# Copyright (c) 2021, 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.
# 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.
import torch

from ..inference import RPNPostProcessor
from ..utils import permute_and_flatten

from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.modeling.utils import cat
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes


class RetinaNetPostProcessor(RPNPostProcessor):
    """
    Performs post-processing on the outputs of the RetinaNet boxes.
    This is only used in the testing.
    """
    def __init__(
        self,
        pre_nms_thresh,
        pre_nms_top_n,
        nms_thresh,
        fpn_post_nms_top_n,
        min_size,
        num_classes,
        box_coder=None,
    ):
        """
        Arguments:
            pre_nms_thresh (float)
            pre_nms_top_n (int)
            nms_thresh (float)
            fpn_post_nms_top_n (int)
            min_size (int)
            num_classes (int)
            box_coder (BoxCoder)
        """
        super(RetinaNetPostProcessor, self).__init__(
            pre_nms_thresh, 0, nms_thresh, min_size
        )
        self.pre_nms_thresh = pre_nms_thresh
        self.pre_nms_top_n = pre_nms_top_n
        self.nms_thresh = nms_thresh
        self.fpn_post_nms_top_n = fpn_post_nms_top_n
        self.min_size = min_size
        self.num_classes = num_classes

        if box_coder is None:
            box_coder = BoxCoder(weights=(10., 10., 5., 5.))
        self.box_coder = box_coder
 
    def add_gt_proposals(self, proposals, targets):
        """
        This function is not used in RetinaNet
        """
        pass

    def forward_for_single_feature_map(
            self, anchors, box_cls, box_regression):
        """
        Arguments:
            anchors: list[BoxList]
            box_cls: tensor of size N, A * C, H, W
            box_regression: tensor of size N, A * 4, H, W
        """
        device = box_cls.device
        N, _, H, W = box_cls.shape
        A = box_regression.size(1) // 4
        C = box_cls.size(1) // A

        # put in the same format as anchors
        box_cls = permute_and_flatten(box_cls, N, A, C, H, W)
        box_cls = box_cls.sigmoid()

        box_regression = permute_and_flatten(box_regression, N, A, 4, H, W)
        box_regression = box_regression.reshape(N, -1, 4)

        num_anchors = A * H * W

        candidate_inds = box_cls > self.pre_nms_thresh

        pre_nms_top_n = candidate_inds.view(N, -1).sum(1)
        pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_top_n)

        results = []
        for per_box_cls, per_box_regression, per_pre_nms_top_n, \
        per_candidate_inds, per_anchors in zip(
            box_cls,
            box_regression,
            pre_nms_top_n,
            candidate_inds,
            anchors):

            # Sort and select TopN
            # TODO most of this can be made out of the loop for
            # all images. 
            # TODO:Yang: Not easy to do. Because the numbers of detections are
            # different in each image. Therefore, this part needs to be done
            # per image. 
            per_box_cls = per_box_cls[per_candidate_inds]
 
            per_box_cls, top_k_indices = \
                    per_box_cls.topk(per_pre_nms_top_n, sorted=False)

            per_candidate_nonzeros = \
                    per_candidate_inds.nonzero()[top_k_indices, :]

            per_box_loc = per_candidate_nonzeros[:, 0]
            per_class = per_candidate_nonzeros[:, 1]
            per_class += 1

            detections = self.box_coder.decode(
                per_box_regression[per_box_loc, :].view(-1, 4),
                per_anchors.bbox[per_box_loc, :].view(-1, 4)
            )

            boxlist = BoxList(detections, per_anchors.size, mode="xyxy")
            boxlist.add_field("labels", per_class)
            boxlist.add_field("scores", per_box_cls)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            results.append(boxlist)

        return results

    # TODO very similar to filter_results from PostProcessor
    # but filter_results is per image
    # TODO Yang: solve this issue in the future. No good solution
    # right now.
    def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results


def make_retinanet_postprocessor(config, rpn_box_coder, is_train):
    pre_nms_thresh = config.MODEL.RETINANET.INFERENCE_TH
    pre_nms_top_n = config.MODEL.RETINANET.PRE_NMS_TOP_N
    nms_thresh = config.MODEL.RETINANET.NMS_TH
    fpn_post_nms_top_n = config.TEST.DETECTIONS_PER_IMG
    min_size = 0

    box_selector = RetinaNetPostProcessor(
        pre_nms_thresh=pre_nms_thresh,
        pre_nms_top_n=pre_nms_top_n,
        nms_thresh=nms_thresh,
        fpn_post_nms_top_n=fpn_post_nms_top_n,
        min_size=min_size,
        num_classes=config.MODEL.RETINANET.NUM_CLASSES,
        box_coder=rpn_box_coder,
    )

    return box_selector
