Added cupy benchmark script

benchmark/ptypy_moonflower_script_cupy.py

@@ -0,0 +1,94 @@
+"""
+This script is a test for ptychographic reconstruction in the absence
+of actual data. It uses the test Scan class
+`ptypy.core.data.MoonFlowerScan` to provide "data".
+"""
+import ptypy
+from ptypy.core import Ptycho
+from ptypy import utils as u
+
+import tempfile
+import argparse
+
+ptypy.load_gpu_engines("cupy")
+tmpdir = tempfile.gettempdir()
+
+def run_benchmark():
+    opt = parse()
+    p = get_params(opt)
+    P = Ptycho(p,level=5)
+    print_results(P)
+
+
+def parse():
+    parser = argparse.ArgumentParser(description="A script to benchmark ptypy using the moonflower simulation")
+    parser.add_argument("-n", "--frames", type=int, help="Nr. of data frames")
+    parser.add_argument("-s", "--shape", type=int, help="1D shape of each data frame")
+    parser.add_argument("-i", "--iterations", type=int, help="Nr. of iterations")
+    parser.add_argument("-f", "--fftlib", type=str, default="cupy")
+    args = parser.parse_args()
+    return args
+
+def get_params(args):
+
+    p = u.Param()
+
+    # for verbose output
+    p.verbose_level = "info"
+
+    # set home path
+    p.io = u.Param()
+    p.io.home = "/".join([tmpdir, "ptypy"])
+
+    # saving intermediate results
+    p.io.autosave = u.Param(active=False)
+
+    # opens plotting GUI if interaction set to active)
+    p.io.autoplot = u.Param(active=False)
+    p.io.interaction = u.Param(active=False)
+
+    # Save benchmark timings
+    p.io.benchmark = "all"
+
+    # max 200 frames (128x128px) of diffraction data
+    p.scans = u.Param()
+    p.scans.MF = u.Param()
+    # now you have to specify which ScanModel to use with scans.XX.name,
+    # just as you have to give 'name' for engines and PtyScan subclasses.
+    p.scans.MF.name = 'BlockVanilla' # or 'BlockFull'
+    p.scans.MF.data= u.Param()
+    p.scans.MF.data.name = 'MoonFlowerScan'
+    p.scans.MF.data.shape = args.shape
+    p.scans.MF.data.num_frames = args.frames
+    p.scans.MF.data.save = None
+
+    # position distance in fraction of illumination frame
+    p.scans.MF.data.density = 0.2
+    # total number of photon in empty beam
+    p.scans.MF.data.photons = 1e8
+    # Gaussian FWHM of possible detector blurring
+    p.scans.MF.data.psf = 0.
+
+    # attach a reconstrucion engine
+    p.engines = u.Param()
+    p.engines.engine00 = u.Param()
+    p.engines.engine00.name = 'DM_cupy'
+    p.engines.engine00.numiter = args.iterations
+    p.engines.engine00.fft_lib = args.fftlib
+
+    return p
+
+def print_results(ptycho):
+    # Print benchmarking results
+    if (ptycho.p.io.benchmark == "all") and u.parallel.master:
+        print("\nBenchmark:")
+        print("==========")
+        total = 0
+        for k,v in ptycho.benchmark.items():
+            total += v
+            print(f"{k}: {v:.02f} s")
+        print(f"Total: {total:.02f} s")
+
+# prepare and run
+if __name__ == "__main__":
+    run_benchmark()

benchmark/ptypy_moonflower_script_numpy.py

@@ -19,12 +19,12 @@ def run_benchmark():
 
 
 def parse():
-     parser = argparse.ArgumentParser(description="A script to benchmark ptypy using the moonflower simulation")
-     parser.add_argument("-n", "--frames", type=int, help="Nr. of data frames")
-     parser.add_argument("-s", "--shape", type=int, help="1D shape of each data frame")
-     parser.add_argument("-i", "--iterations", type=int, help="Nr. of iterations")
-     args = parser.parse_args()
-     return args
+    parser = argparse.ArgumentParser(description="A script to benchmark ptypy using the moonflower simulation")
+    parser.add_argument("-n", "--frames", type=int, help="Nr. of data frames")
+    parser.add_argument("-s", "--shape", type=int, help="1D shape of each data frame")
+    parser.add_argument("-i", "--iterations", type=int, help="Nr. of iterations")
+    args = parser.parse_args()
+    return args
 
 def get_params(args):
 
@@ -72,6 +72,8 @@ def get_params(args):
     p.engines.engine00.name = 'DM'
     p.engines.engine00.numiter = args.iterations
 
+    return p
+
 def print_results(ptycho):
     # Print benchmarking results
     if (ptycho.p.io.benchmark == "all") and u.parallel.master: