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utils.py
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utils.py
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import numpy as np
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data.sampler import SubsetRandomSampler, Sampler
def get_optim_and_scheduler(params, args, run_cfgs):
if not args.adam:
optimizer = torch.optim.SGD(params, args.lr, momentum=0.9,
weight_decay=run_cfgs[args.run_cfg]['weight_decay'])
else:
print('Adam selected, not using weight decay.')
optimizer = torch.optim.Adam(params, args.lr,
weight_decay=run_cfgs[args.run_cfg]['weight_decay'])
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
run_cfgs[args.run_cfg]['lr_schedule'])
return optimizer, lr_scheduler
def train(train_loader, model, criterion, optimizer, epoch):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
for i, (input, target) in enumerate(train_loader):
target = target.cuda(non_blocking=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1 = accuracy(output.data, target, topk=(1,5))
losses.update(loss.item(), input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec1[1], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % 100 == 0:
print('Epoch [Batch]: {0} [{1}/{2}]\t'
'| Loss: {loss.val:.4f} ({loss.avg:.4f})\t'
'| Prec@1: {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch, i, len(train_loader), loss=losses, top1=top1))
return top1.avg, top5.avg, losses.avg
def validate(val_loader, model, criterion, optimizer):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
for i, (input, target) in enumerate(val_loader):
target = target.cuda(non_blocking=True)
# compute output
output = model(input)
loss = criterion(output, target)
# measure accuracy and record loss
prec1 = accuracy(output.data, target, topk=(1,5))
losses.update(loss.item(), input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec1[1], input.size(0))
if i % 100 == 0:
print('Test: {0}/{1}\t'
'| Loss: {loss.val:.4f} ({loss.avg:.4f})\t'
'| Prec@1: {top1.val:.3f} ({top1.avg:.3f})'.format(
i, len(val_loader), loss=losses, top1=top1))
print('\n * Prec@1 {top1.avg:.3f}\n'.format(top1=top1))
return top1.avg, top5.avg, losses.avg
def get_train_test_data(args):
if args.augment:
augmenting_transforms = [transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()]
else:
augmenting_transforms = []
if args.dataset == "cifar10":
normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
elif args.dataset == "cifar100":
normalize = transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))
tensor_and_normalize = [transforms.ToTensor(), normalize]
transform_train = transforms.Compose([ t for t in augmenting_transforms + tensor_and_normalize])
transform_test = transforms.Compose([*tensor_and_normalize])
if args.dataset == "cifar10":
trainset = datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform_train)
testset = datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform_test)
elif args.dataset == "cifar100":
trainset = datasets.CIFAR100(root='./data', train=True,
download=True, transform=transform_train)
testset = datasets.CIFAR100(root='./data', train=False,
download=True, transform=transform_test)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
shuffle=True, num_workers=args.workers)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.test_batch_size,
shuffle=False, num_workers=args.workers)
small_loader = torch.utils.data.DataLoader(testset, batch_size=512,
shuffle=False, num_workers=args.workers,
sampler=SubsetRandomSampler(range(512)))
batch_size_1_loader = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False, num_workers=args.workers,
sampler=SubsetRandomSampler(range(512)))
return trainloader, testloader, small_loader, batch_size_1_loader
def covariance_of_gradient_trace(batch_size_1_loader, model, criterion, optimizer):
# switch to evaluate mode
model.eval()
trace = 0
N = 512 # number of elements in loader
def add_square_grad_to(trace):
k = 0
for group in optimizer.param_groups:
for p in group['params']:
if p.grad is not None:
trace += (p.grad.detach()**2).sum().cpu().item()
k += p.grad.numel()
return trace, k
for i, (input, target) in enumerate(batch_size_1_loader):
target = target.cuda(non_blocking=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
trace, k = add_square_grad_to(trace)
print('there were '+ str(k) + ' parameters')
return trace/N
def pruning_scheduler(layerwise_prune_range_params, epoch):
layerwise_prune_ranges = [range(p[0], p[1], p[2]) for p in layerwise_prune_range_params]
iters = [r.index(epoch)+1 if epoch in r else None for r in layerwise_prune_ranges]
n_iters = [len(r) for r in layerwise_prune_ranges]
return iters, n_iters
def pruned_filters(model):
'''
counts number of pruned filters
'''
zeroed_p_count = 0
for layer in [x for x in model.modules() if hasattr(x, 'prune_frac')]:
zeroed_p_count += len(layer.mask) - int(layer.mask.sum())
return zeroed_p_count
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
def accuracy(output, target, topk=(1,)):
"""
Computes the precision@k for the specified values of k
"""
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)
res.append(correct_k.mul_(100.0 / batch_size))
return res