Source code for mmdet.models.dense_heads.vfnet_head
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale
from mmcv.ops import DeformConv2d
from mmcv.runner import force_fp32
from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator,
build_assigner, build_sampler, distance2bbox,
multi_apply, multiclass_nms, reduce_mean)
from ..builder import HEADS, build_loss
from .atss_head import ATSSHead
from .fcos_head import FCOSHead
INF = 1e8
[docs]@HEADS.register_module()
class VFNetHead(ATSSHead, FCOSHead):
"""Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
Detector.<https://arxiv.org/abs/2008.13367>`_.
The VFNet predicts IoU-aware classification scores which mix the
object presence confidence and object localization accuracy as the
detection score. It is built on the FCOS architecture and uses ATSS
for defining positive/negative training examples. The VFNet is trained
with Varifocal Loss and empolys star-shaped deformable convolution to
extract features for a bbox.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
level points.
center_sampling (bool): If true, use center sampling. Default: False.
center_sample_radius (float): Radius of center sampling. Default: 1.5.
sync_num_pos (bool): If true, synchronize the number of positive
examples across GPUs. Default: True
gradient_mul (float): The multiplier to gradients from bbox refinement
and recognition. Default: 0.1.
bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
'stride'. Default: reg_denom
loss_cls_fl (dict): Config of focal loss.
use_vfl (bool): If true, use varifocal loss for training.
Default: True.
loss_cls (dict): Config of varifocal loss.
loss_bbox (dict): Config of localization loss, GIoU Loss.
loss_bbox (dict): Config of localization refinement loss, GIoU Loss.
norm_cfg (dict): dictionary to construct and config norm layer.
Default: norm_cfg=dict(type='GN', num_groups=32,
requires_grad=True).
use_atss (bool): If true, use ATSS to define positive/negative
examples. Default: True.
anchor_generator (dict): Config of anchor generator for ATSS.
init_cfg (dict or list[dict], optional): Initialization config dict.
Example:
>>> self = VFNetHead(11, 7)
>>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
>>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
>>> assert len(cls_score) == len(self.scales)
""" # noqa: E501
def __init__(self,
num_classes,
in_channels,
regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
(512, INF)),
center_sampling=False,
center_sample_radius=1.5,
sync_num_pos=True,
gradient_mul=0.1,
bbox_norm_type='reg_denom',
loss_cls_fl=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
use_vfl=True,
loss_cls=dict(
type='VarifocalLoss',
use_sigmoid=True,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0),
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
use_atss=True,
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
center_offset=0.0,
strides=[8, 16, 32, 64, 128]),
init_cfg=dict(
type='Normal',
layer='Conv2d',
std=0.01,
override=dict(
type='Normal',
name='vfnet_cls',
std=0.01,
bias_prob=0.01)),
**kwargs):
# dcn base offsets, adapted from reppoints_head.py
self.num_dconv_points = 9
self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
self.dcn_pad = int((self.dcn_kernel - 1) / 2)
dcn_base = np.arange(-self.dcn_pad,
self.dcn_pad + 1).astype(np.float64)
dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
(-1))
self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
super(FCOSHead, self).__init__(
num_classes,
in_channels,
norm_cfg=norm_cfg,
init_cfg=init_cfg,
**kwargs)
self.regress_ranges = regress_ranges
self.reg_denoms = [
regress_range[-1] for regress_range in regress_ranges
]
self.reg_denoms[-1] = self.reg_denoms[-2] * 2
self.center_sampling = center_sampling
self.center_sample_radius = center_sample_radius
self.sync_num_pos = sync_num_pos
self.bbox_norm_type = bbox_norm_type
self.gradient_mul = gradient_mul
self.use_vfl = use_vfl
if self.use_vfl:
self.loss_cls = build_loss(loss_cls)
else:
self.loss_cls = build_loss(loss_cls_fl)
self.loss_bbox = build_loss(loss_bbox)
self.loss_bbox_refine = build_loss(loss_bbox_refine)
# for getting ATSS targets
self.use_atss = use_atss
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
self.anchor_generator = build_anchor_generator(anchor_generator)
self.anchor_center_offset = anchor_generator['center_offset']
self.num_anchors = self.anchor_generator.num_base_anchors[0]
self.sampling = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
def _init_layers(self):
"""Initialize layers of the head."""
super(FCOSHead, self)._init_cls_convs()
super(FCOSHead, self)._init_reg_convs()
self.relu = nn.ReLU(inplace=True)
self.vfnet_reg_conv = ConvModule(
self.feat_channels,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.conv_bias)
self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
self.vfnet_reg_refine_dconv = DeformConv2d(
self.feat_channels,
self.feat_channels,
self.dcn_kernel,
1,
padding=self.dcn_pad)
self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
self.vfnet_cls_dconv = DeformConv2d(
self.feat_channels,
self.feat_channels,
self.dcn_kernel,
1,
padding=self.dcn_pad)
self.vfnet_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
[docs] def forward(self, feats):
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple:
cls_scores (list[Tensor]): Box iou-aware scores for each scale
level, each is a 4D-tensor, the channel number is
num_points * num_classes.
bbox_preds (list[Tensor]): Box offsets for each
scale level, each is a 4D-tensor, the channel number is
num_points * 4.
bbox_preds_refine (list[Tensor]): Refined Box offsets for
each scale level, each is a 4D-tensor, the channel
number is num_points * 4.
"""
return multi_apply(self.forward_single, feats, self.scales,
self.scales_refine, self.strides, self.reg_denoms)
[docs] def forward_single(self, x, scale, scale_refine, stride, reg_denom):
"""Forward features of a single scale level.
Args:
x (Tensor): FPN feature maps of the specified stride.
scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
the bbox prediction.
scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
resize the refined bbox prediction.
stride (int): The corresponding stride for feature maps,
used to normalize the bbox prediction when
bbox_norm_type = 'stride'.
reg_denom (int): The corresponding regression range for feature
maps, only used to normalize the bbox prediction when
bbox_norm_type = 'reg_denom'.
Returns:
tuple: iou-aware cls scores for each box, bbox predictions and
refined bbox predictions of input feature maps.
"""
cls_feat = x
reg_feat = x
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
for reg_layer in self.reg_convs:
reg_feat = reg_layer(reg_feat)
# predict the bbox_pred of different level
reg_feat_init = self.vfnet_reg_conv(reg_feat)
if self.bbox_norm_type == 'reg_denom':
bbox_pred = scale(
self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
elif self.bbox_norm_type == 'stride':
bbox_pred = scale(
self.vfnet_reg(reg_feat_init)).float().exp() * stride
else:
raise NotImplementedError
# compute star deformable convolution offsets
# converting dcn_offset to reg_feat.dtype thus VFNet can be
# trained with FP16
dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
stride).to(reg_feat.dtype)
# refine the bbox_pred
reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
bbox_pred_refine = scale_refine(
self.vfnet_reg_refine(reg_feat)).float().exp()
bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
# predict the iou-aware cls score
cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
cls_score = self.vfnet_cls(cls_feat)
return cls_score, bbox_pred, bbox_pred_refine
[docs] def star_dcn_offset(self, bbox_pred, gradient_mul, stride):
"""Compute the star deformable conv offsets.
Args:
bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
gradient_mul (float): Gradient multiplier.
stride (int): The corresponding stride for feature maps,
used to project the bbox onto the feature map.
Returns:
dcn_offsets (Tensor): The offsets for deformable convolution.
"""
dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
gradient_mul * bbox_pred
# map to the feature map scale
bbox_pred_grad_mul = bbox_pred_grad_mul / stride
N, C, H, W = bbox_pred.size()
x1 = bbox_pred_grad_mul[:, 0, :, :]
y1 = bbox_pred_grad_mul[:, 1, :, :]
x2 = bbox_pred_grad_mul[:, 2, :, :]
y2 = bbox_pred_grad_mul[:, 3, :, :]
bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
N, 2 * self.num_dconv_points, H, W)
bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
return dcn_offset
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
def loss(self,
cls_scores,
bbox_preds,
bbox_preds_refine,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute loss of the head.
Args:
cls_scores (list[Tensor]): Box iou-aware scores for each scale
level, each is a 4D-tensor, the channel number is
num_points * num_classes.
bbox_preds (list[Tensor]): Box offsets for each
scale level, each is a 4D-tensor, the channel number is
num_points * 4.
bbox_preds_refine (list[Tensor]): Refined Box offsets for
each scale level, each is a 4D-tensor, the channel
number is num_points * 4.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
Default: None.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
bbox_preds[0].device)
labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas,
gt_bboxes_ignore)
num_imgs = cls_scores[0].size(0)
# flatten cls_scores, bbox_preds and bbox_preds_refine
flatten_cls_scores = [
cls_score.permute(0, 2, 3,
1).reshape(-1,
self.cls_out_channels).contiguous()
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
for bbox_pred in bbox_preds
]
flatten_bbox_preds_refine = [
bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
for bbox_pred_refine in bbox_preds_refine
]
flatten_cls_scores = torch.cat(flatten_cls_scores)
flatten_bbox_preds = torch.cat(flatten_bbox_preds)
flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
flatten_labels = torch.cat(labels)
flatten_bbox_targets = torch.cat(bbox_targets)
# repeat points to align with bbox_preds
flatten_points = torch.cat(
[points.repeat(num_imgs, 1) for points in all_level_points])
# FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
bg_class_ind = self.num_classes
pos_inds = torch.where(
((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
num_pos = len(pos_inds)
pos_bbox_preds = flatten_bbox_preds[pos_inds]
pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
pos_labels = flatten_labels[pos_inds]
# sync num_pos across all gpus
if self.sync_num_pos:
num_pos_avg_per_gpu = reduce_mean(
pos_inds.new_tensor(num_pos).float()).item()
num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
else:
num_pos_avg_per_gpu = num_pos
pos_bbox_targets = flatten_bbox_targets[pos_inds]
pos_points = flatten_points[pos_inds]
pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds)
pos_decoded_target_preds = distance2bbox(pos_points, pos_bbox_targets)
iou_targets_ini = bbox_overlaps(
pos_decoded_bbox_preds,
pos_decoded_target_preds.detach(),
is_aligned=True).clamp(min=1e-6)
bbox_weights_ini = iou_targets_ini.clone().detach()
bbox_avg_factor_ini = reduce_mean(
bbox_weights_ini.sum()).clamp_(min=1).item()
pos_decoded_bbox_preds_refine = \
distance2bbox(pos_points, pos_bbox_preds_refine)
iou_targets_rf = bbox_overlaps(
pos_decoded_bbox_preds_refine,
pos_decoded_target_preds.detach(),
is_aligned=True).clamp(min=1e-6)
bbox_weights_rf = iou_targets_rf.clone().detach()
bbox_avg_factor_rf = reduce_mean(
bbox_weights_rf.sum()).clamp_(min=1).item()
if num_pos > 0:
loss_bbox = self.loss_bbox(
pos_decoded_bbox_preds,
pos_decoded_target_preds.detach(),
weight=bbox_weights_ini,
avg_factor=bbox_avg_factor_ini)
loss_bbox_refine = self.loss_bbox_refine(
pos_decoded_bbox_preds_refine,
pos_decoded_target_preds.detach(),
weight=bbox_weights_rf,
avg_factor=bbox_avg_factor_rf)
# build IoU-aware cls_score targets
if self.use_vfl:
pos_ious = iou_targets_rf.clone().detach()
cls_iou_targets = torch.zeros_like(flatten_cls_scores)
cls_iou_targets[pos_inds, pos_labels] = pos_ious
else:
loss_bbox = pos_bbox_preds.sum() * 0
loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
if self.use_vfl:
cls_iou_targets = torch.zeros_like(flatten_cls_scores)
if self.use_vfl:
loss_cls = self.loss_cls(
flatten_cls_scores,
cls_iou_targets,
avg_factor=num_pos_avg_per_gpu)
else:
loss_cls = self.loss_cls(
flatten_cls_scores,
flatten_labels,
weight=label_weights,
avg_factor=num_pos_avg_per_gpu)
return dict(
loss_cls=loss_cls,
loss_bbox=loss_bbox,
loss_bbox_rf=loss_bbox_refine)
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
def get_bboxes(self,
cls_scores,
bbox_preds,
bbox_preds_refine,
img_metas,
cfg=None,
rescale=None,
with_nms=True):
"""Transform network outputs for a batch into bbox predictions.
Args:
cls_scores (list[Tensor]): Box iou-aware scores for each scale
level with shape (N, num_points * num_classes, H, W).
bbox_preds (list[Tensor]): Box offsets for each scale
level with shape (N, num_points * 4, H, W).
bbox_preds_refine (list[Tensor]): Refined Box offsets for
each scale level with shape (N, num_points * 4, H, W).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used. Default: None.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before returning boxes.
Default: True.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is an (n, 5) tensor, where the first 4 columns
are bounding box positions (tl_x, tl_y, br_x, br_y) and the
5-th column is a score between 0 and 1. The second item is a
(n,) tensor where each item is the predicted class label of
the corresponding box.
"""
assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
num_levels = len(cls_scores)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
bbox_preds[0].device)
result_list = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_pred_list = [
bbox_preds_refine[i][img_id].detach()
for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
det_bboxes = self._get_bboxes_single(cls_score_list,
bbox_pred_list, mlvl_points,
img_shape, scale_factor, cfg,
rescale, with_nms)
result_list.append(det_bboxes)
return result_list
def _get_bboxes_single(self,
cls_scores,
bbox_preds,
mlvl_points,
img_shape,
scale_factor,
cfg,
rescale=False,
with_nms=True):
"""Transform outputs for a single batch item into bbox predictions.
Args:
cls_scores (list[Tensor]): Box iou-aware scores for a single scale
level with shape (num_points * num_classes, H, W).
bbox_preds (list[Tensor]): Box offsets for a single scale
level with shape (num_points * 4, H, W).
mlvl_points (list[Tensor]): Box reference for a single scale level
with shape (num_total_points, 4).
img_shape (tuple[int]): Shape of the input image,
(height, width, 3).
scale_factor (ndarray): Scale factor of the image arrange as
(w_scale, h_scale, w_scale, h_scale).
cfg (mmcv.Config | None): 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 returning boxes.
Default: True.
Returns:
tuple(Tensor):
det_bboxes (Tensor): BBox predictions in shape (n, 5), where
the first 4 columns are bounding box positions
(tl_x, tl_y, br_x, br_y) and the 5-th column is a score
between 0 and 1.
det_labels (Tensor): A (n,) tensor where each item is the
predicted class label of the corresponding box.
"""
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
mlvl_bboxes = []
mlvl_scores = []
for cls_score, bbox_pred, points in zip(cls_scores, bbox_preds,
mlvl_points):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).contiguous().sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).contiguous()
nms_pre = cfg.get('nms_pre', -1)
if 0 < nms_pre < scores.shape[0]:
max_scores, _ = scores.max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
points = points[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
# BG cat_id: num_class
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
if with_nms:
det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
else:
return mlvl_bboxes, mlvl_scores
def _get_points_single(self,
featmap_size,
stride,
dtype,
device,
flatten=False):
"""Get points according to feature map sizes."""
h, w = featmap_size
x_range = torch.arange(
0, w * stride, stride, dtype=dtype, device=device)
y_range = torch.arange(
0, h * stride, stride, dtype=dtype, device=device)
y, x = torch.meshgrid(y_range, x_range)
# to be compatible with anchor points in ATSS
if self.use_atss:
points = torch.stack(
(x.reshape(-1), y.reshape(-1)), dim=-1) + \
stride * self.anchor_center_offset
else:
points = torch.stack(
(x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2
return points
[docs] def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels,
img_metas, gt_bboxes_ignore):
"""A wrapper for computing ATSS and FCOS targets for points in multiple
images.
Args:
cls_scores (list[Tensor]): Box iou-aware scores for each scale
level with shape (N, num_points * num_classes, H, W).
mlvl_points (list[Tensor]): Points of each fpn level, each has
shape (num_points, 2).
gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
each has shape (num_gt, 4).
gt_labels (list[Tensor]): Ground truth labels of each box,
each has shape (num_gt,).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
Returns:
tuple:
labels_list (list[Tensor]): Labels of each level.
label_weights (Tensor/None): Label weights of all levels.
bbox_targets_list (list[Tensor]): Regression targets of each
level, (l, t, r, b).
bbox_weights (Tensor/None): Bbox weights of all levels.
"""
if self.use_atss:
return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes,
gt_labels, img_metas,
gt_bboxes_ignore)
else:
self.norm_on_bbox = False
return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels)
def _get_target_single(self, *args, **kwargs):
"""Avoid ambiguity in multiple inheritance."""
if self.use_atss:
return ATSSHead._get_target_single(self, *args, **kwargs)
else:
return FCOSHead._get_target_single(self, *args, **kwargs)
[docs] def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list):
"""Compute FCOS regression and classification targets for points in
multiple images.
Args:
points (list[Tensor]): Points of each fpn level, each has shape
(num_points, 2).
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
each has shape (num_gt, 4).
gt_labels_list (list[Tensor]): Ground truth labels of each box,
each has shape (num_gt,).
Returns:
tuple:
labels (list[Tensor]): Labels of each level.
label_weights: None, to be compatible with ATSS targets.
bbox_targets (list[Tensor]): BBox targets of each level.
bbox_weights: None, to be compatible with ATSS targets.
"""
labels, bbox_targets = FCOSHead.get_targets(self, points,
gt_bboxes_list,
gt_labels_list)
label_weights = None
bbox_weights = None
return labels, label_weights, bbox_targets, bbox_weights
[docs] def get_atss_targets(self,
cls_scores,
mlvl_points,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""A wrapper for computing ATSS targets for points in multiple images.
Args:
cls_scores (list[Tensor]): Box iou-aware scores for each scale
level with shape (N, num_points * num_classes, H, W).
mlvl_points (list[Tensor]): Points of each fpn level, each has
shape (num_points, 2).
gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
each has shape (num_gt, 4).
gt_labels (list[Tensor]): Ground truth labels of each box,
each has shape (num_gt,).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4). Default: None.
Returns:
tuple:
labels_list (list[Tensor]): Labels of each level.
label_weights (Tensor): Label weights of all levels.
bbox_targets_list (list[Tensor]): Regression targets of each
level, (l, t, r, b).
bbox_weights (Tensor): Bbox weights of all levels.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = ATSSHead.get_targets(
self,
anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
unmap_outputs=True)
if cls_reg_targets is None:
return None
(anchor_list, labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
bbox_targets_list = [
bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
]
num_imgs = len(img_metas)
# transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
bbox_targets_list = self.transform_bbox_targets(
bbox_targets_list, mlvl_points, num_imgs)
labels_list = [labels.reshape(-1) for labels in labels_list]
label_weights_list = [
label_weights.reshape(-1) for label_weights in label_weights_list
]
bbox_weights_list = [
bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
]
label_weights = torch.cat(label_weights_list)
bbox_weights = torch.cat(bbox_weights_list)
return labels_list, label_weights, bbox_targets_list, bbox_weights
[docs] def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs):
"""Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
Args:
decoded_bboxes (list[Tensor]): Regression targets of each level,
in the form of (x1, y1, x2, y2).
mlvl_points (list[Tensor]): Points of each fpn level, each has
shape (num_points, 2).
num_imgs (int): the number of images in a batch.
Returns:
bbox_targets (list[Tensor]): Regression targets of each level in
the form of (l, t, r, b).
"""
# TODO: Re-implemented in Class PointCoder
assert len(decoded_bboxes) == len(mlvl_points)
num_levels = len(decoded_bboxes)
mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
bbox_targets = []
for i in range(num_levels):
bbox_target = bbox2distance(mlvl_points[i], decoded_bboxes[i])
bbox_targets.append(bbox_target)
return bbox_targets
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
"""Override the method in the parent class to avoid changing para's
name."""
pass