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Make summarize_tensor robust to non-float dtypes (#171)
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@ -187,20 +187,26 @@ def save_to_safetensors(path: Path | str, tensors: dict[str, Tensor], metadata:
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def summarize_tensor(tensor: torch.Tensor, /) -> str:
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return (
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"Tensor("
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+ ", ".join(
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info_list = [
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f"shape=({', '.join(map(str, tensor.shape))})",
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f"dtype={str(object=tensor.dtype).removeprefix('torch.')}",
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f"device={tensor.device}",
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]
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if not tensor.is_complex():
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info_list.extend(
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[
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f"shape=({', '.join(map(str, tensor.shape))})",
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f"dtype={str(object=tensor.dtype).removeprefix('torch.')}",
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f"device={tensor.device}",
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f"min={tensor.min():.2f}", # type: ignore
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f"max={tensor.max():.2f}", # type: ignore
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f"mean={tensor.mean():.2f}",
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f"std={tensor.std():.2f}",
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f"norm={norm(x=tensor):.2f}",
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f"grad={tensor.requires_grad}",
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]
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)
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+ ")"
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info_list.extend(
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[
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f"mean={tensor.float().mean():.2f}",
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f"std={tensor.float().std():.2f}",
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f"norm={norm(x=tensor.float()):.2f}",
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f"grad={tensor.requires_grad}",
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]
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)
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return "Tensor(" + ", ".join(info_list) + ")"
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@ -7,7 +7,14 @@ from PIL import Image
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from torch import device as Device, dtype as DType
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from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur # type: ignore
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from refiners.fluxion.utils import gaussian_blur, image_to_tensor, manual_seed, no_grad, tensor_to_image
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from refiners.fluxion.utils import (
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gaussian_blur,
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image_to_tensor,
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manual_seed,
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no_grad,
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summarize_tensor,
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tensor_to_image,
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)
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@dataclass
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@ -64,6 +71,15 @@ def test_tensor_to_image() -> None:
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assert tensor_to_image(torch.zeros(1, 4, 512, 512)).mode == "RGBA"
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def test_summarize_tensor() -> None:
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).int())
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).float())
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).double())
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assert summarize_tensor(torch.complex(torch.zeros(1, 3, 512, 512), torch.zeros(1, 3, 512, 512)))
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).bfloat16())
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assert summarize_tensor(torch.zeros(1, 3, 512, 512).bool())
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def test_no_grad() -> None:
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x = torch.randn(1, 1, requires_grad=True)
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