mmdet/models/dense_heads/solo_head.py

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple

import mmcv
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmengine.structures import InstanceData
from torch import Tensor

from mmdet.models.utils.misc import floordiv
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType
from ..layers import mask_matrix_nms
from ..utils import center_of_mass, generate_coordinate, multi_apply
from .base_mask_head import BaseMaskHead
from ...structures.mask import mask2bbox


@MODELS.register_module()
class SOLOHead(BaseMaskHead):
    """SOLO mask head used in `SOLO: Segmenting Objects by Locations.

    <https://arxiv.org/abs/1912.04488>`_

    Args:
        num_classes (int): Number of categories excluding the background
            category.
        in_channels (int): Number of channels in the input feature map.
        feat_channels (int): Number of hidden channels. Used in child classes.
            Defaults to 256.
        stacked_convs (int): Number of stacking convs of the head.
            Defaults to 4.
        strides (tuple): Downsample factor of each feature map.
        scale_ranges (tuple[tuple[int, int]]): Area range of multiple
            level masks, in the format [(min1, max1), (min2, max2), ...].
            A range of (16, 64) means the area range between (16, 64).
        pos_scale (float): Constant scale factor to control the center region.
        num_grids (list[int]): Divided image into a uniform grids, each
            feature map has a different grid value. The number of output
            channels is grid ** 2. Defaults to [40, 36, 24, 16, 12].
        cls_down_index (int): The index of downsample operation in
            classification branch. Defaults to 0.
        loss_mask (dict): Config of mask loss.
        loss_cls (dict): Config of classification loss.
        norm_cfg (dict): Dictionary to construct and config norm layer.
            Defaults to norm_cfg=dict(type='GN', num_groups=32,
            requires_grad=True).
        train_cfg (dict): Training config of head.
        test_cfg (dict): Testing config of head.
        init_cfg (dict or list[dict], optional): Initialization config dict.
    """

    def __init__(
        self,
        num_classes: int,
        in_channels: int,
        feat_channels: int = 256,
        stacked_convs: int = 4,
        strides: tuple = (4, 8, 16, 32, 64),
        scale_ranges: tuple = ((8, 32), (16, 64), (32, 128), (64, 256), (128,
                                                                         512)),
        pos_scale: float = 0.2,
        num_grids: list = [40, 36, 24, 16, 12],
        cls_down_index: int = 0,
        loss_mask: ConfigType = dict(
            type='DiceLoss', use_sigmoid=True, loss_weight=3.0),
        loss_cls: ConfigType = dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        norm_cfg: ConfigType = dict(
            type='GN', num_groups=32, requires_grad=True),
        train_cfg: OptConfigType = None,
        test_cfg: OptConfigType = None,
        init_cfg: MultiConfig = [
            dict(type='Normal', layer='Conv2d', std=0.01),
            dict(
                type='Normal',
                std=0.01,
                bias_prob=0.01,
                override=dict(name='conv_mask_list')),
            dict(
                type='Normal',
                std=0.01,
                bias_prob=0.01,
                override=dict(name='conv_cls'))
        ]
    ) -> None:
        super().__init__(init_cfg=init_cfg)
        self.num_classes = num_classes
        self.cls_out_channels = self.num_classes
        self.in_channels = in_channels
        self.feat_channels = feat_channels
        self.stacked_convs = stacked_convs
        self.strides = strides
        self.num_grids = num_grids
        # number of FPN feats
        self.num_levels = len(strides)
        assert self.num_levels == len(scale_ranges) == len(num_grids)
        self.scale_ranges = scale_ranges
        self.pos_scale = pos_scale

        self.cls_down_index = cls_down_index
        self.loss_cls = MODELS.build(loss_cls)
        self.loss_mask = MODELS.build(loss_mask)
        self.norm_cfg = norm_cfg
        self.init_cfg = init_cfg
        self.train_cfg = train_cfg
        self.test_cfg = test_cfg
        self._init_layers()

    def _init_layers(self) -> None:
        """Initialize layers of the head."""
        self.mask_convs = nn.ModuleList()
        self.cls_convs = nn.ModuleList()
        for i in range(self.stacked_convs):
            chn = self.in_channels + 2 if i == 0 else self.feat_channels
            self.mask_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    norm_cfg=self.norm_cfg))
            chn = self.in_channels if i == 0 else self.feat_channels
            self.cls_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    norm_cfg=self.norm_cfg))
        self.conv_mask_list = nn.ModuleList()
        for num_grid in self.num_grids:
            self.conv_mask_list.append(
                nn.Conv2d(self.feat_channels, num_grid**2, 1))

        self.conv_cls = nn.Conv2d(
            self.feat_channels, self.cls_out_channels, 3, padding=1)

    def resize_feats(self, x: Tuple[Tensor]) -> List[Tensor]:
        """Downsample the first feat and upsample last feat in feats.

        Args:
            x (tuple[Tensor]): Features from the upstream network, each is
                a 4D-tensor.

        Returns:
            list[Tensor]: Features after resizing, each is a 4D-tensor.
        """
        out = []
        for i in range(len(x)):
            if i == 0:
                out.append(
                    F.interpolate(x[0], scale_factor=0.5, mode='bilinear'))
            elif i == len(x) - 1:
                out.append(
                    F.interpolate(
                        x[i], size=x[i - 1].shape[-2:], mode='bilinear'))
            else:
                out.append(x[i])
        return out

    def forward(self, x: Tuple[Tensor]) -> tuple:
        """Forward features from the upstream network.

        Args:
            x (tuple[Tensor]): Features from the upstream network, each is
                a 4D-tensor.

        Returns:
            tuple: A tuple of classification scores and mask prediction.

                - mlvl_mask_preds (list[Tensor]): Multi-level mask prediction.
                  Each element in the list has shape
                  (batch_size, num_grids**2 ,h ,w).
                - mlvl_cls_preds (list[Tensor]): Multi-level scores.
                  Each element in the list has shape
                  (batch_size, num_classes, num_grids ,num_grids).
        """
        assert len(x) == self.num_levels
        feats = self.resize_feats(x)
        mlvl_mask_preds = []
        mlvl_cls_preds = []
        for i in range(self.num_levels):
            x = feats[i]
            mask_feat = x
            cls_feat = x
            # generate and concat the coordinate
            coord_feat = generate_coordinate(mask_feat.size(),
                                             mask_feat.device)
            mask_feat = torch.cat([mask_feat, coord_feat], 1)

            for mask_layer in (self.mask_convs):
                mask_feat = mask_layer(mask_feat)

            mask_feat = F.interpolate(
                mask_feat, scale_factor=2, mode='bilinear')
            mask_preds = self.conv_mask_list[i](mask_feat)

            # cls branch
            for j, cls_layer in enumerate(self.cls_convs):
                if j == self.cls_down_index:
                    num_grid = self.num_grids[i]
                    cls_feat = F.interpolate(
                        cls_feat, size=num_grid, mode='bilinear')
                cls_feat = cls_layer(cls_feat)

            cls_pred = self.conv_cls(cls_feat)

            if not self.training:
                feat_wh = feats[0].size()[-2:]
                upsampled_size = (feat_wh[0] * 2, feat_wh[1] * 2)
                mask_preds = F.interpolate(
                    mask_preds.sigmoid(), size=upsampled_size, mode='bilinear')
                cls_pred = cls_pred.sigmoid()
                # get local maximum
                local_max = F.max_pool2d(cls_pred, 2, stride=1, padding=1)
                keep_mask = local_max[:, :, :-1, :-1] == cls_pred
                cls_pred = cls_pred * keep_mask

            mlvl_mask_preds.append(mask_preds)
            mlvl_cls_preds.append(cls_pred)
        return mlvl_mask_preds, mlvl_cls_preds

    def loss_by_feat(self, mlvl_mask_preds: List[Tensor],
                     mlvl_cls_preds: List[Tensor],
                     batch_gt_instances: InstanceList,
                     batch_img_metas: List[dict], **kwargs) -> dict:
        """Calculate the loss based on the features extracted by the mask head.

        Args:
            mlvl_mask_preds (list[Tensor]): Multi-level mask prediction.
                Each element in the list has shape
                (batch_size, num_grids**2 ,h ,w).
            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
                gt_instance. It usually includes ``bboxes``, ``masks``,
                and ``labels`` attributes.
            batch_img_metas (list[dict]): Meta information of multiple images.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        num_levels = self.num_levels
        num_imgs = len(batch_img_metas)

        featmap_sizes = [featmap.size()[-2:] for featmap in mlvl_mask_preds]

        # `BoolTensor` in `pos_masks` represent
        # whether the corresponding point is
        # positive
        pos_mask_targets, labels, pos_masks = multi_apply(
            self._get_targets_single,
            batch_gt_instances,
            featmap_sizes=featmap_sizes)

        # change from the outside list meaning multi images
        # to the outside list meaning multi levels
        mlvl_pos_mask_targets = [[] for _ in range(num_levels)]
        mlvl_pos_mask_preds = [[] for _ in range(num_levels)]
        mlvl_pos_masks = [[] for _ in range(num_levels)]
        mlvl_labels = [[] for _ in range(num_levels)]
        for img_id in range(num_imgs):
            assert num_levels == len(pos_mask_targets[img_id])
            for lvl in range(num_levels):
                mlvl_pos_mask_targets[lvl].append(
                    pos_mask_targets[img_id][lvl])
                mlvl_pos_mask_preds[lvl].append(
                    mlvl_mask_preds[lvl][img_id, pos_masks[img_id][lvl], ...])
                mlvl_pos_masks[lvl].append(pos_masks[img_id][lvl].flatten())
                mlvl_labels[lvl].append(labels[img_id][lvl].flatten())

        # cat multiple image
        temp_mlvl_cls_preds = []
        for lvl in range(num_levels):
            mlvl_pos_mask_targets[lvl] = torch.cat(
                mlvl_pos_mask_targets[lvl], dim=0)
            mlvl_pos_mask_preds[lvl] = torch.cat(
                mlvl_pos_mask_preds[lvl], dim=0)
            mlvl_pos_masks[lvl] = torch.cat(mlvl_pos_masks[lvl], dim=0)
            mlvl_labels[lvl] = torch.cat(mlvl_labels[lvl], dim=0)
            temp_mlvl_cls_preds.append(mlvl_cls_preds[lvl].permute(
                0, 2, 3, 1).reshape(-1, self.cls_out_channels))

        num_pos = sum(item.sum() for item in mlvl_pos_masks)
        # dice loss
        loss_mask = []
        for pred, target in zip(mlvl_pos_mask_preds, mlvl_pos_mask_targets):
            if pred.size()[0] == 0:
                loss_mask.append(pred.sum().unsqueeze(0))
                continue
            loss_mask.append(
                self.loss_mask(pred, target, reduction_override='none'))
        if num_pos > 0:
            loss_mask = torch.cat(loss_mask).sum() / num_pos
        else:
            loss_mask = torch.cat(loss_mask).mean()

        flatten_labels = torch.cat(mlvl_labels)
        flatten_cls_preds = torch.cat(temp_mlvl_cls_preds)
        loss_cls = self.loss_cls(
            flatten_cls_preds, flatten_labels, avg_factor=num_pos + 1)
        return dict(loss_mask=loss_mask, loss_cls=loss_cls)

    def _get_targets_single(self,
                            gt_instances: InstanceData,
                            featmap_sizes: Optional[list] = None) -> tuple:
        """Compute targets for predictions of single image.

        Args:
            gt_instances (:obj:`InstanceData`): Ground truth of instance
                annotations. It should includes ``bboxes``, ``labels``,
                and ``masks`` attributes.
            featmap_sizes (list[:obj:`torch.size`]): Size of each
                feature map from feature pyramid, each element
                means (feat_h, feat_w). Defaults to None.

        Returns:
            Tuple: Usually returns a tuple containing targets for predictions.

                - mlvl_pos_mask_targets (list[Tensor]): Each element represent
                  the binary mask targets for positive points in this
                  level, has shape (num_pos, out_h, out_w).
                - mlvl_labels (list[Tensor]): Each element is
                  classification labels for all
                  points in this level, has shape
                  (num_grid, num_grid).
                - mlvl_pos_masks (list[Tensor]): Each element is
                  a `BoolTensor` to represent whether the
                  corresponding point in single level
                  is positive, has shape (num_grid **2).
        """
        gt_labels = gt_instances.labels
        device = gt_labels.device

        gt_bboxes = gt_instances.bboxes
        gt_areas = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
                              (gt_bboxes[:, 3] - gt_bboxes[:, 1]))

        gt_masks = gt_instances.masks.to_tensor(
            dtype=torch.bool, device=device)

        mlvl_pos_mask_targets = []
        mlvl_labels = []
        mlvl_pos_masks = []
        for (lower_bound, upper_bound), stride, featmap_size, num_grid \
                in zip(self.scale_ranges, self.strides,
                       featmap_sizes, self.num_grids):

            mask_target = torch.zeros(
                [num_grid**2, featmap_size[0], featmap_size[1]],
                dtype=torch.uint8,
                device=device)
            # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
            labels = torch.zeros([num_grid, num_grid],
                                 dtype=torch.int64,
                                 device=device) + self.num_classes
            pos_mask = torch.zeros([num_grid**2],
                                   dtype=torch.bool,
                                   device=device)

            gt_inds = ((gt_areas >= lower_bound) &
                       (gt_areas <= upper_bound)).nonzero().flatten()
            if len(gt_inds) == 0:
                mlvl_pos_mask_targets.append(
                    mask_target.new_zeros(0, featmap_size[0], featmap_size[1]))
                mlvl_labels.append(labels)
                mlvl_pos_masks.append(pos_mask)
                continue
            hit_gt_bboxes = gt_bboxes[gt_inds]
            hit_gt_labels = gt_labels[gt_inds]
            hit_gt_masks = gt_masks[gt_inds, ...]

            pos_w_ranges = 0.5 * (hit_gt_bboxes[:, 2] -
                                  hit_gt_bboxes[:, 0]) * self.pos_scale
            pos_h_ranges = 0.5 * (hit_gt_bboxes[:, 3] -
                                  hit_gt_bboxes[:, 1]) * self.pos_scale

            # Make sure hit_gt_masks has a value
            valid_mask_flags = hit_gt_masks.sum(dim=-1).sum(dim=-1) > 0
            output_stride = stride / 2

            for gt_mask, gt_label, pos_h_range, pos_w_range, \
                valid_mask_flag in \
                    zip(hit_gt_masks, hit_gt_labels, pos_h_ranges,
                        pos_w_ranges, valid_mask_flags):
                if not valid_mask_flag:
                    continue
                upsampled_size = (featmap_sizes[0][0] * 4,
                                  featmap_sizes[0][1] * 4)
                center_h, center_w = center_of_mass(gt_mask)

                coord_w = int(
                    floordiv((center_w / upsampled_size[1]), (1. / num_grid),
                             rounding_mode='trunc'))
                coord_h = int(
                    floordiv((center_h / upsampled_size[0]), (1. / num_grid),
                             rounding_mode='trunc'))

                # left, top, right, down
                top_box = max(
                    0,
                    int(
                        floordiv(
                            (center_h - pos_h_range) / upsampled_size[0],
                            (1. / num_grid),
                            rounding_mode='trunc')))
                down_box = min(
                    num_grid - 1,
                    int(
                        floordiv(
                            (center_h + pos_h_range) / upsampled_size[0],
                            (1. / num_grid),
                            rounding_mode='trunc')))
                left_box = max(
                    0,
                    int(
                        floordiv(
                            (center_w - pos_w_range) / upsampled_size[1],
                            (1. / num_grid),
                            rounding_mode='trunc')))
                right_box = min(
                    num_grid - 1,
                    int(
                        floordiv(
                            (center_w + pos_w_range) / upsampled_size[1],
                            (1. / num_grid),
                            rounding_mode='trunc')))

                top = max(top_box, coord_h - 1)
                down = min(down_box, coord_h + 1)
                left = max(coord_w - 1, left_box)
                right = min(right_box, coord_w + 1)

                labels[top:(down + 1), left:(right + 1)] = gt_label
                # ins
                gt_mask = np.uint8(gt_mask.cpu().numpy())
                # Follow the original implementation, F.interpolate is
                # different from cv2 and opencv
                gt_mask = mmcv.imrescale(gt_mask, scale=1. / output_stride)
                gt_mask = torch.from_numpy(gt_mask).to(device=device)

                for i in range(top, down + 1):
                    for j in range(left, right + 1):
                        index = int(i * num_grid + j)
                        mask_target[index, :gt_mask.shape[0], :gt_mask.
                                    shape[1]] = gt_mask
                        pos_mask[index] = True
            mlvl_pos_mask_targets.append(mask_target[pos_mask])
            mlvl_labels.append(labels)
            mlvl_pos_masks.append(pos_mask)
        return mlvl_pos_mask_targets, mlvl_labels, mlvl_pos_masks

    def predict_by_feat(self, mlvl_mask_preds: List[Tensor],
                        mlvl_cls_scores: List[Tensor],
                        batch_img_metas: List[dict], **kwargs) -> InstanceList:
        """Transform a batch of output features extracted from the head into
        mask results.

        Args:
            mlvl_mask_preds (list[Tensor]): Multi-level mask prediction.
                Each element in the list has shape
                (batch_size, num_grids**2 ,h ,w).
            mlvl_cls_scores (list[Tensor]): Multi-level scores. Each element
                in the list has shape
                (batch_size, num_classes, num_grids ,num_grids).
            batch_img_metas (list[dict]): Meta information of all images.

        Returns:
            list[:obj:`InstanceData`]: Processed results of multiple
            images.Each :obj:`InstanceData` usually contains
            following keys.

                - scores (Tensor): Classification scores, has shape
                  (num_instance,).
                - labels (Tensor): Has shape (num_instances,).
                - masks (Tensor): Processed mask results, has
                  shape (num_instances, h, w).
        """
        mlvl_cls_scores = [
            item.permute(0, 2, 3, 1) for item in mlvl_cls_scores
        ]
        assert len(mlvl_mask_preds) == len(mlvl_cls_scores)
        num_levels = len(mlvl_cls_scores)

        results_list = []
        for img_id in range(len(batch_img_metas)):
            cls_pred_list = [
                mlvl_cls_scores[lvl][img_id].view(-1, self.cls_out_channels)
                for lvl in range(num_levels)
            ]
            mask_pred_list = [
                mlvl_mask_preds[lvl][img_id] for lvl in range(num_levels)
            ]

            cls_pred_list = torch.cat(cls_pred_list, dim=0)
            mask_pred_list = torch.cat(mask_pred_list, dim=0)
            img_meta = batch_img_metas[img_id]

            results = self._predict_by_feat_single(
                cls_pred_list, mask_pred_list, img_meta=img_meta)
            results_list.append(results)

        return results_list

    def _predict_by_feat_single(self,
                                cls_scores: Tensor,
                                mask_preds: Tensor,
                                img_meta: dict,
                                cfg: OptConfigType = None) -> InstanceData:
        """Transform a single image's features extracted from the head into
        mask results.

        Args:
            cls_scores (Tensor): Classification score of all points
                in single image, has shape (num_points, num_classes).
            mask_preds (Tensor): Mask prediction of all points in
                single image, has shape (num_points, feat_h, feat_w).
            img_meta (dict): Meta information of corresponding image.
            cfg (dict, optional): Config used in test phase.
                Defaults to None.

        Returns:
            :obj:`InstanceData`: Processed results of single image.
             it usually contains following keys.

                - scores (Tensor): Classification scores, has shape
                  (num_instance,).
                - labels (Tensor): Has shape (num_instances,).
                - masks (Tensor): Processed mask results, has
                  shape (num_instances, h, w).
        """

        def empty_results(cls_scores, ori_shape):
            """Generate a empty results."""
            results = InstanceData()
            results.scores = cls_scores.new_ones(0)
            results.masks = cls_scores.new_zeros(0, *ori_shape)
            results.labels = cls_scores.new_ones(0)
            results.bboxes = cls_scores.new_zeros(0, 4)
            return results

        cfg = self.test_cfg if cfg is None else cfg
        assert len(cls_scores) == len(mask_preds)

        featmap_size = mask_preds.size()[-2:]

        h, w = img_meta['img_shape'][:2]
        upsampled_size = (featmap_size[0] * 4, featmap_size[1] * 4)

        score_mask = (cls_scores > cfg.score_thr)
        cls_scores = cls_scores[score_mask]
        if len(cls_scores) == 0:
            return empty_results(cls_scores, img_meta['ori_shape'][:2])

        inds = score_mask.nonzero()
        cls_labels = inds[:, 1]

        # Filter the mask mask with an area is smaller than
        # stride of corresponding feature level
        lvl_interval = cls_labels.new_tensor(self.num_grids).pow(2).cumsum(0)
        strides = cls_scores.new_ones(lvl_interval[-1])
        strides[:lvl_interval[0]] *= self.strides[0]
        for lvl in range(1, self.num_levels):
            strides[lvl_interval[lvl -
                                 1]:lvl_interval[lvl]] *= self.strides[lvl]
        strides = strides[inds[:, 0]]
        mask_preds = mask_preds[inds[:, 0]]

        masks = mask_preds > cfg.mask_thr
        sum_masks = masks.sum((1, 2)).float()
        keep = sum_masks > strides
        if keep.sum() == 0:
            return empty_results(cls_scores, img_meta['ori_shape'][:2])
        masks = masks[keep]
        mask_preds = mask_preds[keep]
        sum_masks = sum_masks[keep]
        cls_scores = cls_scores[keep]
        cls_labels = cls_labels[keep]

        # maskness.
        mask_scores = (mask_preds * masks).sum((1, 2)) / sum_masks
        cls_scores *= mask_scores

        scores, labels, _, keep_inds = mask_matrix_nms(
            masks,
            cls_labels,
            cls_scores,
            mask_area=sum_masks,
            nms_pre=cfg.nms_pre,
            max_num=cfg.max_per_img,
            kernel=cfg.kernel,
            sigma=cfg.sigma,
            filter_thr=cfg.filter_thr)
        # mask_matrix_nms may return an empty Tensor
        if len(keep_inds) == 0:
            return empty_results(cls_scores, img_meta['ori_shape'][:2])
        mask_preds = mask_preds[keep_inds]
        mask_preds = F.interpolate(
            mask_preds.unsqueeze(0), size=upsampled_size,
            mode='bilinear')[:, :, :h, :w]
        mask_preds = F.interpolate(
            mask_preds, size=img_meta['ori_shape'][:2],
            mode='bilinear').squeeze(0)
        masks = mask_preds > cfg.mask_thr

        results = InstanceData()
        results.masks = masks
        results.labels = labels
        results.scores = scores
        # create an empty bbox in InstanceData to avoid bugs when
        # calculating metrics.
        bboxes = mask2bbox(masks)
        # results.bboxes = results.scores.new_zeros(len(scores), 4)
        results.bboxes = bboxes
        return results


@MODELS.register_module()
class DecoupledSOLOHead(SOLOHead):
    """Decoupled SOLO mask head used in `SOLO: Segmenting Objects by Locations.

    <https://arxiv.org/abs/1912.04488>`_

    Args:
        init_cfg (dict or list[dict], optional): Initialization config dict.
    """

    def __init__(self,
                 *args,
                 init_cfg: MultiConfig = [
                     dict(type='Normal', layer='Conv2d', std=0.01),
                     dict(
                         type='Normal',
                         std=0.01,
                         bias_prob=0.01,
                         override=dict(name='conv_mask_list_x')),
                     dict(
                         type='Normal',
                         std=0.01,
                         bias_prob=0.01,
                         override=dict(name='conv_mask_list_y')),
                     dict(
                         type='Normal',
                         std=0.01,
                         bias_prob=0.01,
                         override=dict(name='conv_cls'))
                 ],
                 **kwargs) -> None:
        super().__init__(*args, init_cfg=init_cfg, **kwargs)

    def _init_layers(self) -> None:
        self.mask_convs_x = nn.ModuleList()
        self.mask_convs_y = nn.ModuleList()
        self.cls_convs = nn.ModuleList()

        for i in range(self.stacked_convs):
            chn = self.in_channels + 1 if i == 0 else self.feat_channels
            self.mask_convs_x.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    norm_cfg=self.norm_cfg))
            self.mask_convs_y.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    norm_cfg=self.norm_cfg))

            chn = self.in_channels if i == 0 else self.feat_channels
            self.cls_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    norm_cfg=self.norm_cfg))

        self.conv_mask_list_x = nn.ModuleList()
        self.conv_mask_list_y = nn.ModuleList()
        for num_grid in self.num_grids:
            self.conv_mask_list_x.append(
                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
            self.conv_mask_list_y.append(
                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
        self.conv_cls = nn.Conv2d(
            self.feat_channels, self.cls_out_channels, 3, padding=1)

    def forward(self, x: Tuple[Tensor]) -> Tuple:
        """Forward features from the upstream network.

        Args:
            x (tuple[Tensor]): Features from the upstream network, each is
                a 4D-tensor.

        Returns:
            tuple: A tuple of classification scores and mask prediction.

                - mlvl_mask_preds_x (list[Tensor]): Multi-level mask prediction
                  from x branch. Each element in the list has shape
                  (batch_size, num_grids ,h ,w).
                - mlvl_mask_preds_y (list[Tensor]): Multi-level mask prediction
                  from y branch. Each element in the list has shape
                  (batch_size, num_grids ,h ,w).
                - mlvl_cls_preds (list[Tensor]): Multi-level scores.
                  Each element in the list has shape
                  (batch_size, num_classes, num_grids ,num_grids).
        """
        assert len(x) == self.num_levels
        feats = self.resize_feats(x)
        mask_preds_x = []
        mask_preds_y = []
        cls_preds = []
        for i in range(self.num_levels):
            x = feats[i]
            mask_feat = x
            cls_feat = x
            # generate and concat the coordinate
            coord_feat = generate_coordinate(mask_feat.size(),
                                             mask_feat.device)
            mask_feat_x = torch.cat([mask_feat, coord_feat[:, 0:1, ...]], 1)
            mask_feat_y = torch.cat([mask_feat, coord_feat[:, 1:2, ...]], 1)

            for mask_layer_x, mask_layer_y in \
                    zip(self.mask_convs_x, self.mask_convs_y):
                mask_feat_x = mask_layer_x(mask_feat_x)
                mask_feat_y = mask_layer_y(mask_feat_y)

            mask_feat_x = F.interpolate(
                mask_feat_x, scale_factor=2, mode='bilinear')
            mask_feat_y = F.interpolate(
                mask_feat_y, scale_factor=2, mode='bilinear')

            mask_pred_x = self.conv_mask_list_x[i](mask_feat_x)
            mask_pred_y = self.conv_mask_list_y[i](mask_feat_y)

            # cls branch
            for j, cls_layer in enumerate(self.cls_convs):
                if j == self.cls_down_index:
                    num_grid = self.num_grids[i]
                    cls_feat = F.interpolate(
                        cls_feat, size=num_grid, mode='bilinear')
                cls_feat = cls_layer(cls_feat)

            cls_pred = self.conv_cls(cls_feat)

            if not self.training:
                feat_wh = feats[0].size()[-2:]
                upsampled_size = (feat_wh[0] * 2, feat_wh[1] * 2)
                mask_pred_x = F.interpolate(
                    mask_pred_x.sigmoid(),
                    size=upsampled_size,
                    mode='bilinear')
                mask_pred_y = F.interpolate(
                    mask_pred_y.sigmoid(),
                    size=upsampled_size,
                    mode='bilinear')
                cls_pred = cls_pred.sigmoid()
                # get local maximum
                local_max = F.max_pool2d(cls_pred, 2, stride=1, padding=1)
                keep_mask = local_max[:, :, :-1, :-1] == cls_pred
                cls_pred = cls_pred * keep_mask

            mask_preds_x.append(mask_pred_x)
            mask_preds_y.append(mask_pred_y)
            cls_preds.append(cls_pred)
        return mask_preds_x, mask_preds_y, cls_preds

    def loss_by_feat(self, mlvl_mask_preds_x: List[Tensor],
                     mlvl_mask_preds_y: List[Tensor],
                     mlvl_cls_preds: List[Tensor],
                     batch_gt_instances: InstanceList,
                     batch_img_metas: List[dict], **kwargs) -> dict:
        """Calculate the loss based on the features extracted by the mask head.

        Args:
            mlvl_mask_preds_x (list[Tensor]): Multi-level mask prediction
                from x branch. Each element in the list has shape
                (batch_size, num_grids ,h ,w).
            mlvl_mask_preds_y (list[Tensor]): Multi-level mask prediction
                from y branch. Each element in the list has shape
                (batch_size, num_grids ,h ,w).
            mlvl_cls_preds (list[Tensor]): Multi-level scores. Each element
                in the list has shape
                (batch_size, num_classes, num_grids ,num_grids).
            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
                gt_instance. It usually includes ``bboxes``, ``masks``,
                and ``labels`` attributes.
            batch_img_metas (list[dict]): Meta information of multiple images.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        num_levels = self.num_levels
        num_imgs = len(batch_img_metas)
        featmap_sizes = [featmap.size()[-2:] for featmap in mlvl_mask_preds_x]

        pos_mask_targets, labels, xy_pos_indexes = multi_apply(
            self._get_targets_single,
            batch_gt_instances,
            featmap_sizes=featmap_sizes)

        # change from the outside list meaning multi images
        # to the outside list meaning multi levels
        mlvl_pos_mask_targets = [[] for _ in range(num_levels)]
        mlvl_pos_mask_preds_x = [[] for _ in range(num_levels)]
        mlvl_pos_mask_preds_y = [[] for _ in range(num_levels)]
        mlvl_labels = [[] for _ in range(num_levels)]
        for img_id in range(num_imgs):

            for lvl in range(num_levels):
                mlvl_pos_mask_targets[lvl].append(
                    pos_mask_targets[img_id][lvl])
                mlvl_pos_mask_preds_x[lvl].append(
                    mlvl_mask_preds_x[lvl][img_id,
                                           xy_pos_indexes[img_id][lvl][:, 1]])
                mlvl_pos_mask_preds_y[lvl].append(
                    mlvl_mask_preds_y[lvl][img_id,
                                           xy_pos_indexes[img_id][lvl][:, 0]])
                mlvl_labels[lvl].append(labels[img_id][lvl].flatten())

        # cat multiple image
        temp_mlvl_cls_preds = []
        for lvl in range(num_levels):
            mlvl_pos_mask_targets[lvl] = torch.cat(
                mlvl_pos_mask_targets[lvl], dim=0)
            mlvl_pos_mask_preds_x[lvl] = torch.cat(
                mlvl_pos_mask_preds_x[lvl], dim=0)
            mlvl_pos_mask_preds_y[lvl] = torch.cat(
                mlvl_pos_mask_preds_y[lvl], dim=0)
            mlvl_labels[lvl] = torch.cat(mlvl_labels[lvl], dim=0)
            temp_mlvl_cls_preds.append(mlvl_cls_preds[lvl].permute(
                0, 2, 3, 1).reshape(-1, self.cls_out_channels))

        num_pos = 0.
        # dice loss
        loss_mask = []
        for pred_x, pred_y, target in \
                zip(mlvl_pos_mask_preds_x,
                    mlvl_pos_mask_preds_y, mlvl_pos_mask_targets):
            num_masks = pred_x.size(0)
            if num_masks == 0:
                # make sure can get grad
                loss_mask.append((pred_x.sum() + pred_y.sum()).unsqueeze(0))
                continue
            num_pos += num_masks
            pred_mask = pred_y.sigmoid() * pred_x.sigmoid()
            loss_mask.append(
                self.loss_mask(pred_mask, target, reduction_override='none'))
        if num_pos > 0:
            loss_mask = torch.cat(loss_mask).sum() / num_pos
        else:
            loss_mask = torch.cat(loss_mask).mean()

        # cate
        flatten_labels = torch.cat(mlvl_labels)
        flatten_cls_preds = torch.cat(temp_mlvl_cls_preds)

        loss_cls = self.loss_cls(
            flatten_cls_preds, flatten_labels, avg_factor=num_pos + 1)
        return dict(loss_mask=loss_mask, loss_cls=loss_cls)

    def _get_targets_single(self,
                            gt_instances: InstanceData,
                            featmap_sizes: Optional[list] = None) -> tuple:
        """Compute targets for predictions of single image.

        Args:
            gt_instances (:obj:`InstanceData`): Ground truth of instance
                annotations. It should includes ``bboxes``, ``labels``,
                and ``masks`` attributes.
            featmap_sizes (list[:obj:`torch.size`]): Size of each
                feature map from feature pyramid, each element
                means (feat_h, feat_w). Defaults to None.

        Returns:
            Tuple: Usually returns a tuple containing targets for predictions.

                - mlvl_pos_mask_targets (list[Tensor]): Each element represent
                  the binary mask targets for positive points in this
                  level, has shape (num_pos, out_h, out_w).
                - mlvl_labels (list[Tensor]): Each element is
                  classification labels for all
                  points in this level, has shape
                  (num_grid, num_grid).
                - mlvl_xy_pos_indexes (list[Tensor]): Each element
                  in the list contains the index of positive samples in
                  corresponding level, has shape (num_pos, 2), last
                  dimension 2 present (index_x, index_y).
        """
        mlvl_pos_mask_targets, mlvl_labels, mlvl_pos_masks = \
            super()._get_targets_single(gt_instances,
                                        featmap_sizes=featmap_sizes)

        mlvl_xy_pos_indexes = [(item - self.num_classes).nonzero()
                               for item in mlvl_labels]

        return mlvl_pos_mask_targets, mlvl_labels, mlvl_xy_pos_indexes

    def predict_by_feat(self, mlvl_mask_preds_x: List[Tensor],
                        mlvl_mask_preds_y: List[Tensor],
                        mlvl_cls_scores: List[Tensor],
                        batch_img_metas: List[dict], **kwargs) -> InstanceList:
        """Transform a batch of output features extracted from the head into
        mask results.

        Args:
            mlvl_mask_preds_x (list[Tensor]): Multi-level mask prediction
                from x branch. Each element in the list has shape
                (batch_size, num_grids ,h ,w).
            mlvl_mask_preds_y (list[Tensor]): Multi-level mask prediction
                from y branch. Each element in the list has shape
                (batch_size, num_grids ,h ,w).
            mlvl_cls_scores (list[Tensor]): Multi-level scores. Each element
                in the list has shape
                (batch_size, num_classes ,num_grids ,num_grids).
            batch_img_metas (list[dict]): Meta information of all images.

        Returns:
            list[:obj:`InstanceData`]: Processed results of multiple
            images.Each :obj:`InstanceData` usually contains
            following keys.

                - scores (Tensor): Classification scores, has shape
                  (num_instance,).
                - labels (Tensor): Has shape (num_instances,).
                - masks (Tensor): Processed mask results, has
                  shape (num_instances, h, w).
        """
        mlvl_cls_scores = [
            item.permute(0, 2, 3, 1) for item in mlvl_cls_scores
        ]
        assert len(mlvl_mask_preds_x) == len(mlvl_cls_scores)
        num_levels = len(mlvl_cls_scores)

        results_list = []
        for img_id in range(len(batch_img_metas)):
            cls_pred_list = [
                mlvl_cls_scores[i][img_id].view(
                    -1, self.cls_out_channels).detach()
                for i in range(num_levels)
            ]
            mask_pred_list_x = [
                mlvl_mask_preds_x[i][img_id] for i in range(num_levels)
            ]
            mask_pred_list_y = [
                mlvl_mask_preds_y[i][img_id] for i in range(num_levels)
            ]

            cls_pred_list = torch.cat(cls_pred_list, dim=0)
            mask_pred_list_x = torch.cat(mask_pred_list_x, dim=0)
            mask_pred_list_y = torch.cat(mask_pred_list_y, dim=0)
            img_meta = batch_img_metas[img_id]

            results = self._predict_by_feat_single(
                cls_pred_list,
                mask_pred_list_x,
                mask_pred_list_y,
                img_meta=img_meta)
            results_list.append(results)
        return results_list

    def _predict_by_feat_single(self,
                                cls_scores: Tensor,
                                mask_preds_x: Tensor,
                                mask_preds_y: Tensor,
                                img_meta: dict,
                                cfg: OptConfigType = None) -> InstanceData:
        """Transform a single image's features extracted from the head into
        mask results.

        Args:
            cls_scores (Tensor): Classification score of all points
                in single image, has shape (num_points, num_classes).
            mask_preds_x (Tensor): Mask prediction of x branch of
                all points in single image, has shape
                (sum_num_grids, feat_h, feat_w).
            mask_preds_y (Tensor): Mask prediction of y branch of
                all points in single image, has shape
                (sum_num_grids, feat_h, feat_w).
            img_meta (dict): Meta information of corresponding image.
            cfg (dict): Config used in test phase.

        Returns:
            :obj:`InstanceData`: Processed results of single image.
             it usually contains following keys.

                - scores (Tensor): Classification scores, has shape
                  (num_instance,).
                - labels (Tensor): Has shape (num_instances,).
                - masks (Tensor): Processed mask results, has
                  shape (num_instances, h, w).
        """

        def empty_results(cls_scores, ori_shape):
            """Generate a empty results."""
            results = InstanceData()
            results.scores = cls_scores.new_ones(0)
            results.masks = cls_scores.new_zeros(0, *ori_shape)
            results.labels = cls_scores.new_ones(0)
            results.bboxes = cls_scores.new_zeros(0, 4)
            return results

        cfg = self.test_cfg if cfg is None else cfg

        featmap_size = mask_preds_x.size()[-2:]

        h, w = img_meta['img_shape'][:2]
        upsampled_size = (featmap_size[0] * 4, featmap_size[1] * 4)

        score_mask = (cls_scores > cfg.score_thr)
        cls_scores = cls_scores[score_mask]
        inds = score_mask.nonzero()
        lvl_interval = inds.new_tensor(self.num_grids).pow(2).cumsum(0)
        num_all_points = lvl_interval[-1]
        lvl_start_index = inds.new_ones(num_all_points)
        num_grids = inds.new_ones(num_all_points)
        seg_size = inds.new_tensor(self.num_grids).cumsum(0)
        mask_lvl_start_index = inds.new_ones(num_all_points)
        strides = inds.new_ones(num_all_points)

        lvl_start_index[:lvl_interval[0]] *= 0
        mask_lvl_start_index[:lvl_interval[0]] *= 0
        num_grids[:lvl_interval[0]] *= self.num_grids[0]
        strides[:lvl_interval[0]] *= self.strides[0]

        for lvl in range(1, self.num_levels):
            lvl_start_index[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
                lvl_interval[lvl - 1]
            mask_lvl_start_index[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
                seg_size[lvl - 1]
            num_grids[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
                self.num_grids[lvl]
            strides[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
                self.strides[lvl]

        lvl_start_index = lvl_start_index[inds[:, 0]]
        mask_lvl_start_index = mask_lvl_start_index[inds[:, 0]]
        num_grids = num_grids[inds[:, 0]]
        strides = strides[inds[:, 0]]

        y_lvl_offset = (inds[:, 0] - lvl_start_index) // num_grids
        x_lvl_offset = (inds[:, 0] - lvl_start_index) % num_grids
        y_inds = mask_lvl_start_index + y_lvl_offset
        x_inds = mask_lvl_start_index + x_lvl_offset

        cls_labels = inds[:, 1]
        mask_preds = mask_preds_x[x_inds, ...] * mask_preds_y[y_inds, ...]

        masks = mask_preds > cfg.mask_thr
        sum_masks = masks.sum((1, 2)).float()
        keep = sum_masks > strides
        if keep.sum() == 0:
            return empty_results(cls_scores, img_meta['ori_shape'][:2])

        masks = masks[keep]
        mask_preds = mask_preds[keep]
        sum_masks = sum_masks[keep]
        cls_scores = cls_scores[keep]
        cls_labels = cls_labels[keep]

        # maskness.
        mask_scores = (mask_preds * masks).sum((1, 2)) / sum_masks
        cls_scores *= mask_scores

        scores, labels, _, keep_inds = mask_matrix_nms(
            masks,
            cls_labels,
            cls_scores,
            mask_area=sum_masks,
            nms_pre=cfg.nms_pre,
            max_num=cfg.max_per_img,
            kernel=cfg.kernel,
            sigma=cfg.sigma,
            filter_thr=cfg.filter_thr)
        # mask_matrix_nms may return an empty Tensor
        if len(keep_inds) == 0:
            return empty_results(cls_scores, img_meta['ori_shape'][:2])
        mask_preds = mask_preds[keep_inds]
        mask_preds = F.interpolate(
            mask_preds.unsqueeze(0), size=upsampled_size,
            mode='bilinear')[:, :, :h, :w]
        mask_preds = F.interpolate(
            mask_preds, size=img_meta['ori_shape'][:2],
            mode='bilinear').squeeze(0)
        masks = mask_preds > cfg.mask_thr

        results = InstanceData()
        results.masks = masks
        results.labels = labels
        results.scores = scores
        # create an empty bbox in InstanceData to avoid bugs when
        # calculating metrics.
        bboxes = mask2bbox(masks)
        # results.bboxes = results.scores.new_zeros(len(scores), 4)
        results.bboxes = bboxes

        return results


@MODELS.register_module()
class DecoupledSOLOLightHead(DecoupledSOLOHead):
    """Decoupled Light SOLO mask head used in `SOLO: Segmenting Objects by
    Locations <https://arxiv.org/abs/1912.04488>`_

    Args:
        with_dcn (bool): Whether use dcn in mask_convs and cls_convs,
            Defaults to False.
        init_cfg (dict or list[dict], optional): Initialization config dict.
    """

    def __init__(self,
                 *args,
                 dcn_cfg: OptConfigType = None,
                 init_cfg: MultiConfig = [
                     dict(type='Normal', layer='Conv2d', std=0.01),
                     dict(
                         type='Normal',
                         std=0.01,
                         bias_prob=0.01,
                         override=dict(name='conv_mask_list_x')),
                     dict(
                         type='Normal',
                         std=0.01,
                         bias_prob=0.01,
                         override=dict(name='conv_mask_list_y')),
                     dict(
                         type='Normal',
                         std=0.01,
                         bias_prob=0.01,
                         override=dict(name='conv_cls'))
                 ],
                 **kwargs) -> None:
        assert dcn_cfg is None or isinstance(dcn_cfg, dict)
        self.dcn_cfg = dcn_cfg
        super().__init__(*args, init_cfg=init_cfg, **kwargs)

    def _init_layers(self) -> None:
        self.mask_convs = nn.ModuleList()
        self.cls_convs = nn.ModuleList()

        for i in range(self.stacked_convs):
            if self.dcn_cfg is not None \
                    and i == self.stacked_convs - 1:
                conv_cfg = self.dcn_cfg
            else:
                conv_cfg = None

            chn = self.in_channels + 2 if i == 0 else self.feat_channels
            self.mask_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    conv_cfg=conv_cfg,
                    norm_cfg=self.norm_cfg))

            chn = self.in_channels if i == 0 else self.feat_channels
            self.cls_convs.append(
                ConvModule(
                    chn,
                    self.feat_channels,
                    3,
                    stride=1,
                    padding=1,
                    conv_cfg=conv_cfg,
                    norm_cfg=self.norm_cfg))

        self.conv_mask_list_x = nn.ModuleList()
        self.conv_mask_list_y = nn.ModuleList()
        for num_grid in self.num_grids:
            self.conv_mask_list_x.append(
                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
            self.conv_mask_list_y.append(
                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
        self.conv_cls = nn.Conv2d(
            self.feat_channels, self.cls_out_channels, 3, padding=1)

    def forward(self, x: Tuple[Tensor]) -> Tuple:
        """Forward features from the upstream network.

        Args:
            x (tuple[Tensor]): Features from the upstream network, each is
                a 4D-tensor.

        Returns:
            tuple: A tuple of classification scores and mask prediction.

                - mlvl_mask_preds_x (list[Tensor]): Multi-level mask prediction
                  from x branch. Each element in the list has shape
                  (batch_size, num_grids ,h ,w).
                - mlvl_mask_preds_y (list[Tensor]): Multi-level mask prediction
                  from y branch. Each element in the list has shape
                  (batch_size, num_grids ,h ,w).
                - mlvl_cls_preds (list[Tensor]): Multi-level scores.
                  Each element in the list has shape
                  (batch_size, num_classes, num_grids ,num_grids).
        """
        assert len(x) == self.num_levels
        feats = self.resize_feats(x)
        mask_preds_x = []
        mask_preds_y = []
        cls_preds = []
        for i in range(self.num_levels):
            x = feats[i]
            mask_feat = x
            cls_feat = x
            # generate and concat the coordinate
            coord_feat = generate_coordinate(mask_feat.size(),
                                             mask_feat.device)
            mask_feat = torch.cat([mask_feat, coord_feat], 1)

            for mask_layer in self.mask_convs:
                mask_feat = mask_layer(mask_feat)

            mask_feat = F.interpolate(
                mask_feat, scale_factor=2, mode='bilinear')

            mask_pred_x = self.conv_mask_list_x[i](mask_feat)
            mask_pred_y = self.conv_mask_list_y[i](mask_feat)

            # cls branch
            for j, cls_layer in enumerate(self.cls_convs):
                if j == self.cls_down_index:
                    num_grid = self.num_grids[i]
                    cls_feat = F.interpolate(
                        cls_feat, size=num_grid, mode='bilinear')
                cls_feat = cls_layer(cls_feat)

            cls_pred = self.conv_cls(cls_feat)

            if not self.training:
                feat_wh = feats[0].size()[-2:]
                upsampled_size = (feat_wh[0] * 2, feat_wh[1] * 2)
                mask_pred_x = F.interpolate(
                    mask_pred_x.sigmoid(),
                    size=upsampled_size,
                    mode='bilinear')
                mask_pred_y = F.interpolate(
                    mask_pred_y.sigmoid(),
                    size=upsampled_size,
                    mode='bilinear')
                cls_pred = cls_pred.sigmoid()
                # get local maximum
                local_max = F.max_pool2d(cls_pred, 2, stride=1, padding=1)
                keep_mask = local_max[:, :, :-1, :-1] == cls_pred
                cls_pred = cls_pred * keep_mask

            mask_preds_x.append(mask_pred_x)
            mask_preds_y.append(mask_pred_y)
            cls_preds.append(cls_pred)
        return mask_preds_x, mask_preds_y, cls_preds