Python cfg.ANCHOR_SCALES屬性代碼示例

本文整理匯總了Python中model.utils.config.cfg.ANCHOR_SCALES屬性的典型用法代碼示例。如果您正苦於以下問題：Python cfg.ANCHOR_SCALES屬性的具體用法？Python cfg.ANCHOR_SCALES怎麽用？Python cfg.ANCHOR_SCALES使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類model.utils.config.cfg的用法示例。

在下文中一共展示了cfg.ANCHOR_SCALES屬性的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: init

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import ANCHOR_SCALES [as 別名]
def __init__(self, din):
        super(_RPN_FPN, self).__init__()

        self.din = din  # get depth of input feature map, e.g., 512
        self.anchor_ratios = cfg.ANCHOR_RATIOS
        self.anchor_scales = cfg.ANCHOR_SCALES
        self.feat_stride = cfg.FEAT_STRIDE[0]

        # define the convrelu layers processing input feature map
        self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)

        # define bg/fg classifcation score layer
        # self.nc_score_out = len(self.anchor_scales) * len(self.anchor_ratios) * 2 # 2(bg/fg) * 9 (anchors)
        self.nc_score_out = 1 * len(self.anchor_ratios) * 2 # 2(bg/fg) * 3 (anchor ratios) * 1 (anchor scale)
        self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)

        # define anchor box offset prediction layer
        # self.nc_bbox_out = len(self.anchor_scales) * len(self.anchor_ratios) * 4 # 4(coords) * 9 (anchors)
        self.nc_bbox_out = 1 * len(self.anchor_ratios) * 4 # 4(coords) * 3 (anchors) * 1 (anchor scale)
        self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)

        # define proposal layer
        self.RPN_proposal = _ProposalLayer_FPN(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        # define anchor target layer
        self.RPN_anchor_target = _AnchorTargetLayer_FPN(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        self.rpn_loss_cls = 0
        self.rpn_loss_box = 0

開發者ID:guoruoqian，項目名稱:cascade-rcnn_Pytorch，代碼行數:31，代碼來源:rpn_fpn.py

示例2: init

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import ANCHOR_SCALES [as 別名]
def __init__(self, din):
        super(_RPN, self).__init__()
        
        self.din = din  # get depth of input feature map, e.g., 512
        self.anchor_scales = cfg.ANCHOR_SCALES
        self.anchor_ratios = cfg.ANCHOR_RATIOS
        self.feat_stride = cfg.FEAT_STRIDE[0]

        # define the convrelu layers processing input feature map
        self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)

        # define bg/fg classifcation score layer
        self.nc_score_out = len(self.anchor_scales) * len(self.anchor_ratios) * 2 # 2(bg/fg) * 9 (anchors)
        self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)

        # define anchor box offset prediction layer
        self.nc_bbox_out = len(self.anchor_scales) * len(self.anchor_ratios) * 4 # 4(coords) * 9 (anchors)
        self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)

        # define proposal layer
        self.RPN_proposal = _ProposalLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        # define anchor target layer
        self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        self.rpn_loss_cls = 0
        self.rpn_loss_box = 0

開發者ID:Feynman27，項目名稱:pytorch-detect-to-track，代碼行數:29，代碼來源:rpn.py

示例3: init

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import ANCHOR_SCALES [as 別名]
def __init__(self, din):
        super(_RPN, self).__init__()

        self.din = din  # get depth of input feature map, e.g., 512
        self.anchor_scales = cfg.ANCHOR_SCALES
        self.anchor_ratios = cfg.ANCHOR_RATIOS
        self.feat_stride = cfg.FEAT_STRIDE[0]

        # define the convrelu layers processing input feature map
        self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)

        # define bg/fg classifcation score layer
        self.nc_score_out = len(self.anchor_scales) * len(
            self.anchor_ratios) * 2  # 2(bg/fg) * 9 (anchors)
        self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)

        # define anchor box offset prediction layer
        self.nc_bbox_out = len(self.anchor_scales) * len(
            self.anchor_ratios) * 4  # 4(coords) * 9 (anchors)
        self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)

        # define proposal layer
        self.RPN_proposal = _ProposalLayer(self.feat_stride, self.anchor_scales,
                                           self.anchor_ratios)

        # define anchor target layer
        self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride,
                                                    self.anchor_scales,
                                                    self.anchor_ratios)

        self.rpn_loss_cls = 0
        self.rpn_loss_box = 0

開發者ID:ucbdrive，項目名稱:3d-vehicle-tracking，代碼行數:34，代碼來源:rpn.py

示例4: init

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import ANCHOR_SCALES [as 別名]
def __init__(self, din):
        super(_RPN, self).__init__()

        self.din = din  # get depth of input feature map, e.g., 512
        self.anchor_scales = cfg.ANCHOR_SCALES
        self.anchor_ratios = cfg.ANCHOR_RATIOS
        self.feat_stride = cfg.FEAT_STRIDE[0]

        # define the convrelu layers processing input feature map
        self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)

        # define bg/fg classifcation score layer
        self.nc_score_out = len(self.anchor_scales) * len(self.anchor_ratios) * 2  # 2(bg/fg) * 9 (anchors)
        self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)

        # define anchor box offset prediction layer
        self.nc_bbox_out = len(self.anchor_scales) * len(self.anchor_ratios) * 4  # 4(coords) * 9 (anchors)
        self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)

        # define proposal layer
        self.RPN_proposal = _ProposalLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        # define anchor target layer
        self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        self.rpn_loss_cls = 0
        self.rpn_loss_box = 0

開發者ID:chengsq，項目名稱:pytorch-lighthead，代碼行數:29，代碼來源:rpn.py

示例5: init

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import ANCHOR_SCALES [as 別名]
def __init__(self, din):
        super(_RPN, self).__init__()
        
        self.din = din  # get depth of input feature map, e.g., 512
        self.anchor_scales = cfg.ANCHOR_SCALES
        self.anchor_ratios = cfg.ANCHOR_RATIOS
        self.feat_stride = cfg.FEAT_STRIDE[0]

        # define the convrelu layers processing input feature map
        # self.mix_Conv = nn.Sequential(
        #         nn.Conv2d(self.din, 512, 3, 1, 1, bias=True),
        #         nn.BatchNorm2d(512),
        #         nn.ReLU(inplace=True)
        #     )
        self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)

        # define bg/fg classifcation score layer
        self.nc_score_out = len(self.anchor_scales) * len(self.anchor_ratios) * 2 # 2(bg/fg) * 9 (anchors)
        self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)

        # define anchor box offset prediction layer
        self.nc_bbox_out = len(self.anchor_scales) * len(self.anchor_ratios) * 4 # 4(coords) * 9 (anchors)

        self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)

        # define proposal layer
        self.RPN_proposal = _ProposalLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        # define anchor target layer
        self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        self.rpn_loss_cls = 0
        self.rpn_loss_box = 0

開發者ID:timy90022，項目名稱:One-Shot-Object-Detection，代碼行數:35，代碼來源:rpn.py

示例6: init

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import ANCHOR_SCALES [as 別名]
def __init__(self, din):
        super(_RPN, self).__init__()
        
        self.din = din  # get depth of input feature map, e.g., 512
        self.anchor_scales =  [8, 16, 32] #cfg.ANCHOR_SCALES  #[4, 8, 16, 32] #
        self.anchor_ratios = cfg.ANCHOR_RATIOS
        self.feat_stride = cfg.FEAT_STRIDE[0]

        # define the convrelu layers processing input feature map
        self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)

        # define bg/fg classifcation score layer
        self.nc_score_out = len(self.anchor_scales) * len(self.anchor_ratios) * 2 # 2(bg/fg) * 9 (anchors)
        self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)

        # define anchor box offset prediction layer
        self.nc_bbox_out = len(self.anchor_scales) * len(self.anchor_ratios) * 4 # 4(coords) * 9 (anchors)
        self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)

        # define proposal layer
        self.RPN_proposal = _ProposalLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        # define anchor target layer
        self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)

        self.rpn_loss_cls = 0
        self.rpn_loss_box = 0

開發者ID:TKKim93，項目名稱:DivMatch，代碼行數:29，代碼來源:rpn_origin.py

注：本文中的model.utils.config.cfg.ANCHOR_SCALES屬性示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。

示例1: __init__

示例2: __init__

示例3: __init__

示例4: __init__