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_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 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 __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)
class TestDatasetPolicy:
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 test_run_on_dataset(self):
# run on test dataset
dets, clses, samples = self.dp.run_on_dataset(force=True)
assert (len(samples) == clses.shape[0])
assert (len(samples) == self.dp.dataset.num_images() *
len(self.dp.actions))
train_dets, train_clses, train_samples = self.dp.run_on_dataset(
train=True, force=True)
assert (len(train_samples) == train_clses.shape[0])
assert (len(train_samples) == self.dp.train_dataset.num_images() *
len(self.dp.actions))
def test_unique_samples(self):
"Test the correctness of making a list of samples unique."
dets, clses, samples = self.dp.run_on_dataset(force=True)
new_sample = copy.deepcopy(samples[11])
new_sample2 = copy.deepcopy(samples[11])
new_sample2.dt = -40 # an unreasonable value
assert (new_sample in samples)
assert (new_sample2 not in samples)
def test_output_det_statistics(self):
self.dp.output_det_statistics()
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 test_regress(self):
dets, clses, samples = self.dp.run_on_dataset(force=True)
weights, error = self.dp.regress(samples, 'greedy')
print "Weights after %d samples:\n %s" % (len(samples), weights)
print "Error after %d samples: %s" % (len(samples), error)
samples += samples
weights, error = self.dp.regress(samples, 'greedy')
print "Weights after %d samples:\n %s" % (len(samples), weights)
print "Error after %d samples: %s" % (len(samples), error)
samples += samples
weights, error = self.dp.regress(samples, 'greedy')
print "Weights after %d samples:\n %s" % (len(samples), weights)
print "Error after %d samples: %s" % (len(samples), error)
def test_load_weights(self):
modes = ['manual_1', 'manual_2', 'manual_3']
for mode in modes:
print "%s weights:" % mode
self.dp.weights_mode = mode
self.dp.load_weights()
print self.dp.get_reshaped_weights()
assert (self.dp.weights.shape[0] == len(self.dp.actions) *
BeliefState.num_features)
self.dp.write_weights_image('temp_weights_%s.png' % mode)
def test_perfect_detector(self):
dets, clses, samples = self.dp.run_on_dataset(force=True)
#embed()
dets = dets.subset(['x', 'y', 'w', 'h', 'cls_ind', 'img_ind'])
gt = self.dataset.get_det_gt()
gt = gt.subset(['x', 'y', 'w', 'h', 'cls_ind', 'img_ind'])
# TODO: does this make sense?
dets.sort_by_column('x')
gt.sort_by_column('x')
print dets
print gt
assert (dets == gt)
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 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)
class TestDatasetPolicy:
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 test_run_on_dataset(self):
# run on test dataset
dets, clses, samples = self.dp.run_on_dataset(force=True)
assert len(samples) == clses.shape[0]
assert len(samples) == self.dp.dataset.num_images() * len(self.dp.actions)
train_dets, train_clses, train_samples = self.dp.run_on_dataset(train=True, force=True)
assert len(train_samples) == train_clses.shape[0]
assert len(train_samples) == self.dp.train_dataset.num_images() * len(self.dp.actions)
def test_unique_samples(self):
"Test the correctness of making a list of samples unique."
dets, clses, samples = self.dp.run_on_dataset(force=True)
new_sample = copy.deepcopy(samples[11])
new_sample2 = copy.deepcopy(samples[11])
new_sample2.dt = -40 # an unreasonable value
assert new_sample in samples
assert new_sample2 not in samples
def test_output_det_statistics(self):
self.dp.output_det_statistics()
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 test_regress(self):
dets, clses, samples = self.dp.run_on_dataset(force=True)
weights, error = self.dp.regress(samples, "greedy")
print "Weights after %d samples:\n %s" % (len(samples), weights)
print "Error after %d samples: %s" % (len(samples), error)
samples += samples
weights, error = self.dp.regress(samples, "greedy")
print "Weights after %d samples:\n %s" % (len(samples), weights)
print "Error after %d samples: %s" % (len(samples), error)
samples += samples
weights, error = self.dp.regress(samples, "greedy")
print "Weights after %d samples:\n %s" % (len(samples), weights)
print "Error after %d samples: %s" % (len(samples), error)
def test_load_weights(self):
modes = ["manual_1", "manual_2", "manual_3"]
for mode in modes:
print "%s weights:" % mode
self.dp.weights_mode = mode
self.dp.load_weights()
print self.dp.get_reshaped_weights()
assert self.dp.weights.shape[0] == len(self.dp.actions) * BeliefState.num_features
self.dp.write_weights_image("temp_weights_%s.png" % mode)
def test_perfect_detector(self):
dets, clses, samples = self.dp.run_on_dataset(force=True)
# embed()
dets = dets.subset(["x", "y", "w", "h", "cls_ind", "img_ind"])
gt = self.dataset.get_det_gt()
gt = gt.subset(["x", "y", "w", "h", "cls_ind", "img_ind"])
# TODO: does this make sense?
dets.sort_by_column("x")
gt.sort_by_column("x")
print dets
print gt
assert dets == gt
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 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)
