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Drop check of monai.utils.get_torch_version_tuple() >= (1, 6) #1072

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Merged
merged 1 commit into from
Nov 28, 2022
Merged

Drop check of monai.utils.get_torch_version_tuple() >= (1, 6) #1072

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3 changes: 1 addition & 2 deletions acceleration/distributed_training/unet_evaluation_workflows.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,8 +173,7 @@ def evaluate(args):
},
additional_metrics={"val_acc": Accuracy(output_transform=from_engine(["pred", "label"]), device=device)},
val_handlers=val_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
amp=True,
)
evaluator.run()
dist.destroy_process_group()
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3 changes: 1 addition & 2 deletions acceleration/distributed_training/unet_training_workflows.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,8 +177,7 @@ def train(args):
optimizer=opt,
loss_function=loss,
inferer=SimpleInferer(),
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
amp=True,
postprocessing=train_post_transforms,
key_train_metric={"train_acc": Accuracy(output_transform=from_engine(["pred", "label"]), device=device)},
train_handlers=train_handlers,
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3 changes: 1 addition & 2 deletions modules/engines/unet_evaluation_dict.py
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Expand Up @@ -112,8 +112,7 @@ def main(tempdir):
},
additional_metrics={"val_acc": Accuracy(output_transform=from_engine(["pred", "label"]))},
val_handlers=val_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
amp=True,
)
evaluator.run()

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5 changes: 2 additions & 3 deletions modules/engines/unet_training_dict.py
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Expand Up @@ -147,8 +147,7 @@ def main(tempdir):
},
additional_metrics={"val_acc": Accuracy(output_transform=from_engine(["pred", "label"]))},
val_handlers=val_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
amp=True,
)

train_post_transforms = Compose(
Expand Down Expand Up @@ -182,7 +181,7 @@ def main(tempdir):
key_train_metric={"train_acc": Accuracy(output_transform=from_engine(["pred", "label"]))},
train_handlers=train_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP training
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
amp=True,
)
# set initialized trainer for "early stop" handlers
val_handlers[0].set_trainer(trainer=trainer)
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6 changes: 0 additions & 6 deletions pathology/tumor_detection/ignite/camelyon_train_evaluate.py
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Expand Up @@ -184,12 +184,6 @@ def train(cfg):
else:
optimizer = SGD(model.parameters(), lr=cfg["lr"], momentum=0.9)

# AMP scaler
if cfg["amp"]:
cfg["amp"] = True if monai.utils.get_torch_version_tuple() >= (1, 6) else False
else:
cfg["amp"] = False

scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=cfg["n_epochs"])

# --------------------------------------------
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6 changes: 0 additions & 6 deletions pathology/tumor_detection/ignite/camelyon_train_evaluate_nvtx_profiling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,12 +187,6 @@ def train(cfg):
else:
optimizer = SGD(model.parameters(), lr=cfg["lr"], momentum=0.9)

# AMP scaler
if cfg["amp"]:
cfg["amp"] = True if monai.utils.get_torch_version_tuple() >= (1, 6) else False
else:
cfg["amp"] = False

scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=cfg["n_epochs"])

# --------------------------------------------
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1 change: 0 additions & 1 deletion pathology/tumor_detection/torch/camelyon_train_evaluate_pytorch_gpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -375,7 +375,6 @@ def main(cfg):
optimizer = SGD(model.parameters(), lr=cfg["lr"], momentum=0.9)

# AMP scaler
cfg["amp"] = cfg["amp"] and monai.utils.get_torch_version_tuple() >= (1, 6)
if cfg["amp"] is True:
scaler = GradScaler()
else:
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1 change: 0 additions & 1 deletion performance_profiling/pathology/train_evaluate_nvtx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -382,7 +382,6 @@ def main(cfg):
optimizer = SGD(model.parameters(), lr=cfg["lr"], momentum=0.9)

# AMP scaler
cfg["amp"] = cfg["amp"] and monai.utils.get_torch_version_tuple() >= (1, 6)
if cfg["amp"] is True:
scaler = GradScaler()
else:
Expand Down