def test_very_small_Width_nan_after_encoding(self): boxes = [[10.0, 10.0, 10.0000001, 20.0]] anchors = [[15.0, 12.0, 30.0, 18.0]] expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826]] boxes = box_list.BoxList(tf.constant(boxes)) anchors = box_list.BoxList(tf.constant(anchors)) coder = faster_rcnn_box_coder.FasterRcnnBoxCoder() 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_boxes_after_decoding(self): anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]] rel_codes = [[-0.5, -0.416666, -0.405465, -0.182321], [-0.083333, -0.222222, -0.693147, -1.098612]] expected_boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]] anchors = box_list.BoxList(tf.constant(anchors)) coder = faster_rcnn_box_coder.FasterRcnnBoxCoder() boxes = coder.decode(rel_codes, anchors) with self.test_session() as sess: boxes_out, = sess.run([boxes.get()]) self.assertAllClose(boxes_out, expected_boxes)
def test_get_correct_boxes_after_decoding_with_scaling(self): anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]] rel_codes = [[-1., -1.25, -1.62186, -0.911608], [-0.166667, -0.666667, -2.772588, -5.493062]] scale_factors = [2, 3, 4, 5] expected_boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]] anchors = box_list.BoxList(tf.constant(anchors)) coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=scale_factors) boxes = coder.decode(rel_codes, anchors) with self.test_session() as sess: boxes_out, = sess.run([boxes.get()]) self.assertAllClose(boxes_out, expected_boxes)
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. """ 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 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 == '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)