本文整理汇总了Python中modeling.fast_rcnn_heads.fast_rcnn_outputs方法的典型用法代码示例。如果您正苦于以下问题：Python fast_rcnn_heads.fast_rcnn_outputs方法的具体用法？Python fast_rcnn_heads.fast_rcnn_outputs怎么用？Python fast_rcnn_heads.fast_rcnn_outputs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.fast_rcnn_heads的用法示例。
在下文中一共展示了fast_rcnn_heads.fast_rcnn_outputs方法的2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 fast_rcnn_heads [as 别名]
# 或者: from modeling.fast_rcnn_heads import fast_rcnn_outputs [as 别名]
def __init__(self):
super().__init__()
# For cache
self.mapping_to_detectron = None
self.orphans_in_detectron = None
# Backbone for feature extraction
self.Conv_Body = get_func(cfg.MODEL.CONV_BODY)()
# Region Proposal Network
if cfg.RPN.RPN_ON:
self.RPN = rpn_heads.generic_rpn_outputs(
self.Conv_Body.dim_out, self.Conv_Body.spatial_scale)
if cfg.FPN.FPN_ON:
# Only supports case when RPN and ROI min levels are the same
assert cfg.FPN.RPN_MIN_LEVEL == cfg.FPN.ROI_MIN_LEVEL
# RPN max level can be >= to ROI max level
assert cfg.FPN.RPN_MAX_LEVEL >= cfg.FPN.ROI_MAX_LEVEL
# FPN RPN max level might be > FPN ROI max level in which case we
# need to discard some leading conv blobs (blobs are ordered from
# max/coarsest level to min/finest level)
self.num_roi_levels = cfg.FPN.ROI_MAX_LEVEL - cfg.FPN.ROI_MIN_LEVEL + 1
# Retain only the spatial scales that will be used for RoI heads. `Conv_Body.spatial_scale`
# may include extra scales that are used for RPN proposals, but not for RoI heads.
self.Conv_Body.spatial_scale = self.Conv_Body.spatial_scale[-self.num_roi_levels:]
# BBOX Branch
if not cfg.MODEL.RPN_ONLY:
self.Box_Head = get_func(cfg.FAST_RCNN.ROI_BOX_HEAD)(
self.RPN.dim_out, self.roi_feature_transform, self.Conv_Body.spatial_scale)
self.Box_Outs = fast_rcnn_heads.fast_rcnn_outputs(
self.Box_Head.dim_out)
# Mask Branch
if cfg.MODEL.MASK_ON:
self.Mask_Head = get_func(cfg.MRCNN.ROI_MASK_HEAD)(
self.RPN.dim_out, self.roi_feature_transform, self.Conv_Body.spatial_scale)
if getattr(self.Mask_Head, 'SHARE_RES5', False):
self.Mask_Head.share_res5_module(self.Box_Head.res5)
self.Mask_Outs = mask_rcnn_heads.mask_rcnn_outputs(self.Mask_Head.dim_out)
# Keypoints Branch
if cfg.MODEL.KEYPOINTS_ON:
self.Keypoint_Head = get_func(cfg.KRCNN.ROI_KEYPOINTS_HEAD)(
self.RPN.dim_out, self.roi_feature_transform, self.Conv_Body.spatial_scale)
if getattr(self.Keypoint_Head, 'SHARE_RES5', False):
self.Keypoint_Head.share_res5_module(self.Box_Head.res5)
self.Keypoint_Outs = keypoint_rcnn_heads.keypoint_outputs(self.Keypoint_Head.dim_out)
self._init_modules() 示例2: __init__
# 需要导入模块: from modeling import fast_rcnn_heads [as 别名]
# 或者: from modeling.fast_rcnn_heads import fast_rcnn_outputs [as 别名]
def __init__(self):
super().__init__()
# For cache
self.mapping_to_detectron = None
self.orphans_in_detectron = None
# Backbone for feature extraction
self.Conv_Body = get_func(cfg.MODEL.CONV_BODY)()
# Region Proposal Network
if cfg.RPN.RPN_ON:
self.RPN = rpn_heads.generic_rpn_outputs(
self.Conv_Body.dim_out, self.Conv_Body.spatial_scale)
if cfg.FPN.FPN_ON:
# Only supports case when RPN and ROI min levels are the same
assert cfg.FPN.RPN_MIN_LEVEL == cfg.FPN.ROI_MIN_LEVEL
# RPN max level can be >= to ROI max level
assert cfg.FPN.RPN_MAX_LEVEL >= cfg.FPN.ROI_MAX_LEVEL
# FPN RPN max level might be > FPN ROI max level in which case we
# need to discard some leading conv blobs (blobs are ordered from
# max/coarsest level to min/finest level)
self.num_roi_levels = cfg.FPN.ROI_MAX_LEVEL - cfg.FPN.ROI_MIN_LEVEL + 1
# Retain only the spatial scales that will be used for RoI heads. `Conv_Body.spatial_scale`
# may include extra scales that are used for RPN proposals, but not for RoI heads.
self.Conv_Body.spatial_scale = self.Conv_Body.spatial_scale[-self.num_roi_levels:]
# BBOX Branch
self.Box_Head = get_func(cfg.FAST_RCNN.ROI_BOX_HEAD)(
self.RPN.dim_out, self.roi_feature_transform, self.Conv_Body.spatial_scale)
self.Box_Outs = fast_rcnn_heads.fast_rcnn_outputs(
self.Box_Head.dim_out)
self.Prd_RCNN = copy.deepcopy(self)
del self.Prd_RCNN.RPN
del self.Prd_RCNN.Box_Outs
# initialize word vectors
ds_name = cfg.TRAIN.DATASETS[0] if len(cfg.TRAIN.DATASETS) else cfg.TEST.DATASETS[0]
self.obj_vecs, self.prd_vecs = get_obj_prd_vecs(ds_name)
# RelPN
self.RelPN = relpn_heads.generic_relpn_outputs()
# RelDN
self.RelDN = reldn_heads.reldn_head(self.Box_Head.dim_out * 3, self.obj_vecs, self.prd_vecs) # concat of SPO
self._init_modules()