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fix FutureWarning: torch.cuda.amp.autocast(args...) is deprecated #5486

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fix FutureWarning: torch.cuda.amp.autocast(args...) is deprecated #5486

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4572b5a
fix FutureWarning: torch.cuda.amp.autocast(args...) is deprecated
yaqiangsun Aug 22, 2025
1975523
fit for linter
yaqiangsun Aug 22, 2025
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37 changes: 27 additions & 10 deletions detectron2/engine/train_loop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -469,9 +469,14 @@ def __init__(
)

if grad_scaler is None:
from torch.cuda.amp import GradScaler
if torch.__version__ >= "2.4.0":
from torch.amp import GradScaler

grad_scaler = GradScaler()
grad_scaler = GradScaler("cuda")
else:
from torch.cuda.amp import GradScaler

grad_scaler = GradScaler()
self.grad_scaler = grad_scaler
self.precision = precision
self.log_grad_scaler = log_grad_scaler
Expand All @@ -482,21 +487,33 @@ def run_step(self):
"""
assert self.model.training, "[AMPTrainer] model was changed to eval mode!"
assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!"
from torch.cuda.amp import autocast
if torch.__version__ >= "2.4.0":
from torch.amp import autocast
else:
from torch.cuda.amp import autocast

start = time.perf_counter()
data = next(self._data_loader_iter)
data_time = time.perf_counter() - start

if self.zero_grad_before_forward:
self.optimizer.zero_grad()
with autocast(dtype=self.precision):
loss_dict = self.model(data)
if isinstance(loss_dict, torch.Tensor):
losses = loss_dict
loss_dict = {"total_loss": loss_dict}
else:
losses = sum(loss_dict.values())
if torch.__version__ >= "2.4.0":
with autocast("cuda", dtype=self.precision):
loss_dict = self.model(data)
if isinstance(loss_dict, torch.Tensor):
losses = loss_dict
loss_dict = {"total_loss": loss_dict}
else:
losses = sum(loss_dict.values())
else:
with autocast(dtype=self.precision):
loss_dict = self.model(data)
if isinstance(loss_dict, torch.Tensor):
losses = loss_dict
loss_dict = {"total_loss": loss_dict}
else:
losses = sum(loss_dict.values())

if not self.zero_grad_before_forward:
self.optimizer.zero_grad()
Expand Down