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Python gpuarray.GPUArray方法代码示例

本文整理汇总了Python中pycuda.gpuarray.GPUArray方法的典型用法代码示例。如果您正苦于以下问题:Python gpuarray.GPUArray方法的具体用法?Python gpuarray.GPUArray怎么用?Python gpuarray.GPUArray使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在pycuda.gpuarray的用法示例。


在下文中一共展示了gpuarray.GPUArray方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: setOrig

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def setOrig(self, img):
    """
    To set the original image from a given CPU or GPU array.

    If it is a GPU array, it will NOT be copied.
    Note that the most efficient method is to write directly over
    self.devOrig with some kernel and then run self.updateOrig()
    """
    assert img.shape == (self.h, self.w), \
      "Got a {} image in a {} correlation routine!".format(
        img.shape, (self.h, self.w))
    if isinstance(img, np.ndarray):
      self.debug(3, "Setting original image from ndarray")
      self.devOrig.set(img)
    elif isinstance(img, gpuarray.GPUArray):
      self.debug(3, "Setting original image from GPUArray")
      self.devOrig = img
    else:
      self.debug(0, "Error ! Unknown type of data given to setOrig()")
      raise ValueError
    self.updateOrig() 
开发者ID:LaboratoireMecaniqueLille,项目名称:crappy,代码行数:23,代码来源:gpucorrel.py

示例2: setFields

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def setFields(self, fieldsX, fieldsY):
    """
    Method to give the fields to identify with the routine.

    This is necessary only once and can be done multiple times, but the routine
    have to be initialized with .prepare(), causing a slight overhead
    Takes a tuple/list of 2 (gpu)arrays[Nfields,x,y] (one for displacement
    along x and one along y)
    """
    self.debug(2, "Setting fields")
    if isinstance(fieldsX, np.ndarray):
      self.devFieldsX.set(fieldsX)
      self.devFieldsY.set(fieldsY)
    elif isinstance(fieldsX, gpuarray.GPUArray):
      self.devFieldsX = fieldsX
      self.devFieldsY = fieldsY
    self.fields = True 
开发者ID:LaboratoireMecaniqueLille,项目名称:crappy,代码行数:19,代码来源:gpucorrel.py

示例3: setImage

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def setImage(self, img_d):
    """
    Set the image to compare with the original

    Note that calling this method is not necessary: you can do .getDisp(image)
    This will automatically call this method first
    """
    assert img_d.shape == (self.h, self.w), \
      "Got a {} image in a {} correlation routine!".format(
        img_d.shape, (self.h, self.w))
    if isinstance(img_d, np.ndarray):
      self.debug(3, "Creating texture from numpy array")
      self.array_d = cuda.matrix_to_array(img_d, "C")
    elif isinstance(img_d, gpuarray.GPUArray):
      self.debug(3, "Creating texture from gpuarray")
      self.array_d = cuda.gpuarray_to_array(img_d, "C")
    else:
      self.debug(0, "Error ! Unknown type of data given to .setImage()")
      raise ValueError
    self.tex_d.set_array(self.array_d)
    self.devX.set(np.zeros(self.Nfields, dtype=np.float32)) 
开发者ID:LaboratoireMecaniqueLille,项目名称:crappy,代码行数:23,代码来源:gpucorrel.py

示例4: as_contiguous

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def as_contiguous(self):
        """
        Creates a new GPUArray with the same dimensions, but using contiguous memory

        Returns:
            New contiguous GPUArray with separate underlying device allocation
        """
        contig_tensor = GPUArray(self.tensor.shape, dtype=self.tensor.dtype)
        src_strides = [s // self.tensor.dtype.itemsize for s in self.tensor.strides]
        dst_strides = [s // contig_tensor.dtype.itemsize for s in contig_tensor.strides]
        kernel = _get_copy_transpose_kernel(self.tensor.dtype,
                                            self.tensor.shape,
                                            range(len(self.tensor.shape)))
        params = [contig_tensor.gpudata, self.tensor.gpudata] + list(kernel.args)
        params = params + src_strides + dst_strides
        kernel.prepared_async_call(kernel.grid, kernel.block, None, *params)
        return contig_tensor 
开发者ID:NervanaSystems,项目名称:ngraph-python,代码行数:19,代码来源:gputransform.py

示例5: rand_cuda

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def rand_cuda(shape):
    """Create random values in the range [0, 1] in a GPUArray.

    Parameters
    ----------
    shape : tuple
        Size of the random array.

    Returns
    -------
    gpuarray
        Random floats from 0 to 1 in GPUArray.

    Examples
    --------
    >>> a = rand_cuda((2, 2))
    [[ 0.80916596,  0.82687163],
     [ 0.03921388,  0.44197764]]

    >>> type(a)
    <class 'pycuda.gpuarray.GPUArray'>
    """
    return pycuda.curandom.rand(shape, dtype=float32) 
开发者ID:compas-dev,项目名称:compas,代码行数:25,代码来源:cuda.py

示例6: get_cuda

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def get_cuda(a):
    """Return GPUArray from GPU memory as NumPy array.

    Parameters
    ----------
    a : gpuarray
        Data on the GPU memory to retrieve.

    Returns
    -------
    array
        The GPUArray returned to RAM as a NumPy array.

    Examples
    --------
    >>> a = give_cuda([1, 2, 3])
    >>> b = get_cuda(a)
    [ 1.,  2.,  3.]

    >>> type(b)
    <class 'numpy.ndarray'>
    """
    return a.get() 
开发者ID:compas-dev,项目名称:compas,代码行数:25,代码来源:cuda.py

示例7: zeros_cuda

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def zeros_cuda(shape):
    """Create GPUArray of zeros directly on GPU memory.

    Parameters
    ----------
    shape : tuple
        Dimensions of the GPUArray.

    Returns
    -------
    gpuarray
        GPUArray of zeros.

    Examples
    --------
    >>> a = zeros_cuda((3, 2))
    [[ 0.,  0.],
     [ 0.,  0.],
     [ 0.,  0.]]

    >>> type(a)
    <class 'pycuda.gpuarray.GPUArray'>
    """
    return cuda_array.zeros(shape, dtype=float32) 
开发者ID:compas-dev,项目名称:compas,代码行数:26,代码来源:cuda.py

示例8: hstack_cuda

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def hstack_cuda(a, b, dim=4):
    """Stack two GPUArrays horizontally.

    Parameters
    ----------
    a : gpuarray
        First GPUArray.
    b : gpuarray
        Second GPUArray.

    Returns
    -------
    gpuarray
        Horizontally stacked GPUArrays.
    """
    m, n = a.shape
    o = b.shape[1]
    nx = int(ceil((n + o) / dim))
    ny = int(ceil(m / dim))

    func = mod.get_function('hstack_cuda')
    c = pycuda.gpuarray.empty((m, n + o), dtype=float32)
    func(int32(m), int32(n), int32(o), a, b, c, block=(dim, dim, 1), grid=(nx, ny, 1))

    return c 
开发者ID:compas-dev,项目名称:compas,代码行数:27,代码来源:cuda.py

示例9: setMask

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def setMask(self, mask):
    self.debug(3, "Setting the mask")
    assert mask.shape == (self.h, self.w), \
      "Got a {} mask in a {} routine.".format(mask.shape, (self.h, self.w))
    if not mask.dtype == np.float32:
      self.debug(2, "Converting the mask to float32")
      mask = mask.astype(np.float32)
    if isinstance(mask, np.ndarray):
      self.maskArray = cuda.matrix_to_array(mask, 'C')
    elif isinstance(mask, gpuarray.GPUArray):
      self.maskArray = cuda.gpuarray_to_array(mask, 'C')
    else:
      self.debug(0, "Error! Mask data type not understood")
      raise ValueError
    self.texMask.set_array(self.maskArray) 
开发者ID:LaboratoireMecaniqueLille,项目名称:crappy,代码行数:17,代码来源:gpucorrel.py

示例10: setDisp

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def setDisp(self, X):
    assert X.shape == (self.Nfields,), \
      "Incorrect initialization of the parameters"
    if isinstance(X, gpuarray.GPUArray):
      self.devX = X
    elif isinstance(X, np.ndarray):
      self.devX.set(X)
    else:
      self.debug(0, "Error! Unknown type of data given to CorrelStage.setDisp")
      raise ValueError 
开发者ID:LaboratoireMecaniqueLille,项目名称:crappy,代码行数:12,代码来源:gpucorrel.py

示例11: __call__

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def __call__(self, buffer_alloc):
        """
        Allocates the GPUTensor object as a view of a pre-allocated buffer.

        Arguments:
            buffer_alloc (DeviceAllocation): Memory handle returned by pycuda
                allocator
        """
        tensor_description = self.tensor_description
        layout = tensor_description.layout
        dtype = self.transformer.storage_dtype(tensor_description.dtype)

        if layout:
            gpudata = int(buffer_alloc) + (layout.offset * dtype.itemsize)
            strides = tuple([s * dtype.itemsize for s in layout.strides])
            new_tensor = GPUArray(layout.shape,
                                  dtype,
                                  gpudata=gpudata,
                                  strides=strides)
        else:
            gpudata = int(buffer_alloc) + tensor_description.offset
            new_tensor = GPUArray(tensor_description.shape,
                                  dtype,
                                  gpudata=gpudata,
                                  strides=tensor_description.strides)
        self._tensor = new_tensor
        self.transformer.tensors[self.tensor_name] = self._tensor 
开发者ID:NervanaSystems,项目名称:ngraph-python,代码行数:29,代码来源:gputransform.py

示例12: get

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def get(self, tensor):
        """
        Copy the device tensor to a numpy array.

        Arguments:
            tensor (np.ndarray): Optional output array

        Returns:
            Numpy array containing tensor data
        """
        if np.sum(self.tensor.strides) != 0:
            if self.is_contiguous or self.tensor.shape == () or np.prod(self.tensor.shape) == 1:
                contig_tensor = self.tensor
            else:
                # Need to do memcpy from contiguous device memory
                contig_tensor = self.as_contiguous()

            if tensor is None:
                if contig_tensor.shape != self.tensor_description.shape:
                    return contig_tensor.get().reshape(self.tensor_description.shape)
                else:
                    return contig_tensor.get()
            tensor[:] = contig_tensor.get()
        else:
            # Tensor is just a broadcasted scalar, get scalar value and fill output array
            view = GPUArray((1, ), dtype=self.tensor.dtype, gpudata=self.tensor.gpudata)[0]
            value = view.get()

            if tensor is None:
                out = np.ndarray(self.tensor.shape, dtype=self.tensor.dtype)
                out.fill(value)
                return out
            tensor.fill(value)

        return tensor 
开发者ID:NervanaSystems,项目名称:ngraph-python,代码行数:37,代码来源:gputransform.py

示例13: to_array

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def to_array(in_array, copy = True):
    """
    Helper function to convert input from a different module to af.Array

    Parameters
    -------------

    in_array : array like object
             Can be one of the following:
             - numpy.ndarray
             - pycuda.GPUArray
             - pyopencl.Array
             - numba.cuda.cudadrv.devicearray.DeviceNDArray
             - array.array
             - list
    copy : Bool specifying if array is to be copied.
          Default is true.
          Can only be False if array is fortran contiguous.

    Returns
    --------------
    af.Array of same dimensions as input after copying the data from the input

    """
    if AF_NUMPY_FOUND and isinstance(in_array, NumpyArray):
        return np_to_af_array(in_array, copy)
    if AF_PYCUDA_FOUND and isinstance(in_array, CudaArray):
        return pycuda_to_af_array(in_array, copy)
    if AF_PYOPENCL_FOUND and isinstance(in_array, OpenclArray):
        return pyopencl_to_af_array(in_array, copy)
    if AF_NUMBA_FOUND and isinstance(in_array, NumbaCudaArray):
        return numba_to_af_array(in_array, copy)
    return Array(src=in_array) 
开发者ID:arrayfire,项目名称:arrayfire-python,代码行数:35,代码来源:interop.py

示例14: fill_normal

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def fill_normal(self, ary, mean=0, stdv=1):
        """
        Fill ary with normally distributed random numbers.

        Arguments:
            ary (Tensor): Tensor to fill with random values
            mean (float): Mean value. Default 0
            stdv (float): standard deviation value.  Default 1
        """
        self.pcg.fill_normal(p_gpuarray(ary.shape, ary.dtype, gpudata=ary.gpudata))
        if not all([mean == 0, stdv == 1]):
            ary[:] = ary * stdv + mean 
开发者ID:NervanaSystems,项目名称:neon,代码行数:14,代码来源:nervanagpu.py

示例15: give_cuda

# 需要导入模块: from pycuda import gpuarray [as 别名]
# 或者: from pycuda.gpuarray import GPUArray [as 别名]
def give_cuda(a, type='real'):
    """Give a list or an array to GPU memory.

    Parameters
    ----------
    a : array, list
        Data to send to the GPU memory.
    type : str
        'real' or 'complex'.

    Returns
    -------
    gpuarray
        GPUArray version of the input array.

    Examples
    --------
    >>> a = give_cuda([[1., 2., 3.], [4., 5., 6.]])
    [[ 1.,  2.,  3.],
     [ 4.,  5.,  6.]]

    >>> type(a)
    <class 'pycuda.gpuarray.GPUArray'>

    >>> a.shape
    (2, 3)

    >>> a.dtype
    'float32'

    >>> a.reshape((1, 6))
    [[ 1.,  2.,  3.,  4.,  5.,  6.]]
    """
    if type == 'real':
        return cuda_array.to_gpu(array(a).astype(float32))
    elif type == 'complex':
        return cuda_array.to_gpu(array(a).astype(complex64))
    else:
        raise NotImplementedError 
开发者ID:compas-dev,项目名称:compas,代码行数:41,代码来源:cuda.py


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