Merge branch 'tmpdir_routine' of github.com:ericspod/MONAI into tmpdir_routine

Author: ericspod

monai/data/meta_tensor.py

@@ -495,7 +495,8 @@ def astype(self, dtype, device=None, *_args, **_kwargs):
             _kwargs: additional kwargs (currently unused).
 
         Returns:
-            data array instance
+            ``MetaTensor`` when a torch dtype is given (metadata is preserved),
+            or ``np.ndarray`` when a numpy dtype is given.
         """
         if isinstance(dtype, str):
             mod_str, *dtype = dtype.split(".", 1)
@@ -506,7 +507,7 @@ def astype(self, dtype, device=None, *_args, **_kwargs):
 
         out_type: type[torch.Tensor] | type[np.ndarray] | None
         if mod_str == "torch":
-            out_type = torch.Tensor
+            out_type = type(self)
         elif mod_str in ("numpy", "np"):
             out_type = np.ndarray
         else:

monai/handlers/utils.py

@@ -122,15 +122,15 @@ class    mean    median    max    5percentile 95percentile  notnans
 
             # add the average value of all classes to v
             if class_labels is None:
-                class_labels = ["class" + str(i) for i in range(v.shape[1])]
+                labels = ["class" + str(i) for i in range(v.shape[1])]
             else:
-                class_labels = [str(i) for i in class_labels]  # ensure to have a list of str
+                labels = [str(i) for i in class_labels]  # ensure to have a list of str
 
-            class_labels += ["mean"]
+            labels += ["mean"]
             v = np.concatenate([v, np.nanmean(v, axis=1, keepdims=True)], axis=1)
 
             with open(os.path.join(save_dir, f"{k}_raw.csv"), "w") as f:
-                f.write(f"filename{deli}{deli.join(class_labels)}\n")
+                f.write(f"filename{deli}{deli.join(labels)}\n")
                 for i, b in enumerate(v):
                     f.write(
                         f"{images[i] if images is not None else str(i)}{deli}"
@@ -164,7 +164,7 @@ def _compute_op(op: str, d: np.ndarray) -> Any:
                 with open(os.path.join(save_dir, f"{k}_summary.csv"), "w") as f:
                     f.write(f"class{deli}{deli.join(ops)}\n")
                     for i, c in enumerate(np.transpose(v)):
-                        f.write(f"{class_labels[i]}{deli}{deli.join([f'{_compute_op(k, c):.4f}' for k in ops])}\n")
+                        f.write(f"{labels[i]}{deli}{deli.join([f'{_compute_op(k, c):.4f}' for k in ops])}\n")
 
 
 def from_engine(keys: KeysCollection, first: bool = False) -> Callable:

tests/data/meta_tensor/test_meta_tensor.py

@@ -435,8 +435,12 @@ def test_astype(self):
         for np_types in ("float32", "np.float32", "numpy.float32", np.float32, float, "int", np.uint16):
             self.assertIsInstance(t.astype(np_types), np.ndarray)
         for pt_types in ("torch.float", torch.float, "torch.float64"):
-            self.assertIsInstance(t.astype(pt_types), torch.Tensor)
-        self.assertIsInstance(t.astype("torch.float", device="cpu"), torch.Tensor)
+            result = t.astype(pt_types)
+            self.assertIsInstance(result, MetaTensor)
+            self.assertEqual(result.meta.get("fname"), "filename")
+        result = t.astype("torch.float", device="cpu")
+        self.assertIsInstance(result, MetaTensor)
+        self.assertEqual(result.meta.get("fname"), "filename")
 
     def test_transforms(self):
         key = "im"

tests/handlers/test_write_metrics_reports.py

@@ -63,6 +63,28 @@ def test_content(self):
             self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_raw.csv")))
             self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_summary.csv")))
 
+    def test_multi_metric_details_headers(self):
+        with tempfile.TemporaryDirectory() as tempdir:
+            write_metrics_reports(
+                save_dir=Path(tempdir),
+                images=["img1", "img2"],
+                metrics=None,
+                metric_details={
+                    "m1": torch.tensor([[1, 2, 3], [4, 5, 6]]),
+                    "m2": torch.tensor([[7, 8], [9, 10]]),
+                    "m3": torch.tensor([[11, 12, 13, 14], [15, 16, 17, 18]]),
+                },
+                summary_ops=None,
+                deli=",",
+                output_type="csv",
+            )
+            for name, nclass in [("m1", 3), ("m2", 2), ("m3", 4)]:
+                path = os.path.join(tempdir, f"{name}_raw.csv")
+                self.assertTrue(os.path.exists(path))
+                with open(path) as f:
+                    header = f.readline().strip().split(",")
+                self.assertEqual(header, ["filename"] + [f"class{i}" for i in range(nclass)] + ["mean"])
+
 
 if __name__ == "__main__":
     unittest.main()