Python detectron.utils.c2.SuffixNet() Examples
The following are 12
code examples of detectron.utils.c2.SuffixNet().
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Example #1
| Source File: model_builder.py From KL-Loss with Apache License 2.0 | 5 votes |
|
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
Example #2
| Source File: model_builder.py From KL-Loss with Apache License 2.0 | 5 votes |
|
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
Example #3
| Source File: model_builder.py From Clustered-Object-Detection-in-Aerial-Image with Apache License 2.0 | 5 votes |
|
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
Example #4
| Source File: model_builder.py From Clustered-Object-Detection-in-Aerial-Image with Apache License 2.0 | 5 votes |
|
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
Example #5
| Source File: model_builder.py From Detectron-Cascade-RCNN with Apache License 2.0 | 5 votes |
|
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
Example #6
| Source File: model_builder.py From Detectron-Cascade-RCNN with Apache License 2.0 | 5 votes |
|
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
Example #7
| Source File: model_builder.py From Detectron with Apache License 2.0 | 5 votes |
|
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
Example #8
| Source File: model_builder.py From Detectron with Apache License 2.0 | 5 votes |
|
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
Example #9
| Source File: model_builder.py From Detectron-DA-Faster-RCNN with Apache License 2.0 | 5 votes |
|
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
Example #10
| Source File: model_builder.py From Detectron-DA-Faster-RCNN with Apache License 2.0 | 5 votes |
|
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
Example #11
| Source File: model_builder.py From CBNet with Apache License 2.0 | 5 votes |
|
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
Example #12
| Source File: model_builder.py From CBNet with Apache License 2.0 | 5 votes |
|
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
