✨ Update Lung model parameters

.gitignore

@@ -167,3 +167,7 @@ cifar-10-python.tar.gz
 
 .amltconfig
 amlt_job.yml
+
+## VS Code
+
+.vscode/launch.json

InnerEye/ML/configs/segmentation/Lung.py

@@ -25,48 +25,51 @@ class Lung(SegmentationModelBase):
     def __init__(self, **kwargs: Any) -> None:
         fg_classes = ["spinalcord", "lung_r", "lung_l", "heart", "esophagus"]
         fg_display_names = ["SpinalCord", "Lung_R", "Lung_L", "Heart", "Esophagus"]
+
+        azure_dataset_id = kwargs.pop("azure_dataset_id", LUNG_AZURE_DATASET_ID)
+
         super().__init__(
+            adam_betas=(0.9, 0.999),
             architecture="UNet3D",
-            feature_channels=[32],
-            kernel_size=3,
-            azure_dataset_id=LUNG_AZURE_DATASET_ID,
-            crop_size=(64, 224, 224),
-            test_crop_size=(128, 512, 512),
-            image_channels=["ct"],
-            ground_truth_ids=fg_classes,
-            ground_truth_ids_display_names=fg_display_names,
+            azure_dataset_id=azure_dataset_id,
+            check_exclusive=False,
+            class_weights=equally_weighted_classes(fg_classes, background_weight=0.02),
             colours=[(255, 255, 255)] * len(fg_classes),
+            crop_size=(64, 256, 256),
+            feature_channels=[32],
             fill_holes=[False] * len(fg_classes),
-            roi_interpreted_types=["ORGAN"] * len(fg_classes),
-            largest_connected_component_foreground_classes=["lung_r", "lung_l", "heart"],
-            num_dataload_workers=2,
-            norm_method=PhotometricNormalizationMethod.CtWindow,
-            level=40,
-            window=400,
-            class_weights=equally_weighted_classes(fg_classes, background_weight=0.02),
-            train_batch_size=8,
+            ground_truth_ids_display_names=fg_display_names,
+            ground_truth_ids=fg_classes,
+            image_channels=["ct"],
             inference_batch_size=1,
             inference_stride_size=(64, 256, 256),
-            num_epochs=140,
+            kernel_size=3,
+            l_rate_polynomial_gamma=0.9,
             l_rate=1e-3,
+            largest_connected_component_foreground_classes=["lung_l", "lung_r", "heart"],
+            level=-500,
+            loss_type=SegmentationLoss.SoftDice,
             min_l_rate=1e-5,
-            l_rate_polynomial_gamma=0.9,
-            optimizer_type=OptimizerType.Adam,
-            opt_eps=1e-4,
-            adam_betas=(0.9, 0.999),
             momentum=0.9,
-            weight_decay=1e-4,
+            monitoring_interval_seconds=0,
+            norm_method=PhotometricNormalizationMethod.CtWindow,
+            num_dataload_workers=10,
+            num_epochs=300,
+            opt_eps=1e-4,
+            optimizer_type=OptimizerType.Adam,
+            roi_interpreted_types=["ORGAN"] * len(fg_classes),
+            test_crop_size=(128, 512, 512),
+            train_batch_size=10,
             use_mixed_precision=True,
             use_model_parallel=True,
-            monitoring_interval_seconds=0,
-            loss_type=SegmentationLoss.SoftDice,
-            check_exclusive=False,
+            weight_decay=1e-4,
+            window=2200,
         )
         self.add_and_validate(kwargs)
 
+
     def get_model_train_test_dataset_splits(self, dataset_df: pd.DataFrame) -> DatasetSplits:
-        # The first 24 subject IDs are the designated test subjects in this dataset.
-        test = list(map(str, range(0, 24)))
+        test = list(map(str, range(0, 9)))
         train_val = list(dataset_df[~dataset_df.subject.isin(test)].subject.unique())
 
         val = list(map(str, numpy.random.choice(train_val, int(len(train_val) * 0.1), replace=False)))