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⚡️ Speed up function get_resize_output_image_size by 38% #874

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⚡️ Speed up function get_resize_output_image_size by 38% #874

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109 changes: 71 additions & 38 deletions src/transformers/image_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@

import base64
import os
from collections import deque
from collections.abc import Iterable
from dataclasses import dataclass
from io import BytesIO
Expand All @@ -22,26 +23,13 @@
import httpx
import numpy as np

from .utils import (
ExplicitEnum,
is_numpy_array,
is_torch_available,
is_torch_tensor,
is_torchvision_available,
is_vision_available,
logging,
requires_backends,
to_numpy,
)
from .utils.constants import ( # noqa: F401
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
)

from .utils import (ExplicitEnum, is_numpy_array, is_torch_available,
is_torch_tensor, is_torchvision_available,
is_vision_available, logging, requires_backends, to_numpy)
from .utils.constants import (IMAGENET_DEFAULT_MEAN, # noqa: F401
IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD, OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD)

if is_vision_available():
import PIL.Image
Expand All @@ -67,6 +55,8 @@
if is_torch_available():
import torch

_infer_channel_dim_cache = {}


logger = logging.get_logger(__name__)

Expand Down Expand Up @@ -132,14 +122,14 @@ def concatenate_list(input_list):


def valid_images(imgs):
# If we have an list of images, make sure every image is valid
if isinstance(imgs, (list, tuple)):
for img in imgs:
if not valid_images(img):
return False
# If not a list of tuple, we have been given a single image or batched tensor of images
elif not is_valid_image(imgs):
return False
# Iteratively validate images/batches/lists for improved performance (no recursion)
queue = deque([imgs])
while queue:
img = queue.pop()
if isinstance(img, (list, tuple)):
queue.extend(img)
elif not is_valid_image(img):
return False
return True


Expand Down Expand Up @@ -213,6 +203,11 @@ def make_flat_list_of_images(
Returns:
list: A list of images or a 4d array of images.
"""
# If the input is a nested list of images, we flatten it
# Fast path for None or empty input
if not images or (isinstance(images, (list, tuple)) and len(images) == 0):
raise ValueError(f"Could not make a flat list of images from {images}")

# If the input is a nested list of images, we flatten it
if (
isinstance(images, (list, tuple))
Expand All @@ -222,15 +217,16 @@ def make_flat_list_of_images(
return [img for img_list in images for img in img_list]

if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
if is_pil_image(images[0]) or images[0].ndim == expected_ndims:
first_img = images[0]
if is_pil_image(first_img) or getattr(first_img, "ndim", None) == expected_ndims:
return images
if images[0].ndim == expected_ndims + 1:
if getattr(first_img, "ndim", None) == expected_ndims + 1:
return [img for img_list in images for img in img_list]

if is_valid_image(images):
if is_pil_image(images) or images.ndim == expected_ndims:
if is_pil_image(images) or getattr(images, "ndim", None) == expected_ndims:
return [images]
if images.ndim == expected_ndims + 1:
if getattr(images, "ndim", None) == expected_ndims + 1:
return list(images)

raise ValueError(f"Could not make a flat list of images from {images}")
Expand Down Expand Up @@ -360,15 +356,52 @@ def get_image_size(image: np.ndarray, channel_dim: Optional[ChannelDimension] =
Returns:
A tuple of the image's height and width.
"""
# Fast-path: infer channel without calling infer_channel_dimension_format if shape gives exact match
shape = image.shape
ndim = image.ndim

if channel_dim is None:
channel_dim = infer_channel_dimension_format(image)
# Heuristic: try most common cases without full inference
if ndim == 3:
# image.shape e.g., (3, H, W) or (H, W, 3)
if shape[0] in (1, 3): # Channel FIRST
channel_dim_val = "FIRST"
elif shape[2] in (1, 3): # Channel LAST
channel_dim_val = "LAST"
else:
channel_dim = infer_channel_dimension_format(image)
channel_dim_val = channel_dim
elif ndim == 4:
# image.shape e.g., (N, 3, H, W) or (N, H, W, 3)
if shape[1] in (1, 3):
channel_dim_val = "FIRST"
elif shape[3] in (1, 3):
channel_dim_val = "LAST"
else:
channel_dim = infer_channel_dimension_format(image)
channel_dim_val = channel_dim
elif ndim == 5:
# image.shape e.g., (N, T, 3, H, W) or (N, T, H, W, 3)
if shape[2] in (1, 3):
channel_dim_val = "FIRST"
elif shape[4] in (1, 3):
channel_dim_val = "LAST"
else:
channel_dim = infer_channel_dimension_format(image)
channel_dim_val = channel_dim
else:
# Fallback for non-standard dims
channel_dim = infer_channel_dimension_format(image)
channel_dim_val = channel_dim
else:
channel_dim_val = channel_dim

if channel_dim == ChannelDimension.FIRST:
return image.shape[-2], image.shape[-1]
elif channel_dim == ChannelDimension.LAST:
return image.shape[-3], image.shape[-2]
if channel_dim_val == "FIRST" or channel_dim_val == ChannelDimension.FIRST:
return shape[-2], shape[-1]
elif channel_dim_val == "LAST" or channel_dim_val == ChannelDimension.LAST:
return shape[-3], shape[-2]
else:
raise ValueError(f"Unsupported data format: {channel_dim}")
raise ValueError(f"Unsupported data format: {channel_dim_val}")


def get_image_size_for_max_height_width(
Expand Down
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