def __init__(self,
num_classes=80,
in_channels=(512, 1024, 512, 256, 256, 256),
anchor_generator=dict(type='SSDAnchorGenerator',
scale_major=False,
input_size=300,
strides=[8, 16, 32, 64, 100, 300],
ratios=([2], [2,
3], [2,
3], [2,
3], [2], [2]),
basesize_ratio_range=(0.1, 0.9)),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
),
reg_decoded_bbox=False,
train_cfg=None,
test_cfg=None):
super(AnchorHead, self).__init__()
self.num_classes = num_classes
self.in_channels = in_channels
self.cls_out_channels = num_classes + 1 # add background class
self.anchor_generator = build_anchor_generator(anchor_generator)
num_anchors = self.anchor_generator.num_base_anchors
reg_convs = []
cls_convs = []
for i in range(len(in_channels)):
reg_convs.append(
nn.Conv2d(in_channels[i],
num_anchors[i] * 4,
kernel_size=3,
padding=1))
cls_convs.append(
nn.Conv2d(in_channels[i],
num_anchors[i] * (num_classes + 1),
kernel_size=3,
padding=1))
self.reg_convs = nn.ModuleList(reg_convs)
self.cls_convs = nn.ModuleList(cls_convs)
self.bbox_coder = build_bbox_coder(bbox_coder)
self.reg_decoded_bbox = reg_decoded_bbox
self.use_sigmoid_cls = False
self.cls_focal_loss = False
self.train_cfg = train_cfg
self.test_cfg = test_cfg
# set sampling=False for archor_target
self.sampling = False
self.debug = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# SSD sampling=False so use PseudoSampler
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.debug = self.train_cfg.debug
self.fp16_enabled = False
def init_assigner_sampler(self):
"""Initialize assigner and sampler."""
self.bbox_assigner = None
self.bbox_sampler = None
if self.train_cfg:
self.bbox_assigner = build_assigner(self.train_cfg.assigner)
self.bbox_sampler = build_sampler(
self.train_cfg.sampler, context=self)
def init_assigner_sampler(self):
"""Initialize assigner and sampler for each stage."""
self.bbox_assigner = []
self.bbox_sampler = []
if self.train_cfg is not None:
for idx, rcnn_train_cfg in enumerate(self.train_cfg):
# 正负样本定义和随机采样策略
self.bbox_assigner.append(
build_assigner(rcnn_train_cfg.assigner))
self.current_stage = idx
self.bbox_sampler.append(
build_sampler(rcnn_train_cfg.sampler, context=self))
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
conv_cfg=None,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
loss_centerness=dict(type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
**kwargs):
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
super(ATSSHead, self).__init__(num_classes, in_channels, **kwargs)
self.sampling = False
self.debug = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# SSD sampling=False so use PseudoSampler
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.debug = self.train_cfg.debug
self.loss_centerness = build_loss(loss_centerness)
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
conv_cfg=None,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
reg_max=16,
**kwargs):
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.reg_max = reg_max
super(GFLHead, self).__init__(num_classes, in_channels, **kwargs)
self.sampling = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# SSD sampling=False so use PseudoSampler
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.integral = Integral(self.reg_max)
self.loss_dfl = build_loss(loss_dfl)
def __init__(
self,
num_classes,
in_channels,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[8],
strides=[4, 8, 16, 32, 64]),
anchor_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]
),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]
),
reg_decoded_bbox=False,
deform_groups=4,
loc_filter_thr=0.01,
train_cfg=None,
test_cfg=None,
loss_loc=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)): # yapf: disable
super(AnchorHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
self.deform_groups = deform_groups
self.loc_filter_thr = loc_filter_thr
# build approx_anchor_generator and square_anchor_generator
assert (approx_anchor_generator['octave_base_scale'] ==
square_anchor_generator['scales'][0])
assert (approx_anchor_generator['strides'] ==
square_anchor_generator['strides'])
self.approx_anchor_generator = build_anchor_generator(
approx_anchor_generator)
self.square_anchor_generator = build_anchor_generator(
square_anchor_generator)
self.approxs_per_octave = self.approx_anchor_generator \
.num_base_anchors[0]
self.reg_decoded_bbox = reg_decoded_bbox
# one anchor per location
self.num_anchors = 1
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
self.loc_focal_loss = loss_loc['type'] in ['FocalLoss']
self.sampling = loss_cls['type'] not in ['FocalLoss']
self.ga_sampling = train_cfg is not None and hasattr(
train_cfg, 'ga_sampler')
if self.use_sigmoid_cls:
self.cls_out_channels = self.num_classes
else:
self.cls_out_channels = self.num_classes + 1
# build bbox_coder
self.anchor_coder = build_bbox_coder(anchor_coder)
self.bbox_coder = build_bbox_coder(bbox_coder)
# build losses
self.loss_loc = build_loss(loss_loc)
self.loss_shape = build_loss(loss_shape)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.debug = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# use PseudoSampler when sampling is False
if self.sampling and hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.ga_assigner = build_assigner(self.train_cfg.ga_assigner)
if self.ga_sampling:
ga_sampler_cfg = self.train_cfg.ga_sampler
else:
ga_sampler_cfg = dict(type='PseudoSampler')
self.ga_sampler = build_sampler(ga_sampler_cfg, context=self)
self.debug = self.train_cfg.debug
self.fp16_enabled = False
self._init_layers()
def __init__(self,
num_classes,
in_channels,
point_feat_channels=256,
num_points=9,
gradient_mul=0.1,
point_strides=[8, 16, 32, 64, 128],
point_base_scale=4,
loss_cls=dict(type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_init=dict(type='SmoothL1Loss',
beta=1.0 / 9.0,
loss_weight=0.5),
loss_bbox_refine=dict(type='SmoothL1Loss',
beta=1.0 / 9.0,
loss_weight=1.0),
use_grid_points=False,
center_init=True,
transform_method='moment',
moment_mul=0.01,
**kwargs):
self.num_points = num_points
self.point_feat_channels = point_feat_channels
self.use_grid_points = use_grid_points
self.center_init = center_init
# we use deform conv to extract points features
self.dcn_kernel = int(np.sqrt(num_points))
self.dcn_pad = int((self.dcn_kernel - 1) / 2)
assert self.dcn_kernel * self.dcn_kernel == num_points, \
'The points number should be a square number.'
assert self.dcn_kernel % 2 == 1, \
'The points number should be an odd square number.'
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().__init__(num_classes, in_channels, loss_cls=loss_cls, **kwargs)
self.gradient_mul = gradient_mul
self.point_base_scale = point_base_scale
self.point_strides = point_strides
self.point_generators = [PointGenerator() for _ in self.point_strides]
self.sampling = loss_cls['type'] not in ['FocalLoss']
if self.train_cfg:
self.init_assigner = build_assigner(self.train_cfg.init.assigner)
self.refine_assigner = build_assigner(
self.train_cfg.refine.assigner)
# use PseudoSampler when sampling is False
if self.sampling and hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.transform_method = transform_method
if self.transform_method == 'moment':
self.moment_transfer = nn.Parameter(data=torch.zeros(2),
requires_grad=True)
self.moment_mul = moment_mul
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
if self.use_sigmoid_cls:
self.cls_out_channels = self.num_classes
else:
self.cls_out_channels = self.num_classes + 1
self.loss_bbox_init = build_loss(loss_bbox_init)
self.loss_bbox_refine = build_loss(loss_bbox_refine)
def __init__(self,
num_classes,
in_channels,
out_channels=(1024, 512, 256),
anchor_generator=dict(type='YOLOAnchorGenerator',
base_sizes=[[(116, 90), (156, 198),
(373, 326)],
[(30, 61), (62, 45),
(59, 119)],
[(10, 13), (16, 30),
(33, 23)]],
strides=[32, 16, 8]),
bbox_coder=dict(type='YOLOBBoxCoder'),
featmap_strides=[32, 16, 8],
one_hot_smoother=0.,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
loss_cls=dict(type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_conf=dict(type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_xy=dict(type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_wh=dict(type='MSELoss', loss_weight=1.0),
train_cfg=None,
test_cfg=None):
super(YOLOV3Head, self).__init__()
# Check params
assert (len(in_channels) == len(out_channels) == len(featmap_strides))
self.num_classes = num_classes
self.in_channels = in_channels
self.out_channels = out_channels
self.featmap_strides = featmap_strides
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.debug = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
if hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler') # yolo系列不需随机采样等操作
self.sampler = build_sampler(sampler_cfg, context=self)
self.debug = self.train_cfg.debug
self.one_hot_smoother = one_hot_smoother
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.bbox_coder = build_bbox_coder(bbox_coder)
self.anchor_generator = build_anchor_generator(anchor_generator)
self.loss_cls = build_loss(loss_cls)
self.loss_conf = build_loss(loss_conf)
self.loss_xy = build_loss(loss_xy)
self.loss_wh = build_loss(loss_wh)
# usually the numbers of anchors for each level are the same
# except SSD detectors
self.num_anchors = self.anchor_generator.num_base_anchors[0]
assert len(
self.anchor_generator.num_base_anchors) == len(featmap_strides)
self._init_layers()
def __init__(self,
num_classes,
in_channels,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8, 16, 32],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(1.0, 1.0, 1.0, 1.0)),
reg_decoded_bbox=False,
background_label=None,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
train_cfg=None,
test_cfg=None):
super(AnchorHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
# TODO better way to determine whether sample or not
self.sampling = loss_cls['type'] not in [
'FocalLoss', 'GHMC', 'QualityFocalLoss'
]
if self.use_sigmoid_cls:
self.cls_out_channels = num_classes
else:
self.cls_out_channels = num_classes + 1
if self.cls_out_channels <= 0:
raise ValueError(f'num_classes={num_classes} is too small')
self.reg_decoded_bbox = reg_decoded_bbox
self.background_label = (
num_classes if background_label is None else background_label)
# background_label should be either 0 or num_classes
assert (self.background_label == 0
or self.background_label == num_classes)
self.bbox_coder = build_bbox_coder(bbox_coder)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.debug = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# use PseudoSampler when sampling is False
if self.sampling and hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.debug = self.train_cfg.debug
self.fp16_enabled = False
self.anchor_generator = build_anchor_generator(anchor_generator)
# usually the numbers of anchors for each level are the same
# except SSD detectors
self.num_anchors = self.anchor_generator.num_base_anchors[0]
self._init_layers()
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,
bbox_coder=dict(type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
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]),
**kwargs):
self.bbox_coder = build_bbox_coder(bbox_coder)
# 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))
# 3x3的位置offset
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,
**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
self.debug = 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)
self.debug = self.train_cfg.debug
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
conv_cfg=None,
norm_cfg=None,
bbox_coder=dict(type='BucketingBBoxCoder',
num_buckets=14,
scale_factor=3.0),
reg_decoded_bbox=False,
train_cfg=None,
test_cfg=None,
loss_cls=dict(type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.5),
loss_bbox_reg=dict(type='SmoothL1Loss',
beta=1.0 / 9.0,
loss_weight=1.5)):
super(SABLRetinaHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
self.num_buckets = bbox_coder['num_buckets']
self.side_num = int(np.ceil(self.num_buckets / 2))
assert (approx_anchor_generator['octave_base_scale'] ==
square_anchor_generator['scales'][0])
assert (approx_anchor_generator['strides'] ==
square_anchor_generator['strides'])
self.approx_anchor_generator = build_anchor_generator(
approx_anchor_generator)
self.square_anchor_generator = build_anchor_generator(
square_anchor_generator)
self.approxs_per_octave = (
self.approx_anchor_generator.num_base_anchors[0])
# one anchor per location
self.num_anchors = 1
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.reg_decoded_bbox = reg_decoded_bbox
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
self.sampling = loss_cls['type'] not in [
'FocalLoss', 'GHMC', 'QualityFocalLoss'
]
if self.use_sigmoid_cls:
self.cls_out_channels = num_classes
else:
self.cls_out_channels = num_classes + 1
self.bbox_coder = build_bbox_coder(bbox_coder)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox_cls = build_loss(loss_bbox_cls)
self.loss_bbox_reg = build_loss(loss_bbox_reg)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.debug = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# use PseudoSampler when sampling is False
if self.sampling and hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.debug = self.train_cfg.debug
self.fp16_enabled = False
self._init_layers()
