/
augs.py
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/
augs.py
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import torchvision
import skimage
import utils
import torch
from torchvision import transforms
from torchvision.transforms import functional as F
import numpy as np
from PIL import Image
import math
import numbers
IMG_MEAN = (0.4914, 0.4822, 0.4465)
IMG_STD = (0.2023, 0.1994, 0.2010)
NORM = [transforms.ToTensor(),
transforms.Normalize(IMG_MEAN, IMG_STD)]
# GRID_global_crops_size = 224 # 224, 256, 288
class ParamsWrapper(object):
# crop and return parameterss
def __init__(self, transform_list):
self.transform_list = transform_list
def __call__(self, img):
# return transformed img and params
for t in self.transform_list:
if isinstance(t, RandomResizedCropParams):
img, params = t(img)
else:
img = t(img)
return img, params
def _get_image_size(img):
if F._is_pil_image(img):
return img.size
elif isinstance(img, torch.Tensor) and img.dim() > 2:
return img.shape[-2:][::-1]
else:
raise TypeError("Unexpected type {}".format(type(img)))
def _setup_size(size, error_msg):
if isinstance(size, numbers.Number):
return int(size), int(size)
if isinstance(size, Sequence) and len(size) == 1:
return size[0], size[0]
if len(size) != 2:
raise ValueError(error_msg)
return size
class RandomResizedCropParams(torch.nn.Module):
def __init__(self, size, scale, ratio=(3. / 4., 4. / 3.), interpolation=Image.BICUBIC):
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
def get_params(self, img, scale, ratio):
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image or Tensor): Input image.
scale (list): range of scale of the origin size cropped
ratio (list): range of aspect ratio of the origin aspect ratio cropped #NOTE: w/h!!
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for a random
sized crop.
"""
width, height = _get_image_size(img)
# print(img.size) # w, h
# assert False
# print(width,height)
# assert False
area = height * width
log_ratio = torch.log(torch.tensor(ratio))
for kk in range(10):
# print(kk)
r = torch.empty(1).uniform_(scale[0], scale[1]).item()
# print(scale[0], scale[1])
# print(r)
target_area = area * r
aspect_ratio = torch.exp(
torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
).item()
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
# print(area, aspect_ratio)
# print('asd', w,h, width, height)
if 0 < w <= width and 0 < h <= height:
i = torch.randint(0, height - h + 1, size=(1,)).item()
j = torch.randint(0, width - w + 1, size=(1,)).item()
return i, j, h, w
# else:
# print('fail!_{}'.format(kk))
# Fallback to central crop
in_ratio = float(width) / float(height)
if in_ratio < min(ratio):
w = width
h = int(round(w / min(ratio)))
elif in_ratio > max(ratio):
h = height
w = int(round(h * max(ratio)))
else: # whole image
w = width
h = height
i = (height - h) // 2
j = (width - w) // 2
return i, j, h, w
def forward(self, img):
# return crop and crop params
i, j, h, w = self.get_params(img, self.scale, self.ratio)
crop = F.resized_crop(img, i, j, h, w, self.size, self.interpolation)
return crop, (i,j,h,w)
class DataAugmentationDINO(object):
def __init__(self, args, global_crops_scale, local_crops_scale, local_crops_number):
self.args = args
self.flip_and_color_jitter = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
# NOTE: ablation- using color aug or not
transforms.RandomApply(
[transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)],
p=0.8
),
transforms.RandomGrayscale(p=0.2),
])
self.normalize = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# ====== transformation for the local crops ======
# local RRC + F&C + Normalize
self.local_transfo = ParamsWrapper([
RandomResizedCropParams(self.args.local_crops_size, scale=local_crops_scale, interpolation=Image.BICUBIC),
self.flip_and_color_jitter,
utils.GaussianBlur(p=0.5),
self.normalize,
])
# global RRC + Normalize (no color jitter is used)
ratio = (3. / 4., 4. / 3.)
# self.random_resize_crop_params = RandomResizedCropParams(self.args.global_crops_size, scale=global_crops_scale, ratio=ratio, interpolation=Image.BICUBIC)
self.global_transfo = ParamsWrapper([
RandomResizedCropParams(self.args.global_crops_size, scale=global_crops_scale, ratio=ratio, interpolation=Image.BICUBIC),
self.normalize
])
# =======
ori_size = self.args.ori_resize
at_size = (int(ori_size[0]*self.args.at_downscale_factor), int(ori_size[1]*self.args.at_downscale_factor))
# print(ori_size, at_size) # (720, 1280) (360, 640)
self.resize_ori = transforms.Resize(ori_size)
self.resize_at = transforms.Resize(at_size)
def __call__(self, image):
if isinstance(image, torch.Tensor):
image = Image.fromarray(image.numpy())
assert isinstance(image, Image.Image)
# globals
globs = []
for _ in range(self.args.global_crops_number):
globs.append(self.global_transfo(image)[0])
# locals
locals = []
for _ in range(self.args.local_crops_number):
locals.append(self.local_transfo(image)[0])
crops = globs + locals
return crops + [self.normalize(self.resize_at(image))]