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# coding=utf-8
# Copyright 2024 LY Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Literal, Optional
import torch
import torch.nn as nn
import torchvision.transforms as T
import torchvision.transforms.functional as F
from PIL import Image
from timm.data import (
IMAGENET_INCEPTION_MEAN,
IMAGENET_INCEPTION_STD,
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
)
from timm.data.transforms_factory import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_utils import ImageInput, make_list_of_images
from transformers.utils import TensorType
NormalizationType = Literal["imagenet", "imagenet_inception", "openai_clip"]
class CLYPImageProcessor(BaseImageProcessor):
def __init__(
self,
image_size: int = 224,
normalization_type: NormalizationType = "imagenet",
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.normalization_type: NormalizationType = normalization_type
def preprocess(
self,
images: ImageInput | list[ImageInput],
return_tensors: Optional[str | TensorType] = None,
**kwargs,
) -> BatchFeature:
images = make_list_of_images(images, expected_ndims=3)
# TODO: Support train
transforms = TestTransform(
self.image_size, normalization_type=self.normalization_type
)
images = [transforms(image).numpy() for image in images]
return BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
class TrainTransform:
def __init__(
self,
image_size: int,
scale_range_min: float,
scale_range_max: float,
normalization_type: NormalizationType = "imagenet",
) -> None:
"""
Args:
image_size (int): output-image size.
scale_range_min (float): minimum value of the scale to crop an input image.
scale_range_max (float): maximum value of the scale to crop an input image.
normalization_type (str): select mean and std for normalization (see get_mean_and_std).
"""
scale = (scale_range_min, scale_range_max)
mean_and_std = get_mean_and_std(normalization_type)
self.transform = T.Compose(
[
T.RandomResizedCrop(
image_size, scale=scale, interpolation=T.InterpolationMode.BICUBIC
),
_convert_to_rgb,
T.ToTensor(),
T.Normalize(**mean_and_std),
]
)
def __call__(self, img):
return self.transform(img)
class TestTransform:
def __init__(
self, image_size: int, normalization_type: NormalizationType = "imagenet"
) -> None:
"""
Args:
image_size (int): output-image size.
normalization_type (str): select mean and std for normalization (see get_mean_and_std).
"""
mean_and_std = get_mean_and_std(normalization_type)
self.transform = T.Compose(
[
ResizeMaxSize(image_size, fill=0),
T.CenterCrop(image_size),
_convert_to_rgb,
T.ToTensor(),
T.Normalize(**mean_and_std),
]
)
def __call__(self, img):
return self.transform(img)
class SmallestMaxSize(T.Resize):
"""Resize shorter side of an input image.
The shorter side of an input image is resized to the max_size.
Note that an large part of the input image is discarded when an aspect-ratio value of the input image is extremely small or large.
"""
def __init__(self, max_size: int, **kwargs):
super().__init__(max_size, **kwargs)
@staticmethod
def target_size(w: int, h: int, size: int) -> tuple[int, int]:
if h < w:
w, h = int(size * w / h), size
else:
w, h = size, int(size * h / w)
return (h, w)
def __call__(self, img):
size = self.size
assert isinstance(size, int)
w, h = img.size
target_size = self.target_size(w, h, size)
return F.resize(img, list(target_size), self.interpolation)
class ResizeMaxSize(nn.Module):
"""Resize longer side of an input image.
The longer side of an input image is resized to the max_size.
Note that an large part of the output image is padded when an aspect-ration value of the input image is extremely small or large.
Adapted from https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transform.py
"""
def __init__(
self,
max_size: int,
interpolation: T.InterpolationMode = T.InterpolationMode.BICUBIC,
fn: str = "max",
fill: int = 0,
):
super().__init__()
if not isinstance(max_size, int):
raise TypeError(f"Size should be int. Got {type(max_size)}")
self.max_size = max_size
self.interpolation = interpolation
self.fn = min if fn == "min" else min
self.fill = fill
def forward(self, img):
if isinstance(img, torch.Tensor):
height, width = img.shape[:2]
else:
width, height = img.size
scale = self.max_size / float(max(height, width))
if scale != 1.0:
new_size = tuple(round(dim * scale) for dim in (height, width))
img = F.resize(img, new_size, self.interpolation) # type: ignore
pad_h = self.max_size - new_size[0]
pad_w = self.max_size - new_size[1]
img = F.pad(
img,
padding=[
pad_w // 2,
pad_h // 2,
pad_w - pad_w // 2,
pad_h - pad_h // 2,
],
fill=self.fill,
)
return img
def get_mean_and_std(normalization_type: NormalizationType) -> dict:
"""Return mean and std tensors for T.Normalize()
NOTE:
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
OPENAI_CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073)
OPENAI_CLIP_STD = (0.26862954, 0.26130258, 0.27577711)
"""
if normalization_type == "imagenet":
return {
"mean": torch.tensor(IMAGENET_DEFAULT_MEAN),
"std": torch.tensor(IMAGENET_DEFAULT_STD),
}
elif normalization_type == "imagenet_inception":
return {
"mean": torch.tensor(IMAGENET_INCEPTION_MEAN),
"std": torch.tensor(IMAGENET_INCEPTION_STD),
}
elif normalization_type == "openai_clip":
return {
"mean": torch.tensor(OPENAI_CLIP_MEAN),
"std": torch.tensor(OPENAI_CLIP_STD),
}
else:
raise ValueError(normalization_type)
def _convert_to_rgb(image: Image.Image) -> Image.Image:
return image.convert("RGB")
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