from __future__ import print_function
import os
import argparse
import shutil
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import models
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR training')
parser.add_argument('--dataset', type=str, default='cifar100',
help='training dataset (default: cifar100)')
parser.add_argument('--sparsity-regularization', '-sr', dest='sr', action='store_true',
help='train with channel sparsity regularization')
parser.add_argument('--s', type=float, default=0.0001,
help='scale sparse rate (default: 0.0001)')
parser.add_argument('--refine', default='', type=str, metavar='PATH',
help='path to the pruned model to be fine tuned')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=256, metavar='N',
help='input batch size for testing (default: 256)')
parser.add_argument('--epochs', type=int, default=160, metavar='N',
help='number of epochs to train (default: 160)')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
help='learning rate (default: 0.1)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save', default='./logs', type=str, metavar='PATH',
help='path to save prune model (default: current directory)')
parser.add_argument('--arch', default='vgg', type=str,
help='architecture to use')
parser.add_argument('--depth', default=19, type=int,
help='depth of the neural network')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if not os.path.exists(args.save):
os.makedirs(args.save)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
if args.dataset == 'cifar10':
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=True, download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
else:
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('./data.cifar100', train=True, download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
if args.refine:
checkpoint = torch.load(args.refine)
model = models.__dict__[args.arch](dataset=args.dataset, depth=args.depth, cfg=checkpoint['cfg'])
model.load_state_dict(checkpoint['state_dict'])
else:
model = models.__dict__[args.arch](dataset=args.dataset, depth=args.depth)
if args.cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {}) Prec1: {:f}"
.format(args.resume, checkpoint['epoch'], best_prec1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# additional subgradient descent on the sparsity-induced penalty term
def updateBN():
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.grad.data.add_(args.s*torch.sign(m.weight.data)) # L1
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output, target)
pred = output.data.max(1, keepdim=True)[1]
loss.backward()
if args.sr:
updateBN()
optimizer.step()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.1f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data[0]))
def test():
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += F.cross_entropy(output, target, size_average=False).data[0] # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.1f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
return correct / float(len(test_loader.dataset))
def save_checkpoint(state, is_best, filepath):
torch.save(state, os.path.join(filepath, 'checkpoint.pth.tar'))
if is_best:
shutil.copyfile(os.path.join(filepath, 'checkpoint.pth.tar'), os.path.join(filepath, 'model_best.pth.tar'))
best_prec1 = 0.
for epoch in range(args.start_epoch, args.epochs):
if epoch in [args.epochs*0.5, args.epochs*0.75]:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.1
train(epoch)
prec1 = test()
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}, is_best, filepath=args.save)
print("Best accuracy: "+str(best_prec1))
