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How to use d_x2 method in fMBT

Best Python code snippet using fMBT_python

gpu.py

Source:gpu.py

1from __future__ import division2from numbapro import cuda3import numpy as np4import numbapro.cudalib.cublas5import numpy.random6import math7import scipy.sparse.linalg8import scipy.sparse as sps9def fista(I, Phi, lambdav, L=None, tol=10e-6, max_iterations=200, display=True, verbose=False):10	"""11	I: Images 12 	Phi: Dictionary 13 	lambdav: Sparse Penalty 14 	L = Largest eigenvalue of Phi 15 	"""16	b = numbapro.cudalib.cublas.Blas()17	(m, n) = Phi.shape18	(m, batch) = I.shape19	if L == None:20		L = scipy.sparse.linalg.svds(Phi, 1, which='LM', return_singular_vectors=False)21		print "Max eigenvalue: ." + str(L)22	L = (L**2)*2 # L = svd(Phi) -> eig(2*(Phi.T*Phi))23	invL = 1/L24	t = 1.25	if sps.issparse(Phi):26		Phi = np.array(Phi.todense())27	d_I = cuda.to_device(np.array(I, dtype=np.float32, order='F'))28	d_Phi = cuda.to_device(np.array(Phi, dtype=np.float32, order='F'))29	d_Q = cuda.device_array((n, n), dtype=np.float32, order='F')30	d_c = cuda.device_array((n, batch), dtype=np.float32, order='F')31	d_x = cuda.to_device(np.array(np.zeros((n, batch), dtype=np.float32), order='F'))32	d_y = cuda.to_device(np.array(np.zeros((n, batch), dtype=np.float32), order='F'))33	d_x2 = cuda.to_device(np.array(np.zeros((n, batch), dtype=np.float32), order='F'))34	# Temporary array variables35	d_t = cuda.device_array((m, batch), dtype=np.float32, order='F')36	d_t2 = cuda.device_array(n*batch, dtype=np.float32, order='F')37	b.gemm('T', 'N', n, n, m, 1, d_Phi, d_Phi, 0, d_Q) 	# Q = Phi^T * Phi38	b.gemm('T', 'N', n, batch, m, -2, d_Phi, d_I, 0, d_c) # c = -2*Phi^T * y39	blockdim = 32, 3240	griddim = int(math.ceil(n/blockdim[0])), int(math.ceil(batch/blockdim[1]))41	blockdim_1d = 25642	griddim_1d = int(math.ceil(n*batch/blockdim_1d))43	start = l2l1obj(b, d_I, d_Phi, d_x, d_t, d_t2, lambdav, blockdim_1d, griddim_1d)44	obj2 = start45	for i in xrange(max_iterations):46		# x2 = 2*Q*y + c47		b.symm('L', 'U', n, batch, 2, d_Q, d_y, 0, d_x2)48		b.geam('N', 'N', n, batch, 1, d_c, 1, d_x2, d_x2)49		50		# x2 = y - invL * x251		b.geam('N', 'N', n, batch, 1, d_y, -invL, d_x2, d_x2)52		# proxOp()						53		l1prox[griddim, blockdim](d_x2, invL*lambdav, d_x2)54		t2 = (1+math.sqrt(1+4*(t**2)))/2.055		56		# y = x2 + ((t-1)/t2)*(x2-x)57		b.geam('N', 'N', n, batch, 1+(t-1)/t2, d_x2, (1-t)/t2, d_x, d_y)58		# x = x259		b.geam('N', 'N', n, batch, 1, d_x2, 0, d_x, d_x)60		t = t261		# update objective62		obj = obj263		obj2 = l2l1obj(b, d_I, d_Phi, d_x2, d_t, d_t2, lambdav, blockdim_1d, griddim_1d)64		if verbose:65			x2 = d_x2.copy_to_host()66			print "L1 Objective: " + str(obj2)67		if np.abs(obj-obj2)/float(obj) < tol:68			break69	x2 = d_x2.copy_to_host()70	if display:71		print "FISTA Iterations: " + str(i)72		print "L1 Objective: " +  str(lambdav*np.sum(np.abs(x2)) + np.sum((I-Phi.dot(x2))**2))73		print "Objective delta: " + str(obj2-start)74	return x275def l2l1obj(b, d_I, d_Phi, d_x2, d_t, d_t2, lambdav, blockdim, griddim):76	(m, n) = d_Phi.shape77	(m, batch) = d_I.shape78	b.gemm('N', 'N', m, batch, n, 1, d_Phi, d_x2, 0, d_t)79	b.geam('N', 'N', m, batch, 1, d_I, -1, d_t, d_t)80 	l2 = b.nrm2(d_t.ravel(order='F'))**281 	82 	gabs[griddim, blockdim](d_x2.ravel(order='F'), d_t2)83 	84 	l1 = lambdav*b.asum(d_t2)85 	return l2 + l186@cuda.jit('void(float32[:,:], float64, float32[:,:])')87def l1prox(A, t, C):88	""" l1 Proximal operator: C = np.fmax(A-t, 0) + np.fmin(A+t, 0) 89	A: coefficients matrix (dim, batch)90	t: threshold91	C: output (dim, batch) """92	i, j = cuda.grid(2)93	if i >= A.shape[0] or j >= A.shape[1]:94		return95	if A[i, j] >= t:96		C[i, j] = A[i, j] - t 97	elif A[i, j] <= -t: 98		C[i, j] = A[i, j] + t    99	else:100		C[i, j] = 0101	return102@cuda.jit('void(float32[:], float32[:])')103def gabs(x, y):104	i = cuda.grid(1)105	if i >= x.size:106		return107	if x[i] < 0:108		y[i] = -x[i]109	else:110		y[i] = x[i]...

gpu 2.py

Source:gpu 2.py

1from __future__ import division2from numbapro import cuda3import numpy as np4import numbapro.cudalib.cublas5import numpy.random6import math7import scipy.sparse.linalg8import scipy.sparse as sps9def fista(I, Phi, lambdav, L=None, tol=10e-6, max_iterations=200, display=True, verbose=False):10	"""11	I: Images 12 	Phi: Dictionary 13 	lambdav: Sparse Penalty 14 	L = Largest eigenvalue of Phi 15 	"""16	b = numbapro.cudalib.cublas.Blas()17	(m, n) = Phi.shape18	(m, batch) = I.shape19	if L == None:20		L = scipy.sparse.linalg.svds(Phi, 1, which='LM', return_singular_vectors=False)21		print "Max eigenvalue: ." + str(L)22	L = (L**2)*2 # L = svd(Phi) -> eig(2*(Phi.T*Phi))23	invL = 1/L24	t = 1.25	if sps.issparse(Phi):26		Phi = np.array(Phi.todense())27	d_I = cuda.to_device(np.array(I, dtype=np.float32, order='F'))28	d_Phi = cuda.to_device(np.array(Phi, dtype=np.float32, order='F'))29	d_Q = cuda.device_array((n, n), dtype=np.float32, order='F')30	d_c = cuda.device_array((n, batch), dtype=np.float32, order='F')31	d_x = cuda.to_device(np.array(np.zeros((n, batch), dtype=np.float32), order='F'))32	d_y = cuda.to_device(np.array(np.zeros((n, batch), dtype=np.float32), order='F'))33	d_x2 = cuda.to_device(np.array(np.zeros((n, batch), dtype=np.float32), order='F'))34	# Temporary array variables35	d_t = cuda.device_array((m, batch), dtype=np.float32, order='F')36	d_t2 = cuda.device_array(n*batch, dtype=np.float32, order='F')37	b.gemm('T', 'N', n, n, m, 1, d_Phi, d_Phi, 0, d_Q) 	# Q = Phi^T * Phi38	b.gemm('T', 'N', n, batch, m, -2, d_Phi, d_I, 0, d_c) # c = -2*Phi^T * y39	blockdim = 32, 3240	griddim = int(math.ceil(n/blockdim[0])), int(math.ceil(batch/blockdim[1]))41	blockdim_1d = 25642	griddim_1d = int(math.ceil(n*batch/blockdim_1d))43	start = l2l1obj(b, d_I, d_Phi, d_x, d_t, d_t2, lambdav, blockdim_1d, griddim_1d)44	obj2 = start45	for i in xrange(max_iterations):46		# x2 = 2*Q*y + c47		b.symm('L', 'U', n, batch, 2, d_Q, d_y, 0, d_x2)48		b.geam('N', 'N', n, batch, 1, d_c, 1, d_x2, d_x2)49		50		# x2 = y - invL * x251		b.geam('N', 'N', n, batch, 1, d_y, -invL, d_x2, d_x2)52		# proxOp()						53		l1prox[griddim, blockdim](d_x2, invL*lambdav, d_x2)54		t2 = (1+math.sqrt(1+4*(t**2)))/2.055		56		# y = x2 + ((t-1)/t2)*(x2-x)57		b.geam('N', 'N', n, batch, 1+(t-1)/t2, d_x2, (1-t)/t2, d_x, d_y)58		# x = x259		b.geam('N', 'N', n, batch, 1, d_x2, 0, d_x, d_x)60		t = t261		# update objective62		obj = obj263		obj2 = l2l1obj(b, d_I, d_Phi, d_x2, d_t, d_t2, lambdav, blockdim_1d, griddim_1d)64		if verbose:65			x2 = d_x2.copy_to_host()66			print "L1 Objective: " + str(obj2)67		if np.abs(obj-obj2)/float(obj) < tol:68			break69	x2 = d_x2.copy_to_host()70	if display:71		print "FISTA Iterations: " + str(i)72		print "L1 Objective: " +  str(lambdav*np.sum(np.abs(x2)) + np.sum((I-Phi.dot(x2))**2))73		print "Objective delta: " + str(obj2-start)74	return x275def l2l1obj(b, d_I, d_Phi, d_x2, d_t, d_t2, lambdav, blockdim, griddim):76	(m, n) = d_Phi.shape77	(m, batch) = d_I.shape78	b.gemm('N', 'N', m, batch, n, 1, d_Phi, d_x2, 0, d_t)79	b.geam('N', 'N', m, batch, 1, d_I, -1, d_t, d_t)80 	l2 = b.nrm2(d_t.ravel(order='F'))**281 	82 	gabs[griddim, blockdim](d_x2.ravel(order='F'), d_t2)83 	84 	l1 = lambdav*b.asum(d_t2)85 	return l2 + l186@cuda.jit('void(float32[:,:], float64, float32[:,:])')87def l1prox(A, t, C):88	""" l1 Proximal operator: C = np.fmax(A-t, 0) + np.fmin(A+t, 0) 89	A: coefficients matrix (dim, batch)90	t: threshold91	C: output (dim, batch) """92	i, j = cuda.grid(2)93	if i >= A.shape[0] or j >= A.shape[1]:94		return95	if A[i, j] >= t:96		C[i, j] = A[i, j] - t 97	elif A[i, j] <= -t: 98		C[i, j] = A[i, j] + t    99	else:100		C[i, j] = 0101	return102@cuda.jit('void(float32[:], float32[:])')103def gabs(x, y):104	i = cuda.grid(1)105	if i >= x.size:106		return107	if x[i] < 0:108		y[i] = -x[i]109	else:110		y[i] = x[i]...

corrCUDA.py

Source:corrCUDA.py

1import numpy as np2import accelerate.cuda.blas as blas3import accelerate.cuda.fft as ft4from numba import cuda5def corr_td_single (x1,x2):6    c_12 = blas.dot(x1,x2)7    return c_128def best_grid_size(size, tpb):9    bpg = np.ceil(np.array(size, dtype=np.float) / tpb).astype(np.int).tolist()10    return tuple(bpg)11@cuda.jit('void(float32[:], float32[:])')12def mult_inplace(img, resp):13    i = cuda.grid(1)14    img[i] *= resp[i]15def corr_FD(x1,x2):16    threadperblock = 32, 817    blockpergrid = best_grid_size(tuple(reversed(x1.shape)), threadperblock)18    print('kernel config: %s x %s' % (blockpergrid, threadperblock))19    # Trigger initialization the cuFFT system.20    # This takes significant time for small dataset.21    # We should not be including the time wasted here22    #ft.FFTPlan(shape=x1.shape, itype=np.float32, otype=np.complex64)23    X1 = x1.astype(np.float32)24    X2 = x2.astype(np.float32)25    stream1 = cuda.stream()26    stream2 = cuda.stream()27    fftplan1 = ft.FFTPlan(shape=x1.shape, itype=np.float32,28                       otype=np.complex64, stream=stream1)29    fftplan2 = ft.FFTPlan(shape=x2.shape, itype=np.float32,30                       otype=np.complex64, stream=stream2)31    # pagelock memory32    with cuda.pinned(X1, X2):33        # We can overlap the transfer of response_complex with the forward FFT34        # on image_complex.35        d_X1 = cuda.to_device(X1, stream=stream1)36        d_X2 = cuda.to_device(X2, stream=stream2)37        fftplan1.forward(d_X1, out=d_X1)38        fftplan2.forward(d_X2, out=d_X2)39        print ('d_X1 is ',np.shape(d_X1),type(d_X1),np.max(d_X1))40        print ('d_X2 is ',np.shape(d_X2),type(d_X2),np.max(d_X2))41        stream2.synchronize()42        mult_inplace[blockpergrid, threadperblock, stream1](d_X1, d_X2)43        fftplan1.inverse(d_X1, out=d_X1)44        # implicitly synchronizes the streams45        c = d_X1.copy_to_host().real / np.prod(x1.shape)...

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