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."""
if model.train:
train=model.train
switch_to_teacher()
model.train=False
init_params=model.init_params
with c2_utils.NamedTeacherScope():
blobs, dim, spatial_scales = get_func(cfg.MODEL.CONV_BODY)(model)
retinanet_heads.add_fpn_retinanet_outputs(
model, blobs, dim, spatial_scales
)
model.train=train
model.init_params=init_params
switch_to_student()
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
)
loss_gradients_distill=retinanet_heads.add_distill_loss(model,'','teacher/')
loss_gradients.update(loss_gradients_distill)
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)
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)
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