def test_compute_cls_map_gt_half(self): rand_inds = np.random.permutation(range(5011))[:2500] table_gt_half = Table() table_gt_half.arr = np.hstack((self.trainval_gt.arr,np.array(np.arange(5011), ndmin=2).T)) table_gt_half.arr = table_gt_half.arr[rand_inds,:] table_gt_half.cols = list(self.trainval_gt.cols) + ['img_ind'] res = Evaluation.compute_cls_map(table_gt_half, self.trainval_gt) assert(round(res,6) == 1)
def test_compute_cls_map_half(self):
table_csc_half = Table()
table_csc_half.cols = list(self.csc_trainval.cols)
for _ in range(10):
rand_inds = np.random.permutation(range(5011))[:2500]
table_csc_half.arr = self.csc_trainval.arr[rand_inds,:]
res = Evaluation.compute_cls_map(table_csc_half, self.trainval_gt)
assert(round(res,6) > .45)
def test_compute_cls_map_half(self):
table_csc_half = Table()
table_csc_half.cols = list(self.csc_trainval.cols)
for _ in range(10):
rand_inds = np.random.permutation(range(5011))[:2500]
table_csc_half.arr = self.csc_trainval.arr[rand_inds, :]
res = Evaluation.compute_cls_map(table_csc_half, self.trainval_gt)
assert (round(res, 6) > .45)
def test_compute_cls_map_gt_half(self):
rand_inds = np.random.permutation(range(5011))[:2500]
table_gt_half = Table()
table_gt_half.arr = np.hstack(
(self.trainval_gt.arr, np.array(np.arange(5011), ndmin=2).T))
table_gt_half.arr = table_gt_half.arr[rand_inds, :]
table_gt_half.cols = list(self.trainval_gt.cols) + ['img_ind']
res = Evaluation.compute_cls_map(table_gt_half, self.trainval_gt)
assert (round(res, 6) == 1)
def test_compute_cls_map_random_clf(self):
clf_table = Table()
num_test = 10
ress = np.zeros((num_test,))
for idx in range(num_test):
clf_table.arr = np.hstack((np.random.rand(5011, 20),np.array(np.arange(5011), ndmin=2).T))
clf_table.cols = list(self.trainval_gt.cols) + ['img_ind']
res = Evaluation.compute_cls_map(clf_table, self.trainval_gt)
ress[idx] = res
assert(np.mean(ress) < 0.09)
def test_compute_cls_map_random_clf(self):
clf_table = Table()
num_test = 10
ress = np.zeros((num_test, ))
for idx in range(num_test):
clf_table.arr = np.hstack(
(np.random.rand(5011, 20), np.array(np.arange(5011),
ndmin=2).T))
clf_table.cols = list(self.trainval_gt.cols) + ['img_ind']
res = Evaluation.compute_cls_map(clf_table, self.trainval_gt)
ress[idx] = res
assert (np.mean(ress) < 0.09)
train_gt = d_train.get_cls_ground_truth()
val_gt = d_val.get_cls_ground_truth()
if mpi.comm_rank == 0:
filename = os.path.join(config.get_classifier_dataset_dirname(CSCClassifier('default','dog', d_train, d_val), d_train),'crossval.txt')
kernels = ['linear']
Cs = [50]
settings = list(itertools.product(kernels, Cs))
for setin in settings:
kernel = setin[0]
C = setin[1]
#train_csc_svms(d_train, d_val, kernel, C)
safebarrier(comm)
table_arr = test_csc_svm(d_train, d_val)
safebarrier(comm)
if mpi.comm_rank == 0:
table = conv(d_val, table_arr)
cPickle.dump(table, open(os.path.join(config.get_ext_dets_foldname(d_val),'table'),'w'))
res = Evaluation.compute_cls_map(table, val_gt)
res_file = open(filename,'a')
res_file.write('%s, C=%d - %f\n'%(kernel, C, res))
res_file.close()
print res
'crossval.txt')
kernels = ['linear']
Cs = [50]
settings = list(itertools.product(kernels, Cs))
for setin in settings:
kernel = setin[0]
C = setin[1]
#train_csc_svms(d_train, d_val, kernel, C)
safebarrier(comm)
table_arr = test_csc_svm(d_train, d_val)
safebarrier(comm)
if mpi.comm_rank == 0:
table = conv(d_val, table_arr)
cPickle.dump(
table,
open(
os.path.join(config.get_ext_dets_foldname(d_val), 'table'),
'w'))
res = Evaluation.compute_cls_map(table, val_gt)
res_file = open(filename, 'a')
res_file.write('%s, C=%d - %f\n' % (kernel, C, res))
res_file.close()
print res
def test_other_scores(self):
print 'csc_test', Evaluation.compute_cls_map(self.csc_test, self.test_gt)
print 'csc_trainval', Evaluation.compute_cls_map(self.csc_trainval, self.trainval_gt)
print 'ext_test', Evaluation.compute_cls_map(self.ext_csc_test, self.test_gt)
print 'ext_trainval', Evaluation.compute_cls_map(self.ext_csc_trainval, self.trainval_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_test), 'dp', 'table_chi2_20')
ext_table_chi2_20 = cPickle.load(open(filename, 'r'))
print 'ext_chi2_20_test', Evaluation.compute_cls_map(ext_table_chi2_20, self.test_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_train), 'dp', 'table_chi2_20')
ext_table_chi2_20_tv = cPickle.load(open(filename, 'r'))
print 'ext_chi2_20_trainval', Evaluation.compute_cls_map(ext_table_chi2_20_tv, self.trainval_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_test), 'dp', 'table_rbf_20')
ext_table_rbf_20 = cPickle.load(open(filename, 'r'))
print 'ext_rbf_20_test', Evaluation.compute_cls_map(ext_table_rbf_20, self.test_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_train), 'dp', 'table_rbf_20')
ext_table_rbf_20_tv = cPickle.load(open(filename, 'r'))
print 'ext_rbf_20_trainval', Evaluation.compute_cls_map(ext_table_rbf_20_tv, self.trainval_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_test), 'dp', 'table_linear_20')
ext_linear_20_test = cPickle.load(open(filename, 'r'))
print 'ext_linear_test', Evaluation.compute_cls_map(ext_linear_20_test, self.test_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_train), 'dp', 'table_linear_20')
ext_table_linear_20 = cPickle.load(open(filename, 'r'))
print 'ext_linear_20_trainval', Evaluation.compute_cls_map(ext_table_linear_20, self.trainval_gt)
filename = 'tab_linear_5'
ext_tab_lin_5 = cPickle.load(open(filename, 'r'))
print 'ext_tab_lin_5_trainval', Evaluation.compute_cls_map(ext_tab_lin_5, self.trainval_gt)
def test_compute_cls_map_gt(self): res = Evaluation.compute_cls_map(self.trainval_gt, self.trainval_gt) assert(round(res,6) == 1)
def test_compute_cls_map(self): res = Evaluation.compute_cls_map(self.csc_trainval, self.trainval_gt) assert(round(res,11) == 0.47206391958)
def test_other_scores(self):
print 'csc_test', Evaluation.compute_cls_map(self.csc_test,
self.test_gt)
print 'csc_trainval', Evaluation.compute_cls_map(
self.csc_trainval, self.trainval_gt)
print 'ext_test', Evaluation.compute_cls_map(self.ext_csc_test,
self.test_gt)
print 'ext_trainval', Evaluation.compute_cls_map(
self.ext_csc_trainval, self.trainval_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_test),
'dp', 'table_chi2_20')
ext_table_chi2_20 = cPickle.load(open(filename, 'r'))
print 'ext_chi2_20_test', Evaluation.compute_cls_map(
ext_table_chi2_20, self.test_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_train),
'dp', 'table_chi2_20')
ext_table_chi2_20_tv = cPickle.load(open(filename, 'r'))
print 'ext_chi2_20_trainval', Evaluation.compute_cls_map(
ext_table_chi2_20_tv, self.trainval_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_test),
'dp', 'table_rbf_20')
ext_table_rbf_20 = cPickle.load(open(filename, 'r'))
print 'ext_rbf_20_test', Evaluation.compute_cls_map(
ext_table_rbf_20, self.test_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_train),
'dp', 'table_rbf_20')
ext_table_rbf_20_tv = cPickle.load(open(filename, 'r'))
print 'ext_rbf_20_trainval', Evaluation.compute_cls_map(
ext_table_rbf_20_tv, self.trainval_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_test),
'dp', 'table_linear_20')
ext_linear_20_test = cPickle.load(open(filename, 'r'))
print 'ext_linear_test', Evaluation.compute_cls_map(
ext_linear_20_test, self.test_gt)
filename = os.path.join(config.get_ext_dets_foldname(self.d_train),
'dp', 'table_linear_20')
ext_table_linear_20 = cPickle.load(open(filename, 'r'))
print 'ext_linear_20_trainval', Evaluation.compute_cls_map(
ext_table_linear_20, self.trainval_gt)
filename = 'tab_linear_5'
ext_tab_lin_5 = cPickle.load(open(filename, 'r'))
print 'ext_tab_lin_5_trainval', Evaluation.compute_cls_map(
ext_tab_lin_5, self.trainval_gt)
def test_compute_cls_map_gt(self):
res = Evaluation.compute_cls_map(self.trainval_gt, self.trainval_gt)
assert (round(res, 6) == 1)
def test_compute_cls_map(self):
res = Evaluation.compute_cls_map(self.csc_trainval, self.trainval_gt)
assert (round(res, 11) == 0.47206391958)