parser = ArgumentParser(argv[0], description=__doc__)

parser.add_argument('model', type=str)

parser.add_argument('--data', '-d', type=str, default='data/deadleaves_test.mat')

parser.add_argument('--patch_size', '-p', type=int, default=64,

help='Images are split into patches of this size and evaluated separately.')

parser.add_argument('--fraction', '-f', type=float, default=1.,

help='Only use a fraction of the data for a faster estimate.')

parser.add_argument('--mode', '-q', type=str, default='CPU', choices=['CPU', 'GPU'])

parser.add_argument('--device', '-D', type=int, default=0)

parser.add_argument('--stride', '-S', type=int, default=1)

args = parser.parse_args(argv[1:])

# select CPU or GPU for caffe

if args.mode.upper() == 'GPU':

caffe.set_mode_gpu()

caffe.set_device(args.device)

else:

caffe.set_mode_cpu()

# load data

data = loadmat(args.data)['data']

# load model

experiment = Experiment(args.model)

model = experiment['model']

if isinstance(model, PatchRIDE):

if args.patch_size != model.num_rows or args.patch_size != model.num_cols:

print 'Model is for {0}x{1} patches but data is {2}x{2}.'.format(

model.num_rows, model.num_cols, args.patch_size)

return 0

# apply model to data

logloss = []

for i in range(0, data.shape[1] - args.patch_size + 1, args.patch_size * args.stride):

for j in range(0, data.shape[2] - args.patch_size + 1, args.patch_size * args.stride):

# select random subset

idx = random_select(int(ceil(args.fraction * data.shape[0]) + .5), data.shape[0])

logloss.append(

model.evaluate(

data[:, i:i + args.patch_size, j:j + args.patch_size][idx]))

loglik_avg = -mean(logloss)

loglik_err = std(logloss, ddof=1) / sqrt(len(logloss)) if len(logloss) > 1 else inf

print 'Avg. log-likelihood: {0:.5f} +- {1:.5f} [bit/px]'.format(loglik_avg, loglik_err)