Python cupy.arange方法代码示例

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def _non_maximum_suppression_gpu(bbox, thresh, score=None, limit=None):
    if len(bbox) == 0:
        return cp.zeros((0,), dtype=np.int32)

    n_bbox = bbox.shape[0]

    if score is not None:
        order = score.argsort()[::-1].astype(np.int32)
    else:
        order = cp.arange(n_bbox, dtype=np.int32)

    sorted_bbox = bbox[order, :]
    selec, n_selec = _call_nms_kernel(
        sorted_bbox, thresh)
    selec = selec[:n_selec]
    selec = order[selec]
    if limit is not None:
        selec = selec[:limit]
    return selec 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_cupy_to_chainerx_delete_cupy_first():
    dtype = numpy.float32
    a_cupy = cupy.arange(6, dtype=dtype).reshape((2, 3))
    a_chx = _fromrawpointer(
        a_cupy.data.mem.ptr,
        a_cupy.shape,
        a_cupy.dtype,
        a_cupy.strides,
        'cuda:0',
        0,
        a_cupy)

    del a_cupy

    a_chx += 1
    chainerx.testing.assert_array_equal_ex(
        a_chx, numpy.array([[1, 2, 3], [4, 5, 6]], dtype)) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_cupy_to_chainerx_delete_chainerx_first():
    dtype = numpy.float32
    a_cupy = cupy.arange(6, dtype=dtype).reshape((2, 3))
    a_chx = _fromrawpointer(
        a_cupy.data.mem.ptr,
        a_cupy.shape,
        a_cupy.dtype,
        a_cupy.strides,
        'cuda:0',
        0,
        a_cupy)

    del a_chx

    a_cupy += 1
    chainerx.testing.assert_array_equal_ex(
        a_cupy.get(), numpy.array([[1, 2, 3], [4, 5, 6]], dtype)) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_chainerx_to_cupy_delete_cupy_first():
    dtype = 'float32'
    a_chx = chainerx.arange(6, dtype=dtype, device='cuda:0').reshape((2, 3))
    a_cupy = cupy.ndarray(
        a_chx.shape,
        cupy.dtype(a_chx.dtype.name),
        cupy.cuda.MemoryPointer(cupy.cuda.UnownedMemory(
            a_chx.data_ptr + a_chx.offset,
            a_chx.data_size,
            a_chx,
            0), 0),
        strides=a_chx.strides,
    )

    del a_cupy

    a_chx += 1
    chainerx.testing.assert_array_equal_ex(
        a_chx, numpy.array([[1, 2, 3], [4, 5, 6]], dtype)) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_chainerx_to_cupy_delete_chainerx_first():
    dtype = 'float32'
    a_chx = chainerx.arange(6, dtype=dtype, device='cuda:0').reshape((2, 3))
    a_cupy = cupy.ndarray(
        a_chx.shape,
        cupy.dtype(a_chx.dtype.name),
        cupy.cuda.MemoryPointer(cupy.cuda.UnownedMemory(
            a_chx.data_ptr + a_chx.offset,
            a_chx.data_size,
            a_chx,
            0), 0),
        strides=a_chx.strides,
    )

    del a_chx

    a_cupy += 1
    chainerx.testing.assert_array_equal_ex(
        a_cupy.get(), numpy.array([[1, 2, 3], [4, 5, 6]], dtype)) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_chainerx_to_cupy_nondefault_device():
    dtype = 'float32'
    a_chx = chainerx.arange(6, dtype=dtype, device='cuda:1').reshape((2, 3))
    a_cupy = cupy.ndarray(
        a_chx.shape,
        cupy.dtype(a_chx.dtype.name),
        cupy.cuda.MemoryPointer(cupy.cuda.UnownedMemory(
            a_chx.data_ptr + a_chx.offset,
            a_chx.data_size,
            a_chx,
            -1), 0),
        strides=a_chx.strides,
    )

    assert a_cupy.device.id == 1
    chainerx.testing.assert_array_equal_ex(a_chx, a_cupy.get()) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def _non_maximum_suppression_gpu(bbox, thresh, score=None, limit=None):
    if len(bbox) == 0:
        return cp.zeros((0,), dtype=np.int32)

    n_bbox = bbox.shape[0]

    if score is not None:
        order = score.argsort()[::-1].astype(np.int32)
    else:
        order = cp.arange(n_bbox, dtype=np.int32)

    sorted_bbox = bbox[order, :]
    selec, n_selec = _call_nms_kernel(
        sorted_bbox, thresh)
    selec = selec[:n_selec]
    selec = order[selec]
    if limit is not None:
        selec = selec[:limit]
    return cp.asnumpy(selec) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def fit_custom(X, n_clusters, max_iter):
    assert X.ndim == 2

    n_samples = len(X)

    pred = cupy.zeros(n_samples)

    initial_indexes = cupy.random.choice(n_samples, n_clusters, replace=False)
    centers = X[initial_indexes]

    for _ in range(max_iter):
        distances = var_kernel(X[:, None, 0], X[:, None, 1],
                               centers[None, :, 1], centers[None, :, 0])
        new_pred = cupy.argmin(distances, axis=1)
        if cupy.all(new_pred == pred):
            break
        pred = new_pred

        i = cupy.arange(n_clusters)
        mask = pred == i[:, None]
        sums = sum_kernel(X, mask[:, :, None], axis=1)
        counts = count_kernel(mask, axis=1).reshape((n_clusters, 1))
        centers = sums / counts

    return centers, pred 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_ctxGetCurrent_thread(self):
        # Make sure to create context in main thread.
        cupy.arange(1)

        def f(self):
            self._result0 = driver.ctxGetCurrent()
            cupy.arange(1)
            self._result1 = driver.ctxGetCurrent()

        self._result0 = None
        self._result1 = None
        t = threading.Thread(target=f, args=(self,))
        t.daemon = True
        t.start()
        t.join()

        # The returned context pointer must be NULL on sub thread
        # without valid context.
        self.assertEqual(0, self._result0)

        # After the context is created, it should return the valid
        # context pointer.
        self.assertNotEqual(0, self._result1) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None):
    """Returns an array with evenly-spaced values on a log-scale.

    Instead of specifying the step width like :func:`cupy.arange`, this
    function requires the total number of elements specified.

    Args:
        start: Start of the interval.
        stop: End of the interval.
        num: Number of elements.
        endpoint (bool): If ``True``, the stop value is included as the last
            element. Otherwise, the stop value is omitted.
        base (float): Base of the log space. The step sizes between the
            elements on a log-scale are the same as ``base``.
        dtype: Data type specifier. It is inferred from the start and stop
            arguments by default.

    Returns:
        cupy.ndarray: The 1-D array of ranged values.

    """
    y = linspace(start, stop, num=num, endpoint=endpoint)
    if dtype is None:
        return core.power(base, y)
    return core.power(base, y).astype(dtype) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def polyvander(x, deg):
    """Computes the Vandermonde matrix of given degree.

    Args:
        x (cupy.ndarray): array of points
        deg (int): degree of the resulting matrix.

    Returns:
        cupy.ndarray: The Vandermonde matrix

    .. seealso:: :func:`numpy.polynomial.polynomial.polyvander`

    """
    deg = cupy.polynomial.polyutils._deprecate_as_int(deg, 'deg')
    if deg < 0:
        raise ValueError('degree must be non-negative')
    if x.ndim == 0:
        x = x.ravel()
    dtype = cupy.float64 if x.dtype.kind in 'biu' else x.dtype
    out = x ** cupy.arange(deg + 1, dtype=dtype).reshape((-1,) + (1,) * x.ndim)
    return cupy.moveaxis(out, 0, -1) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def forward_gpu(self, inputs):
        a, b = inputs
        c = cp.zeros_like(a, 'float32')
        chainer.cuda.elementwise(
            'int32 j, raw T a, raw T b',
            'raw T c',
            '''
                float* ap = (float*)&a[j * 3];
                float* bp = (float*)&b[j * 3];
                float* cp = (float*)&c[j * 3];
                cp[0] = ap[1] * bp[2] - ap[2] * bp[1];
                cp[1] = ap[2] * bp[0] - ap[0] * bp[2];
                cp[2] = ap[0] * bp[1] - ap[1] * bp[0];
            ''',
            'function',
        )(
            cp.arange(a.size / 3).astype('int32'), a, b, c,
        )
        return c, 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def _voxelize_sub2(faces, size):
    bs, nf = faces.shape[:2]
    faces = cp.ascontiguousarray(faces)
    voxels = cp.zeros((faces.shape[0], size, size, size), 'int32')
    chainer.cuda.elementwise(
        'int32 j, raw T faces, raw int32 bs, raw int32 nf, raw int32 vs',
        'raw int32 voxels',
        '''
            int fn = j % nf;
            int bn = j / nf;
            float* face = &faces[(bn * nf + fn) * 9];
            for (int k = 0; k < 3; k++) {
                int yi = face[3 * k + 0];
                int xi = face[3 * k + 1];
                int zi = face[3 * k + 2];
                if ((0 <= yi) && (yi < vs) && (0 <= xi) && (xi < vs) && (0 <= zi) && (zi < vs))
                    voxels[bn * vs * vs * vs + yi * vs * vs + xi * vs + zi] = 1;
            }
        ''',
        'function',
    )(cp.arange(bs * nf).astype('int32'), faces, bs, nf, size, voxels)
    return voxels 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_kernel(self):
        import cupy as cp
        x = cp.arange(6, dtype='f').reshape(2, 3)
        y = cp.arange(3, dtype='f')
        kernel = cp.ElementwiseKernel(
            'float32 x, float32 y', 'float32 z',
            '''if (x - 2 > y) {
                z = x * y;
            } else {
                z = x + y;
            }''',
            'my_kernel')
        r = kernel(x, y)
        
        self.assertEqual((2, 3), r.shape) 

# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import arange [as 别名]
def test_cupy_to_chainerx_contiguous():
    dtype = numpy.float32
    a_cupy = cupy.arange(6, dtype=dtype).reshape((2, 3))
    a_cupy_refcount_before = sys.getrefcount(a_cupy)

    a_chx = _fromrawpointer(
        a_cupy.data.mem.ptr,
        a_cupy.shape,
        a_cupy.dtype,
        a_cupy.strides,
        'cuda:0',
        0,
        a_cupy)

    assert sys.getrefcount(a_cupy) == a_cupy_refcount_before + 1
    assert a_chx.device.name == 'cuda:0'
    chainerx.testing.assert_array_equal_ex(a_chx, a_cupy.get())

    # Write to a_cupy
    a_cupy[0, 1] = 8
    chainerx.testing.assert_array_equal_ex(
        a_chx, numpy.array([[0, 8, 2], [3, 4, 5]], dtype))

    # Write to a_chx
    a_chx += 1
    chainerx.testing.assert_array_equal_ex(
        a_cupy.get(), numpy.array([[1, 9, 3], [4, 5, 6]], dtype))