Python cfg.ANCHOR_RATIOS属性代码示例

本文整理汇总了Python中model.utils.config.cfg.ANCHOR_RATIOS属性的典型用法代码示例。如果您正苦于以下问题：Python cfg.ANCHOR_RATIOS属性的具体用法？Python cfg.ANCHOR_RATIOS怎么用？Python cfg.ANCHOR_RATIOS使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类model.utils.config.cfg的用法示例。

在下文中一共展示了cfg.ANCHOR_RATIOS属性的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: init

# 需要导入模块: from model.utils.config import cfg [as 别名]
# 或者: from model.utils.config.cfg import ANCHOR_RATIOS [as 别名]
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

开发者ID:guoruoqian，项目名称:cascade-rcnn_Pytorch，代码行数:31，代码来源:rpn_fpn.py

示例2: init

# 需要导入模块: from model.utils.config import cfg [as 别名]
# 或者: from model.utils.config.cfg import ANCHOR_RATIOS [as 别名]
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

开发者ID:Feynman27，项目名称:pytorch-detect-to-track，代码行数:29，代码来源:rpn.py

示例3: init

# 需要导入模块: from model.utils.config import cfg [as 别名]
# 或者: from model.utils.config.cfg import ANCHOR_RATIOS [as 别名]
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

开发者ID:ucbdrive，项目名称:3d-vehicle-tracking，代码行数:34，代码来源:rpn.py

示例4: init

# 需要导入模块: from model.utils.config import cfg [as 别名]
# 或者: from model.utils.config.cfg import ANCHOR_RATIOS [as 别名]
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

开发者ID:chengsq，项目名称:pytorch-lighthead，代码行数:29，代码来源:rpn.py

示例5: init

# 需要导入模块: from model.utils.config import cfg [as 别名]
# 或者: from model.utils.config.cfg import ANCHOR_RATIOS [as 别名]
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

开发者ID:timy90022，项目名称:One-Shot-Object-Detection，代码行数:35，代码来源:rpn.py

示例6: init

# 需要导入模块: from model.utils.config import cfg [as 别名]
# 或者: from model.utils.config.cfg import ANCHOR_RATIOS [as 别名]
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

开发者ID:TKKim93，项目名称:DivMatch，代码行数:29，代码来源:rpn_origin.py

示例7: init

# 需要导入模块: from model.utils.config import cfg [as 别名]
# 或者: from model.utils.config.cfg import ANCHOR_RATIOS [as 别名]
def __init__(self, din):
        super(_Stereo_RPN, self).__init__()

        self.din = din  # get depth of input feature map, e.g., 512
        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 = 1 * len(self.anchor_ratios) * 2 # 2(bg/fg) * 3 (anchor ratios) * 1 (anchor scale)
        self.RPN_cls_score = nn.Conv2d(512*2, self.nc_score_out, 1, 1, 0)

        # define anchor box offset prediction layer
        self.nc_bbox_out = 1 * len(self.anchor_ratios) * 6 # 6(coords) * 3 (anchors) * 1 (anchor scale)
        self.RPN_bbox_pred_left_right = nn.Conv2d(512*2, self.nc_bbox_out, 1, 1, 0)

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

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

        self.rpn_loss_cls = 0
        self.rpn_loss_box_left_right = 0

开发者ID:HKUST-Aerial-Robotics，项目名称:Stereo-RCNN，代码行数:28，代码来源:stereo_rpn.py

注：本文中的model.utils.config.cfg.ANCHOR_RATIOS属性示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

示例1: __init__

示例2: __init__

示例3: __init__

示例4: __init__

示例5: __init__

示例6: __init__