# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================
"""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.
"""
import tensorflow.compat.v1 as tf

from nvidia_tao_tf2.cv.core import box_list
from nvidia_tao_tf2.cv.core import shape_utils


KEYPOINTS_FIELD_NAME = 'keypoints'


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

  def __init__(self, similarity_calc, matcher, box_coder,
               negative_class_weight=1.0, unmatched_cls_target=None):
    """Construct Object Detection Target Assigner.

    Args:
      similarity_calc: a RegionSimilarityCalculator
      matcher: Matcher used to match groundtruth to anchors.
      box_coder: 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.].
      unmatched_cls_target: 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.

    Raises:
      ValueError: if similarity_calc is not a RegionSimilarityCalculator or
        if matcher is not a Matcher or if box_coder is not a BoxCoder
    """
    self._similarity_calc = similarity_calc
    self._matcher = matcher
    self._box_coder = box_coder
    self._negative_class_weight = negative_class_weight
    if unmatched_cls_target is None:
      self._unmatched_cls_target = tf.constant([0], tf.float32)
    else:
      self._unmatched_cls_target = unmatched_cls_target

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

  def assign(self, anchors, groundtruth_boxes, groundtruth_labels=None,
             groundtruth_weights=None, **params):
    """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).
      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.
      **params: Additional keyword arguments for specific implementations of
              the Matcher.

    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]
      reg_targets: a float32 tensor with shape [num_anchors, box_code_dimension]
      reg_weights: a float32 tensor with shape [num_anchors]
      match: a matcher.Match object encoding the match between anchors and
        groundtruth boxes, with rows corresponding to groundtruth boxes
        and columns corresponding to anchors.

    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 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(
            self._unmatched_cls_target))
    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)
    with tf.control_dependencies(
        [unmatched_shape_assert, labels_and_box_shapes_assert]):
      match_quality_matrix = self._similarity_calc.compare(groundtruth_boxes,
                                                           anchors)
      match = self._matcher.match(match_quality_matrix, **params)
      reg_targets = self._create_regression_targets(anchors,
                                                    groundtruth_boxes,
                                                    match)
      cls_targets = self._create_classification_targets(groundtruth_labels,
                                                        match)
      reg_weights = self._create_regression_weights(match, groundtruth_weights)
      cls_weights = self._create_classification_weights(match,
                                                        groundtruth_weights)

    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

  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(KEYPOINTS_FIELD_NAME):
      groundtruth_keypoints = groundtruth_boxes.get_field(KEYPOINTS_FIELD_NAME)
      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(KEYPOINTS_FIELD_NAME, 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, 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).
      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=self._unmatched_cls_target,
        ignored_value=self._unmatched_cls_target)

  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
