Esempio n. 1
0
    def __init__(self, shape, in_dtype, out_dtype, batch=1, stream=None,
                 mode=0x01):

        if np.isscalar(shape):
            self.shape = (shape, )
        else:
            self.shape = shape

        self.in_dtype = in_dtype
        self.out_dtype = out_dtype

        if batch <= 0:
            raise ValueError('batch size must be greater than 0')
        self.batch = batch

        # Determine type of transformation:
        if in_dtype == np.float32 and out_dtype == np.complex64:
            self.fft_type = cufft.CUFFT_R2C
            self.fft_func = cufft.cufftExecR2C
        elif in_dtype == np.complex64 and out_dtype == np.float32:
            self.fft_type = cufft.CUFFT_C2R
            self.fft_func = cufft.cufftExecC2R
        elif in_dtype == np.complex64 and out_dtype == np.complex64:
            self.fft_type = cufft.CUFFT_C2C
            self.fft_func = cufft.cufftExecC2C
        elif in_dtype == np.float64 and out_dtype == np.complex128:
            self.fft_type = cufft.CUFFT_D2Z
            self.fft_func = cufft.cufftExecD2Z
        elif in_dtype == np.complex128 and out_dtype == np.float64:
            self.fft_type = cufft.CUFFT_Z2D
            self.fft_func = cufft.cufftExecZ2D
        elif in_dtype == np.complex128 and out_dtype == np.complex128:
            self.fft_type = cufft.CUFFT_Z2Z
            self.fft_func = cufft.cufftExecZ2Z
        else:
            raise ValueError('unsupported input/output type combination')

        # Check for double precision support:
        capability = misc.get_compute_capability(misc.get_current_device())
        if capability < 1.3 and \
           (misc.isdoubletype(in_dtype) or misc.isdoubletype(out_dtype)):
            raise RuntimeError('double precision requires compute capability '
                               '>= 1.3 (you have %g)' % capability)

        # Set up plan:
        if len(self.shape) > 0:
            n = np.asarray(self.shape, np.int32)
            self.handle = cufft.cufftPlanMany(len(self.shape), n.ctypes.data,
                                              None, 1, 0, None, 1, 0,
                                              self.fft_type, self.batch)
        else:
            raise ValueError('invalid transform size')

        # Set FFTW compatibility mode:
        cufft.cufftSetCompatibilityMode(self.handle, mode)

        # Associate stream with plan:
        if stream != None:
            cufft.cufftSetStream(self.handle, stream.handle)
Esempio n. 2
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    def __init__(self, shape, in_dtype, out_dtype, batch=1, stream=None,
                 mode=0x01):

        if np.isscalar(shape):
            self.shape = (shape, )
        else:
            self.shape = shape

        self.in_dtype = in_dtype
        self.out_dtype = out_dtype

        if batch <= 0:
            raise ValueError('batch size must be greater than 0')
        self.batch = batch

        # Determine type of transformation:
        if in_dtype == np.float32 and out_dtype == np.complex64:
            self.fft_type = cufft.CUFFT_R2C
            self.fft_func = cufft.cufftExecR2C
        elif in_dtype == np.complex64 and out_dtype == np.float32:
            self.fft_type = cufft.CUFFT_C2R
            self.fft_func = cufft.cufftExecC2R
        elif in_dtype == np.complex64 and out_dtype == np.complex64:
            self.fft_type = cufft.CUFFT_C2C
            self.fft_func = cufft.cufftExecC2C
        elif in_dtype == np.float64 and out_dtype == np.complex128:
            self.fft_type = cufft.CUFFT_D2Z
            self.fft_func = cufft.cufftExecD2Z
        elif in_dtype == np.complex128 and out_dtype == np.float64:
            self.fft_type = cufft.CUFFT_Z2D
            self.fft_func = cufft.cufftExecZ2D
        elif in_dtype == np.complex128 and out_dtype == np.complex128:
            self.fft_type = cufft.CUFFT_Z2Z
            self.fft_func = cufft.cufftExecZ2Z
        else:
            raise ValueError('unsupported input/output type combination')

        # Check for double precision support:
        capability = misc.get_compute_capability(misc.get_current_device())
        if capability < 1.3 and \
           (misc.isdoubletype(in_dtype) or misc.isdoubletype(out_dtype)):
            raise RuntimeError('double precision requires compute capability '
                               '>= 1.3 (you have %g)' % capability)

        # Set up plan:
        if len(self.shape) > 0:
            n = np.asarray(self.shape, np.int32)
            self.handle = cufft.cufftPlanMany(len(self.shape), n.ctypes.data,
                                              None, 1, 0, None, 1, 0,
                                              self.fft_type, self.batch)
        else:
            raise ValueError('invalid transform size')

        # Set FFTW compatibility mode:
        cufft.cufftSetCompatibilityMode(self.handle, mode)

        # Associate stream with plan:
        if stream != None:
            cufft.cufftSetStream(self.handle, stream.handle)
Esempio n. 3
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    def __init__(self, shape, in_dtype, out_dtype, batch=1, stream=None,
                 mode=0x01):

        if np.isscalar(shape):
            self.shape = (shape, )
        else:
            self.shape = shape

        self.in_dtype = in_dtype
        self.out_dtype = out_dtype

        if batch <= 0:
            raise ValueError('batch size must be greater than 0')
        self.batch = batch

        # Determine type of transformation:
        if in_dtype == np.float32 and out_dtype == np.complex64:
            self.fft_type = cufft.CUFFT_R2C
            self.fft_func = cufft.cufftExecR2C
        elif in_dtype == np.complex64 and out_dtype == np.float32:
            self.fft_type = cufft.CUFFT_C2R
            self.fft_func = cufft.cufftExecC2R
        elif in_dtype == np.complex64 and out_dtype == np.complex64:
            self.fft_type = cufft.CUFFT_C2C
            self.fft_func = cufft.cufftExecC2C
        elif in_dtype == np.float64 and out_dtype == np.complex128:
            self.fft_type = cufft.CUFFT_D2Z
            self.fft_func = cufft.cufftExecD2Z
        elif in_dtype == np.complex128 and out_dtype == np.float64:
            self.fft_type = cufft.CUFFT_Z2D
            self.fft_func = cufft.cufftExecZ2D
        elif in_dtype == np.complex128 and out_dtype == np.complex128:
            self.fft_type = cufft.CUFFT_Z2Z
            self.fft_func = cufft.cufftExecZ2Z
        else:
            raise ValueError('unsupported input/output type combination')

        # Set up plan:
        if len(self.shape) > 0:
            n = np.asarray(self.shape, np.int32)
            self.handle = cufft.cufftPlanMany(len(self.shape), n.ctypes.data,
                                              None, 1, 0, None, 1, 0,
                                              self.fft_type, self.batch)
        else:
            raise ValueError('invalid transform size')

        # Set FFTW compatibility mode:
        cufft.cufftSetCompatibilityMode(self.handle, mode)

        # Associate stream with plan:
        if stream != None:
            cufft.cufftSetStream(self.handle, stream.handle)