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)