本文整理汇总了Python中modeling.generate_proposals.GenerateProposalsOp方法的典型用法代码示例。如果您正苦于以下问题：Python generate_proposals.GenerateProposalsOp方法的具体用法？Python generate_proposals.GenerateProposalsOp怎么用？Python generate_proposals.GenerateProposalsOp使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类modeling.generate_proposals的用法示例。
在下文中一共展示了generate_proposals.GenerateProposalsOp方法的3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 modeling import generate_proposals [as 别名]
# 或者: from modeling.generate_proposals import GenerateProposalsOp [as 别名]
def __init__(self, dim_in, spatial_scale):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_in if cfg.RPN.OUT_DIM_AS_IN_DIM else cfg.RPN.OUT_DIM
anchors = generate_anchors(
stride=1. / spatial_scale,
sizes=cfg.RPN.SIZES,
aspect_ratios=cfg.RPN.ASPECT_RATIOS)
num_anchors = anchors.shape[0]
# RPN hidden representation
self.RPN_conv = nn.Conv2d(self.dim_in, self.dim_out, 3, 1, 1)
# Proposal classification scores
self.n_score_out = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
else num_anchors
self.RPN_cls_score = nn.Conv2d(self.dim_out, self.n_score_out, 1, 1, 0)
# Proposal bbox regression deltas
self.RPN_bbox_pred = nn.Conv2d(self.dim_out, num_anchors * 4, 1, 1, 0)
self.RPN_GenerateProposals = GenerateProposalsOp(anchors, spatial_scale)
self.RPN_GenerateProposalLabels = GenerateProposalLabelsOp()
self._init_weights() 示例2: __init__
# 需要导入模块: from modeling import generate_proposals [as 别名]
# 或者: from modeling.generate_proposals import GenerateProposalsOp [as 别名]
def __init__(self, dim_in, spatial_scales):
super().__init__()
self.dim_in = dim_in
self.spatial_scales = spatial_scales
self.dim_out = self.dim_in
num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS)
# Create conv ops shared by all FPN levels
self.FPN_RPN_conv = nn.Conv2d(dim_in, self.dim_out, 3, 1, 1)
dim_score = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
else num_anchors
self.FPN_RPN_cls_score = nn.Conv2d(self.dim_out, dim_score, 1, 1, 0)
self.FPN_RPN_bbox_pred = nn.Conv2d(self.dim_out, 4 * num_anchors, 1, 1, 0)
self.GenerateProposals_modules = nn.ModuleList()
k_max = cfg.FPN.RPN_MAX_LEVEL # coarsest level of pyramid
k_min = cfg.FPN.RPN_MIN_LEVEL # finest level of pyramid
for lvl in range(k_min, k_max + 1):
sc = self.spatial_scales[k_max - lvl] # in reversed order
lvl_anchors = generate_anchors(
stride=2.**lvl,
sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * 2.**(lvl - k_min), ),
aspect_ratios=cfg.FPN.RPN_ASPECT_RATIOS
)
self.GenerateProposals_modules.append(GenerateProposalsOp(lvl_anchors, sc))
self.CollectAndDistributeFpnRpnProposals = CollectAndDistributeFpnRpnProposalsOp()
self._init_weights() 示例3: __init__
# 需要导入模块: from modeling import generate_proposals [as 别名]
# 或者: from modeling.generate_proposals import GenerateProposalsOp [as 别名]
def __init__(self, dim_in, spatial_scales):
super().__init__()
self.dim_in = dim_in
self.spatial_scales = spatial_scales
self.dim_out = self.dim_in
num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS)
# Create conv ops shared by all FPN levels
self.FPN_RPN_conv = nn.Conv2d(dim_in, self.dim_out, 3, 1, 1)
dim_score = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
else num_anchors
self.FPN_RPN_cls_score = nn.Conv2d(self.dim_out, dim_score, 1, 1, 0)
self.FPN_RPN_bbox_pred = nn.Conv2d(self.dim_out, 4 * num_anchors, 1, 1, 0)
self.GenerateProposals_modules = nn.ModuleList()
k_max = cfg.FPN.RPN_MAX_LEVEL # coarsest level of pyramid
k_min = cfg.FPN.RPN_MIN_LEVEL # finest level of pyramid
for lvl in range(k_min, k_max + 1):
sc = self.spatial_scales[k_max - lvl] # in reversed order
lvl_anchors = generate_anchors(
stride=2. ** lvl,
sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * 2. ** (lvl - k_min),),
aspect_ratios=cfg.FPN.RPN_ASPECT_RATIOS
)
self.GenerateProposals_modules.append(GenerateProposalsOp(lvl_anchors, sc))
self.CollectAndDistributeFpnRpnProposals = CollectAndDistributeFpnRpnProposalsOp()
self._init_weights()