Merge branch 'dev' into 7047-simplify-resnet-pretrained

monai/apps/auto3dseg/bundle_gen.py

@@ -42,9 +42,11 @@
 from monai.config import PathLike
 from monai.utils import ensure_tuple, look_up_option, run_cmd
 from monai.utils.enums import AlgoKeys
+from monai.utils.misc import MONAIEnvVars
 
 logger = get_logger(module_name=__name__)
-ALGO_HASH = os.environ.get("MONAI_ALGO_HASH", "3e11dd0")
+ALGO_HASH = MONAIEnvVars.algo_hash()
+
 
 __all__ = ["BundleAlgo", "BundleGen"]
 

monai/metrics/hausdorff_distance.py

@@ -208,7 +208,7 @@ def _compute_percentile_hausdorff_distance(
 
     # for both pred and gt do not have foreground
     if surface_distance.shape == (0,):
-        return torch.tensor(torch.nan, dtype=torch.float, device=surface_distance.device)
+        return torch.tensor(np.nan, dtype=torch.float, device=surface_distance.device)
 
     if not percentile:
         return surface_distance.max()

monai/metrics/surface_dice.py

@@ -275,7 +275,7 @@ def compute_surface_dice(
             boundary_correct = gt_true + pred_true
         if boundary_complete == 0:
             # the class is neither present in the prediction, nor in the reference segmentation
-            nsd[b, c] = torch.nan
+            nsd[b, c] = torch.tensor(np.nan)
         else:
             nsd[b, c] = boundary_correct / boundary_complete
 

monai/metrics/surface_distance.py

@@ -181,6 +181,6 @@ def compute_average_surface_distance(
             class_index=c,
         )
         surface_distance = torch.cat(distances)
-        asd[b, c] = torch.nan if surface_distance.shape == (0,) else surface_distance.mean()
+        asd[b, c] = torch.tensor(np.nan) if surface_distance.shape == (0,) else surface_distance.mean()
 
     return convert_data_type(asd, output_type=torch.Tensor, device=y_pred.device, dtype=torch.float)[0]

monai/metrics/utils.py

@@ -275,10 +275,10 @@ def get_surface_distance(
     """
     lib: ModuleType = torch if isinstance(seg_pred, torch.Tensor) else np
     if not seg_gt.any():
-        dis = lib.inf * lib.ones_like(seg_gt, dtype=lib.float32)
+        dis = np.inf * lib.ones_like(seg_gt, dtype=lib.float32)
     else:
         if not lib.any(seg_pred):
-            dis = lib.inf * lib.ones_like(seg_gt, dtype=lib.float32)
+            dis = np.inf * lib.ones_like(seg_gt, dtype=lib.float32)
             dis = dis[seg_gt]
             return convert_to_dst_type(dis, seg_pred, dtype=dis.dtype)[0]
         if distance_metric == "euclidean":

monai/networks/utils.py

@@ -552,7 +552,7 @@ def copy_model_state(
     if inplace and isinstance(dst, torch.nn.Module):
         if isinstance(dst, (nn.DataParallel, nn.parallel.DistributedDataParallel)):
             dst = dst.module
-        dst.load_state_dict(dst_dict)
+        dst.load_state_dict(dst_dict)  # type: ignore
     return dst_dict, updated_keys, unchanged_keys
 
 

monai/optimizers/novograd.py

@@ -70,7 +70,7 @@ def __setstate__(self, state):
         for group in self.param_groups:
             group.setdefault("amsgrad", False)
 
-    def step(self, closure: Callable[[], T] | None = None) -> T | None:
+    def step(self, closure: Callable[[], T] | None = None) -> T | None:  # type: ignore
         """Performs a single optimization step.
 
         Arguments:

monai/transforms/inverse.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-import os
 import warnings
 from collections.abc import Hashable, Mapping
 from contextlib import contextmanager
@@ -34,6 +33,7 @@
     convert_to_numpy,
     convert_to_tensor,
 )
+from monai.utils.misc import MONAIEnvVars
 
 __all__ = ["TraceableTransform", "InvertibleTransform"]
 
@@ -70,7 +70,7 @@ class TraceableTransform(Transform):
     `MONAI_TRACE_TRANSFORM` when initializing the class.
     """
 
-    tracing = os.environ.get("MONAI_TRACE_TRANSFORM", "1") != "0"
+    tracing = MONAIEnvVars.trace_transform() != "0"
 
     def set_tracing(self, tracing: bool) -> None:
         """Set whether to trace transforms."""

monai/transforms/signal/array.py

@@ -380,7 +380,7 @@ def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor:
         Args:
             signal: signal to be filled
         """
-        signal = torch.nan_to_num(convert_to_tensor(signal), nan=self.replacement)
+        signal = torch.nan_to_num(convert_to_tensor(signal, track_meta=True), nan=self.replacement)
         return signal
 
 

monai/utils/misc.py

@@ -527,6 +527,30 @@ def debug() -> bool:
     def doc_images() -> str | None:
         return os.environ.get("MONAI_DOC_IMAGES")
 
+    @staticmethod
+    def algo_hash() -> str | None:
+        return os.environ.get("MONAI_ALGO_HASH", "249bf4b")
+
+    @staticmethod
+    def trace_transform() -> str | None:
+        return os.environ.get("MONAI_TRACE_TRANSFORM", "1")
+
+    @staticmethod
+    def eval_expr() -> str | None:
+        return os.environ.get("MONAI_EVAL_EXPR", "1")
+
+    @staticmethod
+    def allow_missing_reference() -> str | None:
+        return os.environ.get("MONAI_ALLOW_MISSING_REFERENCE", "1")
+
+    @staticmethod
+    def extra_test_data() -> str | None:
+        return os.environ.get("MONAI_EXTRA_TEST_DATA", "1")
+
+    @staticmethod
+    def testing_algo_template() -> str | None:
+        return os.environ.get("MONAI_TESTING_ALGO_TEMPLATE", None)
+
 
 class ImageMetaKey:
     """

tests/min_tests.py

@@ -188,6 +188,7 @@ def run_testsuit():
         "test_unetr_block",
         "test_vit",
         "test_vitautoenc",
+        "test_vnet",
         "test_write_metrics_reports",
         "test_wsireader",
         "test_zoom",

tests/test_monai_env_vars.py

@@ -21,15 +21,15 @@ class TestMONAIEnvVars(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         super(__class__, cls).setUpClass()
-        cls.orig_value = os.environ.get("MONAI_DEBUG")
+        cls.orig_value = str(MONAIEnvVars.debug())
 
     @classmethod
     def tearDownClass(cls):
         if cls.orig_value is not None:
             os.environ["MONAI_DEBUG"] = cls.orig_value
         else:
             os.environ.pop("MONAI_DEBUG")
-        print("MONAI debug value:", os.environ.get("MONAI_DEBUG"))
+        print("MONAI debug value:", str(MONAIEnvVars.debug()))
         super(__class__, cls).tearDownClass()
 
     def test_monai_env_vars(self):

tests/test_monai_utils_misc.py

@@ -16,7 +16,7 @@
 
 from parameterized import parameterized
 
-from monai.utils.misc import check_kwargs_exist_in_class_init, run_cmd, to_tuple_of_dictionaries
+from monai.utils.misc import MONAIEnvVars, check_kwargs_exist_in_class_init, run_cmd, to_tuple_of_dictionaries
 
 TO_TUPLE_OF_DICTIONARIES_TEST_CASES = [
     ({}, tuple(), tuple()),
@@ -75,7 +75,7 @@ def _custom_user_function(self, cls, *args, **kwargs):
 
 class TestCommandRunner(unittest.TestCase):
     def setUp(self):
-        self.orig_flag = os.environ.get("MONAI_DEBUG")
+        self.orig_flag = str(MONAIEnvVars.debug())
 
     def tearDown(self):
         if self.orig_flag is not None:

tests/test_network_consistency.py

@@ -23,9 +23,10 @@
 
 import monai.networks.nets as nets
 from monai.utils import set_determinism
+from monai.utils.misc import MONAIEnvVars
 from tests.utils import assert_allclose
 
-extra_test_data_dir = os.environ.get("MONAI_EXTRA_TEST_DATA")
+extra_test_data_dir = MONAIEnvVars.extra_test_data()
 
 TESTS = []
 if extra_test_data_dir is not None:

tests/test_transform.py

@@ -16,6 +16,7 @@
 
 import monai.transforms as mt
 from monai.data import Dataset
+from monai.utils.misc import MONAIEnvVars
 
 
 class FaultyTransform(mt.Transform):
@@ -31,7 +32,7 @@ class TestTransform(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         super(__class__, cls).setUpClass()
-        cls.orig_value = os.environ.get("MONAI_DEBUG")
+        cls.orig_value = str(MONAIEnvVars.debug())
 
     @classmethod
     def tearDownClass(cls):

tests/utils.py

@@ -46,6 +46,7 @@
 from monai.data.meta_tensor import MetaTensor, get_track_meta
 from monai.networks import convert_to_onnx, convert_to_torchscript
 from monai.utils import optional_import
+from monai.utils.misc import MONAIEnvVars
 from monai.utils.module import pytorch_after
 from monai.utils.tf32 import detect_default_tf32
 from monai.utils.type_conversion import convert_data_type
@@ -75,7 +76,7 @@ def get_testing_algo_template_path():
 
     https://github.com/Project-MONAI/MONAI/blob/1.1.0/monai/apps/auto3dseg/bundle_gen.py#L380-L381
     """
-    return os.environ.get("MONAI_TESTING_ALGO_TEMPLATE", None)
+    return MONAIEnvVars.testing_algo_template()
 
 
 def clone(data: NdarrayTensor) -> NdarrayTensor: