def test_get_correct_counts(self):
print('\n==========================================================')
print('test_get_correct_counts')
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 graph_fn():
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)
match = matcher.Match(match_results)
gathered_tensor = match.gather_based_on_match(input_tensor,
unmatched_value=100.,
ignored_value=200.)
return gathered_tensor
def graph_fn():
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()
return (matched_column_indices, unmatched_column_indices,
ignored_column_indices)
def graph_fn():
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)
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))
return gathered_tensor
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 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 graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 1, -2])
match = matcher.Match(match_results)
num_matched_columns = match.num_matched_columns()
num_unmatched_columns = match.num_unmatched_columns()
num_ignored_columns = match.num_ignored_columns()
num_matched_rows = match.num_matched_rows()
return [num_matched_columns, num_unmatched_columns, num_ignored_columns,
num_matched_rows]
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_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.assertEqual(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_unmatched_ignored_column_indices(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indices = [2, 4, 6]
unmatched_ignored_column_indices = (
match.unmatched_or_ignored_column_indices())
self.assertEqual(unmatched_ignored_column_indices.dtype, tf.int32)
with self.test_session() as sess:
unmatched_ignored_column_indices = sess.run(
unmatched_ignored_column_indices)
self.assertAllEqual(unmatched_ignored_column_indices,
expected_column_indices)
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 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_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)
def test_get_correct_matched_columnIndices(self):
print(
'\n==============================================================='
)
print('test_get_correct_matched_columnIndices')
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indices = [0, 1, 3, 5]
matched_column_indices = match.matched_column_indices()
self.assertEquals(matched_column_indices.dtype, tf.int32)
with self.test_session() as sess:
matched_column_indices_out = sess.run(matched_column_indices)
self.assertAllEqual(matched_column_indices_out,
expected_column_indices)
def test_scalar_gather_based_on_match(self):
print(
'\n=============================================================')
print('test_scalar_gather_based_on_match')
input_tensor = tf.constant([7, 6, 5, 4, 3, 2, 1, 0], dtype=tf.float32)
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
expected_gathered_tensor = [4, 6, 100, 7, 100, 2, 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()
print('gathered_tensor_out: {}'.format(gathered_tensor_out))
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
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 batch_get_targets(batch_match, groundtruth_tensor_list,
groundtruth_weights_list, unmatched_value,
unmatched_weight):
"""Returns targets based on anchor-groundtruth box matching results.
Args:
batch_match: An int32 tensor of shape [batch, num_anchors] containing the
result of target assignment returned by TargetAssigner.assign(..).
groundtruth_tensor_list: A list of groundtruth tensors of shape
[num_groundtruth, d_1, d_2, ..., d_k]. The tensors can be of any type.
groundtruth_weights_list: A list of weights, one per groundtruth tensor, of
shape [num_groundtruth].
unmatched_value: A tensor of shape [d_1, d_2, ..., d_k] of the same type as
groundtruth tensor containing target value for anchors that remain
unmatched.
unmatched_weight: Scalar weight to assign to anchors that remain unmatched.
Returns:
targets: A tensor of shape [batch, num_anchors, d_1, d_2, ..., d_k]
containing targets for anchors.
weights: A float tensor of shape [batch, num_anchors] containing the weights
to assign to each target.
"""
match_list = tf.unstack(batch_match)
targets_list = []
weights_list = []
for match_tensor, groundtruth_tensor, groundtruth_weight in zip(
match_list, groundtruth_tensor_list, groundtruth_weights_list):
match_object = mat.Match(match_tensor)
targets = match_object.gather_based_on_match(
groundtruth_tensor,
unmatched_value=unmatched_value,
ignored_value=unmatched_value)
targets_list.append(targets)
weights = match_object.gather_based_on_match(
groundtruth_weight,
unmatched_value=unmatched_weight,
ignored_value=tf.zeros_like(unmatched_weight))
weights_list.append(weights)
return tf.stack(targets_list), tf.stack(weights_list)
def graph_fn(): match_results = tf.constant([3, 1, -1, 0, -1, 5, -2]) match = matcher.Match(match_results) matched_row_indices = match.matched_row_indices() return matched_row_indices
def graph_fn(): match_results = tf.constant([3, 1, -1, 0, -1, 5, -2]) match = matcher.Match(match_results) ignored_column_indices = match.ignored_column_indices() return ignored_column_indices
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
unmatched_ignored_column_indices = (match.
unmatched_or_ignored_column_indices())
return unmatched_ignored_column_indices
def graph_fn(): match_results = tf.constant([3, 1, -1, 0, -1, 5, -2]) match = matcher.Match(match_results) unmatched_column_indicator = match.unmatched_column_indicator() return unmatched_column_indicator
def loss(self, prediction_dict, true_image_shapes, scope=None):
"""Computes scalar loss tensors with respect to provided groundtruth.
Calling this function requires that groundtruth tensors have been
provided via the provide_groundtruth function.
Args:
prediction_dict: a dictionary holding prediction tensors with
1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors,
box_code_dimension] containing predicted boxes.
2) class_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, num_classes+1] containing class predictions
(logits) for each of the anchors. Note that this tensor *includes*
background class predictions.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
scope: Optional scope name.
Returns:
a dictionary mapping loss keys (`localization_loss` and
`classification_loss`) to scalar tensors representing corresponding loss
values.
"""
with tf.name_scope(scope, 'Loss', prediction_dict.values()):
keypoints = None
if self.groundtruth_has_field(fields.BoxListFields.keypoints):
keypoints = self.groundtruth_lists(
fields.BoxListFields.keypoints)
weights = None
if self.groundtruth_has_field(fields.BoxListFields.weights):
weights = self.groundtruth_lists(fields.BoxListFields.weights)
(batch_cls_targets, batch_cls_weights, batch_reg_targets,
batch_reg_weights, batch_match) = self._assign_targets(
self.groundtruth_lists(fields.BoxListFields.boxes),
self.groundtruth_lists(fields.BoxListFields.classes),
keypoints, weights)
match_list = [
matcher.Match(match) for match in tf.unstack(batch_match)
]
if self._add_summaries:
self._summarize_target_assignment(
self.groundtruth_lists(fields.BoxListFields.boxes),
match_list)
location_losses = self._localization_loss(
prediction_dict['box_encodings'],
batch_reg_targets,
ignore_nan_targets=True,
weights=batch_reg_weights)
cls_losses = ops.reduce_sum_trailing_dimensions(
self._classification_loss(
prediction_dict['class_predictions_with_background'],
batch_cls_targets,
weights=batch_cls_weights),
ndims=2)
if self._hard_example_miner:
(loc_loss_list, cls_loss_list) = self._apply_hard_mining(
location_losses, cls_losses, prediction_dict, match_list)
localization_loss = tf.reduce_sum(tf.stack(loc_loss_list))
classification_loss = tf.reduce_sum(tf.stack(cls_loss_list))
if self._add_summaries:
self._hard_example_miner.summarize()
else:
if self._add_summaries:
class_ids = tf.argmax(batch_cls_targets, axis=2)
flattened_class_ids = tf.reshape(class_ids, [-1])
flattened_classification_losses = tf.reshape(
cls_losses, [-1])
self._summarize_anchor_classification_loss(
flattened_class_ids, flattened_classification_losses)
localization_loss = tf.reduce_sum(location_losses)
classification_loss = tf.reduce_sum(cls_losses)
# Optionally normalize by number of positive matches
normalizer = tf.constant(1.0, dtype=tf.float32)
if self._normalize_loss_by_num_matches:
normalizer = tf.maximum(
tf.to_float(tf.reduce_sum(batch_reg_weights)), 1.0)
with tf.name_scope('localization_loss'):
localization_loss_normalizer = normalizer
if self._normalize_loc_loss_by_codesize:
localization_loss_normalizer *= self._box_coder.code_size
localization_loss = ((self._localization_loss_weight /
(localization_loss_normalizer)) *
localization_loss)
with tf.name_scope('classification_loss'):
classification_loss = (
(self._classification_loss_weight / normalizer) *
classification_loss)
loss_dict = {
'localization_loss': localization_loss,
'classification_loss': classification_loss
}
return loss_dict
