def conv(d_train, table_arr):
table = Table()
#table_arr = cPickle.load(open('table_linear_5','r'))
table.arr = np.hstack(
(table_arr, np.array(np.arange(table_arr.shape[0]), ndmin=2).T))
table.cols = d_train.classes + ['img_ind']
print table
#cPickle.dump(table, open('tab_linear_5','w'))
return table
if __name__ == '__main__':
d_train = Dataset('full_pascal_trainval')
d_val = Dataset('full_pascal_val')
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]
return table
def conv(d_train, table_arr):
table = Table()
#table_arr = cPickle.load(open('table_linear_5','r'))
table.arr = np.hstack((table_arr, np.array(np.arange(table_arr.shape[0]),ndmin=2).T))
table.cols = d_train.classes + ['img_ind']
print table
#cPickle.dump(table, open('tab_linear_5','w'))
return table
if __name__=='__main__':
d_train = Dataset('full_pascal_trainval')
d_val = Dataset('full_pascal_val')
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)
class TestDatasetJson(object):
def setup(self):
self.d = Dataset(test_config, 'test_data1')
self.d.load_from_json(test_data1)
self.classes = ["A", "B", "C"]
def test_load(self):
assert(self.d.num_images() == 4)
assert(self.d.classes == self.classes)
def test_get_det_gt(self):
gt = self.d.get_det_gt(with_diff=True, with_trun=False)
df = Table(
np.array([[0., 0., 0., 0., 0., 0, 0, 0.],
[1., 1., 1., 1., 1., 0, 0, 0.],
[1., 1., 1., 0., 0., 0, 0, 1.],
[0., 0., 0., 0., 1., 0, 0, 2.],
[0., 0., 0., 0., 2., 0, 0, 3.],
[1., 1., 1., 1., 2., 0, 0, 3.]]),
['x', 'y', 'w', 'h', 'cls_ind', 'diff', 'trun', 'img_ind'])
print(gt)
print(df)
assert(gt == df)
def test_get_cls_counts(self):
arr = np.array(
[[1, 1, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 2]])
print(self.d.get_cls_counts())
assert(np.all(self.d.get_cls_counts() == arr))
def test_get_cls_ground_truth(self):
table = Table(
np.array([[True, True, False],
[True, False, False],
[False, True, False],
[False, False, True]]), ["A", "B", "C"])
assert(self.d.get_cls_ground_truth() == table)
def test_det_ground_truth_for_class(self):
gt = self.d.get_det_gt_for_class("A", with_diff=True, with_trun=True)
arr = np.array(
[[0., 0., 0., 0., 0., 0., 0, 0.],
[1., 1., 1., 0., 0., 0., 0., 1.]])
cols = ['x', 'y', 'w', 'h', 'cls_ind', 'diff', 'trun', 'img_ind']
print(gt.arr)
assert(np.all(gt.arr == arr))
assert(gt.cols == cols)
# no diff or trun
gt = self.d.get_det_gt_for_class("A", with_diff=False, with_trun=False)
arr = np.array(
[[0., 0., 0., 0., 0., 0., 0, 0.],
[1., 1., 1., 0., 0., 0., 0., 1.]])
cols = ['x', 'y', 'w', 'h', 'cls_ind', 'diff', 'trun', 'img_ind']
print(gt.arr)
assert(np.all(gt.arr == arr))
assert(gt.cols == cols)
def test_set_class_values(self):
assert(np.all(self.d.values == 1 / 3. * np.ones(len(self.classes))))
self.d.set_class_values('uniform')
assert(np.all(self.d.values == 1 / 3. * np.ones(len(self.classes))))
self.d.set_class_values('inverse_prior')
print(self.d.values)
assert(np.all(self.d.values == np.array([0.25, 0.25, 0.5])))
