# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F

from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import (
    BalancedPositiveNegativeSampler,
)
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.utils import cat
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou


class FastRCNNLossComputation(object):
    """
    Computes the loss for Faster R-CNN.
    Also supports FPN
    """

    def __init__(self, proposal_matcher, fg_bg_sampler, box_coder, has_attributes=False):
        """
        Arguments:
            proposal_matcher (Matcher)
            fg_bg_sampler (BalancedPositiveNegativeSampler)
            box_coder (BoxCoder)
        """
        self.proposal_matcher = proposal_matcher
        self.fg_bg_sampler = fg_bg_sampler
        self.box_coder = box_coder
        self.has_attributes = has_attributes

    def match_targets_to_proposals(self, proposal, target):
        match_quality_matrix = boxlist_iou(target, proposal)
        matched_idxs = self.proposal_matcher(match_quality_matrix)
        # Fast RCNN only need "labels" field for selecting the targets
        if self.has_attributes:
            target = target.copy_with_fields(("labels", "attributes"))
        else:
            target = target.copy_with_fields("labels")
        # get the targets corresponding GT for each proposal
        # NB: need to clamp the indices because we can have a single
        # GT in the image, and matched_idxs can be -2, which goes
        # out of bounds
        matched_targets = target[matched_idxs.clamp(min=0)]
        matched_targets.add_field("matched_idxs", matched_idxs)
        return matched_targets

    def prepare_targets(self, proposals, targets):
        labels = []
        regression_targets = []
        attribute_labels = []
        for proposals_per_image, targets_per_image in zip(proposals, targets):
            matched_targets = self.match_targets_to_proposals(
                proposals_per_image, targets_per_image
            )
            matched_idxs = matched_targets.get_field("matched_idxs")

            labels_per_image = matched_targets.get_field("labels")
            labels_per_image = labels_per_image.to(dtype=torch.int64)

            # Label background (below the low threshold)
            bg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD
            labels_per_image[bg_inds] = 0

            # Label ignore proposals (between low and high thresholds)
            ignore_inds = matched_idxs == Matcher.BETWEEN_THRESHOLDS
            labels_per_image[ignore_inds] = -1  # -1 is ignored by sampler

            # compute regression targets
            regression_targets_per_image = self.box_coder.encode(
                matched_targets.bbox, proposals_per_image.bbox
            )

            labels.append(labels_per_image)
            regression_targets.append(regression_targets_per_image)

            if self.has_attributes:
                attr_labels_per_image = matched_targets.get_field("attributes")
                attr_labels_per_image = attr_labels_per_image.to(dtype=torch.int64)

                attr_labels_per_image[bg_inds] = -1
                attr_labels_per_image[ignore_inds] = -1

                attribute_labels.append(attr_labels_per_image)

        if self.has_attributes:
            return labels, regression_targets, attribute_labels

        return labels, regression_targets

    def subsample(self, proposals, targets):
        """
        This method performs the positive/negative sampling, and return
        the sampled proposals.
        Note: this function keeps a state.

        Arguments:
            proposals (list[BoxList])
            targets (list[BoxList])
        """

        if self.has_attributes:
            labels, regression_targets, attr_labels = self.prepare_targets(proposals, targets)
        else:
            labels, regression_targets = self.prepare_targets(proposals, targets)
        sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)

        proposals = list(proposals)

        if self.has_attributes:
            # add corresponding label and regression_targets information to the bounding boxes
            for (
                labels_per_image,
                regression_targets_per_image,
                attr_labels_per_image,
                proposals_per_image,
            ) in zip(labels, regression_targets, attr_labels, proposals):
                proposals_per_image.add_field("labels", labels_per_image)
                proposals_per_image.add_field("attr_labels", attr_labels_per_image)
                proposals_per_image.add_field("regression_targets", regression_targets_per_image)
        else:
            for labels_per_image, regression_targets_per_image, proposals_per_image in zip(
                labels, regression_targets, proposals
            ):
                proposals_per_image.add_field("labels", labels_per_image)
                proposals_per_image.add_field("regression_targets", regression_targets_per_image)
        # distributed sampled proposals, that were obtained on all feature maps
        # concatenated via the fg_bg_sampler, into individual feature map levels
        for img_idx, (pos_inds_img, neg_inds_img) in enumerate(
            zip(sampled_pos_inds, sampled_neg_inds)
        ):
            img_sampled_inds = torch.nonzero(pos_inds_img | neg_inds_img).squeeze(1)
            proposals_per_image = proposals[img_idx][img_sampled_inds]
            proposals[img_idx] = proposals_per_image

        self._proposals = proposals
        return proposals

    def __call__(self, class_logits, box_regression, attribute_logits=None):
        """
        Computes the loss for Faster R-CNN.
        This requires that the subsample method has been called beforehand.

        Arguments:
            class_logits (list[Tensor])
            box_regression (list[Tensor])

        Returns:
            classification_loss (Tensor)
            box_loss (Tensor)
        """

        class_logits = cat(class_logits, dim=0)
        box_regression = cat(box_regression, dim=0)
        device = class_logits.device

        if not hasattr(self, "_proposals"):
            raise RuntimeError("subsample needs to be called before")

        proposals = self._proposals

        labels = cat([proposal.get_field("labels") for proposal in proposals], dim=0)
        regression_targets = cat(
            [proposal.get_field("regression_targets") for proposal in proposals], dim=0
        )

        classification_loss = F.cross_entropy(class_logits, labels)

        # get indices that correspond to the regression targets for
        # the corresponding ground truth labels, to be used with
        # advanced indexing
        sampled_pos_inds_subset = torch.nonzero(labels > 0).squeeze(1)
        labels_pos = labels[sampled_pos_inds_subset]
        map_inds = 4 * labels_pos[:, None] + torch.tensor([0, 1, 2, 3], device=device)

        box_loss = smooth_l1_loss(
            box_regression[sampled_pos_inds_subset[:, None], map_inds],
            regression_targets[sampled_pos_inds_subset],
            size_average=False,
            beta=1,
        )
        box_loss = box_loss / labels.numel()

        # Attribute loss
        if attribute_logits is not None:
            attributes = cat([proposal.get_field("attr_labels") for proposal in proposals], dim=0)
            attributes = attributes[sampled_pos_inds_subset]

            attribute_logits = cat(attribute_logits, dim=0)
            n_boxes = attribute_logits.shape[0]

            # N_BOXES, N_ATTR -> N_BOXES, 1, N_ATTR
            attribute_logits = attribute_logits.unsqueeze(1)

            # N_BOXES, 1, N_ATTR -> N_BOXES, MAX_ATTR_PER_INST, N_ATTR -> N_BOXES * MAX_ATTR_PER_INST, N_ATTR
            attribute_logits = attribute_logits.expand(n_boxes, 16, 400).contiguous().view(-1, 400)

            # Normalize each box loss by # of valid GT attributes (ie attr != -1)
            # Repeat number of valid attributes per box along the rows and take transpose
            inv_per_box_weights = (
                (attributes >= 0).sum(dim=1).repeat(16, 1).transpose(0, 1).flatten()
            )
            per_box_weights = inv_per_box_weights.float().reciprocal()
            per_box_weights[per_box_weights > 1] = 0.0

            attributes = attributes.view(-1)
            attribute_loss = 0.5 * F.cross_entropy(
                attribute_logits, attributes, reduction="none", ignore_index=-1
            )

            attribute_loss = (attribute_loss * per_box_weights).view(n_boxes, -1).sum(dim=1)

            # Find number of boxes with atleast valid attribute
            n_valid_boxes = len(attribute_loss.nonzero())

            if n_valid_boxes > 0:
                attribute_loss = (attribute_loss / n_valid_boxes).sum()
            else:
                attribute_loss = (attribute_loss * 0.0).sum()

            return classification_loss, box_loss, attribute_loss

        return classification_loss, box_loss


def make_roi_box_loss_evaluator(cfg):
    matcher = Matcher(
        cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD,
        cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD,
        allow_low_quality_matches=False,
    )

    bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS
    box_coder = BoxCoder(weights=bbox_reg_weights)

    fg_bg_sampler = BalancedPositiveNegativeSampler(
        cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION
    )

    has_attributes = cfg.MODEL.ROI_BOX_HEAD.ATTR

    loss_evaluator = FastRCNNLossComputation(matcher, fg_bg_sampler, box_coder, has_attributes)

    return loss_evaluator
