Skip to content
This repository was archived by the owner on Jul 1, 2024. It is now read-only.

Create function to see if loss has learable parameters #641

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Create function to see if loss has learable parameters #641

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 8 additions & 4 deletions classy_vision/tasks/classification_task.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -719,10 +719,7 @@ def init_distributed_data_parallel_model(self):
broadcast_buffers=broadcast_buffers,
find_unused_parameters=self.find_unused_parameters,
)
if (
isinstance(self.base_loss, ClassyLoss)
and self.base_loss.has_learned_parameters()
):
if self._loss_has_learnable_params():
logging.info("Initializing distributed loss")
self.distributed_loss = init_distributed_data_parallel_model(
self.base_loss,
Expand Down Expand Up @@ -1014,6 +1011,13 @@ def _broadcast_buffers(self):
for buffer in buffers:
broadcast(buffer, 0, group=self.distributed_model.process_group)

def _loss_has_learnable_params(self):
"""Returns True if the loss has any learnable parameters"""
return (
isinstance(self.base_loss, ClassyLoss)
and self.base_loss.has_learned_parameters()
)

# TODO: Functions below should be better abstracted into the dataloader
# abstraction
def get_batchsize_per_replica(self):
Expand Down