Move get_device to a separate location

examples/transit_infer.py

@@ -5,7 +5,8 @@
 
 from qusi.finite_standard_light_curve_dataset import FiniteStandardLightCurveDataset
 from qusi.hadryss_model import Hadryss
-from qusi.infer_session import get_device, infer_session
+from qusi.infer_session import infer_session
+from qusi.device import get_device
 from qusi.light_curve_collection import LightCurveCollection
 from ramjet.photometric_database.tess_two_minute_cadence_light_curve import TessMissionLightCurve
 

examples/transit_infinite_dataset_test.py

@@ -8,6 +8,7 @@
 from torchmetrics.classification import BinaryAccuracy
 
 from qusi.hadryss_model import Hadryss
+from qusi.device import get_device
 from qusi.light_curve_collection import LabeledLightCurveCollection
 from qusi.light_curve_dataset import LightCurveDataset
 from ramjet.photometric_database.tess_two_minute_cadence_light_curve import TessMissionLightCurve
@@ -71,14 +72,6 @@ def infinite_datasets_test_session(test_datasets: list[LightCurveDataset], model
     return results
 
 
-def get_device():
-    if torch.cuda.is_available():
-        device = torch.device('cuda')
-    else:
-        device = torch.device('cpu')
-    return device
-
-
 def infinite_dataset_test_phase(dataloader, model: Module, metric_functions: list[Module], device: Device, steps: int):
     batch_count = 0
     metric_totals = torch.zeros(size=[len(metric_functions)])

src/qusi/device.py

@@ -0,0 +1,10 @@
+import torch
+from torch.types import Device
+
+
+def get_device() -> Device:
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+    return device

src/qusi/infer_session.py

@@ -22,14 +22,6 @@ def infer_session(
     return results
 
 
-def get_device() -> Device:
-    if torch.cuda.is_available():
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-    return device
-
-
 def infer_phase(dataloader, model: Module, device: Device):
     batch_count = 0
     batches_of_predicted_targets = []

tests/end_to_end_tests/test_toy_infer_session.py

@@ -3,7 +3,8 @@
 
 import numpy as np
 
-from qusi.infer_session import get_device, infer_session
+from qusi.infer_session import infer_session
+from qusi.device import get_device
 from qusi.light_curve_dataset import (
     default_light_curve_post_injection_transform,
 )