770 lines
33 KiB
Python
770 lines
33 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, bias_init_with_prob, normal_init
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from mmcv.ops import deform_conv2d
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from mmcv.runner import force_fp32
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from mmdet.core import (anchor_inside_flags, build_assigner, distance2bbox,
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images_to_levels, multi_apply, reduce_mean, unmap)
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from mmdet.core.utils import filter_scores_and_topk
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from mmdet.models.utils import sigmoid_geometric_mean
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from ..builder import HEADS, build_loss
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from .atss_head import ATSSHead
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class TaskDecomposition(nn.Module):
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"""Task decomposition module in task-aligned predictor of TOOD.
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Args:
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feat_channels (int): Number of feature channels in TOOD head.
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stacked_convs (int): Number of conv layers in TOOD head.
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la_down_rate (int): Downsample rate of layer attention.
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conv_cfg (dict): Config dict for convolution layer.
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norm_cfg (dict): Config dict for normalization layer.
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"""
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def __init__(self,
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feat_channels,
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stacked_convs,
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la_down_rate=8,
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conv_cfg=None,
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norm_cfg=None):
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super(TaskDecomposition, self).__init__()
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self.feat_channels = feat_channels
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self.stacked_convs = stacked_convs
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self.in_channels = self.feat_channels * self.stacked_convs
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self.norm_cfg = norm_cfg
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self.layer_attention = nn.Sequential(
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nn.Conv2d(self.in_channels, self.in_channels // la_down_rate, 1),
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nn.ReLU(inplace=True),
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nn.Conv2d(
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self.in_channels // la_down_rate,
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self.stacked_convs,
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1,
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padding=0), nn.Sigmoid())
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self.reduction_conv = ConvModule(
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self.in_channels,
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self.feat_channels,
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1,
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stride=1,
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padding=0,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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bias=norm_cfg is None)
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def init_weights(self):
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for m in self.layer_attention.modules():
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if isinstance(m, nn.Conv2d):
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normal_init(m, std=0.001)
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normal_init(self.reduction_conv.conv, std=0.01)
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def forward(self, feat, avg_feat=None):
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b, c, h, w = feat.shape
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if avg_feat is None:
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avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
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weight = self.layer_attention(avg_feat)
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# here we first compute the product between layer attention weight and
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# conv weight, and then compute the convolution between new conv weight
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# and feature map, in order to save memory and FLOPs.
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conv_weight = weight.reshape(
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b, 1, self.stacked_convs,
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1) * self.reduction_conv.conv.weight.reshape(
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1, self.feat_channels, self.stacked_convs, self.feat_channels)
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conv_weight = conv_weight.reshape(b, self.feat_channels,
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self.in_channels)
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feat = feat.reshape(b, self.in_channels, h * w)
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feat = torch.bmm(conv_weight, feat).reshape(b, self.feat_channels, h,
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w)
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if self.norm_cfg is not None:
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feat = self.reduction_conv.norm(feat)
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feat = self.reduction_conv.activate(feat)
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return feat
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@HEADS.register_module()
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class TOODHead(ATSSHead):
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"""TOODHead used in `TOOD: Task-aligned One-stage Object Detection.
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<https://arxiv.org/abs/2108.07755>`_.
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TOOD uses Task-aligned head (T-head) and is optimized by Task Alignment
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Learning (TAL).
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Args:
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num_dcn (int): Number of deformable convolution in the head.
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Default: 0.
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anchor_type (str): If set to `anchor_free`, the head will use centers
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to regress bboxes. If set to `anchor_based`, the head will
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regress bboxes based on anchors. Default: `anchor_free`.
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initial_loss_cls (dict): Config of initial loss.
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Example:
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>>> self = TOODHead(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 = self.forward(feats)
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>>> assert len(cls_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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num_dcn=0,
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anchor_type='anchor_free',
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initial_loss_cls=dict(
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type='FocalLoss',
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use_sigmoid=True,
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activated=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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**kwargs):
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assert anchor_type in ['anchor_free', 'anchor_based']
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self.num_dcn = num_dcn
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self.anchor_type = anchor_type
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self.epoch = 0 # which would be update in SetEpochInfoHook!
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super(TOODHead, self).__init__(num_classes, in_channels, **kwargs)
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if self.train_cfg:
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self.initial_epoch = self.train_cfg.initial_epoch
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self.initial_assigner = build_assigner(
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self.train_cfg.initial_assigner)
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self.initial_loss_cls = build_loss(initial_loss_cls)
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self.assigner = self.initial_assigner
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self.alignment_assigner = build_assigner(self.train_cfg.assigner)
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self.alpha = self.train_cfg.alpha
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self.beta = self.train_cfg.beta
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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.inter_convs = nn.ModuleList()
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for i in range(self.stacked_convs):
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if i < self.num_dcn:
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conv_cfg = dict(type='DCNv2', deform_groups=4)
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else:
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conv_cfg = self.conv_cfg
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chn = self.in_channels if i == 0 else self.feat_channels
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self.inter_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=conv_cfg,
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norm_cfg=self.norm_cfg))
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self.cls_decomp = TaskDecomposition(self.feat_channels,
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self.stacked_convs,
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self.stacked_convs * 8,
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self.conv_cfg, self.norm_cfg)
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self.reg_decomp = TaskDecomposition(self.feat_channels,
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self.stacked_convs,
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self.stacked_convs * 8,
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self.conv_cfg, self.norm_cfg)
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self.tood_cls = nn.Conv2d(
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self.feat_channels,
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self.num_base_priors * self.cls_out_channels,
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3,
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padding=1)
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self.tood_reg = nn.Conv2d(
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self.feat_channels, self.num_base_priors * 4, 3, padding=1)
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self.cls_prob_module = nn.Sequential(
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nn.Conv2d(self.feat_channels * self.stacked_convs,
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self.feat_channels // 4, 1), nn.ReLU(inplace=True),
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nn.Conv2d(self.feat_channels // 4, 1, 3, padding=1))
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self.reg_offset_module = nn.Sequential(
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nn.Conv2d(self.feat_channels * self.stacked_convs,
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self.feat_channels // 4, 1), nn.ReLU(inplace=True),
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nn.Conv2d(self.feat_channels // 4, 4 * 2, 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 init_weights(self):
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"""Initialize weights of the head."""
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bias_cls = bias_init_with_prob(0.01)
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for m in self.inter_convs:
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normal_init(m.conv, std=0.01)
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for m in self.cls_prob_module:
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if isinstance(m, nn.Conv2d):
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normal_init(m, std=0.01)
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for m in self.reg_offset_module:
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if isinstance(m, nn.Conv2d):
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normal_init(m, std=0.001)
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normal_init(self.cls_prob_module[-1], std=0.01, bias=bias_cls)
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self.cls_decomp.init_weights()
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self.reg_decomp.init_weights()
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normal_init(self.tood_cls, std=0.01, bias=bias_cls)
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normal_init(self.tood_reg, std=0.01)
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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]): Decoded box for all scale levels,
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each is a 4D-tensor, the channels number is
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num_anchors * 4. In [tl_x, tl_y, br_x, br_y] format.
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"""
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cls_scores = []
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bbox_preds = []
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for idx, (x, scale, stride) in enumerate(
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zip(feats, self.scales, self.prior_generator.strides)):
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b, c, h, w = x.shape
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anchor = self.prior_generator.single_level_grid_priors(
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(h, w), idx, device=x.device)
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anchor = torch.cat([anchor for _ in range(b)])
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# extract task interactive features
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inter_feats = []
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for inter_conv in self.inter_convs:
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x = inter_conv(x)
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inter_feats.append(x)
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feat = torch.cat(inter_feats, 1)
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# task decomposition
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avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
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cls_feat = self.cls_decomp(feat, avg_feat)
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reg_feat = self.reg_decomp(feat, avg_feat)
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# cls prediction and alignment
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cls_logits = self.tood_cls(cls_feat)
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cls_prob = self.cls_prob_module(feat)
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cls_score = sigmoid_geometric_mean(cls_logits, cls_prob)
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# reg prediction and alignment
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if self.anchor_type == 'anchor_free':
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reg_dist = scale(self.tood_reg(reg_feat).exp()).float()
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reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
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reg_bbox = distance2bbox(
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self.anchor_center(anchor) / stride[0],
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reg_dist).reshape(b, h, w, 4).permute(0, 3, 1,
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2) # (b, c, h, w)
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elif self.anchor_type == 'anchor_based':
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reg_dist = scale(self.tood_reg(reg_feat)).float()
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reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
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reg_bbox = self.bbox_coder.decode(anchor, reg_dist).reshape(
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b, h, w, 4).permute(0, 3, 1, 2) / stride[0]
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else:
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raise NotImplementedError(
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f'Unknown anchor type: {self.anchor_type}.'
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f'Please use `anchor_free` or `anchor_based`.')
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reg_offset = self.reg_offset_module(feat)
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bbox_pred = self.deform_sampling(reg_bbox.contiguous(),
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reg_offset.contiguous())
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cls_scores.append(cls_score)
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bbox_preds.append(bbox_pred)
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return tuple(cls_scores), tuple(bbox_preds)
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def deform_sampling(self, feat, offset):
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"""Sampling the feature x according to offset.
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Args:
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feat (Tensor): Feature
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offset (Tensor): Spatial offset for for feature sampliing
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"""
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# it is an equivalent implementation of bilinear interpolation
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b, c, h, w = feat.shape
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weight = feat.new_ones(c, 1, 1, 1)
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y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
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return y
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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, alignment_metrics, stride):
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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): 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): Decoded bboxes for each scale
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level with shape (N, num_anchors * 4, H, W).
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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 with
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shape (N, num_total_anchors, 4).
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alignment_metrics (Tensor): Alignment metrics with shape
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(N, num_total_anchors).
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stride (tuple[int]): Downsample stride of the feature map.
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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, 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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bbox_targets = bbox_targets.reshape(-1, 4)
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labels = labels.reshape(-1)
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alignment_metrics = alignment_metrics.reshape(-1)
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label_weights = label_weights.reshape(-1)
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targets = labels if self.epoch < self.initial_epoch else (
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labels, alignment_metrics)
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cls_loss_func = self.initial_loss_cls \
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if self.epoch < self.initial_epoch else self.loss_cls
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loss_cls = cls_loss_func(
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cls_score, targets, label_weights, avg_factor=1.0)
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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_decode_bbox_pred = pos_bbox_pred
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pos_decode_bbox_targets = pos_bbox_targets / stride[0]
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# regression loss
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pos_bbox_weight = self.centerness_target(
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pos_anchors, pos_bbox_targets
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) if self.epoch < self.initial_epoch else alignment_metrics[
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pos_inds]
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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=pos_bbox_weight,
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avg_factor=1.0)
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else:
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loss_bbox = bbox_pred.sum() * 0
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pos_bbox_weight = bbox_targets.new_tensor(0.)
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return loss_cls, loss_bbox, alignment_metrics.sum(
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), pos_bbox_weight.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]): 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]): Decoded box for each scale
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level with shape (N, num_anchors * 4, H, W) in
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[tl_x, tl_y, br_x, br_y] format.
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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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num_imgs = len(img_metas)
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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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flatten_cls_scores = torch.cat([
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cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
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self.cls_out_channels)
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for cls_score in cls_scores
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], 1)
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flatten_bbox_preds = torch.cat([
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bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4) * stride[0]
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for bbox_pred, stride in zip(bbox_preds,
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self.prior_generator.strides)
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], 1)
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cls_reg_targets = self.get_targets(
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flatten_cls_scores,
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flatten_bbox_preds,
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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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(anchor_list, labels_list, label_weights_list, bbox_targets_list,
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alignment_metrics_list) = cls_reg_targets
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losses_cls, losses_bbox,\
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cls_avg_factors, bbox_avg_factors = 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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alignment_metrics_list,
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self.prior_generator.strides)
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cls_avg_factor = reduce_mean(sum(cls_avg_factors)).clamp_(min=1).item()
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losses_cls = list(map(lambda x: x / cls_avg_factor, losses_cls))
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bbox_avg_factor = reduce_mean(
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sum(bbox_avg_factors)).clamp_(min=1).item()
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losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
|
|
return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
|
|
|
|
def _get_bboxes_single(self,
|
|
cls_score_list,
|
|
bbox_pred_list,
|
|
score_factor_list,
|
|
mlvl_priors,
|
|
img_meta,
|
|
cfg,
|
|
rescale=False,
|
|
with_nms=True,
|
|
**kwargs):
|
|
"""Transform outputs of a single image into bbox predictions.
|
|
|
|
Args:
|
|
cls_score_list (list[Tensor]): Box scores from all scale
|
|
levels of a single image, each item has shape
|
|
(num_priors * num_classes, H, W).
|
|
bbox_pred_list (list[Tensor]): Box energies / deltas from
|
|
all scale levels of a single image, each item has shape
|
|
(num_priors * 4, H, W).
|
|
score_factor_list (list[Tensor]): Score factor from all scale
|
|
levels of a single image, each item has shape
|
|
(num_priors * 1, H, W).
|
|
mlvl_priors (list[Tensor]): Each element in the list is
|
|
the priors of a single level in feature pyramid. In all
|
|
anchor-based methods, it has shape (num_priors, 4). In
|
|
all anchor-free methods, it has shape (num_priors, 2)
|
|
when `with_stride=True`, otherwise it still has shape
|
|
(num_priors, 4).
|
|
img_meta (dict): Image meta info.
|
|
cfg (mmcv.Config): Test / postprocessing configuration,
|
|
if None, test_cfg would be used.
|
|
rescale (bool): If True, return boxes in original image space.
|
|
Default: False.
|
|
with_nms (bool): If True, do nms before return boxes.
|
|
Default: True.
|
|
|
|
Returns:
|
|
tuple[Tensor]: Results of detected bboxes and labels. If with_nms
|
|
is False and mlvl_score_factor is None, return mlvl_bboxes and
|
|
mlvl_scores, else return mlvl_bboxes, mlvl_scores and
|
|
mlvl_score_factor. Usually with_nms is False is used for aug
|
|
test. If with_nms is True, then return the following format
|
|
|
|
- det_bboxes (Tensor): Predicted bboxes with shape \
|
|
[num_bboxes, 5], where the first 4 columns are bounding \
|
|
box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
|
|
column are scores between 0 and 1.
|
|
- det_labels (Tensor): Predicted labels of the corresponding \
|
|
box with shape [num_bboxes].
|
|
"""
|
|
|
|
cfg = self.test_cfg if cfg is None else cfg
|
|
nms_pre = cfg.get('nms_pre', -1)
|
|
|
|
mlvl_bboxes = []
|
|
mlvl_scores = []
|
|
mlvl_labels = []
|
|
for cls_score, bbox_pred, priors, stride in zip(
|
|
cls_score_list, bbox_pred_list, mlvl_priors,
|
|
self.prior_generator.strides):
|
|
|
|
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
|
|
|
|
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) * stride[0]
|
|
scores = cls_score.permute(1, 2,
|
|
0).reshape(-1, self.cls_out_channels)
|
|
|
|
# After https://github.com/open-mmlab/mmdetection/pull/6268/,
|
|
# this operation keeps fewer bboxes under the same `nms_pre`.
|
|
# There is no difference in performance for most models. If you
|
|
# find a slight drop in performance, you can set a larger
|
|
# `nms_pre` than before.
|
|
results = filter_scores_and_topk(
|
|
scores, cfg.score_thr, nms_pre,
|
|
dict(bbox_pred=bbox_pred, priors=priors))
|
|
scores, labels, keep_idxs, filtered_results = results
|
|
|
|
bboxes = filtered_results['bbox_pred']
|
|
|
|
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,
|
|
with_nms, None, **kwargs)
|
|
|
|
def get_targets(self,
|
|
cls_scores,
|
|
bbox_preds,
|
|
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):
|
|
"""Compute regression and classification targets for anchors in
|
|
multiple images.
|
|
|
|
Args:
|
|
cls_scores (Tensor): Classification predictions of images,
|
|
a 3D-Tensor with shape [num_imgs, num_priors, num_classes].
|
|
bbox_preds (Tensor): Decoded bboxes predictions of one image,
|
|
a 3D-Tensor with shape [num_imgs, num_priors, 4] in [tl_x,
|
|
tl_y, br_x, br_y] format.
|
|
anchor_list (list[list[Tensor]]): Multi level anchors of each
|
|
image. The outer list indicates images, and the inner list
|
|
corresponds to feature levels of the image. Each element of
|
|
the inner list is a tensor of shape (num_anchors, 4).
|
|
valid_flag_list (list[list[Tensor]]): Multi level valid flags of
|
|
each image. The outer list indicates images, and the inner list
|
|
corresponds to feature levels of the image. Each element of
|
|
the inner list is a tensor of shape (num_anchors, )
|
|
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
|
|
img_metas (list[dict]): Meta info of each image.
|
|
gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
|
|
ignored.
|
|
gt_labels_list (list[Tensor]): Ground truth labels of each box.
|
|
label_channels (int): Channel of label.
|
|
unmap_outputs (bool): Whether to map outputs back to the original
|
|
set of anchors.
|
|
|
|
Returns:
|
|
tuple: a tuple containing learning targets.
|
|
|
|
- anchors_list (list[list[Tensor]]): Anchors of each level.
|
|
- labels_list (list[Tensor]): Labels of each level.
|
|
- label_weights_list (list[Tensor]): Label weights of each
|
|
level.
|
|
- bbox_targets_list (list[Tensor]): BBox targets of each level.
|
|
- norm_alignment_metrics_list (list[Tensor]): Normalized
|
|
alignment metrics of each level.
|
|
"""
|
|
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)]
|
|
# anchor_list: list(b * [-1, 4])
|
|
|
|
if self.epoch < self.initial_epoch:
|
|
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
|
|
all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
|
|
super()._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)
|
|
all_assign_metrics = [
|
|
weight[..., 0] for weight in all_bbox_weights
|
|
]
|
|
else:
|
|
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
|
|
all_assign_metrics) = multi_apply(
|
|
self._get_target_single,
|
|
cls_scores,
|
|
bbox_preds,
|
|
anchor_list,
|
|
valid_flag_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
|
|
|
|
# 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)
|
|
norm_alignment_metrics_list = images_to_levels(all_assign_metrics,
|
|
num_level_anchors)
|
|
|
|
return (anchors_list, labels_list, label_weights_list,
|
|
bbox_targets_list, norm_alignment_metrics_list)
|
|
|
|
def _get_target_single(self,
|
|
cls_scores,
|
|
bbox_preds,
|
|
flat_anchors,
|
|
valid_flags,
|
|
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:
|
|
cls_scores (list(Tensor)): Box scores for each image.
|
|
bbox_preds (list(Tensor)): Box energies / deltas for each image.
|
|
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,).
|
|
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).
|
|
norm_alignment_metrics (Tensor): Normalized alignment metrics
|
|
of all priors in the image with shape (N,).
|
|
"""
|
|
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, :]
|
|
assign_result = self.alignment_assigner.assign(
|
|
cls_scores[inside_flags, :], bbox_preds[inside_flags, :], anchors,
|
|
gt_bboxes, gt_bboxes_ignore, gt_labels, self.alpha, self.beta)
|
|
assign_ious = assign_result.max_overlaps
|
|
assign_metrics = assign_result.assign_metrics
|
|
|
|
sampling_result = self.sampler.sample(assign_result, anchors,
|
|
gt_bboxes)
|
|
|
|
num_valid_anchors = anchors.shape[0]
|
|
bbox_targets = 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)
|
|
norm_alignment_metrics = 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:
|
|
# point-based
|
|
pos_bbox_targets = sampling_result.pos_gt_bboxes
|
|
bbox_targets[pos_inds, :] = pos_bbox_targets
|
|
|
|
if gt_labels is None:
|
|
# Only rpn gives gt_labels as None
|
|
# 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
|
|
|
|
class_assigned_gt_inds = torch.unique(
|
|
sampling_result.pos_assigned_gt_inds)
|
|
for gt_inds in class_assigned_gt_inds:
|
|
gt_class_inds = pos_inds[sampling_result.pos_assigned_gt_inds ==
|
|
gt_inds]
|
|
pos_alignment_metrics = assign_metrics[gt_class_inds]
|
|
pos_ious = assign_ious[gt_class_inds]
|
|
pos_norm_alignment_metrics = pos_alignment_metrics / (
|
|
pos_alignment_metrics.max() + 10e-8) * pos_ious.max()
|
|
norm_alignment_metrics[gt_class_inds] = pos_norm_alignment_metrics
|
|
|
|
# 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)
|
|
norm_alignment_metrics = unmap(norm_alignment_metrics,
|
|
num_total_anchors, inside_flags)
|
|
return (anchors, labels, label_weights, bbox_targets,
|
|
norm_alignment_metrics)
|