'DataType': np.float32, 'CBPDN': { 'rho': 5.0, 'AutoRho': { 'Enabled': True }, 'RelaxParam': 1.8, 'RelStopTol': 1e-7, 'MaxMainIter': 50, 'FastSolve': False, 'DataType': np.float32 } }) lmbda = 0.2 if not cupy_enabled(): print( 'CuPy/GPU device not available: running without GPU acceleration\n' ) else: id = select_device_by_load() info = gpu_info() if info: print('Running on GPU %d (%s)\n' % (id, info[id].name)) data_ra_new = [] for i in range(1, int(data_ra.shape[0] / 1000)): data_ra_new.append(data_ra[(i - 1) * 1000:i * 1000, :]) data_ra = np.asarray(data_ra_new) print(data_ra.shape, "New data_ra shape")
""" Set :class:`.admm.pdcsc.ConvProdDictL1L1Grd` solver options. """ opt = pdcsc.ConvProdDictL1L1Grd.Options( {'Verbose': True, 'MaxMainIter': 100, 'RelStopTol': 5e-3, 'AuxVarObj': False, 'rho': 1e1, 'RelaxParam': 1.8, 'L1Weight': np2cp(wl1), 'GradWeight': np2cp(wgr)}) """ Initialise the :class:`.admm.pdcsc.ConvProdDictL1L1Grd` object and call the ``solve`` method. """ if not cupy_enabled(): print('CuPy/GPU device not available: running without GPU acceleration\n') else: id = select_device_by_load() info = gpu_info() if info: print('Running on GPU %d (%s)\n' % (id, info[id].name)) b = pdcsc.ConvProdDictL1L1Grd(np2cp(D), np2cp(B), np2cp(pad(imgn)), lmbda, mu, opt=opt, dimK=0) X = cp2np(b.solve()) """ The denoised estimate of the image is just the reconstruction from all coefficient maps. """