cfg.idx = a else: assert False, "unhandled option" if not hasattr(cfg, "basename"): usage() sys.exit() if not hasattr(cfg, "idx"): cfg.idx = "pretrain" fn = os.path.join(cfg.basename, "info-0-%s.pickle" % cfg.idx) if not cfg.overwrite and os.path.exists(fn): with open(fn, "r") as f: stats = cPickle.load(f) if "nats" in stats: print "pickle already has a value for partition function in it, exiting." sys.exit(0) cp.initCUDA(cfg.device) # load pickle with rbm cfg fn = os.path.join(cfg.basename, "info-0.pickle") with open(fn, "r") as f: rbmcfg = cPickle.load(f) cfg.dataset = rbmcfg["dataset"] start_time = time.clock() bv, bh, W = read_data(cfg.basename, cfg.idx) mbp = get_mbp(cfg) pf = PartitionFunction(W, bv, bh) print "\nCalculating Probability of Data." if cfg.dataset == Dataset.mnist: num = pf.numerator(mbp, 1000)
pig = Image.open("tests/data/gray_square.gif").resize((640,480)).convert("L") #test_cuda_array(np.asarray(pig) def test_gaussian_pyramid_construction(): print "Color Pyramid Construction on GPU: ", global pic w, h = 640,480 pic = Image.open("tests/data/gray_square.gif").resize((w,h)).convert("RGBA") pic = np.asarray(pic).reshape(h,w*4) for x in xrange(1): # warmup build_pyramid_GPU(pic,input_channels=4,pyramid_channels=3) t = Timer('build_pyramid_GPU(pic,4,3)','from %s import build_pyramid_GPU, cp, pic'%__name__) print t.timeit(number=100)/100 print "Grayscale Pyramid Construction on CPU: ", pic = Image.open("tests/data/gray_square.gif").resize((w,h)).convert("RGB") pic = np.asarray(pic) t = Timer('build_pyramid_CPU(pic)','from %s import build_pyramid_CPU, pic'%__name__) print t.timeit(number=100)/100 if __name__ == "__main__": cp.initCUDA(0) run() test_pixel_classes() test_gaussian_pyramid_construction() cp.exitCUDA()
def initialize(cfg): cp.initCUDA(cfg.device) cp.initialize_mersenne_twister_seeds(cfg.seed)
def setUpModule(): cp.initCUDA(-1)
cfg.idx = a else: assert False, "unhandled option" if not hasattr(cfg, "basename"): usage() sys.exit() if not hasattr(cfg, "idx"): cfg.idx = "pretrain" fn = os.path.join(cfg.basename, "info-0-%s.pickle" % cfg.idx) if not cfg.overwrite and os.path.exists(fn): with open(fn, "r") as f: stats = cPickle.load(f) if "nats" in stats: print "pickle already has a value for partition function in it, exiting." sys.exit(0) cp.initCUDA(cfg.device) #load pickle with rbm cfg fn = os.path.join(cfg.basename, "info-0.pickle") with open(fn, "r") as f: rbmcfg = cPickle.load(f) cfg.dataset = rbmcfg['dataset'] start_time = time.clock() bv, bh, W = read_data(cfg.basename, cfg.idx) mbp = get_mbp(cfg) pf = PartitionFunction(W, bv, bh) print "\nCalculating Probability of Data." if cfg.dataset == Dataset.mnist: num = pf.numerator(mbp, 1000)
def test_gaussian_pyramid_construction(): print "Color Pyramid Construction on GPU: ", global pic w, h = 640, 480 pic = Image.open("tests/data/gray_square.gif").resize( (w, h)).convert("RGBA") pic = np.asarray(pic).reshape(h, w * 4) for x in xrange(1): # warmup build_pyramid_GPU(pic, input_channels=4, pyramid_channels=3) t = Timer('build_pyramid_GPU(pic,4,3)', 'from %s import build_pyramid_GPU, cp, pic' % __name__) print t.timeit(number=100) / 100 print "Grayscale Pyramid Construction on CPU: ", pic = Image.open("tests/data/gray_square.gif").resize( (w, h)).convert("RGB") pic = np.asarray(pic) t = Timer('build_pyramid_CPU(pic)', 'from %s import build_pyramid_CPU, pic' % __name__) print t.timeit(number=100) / 100 if __name__ == "__main__": cp.initCUDA(0) run() test_pixel_classes() test_gaussian_pyramid_construction() cp.exitCUDA()