Make summarize_tensor robust to non-float dtypes (#171)

src/refiners/fluxion/utils.py

@@ -187,20 +187,26 @@ def save_to_safetensors(path: Path | str, tensors: dict[str, Tensor], metadata:
 
 
 def summarize_tensor(tensor: torch.Tensor, /) -> str:
-    return (
-        "Tensor("
-        + ", ".join(
-            [
+    info_list = [
         f"shape=({', '.join(map(str, tensor.shape))})",
         f"dtype={str(object=tensor.dtype).removeprefix('torch.')}",
         f"device={tensor.device}",
+    ]
+    if not tensor.is_complex():
+        info_list.extend(
+            [
                 f"min={tensor.min():.2f}",  # type: ignore
                 f"max={tensor.max():.2f}",  # type: ignore
-                f"mean={tensor.mean():.2f}",
-                f"std={tensor.std():.2f}",
-                f"norm={norm(x=tensor):.2f}",
+            ]
+        )
+
+    info_list.extend(
+        [
+            f"mean={tensor.float().mean():.2f}",
+            f"std={tensor.float().std():.2f}",
+            f"norm={norm(x=tensor.float()):.2f}",
             f"grad={tensor.requires_grad}",
         ]
     )
-        + ")"
-    )
+
+    return "Tensor(" + ", ".join(info_list) + ")"

tests/fluxion/test_utils.py

@@ -7,7 +7,14 @@ from PIL import Image
 from torch import device as Device, dtype as DType
 from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur  # type: ignore
 
-from refiners.fluxion.utils import gaussian_blur, image_to_tensor, manual_seed, no_grad, tensor_to_image
+from refiners.fluxion.utils import (
+    gaussian_blur,
+    image_to_tensor,
+    manual_seed,
+    no_grad,
+    summarize_tensor,
+    tensor_to_image,
+)
 
 
 @dataclass
@@ -64,6 +71,15 @@ def test_tensor_to_image() -> None:
     assert tensor_to_image(torch.zeros(1, 4, 512, 512)).mode == "RGBA"
 
 
+def test_summarize_tensor() -> None:
+    assert summarize_tensor(torch.zeros(1, 3, 512, 512).int())
+    assert summarize_tensor(torch.zeros(1, 3, 512, 512).float())
+    assert summarize_tensor(torch.zeros(1, 3, 512, 512).double())
+    assert summarize_tensor(torch.complex(torch.zeros(1, 3, 512, 512), torch.zeros(1, 3, 512, 512)))
+    assert summarize_tensor(torch.zeros(1, 3, 512, 512).bfloat16())
+    assert summarize_tensor(torch.zeros(1, 3, 512, 512).bool())
+
+
 def test_no_grad() -> None:
     x = torch.randn(1, 1, requires_grad=True)