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utils.py
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import os
import logging
import numpy as np
import torch
import torchvision.transforms as transforms
import torch.utils.data as data
from torch.autograd import Variable
import torch.nn.functional as F
import random
from sklearn.metrics import confusion_matrix, roc_auc_score
from torch.utils.data import DataLoader
import copy
from model import *
from datasets import MNIST_truncated, CIFAR10_truncated, CIFAR100_truncated, ImageFolder_custom, SVHN_custom, FashionMNIST_truncated, CustomTensorDataset, CelebA_custom, FEMNIST, Generated, genData
from math import sqrt
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as vutils
import time
import random
from models.mnist_model import Generator, Discriminator, DHead, QHead
from config import params
import sklearn.datasets as sk
from sklearn.datasets import load_svmlight_file
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def mkdirs(dirpath):
try:
os.makedirs(dirpath)
except Exception as _:
pass
def load_mnist_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
mnist_train_ds = MNIST_truncated(datadir, train=True, download=True, transform=transform)
mnist_test_ds = MNIST_truncated(datadir, train=False, download=True, transform=transform)
X_train, y_train = mnist_train_ds.data, mnist_train_ds.target
X_test, y_test = mnist_test_ds.data, mnist_test_ds.target
X_train = X_train.data.numpy()
y_train = y_train.data.numpy()
X_test = X_test.data.numpy()
y_test = y_test.data.numpy()
return (X_train, y_train, X_test, y_test)
def load_fmnist_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
mnist_train_ds = FashionMNIST_truncated(datadir, train=True, download=True, transform=transform)
mnist_test_ds = FashionMNIST_truncated(datadir, train=False, download=True, transform=transform)
X_train, y_train = mnist_train_ds.data, mnist_train_ds.target
X_test, y_test = mnist_test_ds.data, mnist_test_ds.target
X_train = X_train.data.numpy()
y_train = y_train.data.numpy()
X_test = X_test.data.numpy()
y_test = y_test.data.numpy()
return (X_train, y_train, X_test, y_test)
def load_svhn_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
svhn_train_ds = SVHN_custom(datadir, train=True, download=True, transform=transform)
svhn_test_ds = SVHN_custom(datadir, train=False, download=True, transform=transform)
X_train, y_train = svhn_train_ds.data, svhn_train_ds.target
X_test, y_test = svhn_test_ds.data, svhn_test_ds.target
# X_train = X_train.data.numpy()
# y_train = y_train.data.numpy()
# X_test = X_test.data.numpy()
# y_test = y_test.data.numpy()
return (X_train, y_train, X_test, y_test)
def load_cifar10_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
cifar10_train_ds = CIFAR10_truncated(datadir, train=True, download=True, transform=transform)
cifar10_test_ds = CIFAR10_truncated(datadir, train=False, download=True, transform=transform)
X_train, y_train = cifar10_train_ds.data, cifar10_train_ds.target
X_test, y_test = cifar10_test_ds.data, cifar10_test_ds.target
# y_train = y_train.numpy()
# y_test = y_test.numpy()
return (X_train, y_train, X_test, y_test)
def load_celeba_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
celeba_train_ds = CelebA_custom(datadir, split='train', target_type="attr", download=True, transform=transform)
celeba_test_ds = CelebA_custom(datadir, split='test', target_type="attr", download=True, transform=transform)
gender_index = celeba_train_ds.attr_names.index('Male')
y_train = celeba_train_ds.attr[:,gender_index:gender_index+1].reshape(-1)
y_test = celeba_test_ds.attr[:,gender_index:gender_index+1].reshape(-1)
# y_train = y_train.numpy()
# y_test = y_test.numpy()
return (None, y_train, None, y_test)
def load_femnist_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
mnist_train_ds = FEMNIST(datadir, train=True, transform=transform, download=True)
mnist_test_ds = FEMNIST(datadir, train=False, transform=transform, download=True)
X_train, y_train, u_train = mnist_train_ds.data, mnist_train_ds.targets, mnist_train_ds.users_index
X_test, y_test, u_test = mnist_test_ds.data, mnist_test_ds.targets, mnist_test_ds.users_index
X_train = X_train.data.numpy()
y_train = y_train.data.numpy()
u_train = np.array(u_train)
X_test = X_test.data.numpy()
y_test = y_test.data.numpy()
u_test = np.array(u_test)
return (X_train, y_train, u_train, X_test, y_test, u_test)
def load_cifar100_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
cifar100_train_ds = CIFAR100_truncated(datadir, train=True, download=True, transform=transform)
cifar100_test_ds = CIFAR100_truncated(datadir, train=False, download=True, transform=transform)
X_train, y_train = cifar100_train_ds.data, cifar100_train_ds.target
X_test, y_test = cifar100_test_ds.data, cifar100_test_ds.target
# y_train = y_train.numpy()
# y_test = y_test.numpy()
return (X_train, y_train, X_test, y_test)
def load_tinyimagenet_data(datadir):
transform = transforms.Compose([transforms.ToTensor()])
xray_train_ds = ImageFolder_custom(datadir+'./train/', transform=transform)
xray_test_ds = ImageFolder_custom(datadir+'./val/', transform=transform)
X_train, y_train = np.array([s[0] for s in xray_train_ds.samples]), np.array([int(s[1]) for s in xray_train_ds.samples])
X_test, y_test = np.array([s[0] for s in xray_test_ds.samples]), np.array([int(s[1]) for s in xray_test_ds.samples])
return (X_train, y_train, X_test, y_test)
def record_net_data_stats(y_train, net_dataidx_map, logdir):
net_cls_counts = {}
for net_i, dataidx in net_dataidx_map.items():
unq, unq_cnt = np.unique(y_train[dataidx], return_counts=True)
tmp = {unq[i]: unq_cnt[i] for i in range(len(unq))}
net_cls_counts[net_i] = tmp
logger.info('Data statistics: %s' % str(net_cls_counts))
return net_cls_counts
def partition_data(dataset, datadir, logdir, partition, n_parties, beta=0.4):
#np.random.seed(2020)
#torch.manual_seed(2020)
if dataset == 'mnist':
X_train, y_train, X_test, y_test = load_mnist_data(datadir)
elif dataset == 'fmnist':
X_train, y_train, X_test, y_test = load_fmnist_data(datadir)
elif dataset == 'cifar10':
X_train, y_train, X_test, y_test = load_cifar10_data(datadir)
elif dataset == 'svhn':
X_train, y_train, X_test, y_test = load_svhn_data(datadir)
elif dataset == 'celeba':
X_train, y_train, X_test, y_test = load_celeba_data(datadir)
elif dataset == 'femnist':
X_train, y_train, u_train, X_test, y_test, u_test = load_femnist_data(datadir)
elif dataset == 'cifar100':
X_train, y_train, X_test, y_test = load_cifar100_data(datadir)
elif dataset == 'tinyimagenet':
X_train, y_train, X_test, y_test = load_tinyimagenet_data(datadir)
elif dataset == 'generated':
X_train, y_train = [], []
for loc in range(4):
for i in range(1000):
p1 = random.random()
p2 = random.random()
p3 = random.random()
if loc > 1:
p2 = -p2
if loc % 2 ==1:
p3 = -p3
if i % 2 == 0:
X_train.append([p1, p2, p3])
y_train.append(0)
else:
X_train.append([-p1, -p2, -p3])
y_train.append(1)
X_test, y_test = [], []
for i in range(1000):
p1 = random.random() * 2 - 1
p2 = random.random() * 2 - 1
p3 = random.random() * 2 - 1
X_test.append([p1, p2, p3])
if p1>0:
y_test.append(0)
else:
y_test.append(1)
X_train = np.array(X_train, dtype=np.float32)
X_test = np.array(X_test, dtype=np.float32)
y_train = np.array(y_train, dtype=np.int32)
y_test = np.array(y_test, dtype=np.int64)
idxs = np.linspace(0,3999,4000,dtype=np.int64)
batch_idxs = np.array_split(idxs, n_parties)
net_dataidx_map = {i: batch_idxs[i] for i in range(n_parties)}
mkdirs("data/generated/")
np.save("data/generated/X_train.npy",X_train)
np.save("data/generated/X_test.npy",X_test)
np.save("data/generated/y_train.npy",y_train)
np.save("data/generated/y_test.npy",y_test)
#elif dataset == 'covtype':
# cov_type = sk.fetch_covtype('./data')
# num_train = int(581012 * 0.75)
# idxs = np.random.permutation(581012)
# X_train = np.array(cov_type['data'][idxs[:num_train]], dtype=np.float32)
# y_train = np.array(cov_type['target'][idxs[:num_train]], dtype=np.int32) - 1
# X_test = np.array(cov_type['data'][idxs[num_train:]], dtype=np.float32)
# y_test = np.array(cov_type['target'][idxs[num_train:]], dtype=np.int32) - 1
# mkdirs("data/generated/")
# np.save("data/generated/X_train.npy",X_train)
# np.save("data/generated/X_test.npy",X_test)
# np.save("data/generated/y_train.npy",y_train)
# np.save("data/generated/y_test.npy",y_test)
elif dataset in ('rcv1', 'SUSY', 'covtype'):
X_train, y_train = load_svmlight_file(datadir+dataset)
X_train = X_train.todense()
num_train = int(X_train.shape[0] * 0.75)
if dataset == 'covtype':
y_train = y_train-1
else:
y_train = (y_train+1)/2
idxs = np.random.permutation(X_train.shape[0])
X_test = np.array(X_train[idxs[num_train:]], dtype=np.float32)
y_test = np.array(y_train[idxs[num_train:]], dtype=np.int32)
X_train = np.array(X_train[idxs[:num_train]], dtype=np.float32)
y_train = np.array(y_train[idxs[:num_train]], dtype=np.int32)
mkdirs("data/generated/")
np.save("data/generated/X_train.npy",X_train)
np.save("data/generated/X_test.npy",X_test)
np.save("data/generated/y_train.npy",y_train)
np.save("data/generated/y_test.npy",y_test)
elif dataset in ('a9a'):
X_train, y_train = load_svmlight_file(datadir+"a9a")
X_test, y_test = load_svmlight_file(datadir+"a9a.t")
X_train = X_train.todense()
X_test = X_test.todense()
X_test = np.c_[X_test, np.zeros((len(y_test), X_train.shape[1] - np.size(X_test[0, :])))]
X_train = np.array(X_train, dtype=np.float32)
X_test = np.array(X_test, dtype=np.float32)
y_train = (y_train+1)/2
y_test = (y_test+1)/2
y_train = np.array(y_train, dtype=np.int32)
y_test = np.array(y_test, dtype=np.int32)
mkdirs("data/generated/")
np.save("data/generated/X_train.npy",X_train)
np.save("data/generated/X_test.npy",X_test)
np.save("data/generated/y_train.npy",y_train)
np.save("data/generated/y_test.npy",y_test)
n_train = y_train.shape[0]
if partition == "homo":
idxs = np.random.permutation(n_train)
batch_idxs = np.array_split(idxs, n_parties)
net_dataidx_map = {i: batch_idxs[i] for i in range(n_parties)}
elif partition == "noniid-labeldir":
min_size = 0
min_require_size = 10
K = 10
if dataset in ('celeba', 'covtype', 'a9a', 'rcv1', 'SUSY'):
K = 2
# min_require_size = 100
if dataset == 'cifar100':
K = 100
elif dataset == 'tinyimagenet':
K = 200
N = y_train.shape[0]
#np.random.seed(2020)
net_dataidx_map = {}
while min_size < min_require_size:
idx_batch = [[] for _ in range(n_parties)]
for k in range(K):
idx_k = np.where(y_train == k)[0]
np.random.shuffle(idx_k)
proportions = np.random.dirichlet(np.repeat(beta, n_parties))
# logger.info("proportions1: ", proportions)
# logger.info("sum pro1:", np.sum(proportions))
## Balance
proportions = np.array([p * (len(idx_j) < N / n_parties) for p, idx_j in zip(proportions, idx_batch)])
# logger.info("proportions2: ", proportions)
proportions = proportions / proportions.sum()
# logger.info("proportions3: ", proportions)
proportions = (np.cumsum(proportions) * len(idx_k)).astype(int)[:-1]
# logger.info("proportions4: ", proportions)
idx_batch = [idx_j + idx.tolist() for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))]
min_size = min([len(idx_j) for idx_j in idx_batch])
# if K == 2 and n_parties <= 10:
# if np.min(proportions) < 200:
# min_size = 0
# break
for j in range(n_parties):
np.random.shuffle(idx_batch[j])
net_dataidx_map[j] = idx_batch[j]
elif partition > "noniid-#label0" and partition <= "noniid-#label9":
num = eval(partition[13:])
if dataset in ('celeba', 'covtype', 'a9a', 'rcv1', 'SUSY'):
num = 1
K = 2
else:
K = 10
if dataset == "cifar100":
K = 100
elif dataset == "tinyimagenet":
K = 200
if num == 10:
net_dataidx_map ={i:np.ndarray(0,dtype=np.int64) for i in range(n_parties)}
for i in range(10):
idx_k = np.where(y_train==i)[0]
np.random.shuffle(idx_k)
split = np.array_split(idx_k,n_parties)
for j in range(n_parties):
net_dataidx_map[j]=np.append(net_dataidx_map[j],split[j])
else:
times=[0 for i in range(K)]
contain=[]
for i in range(n_parties):
current=[i%K]
times[i%K]+=1
j=1
while (j<num):
ind=random.randint(0,K-1)
if (ind not in current):
j=j+1
current.append(ind)
times[ind]+=1
contain.append(current)
net_dataidx_map ={i:np.ndarray(0,dtype=np.int64) for i in range(n_parties)}
for i in range(K):
idx_k = np.where(y_train==i)[0]
np.random.shuffle(idx_k)
split = np.array_split(idx_k,times[i])
ids=0
for j in range(n_parties):
if i in contain[j]:
net_dataidx_map[j]=np.append(net_dataidx_map[j],split[ids])
ids+=1
elif partition == "iid-diff-quantity":
idxs = np.random.permutation(n_train)
min_size = 0
while min_size < 10:
proportions = np.random.dirichlet(np.repeat(beta, n_parties))
proportions = proportions/proportions.sum()
min_size = np.min(proportions*len(idxs))
proportions = (np.cumsum(proportions)*len(idxs)).astype(int)[:-1]
batch_idxs = np.split(idxs,proportions)
net_dataidx_map = {i: batch_idxs[i] for i in range(n_parties)}
elif partition == "mixed":
min_size = 0
min_require_size = 10
K = 10
if dataset in ('celeba', 'covtype', 'a9a', 'rcv1', 'SUSY'):
K = 2
# min_require_size = 100
N = y_train.shape[0]
net_dataidx_map = {}
times=[1 for i in range(10)]
contain=[]
for i in range(n_parties):
current=[i%K]
j=1
while (j<2):
ind=random.randint(0,K-1)
if (ind not in current and times[ind]<2):
j=j+1
current.append(ind)
times[ind]+=1
contain.append(current)
net_dataidx_map ={i:np.ndarray(0,dtype=np.int64) for i in range(n_parties)}
min_size = 0
while min_size < 10:
proportions = np.random.dirichlet(np.repeat(beta, n_parties))
proportions = proportions/proportions.sum()
min_size = np.min(proportions*n_train)
for i in range(K):
idx_k = np.where(y_train==i)[0]
np.random.shuffle(idx_k)
proportions_k = np.random.dirichlet(np.repeat(beta, 2))
#proportions_k = np.ndarray(0,dtype=np.float64)
#for j in range(n_parties):
# if i in contain[j]:
# proportions_k=np.append(proportions_k ,proportions[j])
proportions_k = (np.cumsum(proportions_k)*len(idx_k)).astype(int)[:-1]
split = np.split(idx_k, proportions_k)
ids=0
for j in range(n_parties):
if i in contain[j]:
net_dataidx_map[j]=np.append(net_dataidx_map[j],split[ids])
ids+=1
elif partition == "real" and dataset == "femnist":
num_user = u_train.shape[0]
user = np.zeros(num_user+1,dtype=np.int32)
for i in range(1,num_user+1):
user[i] = user[i-1] + u_train[i-1]
no = np.random.permutation(num_user)
batch_idxs = np.array_split(no, n_parties)
net_dataidx_map = {i:np.zeros(0,dtype=np.int32) for i in range(n_parties)}
for i in range(n_parties):
for j in batch_idxs[i]:
net_dataidx_map[i]=np.append(net_dataidx_map[i], np.arange(user[j], user[j+1]))
elif partition == "transfer-from-femnist":
stat = np.load("femnist-dis.npy")
n_total = stat.shape[0]
chosen = np.random.permutation(n_total)[:n_parties]
stat = stat[chosen,:]
if dataset in ('celeba', 'covtype', 'a9a', 'rcv1', 'SUSY'):
K = 2
else:
K = 10
N = y_train.shape[0]
#np.random.seed(2020)
net_dataidx_map = {}
idx_batch = [[] for _ in range(n_parties)]
for k in range(K):
idx_k = np.where(y_train == k)[0]
np.random.shuffle(idx_k)
proportions = stat[:,k]
# logger.info("proportions2: ", proportions)
proportions = proportions / proportions.sum()
# logger.info("proportions3: ", proportions)
proportions = (np.cumsum(proportions) * len(idx_k)).astype(int)[:-1]
# logger.info("proportions4: ", proportions)
idx_batch = [idx_j + idx.tolist() for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))]
for j in range(n_parties):
np.random.shuffle(idx_batch[j])
net_dataidx_map[j] = idx_batch[j]
elif partition == "transfer-from-criteo":
stat0 = np.load("criteo-dis.npy")
n_total = stat0.shape[0]
flag=True
while (flag):
chosen = np.random.permutation(n_total)[:n_parties]
stat = stat0[chosen,:]
check = [0 for i in range(10)]
for ele in stat:
for j in range(10):
if ele[j]>0:
check[j]=1
flag=False
for i in range(10):
if check[i]==0:
flag=True
break
if dataset in ('celeba', 'covtype', 'a9a', 'rcv1', 'SUSY'):
K = 2
stat[:,0]=np.sum(stat[:,:5],axis=1)
stat[:,1]=np.sum(stat[:,5:],axis=1)
else:
K = 10
N = y_train.shape[0]
#np.random.seed(2020)
net_dataidx_map = {}
idx_batch = [[] for _ in range(n_parties)]
for k in range(K):
idx_k = np.where(y_train == k)[0]
np.random.shuffle(idx_k)
proportions = stat[:,k]
# logger.info("proportions2: ", proportions)
proportions = proportions / proportions.sum()
# logger.info("proportions3: ", proportions)
proportions = (np.cumsum(proportions) * len(idx_k)).astype(int)[:-1]
# logger.info("proportions4: ", proportions)
idx_batch = [idx_j + idx.tolist() for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))]
for j in range(n_parties):
np.random.shuffle(idx_batch[j])
net_dataidx_map[j] = idx_batch[j]
traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir)
return (X_train, y_train, X_test, y_test, net_dataidx_map, traindata_cls_counts)
def get_trainable_parameters(net):
'return trainable parameter values as a vector (only the first parameter set)'
trainable=filter(lambda p: p.requires_grad, net.parameters())
# logger.info("net.parameter.data:", list(net.parameters()))
paramlist=list(trainable)
N=0
for params in paramlist:
N+=params.numel()
# logger.info("params.data:", params.data)
X=torch.empty(N,dtype=torch.float64)
X.fill_(0.0)
offset=0
for params in paramlist:
numel=params.numel()
with torch.no_grad():
X[offset:offset+numel].copy_(params.data.view_as(X[offset:offset+numel].data))
offset+=numel
# logger.info("get trainable x:", X)
return X
def put_trainable_parameters(net,X):
'replace trainable parameter values by the given vector (only the first parameter set)'
trainable=filter(lambda p: p.requires_grad, net.parameters())
paramlist=list(trainable)
offset=0
for params in paramlist:
numel=params.numel()
with torch.no_grad():
params.data.copy_(X[offset:offset+numel].data.view_as(params.data))
offset+=numel
def compute_accuracy_vote(model_list, threshold_list, dataloader, accepted_vote, normalize = True, factor=1, mode=1, device="cpu"):
for model in model_list:
model.eval()
model.to(device)
true_labels_list, pred_labels_list = np.array([]), np.array([])
if type(dataloader) == type([1]):
pass
else:
dataloader = [dataloader]
correct, total = 0, 0
with torch.no_grad():
for tmp in dataloader:
for batch_idx, (x, target) in enumerate(tmp):
x, target = x.to(device), target.to(device,dtype=torch.int64)
out = [model(x)[0].cpu() for model in model_list]
for i in range(len(out)):
#out_del = out[i].numpy()
#out_max = np.repeat(np.max(out_del[:,:-1], axis=1), out_del.shape[1]).reshape(-1, out_del.shape[1])
#out_del = np.where(out_del > out_max - 1e-5 ,-10000, out_del)
#out_del = torch.softmax(torch.Tensor(out_del), dim=1).numpy()
#confidence = out_del[:,-1]
saved = torch.softmax(out[i][:,:-1], dim=1).numpy()
out[i] = torch.softmax(out[i], dim=1).numpy()
#out[i][:,:-1] = saved # new added, just calculate existing class
if normalize:
out[i][:,:-1] = saved
#out[i][:,:-1] *= np.repeat(confidence,out[i].shape[1]-1).reshape(-1, out[i].shape[1]-1)
#out[i][:,-1] = -np.max(out[i][:,:-1], axis=1)
out[i][:,-1] = (np.log(out[i][:,-1]) - threshold_list[i][0]) / (threshold_list[i][mode] - threshold_list[i][0])
out[i][:,-1] = np.where(out[i][:,-1]<0, 0, out[i][:,-1])
out[i][:,-1] = np.where(out[i][:,-1]>1, 1, out[i][:,-1])
out[i][:,:-1] = out[i][:,:-1] - out[i][:,:-1] * np.repeat(out[i][:,-1],out[i].shape[1]-1).reshape(-1, out[i].shape[1]-1)
out[i] = out[i] ** factor
out[i] = out[i].tolist()
pred_label = []
for ind in range(len(out[0])):
vote = [result[ind] for result in out]
vote = np.array(vote)
index = np.argsort(vote[:,-1])
sorted_vote = vote[index]
final_vote = np.sum(sorted_vote[:accepted_vote, :-1], axis=0)
pred = int(np.argmax(final_vote))
pred_label.append(pred)
'''
if batch_idx == 0:
logger.info(target[ind])
logger.info(pred)
logger.info(sorted_vote)
'''
pred_label = torch.LongTensor(pred_label).to(device)
total += x.data.size()[0]
correct += (pred_label == target.data).sum().item()
if device == "cpu":
pred_labels_list = np.append(pred_labels_list, pred_label.numpy())
true_labels_list = np.append(true_labels_list, target.data.numpy())
else:
pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy())
true_labels_list = np.append(true_labels_list, target.data.cpu().numpy())
half = int(batch_idx / 2)
return correct/float(total), half, pred_labels_list
def compute_accuracy(model, dataloader, get_confusion_matrix=False, calc=False, device="cpu", add=0):
was_training = False
if model.training:
model.eval()
was_training = True
true_labels_list, pred_labels_list = np.array([]), np.array([])
if type(dataloader) == type([1]):
pass
else:
dataloader = [dataloader]
correct, total = 0, 0
outlier_prob, num = 0.0, 0
max_prob = 0
avg_max, avg_num = 0.0, 0
max_tmp = 0
flag = False
ftrs = None
lbs = None
with torch.no_grad():
for tmp in dataloader:
for batch_idx, (x, target) in enumerate(tmp):
x, target = x.to(device), target.to(device,dtype=torch.int64)
out, mid = model(x)
'''
if not flag:
ftrs = mid.cpu().numpy()
lbs = target.cpu().numpy()
flag = True
else:
ftrs = np.concatenate((ftrs,mid.cpu().numpy()))
lbs = np.concatenate((lbs,target.cpu().numpy()))
'''
prob = torch.softmax(out, dim=1)
if calc:
if torch.max(prob[:,-1]) > max_prob:
max_prob = torch.max(prob[:,-1])
if torch.sum(torch.log(prob[:,-1])) > -10000:
outlier_prob += torch.sum(torch.log(prob[:,-1]))
num += x.shape[0]
if torch.max(prob[:,-1]) > max_tmp:
max_tmp = torch.max(prob[:,-1])
if batch_idx % 4 == 0:
avg_max += torch.log(max_tmp)
avg_num += 1
max_tmp = 0
_, pred_label = torch.max(out.data, 1)
total += x.data.size()[0]
correct += (pred_label == target.data).sum().item()
if device == "cpu":
pred_labels_list = np.append(pred_labels_list, pred_label.numpy())
true_labels_list = np.append(true_labels_list, target.data.numpy())
else:
pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy())
true_labels_list = np.append(true_labels_list, target.data.cpu().numpy())
''''
if not calc:
ftrs = np.concatenate((ftrs,add))
lbs = np.concatenate((lbs,np.ones(add.shape[0],dtype=np.int32)*10))
tsne = TSNE()
result = tsne.fit_transform(ftrs)
np.save('ft.npy',result)
np.save('lb.npy',lbs)
'''
#if get_confusion_matrix:
# conf_matrix = confusion_matrix(true_labels_list, pred_labels_list)
if was_training:
model.train()
#if get_confusion_matrix:
# return correct/float(total), conf_matrix
if calc:
return correct/float(total), 1,1,1 #outlier_prob / num, torch.log(max_prob), avg_max / avg_num
else:
return correct/float(total)
def save_model(model, model_index, args):
logger.info("saving local model-{}".format(model_index))
with open(args.modeldir+"trained_local_model"+str(model_index), "wb") as f_:
torch.save(model.state_dict(), f_)
return
def load_model(model, model_index, device="cpu"):
#
with open("trained_local_model"+str(model_index), "rb") as f_:
model.load_state_dict(torch.load(f_))
model.to(device)
return model
class AddGaussianNoise(object):
def __init__(self, mean=0., std=1., net_id=None, total=0):
self.std = std
self.mean = mean
self.net_id = net_id
self.num = int(sqrt(total))
if self.num * self.num < total:
self.num = self.num + 1
def __call__(self, tensor):
if self.net_id is None:
return tensor + torch.randn(tensor.size()) * self.std + self.mean
else:
tmp = torch.randn(tensor.size())
filt = torch.zeros(tensor.size())
size = int(28 / self.num)
row = int(self.net_id / size)
col = self.net_id % size
for i in range(size):
for j in range(size):
filt[:,row*size+i,col*size+j] = 1
tmp = tmp * filt
return tensor + tmp * self.std + self.mean
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None, noise_level=0, net_id=None, total=0):
if dataset in ('mnist', 'femnist', 'fmnist', 'cifar10', 'svhn', 'generated', 'covtype', 'a9a', 'rcv1', 'SUSY', 'cifar100', 'tinyimagenet'):
if dataset == 'mnist':
dl_obj = MNIST_truncated
transform_train = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
transform_test = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
elif dataset == 'femnist':
dl_obj = FEMNIST
transform_train = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
transform_test = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
elif dataset == 'fmnist':
dl_obj = FashionMNIST_truncated
transform_train = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
transform_test = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
elif dataset == 'svhn':
dl_obj = SVHN_custom
transform_train = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
transform_test = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
elif dataset == 'cifar10':
dl_obj = CIFAR10_truncated
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: F.pad(
Variable(x.unsqueeze(0), requires_grad=False),
(4, 4, 4, 4), mode='reflect').data.squeeze()),
transforms.ToPILImage(),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)
])
# data prep for test set
transform_test = transforms.Compose([
transforms.ToTensor(),
AddGaussianNoise(0., noise_level, net_id, total)])
elif dataset == 'cifar100':
dl_obj = CIFAR100_truncated
normalize = transforms.Normalize(mean=[0.5070751592371323, 0.48654887331495095, 0.4409178433670343],
std=[0.2673342858792401, 0.2564384629170883, 0.27615047132568404])
# transform_train = transforms.Compose([
# transforms.RandomCrop(32),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# normalize
# ])
transform_train = transforms.Compose([
# transforms.ToPILImage(),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ToTensor(),
normalize
])
# data prep for test set
transform_test = transforms.Compose([
transforms.ToTensor(),
normalize])
elif dataset == 'tinyimagenet':
dl_obj = ImageFolder_custom
transform_train = transforms.Compose([
transforms.Resize(32),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
transform_test = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
else:
dl_obj = Generated
transform_train = None
transform_test = None
if dataset == "tinyimagenet":
train_ds = dl_obj(datadir+'./train/', dataidxs=dataidxs, transform=transform_train)
test_ds = dl_obj(datadir+'./val/', transform=transform_test)
else:
train_ds = dl_obj(datadir, dataidxs=dataidxs, train=True, transform=transform_train, download=True)
test_ds = dl_obj(datadir, train=False, transform=transform_test, download=True)
train_dl = data.DataLoader(dataset=train_ds, batch_size=train_bs, shuffle=True, drop_last=False)
test_dl = data.DataLoader(dataset=test_ds, batch_size=test_bs, shuffle=False, drop_last=False)
return train_dl, test_dl, train_ds, test_ds
def weights_init(m):
"""
Initialise weights of the model.
"""
if(type(m) == nn.ConvTranspose2d or type(m) == nn.Conv2d):
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif(type(m) == nn.BatchNorm2d):
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
class NormalNLLLoss:
"""
Calculate the negative log likelihood
of normal distribution.
This needs to be minimised.
Treating Q(cj | x) as a factored Gaussian.
"""
def __call__(self, x, mu, var):
logli = -0.5 * (var.mul(2 * np.pi) + 1e-6).log() - (x - mu).pow(2).div(var.mul(2.0) + 1e-6)
nll = -(logli.sum(1).mean())
return nll
def noise_sample(choice, n_dis_c, dis_c_dim, n_con_c, n_z, batch_size, device):
"""
Sample random noise vector for training.
INPUT
--------
n_dis_c : Number of discrete latent code.
dis_c_dim : Dimension of discrete latent code.
n_con_c : Number of continuous latent code.
n_z : Dimension of iicompressible noise.
batch_size : Batch Size
device : GPU/CPU
"""
z = torch.randn(batch_size, n_z, 1, 1, device=device)
idx = np.zeros((n_dis_c, batch_size))
if(n_dis_c != 0):
dis_c = torch.zeros(batch_size, n_dis_c, dis_c_dim, device=device)
c_tmp = np.array(choice)
for i in range(n_dis_c):
idx[i] = np.random.randint(len(choice), size=batch_size)
for j in range(batch_size):
idx[i][j] = c_tmp[int(idx[i][j])]
dis_c[torch.arange(0, batch_size), i, idx[i]] = 1.0
dis_c = dis_c.view(batch_size, -1, 1, 1)
if(n_con_c != 0):
# Random uniform between -1 and 1.
con_c = torch.rand(batch_size, n_con_c, 1, 1, device=device) * 2 - 1
noise = z
if(n_dis_c != 0):
noise = torch.cat((z, dis_c), dim=1)
if(n_con_c != 0):
noise = torch.cat((noise, con_c), dim=1)
return noise, idx
def compute_auc_outlier_detection(inlier, model, dataloader, get_confusion_matrix=False, calc=False, device="cpu", add=0):
was_training = False
if model.training:
model.eval()
was_training = True
true_labels_list, pred_labels_list = np.array([]), np.array([])
if type(dataloader) == type([1]):
pass
else:
dataloader = [dataloader]
correct, total = 0, 0
outlier_prob, num = 0.0, 0
max_prob = 0
avg_max, avg_num = 0.0, 0
max_tmp = 0
flag = False
ftrs = None
lbs = None
with torch.no_grad():
for tmp in dataloader:
for batch_idx, (x, target) in enumerate(tmp):
x, target = x.to(device), target.to(device,dtype=torch.int64)
out, mid = model(x)
prob = torch.softmax(out, dim=1)