def __init__(self,
num_classes=81,
fpn_stride=[8, 16, 32, 64, 128],
prior_prob=0.01,
num_convs=4,
norm_type="gn",
fcos_loss=None,
norm_reg_targets=False,
centerness_on_reg=False,
use_dcn_in_tower=False,
nms=MultiClassNMS(score_threshold=0.01,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
background_label=-1).__dict__):
self.num_classes = num_classes - 1
self.fpn_stride = fpn_stride[::-1]
self.prior_prob = prior_prob
self.num_convs = num_convs
self.norm_reg_targets = norm_reg_targets
self.centerness_on_reg = centerness_on_reg
self.use_dcn_in_tower = use_dcn_in_tower
self.norm_type = norm_type
self.fcos_loss = fcos_loss
self.batch_size = 8
self.nms = nms
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
def __init__(self, head, nms=MultiClassNMS().__dict__, num_classes=81):
super(CascadeBBoxHead, self).__init__()
self.head = head
self.nms = nms
self.num_classes = num_classes
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
def __init__(self,
norm_decay=0.,
num_classes=80,
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
drop_block=False,
block_size=3,
keep_prob=0.9,
yolo_loss="YOLOv3Loss",
nms=MultiClassNMS(
score_threshold=0.01,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
background_label=-1).__dict__,
weight_prefix_name=''):
self.norm_decay = norm_decay
self.num_classes = num_classes
self.anchor_masks = anchor_masks
self._parse_anchors(anchors)
self.yolo_loss = yolo_loss
self.nms = nms
self.prefix_name = weight_prefix_name
self.drop_block = drop_block
self.block_size = block_size
self.keep_prob = keep_prob
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
def __init__(self,
norm_decay=0.,
num_classes=80,
ignore_thresh=0.7,
label_smooth=True,
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
nms=MultiClassNMS(
score_threshold=0.01,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
background_label=-1).__dict__,
weight_prefix_name=''):
self.norm_decay = norm_decay
self.num_classes = num_classes
self.ignore_thresh = ignore_thresh
self.label_smooth = label_smooth
self.anchor_masks = anchor_masks
self._parse_anchors(anchors)
self.nms = nms
self.prefix_name = weight_prefix_name
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
def __init__(self,
head,
box_coder=BoxCoder().__dict__,
nms=MultiClassNMS().__dict__,
num_classes=81):
super(BBoxHead, self).__init__()
self.head = head
self.num_classes = num_classes
self.box_coder = box_coder
self.nms = nms
if isinstance(box_coder, dict):
self.box_coder = BoxCoder(**box_coder)
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
self.head_feat = None
def __init__(self,
anchors=[[12, 16], [19, 36], [40, 28], [36, 75], [76, 55],
[72, 146], [142, 110], [192, 243], [459, 401]],
anchor_masks=[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
nms=MultiClassNMS(
score_threshold=0.01,
nms_top_k=-1,
keep_top_k=-1,
nms_threshold=0.45,
background_label=-1).__dict__,
spp_stage=5,
num_classes=80,
weight_prefix_name='',
downsample=[8, 16, 32],
scale_x_y=1.0,
yolo_loss="YOLOv3Loss",
iou_aware=False,
iou_aware_factor=0.4,
clip_bbox=False):
super(YOLOv4Head, self).__init__(
anchors=anchors,
anchor_masks=anchor_masks,
nms=nms,
num_classes=num_classes,
weight_prefix_name=weight_prefix_name,
downsample=downsample,
scale_x_y=scale_x_y,
yolo_loss=yolo_loss,
iou_aware=iou_aware,
iou_aware_factor=iou_aware_factor,
clip_bbox=clip_bbox)
self.spp_stage = spp_stage
def __init__(
self,
head,
nms=MultiClassNMS().__dict__,
bbox_loss=SmoothL1Loss().__dict__,
num_classes=81,
):
super(CascadeBBoxHead, self).__init__()
self.head = head
self.nms = nms
self.bbox_loss = bbox_loss
self.num_classes = num_classes
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
if isinstance(bbox_loss, dict):
self.bbox_loss = SmoothL1Loss(**bbox_loss)
def __init__(self,
conv_block_num=2,
norm_decay=0.,
num_classes=80,
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
drop_block=False,
coord_conv=False,
iou_aware=False,
iou_aware_factor=0.4,
block_size=3,
keep_prob=0.9,
yolo_loss="YOLOv3Loss",
spp=False,
nms=MultiClassNMS(
score_threshold=0.01,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
background_label=-1).__dict__,
weight_prefix_name='',
downsample=[32, 16, 8],
scale_x_y=1.0,
clip_bbox=True):
check_version("1.8.4")
self.conv_block_num = conv_block_num
self.norm_decay = norm_decay
self.num_classes = num_classes
self.anchor_masks = anchor_masks
self._parse_anchors(anchors)
self.yolo_loss = yolo_loss
self.nms = nms
self.prefix_name = weight_prefix_name
self.drop_block = drop_block
self.iou_aware = iou_aware
self.coord_conv = coord_conv
self.iou_aware_factor = iou_aware_factor
self.block_size = block_size
self.keep_prob = keep_prob
self.use_spp = spp
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
self.downsample = downsample
self.scale_x_y = scale_x_y
self.clip_bbox = clip_bbox
def __init__(self,
head,
nms=MultiClassNMS().__dict__,
bbox_loss=SmoothL1Loss().__dict__,
num_classes=81,
lr_ratio=2.0):
super(HTCBBoxHead, self).__init__()
self.head = head
self.nms = nms
self.bbox_loss = bbox_loss
self.num_classes = num_classes
self.lr_ratio = lr_ratio
if isinstance(nms, dict):
self.nms = MultiClassNMS(**nms)
if isinstance(bbox_loss, dict):
self.bbox_loss = SmoothL1Loss(**bbox_loss)
def __init__(self,
conv_block_num=3,
norm_decay=0.,
num_classes=80,
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
drop_block=False,
iou_aware=False,
iou_aware_factor=0.4,
block_size=3,
keep_prob=0.9,
yolo_loss="YOLOv3Loss",
spp=False,
nms=MultiClassNMS(score_threshold=0.01,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
background_label=-1).__dict__,
weight_prefix_name='',
downsample=[32, 16, 8],
scale_x_y=1.0,
clip_bbox=True,
act='mish'):
super(YOLOv3PANHead,
self).__init__(conv_block_num=conv_block_num,
norm_decay=norm_decay,
num_classes=num_classes,
anchors=anchors,
anchor_masks=anchor_masks,
drop_block=drop_block,
iou_aware=iou_aware,
iou_aware_factor=iou_aware_factor,
block_size=block_size,
keep_prob=keep_prob,
yolo_loss=yolo_loss,
spp=spp,
nms=nms,
weight_prefix_name=weight_prefix_name,
downsample=downsample,
scale_x_y=scale_x_y,
clip_bbox=clip_bbox)
self.act = act
def __init__(self,
norm_decay=0.,
num_classes=80,
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
detection_block_channels=[128, 96],
drop_block=False,
block_size=3,
keep_prob=0.9,
yolo_loss="YOLOv3Loss",
spp=False,
nms=MultiClassNMS(score_threshold=0.01,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.45,
background_label=-1).__dict__,
weight_prefix_name='',
downsample=[32, 16, 8],
scale_x_y=1.0,
clip_bbox=True):
super(PPYOLOTinyHead,
self).__init__(norm_decay=norm_decay,
num_classes=num_classes,
anchors=anchors,
anchor_masks=anchor_masks,
drop_block=drop_block,
block_size=block_size,
keep_prob=0.9,
spp=spp,
yolo_loss=yolo_loss,
nms=nms,
weight_prefix_name=weight_prefix_name,
downsample=downsample,
scale_x_y=scale_x_y,
clip_bbox=clip_bbox)
self.detection_block_channels = detection_block_channels
