remove box coders

research/object_detection/box_coders/detr_box_coder.py

-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""DETR box coder.
-
-DETR box coder follows the coding schema described below:
-  ty = y
-  tx = x
-  th = h
-  tw = w
-  where x, y, w, h denote the box's center coordinates, width and height
-  respectively. Similarly, xa, ya, wa, ha denote the anchor's center
-  coordinates, width and height. tx, ty, tw and th denote the anchor-encoded
-  center, width and height respectively.
-
-  See http://arxiv.org/abs/1506.01497 for details.
-"""
-
-import tensorflow.compat.v1 as tf
-
-from object_detection.core import box_coder
-from object_detection.core import box_list
-
-EPSILON = 1e-8
-
-
-class DETRBoxCoder(box_coder.BoxCoder):
-  """DETR box coder."""
-
-  def __init__(self):
-    """Constructor for DETRBoxCoder."""
-    pass
-
-  @property
-  def code_size(self):
-    return 4
-
-  def _encode(self, boxes, anchors):
-    """Encode a box collection with respect to anchor collection.
-
-    Args:
-      boxes: BoxList holding N boxes to be encoded.
-      anchors: BoxList of anchors, ignored for DETR.
-
-    Returns:
-      a tensor representing N encoded boxes of the format
-      [ty, tx, th, tw].
-    """
-    # Convert anchors to the center coordinate representation.
-    ty, tx, th, tw = boxes.get_center_coordinates_and_sizes()
-    return tf.transpose(tf.stack([ty, tx, th, tw]))
-
-  def _decode(self, rel_codes, anchors):
-    """Decode relative codes to boxes.
-
-    Args:
-      rel_codes: a tensor representing N encoded boxes.
-      anchors: BoxList of anchors, ignored for DETR.
-
-    Returns:
-      boxes: BoxList holding N bounding boxes.
-    """
-    ty, tx, th, tw = tf.unstack(tf.transpose(rel_codes))
-    ymin = ty - th / 2.
-    xmin = tx - tw / 2.
-    ymax = ty + th / 2.
-    xmax = tx + tw / 2.
-    return box_list.BoxList(tf.transpose(tf.stack([ymin, xmin, ymax, xmax])))
-

research/object_detection/box_coders/detr_box_coder_test.py

-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Tests for object_detection.box_coder.detr_box_coder."""
-import numpy as np
-import tensorflow.compat.v1 as tf
-
-from object_detection.box_coders import detr_box_coder
-from object_detection.core import box_list
-from object_detection.utils import test_case
-
-
-class DETRBoxCoder(test_case.TestCase):
-
-  def test_get_correct_relative_codes_after_encoding(self):
-    boxes = np.array([[0.0, 2.5, 20.0, 17.5], [-0.05, -0.1, 0.45, 0.3]],
-                     np.float32)
-    expected_rel_codes = [[10.0, 10.0, 20.0, 15.0],
-                          [0.2, 0.1, 0.5, 0.4]]
-    def graph_fn(boxes):
-      boxes = box_list.BoxList(boxes)
-      coder = detr_box_coder.DETRBoxCoder()
-      rel_codes = coder.encode(boxes, None)
-      return rel_codes
-    rel_codes_out = self.execute(graph_fn, [boxes])
-    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
-                        atol=1e-04)
-
-  def test_get_correct_boxes_after_decoding(self):
-    rel_codes = np.array([[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]],
-                     np.float32)
-    expected_boxes = [[0.0, 2.5, 20.0, 17.5], [-0.05, -0.1, 0.45, 0.3]]
-    def graph_fn(rel_codes):
-      coder = detr_box_coder.DETRBoxCoder()
-      boxes = coder.decode(rel_codes, None)
-      return boxes.get()
-    boxes_out = self.execute(graph_fn, [rel_codes])
-    self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
-                        atol=1e-04)
-
-if __name__ == '__main__':
-  tf.test.main()