def graph_fn():
window = tf.constant([0, 0, 9, 14], tf.float32)
corners = tf.constant([[5.0, 5.0, 6.0, 6.0],
[-1.0, -2.0, 4.0, 5.0],
[2.0, 3.0, 5.0, 9.0],
[0.0, 0.0, 9.0, 14.0],
[-10.0, -10.0, -9.0, -9.0],
[-100.0, -100.0, 300.0, 600.0]])
boxes = box_list.BoxList(corners)
boxes.add_field('extra_data', tf.constant([[1], [2], [3], [4], [5], [6]]))
pruned, keep_indices = box_list_ops.prune_outside_window(boxes, window)
return pruned.get(), pruned.get_field('extra_data'), keep_indices
def test_prune_outside_window_filters_boxes_which_fall_outside_the_window(
self):
window = tf.constant([0, 0, 9, 14], tf.float32)
corners = tf.constant([[5.0, 5.0, 6.0, 6.0], [-1.0, -2.0, 4.0, 5.0],
[2.0, 3.0, 5.0, 9.0], [0.0, 0.0, 9.0, 14.0],
[-10.0, -10.0, -9.0, -9.0],
[-100.0, -100.0, 300.0, 600.0]])
boxes = box_list.BoxList(corners)
boxes.add_field('extra_data',
tf.constant([[1], [2], [3], [4], [5], [6]]))
exp_output = [[5.0, 5.0, 6.0, 6.0], [2.0, 3.0, 5.0, 9.0],
[0.0, 0.0, 9.0, 14.0]]
pruned, keep_indices = box_list_ops.prune_outside_window(boxes, window)
with self.test_session() as sess:
pruned_output = sess.run(pruned.get())
self.assertAllClose(pruned_output, exp_output)
keep_indices_out = sess.run(keep_indices)
self.assertAllEqual(keep_indices_out, [0, 2, 3])
extra_data_out = sess.run(pruned.get_field('extra_data'))
self.assertAllEqual(extra_data_out, [[1], [3], [4]])
def test_prune_outside_window_filters_boxes_which_fall_outside_the_window(
self):
window = tf.constant([0, 0, 9, 14], tf.float32)
corners = tf.constant([[5.0, 5.0, 6.0, 6.0],
[-1.0, -2.0, 4.0, 5.0],
[2.0, 3.0, 5.0, 9.0],
[0.0, 0.0, 9.0, 14.0],
[-10.0, -10.0, -9.0, -9.0],
[-100.0, -100.0, 300.0, 600.0]])
boxes = box_list.BoxList(corners)
boxes.add_field('extra_data', tf.constant([[1], [2], [3], [4], [5], [6]]))
exp_output = [[5.0, 5.0, 6.0, 6.0],
[2.0, 3.0, 5.0, 9.0],
[0.0, 0.0, 9.0, 14.0]]
pruned, keep_indices = box_list_ops.prune_outside_window(boxes, window)
with self.test_session() as sess:
pruned_output = sess.run(pruned.get())
self.assertAllClose(pruned_output, exp_output)
keep_indices_out = sess.run(keep_indices)
self.assertAllEqual(keep_indices_out, [0, 2, 3])
extra_data_out = sess.run(pruned.get_field('extra_data'))
self.assertAllEqual(extra_data_out, [[1], [3], [4]])
Faster-Rcnn full process:
A: prediction_dict = detection_model.predict()
a: Train RPN (first stage)
0. preprocessed inputs
1. self._extract_rpn_feature_maps()
I: rpn_features_to_crop = self._feature_extractor.extract_proposal_features()
II: anchors = self._first_stage_anchor_generator.generate()
-->GridAnchorGenerator._generate() -->GridAnchorGenerator.tile_anchors()
III: rpn_box_predictor_features = slim.conv2d()
2. self._predict_rpn_proposals()
I: box_predictions = self._first_stage_box_predictor.predict()
II: objectness_predictions_with_background =
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND()
3. _remove_invalid_anchors_and_predictions
I: pruned_anchors_boxlist, keep_indices = box_list_ops.prune_outside_window()
II: _batch_gather_kept_indices(box_encodings)
_batch_gather_kept_indices(objectness_predictions_with_background)
'Extremely hard to get through!!!!!!'
b: Classification (second stage)
1. _predict_second_stage
I: flattened_proposal_feature_maps = self._postprocess_rpn()
'Very complicate function!!!!!!'
i: self._format_groundtruth_data():
ii: decoded_boxes = self._box_coder.decode(rpn_box_encodings, box_list.BoxList(anchors))
--> faster_rcnn_box_coder.FasterRcnnBoxCoder._decode()
objectness_scores = tf.nn.softmax(rpn_objectness_predictions_with_background)
iii:proposal_boxlist = post_processing.multiclass_non_max_suppression()
iv: padded_proposals = box_list_ops.pad_or_clip_box_list()
II: self._compute_second_stage_input_feature_maps()
