def multip_grabCut():
raw_example_list = _load_grabcut_unary_pairwise_cliques()
parser = BatchExamplesParser()
selected_num = 50
examples_list_all = parser.parse_grabcut_pickle(raw_example_list)
options = Options()
batches_num = len(examples_list_all)
inf_latent_method = ''
init_method = 'clique_by_clique'
Tasks = list()
for i in range(selected_num):
examples_list = examples_list_all[:i] + examples_list_all[i + 1:]
Tasks.append((calcGrabCut, (examples_list, examples_list_all[i].name,
inf_latent_method, init_method, options)))
process_num = min(selected_num, 20)
pool = multiprocessing.Pool(process_num)
r = pool.map(calcFun, Tasks)
pool.close()
pool.join()
print(r)
print('finished')
def laipi_grabCut():
raw_example_list = _load_grabcut_unary_pairwise_cliques()
parser = BatchExamplesParser()
selected_num = 50
examples_list_all = parser.parse_grabcut_pickle(raw_example_list)
options = Options()
batches_num = len(examples_list_all)
inf_latent_method = 'remove_redundancy'
init_method = 'exlarge'
laipi_name = [
'tennis', 'person6', 'cross', '65019', 'elefant', 'bool', '37073',
'banana1', '153077', 'book', '153093', '24077', '189080', '69020'
]
Tasks = list()
for i in range(selected_num):
ex_test = examples_list_all[i]
if ex_test.name.split('.')[0] in laipi_name:
examples_list = examples_list_all[:i] + examples_list_all[i + 1:]
Tasks.append(
(laipiDog, (examples_list, examples_list_all[i].name,
inf_latent_method, init_method, options, ex_test)))
if len(Tasks) != len(laipi_name):
raise ValueError('Laipi doesn\'t match!')
pool = multiprocessing.Pool(len(laipi_name))
r = pool.map(calcFun, Tasks)
pool.close()
pool.join()
print(r)
print('laipi_finished')
def calcCheckboard(prefix_str, miu):
instance = Instance('gaussian_portions',
portion_miu=miu,
is_gaussian=False)
examples_list = parser.parse_checkboard(instance)
options = Options()
outer_history = cccp_outer_loop([examples_list[0]], options, init_method,
inf_latent_method, prefix_str)
with open(
'./expData/batchResult/training_result/'
'image_%s_outer_history.pickle' % prefix_str, 'wb') as f:
pickle.dump([outer_history, examples_list, prefix_str], f)
sys.stdout.flush()
path = './expData/batchResult/temp/bush.pickle_outer_iter0.pickle'
theta_list = list()
loss_list = list()
with open(path, 'rb') as f:
data = pickle.load(f)
inner_history = data[-1]['inner_history']
for i in inner_history:
theta_list.append(i['theta'])
loss_list.append(i['loss_aug'])
batches_num = 25
raw_example_list = _load_grabcut_unary_pairwise_cliques()
parser = BatchExamplesParser()
examples_list_all = parser.parse_grabcut_pickle(raw_example_list)
options = Options()
inf_latent_method = ''
init_method = 'clique_by_clique'
Tasks = list()
# for i in range(batches_num):
i = 25
examples_list = examples_list_all[:i] + examples_list_all[i + 1:]
y_hat_loss = list()
for theta in theta_list:
error = 0
for ex in examples_list:
y_hat = inf_label_latent_helper(ex.unary_observed, ex.pairwise,
ex.clique_indexes, theta, options,