make_dirichlet_dataset.py

from torchvision.datasets import ImageFolder
from PIL import Image
from cifar10c_dataset import CIFAR10C_preprocessed

import os
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms

import sys


class DatasetObject:
    def __init__(self, dataset, n_client, rule, unbalanced_sgm=0, rule_arg=''):
        self.dataset  = dataset
        self.n_client = n_client
        self.rule     = rule
        self.rule_arg = rule_arg
        rule_arg_str  = rule_arg if isinstance(rule_arg, str) else '%.3f' % rule_arg
        self.name = "%s_%d_%s_%s" %(self.dataset, self.n_client, self.rule, rule_arg_str)
        self.name += '_%f' %unbalanced_sgm if unbalanced_sgm!=0 else ''
        self.unbalanced_sgm = unbalanced_sgm
        self.data_path = 'data'
        self.set_data()
        
    def set_data(self):
        # Prepare data if not ready
        if not os.path.exists('%s/%s' %(self.data_path, self.name)):
        # if False:
            # Get Raw data                
            if self.dataset == 'mnist':
                transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
                trnset = torchvision.datasets.MNIST(root='%s/Raw' %self.data_path, 
                                                    train=True , download=True, transform=transform)
                tstset = torchvision.datasets.MNIST(root='%s/Raw' %self.data_path, 
                                                    train=False, download=True, transform=transform)
                
                trn_load = torch.utils.data.DataLoader(trnset, batch_size=60000, shuffle=False, num_workers=1)
                tst_load = torch.utils.data.DataLoader(tstset, batch_size=10000, shuffle=False, num_workers=1)
                self.channels = 1; self.width = 28; self.height = 28; self.n_cls = 10;
            
            if self.dataset == 'CIFAR10':
                transform = transforms.Compose([transforms.ToTensor(),
                                                transforms.Normalize(mean=[0.491, 0.482, 0.447], std=[0.247, 0.243, 0.262])])

                trnset = torchvision.datasets.CIFAR10(root='%s/Raw' %self.data_path,
                                                      train=True , download=True, transform=transform)
                tstset = torchvision.datasets.CIFAR10(root='%s/Raw' %self.data_path,
                                                      train=False, download=True, transform=transform)
                
                trn_load = torch.utils.data.DataLoader(trnset, batch_size=50000, shuffle=False, num_workers=1)
                tst_load = torch.utils.data.DataLoader(tstset, batch_size=10000, shuffle=False, num_workers=1)
                self.channels = 3; self.width = 32; self.height = 32; self.n_cls = 10;
                
            if self.dataset == 'CIFAR100':
                print(self.dataset)
                # mean and std are validated here: https://gist.github.com/weiaicunzai/e623931921efefd4c331622c344d8151
                transform = transforms.Compose([transforms.ToTensor(),
                                                transforms.Normalize(mean=[0.5071, 0.4867, 0.4408], 
                                                                     std=[0.2675, 0.2565, 0.2761])])
                trnset = torchvision.datasets.CIFAR100(root='%s/Raw' %self.data_path,
                                                      train=True , download=True, transform=transform)
                tstset = torchvision.datasets.CIFAR100(root='%s/Raw' %self.data_path,
                                                      train=False, download=True, transform=transform)
                trn_load = torch.utils.data.DataLoader(trnset, batch_size=50000, shuffle=False, num_workers=0)
                tst_load = torch.utils.data.DataLoader(tstset, batch_size=10000, shuffle=False, num_workers=0)
                self.channels = 3; self.width = 32; self.height = 32; self.n_cls = 100;
            
            if self.dataset != 'emnist':
                trn_itr = trn_load.__iter__(); tst_itr = tst_load.__iter__() 
                # labels are of shape (n_data,)
                trn_x, trn_y = trn_itr.__next__()
                tst_x, tst_y = tst_itr.__next__()

                trn_x = trn_x.numpy(); trn_y = trn_y.numpy().reshape(-1,1)
                tst_x = tst_x.numpy(); tst_y = tst_y.numpy().reshape(-1,1)
            
            
            if self.dataset == 'emnist':
                emnist = io.loadmat(self.data_path + "/Raw/matlab/emnist-letters.mat")
                # load training dataset
                x_train = emnist["dataset"][0][0][0][0][0][0]
                x_train = x_train.astype(np.float32)

                # load training labels
                y_train = emnist["dataset"][0][0][0][0][0][1] - 1 # make first class 0

                # take first 10 classes of letters
                trn_idx = np.where(y_train < 10)[0]

                y_train = y_train[trn_idx]
                x_train = x_train[trn_idx]

                mean_x = np.mean(x_train)
                std_x = np.std(x_train)

                # load test dataset
                x_test = emnist["dataset"][0][0][1][0][0][0]
                x_test = x_test.astype(np.float32)

                # load test labels
                y_test = emnist["dataset"][0][0][1][0][0][1] - 1 # make first class 0

                tst_idx = np.where(y_test < 10)[0]

                y_test = y_test[tst_idx]
                x_test = x_test[tst_idx]
                
                x_train = x_train.reshape((-1, 1, 28, 28))
                x_test  = x_test.reshape((-1, 1, 28, 28))
                
                # normalise train and test features

                trn_x = (x_train - mean_x) / std_x
                trn_y = y_train
                
                tst_x = (x_test  - mean_x) / std_x
                tst_y = y_test
                
                self.channels = 1; self.width = 28; self.height = 28; self.n_cls = 10;
            
            # Shuffle Data
            rand_perm = np.random.permutation(len(trn_y))
            trn_x = trn_x[rand_perm]
            trn_y = trn_y[rand_perm]
            
            self.trn_x = trn_x
            self.trn_y = trn_y
            self.tst_x = tst_x
            self.tst_y = tst_y
            
            
            ###
            n_data_per_clnt = int((len(trn_y)) / self.n_client)
            if self.unbalanced_sgm != 0:
                # Draw from lognormal distribution
                clnt_data_list = (np.random.lognormal(mean=np.log(n_data_per_clnt), sigma=self.unbalanced_sgm, size=self.n_client))
                clnt_data_list = (clnt_data_list/np.sum(clnt_data_list)*len(trn_y)).astype(int)
                diff = np.sum(clnt_data_list) - len(trn_y)

                # Add/Subtract the excess number starting from first client
                if diff!= 0:
                    for clnt_i in range(self.n_client):
                        if clnt_data_list[clnt_i] > diff:
                            clnt_data_list[clnt_i] -= diff
                            break
            else:
                clnt_data_list = (np.ones(self.n_client) * n_data_per_clnt).astype(int)
            ###     
            
            if self.rule == 'Dirichlet':
                cls_priors   = np.random.dirichlet(alpha=[self.rule_arg]*self.n_cls,size=self.n_client)
                prior_cumsum = np.cumsum(cls_priors, axis=1)
                idx_list = [np.where(trn_y==i)[0] for i in range(self.n_cls)]
                cls_amount = [len(idx_list[i]) for i in range(self.n_cls)]

                clnt_x = [ np.zeros((clnt_data_list[clnt__], self.channels, self.height, self.width)).astype(np.float32) for clnt__ in range(self.n_client) ]
                clnt_y = [ np.zeros((clnt_data_list[clnt__], 1)).astype(np.int64) for clnt__ in range(self.n_client) ]
    
                while(np.sum(clnt_data_list)!=0):
                    curr_clnt = np.random.randint(self.n_client)
                    # If current node is full resample a client
                    print('Remaining Data: %d' %np.sum(clnt_data_list))
                    if clnt_data_list[curr_clnt] <= 0:
                        continue
                    clnt_data_list[curr_clnt] -= 1
                    curr_prior = prior_cumsum[curr_clnt]
                    while True:
                        cls_label = np.argmax(np.random.uniform() <= curr_prior)
                        # Redraw class label if trn_y is out of that class
                        if cls_amount[cls_label] <= 0:
                            continue
                        cls_amount[cls_label] -= 1
                        clnt_x[curr_clnt][clnt_data_list[curr_clnt]] = trn_x[idx_list[cls_label][cls_amount[cls_label]]]
                        clnt_y[curr_clnt][clnt_data_list[curr_clnt]] = trn_y[idx_list[cls_label][cls_amount[cls_label]]]

                        break
                
                clnt_x = np.asarray(clnt_x)
                clnt_y = np.asarray(clnt_y)
                
                cls_means = np.zeros((self.n_client, self.n_cls))
                for clnt in range(self.n_client):
                    for cls in range(self.n_cls):
                        cls_means[clnt,cls] = np.mean(clnt_y[clnt]==cls)
                prior_real_diff = np.abs(cls_means-cls_priors)
                print('--- Max deviation from prior: %.4f' %np.max(prior_real_diff))
                print('--- Min deviation from prior: %.4f' %np.min(prior_real_diff))
            
            elif self.rule == 'iid' and self.dataset == 'CIFAR100' and self.unbalanced_sgm==0:
                assert len(trn_y)//100 % self.n_client == 0 
                # Only have the number clients if it divides 500
                # Perfect IID partitions for cifar100 instead of shuffling
                idx = np.argsort(trn_y[:, 0])
                n_data_per_clnt = len(trn_y) // self.n_client
                # clnt_x dtype needs to be float32, the same as weights
                clnt_x = np.zeros((self.n_client, n_data_per_clnt, 3, 32, 32), dtype=np.float32)
                clnt_y = np.zeros((self.n_client, n_data_per_clnt, 1), dtype=np.float32)
                trn_x = trn_x[idx] # 50000*3*32*32
                trn_y = trn_y[idx]
                n_cls_sample_per_device = n_data_per_clnt // 100
                for i in range(self.n_client): # devices
                    for j in range(100): # class
                        clnt_x[i, n_cls_sample_per_device*j:n_cls_sample_per_device*(j+1), :, :, :] = trn_x[500*j+n_cls_sample_per_device*i:500*j+n_cls_sample_per_device*(i+1), :, :, :]
                        clnt_y[i, n_cls_sample_per_device*j:n_cls_sample_per_device*(j+1), :] = trn_y[500*j+n_cls_sample_per_device*i:500*j+n_cls_sample_per_device*(i+1), :] 
            
            
            elif self.rule == 'iid':
                                
                clnt_x = [ np.zeros((clnt_data_list[clnt__], self.channels, self.height, self.width)).astype(np.float32) for clnt__ in range(self.n_client) ]
                clnt_y = [ np.zeros((clnt_data_list[clnt__], 1)).astype(np.int64) for clnt__ in range(self.n_client) ]
            
                clnt_data_list_cum_sum = np.concatenate(([0], np.cumsum(clnt_data_list)))
                for clnt_idx_ in range(self.n_client):
                    clnt_x[clnt_idx_] = trn_x[clnt_data_list_cum_sum[clnt_idx_]:clnt_data_list_cum_sum[clnt_idx_+1]]
                    clnt_y[clnt_idx_] = trn_y[clnt_data_list_cum_sum[clnt_idx_]:clnt_data_list_cum_sum[clnt_idx_+1]]
                
                
                clnt_x = np.asarray(clnt_x)
                clnt_y = np.asarray(clnt_y)

            
            self.clnt_x = clnt_x; self.clnt_y = clnt_y

            self.tst_x  = tst_x;  self.tst_y  = tst_y
            
            # Save data
            os.mkdir('%s/%s' %(self.data_path, self.name))
            
            np.save('%s/%s/clnt_x.npy' %(self.data_path, self.name), clnt_x)
            np.save('%s/%s/clnt_y.npy' %(self.data_path, self.name), clnt_y)

            np.save('%s/%s/tst_x.npy'  %(self.data_path, self.name),  tst_x)
            np.save('%s/%s/tst_y.npy'  %(self.data_path, self.name),  tst_y)

        else:
            print("Data is already downloaded in the folder.")
            self.clnt_x = np.load('%s/%s/clnt_x.npy' %(self.data_path, self.name), allow_pickle=True)
            self.clnt_y = np.load('%s/%s/clnt_y.npy' %(self.data_path, self.name), allow_pickle=True)
            self.n_client = len(self.clnt_x)

            tst_x  = np.load('%s/%s/tst_x.npy'  %(self.data_path, self.name), allow_pickle=True)
            tst_y  = np.load('%s/%s/tst_y.npy'  %(self.data_path, self.name), allow_pickle=True)
            print(tst_x.shape)

            
            if self.dataset == 'mnist':
                self.channels = 1; self.width = 28; self.height = 28; self.n_cls = 10;
            if self.dataset == 'CIFAR10':
                self.channels = 3; self.width = 32; self.height = 32; self.n_cls = 10;
            if self.dataset == 'CIFAR100':
                self.channels = 3; self.width = 32; self.height = 32; self.n_cls = 100;
            if self.dataset == 'fashion_mnist':
                self.channels = 1; self.width = 28; self.height = 28; self.n_cls = 10;
            if self.dataset == 'emnist':
                self.channels = 1; self.width = 28; self.height = 28; self.n_cls = 10;
        
            self.tst_x = []
            self.tst_y = []
            num_test_per_client = int(len(tst_y)/self.n_cls)
            for i in range(self.n_cls):
                self.tst_x.append(tst_x[i*num_test_per_client:(i+1)*num_test_per_client])
                self.tst_y.append(tst_y[i*num_test_per_client:(i+1)*num_test_per_client])
            self.tst_x = np.array(self.tst_x)
            self.tst_y = np.array(self.tst_y)

                
        print('Class frequencies:')
        count = 0
        for clnt in range(self.n_client):
            print("Client %3d: " %clnt + 
                  ', '.join(["%f" %np.mean(self.clnt_y[clnt]==cls) for cls in range(self.n_cls)]) + 
                  ', Amount:%d' %self.clnt_y[clnt].shape[0])
            count += self.clnt_y[clnt].shape[0]

        for clnt in range(self.n_client):
            print("Client %3d: " %clnt + 
                  ', '.join(["%f" %np.mean(self.tst_y[clnt]==cls) for cls in range(self.n_cls)]) + 
                  ', Amount:%d' %self.tst_y[clnt].shape[0])
            count += self.tst_y[clnt].shape[0]
        
        
        print('Total Amount:%d' %count)
        print('--------')

        print("      Test: " + 
              ', '.join(["%.3f" %np.mean(self.tst_y==cls) for cls in range(self.n_cls)]) + 
              ', Amount:%d' %self.tst_y.shape[0])
        
        # sys.exit()
        

    
class Dataset(torch.utils.data.Dataset):
    
    def __init__(self, data_x, data_y=True, train=False, dataset_name=''):
        self.name = dataset_name
        if self.name == 'mnist' or self.name == 'synt' or self.name == 'emnist' or self.name == 'digits' or self.name == 'retina' or self.name == 'cifar10c':
            self.X_data = torch.tensor(data_x).float()
            self.y_data = data_y
            if not isinstance(data_y, bool):
                self.y_data = torch.tensor(data_y).float()
            
        elif self.name == 'CIFAR10' or self.name == 'CIFAR100':
            self.train = train
            self.transform = transforms.Compose([transforms.ToTensor()])
        
            self.X_data = data_x
            self.y_data = data_y
            if not isinstance(data_y, bool):
                self.y_data = data_y.astype('int32')
                
        elif self.name == 'shakespeare':
            
            self.X_data = data_x
            self.y_data = data_y
                
            self.X_data = torch.tensor(self.X_data).long()
            if not isinstance(data_y, bool):
                self.y_data = torch.tensor(self.y_data).float()

    def __len__(self):
        return len(self.X_data)

    def __getitem__(self, idx):
        if self.name == 'mnist' or self.name == 'synt' or self.name == 'emnist' or self.name == 'digits' or self.name == 'retina' or self.name == 'cifar10c':
            X = self.X_data[idx, :]
            if isinstance(self.y_data, bool):
                return X
            else:
                y = self.y_data[idx]
                return X, y
        
        elif self.name == 'CIFAR10' or self.name == 'CIFAR100':
            img = self.X_data[idx]
            if self.train:
                img = np.flip(img, axis=2).copy() if (np.random.rand() > .5) else img # Horizontal flip
                if (np.random.rand() > .5):
                # Random cropping 
                    pad = 4
                    extended_img = np.zeros((3,32 + pad *2, 32 + pad *2)).astype(np.float32)
                    extended_img[:,pad:-pad,pad:-pad] = img
                    dim_1, dim_2 = np.random.randint(pad * 2 + 1, size=2)
                    img = extended_img[:,dim_1:dim_1+32,dim_2:dim_2+32]
            img = np.moveaxis(img, 0, -1)
            img = self.transform(img)
            if isinstance(self.y_data, bool):
                return img
            else:
                y = torch.tensor(self.y_data[idx], dtype=torch.long)
                return img, y
            
        elif self.name == 'shakespeare':
            x = self.X_data[idx]
            y = self.y_data[idx] 
            return x, y