def _single_gpu_build_func(model):
"""Builds 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."""
blob, dim, spatial_scale = add_conv_body_func(model)
if not model.train:
model.conv_body_net = model.net.Clone('conv_body_net')
rfcn_heads.add_rfcn_outputs(model, blob, dim, dim_reduce, spatial_scale)
if model.train:
loss_gradients = fast_rcnn_heads.add_fast_rcnn_losses(model)
return loss_gradients if model.train else None
def _single_gpu_build_func(model):
"""Builds 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."""
blob, dim, spatial_scale = add_conv_body_func(model)
if not model.train:
model.conv_body_net = model.net.Clone('conv_body_net')
rfcn_heads.add_rfcn_outputs(model, blob, dim, dim_reduce, spatial_scale)
if model.train:
loss_gradients = fast_rcnn_heads.add_fast_rcnn_losses(model)
return loss_gradients if model.train else None
def _add_rfcn_head(
model, add_roi_box_head_func, blob_in, dim_in, spatial_scale_in, dim_reduce
):
"""Add a Fast R-CNN head to the model."""
blob_rfcn, dim_rfcn = add_roi_box_head_func(
model, blob_in, dim_in, spatial_scale_in, dim_reduce
)
rfcn_heads.add_rfcn_outputs(model)
if model.train:
loss_gradients = rfcn_heads.add_rfcn_losses(model)
else:
loss_gradients = None
return loss_gradients
