def test_ground_truth_test(self):
d = Dataset(test_config, 'test_pascal_val')
d.load_from_pascal('val')
gt = d.get_det_gt(with_diff=False, with_trun=False)
correct = np.matrix(
[[139., 200., 69., 102., 18., 0., 0., 0.],
[123., 155., 93., 41., 17., 0., 0., 1.],
[239., 156., 69., 50., 8., 0., 0., 1.]])
print(gt)
assert np.all(gt.arr == correct)
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)
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
class TestEvaluationSynthetic(unittest.TestCase):
def setUp(self):
self.d = Dataset(test_config, 'test_data2')
self.d.load_from_json(test_data2)
self.classes = self.d.classes
det_gt = self.d.get_det_gt()
# perfect detections
scores = np.ones(det_gt.shape[0])
self.full_dets = det_gt.append_column('score', scores)
# perfect detections, but only for class 'A'
dets_just_A = self.d.get_det_gt_for_class('A')
scores = np.ones(dets_just_A.shape[0])
self.partial_dets = dets_just_A.append_column('score', scores)
def test_values(self):
det_gt = self.d.get_det_gt()
self.d.set_class_values('uniform')
assert(np.all(self.d.values == 1. / 3 * np.ones(len(self.classes))))
ap = evaluation.compute_det_map(self.full_dets, det_gt, self.d.values)
assert(ap == 1)
ap = evaluation.compute_det_map(
self.partial_dets, det_gt, self.d.values)
assert_almost_equal(ap, 1 / 3.)
self.d.set_class_values('inverse_prior')
assert(np.all(self.d.values == np.array([0.25, 0.25, 0.5])))
ap = evaluation.compute_det_map(self.full_dets, det_gt, self.d.values)
assert(ap == 1)
ap = evaluation.compute_det_map(
self.partial_dets, det_gt, self.d.values)
assert_almost_equal(ap, 0.25)
def test_compute_pr_multiclass(self):
cols = ['x', 'y', 'w', 'h', 'cls_ind', 'img_ind', 'diff']
dets_cols = ['x', 'y', 'w', 'h', 'score', 'time', 'cls_ind', 'img_ind']
# two objects of different classes in the image, perfect detection
arr = np.array(
[[0, 0, 10, 10, 0, 0, 0],
[10, 10, 10, 10, 1, 0, 0]])
gt = Table(arr, cols)
dets_arr = np.array(
[[0, 0, 10, 10, -1, -1, 0, 0],
[10, 10, 10, 10, -1, -1, 1, 0]])
dets = Table(dets_arr, dets_cols)
# make sure gt and gt_cols aren't modified
gt_arr_copy = gt.arr.copy()
gt_cols_copy = list(gt.cols)
ap, rec, prec = evaluation.compute_det_pr(dets, gt)
assert(np.all(gt.arr == gt_arr_copy))
assert(gt_cols_copy == gt.cols)
correct_ap = 1
correct_rec = np.array([0.5, 1])
correct_prec = np.array([1, 1])
print((ap, rec, prec))
assert(correct_ap == ap)
assert(np.all(correct_rec == rec))
assert(np.all(correct_prec == prec))
# some extra detections to generate false positives
dets_arr = np.array(
[[0, 0, 10, 10, -1, -1, 0, 0],
[0, 0, 10, 10, 0, -1, 0, 0],
[10, 10, 10, 10, 0, -1, 1, 0],
[10, 10, 10, 10, -1, -1, 1, 0]])
dets = Table(dets_arr, dets_cols)
ap, rec, prec = evaluation.compute_det_pr(dets, gt)
correct_rec = np.array([0.5, 1, 1, 1])
correct_prec = np.array([1, 1, 2. / 3, 0.5])
print((ap, rec, prec))
assert(np.all(correct_rec == rec))
assert(np.all(correct_prec == prec))
# confirm that running on the same dets gives the same answer
ap, rec, prec = evaluation.compute_det_pr(dets, gt)
correct_rec = np.array([0.5, 1, 1, 1])
correct_prec = np.array([1, 1, 2. / 3, 0.5])
print((ap, rec, prec))
assert(np.all(correct_rec == rec))
assert(np.all(correct_prec == prec))
# now let's add two objects of a different class to gt to lower recall
arr = np.array(
[[0, 0, 10, 10, 0, 0, 0],
[10, 10, 10, 10, 1, 0, 0],
[20, 20, 10, 10, 2, 0, 0],
[30, 30, 10, 10, 2, 0, 0]])
gt = Table(arr, cols)
ap, rec, prec = evaluation.compute_det_pr(dets, gt)
correct_rec = np.array([0.25, 0.5, 0.5, 0.5])
correct_prec = np.array([1, 1, 2. / 3, 0.5])
print((ap, rec, prec))
assert(np.all(correct_rec == rec))
assert(np.all(correct_prec == prec))
# now call it with empty detections
dets_arr = np.array([])
dets = Table(dets_arr, dets_cols)
ap, rec, prec = evaluation.compute_det_pr(dets, gt)
correct_ap = 0
correct_rec = np.array([0])
correct_prec = np.array([0])
print((ap, rec, prec))
assert(np.all(correct_ap == ap))
assert(np.all(correct_rec == rec))
assert(np.all(correct_prec == prec))
def test_plots(self):
full_results_dirname = os.path.join(res_dir, 'full_dets_eval')
partial_results_dirname = os.path.join(res_dir, 'partial_dets_eval')
evaluation.evaluate_detections_whole(
self.d, self.partial_dets, partial_results_dirname, force=True)
assert(os.path.exists(
os.path.join(partial_results_dirname, 'whole_dashboard.html')))
pngs = glob.glob(os.path.join(partial_results_dirname, '*.png'))
assert(len(pngs) == 4) # 3 classes + 1 multiclass
evaluation.evaluate_detections_whole(
self.d, self.full_dets, full_results_dirname, force=True)
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])))
