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
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
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
if cfg.MODEL.SIBLING_BACKBONE_ON and 'keypoint' in cfg.SIBLING.HEADS:
keypoint_net_temp, _ = c2_utils.SuffixNet(
'keypoint_net_temp', model.net, len(bbox_net.op), blob_keypoint
)
model.keypoint_net, _ = c2_utils.RenameNet(
"keypoint_net", keypoint_net_temp, cfg.SIBLING.PREFFIX, excluded_nodes=[core.ScopedName("keypoint_rois_fpn{}".format(i)) for i in xrange(cfg.FPN.ROI_MIN_LEVEL, cfg.FPN.ROI_MAX_LEVEL + 1)] + [core.ScopedName("keypoint_rois_idx_restore_int32"), str(blob_keypoint)]
)
model.AddParams([core.BlobReference(input_name) for op in model.keypoint_net.Proto().op for input_name in op.input if input_name[-2] == "_"])
del keypoint_net_temp
else:
model.keypoint_net, _ = 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
