mirror of https://github.com/hero-y/BHRL
649 lines
29 KiB
Python
649 lines
29 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from mmcv.cnn import Scale
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from mmcv.runner import force_fp32
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from mmdet.core import distance2bbox, multi_apply, multiclass_nms, reduce_mean
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from ..builder import HEADS, build_loss
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from .anchor_free_head import AnchorFreeHead
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INF = 1e8
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@HEADS.register_module()
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class FCOSHead(AnchorFreeHead):
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"""Anchor-free head used in `FCOS <https://arxiv.org/abs/1904.01355>`_.
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The FCOS head does not use anchor boxes. Instead bounding boxes are
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predicted at each pixel and a centerness measure is used to suppress
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low-quality predictions.
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Here norm_on_bbox, centerness_on_reg, dcn_on_last_conv are training
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tricks used in official repo, which will bring remarkable mAP gains
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of up to 4.9. Please see https://github.com/tianzhi0549/FCOS for
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more detail.
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Args:
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num_classes (int): Number of categories excluding the background
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category.
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in_channels (int): Number of channels in the input feature map.
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strides (list[int] | list[tuple[int, int]]): Strides of points
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in multiple feature levels. Default: (4, 8, 16, 32, 64).
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regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
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level points.
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center_sampling (bool): If true, use center sampling. Default: False.
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center_sample_radius (float): Radius of center sampling. Default: 1.5.
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norm_on_bbox (bool): If true, normalize the regression targets
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with FPN strides. Default: False.
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centerness_on_reg (bool): If true, position centerness on the
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regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042.
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Default: False.
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conv_bias (bool | str): If specified as `auto`, it will be decided by the
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norm_cfg. Bias of conv will be set as True if `norm_cfg` is None, otherwise
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False. Default: "auto".
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loss_cls (dict): Config of classification loss.
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loss_bbox (dict): Config of localization loss.
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loss_centerness (dict): Config of centerness loss.
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norm_cfg (dict): dictionary to construct and config norm layer.
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Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True).
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Example:
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>>> self = FCOSHead(11, 7)
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>>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
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>>> cls_score, bbox_pred, centerness = self.forward(feats)
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>>> assert len(cls_score) == len(self.scales)
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""" # noqa: E501
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def __init__(self,
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num_classes,
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in_channels,
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regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
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(512, INF)),
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center_sampling=False,
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center_sample_radius=1.5,
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norm_on_bbox=False,
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centerness_on_reg=False,
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loss_cls=dict(
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type='FocalLoss',
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use_sigmoid=True,
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gamma=2.0,
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alpha=0.25,
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loss_weight=1.0),
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loss_bbox=dict(type='IoULoss', loss_weight=1.0),
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loss_centerness=dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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loss_weight=1.0),
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norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
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init_cfg=dict(
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type='Normal',
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layer='Conv2d',
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std=0.01,
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override=dict(
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type='Normal',
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name='conv_cls',
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std=0.01,
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bias_prob=0.01)),
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**kwargs):
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self.regress_ranges = regress_ranges
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self.center_sampling = center_sampling
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self.center_sample_radius = center_sample_radius
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self.norm_on_bbox = norm_on_bbox
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self.centerness_on_reg = centerness_on_reg
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super().__init__(
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num_classes,
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in_channels,
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loss_cls=loss_cls,
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loss_bbox=loss_bbox,
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norm_cfg=norm_cfg,
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init_cfg=init_cfg,
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**kwargs)
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self.loss_centerness = build_loss(loss_centerness)
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def _init_layers(self):
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"""Initialize layers of the head."""
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super()._init_layers()
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self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1)
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self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
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def forward(self, feats):
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"""Forward features from the upstream network.
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Args:
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feats (tuple[Tensor]): Features from the upstream network, each is
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a 4D-tensor.
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Returns:
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tuple:
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cls_scores (list[Tensor]): Box scores for each scale level, \
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each is a 4D-tensor, the channel number is \
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num_points * num_classes.
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bbox_preds (list[Tensor]): Box energies / deltas for each \
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scale level, each is a 4D-tensor, the channel number is \
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num_points * 4.
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centernesses (list[Tensor]): centerness for each scale level, \
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each is a 4D-tensor, the channel number is num_points * 1.
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"""
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return multi_apply(self.forward_single, feats, self.scales,
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self.strides)
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def forward_single(self, x, scale, stride):
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"""Forward features of a single scale level.
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Args:
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x (Tensor): FPN feature maps of the specified stride.
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scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
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the bbox prediction.
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stride (int): The corresponding stride for feature maps, only
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used to normalize the bbox prediction when self.norm_on_bbox
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is True.
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Returns:
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tuple: scores for each class, bbox predictions and centerness \
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predictions of input feature maps.
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"""
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cls_score, bbox_pred, cls_feat, reg_feat = super().forward_single(x)
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if self.centerness_on_reg:
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centerness = self.conv_centerness(reg_feat)
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else:
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centerness = self.conv_centerness(cls_feat)
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# scale the bbox_pred of different level
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# float to avoid overflow when enabling FP16
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bbox_pred = scale(bbox_pred).float()
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if self.norm_on_bbox:
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bbox_pred = F.relu(bbox_pred)
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if not self.training:
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bbox_pred *= stride
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else:
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bbox_pred = bbox_pred.exp()
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return cls_score, bbox_pred, centerness
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@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
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def loss(self,
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cls_scores,
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bbox_preds,
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centernesses,
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gt_bboxes,
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gt_labels,
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img_metas,
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gt_bboxes_ignore=None):
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"""Compute loss of the head.
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Args:
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cls_scores (list[Tensor]): Box scores for each scale level,
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each is a 4D-tensor, the channel number is
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num_points * num_classes.
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bbox_preds (list[Tensor]): Box energies / deltas for each scale
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level, each is a 4D-tensor, the channel number is
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num_points * 4.
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centernesses (list[Tensor]): centerness for each scale level, each
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is a 4D-tensor, the channel number is num_points * 1.
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gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
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shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
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gt_labels (list[Tensor]): class indices corresponding to each box
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img_metas (list[dict]): Meta information of each image, e.g.,
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image size, scaling factor, etc.
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gt_bboxes_ignore (None | list[Tensor]): specify which bounding
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boxes can be ignored when computing the loss.
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Returns:
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dict[str, Tensor]: A dictionary of loss components.
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"""
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assert len(cls_scores) == len(bbox_preds) == len(centernesses)
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featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
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all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
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bbox_preds[0].device)
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labels, bbox_targets = self.get_targets(all_level_points, gt_bboxes,
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gt_labels)
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num_imgs = cls_scores[0].size(0)
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# flatten cls_scores, bbox_preds and centerness
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flatten_cls_scores = [
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cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
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for cls_score in cls_scores
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]
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flatten_bbox_preds = [
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bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
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for bbox_pred in bbox_preds
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]
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flatten_centerness = [
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centerness.permute(0, 2, 3, 1).reshape(-1)
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for centerness in centernesses
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]
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flatten_cls_scores = torch.cat(flatten_cls_scores)
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flatten_bbox_preds = torch.cat(flatten_bbox_preds)
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flatten_centerness = torch.cat(flatten_centerness)
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flatten_labels = torch.cat(labels)
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flatten_bbox_targets = torch.cat(bbox_targets)
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# repeat points to align with bbox_preds
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flatten_points = torch.cat(
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[points.repeat(num_imgs, 1) for points in all_level_points])
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# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
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bg_class_ind = self.num_classes
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pos_inds = ((flatten_labels >= 0)
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& (flatten_labels < bg_class_ind)).nonzero().reshape(-1)
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num_pos = torch.tensor(
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len(pos_inds), dtype=torch.float, device=bbox_preds[0].device)
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num_pos = max(reduce_mean(num_pos), 1.0)
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loss_cls = self.loss_cls(
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flatten_cls_scores, flatten_labels, avg_factor=num_pos)
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pos_bbox_preds = flatten_bbox_preds[pos_inds]
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pos_centerness = flatten_centerness[pos_inds]
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pos_bbox_targets = flatten_bbox_targets[pos_inds]
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pos_centerness_targets = self.centerness_target(pos_bbox_targets)
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# centerness weighted iou loss
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centerness_denorm = max(
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reduce_mean(pos_centerness_targets.sum().detach()), 1e-6)
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if len(pos_inds) > 0:
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pos_points = flatten_points[pos_inds]
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pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds)
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pos_decoded_target_preds = distance2bbox(pos_points,
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pos_bbox_targets)
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loss_bbox = self.loss_bbox(
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pos_decoded_bbox_preds,
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pos_decoded_target_preds,
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weight=pos_centerness_targets,
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avg_factor=centerness_denorm)
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loss_centerness = self.loss_centerness(
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pos_centerness, pos_centerness_targets, avg_factor=num_pos)
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else:
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loss_bbox = pos_bbox_preds.sum()
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loss_centerness = pos_centerness.sum()
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return dict(
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loss_cls=loss_cls,
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loss_bbox=loss_bbox,
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loss_centerness=loss_centerness)
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@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
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def get_bboxes(self,
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cls_scores,
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bbox_preds,
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centernesses,
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img_metas,
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cfg=None,
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rescale=False,
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with_nms=True):
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"""Transform network output for a batch into bbox predictions.
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Args:
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cls_scores (list[Tensor]): Box scores for each scale level
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with shape (N, num_points * num_classes, H, W).
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bbox_preds (list[Tensor]): Box energies / deltas for each scale
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level with shape (N, num_points * 4, H, W).
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centernesses (list[Tensor]): Centerness for each scale level with
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shape (N, num_points * 1, H, W).
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img_metas (list[dict]): Meta information of each image, e.g.,
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image size, scaling factor, etc.
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cfg (mmcv.Config | None): Test / postprocessing configuration,
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if None, test_cfg would be used. Default: None.
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rescale (bool): If True, return boxes in original image space.
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Default: False.
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with_nms (bool): If True, do nms before return boxes.
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Default: True.
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Returns:
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list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
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The first item is an (n, 5) tensor, where 5 represent
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(tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
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The shape of the second tensor in the tuple is (n,), and
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each element represents the class label of the corresponding
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box.
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"""
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assert len(cls_scores) == len(bbox_preds)
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num_levels = len(cls_scores)
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featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
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mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
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bbox_preds[0].device)
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cls_score_list = [cls_scores[i].detach() for i in range(num_levels)]
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bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)]
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centerness_pred_list = [
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centernesses[i].detach() for i in range(num_levels)
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]
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if torch.onnx.is_in_onnx_export():
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assert len(
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img_metas
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) == 1, 'Only support one input image while in exporting to ONNX'
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img_shapes = img_metas[0]['img_shape_for_onnx']
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else:
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img_shapes = [
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img_metas[i]['img_shape']
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for i in range(cls_scores[0].shape[0])
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]
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scale_factors = [
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img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0])
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]
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result_list = self._get_bboxes(cls_score_list, bbox_pred_list,
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centerness_pred_list, mlvl_points,
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img_shapes, scale_factors, cfg, rescale,
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with_nms)
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return result_list
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def _get_bboxes(self,
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cls_scores,
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bbox_preds,
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centernesses,
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mlvl_points,
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img_shapes,
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scale_factors,
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cfg,
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rescale=False,
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with_nms=True):
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"""Transform outputs for a single batch item into bbox predictions.
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Args:
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cls_scores (list[Tensor]): Box scores for a single scale level
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with shape (N, num_points * num_classes, H, W).
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bbox_preds (list[Tensor]): Box energies / deltas for a single scale
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level with shape (N, num_points * 4, H, W).
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centernesses (list[Tensor]): Centerness for a single scale level
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with shape (N, num_points, H, W).
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mlvl_points (list[Tensor]): Box reference for a single scale level
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with shape (num_total_points, 4).
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img_shapes (list[tuple[int]]): Shape of the input image,
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list[(height, width, 3)].
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scale_factors (list[ndarray]): Scale factor of the image arrange as
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(w_scale, h_scale, w_scale, h_scale).
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cfg (mmcv.Config | None): Test / postprocessing configuration,
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if None, test_cfg would be used.
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rescale (bool): If True, return boxes in original image space.
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Default: False.
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with_nms (bool): If True, do nms before return boxes.
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Default: True.
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Returns:
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tuple(Tensor):
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det_bboxes (Tensor): BBox predictions in shape (n, 5), where
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the first 4 columns are bounding box positions
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(tl_x, tl_y, br_x, br_y) and the 5-th column is a score
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between 0 and 1.
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det_labels (Tensor): A (n,) tensor where each item is the
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predicted class label of the corresponding box.
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"""
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cfg = self.test_cfg if cfg is None else cfg
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assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
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device = cls_scores[0].device
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batch_size = cls_scores[0].shape[0]
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# convert to tensor to keep tracing
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nms_pre_tensor = torch.tensor(
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cfg.get('nms_pre', -1), device=device, dtype=torch.long)
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mlvl_bboxes = []
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mlvl_scores = []
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mlvl_centerness = []
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for cls_score, bbox_pred, centerness, points in zip(
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cls_scores, bbox_preds, centernesses, mlvl_points):
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assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
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scores = cls_score.permute(0, 2, 3, 1).reshape(
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batch_size, -1, self.cls_out_channels).sigmoid()
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centerness = centerness.permute(0, 2, 3,
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1).reshape(batch_size,
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-1).sigmoid()
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bbox_pred = bbox_pred.permute(0, 2, 3,
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1).reshape(batch_size, -1, 4)
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points = points.expand(batch_size, -1, 2)
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# Get top-k prediction
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from mmdet.core.export import get_k_for_topk
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nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1])
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if nms_pre > 0:
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max_scores, _ = (scores * centerness[..., None]).max(-1)
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_, topk_inds = max_scores.topk(nms_pre)
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batch_inds = torch.arange(batch_size).view(
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-1, 1).expand_as(topk_inds).long()
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# Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
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if torch.onnx.is_in_onnx_export():
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transformed_inds = bbox_pred.shape[
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1] * batch_inds + topk_inds
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points = points.reshape(-1,
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2)[transformed_inds, :].reshape(
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batch_size, -1, 2)
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bbox_pred = bbox_pred.reshape(
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-1, 4)[transformed_inds, :].reshape(batch_size, -1, 4)
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scores = scores.reshape(
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-1, self.num_classes)[transformed_inds, :].reshape(
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batch_size, -1, self.num_classes)
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centerness = centerness.reshape(
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-1, 1)[transformed_inds].reshape(batch_size, -1)
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else:
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points = points[batch_inds, topk_inds, :]
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bbox_pred = bbox_pred[batch_inds, topk_inds, :]
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scores = scores[batch_inds, topk_inds, :]
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centerness = centerness[batch_inds, topk_inds]
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bboxes = distance2bbox(points, bbox_pred, max_shape=img_shapes)
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mlvl_bboxes.append(bboxes)
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mlvl_scores.append(scores)
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mlvl_centerness.append(centerness)
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batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
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if rescale:
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batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
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scale_factors).unsqueeze(1)
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batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
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batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1)
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# Replace multiclass_nms with ONNX::NonMaxSuppression in deployment
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if torch.onnx.is_in_onnx_export() and with_nms:
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from mmdet.core.export import add_dummy_nms_for_onnx
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batch_mlvl_scores = batch_mlvl_scores * (
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batch_mlvl_centerness.unsqueeze(2))
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max_output_boxes_per_class = cfg.nms.get(
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'max_output_boxes_per_class', 200)
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iou_threshold = cfg.nms.get('iou_threshold', 0.5)
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score_threshold = cfg.score_thr
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nms_pre = cfg.get('deploy_nms_pre', -1)
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return add_dummy_nms_for_onnx(batch_mlvl_bboxes, batch_mlvl_scores,
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max_output_boxes_per_class,
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iou_threshold, score_threshold,
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nms_pre, cfg.max_per_img)
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# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
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# BG cat_id: num_class
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padding = batch_mlvl_scores.new_zeros(batch_size,
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batch_mlvl_scores.shape[1], 1)
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batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
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|
|
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if with_nms:
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det_results = []
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for (mlvl_bboxes, mlvl_scores,
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mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores,
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batch_mlvl_centerness):
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det_bbox, det_label = multiclass_nms(
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|
mlvl_bboxes,
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|
mlvl_scores,
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cfg.score_thr,
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|
cfg.nms,
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cfg.max_per_img,
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score_factors=mlvl_centerness)
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det_results.append(tuple([det_bbox, det_label]))
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else:
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|
det_results = [
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tuple(mlvl_bs)
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for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores,
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|
batch_mlvl_centerness)
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|
]
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|
return det_results
|
|
|
|
def _get_points_single(self,
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featmap_size,
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|
stride,
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|
dtype,
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|
device,
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|
flatten=False):
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|
"""Get points according to feature map sizes."""
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|
y, x = super()._get_points_single(featmap_size, stride, dtype, device)
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|
points = torch.stack((x.reshape(-1) * stride, y.reshape(-1) * stride),
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|
dim=-1) + stride // 2
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|
return points
|
|
|
|
def get_targets(self, points, gt_bboxes_list, gt_labels_list):
|
|
"""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).
|
|
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
|
|
each has shape (num_gt, 4).
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|
gt_labels_list (list[Tensor]): Ground truth labels of each box,
|
|
each has shape (num_gt,).
|
|
|
|
Returns:
|
|
tuple:
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|
concat_lvl_labels (list[Tensor]): Labels of each level. \
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|
concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
|
|
level.
|
|
"""
|
|
assert len(points) == len(self.regress_ranges)
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|
num_levels = len(points)
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|
# 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_target_single,
|
|
gt_bboxes_list,
|
|
gt_labels_list,
|
|
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_target_single(self, gt_bboxes, gt_labels, points, regress_ranges,
|
|
num_points_per_lvl):
|
|
"""Compute regression and classification targets for a single image."""
|
|
num_points = points.size(0)
|
|
num_gts = gt_labels.size(0)
|
|
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):
|
|
"""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)
|