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import torch
import torchvision
from torchvision import transforms
import torch.nn.functional as F
import torchvision.transforms.functional as TF
import numpy as np
from PIL import Image, ImageFilter
import random
class BaseTransform(object):
"""
Resize and center crop.
"""
def __init__(self, res):
self.res = res
def __call__(self, index, image):
image = TF.resize(image, self.res, Image.BILINEAR)
w, h = image.size
left = int(round((w - self.res) / 2.))
top = int(round((h - self.res) / 2.))
return TF.crop(image, top, left, self.res, self.res)
class ComposeTransform(object):
def __init__(self, tlist):
self.tlist = tlist
def __call__(self, index, image):
for trans in self.tlist:
image = trans(index, image)
return image
class RandomResize(object):
def __init__(self, rmin, rmax, N):
self.reslist = [random.randint(rmin, rmax) for _ in range(N)]
def __call__(self, index, image):
return TF.resize(image, self.reslist[index], Image.BILINEAR)
class RandomCrop(object):
def __init__(self, res, N):
self.res = res
self.cons = [(np.random.uniform(0, 1), np.random.uniform(0, 1)) for _ in range(N)]
def __call__(self, index, image):
ws, hs = self.cons[index]
w, h = image.size
left = int(round((w-self.res)*ws))
top = int(round((h-self.res)*hs))
return TF.crop(image, top, left, self.res, self.res)
class RandomHorizontalFlip(object):
def __init__(self, N, p=0.5):
self.p_ref = p
self.plist = np.random.random_sample(N)
def __call__(self, index, image):
if self.plist[index.cpu()] < self.p_ref:
return TF.hflip(image)
else:
return image
class TensorTransform(object):
def __init__(self):
self.to_tensor = transforms.ToTensor()
#self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
def __call__(self, image):
image = self.to_tensor(image)
#image = self.normalize(image)
return image
class RandomGaussianBlur(object):
def __init__(self, sigma, p, N):
self.min_x = sigma[0]
self.max_x = sigma[1]
self.del_p = 1 - p
self.p_ref = p
self.plist = np.random.random_sample(N)
def __call__(self, index, image):
if self.plist[index] < self.p_ref:
x = self.plist[index] - self.p_ref
m = (self.max_x - self.min_x) / self.del_p
b = self.min_x
s = m * x + b
return image.filter(ImageFilter.GaussianBlur(radius=s))
else:
return image
class RandomGrayScale(object):
def __init__(self, p, N):
self.grayscale = transforms.RandomGrayscale(p=1.) # Deterministic (We still want flexible out_dim).
self.p_ref = p
self.plist = np.random.random_sample(N)
def __call__(self, index, image):
if self.plist[index] < self.p_ref:
return self.grayscale(image)
else:
return image
class RandomColorBrightness(object):
def __init__(self, x, p, N):
self.min_x = max(0, 1 - x)
self.max_x = 1 + x
self.p_ref = p
self.plist = np.random.random_sample(N)
self.rlist = [random.uniform(self.min_x, self.max_x) for _ in range(N)]
def __call__(self, index, image):
if self.plist[index] < self.p_ref:
return TF.adjust_brightness(image, self.rlist[index])
else:
return image
class RandomColorContrast(object):
def __init__(self, x, p, N):
self.min_x = max(0, 1 - x)
self.max_x = 1 + x
self.p_ref = p
self.plist = np.random.random_sample(N)
self.rlist = [random.uniform(self.min_x, self.max_x) for _ in range(N)]
def __call__(self, index, image):
if self.plist[index] < self.p_ref:
return TF.adjust_contrast(image, self.rlist[index])
else:
return image
class RandomColorSaturation(object):
def __init__(self, x, p, N):
self.min_x = max(0, 1 - x)
self.max_x = 1 + x
self.p_ref = p
self.plist = np.random.random_sample(N)
self.rlist = [random.uniform(self.min_x, self.max_x) for _ in range(N)]
def __call__(self, index, image):
if self.plist[index] < self.p_ref:
return TF.adjust_saturation(image, self.rlist[index])
else:
return image
class RandomColorHue(object):
def __init__(self, x, p, N):
self.min_x = -x
self.max_x = x
self.p_ref = p
self.plist = np.random.random_sample(N)
self.rlist = [random.uniform(self.min_x, self.max_x) for _ in range(N)]
def __call__(self, index, image):
if self.plist[index] < self.p_ref:
return TF.adjust_hue(image, self.rlist[index])
else:
return image
class RandomVerticalFlip(object):
def __init__(self, N, p=0.5):
self.p_ref = p
self.plist = np.random.random_sample(N)
def __call__(self, indice, image):
I = np.nonzero(self.plist[indice] < self.p_ref)[0]
if len(image.size()) == 3:
image_t = image[I].flip([1])
else:
image_t = image[I].flip([2])
return torch.stack([image_t[np.where(I==i)[0][0]] if i in I else image[i] for i in range(image.size(0))])
class RandomHorizontalTensorFlip(object):
def __init__(self, N, p=0.5):
self.p_ref = p
self.plist = np.random.random_sample(N)
def __call__(self, indice, image, is_label=False):
I = np.nonzero(self.plist[indice] < self.p_ref)[0]
if len(image.size()) == 3:
image_t = image[I].flip([2])
else:
image_t = image[I].flip([3])
return torch.stack([image_t[np.where(I==i)[0][0]] if i in I else image[i] for i in range(image.size(0))])
class RandomResizedCrop(object):
def __init__(self, N, res, scale=(0.5, 1.0)):
self.res = res
self.scale = scale
self.rscale = [np.random.uniform(*scale) for _ in range(N)]
self.rcrop = [(np.random.uniform(0, 1), np.random.uniform(0, 1)) for _ in range(N)]
def random_crop(self, idx, img):
ws, hs = self.rcrop[idx]
res1 = int(img.size(-1))
res2 = int(self.rscale[idx]*res1)
i1 = int(round((res1-res2)*ws))
j1 = int(round((res1-res2)*hs))
return img[:, :, i1:i1+res2, j1:j1+res2]
def __call__(self, indice, image):
new_image = []
res_tar = self.res // 4 if image.size(1) > 5 else self.res # View 1 or View 2?
for i, idx in enumerate(indice):
img = image[[i]]
img = self.random_crop(idx, img)
img = F.interpolate(img, res_tar, mode='bilinear', align_corners=False)
new_image.append(img)
new_image = torch.cat(new_image)
return new_image
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