# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved

from typing import Tuple

import torch
from torch import nn
from torch.nn import functional as F

from detectron2.config import configurable
from detectron2.data import MetadataCatalog
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.postprocessing import sem_seg_postprocess
from detectron2.structures import ImageList

from .modeling.criterion import SetCriterion
from .modeling.matcher import HungarianMatcher


@META_ARCH_REGISTRY.register()
class MaskFormer(nn.Module):
    """
    Main class for mask classification semantic segmentation architectures.
    """

    @configurable
    def __init__(
        self,
        *,
        backbone: Backbone,
        sem_seg_head: nn.Module,
        criterion: nn.Module,
        num_queries: int,
        panoptic_on: bool,
        object_mask_threshold: float,
        overlap_threshold: float,
        metadata,
        size_divisibility: int,
        sem_seg_postprocess_before_inference: bool,
        pixel_mean: Tuple[float],
        pixel_std: Tuple[float],
    ):
        """
        Args:
            backbone: a backbone module, must follow detectron2's backbone interface
            sem_seg_head: a module that predicts semantic segmentation from backbone features
            criterion: a module that defines the loss
            num_queries: int, number of queries
            panoptic_on: bool, whether to output panoptic segmentation prediction
            object_mask_threshold: float, threshold to filter query based on classification score
                for panoptic segmentation inference
            overlap_threshold: overlap threshold used in general inference for panoptic segmentation
            metadata: dataset meta, get `thing` and `stuff` category names for panoptic
                segmentation inference
            size_divisibility: Some backbones require the input height and width to be divisible by a
                specific integer. We can use this to override such requirement.
            sem_seg_postprocess_before_inference: whether to resize the prediction back
                to original input size before semantic segmentation inference or after.
                For high-resolution dataset like Mapillary, resizing predictions before
                inference will cause OOM error.
            pixel_mean, pixel_std: list or tuple with #channels element, representing
                the per-channel mean and std to be used to normalize the input image
        """
        super().__init__()
        self.backbone = backbone
        self.sem_seg_head = sem_seg_head
        self.criterion = criterion
        self.num_queries = num_queries
        self.overlap_threshold = overlap_threshold
        self.panoptic_on = panoptic_on
        self.object_mask_threshold = object_mask_threshold
        self.metadata = metadata
        if size_divisibility < 0:
            # use backbone size_divisibility if not set
            size_divisibility = self.backbone.size_divisibility
        self.size_divisibility = size_divisibility
        self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference
        self.register_buffer(
            "pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False
        )
        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)

    @classmethod
    def from_config(cls, cfg):
        backbone = build_backbone(cfg)
        sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())

        # Loss parameters:
        deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
        no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT
        dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT
        mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT

        # building criterion
        matcher = HungarianMatcher(
            cost_class=1,
            cost_mask=mask_weight,
            cost_dice=dice_weight,
        )

        weight_dict = {"loss_ce": 1, "loss_mask": mask_weight, "loss_dice": dice_weight}
        if deep_supervision:
            dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS
            aux_weight_dict = {}
            for i in range(dec_layers - 1):
                aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
            weight_dict.update(aux_weight_dict)

        losses = ["labels", "masks"]

        criterion = SetCriterion(
            sem_seg_head.num_classes,
            matcher=matcher,
            weight_dict=weight_dict,
            eos_coef=no_object_weight,
            losses=losses,
        )

        return {
            "backbone": backbone,
            "sem_seg_head": sem_seg_head,
            "criterion": criterion,
            "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES,
            "panoptic_on": cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON,
            "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD,
            "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD,
            "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
            "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY,
            "sem_seg_postprocess_before_inference": (
                cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE
                or cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON
            ),
            "pixel_mean": cfg.MODEL.PIXEL_MEAN,
            "pixel_std": cfg.MODEL.PIXEL_STD,
        }

    @property
    def device(self):
        return self.pixel_mean.device

    def forward(self, batched_inputs):
        """
        Args:
            batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
                Each item in the list contains the inputs for one image.
                For now, each item in the list is a dict that contains:
                   * "image": Tensor, image in (C, H, W) format.
                   * "instances": per-region ground truth
                   * Other information that's included in the original dicts, such as:
                     "height", "width" (int): the output resolution of the model (may be different
                     from input resolution), used in inference.
        Returns:
            list[dict]:
                each dict has the results for one image. The dict contains the following keys:

                * "sem_seg":
                    A Tensor that represents the
                    per-pixel segmentation prediced by the head.
                    The prediction has shape KxHxW that represents the logits of
                    each class for each pixel.
                * "panoptic_seg":
                    A tuple that represent panoptic output
                    panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.
                    segments_info (list[dict]): Describe each segment in `panoptic_seg`.
                        Each dict contains keys "id", "category_id", "isthing".
        """
        images = [x["image"].to(self.device) for x in batched_inputs]
        images = [(x - self.pixel_mean) / self.pixel_std for x in images]
        images = ImageList.from_tensors(images, self.size_divisibility)

        features = self.backbone(images.tensor)
        outputs = self.sem_seg_head(features)

        if self.training:
            # mask classification target
            if "instances" in batched_inputs[0]:
                gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
                targets = self.prepare_targets(gt_instances, images)
            else:
                targets = None

            # bipartite matching-based loss
            losses = self.criterion(outputs, targets)

            for k in list(losses.keys()):
                if k in self.criterion.weight_dict:
                    losses[k] *= self.criterion.weight_dict[k]
                else:
                    # remove this loss if not specified in `weight_dict`
                    losses.pop(k)

            return losses
        else:
            mask_cls_results = outputs["pred_logits"]
            mask_pred_results = outputs["pred_masks"]
            # upsample masks
            mask_pred_results = F.interpolate(
                mask_pred_results,
                size=(images.tensor.shape[-2], images.tensor.shape[-1]),
                mode="bilinear",
                align_corners=False,
            )

            processed_results = []
            for mask_cls_result, mask_pred_result, input_per_image, image_size in zip(
                mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
            ):
                height = input_per_image.get("height", image_size[0])
                width = input_per_image.get("width", image_size[1])

                if self.sem_seg_postprocess_before_inference:
                    mask_pred_result = sem_seg_postprocess(
                        mask_pred_result, image_size, height, width
                    )

                # semantic segmentation inference
                r = self.semantic_inference(mask_cls_result, mask_pred_result)
                if not self.sem_seg_postprocess_before_inference:
                    r = sem_seg_postprocess(r, image_size, height, width)
                processed_results.append({"sem_seg": r})

                # panoptic segmentation inference
                if self.panoptic_on:
                    panoptic_r = self.panoptic_inference(
                        mask_cls_result, mask_pred_result
                    )
                    processed_results[-1]["panoptic_seg"] = panoptic_r

            return processed_results

    def prepare_targets(self, targets, images):
        h, w = images.tensor.shape[-2:]
        new_targets = []
        for targets_per_image in targets:
            # pad gt
            gt_masks = targets_per_image.gt_masks
            padded_masks = torch.zeros(
                (gt_masks.shape[0], h, w), dtype=gt_masks.dtype, device=gt_masks.device
            )
            padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks
            new_targets.append(
                {
                    "labels": targets_per_image.gt_classes,
                    "masks": padded_masks,
                }
            )
        return new_targets

    def semantic_inference(self, mask_cls, mask_pred):
        mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]
        mask_pred = mask_pred.sigmoid()
        semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred)
        return semseg