data/util.py

# adapted from
# https://github.com/VICO-UoE/DatasetCondensation

import os
import time

import kornia as K
import matplotlib.pyplot as plt
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import tqdm
from PIL import Image
from scipy.ndimage.interpolation import rotate as scipyrotate
from torch.utils.data import Dataset
from torchvision import datasets, transforms

from networks import (MLP, VGG11, VGG11BN, AlexNet, AttnUnet, ConvNet, LeNet,
                      R2AttnUnet, ResNet18, ResNet18_AP, ResNet18BN_AP,
                      TransUnet, Unet)


class Config:
    imagenette = [0, 217, 482, 491, 497, 566, 569, 571, 574, 701]

    # ["australian_terrier", "border_terrier", "samoyed", "beagle", "shih-tzu", "english_foxhound", "rhodesian_ridgeback", "dingo", "golden_retriever", "english_sheepdog"]
    imagewoof = [193, 182, 258, 162, 155, 167, 159, 273, 207, 229]

    # ["tabby_cat", "bengal_cat", "persian_cat", "siamese_cat", "egyptian_cat", "lion", "tiger", "jaguar", "snow_leopard", "lynx"]
    imagemeow = [281, 282, 283, 284, 285, 291, 292, 290, 289, 287]

    # ["peacock", "flamingo", "macaw", "pelican", "king_penguin", "bald_eagle", "toucan", "ostrich", "black_swan", "cockatoo"]
    imagesquawk = [84, 130, 88, 144, 145, 22, 96, 9, 100, 89]

    # ["pineapple", "banana", "strawberry", "orange", "lemon", "pomegranate", "fig", "bell_pepper", "cucumber", "green_apple"]
    imagefruit = [953, 954, 949, 950, 951, 957, 952, 945, 943, 948]

    # ["bee", "ladys slipper", "banana", "lemon", "corn", "school_bus", "honeycomb", "lion", "garden_spider", "goldfinch"]
    imageyellow = [309, 986, 954, 951, 987, 779, 599, 291, 72, 11]

    dict = {
        "imagenette": imagenette,
        "imagewoof": imagewoof,
        "imagefruit": imagefruit,
        "imageyellow": imageyellow,
        "imagemeow": imagemeow,
        "imagesquawk": imagesquawk,
    }


config = Config()


@torch.no_grad()
def cutmix(
        x: torch.tensor, y: torch.tensor = None, cutmix_prob: int = 0.1, beta: int = 0.3,
) -> torch.tensor:
    if y == None:
        y = torch.zeros_like(x).to(x.device)
    if np.random.rand() > cutmix_prob:
        return x, y
    N, _, H, W = x.shape
    indices = torch.randperm(N).to(x.device)
    x1 = x[indices, :, :, :]
    y1 = y[indices, :, :, :]
    lam = np.random.beta(beta, beta)
    rate = np.sqrt(1 - lam)
    cut_x, cut_y = int((H * rate) // 2), int((W * rate) // 2)
    if cut_x == H // 2 or cut_y == W // 2:
        return x, y
    cx, cy = int(np.random.randint(cut_x, H - cut_x)), int(np.random.randint(cut_y, W - cut_x))
    bx1, bx2 = cx - cut_x, cx + cut_x
    by1, by2 = cy - cut_y, cy + cut_y
    x[:, :, bx1:bx2, by1:by2] = x1[:, :, bx1:bx2, by1:by2].clone()
    y[:, :, bx1:bx2, by1:by2] = y1[:, :, bx1:bx2, by1:by2].clone()
    return x, (y > 0.5).float()


def get_dataset(dataset, data_path, batch_size=1, subset="imagenette", args=None):
    class_map = None
    loader_train_dict = None
    class_map_inv = None

    if dataset == 'CIFAR10':
        channel = 3
        im_size = (32, 32)
        num_classes = 10
        mean = [0.4914, 0.4822, 0.4465]
        std = [0.2023, 0.1994, 0.2010]
        if args.zca:
            transform = transforms.Compose([transforms.ToTensor()])
        else:
            transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
        dst_train = datasets.CIFAR10(data_path, train=True, download=True, transform=transform)  # no augmentation
        dst_test = datasets.CIFAR10(data_path, train=False, download=True, transform=transform)
        class_names = dst_train.classes
        class_map = {x: x for x in range(num_classes)}


    elif dataset == 'Tiny':
        channel = 3
        im_size = (64, 64)
        num_classes = 200
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        if args.zca:
            transform = transforms.Compose([transforms.ToTensor()])
        else:
            transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
        dst_train = datasets.ImageFolder(os.path.join(data_path, "train"), transform=transform)  # no augmentation
        dst_test = datasets.ImageFolder(os.path.join(data_path, "val", "images"), transform=transform)
        class_names = dst_train.classes
        class_map = {x: x for x in range(num_classes)}


    elif dataset == 'ImageNet':
        channel = 3
        im_size = (128, 128)
        num_classes = 10

        config.img_net_classes = config.dict[subset]

        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        if args.zca:
            transform = transforms.Compose([transforms.ToTensor(),
                                            transforms.Resize(im_size),
                                            transforms.CenterCrop(im_size)])
        else:
            transform = transforms.Compose([transforms.ToTensor(),
                                            transforms.Normalize(mean=mean, std=std),
                                            transforms.Resize(im_size),
                                            transforms.CenterCrop(im_size)])

        dst_train = datasets.ImageNet(data_path, split="train", transform=transform)  # no augmentation
        dst_train_dict = {c: torch.utils.data.Subset(dst_train, np.squeeze(
            np.argwhere(np.equal(dst_train.targets, config.img_net_classes[c])))) for c in
                          range(len(config.img_net_classes))}
        dst_train = torch.utils.data.Subset(dst_train,
                                            np.squeeze(np.argwhere(np.isin(dst_train.targets, config.img_net_classes))))
        loader_train_dict = {
            c: torch.utils.data.DataLoader(dst_train_dict[c], batch_size=batch_size, shuffle=True, num_workers=16) for c
            in range(len(config.img_net_classes))}
        dst_test = datasets.ImageNet(data_path, split="val", transform=transform)
        dst_test = torch.utils.data.Subset(dst_test,
                                           np.squeeze(np.argwhere(np.isin(dst_test.targets, config.img_net_classes))))
        for c in range(len(config.img_net_classes)):
            dst_test.dataset.targets[dst_test.dataset.targets == config.img_net_classes[c]] = c
            dst_train.dataset.targets[dst_train.dataset.targets == config.img_net_classes[c]] = c
        class_map = {x: i for i, x in enumerate(config.img_net_classes)}
        class_map_inv = {i: x for i, x in enumerate(config.img_net_classes)}
        class_names = None


    elif dataset.startswith('CIFAR100'):
        channel = 3
        im_size = (32, 32)
        num_classes = 100
        mean = [0.4914, 0.4822, 0.4465]
        std = [0.2023, 0.1994, 0.2010]

        if args.zca:
            transform = transforms.Compose([transforms.ToTensor()])
        else:
            transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
        dst_train = datasets.CIFAR100(data_path, train=True, download=True, transform=transform)  # no augmentation
        dst_test = datasets.CIFAR100(data_path, train=False, download=True, transform=transform)
        class_names = dst_train.classes
        class_map = {x: x for x in range(num_classes)}

    elif dataset.startswith("COVID19"):
        channel = 1
        im_size = (256, 256)
        num_classes = 1
        mean = [0.5]
        std = [0.5]
        train_ratio = 0.9

        from utils.covid19_dataset import COVID19Dataset, clean_dataset
        assert args.csv_path != "no", "COVID-19 Segmentation task need csv metadata!"
        dst = COVID19Dataset(imgpath=data_path, csvpath=args.csv_path, semantic_masks=True)
        dst = clean_dataset(dst)
        from sklearn.model_selection import StratifiedShuffleSplit
        labels = [0 for i in range(len(dst))]
        ss = StratifiedShuffleSplit(n_splits=1, test_size=1 - train_ratio, random_state=0)
        train_indices, valid_indices = list(ss.split(np.array(labels)[:, np.newaxis], labels))[0]
        dst_train = torch.utils.data.Subset(dst, train_indices)
        dst_test = torch.utils.data.Subset(dst, valid_indices)
        # idx = 0
        # for sample in dst_train:
        #     img = sample["img"]
        #     print(img.mean(),img.min(),img.max())
        #     pil_sample = transforms.ToPILImage()(img)
        #     pil_sample.save(f"{idx}_image.png")
        #     seg_label = sample["semantic_masks"]["Lungs"]
        #     print(seg_label.mean())
        #     pil_sample = transforms.ToPILImage()(seg_label)
        #     pil_sample.save(f"{idx}_label.png")
        #     idx+=1

        class_names = ["segmentation"]
        class_map = None
    else:
        exit('unknown dataset: %s' % dataset)
        raise KeyError

    if args.zca and not dataset.startswith("COVID19"):
        images = []
        labels = []
        print("Train ZCA")
        for i in tqdm.tqdm(range(len(dst_train))):
            im, lab = dst_train[i]
            images.append(im)
            labels.append(lab)
        images = torch.stack(images, dim=0).to(args.device)
        labels = torch.tensor(labels, dtype=torch.long, device="cpu")
        zca = K.enhance.ZCAWhitening(eps=0.1, compute_inv=True)
        zca.fit(images)
        zca_images = zca(images).to("cpu")
        dst_train = TensorDataset(zca_images, labels)

        images = []
        labels = []
        print("Test ZCA")
        for i in tqdm.tqdm(range(len(dst_test))):
            im, lab = dst_test[i]
            images.append(im)
            labels.append(lab)
        images = torch.stack(images, dim=0).to(args.device)
        labels = torch.tensor(labels, dtype=torch.long, device="cpu")

        zca_images = zca(images).to("cpu")
        dst_test = TensorDataset(zca_images, labels)

        args.zca_trans = zca

    testloader = torch.utils.data.DataLoader(dst_test, batch_size=128, shuffle=False, num_workers=2)

    return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test, testloader, loader_train_dict, class_map, class_map_inv


class TensorDataset(Dataset):
    def __init__(self, images, labels):  # images: n x c x h x w tensor
        self.images = images.detach().float()
        self.labels = labels.detach()

    def __getitem__(self, index):
        return self.images[index], self.labels[index]

    def __len__(self):
        return self.images.shape[0]


def get_default_convnet_setting():
    net_width, net_depth, net_act, net_norm, net_pooling = 128, 3, 'relu', 'instancenorm', 'avgpooling'
    return net_width, net_depth, net_act, net_norm, net_pooling


def get_network(model, channel, num_classes, im_size=(32, 32), dist=True):
    torch.random.manual_seed(int(time.time() * 1000) % 100000)
    net_width, net_depth, net_act, net_norm, net_pooling = get_default_convnet_setting()

    if model == 'MLP':
        net = MLP(channel=channel, num_classes=num_classes)
    elif model == 'ConvNet':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'LeNet':
        net = LeNet(channel=channel, num_classes=num_classes)
    elif model == 'AlexNet':
        net = AlexNet(channel=channel, num_classes=num_classes)
    elif model == 'VGG11':
        net = VGG11(channel=channel, num_classes=num_classes)
    elif model == 'VGG11BN':
        net = VGG11BN(channel=channel, num_classes=num_classes)
    elif model == 'ResNet18':
        net = ResNet18(channel=channel, num_classes=num_classes)
    elif model == 'ResNet18BN_AP':
        net = ResNet18BN_AP(channel=channel, num_classes=num_classes)
    elif model == 'ResNet18_AP':
        net = ResNet18_AP(channel=channel, num_classes=num_classes)
    elif model == 'ConvNetD1':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=1, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetD2':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=2, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetD3':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=3, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetD4':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=4, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetD5':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=5, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetD6':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=6, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetD7':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=7, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetD8':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=8, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetW32':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=32, net_depth=net_depth, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling)
    elif model == 'ConvNetW64':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=64, net_depth=net_depth, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling)
    elif model == 'ConvNetW128':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=128, net_depth=net_depth, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling)
    elif model == 'ConvNetW256':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=256, net_depth=net_depth, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling)
    elif model == 'ConvNetW512':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=512, net_depth=net_depth, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling)
    elif model == 'ConvNetW1024':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=1024, net_depth=net_depth, net_act=net_act,
                      net_norm=net_norm, net_pooling=net_pooling)

    elif model == "ConvNetKIP":
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=1024, net_depth=net_depth, net_act=net_act,
                      net_norm="none", net_pooling=net_pooling)
    elif model == 'ConvNetAS':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act='sigmoid', net_norm=net_norm, net_pooling=net_pooling)
    elif model == 'ConvNetAR':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act='relu', net_norm=net_norm, net_pooling=net_pooling)
    elif model == 'ConvNetAL':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act='leakyrelu', net_norm=net_norm, net_pooling=net_pooling)

    elif model == 'ConvNetNN':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm='none', net_pooling=net_pooling)
    elif model == 'ConvNetBN':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm='batchnorm', net_pooling=net_pooling)
    elif model == 'ConvNetLN':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm='layernorm', net_pooling=net_pooling)
    elif model == 'ConvNetIN':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm='instancenorm', net_pooling=net_pooling)
    elif model == 'ConvNetGN':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm='groupnorm', net_pooling=net_pooling)

    elif model == 'ConvNetNP':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm=net_norm, net_pooling='none')
    elif model == 'ConvNetMP':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm=net_norm, net_pooling='maxpooling')
    elif model == 'ConvNetAP':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth,
                      net_act=net_act, net_norm=net_norm, net_pooling='avgpooling')
    elif model == 'Unet':
        net = Unet(channel=channel, num_classes=num_classes)
    elif model == "AttnUnet":
        net = AttnUnet(channel=channel, num_classes=num_classes)
    elif model == "R2AttnUnet":
        net = R2AttnUnet(channel=channel, num_classes=num_classes)
    elif model == "TransUnet":
        net = TransUnet(channel=channel, num_classes=num_classes)
    else:
        net = None
        exit('DC error: unknown model')

    if dist:
        gpu_num = torch.cuda.device_count()
        if gpu_num > 0:
            device = 'cuda'
            if gpu_num > 1:
                net = nn.DataParallel(net)
        else:
            device = 'cpu'
        net = net.to(device)

    return net


def get_time():
    return str(time.strftime("[%Y-%m-%d %H:%M:%S]", time.localtime()))


def psnr(target, ref):
    diff = F.mse_loss(target, ref)
    return 10 * torch.log10(1.0 / diff)


def epoch(mode, dataloader, net, optimizer, scheduler, iter, scaler, criterion, args, texture=False):
    loss_avg, acc_avg, num_exp = 0, 0, 0
    net = net.to(args.device)
    if mode == 'train':
        net.train()
    else:
        net.eval()
    for i_batch, datum in enumerate(dataloader):
        img = datum[0].float().to(args.device)
        lab = datum[1].float().to(args.device)
        if mode == "train" and texture:
            img = torch.cat([torch.stack([torch.roll(im, (torch.randint(args.im_size[0] * args.canvas_size, (1,)),
                                                          torch.randint(args.im_size[0] * args.canvas_size, (1,))),
                                                     (1, 2))[:, :args.im_size[0], :args.im_size[1]] for im in img]) for
                             _ in range(args.canvas_samples)])
            lab = torch.cat([lab for _ in range(args.canvas_samples)])

        n_b = lab.shape[0]
        with torch.cuda.amp.autocast():
            output = net(img)
        output = output.float()
        loss = criterion(output, lab)
        acc = psnr(torch.sigmoid(output / 0.05), lab).item()
        # acc = psnr(output,lab).item()
        loss_avg += loss.item() * n_b
        acc_avg += acc * n_b
        num_exp += n_b

        if mode == 'train':
            optimizer.zero_grad()
            scaler.scale(loss).backward()
            # scaler.unscale_(optimizer)
            # nn.utils.clip_grad_value_(net.parameters(), 1.)
            scaler.step(optimizer)
            scaler.update()
            scheduler.step(iter)
            iter += 1

    loss_avg /= num_exp
    acc_avg /= num_exp

    return loss_avg, acc_avg


def epoch2(mode, dataloader, net, optimizer, scheduler, iter, scaler, criticion, criticion_dice, args, texture=False):
    loss_avg, dice_avg, psnr_avg, num_exp = 0, 0, 0, 0
    net = net.to(args.device)
    if mode == 'train':
        net.train()
    else:
        net.eval()
    for i_batch, datum in enumerate(dataloader):
        img = datum[0].float().to(args.device)
        lab = datum[1].float().to(args.device)
        # if mode == "train":
        #     img, lab = cutmix(img, lab)
        if mode == "train" and texture:
            img = torch.cat([torch.stack([torch.roll(im, (torch.randint(args.im_size[0] * args.canvas_size, (1,)),
                                                          torch.randint(args.im_size[0] * args.canvas_size, (1,))),
                                                     (1, 2))[:, :args.im_size[0], :args.im_size[1]] for im in img]) for
                             _ in range(args.canvas_samples)])
            lab = torch.cat([lab for _ in range(args.canvas_samples)])

        n_b = lab.shape[0]
        with torch.cuda.amp.autocast():
            output = net(img)
        output = output.float()
        if mode == "train":
            _dice = criticion_dice(torch.sigmoid(output), lab)
        else:
            _dice = criticion_dice(((torch.sigmoid(output)) > 0.5).float(), lab)
        loss = criticion(output, lab) + _dice
        a_psnr = psnr((torch.sigmoid(output)).float(), lab).item()
        a_dice = _dice.item()

        # acc = psnr(output,lab).item()
        loss_avg += loss.item() * n_b
        dice_avg += a_dice * n_b
        psnr_avg += a_psnr * n_b
        num_exp += n_b

        if mode == 'train':
            optimizer.zero_grad()
            scaler.scale(loss).backward()
            # scaler.unscale_(optimizer)
            # nn.utils.clip_grad_value_(net.parameters(), 1.)
            scaler.step(optimizer)
            scaler.update()
            scheduler.step(iter)
            iter += 1

    loss_avg /= num_exp
    dice_avg /= num_exp
    psnr_avg /= num_exp

    return loss_avg, dice_avg, psnr_avg


def evaluate_synset(it_eval, net, images_train, labels_train, testloader, args, return_loss=False, texture=False):
    net = net.to(args.device)
    images_train = images_train.to(args.device)
    labels_train = labels_train.to(args.device)
    lr = float(args.lr_net)
    Epoch = int(args.epoch_eval_train)
    lr_schedule = [Epoch // 2 + 1]
    optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=0.0005)

    criterion = nn.CrossEntropyLoss().to(args.device)

    dst_train = TensorDataset(images_train, labels_train)
    trainloader = torch.utils.data.DataLoader(dst_train, batch_size=args.batch_train, shuffle=True, num_workers=0)

    start = time.time()
    acc_train_list = []
    loss_train_list = []

    for ep in tqdm.tqdm(range(Epoch + 1)):
        loss_train, acc_train = epoch('train', trainloader, net, optimizer, criterion, args, aug=True, texture=texture)
        acc_train_list.append(acc_train)
        loss_train_list.append(loss_train)
        if ep == Epoch:
            with torch.no_grad():
                loss_test, acc_test = epoch('test', testloader, net, optimizer, criterion, args, aug=False)
        if ep in lr_schedule:
            lr *= 0.1
            optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=0.0005)

    time_train = time.time() - start

    print('%s Evaluate_%02d: epoch = %04d train time = %d s train loss = %.6f train acc = %.4f, test acc = %.4f' % (
        get_time(), it_eval, Epoch, int(time_train), loss_train, acc_train, acc_test))

    if return_loss:
        return net, acc_train_list, acc_test, loss_train_list, loss_test
    else:
        return net, acc_train_list, acc_test


def augment(images, dc_aug_param, device):
    # This can be sped up in the future.

    if dc_aug_param != None and dc_aug_param['strategy'] != 'none':
        scale = dc_aug_param['scale']
        crop = dc_aug_param['crop']
        rotate = dc_aug_param['rotate']
        noise = dc_aug_param['noise']
        strategy = dc_aug_param['strategy']

        shape = images.shape
        mean = []
        for c in range(shape[1]):
            mean.append(float(torch.mean(images[:, c])))

        def cropfun(i):
            im_ = torch.zeros(shape[1], shape[2] + crop * 2, shape[3] + crop * 2, dtype=torch.float, device=device)
            for c in range(shape[1]):
                im_[c] = mean[c]
            im_[:, crop:crop + shape[2], crop:crop + shape[3]] = images[i]
            r, c = np.random.permutation(crop * 2)[0], np.random.permutation(crop * 2)[0]
            images[i] = im_[:, r:r + shape[2], c:c + shape[3]]

        def scalefun(i):
            h = int((np.random.uniform(1 - scale, 1 + scale)) * shape[2])
            w = int((np.random.uniform(1 - scale, 1 + scale)) * shape[2])
            tmp = F.interpolate(images[i:i + 1], [h, w], )[0]
            mhw = max(h, w, shape[2], shape[3])
            im_ = torch.zeros(shape[1], mhw, mhw, dtype=torch.float, device=device)
            r = int((mhw - h) / 2)
            c = int((mhw - w) / 2)
            im_[:, r:r + h, c:c + w] = tmp
            r = int((mhw - shape[2]) / 2)
            c = int((mhw - shape[3]) / 2)
            images[i] = im_[:, r:r + shape[2], c:c + shape[3]]

        def rotatefun(i):
            im_ = scipyrotate(images[i].cpu().data.numpy(), angle=np.random.randint(-rotate, rotate), axes=(-2, -1),
                              cval=np.mean(mean))
            r = int((im_.shape[-2] - shape[-2]) / 2)
            c = int((im_.shape[-1] - shape[-1]) / 2)
            images[i] = torch.tensor(im_[:, r:r + shape[-2], c:c + shape[-1]], dtype=torch.float, device=device)

        def noisefun(i):
            images[i] = images[i] + noise * torch.randn(shape[1:], dtype=torch.float, device=device)

        augs = strategy.split('_')

        for i in range(shape[0]):
            choice = np.random.permutation(augs)[0]  # randomly implement one augmentation
            if choice == 'crop':
                cropfun(i)
            elif choice == 'scale':
                scalefun(i)
            elif choice == 'rotate':
                rotatefun(i)
            elif choice == 'noise':
                noisefun(i)

    return images


def get_daparam(dataset, model, model_eval, ipc):
    # We find that augmentation doesn't always benefit the performance.
    # So we do augmentation for some of the settings.

    dc_aug_param = dict()
    dc_aug_param['crop'] = 4
    dc_aug_param['scale'] = 0.2
    dc_aug_param['rotate'] = 45
    dc_aug_param['noise'] = 0.001
    dc_aug_param['strategy'] = 'none'

    if dataset == 'MNIST':
        dc_aug_param['strategy'] = 'crop_scale_rotate'

    if model_eval in ['ConvNetBN']:  # Data augmentation makes model training with Batch Norm layer easier.
        dc_aug_param['strategy'] = 'crop_noise'

    return dc_aug_param


def get_eval_pool(eval_mode, model, model_eval):
    if eval_mode == 'M':  # multiple architectures
        # model_eval_pool = ['MLP', 'ConvNet', 'AlexNet', 'VGG11', 'ResNet18', 'LeNet']
        model_eval_pool = ['ConvNet', 'AlexNet', 'VGG11', 'ResNet18_AP', 'ResNet18']
        # model_eval_pool = ['MLP', 'ConvNet', 'AlexNet', 'VGG11', 'ResNet18']
    elif eval_mode == 'W':  # ablation study on network width
        model_eval_pool = ['ConvNetW32', 'ConvNetW64', 'ConvNetW128', 'ConvNetW256']
    elif eval_mode == 'D':  # ablation study on network depth
        model_eval_pool = ['ConvNetD1', 'ConvNetD2', 'ConvNetD3', 'ConvNetD4']
    elif eval_mode == 'A':  # ablation study on network activation function
        model_eval_pool = ['ConvNetAS', 'ConvNetAR', 'ConvNetAL']
    elif eval_mode == 'P':  # ablation study on network pooling layer
        model_eval_pool = ['ConvNetNP', 'ConvNetMP', 'ConvNetAP']
    elif eval_mode == 'N':  # ablation study on network normalization layer
        model_eval_pool = ['ConvNetNN', 'ConvNetBN', 'ConvNetLN', 'ConvNetIN', 'ConvNetGN']
    elif eval_mode == 'S':  # itself
        model_eval_pool = [model[:model.index('BN')]] if 'BN' in model else [model]
    elif eval_mode == 'C':
        model_eval_pool = [model, 'ConvNet']
    else:
        model_eval_pool = [model_eval]
    return model_eval_pool


class ParamDiffAug():
    def __init__(self):
        self.aug_mode = 'S'  # 'multiple or single'
        self.prob_flip = 0.5
        self.ratio_scale = 1.2
        self.ratio_rotate = 15.0
        self.ratio_crop_pad = 0.125
        self.ratio_cutout = 0.5  # the size would be 0.5x0.5
        self.ratio_noise = 0.05
        self.brightness = 1.0
        self.saturation = 2.0
        self.contrast = 0.5


def set_seed_DiffAug(param):
    if param.latestseed == -1:
        return
    else:
        torch.random.manual_seed(param.latestseed)
        param.latestseed += 1


def DiffAugment(x, strategy='', seed=-1, param=None):
    if seed == -1:
        param.batchmode = False
    else:
        param.batchmode = True

    param.latestseed = seed

    if strategy == 'None' or strategy == 'none':
        return x

    if strategy:
        if param.aug_mode == 'M':  # original
            for p in strategy.split('_'):
                for f in AUGMENT_FNS[p]:
                    x = f(x, param)
        elif param.aug_mode == 'S':
            pbties = strategy.split('_')
            set_seed_DiffAug(param)
            p = pbties[torch.randint(0, len(pbties), size=(1,)).item()]
            for f in AUGMENT_FNS[p]:
                x = f(x, param)
        else:
            exit('Error ZH: unknown augmentation mode.')
        x = x.contiguous()
    return x


# We implement the following differentiable augmentation strategies based on the code provided in https://github.com/mit-han-lab/data-efficient-gans.
def rand_scale(x, param):
    # x>1, max scale
    # sx, sy: (0, +oo), 1: orignial size, 0.5: enlarge 2 times
    ratio = param.ratio_scale
    set_seed_DiffAug(param)
    sx = torch.rand(x.shape[0]) * (ratio - 1.0 / ratio) + 1.0 / ratio
    set_seed_DiffAug(param)
    sy = torch.rand(x.shape[0]) * (ratio - 1.0 / ratio) + 1.0 / ratio
    theta = [[[sx[i], 0, 0],
              [0, sy[i], 0], ] for i in range(x.shape[0])]
    theta = torch.tensor(theta, dtype=torch.float)
    if param.batchmode:  # batch-wise:
        theta[:] = theta[0]
    grid = F.affine_grid(theta, x.shape, align_corners=True).to(x.device)
    x = F.grid_sample(x, grid, align_corners=True)
    return x


def rand_rotate(x, param):  # [-180, 180], 90: anticlockwise 90 degree
    ratio = param.ratio_rotate
    set_seed_DiffAug(param)
    theta = (torch.rand(x.shape[0]) - 0.5) * 2 * ratio / 180 * float(np.pi)
    theta = [[[torch.cos(theta[i]), torch.sin(-theta[i]), 0],
              [torch.sin(theta[i]), torch.cos(theta[i]), 0], ] for i in range(x.shape[0])]
    theta = torch.tensor(theta, dtype=torch.float)
    if param.batchmode:  # batch-wise:
        theta[:] = theta[0]
    grid = F.affine_grid(theta, x.shape, align_corners=True).to(x.device)
    x = F.grid_sample(x, grid, align_corners=True)
    return x


def rand_flip(x, param):
    prob = param.prob_flip
    set_seed_DiffAug(param)
    randf = torch.rand(x.size(0), 1, 1, 1, device=x.device)
    if param.batchmode:  # batch-wise:
        randf[:] = randf[0]
    return torch.where(randf < prob, x.flip(3), x)


def rand_brightness(x, param):
    ratio = param.brightness
    set_seed_DiffAug(param)
    randb = torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device)
    if param.batchmode:  # batch-wise:
        randb[:] = randb[0]
    x = x + (randb - 0.5) * ratio
    return x


def rand_saturation(x, param):
    ratio = param.saturation
    x_mean = x.mean(dim=1, keepdim=True)
    set_seed_DiffAug(param)
    rands = torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device)
    if param.batchmode:  # batch-wise:
        rands[:] = rands[0]
    x = (x - x_mean) * (rands * ratio) + x_mean
    return x


def rand_contrast(x, param):
    ratio = param.contrast
    x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
    set_seed_DiffAug(param)
    randc = torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device)
    if param.batchmode:  # batch-wise:
        randc[:] = randc[0]
    x = (x - x_mean) * (randc + ratio) + x_mean
    return x


def rand_crop(x, param):
    # The image is padded on its surrounding and then cropped.
    ratio = param.ratio_crop_pad
    shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
    set_seed_DiffAug(param)
    translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
    set_seed_DiffAug(param)
    translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
    if param.batchmode:  # batch-wise:
        translation_x[:] = translation_x[0]
        translation_y[:] = translation_y[0]
    grid_batch, grid_x, grid_y = torch.meshgrid(
        torch.arange(x.size(0), dtype=torch.long, device=x.device),
        torch.arange(x.size(2), dtype=torch.long, device=x.device),
        torch.arange(x.size(3), dtype=torch.long, device=x.device),
    )
    grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
    grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
    x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
    x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
    return x


def rand_cutout(x, param):
    ratio = param.ratio_cutout
    cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
    set_seed_DiffAug(param)
    offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
    set_seed_DiffAug(param)
    offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
    if param.batchmode:  # batch-wise:
        offset_x[:] = offset_x[0]
        offset_y[:] = offset_y[0]
    grid_batch, grid_x, grid_y = torch.meshgrid(
        torch.arange(x.size(0), dtype=torch.long, device=x.device),
        torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
        torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
    )
    grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
    grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
    mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
    mask[grid_batch, grid_x, grid_y] = 0
    x = x * mask.unsqueeze(1)
    return x


AUGMENT_FNS = {
    'color': [rand_brightness, rand_saturation, rand_contrast],
    'crop': [rand_crop],
    'cutout': [rand_cutout],
    'flip': [rand_flip],
    'scale': [rand_scale],
    'rotate': [rand_rotate],
}