649 lines
27 KiB
Python
649 lines
27 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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import torch.nn.functional as F
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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, bbox_overlaps, build_assigner,
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build_sampler, images_to_levels, multi_apply,
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reduce_mean, unmap)
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from mmdet.core.utils import filter_scores_and_topk
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from ..builder import HEADS, build_loss
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from .anchor_head import AnchorHead
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class Integral(nn.Module):
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"""A fixed layer for calculating integral result from distribution.
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This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
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P(y_i) denotes the softmax vector that represents the discrete distribution
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y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}
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Args:
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reg_max (int): The maximal value of the discrete set. Default: 16. You
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may want to reset it according to your new dataset or related
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settings.
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"""
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def __init__(self, reg_max=16):
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super(Integral, self).__init__()
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self.reg_max = reg_max
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self.register_buffer('project',
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torch.linspace(0, self.reg_max, self.reg_max + 1))
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def forward(self, x):
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"""Forward feature from the regression head to get integral result of
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bounding box location.
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Args:
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x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
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n is self.reg_max.
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Returns:
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x (Tensor): Integral result of box locations, i.e., distance
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offsets from the box center in four directions, shape (N, 4).
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"""
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x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
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x = F.linear(x, self.project.type_as(x)).reshape(-1, 4)
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return x
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@HEADS.register_module()
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class GFLHead(AnchorHead):
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"""Generalized Focal Loss: Learning Qualified and Distributed Bounding
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Boxes for Dense Object Detection.
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GFL head structure is similar with ATSS, however GFL uses
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1) joint representation for classification and localization quality, and
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2) flexible General distribution for bounding box locations,
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which are supervised by
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Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively
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https://arxiv.org/abs/2006.04388
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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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stacked_convs (int): Number of conv layers in cls and reg tower.
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Default: 4.
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conv_cfg (dict): dictionary to construct and config conv layer.
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Default: None.
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norm_cfg (dict): dictionary to construct and config norm layer.
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Default: dict(type='GN', num_groups=32, requires_grad=True).
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loss_qfl (dict): Config of Quality Focal Loss (QFL).
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bbox_coder (dict): Config of bbox coder. Defaults
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'DistancePointBBoxCoder'.
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reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`
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in QFL setting. Default: 16.
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Example:
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>>> self = GFLHead(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_quality_score, bbox_pred = self.forward(feats)
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>>> assert len(cls_quality_score) == len(self.scales)
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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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loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
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bbox_coder=dict(type='DistancePointBBoxCoder'),
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reg_max=16,
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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='gfl_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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self.reg_max = reg_max
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super(GFLHead, self).__init__(
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num_classes,
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in_channels,
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bbox_coder=bbox_coder,
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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.integral = Integral(self.reg_max)
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self.loss_dfl = build_loss(loss_dfl)
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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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assert self.num_anchors == 1, 'anchor free version'
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self.gfl_cls = nn.Conv2d(
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self.feat_channels, self.cls_out_channels, 3, padding=1)
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self.gfl_reg = nn.Conv2d(
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self.feat_channels, 4 * (self.reg_max + 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 and quality (IoU)
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joint scores for all scale levels, each is a 4D-tensor,
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the channel number is num_classes.
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bbox_preds (list[Tensor]): Box distribution logits for all
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scale levels, each is a 4D-tensor, the channel number is
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4*(n+1), n is max value of integral set.
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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 and quality joint scores for a single
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scale level the channel number is num_classes.
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bbox_pred (Tensor): Box distribution logits for a single scale
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level, the channel number is 4*(n+1), n is max value of
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integral set.
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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.gfl_cls(cls_feat)
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bbox_pred = scale(self.gfl_reg(reg_feat)).float()
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return cls_score, bbox_pred
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def anchor_center(self, anchors):
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"""Get anchor centers from anchors.
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Args:
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anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
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Returns:
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Tensor: Anchor centers with shape (N, 2), "xy" format.
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"""
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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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return torch.stack([anchors_cx, anchors_cy], dim=-1)
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def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
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bbox_targets, stride, num_total_samples):
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"""Compute loss of a single scale level.
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Args:
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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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cls_score (Tensor): Cls and quality joint scores for each scale
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level has shape (N, num_classes, H, W).
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bbox_pred (Tensor): Box distribution logits for each scale
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level with shape (N, 4*(n+1), H, W), n is max value of integral
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set.
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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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stride (tuple): Stride in this scale level.
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num_total_samples (int): Number of 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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assert stride[0] == stride[1], 'h stride is not equal to w stride!'
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anchors = anchors.reshape(-1, 4)
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cls_score = cls_score.permute(0, 2, 3,
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1).reshape(-1, self.cls_out_channels)
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bbox_pred = bbox_pred.permute(0, 2, 3,
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1).reshape(-1, 4 * (self.reg_max + 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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# 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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score = label_weights.new_zeros(labels.shape)
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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_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
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weight_targets = cls_score.detach().sigmoid()
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weight_targets = weight_targets.max(dim=1)[0][pos_inds]
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pos_bbox_pred_corners = self.integral(pos_bbox_pred)
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pos_decode_bbox_pred = self.bbox_coder.decode(
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pos_anchor_centers, pos_bbox_pred_corners)
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pos_decode_bbox_targets = pos_bbox_targets / stride[0]
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score[pos_inds] = bbox_overlaps(
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pos_decode_bbox_pred.detach(),
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pos_decode_bbox_targets,
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is_aligned=True)
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pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
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target_corners = self.bbox_coder.encode(pos_anchor_centers,
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pos_decode_bbox_targets,
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self.reg_max).reshape(-1)
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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_decode_bbox_targets,
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weight=weight_targets,
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avg_factor=1.0)
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# dfl loss
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loss_dfl = self.loss_dfl(
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pred_corners,
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target_corners,
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weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
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avg_factor=4.0)
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else:
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loss_bbox = bbox_pred.sum() * 0
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loss_dfl = bbox_pred.sum() * 0
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weight_targets = bbox_pred.new_tensor(0)
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# cls (qfl) loss
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loss_cls = self.loss_cls(
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cls_score, (labels, score),
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weight=label_weights,
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avg_factor=num_total_samples)
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return loss_cls, loss_bbox, loss_dfl, weight_targets.sum()
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@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
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def loss(self,
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cls_scores,
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bbox_preds,
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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]): Cls and quality scores for each scale
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level has shape (N, num_classes, H, W).
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bbox_preds (list[Tensor]): Box distribution logits for each scale
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level with shape (N, 4*(n+1), H, W), n is max value of integral
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set.
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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, losses_dfl,\
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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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labels_list,
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label_weights_list,
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bbox_targets_list,
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self.prior_generator.strides,
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num_total_samples=num_total_samples)
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avg_factor = sum(avg_factor)
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avg_factor = reduce_mean(avg_factor).clamp_(min=1).item()
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losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox))
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losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl))
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return dict(
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loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl)
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def _get_bboxes_single(self,
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cls_score_list,
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bbox_pred_list,
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score_factor_list,
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mlvl_priors,
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img_meta,
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cfg,
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rescale=False,
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with_nms=True,
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**kwargs):
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"""Transform outputs of a single image into bbox predictions.
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Args:
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cls_score_list (list[Tensor]): Box scores from all scale
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levels of a single image, each item has shape
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(num_priors * num_classes, H, W).
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bbox_pred_list (list[Tensor]): Box energies / deltas from
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all scale levels of a single image, each item has shape
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(num_priors * 4, H, W).
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score_factor_list (list[Tensor]): Score factor from all scale
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levels of a single image. GFL head does not need this value.
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mlvl_priors (list[Tensor]): Each element in the list is
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the priors of a single level in feature pyramid, has shape
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(num_priors, 4).
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img_meta (dict): Image meta info.
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cfg (mmcv.Config): Test / postprocessing configuration,
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if None, test_cfg would be used.
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rescale (bool): If True, return boxes in original image space.
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Default: False.
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with_nms (bool): If True, do nms before return boxes.
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Default: True.
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Returns:
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tuple[Tensor]: Results of detected bboxes and labels. If with_nms
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is False and mlvl_score_factor is None, return mlvl_bboxes and
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mlvl_scores, else return mlvl_bboxes, mlvl_scores and
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mlvl_score_factor. Usually with_nms is False is used for aug
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test. If with_nms is True, then return the following format
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- det_bboxes (Tensor): Predicted bboxes with shape \
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[num_bboxes, 5], where the first 4 columns are bounding \
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box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
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column are scores between 0 and 1.
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- det_labels (Tensor): Predicted labels of the corresponding \
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box with shape [num_bboxes].
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"""
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cfg = self.test_cfg if cfg is None else cfg
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img_shape = img_meta['img_shape']
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nms_pre = cfg.get('nms_pre', -1)
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mlvl_bboxes = []
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mlvl_scores = []
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mlvl_labels = []
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for level_idx, (cls_score, bbox_pred, stride, priors) in enumerate(
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zip(cls_score_list, bbox_pred_list,
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self.prior_generator.strides, mlvl_priors)):
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assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
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assert stride[0] == stride[1]
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bbox_pred = bbox_pred.permute(1, 2, 0)
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bbox_pred = self.integral(bbox_pred) * stride[0]
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scores = cls_score.permute(1, 2, 0).reshape(
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-1, self.cls_out_channels).sigmoid()
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# After https://github.com/open-mmlab/mmdetection/pull/6268/,
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# this operation keeps fewer bboxes under the same `nms_pre`.
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# There is no difference in performance for most models. If you
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# find a slight drop in performance, you can set a larger
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# `nms_pre` than before.
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results = filter_scores_and_topk(
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scores, cfg.score_thr, nms_pre,
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dict(bbox_pred=bbox_pred, priors=priors))
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scores, labels, _, filtered_results = results
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bbox_pred = filtered_results['bbox_pred']
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priors = filtered_results['priors']
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bboxes = self.bbox_coder.decode(
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self.anchor_center(priors), bbox_pred, max_shape=img_shape)
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mlvl_bboxes.append(bboxes)
|
|
mlvl_scores.append(scores)
|
|
mlvl_labels.append(labels)
|
|
|
|
return self._bbox_post_process(
|
|
mlvl_scores,
|
|
mlvl_labels,
|
|
mlvl_bboxes,
|
|
img_meta['scale_factor'],
|
|
cfg,
|
|
rescale=rescale,
|
|
with_nms=with_nms)
|
|
|
|
def get_targets(self,
|
|
anchor_list,
|
|
valid_flag_list,
|
|
gt_bboxes_list,
|
|
img_metas,
|
|
gt_bboxes_ignore_list=None,
|
|
gt_labels_list=None,
|
|
label_channels=1,
|
|
unmap_outputs=True):
|
|
"""Get targets for GFL head.
|
|
|
|
This method is almost the same as `AnchorHead.get_targets()`. Besides
|
|
returning the targets as the parent method does, it also returns the
|
|
anchors as the first element of the returned tuple.
|
|
"""
|
|
num_imgs = len(img_metas)
|
|
assert len(anchor_list) == len(valid_flag_list) == num_imgs
|
|
|
|
# anchor number of multi levels
|
|
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
|
|
num_level_anchors_list = [num_level_anchors] * num_imgs
|
|
|
|
# concat all level anchors and flags to a single tensor
|
|
for i in range(num_imgs):
|
|
assert len(anchor_list[i]) == len(valid_flag_list[i])
|
|
anchor_list[i] = torch.cat(anchor_list[i])
|
|
valid_flag_list[i] = torch.cat(valid_flag_list[i])
|
|
|
|
# compute targets for each image
|
|
if gt_bboxes_ignore_list is None:
|
|
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
|
|
if gt_labels_list is None:
|
|
gt_labels_list = [None for _ in range(num_imgs)]
|
|
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
|
|
all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
|
|
self._get_target_single,
|
|
anchor_list,
|
|
valid_flag_list,
|
|
num_level_anchors_list,
|
|
gt_bboxes_list,
|
|
gt_bboxes_ignore_list,
|
|
gt_labels_list,
|
|
img_metas,
|
|
label_channels=label_channels,
|
|
unmap_outputs=unmap_outputs)
|
|
# no valid anchors
|
|
if any([labels is None for labels in all_labels]):
|
|
return None
|
|
# sampled anchors of all images
|
|
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
|
|
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
|
|
# split targets to a list w.r.t. multiple levels
|
|
anchors_list = images_to_levels(all_anchors, num_level_anchors)
|
|
labels_list = images_to_levels(all_labels, num_level_anchors)
|
|
label_weights_list = images_to_levels(all_label_weights,
|
|
num_level_anchors)
|
|
bbox_targets_list = images_to_levels(all_bbox_targets,
|
|
num_level_anchors)
|
|
bbox_weights_list = images_to_levels(all_bbox_weights,
|
|
num_level_anchors)
|
|
return (anchors_list, labels_list, label_weights_list,
|
|
bbox_targets_list, bbox_weights_list, num_total_pos,
|
|
num_total_neg)
|
|
|
|
def _get_target_single(self,
|
|
flat_anchors,
|
|
valid_flags,
|
|
num_level_anchors,
|
|
gt_bboxes,
|
|
gt_bboxes_ignore,
|
|
gt_labels,
|
|
img_meta,
|
|
label_channels=1,
|
|
unmap_outputs=True):
|
|
"""Compute regression, classification targets for anchors in a single
|
|
image.
|
|
|
|
Args:
|
|
flat_anchors (Tensor): Multi-level anchors of the image, which are
|
|
concatenated into a single tensor of shape (num_anchors, 4)
|
|
valid_flags (Tensor): Multi level valid flags of the image,
|
|
which are concatenated into a single tensor of
|
|
shape (num_anchors,).
|
|
num_level_anchors Tensor): Number of anchors of each scale level.
|
|
gt_bboxes (Tensor): Ground truth bboxes of the image,
|
|
shape (num_gts, 4).
|
|
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
|
|
ignored, shape (num_ignored_gts, 4).
|
|
gt_labels (Tensor): Ground truth labels of each box,
|
|
shape (num_gts,).
|
|
img_meta (dict): Meta info of the image.
|
|
label_channels (int): Channel of label.
|
|
unmap_outputs (bool): Whether to map outputs back to the original
|
|
set of anchors.
|
|
|
|
Returns:
|
|
tuple: N is the number of total anchors in the image.
|
|
anchors (Tensor): All anchors in the image with shape (N, 4).
|
|
labels (Tensor): Labels of all anchors in the image with shape
|
|
(N,).
|
|
label_weights (Tensor): Label weights of all anchor in the
|
|
image with shape (N,).
|
|
bbox_targets (Tensor): BBox targets of all anchors in the
|
|
image with shape (N, 4).
|
|
bbox_weights (Tensor): BBox weights of all anchors in the
|
|
image with shape (N, 4).
|
|
pos_inds (Tensor): Indices of positive anchor with shape
|
|
(num_pos,).
|
|
neg_inds (Tensor): Indices of negative anchor with shape
|
|
(num_neg,).
|
|
"""
|
|
inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
|
|
img_meta['img_shape'][:2],
|
|
self.train_cfg.allowed_border)
|
|
if not inside_flags.any():
|
|
return (None, ) * 7
|
|
# assign gt and sample anchors
|
|
anchors = flat_anchors[inside_flags, :]
|
|
|
|
num_level_anchors_inside = self.get_num_level_anchors_inside(
|
|
num_level_anchors, inside_flags)
|
|
assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
|
|
gt_bboxes, gt_bboxes_ignore,
|
|
gt_labels)
|
|
|
|
sampling_result = self.sampler.sample(assign_result, anchors,
|
|
gt_bboxes)
|
|
|
|
num_valid_anchors = anchors.shape[0]
|
|
bbox_targets = torch.zeros_like(anchors)
|
|
bbox_weights = torch.zeros_like(anchors)
|
|
labels = anchors.new_full((num_valid_anchors, ),
|
|
self.num_classes,
|
|
dtype=torch.long)
|
|
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
|
|
|
|
pos_inds = sampling_result.pos_inds
|
|
neg_inds = sampling_result.neg_inds
|
|
if len(pos_inds) > 0:
|
|
pos_bbox_targets = sampling_result.pos_gt_bboxes
|
|
bbox_targets[pos_inds, :] = pos_bbox_targets
|
|
bbox_weights[pos_inds, :] = 1.0
|
|
if gt_labels is None:
|
|
# Only rpn gives gt_labels as None
|
|
# Foreground is the first class
|
|
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
|