mmdet/models/dense_heads/fcos_head.py

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

import torch
import torch.nn as nn
from mmcv.cnn import Scale
from mmengine.structures import InstanceData
from torch import Tensor

from mmdet.registry import MODELS
from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
                         OptInstanceList, RangeType, reduce_mean)
from ..utils import multi_apply
from .anchor_free_head import AnchorFreeHead

INF = 1e8


@MODELS.register_module()
class FCOSHead(AnchorFreeHead):
    """Anchor-free head used in `FCOS <https://arxiv.org/abs/1904.01355>`_.

    The FCOS head does not use anchor boxes. Instead bounding boxes are
    predicted at each pixel and a centerness measure is used to suppress
    low-quality predictions.
    Here norm_on_bbox, centerness_on_reg, dcn_on_last_conv are training
    tricks used in official repo, which will bring remarkable mAP gains
    of up to 4.9. Please see https://github.com/tianzhi0549/FCOS for
    more detail.

    Args:
        num_classes (int): Number of categories excluding the background
            category.
        in_channels (int): Number of channels in the input feature map.
        strides (Sequence[int] or Sequence[Tuple[int, int]]): Strides of points
            in multiple feature levels. Defaults to (4, 8, 16, 32, 64).
        regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
            level points.
        center_sampling (bool): If true, use center sampling.
            Defaults to False.
        center_sample_radius (float): Radius of center sampling.
            Defaults to 1.5.
        norm_on_bbox (bool): If true, normalize the regression targets with
            FPN strides. Defaults to False.
        centerness_on_reg (bool): If true, position centerness on the
            regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042.
            Defaults to False.
        conv_bias (bool or str): If specified as `auto`, it will be decided by
            the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
            None, otherwise False. Defaults to "auto".
        loss_cls (:obj:`ConfigDict` or dict): Config of classification loss.
        loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss.
        loss_centerness (:obj:`ConfigDict`, or dict): Config of centerness
            loss.
        norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
            config norm layer.  Defaults to
            ``norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)``.
        init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
            dict]): Initialization config dict.

    Example:
        >>> self = FCOSHead(11, 7)
        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
        >>> cls_score, bbox_pred, centerness = self.forward(feats)
        >>> assert len(cls_score) == len(self.scales)
    """  # noqa: E501

    def __init__(self,
                 num_classes: int,
                 in_channels: int,
                 regress_ranges: RangeType = ((-1, 64), (64, 128), (128, 256),
                                              (256, 512), (512, INF)),
                 center_sampling: bool = False,
                 center_sample_radius: float = 1.5,
                 norm_on_bbox: bool = False,
                 centerness_on_reg: bool = False,
                 loss_cls: ConfigType = dict(
                     type='FocalLoss',
                     use_sigmoid=True,
                     gamma=2.0,
                     alpha=0.25,
                     loss_weight=1.0),
                 loss_bbox: ConfigType = dict(type='IoULoss', loss_weight=1.0),
                 loss_centerness: ConfigType = dict(
                     type='CrossEntropyLoss',
                     use_sigmoid=True,
                     loss_weight=1.0),
                 norm_cfg: ConfigType = dict(
                     type='GN', num_groups=32, requires_grad=True),
                 init_cfg: MultiConfig = dict(
                     type='Normal',
                     layer='Conv2d',
                     std=0.01,
                     override=dict(
                         type='Normal',
                         name='conv_cls',
                         std=0.01,
                         bias_prob=0.01)),
                 **kwargs) -> None:
        self.regress_ranges = regress_ranges
        self.center_sampling = center_sampling
        self.center_sample_radius = center_sample_radius
        self.norm_on_bbox = norm_on_bbox
        self.centerness_on_reg = centerness_on_reg
        super().__init__(
            num_classes=num_classes,
            in_channels=in_channels,
            loss_cls=loss_cls,
            loss_bbox=loss_bbox,
            norm_cfg=norm_cfg,
            init_cfg=init_cfg,
            **kwargs)
        self.loss_centerness = MODELS.build(loss_centerness)

    def _init_layers(self) -> None:
        """Initialize layers of the head."""
        super()._init_layers()
        self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1)
        self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])

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

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

        Returns:
            tuple: A tuple of each level outputs.

            - cls_scores (list[Tensor]): Box scores for each scale level, \
            each is a 4D-tensor, the channel number is \
            num_points * num_classes.
            - bbox_preds (list[Tensor]): Box energies / deltas for each \
            scale level, each is a 4D-tensor, the channel number is \
            num_points * 4.
            - centernesses (list[Tensor]): centerness for each scale level, \
            each is a 4D-tensor, the channel number is num_points * 1.
        """
        return multi_apply(self.forward_single, x, self.scales, self.strides)

    def forward_single(self, x: Tensor, scale: Scale,
                       stride: int) -> Tuple[Tensor, Tensor, Tensor]:
        """Forward features of a single scale level.

        Args:
            x (Tensor): FPN feature maps of the specified stride.
            scale (:obj:`mmcv.cnn.Scale`): Learnable scale module to resize
                the bbox prediction.
            stride (int): The corresponding stride for feature maps, only
                used to normalize the bbox prediction when self.norm_on_bbox
                is True.

        Returns:
            tuple: scores for each class, bbox predictions and centerness
            predictions of input feature maps.
        """
        cls_score, bbox_pred, cls_feat, reg_feat = super().forward_single(x)
        if self.centerness_on_reg:
            centerness = self.conv_centerness(reg_feat)
        else:
            centerness = self.conv_centerness(cls_feat)
        # scale the bbox_pred of different level
        # float to avoid overflow when enabling FP16
        bbox_pred = scale(bbox_pred).float()
        if self.norm_on_bbox:
            # bbox_pred needed for gradient computation has been modified
            # by F.relu(bbox_pred) when run with PyTorch 1.10. So replace
            # F.relu(bbox_pred) with bbox_pred.clamp(min=0)
            bbox_pred = bbox_pred.clamp(min=0)
            if not self.training:
                bbox_pred *= stride
        else:
            bbox_pred = bbox_pred.exp()
        return cls_score, bbox_pred, centerness

    def loss_by_feat(
        self,
        cls_scores: List[Tensor],
        bbox_preds: List[Tensor],
        centernesses: List[Tensor],
        batch_gt_instances: InstanceList,
        batch_img_metas: List[dict],
        batch_gt_instances_ignore: OptInstanceList = None
    ) -> Dict[str, Tensor]:
        """Calculate the loss based on the features extracted by the detection
        head.

        Args:
            cls_scores (list[Tensor]): Box scores for each scale level,
                each is a 4D-tensor, the channel number is
                num_points * num_classes.
            bbox_preds (list[Tensor]): Box energies / deltas for each scale
                level, each is a 4D-tensor, the channel number is
                num_points * 4.
            centernesses (list[Tensor]): centerness for each scale level, each
                is a 4D-tensor, the channel number is num_points * 1.
            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
                gt_instance.  It usually includes ``bboxes`` and ``labels``
                attributes.
            batch_img_metas (list[dict]): Meta information of each image, e.g.,
                image size, scaling factor, etc.
            batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
                Batch of gt_instances_ignore. It includes ``bboxes`` attribute
                data that is ignored during training and testing.
                Defaults to None.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        assert len(cls_scores) == len(bbox_preds) == len(centernesses)
        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
        all_level_points = self.prior_generator.grid_priors(
            featmap_sizes,
            dtype=bbox_preds[0].dtype,
            device=bbox_preds[0].device)
        labels, bbox_targets = self.get_targets(all_level_points,
                                                batch_gt_instances)

        num_imgs = cls_scores[0].size(0)
        # flatten cls_scores, bbox_preds and centerness
        flatten_cls_scores = [
            cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
            for cls_score in cls_scores
        ]
        flatten_bbox_preds = [
            bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
            for bbox_pred in bbox_preds
        ]
        flatten_centerness = [
            centerness.permute(0, 2, 3, 1).reshape(-1)
            for centerness in centernesses
        ]
        flatten_cls_scores = torch.cat(flatten_cls_scores)
        flatten_bbox_preds = torch.cat(flatten_bbox_preds)
        flatten_centerness = torch.cat(flatten_centerness)
        flatten_labels = torch.cat(labels)
        flatten_bbox_targets = torch.cat(bbox_targets)
        # repeat points to align with bbox_preds
        flatten_points = torch.cat(
            [points.repeat(num_imgs, 1) for points in all_level_points])

        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
        bg_class_ind = self.num_classes
        pos_inds = ((flatten_labels >= 0)
                    & (flatten_labels < bg_class_ind)).nonzero().reshape(-1)
        num_pos = torch.tensor(
            len(pos_inds), dtype=torch.float, device=bbox_preds[0].device)
        num_pos = max(reduce_mean(num_pos), 1.0)
        loss_cls = self.loss_cls(
            flatten_cls_scores, flatten_labels, avg_factor=num_pos)

        pos_bbox_preds = flatten_bbox_preds[pos_inds]
        pos_centerness = flatten_centerness[pos_inds]
        pos_bbox_targets = flatten_bbox_targets[pos_inds]
        pos_centerness_targets = self.centerness_target(pos_bbox_targets)
        # centerness weighted iou loss
        centerness_denorm = max(
            reduce_mean(pos_centerness_targets.sum().detach()), 1e-6)

        if len(pos_inds) > 0:
            pos_points = flatten_points[pos_inds]
            pos_decoded_bbox_preds = self.bbox_coder.decode(
                pos_points, pos_bbox_preds)
            pos_decoded_target_preds = self.bbox_coder.decode(
                pos_points, pos_bbox_targets)
            loss_bbox = self.loss_bbox(
                pos_decoded_bbox_preds,
                pos_decoded_target_preds,
                weight=pos_centerness_targets,
                avg_factor=centerness_denorm)
            loss_centerness = self.loss_centerness(
                pos_centerness, pos_centerness_targets, avg_factor=num_pos)
        else:
            loss_bbox = pos_bbox_preds.sum()
            loss_centerness = pos_centerness.sum()

        return dict(
            loss_cls=loss_cls,
            loss_bbox=loss_bbox,
            loss_centerness=loss_centerness)

    def get_targets(
            self, points: List[Tensor], batch_gt_instances: InstanceList
    ) -> Tuple[List[Tensor], List[Tensor]]:
        """Compute regression, classification and centerness targets for points
        in multiple images.

        Args:
            points (list[Tensor]): Points of each fpn level, each has shape
                (num_points, 2).
            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
                gt_instance.  It usually includes ``bboxes`` and ``labels``
                attributes.

        Returns:
            tuple: Targets of each level.

            - concat_lvl_labels (list[Tensor]): Labels of each level.
            - concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
            level.
        """
        assert len(points) == len(self.regress_ranges)
        num_levels = len(points)
        # expand regress ranges to align with points
        expanded_regress_ranges = [
            points[i].new_tensor(self.regress_ranges[i])[None].expand_as(
                points[i]) for i in range(num_levels)
        ]
        # concat all levels points and regress ranges
        concat_regress_ranges = torch.cat(expanded_regress_ranges, dim=0)
        concat_points = torch.cat(points, dim=0)

        # the number of points per img, per lvl
        num_points = [center.size(0) for center in points]

        # get labels and bbox_targets of each image
        labels_list, bbox_targets_list = multi_apply(
            self._get_targets_single,
            batch_gt_instances,
            points=concat_points,
            regress_ranges=concat_regress_ranges,
            num_points_per_lvl=num_points)

        # split to per img, per level
        labels_list = [labels.split(num_points, 0) for labels in labels_list]
        bbox_targets_list = [
            bbox_targets.split(num_points, 0)
            for bbox_targets in bbox_targets_list
        ]

        # concat per level image
        concat_lvl_labels = []
        concat_lvl_bbox_targets = []
        for i in range(num_levels):
            concat_lvl_labels.append(
                torch.cat([labels[i] for labels in labels_list]))
            bbox_targets = torch.cat(
                [bbox_targets[i] for bbox_targets in bbox_targets_list])
            if self.norm_on_bbox:
                bbox_targets = bbox_targets / self.strides[i]
            concat_lvl_bbox_targets.append(bbox_targets)
        return concat_lvl_labels, concat_lvl_bbox_targets

    def _get_targets_single(
            self, gt_instances: InstanceData, points: Tensor,
            regress_ranges: Tensor,
            num_points_per_lvl: List[int]) -> Tuple[Tensor, Tensor]:
        """Compute regression and classification targets for a single image."""
        num_points = points.size(0)
        num_gts = len(gt_instances)
        gt_bboxes = gt_instances.bboxes
        gt_labels = gt_instances.labels

        if num_gts == 0:
            return gt_labels.new_full((num_points,), self.num_classes), \
                   gt_bboxes.new_zeros((num_points, 4))

        areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (
            gt_bboxes[:, 3] - gt_bboxes[:, 1])
        # TODO: figure out why these two are different
        # areas = areas[None].expand(num_points, num_gts)
        areas = areas[None].repeat(num_points, 1)
        regress_ranges = regress_ranges[:, None, :].expand(
            num_points, num_gts, 2)
        gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4)
        xs, ys = points[:, 0], points[:, 1]
        xs = xs[:, None].expand(num_points, num_gts)
        ys = ys[:, None].expand(num_points, num_gts)

        left = xs - gt_bboxes[..., 0]
        right = gt_bboxes[..., 2] - xs
        top = ys - gt_bboxes[..., 1]
        bottom = gt_bboxes[..., 3] - ys
        bbox_targets = torch.stack((left, top, right, bottom), -1)

        if self.center_sampling:
            # condition1: inside a `center bbox`
            radius = self.center_sample_radius
            center_xs = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) / 2
            center_ys = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) / 2
            center_gts = torch.zeros_like(gt_bboxes)
            stride = center_xs.new_zeros(center_xs.shape)

            # project the points on current lvl back to the `original` sizes
            lvl_begin = 0
            for lvl_idx, num_points_lvl in enumerate(num_points_per_lvl):
                lvl_end = lvl_begin + num_points_lvl
                stride[lvl_begin:lvl_end] = self.strides[lvl_idx] * radius
                lvl_begin = lvl_end

            x_mins = center_xs - stride
            y_mins = center_ys - stride
            x_maxs = center_xs + stride
            y_maxs = center_ys + stride
            center_gts[..., 0] = torch.where(x_mins > gt_bboxes[..., 0],
                                             x_mins, gt_bboxes[..., 0])
            center_gts[..., 1] = torch.where(y_mins > gt_bboxes[..., 1],
                                             y_mins, gt_bboxes[..., 1])
            center_gts[..., 2] = torch.where(x_maxs > gt_bboxes[..., 2],
                                             gt_bboxes[..., 2], x_maxs)
            center_gts[..., 3] = torch.where(y_maxs > gt_bboxes[..., 3],
                                             gt_bboxes[..., 3], y_maxs)

            cb_dist_left = xs - center_gts[..., 0]
            cb_dist_right = center_gts[..., 2] - xs
            cb_dist_top = ys - center_gts[..., 1]
            cb_dist_bottom = center_gts[..., 3] - ys
            center_bbox = torch.stack(
                (cb_dist_left, cb_dist_top, cb_dist_right, cb_dist_bottom), -1)
            inside_gt_bbox_mask = center_bbox.min(-1)[0] > 0
        else:
            # condition1: inside a gt bbox
            inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0

        # condition2: limit the regression range for each location
        max_regress_distance = bbox_targets.max(-1)[0]
        inside_regress_range = (
            (max_regress_distance >= regress_ranges[..., 0])
            & (max_regress_distance <= regress_ranges[..., 1]))

        # if there are still more than one objects for a location,
        # we choose the one with minimal area
        areas[inside_gt_bbox_mask == 0] = INF
        areas[inside_regress_range == 0] = INF
        min_area, min_area_inds = areas.min(dim=1)

        labels = gt_labels[min_area_inds]
        labels[min_area == INF] = self.num_classes  # set as BG
        bbox_targets = bbox_targets[range(num_points), min_area_inds]

        return labels, bbox_targets

    def centerness_target(self, pos_bbox_targets: Tensor) -> Tensor:
        """Compute centerness targets.

        Args:
            pos_bbox_targets (Tensor): BBox targets of positive bboxes in shape
                (num_pos, 4)

        Returns:
            Tensor: Centerness target.
        """
        # only calculate pos centerness targets, otherwise there may be nan
        left_right = pos_bbox_targets[:, [0, 2]]
        top_bottom = pos_bbox_targets[:, [1, 3]]
        if len(left_right) == 0:
            centerness_targets = left_right[..., 0]
        else:
            centerness_targets = (
                left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * (
                    top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])
        return torch.sqrt(centerness_targets)