OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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455 lines
19 KiB
455 lines
19 KiB
# 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 needed for gradient computation has been modified |
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# by F.relu(bbox_pred) when run with PyTorch 1.10. So replace |
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# F.relu(bbox_pred) with bbox_pred.clamp(min=0) |
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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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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) |
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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 size. |
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This function will be deprecated soon. |
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""" |
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warnings.warn( |
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'`_get_points_single` in `FCOSHead` will be ' |
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'deprecated soon, we support a multi level point generator now' |
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'you can get points of a single level feature map ' |
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'with `self.prior_generator.single_level_grid_priors` ') |
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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
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