# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import logging
from typing import Dict, Union
import torch
from torch import nn
from torch.nn import functional as F
from fvcore.nn import smooth_l1_loss

from detectron2.utils.events import get_event_storage

from detectron2.config import configurable
from detectron2.layers import Linear, ShapeSpec, cat, nonzero_tuple, batched_nms_rotated
from detectron2.modeling.box_regression import Box2BoxTransformRotated
from detectron2.structures import Boxes, Instances, RotatedBoxes

from ...utils.rotated_box_utils import overwrite_orientations_on_boxes
from ..losses.rotated_box_losses import sine_loss, cosine_loss, sine_square_loss


logger = logging.getLogger(__name__)

"""
Shape shorthand in this module:

    N: number of images in the minibatch
    R: number of ROIs, combined over all images, in the minibatch
    Ri: number of ROIs in image i
    K: number of foreground classes. E.g.,there are 80 foreground classes in COCO.

Naming convention:

    deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box
    transform (see :class:`box_regression.Box2BoxTransform`).

    pred_class_logits: predicted class scores in [-inf, +inf]; use
        softmax(pred_class_logits) to estimate P(class).

    gt_classes: ground-truth classification labels in [0, K], where [0, K) represent
        foreground object classes and K represents the background class.

    pred_proposal_deltas: predicted box2box transform deltas for transforming proposals
        to detection box predictions.

    gt_proposal_deltas: ground-truth box2box transform deltas
"""


def fast_rcnn_inference(boxes, scores, orientations, image_shapes, score_thresh, nms_thresh,
                        topk_per_image):
    """
    Call `fast_rcnn_inference_single_image` for all images.

    Args:
        boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
            boxes for each image. Element i has shape (Ri, K * 4) if doing
            class-specific regression, or (Ri, 4) if doing class-agnostic
            regression, where Ri is the number of predicted objects for image i.
            This is compatible with the output of :meth:`FastRCNNOutputs.predict_boxes`.
        scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
            Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
            for image i. Compatible with the output of :meth:`FastRCNNOutputs.predict_probs`.
        image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.
        score_thresh (float): Only return detections with a confidence score exceeding this
            threshold.
        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].
        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
            all detections.

    Returns:
        instances: (list[Instances]): A list of N instances, one for each image in the batch,
            that stores the topk most confidence detections.
        kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates
            the corresponding boxes/scores index in [0, Ri) from the input, for image i.
    """
    result_per_image = [
        fast_rcnn_inference_single_image_rotated(
            boxes_per_image, scores_per_image, orientation_per_image, image_shape,
            score_thresh, nms_thresh, topk_per_image
        )
        for boxes_per_image, scores_per_image, orientation_per_image, image_shape in
        zip(boxes, scores, orientations, image_shapes)
    ]
    return [x[0] for x in result_per_image], [x[1] for x in result_per_image]


def fast_rcnn_inference_single_image_rotated(
        boxes, scores, orientations, image_shape, score_thresh, nms_thresh, topk_per_image
):
    """
    Single-image inference. Return bounding-box detection results by thresholding
    on scores and applying non-maximum suppression (NMS).

    Args:
        Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes
        per image.

    Returns:
        Same as `fast_rcnn_inference`, but for only one image.
    """
    valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
    invalid_preds_are_found = not valid_mask.all()
    if invalid_preds_are_found:
        boxes = boxes[valid_mask]
        scores = scores[valid_mask]
        orientations = orientations[valid_mask] if orientations is not None else None

    scores = scores[:, :-1]  # Removing the score of the background class
    num_bbox_reg_classes = boxes.shape[1] // 5
    # Convert to Boxes to use the `clip` function ...
    boxes = RotatedBoxes(boxes.reshape(-1, 5))
    boxes.clip(image_shape)
    boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 5)  # R x C x 5

    # Filter results based on detection scores
    filter_mask = scores > score_thresh  # R x K
    # R' x 2. First column contains indices of the R predictions;
    # Second column contains indices of classes.
    filter_inds = torch.nonzero(filter_mask)
    if num_bbox_reg_classes == 1:
        boxes = boxes[filter_inds[:, 0], 0]
    else:
        boxes = boxes[filter_mask]
    scores = scores[filter_mask]

    # Masking according to score for the custom attribute/orientation heads
    orientations = orientations[filter_inds[:, 0]] if orientations is not None else None

    # Apply per-class NMS
    keep = batched_nms_rotated(boxes, scores, filter_inds[:, 1], nms_thresh)
    if topk_per_image >= 0:
        keep = keep[:topk_per_image]
    boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]

    # In the below approach we return the confidences for all of the attributes (even with low score)
    # we remove the background class (0) and concat to a big matrix
    orientations = orientations[keep] if orientations is not None else None

    # Processing attributes to predictions
    result = Instances(image_shape)
    result.pred_boxes = RotatedBoxes(boxes)
    result.scores = scores
    result.pred_classes = filter_inds[:, 1]
    if orientations is not None:
        result.orientations = orientations

    return result, filter_inds[:, 0]


class RotatedFastRCNNOutputs:
    """
    A class that stores information about outputs of a Fast R-CNN head.
    It provides methods that are used to decode the outputs of a Fast R-CNN head.
    """

    def __init__(
            self,
            box2box_transform,
            pred_class_logits,
            pred_proposal_deltas,
            pred_orientation_logits,
            proposals,
            smooth_l1_beta=0,
            box_reg_loss_type="smooth_l1",
            class_names=None,
            orientation_loss_weight=1.,
            apply_orientation_to_boxes=False,
    ):
        """
        Args:
            box2box_transform (Box2BoxTransform/Box2BoxTransformRotated):
                box2box transform instance for proposal-to-detection transformations.
            pred_class_logits (Tensor): A tensor of shape (R, K + 1) storing the predicted class
                logits for all R predicted object instances.
                Each row corresponds to a predicted object instance.
            pred_proposal_deltas (Tensor): A tensor of shape (R, K * B) or (R, B) for
                class-specific or class-agnostic regression. It stores the predicted deltas that
                transform proposals into final box detections.
                B is the box dimension (4 or 5).
                When B is 4, each row is [dx, dy, dw, dh (, ....)].
                When B is 5, each row is [dx, dy, dw, dh, da (, ....)].
            proposals (list[Instances]): A list of N Instances, where Instances i stores the
                proposals for image i, in the field "proposal_boxes".
                When training, each Instances must have ground-truth labels
                stored in the field "gt_classes" and "gt_boxes".
                The total number of all instances must be equal to R.
            smooth_l1_beta (float): The transition point between L1 and L2 loss in
                the smooth L1 loss function. When set to 0, the loss becomes L1. When
                set to +inf, the loss becomes constant 0.
        """
        self.box2box_transform = box2box_transform
        self.num_preds_per_image = [len(p) for p in proposals]
        self.pred_class_logits = pred_class_logits
        self.pred_proposal_deltas = pred_proposal_deltas
        self.pred_orientation_logits = pred_orientation_logits
        self.smooth_l1_beta = smooth_l1_beta
        self.box_reg_loss_type = box_reg_loss_type
        self.apply_orientation_to_boxes = apply_orientation_to_boxes if \
            self.pred_orientation_logits is not None else False

        self.image_shapes = [x.image_size for x in proposals]

        self.orientation_loss_weight = orientation_loss_weight

        self.class_names = class_names or []
        if 'line' in self.class_names:
            line_index = self.class_names.index('line') + 1  # First gt_class is background, hence +1
        else:
            line_index = None

        if len(proposals):
            box_type = type(proposals[0].proposal_boxes)
            # cat(..., dim=0) concatenates over all images in the batch
            self.proposals = box_type.cat([p.proposal_boxes for p in proposals])
            assert (
                not self.proposals.tensor.requires_grad
            ), "Proposals should not require gradients!"

            # The following fields should exist only when training.
            if proposals[0].has("gt_boxes"):
                self.gt_boxes = box_type.cat([p.gt_boxes for p in proposals])
                assert proposals[0].has("gt_classes")
                self.gt_classes = cat([p.gt_classes for p in proposals], dim=0)
                bg_ind = pred_class_logits.shape[1] - 1

                # Computing the mask of valid orientation instances for back-propagation
                if pred_orientation_logits is not None:
                    # TODO(tsiper): Maybe we do want to label the orientation of lines?
                    self.valid_orientation_mask = self.gt_classes != bg_ind
                    if line_index is not None:  # We ignore lines for orientation detection
                        self.valid_orientation_mask = (self.gt_classes != line_index) & self.valid_orientation_mask
                    # self.gt_orientation = cat([p.gt_orientation for p in proposals], dim=0).to(torch.int64)

                if proposals[0].has('gt_orientation'):
                    self.gt_orientation = cat([p.gt_orientation for p in proposals], dim=0).to(torch.int64)
        else:
            self.proposals = Boxes(torch.zeros(0, 4, device=self.pred_proposal_deltas.device))
        self._no_instances = len(proposals) == 0  # no instances found


    def orientation_loss(self):
        if self._no_instances or self.valid_orientation_mask.sum() == 0:
            return 0.0 * self.pred_orientation_logits.sum()
        else:
            # angle_diff = self.proposals.tensor[:, 4] - self.gt_boxes.tensor[:, 4]
            gt_orientation = self.gt_orientation # (torch.round(angle_diff / 90) % 4).to(dtype=torch.long)
            valid_gt_orientation = gt_orientation[self.valid_orientation_mask]
            valid_orientation_logits = self.pred_orientation_logits[self.valid_orientation_mask]
            return (F.cross_entropy(valid_orientation_logits, valid_gt_orientation, reduction="mean")
                    * self.orientation_loss_weight)

    def losses(self):
        """
        Compute the default losses for box head in Fast(er) R-CNN,
        with softmax cross entropy loss and smooth L1 loss.

        Returns:
            A dict of losses (scalar tensors) containing keys "loss_cls" and "loss_box_reg".
        """
        losses = {
            "loss_cls": self.softmax_cross_entropy_loss(),
            "loss_box_reg": self.box_reg_loss(),
        }
        if self.pred_orientation_logits is not None:
            losses['loss_orientation'] = self.orientation_loss()
        return losses

    def box_reg_loss(self):
        """
        Compute the smooth L1 loss for box regression.

        Returns:
            scalar Tensor
        """
        if self._no_instances:
            return 0.0 * self.pred_proposal_deltas.sum()

        box_dim = self.gt_boxes.tensor.size(1)  # 4 or 5
        cls_agnostic_bbox_reg = self.pred_proposal_deltas.size(1) == box_dim
        device = self.pred_proposal_deltas.device

        bg_class_ind = self.pred_class_logits.shape[1] - 1

        # Box delta loss is only computed between the prediction for the gt class k
        # (if 0 <= k < bg_class_ind) and the target; there is no loss defined on predictions
        # for non-gt classes and background.
        # Empty fg_inds produces a valid loss of zero as long as the size_average
        # arg to smooth_l1_loss is False (otherwise it uses torch.mean internally
        # and would produce a nan loss).
        fg_inds = nonzero_tuple((self.gt_classes >= 0) & (self.gt_classes < bg_class_ind))[0]
        if cls_agnostic_bbox_reg:
            # pred_proposal_deltas only corresponds to foreground class for agnostic
            gt_class_cols = torch.arange(box_dim, device=device)
        else:
            fg_gt_classes = self.gt_classes[fg_inds]
            # pred_proposal_deltas for class k are located in columns [b * k : b * k + b],
            # where b is the dimension of box representation (4 or 5)
            # Note that compared to Detectron1,
            # we do not perform bounding box regression for background classes.
            gt_class_cols = box_dim * fg_gt_classes[:, None] + torch.arange(box_dim, device=device)

        pred_deltas = self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols]
        gt_proposal_deltas = self.box2box_transform.get_deltas(self.proposals.tensor, self.gt_boxes.tensor)
        gt_fg_deltas = gt_proposal_deltas[fg_inds]

        if self.box_reg_loss_type == 'smooth_l1':
            loss_box_reg = smooth_l1_loss(pred_deltas, gt_fg_deltas, self.smooth_l1_beta, reduction="sum")
        elif self.box_reg_loss_type == 'sine_loss':
            loss_box_reg = sine_loss(pred_deltas, gt_fg_deltas, weights=self.box2box_transform.weights,
                                     smooth_l1_beta=self.smooth_l1_beta, reduction="sum")
        elif self.box_reg_loss_type == 'cosine_loss':
            loss_box_reg = cosine_loss(pred_deltas, gt_fg_deltas, weights=self.box2box_transform.weights,
                                       smooth_l1_beta=self.smooth_l1_beta, reduction="sum")
        elif self.box_reg_loss_type == 'sine_square_loss':
            loss_box_reg = sine_square_loss(pred_deltas, gt_fg_deltas, weights=self.box2box_transform.weights,
                                            smooth_l1_beta=self.smooth_l1_beta, reduction="sum")
        else:
            raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")

        # The loss is normalized using the total number of regions (R), not the number
        # of foreground regions even though the box regression loss is only defined on
        # foreground regions. Why? Because doing so gives equal training influence to
        # each foreground example. To see how, consider two different minibatches:
        #  (1) Contains a single foreground region
        #  (2) Contains 100 foreground regions
        # If we normalize by the number of foreground regions, the single example in
        # minibatch (1) will be given 100 times as much influence as each foreground
        # example in minibatch (2). Normalizing by the total number of regions, R,
        # means that the single example in minibatch (1) and each of the 100 examples
        # in minibatch (2) are given equal influence.
        loss_box_reg = loss_box_reg / self.gt_classes.numel()
        return loss_box_reg

    def predict_boxes(self):
        """
        Returns:
            Tensor: A Tensors of predicted class-specific or class-agnostic boxes
                for all images in a batch. Element i has shape (Ri, K * B) or (Ri, B), where Ri is
                the number of predicted objects for image i and B is the box dimension (4 or 5)
        """
        return self.box2box_transform.apply_deltas(self.pred_proposal_deltas, self.proposals.tensor)

    def inference(self, score_thresh, nms_thresh, topk_per_image):
        """
        Args:
            score_thresh (float): same as fast_rcnn_inference.
            nms_thresh (float): same as fast_rcnn_inference.
            topk_per_image (int): same as fast_rcnn_inference.
        Returns:
            list[Instances]: same as fast_rcnn_inference.
            list[Tensor]: same as fast_rcnn_inference.
        """
        boxes = self.predict_boxes()
        scores = self.predict_probs()
        orientations = self.predict_orientations()

        # # We apply the orientation prediction directly to the predicted box
        if self.apply_orientation_to_boxes:
            oriented_boxes = list()
            for box, orientation in zip(boxes, orientations):
                orientation_values = orientation[:, 0]
                oriented_box = overwrite_orientations_on_boxes(box, orientation_values)
                oriented_boxes.append(oriented_box)
            boxes = tuple(oriented_boxes)
        else:
            boxes = [boxes]

        image_shapes = self.image_shapes

        return fast_rcnn_inference(
            boxes, scores, orientations, image_shapes, score_thresh, nms_thresh, topk_per_image
        )

    def _log_accuracy(self):
        """
        Log the accuracy metrics to EventStorage.
        """
        num_instances = self.gt_classes.numel()
        pred_classes = self.pred_class_logits.argmax(dim=1)
        bg_class_ind = self.pred_class_logits.shape[1] - 1

        fg_inds = (self.gt_classes >= 0) & (self.gt_classes < bg_class_ind)
        num_fg = len(torch.nonzero(fg_inds))
        fg_gt_classes = self.gt_classes[fg_inds]
        fg_pred_classes = pred_classes[fg_inds]

        num_false_negative = len(torch.nonzero(fg_pred_classes == bg_class_ind))
        num_accurate = len(torch.nonzero(pred_classes == self.gt_classes))
        fg_num_accurate = len(torch.nonzero(fg_pred_classes == fg_gt_classes))

        storage = get_event_storage()
        if num_instances > 0:
            storage.put_scalar("fast_rcnn/cls_accuracy", num_accurate / num_instances)
            if num_fg > 0:
                storage.put_scalar("fast_rcnn/fg_cls_accuracy", fg_num_accurate / num_fg)
                storage.put_scalar("fast_rcnn/false_negative", num_false_negative / num_fg)

    def softmax_cross_entropy_loss(self):
        """
        Compute the softmax cross entropy loss for box classification.

        Returns:
            scalar Tensor
        """
        if self._no_instances:
            return 0.0 * self.pred_class_logits.sum()
        else:
            self._log_accuracy()
            return F.cross_entropy(self.pred_class_logits, self.gt_classes, reduction="mean")

    def box_reg_loss(self):
        """
        Compute the smooth L1 loss for box regression.

        Returns:
            scalar Tensor
        """
        if self._no_instances:
            return 0.0 * self.pred_proposal_deltas.sum()

        box_dim = self.gt_boxes.tensor.size(1)  # 4 or 5
        cls_agnostic_bbox_reg = self.pred_proposal_deltas.size(1) == box_dim
        device = self.pred_proposal_deltas.device

        bg_class_ind = self.pred_class_logits.shape[1] - 1

        # Box delta loss is only computed between the prediction for the gt class k
        # (if 0 <= k < bg_class_ind) and the target; there is no loss defined on predictions
        # for non-gt classes and background.
        # Empty fg_inds produces a valid loss of zero as long as the size_average
        # arg to smooth_l1_loss is False (otherwise it uses torch.mean internally
        # and would produce a nan loss).
        fg_inds = nonzero_tuple((self.gt_classes >= 0) & (self.gt_classes < bg_class_ind))[0]
        if cls_agnostic_bbox_reg:
            # pred_proposal_deltas only corresponds to foreground class for agnostic
            gt_class_cols = torch.arange(box_dim, device=device)
        else:
            fg_gt_classes = self.gt_classes[fg_inds]
            # pred_proposal_deltas for class k are located in columns [b * k : b * k + b],
            # where b is the dimension of box representation (4 or 5)
            # Note that compared to Detectron1,
            # we do not perform bounding box regression for background classes.
            gt_class_cols = box_dim * fg_gt_classes[:, None] + torch.arange(box_dim, device=device)

        gt_proposal_deltas = self.box2box_transform.get_deltas(
            self.proposals.tensor, self.gt_boxes.tensor
        )
        if self.box_reg_loss_type == "smooth_l1":
            loss_box_reg = smooth_l1_loss(
                self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols],
                gt_proposal_deltas[fg_inds],
                self.smooth_l1_beta,
                reduction="sum",
            )
        elif self.box_reg_loss_type == "sine_square_loss":
            loss_box_reg = sine_square_loss(
                self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols],
                gt_proposal_deltas[fg_inds],
                self.smooth_l1_beta, reduction="sum",
                weights=self.box2box_transform.weights)

        else:
            raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")

        # The loss is normalized using the total number of regions (R), not the number
        # of foreground regions even though the box regression loss is only defined on
        # foreground regions. Why? Because doing so gives equal training influence to
        # each foreground example. To see how, consider two different minibatches:
        #  (1) Contains a single foreground region
        #  (2) Contains 100 foreground regions
        # If we normalize by the number of foreground regions, the single example in
        # minibatch (1) will be given 100 times as much influence as each foreground
        # example in minibatch (2). Normalizing by the total number of regions, R,
        # means that the single example in minibatch (1) and each of the 100 examples
        # in minibatch (2) are given equal influence.
        loss_box_reg = loss_box_reg / self.gt_classes.numel()
        return loss_box_reg

    def predict_probs(self):
        probs = F.softmax(self.pred_class_logits, dim=-1)
        return probs.split(self.num_preds_per_image, dim=0)

    def predict_orientations(self):
        if self.pred_orientation_logits is not None:
            orientation_probs = F.softmax(self.pred_orientation_logits, dim=-1)
            orientations_max = orientation_probs.max(dim=1)
            orientations = torch.stack((orientations_max[1], orientations_max[0]), 1) # tensor with prediction, score
            return orientations.split(self.num_preds_per_image, dim=0)
        else:
            return [None] * len(self.image_shapes)


class RotatedFastRCNNOutputLayers(nn.Module):
    """
    Two linear layers for predicting Fast R-CNN outputs:
      (1) proposal-to-detection box regression deltas
      (2) classification scores
    """

    @configurable
    def __init__(
            self,
            input_shape,
            box2box_transform,
            num_classes,
            class_names,
            cls_agnostic_bbox_reg=False,
            smooth_l1_beta=0.0,
            test_score_thresh=0.0,
            test_nms_thresh=0.5,
            test_topk_per_image=100,
            box_reg_loss_type: str = "smooth_l1",
            loss_weight: Union[float, Dict[str, float]] = 1.0,
            orientation_on=False,
            orientation_loss_weight=1.0,
    ):
        """
        Args:
            input_shape (ShapeSpec): shape of the input feature to this module
            box2box_transform (Box2BoxTransform or Box2BoxTransformRotated):
            num_classes (int): number of foreground classes
            class_names (List[str]): A list containing the ordered names of the classes ['word', 'line'] etc..
            cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression
            smooth_l1_beta (float): transition point from L1 to L2 loss.
            test_score_thresh (float): threshold to filter predictions results.
            test_nms_thresh (float): NMS threshold for prediction results.
            test_topk_per_image (int): number of top predictions to produce per image.
        """
        super().__init__()
        if isinstance(input_shape, int):  # some backward compatibility
            input_shape = ShapeSpec(channels=input_shape)
        input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1)
        # The prediction layer for num_classes foreground classes and one background class
        # (hence + 1)
        self.cls_score = Linear(input_size, num_classes + 1)
        num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes
        box_dim = len(box2box_transform.weights)
        self.bbox_pred = Linear(input_size, num_bbox_reg_classes * box_dim)

        nn.init.normal_(self.cls_score.weight, std=0.01)
        nn.init.normal_(self.bbox_pred.weight, std=0.001)
        for l in [self.cls_score, self.bbox_pred]:
            nn.init.constant_(l.bias, 0)

        self.orientation_on = orientation_on
        if orientation_on:
            self.orientation_pred = Linear(input_size, 4)  # We have 4 cardinal directions
            nn.init.normal_(self.orientation_pred.weight, std=0.01)
            nn.init.constant_(self.orientation_pred.bias, 0)

        self.class_names = [name.lower() for name in class_names]

        # Loss weights
        self.orientation_loss_weight = orientation_loss_weight

        self.box2box_transform = box2box_transform
        self.smooth_l1_beta = smooth_l1_beta
        self.test_score_thresh = test_score_thresh
        self.test_nms_thresh = test_nms_thresh
        self.test_topk_per_image = test_topk_per_image
        self.box_reg_loss_type = box_reg_loss_type
        if isinstance(loss_weight, float):
            loss_weight = {"loss_cls": loss_weight, "loss_box_reg": loss_weight}
        self.loss_weight = loss_weight

    @classmethod
    def from_config(cls, cfg, input_shape):
        return {
            "input_shape": input_shape,
            "box2box_transform": Box2BoxTransformRotated(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS),
            "num_classes": cfg.MODEL.ROI_HEADS.NUM_CLASSES,
            "class_names": cfg.MODEL.ROI_HEADS.CLASS_NAMES,
            "cls_agnostic_bbox_reg": cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,
            "smooth_l1_beta": cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA,
            "test_score_thresh": cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,
            "test_nms_thresh": cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
            "test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
            "box_reg_loss_type": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE,
            "loss_weight": {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT},

            # Orientation params
            "orientation_on": cfg.MODEL.ORIENTATION_ON,
            "orientation_loss_weight": cfg.MODEL.ROI_ORIENTATION_HEAD.LOSS_WEIGHT,
        }

    def forward(self, x):
        """
        Returns:
            Tensor: Nx(K+1) scores for each box
            Tensor: Nx4 or Nx(Kx4) bounding box regression deltas.
            List[Tensor]: Each Nx(K+1) For the attribute predictions (with attribute group of K elements)
        """
        if x.dim() > 2:
            x = torch.flatten(x, start_dim=1)
        scores = self.cls_score(x)
        proposal_deltas = self.bbox_pred(x)
        orientation_scores = self.orientation_pred(x) if self.orientation_on else None
        return scores, proposal_deltas, orientation_scores

    def losses(self, predictions, proposals):
        """
        Args:
            predictions: return values of :meth:`forward()`.
            proposals (list[Instances]): proposals that match the features
                that were used to compute predictions.
        """
        scores, proposal_deltas, orientation_scores = predictions
        return RotatedFastRCNNOutputs(
            self.box2box_transform, scores, proposal_deltas, orientation_scores, proposals,
            self.smooth_l1_beta, self.box_reg_loss_type,
            self.class_names, self.orientation_loss_weight,
        ).losses()

    def inference(self, predictions, proposals):
        scores, proposal_deltas, orientation_scores = predictions
        return RotatedFastRCNNOutputs(
            self.box2box_transform, scores, proposal_deltas, orientation_scores, proposals,
            self.smooth_l1_beta, self.box_reg_loss_type,
            self.class_names, self.orientation_loss_weight,
        ).inference(self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image)

    def predict_boxes(self, predictions, proposals):
        # scores, proposal_deltas, att_scores, orientation_scores = predictions
        scores, proposal_deltas, orientation_scores = predictions
        return RotatedFastRCNNOutputs(
            self.box2box_transform, scores, proposal_deltas, orientation_scores, proposals,
            self.smooth_l1_beta, self.box_reg_loss_type,
            self.class_names, self.orientation_loss_weight
        ).predict_boxes()

    def predict_probs(self, predictions, proposals):
        scores, proposal_deltas, att_scores, orientation_scores = predictions
        return RotatedFastRCNNOutputs(
            self.box2box_transform, scores, proposal_deltas, orientation_scores, proposals,
            self.smooth_l1_beta, self.box_reg_loss_type,
            self.class_names, self.orientation_loss_weight
        ).predict_probs()

