downstream/ProxyCLIP_TPAMI/clipself/src/open_clip/transform.py

import warnings
from dataclasses import dataclass, asdict
from typing import Any, Dict, Optional, Sequence, Tuple, Union

import torch
import torch.nn as nn
import torchvision.transforms.functional as F

from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
    CenterCrop
from torchvision import transforms
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD

@dataclass
class AugmentationCfg:
    scale: Tuple[float, float] = (0.9, 1.0)
    ratio: Optional[Tuple[float, float]] = None
    color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None
    interpolation: Optional[str] = None
    re_prob: Optional[float] = None
    re_count: Optional[int] = None
    use_timm: bool = False


class ResizeMaxSize(nn.Module):

    def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=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))
        new_size = tuple(round(dim * scale) for dim in (height, width))
        img = F.resize(img, new_size, self.interpolation)
        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 _convert_to_rgb(image):
    return image.convert('RGB')


def image_transform(
        image_size: int,
        is_train: bool,
        mean: Optional[Tuple[float, ...]] = None,
        std: Optional[Tuple[float, ...]] = None,
        resize_longest_max: bool = False,
        fill_color: int = 0,
        aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
):
    mean = mean or OPENAI_DATASET_MEAN
    if not isinstance(mean, (list, tuple)):
        mean = (mean,) * 3

    std = std or OPENAI_DATASET_STD
    if not isinstance(std, (list, tuple)):
        std = (std,) * 3

    if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
        # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
        image_size = image_size[0]

    if isinstance(aug_cfg, dict):
        aug_cfg = AugmentationCfg(**aug_cfg)
    else:
        aug_cfg = aug_cfg or AugmentationCfg()
    normalize = Normalize(mean=mean, std=std)
    if is_train:
        aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None}
        use_timm = aug_cfg_dict.pop('use_timm', False)
        if use_timm:
            from timm.data import create_transform  # timm can still be optional
            if isinstance(image_size, (tuple, list)):
                assert len(image_size) >= 2
                input_size = (3,) + image_size[-2:]
            else:
                input_size = (3, image_size, image_size)
            # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time
            aug_cfg_dict.setdefault('interpolation', 'random')
            aug_cfg_dict.setdefault('color_jitter', None)  # disable by default
            train_transform = create_transform(
                input_size=input_size,
                is_training=True,
                hflip=0.,
                mean=mean,
                std=std,
                re_mode='pixel',
                **aug_cfg_dict,
            )
        else:
            train_transform = Compose([
                RandomResizedCrop(
                    image_size,
                    scale=aug_cfg_dict.pop('scale'),
                    interpolation=InterpolationMode.BICUBIC,
                ),
                _convert_to_rgb,
                ToTensor(),
                normalize,
            ])
            if aug_cfg_dict:
                warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).')
        return train_transform
    else:
        if resize_longest_max:
            transforms = [
                ResizeMaxSize(image_size, fill=fill_color)
            ]
        else:
            transforms = [
                Resize(image_size, interpolation=InterpolationMode.BICUBIC),
                CenterCrop(image_size),
            ]
        transforms.extend([
            _convert_to_rgb,
            ToTensor(),
            normalize,
        ])
        return Compose(transforms)


def det_image_transform(
        image_size: int,
        is_train: bool,
        mean: Optional[Tuple[float, ...]] = None,
        std: Optional[Tuple[float, ...]] = None,
        fill_color: int = 0,
        aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
):
    mean = mean or OPENAI_DATASET_MEAN
    if not isinstance(mean, (list, tuple)):
        mean = (mean,) * 3

    std = std or OPENAI_DATASET_STD
    if not isinstance(std, (list, tuple)):
        std = (std,) * 3

    if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
        # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
        image_size = image_size[0]

    normalize = Normalize(mean=mean, std=std)
    if is_train:
        raise NotImplementedError
    else:
        transforms = [
            ResizeLongest(image_size, fill=fill_color),
            _convert_to_rgb,
            ToTensor(),
            normalize,
        ]
        return Compose(transforms)


class ResizeLongest(nn.Module):
    def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fill=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.fill = fill

    def forward(self, img):
        if isinstance(img, torch.Tensor):
            height, width = img.shape[1:]
        else:
            width, height = img.size
        scale = self.max_size / float(max(height, width))
        new_height, new_width = round(height * scale), round(width * scale)

        img = F.resize(img, [new_height, new_width], self.interpolation, antialias=None)
        pad_h = self.max_size - new_height
        pad_w = self.max_size - new_width
        img = F.pad(img, padding=[0, 0, pad_w, pad_h], fill=self.fill)

        return img


def get_scale(img, new_image):
    if isinstance(img, torch.Tensor):
        height, width = new_image.shape[-2:]
    else:
        width, height = img.size

    if isinstance(new_image, torch.Tensor):
        new_height, new_width = new_image.shape[-2:]
    else:
        new_width, new_height = new_image.size

    scale = min(new_height/height, new_width/width)

    return scale



class MultiViewAugmentation(object):
    def __init__(self,
                image_size: int,
                mean: Optional[Tuple[float, ...]] = None,
                std: Optional[Tuple[float, ...]] = None,
                resize_longest_max: bool = False,
                fill_color: int = 0,
                global_crops_scale=(0.32,1.0),
                ):
        
        normalize = Normalize(mean=mean, std=std)
        if resize_longest_max:
            self.vanilla_transfo = [ResizeMaxSize(image_size, fill=fill_color)]
        else:
            self.vanilla_transfo = [
                Resize(image_size, interpolation=InterpolationMode.BICUBIC),
                CenterCrop(image_size),
            ]
        self.vanilla_transfo.extend([
                    _convert_to_rgb,
                    ToTensor(),
                    normalize,
                    ])
        self.vanilla_transfo=Compose(self.vanilla_transfo)

        self.geometric_augmentation_global = transforms.Compose(
            [
                transforms.RandomResizedCrop(
                    image_size, scale=global_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
                ),
                transforms.RandomHorizontalFlip(p=0.5),
            ]
        )
        color_jittering = transforms.Compose(
            [
                transforms.RandomApply(
                    [transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)],
                    p=0.8,
                ),
                transforms.RandomGrayscale(p=0.2),
            ]
        )

        global_transfo_extra = transforms.Compose(
            [
                GaussianBlur(p=0.1),
                transforms.RandomSolarize(threshold=128, p=0.2),
            ]
        )
        self.global_transfo = transforms.Compose([self.geometric_augmentation_global, color_jittering, global_transfo_extra,_convert_to_rgb,ToTensor(),normalize])

    def __call__(self, image):
        global_view=self.global_transfo(image)
        vanilla_view = self.vanilla_transfo(image)
        return vanilla_view, global_view
        


class GaussianBlur(transforms.RandomApply):
    """
    Apply Gaussian Blur to the PIL image.
    """

    def __init__(self, *, p: float = 0.5, radius_min: float = 0.1, radius_max: float = 2.0):
        # NOTE: torchvision is applying 1 - probability to return the original image
        keep_p = 1 - p
        transform = transforms.GaussianBlur(kernel_size=9, sigma=(radius_min, radius_max))
        super().__init__(transforms=[transform], p=keep_p)