/
githubmirror
/
models
Обзор
Документация
Войти
/
githubmirror
/
models
Код
Запросы
0
Пакеты
0
Релизы
0
Аналитика
Безопасность
master
research/object_detection/box_coders/keypoint_box_coder_test.py
151 строка
7 KB
pkulzc
Release MobileDet code and model, and require tf_slim installation for OD API. (#8562)
27 май 2020, 02:19
Не верифицирован
27 май 2020, 02:19
451906e
Код
Авторство
О чём код?
# Copyright 2017 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.keypoint_box_coder.""" import numpy as np import tensorflow.compat.v1 as tf from object_detection.box_coders import keypoint_box_coder from object_detection.core import box_list from object_detection.core import standard_fields as fields from object_detection.utils import test_case class KeypointBoxCoderTest(test_case.TestCase): def test_get_correct_relative_codes_after_encoding(self): boxes = np.array([[10., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]], np.float32) keypoints = np.array([[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]], np.float32) num_keypoints = len(keypoints[0]) anchors = np.array([[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]], np.float32) 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] ] 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 rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors]) self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04, atol=1e-04) def test_get_correct_relative_codes_after_encoding_with_scaling(self): boxes = np.array([[10., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]], np.float32) keypoints = np.array([[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]], np.float32) num_keypoints = len(keypoints[0]) anchors = np.array([[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]], np.float32) 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] ] def graph_fn(boxes, keypoints, anchors): scale_factors = [2, 3, 4, 5] boxes = box_list.BoxList(boxes) boxes.add_field(fields.BoxListFields.keypoints, keypoints) anchors = box_list.BoxList(anchors) coder = keypoint_box_coder.KeypointBoxCoder( num_keypoints, scale_factors=scale_factors) rel_codes = coder.encode(boxes, anchors) return rel_codes rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors]) self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04, atol=1e-04) def test_get_correct_boxes_after_decoding(self): anchors = np.array([[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]], np.float32) rel_codes = np.array([ [-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] ], np.float32) expected_boxes = [[10., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]] expected_keypoints = [[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]] num_keypoints = len(expected_keypoints[0]) def graph_fn(rel_codes, anchors): anchors = box_list.BoxList(anchors) coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints) boxes = coder.decode(rel_codes, anchors) return boxes.get(), boxes.get_field(fields.BoxListFields.keypoints) boxes_out, keypoints_out = self.execute(graph_fn, [rel_codes, anchors]) self.assertAllClose(keypoints_out, expected_keypoints, rtol=1e-04, atol=1e-04) self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04, atol=1e-04) def test_get_correct_boxes_after_decoding_with_scaling(self): anchors = np.array([[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]], np.float32) rel_codes = np.array([ [-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] ], np.float32) expected_boxes = [[10., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]] expected_keypoints = [[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]] num_keypoints = len(expected_keypoints[0]) 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) boxes_out, keypoints_out = self.execute(graph_fn, [rel_codes, anchors]) self.assertAllClose(keypoints_out, expected_keypoints, rtol=1e-04, atol=1e-04) self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04, atol=1e-04) def test_very_small_width_nan_after_encoding(self): boxes = np.array([[10., 10., 10.0000001, 20.]], np.float32) keypoints = np.array([[[10., 10.], [10.0000001, 20.]]], np.float32) anchors = np.array([[15., 12., 30., 18.]], np.float32) expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826, -0.833333, -0.833333, -0.833333, 0.833333]] 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(2) rel_codes = coder.encode(boxes, anchors) return rel_codes rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors]) self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04, atol=1e-04) if __name__ == '__main__': tf.test.main()