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perf: skip redundant convert_to_tensor for MetaTensor strip #8846

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perf: skip redundant convert_to_tensor for MetaTensor strip #8846

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c575f22
perf: skip redundant convert_to_tensor for MetaTensor strip
aymuos15 May 7, 2026
9478c51
Fix mypy errors from convert_to_tensor → as_tensor swap
aymuos15 May 8, 2026
217ddb8
Merge branch 'dev' into fix/redundant-convert-to-tensor
aymuos15 May 8, 2026
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17 changes: 9 additions & 8 deletions monai/transforms/intensity/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
from monai.config import DtypeLike
from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
from monai.data.meta_obj import get_track_meta
from monai.data.meta_tensor import MetaTensor
from monai.data.ultrasound_confidence_map import UltrasoundConfidenceMap
from monai.data.utils import get_random_patch, get_valid_patch_size
from monai.networks.layers import GaussianFilter, HilbertTransform, MedianFilter, SavitzkyGolayFilter
Expand Down Expand Up @@ -483,7 +484,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:

"""
img = convert_to_tensor(img, track_meta=get_track_meta())
img_t = convert_to_tensor(img, track_meta=False)
img_t: NdarrayOrTensor = img.as_tensor() if isinstance(img, MetaTensor) else img
ret: NdarrayOrTensor
if self.minv is not None or self.maxv is not None:
if self.channel_wise:
Expand Down Expand Up @@ -542,7 +543,7 @@ def __call__(self, img: NdarrayOrTensor, factor=None) -> NdarrayOrTensor:
factor = factor if factor is not None else self.factor

img = convert_to_tensor(img, track_meta=get_track_meta())
img_t = convert_to_tensor(img, track_meta=False)
img_t: NdarrayOrTensor = img.as_tensor() if isinstance(img, MetaTensor) else img
ret: NdarrayOrTensor
if self.channel_wise:
out = []
Expand Down Expand Up @@ -1168,7 +1169,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
Apply the transform to `img`.
"""
img = convert_to_tensor(img, track_meta=get_track_meta())
img_t = convert_to_tensor(img, track_meta=False)
img_t: NdarrayOrTensor = img.as_tensor() if isinstance(img, MetaTensor) else img
if self.channel_wise:
img_t = torch.stack([self._clip(img=d) for d in img_t]) # type: ignore
else:
Expand Down Expand Up @@ -1433,7 +1434,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
Apply the transform to `img`.
"""
img = convert_to_tensor(img, track_meta=get_track_meta())
img_t = convert_to_tensor(img, track_meta=False)
img_t: NdarrayOrTensor = img.as_tensor() if isinstance(img, MetaTensor) else img
if self.channel_wise:
img_t = torch.stack([self._normalize(img=d) for d in img_t]) # type: ignore
else:
Expand Down Expand Up @@ -1530,7 +1531,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:

"""
img = convert_to_tensor(img, track_meta=get_track_meta())
self.img_t = convert_to_tensor(img, track_meta=False)
self.img_t = img.as_tensor() if isinstance(img, MetaTensor) else cast(torch.Tensor, img)

# add one to transform axis because a batch axis will be added at dimension 0
savgol_filter = SavitzkyGolayFilter(self.window_length, self.order, self.axis + 1, self.mode)
Expand Down Expand Up @@ -1907,7 +1908,7 @@ def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTen

if self.reference_control_points is None or self.floating_control_points is None:
raise RuntimeError("please call the `randomize()` function first.")
img_t = convert_to_tensor(img, track_meta=False)
img_t: NdarrayOrTensor = img.as_tensor() if isinstance(img, MetaTensor) else img
img_min, img_max = img_t.min(), img_t.max()
if img_min == img_max:
warn(
Expand Down Expand Up @@ -1952,7 +1953,7 @@ def __init__(self, alpha: float = 0.1) -> None:

def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
img = convert_to_tensor(img, track_meta=get_track_meta())
img_t = convert_to_tensor(img, track_meta=False)
img_t: NdarrayOrTensor = img.as_tensor() if isinstance(img, MetaTensor) else img
n_dims = len(img_t.shape[1:])

# FT
Expand Down Expand Up @@ -2604,7 +2605,7 @@ def __call__(self, img: torch.Tensor) -> torch.Tensor:
img: image to remap.
"""
img = convert_to_tensor(img, track_meta=get_track_meta())
img_ = convert_to_tensor(img, track_meta=False)
img_ = img.as_tensor() if isinstance(img, MetaTensor) else img
# sample noise
vals_to_sample = torch.unique(img_).tolist()
noise = torch.from_numpy(self.R.choice(vals_to_sample, len(vals_to_sample) - 1 + self.kernel_size))
Expand Down
7 changes: 4 additions & 3 deletions monai/transforms/post/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@

import warnings
from collections.abc import Callable, Iterable, Sequence
from typing import cast

import numpy as np
import torch
Expand Down Expand Up @@ -338,7 +339,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
else:
applied_labels = tuple(get_unique_labels(img, is_onehot, discard=0))
img = convert_to_tensor(img, track_meta=get_track_meta())
img_: torch.Tensor = convert_to_tensor(img, track_meta=False)
img_: torch.Tensor = img.as_tensor() if isinstance(img, MetaTensor) else cast(torch.Tensor, img)
if self.independent:
for i in applied_labels:
foreground = img_[i] > 0 if is_onehot else img_[0] == i
Expand Down Expand Up @@ -497,7 +498,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:

if isinstance(img, torch.Tensor):
img = convert_to_tensor(img, track_meta=get_track_meta())
img_ = convert_to_tensor(img, track_meta=False)
img_ = img.as_tensor() if isinstance(img, MetaTensor) else cast(torch.Tensor, img)
if hasattr(torch, "isin"): # `isin` is new in torch 1.10.0
appl_lbls = torch.as_tensor(self.applied_labels, device=img_.device)
out = torch.where(torch.isin(img_, appl_lbls), img_, torch.tensor(0.0).to(img_))
Expand Down Expand Up @@ -623,7 +624,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:

"""
img = convert_to_tensor(img, track_meta=get_track_meta())
img_: torch.Tensor = convert_to_tensor(img, track_meta=False)
img_: torch.Tensor = img.as_tensor() if isinstance(img, MetaTensor) else cast(torch.Tensor, img)
spatial_dims = len(img_.shape) - 1
img_ = img_.unsqueeze(0) # adds a batch dim
if spatial_dims == 2:
Expand Down
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