def test_18(self): N = 16 Nd = 5 M = 4 D = np.random.randn(Nd, Nd, M) s = np.random.randn(N, N) lmbda = 1e-1 mu = 1e-2 try: b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu) b.solve() except Exception as e: print(e) assert(0)
def test_05(self): Nr = 32 Nc = 31 Nd = 5 M = 4 D = np.random.randn(Nd, Nd, M).astype(np.float32) s = np.random.randn(Nr, Nc).astype(np.float32) lmbda = 1e-1 mu = 1e-2 opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 50, 'AutoRho': {'Enabled': False}}) b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt) X1 = b.solve() X2 = cucbpdn.cbpdngrd(D, s, lmbda, mu, opt) assert(sm.mse(X1, X2) < 1e-10)
def test_19(self): N = 16 Nd = 5 K = 2 M = 4 D = np.random.randn(Nd, Nd, M) s = np.random.randn(N, N, K) dt = np.float32 opt = cbpdn.ConvBPDNGradReg.Options({'Verbose' : False, 'LinSolveCheck' : True, 'MaxMainIter' : 20, 'AutoRho' : {'Enabled' : True}, 'DataType' : dt}) lmbda = 1e-1 mu = 1e-2 b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt=opt) b.solve() assert(b.X.dtype == dt) assert(b.Y.dtype == dt) assert(b.U.dtype == dt)
mu = 3e-1 w1 = np.ones((1, 1, 1, 1, D.shape[-1])) w1[..., 0:3] = 0.33 w1[..., 3:6] = 0.0 wg = np.zeros((D.shape[-1])) wg[..., 3:6] = 1.0 opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': True, 'MaxMainIter': 100, 'RelStopTol': 5e-3, 'AuxVarObj': False, 'L1Weight': w1, 'GradWeight': wg}) """ Initialise the :class:`.admm.cbpdn.ConvBPDNGradReg` object and call the ``solve`` method. """ b = cbpdn.ConvBPDNGradReg(D, pad(imgn), lmbda, mu, opt, dimK=0) X = b.solve() """ The denoised estimate of the image is just the reconstruction from all coefficient maps except those that represent the impulse noise, which is why we subtract the slice of ``X`` corresponding the impulse noise representing filters from the result of ``reconstruct``. """ imgdp = b.reconstruct().squeeze() - X[..., 0, 0:3].squeeze() imgd = crop(imgdp) """ Keep a copy of the low-frequency component estimate from this solution for use in the next approach. """
wgr = np.zeros((D.shape[2])) wgr[0] = 1.0 # Set up ConvBPDNGradReg options lmbda = 1e-2 mu = 1e1 opt = cbpdn.ConvBPDNGradReg.Options({'Verbose' : True, 'MaxMainIter' : 100, 'HighMemSolve' : True, 'LinSolveCheck' : True, 'RelStopTol' : 1e-3, 'AuxVarObj' : False, 'rho' : 1e0, 'AutoRho' : {'Enabled' : True, 'Period' : 10, 'RsdlTarget' : 0.02}, 'L1Weight' : wl1, 'GradWeight' : wgr}) # Initialise and run ConvBPDNGradReg object b = cbpdn.ConvBPDNGradReg(D, img, lmbda, mu, opt) X = b.solve() print("ConvBPDNGradReg solve time: %.2fs" % b.runtime) # Reconstruct representation imgr = b.reconstruct().squeeze() print(" reconstruction PSNR: %.2fdB\n" % sm.psnr(img, imgr)) # Display representation and reconstructed image fig1 = plot.figure(1, figsize=(14,14)) plot.subplot(2,2,1) plot.imview(b.Y[...,0].squeeze(), fgrf=fig1, cmap=plot.cm.Blues, title='Lowpass component') plot.subplot(2,2,2) plot.imview(np.sum(abs(b.Y[...,1:]), axis=b.cri.axisM).squeeze(), fgrf=fig1,
zm=optd['ZeroMean']) """ Update D update options to include initial values for Y and U. """ optd.update({'Y0': cnvrep.zpad(cnvrep.stdformD(D0n, cri.Cd, cri.M), cri.Nv), 'U0': np.zeros(cri.shpD + (cri.K,))}) """ Create X update object. """ xstep = cbpdn.ConvBPDNGradReg(D0n, S, lmbda, mu, optx) """ Create D update object. """ dstep = ccmod.ConvCnstrMOD(None, S, D0.shape, optd, method='cns') """ Create DictLearn object and solve. """ opt = dictlrn.DictLearn.Options({'Verbose': True, 'MaxMainIter': 200}) d = dictlrn.DictLearn(xstep, dstep, opt)
def gengraphs(pth, nopyfftw): """ Generate call graph images when necessary. Parameter pth is the path to the directory in which images are to be created. Parameter nopyfftw is a flag indicating whether it is necessary to avoid using pyfftw. """ srcmodflt = '^sporco.admm' srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$' dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$' fnmsub = ('^sporco.admm.', '') grpflt = r'^[^\.]*.[^\.]*' lnksub = (r'^([^\.]*).(.*)', r'../../sporco.admm.\1.html#sporco.admm.\1.\2') fntsz = 9 fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans' kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True} ct = jonga.CallTracer(srcmodflt=srcmodflt, srcqnmflt=srcqnmflt, dstqnmflt=dstqnmflt, fnmsub=fnmsub, grpflt=grpflt, lnksub=lnksub) # Make destination directory if it doesn't exist if not os.path.exists(pth): os.makedirs(pth, exist_ok=True) # Handle environment in which pyfftw is unavailable if nopyfftw: import numpy.fft as npfft import sporco.linalg as spl def empty(shape, dtype, order='C', n=None): return np.zeros(shape, dtype=dtype) spl.pyfftw_empty_aligned = empty def rfftn_empty(shape, axes, dtype, order='C', n=None): ashp = list(shape) raxis = axes[-1] ashp[raxis] = ashp[raxis] // 2 + 1 cdtype = spl.complex_dtype(dtype) return np.zeros(ashp, dtype=cdtype) spl.pyfftw_rfftn_empty_aligned = rfftn_empty spl.fftn = npfft.fftn spl.ifftn = npfft.ifftn spl.rfftn = npfft.rfftn spl.irfftn = npfft.irfftn import numpy as np np.random.seed(12345) #### bpdn module from sporco.admm import bpdn mdnm = 'sporco.admm.bpdn' D = np.random.randn(8, 16) s = np.random.randn(8, 1) lmbda = 0.1 ## BPDN class opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs): b = bpdn.BPDN(D, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs): b.solve() ## BPDNJoint class opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1}) mu = 0.01 with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs): b = bpdn.BPDNJoint(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs): b.solve() ## ElasticNet class opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs): b = bpdn.ElasticNet(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs): b.solve() # BPDNProjL1 class opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1}) gamma = 2.0 with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs): b = bpdn.BPDNProjL1(D, s, gamma, opt) with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs): b.solve() ## MinL1InL2Ball class opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1}) epsilon = 1.0 with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs): b = bpdn.MinL1InL2Ball(D, s, epsilon, opt) with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs): b.solve() #### cbpdn module from sporco.admm import cbpdn mdnm = 'sporco.admm.cbpdn' D = np.random.randn(4, 4, 16) s = np.random.randn(8, 8) lmbda = 0.1 ## ConvBPDN class opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs): b = cbpdn.ConvBPDN(D, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs): b.solve() ## ConvBPDNJoint class opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1}) mu = 0.01 with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs): b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs): b.solve() ## ConvElasticNet class opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs): b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs): b.solve() ## ConvBPDNGradReg class opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs): b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs): b.solve() ## ConvBPDNProjL1 class opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1}) gamma = 0.5 with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs): b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt) with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs): b.solve() ## ConvMinL1InL2Ball class opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1}) epsilon = 0.5 with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs): b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt) with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs): b.solve() ## ConvBPDNMaskDcpl class opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1}) W = np.ones(s.shape) with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs): b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt) with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs): b.solve() #### cbpdntv module from sporco.admm import cbpdntv mdnm = 'sporco.admm.cbpdntv' D = np.random.randn(4, 4, 16) s = np.random.randn(8, 8) lmbda = 0.1 mu = 0.01 ## ConvBPDNScalarTV class opt = cbpdntv.ConvBPDNScalarTV.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs): b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs): b.solve() ## ConvBPDNVectorTV class opt = cbpdntv.ConvBPDNVectorTV.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs): b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs): b.solve() ## ConvBPDNRecTV class opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs): b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs): b.solve() #### cmod module from sporco.admm import cmod mdnm = 'sporco.admm.cmod' X = np.random.randn(8, 16) S = np.random.randn(8, 16) ## CnstrMOD class opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs): b = cmod.CnstrMOD(X, S, opt=opt) with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs): b.solve() #### ccmod module from sporco.admm import ccmod mdnm = 'sporco.admm.ccmod' X = np.random.randn(8, 8, 1, 2, 1) S = np.random.randn(8, 8, 2) dsz = (4, 4, 1) ## ConvCnstrMOD_IterSM class opt = ccmod.ConvCnstrMOD_IterSM.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs): b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs): b.solve() ## ConvCnstrMOD_CG class opt = ccmod.ConvCnstrMOD_CG.Options({ 'Verbose': False, 'MaxMainIter': 1, 'CG': { 'MaxIter': 1 } }) with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs): b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs): b.solve() ## ConvCnstrMOD_Consensus class opt = ccmod.ConvCnstrMOD_Consensus.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs): b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs): b.solve() #### ccmodmd module from sporco.admm import ccmodmd mdnm = 'sporco.admm.ccmodmd' X = np.random.randn(8, 8, 1, 2, 1) S = np.random.randn(8, 8, 2) W = np.array([1.0]) dsz = (4, 4, 1) ## ConvCnstrMODMaskDcpl_IterSM class opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs): b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs): b.solve() ## ConvCnstrMODMaskDcpl_CG class opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({ 'Verbose': False, 'MaxMainIter': 1, 'CG': { 'MaxIter': 1 } }) with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs): b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs): b.solve() ## ConvCnstrMODMaskDcpl_Consensus class opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs): b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs): b.solve() #### bpdndl module from sporco.admm import bpdndl mdnm = 'sporco.admm.bpdndl' D0 = np.random.randn(8, 8) S = np.random.randn(8, 16) lmbda = 0.1 ## BPDNDictLearn class opt = bpdndl.BPDNDictLearn.Options({ 'Verbose': False, 'MaxMainIter': 1, 'AccurateDFid': True }) with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs): b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt) with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs): b.solve() #### cbpdndl module from sporco.admm import cbpdndl mdnm = 'sporco.admm.cbpdndl' D0 = np.random.randn(4, 4, 16) s = np.random.randn(8, 8, 10) lmbda = 0.1 ## ConvBPDNDictLearn class opt = cbpdndl.ConvBPDNDictLearn.Options({ 'Verbose': False, 'MaxMainIter': 1, 'AccurateDFid': True }) with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs): b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs): b.solve() ## ConvBPDNMaskDcplDictLearn class W = np.array([1.0]) opt = cbpdndl.ConvBPDNMaskDcplDictLearn.Options({ 'Verbose': False, 'MaxMainIter': 1, 'AccurateDFid': True }) with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs): b = cbpdndl.ConvBPDNMaskDcplDictLearn(D0, s, lmbda, W, opt) with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs): b.solve() #### tvl1 module from sporco.admm import tvl1 mdnm = 'sporco.admm.tvl1' s = np.random.randn(16, 16) lmbda = 0.1 ## TVL1Denoise class opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs): b = tvl1.TVL1Denoise(s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs): b.solve() ## TVL1Deconv class opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1}) h = np.random.randn(3, 3) with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs): b = tvl1.TVL1Deconv(h, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs): b.solve() #### tvl2 module from sporco.admm import tvl2 mdnm = 'sporco.admm.tvl2' s = np.random.randn(16, 16) lmbda = 0.1 ## TVL2Denoise class opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs): b = tvl2.TVL2Denoise(s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs): b.solve() ## TVL2Deconv class opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1}) h = np.random.randn(3, 3) with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs): b = tvl2.TVL2Deconv(h, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs): b.solve() srcmodflt = '^sporco.fista' fnmsub = ('^sporco.fista.', '') lnksub = (r'^([^\.]*).(.*)', r'../../sporco.fista.\1.html#sporco.fista.\1.\2') ct = jonga.CallTracer(srcmodflt=srcmodflt, srcqnmflt=srcqnmflt, dstqnmflt=dstqnmflt, fnmsub=fnmsub, grpflt=grpflt, lnksub=lnksub) #### fista.cbpdn module from sporco.fista import cbpdn mdnm = 'sporco.fista.cbpdn' D = np.random.randn(4, 4, 16) s = np.random.randn(8, 8) lmbda = 0.1 ## ConvBPDN class opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs): b = cbpdn.ConvBPDN(D, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs): b.solve() #### fista.ccmod module from sporco.fista import ccmod mdnm = 'sporco.fista.ccmod' X = np.random.randn(8, 8, 1, 2, 1) S = np.random.randn(8, 8, 2) dsz = (4, 4, 1) ## ConvCnstrMOD class opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs): b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs): b.solve() ## ConvCnstrMODMaskDcpl class opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs): b = ccmod.ConvCnstrMODMask(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs): b.solve()
def gengraphs(pth): """ Generate call graph images when necessary. Parameter pth is the path to the directory in which images are to be created. """ srcmodflt = '^sporco.admm' srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$' dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$' fnmsub = ('^sporco.admm.', '') grpflt = r'^[^\.]*.[^\.]*' lnkpfx = '../../modules/' lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))', lnkpfx + r'sporco.admm.\1.html#sporco.admm.\1.\2\4') fntsz = 9 fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans' kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True} ct = jonga.CallTracer(srcmodflt=srcmodflt, srcqnmflt=srcqnmflt, dstqnmflt=dstqnmflt, fnmsub=fnmsub, grpflt=grpflt, lnksub=lnksub) # Make destination directory if it doesn't exist if not os.path.exists(pth): os.makedirs(pth, exist_ok=True) import numpy as np np.random.seed(12345) #### bpdn module from sporco.admm import bpdn mdnm = 'sporco.admm.bpdn' D = np.random.randn(8, 16) s = np.random.randn(8, 1) lmbda = 0.1 ## BPDN class opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs): b = bpdn.BPDN(D, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs): b.solve() ## BPDNJoint class opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1}) mu = 0.01 with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs): b = bpdn.BPDNJoint(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs): b.solve() ## ElasticNet class opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs): b = bpdn.ElasticNet(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs): b.solve() # BPDNProjL1 class opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1}) gamma = 2.0 with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs): b = bpdn.BPDNProjL1(D, s, gamma, opt) with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs): b.solve() ## MinL1InL2Ball class opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1}) epsilon = 1.0 with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs): b = bpdn.MinL1InL2Ball(D, s, epsilon, opt) with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs): b.solve() #### cbpdn module from sporco.admm import cbpdn mdnm = 'sporco.admm.cbpdn' D = np.random.randn(4, 4, 16) s = np.random.randn(8, 8) lmbda = 0.1 ## ConvBPDN class opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs): b = cbpdn.ConvBPDN(D, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs): b.solve() ## ConvBPDNJoint class opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1}) mu = 0.01 with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs): b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs): b.solve() ## ConvElasticNet class opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs): b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs): b.solve() ## ConvBPDNGradReg class opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs): b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs): b.solve() ## ConvBPDNProjL1 class opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1}) gamma = 0.5 with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs): b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt) with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs): b.solve() ## ConvMinL1InL2Ball class opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1}) epsilon = 0.5 with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs): b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt) with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs): b.solve() ## ConvBPDNMaskDcpl class opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1}) W = np.ones(s.shape) with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs): b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt) with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs): b.solve() ## ConvL1L1Grd class opt = cbpdn.ConvL1L1Grd.Options({'Verbose': False, 'MaxMainIter': 1}) mu = 1e-2 with CallGraph(ct, mdnm, pth, 'cl1l1grd_init.svg', **kwargs): b = cbpdn.ConvL1L1Grd(D, s, lmbda, mu, W, opt) with CallGraph(ct, mdnm, pth, 'cl1l1grd_solve.svg', **kwargs): b.solve() #### cbpdntv module from sporco.admm import cbpdntv mdnm = 'sporco.admm.cbpdntv' D = np.random.randn(4, 4, 16) s = np.random.randn(8, 8) lmbda = 0.1 mu = 0.01 ## ConvBPDNScalarTV class opt = cbpdntv.ConvBPDNScalarTV.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs): b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs): b.solve() ## ConvBPDNVectorTV class opt = cbpdntv.ConvBPDNVectorTV.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs): b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs): b.solve() ## ConvBPDNRecTV class opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs): b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt) with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs): b.solve() #### cbpdnin module from sporco.admm import cbpdnin mdnm = 'sporco.admm.cbpdnin' D = np.random.randn(4, 4, 32) s = np.random.randn(8, 8) lmbda = 0.1 mu = 0.01 Wg = np.append(np.eye(16), np.eye(16), axis=-1) ## ConvBPDNInhib class opt = cbpdnin.ConvBPDNInhib.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cbpdnin_init.svg', **kwargs): b = cbpdnin.ConvBPDNInhib(D, s, Wg, Whn=4, lmbda=lmbda, mu=mu, gamma=None, opt=opt) with CallGraph(ct, mdnm, pth, 'cbpdnin_solve.svg', **kwargs): b.solve() #### cmod module from sporco.admm import cmod mdnm = 'sporco.admm.cmod' X = np.random.randn(8, 16) S = np.random.randn(8, 16) ## CnstrMOD class opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs): b = cmod.CnstrMOD(X, S, opt=opt) with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs): b.solve() #### ccmod module from sporco.admm import ccmod mdnm = 'sporco.admm.ccmod' X = np.random.randn(8, 8, 1, 2, 1) S = np.random.randn(8, 8, 2) dsz = (4, 4, 1) ## ConvCnstrMOD_IterSM class opt = ccmod.ConvCnstrMOD_IterSM.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs): b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs): b.solve() ## ConvCnstrMOD_CG class opt = ccmod.ConvCnstrMOD_CG.Options({ 'Verbose': False, 'MaxMainIter': 1, 'CG': { 'MaxIter': 1 } }) with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs): b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs): b.solve() ## ConvCnstrMOD_Consensus class opt = ccmod.ConvCnstrMOD_Consensus.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs): b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs): b.solve() #### ccmodmd module from sporco.admm import ccmodmd mdnm = 'sporco.admm.ccmodmd' X = np.random.randn(8, 8, 1, 2, 1) S = np.random.randn(8, 8, 2) W = np.array([1.0]) dsz = (4, 4, 1) ## ConvCnstrMODMaskDcpl_IterSM class opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs): b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs): b.solve() ## ConvCnstrMODMaskDcpl_CG class opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({ 'Verbose': False, 'MaxMainIter': 1, 'CG': { 'MaxIter': 1 } }) with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs): b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs): b.solve() ## ConvCnstrMODMaskDcpl_Consensus class opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({ 'Verbose': False, 'MaxMainIter': 1 }) with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs): b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs): b.solve() #### tvl1 module from sporco.admm import tvl1 mdnm = 'sporco.admm.tvl1' s = np.random.randn(16, 16) lmbda = 0.1 ## TVL1Denoise class opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs): b = tvl1.TVL1Denoise(s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs): b.solve() ## TVL1Deconv class opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1}) h = np.random.randn(3, 3) with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs): b = tvl1.TVL1Deconv(h, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs): b.solve() #### tvl2 module from sporco.admm import tvl2 mdnm = 'sporco.admm.tvl2' s = np.random.randn(16, 16) lmbda = 0.1 ## TVL2Denoise class opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs): b = tvl2.TVL2Denoise(s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs): b.solve() ## TVL2Deconv class opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1}) h = np.random.randn(3, 3) with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs): b = tvl2.TVL2Deconv(h, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs): b.solve() srcmodflt = '^sporco.fista' fnmsub = ('^sporco.fista.', '') lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))', lnkpfx + r'sporco.fista.\1.html#sporco.fista.\1.\2\4') ct = jonga.CallTracer(srcmodflt=srcmodflt, srcqnmflt=srcqnmflt, dstqnmflt=dstqnmflt, fnmsub=fnmsub, grpflt=grpflt, lnksub=lnksub) #### fista.cbpdn module from sporco.fista import cbpdn mdnm = 'sporco.fista.cbpdn' D = np.random.randn(4, 4, 16) s = np.random.randn(8, 8) lmbda = 0.1 ## ConvBPDN class opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs): b = cbpdn.ConvBPDN(D, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs): b.solve() #### fista.ccmod module from sporco.fista import ccmod mdnm = 'sporco.fista.ccmod' X = np.random.randn(8, 8, 1, 2, 1) S = np.random.randn(8, 8, 2) dsz = (4, 4, 1) ## ConvCnstrMOD class opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs): b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs): b.solve() ## ConvCnstrMODMask class opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False, 'MaxMainIter': 1}) with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs): b = ccmod.ConvCnstrMODMask(X, S, W, dsz=dsz, opt=opt) with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs): b.solve() srcmodflt = '^sporco.dictlrn' fnmsub = ('^sporco.dictlrn.', '') lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))', lnkpfx + r'sporco.dictlrn.\1.html#sporco.dictlrn.\1.\2\4') ct = jonga.CallTracer(srcmodflt=srcmodflt, srcqnmflt=srcqnmflt, dstqnmflt=dstqnmflt, fnmsub=fnmsub, grpflt=grpflt, lnksub=lnksub) #### bpdndl module from sporco.dictlrn import bpdndl mdnm = 'sporco.dictlrn.bpdndl' D0 = np.random.randn(8, 8) S = np.random.randn(8, 16) lmbda = 0.1 ## BPDNDictLearn class opt = bpdndl.BPDNDictLearn.Options({ 'Verbose': False, 'MaxMainIter': 1, 'AccurateDFid': True }) with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs): b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt) with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs): b.solve() #### cbpdndl module from sporco.dictlrn import cbpdndl mdnm = 'sporco.dictlrn.cbpdndl' D0 = np.random.randn(4, 4, 16) s = np.random.randn(8, 8, 10) lmbda = 0.1 ## ConvBPDNDictLearn class opt = cbpdndl.ConvBPDNDictLearn.Options({ 'Verbose': False, 'MaxMainIter': 1, 'AccurateDFid': True }) with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs): b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt) with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs): b.solve() #### cbpdndlmd module from sporco.dictlrn import cbpdndlmd mdnm = 'sporco.dictlrn.cbpdndlmd' ## ConvBPDNMaskDcplDictLearn class W = np.array([1.0]) opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({ 'Verbose': False, 'MaxMainIter': 1, 'AccurateDFid': True }) with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs): b = cbpdndlmd.ConvBPDNMaskDictLearn(D0, s, lmbda, W, opt) with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs): b.solve()