#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import torch
from detr.util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from scipy.optimize import linear_sum_assignment
from torch import nn


class HungarianMatcher(nn.Module):
    """This class computes an assignment between the targets and the predictions of the network

    For efficiency reasons, the targets don't include the no_object. Because of this, in general,
    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
    while the others are un-matched (and thus treated as non-objects).
    """

    def __init__(
        self,
        cost_class: float = 1,
        cost_bbox: float = 1,
        cost_giou: float = 1,
        use_focal_loss=False,
    ):
        """Creates the matcher

        Params:
            cost_class: This is the relative weight of the classification error in the matching cost
            cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
            cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
        """
        super().__init__()
        self.cost_class = cost_class
        self.cost_bbox = cost_bbox
        self.cost_giou = cost_giou
        assert (
            cost_class != 0 or cost_bbox != 0 or cost_giou != 0
        ), "all costs cant be 0"
        self.use_focal_loss = use_focal_loss

    @torch.no_grad()
    def forward(self, outputs, targets):
        """Performs the matching

        Params:
            outputs: This is a dict that contains at least these entries:
                 "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
                 "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates

            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
                 "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
                           objects in the target) containing the class labels
                 "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates

        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        """
        bs, num_queries = outputs["pred_logits"].shape[:2]

        # We flatten to compute the cost matrices in a batch
        if self.use_focal_loss:
            out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid()
        else:
            out_prob = (
                outputs["pred_logits"].flatten(0, 1).softmax(-1)
            )  # [batch_size * num_queries, num_classes]
        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]

        # Also concat the target labels and boxes
        tgt_ids = torch.cat([v["labels"] for v in targets])  # [\sum_b NUM-BOX_b,]
        tgt_bbox = torch.cat([v["boxes"] for v in targets])  # [\sum_b NUM-BOX_b, 4]

        # Compute the classification cost. Contrary to the loss, we don't use the NLL,
        # but approximate it in 1 - proba[target class].
        # The 1 is a constant that doesn't change the matching, it can be omitted.
        if self.use_focal_loss:
            alpha = 0.25
            gamma = 2.0
            neg_cost_class = (
                (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())
            )
            pos_cost_class = (
                alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
            )
            cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]
        else:
            cost_class = -out_prob[
                :, tgt_ids
            ]  # shape [batch_size * num_queries, \sum_b NUM-BOX_b]

        # Compute the L1 cost between boxes
        cost_bbox = torch.cdist(
            out_bbox, tgt_bbox, p=1
        )  # shape [batch_size * num_queries,\sum_b NUM-BOX_b]

        # Compute the giou cost betwen boxes
        # shape [batch_size * num_queries, \sum_b NUM-BOX_b]
        cost_giou = -generalized_box_iou(
            box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)
        )

        # Final cost matrix
        C = (
            self.cost_bbox * cost_bbox
            + self.cost_class * cost_class
            + self.cost_giou * cost_giou
        )
        C = C.view(
            bs, num_queries, -1
        ).cpu()  # shape [batch_size, num_queries, \sum_b NUM-BOX_b]

        sizes = [len(v["boxes"]) for v in targets]  # shape [batch_size,]
        # each split c shape [batch_size, num_queries, NUM-BOX_b]
        indices = [
            linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))
        ]
        # A list where each item is [row_indices, col_indices]
        return [
            (
                torch.as_tensor(i, dtype=torch.int64),
                torch.as_tensor(j, dtype=torch.int64),
            )
            for i, j in indices
        ]


def build_matcher(args):
    return HungarianMatcher(
        cost_class=args.set_cost_class,
        cost_bbox=args.set_cost_bbox,
        cost_giou=args.set_cost_giou,
    )
