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Commit de3a34b1 authored by Kaushik Shivakumar's avatar Kaushik Shivakumar
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fix issues with target assigner for pr

parent 111c9d39
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Showing with 59 additions and 46 deletions
research/object_detection/core/region_similarity_calculator.py
View file @ de3a34b1
......@@ -100,11 +100,13 @@ class DETRSimilarity(RegionSimilarityCalculator):
"""
groundtruth_labels = boxlist1.get_field(fields.BoxListFields.classes)
predicted_labels = boxlist2.get_field(fields.BoxListFields.classes)
# Currently supporting only softmax
classification_scores = tf.matmul(groundtruth_labels,
tf.nn.softmax(predicted_labels), transpose_b=True)
predicted_labels,
transpose_b=True)
return -self.l1_weight * box_list_ops.l1(
boxlist1, boxlist2) + self.giou_weight * box_list_ops.giou(
boxlist1, boxlist2) + classification_scores
boxlist1, boxlist2) + classification_scores
class NegSqDistSimilarity(RegionSimilarityCalculator):
......
research/object_detection/core/target_assigner.py
View file @ de3a34b1
......@@ -1917,21 +1917,21 @@ class DETRTargetAssigner(object):
self._matcher = hungarian_matcher.HungarianBipartiteMatcher()
def batch_assign(self,
pred_boxes_batch,
pred_box_batch,
gt_box_batch,
class_predictions,
pred_class_batch,
gt_class_targets_batch,
gt_weights_batch=None):
"""Batched assignment of classification and regression targets.
Args:
pred_boxes_batch: list of BoxList objects with length batch_size
representing predicted box sets.
gt_box_batch: a list of BoxList objects with length batch_size
representing groundtruth boxes for each image in the batch
class_predictions: A list of tensors with length batch_size, where each
each tensor has shape [max_num_boxes, num_classes] to be used
pred_box_batch: a tensor of shape [batch_size, num_queries, 4]
representing predicted bounding boxes.
gt_box_batch: a tensor of shape [batch_size, num_queries, 4]
representing groundtruth bounding boxes.
pred_class_batch: A list of tensors with length batch_size, where each
each tensor has shape [num_queries, num_classes] to be used
by certain similarity calculators.
gt_class_targets_batch: a list of tensors with length batch_size, where
each tensor has shape [num_gt_boxes_i, num_classes] and
......@@ -1949,19 +1949,27 @@ class DETRTargetAssigner(object):
box_code_dimension]
batch_reg_weights: a tensor with shape [batch_size, num_pred_boxes].
"""
pred_box_batch = [
box_list.BoxList(pred_box)
for pred_box in tf.unstack(pred_box_batch)]
gt_box_batch = [
box_list.BoxList(gt_box)
for gt_box in tf.unstack(gt_box_batch)]
cls_targets_list = []
cls_weights_list = []
reg_targets_list = []
reg_weights_list = []
if gt_weights_batch is None:
gt_weights_batch = [None] * len(gt_class_targets_batch)
class_predictions = tf.unstack(class_predictions)
for pred_boxes, gt_boxes, class_preds, gt_class_targets, gt_weights in zip(
pred_boxes_batch, gt_box_batch, class_predictions,
gt_class_targets_batch, gt_weights_batch):
pred_class_batch = tf.unstack(pred_class_batch)
for (pred_boxes, gt_boxes, pred_class_batch,
gt_class_targets, gt_weights) in zip(
pred_box_batch, gt_box_batch, pred_class_batch,
gt_class_targets_batch, gt_weights_batch):
(cls_targets, cls_weights,
reg_targets, reg_weights) = self.assign(
pred_boxes, gt_boxes, class_preds, gt_class_targets, gt_weights)
reg_targets, reg_weights) = self.assign(
pred_boxes, gt_boxes, pred_class_batch, gt_class_targets, gt_weights)
cls_targets_list.append(cls_targets)
cls_weights_list.append(cls_weights)
reg_targets_list.append(reg_targets)
......@@ -1976,7 +1984,7 @@ class DETRTargetAssigner(object):
def assign(self,
box_preds,
groundtruth_boxes,
class_predictions,
pred_class_batch,
groundtruth_labels,
groundtruth_weights=None):
"""Assign classification and regression targets to each box_pred.
......@@ -1992,7 +2000,7 @@ class DETRTargetAssigner(object):
Args:
box_preds: a BoxList representing N box_preds
groundtruth_boxes: a BoxList representing M groundtruth boxes
class_predictions: A tensor with shape [max_num_boxes, num_classes]
pred_class_batch: A tensor with shape [max_num_boxes, num_classes]
to be used by certain similarity calculators.
groundtruth_labels: a tensor of shape [M, num_classes]
with labels for each of the ground_truth boxes. The subshape
......@@ -2000,12 +2008,12 @@ class DETRTargetAssigner(object):
to None, groundtruth_labels assumes a binary problem where all
ground_truth boxes get a positive label (of 1).
groundtruth_weights: a float tensor of shape [M] indicating the weight to
assign to all box_preds match to a particular groundtruth box. The weights
must be in [0., 1.]. If None, all weights are set to 1. Generally no
groundtruth boxes with zero weight match to any box_preds as matchers are
aware of groundtruth weights. Additionally, `cls_weights` and
`reg_weights` are calculated using groundtruth weights as an added
safety.
assign to all box_preds match to a particular groundtruth box. The
weights must be in [0., 1.]. If None, all weights are set to 1.
Generally no groundtruth boxes with zero weight match to any box_preds
as matchers are aware of groundtruth weights. Additionally,
`cls_weights` and `reg_weights` are calculated using groundtruth
weights as an added safety.
Returns:
cls_targets: a float32 tensor with shape [num_box_preds, num_classes],
......@@ -2013,11 +2021,13 @@ class DETRTargetAssigner(object):
which has shape [num_gt_boxes, num_classes].
cls_weights: a float32 tensor with shape [num_box_preds, num_classes],
representing weights for each element in cls_targets.
reg_targets: a float32 tensor with shape [num_box_preds, box_code_dimension]
reg_targets: a float32 tensor with shape [num_box_preds,
box_code_dimension]
reg_weights: a float32 tensor with shape [num_box_preds]
"""
unmatched_class_label = tf.constant([1] + [0] * (groundtruth_labels.shape[1] - 1), tf.float32)
unmatched_class_label = tf.constant(
[1] + [0] * (groundtruth_labels.shape[1] - 1), tf.float32)
if groundtruth_weights is None:
num_gt_boxes = groundtruth_boxes.num_boxes_static()
......@@ -2025,8 +2035,9 @@ class DETRTargetAssigner(object):
num_gt_boxes = groundtruth_boxes.num_boxes()
groundtruth_weights = tf.ones([num_gt_boxes], dtype=tf.float32)
groundtruth_boxes.add_field(fields.BoxListFields.classes, groundtruth_labels)
box_preds.add_field(fields.BoxListFields.classes, class_predictions)
groundtruth_boxes.add_field(fields.BoxListFields.classes,
groundtruth_labels)
box_preds.add_field(fields.BoxListFields.classes, pred_class_batch)
match_quality_matrix = self._similarity_calc.compare(
groundtruth_boxes,
......
research/object_detection/core/target_assigner_test.py
View file @ de3a34b1
......@@ -504,6 +504,7 @@ class BatchTargetAssignerTest(test_case.TestCase):
return targetassigner.TargetAssigner(similarity_calc, matcher, box_coder)
def test_batch_assign_targets(self):
def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2):
box_list1 = box_list.BoxList(groundtruth_boxlist1)
box_list2 = box_list.BoxList(groundtruth_boxlist2)
......@@ -1922,6 +1923,7 @@ class CenterNetMaskTargetAssignerTest(test_case.TestCase):
np.testing.assert_array_almost_equal(
expected_seg_target, segmentation_target)
class CenterNetDensePoseTargetAssignerTest(test_case.TestCase):
def test_assign_part_and_coordinate_targets(self):
......@@ -2213,7 +2215,7 @@ class DETRTargetAssignerTest(test_case.TestCase):
dtype=np.float32)
groundtruth_labels = np.array([[0.0, 1.0], [0.0, 1.0]],
dtype=np.float32)
exp_cls_targets = [[0, 1], [0, 1], [1, 0]]
exp_cls_weights = [[1, 1], [1, 1], [1, 1]]
exp_reg_targets = [[0.25, 0.25, 0.5, 0.5],
......@@ -2237,37 +2239,35 @@ class DETRTargetAssignerTest(test_case.TestCase):
def test_batch_assign_detr(self):
def graph_fn(pred_corners, groundtruth_box_corners,
groundtruth_labels, predicted_labels):
groundtruth_labels, predicted_labels):
detr_target_assigner = targetassigner.DETRTargetAssigner()
pred_boxlist = [box_list.BoxList(pred_corners)]
groundtruth_boxlist = [box_list.BoxList(groundtruth_box_corners)]
result = detr_target_assigner.batch_assign(
pred_boxlist, groundtruth_boxlist,
pred_corners, groundtruth_box_corners,
[predicted_labels], [groundtruth_labels])
(cls_targets, cls_weights, reg_targets, reg_weights) = result
return (cls_targets, cls_weights, reg_targets, reg_weights)
pred_corners = np.array([[0.25, 0.25, 0.4, 0.2],
[0.5, 0.8, 1.0, 0.8],
[0.9, 0.5, 0.1, 1.0]], dtype=np.float32)
groundtruth_box_corners = np.array([[0.0, 0.0, 0.5, 0.5],
[0.5, 0.5, 0.9, 0.9]],
dtype=np.float32)
pred_corners = np.array([[[0.25, 0.25, 0.4, 0.2],
[0.5, 0.8, 1.0, 0.8],
[0.9, 0.5, 0.1, 1.0]]], dtype=np.float32)
groundtruth_box_corners = np.array([[[0.0, 0.0, 0.5, 0.5],
[0.5, 0.5, 0.9, 0.9]]],
dtype=np.float32)
predicted_labels = np.array([[-3.0, 3.0], [2.0, 9.4], [5.0, 1.0]],
dtype=np.float32)
groundtruth_labels = np.array([[0.0, 1.0], [0.0, 1.0]],
dtype=np.float32)
exp_cls_targets = [[[0, 1], [0, 1], [1, 0]]]
exp_cls_weights = [[[1, 1], [1, 1], [1, 1]]]
exp_reg_targets = [[[0.25, 0.25, 0.5, 0.5],
[0.7, 0.7, 0.4, 0.4],
[0, 0, 0, 0]]]
[0.7, 0.7, 0.4, 0.4],
[0, 0, 0, 0]]]
exp_reg_weights = [[1, 1, 0]]
(cls_targets_out,
cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
graph_fn, [pred_corners, groundtruth_box_corners,
cls_weights_out, reg_targets_out, reg_weights_out) = self.execute(
graph_fn, [pred_corners, groundtruth_box_corners,
groundtruth_labels, predicted_labels])
self.assertAllClose(cls_targets_out, exp_cls_targets)
......@@ -2280,4 +2280,4 @@ class DETRTargetAssignerTest(test_case.TestCase):
self.assertEqual(reg_weights_out.dtype, np.float32)
if __name__ == '__main__':
tf.test.main()
\ No newline at end of file
tf.test.main()
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