# 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.
# ==============================================================================

"""Base target assigner module.

The job of a TargetAssigner is, for a given set of anchors (bounding boxes) and
groundtruth detections (bounding boxes), to assign classification and regression
targets to each anchor as well as weights to each anchor (specifying, e.g.,
which anchors should not contribute to training loss).

It assigns classification/regression targets by performing the following steps:
1) Computing pairwise similarity between anchors and groundtruth boxes using a
  provided RegionSimilarity Calculator
2) Computing a matching based on the similarity matrix using a provided Matcher
3) Assigning regression targets based on the matching and a provided BoxCoder
4) Assigning classification targets based on the matching and groundtruth labels

Note that TargetAssigners only operate on detections from a single
image at a time, so any logic for applying a TargetAssigner to multiple
images must be handled externally.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from six.moves import range
from six.moves import zip
import tensorflow.compat.v1 as tf
import tensorflow.compat.v2 as tf2

from object_detection.box_coders import faster_rcnn_box_coder
from object_detection.box_coders import mean_stddev_box_coder
from object_detection.core import box_coder
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import densepose_ops
from object_detection.core import keypoint_ops
from object_detection.core import matcher as mat
from object_detection.core import region_similarity_calculator as sim_calc
from object_detection.core import standard_fields as fields
from object_detection.matchers import argmax_matcher
from object_detection.utils import shape_utils
from object_detection.utils import target_assigner_utils as ta_utils
from object_detection.utils import tf_version

if tf_version.is_tf1():
  from object_detection.matchers import bipartite_matcher  # pylint: disable=g-import-not-at-top
elif tf_version.is_tf2():
  from object_detection.matchers import hungarian_matcher

ResizeMethod = tf2.image.ResizeMethod

_DEFAULT_KEYPOINT_OFFSET_STD_DEV = 1.0


class TargetAssigner(object):
  """Target assigner to compute classification and regression targets."""

  def __init__(self,
               similarity_calc,
               matcher,
               box_coder_instance,
               negative_class_weight=1.0):
    """Construct Object Detection Target Assigner.

    Args:
      similarity_calc: a RegionSimilarityCalculator
      matcher: an object_detection.core.Matcher used to match groundtruth to
        anchors.
      box_coder_instance: an object_detection.core.BoxCoder used to encode
        matching groundtruth boxes with respect to anchors.
      negative_class_weight: classification weight to be associated to negative
        anchors (default: 1.0). The weight must be in [0., 1.].

    Raises:
      ValueError: if similarity_calc is not a RegionSimilarityCalculator or
        if matcher is not a Matcher or if box_coder is not a BoxCoder
    """
    if not isinstance(similarity_calc, sim_calc.RegionSimilarityCalculator):
      raise ValueError('similarity_calc must be a RegionSimilarityCalculator')
    if not isinstance(matcher, mat.Matcher):
      raise ValueError('matcher must be a Matcher')
    if not isinstance(box_coder_instance, box_coder.BoxCoder):
      raise ValueError('box_coder must be a BoxCoder')
    self._similarity_calc = similarity_calc
    self._matcher = matcher
    self._box_coder = box_coder_instance
    self._negative_class_weight = negative_class_weight

  @property
  def box_coder(self):
    return self._box_coder

  # TODO(rathodv): move labels, scores, and weights to groundtruth_boxes fields.
  def assign(self,
             anchors,
             groundtruth_boxes,
             groundtruth_labels=None,
             unmatched_class_label=None,
             groundtruth_weights=None,
             class_predictions=None):
    """Assign classification and regression targets to each anchor.

    For a given set of anchors and groundtruth detections, match anchors
    to groundtruth_boxes and assign classification and regression targets to
    each anchor as well as weights based on the resulting match (specifying,
    e.g., which anchors should not contribute to training loss).

    Anchors that are not matched to anything are given a classification target
    of self._unmatched_cls_target which can be specified via the constructor.

    Args:
      anchors: a BoxList representing N anchors
      groundtruth_boxes: a BoxList representing M groundtruth boxes
      groundtruth_labels:  a tensor of shape [M, d_1, ... d_k]
        with labels for each of the ground_truth boxes. The subshape
        [d_1, ... d_k] can be empty (corresponding to scalar inputs).  When set
        to None, groundtruth_labels assumes a binary problem where all
        ground_truth boxes get a positive label (of 1).
      unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
        which is consistent with the classification target for each
        anchor (and can be empty for scalar targets).  This shape must thus be
        compatible with the groundtruth labels that are passed to the "assign"
        function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
        If set to None, unmatched_cls_target is set to be [0] for each anchor.
      groundtruth_weights: a float tensor of shape [M] indicating the weight to
        assign to all anchors match to a particular groundtruth box. The weights
        must be in [0., 1.]. If None, all weights are set to 1. Generally no
        groundtruth boxes with zero weight match to any anchors as matchers are
        aware of groundtruth weights. Additionally, `cls_weights` and
        `reg_weights` are calculated using groundtruth weights as an added
        safety.
      class_predictions: a float tensor of shape [N, num_classes] containing class
        predictions from the model, to be used in certain matchers.

    Returns:
      cls_targets: a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k],
        where the subshape [d_1, ..., d_k] is compatible with groundtruth_labels
        which has shape [num_gt_boxes, d_1, d_2, ... d_k].
      cls_weights: a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k],
        representing weights for each element in cls_targets.
      reg_targets: a float32 tensor with shape [num_anchors, box_code_dimension]
      reg_weights: a float32 tensor with shape [num_anchors]
      match: an int32 tensor of shape [num_anchors] containing result of anchor
        groundtruth matching. Each position in the tensor indicates an anchor
        and holds the following meaning:
        (1) if match[i] >= 0, anchor i is matched with groundtruth match[i].
        (2) if match[i]=-1, anchor i is marked to be background .
        (3) if match[i]=-2, anchor i is ignored since it is not background and
            does not have sufficient overlap to call it a foreground.

    Raises:
      ValueError: if anchors or groundtruth_boxes are not of type
        box_list.BoxList
    """
    if not isinstance(anchors, box_list.BoxList):
      raise ValueError('anchors must be an BoxList')
    if not isinstance(groundtruth_boxes, box_list.BoxList):
      raise ValueError('groundtruth_boxes must be an BoxList')

    if unmatched_class_label is None:
      unmatched_class_label = tf.constant([0], tf.float32)

    if groundtruth_labels is None:
      groundtruth_labels = tf.ones(tf.expand_dims(groundtruth_boxes.num_boxes(),
                                                  0))
      groundtruth_labels = tf.expand_dims(groundtruth_labels, -1)

    unmatched_shape_assert = shape_utils.assert_shape_equal(
        shape_utils.combined_static_and_dynamic_shape(groundtruth_labels)[1:],
        shape_utils.combined_static_and_dynamic_shape(unmatched_class_label))
    labels_and_box_shapes_assert = shape_utils.assert_shape_equal(
        shape_utils.combined_static_and_dynamic_shape(
            groundtruth_labels)[:1],
        shape_utils.combined_static_and_dynamic_shape(
            groundtruth_boxes.get())[:1])

    if groundtruth_weights is None:
      num_gt_boxes = groundtruth_boxes.num_boxes_static()
      if not num_gt_boxes:
        num_gt_boxes = groundtruth_boxes.num_boxes()
      groundtruth_weights = tf.ones([num_gt_boxes], dtype=tf.float32)

    # set scores on the gt boxes
    scores = 1 - groundtruth_labels[:, 0]
    groundtruth_boxes.add_field(fields.BoxListFields.scores, scores)

    with tf.control_dependencies(
        [unmatched_shape_assert, labels_and_box_shapes_assert]):
      
      match_quality_matrix = self._similarity_calc.compare(groundtruth_boxes,
                                                           anchors,
                                                           groundtruth_labels=groundtruth_labels,
                                                           predicted_labels=class_predictions)
      match = self._matcher.match(match_quality_matrix,
                                  valid_rows=tf.greater(groundtruth_weights, 0))

      reg_targets = self._create_regression_targets(anchors,
                                                    groundtruth_boxes,
                                                    match)
      cls_targets = self._create_classification_targets(groundtruth_labels,
                                                        unmatched_class_label,
                                                        match)
      reg_weights = self._create_regression_weights(match, groundtruth_weights)

      cls_weights = self._create_classification_weights(match,
                                                        groundtruth_weights)
      # convert cls_weights from per-anchor to per-class.
      class_label_shape = tf.shape(cls_targets)[1:]
      weights_shape = tf.shape(cls_weights)
      weights_multiple = tf.concat(
          [tf.ones_like(weights_shape), class_label_shape],
          axis=0)
      for _ in range(len(cls_targets.get_shape()[1:])):
        cls_weights = tf.expand_dims(cls_weights, -1)
      cls_weights = tf.tile(cls_weights, weights_multiple)

    num_anchors = anchors.num_boxes_static()
    if num_anchors is not None:
      reg_targets = self._reset_target_shape(reg_targets, num_anchors)
      cls_targets = self._reset_target_shape(cls_targets, num_anchors)
      reg_weights = self._reset_target_shape(reg_weights, num_anchors)
      cls_weights = self._reset_target_shape(cls_weights, num_anchors)

    return (cls_targets, cls_weights, reg_targets, reg_weights,
            match.match_results)

  def _reset_target_shape(self, target, num_anchors):
    """Sets the static shape of the target.

    Args:
      target: the target tensor. Its first dimension will be overwritten.
      num_anchors: the number of anchors, which is used to override the target's
        first dimension.

    Returns:
      A tensor with the shape info filled in.
    """
    target_shape = target.get_shape().as_list()
    target_shape[0] = num_anchors
    target.set_shape(target_shape)
    return target

  def _create_regression_targets(self, anchors, groundtruth_boxes, match):
    """Returns a regression target for each anchor.

    Args:
      anchors: a BoxList representing N anchors
      groundtruth_boxes: a BoxList representing M groundtruth_boxes
      match: a matcher.Match object

    Returns:
      reg_targets: a float32 tensor with shape [N, box_code_dimension]
    """
    matched_gt_boxes = match.gather_based_on_match(
        groundtruth_boxes.get(),
        unmatched_value=tf.zeros(4),
        ignored_value=tf.zeros(4))
    matched_gt_boxlist = box_list.BoxList(matched_gt_boxes)
    if groundtruth_boxes.has_field(fields.BoxListFields.keypoints):
      groundtruth_keypoints = groundtruth_boxes.get_field(
          fields.BoxListFields.keypoints)
      matched_keypoints = match.gather_based_on_match(
          groundtruth_keypoints,
          unmatched_value=tf.zeros(groundtruth_keypoints.get_shape()[1:]),
          ignored_value=tf.zeros(groundtruth_keypoints.get_shape()[1:]))
      matched_gt_boxlist.add_field(fields.BoxListFields.keypoints,
                                   matched_keypoints)
    matched_reg_targets = self._box_coder.encode(matched_gt_boxlist, anchors)
    match_results_shape = shape_utils.combined_static_and_dynamic_shape(
        match.match_results)

    # Zero out the unmatched and ignored regression targets.
    unmatched_ignored_reg_targets = tf.tile(
        self._default_regression_target(), [match_results_shape[0], 1])
    matched_anchors_mask = match.matched_column_indicator()
    reg_targets = tf.where(matched_anchors_mask,
                           matched_reg_targets,
                           unmatched_ignored_reg_targets)
    return reg_targets

  def _default_regression_target(self):
    """Returns the default target for anchors to regress to.

    Default regression targets are set to zero (though in
    this implementation what these targets are set to should
    not matter as the regression weight of any box set to
    regress to the default target is zero).

    Returns:
      default_target: a float32 tensor with shape [1, box_code_dimension]
    """
    return tf.constant([self._box_coder.code_size*[0]], tf.float32)

  def _create_classification_targets(self, groundtruth_labels,
                                     unmatched_class_label, match):
    """Create classification targets for each anchor.

    Assign a classification target of for each anchor to the matching
    groundtruth label that is provided by match.  Anchors that are not matched
    to anything are given the target self._unmatched_cls_target

    Args:
      groundtruth_labels:  a tensor of shape [num_gt_boxes, d_1, ... d_k]
        with labels for each of the ground_truth boxes. The subshape
        [d_1, ... d_k] can be empty (corresponding to scalar labels).
      unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
        which is consistent with the classification target for each
        anchor (and can be empty for scalar targets).  This shape must thus be
        compatible with the groundtruth labels that are passed to the "assign"
        function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
      match: a matcher.Match object that provides a matching between anchors
        and groundtruth boxes.

    Returns:
      a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the
      subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has
      shape [num_gt_boxes, d_1, d_2, ... d_k].
    """
    return match.gather_based_on_match(
        groundtruth_labels,
        unmatched_value=unmatched_class_label,
        ignored_value=unmatched_class_label)

  def _create_regression_weights(self, match, groundtruth_weights):
    """Set regression weight for each anchor.

    Only positive anchors are set to contribute to the regression loss, so this
    method returns a weight of 1 for every positive anchor and 0 for every
    negative anchor.

    Args:
      match: a matcher.Match object that provides a matching between anchors
        and groundtruth boxes.
      groundtruth_weights: a float tensor of shape [M] indicating the weight to
        assign to all anchors match to a particular groundtruth box.

    Returns:
      a float32 tensor with shape [num_anchors] representing regression weights.
    """
    return match.gather_based_on_match(
        groundtruth_weights, ignored_value=0., unmatched_value=0.)

  def _create_classification_weights(self,
                                     match,
                                     groundtruth_weights):
    """Create classification weights for each anchor.

    Positive (matched) anchors are associated with a weight of
    positive_class_weight and negative (unmatched) anchors are associated with
    a weight of negative_class_weight. When anchors are ignored, weights are set
    to zero. By default, both positive/negative weights are set to 1.0,
    but they can be adjusted to handle class imbalance (which is almost always
    the case in object detection).

    Args:
      match: a matcher.Match object that provides a matching between anchors
        and groundtruth boxes.
      groundtruth_weights: a float tensor of shape [M] indicating the weight to
        assign to all anchors match to a particular groundtruth box.

    Returns:
      a float32 tensor with shape [num_anchors] representing classification
      weights.
    """
    return match.gather_based_on_match(
        groundtruth_weights,
        ignored_value=0.,
        unmatched_value=self._negative_class_weight)

  def get_box_coder(self):
    """Get BoxCoder of this TargetAssigner.

    Returns:
      BoxCoder object.
    """
    return self._box_coder


# TODO(rathodv): This method pulls in all the implementation dependencies into
# core. Therefore its best to have this factory method outside of core.
def create_target_assigner(reference, stage=None,
                           negative_class_weight=1.0, use_matmul_gather=False):
  """Factory function for creating standard target assigners.

  Args:
    reference: string referencing the type of TargetAssigner.
    stage: string denoting stage: {proposal, detection}.
    negative_class_weight: classification weight to be associated to negative
      anchors (default: 1.0)
    use_matmul_gather: whether to use matrix multiplication based gather which
      are better suited for TPUs.

  Returns:
    TargetAssigner: desired target assigner.

  Raises:
    ValueError: if combination reference+stage is invalid.
  """
  if reference == 'Multibox' and stage == 'proposal':
    if tf_version.is_tf2():
      raise ValueError('GreedyBipartiteMatcher is not supported in TF 2.X.')
    similarity_calc = sim_calc.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder_instance = mean_stddev_box_coder.MeanStddevBoxCoder()

  elif reference == 'FasterRCNN' and stage == 'proposal':
    similarity_calc = sim_calc.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.7,
                                           unmatched_threshold=0.3,
                                           force_match_for_each_row=True,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=[10.0, 10.0, 5.0, 5.0])

  elif reference == 'FasterRCNN' and stage == 'detection':
    similarity_calc = sim_calc.IouSimilarity()
    # Uses all proposals with IOU < 0.5 as candidate negatives.
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           negatives_lower_than_unmatched=True,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=[10.0, 10.0, 5.0, 5.0])

  elif reference == 'FastRCNN':
    similarity_calc = sim_calc.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.1,
                                           force_match_for_each_row=False,
                                           negatives_lower_than_unmatched=False,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder()

  elif reference == 'DETR':
    similarity_calc = sim_calc.DETRSimilarity()
    matcher = hungarian_matcher.HungarianBipartiteMatcher()
    box_coder_instance = detr_box_coder.DETRBoxCoder()

  else:
    raise ValueError('No valid combination of reference and stage.')

  return TargetAssigner(similarity_calc, matcher, box_coder_instance,
                        negative_class_weight=negative_class_weight)


def batch_assign(target_assigner,
                 anchors_batch,
                 gt_box_batch,
                 gt_class_targets_batch,
                 unmatched_class_label=None,
                 gt_weights_batch=None,
                 class_predictions=None):
  """Batched assignment of classification and regression targets.

  Args:
    target_assigner: a target assigner.
    anchors_batch: BoxList representing N box anchors or list of BoxList objects
      with length batch_size representing anchor sets.
    gt_box_batch: a list of BoxList objects with length batch_size
      representing groundtruth boxes for each image in the batch
    gt_class_targets_batch: a list of tensors with length batch_size, where
      each tensor has shape [num_gt_boxes_i, classification_target_size] and
      num_gt_boxes_i is the number of boxes in the ith boxlist of
      gt_box_batch.
    unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
      which is consistent with the classification target for each
      anchor (and can be empty for scalar targets).  This shape must thus be
      compatible with the groundtruth labels that are passed to the "assign"
      function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
    gt_weights_batch: A list of 1-D tf.float32 tensors of shape
      [num_boxes] containing weights for groundtruth boxes.
    class_predictions: A 

  Returns:
    batch_cls_targets: a tensor with shape [batch_size, num_anchors,
      num_classes],
    batch_cls_weights: a tensor with shape [batch_size, num_anchors,
      num_classes],
    batch_reg_targets: a tensor with shape [batch_size, num_anchors,
      box_code_dimension]
    batch_reg_weights: a tensor with shape [batch_size, num_anchors],
    match: an int32 tensor of shape [batch_size, num_anchors] containing result
      of anchor groundtruth matching. Each position in the tensor indicates an
      anchor and holds the following meaning:
      (1) if match[x, i] >= 0, anchor i is matched with groundtruth match[x, i].
      (2) if match[x, i]=-1, anchor i is marked to be background .
      (3) if match[x, i]=-2, anchor i is ignored since it is not background and
          does not have sufficient overlap to call it a foreground.

  Raises:
    ValueError: if input list lengths are inconsistent, i.e.,
      batch_size == len(gt_box_batch) == len(gt_class_targets_batch)
        and batch_size == len(anchors_batch) unless anchors_batch is a single
        BoxList.
  """
  if not isinstance(anchors_batch, list):
    anchors_batch = len(gt_box_batch) * [anchors_batch]
  if not all(
      isinstance(anchors, box_list.BoxList) for anchors in anchors_batch):
    raise ValueError('anchors_batch must be a BoxList or list of BoxLists.')
  if not (len(anchors_batch)
          == len(gt_box_batch)
          == len(gt_class_targets_batch)):
    raise ValueError('batch size incompatible with lengths of anchors_batch, '
                     'gt_box_batch and gt_class_targets_batch.')
  cls_targets_list = []
  cls_weights_list = []
  reg_targets_list = []
  reg_weights_list = []
  match_list = []
  if gt_weights_batch is None:
    gt_weights_batch = [None] * len(gt_class_targets_batch)
  if class_predictions:
    class_predictions = tf.unstack(class_predictions)
  else:
    class_predictions = [None] * len(gt_class_targets_batch)
  for anchors, gt_boxes, gt_class_targets, gt_weights, class_preds in zip(
      anchors_batch, gt_box_batch, gt_class_targets_batch, gt_weights_batch,
      class_predictions):
    (cls_targets, cls_weights,
     reg_targets, reg_weights, match) = target_assigner.assign(
         anchors, gt_boxes, gt_class_targets, unmatched_class_label, gt_weights,
         class_preds)
    cls_targets_list.append(cls_targets)
    cls_weights_list.append(cls_weights)
    reg_targets_list.append(reg_targets)
    reg_weights_list.append(reg_weights)
    match_list.append(match)
  batch_cls_targets = tf.stack(cls_targets_list)
  batch_cls_weights = tf.stack(cls_weights_list)
  batch_reg_targets = tf.stack(reg_targets_list)
  batch_reg_weights = tf.stack(reg_weights_list)
  batch_match = tf.stack(match_list)
  return (batch_cls_targets, batch_cls_weights, batch_reg_targets,
          batch_reg_weights, batch_match)


# Assign an alias to avoid large refactor of existing users.
batch_assign_targets = batch_assign


def batch_get_targets(batch_match, groundtruth_tensor_list,
                      groundtruth_weights_list, unmatched_value,
                      unmatched_weight):
  """Returns targets based on anchor-groundtruth box matching results.

  Args:
    batch_match: An int32 tensor of shape [batch, num_anchors] containing the
      result of target assignment returned by TargetAssigner.assign(..).
    groundtruth_tensor_list: A list of groundtruth tensors of shape
      [num_groundtruth, d_1, d_2, ..., d_k]. The tensors can be of any type.
    groundtruth_weights_list: A list of weights, one per groundtruth tensor, of
      shape [num_groundtruth].
    unmatched_value: A tensor of shape [d_1, d_2, ..., d_k] of the same type as
      groundtruth tensor containing target value for anchors that remain
      unmatched.
    unmatched_weight: Scalar weight to assign to anchors that remain unmatched.

  Returns:
    targets: A tensor of shape [batch, num_anchors, d_1, d_2, ..., d_k]
      containing targets for anchors.
    weights: A float tensor of shape [batch, num_anchors] containing the weights
      to assign to each target.
  """
  match_list = tf.unstack(batch_match)
  targets_list = []
  weights_list = []
  for match_tensor, groundtruth_tensor, groundtruth_weight in zip(
      match_list, groundtruth_tensor_list, groundtruth_weights_list):
    match_object = mat.Match(match_tensor)
    targets = match_object.gather_based_on_match(
        groundtruth_tensor,
        unmatched_value=unmatched_value,
        ignored_value=unmatched_value)
    targets_list.append(targets)
    weights = match_object.gather_based_on_match(
        groundtruth_weight,
        unmatched_value=unmatched_weight,
        ignored_value=tf.zeros_like(unmatched_weight))
    weights_list.append(weights)
  return tf.stack(targets_list), tf.stack(weights_list)


def batch_assign_confidences(target_assigner,
                             anchors_batch,
                             gt_box_batch,
                             gt_class_confidences_batch,
                             gt_weights_batch=None,
                             unmatched_class_label=None,
                             include_background_class=True,
                             implicit_class_weight=1.0):
  """Batched assignment of classification and regression targets.

  This differences between batch_assign_confidences and batch_assign_targets:
   - 'batch_assign_targets' supports scalar (agnostic), vector (multiclass) and
     tensor (high-dimensional) targets. 'batch_assign_confidences' only support
     scalar (agnostic) and vector (multiclass) targets.
   - 'batch_assign_targets' assumes the input class tensor using the binary
     one/K-hot encoding. 'batch_assign_confidences' takes the class confidence
     scores as the input, where 1 means positive classes, 0 means implicit
     negative classes, and -1 means explicit negative classes.
   - 'batch_assign_confidences' assigns the targets in the similar way as
     'batch_assign_targets' except that it gives different weights for implicit
     and explicit classes. This allows user to control the negative gradients
     pushed differently for implicit and explicit examples during the training.

  Args:
    target_assigner: a target assigner.
    anchors_batch: BoxList representing N box anchors or list of BoxList objects
      with length batch_size representing anchor sets.
    gt_box_batch: a list of BoxList objects with length batch_size
      representing groundtruth boxes for each image in the batch
    gt_class_confidences_batch: a list of tensors with length batch_size, where
      each tensor has shape [num_gt_boxes_i, classification_target_size] and
      num_gt_boxes_i is the number of boxes in the ith boxlist of
      gt_box_batch. Note that in this tensor, 1 means explicit positive class,
      -1 means explicit negative class, and 0 means implicit negative class.
    gt_weights_batch: A list of 1-D tf.float32 tensors of shape
      [num_gt_boxes_i] containing weights for groundtruth boxes.
    unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
      which is consistent with the classification target for each
      anchor (and can be empty for scalar targets).  This shape must thus be
      compatible with the groundtruth labels that are passed to the "assign"
      function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
    include_background_class: whether or not gt_class_confidences_batch includes
      the background class.
    implicit_class_weight: the weight assigned to implicit examples.

  Returns:
    batch_cls_targets: a tensor with shape [batch_size, num_anchors,
      num_classes],
    batch_cls_weights: a tensor with shape [batch_size, num_anchors,
      num_classes],
    batch_reg_targets: a tensor with shape [batch_size, num_anchors,
      box_code_dimension]
    batch_reg_weights: a tensor with shape [batch_size, num_anchors],
    match: an int32 tensor of shape [batch_size, num_anchors] containing result
      of anchor groundtruth matching. Each position in the tensor indicates an
      anchor and holds the following meaning:
      (1) if match[x, i] >= 0, anchor i is matched with groundtruth match[x, i].
      (2) if match[x, i]=-1, anchor i is marked to be background .
      (3) if match[x, i]=-2, anchor i is ignored since it is not background and
          does not have sufficient overlap to call it a foreground.

  Raises:
    ValueError: if input list lengths are inconsistent, i.e.,
      batch_size == len(gt_box_batch) == len(gt_class_targets_batch)
      and batch_size == len(anchors_batch) unless anchors_batch is a single
      BoxList, or if any element in gt_class_confidences_batch has rank > 2.
  """
  if not isinstance(anchors_batch, list):
    anchors_batch = len(gt_box_batch) * [anchors_batch]
  if not all(
      isinstance(anchors, box_list.BoxList) for anchors in anchors_batch):
    raise ValueError('anchors_batch must be a BoxList or list of BoxLists.')
  if not (len(anchors_batch)
          == len(gt_box_batch)
          == len(gt_class_confidences_batch)):
    raise ValueError('batch size incompatible with lengths of anchors_batch, '
                     'gt_box_batch and gt_class_confidences_batch.')

  cls_targets_list = []
  cls_weights_list = []
  reg_targets_list = []
  reg_weights_list = []
  match_list = []
  if gt_weights_batch is None:
    gt_weights_batch = [None] * len(gt_class_confidences_batch)
  for anchors, gt_boxes, gt_class_confidences, gt_weights in zip(
      anchors_batch, gt_box_batch, gt_class_confidences_batch,
      gt_weights_batch):

    if (gt_class_confidences is not None and
        len(gt_class_confidences.get_shape().as_list()) > 2):
      raise ValueError('The shape of the class target is not supported. ',
                       gt_class_confidences.get_shape())

    cls_targets, _, reg_targets, _, match = target_assigner.assign(
        anchors, gt_boxes, gt_class_confidences, unmatched_class_label,
        groundtruth_weights=gt_weights)

    if include_background_class:
      cls_targets_without_background = tf.slice(
          cls_targets, [0, 1], [-1, -1])
    else:
      cls_targets_without_background = cls_targets

    positive_mask = tf.greater(cls_targets_without_background, 0.0)
    negative_mask = tf.less(cls_targets_without_background, 0.0)
    explicit_example_mask = tf.logical_or(positive_mask, negative_mask)
    positive_anchors = tf.reduce_any(positive_mask, axis=-1)

    regression_weights = tf.cast(positive_anchors, dtype=tf.float32)
    regression_targets = (
        reg_targets * tf.expand_dims(regression_weights, axis=-1))
    regression_weights_expanded = tf.expand_dims(regression_weights, axis=-1)

    cls_targets_without_background = (
        cls_targets_without_background *
        (1 - tf.cast(negative_mask, dtype=tf.float32)))
    cls_weights_without_background = ((1 - implicit_class_weight) * tf.cast(
        explicit_example_mask, dtype=tf.float32) + implicit_class_weight)

    if include_background_class:
      cls_weights_background = (
          (1 - implicit_class_weight) * regression_weights_expanded
          + implicit_class_weight)
      classification_weights = tf.concat(
          [cls_weights_background, cls_weights_without_background], axis=-1)
      cls_targets_background = 1 - regression_weights_expanded
      classification_targets = tf.concat(
          [cls_targets_background, cls_targets_without_background], axis=-1)
    else:
      classification_targets = cls_targets_without_background
      classification_weights = cls_weights_without_background

    cls_targets_list.append(classification_targets)
    cls_weights_list.append(classification_weights)
    reg_targets_list.append(regression_targets)
    reg_weights_list.append(regression_weights)
    match_list.append(match)
  batch_cls_targets = tf.stack(cls_targets_list)
  batch_cls_weights = tf.stack(cls_weights_list)
  batch_reg_targets = tf.stack(reg_targets_list)
  batch_reg_weights = tf.stack(reg_weights_list)
  batch_match = tf.stack(match_list)
  return (batch_cls_targets, batch_cls_weights, batch_reg_targets,
          batch_reg_weights, batch_match)


def _smallest_positive_root(a, b, c):
  """Returns the smallest positive root of a quadratic equation."""

  discriminant = tf.sqrt(b ** 2 - 4 * a * c)

  # TODO(vighneshb) We are currently using the slightly incorrect
  # CenterNet implementation. The commented lines implement the fixed version
  # in https://github.com/princeton-vl/CornerNet. Change the implementation
  # after verifying it has no negative impact.
  # root1 = (-b - discriminant) / (2 * a)
  # root2 = (-b + discriminant) / (2 * a)

  # return tf.where(tf.less(root1, 0), root2, root1)

  return (-b + discriminant) / (2.0)


def max_distance_for_overlap(height, width, min_iou):
  """Computes how far apart bbox corners can lie while maintaining the iou.

  Given a bounding box size, this function returns a lower bound on how far
  apart the corners of another box can lie while still maintaining the given
  IoU. The implementation is based on the `gaussian_radius` function in the
  Objects as Points github repo: https://github.com/xingyizhou/CenterNet

  Args:
    height: A 1-D float Tensor representing height of the ground truth boxes.
    width: A 1-D float Tensor representing width of the ground truth boxes.
    min_iou: A float representing the minimum IoU desired.

  Returns:
   distance: A 1-D Tensor of distances, of the same length as the input
     height and width tensors.
  """

  # Given that the detected box is displaced at a distance `d`, the exact
  # IoU value will depend on the angle at which each corner is displaced.
  # We simplify our computation by assuming that each corner is displaced by
  # a distance `d` in both x and y direction. This gives us a lower IoU than
  # what is actually realizable and ensures that any box with corners less
  # than `d` distance apart will always have an IoU greater than or equal
  # to `min_iou`

  # The following 3 cases can be worked on geometrically and come down to
  # solving a quadratic inequality. In each case, to ensure `min_iou` we use
  # the smallest positive root of the equation.

  # Case where detected box is offset from ground truth and no box completely
  # contains the other.

  distance_detection_offset = _smallest_positive_root(
      a=1, b=-(height + width),
      c=width * height * ((1 - min_iou) / (1 + min_iou))
  )

  # Case where detection is smaller than ground truth and completely contained
  # in it.
  distance_detection_in_gt = _smallest_positive_root(
      a=4, b=-2 * (height + width),
      c=(1 - min_iou) * width * height
  )

  # Case where ground truth is smaller than detection and completely contained
  # in it.
  distance_gt_in_detection = _smallest_positive_root(
      a=4 * min_iou, b=(2 * min_iou) * (width + height),
      c=(min_iou - 1) * width * height
  )

  return tf.reduce_min([distance_detection_offset,
                        distance_gt_in_detection,
                        distance_detection_in_gt], axis=0)


def get_batch_predictions_from_indices(batch_predictions, indices):
  """Gets the values of predictions in a batch at the given indices.

  The indices are expected to come from the offset targets generation functions
  in this library. The returned value is intended to be used inside a loss
  function.

  Args:
    batch_predictions: A tensor of shape [batch_size, height, width, channels]
      or [batch_size, height, width, class, channels] for class-specific
      features (e.g. keypoint joint offsets).
    indices: A tensor of shape [num_instances, 3] for single class features or
      [num_instances, 4] for multiple classes features.

  Returns:
    values: A tensor of shape [num_instances, channels] holding the predicted
      values at the given indices.
  """
  return tf.gather_nd(batch_predictions, indices)


def _compute_std_dev_from_box_size(boxes_height, boxes_width, min_overlap):
  """Computes the standard deviation of the Gaussian kernel from box size.

  Args:
    boxes_height: A 1D tensor with shape [num_instances] representing the height
      of each box.
    boxes_width: A 1D tensor with shape [num_instances] representing the width
      of each box.
    min_overlap: The minimum IOU overlap that boxes need to have to not be
      penalized.

  Returns:
    A 1D tensor with shape [num_instances] representing the computed Gaussian
    sigma for each of the box.
  """
  # We are dividing by 3 so that points closer than the computed
  # distance have a >99% CDF.
  sigma = max_distance_for_overlap(boxes_height, boxes_width, min_overlap)
  sigma = (2 * tf.math.maximum(tf.math.floor(sigma), 0.0) + 1) / 6.0
  return sigma


class CenterNetCenterHeatmapTargetAssigner(object):
  """Wrapper to compute the object center heatmap."""

  def __init__(self, stride, min_overlap=0.7):
    """Initializes the target assigner.

    Args:
      stride: int, the stride of the network in output pixels.
      min_overlap: The minimum IOU overlap that boxes need to have to not be
        penalized.
    """

    self._stride = stride
    self._min_overlap = min_overlap

  def assign_center_targets_from_boxes(self,
                                       height,
                                       width,
                                       gt_boxes_list,
                                       gt_classes_list,
                                       gt_weights_list=None):
    """Computes the object center heatmap target.

    Args:
      height: int, height of input to the model. This is used to
        determine the height of the output.
      width: int, width of the input to the model. This is used to
        determine the width of the output.
      gt_boxes_list: A list of float tensors with shape [num_boxes, 4]
        representing the groundtruth detection bounding boxes for each sample in
        the batch. The box coordinates are expected in normalized coordinates.
      gt_classes_list: A list of float tensors with shape [num_boxes,
        num_classes] representing the one-hot encoded class labels for each box
        in the gt_boxes_list.
      gt_weights_list: A list of float tensors with shape [num_boxes]
        representing the weight of each groundtruth detection box.

    Returns:
      heatmap: A Tensor of size [batch_size, output_height, output_width,
        num_classes] representing the per class center heatmap. output_height
        and output_width are computed by dividing the input height and width by
        the stride specified during initialization.
    """

    out_height = tf.cast(height // self._stride, tf.float32)
    out_width = tf.cast(width // self._stride, tf.float32)
    # Compute the yx-grid to be used to generate the heatmap. Each returned
    # tensor has shape of [out_height, out_width]
    (y_grid, x_grid) = ta_utils.image_shape_to_grids(out_height, out_width)

    heatmaps = []
    if gt_weights_list is None:
      gt_weights_list = [None] * len(gt_boxes_list)
    # TODO(vighneshb) Replace the for loop with a batch version.
    for boxes, class_targets, weights in zip(gt_boxes_list, gt_classes_list,
                                             gt_weights_list):
      boxes = box_list.BoxList(boxes)
      # Convert the box coordinates to absolute output image dimension space.
      boxes = box_list_ops.to_absolute_coordinates(boxes,
                                                   height // self._stride,
                                                   width // self._stride)
      # Get the box center coordinates. Each returned tensors have the shape of
      # [num_instances]
      (y_center, x_center, boxes_height,
       boxes_width) = boxes.get_center_coordinates_and_sizes()

      # Compute the sigma from box size. The tensor shape: [num_instances].
      sigma = _compute_std_dev_from_box_size(boxes_height, boxes_width,
                                             self._min_overlap)
      # Apply the Gaussian kernel to the center coordinates. Returned heatmap
      # has shape of [out_height, out_width, num_classes]
      heatmap = ta_utils.coordinates_to_heatmap(
          y_grid=y_grid,
          x_grid=x_grid,
          y_coordinates=y_center,
          x_coordinates=x_center,
          sigma=sigma,
          channel_onehot=class_targets,
          channel_weights=weights)
      heatmaps.append(heatmap)

    # Return the stacked heatmaps over the batch.
    return tf.stack(heatmaps, axis=0)


class CenterNetBoxTargetAssigner(object):
  """Wrapper to compute target tensors for the object detection task.

  This class has methods that take as input a batch of ground truth tensors
  (in the form of a list) and return the targets required to train the object
  detection task.
  """

  def __init__(self, stride):
    """Initializes the target assigner.

    Args:
      stride: int, the stride of the network in output pixels.
    """

    self._stride = stride

  def assign_size_and_offset_targets(self,
                                     height,
                                     width,
                                     gt_boxes_list,
                                     gt_weights_list=None):
    """Returns the box height/width and center offset targets and their indices.

    The returned values are expected to be used with predicted tensors
    of size (batch_size, height//self._stride, width//self._stride, 2). The
    predicted values at the relevant indices can be retrieved with the
    get_batch_predictions_from_indices function.

    Args:
      height: int, height of input to the model. This is used to determine the
        height of the output.
      width: int, width of the input to the model. This is used to determine the
        width of the output.
      gt_boxes_list: A list of float tensors with shape [num_boxes, 4]
        representing the groundtruth detection bounding boxes for each sample in
        the batch. The coordinates are expected in normalized coordinates.
      gt_weights_list: A list of tensors with shape [num_boxes] corresponding to
        the weight of each groundtruth detection box.

    Returns:
      batch_indices: an integer tensor of shape [num_boxes, 3] holding the
        indices inside the predicted tensor which should be penalized. The
        first column indicates the index along the batch dimension and the
        second and third columns indicate the index along the y and x
        dimensions respectively.
      batch_box_height_width: a float tensor of shape [num_boxes, 2] holding
        expected height and width of each box in the output space.
      batch_offsets: a float tensor of shape [num_boxes, 2] holding the
        expected y and x offset of each box in the output space.
      batch_weights: a float tensor of shape [num_boxes] indicating the
        weight of each prediction.
    """

    if gt_weights_list is None:
      gt_weights_list = [None] * len(gt_boxes_list)

    batch_indices = []
    batch_box_height_width = []
    batch_weights = []
    batch_offsets = []

    for i, (boxes, weights) in enumerate(zip(gt_boxes_list, gt_weights_list)):
      boxes = box_list.BoxList(boxes)
      boxes = box_list_ops.to_absolute_coordinates(boxes,
                                                   height // self._stride,
                                                   width // self._stride)
      # Get the box center coordinates. Each returned tensors have the shape of
      # [num_boxes]
      (y_center, x_center, boxes_height,
       boxes_width) = boxes.get_center_coordinates_and_sizes()
      num_boxes = tf.shape(x_center)

      # Compute the offsets and indices of the box centers. Shape:
      #   offsets: [num_boxes, 2]
      #   indices: [num_boxes, 2]
      (offsets, indices) = ta_utils.compute_floor_offsets_with_indices(
          y_source=y_center, x_source=x_center)

      # Assign ones if weights are not provided.
      if weights is None:
        weights = tf.ones(num_boxes, dtype=tf.float32)

      # Shape of [num_boxes, 1] integer tensor filled with current batch index.
      batch_index = i * tf.ones_like(indices[:, 0:1], dtype=tf.int32)
      batch_indices.append(tf.concat([batch_index, indices], axis=1))
      batch_box_height_width.append(
          tf.stack([boxes_height, boxes_width], axis=1))
      batch_weights.append(weights)
      batch_offsets.append(offsets)

    batch_indices = tf.concat(batch_indices, axis=0)
    batch_box_height_width = tf.concat(batch_box_height_width, axis=0)
    batch_weights = tf.concat(batch_weights, axis=0)
    batch_offsets = tf.concat(batch_offsets, axis=0)
    return (batch_indices, batch_box_height_width, batch_offsets, batch_weights)


# TODO(yuhuic): Update this class to handle the instance/keypoint weights.
# Currently those weights are used as "mask" to indicate whether an
# instance/keypoint should be considered or not (expecting only either 0 or 1
# value). In reality, the weights can be any value and this class should handle
# those values properly.
class CenterNetKeypointTargetAssigner(object):
  """Wrapper to compute target tensors for the CenterNet keypoint estimation.

  This class has methods that take as input a batch of groundtruth tensors
  (in the form of a list) and returns the targets required to train the
  CenterNet model for keypoint estimation. Specifically, the class methods
  expect the groundtruth in the following formats (consistent with the
  standard Object Detection API). Note that usually the groundtruth tensors are
  packed with a list which represents the batch dimension:

  gt_classes_list: [Required] a list of 2D tf.float32 one-hot
    (or k-hot) tensors of shape [num_instances, num_classes] containing the
    class targets with the 0th index assumed to map to the first non-background
    class.
  gt_keypoints_list: [Required] a list of 3D tf.float32 tensors of
    shape [num_instances, num_total_keypoints, 2] containing keypoint
    coordinates. Note that the "num_total_keypoints" should be the sum of the
    num_keypoints over all possible keypoint types, e.g. human pose, face.
    For example, if a dataset contains both 17 human pose keypoints and 5 face
    keypoints, then num_total_keypoints = 17 + 5 = 22.
    If an intance contains only a subet of keypoints (e.g. human pose keypoints
    but not face keypoints), the face keypoints will be filled with zeros.
    Also note that keypoints are assumed to be provided in normalized
    coordinates and missing keypoints should be encoded as NaN.
  gt_keypoints_weights_list: [Optional] a list 3D tf.float32 tensors of shape
    [num_instances, num_total_keypoints] representing the weights of each
    keypoints. If not provided, then all not NaN keypoints will be equally
    weighted.
  gt_boxes_list: [Optional] a list of 2D tf.float32 tensors of shape
    [num_instances, 4] containing coordinates of the groundtruth boxes.
    Groundtruth boxes are provided in [y_min, x_min, y_max, x_max] format and
    assumed to be normalized and clipped relative to the image window with
    y_min <= y_max and x_min <= x_max.
    Note that the boxes are only used to compute the center targets but are not
    considered as required output of the keypoint task. If the boxes were not
    provided, the center targets will be inferred from the keypoints
    [not implemented yet].
  gt_weights_list: [Optional] A list of 1D tf.float32 tensors of shape
    [num_instances] containing weights for groundtruth boxes. Only useful when
    gt_boxes_list is also provided.
  """

  def __init__(self,
               stride,
               class_id,
               keypoint_indices,
               keypoint_std_dev=None,
               per_keypoint_offset=False,
               peak_radius=0):
    """Initializes a CenterNet keypoints target assigner.

    Args:
      stride: int, the stride of the network in output pixels.
      class_id: int, the ID of the class (0-indexed) that contains the target
        keypoints to consider in this task. For example, if the task is human
        pose estimation, the class id should correspond to the "human" class.
      keypoint_indices: A list of integers representing the indices of the
        keypoints to be considered in this task. This is used to retrieve the
        subset of the keypoints from gt_keypoints that should be considered in
        this task.
      keypoint_std_dev: A list of floats represent the standard deviation of the
        Gaussian kernel used to generate the keypoint heatmap (in the unit of
        output pixels). It is to provide the flexibility of using different
        sizes of Gaussian kernel for each keypoint type. If not provided, then
        all standard deviation will be the same as the default value (10.0 in
        the output pixel space). If provided, the length of keypoint_std_dev
        needs to be the same as the length of keypoint_indices, indicating the
        standard deviation of each keypoint type.
      per_keypoint_offset: boolean, indicating whether to assign offset for
        each keypoint channel. If set False, the output offset target will have
        the shape [batch_size, out_height, out_width, 2]. If set True, the
        output offset target will have the shape [batch_size, out_height,
        out_width, 2 * num_keypoints].
      peak_radius: int, the radius (in the unit of output pixel) around heatmap
        peak to assign the offset targets.
    """

    self._stride = stride
    self._class_id = class_id
    self._keypoint_indices = keypoint_indices
    self._per_keypoint_offset = per_keypoint_offset
    self._peak_radius = peak_radius
    if keypoint_std_dev is None:
      self._keypoint_std_dev = ([_DEFAULT_KEYPOINT_OFFSET_STD_DEV] *
                                len(keypoint_indices))
    else:
      assert len(keypoint_indices) == len(keypoint_std_dev)
      self._keypoint_std_dev = keypoint_std_dev

  def _preprocess_keypoints_and_weights(self, out_height, out_width, keypoints,
                                        class_onehot, class_weights,
                                        keypoint_weights):
    """Preprocesses the keypoints and the corresponding keypoint weights.

    This function performs several common steps to preprocess the keypoints and
    keypoint weights features, including:
      1) Select the subset of keypoints based on the keypoint indices, fill the
         keypoint NaN values with zeros and convert to absoluate coordinates.
      2) Generate the weights of the keypoint using the following information:
         a. The class of the instance.
         b. The NaN value of the keypoint coordinates.
         c. The provided keypoint weights.

    Args:
      out_height: An integer or an interger tensor indicating the output height
        of the model.
      out_width: An integer or an interger tensor indicating the output width of
        the model.
      keypoints: A float tensor of shape [num_instances, num_total_keypoints, 2]
        representing the original keypoint grountruth coordinates.
      class_onehot: A float tensor of shape [num_instances, num_classes]
        containing the class targets with the 0th index assumed to map to the
        first non-background class.
      class_weights: A float tensor of shape [num_instances] containing weights
        for groundtruth instances.
      keypoint_weights: A float tensor of shape
        [num_instances, num_total_keypoints] representing the weights of each
        keypoints.

    Returns:
      A tuple of two tensors:
        keypoint_absolute: A float tensor of shape
          [num_instances, num_keypoints, 2] which is the selected and updated
          keypoint coordinates.
        keypoint_weights: A float tensor of shape [num_instances, num_keypoints]
          representing the updated weight of each keypoint.
    """
    # Select the targets keypoints by their type ids and generate the mask
    # of valid elements.
    valid_mask, keypoints = ta_utils.get_valid_keypoint_mask_for_class(
        keypoint_coordinates=keypoints,
        class_id=self._class_id,
        class_onehot=class_onehot,
        class_weights=class_weights,
        keypoint_indices=self._keypoint_indices)
    # Keypoint coordinates in absolute coordinate system.
    # The shape of the tensors: [num_instances, num_keypoints, 2].
    keypoints_absolute = keypoint_ops.to_absolute_coordinates(
        keypoints, out_height, out_width)
    # Assign default weights for the keypoints.
    if keypoint_weights is None:
      keypoint_weights = tf.ones_like(keypoints[:, :, 0])
    else:
      keypoint_weights = tf.gather(
          keypoint_weights, indices=self._keypoint_indices, axis=1)
    keypoint_weights = keypoint_weights * valid_mask
    return keypoints_absolute, keypoint_weights

  def assign_keypoint_heatmap_targets(self,
                                      height,
                                      width,
                                      gt_keypoints_list,
                                      gt_classes_list,
                                      gt_keypoints_weights_list=None,
                                      gt_weights_list=None,
                                      gt_boxes_list=None):
    """Returns the keypoint heatmap targets for the CenterNet model.

    Args:
      height: int, height of input to the CenterNet model. This is used to
        determine the height of the output.
      width: int, width of the input to the CenterNet model. This is used to
        determine the width of the output.
      gt_keypoints_list: A list of float tensors with shape [num_instances,
        num_total_keypoints, 2]. See class-level description for more detail.
      gt_classes_list: A list of float tensors with shape [num_instances,
        num_classes]. See class-level description for more detail.
      gt_keypoints_weights_list: A list of tensors with shape [num_instances,
        num_total_keypoints] corresponding to the weight of each keypoint.
      gt_weights_list: A list of float tensors with shape [num_instances]. See
        class-level description for more detail.
      gt_boxes_list: A list of float tensors with shape [num_instances, 4]. See
        class-level description for more detail. If provided, the keypoint
        standard deviations will be scaled based on the box sizes.

    Returns:
      heatmap: A float tensor of shape [batch_size, output_height, output_width,
        num_keypoints] representing the per keypoint type center heatmap.
        output_height and output_width are computed by dividing the input height
        and width by the stride specified during initialization. Note that the
        "num_keypoints" is defined by the length of keypoint_indices, which is
        not necessarily equal to "num_total_keypoints".
      num_instances_batch: A 2D int tensor of shape
        [batch_size, num_keypoints] representing number of instances for each
        keypoint type.
      valid_mask: A float tensor with shape [batch_size, output_height,
        output_width] where all values within the regions of the blackout boxes
        are 0.0 and 1.0 else where.
    """
    out_width = tf.cast(width // self._stride, tf.float32)
    out_height = tf.cast(height // self._stride, tf.float32)
    # Compute the yx-grid to be used to generate the heatmap. Each returned
    # tensor has shape of [out_height, out_width]
    y_grid, x_grid = ta_utils.image_shape_to_grids(out_height, out_width)

    if gt_keypoints_weights_list is None:
      gt_keypoints_weights_list = [None] * len(gt_keypoints_list)
    if gt_weights_list is None:
      gt_weights_list = [None] * len(gt_classes_list)
    if gt_boxes_list is None:
      gt_boxes_list = [None] * len(gt_keypoints_list)

    heatmaps = []
    num_instances_list = []
    valid_mask_list = []
    for keypoints, classes, kp_weights, weights, boxes in zip(
        gt_keypoints_list, gt_classes_list, gt_keypoints_weights_list,
        gt_weights_list, gt_boxes_list):
      keypoints_absolute, kp_weights = self._preprocess_keypoints_and_weights(
          out_height=out_height,
          out_width=out_width,
          keypoints=keypoints,
          class_onehot=classes,
          class_weights=weights,
          keypoint_weights=kp_weights)
      num_instances, num_keypoints, _ = (
          shape_utils.combined_static_and_dynamic_shape(keypoints_absolute))

      # A tensor of shape [num_instances, num_keypoints] with
      # each element representing the type dimension for each corresponding
      # keypoint:
      # [[0, 1, ..., k-1],
      #  [0, 1, ..., k-1],
      #          :
      #  [0, 1, ..., k-1]]
      keypoint_types = tf.tile(
          input=tf.expand_dims(tf.range(num_keypoints), axis=0),
          multiples=[num_instances, 1])

      # A tensor of shape [num_instances, num_keypoints] with
      # each element representing the sigma of the Gaussian kernel for each
      # keypoint.
      keypoint_std_dev = tf.tile(
          input=tf.expand_dims(tf.constant(self._keypoint_std_dev), axis=0),
          multiples=[num_instances, 1])

      # If boxes is not None, then scale the standard deviation based on the
      # size of the object bounding boxes similar to object center heatmap.
      if boxes is not None:
        boxes = box_list.BoxList(boxes)
        # Convert the box coordinates to absolute output image dimension space.
        boxes = box_list_ops.to_absolute_coordinates(boxes,
                                                     height // self._stride,
                                                     width // self._stride)
        # Get the box height and width. Each returned tensors have the shape
        # of [num_instances]
        (_, _, boxes_height,
         boxes_width) = boxes.get_center_coordinates_and_sizes()

        # Compute the sigma from box size. The tensor shape: [num_instances].
        sigma = _compute_std_dev_from_box_size(boxes_height, boxes_width, 0.7)
        keypoint_std_dev = keypoint_std_dev * tf.stack(
            [sigma] * num_keypoints, axis=1)

        # Generate the valid region mask to ignore regions with target class but
        # no corresponding keypoints.
        # Shape: [num_instances].
        blackout = tf.logical_and(classes[:, self._class_id] > 0,
                                  tf.reduce_max(kp_weights, axis=1) < 1e-3)
        valid_mask = ta_utils.blackout_pixel_weights_by_box_regions(
            out_height, out_width, boxes.get(), blackout)
        valid_mask_list.append(valid_mask)

      # Apply the Gaussian kernel to the keypoint coordinates. Returned heatmap
      # has shape of [out_height, out_width, num_keypoints].
      heatmap = ta_utils.coordinates_to_heatmap(
          y_grid=y_grid,
          x_grid=x_grid,
          y_coordinates=tf.keras.backend.flatten(keypoints_absolute[:, :, 0]),
          x_coordinates=tf.keras.backend.flatten(keypoints_absolute[:, :, 1]),
          sigma=tf.keras.backend.flatten(keypoint_std_dev),
          channel_onehot=tf.one_hot(
              tf.keras.backend.flatten(keypoint_types), depth=num_keypoints),
          channel_weights=tf.keras.backend.flatten(kp_weights))
      num_instances_list.append(
          tf.cast(tf.reduce_sum(kp_weights, axis=0), dtype=tf.int32))
      heatmaps.append(heatmap)
    return (tf.stack(heatmaps, axis=0), tf.stack(num_instances_list, axis=0),
            tf.stack(valid_mask_list, axis=0))

  def _get_keypoint_types(self, num_instances, num_keypoints, num_neighbors):
    """Gets keypoint type index tensor.

    The function prepares the tensor of keypoint indices with shape
    [num_instances, num_keypoints, num_neighbors]. Each element represents the
    keypoint type index for each corresponding keypoint and tiled along the 3rd
    axis:
    [[0, 1, ..., num_keypoints - 1],
     [0, 1, ..., num_keypoints - 1],
             :
     [0, 1, ..., num_keypoints - 1]]

    Args:
      num_instances: int, the number of instances, used to define the 1st
        dimension.
      num_keypoints: int, the number of keypoint types, used to define the 2nd
        dimension.
      num_neighbors: int, the number of neighborhood pixels to consider for each
        keypoint, used to define the 3rd dimension.

    Returns:
      A integer tensor of shape [num_instances, num_keypoints, num_neighbors].
    """
    keypoint_types = tf.range(num_keypoints)[tf.newaxis, :, tf.newaxis]
    tiled_keypoint_types = tf.tile(keypoint_types,
                                   multiples=[num_instances, 1, num_neighbors])
    return tiled_keypoint_types

  def assign_keypoints_offset_targets(self,
                                      height,
                                      width,
                                      gt_keypoints_list,
                                      gt_classes_list,
                                      gt_keypoints_weights_list=None,
                                      gt_weights_list=None):
    """Returns the offsets and indices of the keypoints for location refinement.

    The returned values are used to refine the location of each keypoints in the
    heatmap. The predicted values at the relevant indices can be retrieved with
    the get_batch_predictions_from_indices function.

    Args:
      height: int, height of input to the CenterNet model. This is used to
        determine the height of the output.
      width: int, width of the input to the CenterNet model. This is used to
        determine the width of the output.
      gt_keypoints_list: A list of tensors with shape [num_instances,
        num_total_keypoints]. See class-level description for more detail.
      gt_classes_list: A list of tensors with shape [num_instances,
        num_classes]. See class-level description for more detail.
      gt_keypoints_weights_list: A list of tensors with shape [num_instances,
        num_total_keypoints] corresponding to the weight of each keypoint.
      gt_weights_list: A list of float tensors with shape [num_instances]. See
        class-level description for more detail.

    Returns:
      batch_indices: an integer tensor of shape [num_total_instances, 3] (or
        [num_total_instances, 4] if 'per_keypoint_offset' is set True) holding
        the indices inside the predicted tensor which should be penalized. The
        first column indicates the index along the batch dimension and the
        second and third columns indicate the index along the y and x
        dimensions respectively. The fourth column corresponds to the channel
        dimension (if 'per_keypoint_offset' is set True).
      batch_offsets: a float tensor of shape [num_total_instances, 2] holding
        the expected y and x offset of each box in the output space.
      batch_weights: a float tensor of shape [num_total_instances] indicating
        the weight of each prediction.
      Note that num_total_instances = batch_size * num_instances *
                                      num_keypoints * num_neighbors
    """

    batch_indices = []
    batch_offsets = []
    batch_weights = []

    if gt_keypoints_weights_list is None:
      gt_keypoints_weights_list = [None] * len(gt_keypoints_list)
    if gt_weights_list is None:
      gt_weights_list = [None] * len(gt_classes_list)
    for i, (keypoints, classes, kp_weights, weights) in enumerate(
        zip(gt_keypoints_list, gt_classes_list, gt_keypoints_weights_list,
            gt_weights_list)):
      keypoints_absolute, kp_weights = self._preprocess_keypoints_and_weights(
          out_height=height // self._stride,
          out_width=width // self._stride,
          keypoints=keypoints,
          class_onehot=classes,
          class_weights=weights,
          keypoint_weights=kp_weights)
      num_instances, num_keypoints, _ = (
          shape_utils.combined_static_and_dynamic_shape(keypoints_absolute))

      # [num_instances * num_keypoints]
      y_source = tf.keras.backend.flatten(keypoints_absolute[:, :, 0])
      x_source = tf.keras.backend.flatten(keypoints_absolute[:, :, 1])

      # All keypoint coordinates and their neighbors:
      # [num_instance * num_keypoints, num_neighbors]
      (y_source_neighbors, x_source_neighbors,
       valid_sources) = ta_utils.get_surrounding_grids(height // self._stride,
                                                       width // self._stride,
                                                       y_source, x_source,
                                                       self._peak_radius)
      _, num_neighbors = shape_utils.combined_static_and_dynamic_shape(
          y_source_neighbors)

      # Update the valid keypoint weights.
      # [num_instance * num_keypoints, num_neighbors]
      valid_keypoints = tf.cast(
          valid_sources, dtype=tf.float32) * tf.stack(
              [tf.keras.backend.flatten(kp_weights)] * num_neighbors, axis=-1)

      # Compute the offsets and indices of the box centers. Shape:
      #   offsets: [num_instances * num_keypoints, num_neighbors, 2]
      #   indices: [num_instances * num_keypoints, num_neighbors, 2]
      offsets, indices = ta_utils.compute_floor_offsets_with_indices(
          y_source=y_source_neighbors,
          x_source=x_source_neighbors,
          y_target=y_source,
          x_target=x_source)
      # Reshape to:
      #   offsets: [num_instances * num_keypoints * num_neighbors, 2]
      #   indices: [num_instances * num_keypoints * num_neighbors, 2]
      offsets = tf.reshape(offsets, [-1, 2])
      indices = tf.reshape(indices, [-1, 2])

      # Prepare the batch indices to be prepended.
      batch_index = tf.fill(
          [num_instances * num_keypoints * num_neighbors, 1], i)
      if self._per_keypoint_offset:
        tiled_keypoint_types = self._get_keypoint_types(
            num_instances, num_keypoints, num_neighbors)
        batch_indices.append(
            tf.concat([batch_index, indices,
                       tf.reshape(tiled_keypoint_types, [-1, 1])], axis=1))
      else:
        batch_indices.append(tf.concat([batch_index, indices], axis=1))
      batch_offsets.append(offsets)
      batch_weights.append(tf.keras.backend.flatten(valid_keypoints))

    # Concatenate the tensors in the batch in the first dimension:
    # shape: [batch_size * num_instances * num_keypoints * num_neighbors, 3] or
    # [batch_size * num_instances * num_keypoints * num_neighbors, 4] if
    # 'per_keypoint_offset' is set to True.
    batch_indices = tf.concat(batch_indices, axis=0)
    # shape: [batch_size * num_instances * num_keypoints * num_neighbors]
    batch_weights = tf.concat(batch_weights, axis=0)
    # shape: [batch_size * num_instances * num_keypoints * num_neighbors, 2]
    batch_offsets = tf.concat(batch_offsets, axis=0)
    return (batch_indices, batch_offsets, batch_weights)

  def assign_joint_regression_targets(self,
                                      height,
                                      width,
                                      gt_keypoints_list,
                                      gt_classes_list,
                                      gt_boxes_list=None,
                                      gt_keypoints_weights_list=None,
                                      gt_weights_list=None):
    """Returns the joint regression from center grid to keypoints.

    The joint regression is used as the grouping cue from the estimated
    keypoints to instance center. The offsets are the vectors from the floored
    object center coordinates to the keypoint coordinates.

    Args:
      height: int, height of input to the CenterNet model. This is used to
        determine the height of the output.
      width: int, width of the input to the CenterNet model. This is used to
        determine the width of the output.
      gt_keypoints_list: A list of float tensors with shape [num_instances,
        num_total_keypoints]. See class-level description for more detail.
      gt_classes_list: A list of float tensors with shape [num_instances,
        num_classes]. See class-level description for more detail.
      gt_boxes_list: A list of float tensors with shape [num_instances, 4]. See
        class-level description for more detail. If provided, then the center
        targets will be computed based on the center of the boxes.
      gt_keypoints_weights_list: A list of float tensors with shape
        [num_instances, num_total_keypoints] representing to the weight of each
        keypoint.
      gt_weights_list: A list of float tensors with shape [num_instances]. See
        class-level description for more detail.

    Returns:
      batch_indices: an integer tensor of shape [num_instances, 4] holding the
        indices inside the predicted tensor which should be penalized. The
        first column indicates the index along the batch dimension and the
        second and third columns indicate the index along the y and x
        dimensions respectively, the last dimension refers to the keypoint type
        dimension.
      batch_offsets: a float tensor of shape [num_instances, 2] holding the
        expected y and x offset of each box in the output space.
      batch_weights: a float tensor of shape [num_instances] indicating the
        weight of each prediction.
      Note that num_total_instances = batch_size * num_instances * num_keypoints

    Raises:
      NotImplementedError: currently the object center coordinates need to be
        computed from groundtruth bounding boxes. The functionality of
        generating the object center coordinates from keypoints is not
        implemented yet.
    """

    batch_indices = []
    batch_offsets = []
    batch_weights = []
    batch_size = len(gt_keypoints_list)
    if gt_keypoints_weights_list is None:
      gt_keypoints_weights_list = [None] * batch_size
    if gt_boxes_list is None:
      gt_boxes_list = [None] * batch_size
    if gt_weights_list is None:
      gt_weights_list = [None] * len(gt_classes_list)
    for i, (keypoints, classes, boxes, kp_weights, weights) in enumerate(
        zip(gt_keypoints_list, gt_classes_list,
            gt_boxes_list, gt_keypoints_weights_list, gt_weights_list)):
      keypoints_absolute, kp_weights = self._preprocess_keypoints_and_weights(
          out_height=height // self._stride,
          out_width=width // self._stride,
          keypoints=keypoints,
          class_onehot=classes,
          class_weights=weights,
          keypoint_weights=kp_weights)
      num_instances, num_keypoints, _ = (
          shape_utils.combined_static_and_dynamic_shape(keypoints_absolute))

      # If boxes are provided, compute the joint center from it.
      if boxes is not None:
        # Compute joint center from boxes.
        boxes = box_list.BoxList(boxes)
        boxes = box_list_ops.to_absolute_coordinates(boxes,
                                                     height // self._stride,
                                                     width // self._stride)
        y_center, x_center, _, _ = boxes.get_center_coordinates_and_sizes()
      else:
        # TODO(yuhuic): Add the logic to generate object centers from keypoints.
        raise NotImplementedError((
            'The functionality of generating object centers from keypoints is'
            ' not implemented yet. Please provide groundtruth bounding boxes.'
        ))

      # Tile the yx center coordinates to be the same shape as keypoints.
      y_center_tiled = tf.tile(
          tf.reshape(y_center, shape=[num_instances, 1]),
          multiples=[1, num_keypoints])
      x_center_tiled = tf.tile(
          tf.reshape(x_center, shape=[num_instances, 1]),
          multiples=[1, num_keypoints])
      # [num_instance * num_keypoints, num_neighbors]
      (y_source_neighbors, x_source_neighbors,
       valid_sources) = ta_utils.get_surrounding_grids(
           height // self._stride, width // self._stride,
           tf.keras.backend.flatten(y_center_tiled),
           tf.keras.backend.flatten(x_center_tiled), self._peak_radius)

      _, num_neighbors = shape_utils.combined_static_and_dynamic_shape(
          y_source_neighbors)
      valid_keypoints = tf.cast(
          valid_sources, dtype=tf.float32) * tf.stack(
              [tf.keras.backend.flatten(kp_weights)] * num_neighbors, axis=-1)

      # Compute the offsets and indices of the box centers. Shape:
      #   offsets: [num_instances * num_keypoints, 2]
      #   indices: [num_instances * num_keypoints, 2]
      (offsets, indices) = ta_utils.compute_floor_offsets_with_indices(
          y_source=y_source_neighbors,
          x_source=x_source_neighbors,
          y_target=tf.keras.backend.flatten(keypoints_absolute[:, :, 0]),
          x_target=tf.keras.backend.flatten(keypoints_absolute[:, :, 1]))
      # Reshape to:
      #   offsets: [num_instances * num_keypoints * num_neighbors, 2]
      #   indices: [num_instances * num_keypoints * num_neighbors, 2]
      offsets = tf.reshape(offsets, [-1, 2])
      indices = tf.reshape(indices, [-1, 2])

      # keypoint type tensor: [num_instances, num_keypoints, num_neighbors].
      tiled_keypoint_types = self._get_keypoint_types(
          num_instances, num_keypoints, num_neighbors)

      batch_index = tf.fill(
          [num_instances * num_keypoints * num_neighbors, 1], i)
      batch_indices.append(
          tf.concat([batch_index, indices,
                     tf.reshape(tiled_keypoint_types, [-1, 1])], axis=1))
      batch_offsets.append(offsets)
      batch_weights.append(tf.keras.backend.flatten(valid_keypoints))

    # Concatenate the tensors in the batch in the first dimension:
    # shape: [batch_size * num_instances * num_keypoints, 4]
    batch_indices = tf.concat(batch_indices, axis=0)
    # shape: [batch_size * num_instances * num_keypoints]
    batch_weights = tf.concat(batch_weights, axis=0)
    # shape: [batch_size * num_instances * num_keypoints, 2]
    batch_offsets = tf.concat(batch_offsets, axis=0)
    return (batch_indices, batch_offsets, batch_weights)


def _resize_masks(masks, height, width, method):
  # Resize segmentation masks to conform to output dimensions. Use TF2
  # image resize because TF1's version is buggy:
  # https://yaqs.corp.google.com/eng/q/4970450458378240
  masks = tf2.image.resize(
      masks[:, :, :, tf.newaxis],
      size=(height, width),
      method=method)
  return masks[:, :, :, 0]


class CenterNetMaskTargetAssigner(object):
  """Wrapper to compute targets for segmentation masks."""

  def __init__(self, stride):
    self._stride = stride

  def assign_segmentation_targets(
      self, gt_masks_list, gt_classes_list,
      mask_resize_method=ResizeMethod.BILINEAR):
    """Computes the segmentation targets.

    This utility produces a semantic segmentation mask for each class, starting
    with whole image instance segmentation masks. Effectively, each per-class
    segmentation target is the union of all masks from that class.

    Args:
      gt_masks_list: A list of float tensors with shape [num_boxes,
        input_height, input_width] with values in {0, 1} representing instance
        masks for each object.
      gt_classes_list: A list of float tensors with shape [num_boxes,
        num_classes] representing the one-hot encoded class labels for each box
        in the gt_boxes_list.
      mask_resize_method: A `tf.compat.v2.image.ResizeMethod`. The method to use
        when resizing masks from input resolution to output resolution.

    Returns:
      segmentation_targets: An int32 tensor of size [batch_size, output_height,
        output_width, num_classes] representing the class of each location in
        the output space.
    """
    # TODO(ronnyvotel): Handle groundtruth weights.
    _, num_classes = shape_utils.combined_static_and_dynamic_shape(
        gt_classes_list[0])

    _, input_height, input_width = (
        shape_utils.combined_static_and_dynamic_shape(gt_masks_list[0]))
    output_height = input_height // self._stride
    output_width = input_width // self._stride

    segmentation_targets_list = []
    for gt_masks, gt_classes in zip(gt_masks_list, gt_classes_list):
      gt_masks = _resize_masks(gt_masks, output_height, output_width,
                               mask_resize_method)
      gt_masks = gt_masks[:, :, :, tf.newaxis]
      gt_classes_reshaped = tf.reshape(gt_classes, [-1, 1, 1, num_classes])
      # Shape: [h, w, num_classes].
      segmentations_for_image = tf.reduce_max(
          gt_masks * gt_classes_reshaped, axis=0)
      # Avoid the case where max of an empty array is -inf.
      segmentations_for_image = tf.maximum(segmentations_for_image, 0.0)
      segmentation_targets_list.append(segmentations_for_image)

    segmentation_target = tf.stack(segmentation_targets_list, axis=0)
    return segmentation_target


class CenterNetDensePoseTargetAssigner(object):
  """Wrapper to compute targets for DensePose task."""

  def __init__(self, stride, num_parts=24):
    self._stride = stride
    self._num_parts = num_parts

  def assign_part_and_coordinate_targets(self,
                                         height,
                                         width,
                                         gt_dp_num_points_list,
                                         gt_dp_part_ids_list,
                                         gt_dp_surface_coords_list,
                                         gt_weights_list=None):
    """Returns the DensePose part_id and coordinate targets and their indices.

    The returned values are expected to be used with predicted tensors
    of size (batch_size, height//self._stride, width//self._stride, 2). The
    predicted values at the relevant indices can be retrieved with the
    get_batch_predictions_from_indices function.

    Args:
      height: int, height of input to the model. This is used to determine the
        height of the output.
      width: int, width of the input to the model. This is used to determine the
        width of the output.
      gt_dp_num_points_list: a list of 1-D tf.int32 tensors of shape [num_boxes]
        containing the number of DensePose sampled points per box.
      gt_dp_part_ids_list: a list of 2-D tf.int32 tensors of shape
        [num_boxes, max_sampled_points] containing the DensePose part ids
        (0-indexed) for each sampled point. Note that there may be padding, as
        boxes may contain a different number of sampled points.
      gt_dp_surface_coords_list: a list of 3-D tf.float32 tensors of shape
        [num_boxes, max_sampled_points, 4] containing the DensePose surface
        coordinates (normalized) for each sampled point. Note that there may be
        padding.
      gt_weights_list: A list of 1-D tensors with shape [num_boxes]
        corresponding to the weight of each groundtruth detection box.

    Returns:
      batch_indices: an integer tensor of shape [num_total_points, 4] holding
        the indices inside the predicted tensor which should be penalized. The
        first column indicates the index along the batch dimension and the
        second and third columns indicate the index along the y and x
        dimensions respectively. The fourth column is the part index.
      batch_part_ids: an int tensor of shape [num_total_points, num_parts]
        holding 1-hot encodings of parts for each sampled point.
      batch_surface_coords: a float tensor of shape [num_total_points, 2]
        holding the expected (v, u) coordinates for each sampled point.
      batch_weights: a float tensor of shape [num_total_points] indicating the
        weight of each prediction.
      Note that num_total_points = batch_size * num_boxes * max_sampled_points.
    """

    if gt_weights_list is None:
      gt_weights_list = [None] * len(gt_dp_num_points_list)

    batch_indices = []
    batch_part_ids = []
    batch_surface_coords = []
    batch_weights = []

    for i, (num_points, part_ids, surface_coords, weights) in enumerate(
        zip(gt_dp_num_points_list, gt_dp_part_ids_list,
            gt_dp_surface_coords_list, gt_weights_list)):
      num_boxes, max_sampled_points = (
          shape_utils.combined_static_and_dynamic_shape(part_ids))
      part_ids_flattened = tf.reshape(part_ids, [-1])
      part_ids_one_hot = tf.one_hot(part_ids_flattened, depth=self._num_parts)
      # Get DensePose coordinates in the output space.
      surface_coords_abs = densepose_ops.to_absolute_coordinates(
          surface_coords, height // self._stride, width // self._stride)
      surface_coords_abs = tf.reshape(surface_coords_abs, [-1, 4])
      # Each tensor has shape [num_boxes * max_sampled_points].
      yabs, xabs, v, u = tf.unstack(surface_coords_abs, axis=-1)

      # Get the indices (in output space) for the DensePose coordinates. Note
      # that if self._stride is larger than 1, this will have the effect of
      # reducing spatial resolution of the groundtruth points.
      indices_y = tf.cast(yabs, tf.int32)
      indices_x = tf.cast(xabs, tf.int32)

      # Assign ones if weights are not provided.
      if weights is None:
        weights = tf.ones(num_boxes, dtype=tf.float32)
      # Create per-point weights.
      weights_per_point = tf.reshape(
          tf.tile(weights[:, tf.newaxis], multiples=[1, max_sampled_points]),
          shape=[-1])
      # Mask out invalid (i.e. padded) DensePose points.
      num_points_tiled = tf.tile(num_points[:, tf.newaxis],
                                 multiples=[1, max_sampled_points])
      range_tiled = tf.tile(tf.range(max_sampled_points)[tf.newaxis, :],
                            multiples=[num_boxes, 1])
      valid_points = tf.math.less(range_tiled, num_points_tiled)
      valid_points = tf.cast(tf.reshape(valid_points, [-1]), dtype=tf.float32)
      weights_per_point = weights_per_point * valid_points

      # Shape of [num_boxes * max_sampled_points] integer tensor filled with
      # current batch index.
      batch_index = i * tf.ones_like(indices_y, dtype=tf.int32)
      batch_indices.append(
          tf.stack([batch_index, indices_y, indices_x, part_ids_flattened],
                   axis=1))
      batch_part_ids.append(part_ids_one_hot)
      batch_surface_coords.append(tf.stack([v, u], axis=1))
      batch_weights.append(weights_per_point)

    batch_indices = tf.concat(batch_indices, axis=0)
    batch_part_ids = tf.concat(batch_part_ids, axis=0)
    batch_surface_coords = tf.concat(batch_surface_coords, axis=0)
    batch_weights = tf.concat(batch_weights, axis=0)
    return batch_indices, batch_part_ids, batch_surface_coords, batch_weights


def filter_mask_overlap_min_area(masks):
  """If a pixel belongs to 2 instances, remove it from the larger instance."""

  num_instances = tf.shape(masks)[0]
  def _filter_min_area():
    """Helper function to filter non empty masks."""
    areas = tf.reduce_sum(masks, axis=[1, 2], keepdims=True)
    per_pixel_area = masks * areas
    # Make sure background is ignored in argmin.
    per_pixel_area = (masks * per_pixel_area +
                      (1 - masks) * per_pixel_area.dtype.max)
    min_index = tf.cast(tf.argmin(per_pixel_area, axis=0), tf.int32)

    filtered_masks = (
        tf.range(num_instances)[:, tf.newaxis, tf.newaxis]
        ==
        min_index[tf.newaxis, :, :]
    )

    return tf.cast(filtered_masks, tf.float32) * masks

  return tf.cond(num_instances > 0, _filter_min_area,
                 lambda: masks)


def filter_mask_overlap(masks, method='min_area'):

  if method == 'min_area':
    return filter_mask_overlap_min_area(masks)
  else:
    raise ValueError('Unknown mask overlap filter type - {}'.format(method))


class CenterNetCornerOffsetTargetAssigner(object):
  """Wrapper to compute corner offsets for boxes using masks."""

  def __init__(self, stride, overlap_resolution='min_area'):
    """Initializes the corner offset target assigner.

    Args:
      stride: int, the stride of the network in output pixels.
      overlap_resolution: string, specifies how we handle overlapping
        instance masks. Currently only 'min_area' is supported which assigns
        overlapping pixels to the instance with the minimum area.
    """

    self._stride = stride
    self._overlap_resolution = overlap_resolution

  def assign_corner_offset_targets(
      self, gt_boxes_list, gt_masks_list):
    """Computes the corner offset targets and foreground map.

    For each pixel that is part of any object's foreground, this function
    computes the relative offsets to the top-left and bottom-right corners of
    that instance's bounding box. It also returns a foreground map to indicate
    which pixels contain valid corner offsets.

    Args:
      gt_boxes_list: A list of float tensors with shape [num_boxes, 4]
        representing the groundtruth detection bounding boxes for each sample in
        the batch. The coordinates are expected in normalized coordinates.
      gt_masks_list: A list of float tensors with shape [num_boxes,
        input_height, input_width] with values in {0, 1} representing instance
        masks for each object.

    Returns:
      corner_offsets: A float tensor of shape [batch_size, height, width, 4]
        containing, in order, the (y, x) offsets to the top left corner and
        the (y, x) offsets to the bottom right corner for each foregroung pixel
      foreground: A float tensor of shape [batch_size, height, width] in which
        each pixel is set to 1 if it is a part of any instance's foreground
        (and thus contains valid corner offsets) and 0 otherwise.

    """
    _, input_height, input_width = (
        shape_utils.combined_static_and_dynamic_shape(gt_masks_list[0]))
    output_height = input_height // self._stride
    output_width = input_width // self._stride
    y_grid, x_grid = tf.meshgrid(
        tf.range(output_height), tf.range(output_width),
        indexing='ij')
    y_grid, x_grid = tf.cast(y_grid, tf.float32), tf.cast(x_grid, tf.float32)

    corner_targets = []
    foreground_targets = []
    for gt_masks, gt_boxes in zip(gt_masks_list, gt_boxes_list):
      gt_masks = _resize_masks(gt_masks, output_height, output_width,
                               method=ResizeMethod.NEAREST_NEIGHBOR)
      gt_masks = filter_mask_overlap(gt_masks, self._overlap_resolution)

      ymin, xmin, ymax, xmax = tf.unstack(gt_boxes, axis=1)
      ymin, ymax = ymin * output_height, ymax * output_height
      xmin, xmax = xmin * output_width, xmax * output_width

      top_y = ymin[:, tf.newaxis, tf.newaxis] - y_grid[tf.newaxis]
      left_x = xmin[:, tf.newaxis, tf.newaxis] - x_grid[tf.newaxis]
      bottom_y = ymax[:, tf.newaxis, tf.newaxis] - y_grid[tf.newaxis]
      right_x = xmax[:, tf.newaxis, tf.newaxis] - x_grid[tf.newaxis]

      foreground_target = tf.cast(tf.reduce_sum(gt_masks, axis=0) > 0.5,
                                  tf.float32)
      foreground_targets.append(foreground_target)

      corner_target = tf.stack([
          tf.reduce_sum(top_y * gt_masks, axis=0),
          tf.reduce_sum(left_x * gt_masks, axis=0),
          tf.reduce_sum(bottom_y * gt_masks, axis=0),
          tf.reduce_sum(right_x * gt_masks, axis=0),
      ], axis=2)

      corner_targets.append(corner_target)

    return (tf.stack(corner_targets, axis=0),
            tf.stack(foreground_targets, axis=0))