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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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