mirror of https://github.com/tensorflow/models.git
96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
# Copyright 2025 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for coco_utils."""
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import os
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import numpy as np
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import tensorflow as tf, tf_keras
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from official.vision.dataloaders import tfexample_utils
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from official.vision.evaluation import coco_utils
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class CocoUtilsTest(tf.test.TestCase):
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def test_scan_and_generator_annotation_file(self):
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num_samples = 10
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example = tfexample_utils.create_detection_test_example(
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image_height=512, image_width=512, image_channel=3, num_instances=10
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)
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tf_examples = [example] * num_samples
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data_file = os.path.join(self.create_tempdir(), 'test.tfrecord')
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tfexample_utils.dump_to_tfrecord(
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record_file=data_file, tf_examples=tf_examples
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)
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annotation_file = os.path.join(self.create_tempdir(), 'annotation.json')
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coco_utils.scan_and_generator_annotation_file(
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file_pattern=data_file,
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file_type='tfrecord',
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num_samples=num_samples,
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include_mask=True,
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annotation_file=annotation_file,
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)
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self.assertTrue(
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tf.io.gfile.exists(annotation_file),
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msg='Annotation file {annotation_file} does not exist.',
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)
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def test_convert_keypoint_predictions_to_coco_annotations(self):
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batch_size = 1
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max_num_detections = 3
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num_keypoints = 3
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image_size = 512
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source_id = [np.array([[1]], dtype=int)]
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detection_boxes = [
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np.random.random([batch_size, max_num_detections, 4]) * image_size
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]
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detection_class = [
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np.random.randint(1, 5, [batch_size, max_num_detections])
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]
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detection_scores = [np.random.random([batch_size, max_num_detections])]
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detection_keypoints = [
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np.random.random([batch_size, max_num_detections, num_keypoints, 2])
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* image_size
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]
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predictions = {
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'source_id': source_id,
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'detection_boxes': detection_boxes,
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'detection_classes': detection_class,
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'detection_scores': detection_scores,
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'detection_keypoints': detection_keypoints,
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}
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anns = coco_utils.convert_predictions_to_coco_annotations(predictions)
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for i in range(max_num_detections):
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expected_keypoint_ann = np.concatenate(
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[
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np.expand_dims(detection_keypoints[0][0, i, :, 1], axis=-1),
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np.expand_dims(detection_keypoints[0][0, i, :, 0], axis=-1),
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np.expand_dims(np.ones(num_keypoints), axis=1),
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],
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axis=1,
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).astype(int)
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expected_keypoint_ann = expected_keypoint_ann.flatten().tolist()
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self.assertAllEqual(anns[i]['keypoints'], expected_keypoint_ann)
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if __name__ == '__main__':
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tf.test.main()
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