def build(box_coder_config): if not isinstance(box_coder_config, box_coder_pb2.BoxCoder): raise ValueError( 'box_coder_config not of type box_coder_pb2.BoxCoder.') if box_coder_config.WhichOneof( 'box_coder_oneof') == 'faster_rcnn_box_coder': return faster_rcnn_box_coder.FasterRcnnBoxCoder(scale_factors=[ box_coder_config.faster_rcnn_box_coder.y_scale, box_coder_config.faster_rcnn_box_coder.x_scale, box_coder_config.faster_rcnn_box_coder.height_scale, box_coder_config.faster_rcnn_box_coder.width_scale ]) if box_coder_config.WhichOneof('box_coder_oneof') == 'keypoint_box_coder': return keypoint_box_coder.KeypointBoxCoder( box_coder_config.keypoint_box_coder.num_keypoints, scale_factors=[ box_coder_config.keypoint_box_coder.y_scale, box_coder_config.keypoint_box_coder.x_scale, box_coder_config.keypoint_box_coder.height_scale, box_coder_config.keypoint_box_coder.width_scale ]) if (box_coder_config.WhichOneof('box_coder_oneof') == 'mean_stddev_box_coder'): return mean_stddev_box_coder.MeanStddevBoxCoder( stddev=box_coder_config.mean_stddev_box_coder.stddev) if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder': return square_box_coder.SquareBoxCoder(scale_factors=[ box_coder_config.square_box_coder.y_scale, box_coder_config.square_box_coder.x_scale, box_coder_config.square_box_coder.length_scale ]) raise ValueError('Empty box coder.')
def graph_fn(boxes, keypoints, anchors): boxes = box_list.BoxList(boxes) boxes.add_field(fields.BoxListFields.keypoints, keypoints) anchors = box_list.BoxList(anchors) coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints) rel_codes = coder.encode(boxes, anchors) return rel_codes
def graph_fn(rel_codes, anchors): scale_factors = [2, 3, 4, 5] anchors = box_list.BoxList(anchors) coder = keypoint_box_coder.KeypointBoxCoder( num_keypoints, scale_factors=scale_factors) boxes = coder.decode(rel_codes, anchors) return boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)
def test_very_small_width_nan_after_encoding(self): boxes = [[10., 10., 10.0000001, 20.]] keypoints = [[[10., 10.], [10.0000001, 20.]]] anchors = [[15., 12., 30., 18.]] expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826, -0.833333, -0.833333, -0.833333, 0.833333]] boxes = box_list.BoxList(tf.constant(boxes)) boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints)) anchors = box_list.BoxList(tf.constant(anchors)) coder = keypoint_box_coder.KeypointBoxCoder(2) rel_codes = coder.encode(boxes, anchors) with self.test_session() as sess: rel_codes_out, = sess.run([rel_codes]) self.assertAllClose(rel_codes_out, expected_rel_codes)
def build(box_coder_config): """Builds a box coder object based on the box coder config. Args: box_coder_config: A box_coder.proto object containing the config for the desired box coder. Returns: BoxCoder based on the config. Raises: ValueError: On empty box coder proto. """ print("CONFIG:{}".format(box_coder_config)) print("PB2:{}".format(box_coder_pb2.BoxCoder)) if not isinstance(box_coder_config, box_coder_pb2.BoxCoder): print("CONFIG:{}".format(box_coder_config)) print("PB2:{}".format(box_coder_pb2.BoxCoder)) raise ValueError( 'box_coder_config not of type box_coder_pb2.BoxCoder.') if box_coder_config.WhichOneof( 'box_coder_oneof') == 'faster_rcnn_box_coder': return faster_rcnn_box_coder.FasterRcnnBoxCoder(scale_factors=[ box_coder_config.faster_rcnn_box_coder.y_scale, box_coder_config.faster_rcnn_box_coder.x_scale, box_coder_config.faster_rcnn_box_coder.height_scale, box_coder_config.faster_rcnn_box_coder.width_scale ]) if box_coder_config.WhichOneof('box_coder_oneof') == 'keypoint_box_coder': return keypoint_box_coder.KeypointBoxCoder( box_coder_config.keypoint_box_coder.num_keypoints, scale_factors=[ box_coder_config.keypoint_box_coder.y_scale, box_coder_config.keypoint_box_coder.x_scale, box_coder_config.keypoint_box_coder.height_scale, box_coder_config.keypoint_box_coder.width_scale ]) if (box_coder_config.WhichOneof('box_coder_oneof') == 'mean_stddev_box_coder'): return mean_stddev_box_coder.MeanStddevBoxCoder() if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder': return square_box_coder.SquareBoxCoder(scale_factors=[ box_coder_config.square_box_coder.y_scale, box_coder_config.square_box_coder.x_scale, box_coder_config.square_box_coder.length_scale ]) raise ValueError('Empty box coder.')
def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_keypoints): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.5) box_coder = keypoint_box_coder.KeypointBoxCoder( num_keypoints=6, scale_factors=[10.0, 10.0, 5.0, 5.0]) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder, unmatched_cls_target=None) anchors_boxlist = box_list.BoxList(anchor_means) groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners) groundtruth_boxlist.add_field(fields.BoxListFields.keypoints, groundtruth_keypoints) result = target_assigner.assign(anchors_boxlist, groundtruth_boxlist) (cls_targets, cls_weights, reg_targets, reg_weights, _) = result return (cls_targets, cls_weights, reg_targets, reg_weights)
def test_get_correct_relative_codes_after_encoding(self): boxes = [[10., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]] keypoints = [[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]] num_keypoints = len(keypoints[0]) anchors = [[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]] expected_rel_codes = [[ -0.5, -0.416666, -0.405465, -0.182321, -0.5, -0.5, -0.833333, 0. ], [ -0.083333, -0.222222, -0.693147, -1.098612, 0.166667, -0.166667, -0.333333, -0.055556 ]] boxes = box_list.BoxList(tf.constant(boxes)) boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints)) anchors = box_list.BoxList(tf.constant(anchors)) coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints) rel_codes = coder.encode(boxes, anchors) with self.test_session() as sess: rel_codes_out, = sess.run([rel_codes]) self.assertAllClose(rel_codes_out, expected_rel_codes)
def test_get_correct_relative_codes_after_encoding_with_scaling(self): boxes = [[10., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]] keypoints = [[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]] num_keypoints = len(keypoints[0]) anchors = [[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]] scale_factors = [2, 3, 4, 5] expected_rel_codes = [[ -1., -1.25, -1.62186, -0.911608, -1.0, -1.5, -1.666667, 0. ], [ -0.166667, -0.666667, -2.772588, -5.493062, 0.333333, -0.5, -0.666667, -0.166667 ]] boxes = box_list.BoxList(tf.constant(boxes)) boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints)) anchors = box_list.BoxList(tf.constant(anchors)) coder = keypoint_box_coder.KeypointBoxCoder( num_keypoints, scale_factors=scale_factors) rel_codes = coder.encode(boxes, anchors) with self.test_session() as sess: rel_codes_out, = sess.run([rel_codes]) self.assertAllClose(rel_codes_out, expected_rel_codes)
def create_target_assigner(reference, stage=None, negative_class_weight=1.0, use_matmul_gather=False): """Factory function for creating standard target assigners. Args: reference: string referencing the type of TargetAssigner. stage: string denoting stage: {proposal, detection}. negative_class_weight: classification weight to be associated to negative anchors (default: 1.0) use_matmul_gather: whether to use matrix multiplication based gather which are better suited for TPUs. Returns: TargetAssigner: desired target assigner. Raises: ValueError: if combination reference+stage is invalid. """ if reference == 'Multibox' and stage == 'proposal': similarity_calc = sim_calc.NegSqDistSimilarity() matcher = bipartite_matcher.GreedyBipartiteMatcher() box_coder = mean_stddev_box_coder.MeanStddevBoxCoder() elif reference == 'FasterRCNN' and stage == 'proposal': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.7, unmatched_threshold=0.3, force_match_for_each_row=True, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FasterRCNN' and stage == 'detection': similarity_calc = sim_calc.IouSimilarity() # Uses all proposals with IOU < 0.5 as candidate negatives. matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.5, negatives_lower_than_unmatched=True, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FasterRCNN' and stage == 'keypoints': similarity_calc = sim_calc.IouSimilarity() # Uses all proposals with IOU < 0.5 as candidate negatives. matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.5, negatives_lower_than_unmatched=True, use_matmul_gather=use_matmul_gather) box_coder = keypoint_box_coder.KeypointBoxCoder( 6, scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FastRCNN': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.5, unmatched_threshold=0.1, force_match_for_each_row=False, negatives_lower_than_unmatched=False, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder() else: raise ValueError('No valid combination of reference and stage.') return TargetAssigner(similarity_calc, matcher, box_coder, negative_class_weight=negative_class_weight)