Merge pull request #132 from mrava87/feature-pnpgeneral

feature: modify PnP signature to allow any proximal solver

pyproximal/optimization/pnp.py

@@ -33,19 +33,19 @@ def prox(self, x, tau):
         return xden.ravel()
 
 
-def PlugAndPlay(proxf, denoiser, dims, x0, tau, niter=10,
-                callback=None, show=False):
-    r"""Plug-and-Play Priors with ADMM optimization
+def PlugAndPlay(proxf, denoiser, dims, x0, solver=ADMM, **kwargs_solver):
+    r"""Plug-and-Play Priors with any proximal algorithm of choice
 
-    Solves the following minimization problem using the ADMM algorithm:
+    Solves the following minimization problem using any proximal a
+    lgorithm of choice:
 
     .. math::
 
         \mathbf{x},\mathbf{z}  = \argmin_{\mathbf{x}}
         f(\mathbf{x}) + \lambda g(\mathbf{x})
 
-    where :math:`f(\mathbf{x})` is a function that has a known proximal
-    operator where :math:`g(\mathbf{x})` is a function acting as implicit
+    where :math:`f(\mathbf{x})` is a function that has a known gradient or
+    proximal operator and :math:`g(\mathbf{x})` is a function acting as implicit
     prior. Implicit means that no explicit function should be defined: instead,
     a denoising algorithm of choice is used. See Notes for details.
 
@@ -62,6 +62,8 @@ def PlugAndPlay(proxf, denoiser, dims, x0, tau, niter=10,
         prior to calling the ``denoiser``
     x0 : :obj:`numpy.ndarray`
         Initial vector
+    solver : :func:`pyproximal.optimization.primal` or :func:`pyproximal.optimization.primaldual`
+        Solver of choice
     tau : :obj:`float`, optional
         Positive scalar weight, which should satisfy the following condition
         to guarantees convergence: :math:`\tau  \in (0, 1/L]` where ``L`` is
@@ -83,7 +85,8 @@ def PlugAndPlay(proxf, denoiser, dims, x0, tau, niter=10,
 
     Notes
     -----
-    Plug-and-Play Priors [1]_ can be expressed by the following recursion:
+    Plug-and-Play Priors [1]_ can be used with any proximal algorithm of choice. For example, when
+    ADMM is selected, the resulting scheme can be expressed by the following recursion:
 
     .. math::
 
@@ -119,6 +122,4 @@ def PlugAndPlay(proxf, denoiser, dims, x0, tau, niter=10,
     # Denoiser
     proxpnp = _Denoise(denoiser, dims=dims)
 
-    return ADMM(proxf, proxpnp, tau=tau, x0=x0,
-                niter=niter, callback=callback,
-                show=show)
+    return solver(proxf, proxpnp, x0=x0, **kwargs_solver)

pyproximal/optimization/primal.py

@@ -213,10 +213,11 @@ def ProximalGradient(proxf, proxg, x0, tau=None, beta=0.5,
               'Proximal operator (f): %s\n'
               'Proximal operator (g): %s\n'
               'tau = %s\tbeta=%10e\n'
-              'epsg = %s\tniter = %d\t'
-              'niterback = %d\n' % (type(proxf), type(proxg),
+              'epsg = %s\tniter = %d\n'
+              ''
+              'niterback = %d\tacceleration = %s\n' % (type(proxf), type(proxg),
                                     'Adaptive' if tau is None else str(tau), beta,
-                                    epsg_print, niter, niterback))
+                                    epsg_print, niter, niterback, acceleration))
         head = '   Itn       x[0]          f           g       J=f+eps*g'
         print(head)
 

tutorials/plugandplay.py

@@ -11,10 +11,15 @@
 
 As an example, we will consider a simplified MRI experiment, where the
 data is created by appling a 2D Fourier Transform to the input model and
-by randomly sampling 60% of its values. We will also use the famous
+by randomly sampling 60% of its values. We will use the famous
 `BM3D <https://pypi.org/project/bm3d>`_ as the denoiser, but any other denoiser
 of choice can be used instead!
 
+Finally, whilst in the original paper, PnP is associated to the ADMM solver, subsequent
+research showed that the same principle can be applied to pretty much any proximal
+solver. We will show how to pass a solver of choice to our
+:func:`pyproximal.optimization.pnp.PlugAndPlay` solver.
+
 """
 import numpy as np
 import matplotlib.pyplot as plt
@@ -67,7 +72,7 @@
 
 ###############################################################################
 # At this point we create a denoiser instance using the BM3D algorithm and use
-# as Plug-and-Play Prior to the ADMM algorithm
+# as Plug-and-Play Prior to the PG and ADMM algorithms
 
 def callback(x, xtrue, errhist):
     errhist.append(np.linalg.norm(x - xtrue))
@@ -83,24 +88,46 @@ def callback(x, xtrue, errhist):
 denoiser = lambda x, tau: bm3d.bm3d(np.real(x), sigma_psd=sigma * tau,
                                     stage_arg=bm3d.BM3DStages.HARD_THRESHOLDING)
 
-errhist = []
-xpnp = pyproximal.optimization.pnp.PlugAndPlay(l2, denoiser, x.shape,
-                                               tau=tau, x0=np.zeros(x.size),
-                                               niter=40, show=True,
-                                               callback=lambda xx: callback(xx, x.ravel(),
-                                                                            errhist))[0]
-xpnp = np.real(xpnp.reshape(x.shape))
+# PG-Pnp
+errhistpg = []
+xpnppg = pyproximal.optimization.pnp.PlugAndPlay(l2, denoiser, x.shape,
+                                                 solver=pyproximal.optimization.primal.ProximalGradient,
+                                                 tau=tau, x0=np.zeros(x.size),
+                                                 niter=40,
+                                                 acceleration='fista',
+                                                 show=True,
+                                                 callback=lambda xx: callback(xx, x.ravel(),
+                                                                              errhistpg))
+xpnppg = np.real(xpnppg.reshape(x.shape))
+
+# ADMM-PnP
+errhistadmm = []
+xpnpadmm = pyproximal.optimization.pnp.PlugAndPlay(l2, denoiser, x.shape,
+                                                   solver=pyproximal.optimization.primal.ADMM,
+                                                   tau=tau, x0=np.zeros(x.size),
+                                                   niter=40, show=True,
+                                                   callback=lambda xx: callback(xx, x.ravel(),
+                                                                                errhistadmm))[0]
+xpnpadmm = np.real(xpnpadmm.reshape(x.shape))
 
-fig, axs = plt.subplots(1, 2, figsize=(9, 5))
+fig, axs = plt.subplots(1, 3, figsize=(14, 5))
 axs[0].imshow(x, vmin=0, vmax=1, cmap="gray")
 axs[0].set_title("Model")
 axs[0].axis("tight")
-axs[1].imshow(xpnp, vmin=0, vmax=1, cmap="gray")
-axs[1].set_title("PnP Inversion")
+axs[1].imshow(xpnppg, vmin=0, vmax=1, cmap="gray")
+axs[1].set_title("PG-PnP Inversion")
 axs[1].axis("tight")
+axs[2].imshow(xpnpadmm, vmin=0, vmax=1, cmap="gray")
+axs[2].set_title("ADMM-PnP Inversion")
+axs[2].axis("tight")
 plt.tight_layout()
 
+###############################################################################
+# Finally, let's compare the error convergence of the two variations of PnP
+
 plt.figure(figsize=(12, 3))
-plt.plot(errhist, 'k', lw=2)
+plt.plot(errhistpg, 'k', lw=2, label='PG')
+plt.plot(errhistadmm, 'r', lw=2, label='ADMM')
 plt.title("Error norm")
+plt.legend()
 plt.tight_layout()