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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)
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