1756 update ignite version to 0.4.4

docs/requirements.txt

@@ -1,6 +1,6 @@
 -f https://download.pytorch.org/whl/cpu/torch-1.6.0%2Bcpu-cp37-cp37m-linux_x86_64.whl
 torch>=1.5
-pytorch-ignite==0.4.2
+pytorch-ignite==0.4.4
 numpy>=1.17
 itk>=5.0
 nibabel

monai/engines/evaluator.py

@@ -26,8 +26,8 @@
     from ignite.engine import Engine
     from ignite.metrics import Metric
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
-    Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
+    Metric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "Metric")
 
 __all__ = ["Evaluator", "SupervisedEvaluator", "EnsembleEvaluator"]
 

monai/engines/multi_gpu_supervised_trainer.py

@@ -19,15 +19,15 @@
 from monai.engines.utils import get_devices_spec
 from monai.utils import exact_version, optional_import
 
-create_supervised_trainer, _ = optional_import("ignite.engine", "0.4.2", exact_version, "create_supervised_trainer")
-create_supervised_evaluator, _ = optional_import("ignite.engine", "0.4.2", exact_version, "create_supervised_evaluator")
-_prepare_batch, _ = optional_import("ignite.engine", "0.4.2", exact_version, "_prepare_batch")
+create_supervised_trainer, _ = optional_import("ignite.engine", "0.4.4", exact_version, "create_supervised_trainer")
+create_supervised_evaluator, _ = optional_import("ignite.engine", "0.4.4", exact_version, "create_supervised_evaluator")
+_prepare_batch, _ = optional_import("ignite.engine", "0.4.4", exact_version, "_prepare_batch")
 if TYPE_CHECKING:
     from ignite.engine import Engine
     from ignite.metrics import Metric
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
-    Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
+    Metric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "Metric")
 
 __all__ = [
     "create_multigpu_supervised_trainer",

monai/engines/trainer.py

@@ -26,8 +26,8 @@
     from ignite.engine import Engine
     from ignite.metrics import Metric
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
-    Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
+    Metric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "Metric")
 
 __all__ = ["Trainer", "SupervisedTrainer", "GanTrainer"]
 

monai/engines/utils.py

@@ -18,7 +18,7 @@
 if TYPE_CHECKING:
     from ignite.engine import EventEnum
 else:
-    EventEnum, _ = optional_import("ignite.engine", "0.4.2", exact_version, "EventEnum")
+    EventEnum, _ = optional_import("ignite.engine", "0.4.4", exact_version, "EventEnum")
 
 __all__ = [
     "IterationEvents",

monai/engines/workflow.py

@@ -20,15 +20,15 @@
 from monai.transforms import apply_transform
 from monai.utils import ensure_tuple, exact_version, optional_import
 
-IgniteEngine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
-State, _ = optional_import("ignite.engine", "0.4.2", exact_version, "State")
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+IgniteEngine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
+State, _ = optional_import("ignite.engine", "0.4.4", exact_version, "State")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
     from ignite.metrics import Metric
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
-    Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
+    Metric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "Metric")
 
 
 class Workflow(IgniteEngine):  # type: ignore[valid-type, misc] # due to optional_import

monai/handlers/checkpoint_loader.py

@@ -16,12 +16,12 @@
 
 from monai.utils import exact_version, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
-Checkpoint, _ = optional_import("ignite.handlers", "0.4.2", exact_version, "Checkpoint")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
+Checkpoint, _ = optional_import("ignite.handlers", "0.4.4", exact_version, "Checkpoint")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class CheckpointLoader:

monai/handlers/checkpoint_saver.py

@@ -15,16 +15,16 @@
 
 from monai.utils import exact_version, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
-Checkpoint, _ = optional_import("ignite.handlers", "0.4.2", exact_version, "Checkpoint")
-BaseSaveHandler, _ = optional_import("ignite.handlers.checkpoint", "0.4.2", exact_version, "BaseSaveHandler")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
+Checkpoint, _ = optional_import("ignite.handlers", "0.4.4", exact_version, "Checkpoint")
+BaseSaveHandler, _ = optional_import("ignite.handlers.checkpoint", "0.4.4", exact_version, "BaseSaveHandler")
 
 if TYPE_CHECKING:
     from ignite.engine import Engine
     from ignite.handlers import DiskSaver
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
-    DiskSaver, _ = optional_import("ignite.handlers", "0.4.2", exact_version, "DiskSaver")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
+    DiskSaver, _ = optional_import("ignite.handlers", "0.4.4", exact_version, "DiskSaver")
 
 
 class CheckpointSaver:

monai/handlers/classification_saver.py

@@ -17,12 +17,12 @@
 from monai.utils import ImageMetaKey as Key
 from monai.utils import exact_version, optional_import
 
-idist, _ = optional_import("ignite", "0.4.2", exact_version, "distributed")
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+idist, _ = optional_import("ignite", "0.4.4", exact_version, "distributed")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class ClassificationSaver:

monai/handlers/confusion_matrix.py

@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Union
 
 import torch
 
@@ -28,7 +28,7 @@ def __init__(
         include_background: bool = True,
         metric_name: str = "hit_rate",
         output_transform: Callable = lambda x: x,
-        device: Optional[torch.device] = None,
+        device: Union[str, torch.device] = "cpu",
         save_details: bool = True,
     ) -> None:
         """

monai/handlers/hausdorff_distance.py

@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Callable, Optional
+from typing import Callable, Optional, Union
 
 import torch
 
@@ -30,7 +30,7 @@ def __init__(
         percentile: Optional[float] = None,
         directed: bool = False,
         output_transform: Callable = lambda x: x,
-        device: Optional[torch.device] = None,
+        device: Union[str, torch.device] = "cpu",
         save_details: bool = True,
     ) -> None:
         """

monai/handlers/iteration_metric.py

@@ -9,21 +9,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence
+from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence, Union
 
 import torch
 
 from monai.handlers.utils import evenly_divisible_all_gather
 from monai.metrics import do_metric_reduction
 from monai.utils import MetricReduction, exact_version, optional_import
 
-idist, _ = optional_import("ignite", "0.4.2", exact_version, "distributed")
-Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
-reinit__is_reduced, _ = optional_import("ignite.metrics.metric", "0.4.2", exact_version, "reinit__is_reduced")
+idist, _ = optional_import("ignite", "0.4.4", exact_version, "distributed")
+Metric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "Metric")
+reinit__is_reduced, _ = optional_import("ignite.metrics.metric", "0.4.4", exact_version, "reinit__is_reduced")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class IterationMetric(Metric):  # type: ignore[valid-type, misc] # due to optional_import
@@ -46,7 +46,7 @@ def __init__(
         self,
         metric_fn: Callable,
         output_transform: Callable = lambda x: x,
-        device: Optional[torch.device] = None,
+        device: Union[str, torch.device] = "cpu",
         save_details: bool = True,
     ) -> None:
         self._is_reduced: bool = False
@@ -77,7 +77,7 @@ def update(self, output: Sequence[torch.Tensor]) -> None:
         score = self.metric_fn(y_pred, y)
         if isinstance(score, (tuple, list)):
             score = score[0]
-        self._scores.append(score)
+        self._scores.append(score.to(self._device))
 
     def compute(self) -> Any:
         """

monai/handlers/lr_schedule_handler.py

@@ -16,11 +16,11 @@
 
 from monai.utils import ensure_tuple, exact_version, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class LrScheduleHandler:

monai/handlers/mean_dice.py

@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Callable, Optional
+from typing import Callable, Union
 
 import torch
 
@@ -27,7 +27,7 @@ def __init__(
         self,
         include_background: bool = True,
         output_transform: Callable = lambda x: x,
-        device: Optional[torch.device] = None,
+        device: Union[str, torch.device] = "cpu",
         save_details: bool = True,
     ) -> None:
         """

monai/handlers/metric_logger.py

@@ -14,11 +14,11 @@
 
 from monai.utils import exact_version, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class MetricLogger:

monai/handlers/metrics_saver.py

@@ -15,12 +15,12 @@
 from monai.utils import ImageMetaKey as Key
 from monai.utils import ensure_tuple, exact_version, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
-idist, _ = optional_import("ignite", "0.4.2", exact_version, "distributed")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
+idist, _ = optional_import("ignite", "0.4.4", exact_version, "distributed")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class MetricsSaver:

monai/handlers/roc_auc.py

@@ -17,8 +17,8 @@
 from monai.metrics import compute_roc_auc
 from monai.utils import Average, exact_version, optional_import
 
-idist, _ = optional_import("ignite", "0.4.2", exact_version, "distributed")
-EpochMetric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "EpochMetric")
+idist, _ = optional_import("ignite", "0.4.4", exact_version, "distributed")
+EpochMetric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "EpochMetric")
 
 
 class ROCAUC(EpochMetric):  # type: ignore[valid-type, misc]  # due to optional_import
@@ -61,7 +61,7 @@ def __init__(
         other_act: Optional[Callable] = None,
         average: Union[Average, str] = Average.MACRO,
         output_transform: Callable = lambda x: x,
-        device: Optional[torch.device] = None,
+        device: Union[str, torch.device] = "cpu",
     ) -> None:
         def _compute_fn(pred, label):
             return compute_roc_auc(

monai/handlers/segmentation_saver.py

@@ -18,11 +18,11 @@
 from monai.transforms import SaveImage
 from monai.utils import GridSampleMode, GridSamplePadMode, InterpolateMode, exact_version, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class SegmentationSaver:

monai/handlers/smartcache_handler.py

@@ -14,11 +14,11 @@
 from monai.data import SmartCacheDataset
 from monai.utils import exact_version, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 
 class SmartCacheHandler:

monai/handlers/stats_handler.py

@@ -17,11 +17,11 @@
 
 from monai.utils import exact_version, is_scalar, optional_import
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
 
 DEFAULT_KEY_VAL_FORMAT = "{}: {:.4f} "
 DEFAULT_TAG = "Loss"

monai/handlers/surface_distance.py

@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Callable, Optional
+from typing import Callable, Union
 
 import torch
 
@@ -29,7 +29,7 @@ def __init__(
         symmetric: bool = False,
         distance_metric: str = "euclidean",
         output_transform: Callable = lambda x: x,
-        device: Optional[torch.device] = None,
+        device: Union[str, torch.device] = "cpu",
         save_details: bool = True,
     ) -> None:
         """

monai/handlers/tensorboard_handlers.py

@@ -18,12 +18,12 @@
 from monai.utils import exact_version, is_scalar, optional_import
 from monai.visualize import plot_2d_or_3d_image
 
-Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
+Events, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Events")
 if TYPE_CHECKING:
     from ignite.engine import Engine
     from torch.utils.tensorboard import SummaryWriter
 else:
-    Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
+    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")
     SummaryWriter, _ = optional_import("torch.utils.tensorboard", name="SummaryWriter")
 
 DEFAULT_TAG = "Loss"