def test_get_correct_unmatched_column_indicator(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indicator = [False, False, True, False, True, False, False]
unmatched_column_indicator = match.unmatched_column_indicator()
self.assertEquals(unmatched_column_indicator.dtype, tf.bool)
with self.test_session() as sess:
unmatched_column_indicator = sess.run(unmatched_column_indicator)
self.assertAllEqual(unmatched_column_indicator, expected_column_indicator)
def testGetCorrectMatchedRowIndices(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_row_indices = [3, 1, 0, 5]
matched_row_indices = match.matched_row_indices()
self.assertEquals(matched_row_indices.dtype, tf.int32)
with self.test_session() as sess:
matched_row_inds = sess.run(matched_row_indices)
self.assertAllEqual(matched_row_inds, expected_row_indices)
def test_get_correct_ignored_column_indices(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indices = [6]
ignored_column_indices = match.ignored_column_indices()
self.assertEquals(ignored_column_indices.dtype, tf.int32)
with self.test_session() as sess:
ignored_column_indices = sess.run(ignored_column_indices)
self.assertAllEqual(ignored_column_indices, expected_column_indices)
def testGetCorrectUnmatchedColumnIndices(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indices = [2, 4]
unmatched_column_indices = match.unmatched_column_indices()
self.assertEquals(unmatched_column_indices.dtype, tf.int32)
with self.test_session() as sess:
unmatched_column_indices = sess.run(unmatched_column_indices)
self.assertAllEqual(unmatched_column_indices, expected_column_indices)
def test_scalar_gather_based_on_match(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
input_tensor = tf.constant([0, 1, 2, 3, 4, 5, 6, 7], dtype=tf.float32)
expected_gathered_tensor = [3, 1, 100, 0, 100, 5, 200]
match = matcher.Match(match_results)
gathered_tensor = match.gather_based_on_match(input_tensor,
unmatched_value=100.,
ignored_value=200.)
self.assertEquals(gathered_tensor.dtype, tf.float32)
with self.test_session():
gathered_tensor_out = gathered_tensor.eval()
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
def testEnforceNegativesPerPositiveRatio_rbox(self):
location_losses = tf.constant(
[[100, 90, 80, 0, 1, 2, 3, 10, 20, 100, 20, 3]], tf.float32)
cls_losses = tf.constant([[0, 0, 100, 0, 90, 70, 0, 60, 0, 17, 13, 0]],
tf.float32)
rbox = tf.constant(
[[0.0, 0.0, 0.2, 0.1, 0.0], [0.0, 0.0, 0.2, 0.1, 0.0],
[0.0, 0.0, 0.2, 0.1, 0.0], [0.0, 0.0, 0.2, 0.1, 0.0],
[0.0, 0.0, 0.5, 0.1, 0.0], [0.0, 0.0, 0.6, 0.1, 0.0],
[0.0, 0.0, 0.2, 0.1, 0.0], [0.0, 0.0, 0.8, 0.1, 0.0],
[0.0, 0.0, 0.2, 0.1, 0.0], [0.0, 0.0, 1.0, 0.1, 0.0],
[0.0, 0.0, 1.1, 0.1, 0.0], [0.0, 0.0, 0.2, 0.1, 0.0]], tf.float32)
match_results = tf.constant(
[2, -1, 0, -1, -1, 1, -1, -1, -1, -1, -1, 3])
match_list = [matcher.Match(match_results)]
decoded_boxlist_list = []
decoded_boxlist_list.append(rbox_list.RBoxList(rbox))
max_negatives_per_positive_list = [0.0, 0.5, 1.0, 1.5, 10]
exp_loc_loss_list = [
100 + 80 + 2 + 3, 100 + 80 + 1 + 2 + 10 + 3,
100 + 80 + 1 + 2 + 10 + 3 + 100 + 20,
100 + 90 + 80 + 0 + 1 + 2 + 10 + 3 + 100 + 20,
100 + 90 + 80 + 0 + 1 + 2 + 3 + 10 + 20 + 100 + 20 + 3
]
exp_cls_loss_list = [
0 + 100 + 70 + 0, 0 + 100 + 90 + 70 + 60 + 0,
0 + 100 + 90 + 70 + 60 + 0 + 17 + 13,
0 + 0 + 100 + 0 + 90 + 70 + 60 + 0 + 17 + 13,
0 + 0 + 100 + 0 + 90 + 70 + 0 + 60 + 0 + 17 + 13 + 0
]
for max_negatives_per_positive, exp_loc_loss, exp_cls_loss in zip(
max_negatives_per_positive_list, exp_loc_loss_list,
exp_cls_loss_list):
loss_op = losses.HardExampleMiner(
num_hard_examples=None,
iou_threshold=1.0,
loss_type='cls',
cls_loss_weight=1,
loc_loss_weight=1,
max_negatives_per_positive=max_negatives_per_positive,
box_type='rbbox')
(loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
decoded_boxlist_list, match_list)
loss_op.summarize()
with self.test_session() as sess:
loc_loss_output = sess.run(loc_loss)
self.assertAllClose(loc_loss_output, exp_loc_loss)
cls_loss_output = sess.run(cls_loss)
self.assertAllClose(cls_loss_output, exp_cls_loss)
def testEnforceNegativesPerPositiveRatioWithMinNegativesPerImage(self):
location_losses = tf.constant([[100, 90, 80, 0, 1, 2,
3, 10, 20, 100, 20, 3]], tf.float32)
cls_losses = tf.constant([[0, 0, 100, 0, 90, 70,
0, 60, 0, 17, 13, 0]], tf.float32)
box_corners = tf.constant([[0.0, 0.0, 0.2, 0.1],
[0.0, 0.0, 0.2, 0.1],
[0.0, 0.0, 0.2, 0.1],
[0.0, 0.0, 0.2, 0.1],
[0.0, 0.0, 0.5, 0.1],
[0.0, 0.0, 0.6, 0.1],
[0.0, 0.0, 0.2, 0.1],
[0.0, 0.0, 0.8, 0.1],
[0.0, 0.0, 0.2, 0.1],
[0.0, 0.0, 1.0, 0.1],
[0.0, 0.0, 1.1, 0.1],
[0.0, 0.0, 0.2, 0.1]], tf.float32)
match_results = tf.constant([-1] * 12)
match_list = [matcher.Match(match_results)]
decoded_boxlist_list = []
decoded_boxlist_list.append(box_list.BoxList(box_corners))
min_negatives_per_image_list = [0, 1, 2, 4, 5, 6]
exp_loc_loss_list = [0,
80,
80 + 1,
80 + 1 + 2 + 10,
80 + 1 + 2 + 10 + 100,
80 + 1 + 2 + 10 + 100 + 20]
exp_cls_loss_list = [0,
100,
100 + 90,
100 + 90 + 70 + 60,
100 + 90 + 70 + 60 + 17,
100 + 90 + 70 + 60 + 17 + 13]
for min_negatives_per_image, exp_loc_loss, exp_cls_loss in zip(
min_negatives_per_image_list, exp_loc_loss_list, exp_cls_loss_list):
loss_op = losses.HardExampleMiner(
num_hard_examples=None, iou_threshold=0.9999, loss_type='cls',
cls_loss_weight=1, loc_loss_weight=1,
max_negatives_per_positive=3,
min_negatives_per_image=min_negatives_per_image)
(loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
decoded_boxlist_list, match_list)
with self.test_session() as sess:
loc_loss_output = sess.run(loc_loss)
self.assertAllClose(loc_loss_output, exp_loc_loss)
cls_loss_output = sess.run(cls_loss)
self.assertAllClose(cls_loss_output, exp_cls_loss)
def test_multidimensional_gather_based_on_match(self):
match_results = tf.constant([1, -1, -2])
input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
dtype=tf.float32)
expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0],
[0, 0, 0, 0]]
match = matcher.Match(match_results)
gathered_tensor = match.gather_based_on_match(
input_tensor,
unmatched_value=tf.zeros(4),
ignored_value=tf.zeros(4))
self.assertEquals(gathered_tensor.dtype, tf.float32)
with self.test_session():
gathered_tensor_out = gathered_tensor.eval()
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
def test_get_correct_counts(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
exp_num_matched_columns = 4
exp_num_unmatched_columns = 2
exp_num_ignored_columns = 1
num_matched_columns = match.num_matched_columns()
num_unmatched_columns = match.num_unmatched_columns()
num_ignored_columns = match.num_ignored_columns()
self.assertEquals(num_matched_columns.dtype, tf.int32)
self.assertEquals(num_unmatched_columns.dtype, tf.int32)
self.assertEquals(num_ignored_columns.dtype, tf.int32)
with self.test_session() as sess:
(num_matched_columns_out, num_unmatched_columns_out,
num_ignored_columns_out) = sess.run(
[num_matched_columns, num_unmatched_columns, num_ignored_columns])
self.assertAllEqual(num_matched_columns_out, exp_num_matched_columns)
self.assertAllEqual(num_unmatched_columns_out, exp_num_unmatched_columns)
self.assertAllEqual(num_ignored_columns_out, exp_num_ignored_columns)
def test_all_columns_accounted_for(self):
# Note: deliberately setting to small number so not always
# all possibilities appear (matched, unmatched, ignored)
num_matches = 10
match_results = tf.random_uniform(
[num_matches], minval=-2, maxval=5, dtype=tf.int32)
match = matcher.Match(match_results)
matched_column_indices = match.matched_column_indices()
unmatched_column_indices = match.unmatched_column_indices()
ignored_column_indices = match.ignored_column_indices()
with self.test_session() as sess:
matched, unmatched, ignored = sess.run([
matched_column_indices, unmatched_column_indices,
ignored_column_indices
])
all_indices = np.hstack((matched, unmatched, ignored))
all_indices_sorted = np.sort(all_indices)
self.assertAllEqual(all_indices_sorted,
np.arange(num_matches, dtype=np.int32))
def test_multidimensional_gather_based_on_match_with_matmul_gather_op(
self):
match_results = tf.constant([1, -1, -2])
input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
dtype=tf.float32)
expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0],
[0, 0, 0, 0]]
match = matcher.Match(match_results, use_matmul_gather=True)
gathered_tensor = match.gather_based_on_match(
input_tensor,
unmatched_value=tf.zeros(4),
ignored_value=tf.zeros(4))
self.assertEquals(gathered_tensor.dtype, tf.float32)
with self.test_session() as sess:
self.assertTrue(
all([
op.name is not 'Gather'
for op in sess.graph.get_operations()
]))
gathered_tensor_out = gathered_tensor.eval()
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
