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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Tests for Centernet Head."""

from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras

from official.projects.centernet.modeling.heads import centernet_head


class CenterNetHeadTest(tf.test.TestCase, parameterized.TestCase):

  def test_decoder_shape(self):
    task_config = {
        'ct_heatmaps': 90,
        'ct_offset': 2,
        'ct_size': 2,
    }
    input_specs = {
        '2_0': tf_keras.layers.InputSpec(shape=(None, 128, 128, 256)).shape,
        '2': tf_keras.layers.InputSpec(shape=(None, 128, 128, 256)).shape,
    }

    input_levels = ['2', '2_0']

    head = centernet_head.CenterNetHead(
        task_outputs=task_config,
        input_specs=input_specs,
        input_levels=input_levels)

    config = head.get_config()
    self.assertEqual(config['heatmap_bias'], -2.19)

    # Output shape tests
    outputs = head([np.zeros((2, 128, 128, 256), dtype=np.float32),
                    np.zeros((2, 128, 128, 256), dtype=np.float32)])
    self.assertLen(outputs, 3)
    self.assertEqual(outputs['ct_heatmaps'][0].shape, (2, 128, 128, 90))
    self.assertEqual(outputs['ct_offset'][0].shape, (2, 128, 128, 2))
    self.assertEqual(outputs['ct_size'][0].shape, (2, 128, 128, 2))

    # Weight initialization tests
    hm_bias_vector = np.asarray(head.layers[2].weights[-1])
    off_bias_vector = np.asarray(head.layers[4].weights[-1])
    size_bias_vector = np.asarray(head.layers[6].weights[-1])

    self.assertArrayNear(hm_bias_vector,
                         np.repeat(-2.19, repeats=90), err=1.00e-6)
    self.assertArrayNear(off_bias_vector,
                         np.repeat(0, repeats=2), err=1.00e-6)
    self.assertArrayNear(size_bias_vector,
                         np.repeat(0, repeats=2), err=1.00e-6)


if __name__ == '__main__':
  tf.test.main()
