本文整理汇总了Python中detectron.utils.c2.SuffixNet方法的典型用法代码示例。如果您正苦于以下问题：Python c2.SuffixNet方法的具体用法？Python c2.SuffixNet怎么用？Python c2.SuffixNet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detectron.utils.c2的用法示例。
在下文中一共展示了c2.SuffixNet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: _add_roi_mask_head
# 需要导入模块: from detectron.utils import c2 [as 别名]
# 或者: from detectron.utils.c2 import SuffixNet [as 别名]
def _add_roi_mask_head(
model, add_roi_mask_head_func, blob_in, dim_in, spatial_scale_in
):
"""Add a mask prediction head to the model."""
# Capture model graph before adding the mask head
bbox_net = copy.deepcopy(model.net.Proto())
# Add the mask head
blob_mask_head, dim_mask_head = add_roi_mask_head_func(
model, blob_in, dim_in, spatial_scale_in
)
# Add the mask output
blob_mask = mask_rcnn_heads.add_mask_rcnn_outputs(
model, blob_mask_head, dim_mask_head
)
if not model.train: # == inference
# Inference uses a cascade of box predictions, then mask predictions.
# This requires separate nets for box and mask prediction.
# So we extract the mask prediction net, store it as its own network,
# then restore model.net to be the bbox-only network
model.mask_net, blob_mask = c2_utils.SuffixNet(
'mask_net', model.net, len(bbox_net.op), blob_mask
)
model.net._net = bbox_net
loss_gradients = None
else:
loss_gradients = mask_rcnn_heads.add_mask_rcnn_losses(model, blob_mask)
return loss_gradients 示例2: _add_roi_keypoint_head
# 需要导入模块: from detectron.utils import c2 [as 别名]
# 或者: from detectron.utils.c2 import SuffixNet [as 别名]
def _add_roi_keypoint_head(
model, add_roi_keypoint_head_func, blob_in, dim_in, spatial_scale_in
):
"""Add a keypoint prediction head to the model."""
# Capture model graph before adding the mask head
bbox_net = copy.deepcopy(model.net.Proto())
# Add the keypoint head
blob_keypoint_head, dim_keypoint_head = add_roi_keypoint_head_func(
model, blob_in, dim_in, spatial_scale_in
)
# Add the keypoint output
blob_keypoint = keypoint_rcnn_heads.add_keypoint_outputs(
model, blob_keypoint_head, dim_keypoint_head
)
if not model.train: # == inference
# Inference uses a cascade of box predictions, then keypoint predictions
# This requires separate nets for box and keypoint prediction.
# So we extract the keypoint prediction net, store it as its own
# network, then restore model.net to be the bbox-only network
model.keypoint_net, keypoint_blob_out = c2_utils.SuffixNet(
'keypoint_net', model.net, len(bbox_net.op), blob_keypoint
)
model.net._net = bbox_net
loss_gradients = None
else:
loss_gradients = keypoint_rcnn_heads.add_keypoint_losses(model)
return loss_gradients