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
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
def _add_roi_mask_head(model, add_roi_mask_head_func, blob_in, dim_in,
spatial_scale_in, blob_boundary_attention):
"""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)
# attention
if cfg.MODEL.BOUNDARY_ON and cfg.BOUNDARY.CONCAT_MASK:
# s = model.net.Sum([blob_mask_head, blob_boundary_attention], 'm_b_sum')
bo = 'm_b_concat'
concat_F, concat_dims = model.net.Concat(
[blob_mask_head, blob_boundary_attention],
[bo, "_" + bo + "_concat_dims"])
concat_dims = dim_mask_head * 2
else:
concat_F = blob_mask_head
concat_dims = dim_mask_head
# Add the mask output
blob_mask = mask_rcnn_heads.add_mask_rcnn_outputs(model, concat_F,
concat_dims)
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
