def _single_gpu_build_func(model):
"""Build the model on a single GPU. Can be called in a loop over GPUs
with name and device scoping to create a data parallel model.
"""
# Add the conv body (called "backbone architecture" in papers)
# E.g., ResNet-50, ResNet-50-FPN, ResNeXt-101-FPN, etc.
blob_conv, dim_conv, spatial_scale_conv = add_conv_body_func(model)
if freeze_conv_body:
for b in c2_utils.BlobReferenceList(blob_conv):
model.StopGradient(b, b)
if not model.train: # == inference
# Create a net that can be used to execute the conv body on an image
# (without also executing RPN or any other network heads)
model.conv_body_net = model.net.Clone('conv_body_net')
head_loss_gradients = {
'rpn': None,
'box': None,
'mask': None,
'keypoints': None,
}
if cfg.RPN.RPN_ON:
# Add the RPN head
head_loss_gradients['rpn'] = rpn_heads.add_generic_rpn_outputs(
model, blob_conv, dim_conv, spatial_scale_conv)
if cfg.FPN.FPN_ON:
# After adding the RPN head, restrict FPN blobs and scales to
# those used in the RoI heads
blob_conv, spatial_scale_conv = _narrow_to_fpn_roi_levels(
blob_conv, spatial_scale_conv)
if not cfg.MODEL.RPN_ONLY:
# Add the Fast R-CNN head
head_loss_gradients['box'] = _add_fast_rcnn_head(
model, add_roi_box_head_func, blob_conv, dim_conv,
spatial_scale_conv)
if cfg.MODEL.MASK_ON:
# Add the mask head
head_loss_gradients['mask'] = _add_roi_mask_head(
model, add_roi_mask_head_func, blob_conv, dim_conv,
spatial_scale_conv)
if cfg.MODEL.KEYPOINTS_ON:
# Add the keypoint head
head_loss_gradients['keypoint'] = _add_roi_keypoint_head(
model, add_roi_keypoint_head_func, blob_conv, dim_conv,
spatial_scale_conv)
if model.train:
loss_gradients = {}
for lg in head_loss_gradients.values():
if lg is not None:
loss_gradients.update(lg)
return loss_gradients
else:
return None
def _single_gpu_build_func(model):
"""Build the model on a single GPU. Can be called in a loop over GPUs
with name and device scoping to create a data parallel model.
"""
# Add the conv body (called "backbone architecture" in papers)
# E.g., ResNet-50, ResNet-50-FPN, ResNeXt-101-FPN, etc.
blob_conv, dim_conv, spatial_scale_conv = add_conv_body_func(model)
if freeze_conv_body:
for b in c2_utils.BlobReferenceList(blob_conv):
model.StopGradient(b, b)
if not model.train: # == inference
# Create a net that can be used to execute the conv body on an image
# (without also executing RPN or any other network heads)
model.conv_body_net = model.net.Clone('conv_body_net')
head_loss_gradients = {
'rpn': None,
'box': None,
'mask': None,
'keypoints': None,
}
if cfg.RPN.RPN_ON:
# Add the RPN head
head_loss_gradients['rpn'] = rpn_heads.add_generic_rpn_outputs(
model, blob_conv, dim_conv, spatial_scale_conv
)
if cfg.FPN.FPN_ON:
# After adding the RPN head, restrict FPN blobs and scales to
# those used in the RoI heads
blob_conv, spatial_scale_conv = _narrow_to_fpn_roi_levels(
blob_conv, spatial_scale_conv
)
if not cfg.MODEL.RPN_ONLY:
# Add the Fast R-CNN head
head_loss_gradients['box'] = _add_fast_rcnn_head(
model, add_roi_box_head_func, blob_conv, dim_conv,
spatial_scale_conv
)
if cfg.MODEL.MASK_ON:
# Add the mask head
head_loss_gradients['mask'] = _add_roi_mask_head(
model, add_roi_mask_head_func, blob_conv, dim_conv,
spatial_scale_conv
)
if cfg.MODEL.KEYPOINTS_ON:
# Add the keypoint head
head_loss_gradients['keypoint'] = _add_roi_keypoint_head(
model, add_roi_keypoint_head_func, blob_conv, dim_conv,
spatial_scale_conv
)
if model.train:
loss_gradients = {}
for lg in head_loss_gradients.values():
if lg is not None:
loss_gradients.update(lg)
return loss_gradients
else:
return None