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."""
blobs, dim, spatial_scales = add_conv_body_func(model)
if not model.train:
model.conv_body_net = model.net.Clone('conv_body_net')
retinanet_heads.add_fpn_retinanet_outputs(
model, blobs, dim, spatial_scales
)
if cfg.WSL.WSL_ON:
retinanet_heads.add_wsl_fpn_retinanet_outputs(
model, blobs, dim, spatial_scales
)
if model.train:
loss_gradients = retinanet_heads.add_wsl_fpn_retinanet_losses(
model
)
return loss_gradients if model.train else None
if model.train:
loss_gradients = retinanet_heads.add_fpn_retinanet_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."""
blobs, dim, spatial_scales = add_conv_body_func(model)
if not model.train:
model.conv_body_net = model.net.Clone('conv_body_net')
retinanet_heads.add_fpn_retinanet_outputs(
model, blobs, dim, spatial_scales
)
if model.train:
loss_gradients = retinanet_heads.add_fpn_retinanet_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."""
blobs, dim, spatial_scales = 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:
model.conv_body_net = model.net.Clone('conv_body_net')
retinanet_heads.add_fpn_retinanet_outputs(model, blobs, dim,
spatial_scales)
if model.train:
loss_gradients = retinanet_heads.add_fpn_retinanet_losses(model)
return loss_gradients if model.train else None
