def _prep_texture(self):
width, height, depth = self.dimensions
dim = 3 if depth != 0 else 2 if height != 0 else 1
# generate input data and allocate output buffer
shape = (depth, height, width) if dim == 3 else \
(height, width) if dim == 2 else \
(width,)
self.shape = shape
# prepare input, output, and texture memory
# self.data holds the data stored in the texture memory
tex_data = cupy.random.random(shape, dtype=cupy.float32)
ch = ChannelFormatDescriptor(32, 0, 0, 0,
runtime.cudaChannelFormatKindFloat)
arr = CUDAarray(ch, width, height, depth)
arr.copy_from(tex_data)
self.data = tex_data
# create resource and texture descriptors
res = ResourceDescriptor(runtime.cudaResourceTypeArray, cuArr=arr)
address_mode = (runtime.cudaAddressModeClamp,
runtime.cudaAddressModeClamp)
tex = TextureDescriptor(address_mode, runtime.cudaFilterModePoint,
runtime.cudaReadModeElementType)
# create a texture object
return TextureObject(res, tex)
def SART2DBackWard(grad, order, sp):
grad_ = grad.copy()
block1D = (8, 1)
grid1D = ((sp['nBins'] + block1D[0] - 1) // block1D[0], 1)
block2D = (8, 8)
grid2D = ((sp['nWidth'] + block2D[0] - 1) // block2D[0],
(sp['nHeight'] + block2D[1] - 1) // block2D[1])
mod = cupy.RawModule(code=source_texref)
AssignResidualError = mod.get_function('AssignResidualError_kernel')
FpKernel = mod.get_function('fGetFp_kernel')
# 2D texture
channelDescImg = ChannelFormatDescriptor(
32, 0, 0, 0, runtime.cudaChannelFormatKindFloat)
cuArrayImg = CUDAarray(channelDescImg, sp['nWidth'], sp['nHeight'])
resourceDescImg = ResourceDescriptor(runtime.cudaResourceTypeArray,
cuArrayImg)
address_modeImg = (runtime.cudaAddressModeClamp,
runtime.cudaAddressModeClamp)
texDescImg = TextureDescriptor(address_modeImg,
runtime.cudaFilterModePoint,
runtime.cudaReadModeElementType)
# 1D texture
channelDesc1D = ChannelFormatDescriptor(32, 0, 0, 0,
runtime.cudaChannelFormatKindFloat)
cuArray1D = CUDAarray(channelDesc1D, sp['nBins'])
resourceDesc1D = ResourceDescriptor(runtime.cudaResourceTypeArray,
cuArray1D)
address_mode1D = (runtime.cudaAddressModeClamp,
runtime.cudaAddressModeClamp)
texDesc1D = TextureDescriptor(address_mode1D, runtime.cudaFilterModePoint,
runtime.cudaReadModeElementType)
d_fOneProj = cupy.zeros(sp['nBins'], cupy.float32)
for v in range(sp['nViews']):
nView = order[sp['nViews'] - 1 - v]
fLambda = sp['fRotateDir'] * 2.0 * np.pi / float(
sp['nNumAngle']) * float(nView + sp['nStartAngle'])
fCosLambda = np.cos(fLambda)
fSinLambda = np.sin(fLambda)
# A*x
cuArrayImg.copy_from(grad)
TextureReference(mod.get_texref('texImage'), resourceDescImg,
texDescImg)
args = (d_fOneProj, sp['nBins'], sp['fSod'], sp['fOdd'],
sp['fCellSize'], sp['fPixelSize'], sp['fFovRadius'],
fCosLambda, fSinLambda, nView, sp['fOffSet'],
sp['fAngleOfSlope'])
FpKernel(grid1D, block1D, args)
# AT*A*x
cuArray1D.copy_from(d_fOneProj)
TextureReference(mod.get_texref('texFP'), resourceDesc1D, texDesc1D)
AssignResidualErrorArgs = (grad, sp['nWidth'], sp['nHeight'],
sp['nBins'], sp['fSod'], sp['fOdd'],
sp['fCellSize'], sp['fPixelSize'],
fCosLambda, fSinLambda, sp['fOffSet'],
sp['fAngleOfSlope'], sp['relax_factor'])
AssignResidualError(grid2D, block2D, AssignResidualErrorArgs)
grad = grad_ - sp['relax_factor'] * grad
return grad
def SART2D(p, sp, order, x0):
# x0 = xinit.copy()
block1D = (8, 1)
grid1D = ((sp['nBins'] + block1D[0] - 1) // block1D[0], 1)
block2D = (8, 8)
grid2D = ((sp['nWidth'] + block2D[0] - 1) // block2D[0],
(sp['nHeight'] + block2D[1] - 1) // block2D[1])
mod = cupy.RawModule(code=source_texref)
fGetResiduals = mod.get_function('fGetResiduals')
AssignResidualError = mod.get_function('AssignResidualError_kernel')
channelDescImg = ChannelFormatDescriptor(
32, 0, 0, 0, runtime.cudaChannelFormatKindFloat)
cuArrayImg = CUDAarray(channelDescImg, sp['nWidth'], sp['nHeight'])
resourceDescImg = ResourceDescriptor(runtime.cudaResourceTypeArray,
cuArrayImg)
address_modeImg = (runtime.cudaAddressModeClamp,
runtime.cudaAddressModeClamp)
texDescImg = TextureDescriptor(address_modeImg,
runtime.cudaFilterModePoint,
runtime.cudaReadModeElementType)
# 1D texture
channelDesc1D = ChannelFormatDescriptor(32, 0, 0, 0,
runtime.cudaChannelFormatKindFloat)
cuArray1D = CUDAarray(channelDesc1D, sp['nBins'])
resourceDesc1D = ResourceDescriptor(runtime.cudaResourceTypeArray,
cuArray1D)
address_mode1D = (runtime.cudaAddressModeClamp,
runtime.cudaAddressModeClamp)
texDesc1D = TextureDescriptor(address_mode1D, runtime.cudaFilterModePoint,
runtime.cudaReadModeElementType)
d_fResidualsData = cupy.zeros(sp['nBins'], cupy.float32)
for v in range(sp['nViews']):
# print('{}\n'.format(v))
nView = order[v]
fLambda = sp['fRotateDir'] * 2.0 * np.pi / float(
sp['nNumAngle']) * float(nView + sp['nStartAngle'])
fCosLambda = np.cos(fLambda)
fSinLambda = np.sin(fLambda)
cuArrayImg.copy_from(x0)
TextureReference(mod.get_texref('texImage'), resourceDescImg,
texDescImg)
getErrArgs = (d_fResidualsData, p, sp['nBins'], sp['fSod'], sp['fOdd'],
sp['fCellSize'], sp['fPixelSize'], sp['fFovRadius'],
fCosLambda, fSinLambda, nView, sp['fOffSet'],
sp['fAngleOfSlope'])
fGetResiduals(grid1D, block1D, getErrArgs)
cuArray1D.copy_from(d_fResidualsData)
TextureReference(mod.get_texref('texFP'), resourceDesc1D, texDesc1D)
AssignResidualErrorArgs = (x0, sp['nWidth'], sp['nHeight'],
sp['nBins'], sp['fSod'], sp['fOdd'],
sp['fCellSize'], sp['fPixelSize'],
fCosLambda, fSinLambda, sp['fOffSet'],
sp['fAngleOfSlope'], sp['relax_factor'])
AssignResidualError(grid2D, block2D, AssignResidualErrorArgs)
return x0
def deskew(data, angle, dx, dz, rotate=True, return_resolution=True, out=None):
"""
Args:
data (ndarray): 3-D array to apply deskew
angle (float): angle between the objective and coverslip, in degree
dx (float): X resolution
dz (float): Z resolution
rotate (bool, optional): rotate and crop the output
return_resolution (bool, optional): return deskewed X/Z resolution
out (ndarray, optional): array to store the result
"""
angle = radians(angle)
# shift along X axis, in pixels
shift = dz * cos(angle) / dx
logger.debug(f"layer shift: {shift:.04f} px")
# estimate new size
nw, nv, nu = data.shape
nz, ny, nx = nw, nv, nu + ceil(shift * (nw - 1))
# upload texture
ch = ChannelFormatDescriptor(32, 0, 0, 0,
runtime.cudaChannelFormatKindFloat)
arr = CUDAarray(ch, nu, nw)
res = ResourceDescriptor(runtime.cudaResourceTypeArray, cuArr=arr)
address_mode = (runtime.cudaAddressModeBorder,
runtime.cudaAddressModeBorder)
tex = TextureDescriptor(address_mode, runtime.cudaFilterModeLinear,
runtime.cudaReadModeElementType)
# transpose
data = np.swapaxes(data, 0, 1)
data = np.ascontiguousarray(data)
data_in = data.astype(np.float32)
data_out = cp.empty((ny, nz, nx), np.float32)
for i, layer in enumerate(data_in):
arr.copy_from(layer) # TODO use stream
texobj = TextureObject(res, tex)
kernels["shear_kernel"](
(ceil(nx / 16), ceil(nz / 16)),
(16, 16),
(data_out[i, ...], texobj, nx, nz, nu, np.float32(shift)),
)
data_out = cp.swapaxes(data_out, 0, 1)
data_out = cp.asnumpy(data_out)
data_out = data_out.astype(data.dtype)
if return_resolution:
# new resolution
dz *= sin(angle)
return data_out, (dz, dx)
else:
return data_out
def test_fetch_float4_texture(self):
width = 47
height = 39
depth = 11
n_channel = 4
# generate input data and allocate output buffer
in_shape = (depth, height, n_channel * width)
out_shape = (depth, height, width)
# prepare input, output, and texture memory
tex_data = cupy.random.random(in_shape, dtype=cupy.float32)
real_output_x = cupy.zeros(out_shape, dtype=cupy.float32)
real_output_y = cupy.zeros(out_shape, dtype=cupy.float32)
real_output_z = cupy.zeros(out_shape, dtype=cupy.float32)
real_output_w = cupy.zeros(out_shape, dtype=cupy.float32)
ch = ChannelFormatDescriptor(32, 32, 32, 32,
runtime.cudaChannelFormatKindFloat)
arr = CUDAarray(ch, width, height, depth)
arr.copy_from(tex_data)
# create resource and texture descriptors
res = ResourceDescriptor(runtime.cudaResourceTypeArray, cuArr=arr)
address_mode = (runtime.cudaAddressModeClamp,
runtime.cudaAddressModeClamp)
tex = TextureDescriptor(address_mode, runtime.cudaFilterModePoint,
runtime.cudaReadModeElementType)
if self.target == 'object':
# create a texture object
texobj = TextureObject(res, tex)
mod = cupy.RawModule(code=source_texobj)
else: # self.target == 'reference'
mod = cupy.RawModule(code=source_texref)
texrefPtr = mod.get_texref('texref3Df4')
# bind texture ref to resource
texref = TextureReference(texrefPtr, res, tex) # noqa
# get and launch the kernel
ker_name = 'copyKernel3D_4ch'
ker = mod.get_function(ker_name)
block = (4, 4, 2)
grid = ((width + block[0] - 1) // block[0],
(height + block[1] - 1) // block[1],
(depth + block[2] - 1) // block[2])
args = (real_output_x, real_output_y, real_output_z, real_output_w)
if self.target == 'object':
args = args + (texobj, )
args = args + (width, height, depth)
ker(grid, block, args)
# validate result
assert (real_output_x == tex_data[..., 0::4]).all()
assert (real_output_y == tex_data[..., 1::4]).all()
assert (real_output_z == tex_data[..., 2::4]).all()
assert (real_output_w == tex_data[..., 3::4]).all()
def test_array_gen_cpy(self):
xp = numpy if self.xp == 'numpy' else cupy
stream = None if not self.stream else cupy.cuda.Stream()
width, height, depth = self.dimensions
n_channel = self.n_channels
dim = 3 if depth != 0 else 2 if height != 0 else 1
shape = (depth, height, n_channel*width) if dim == 3 else \
(height, n_channel*width) if dim == 2 else \
(n_channel*width,)
# generate input data and allocate output buffer
if self.dtype in (numpy.float16, numpy.float32):
arr = xp.random.random(shape).astype(self.dtype)
kind = runtime.cudaChannelFormatKindFloat
else: # int
arr = xp.random.randint(100, size=shape, dtype=self.dtype)
if self.dtype in (numpy.int8, numpy.int16, numpy.int32):
kind = runtime.cudaChannelFormatKindSigned
else:
kind = runtime.cudaChannelFormatKindUnsigned
if self.c_contiguous:
arr2 = xp.zeros_like(arr)
assert arr.flags.c_contiguous
assert arr2.flags.c_contiguous
else:
arr = arr[..., ::2]
arr2 = xp.zeros_like(arr)
width = arr.shape[-1] // n_channel
assert not arr.flags.c_contiguous
assert arr2.flags.c_contiguous
assert arr.shape[-1] == n_channel*width
# create a CUDA array
ch_bits = [0, 0, 0, 0]
for i in range(n_channel):
ch_bits[i] = arr.dtype.itemsize*8
ch = ChannelFormatDescriptor(*ch_bits, kind)
cu_arr = CUDAarray(ch, width, height, depth)
# need to wait for the current stream to finish initialization
if stream is not None:
s = cupy.cuda.get_current_stream()
e = s.record()
stream.wait_event(e)
# copy from input to CUDA array, and back to output
cu_arr.copy_from(arr, stream)
cu_arr.copy_to(arr2, stream)
# check input and output are identical
if stream is not None:
stream.synchronize()
assert (arr == arr2).all()
def test_write_float_surface(self):
width, height, depth = self.dimensions
dim = 3 if depth != 0 else 2 if height != 0 else 1
# generate input data and allocate output buffer
shape = (depth, height, width) if dim == 3 else \
(height, width) if dim == 2 else \
(width,)
# prepare input, output, and surface memory
real_output = cupy.zeros(shape, dtype=cupy.float32)
assert real_output.flags['C_CONTIGUOUS']
ch = ChannelFormatDescriptor(32, 0, 0, 0,
runtime.cudaChannelFormatKindFloat)
expected_output = cupy.arange(numpy.prod(shape), dtype=cupy.float32)
expected_output = expected_output.reshape(shape) * 3.0
assert expected_output.flags['C_CONTIGUOUS']
# create resource descriptor
# note that surface memory only support CUDA array
arr = CUDAarray(ch, width, height, depth,
runtime.cudaArraySurfaceLoadStore)
arr.copy_from(real_output) # init to zero
res = ResourceDescriptor(runtime.cudaResourceTypeArray, cuArr=arr)
# create a surface object; currently we don't support surface reference
surfobj = SurfaceObject(res)
mod = cupy.RawModule(code=source_surfobj)
# get and launch the kernel
ker_name = 'writeKernel'
ker_name += '3D' if dim == 3 else '2D' if dim == 2 else '1D'
ker = mod.get_function(ker_name)
block = (4, 4, 2) if dim == 3 else (4, 4) if dim == 2 else (4, )
grid = ()
args = (surfobj, )
if dim >= 1:
grid_x = (width + block[0] - 1) // block[0]
grid = grid + (grid_x, )
args = args + (width, )
if dim >= 2:
grid_y = (height + block[1] - 1) // block[1]
grid = grid + (grid_y, )
args = args + (height, )
if dim == 3:
grid_z = (depth + block[2] - 1) // block[2]
grid = grid + (grid_z, )
args = args + (depth, )
ker(grid, block, args)
# validate result
arr.copy_to(real_output)
assert (real_output == expected_output).all()
def test_array_gen_cpy(self):
xp = numpy if self.xp == 'numpy' else cupy
stream = None if not self.stream else cupy.cuda.Stream()
width, height, depth = self.dimensions
n_channel = self.n_channels
dim = 3 if depth != 0 else 2 if height != 0 else 1
shape = (depth, height, n_channel*width) if dim == 3 else \
(height, n_channel*width) if dim == 2 else \
(n_channel*width,)
# generate input data and allocate output buffer
if self.dtype in (numpy.float16, numpy.float32):
arr = xp.random.random(shape).astype(self.dtype)
kind = runtime.cudaChannelFormatKindFloat
else: # int
# randint() in NumPy <= 1.10 does not have the dtype argument...
arr = xp.random.randint(100, size=shape).astype(self.dtype)
if self.dtype in (numpy.int8, numpy.int16, numpy.int32):
kind = runtime.cudaChannelFormatKindSigned
else:
kind = runtime.cudaChannelFormatKindUnsigned
arr2 = xp.zeros_like(arr)
assert arr.flags['C_CONTIGUOUS']
assert arr2.flags['C_CONTIGUOUS']
# create a CUDA array
ch_bits = [0, 0, 0, 0]
for i in range(n_channel):
ch_bits[i] = arr.dtype.itemsize * 8
# unpacking arguments using *ch_bits is not supported before PY35...
ch = ChannelFormatDescriptor(ch_bits[0], ch_bits[1], ch_bits[2],
ch_bits[3], kind)
cu_arr = CUDAarray(ch, width, height, depth)
# copy from input to CUDA array, and back to output
cu_arr.copy_from(arr, stream)
cu_arr.copy_to(arr2, stream)
# check input and output are identical
if stream is not None:
dev.synchronize()
assert (arr == arr2).all()
def test_fetch_float_texture(self):
width, height, depth = self.dimensions
dim = 3 if depth != 0 else 2 if height != 0 else 1
if (self.mem_type == 'linear' and dim != 1) or \
(self.mem_type == 'pitch2D' and dim != 2):
pytest.skip('The test case {0} is inapplicable for {1} and thus '
'skipped.'.format(self.dimensions, self.mem_type))
# generate input data and allocate output buffer
shape = (depth, height, width) if dim == 3 else \
(height, width) if dim == 2 else \
(width,)
# prepare input, output, and texture memory
tex_data = cupy.random.random(shape, dtype=cupy.float32)
real_output = cupy.zeros_like(tex_data)
ch = ChannelFormatDescriptor(32, 0, 0, 0,
runtime.cudaChannelFormatKindFloat)
assert tex_data.flags['C_CONTIGUOUS']
assert real_output.flags['C_CONTIGUOUS']
if self.mem_type == 'CUDAarray':
arr = CUDAarray(ch, width, height, depth)
expected_output = cupy.zeros_like(tex_data)
assert expected_output.flags['C_CONTIGUOUS']
# test bidirectional copy
arr.copy_from(tex_data)
arr.copy_to(expected_output)
else: # linear are pitch2D are backed by ndarray
arr = tex_data
expected_output = tex_data
# create resource and texture descriptors
if self.mem_type == 'CUDAarray':
res = ResourceDescriptor(runtime.cudaResourceTypeArray, cuArr=arr)
elif self.mem_type == 'linear':
res = ResourceDescriptor(runtime.cudaResourceTypeLinear,
arr=arr,
chDesc=ch,
sizeInBytes=arr.size * arr.dtype.itemsize)
else: # pitch2D
# In this case, we rely on the fact that the hand-picked array
# shape meets the alignment requirement. This is CUDA's limitation,
# see CUDA Runtime API reference guide. "TexturePitchAlignment" is
# assumed to be 32, which should be applicable for most devices.
res = ResourceDescriptor(runtime.cudaResourceTypePitch2D,
arr=arr,
chDesc=ch,
width=width,
height=height,
pitchInBytes=width * arr.dtype.itemsize)
address_mode = (runtime.cudaAddressModeClamp,
runtime.cudaAddressModeClamp)
tex = TextureDescriptor(address_mode, runtime.cudaFilterModePoint,
runtime.cudaReadModeElementType)
if self.target == 'object':
# create a texture object
texobj = TextureObject(res, tex)
mod = cupy.RawModule(code=source_texobj)
else: # self.target == 'reference'
mod = cupy.RawModule(code=source_texref)
texref_name = 'texref'
texref_name += '3D' if dim == 3 else '2D' if dim == 2 else '1D'
texrefPtr = mod.get_texref(texref_name)
# bind texture ref to resource
texref = TextureReference(texrefPtr, res, tex) # noqa
# get and launch the kernel
ker_name = 'copyKernel'
ker_name += '3D' if dim == 3 else '2D' if dim == 2 else '1D'
ker_name += 'fetch' if self.mem_type == 'linear' else ''
ker = mod.get_function(ker_name)
block = (4, 4, 2) if dim == 3 else (4, 4) if dim == 2 else (4, )
grid = ()
args = (real_output, )
if self.target == 'object':
args = args + (texobj, )
if dim >= 1:
grid_x = (width + block[0] - 1) // block[0]
grid = grid + (grid_x, )
args = args + (width, )
if dim >= 2:
grid_y = (height + block[1] - 1) // block[1]
grid = grid + (grid_y, )
args = args + (height, )
if dim == 3:
grid_z = (depth + block[2] - 1) // block[2]
grid = grid + (grid_z, )
args = args + (depth, )
ker(grid, block, args)
# validate result
assert (real_output == expected_output).all()