456 lines
19 KiB
Python
456 lines
19 KiB
Python
# All modification made by Kneron Corp.: Copyright (c) 2022 Kneron Corp.
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# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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import torch
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import torch.nn as nn
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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 multi_apply, 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 = bbox_pred.clamp(min=0)
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if not self.training:
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bbox_pred *= stride
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elif torch.onnx.is_in_onnx_export():
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bbox_pred = bbox_pred
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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.prior_generator.grid_priors(
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featmap_sizes,
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dtype=bbox_preds[0].dtype,
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device=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 = self.bbox_coder.decode(
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pos_points, pos_bbox_preds)
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pos_decoded_target_preds = self.bbox_coder.decode(
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pos_points, 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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def get_targets(self, points, gt_bboxes_list, gt_labels_list):
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"""Compute regression, classification and centerness targets for points
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in multiple images.
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Args:
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points (list[Tensor]): Points of each fpn level, each has shape
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(num_points, 2).
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gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
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each has shape (num_gt, 4).
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gt_labels_list (list[Tensor]): Ground truth labels of each box,
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each has shape (num_gt,).
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Returns:
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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 \
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level.
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"""
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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
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expanded_regress_ranges = [
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points[i].new_tensor(self.regress_ranges[i])[None].expand_as(
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points[i]) for i in range(num_levels)
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]
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# concat all levels points and regress ranges
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concat_regress_ranges = torch.cat(expanded_regress_ranges, dim=0)
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concat_points = torch.cat(points, dim=0)
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# the number of points per img, per lvl
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num_points = [center.size(0) for center in points]
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# get labels and bbox_targets of each image
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labels_list, bbox_targets_list = multi_apply(
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self._get_target_single,
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gt_bboxes_list,
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gt_labels_list,
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points=concat_points,
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regress_ranges=concat_regress_ranges,
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num_points_per_lvl=num_points)
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# split to per img, per level
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labels_list = [labels.split(num_points, 0) for labels in labels_list]
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bbox_targets_list = [
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bbox_targets.split(num_points, 0)
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for bbox_targets in bbox_targets_list
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]
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# concat per level image
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concat_lvl_labels = []
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concat_lvl_bbox_targets = []
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for i in range(num_levels):
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concat_lvl_labels.append(
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torch.cat([labels[i] for labels in labels_list]))
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bbox_targets = torch.cat(
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[bbox_targets[i] for bbox_targets in bbox_targets_list])
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if self.norm_on_bbox:
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bbox_targets = bbox_targets / self.strides[i]
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concat_lvl_bbox_targets.append(bbox_targets)
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return concat_lvl_labels, concat_lvl_bbox_targets
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def _get_target_single(self, gt_bboxes, gt_labels, points, regress_ranges,
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num_points_per_lvl):
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"""Compute regression and classification targets for a single image."""
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num_points = points.size(0)
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num_gts = gt_labels.size(0)
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if num_gts == 0:
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return gt_labels.new_full((num_points,), self.num_classes), \
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gt_bboxes.new_zeros((num_points, 4))
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areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (
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gt_bboxes[:, 3] - gt_bboxes[:, 1])
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# TODO: figure out why these two are different
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# areas = areas[None].expand(num_points, num_gts)
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areas = areas[None].repeat(num_points, 1)
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regress_ranges = regress_ranges[:, None, :].expand(
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num_points, num_gts, 2)
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gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4)
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xs, ys = points[:, 0], points[:, 1]
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xs = xs[:, None].expand(num_points, num_gts)
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ys = ys[:, None].expand(num_points, num_gts)
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left = xs - gt_bboxes[..., 0]
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right = gt_bboxes[..., 2] - xs
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top = ys - gt_bboxes[..., 1]
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bottom = gt_bboxes[..., 3] - ys
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bbox_targets = torch.stack((left, top, right, bottom), -1)
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if self.center_sampling:
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# condition1: inside a `center bbox`
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radius = self.center_sample_radius
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center_xs = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) / 2
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center_ys = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) / 2
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center_gts = torch.zeros_like(gt_bboxes)
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stride = center_xs.new_zeros(center_xs.shape)
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# project the points on current lvl back to the `original` sizes
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lvl_begin = 0
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for lvl_idx, num_points_lvl in enumerate(num_points_per_lvl):
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lvl_end = lvl_begin + num_points_lvl
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stride[lvl_begin:lvl_end] = self.strides[lvl_idx] * radius
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lvl_begin = lvl_end
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x_mins = center_xs - stride
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y_mins = center_ys - stride
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x_maxs = center_xs + stride
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y_maxs = center_ys + stride
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center_gts[..., 0] = torch.where(x_mins > gt_bboxes[..., 0],
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x_mins, gt_bboxes[..., 0])
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center_gts[..., 1] = torch.where(y_mins > gt_bboxes[..., 1],
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y_mins, gt_bboxes[..., 1])
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center_gts[..., 2] = torch.where(x_maxs > gt_bboxes[..., 2],
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gt_bboxes[..., 2], x_maxs)
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center_gts[..., 3] = torch.where(y_maxs > gt_bboxes[..., 3],
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gt_bboxes[..., 3], y_maxs)
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cb_dist_left = xs - center_gts[..., 0]
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cb_dist_right = center_gts[..., 2] - xs
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cb_dist_top = ys - center_gts[..., 1]
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cb_dist_bottom = center_gts[..., 3] - ys
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center_bbox = torch.stack(
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(cb_dist_left, cb_dist_top, cb_dist_right, cb_dist_bottom), -1)
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inside_gt_bbox_mask = center_bbox.min(-1)[0] > 0
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else:
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# condition1: inside a gt bbox
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inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0
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# condition2: limit the regression range for each location
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max_regress_distance = bbox_targets.max(-1)[0]
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inside_regress_range = (
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(max_regress_distance >= regress_ranges[..., 0])
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& (max_regress_distance <= regress_ranges[..., 1]))
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# if there are still more than one objects for a location,
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# we choose the one with minimal area
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areas[inside_gt_bbox_mask == 0] = INF
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areas[inside_regress_range == 0] = INF
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min_area, min_area_inds = areas.min(dim=1)
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labels = gt_labels[min_area_inds]
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labels[min_area == INF] = self.num_classes # set as BG
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bbox_targets = bbox_targets[range(num_points), min_area_inds]
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return labels, bbox_targets
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def centerness_target(self, pos_bbox_targets):
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"""Compute centerness targets.
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Args:
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pos_bbox_targets (Tensor): BBox targets of positive bboxes in shape
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(num_pos, 4)
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Returns:
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Tensor: Centerness target.
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"""
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# only calculate pos centerness targets, otherwise there may be nan
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left_right = pos_bbox_targets[:, [0, 2]]
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top_bottom = pos_bbox_targets[:, [1, 3]]
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if len(left_right) == 0:
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centerness_targets = left_right[..., 0]
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else:
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centerness_targets = (
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left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * (
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top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])
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|
return torch.sqrt(centerness_targets)
|
|
|
|
def _get_points_single(self,
|
|
featmap_size,
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|
stride,
|
|
dtype,
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|
device,
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|
flatten=False):
|
|
"""Get points according to feature map size.
|
|
|
|
This function will be deprecated soon.
|
|
"""
|
|
warnings.warn(
|
|
'`_get_points_single` in `FCOSHead` will be '
|
|
'deprecated soon, we support a multi level point generator now'
|
|
'you can get points of a single level feature map '
|
|
'with `self.prior_generator.single_level_grid_priors` ')
|
|
|
|
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),
|
|
dim=-1) + stride // 2
|
|
return points
|