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#
# 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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#
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#
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# Lint as: python3
"""Tests for dense_prediction_heads.py."""

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

from official.projects.qat.vision.modeling.heads import dense_prediction_heads


def get_attribute_heads(att_head_type):
  if att_head_type == 'regression_head':
    return [
        dict(name='depth', type='regression', size=1, prediction_tower_name='')
    ]
  elif att_head_type == 'shared_prediction_tower_attribute_heads':
    return [
        dict(
            name='attr_1', type='regression', size=1, prediction_tower_name=''),
        dict(
            name='attr_2',
            type='classification',
            size=1,
            prediction_tower_name='tower_1'),
        dict(
            name='attr_3',
            type='regression',
            size=1,
            prediction_tower_name='tower_1')
    ]
  else:
    raise ValueError('Undefined attribute type.')


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

  @parameterized.parameters(
      (False, False, False, None),
      (False, True, False, None),
      (True, False, True, 'regression_head'),
      (True, True, True, 'regression_head'),
      (True, True, True, 'shared_prediction_tower_attribute_heads'),
  )
  def test_forward(self, use_separable_conv, use_sync_bn, has_att_heads,
                   att_head_type):
    if has_att_heads:
      attribute_heads = get_attribute_heads(att_head_type)
    else:
      attribute_heads = None

    retinanet_head = dense_prediction_heads.RetinaNetHeadQuantized(
        min_level=3,
        max_level=4,
        num_classes=3,
        num_anchors_per_location=3,
        num_convs=2,
        num_filters=256,
        attribute_heads=attribute_heads,
        use_separable_conv=use_separable_conv,
        activation='relu',
        use_sync_bn=use_sync_bn,
        norm_momentum=0.99,
        norm_epsilon=0.001,
        kernel_regularizer=None,
        bias_regularizer=None,
    )
    features = {
        '3': np.random.rand(2, 128, 128, 16),
        '4': np.random.rand(2, 64, 64, 16),
    }
    scores, boxes, attributes = retinanet_head(features)
    self.assertAllEqual(scores['3'].numpy().shape, [2, 128, 128, 9])
    self.assertAllEqual(scores['4'].numpy().shape, [2, 64, 64, 9])
    self.assertAllEqual(boxes['3'].numpy().shape, [2, 128, 128, 12])
    self.assertAllEqual(boxes['4'].numpy().shape, [2, 64, 64, 12])
    if has_att_heads:
      for att in attributes.values():
        self.assertAllEqual(att['3'].numpy().shape, [2, 128, 128, 3])
        self.assertAllEqual(att['4'].numpy().shape, [2, 64, 64, 3])

  def test_serialize_deserialize(self):
    retinanet_head = dense_prediction_heads.RetinaNetHeadQuantized(
        min_level=3,
        max_level=7,
        num_classes=3,
        num_anchors_per_location=9,
        num_convs=2,
        num_filters=16,
        attribute_heads=None,
        use_separable_conv=False,
        activation='relu',
        use_sync_bn=False,
        norm_momentum=0.99,
        norm_epsilon=0.001,
        kernel_regularizer=None,
        bias_regularizer=None,
    )
    config = retinanet_head.get_config()
    new_retinanet_head = (
        dense_prediction_heads.RetinaNetHead.from_config(config))
    self.assertAllEqual(
        retinanet_head.get_config(), new_retinanet_head.get_config())
