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fix: torch.cuda.amp imports #5371

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fix: torch.cuda.amp imports #5371

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4 changes: 2 additions & 2 deletions detectron2/engine/train_loop.py
View file
Original file line number Diff line number Diff line change
@@ -469,7 +469,7 @@ def __init__(
)

if grad_scaler is None:
from torch.cuda.amp import GradScaler
from torch.amp import GradScaler

grad_scaler = GradScaler()
self.grad_scaler = grad_scaler
@@ -482,7 +482,7 @@ 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
from torch.amp import autocast

start = time.perf_counter()
data = next(self._data_loader_iter)
2 changes: 1 addition & 1 deletion tests/layers/test_blocks.py
View file
Original file line number Diff line number Diff line change
@@ -24,7 +24,7 @@ def test_aspp(self):

@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_frozen_batchnorm_fp16(self):
from torch.cuda.amp import autocast
from torch.amp import autocast

C = 10
input = torch.rand(1, C, 10, 10).cuda()
4 changes: 2 additions & 2 deletions tests/modeling/test_model_e2e.py
View file
Original file line number Diff line number Diff line change
@@ -155,7 +155,7 @@ def test_roiheads_inf_nan_data(self):

@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_autocast(self):
from torch.cuda.amp import autocast
from torch.amp import autocast

inputs = [{"image": torch.rand(3, 100, 100)}]
self.model.eval()
@@ -195,7 +195,7 @@ def test_inf_nan_data(self):

@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_autocast(self):
from torch.cuda.amp import autocast
from torch.amp import autocast

inputs = [{"image": torch.rand(3, 100, 100)}]
self.model.eval()