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datasets/new_da.py
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567
datasets/new_da.py
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import torch
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from torchvision.transforms import RandomResizedCrop,InterpolationMode
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from torchvision.transforms import functional as F
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import numpy as np
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import random
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import torch
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import torchvision.transforms.functional as F
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from torchvision.transforms import (
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Resize, Compose, ToTensor, Normalize, CenterCrop, RandomCrop, ColorJitter,
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RandomApply, GaussianBlur, RandomGrayscale, RandomResizedCrop,
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RandomHorizontalFlip
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)
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from torchvision.transforms.functional import InterpolationMode
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from dassl.data.transforms.transforms import SVHNPolicy, CIFAR10Policy, ImageNetPolicy
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from dassl.data.transforms.transforms import RandAugment, RandAugment2, RandAugmentFixMatch
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from PIL import Image, ImageFilter
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class RandomResizedCropPair(RandomResizedCrop):
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def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=InterpolationMode.BILINEAR):
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super(RandomResizedCropPair, self).__init__(size, scale, ratio, interpolation)
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def __call__(self, img,mask):
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i,j,h,w = self.get_params(img,self.scale,self.ratio)
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return F.resized_crop(img,i,j,h,w,self.size,self.interpolation),F.resized_crop(mask,i,j,h,w,self.size,self.interpolation)
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class ComposePair:
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def __init__(self, transforms):
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self.transforms = transforms
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def __call__(self, img,mask):
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for t in self.transforms:
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if isinstance(t,Normalize):
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img = t(img)
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elif isinstance(t,ToTensor):
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img = t(img)
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mask = torch.from_numpy(np.array(mask,dtype=np.float16)).permute(2,0,1)[:1]
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###design the mask split
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mask[mask==255] = 0
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mask[mask > 1] = 1
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else:
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img,mask = t(img,mask)
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return img,mask
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def __repr__(self):
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format_string = self.__class__.__name__ + '('
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for t in self.transforms:
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format_string += '\n'
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format_string += ' {0}'.format(t)
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format_string += '\n)'
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return format_string
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class RandomHorizontalFlipPair(RandomHorizontalFlip):
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def __init__(self, p=0.5):
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super().__init__(p)
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def __call__(self, img, mask):
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if torch.rand(1) < self.p:
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return F.hflip(img),F.hflip(mask)
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return img,mask
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AVAI_CHOICES = [
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"random_flip",
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"random_resized_crop",
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"normalize",
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"instance_norm",
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"random_crop",
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"random_translation",
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"center_crop", # This has become a default operation during testing
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"cutout",
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"imagenet_policy",
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"cifar10_policy",
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"svhn_policy",
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"randaugment",
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"randaugment_fixmatch",
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"randaugment2",
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"gaussian_noise",
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"colorjitter",
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"randomgrayscale",
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"gaussian_blur",
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"random_flip_pair",
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"random_resized_crop_pair",
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]
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INTERPOLATION_MODES = {
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"bilinear": InterpolationMode.BILINEAR,
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"bicubic": InterpolationMode.BICUBIC,
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"nearest": InterpolationMode.NEAREST,
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}
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class Random2DTranslation:
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"""Given an image of (height, width), we resize it to
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(height*1.125, width*1.125), and then perform random cropping.
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Args:
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height (int): target image height.
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width (int): target image width.
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p (float, optional): probability that this operation takes place.
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Default is 0.5.
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interpolation (int, optional): desired interpolation. Default is
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``torchvision.transforms.functional.InterpolationMode.BILINEAR``
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"""
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def __init__(
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self, height, width, p=0.5, interpolation=InterpolationMode.BILINEAR
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):
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self.height = height
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self.width = width
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self.p = p
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self.interpolation = interpolation
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def __call__(self, img):
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if random.uniform(0, 1) > self.p:
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return F.resize(
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img=img,
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size=[self.height, self.width],
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interpolation=self.interpolation
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)
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new_width = int(round(self.width * 1.125))
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new_height = int(round(self.height * 1.125))
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resized_img = F.resize(
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img=img,
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size=[new_height, new_width],
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interpolation=self.interpolation
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)
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x_maxrange = new_width - self.width
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y_maxrange = new_height - self.height
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x1 = int(round(random.uniform(0, x_maxrange)))
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y1 = int(round(random.uniform(0, y_maxrange)))
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croped_img = F.crop(
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img=resized_img,
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top=y1,
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left=x1,
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height=self.height,
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width=self.width
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)
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return croped_img
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class InstanceNormalization:
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"""Normalize data using per-channel mean and standard deviation.
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Reference:
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- Ulyanov et al. Instance normalization: The missing in- gredient
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for fast stylization. ArXiv 2016.
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- Shu et al. A DIRT-T Approach to Unsupervised Domain Adaptation.
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ICLR 2018.
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"""
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def __init__(self, eps=1e-8):
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self.eps = eps
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def __call__(self, img):
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C, H, W = img.shape
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img_re = img.reshape(C, H * W)
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mean = img_re.mean(1).view(C, 1, 1)
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std = img_re.std(1).view(C, 1, 1)
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return (img-mean) / (std + self.eps)
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class Cutout:
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"""Randomly mask out one or more patches from an image.
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https://github.com/uoguelph-mlrg/Cutout
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Args:
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n_holes (int, optional): number of patches to cut out
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of each image. Default is 1.
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length (int, optinal): length (in pixels) of each square
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patch. Default is 16.
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"""
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def __init__(self, n_holes=1, length=16):
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self.n_holes = n_holes
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self.length = length
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def __call__(self, img):
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"""
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Args:
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img (Tensor): tensor image of size (C, H, W).
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Returns:
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Tensor: image with n_holes of dimension
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length x length cut out of it.
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"""
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h = img.size(1)
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w = img.size(2)
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mask = np.ones((h, w), np.float32)
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for n in range(self.n_holes):
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y = np.random.randint(h)
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x = np.random.randint(w)
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y1 = np.clip(y - self.length // 2, 0, h)
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y2 = np.clip(y + self.length // 2, 0, h)
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x1 = np.clip(x - self.length // 2, 0, w)
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x2 = np.clip(x + self.length // 2, 0, w)
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mask[y1:y2, x1:x2] = 0.0
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mask = torch.from_numpy(mask)
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mask = mask.expand_as(img)
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return img * mask
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class GaussianNoise:
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"""Add gaussian noise."""
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def __init__(self, mean=0, std=0.15, p=0.5):
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self.mean = mean
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self.std = std
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self.p = p
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def __call__(self, img):
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if random.uniform(0, 1) > self.p:
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return img
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noise = torch.randn(img.size()) * self.std + self.mean
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return img + noise
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def build_transform(cfg, is_train=True, choices=None):
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"""Build transformation function.
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Args:
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cfg (CfgNode): config.
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is_train (bool, optional): for training (True) or test (False).
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Default is True.
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choices (list, optional): list of strings which will overwrite
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cfg.INPUT.TRANSFORMS if given. Default is None.
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"""
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if cfg.INPUT.NO_TRANSFORM:
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print("Note: no transform is applied!")
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return None
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if choices is None:
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choices = cfg.INPUT.TRANSFORMS
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for choice in choices:
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assert choice in AVAI_CHOICES
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target_size = f"{cfg.INPUT.SIZE[0]}x{cfg.INPUT.SIZE[1]}"
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normalize = Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD)
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if is_train:
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return _build_transform_train(cfg, choices, target_size, normalize)
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else:
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return _build_transform_test(cfg, choices, target_size, normalize)
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def build_transform_pair(cfg, is_train=True, choices=None):
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"""Build transformation function.
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Args:
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cfg (CfgNode): config.
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is_train (bool, optional): for training (True) or test (False).
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Default is True.
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choices (list, optional): list of strings which will overwrite
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cfg.INPUT.TRANSFORMS if given. Default is None.
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"""
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if cfg.INPUT.NO_TRANSFORM:
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print("Note: no transform is applied!")
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return None
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if choices is None:
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choices = cfg.INPUT.TRANSFORMS
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for choice in choices:
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assert choice in AVAI_CHOICES
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target_size = f"{cfg.INPUT.SIZE[0]}x{cfg.INPUT.SIZE[1]}"
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normalize = Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD)
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if is_train:
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return _build_transform_train_pair(cfg, choices, target_size, normalize)
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else:
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return _build_transform_test(cfg, choices, target_size, normalize)
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def _build_transform_train_pair(cfg, choices, target_size, normalize):
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print("Building transform_train_pair")
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tfm_train = []
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interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
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input_size = cfg.INPUT.SIZE
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# Make sure the image size matches the target size
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conditions = []
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conditions += ["random_crop" not in choices]
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conditions += ["random_resized_crop" not in choices]
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if all(conditions):
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print(f"+ resize to {target_size}")
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tfm_train += [Resize(input_size, interpolation=interp_mode)]
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# if "random_translation" in choices:
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# print("+ random translation")
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# tfm_train += [Random2DTranslation(input_size[0], input_size[1])]
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#
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# if "random_crop" in choices:
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# crop_padding = cfg.INPUT.CROP_PADDING
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# print(f"+ random crop (padding = {crop_padding})")
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# tfm_train += [RandomCrop(input_size, padding=crop_padding)]
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if "random_resized_crop" in choices:
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s_ = cfg.INPUT.RRCROP_SCALE
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print(f"+ random resized crop pair (size={input_size}, scale={s_})")
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tfm_train += [
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RandomResizedCropPair(input_size, scale=s_, interpolation=interp_mode)
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]
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if "random_flip" in choices:
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print("+ random flip pair")
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tfm_train += [RandomHorizontalFlipPair()]
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if "imagenet_policy" in choices:
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print("+ imagenet policy")
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tfm_train += [ImageNetPolicy()]
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if "cifar10_policy" in choices:
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print("+ cifar10 policy")
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tfm_train += [CIFAR10Policy()]
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if "svhn_policy" in choices:
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print("+ svhn policy")
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tfm_train += [SVHNPolicy()]
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if "randaugment" in choices:
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n_ = cfg.INPUT.RANDAUGMENT_N
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m_ = cfg.INPUT.RANDAUGMENT_M
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print(f"+ randaugment (n={n_}, m={m_})")
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tfm_train += [RandAugment(n_, m_)]
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if "randaugment_fixmatch" in choices:
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n_ = cfg.INPUT.RANDAUGMENT_N
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print(f"+ randaugment_fixmatch (n={n_})")
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tfm_train += [RandAugmentFixMatch(n_)]
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if "randaugment2" in choices:
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n_ = cfg.INPUT.RANDAUGMENT_N
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print(f"+ randaugment2 (n={n_})")
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tfm_train += [RandAugment2(n_)]
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if "colorjitter" in choices:
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b_ = cfg.INPUT.COLORJITTER_B
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c_ = cfg.INPUT.COLORJITTER_C
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s_ = cfg.INPUT.COLORJITTER_S
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h_ = cfg.INPUT.COLORJITTER_H
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print(
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f"+ color jitter (brightness={b_}, "
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f"contrast={c_}, saturation={s_}, hue={h_})"
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)
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tfm_train += [
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ColorJitter(
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brightness=b_,
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contrast=c_,
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saturation=s_,
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hue=h_,
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)
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]
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if "randomgrayscale" in choices:
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print("+ random gray scale")
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tfm_train += [RandomGrayscale(p=cfg.INPUT.RGS_P)]
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if "gaussian_blur" in choices:
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print(f"+ gaussian blur (kernel={cfg.INPUT.GB_K})")
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gb_k, gb_p = cfg.INPUT.GB_K, cfg.INPUT.GB_P
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tfm_train += [RandomApply([GaussianBlur(gb_k)], p=gb_p)]
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print("+ to torch tensor of range [0, 1]")
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tfm_train += [ToTensor()]
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if "cutout" in choices:
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cutout_n = cfg.INPUT.CUTOUT_N
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cutout_len = cfg.INPUT.CUTOUT_LEN
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print(f"+ cutout (n_holes={cutout_n}, length={cutout_len})")
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tfm_train += [Cutout(cutout_n, cutout_len)]
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if "normalize" in choices:
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print(
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f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})"
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)
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tfm_train += [normalize]
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if "gaussian_noise" in choices:
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print(
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f"+ gaussian noise (mean={cfg.INPUT.GN_MEAN}, std={cfg.INPUT.GN_STD})"
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)
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tfm_train += [GaussianNoise(cfg.INPUT.GN_MEAN, cfg.INPUT.GN_STD)]
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if "instance_norm" in choices:
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print("+ instance normalization")
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tfm_train += [InstanceNormalization()]
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tfm_train = ComposePair(tfm_train)
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return tfm_train
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def _build_transform_train(cfg, choices, target_size, normalize):
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print("Building transform_train")
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tfm_train = []
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interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
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input_size = cfg.INPUT.SIZE
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# Make sure the image size matches the target size
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conditions = []
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conditions += ["random_crop" not in choices]
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conditions += ["random_resized_crop" not in choices]
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if all(conditions):
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print(f"+ resize to {target_size}")
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tfm_train += [Resize(input_size, interpolation=interp_mode)]
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if "random_translation" in choices:
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print("+ random translation")
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tfm_train += [Random2DTranslation(input_size[0], input_size[1])]
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if "random_crop" in choices:
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crop_padding = cfg.INPUT.CROP_PADDING
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print(f"+ random crop (padding = {crop_padding})")
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tfm_train += [RandomCrop(input_size, padding=crop_padding)]
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if "random_resized_crop" in choices:
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s_ = cfg.INPUT.RRCROP_SCALE
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print(f"+ random resized crop (size={input_size}, scale={s_})")
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tfm_train += [
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RandomResizedCrop(input_size, scale=s_, interpolation=interp_mode)
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]
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if "random_flip" in choices:
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print("+ random flip")
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tfm_train += [RandomHorizontalFlip()]
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if "imagenet_policy" in choices:
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print("+ imagenet policy")
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tfm_train += [ImageNetPolicy()]
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if "cifar10_policy" in choices:
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print("+ cifar10 policy")
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tfm_train += [CIFAR10Policy()]
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if "svhn_policy" in choices:
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print("+ svhn policy")
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tfm_train += [SVHNPolicy()]
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if "randaugment" in choices:
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n_ = cfg.INPUT.RANDAUGMENT_N
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m_ = cfg.INPUT.RANDAUGMENT_M
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print(f"+ randaugment (n={n_}, m={m_})")
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tfm_train += [RandAugment(n_, m_)]
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if "randaugment_fixmatch" in choices:
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n_ = cfg.INPUT.RANDAUGMENT_N
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print(f"+ randaugment_fixmatch (n={n_})")
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tfm_train += [RandAugmentFixMatch(n_)]
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if "randaugment2" in choices:
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n_ = cfg.INPUT.RANDAUGMENT_N
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print(f"+ randaugment2 (n={n_})")
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tfm_train += [RandAugment2(n_)]
|
||||
|
||||
if "colorjitter" in choices:
|
||||
b_ = cfg.INPUT.COLORJITTER_B
|
||||
c_ = cfg.INPUT.COLORJITTER_C
|
||||
s_ = cfg.INPUT.COLORJITTER_S
|
||||
h_ = cfg.INPUT.COLORJITTER_H
|
||||
print(
|
||||
f"+ color jitter (brightness={b_}, "
|
||||
f"contrast={c_}, saturation={s_}, hue={h_})"
|
||||
)
|
||||
tfm_train += [
|
||||
ColorJitter(
|
||||
brightness=b_,
|
||||
contrast=c_,
|
||||
saturation=s_,
|
||||
hue=h_,
|
||||
)
|
||||
]
|
||||
|
||||
if "randomgrayscale" in choices:
|
||||
print("+ random gray scale")
|
||||
tfm_train += [RandomGrayscale(p=cfg.INPUT.RGS_P)]
|
||||
|
||||
if "gaussian_blur" in choices:
|
||||
print(f"+ gaussian blur (kernel={cfg.INPUT.GB_K})")
|
||||
gb_k, gb_p = cfg.INPUT.GB_K, cfg.INPUT.GB_P
|
||||
tfm_train += [RandomApply([GaussianBlur(gb_k)], p=gb_p)]
|
||||
|
||||
print("+ to torch tensor of range [0, 1]")
|
||||
tfm_train += [ToTensor()]
|
||||
|
||||
if "cutout" in choices:
|
||||
cutout_n = cfg.INPUT.CUTOUT_N
|
||||
cutout_len = cfg.INPUT.CUTOUT_LEN
|
||||
print(f"+ cutout (n_holes={cutout_n}, length={cutout_len})")
|
||||
tfm_train += [Cutout(cutout_n, cutout_len)]
|
||||
|
||||
if "normalize" in choices:
|
||||
print(
|
||||
f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})"
|
||||
)
|
||||
tfm_train += [normalize]
|
||||
|
||||
if "gaussian_noise" in choices:
|
||||
print(
|
||||
f"+ gaussian noise (mean={cfg.INPUT.GN_MEAN}, std={cfg.INPUT.GN_STD})"
|
||||
)
|
||||
tfm_train += [GaussianNoise(cfg.INPUT.GN_MEAN, cfg.INPUT.GN_STD)]
|
||||
|
||||
if "instance_norm" in choices:
|
||||
print("+ instance normalization")
|
||||
tfm_train += [InstanceNormalization()]
|
||||
|
||||
tfm_train = Compose(tfm_train)
|
||||
|
||||
return tfm_train
|
||||
|
||||
|
||||
def _build_transform_test(cfg, choices, target_size, normalize):
|
||||
print("Building transform_test")
|
||||
tfm_test = []
|
||||
|
||||
interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
|
||||
input_size = cfg.INPUT.SIZE
|
||||
|
||||
print(f"+ resize the smaller edge to {max(input_size)}")
|
||||
tfm_test += [Resize(max(input_size), interpolation=interp_mode)]
|
||||
|
||||
print(f"+ {target_size} center crop")
|
||||
tfm_test += [CenterCrop(input_size)]
|
||||
|
||||
print("+ to torch tensor of range [0, 1]")
|
||||
tfm_test += [ToTensor()]
|
||||
|
||||
if "normalize" in choices:
|
||||
print(
|
||||
f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})"
|
||||
)
|
||||
tfm_test += [normalize]
|
||||
|
||||
if "instance_norm" in choices:
|
||||
print("+ instance normalization")
|
||||
tfm_test += [InstanceNormalization()]
|
||||
|
||||
tfm_test = Compose(tfm_test)
|
||||
|
||||
return tfm_test
|
||||
|
||||
Reference in New Issue
Block a user