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')
print('converge at iter: %d' % t)
print('classification error: %d' % total_error)
break
return outer_history
if __name__ == "__main__":
init_method = 'clique_by_clique'
inf_latent_method = 'remove_redund'
options = Options()
instance = Instance()
from Utils.IOhelpers import _load_grabcut_unary_pairwise_cliques
from MrfTypes import Example, BatchExamplesParser
parser = BatchExamplesParser()
examples_list = parser.parse_grabcut_pickle(
_load_grabcut_unary_pairwise_cliques())
example = examples_list[0] # type: Example
options.H = example.rows # rows image height
options.W = example.cols # cols image width
options.numCliques = example.numCliques # number of clique_indexes
options.rowsPairwise = example.rowsPairwise
instance.clique_indexes = example.clique_indexes
instance.pairwise = example.pairwise
instance.unary_observed = example.unary_observed
instance.y = example.y
# Image Configs
options.gridStep = np.inf # grid size for defining clique_indexes
options.numVariables = example.numVariables
__author__ = 'spacegoing'
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:
import numpy as np
from Batch_CCCP import cccp_outer_loop
from Checkboard import Instance
from MrfTypes import Example, Options, BatchExamplesParser
from Utils.IOhelpers import _load_grabcut_unary_pairwise_cliques
from Batch_MRF_Helpers import inf_label_latent_helper
import multiprocessing
import sys
import pickle
__author__ = 'spacegoing'
inf_latent_method = ''
init_method = 'clique_by_clique'
parser = BatchExamplesParser()
root = './expData/unbalaced_portions/'
def calcFun(args):
func, arg = args
result = func(*arg)
return result
def calcCheckboard(prefix_str, miu):
instance = Instance('gaussian_portions',
portion_miu=miu,
is_gaussian=False)
examples_list = parser.parse_checkboard(instance)
print('converge at iter: %d' % t)
print('classification error: %d' % total_error)
break
return outer_history
if __name__ == "__main__":
init_method = 'clique_by_clique'
inf_latent_method = 'remove_redund'
options = Options()
instance = Instance()
from Utils.IOhelpers import _load_grabcut_unary_pairwise_cliques
from MrfTypes import Example, BatchExamplesParser
parser = BatchExamplesParser()
examples_list = parser.parse_grabcut_pickle(
_load_grabcut_unary_pairwise_cliques())
example = examples_list[0] # type: Example
options.H = example.rows # rows image height
options.W = example.cols # cols image width
options.numCliques = example.numCliques # number of clique_indexes
options.rowsPairwise = example.rowsPairwise
instance.clique_indexes = example.clique_indexes
instance.pairwise = example.pairwise
instance.unary_observed = example.unary_observed
instance.y = example.y
# Image Configs
options.gridStep = np.inf # grid size for defining clique_indexes
options.numVariables = example.numVariables
break
eng.exit()
return outer_history
if __name__ == '__main__':
from MrfTypes import BatchExamplesParser
from Utils.IOhelpers import _load_grabcut_unary_pairwise_cliques
import time
time_list = list()
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 = 'remove_redundancy'
init_method = 'clique_by_clique'
for i in range(50):
time_list.append(time.time())
examples_list = examples_list_all[:i] + examples_list_all[i + 1:]
outer_history = cccp_outer_loop(examples_list, options, inf_latent_method, init_method)
with open('./expData/batchResult/training_result/'
'image_%d_%s_outer_history.pickle' % (i, examples_list_all[i].name), 'wb') as f:
pickle.dump([examples_list, outer_history, examples_list_all[i].name, time_list], f)