def test_load_dpm_detections(self):
conf = dict(self.config)
conf['detectors'] = ['dpm']
policy = DatasetPolicy(self.dataset, self.train_dataset, **conf)
assert (policy.detectors == ['dpm'])
dets = policy.load_ext_detections(self.dataset,
'dpm_may25',
force=True)
dets = dets.with_column_omitted('time')
# load the ground truth dets, processed in Matlab
# (timely/data/test_support/concat_dets.m)
filename = os.path.join(config.test_support_dir, 'val_dets.mat')
dets_correct = Table(
scipy.io.loadmat(filename)['dets'], [
'x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy', 'dummy',
'score', 'cls_ind', 'img_ind'
], 'dets_correct')
dets_correct = dets_correct.subset(
['x1', 'y1', 'x2', 'y2', 'score', 'cls_ind', 'img_ind'])
dets_correct.arr[:, :4] -= 1
dets_correct.arr[:, :4] = BoundingBox.convert_arr_from_corners(
dets_correct.arr[:, :4])
dets_correct.cols = ['x', 'y', 'w', 'h', 'score', 'cls_ind', 'img_ind']
print('----mine:')
print(dets)
print('----correct:')
print(dets_correct)
assert (dets_correct == dets)
def test_learn_weights(self):
dataset = Dataset('full_pascal_val')
train_dataset = Dataset('full_pascal_train')
dataset.images = dataset.images[:20]
train_dataset.images = train_dataset.images[:20]
dp = DatasetPolicy(dataset, train_dataset, self.weights_dataset_name,
**self.config)
weights = dp.learn_weights()
def __init__(self):
self.dataset = Dataset('test_pascal_val')
self.train_dataset = Dataset('test_pascal_train')
self.weights_dataset_name = 'test_pascal_val'
self.config = {
'suffix': 'default',
'detectors':
['perfect'], # perfect,perfect_with_noise,dpm,csc_default,csc_half
'policy_mode': 'random',
'bounds': None,
'weights_mode':
'manual_1' # manual_1, manual_2, manual_3, greedy, rl
}
self.dp = DatasetPolicy(self.dataset, self.train_dataset,
self.weights_dataset_name, **self.config)
def setup(self):
train_dataset = Dataset('test_pascal_train',force=True)
dataset = Dataset('test_pascal_val',force=True)
self.dp = DatasetPolicy(dataset,train_dataset,detector='perfect')
self.evaluation = Evaluation(test_config, self.dp)
def setUp(self):
d = Dataset(test_config, 'test_pascal_trainval').load_from_pascal('trainval', force=True)
d2 = Dataset(test_config, 'test_pascal_test').load_from_pascal('test', force=True)
config = {'detectors': ['csc_default']}
self.dp = DatasetPolicy(test_config, d, d2, **config)
self.bs = BeliefState(d, self.dp.actions)
