datasets.py

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import os
import json

from torchvision import datasets, transforms
from torchvision.datasets.folder import ImageFolder, default_loader

from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import create_transform
import numpy as np
from PIL import Image


class INatDataset(ImageFolder):
    def __init__(self, root, train=True, year=2018, transform=None, target_transform=None,
                 category='name', loader=default_loader):
        self.transform = transform
        self.loader = loader
        self.target_transform = target_transform
        self.year = year
        # assert category in ['kingdom','phylum','class','order','supercategory','family','genus','name']
        path_json = os.path.join(root, f'{"train" if train else "val"}{year}.json')
        with open(path_json) as json_file:
            data = json.load(json_file)

        with open(os.path.join(root, 'categories.json')) as json_file:
            data_catg = json.load(json_file)

        path_json_for_targeter = os.path.join(root, f"train{year}.json")

        with open(path_json_for_targeter) as json_file:
            data_for_targeter = json.load(json_file)

        targeter = {}
        indexer = 0
        for elem in data_for_targeter['annotations']:
            king = []
            king.append(data_catg[int(elem['category_id'])][category])
            if king[0] not in targeter.keys():
                targeter[king[0]] = indexer
                indexer += 1
        self.nb_classes = len(targeter)

        self.samples = []
        for elem in data['images']:
            cut = elem['file_name'].split('/')
            target_current = int(cut[2])
            path_current = os.path.join(root, cut[0], cut[2], cut[3])

            categors = data_catg[target_current]
            target_current_true = targeter[categors[category]]
            self.samples.append((path_current, target_current_true))

    # __getitem__ and __len__ inherited from ImageFolder


def build_dataset(is_train, args):
    transform = build_transform(is_train, args)

    if args.data_set == 'CIFAR':
        dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform)
        nb_classes = 100
    elif args.data_set == 'CIFAR10':
        dataset = datasets.CIFAR10(args.data_path, train=is_train, transform=transform)
        nb_classes = 10
    elif args.data_set == 'IMNET':
        root = os.path.join(args.data_path, 'train' if is_train else 'val')
        dataset = datasets.ImageFolder(root, transform=transform)
        nb_classes = 1000
    elif args.data_set == 'IMNETV2':
        dataset = datasets.ImageFolder(args.data_path, transform=transform)
        nb_classes = 1000
    elif args.data_set == 'INAT':
        dataset = INatDataset(args.data_path, train=is_train, year=2018,
                              category=args.inat_category, transform=transform)
        nb_classes = dataset.nb_classes
    elif args.data_set == 'INAT19':
        dataset = INatDataset(args.data_path, train=is_train, year=2019,
                              category=args.inat_category, transform=transform)
        nb_classes = dataset.nb_classes

    return dataset, nb_classes

class AddSaltPepperNoise(object):
    def __init__(self, density=0):
        self.density = density

    def __call__(self, img):
        img = np.array(img)                                                             # 图片转numpy
        h, w, c = img.shape
        Nd = self.density
        Sd = 1 - Nd
        mask = np.random.choice((0, 1, 2), size=(h, w, 1), p=[Nd/2.0, Nd/2.0, Sd])      # 生成一个通道的mask
        mask = np.repeat(mask, c, axis=2)                                               # 在通道的维度复制，生成彩色的mask
        img[mask == 0] = 0                                                              # 椒
        img[mask == 1] = 255                                                            # 盐
        img= Image.fromarray(img.astype('uint8')).convert('RGB')                        # numpy转图片
        return img

class AddGaussianNoise(object):
    def __init__(self, mean=0.0, variance=1.0, density=1.0):

        self.mean = mean
        self.variance = variance
        self.density = density

    def __call__(self, img):
        img = np.array(img)
        h, w, c = img.shape
        N = self.density * 255 * np.random.normal(loc=self.mean, scale=self.variance, size=(h, w, 1))
        N = np.repeat(N, c, axis=2)
        img = N + img
        img[img > 255] = 255 
        img[img < 0] = 0
        img = Image.fromarray(img.astype('uint8')).convert('RGB')
        return img

class AddUniformNoise(object):
    def __init__(self, density=1.0):
        self.density = density

    def __call__(self, img):
        img = np.array(img)
        h, w, c = img.shape
        N = np.random.uniform(0, self.density * 255, size=(h, w, 1))
        N = np.repeat(N, c, axis=2)
        img = N + img
        img[img > 255] = 255 
        img[img < 0] = 0
        img = Image.fromarray(img.astype('uint8')).convert('RGB')
        return img

class AddExpNoise(object):
    def __init__(self, density=1.0):
        self.density = density

    def __call__(self, img):
        img = np.array(img)
        h, w, c = img.shape
        N = np.random.exponential(self.density * 255, size=(h, w, 1))
        N = np.repeat(N, c, axis=2)
        img = N + img
        img[img > 255] = 255 
        img[img < 0] = 0
        img = Image.fromarray(img.astype('uint8')).convert('RGB')
        return img

class FFTFilter(object):
    def __init__(self, s=0.1):

        self.s = s

    def __call__(self, img):
        img = np.array(img)
        h, w, c = img.shape
        h_s, w_s = int(h*self.s), int(w*self.s)
        fft = np.fft.fft2(img)
        N = self.density * 255 * np.random.normal(loc=self.mean, scale=self.variance, size=(h, w, 1))
        N = np.repeat(N, c, axis=2)
        img = N + img
        img[img > 255] = 255 
        img[img < 0] = 0
        img = Image.fromarray(img.astype('uint8')).convert('RGB')
        return img

def build_transform(is_train, args):
    resize_im = args.input_size > 32
    if is_train and args.for_train:
        # this should always dispatch to transforms_imagenet_train
        transform = create_transform(
            input_size=args.input_size,
            is_training=True,
            color_jitter=args.color_jitter,
            auto_augment=args.aa,
            interpolation=args.train_interpolation,
            re_prob=args.reprob,
            re_mode=args.remode,
            re_count=args.recount,
        )
        if not resize_im:
            # replace RandomResizedCropAndInterpolation with
            # RandomCrop
            transform.transforms[0] = transforms.RandomCrop(
                args.input_size, padding=4)
        return transform

    t = []
    if resize_im:
        size = int((256 / 224) * args.input_size)
        t.append(
            transforms.Resize(size, interpolation=3),  # to maintain same ratio w.r.t. 224 images
        )
        t.append(transforms.CenterCrop(args.input_size))

    # if args.gaussian:
    #     t.append(transforms.GaussianBlur(7))
    # if args.noise:
    #     t.append(AddSaltPepperNoise(density=args.noise_density))
    #     # t.append(AddGaussianNoise(density=args.noise_density))
    #     # t.append(AddUniformNoise(density=args.noise_density))
    #     # t.append(AddExpNoise(density=args.noise_density))

    t.append(transforms.ToTensor())

    if args.normalize:
        t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
    return transforms.Compose(t)