measured
for (auto _ : state)
{
std::string data = "#########################################################################################################################################################################################";
char * c_str = (char*) malloc(data.size() + 1);
strcpy(c_str, data.data());
free(c_str);
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(c_str);
}
}
BENCHMARK(string_strcpy);
static void string_strdup(benchmark::State& state)
{
// Code before the loop is not measured
for (auto _ : state)
{
std::string data = "#########################################################################################################################################################################################";
char * c_str = strdup(data.data());
free(c_str);
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(c_str);
}
}
BENCHMARK(string_strdup);
static void alloc_malloc(benchmark::State& state)
{
// Code before the loop is not measured
for (auto _ : state)
{
char* arr[30000];
for (int i = 0; i < 30000; i++)
{
arr[i] = (char*)malloc(10);
arr[i][0] = i;
}
for (int i = 0; i < 30000; i++)
{
free(arr[i]);
}
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(arr);
}
}
BENCHMARK(alloc_malloc);//->Iterations(100);
static void alloc_pmr(benchmark::State& state)
{
// Code before the loop is not measured
for (auto _ : state)
{
char* arr[30000];
for (int i = 0; i < 30000; i++)
{
arr[i] = static_cast<char*>(cucim_malloc(10));
arr[i][0] = i;
}
for (int i = 0; i < 30000; i++)
{
cucim_free(arr[i]);
}
// Make sure the variable is not optimized away by compiler
benchmark::DoNotOptimize(arr);
}
}
BENCHMARK(alloc_pmr);//->Iterations(100);
BENCHMARK_MAIN();
// Debug
// ```
// --------------------------------------------------------------------
// Benchmark Time CPU Iterations
// --------------------------------------------------------------------
// vector_copy_push_back 591517 ns 591510 ns 1267
// vector_copy_insert 8488 ns 8488 ns 85160
// vector_copy_vector_vector 225441 ns 225439 ns 3069
// string_memcpy 169 ns 169 ns 3854598
// string_strcpy 202 ns 202 ns 4114834
// string_strdup 184 ns 184 ns 3666944
// ```
// Release
// ```
// --------------------------------------------------------------------
// Benchmark Time CPU Iterations
// --------------------------------------------------------------------
// vector_copy_push_back 118518 ns 118518 ns 5745
// vector_copy_insert 7779 ns 7779 ns 92190
// vector_copy_vector_vector 198800 ns 198793 ns 3347
// string_memcpy 20.3 ns 20.3 ns 32102053
// string_strcpy 24.8 ns 24.8 ns 27352024
// string_strdup 32.4 ns 32.4 ns 21458177
// ```
| 0 |
rapidsai_public_repos/cucim/benchmarks
|
rapidsai_public_repos/cucim/benchmarks/skimage/cucim_exposure_bench.py
|
import argparse
import os
import pickle
import cupy
import cupy as cp
import numpy as np
import pandas as pd
import skimage
import skimage.exposure
from _image_bench import ImageBench
import cucim.skimage
import cucim.skimage.exposure
class ExposureBench(ImageBench):
def set_args(self, dtype):
if np.dtype(dtype).kind in "iu":
scale = 256
else:
scale = 1.0
imaged = cupy.testing.shaped_random(
self.shape, xp=cp, dtype=dtype, scale=scale
)
image = cp.asnumpy(imaged)
self.args_cpu = (image,)
self.args_gpu = (imaged,)
class MatchHistogramBench(ImageBench):
def set_args(self, dtype):
if np.dtype(dtype).kind in "iu":
scale = 256
else:
scale = 1.0
imaged = cupy.testing.shaped_random(
self.shape, xp=cp, dtype=dtype, scale=scale
)
imaged2 = cupy.testing.shaped_random(
self.shape, xp=cp, dtype=dtype, scale=scale
)
image = cp.asnumpy(imaged)
image2 = cp.asnumpy(imaged2)
self.args_cpu = (image, image2)
self.args_gpu = (imaged, imaged2)
def main(args):
pfile = "cucim_exposure_results.pickle"
if os.path.exists(pfile):
with open(pfile, "rb") as f:
all_results = pickle.load(f)
else:
all_results = pd.DataFrame()
dtypes = [np.dtype(args.dtype)]
# image sizes/shapes
shape = tuple(list(map(int, (args.img_size.split(",")))))
run_cpu = not args.no_cpu
for function_name, fixed_kwargs, var_kwargs, allow_color in [
("equalize_adapthist", dict(clip_limit=0.01, nbins=256), dict(), True),
(
"histogram",
dict(source_range="image"),
dict(nbins=[16, 256], normalize=[True, False]),
False,
),
("cumulative_distribution", dict(), dict(nbins=[16, 256]), False),
("equalize_hist", dict(mask=None), dict(nbins=[16, 256]), False),
(
"rescale_intensity",
dict(in_range="image", out_range="dtype"),
dict(),
False,
),
("adjust_gamma", dict(), dict(), False),
("adjust_log", dict(), dict(), False),
("adjust_sigmoid", dict(), dict(inv=[False, True]), False),
("is_low_contrast", dict(), dict(), False),
]:
if function_name != args.func_name:
continue
if function_name == "match_histograms":
channel_axis = -1 if shape[-1] in [3, 4] else None
B = MatchHistogramBench(
function_name="match_histograms",
shape=shape,
dtypes=dtypes,
fixed_kwargs=dict(channel_axis=channel_axis),
var_kwargs=dict(),
module_cpu=skimage.exposure,
module_gpu=cucim.skimage.exposure,
run_cpu=run_cpu,
)
results = B.run_benchmark(duration=args.duration)
all_results = pd.concat([all_results, results["full"]])
else:
if shape[-1] == 3 and not allow_color:
continue
if function_name == "equalize_adapthist":
# TODO: fix equalize_adapthist for size (3840, 2160)
# and kernel_size = [16, 16]
size_factors = [4, 8, 16]
kernel_sizes = []
for size_factor in size_factors:
kernel_sizes.append(
[max(s // size_factor, 1) for s in shape if s != 3]
)
var_kwargs.update(dict(kernel_size=kernel_sizes))
B = ExposureBench(
function_name=function_name,
shape=shape,
dtypes=dtypes,
fixed_kwargs=fixed_kwargs,
var_kwargs=var_kwargs,
module_cpu=skimage.exposure,
module_gpu=cucim.skimage.exposure,
run_cpu=run_cpu,
)
results = B.run_benchmark(duration=args.duration)
all_results = pd.concat([all_results, results["full"]])
fbase = os.path.splitext(pfile)[0]
all_results.to_csv(fbase + ".csv")
all_results.to_pickle(pfile)
with open(fbase + ".md", "wt") as f:
f.write(all_results.to_markdown())
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Benchmarking cuCIM exposure functions"
)
func_name_choices = [
"equalize_adapthist",
"cumulative_distribution",
"equalize_hist",
"rescale_intensity",
"adjust_gamma",
"adjust_log",
"adjust_sigmoid",
"is_low_contrast",
"match_histograms",
]
dtype_choices = [
"float16",
"float32",
"float64",
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
]
parser.add_argument(
"-i", "--img_size", type=str, help="Size of input image", required=True
)
parser.add_argument(
"-d",
"--dtype",
type=str,
help="Dtype of input image",
choices=dtype_choices,
required=True,
)
parser.add_argument(
"-f",
"--func_name",
type=str,
help="function to benchmark",
choices=func_name_choices,
required=True,
)
parser.add_argument(
"-t",
"--duration",
type=int,
help="time to run benchmark",
required=True,
)
parser.add_argument(
"--no_cpu",
action="store_true",
help="disable cpu measurements",
default=False,
)
args = parser.parse_args()
main(args)
| 0 |
rapidsai_public_repos/cucim/benchmarks
|
rapidsai_public_repos/cucim/benchmarks/skimage/run-nv-bench-exposure.sh
|
#!/bin/bash
param_shape=(512,512 3840,2160 3840,2160,3 192,192,192)
param_filt=(equalize_adapthist cumulative_distribution equalize_hist rescale_intensity adjust_gamma adjust_log adjust_sigmoid is_low_contrast match_histograms)
param_dt=(float32 uint8)
for shape in "${param_shape[@]}"; do
for filt in "${param_filt[@]}"; do
for dt in "${param_dt[@]}"; do
python cucim_exposure_bench.py -f $filt -i $shape -d $dt -t 10
done
done
done
| 0 |
rapidsai_public_repos/cucim/benchmarks
|
rapidsai_public_repos/cucim/benchmarks/skimage/cupyx_scipy_ndimage_filter_bench.py
|
import os
import pickle
import cupy
import cupy as cp
import cupyx.scipy.ndimage
import numpy as np
import pandas as pd
import scipy
from _image_bench import ImageBench
class ConvolveBench(ImageBench):
def __init__(
self,
function_name,
shape,
weights_shape,
dtypes=[np.float32],
fixed_kwargs={},
var_kwargs={},
module_cpu=scipy.ndimage,
module_gpu=cupyx.scipy.ndimage,
):
self.weights_shape = weights_shape
super().__init__(
function_name=function_name,
shape=shape,
dtypes=dtypes,
fixed_kwargs=fixed_kwargs,
var_kwargs=var_kwargs,
module_cpu=module_cpu,
module_gpu=module_gpu,
)
def set_args(self, dtype):
imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype)
image = cp.asnumpy(imaged)
wd = cupy.testing.shaped_random(self.weights_shape, xp=cp, dtype=dtype)
w = cp.asnumpy(wd)
self.args_cpu = (image, w)
self.args_gpu = (imaged, wd)
class FilterBench(ImageBench):
def set_args(self, dtype):
imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype)
image = cp.asnumpy(imaged)
self.args_cpu = (image,)
self.args_gpu = (imaged,)
pfile = "filter_results.pickle"
if os.path.exists(pfile):
with open(pfile, "rb") as f:
all_results = pickle.load(f)
else:
all_results = pd.DataFrame()
modes = ["constant", "mirror"]
prefilter = True
dtypes = [np.float32]
for shape in [(512, 512), (3840, 2160), (192, 192, 192)]:
ndim = len(shape)
weights_shape = (3,) * ndim
weights_shape1d = weights_shape[:1]
# TODO: add cases for generic_filter and generic_filter1d?
for fname, var_kwargs in [
("uniform_filter", dict(mode=["nearest"], size=[3, 5, 7, 9])),
("uniform_filter1d", dict(mode=["nearest"], size=[3, 7], axis=[0, -1])),
("gaussian_filter", dict(mode=["nearest"], sigma=[0.33, 1, 3, 4, 9])),
(
"gaussian_filter1d",
dict(
mode=["nearest"], sigma=[0.33, 3, 9], axis=[0, -1], order=[0, 1]
),
),
("maximum_filter", dict(mode=["nearest"], size=[3, 5, 7])),
("maximum_filter1d", dict(mode=["nearest"], size=[3, 7], axis=[0, -1])),
("minimum_filter", dict(mode=["nearest"], size=[3, 5, 7])),
("minimum_filter1d", dict(mode=["nearest"], size=[3, 7], axis=[0, -1])),
("median_filter", dict(mode=["nearest"], size=[3, 5, 7])),
(
"percentile_filter",
dict(mode=["nearest"], size=[3, 5, 7], percentile=[30]),
),
("rank_filter", dict(mode=["nearest"], size=[3, 5, 7], rank=[-2])),
("prewitt", dict(mode=["nearest"], axis=[0, -1])),
("sobel", dict(mode=["nearest"], axis=[0, -1])),
("laplace", dict(mode=["nearest"])),
("gaussian_laplace", dict(mode=["nearest"], sigma=[0.33, 3, 9])),
(
"gaussian_gradient_magnitude",
dict(mode=["nearest"], sigma=[0.33, 3, 9]),
),
]:
B = FilterBench(
function_name=fname,
shape=shape,
dtypes=dtypes,
fixed_kwargs=dict(output=None),
var_kwargs=var_kwargs,
)
results = B.run_benchmark(duration=1)
all_results = pd.concat([all_results, results["full"]])
for fname, wshape, var_kwargs in [
("convolve", weights_shape, dict(mode=modes)),
("correlate", weights_shape, dict(mode=modes)),
("convolve1d", weights_shape1d, dict(mode=modes, axis=[0, -1])),
("correlate1d", weights_shape1d, dict(mode=modes, axis=[0, -1])),
]:
B = ConvolveBench(
function_name=fname,
shape=shape,
weights_shape=wshape,
dtypes=dtypes,
fixed_kwargs=dict(output=None, origin=0),
var_kwargs=var_kwargs,
)
results = B.run_benchmark(duration=1)
all_results = pd.concat([all_results, results["full"]])
fbase = os.path.splitext(pfile)[0]
all_results.to_csv(fbase + ".csv")
all_results.to_pickle(pfile)
with open(fbase + ".md", "wt") as f:
f.write(all_results.to_markdown())
| 0 |
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