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Using 8bit images

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This commit is contained in:
Martin Laasmaa
2026-01-16 10:38:34 +02:00
parent 0c19ea2557
commit 31cb6a6c8e
1 changed files with 4 additions and 3 deletions
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7
src/utils/ultralytics_16bit_patch.py
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@@ -72,8 +72,9 @@ def apply_ultralytics_16bit_tiff_patches(*, force: bool = False) -> None:
# logger.info(f"Loading with monkey-patched imread: {filename}")
arr = arr.astype(np.float32)
arr /= arr.max()
arr *= 2**16 - 1
arr = arr.astype(np.uint16)
arr *= 2**8 - 1
arr = arr.astype(np.uint8)
# print(arr.shape, arr.dtype, any(np.isnan(arr).flatten()), np.where(np.isnan(arr)), arr.min(), arr.max())
return np.ascontiguousarray(arr)
# logger.info(f"Loading with original imread: {filename}")
@@ -105,7 +106,7 @@ def apply_ultralytics_16bit_tiff_patches(*, force: bool = False) -> None:
def preprocess_batch_16bit(self, batch: dict) -> dict: # type: ignore[override]
# Start from upstream behavior to keep device placement + multiscale identical,
# but replace the 255 division with dtype-aware scaling.
logger.info(f"Preprocessing batch with monkey-patched preprocess_batch")
# logger.info(f"Preprocessing batch with monkey-patched preprocess_batch")
for k, v in batch.items():
if isinstance(v, torch.Tensor):
batch[k] = v.to(self.device, non_blocking=self.device.type == "cuda")