utils.py

import time
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision import datasets, transforms
from scipy.ndimage.interpolation import rotate as scipyrotate
from networks import MLP, ConvNet, LeNet, AlexNet, AlexNetBN, VGG11, VGG11BN, ResNet18, ResNet18BN_AP, ResNet18BN

def get_dataset(dataset, data_path):
    if dataset == 'MNIST':
        channel = 1
        im_size = (28, 28)
        num_classes = 10
        mean = [0.1307]
        std = [0.3081]
        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
        dst_train = datasets.MNIST(data_path, train=True, download=False, transform=transform) # no augmentation
        dst_test = datasets.MNIST(data_path, train=False, download=False, transform=transform)
        class_names = [str(c) for c in range(num_classes)]

    elif dataset == 'FashionMNIST':
        channel = 1
        im_size = (28, 28)
        num_classes = 10
        mean = [0.2861]
        std = [0.3530]
        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
        dst_train = datasets.FashionMNIST(data_path, train=True, download=True, transform=transform) # no augmentation
        dst_test = datasets.FashionMNIST(data_path, train=False, download=True, transform=transform)
        class_names = dst_train.classes

    elif dataset == 'SVHN':
        channel = 3
        im_size = (32, 32)
        num_classes = 10
        mean = [0.4377, 0.4438, 0.4728]
        std = [0.1980, 0.2010, 0.1970]
        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
        dst_train = datasets.SVHN(data_path, split='train', download=True, transform=transform)  # no augmentation
        dst_test = datasets.SVHN(data_path, split='test', download=True, transform=transform)
        class_names = [str(c) for c in range(num_classes)]

    elif 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]
        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
        dst_train = datasets.CIFAR10(data_path, train=True, download=False, transform=transform) # no augmentation
        dst_test = datasets.CIFAR10(data_path, train=False, download=False, transform=transform)
        class_names = dst_train.classes

    elif dataset == 'CIFAR100':
        channel = 3
        im_size = (32, 32)
        num_classes = 100
        mean = [0.5071, 0.4866, 0.4409]
        std = [0.2673, 0.2564, 0.2762]
        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

    elif dataset == 'TinyImageNet':
        channel = 3
        im_size = (64, 64)
        num_classes = 200
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        data = torch.load(os.path.join(data_path, 'tinyimagenet.pt'), map_location='cpu')

        class_names = data['classes']

        images_train = data['images_train']
        labels_train = data['labels_train']
        images_train = images_train.detach().float() / 255.0
        labels_train = labels_train.detach()
        for c in range(channel):
            images_train[:,c] = (images_train[:,c] - mean[c])/std[c]
        dst_train = TensorDataset(images_train, labels_train)  # no augmentation

        images_val = data['images_val']
        labels_val = data['labels_val']
        images_val = images_val.detach().float() / 255.0
        labels_val = labels_val.detach()

        for c in range(channel):
            images_val[:, c] = (images_val[:, c] - mean[c]) / std[c]

        dst_test = TensorDataset(images_val, labels_val)  # no augmentation

    else:
        exit('unknown dataset: %s'%dataset)



    testloader = torch.utils.data.DataLoader(dst_test, batch_size=256, shuffle=False, num_workers=0)
    return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test, testloader



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)):
    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 == 'AlexNetBN':
        net = AlexNetBN(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 == 'ResNet18BN':
        net = ResNet18BN(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 == '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, im_size=im_size)
    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, im_size=im_size)
    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, im_size=im_size)
    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, im_size=im_size)

    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, im_size=im_size)
    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, im_size=im_size)
    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, im_size=im_size)
    elif model == 'ConvNetASwish':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth, net_act='swish', net_norm=net_norm, net_pooling=net_pooling, im_size=im_size)
    elif model == 'ConvNetASwishBN':
        net = ConvNet(channel=channel, num_classes=num_classes, net_width=net_width, net_depth=net_depth, net_act='swish', net_norm='batchnorm', net_pooling=net_pooling, im_size=im_size)

    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, im_size=im_size)
    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, im_size=im_size)
    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, im_size=im_size)
    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, im_size=im_size)
    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, im_size=im_size)

    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', im_size=im_size)
    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', im_size=im_size)
    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', im_size=im_size)

    else:
        net = None
        exit('unknown model: %s'%model)

    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 distance_wb(gwr, gws):
    shape = gwr.shape
    if len(shape) == 4: # conv, out*in*h*w
        gwr = gwr.reshape(shape[0], shape[1] * shape[2] * shape[3])
        gws = gws.reshape(shape[0], shape[1] * shape[2] * shape[3])
    elif len(shape) == 3:  # layernorm, C*h*w
        gwr = gwr.reshape(shape[0], shape[1] * shape[2])
        gws = gws.reshape(shape[0], shape[1] * shape[2])
    elif len(shape) == 2: # linear, out*in
        tmp = 'do nothing'
    elif len(shape) == 1: # batchnorm/instancenorm, C; groupnorm x, bias
        gwr = gwr.reshape(1, shape[0])
        gws = gws.reshape(1, shape[0])
        return torch.tensor(0, dtype=torch.float, device=gwr.device)

    dis_weight = torch.sum(1 - torch.sum(gwr * gws, dim=-1) / (torch.norm(gwr, dim=-1) * torch.norm(gws, dim=-1) + 0.000001))
    dis = dis_weight
    return dis



def match_loss(gw_syn, gw_real, args):
    dis = torch.tensor(0.0).to(args.device)

    if args.dis_metric == 'ours':
        for ig in range(len(gw_real)):
            gwr = gw_real[ig]
            gws = gw_syn[ig]
            dis += distance_wb(gwr, gws)

    elif args.dis_metric == 'mse':
        gw_real_vec = []
        gw_syn_vec = []
        for ig in range(len(gw_real)):
            gw_real_vec.append(gw_real[ig].reshape((-1)))
            gw_syn_vec.append(gw_syn[ig].reshape((-1)))
        gw_real_vec = torch.cat(gw_real_vec, dim=0)
        gw_syn_vec = torch.cat(gw_syn_vec, dim=0)
        dis = torch.sum((gw_syn_vec - gw_real_vec)**2)

    elif args.dis_metric == 'cos':
        gw_real_vec = []
        gw_syn_vec = []
        for ig in range(len(gw_real)):
            gw_real_vec.append(gw_real[ig].reshape((-1)))
            gw_syn_vec.append(gw_syn[ig].reshape((-1)))
        gw_real_vec = torch.cat(gw_real_vec, dim=0)
        gw_syn_vec = torch.cat(gw_syn_vec, dim=0)
        dis = 1 - torch.sum(gw_real_vec * gw_syn_vec, dim=-1) / (torch.norm(gw_real_vec, dim=-1) * torch.norm(gw_syn_vec, dim=-1) + 0.000001)

    else:
        exit('unknown distance function: %s'%args.dis_metric)

    return dis



def get_loops(ipc):
    # Get the two hyper-parameters of outer-loop and inner-loop.
    # The following values are empirically good.
    if ipc == 1:
        outer_loop, inner_loop = 1, 1
    elif ipc == 7: # Only LoRa
        outer_loop, inner_loop = 1, 1
    elif ipc == 8: # Only LoRa
        outer_loop, inner_loop = 1, 1
    elif ipc == 10:
        outer_loop, inner_loop = 10, 50
    elif ipc == 20: # Only LoRa
        outer_loop, inner_loop = 10, 50
    elif ipc == 70: # Only LoRa
        outer_loop, inner_loop = 10, 50
    elif ipc == 80: # Only LoRa
        outer_loop, inner_loop = 10, 50
    elif ipc == 20:
        outer_loop, inner_loop = 20, 25
    elif ipc == 160: # Only LoRa
        outer_loop, inner_loop = 20, 25
    elif ipc == 30:
        outer_loop, inner_loop = 30, 20
    elif ipc == 40:
        outer_loop, inner_loop = 40, 15
    elif ipc == 50:
        outer_loop, inner_loop = 50, 10
    elif ipc == 88: # Only LoRa mnist
        outer_loop, inner_loop = 50, 10
    elif ipc == 100: # Only LoRa mnist
        outer_loop, inner_loop = 50, 10
    elif ipc == 400: # Only LoRa
        outer_loop, inner_loop = 50, 10
    elif ipc == 350: # Only LoRa
        outer_loop, inner_loop = 50, 10
    else:
        outer_loop, inner_loop = 0, 0
        exit('loop hyper-parameters are not defined for %d ipc'%ipc)
    return outer_loop, inner_loop



def epoch(mode, dataloader, net, optimizer, criterion, args, aug):
    loss_avg, acc_avg, num_exp = 0, 0, 0
    net = net.to(args.device)
    criterion = criterion.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)
        if aug:
            if args.dsa:
                img = DiffAugment(img, args.dsa_strategy, param=args.dsa_param)
            else:
                img = augment(img, args.dc_aug_param, device=args.device)
        lab = datum[1].long().to(args.device)
        n_b = lab.shape[0]

        output = net(img)
        loss = criterion(output, lab)
        acc = np.sum(np.equal(np.argmax(output.cpu().data.numpy(), axis=-1), lab.cpu().data.numpy()))

        loss_avg += loss.item()*n_b
        acc_avg += acc
        num_exp += n_b

        if mode == 'train':
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

    loss_avg /= num_exp
    acc_avg /= num_exp

    return loss_avg, acc_avg



def evaluate_synset(it_eval, net, images_train, labels_train, testloader, args):
    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()
    for ep in range(Epoch+1):
        loss_train, acc_train = epoch('train', trainloader, net, optimizer, criterion, args, aug = True)
        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
    loss_test, acc_test = epoch('test', testloader, net, optimizer, criterion, args, aug = False)
    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))

    return net, acc_train, 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', 'LeNet', 'AlexNet', 'VGG11', 'ResNet18']
    elif eval_mode == 'B':  # multiple architectures with BatchNorm for DM experiments
        model_eval_pool = ['ConvNetBN', 'ConvNetASwishBN', 'AlexNetBN', 'VGG11BN', 'ResNet18BN']
    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', 'ConvNetASwish']
    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
        if 'BN' in model:
            print('Attention: Here I will replace BN with IN in evaluation, as the synthetic set is too small to measure BN hyper-parameters.')
        model_eval_pool = [model[:model.index('BN')]] if 'BN' in model else [model]
    elif eval_mode == 'SS':  # itself
        model_eval_pool = [model]
    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.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 strategy == 'None' or strategy == 'none' or strategy == '':
        return x

    if seed == -1:
        param.Siamese = False
    else:
        param.Siamese = True

    param.latestseed = seed

    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('unknown augmentation mode: %s'%param.aug_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.Siamese: # Siamese augmentation:
        b = theta.clone()
        theta[:] = b[0]
    grid = F.affine_grid(theta, x.shape).to(x.device)
    x = F.grid_sample(x, grid)
    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.Siamese: # Siamese augmentation:
        b = theta.clone()
        theta[:] = b[0]
    grid = F.affine_grid(theta, x.shape).to(x.device)
    x = F.grid_sample(x, grid)
    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.Siamese: # Siamese augmentation:
        b = randf.clone()
        randf[:] = b[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.Siamese:  # Siamese augmentation:
        b = randb.clone()
        randb[:] = b[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.Siamese:  # Siamese augmentation:
        b = rands.clone()
        rands[:] = b[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.Siamese:  # Siamese augmentation:
        b = randc.clone()
        randc[:] = b[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.Siamese:  # Siamese augmentation:
        b = translation_x.clone()
        translation_x[:] = b[0]
        b = translation_y.clone()
        translation_y[:] = b[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.Siamese:  # Siamese augmentation:
        b = offset_x.clone()
        offset_x[:] = b[0]
        b = offset_y.clone()
        offset_y[:] = b[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],
}