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util.py
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util.py
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import os
import numpy as np
import time
import argparse
from mpi4py import MPI
from math import ceil
from random import Random
import networkx as nx
import torch
import torch.distributed as dist
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.multiprocessing import Process
from torch.autograd import Variable
import torchvision
from torchvision import datasets, transforms
import torch.backends.cudnn as cudnn
import torchvision.models as models
from models import *
import GraphPreprocess
class Partition(object):
""" Dataset-like object, but only access a subset of it. """
def __init__(self, data, index):
self.data = data
self.index = index
def __len__(self):
return len(self.index)
def __getitem__(self, index):
data_idx = self.index[index]
return self.data[data_idx]
class DataPartitioner(object):
""" Partitions a dataset into different chuncks. """
def __init__(self, data, sizes=[0.7, 0.2, 0.1], seed=1234, isNonIID=False):
self.data = data
self.partitions = []
rng = Random()
rng.seed(seed)
data_len = len(data)
indexes = [x for x in range(0, data_len)]
rng.shuffle(indexes)
for frac in sizes:
part_len = int(frac * data_len)
self.partitions.append(indexes[0:part_len])
indexes = indexes[part_len:]
if isNonIID:
self.partitions = __getNonIIDdata__(self, data, sizes, seed)
def use(self, partition):
return Partition(self.data, self.partitions[partition])
def __getNonIIDdata__(self, data, sizes, seed):
labelList = data.train_labels
rng = Random()
rng.seed(seed)
a = [(label, idx) for idx, label in enumerate(labelList)]
# Same Part
labelIdxDict = dict()
for label, idx in a:
labelIdxDict.setdefault(label,[])
labelIdxDict[label].append(idx)
labelNum = len(labelIdxDict)
labelNameList = [key for key in labelIdxDict]
labelIdxPointer = [0] * labelNum
partitions = [list() for i in range(len(sizes))]
eachPartitionLen= int(len(labelList)/len(sizes))
majorLabelNumPerPartition = ceil(labelNum/len(partitions))
basicLabelRatio = 0.4
interval = 1
labelPointer = 0
#basic part
for partPointer in range(len(partitions)):
requiredLabelList = list()
for _ in range(majorLabelNumPerPartition):
requiredLabelList.append(labelPointer)
labelPointer += interval
if labelPointer > labelNum - 1:
labelPointer = interval
interval += 1
for labelIdx in requiredLabelList:
start = labelIdxPointer[labelIdx]
idxIncrement = int(basicLabelRatio*len(labelIdxDict[labelNameList[labelIdx]]))
partitions[partPointer].extend(labelIdxDict[labelNameList[labelIdx]][start:start+ idxIncrement])
labelIdxPointer[labelIdx] += idxIncrement
#random part
remainLabels = list()
for labelIdx in range(labelNum):
remainLabels.extend(labelIdxDict[labelNameList[labelIdx]][labelIdxPointer[labelIdx]:])
rng.shuffle(remainLabels)
for partPointer in range(len(partitions)):
idxIncrement = eachPartitionLen - len(partitions[partPointer])
partitions[partPointer].extend(remainLabels[:idxIncrement])
rng.shuffle(partitions[partPointer])
remainLabels = remainLabels[idxIncrement:]
return partitions
def partition_dataset(rank, size, args):
print('==> load train data')
if args.dataset == 'cifar10':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
trainset = torchvision.datasets.CIFAR10(root=args.datasetRoot,
train=True,
download=True,
transform=transform_train)
partition_sizes = [1.0 / size for _ in range(size)]
partition = DataPartitioner(trainset, partition_sizes, isNonIID=False)
partition = partition.use(rank)
train_loader = torch.utils.data.DataLoader(partition,
batch_size=args.bs,
shuffle=True,
pin_memory=True)
print('==> load test data')
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
testset = torchvision.datasets.CIFAR10(root=args.datasetRoot,
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=size)
if args.dataset == 'cifar100':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)),
])
trainset = torchvision.datasets.CIFAR100(root=args.datasetRoot,
train=True,
download=True,
transform=transform_train)
partition_sizes = [1.0 / size for _ in range(size)]
partition = DataPartitioner(trainset, partition_sizes, isNonIID=False)
partition = partition.use(rank)
train_loader = torch.utils.data.DataLoader(partition,
batch_size=args.bs,
shuffle=True,
pin_memory=True)
print('==> load test data')
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)),
])
testset = torchvision.datasets.CIFAR100(root=args.datasetRoot,
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=size)
elif args.dataset == 'imagenet':
datadir = args.datasetRoot
traindir = os.path.join(datadir, 'CLS-LOC/train/')
#valdir = os.path.join(datadir, 'CLS-LOC/')
#testdir = os.path.join(datadir, 'CLS-LOC/')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
partition_sizes = [1.0 / size for _ in range(size)]
partition = DataPartitioner(train_dataset, partition_sizes, isNonIID=False)
partition = partition.use(rank)
train_loader = torch.utils.data.DataLoader(
partition, batch_size=args.bs, shuffle=True,
pin_memory=True)
'''
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.bs, shuffle=False,
pin_memory=True)
val_loader = None
'''
test_loader = None
if args.dataset == 'emnist':
transform_train = transforms.Compose([
transforms.ToTensor(),
])
train_dataset = torchvision.datasets.EMNIST(root=args.datasetRoot,
split = 'balanced',
train=True,
download=True,
transform=transform_train)
partition_sizes = [1.0 / size for _ in range(size)]
partition = DataPartitioner(train_dataset, partition_sizes, isNonIID=False)
partition = partition.use(rank)
train_loader = torch.utils.data.DataLoader(
partition, batch_size=args.bs, shuffle=True,
pin_memory=True)
transform_test = transforms.Compose([
transforms.ToTensor(),
])
testset = torchvision.datasets.EMNIST(root=args.datasetRoot,
split = 'balanced',
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=size)
return train_loader, test_loader
def select_model(num_class, args):
if args.model == 'VGG':
model = vggnet.VGG(16, num_class)
elif args.model == 'res':
if args.dataset == 'cifar10':
# model = large_resnet.ResNet18()
model = resnet.ResNet(50, num_class)
elif args.dataset == 'imagenet':
model = models.resnet18()
elif args.model == 'wrn':
model = wrn.Wide_ResNet(28,10,0,num_class)
elif args.model == 'mlp':
if args.dataset == 'emnist':
model = MLP.MNIST_MLP(47)
return model
def select_graph(graphid):
# pre-defined base network topologies
# you can add more by extending the list
Graphs =[
# graph 0:
# 8-node erdos-renyi graph as shown in Fig. 1(a) in the main paper
[[(1, 5), (6, 7), (0, 4), (2, 3)],
[(1, 7), (3, 6)],
[(1, 0), (3, 7), (5, 6)],
[(1, 2), (7, 0)],
[(3, 1)]],
# graph 1:
# 16-node gemetric graph as shown in Fig. A.3(a) in Appendix
[[(4, 8), (6, 11), (7, 13), (0, 12), (5, 14), (10, 15), (2, 3), (1, 9)],
[(11, 13), (14, 2), (5, 6), (15, 3), (10, 9)],
[(11, 8), (2, 5), (13, 4), (14, 3), (0, 10)],
[(11, 5), (15, 14), (13, 8)],
[(2, 11)]],
# graph 2:
# 16-node gemetric graph as shown in Fig. A.3(b) in Appendix
[[(2, 7), (12, 15), (3, 13), (5, 6), (8, 0), (9, 4), (11, 14), (1, 10)],
[(8, 6), (0, 11), (3, 2), (5, 4), (15, 14), (1, 9)],
[(8, 3), (0, 6), (11, 2), (4, 1), (12, 14)],
[(8, 11), (6, 3), (0, 5)],
[(8, 2), (0, 3), (6, 7), (11, 12)],
[(8, 5), (6, 4), (0, 2), (11, 7)],
[(8, 15), (3, 7), (0, 4), (6, 2)],
[(8, 14), (5, 3), (11, 6), (0, 9)],
[(8, 7), (15, 11), (2, 5), (4, 3), (1, 0), (13, 6)],
[(12, 8)]],
# graph 3:
# 16-node gemetric graph as shown in Fig. A.3(c) in Appendix
[[(3, 12), (4, 8), (1, 13), (5, 7), (9, 10), (11, 14), (6, 15), (0, 2)],
[(7, 14), (2, 6), (5, 13), (8, 10), (1, 15), (0, 11), (3, 9), (4, 12)],
[(2, 7), (3, 15), (9, 13), (6, 11), (4, 14), (10, 12), (1, 8), (0, 5)],
[(5, 14), (1, 12), (13, 8), (9, 4), (2, 11), (7, 0)],
[(5, 1), (14, 8), (13, 12), (10, 4), (6, 7)],
[(5, 9), (14, 1), (13, 3), (8, 2), (11, 7)],
[(5, 12), (14, 13), (1, 9), (8, 0)],
[(5, 2), (14, 10), (1, 3), (9, 8), (13, 15)],
[(5, 8), (14, 12), (1, 4), (13, 10)],
[(5, 3), (14, 2), (9, 12), (1, 10), (13, 4)],
[(5, 6), (14, 0), (8, 12), (1, 2)],
[(5, 15), (9, 14)],
[(11, 5)]],
# graph 4:
# 16-node erdos-renyi graph as shown in Fig 3.(b) in the main paper
[[(2, 7), (3, 15), (13, 14), (8, 9), (1, 5), (0, 10), (6, 12), (4, 11)],
[(12, 11), (5, 6), (14, 1), (9, 10), (15, 2), (8, 13)],
[(12, 5), (11, 6), (1, 8), (9, 3), (2, 10)],
[(12, 14), (11, 9), (5, 15), (0, 6), (1, 7)],
[(12, 8), (5, 2), (11, 14), (1, 6)],
[(12, 15), (13, 11), (10, 5), (3, 14)],
[(12, 9)],
[(0, 12)]],
# graph 5, 8-node ring
[[(0, 1), (2, 3), (4, 5), (6, 7)],
[(0, 7), (2, 1), (4, 3), (6, 5)]]
]
return Graphs[graphid]
def comp_accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class Recorder(object):
def __init__(self, args, rank):
self.record_accuracy = list()
self.record_timing = list()
self.record_comp_timing = list()
self.record_comm_timing = list()
self.record_losses = list()
self.record_trainacc = list()
self.total_record_timing = list()
self.args = args
self.rank = rank
self.saveFolderName = args.savePath + args.name + '_' + args.model
if rank == 0 and os.path.isdir(self.saveFolderName)==False and self.args.save:
os.mkdir(self.saveFolderName)
def add_new(self,record_time,comp_time,comm_time,epoch_time,top1,losses,test_acc):
self.total_record_timing.append(record_time)
self.record_timing.append(epoch_time)
self.record_comp_timing.append(comp_time)
self.record_comm_timing.append(comm_time)
self.record_trainacc.append(top1)
self.record_losses.append(losses)
self.record_accuracy.append(test_acc)
def save_to_file(self):
np.savetxt(self.saveFolderName+'/dsgd-lr'+str(self.args.lr)+'-budget'+str(self.args.budget)+'-r'+str(self.rank)+'-recordtime.log', self.total_record_timing, delimiter=',')
np.savetxt(self.saveFolderName+'/dsgd-lr'+str(self.args.lr)+'-budget'+str(self.args.budget)+'-r'+str(self.rank)+'-time.log', self.record_timing, delimiter=',')
np.savetxt(self.saveFolderName+'/dsgd-lr'+str(self.args.lr)+'-budget'+str(self.args.budget)+'-r'+str(self.rank)+'-comptime.log', self.record_comp_timing, delimiter=',')
np.savetxt(self.saveFolderName+'/dsgd-lr'+str(self.args.lr)+'-budget'+str(self.args.budget)+'-r'+str(self.rank)+'-commtime.log', self.record_comm_timing, delimiter=',')
np.savetxt(self.saveFolderName+'/dsgd-lr'+str(self.args.lr)+'-budget'+str(self.args.budget)+'-r'+str(self.rank)+'-acc.log', self.record_accuracy, delimiter=',')
np.savetxt(self.saveFolderName+'/dsgd-lr'+str(self.args.lr)+'-budget'+str(self.args.budget)+'-r'+str(self.rank)+'-losses.log', self.record_losses, delimiter=',')
np.savetxt(self.saveFolderName+'/dsgd-lr'+str(self.args.lr)+'-budget'+str(self.args.budget)+'-r'+str(self.rank)+'-tacc.log', self.record_trainacc, delimiter=',')
with open(self.saveFolderName+'/ExpDescription', 'w') as f:
f.write(str(self.args)+ '\n')
f.write(self.args.description + '\n')
def test(model, test_loader):
model.eval()
top1 = AverageMeter()
# correct = 0
# total = 0
for batch_idx, (inputs, targets) in enumerate(test_loader):
inputs, targets = inputs.cuda(non_blocking=True), targets.cuda(non_blocking=True)
outputs = model(inputs)
acc1 = comp_accuracy(outputs, targets)
top1.update(acc1[0], inputs.size(0))
return top1.avg