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transformers.image_transforms.normalize wrong types #35773

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merged 1 commit into from
Jan 20, 2025
Merged

transformers.image_transforms.normalize wrong types #35773

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14 changes: 7 additions & 7 deletions src/transformers/image_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@

import warnings
from math import ceil
from typing import Iterable, List, Optional, Tuple, Union
from typing import Iterable, List, Optional, Sequence, Tuple, Union

import numpy as np

Expand Down Expand Up @@ -357,8 +357,8 @@ def resize(

def normalize(
image: np.ndarray,
mean: Union[float, Iterable[float]],
std: Union[float, Iterable[float]],
mean: Union[float, Sequence[float]],
std: Union[float, Sequence[float]],
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
Expand All @@ -370,9 +370,9 @@ def normalize(
Args:
image (`np.ndarray`):
The image to normalize.
mean (`float` or `Iterable[float]`):
mean (`float` or `Sequence[float]`):
The mean to use for normalization.
std (`float` or `Iterable[float]`):
std (`float` or `Sequence[float]`):
The standard deviation to use for normalization.
data_format (`ChannelDimension`, *optional*):
The channel dimension format of the output image. If unset, will use the inferred format from the input.
Expand All @@ -393,14 +393,14 @@ def normalize(
if not np.issubdtype(image.dtype, np.floating):
image = image.astype(np.float32)

if isinstance(mean, Iterable):
if isinstance(mean, Sequence):
if len(mean) != num_channels:
raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(mean)}")
else:
mean = [mean] * num_channels
mean = np.array(mean, dtype=image.dtype)

if isinstance(std, Iterable):
if isinstance(std, Sequence):
if len(std) != num_channels:
raise ValueError(f"std must have {num_channels} elements if it is an iterable, got {len(std)}")
else:
Expand Down