official/projects/yolo/modeling/layers/detection_generator_test.py
# Copyright 2024 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.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""Tests for yolo detection generator."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.projects.yolo.modeling.layers import detection_generator
class YoloDecoderTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
('v1', None),
('v2', False),
('v2', True),
('greedy', None),
)
def test_network_creation(self, nms_version, use_class_agnostic_nms):
"""Test creation of ResNet family models."""
tf_keras.backend.set_image_data_format('channels_last')
input_shape = {
'3': [1, 52, 52, 255],
'4': [1, 26, 26, 255],
'5': [1, 13, 13, 255]
}
classes = 80
anchors = {
'3': [[12.0, 19.0], [31.0, 46.0], [96.0, 54.0]],
'4': [[46.0, 114.0], [133.0, 127.0], [79.0, 225.0]],
'5': [[301.0, 150.0], [172.0, 286.0], [348.0, 340.0]]
}
box_type = {key: 'scaled' for key in anchors.keys()}
layer = detection_generator.YoloLayer(
anchors,
classes,
box_type=box_type,
max_boxes=10,
use_class_agnostic_nms=use_class_agnostic_nms,
nms_version=nms_version,
)
inputs = {}
for key in input_shape:
inputs[key] = tf.ones(input_shape[key], dtype=tf.float32)
endpoints = layer(inputs)
boxes = endpoints['bbox']
classes = endpoints['classes']
self.assertAllEqual(boxes.shape.as_list(), [1, 10, 4])
self.assertAllEqual(classes.shape.as_list(), [1, 10])
if __name__ == '__main__':
tf.test.main()