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