def compute_error_vs_iterations(suffix, num_images, dataset):
# assemble truth
d = Dataset(dataset)
truth = d.get_cls_ground_truth().arr
truth = np.random.permutation(truth)[:num_images, :]
num_classes = truth.shape[1]
tt = ut.TicToc()
lbp_times = [0] + [10**x for x in range(3)]
lbp_times += [1000 + 1000 * x for x in range(10)]
lbp_times += [10**x for x in [5]]
#lbp_times = [3000]
all_scores = np.zeros((num_classes, len(lbp_times), num_classes))
all_times = np.zeros((num_classes, len(lbp_times)))
counter = 0
# do inference
for itdex in range(len(lbp_times)):
fm = FastinfModel(d, 'perfect', num_classes, lbp_time=lbp_times[itdex])
for rowdex in range(comm_rank, truth.shape[0], comm_size): # parallel
obs = truth[rowdex, :].astype(int)
taken = np.zeros(num_classes).astype(int)
for num_obser in range(num_classes):
counter += 1
taken[np.argmax(fm.p_c - taken)] = 1
tt.tic()
fm.update_with_observations(taken, obs)
utime = tt.toc(quiet=True)
curr_score = compute_sq_error(obs, fm.p_c)
all_scores[num_obser,
itdex, :] = np.add(all_scores[num_obser, itdex, :],
curr_score)
all_times[num_obser, itdex] += utime
print '%d is at %d / %d :'%(comm_rank, counter, len(lbp_times)* \
num_classes*num_images/float(comm_size)),curr_score
all_scores /= num_images
all_times /= num_images
safebarrier(comm)
all_scores = comm.reduce(all_scores)
all_times = comm.reduce(all_times)
if comm_rank == 0: #parallel
outfile = open('all_scores_' + suffix, 'w')
cPickle.dump(all_scores, outfile)
outfile.close()
outfile = open('all_times_' + suffix, 'w')
cPickle.dump(all_times, outfile)
outfile.close()
def test():
dataset = Dataset('full_pascal_trainval')
fm = FastinfModel(dataset, 'perfect', 20)
# NOTE: just took values from a run of the thing
prior_correct = [
float(x) for x in
"0.050543 0.053053 0.073697 0.038331 0.050954 0.041879 0.16149\
0.068721 0.10296 0.026837 0.043779 0.087683 0.063447 0.052205\
0.41049 0.051664 0.014211 0.068361 0.056969 0.05046".split()
]
np.testing.assert_almost_equal(fm.p_c, prior_correct, 4)
observations = np.zeros(20)
taken = np.zeros(20)
fm.update_with_observations(taken, observations)
np.testing.assert_almost_equal(fm.p_c, prior_correct, 4)
observations[5] = 1
taken[5] = 1
fm.update_with_observations(taken, observations)
print fm.p_c
correct = [
float(x) for x in
"0.027355 0.11855 0.027593 0.026851 0.012569 0.98999 0.52232\
0.017783 0.010806 0.015199 0.0044641 0.02389 0.033602 0.089089\
0.50297 0.0083272 0.0088274 0.0098522 0.034259 0.0086298".split()
]
np.testing.assert_almost_equal(fm.p_c, correct, 4)
observations[15] = 0
taken[15] = 1
fm.update_with_observations(taken, observations)
correct = [
float(x) for x in
"2.73590000e-02 1.19030000e-01 2.75500000e-02 2.68760000e-02 \
1.23920000e-02 9.90200000e-01 5.25320000e-01 1.76120000e-02 \
1.05030000e-02 1.52130000e-02 4.26410000e-03 2.38250000e-02 \
3.36870000e-02 8.96450000e-02 5.04300000e-01 8.71880000e-05 \
8.82630000e-03 9.55290000e-03 3.43240000e-02 8.44510000e-03".split()
]
np.testing.assert_almost_equal(fm.p_c, correct)
# reinit_marginals
fm.reset()
np.testing.assert_equal(fm.p_c, prior_correct)
print(fm.cache)
def __init__(self,dataset,actions,mode='fixed_order',bounds=None,model=None,fastinf_model_name='perfect'):
assert(mode in self.accepted_modes)
self.mode = mode
self.dataset = dataset
self.actions = actions
self.bounds = bounds
self.fastinf_model_name = fastinf_model_name
# Is GIST in the actions? Need to behave differently if so.
self.gist_mode = ('gist' in [action.name for action in self.actions])
self.num_obs_vars = len(self.actions)
if self.gist_mode:
assert(self.actions[0].name == 'gist')
self.num_obs_vars = len(self.actions)-1+len(self.dataset.classes)
if mode == 'random':
if model:
assert(isinstance(model,RandomModel))
self.model = model
else:
self.model = RandomModel(len(self.dataset.classes))
elif mode=='no_smooth' or mode=='backoff':
if model:
assert(isinstance(model,NGramModel))
self.model = model
else:
self.model = NGramModel(dataset,mode)
elif mode=='fixed_order':
if model:
assert(isinstance(model,FixedOrderModel))
self.model = model
else:
self.model = FixedOrderModel(dataset)
elif mode=='fastinf':
if model:
assert(isinstance(model,FastinfModel))
self.model = model
else:
self.model = FastinfModel(dataset, self.fastinf_model_name, self.num_obs_vars)
else:
raise RuntimeError("Unknown mode")
self.reset()
self.orig_p_c = self.get_p_c()
