493 lines
20 KiB
Python
493 lines
20 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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import torch.nn as nn
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from mmcv.cnn import ConvModule, Scale
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from mmcv.runner import force_fp32
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from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
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images_to_levels, multi_apply, reduce_mean, unmap)
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from ..builder import HEADS, build_loss
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from .anchor_head import AnchorHead
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@HEADS.register_module()
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class ATSSHead(AnchorHead):
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"""Bridging the Gap Between Anchor-based and Anchor-free Detection via
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Adaptive Training Sample Selection.
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ATSS head structure is similar with FCOS, however ATSS use anchor boxes
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and assign label by Adaptive Training Sample Selection instead max-iou.
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https://arxiv.org/abs/1912.02424
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"""
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def __init__(self,
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num_classes,
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in_channels,
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stacked_convs=4,
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conv_cfg=None,
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norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
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reg_decoded_bbox=True,
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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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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='atss_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.stacked_convs = stacked_convs
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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super(ATSSHead, self).__init__(
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num_classes,
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in_channels,
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reg_decoded_bbox=reg_decoded_bbox,
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init_cfg=init_cfg,
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**kwargs)
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self.sampling = False
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if self.train_cfg:
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self.assigner = build_assigner(self.train_cfg.assigner)
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# SSD sampling=False so use PseudoSampler
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sampler_cfg = dict(type='PseudoSampler')
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self.sampler = build_sampler(sampler_cfg, context=self)
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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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self.relu = nn.ReLU(inplace=True)
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self.cls_convs = nn.ModuleList()
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self.reg_convs = nn.ModuleList()
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for i in range(self.stacked_convs):
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chn = self.in_channels if i == 0 else self.feat_channels
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self.cls_convs.append(
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ConvModule(
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chn,
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self.feat_channels,
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3,
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stride=1,
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padding=1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg))
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self.reg_convs.append(
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ConvModule(
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chn,
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self.feat_channels,
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3,
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stride=1,
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padding=1,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg))
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self.atss_cls = nn.Conv2d(
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self.feat_channels,
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self.num_anchors * self.cls_out_channels,
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3,
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padding=1)
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self.atss_reg = nn.Conv2d(
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self.feat_channels, self.num_base_priors * 4, 3, padding=1)
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self.atss_centerness = nn.Conv2d(
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self.feat_channels, self.num_base_priors * 1, 3, padding=1)
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self.scales = nn.ModuleList(
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[Scale(1.0) for _ in self.prior_generator.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: Usually a tuple of classification scores and bbox prediction
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cls_scores (list[Tensor]): Classification scores for all scale
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levels, each is a 4D-tensor, the channels number is
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num_anchors * num_classes.
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bbox_preds (list[Tensor]): Box energies / deltas for all scale
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levels, each is a 4D-tensor, the channels number is
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num_anchors * 4.
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"""
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return multi_apply(self.forward_single, feats, self.scales)
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def forward_single(self, x, scale):
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"""Forward feature of a single scale level.
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Args:
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x (Tensor): Features of a single scale level.
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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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Returns:
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tuple:
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cls_score (Tensor): Cls scores for a single scale level
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the channels number is num_anchors * num_classes.
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bbox_pred (Tensor): Box energies / deltas for a single scale
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level, the channels number is num_anchors * 4.
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centerness (Tensor): Centerness for a single scale level, the
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channel number is (N, num_anchors * 1, H, W).
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"""
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cls_feat = x
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reg_feat = x
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for cls_conv in self.cls_convs:
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cls_feat = cls_conv(cls_feat)
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for reg_conv in self.reg_convs:
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reg_feat = reg_conv(reg_feat)
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cls_score = self.atss_cls(cls_feat)
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# we just follow atss, not apply exp in bbox_pred
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bbox_pred = scale(self.atss_reg(reg_feat)).float()
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centerness = self.atss_centerness(reg_feat)
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return cls_score, bbox_pred, centerness
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def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
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label_weights, bbox_targets, num_total_samples):
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"""Compute loss of a single scale level.
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Args:
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cls_score (Tensor): Box scores for each scale level
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Has shape (N, num_anchors * num_classes, H, W).
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bbox_pred (Tensor): Box energies / deltas for each scale
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level with shape (N, num_anchors * 4, H, W).
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anchors (Tensor): Box reference for each scale level with shape
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(N, num_total_anchors, 4).
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labels (Tensor): Labels of each anchors with shape
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(N, num_total_anchors).
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label_weights (Tensor): Label weights of each anchor with shape
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(N, num_total_anchors)
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bbox_targets (Tensor): BBox regression targets of each anchor
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weight shape (N, num_total_anchors, 4).
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num_total_samples (int): Number os positive samples that is
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reduced over all GPUs.
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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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anchors = anchors.reshape(-1, 4)
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cls_score = cls_score.permute(0, 2, 3, 1).reshape(
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-1, self.cls_out_channels).contiguous()
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bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
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centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
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bbox_targets = bbox_targets.reshape(-1, 4)
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labels = labels.reshape(-1)
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label_weights = label_weights.reshape(-1)
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# classification loss
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loss_cls = self.loss_cls(
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cls_score, labels, label_weights, avg_factor=num_total_samples)
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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 = ((labels >= 0)
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& (labels < bg_class_ind)).nonzero().squeeze(1)
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if len(pos_inds) > 0:
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pos_bbox_targets = bbox_targets[pos_inds]
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pos_bbox_pred = bbox_pred[pos_inds]
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pos_anchors = anchors[pos_inds]
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pos_centerness = centerness[pos_inds]
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centerness_targets = self.centerness_target(
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pos_anchors, pos_bbox_targets)
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pos_decode_bbox_pred = self.bbox_coder.decode(
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pos_anchors, pos_bbox_pred)
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# regression loss
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loss_bbox = self.loss_bbox(
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pos_decode_bbox_pred,
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pos_bbox_targets,
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weight=centerness_targets,
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avg_factor=1.0)
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# centerness loss
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loss_centerness = self.loss_centerness(
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pos_centerness,
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centerness_targets,
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avg_factor=num_total_samples)
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else:
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loss_bbox = bbox_pred.sum() * 0
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loss_centerness = centerness.sum() * 0
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centerness_targets = bbox_targets.new_tensor(0.)
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return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum()
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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 losses 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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Has shape (N, num_anchors * 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_anchors * 4, H, W)
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centernesses (list[Tensor]): Centerness for each scale
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level with shape (N, num_anchors * 1, H, W)
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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 (list[Tensor] | None): 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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featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
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assert len(featmap_sizes) == self.prior_generator.num_levels
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device = cls_scores[0].device
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anchor_list, valid_flag_list = self.get_anchors(
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featmap_sizes, img_metas, device=device)
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label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
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cls_reg_targets = self.get_targets(
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anchor_list,
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valid_flag_list,
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gt_bboxes,
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img_metas,
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gt_bboxes_ignore_list=gt_bboxes_ignore,
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gt_labels_list=gt_labels,
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label_channels=label_channels)
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if cls_reg_targets is None:
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return None
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(anchor_list, labels_list, label_weights_list, bbox_targets_list,
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bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
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num_total_samples = reduce_mean(
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torch.tensor(num_total_pos, dtype=torch.float,
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device=device)).item()
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num_total_samples = max(num_total_samples, 1.0)
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losses_cls, losses_bbox, loss_centerness,\
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bbox_avg_factor = multi_apply(
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self.loss_single,
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anchor_list,
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cls_scores,
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bbox_preds,
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centernesses,
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labels_list,
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label_weights_list,
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bbox_targets_list,
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num_total_samples=num_total_samples)
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bbox_avg_factor = sum(bbox_avg_factor)
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bbox_avg_factor = reduce_mean(bbox_avg_factor).clamp_(min=1).item()
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losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
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return dict(
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loss_cls=losses_cls,
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loss_bbox=losses_bbox,
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loss_centerness=loss_centerness)
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def centerness_target(self, anchors, gts):
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# only calculate pos centerness targets, otherwise there may be nan
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anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
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anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
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l_ = anchors_cx - gts[:, 0]
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t_ = anchors_cy - gts[:, 1]
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r_ = gts[:, 2] - anchors_cx
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b_ = gts[:, 3] - anchors_cy
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left_right = torch.stack([l_, r_], dim=1)
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top_bottom = torch.stack([t_, b_], dim=1)
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centerness = torch.sqrt(
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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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assert not torch.isnan(centerness).any()
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return centerness
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def get_targets(self,
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anchor_list,
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valid_flag_list,
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gt_bboxes_list,
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img_metas,
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gt_bboxes_ignore_list=None,
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gt_labels_list=None,
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label_channels=1,
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unmap_outputs=True):
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"""Get targets for ATSS head.
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This method is almost the same as `AnchorHead.get_targets()`. Besides
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returning the targets as the parent method does, it also returns the
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anchors as the first element of the returned tuple.
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"""
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num_imgs = len(img_metas)
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assert len(anchor_list) == len(valid_flag_list) == num_imgs
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# anchor number of multi levels
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num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
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num_level_anchors_list = [num_level_anchors] * num_imgs
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# concat all level anchors and flags to a single tensor
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for i in range(num_imgs):
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assert len(anchor_list[i]) == len(valid_flag_list[i])
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anchor_list[i] = torch.cat(anchor_list[i])
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valid_flag_list[i] = torch.cat(valid_flag_list[i])
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# compute targets for each image
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if gt_bboxes_ignore_list is None:
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gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
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if gt_labels_list is None:
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gt_labels_list = [None for _ in range(num_imgs)]
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(all_anchors, all_labels, all_label_weights, all_bbox_targets,
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all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
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self._get_target_single,
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anchor_list,
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valid_flag_list,
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num_level_anchors_list,
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gt_bboxes_list,
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gt_bboxes_ignore_list,
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gt_labels_list,
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img_metas,
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label_channels=label_channels,
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unmap_outputs=unmap_outputs)
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# no valid anchors
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if any([labels is None for labels in all_labels]):
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return None
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# sampled anchors of all images
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num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
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num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
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# split targets to a list w.r.t. multiple levels
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anchors_list = images_to_levels(all_anchors, num_level_anchors)
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labels_list = images_to_levels(all_labels, num_level_anchors)
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label_weights_list = images_to_levels(all_label_weights,
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num_level_anchors)
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bbox_targets_list = images_to_levels(all_bbox_targets,
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num_level_anchors)
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bbox_weights_list = images_to_levels(all_bbox_weights,
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num_level_anchors)
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return (anchors_list, labels_list, label_weights_list,
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bbox_targets_list, bbox_weights_list, num_total_pos,
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num_total_neg)
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def _get_target_single(self,
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flat_anchors,
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valid_flags,
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num_level_anchors,
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gt_bboxes,
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gt_bboxes_ignore,
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gt_labels,
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img_meta,
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label_channels=1,
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unmap_outputs=True):
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"""Compute regression, classification targets for anchors in a single
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image.
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Args:
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flat_anchors (Tensor): Multi-level anchors of the image, which are
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concatenated into a single tensor of shape (num_anchors ,4)
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valid_flags (Tensor): Multi level valid flags of the image,
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which are concatenated into a single tensor of
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shape (num_anchors,).
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num_level_anchors Tensor): Number of anchors of each scale level.
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gt_bboxes (Tensor): Ground truth bboxes of the image,
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shape (num_gts, 4).
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gt_bboxes_ignore (Tensor): Ground truth bboxes to be
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ignored, shape (num_ignored_gts, 4).
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gt_labels (Tensor): Ground truth labels of each box,
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shape (num_gts,).
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img_meta (dict): Meta info of the image.
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label_channels (int): Channel of label.
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unmap_outputs (bool): Whether to map outputs back to the original
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set of anchors.
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Returns:
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tuple: N is the number of total anchors in the image.
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labels (Tensor): Labels of all anchors in the image with shape
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(N,).
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label_weights (Tensor): Label weights of all anchor in the
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image with shape (N,).
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bbox_targets (Tensor): BBox targets of all anchors in the
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image with shape (N, 4).
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bbox_weights (Tensor): BBox weights of all anchors in the
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image with shape (N, 4)
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pos_inds (Tensor): Indices of positive anchor with shape
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(num_pos,).
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neg_inds (Tensor): Indices of negative anchor with shape
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(num_neg,).
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"""
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inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
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img_meta['img_shape'][:2],
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self.train_cfg.allowed_border)
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if not inside_flags.any():
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return (None, ) * 7
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# assign gt and sample anchors
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anchors = flat_anchors[inside_flags, :]
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num_level_anchors_inside = self.get_num_level_anchors_inside(
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num_level_anchors, inside_flags)
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assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
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gt_bboxes, gt_bboxes_ignore,
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gt_labels)
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sampling_result = self.sampler.sample(assign_result, anchors,
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gt_bboxes)
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num_valid_anchors = anchors.shape[0]
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bbox_targets = torch.zeros_like(anchors)
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bbox_weights = torch.zeros_like(anchors)
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labels = anchors.new_full((num_valid_anchors, ),
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self.num_classes,
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dtype=torch.long)
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label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
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pos_inds = sampling_result.pos_inds
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neg_inds = sampling_result.neg_inds
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if len(pos_inds) > 0:
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if self.reg_decoded_bbox:
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pos_bbox_targets = sampling_result.pos_gt_bboxes
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else:
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pos_bbox_targets = self.bbox_coder.encode(
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sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
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bbox_targets[pos_inds, :] = pos_bbox_targets
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bbox_weights[pos_inds, :] = 1.0
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if gt_labels is None:
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# Only rpn gives gt_labels as None
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# Foreground is the first class since v2.5.0
|
|
labels[pos_inds] = 0
|
|
else:
|
|
labels[pos_inds] = gt_labels[
|
|
sampling_result.pos_assigned_gt_inds]
|
|
if self.train_cfg.pos_weight <= 0:
|
|
label_weights[pos_inds] = 1.0
|
|
else:
|
|
label_weights[pos_inds] = self.train_cfg.pos_weight
|
|
if len(neg_inds) > 0:
|
|
label_weights[neg_inds] = 1.0
|
|
|
|
# map up to original set of anchors
|
|
if unmap_outputs:
|
|
num_total_anchors = flat_anchors.size(0)
|
|
anchors = unmap(anchors, num_total_anchors, inside_flags)
|
|
labels = unmap(
|
|
labels, num_total_anchors, inside_flags, fill=self.num_classes)
|
|
label_weights = unmap(label_weights, num_total_anchors,
|
|
inside_flags)
|
|
bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
|
|
bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
|
|
|
|
return (anchors, labels, label_weights, bbox_targets, bbox_weights,
|
|
pos_inds, neg_inds)
|
|
|
|
def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
|
|
split_inside_flags = torch.split(inside_flags, num_level_anchors)
|
|
num_level_anchors_inside = [
|
|
int(flags.sum()) for flags in split_inside_flags
|
|
]
|
|
return num_level_anchors_inside
|